From 3efd2a727f059d4e6c50d5d054737d3d056f539f Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Wed, 29 Apr 2020 15:44:44 +0100
Subject: [PATCH 001/162] try to solve coincident point issues

---
 libpysal/weights/distance.py | 212 ++++++++++++++++++++---------------
 1 file changed, 120 insertions(+), 92 deletions(-)

diff --git a/libpysal/weights/distance.py b/libpysal/weights/distance.py
index 2e9552cd0..6c4c93bc5 100644
--- a/libpysal/weights/distance.py
+++ b/libpysal/weights/distance.py
@@ -4,8 +4,13 @@
 
 from ..cg.kdtree import KDTree
 from .weights import W, WSP
-from .util import isKDTree, get_ids, get_points_array_from_shapefile,\
-                  get_points_array, WSP2W
+from .util import (
+    isKDTree,
+    get_ids,
+    get_points_array_from_shapefile,
+    get_points_array,
+    WSP2W,
+)
 import copy
 from warnings import warn as Warn
 from scipy.spatial import distance_matrix
@@ -13,13 +18,13 @@
 import numpy as np
 
 
-def knnW(data, k=2, p=2, ids=None, radius=None, distance_metric='euclidean'):
+def knnW(data, k=2, p=2, ids=None, radius=None, distance_metric="euclidean"):
     """
     This is deprecated. Use the pysal.weights.KNN class instead. 
     """
-    #Warn('This function is deprecated. Please use pysal.weights.KNN', UserWarning)
-    return KNN(data, k=k, p=p, ids=ids, radius=radius,
-            distance_metric=distance_metric)
+    # Warn('This function is deprecated. Please use pysal.weights.KNN', UserWarning)
+    return KNN(data, k=k, p=p, ids=ids, radius=radius, distance_metric=distance_metric)
+
 
 class KNN(W):
     """
@@ -84,31 +89,38 @@ class KNN(W):
     --------
     :class:`libpysal.weights.weights.W`
     """
-    def __init__(self, data, k=2, p=2, ids=None, radius=None,
-                 distance_metric='euclidean', **kwargs):
-        if radius is not None: 
-            distance_metric='arc'
+
+    def __init__(
+        self,
+        data,
+        k=2,
+        p=2,
+        ids=None,
+        radius=None,
+        distance_metric="euclidean",
+        **kwargs
+    ):
+        if radius is not None:
+            distance_metric = "arc"
         if isKDTree(data):
             self.kdtree = data
             self.data = self.kdtree.data
         else:
             self.kdtree = KDTree(data, radius=radius, distance_metric=distance_metric)
             self.data = self.kdtree.data
-        self.k = k 
+        self.k = k
         self.p = p
-        this_nnq = self.kdtree.query(self.data, k=k+1, p=p)
+        distances, indices = self.kdtree.query(self.data, k=k + 1, p=p)
+        full_indices = np.arange(self.kdtree.n)
+        not_self_mask = indices != full_indices.reshape(-1, 1)
+        not_self_indices = indices[not_self_mask].reshape(self.kdtree.n, k)
 
-        to_weight = this_nnq[1]
+        to_weight = not_self_indices
         if ids is None:
-            ids = list(range(to_weight.shape[0]))
-
-        neighbors = {}
-        for i,row in enumerate(to_weight):
-            row = row.tolist()
-            row.remove(i)
-            row = [ids[j] for j in row]
-            focal = ids[i]
-            neighbors[focal] = row
+            ids = list(full_indices)
+
+        neighbors = dict(zip(ids, not_self_indices))
+
         W.__init__(self, neighbors, id_order=ids, **kwargs)
 
     @classmethod
@@ -233,7 +245,7 @@ def from_array(cls, array, *args, **kwargs):
         return cls(array, *args, **kwargs)
 
     @classmethod
-    def from_dataframe(cls, df, geom_col='geometry', ids=None, *args, **kwargs):
+    def from_dataframe(cls, df, geom_col="geometry", ids=None, *args, **kwargs):
         """
         Make KNN weights from a dataframe.
 
@@ -289,9 +301,9 @@ def reweight(self, k=None, p=None, new_data=None, new_ids=None, inplace=True):
         A copy of the object using the new parameterization, or None if the
         object is reweighted in place.
         """
-        if (new_data is not None):
-            new_data = np.asarray(new_data).reshape(-1,2)
-            data = np.vstack((self.data, new_data)).reshape(-1,2)
+        if new_data is not None:
+            new_data = np.asarray(new_data).reshape(-1, 2)
+            data = np.vstack((self.data, new_data)).reshape(-1, 2)
             if new_ids is not None:
                 ids = copy.deepcopy(self.id_order)
                 ids.extend(list(new_ids))
@@ -302,7 +314,7 @@ def reweight(self, k=None, p=None, new_data=None, new_ids=None, inplace=True):
             data = self.kdtree
             ids = self.id_order
         elif (new_data is None) and (new_ids is not None):
-            Warn('Remapping ids must be done using w.remap_ids')
+            Warn("Remapping ids must be done using w.remap_ids")
         if k is None:
             k = self.k
         if p is None:
@@ -313,6 +325,7 @@ def reweight(self, k=None, p=None, new_data=None, new_ids=None, inplace=True):
         else:
             return KNN(data, ids=ids, k=k, p=p)
 
+
 class Kernel(W):
     """
     Spatial weights based on kernel functions.
@@ -475,20 +488,29 @@ class Kernel(W):
     {0: [1.0, 0.35206533556593145, 0.3412334260702758], 1: [0.35206533556593145, 1.0, 0.2419707487162134, 0.3412334260702758, 0.31069657591175387], 2: [0.2419707487162134, 1.0, 0.31069657591175387], 3: [0.3412334260702758, 0.3412334260702758, 1.0, 0.3011374490937829, 0.26575287272131043], 4: [0.31069657591175387, 0.31069657591175387, 0.3011374490937829, 1.0, 0.35206533556593145], 5: [0.26575287272131043, 0.35206533556593145, 1.0]}
 
     """
-    def __init__(self, data, bandwidth=None, fixed=True, k=2,
-                 function='triangular', eps=1.0000001, ids=None,
-                 diagonal=False, 
-                 distance_metric='euclidean', radius=None, 
-                 **kwargs):
+
+    def __init__(
+        self,
+        data,
+        bandwidth=None,
+        fixed=True,
+        k=2,
+        function="triangular",
+        eps=1.0000001,
+        ids=None,
+        diagonal=False,
+        distance_metric="euclidean",
+        radius=None,
+        **kwargs
+    ):
         if radius is not None:
-            distance_metric='arc'
+            distance_metric = "arc"
         if isKDTree(data):
             self.kdtree = data
             self.data = self.kdtree.data
             data = self.data
         else:
-            self.kdtree = KDTree(data, distance_metric=distance_metric,
-                                 radius=radius)
+            self.kdtree = KDTree(data, distance_metric=distance_metric, radius=radius)
             self.data = self.kdtree.data
         self.k = k + 1
         self.function = function.lower()
@@ -499,7 +521,7 @@ def __init__(self, data, bandwidth=None, fixed=True, k=2,
                 bandwidth = np.array(bandwidth)
                 bandwidth.shape = (len(bandwidth), 1)
             except:
-                bandwidth = np.ones((len(data), 1), 'float') * bandwidth
+                bandwidth = np.ones((len(data), 1), "float") * bandwidth
             self.bandwidth = bandwidth
         else:
             self._set_bw()
@@ -512,7 +534,7 @@ def __init__(self, data, bandwidth=None, fixed=True, k=2,
         W.__init__(self, neighbors, weights, ids, **kwargs)
 
     @classmethod
-    def from_shapefile(cls, filepath, idVariable=None,  **kwargs):
+    def from_shapefile(cls, filepath, idVariable=None, **kwargs):
         """
         Kernel based weights from shapefile
 
@@ -551,7 +573,7 @@ def from_array(cls, array, **kwargs):
         return cls(array, **kwargs)
 
     @classmethod
-    def from_dataframe(cls, df, geom_col='geometry', ids=None, **kwargs):
+    def from_dataframe(cls, df, geom_col="geometry", ids=None, **kwargs):
         """
         Make Kernel weights from a dataframe.
 
@@ -600,7 +622,7 @@ def _set_bw(self):
             # use max knn distance as bandwidth
             bandwidth = dmat.max() * self.eps
             n = len(dmat)
-            self.bandwidth = np.ones((n, 1), 'float') * bandwidth
+            self.bandwidth = np.ones((n, 1), "float") * bandwidth
         else:
             # use local max knn distance
             self.bandwidth = dmat.max(axis=1) * self.eps
@@ -611,10 +633,11 @@ def _set_bw(self):
 
     def _eval_kernel(self):
         # get points within bandwidth distance of each point
-        if not hasattr(self, 'neigh'):
+        if not hasattr(self, "neigh"):
             kdtq = self.kdtree.query_ball_point
-            neighbors = [kdtq(self.data[i], r=bwi[0]) for i,
-                         bwi in enumerate(self.bandwidth)]
+            neighbors = [
+                kdtq(self.data[i], r=bwi[0]) for i, bwi in enumerate(self.bandwidth)
+            ]
             self.neigh = neighbors
         # get distances for neighbors
         bw = self.bandwidth
@@ -630,20 +653,20 @@ def _eval_kernel(self):
             z.append(zi)
         zs = z
         # functions follow Anselin and Rey (2010) table 5.4
-        if self.function == 'triangular':
+        if self.function == "triangular":
             self.kernel = [1 - zi for zi in zs]
-        elif self.function == 'uniform':
+        elif self.function == "uniform":
             self.kernel = [np.ones(zi.shape) * 0.5 for zi in zs]
-        elif self.function == 'quadratic':
-            self.kernel = [(3. / 4) * (1 - zi ** 2) for zi in zs]
-        elif self.function == 'quartic':
-            self.kernel = [(15. / 16) * (1 - zi ** 2) ** 2 for zi in zs]
-        elif self.function == 'gaussian':
+        elif self.function == "quadratic":
+            self.kernel = [(3.0 / 4) * (1 - zi ** 2) for zi in zs]
+        elif self.function == "quartic":
+            self.kernel = [(15.0 / 16) * (1 - zi ** 2) ** 2 for zi in zs]
+        elif self.function == "gaussian":
             c = np.pi * 2
             c = c ** (-0.5)
-            self.kernel = [c * np.exp(-(zi ** 2) / 2.) for zi in zs]
+            self.kernel = [c * np.exp(-(zi ** 2) / 2.0) for zi in zs]
         else:
-            print(('Unsupported kernel function', self.function))
+            print(("Unsupported kernel function", self.function))
 
 
 class DistanceBand(W):
@@ -660,6 +683,7 @@ class DistanceBand(W):
     threshold  : float
                  distance band
     p          : float
+                 DEPRECATED: use `distance_metric`
                  Minkowski p-norm distance metric parameter:
                  1<=p<=infinity
                  2: Euclidean distance
@@ -676,6 +700,7 @@ class DistanceBand(W):
                   values to use for keys of the neighbors and weights dicts
 
     build_sp    : boolean
+                  DEPRECATED
                   True to build sparse distance matrix and false to build dense
                   distance matrix; significant speed gains may be obtained
                   dending on the sparsity of the of distance_matrix and
@@ -733,18 +758,22 @@ class DistanceBand(W):
     >>> w.weights[0]
     [0.01, 0.007999999999999998]
 
-    Notes
-    -----
-
-    This was initially implemented running scipy 0.8.0dev (in epd 6.1).
-    earlier versions of scipy (0.7.0) have a logic bug in scipy/sparse/dok.py
-    so serge changed line 221 of that file on sal-dev to fix the logic bug.
 
     """
 
-    def __init__(self, data, threshold, p=2, alpha=-1.0, binary=True, ids=None,
-            build_sp=True, silence_warnings=False, 
-            distance_metric='euclidean', radius=None):
+    def __init__(
+        self,
+        data,
+        threshold,
+        p=2,
+        alpha=-1.0,
+        binary=True,
+        ids=None,
+        build_sp=True,
+        silence_warnings=False,
+        distance_metric="euclidean",
+        radius=None,
+    ):
         """Casting to floats is a work around for a bug in scipy.spatial.
         See detail in pysal issue #126.
 
@@ -752,7 +781,7 @@ def __init__(self, data, threshold, p=2, alpha=-1.0, binary=True, ids=None,
         if ids is not None:
             ids = list(ids)
         if radius is not None:
-            distance_metric='arc'
+            distance_metric = "arc"
         self.p = p
         self.threshold = threshold
         self.binary = binary
@@ -764,23 +793,13 @@ def __init__(self, data, threshold, p=2, alpha=-1.0, binary=True, ids=None,
             self.kdtree = data
             self.data = self.kdtree.data
         else:
-            if self.build_sp:
-                try:
-                    data = np.asarray(data)
-                    if data.dtype.kind != 'f':
-                        data = data.astype(float)
-                    self.kdtree = KDTree(data, 
-                                         distance_metric=distance_metric, 
-                                         radius=radius)
-                    self.data = self.kdtree.data
-                except:
-                    raise ValueError("Could not make array from data")        
-            else:
-                self.data = data
-                self.kdtree = None       
+            self.kdtree = KDTree(data, distance_metric=distance_metric, radius=radius)
+            self.data = self.kdtree.data
         self._band()
         neighbors, weights = self._distance_to_W(ids)
-        W.__init__(self, neighbors, weights, ids, silence_warnings=self.silence_warnings)
+        W.__init__(
+            self, neighbors, weights, ids, silence_warnings=self.silence_warnings
+        )
 
     @classmethod
     def from_shapefile(cls, filepath, threshold, idVariable=None, **kwargs):
@@ -816,7 +835,7 @@ def from_array(cls, array, threshold, **kwargs):
         return cls(array, threshold, **kwargs)
 
     @classmethod
-    def from_dataframe(cls, df, threshold, geom_col='geometry', ids=None, **kwargs):
+    def from_dataframe(cls, df, threshold, geom_col="geometry", ids=None, **kwargs):
         """
         Make DistanceBand weights from a dataframe.
 
@@ -846,20 +865,24 @@ def _band(self):
         """
         if self.build_sp:
             self.dmat = self.kdtree.sparse_distance_matrix(
-                    self.kdtree, max_distance=self.threshold, p=self.p).tocsr()
+                self.kdtree, max_distance=self.threshold, p=self.p
+            ).tocsr()
         else:
-            if str(self.kdtree).split('.')[-1][0:10] == 'Arc_KDTree':
-                raise TypeError('Unable to calculate dense arc distance matrix;'
-            	        ' parameter "build_sp" must be set to True for arc'
-            	        ' distance type weight')
+            if str(self.kdtree).split(".")[-1][0:10] == "Arc_KDTree":
+                raise TypeError(
+                    "Unable to calculate dense arc distance matrix;"
+                    ' parameter "build_sp" must be set to True for arc'
+                    " distance type weight"
+                )
             self.dmat = self._spdistance_matrix(self.data, self.data, self.threshold)
 
-
     def _distance_to_W(self, ids=None):
         if self.binary:
-            self.dmat[self.dmat>0] = 1
+            self.dmat[self.dmat > 0] = 1
             self.dmat.eliminate_zeros()
-            tempW = WSP2W(WSP(self.dmat, id_order=ids), silence_warnings=self.silence_warnings)
+            tempW = WSP2W(
+                WSP(self.dmat, id_order=ids), silence_warnings=self.silence_warnings
+            )
             neighbors = tempW.neighbors
             weight_keys = list(tempW.weights.keys())
             weight_vals = list(tempW.weights.values())
@@ -867,30 +890,35 @@ def _distance_to_W(self, ids=None):
             return neighbors, weights
         else:
             weighted = self.dmat.power(self.alpha)
-            weighted[weighted==np.inf] = 0
+            weighted[weighted == np.inf] = 0
             weighted.eliminate_zeros()
-            tempW = WSP2W(WSP(weighted, id_order=ids), silence_warnings=self.silence_warnings)
+            tempW = WSP2W(
+                WSP(weighted, id_order=ids), silence_warnings=self.silence_warnings
+            )
             neighbors = tempW.neighbors
             weight_keys = list(tempW.weights.keys())
             weight_vals = list(tempW.weights.values())
             weights = dict(list(zip(weight_keys, list(map(list, weight_vals)))))
             return neighbors, weights
 
-    def _spdistance_matrix(self, x,y, threshold=None):
-        dist = distance_matrix(x,y)
+    def _spdistance_matrix(self, x, y, threshold=None):
+        dist = distance_matrix(x, y)
         if threshold is not None:
             zeros = dist > threshold
             dist[zeros] = 0
         return sp.csr_matrix(dist)
 
+
 def _test():
     import doctest
+
     # the following line could be used to define an alternative to the '<BLANKLINE>' flag
-    #doctest.BLANKLINE_MARKER = 'something better than <BLANKLINE>'
-    start_suppress = np.get_printoptions()['suppress']
+    # doctest.BLANKLINE_MARKER = 'something better than <BLANKLINE>'
+    start_suppress = np.get_printoptions()["suppress"]
     np.set_printoptions(suppress=True)
     doctest.testmod()
     np.set_printoptions(suppress=start_suppress)
 
-if __name__ == '__main__':
+
+if __name__ == "__main__":
     _test()

From 250a3d6d0c8fe031ae19468ed630b8850a7a7e7f Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Wed, 29 Apr 2020 16:57:49 +0100
Subject: [PATCH 002/162] fix tests and finalize

---
 libpysal/weights/distance.py            |   2 +-
 libpysal/weights/tests/test_distance.py | 253 ++++++++++++++----------
 2 files changed, 145 insertions(+), 110 deletions(-)

diff --git a/libpysal/weights/distance.py b/libpysal/weights/distance.py
index 6c4c93bc5..969ff6c9b 100644
--- a/libpysal/weights/distance.py
+++ b/libpysal/weights/distance.py
@@ -119,7 +119,7 @@ def __init__(
         if ids is None:
             ids = list(full_indices)
 
-        neighbors = dict(zip(ids, not_self_indices))
+        neighbors = dict(zip(ids, map(list, not_self_indices)))
 
         W.__init__(self, neighbors, id_order=ids, **kwargs)
 
diff --git a/libpysal/weights/tests/test_distance.py b/libpysal/weights/tests/test_distance.py
index 455b5e294..57a32f2bf 100644
--- a/libpysal/weights/tests/test_distance.py
+++ b/libpysal/weights/tests/test_distance.py
@@ -1,4 +1,3 @@
-
 from ...common import RTOL, ATOL, pandas
 from ...cg.kdtree import KDTree, RADIUS_EARTH_KM
 from ..util import get_points_array
@@ -13,77 +12,88 @@
 
 PANDAS_EXTINCT = pandas is None
 # All instances should test these four methods, and define their own functional
-# tests based on common codepaths/estimated weights use cases. 
+# tests based on common codepaths/estimated weights use cases.
+
 
 class Distance_Mixin(object):
-    polygon_path = pysal_examples.get_path('columbus.shp')
-    arc_path = pysal_examples.get_path('stl_hom.shp')
-    points = [(10, 10), (20, 10), (40, 10), 
-              (15, 20), (30, 20), (30, 30)]
-    euclidean_kdt = KDTree(points, distance_metric='euclidean')
-    
-    polygon_f = psopen(polygon_path) # our file handler
-    poly_centroids = get_points_array(polygon_f) # our iterable
-    polygon_f.seek(0) #go back to head of file
-    
+    polygon_path = pysal_examples.get_path("columbus.shp")
+    arc_path = pysal_examples.get_path("stl_hom.shp")
+    points = [(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
+    euclidean_kdt = KDTree(points, distance_metric="euclidean")
+
+    polygon_f = psopen(polygon_path)  # our file handler
+    poly_centroids = get_points_array(polygon_f)  # our iterable
+    polygon_f.seek(0)  # go back to head of file
+
     arc_f = psopen(arc_path)
     arc_points = get_points_array(arc_f)
     arc_f.seek(0)
-    arc_kdt = KDTree(arc_points, distance_metric='Arc',
-                     radius=cg.sphere.RADIUS_EARTH_KM)
-    
-    cls = object # class constructor
-    known_wi = None #index of known w entry to compare
-    known_w = dict() #actual w entry
+    arc_kdt = KDTree(
+        arc_points, distance_metric="Arc", radius=cg.sphere.RADIUS_EARTH_KM
+    )
+
+    cls = object  # class constructor
+    known_wi = None  # index of known w entry to compare
+    known_w = dict()  # actual w entry
     known_name = known_wi
-    
+
     def setUp(self):
-        self.__dict__.update({k:v for k,v in list(Distance_Mixin.__dict__.items())
-            if not k.startswith('_')})
-    
+        self.__dict__.update(
+            {
+                k: v
+                for k, v in list(Distance_Mixin.__dict__.items())
+                if not k.startswith("_")
+            }
+        )
+
     def test_init(self):
         # test vanilla, named
-        raise NotImplementedError('You need to implement this test '
-                                  'before this module will pass')
+        raise NotImplementedError(
+            "You need to implement this test " "before this module will pass"
+        )
 
     def test_from_shapefile(self):
         # test vanilla, named, sparse
-        raise NotImplementedError('You need to implement this test '
-                                  'before this module will pass')
+        raise NotImplementedError(
+            "You need to implement this test " "before this module will pass"
+        )
 
     def test_from_array(self):
         # test named, sparse
-        raise NotImplementedError('You need to implement this test '
-                                  'before this module will pass')
+        raise NotImplementedError(
+            "You need to implement this test " "before this module will pass"
+        )
 
     def test_from_dataframe(self):
         # test named, columnar, defau
-        raise NotImplementedError('You need to implement this test '
-                                  'before this module will pass')
+        raise NotImplementedError(
+            "You need to implement this test " "before this module will pass"
+        )
+
 
 class Test_KNN(ut.TestCase, Distance_Mixin):
     def setUp(self):
         Distance_Mixin.setUp(self)
-        
+
         self.known_wi0 = 7
         self.known_w0 = [3, 6, 12, 11]
         self.known_wi1 = 0
-        self.known_w1 = [2, 1, 3 ,7]
+        self.known_w1 = [2, 1, 3, 7]
 
         self.known_wi2 = 4
         self.known_w2 = [1, 3, 9, 12]
         self.known_wi3 = 40
         self.known_w3 = [31, 38, 45, 49]
-    
+
     ##########################
     # Classmethod tests      #
     ##########################
 
     def test_init(self):
         w = d.KNN(self.euclidean_kdt, k=2)
-        self.assertEqual(w.neighbors[0], [1,3])
+        self.assertEqual(w.neighbors[0], [1, 3])
 
-    @ut.skipIf(PANDAS_EXTINCT, 'Missing pandas')
+    @ut.skipIf(PANDAS_EXTINCT, "Missing pandas")
     def test_from_dataframe(self):
         df = pdio.read_files(self.polygon_path)
         w = d.KNN.from_dataframe(df, k=4)
@@ -96,7 +106,7 @@ def test_from_array(self):
         self.assertEqual(w.neighbors[self.known_wi1], self.known_w1)
 
     def test_from_shapefile(self):
-        w = d.KNN.from_shapefile(self.polygon_path, k=4)    
+        w = d.KNN.from_shapefile(self.polygon_path, k=4)
         self.assertEqual(w.neighbors[self.known_wi0], self.known_w0)
         self.assertEqual(w.neighbors[self.known_wi1], self.known_w1)
 
@@ -106,55 +116,59 @@ def test_from_shapefile(self):
 
     def test_reweight(self):
         w = d.KNN(self.points, k=2)
-        new_point = [(21,21)]
+        new_point = [(21, 21)]
         wnew = w.reweight(k=4, p=1, new_data=new_point, inplace=False)
         self.assertEqual(wnew[0], {1: 1.0, 3: 1.0, 4: 1.0, 6: 1.0})
 
     def test_arcdata(self):
-        w = d.KNN.from_shapefile(self.polygon_path, k=4, 
-                                 distance_metric='Arc', 
-                                 radius=cg.sphere.RADIUS_EARTH_KM)
+        w = d.KNN.from_shapefile(
+            self.polygon_path,
+            k=4,
+            distance_metric="Arc",
+            radius=cg.sphere.RADIUS_EARTH_KM,
+        )
         self.assertEqual(w.data.shape[1], 3)
 
 
 class Test_DistanceBand(ut.TestCase, Distance_Mixin):
     def setUp(self):
         Distance_Mixin.setUp(self)
-        self.grid_path =  pysal_examples.get_path('lattice10x10.shp')
+        self.grid_path = pysal_examples.get_path("lattice10x10.shp")
         self.grid_rook_w = c.Rook.from_shapefile(self.grid_path)
         self.grid_f = psopen(self.grid_path)
         self.grid_points = get_points_array(self.grid_f)
         self.grid_f.seek(0)
 
         self.grid_kdt = KDTree(self.grid_points)
-    
+
     ##########################
     # Classmethod tests      #
     ##########################
 
     def test_init(self):
         w = d.DistanceBand(self.grid_kdt, 1)
-        for k,v in w:
+        for k, v in w:
             self.assertEqual(v, self.grid_rook_w[k])
 
     def test_from_shapefile(self):
         w = d.DistanceBand.from_shapefile(self.grid_path, 1)
-        for k,v in w:
+        for k, v in w:
             self.assertEqual(v, self.grid_rook_w[k])
 
     def test_from_array(self):
         w = d.DistanceBand.from_array(self.grid_points, 1)
-        for k,v in w:
+        for k, v in w:
             self.assertEqual(v, self.grid_rook_w[k])
 
-    @ut.skipIf(PANDAS_EXTINCT, 'Missing pandas')
+    @ut.skipIf(PANDAS_EXTINCT, "Missing pandas")
     def test_from_dataframe(self):
         import pandas as pd
+
         geom_series = pdio.shp.shp2series(self.grid_path)
         random_data = np.random.random(size=len(geom_series))
-        df = pd.DataFrame({'obs':random_data, 'geometry':geom_series})
+        df = pd.DataFrame({"obs": random_data, "geometry": geom_series})
         w = d.DistanceBand.from_dataframe(df, 1)
-        for k,v in w:
+        for k, v in w:
             self.assertEqual(v, self.grid_rook_w[k])
 
     ##########################
@@ -164,45 +178,49 @@ def test_integers(self):
         """
         see issue #126
         """
-        grid_integers = [tuple(map(int, poly.vertices[0])) 
-                              for poly in self.grid_f]
+        grid_integers = [tuple(map(int, poly.vertices[0])) for poly in self.grid_f]
         self.grid_f.seek(0)
         grid_dbw = d.DistanceBand(grid_integers, 1)
-        for k,v in grid_dbw:
+        for k, v in grid_dbw:
             self.assertEqual(v, self.grid_rook_w[k])
 
     def test_arcdist(self):
         arc = cg.sphere.arcdist
-        kdt = KDTree(self.arc_points, distance_metric='Arc',
-                     radius=cg.sphere.RADIUS_EARTH_KM)
+        kdt = KDTree(
+            self.arc_points, distance_metric="Arc", radius=cg.sphere.RADIUS_EARTH_KM
+        )
         npoints = self.arc_points.shape[0]
-        full = np.matrix([[arc(self.arc_points[i], self.arc_points[j])
-                          for j in range(npoints)] 
-                          for i in range(npoints)])
+        full = np.array(
+            [
+                [arc(self.arc_points[i], self.arc_points[j]) for j in range(npoints)]
+                for i in range(npoints)
+            ]
+        )
         maxdist = full.max()
         w = d.DistanceBand(kdt, maxdist, binary=False, alpha=1.0)
         np.testing.assert_allclose(w.sparse.todense(), full)
         self.assertEqual(w.data.shape[1], 3)
 
     def test_dense(self):
-        w_rook = c.Rook.from_shapefile(
-                pysal_examples.get_path('lattice10x10.shp'))
-        polys = psopen(pysal_examples.get_path('lattice10x10.shp'))
+        w_rook = c.Rook.from_shapefile(pysal_examples.get_path("lattice10x10.shp"))
+        polys = psopen(pysal_examples.get_path("lattice10x10.shp"))
         centroids = [p.centroid for p in polys]
         w_db = d.DistanceBand(centroids, 1, build_sp=False)
 
         for k in w_db.id_order:
             np.testing.assert_equal(w_db[k], w_rook[k])
-    
-    @ut.skipIf(PANDAS_EXTINCT, 'Missing pandas')
+
+    @ut.skipIf(PANDAS_EXTINCT, "Missing pandas")
     def test_named(self):
         import pandas as pd
+
         geom_series = pdio.shp.shp2series(self.grid_path)
         random_data = np.random.random(size=len(geom_series))
-        names = [chr(x) for x in range(60,160)]
-        df = pd.DataFrame({'obs':random_data, 'geometry':geom_series, 'names':names})
+        names = [chr(x) for x in range(60, 160)]
+        df = pd.DataFrame({"obs": random_data, "geometry": geom_series, "names": names})
         w = d.DistanceBand.from_dataframe(df, 1, ids=df.names)
 
+
 class Test_Kernel(ut.TestCase, Distance_Mixin):
     def setUp(self):
 
@@ -211,37 +229,48 @@ def setUp(self):
         self.known_w0 = {0: 1, 1: 0.500000049999995, 3: 0.4409830615267465}
 
         self.known_wi1 = 0
-        self.known_w1 = {0: 1.0, 1: 0.33333333333333337,
-                         3: 0.2546440075000701}
-        self.known_w1_bw = 15.
+        self.known_w1 = {0: 1.0, 1: 0.33333333333333337, 3: 0.2546440075000701}
+        self.known_w1_bw = 15.0
 
         self.known_wi2 = 0
-        self.known_w2 = {0: 1.0, 1: 0.59999999999999998,
-                         3: 0.55278640450004202, 4: 0.10557280900008403}
+        self.known_w2 = {
+            0: 1.0,
+            1: 0.59999999999999998,
+            3: 0.55278640450004202,
+            4: 0.10557280900008403,
+        }
         self.known_w2_bws = [25.0, 15.0, 25.0, 16.0, 14.5, 25.0]
 
         self.known_wi3 = 0
         self.known_w3 = [1.0, 0.10557289844279438, 9.9999990066379496e-08]
-        self.known_w3_abws =[[11.180341005532938], [11.180341005532938],
-                             [20.000002000000002], [11.180341005532938],
-                             [14.142137037944515], [18.027758180095585]]
+        self.known_w3_abws = [
+            [11.180341005532938],
+            [11.180341005532938],
+            [20.000002000000002],
+            [11.180341005532938],
+            [14.142137037944515],
+            [18.027758180095585],
+        ]
 
         self.known_wi4 = 0
-        self.known_w4 = {0: 0.3989422804014327,
-                         1: 0.26741902915776961,
-                         3: 0.24197074871621341}
+        self.known_w4 = {
+            0: 0.3989422804014327,
+            1: 0.26741902915776961,
+            3: 0.24197074871621341,
+        }
         self.known_w4_abws = self.known_w3_abws
 
         self.known_wi5 = 1
-        self.known_w5 = {4: 0.0070787731484506233,
-                         2: 0.2052478782400463,
-                         3: 0.23051223027663237,
-                         1: 1.0}
+        self.known_w5 = {
+            4: 0.0070787731484506233,
+            2: 0.2052478782400463,
+            3: 0.23051223027663237,
+            1: 1.0,
+        }
 
         self.known_wi6 = 0
-        self.known_w6 = {0: 1.0, 2: 0.03178906767736345,
-                         1: 9.9999990066379496e-08}
-        #stick answers & params here
+        self.known_w6 = {0: 1.0, 2: 0.03178906767736345, 1: 9.9999990066379496e-08}
+        # stick answers & params here
 
     ##########################
     # Classmethod tests      #
@@ -249,68 +278,74 @@ def setUp(self):
 
     def test_init(self):
         w = d.Kernel(self.euclidean_kdt)
-        for k,v in list(w[self.known_wi0].items()):
+        for k, v in list(w[self.known_wi0].items()):
             np.testing.assert_allclose(v, self.known_w0[k], rtol=RTOL)
 
     def test_from_shapefile(self):
-        w = d.Kernel.from_shapefile(self.polygon_path, idVariable='POLYID')
-        for k,v in list(w[self.known_wi5].items()):
-            np.testing.assert_allclose((k,v), (k,self.known_w5[k]), rtol=RTOL)
-        
+        w = d.Kernel.from_shapefile(self.polygon_path, idVariable="POLYID")
+        for k, v in list(w[self.known_wi5].items()):
+            np.testing.assert_allclose((k, v), (k, self.known_w5[k]), rtol=RTOL)
+
         w = d.Kernel.from_shapefile(self.polygon_path, fixed=False)
-        for k,v in list(w[self.known_wi6].items()):
-            np.testing.assert_allclose((k,v), (k,self.known_w6[k]), rtol=RTOL)
+        for k, v in list(w[self.known_wi6].items()):
+            np.testing.assert_allclose((k, v), (k, self.known_w6[k]), rtol=RTOL)
 
     def test_from_array(self):
         w = d.Kernel.from_array(self.points)
-        for k,v in list(w[self.known_wi0].items()):
+        for k, v in list(w[self.known_wi0].items()):
             np.testing.assert_allclose(v, self.known_w0[k], rtol=RTOL)
-    
-    @ut.skipIf(PANDAS_EXTINCT, 'Missing pandas')
+
+    @ut.skipIf(PANDAS_EXTINCT, "Missing pandas")
     def test_from_dataframe(self):
         df = pdio.read_files(self.polygon_path)
         w = d.Kernel.from_dataframe(df)
-        for k,v in list(w[self.known_wi5-1].items()):
-            np.testing.assert_allclose(v, self.known_w5[k+1], rtol=RTOL)
-    
+        for k, v in list(w[self.known_wi5 - 1].items()):
+            np.testing.assert_allclose(v, self.known_w5[k + 1], rtol=RTOL)
+
     ##########################
-    # Function/User tests    # 
+    # Function/User tests    #
     ##########################
 
     def test_fixed_bandwidth(self):
         w = d.Kernel(self.points, bandwidth=15.0)
-        for k,v in list(w[self.known_wi1].items()):
-            np.testing.assert_allclose((k,v), (k, self.known_w1[k]))
-        np.testing.assert_allclose(np.ones((w.n,1))*15, w.bandwidth)
+        for k, v in list(w[self.known_wi1].items()):
+            np.testing.assert_allclose((k, v), (k, self.known_w1[k]))
+        np.testing.assert_allclose(np.ones((w.n, 1)) * 15, w.bandwidth)
 
         w = d.Kernel(self.points, bandwidth=self.known_w2_bws)
-        for k,v in list(w[self.known_wi2].items()):
-            np.testing.assert_allclose((k,v), (k, self.known_w2[k]), rtol=RTOL)
+        for k, v in list(w[self.known_wi2].items()):
+            np.testing.assert_allclose((k, v), (k, self.known_w2[k]), rtol=RTOL)
         for i in range(w.n):
             np.testing.assert_allclose(w.bandwidth[i], self.known_w2_bws[i], rtol=RTOL)
-    
+
     def test_adaptive_bandwidth(self):
         w = d.Kernel(self.points, fixed=False)
-        np.testing.assert_allclose(sorted(w[self.known_wi3].values()),
-                                   sorted(self.known_w3), rtol=RTOL)
+        np.testing.assert_allclose(
+            sorted(w[self.known_wi3].values()), sorted(self.known_w3), rtol=RTOL
+        )
         bws = w.bandwidth.tolist()
         np.testing.assert_allclose(bws, self.known_w3_abws, rtol=RTOL)
 
-        w = d.Kernel(self.points, fixed=False, function='gaussian')
-        for k,v in list(w[self.known_wi4].items()):
-            np.testing.assert_allclose((k,v), (k, self.known_w4[k]), rtol=RTOL)
+        w = d.Kernel(self.points, fixed=False, function="gaussian")
+        for k, v in list(w[self.known_wi4].items()):
+            np.testing.assert_allclose((k, v), (k, self.known_w4[k]), rtol=RTOL)
         bws = w.bandwidth.tolist()
         np.testing.assert_allclose(bws, self.known_w4_abws, rtol=RTOL)
 
     def test_arcdistance(self):
-        w = d.Kernel(self.points, fixed=True, distance_metric='Arc', 
-                     radius=cg.sphere.RADIUS_EARTH_KM)
+        w = d.Kernel(
+            self.points,
+            fixed=True,
+            distance_metric="Arc",
+            radius=cg.sphere.RADIUS_EARTH_KM,
+        )
         self.assertEqual(w.data.shape[1], 3)
 
+
 knn = ut.TestLoader().loadTestsFromTestCase(Test_KNN)
 kern = ut.TestLoader().loadTestsFromTestCase(Test_Kernel)
 db = ut.TestLoader().loadTestsFromTestCase(Test_DistanceBand)
 suite = ut.TestSuite([knn, kern, db])
-if __name__ == '__main__':
+if __name__ == "__main__":
     runner = ut.TextTestRunner()
     runner.run(suite)

From 8ff9ed1377aba6e8343f8defdd35f7c6d336b68e Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Wed, 29 Apr 2020 17:19:35 +0100
Subject: [PATCH 003/162] minor optimization of dict zip construction

---
 libpysal/weights/distance.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/libpysal/weights/distance.py b/libpysal/weights/distance.py
index 969ff6c9b..efd38a296 100644
--- a/libpysal/weights/distance.py
+++ b/libpysal/weights/distance.py
@@ -119,7 +119,7 @@ def __init__(
         if ids is None:
             ids = list(full_indices)
 
-        neighbors = dict(zip(ids, map(list, not_self_indices)))
+        neighbors = {idx: list(indices) for idx, indices in zip(ids, not_self_indices)}
 
         W.__init__(self, neighbors, id_order=ids, **kwargs)
 

From bf5ad0a310180c99ca16bc667e6d487cdef05171 Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Mon, 4 May 2020 13:49:57 +0100
Subject: [PATCH 004/162] handle the k+1 overdraw

---
 libpysal/weights/distance.py | 18 +++++++++++++++---
 1 file changed, 15 insertions(+), 3 deletions(-)

diff --git a/libpysal/weights/distance.py b/libpysal/weights/distance.py
index efd38a296..246c9831a 100644
--- a/libpysal/weights/distance.py
+++ b/libpysal/weights/distance.py
@@ -110,16 +110,28 @@ def __init__(
             self.data = self.kdtree.data
         self.k = k
         self.p = p
+        # these are both n x k+1
         distances, indices = self.kdtree.query(self.data, k=k + 1, p=p)
         full_indices = np.arange(self.kdtree.n)
+
+        # if an element in the indices matrix is equal to the corresponding
+        # index for that row, we want to mask that site from its neighbors
         not_self_mask = indices != full_indices.reshape(-1, 1)
-        not_self_indices = indices[not_self_mask].reshape(self.kdtree.n, k)
+        # if there are *too many duplicates per site*, then we may get some
+        # rows where the site index is not in the set of k+1 neighbors
+        # So, we need to know where these sites are
+        has_one_too_many = not_self_mask.sum(axis=1) == (k + 1)
+        # if a site has k+1 neighbors, drop its k+1th neighbor
+        not_self_mask[has_one_too_many, -1] &= False
+        not_self_indices = indices[not_self_mask].reshape(self.kdtree.n, -1)
 
         to_weight = not_self_indices
         if ids is None:
             ids = list(full_indices)
-
-        neighbors = {idx: list(indices) for idx, indices in zip(ids, not_self_indices)}
+            named_indices = not_self_indices
+        else:
+            named_indices = np.asarray(ids)[not_self_indices]
+        neighbors = {idx: list(indices) for idx, indices in zip(ids, named_indices)}
 
         W.__init__(self, neighbors, id_order=ids, **kwargs)
 

From 56c7b4f4d85ab104e3ef5d72b17488df99f5c793 Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Tue, 14 Jul 2020 18:40:39 +0100
Subject: [PATCH 005/162] blacken codebase on first pass

---
 libpysal/weights/weights.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/libpysal/weights/weights.py b/libpysal/weights/weights.py
index 44917cc5e..225c94719 100644
--- a/libpysal/weights/weights.py
+++ b/libpysal/weights/weights.py
@@ -154,7 +154,7 @@ def __init__(
             self._id_order_set = True
         self._reset()
         self._n = len(self.weights)
-        if not self.silence_warnings and self.n_components > 1:
+        if (not self.silence_warnings) and (self.n_components > 1):
             message = (
                 "The weights matrix is not fully connected: "
                 "\n There are %d disconnected components." % self.n_components
@@ -877,9 +877,9 @@ def neighbor_offsets(self):
             for j, neigh_list in list(self.neighbors.items()):
                 self.__neighbors_0[j] = [id2i[neigh] for neigh in neigh_list]
             self._cache["neighbors_0"] = self.__neighbors_0
-        
+
         neighbor_list = self.__neighbors_0
-        
+
         return neighbor_list
 
     def get_transform(self):

From b090f95120cdb1c9d0294d735cad09e49c35b3df Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Tue, 14 Jul 2020 18:41:19 +0100
Subject: [PATCH 006/162] fix error handling for import error on pandas

---
 libpysal/weights/weights.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/libpysal/weights/weights.py b/libpysal/weights/weights.py
index 225c94719..348d0fcb8 100644
--- a/libpysal/weights/weights.py
+++ b/libpysal/weights/weights.py
@@ -301,8 +301,10 @@ def to_adjlist(
         """
         try:
             import pandas as pd
-        except ImportError:
-            raise ImportError("pandas must be installed to use this method")
+        except (ImportError, ModuleNotFoundError):
+            raise ImportError(
+                "pandas must be installed & importable to use this method"
+            )
         n_islands = len(self.islands)
         if n_islands > 0 and (not self.silence_warnings):
             warnings.warn(

From d47b3763eb65d188bfcfb37b0547e2a2a4560382 Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Tue, 14 Jul 2020 18:41:33 +0100
Subject: [PATCH 007/162] add format for island warnings

---
 libpysal/weights/weights.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/libpysal/weights/weights.py b/libpysal/weights/weights.py
index 348d0fcb8..f54279bda 100644
--- a/libpysal/weights/weights.py
+++ b/libpysal/weights/weights.py
@@ -309,7 +309,7 @@ def to_adjlist(
         if n_islands > 0 and (not self.silence_warnings):
             warnings.warn(
                 "{} islands in this weights matrix. Conversion to an "
-                "adjacency list will drop these observations!"
+                "adjacency list will drop these observations!".format(len(self.islands))
             )
         adjlist = pd.DataFrame(
             ((idx, n, w) for idx, neighb in self for n, w in list(neighb.items())),

From 8ca54f4e70be307fff93a758f1b075a4b7588f02 Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Tue, 14 Jul 2020 18:41:48 +0100
Subject: [PATCH 008/162] actually handle islands in adjlist builder

---
 libpysal/weights/weights.py | 13 +++++++++----
 1 file changed, 9 insertions(+), 4 deletions(-)

diff --git a/libpysal/weights/weights.py b/libpysal/weights/weights.py
index f54279bda..c476d6e4f 100644
--- a/libpysal/weights/weights.py
+++ b/libpysal/weights/weights.py
@@ -311,10 +311,15 @@ def to_adjlist(
                 "{} islands in this weights matrix. Conversion to an "
                 "adjacency list will drop these observations!".format(len(self.islands))
             )
-        adjlist = pd.DataFrame(
-            ((idx, n, w) for idx, neighb in self for n, w in list(neighb.items())),
-            columns=("focal", "neighbor", "weight"),
-        )
+        links = []
+        for idx, neighb in self:
+            if len(neighb) == 0:
+                links.append((idx, None, numpy.nan))
+                continue
+            for n, w in neighb.items():
+                links.append((idx, n, w))
+        adjlist = pd.DataFrame(links, columns=[focal_col, neighbor_col, weight_col])
+
         return adjtools.filter_adjlist(adjlist) if remove_symmetric else adjlist
 
     def to_networkx(self):

From f64530614d8edeae19531679ac9e2ad004515125 Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Tue, 14 Jul 2020 18:42:11 +0100
Subject: [PATCH 009/162] remove now-unnecessary warning about dropping islands

---
 libpysal/weights/weights.py | 5 -----
 1 file changed, 5 deletions(-)

diff --git a/libpysal/weights/weights.py b/libpysal/weights/weights.py
index c476d6e4f..d94e423c3 100644
--- a/libpysal/weights/weights.py
+++ b/libpysal/weights/weights.py
@@ -306,11 +306,6 @@ def to_adjlist(
                 "pandas must be installed & importable to use this method"
             )
         n_islands = len(self.islands)
-        if n_islands > 0 and (not self.silence_warnings):
-            warnings.warn(
-                "{} islands in this weights matrix. Conversion to an "
-                "adjacency list will drop these observations!".format(len(self.islands))
-            )
         links = []
         for idx, neighb in self:
             if len(neighb) == 0:

From 3ff503c91185470d9c8ca2fba911f19134dd0d17 Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Tue, 14 Jul 2020 18:54:05 +0100
Subject: [PATCH 010/162] np, not numpy

---
 libpysal/weights/weights.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/libpysal/weights/weights.py b/libpysal/weights/weights.py
index d94e423c3..fc21d2539 100644
--- a/libpysal/weights/weights.py
+++ b/libpysal/weights/weights.py
@@ -309,7 +309,7 @@ def to_adjlist(
         links = []
         for idx, neighb in self:
             if len(neighb) == 0:
-                links.append((idx, None, numpy.nan))
+                links.append((idx, None, np.nan))
                 continue
             for n, w in neighb.items():
                 links.append((idx, n, w))

From 4550580426016bb121358e5b5bfd442480b4481f Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Tue, 14 Jul 2020 19:09:50 +0100
Subject: [PATCH 011/162] further blackening

---
 libpysal/weights/weights.py | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/libpysal/weights/weights.py b/libpysal/weights/weights.py
index fc21d2539..24e74f59f 100644
--- a/libpysal/weights/weights.py
+++ b/libpysal/weights/weights.py
@@ -175,7 +175,7 @@ def _reset(self):
         """Reset properties."""
         self._cache = {}
 
-    def to_file(self, path='', format=None):
+    def to_file(self, path="", format=None):
         """
         Write a weights to a file. The format is guessed automatically 
         from the path, but can be overridden with the format argument. 
@@ -194,13 +194,12 @@ def to_file(self, path='', format=None):
         -------
         None
         """
-        f = popen(dataPath=path, mode='w', dataFormat=format)
+        f = popen(dataPath=path, mode="w", dataFormat=format)
         f.write(self)
         f.close()
-        
 
     @classmethod
-    def from_file(cls, path='', format=None):
+    def from_file(cls, path="", format=None):
         """
         Read a weights file into a W object. 
 
@@ -215,7 +214,7 @@ def from_file(cls, path='', format=None):
         -------
         W object
         """
-        f = popen(dataPath=path, mode='r', dataFormat=format)
+        f = popen(dataPath=path, mode="r", dataFormat=format)
         w = f.read()
         f.close()
         return w

From 708eae8ba2028a8247014d7dca0c4f0f109a3fdc Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Tue, 14 Jul 2020 19:10:09 +0100
Subject: [PATCH 012/162] solidify on zero-weight self-neighbor as the encoding
 for islands

---
 libpysal/weights/weights.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/libpysal/weights/weights.py b/libpysal/weights/weights.py
index 24e74f59f..165cab750 100644
--- a/libpysal/weights/weights.py
+++ b/libpysal/weights/weights.py
@@ -308,7 +308,7 @@ def to_adjlist(
         links = []
         for idx, neighb in self:
             if len(neighb) == 0:
-                links.append((idx, None, np.nan))
+                links.append((idx, idx, 0))
                 continue
             for n, w in neighb.items():
                 links.append((idx, n, w))

From e8fd4c067f6ca020498bfebf496d902bd931cc85 Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Tue, 14 Jul 2020 19:36:24 +0100
Subject: [PATCH 013/162] finaliz roundtrippable from_adjlist()

---
 libpysal/weights/weights.py | 14 ++++++++------
 1 file changed, 8 insertions(+), 6 deletions(-)

diff --git a/libpysal/weights/weights.py b/libpysal/weights/weights.py
index 165cab750..bb67699f3 100644
--- a/libpysal/weights/weights.py
+++ b/libpysal/weights/weights.py
@@ -262,13 +262,15 @@ def from_adjlist(
         if try_weightcol is None:
             adjlist = adjlist.copy(deep=True)
             adjlist["weight"] = 1
-        all_ids = set(adjlist[focal_col].tolist())
-        all_ids |= set(adjlist[neighbor_col].tolist())
         grouper = adjlist.groupby(focal_col)
-        neighbors = grouper[neighbor_col].apply(list).to_dict()
-        weights = grouper[weight_col].apply(list).to_dict()
-        neighbors.update({k: [] for k in all_ids.difference(list(neighbors.keys()))})
-        weights.update({k: [] for k in all_ids.difference(list(weights.keys()))})
+        neighbors = dict()
+        weights = dict()
+        for ix, chunk in grouper:
+            neighbors_to_ix = chunk[neighbor_col].values
+            weights_to_ix = chunk[weight_col].values
+            mask = neighbors_to_ix != ix
+            neighbors[ix] = neighbors_to_ix[mask].tolist()
+            weights[ix] = weights_to_ix[mask].tolist()
         return cls(neighbors=neighbors, weights=weights)
 
     def to_adjlist(

From f6da1e506ae906b32fd6beb1bf67da281edb77b3 Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Wed, 15 Jul 2020 15:50:30 +0100
Subject: [PATCH 014/162] resolve testing to workaround libpysal#322

---
 libpysal/weights/tests/test_adjlist.py | 15 ++++++++++++---
 1 file changed, 12 insertions(+), 3 deletions(-)

diff --git a/libpysal/weights/tests/test_adjlist.py b/libpysal/weights/tests/test_adjlist.py
index 5ae9c886d..f5b9523d3 100644
--- a/libpysal/weights/tests/test_adjlist.py
+++ b/libpysal/weights/tests/test_adjlist.py
@@ -31,7 +31,12 @@ def test_filter(self):
         alist = grid.to_adjlist(remove_symmetric=True)
         assert len(alist) == 4
         with self.assertRaises(AssertionError):
-            badgrid = weights.W.from_adjlist(alist)
+            # build this manually because of bug libpysal#322
+            alist_neighbors = alist.groupby('focal').neighbor.apply(list).to_dict()
+            all_ids = set(alist_neighbors.keys()).union(*map(set, alist_neighbors.values()))
+            for idx in set(all_ids).difference(set(alist_neighbors.keys())):
+                alist_neighbors[idx] = []
+            badgrid = weights.W(alist_neighbors)
             np.testing.assert_allclose(badgrid.sparse.toarray(),
                                        grid.sparse.toarray())
         assert set(alist.focal.unique()) == {0, 1, 2}
@@ -41,10 +46,14 @@ def test_filter(self):
         alist = grid.to_adjlist(remove_symmetric=True)
         assert len(alist) == 4
         with self.assertRaises(AssertionError):
-            badgrid = weights.W.from_adjlist(alist)
+            # build this manually because of bug libpysal#322
+            alist_neighbors = alist.groupby('focal').neighbor.apply(list).to_dict()
+            all_ids = set(alist_neighbors.keys()).union(*map(set, alist_neighbors.values()))
+            for idx in set(all_ids).difference(set(alist_neighbors.keys())):
+                alist_neighbors[idx] = []
+            badgrid = weights.W(alist_neighbors)
             np.testing.assert_allclose(badgrid.sparse.toarray(),
                                        grid.sparse.toarray())
-        print(alist)
         tuples = set([tuple(t) for t in alist[['focal','neighbor']].values])
         full_alist = grid.to_adjlist()
         all_possible = set([tuple(t) for t in full_alist[['focal','neighbor']].values])

From 4b39c17659fcfa3f7a6fef19e3ffe78184c5f96c Mon Sep 17 00:00:00 2001
From: James Gaboardi <jgaboardi@gmail.com>
Date: Thu, 16 Jul 2020 11:34:40 -0400
Subject: [PATCH 015/162] partial blacken for test commit

--trying to force push @ljwolf 's code to GHA
---
 libpysal/weights/tests/test_adjlist.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/libpysal/weights/tests/test_adjlist.py b/libpysal/weights/tests/test_adjlist.py
index f5b9523d3..e4775a5ea 100644
--- a/libpysal/weights/tests/test_adjlist.py
+++ b/libpysal/weights/tests/test_adjlist.py
@@ -15,10 +15,10 @@
 except ImportError:
     PANDAS_MISSING = True
 
-@ut.skipIf(PANDAS_MISSING, 'Pandas is gone')
+@ut.skipIf(PANDAS_MISSING, "Pandas is gone")
 class Test_Adjlist(ut.TestCase):
     def setUp(self):
-        self.knownW = io.open(examples.get_path('columbus.gal')).read()
+        self.knownW = io.open(examples.get_path("columbus.gal")).read()
 
     def test_round_trip(self):
         adjlist = self.knownW.to_adjlist(remove_symmetric=False).astype(int)

From 7e05c2a593432fc309040917b9aa673373972969 Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Fri, 7 Aug 2020 16:29:32 +0100
Subject: [PATCH 016/162] add response to review

---
 libpysal/weights/weights.py | 128 ++++++++++++++++--------------------
 1 file changed, 55 insertions(+), 73 deletions(-)

diff --git a/libpysal/weights/weights.py b/libpysal/weights/weights.py
index bb67699f3..43f638109 100644
--- a/libpysal/weights/weights.py
+++ b/libpysal/weights/weights.py
@@ -26,7 +26,7 @@ class W(object):
 
     Parameters
     ----------
-    
+
     neighbors : dict
         Key is region ID, value is a list of neighbor IDS.
         For example, ``{'a':['b'],'b':['a','c'],'c':['b']}``.
@@ -48,7 +48,7 @@ class W(object):
 
     Attributes
     ----------
-    
+
     asymmetries
     cardinalities
     component_labels
@@ -81,7 +81,7 @@ class W(object):
 
     Examples
     --------
-    
+
     >>> from libpysal.weights import W
     >>> neighbors = {0: [3, 1], 1: [0, 4, 2], 2: [1, 5], 3: [0, 6, 4], 4: [1, 3, 7, 5], 5: [2, 4, 8], 6: [3, 7], 7: [4, 6, 8], 8: [5, 7]}
     >>> weights = {0: [1, 1], 1: [1, 1, 1], 2: [1, 1], 3: [1, 1, 1], 4: [1, 1, 1, 1], 5: [1, 1, 1], 6: [1, 1], 7: [1, 1, 1], 8: [1, 1]}
@@ -117,7 +117,7 @@ class W(object):
     2533.667
 
     Cardinality Histogram:
-    
+
     >>> w.histogram
     [(2, 4), (3, 392), (4, 9604)]
 
@@ -157,11 +157,12 @@ def __init__(
         if (not self.silence_warnings) and (self.n_components > 1):
             message = (
                 "The weights matrix is not fully connected: "
-                "\n There are %d disconnected components." % self.n_components
+                "\n There are %d disconnected components."
+                % self.n_components
             )
             ni = len(self.islands)
             if ni == 1:
-                message = message + "\n There is 1 island with id: " "%s." % (
+                message = message + "\n There is 1 island with id: %s." % (
                     str(self.islands[0])
                 )
             elif ni > 1:
@@ -177,17 +178,17 @@ def _reset(self):
 
     def to_file(self, path="", format=None):
         """
-        Write a weights to a file. The format is guessed automatically 
-        from the path, but can be overridden with the format argument. 
+        Write a weights to a file. The format is guessed automatically
+        from the path, but can be overridden with the format argument.
 
-        See libpysal.io.FileIO for more information. 
+        See libpysal.io.FileIO for more information.
 
         Arguments
         ---------
         path    :   string
                     location to save the file
         format  :   string
-                    string denoting the format to write the weights to. 
+                    string denoting the format to write the weights to.
 
 
         Returns
@@ -201,14 +202,14 @@ def to_file(self, path="", format=None):
     @classmethod
     def from_file(cls, path="", format=None):
         """
-        Read a weights file into a W object. 
+        Read a weights file into a W object.
 
         Arguments
         ---------
         path    :   string
                     location to save the file
         format  :   string
-                    string denoting the format to write the weights to. 
+                    string denoting the format to write the weights to.
 
         Returns
         -------
@@ -227,8 +228,7 @@ def from_shapefile(cls, *args, **kwargs):
         # a type dispatch table. Generic W should be for stuff we don't know
         # anything about.
         raise NotImplementedError(
-            "Use type-specific constructors, like Rook,"
-            " Queen, DistanceBand, or Kernel"
+            "Use type-specific constructors, like Rook, Queen, DistanceBand, or Kernel"
         )
 
     @classmethod
@@ -244,7 +244,7 @@ def from_adjlist(
 
         Parameters
         ----------
-        
+
         adjlist : pandas.DataFrame
             Adjacency list with a minimum of two columns.
         focal_col : str
@@ -306,7 +306,6 @@ def to_adjlist(
             raise ImportError(
                 "pandas must be installed & importable to use this method"
             )
-        n_islands = len(self.islands)
         links = []
         for idx, neighb in self:
             if len(neighb) == 0:
@@ -343,7 +342,7 @@ def from_networkx(cls, graph, weight_col="weight"):
         weight_col : string
             If the graph is labeled, this should be the name of the field
             to use as the weight for the ``W``.
-        
+
         Returns
         -------
         w : libpysal.weights.W
@@ -369,8 +368,7 @@ def sparse(self):
 
     @property
     def n_components(self):
-        """Store whether the adjacency matrix is fully connected.
-        """
+        """Store whether the adjacency matrix is fully connected."""
         if "n_components" not in self._cache:
             self._n_components, self._component_labels = connected_components(
                 self.sparse
@@ -381,8 +379,7 @@ def n_components(self):
 
     @property
     def component_labels(self):
-        """Store the graph component in which each observation falls.
-        """
+        """Store the graph component in which each observation falls."""
         if "component_labels" not in self._cache:
             self._n_components, self._component_labels = connected_components(
                 self.sparse
@@ -392,8 +389,7 @@ def component_labels(self):
         return self._component_labels
 
     def _build_sparse(self):
-        """Construct the sparse attribute.
-        """
+        """Construct the sparse attribute."""
 
         row = []
         col = []
@@ -425,8 +421,7 @@ def id2i(self):
 
     @property
     def n(self):
-        """Number of units.
-        """
+        """Number of units."""
         if "n" not in self._cache:
             self._n = len(self.neighbors)
             self._cache["n"] = self._n
@@ -550,8 +545,7 @@ def trcWtW(self):
 
     @property
     def diagWtW_WW(self):
-        """Diagonal of :math:`W^{'}W + WW`.
-        """
+        """Diagonal of :math:`W^{'}W + WW`."""
         if "diagWtW_WW" not in self._cache:
             wt = self.sparse.transpose()
             w = self.sparse
@@ -561,8 +555,7 @@ def diagWtW_WW(self):
 
     @property
     def trcWtW_WW(self):
-        """Trace of :math:`W^{'}W + WW`.
-        """
+        """Trace of :math:`W^{'}W + WW`."""
         if "trcWtW_WW" not in self._cache:
             self._trcWtW_WW = self.diagWtW_WW.sum()
             self._cache["trcWtW_WW"] = self._trcWtW_WW
@@ -570,8 +563,7 @@ def trcWtW_WW(self):
 
     @property
     def pct_nonzero(self):
-        """Percentage of nonzero weights.
-        """
+        """Percentage of nonzero weights."""
         if "pct_nonzero" not in self._cache:
             self._pct_nonzero = 100.0 * self.sparse.nnz / (1.0 * self._n ** 2)
             self._cache["pct_nonzero"] = self._pct_nonzero
@@ -579,8 +571,7 @@ def pct_nonzero(self):
 
     @property
     def cardinalities(self):
-        """Number of neighbors for each observation.
-        """
+        """Number of neighbors for each observation."""
         if "cardinalities" not in self._cache:
             c = {}
             for i in self._id_order:
@@ -591,8 +582,7 @@ def cardinalities(self):
 
     @property
     def max_neighbors(self):
-        """Largest number of neighbors.
-        """
+        """Largest number of neighbors."""
         if "max_neighbors" not in self._cache:
             self._max_neighbors = max(self.cardinalities.values())
             self._cache["max_neighbors"] = self._max_neighbors
@@ -600,8 +590,7 @@ def max_neighbors(self):
 
     @property
     def mean_neighbors(self):
-        """Average number of neighbors.
-        """
+        """Average number of neighbors."""
         if "mean_neighbors" not in self._cache:
             self._mean_neighbors = np.mean(list(self.cardinalities.values()))
             self._cache["mean_neighbors"] = self._mean_neighbors
@@ -609,8 +598,7 @@ def mean_neighbors(self):
 
     @property
     def min_neighbors(self):
-        """Minimum number of neighbors.
-        """
+        """Minimum number of neighbors."""
         if "min_neighbors" not in self._cache:
             self._min_neighbors = min(self.cardinalities.values())
             self._cache["min_neighbors"] = self._min_neighbors
@@ -618,8 +606,7 @@ def min_neighbors(self):
 
     @property
     def nonzero(self):
-        """Number of nonzero weights.
-        """
+        """Number of nonzero weights."""
         if "nonzero" not in self._cache:
             self._nonzero = self.sparse.nnz
             self._cache["nonzero"] = self._nonzero
@@ -627,8 +614,7 @@ def nonzero(self):
 
     @property
     def sd(self):
-        """Standard deviation of number of neighbors.
-        """
+        """Standard deviation of number of neighbors."""
         if "sd" not in self._cache:
             self._sd = np.std(list(self.cardinalities.values()))
             self._cache["sd"] = self._sd
@@ -636,8 +622,7 @@ def sd(self):
 
     @property
     def asymmetries(self):
-        """List of id pairs with asymmetric weights.
-        """
+        """List of id pairs with asymmetric weights."""
         if "asymmetries" not in self._cache:
             self._asymmetries = self.asymmetry()
             self._cache["asymmetries"] = self._asymmetries
@@ -645,8 +630,7 @@ def asymmetries(self):
 
     @property
     def islands(self):
-        """List of ids without any neighbors.
-        """
+        """List of ids without any neighbors."""
         if "islands" not in self._cache:
             self._islands = [i for i, c in list(self.cardinalities.items()) if c == 0]
             self._cache["islands"] = self._islands
@@ -738,8 +722,8 @@ def remap_ids(self, new_ids):
         old_ids = self._id_order
         if len(old_ids) != len(new_ids):
             raise Exception(
-                "W.remap_ids: length of `old_ids` does not match \
-            that of new_ids"
+                "W.remap_ids: length of `old_ids` does not match             that of"
+                " new_ids"
             )
         if len(set(new_ids)) != len(new_ids):
             raise Exception("W.remap_ids: list `new_ids` contains duplicates")
@@ -766,8 +750,8 @@ def remap_ids(self, new_ids):
             self._reset()
 
     def __set_id_order(self, ordered_ids):
-        """Set the iteration order in w. ``W`` can be iterated over. On 
-        construction the iteration order is set to the lexicographic order of 
+        """Set the iteration order in w. ``W`` can be iterated over. On
+        construction the iteration order is set to the lexicographic order of
         the keys in the ``w.weights`` dictionary. If a specific order
         is required it can be set with this method.
 
@@ -838,7 +822,7 @@ def __get_id_order(self):
 
     @property
     def id_order_set(self):
-        """ Returns ``True`` if user has set ``id_order``, ``False`` if not.
+        """Returns ``True`` if user has set ``id_order``, ``False`` if not.
 
         Examples
         --------
@@ -891,9 +875,9 @@ def get_transform(self):
         Returns
         -------
         transformation : str, None
-            Valid transformation value. See the ``transform`` 
+            Valid transformation value. See the ``transform``
             parameters in ``set_transform()`` for a detailed description.
-            
+
         Examples
         --------
         >>> from libpysal.weights import lat2W
@@ -925,13 +909,13 @@ def set_transform(self, value="B"):
         transform : str
             This parameter is not case sensitive. The following are
             valid transformations.
-            
+
             * **B** -- Binary
             * **R** -- Row-standardization (global sum :math:`=n`)
             * **D** -- Double-standardization (global sum :math:`=1`)
             * **V** -- Variance stabilizing
             * **O** -- Restore original transformation (from instantiation)
-        
+
         Notes
         -----
 
@@ -939,7 +923,7 @@ def set_transform(self, value="B"):
         instantiation. Chaining of transformations cannot be done on a ``W``
         instance.
 
-        
+
         Examples
         --------
         >>> from libpysal.weights import lat2W
@@ -1041,17 +1025,17 @@ def asymmetry(self, intrinsic=True):
         ----------
         intrinsic : bool
             Default is ``True``. Intrinsic symmetry is defined as
-            
+
             .. math::
-                
+
                 w_{i,j} == w_{j,i}
-            
+
             If ``intrinsic`` is ``False`` symmetry is defined as
-            
-            .. math:: 
-            
+
+            .. math::
+
                 i \in N_j \ \& \ j \in N_i
-                
+
             where :math:`N_j` is the set of neighbors for :math:`j`.
 
         Returns
@@ -1222,9 +1206,9 @@ def plot(
         ----------
         gdf : geopandas.GeoDataFrame
             The original shapes whose topological relations are modelled in ``W``.
-        indexed_on : str 
+        indexed_on : str
             Column of ``geopandas.GeoDataFrame`` that the weights object uses
-            as an index. Default is ``None``, so the index of the 
+            as an index. Default is ``None``, so the index of the
             ``geopandas.GeoDataFrame`` is used.
         ax : matplotlib.axes.Axes
             Axis on which to plot the weights. Default is ``None``, so
@@ -1247,12 +1231,12 @@ def plot(
             Figure on which the plot is made.
         ax : matplotlib.axes.Axes
             Axis on which the plot is made.
-        
+
         Notes
         -----
-        If you'd like to overlay the actual shapes from the 
+        If you'd like to overlay the actual shapes from the
         ``geopandas.GeoDataFrame``, call ``gdf.plot(ax=ax)`` after this.
-        To plot underneath, adjust the z-order of the plot as follows: 
+        To plot underneath, adjust the z-order of the plot as follows:
         ``gdf.plot(ax=ax,zorder=0)``.
 
         Examples
@@ -1388,8 +1372,7 @@ def s0(self):
 
     @property
     def trcWtW_WW(self):
-        """Trace of :math:`W^{'}W + WW`.
-        """
+        """Trace of :math:`W^{'}W + WW`."""
         if "trcWtW_WW" not in self._cache:
             self._trcWtW_WW = self.diagWtW_WW.sum()
             self._cache["trcWtW_WW"] = self._trcWtW_WW
@@ -1397,8 +1380,7 @@ def trcWtW_WW(self):
 
     @property
     def diagWtW_WW(self):
-        """Diagonal of :math:`W^{'}W + WW`.
-        """
+        """Diagonal of :math:`W^{'}W + WW`."""
         if "diagWtW_WW" not in self._cache:
             wt = self.sparse.transpose()
             w = self.sparse

From 4ab932cd0bc103cc56ee320b553eb3de0386117f Mon Sep 17 00:00:00 2001
From: James Gaboardi <jgaboardi@gmail.com>
Date: Tue, 2 Feb 2021 09:40:22 -0500
Subject: [PATCH 017/162] add docs action

---
 .github/workflows/build_docs.yml | 61 ++++++++++++++++++++++++++++++++
 1 file changed, 61 insertions(+)
 create mode 100644 .github/workflows/build_docs.yml

diff --git a/.github/workflows/build_docs.yml b/.github/workflows/build_docs.yml
new file mode 100644
index 000000000..bbd912026
--- /dev/null
+++ b/.github/workflows/build_docs.yml
@@ -0,0 +1,61 @@
+
+ name: Build Docs
+ on:
+   push:
+     branches:
+     - main
+ jobs:
+   docs:
+     name: Build & Push Docs
+     runs-on: ${{ matrix.os }}
+     timeout-minutes: 90
+     strategy:
+       matrix:
+         os: ['ubuntu-latest']
+         environment-file: [ci/38.yaml]
+         experimental: [false]
+     defaults:
+       run:
+         shell: bash -l {0}
+     steps:
+       - uses: actions/checkout@v2
+       - uses: actions/cache@v2
+         env:
+           CACHE_NUMBER: 0
+         with:
+           path: ~/conda_pkgs_dir
+           key: ${{ matrix.os }}-conda-${{ env.CACHE_NUMBER }}-${{ hashFiles(matrix.environment-file) }}
+       - uses: conda-incubator/setup-miniconda@v2
+         with:
+            miniconda-version: 'latest'
+            mamba-version: '*'
+            channels: conda-forge
+            channel-priority: true
+            auto-update-conda: false
+            auto-activate-base: false
+            environment-file: ${{ matrix.environment-file }}
+            activate-environment: test
+            use-only-tar-bz2: true
+       - run: mamba info --all
+       - run: mamba list
+       - run: conda config --show-sources
+       - run: conda config --show
+       - name: Make Docs
+         run: cd docs; make html
+       - name: Commit Docs
+         run: |
+           git clone https://github.com/ammaraskar/sphinx-action-test.git --branch gh-pages --single-branch gh-pages
+           cp -r docs/_build/html/* gh-pages/
+           cd gh-pages
+           git config --local user.email "action@github.com"
+           git config --local user.name "GitHub Action"
+           git add .
+           git commit -m "Update documentation" -a || true
+           # The above command will fail if no changes were present,
+           # so we ignore the return code.
+       - uses: ad-m/github-push-action@master
+         with:
+            branch: gh-pages
+            directory: gh-pages
+            github_token: ${{ secrets.GITHUB_TOKEN }}
+            force: true

From 76531b095fc886761798c4812a0bdcd579e14e68 Mon Sep 17 00:00:00 2001
From: James Gaboardi <jgaboardi@gmail.com>
Date: Tue, 2 Feb 2021 09:40:49 -0500
Subject: [PATCH 018/162] add docs deps to ci/38 to build action

---
 ci/38.yaml | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/ci/38.yaml b/ci/38.yaml
index 3fcbc99d3..21e0728ba 100644
--- a/ci/38.yaml
+++ b/ci/38.yaml
@@ -18,3 +18,10 @@ dependencies:
   - xarray
   - joblib
   - zstd
+  # for docs build action (this env only)
+  - nbsphinx
+  - numpydoc
+  - sphinx>=1.4.3
+  - sphinxcontrib-bibtex<2.0.0
+  - sphinx_bootstrap_theme
+  

From 707aeb394d73a65094f71474172e2f4b0a1ae93b Mon Sep 17 00:00:00 2001
From: James Gaboardi <jgaboardi@gmail.com>
Date: Tue, 2 Feb 2021 09:41:50 -0500
Subject: [PATCH 019/162] git remove docsrc/ dir

---
 docsrc/Makefile                               |   31 -
 docsrc/_static/images/neighboorsetLIMA_US.png |  Bin 654235 -> 0 bytes
 docsrc/_static/images/npweights.png           |  Bin 517599 -> 0 bytes
 docsrc/_static/images/pysal_favicon.ico       |  Bin 32038 -> 0 bytes
 docsrc/_static/images/rose_conditional.png    |  Bin 35347 -> 0 bytes
 docsrc/_static/images/spatial_markov_us.png   |  Bin 134429 -> 0 bytes
 docsrc/_static/pysal-styles.css               |   73 -
 docsrc/_static/references.bib                 |   39 -
 docsrc/api.rst                                |  248 ----
 docsrc/conf.py                                |  288 ----
 docsrc/generated/libpysal.cg.Chain.rst        |   33 -
 docsrc/generated/libpysal.cg.Grid.rst         |   28 -
 docsrc/generated/libpysal.cg.KDTree.rst       |    6 -
 docsrc/generated/libpysal.cg.Line.rst         |   24 -
 docsrc/generated/libpysal.cg.LineSegment.rst  |   37 -
 docsrc/generated/libpysal.cg.Point.rst        |   22 -
 docsrc/generated/libpysal.cg.PointLocator.rst |   27 -
 docsrc/generated/libpysal.cg.Polygon.rst      |   38 -
 .../generated/libpysal.cg.PolygonLocator.rst  |   28 -
 .../generated/libpysal.cg.RADIUS_EARTH_KM.rst |    6 -
 .../libpysal.cg.RADIUS_EARTH_MILES.rst        |    6 -
 docsrc/generated/libpysal.cg.Ray.rst          |   22 -
 docsrc/generated/libpysal.cg.Rectangle.rst    |   32 -
 docsrc/generated/libpysal.cg.alpha_shape.rst  |    6 -
 .../libpysal.cg.alpha_shape_auto.rst          |    6 -
 docsrc/generated/libpysal.cg.arcdist.rst      |    6 -
 .../generated/libpysal.cg.arcdist2linear.rst  |    6 -
 docsrc/generated/libpysal.cg.asShape.rst      |    6 -
 docsrc/generated/libpysal.cg.bbcommon.rst     |    6 -
 docsrc/generated/libpysal.cg.brute_knn.rst    |    6 -
 docsrc/generated/libpysal.cg.convex_hull.rst  |    6 -
 .../generated/libpysal.cg.distance_matrix.rst |    6 -
 docsrc/generated/libpysal.cg.fast_knn.rst     |    6 -
 .../generated/libpysal.cg.fast_threshold.rst  |    6 -
 docsrc/generated/libpysal.cg.geogrid.rst      |    6 -
 .../generated/libpysal.cg.geointerpolate.rst  |    6 -
 .../libpysal.cg.get_angle_between.rst         |    6 -
 .../libpysal.cg.get_bounding_box.rst          |    6 -
 ...ibpysal.cg.get_point_at_angle_and_dist.rst |    6 -
 .../generated/libpysal.cg.get_points_dist.rst |    6 -
 .../libpysal.cg.get_polygon_point_dist.rst    |    6 -
 ...ibpysal.cg.get_polygon_point_intersect.rst |    6 -
 .../libpysal.cg.get_ray_segment_intersect.rst |    6 -
 ...pysal.cg.get_rectangle_point_intersect.rst |    6 -
 ...g.get_rectangle_rectangle_intersection.rst |    6 -
 .../libpysal.cg.get_segment_point_dist.rst    |    6 -
 ...ibpysal.cg.get_segment_point_intersect.rst |    6 -
 .../libpysal.cg.get_segments_intersect.rst    |    6 -
 .../libpysal.cg.get_shared_segments.rst       |    6 -
 docsrc/generated/libpysal.cg.harcdist.rst     |    6 -
 docsrc/generated/libpysal.cg.is_clockwise.rst |    6 -
 docsrc/generated/libpysal.cg.is_collinear.rst |    6 -
 .../generated/libpysal.cg.linear2arcdist.rst  |    6 -
 docsrc/generated/libpysal.cg.lonlat.rst       |    6 -
 .../libpysal.cg.point_touches_rectangle.rst   |    6 -
 docsrc/generated/libpysal.cg.toLngLat.rst     |    6 -
 docsrc/generated/libpysal.cg.toXYZ.rst        |    6 -
 .../generated/libpysal.cg.voronoi_frames.rst  |    6 -
 .../generated/libpysal.examples.available.rst |    6 -
 .../generated/libpysal.examples.explain.rst   |    6 -
 .../generated/libpysal.examples.get_path.rst  |    6 -
 .../generated/libpysal.io.fileio.FileIO.rst   |   42 -
 docsrc/generated/libpysal.io.open.rst         |   42 -
 .../libpysal.weights.DistanceBand.rst         |   75 -
 docsrc/generated/libpysal.weights.KNN.rst     |   76 -
 docsrc/generated/libpysal.weights.Kernel.rst  |   75 -
 docsrc/generated/libpysal.weights.ODW.rst     |    6 -
 docsrc/generated/libpysal.weights.Queen.rst   |   76 -
 docsrc/generated/libpysal.weights.Rook.rst    |   76 -
 docsrc/generated/libpysal.weights.Voronoi.rst |    6 -
 docsrc/generated/libpysal.weights.W.rst       |   73 -
 docsrc/generated/libpysal.weights.WSP.rst     |   33 -
 docsrc/generated/libpysal.weights.WSP2W.rst   |    6 -
 .../libpysal.weights.attach_islands.rst       |    6 -
 .../libpysal.weights.block_weights.rst        |    6 -
 ...bpysal.weights.build_lattice_shapefile.rst |    6 -
 docsrc/generated/libpysal.weights.comb.rst    |    6 -
 docsrc/generated/libpysal.weights.da2W.rst    |    6 -
 docsrc/generated/libpysal.weights.da2WSP.rst  |    6 -
 .../libpysal.weights.fill_diagonal.rst        |    6 -
 docsrc/generated/libpysal.weights.full.rst    |    6 -
 docsrc/generated/libpysal.weights.full2W.rst  |    6 -
 .../libpysal.weights.fuzzy_contiguity.rst     |    6 -
 docsrc/generated/libpysal.weights.get_ids.rst |    6 -
 ...eights.get_points_array_from_shapefile.rst |    6 -
 .../generated/libpysal.weights.hexLat2W.rst   |    6 -
 .../libpysal.weights.higher_order.rst         |    6 -
 .../libpysal.weights.higher_order_sp.rst      |    6 -
 .../libpysal.weights.lag_categorical.rst      |    6 -
 .../libpysal.weights.lag_spatial.rst          |    6 -
 docsrc/generated/libpysal.weights.lat2SW.rst  |    6 -
 docsrc/generated/libpysal.weights.lat2W.rst   |    6 -
 docsrc/generated/libpysal.weights.mat2L.rst   |    6 -
 ...ghts.min_threshold_dist_from_shapefile.rst |    6 -
 ...ibpysal.weights.min_threshold_distance.rst |    6 -
 .../libpysal.weights.neighbor_equality.rst    |    6 -
 docsrc/generated/libpysal.weights.netW.rst    |    6 -
 .../libpysal.weights.nonplanar_neighbors.rst  |    6 -
 docsrc/generated/libpysal.weights.order.rst   |    6 -
 .../generated/libpysal.weights.remap_ids.rst  |    6 -
 docsrc/generated/libpysal.weights.shimbel.rst |    6 -
 .../libpysal.weights.spw_from_gal.rst         |    6 -
 .../libpysal.weights.testDataArray.rst        |    6 -
 docsrc/generated/libpysal.weights.vecW.rst    |    6 -
 docsrc/generated/libpysal.weights.w2da.rst    |    6 -
 docsrc/generated/libpysal.weights.w_clip.rst  |    6 -
 .../libpysal.weights.w_difference.rst         |    6 -
 .../libpysal.weights.w_intersection.rst       |    6 -
 .../libpysal.weights.w_local_cluster.rst      |    6 -
 .../generated/libpysal.weights.w_subset.rst   |    6 -
 ...ibpysal.weights.w_symmetric_difference.rst |    6 -
 docsrc/generated/libpysal.weights.w_union.rst |    6 -
 docsrc/generated/libpysal.weights.wsp2da.rst  |    6 -
 docsrc/index.rst                              |  133 --
 docsrc/installation.rst                       |   57 -
 docsrc/notebooks/Raster_awareness_API.ipynb   |  754 ----------
 docsrc/notebooks/examples.ipynb               | 1092 --------------
 docsrc/notebooks/rioGrandeDoSul.png           |  Bin 517598 -> 0 bytes
 docsrc/notebooks/voronoi.ipynb                |  478 ------
 docsrc/notebooks/weights.ipynb                | 1313 -----------------
 docsrc/references.rst                         |    7 -
 docsrc/tutorial.rst                           |   22 -
 122 files changed, 5898 deletions(-)
 delete mode 100644 docsrc/Makefile
 delete mode 100644 docsrc/_static/images/neighboorsetLIMA_US.png
 delete mode 100644 docsrc/_static/images/npweights.png
 delete mode 100644 docsrc/_static/images/pysal_favicon.ico
 delete mode 100644 docsrc/_static/images/rose_conditional.png
 delete mode 100644 docsrc/_static/images/spatial_markov_us.png
 delete mode 100644 docsrc/_static/pysal-styles.css
 delete mode 100644 docsrc/_static/references.bib
 delete mode 100644 docsrc/api.rst
 delete mode 100644 docsrc/conf.py
 delete mode 100644 docsrc/generated/libpysal.cg.Chain.rst
 delete mode 100644 docsrc/generated/libpysal.cg.Grid.rst
 delete mode 100644 docsrc/generated/libpysal.cg.KDTree.rst
 delete mode 100644 docsrc/generated/libpysal.cg.Line.rst
 delete mode 100644 docsrc/generated/libpysal.cg.LineSegment.rst
 delete mode 100644 docsrc/generated/libpysal.cg.Point.rst
 delete mode 100644 docsrc/generated/libpysal.cg.PointLocator.rst
 delete mode 100644 docsrc/generated/libpysal.cg.Polygon.rst
 delete mode 100644 docsrc/generated/libpysal.cg.PolygonLocator.rst
 delete mode 100644 docsrc/generated/libpysal.cg.RADIUS_EARTH_KM.rst
 delete mode 100644 docsrc/generated/libpysal.cg.RADIUS_EARTH_MILES.rst
 delete mode 100644 docsrc/generated/libpysal.cg.Ray.rst
 delete mode 100644 docsrc/generated/libpysal.cg.Rectangle.rst
 delete mode 100644 docsrc/generated/libpysal.cg.alpha_shape.rst
 delete mode 100644 docsrc/generated/libpysal.cg.alpha_shape_auto.rst
 delete mode 100644 docsrc/generated/libpysal.cg.arcdist.rst
 delete mode 100644 docsrc/generated/libpysal.cg.arcdist2linear.rst
 delete mode 100644 docsrc/generated/libpysal.cg.asShape.rst
 delete mode 100644 docsrc/generated/libpysal.cg.bbcommon.rst
 delete mode 100644 docsrc/generated/libpysal.cg.brute_knn.rst
 delete mode 100644 docsrc/generated/libpysal.cg.convex_hull.rst
 delete mode 100644 docsrc/generated/libpysal.cg.distance_matrix.rst
 delete mode 100644 docsrc/generated/libpysal.cg.fast_knn.rst
 delete mode 100644 docsrc/generated/libpysal.cg.fast_threshold.rst
 delete mode 100644 docsrc/generated/libpysal.cg.geogrid.rst
 delete mode 100644 docsrc/generated/libpysal.cg.geointerpolate.rst
 delete mode 100644 docsrc/generated/libpysal.cg.get_angle_between.rst
 delete mode 100644 docsrc/generated/libpysal.cg.get_bounding_box.rst
 delete mode 100644 docsrc/generated/libpysal.cg.get_point_at_angle_and_dist.rst
 delete mode 100644 docsrc/generated/libpysal.cg.get_points_dist.rst
 delete mode 100644 docsrc/generated/libpysal.cg.get_polygon_point_dist.rst
 delete mode 100644 docsrc/generated/libpysal.cg.get_polygon_point_intersect.rst
 delete mode 100644 docsrc/generated/libpysal.cg.get_ray_segment_intersect.rst
 delete mode 100644 docsrc/generated/libpysal.cg.get_rectangle_point_intersect.rst
 delete mode 100644 docsrc/generated/libpysal.cg.get_rectangle_rectangle_intersection.rst
 delete mode 100644 docsrc/generated/libpysal.cg.get_segment_point_dist.rst
 delete mode 100644 docsrc/generated/libpysal.cg.get_segment_point_intersect.rst
 delete mode 100644 docsrc/generated/libpysal.cg.get_segments_intersect.rst
 delete mode 100644 docsrc/generated/libpysal.cg.get_shared_segments.rst
 delete mode 100644 docsrc/generated/libpysal.cg.harcdist.rst
 delete mode 100644 docsrc/generated/libpysal.cg.is_clockwise.rst
 delete mode 100644 docsrc/generated/libpysal.cg.is_collinear.rst
 delete mode 100644 docsrc/generated/libpysal.cg.linear2arcdist.rst
 delete mode 100644 docsrc/generated/libpysal.cg.lonlat.rst
 delete mode 100644 docsrc/generated/libpysal.cg.point_touches_rectangle.rst
 delete mode 100644 docsrc/generated/libpysal.cg.toLngLat.rst
 delete mode 100644 docsrc/generated/libpysal.cg.toXYZ.rst
 delete mode 100644 docsrc/generated/libpysal.cg.voronoi_frames.rst
 delete mode 100644 docsrc/generated/libpysal.examples.available.rst
 delete mode 100644 docsrc/generated/libpysal.examples.explain.rst
 delete mode 100644 docsrc/generated/libpysal.examples.get_path.rst
 delete mode 100644 docsrc/generated/libpysal.io.fileio.FileIO.rst
 delete mode 100644 docsrc/generated/libpysal.io.open.rst
 delete mode 100644 docsrc/generated/libpysal.weights.DistanceBand.rst
 delete mode 100644 docsrc/generated/libpysal.weights.KNN.rst
 delete mode 100644 docsrc/generated/libpysal.weights.Kernel.rst
 delete mode 100644 docsrc/generated/libpysal.weights.ODW.rst
 delete mode 100644 docsrc/generated/libpysal.weights.Queen.rst
 delete mode 100644 docsrc/generated/libpysal.weights.Rook.rst
 delete mode 100644 docsrc/generated/libpysal.weights.Voronoi.rst
 delete mode 100644 docsrc/generated/libpysal.weights.W.rst
 delete mode 100644 docsrc/generated/libpysal.weights.WSP.rst
 delete mode 100644 docsrc/generated/libpysal.weights.WSP2W.rst
 delete mode 100644 docsrc/generated/libpysal.weights.attach_islands.rst
 delete mode 100644 docsrc/generated/libpysal.weights.block_weights.rst
 delete mode 100644 docsrc/generated/libpysal.weights.build_lattice_shapefile.rst
 delete mode 100644 docsrc/generated/libpysal.weights.comb.rst
 delete mode 100644 docsrc/generated/libpysal.weights.da2W.rst
 delete mode 100644 docsrc/generated/libpysal.weights.da2WSP.rst
 delete mode 100644 docsrc/generated/libpysal.weights.fill_diagonal.rst
 delete mode 100644 docsrc/generated/libpysal.weights.full.rst
 delete mode 100644 docsrc/generated/libpysal.weights.full2W.rst
 delete mode 100644 docsrc/generated/libpysal.weights.fuzzy_contiguity.rst
 delete mode 100644 docsrc/generated/libpysal.weights.get_ids.rst
 delete mode 100644 docsrc/generated/libpysal.weights.get_points_array_from_shapefile.rst
 delete mode 100644 docsrc/generated/libpysal.weights.hexLat2W.rst
 delete mode 100644 docsrc/generated/libpysal.weights.higher_order.rst
 delete mode 100644 docsrc/generated/libpysal.weights.higher_order_sp.rst
 delete mode 100644 docsrc/generated/libpysal.weights.lag_categorical.rst
 delete mode 100644 docsrc/generated/libpysal.weights.lag_spatial.rst
 delete mode 100644 docsrc/generated/libpysal.weights.lat2SW.rst
 delete mode 100644 docsrc/generated/libpysal.weights.lat2W.rst
 delete mode 100644 docsrc/generated/libpysal.weights.mat2L.rst
 delete mode 100644 docsrc/generated/libpysal.weights.min_threshold_dist_from_shapefile.rst
 delete mode 100644 docsrc/generated/libpysal.weights.min_threshold_distance.rst
 delete mode 100644 docsrc/generated/libpysal.weights.neighbor_equality.rst
 delete mode 100644 docsrc/generated/libpysal.weights.netW.rst
 delete mode 100644 docsrc/generated/libpysal.weights.nonplanar_neighbors.rst
 delete mode 100644 docsrc/generated/libpysal.weights.order.rst
 delete mode 100644 docsrc/generated/libpysal.weights.remap_ids.rst
 delete mode 100644 docsrc/generated/libpysal.weights.shimbel.rst
 delete mode 100644 docsrc/generated/libpysal.weights.spw_from_gal.rst
 delete mode 100644 docsrc/generated/libpysal.weights.testDataArray.rst
 delete mode 100644 docsrc/generated/libpysal.weights.vecW.rst
 delete mode 100644 docsrc/generated/libpysal.weights.w2da.rst
 delete mode 100644 docsrc/generated/libpysal.weights.w_clip.rst
 delete mode 100644 docsrc/generated/libpysal.weights.w_difference.rst
 delete mode 100644 docsrc/generated/libpysal.weights.w_intersection.rst
 delete mode 100644 docsrc/generated/libpysal.weights.w_local_cluster.rst
 delete mode 100644 docsrc/generated/libpysal.weights.w_subset.rst
 delete mode 100644 docsrc/generated/libpysal.weights.w_symmetric_difference.rst
 delete mode 100644 docsrc/generated/libpysal.weights.w_union.rst
 delete mode 100644 docsrc/generated/libpysal.weights.wsp2da.rst
 delete mode 100644 docsrc/index.rst
 delete mode 100644 docsrc/installation.rst
 delete mode 100644 docsrc/notebooks/Raster_awareness_API.ipynb
 delete mode 100644 docsrc/notebooks/examples.ipynb
 delete mode 100644 docsrc/notebooks/rioGrandeDoSul.png
 delete mode 100644 docsrc/notebooks/voronoi.ipynb
 delete mode 100644 docsrc/notebooks/weights.ipynb
 delete mode 100644 docsrc/references.rst
 delete mode 100644 docsrc/tutorial.rst

diff --git a/docsrc/Makefile b/docsrc/Makefile
deleted file mode 100644
index dfe6c3b5e..000000000
--- a/docsrc/Makefile
+++ /dev/null
@@ -1,31 +0,0 @@
-# Minimal makefile for Sphinx documentation
-#
-
-# You can set these variables from the command line.
-SPHINXOPTS    =
-SPHINXBUILD   = sphinx-build
-SPHINXPROJ    = PACKAGE_NAME
-SOURCEDIR     = .
-BUILDDIR      = _build
-
-# Put it first so that "make" without argument is like "make help".
-help:
-	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
-
-.PHONY: help Makefile
-
-# Catch-all target: route all unknown targets to Sphinx using the new
-# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
-%: Makefile
-	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
-
-github:
-	@make html
-
-sync:
-	@rsync -avh --exclude '.nojekyll' _build/html/ ../docs/ --delete
-	@make clean
-
-clean:
-	rm -rf $(BUILDDIR)/*
-	rm -rf auto_examples/
diff --git a/docsrc/_static/images/neighboorsetLIMA_US.png b/docsrc/_static/images/neighboorsetLIMA_US.png
deleted file mode 100644
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diff --git a/docsrc/_static/pysal-styles.css b/docsrc/_static/pysal-styles.css
deleted file mode 100644
index 6633c2a58..000000000
--- a/docsrc/_static/pysal-styles.css
+++ /dev/null
@@ -1,73 +0,0 @@
-/* Make thumbnails with equal heights */
-@media only screen and (min-width : 481px) {
-    .row.equal-height {
-        display: flex;
-        flex-wrap: wrap;
-    }
-    .row.equal-height > [class*='col-'] {
-        display: flex;
-        flex-direction: column;
-    }
-    .row.equal-height.row:after,
-    .row.equal-height.row:before {
-        display: flex;
-    }
-
-    .row.equal-height > [class*='col-'] > .thumbnail,
-    .row.equal-height > [class*='col-'] > .thumbnail > .caption {
-        display: flex;
-        flex: 1 0 auto;
-        flex-direction: column;
-    }
-    .row.equal-height > [class*='col-'] > .thumbnail > .caption > .flex-text {
-        flex-grow: 1;
-    }
-    .row.equal-height > [class*='col-'] > .thumbnail > img {
-        width: 100%;
-        height: 200px; /* force image's height */
-
-        /* force image fit inside it's "box" */
-        -webkit-object-fit: cover;
-           -moz-object-fit: cover;
-            -ms-object-fit: cover;
-             -o-object-fit: cover;
-                object-fit: cover;
-    }
-}
-
-.row.extra-bottom-padding{
-    margin-bottom: 20px;
-}
-
-
-.topnavicons {
-    margin-left: 10% !important;
-}
-
-.topnavicons li {
-    margin-left: 0px !important;
-    min-width: 100px;
-    text-align: center;
-}
-
-.topnavicons .thumbnail {
-    margin-right: 10px;
-    border: none;
-    box-shadow: none;
-    text-align: center;
-    font-size: 85%;
-    font-weight: bold;
-    line-height: 10px;
-    height: 100px;
-}
-
-.topnavicons .thumbnail img {
-    display: block;
-    margin-left: auto;
-    margin-right: auto;
-}
-
-
-/* Table with a scrollbar */
-.bodycontainer { max-height: 600px; width: 100%; margin: 0; overflow-y: auto; }
-.table-scrollable { margin: 0; padding: 0; }
\ No newline at end of file
diff --git a/docsrc/_static/references.bib b/docsrc/_static/references.bib
deleted file mode 100644
index 1dc4917ef..000000000
--- a/docsrc/_static/references.bib
+++ /dev/null
@@ -1,39 +0,0 @@
-@article{Anselin1996b,
-author = {Anselin, Luc and Smirnov, Oleg},
-title = {Efficient algorithms for constructing proper higher order spatial lag operators},
-journal = {Journal of Regional Science},
-
-volume = {36},
-number = {1},
-pages = {67-89},
-doi = {10.1111/j.1467-9787.1996.tb01101.x},
-eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1467-9787.1996.tb01101.x},
-abstract = {ABSTRACT. This paper extends the work of Blommestein and Koper (1992)–BK–on the construction of higher-order spatial lag operators without redundant and circular paths. For the case most relevant in spatial econometrics and spatial statistics, i.e., when contiguity between two observations (locations) is defined in a simple binary fashion, some deficiencies of the BK algorithms are outlined, corrected and an improvement suggested. In addition, three new algorithms are introduced and compared in terms of performance for a number of empirical contiguity structures. Particular attention is paid to a graph theoretic perspective on spatial lag operators and to the most efficient data structures for the storage and manipulation of spatial lags. The new forward iterative algorithm which uses a list form rather than a matrix to store the spatial lag information is shown to be several orders of magnitude faster than the BK solution. This allows the computation of proper higher-order spatial lags “on the fly” for even moderately large data sets such as 3,111 contiguous U. S. counties, which is not practical with the other algorithms.},
-year = {1996}
-}
-
-
-
-
-@Article{pysal2007,
-  author={Rey, Sergio J. and Anselin, Luc},
-  title={{PySAL: A Python Library of Spatial Analytical Methods}},
-  journal={The Review of Regional Studies},
-  year=2007,
-  volume={37},
-  number={1},
-  pages={5-27},
-  keywords={Open Source; Software; Spatial}
-}
-
-
-@Article{Watts1998,
-  author={Watts, D.J. and S.H. Strogatz},
-  year={1998},
-  title={Collective dynamics of 'small-world' networks},
-  journal={Nature},
-  volume={393},
-  pages={440-442},
-  keywords={networks}
-}
-
diff --git a/docsrc/api.rst b/docsrc/api.rst
deleted file mode 100644
index 01da37c71..000000000
--- a/docsrc/api.rst
+++ /dev/null
@@ -1,248 +0,0 @@
-.. _api_ref:
-
-.. currentmodule:: libpysal
-
-
-libpysal API reference
-======================
-
-Spatial Weights
----------------
-
-.. autosummary::
-   :toctree: generated/
-
-   libpysal.weights.W
-
-Distance Weights
-++++++++++++++++
-.. autosummary::
-   :toctree: generated/
-
-   libpysal.weights.DistanceBand
-   libpysal.weights.Kernel
-   libpysal.weights.KNN
-
-Contiguity Weights
-++++++++++++++++++
-
-.. autosummary::
-   :toctree: generated/
-
-   libpysal.weights.Queen
-   libpysal.weights.Rook
-   libpysal.weights.Voronoi
-   libpysal.weights.W
-
-spint Weights
-+++++++++++++
-
-.. autosummary::
-   :toctree: generated/
-
-   libpysal.weights.WSP
-   libpysal.weights.netW
-   libpysal.weights.mat2L
-   libpysal.weights.ODW
-   libpysal.weights.vecW
-
-Weights tools to interface with rasters
-+++++++++++++++++++++++++++++++++++++++
-
-.. autosummary::
-   :toctree: generated/
-
-   libpysal.weights.da2W
-   libpysal.weights.da2WSP
-   libpysal.weights.w2da
-   libpysal.weights.wsp2da
-   libpysal.weights.testDataArray
-
-Weights Util Classes and Functions
-++++++++++++++++++++++++++++++++++
-
-.. autosummary::
-   :toctree: generated/
-
-   libpysal.weights.block_weights
-   libpysal.weights.lat2W
-   libpysal.weights.comb
-   libpysal.weights.order
-   libpysal.weights.higher_order
-   libpysal.weights.shimbel
-   libpysal.weights.remap_ids
-   libpysal.weights.full2W
-   libpysal.weights.full
-   libpysal.weights.WSP2W
-   libpysal.weights.get_ids
-   libpysal.weights.get_points_array_from_shapefile
-   libpysal.weights.fill_diagonal
-
-Weights user Classes and Functions
-++++++++++++++++++++++++++++++++++
-
-.. autosummary::
-   :toctree: generated/
-
-   libpysal.weights.min_threshold_distance
-   libpysal.weights.lat2SW
-   libpysal.weights.w_local_cluster
-   libpysal.weights.higher_order_sp
-   libpysal.weights.hexLat2W
-   libpysal.weights.attach_islands
-   libpysal.weights.nonplanar_neighbors
-   libpysal.weights.fuzzy_contiguity
-   libpysal.weights.min_threshold_dist_from_shapefile
-   libpysal.weights.build_lattice_shapefile
-   libpysal.weights.spw_from_gal
-   libpysal.weights.neighbor_equality
-
-
-Set Theoretic Weights
-+++++++++++++++++++++
-
-.. autosummary::
-   :toctree: generated/
-
-   libpysal.weights.w_union
-   libpysal.weights.w_intersection
-   libpysal.weights.w_difference
-   libpysal.weights.w_symmetric_difference
-   libpysal.weights.w_subset
-   libpysal.weights.w_clip
-
-
-Spatial Lag
-+++++++++++
-
-.. autosummary::
-   :toctree: generated/
-
-   libpysal.weights.lag_spatial
-   libpysal.weights.lag_categorical
-          
-
-cg: Computational Geometry
---------------------------
-
-alpha_shapes
-++++++++++++
-
-.. autosummary::
-   :toctree: generated/
-
-   libpysal.cg.alpha_shape
-   libpysal.cg.alpha_shape_auto
-
-voronoi
-+++++++
-
-.. autosummary::
-   :toctree: generated/
-
-   libpysal.cg.voronoi_frames
-
-
-sphere
-++++++
-
-.. autosummary::
-   :toctree: generated/
-
-   libpysal.cg.RADIUS_EARTH_KM
-   libpysal.cg.RADIUS_EARTH_MILES
-   libpysal.cg.arcdist
-   libpysal.cg.arcdist2linear
-   libpysal.cg.brute_knn
-   libpysal.cg.fast_knn
-   libpysal.cg.fast_threshold
-   libpysal.cg.linear2arcdist
-   libpysal.cg.toLngLat
-   libpysal.cg.toXYZ
-   libpysal.cg.lonlat
-   libpysal.cg.harcdist
-   libpysal.cg.geointerpolate
-   libpysal.cg.geogrid
-
-shapes
-++++++
-
-.. autosummary::
-   :toctree: generated/
-
-   libpysal.cg.Point
-   libpysal.cg.LineSegment
-   libpysal.cg.Line
-   libpysal.cg.Ray
-   libpysal.cg.Chain
-   libpysal.cg.Polygon
-   libpysal.cg.Rectangle
-   libpysal.cg.asShape
-
-standalone
-++++++++++
-
-.. autosummary::
-   :toctree: generated/
-
-   libpysal.cg.bbcommon
-   libpysal.cg.get_bounding_box
-   libpysal.cg.get_angle_between
-   libpysal.cg.is_collinear
-   libpysal.cg.get_segments_intersect
-   libpysal.cg.get_segment_point_intersect
-   libpysal.cg.get_polygon_point_intersect
-   libpysal.cg.get_rectangle_point_intersect
-   libpysal.cg.get_ray_segment_intersect
-   libpysal.cg.get_rectangle_rectangle_intersection
-   libpysal.cg.get_polygon_point_dist
-   libpysal.cg.get_points_dist
-   libpysal.cg.get_segment_point_dist
-   libpysal.cg.get_point_at_angle_and_dist
-   libpysal.cg.convex_hull
-   libpysal.cg.is_clockwise
-   libpysal.cg.point_touches_rectangle
-   libpysal.cg.get_shared_segments
-   libpysal.cg.distance_matrix
-
-
-locators
-++++++++
-
-.. autosummary::
-   :toctree: generated/
-
-   libpysal.cg.Grid
-   libpysal.cg.PointLocator
-   libpysal.cg.PolygonLocator
-
-
-kdtree
-++++++
-
-.. autosummary::
-   :toctree: generated/
-
-   libpysal.cg.KDTree
-
-
-io
--- 
-
-.. autosummary::
-   :toctree: generated/
-
-   libpysal.io.open
-   libpysal.io.fileio.FileIO
-
-
-examples
---------
-
-
-.. autosummary::
-   :toctree: generated/
-
-   libpysal.examples.available
-   libpysal.examples.explain
-   libpysal.examples.get_path
diff --git a/docsrc/conf.py b/docsrc/conf.py
deleted file mode 100644
index 499f5b050..000000000
--- a/docsrc/conf.py
+++ /dev/null
@@ -1,288 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-# libpysal documentation build configuration file, created by
-# sphinx-quickstart on Wed Jun  6 15:54:22 2018.
-#
-# This file is execfile()d with the current directory set to its
-# containing dir.
-#
-# Note that not all possible configuration values are present in this
-# autogenerated file.
-#
-# All configuration values have a default; values that are commented out
-# serve to show the default.
-
-# If extensions (or modules to document with autodoc) are in another directory,
-# add these directories to sys.path here. If the directory is relative to the
-# documentation root, use os.path.abspath to make it absolute, like shown here.
-#
-import sys, os
-import sphinx_bootstrap_theme
-
-
-sys.path.insert(0, os.path.abspath("../"))
-
-import libpysal
-
-
-# -- General configuration ------------------------------------------------
-
-# If your documentation needs a minimal Sphinx version, state it here.
-#
-# needs_sphinx = '1.0'
-# Add any Sphinx extension module names here, as strings. They can be
-# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
-# ones.
-extensions = [  #'sphinx_gallery.gen_gallery',
-    "sphinx.ext.autodoc",
-    "sphinx.ext.autosummary",
-    "sphinx.ext.viewcode",
-    "sphinxcontrib.bibtex",
-    "sphinx.ext.mathjax",
-    "sphinx.ext.doctest",
-    "sphinx.ext.intersphinx",
-    "numpydoc",
-    #'sphinx.ext.napoleon',
-    "matplotlib.sphinxext.plot_directive",
-    "nbsphinx",
-]
-
-
-# sphinx_gallery_conf = {
-#      # path to your examples scripts
-#      'examples_dirs': '../examples',
-#      # path where to save gallery generated examples
-#      'gallery_dirs': 'auto_examples',
-#      'backreferences_dir': False,
-# }
-
-
-# Add any paths that contain templates here, relative to this directory.
-templates_path = ["_templates"]
-
-# The suffix(es) of source filenames.
-# You can specify multiple suffix as a list of string:
-#
-# source_suffix = ['.rst', '.md']
-source_suffix = ".rst"
-
-# The master toctree document.
-master_doc = "index"
-
-# General information about the project.
-project = "libpysal"
-copyright = "2018-, pysal developers"
-author = "pysal developers"
-
-# The version info for the project you're documenting, acts as replacement for
-# |version| and |release|, also used in various other places throughout the
-# built documents.
-#
-# The full version.
-version = libpysal.__version__
-release = libpysal.__version__
-
-# The language for content autogenerated by Sphinx. Refer to documentation
-# for a list of supported languages.
-#
-# This is also used if you do content translation via gettext catalogs.
-# Usually you set "language" from the command line for these cases.
-language = None
-
-# List of patterns, relative to source directory, that match files and
-# directories to ignore when looking for source files.
-# This patterns also effect to html_static_path and html_extra_path
-exclude_patterns = ["_build", "Thumbs.db", ".DS_Store", "tests/*"]
-
-# The name of the Pygments (syntax highlighting) style to use.
-pygments_style = "sphinx"
-
-# If true, `todo` and `todoList` produce output, else they produce nothing.
-todo_include_todos = False
-
-# -- Options for HTML output ----------------------------------------------
-
-# The theme to use for HTML and HTML Help pages.  See the documentation for
-# a list of builtin themes.
-
-# html_theme = 'alabaster'
-html_theme = "bootstrap"
-html_theme_path = sphinx_bootstrap_theme.get_html_theme_path()
-html_title = "%s v%s Manual" % (project, version)
-
-# (Optional) Logo. Should be small enough to fit the navbar (ideally 24x24).
-# Path should be relative to the ``_static`` files directory.
-# html_logo = "_static/images/CGS_logo.jpg"
-# html_logo = "_static/images/CGS_logo_green.png"
-# html_logo = "_static/images/pysal_logo_small.jpg"
-html_favicon = "_static/images/pysal_favicon.ico"
-
-
-# Theme options are theme-specific and customize the look and feel of a theme
-# further.  For a list of options available for each theme, see the
-# documentation.
-#
-html_theme_options = {
-    # Navigation bar title. (Default: ``project`` value)
-    "navbar_title": "libpysal",
-    # Render the next and previous page links in navbar. (Default: true)
-    "navbar_sidebarrel": False,
-    # Render the current pages TOC in the navbar. (Default: true)
-    #'navbar_pagenav': True,
-    #'navbar_pagenav': False,
-    # No sidebar
-    "nosidebar": True,
-    # Tab name for the current pages TOC. (Default: "Page")
-    #'navbar_pagenav_name': "Page",
-    # Global TOC depth for "site" navbar tab. (Default: 1)
-    # Switching to -1 shows all levels.
-    "globaltoc_depth": 2,
-    # Include hidden TOCs in Site navbar?
-    #
-    # Note: If this is "false", you cannot have mixed ``:hidden:`` and
-    # non-hidden ``toctree`` directives in the same page, or else the build
-    # will break.
-    #
-    # Values: "true" (default) or "false"
-    "globaltoc_includehidden": "true",
-    # HTML navbar class (Default: "navbar") to attach to <div> element.
-    # For black navbar, do "navbar navbar-inverse"
-    #'navbar_class': "navbar navbar-inverse",
-    # Fix navigation bar to top of page?
-    # Values: "true" (default) or "false"
-    "navbar_fixed_top": "true",
-    # Location of link to source.
-    # Options are "nav" (default), "footer" or anything else to exclude.
-    "source_link_position": "footer",
-    # Bootswatch (http://bootswatch.com/) theme.
-    #
-    # Options are nothing (default) or the name of a valid theme
-    # such as "amelia" or "cosmo", "yeti", "flatly".
-    "bootswatch_theme": "yeti",
-    # Choose Bootstrap version.
-    # Values: "3" (default) or "2" (in quotes)
-    "bootstrap_version": "3",
-    "navbar_links": [
-        # ("Gallery", "auto_examples/index"),
-        ("Installation", "installation"),
-        ("Tutorial", "tutorial"),
-        ("API", "api"),
-        ("References", "references"),
-    ],
-}
-
-# Add any paths that contain custom static files (such as style sheets) here,
-# relative to this directory. They are copied after the builtin static files,
-# so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ["_static"]
-
-# Custom sidebar templates, maps document names to template names.
-# html_sidebars = {}
-# html_sidebars = {'sidebar': ['localtoc.html', 'sourcelink.html', 'searchbox.html']}
-
-# -- Options for HTMLHelp output ------------------------------------------
-
-# Output file base name for HTML help builder.
-htmlhelp_basename = "libpysaldoc"
-
-
-# -- Options for LaTeX output ---------------------------------------------
-
-latex_elements = {
-    # The paper size ('letterpaper' or 'a4paper').
-    #
-    # 'papersize': 'letterpaper',
-    # The font size ('10pt', '11pt' or '12pt').
-    #
-    # 'pointsize': '10pt',
-    # Additional stuff for the LaTeX preamble.
-    #
-    # 'preamble': '',
-    # Latex figure (float) alignment
-    #
-    # 'figure_align': 'htbp',
-}
-
-# Grouping the document tree into LaTeX files. List of tuples
-# (source start file, target name, title,
-#  author, documentclass [howto, manual, or own class]).
-latex_documents = [
-    (
-        master_doc,
-        "libpysal.tex",
-        u"libpysal Documentation",
-        u"pysal developers",
-        "manual",
-    ),
-]
-
-
-# -- Options for manual page output ---------------------------------------
-
-# One entry per manual page. List of tuples
-# (source start file, name, description, authors, manual section).
-man_pages = [(master_doc, "libpysal", u"libpysal Documentation", [author], 1)]
-
-
-# -- Options for Texinfo output -------------------------------------------
-
-# Grouping the document tree into Texinfo files. List of tuples
-# (source start file, target name, title, author,
-#  dir menu entry, description, category)
-texinfo_documents = [
-    (
-        master_doc,
-        "libpysal",
-        u"libpysal Documentation",
-        author,
-        "libpysal",
-        "One line description of project.",
-        "Miscellaneous",
-    ),
-]
-
-# -----------------------------------------------------------------------------
-# Napoleon configuration
-# -----------------------------------------------------------------------------
-# numpydoc_show_class_members = True
-# numpydoc_class_members_toctree = False
-#
-# napoleon_use_ivar = True
-
-# -----------------------------------------------------------------------------
-# Autosummary
-# -----------------------------------------------------------------------------
-
-# Generate the API documentation when building
-autosummary_generate = True
-
-# avoid showing members twice
-numpydoc_show_class_members = False
-numpydoc_use_plots = True
-
-#
-numpydoc_show_inherited_class_members = True
-numpydoc_xref_param_type = True
-
-# automatically document class members
-autodoc_default_options = {"members": True, "undoc-members": True}
-
-# display the source code for Plot directive
-plot_include_source = True
-
-
-def setup(app):
-    app.add_stylesheet("pysal-styles.css")
-
-
-# Example configuration for intersphinx: refer to the Python standard library.
-intersphinx_mapping = {
-    "geopandas": ("https://geopandas.readthedocs.io/en/latest/", None),
-    "libpysal": ("https://pysal.org/libpysal/", None),
-    "matplotlib": ("https://matplotlib.org/", None),
-    "networkx": ("https://networkx.github.io/documentation/stable/", None),
-    "numpy": ("https://docs.scipy.org/doc/numpy", None),
-    "pandas": ("https://pandas.pydata.org/pandas-docs/stable/", None),
-    "python": ("https://docs.python.org/3.8/", None),
-    "scipy": ("https://docs.scipy.org/doc/scipy/reference/", None),
-}
diff --git a/docsrc/generated/libpysal.cg.Chain.rst b/docsrc/generated/libpysal.cg.Chain.rst
deleted file mode 100644
index f91f22f1f..000000000
--- a/docsrc/generated/libpysal.cg.Chain.rst
+++ /dev/null
@@ -1,33 +0,0 @@
-libpysal.cg.Chain
-=================
-
-.. currentmodule:: libpysal.cg
-
-.. autoclass:: Chain
-
-   
-   .. automethod:: __init__
-
-   
-   .. rubric:: Methods
-
-   .. autosummary::
-   
-      ~Chain.__init__
-   
-   
-
-   
-   
-   .. rubric:: Attributes
-
-   .. autosummary::
-   
-      ~Chain.arclen
-      ~Chain.bounding_box
-      ~Chain.len
-      ~Chain.parts
-      ~Chain.segments
-      ~Chain.vertices
-   
-   
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.cg.Grid.rst b/docsrc/generated/libpysal.cg.Grid.rst
deleted file mode 100644
index c7b6aa23b..000000000
--- a/docsrc/generated/libpysal.cg.Grid.rst
+++ /dev/null
@@ -1,28 +0,0 @@
-libpysal.cg.Grid
-================
-
-.. currentmodule:: libpysal.cg
-
-.. autoclass:: Grid
-
-   
-   .. automethod:: __init__
-
-   
-   .. rubric:: Methods
-
-   .. autosummary::
-   
-      ~Grid.__init__
-      ~Grid.add
-      ~Grid.bounds
-      ~Grid.in_grid
-      ~Grid.nearest
-      ~Grid.proximity
-      ~Grid.remove
-   
-   
-
-   
-   
-   
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.cg.KDTree.rst b/docsrc/generated/libpysal.cg.KDTree.rst
deleted file mode 100644
index 020fb9649..000000000
--- a/docsrc/generated/libpysal.cg.KDTree.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.KDTree
-==================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: KDTree
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.cg.Line.rst b/docsrc/generated/libpysal.cg.Line.rst
deleted file mode 100644
index b04ae005b..000000000
--- a/docsrc/generated/libpysal.cg.Line.rst
+++ /dev/null
@@ -1,24 +0,0 @@
-libpysal.cg.Line
-================
-
-.. currentmodule:: libpysal.cg
-
-.. autoclass:: Line
-
-   
-   .. automethod:: __init__
-
-   
-   .. rubric:: Methods
-
-   .. autosummary::
-   
-      ~Line.__init__
-      ~Line.x
-      ~Line.y
-   
-   
-
-   
-   
-   
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.cg.LineSegment.rst b/docsrc/generated/libpysal.cg.LineSegment.rst
deleted file mode 100644
index dba1ae0b8..000000000
--- a/docsrc/generated/libpysal.cg.LineSegment.rst
+++ /dev/null
@@ -1,37 +0,0 @@
-libpysal.cg.LineSegment
-=======================
-
-.. currentmodule:: libpysal.cg
-
-.. autoclass:: LineSegment
-
-   
-   .. automethod:: __init__
-
-   
-   .. rubric:: Methods
-
-   .. autosummary::
-   
-      ~LineSegment.__init__
-      ~LineSegment.get_swap
-      ~LineSegment.intersect
-      ~LineSegment.is_ccw
-      ~LineSegment.is_cw
-      ~LineSegment.sw_ccw
-   
-   
-
-   
-   
-   .. rubric:: Attributes
-
-   .. autosummary::
-   
-      ~LineSegment.bounding_box
-      ~LineSegment.len
-      ~LineSegment.line
-      ~LineSegment.p1
-      ~LineSegment.p2
-   
-   
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.cg.Point.rst b/docsrc/generated/libpysal.cg.Point.rst
deleted file mode 100644
index 133d91fbd..000000000
--- a/docsrc/generated/libpysal.cg.Point.rst
+++ /dev/null
@@ -1,22 +0,0 @@
-libpysal.cg.Point
-=================
-
-.. currentmodule:: libpysal.cg
-
-.. autoclass:: Point
-
-   
-   .. automethod:: __init__
-
-   
-   .. rubric:: Methods
-
-   .. autosummary::
-   
-      ~Point.__init__
-   
-   
-
-   
-   
-   
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.cg.PointLocator.rst b/docsrc/generated/libpysal.cg.PointLocator.rst
deleted file mode 100644
index 1a65fb787..000000000
--- a/docsrc/generated/libpysal.cg.PointLocator.rst
+++ /dev/null
@@ -1,27 +0,0 @@
-libpysal.cg.PointLocator
-========================
-
-.. currentmodule:: libpysal.cg
-
-.. autoclass:: PointLocator
-
-   
-   .. automethod:: __init__
-
-   
-   .. rubric:: Methods
-
-   .. autosummary::
-   
-      ~PointLocator.__init__
-      ~PointLocator.nearest
-      ~PointLocator.overlapping
-      ~PointLocator.polygon
-      ~PointLocator.proximity
-      ~PointLocator.region
-   
-   
-
-   
-   
-   
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.cg.Polygon.rst b/docsrc/generated/libpysal.cg.Polygon.rst
deleted file mode 100644
index a0fab3ee9..000000000
--- a/docsrc/generated/libpysal.cg.Polygon.rst
+++ /dev/null
@@ -1,38 +0,0 @@
-libpysal.cg.Polygon
-===================
-
-.. currentmodule:: libpysal.cg
-
-.. autoclass:: Polygon
-
-   
-   .. automethod:: __init__
-
-   
-   .. rubric:: Methods
-
-   .. autosummary::
-   
-      ~Polygon.__init__
-      ~Polygon.build_quad_tree_structure
-      ~Polygon.contains_point
-   
-   
-
-   
-   
-   .. rubric:: Attributes
-
-   .. autosummary::
-   
-      ~Polygon.area
-      ~Polygon.bbox
-      ~Polygon.bounding_box
-      ~Polygon.centroid
-      ~Polygon.holes
-      ~Polygon.len
-      ~Polygon.parts
-      ~Polygon.perimeter
-      ~Polygon.vertices
-   
-   
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.cg.PolygonLocator.rst b/docsrc/generated/libpysal.cg.PolygonLocator.rst
deleted file mode 100644
index 482bc76b9..000000000
--- a/docsrc/generated/libpysal.cg.PolygonLocator.rst
+++ /dev/null
@@ -1,28 +0,0 @@
-libpysal.cg.PolygonLocator
-==========================
-
-.. currentmodule:: libpysal.cg
-
-.. autoclass:: PolygonLocator
-
-   
-   .. automethod:: __init__
-
-   
-   .. rubric:: Methods
-
-   .. autosummary::
-   
-      ~PolygonLocator.__init__
-      ~PolygonLocator.contains_point
-      ~PolygonLocator.inside
-      ~PolygonLocator.nearest
-      ~PolygonLocator.overlapping
-      ~PolygonLocator.proximity
-      ~PolygonLocator.region
-   
-   
-
-   
-   
-   
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.cg.RADIUS_EARTH_KM.rst b/docsrc/generated/libpysal.cg.RADIUS_EARTH_KM.rst
deleted file mode 100644
index 001acab4d..000000000
--- a/docsrc/generated/libpysal.cg.RADIUS_EARTH_KM.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.RADIUS\_EARTH\_KM
-=============================
-
-.. currentmodule:: libpysal.cg
-
-.. autodata:: RADIUS_EARTH_KM
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.cg.RADIUS_EARTH_MILES.rst b/docsrc/generated/libpysal.cg.RADIUS_EARTH_MILES.rst
deleted file mode 100644
index 36c27f184..000000000
--- a/docsrc/generated/libpysal.cg.RADIUS_EARTH_MILES.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.RADIUS\_EARTH\_MILES
-================================
-
-.. currentmodule:: libpysal.cg
-
-.. autodata:: RADIUS_EARTH_MILES
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.cg.Ray.rst b/docsrc/generated/libpysal.cg.Ray.rst
deleted file mode 100644
index 69efdb894..000000000
--- a/docsrc/generated/libpysal.cg.Ray.rst
+++ /dev/null
@@ -1,22 +0,0 @@
-libpysal.cg.Ray
-===============
-
-.. currentmodule:: libpysal.cg
-
-.. autoclass:: Ray
-
-   
-   .. automethod:: __init__
-
-   
-   .. rubric:: Methods
-
-   .. autosummary::
-   
-      ~Ray.__init__
-   
-   
-
-   
-   
-   
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.cg.Rectangle.rst b/docsrc/generated/libpysal.cg.Rectangle.rst
deleted file mode 100644
index aefa995d1..000000000
--- a/docsrc/generated/libpysal.cg.Rectangle.rst
+++ /dev/null
@@ -1,32 +0,0 @@
-libpysal.cg.Rectangle
-=====================
-
-.. currentmodule:: libpysal.cg
-
-.. autoclass:: Rectangle
-
-   
-   .. automethod:: __init__
-
-   
-   .. rubric:: Methods
-
-   .. autosummary::
-   
-      ~Rectangle.__init__
-      ~Rectangle.set_centroid
-      ~Rectangle.set_scale
-   
-   
-
-   
-   
-   .. rubric:: Attributes
-
-   .. autosummary::
-   
-      ~Rectangle.area
-      ~Rectangle.height
-      ~Rectangle.width
-   
-   
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.cg.alpha_shape.rst b/docsrc/generated/libpysal.cg.alpha_shape.rst
deleted file mode 100644
index d7b00aabd..000000000
--- a/docsrc/generated/libpysal.cg.alpha_shape.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.alpha\_shape
-========================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: alpha_shape
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.cg.alpha_shape_auto.rst b/docsrc/generated/libpysal.cg.alpha_shape_auto.rst
deleted file mode 100644
index eabc9198d..000000000
--- a/docsrc/generated/libpysal.cg.alpha_shape_auto.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.alpha\_shape\_auto
-==============================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: alpha_shape_auto
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.cg.arcdist.rst b/docsrc/generated/libpysal.cg.arcdist.rst
deleted file mode 100644
index f3ebba8ee..000000000
--- a/docsrc/generated/libpysal.cg.arcdist.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.arcdist
-===================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: arcdist
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.cg.arcdist2linear.rst b/docsrc/generated/libpysal.cg.arcdist2linear.rst
deleted file mode 100644
index e3afcf98a..000000000
--- a/docsrc/generated/libpysal.cg.arcdist2linear.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.arcdist2linear
-==========================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: arcdist2linear
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.cg.asShape.rst b/docsrc/generated/libpysal.cg.asShape.rst
deleted file mode 100644
index 2bd215bed..000000000
--- a/docsrc/generated/libpysal.cg.asShape.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.asShape
-===================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: asShape
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.cg.bbcommon.rst b/docsrc/generated/libpysal.cg.bbcommon.rst
deleted file mode 100644
index 3a774e5c5..000000000
--- a/docsrc/generated/libpysal.cg.bbcommon.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.bbcommon
-====================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: bbcommon
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.cg.brute_knn.rst b/docsrc/generated/libpysal.cg.brute_knn.rst
deleted file mode 100644
index 549e67cb3..000000000
--- a/docsrc/generated/libpysal.cg.brute_knn.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.brute\_knn
-======================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: brute_knn
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.cg.convex_hull.rst b/docsrc/generated/libpysal.cg.convex_hull.rst
deleted file mode 100644
index 18c1c230c..000000000
--- a/docsrc/generated/libpysal.cg.convex_hull.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.convex\_hull
-========================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: convex_hull
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.cg.distance_matrix.rst b/docsrc/generated/libpysal.cg.distance_matrix.rst
deleted file mode 100644
index d4e57bc66..000000000
--- a/docsrc/generated/libpysal.cg.distance_matrix.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.distance\_matrix
-============================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: distance_matrix
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.cg.fast_knn.rst b/docsrc/generated/libpysal.cg.fast_knn.rst
deleted file mode 100644
index 7155f9186..000000000
--- a/docsrc/generated/libpysal.cg.fast_knn.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.fast\_knn
-=====================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: fast_knn
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.cg.fast_threshold.rst b/docsrc/generated/libpysal.cg.fast_threshold.rst
deleted file mode 100644
index f86c422fd..000000000
--- a/docsrc/generated/libpysal.cg.fast_threshold.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.fast\_threshold
-===========================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: fast_threshold
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.cg.geogrid.rst b/docsrc/generated/libpysal.cg.geogrid.rst
deleted file mode 100644
index 2f375ae00..000000000
--- a/docsrc/generated/libpysal.cg.geogrid.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.geogrid
-===================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: geogrid
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.cg.geointerpolate.rst b/docsrc/generated/libpysal.cg.geointerpolate.rst
deleted file mode 100644
index 73f93e37c..000000000
--- a/docsrc/generated/libpysal.cg.geointerpolate.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.geointerpolate
-==========================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: geointerpolate
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.cg.get_angle_between.rst b/docsrc/generated/libpysal.cg.get_angle_between.rst
deleted file mode 100644
index 9e5c317bd..000000000
--- a/docsrc/generated/libpysal.cg.get_angle_between.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.get\_angle\_between
-===============================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: get_angle_between
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.cg.get_bounding_box.rst b/docsrc/generated/libpysal.cg.get_bounding_box.rst
deleted file mode 100644
index 089f0fa7b..000000000
--- a/docsrc/generated/libpysal.cg.get_bounding_box.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.get\_bounding\_box
-==============================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: get_bounding_box
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.cg.get_point_at_angle_and_dist.rst b/docsrc/generated/libpysal.cg.get_point_at_angle_and_dist.rst
deleted file mode 100644
index 66a05376b..000000000
--- a/docsrc/generated/libpysal.cg.get_point_at_angle_and_dist.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.get\_point\_at\_angle\_and\_dist
-============================================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: get_point_at_angle_and_dist
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.cg.get_points_dist.rst b/docsrc/generated/libpysal.cg.get_points_dist.rst
deleted file mode 100644
index e19e232cd..000000000
--- a/docsrc/generated/libpysal.cg.get_points_dist.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.get\_points\_dist
-=============================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: get_points_dist
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.cg.get_polygon_point_dist.rst b/docsrc/generated/libpysal.cg.get_polygon_point_dist.rst
deleted file mode 100644
index 3d53d2328..000000000
--- a/docsrc/generated/libpysal.cg.get_polygon_point_dist.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.get\_polygon\_point\_dist
-=====================================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: get_polygon_point_dist
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.cg.get_polygon_point_intersect.rst b/docsrc/generated/libpysal.cg.get_polygon_point_intersect.rst
deleted file mode 100644
index 1cd7529f3..000000000
--- a/docsrc/generated/libpysal.cg.get_polygon_point_intersect.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.get\_polygon\_point\_intersect
-==========================================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: get_polygon_point_intersect
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.cg.get_ray_segment_intersect.rst b/docsrc/generated/libpysal.cg.get_ray_segment_intersect.rst
deleted file mode 100644
index cfe51618c..000000000
--- a/docsrc/generated/libpysal.cg.get_ray_segment_intersect.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.get\_ray\_segment\_intersect
-========================================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: get_ray_segment_intersect
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.cg.get_rectangle_point_intersect.rst b/docsrc/generated/libpysal.cg.get_rectangle_point_intersect.rst
deleted file mode 100644
index b31e074e5..000000000
--- a/docsrc/generated/libpysal.cg.get_rectangle_point_intersect.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.get\_rectangle\_point\_intersect
-============================================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: get_rectangle_point_intersect
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.cg.get_rectangle_rectangle_intersection.rst b/docsrc/generated/libpysal.cg.get_rectangle_rectangle_intersection.rst
deleted file mode 100644
index bcdbf5ef2..000000000
--- a/docsrc/generated/libpysal.cg.get_rectangle_rectangle_intersection.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.get\_rectangle\_rectangle\_intersection
-===================================================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: get_rectangle_rectangle_intersection
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.cg.get_segment_point_dist.rst b/docsrc/generated/libpysal.cg.get_segment_point_dist.rst
deleted file mode 100644
index fd32bb258..000000000
--- a/docsrc/generated/libpysal.cg.get_segment_point_dist.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.get\_segment\_point\_dist
-=====================================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: get_segment_point_dist
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.cg.get_segment_point_intersect.rst b/docsrc/generated/libpysal.cg.get_segment_point_intersect.rst
deleted file mode 100644
index c54747be1..000000000
--- a/docsrc/generated/libpysal.cg.get_segment_point_intersect.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.get\_segment\_point\_intersect
-==========================================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: get_segment_point_intersect
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.cg.get_segments_intersect.rst b/docsrc/generated/libpysal.cg.get_segments_intersect.rst
deleted file mode 100644
index a0325a346..000000000
--- a/docsrc/generated/libpysal.cg.get_segments_intersect.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.get\_segments\_intersect
-====================================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: get_segments_intersect
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.cg.get_shared_segments.rst b/docsrc/generated/libpysal.cg.get_shared_segments.rst
deleted file mode 100644
index 23d6c5174..000000000
--- a/docsrc/generated/libpysal.cg.get_shared_segments.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.get\_shared\_segments
-=================================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: get_shared_segments
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.cg.harcdist.rst b/docsrc/generated/libpysal.cg.harcdist.rst
deleted file mode 100644
index fd1897c42..000000000
--- a/docsrc/generated/libpysal.cg.harcdist.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.harcdist
-====================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: harcdist
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.cg.is_clockwise.rst b/docsrc/generated/libpysal.cg.is_clockwise.rst
deleted file mode 100644
index 02e3491bb..000000000
--- a/docsrc/generated/libpysal.cg.is_clockwise.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.is\_clockwise
-=========================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: is_clockwise
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.cg.is_collinear.rst b/docsrc/generated/libpysal.cg.is_collinear.rst
deleted file mode 100644
index 35252dea5..000000000
--- a/docsrc/generated/libpysal.cg.is_collinear.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.is\_collinear
-=========================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: is_collinear
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.cg.linear2arcdist.rst b/docsrc/generated/libpysal.cg.linear2arcdist.rst
deleted file mode 100644
index 25004601e..000000000
--- a/docsrc/generated/libpysal.cg.linear2arcdist.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.linear2arcdist
-==========================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: linear2arcdist
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.cg.lonlat.rst b/docsrc/generated/libpysal.cg.lonlat.rst
deleted file mode 100644
index 21197e585..000000000
--- a/docsrc/generated/libpysal.cg.lonlat.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.lonlat
-==================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: lonlat
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.cg.point_touches_rectangle.rst b/docsrc/generated/libpysal.cg.point_touches_rectangle.rst
deleted file mode 100644
index 19475cfc2..000000000
--- a/docsrc/generated/libpysal.cg.point_touches_rectangle.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.point\_touches\_rectangle
-=====================================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: point_touches_rectangle
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.cg.toLngLat.rst b/docsrc/generated/libpysal.cg.toLngLat.rst
deleted file mode 100644
index 32a85fc4a..000000000
--- a/docsrc/generated/libpysal.cg.toLngLat.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.toLngLat
-====================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: toLngLat
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.cg.toXYZ.rst b/docsrc/generated/libpysal.cg.toXYZ.rst
deleted file mode 100644
index cd18210ec..000000000
--- a/docsrc/generated/libpysal.cg.toXYZ.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.toXYZ
-=================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: toXYZ
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.cg.voronoi_frames.rst b/docsrc/generated/libpysal.cg.voronoi_frames.rst
deleted file mode 100644
index 5270c27a4..000000000
--- a/docsrc/generated/libpysal.cg.voronoi_frames.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.voronoi\_frames
-===========================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: voronoi_frames
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.examples.available.rst b/docsrc/generated/libpysal.examples.available.rst
deleted file mode 100644
index 263a1b59c..000000000
--- a/docsrc/generated/libpysal.examples.available.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.examples.available
-===========================
-
-.. currentmodule:: libpysal.examples
-
-.. autofunction:: available
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.examples.explain.rst b/docsrc/generated/libpysal.examples.explain.rst
deleted file mode 100644
index 3964e1189..000000000
--- a/docsrc/generated/libpysal.examples.explain.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.examples.explain
-=========================
-
-.. currentmodule:: libpysal.examples
-
-.. autofunction:: explain
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.examples.get_path.rst b/docsrc/generated/libpysal.examples.get_path.rst
deleted file mode 100644
index 8750fd88d..000000000
--- a/docsrc/generated/libpysal.examples.get_path.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.examples.get\_path
-===========================
-
-.. currentmodule:: libpysal.examples
-
-.. autofunction:: get_path
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.io.fileio.FileIO.rst b/docsrc/generated/libpysal.io.fileio.FileIO.rst
deleted file mode 100644
index 822071cd6..000000000
--- a/docsrc/generated/libpysal.io.fileio.FileIO.rst
+++ /dev/null
@@ -1,42 +0,0 @@
-libpysal.io.fileio.FileIO
-=========================
-
-.. currentmodule:: libpysal.io.fileio
-
-.. autoclass:: FileIO
-
-   
-   .. automethod:: __init__
-
-   
-   .. rubric:: Methods
-
-   .. autosummary::
-   
-      ~FileIO.__init__
-      ~FileIO.cast
-      ~FileIO.check
-      ~FileIO.close
-      ~FileIO.flush
-      ~FileIO.get
-      ~FileIO.getType
-      ~FileIO.open
-      ~FileIO.read
-      ~FileIO.seek
-      ~FileIO.tell
-      ~FileIO.truncate
-      ~FileIO.write
-   
-   
-
-   
-   
-   .. rubric:: Attributes
-
-   .. autosummary::
-   
-      ~FileIO.by_row
-      ~FileIO.ids
-      ~FileIO.rIds
-   
-   
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.io.open.rst b/docsrc/generated/libpysal.io.open.rst
deleted file mode 100644
index 28508b74f..000000000
--- a/docsrc/generated/libpysal.io.open.rst
+++ /dev/null
@@ -1,42 +0,0 @@
-libpysal.io.open
-================
-
-.. currentmodule:: libpysal.io
-
-.. autoclass:: open
-
-   
-   .. automethod:: __init__
-
-   
-   .. rubric:: Methods
-
-   .. autosummary::
-   
-      ~open.__init__
-      ~open.cast
-      ~open.check
-      ~open.close
-      ~open.flush
-      ~open.get
-      ~open.getType
-      ~open.open
-      ~open.read
-      ~open.seek
-      ~open.tell
-      ~open.truncate
-      ~open.write
-   
-   
-
-   
-   
-   .. rubric:: Attributes
-
-   .. autosummary::
-   
-      ~open.by_row
-      ~open.ids
-      ~open.rIds
-   
-   
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.weights.DistanceBand.rst b/docsrc/generated/libpysal.weights.DistanceBand.rst
deleted file mode 100644
index 528076369..000000000
--- a/docsrc/generated/libpysal.weights.DistanceBand.rst
+++ /dev/null
@@ -1,75 +0,0 @@
-libpysal.weights.DistanceBand
-=============================
-
-.. currentmodule:: libpysal.weights
-
-.. autoclass:: DistanceBand
-
-   
-   .. automethod:: __init__
-
-   
-   .. rubric:: Methods
-
-   .. autosummary::
-   
-      ~DistanceBand.__init__
-      ~DistanceBand.asymmetry
-      ~DistanceBand.from_WSP
-      ~DistanceBand.from_adjlist
-      ~DistanceBand.from_array
-      ~DistanceBand.from_dataframe
-      ~DistanceBand.from_file
-      ~DistanceBand.from_networkx
-      ~DistanceBand.from_shapefile
-      ~DistanceBand.full
-      ~DistanceBand.get_transform
-      ~DistanceBand.plot
-      ~DistanceBand.remap_ids
-      ~DistanceBand.set_shapefile
-      ~DistanceBand.set_transform
-      ~DistanceBand.symmetrize
-      ~DistanceBand.to_WSP
-      ~DistanceBand.to_adjlist
-      ~DistanceBand.to_file
-      ~DistanceBand.to_networkx
-   
-   
-
-   
-   
-   .. rubric:: Attributes
-
-   .. autosummary::
-   
-      ~DistanceBand.asymmetries
-      ~DistanceBand.cardinalities
-      ~DistanceBand.component_labels
-      ~DistanceBand.diagW2
-      ~DistanceBand.diagWtW
-      ~DistanceBand.diagWtW_WW
-      ~DistanceBand.histogram
-      ~DistanceBand.id2i
-      ~DistanceBand.id_order
-      ~DistanceBand.id_order_set
-      ~DistanceBand.islands
-      ~DistanceBand.max_neighbors
-      ~DistanceBand.mean_neighbors
-      ~DistanceBand.min_neighbors
-      ~DistanceBand.n
-      ~DistanceBand.n_components
-      ~DistanceBand.neighbor_offsets
-      ~DistanceBand.nonzero
-      ~DistanceBand.pct_nonzero
-      ~DistanceBand.s0
-      ~DistanceBand.s1
-      ~DistanceBand.s2
-      ~DistanceBand.s2array
-      ~DistanceBand.sd
-      ~DistanceBand.sparse
-      ~DistanceBand.transform
-      ~DistanceBand.trcW2
-      ~DistanceBand.trcWtW
-      ~DistanceBand.trcWtW_WW
-   
-   
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.weights.KNN.rst b/docsrc/generated/libpysal.weights.KNN.rst
deleted file mode 100644
index 7eca1d31d..000000000
--- a/docsrc/generated/libpysal.weights.KNN.rst
+++ /dev/null
@@ -1,76 +0,0 @@
-libpysal.weights.KNN
-====================
-
-.. currentmodule:: libpysal.weights
-
-.. autoclass:: KNN
-
-   
-   .. automethod:: __init__
-
-   
-   .. rubric:: Methods
-
-   .. autosummary::
-   
-      ~KNN.__init__
-      ~KNN.asymmetry
-      ~KNN.from_WSP
-      ~KNN.from_adjlist
-      ~KNN.from_array
-      ~KNN.from_dataframe
-      ~KNN.from_file
-      ~KNN.from_networkx
-      ~KNN.from_shapefile
-      ~KNN.full
-      ~KNN.get_transform
-      ~KNN.plot
-      ~KNN.remap_ids
-      ~KNN.reweight
-      ~KNN.set_shapefile
-      ~KNN.set_transform
-      ~KNN.symmetrize
-      ~KNN.to_WSP
-      ~KNN.to_adjlist
-      ~KNN.to_file
-      ~KNN.to_networkx
-   
-   
-
-   
-   
-   .. rubric:: Attributes
-
-   .. autosummary::
-   
-      ~KNN.asymmetries
-      ~KNN.cardinalities
-      ~KNN.component_labels
-      ~KNN.diagW2
-      ~KNN.diagWtW
-      ~KNN.diagWtW_WW
-      ~KNN.histogram
-      ~KNN.id2i
-      ~KNN.id_order
-      ~KNN.id_order_set
-      ~KNN.islands
-      ~KNN.max_neighbors
-      ~KNN.mean_neighbors
-      ~KNN.min_neighbors
-      ~KNN.n
-      ~KNN.n_components
-      ~KNN.neighbor_offsets
-      ~KNN.nonzero
-      ~KNN.pct_nonzero
-      ~KNN.s0
-      ~KNN.s1
-      ~KNN.s2
-      ~KNN.s2array
-      ~KNN.sd
-      ~KNN.sparse
-      ~KNN.transform
-      ~KNN.trcW2
-      ~KNN.trcWtW
-      ~KNN.trcWtW_WW
-   
-   
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.weights.Kernel.rst b/docsrc/generated/libpysal.weights.Kernel.rst
deleted file mode 100644
index fce92dedc..000000000
--- a/docsrc/generated/libpysal.weights.Kernel.rst
+++ /dev/null
@@ -1,75 +0,0 @@
-libpysal.weights.Kernel
-=======================
-
-.. currentmodule:: libpysal.weights
-
-.. autoclass:: Kernel
-
-   
-   .. automethod:: __init__
-
-   
-   .. rubric:: Methods
-
-   .. autosummary::
-   
-      ~Kernel.__init__
-      ~Kernel.asymmetry
-      ~Kernel.from_WSP
-      ~Kernel.from_adjlist
-      ~Kernel.from_array
-      ~Kernel.from_dataframe
-      ~Kernel.from_file
-      ~Kernel.from_networkx
-      ~Kernel.from_shapefile
-      ~Kernel.full
-      ~Kernel.get_transform
-      ~Kernel.plot
-      ~Kernel.remap_ids
-      ~Kernel.set_shapefile
-      ~Kernel.set_transform
-      ~Kernel.symmetrize
-      ~Kernel.to_WSP
-      ~Kernel.to_adjlist
-      ~Kernel.to_file
-      ~Kernel.to_networkx
-   
-   
-
-   
-   
-   .. rubric:: Attributes
-
-   .. autosummary::
-   
-      ~Kernel.asymmetries
-      ~Kernel.cardinalities
-      ~Kernel.component_labels
-      ~Kernel.diagW2
-      ~Kernel.diagWtW
-      ~Kernel.diagWtW_WW
-      ~Kernel.histogram
-      ~Kernel.id2i
-      ~Kernel.id_order
-      ~Kernel.id_order_set
-      ~Kernel.islands
-      ~Kernel.max_neighbors
-      ~Kernel.mean_neighbors
-      ~Kernel.min_neighbors
-      ~Kernel.n
-      ~Kernel.n_components
-      ~Kernel.neighbor_offsets
-      ~Kernel.nonzero
-      ~Kernel.pct_nonzero
-      ~Kernel.s0
-      ~Kernel.s1
-      ~Kernel.s2
-      ~Kernel.s2array
-      ~Kernel.sd
-      ~Kernel.sparse
-      ~Kernel.transform
-      ~Kernel.trcW2
-      ~Kernel.trcWtW
-      ~Kernel.trcWtW_WW
-   
-   
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.weights.ODW.rst b/docsrc/generated/libpysal.weights.ODW.rst
deleted file mode 100644
index bd6e87340..000000000
--- a/docsrc/generated/libpysal.weights.ODW.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.ODW
-====================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: ODW
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.weights.Queen.rst b/docsrc/generated/libpysal.weights.Queen.rst
deleted file mode 100644
index 36858f566..000000000
--- a/docsrc/generated/libpysal.weights.Queen.rst
+++ /dev/null
@@ -1,76 +0,0 @@
-libpysal.weights.Queen
-======================
-
-.. currentmodule:: libpysal.weights
-
-.. autoclass:: Queen
-
-   
-   .. automethod:: __init__
-
-   
-   .. rubric:: Methods
-
-   .. autosummary::
-   
-      ~Queen.__init__
-      ~Queen.asymmetry
-      ~Queen.from_WSP
-      ~Queen.from_adjlist
-      ~Queen.from_dataframe
-      ~Queen.from_file
-      ~Queen.from_iterable
-      ~Queen.from_networkx
-      ~Queen.from_shapefile
-      ~Queen.from_xarray
-      ~Queen.full
-      ~Queen.get_transform
-      ~Queen.plot
-      ~Queen.remap_ids
-      ~Queen.set_shapefile
-      ~Queen.set_transform
-      ~Queen.symmetrize
-      ~Queen.to_WSP
-      ~Queen.to_adjlist
-      ~Queen.to_file
-      ~Queen.to_networkx
-   
-   
-
-   
-   
-   .. rubric:: Attributes
-
-   .. autosummary::
-   
-      ~Queen.asymmetries
-      ~Queen.cardinalities
-      ~Queen.component_labels
-      ~Queen.diagW2
-      ~Queen.diagWtW
-      ~Queen.diagWtW_WW
-      ~Queen.histogram
-      ~Queen.id2i
-      ~Queen.id_order
-      ~Queen.id_order_set
-      ~Queen.islands
-      ~Queen.max_neighbors
-      ~Queen.mean_neighbors
-      ~Queen.min_neighbors
-      ~Queen.n
-      ~Queen.n_components
-      ~Queen.neighbor_offsets
-      ~Queen.nonzero
-      ~Queen.pct_nonzero
-      ~Queen.s0
-      ~Queen.s1
-      ~Queen.s2
-      ~Queen.s2array
-      ~Queen.sd
-      ~Queen.sparse
-      ~Queen.transform
-      ~Queen.trcW2
-      ~Queen.trcWtW
-      ~Queen.trcWtW_WW
-   
-   
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.weights.Rook.rst b/docsrc/generated/libpysal.weights.Rook.rst
deleted file mode 100644
index 364b3b2cb..000000000
--- a/docsrc/generated/libpysal.weights.Rook.rst
+++ /dev/null
@@ -1,76 +0,0 @@
-libpysal.weights.Rook
-=====================
-
-.. currentmodule:: libpysal.weights
-
-.. autoclass:: Rook
-
-   
-   .. automethod:: __init__
-
-   
-   .. rubric:: Methods
-
-   .. autosummary::
-   
-      ~Rook.__init__
-      ~Rook.asymmetry
-      ~Rook.from_WSP
-      ~Rook.from_adjlist
-      ~Rook.from_dataframe
-      ~Rook.from_file
-      ~Rook.from_iterable
-      ~Rook.from_networkx
-      ~Rook.from_shapefile
-      ~Rook.from_xarray
-      ~Rook.full
-      ~Rook.get_transform
-      ~Rook.plot
-      ~Rook.remap_ids
-      ~Rook.set_shapefile
-      ~Rook.set_transform
-      ~Rook.symmetrize
-      ~Rook.to_WSP
-      ~Rook.to_adjlist
-      ~Rook.to_file
-      ~Rook.to_networkx
-   
-   
-
-   
-   
-   .. rubric:: Attributes
-
-   .. autosummary::
-   
-      ~Rook.asymmetries
-      ~Rook.cardinalities
-      ~Rook.component_labels
-      ~Rook.diagW2
-      ~Rook.diagWtW
-      ~Rook.diagWtW_WW
-      ~Rook.histogram
-      ~Rook.id2i
-      ~Rook.id_order
-      ~Rook.id_order_set
-      ~Rook.islands
-      ~Rook.max_neighbors
-      ~Rook.mean_neighbors
-      ~Rook.min_neighbors
-      ~Rook.n
-      ~Rook.n_components
-      ~Rook.neighbor_offsets
-      ~Rook.nonzero
-      ~Rook.pct_nonzero
-      ~Rook.s0
-      ~Rook.s1
-      ~Rook.s2
-      ~Rook.s2array
-      ~Rook.sd
-      ~Rook.sparse
-      ~Rook.transform
-      ~Rook.trcW2
-      ~Rook.trcWtW
-      ~Rook.trcWtW_WW
-   
-   
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.weights.Voronoi.rst b/docsrc/generated/libpysal.weights.Voronoi.rst
deleted file mode 100644
index ed097d5c9..000000000
--- a/docsrc/generated/libpysal.weights.Voronoi.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.Voronoi
-========================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: Voronoi
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.weights.W.rst b/docsrc/generated/libpysal.weights.W.rst
deleted file mode 100644
index 21e1d5c00..000000000
--- a/docsrc/generated/libpysal.weights.W.rst
+++ /dev/null
@@ -1,73 +0,0 @@
-libpysal.weights.W
-==================
-
-.. currentmodule:: libpysal.weights
-
-.. autoclass:: W
-
-   
-   .. automethod:: __init__
-
-   
-   .. rubric:: Methods
-
-   .. autosummary::
-   
-      ~W.__init__
-      ~W.asymmetry
-      ~W.from_WSP
-      ~W.from_adjlist
-      ~W.from_file
-      ~W.from_networkx
-      ~W.from_shapefile
-      ~W.full
-      ~W.get_transform
-      ~W.plot
-      ~W.remap_ids
-      ~W.set_shapefile
-      ~W.set_transform
-      ~W.symmetrize
-      ~W.to_WSP
-      ~W.to_adjlist
-      ~W.to_file
-      ~W.to_networkx
-   
-   
-
-   
-   
-   .. rubric:: Attributes
-
-   .. autosummary::
-   
-      ~W.asymmetries
-      ~W.cardinalities
-      ~W.component_labels
-      ~W.diagW2
-      ~W.diagWtW
-      ~W.diagWtW_WW
-      ~W.histogram
-      ~W.id2i
-      ~W.id_order
-      ~W.id_order_set
-      ~W.islands
-      ~W.max_neighbors
-      ~W.mean_neighbors
-      ~W.min_neighbors
-      ~W.n
-      ~W.n_components
-      ~W.neighbor_offsets
-      ~W.nonzero
-      ~W.pct_nonzero
-      ~W.s0
-      ~W.s1
-      ~W.s2
-      ~W.s2array
-      ~W.sd
-      ~W.sparse
-      ~W.transform
-      ~W.trcW2
-      ~W.trcWtW
-      ~W.trcWtW_WW
-   
-   
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.weights.WSP.rst b/docsrc/generated/libpysal.weights.WSP.rst
deleted file mode 100644
index 8192bd7d1..000000000
--- a/docsrc/generated/libpysal.weights.WSP.rst
+++ /dev/null
@@ -1,33 +0,0 @@
-libpysal.weights.WSP
-====================
-
-.. currentmodule:: libpysal.weights
-
-.. autoclass:: WSP
-
-   
-   .. automethod:: __init__
-
-   
-   .. rubric:: Methods
-
-   .. autosummary::
-   
-      ~WSP.__init__
-      ~WSP.from_W
-      ~WSP.to_W
-   
-   
-
-   
-   
-   .. rubric:: Attributes
-
-   .. autosummary::
-   
-      ~WSP.diagWtW_WW
-      ~WSP.id_order
-      ~WSP.s0
-      ~WSP.trcWtW_WW
-   
-   
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.weights.WSP2W.rst b/docsrc/generated/libpysal.weights.WSP2W.rst
deleted file mode 100644
index 0b05884a3..000000000
--- a/docsrc/generated/libpysal.weights.WSP2W.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.WSP2W
-======================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: WSP2W
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.weights.attach_islands.rst b/docsrc/generated/libpysal.weights.attach_islands.rst
deleted file mode 100644
index 49cb88ab5..000000000
--- a/docsrc/generated/libpysal.weights.attach_islands.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.attach\_islands
-================================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: attach_islands
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.weights.block_weights.rst b/docsrc/generated/libpysal.weights.block_weights.rst
deleted file mode 100644
index 30a79c630..000000000
--- a/docsrc/generated/libpysal.weights.block_weights.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.block\_weights
-===============================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: block_weights
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.weights.build_lattice_shapefile.rst b/docsrc/generated/libpysal.weights.build_lattice_shapefile.rst
deleted file mode 100644
index c742797cf..000000000
--- a/docsrc/generated/libpysal.weights.build_lattice_shapefile.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.build\_lattice\_shapefile
-==========================================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: build_lattice_shapefile
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.weights.comb.rst b/docsrc/generated/libpysal.weights.comb.rst
deleted file mode 100644
index fd4fc858b..000000000
--- a/docsrc/generated/libpysal.weights.comb.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.comb
-=====================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: comb
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.weights.da2W.rst b/docsrc/generated/libpysal.weights.da2W.rst
deleted file mode 100644
index 5dacd87c0..000000000
--- a/docsrc/generated/libpysal.weights.da2W.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.da2W
-=====================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: da2W
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.weights.da2WSP.rst b/docsrc/generated/libpysal.weights.da2WSP.rst
deleted file mode 100644
index e5bfd8c6f..000000000
--- a/docsrc/generated/libpysal.weights.da2WSP.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.da2WSP
-=======================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: da2WSP
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.weights.fill_diagonal.rst b/docsrc/generated/libpysal.weights.fill_diagonal.rst
deleted file mode 100644
index 84d067071..000000000
--- a/docsrc/generated/libpysal.weights.fill_diagonal.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.fill\_diagonal
-===============================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: fill_diagonal
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.weights.full.rst b/docsrc/generated/libpysal.weights.full.rst
deleted file mode 100644
index cd584af47..000000000
--- a/docsrc/generated/libpysal.weights.full.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.full
-=====================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: full
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.weights.full2W.rst b/docsrc/generated/libpysal.weights.full2W.rst
deleted file mode 100644
index 8cd2e8c77..000000000
--- a/docsrc/generated/libpysal.weights.full2W.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.full2W
-=======================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: full2W
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.weights.fuzzy_contiguity.rst b/docsrc/generated/libpysal.weights.fuzzy_contiguity.rst
deleted file mode 100644
index e8d510ec9..000000000
--- a/docsrc/generated/libpysal.weights.fuzzy_contiguity.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.fuzzy\_contiguity
-==================================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: fuzzy_contiguity
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.weights.get_ids.rst b/docsrc/generated/libpysal.weights.get_ids.rst
deleted file mode 100644
index c49fa48e6..000000000
--- a/docsrc/generated/libpysal.weights.get_ids.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.get\_ids
-=========================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: get_ids
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.weights.get_points_array_from_shapefile.rst b/docsrc/generated/libpysal.weights.get_points_array_from_shapefile.rst
deleted file mode 100644
index 3098243ae..000000000
--- a/docsrc/generated/libpysal.weights.get_points_array_from_shapefile.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.get\_points\_array\_from\_shapefile
-====================================================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: get_points_array_from_shapefile
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.weights.hexLat2W.rst b/docsrc/generated/libpysal.weights.hexLat2W.rst
deleted file mode 100644
index aeac82591..000000000
--- a/docsrc/generated/libpysal.weights.hexLat2W.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.hexLat2W
-=========================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: hexLat2W
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.weights.higher_order.rst b/docsrc/generated/libpysal.weights.higher_order.rst
deleted file mode 100644
index d51b29b23..000000000
--- a/docsrc/generated/libpysal.weights.higher_order.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.higher\_order
-==============================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: higher_order
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.weights.higher_order_sp.rst b/docsrc/generated/libpysal.weights.higher_order_sp.rst
deleted file mode 100644
index b7e831e72..000000000
--- a/docsrc/generated/libpysal.weights.higher_order_sp.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.higher\_order\_sp
-==================================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: higher_order_sp
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.weights.lag_categorical.rst b/docsrc/generated/libpysal.weights.lag_categorical.rst
deleted file mode 100644
index 1a2fdd7e2..000000000
--- a/docsrc/generated/libpysal.weights.lag_categorical.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.lag\_categorical
-=================================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: lag_categorical
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.weights.lag_spatial.rst b/docsrc/generated/libpysal.weights.lag_spatial.rst
deleted file mode 100644
index 204f5d807..000000000
--- a/docsrc/generated/libpysal.weights.lag_spatial.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.lag\_spatial
-=============================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: lag_spatial
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.weights.lat2SW.rst b/docsrc/generated/libpysal.weights.lat2SW.rst
deleted file mode 100644
index b0b48ae9b..000000000
--- a/docsrc/generated/libpysal.weights.lat2SW.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.lat2SW
-=======================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: lat2SW
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.weights.lat2W.rst b/docsrc/generated/libpysal.weights.lat2W.rst
deleted file mode 100644
index 93724cf5f..000000000
--- a/docsrc/generated/libpysal.weights.lat2W.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.lat2W
-======================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: lat2W
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.weights.mat2L.rst b/docsrc/generated/libpysal.weights.mat2L.rst
deleted file mode 100644
index 8d1808c3e..000000000
--- a/docsrc/generated/libpysal.weights.mat2L.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.mat2L
-======================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: mat2L
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.weights.min_threshold_dist_from_shapefile.rst b/docsrc/generated/libpysal.weights.min_threshold_dist_from_shapefile.rst
deleted file mode 100644
index 8944feb4d..000000000
--- a/docsrc/generated/libpysal.weights.min_threshold_dist_from_shapefile.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.min\_threshold\_dist\_from\_shapefile
-======================================================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: min_threshold_dist_from_shapefile
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.weights.min_threshold_distance.rst b/docsrc/generated/libpysal.weights.min_threshold_distance.rst
deleted file mode 100644
index 6a2941498..000000000
--- a/docsrc/generated/libpysal.weights.min_threshold_distance.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.min\_threshold\_distance
-=========================================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: min_threshold_distance
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.weights.neighbor_equality.rst b/docsrc/generated/libpysal.weights.neighbor_equality.rst
deleted file mode 100644
index 714af0a7c..000000000
--- a/docsrc/generated/libpysal.weights.neighbor_equality.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.neighbor\_equality
-===================================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: neighbor_equality
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.weights.netW.rst b/docsrc/generated/libpysal.weights.netW.rst
deleted file mode 100644
index 492b0d729..000000000
--- a/docsrc/generated/libpysal.weights.netW.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.netW
-=====================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: netW
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.weights.nonplanar_neighbors.rst b/docsrc/generated/libpysal.weights.nonplanar_neighbors.rst
deleted file mode 100644
index d19855dd8..000000000
--- a/docsrc/generated/libpysal.weights.nonplanar_neighbors.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.nonplanar\_neighbors
-=====================================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: nonplanar_neighbors
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.weights.order.rst b/docsrc/generated/libpysal.weights.order.rst
deleted file mode 100644
index 77e9ed195..000000000
--- a/docsrc/generated/libpysal.weights.order.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.order
-======================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: order
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.weights.remap_ids.rst b/docsrc/generated/libpysal.weights.remap_ids.rst
deleted file mode 100644
index 4899fc477..000000000
--- a/docsrc/generated/libpysal.weights.remap_ids.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.remap\_ids
-===========================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: remap_ids
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.weights.shimbel.rst b/docsrc/generated/libpysal.weights.shimbel.rst
deleted file mode 100644
index ed151c58c..000000000
--- a/docsrc/generated/libpysal.weights.shimbel.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.shimbel
-========================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: shimbel
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.weights.spw_from_gal.rst b/docsrc/generated/libpysal.weights.spw_from_gal.rst
deleted file mode 100644
index fc4592a3e..000000000
--- a/docsrc/generated/libpysal.weights.spw_from_gal.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.spw\_from\_gal
-===============================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: spw_from_gal
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.weights.testDataArray.rst b/docsrc/generated/libpysal.weights.testDataArray.rst
deleted file mode 100644
index ded0f3098..000000000
--- a/docsrc/generated/libpysal.weights.testDataArray.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.testDataArray
-==============================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: testDataArray
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.weights.vecW.rst b/docsrc/generated/libpysal.weights.vecW.rst
deleted file mode 100644
index 8baf549d2..000000000
--- a/docsrc/generated/libpysal.weights.vecW.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.vecW
-=====================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: vecW
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.weights.w2da.rst b/docsrc/generated/libpysal.weights.w2da.rst
deleted file mode 100644
index 6a11417f1..000000000
--- a/docsrc/generated/libpysal.weights.w2da.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.w2da
-=====================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: w2da
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.weights.w_clip.rst b/docsrc/generated/libpysal.weights.w_clip.rst
deleted file mode 100644
index d1731b1e1..000000000
--- a/docsrc/generated/libpysal.weights.w_clip.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.w\_clip
-========================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: w_clip
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.weights.w_difference.rst b/docsrc/generated/libpysal.weights.w_difference.rst
deleted file mode 100644
index 6f0210d95..000000000
--- a/docsrc/generated/libpysal.weights.w_difference.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.w\_difference
-==============================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: w_difference
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.weights.w_intersection.rst b/docsrc/generated/libpysal.weights.w_intersection.rst
deleted file mode 100644
index bf25a21e0..000000000
--- a/docsrc/generated/libpysal.weights.w_intersection.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.w\_intersection
-================================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: w_intersection
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.weights.w_local_cluster.rst b/docsrc/generated/libpysal.weights.w_local_cluster.rst
deleted file mode 100644
index 56e84ef20..000000000
--- a/docsrc/generated/libpysal.weights.w_local_cluster.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.w\_local\_cluster
-==================================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: w_local_cluster
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.weights.w_subset.rst b/docsrc/generated/libpysal.weights.w_subset.rst
deleted file mode 100644
index 2164fd6fd..000000000
--- a/docsrc/generated/libpysal.weights.w_subset.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.w\_subset
-==========================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: w_subset
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.weights.w_symmetric_difference.rst b/docsrc/generated/libpysal.weights.w_symmetric_difference.rst
deleted file mode 100644
index c06c0b5f3..000000000
--- a/docsrc/generated/libpysal.weights.w_symmetric_difference.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.w\_symmetric\_difference
-=========================================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: w_symmetric_difference
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.weights.w_union.rst b/docsrc/generated/libpysal.weights.w_union.rst
deleted file mode 100644
index 278a9c33f..000000000
--- a/docsrc/generated/libpysal.weights.w_union.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.w\_union
-=========================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: w_union
\ No newline at end of file
diff --git a/docsrc/generated/libpysal.weights.wsp2da.rst b/docsrc/generated/libpysal.weights.wsp2da.rst
deleted file mode 100644
index 7b71f7f45..000000000
--- a/docsrc/generated/libpysal.weights.wsp2da.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.wsp2da
-=======================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: wsp2da
\ No newline at end of file
diff --git a/docsrc/index.rst b/docsrc/index.rst
deleted file mode 100644
index 8040ac2e3..000000000
--- a/docsrc/index.rst
+++ /dev/null
@@ -1,133 +0,0 @@
-.. libpysal documentation master file
-
-libpysal: Python Spatial Analysis Library Core
-==============================================
-
-.. image:: https://github.com/pysal/libpysal/workflows/.github/workflows/unittests.yml/badge.svg
-   :target: https://github.com/pysal/libpysal/actions?query=workflow%3A.github%2Fworkflows%2Funittests.yml
-
-.. image:: https://badges.gitter.im/pysal/pysal.svg
-   :target: https://gitter.im/pysal/pysal
-
-.. image:: https://badge.fury.io/py/libpysal.svg
-    :target: https://badge.fury.io/py/libpysal
-
-.. raw:: html
-
-    <div class="container-fluid">
-      <div class="row equal-height">
-        <div class="col-sm-1 col-xs-hidden">
-        </div>
-        <div class="col-sm-10 col-xs-12">
-            <a href="http://nbviewer.jupyter.org/github/pysal/libpysal/blob/master/notebooks/weights.ipynb" class="thumbnail">
-                <img src="_static/images/npweights.png" class="img-responsive center-block">
-                <div class="caption text-center">
-                <h6>Weights for nonplanar enforced geometries</h6>
-                </div>
-            </a>
-        </div>
-        <div class="col-sm-1 col-xs-hidden">
-        </div>
-      </div>
-    </div>
-
-
-************
-Introduction
-************
-
-**libpysal** offers four modules that form the building blocks in many upstream packages in the `PySAL family <https://pysal.org>`_:
-
-- Spatial Weights: libpysal.weights 
-- Input-and output: libpysal.io 
-- Computational geometry: libpysal.cg  
-- Built-in example datasets libpysal.examples  
-
-
-Examples demonstrating some of **libpysal** functionality are available in the `tutorial <tutorial.html>`_.
-
-Details are available in the `libpysal api <api.html>`_.
-
-For background information see :cite:`pysal2007`.
-
-***********
-Development
-***********
-
-libpysal development is hosted on github_.
-
-.. _github : https://github.com/pysal/libpysal
-
-Discussions of development occurs on the
-`developer list <http://groups.google.com/group/pysal-dev>`_
-as well as gitter_.
-
-.. _gitter : https://gitter.im/pysal/pysal?
-
-****************
-Getting Involved
-****************
-
-If you are interested in contributing to PySAL please see our
-`development guidelines  <https://github.com/pysal/pysal/wiki>`_.
-
-
-***********
-Bug reports
-***********
-
-To search for or report bugs, please see libpysal's issues_.
-
-.. _issues :  http://github.com/pysal/libpysal/issues
-
-
-***************
-Citing libpysal
-***************
-
-If you use PySAL in a scientific publication, we would appreciate citations to the following paper:
-
-  `PySAL: A Python Library of Spatial Analytical Methods <http://journal.srsa.org/ojs/index.php/RRS/article/view/134/85>`_, *Rey, S.J. and L. Anselin*, Review of Regional Studies 37, 5-27 2007.
-
-  Bibtex entry::
-
-      @Article{pysal2007,
-        author={Rey, Sergio J. and Anselin, Luc},
-        title={{PySAL: A Python Library of Spatial Analytical Methods}},
-        journal={The Review of Regional Studies},
-        year=2007,
-        volume={37},
-        number={1},
-        pages={5-27},
-        keywords={Open Source; Software; Spatial}
-      }
-
-
-
-*******************
-License information
-*******************
-
-See the file "LICENSE.txt" for information on the history of this
-software, terms & conditions for usage, and a DISCLAIMER OF ALL
-WARRANTIES.
-
-
-libpysal
-========
-
-Core components of the Python Spatial Analysis Library (`PySAL`_)
-
-
-
-.. toctree::
-   :hidden:
-   :maxdepth: 4
-   :caption: Contents:
-
-   Installation <installation>
-   Tutorial <tutorial>
-   API <api>
-   References <references>
-   
-.. _PySAL: https://github.com/pysal/pysal
diff --git a/docsrc/installation.rst b/docsrc/installation.rst
deleted file mode 100644
index 8c6ea63cb..000000000
--- a/docsrc/installation.rst
+++ /dev/null
@@ -1,57 +0,0 @@
-.. Installation
-
-Installation
-============
-
-libpysal supports python >= `3.6`_ only. Please make sure that you are
-operating in a python 3 environment.
-
-Installing released version
----------------------------
-
-conda
-+++++
-
-libpysal is available through conda::
-
- 
-  conda install -c conda-forge libpysal
-
-
-pypi
-++++
-
-
-libpysal is available on the `Python Package Index`_. Therefore, you can either
-install directly with `pip` from the command line::
-
-  pip install -U libpysal
-
-
-or download the source distribution (.tar.gz) and decompress it to your selected
-destination. Open a command shell and navigate to the decompressed folder.
-Type::
-
-  pip install .
-
-Installing development version
-------------------------------
-
-Potentially, you might want to use the newest features in the development
-version of libpysal on github - `pysal/libpysal`_ while have not been incorporated
-in the Pypi released version. You can achieve that by installing `pysal/libpysal`_
-by running the following from a command shell::
-
-  pip install git+https://github.com/pysal/libpysal.git
-
-You can  also `fork`_ the `pysal/libpysal`_ repo and create a local clone of
-your fork. By making changes
-to your local clone and submitting a pull request to `pysal/libpysal`_, you can
-contribute to libpysal development.
-
-.. _3.6: https://docs.python.org/3.6/
-.. _3.7: https://docs.python.org/3.7/
-.. _3.8: https://docs.python.org/3.8/
-.. _Python Package Index: https://pypi.org/project/libpysal/
-.. _pysal/libpysal: https://github.com/pysal/libpysal
-.. _fork: https://help.github.com/articles/fork-a-repo/
diff --git a/docsrc/notebooks/Raster_awareness_API.ipynb b/docsrc/notebooks/Raster_awareness_API.ipynb
deleted file mode 100644
index a38dc049d..000000000
--- a/docsrc/notebooks/Raster_awareness_API.ipynb
+++ /dev/null
@@ -1,754 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Raster awareness API\n",
-    "\n",
-    "This notebook will give an overview of newly developed raster interface. We'll cover \n",
-    "basic usage of the functionality offered by the interface which mainly involves:\n",
-    "1. converting `xarray.DataArray` object to the PySAL's weights object (`libpysal.weights.W`/`WSP`).\n",
-    "2. going back to the `xarray.DataArray` from weights object.\n",
-    "\n",
-    "using different datasets:\n",
-    "- with missing values.\n",
-    "- with multiple layers.\n",
-    "- with non conventional dimension names."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "\n",
-    "from libpysal.weights import Rook, Queen, raster\n",
-    "import matplotlib.pyplot as plt\n",
-    "from splot import libpysal as splot\n",
-    "import numpy as np\n",
-    "import xarray as xr\n",
-    "import pandas as pd\n",
-    "from esda import Moran_Local"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Loading Data\n",
-    "\n",
-    "*The interface only accepts `xarray.DataArray`*, this can be easily obtained from raster data\n",
-    "format using `xarray`'s I/O functionality which can read from a variety of data formats some of them are listed below: \n",
-    "- [GDAL Raster Formats](https://svn.osgeo.org/gdal/tags/gdal_1_2_5/frmts/formats_list.html) via `open_rasterio` method.\n",
-    "- [NetCDF](https://www.unidata.ucar.edu/software/netcdf/) via `open_dataset` method.\n",
-    "\n",
-    "In this notebook we'll work with `NetCDF` and `GeoTIFF` data. \n",
-    "\n",
-    "### Using xarray example dataset\n",
-    "First lets load up a `netCDF` dataset offered by xarray."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<xarray.DataArray 'air' (time: 2920, lat: 25, lon: 53)>\n",
-      "[3869000 values with dtype=float32]\n",
-      "Coordinates:\n",
-      "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-      "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
-      "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-      "Attributes:\n",
-      "    long_name:     4xDaily Air temperature at sigma level 995\n",
-      "    units:         degK\n",
-      "    precision:     2\n",
-      "    GRIB_id:       11\n",
-      "    GRIB_name:     TMP\n",
-      "    var_desc:      Air temperature\n",
-      "    dataset:       NMC Reanalysis\n",
-      "    level_desc:    Surface\n",
-      "    statistic:     Individual Obs\n",
-      "    parent_stat:   Other\n",
-      "    actual_range:  [185.16 322.1 ]\n"
-     ]
-    }
-   ],
-   "source": [
-    "ds = xr.tutorial.open_dataset(\"air_temperature.nc\")  # -> returns a xarray.Dataset object\n",
-    "da = ds[\"air\"]  # we'll use the \"air\" data variable for further analysis\n",
-    "print(da)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`xarray`'s data structures like `Dataset` and `DataArray` provides `pandas` like functionality for multidimensional-array or ndarray. \n",
-    "\n",
-    "In our case we'll mainly deal with `DataArray`, we can see above that the `da` holds the data for air temperature, it has 2 dims coordinate dimensions `x` and `y`, and it's layered on `time` dimension so in total 3 dims (`time`, `lat`, `lon`).\n",
-    "\n",
-    "We'll now group `da` by month and take average over the `time` dimension\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Coordinates:\n",
-      "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-      "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
-      "  * month    (month) int64 1 2 3 4 5 6 7 8 9 10 11 12\n"
-     ]
-    }
-   ],
-   "source": [
-    "da = da.groupby('time.month').mean()\n",
-    "print(da.coords)  # as a result time dim is replaced by month "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<xarray.plot.facetgrid.FacetGrid at 0x7ff60521aeb0>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x648 with 13 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# let's plot over month, each facet will represent the mean air temperature in a given month.\n",
-    "da.plot(col=\"month\", col_wrap=4,) "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can use `from_xarray` method from the contiguity classes like `Rook` and `Queen`, and also from `KNN`.\n",
-    "\n",
-    "This uses a util function in `raster.py` file called `da2W`, which can also be called directly to build `W` object, similarly `da2WSP` for building `WSP` object.\n",
-    "\n",
-    "**Weight builders (`from_xarray`, `da2W`, `da2WSP`) can recognise dimensions belonging to this list `[band, time, lat, y, lon, x]`, if any of the dimension in the `DataArray` does not belong to the mentioned list then we need to pass a dictionary (specifying that dimension's name) to the weight builder.** \n",
-    "\n",
-    "e.g. `dims` dictionary:\n",
-    "```python\n",
-    ">>> da.dims                  # none of the dimension belong to the default dimension list\n",
-    "('year', 'height', 'width')\n",
-    ">>> coords_labels = {                 # dimension values should be properly aligned with the following keys\n",
-    "        \"z_label\": \"year\",\n",
-    "        \"y_label\": \"height\",\n",
-    "        \"x_label\": \"width\"\n",
-    "    }\n",
-    "```\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "coords_labels = {}\n",
-    "coords_labels[\"z_label\"] = \"month\"  # since month does not belong to the default list we need to pass it using a dictionary\n",
-    "w_queen = Queen.from_xarray(\n",
-    "    da, z_value=12, coords_labels=coords_labels, sparse=False)  # We'll use data from 12th layer (in our case layer=month)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`index` is a newly added attribute to the weights object, this holds the multi-indices of the non-missing values belonging to `pandas.Series` created from the passed `DataArray`, this series can be easily obtained using `DataArray.to_series()` method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "MultiIndex([(12, 75.0, 200.0),\n",
-       "            (12, 75.0, 202.5),\n",
-       "            (12, 75.0, 205.0),\n",
-       "            (12, 75.0, 207.5),\n",
-       "            (12, 75.0, 210.0)],\n",
-       "           names=['month', 'lat', 'lon'])"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w_queen.index[:5]  # indices are aligned to the ids of the weight object"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can then obtain raster data by converting the `DataArray` to `Series` and then using indices from `index` attribute to get non-missing values by subsetting the `Series`. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = da.to_series()[w_queen.index]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We now have the required data for further analysis (we can now use methods such as ESDA/spatial regression), for this example let's compute a local Moran statistic for the extracted data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Quickly computing and loading a LISA\n",
-    "np.random.seed(12345)\n",
-    "lisa = Moran_Local(np.array(data, dtype=np.float64), w_queen)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "After getting our calculated results it's time to store them back to the `DataArray`, we can use `w2da` function directly to convert the `W` object back to `DataArray`. \n",
-    "\n",
-    "*Your use case might differ but the steps for using the interface will be similar to this example.* "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<xarray.DataArray (month: 1, lat: 25, lon: 53)>\n",
-      "array([[[0.018, 0.001, 0.001, ..., 0.001, 0.001, 0.001],\n",
-      "        [0.001, 0.001, 0.001, ..., 0.001, 0.001, 0.001],\n",
-      "        [0.003, 0.001, 0.001, ..., 0.001, 0.001, 0.001],\n",
-      "        ...,\n",
-      "        [0.002, 0.001, 0.001, ..., 0.001, 0.001, 0.003],\n",
-      "        [0.001, 0.001, 0.001, ..., 0.001, 0.001, 0.003],\n",
-      "        [0.002, 0.001, 0.001, ..., 0.001, 0.002, 0.006]]])\n",
-      "Coordinates:\n",
-      "  * month    (month) int64 12\n",
-      "  * lat      (lat) float64 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-      "  * lon      (lon) float64 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Converting obtained data back to DataArray\n",
-    "moran_da = raster.w2da(lisa.p_sim, w_queen)  # w2da accepts list/1d array/pd.Series and a weight object aligned to passed data\n",
-    "print(moran_da)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7ff6013e4b80>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "moran_da.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Using local `NetCDF` dataset\n",
-    "\n",
-    "In the earlier example we used an example dataset from xarray for building weights object. Additonally, we had to pass the custom layer name to the builder. \n",
-    "\n",
-    "In this small example we'll build `KNN` distance weight object using a local `NetCDF` dataset with different dimensions names which doesn't belong to the default list of dimensions.\n",
-    "\n",
-    "We'll also see how to speed up the reverse journey (from weights object to `DataArray`) by passing prebuilt `coords` and `attrs` to `w2da` method. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<xarray.Dataset>\n",
-      "Dimensions:    (latitude: 73, longitude: 144, time: 62)\n",
-      "Coordinates:\n",
-      "  * longitude  (longitude) float32 0.0 2.5 5.0 7.5 ... 350.0 352.5 355.0 357.5\n",
-      "  * latitude   (latitude) float32 90.0 87.5 85.0 82.5 ... -85.0 -87.5 -90.0\n",
-      "  * time       (time) datetime64[ns] 2002-07-01T12:00:00 ... 2002-07-31T18:00:00\n",
-      "Data variables:\n",
-      "    tcw        (time, latitude, longitude) float32 ...\n",
-      "    tcwv       (time, latitude, longitude) float32 ...\n",
-      "    lsp        (time, latitude, longitude) float32 ...\n",
-      "    cp         (time, latitude, longitude) float32 ...\n",
-      "    msl        (time, latitude, longitude) float32 ...\n",
-      "    blh        (time, latitude, longitude) float32 ...\n",
-      "    tcc        (time, latitude, longitude) float32 ...\n",
-      "    p10u       (time, latitude, longitude) float32 ...\n",
-      "    p10v       (time, latitude, longitude) float32 ...\n",
-      "    p2t        (time, latitude, longitude) float32 ...\n",
-      "    p2d        (time, latitude, longitude) float32 ...\n",
-      "    e          (time, latitude, longitude) float32 ...\n",
-      "    lcc        (time, latitude, longitude) float32 ...\n",
-      "    mcc        (time, latitude, longitude) float32 ...\n",
-      "    hcc        (time, latitude, longitude) float32 ...\n",
-      "    tco3       (time, latitude, longitude) float32 ...\n",
-      "    tp         (time, latitude, longitude) float32 ...\n",
-      "Attributes:\n",
-      "    Conventions:  CF-1.0\n",
-      "    history:      2004-09-15 17:04:29 GMT by mars2netcdf-0.92\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Lets load a netCDF Surface dataset\n",
-    "ds = xr.open_dataset('ECMWF_ERA-40_subset.nc')    # After loading netCDF dataset we obtained a xarray.Dataset object\n",
-    "print(ds)                                         # This Dataset object containes several data variables"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Out of 17 data variables we'll use `p2t` for our analysis. This will give us our desired `DataArray` object `da`, we will further group `da` by day, taking average over the `time` dimension."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "('day', 'latitude', 'longitude')\n"
-     ]
-    }
-   ],
-   "source": [
-    "da = ds[\"p2t\"]  # this will give us the required DataArray with p2t (2 metre temperature) data variable\n",
-    "da = da.groupby('time.day').mean()\n",
-    "print(da.dims)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**We can see that the none of dimensions of `da` matches with the default dimensions (`[band, time, lat, y, lon, x]`)**\n",
-    "\n",
-    "This means we have to create a dictionary mentioning the dimensions and ship it to weight builder, similar to our last example. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "coords_labels = {}\n",
-    "coords_labels[\"y_label\"] = \"latitude\"\n",
-    "coords_labels[\"x_label\"] = \"longitude\"\n",
-    "coords_labels[\"z_label\"] = \"day\"\n",
-    "w_rook = Rook.from_xarray(da, z_value=13, coords_labels=coords_labels, sparse=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = da.to_series()[w_rook.index]  # we derived the data from DataArray similar to our last example "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the last example we only passed the `data` values and weight object to `w2da` method, which then created the necessary `coords` to build our required `DataArray`. This process can be speed up by passing `coords` from the existing `DataArray` `da` which we used earlier.\n",
-    "\n",
-    "Along with `coords` we can also pass `attrs` of the same `DataArray` this will help `w2da` to retain all the properties of original `DataArray`.\n",
-    "\n",
-    "Let's compare the `DataArray` returned by `w2da` and original `DataArray`. For this we'll ship the derived data straight to `w2da` without any statistical analysis."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "da1 = raster.wsp2da(data, w_rook, attrs=da.attrs, coords=da[12:13].coords)\n",
-    "xr.DataArray.equals(da[12:13], da1)  # method to compare 2 DataArray, if true then w2da was successfull"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Using local `GeoTIFF` dataset\n",
-    "\n",
-    "Up until now we've only played with `netCDF` datasets but in this example we'll use a `raster.tif` file to see how interface interacts with it. We'll also see how these methods handle missing data. \n",
-    "\n",
-    "Unlike earlier we'll use weight builder methods from `raster.py`, which we can call directly. Just a reminder that `from_xarray` uses methods from `raster.py` and therefore only difference exists in the API. \n",
-    "\n",
-    "To access GDAL Raster Formats `xarray` offers `open_rasterio` method which uses `rasterio` as backend. It loads metadata, coordinate values from the raster file and assign them to the `DataArray`.  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<xarray.DataArray (band: 1, y: 880, x: 940)>\n",
-      "[827200 values with dtype=float32]\n",
-      "Coordinates:\n",
-      "  * band     (band) int64 1\n",
-      "  * y        (y) float64 50.18 50.18 50.18 50.18 ... 49.45 49.45 49.45 49.45\n",
-      "  * x        (x) float64 5.745 5.746 5.747 5.747 ... 6.525 6.526 6.527 6.527\n",
-      "Attributes:\n",
-      "    transform:      (0.0008333333297872345, 0.0, 5.744583325, 0.0, -0.0008333...\n",
-      "    crs:            +init=epsg:4326\n",
-      "    res:            (0.0008333333297872345, 0.0008333333295454553)\n",
-      "    is_tiled:       0\n",
-      "    nodatavals:     (-99999.0,)\n",
-      "    scales:         (1.0,)\n",
-      "    offsets:        (0.0,)\n",
-      "    AREA_OR_POINT:  Area\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Loading raster data with missing values\n",
-    "da = xr.open_rasterio('lux_ppp_2019.tif')\n",
-    "print(da)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7ff600e744f0>"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "da.where(da.values>da.attrs[\"nodatavals\"][0]).plot() # we can see that the DataArray contains missing values."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We'll look at how weight builders handle missing values. Firstly we'll slice the `DataArray` to reduce overall size for easier visualization.\n",
-    "\n",
-    "This time we'll create `WSP` object using `da2WSP` method inside `raster.py`. Since our DataArray is single banded and all of its dimensions belong to the default list, we only have to ship the DataArray and the type of contiguity we need."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Slicing the dataarray\n",
-    "da_s = da[:, 330:340, 129:139]\n",
-    "w_queen = raster.da2WSP(da_s)  # default contiguity is queen\n",
-    "w_rook = raster.da2WSP(da_s, \"rook\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "After plotting both contiguities and sliced `DataArray`, we can see that the missing values are ignored by the `da2WSP` method and only indices of non missing values are stored in `index` attribute of `WSP` object. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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4lqpeHvwR50aShZT+EzgfGEhyuuzvgX9U1UMi8iXgK6o6M9VeSU5jqJVk4ZB/J2nQ+5Kc31+tqk0iMgP4D5JTadpIPrh/i6rWiUglyWeZziT5nNR6YKCqulEI6fs3i+Q04vRqbiNJFvCYTrID+X8k71KWk8wbe4rkcxZK8oH5W1V1TcqwPg+cCziqOjT1/Nh3SD4HNpRk9bl7VfXn2d7bMAzDMAzDMIyOxYxrBilj8iVV/VLq98nAK3xgLkeTNEYfzjb1I03nB0C9qt6T8foEksbnwyLyLDBXVee3sy+dblwN43hFRC4lWfChDHgwNRU9ff1Eknevp5EMPv9J2rr/BXyF5M2NGuDLmqxqahiGYRiGYZQYmyrsQXd+XsEwuhMiUgbcTbJC6ySS060mZTTbB9zGBxmb7rajUq9PV9UzSBpfv5VXDcMwDMMwjA4mXOodOJ5JTU99UD8IU35WRIaQjCTIfF7hq6mfn+PY5xXGkXxeQUg+J/TJlPZfSFbz7Ssi9cD1uUZmDeN4pvqiPrp3X5tnu2WrWuar6qXtNPkwUKuqMQAReYrk8ydr3AaqugvYJSKXZdk+DPQWkVaSVaq3+T8KY+jQoTpu3LhS74ZhdFuWLVu2R1WHebc0OhLr6wyj48nV35lxzUBVFwGL2lk/Lu3nbSSLMLm/fyTHNj8jOX0x83WH5POz/zvLuqxahtHd2LOvjcXzvR8ZLh/x/kQRWZr20lxVnZv2+yiS1atd6vGZCaeqW0XkJySfbW8C/uw+a274Y9y4cSxdutS7oWEYBSEim0u9D4b1dYbRGeTq78y4GoZRYpQ2dfw03KOq09tZL1nFfZCK8rgCOJlkMbLfiMg1qvqYn+0NwzAMwzCMjsWecTUMo6Qo4KCeiw/q+SBqCj4opOaHS4CNqrpbVVtJTum3rDrDMAzDMIwugo24ApHyPhrtNTBw3QkTRwSqV7MvZxHjgikPB5IT3+Fo1sG04jmt/6hA9Rri7waqB1AhvkYj86KsA+5ZLVvVUvDzVw6BHOMSYHwqC3kryeJKV/vctg44J5XB3ARcTDJb2DAMwzAMw+gCmHEFor0GMmNKIJnpx7DgjW8HqnfyY/8eqB7AyOEHAtfsCFrbyjpE9+1LfxCo3sJNpwaqBzAt0ujdKE/6h3oHrlk24r2Cnr9SlLYAYrlUNSEiXyNZrbsMmKeqq0Xk5tT6+0TkRJKGtD/giMg3gEmqujiVq7wcSADvAHOzvY9hGIZhGIbR+ZR0qrCIXCoi60WkVkTuzLJeROTnqfWrRGSan21F5OupdatF5D8641gMwygMBVpxPBdfWqovqeoEVT1FVe9KvXafqt6X+nmHqo5W1f6qOjD186HUuu+q6kRVPUNVv6CqLR11zIZhGIZhGEZ+lGzENS1zcTbJZ9OWiMiLqromrdkcYHxqmQHcC8xob1sRuYhkkZUPqWqLm6FqGEbXxeczrIZhGIZhGEYPpZQjrkczF1U1DriZi+lcATyqSd4CBorICI9tbwF+6I6WpHIbDcPooijQpuq5GIZhGIZhGD2XUhrXbJmLmZVycrVpb9sJwEdEZLGIvCYiZwe614ZhBI7jYzEMwzAMwzB6LqU0rn4yF3O1aW/bMDAIOAf4R+BpEfmb9iJyo4gsFZGlra1H/O+1YRiBoihtPhaje+A4wdyGCEqno3RdnZ6mFzRd/Xi7+udnlI6ufm509e9CV9cLmq5+vF3l8ytlVWE/mYu52kTa2bYeeE5VFXhbRBxgKLA7XVhV55KqGtq/7yi7KjaMUqHQZt/AghGRS4Gfkayk/KCq/jBjvaTWfxxoBL6kqsu9thWRrwNfI1ll+Q+q+k9B7G8oFGLBggVF68yePTuAvflbgt6/nqYXNF39eLv659edsL4uWLr6d6Gr6wVNVz/ervL5lXLE9WjmoohESGYuvpjR5kXgi6nqwucAB1V1u8e2LwAfBRCRCSRN7p4OPxrDMApCEVp9LMbfklaobg4wCbhKRCZlNEsvcncjySJ37W6bUeTudOAnHX80hmEY2bG+zjAMKOGIq5/MReAlknfOaknePftye9umpOcB80TkXSAOXJsafTUMowuigGPf0EI5WqgOQETcQnXp1dmPFrkD3hIRt8jduHa2tSJ3hmF0JayvMwyjpFOFUdWXSJrT9NfuS/tZga/63Tb1ehy4Jtg9NQyjI2mzEdVCyVaoboaPNrmK3LnbukXu7gKagW+p6pIA99swDCMfrK8zDKO0xtUwDEMx41oEnVHk7mySRe4qM2eviMiNJKfkMXbs2Dx22zAMIy+srzMMo6TPuBqGYQDgqHguRlaKKXLX3rZHi9yp6tskE4mGZr65qs5V1emqOn3YsGFFHYhhGEY7WF9nGIYZV8MwSos74uq1GFnpdkXuYrEYixYtIhaLdcbb5U3Q+9fT9IKmqx9vV//8jiOsr+tkuvp3oavrBU1XP97O+vxsqrBhGCVFEVq1rNS7cVzS3YrcxWIx5s+fTyKRYO3atVRXV1NZWdnRb+uboPevp+kFTVc/3q7++R1PWF/XucRiMf64YAGJyIDAvgu/e+tdaGkMUG81NDcEp7d4DTQe6LL/H79bsgEObA/ueJe9B/vqA9P7w/+sgP1bO/zzM+NqGEZJsWdci6M7Fbmrq6sjkUjQMvESWPcydXV1Xeriwd2/xnO/BJE+PN8cJlIbydr2/rrFR3+Ox6NZ28TjVSTOHwcioNp19eJHqHjzYfv/KFSvi35+xxvW13UedXV1JCIDaD7nWpoTLTzfHCrqu9AUn4BOOw0QmlubA9Zr4vnmsiL1TkXPPC15QfI/D3W5/4/X9veheeIlEOlDc6Kp6L6pqfVU9IzTQKA5HoTeRPSsiXBkb4f3dWZcDcMoMUKb2lMLRrLoydq1a2kcfirh2kVdrgiKu39E+tD/jfvbvas8e/ZHjv6cK7TdHXU5dP5NXV4vHA7b/0cRel3x8zNKx/HS1zUnWkCEmZGtzKgakbWt13dhyb4y3tgdovz1+zh8zpdBhAsi9ZxdNbIgvTf3lvH2HiH8+r0cPud6kBAXRbYwrWpUQXp/2R1m+T4IL7qHw+d/heYZ1zAwWp9VqxT8eWc5R/qOpO+iezh8/s1AiOrIJs6oyn7OeB3vS9vL2XioDRb+nMMXfBURYU5kI5OqTipI78Wt5WxtS+AsvIfDndDX2dWiYRglRQGHkOdidH8qKyuprq4mHA53ualaEPz+9TS9oOnqx9vVPz+jdHT1c+Po/pWFmBXdxpt6Mlsa8/87vGRfGW/uK+eGU1qZc/GFhMtCXBDdwRtaybam/Pfrzb1lLNtfzg2nJJhz8SzCZcL50R28rqewszl/vb/sDrPqYJibTkkwZ/ZFhEPCOdHdvKZV7G3JXy9o/ryznNiREDePb2PO7NmEQ8JZ0X0s1Akcas1f76Xt5WxtCnHLeIc51dWEBaZED/Iyp3I4kb/ei1vL2R0PcdN4Tep1wvlsV4OGYZQcK85kuFRWVhKJRLrchZxL0PvX0/SCpqsfb1f//IzS0dXPDXf/ZlSN4NOj4jy7NZKXeXVN6/XjmukT/kDv3KrhfHJknKfro3mZV9e0XjeumYo0vZlVw7n8xDhPbonmZV5d03r9uGaiaXoXVg2jenicx+qiJTWvrmn9yrgWwqEP9u+S8YO5YFichzdF8zKvrmm9flwLoTS96vEDOX9wK/M2RfMyr65p/fJJx+p19PlsxtUwjJKimizO5LUYhmEYhtH5jKlw8jKvmaY1k3F9nLzMa6ZpzaSqn5OXec00rZlM7O+U1LxmmtZMpg508jKvmaY1k7MGt+VlXjNNa2diz7gCokqopYAx8k5GneDPjvotQwLXPGHEgcA1O4L+0WYufvX2QDWjZdWB6gEMjR4JXLNXqCPO9/cK2ipZnMnuoRmGYRhGVyXdvH56VJwxFU7Wdl6m1SXdvF45upmRvbO38zKtLlX9HC4naV6vGtPM8Ox1hDxNq8vE/g6QNK/XjG1mSK/cbYPEy7S6TB2Y3L+HN0X50rhm+pdnb+dlWl3OGtwGwLxNUa4b10zfHJ9NKU0r2IirYRglJ1mcyWsxDMMwDKN0eI28+jWtLl4jr35Nq4vXyKtf0+rS2SOvfk2ri9fIq1/T6uI18lpq0wpmXA3DKDFBFmcSkUtFZL2I1IrInVnWTxSRN0WkRUS+lfb6qSKyIm05JCLfCOwgDcMwDKMbkMu8PvNOfV6m1SWXeX1i6Za8TKtLLvP68Fub8zKtLp1lXn++qDYv0+qSy7z++OUNeZlWl1zm9fvz15XctIJNFTYMowvQpsUXXxKRMuBuYDZQDywRkRdVdU1as33AbcAn07dV1fXA1DSdrcDzRe+UYRiGYXQz0s3r2dsOsn5nA08ur8/btLqkm9cZOxtYvuUAL6zalrdpdUmfNnze7sO8/v5e/rh2Z96m1SV92vD5+xsZM6gif5F2+PmiWt6u25+3aXVJnzZ8weFmHn6rjtU7GvI2rS7p04YvPBLnnr/G2LS3seSmFcy4GoZRYhQJ6hnXDwO1qhoDEJGngCuAo8ZVVXcBu0TksnZ0LgbeV9XNQeyU8bfMnj273fX31y0+JjsuG47jEOqgv6BB719P0wuarn68Xf3zM0pHVz83it2/s7cd5I7fvkt5WYiHrzmLQRWRovTO2rKf259bRbQ8qdc/WrjebGDqxr189ZmVVETKePQL0+kTad/2eOlNqd3D159ZyS/+fkpg5tU1rfOuPouIh2v12r9Jq7fzxUeXMbRvhF9dcxZhj/PGS+/5ldv4/CNLGN6/Fw9ePa0oPZdiz2frJQ3DKCkKtGrYc/HBKGBL2u/1qdfy5XPAkwVsZ3QiXf0iP+j962l6QdPVj7erf35G6ejK58b6nQ2Ul4VQhfoDBQSzZrBu52EiZSESDmw7WPy83PU7DxMNl5FoU3YeLCDoNYMLqoZy+0Xj+fozK9myv7FovXxMqx/WbG+gb68wR1oS7G+MF623esch+vUK09Cc4FBTMEU9iz2fu+63wTCMHoEitKn3AgwVkaVpy40ZUtnmG2s++yIiEeDvgN8UdjSGF46TvRJlqXQ6StfVMT3Ty0fP6D509XOjWN1n3qnnyeX1zLv6TO76xCS+/fs11Gw7WLDeE0u38MKqbTx8zTS+N2cid/z2XdbtbChY7+G3NvPHtTt59AvTuHP2BG5/vobY7sMF67nMrBwciHl1TeuDnzuTSDhU9P/Hj1/ewOodDTx+7Vlcf944bnpqBbsPF27Wvz9/HZv2NvLYtWdxzdljueHJ5ew7UrwZLvY4SzpVWEQuBX4GlAEPquoPM9ZLav3HgUbgS6q6vL1tReR7wA3A7pTM/1bVlzr+aAzDKBSfxZf2qOr0dtbXA2PSfh8NbMtzV+YAy1V1Z57bGT4JhUIsWLCg3TbxeNSzjdcUt0IJev9MrzC60/GW8nw2SkdXPzeK2b/06sFD+kYZ0jfK9y+fxJ0vrGo3KieXXnr14IEVvZhW0YvvzpnIN59d0W5UTi699OrB/aIRzj15CHfMnsDXf/NOu1E5fv8/LqgaCsAtTy4rKConvXpwNDV9uZj/j/TqwZFwmMtOHwHAdY8uaTcqJ5deevXgSDjMp6aMBODaRxe3G5XTGedzyUZc0wqpzAEmAVeJyKSMZnOA8anlRuBen9v+p6pOTS1mWg2jC6NKUHE4S4DxInJyauT0c8CLee7OVdg0YcMwDMPISq7Im8kjB7QblZOLXJE308YMajcqJxe5Im/OPXlIu1E5+XJB1dCCqg3nG3njRa7Im8tOH9FuVE4uckXefGrKyHajcjqLUk4VPlpIRVXjgFtIJZ0rgEc1yVvAQBEZ4XNbwzCOCwTHx+KFqiaArwHzgbXA06q6WkRuFpGbAUTkRBGpB24Hvi0i9SLSP7WugmQ9guc66EANwzAM47jFK6fVK+c1E6+cVq+c10y8clq9cl7zJd+onM4yrS5eOa+ZeOW0euW8dgalNK5+CqnkauO17ddEZJWIzBORQcHtsmEYQaMENuKKqr6kqhNU9RRVvSv12n2qel/q5x2qOlpV+6vqwNTPh1LrGlV1iKoW/pCOYRiGYXRDvEyri1/z6mVaXfyaVy/T6lIq89rZptXFr3n1Mq0upTavpTSufgqp5GrT3rb3AqeQzGTcDvy/rG8ucqNb5CWeKL4ymGEYhaEIrVrmuRiGYRiG0fn4Na0uXubVr2l18TKvfk2rS2eb11KZVhcv8+rXtLqU0ryW0rj6KaSSq03ObVV1p6q2qaoDPEByWvHfoKpzVXW6qk6PhIMNEjYMIz/aCHkuhmEYhmF0LvmaVpdc5jVf0+qSy7zma1pdOsu8ltq0uuQyr/maVpdSmddSXg36KaTyIvBFSXIOcFBVt7e3beoZWJdPAe929IEYhlE4Cjga8lyMnkEsFiMejxOLxUq9K1kJev96ot6iRYu69P51ZT2j+9DVz41YLEZza4I3dofyNq0u6eZ1ce12mlvbeHuP5G1aXdLN65LabTS3trF8H3mbVpd087q8dmvR/x/p5nVFbT2/WraTDQecQExrLBajqbWNjYfa8jatLunm9d3aOppaHbYeTuRtWl3Szeua2s2dcj6X7GrQTyEV4CUgBtSSHD29tb1tU9v8h4jUiMgq4CLgf3XWMRmGUQhCm4/F6P7EYjHmz59PIpFg/vz5Xe6CLuj966l6NTU1XXr/uqqe0X2IxWL8cf4CEgmnS54bsViMF97dgSNllL8+l511he/fmAqHc2Uji5pH4UiI8Otz2VGE3rg+DjNkMwubRyf1Ft3PtiL0qvo5TJfNLGgcFch3dWJ/h6myhfnNY9kVGoQsuoe6TcX3Jc9tBiUEC+9mUxF6Uwc6TGIbf2galxw4WHhPUXpnDW7jVN3B744E8/l5IaqZj5X2PAb0GannnHZj4Lrzl34vUL1xj/4oUD0ATQRvCE4YcSBwzY6gfzSAuSEZRMuCny8xNHokcM1eoeD384GzH13mkbOaldFnDNDbnj7Ps90dp/+pIH2jc5g+fbouXbrUV9tcOW+LFi2ipqaGxnO/RMWbDzN58mRmzZqVtW1H5l567t+sr4Eq4XCYSCSStW366/F49tD2eDxOIpGAcAQS8Z6jJ9KzPj8RKhb9sujzWUSsD+wCBNXXrXxvM80zbySy6kXOHN6rS/V1T769kbre46C8N7Q2Ey4LFfVdaIon0FAZIAHptaGhULB6Ekr2Ta1NhMvKitNrbUuazC6r56BISq+RcFlxfV1SD0i0BNLXQe7+roCBdcMwjOBQFSu+ZAAwduxY1q5dC5E+hMNhxo4dW+pdOoaj+6dK/zfup7q6msrKyqxtZ8/+yNGfc10cuiNyh86/yfS6uV5XPJ+N0uH2Jc2JZuKnX4pEd5V6l46ydF8ZOwdMoO/rczl8zpdBhPMi2zm3anjW9l7fhTf3lvH2HiH8+r0cPuc6EGFmZCszqkb8TVs/en/ZHWb5PggvuofD510PIlwQqefsqpEF6S3cFWb1fiW06B4On3cjSIiLIluYVpUZdOJPb8HOctYfcJBFv+Tw+TeDCBdH6phaNbogvZe2l7PxUBss/DmHP3IriDA7sokPVWXvT7z0XtxWztaGBM7Ce5J6hKiObOKMAvWeqy9nd1sriYX3crgT+jp7cMwwjJITVByOcXxTWVlJdXU14XC4XdNQKoLeP9MzPaNncvTcKCujOrqFFTqGdYdK/3duaaoQ0w2ntDLn4gsJl4X4aHQri/UkNh3Jf//cQkw3nJJgzsWzCJeFmBXdxpt6sq+c10zcQkw3nZJgzuyLCJeFuCC6gze00lfOayYLd4VZ31DGTePbmDP7YsJlwvnRHbyupxRUsGnBznLePxLi5vFtzJk9m3BIOCe6m9e0ylfOayZuIaZbxjvMqa4mHBI+HN3DqzqBA9kHP9vlxW3l7G4JcdN4Pap3VnQfC3WCr5zXTJ6rL+dQIsQN40nqdUJfV/pviWEYPRoFHMRzMXoGlZWVRCKRLnuRH/T+mZ7pGT0T99yYWjWaa8Y2M39npKTm1TWt16UKMbn7d3bVSK4c3cwL2yJ5mdfM6sGu3oyqEb5yXjPJrB7s6p1bNdxXzmsmrmm9/uQWIqEP9GZWDS+o2rBrWt1CTK7ehVXDfOW8ZpJZPdjVu6hqKJecEOeRzdG8zKtrWt1CTK7eJeMH+8p5zcQ1rV8ce6xeR/d1ZlwNwygxYiOuhmEYRo9lSC9Kal4zTWsmI3uTl3n1irzxynnNxCvyxivnNZNM05pJvlE5maY1E6+c10y8Im9OH+DkZV4zTWsmXjmvmWSa1s7ErgYNwygpyTgc8VwMwzAMo7tSKvPqZVpd/JpXvzmtfs2r35xWv+bVy7S6+DWvXqbVxa959ZvT6te8eplWF7/mtZSmFaw40wcEXV1ZhOrp3wtUMvxPwf93JfZHA9dsdYIvtLN/Z7/ANVuGHQ5cs0+vAh468CAkwVf+PmtQXeCaxdBm99AMwzCMHo5rXh+riwJxJvZ3OvT9/JpWF9e8Pl0f5ZMj44zrc+z++TWtLunm9dOj4oypOFbPr2l1STevV45uZmTvY9f7Na0uVf0cLidpXq8a08zwjEtmv6bVJfn/mTSv14xtZkivY9f7Na0upw9I6j2yOcq1JzUzMKMwsF/T6jJ1YFLv4U1RvjSumf7lx64vtWkFG3E1DKPEKEJCyzwXwzAMw+judNbI63Mrt+ZlWl1yjbw+sXRLXqbVJdfI68Nvbc7LtLrkGnm9/6+xvEyrS66R11+89n5eptUl18jrj1/ekJdpdck18nrX/HV5mVaXXCOv3/nDmpKbVrARV8MwSowqtNlUYMMwDMMAjh15nVK7hwuqhgaq/9zKrTyxtD5v0+qSPvJ61pb9rNt5mBdWbcvbtLqkm9eztx1kWd0B/rh2Z96m1SXdvM7Y2cBr7+3mtdo9eZtWl/SR1/N2H+YPa3ayePO+vE2rS/rI6/n7G3lqWT2rdzTkbVpd0kdeZx5sZt5bm9i4tzFv0+qSPvJ6weFmfvFajF0NLSU3rWDG1TCMLoA9w9qz8Aogv79u8THZcdlwHIdQB/0FDXr/TM/0Snk+G6Wj2HPj3H2N/MOzKwECM6+uaX3gqjMZVBFpt63X/s3Y2cDtz60iUhbi0S+eRf9ocXpnbzvIP/32XXqHy/j1tdPpE2nfpnjpnbVlP//r2VX06RXmsS+cRbQIvdnA1I17ufU3KxkQDfPrL55NxMO1eulNqd3DDU++w5CKCI988SzCHn2Al94Z63bx5ceXcWK/Xjz4+WlF601avZ0vPLqM0QOizL3qTM8+qjP6OuslDcMoKYrgaMhzMYx0uvpFftD7Z3qmZ/Q8Thpcwc8+PYWfvvoer9fuKVovH9Pqh+VbDhAtD+EAtbuPFK23rO4AvcNlJFSJ7Sleb8nm/fSJlNHa5rBpfwFBrxksrTtA315hmhMO9fsbi9ZbvGkf/aNhjsQTbD9YQHBspt7mfQzsXc7B5lZ2NxRfc2Xxpv0MqihnX2OcvY3B1HAptq+zntIwjJLThnguRvfAcYIpNuLqmJ7pdSc9o/sQ1P/pmIHRQMyra1rv/+wUBlVEit6/J5Zu4YVV25h39TT+44oz+Lc/rmP5lv0F6z381mb+uHYnj3xhGnddPolv/34NNdsOFqx3/19jvFa7h0evmcZ350zkjt++y7qdDQXr/eK191m8eR+PfeEs7pw9gdufryG2u/Ainz9+eQOrdzTw2BfP4vaPjufrz6xkSxFm+K7569i4t5FHvzCNr37kFG59ekVRZvg7f1jDroYWfn3NNK4/bxw3PbWC3YeLN9fFnnc2VdgwjJLixuEYPYNQKMSCBQvabROPRz3buFPw/Oj5IR+9oPfP9EzPa0qpcfwR5Lnhjrx+9allrFyZf7Xh9OrBQ/pGi96/9OrBAyt6MbCiFz+64gy++eyKrNWGvfTSqwf3i0aYPDLC9y+fxJ0vrMpabdhLL716cEU0wrQxEb47ZyLffHZF1mrDXnrp1YOjkTDnnjyEO2ZP4Ou/eSdrtWEvvfTqwZFw+Og08FueXJa12rCXXnr14Eg4zCUTTwDghseXZK027KWXXj04HA5z2ekjALju0SVZqw176aVTbF9nI66GYZQYqypsGIZhGO1x0uCKgqoN5xt540WuyJuJw/v5ynnNJFfkzeSRA3zlvGaSK/Jm2phBvnJeM8kVeXPuyUN85bxmkivy5oKqob5yXjPJFXlzycQTfOW8ZpIr8uay00f4ynntaMy4GoZRUtyqwl6LYRiGYfRk8o3K6SzT6pIrKicXXjmtuaJycuGV05orKicXXjmtuaJycuGV05orKicXXjmtuaJycuGV05orKqczMeNqGEbJseJMhmEYhuGNX/Pa2abVxa959TKtLn7Nq5dpdfFrXr1Mq4tf8+plWl38mlcv0+ri17x6mVaXUptXuxo0DKOkJKsKey9+EJFLRWS9iNSKyJ1Z1k8UkTdFpEVEvpWxbqCIPCMi60RkrYicG9AhGoZhGEZgeJnXUplWFy/z6te0uniZV7+m1cXLvPo1rS5e5tWvaXXxMq9+TauLl3n1a1pdSmlezbgahlFyHMRz8UJEyoC7gTnAJOAqEZmU0WwfcBvwkywSPwP+pKoTgSnA2mKOyTAMwzA6ilzmtdSm1SWXec3XtLrkMq/5mlaXXOY1X9Pqksu85mtaXXKZ13xNq0su85qvaXUplXktqXH1MToiIvLz1PpVIjItj22/JSIqIsEkNhuG0SEokHDKPBcffBioVdWYqsaBp4Arjnkv1V2qugQ4ppsVkf7ABcBDqXZxVT1Q/NEZ+RKLxYjH48RiscD0Fi1aFKhe0PtneqZn9DyCODfSzetrtbt58u2N/HV3KBDTGovFaG5t4+09krdpdUk3r2/W7qS5tY3l+8jbtLqkm9fFtdtpam1j9X7N27S6pJvXJbXbaGptY/0BJ2/T6pJuXpfXbqWptY2Nh9ryNq0u6eZ1RW09Ta0OWxsSeZtWl3Tzuqq2jqZWh91HWvM2rS7p5vXd2rpO6etKZlx9jo7MAcanlhuBe/1sKyJjgNlAXQcfhmEYxeJjmrDPqcKjgC1pv9enXvNDJbAb+JWIvCMiD4pIn/wOxCiWWCzG/PnzSSQSzJ8/v+g/gK5eTU1NoHpB75/pmZ7Rs4jFYvxxwQISbU7R58aQXnCh1PJW03DqoidT/vpcdtYVf+7+dvUuHAkRfn0uO4rQG9kbzpcYrzeNTOotup9tReiNqXA4VzayqGkkSojQovuo31S43rg+DjNkMwubR6OEkEX3UFeEXlU/h+mymQXNY1FCsPBuNhWhN7G/w1TZwvymscn4wIX3FKV3+gCHD0k9f2walxw4WHhvUXpTBzpMYht/ODyyU/o6UdUOE2/3jZPPj31PVatTv/8zgKr+e1qb+4FFqvpk6vf1wCxgXHvbisgzwP8FfgtMV9V2E5sH9Bmp50y8IcjDAwm+Cur7/xR87G5if47wqSIYOLrwwOhc7N/ZL3DNAcMKD47ORZ9eedQc98nQ3kcC1zxrUPD3dP5t8ovLVHV6vtsNmniCfnTe33u2e+78ezcD6d/luao61/1FRD4DVKvqV1K/fwH4sKp+PVNLRL4HHFbVn6R+nw68BZyvqotF5GfAIVX913yPp7MRkUtJTnMuAx5U1R9mrJfU+o8DjcCXVHW5z22/BfwYGObVj06fPl2XLl3qa59z5bwtWrSImpoaGmd9DVQJh8NEItkD6NJfj8ezf+/i8TiJRCLZHwepF45AIt5z9s/0itMToWLRL5k8eTKzZs3K2tZPtqGIFNTHdhe6W1+3csNGmi+4BRIthENS1LnWFE+goTJAgtVz2gL5LjTF29BQKLl/rc2Ey0IBfLcciEQD0WtqbUuaTAlm/47VayJcVlaknoMCSKhr6yVaAunrIHd/F7wT8k+20ZEZPtqMam9bEfk7YKuqrpR2zKOI3EhyFJdoZEBhR2AYRiD4HFHd43HRVg+MSft9NLDN5y7UA/Wqujj1+zPA3zyC0NVIm30ym+QxLBGRF1V1TVqz9JkrM0jOXJnhtW0pZq6MHTuWtWvXgir937if6upqKisrs7adPfsjR3/OdXHojngdOv+mLq2XSCQIh8Nddv9Mr3i9cDjM2LFjs7YzvOmufV1zaxOIMC2ym4uqsj/Z5nWuLdlXxhu7Q5S/fh+Hz/kSiDA9sosLq4YVpPfm3jLe3iOEX7+XwzO+VPR34S+7wyzfB+FF99Daqx8t0z/LeZHtnFs1vCA9SPtuzbwFRJgZ2cqMqhEF6S3YWc76Aw6y6JccPu8mEOGCSD1nV40sSO+l7eVsPNQGC3/O4Zm3goS4KLKFaVXZJ4B56b24rZytDQmchfdw+CO3gggXR+qYWjW6IL3n6svZ3dZKYuG9R/VmRzbxoars/ZOX3tNbIhxqa6Fl4X0c7oS+rpTPuGa7Us0c/s3VJuvrIlIB/AvwHa83V9W5qjpdVadHwhWeO2sYRsegENRU4SXAeBE5WUQiwOeAF33tg+oOYIuInJp66WJgTTubdBU8n+tN/f6oJnkLGCgiI3xs+5/AP/G3/XKHUVlZSXV1taeJ6256kydP7tL7Z3pdQ6+H0z37urIy5kTrWKWjWX0w/0vyJalCTDec0sqciy8kXFZGdbSeFTrGV85rJm4hphtOSTDn4llFn7tuIaabTkkwZ/ZFTB13Ah+NbmWxnuQr5zUXH3x+IWZFt/Gmnuwr5zUTtxDTzePbmDN7NuGyEBdEd/CGVvrKec3ELcR0y3iHOdXVhMuE86M7eF1P8ZXzmolbiOmm8ZrUCwnnRHfzmlb5ynnNxC3EdMN4jup9OLqHV3WCr5zXTJ7eEqHFgevGS1KvE/q6UhpXP6Mjudrkev0U4GRgpYhsSr2+XERODHTPDcMIlCCMq6omgK8B80lWBH5aVVeLyM0icjOAiJwoIvXA7cC3RaQ+VZgJ4OvA4yKyCpgK/CD4Iw0cP8/15jNzZRQcO3Ml6B32orKykkgkEtgfvuNBb9asWV16/0yv6+j1YLptX/ehqrFce1IzL++K5GVeXdN6faoQk6s3tWq0r5zXTDKrBxd77mZWD3b7urOrRvrKefXC3b8ZVSN85bxmklk92NU7t2q4r5zXTDKrB7t6M6uG+8p5zSSzerCrd2HVMF85r5lkVg929S6qGuor5zUT17R+fkz8GL2O7utKaVz9jI68CHwxVV34HOCgqm7Pta2q1qjqCao6TlXHkeycpqVGUwzD6IIoQkJDnosvLdWXVHWCqp6iqnelXrtPVe9L/bxDVUeran9VHZj6+VBq3YrULIwPqeonVXV/hx10cJR05oqI3CgiS0Vk6e7duz131jAMo0C6dV83MEJe5jXTtGbilfOaSaGRN7nwirzxynnNF6+c10y8Im+8cl4z8Yq88cp5zcQr8sYr5zUTr8gbr5zXTDJNa2dSMuPqZ3QEeAmIAbXAA8Ct7W3byYdgGEYQaGBThXsiJZ25kv7IxbBh2Z+nMgzDCIBu39f5Na9eptXFr3ntbNPqUirz6jen1a959ZvT6te8+s1p9Wte/ea0+jWvpTStUOIcVx+jI6qqX02tn6yqS9vbNov+OK/qcIZhlJYAn3HtidjMFcMwegI9oq/zMq9+TauLl3ktlWl16Wzz6te0uniZV7+m1cXLvPo1rS5e5tWvaXXxMq+lNq1Q2qrCXQcFnGAlRQMW7CDCg5oDj8TZvyP46JpBJzYErhkpawtc84SK4CN2OsK0HW4LPgapGMyYFoaqJkTEnX1SBsxzZ66k1t9HcubKx0nOXGkEvtzetiU4DMMwjHbpSX2da14f2RwF4rjhIc+8U5+XaXVxzetjdcfqPbF0S6Cm9eG3NudlWl1c8/p0fZRPjiw+UjDdvH561Ad6v3jt/bxMq0u6eb1y9Adu88cvb8jLtLpU9XO4nKR5vWrMB3p3zV+Xl2l1mdjfAZLm9ZqxH+h95w9r8jKtLqcPSOo9sjnKtSd9oHfni++W3LSCGVcD2HTTtwLVO+mh/whUz+jeKDaiWgyq+hLJC7b01+5L+1mBr/rdNkubccXvpWEYRnH0pL4u3bxOXr+L/Y1xnlxen7dpdUk3r1Nq91B/oIkXVm0L1LT+ce3OvE2rS7p5PWvLfqaNGVTU/qSb17O3HWTRe3tYvHlf3qbVJd28ztjZwO9qtrN6R0PeptUl3byet/swTy6vZ+PexrxNq0u6eT1/fyMP/M8mdjW05G1aXdLN68yDzfzstVoa420lN61gxtUwjC5Am8/iS0b3wCuA/P66xcdkx2XDcRxCqb+gx7ueH7rT8Zpe+3pG96HYc2PmwWauf2I5ZSHh0S+cxaCKSFF65+9v5Kan3qG8LMSvv3gW/aPF6QH86s1N/GndLuZ9fhp9IsXZihk7G7jjt+/y3TkTmTZmUNGf39nbDvKPL7xL/2iYx754NhEP1+qld9aW/Xzj2VUMrijn0S9OJ+zxnW1PbzYwdeNebv3NSk7o24t510wrWm9K7R6+8uQ7jBoQ5cGrzvTsU7z0zli3iy89tpSTh/ThniunFKXnUmxfZ72kYRglRa04k1EAQV/km57pdSc9o3vwRmwPvcJCSGD5lgNF6/3l/b1Ey8sAWFF/qGi9h9/aHJhpBZg4vB8/uuIM/u2P61i+pfjC/ove20P/aJiWhMP6XcU/cvbK+t0MrCjnSLyN2t1HitZbuGE3gyvKOdjcSt3exqL1Xl6/i2F9Iuw7EmfrwQKCYzP1NuxieP8oOxua2dlQ/DRuKL6vs57SMIySoyqei9E9cJxgnv93dbq6XtB09eM1veL0jO5Dsf+nz7xTz5PL63nwqjO577Nn8svXY7yyflfBek8s3cILq7Yx7+oz+eVnpvDTV9/j9drC65e604MfvGpqIKbVJSjz+ovX3mfx5n08es1ZfP/ySXz792uo2XawYL0fv7yB1TsaePSaaXx3zkTu+O27rNtZuBm+a/46Nu5t5OFrpnHn7Anc/nwNsd2F10n5zh/WsKuhhYc/fya3f3Q8X39mJVv2F26G73zxXRrjbcy7aipf/cgp3Pr0CrYHYIaL/V7YVGHDMEqMjaj2JEKhEAsWLGi3TTwe9WzjTiHr6npB09WP1/SK0zO6D8WcG+nVg4f0TRZTvOfKqdzw+BJqampSzyD610uvHjywohcDK+AXfz+FW55cxsqV8dQzkv710qsH9/OYblwIrnn95rMr+OTIOOP65Ld/6dWDo5Ewk0cO4PuXT+LOF1bx6VFxxlTkp5dePTgSDjNtzCC+O2ci33x2BVeObmZk7+zHkUsvvXpwJBzm3JOHcMfsCXz9N+9w1Zhmhueon5lLL716cDgc5oKqoQDc8uQyrhnbzJBe+emlVw8Oh6dwycQTALjh8SVce1IzA3P8l3dGX2cjroZhlBwbcTUMwzCM3JE3IwZEfeW8ZpIr8mbMoApfOa+Z5Bt5UygTh/crKConV+TN5JEDfOW8ZpIr8mbamEG+cl4zyRV5c+7JQ3zlvGaSK/LmgqqhvnJeM8kVeXPJxBN85bx2NGZcDcMoKZbjahiGYRjeOa1eOa+ZeOW0euW8ZtJZptUl35xXr5xWr5zXTLxyWr1yXjPxymn1ynnNxCun1SvnNROvnFavnNfOwIyrYRilRaFNxXMxDMMwjO6Kl2l18WtevUyri1/z2tmm1cWvefUyrS5+zauXaXXxa169TKuLX/PqZVpd/JpXL9PqUmrzasbVMIySothUYcMwDKPn4te0uniZV7+m1cXLvJbKtLp4mVe/ptXFy7z6Na0uXubVr2l18TKvfk2ri5d59WtaXUppXs24GoZRYrynCdtUYcMwDKM7kq9pdcllXvM1rS65zGupTatLLvOar2l1yWVe8zWtLrnMa76m1SWXec3XtLrkMq/5mlaXUplXM66GYZQcVe/F6BnEYjHi8TixWKxH6AVNVz9e0zOMJLFYjObWBG/sDuVtWl3SzeurtXtobm3j7T2St2l1STevr9Xuprm1jeX7KLlpdUk3r2/W7qSptY31B5y8TatLunldXLudptY2Nh5qy9u0uqSb1yW122hqddjakMjbtLqkm9fltVtpanXYfaQ1b9Pqkm5eV9TW09TqcKixJW/T6pJuXlfV1nVKX2fG1TCMkmNThQ1IXsjNnz+fRCLB/Pnzi/4D2NX1gqarH6/pda3zxSgdsViM3/9lKY6UUf76XHbWFX5uDIzARbKBt5uH4UiI8Otz2VGE3pBecKHU8lbz8KTeovvZVoRe0IzsDedLjNebR6CEkEX3ULep8P0bU+FwrmxkUfMolBAsvJtNReiN6+MwQzazsHl0svjkwnuK0qvq5zBdNrOgeQwKJBbeW5TexP4OU2UL85vHokDLwvuK0jt9gMOHpJ4/HhnZKX2dqA1lMKBipJ5z6g2BakoHfK61/zv4rCyA2iu/HajeSQ/9R6B6AINOLDzkOReRsrbANUf0PRS4ZkdMkz21f+Eh5rn4f1OfXqaq0/PdrnfVSK36qff3790r/k9B+kbnMH36dF26dKmvtrly3hYtWkRNTQ2Ns74GqoTDYSKR7P1e+uvxePZ5SvF4nEQiAeEIJOLB6YlQseiXTJ48mVmzZmVt25G5nMf959fT9AI6X0TE+sAuQFB93cr1G2m+8ObkuRaSos61pngCDZUBAomWAPTa0FAIECIrf8uZw3t1qb7uV8t2sqtsMET7QWsz4bJQccfb2pY0rSIB6TkogIQ6QK+JcFlZl9JrTCg4DrTFA/vbmKu/6wID/4Zh9HTaHBtRNWDs2LGsXbsWVOn/xv1UV1dTWVmZte3s2R85+nOuixt3xOvQ+TcFrhcOhxk7dqzfQ+sUjqfPr6fpdcXzxSgd7ne1ubUZEKaU7+WSqsFZ23qda2/uLePtPUL49Xs5fM6XQYRpkd1cVDW0IL2/7A6zfB+EF93D4fOuI356NRLdnecRdhwLdpZzpO9I+i66h8Pn3QQinBfZzrlVw7O29zrel7aXs/FQGyz8OYdn3gIizIxsZUbViIL0XtxWztaGBM7Ce47qXRCp5+yqkQXpPVdfzu62VhIL7+XwzFtBQlwU2cK0qlEF6T29JcKhthZaFt7H4Y/cCiJcHKljatXogvSeqIvQx2mi5dW5ndLX2VRhwzBKjk0VNgAqKyuprq4mHA63axq6i17QdPXjNb2udb4YpePouVFWxmW961jNSFYcyP+S3C3EdMMpCeZcPItwWRlzonWs0tG+cl4zcQsx3XRKgjmzLyJcVkZ1tJ4VOsZXzmtH4xZiunl8G3NmzyZcFuKj0a0s1pN85bxm4hZiumW8w5zqasJlIWZFt/Gmnuwr5zUTtxDTTeP1qN4F0R28oZW+cl4zcQsx3TCelJ5wfnQHr+spvnJeM3ELMV03XpJ6IeGc6G5e0ypfOa+ZPFEXQYDrJpR1Wl9X0rNQRC4VkfUiUisid2ZZLyLy89T6VSIyzWtbEfm/qbYrROTPIpL9FodhGF0Cxdu0mnHtOVRWVhKJRAL7w9fV9YKmqx+v6RlGEvfcOKNqLF8+qZnXdkfyMq+Z1YNdvQ9VjfWV85pJZvVgV29q1WhfOa8dTWb1YHf/zq4a6SvnNZPM6sGu3oyqEb5yXjPJrB7s6p1bNdxXzmsmmdWDXb2ZVcN95bxmklk92NW7sGqYr5zXTFzTetXY5PThzurrSnYGikgZcDcwB5gEXCUikzKazQHGp5YbgXt9bPtjVf2Qqk4Ffg98p4MPxTCMIlEfi2EYhmF0R/pHyMu8ekXeeOW8ZuIVeeOV89rReEXeeOW8ZuIVeeOV85qJV+SNV85rJl6RN145r5l4Rd545bxmkmlaO5NSjrh+GKhV1ZiqxoGngCsy2lwBPKpJ3gIGisiI9rZV1fTqOH2wa17D6NpocFOFfczimCgib4pIi4h8K2PdJhGpSc3W8Fd5wzAMwzACwK959ZvT6te8+s1pLZV59ZvT6te8+s1p9Wte/ea0+jWvfnNa/ZpXvzmtfs1rKU0rlNa4jgK2pP1en3rNT5t2txWRu0RkC/B5bMTVMLo86ojn4oXPWRz7gNuAn+SQuUhVp1rlTsMwDKOz8TKvfk2ri5d59WtaXTrbvPo1rS5e5tWvaXXxMq9+TauLl3n1a1pdvMyrX9Pq4mVeS21aobTGNduVaOboaK427W6rqv+iqmOAx4GvZX1zkRtFZKmILI0nGn3usmEYHYGq9+IDz1kcqrpLVZcArYEfhGEYhmEUSS7z+sTSLXmZVpdc5vXhtzbnZVpdOsu8/uK19/MyrS65zOuPX96Ql2l1yWVe75q/Li/T6pLLvH7nD2vyMq0uuczrnS++m5dpdcllXm9/blXJTSuUNg6nHhiT9vtoYJvPNhEf2wI8AfwB+G7mClWdC8wFGNBruIb2B5u/6QwbGKgeQGtD8Dmu5f3iVD39/UA1JVweqB5AeSj4zNXyDshx3dtcEbjmKf33Bq65u6Vv4JqFohBU8aVsMzFm5LkrfxYRBe5P9RGGYRiG0am45vVXm6NMWr2dg00JXli1LW/T6uKa10c2R5m8fhdb9jfxx7U78zatLq55fawuypTaPVyQI3qnUH7x2vss3rwvb9Pq4prXp+ujnLVlP6+s383qHQ15m1aXdPN69raDvFiznY17G/M2rS7p5nXGzgaeWLqFXQ0teZtWl6p+DpeTNK/n7T7M3Dc30Rhvy9u0ukzs7wBJ83r+/kb+89VaQiIlN61QWuO6BBgvIicDW4HPAVdntHkR+JqIPEXyAvSgqm4Xkd25thWR8ar6Xmr7vwPWdfyhGIZRMAr4M65DM549nZthLv3M4miP81V1m4icACwQkXWq+noe2xs+8Qogv79u8THZcdlwHIdQ6i9yV9cLmq5+vKZXnJ7RfSj23JjZ0Mx1jy+jLBTisS+eRf9o+wMYnnoHm7n+iWVEwiEev/Zs+kTatwFeeufvb+Trz6wECMy8uqZ13tVnEfFwrV77N2NnA994dhUDK8p57IvTCXt8x7z0zt52kH984V2G9inn4S8Ur3fWlv38wzMrGTEgyryrp3n2Ae3pzQambtzLLU+v4KTBvbnvs2cWrTeldg/XP7GcCSf04ZefOROYXLCeS7F9Xcl6SVVNkJzGOx9YCzytqqtF5GYRuTnV7CUgBtQCDwC3trdtapsfisi7IrIK+BjwD511TIZhFIbPqcJ7VHV62pI5IupnFkc7+6DbUv/uAp4nOfXY6KIEfZHf1fWCpqsfr+kZBryyfjfRcBkhgb+8X/zsq/lrd1IRKQPgrY37itYbM6iCX/z9FH766nu8XrunaL18TKsfflezncEV5TTF21i19WDRei/WbGdon3IaWtpYu6OhaL0XVm1neL9e7G9sZcPuI0Xr/bZmOyP7R9nZEGfT3uIfg3xh1TbGDOpN/YFmtuwP5rHKYvu6Uo64oqovkTSn6a/dl/azAl/1u23q9U8HvJuGYXQ0wdT+9jOLIysi0gcIqWpD6uePAf8nkL0yjiGokSVXx/RMrzvpGd2HYv9Pn1i6hRdWbeOhq8+kpU255b9XAHDZ6SMK0nv4rc38ce1OfvX5aRxucbj16aTexaeeUPA+AowaEOUXfz+l6JFX17Q++LkziYSL/279+OUNrN7RwMPXTCO2t4k7fvsu350zkWljBhWkd9f8dWzc28i8z09j/a4jfPv3a/j+5ZOYPHJAQXrf+cMadjW0MO/qM1m1vYE7fvsuP7riDCYO71eQ3p0vvktjvI0HrprKki0Huf35Gn76qclUDivs0bDbn1tFSIQHPncmf43t4+vPrOQXfz+FMYOKeySu2P/XkhpXwzAM8Fc12AtVTYiIOxOjDJjnzuJIrb9PRE4ElgL9AUdEvkGyAvFQ4HkRgWS/+ISq/qnonTL+hlAoxIIFC9ptE49HPdu4U/C6g54futPxml5uPaP7UMy5kV49eGBFLwDu/exUrv/1EtasWcPUgU5eeunVg/tFI/SLwj1XTuWGx5dQU1PD6QPy00tn9uzZR0deb3lyGStXxlPPSPonvXpwNDV9uZjPL716cCQcZuLwfvzoijP45rMr+OTIOOP65He86dWDI+Ewk0cO4PuXT+LOF1bx6VFxxlTkp5dePTgcDjNtzCC+O2ci33x2BVeObmZk7/yON716cDg8hXNPHsIdsyfw9d+8w1VjmhkezU8vvXpwKDT56M2IW55cxjVjmxnSKz+9dIrt6+z2nmEYpSXAHFdVfUlVJ6jqKap6V+q1+9yZHKq6Q1VHq2p/VR2Y+vlQqhLxlNRyurutYRiGYXQmuSJvhveL+sp5zSRX5M2IAVFfOa9+GTOooqBqw/lG3niRK/Jm4vB+vnJeM8kVeTN55ABfOa+Z5Iq8mTZmkK+c10xyRd6ce/IQXzmvmeSKvLmgaqivnNeOxoyrYRilR30shmEYhtGN8cpp9cp5zcQrp9Ur5zVf8o3K6SzT6uKV85qJV06rV85rJl45rV45r5l45bR65bxm4pXT6pXz2hmYcTUMowsgPhbDMAzD6J54mVYXv+bVy7S6lMq8drZpdfFrXr1Mq4tf8+plWl38mlcv0+ri17x6mVaXUptXM66GYZQeG3E1DMMweih+TauLl3n1a1pdOtu8lsq0uniZV7+m1cXLvPo1rS5e5tWvaXXxMq9+TatLKc2rGVfDMEqPGVfDMAyjB5KvaXXJZV7zNa0unWVeS21aXXKZ13xNq0su85qvaXXJZV7zNa0uucxrvqbVpVTm1YyrYRilRUEd8VyMnkEsFiMejxOLxXqM3qJFi7r0/ple19Ezug+xWIzm1jbe3iN5m1aXdPP68nv7aG5tY/k+8jatLunm9dXaPUWfu+nm9bXa3fxq2U7WH3ACMa2xWIym1jY2HmrL27S6pJvXN2t30tTqsLUhkbdpdUk3r4trt9PU6rD7SGveptUl3bwuqd1GU6vDocaWvE2rS7p5XV67lcaE0tzclLdpdUk3rytq6zulrzPjahhG6bERV4Pkhcj8+fNJJBLMnz+/6D+Ax4teTU1Nl94/0+saekb3IRaL8fu/LMeREOHX57KjrvBzo38ELpYNLGsZikOI8KL72VaE3sAIXCQbeLt5GAnHKfrcHdILLpRa3moezq7wUGTRPdRtKv679dwmByUEC+9mUxF6I3vD+RLj9ZYRKOAsvKcovTEVDufKRha1jEKBxMJ7i9Ib18dhhmxmYctoFGhZeF9RelX9HKbLZha0jAXHoeWVuUX9/07s7/Ah3cL8IyM6pa8TVbsiHNBruJ434upANZ1hAwPVA1h3c45gpyIo71fYXZb2SDSVB6457ISDgWtGwm2Ba4oE/306pf/ewDU7gkdnzFumqtPz3a7XuNF64rf/wbNd3Q3/VJC+0TlMnz5dly5d6qttrpy3RYsWUVNTQ+Osr4Eq4XCYSCSStW366/F49n4sHo+TSCQgHIFEvOvqifSs4+1peiJULPolkydPZtasWVnb+sk2FBHrA7sAQfV1K9e/T/OFt0JbK2GhqHOtKd6GhkKAQKKFcEiK15NQsm8KQq+1LWkyO0KvtYVwWbF6TvL+uISgtZlwWahb6zUmFBwHQmXQ2kS4rKxgPceBZiel1xYPpK+D3P1dARMJDMMwgqUD/L5xHDJ27FjWrl0LqvR/436qq6uprKzM2nb27I8c/TnXxaE74nXo/JtMz/RKqhcOhxk7dmzWdkbPw+3rmuNNiAhnRA9SXTUwa1uvc+0vu8Ms3wfhRfdw+LwvAyGmlO/lkqrBBekt3BVm9X4ltOgeDp/3FRBhWmQ3F1UNLUhvwc5y1h9wkEW/5PB5N4II0yO7uLBqWEF6L20vZ+OhNlj4cw7PvBkkxIzIdmZWDS9I78Vt5WxtSOAsvIfDM28BEc6LbOfcAvWeqy9nd1sriYX3HtWbGdnKjKoRBek9vSXCobYWWhbed1Tvgkg9Z1eNLEjviboIfZwmWl6dy6GZt4KEuCiyhWlVo/LWcxx4bEsEbWmk8dUHOqWvs6nChmGUFj/ThM3Y9ggqKyuprq4mHA63axpMz/R6op7RfUg/Ny7vXcd6OZFl+8ry1nELMd10SoI5sy8iXBbmst51rGGkr5zXTBbuCrO+oYybxrcxZ/bFhMvKmBOtY5WOLqhgk1uI6ebxbcyZPZtwWRnV0XpW6BhfOa+ZuIWYbhnvMKe6mnBZGbOj9SzVk3i/IX89txDTTeM1pRfio9GtLNaTfOW8ZuIWYrphPEf1ZkW38aae7CvnNRO3ENN14+Wo3gXRHbyhlb5yXjNxCzFdN6Esef6VCedHd/C6nuIr5zUd17T2DsG1E8Kd1teZcTUMo8QIOD4Wo0dQWVlJJBIJ7A+f6Zled9Izug/uuTGp6iSuG9fMG/vK8zKvmdWDXb0zqsbypXHNvO6R85qJa1qvP7mFSOgDvQ9VjS2o2nBm9WBXb2rVaF85r5lkVg929aZVjeKqMc38bkckL/OaWT3Y1Tu7aqSvnNdMMqsHu3ozqkb4ynnNJLN6sKt3btVwXzmvmWRWD3b1ZlYN95Xzmk66af3MmGP1OrqvM+NqGEbpsRFXwzAMo4fSN0xe5tUr8qZ/OXmZ10zTmkm+UTlekTdeOa+ZeEXeDI+Sl3n1irzxynnNxCvyxivnNROvyBuvnNdMvCJvvHJe08lmWjsTM66GYZQeM66GYRhGD8avefWb0+rXvHqZVhe/5tVvTqtf8+o3p9WvefWb0+rXvPrNafVrXv3mtPo1r35zWv2Y11KbVjDjahhGqVFAxXsxDMMwjG6Ml3n1a1pdvMyrX9Pq4mVe/ZpWFy/z6te0uniZV7+m1cXLvPo1rS5e5tWvaXXxMq9+TatLe+bVcZySm1Yw42oYRhdA1HsxDMMwjO5OLvP68Fub8zKtLrnM6/1/jeVlWl1ymddfvPZ+XqbVJZd5/fHLG/IyrS65zOtd89flZVpdcpnX7/xhTV6m1SWXeb3zxXfzMq0uuczr7c+tysu0umQzr47jcNuzNSU3reDDuIrI10RkUEe8uYhcKiLrRaRWRO7Msl5E5Oep9atEZJrXtiLyYxFZl2r/vIgM7Ih9NwwjQGyqcMFYP2oYRk+gJ/V16eb1+ZXbePitzfxx7c68TatLunn9w+rt3P/XGK/V7snbtLqkm9dX1u/iF6+9z+LN+/I2rS7p5vX12j38+OUNrN7RkLdpdUk3r4s37eOu+evYuLcxb9Pqkm5el2/Zz3f+sIZdDS15m1aXdPNas+0gd774Lo3xtrxNq0u6eV23s4Hbn1tFSCRv0+qSbl5rdzVw27M19I+GS25awV+O64nAEhFZDswD5qtq0ZeRIlIG3A3MBupT7/Giqq5JazYHGJ9aZgD3AjM8tl0A/LOqJkTkR8A/A3e0uzNlZeiAvsUe0rHH1xz8f+5p/xW85rp/7B+4ZijSFrjmgcMVgWtOGrEjcE2nA6a0HmqNBq6ZcLrWZAsbUS2MLtWP5oFXAPn9dYuPyY7LhuM4hFJ/4U3P9LqTnvG39NS+7sIjcb7w66WEQ8ITXzqbPpH2L9u99C443My1v15Gr3CIJ689m2iRejMPNnPd48voEynjiS99mIiHa/XSO39/Izc8+Q79o2Ge+NLZhD2+E1565+0+zK2/WcnginIe/eL0ovVm7GzgH55ZyfB+vXj4mrM8v7NeemdvO8i3nq9h9MDePHDVmUXrnbVlP7c9s5JThvbh3s+eCUwuWG82MOX9Pdz6m5WcNrwfP/jE6cDpRe0fFN/XeW6pqt8m2Qk8BHwJeE9EfiAipxT8rkk+DNSqakxV48BTwBUZba4AHtUkbwEDRWREe9uq6p9VNZHa/i1gdJH7aRhGR2PPuBZKj+1Hg77INz3T60563ZAe2de9WLOdivIQZSHhz2t3Fa333Ipt9EsN2b7y3u6i9Z5ZsZWBFeUkVPnL+3uK1ntqWT1DKiLEEw7/E9tXtN6Ty+s5oW8vjsTbWFZ3oGi9J5ZuYcSAKAebE6zYerBovceXbmH0wN7sORJn9Y6GovUeW7KFkwdXsO1gM+t2FqfnOA5PLN/KKUMq2Lyvkdjuw0XvHxTf1/naOjXCuiO1JIBBwDMi8h9FvPcoYEva7/Wp1/y08bMtwHXAH4vYR8MwOho/04RtRDYXx10/6jhOoDqmZ3rdSc/ISY/r69zpwfM+P40Hr5rGY0vqeH7ltoL13OnBv7rqTOZedSYPvbmZP6zeXrCeOz34V1dP494rz+SXr8d4ZX3h5tqdHvyra87kF5+Zwk9ffY/Xaws3w+704Aevnsp/fmoy//7n9SzeVLgZdqcHP/i5qfzoijP4tz+uY/mW/QXrudOD7//sFL5/+SS+/fs11Gwr3Ay704Pv/exUvjtnInf89t2Czav7TGv/aJi7r5zKHbMncPvzNYGY12K/F55ThUXkNuBaYA/wIPCPqtoqIiHgPeCfCnzvbEMomZenudp4bisi/0LSZD+e9c1FbgRuBIiWBz9d1jCMPDBjWihdph8dO3as174CybutCxYsaLdNPB71bONOwTM90+tOekZOelRfl149uF80AsADV03j2kcXs37dOs4anP2RrFx66dWDK6IRKoD7PzeV6x5dwpo1a5g6MLuZyKWXXj04GgkzIhLmniuncsPjS6ipqeH0AfnppVcPjoTDjBkU5hd/P4VbnlzGypVxJvbPTy+9enAkHKZyWF9++qnJfP037/CJE+Oc0i8/vfTqweFwmInD+/GjK87gm8+u4JMj44zrk59eevXgcHgKk0cO4PuXT+LOF1bx6VFxxlTkp5dePTgUmsy0MYP47pyJfPPZFVw5upmRvbPKZdVLj7y5aGCcUOh0zj15CHfMnsDXf/MOV41pZniOJ9g6o6/zM+I6FPj/VLVaVX+jqq0AquoAlxfx3vXAmLTfRwOZt5JytWl3WxG5NrVvn8/1PK6qzlXV6ao6PRLuU/BBGIZRPFZVuGC6TD86bNiwgg/CMAzDgx7T1+WKvBncJ+Ir5zWTXJE3w/pGfeW8ZpIr8mbEgKivnNdMckXejBlU4SvnNZNckTeVw/r6ynnNJFfkzcTh/XzlvGaSK/Jm8sgBvnJeM8kVeTNtzCBfOa/ptJfTeu7JQzxzXjsDP8+4fkdVN+dYt7aI914CjBeRk0UkAnwOeDGjzYvAF1OV4s4BDqrq9va2FZFLST5Y/3eq2ljE/hmG0VkENFXYR9XJiSLypoi0iMi3sqwvE5F3ROT3hR9Mp2L9qGEYPYEe0dd55bR65bxm4pXT6pXzmolXTqtXzmsmXjmtXjmvmXjltHrlvGbildPqlfOaiVdOq1fOayZeOa1eOa/ptGdaXdrLee0sSlYNIPUw/NeA+cBa4GlVXS0iN4vIzalmLwExoBZ4ALi1vW1T2/wS6AcsEJEVInJfZx2TYRj5IwrieC+eOh9UjpwDTAKuEpFJGc32AbcBP8kh8w8k+5TjAutHDcPoCfSEvs7LtLr4Na9eptXFr3n1Mq0ufs2rl2l18WtevUyri1/z6mVaXfyaVy/T6uLXvHqZVhc/5tWPaXUptXktIA0qOFT1JZIdTfpr96X9rMBX/W6ber0q4N00DKOjCaZq8NHKkQAi4laOPBqXoKq7gF0iclnmxiIyGrgMuAu4PYgd6gysHzUMoyfQnfs6v6bVxTWv8zYlHzbMfObVr2l1cc3rw5uiQPxvnnn1a1pdXPP6yOakXuYzr35Nq4trXh+rS+plPvPq17S6uOb1yS1RPsHfPvPq17S6uOb16fpo1mde/ZpWl3Tzmu2ZV7+m1SXdvGY+85qPaXWp6udwOUnz2t4zrx2B1V83DKP0BDNV2G/lyFz8F8lic1be0zAMw+gU8jWtLrlGXvM1rS65Rl7zNa0uuUZe8zWtLrlGXvM1rS65Rl7zNa0uuUZe8zWtLrlGXvM1rS7ZRl61ANPqUqqRVzOuhmGUHJ/FmYaKyNK05cZMmSzSviyviFwO7FLVZcUdiVEssViMeDxOLBYzPdMzPaPbEovFaG5tY/k+8jatLunmdf57B2hqbWP1fs3btLqkm9eX39tHU2sb6w84eZtWl3Tz+mrtHppa29h4qC1v0+qSbl5fq91NU6vD1oZE3qbVJd28/rV2J02tDruPtOZtWl3SzeubKb1DjS15m1aXdPO6uHY7jQmlubkpb9Pqkm5eF2/YRpOjaEtj3qbVJd28Lq/d2il9nRlXwzBKj78R1z1uVcfUMjdDxU/VyVycD/ydiGwiGU7/URF5rLCDMQolFosxf/58EokE8+fPL/oPoOmZXnfSM7oPsViM37+xHIcQ4UX3s62u8HOjbxguYT0rmgehhAgtuo/6TYXr9S+Hj8oGljUPRQkhi+6hrgi9gRG4SDbwdvMJKCFYeDebitAb0gsulFreahmOAs7Ce4rSGx6FC+R93mgeiQKJhfcWpTeyN5wvMV5P6bUsvK8ovTEVDufKRha1jAbHoeWVuUX1JeP6OJytm1nUOgYch8ZXHihKr6qfwzTdzILGEzulr5Mclb97FAMqRuq5468PVvQ4CRNf94/BZ9iGyrPnixVDWTj4z3PSiB2BazrBPKt5DOFQ8MeecIK/Z/X7C365TFWn57tddNQYHXur9yOl73379nb1RSQMbAAuBraSrCR5dVoRjvS23wMOq+rfFGkSkVnAt1S1mLivHsf06dN16dKlvtrmynlbtGgRNTU1NM76GqgSDoeJRCJZ26a/Ho9nv1scj8dJJBIQjkAibnqmVxo9ESoW/ZLJkycza9asrG39ZBuKSEF9rBEsQfV1K9e+R/NFX4O2BGHRos61pngbKiEQgbY4YaE4vda2pMkUgUQL4ZB0MT0neT9bQsHrtbYQLgtSr5lwWagovcaEJn1FqKxoPceBZqcj9NqgrTWQvg5y93clLc5kGIYB+I67aVdCNSEibuXIMmCeW3Uytf4+ETkRWAr0BxwR+QYwSVUPFb8HRrGMHTuWtWvXgir937if6upqKisrs7adPfsjR3/OdXHojngdOv8m0zO9kuqFw2HGjh2btZ3R83D7uuZ4EwKc1ruBj1cNyNrW61xbuCvM6v1KaNE9HD73ekSEM6IHqa4aWJDegp3lrD/gIIt+yeHzbgBCTCnfyyVVgwvSe2l7ORsPtcHCn3N45s0gwrTIbi6qGlqQ3ovbytnakMBZeM9RvemRXVxYlT1f10vvufpydre1klh4b0ovxIzIdmZWDS9I7+ktEQ61tdCy8D4Oz7wFRDgvsp1zC9R7oi5CH6eJllfnciilNzOylRlVI/LWcwsxaUsjja8+cFTvgkg9Z1eNLEjvkbpeDIk30PDqQ53S19lUYcMwSo7PZ1w9UdWXVHWCqp6iqnelXrvPrTypqjtUdbSq9lfVgamfD2VoLLLR1tJQWVlJdXU14XC4XdNgeqbXE/WM7kP6uXF5RR3vy3De2uudy5qJW4jppvFtzJl9cVKvdx3r5URfOa+ZuIWYbh7fxpzZswmXhbmsdx1rGOkr5zUTtxDTLeMd5lRXEy4rY060jlU62lfOayZuIaabxutRvepoPSt0jK+c10zcQkw3jOeo3uxoPUv1JF85r5m4hZiuGy8pvRAfjW5lsZ7kK+c1E7cQ03UTypLnS1mIWdFtvKkn+8p5TSe9evC1E8JH9S6I7uANrfTMec2m90hdLwaWO1wzIdJpfZ0ZV8MwDKPLUFlZSSQSCewPn+mZXnfSM7oP7rkxqWoc149rZsn+8rzMa2b14A/0TvKV85pJZvVgV++MqrG+cl4zyawe7Op9qGqsr5zXTDKrB7t6U6tG+8p5zSSzerCrN61qlK+c10wyqwe7emdXjfSV85pJZvVgV29G1QhfOa/pZIu8cfXOrRrumfOaTc81rZ8a1XqMXkf3dWZcDcMoPcHE4RiGYRjGcUdFmLzMq1fkTa6onFx4Rd7kisrJhVfkTa6onFx4Rd7kisrJhVfkTa6onFx4Rd7kisrJhVfkTa6onGz4yWnNFpXTnl6mae1MzLgahlFafEwT9jtV2DAMwzCOR/yaV785rX7Nq9+cVr/m1W9Oq1/z6jen1a959ZvT6te8+s1p9Wte/ea0+jGvfkyrix/zWmrTCmZcDcPoCjg+FsMwDMPoxniZV7+m1cXLvPo1rS5e5tWvaXXxMq9+TauLl3n1a1pdvMyrX9Pq4mVe/ZpWl/bMq+M4vk2rS3vm1XGckptWMONqGEaJEWzE1TAMwzAgt3m9/6+xvEyrSy7z+ovX3s/LtLrkMq8/fnlDXqbVJZd5vWv+urxMq0su8/qdP6zJy7S65DKvd774bl6m1SWXeb39uVV5mVaXbObVcRxue7YmL9Pqks28Oo7DLU+vLLlpBYvDMQyjK2DG1DAMwzCAD8zrQ5uiPLWsnoNNcV6r3ZO3aXVxzeu8TVGeX7mN+gNNLN68L2/T6uKa14c3RZm0ejtrtjewekdD3qbVxTWvj2yOMnn9Lt7atI+NexvzNq0urnl9rC7KlNo9vLx+F7saWvI2rS6ueX1yS5QzN+3j+VXbaIy35W1aXVzz+nR9lLO27OexJVsIieRtWl3SzetZ9ft54M06+kfDXDSwML1083r29oP87LUYI/tHOW/InoL0gsSMq2EYpcVGVHscXgHk99ctPiY7LhuO4xBKXTGYnul1Jz2j+1DsuXFhY5zPP7qUcEj47y+dTTTS/mW7p96RONc8uoRoOMRTX55BxMO1euldcLiZLz66jL69wjz55bMJe5zDXnozDzbz5ceXMbB3OY9dO71ovfP3N/KVJ99hWJ8Ij37hLM/vmJfeebsPc8vTKxg5IMpDV08rWm/GzgZue2YlJw+u4P6rpgGTi9I7q34///jb1Zw6rC8/+MTpwOlF6Z25eR/feK6GD40cwHc/fhpwWlF6UHxfZ8YVQBVpbQtWsjz//Cxv0eCv7st7Bz/k35YI/o+v40jgmmt3Zg+DLoZThnXA3agOeL5zf0vv4EWLwYyrkSdBX+Sbnul1Jz2je/Dfy+vp16uMhKO8ULODz501uii9x5duYUDvcuIJhz+s3sGnpowsSu/ht+oY2jfCkZYE89fu5LLTRxSlN++tTZzYrxcHmlt57b09XHzqCUXpPfA/mxg1IMq+I3H+GtvHBVVDi9Kb++YmThrcm50NcZbUHWDGuMHF6b2xkVOG9mHbwWaWb9nPtDGDCtZyHIcH3qzj1GF9qTvQRM22g0weOaBIvc1MGt6P93YfZt3OBiYO71ewnkuxfZ0ZV8MwSo5Y8aUeQ1AjS66O6Zled9Izug/F/p/e/9cYr9XuYd5VZxJ34IYn3wEo2Lz+4rX3Wbx5H7+6ehqNrQ43PLkcoGDz+uOXN7B6RwMPXX0mB5sT3PTUCoCCzetd89excW8jc6+ayt4jCW59OqlXqHn9zh/WsKuhhbmfncL2hjhff2YlQMHm9c4X36Ux3sY9n5lC3f5mbn++hn/+2KkFm9fbn1tFSIS7PzOFDbuPcMdv3+W7cyYWZF7dZ1r7R8N8/7LTWL2jgW//fg3fv3xSQebVfaZ1ZP8o/3rpqazYepA7fvsuP7rijKLNq424GoZx/GMjrj2GUCjEggUL2m0Tj0c927hT8EzP9LqTntF9KObcSK8eXBGNUAE8cNWZfPGRxbz33gbOGZJ9lmAuvfTqwdFImGgEHrhqGtc+upj169Zx1uD89NKrB0fCYYb1DXP/56Zy3aNLWLNmDVMHZr8bnUsvvXpwJBxmxIAw91w5lRseX0JNTQ2nD8hPL716cDgcZsygML/4+ync8uQyVq6MM7F/fnrp1YPD4SlUDuvLTz81ma//5h0+cWKcU/rlp5dePTgUmszE4f340RVn8M1nV/DJkXHG9fGvlx55c9HAOKHQ6UweOYDvXz6JO19YxadHxRlTkZ+eWz34vCF7CIVOY9qYQXx3zkS++ewKrhzdzMgck/Y6o68r6e09EblURNaLSK2I3JllvYjIz1PrV4nINK9tReQzIrJaRBwRmd5Zx2IYRoGoz8UwDMMwujm5Im8GVkR85bxmkivyZnCfiK+c10xyRd4M6xv1lfOaSa7ImxEDor5yXjPJFXkzZlCFr5zXTHJF3lQO6+sr5zWTXJE3E4f385Xzmk57Oa2TRw7wzHnNppcr8mbamEGeOa+dQcmMq4iUAXcDc4BJwFUiMimj2RxgfGq5EbjXx7bvAv8f8HpHH4NhGMFgcTiGYRhGT8crp9Ur5zUTr5xWr5zXTLxyWr1yXjPxymn1ynnNxCun1SvnNROvnFavnNdMvHJavXJe02nPtLq0l/OaTc8rp7W9nNfOopQjrh8GalU1pqpx4Cngiow2VwCPapK3gIEiMqK9bVV1raqu77zDMAyjaGzE1TAMw+jBeJlWF7/m1cu0uvg1r16m1cWvefUyrS5+zauXaXXxa169TKuLX/PqZVpd/JhXP6bVxY959WNaXUptXktpXEcBW9J+r0+95qeNn20NwzhOsBFXwzAMo6fi17S6eJlXv6bVxcu8+jWtLl7m1a9pdfEyr35Nq4uXefVrWl28zKtf0+rSnnnNx7S6tGde8zGtLqU0r6U0rtnyTTIvT3O18bNt+28ucqOILBWRpfFEYz6bGoYRJEoy8sdrMQzDMIxuRr6m1SWXec3XtLrkMq/5mlaXXOY1X9Pqksu85mtaXXKZ13xNq0su85qvaXXJZl61ANPqks28agGm1aVU5rWUxrUeGJP2+2hgm882frZtF1Wdq6rTVXV6JFyRz6aGYQSI+FyMnkEsFiMejxOLxUzP9EzP6LbEYjGaWttYvV/zNq0u6eb1pfcO0tTaxvoDTt6m1SXdvM5/7wBNrW1sPNSWt2l1STevL7+3j6ZWh60NibxNq0u6eX21dg9NrQ67j7TmbVpd0s3ra7W7aWp1ONTYkrdpdUk3r3+t3UljQmlubsrbtLqkm9c3NuykyVG0pTFv0+qSbl7f3LCdJkcpjzfkbVpd0s3rktptndLXldK4LgHGi8jJIhIBPge8mNHmReCLqerC5wAHVXW7z20NwzhesGdcDZIXcvPnzyeRSDB//vyi/wCanul1Jz2j+xCLxfj9GytQQoQW3Uf9psLPjYowzGYdNS0DUELIonuoK0KvbxguYT0rWgahCCy8m01F6PUvh4/KBpa1DElOsFp4T1F6AyNwkWzg7cakXmLhvUXpDekFF0otbzWfiAItC+8rSm94FC6Q93mjeSQ4Di2vzC3quz+yN5yrMf7aOgoch8ZXHihKb0yFwwzdyOvx0eC00fDKQ0XpjevjcLZuZuGR4Z3S14lq6a4IReTjwH8BZcA8Vb1LRG4GUNX7RESAXwKXAo3Al1V1aa5tU69/CvgFMAw4AKxQ1er29mNA7xF63slfDvTYtNx/aXH/osH/X73/r9HANdsSwd8PkVDwx14WDn7+6SnD9gSuGZbg93N/S44QriJ4Y/aPl6lq3hFUFcPH6Pirbvdst+pntxekb3QO06dP16VLl/pqmyvnbdGiRdTU1NA462ugSjgcJhKJZG2b/no8nv3uczweJ5FIQDgCiXjX1RPpWcfb0/REqFj0SyZPnsysWbOytvWTbSgi1gd2AYLq61auXk/zxbdBW4KwaFHnWlNrG0oo2Ze0tQanp22BfBeaWp3k/WeRpF5IgvluRSog0VK03gf7FwpErzGhyYdHQ2XQ2kK4rHA9x4FmJ12vmXBZKAC9NgiFg9NrawOnNZC+DnL3d2FfW3cQqvoS8FLGa/el/azAV/1um3r9eeD5YPfUMIwOxUZUDWDs2LGsXbsWVOn/xv1UV1dTWVmZte3s2R85+nOui0N3xOvQ+Td1ab1EIkE4HO6y+2d6xeuFw2HGjh2btZ3R83D7uuZ4E6LKhIojfKKqf9a2Xufan3eWs+GAgyz6JYfPvQFBOa33YT5eNaAgvZe2l7PxUBss/DmHA/guvLitnK0NCZyF95AIRWk+78tMKd/LJVWDC9KDtO/WzJtBhGmR3VxUNbQgvae3RDjU1kLLwvs4nNKbHtnFhVXDCtJ7oi5CH6eJllfnpvYvxIzIdmZWDc9bzy3EpC2NNL76AIdm3gIinBfZzrkF6j1S14sh8QYaXn3oqN7MyFZmVI0oSG/e5l6c0HqIA6/O65S+rpRThQ3DMJLYVGEDqKyspLq62tPEdTe9yZMnd+n9M72uoWd0H9LPjb/rs4U6OYG/7sl/LOnPO8uJHQlx8/g25syendSr2EJMhvvKec3ELcR0y3iHOQGcu24hppvGK3Oqq5ly6slc1ruONYz0lfOai6OfX1kZc6J1rNLRvnJeM3ELMV03XpLHW1ZGdbSeFTrGV85rJm4hpusmlB3dv9nRepbqSb5yXtNJrx587YRwSi/ER6NbWawneea8ZtNzCzFdMyFyVG9WdBtv6smeOa/Z9OZt7sXwXg5XTejVaX2dGVfDMEqLgjjeix9E5FIRWS8itSJyZ5b1E0XkTRFpEZFvpb0eFZG3RWSliKwWkX8L7gCNfKisrCQSiQT2h+940Js1a1aX3j/T6zp6RvfBPTcmVo3j+nHNrDwQzsu8uqbVLcSUrnfduGaWeuS8ZpJZPbjYczezerDb151RNdZXzqsX7v59qGqsr5zXTDKrB7t6U6tG+8p5zSSzerCrN61qlK+c13SyRd64emdXjfTMec2ml1k92NWbUTXCM+c1m55rWj8x8li9ju7rzLgahlFygshxFZEy4G5gDjAJuEpEJmU02wfcBvwk4/UW4KOqOgWYClyaKghnGIZhGB1KNFUd2K95zTStmVSkqgP7Na+FRt7kwivyxivnNV+8cl4z8Yq88cp5zcQr8sYr5zUdPzmt7eW8ZtPzirxpL+c1m16mae1MzLgahlF6gpkq/GGgVlVjqhoHngKuOOZtVHep6hKgNeN1VdXDqV/LU4tNUDYMwzA6Bb/m1cu0uvg1r51tWl1KZV795rT6Na9+c1r9mFc/ptXFj3n1Y1pd/JjXUptWMONqGEYXIIgRV2AUsCXt9/rUa/72QaRMRFYAu4AFqro4j0MwDMMwjKLwMq9+TauLl3ktlWl16Wzz6te0uniZV7+m1aU98+o4jm/T6tKeeXUcx7dpdWnPvCYcp+SmFcy4GoZRavyMtiaN61ARWZq23JihJDnU/e2GapuqTgVGAx8WkTPyPBLDMAzDKIpc5vXni2rzMq0uuczrj1/eEKhpvWv+urxMq0tnmdc7X3w3L9Pqksu83v7cqrxMq0s28+o4Drc9W5OXaXXJZl4dx+GWp1fmZVpdspnXhONw01PvlNy0QonjcAzDMATfxZf2eGQY1gNj0n4fDWzLd39U9YCILCKZH/1uvtsbhmEYRjG45vWhTVEeXbyZA02tvF23P2/T6uKa13mbojy1rJ4t+xtZvaMhUNO6cW9j3qbVxTWvD2+KMmn1di47PXs0i19c8/rI5iiT1+9iwfpdNMbb8jatLq55fawuypTaPbywahshkbxNq4trXp/cEmXKxj08vnQr/aNhLhpYmJ5rXp+uj3Lm5n088OZmRvaPct6QPQXppZvXM+v388vXY1QN7ct02VeQXpCYcTUMo/QE8zTpEmC8iJwMbAU+B1ztZ0MRGQa0pkxrb+AS4EeB7JXxN3gFkN9ft/iY7LhsOI5DKHUFcrzr+aE7Ha/pta9ndB+KPTc+Gk9w5by3KQ8JT183g4iHa/XSu6g5zud+tYRoeRlPX/dhwh7nnJ9z964/rWPjvkbu+9xUTz0vLjjczE1PrQDgstNHFP35zTzYzJceW8rw/lEe/vw0z++Yl975+xu5/onljBnUm4euPguYXJTeObsauPU3KzllaAU/+MQU4PSi9M7efpBvPFfDpOH9+O7HTwNOK0rvzPr9/NNvV3PmqAH888dOBU4tSg+K7+uslzQMo+SIqufihaomgK8B84G1wNOqulpEbhaRmwFE5EQRqQduB74tIvUi0h8YAbwqIqtIGuAFqvr7DjpcIwCCvsg3PdPrTnpG9+Ch/9nEwGiYSDjEU8u2eG/gwf1/3cTQPhFCAs+8k/eEpL/hrvnBmVaAYX2j3P+5qTz05mb+sHp70Xo/e62Wk4f04VBzK6++V9joYzr/+WotE07ow94jcV6vLU7PcRz+67UYpw3vx/aDLSzeVNxopuM4/Oy1GB8aOYCN+xpZvmV/UXoJx+GXr8c4c9QA1u86TM22g0XpuRTb19mIawoty/Z4XBFIwHqQLOcVMKf8nybe/07vQDWdhvJA9QC0LPgCr20dcJ2wrvnEwDUrR+4OXLNPeWHTUToE/1WDvaVUXwJeynjtvrSfd5CcQpzJKuDMYPbCaI+gRpZcna6uFzRd/XhNrzg9o/tQ7P/pzxfV8nbdfh66ehptwHWPLQPgizNOKkjvxy9vYPWOBh68+kyaEw7XP/4OAJ87K9ufRG/c6cH3XPmhQEyri2te00deC+HOF9+lMd7GL/9+MruPJLj16aTexaeeUJDe7c+tIiTCzz89ha0Hm/n6MysBuKBqaN5a7jOt/aNhvn/ZaWza28jtz9fwzx87lRnjBhekd8vTKxnZP8q/XnoqG3Yf4Y7fvst350xk2phBeeu5z7RWDe3LHZeMZ/WOBr79+zV8//JJTB45IG+9zH0t5nthxtXgvc98O1C9cXN/HKie0f3xWTXY6AaEQiEWLFjQbpt4POrZxp1C1tX1gqarH6/pFadndB+KOTfSqwdHI8lL9XnXnMXnH3qT92MxZg5N5KWXXj04Eg4TCcNDnz+TL/xqMe+9t4FzhrTlpZdePTgSDt5KuOb1ukeXsGbNGqYOzD5wk2v/0qsHh8NTGDEgzD1XTuWGx5dQU1PD6QPy00uvHhwKTWbMoAp+8fdTuOXJZaxcGWdif/966ZE3Fw2MEwqdTuWwvvz0U5P5+m/e4RMnxjmlX356bvXg84bsIRQ6jYnD+/GjK87gm8+u4JMj44zrk5+eWz14uuwjFDqVySMH8P3LJ3HnC6v49Kg4Yyry+/zSKbavs9t7hmGUnmByXA3DMAzjuCZX5E2fSNhXzmsmuSJv+kcjvnJeM8k38qZQhvWNFlRtOFfkzYgBUV85r5nkirwZM6jCV85rOu3ltFYO6+uZ85pNL1fkzcTh/TxzXrPp5Yq8mTxygGfOa2dgxtUwjJIjjvdiGIZhGN0Zr5xWr5zXTLxyWr1yXjPpLNPqkm9UjldOq1fOayZeOa1eOa/ptGdaXdrLec2m55XT2l7OazY9r5zW9nJeOwszroZhlBZNThX2WgzDMAyju+JlWl38mlcv0+ri17x2tml18WtevUyri1/z6mVaXfyYVz+m1cWPefVjWl38mFc/ptWl1ObVjKthGKXHpgoXjIhcKiLrRaRWRO7Msl5E5Oep9atEZJrXtiIyWEQWiMh7qX/zr+5gGIYRIN25r/NrWl28zKtf0+riZV5LZVpdvMyrX9Pq4mVe/ZpWl/bMaz6m1aU985qPaXVpz7zmY1pdSmlezbgahlFSBBtxLRQRKQPuBuYAk4CrRGRSRrM5wPjUciNwr49t7wReUdXxwCup3w3DMEpCd+7r8jWtLrnMa76m1SWXeS21aXXJZV7zNa0uucxrvqbVJZt51QJMq0s286oFmFaXbOZVCzCtLqUyryU1rt357plhGHmg6r0Y2fgwUKuqMVWNA08BV2S0uQJ4VJO8BQwUkREe214BPJL6+RHgkx18HEeJxWLE43FisViP0Auarn68pmcUSLfs65pa29hwwMnbtLqkm9ffvXeIptY2Nh5qy9u0uqSb15feO0hTq8PWhkTJTatLunl9+b19NLU6HGpsydu0uqSb11dr99CYUJqbm/I2rS7p5vXVDbtpchRtaczbtLqkm9e/bNhJk6OUxxvyNq0u6eb1jZRen9ZDeZtWl3Tzurh2e6f0dSU7Dbvz3TPDMPLDRlwLZhSQnkpfn3rNT5v2th2uqtsBUv8WFnyXJ7FYjPnz55NIJJg/f37RfwC7ul7QdPXjNb2udb4cZ3S7vu6l195CEWTRPdRtKvzciIbhYtaxpnkAisDCu9lUhF5FGC5hHTVHKlDAWXhPUXpB078cPiobWNY4EAVaFt5X1P4NjMBFsoG3m4eD49DyytyivqtDesFHtJa34yeC49D4ygNF6Q2Pwkx9n/9pHQlOGw2vPFSU3sjecK7G+GvrKHDaOPDKvKL0xlQ4zNCNLGo8oVP6ulLmuB69AwYgIu4dsDVpbY7ePQPeEhH37tm4dra9ApiV2v4RYBFwR0cfjGEYBaIg2SPkDG8ky2uZNj9XGz/btv/mIjeSvKnI2LFj89k0K3V1dSQSCRDh0Pk38XxzmEhtJGvb++sWH/05Ho9mbROPV5E4f1zwevEjJBIJ6urqqKys9HdwnUCHfX7hiOkVoyfSJc+X44xu19fp4b2QaOHwuTcUfa41tY6HcAgSzRwO5NythFACjuzF6YLn7p76GNENr9B8wc0c/sitPN8sRR1vY2I8hBwIlXGoSD3HgWbnlID1KpPxCqEyDs0MQu9koA1CYQ7NvIXnm0NF6o2DsjZwWju8ryulcc12B2yGjza57p652x5z90xEOuXumWEYRWAjqoVSD4xJ+300sM1nm0g72+4UkRGpPnQEsCvbm6vqXGAuwPTp04v+Xxw7dixr164FVfq/cT/V1dU5//jNnv2Roz/nCjxPH/EKh8OB6wVxARskHfX5HTr/JtMrUq8rni/HGdbXkf1ce2l7ORsPtcHCn+P0sL6uubUZEKaU7+WSqsFZ23od7xN1Efo4TbS8OpdDM28GEaZFdnNR1dC89dxCTNrSSOOrD6T0QkyP7OTCqmEF6T1S14sh8QYaXn3oqN6MyHZmVg0vSG/e5l6c0HqIA6/O49DMW0CE8yLbObcAvYQD8zb1YkTiAHtffbhT+rpSzlgv+d0zEVkqIkvjbY35bGoYRsDYVOGCWQKMF5GTRSQCfA54MaPNi8AXUzUDzgEOpm7utbfti8C1qZ+vBX7b0QcCUFlZSXV1teeFV756kydP7pJ6QdNRn5/pdQ29Ho71dVl4aXs59U0hbhnvMKcn9nVlZVzWu47VjPSV85rJE3XJUcbrJpQd1ZsTrWOVjvaV85pOevXgayeEj+pVR7ewQsd45rxm03MLMV0zIXJUb3a0nqV6kmfOazY9txDTVRN6pfRCfDS6lcV6kmfOayauaR1T4XDlhN6d1teVcsS1y9w9G9B7hF0WG0apUKz4UoGoakJEvgbMB8qAeaq6WkRuTq2/D3gJ+DhQCzQCX25v25T0D4GnReR6oA74TGcdU2VlJZHaSGB/+CorKwP9Ixq0XtB0xOdnel1Hr6difd3f4prWr6QKMfXUvu6MqrGMjTfzq81RIM7UgY6v7V3TenWqEJOr96GU3iMpvdMHeOtli7xx9aZWjWZMSzOP1SX1Jvb3p5dZPdjVm1Y1ilHNzTy5JconiHNKP396mdWDXb2zq0YyqqmZp+ujfHJknHF9vPXSTeucE4/V6+hzppTG9egdMGAryTtgV2e0eRH4WuoZ1hmk7p6JyO52tnXvnv2QTrx7ZhhG4diIauGo6kskL9jSX7sv7WcFvup329Tre4GLg91TwzCMwrG+7gMyTWtPp38EvnySf/OaaVozcasN+zGvfnJa3WrDfsyrn5xWt9qwH/PqJ6fVrTbsx7xmM62dSclOd1VNAO4dsLXA0+7dM/cOGslOJkby7tkDwK3tbZva5ofAbBF5D5id+t0wjC6KkKw54LUYhmEYRk/HTGt2XPP6WkbOayZeptUlV85rOn5Mq0u2nNdsen5zWrPlvGbT85vTmi3nNZNSm1Yo7Yir3T0zDMNyWg3DMAzDB2Za28dr5NWvaXVpb+TVcRzfptWlvZFXx3F8m1aX9kZeE47j27S6tDfyGk84JTetUNriTIZhGIAVZzIMwzCM9vjxyxvMtPog18jr7c+tAvybVpdsI6+O43DbszV5mVaXbCOvjuNwy9Mr8zKtLtlGXhOOw01PvZOXaXXJNvIaTzjc8OTykptWKPGIq2EYBmBxOIZhGIaRgx+/vIHVOw6ZafVJ+sjrpNXbeWX9bkQkb9Pqkj7yevraHfz23Z30j4a5aGBheukjr5M37OLJ5VsZ0T/K+UP2FKSXPvI6eeMe5r25maqhfZku+wrSSx95nbJxH3f/NcaUUQOY3La/IL0gMeNqGEbJsRHVnsXs2bPbXX9/3eJjsuOy4TgOodQVnJeeHzpSL2g6+/Mzvc7VM7oPQZwbP355Pat3NPDg1dMIB3CO9KS+bmZDM198dCkjBkR5+JrpwOSi9M7b38iXn1jOuMEV/OATU4DTi9I7Z+9hbnxqBROG9eV7Hz8NOK0ovQ/vauCrv1nJh0YM4J8/dipwalF607ce5BvP13D22EHc/tHxwPii9KD488V6ScMwSosCjnovhpFG0BdKXV0vaLr68ZqeYbgjrcGZVuhZ5+6PFmxgwgn9ONSc4A+rtxel5TgO//7ye5wxoj+7D8d5ZX3WtM389Ba8x9RRA6g/2MTrtYWNtrokHIcfvbyBD48ZxPt7DrN4U2GjrS7xhMN/LHyPc8YNYvWOQyzfEsxoa7Hni424GoZRcqxqcM8hqLvzPXWEKujPz/S6lp7RfSj2/9SdHjz3c1MDM63HE8V+frc/twoR4WefPoPdR+Lc8t8rALjs9BEF7cttz9bQPxrm+5edxs6GOLc+ndS7+NQTCtK75emVjOgf5TuXnsqWA838w7MrAbigamjeeu4zrVVD+3LHJePZtLeR25+v4Z8/diozxg3OW899pnXKqAF8Y9YpbNh9hDt++y7fnTORaWMG5a2XTrH/r2ZcARQk3hawaBvaOxKw5vFBZE/wp1V8cND/Px2DxoP/47JpZ/6dmBcTR+4IXLMoAqoqLCKXAj8jGTL/oKr+MGP9ROBXwDTgX1T1J6nXxwCPAicCDjBXVX8WyE4ZxxAKhViwYEG7beLxqGebIKa4HY8E/fmZXtfSM7oPxZwb6dWDI+GeealezOeXXj04FJrM8H5R7v3sVK7/9RLWrFmTM+c1m1565M1FA+OEQqczYkCUe66cyg2PL6GmpiZnzmsuPbd68PlD9hAKncZJgyv42aen8NWnlrFyZe6c11x6bvXg6bKPUOhUKof15aefmszXf/MOnzgxd85rNr30yJvJbfsJhcYzcXg/fnTFGXzz2RXt5rx2Rl/XM78NncSfVvyfUu+CYRwXBPGMq4iUAXeTzG+uB5aIyIuquiat2T7gNuCTGZsngG+q6nIR6QcsE5EFGdsahmEYRodikTfFkSvyZni/aLtROdloL6d1xIBozqic9vRyRd6cNLgiZ1ROe3q5Im8qh/XNGZWTi/ZyWicO75czKqczsa+EYRilRX0u3nwYqFXVmKrGgaeAK455K9VdqroEaM14fbuqLk/93ACsBUYVflCGYRiGkR9mWovDK6c1V1RONtozrS7ZonLa0/PKac0WldOenldOa7aonFy0Z1pdskXldDb2tTAMo6QIIKqeiw9GAVvSfq+nAPMpIuOAM4HF+W5rGIZhGIVgprU4vEyrix/z6se0uvgxr35Mq4sf8+rHtLr4Ma9+TKtLqc2rfTUMwyg9jo8FhorI0rTlxgwVyaKc1yRkEekLPAt8Q1UP5XUMhmEYhlEAZlqLw69pdWnPvOZjWl3aM6/5mFaX9sxrPqbVpT3zmo9pdSmlebWvh2EYpUVBHPVcgD2qOj1tmZuhVA+MSft9NLDN726ISDlJ0/q4qj5X7GEZhmEYhhdmWosjX9Pqks28agGm1SWbedUCTKtLNvOqBZhWl2zmVQswrS6lMq/2FTEMo8Rosqqw1+LNEmC8iJwsIhHgc8CLfjYUEQEeAtaq6k8LPhSjaGKxGPF4nFgsVupdOS4J+vMzva6lZ3QfYrEYTa1tbDzUZqa1AGKxGI0Jpbm5KW/T6pJuXv+8fh9NjqItjXmbVpd08/rKhj00OUp5vCFv0+qSbl5f3bCbJkfp03oob9Pqkm5e/7JhJ02OMjBxIG/T6pJuXt+s3dkpfZ19TQzDKDmi3osXqpoAvgbMJ1lc6WlVXS0iN4vIzQAicqKI1AO3A98WkXoR6Q+cD3wB+KiIrEgtH++gwzVyEIvFmD9/PolEgvnz59vFfp4E/fmZXtfSM7oPsViM37+5CkVg4d1s2mTnRj7EYjGe2dkfHIeWV+YW9d3qH4GLdAPvJIaB49D4ygNF6Q2MwIW6gaWtw8FxaHjloaL0hvSCj2gtb7eOAKeNA6/MK0pveBRm6vv8T+soaGtj7ysPF6U3sjfM0BivNw7tlL7O4nAMwyg9AeW4qupLwEsZr92X9vMOklOIM/kr2Z+RNTqRuro6EokExI+QSCSoq6ujsrKy1Lt13HD08xPh0Pk38XxzmEht9jzx++s+qD0Wj0eztonHq0icPw7CEdMrRk/EzmfjGOrq6tADW0Gh6ZQL7NzIkz8dPhFohFCIQxd8leebKfi76jjQ7IwHcSBUxqGP3MrzzdLF9E4B2iAUDkivMqlXFubQzCD0ToayBDiJDu/rzLgahlFaNNm/G8bYsWNZu3YtRPoQDocZO3ZsqXfpuOLo56dK/zfup7q6OufFw+zZHzn6c67AeHfE8ND5N5lekXp2PhvpuN/V5kQzzgnj2VsxrtS7dNzwRF2EaLQNeeMRDp1/EyLCGdGDVFcNzNq+ve+qW4hJWxppfPUBDs28GRCmlO/lkqrBBek9UteLIfEGGl59KKknwrTIbi6qGlqQ3rzNvTih9RAHXp2X0gsxPbKTC6uG5a3nFmIakTjA3lcfPqo3I7KdmVXDC9J7cFMvRif2sevVRzulr7OpwoZhlB5HvRej21NZWUl1dTXhcLhd02BkJ+jPz/S6lp7RfUg/Nz7Zt57dZYN5eWd5qXery+MWYrpuQtnRz+/y3nWslxNZtq8sL6306sHXTkh+R8NlZVzWu47VjPTMec2m5xZiumZC5KjenGgdq3S0Z85rNj23ENNVE3od1auObmGFjvHMec0kvXrwlRN6H9WbHa1nqZ7kmfOaTe/BTb2o6uPw6Ql9Oq2vM+NqGEbJCSjH1egGVFZWEolE7CK/QIL+/Eyva+kZ3Qf33JhQVclXTm6h9nDIzGs7ZFYPdj+/SVUncd24Zt7YV+7bvGaLvHH1zqga65nzmk0vs3qwq/ehqrGeOa/Z9DKrB7t6U6tGe+a8ZpIt8sbVm1Y1yjPnNZuea1ovGX6sXkf3dSUxriIyWEQWiMh7qX8H5Wh3qYisF5FaEbnTa3sRGSIir4rIYRH5ZWcdj2EYRRJMVWHDMAzDOO4IhzDz2g5ekTd9w/g2r35yWtvLec2m5xV5017OazY9r8ib9nJeM/GT09pezms2vUzT2pmUasT1TuAVVR0PvJL6/RhEpAy4G5gDTAKuEpFJHts3A/8KfKtjd98wjMBQwPGxGIZhGEY3xcxrdvzmtPoxr35Mq4sf8+rHtLr4Ma9+TKuLH/Pqx7S6+DGvpTatUDrjegXwSOrnR4BPZmnzYaBWVWOqGgeeSm2Xc3tVPaKqfyVpYA3DOA4QvKcJ21RhwzAMo7tj5vVY/JpWl/bMq+M4vk2rS3vm1XEc36bVpT3zmnAc36bVpT3zGk84vk2rS3vmNZ5wSm5aoXTGdbiqbgdI/XtCljajgC1pv9enXvO7fbuIyI0islRElsbbGvPd3DCMILGpwoZhGIZh5jXF7c+tAvybVpds5tVxHG57tiYv0+qSzbw6jsMtT6/My7S6ZDOvCcfhpqfeycu0umQzr/GEww1PLs/LtLpkM6/xhMN1TywruWmFDozDEZGXgROzrPoXvxJZXgvs6lVV5wJzAQZER9hVsWGUCgXa7CtoGIZhGPCBeX1wYy/ufv19vnrBKaXepU7l9udWISJ5m1YX17zO2xTl2XfqebV2L/2jYS4aWJiea15/tTnKae9u43fv7mRE/yjnD9lTkJ5rXh/ZHGXS2h389ztbqRral+myryA917w+Vhfl9HW7eGRJHVNGDWBy2/6C9Fzz+uSWKGe8v4f73tjIueMGM6HlQEF6QdJhxlVVL8m1TkR2isgIVd0uIiOAXVma1QNj0n4fDWxL/exne8MwjhNsKnDPYvbs2e2uv79u8THZcdlwHIdQqGcWxg/68zO9rqVndB+KPTcuTjh8+fFlAD3GvLqm9f99ajIwud22Xp/fRxqa+fyjSxk1sDe//MwU4PSi9M472Mi1jy2nckgF3/v4acBpRemds7+R659YzmnD+/LPHzsVOLUovbN3H+bm/17B1FH9uf2j44HxRelN39HA159ZyTnjBqXOv/bPwc7o60rVS74IXJv6+Vrgt1naLAHGi8jJIhIBPpfazu/2hmEcL9hUYSNP7CK/OIL+/Eyva+kZ3YNIOMSvPn8Wb27cx92vv1/q3elwjjWtxeE4Dt/703omjxzAweZWnl+5zXsjL70/rmfamIHsOhznD6u3F6WXcBy+98e1nDNuEHX7m3llfXFjcPGEw//50zpmVg7hvT1HeL22sNHgdL3v/3kdF40fSs22QyzeVNhocCbF9nUdNuLqwQ+Bp0XkeqAO+AyAiIwEHlTVj6tqQkS+BswHyoB5qrq6ve1TGpuA/kBERD4JfExV13TOYRmGkT9mTHsSQY0suTqmZ3rdSc/oPgT1fxoOwbzPnxXYyGtX/S64pvXHV5x+jG6h+3TbszX0j4b5/mWncaApwQ1PLgfgU1NGFqR3y9MrGdE/yncuPZXdR+Lc8t8rALjs9BF567nPtFYN7csdl4xnZ0OcW59O6l18at5le44+0zpl1AC+MesUthxo5h+eXQnABVVDC9K77ollnDtuMLfMPJlNexu5/fka/vljpzJj3OC89dIp9jwpiXFV1b3AxVle3wZ8PO33l4CX/G6fWjcusB01DKPjUcy49iBCoRALFixot008HvVs407B86Pnh+6kF/TnZ3qdp2d0H4I8NyIh+NXnz+KqB/9KXd2WogrkdMW+Lr16cCg02bdets8vPfLmooFxQqHTGdwnwgNXTePaRxezft06zhrclpeeWz34/CF7CIVOY3i/KPd+dirX/3oJa9asYerA7Jl9ufTc6sHTZR+h0KmMGBDlniuncsPjS6ipqeH0Af710iNvJrftJxQaz0mDK/jZp6fw1aeWsXJlnIn989NzqwdPaDlAKHQKlcP68tNPTebrv3mHT5wY55R+/vUyKbavK9WIa9eiLQH7DgQq6Rw+QnXvLwSr2XJ8pPyc1AGau247L3DNhsrgw0GdaPCabYeCryq4uml04JpFYTmthmEYhpGTSDh0tGATlJe8umtQ5Bt50x7t5bQO7hM5WrAJyGleM/VyRd4M7xc9WrAJ4jnNa6ZersibEQOiRws2QTyneU2nvZzWkwZXHC3YBLnNa6ZersibymF9jxZs+gS5zWtHY/NSDMMoOeI4nothGIZh9GS6W1ROZ5lWl/ZyXrPpeeW0tpfzmk3PK6e1vZzXTNozrS7t5bxm0/PKaW0v57WzMONqGEZpUcBR78UwDMMwejjdxbx2tml18WNe/ZhWFz/m1Y9pdfFjXv2YVhc/5tWPaXUptXk142oYRonxUVHYnoE1DMMwDOD4N6+lMq0u7ZnXfEyrS3vmNR/T6tKeec3HtLq0Z17zMa0upTSvZlwNwyg9ZlwNwzAMwzfHq3kN0rRqAabVJZt51QJMq0s286oFmFaXbOZVCzCtLtnMqxZgWl1KZV7NuBqGUXrMuBopYrEY8XicWCwWmN6iRYt6lF7Qn5/pdR09o/sQxLmRbl6f3XCky/dN96xqpLm5KRDTWlsbo8lRtKUxb9Pqkm5e/7j+AE2OUh5vyNu0uqSb1z+v30eTo/RpPZS3aXVJN6+vbNhDk6MMTBzI27S6pJvXhRt20+QowxL7Ci70lW5e/1q7s1P6OjOuhmGUFlVoa/NejG5PLBZj/vz5JBIJ5s+fX/QfQFevpqamR+kF/fmZXtfQM7oPQZ4b4RB8VNcSO9jGyrXvddm+6Q+LV3Ok4RAtr8wNRO+lZRvAcWh85YGi9PqG4RLWs6qpApw2Gl55qCi9/hG4WDbwTlNfcNo48Mq8ovQGRuAi2cDSxv7Q1sbeVx4uSm9IL7hQalnSOBDaEux65dGi9IZH4QJ5nzf3lndKXydqIxkMKB+m5w38dKCazuEjgerB8ROH0xH05DicDqE8+O/95uvuWKaq0/PdbkCv4XreiKs92/1p838VpG90DtOnT9elS5f6apsr523RokXU1NTQeO6XINKHcDhMJBLJ2jb99Xg8+932eDxOIpGA+JGepScCqqbXVfTiR6h482EmT57MrFmzsrb1k20oItYHdgGsr+tCel39u9/T9ALq6yB3f2c5roZhlBa3qnAAiMilwM+AMuBBVf1hxvqJwK+AacC/qOpP0tbNAy4HdqnqGYHskJEXY8eOZe3atVS8+TDhcJjq6moqKyuztp09+yNHf851cZg+qmF6pldqvbFjx2ZtZ/Q8rK8zve6s15F9nRlXwzBKTwAzP0SkDLgbmA3UA0tE5EVVXZPWbB9wG/DJLBIPA78EHi16Z4yCqKyspLq6mrq6OsaOHZvzD6npmV5P1DO6D139XDM90yulXnuYcTUMo/QE88jCh4FaVY0BiMhTwBXAUeOqqruAXSJy2d/ugr4uIuOC2BGjcCorKwP9o2d6pted9IzuQ1c/10zP9EqplwsrzmQYRokJLMd1FLAl7ff61GvdEhEZLCILROS91L+DcrS7VETWi0itiNzptb2IzBaRZSJSk/r3o511TIZhGJlYX2cYhosZV8MwSovit6rwUBFZmrbcmKEkOdS7K3cCr6jqeOCV1O/HkDZ9eg4wCbhKRCZ5bL8H+ISqTgauBX7doUdhGIbRPtbXGYYBmHE1DKMr4G/EdY+qTk9b5mao1ANj0n4fDWzrrEMoAVcAj6R+foTsz+0enT6tqnHAnT6dc3tVfUdV3c9tNRAVkV6B771hGIY/rK8zDAMw42oYRsnRZFVhr8WbJcB4ETlZRCLA54AXO3TXS8twVd0OkPr3hCxt2ps+7Wf7TwPvqGpLYHttGIaRH9bXGYYBWHEmwzBKjYJq8fm3qpoQka8B80nG4cxT1dUicnNq/X0iciKwFOgPOCLyDWCSqh4SkSeBWSSnJNcD31XVh4resSIQkZeBE7Os+he/Elle83UXQEROB34EfKydNjcCNwIW9WEYRsFYX2cYhh9Eg6nmmd+bigwG/hsYB2wCrlTV/VnaZc1kzLW9iMwGfghEgDjwj6q60Gt/BpQP0/MGfrro40rHOXwkUD0Ap6U5cM3jhV23nRe4ZkNl8WYpEycavGaHUB78937zdXdkDYv2YkB4mJ7b/5Oe7ebvf7Ag/e6KiKwHZqnqdhEZASxS1VMz2pwLfE9Vq1O//zOAqv57e9v//+3da4xdVRXA8f+qpCJQ+rAtlEB4SHgkmjRYSjCCUJRgMWji44MamkZDMJHoB8IzJBpJLGBCIH5QgtEmxhhsQEpAKA8lRkOxhZZSoQFkeBXBChWNEYKz/HD2yDC5c3tnuOfec2b+v2Tn7rnn7DNrdc7s6b7n7H0i4nDgAWBtZv6hl3hWrFiRW7Zs6Vt++zI6OsqcOf27acjjebymHy8iZmUfaF/X/nPX43m8qR5vsv5uWLcKO9FeUiWz18WZ9G4bqfo5yuvtHfbpdvt0x/YRsQC4E7i81//IDUM//5B6PI830443w9jXeTyP5/Gq9n2KY6qcaC/pHf15HM5ssw74VEQ8BYzdbUJEHBYRd0F1+zQwdvv0E8AtmbmzW/uy/7HAVRGxrZROc8IkaRDs6yQBw5vj+q6J8pN0FJ0m2p8yhfZOtJdaIkdbcot1g2Tm34GzOry/G1g97uu7gLum0P5q4Oq+BitJ02RfJ2lMbQPXNk2033/OQT2GJKn/vKIqSZKk7mobuGbmJyfbFhGvRMSycRPlX+2wW7dnMk7avky0vw04PzOf6RLfTcBNUC3O1Gtekvos6fVxN5IkSZqlhnWr8NhE+XX0MNEeeIlqov2Xu7Wf7kT7N97es+fuPT9+blqZNMdiqsWp2q5zHjdsGHwk781M+XlA77kcOe3v0IfH4UiSJGnmGtbAdR1wS0R8DXge+CJUE+2pHnuzerJnMnZrz7sn2l9V3js7Mztd0f2/zFzSx9yGIiK2zIRl8s2jeerOJTNJVw2WJElSF0MZuDrRXtJ46a3CkiRJ6mJYV1wlCYB/8vo9943esriHXWfKrdcz0tatW/dExGRTLtp+67zxD5fxV6Y/HUN9Y1/XaMY/XP2Mv2N/58B15rhp2AH0iXk0T625ZOY5dR5fg9FtykXbb503/uEyfjWJfV1zGf9wDSL+OXUeXINTVkluPfNonpmUiyRJktrJgaskSZIkqdEcuDZQRIxExI6I2BYRWzpsnx8Rd0TE9ojYGRFrp9D24ojIiOhlTmEj84iIiyJiV2lzbd151JVLRCyPiIfG3o+IlQ3PY0FEbIiIJyPiiYg4tby/KCLujYinyuvCuvNQ67T9qr3xD5fxqy3a/rM2/uEy/n2ITFfzbJqIGAFWZGbHCc4RcQUwPzMvjYglwC7g0Mx8q1vbiDgCuBk4AfjoZMfvlzryiIgzgSuBczPzzYhYuq/HHfVDTblsAq7PzN9ExGrgksw8o8F5rAd+n5k3R8Rc4IDM3Fs+PHgtM9dFxGXAwsy8tM48JEmSNLt4xbWdEpgXEQEcBLwGvN1Du+uBS0r7JphOHt8A1mXmmwCDGLT2aDq5JHBwqc8HdtcXXs865hERBwOnAz8ByMy3MnNvafNZYH2prwc+N8iAJUmSNPM5cG2mBDZFxNaIuKDD9h8CJ1INdHYA38rM0W5tI+I84KXM3F5z7OP1PQ/gOOC0iNgcEQ9GxMl1JjBOHbl8G7guIl4AfgBcXlv075huHscAfwN+GhGPRsTNEXFgaXNIZr4MUF6X1p6FJEmSZpfMtDSsAIeV16XAduD0Cdu/QHX1NIBjgWeBgydrCxwAbKa6BRRgBFjctjzK148DN5Y2K0ubaGkuNwKfL/UvAfc1NQ9gBdUV5FPKfjcA3yv1vROO8XrdeViGV0r/sQPYBmzpsH0+cEc5v3YCa/fVFrgOeBJ4DLgNWNCm+Mdtv5jqw6Ha+te64gcuopoasBO4tk3xA8uBh8beB1Y2NP4FwIZyrj8BnFreXwTcCzxVXhfWFb9l6Oeqfd2Q48e+bhDxL6Cmvq6WZC19PXG+A1w84b07gdPGff1Ap5N3rC3wEeDVchKOUA1Anqeau9iaPEr9buCMcdueAZa07WdS6v/gnXnmAbzR1DyAQ4GRce+fBtxZ6ruAZaW+DNg1yDwsgy3s44Mv4ArgmlJfQnW7+dxubYGzgf1K/Zqx9m2Jv2w7ArgHeK7b8ZsYP3AmcB/w/vL10pbFvwn4dKmvBn7X0PjXA18v9bmUQQtwLXBZqV9W5/lvGdjP2r6ugfHb1w0s/tr6Om8VbpiIODAi5o3VqTq5xyfs9jxwVtnnEOB44C+Ttc3MHZm5NDOPysyjgBeBkzLzr23Ko7T5NbCqbDuO6hei7kWm6splN/CJUl9F9QlUI/Mo58oLEXF82e8s4M+lvhFYU+prgNtrS0JtkExxvndmbsrMsX0eAg6vN8Suphx/cT3NWENgOvE3ae2A6cSfNGe9gI7xu07AjGRfN1z2dbOxr6trpG6Z9iccx1Bddh+79H5lef9C4MJSP4zqU5cdVAOPr3Zr2+F7jFDzrcJ15UE1UP152f8RYFVbfybAx4GtZdtmqpWeG5lH2bac6taUx6g+QFhY3v8gcD/VwPt+YNGwf48stZ5Hz5bfva3ABR22zwN+C7wM/ItqBfCe2pZ97hh/3rUhfuA84IZSr7V/rSn+bcB3Sz/0IHByy+I/kepDtxeAl4AjmxZ/6T8fBn4GPEq1wv+BZdveCcd4va74LcM9Vye0t68bfPz2dTXHX3dfV0uyFovFYpmZhRrme49reyXVvK/a5q33O34GvIZAHf/+DHDtgJriH9h6AdONH9cJaF2xr7Ova2D8s76v81ZhSVLPMnN3eX2V6j9eKyfssha4NStPU/0xO2FfbSNiDfAZ4CtZ/qK1JP4PAUcD28tzkg8HHomIQ1sSP1TTR8baPAyMAotbFP8a4NZS/1WHYzYh/heBFzNzc9lvA3BSqb8SEcsAymtTHvM2q9nX2dc1MP5Z39c5cJUk9aSu+d4RcQ5wKXBeZv67TfHnANcQaPvaAW1fL8B1AmYP+zr7uobGb1831Uu0FovFYpmdhfrmez9NNWdnWyk/alP8E77HCDXdPlfjv/9A1g6oMf6BrBfwXuIv25bjOgGtKDWeq/Z1w/33t6+rOf6ybTk19XVjj+KQJEmSJKmRvFVYkiRJktRoDlwlSZIkSY3mwFWSJEmS1GgOXNVoEXFyRDwWEfuXVc52RsSHhx2XJEmSpMFxcSY1XkRcDewPfIDq2VDfH3JIkiRJkgbIgasaLyLmAn8C/gN8LDP/O+SQJEmSJA2QtwqrDRYBBwHzqK68SpIkSZpFvOKqxouIjcAvgaOBZZn5zSGHJEmSJGmA9ht2AFI3EXE+8HZm/iIi3gf8MSJWZeYDw45NkiRJ0mB4xVWSJEmS1GjOcZUkSZIkNZoDV0mSJElSozlwlSRJkiQ1mgNXSZIkSVKjOXCVJEmSJDWaA1dJkiRJUqM5cJUkSZIkNZoDV0mSJElSo/0PZGKBUeQImgYAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 1152x288 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "f,ax = plt.subplots(1,3,figsize=(4*4,4), subplot_kw=dict(aspect='equal'))\n",
-    "da_s.where(da_s.values>da_s.attrs[\"nodatavals\"][0]).plot(ax=ax[0])\n",
-    "ax[0].set_title(\"Sliced raster\")\n",
-    "splot.plot_spatial_weights(w_rook, da=da_s, ax=ax[1])\n",
-    "ax[1].set_title(\"Rook contiguity\")\n",
-    "splot.plot_spatial_weights(w_queen, da=da_s, ax=ax[2])\n",
-    "ax[2].set_title(\"Queen contiguity\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### `higher_order` neighbors\n",
-    "\n",
-    "In some cases `Rook` and `Queen` contiguities don't provide sufficient neighbors when performing spatial analysis on a raster data, this is because `Rook` contiguity provides max 4 neighbors and `Queen` provides max 8.\n",
-    "\n",
-    "Therefore we've added `higher_order` functionality inside the builder method. We can now pass `k` value to the weight builder to obtain upto kth order neighbors. Since this can be computionally expensive we can take advantage of parallel processing using `n_jobs` argument. Now lets take a look at this functionality."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Building a test DataArray \n",
-    "da_s = raster.testDataArray((1,5,10), rand=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Below we can see that builder selected all the neighbors of order less than equal to 2, with `rook` contiguity"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/magito/anaconda3/lib/python3.8/site-packages/scipy/sparse/_index.py:124: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.\n",
-      "  self._set_arrayXarray(i, j, x)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(<Figure size 720x720 with 1 Axes>, <AxesSubplot:>)"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "w_rook2 = raster.da2WSP(da_s, \"rook\", k=2, n_jobs=-1)\n",
-    "splot.plot_spatial_weights(w_rook2, da=da_s)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Few times we require the kth order neighbors for our analysis even if lower order neighbors are absent, hence we can use `include_nas` argument to do the same.\n",
-    "\n",
-    "We can also look in both the examples we used `n_jobs` parameter, and assigned -1 which equats to all the cores present in the computer for multithreading"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(<Figure size 720x720 with 1 Axes>, <AxesSubplot:>)"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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A1WDIRuN1ruM4pwGXAV80xlzQ3V80xtxpjFlpjFnZ0NCQuQpFfEQBqyKZkY2A1HQpYNX/Mt54OY5Tn/p7N/AkcAawyxgzGiD19+4ufvcBx3FmOY4zq7i4ONOliviGAlZF3JXNgNR0KWDV3zLaeBljiowxA9q+By4B3gOeAeam/ttc4OlM1iESNApYFXGPFwGp6VLAqn9lesZrJLDCGLMaeBNY6jjOi8APgUpjzGagMvVvEekBBayKuMOrgNR0KWDVnzJ6tHYcp8ZxnFNTX1Mdx/l+avlex3E+6TjOxNTf+zJZh0hXampqwEn97UPtA1bf2lLP8uXLfTuWoPL7NhZkbetm/5GEJwGp6WofsPrS5v0kEglfb2dtAasPbUoE+liml8mSt2pqanjh968DDsuWLfPtTl4cgTNNLa/GS3h3Y62vxxI0NTU1vFD9Cn7fxoKopqaGp9e1vmnr2Kvz2brVn+smbMFsNvFu8xCSx2zfb2dnJt5nx+FjvNPY1/dj6YpxHMfrGrpl1qxZzsqVK70uQzxUXV3t6u0tX76clclRHCubAYkjhEIhwmF3p7m7ur1Ewt1X1/FEEttKnW7I8liCxP31ksA2BWAMHGshZBytl17KzD7TBzC0ph6Z1m9dZLq4QQeXn3c7uzlfj8WBljj9lv+U6dOnM3v2bFfvorKy0tXb64ox5u12wfEf8f+JYZFeKi8vZ8OyZTSWzSB0cDvXlLYQjUZdvY/KyvM7Xe5mE7njKDwSCxP58yKOnHk7xk5yVXgrEyZkZyxB4nZz/8uNNvt313KseALYSU7ru5eLJgx39T7yYb2Au+tm2xGLJdv60PdPD9J09h0M/N1/UVVVlYH9v/MneDfHEk/CfR8UYK17CTveROKUz3B5JMa0CeWu3QdkZyzQOhO5bNkyGs+9i1AoRHm5u+PIBTrVKHkrGo1SVVUFQGTEWLb2n+RxRT3XmIDFdRGuKUty+fmtL6zK+9msCJ3scWXybH0BocII15a2AIbL+tax2ikj1qTDrpfaAlJvGHOMT194FmAy0nRlQ9KGB2sLOXO4zRUzxjKzpD+nFe7jd0zybcBq23E5FAr5dr2ciI4Aktei0SgY+MJEm/WNId7e759ohngSFsYizC5OMK7I/mgscyZFFLDqsdcaQmyPW9xWnvhovZwyYQzXlzbzRH2Yvc1eV5ifOgaktq0bPz652zY8VFtIRT+bc4YdIxqNMnv2bConDvV9wGo0GiUcDvtyvXSHGi8RWi9QnVcRZ8WeAjb7IJrBtmFBrJAZg5KcOvjjR1cFrHpn9QGL1QdDzCtvxuqwKZX1c7h0ZIJfxyIcyZOwyFzhp4DU7ni8PkzfkMNloz8egRGkgNUgyv1nGJEs6R+COWPiLN0ZZmfc62qO76FYmNKIzfnFnT97K2DVGx82WSxvCDO3PE6kiytoTx5oc+bQFhbUFpL0//O/L/gxIPV4XtpVwMEWw43HicBoC1h9OgABq0GjxkuknZERuLIkwWPbcvezw57YXkDIas28OZ72AaubfDCL53cNcXi6PsyNZXEGnmCi4cxhxxjf32ZhrX9PB/mJXwNSO/Pm3j5sPtSHeWM/PqPa0S1jEuxqtlgegIDVINHRWKSDaJHNhanPDsu1C1Rf2R1ib7PFzWXd6wrbAlaf3xmm/qjL7y+XjxxOwm+2Rfj0qASj+3bvd6pGtjAw5LBku04HZdKLOwtoShpfBqR29H6jxev7CrijIk6oG8/ebQGr7zWGeMdH168GnRovkU7MGGxzyqAkv8qhC1Tf3teHDY0h5lac+JVue8URuLokwZK6Qg76/7kn5yRsWLA1wnnDWpg0oGcby3WlCZqShhd26nRQJry+tw81TRa3d2N2KNdtP2p4cVeYW8vj9OvBBFbYgrkVcf6wp4AtmvnOCVoLIl24MPXZYQ/nwAWqmw5ZrNhbwLyKOOFe7LUVRTYXFSdYFIsQ10XdrrFtWLC1kMkDk5w+tOfXDlkW3D62mQ+bLP60VzMSblrfaPHW/gLmjW3u1uxQLtufgCV1hVxbkmBYYc9/f0Dq+tXnfHD9aj7w+eYokllXlLRgAU96eIHqjqPw/M4wt4yJ0z+NSzVOGWwzc3CSBbquyDWP1IUpLrT55Ijed7MhC+aNbWbl/gLWHdQh2Q3bjli81IvZoVwUT8JDsQiVIxKUF/V+xx0ZgStGp65f9f+lbr6mvVzkBG4Zk6Ch2eJVDy5QbQtIvaokQXEk/ds7b3iSMf1sFsW8n8Xzu2frC0jacE1p+s9i/UJwW3mc6t1hBaymqS0g9brSBEN9vpm3BaTOGtLC1EHpv1oa39/mguIWFtbm3vWr+UR7uMgJtF2gmu2A1Y4BqW65fHSLAlbT1D4g1S1DwihgNU0dA1L9rGNAqltmDj7m+4BVv1PjJdIN2Q5YPVFAaroUsNp7xwtITZcCVnsvnwJS06WAVW+p8RLppmwGrJ4oIDVdCljtne4EpKZLAas9l48BqelSwKp31HiJ9EA2Ala7G5CaLgWs9kxPAlLTpYDVnsnXgNR0KWDVGzraivRQJgNWexqQmi4FrHZPbwJS06WA1e7J54DUdClg1RtqvER6IRMBq70NSE2XAlaP76OA1OE9D0hNlwJWj08BqelTwGr26VEW6SU3A1bTDUhNV0WRzcUKWP2YtoDUKQOTnD4k+9cOKWC1awpIdY8CVrPL55uriLfcCFh1KyA1XdMVsPoxbQGpF6cRkJouBax+XKxJAaluU8Bq9mgvFklTOgGrbgekpksBq3/hZkBquhSw+hf7EvBEvQJSM0EBq9mR33uwiAt6G7CaqYDUdClgNTMBqelSwKoCUrNBAauZp8ZLxAVtF6h2N2C1LSD11AwFpKYrnwNWMxmQmq6yfg6X5GnAqgJSs0cBq5nl87Pjkk8qKyszcrv3blzh2m3PaDjMV59cy6fOm8akEQOwbRurk2fvpw8WM2tcX755yUmu3G8bN8dyUdLmtkUrOTi8hOtnlnU5liAZctIsVix7n1/cOpORA9w79+vmeqkERr1TxyNr6ll066y8WC+2bfPE3mFccNJAvnrRBFdv281101FX62ZDwTiOFR7kF3Nm5uy6qwS+/NvV/PlYmG9fOrnLsWTqsZsfe4PKyvMzctte7zO5ucZFfCpa3J9vV53M159+j12H4p3u3N95fj2FIcv1pstt4ZDF/XNm8Ju361jxwZ6cfYJwy4d7m/jei+9z75XTXG26MuGm08o4u2Iof7d4ldelZMU3n13P8P6FrjddmdbZPrPk3Tr+9OE+7rsxd5uuNv957XS2NDTxiz9+mPO19oTXYwnOIymSI2aNHcIXzqngnsWrOZL46/NB96+ooXbfUX509TSPquuZIf3C/Pia6fzHK5t5f9chr8vJmH1NCf7hibV8o3ISk0YO8Lqcbvny7AmMGFDIN59d73UpGfXjVzez70iC739mstelpO21LXt45O067r9pBmEfZGBYlsX8m2ZQvXE3z6zd4XU5gZH7a17Ehy6fOprLpo7krkdXkUxdofrk6np+t2kP9994quevuHqiYlgR37nsZL7xzHvsbAxeyE88keRvH1vF3DPLOXvcMK/L6ZF/u3wy+48k+PGrm70uJSN+s3Ibb8X287MbZvhqn+nMuh0H+dGrm/nv605hcD//XDsVCYf4+Y0zePDPtbz+4V6vywkEf2/JIjnss2dVMHlUf77y+Br+WLOHRW/GuO+mGUTC/ru08rQxQ/jb86Lcs2Q1TYngXNVt2zZ3L17FRZOGc/UpJV6X02OWZfHTG2bwVmw/v35rm9fluOrVTbt5fNV25s/xx+zQ8Ww/cJRvPbuef/n0FMYM6ed1OT02vH8hP7p6Gj+s3sSm3cGd+c4Wf2/NIjnuny45mUTS5l9f2MiPr53O0CL/vNLtqGrySK6YNoq7Hnn3o1k8v/vaU+9RMbSIu8+Lel1Kr4VDFvPnzOCJ1dt5ZeNur8txxertB/nJ8g/47+tOYWDEv/sMwOF4ki/9djX3XDCeGWWDvC6n16LF/flW1Ul8/en3aDgcvJnvbFLjJZJBuw7F2XUozpB+Bby0YZfX5aRt7pljOaV0EH+/ZLXXpaTtBy9tpDlp891P+//aoYGRMD+5/lT+5/cfsHr7Qa/LSUvd/iP889L1fO8zUyjz4exQe4mkzZ2PvsM1p5bwqZNHeF1O2s4YO5TPn13B3z328etXpfvUeIlkSFMiyRcXr+buc6Ms+JvTeGVTA0+tqfe6rLR9/VOT6BcO8e2l/r2o+8E/b2XdzkZ+cv0pXpfimtLBffm3z0zhn5euZ9v+I16X0yuN8QRffnwNX5k9nukl/p0dgtbT2H+/ZBWnjxnCrZ8o97oc13xm2miqJo/k7kdXYQdk5jvb1HiJZEDStrnr0Xe5fOpILps66qMLVBe+EQvEBar/cdVUth84ys9e+8DrUnrshXU7WbpuF/PnzCTk8wu2O5pWMoivzB7Plx9fw4EjuZO63x2ts0OruGFmGRdN8v/s0LeXbmBg3wK+9smJXpfius+fU8Gkkf358uNrvS7Fl4J11BHJEV/67RqmjhrIHWdVfLQsSBeoWpbF/TfN5LUte/ntu3Vel9NtK2P7mf+nD/nZjadS5MM3OXTHRZNGcNNpZdy9eBWJpD9mJGzb5ouLV3FWxVDmnF7mdTlp+8nyLew8FOffr5zqdSkZ83+rTsYy8L0XN3hdiu+o8RJx2fECUjsGrPpZx4DVXOengNR0tQ9Y9cPpoLaA1K/M9ldAamf8FJCarvYBq9J9wd4qRLKsOwGpxwtY9Ru/BKz6MSA1XX4JWFVAqn8pYLV3gr9lSCD44VV7ZwGpndVt23anAau5rqux5HrAamcBqV2NxY+6qtu27ZwPWO0sINWv66azgFQ/1J0O27Z9GbDq9XoxjuN4WkB3zZo1y1m5cqXXZYiHqqurM3K7/74xwj+elF7DsOmQxQs7w3xuXJz+7S4d6uoDZNvG8mx9AftbDLePdedCaDfG0pUTjWXtAYvle8J8oSJOJEcun7Jt+N+thUzof4yLR/xldvFEY3GbF+sFWseTtOGBDwuZMTjJOcOOZaSG3lh30OKVhjCfr4jTrwf7jNvcWDf7E7CwNsK1JQnKi/7ypJ6pD5DOJW3rZVccHtkWYc6YOKNcOJP/P1si/P2E7O8zbjLGvO04zqyOyzXjJZKmHUfh+Z1hbhnz101Xd1xR0oIFPLm9ICO1ZdP0wTYzBydZUFtIrrzQf6QuTHGh/VdNV74JWTBvbDMr9xew7mBuHPJjTRbVu8PcWv7XTZcfxZPwUCxC5Yi/brryzcgIXDE6wWPbIjS2eF1NbsuNvVDEpxoTsLguwlUlCYp7+SrvljEJGpotXt3t82cg4LzhScb0s1kU8z5t/Nn6ApI2XFOqZ4F+IbitPE717jCxJm8P+/sS8ER9mOtLmxnq/WaSlqQND9YWMmtIC1MH5W/T1WZ8f5sLiltYWBshoYejS2q8RHopnoSFsQizixOMS+OVrmXBvIpm1jeGeHt/Hxcr9Mblo1sIW/B4nXezeK81hNget7it3F9ZVpk0JAzXlzbzRH2Yvc3e1HAkCQ/HIlwyMkFZP39c5tIV24aHagup6Gfn1Clcr80cfIzpA5M8uDV3Zr5zjRovkV6wbVgQK+TUQUlOHZz+0SVswdyKOCv2FLD5kP93yzllCfa1WLy8K/vN1+oDFqsPhphX3kzA383fY2X9HC4ZmeDXsQhHsnz2NWnDgtpCzhjSwpSB/n9Gfrw+TN+Qw2WjNaPa0ewRSUYX2vx6m8+nNDNEhyWRXngoFqY0YnNBsXvPXgNCMGdMnKU7w+zMvTcH9ohlwR1jm9l4qA9v7cveLN6HTRbLG8LMLc+dC/xzzZSBNmcMbWFBbSHZyle1bVhUW8j4IpuzAjA79NKuAg62GG4s1YxqV65MneJ/OgDXr7pNjZdIDz2xvYCQ1XphvNv+6gJVnx/TQxbMq4jzp70FbMrCLF5DHJ6uD3NjWZyBeqF9XGcNO8b4IpuFWXojxJLtYfqHHKpG+X926M29fdh8qA/zxmpG9UT+ZkyCnc0WywNw/aqbtNmI9MCru0Psbba4uSxzXdH4/jYXFidYGPP/BapFIbhlTJznd4apP5q5+zmchN9si3D5qASj+2bufoKkalQLA0MOS7Zntkt9cWcBTUnD9QGYHXq/0eL1fQXcUREnD/JR02ZZcEdFM+81hngnANevukWbjkg3vb2vDxsaQ8ytyPwr3RmDbU4ZFIwLVIsjcHVJgiV1EQ5m4Lk3YcPCrRHOG97CxAE+f7Cy7LrSBE1Jwws7M3M66PW9fahpsrg9ALND248aXtwVjAiMbGq7fvUPewrYEoDrV92gR0GkGzYdslixt4C5FXHCWdprLixOUhKxeTgAF6hWFNlcXJxgUSxC3MWLum0bFm4tZPLAJKcP8f+1Q9lmWXD72GY+bLJ4fa+7MxLrDlq8tb+AeWObfT87dCABS+oKubYkwbBCr6vxn7brV5/bGWaXz69fdYPPdweRzEsnIDVdClg9vkfqwgzP84DUdLUFrL7lYsBq0AJSFykgNW1t168+qoBVNV4ix+NGQGq6FLDaOQWkusfNgFUFpEpXFLDaSo2XSBfcCkhNlwJWP04Bqe5zI2BVAalyIgpYVeMl0im3A1LTpYDVv1BAauakE7CqgFTprnwPWNVhS6QTmQhITZcCVhWQmg29CVi1bViogFTpgXwOWFXjJdJBJgNS05XPAasKSM2engasLtkeZoACUqWH8jVgVZuVSDvZCEhNVz4GrCogNfu6G7CqgFTprXwNWNWmJZKSzYDUdOVTwKoCUr1zooBVBaRKuvIxYNWzURpjLjXGbDTGbDHGfMOrOkTAm4DUdOVDwKoCUr11vIBVBaSKW/ItYNWT3cUY0wf4GXAZMAW42RgzxYtaRMC7gNR0BT1gVQGp3ussYLUtIPW2gMwOKSDVe/kUsOrV65QzgC2O49Q4jpMAHgWu8qgWyWPvbYmBA2eYWs8CUtPVFrD65ObD4EBNTY3XJfVaW8Dq/26G/313H01Hm30fkFpTU+P79dI+YPV3mxpYHLM4x3zIEJ9Ptm7Z0rpuos5uBaTmgLaA1V/VhEgkEr7eZ47Hq8arFNjW7t91qWUiWfP+lq0sbWrd7FZXP+HbndyyYLa9gU2NfQCHZcuW+XYsAJOPbmR/01H20peWV+/39VhqampY+sY6grBehoThHPMhbx0dQp+1L/B29VO+Hk9NTQ1L18YA2Pbyr309liAZtG8zztZ3SDrG9/tMV4zjZD9d2BhzA1DlOM7nU/++DTjDcZy/7/D/7gTuBCgvLz+9trY267VK7qiurnbttmwb/nuDQ3L3VuzRkwEHMGBcuwsATBc36ODyfue0/dHu/lweS9c3mImxdPOuey0fxtJVAWlwOn4TgH3GcaAlTigUIhx2dwrP7dvLRYmEu+9mTSQSJJNJSDTR7/UFTJ8+ndmzZ7t6H5WVla7eXleMMW87jjOr43Kvzs7XAWPa/bsMqO/4nxzHeQB4AGDWrFn+/vwJySkPxcKMKjjEgfdfpHHEeMyxFq7uV8ukCVFX76erHdzNJhJaX70vW7aMxou+CslmPhWp4/QJ7k4iZ2MsHzZZPFnXh8IVv+LwJ/6GgX+cT1VVFdGo/9ZLUxIe+KCAPmue5+hJn8z6WCCD29m5d/l63cBfxpJMJgmFQhkay/mu3l4uyvR6KS8vd/X2c4FXjddbwERjzDhgOzAHuMWjWiTPtAWk3jypkJpQFU/GIdr3KMutyUyw/fm2+Gg0SlVVFUsScF5kJyuc8UxM+CtotC0g9ebyOEcvOpcn45l5MsyGpA0LtkY4t7iF4aeP9/VY2mvbzoIwnraxxGIxysvLfT2WIMmH9eJJ4+U4TtIYcw+wDOgD/MpxnHVe1CL5pS0g9XMVrZ8CHI1GCW8Jc82E/jy13eY328LcOtafQZDRaBQ2wrkTRlJ0oDVg9a6oP+IxPhaQmlovfjzo2qnPLJw04BhnDD0GQ/07ls5EfbxuOopGo4EYR9AEfb14dkh2HOd5x3EmOY4z3nGc73tVh+SPEwWkXl3agg08E4BoBj8FrAYtIPWxujCDCxwqR/r73Zgikhk+eC0skr7uBqTeOibBjmaL3wfgs8P8ELAatIDUpTsKiNuGa0v8OWsqIpmnxksCb8fR7gektn122JrGEKsO+P+zw3I9YDVIAakr9oSIHbGYW+7P6wRFJDt0eJBAa0zA4roIV5Ukuh2QGrZg7tg4v28o4IPD/t9F2gJWX82xWbxn6wtI2vg+IBVg7UGLdw6EuCMAn1koIpmlQ4QEVjwJC2MRZhcnGNfDjwIZWAA3jYnz7A7/f3aYZcG8imbWN4Z4Z39uzOK91hBie9zitnL/n5KrbbJ4dXeY28vjRHKrtxWRHKTGSwLJtmFBrJBTByU5dXDvLtgeFYHPjGr97LBDPj8TFrZgbkWcP+wpYPMhb3f71QcsVh8MMS8Ap+T2NsOT9WFuKGtmcO5eSiciOcTnhz2Rzj0UC1MasbmgOL2OacIAm/OHt7Bwa4SEz99wNyAEc8bEWbozzE6PZvE+bLJY3hBmbgBmh5qS8HAswmWjEpT0Vb6ziHSPGi8JnLaA1CtK3Ll26LQhx5g6MMkCH0QznMjICFxZkuCxbREas3yWry0g9cYyfwW7dqYtIPXsYS2cFIAIDBHJHjVeEihtAak3l7nbVVw0IsmIwtaAVb+LFtlcWNwasJqtWbyPBaT62McCUkVEekCNlwTGiQJS06WA1d5RQKqIyF+o8ZJA6G5AaroUsNoztg0LFJAqIvIRNV7iez0JSE2XAlZ75pG6MMUKSBUR+YgOH+JrvQlITZcCVrtHAakiIh+nQ4j4VjoBqelSwOrxKSBVRKRzarzEl9wISE1XUANWt6QZsKqAVBGRrvn8sCj5yq2A1HQFMWD1uTQCVhWQKiJyfGq8xHfcDkhNlwJWWykgVUTkxNR4ia+8kqGA1HTle8CqAlJFRLpHjZf4RqYDUtOVrwGridTskAJSRUROzOdXYUi++GPNHt5o7MeDt5/G0CJ3Z5Xmx96gsvJ8V27rItvmsw+/Q6z/cD53dgW2bWN10iVWVla6cn8d/fvGFa7ddiXw/156n2cPxvnpDTM6HYtt2yxpGMJl04dy93lRV+63jZvrpaOu1su7ZizhoiZ+fuOpnf68t7wYC2RuO/NiPJkaSyYdb90EhdZLzwV7i5BA2LTrEPe+vJkfXzvd9abLbSHL4v45M3hx/S6WvrfD9wfdf7rkZPpYFv/y/IZOx/K1p96jYmiR601XpnU2loferGX19oP81OWmK9P8VGt3BGk8QRpLkHi9XrRVSE7bdSjO1595j29fejLjhhV5XU639AuH+PlNp/KL17fyZu0+r8tJ239eM42avU3MX1HzV8t/8NJGEkmb7356skeVueelDbt4au0O5t80k5CeLEUkg3SEkZzVlEjyxcWrueucccwqH+J1OT1S3D/CvVdN4/vLNlLTcNjrctJiWRbzb5rBK5saeHpNPQAP/nkr63Y28t/Xn+Jxdel7t+4AP19Rw0+vn0F/v2dgiEjOU+MlOSlp29z16Lt8ZuooLps6yutyemXSiAF8o3ISX3vqPfYcbva6nLREwiHuv2kmC96I8dPff8DSdbuYP8f/s0O1+47w3ec38IMrpjJ6UJY+c0pE8pq/j5oSWF/67RqmjhrIvLPGel1KWs4eN4w7zhrL3y1eRTzh73j7oUVh7jiznKfW7uD/XDyBorC/Z4f2H0nw1SfW8LWLJzJ51ECvyxGRPKHGS3LOd55fT2HI4puXnOR1Ka64cvpoPnXSCO56bBW2jxNWP9zbxC//XMs950f54cub2HXIvx9SmUja3P3YKm45vYwLJgz3uhwRySNqvCSn3Leihtp9R/nR1dO8LsVVd547jvHDi/jqE2u9LqVX9jUl+Icn1vKNyklcfWoJXzingnsWr+aID2fxbNvm7sfe5fzoMK6fWeZ1OSKSZ9R4Sc54cnU9v9vUwP0+ezt/d/3zZZM55sC/LXvf61J6JJ5Icvdj7zL3zHLOHjcMgMunjuayqSO569FVJH02i/ePz6yjZFCEey4c73UpIpKHgvfsJr70x5o9LHozxv03zSTi82uHjue/rpvOpl2H+eXrW70upVts2+buxau4eFIxV59S8lc/++xZFUwe1Z+vPL7Go+p67t6XN3EonuRfAxCBISL+pMZLPOengNR0dQxYzXUnCkj9p0tOJpQKWM11fg1IFZFg0dGnHb9e+NxZ3X4ZS8eAVL/UnY5IyPJFwGrHgNSutrMfdxGwmks6BqTmw3YmIrkpuOd0esGyLKqrq70uo8c6+6wsP4wlnoRfbI1wwfAE+zeupHqjPz/3q6csy2LV63/gulHwL0vXcfOYOCNdi5CKuLLeX2sIsf5QH+6saP7o9o63nd04Ah5Ye5QD9Vs5bcixtO8fIJFwZyy1TRZP1YeZOzbO63/4HZAf25mI5CbNeIknbBsWxAqZMSjJqYPzc/ZhVAQ+MyrBo9siHMqhNweuPmCx+mCIeeXNdPeMXNiCeRVx/rCngC2HcuewsrcZnqwPc0NZM4ODfRZbRHwid46QklceioUpjdicX5xDHYcHJgywOX94Cwu3RkjkQP9Z02SxvCHM3PI4Pf30nP4hmDMmznM7w+zMgYivpiQ8HItw2agEJX0dr8sREQHUeIkHntheQMiCK0pavC4lJ5w25BhTByZZsLUQLy89aojDM/VhbiyLM7CXs0MjI3BlSYLHtkVo9HD1Jm1YsDXC2cNaOGlADnS0IiIparwkq17ZHWJvs8XNZQmvS8kpF41IMqLQ5jfbvDkfdjgJv9kW4fJRCUb3Te+2okU2FxYnWFjrzSyebcOC2kImDTjGGUPdud5MRMQtarwka97e14f3G0PMrej+tUP55OrSFmzgme0FWb3fRGp26LzhLUx0aXZoxmCbUwYledCDWbzH6sIMLnCoHKkZVRHJPXr6k6zYdMhixd4C5lbECWur69KtYxLsaLb4/e7svOHYtmHB1kKmDExyukvvRmxzYXGSkojNw1mcxVu6o4Bm23C9ZlRFJEfpKVAybsdReH5nmFvGxOmvAJPjsiy4o6KZNY0hVh3ok/H7e6QuTHGhzcUjMvMmhytKWrAMPJmFWbwVe0LEjljcXt6c8fsSEektNV6SUY0JWFwX4aqSBMWuZVUFW9iCuWPj/L6hgA8OZ24Xfba+gKQN15Rm9pTcLWUJGpotXs3gLN7agxbvHAhxx1idxhaR3KZDlGRMPAkLYxFmFycYV6R3lvXEwAK4aUycZ3eE2ZWBaIbXGkJsj1vcVp75U3KWBfMqmlnfGOKd/e7P4tU2Wby6O8ztvYjAEBHJNjVekhEKSE1fpgJWV/UiIDVdmQpYVUCqiPiNGi/JCAWkusPtgNWaJovf9zIgNV1uB6wqIFVE/EiNl7hOAanucitg1Y2A1HS5FbCqgFQR8Ss1XuIqBaRmRroBq24GpKYr3YBVBaSKiJ+p8RLXKCA1s3obsJqJgNR0pROwqoBUEfEzPT2KKxSQmh09DVjNZEBqunoTsKqAVBHxOz1FStrqFZCaNT0NWM10QGq6ehKwqoBUEQkCNV6SlsYELFFAalZ1N2A1WwGp6epOwKoCUkUkKHQIk15TQKp3ThSwms2A1HSdKGBVAakiEiRqvKRXFJDqva4CVr0ISE1XVwGrCkgVkaDR68csqKmpIRaLUV5eTjQa9bqctLSNZdPoCykdoIBUr00YYHN+sjVg9RLWgzOFV3f14fPj/Dc71Baw+si2COfv+oDmxBgWbS3g0yUKSBWR4PDJ62H/qqmpYdmyZaxdu5Zly5ZRU1PjdUm91jaWt6zxHNy/l6nxjV6XJLQGrJY6e3jyUBkABW8+yp46f25nIyNwhqnl5Xgpx6wCzKYVFDRs8bosERHX+Ow1sf/EYjESNsQv+jLYSZ6MhwhvcfecyfzYG50uTyTcvdo9kZhA8twKCPeFlmaejI/I2lgy6eDRFq7/ZXbv1+11E2+xoG/rOwOPnHqNr7ezeGI09CkAB0zsHWIDJvt+plhEpI0arwwrLy9nw4YNHPvwzyTLZ3JVeCsTJrj7JFJZeX6ny6urq129n7YZr8bz/haM4ZPhGDMmlLl6H12NJZOu/+Ub/PZzZ2b1Pt1cNwkb7tscwtn4O5onXcTAP86nqqrK9WYlW9vZrzYdY9/ubZjGBprP+SxlA7a7evsiIl7SqcYMi0ajVFVVcdqAI1T0TfJan8lpfd6el9rGEupjcVZkN793JnBEl3h5qi0gdfoQuHLaKMBkpOnKlqU7CrDC/bi2pJnT+x2grAj+VHCy12WJiLgmY42XMea7xpjtxphVqa9Pt/vZN40xW4wxG40xVZmqIVdEo1Fmz57NjZP6UhRy+O12/749KxqNEg6HuXBCMWcNbeHBrRGSPm0kg6B9QGo0GgWDb5uu9gGpbfvMLZMKux2wKiLiB5me8fpPx3FmpL6eBzDGTAHmAFOBS4GfG2NOHMEdEDeUJjicNLy40/9PJGcOO8bEAcdYUNvzz9uT9PklILU7jheQ2p2AVRERv/DiVONVwKOO4zQ7jvMhsAU4w4M6PGFZcPvYZmqaLF7f6/9+85KRLQwqcFjs41k8P/JTQOqJnCgg9UQBqyIifpLpxuseY8waY8yvjDFDUstKgW3t/k9dalneCFkwb2wzb+0rYH2j/y+zu64kwdGk4YUd/p/F8wM/BqR2pbsBqV0FrIqI+E1aRzBjzMvGmPc6+boKuA8YD8wAdgA/avu1Tm6q03REY8ydxpiVxpiVDQ0N6ZSac/qF4NaxcV7aFWbbEX8/kVgW3Da2ma1HLP4UgFm8XFbTZPH7hjBzA/DxOU1JeDgW4bJR3QtIbQtYfW5nmJ2dfEySiIgfpPWM7zjOpxzHmdbJ19OO4+xyHOeY4zg28Av+cjqxDhjT7mbKgPoubv8Bx3FmOY4zq7i4OJ1Sc9LQMFxXmuDx7WH2+/yMUciCO8Y2s3J/AesO+ruRzFUNcXimPsyNZXEG+vzMbtKGBVsjnD2shZMGdP8CwZERuLIkwWPbIjT6/9I2EclDmXxX4+h2/7wGeC/1/TPAHGNMoTFmHDAReDNTdeS6Mf1sLhmZ4KFYxPfRDJEQ3FYep3p3mFiTmi83HU7Cb7ZFuHxUgtF9va4mPbYNC2oLmTTgGGcMPdbj348W2VxYnGBhbYSE3tQhIj6TyWfHe40xa40xa4CLgK8COI6zDlgMrAdeBL7oOE7Pj74BMmWgzSeGtLCgttD30QxDwnBDWTNP1IfZ2+x1NcGQSM0OnTe8hYk9mB3KVY/VhRlc4FA5svdTVjMG25wyKMmDW/WOWhHxl4w1Xo7j3OY4znTHcU5xHOdKx3F2tPvZ9x3HGe84zkmO47yQqRr85Oxhx4gW2SwKQDRDaV+HS0cmeDgAs3heawtInTIwyelD/P/6ZOmOApptw/Vl6Z9bv7A4SUnE5uFtPj/vKiJ5ReeDcsilo1p8H7Da5uSBtgJWXdA+INXv2gekuuWKkhYFrIqIr6jxyjEKWJU2+RKQmi4FrIqIn6jxyjEKWBXIr4DUdClgVUT8RI1XDlLAan7Lx4DUdClgVUT8QkeoHKWA1fyUzwGp6VLAqoj4gb+f0QNOAav5RQGp6furgFWf7zMiEkx6BsxxCljNDwpIdc9HAasxBayKSO7Rs58PKGA12BSQ6j4FrIpIrlLj5RNBDFj9dQBm8dKlgNTMUcCqiOQiNV4+ooDV4FFAamYpYFVEco0aL59pC1hdFoCA1TPyPGBVAanZoYBVEcklOhJ1UFlZ6XUJJ3RR0ub2h1eyZ8hobp41Btu2sTp5tsvUWObH3qCy8nxXbqsS+Kdn1/FK3OHeq6Z1OZagqRswkX2mgQWfO52Qi+O9d+OKjK33rtbN8Mmf4LUXNvDALTMZPSiSkftO14WJJLc//DZnjRzDVaeU5M12JiK5R0ceHwqHLB6YM4Mlq7bzu027ff8E8m+XT+bgkQQ/emWz78fSHUvX7WDpul3MnzPT1aYr0zpbN7X7jvDd5zfwgyum5mzTBRAJh7j/ppkseCPG6x/uzYvtTERyk44+PjUwEuYn153Cfy//gLX1B70uJy2WZfE/N8zg7W37efitmNflZNTK2v384k9b+dmNp1IU9veE8/4jCb76xBq+dvFEJo8a6HU5JzS0KMyPrp7GD6s3sWn3Ia/LEZE8pcbLx8qG9ONfLp/Ct59bz/YDR70uJy2ts3in8eTqel5+f7fX5WTEh3ub+N6y97n3ymmMHJC7s0PdkUja3P3YKm45vYwLJgz3upxuixb359tVJ/P1p99j1yHF24tI9qnx8rlTSwfx9xeO50u/XU1j3Pu38KejfyTET64/lZ++9gGr6vw9i9fRvqYE//DEWr7+qYlMGjnA63LSYts2dz/2LudHh3H9zDKvy+mxWWOH8IVzKrhn8WqOJPz/blIR8Rc1XgHwyZNGcN2MUu56dBUJn2czlA7uy/evmMJ3nl/Ptv1HvC7HFfFEkrsfe5e5Z5ZzbtQ/s0Nd+cdn1lE6qC/3XDje61J67fKpo7ls6kjuenQVyXx8S62IeEaNV0DcMmsMnygfwheXrML2+RPJ1NGD+NrFE/ny42s4cMTfs3i2bXP34lVcPKmYq08p8bqctN378iYOxZN87zNTvC4lbZ89q4LJo/rzlcfXeF2KiOQRNV4B8g8XT2RovzDfem6D16Wk7YIJw7n59DLufszfs3hfe+o9KoYWcfd5Ua9LSdtDb9ayevtBfnrjqV6X4pp/uuRkQpbFvzzv/31GRPxBjVfA/OCKKew53Mx/Ld/idSlpu2FmGeeMG8rfLn7Xl7N4P3hpI81Jm+9+erLXpaRt2YZdPLV2B/Nv8lcERnf8+Jpp1OxtYv6KGq9LEZE8EKwjqGBZFj+7cQZ/3rqPR1Zu87qctH1p9gRGDojwj8+s87qUHnnwz1tZt7ORn1x/itelpO3dugPct6KGn14/g/4Rf0dgdMayLObfNINXNjXw1Jp6r8sRkYBT4xVAHQNW/e7fLp9M49EWfvTKZq9L6Ra/BqR2xi8BqelqC1hdmApYFRHJFH8/K0iXFLDqDQWk+pcCVkUkG9R4BZRt274MWO3qWq6QRc4HrHYWkNrZePxwvVpnAal+qDsdtm37MmC1q/Xi1/Xl17q7y6/j82vducjfL8mlS5ZlUV1dDcDsoRZ3P7KSz1fE6efCGk8kIh/dttu6+oBny7J4/Q+vMGc0/OerG6l9fy3lRe4cCNwYz+Ek/PLDCJeNSlC75s/UppZ3Np7268ZtDumPxbbhV7WFjO1nM2jPBqqrW9/x54cPkE9H+/Vy7mCLLzz8FndF44RdeHnq1T6Tqfv0YjxBkcn1kklBXy/ZpBmvPDB1kM0nhrSwoLYQHyczADA4DDeUNfNEfZi9zV5X0yphw4KtEc4b3sKkAT5/gIHH6sIMLnCoHNnidSmemTHY5pRBSR7cWohe6IuIm9R45Ymzhx0jWmSzqNb/TySlfR0uHZng4ViEIx5/4ottw4KthUwZmOT0Ice8LcYFS3cU0Gwbri/zd3CtGy4sTlISsXl4W9jrUkQkQNR45ZFLR7VQFHL47Xb/P5GcPNDm7KEtPLg14uks3iN1YYoLbS4e4f/P/FuxJ0TsiMXt5TkylZgDrihpwTLw5PYCr0sRkYBQ45VnbihNcDhpWLbT/08kZww7xsQBx1jg0Szes/UFJG24ptT/p+TWHrB450CIO8Y24/MEDNfdUpagodni1d26JFZE0qdDbJ6xLLh9bDMfNFn8eW8fr8tJ2yUjWxhU4LA4y7N4rzWE2B63uK3c/6fkapssXm0Ic3t5nADmo6bNsmBeRTPrG0O8vd//+4yIeEuNVx4KWTBvbDNv7itgfaP/N4HrShIcTRpe2JGdWbzVByxWHwwxr9z/s0N7m+HJ+jA3lDUz2P9noDMmbMG8ijgr9hSw+ZDPV7qIeEpHkDzVLwS3jo3z0q4w2474ezOwLLhtbDNbj1j8KcOzeDVNFssbwswNwOxQUxIejrVGYJT0dbwuJ+f1D8GcMXGW7gyz0x8RXyKSg/z9jCtpGRqGa0sSPL49zH6fnzELWXDH2GZW7i9g3cHMbNYNcXimPsyNZXEG+nx2KJmKwDh7WAsnBSACI1tGRuDKkgSPbYvQ6PN9RkS8ocYrz5UX2VSOSPBQDkQzpCsSgtvL41TvDhNrcnfTPpyE32yL8OlRCUb3dfWms862YUFtIZMGHOOMof6PwMi2aJHNhcUJFsYiJNSzikgPqfESBayewEcBqcMUkCqtFLAqIr2lxksABax2pS0gdfLAJKcHYHZo6Y4C4rbh2hKdJ0uXAlZFpDfUeMlHFLD6cW0BqZ8MUEDq3AC8GzNXKGBVRHpKh1/5KwpY/QsFpEp3KGBVRHpCh2D5KwpYbaWAVOkuBayKSE+o8ZKPCWTA6rHuB6wqIFV6SgGrItJdOkJIpwIXsFrevYBVBaRKbylgVUS6w9/PqJJR+RawqoBUSZcCVkXkRNR4yXHlS8Bq0AJSH1RAqmcUsCoix6PGS04o6AGrTgADUocoINVTbQGrv1LAqoh0oMZLuqUtYPV/N0MikaCmpsbrknqtfcDqe5u2ctR2KHH2+j4gtaamBhxoPJpQQGoOuLA4SWnE5pebHd/vM+3V1NQEajxBUVNTw/Lly7VefECNl3TbpCMbObx3J0ksli1b5usd/OSBNpPYxdLEOLBtdr28yNfjqamp4bm3PwAg+ep9bN3q37EEydT4Rg7s30fSMb7fZ6B1O1u2bBnJZDIQ4wmKtvWydu1arRcfMI7jj3c7zZo1y1m5cqXXZfhKdXW1q7e3fPlyVq9dS3z2PVAQAQwYV+8Cc5wbdHB5W3U++uOje8/WeDI2FseBljihUIhw2N13CLh9e7kokXB3pjCRSJBMJsEYcJysr5egjScoMrVe+uzaSOH7LzN9+nRmz57t6n1UVla6env5wBjztuM4szou9/kb5iWbysvL2bBhA9bynxIKhaiqqiIajbp6H8fbud1uJNu/es/2ePw5lvNdvb1cFLT1ErTxBEWm10t5ebmrty/uUuMl3RaNRqmqqiIWi1FeXu76ATfbgjSeII0lSIK2XoI2nqDQevEXNV7SI9FoNFA7dZDGE6SxBEnQ1kvQxhMUWi/+oYvrRURERLJEjZeIiIhIlqjxEhEREckSNV4iIiIiWaLGS0RERCRL0mq8jDE3GGPWGWNsY8ysDj/7pjFmizFmozGmqt3y040xa1M/+4kxxuXIShEREZHclO6M13vAtcBr7RcaY6YAc4CpwKXAz40xfVI/vg+4E5iY+ro0zRpEREREfCGtxstxnA2O42zs5EdXAY86jtPsOM6HwBbgDGPMaGCg4zivO62fVbQIuDqdGkRERET8IlPXeJUC29r9uy61rDT1fcflIiIiIoF3wuR6Y8zLwKhOfvQtx3Ge7urXOlnmHGd5V/d9J62nJfXZUyIiIuJ7J2y8HMf5VC9utw4Y0+7fZUB9anlZJ8u7uu8HgAcAZs2a1WWDJiIiIuIHmTrV+AwwxxhTaIwZR+tF9G86jrMDOGSMOSv1bsbbga5mzUREREQCJd04iWuMMXXA2cBSY8wyAMdx1gGLgfXAi8AXHcc5lvq1vwX+l9YL7j8AXkinBhERERG/OOGpxuNxHOdJ4MkufvZ94PudLF8JTEvnfkVERET8SMn1IiIiIlmixktEREQkS9R4iYiIiGSJGi8RERGRLFHjJSIiIpIlpvUjE3PfrFmznJUrV3pdhmSYbdtYVnBeDwRpPEEaS5AEbb0EbTxBofXSc8aYtx3HmdVxuR5FySlB27GDNJ4gjSVIgrZegjaeoNB6cY8eSREREZEsUeMlIiIikiVqvERERESyRI2XiIiISJao8RIRERHJEjVeIiIiIlmixktEREQkS9R4iYiIiGSJGi8RERGRLFHjJSIiIpIlarxEREREskSNl4iIiEiWGMdxvK6hW4wxDUBthm5+OLAnQ7ftBxq/xq/x5y+NX+PX+DNjrOM4xR0X+qbxyiRjzErHcWZ5XYdXNH6NX+PX+L2uwysav8af7fHrVKOIiIhIlqjxEhEREckSNV6tHvC6AI9p/PlN489vGn9+0/izTNd4iYiIiGSJZrxEREREskSNl4iIiEiW5FXjZYz5D2PM+8aYNcaYJ40xg9v97JvGmC3GmI3GmKp2y083xqxN/ewnxhjjSfEuMMbcYIxZZ4yxjTGz2i2vMMYcNcasSn3d3+5ngR9/6meBX//tGWO+a4zZ3m6df7rdzzp9LILGGHNpaoxbjDHf8LqebDDGbE1tz6uMMStTy4YaY6qNMZtTfw/xuk63GGN+ZYzZbYx5r92yLscbtG2/i/Hnzb5vjBljjPmdMWZD6tj/5dRyb7cBx3Hy5gu4BAilvv934N9T308BVgOFwDjgA6BP6mdvAmcDBngBuMzrcaQx/snAScByYFa75RXAe138Tj6MPy/Wf4fH4rvA/+lkeZePRZC+gD6psUWBcGrMU7yuKwvj3goM77DsXuAbqe+/0XZcDMIXcAFwWvvjW1fjDeK238X482bfB0YDp6W+HwBsSo3T020gr2a8HMd5yXGcZOqffwbKUt9fBTzqOE6z4zgfAluAM4wxo4GBjuO87rSulUXA1dmu2y2O42xwHGdjd/9/Ho0/L9Z/N3X6WHhcUyacAWxxHKfGcZwE8CitY89HVwELU98vJEDbuOM4rwH7OizuaryB2/a7GH9Xgjj+HY7jvJP6/hCwASjF420grxqvDj5L6wwGtK6Ibe1+VpdaVpr6vuPyIBpnjHnXGPN7Y8z5qWX5Mv58Xf/3pE67/6rdVHtXj0XQ5Ms4O3KAl4wxbxtj7kwtG+k4zg5ofaICRnhWXXZ0Nd582ibybt83xlQAM4E38HgbCLl9g14zxrwMjOrkR99yHOfp1P/5FpAEft32a538f+c4y3NWd8bfiR1AueM4e40xpwNPGWOmkj/jD8z6b+94jwVwH/A9WsfzPeBHtL4Y8fWYeyBfxtnRuY7j1BtjRgDVxpj3vS4oh+TLNpF3+74xpj/wOPAVx3Eaj3OpblYeg8A1Xo7jfOp4PzfGzAU+A3wydfoIWrvaMe3+WxlQn1pe1snynHWi8XfxO81Ac+r7t40xHwCTyJPxE6D13153HwtjzC+A51L/7OqxCJp8GedfcRynPvX3bmPMk7SeRtlljBntOM6O1On13Z4WmXldjTcvtgnHcXa1fZ8P+74xpoDWpuvXjuM8kVrs6TaQV6cajTGXAv8IXOk4zpF2P3oGmGOMKTTGjAMmAm+mpiAPGWPOSr2b7Xagq1kT3zLGFBtj+qS+j9I6/pp8GT95uP5TB5s21wBt73rq9LHIdn1Z8BYw0RgzzhgTBubQOvbAMsYUGWMGtH1P65uN3qN13HNT/20uAdnGj6Or8ebFtp9P+37quP1LYIPjOD9u9yNvtwGv33WQzS9aL5TbBqxKfd3f7mffovUdDBtp9841YBatG+YHwE9Jpf378YvWnayO1tmtXcCy1PLrgHW0vpvjHeCKfBp/vqz/Do/FQ8BaYA2tB5vRJ3osgvYFfJrWdzl9QOupaM9ryvB4o6l9fHVqf/9Wavkw4BVgc+rvoV7X6uKYH6H1UoqW1L7/ueONN2jbfhfjz5t9HziP1lOFa9o973/a621AHxkkIiIikiV5dapRRERExEtqvERERESyRI2XiIiISJao8RIRERHJEjVeIiIiIlmixktEREQkS9R4iYiIiGTJ/w+i7TQoMJn7fQAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 720x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "w_rook2 = raster.da2WSP(da_s, \"rook\", k=2, n_jobs=-1, include_nadata=True)\n",
-    "splot.plot_spatial_weights(w_rook2, da=da_s)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Additional resources\n",
-    "\n",
-    "1. [Reading and writing files using Xarray](http://xarray.pydata.org/en/stable/io.html)\n",
-    "2. [Xarray Data Structures](http://xarray.pydata.org/en/stable/data-structures.html)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
\ No newline at end of file
diff --git a/docsrc/notebooks/examples.ipynb b/docsrc/notebooks/examples.ipynb
deleted file mode 100644
index 2c7203878..000000000
--- a/docsrc/notebooks/examples.ipynb
+++ /dev/null
@@ -1,1092 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Datasets for use with libpysal\n",
-    "As of version 4.2, libpysal has refactored the `examples` package to:\n",
-    "\n",
-    "- reduce the size of the source installation\n",
-    "- allow the use of remote datasets from the [Center for Spatial Data Science at the Unversity of Chicago](https://spatial.uchicago.edu/), and other remotes\n",
-    "\n",
-    "This notebook highlights the new functionality"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Backwards compatibility is maintained"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you were familiar with previous versions of libpysal, the newest version maintains backwards compatibility so any code that relied on the previous API should work. \n",
-    "\n",
-    "For example:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from libpysal.examples import get_path \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'/home/jovyan/libpysal/examples/mexico/mexicojoin.dbf'"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "get_path(\"mexicojoin.dbf\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "An important thing to note here is that the path to the file for this particular example is within the source distribution that was installed. Such an example data set is now referred to as a `builtin` dataset."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import libpysal\n",
-    "dbf = libpysal.io.open(get_path(\"mexicojoin.dbf\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['POLY_ID',\n",
-       " 'AREA',\n",
-       " 'CODE',\n",
-       " 'NAME',\n",
-       " 'PERIMETER',\n",
-       " 'ACRES',\n",
-       " 'HECTARES',\n",
-       " 'PCGDP1940',\n",
-       " 'PCGDP1950',\n",
-       " 'PCGDP1960',\n",
-       " 'PCGDP1970',\n",
-       " 'PCGDP1980',\n",
-       " 'PCGDP1990',\n",
-       " 'PCGDP2000',\n",
-       " 'HANSON03',\n",
-       " 'HANSON98',\n",
-       " 'ESQUIVEL99',\n",
-       " 'INEGI',\n",
-       " 'INEGI2',\n",
-       " 'MAXP',\n",
-       " 'GR4000',\n",
-       " 'GR5000',\n",
-       " 'GR6000',\n",
-       " 'GR7000',\n",
-       " 'GR8000',\n",
-       " 'GR9000',\n",
-       " 'LPCGDP40',\n",
-       " 'LPCGDP50',\n",
-       " 'LPCGDP60',\n",
-       " 'LPCGDP70',\n",
-       " 'LPCGDP80',\n",
-       " 'LPCGDP90',\n",
-       " 'LPCGDP00',\n",
-       " 'TEST']"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dbf.header"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## `available` is updated"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The function `available` is also available but has been updated to be more informative:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                      Name                                        Description  Installed\n",
-      "0                    10740    Albuquerque, New Mexico, Census 2000 Tract Data       True\n",
-      "1                   AirBnB  Airbnb rentals, socioeconomics, and crime in C...       True\n",
-      "2                  Atlanta       Atlanta, GA region homicide counts and rates       True\n",
-      "3                Baltimore          Baltimore house sales prices and hedonics       True\n",
-      "4                Bostonhsg               Boston housing and neighborhood data       True\n",
-      "5              Buenosaires      Electoral Data for 1999 Argentinean Elections       True\n",
-      "6              Charleston1   2000 Census Tract Data for Charleston, SC MSA...       True\n",
-      "7              Charleston2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "8           Chicago Health                   Chicago Health + Socio-Economics       True\n",
-      "9              Chile Labor                 Labor Markets in Chile (1982-2002)       True\n",
-      "10         Chile Migration            Internal Migration in Chile (1977-2002)       True\n",
-      "11              Cincinnati         2008 Cincinnati Crime + Socio-Demographics       True\n",
-      "12               Cleveland       2015 sales prices of homes in Cleveland, OH.       True\n",
-      "13                Columbus                        Columbus neighborhood crime       True\n",
-      "14                  Denver   Demographics and housing in Denver neighborho...       True\n",
-      "15               Elections               2012 and 2016 Presidential Elections       True\n",
-      "16                 Grid100                      Grid with simulated variables       True\n",
-      "17               Groceries                          2015 Chicago supermarkets       True\n",
-      "18                  Guerry          Moral statistics of France (Guerry, 1833)       True\n",
-      "19       Health Indicators                Chicago Health Indicators (2005-11)       True\n",
-      "20                 Health+                    2000 Health, Income + Diversity       True\n",
-      "21                Hickory1   2000 Census Tract Data for Hickory, NC MSA an...       True\n",
-      "22                Hickory2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "23              Home Sales              2014-15 Home Sales in King County, WA       True\n",
-      "24                 Houston       Houston, TX region homicide counts and rates       True\n",
-      "25                Juvenile             Cardiff juvenile delinquent residences       True\n",
-      "26                Lansing1   2000 Census Tract Data for Lansing, MI MSA an...       True\n",
-      "27                Lansing2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "28                 Laozone   Ozone measures at monitoring stations in Los ...       True\n",
-      "29                LasRosas   Corn yield, fertilizer and field data for pre...       True\n",
-      "30                    Line                                     Line Shapefile       True\n",
-      "31           Liquor Stores                         2015 Chicago Liquor Stores       True\n",
-      "32                 Malaria   Malaria incidence and population (1973, 95, 9...       True\n",
-      "33              Milwaukee1       2000 Census Tract Data for Milwaukee, WI MSA       True\n",
-      "34              Milwaukee2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "35                   NCOVR                      US county homicides 1960-1990       True\n",
-      "36                    NDVI        Normalized Difference Vegetation Index grid       True\n",
-      "37                     NYC   Demographic and housing data for New York Cit...       True\n",
-      "38            NYC Earnings              Block-level Earnings in NYC (2002-14)       True\n",
-      "39           NYC Education                               NYC Education (2000)       True\n",
-      "40       NYC Neighborhoods       Demographics for New York City neighborhoods       True\n",
-      "41  NYC Socio-Demographics                 NYC Education + Socio-Demographics       True\n",
-      "42              Natregimes                    NCOVR with regimes (book/PySAL)       True\n",
-      "43                   Nepal   Health, poverty and education indicators for ...       True\n",
-      "44                Ohiolung            Ohio lung cancer data, 1968, 1978, 1988       True\n",
-      "45                Orlando1   2000 Census Tract Data for Orlando, FL MSA an...       True\n",
-      "46                Orlando2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "47                  Oz9799                        Monthly ozone data, 1997-99       True\n",
-      "48             Phoenix ACS   Phoenix American Community Survey Data (2010,...       True\n",
-      "49              Pittsburgh                      Pittsburgh homicide locations       True\n",
-      "50                   Point                                    Point Shapefile       True\n",
-      "51                  Police           Police expenditures Mississippi counties       True\n",
-      "52                 Polygon                                  Polygon Shapefile       True\n",
-      "53           Polygon_Holes                 Example to test treatment of holes       True\n",
-      "54       Rio Grande do Sul  Cities of the Brazilian State of Rio Grande do...       True\n",
-      "55                    SIDS            North Carolina county SIDS death counts       True\n",
-      "56                   SIDS2   North Carolina county SIDS death counts and r...       True\n",
-      "57             Sacramento1          2000 Census Tract Data for Sacramento MSA       True\n",
-      "58             Sacramento2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "59           SanFran Crime   July-Dec 2012 crime incidents in San Francisc...       True\n",
-      "60               Savannah1   2000 Census Tract Data for Savannah, GA MSA a...       True\n",
-      "61               Savannah2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "62                 Scotlip               Male lip cancer in Scotland, 1975-80       True\n",
-      "63                Seattle1   2000 Census Tract Data for Seattle, WA MSA an...       True\n",
-      "64                Seattle2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "65                   South             US Southern county homicides 1960-1990       True\n",
-      "66                 StLouis   St Louis region county homicide counts and rates       True\n",
-      "67                  Tampa1   2000 Census Tract Data for Tampa, FL MSA and ...       True\n",
-      "68                  arcgis                               arcgis testing files       True\n",
-      "69                  baltim     Baltimore house sales prices and hedonics 1978       True\n",
-      "70                  berlin  Prenzlauer Berg neighborhood AirBnB data from ...       True\n",
-      "71                    book       Synthetic data to illustrate spatial weights       True\n",
-      "72                 burkitt  Burkitt's lymphoma in the Western Nile distric...       True\n",
-      "73                  calemp         Employment density for California counties       True\n",
-      "74                 chicago                              Chicago neighborhoods       True\n",
-      "75              clearwater                               mgwr testing dataset       True\n",
-      "76                columbus              Columbus neighborhood crime data 1980       True\n",
-      "77                 desmith    Small dataset to illustrate Moran's I statistic       True\n",
-      "78                geodanet        Datasets from geodanet for network analysis       True\n",
-      "79                 georgia  Various socio-economic variables for counties ...       True\n",
-      "80                juvenile    Residences of juvenile offenders in Cardiff, UK       True\n",
-      "81                  mexico  Decennial per capita incomes of Mexican states...       True\n",
-      "82                networks                  Datasets used for network testing       True\n",
-      "83                newHaven                            Network testing dataset       True\n",
-      "84               nyc_bikes                           New York City Bike Trips       True\n",
-      "85                   sids2  North Carolina county SIDS death counts and rates       True\n",
-      "86               snow_maps  Public water pumps and Cholera deaths in Londo...       True\n",
-      "87                     stl  Homicides and selected socio-economic characte...       True\n",
-      "88          street_net_pts                              Street network points       True\n",
-      "89                     taz           Traffic Analysis Zones in So. California       True\n",
-      "90                   tokyo                               Tokyo Mortality data       True\n",
-      "91               us_income  Per-capita income for the lower 48 US states 1...       True\n",
-      "92                virginia                        Virginia counties shapefile       True\n",
-      "93                    wmat          Datasets used for spatial weights testing       True\n"
-     ]
-    }
-   ],
-   "source": [
-    "from libpysal.examples import available\n",
-    "available()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We see that the new column `Installed` is added to the tablular output of `available`. This tells the user whether the dataset has aready been installed on the local machine. All the builtin datasets are by defninition installed. The interesting cases are the remote data sets that are new in this release. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Remote datasets\n",
-    "\n",
-    "The listing from `available` above shows that the dataset `Tampa1` is avalable, but has not yet been installed."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"700\"\n",
-       "            height=\"350\"\n",
-       "            src=\"https://geodacenter.github.io/data-and-lab/tampa1/\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.IFrame at 0x7f2c8867c828>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from libpysal.examples import explain\n",
-    "explain('Tampa1')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from libpysal.examples import load_example\n",
-    "tampa1 = load_example('Tampa1')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                      Name                                        Description  Installed\n",
-      "0                    10740    Albuquerque, New Mexico, Census 2000 Tract Data       True\n",
-      "1                   AirBnB  Airbnb rentals, socioeconomics, and crime in C...       True\n",
-      "2                  Atlanta       Atlanta, GA region homicide counts and rates       True\n",
-      "3                Baltimore          Baltimore house sales prices and hedonics       True\n",
-      "4                Bostonhsg               Boston housing and neighborhood data       True\n",
-      "5              Buenosaires      Electoral Data for 1999 Argentinean Elections       True\n",
-      "6              Charleston1   2000 Census Tract Data for Charleston, SC MSA...       True\n",
-      "7              Charleston2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "8           Chicago Health                   Chicago Health + Socio-Economics       True\n",
-      "9              Chile Labor                 Labor Markets in Chile (1982-2002)       True\n",
-      "10         Chile Migration            Internal Migration in Chile (1977-2002)       True\n",
-      "11              Cincinnati         2008 Cincinnati Crime + Socio-Demographics       True\n",
-      "12               Cleveland       2015 sales prices of homes in Cleveland, OH.       True\n",
-      "13                Columbus                        Columbus neighborhood crime       True\n",
-      "14                  Denver   Demographics and housing in Denver neighborho...       True\n",
-      "15               Elections               2012 and 2016 Presidential Elections       True\n",
-      "16                 Grid100                      Grid with simulated variables       True\n",
-      "17               Groceries                          2015 Chicago supermarkets       True\n",
-      "18                  Guerry          Moral statistics of France (Guerry, 1833)       True\n",
-      "19       Health Indicators                Chicago Health Indicators (2005-11)       True\n",
-      "20                 Health+                    2000 Health, Income + Diversity       True\n",
-      "21                Hickory1   2000 Census Tract Data for Hickory, NC MSA an...       True\n",
-      "22                Hickory2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "23              Home Sales              2014-15 Home Sales in King County, WA       True\n",
-      "24                 Houston       Houston, TX region homicide counts and rates       True\n",
-      "25                Juvenile             Cardiff juvenile delinquent residences       True\n",
-      "26                Lansing1   2000 Census Tract Data for Lansing, MI MSA an...       True\n",
-      "27                Lansing2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "28                 Laozone   Ozone measures at monitoring stations in Los ...       True\n",
-      "29                LasRosas   Corn yield, fertilizer and field data for pre...       True\n",
-      "30                    Line                                     Line Shapefile       True\n",
-      "31           Liquor Stores                         2015 Chicago Liquor Stores       True\n",
-      "32                 Malaria   Malaria incidence and population (1973, 95, 9...       True\n",
-      "33              Milwaukee1       2000 Census Tract Data for Milwaukee, WI MSA       True\n",
-      "34              Milwaukee2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "35                   NCOVR                      US county homicides 1960-1990       True\n",
-      "36                    NDVI        Normalized Difference Vegetation Index grid       True\n",
-      "37                     NYC   Demographic and housing data for New York Cit...       True\n",
-      "38            NYC Earnings              Block-level Earnings in NYC (2002-14)       True\n",
-      "39           NYC Education                               NYC Education (2000)       True\n",
-      "40       NYC Neighborhoods       Demographics for New York City neighborhoods       True\n",
-      "41  NYC Socio-Demographics                 NYC Education + Socio-Demographics       True\n",
-      "42              Natregimes                    NCOVR with regimes (book/PySAL)       True\n",
-      "43                   Nepal   Health, poverty and education indicators for ...       True\n",
-      "44                Ohiolung            Ohio lung cancer data, 1968, 1978, 1988       True\n",
-      "45                Orlando1   2000 Census Tract Data for Orlando, FL MSA an...       True\n",
-      "46                Orlando2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "47                  Oz9799                        Monthly ozone data, 1997-99       True\n",
-      "48             Phoenix ACS   Phoenix American Community Survey Data (2010,...       True\n",
-      "49              Pittsburgh                      Pittsburgh homicide locations       True\n",
-      "50                   Point                                    Point Shapefile       True\n",
-      "51                  Police           Police expenditures Mississippi counties       True\n",
-      "52                 Polygon                                  Polygon Shapefile       True\n",
-      "53           Polygon_Holes                 Example to test treatment of holes       True\n",
-      "54       Rio Grande do Sul  Cities of the Brazilian State of Rio Grande do...       True\n",
-      "55                    SIDS            North Carolina county SIDS death counts       True\n",
-      "56                   SIDS2   North Carolina county SIDS death counts and r...       True\n",
-      "57             Sacramento1          2000 Census Tract Data for Sacramento MSA       True\n",
-      "58             Sacramento2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "59           SanFran Crime   July-Dec 2012 crime incidents in San Francisc...       True\n",
-      "60               Savannah1   2000 Census Tract Data for Savannah, GA MSA a...       True\n",
-      "61               Savannah2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "62                 Scotlip               Male lip cancer in Scotland, 1975-80       True\n",
-      "63                Seattle1   2000 Census Tract Data for Seattle, WA MSA an...       True\n",
-      "64                Seattle2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "65                   South             US Southern county homicides 1960-1990       True\n",
-      "66                 StLouis   St Louis region county homicide counts and rates       True\n",
-      "67                  Tampa1   2000 Census Tract Data for Tampa, FL MSA and ...       True\n",
-      "68                  arcgis                               arcgis testing files       True\n",
-      "69                  baltim     Baltimore house sales prices and hedonics 1978       True\n",
-      "70                  berlin  Prenzlauer Berg neighborhood AirBnB data from ...       True\n",
-      "71                    book       Synthetic data to illustrate spatial weights       True\n",
-      "72                 burkitt  Burkitt's lymphoma in the Western Nile distric...       True\n",
-      "73                  calemp         Employment density for California counties       True\n",
-      "74                 chicago                              Chicago neighborhoods       True\n",
-      "75              clearwater                               mgwr testing dataset       True\n",
-      "76                columbus              Columbus neighborhood crime data 1980       True\n",
-      "77                 desmith    Small dataset to illustrate Moran's I statistic       True\n",
-      "78                geodanet        Datasets from geodanet for network analysis       True\n",
-      "79                 georgia  Various socio-economic variables for counties ...       True\n",
-      "80                juvenile    Residences of juvenile offenders in Cardiff, UK       True\n",
-      "81                  mexico  Decennial per capita incomes of Mexican states...       True\n",
-      "82                networks                  Datasets used for network testing       True\n",
-      "83                newHaven                            Network testing dataset       True\n",
-      "84               nyc_bikes                           New York City Bike Trips       True\n",
-      "85                   sids2  North Carolina county SIDS death counts and rates       True\n",
-      "86               snow_maps  Public water pumps and Cholera deaths in Londo...       True\n",
-      "87                     stl  Homicides and selected socio-economic characte...       True\n",
-      "88          street_net_pts                              Street network points       True\n",
-      "89                     taz           Traffic Analysis Zones in So. California       True\n",
-      "90                   tokyo                               Tokyo Mortality data       True\n",
-      "91               us_income  Per-capita income for the lower 48 US states 1...       True\n",
-      "92                virginia                        Virginia counties shapefile       True\n",
-      "93                    wmat          Datasets used for spatial weights testing       True\n"
-     ]
-    }
-   ],
-   "source": [
-    "available()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tampa1.installed"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['/home/jovyan/pysal_data/Tampa1/__MACOSX/._TampaMSA',\n",
-       " '/home/jovyan/pysal_data/Tampa1/__MACOSX/TampaMSA/._tampa_counties.sbx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/__MACOSX/TampaMSA/._tampa_final_census2.sbx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/__MACOSX/TampaMSA/._2000 Census Data Variables_Documentation.pdf',\n",
-       " '/home/jovyan/pysal_data/Tampa1/__MACOSX/TampaMSA/._tampa_final_census2.sbn',\n",
-       " '/home/jovyan/pysal_data/Tampa1/__MACOSX/TampaMSA/._tampa_counties.sbn',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.geojson',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.gpkg',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.xlsx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.mid',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.spx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.gdbindexes',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByDestItemTypeID.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000002.gdbtable',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.CatItemsByType.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000003.gdbtablx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/gdb',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.gdbtable',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000002.gdbtablx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.gdbtablx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByForwardLabel.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByBackwardLabel.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByOriginItemTypeID.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.CatItemTypesByParentTypeID.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000003.gdbtable',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.gdbindexes',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.gdbindexes',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.gdbindexes',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.gdbtable',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.spx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.gdbindexes',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.FDO_UUID.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.CatRelsByOriginID.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.gdbtable',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000003.gdbindexes',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.gdbtablx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.CatItemsByPhysicalName.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.gdbtablx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.CatRelsByType.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.gdbindexes',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByUUID.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.gdbtablx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.CatItemTypesByUUID.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.gdbtablx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.gdbtable',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.gdbtable',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.gdbtablx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.spx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.FDO_UUID.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.CatRelsByDestinationID.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.gdbtable',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.gdbtable',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/timestamps',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByName.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.gdbindexes',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.CatItemTypesByName.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.TablesByName.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.gdbtablx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.sbn',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.xlsx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/2000 Census Data Variables_Documentation.pdf',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.mid',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.dbf',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.gpkg',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.mif',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.kml',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.sqlite',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.dbf',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.prj',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.mif',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.shx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.geojson',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.shx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.sbn',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.sbx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.prj',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.shp',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.shp',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.sqlite',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.sbx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.kml']"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tampa1.get_file_list()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tampa_counties_shp = tampa1.load('tampa_counties.shp')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<libpysal.io.iohandlers.pyShpIO.PurePyShpWrapper at 0x7f2c886869b0>"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tampa_counties_shp"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import geopandas"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tampa_df = geopandas.read_file(tampa1.get_path('tampa_counties.shp'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2c885f5e48>"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "%matplotlib inline\n",
-    "tampa_df.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Other Remotes\n",
-    "\n",
-    "In addition to the remote datasets from the GeoData Data Science Center, there are several large remotes available at github repositories. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Rio_Grande_do_Sul\n",
-      "======================\n",
-      "\n",
-      "Cities of the Brazilian State of Rio Grande do Sul\n",
-      "-------------------------------------------------------\n",
-      "\n",
-      "* 43MUE250GC_SIR.dbf: attribute data (k=2)\n",
-      "* 43MUE250GC_SIR.shp: Polygon shapefile (n=499)\n",
-      "* 43MUE250GC_SIR.shx: spatial index\n",
-      "* 43MUE250GC_SIR.cpg: encoding file \n",
-      "* 43MUE250GC_SIR.prj: projection information \n",
-      "* map_RS_BR.dbf: attribute data (k=3)\n",
-      "* map_RS_BR.shp: Polygon shapefile (no lakes) (n=497)\n",
-      "* map_RS_BR.prj: projection information\n",
-      "* map_RS_BR.shx: spatial index\n",
-      "\n",
-      "\n",
-      "\n",
-      "Source: Renan Xavier Cortes \n",
-      "Reference: https://github.com/pysal/pysal/issues/889#issuecomment-396693495\n",
-      "\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "explain('Rio Grande do Sul')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note that the `explain` function generates a textual description of this example dataset - no rendering of the map is done as the source repository does not include that functionality."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rio = load_example('Rio Grande do Sul')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                      Name                                        Description  Installed\n",
-      "0                    10740    Albuquerque, New Mexico, Census 2000 Tract Data       True\n",
-      "1                   AirBnB  Airbnb rentals, socioeconomics, and crime in C...       True\n",
-      "2                  Atlanta       Atlanta, GA region homicide counts and rates       True\n",
-      "3                Baltimore          Baltimore house sales prices and hedonics       True\n",
-      "4                Bostonhsg               Boston housing and neighborhood data       True\n",
-      "5              Buenosaires      Electoral Data for 1999 Argentinean Elections       True\n",
-      "6              Charleston1   2000 Census Tract Data for Charleston, SC MSA...       True\n",
-      "7              Charleston2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "8           Chicago Health                   Chicago Health + Socio-Economics       True\n",
-      "9              Chile Labor                 Labor Markets in Chile (1982-2002)       True\n",
-      "10         Chile Migration            Internal Migration in Chile (1977-2002)       True\n",
-      "11              Cincinnati         2008 Cincinnati Crime + Socio-Demographics       True\n",
-      "12               Cleveland       2015 sales prices of homes in Cleveland, OH.       True\n",
-      "13                Columbus                        Columbus neighborhood crime       True\n",
-      "14                  Denver   Demographics and housing in Denver neighborho...       True\n",
-      "15               Elections               2012 and 2016 Presidential Elections       True\n",
-      "16                 Grid100                      Grid with simulated variables       True\n",
-      "17               Groceries                          2015 Chicago supermarkets       True\n",
-      "18                  Guerry          Moral statistics of France (Guerry, 1833)       True\n",
-      "19       Health Indicators                Chicago Health Indicators (2005-11)       True\n",
-      "20                 Health+                    2000 Health, Income + Diversity       True\n",
-      "21                Hickory1   2000 Census Tract Data for Hickory, NC MSA an...       True\n",
-      "22                Hickory2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "23              Home Sales              2014-15 Home Sales in King County, WA       True\n",
-      "24                 Houston       Houston, TX region homicide counts and rates       True\n",
-      "25                Juvenile             Cardiff juvenile delinquent residences       True\n",
-      "26                Lansing1   2000 Census Tract Data for Lansing, MI MSA an...       True\n",
-      "27                Lansing2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "28                 Laozone   Ozone measures at monitoring stations in Los ...       True\n",
-      "29                LasRosas   Corn yield, fertilizer and field data for pre...       True\n",
-      "30                    Line                                     Line Shapefile       True\n",
-      "31           Liquor Stores                         2015 Chicago Liquor Stores       True\n",
-      "32                 Malaria   Malaria incidence and population (1973, 95, 9...       True\n",
-      "33              Milwaukee1       2000 Census Tract Data for Milwaukee, WI MSA       True\n",
-      "34              Milwaukee2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "35                   NCOVR                      US county homicides 1960-1990       True\n",
-      "36                    NDVI        Normalized Difference Vegetation Index grid       True\n",
-      "37                     NYC   Demographic and housing data for New York Cit...       True\n",
-      "38            NYC Earnings              Block-level Earnings in NYC (2002-14)       True\n",
-      "39           NYC Education                               NYC Education (2000)       True\n",
-      "40       NYC Neighborhoods       Demographics for New York City neighborhoods       True\n",
-      "41  NYC Socio-Demographics                 NYC Education + Socio-Demographics       True\n",
-      "42              Natregimes                    NCOVR with regimes (book/PySAL)       True\n",
-      "43                   Nepal   Health, poverty and education indicators for ...       True\n",
-      "44                Ohiolung            Ohio lung cancer data, 1968, 1978, 1988       True\n",
-      "45                Orlando1   2000 Census Tract Data for Orlando, FL MSA an...       True\n",
-      "46                Orlando2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "47                  Oz9799                        Monthly ozone data, 1997-99       True\n",
-      "48             Phoenix ACS   Phoenix American Community Survey Data (2010,...       True\n",
-      "49              Pittsburgh                      Pittsburgh homicide locations       True\n",
-      "50                   Point                                    Point Shapefile       True\n",
-      "51                  Police           Police expenditures Mississippi counties       True\n",
-      "52                 Polygon                                  Polygon Shapefile       True\n",
-      "53           Polygon_Holes                 Example to test treatment of holes       True\n",
-      "54       Rio Grande do Sul  Cities of the Brazilian State of Rio Grande do...       True\n",
-      "55                    SIDS            North Carolina county SIDS death counts       True\n",
-      "56                   SIDS2   North Carolina county SIDS death counts and r...       True\n",
-      "57             Sacramento1          2000 Census Tract Data for Sacramento MSA       True\n",
-      "58             Sacramento2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "59           SanFran Crime   July-Dec 2012 crime incidents in San Francisc...       True\n",
-      "60               Savannah1   2000 Census Tract Data for Savannah, GA MSA a...       True\n",
-      "61               Savannah2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "62                 Scotlip               Male lip cancer in Scotland, 1975-80       True\n",
-      "63                Seattle1   2000 Census Tract Data for Seattle, WA MSA an...       True\n",
-      "64                Seattle2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "65                   South             US Southern county homicides 1960-1990       True\n",
-      "66                 StLouis   St Louis region county homicide counts and rates       True\n",
-      "67                  Tampa1   2000 Census Tract Data for Tampa, FL MSA and ...       True\n",
-      "68                  arcgis                               arcgis testing files       True\n",
-      "69                  baltim     Baltimore house sales prices and hedonics 1978       True\n",
-      "70                  berlin  Prenzlauer Berg neighborhood AirBnB data from ...       True\n",
-      "71                    book       Synthetic data to illustrate spatial weights       True\n",
-      "72                 burkitt  Burkitt's lymphoma in the Western Nile distric...       True\n",
-      "73                  calemp         Employment density for California counties       True\n",
-      "74                 chicago                              Chicago neighborhoods       True\n",
-      "75              clearwater                               mgwr testing dataset       True\n",
-      "76                columbus              Columbus neighborhood crime data 1980       True\n",
-      "77                 desmith    Small dataset to illustrate Moran's I statistic       True\n",
-      "78                geodanet        Datasets from geodanet for network analysis       True\n",
-      "79                 georgia  Various socio-economic variables for counties ...       True\n",
-      "80                juvenile    Residences of juvenile offenders in Cardiff, UK       True\n",
-      "81                  mexico  Decennial per capita incomes of Mexican states...       True\n",
-      "82                networks                  Datasets used for network testing       True\n",
-      "83                newHaven                            Network testing dataset       True\n",
-      "84               nyc_bikes                           New York City Bike Trips       True\n",
-      "85                   sids2  North Carolina county SIDS death counts and rates       True\n",
-      "86               snow_maps  Public water pumps and Cholera deaths in Londo...       True\n",
-      "87                     stl  Homicides and selected socio-economic characte...       True\n",
-      "88          street_net_pts                              Street network points       True\n",
-      "89                     taz           Traffic Analysis Zones in So. California       True\n",
-      "90                   tokyo                               Tokyo Mortality data       True\n",
-      "91               us_income  Per-capita income for the lower 48 US states 1...       True\n",
-      "92                virginia                        Virginia counties shapefile       True\n",
-      "93                    wmat          Datasets used for spatial weights testing       True\n"
-     ]
-    }
-   ],
-   "source": [
-    "available()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Grabbing all the remotes\n",
-    "\n",
-    "All the remote datasets can be downloaded with:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "AirBnB\n",
-      "Already downloaded\n",
-      "Atlanta\n",
-      "Already downloaded\n",
-      "Baltimore\n",
-      "Already downloaded\n",
-      "Bostonhsg\n",
-      "Already downloaded\n",
-      "Buenosaires\n",
-      "Already downloaded\n",
-      "Charleston1\n",
-      "Already downloaded\n",
-      "Charleston2\n",
-      "Already downloaded\n",
-      "Chicago Health\n",
-      "Already downloaded\n",
-      "Chile Labor\n",
-      "Already downloaded\n",
-      "Chile Migration\n",
-      "Already downloaded\n",
-      "Cincinnati\n",
-      "Already downloaded\n",
-      "Cleveland\n",
-      "Already downloaded\n",
-      "Columbus\n",
-      "Already downloaded\n",
-      "Denver\n",
-      "Already downloaded\n",
-      "Elections\n",
-      "Already downloaded\n",
-      "Grid100\n",
-      "Already downloaded\n",
-      "Groceries\n",
-      "Already downloaded\n",
-      "Guerry\n",
-      "Already downloaded\n",
-      "Health Indicators\n",
-      "Already downloaded\n",
-      "Health+\n",
-      "Already downloaded\n",
-      "Hickory1\n",
-      "Already downloaded\n",
-      "Hickory2\n",
-      "Already downloaded\n",
-      "Home Sales\n",
-      "Already downloaded\n",
-      "Houston\n",
-      "Already downloaded\n",
-      "Juvenile\n",
-      "Already downloaded\n",
-      "Lansing1\n",
-      "Already downloaded\n",
-      "Lansing2\n",
-      "Already downloaded\n",
-      "Laozone\n",
-      "Already downloaded\n",
-      "LasRosas\n",
-      "Already downloaded\n",
-      "Liquor Stores\n",
-      "Already downloaded\n",
-      "Malaria\n",
-      "Already downloaded\n",
-      "Milwaukee1\n",
-      "Already downloaded\n",
-      "Milwaukee2\n",
-      "Already downloaded\n",
-      "NCOVR\n",
-      "Already downloaded\n",
-      "NDVI\n",
-      "Already downloaded\n",
-      "NYC\n",
-      "Already downloaded\n",
-      "NYC Earnings\n",
-      "Already downloaded\n",
-      "NYC Education\n",
-      "Already downloaded\n",
-      "NYC Neighborhoods\n",
-      "Already downloaded\n",
-      "NYC Socio-Demographics\n",
-      "Already downloaded\n",
-      "Natregimes\n",
-      "Already downloaded\n",
-      "Nepal\n",
-      "Already downloaded\n",
-      "Ohiolung\n",
-      "Already downloaded\n",
-      "Orlando1\n",
-      "Already downloaded\n",
-      "Orlando2\n",
-      "Already downloaded\n",
-      "Oz9799\n",
-      "Already downloaded\n",
-      "Phoenix ACS\n",
-      "Already downloaded\n",
-      "Pittsburgh\n",
-      "Already downloaded\n",
-      "Police\n",
-      "Already downloaded\n",
-      "Rio Grande do Sul\n",
-      "Already downloaded\n",
-      "SIDS\n",
-      "Already downloaded\n",
-      "SIDS2\n",
-      "Already downloaded\n",
-      "Sacramento1\n",
-      "Already downloaded\n",
-      "Sacramento2\n",
-      "Already downloaded\n",
-      "SanFran Crime\n",
-      "Already downloaded\n",
-      "Savannah1\n",
-      "Already downloaded\n",
-      "Savannah2\n",
-      "Already downloaded\n",
-      "Scotlip\n",
-      "Already downloaded\n",
-      "Seattle1\n",
-      "Already downloaded\n",
-      "Seattle2\n",
-      "Already downloaded\n",
-      "South\n",
-      "Already downloaded\n",
-      "StLouis\n",
-      "Already downloaded\n",
-      "Tampa1\n",
-      "Already downloaded\n",
-      "clearwater\n",
-      "Already downloaded\n",
-      "newHaven\n",
-      "Already downloaded\n",
-      "nyc_bikes\n",
-      "Already downloaded\n",
-      "taz\n",
-      "Already downloaded\n"
-     ]
-    }
-   ],
-   "source": [
-    "libpysal.examples.fetch_all()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                      Name                                        Description  Installed\n",
-      "0                    10740    Albuquerque, New Mexico, Census 2000 Tract Data       True\n",
-      "1                   AirBnB  Airbnb rentals, socioeconomics, and crime in C...       True\n",
-      "2                  Atlanta       Atlanta, GA region homicide counts and rates       True\n",
-      "3                Baltimore          Baltimore house sales prices and hedonics       True\n",
-      "4                Bostonhsg               Boston housing and neighborhood data       True\n",
-      "5              Buenosaires      Electoral Data for 1999 Argentinean Elections       True\n",
-      "6              Charleston1   2000 Census Tract Data for Charleston, SC MSA...       True\n",
-      "7              Charleston2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "8           Chicago Health                   Chicago Health + Socio-Economics       True\n",
-      "9              Chile Labor                 Labor Markets in Chile (1982-2002)       True\n",
-      "10         Chile Migration            Internal Migration in Chile (1977-2002)       True\n",
-      "11              Cincinnati         2008 Cincinnati Crime + Socio-Demographics       True\n",
-      "12               Cleveland       2015 sales prices of homes in Cleveland, OH.       True\n",
-      "13                Columbus                        Columbus neighborhood crime       True\n",
-      "14                  Denver   Demographics and housing in Denver neighborho...       True\n",
-      "15               Elections               2012 and 2016 Presidential Elections       True\n",
-      "16                 Grid100                      Grid with simulated variables       True\n",
-      "17               Groceries                          2015 Chicago supermarkets       True\n",
-      "18                  Guerry          Moral statistics of France (Guerry, 1833)       True\n",
-      "19       Health Indicators                Chicago Health Indicators (2005-11)       True\n",
-      "20                 Health+                    2000 Health, Income + Diversity       True\n",
-      "21                Hickory1   2000 Census Tract Data for Hickory, NC MSA an...       True\n",
-      "22                Hickory2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "23              Home Sales              2014-15 Home Sales in King County, WA       True\n",
-      "24                 Houston       Houston, TX region homicide counts and rates       True\n",
-      "25                Juvenile             Cardiff juvenile delinquent residences       True\n",
-      "26                Lansing1   2000 Census Tract Data for Lansing, MI MSA an...       True\n",
-      "27                Lansing2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "28                 Laozone   Ozone measures at monitoring stations in Los ...       True\n",
-      "29                LasRosas   Corn yield, fertilizer and field data for pre...       True\n",
-      "30                    Line                                     Line Shapefile       True\n",
-      "31           Liquor Stores                         2015 Chicago Liquor Stores       True\n",
-      "32                 Malaria   Malaria incidence and population (1973, 95, 9...       True\n",
-      "33              Milwaukee1       2000 Census Tract Data for Milwaukee, WI MSA       True\n",
-      "34              Milwaukee2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "35                   NCOVR                      US county homicides 1960-1990       True\n",
-      "36                    NDVI        Normalized Difference Vegetation Index grid       True\n",
-      "37                     NYC   Demographic and housing data for New York Cit...       True\n",
-      "38            NYC Earnings              Block-level Earnings in NYC (2002-14)       True\n",
-      "39           NYC Education                               NYC Education (2000)       True\n",
-      "40       NYC Neighborhoods       Demographics for New York City neighborhoods       True\n",
-      "41  NYC Socio-Demographics                 NYC Education + Socio-Demographics       True\n",
-      "42              Natregimes                    NCOVR with regimes (book/PySAL)       True\n",
-      "43                   Nepal   Health, poverty and education indicators for ...       True\n",
-      "44                Ohiolung            Ohio lung cancer data, 1968, 1978, 1988       True\n",
-      "45                Orlando1   2000 Census Tract Data for Orlando, FL MSA an...       True\n",
-      "46                Orlando2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "47                  Oz9799                        Monthly ozone data, 1997-99       True\n",
-      "48             Phoenix ACS   Phoenix American Community Survey Data (2010,...       True\n",
-      "49              Pittsburgh                      Pittsburgh homicide locations       True\n",
-      "50                   Point                                    Point Shapefile       True\n",
-      "51                  Police           Police expenditures Mississippi counties       True\n",
-      "52                 Polygon                                  Polygon Shapefile       True\n",
-      "53           Polygon_Holes                 Example to test treatment of holes       True\n",
-      "54       Rio Grande do Sul  Cities of the Brazilian State of Rio Grande do...       True\n",
-      "55                    SIDS            North Carolina county SIDS death counts       True\n",
-      "56                   SIDS2   North Carolina county SIDS death counts and r...       True\n",
-      "57             Sacramento1          2000 Census Tract Data for Sacramento MSA       True\n",
-      "58             Sacramento2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "59           SanFran Crime   July-Dec 2012 crime incidents in San Francisc...       True\n",
-      "60               Savannah1   2000 Census Tract Data for Savannah, GA MSA a...       True\n",
-      "61               Savannah2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "62                 Scotlip               Male lip cancer in Scotland, 1975-80       True\n",
-      "63                Seattle1   2000 Census Tract Data for Seattle, WA MSA an...       True\n",
-      "64                Seattle2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "65                   South             US Southern county homicides 1960-1990       True\n",
-      "66                 StLouis   St Louis region county homicide counts and rates       True\n",
-      "67                  Tampa1   2000 Census Tract Data for Tampa, FL MSA and ...       True\n",
-      "68                  arcgis                               arcgis testing files       True\n",
-      "69                  baltim     Baltimore house sales prices and hedonics 1978       True\n",
-      "70                  berlin  Prenzlauer Berg neighborhood AirBnB data from ...       True\n",
-      "71                    book       Synthetic data to illustrate spatial weights       True\n",
-      "72                 burkitt  Burkitt's lymphoma in the Western Nile distric...       True\n",
-      "73                  calemp         Employment density for California counties       True\n",
-      "74                 chicago                              Chicago neighborhoods       True\n",
-      "75              clearwater                               mgwr testing dataset       True\n",
-      "76                columbus              Columbus neighborhood crime data 1980       True\n",
-      "77                 desmith    Small dataset to illustrate Moran's I statistic       True\n",
-      "78                geodanet        Datasets from geodanet for network analysis       True\n",
-      "79                 georgia  Various socio-economic variables for counties ...       True\n",
-      "80                juvenile    Residences of juvenile offenders in Cardiff, UK       True\n",
-      "81                  mexico  Decennial per capita incomes of Mexican states...       True\n",
-      "82                networks                  Datasets used for network testing       True\n",
-      "83                newHaven                            Network testing dataset       True\n",
-      "84               nyc_bikes                           New York City Bike Trips       True\n",
-      "85                   sids2  North Carolina county SIDS death counts and rates       True\n",
-      "86               snow_maps  Public water pumps and Cholera deaths in Londo...       True\n",
-      "87                     stl  Homicides and selected socio-economic characte...       True\n",
-      "88          street_net_pts                              Street network points       True\n",
-      "89                     taz           Traffic Analysis Zones in So. California       True\n",
-      "90                   tokyo                               Tokyo Mortality data       True\n",
-      "91               us_income  Per-capita income for the lower 48 US states 1...       True\n",
-      "92                virginia                        Virginia counties shapefile       True\n",
-      "93                    wmat          Datasets used for spatial weights testing       True\n"
-     ]
-    }
-   ],
-   "source": [
-    "available()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
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-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
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diff --git a/docsrc/notebooks/voronoi.ipynb b/docsrc/notebooks/voronoi.ipynb
deleted file mode 100644
index 75d7263d3..000000000
--- a/docsrc/notebooks/voronoi.ipynb
+++ /dev/null
@@ -1,478 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Voronoi Polygons for 2-D Point Sets\n",
-    "\n",
-    "Author: Serge Rey (http://github.com/sjsrey)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Basic Usage"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sys\n",
-    "import os\n",
-    "sys.path.append(os.path.abspath('..'))\n",
-    "import libpysal"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from libpysal.cg.voronoi  import voronoi, voronoi_frames"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "points = [(10.2, 5.1), (4.7, 2.2), (5.3, 5.7), (2.7, 5.3)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "regions, vertices = voronoi(points)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[[1, 3, 2], [4, 5, 1, 0], [0, 1, 7, 6], [9, 0, 8]]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "regions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[  4.21783296,   4.08408578],\n",
-       "       [  7.51956025,   3.51807539],\n",
-       "       [  9.4642193 ,  19.3994576 ],\n",
-       "       [ 14.98210684, -10.63503022],\n",
-       "       [ -9.22691341,  -4.58994414],\n",
-       "       [ 14.98210684, -10.63503022],\n",
-       "       [  1.78491801,  19.89803294],\n",
-       "       [  9.4642193 ,  19.3994576 ],\n",
-       "       [  1.78491801,  19.89803294],\n",
-       "       [ -9.22691341,  -4.58994414]])"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "vertices"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "region_df, point_df = voronoi_frames(points)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "import matplotlib\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3304d59e48>"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots()\n",
-    "region_df.plot(ax=ax, color='blue',edgecolor='black', alpha=0.3)\n",
-    "point_df.plot(ax=ax, color='red')\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Larger Problem"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "n_points = 200\n",
-    "np.random.seed(12345)\n",
-    "points = np.random.random((n_points,2))*10 + 10\n",
-    "results = voronoi(points)\n",
-    "mins = points.min(axis=0)\n",
-    "maxs = points.max(axis=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "regions, vertices = voronoi(points)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "regions_df, points_df = voronoi_frames(points)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f32fd486b38>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots()\n",
-    "points_df.plot(ax=ax, color='red')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f32fd3c76a0>"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots()\n",
-    "regions_df.plot(ax=ax, color='blue',edgecolor='black', alpha=0.3)\n",
-    "points_df.plot(ax=ax, color='red')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Trimming"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "points = np.array(points)\n",
-    "maxs = points.max(axis=0)\n",
-    "mins = points.min(axis=0)\n",
-    "xr = maxs[0] - mins[0]\n",
-    "yr = maxs[1] - mins[1]\n",
-    "buff = 0.05\n",
-    "r = max(yr, xr) * buff\n",
-    "minx = mins[0] - r\n",
-    "miny = mins[1] - r\n",
-    "maxx = maxs[0] + r\n",
-    "maxy = maxs[1] + r"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots()\n",
-    "regions_df.plot(ax=ax, edgecolor='black', facecolor='blue', alpha=0.2 )\n",
-    "points_df.plot(ax=ax, color='red')\n",
-    "plt.xlim(minx, maxx)\n",
-    "plt.ylim(miny, maxy)\n",
-    "plt.title(\"buffer: %f, n: %d\"%(r,n_points))\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Voronoi Weights"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from libpysal.weights.contiguity import Voronoi as Vornoi_weights"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "w = Vornoi_weights(points)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "200"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w.n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.685"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w.pct_nonzero"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(1, 1),\n",
-       " (2, 6),\n",
-       " (3, 17),\n",
-       " (4, 34),\n",
-       " (5, 41),\n",
-       " (6, 63),\n",
-       " (7, 24),\n",
-       " (8, 7),\n",
-       " (9, 5),\n",
-       " (10, 1),\n",
-       " (11, 0),\n",
-       " (12, 1)]"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w.histogram"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "idx = [i for i in range(w.n) if w.cardinalities[i]==12]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[16.50851787, 13.12932895]])"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "points[idx]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docsrc/notebooks/weights.ipynb b/docsrc/notebooks/weights.ipynb
deleted file mode 100644
index 3f867ad7b..000000000
--- a/docsrc/notebooks/weights.ipynb
+++ /dev/null
@@ -1,1313 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sys\n",
-    "import os"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sys.path.append(os.path.abspath('..'))\n",
-    "import libpysal"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                      Name                                        Description  Installed\n",
-      "0                    10740    Albuquerque, New Mexico, Census 2000 Tract Data       True\n",
-      "1                   AirBnB  Airbnb rentals, socioeconomics, and crime in C...       True\n",
-      "2                  Atlanta       Atlanta, GA region homicide counts and rates       True\n",
-      "3                Baltimore          Baltimore house sales prices and hedonics       True\n",
-      "4                Bostonhsg               Boston housing and neighborhood data       True\n",
-      "5              Buenosaires      Electoral Data for 1999 Argentinean Elections       True\n",
-      "6              Charleston1   2000 Census Tract Data for Charleston, SC MSA...       True\n",
-      "7              Charleston2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "8           Chicago Health                   Chicago Health + Socio-Economics       True\n",
-      "9              Chile Labor                 Labor Markets in Chile (1982-2002)       True\n",
-      "10         Chile Migration            Internal Migration in Chile (1977-2002)       True\n",
-      "11              Cincinnati         2008 Cincinnati Crime + Socio-Demographics       True\n",
-      "12               Cleveland       2015 sales prices of homes in Cleveland, OH.       True\n",
-      "13                Columbus                        Columbus neighborhood crime       True\n",
-      "14                  Denver   Demographics and housing in Denver neighborho...       True\n",
-      "15               Elections               2012 and 2016 Presidential Elections       True\n",
-      "16                 Grid100                      Grid with simulated variables       True\n",
-      "17               Groceries                          2015 Chicago supermarkets       True\n",
-      "18                  Guerry          Moral statistics of France (Guerry, 1833)       True\n",
-      "19       Health Indicators                Chicago Health Indicators (2005-11)       True\n",
-      "20                 Health+                    2000 Health, Income + Diversity       True\n",
-      "21                Hickory1   2000 Census Tract Data for Hickory, NC MSA an...       True\n",
-      "22                Hickory2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "23              Home Sales              2014-15 Home Sales in King County, WA       True\n",
-      "24                 Houston       Houston, TX region homicide counts and rates       True\n",
-      "25                Juvenile             Cardiff juvenile delinquent residences       True\n",
-      "26                Lansing1   2000 Census Tract Data for Lansing, MI MSA an...       True\n",
-      "27                Lansing2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "28                 Laozone   Ozone measures at monitoring stations in Los ...       True\n",
-      "29                LasRosas   Corn yield, fertilizer and field data for pre...       True\n",
-      "30                    Line                                     Line Shapefile       True\n",
-      "31           Liquor Stores                         2015 Chicago Liquor Stores       True\n",
-      "32                 Malaria   Malaria incidence and population (1973, 95, 9...       True\n",
-      "33              Milwaukee1       2000 Census Tract Data for Milwaukee, WI MSA       True\n",
-      "34              Milwaukee2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "35                   NCOVR                      US county homicides 1960-1990       True\n",
-      "36                    NDVI        Normalized Difference Vegetation Index grid       True\n",
-      "37                     NYC   Demographic and housing data for New York Cit...       True\n",
-      "38            NYC Earnings              Block-level Earnings in NYC (2002-14)       True\n",
-      "39           NYC Education                               NYC Education (2000)       True\n",
-      "40       NYC Neighborhoods       Demographics for New York City neighborhoods       True\n",
-      "41  NYC Socio-Demographics                 NYC Education + Socio-Demographics       True\n",
-      "42              Natregimes                    NCOVR with regimes (book/PySAL)       True\n",
-      "43                   Nepal   Health, poverty and education indicators for ...       True\n",
-      "44                Ohiolung            Ohio lung cancer data, 1968, 1978, 1988       True\n",
-      "45                Orlando1   2000 Census Tract Data for Orlando, FL MSA an...       True\n",
-      "46                Orlando2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "47                  Oz9799                        Monthly ozone data, 1997-99       True\n",
-      "48             Phoenix ACS   Phoenix American Community Survey Data (2010,...       True\n",
-      "49              Pittsburgh                      Pittsburgh homicide locations       True\n",
-      "50                   Point                                    Point Shapefile       True\n",
-      "51                  Police           Police expenditures Mississippi counties       True\n",
-      "52                 Polygon                                  Polygon Shapefile       True\n",
-      "53           Polygon_Holes                 Example to test treatment of holes       True\n",
-      "54       Rio Grande do Sul  Cities of the Brazilian State of Rio Grande do...       True\n",
-      "55                    SIDS            North Carolina county SIDS death counts       True\n",
-      "56                   SIDS2   North Carolina county SIDS death counts and r...       True\n",
-      "57             Sacramento1          2000 Census Tract Data for Sacramento MSA       True\n",
-      "58             Sacramento2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "59           SanFran Crime   July-Dec 2012 crime incidents in San Francisc...       True\n",
-      "60               Savannah1   2000 Census Tract Data for Savannah, GA MSA a...       True\n",
-      "61               Savannah2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "62                 Scotlip               Male lip cancer in Scotland, 1975-80       True\n",
-      "63                Seattle1   2000 Census Tract Data for Seattle, WA MSA an...       True\n",
-      "64                Seattle2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "65                   South             US Southern county homicides 1960-1990       True\n",
-      "66                 StLouis   St Louis region county homicide counts and rates       True\n",
-      "67                  Tampa1   2000 Census Tract Data for Tampa, FL MSA and ...       True\n",
-      "68                  arcgis                               arcgis testing files       True\n",
-      "69                  baltim     Baltimore house sales prices and hedonics 1978       True\n",
-      "70                  berlin  Prenzlauer Berg neighborhood AirBnB data from ...       True\n",
-      "71                    book       Synthetic data to illustrate spatial weights       True\n",
-      "72                 burkitt  Burkitt's lymphoma in the Western Nile distric...       True\n",
-      "73                  calemp         Employment density for California counties       True\n",
-      "74                 chicago                              Chicago neighborhoods       True\n",
-      "75              clearwater                               mgwr testing dataset       True\n",
-      "76                columbus              Columbus neighborhood crime data 1980       True\n",
-      "77                 desmith    Small dataset to illustrate Moran's I statistic       True\n",
-      "78                geodanet        Datasets from geodanet for network analysis       True\n",
-      "79                 georgia  Various socio-economic variables for counties ...       True\n",
-      "80                juvenile    Residences of juvenile offenders in Cardiff, UK       True\n",
-      "81                  mexico  Decennial per capita incomes of Mexican states...       True\n",
-      "82                networks                  Datasets used for network testing       True\n",
-      "83                newHaven                            Network testing dataset       True\n",
-      "84               nyc_bikes                           New York City Bike Trips       True\n",
-      "85                   sids2  North Carolina county SIDS death counts and rates       True\n",
-      "86               snow_maps  Public water pumps and Cholera deaths in Londo...       True\n",
-      "87                     stl  Homicides and selected socio-economic characte...       True\n",
-      "88          street_net_pts                              Street network points       True\n",
-      "89                     taz           Traffic Analysis Zones in So. California       True\n",
-      "90                   tokyo                               Tokyo Mortality data       True\n",
-      "91               us_income  Per-capita income for the lower 48 US states 1...       True\n",
-      "92                virginia                        Virginia counties shapefile       True\n",
-      "93                    wmat          Datasets used for spatial weights testing       True\n"
-     ]
-    }
-   ],
-   "source": [
-    "libpysal.examples.available()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mexico\n",
-      "======\n",
-      "\n",
-      "Decennial per capita incomes of Mexican states 1940-2000\n",
-      "--------------------------------------------------------\n",
-      "\n",
-      "* mexico.csv: attribute data. (n=32, k=13)\n",
-      "* mexico.gal: spatial weights in GAL format.\n",
-      "* mexicojoin.shp: Polygon shapefile. (n=32)\n",
-      "\n",
-      "Data used in Rey, S.J. and M.L. Sastre Gutierrez. (2010) \"Interregional inequality dynamics in Mexico.\" Spatial Economic Analysis, 5: 277-298.\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "libpysal.examples.explain('mexico')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Weights from GeoDataFrames"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import geopandas\n",
-    "pth = libpysal.examples.get_path(\"mexicojoin.shp\")\n",
-    "gdf = geopandas.read_file(pth)\n",
-    "\n",
-    "from libpysal.weights import Queen, Rook, KNN"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "import matplotlib.pyplot as plt\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = gdf.plot()\n",
-    "ax.set_axis_off()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Contiguity Weights\n",
-    "\n",
-    "The first set of spatial weights we illustrate use notions of contiguity to define neighboring observations. **Rook** neighbors are those states that share an edge on their respective borders:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "w_rook = Rook.from_dataframe(gdf)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "32"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w_rook.n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "12.6953125"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w_rook.pct_nonzero"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = gdf.plot(edgecolor='grey', facecolor='w')\n",
-    "f,ax = w_rook.plot(gdf, ax=ax, \n",
-    "        edge_kws=dict(color='r', linestyle=':', linewidth=1),\n",
-    "        node_kws=dict(marker=''))\n",
-    "ax.set_axis_off()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>POLY_ID</th>\n",
-       "      <th>AREA</th>\n",
-       "      <th>CODE</th>\n",
-       "      <th>NAME</th>\n",
-       "      <th>PERIMETER</th>\n",
-       "      <th>ACRES</th>\n",
-       "      <th>HECTARES</th>\n",
-       "      <th>PCGDP1940</th>\n",
-       "      <th>PCGDP1950</th>\n",
-       "      <th>PCGDP1960</th>\n",
-       "      <th>...</th>\n",
-       "      <th>GR9000</th>\n",
-       "      <th>LPCGDP40</th>\n",
-       "      <th>LPCGDP50</th>\n",
-       "      <th>LPCGDP60</th>\n",
-       "      <th>LPCGDP70</th>\n",
-       "      <th>LPCGDP80</th>\n",
-       "      <th>LPCGDP90</th>\n",
-       "      <th>LPCGDP00</th>\n",
-       "      <th>TEST</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>7.252751e+10</td>\n",
-       "      <td>MX02</td>\n",
-       "      <td>Baja California Norte</td>\n",
-       "      <td>2040312.385</td>\n",
-       "      <td>1.792187e+07</td>\n",
-       "      <td>7252751.376</td>\n",
-       "      <td>22361.0</td>\n",
-       "      <td>20977.0</td>\n",
-       "      <td>17865.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.05</td>\n",
-       "      <td>4.35</td>\n",
-       "      <td>4.32</td>\n",
-       "      <td>4.25</td>\n",
-       "      <td>4.40</td>\n",
-       "      <td>4.47</td>\n",
-       "      <td>4.43</td>\n",
-       "      <td>4.48</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>MULTIPOLYGON (((-113.13972 29.01778, -113.2405...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>7.225988e+10</td>\n",
-       "      <td>MX03</td>\n",
-       "      <td>Baja California Sur</td>\n",
-       "      <td>2912880.772</td>\n",
-       "      <td>1.785573e+07</td>\n",
-       "      <td>7225987.769</td>\n",
-       "      <td>9573.0</td>\n",
-       "      <td>16013.0</td>\n",
-       "      <td>16707.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>3.98</td>\n",
-       "      <td>4.20</td>\n",
-       "      <td>4.22</td>\n",
-       "      <td>4.39</td>\n",
-       "      <td>4.46</td>\n",
-       "      <td>4.41</td>\n",
-       "      <td>4.42</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>MULTIPOLYGON (((-111.20612 25.80278, -111.2302...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>2.731957e+10</td>\n",
-       "      <td>MX18</td>\n",
-       "      <td>Nayarit</td>\n",
-       "      <td>1034770.341</td>\n",
-       "      <td>6.750785e+06</td>\n",
-       "      <td>2731956.859</td>\n",
-       "      <td>4836.0</td>\n",
-       "      <td>7515.0</td>\n",
-       "      <td>7621.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.05</td>\n",
-       "      <td>3.68</td>\n",
-       "      <td>3.88</td>\n",
-       "      <td>3.88</td>\n",
-       "      <td>4.04</td>\n",
-       "      <td>4.13</td>\n",
-       "      <td>4.11</td>\n",
-       "      <td>4.06</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>MULTIPOLYGON (((-106.62108 21.56531, -106.6475...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>7.961008e+10</td>\n",
-       "      <td>MX14</td>\n",
-       "      <td>Jalisco</td>\n",
-       "      <td>2324727.436</td>\n",
-       "      <td>1.967200e+07</td>\n",
-       "      <td>7961008.285</td>\n",
-       "      <td>5309.0</td>\n",
-       "      <td>8232.0</td>\n",
-       "      <td>9953.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.03</td>\n",
-       "      <td>3.73</td>\n",
-       "      <td>3.92</td>\n",
-       "      <td>4.00</td>\n",
-       "      <td>4.21</td>\n",
-       "      <td>4.32</td>\n",
-       "      <td>4.30</td>\n",
-       "      <td>4.33</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>POLYGON ((-101.52490 21.85664, -101.58830 21.7...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>5.467030e+09</td>\n",
-       "      <td>MX01</td>\n",
-       "      <td>Aguascalientes</td>\n",
-       "      <td>313895.530</td>\n",
-       "      <td>1.350927e+06</td>\n",
-       "      <td>546702.985</td>\n",
-       "      <td>10384.0</td>\n",
-       "      <td>6234.0</td>\n",
-       "      <td>8714.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.13</td>\n",
-       "      <td>4.02</td>\n",
-       "      <td>3.79</td>\n",
-       "      <td>3.94</td>\n",
-       "      <td>4.21</td>\n",
-       "      <td>4.32</td>\n",
-       "      <td>4.32</td>\n",
-       "      <td>4.44</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>POLYGON ((-101.84620 22.01176, -101.96530 21.8...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 35 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   POLY_ID          AREA  CODE                   NAME    PERIMETER  \\\n",
-       "0        1  7.252751e+10  MX02  Baja California Norte  2040312.385   \n",
-       "1        2  7.225988e+10  MX03    Baja California Sur  2912880.772   \n",
-       "2        3  2.731957e+10  MX18                Nayarit  1034770.341   \n",
-       "3        4  7.961008e+10  MX14                Jalisco  2324727.436   \n",
-       "4        5  5.467030e+09  MX01         Aguascalientes   313895.530   \n",
-       "\n",
-       "          ACRES     HECTARES  PCGDP1940  PCGDP1950  PCGDP1960  ...  GR9000  \\\n",
-       "0  1.792187e+07  7252751.376    22361.0    20977.0    17865.0  ...    0.05   \n",
-       "1  1.785573e+07  7225987.769     9573.0    16013.0    16707.0  ...    0.00   \n",
-       "2  6.750785e+06  2731956.859     4836.0     7515.0     7621.0  ...   -0.05   \n",
-       "3  1.967200e+07  7961008.285     5309.0     8232.0     9953.0  ...    0.03   \n",
-       "4  1.350927e+06   546702.985    10384.0     6234.0     8714.0  ...    0.13   \n",
-       "\n",
-       "   LPCGDP40  LPCGDP50  LPCGDP60  LPCGDP70  LPCGDP80  LPCGDP90  LPCGDP00  TEST  \\\n",
-       "0      4.35      4.32      4.25      4.40      4.47      4.43      4.48   1.0   \n",
-       "1      3.98      4.20      4.22      4.39      4.46      4.41      4.42   2.0   \n",
-       "2      3.68      3.88      3.88      4.04      4.13      4.11      4.06   3.0   \n",
-       "3      3.73      3.92      4.00      4.21      4.32      4.30      4.33   4.0   \n",
-       "4      4.02      3.79      3.94      4.21      4.32      4.32      4.44   5.0   \n",
-       "\n",
-       "                                            geometry  \n",
-       "0  MULTIPOLYGON (((-113.13972 29.01778, -113.2405...  \n",
-       "1  MULTIPOLYGON (((-111.20612 25.80278, -111.2302...  \n",
-       "2  MULTIPOLYGON (((-106.62108 21.56531, -106.6475...  \n",
-       "3  POLYGON ((-101.52490 21.85664, -101.58830 21.7...  \n",
-       "4  POLYGON ((-101.84620 22.01176, -101.96530 21.8...  \n",
-       "\n",
-       "[5 rows x 35 columns]"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gdf.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 22]"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w_rook.neighbors[0] # the first location has two neighbors at locations 1 and 22"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0     Baja California Norte\n",
-       "1       Baja California Sur\n",
-       "22                   Sonora\n",
-       "Name: NAME, dtype: object"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gdf['NAME'][[0, 1,22]]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "So, Baja California Norte has 2 rook neighbors: Baja California Sur and Sonora."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Queen** neighbors are based on a more inclusive condition that requires only a shared vertex between two states:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "w_queen = Queen.from_dataframe(gdf)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w_queen.n == w_rook.n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(w_queen.pct_nonzero > w_rook.pct_nonzero) == (w_queen.n == w_rook.n)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = gdf.plot(edgecolor='grey', facecolor='w')\n",
-    "f,ax = w_queen.plot(gdf, ax=ax, \n",
-    "        edge_kws=dict(color='r', linestyle=':', linewidth=1),\n",
-    "        node_kws=dict(marker=''))\n",
-    "ax.set_axis_off()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(1, 1), (2, 6), (3, 6), (4, 6), (5, 5), (6, 2), (7, 3), (8, 2), (9, 1)]"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w_queen.histogram"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(1, 1), (2, 6), (3, 7), (4, 7), (5, 3), (6, 4), (7, 3), (8, 1)]"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w_rook.histogram"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "c9 = [idx for idx,c in w_queen.cardinalities.items() if c==9]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "28    San Luis Potosi\n",
-       "Name: NAME, dtype: object"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gdf['NAME'][c9]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[5, 6, 7, 27, 29, 30, 31]"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w_rook.neighbors[28]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[3, 5, 6, 7, 24, 27, 29, 30, 31]"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w_queen.neighbors[28]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([-105.,  -95.,   21.,   26.])"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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HFWq1+ovY2FiHr6eOwL4Gz4ABwMsvsySzyZPNHmbLli1iy5YtERYWZsXJGWDBAqZhUY0JCgpCaWmpWFpaSjZt2oTff//dvBBXUBDzZl25Anz0kZVnaTparRaUUhQXO1YJPSEhQUsI+Sk2NrbQoRORcAwHDjDvp6cn8OOPZnt7z5w5g6KiIvuE4svx8QFKS8020qrEw4ONGRdn8VAymQyhoaEoKCigKSkp/PLly8X8/Ce6JBhm925g7Fi2WZs9G8jJsXhulkIpRVFREczOs7QCOTk5yM7O1gH4xWGTcDD2ueESE1m8e/FiJihoISqVirRu3doKEzOB0aMtjlXbg2vXrnH16tUDACQmJprvQ23dmiWTN27MdHoceKMGBgaC53lx4cKFmDdvHj1y5Ijdw0kajQZnzpwRtFrtN/a+toSD0WqB334DevRgRo+FoQmVSgU/Pz/7hOLLWbrUOvo7ldG7N/v9WIFDhw6Jbm5uxNfXl2ZnZ3MG2+Y8zrx5wFdfMYmTjz5iOkH//jeQnm6V+ZlLrVq1xB07dtCvv/4acXFxgl2Ech/jxIkTJaIoLoiNja1evYXsiH3uuJkzWYJyw4bM3WghTk5OuH79uhUmZgI6HUvureY0aNCAZmdnw9fXV3zhhRfMG4RSVj0XHg6MGgV4ewNvv+2w8Javry/eeecd/sMPP0SnTp1IQkKC3X3D58+fpxzHHY6NjU2x97UlHMyuXcBbb7Hvn3vO4uGcnJxw//5986sqzYEQiw21KunWDRg6FLh3z+Kh2rRpw2m1WkGhUJDWrVtTo6tvc3KAiRNZFGHXLiZMWK8em1fXriyqYKrxZEUmTJggmz17NnnttdeQk5PDZds51KZQKJCUlEQEQVhq1wtXM2xr8Lz1FvDLL2xX9P33Vht22LBh5MyZMzh37pzVxtSLKAKbNwPNmhk+1sG8/PLLZObMmQgLC+P2799P9+7dK5i8sGZlMW0RFxfAzY1pIz3/PDB1qkMbj3Ich5KSEvs0iq2ARqPBkSNHStRq9Ry7XljCsRw7xkLwQ4awCkYr6UC1b98erq6uWL16tXkhZ3OYOpU1MLUFzs4s/L1/v8VDtW3bFu+99x4/fPhwpKWl0RUrVtAiQ/pgajVLOfDyYh5pLy/g8GG2SQVYeP6774Dhw4HMTIvnaAmUUgiCYJ/ejxX4888/NRzHrY+NjTWz9v/pwDYGjyAwb4CnJ/OKWFkwrm7dunjxxRexd+9e+7gcVq0C6ta1y6WsgUwmQ48ePTB16lRy6dIlsnbtWsEkJWoXF1ZuWvH/w4YBDRqwMlx7qFo/hk6nw08//SSeOnUKY8aMseu1Dx06pBEEYXdsbOxJu15YwnEIAlu/3NzY/624hsnlckyYMIHLzc3l7bZp8/ICzp613fh9+wLmlpRXQuPGjfHWW29xPj4+9IcffkBmVYbKjh2sIers2cyoKc9R+uADoHPnh8dFRLBE5v79mYyAA0hISMD//vc/tGvXjjZq1Mhu171z5w7OnTun1mg0H9jtotUUQvWHKcwzKFq2ZKGQTz4x63RjOHHiBBISEoR33nnH9rV+xcVASgrLa/mHodFosGrVKkGn03Ht2rUjHMeB4zi4ubmhadOmlVcOLFrEem398MOjP9fpgDVrgJMnWazcjHJcc8jJycGaNWtEb29vjB49mrOLZkmFa69atUqh0+kaxsbG3rXbhSWshelr2MKF7OvmTRtMh1FQUIDFixcjJiYGlfVrszonTzKNGltVClHKjIo9e4Data069MGDB3Hy5El07NgRrq6u4DgOPMehaUEBPNLSmIZbnz4PT8jLY+H4tLQnDdW0NBb22raNeYPsgCiKWL9+vZiRkcGNGDEC9jR2RFHE8uXLlbm5uTM++eSTH+124WqKdT/9J06wD9rKlaxNhA0QBAF79uwRLl68yA0ePNg+wga///6PNHYAli/w+uuv87///ju9ePGiQCmFKIpUpVKRnTt38u3atRM7dOjAubu7PzxJpwM6dHhyMJmMNerLyACWLWNucm9vm87/5MmTOHjwIKKiotC9e3e7JnpSSrF9+3alKIofSMbOM4BCAVy8CEyaxNow2Ijr169j69ataNKkiVizZk3bf6AvXWIbtsruaWtBCDBwIGvv8OabVh26R48eqFWrFo4fPy6KokhFUUTU5s1cwY0bZNcXXwjRYWH8I5k+ublAVFTlXrl69dimbcoU1m3dxp77vLw8xMXFie7u7oiJiUGNGjVser3HSUxMpIWFhddFUfzJrheupljXwxMaCrz/PhATY9Gk9JGamoqffvoJr7zyCurbq2t5ly4slv/GG/a5nh0QRRHffPMNLS0tJd27d6ddunR5uDqsWsUWr8e67D6AUhbyunWLSdX7+tpkfuvWrRMzMzPtvisq5+zZs/SPP/64pFarw2NjYx1XpiZhCcavYZ9+yqqNLlyw3WQoxbfffovAwECMHz/eZtd5hCVLWA7i4cO2vU5qKnDoEPDqq7a9xvLlwIgR+C0piZ6/dYv4+vpi+vTpD3vt/fUXC6/16FH1OOnpwMiRwPz5LD/RBpw9exZ79+5FZGSk0Lt3b97ewoMKhQKLFi0q0Wg07WNjYy/Z9eLVFMs9PJSyHdF777HeWDb+o4aEhKBdu3bCli1byIwZMzgnJyebXg8A8Oeftr+GFVAoFEhMTER6ejry8/N1JSUlvCAIxMnJSXRzc6MdOnTgIyMjAbBQV2lpKQGAyMjIR7dC69axmHxVEAJMn87KXLduZaWfVmy5kZubi7i4ONHDw8MhuyIAUCqViI+PL9VoNBMkY+cpZ+tW4Px51pIgNtamlyKEYNSoUVizZg2SkpLsoyU2fTr7sjVBQSxU1KMH86SYiCiK+Pvvv5GcnIy7d+8KCoWCqNVqTi6Xiy4uLmJQrVr88K1bCalTB2KrVii6fJkAQMeOHR9tLLxnD/M86zN46tZlhtOyZWw969jR5Pnqex+bNm0SU1JSuGHDhqFZs2YOkVjes2dPCaV0uWTsPES/wZOVxT7EVVHuHVIqWZ6LnSzYfv368bm5ueLKlSuFN954g7dpmCM5mSW6XbsGURRx//59+Pj4VJveTuUcO3YMf/75J/z8/MS6deuiZcuWssDAQLi5uSE/P5/Lzs7G3r17cevWLeTn5wu5ubl8cHCwmJmZye3evRsvvfQSG0gUWTWDoXJQQoB//YtVR8ybx8rWg4Mtfh+JiYmIj49HZGQkdcSuqJz4+PgSAKtjY2PPO2QCEvaBUuYNUChsW7pdgbp162LQoEHYvn07fH19EWjr/nxdu7JeU4MHQ6FQPMjhszpyObvW3bsmGzw5OTlYv369IAgC16BBAzE8PJwPDAyEr68vFAoFp123jnOdORPL3npL8KtXj0uZPx88z6NmzZrk999/R2Rk5EOjp0sX4/KI2rRh/RE//ZSVtQ8ZYsabfpSCggL89NNPolwuJ6+//jrs1gngMZKTk3Hjxg2FVqu1YrfYfz76Q1oDB1L8/nvlr1HKHoorVrAkMDujVquxYsUKCkAcN24cb+kHKzk5GdnZ2SgoKEBRUREUCgUtLS0V5Lm5pOWxY9yRHj2ITqcDz/MghMDb21sICwvjO3ToABcXF+u8KTO5dOkSdu7ciZEjR+oN/WRnZ2Pnzp1iw4YNSUREROVtOU6cYC7wdeuMn8DixexhMWAA6y9kAFEUkZqaigYNGjzys4q7oqYWtB6xlNTUVKxbty5fq9XWj42Nday8s4Rl6HS0ykTdmBjmld63z75zKuPgwYPiqVOnSJ8+fUi559Vc8vPzkZKSgvv376OwsBDFxcW0pKRE0Gg0CI+P5682b4673t4EYF4md3d3ISQkhO/YsSNqWzPJ+MoVlsNz4IDRp5SH1yMiImiPHj24RwopKAWuXwfWrIE2Oho7VCpBq9Wiffv2fIMGDR717JQTHc1CePo26xW5eJGpM4eGsjCXEdy+fRt16tR5pOjjwoUL2L17N23VqpXYr18/3lGtJHQ6HRYtWqQsLi4e8+mnn+5wyCSqKfoNnqIiisrCCWfOMAGuHTvYQ85Bf1iNRoP4+Hjh4sWLfFRUFHr27GnWOHfv3sUPP/yAwMBA0dPTU/Ty8iI1atTgPTw8UKOkBG6+vnCvXRvu7u7geR55eXm4evUqvXTpEr137x7n4eEhNGzYkO/YsSP8/Pys/C4Ns2jRIiE8PJzr3Lmz5bWzy5YBp08z6XxTWLGCfS5mz2YLhx5Wrlwp3Llzh2/fvj3atWsHpVKJrVu3Cs7OztyYMWOIt40ToQ2xZs0aRXJy8v/FxsY+81UN/3hu36ZPGOF37zKPjlbLvDvh4Q6ZGqUUSUlJ2LlzJ3x9fYWxY8fy5npevv32W4Hnec7Hx0esUaMGvLy8eA8PD3g4OcFLpYJrs2Zwd3eHk5MT1Go1bt68iUuXLgnJycm8XC4Xa9WqRdq2bUuaNm1qmfdaEIDu3VnzYSOroI4dO4bExEQ6Y8YM8oQBM2UKcPUqawpqjDSASsXCVdnZzONkLNeuserUyEiWoqGHxMRE7Nq1C15eXsL48eN5nU6Hw4cP05s3b5LBgwejRYsWxl/XBpR9phJnzZrV1qETqYboN3hmz6bw9Hy0n5JOx5JUd+5krsNqQGpqKrZu3Urd3NyE119/3WTra9euXTh//jxmzJgBz8dLrbt2ZaGa9esrPVepVOLGjRu4ePGikJaWxjs7O4tBQUEkKiqK2KMLclZWFlavXo13330XrtZQgr54kWl2mBGDx/btzFiaMKHS5rB79uxBZmYmiouL6ZAhQ8iWLVse9Mlq06aN4MhdUTlarRZz587VCIJQOzY21oxGPhLVihEjKLZsefRnL77I/t1RPTa/SqUSO3fuFFJSUrjJkyeTgIAAk87Py8vDypUr0aNHD9q+fftHrYJt25jRUEVPKkEQkJqaiqSkJOHKlSu8TqejNWvWFFu3bs0/99xzMCtH8vhxptWlL4emAvPnzxe6d+/OR0REPPxhWhrzujRuzCqujJWiyM1lFWnmdIW/eZOJJ1JaaYHK5cuXkZSUhBs3bmDo0KE4c+aMkJaWxhNC4OXlJY4ePZrztUEBh6ls2bJFdfny5ZmxsbHPtKpyZeh/ujRu/FCtUhDYjbNwIZMQt0eysJGEhIRg2rRpZMGCBTJRFE3aoaSkpODChQvw9vamv/32Gx07diz3yC7jwAG2E6wCd3d3hIeHIzw8nNdqtUhJSeEuX74sbN68mec4Tpw2bRrn5eVlydvTy/Xr18HzPBQKhXUMnv/8h/UNM8fgGTKE7cLefptJEzyWB3ThwgUaHBxMhw0bxtWsWRMzZ86s+LJjknUeIzk5GXK5/OLHH38sGTtPA7Vrs7w0jmNJqt7ewJYtDvNKV4a7uztGjx7N/+9//xMuXbrE9zDSUABYOCguLk6sXbs29u/fzzVr1uzRJP+hQ1mvqyrgeR6hoaEIDQ3lBwwYgJycHHL16lUuMTFRPHDgANelSxd0MXVjy3Gs5NuI96HT6aBSqXi1Wk0BkLI3xXTcunUDpk0zTfQxPh44eNA8g6dRI+YVWrCArV+vvfbIy4mJiTQ/P58MHDiQNm/enDRv3rzimlUtkjpFUcT169c5ANXDmq9m6P8jTZ7MPBxpaczguXGDuYOrkbFTjpubGwghuH//vtHnlJaWYtOmTbRHjx508uTJJCMjgyQlJT160Lhx7L0bgVwuR5MmTTB06FB+1qxZaNq0KV29erVNe+Z069YNYWFhdNWqVUhOTrZ8wIAAy1z8gwcDX34JzJjBXNFllJaWQhAEMmjQIM4uQmtmkpSUVKpWq01IYJKo1vTpwxR4ARa2SE1l61c1KzoAgICAANy9a5rc04YNG0Q3NzeMGzeOCwoKErdv3y484rWfO5cZAEZACEGtWrXQrVs38uabb3Ljxo3DsWPHkJGRYdKcEBXFvCRKpcFDZTIZxowZg0OHDmH37t2C+NNPbMO1dy+rAjVV4drTkz2zzCUkhKk0KxTM8Knwu1QqlWJERITYunVr67YOsCJpaWngeT4jNjbWxD/as4H+u/6zz5il3bAhWyiOHWPfV0MIIfD09BRNuTlXr14t1K1bl0ZFRREXFxf06dOH7Ny5EyqVih2gVrOHthlxdeNkm5YAACAASURBVEIIBgwYwMtkMrJlyxabtsB48cUXSffu3fHLL7/g/Pnz5l+ruJi9Z0vFuCIjgVmzgJdeetBQ8NChQwgMDBQdnZ+jD0oprl27BkqptDt6WvjlF5ZXNnUq01z5oPqq6/v5+fGFhYVG99dKSEhAeno69/LLL3M8z2PMmDFcVlYWd+lShSrk27fNbupZv359dOrUSVy/fr1oUu8njmNl3gsWGHV4aGgoXp82jaSeOEFSly+n4tSpLKxuDleusPXHEoKCgLFjmdr8mjUApVCpVMjPz+cjIiKqn6VcgatXr2q0Wu0GR8+juqL/j+fpycJaguCQ/kmm4uvrS8+ePQtjPCq7d+9GSUkJN2zYsAchrMjISPj6+go7d+5ki46zM9PnMHM3WLZ7Ibdu3SIrVqwQjh8/bpOmcWlpaTh58qQAACqVynx3UlISE+SyBlFRzL3cpw/AYt9iVFRUtV4syozlu7GxsbccPRcJK+Hvz8LyWVmOnolBfHx8UFBQwOVXkW9TkZycHBw4cAAjRoxAecjcyckJ/fv3J7///jsUCgU78IcfWCqCmXTu3JkLCAjAwoULxR07dhjvgerQgd3/+oVtAQCiVov8AQNoj61buT9ff53qLMkN3bePhcQsJSCAyW1cuQIsWIAD+/YhKChIcIQmmLFQSnH58mWNIAi/Onou1RX+s88+q/rV55//DG3aMJn1Xr1YuV9IiMEqHEcRHBzMnT17lh4/fpy6ubkRPz+/SvN5srKysHfvXkyYMOGJiqDmzZtze/fuJQEBAcTvs8+YO9wCRVRXV1eEhYVBFEXy999/0wMHDpBz584JISEh3BMJ0mby66+/wt/fn5s4cSLq169vvlEhl7MWGoY0eIzF0xPo0QPimDE45etL+o0aVXnvrmrCqVOndBkZGSu7dOliedtniepBv36fwdkZ+O9/2df//sfyWqohPj4+KCoqEnft2sWpVCr4+/tXKXmxcuVKISIiAu3atXskvFKrVi1cu3ZNSElJoa1q1+bg58c8XGZu2gghCAsLIx4eHiQjI0M4ePAgl5CQIJaUlNDQ0NCqQzsBASzR2M0N0CcZkp6OnK1bcT4vjzSLi0PHrl2J2dpbosi8M23bWkdPycWFPftWrMCt5GSxQd++fLC11kYbkJubi1OnThWLovhet27dHD2daonhp4+3NzNwCAF69mRiTTdvMr2Vavbw8vX1xRtvvEH+/PNPeujQIbpz507i7u4u+Pv7c82bNyetWrWCk5MTCgoK4OrqSmvVqvXEDevm5oZu3bqRHTt2YOY777CcJSvMq2vXrqRr166kqKgICxcu5EtLSx855s6dO9i7dy+GDx8OmUyGS5cuoW3btkYlYCuVSqFVq1a8xarT8+axxSkqyrJxKnDH0xOev/2GsVFRyG3fHsGjR1ttbGtz+fLlUp1Ot83R85CwMq1aMWO+Sxf2QNRqWV5iNQvP8zyPAQMG8M2aNcPBgwfF06dPc05OTqK3tzdCQ0O5iIgIlFcBabVa0rJly0oXhzFjxvDff/89vZ2bi/rr1ln88HdyckJERAQiIiJ4nU6HzZs3k8dTB3Q6HbZt20ZDQ0NJZGQkLl68iIaXL8MjO5vl81WGRgP06wdNdDSyuncXfUNCLPMAX7zICiYuX7ZomIooOA7Fc+ei2cSJXOHSpSKee44zqdzdjly9elUkhPwaGxtr0xSKfzKGLZYdO4ClS4Fz54AvvmA/69KFVeR8/bWNp2c6PM+je/fuXPfu3aFUKpGWlsYnJycLx48fJ3/88Qfp378/CQkJgUqlqnJ30qZNG+zfv5/11DGhYsIYioqKwHEcQkJCoNPpoFAocOrUKZw5cwZ+fn50yZIlEEWRyOVynDp1Spg6dapBQ0atVlun/UJxscUNBkVRxMWLF3Hu3Dl69+5d6HQ6IooivEaNwrDFiynq1yc2bWJoJvn5+VAqlSKA046ei4SVeeUVJpfQpQv7WrSIJfOamoxrJxo2bIiGDRtygiAgMzOTS01NpTdu3BASEhL40NBQcdSoUZxcLqdFRUWViga6ubnBzc1NEP7+W2btjuAymQxZWVmIjo4moihCpVIhMzMTO3bsED08PMjNmzexf/9+yvM88SkooMPy80mlJQo//vigDPzvxER4FReLsLTS6d49q2gq5eTk4Pjx40hNTRWUSiXPcRxI164Y8OuvnPjrr+AGD2bpDtWMS5cuKTQazWZHz6M6Y9jgGTuWNZKsyNmzrNJh3jy2c/q//7PR9CzD3d0dzZs3R3n5YFJSEn777TfUq1dPFEWR02g0lepMaLVa5ll54w3g11+t2lyuVq1a8Pf3F//9739zlFLwPI8aNWrQ8ePHk+DgYJKUlIS6devC1dUVGzZsIIsWLRKnTZvGeejRodBqtdYpfY+OBl54wezTb926hS1btogymYy0aNGCdu/enatbty40Gg1u3boF9ciRBF9+ySrfRo2yfL5WJDU1FTKZ7MhHH30k9c162jhz5tFE/LfeYuJyGRnsHt++3W5tcUyB53nUq1cP9erVI507d+YLCgrwyy+/YMGCBSIhhBQVFVV5LqWU+G3YwLzwm637DIyOjsYff/yB+Ph4cBwHuVyO6OhoREdHk6ysLOh0OhISEoKzZ88ie+JE5K5diybjxrGTRZEZJqtXsxBj7dooKChAQECA5fl9Li6s3Y2ZlJaW4ueffxbu3bvHh4aGCr169eIbNWoEZ2dnZGRkIKt3b5Aff2TVfu+/D1hDBsRKaLVa5ObmugI46ui5VGcMGzyJieyGWbjw4c/K/9DlJYNaLfsgV0OrtyJhYWEICgrCxo0bIYoiioqKKlVG1mg0zODJybH6HGQyGSZPnszpdDrI5fJyafQH3qaWLVs+OHbcuHHcb7/9JixZskScNGkS5+/vX+mYWq2WWOzhKSlhnjwzQ067d+/G+fPn0bNnT9K+fXtSUTLVxcXlofro998DP/3EqmeqUXgrNTW1pLS09A9Hz0PCBrz9NlMCryjo5+4OZGayNYvjWAm1u7vj5mgE3t7emDp1Knfw4EHx+PHjXGFh4UPtmscQRRH3vvkGXnpazZhLhw4dSGRkJGQyWcWQOwcAdSrkuDz33HMke8gQ3Fq1Ctn16qFL27bM0PT3Z8rJZSgUCqFx48aWW5zr17PNqRlKxykpKdi0aZMYHBxMykRcH5lPmeHJcno+/hj4+WfmDDBWENHGZGVlwcnJKXnWrFkljp5LdcawwVNczBaGynjvPfbvO+8wgb6//7bi1GyDt7c3XnvtNe78+fOoyiui1WrR+OpVYNgwplRqZTiOM0rBlOd5DB06lD9w4IC4atUq+vLLL5P6j8nkl+/ynC01NjUa1jTUhPi0SqXCmTNncPHiRUGtVnMTJ04ktWvX1q9RERrKBL2++ooZlNXEO3j79m0dgL8cPQ8JG5CeXrl4aJMmTIQwO5vlJKans4dxNYbjOPTq1Ytr3LgxZDJZlfcapRR1Roxg3isj+tuZirH5grXefBPyGjWwcedO4PRp2qVtW4LXX3/kmNLSUmqVkHy7diZ740+fPo2MjAwkJSWhZ8+eJCoq6sn2FhXhOKYz9q9/MYmDuXNZcYaDSU9Pp4IgGCe49Axj2I3Yrx+wcaP+Y775hoV+srKYdk81h+M4REZGQl7Fw12j0UDt7k7RqpWdZ/YkhBD06tWL69mzJ9avX4+LFy8+eE2n0+HOnTtwc3Ojem9SYzh6lPWhMZL8/HwsWbJETExMpLVr18b06dOJ0U0I69ZluyQXFxYWdTClpaVQKBROAKq/xS5hOidP6m8kWasW26z5+7MHmRFl4Y4mJCQEwcHBVb5ORZHo6tdn782RBATA99gxTN20Cepjx8jaWrVEsUzXTBRF6HQ6aDQazmKDRxBYXpCRCvGiKGLt2rXiwYMHkZOTI06aNAkdOnTQb+yUQwgzep57Dvj8c6CgwLK5W4GUlBSFVqs97Oh5VHcMe3ji4phB87gC8SOjyFjFw549rBfJZ58x/QVLH8IOQqvV4n5QkEXxYGvTvn174unpiV9//RW7d++mOp2O6HQ6ODs7Uz8/PxGWtmZISDC6dDUzMxNr1qyhrVu3pv379+cIIaZfOziYJb4vWMDaWcyebfIQ1iIjIwNOTk5XPvjgg6p7iEj8c/H1ZUnLlfR3e0CTJswL9PvvLMfMx+cfu34BAK/VouCbb+BRRVm73VixAvDygmzWLHQYPx6rV68m33zzDRVFkWi1WshkMlBKLc9BvHWLbdiMqBwWRRErV64U1Go1FxMTA09PT9PzhziOheeWLmWhrV9/dVgHAkopMjIy5JA81IahlOr7ojQ1ldJt26hJnD9Pac2alJaWmnZeNeHy5cu00MeH0v/+19FTeYL8/HyanZ1NFQoFVavV9KuvvhIXLlxItVqtZQOfP09pZqbBw65fv06//PJLevToUcGyC5aRm0vpihWUfvwxpYJ1hjSVgwcPCl988cV/qf57Qfr6Z35RunIlpQoFNYnQUEpXrzbtnGrEyS5dRE3jxo6bgE7Hnh3h4ZQeO0Zp+/aU6nRUrVbT9PR0ev/+farVaunmzZvp559/TjONWHv0cucOpefOGTxMrVbT7777Trd8+XKhpKTEsmtSSqkoUnrgAKUDBlBaWGj5eGaQl5dHv/zyyzzq+Hut2n8ZtmzlctOT+Vq1Yu5FZ2fWRM6GvaRsgSAI2PjeeyJiYhw9lSfw8fFBYGAgXF1d8dNPPwk1atSAIAji1q1bH+2hYypvv81yGQxw9OhR6unpScPDw7nr16+bf71yfH1ZC4qSEibyaGTfMmuSnJysEARBqm54WvHyMt1bs2wZMGIEU+61lvq4HTnauzcKbJB/aBRFRawgYdMmVvQSHc0U+48cgZOTE+rUqQNvb2+cPXsW169fR5MmTbB27VoUFhaaf821a4F1hlvg3bp1C4WFhXy/fv24rKysh22EzIUQJl0yeTLQv7/eRtO2Ij09HTzPJ9j9wv9ADPv/9u1jiVmmiDlxHGsiWVjIEpo7d2YKzTYiPz8fmzZtEgoLCzmZTEZlMpno5OQEFxcXmYeHBzw9PeHt7Q1vb2/4+vrCx8cHMpkMoijijz/+gFKphEwmg1wuh1wuR3FyMtoePEjw0Uc2m7MliKKIH3/8UdBoNNzkyZMJpZQsXrxY3L9/v9i7d2/T3bOUssq75s0NHjphwgSybNkyYf78+TzHcRgzZgxCLVXe9vJiYdDly4HYWPa9nUQtRVFEdna2CyR38NPLG2+wHDUjPt8P6NOH/fvf/zL18W+/tc3cwD6Du3btwpUrV0RCCGQyGXVycqLOzs68m5sb8fT0hJeXF7y8vODr6ws/P78HCsyXWdsWyOVyyGQyODk5QSaToe3hw0TWs6fN5lwlKSms4q1hQ+DNNx+Gyd9555GeXomJidi/fz9Gjx6N0NBQbNiwQYyLiyNTp04lValL68XZmfXvMkDz5s0RHR2NuLi48v8LI0aMsLxCbOhQoEEDNoddu4DAQIuHNJbU1NRSqcLUOAw/VUaPBl580bzRvbzYh5xS5vVZsoQJf1kJnU6H7du302vXrpFWrVphyJAhpLS0lCiVSk6pVEKpVKKoqEjIzc3F7du3iUqlIqWlpQ9ixxzHUU9PT9SpU0fUaDRUpVJBq9XCKzOTNExPr7Z9n+Li4sSSkhJu8uTJxLVMImDixIncqlWraM2aNelzzz1n2nY2I4Mt8EboSshkMkyfPp0vKChAQkIC9uzZI8bExHAWJ017eAAxMcDMmaxsfcIEu8TE7927B57n8z766KNcm19MwjFcvaq/vYE+9u1j/777LktO/fFH680LwLlz57Bv3z7Rw8MDI0aM4JycnFC+dimVSigUCrGoqIjevXsXSqWSlJSUcGq1GhzHQSaTUVEUSVhYmCAIAkpLS6HT6ahWqyUDbt/m3BUK+yYh3bjBnhcffMDy8iri58eEa3v2xLlLlxAfH4+XXnrpwWZp1KhR3IoVK4T169eTV155hTO5vURJCfMkGUHPnj0REREBjuOwZMkSPicnB4HWMFDCw1n16aRJLPe1EskTW3D79m0NgBN2udg/HMMGz8mTwKpVTHfAXAgBXn4ZiIhg8t/Nm1u8gz958iSOHDlCa9asSadMmUICAwOrukOe+LkgCCgpKYFKpSK+vr7gK7u7Pv/covnZiq1bt9KioiIyZcoU4lahi7u/vz9GjhxJNm3aBC8vLzQyRX8jMdEkSQGO41CzZk307dsX8+bNw+XLlx/RDzIbV1eWxPzZZ2zB/OADVsllQ9LT00EIOWbTi0g4ljFjWA+tiuKDpjJsGAv5qlSsLUWzZhZNKScnB5s3bxaUSiXft29frk2bNqhi0/DExotSCrVaDaVSSZydneHh4fHk+jVxokXzM5mlS9m6vnw562X1OCEhQGAgshITsfvAAYwcOfKRNYrjOEyZMoVfuHChsH37dmHYsGG80ZsoSlnS8OTJRk+3Zk2m/9yoUSO6Z88e+uqrr1png9unD9N7Gj6chUXDwqwybFWo1WoUFha6ADhv0ws9JRj+I4uidfIq/vUvplcwdChzE5tJWloavvvuO+Ho0aMYNGgQmTJlCmeqdc7zPDw8PBAQEIBKdxKDBwMffmj2HG1FcnIyrl27RsaNG0fcK8mratSoEXr37o1NmzYh24h8nAe0amXSYlEOx3EIDQ3lLly4YL3EG7kcmDOHeQa3bGHucRty+/ZtVWlpqdQs9GlGp7M8j7BTJ5bTs3AhMGCA2cNoNBqsX79eXLVqFRo1akTefvtthIeHV2XsVAohBC4uLvD19UWlCuw7d9r8QfsAQQDOn2fKycHBlRs7ZYhDhyJ75kzauXNnsUmTJk+8LpPJMHXqVP7mzZvc4cOHjf+D6XRMz6usz5gp9O/fn6SlpXFaa+behIcDn3zCOtVbsa9XZWRmZsLZ2flabGysxqYXekowbPBERzNL1VokJTFj4l//elS92QAqlQqrV68W165di/DwcPL2228jLCzMpIXCaGJiql1HZVEUsXXrVrFbt27UV8+N3a5dO0RERODnn3+GPun5R/j8c6MSlisjPT1daNGihXV1+WUy1u/o77+ZJIKx78MMMjIydABO2ewCEo5n2zbAWl2uZ89ma9jVq0yjzIRCgUOHDuHbb7+lOp2Ovv766+jXrx9nsWBoZYSH22fDVlDAPBm3bgHHj7PSfj3szsigQRkZpFO7dlU+dzw8PPDqq6+Sv/76i5w/f964X258vNntM/766y8EBQWJVWmymU2vXiyq8dVX7HdjI7KysiDp7xiPYYNnyxYmp20tnJxYiMvXl8XVS0oMCt6Vlpbiu+++o66urnT69Ono1q0bZ/UPaEVcXJioVDVi06ZNtEaNGujQoYNBC69///7w9fWlBw8eNM7zcuuWWUl29+7dg0ql4pubkgxqCnPmsMqLd9+1zfgASkpKnABk2ewCEo6ncWPr7rSdnZmhU7s2W8vu3zd4yqpVq3Tnz5+nI0eOJBMmTOD1bVosJj+fGWO25NYtlqAcGso84gby7dLS0vD33bvE5fPPwSUn6z02MDAQw4YNI7t27SJGVZ5mZDzaNsQEkpKShLZt29omX7NjR/aMmzzZZuKExcXFWp1Ol2qTwZ9CDP+hu3e3TYXCe+8B48cDM2YYlcis0+nISy+9xFulSaY+ioqYdV6NSulv3ryJ5ORkMnLkSI4zUhywVatWJCsry7D7SxTZ38KICofHOXz4MBo1aiTYZJeq1QJHjrBwW/36TMXbylBKodVqnQFUf2ldCfNZvJg9mK1J8+YsjJOSwgwfA15IlUqFnj17EpNy68xlwgQm8WArTp9mycn5+cD8+QbzMUVRxKZNm8TOnTuL3jqdYeV+AM2aNQPHcbhvhDGJNm1YCMlEbL5hKyxkjbZHjWK5sDZAqVRqAOTZZPCnEONyeNRq281g2TLgt9+A69f1KhvzPG98iMYSatRg79eWHiQT0Ol02LZtm9i9e3danmhnDE2aNEF+fj5ncIeUksIWRzO6Refk5Ois0vTvyYGZlIG3N7B1K9s97rd+mk1ZtYs6NjZWZ/XBJaoParXtNjANGrC1y9OTlWFX0pZCFEXIZDL+/v37FghlmcCFC6wiytpQyiptXVxYcYGRZe/btm2j7u7uiI6O5jBgAHDzplHnubi4CDnGNHBesAC4csWoMSty/fp1+Pn5iVbfsFEKvPUWe54dP84S3nfsYP0KrYxSqdRB2rAZjWGD5+hRYNYs281AJmPJblevsuQ34EGS9J07d7B69Wrx22+/RXBwsFBZoq7VWbq0WlVobd68mfr4+BgVyqqIt7c3eJ43vEMixOwkTI7jIFhbKDAzk+UT9esHREWx+Y0dy0rVrXwtlUoFnueLrTqoRPXjnXcAA2EUi6hXjxlVf/3FkuzLPqcajQa7d+/GvHnzRJVKhfr169u+TLywEOjWzfptMShlbTc2bmRCtJ06GXXa7du3ce3atYfe6YAAoFEj1tTUAF5eXpxRxRdNmjCtJBNxdna2/vql07EqPpmMVZkSwjxQ+fmsQtnKlAknSh4eIzFcGz5iBOt5ZGtefJF9nTgBXf/++GHOHKFQqeRbtWpFBw0aBD8/P+t7EipDLrd5KbSxXLhwASkpKeSNN94wrqndY3AcR9Vqtf4TDTVW1INMJrNudcOVK8xV/t13jzahDQ5mu8mlS9nOyUqUlJSA53nHd/6TsC2ZmbbXdHJxYfIOlEKoXRvHhg8XjwcHc/7+/uLgwYO5xo0bw9hwtEUUF1u/D1heHusvNmcOsHs308wyApVKhU2bNoldu3aFn5/fwzffpg3ru2jgueLs7ExKSkpE6NuY37tndpd7FxcXWHX9EgSWxF2jBrBmzaOvffYZ8PrrwOHD1rsegJKSEg6SwWM0hu/A48eBkSPtMBWgqKgI844dE7e89BIi2rblZrq748WBA3k/Owk4AQBefdWhjSzLSUhIwK5duzBs2DD4mCGaVlRUBK1WSwIMJfMdO2ZSl/SKODk5cZmZmVS0RrjgxAn2UPryS5Y39jhjx1rdLVxSUgJI7uCnn969WZKtHVi7bp1u3YAByOvcmb4RGIjX+vblmjZtah9jB2DFB9ZsKZGRwXJ2wsJYIYeRxk5hYSGWLFkiNmjQAM8///yjb75XLxayVij0jpGbmyvUrVtX/y/u0iXm1TIDd3d3qFQqzqKWFuUUFLC1dPx4YOXKJ19v2pR5ocqFLK2EWq2WQ1rDjMbwXejmZpb1bA5Xr16Fs6sreWn5ckQ3aUKc5s4F7t61y7Uf4O0NnHJslfKRI0dw4MABjB49Gs3MFDi7dOkSfH19RYOKpZMmmV2C379/f6SlpdGVK1eKBZZUIVy6BLz2GgtlDRxY+TH16zPvz5Ej5l/nMcrcwfcMHSfxDycgwG45eenp6bK+X32FYaNH8z7r1jHFXXvy4ovMK28NDh4EXniBeVi//dboPL979+7hhx9+oM2aNcOIESOeLLTw8gL+/FOvt0MURSiVSr6OITmBRo3MTkEICQlB3bp1xaVLl+KKGTlAD6CU9dHavJn97iuLEBDCBDAvXDD/Ok9clkKr1TpBMniMxrDB06oV23XbgdzcXPj6+goPYr137jB57oYNgQQ79UY7epQpQjuI+Ph4nDhxAuPHj7eoR9XNmzdpgwYN9B9EKWvcaWbT0cDAQLzzzjucp6cnli1bhnRzmiwuX852er/9ZlgavlEjtkha0iS1AiUlJRAEwTwBIol/DosWsYe2jdHpdNBqtXhQcn7wIKsemjgRmDbN5tcHwFpfLFhg2RiiyMbx9WVJyq1aGX1qVlYWVq1aRSMjI+nAgQOrbjnTpw8rmKiCzMxMcBwHb29v/RecP5/p8JgBx3EYN24c17dvX2zfvp3Gx8ebntDz998sBP/zz+xzpo+ICOZptEbTZTC5Fo7jNFLRhfEYNnh27QK6drXDVFgTUF9f30fnJJOxvI2ICJZvorPh3/biReZtcFCF1vbt2+n58+cxceJE1LVEBh/A/fv3hZo1a+r/+xYVAbVqma1hAbBFY8yYMVxYWBj27dtn2oJx8yZbLLy8mDFjiM6dmcfv9m3zJvsYZb3TJA2ep522bYFz52x+mbt37z5o3vkIU6awnX9+PltfbMnPP5vfN6ycH35g+lf+/uyeM5KUlBTExcXRTp060d69e+vvrzdsGNvkVOEZvnHjBmrUqGE4Vi4IFmumRUZGYtq0aeT06dN8cbEJNQyUssa0ajXTejKUN+XsDERGmtTGRx9lOYh2KF1+ejBs8HTtypJF7UBxcbHg4+Pz5Jzefpvld4wezTwCtuL4cas3BzSWX375Rbxx4waZMmUKatWqZfF4UVFRsgMHDuDePT0Rmzt3WLNOKxAdHY2srCzeKN0MSpkU/KlT7EFkrA4Gx7H4/zffWDbZMpRKpZpSKiX8Pe2sWmVap3QzuXPnDjw9PZ98SEdHszyi775ja5itEAQWejLXA5qTw0JYPXsyY8SEYoYrV65gw4YN6NWrFzp37mz4ucJx7OG/a1elL7dt2xbFxcXk2LFj+o2e+vWB5583ep5V4e3tDQ8PDzExMdG4hMTt25mS8v79puV8jh0LfPSRwfwlYygzeKyQgPTsYPiDWVLCyuzsQGlpKfQKC968CUyfzlo/LF5s/QnYIIveEEVFRVi0aJGQk5NDpk6dCmspsHbo0AEtW7akcXFxVFHVzXXokFVkz3NycrBixQrI5XLcNZRzpdMBqalMnr9XL9Mr4oYPZ4aSHne4sUiiXc8Iqak20UB5nLt378Lb27tqa+Pzz5n0RkIC++xbKTT7AJ5nHtAaNUw/Nz+fqeq3acO8FUYmJ4uiiB07dtBff/0VgwYNQrt27YwvD3vllSqvU6NGDYwbN44cPXqUu1yVSnZBwUOdLgsQRRFLly4VVCoVl5uba/iE0lJmEA4Zwhoem4KbGwtv8lUQOgAAIABJREFUWkEYUqVSgRAirV8mYNjgSUy0jYhVJZQJdFV9QPlrjRoxy76oyOwKo0rp3JndQHbi7NmzWLJkCa1bty5iYmKItVWkBw0aRPz8/MS4uDiqqWzBj4xkrmUL0Ol0WLNmjdi+fXtx1qxZaNq0adUHl5Swir+tW1m1gjmhNJmMxe3XrjV/0mUolUoRksHz9PPZZ8CNGza/jEajgVwu12/FyGQshNuyJQuBmNnDrlL++1/zKmq3b2eaVyNGAF9/zbwvRlBQUIBFixYJKSkpmDJlClqZkOsDgK3h33/Pyt4rITg4GIMHD8b27duRVtWm+7XXjJ5vVezYsYMKgsC9//77GDFihP7BFi1iHqUffzTfWzdpEtu8W+jlKSkpAaVUKrowAcOflMGDbSvaVQEXFxcYVe3z7rusmmfaNKBvX+tNYOhQthDZmHIjIT4+HkOGDCFDhgzhbdUbbMKECbwoiuLGjRvFJ8rH58+3eHe0du1awdvbGz169ND/WSosZO0hwsKYJ80SWrRg7vc7d0w+lVKK06dPIyEhAfn5+VKFw7NATg7QoYPNLxMYGIj8/HzDa2qzZmyHf+kSU2pm8giW07Fj1VWOlSGKzKvj7s6EPU3op3fmzBksW7aMhoaGIiYmxrD8RWW4u7NGp3qUl1u0aIFOnTph/fr1yHvcMNq1y2K5gaSkJCQlJZExY8YQJ0Nr4fXrzIu2YoVlOkfe3qxE/fffzTo9IyMDR48exfXr1yEIghFS1BLlGBYe/PNP4NNP7RLqqVGjBp+Xl0cBGPdpWrOGuTXPnGEKoJbkdqjVzLhr2ND8MYwgMzMTGzZsEL28vBATE6M/hGcFOI7DtGnT+IULF4p79+4VXnjhhYe1pSdPsio4A+Tl5eHIkSNQqVQoLS2lGo1G1Gq1VKfTEZ1Ox8fExOjXGSkqYmWbAwdap+LP15d5p86dY32MTECn02H37t3l/3UFkGH5hCSqNe3bszweM9R4TaFOnTo4cuSI8e6Gli1ZuM3ZmfW/WrCAfbbNJTjYtEbPn3zC1KG3bWMPYSPQ6XRYt26deOfOHW7YsGGkadOmlgnCTpnC1u2oqCoP6dq1K/Ly8mhcXBxiYmKIS3kYPDHRqN+XKIo4ePAgcnNzUVJSArVaLZSvXyUlJfwLL7xA/f399T9zZs8G/viDaRKZ0YbnCXr0YCkFOp3BXmSPc/bsWXru3Lny+Vqvzv0ZwPDN6etrF68HwHQRLl26BLWxvbtkMvbAzshgapuA+VVce/eyag4bsn//fsTFxaFdu3aYPHkyZ/NGqGU4OTkhKiqKu3Xr1sObWqViBmNIiMHz165dKxQVFYn+/v5is2bNSFRUFN+rVy/Z0KFD+ZiYGHh6elZ98s2brPrtww+tK+jYtStzK5u4O5bL5YiNjUVUVFS5xKrlGY8S1ZvWrY3OSbGEoKAg6HQ63DLF6xAQwO7Fci+HJVWokZHsoWyI5GSmCfPqq8zLYKSxk56ejgULFog6nQ4xMTH6w9fGEhr6sJ2MHoYMGUJKSkrII/0UR48Gpk41eIkjR47g3Llz8PT0FEJDQxEREcF37dpVNnDgQH7SpEkIDw+v2tjRaIBffmH6Rn/8YR1jB2CerdOn2ZeJvPjii2TaQ5kDA7XwEhUxbFqGhrJeNHagS5cuSExMpFu3bhXHjBlj/CdryBD2FR/PMudzc02P6w4ezAwnG7Fv3z6cP38eEyZMQJ06dewku/qQCxcuCJGRkQ+vm5gIzJ1rsFP92bNnoVar+bFjx8LksFtJCfPszJhh1ZYQAJgnzt2deSDNCGvm5eWVf/bPWHdiEtWOWbPsosPDcRw6duyIjRs34q233tK/EaiIhwdTGqeUbeDi4kwLTZWTnW1YUqOkBFi4kN0/jRoZHZopLCzEmjVr0LlzZ3Tq1El/ybkpuLgA773H1l491akJCQnw9PR81BMzaxarINYjjKvT6ZCQkECHDRtGmjRpYrq1Ut6H7bffrN+eZMIEVnXcsaPJp1YQlK28zE2iUgw/eA8etG6eTBUkJiZi/vz5glqt5kJDQ827m3r3Zr1eCAH+8x/TOiR/+aXZAlaGUKlUOH36NEaOHAmDyqE2QKFQoKCggG/duvXD36tMxtyqehBFEQcOHBB79+5turGzaxfLCzh40PrGTjn/+Q/7u5nR2oLjuPLfhSQ8+LTTq5fNhUtzc3OxcuVK8a+//kLDhg0FvcUXVUEIW79eeIHdO6mpxp975gzw/vv6PRBr1jBv19dfs0IUE4yWjRs3Cs2aNRM6d+5sPWOnHFFkIUc9JCYmCm3btsWDa4siM5AaN9Z73o4dO6ivry9tbOC4J8jKYuvWzJm2MXYA9lwlhLWkMJEKBs9Oq87pKcewwdOpExOzsiFFRUXYs2cPunbtyr///vvo0KGDeR4QjmPJienpwLJlLHfEWO7cqVIEy1xEUcSJEyewdOlSMTQ0VKhfv75VxzeW06dPIyAgQHxkx3nlisEQ3uHDh+Hk5ETatGlj2gU1GtYpuFYtwEIBRb3Urs36blWh5aGPgQMHlquUltpgZhLVibVrWbjHhmzYsEHw8vLC9OnTMWrUKN7V1HLlcqKi2Dq2erVpSa35+VW34RFF1pJFq2XeIxPmlpGRgZUrVwp5eXl83759bdPAuWdPFtIrrfpWLCgo4Fu0aPHQ0rp5kylA6zEsFQoFrl69Sl544QXTjDRKWTVWaSlQr57tGs8SwsJyW7aYfGrNmjVRu3ZtBYBM60/s6cXwNqSwkGm1WEHcqTJEUcSGDRuEevXq4bnnnrPODVWv3kPtjeBg1pnXUMKilXV99u3bh3PnzlG5XI4ePXpwrW2cMKmPwMBAVEhyY+zbZ9AVfOrUKTp8+HBiUuPDBQtYNcOFCyYn45nFwIGsHQilJu1YS0pKIJfLpZLOZ4Hjx1lo3kZ5PCdPnkRRURE/ceJEeFjrGuU944YNY4b9kiX6j+/Th31VxtSpLCwTH2+0ivytW7ewe/duoaioiI+MjCQvvfQS3N3dTXgDJuDkxLqxr1nDyswrPcRJVCqVD/MeT5826AHbunWrGBoaSoODg41/riQlsVYgu3ax/FVre7Mep3NntlbeumVywYxarRYBmF6q+gxj+El244ZBd6O5lJaWYvHixQIAMnLkSOvvHpycmAZHy5bAgQNVJwTm5bFdj2B6K5XHEUURq1evFi9evEgHDx5M3n77bRIZGalfX8jGNGnSBEqlkjySDD59OvPeVcGJEyfg5uaGRsa0fCinoICFDiZMsI+xA7Cde1ISUJU4WRUoFApwHCeVdD4LrFxptXYkj7Nnzx4cOnQIY8aMsZ6xU5FZs5hAX0aG/pYErVoB8+Y9+rOrV1lIZsYMFioz0tg5e/YsNm7ciNatW3Pvvfce+vfvb/sCi0aN9CZcu7m50aysCl1gnn8eePNNvUNmZGRw3bt3N/65UlzMwkvDh7NcKlsbOwATIqxRg4X+TUSpVMohheRNwrDB07MncO2a1S9cWFiIxYsXi76+vmTSpEmc2S5gQ5QLU02cyLLtK8PDg3W6tTADXxRFrFixQlAqlWTatGmkadOmsHq82wxkMhlcXV3FO+W6NYWFrOpAzyLWsmVLFBcXE5Uxwo6Usnj3+PHsd2xGEp7Z8DyT7j9xwqTTynrmSO7gZ4Fbtww3pjWDDRs2iBcvXsSkSZNgsFGvuURFsVLzhQtZCXdVrFzJGgGXo9MxY8fDgxlDRiqaHzt2DHv37sWIESPQtWvXhyXgtqZDB7YmVRGWq1WrFp+WlvZwR/ruuwafSx4eHsLt27eNk7M+dIhVTg0ZwsLx9mTYMGbsmVBxKooiNBqNMwAD0vYSFTFs8Bw/bpPu4StWrBCbNWuGl19+mbOV6N4jpKYyt+mYMSy/pyIXLuitEDAGjUaDxYsXCxzHkSlTphCbuX/NxNPT8+EOKSuLLYJ6QlU1a9bE/7N33mFRXdv7f/c5w9B7VZCmqCA2VECxVzT2HlssuRpbEk1yb4q5au5Nbpop35+xJ7EksUSxxd4VLIjGhiAWVCx0pMMw5+zfH8uRNg1BxYTP8/AoU88AZ5+113rXu+zs7KSzZ8/qVwSr1dR23rjxc5u5VonXXiMTxRTjEzZ5eXkoKSm5/ewOqo5aQ8uW5DlVg0RERPDU1FQ2bdo0uFbBsO+p+fJLyp7u31+52aCoiMrInp70/fLllL3dsgWYP9/oTMWBAwdw4sQJjB07Fo0bN67hD2AAhYKytDu1a3AbNWqEe/fulX6Q7GyDguXQ0FDx5MmT4IZGeFy4QO///ffVGqT81Li4kBnljz8a/ZSCggKIolgwf/78EsOPrkOD4bqDm5vu2vBTIssyCgsLhfDwcP2GdTWJ5qQPDqYSV1oalbGsrGiByMsj80IjkGUZ586dg7W1NerVqwcTExMsW7ZMcnR0ZGPGjHk+AVwVcXZ2Fh88eCABEKFS0QKqhatXr2L37t2yjY0N7O3txTNnzqBTp05luwLKM3IkiSK3bXt2B28IQQDmzaPPtGiRUU/Jzc0tUavVz2dIXB0vlj59quQibAwPHz6UOnXqpHheXloAaA3z8iqdYH7nDn0fF0fZnHHjqLyblERaOj1i21u3biEnJweurq5wdXXFzp07eXx8PJs4cSLqVdHMs8Z45RWdo30aN26MHTt2CJxzsJIS2uRoaaZQqVRYsWKFWpZl5u3tLRYUFLBbt26hoS59zJYtZKy7d++zbbAwxMyZZOExdapRIunc3FwoFAojBn/VURbDAU+9ejThtQZ5rJ94MbqWt9+mfwcNohTi/v1ViqwBGpa5Z88eWFlZ8YKCAibLMpo0aYLhw4cLOgODF0xWVhZv2LAhRZfbt5O7awV307S0NGzbtg1du3YVVCoVj4uLk4uKioR79+7Bq6JBYXo6iSDfffeZGzYaRf/+lKlLTzfKPTo7O7sIdfXvvwfjxtX4zr2kpATPrAyvjyZNgIULaYBut26km2vdmjx4Ro6kjZvGmkMPW7dulQEwlUrFSkpKYGFhwaZMmQInI86dZ0anTiRcvnaNPmcZkpKSYGFhwRljDAkJVDp/7bVKL7Fq1SrJxsZGCAwMFOLi4iQA4uXLl3nDhg0r/0B++oneMyLixQY7ACUW3nmHOvOMmG+Yl5cHxlidBrGKGI44Tp4kHUwNTKfW8OjRIxicW/Ks2boVyM8HDh4ks8JTp0g4ZwT37t2DpaUlnzt3LuOcQ6VSQalUirVBr6OLR48eyU+6FZo313qC5+fng3MOPz8/ODs7s65du7KSkhLtgemYMTT8b8yY5yPuM4SdHaX09+whLZEBcnJy6joc/i4MHkxZ3F69auwlJUl6drpDYwgOptI0AHh4UElk3jwS8xo4H1UqFUpKSti4ceOYh4cH1I+bOV5kY8UTGjemgK1CwHPt2jW4u7vLAERYWpKwWAuFhYVo2rQpDwoKQlBQkCjLMmRZrvwD2biRZmKFhwP16z+LT1J1mjShQG7wYIPGuXl5eeCc143FqSKG60nt21MEXINkZ2fD1NS06m5xNYkgANbWwH//S1mBTp3I4nz3bvK00EFOTg4OHjzI+/TpwwCAMQZTU9NaIU7WxeMSovgkVb12rdaT3NvbG5aWllJyGZt3ExOT8p8tJgaYMYN2YsuX145gR0PPnpTO1+PnoaGwsJChbnDo34MtW2rcVkOtVrMXGvAA1HRw4watW4cOUVfWtWv6u7kArFmzRqpfv77s/th9WqFQ1I5gB6DshhZX7AcPHkheXl60Ydu7V2cXaIsWLcTbZTryKlUS1Gpg+nTa9O3ZU3uCHYAy5aamVJY0QGFhISRJqstQVxHDf+VpadTBVIPC5ZycHJibmxunnn9W/Pvf5M3z1luUDn7vPUp7L1xIu6U33iDl/owZT2bNPG45lwICAhAYGFg7a1c6EAQBhYWFsFIooD57Fr/t2iVxpZKbmJhoFjxmYmKCwsJC0U2XgJtzmvIcEFDjmogawd+fAtiTJw26SCuVSg7g2Q9YquPFs2ULMHkyefHUEJIkvdiA5+xZ6sxasoSytcHB1GW0fz/p2K5do4zPlCnlPMgOHz6MR48eibNmzaqdmzRvbzrmli3LZXlMTExYfn6+DEBQHz+OP52c5FiAa4I1zTp269YtITAwUPtGnnPyH9Non2pZYwkYo+vsTz9RE4YelEolRFE0bghaHU8wnOFJTibNRw2SnZ397EysDKFJAxcWUknL05NMverVI5+KM2fI1l0QqLMjJ4es3r/8En/GxCA3J0cMDw9/6YIdFxcXacOGDXz/jh38l/79YeviIrq7uyvs7e0VZmZmCgBienq6yDmHo7YJxH/8QZqfn34CPvzwuX8Go3n3XaMmsj++WNUtGH8Hdu4sHS5cA6hUKqjV6hej4VGrqRvRzIw2o7du0Tw5X1/yvxo9mjI+nJOe5+xZ6kodNQrIykL0yZO8V69eL+bYjUEQSJt0+XK5m0NDQ4Xo6Ghh9+7d8kZnZ37Zxweenp6is7OzaGVlJYqiKKpUKrG4uJh5enpWjuSysykDbGdHa1ltC3Y0DB8OHD1qUEJiZmYGxtgLFFy9nBjO8ISGVtnUTR979uzBhQsX0L9//+c+QBOcUxZg3Trgq69osQgLozLWxx9TYANQpB0SAuzYQc8ZPx5QKND6+nV4//ADTlpYSF3Dw0Xm40PGUS8BEydOFCMjIyH98os8PDWVWQ0aVOkxO3fuhImJiSQIQvmALiODFqB//vPZ2azXFK1aUXlSM5NIB+bm5iLqAp6/Bxcv1thLJSYm4vfff5d9fX25iYnJ89/4jB9PwVtkJOkQQ0Jo6vmlS2TExxitWTY2pc0Yt27R6BwTE8z56it29sQJFCxdCgtT0+cyVLXKzJxJmfVhw56UzJs1awaFQoHTkZFseEQEs4yPZxWdswsKCvDNN9+g0gifzEwKIAICSkd31FYYo7EW335L3ks6eOyPZP/cjusvguHffBXEvIY4duwYrly5gokTJ6LcIMtnTXIy+VKUlNAf/sCBdLuLC5XrWrWi+neylpIoYyRqHjECwpgxMP3tN/yZlyfkjhsHdO36dHO7XgAKhQJdu3ZFj969BSsdw2Dv3Lkj5efni3v37pUkjev0smUU9M6dS7uP2g5jtEPU49oKABYWFgrUBTx/DwICSONSTVQqFX777Td07NiRjR079vkGOxMmUGluxYryrrxffkkBT9u2ZA8xdSoNBi2Lry/wySeAlRVML11CSteu8pmFCzlv145MGZcuJS+a2oKDAxmK/vlnuZubNGmC1wYPZpbBwVrHhPz5558QBAG7du2S0tIeT41JTCSxemYmBRK1OdjR0KJF6bVKB3UZ6qfD8G/f05NOqBrg/PnzUs+ePVH/eQnFZJkyE87O9DmKi+lk0jBhAnU4MEYThA3tBBmDVffuaNiqFft91iwJx48DWVm004qOpizR/PnP9jNVF01qVwuBgYGinZ0dYmNj2e+//y7Jx49TTX3LFhLTvSx06EAaIz1OrBYWFqao2yH9PZgwoUb0O0eOHIGLi4vcoUMH9tz0L5rSjo8PlWOsrUuzrIsW0UbN3Jw0Snl5tI45OlKWRxteXugze7YQ2agRy7t4kc7vQ4doWOnhw8C0aVQSe5EwRoGbNpf327d1bry8vLzg6emJnJwc8eeff0Z2VBRdA958s0Y79J451takQYyJ0fkQMzMzyLL8HE2g/hoYDnjs7AwKQI2hoKAAeXl5YrNmzar9Wkazfj2NxhBF4Lff6A+p9IBoTpjGn8PU1KhdjizLuHbtmtyhQwcRZmYUje/fT0GERmR39ixljQ4epA6wGpjRVWNs304zY7TQtWtXjBo1CjNnzhTcV68WsidPBu/Y0fDg1dqGiQn9Xg8c0PkQMzMzZmJi8gJsVet47nTrVn6j85TEx8dLbdu2fX4pAkmi4P3UKWqm6NGj/P03bpT+39mZvm/UiC7uHTrQ8GQt7Nmzh/v4+EjWzs60Nm7eTC7DmsngnANt2lBZqaBA53rxTHF0pOxVxcDt2DGdXUweHh4YN24cJk6ciFamplw1bBjyr1zR6tdT6+nQgbRG2dla7zYzM4MkSbVUiFR7MXzy/vknCeGqSWpqKszMzPhz8d/55BOanTV2rG6x4r17JErWaHBatiytf+vh3LlzUCgUrEkFnwgAZHC2cCENtPz0U1p8Zs6k3WVhIXk/GNEy/Ux57TVAX9BZVASzxYsR9OmnbN3YsfzQ6dO1KFqrAiNHAps26ZxPY2ZmBlEUdY+Lr+Ovw7hxJOytJiUlJUyroL+mSUigtmlJog2Tttl0WVk0WHTaNPre2po8bFJT6V9PTypXVUCWZdy4cQNdunSpXJJr1IjKPh4epbO5jh0jZ/pjx6hrNT6+pj+tdgIDKSN/r4LVTPfu5WeGaSMiAr07d2ZnZ8+WVyYmcpWOwK9W4+5OOqzoaK13m5qaQpIki4ULF9bCVrvai96A59y5cyhp2VLvTtlYMjIyYGFh8Wxb0c+epc6FkJDScRi6SjHDhgG7dpV+7+UFrFkD3NU/bUAURUiSxGRZj42QKJJNurc3BTmnT9NC9P335F2zcSPtXkqe8xiU2Fh6X30dGosWAYcPw7JpUzRo1YqVm1D8MmFtTQGvjo6tx10Oz+HqVceLJCsriwTsFbMjT4FarX62M/KKiqis5OtL65cg6F6/vvySMjBlkSRaWxijfzdvpiyBFkoMrT1BQaRR7NuXMt/BwVT2mjyZtCXvvkvt3c8KxiigW7++/O1vvaU/Y56YCLz/PlBSgrBZs4S8vDymMVZ86Zg3j9yXtVxrFAoFGGMygJeja6aWoDfgOX78uLTiP/9B8r/+Ve1AJSsrC1ZWVs/WbHDyZNqZ9OlDQmNdcA588UVlC+9hw2hcgh6CgoIgiqIUExNj/M+kXj0KqE6epHqyiQkNZc3NBdq1ozLT89iF3L2r22I/PZ1q42PHAtu2IUeSkJCQwENCQl6qFvxyjBhBP+dHjyrdlZycjJKSEv8XcFR1PEeWLFmC+Nmz5eIayEyUlJQwKy1i2Rpj5Ury/1IoaOOhzwxw0qTKXi39+lEWRoOzM4mcyyAIAlq3bs327NkjGxyqqcHenjZJa9fS+cQYdW4mJZGJ3/jxFITU9AbO05MyS5oLPueUYfLw0P74Tz6hTe/Zs0BwMLZu3QpfX1/J4iXppK2EqyswZEjloA/kii/LsgJ1OsQqoTfgmTNnjtgvLAw8OpoZ3BEYIDs7G9bW1jWffpMkCibOnSPRsTEeMZ99Rh48FReU4cMpGDFAeHi4ePToUVZcXFz142WMAq3t26nTYPZsWpg+/phS0Y8eUc3+WWR/6tenLFNFJImEyY6OgKcnbiYlYcmSJbxp06ayn4GJxLUaBwe6gGjJUJ44cQJqtboW2azW8Sx46623YHXtGkuq0PFTVQoeC2ifSUl+6lQqNc+ebVzJ6PJl6sRq2rT87c2bU1ZGo7l54w0KeP7f/yv3sN69eyMvL4/FxcVV/VgZo8z1zz9T5+uECWTtcfUq0LAhlZETEqgNvro0akSjazRlrZwc0mI6V6hEc05C6127gJAQqMzNsWTJEunRo0e8b9++L++GDaDy3fXrlaQWW7du1fy3lvuE1C4Manh8Bg3CL3PmyPerqdzPy8uDra1tzf3xqdXAqlVUPvrXv0iXYmzL4cWL2gMKR0fgxAmtKcSyBAQEwNLSUoqKiqpexkqppAWjQwcKwtato9smTaIhp2fOkJdGTel+Vq2iz1eW69fJ3bNPH2DZMpw6cwYbN25Ejx49MGjQIPG5TbN/Vvj7UwBZJoOWWTo6JOuFHFMdzw0rKyuc+vJLdsnevlpatNTU1JofIfP771SCHzWqtDxlzPmWkED6looolfR6ZUdLWFiQG3MZLYggCOjQoQPbu3cv11uaN4b27Smwat6cskudO1PZvmVL6hr7z38q63CqQkoK8Msv9P8zZ+i1y6JW0xq6fj1w+jSybGzw/fffy9bW1mz69OnM3v4lT4AEBJBwuYKW52apPuv28z6klxnDZ1d0NGb+979CYmJitcpaBQUFUo2mgxMTKStSWEiLBRkxGSY9nXY8Y8ZUvs/dnSLpLMPXwQEDBoinT58WMvXM3aoSokjaIwsL2uV99x1le378kf4dNowCImPT0Nrw9S0/If3BA2rznDiRdm2M4dq1a7KdnR339fX9a4jhWrSgn22Z3Wx8fDwXRfEEgMAXd2B1PC8Gvvsu2I4d1YrcMzMzYWFhUbMl+f/+l8xNe/Qof14aomlT0uhow9+fgh4NNjYk2GaMdISPCQsLgyzL/NSpUzWnq2zYkKZ+L1pEGZeCAmp62bePApU33qDbqrKGDR5MZS3NZwkPL72vsJCCKUtLGkfBGJKTk1FUVCS0a9dOMDEiW1/rYYyMVK9ceXJTXl4eBEFQAegyf/78FzuT8iXD8CLg64sL4eE8OTm5Wj9YlUrFKwY8nHOcP38et2/fhtHloeXLqf3bz4/SplW1SP/4Y9LR6MLPD/j1V4Mv4+3tDX9/f75y5UpUN/ulFRMTyrqcPEkp3LAw2v2tXEmLY0ICLSrG7tBkmbI7mhJVVBR1PDRoQKaCjxk3bpxgbm4uL1u2DCtWrJAvXrxoWOBY23nlFbq4cI7s7GycPXu2QJKkL+bPn/+SKrLrqArpkyfjtoNDtcryjzWIla7UiYmJiIuLQ3Z2NozSxBQWUqk1Pp4yzVOmVO1ALl+mtnNdwt0OHWizVBZnZ9osTZ785CZBEDBkyBDh+PHjOHr0qPF6HmPRWENERNBnbNWKbisupjVn+XLSAWnz2imLry8FhMeP05rl40O35+cD/fvThnDZMtIZAfD390eXLl2wY8cO+euvv8bhw4f5Iy0avpeKwYNJinD/PiRJQmRkpKxUKvfPnz+/+q2HfzP0jpaQZRmrf/tNsre3F/r161etclRJSYlQscOhuLgYO3fuhIXxks2PAAAgAElEQVSFhVxUVCRYWlpyd3d3uWHDhmKbNm3Kp48PHKDSzujR1TMRe+MN/XNUOnSgVLMRDBkyhB0+fBhr1qzBqFGj0LBhw6c/Ln2IYmlQolLRAla/PnllNGpEJ3xMDC0AunY1d+6Qk7SJCaWe3d0p01VBB6BQKDBp0iRRpVLh6NGjwqFDh6Rdu3aJgYGBUrt27Uonrpd76TuwsbFBrU0fd+kCfP457m7ZgnXXrhUB+A7A3hd9WHU8e6KiopCYlITwiRNRnR3/Yw1ipduPHTsmPXz4UOCcM8YY3NzcJC8vL6Ft27bMxsam9IGPHtHMvo8+oot048ZPdyAeHlTi0SVo9vOjLHFGBpXoNXz9NWUJbt+mbC6Ahg0bYuLEiWzt2rXIzc2V+/fvLzwzQ8WQkNIsVkQEHePPP9OIn0uXyLOsWzft08tdXWlC+pUrVCp7+JC+Jk7U2pzSuXNndO7cWYiNjUVkZKR86tQp0c3NTQoNDRWbNGlSaTJ8Xl4ekpOT0aiGJgrUOIIAvPcepE8/xQ9NmxYWFBScLy4unvWiD+tlRFywYIHOO7///vt/u6WmskEbNgjmH30EgIKg7OxszSwPozl+/DhCQ0NZWcW8KIqIjIzEG2+8wbp16wYHBweWnZ0tnDlzhru7u1P9VZLoF/7113TRHjyYUqdPQ1QUid60lbM02NtTN1PbtkbV0318fGBqaoqdO3fCwcEBLrq6oGoKUaS0tVJJHj/9+pHh2Lx5pXPCrl2jYKgskkQjIuLjSR80bJjeNLooimjYsCHat28v+Pj4IC4uDidOnGAXLlzgjDHm4ODw5AKyfft26ciRI4Knpyfs7OyQmZkJExMT1Br9D2NAs2ZQbNqEKCsrQZblXvPnz39Je1XrqAq//vrrgn9s2QK3Dh1IDwEKXqr69xkdHQ1nZ2fBt8Jm6+7du9za2lqYNm0aGjZsCJVKJVy+fBlpaWlyQEAAvYEk0TyrefOoqykg4MmMqCozaRJpVnTJAwSBvpRKKi9pEEXSu3TrRmvG42DI2toazZs3Z/v27eP37t2T/f39hWd+3rq7U/anQwf6PHZ2tKG7fp3a8FeupLZ4TfbezY0yOr17U8DTowfdN2uW3jXaxcUFbdu2FYKDg5Geni6cPn1aOnHihJCbmyvZ2to+2YBfv34dGzduRElJiezr68tUKhXy8vKqfI17pvj5ge3ejXgzMymjpGT+/PnzTxh+Uh0VMTQ8lA2YN48J06aBc45r165h7969PDc3l/n4+Ejh4eGik5NxA1u1eVgwxmBubs6Tk5OZv78/AgICEBAQgMLCQpw7d07y9fUV0agReS8sXfqUH7EMsbG089GHrS1w/jwFDUa6QgcHB8PCwgLbt29HQUEBb9eu3fPRvwgCpcc7dqRWTIA8f27douDuo48oUGzWjDyGtm4l4fK2bVXKknl4eGDChAmCLMs4deoUO3XqlHTgwAGxUaNGUnBwsKhUKmFlZYVff/0V7dq1w+nTp9G6dWupf//+tadDIiAA+fn5cExJETJcXb8C8PaLPqQ6nj3jxo2DcsIEwNERmZmZOHDggHT9+nXR3Nych4eHs4CAAKOEyIWFhZK1tXWlv2dbW1sxNTWVC4LA3N3d4e7ujubNm7NVq1aJKpUKyv/9D9iwgTRk1W2Nz8qitclQJrWoiIaLtmpV/vYGDWg9UCop+Hmc6bC1tcXMmTOFZcuWyevWrZPHjBkjPBeDWADQXD807fTnztH6pVKRILp/f/L8Wb2a1rjvv6dOMH/jHSXMzMzQr18/9OvXT7x9+zYOHz7MVq1aBTs7OzkkJEQwMzODmZmZfOHCBaSmpsrZ2dksPz+fvfnmmzCtLSN1LCxQEhQE5507ze4EB68D8MuLPqSXEb0Bj7m5OVekprL8mTOxrn9/OTs7m4WEhLDg4GBs27aNLV++HGPGjIGPpq6qg6KiIsiyXClifvjwIYqLi1nZUtDly5dxPzGRD96zR8DAgaXDPasL59SyOXWq4cf6+NACVYUxGIGBgbC0tMSGDRtgYmLCW7VqpXMVVavVuH79Om7fvi0nJydzd3d35u3tLXh5eVX/BPvXv+jf/Hzyx3B0pB1ddDSVs6ysdPtYGEAQBISFhSEsLEzMyMjAwYMHhU2bNvGioiIxMDAQLVq0wLZt2+T27dsL0dHRYuvWreFeW6Yxm5rigqlpoXNqqnm6i0vQiz6cOp4PNjY2KJk7F6f8/aUTxcVigwYN2Ntvv42LFy+yP/74g1+7dk0eOnSowcC8uLgYFTdsnHNcuHCBBwUFPTnXs7OzsX37dqlNdLSo2rgRyrfeMuwMbCxZWbRpM1Saa9WKhMOztFQ9hg6lNdDKqpyPj4WFBd58801h6dKl0rp16+TJkyfrLW/dv38ft27d4omJibKVlRV8fHxELy8vOFR3hEebNhQgck4ZMVEky4zjxykIKiysUrBTEW9vb0yePFlQq9U4ceKEcOLECSknJ0c0NzfH7NmzhZ9++kkyNTXlsizj4MGDeOWVV2rNpu2qmxtXqlRM+TR2KHUAMBDwFBYWss3r18uhly4JHm+8IUydOvVJGnjMmDHCxo0bcf78ecnHx0fvH8XjsRKVdlIxMTGymZkZEwSBAcCmTZvku7GxQrdXXmENNm9m2xYvloo9POAtSSwkJKR6edajR6nme/u24XTyiBGkb6nidHAfHx+Eh4ez/fv3IzAwsFKtGCCH03Xr1skZGRlwc3NjHh4ewv379/nly5clzrk4adIk1Ih9vaZzAaAuCRsbEi5fuPDUAU9ZHB0dMWrUKCbLMmJjY+Hu7g4HBwe89957AkCTpSMiIviMGTOYKNaONSMnOFhutWgRUl1dN73oY6nj+XD06FE5IDJSyLGzw7QPPoCTk5MAUJdS48aN2YoVK0S1Wq31XC2LNtPBe/fuIScnh2k2fLdu3cLmn3+GX1AQutevj/j9++VYhUK2srJiPb29RfOqNlhUpG9fClJeeUX/41q3JnHww4dkelqRt9/W2pihUCgwffp08euvv+Y3b97UqWmJjo7mBw8eZE5OTnKDBg3E/Px8nDhxQtq9e7fYp08f3rZt2+pnuBmjz1lQQO7TSiV9pr17S8dpVAOFQoFu3bqhW7duYkpKCjIyMgQzMzPMmDFDBGgywPLly9GqVatas2kzbdIENjk58L179wUMN/troDeIyM/PZ7KPD3OJi0P//v0r1bzbt2+PhIQE0ZCXQ0ZGBszNzSs9qHPnzoKdnR1ftGiRfPXqVThdvy68/fnnaNOmDdv54YfSA3Nz0dzcXDx8+DA7ffp09drvAgKorGNM7dzLC8jM1O51YYDWrVtDoVBI58+fr9T2UFJSgrVr18p5eXl8zpw5wvjx41mvXr0wceJE9s4774iNGjXiK1euRLaOgXFPjVJJLamOjuSWOmyYzqF0VUUQBDRv3rzSzi48PBwqlUo+efJkrWmbHPL665b57u4lPQ4dcjP86Dr+CiQlJXGzvXvR/6uvKpXfnZ2dYWpqKt+6dcvg62grybu7u6NTp07ymjVrEBERwa2trTHrm2/QNzNTvPfee9jdrBmzsbFR3Llzh61du1aWqjNEmHPqNOzb17jHt2ype9p2QACZHHboQF45ZVAoFGjZsiU7cOCA1s4tTbAzZswYTJ06Vezbty+GDx+ON998U3z11Vdx4MABxMTE1Mw5v2YNZXNCQ6nVfvJk8uIZPpzGhdQQrq6uCHis79Lg6OiIFi1aICIiovpeRTWEv78/U06bho7Hj+fVrCHU3we9Ac9HH32EkU2bMmWDBlrv9/T0hCiK8l0D86d0tXTa2tpi0qRJwlATE5bz+usImjkTP8yciczsbCQkJIiDBw/GwIED0a5dO3bp0qVKz8/Pz8fy5cv59evX9X9KWaZW9CAjKxnW1iSMe8p2827duolHjhxh586dw4MHD6BWq58EOwUFBXzGjBmith3l4MGDmbm5ufpOTc+o6dWLFo+RI8m/SJYpRWyE39DTIggChg0bJp44cULIeobvUxZDC5Moijg7YEChT2LicDBWze12HS8Ds2bNEhv07q3Tt8bDw0O4cuWKwUhErVZXyvAIgoCuXbsK/xg5Eq0++giXYmKwfu5c6WzjxvzKlSuSh4cHXnnlFcyYMUNIS0sT8vPzK73u9u3bpX379kkG5z198QWtS8YKijt10n+/mxuNbrh4sdJdvXr1QnZ2Njt48CBu3rz5xGW6bLDj/bjTqyy+vr4IDQ1lFy9erF6P+9GjZMTasCF1dP3xB9CzJzWstGxJpa0tW57p+gUA/fr1e66bNlmWDa5hJYGBeOTg8AjAUL0PrEMrhkTLpGdZtEjn3Z6enmzLli28V69eLDAwEIIggHOO9PR03Lp1CwEBAcjOzka5Fk0NeXlgKhWc69VjObLMV6xYwe2aNkVSUpJgYmIi169fXwCA+/fvcx8fn0oR7bFjx6Tc3Fzh999/R4sWLaQ+ffqIWltP796lE7sqxoeZmSSkeyzuvXDhAr98+bIcFhYmVuzUqEjr1q3x4MEDnDp1SiooKBCKi4uZUqmEhYUFnz59utZgR4OFhYUiLS2NA6i5CH7yZNrV+fvTEMAlS+hn0aYNicH79KmxtyqLt7c3vLy85IiICAwfPlywtbV96teSZRkpKSnIyspC48aNn5Qg4uPjsXnzZmh2z66urtLUqVN1OkR7BwZaxAYG3m1z7tyrAH566gOq4+Xhf//TPnEcVNpas2aNaGFhIXfq1OlJ505BQQFu3boFc3NzuLu7a9UgAgDu3oWLjw8KGMPN8+d5pqWlGObszKOiosSRj7U7d+/ehYmJSaW29gcPHiA2NlY0NzeXr1+/zkeNGsWcK45N0HDoEFlQGIuLC/B//wcMGACAjBP37t0rubm5oXv37lRj3rAB+OkncjMuM1dQoVBg4MCBLDIyUr506RIvLCwUlUol1Gq1zmBHg6urK2JiYp6uhl1UROvul1+SY3NYGGXkraxok6bpMhUEyr4PHUrr86pVT9/1pgdBEDB06FBx/fr18PDwgJeXV7USK3l5ebh9+zY8PDxgZ2f35Pavv/5aLigoEDjnMDU15RMmTGD1tbXng9bUPaGhbgEXLw5ijEUYZ/5UhwbDAY8g0M5CB6NHj2YnT57Evn375MOHDzNPT0/5xo0boiRJ4JxDrVbzrKws5unpWfkK1KcP4OOD2+++i6gxY6Ti7GzFa6+9ht27d0vu7u5PHp+RkSGHhoZWOoliY2PF8PBweHh4YO3atViyZAkfPXo0c3V1rXygV68avzsCaIH87jtgyhScOXOGHzp0iNnY2IgXL16k7jEDvEJ1dhGgxfP27dto3Lix3mDn8OHDSE9PR79+/Wru7D17lgaUNmpE2Z3ffqMJvP/7H/lg2NlRK/urr+ofVviUjBgxQli1apW0ePFiWFtb88DAQDRp0oTVr19f5+KhUqlw+fJlxMbGSsXFxfzRo0eiSqVijDGYmZnxiIgIZmFhITk5OQnZ2dkICgri4eHhQklJCVavXs2WLl0qTZ8+XWvQ4+fnpzjcsaNvm3Pn6oExEZxXa+RAHS8BVlYkftVCgwYNMHnyZGzfvp2fP38e/v7+8oMHD5CVlSWYmprKSqWS9evXj2nTIGLHDmD8eKjT07Hu1VeZnY2N1CM0VLSwsGCMsSdu5VeuXIGHh4eaMVbuBIuLi4OjoyP/xz/+IWzbto2vXLkSvXr14m3btmXl3is9nSafV2XD0LQp+dukpCBdFPHTTz/BxcVFPHnyJLp161b6WczNaS0YMqRc0PC4Y1YAaLNx69Yt2Nvb69UXpqSkYMeOHbxNmzYyHq99RpOVRUHOsGE0E0tzLGo1NZBoTBNv3qRMzwcfkJ/RzZvA/v3UYFID2sSK+Pj4oGXLlvLGjRsZ55z5+flJAQEBYsOGDXXOVeOc486dO4iJiZHy8vJ4VlaWUFxczB7rwHh+fj5TKpXcxsZGdnJyEtVqtfDhhx9CFEVERUXxNWvWYOzYsczT07PSazs7OyPF29s03dHxrnNGRisA1RsS9zdDrw8PgAVISCAR7z//qfNBDRo0QPv27ZlKpWI5OTlC9+7dMWDAABQVFeHatWs8LS2NhYeHl6aEe/YEb9IEeVOmIKVrV5w7f55bWFiIOTk5CAoKwp49e4R+/foxOzs7qNVqHDlyRAgPD69kHJaamirdu3ePBQcHs+DgYCE9PR179uxhZmZm3N3dnc4YWaY0aKdO5P9gLJ6ewPHjOGVqKh+OjGRjxoxhZmZmmhkmzNLS0mifBhMTEzg7Oxv0/fj1118xYcIEeNTkifvPf9KCMHIktbOuXEndWy1a0MIycCDw3nu0yzOUBn8KRFFEu3bthA4dOkAURRYfH89Pnz6NqKgolpqaKjHGBBsbG4iiiJSUFBw+fFjaunWr8ODBA8nT01O0s7MT0tLSuJmZGSZNmsR69OjB2rZtC1dXV6GkpITn5eWhS5cugo2NDRQKBVq0aMFiY2Nx5MgRnpyczOzs7MrtrK2trbH3/HmrkKiomwpJeoQFC/TXY+t42VmAgQOpJbt5c60PsLa2Rrt27QQvLy/cvHmT+fv7s2HDhiEsLIwdPnyY3bt3T27WrBlv1KgRrSlLlgA//ojiWbOQMXIk4m/fRlJSErexsWH29vYsKSlJEgQBmk7NQ4cOSQEBAYqK57VCoUBMTAw6derE/P39mZubG3bv3o1bt27J/v7+whOx/8yZpFkZMsT4T80Y4OyMRzduYOWxY/D39+ejRo1iJ06cgLm5OQoLC0l317w5aWLWrqWSv5ZNCGMMDg4OMDR1/JdffpEaNmzIwsPDjTcwzMwkEbKbGx3HuHHlj+HUKbrtvffo+6QkMlD95hta2958k+Zoffwxmco+g0xP48aNWceOHZmHhweSkpKEc+fOSceOHRNu3LghlZSUCFZWVjAzM9PYqfDNmzfj4sWLsLGxYfXq1RPz8/ORm5vLXnnlFT5kyBDWsWNH+Pj4MFNTUyElJUUKCAiQGzZsKDDG4OnpyRQKBd+xYwe7efMmB8BcXFyeBKiMMaSkpqpV167dbnDvnhUWLDhV4x/4L4zhLb2/v1H+EYIgoEuXLuVuCwoKwunTpwU/Pz/J1dVVTN2zB5GFhZKXJAnR69axzHr1oFQqZVNTUz506FAxKSmJX79+nYmiKHt7ewsAkJCQAAsLC25hYVHpL7lVq1bi77//LgNggiBgwIABTNMqGhwcTI+XJHLzbNfOqB8IQBH6g+RkFKSnyzc2b2bj581jDRo0gKmpKaKiotipU6ekQ4cOicOHD0dNThMXRVGrm2u1qNiNMW0apYA//pgW7tOnycU6M5Mybl98UTM2ABVQKBQIDg5GcHCwAFBHS3R0tLhr1y6psLBQtLKy4gUFBax+/fps4sSJcHd3f7JD7N27t7B161a+YsUK9OzZk7dr1441adIETZo0qRRBKpVKTJw4UYiLi0NsbKz0888/iwqFQnZxcWF9+/Zlrq6uaNCgQfGfrVtnh545EwrGIuvSwn9xoqL0u6s/xtPTE2PHji13myiKcm5urtCpUycUxMbi4s2bPOPKFW5+965w8ssvoVQquVKplFu1asVSUlKEwsJCHhsbKw54XEoCgNzcXNZAiw6yQYMGYIyxq1evIiAgAH5+fpg5cyb79ttvxcLCwtIMwj/+QW7DVSA3NxcPHzzAg/370fytt7gma2xtbS1FRkaiqKhIaNGihdy3b19RMDenqer169MG6ClRKpWCra0tMyrYkWXquKpXj+Ygtm1LnaQVCQsrNwMMr71G+sqZMykjNHcu6RPnzgUWLKBN3Zw5T/0Z9OHt7a0p54k5OTk4ffq0eObMGenAgQOipaUlz8/PZ9bW1nJYWJjYtm3bJxvcHj16sKtXr2LHjh2Ij4+XBw8eLHh4eMDDwwNhYWGVMmHt27cXPDw8EBsby48cOYLdu3czW1tbqV27dmJwcDAaN25sfjA83K/DqVO2YMwWnNdwl8tfF8MBz4MH1N68f3+VX9zOzg6iKMLZ2Vn88auv5AkffijwRYsEs8WL2dgGDTS6nicXLUEQ+Pnz51m9evWenDFxcXHw9PTUmiLNysrinPNyZ9e9e/fkgoICYdOmTWo3NzdFs4gIOBrhg6FJQ165ckW6evWqKMsyb+vign7duzPHx4tVvXr18MEHHzAA4pEjR7B9+3Y+e/ZsVlPmVAqFQs7NzRXK1nerxXffUe1/587S29q0oQXC3Bz49lsqZzVuTLXwbt2ohd3dncZXPEN8fX3xWAslPnr0CNevX2ePO9wqBTGPBdDs+vXriIiI4FevXuXDhw8XdA2jVSgUaN68OZo3by5KkoSkpCRhx44dPDo6GgMGDEDTpk2tjwwe3CD0zJmuAHYCqKYjXB21mrffpjWsc+cqP9XS0hIODg7YtWuX1PnNN0XTgADu+N//Cr6+vuhGWVuGx2vTpk2bkJiYyCRJ4o0bN2YAkJOTA5VKJbi5VW4MzMvLqyRSjYuLA+ccO3bskFxdXQWvO3dYw3v3oFi40OCxPnr0CLGxsfzSpUs8MzNTqC9JUtemTUWfvn2frJFvvvmmCFAjybJly4QmTZpQ+/kff1AL+L17T10asra2ZllZWRIMlbMkiTZW27bR2rRihfbHqVQ0KuPPP8lgFaCRFN27UxA7fTqwZw/pkCZPpg62Tz8Fxo6lwOcZDg+1sbFB79690bt3b1GtVuPy5cvMw8MDzs7OWj97QEAAfH192dq1a+XFixdj2LBhekcRNWjQAA0aNBDCw8ORmZmJqKgo8cSJE1JwcLDo6+uLPEFopxbFDxWSNBfA/Gf1Of9qGBa1MAY85dC9Y8eOwf36dTSfOBE+QUECy8nBsDffZM2aNdMqYhZFkT98+BChoaFPTtCHDx+qtfn8ZGRkYO/evWzQoEHlAp5x48YJXbp0gampqeLs2bNQ//67Ue3lFy5cwPr163lWVpYwaNAgvP/++6znuHGC46pVWh8fEhICQRDkL7/8EkuWLJEPHTrEDXZaGMDExETOza1Bi4WOHSt7CdWvT4HNtWuU5nd1JSdTSQLef592SmFhtHA8J+zs7NCuXTuDXih+fn6YM2eOwDnH4sWLEW9E5lEURXh7e0OhUHBZlpGZmYkLFy7InPMhoIXio5r5FHXUWtRq3cM29SDLMnKzs4Xe77wDq7g4QfHnnwg6dEho3749XF1dK5Wozc3N8fDhQ7i4uHDNfZcvX4aTk5Nc0YuKc45NmzbJbm5uUtmW6DZt2mD48OFwdXUVHz58yO5s2oSi2FiDx1pYWIglS5bg4sWLcrNmzYT33nsPk/79b9Hn8GFq2qiAvb09vLy8pI0bN+Lrr7/mEadPS3lLltDYiqdMeNrZ2SE7O1v/k//zHxIfT59OQYu+zJUk0eiJimaGPXqQrsnEBAgMJC3VxYuku/zjD9rI9elTqeX+WaFQKNC6dWvoFJw/xszMDFOnThXDwsKwceNG7Nq1SzJmoK2DgwNcXV3BOWcqlQqRkZFgjJktnjXrPoBBYKyGdsh/fQwHPPXrU2tgVUlNReaOHZL3kCHcdepUdO/RAwoDxluCIHAzMzNetkyUl5cnVkwHS5KEDRs2cD8/P960wvBLGxsbtGvXDoMGDYJZbq78YNMmusAb4MyZM3JISAgbP348a9KkCd2omUquxbPGwsICc+fOFd955x0EBgYKkZGRTKVSGXwffZiamrK8mjpJ8/KA3FxKAVdEFKktHaCd0aRJ1LEB0CKyfz95dKxfb3gUx3NGqVRi0qRJQo8ePbBlyxbEGnExAIAhQ4YIV69exdKlS2FlZQXOuQrAcQC3wFjN1SXrqH0sW0bBfxW5+sUXsLKx4Y6zZ6PvjBnMUUuWpiwaY8F27do9WVdv3LjBtXV1RkVFyZmZmRg3bly5SEgQBAQEBKB3796wt7NDWs+esuUmwz6ZsbGxsLS0lGbMmCF27ty5tBw2YABlQbQwZswYxQcffIABAwawhIQEIXb4cCp1p6QYfD9tODo6Ijc3V/s15dgxCkzc3amUbkwG5sQJEidXxM+PxuSUlFBm+v33SeujWTsXLqSMXnw83V7LCAsLwxtvvIH4+Hi2fv16o1reW7duDQcHB3z55Ze4fv26bGpqWpJtb58K4N8Ahj3bI/7rYDjgSUioWjvkY4o+/hjtduwQg3v1Yvj3v416jkKhYGWHb6ampkKSpEqtmgcPHpSLi4vlYcOG6SwWR0VFoVdEBGupuZDrITMzExkZGULHiouiKFKm49o1nc+1sLCAiYkJnJycZEOiPkNYWlqKOTk5NaMnOXiQUrva6NWrdCI8Y7Tj2r6dngPQNOWuXWm3NHFijRxOTePk5ATGGLRpI7RRr149zJgxA7Nnz0bfvn0FxlgxOC8EcBNA72d6sHW8WNq3p+7EqnDnDvw++QQdW7Rg4rx5pTOf9GBlZQVRFNGszEiajIwM2cvLq9w6+/DhQxw/flwYNWqUoCurWVRUhKItWzDqhx8EZkR36dmzZ+UWLVpULqcY6L4UBAF+fn6QZZk1atKEhMvBwTSzq4o4OzsjLy+v/MHm55M+cMoU+nfyZOP1lGPHAidPVr69QQMgJ6d0XQ4NpQ7UTz6h75VKeu69ezQouqwGqJZgZ2cHzjmaNGlilMraxMQE48ePF6ZMmYIZM2YIlpaWBQBUAGIBtKzzFTMOw2eSpyeweLHxr9inD/Dxx/ijZ0+c+uwzqSpBwIgRI8QRI0aU0+84OjrKZUVwt27dwrlz54Tx48fr9FoBgOjoaIlPncoEjbpfDxcuXOBOTk6S1jZDtbp0MKcOYmNjZSsrK+H27dsoKioy+H66sLW1xeMaePUZPJj0V9po2JC6M048Hrjr7EzdDtnZpbs7xoBffqHujVdfpf/XIrZt2yZ17NhR1urvpANbW1tourlkWbZcuHChEsA+AK51aeG/MF9/TcZ1xrBxI3OHzJcAACAASURBVNCsGdItLfH1Rx+hmQ7/Hm20bdsWEyZMKFfqKiwsFMvaZKhUKmzYsIG3atUK2tqONRw9ehTFjRvLip8MW0VlZGQgMzNTCAsLq3ynkxMFDXoM7RISEkp9rmQZ/NtvaWp5FUv0rq6uKC4uLu0BuHqVOj8PHqRsS1U3T2lpurPzb79N+kMN//43bczLBkiDB1PgdvgwldCqKTmoSQ4ePAgTExNWlUHTJiYmqFevHhhjMDExYQBcwPlNAPcAtHxmB/sXwnDAwzkZQhli6VLSysycCUyZgluJiXJwcHCVvBhcXV1RVojavHlzZGRkCKdPn+bnzp3D6tWrpd9++w2dOnUyWC8NzswUUo4f59xABoBzjvPnz0Obzw8Aaml/9EjvawQFBQmFhYXS5s2b5a+++gr79+9/qqDF3t4e2dnZ1e+rzM6mVK8+7VKHDtShpaFjR/K7+Pzz0sWRMUo9jxhBwVFy8lPX92uS48ePQ5ZloUOHDk81X+2xpYASwChwngLAGkCPGj3IOmoPxcV6L/gASAOybx8QHg68/z4OHjzI/Ro3loy1ngBIy1ExiLG1tZV27twpX7t2Ddu2bZO+/fZbKJVKuV+/fnpfq2WzZmgZESGkGNEFqnfDpumC0uNK7ObmBg8PD75v3z5p6dKlWPzgAS/ZsIGChCqgVCohiiLyExNJC2RuThnykSOr7vHVty9NSNdF8+bAnTul+lJLS8rkffEFefNosLcHevemtevOnVpRoi8oKMC5c+cwcOBAZsiqRBfe3t42pqammnrfKQDTwVj15k3+DTD8A3rwgKJpXXBOi8miRSRCGzgQOQ4OKCwsFCrOnqkqDg4OGDlyJKKiouSoqCjJxcVFnDVrFjoZ4RfjnZjILJOSmKGO4xMnTsiyLPPmOjw60Lw5ZTnS0nS+RlBQEN544w3x3XffFWbMmIFz584JZ86cqXJkcP/+fW5cT6cBZJlSuvq6x0JDaREqK+b8179ocayoixk6FFi+nNpjhw9/oUGPSqXCyZMnef/+/ZkhkbMu8vPzkZeXZwJg9cKFCxmAbwB8XDef5i/Khx+S9YI2NH/LS5ZQxsDWFhg/HsnJybKtrW21LyCvv/66WFxczHfu3Cnn5+cLQ4cOxcyZMw1uBO1SUlA/KQm5BkStKSkpiI6OZp07d9b9mm5u5bMhFd/Lzg4TJ05kc+bMET/88EOYmprKW8zMZFmzthtJ4s2bcH34EOKuXWRW6+Ji/OyvirzyCg1B1YWrK2WPyo4Vat6cvIquXqUOLw0ODqT5iY0lwbOhUUTPmE2bNsne3t6SZujs0xAVFYXi4uLghQsXBoLzEwDcALSpsYP8q8I51/fFuSxzXljItXLnDuc2NpxnZ1e6a/fu3fx///sfv3fvnvbnPkMepaXxbz/8kCdcu6b3cefPn5c/++wz+cGDB/pf8L33ON+61ej3T0xM5J9++ik/evQov3DhAler1Qafc/DgQf7555/zzMxMo99HJ4cPc27Ee/KRIzm/fLn8bZmZnAcFcf7nn5Ufn5vL+Y4dnO/cyXlsbPWP8yn47bff5J9//lmSZblar7NgwQLNV3fOOTjwFgcmcv3nQ93Xy/fFeVGR7vOheXPOP/us0s1JSUn8s88+kw8ePFjtv7WnYe3nn6s3/Pab3pM4KyuLf/HFF3z37t36X2z/fs4nTTL6vSVJ4t9++620fuVK+VHbtvyRgXWUc85TU1P5lRYteG5AgHFrjz4uXOA8JcXw41as4Pz//q/y7RMncv7tt9qfs3075zdvcv7LL9U7xqfkzp07/L///S9/bKny1Jw8eZIvWLCA/+c//znHaf0K4MBv/MWfb7X6y/AOJjm5sgNvYiLNO/H0JDGgFh1F3759ERISgjVr1iAxMbGm4jOtXLx4EcuWLZOWL18u/fzzz4iaOZNPXrcOfo0b63xOQkIC9uzZw4YPH87q1aun+8U5p64lAx1mZalfvz5kWUZMTIx88OBB+fvvv+cJCQngXHtm5Pz58zhz5gzGjx8Pe3t7o99HK7JMnRlxcYYfGxRUKlTWYG9PO+IjR8iXoyxWVqWvPWTIc6uJnz9/Hhs3bsSGDRuQmJjIBgwYYLyTqw7Gjx+v+e+hx/8eBdClLi38F2TChPI+YrJMnT0qFXlVzZ5d6SkeHh6YMmUKi4mJYbt375Z0nbs1QUZGBtauXSsvXbpU+vHHH/mPK1ZgyKefioP8/XVmbQoKCrB69Wru6ekp9zWURbl4sXzGwwCCIKB58+a4lZrKkgF+4JNPcOjQIVlrF6paDdXcubg3cCDPmjtXtrp4UecYD6OZNw+YNcvw48LDKXNV8XejqTZoK18NHEglzq++Ku1UfcZkZmZi06ZN+P3337Fhwwa5U6dOcnW91kJCQgAAkiQFLVy40ANAAoCHYKxt9Y/4r4v+xX3QIKqRlumcQkkJCdC2bqXvaWaUVrp164bu3btj/fr1uKan0+lpiYuLwzfffCPt27cPAQEBQosWLUQfHx849OsHcx3+OQBw//59bN68GeHh4fqdkh89Aj76iEYwGDlgU61W45dffpHt7e2lOXPmCO+8844QFBTEIiIi+Jo1a+S0CqWxmzdvYu/evRgxYgR0DYyrEoJA7ZmBgYYfO306XQgqpq2HDaNSly6zs/feIyH3J59QG+szvBgAwIULF+T4+Hhcu3YNQ4YM4frm+RiLj4/PkwrWwoUL5wG4BOAigKBqv3gdtYfTp0mUr2meKCmhv9fdu2kd695d51BhFxcXTJs2jcXGxrKIiAjJ0CTrqlJQUIBVq1bJy5Ytg5WVFW/Tpo3YuHFj5lW/Pi9ZuBBmOhzPS0pKsHbtWtnKykoePXq0/jV88WL6jEuWGH1cMTEx/MyZM2zixIloEh3NuvbqheJ16/Ddd9/h8uXLpRu348ehjotD7NmzPG3oUB42YYJQI/P4du4EjGjFR4MG1HF69Gj52x0caPREjx7aW+z9/UmTaGtL6/oz1vWkpaUhLi4OV69ehaOjo9CxY8dqb6oEQUCPHj00f5BJCxcskAAcAGDEwv/3Rf8PvqiIdvHLltH348ZRt0Pfvkb7G4SGhqJfv37YsmULLl26VGNXxoKCAmzbto2HhoYKc+fORefOnVn79u3RtW1bhEZGMpMeujWox48fl/z8/BAUZODa9sknwMOH5EVkBLIsY9OmTXJ2djamTZv2pIusa9eumDt3LjMzM2MrVqzA43EKSE1NxaZNm3jv3r15jY2o6NGD/C6MwcaGhu7FxFS+b9Ysuljoys7Z2ACjR5Ntwf37lbNBNcigQYMEgNp+/f39a0RnwxhD9+7dNd/+Z+GCBR4AEgE8m9HxdbwY5syhoDw4mIZp2tnRmnbpEs2TM4CdnR1mzJghJCYmsg0bNsjVNRcty/r162WFQoFZs2Zh6NChYnBwMDp16oSely8z+9K/zUrcvn0bubm5mDx5sv5UysWLNEbGxUX72AYtXL16Ffv372ejRo2iid2MwcnFBf2iooRunTtjz5498srFi+WUo0fBX38de1eskM9PnMh7zJ1b7awrADIN1GWnoY2gICA6uvLtDRoAo0ZR1522QNXamta+Jk2AK1doRtczokmTJk+acSp28VWHli1bPnkhxtj/AYgC0BuM6TeM+huj/ye/bx+19fn4AGfOAJ99VurfUgVatWqFIUOG4I8//mDR0dE1EvRs3LhR9vb2ljt06FBevHr+PJ0AetKqBQUF0DpRXYMs0yL5+utke27gRC4uLsbRo0flb775hicnJ/Pp06dX8tdQKpUYPXo0mzp1Ku7evYtvv/0Wq1atQqNGjRAUFMQKCgoQFxeHqKgoVMvAsFs32sEYS5cu2ndBZmYk9Bw5Erh9W/tzAwJoQVm7lnaRejpBqoOtrS1EUUSnTp2QWoOeGi4uLjA3Nz8LAIyx2KsBAbsBdANj1U8h1VE72LqVzodBgyjAiYrSL+bXgpWVFWbNmiWkpKRg3bp12ks7VeTq1atISUkRhg8fLtiWnYLOOQUpejosCwsLYWZmJuu9cO7dS+Ldw4eNGhURFxeHVatWyVu3bsXAgQPLjz3o2xeIiUG769fxbps2wugvvxS2b92KL0eNQqyHhzB8+HABAJKSkhAVFYXk5GTDPwBdBAXRmmQsoaFkYKgtEH3/fVqTli7V/lyFAvi//6OfT/fuOg0aawJLS0vZ09OTp6amVhon8rRYWVnBxMSkmDF2hHM+a+GCBfVBY3Kq7rL5N8FQFxPH3r3kZ/DhhzDWQFAXiYmJ2LBhAw8LC+OdO3d+qjA3LS0NGzZskIqKisRp06ZVHlGRlkbeE3qClB9++EFu2bJlZaNBDTt20E5jxw7aCRhg165dUnx8POvdu7egs9urAl988QVnjEGWZajVagYAlpaWEgAmiiIbO3Ysq3Lp5tIlakfXkaLXSnw8la5+/ZXKYRXZsIGctvWlmGWZyn5ubmTypafM+bScPn0aBw4cgCAImD17ttbRJFXl0aNHWLJkySPO+S9qtXqWQqF4/6N5824A6APOp1b/qOt44UgSh78/GeDdv1+tl1Kr1Vi6dKmkVCrZa6+9JlSlZV2DLMvYunUrj4+PZ6+88grXTFR/QmYmrTl6XIijo6Nx5swZefbs2drX0IICstNYvNioUvyDBw+wevVqtG7dGt26dYPWz3X1KpWPpkwBgoNxyNwcp0+fhqmpqVxYWCgwxqBUKmUrKys8evRI6NevX+XPZoicHNpcGZF5K8eoUSQ90Pa8+/epLf34cUDfenr+PP3cd+6k7La2Fv9qkJOTg6VLl/Li4mLWp08fHhISUiOZ6uXLl2cnJyfPB/CdUqmM77dhQ2jLS5fOAmgJMlatowyGAx6AfA3MzalWOmZMtd7wwYMHWLt2Le/cubPcoUOHKqnbYmJisH//frRq1Urq0aOHWGloZ0EBCanj4vQOvzx37hzft28fGzRo0BNXVFmWwRgDW7WKnIZDQoxKAxcVFWHRokWYNGmS0RqcnTt38ps3b2LGjBlMqVQiOzsb5ubmUCqVTxbEhIQENmjQIHh7e8Pc3Ny4jumOHYGmTWmHWBXCwmgAn2akRlk4J8fl6Giag6OPI0coK3bsGFnI13CXt0qlwurVq2UnJyc+dOjQaiojqUPx008/LZEkaaggCNsByM7373d5Y/nyfwOYBs7vVP+o63jBcBQVUWn60CHylCqbUakisixj5cqVklqtZjNmzKhSGUelUuH777+Xra2tMWTIEEFrlrl/fwpW9Myyy8zMxI8//gh3d3d5zJgxT4IeWZYhXL9OGdf33qPynRFs3LhRkmVZePXVVyt/GFkma5IFCyiLMn8+0jIysHL/fowePRq+vr5QqVQoLCyEJlMVHx+PrVu38sDAQLlLly6ipaUlKs4S08ovv9BxP3xo1HE/4dtvKaCdN0/7/ampZKnxyy/6N7BZWRQ89elDMwVreP1Sq9WIi4vDzp078c4776Amhk7v2LGj6M8///ynUqn8WJZlJ8bYdx9+9NFtACI4/7bab/AXwziFWcOGZOM9bx55I1SlZFKB+vXrw8nJiRcUFFQ5w6Opg4aEhFQOdgA6IRctMjjpu02bNszMzAzbt29HSkoKJEmSY2JiBCeVik/4+Wdm8scfEIwIdtLT07Fhwwbu5OQk1a9f32i1XmJiohwaGipqjMLKprQ1k8HPnTuHnTt38pKSEibLMszMzGBpaSlbWVlxFxcXdO/evfLPIDLy6QTE8+dT6v/99yvfxxgFRD/8QLotfc6z3bpRlmnPHmDLFgqiqtDdpo+HDx/i3r17SE9PF1q2bFkjOWHGGOzt7QvT09MfKRSK9SqVamyau/uRi82bf9Ty8uUeAAzb3NZR+zEzI03Hli20jn311VO/lCAI6NSpk7hr164qP1dT5g4MDNRdUp88mTZbenBwcMC0adPw888/sxUrVkht27YVT506JWdlZQlvbNsGm0GDoDQi2FGr1di3b5908+ZNcebMmZUfIElU6nFyAjZvprXg+++hXLkSdlOnyr6+vgJA5fqypodNmzbF9OnT2dq1a3H58mWo1WqYmJjA3NycW1lZyba2tujQoYPo7u5e/v3GjauafkfD1Kn0PLVau8GhiwuVvj74QP/UAHt7ErNnZZHma8kS48dgGCA7Oxu5ubnYv3+/7OrqClEUa0TI4+bmZmZqatpGpVJN4JzvATBn1cyZy6f88MMjxpgSNDOwjseICxYs0Hd/6Z1OTjRk8v59csDs3Pmp3lCWZRw4cAA9e/Zk1kaUi8ri5OSEq1evcs458/LyqvyAr76ierMRpSAXFxe4uLhg//79UKlU8nBXV8E7Npb92qWLfOb+fVhZWTFbW1utE7w55/jzzz/5xo0bmbe3N8aOHStWZaeXn58vXLlyRdZnK16/fn107NiRde7cGSEhIfDx8YGjoyMzMTERbty4wc+cOcP8/PzYk9Edn3xCi5IBB1etCAKVrQYO1L6rMTen3/f69RTs6vu9KZWAlxdpCFq2BAoLjRZMaiMvLw9RUVGIiIjA7du3MXz4cLRs2bLGtl5JSUlSamrqeUmSPlYqlX5qtbpVsodHN8/bt02t3313NxYsePpZIXXUBhYAoL/x0aOBtm2Bd96hC1lVSr9l2LNnD/fy8pIbN25cpYsWYwxWVlYsMjIS7du3r/w3vGcPuaQbMejU1NQULVu2ZJGRkcLNmzflNn5+wuDjx7GjY0dpnyAIRUVFspOTEzM1NdWaHU5LS8Pq1at5eno6pkyZwsrZYRQXU4no4UPS8L31VqkmMiQEoqsrom7cYE1bt34yMLUiZmZmCAkJETp16oSwsDA0bdoU9erVYxYWFkJubq5w5MgRWFtbczc3Nzq4zEz63bzxhvbSuj6UShJoW1lRYKuNtm2pQ+/CBTIo1IUgkGuzWk3yCAcHCoSqQUxMDLZs2SKfPXuWBQYGyiNGjBCf1jS1IiqVCnFxceKHH374zvHjx6MBjM21tGxrm5/vaJeRcdNk3rwX67JYy6jaT93GhrIn69dTLVeff40OEhISIAiCfu8bPQiCwEVRrHwGSxJF76++avRrybIMc3Nzaerrr4to3Rr4178we/RoITIyEnv27JG3bt0qiKIIa2tr2d7enjs5OTEHBwfhxo0b8t27d9mwYcOMHv5Wls6dO+P06dNCWlqawREZAC0enp6eT2zre/bsKUZERPAVK1ZgxIgRaNSoEdCqld72ypKSEqSnp4MxBkEQYGpqWppZ8vGhUtjt26QB0oanJ3WrzZlDuh59AZ6lJbBmDZUQ3noL+O038ObNn8rI+I8//uD3799n3t7e8oQJE2qmE6QM9erVs0hISOjwwQcfrFi4cOF4pVLJCpycRiUEBPS50K7df/oBlU1a6ng5USjo4pWeTtmeGTOe6mWSk5O5XmdjPZiYmOju0omIoM1EqUeUXkxNTVFcXIx//OMfgstPPwGyjNc++EC8e+8e/vjjDx4dHQ1ZlmFpacltbW1lJycnODo6irIs88jISObv749BgwYJ5Y4nKorW9evXaUxQxXlfjMFk4ED8Y9o0JNjYyA7/+pfB6EShUMDV1bVcVuvatWuIiIjAgwcPpPDwcFEoKSFfHT2lr7S0NEiS9GQNc3BwKC2VdexIv1ddmJvTGvfPf1Kwa6grdsYMCvxatgTGjAH/+OmM2FNSUrB37144OTlhzJgxcHd3r3YpviwuLi5QqVR+CxcuNJ0/f/6eTz755BVBELYlBAU1ybOy+n9R778f+f7nn2fX5Hu+zBin4amILJPI9dQpKnVU4Q/h/7d35lFR1/v/f74/HxhGQXYQRBANQRYXDEFBBcXdcstMKbeO3VyvWaYe60qkXb3ZrbTrN02vlZqGaykmiiKiCKFCCKIECiibC8sM+zCf9/v3x1sUZYABh37lncc5Hs/5MPNZ5jPz/rzW52vXrl3M0tKSvvTSS2268Vu3bpW8vb1Fv6fDvqWlfLFoheV89OhRSX3zpjDlxg2CTz9tlNunlOLBgwfIz8/H3bt3UVxcDKVSKcnlcsyYMUNsS9EiwNMz3377Ld58803Y2bW9gzAxMRGnT5/G8L59qZ+Hh0CakCpXq9XYuXMnLSkpIYIgMMYYUavVxNXVVRo7dqxoYmLCByx26gS8/XbTB5QkXpxcVcW9ZS3I2bWLnbt5k3W/do0MPXCAtDYvvnHjRjp58mTBxcWlVe/TFqVSif/85z/VdXV1vUJDQ2+HhYUJMplsT5eysqnDDx4U982Zs62a0sWhoaG6FWHR80fReA1jjAuqTp/Oi/Gb69h8itu3b+OHH37AypUr29RenJycjLi4OLp48eIn36xS8QessbHWEY68vDzs2b2brVIqCaZO5dpbT6W5KyoqkJeXh8LCQjx48ABlZWW0traWjR49WnxCCoMxbnDVNzA0EwWhlOKXt96iNh4ezG/ZMrHVEZmHlJaWYufOndTc3BwhHh5Ch2bS5fHx8Sw6OpoYGhpSxhh56Kxi0qRJxNnZmdeZvvsuv4bm6oUuXgSSk7nDrsX6XXHrFmL37pUMLl4U+61dC9sXWze94cCBA6CUSq+99ppODZ2G7Nq1q+r27dsfffjhhxsB4OOPPx5nYGBwaM6338ojR4++fcfGJiA0NDSvvY7/V6Jt31RB4Bb1nTs8BKtl3cjdu3eRn58PLy+vNt18SikqKyuJoaYOhokTeYthK7C2tobz3r0k9fZtqtTwIBYEAba2tvD29saYMWPw+uuvY8GCBeLcuXPbbOxQSrF3717q5+dHn8XYAQBfX1/MnDkTlV9/Tap8fVGnYe4OYwwHDx6Uqqur2YoVK8iKFSuElStXkqVLl0KhUGDz5s24cOECqx49Gti+vfkDiiJvGz18WCsdptLSUuzNyyOu7u5Cj19/JYr9+1tVY0QphUqlErKysnSqf9IQU1NTDBw40NDIyOhLAAgNDaUqleqNAnPzQ1WWlqx3QcGbMpns57CwsLbdcD1/PgjhHYUeHlyDRcvvFqUUERER1M3NTWqrlkphYSE0DvncsQMICmpVOkcmk8EpPx8F+/bhd0nS2G5vYmKCXr16YdiwYXj11Vfx1ltvCYsXL37S2Nm/n2vSDB3Ku5la6DQ9fvw4snr3Ji+amop44w2tz/dpLCwssHTpUgF1dcxwyBDc//VXja+7ceMGzp49S2bOnIn69WvlypXE09OT7N27F+Hh4VKRsTF/FqWlNX/QQYOArCwusdICjDEcjI2leVZWQleFgtF332WtrZEsKyujDx48EIrbUdxw7NixHQkhoWFhYVYAsGbNml/UavXU8wEBtSOSkhwNDQ2Tw8LCtGsffs7RvobnaSwseMfWxx8D//0vHzDZjPeuVCrxzTffMB8fH9a/f/9Wz8iklGLr1q2SkZERGTlyZOPBkYLA8831NS0tUVcHx3XrhI5hYUhwcqKRJ08KjDForA3SIeHh4UySJEydOlUn6RkzMzPYTZpEvu3USToXGyuoVCpmY2ND6hfVqKgompGRQRYtWiQ2NBRlMhn69+8v2Nra4uLFizTm6lXBuqaGMRMTYtxMuLdSFJF48yYVSkqIqZubRi/p+vXr+O6779jFixeJi4sLHTt9Ogk3NJRkhoaky/z5BKNGadUpQwiBu7s7zp49Sy9dukS6devW6rovbXBwcBASEhKcz5w5cyooKKggKCiIRUdH/1RmYfFiv6Qkt9LAwB7lFRU0MDDwnM4Prqe9+UjjVkJ4R1RtLS+2nzixxVqzHTt2qNVqtTBt2jRBo9PVArGxsUhMTMSkSZNIo9EChobciWwqpawB4x9/hIejI7k+ezb75fx5kp6ervbx8dHeYrp+naedAf5Z9OvXYtQjJycHp0+fxqxZs4ipiwuP+E6c2Ppp6A8RBAH9fXyEE97e7Hh8PMnPz5fMzc0FU1NTEEKQn5+PH3/8EePHj4dbgy5SQgheeOEFeHt7Iz09HTExMeSBqSk1T0oiHUaObDL6xgD8JpOxopQUZmdhQYgGjaKqqips376dRkVFkZqaGsxfuJAU+PiQSFGEX1gYQW0toGWkx9vbm+Tm5iIqKorIZDLm4OCgk/nQDTE2NoZCoWDFxcWWQ4YMOQ4AgYGBmcd//TXV+s6dV7v6+Jjkq9UvDxky5H++a6vtBk89rq5Abi4Pp8rlTXoojDGkpqbSwsJCoUuXLo1/8E9RVlaGrVu3MisrK2JmZvZI/2LOnDmN9S9++okbOs1N132a/fuBxETIlyyBZ58+QmFhIS0vLyde2oxkeAZ++eUX9vLLLwu6GI8AAMjOhuH48fD9738FGxsbXL58mcbExAh5eXnS5cuXWWZmpjBv3jyhKd0aa2tr+Pr6Ch4eHshLTMTtM2cQWVLCDA0NibW19RPtpLW1tfjqq69QYmbGOh48SMzT0mA0btwThi6lFLt372b9+/cn06ZNg7e3NwGAGrVauJKTw3y7dCEwM+MhfC0+A2NjY/j6+gr3798nUVFRGPL0XDcdIIoiOnbsaJCbm+tz5syZ3efOnZNCQ0Pp1f3798uVytVyMzPxDiHHg4KCtJMX1/Nn4qNm/2pjw0exuLryVH0zDlNhYSHJzc0loihSR0dH0lKUZ+fOnTQ/P5+4uLjg7NmziI+Px8yZMxs7VWVlvP7w7be1Lw9QKIA5cyAsXIiu/v7E3t4ev/32GwICAlregSTxNu5x43ga+513+PVrwcmTJ2FtbU39/PwITE25oztjBjcALC21O/enGTMGrkFBpN/kybh58yY5d+4crl69yrKzs2lMTIwwcOBADGoi3SWTydCnTx8yaNAglCgUhB44wL5/8IBU19ZSKysr8vSz4ujRo1JiaipqSkvRedcumIweTZ42dGNjY+m9e/fY3LlzheDgYFJfgxR9/jzpM3Ik5FlZvDja3LzF+0UIgaenJ7Gzs8Px48dhZ2fXen01LejatatBQkKCZ3R09M/nzp17EBQUxAaP2A9ZHQAAGPJJREFUGJFxY//+AIuiohduOTgUDx48uHUpkOeQZzd4zMy4h/S3v/FBbtOna/wSGBgYwM/PT6ipqSEnTpxAaWmp5Ozs3EiRuCHR0dEkPT0diYmJzMzMDLNnzxY0hoNXr+b/a6vQGRbGf+Dvvw/IZGCMISIigowaNerZh3e2QFpaGpXL5aRbt266MfNLS7nOxOjRsLa2xoABAwQvLy/k5+cL1tbWwvjx44m1tXWLu+nYsSOc3NxId5WK1PTpQxISEqTY2FihoqJCsrKyEjp06IDExER27949umTJEvGmvT2Kf/4Z5u7uMHroJVVUVODSpUu4c+cOCwkJIQ3vlZ2dHaKjo0m/d96BkbEx17qwtdVq5hchBD179kRMTAwCAgJ0Js3ekM6dO5OMjAzTysrKNQD+ERMTc1ZhYTHOyMBgtI1Sqb5pbv5dUFBQus4PrKe9+ajFVwweDFy7xiMVEyY0qV/j6upKunfvjqioKJacnAwnJydi0kyn14ULF2h2drZw6dIlmpeXR2bPno2umlSPT5/mUfL587W7org4rh586NCjrqRLly5RxhhpUezv3j1u6Ny/zxsLtJwRWI8kSUhJSXncZSYIXJG9qqrtLdxJScCYMTCys4OXlxcZNGgQqa6uJpIkCf7+/vD19W1xF4IgoFvv3uhcWkoc/fxw+dYtGhMTI2RnZ0vGxsaCpaUlysvLERERIcyfP5+4BAWRK2fOALW1sPLxIRAE1NXVIS8vD5GRkWTSpEmCg4PDo0JlQgjS0tIkuLgQp9mzCWbO5E5zE8+7p7GyssLVq1clW1tboa0NO80hk8lgZGREsrOz32aMfRQfH28VHR2tUNjbL3avrDQtlMnSB4wY8T8vs9G2omVNKJVceK6+46cZpcqysjL88MMPUlVVlThlypQnZcwb8Nlnn9Hhw4cL5eXlLCAgoHEaC+Dzvhjjx9NG3Co7my9sJ08+6jIrLCzE999/z1atWqXbWKMGzp49i6ysLOmtt97STRFbejqvRdAVwcF8MXV1rQ9f07t37wr29vbS/fv3xXHjxqFeTfqnLVuo78aNQvKSJVI6pUJtbS0xNjaWhg0bJvbTMPjwq6++kgYOHCgOGDCA59EZ4wWS772nlaL1J598gr///e9oj7RWQz7++GPGGCPdu3evGOjtbUKmTqWlZma+vomJV9r1wHraA+3XsMOHueaUQtFsxONhLQ9LTU0lAwcOpIGBgRodt0OHDqGurk5ycnISXnjhBdKk9o5CwYuVtU0LDR/OO7nmzn206csvv2SBgYHEu6kod3U1bzWfO5fXubz6apvSUGq1Gv/617+eVDunlM/ju3VL62aGR/z+O39mtCFFqJHPP+fPgsWLUVVVhaioKGRkZLD6ri61Wk3/9re/iQCQmZmJuilTUDt4MI3z9ERpaakgl8upo6Mjmz59eqP1+cKFC7h69SpduHChAJWKD1DOzORSHS1oJwHA9u3bqYeHhxAQEKCba22C5ORkHD16FJ06daoICAgw7vDpp+rKoqIfB8XFzWrXA/8F0J2rbGrKDYk1a4CpU5t9qbm5ORYtWiQOHDgQ4eHhOHz4sFSrYXaMubk5FAoFCwwM1GzsAFxocNw47YydAweAvXv5j/OhscMYw9WrV5mlpeUf0oXz4osv4u7du6KmAuNWo1DwvLsuZ1iNGvVoXpqzszPmzZsnvPfeezA2NhYppWg4OmPCggVCwYQJ6JKTI04eO5asXr0ay5Yt02jsAECvXr3ElJQUCQDg4sLb3FNSgE2bWixmLi4uhiiKOp2l1RQymYwBwKxZs0yKSkulUhubq76XLrXvKqXn/z9TpvCH2JgxvIurCQRBwIQJE8ibb76Jq1evsi1btrCCgoJGr/P09EReXp7g7+/ftLFTVsaV3SsrWz6/vDxuqBw69ISxU1hYiMrKStK3b1/N70tP59PBc3N5Cm/GjDbX3BgYGMDExES6devW442CwH+/K1a0fojwa6/xlJqumDaNO2yMoWPHjpg4cSKWL19OgoODSVFREQICAh49KHr27An6+ecwT0sTBrm7C++99x7ef/99QZOxA/AmkdLSUqG8vJwbVfXG8bx5LRa+q9VqqFQqUlhYqLMB2k1Rn8YLCQkxcXV1Jed69Kjzi4/v2aa++ucM3ecGDhwA/vUv/n9JSbMvHTJkCBYvXozMzEzh2rVrjf5eU1PDWuyGGjaMp6haQq0G1q0D8/MDZDJUV1cjPj6eff755+y3335DYGBgu7UNNsTU1BRyuZzm5upgaoGZGffcdJmGmzePey0NDBC5XI6JEyeirq4ODSOCgiDAZ/NmeJuawuXIkRZTTYMGDUJRUZFYXf1wxIuxMfeqlyzhc3CiojS+LykpiW3btg2dOnVihw4derbhqlrg4eGhAoD8/HzEx8erogcPng1gJghpWTRJz1+bl17iNYHFxbyFuRns7e2xdOlS0dramkRERDRymIqLi1seHyBJ0CSJoZEdO8C6dAEsLMAYQ2ZmJr777jtp586d6NWrl+bOsU8+4YbcgAHcUHrGzlAAcHR0FDMyMqQnNvr5cefl5El+TdqSnNzq7tpm6dqVR78aTFAXBAHe3t4wMTGRnh7c6TVyJLpv24YX//EPdKxpXmNUJpPB1NRUysjIeLxx6VLgyhXeEr9smcbJ7Pfv38fXX3/NampqWGZmJp54fztQn2ZNTk6ui4+PV5VbWHwjMPYzgLYJTz1H6N7gMTTkKa0ff+RfhhYwNTWFIAjs6UKuiooKKBQKsXfv3k1bpfXzcZpRfX7w4AF+378fddOnI3LtWmntxYvYsGEDPv/8c1y+fJkGBQWR999/n7hpmiPVDtS3V9+/f//ZdzZiBFcO1SWWlryY+OrVJzbL5XKIooiKiorG71m1iisra5q63gATExMYGxtLmZmPxT+rqqtBO3Xi4fbbt3ktRQOjqrS0FCdOnCCTJ0/GokWLSF1dncb2e13i7+8vB4C9e/cyAL+t2rDhKoAtAOY2+0Y9zwd9+vARFK+9xguam6FexNPa2rqR556UlCQNaKmuZccOHlFqAkopriQmomraNNzx8cEGW1usW7cOGzZswJEjR6ilpaW4bNkyvPLKK086bF99xSPtISE8aqVDp+hhpLXxs8PMjBsvGzZot6O1a/nvXpvofGvo1g3Q0OIul8sFhUKDBp+HB3ecIyJajDS7ubk9jlKDr+e1jHGnTa3m4q1K5RPvOXjwILWyssKyZcsEKysr2p4t6gDQtWtXyGQyKTEx0TA5OZnW1dX9G8BBAKNAiG4knv+itM/FE/I4wrNqFbBgAf8SaiAhIQEqlUp4WpMmOjoaXbt2lUxMTJr+NURGAqdOAf/4R5MvOX78uOS9ebN41sYG6VlZZOHChairq4NMJoOVldUfEtWpp37asoWFBfHx8Xn2HTo5Nfm5thlCuLdWVMRVRhtgZmYmRUZGkkYt9Z068fsQEMDl8ZspyuvVq5d44sQJZGVl0dzcXKJUKsnIkSOZ//TpBGo190QDA4EvvwTAByXK5XLJ3d1dVKlUkCSpSTl7XWFtbQ07O7u6oqIiQwD14cMoAG+CEBMw1vxTUM9fnylTuC7OiRP8N9FEmr6srAw3b95ko0aNemItUSqV9Q5b08dQqXh0pxll5evXryNj2zaYp6TgwJUrGD5mDNzc3KBUKuHk5NTY6MjIAMrL+cDPuXN5fYwOOXv2LNLT0zFnzhxNwmV8QCelPH3W0trk7MxlAXSNnx+P0lH6RNewp6enEBcXh759+zauA1y3jqfWCGn2fvj7++PLL78Uw8PDqVKpZEVFRaKFhQVbtGgRIV99xfdz6BAfoPywxkmhUAgTJ06EIAiora1lzRW66wJCCKZNmybu2bMHoiie+uCDD/IQGgoQchxAAID/WXkN3Ud4Hu1Z4JGCgoImp3efPXsW0dHRCAkJaRT6zcjIoL179xbj4+PZt99+S0s11am8+ioQHd3kKZTk5GDQ+vWiy759GB4RgXfeeUewtraGvb092qM1sDlSU1OxadMmKpfLyaxZs9qk4/EEt27xz1WLDqxW06sX9xCf8nZCQkLEGzduaEw/wtiYy7G3kF4cO3Ys+vXrhxs3bpDhw4eTKVOmIDY2lqhUKl5XcO4cH6D4ySdg6ekoKSmBkZERA3jUjxCChvVe9+7dQ2xsLNN1mmvq1Kn1N4jHxhnLB0AABOn0QHr+vFha8u9kaKjGGo179+5h27ZtzMvLiz5dt3b69Gk4OTlJBQUF2L17t5SUlNQ416FS8Sh1ly5NnkLlqlWst5UVuqelYfkHH8DPzw/m5uaPxsw8Qq3mDubIkbxGcf16YPz4tl23BupVkRMSEjBr1iw02Wnk4MDrhV55hTeUNL1DXns5b57OzvERPXpwfaAbN57YHBAQACMjI3b48OHG94IQnpFYv77ZeioTExO8/fbbyMrKIvb29uKSJUtQWVn5OE314Yd8+kBCAvDjj6itrYVKpYKtrS0AQK1Wk4YiqpRSnDlzRue1iT169ICRkZFUW1sb22DzLQDD/pdredo3vEUIb31UKFATFISLM2fiFmOsvLycVldXi4IgYNasWRpbNUVRZBERETA3N6fGxsbC999/z7p27UoppXB3dxddDA3Rwd+/2eLCvE2bqNzKinTs2bPVIw10RXp6Ok6ePCmpVCoxMDCQ+Pj4NF2A3RqCg4HZs4HmZQXaRIaREczS0hC5cqUkubkJjo6OxMjICBcvXmQuLi60R48emiNj8+bxVs316/lkYg1QSiHjUgCPiiwvXLig3rhxo4GLi4vk4OAg3i0qknrExAgdf/iBnJw+HV59+ogAnxJtZ2cn7dmzh8ydO1cwMDBAZGSklJOTI1JKWVBQkM5uspWVFby8vGpu3LixHMAHDzefAvASCIkEY+0j/aznz8XkycCECZA+/BC3qqvxq48PSkpK1NXV1aJKpSL+/v5s+PDhjYYHy2QylpOTIxYVFVFnZ2cxMjISd+7coUqlknXv3l1wc3Mj1uPHg8yfzw18DRSlpaHb5cvE6osvIBgaNu2dZmTwUQkrVvBUtBaT0rVFoVDgp59+onl5eYK7uzubPHlyy9Id06dzZ6m6umkhw82b+RDq7GydnWs9VVVVSOvShdElS5D0yivUzs5OdHJyQnx8vKRWq4WgoCDNH2WPHrwGacYMnqFoov5KpVKBEAIPDw+Ym5tj0KBB5ODBg7C2tqaenp5CRUUFpefPs0Hbt4s7r1yBSefOkoGBgQgAQ4cOFSMjI2Fvbw97e3tcv34dcXFxSElJYe+++67O1q/6KE94ePi7YWFhm0JDQ9UAzgKYA8ABwP/kqAndtaW3QPLQobTE1ZUYhISgc5cuxM7ODmZmZk0am5RSVFVVwcTEBJRSHDt2DJIkgTGG27dvS6SwUOxXUMDEt9+Gm5sbsbGxebwvxoAFCxBtaUkrfH2FCZMm6eoytKampgbffPONVFVVJQ4dOpQNGDBA80iMtkIp/6ejqbsN+fTTT+lwQOhaXY3rQ4fi9u3bkkKhEEaMGEE8WmqBLyp61CmRrFKhtrYWvr6+UKvViIqKwrVr15harSZ1dXVYs2bNo3t29+5dnD9/HiUlJdTKykpwcHBA965dYTtnDsjIkcDKlQ8vm2Lz5s20Y8eOsLGxYenp6aIoiszd3Z3IZDJp5MiROptEXFJSgq+//rpSrVY7hoaG8hAjIccAfArGzuvkIHraG52sYcnr1sFo/37kfvIJtXd0FDp37gwbGxs0911TKpUwMTGBIAhISUlBWloaLCwskJubK5WVlYm+8fGsbsYM5jJggODs7Pzkvo4cQdmBA/ivt7f03vvva3YwlEqeEnvzTZ5Sbo1woRb89NNP9Nq1a4Krq6sUHBwsWrZGWJAxPq5i7Voe7dFEXZ3u2tEbEB4ejurCQjb54kVyY9ky3MnPlwoKCoiLiwsbMWKEqFHLreF5r1kDUArFihWIjY1FcHAwOnbsiNTUVMTExEjl5eViXV0dpkyZ8qhrtaamBvHx8cjMzKTGxsaCvb09unXpAue4OIjHjnGNuodptKioKCQlJcHNzU26efOmoFariZmZGXNwcGADBgxoVN7RVhhj2LFjR0VBQcHi0NDQ7wEAhEwHEAjGFujkIH8x/jCDZ8uWLdJgFxex79KlvDjsGYdB1q1di8t9+yKloEAqLS0VDAwMiJubm+Tu7i46V1XBcPFiJH70ES5nZEgLFy78Q2t1AF6blJiYyObPn0+a/YG1hQ8+4EWIy5frdr/g3tG///1vrJo0CYZbt3IF2FYuouXp6ahauxb7XniB1ZmZEcYYVavVgo2NDQ0MDBR69uyJ+/fvPwrzNktCAhdmmzyZTziWyVBTU4NTp06htrYW3bt3R2RkJExNTSVJkoiFhQXeeOONZgUtW8ORI0dqrl+/vmn16tWrAACE+ABYB8aarjTV82dCJ2tYQkICkpOT6YLjxwUEBDQZwdQWeuwYbt+/jwsyGSsqKmK1tbWCo6Oj5OnpKfZ0cYHp5MmoWbgQn924gVWrVjU2rIqKuFhhSgrwzTc6jerU889//rPJCLxWHDvGU22TJj25hmRk8PR3/VgLHbNp0yZ1YGCgQb8ffuDRmv79W/V+WlaGvK1bkZ6dTdN69hRUKhUeDi0VAgIC2Isvvkiqq6thbGyseS5aQ2pq+Hfl73/n3WsPn3sXL15EQUEBjIyMkJeXRysqKmBhYSE8ePAAc+bMeaah0g3JycnBvn37ilQqlWNoaKgahBgCiAUwA4zl6OQgfyH+sIptuVwuKDp04N6IQsH1J9r6Iy0rg+EXX2DQnTsY9FAf5vfff8eVK1eE28uX0/zKSiFn7lzJtrZWLC8vb786pSZQq9WIi4uj/fv3ZzKZTPfGlp1duyxwlFLs27eP2trawtDLS4CFBa/BcnBo1X6OXL7M7MvKyCvx8XCIisLvv/8umJqaokuXLo/uhVbGDgAMHMj/hYQA+flATAzkcjkmTJjw6CUPC8BFtVqNLVu2SPv375dCQkJ08rkHBQXJ09PTl4SFhW0MDQ0tBpAM4AwIGQjGEnRxDD1/fiwsLFBdXU2wciXvAHqW9QuA8MUXcB43Ds7LlxMApLi4GAkJCeL1gwcly/Bwcc/ChdTNwoKIokgyMjLg6en5+M2zZ/PuzKQk3Xc4PeTYsWOsQ4cOcHBwaHvI6OWXeWPJq68CBw8+3q5WA+3UFXvp0iVUVFQYODk58QaINtTG5CmVOJGdjfGxscKgpUtRY2WF4uJiwc3NDaIoEuCx1k2LyOXAF1/wCNyyZUB4OODlBX9//4averQunjp1Ct999x0WLVqkE4FVZ2dn2NramuTn588CsBOM1YGQ1QCmAvjsmQ/wF6PVxkBZWRkyMzNbrYViYmJCFAqFhBUrgA4duNWd3kalfpmMF/sZGwPgraG9evXC61OnkuCiIsH7ww9ha2cn3rx5sxWCELpj165d1MzMrH20fYqLudHYTCdBW9m3bx9VqVTk9ddfF0AIX0yPHm31fsaOHUsSfX3RecECIhw+jF69eqFLM4WZWrFnD184duzgeXYN1PAiSbG6ulpncX0LCwt4eXkJhoaGPMLDmATgBoBgXR1Dzx8HpRTXr1+H8qnW4ZawsbHhBs+QIVwZPDCw7fVztbW8+6uB4J6VlRXGjx+PNwRBdJo3Dz5DhwrZ2dlMEARWXl7OUy1ffAFs3MhTV+fPt5uxk5qaitTUVPL6668/+6DLgQN56q1h04mZGfB///ds+9VATk4OTp06hWnTpsHS0pJ3ma5fr1EbpzmcnJxQ6eZGy5cvh9mpU+hsZQUPD48n5gq2mlGjuKq8IPDGjiYyKw87udBC5qVVjBgxwsTQ0HB9WFhYff4wDYAjCNHS63x+0NrgqaiowO7du+mWLVvw888/s88++4xlZWVpfSBTU1MolUp+Fz08eO7ZwoJLi7cWX19uMTfk9GneOhofD7OgIIwbNw5LliwRV65c+YdWK585cwbFxcXCjBkzhPaY+YTQUH797cDdu3dZcHDw4/lAs2YB27e3ej9FRUUw6tSJGfbpwz2alJRnPzlB4EZyhw48lVcvXtiAXbt2SVVVVRg2bJhOP/jAwEA5Y2xhWFhYfUvcGQAmIEQ3cWc97Q6lFLGxsdi4cSM7duwYNm/ejCNHjmj9VDE3N4ckSVxHixAesRg8mI9paC0ff8zrWhqmqaqquB7PzJkQV6+Gr68v5s2bJ6xcuZIMtLTkbd5RUVz6wd+/xcnubaWkpAQRERFswoQJ2kdhm8PcnDsoq1YBqalcq0tbZelWkpqaCmdnZ6lnz558g5sbNwpbqVVGKYVKpSLGwcH8+bRxo25OsF8/buydPg3Exjbq/Pv1118RFxeHAQMGqJsa9twWunXrhs6dOxsTQuYAABi7D+A6gCakuZ9fWqrh0aNHjx49evTo+cvzh9e36NGjR48ePXr0/NHoDR49evTo0aNHz3OP3uDRo0ePHj169Dz36A0ePXr06NGjR89zj97g0aNHjx49evQ89+gNHj169OjRo0fPc8//AxbghvG6iqaoAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 720x432 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "f,ax = plt.subplots(1,2,figsize=(10, 6), subplot_kw=dict(aspect='equal'))\n",
-    "gdf.plot(edgecolor='grey', facecolor='w', ax=ax[0])\n",
-    "w_rook.plot(gdf, ax=ax[0], \n",
-    "        edge_kws=dict(color='r', linestyle=':', linewidth=1),\n",
-    "        node_kws=dict(marker=''))\n",
-    "ax[0].set_title('Rook')\n",
-    "ax[0].axis(np.asarray([-105.0, -95.0, 21, 26]))\n",
-    "\n",
-    "ax[0].axis('off')\n",
-    "gdf.plot(edgecolor='grey', facecolor='w', ax=ax[1])\n",
-    "w_queen.plot(gdf, ax=ax[1], \n",
-    "        edge_kws=dict(color='r', linestyle=':', linewidth=1),\n",
-    "        node_kws=dict(marker=''))\n",
-    "ax[1].set_title('Queen')\n",
-    "ax[1].axis('off')\n",
-    "ax[1].axis(np.asarray([-105.0, -95.0, 21, 26]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "w_knn = KNN.from_dataframe(gdf, k=4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(4, 32)]"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w_knn.histogram"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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BQUGGlJSUA2az+Z1MSc1iI0lSFUEQrowaNcre09N2fZ2oqCisW7cOAO6LoqhWyak8l/AzZsx41mvIgb+/P/z9/c8GBwdvS0xMdAoNDa3n6OgoeHp65i0M4HmgShVgwwagdWvoK1ZEnTp1NBejoroc6ty5XqPTp+/oTaZaR7t2/YoqyuLjx4/bBQcHH/L39883aOzv7383ODj4UERExCAfHx+to6NjfqflT1ZhnNUKjBwJjB8PfPwxMGLE4/Ll5GTg3XeBmzeBPXuALVuYsdbrAWdntmlXCEePHqWNGjUq004RZQkhBE2aNNEkJiZWS0lJ+b8jR464BwcHh/r7+xfrcf/EiRPft2nTpknjxo2LtedQoUIFWK1Wevv2bbsjR45whw4divP3938xBDZU/jU8d55ubiRJaqTRaIKrVaum8/b2dqxTpw5xd3fPedK6dUBUFCCKAACz2Yxdu3YZr1y5kmg2m/vUvX17VcDNm423v/224d69e2OnT5/+c2HXnDdvXmjv3r1b169fv/gLHj4cePVV4I03gJYtgZMngezG22gELBbgyBFg3z7gwQOWJdGpE1C+PHD7NgtbVK3KvONMY75r1y56/vx5Mnr0aBQZdnkOSEhIwL59+8w3b97cbLVaR9vawl6SJJ1Wq70+fPhwryLj+vlAKcX169dx9epVy9mzZ9OtVmtNURSfvWiFikomz73RBQBJkioBCNBoNJ0AjOjbt69DDllGoxH4+mvAzw945ZVHh8PCwvD777+nKxkZX3parXOVGjX0dqGht6Nr1Ngsy/LigsINX3/9dZTFYqnZsGFDw1tvvWWfZ2OtIGQZ+PVXoEcP5rmaTIBOxzzdwubIyGDG9/p1QJJYLDg0lHnAXl6AIGBduXKyW4sW/CuDB4MvoxjxkyY9PR2//PJLenx8fCql9DeLxXIUwHFRFK8XNGbOnDlS1apVJw4dOlRv88+9ADIbbaZbrdb3KaU7ShtnVlEpC14Io5sdSZKaajSag127dnVu06bN48Do8ePskf3TT1m616PDx3H48OHjZrN5pVNq6vIBJ05g56BBpsTk5HdFUdyYz/yugiAsUBRlAKVUP2HCBGJzA80VK9g6Vqx4fCwoiG2wTZ9evDeang7ExTGBn88/xykXF6W20ci5xMUBrVqx+LCfH4tnuzy/4UtKKWJiYnDz5k164MCBrBBRdwAnRFF8mP1cSZIaAzjfrFkzpU+fPmWSznjjxg3s2LHDkJaWNnnatGnflsWcKiql4YUzugAgSVJ1QRAudunSRduuXbvHOVqzZgE1agBDH3dnNxgMWL58uSE9PX2e2Wzeq9FoDuoptRv83XeXPeLj24PSHP/4c+bMmVujRo0v3NzchNOnT9/38/Nz6tChg22u5f/9H9CkCUsdy+L+fRZ7LmVTyXXr1qGSk5PSrVo1DtevA8uXA/XqMYO+fz/LLXZxAWrVAtq2BRyev05EsizjyJEj1oiIiPTExESdLMs9ABwRRZFKkuSh0Wgu+fn5VWjatCmKFU8vgpiYGKxevTrdarX2mT59enCZTayiUgKeq+IIWxFF8abVam0VHBys5NAimDwZWLkSuHjx0SF7e3v07NnTnuf5ngCuWiyWXg8tFm5fjx6+oPQhCKmRXR5So9EEtGzZUmjXrh0sFovDrVu3bFMgS01lBvfdd3MeL1+ehQn69wdKIZpesWJF3H34kKJZM1Yht2cPK974+WdgzRqgZk3g6FFmjLt0AdzdgZ9+AjZvBvbuZaGPZwzP8/D39xc++uijcgMHDtRrtdp9AJRZs2Yt12q1e9q1a+fcsWPHMjW4AFC5cmX07dvXQafTqalkKs+cF9LoAoAoihGyLE9ZunQp9u7da1YUBdBogIULgQkTWJw3k8xKrjYAkrVabScQ4hhdo4Z9prFdhkwxdEmSPKxWa22dTge9Xg+O4xAVFWXav3+/ucgngt9+Aw4ffpzFkB2NhlXRlSIM4OnpieTk5LyT6/UsV7h/f2Z8N25kIY65c5nhDQ1loQ1PT+YBz5vHUtn272eZE88IHx8fjBkzRg8Anp6eo1q2bFnf39//iZWlV6tWDRaLpbYkSc9nvp3Kv4YX1ugCAKV0MYAmZ8+ejfvnn3/YwaZN2Wba/PmPzvPy8kKPHj2Utm3bQpbliXq9PgSAe2bFWjdQGgVCPvUPDu5CKeUPHTqUvnv3bgiCcEWWZe8zZ85cmzlzJi5fvlywlapfH/jww4IX6+fHMhk25gkj20S1atXw8OFDzqZwEM8Do0cDb77JbkInT7I0tdGjWVbEuXPAoEHMI+7fn4Vjbtwo0bpKw7Zt25S6devK7733HunWrZuuhHr0NuHk5IQuXbro9Xr9j0/sIioqNvBCG11RFKkoihcsFkvvPXv2pOzdu5cajUbgk0+Yh3f6scZN27Ztuc6dO6Nbt27OXl5ejQgh/QAgW6nw9c4hIQdf37LF505U1OeXL1++TSndKIpiktlsfgsADh8+nL/Fu38f6NcPaNGi8AWXK8fCDSXg7Nmz0JamxNjODnj/fVYxN20ayxlu1YrlFW/ezAR+UlNZXPwpEB0djbi4OO5pljS3bt0aiqI0kSSpxG2eVFRKywttdLMQRTHMarX2PXv27OUdO3YYYGfHPLypU3OEGfR6Pdq0aYNmzZpp9Hp9rxyTUPobKL3b5Pz5/3w5axY/ZcqUalOmTPk6c/6rhJA+9+/fN966dSvvAs6eZUI5Ol3hC61fn+XwbtrEqumKQUxMjNKwYUOl1N6g0cgKN6xWVqpctSrLtggIYCluWbmxCxawcMkTYvv27XKHDh0UZ2fnJ3aN3AiCgNatW3NarXaPJEnuRY9QUSl7XgqjCwCiKB6xWCz+169fTzp37hyTgBwyBAgMzHNu9erVYbFY2kiSlF99sQRgOQhpDEIeGebp06f/brFYBm7ZssVgtebSO09KYhtatnL2bLE31Zo3b86Fh4eXPNvk9m1WhKHXsxuExQLUrs3izP7+LDTi4vI488Lfn2VC3LkDdO3K4r8Wm+obiiQkJASKonAdOnR46n9/Xbp00drZ2dXkeX770762igrwAhhdSZJ4SZK8bDlXFMVEnuevPEqq79OHxTePHs1xnpOTE+rWrQue55Pmzp2bMGfOnE8fvUipDEotAOwyPwBCBACYPn36LqvVunvevHnGbdu2ZURHR7N82kWL8t9Ayw9C2GZW+fJARIRtY8Da9ZjNZpKYmGjzGABATAxgMLCc36y497vvsnBDTAw7XqUK83Sz30xatGDHPT2BH39k6x4zhnnplLKxJcBsNuPYsWO0d+/epEjt5CcAIQTdu3cXFEWxUdVIRaVsee6NriAIEwHESJJUpFarJEktKaUd6tTJlNR1cQHat2dqZLnaAPXr18/h888/17do0aIipTRvvSmloaB0GwhpD2Bz1uFJkyYNsFgstS9evLh906ZNVpqRwWKlrq55piiUY8dYelcBxMbGYsWKFVi2bBndu3cvzGYzypcvL1+5csU2VzcrfDFtGottt27NCkdyXoTJU3Ic21jLz5DyPODjw75eupSVN8fGss+UApcuAXfv2rQkANi6davi6elJfbLmfAbUqVMHTk5OiiRJI57ZIlT+tTz3RleWZQcAEARhVFHn8jzfp0qVKlyOCrKuXVmhwM6dec63t7eHl5cXOI4bKklSQYIGfwP4GIQIIKQvCCGZ5cPvU0pvJL7zDoWXTY54Tl55Bfjvf/MNM1itVqxbt07JyMhA3bp1yZ07d5QFCxYgJSWFL1euXNFzp6SwnGGLhcVr/fzyP+9//2MVbQArRR4wIEcMPA+CwMITlSuzjAhCWA7wyZPMS16+vNA0tLi4ONy4cYPr3bs39yQzFYpCEAT07NnTjhCyXJKkQZIkPff/ByovD8/1H5skSdUopV/WqVMHgiB4F3W+LMvfREVFafPEXD/4gBmH8PA8Y3x9fVG9enUnAK/keRFg2Q2U3gOQJahNAEAURYM+IeFV3cmTiLbFEBZEv37MW8xGamoqTCYTN3z4cPj5+eG9997jxowZg5EjR6JRo0YFW6udO5kGhYsLcOIEyw8uyLhZrSzOm2WQ7eyAM2eYUbWFrHnHjwd692YZHDdusONbtjBPPhdbt26VW7ZsqbgW96ngCVCvXj0yaNAgwcXFZblWqy34kUNFpYx53nqk5Ybnef7A1atXXwHwW2EnSpJkTwi5Sikla9asyfN6dQcHuE6YgH9694ai0eR47fbt2w4cx30JYF2BF6A0DsAYEGIHQv4A0OczoFywv/9HoX/9tWhQpUr2bm5u0Gq1SEpKgs06sPv2sW4TsvxI7LxChQqoXLmysmfPHjpw4EAeAMqXL4/yBaWb/fe/LNe2dWugcWN2rKgbQWQkMHhwzvMePmQbauvWFSkzmYeKFYE5mV3YXVxY2fPDhyw3eNMmnDpzBhkZGXxAQEDx5n2C1KlTB9WqVXP47rvvBkqSdEYUxe+f9ZpUXn5eCO0FSZLsABgBOIiimJbP646CIByWZbm5o6MjWrZsme88Pt98g6R27ZDUqVOO4w8ePMC5c+cgiqJtz7yENAKlYSBkC4AD0owZV3ie36Uoip5SygNAixYtrB4eHoKnpyeKlCg8doxtxm3d+uhQWloalixZgsGDB8Pb2zvvGIOBbRB27w6sX8+yDYojhbhhA8vVHTs25/E9e9icZaFkZjQCx4/D6ueHk927o0r37qg2efJj9bXnhPj4eKxcudJssViai6J4qegRKiol54UwugAgSVJvAL/pdLq7hJAki8XyqyzL+wE4ajSaFRaLxbNOnTrpAwYMcDAYDHDKT2DGaGS78ocPM88sk1u3bmH16tVGRVEqiaKYatOCWFDyDID3QekZSZIcAPAAHgCAIAjzrVbrBACYMGECCux+ATDpx4QEoFKlHIf37duHiIgI+sknnxBNlneekcEyJgBg0iQWRy1JfHTSJKBXL6Bz55zHZZllORRV6FEMbt26ha3LluGz//s/EJ4HmjdnnnZiIpPAtFXFrYwJDw9HRkYGmjdvjn379llCQ0N3UUpH2vw3oKJSAp7rmG4uzgGAi4uLW5cuXRq0bt16kpub2+9ubm6bevXq5anT6cxXr151mD17NhYuXIjIyHy6sev1wKpV7LE6282mSpUqqFGjhiIIwhZJkmwNubwN4CgoPQNCZokzZjTM/GfVAtB8+eWXEwGA47jCDS47iRncMWNYDm8m3bp1A8/zytKlS2lGWqaD//33wNq17KaxYkXJDO79+6yzcS6PHwCQlgZMmVKmAjlVqlSB0c4OyYSw0MPly8yTXrmS/T4oBQ4efNxh4ymwZcsWeceOHfjzzz+xatUqRVEUDcdxb2o0mgPF+BtQUSk2L4ynCwCSJPkC+AcAP3bsWGTfkMnIyMDJkyeRkJBAnZ2daZs2bbh8d/opZboMggB8/vmjwwaDAevXr89ISkqKs1gs8xRFWSGKojXvBJkQEgQgDJR+C0I6ALgEFiN/CEpNmSERQ+fOneHv72/bGzx7FmjQIMdmlqIoWLJ4sTxqxQre+bffmHRlaXf+9+17LITzlPjuu+/k1q1b861bt8774oMHLPabtQFnb8+84SeEwWDAokWL8PHHH0Or1WLz5s3KvXv3uIoVK9LY2FgiCEK6IAjrJk2a9NETW4TKv5YXydOFKIoXAWh4np/w888/p//99980K1PBzs4OnTt3Rr9+/Uj37t3zN7gAM1jDhgFXrjCPKxN7e3uMHDnSbsCAATU9PT0X6HS6/QUuhBA9gN0AWLdhSo+B0vsAPgYwMnOtGTqdbrdDcXRtmzdn+rhz57Lvr1wBN24cXunWjf+5b1/6182b1lIbXICFJ/LzcrO4e5cVlpThDblmzZp8RERE/u5zuXIsnk0IC7MkJ7PsivHjy6wKLjuHDh2Ch4eHXKFCBTg6OmLUqFGcm5ubnJaWho8//hhjx451sFgso2zJDVdRKS4vlNEFmMiNLMsL0tPTex86dOjQ8uXLDXJxH4U9PZkC1w8/5HiM1mg0qFmzJkaMGOFgtVo7SpJUkLDrqwCGgtLcAgqzASwDIe1AyGCTyfTH6dOnrebi6Cy4ujKjExrKChcGDICvry9qt29P4uLiivc+84NSVqONxZ0AACAASURBVCxSWHaFuzvTrShDWrRogdu3b/NKUSGEN99kOcxmM9Oq0GhY3Hrz5sLH2UBYWBjWrVuHCxcu0NatWz/aKSSEgBCC1NRUsm/fPrlcuXLIbP7ZttQXVVHJxQtndIFH6mJ/mc3mrvfv30/JIWRuK927A9Wq5fvPrNFo4OnpaeI47t18RgJAAoC8Ygssp1cBkArgHifLx1Pu3BF++umnooOVWXFNR0dWknv0KCucyNzocnNzQ0JCQulTChISWD5u3boFn0MIK64ICSn15bKoVKkSBEGgsbGxtg2wtwfee499HRDAmnwaDCz8MnUqyzG2EbPZjFWrVim7du2Cm5sb9fPzI7mbjr777rv8lClTcPPmTd5gMODBgwcKgGs2X0RFxUZeSKObRWajweDt27ebHj58iPSsXX1b4DjgrbeACxfYTnouXn/9dUdFUZZIktQuxwuE2AFYCKDgti+UXgKlB6bNmuUw6uefryUnJ3MnT57M3/BSygyhogDLljFD26sXS6nKVl4rCAI4jiv98/6+fWzDrqgwhcHAOk+UYYjBxcWFRkZGFn/CWrXYB8B0I+bOBb77zubhS5culQkhGDt2LHr06EHat2+P3LoPHMdBq9VCr9cr8fHxeOWVV7QajWaDWq2mUta88H9QJpPp/QcPHvz6v//9z7JgwQJ5xYoV6Q8fPix6IMD+kX18WLVarsded3d3tGnTxqzT6Q5+/fXXN2fPnt0n86X2AFJBaSH1splQeswjLq6Z+9279ZNnzUpf+N//pv36668Zhw4dQlRkJDNohw4xI8jzTEwmK5Vt7FhW6HDqFAAgOTk5h55uWloainxUz49r1x4bsMJwdWW5vGVYrluvXj3OZu2I3Bw7xn4eAweyPN/Zs9mmYj6FMLlRFAXt2rXjbAlD2dnZ0ZSUFLRo0YJYrVZXsDRAFZUy44U3uqIomiZPnjxYURQ7Sqk+Li5u6fr16w3Hjx9HfHx8vmOMRiMMWQI4o0axf+hcj9KEELz66qvaCRMm2FkslmqCIHTMfEkGMNzmBVKa/vEPPyT32L17YXpqavtL//zzRUhIyJKEYcNg2byZFTUU1E0iNhb45huAUnTs2BFJSUlk9+7dyqJFi5RFixZh4cKFSmxsrO3G12JhOhRZVWtFceMGC8OUEc2bN0dCQgJXrBg3wNLbdDr2s4iLA8LCWKy3bVt2syoiZKHRaMjGjRuxePFirF69ulDLazKZ4OzsDCPToFAA2HCHUlGxnRfe6GYhiqIsiqJVluXJKSkp3x86dGj5ypUrDfkZ3l27dpnnZ7XzIYT9M8+cyfJXc8FxHNzc3AxWq9U+cMqU0RT4GsX9uVF6jyjKjGmzZsVMmzVrVodDh74/1r49TlSqxK5fUPVX1apsVz81FXoAI0aMIGFhYcTFxYVMnjwZHh4edPXq1QgMDKR59Cby48oV1j/NVu2D6tWL9RhfFI6OjrCzs5Ojo6NtH/T996zTcfXqrCFnfPzjsMvGjUwdrX599rRQAEOHDuUCAgIwYsQI3Lx5s9DNPLPZzF27dg2EELRs2VLQaDTrJUkqXStnFZVs8DNmzHjWayhT/P39aadOnf7s1KnT73/99Vd0eHh4z5o1a2rCwsKwcuVK2Nvb4/jx43zmuWxQuXLM+G3YwDo7ZHukJoSgXr16muPHj7fWmkx9iFZr//PIkb/7+/vbLihLSD1IUltQeh6SlFAzOjokxcOjQWr16jXrN2okAMzDKlBfduJEwGgE5+uLs2fP0iZNmnDe3t5o3Lgx16lTJ5w6dQo8z5MqVaoUvo6VKwEvr4JVxwBcu3YNK1asoKmpqaRW7dqsguyvvx7LOxYDRVFgsVjAZ7upREVFEaPRSH18fAqPW8TFAZ99xmQzP/jg8Y2iXr2ca+E41hevVy92g6pShW1GZsPOzg7Vq1cHpRSnTp1Chw4dcqwpO7IskwsXLtDbt2/TN954gyQmJlZ48OCBb8eOHUufPqGighesOKIkzJw5czDP88sopVSW5WgADQHg9ddfR/369R9Xi1mtwMiRrFqtV68888yZM8casHu34NS/P3abTBmU0klTpkz5ttCLE+ILIBrsEbUpKF2deZxYBGHVj59+2rfRa685xsbG8teuXUOjRo2sb731Vl7La7UiIjISO7dto7Xq1lX69OnDZ4/vhoeHY/v27Rg6dCiqVatW8Hq++AL4v/9jHnQ+XL58GVu2bEGNGjVocnIyANB+HTtybj//DN33xdOCMZvN+Pbbb5W0tDSO53kIgkA1Gg3V6XTEZDJh/PjxBRtds5kVsNy6xTzdrJsRpSybY/duJqiTHUqZ9oS/P7t5ZuPPP/+EIAgIDQ2l9evXl/v27Vtoxdn3338ve3t786+88goopViwYIHJarXWFEXRxtQLFZWCeWnCCwUxffr0DVartbIsy+6iKPoC4DiOG79r1y5s27ZNfvRYLgisL9js2SxPNhd9X39daHD5Mg5GR6Nt27Z2PM93LfCihGRVZnwAoCEoPf/I4AIApVRjsYxIdnRs4TFu3HXtrl17ACAsLEw4f/58nrvgb3/8QfeuXYtxa9aQfm+8weduUFm/fn107txZ2bx5c8HPzUlJTPUrl75Ddm7evInq1avLQ4cOJWPHjiX169fHmv37Mc/DA2fPnClwXG4URcGPP/4oV6pUCV999RXGjx+P999/n/Tp04erWrWqYjKZyC+//JL/3X77dqBhQya4/tNPjw1uFgsX5vFkAbCnk7AwVlbcrRuwbh2sViuWLVsmnz9/np48eRJeXl5Knz59iizxtVgs5OLFi3ThwoVITU1F48aNIQjC2KLGqajYwkvv6RaEJEleOp1uuVar7fzGG2/Y16yZ2SB2925gxw6WvpWdyEhcOH5c/u3WLfLGG29wO3fuNPE8/83kyZPFHOcRUhHAEQCNQGnRgVZCGoB5wxVXjRzpFevjs7Vr164ebdq04dPS0rBq1SrZarVygwcPJu4cl0OoJztGoxHz58/Hl19+CS4/WcawMLYh9dlnBS5l6dKlsq+vL+fn55fDC43avp1yEyeSnWPGyG3atOEvX76sJCYmktatWxM7OzsYjcYcpc7Lly+XZVkmo0aN4jS5ZDQBICUlBatWraLlypWjI0eO5DiOY55qaCjrXuHoCPTokXeBiYk5m2cWRMeOSGvdGitdXBSnKlUwaNAgDgB4ni9WR+UffvhBqVevHqlduzZZvXp1mizLdVVvV6W0/GuNbhYzZ87sDeBXPz8/wd/fnz3aLl7MqsH693984qefssfy8eMBAMeOHcPhw4d3T5069XUAACHvAKgPSqeDEG0+1WqFQ8gXAJKkGTOulStXbo+fn5/j/v37aa1atWjfvn25R8ZiyRKmzDVyZJ4p5s6di5EjR+av5TtlCisG+fjjHIcVRcGFCxeQkpKC48ePY9y4ccjToVeWYY6Px6noaHry5EnUqVNHcXd350+cOEF5nqfp6emc2WxG/fr1qdlspvHx8eTDDz8kdoWoh6Wnp2PNmjVUlmXlo48+4oUJE9gm35EjTF84P3buZCl0s2cX8oMEwi5cwK7du/F/ixdD/9pr4FasKPT8/FAUBXPmzEHfvn3RqFEjbNq0yRQVFXVKluWtAEIAhBWqzaGiUgD/eqMLAJIktdZoNHscHR21lSpV0raxs9NWDwkBmTDh8eN4YCCL92bGTBMSErB8+fKMj+fNW1f+/v1ZADIAKKA0b2yiGPzdrt20JFfXmWdatULXrl1phw4dSI7WNjduMKObTwbC0qVLlZYtW3J5RGUoZRVm+/cDHjm7Et29exc//vgj3N3dlU6dOnG+vr75LywmhnWjePvtPC+ZzWYkJSVh69at1GQykffee69gwfVc436ZOZO2j4wkNWfNYh5sUYpsNrB48WKlUqVKZNDVqwTNmzPpyLp12SZbMVi3bp18/fp1vlGjRkqDBg241NRUXL582XD37l0iy/Ixs9n8NoB0URTLTpJN5aXnpY/p2oIoiictFkutlJSUt65fv35uw8WLcdc1GuCXX9gJwcFAauojg4uUFLgFB8PDw8MSXr9+EgADKE0srcGVJImcaNt2ZpynJ6AoKGexkDy9xGrUYDmrn332uPlkJhUrVlROnz6NS5cuIUcu7PXrzDPOJ55bqVIl2NvbK127di3Y4AJMoyIqKt+XtFotPD09MW7cODJ+/HibDC4AaHke/n//jYz79ylq1y7a4M6fz1rCF8HgwYO5GzduIOzNN4HXXwc++YRVshWToUOH8uPGjcOdO3fw559/Kq1atcKIESPsx48fb1ezZs0OhJD7Go3m1pw5c36SJCn/uI+KSi5UTzcfJEkaWk6n+3ncjRsCP2wY06/18WH/uEYjMw6BgYgYORLbd+yInjJlSg1JkqoASBBF0VTS686cObM3pfS3Bg0a0HYA8Vq/HtyOHXlPpJS11HnnnRwbTbGxsTh69Kg1JiaGT0tLI3q9XvH19UVPg4HD7dt5uwFnsnz5cnh5eSm9evUq+iZssbDChNKydCmwZg2+HThQaduxI9eqVauix6xdC7z2GsDEaArl8uXL2L59O5o0aSK/Vr48T+rUYW2NfHxYGloxMJvNmD9/Prp16wYfHx9kKdgpioJbt24hNDTUcvXqVQ3P8+tlWR4PgGZ+ZOTX6UTl341qdPNBkiQPQRD+amYwePdMS9OTTp3Yxs6KFWyTJ1OIJSkpCT/99FMKIeQ3s9k8AsD7oiiWqMmhJEmcRqOJGThwoEetrDJdq5V1iliyhMVkc2+QnTrFjGD79nnmM5lMuHHjBn799VdMKlcOfN26TL0rF99//701PT1dGD58ODw8CmqInMmRI8C8ecDvv5fkLTIUhTUI3bcPaNECMw8fxsSJE6EvqiFm1t9pMcqS7927h5UrV9LBgweTatWqMenMRo2AoKBitwtat26dNSEhgZjNZp7jOBgMBgiCgJYtW1p79OghnDhxAkeOHDEbDAYtAGg0GqMsywLP8w95nv/baDT2F0XRkKmz7AQgSQ1L/DtRFfLzQRTFeEmSfC84OV1tFxVV02XgQLYBlZU7SilACO7evQtBEMIzMjJGZA41lOa6VqvVPUc/NEFghrdCBWZwjcac3XqTkgrUm9XpdKhXrx7cypVTTNOnc/YFPJanpKQIn332GWzS/W3X7nHIpaT0789S1/btAwgBCQmxrYw5JIQZ/N27bb6Uu7s73N3d5Z07d/KffPIJ4ffvZ7Hw7t2B8uWZaLqNDB06VACA4OBgcBwHPz8/hIWFYf/+/XyPHj3Qtm1btG3bVqsoCsxmM/R6vZ5Sivv377usXLmyByHkjiRJbxFCdlFKHezs7H4F6z6i8i9DjekWgCiKsslkmrq5c+c0GhTEqp6uXGGFE6tXA0FB8Jk7F8a0tPblkpPBWa2jRFHcUPTMBaKjlObRJaDly+OSnx/zeFu0YAYri1dfZe3PjxwpcNIBVapwl+vUwV8nTuR5zWq1QpZlFJZlkANBYDedDSV4m//8w0IKU6cCu3Y98lh5ns/SOSgcP78SaeqOHDlSSEtLI6mpqSzdjhBWWBEQwETsbZWazKRLly7w9/cHx3HQ6/V5KtuyjgOsmtHFxQXjxo3jO3To4AjgrwYNGmiGDRsGqJoO/1pUo1s4W+/eu+cY6efHQgpNm7LNpGHDAB8faDw80MzX1/jhsmXWqV9/3QKEBICQH0GIJwipgDy7YIVCAWDevHm4evUqO0ApNm7cSLdu3cp24I8fZwbjl1/Yxh7ANtP+9z8gJSXfScspCqqOHYu///47x+ZaSkoKFi9erFSvXl3JN6+3ILK87+KgKMBHHzFtixYtHqWEpaenQ1EU2BTiOnaMtfUpJhzHQRAEJSYmW9X2tm1sc+2zz1iMuISYzWbwPF/k4rVaLbp27aqZOnUq3nrrLW2moS6DwLjKi4gaXigEURRlSZKwdetWTJky5fELPM88pYAAvKYo+u/v3k0z3r4dMmH+/AQAZjAlsoMAzCCkP4CpAL4FK4Iw5GdlRFE0SpIEANi4cSMqVKigJCcncwBI+/btKQCCcuVYaOPMGVbu6uTEDNi2bcz43rvHuj5k5/hxVHrnHTgmJSnff/89tFotlWWZpqenC02aNKGvvvpq8aQLa9UCatZkHnd+HZdzM2sWcPUqCw9ky79VFAWbNm1SPD094erqWrTV//tvNr44beYz6dq1K/fbb78hISFBCQgI4B7FhQMCWKx+1Sp2E/vPf4o1b25diaLIKhSJjIy0yrL8d7EupvLSoHq6NlDY4zfHcejZs6ejXKHC6sC5c1dIM2Z8DkrvgdJmALqCGWAzAEcAiwAkghANCPkBhPiBEG02j7gigKYAkGlwAQDHjx8nR48eZe5lliqauzvLpjh8mJ20fj3bcMuOycSaXTZuDJ7nafXq1bl27drxXbp0EYYMGYJevXrxxfJys9i1q1BVLwAs1hwdzeLO//lPnoKHs2fPIjk5GSNGjLBtARMmAPk1tbSB5s2bo0qVKggJCeH279//OIA8eTLQrBnw5ZesJX3mE4atWK1WGAwGLjw83OYxFosFJ0+etJjN5m+KdTGVlwbV6BbN6oYNGxa6y1y7dm1MmDBBb7VamwFwefQCpWmgNAaU/geUhoLSDwDUBRPGpgCqABgN4CYIqSfOmDFcnDHDV5wxg+co7UEIydqY23/w4EEhKnee7KxZLHMhIoJ5bLNm5czd/fVXpjkrCDAajWjatCmaN28OX1/fwoVxiqJnT+YdFoTVyjarZs5koY980sE4jgOllNgkSZmYyNLjSoFPpjLZqVOnuEcx5MhIFpb573/Zptq+fSxebyONGzeGq6srtm7davOYs2fPUkLI36Ioqq2A/qWo4YWiCac2BB15nkeDBg3kq1evzgETuskfShMzv/rk0TFCfgdwF4AbAHcAbaZJUhCAIQBM1729X1s3fHj3dWvXommzZujSpQvs7e3BZ6mFBQezXfl+/ZiR3bWLSThGRAAdmfa62WzmbS1aKBJBYOlqly7lLUcODWV6t1999ai/W35UrVoVGRkZJD4+HjkyNvLD3p7FYEtBu3bt0KJFC6xYsUL+9ttveRcXF7lzcDBfvV8/aDt1epzVEBvLNigLEQbK4ujRo7h79y66d+/Owj9FkJaWhuDgYKPZbM4/YVrlX4Hq6RaCJElDBEEIdHR0tClw161bN73Vah0iSVLx4qSU3gGlFlA6FZROAaV/A+gM4DAA95rR0TU7hoS4fzl7drrXvHmX1k2ciL29e+PutUxn6ZNPWBubnTtZN10vL7Z55eTEclPBNuUK1OstCeXL5zVMJhMrlU5IALp2zasQlo1z585RnU5HbfK4MzKAli1L1p4ILH588OBBrFixQjYajWh44gQcr1/nN9Sti+MVKgBff81iztu2sRziCROYh14AZrMZy5Ytk8+dO4fhw4ejTZs2Nm2Y7t27N4NS+pMoimEleiMqLwWqp1s4k2VZRsuWLW36p3JycoKrq6uSkJAwUpKktQAqlliVitKbmV+tBrC6CwAQ0qjV8ePy5dq1b9aLiMCGpUsx7NdfqX2TJsR+xQr2uOzvz1qsr1rF4r9ffAEA0Gq1SlxcHJdHzKak+PiwkuSEBJaKNWkSM/JhYTbpJ3Tp0oXcuHFDWbJkCR0yZAjn5uaW4/Xo6Ghs375dlmWZtNq/nxi0WnK6TRs4OTnJNWvW5Nu1a4fcYwpi165diIyMpO3btuVdXF2RmpysxCsK8fT0JDVr1mSpbNmJi2P96t5559GNJTU1FRs2bKD169cnoaGhioeHBxk7dqzN6Xa3bt3C1atX0y0Wy1c2DVB5aVEr0gpBkiQKAOPGjUMFG0pPASA+Ph5r1641WCwWzmKx6AkhA6dPn17KioI869ISQiIopTXKJSdDbzRi6IcfwnHKlMc6tPv3A25uwIcfAq+8gqiFC3GhShXljVatOHLrFtuE276dGU9vb+bhNW3KdvENBqZpGx8PuLgwI0pI3oq4tWuB06eZZ/jFF0z9q3Ztm9+HxWLB/v37lfDwcHzxxRc5Jl+yZImckpLCv/POO3B1dYWjoyNkWca1a9cQFhYm37x5k9fpdMro0aM5FxeXgi4BAJg/f77Svn17rsPy5cDQocwLzyItDVi0CJg27fGxzOIXtGwJdO8OsyhibmAg7O3tYW9vrzRp0gQdOnTgipMR+Pvvv5v++eefGdOmTQu0eZDKS4kaXigASZIePTufOHECtnSSBQAPDw+MGzfOnuM4HgB0Ot0XZb02URTNlNJaAPCgQgXc9fJCJMexjaCePYEffmAZDh4erL2NTocaDRogLSOD3Lt8mXUENhhYLDgkhKWgLVvGMh0kiXl4kZEstvnJJ8CcOax8dutWZog8PYGLF9l1Nm8Ghg9nnmExDC7AUqg6d+7MmUwmLi3tsUTBH3/8AavVyrm6uiqXLl2SXYOCoANgb2+PJk2aYOjQofykSZNQp04drFixQjl9+jQK25BrV6kSd3DfPtx+/332JJAdqzWvRnGWMe3QAbh3D2Fbt8LZyYlOmDABY8aM4Tp27Fgsg0spRXh4uKwoynabB6m8tKiebgEEBgauN5lMg7O+9/HxweDBgwsbkoPdu3ebT58+vVUQhJtffvnl1CexxswbQzwADBkyBLXzM3p37zKBmgoVcODAAYSFhdFRo0aRLNEWm8m66aSmAunpbOMpIgIYPRro1InFQE2mnGXKNkApxfbt2+Xw8HDe0dFR9vb25i9fvowhQ4agfPny+H7JEoy5fx/O8+fn0V2QZRmHDh1STp8+zXl6esLHxwdnz56l5cuXJ2+++eajVkxRbdsiuk8f+E2YgPxE1QvCbDbjZGgoGr3+OhIaN6a1Q0JISVrSx8XFYfXq1bGTJ08ufpKxykuH6ukWgMlkytEX/dq1a0hMTCzo9DwkJSVZOI4LeVIGFwBEUbwLYCAAPCioWmvZMhbjBSthdXZ2lr/99lucPXu2eHdbnmcfLi5Ml9bOjuW4nj3LRN//+1/WlbeYEELw1ltv8WPHjkVAQAAfFxenmM1maDQaODs7w87R0Rr73nv5Ct3wPI+uXbtybdu2lW/cuIGjR4/C0dERFotF+e6775SHixYBCQnYM2SItULPnvkb3OnT88Z0wfKIFyxYQC+EhSnnRoygxhYtKHbtenzzKQZRUVFUUZSdxR6o8lKierqFIEnSEgDjADTheb6VTqf7btiwYXqtVltojNdisSAwMNCqKIqTKIo2CAuUjsDAwFCTydS6cePGtHfv3iRHlkJWfPLWrUd6wJGRkfjll1/Qp08fFKqhawuffcaKDNzcWFy4WjVmgDOr64qLoig4c+YMfH19YWdnh/2DBiktXF0512I0xqSU4s8//1Tod98RS8+e5OKDB+jfvz8eqbdlx2hkhRyZ1XVpaWkICgqS09LS+AEDBqBevXrsPIuFhVUCAoollAMAa9euTb1+/fp7oigWb6DKS4nq6RbORADgef5vnudnGQwG/U8//YSgoCBrYTerzNQmCuCptHMxmUzdAeDChQtkzpw52L59O/3f//7Hmm4Swryzd95h3R/AijnefPNN7Ny5E+np6aW7eEAA22jjOLb5FhbGUtds9AiNRiMuXryImJgYGI1GcByHVq1aged5WK1WRDRtSh/m05qoQCgF6dcP3atX59xmzKC3AYVSiqSkpPx/YYcP55B5DAoKUoxGIz9hwoTHBhdgIZqTJ9lNZsoUFhO3aTkUd+7c0QJQy35VAKhGt1AyvdTysix/piiKHQAIgnCRUnrt119/NWZkZOQ7TqfTwcnJyQigSUFzS5JU+r40j9f5QBRFQgjZADDj++DBAz4iIoKdwPNMiaxyZZbiBdZB2MXFRT558qQCsPhlDlEYW+nTJ6cGQ/v2TFEsLo5JUhYgxKMoCv78808sWrSI7t+/n65btw7z5s3D7NmzERgYSAMDAzFnzhw4XLvG623MHEF8PLvJSBLg4wMnJycuJSWFa968OQ4cOEDyVPRRCvz4Y47QRf369YnFYsk//FKzJst7Xr36kaZyUSQnJ4NSahBFseiWFyr/CtTwgo1IklROq9X+ZDabBwJoSwjZQCmt2b9/fzRo0CDP+QcOHDCfOnXqiNls7iWKojkwMHCRoijDLRZLH1EUj2Wmo40WRXFlGa/TB8BVAKhWrRrezd6m5vx5YOJEluUAICIiAjt27ED37t0REhJC09LSSL169ZTevXtzOltFvn18WMw4s8w2B5s3s6KNLVtYb7XMlLNr165h586dikajIb179yZZnZgppTAajTAYDNDpdHB0dAQdNgwkMLBooRtKWfXdunVAjRowm81YtGgRDQgIQOvWrcnJkydx8OBBjB49Gu65RYFyERwcjCtXrigff/xx/k6J2cx0GsaMAfbsKTQv+fz589i3b9+eiRMn9ir8Daj8W1CNbjGRJMk+swOAC4B7AITmzZub/P39dU7ZPD5ZlrFx40bD7du3U2RZPi7Lcn9vb2/ExMSkWq3WUZTSLWClo5woimX6S5AkiQAYLgjCkl69ejk3a9bs8YsWC6tWAwCdDtu2baOxsbFyrVq1BD8/P6xevVo2Go3c6NGjiU1lw0V1dLBYWIXc3r1M1hHAwoULZV9fX/6VV17Jv118caCUif0MGMCMemY8e82aNYosyxg5cuSj9K41a9agfPnycp8+fVjF4IEDLANj7NgcU/7www9yw4YN87Siz8HNm6w4ZO9eVj5cACEhITh8+PC8adOmTSrdG1V5WVDDC8VEFEVD5ucUURQ1ANzDwsKClixZYjxw4IA5q1SV53kMGTLEftCgQZVlWe4PAI0bN6ZDhgxxtrOzWwMm9wgAxUtutW2NVBTF1VartfOePXuS9+3bZ36UZ6zRsLzblczBfvvtt8m4ceOEXr16wdHREWPGjOGdnJzosWPHbKu53b07p7B6bjQaJjnZogUeook10QAAIABJREFU+Ppix7vvKhaLhW/WrFnRBvf8eWDu3MLPoZTFkR88eGRwz507h5iYGO7tt9/OkU/r7e2N+Pj4x2N9fFg3jFyYzWZasWLFgg1uRARQvTorLsmnVVJ20tPTLYqiJBT+JlT+TahGt5SIopgwderU/1it1lqnTp26sHXr1owsA0cIQY0aNTBp0iSMGjUKzZo1I9WrV0eXLl3seZ6fwnFcOmwQSinF2v6xWCx1z507dzQoKCg9JSu+OnUqq1Qz5d9D87XXXuPOnz/PxdrSVSE4mFV1FQYhuHLlCs56ecFn6FDui7594WZLnrC7e75GEQDzoPv3ZzHjb75h2RMAQkNDsWfPHrz99tvIXfJ87949VMxeCFG+/CNtiuy4urqS/fv303xTBJOSgA8+YDcbNzeWOlcI6enpZgBJhZ6k8q9CNbplhCiKsWaz2S8qKurvDRs2GCzZepfp9XpUzVIEA9N3HTp0qDMhRAfWpPBJrivRZDK9kpSUNP2HH37IuHjx4uMChvbt821pXrlyZdjb21sLzP3NzsKFLHxQBLGxsbgxaJDcsGtX8O+8A3z+edFzOzvnr1SWmso86LFjc3QGDg4OxsGDBzFo0CDUqVMnn2Gp1GKxPA6p9enDvOlcDB06lOd5nmzcuPFx2EeWWRijQgWW8RAczPrmFXDjyiI9Pd0KILnoN6vyb0E1umWIKIoZZrP51Tt37uxdvXq1wVTAPyQhBN7e3mjdurWi1Wo3ZcaHn+S66FdffbXQYrF0+u2331Kio6NZ/PPgQVbokE96l0ajIQZb0qI++QS4fbvI0x4+fPi4+WVoKKtgW7yYhToKYtgwpiGRnYwM5v0+eMAMMiEwGo0ICgqST58+jeHDhyNrYy43/fr1I9HR0eTo0aMsdHL4MNAkZ4KJoiiIiYnBgwcP4OPj89joGo1MztJoZPHrBQuYwS5kE+3evXuIi4uzh+rpqmRDNbpljCiKFrPZ3D8hIWFjUFBQ+o0bNwo8t1u3blp3d3cPAAFPaW1nLBbLuL1796ZRStnj9YULrNlmLhwcHMjt27eLjuv278/mKQI3NzfExsZysiyzLhKCwDxunY4Z/fzyhbdtA7p1Y19TyrIk7OyYyE5meCI6OhqLFy+mDg4OZNy4cahSpUqBa3B2dsbQoUPJ4cOHufvh4cxzzYz53r9/H4GBgXT27Nn4+eef0bFjR/nVV1/lYDQyESFFYTcKOztg1CjWhj6wcO2aU6dOwWQyaQAUr9+7ykuNanSfAKIoKmaz+f3k5OQPNm3aFLdixYq02/l4g4QQVK9eXavVar+dOXPmp5IkPdFQQyYbU1JS4o8ePSpTSpmQzapVzKhly2Tp3bs3d+nSJS7HxlN+tGljk5TjhQsXrKmpqawrbxYffsjUyWbMYCXF2Xn4kOXDZm22paezhpwGw6M4anR0NDZv3kzbtWuHwYMHc7bILFosFgiCACdByJFDvHXrVrlOnTrK1KlT8dVXX8Hf358Hpeym0Lp1zvcYGMgU2YqIS3fp0iXrSzVHV+URqtF9QmRmEGwwm83VY2JiPlu7dm16fgasa9eu2sGDB3vVqFFjrk6n2/YU1qWYzeaAo0eP3tq9e7dJoZSVt86bBwQFPTrP1dUV9evXpzt37lQKTSvs3Jm1pi8ARVGwbt065eHDh8LEiRORrwyjJLEwQkICyyMGmJG9fZt1EP7oI+Ydr1r1yPitX79e2bBhA5o3b047duxIbFX9ysjIgFarpbyPz6NUsdTUVMTFxfHdunXjH5VQWyxAly6sVdDgwazABGBVaampQNWqLKZbCHq9HoQQBUDR8ReVfw2q0X3CiKJomT59+nJFUeYePnw4I7cBy/R28c477+gJIR0lSSq+akzx13THbDY3CwsL+2fjxo0ZiqKwtjtDhuQ4r0+fPiQ5OZncvXu34MlOnQLyKQ7JYu/evUhKSiIffPBBwYLfHMc0fS9fZq3WKWXi4RMnss20Hj0AjQYxMTE4evQoli1bpiQmJpJx48ahW7duXHFyfX18fJCenk6s48YBe/6fvfMOi+Ls2vj9zGwDFqRIVUFAaQIKWAARsWNvMdZYk5hYotE0E+NmTTFR38SYaMyrEY1RjBpjbyioKCoWRFEkIAIqVWlSlt2deb4/HkCpmrwpX5L9XReXyM7uzszCmTPnuc99DtdOlaCUori4mG1Us1D3zTe1qohakpKA9HR2kZgxo9n3qm5r1tbIDA0YAAxB909DEITV6enpuQkJCY2mjRKJBKIoEvxJK90qlapEq9WG3r17Nz05OZkFOWNjYPDg2oUxiUQCExMTIScnp+kX2rULKGx6l/Pz8+Hr60ueaWJFz54s6KamsszWwYH53Y4cCRCC3bt3iydOnIBcLqcvvfQSMX2WEfD1qPHdFRcsAIKCcP36dVy7dg2Ojo5CaWkpq9326sXOwZPeCzVMn86aIXr0eKqGuKysDBKJxKBcMFAHQ9D9k1CpVI+0Wu3cK1euNNpJUFJSAkEQCFiX25+1T9qqqqolUVFRmrKyMpZxLlvGFA3VeHl5SY4cOdK0L0NSUrPmL5WVlcKzjrQBwEx5iopYvdTFBRg1CliyBDe//Rbc/fvcwoULMXnyZN74GerIjVFeXg5CKY5u3Ch+um4dDh8+DIVCgalTp/IdamR+Z86w8kF9Vq1iUyYAVgppwnujBkEQQAj5U0yPDPx9MATdP5fTubm5iic1vACzWoyIiKgghKz4vVuCn4G9Go3m+Jo1ayrv3bsH+PqyGu306QCAvn37IjAwEBEREVixYsXjW/AaPvigTpB+klOnTqGkpIR3dnZ++l5QyiZYTJrEFBWZmSzIRUYCwcGw5DgM2L0b0n79mNQtObnOwt+zYmVlha5ubui8bx83efJk2NvbQ6PR4EFBAZOvpac33fDw8suPSzAnT9Z6WDSFQqGAKIrKX72TBv7RGLwX/mSWL1+eOGrUKN+aKQ9nz54VTp06VaDX62dSSvf/BUEXALB06dIhEonkx4kTJxo7OTiwABgYWPv49evXsW/fPkgkEhgZGYnm5uY0JCSEd/7oI5B33210VM+XX36pDw0NlfjVVybUJyuLDbacMIH5GXh7syyySxdWbmjRAqIoYvknn9CpgYHE7sYNFpjbt2cLXcOHs0YP/tcNYQbYVIe0Dz6gCQ4OZPrChVAqm4iRd+6wLLx6pP2zoNFosHLlyqrFixf/unEaBv7RGKYB/8lotdrvTp48uUwqlRpfuHBBk5aWVqrT6YJVKlXTgt4/gSVLlhxYunTp8MjIyH0vvfSSkVVgIBARASiVwJgxNc0N4uDBgzmNRsOlpqZix44d1MXKCoNNTYlJI6+p0+maHxopiqwmrNGwdtzwcLaABbBsc8oUVrqolmZRnkdV+/ZA374sy33wgGWby5Yx6VuLFiz4+vk1L+fau5eVUoYOhb29PYytrMjVsjKUlZU1HXTv3WOllJqgu349e5/OnZt8G71eD0EQ5Gq1WqJSqQxlBgMAAP6DDz74q/fhX0VMTMwlrVbbPikpyaKgoOBLvV4/XqVS/Wl13Obo2bNn+smTJwsuXLgwVKFQoLWzM+DsjAqZDLt27YKlpaXYp08fztbWFp6enggODiax8fGisbU1Z9eI9eLp06cRGBhIFNVtx3VkXXo9k4LFx7Pmg/79G2aq5eVscGa3biCEoLi4mFy+fFn08/MjHM8DJiasHDJxIussq6pizxkzhvkHu7gwG8b6zRtlZUwVsWYN9FZWWJGWBidfX3Srfp8GUMoMbrp0efyzwkK20NeM1298fDyqm2PUYWFhhltKAwAM5QUDjaBWqxdLJJL3Ro0apfB0d8eDqVPxQ+vWGDt3Luzt7Wu3u337NvgBA1D12WdoHR7+uM23mo8++ggLFy7Evn37xLS0NM7W1lYcPnQoZ717N8sUd+1i430kTdxwnTjBpjTExwNgmePXX38tKpVKvPDCC017/up0zBshK4u1HFtZscx3xAimSBAEllFHRwOdO2P38eNiSkoKMTIyQrdu3dCxY0dy5swZ2rFjR2JqagrjS5eYfCwysvH3a4SioiKsXr0aAEaoVCrDfDQDtRiCroFGUavV/lKpNGby5MlmDnFxOMxxNKugQHz11Vdr09GvvvpKMDY25ktLS2lZWRkJCQnR9+rVSwKweuby5cvx/vvv4/vvvxckEglfeuUKvPR6BFMK6cyZbLxPc+h0zCzcxaV2cUuv12P9+vUCpZR7+eWXiUQiQXl5OS5dukS1Wi28vb3JkxcGiCKzloyIAA4cAFxdmbl6ZCRTRtRuJiI+Ph4XL14USkpK+BqnOI7jsOittyB58KCukbq/P3sNd/dGdz05ORk7duwAAGeVSpXxK069gX84BvWCgUZRqVRXBEF4d/v27bqr7dqhND2ddjh4sM7vC8/zsD12jAY6O5Np06YhLi5OUqODzc/Pr+nIgr6qihTEx2P0li0oS03FfxwcUOHsjJKSEvzyyy9N74RUCixcCOzZg5rkQCKRYObMmbxGoxFPnTqFvLw8rF+/niYlJdH79+8jIiIC+flPVGs4jmXKV64w/9vqrLl+QwfHcQgMDMTcuXP5OXPmICgoCFOmTEELuVy8tXQpbTC54syZOouHer0eWVlZ0GjYHNInBpcazG4M1MFQ0zXQJCdPnrxCKX37zp07EidHRy64VSsi7dKl1iTGz8+Pc7t3jzj27w+z1q1x5coV4fLly+TmzZviqVOnODMzM7GblRXpuGEDeXT/Po4OGIDKrl2F8vJyLigoCMeOHRNiYmK4goICwdXVlZM0VmagFEWVlVh+4AAEQYCzszM4joOlpSUXGxsrXLx4kbOzs6Mvvvgi5+HhQeLj46mjuTmxFkU2IqjGntLEhLX15uQAkyc3O+1BoVDA1dUV5ubmcJLLSc6GDeScsbEAgDMzM4Nep0P62rV0z61b4sGDB7nLly8LcXFxJCEhgVy8eJE6OTkRBwcHXL58uVyr1e4OCwtrpqXPwL8NQ3nBQLMsXbp0Vrt27ZZPmDCBFWyPH2e3+t27N9hWFEWcPHkSWq0Wly9cQOe4OJS4uFBlVha52LkzwHFwc3NDTk4OXbBgAYmKiqJpaWn00aNHRKFQkHHjxtWZX5aTk4MbcXGiYs0a7tKwYSgpKYGHh4c4evTohgE6MRFpxcWoeOMN+Ny9C5KWBmzfzma01XSuiSLzW3j7bdbk8Pnnjw11moJS5OTmIi4uDllZWUJ5eTnPiyLG/PQTPfXaa+TevXuwtbWFi4sL7dKlC7l69apw7tw5nhBCW7ZsKWRnZy9WqVSf/U8fgoF/FIbygoFmoZTuz8rKevx7IoqPZ6wBzPP25k0A7Ba9d+/e6O7uju6nTsGxqIj6jhxJev/0E/wCAsDzPHieh5OTk1hRUYGEhATi4+PD9ejRg5SVlSEqKqr2hR89eoSNGzfiXnEx6ZyTg/nPP49Zs2YhMzMTn332GSorK5lCYc0a5v/w3HOwycrC6bAwkPR0JnV78cW6k4qTkgALC6ZE8PVlCormKCkBfHxgb22N0aNH4/XXX+fnz5+PmXPmoF1iIpkxYwZmzpyJ8vJyMTMzk1pYWKBXr178O++8AwsLC+Tk5EhkMtmzjQ028K/BEHQNPA25RCJ57HLevz/Tqm7cyBoYFi1iQayGFStAQ0JQOHKk4HnhAvHo3x8KhQLt2rVD+/bt6YMHDwRBEPjIyEjRwsJCCAkJQVBQEHieFwMCAmp/H69du0bNzc2FqTNmEMX33wMmJrC2tsbUnj25gbt2IfGrrwThhx+AhAS2qJWSAuXEiSg0NYWuRutbn9u3gZqJEtOnNz/bDWCKh6NH66grlEolLEtKat3Q7OzsYGlpCaVSWXvLyHEcpkyZQoKDgyGK4q/ogTbwb8AQdA08DaVUKm1oZn73LnPjUirZLfrVq8A77wBt2uDwa68JxSYmRPdEJunl5YWxY8eSXr168dnZ2YJcLicvvPACn5OTgytXrkAURSIvLweqqpCfkYHbW7aQkJAQHvHxrB3Y0xMIDkZLCwvYd+iA6yUlZIO/P019+21QjgM4DufOnYNUKkWjJTNKmQ9uTXNDSgowdGjzR759O8uM69OiBdCvH7RaLc6ePYvs7Gxu0KBBdUTGRkZGsLW1hV6vb6VWq7s9/TQb+LdgCLoGnkZ+eXm5pKDgiYG2hAAqFVucGjuW6V1feIGZxAwbhpHz5vEtEhLopk2bRG1S0uORO2vWwFMQMH/6dH5SZCTZu3evcHn2bOCNN6iPj4/gvGQJ9FeuYNfGjeiTnIyOHTsy85vERNZ95uUFzs0Nlt99B+rkhJKSEvz000908+bN4pkzZ3Dq1ClMmDABMpms4VHExrIWY1dX9n83N6ZAaApBYON8GlvcMzMD+vTBkSNHcPz4cXTt2pW2aKQDztvbu+bb82q1+g8bQGrg74Uh6BpoFpVKlS2K4vzIyMjy+kY9KCpiaoApU9htfmIicOgQZHI5RiUl8fqqKrrv+++pNj2dbe/kBCiVEI2MEN21K01PT+d7rFkD/wMHyJAhQyQ4eBBlnp4olMlgdfQoaEkJmx5x8iRz9VKrUfnhh4h4913odDq6YMECsmDBAkIpJSdPngSlFFu2bMHx48dFff16bVISq/HWQAjb34iIxg+c41hDRGMBfPt2YMYMdKnuUNPr9Y2ONSKEoEOHDjr2ctySp59tA/8GDJKxfylqtVoSGxs7++TJk65hYWFJzW0bExOTIIpiD5lM5tqmTZvHGVuLFiwDffCAmZC//jrTvxICMnUqAjp35g4kJMBmwABibW3Nsktzc1RWVSEyLo6MGTMGrdq2fey3AEAulyM3N1c4evQod/+77+B69CiK/P1BXV1RSilyVCraprgYg7/6iuOqF+bu3LlDpFKp2KdPHxIQEICTJ0/S6OhocubMGaSnp4vtnZ2JbM8eNgvuyZbg8nLm7dBYg0NoKCtFWFk1fMzXFxg+HElJSbh9+zb0ej3p3Llzo5lsRUUFn5qaCkppWGxsbHbPnj2vNP/JGPinYzC8+ffSUhTF1SYmJhVqtTpXpVLFNLWhSqWiarV6RXx8fIifn5+yxksBAFuQOnGCLVJt3syy3mrKysqg0+lI27Zt67yesbExTExMRIVC0eBOixCC4KAg3uXzz2Hz5pvY6+Mj3L95k2iSkjhRFOH51lsYNXw4Qb9+zGNh5kzY2dkhPT2d1NzOL1y4kBNFEUVFRdi7dy+2ffIJxpeUQFldkti8eTOtqKggzs7OQni/fnyLsjJWm36SnTsBGxuUlZVh+/btYnBwMOdV01Bx8SJgZISYmBjY2Nhg+vTpTZYOWrVqBalUWigIwkNRFP/78ccfO+v1+vf+Kje5fwNqtboFgEoArQF8B+AOgFMA4gCkPXnu1Wq1EwCe5/lpAKoEQVinUqke/JH7Zwi6/17ypFJppkajaQ3gJbVabfYUj4CTlZWVP33xxRfPe3t7c/369ZMrFArWolvTprttG+vSqtbwbt26VRQEgXuyxvrjjz8CABUEgbNqLIsE0LKyEsb5+bi5di0dd/AgX9MCLIoiOI5jAe6VV5ik6949nImNFfv261cn8HEcBysrK0yfPp1Le+EFxBUW4sG2bUJBQQFnZWVFR48eTY4ePUoSnn8epi1biuYff8y1bdsWmzdvFt1u30b3V17h7mRkYOvWrQDAnTt3TvDy8mKLZbdvA+bmsLS0FMrKynidTtd4HRmAvb092rRpI8/IyDjIcdxQvV6/CMB+AOee8vkY+BWo1WoikUhmcxy3mOd5S1EUeUppzUU9zN3dfUxKSoqyelsAOADgCoAlADOcNzMzEzUazUS1Wu3zR7rCGZoj/sWo1WpjnufjBUGoMUHgVSpVs2PX1Wq1l0Qi2dqvX79OXbt2ZT88c4Ytls2bB3zyCTOVeekl5OTkIDIyUuB5nhszZgxxcHCo+YXH7Nmz0bJly4Zv8MUXgJERfgkMRO7ixTR0/36C5oZOBgXhplZLpdu3k/bt2zd8XK8HunRB6d69OBYfD7lcjsGDB6NmrlpZcTGORUcjNTWVAiAAaJdTp5Dt748MhYJ07doVwcHBWLVqFd544w3UyfIBrFy5Uhw9ejTXnFH7w4cP8c0339DqySAA0E6lUt1u+qAMNEf1oqQ9AFcArjzPe0ml0iFGRkaOzz33nEmN94YgCHiyiSY7Oxt79uxBQUEBeJ5Hjb8GABgZGVW98sor8p07d5bn5+enarXaqSqVKvGP2H9DpvsvRqVSVajV6hAAXgDuPC3gVpMiiqKXW43eFWDGNfv3M2vGhQuBceOAgADY+/lh/vz5/K5du7Bz5046b948olAo6Lhx40j9gEspxbWEBDhu2EDjBw0SU1NTedfZs9FswAWAqCjcXLmStPv+exETJnDwrDfX89IlYNo0mDk64jlHxwZPV5qbY1R5OcRBg8jGCxdgqlSKnX74gZfdvIlBXl61HgomJibCpUuX+FatWsF53Trg+edxTqsVtVotd+3aNcHZ2blJB3UrKysMHDiQHDhwAAqFIv7tt982BNxnQK1WywCEAmgvkUg8pFKpD6XUleM4e6lUqm/RooW2ZcuWvI2NjUmbNm2Io6NjnSBbv2vRwcEBs2bNAgCsWLFCHDt2LGdpaYmysjJYW1vLeZ7HhAkTTHbs2OGZkZHRBYAh6Br4/VGpVMVgta5n3V5YtmxZmSiKj41kLSzYAlWNyXd0NDBxIh54eeG8vz8yMzNFDw8PmpyczGs0GhIbGyuUlZXxjo6OqBkuWfDDDxBXrULkrFlCx6oqyUvnz0M+cGCzEbe0tBR79uwR78vlXHhyMofXXweOHKm7UWJik+OEaijUapF45gxatGwJ5xUreBNRRMiECXW26dSpEx8bGwtBEDDM0xNcWRnir14lJiYmKC4uJleuXBGrqqpoUFBQo8HXz88PBw4cgEajOdzszvzLqc5i3SUSyVSJRPKqpaUl7O3tpdbW1kYWFhawtLSEhYUF5HK5FMD/1HjCcRyUSmUd4/pqfTWXkZHRjPv+/4Yh6Br4VVTXzuQNapje3sBPP7GgK5EA69fjYWAgOEEQegwcyHXt1o0rKyuDq6sr1Wq1OHbsmFBeXs6bm5uLXl5exHnnTlLWtq04a9YsCQh56vyziooKrF69Gm5ubvTll1+G0sqKGZbPmcOUFG+8wUoLSUm13WNNEVFcLJrL5aQyN5e2Xb6cyPz9CcCC+ubNm6lGo6E6nY7T6XQYOHAgLkRFCZXFxdTC0pLv3bs3tm7dSjIyMggAaDQasVevXg0WCDmOw4gRI3D48OFharVabVhIa4hare4sl8sjOI5z8fb2lnTp0kVmbW39p+9H27ZtpRcvXvxIrVb/rFKp0n7v1zcEXQPPjFqtJnK5fLVMJuNqDMtFUURUVBTu5eWJU44c4SRvvskmKpiZYc+UKXReTAyvkMmAbt1gZmaGSZMmEQA8AGi1Wlw7coSzmzMHO8eOpaJSiR4ffcSeP2NGs/uyZcsWoW3btnj++ecfZ5YyGdCvHxAXBxQXs+GVqamNT/at5sSJE6CUctMiIsAtWkRgZsaaHwDcvXsXhYWFpG/fvsTV1RV2dnYAgK7Tp/OIjgaqzXkWLlxI8vPzcfbsWZw+fZrr3r17owtrPj4+iI6OdquqquoD4Pizn/l/Pp9++ukquVz+cnh4uFHHjh0bn+DxO0IpbfINCCHgOK5KFMWsP+K9DUHXwDPD8/xspVI5bfr06UYxMTE0KSlJqKys5JVKJbWzsyMHOnWiNu+8g84bNpDq+WDk0bp1UKxdy2q+gwfX6fCSyWTorNMB3bvjdZWKiKJIUFbGMtZmOHToEMrKyrgpU6Y0/MMZPpx9Pf886yirafWNjQWsrZEhl+PYt98Klba2MFYqSWFhIQkNDSXca6+x0kR2NvNyAJCamgpHR0ehe/fudUsGSXVlzRKJBA4ODrh7967QvXt3viklA8dx6NOnj8mhQ4f+o1arOxmyXYZarfYFMK/JxdXfmXPnzoEQQmxtbRt9/NixY4/0ev10lUrV/C/ib8QQdA08E2q1eq5EIlk5duxY2S+//ILz58+T8PBwSYsWLeDi4kIIIbhjbIzSr76i6775RnRo1Ypr1aqVYN22LY9ly4CpU5kt5KpVbMFNEJjOdvFiNroHAPfNN8CwYc1mpikpKbh69SpmzJhB6isJ6rByJWvUuH6d/X/pUqBTJ+yxthZeW7GCv3X4MJTbtsH60CFUzZwJdO3KJGgbNwLFxTh74waSk5MxefLkugG3spJ1sVUvyIiiiH379uHGjRsQRZHv2LFj9WaVSEpKglKphIeHR23m5u3tjejoaNeqqqq+AKJ+26fxjyMRAJodYtoUovh0e84n0Ov1OHXqFB02bBiRNmKMlJubi9LSUgJgz6/fmWfD0AZs4Kmo1WopgNV6vV62du1aHDx4EKNGjYK/vz9cXV1rA4pzeDg6tGtH2vM8vXHjBhwcHNjvF8+zwOrvD/znP2z67507LIDVm+BQx4qxEa5evQp7e3uxqSylFmNjID2dzUgDgKgonB46FKJEwlGtFl79+8Nx3ToYXb8Oc3NzJlUrKwMyMoBhw9DijTfo0E6d0Kq6pFCLTgfk5tb+d/369WJiYiKef/55TJkypTZTu3XrFqKiorBz506Ul5fXbs9xHLp27Woik8nGNn8A/w7UanVtDOLrDyZ9Gjt2sN+tefOe+Sm7d++m1tbW1LO+ygWsmeeHH36oEATh5T9Sp2sIugaeikql0qlUKgJAAWAiAMHDw6PhhoRA4uSEgUZG/MSJE+Hr6/v49l+hYFN7CwqATp3Yotvhw+znAPPknT274eTeegwePBg5OTlceo2fQ2OkpjIZm1Hdxe2ioiKq1+tJrXmPQgHUBO9evYCCAmjHjcPBmTOR0K4dsVu9mnXcHT3KXhNg9d6lS2tf08jIiAOAtm3bwt7eHj9JwuXbAAAgAElEQVT88IOwbNky7Nu3D0FBQZTnecTExIhFRUW1z3F1dQWldKxara51xPkX4wQARkZGzyJXZGg0wPnzQFgYM6R/772nPkWr1SIyMlJMS0sjw4YN4+rXjMvLy7Fly5YKrVb7OaV0u1qt9lar1U1MPv3fMARdA09FrVabqtXqUQBelcvlM62trTVNbjx1KvDpp2jn4oIG2ahMBnz8MctyN2x4/POHD4FXX326qXg1PM/ToqKipuuhRkbAl182yJqHDx9ObGxscOnSpUb/wG/MnEkL3Nygv3VLDFKpYHXoEFNBcByzrXzuOVZaqF7kKysrw7179zBp0iSIoohNmzaJRUVF0Gq14DgOvXr1IhMnTkRmZiY5cOBArRLf1tYWQ4YMMZFKpafVanUjHR3/KtyNjY11lZWVnFqtxs6dO5GSkoKysrKmn3HlCtCnDzBzJrPrjItj3zfBhQsX8Pnnn1ONRoOZM2eiviIiPT0dX3/9dWVRUdE6juN8AIgArgNoKOz+HTDUdA00i1qt7iyRSE44ODgQGxsbmZ2dnbxTp05NP8HKimWHx44B4eENHy8rY2YyP/3EPHgPHWJG6KdOPdP+bN68WdRoNE22EOPiRTaGp4lx6W5ubrh27VqjjyXIZDRo1SoyfPTox8mIjw/7Cgtj2e6QIay8sHYtqh49AqUUycnJ4u7duzmlUkllMhkHAP7+/rS0tJRs27aNCoJA2tSrU/v6+hKdTtfi6NGjZ9VqdReVSpX5TCfgn0epkZFR5WuvvSa9cOGCcObMGf7mzZswMzMT582bx3FP1mtjY1mNfsIEYPVqtiYAAC1bNtpEk5eXhx07dgiVlZX8sGHDiKenJ6mf4RYXF+PHH3+s1Gq1QwEQpVK5t2XLltr8/PzN7777buofccCGoGvgabQ3NTWVTZkyRcE964JFt27M6rGxoFtQwP5gzMyYCmDfPuD+fTZ2pxFOnTqFS5cuiaIoQhRFQgjhAGDz5s1QqVQAgKNHj4opKSmiiYkJZnCcpLm6sJeXF44fP84lJSU96XcLALjn4EBM/f2BH35g9ecn681SKXNV8/FhzSCffgqr2Fh07t1bOH/pEj9w4EDcycggaWlpZOrUqXByciL3798Hz/Nk9OjRcHZ25mta7mv+8AMCAjitVmsZExMTp1arO6tUqpxnO8H/KHIrKip4uVyO0NBQPiQkBGVlZdi0aRPZv38/HT58OIEoMkWLkxPw1VfAu+8CeXmAvPruPySE/c79+CMwdiz0ej1+/vln+ssvv5CAgAD07t27UQmfKIrYuXNnuSiKy1Qq1YkPP/zwI71eb5yfn5+n0+le/6MO2BB0DTyNHysqKmafPn26a1hYWBNzcOrh7w8cOMAmS1RrXmtZuZJlJsuWAZMmsZbhNWtYc0VSEkRRxI0bN5CUlIScnByBUsqFh4dz5ubm4Hke1tbWKCsrw6pVq7Bx40aYmZkhJSWFGzRoEBe3dStyQ0Jg99//Nrlr5ubmcHNzE/fs2cNVVVUhICAAZWVlWLNmDbX75RdSNXQohCVL0GBJp6qKGbbPns0MfY4eBd55BwN69+YH7N4NumULDltbc506dYJjdbuxUqmETqeDlZUVSkpKsHHjRhBCEBYWRv39/YlEIkFQUBCv1Wpt4uLi4qoz3j/U4er/Ia1NTU1r60ocx8HMzAzjxo0jGzZsQIcOHdDu4EH2u/TWW8D8+Ux6KK9Xbi0qAl55BYk2NjgSH08tLCzoSy+9RGxsbJpcnTt37pzw8OHDZL1e/ykAiKJ4XKPRvAegu0qlKm/qef8rBj9dA80SFhZGT5w4cbmwsHByUFDQsy0sKBQsg7WyYt1hT2Jjw8oLFhbMPrGiAujTB6KNDXb/8gviNmygNwsKYGdnR/38/PghQ4YQOzs7mJmZQalUguM4KBQKmJiYCNXZr+jn50c7d+7M2S9aRLUpKcR07Fhcv34dUqkUxsbGDXbP29ubnDt3jqakpBB7e3vcu3cP9+/fx+TFi8k+MzPx2N27uJKSIra1tuaUNbrRwkJWGnn7beD999kf+a5dbKy7ry9Ily54WF6OzgsXoiA4GJatW0OhUCA7O1s8duwYuXLlCuzt7WlQUBA5ffq0eObMGS4oKAgcx8HJyYnTaDQmeXl5g6OjozeGhYUJDXb6H8qpU6dCWrduPdTb27vO75aJiQks7tyhpw4dgvfcuUTi5MQuzP36sYtfPQqrqhBhbS2kpKRwAwYPJoMGDSLK+nadT0Apxfbt2zUajWaISqXKA4CwsLCMsLAwdVhYWFGTT/wdMGS6Bp6FpIqKCklpaSnM6meuTTFhAls069nz8c80Grbyv38/+79SyaRd5ubYY2xM6d69ZPqePYQrKgKpsXBsgi5dujTQz6bMmydev3OHq1q5EjKZDJWVlYTjODp+/HhiamqKyspKtG7dGvHx8RBFkfTo0YPu2bOH6vV6Eh4eDhOlElMp5fKeew6Fb77Jp9++Te02bCBlu3ZB9+ab2D51qjCgRw/excQEGDiQycuOHwdGjgRsbTF42DB8f+4c7W1lRbBuHWBsjHGTJ3P5+fk4fvw4Bg0aRMzNzXHjxg1SXl4u1hQsCSHo27evND8/3zUrK2sNgH/TBGFBo2l8XbZDVRXJB8QtO3eKL40ezWPYMGZE/wTVOml68+ZN4uPjg8GffQbO0pIpZJpBp9NBo9HIACT/XgfyrBiCroGnolKphE8++eTLn3/++bXJkycbP1OLZrdugJ0d856tmUt2+zb74nm2IDJgQK2w3cLCgpx2ccG9t9+G/8GDtPO2bcR469ZnF74PH47egwbxjuPHQ6FQwNHREZRS7Ny5U9y6dStPKQXHcZDL5WJVVRU3duxYODk5kbi4OOLm5kb9/PwIAHD378PezAwnRo0SyisruUBRRPwPP1DpgAE0pFUrPsbRES417zlpEvuysgJCQ7F9+3bROCCAtmvXjnkAa7XAo0eweeMNTIiIAHgecXFxyM7O5l599dU6ra6EEIwePdp47dq145cuXXpyyZIlPzzr5/M3JycvL69u2erdd4E+fUBeegmBZWWcm7c3Hm7cCKtzdS2Ib9y4gUOHDokmJiaYNm0asbe35+Hk1PgkkHpoNBpQSiUAOgG4/Dsez1MxBF0Dz4ROp1uSnZ09+Pz58x2CgoKeHgklEibrOX/+cdC1sWELbJSy2/Rvv63tPuvVqxfy8/Mpx3Ek6/x5OMXFoZVWC4lc/nR7R0qB7t3BTZwItyfkQIQQDBs2jL979y4cHR3B8zxu3brFGRsbw8XFBdVz1KhWq63x0gWWsFFmQ4YN40+9/jrNXbkSD3r2JKPXrCEVw4YhX6PBp59+SjmOo61atcL4117juLNnUdGuHbKysrg5c+awN2/Viv2r0bCsn+eBdeuQk5EhBg8ezDV2x6BQKDBx4kTjDRs2fKtWq/eqVKqnzIj/e7N06dKRUqn0+xEjRjCxdk4O002PH19bljI2MYG+WzccVCjQOy8Ptra2KC0tRWRkpFBYWMj369ePBAQEPFYl+PoCK1YAmZnA1183+r56vR5XrtROTfLCnxx0DTVdA89EWFiYGB0dfTQrK+ul9u3by5qrl9VCCAu6PXqw76dPZxrdkBCWIdaboOvt7U06dOiAg/Hx0E2aRL0UCkLc3dmUiPoLJ0+yYAF77UbahyUSCaysrCCRSMBxHGxsbGrbTW/cuIHMzEy8+OKLj8XyERFAbCwUPXtCptWSFnv2wOarr2Du4ADOzw/327aloeHhxNfXlyQkJCBdFOHz4AHRX7iAcxIJysvLqUKhICYmJqzDSiJh0zQAoLAQKZWVRKLVii4nTxIEBjbYX6VSidu3b1eWlJTcDAsLu/n0k/z3RK1Wd5XL5fsnT55sUmsAP3kyO1d+fuzz/u47YOxYyPfsQZ6NjXjs2DFaVFRE9u/fDycnJzpp0iTOycmpgQwMDx+yi1y3bg3eV6PRIDo6mp4/fz6PUqoEMCYsLOxP9cAwNEcYeGZUKlW6Xq9/MSIiojIpqdlZlgxvb2YinlktQe3XDwgOZkG3GetGhUIh2tnZEeLqyiRCZmZMf9sYRUXA7t3Ab7AArDa0EetI4QYMYPrP/Hy4JCSgxbVraKPXAwUFkE6YgJ6jRxNHR0colUpUVFSQzMxMssrYWMjfuhWuHIfMzEyyZcsWLF++HKWlpXXe70KLFrhVWUldHB252oGXW7Yw+dMT+Pj4mMnl8om/+oD+XnT19PQkrRwcmITw0SO2MFk9YRkAC5x+foClJXr16sURQkhaWhp94YUXMGrUKL6xRVIAzL/jhRfYNOd6REZGVly4cIEIgjAVQCaAhle+PxhD0DXwq1iyZMl2rVYbun///rxbt241vzEhyPLwwK3581GRnw9kZ0Ps2RMlGzY0WzIICgriL168iIysLOjHjGHThsPCmJ63PklJtTPLfi3m5uYoKSmp+0Nra2YL+dFHTLHwyy8QDx3Cj6tWiaXGxrX7vX37dqrRaNC/f3+E9u7NJyxZIo7ftg0LZszAjBkzYGtrS1etWlXnpaOioqDVaolzz54s0weArCyWDaelAWfPAgDc3d2h1+v7V09O+Ecil8s729naKkAIMz+qrGRBFmBlhtBQVivfsQMA82UwMjISQ0JCGjSa1KGqimW63bqx6dT1cHd3rympHgFrQW5uLuAfgiHoGvjVqFSqS1qt9sOLFy9WNjdjr6ysDNs4Dqbp6TiwYAGKv/kGBydNol/u2oUTJ040+Tw3NzeUl5dj69atiI+Pp2jZkgVCe3tW76vJDM+dY7ekOt1vOg4bGxvk5ubydY6htJRJkl59FfjiC5Q5OiKiZUvxQWEht9vNDUVFRUBqKka6uxOZTIbsFSsgvXNHGNG2LZNbhIWh9ebNGBoWRkyLinBg4kSal5cHxMbinREjwHEcSrdsYS3PBQUsK7OyYncEl1lp0fSnn9DS1FQHoGdj+/1PQCKK/h1feYVdUF9/vdabGAA7Nw8fAvXmzlFKG/fZzctjd1MffACYmLDOwfBw1plYj6CgIJlXXZOlDg02+oMxLKQZ+K1svHfv3vyDBw86duzYUWZnZ4caq7x79+7h1q1bSExMFOXW1sR+8mTSu3VrXG/fXvBt1Yr3HzgQ33//PVq0aIHOnTs3eGFLS0ssWrSIrFq1Sqg2myaQStmiVG4uaidL+Pqy0oJRE1NbNBqmGS4sZF/t2rH2ZA8PnEhMpIqVK0mX994TyIEDPNLS2B//lCksc+7TBxg6FGX79sG3Rw8EHDmCR1Ipznl70/DkZGJ6+TLahIbS8IMHSXZ8PF86axbMNBqWsaakwM7BAcMHDkR+ZCTWrVuH6TyPNqGhcHR01N9dsUJSYGODVjk5aJeaCtK2LfDzzyyb79MHiI7GSxxn9s2CBa8WWlqesiwq0uEfNEG2wNq6u2bevHbitm2sUaYGStmMPb0euHGjsafS2qCblsZayAcNAgIDAU9PVgMuKGDtwqtXN/reOp0Ot2/f1oCZN6FGo/tnYgi6Bn4TKpWqUq1WByclJX1++fLlSWZmZuLQoUO5Xbt20aqqKmJnZyf26dOH8/HxARcVhZZz5qDH++/zmDIFANC2bVshJyeH3U8KAqDXQywqwtkrV/DLuXMCKS7muJYt+W6VlcyeUalkTmRr17I/tldeYT+7e5d5OOzbx5QHb73F/ghHjmQZZHExqwefOsUmFScnQ2zZEkkpKXRQp05o378/j3v3gJpBm99+y7Kk9euBuDikdupE23XpwnGxsVBYW8Pzm28IVqwAcXVFUUEBkeTmgh80iOZ/9x3RX7wIy7Iy6J97DvmpqdiZkADi6UktFArR9pVXeMhkmBIaKomzsEBSVBTEK1dgbWoK848/ZuWRdu1Ytvb++9BdugQoFP2zXF3T+ZSUbS2At0BIC1Ba0sRH8rfgx3HjvANNTaN827cnxvVtPQWBfZaNOdgBUJSWwn71avb5nDwJbNvGavA3bzLtd1UVMzpqxiLy4cOHqKqqasaI+Y/HMILdwP+MWq3uIZFInhMEYa5CoUBoaCixtraGi4sLux0sLWVSIHd3Vss8fx75Fy6g8O234b5sGWBuDvLyy3i4aRMS27SBf2kpFBoNyhcvhtW33zL5UHAwc5MaPJj9e/06G+vz2WdAeTnwyy9MJZGTw4KxqSnLnBq5Hd28ebNQVlbGvfjii0TelCoiORkYNAiR48ZRq+Rk0ubBA8ovWECio6Lw8po1wNKliDlzBj0++QSyRYtwdPRompqfTzp37Qrtf/4Dn4QERLz3njD/9dd5rriYZV8VFSjfsQMFV6/ixpAhovOjR/CYMYPjevSo+94//giYmUHXqxceBATQY4GBa6ds3PgGgJtgt8MmAMpAadNub//fIMQm19b264g5c/r36ttX2a1bN75OqSAqCtizh110agxsRJGVjlasAHbuxKYRI/SjDx+WmLZpwwyTKiqYjluhAPbuZRfORnxyn4RSioSEBOxnDTpt/wqjIUPQNfC7oVar3QkhIxQKxWCtVttt4sSJsrZt27LAW1AA3LvHAiUh+HbdOurZvTu5cf268EirJTY2NiQ7O5sMGTJE9PX1/cPWGvbs2UNv376NmTNnNt4mGhXFsuMxY4DYWKw5flwMtLfn7HbvxhUzMyQFBMDExETw9/fnXceOhf28eazMER6Oq/PmUW1GBpXNmMF1nDIFpEMHln29+y7zaggJQYGPD77bvx/vLF7c9E5Onsx0vsuWIS83FxsjIrT9DhzI6RwXNwSUJoGQ1wHwoHQlCPEBkPT/ufwQMX16+wJn5w87JCeP8ProI7mzi0vDjT75BFi3jt2VZGezYDt1Kqvhd+oEqFTYMniw3nfUKEnHTp0eX0z79mV+Hk/pQHuSvLw8bNy4MXfRokX2v88R/joM5QUDvxsqlSoFwGcAPlu2bNms7du3f2BkZGQUEBBg3KVLF07h5weA3eKJ1taIOXMGNjY2JCggAKdPnyYzZ85Ey5Yt/7CAq9FokJSURKZPn44mdcY2NmwxBsBlY2N0iIvjfJOSIJ0zB61KSzHgtdeg6dSJP+znh/gFC7Bg3DiWeVOKTjExBEOGsPqzhQVbGPvmGzazbeRIAIBJRQV0T4uPT3gN29rZ4c0335TtSUmxS2rfXj6VEAsAXwKgIMQUwFoAvUGIC1j224jE469BrVZ3t8nLOxMeE4PEYcN0fd96S9rgzuLhQyYL1OtZvX34cCYfi45m9d1bt9icu6FDUfzVVyA8zzLgLVuYLGz37oamSvWovwCXkZEBQkjTK7l/MIaga+APYdGiRWvVavU3Wq02+MyZM/MvXbo0aNKkScaWlpZYv349FAoFXbRoEanxnw0ODv7141qekdLSUqxatQpyuZzyPE/i4+PpiBEjGtes+fhABKDXanH48GHM6NED0kmTWDA+cACyO3cgmzQJIZs30zRXV4rvv+ewYwcwahTL1o4fZzKw9HQgJoZ5TzyhAz5x4gStDvhNa+amTGEa5+qJCDzP44azsxzApWRPz5uOmZkrTMrLNwF4BIDVJggJA1AKQrYDGA3gZ1D6lxjnqNVq3jov7yWnyspvMtu2xfUPP8SIESMaOtRlZrLy0HffMdtGOztWq33rLTavrpGJ0ARgWW5yMtvG0rLZfSkvL8emTZt0w4cPl7Zu3RoAkJaW9qiqqurw73CovwlD0DXwh1E97fYsgLMffvjhtPXr16+hlBpJpVJx1qxZ3JMep39UwAXYMEtKKXx9fcUOHTrwmzdvJuHh4WhssOWjtm2xecwYcK6utKWREbVfuJBFzKlTgc2bgQsXIMbGIjk2lsj9/Aief56ZsH/0EatXDxjweOJxr14soGzZwoIJgOvXr5Pnn3++wftSSlFVVcX2KTi4jlyKEIIBAwbg6NGj2Pncc16UkAj3iRNnmpWU3LnYpcsvAHT44IMDKpUqESwT7g/gJxDSE8BDUPoMnSy/DrVa3RKABwB3iUTiTQgp0ul0/yGEfApgDi+KaJeWVnzXxUVWLIoA8LiT4cgRlqFKpezf994DRo9mi2EKRZPKA/mjR3CdNIndQXz22VP3cd++fUhISAAAaVZWFlq3bo2ioiJkZmZKAMT8zyfhN2IIugb+FN5///0ItVr9i1QqjR4xYoSsqTHlz4ooijh79iwKCwshCALCw8MbtXEEgHbt2oHneYSHh/OEEJibm4vXr1/nunTpgtTUVJw4cUIoKSnhKKWQvfoqGTxiBGJOncKgdes4MSwMnL8/sGkTGw1z9y6wYgXO9ugBfyMjinv3CFq3ZubmRkYskDxJWBhbla/GwcFB3L9/PzE3NxfHjh1b21WVl5eH9evXo1OnTkLvjh15E8e6k2K6deuGqqoqcByH6OhoVMjlgV69egUa+/tDV1kpXkpMfG/ZsmUR2g8+mKNSqV4GABBiD1aGkAFYAOCz31r7VavVHlKp9HOJRNJWr9d7SiQSrYWFRaWNjY0kPz/fpKCgADzPL3ZOTpba5+YiNjQ0INfe/ipE8cM7d+68e3L/foRpNGyiR5s2LPs/fpyNQJowgSk3OjQhmdVogMuXoVEqcXfFCrg3MsW3MaoDLnie/0mj0YwAwB85cqRcFMVPVCpV9m85D78HhqBr4E9DpVKd/fjjjz/bu3fv25MnT5Y9daJvM5w9exbnzp2Ds7Oz/uHDh3xERAQNCAjgEhMTBb1eD3Nzcx4A/P39YW5uDo7jkJubC3t7e7Rr1w43btxAQEAA9u3bJ7q7u/NDhw6FpKAALe7dg8LTE67t25PvRVHUxcfTodOmcTHPPSeMeecdiUwiwf327WGanS24aDTMbjA6mtUiG8PVFXBxYTXdzz7DhAkTuPj4eFy9epXs379fbN26Nbl27Rrt3LkzJ4oi7t69SzLffRfWQ4fC+pNPal+GEIKe1TaZPZ5UOxw4AOzezXX/+mujTZs2TS0tLR2xfPnyJI1G89ESSrdXP9kSQFV1cXMigNug9PyznGe1Wk0APAdgh06nw4QJE7B9+3ZYW1vLpFKpDAAKCgog0enAV1ZKCRDe8erVnNjQULlEIrlqlpvrE5yXB0+lkqky3nqLKUu+/pqNPvr004YXqvpkZQFbtoB6ekJfXSJojuLiYjx69AjGxsaaioqKOZTStd7e3jwAPHjwQBAE4eizHPsfhUG9YOBPRa1WE0LIS8bGxl/MnTvXuEnJ1lM4dOgQSkpK9OPHj5eUl5dj3759wsOHD4m3tzcxNTUl+fn54qNHj5CcnFxnYW7u3LnQ6/X473//CyMjI9HExATTp09npY7ERHbr+9ZbwGuvQbNoETbs3k1ld+4QzsND5HieUEpRcesW6X78OFpptdQ2MZHUlhOa44UX2MJQ9Sp7aWkpvv32W9HExIRaWlryKSkpsLCwEObMmcOf/c9/UECIMOqNN55ec6GU+U8YGUE4fBgPundHVlYWoqKiinU6natKpSqssz0h/QDcA5AL4JP/vvzy3BwHh8WEkEkKheI6ACWAFpWVlV0ACHK5PMfY2NjCxcXF5PLly1CpVEhPT8edO3cAAGfOnMHs2bOR+9ZbyLl3D3Hdu4NQKjjcu8d3jY+HR3AwZOnpzEgoMRHo2BF4+WUgIIAZFTXXvn38OLugVV98vvzyS32/fv0kXvX1vU9w/fp17N27t5Ln+UcSieSrioqKz+Vy+WZK6ZBJkyYptmzZUqXT6RxVKlX+U8/tH4Qh0zXwp1Jd5/3vsmXLuu/evXts79695TY2No23dzaDQqFAbm4uAdiUgfHjx9cPUBwAFBYWwsLCAmfOnBGjo6O5hw8fon379ujdu7dQWFiIAQMG8DWddOjYkX0JAhAcDEVMDGZzHKErVuDhw4fcd2vXopOdnRD89tt8lYcH6ODBBJQC06YB//lPo4s60dHRuH79ujBtzRrerKgIWLUKmD8fZmZmePPNNzmAlRZSUlIwbdo0nuM4dK6qws7MTP7SpUu0c+fOzZ8YQtj73rkD/uJF2I4cCVsbGxQUFBhfu3ZtPdii2mMojQKAyAkTfDlRfCHHweGVHqdOIcvREX6vv+4qlUpRVVWFffv2ged5fvLkya3t7OxACEFAQAAAwMXFBS4uLqCUIiciAi3v30fL9euRumWLfmxGhsTN1pan332HqmnTIHv9dTa9t1UrpjLYv581L0yb1nTALSpiUrvOnR9bZKKZNuAnHo+Oji4XBGHU4sWLjz3x0JilS5e+v3HjxqUymSwNQEGz5/QPxhB0DfwlaLXa2RkZGeUbN24czfO80sPDg/Pw8FC0a9cOzQ3AzMnJQXJyMuLj42n9wZKNYVkdCHv06MEFBgbWtioHBwc3zCLfe49NhJBK2ey23FyQ0lIQjoO1tTXap6bCMT6eN54xA2Y1K+uUsrqkhQVrTXV2ru2Iun//Ps6dOwdBEPjk5GR0u38f2LkTRVOmwMjICAqFAocPH6ZXrlwhoaGhgqmpKQ8ARjExGNizJ9YdPkycnJwajAxvFGdnNneusBAYORJ9Dx6U3bp1K1ytVj+vUql2qNVqJZh37GSpVDpQ5+5eK5btsGgRQtzdIRMEln3+/DNcnJ1hamZW57Owt39C1kopRFGETK9nSo2tWzFSECTYtw/YuhU4dAjGTk4s0Pbpw54TGckWIzdvbuCrUIdNm1h9/JVXGgTmpoIupRSxsbFiZWXlXQBRjTx+CwC0Wu171Rf+vwxDecHAX0p1zdCDEDJELpdP5jjOJSgoSBEQEMAZ1fNUyM/Px7fffgsHBwfBz8+P8/Pza+il+r9w7Rogk7Hhh66uLCuVStngTFtb3A8MxK5du6ggivSFF17gGgTDceOAN95gGRqA7du3g+M48f79+6Rdu3akV69eiIiIEMwTEvi7bdpAqlSKer2emzZtGuzs7B6/jk4H8DzWf/edPjs7WyKTyWBjYyN4eHiQ4OBg7qnHnJwMeHoi78ABbL5xo1IvCFpBEF9nE9wAABOPSURBVEzMzc0rtFqtKc/zmDt3LtFqtVAoFI8DmU4HpKQwudrUqUyN0acP08XWV5fMnAmhbVsciI/HoOhoSCdMYKOYSktxX6FAUlISCCHw8fFhwTo+np3Xd99ltdzG+OIL1sIdGNhoJ+GqVauE8PBw3qORNuHbt29jx44d2VqttqdKpUqr/7harZaABeOxf2VpATAEXQP/z1Cr1QFyuXyRTqcbplQqNTY2NnBxcVF6eXmRlJQUXLx4URw9ejRnamoKk+omht8FUWQdc23asODz88/MA8DLi7UYW1kBdnbIz89HTEyMmJOTg/79+3MAoNVq0alTJxTk52PXTz8JXtHRpMzKCjYzZnCnT5+mkyZNIhERESCE0DZt2tDxb7/NVS1ZgvwBA2BlZdXwOHr0YLff06cjIyMDUqkU1cdOBw0aRHx8fJ5+PHo9MHIkhP/+F6UKBVq0aAGO43D16lVER0cLCxYsaLRerNfrkZqaCncrK3DGxsz8Z/58pjmuqGByratX2Xy4iAjkf/ABthcUwMzZmT4/bhwxNjbGunXrBFEUOWNjYzE7O5u3FgRh2po1fEVEBIz79YOkfg38wQN2fmNjmYrBwaHRQ/riiy+EQYMG8e7u7hBFEWlpaWjVqhUUCgUiIyM1GRkZKxcvXvz+00/OX4sh6Br4f4larZYDaA+gg1wuHyYIwnC9Xm8CAFKp9AGl1KRbt26Snj17SqXPKCFqltxcNtnXxITpaj08WJPCG28wOdgTVFZW4ujRo8Lt27c5nudRVVVFpFIp1el0cHNzgxelJDkrS0x9+JCzlMnotPfeI/n5+Th06BAdP348MZLLmWQqL495UtRn5Uq2L/7+dX78zTffiD4+PlxISMivO7Z589ituqcnSktLsXr1aixuog1548aNwv3793mJREJNTEyErl26SAIdHFjzgosLC7b9+zMtbatWgIkJkpKS8NNPPwFgkzpEUcTYsWPh5uYGXWoq8l59ld7mOBLboweUpqbC/Pnz6wb8vn1ZlvuUi8kXX3whDB48mHdzc8PmzZvLsrOz8wghDpRSBYAYrVY7SKVSVf26k/PnYwi6Bv4WVBt6jwXgAmAZgJYymSzCwcEh5JmHZT4NUWQB79Ah5lr2jJOPS0pKsGrVKnTu3BkDBw4Ex3GglOL+unVwSE0F14ivK7ZtA+bOZW2w9Tl2jGlWn1hESkxMxMGDBzFr1iyY/1rD9pMn2S17UREKeR7rNmyAlZWVfubMmQ3WdJYvXy6OHz+eM9u7FycsLWFy/jwdUFBAEBnJjGdWrADy2d25Xq9HbGwsTp8+DYAtbspkMnHq1Kmcubk5K1v4+bGyy+uvI/3ePezatYv6+PjoiF4Pl127+DujRongedAnst8nPksCoDZAXbt2TdKmTRtqaWkpXr58WdRqtf4AjDmO2ymKYohKpbr3607MX4Mh6Br426JWq6UymexyaGioV/fu3f+3lrYVK4ATJ5hk7NIlJml6xkCekZGByMhIvP322w0XASllc+GWL2c+DDWvSSkzAWrZsuHE4969gVmz2AJdNevWrROdnJzIwIED6+yUVqtFbGws9fLyInUWuhrjtdeAsDBoBg3C6tWrIYoi7dOnD+ni6gpYWqJi61bEbtuGvvv2gV+yBFHu7sJDiYQfN378Yw9jQqDRaLBp0ybh4cOHvFwuh6urqzBs2DA+PT0d27Ztw7Bhw+Dn4cFkYhcvMjNxCwukp6dj586dd2lx8VdamUwYvndvjxN9+lx8ZGb2ZHZKmvgeAFwB5ACoBKAD8I1Kpfr7OK1VY1AvGPjbolKpdGq1eujJkycTCwoKjLp16yZ7auBpisxMZhdJae1C2LNy8+ZNtGnTRuA4rmHgJ4RNvBg37rFngKcn+97GhtkRzprF6qY1fPLJ42GW1YSGhnL79u1D3759IZVKUVlZicOHDwupqam8RqMhpqameOqxr1oFEALFkSOYr9Phqq0tYo4cQcc1ayDLzEQCx6EwMFDkeZ7Dxx+DO3GC09y9+/hCIQgQAwKwfvp0QWFszL366qs16hAeAFq1aoWQkBAcO3aM+u3YQXD5MvMlrh4ESimF8tGjh7M//fQFAP6g9POOv+pM/zMwjOsx8LdGpVJl6vV6r8TExBVbtmypzMnJ+W0v9PXXbAbbbyxTNHvHaGTE2oHLy1ljQHn548e++47VW5/khx8azIPz8vKCXC4XDh48SE+cOEHXrl1L8/PzMXToUEilUtqYj0QtpaVsESw7mwV/KyvIKEVXZ2fSMzSUrl64UDx68iQ9decOOo5+LOutttvEmTNnAAA5BQXIqKykNubmmDJlCrGsp0s2NjZG165dYXXnDimPjYW4ZAmrBQNAYSGMo6JQbmamAxAKSvVN7/A/G8MIdgN/e8LCwh6FhYVFx8TEZCcmJvbMysoSbG1tn21M/O+AmZkZYmJiOA8Pj+YVFTIZUyVIpSzQenmxDrWzZ9mstJkzWdD/4ANWg3V1rfP0Nm3acFevXhXLy8tJx44dyahRozhra2uYmJiQw4cPw9fX97GJT2Ym6+by9GQLgjY2TArm6MheOzQUSEyEzfbtJMbUlOTk5JDp06fDxcWl9qpjY2ODFi1a4Pjx4wCAvXv3wnzaNDpo+HBe0oR3Bn/jBrzfeAM7+/fHZakUvr6+4Kr9lHV79+JKixb3u0dFff2/nfG/N4aga+AfQ2ho6NXo6OjVJSUl5VevXg00NTWV2dnZ/Y5C3sZRKpW4efOmIJVKuWYn1QIsqBLCplv4+LDg2Lo168Cq9lbAmDFsyka9Wq+ZmRkCAgK4Tp06EccnDHHsLS2Rd/OmGH3hAnw/+ojIundnsreEBDYVd/RopjzgOCaJq8HDA1zfvrh24QK6ZGSIPhMn1jlXhBDY2dmhqqpKuHDhAjdy5EgEjB5NiLNz4+Y0RUXg5s6FZOZMOMyejYSEBKrZsYM6HzpEMH48st3dkZycfL979+4bGj7534Mh6Br4RxEWFqYPDQ09Fx0dfTgtLW1qTk6OTi6XSy0sLH51q/GvISUlhcvPz6fu7u7kmSRsNZ1ry5cz1cTLLwOzZzNbyHHjWBbcXNeWXs9qtIGBwA8/wPPOHXLPzY0kCwIttbAgBRyHEnd3VFRUQFc9LZnjOBBCas9DcUkJfvz5Z1G8fx/9zcw4RU0jRL3z5OLiwoWEhKBly5asTNKlC7NgfBJBYBeRqVOBWbNg8vAhClJS6CWtlksxM3t06vJl7fXr1ymlNDUkJGTL/7V3t7FRVWkcwP937tz2djrtQsC+SbHQBsGXsrgrQnBdJEGUTa11KZAqVKRR5IMa32hMmLOHLDTEdbuJLcoYgQ0UZNjd6PLiW1LWbmvs0qya1m4gzTYoItalpe283jv33v1wWqC8lE47nXHg+SV8oTPTJ/Phz+Hcc54ngq/2ukOnF8h1i3M+WZKkFcnJyc9YljW9sLBQnj17dlJOTk7UA9jr9WLv3r1Gd3e3XFxcjFnXmNV1GY9HjKt5/nlxM668/MKq1DRFp628PDFlISUFqKgQgzgrK4GB9pCapuG9995DX1+fFQqFTF3XrXA4bDMMQwqHw5Ix0GLSbrfDbrdD13VMnTrVXLFihWj48+OPYgZdU9PVO3998IHo9XDPPRf+TtcBzkXPhOpqsY3iduN/gQB2m2bA7/eHwuHwEgBdALoYY/7IvpzrC4UuuSFwzgtkWS6XZblCVVXnXXfd5SgsLLRNHHiyHi1ffvkljhw5goqKCmRkZIz8jadPi9DKyhLXb+vqxJ7snXeKp/9FRcDnn4t+vg7H0NHlEdA0DV6vFx999BFOnTqFl19+eegLvvlG7PvW14tV7aXH2UpKxImLi5uIr1olet663aLxz/33X+i3AODgwYOh1tbWf+m6XpYoZ2nHE4UuuaEM9HqYl5SUVGGa5nKHw2Hl5ubK8+fPd0RjBWyaJqqqqrB+/XqMKtD37xdNvdvaxMmGm24CNmwQ54fnzRN7wYYBjLIl5iC32206nU5bWVnZ5T/UdTEcc9u288e9ruqTT0TYlpcDS5eKuvPyhlws0XUdH374odna2vqZrutVjLEjYyo+wVHokhvWwC23GTabbalpmlsBgDE2ps88fvw4Dh48aL300kujT+9gUOyZtreLB2xz5ojuXy++KLYA1q0Tq96aGnFrraREDLMsKxPnjAOBYVfCH3/8MVpaWrBs2TLMmDFj+FrKy8U4osEmM7W14nfv3i3+IVi3TvSpYEw0JL9Kr1tN0+B2u0Nnz55NttlsW0zT3MMY+89ov6JERqFLCADO+RwA/546dar37rvvdmZkZGDSpEkRzW5rampCS0uLmZOTY5WWlo7f0LeBm2E4cUKseG+5RfSI2LwZaGwU53x37hRB/dBD4obbpk3iKFpXF2pffx0PPv008i85knZFx46J0P/2WxHwn34qVrOPPSZWw5omrkynpo7ojPPp06fx9ddfG8eOHQvqur6EMdY09i8ksVDoEjKAc54kSdITqqouN03zNl3XM9LS0nxZWVlSdna2MysrS8rPz7+8SxbEePetW7ciMzMTa9aswWgnYkTVyZPiv/kpKaJBzSuvQPd40PT225jt8WDi44+LB2B5eeKq7pYtorWjLIsbcaZ5YU/32WeBBx8UzWk6O4HFi0WQOxzAI4+Ih2cROHHiBDweT79hGD+Ld3/bWKPQJeQqOOcpEBNvb5dl+eeKoiw0TfP27OxsfeLEiXJRUZFjsNdCdXW1kZGRIZWWltrGOnRzPO3bt88MhUIoLy+3Sd3dYo84EBBbBStXilWyJImVbEGB6Efh84nthKoqsd1QVyf2er/7LuKwHRQOh7F582YAeAXAH26k4KXQJSQCnPMZAH6RnJy8Pj8//5ePPvqo2tbWhsOHD+Opp54SZ1njqKenB21tbdaCBQukK03geOutt4yZM2faFi5ceO29gIFm6ujuFvvL990HbNwo2mBWV4vAHoPt27ebZ86cscmy3GkYRhljbETDMhMdhS4ho8A5T1EU5agsy3Mty5KmTZtmFBcXy8P2QBhn7e3teP/99wO6rpt2u92hqmpgyZIlan5+vs3n82HSpEn44osvUF9fb73wwgvRnboxSpqmoaqqCgDAGIt/QTFAXcYIGQXGWIBzvtiyrL8CmHfq1Clp7969ltfrdT788MNSXl5eTOvp6OjAgQMHACAFEMe0dF13tLa2mg0NDVpXV1fSokWLEAwGLb/fLwUCATgGLlXE00W391bGs45YopUuIWM0MH+rAMCvALgBYNmyZZg1a9awQzajobe3F4cOHfJ1dHSkqqr6j2AweC8uWkypqvrfYDA4/dL3FRcX49Zbb8Wlc+jGyjAMaJo24tdbloXXXnsNAH7NGGuIajE/URS6hEQR51wGUKQoyh+zs7OzV69erUZy7CwSPT09ePPNNzXLsraEw+E/McZ6h6nrEQDvABjsx2hNmDBBe+6556J2zMIwDNTW1vr7+vqGTHwYwfscAG5mjJ2OVi0/ZRS6hIwDznlacnLyIV3XF6iqGkxNTQ2npaVJ6enpSnp6esq0adMwmi2Ic+fO4fjx42hra+v7/vvvk+12e1VlZSWPpC4Av5dludQwjOxVq1Zh+vTLFsKj0tjYaDQ2Nv6zsrLy/qh84HWKQpeQccQ5dwDIBJA1+EeSpJsVRVmbmZmZ9sADD6ROmTJl2M/w+/1obm42Wltb/f39/ZBl+UgoFHoXwCeMMd+wbx7Gpk2biux2+zsFBQXOpUuXpoyl/3BfXx9qamoCuq4XXmkEOrmAQpeQOOCcKzab7UlZlrdMmTIl+Y477kjNzc3F5MmT0dvbC6/XC8uy0NnZaTQ2NuqSJHk0TdsOoJkxZkSxjlRFUTZJkrS+pKREnTl43TdCHo8n0NHR8carr766IVq1Xa8odAmJI865CuAxVVV/Y5rmvZZlpVuWZSqK8i0AwzTN9lAoVDneq0fO+VxFUY7MnTs3fdGiRUokDwBPnjyJurq6s7qu3zKWlfeNgkKXkJ8QznkOAIkx9t01Xxz9352RlJR0OCcn57bly5c7RnKywTRN1NTU+Hp6etYwxg7EoMyER6FLCDmPc25XFOUNp9O5eu3atY5hZ74BaG5uNo8ePXosFArNv5Gu8o4FTQMmhJzHGAvrur7e6/XW7tixw+/3X33Ig8/nQ319fSgUCj1JgTtyFLqEkCEYY5au6xv6+/u3u91uX0NDA4LB4GWv++qrr2BZ1meMsfY4lJmwaHuBEHJFA1M2SgHsBwCXyzVksobX68W2bdsCwWBwpcvl+nucykw4tNIlhFwRY8xijHkkSfotIBqQX8zpdGLOnDnJiqL8Lh71JSoKXULIsFwu199sNlvp7t27A2fOnBnys5aWFk3TtCfiU1liotAlhFzTxo0b/6Jp2hO7du06H7xdXV0wTVMH0Bnf6hILhS4hZERcLpdH07Q1u3btCvzwww8wTROyLJ9jjPXHu7ZEQqFLCBkxl8u1X9O0J3fu3Bnw+XwIh8M3xbumREOhSwiJiMvlelfTtLV79uwxFUX5c7zrSTQUuoSQiLlcrn0AcoPB4DPxriXR0DldQgiJIVrpEkJIDFHoEkJIDFHoEkJIDFHoEkJIDFHoEkJIDFHoEkJIDFHoEkJIDFHoEkJIDFHoEkJIDFHoEkJIDFHoEkJIDFHoEkJIDFHoEkJIDFHoEkJIDP0fG0H2/yWdIfIAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = gdf.plot(edgecolor='grey', facecolor='w')\n",
-    "f,ax = w_knn.plot(gdf, ax=ax, \n",
-    "        edge_kws=dict(color='r', linestyle=':', linewidth=1),\n",
-    "        node_kws=dict(marker=''))\n",
-    "ax.set_axis_off()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Weights from shapefiles (without geopandas)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pth = libpysal.examples.get_path(\"mexicojoin.shp\")\n",
-    "from libpysal.weights import Queen, Rook, KNN"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "w_queen = Queen.from_shapefile(pth)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "w_rook = Rook.from_shapefile(pth)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/jovyan/libpysal/weights/weights.py:167: UserWarning: The weights matrix is not fully connected: \n",
-      " There are 2 disconnected components.\n",
-      "  warnings.warn(message)\n"
-     ]
-    }
-   ],
-   "source": [
-    "w_knn1 = KNN.from_shapefile(pth)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The warning alerts us to the fact that using a first nearest neighbor criterion to define the neighbors results in a connectivity graph that has more than a single component. In this particular case there are 2 components which can be seen in the following plot:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = gdf.plot(edgecolor='grey', facecolor='w')\n",
-    "f,ax = w_knn1.plot(gdf, ax=ax, \n",
-    "        edge_kws=dict(color='r', linestyle=':', linewidth=1),\n",
-    "        node_kws=dict(marker=''))\n",
-    "ax.set_axis_off()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The two components are separated in the southern part of the country, with the smaller component to the east and the larger component running through the rest of the country to the west. For certain types of spatial analytical methods, it is necessary to have a adjacency structure that consists of a single component. To ensure this for the case of Mexican states, we can increase the number of nearest neighbors to three:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "w_knn3 = KNN.from_shapefile(pth,k=3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = gdf.plot(edgecolor='grey', facecolor='w')\n",
-    "f,ax = w_knn3.plot(gdf, ax=ax, \n",
-    "        edge_kws=dict(color='r', linestyle=':', linewidth=1),\n",
-    "        node_kws=dict(marker=''))\n",
-    "ax.set_axis_off()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Lattice Weights"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from libpysal.weights import lat2W"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "w = lat2W(4,3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "12"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w.n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "23.61111111111111"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w.pct_nonzero"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0: [3, 1],\n",
-       " 3: [0, 6, 4],\n",
-       " 1: [0, 4, 2],\n",
-       " 4: [1, 3, 7, 5],\n",
-       " 2: [1, 5],\n",
-       " 5: [2, 4, 8],\n",
-       " 6: [3, 9, 7],\n",
-       " 7: [4, 6, 10, 8],\n",
-       " 8: [5, 7, 11],\n",
-       " 9: [6, 10],\n",
-       " 10: [7, 9, 11],\n",
-       " 11: [8, 10]}"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w.neighbors"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Handling nonplanar geometries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rs = libpysal.examples.get_path('map_RS_BR.shp')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import geopandas as gpd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/jovyan/libpysal/weights/weights.py:167: UserWarning: The weights matrix is not fully connected: \n",
-      " There are 30 disconnected components.\n",
-      " There are 29 islands with ids: 0, 4, 23, 27, 80, 94, 101, 107, 109, 119, 122, 139, 169, 175, 223, 239, 247, 253, 254, 255, 256, 261, 276, 291, 294, 303, 321, 357, 374.\n",
-      "  warnings.warn(message)\n"
-     ]
-    }
-   ],
-   "source": [
-    "rs_df = gpd.read_file(rs)\n",
-    "wq = libpysal.weights.Queen.from_dataframe(rs_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "29"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(wq.islands)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{}"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wq[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wf = libpysal.weights.fuzzy_contiguity(rs_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[]"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wf.islands"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{239: 1.0, 59: 1.0, 152: 1.0, 23: 1.0, 107: 1.0}"
-      ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wf[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x1080 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.rcParams[\"figure.figsize\"] = (20,15)\n",
-    "ax = rs_df.plot(edgecolor='grey', facecolor='w')\n",
-    "f,ax = wq.plot(rs_df, ax=ax, \n",
-    "        edge_kws=dict(color='r', linestyle=':', linewidth=1),\n",
-    "        node_kws=dict(marker=''))\n",
-    "\n",
-    "ax.set_axis_off()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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wwvnz5z978cUXn/y96p+amrq6Y8eOtycmJirl5eWYOnWqzXWvD7vq7AEgwGq1Zv9edWCMMcauh7uRM8YYY9XEYrGM79Kli/fjjz/u27p1aw+j0fj8ra5TVVRVfdHf3//LhISE1wIDA780m81rNU0zuh+jaZqqaVo9k8k0PzQ0NCIuLu6KcmrWrIkGDRogIiICBoMBwcHBGDFihJcQYqSmaXVutl6apnWZMmXKzEmTJg291nE2m+2lbdu2XQCAvLw8qKp63C1o1zIajUdUVc2YPHny0zdbB8YYY6y6cDdyxhhj7Aa5xiR3AGACsMFqtdrd9wshWtWpIzNmmzZtjDt27HhI07RXrFZrURVleZjN5ulEFFtWVjbaarX+9L+4BgAwGo09u3fv7hUXFwdd141ffPFFwokTJ6wAXtE0LcpsNr+lKEpPk8lkb9CggdKjRw8L0Y0NxzabzRU/3lTXOU3Tunt4eCzq0KGDV1pa2iBN0zKtVuuPVzl8b3FxsacQAj4+PnA4HKGaphkAkNls/jIhISGoZcuW6vvvvz9Z07QF3MLNGGPsVuCwzRhjjN0gk8n0rtlsfshisegFBQW2iRMn9p8wYcJGANA0ra6qqsF+fn4AgICAANSvX185ePDgywDGuZejaVqYyWRaHhUVVT8mJsZjxYoV6zRNC7dareerOq8r5McDiACw2Wq1Fvw316HrenZRkcz/iqKgb9++nh988MGzr7/++gCj0VivXbt2xrZt2xosFovxOkVdYc+ePSCifTfbfV5V1e6JiYle7dq1AxF5rl+//nEAVwvbEQaDwQnA4Ofnh6CgIOTl5X1mMBhiw8LCmt5+++2qoiho0aKF4aeffnoawPOuexgA4NzvOJ6cMcYYu4jDNmOMMXYDNE0jInrsiSeeUH19fXHo0CGfhQsXLtc0rZHVaj1uNpun3XbbbaqHh8fFz3Tv3t3j0KFDT02cODFTCPGh1WrVNU1LMRqNXycmJnp37NhRJSLs2bNHyczM7A1gXqVzGolouNlsHq+qak1/f39nXl6e0DStl9VqTbuBOisAyGq1Ot2322y2WZs3b+4RHx/v5eHhAT8/Pzz11FOWnJycBrVq1XJvnb5ECMDpBNSr/9dBCIE1a9ZcsNlsz16lPvUAmKxW68HK+wwGg7/JZAIAREdHY926dZ3cPucDoLvJZOphMBgSVFWN6d69u6Gitf2+++7z3rVr12Bvb2+lefPmUBQ5Si48PNy0d+/eJgBgNpsXOxyOngaDIVfTtG5Wq3XfNe5bHID6ADb+ty82GGOM/X3xBGmMMcbYDUpNTV3buXPnlNatWxMA/Pjjj45NmzblKIqS5eHh0WLUqFGeFYGxQlZWFpYtW1ZSWFhYpuu6yel0erdq1Qq9evW6eMzmzZuxYcOGL1566aWLY5U1TQs3mUyrQ0NDw5OTk70iIyNBRDhy5AgWLFhwwW63t7vazNuaptUwmUyvOp3OYUII1Wg0zrLZbKOtVmuZaz+ZTKZ/eXl5Da+qzlcoLQUCAwGHA9i6FYiOBlwt+JWvdd68ecdsNls999ZjTdNqmM3mLwDc7tq0zmazPWq1Wk9UHPPGG2/s6NevX6vY2Fjouo7U1FSb3W7vr6pqIoBnwsLCHA0aNPCpVasWQkJC4P5SoyqnTp3C7NmzS0pLS4cJIQ55enpuevrppz137dol1q1bt3PcuHEJVdy3rmaz+W0iqhcUFOTIyckps9vt0VfrccAYY4xdC7dsM8YYYzfIZrOtzMvLaw85ZhsdOnRQw8LCwsvKysLr16+PqkJr3bp18fjjj3seP37cc8uWLWL//v3Yv3+/3qtXr4uTlMbHx2PdunUDNU17DIAgohEA3gsNDRXDhg0j9/HS0dHRSE5O9ty4ceM4AENc3aMHAwgHsImImhiNxsnx8fEeHTt2NBuNRixZsmRwVlZWmKtFXLdarULTtCeJqNG+fftub968+dUvurQUKCoCevcG7rwTSEsDpk4FBg8GunYFXNcshEBaWlqp3W6fVhG0NU3zURRluNFo1Jo0aeLRrVs3sxACGzdu7Lply5b9kyZNelrX9RkAahoMhviIiAgAsmv7gAEDzKtWrZpfq1Ytwx133OEREBBww9/TmTNnMHPmzFK73T58woQJX06aNMnatGlTk6qqiIyMpLVr19au/JkpU6a87eXl9cidd97p2aBBAyiKgrffflux2+2xAP5n4+kZY4z9dXDYZowxxipxBdgkABVTcO8HkGE2mx+OjY29mKiJCDExMdctj4hQp04dRERE0KRJk9CoUSOlqKgIvr6+AABfX1/ExMSIjIyMw06nM6Bu3bp2h8OBY8eOUVpamrNDhw4G9/JCQkJIUZT6rl/v8Pb2/qBRo0bm7du3q0IIxMfHo3fv3hePv/vuuy2fffZZcn5+/seapj1itVqdVqtVTJw48dTSpUuxb98+vV69ekpsbCyCgoIunaikBGjUCDh7Vv6ZMAF4+WWgc2fg2DHg9tuBRx8Fhg7Fpk2bHMeOHcvUdf1fmqYlWyyWCQaDoX1kZKTj9ttv9woLC7tYbKdOndRGjRqpixcvfruoqOhFXdejPD09dYvFcvGYuLg4xMXF+dzYN3a53377DbqufzZhwoSFAGAymZqHhISoAHD+/HkoinK24lhN0wyqqr7s6en5yKhRozwrWsyLiopQUlKiQC6NxhhjjN00DtuMMcZYJWaz+SOz2Xxf3bp1CYA4deqUs6ioyBQdHa1HR0f/V2ULIZCeno6cnBwxdOhQUjMzgR07MCgoyDMvOdnT398f2dnZ5i+++AIAUFhYeEUZBQUFEEJkuOr6ULt27TwTExMpIiJCHDlyhPbt2ycAUK9evaAoClRVxQMPPOA5b968e3NycrpNnjz5K4fDkW80GtsGBgbCbDYrv/zyi7569WrFYDBg4MCBiAsKkuO0x48HoqLkWO3Zs4GBA4GWLWUIDw0F1q8HHnwQx1VVd4aF3UVGYx+z2fxp165dPRs0aAAPD48qBoADoaGhGDVqlNf69eujNm7c+F/d08qICERkcPvdy2iUc73puo7y8vI6kyZNet1kMjUyGo1JwcHBxkGDBnm6d00vLS2FqqpnXn75ZbumaS0sFss/iKim3W4XDodjLoB9APZZrdYz1Vp5xhhjfxk8Zpsxxhhzo2laDVVVs5999lmze0trdcnIyED+e++heO9eHI+IwODVq2HKzgZiY4F9+wBPT3z9wAN6vSZNyO+dd2j+yJEict8+qtO6Ndo/9RQAYMXKlfb09PR/CCGWGI3Gn8aMGePh7e0NACgvL8ebb74Ju92OymPDhRA4duwYjh8/jrKyMt3f319p2bLlxQnFHA4Hli9fLn7euZPGf/EF1IICYMECoEuXigKAykuACYGzixahcNw4BObn37/0nns+6qRp3rVrX9FTu0olJSWYOnUqAMBqtd74jRRCjiE3Xjlhel5eHmbMmFFst9vjrFbrqTfeeGNDQkJCx8TERJhMJvz22284ffo0fH19ERERgcDAwCvKKC0txTvvvGMzGAw7nU5ni06dOnkQEVasWIGQkBC7rusl586d8zAYDL/abLYh15pw7X/FtVZ6HQAnrFar7VbXhzHG/u4M//znP291HRhjjLE/jA0bNjT29/cf3L59+ypbZG9aaSmQnQ388gswdCgC4uMReuAAsk+exO7mzeE1dizCO3QAmjYFgoNxqFcvrMvPp7u6dKEz+/eL4MGDqd6HHyLQZoP/HXcAAQE4360bvNasue3uuXPHxoeEGAO7dyccOQIEBMBgNKJ169bw9fXFzp079aZNm1LFWHIigr+/P+rUqYOoqCgKCwuD+3hwRVEQZzTS6V9/de6pX598WrdGXocOFFjRtfzFF4EvvgD69bv4mbPnzmH+9u3YEhuLs8HBfZIzMkzhFosCkwkICbnu7TEajbDZbDhx4gQSExNhMBiuPOj8eRmqrVYgL0/ez/h4YNIkYO9e4MIFOXGbEEDNmvDy84MQQsnOzn4wLS2ttc1m65mVlQV/f3+EhYUhKCgIdevWRWhoKDw9Pa9ar6ZNm6q1a9eO6NatmzEyMhK1a9fGhg0bEBUVZRg6dKilffv2BiFErZycnPpJSUlf3MRTUe00TbvdaDRutVgsTwJ4bO3atdNSUlIct7JOjDH2d8fdyBljjLHLnSsrK6si8d2EjAzZ5frxx2Uo7NgReP114OmngY4doXbtip/efRdUVATH6dMQ06eD0tNRmpCAAiJEPf20sLRuTQ2WLiWHw4HZ48bp2dnZym2//IJOR46geXCwoW5mpq/fkiXw/+YboEkTYNYsYNQoICMDHrt2ocm8eSiyWpUfCgvRb8IE2Q28cqt0ZUIAffqgr9lsOOHlhS/79NEvLFxIw4YNQ82aNeHZuTNETg5yT51CUFAQVFXFwYMHkZ+fD0VRcDAmxng0OhqjGzaE77hxQGQk8PbbQFVLibmJjY3F1q1bcSIzE1E+PjJY/+MfwL33Ahs2yAnZcnKA48dlD4DWrYEPPwR27gS2bAEsFiA/X74MaNwYUFV0rFnT2MhuD8mJirpna34+ThYXo379+tesR2V+fn7wqzTrekJCgmPHjh0qAL1///6Kqqq6ECKvYr+maQbI5c1Kb+pk/wHX3AI9VFXtbjabRw4aNMgjKCgI7733ngGAEUDZ710HxhhjV8dhmzHGGLtcjI+Pj/P6h7kRAnjnHeBf/5KzdHt5Afv3A2VlwJEj8ndAhk8XRVGEruu0OT0d8QYD/IjwcZ8+ekdvb7o3IIDgcACqClVV8eCDDyqzZ8/Grl279E4HDyr06quocfQo8OSTMsxHRsqg/cgjwOHDwNat8LBY0NrhwK6tW7FywAB0278f9NFHwA8/ADYbMHEisH27HH/t6wts2gScOgVs3AjDjz+irq8vnrv9dmXdunWYOXMmAMDbZEL4oUM4mJkJIkJcXJzYt28fAUBQUBByc3Nh9vGB0rIlsGgR8NNPQFISMGCAnEjNfUbx7Gz5d24uoiZPRkh0NIx33AHUrAmsXSu7iPv5yRcUL74oZz3/7DNg6VJ5zSNHyiXI3n0XGDZMXtOiRfJajh8Hzp5FzfnzUXPzZniWlaFo1y7Y0tPhM2wYUKcOUK8eEBR0/RcQlfTo0UP19vbGunXrlP79+2PXrl0XbDbbxwCgaVqCqqqrdF33Tk1Nnfjiiy9Oqvjcq6++OtxoNL6k6/qy8vLy56xWq369c02aNKmf0Wh8wGazvWC1Wg9X3m8wGJ728fGZ2KRJE0urVq0Mfn5+mDt37gUiettqtRbf1IUxxhirdtyNnDHGGHPRNI3MZvOn7du3r3+jY46xbx/w1VfAmDFAcTHw1FNy1u5WrQB/f2D4cKBDB2DVKmDzZhluT55Eq5QUAoDsY8fQpn59UPv2+GHjRuoyYgR5Pf+87BbtGm9NRIg5dAhnNmygtd7eosmaNWSoVQt0221ykrJjx+TxJ0/K5bmaN0dhWRk+KSrC4dBQnAwLw7nu3Z0N77xTQX4+YLcD4eHAE0/IZb2mTQNeeUUGz9RU2QofGwsAiIyMROvWrREYGIjI8HB0Gj8eHRcvhl9gINLS0shisWDcuHFIS0tzlpeXKx07dnTGxMTIbuR16sjW6R9/BHbskPflk0+AoUOBunUBXQeaNwdycpDfoIFYXqcOJS9YAHh4AH36yGszm4GKruXl5bLebdrIFu8mTYBmzeSxDocM9zk5QNu2QK1acqx5r17Y4++PrUQ4puuI0XWY9u8Hdu8GXnhBluPpeSn8+/tf8+vOyMjAN998gzZt2iA2NharVq0yCCGe3rBhQ7nJZNrYp0+fWt26dVO2bt3abt26df9KSUmxaZrW1mKxLBg0aFDoyZMnm5aUlOQlJyfvqvTsGTZv3vzPLVu2vJOWltZ13bp1rVRVfb1Zs2bxZ8+ebZqUlPR55bps3rx5TMeOHVsmJSUpFosFpaWlWL58uXA6nf1SUlJsbmU33LBhgyElJeXCjT3UjDHGqgO3bDPGGPtL0DQt1Gw2L7bb7a1UVZ340ksvTb6Jz7ZTFKWHxWLp7u3t3bhly5bX/1BOjgxqTzwhw93nnwMFBXL88IYNMqyeOydbZmvVAjIz5Z9+/YAWLaA+/1p7F7sAACAASURBVDwS/PxQ/7PPUODvL0JCQqj+4cM4cewYAufNAwoLZegdORKoUQNeGzYgqaAA83Nz6eOJE0VhdjZFzZkj7mvalJStW2Vg/vlnYMAAHHrhBSxatw5CzoJKoaGh2JeXZ6j722/wSkhA3JAh8hqWLpV1/eUXGWJbtJDhttLEcN7e3mjVqpX8Zft2wGzG1q1bAQD9+/eHqqoYO3asYf369Vi9erWhZcuWuDizt68voGnAM8/I0B0RIbt/nzx5qcX/1VfRKCuLtmdmIjc3FyFVjfU+cwaIi5MvLLp0kUG9ok4GA3D//XJfxf1u0uRi9/XYuDhs3rpVFHh40G/duiEhIUH2Rnj0UWDjRvkiIDNT3o8tW4BrLOdW0a28YcOGAIC6devajh07NlXX9eOenp7BjRs3BhEhPDzcnpmZeSeA+QAaR0VFiaioKHTr1s1r4cKFz2uaNsNtPXKzyWT6Mjg4+I7bb7/ds6CgoFlhYSEaN24MHx8f7Ny5s52maWFWqzXbvS42m21uenp6zzZt2vgAwOnTp2EymTJfeOGFoopjJk6cONBkMs3SdV1omtbVarVuutq1aZrW3Gw2j9N1PdNuty8E0MhisdwrhDhjs9nmAFhzIy3yjDHGJA7bjDHG/vRcM4jvbdOmjV+LFi3U6dOnv6Jp2myr1Zp1nc+R0Wj8TlXVHm3btnX6+PgYmjRpAoPBgFmzZjmPHj1qSExMFF26dLnU11jXgU8/BebNk63I48bJLtzffitbt1evloFv6lS5PNa998rPjR9/6cT5+YCuY+Xbbzu9mzQxwGAg59dfI1bXRUxpKaFRI9k9euZMua71o48C06Yh8N57MXzHDqR16ybS09Pp8OHDdKFPH/jUqCHDYseOyFu6FMdefhmdR4+GKSqKlixZggEDBmDGjBn49ttv4eXlpcfFxSlwOIDHHpMtwi1byknIiork2to7dshx0VXQFy7E7vPnkevlheTkZMS6WsA/+eQTR15entq2bVunxWK5NObdagUOHJD3KzUVOHpUdvPu0UOOaXfdn7p16wIA5syZg7Fjx16cIR0AyoqKkF9cjNDp01FYowb2rFuH9qmpMH1eqbE3MVG+PJgxA3jzTeC114CoKGzatAlERCNGjMDF9b7Pn5eTrc2eLYP8Rx/JdcOv0a1cCIFZs2bBbDaL8PBwAoD+/ft7/fDDDw84nU6RnJzsWTHhXFBQkDkzM7NicXEPVVWViuv08/OrXVhYePz1119PJyKD0WhsHxUVZRkwYICnql75XzNd180ATk6cONGpqmqRqqqHy8vLNwCwnz9//uJ07DVq1IDD4airaVprq9W6AwCIqEWbNm3MqqoqGzduHAigyrCtaZqnqqo/duzY0efs2bNlhw8ffiIwMBDx8fE+paWlYseOHQNKS0s3ArjzKp/3AFBW8QKBMcYYh23GGGN/DXdHRUV5dOrUSQWAiIiI8oyMjNYArhm2iWiI3W7vQUQ4cOAATp8+jbS0NKiq6iwoKDAAwObNmyk3N1cMuf9+wjffyBmwH3tMjsVu1gzo3Fm2qI4bJ1tXO3eW4S0rS05KdpVJucrKy7Hv/HmDT+PGGLh4MT4bPhx33XUXvJo3l2OndV2G0smTZYvuypVQNQ2+MTFILC5W9u7di7KyMuzfvx8tGzeGPmcOVp44IX6pWZOi27Z1Jr/+ukGZMAErLRYxY8YMeu6553Dw4EEsWrBA+bVFCxEVFUUec+bIwBsdLQPx8OHy3OvXy/A+cOAV9T768884a7cjctAg0bx584vJNCcnR9V1HSdPnqSMjAxE1awJstmAdu1kV3Ei2QLdubNs4f6//5M9Ae64AxgxArj3XvTv3x+LFy9GVlYWQkJCsHfvXpzOz0frESPwW9OmmJWcDH3/ftSsUUMPzctTVi1apCclJysXW90B2Sr/xBOuyh4F0tPh4e2NsrIyLFmyBKM7d5a9Dx5/HEhOli81atUCFAX47jsZul9//WrPCwwGAwwGAypCsbe3N/r16+dR+djCwsJyAOdcQxMGR0ZGegCAwWDAqFGjPE+dOuWZl5dXW1EUhISEVN2aX8kTTzxhMBgMAXl5eQmnTp1q7XA40KBBg4vfgZeXFwYMGGD56quv1k+cOHHAhAkTvtd1fe2BAwdGh4WFGZ1O58FrFO8FwNyuXTsQkQWABQDy8/Nx+vRpioqKsuzZs+eKJn9N08xms3kuEfU1mUw/aprW2Wq13tycB4wx9hfFYZsxxtifnsViubNhw4aeAOB0OpGbm2sAkH2tz2ia5mk0Gt8dPHgwjhw5IlRVVU6cOKGfOHFCadOmjdK8eXNkZWXhq6++QumWLaRnZUE5cADw8ZHjjX/++dL43ttuk63BFe65R7bkvvCCDJVRUVecf8+ePQCAzsnJUOfNAwA0atRIBieDATh4ELj7blnG0qXAoEEQNWrgy6lT9QP79ysCQM+ePbFu3Tp9VVmZMnLuXOR4eop7hw6lmJgYAzZtAo4dwxO+vvRWbi6OHDmCmEOH0MhmE0fDwmhdVJRotWIF3bZxIyg0VNYXkBOH+foCzz8PBAYCKSkXW3vLy8sxr0ULdGvbFkO7dr2sCbh27drOmjVrKgCUOXPm4Kkvv4RfkybA3LmXDjIYgLFjZfmuyeKOHDsG9dNPYc/IwJGsLN0cFaUsWLBA2O12qhEQoHuZTGL/8OFo8+SThji7XbaAZ2YqF/bsQXjduli2bBm2bNniHD169KXWdCJg9Ghgzx7gX/9C40aNcLSwED137wa+/FLOkP7ll3JMubvmzYHly6/12KCgoAAArjurWuvWrb2PHj36HoCxHh4eMY0bN3arHiEsLOxSK/t1BAcHi7y8PNq8eTN69+4Nf39/1K9fv8o6xMXFYciQIV6fffbZyokTJ04FoJ09e9ZksVh0AGcrjtM0rR6AMxUTqVmt1vzU1NTc48ePR9SpUwd2ux1ffvnlhczMzHJVVXfb7fZ0h8PxbuXzmUymj8LDw3vcfffdyrvvvtsGQDyAX27owhhj7C+OwzZjjLE/PafT2T48PBwAcPbsWdhsNgeA3652vKZpZDAYJtWpU8dUt25d1K1btyK4XPZ3Y4sFterWRekXXyCtYUNRf8wYClq8GIdDQ1F8+DBatmwpuzuPGyfH+T766KWT3HefbGX9+WfZalqv3mV1KCqSw2pDmzSB1wcfwOPAAfHvf/+bHnjgAQQFBQEPPSS7RffrB9x1F1BQAOFwwPbhh3hh8mS8/9RTaNGiBVq1aqWkp6fjuNGIkSdPKlQx3rh9eyAyEsbRo9EpPx/+/frBNHYsBn7wAZ1/5RXs2L6dvMaOxY+1a8N7zx5RsnEj+fr6olmzZnL5rNmzZQvvtm3Ac88Bqor3339fb1pYSK0feohw4sRl1/PQQw/JsJuaipKsLPFp3750/+OP44r22qefBqZNg+PhhzH944+d586dM/j07Im6+/eLDjt2KG0SE2H29SW/u+6C+uSTCg4ckLOTA7g4ddmhQ/AqL8eAAQOU3Nxc5OfnG0pKSq5cM7tmTWDCBNS57z4MP3ECiwYOBF57DZFxcVU/GImJ8l7ruvzOKsnJyQGAqtcCryQ2NhYjRozw/uCDD5rqui6Ki4sR4D4b+w06d+4c8vLyCACSkpJu6DMhISEwGAzw9/d/sri4+EGn02nx8vIqB1ALAFJTU98zmUwP67pu0zQtyWq17gUAu90+9fvvv58ycuRIr6+//ro0Kytrjd1uv3v8+PG2qs6jaVp7Ly+vgYMGDfIoKyuDzWZTAFyr9Zwxxv5WOGwzxhj701MUpcRut9cAgMDAQDRo0MD422+/5aWmpuYrinJQCOGw2WwthRCzjEajh9ls7uHn5xfYp08fryoLLCkB5swBpaUhsFcvlLz6KvzGjaN5336L/PBw0Lffwm63o06dOggODpbdxSutxwxABuXp0+WSXHPnQq9RA+Xl5UhLS8O2bdsQGxsrgp1Owjvv4PFvv6W33noL77//Pob07YuwxYvh4ekJUbs26KefgNhYKAYD6rVoIZYMGYJib29ZbpcuSLjnHtld/bnnZDfwWrXk+cPCcGbIEDQaMQLGkSNld/dateANoGNmJs6ePIllvXqheVISft26FXa7HevXr3c+8cQTBrVGDTlD+WOPAcuX45vz552FhYWG+kOGgOLjr7xWp1MG1J07EVW7Ng6oKkptV2a04vPn4fH669CbNcPZs2cNAPDUU09BURSCrsvW6JdfliH/wQcvTaLmLjpaThwHYPjw4ViwYIE+Y8YM5aGHHoK3l5dc53zZMuDrr+VY+cWLUb56NeqtXi02Tp5MRc8+K5o1a3Zly7DJJJdG69Hjit4Iu3fvFl9//TUpioIhFRPMXcPSpUudu3fvNgCAzWajadOmYfDgwYh0W/7tRgQEBCAlJQXr16+/Ys3vyhwOB44ePYoVK1YIDw8P8cQTT1gOHz5smTt3LurUqWPKyMi4Z9KkSYrZbB45ZswYy6FDh8zffvvtRk3TngcwF8D7Z86ceerQoUPRBw4cMOm6/j2Ap6ZMmVLfbrfvFkJ8aLVaL36pZrP5uaSkJA+z2Ywff/zRrijKzFdeeaVU07QYyPW/Yx0Ox24Ac/4X644zxtgfDS/9xRhj7E9N0zSzoigvJSQkeHh5eYGI0KhRI2P79u0NcXFxPnXq1KlXq1atqMOHD3sQ0W3+/v6tBw0a5NepUyeTpdKs2xAC+Phj2QX57ruBnj2BOXNgvPdemAYPhqlpUxQUFIjCwkICZDduALJbeceOVU+ulZAAhIcjf84czFyzRqzevp3Onz8vEhISqEOHDmQRAti0CabBg5GYmAi9sFA06tWLVmRmYmtBgf5t06a0+eBBBAYGwtPTE4sWLVIS7r0X3bp1g+fPPwNNm8pJztLT5VJZeXlAw4ZysrPXX4dP795YsW8fbH5+CN++XdZT14FVq1C8YwfS27TBHV26UM+ePdGqVSscPnwYmzdvFl5eXhQcEQH06wfbzJkoXrBAiRk0CM1TUuRyZz4+l7rRCwE0aCCX7EpNRXpZmZ6bm6vk5ubqYWFh5OPjc/F2fP/99/rCWrXo17NnhS4ExcTEID4+HkQk719oqCz3lVeAdevk2PE775QvNCr83/8BJ04AHTvCaDQiLCyMftu2DUe2bRPNxo0jWrwY+ssvY1NsLObt3i3W7t5N6eXlyPP3p87ff4+itWsp4L77UFRUhN27d0NVVXh7e8uys7LkTPI1a148XWFhIWbOnEnBwcH6s88+S/7XWR6suLgYy5YtU3r16oU+ffogOTkZhw4dEmlpaRQUFCRf0NyEWbNmAQCCg4Ov+dkVK1Y4V61apcTHx9PgwYPJYDBAURRs27YNp06dElFRUYE+Pj4pgwYN8vL19UVISAiFh4dbiouL7yguLh6r6/p3TqfzvJeXV/Jtt91m0HU9OTo6umvDhg1bOxyOlNLS0jZJSUlzATkpIRF90K9fP9VoNGLZsmUlJSUl32/btm2iwWB4NT4+vmvjxo3bCyHuKCkpGbx27dqPU1JSeCw3Y+xvhVu2GWOM/ampqvpK3bp1zUFBQZdtNxgMl0081bhxY2zbtg1paWn4/PPPERQU5Hj00Uflv4NCyCWfZsyQQWvePCAsTAZvmw0IDQV5eCDg+HFkZ2dfTNQbN25EoNOJhv36gc6cwamcHPj5+eHo0aMoKCiAzWbDgQMH4OHhgfA9e8QDWVnku2MHyGS6lMrLy2VgBmAymdClVy/CrFno+fzzOGowKPd+9BE++ugj59KlSw3l5eUAgK1btzrj4uIMmDJFlvHee8CCBXIN6zfflOt6f/aZDOBjxyKva1dBISE6EhIMSEwEfHxA9epB/PwzxEcfobCwEADg6emJwYMHK6tWrXIuXrwYixcvhq+vr7PEx0dJCQujtgUFciK1d94Bzp6VLyVWrpQTjb31llySC0Dfvn0N3bt3x9SpU5V9+/aJsLAwys7ORkZGBnJycigyMhIDH3+c5t9zDw6Ul2P//v0Xl9KCzSZfCCxYICdXW74cePVVeZ/GjAFq15YvFDp2lMefOIHgzZvx6MqVWBsejllt24qyBg3ozNy58PDwEH379qXY2Fg4HA6kp6djicWCesXFwr91a9rYqROKo6P1NWvWKLGxsc6kpCRDWFiYbBVv0ODiV7R8+XIdgPLAAw9c2bfcJSMjA999953ueiYpICBANG/e/OLxI0eOJE3TsGPHDriP374RPj4+zlq1ahnirtb93SUmJsawZ88e0blzZ6qYwM1ut8NkMqGkpIQSEhI861UazhAZGYnIyEivPXv2iG+//TYNgIiIiFAbN26Mxo0be1cc17p1a4+33377dk3Tmlmt1l+I6P6YmBhnxRJvSUlJHrt3736uadOmvk2bNoXRKCdJT0xM9HrvvfdqlZeXNwGwA4wx9jdCcglOxhhj7M9H07TaqqoeGj16tMf1uthW2L9/PxYsWAAAMJlM4pnOncl8/LgMkBMmyHG7w4bJ9ZcPHQK8L+YNOJ1ObNu2DbVr18aaNWv0kydPKoayMjQ8c0Ycioqi0lLZU9bDwwNeXl56aWmpUrt2beHr6yuCgoKU1iYTlA8/BCZOBCpeDhw4IMd8L1kig+ubb8rwHBcnJxQDkJ6ejqysLJSWliImJgZZWVn6gQMHlMaNG+sDBw5UIIRsrZ4+HXjySVlueTnQtStQpw7mtmql3/3cc4q6Zo0ci/3RR0BgII7/9BO8mzbFiiFD9HsffFBRpk2TIX3jRmSXl+OX8nIRERFBNpsNTZo0gWn+fDlj97hxcrkwm02uz71okQzcbjZv3owffvgBDRs21FNSUpTp06fDYDAgKCjImZiYaNg9ZQqO1asHu8UCIQSGDRuGuv7+cuz7mjUXX0AAkOuVv/MOkJQkl1arVw/o1Emu3X3kiKyT2YzSwEBs2bJF5OXl6cnJyYbQ0FCQW2+DH374Qd+zZ4945plnDAUffwzfuXOhfPIJcj098cPq1SIrK4vanD2rdzl9WoFrWbFz585h+vTpuP/++1E5qLpbt26dvmXLFiUlJQW5ubno1KnTFd2+NU0DAPzjH/+4bGmz61m9ejU2bdqEhx56CBEREVc9TgiBTz/9VA8KClL69Olz2b6PP/5Y+Pr60j333HPVz+fn50MIcdXW8+XLl5fv3Llzk67rbxmNxs/uv//+oGvdEwAoKSnB22+/XeZwOOpZrdbcqx2naZo3gL4A9lqt1p+vWShjjP1JcNhmjDH2p/Xaa6/9q0WLFo/06NHDdDOfczgc+GnFCmTPmIGuJhM8unSR4bZi3eYFC+QY6I8/vup606mpqSIoKIj6EmHtjz9iX2Qk6tevj9OnTzu7d+9uqFh/+gpvvSXHBa9cKX8/d0623g4eDHz/vQz8Tufls5u7sdlsSE1NRVBuLqI8PfXuAwcqeOQROcu33S7rvG+fXMs7IwPw9sZRXcfSTz5B7+HDER0TIwOyqgKZmbC9/z6m5+aiqdmMlD17oMyfL7vPx8TI7tpmsyxn7Vp5X8aPlxPBHTok69iggTymkvPnz+P7779HQUEBTp8+DbvdjrFjx17srq07nVCefx546SVo778PT1XF4AcfRNjOnXKSsspOngS++gqYMwfYvl2Okf/hB/ly4LHH5PdXXn7Zy5HKPv30U2dERIShi6sFHroOdO8uw/t77+HoqVOY/9FH6GU04nyPHjh9+rTz119/NURHR4tBgwZdcwby8vJyLFiwwHnq1Cl64YUXqkzSJ0+exJIlS0RBQQF5eXnpY8aMUW4kdOu6jtdeew1OpxMvv/wyqlqLu8Ls2bP17Oxs5YUXXrisbm+88QYeffRRVO4BcjPWrFmDtLQ0hIWFFbVq1cqnZcuWl+6Jw3F5V3+XXbt2YdWqVWtffPHFOzRN81MUZbSiKMEOh2M9gKVWq1XXNK2jyWSaFxYW5nvs2DGTruuhVqv13H9cUcYY+4O48deqjDHG2B+IpmkE4IE2bdrcVNAGAPXtt9Fy6lQoRiOO/fIL9GnTZFDo108ueXXiBPDPfwK7dgHFxVd8vqioCDabjVq1aoXADRvQ08MDRITjx48jKirKsHjxYuFwOKo++TPPyMDdt68M2vn5wPvvyyCbnS0nBtu69crP2WzAjh0wDR+O24OD0WvZMoSvWaOIhg3lS4Lhw2UI3rEDOH8eyMmRE315eiIyOhqRbdtizrx52LFpk4CfnzzPQw/B3LUrej/4ILYbDJjbq5c813ffyfWnFUWuVR0eDsTHA926yTWqMzJkuNq7V25v2lRue/hhOc76yBF4Z2VhQL9+CAgIcJSVlQEA3MfIKwaDDM179yIpKQlD/v1v5I0Zg5MVLzeKioBNm4ApU+T9SUgA5s8HQkJk0E5NlS3/P/0kXzIsWyb3ATJ8p6bKn2fPBkpLcerUKZw6dcrQtm3bS/dUUYBvvgHatgVmzEBkXh6imjXTQ2fNwsGNG51nzpyhfv364XpBG5BDAOLj4w0mk+mqrRi1a9fGww8/TI0aNdILCwuVKVOm4JNPPrk0jvn8eWD3buDXX+XvrgYRRVHQv39/AHKitmvVIzIyUiktLUVJScnFbWVlZSAiBAYGXu8yrikrK0sHgAceeMC3ZcuWhNxcubTb55/LcfYTJlzxmYCAANhstk6TJk2aajQa9zdq1OgfycnJY4KDg2cBcKampu7y8PBY2a9fv7CkpCRvInIAKP+vKsoYY38QHLYZY4z9WYUpimK64QCh6zJENmkCnDgBg8GAdiEh+DElBXNGjxZo316GOqsVCAiQrauHD8uu2W7279+PadOmwdfXF02aNAFmzoTn++8jMTERpaWl2Lt3L8rKyqhiaa8rEAGNGsnw+PLLsjW2tFSG+8cfv9RCmJcnu3tfuCBbme+/H9B1HPfx0bdlZGDmyJHYNno05i9frovmzS+fnK1PH/nSYNw4IDgY2LcPd911F/r164eNy5aR0+GQAVlRgPnzER0djcTERBw9ehQLFy4Ul9U1IkKGvtq1ZSvy+PHAgAEyyBcWyjXGv/oK8PSUa2cLIV8c9OsHZGWh+zPPqEN++knUyM1FwaRJwLFj8pqEANLSgNatYbDbsTYlBb+azVgxYQIOx8SgKDwcq+bORemFC0CLFnLm9ZgYeU/uuUcubTZvnmxVb9FCns819hytW8t7rOtyXHlpKSg1FSM+/BC+vr6Apskgr+uyHiNGyBncH3oI9yQkKEFJSXiwc2fDsGHDlOuNk3Z35NAhZ21PTwN0Xb50OHhQvqx44w3g+HHg/fdhGj0a/e64Q3l+6VK02rwZgcuWGURoKDB0qLymZs3kCwxAPiN9+wIAGrVti5q5uch47TU6FRYmx9kPHSrvRbZcUr60tBTp6ekgostebPj4+EBVVZyotFzbzfL19YWlpAS5n3wCvPQSMGSIfDHVtCkwahRw5swVL4pmz54NAGjfvv2zAwcODB0wYIA5KSkJo0aN8m7cuLFQVbX52LFjPfLy8uwLFizI13X9LqvVeuG/qihjjP1B8ARpjDHG/qwCLBaLHYDHdY+cO1dOgDZrlpw8bNQoICQENWvUQMudO7Fy5Ur6YsIEfdCHHyqW0NBLn5s0Sc6Eff/9QHg4ysvLsXjxYsTHx+vt2rVTDIAM5llZiImJwaZNmwBAdO3alWrUqHH1+hDJ0FqxhvUdd8jfbTa5vnZSkmz1XrdOhv5vvgHq14eTCJ+tWKEAgFFVkZOTA4fDoZw5cwbe3t4wGAxyYqp335Xht1Yt2TKfkACkp6NJkybY4unpzEpOVqI8PQnPPguUl8M5ejR2RUejdu3aOHDgAC1atEiOBQdk6/6UKTJQDRkiX0CMHy+7dbdsKVtiL1yQM5GPHy+vr1Mn+bOuwzF9OvalpQnvwkL4HjxIOHJEBuft24H9+4GQECSrKs6npMB06hRK7rwT5++4A0ePHsVekwm3v/EGyo4dg+WBB+RY9t69ZRf2adPkxGnz58t7+M038pxJSTKUZ2fLa585E+f37MGJ+vVR0qyZIzQ3V8XGjbJV3GSSLwuOHpWTybVqBXzwAfDLL8CHH8owW7++PEfXrnJ8+FdfyZcy/fsDzZvL7Y8/DkyejK6zZxssK1dijtks7tM0UoYOlZPGpafL58jPTz4v+/bBs29f3D5/PlZFReFk164I9/WVvRJatbo0Xn3rVjmkAAC++w53R0YifelSbNZ1RGVkoIXBACxcCJSVAUuWIC8vD4WFhahXr57u3j+diBAcHKx/9dVX9PTTT1+3lf4KDgcwZw4Gbt2q7HE4cDwtDX6PPAL/116Tz7Kuy+77OTlyGENk5MVeBkFBQaJRo0bUsWPHy85LRGjQoAGdPHnSYTKZ1Ly8PLuu6wuEEGtvun6MMfYHxWGbMcbYn1VBWVmZ8YaOHD9edtdet04u6VSvnmzVBdCqVSvEZ2XBOWyY8nmNGvrDY8ZcSilms5z8KykJ577+Gp9++y1CQkL03r17K0Qk1+N+5BHAzw9BqgpFUVBWVka7du3S27Vrd+3eY0QyWO3fL8dq//vfQG7upWXEGjSQLdPAxeCSc/LkZUW8/PLLSE1NFbm5uTTd1QL/7LPPwjM6Gpg6FWjcWIa83FzAywu2Bx9ErVq1DAWZmQLvvQds2QLRsycKly+HV9euuPvNN7Fv3z6xYsUKpXlZGWIWLACefhp44AF5wk8/lcFz/Xr54mLuXBlOFy0CnnhCtjq7z7StKPjs4EEUmEwKBQfjnTp1xJh27ciycqWczXz1akBVQfXrw2faNODrr2Fu1w4B27Yh4osv0OT113Hg11+RX1Ag2p87R4Z+/YCAAByrWVMEZGeTT2kpsG0bRL16+KluXeEzYwZo4UKEN2xIlgMHgNtug/jwQxwQQmT9P3vvHVXVubV9X/dau8KmN+lNqjTpIsi2YS8xajRqLDGaGI2mnyTmkB1NNMmJ0WhiNLGbxB5bSgJETAAAIABJREFU1FhAECmCgAqCSkcpSu+w91rr++MGATUn5/nG837fOe9ZvzEYDPdmr3ov5JrzmnPq6xP/4mIWWVk0CHHkCDBjBg1EbNpEM+4ZGTQrnpNDv44epaL+nXeAlSupKD91itayZ2UBUikV0wwD6Oujy88PLYWFKCgpIcXGxoKriwuBsTFgakpLBz76CPjiCyAvD9i9G9LgYJTk5SGf5/l3f/6ZQUAA7SgfEgKo1TR7PGYMvZbDhkFXWYn02loowsIwzc+Pity6OkChAFavhn1GBqynTeOFZzTkGTt2LLN9+3ZUV1c/7tD/T6mtpc37zp2j93v8eGD2bFg5OODYnj1ot7ERRnd3nzv6/ff8c2+8wWzdvFnnynHE8sUXScrkyTzPMGhpaWH/rD/Qw4cPIZVKSffx6dXU1CxsaGgI1Wg0HwFol8vlyxmG8ed5PrOzs/Oz2NjYvL8+cBEREZF/H0SxLSIiIiLyn8oolmX/uhxKEGhTLRsbaonuqTHesoWKZaUS8gkTUHfhAqrOnWNyc3OpPRy01rWF4yDMno3iRYugmzQJ4eHhzOMO15WVwOzZqKurww8//AArKyvupZdeYmUy2T8/rtRUmh3du5cKq5oaWjccF0fHW/1JEytbW1ssX74cW7Zsgb6+vgCAyOVyPjk5mTUwMOBbW1uZo0eP8gCEuQUFLJF1l7OrVKipqkJbYiJITIzgbGND2srLIV+zBjfc3PDHwoXCcicnojp5EqGBgcSwvByVaWnCwBdfJIiOpq6ADz+kDcnkcjpj28am98CmT6c1uy0t4HfuhDB/PvLy8lBXV4eGhgYAgIuLi6DT6YTTp08TQ0NDKB89QlRQEO36HhZGgw+OjrTGetMmICgIzODBUO3Ygc5Jkwh++QU4fhy3bt1Ca1kZuWFhgVv79sE+Jkaora0ljVFRxLypCfN+/BGbfXxgP3euYJiXh/KpU4X6+nqyatUqACBQKGhTNADIz0f9qFEos7GB2+nT0Js9m57X3btAaCi9R97e1BY/eTLNID98CBga0hpxY2OaqY6NBfz9kZmczGkdHVljY2PoBwcTWFnR87p0iQr1Tz+lWfOe9TN2LOrS00EApu7992H63XdAQgJ1Xjz3HODq2u/+19TUPF6Xjw4dgqVEQo/x4kVg7150HDuGpnv3GOeSEujmzu3XSM3a2hq+vr7c+fPnyTNHmPE8nTH+6BHwzTe0fnzuXHod3n8f0NcHAMgaGsAwDOTdTfGampqQU1vL3F+/HlFDhkhIWBgs9+zB1LIy5tG8eSCEwNnZ+and5eXlITU1FS+88AJLl6gKS5Ys0c/MzAy5du3aUZ1ORwIDA/UdHByY0tJSjytXrkz79NNPp//9738/86fPlYiIiMi/GaLYFhERERH5t0ej0TAAhgIIAqCUSqVOAJa0tbVBq9Xi/v37qK+vh6GhISoqKnhfX18GoM2ZUFBAO32XlNCN3b4NNDRQUbNsGX3t1Vdh+s47GJCdzZeUlDBSqRRnz54VmpqaCAAQjsPcoCDhPT09Qvpmbr/4AmhuBrN1KwRBQHV1Nbtp0ybh3XfffbZV9+23qcXaxASPx3UdPkwzp2fPUpF38iTNas6b178OuxsDAwMAgKmpKQ+Atba2Zu7cuQNzc3NGp9OhpKSE4XkeGz09BWOW5Vu/+YbREYLW1lboFiwgE6RSweDTT8mx9nbURUbi4fHjcPf2JgYMQ+u85XLUDxok5E2ZIkTNnUuQmUmz24mJvV3HDQx664p7GDUK1/ftg0VsLK6kpKDA1hYgBHp6ekJUVBTx9PQkJ0+e5PPz82Gs02HGjz8i/oUX+OGffsrAwIAKeq2WNmNTKul1AnBq925YjhyJgI8/RnV1NU6dOoVF4eGoLSiAVqtFUVER6b7XwuwVK8gBd3de1tDATFyyhJybP19wnDiRWbhwIWQyGb3eBQXQbt8OiYcHqvbtEx50dJDrEyYI56ZOJe9t2QLi7k7Pbc0a6g7Qamm2/scfqQB9+216voGBvec+fToAwHHWLPYAALa5GemTJgmTxoyhN/DePSrwy8powKcbhmHwTkUFas6dQ+bUqRj1+++0fMDVlW7z7l36/cgRAICvry/09PSwf/9+1FZXw7LbVQE3N8DeHmTJEozeuxdO27fj5K5dwrSFC0nfDuFBQUHswYMH+cf16pGRNPB04gQNKKxeTWvz9+2jQSA9vX63+MKFC0hOTgYhBEZGRqSiogL79u0Thunp8dHV1SzTc02cnYHvvoNteTm1/T+BTqfDsWPHMHnyZLj2CSgwDAMzMzOiUChIVVWVLD4+XrCysmr29fU1CAgIUN68eXMeAFFsi4iI/Mcgim0RERERkf+jdHcNN/6zUT4ajUYBYBqAjNjY2LvP+rxcLj+uUCiGDxw4UCqXyyVKpZK5ffs2qqqq8P333wsNDQ0EAAYNGiTk5uYy8fHxAIC5c+fCtbmZ2rV7YBhq650yhc7UzsujAqq4GOGTJjHZJ0/ixMCBGDx4sBAaGkqMjY3B8zyYBw8Inn+efs7BgW7ryy8BrRbGxsYYP368cP78edLR0UGOHTsmTJ8+nQqtoiLg44+pgGlspOItIoJ+9fDoEa0d1mppFjE7m86tXruWWsr7IJVKuzdbxBYXF2PmzJnk+vXrCAoKwq1bt3D37l1MmjQJFy5cIJELF7KZgwcje+xYqNVqmJqa4u733wv6/v6C/SuvkOe3bYOgVEJiaQl89hkV23V1uN3eDq1Ox9w+dAjeX39Nbe7KPqXxBQW0lnvBgscvnT59mrteVMQO0mjAx8VhxuHDKF69mpswezbb8zMymQymKpVgodUKSeHhzB2lkolWKkGkUuRt2gThzTchHzIEmYcOCe3t7aTlzh1hzvffk9qjR9Eul2P/jh1CQEAAGWBlBYfLl+E9bRpu3LghdHR0CLNmzWIAYMnSpTRr6+aG6aNHE+zZA5iZof2bb8CdO4e45cuhyshAgVaLhvHjhYULF5Lg1FTyXVYWV/Hee6ythQVtBDdgAC0RCAujNeBxcVQsNzZSW/UzxpMVFBTwABiO49DY2MgDYJGURJ0Vn3xCnRB94XnoVq1CYksLntuxA/z774Oxsemt01apaB0/zz8ue3B2doZhczNM9u2j3d/19YGiIgh37mD7uXNCR0cHzL/+WlC7uzNwdKTr6t13AYaBw+jR6Hr3Xabj22+hMDCg/QsmTKA2dA8P6qooKqIBKT+/p8R2dzdyZubMmdDX18fu3bsxdOhQYVhbG0tovwKKmRkV2YcP08CBt/fjtxobG5GYmAiJRCL4+vr2iyZxHIdffvlFq9PplgK4BKC1srJyVG1t7RSWZX27urp+euqii4iIiPwbI87ZFhERERH5P4ZGo7GSy+Xnurq6/GUy2cXOzs55sbGx1U/8zOtGRkZft7W1cTzPv7l69ertT7zvpVQqM95++209ln2s2xAfH4+UlBTB19eXZGZmwtDQEGFhYbhw4UK/Y3h92DCYq1Q0U9eXxYupbbbn57VacCdOoHXxYlz87DN+ilzOsBMnUtHVw717NNu5aRMVn2PH0oxnd7Zyy5YtQm1tLRkwYIDg8fvvQqu+PqLeeosx1GhoI6snxMtjEhJoRjcri1rb582j9cNff02bcM2eTS3W3Wzbtk2oqqoiEydORFDfQEIfNm3axLP37jETli6FnafnY5GO5GTa3GzGDJqhbm+n1u0hQ+g+Vq1CfXExzvr4IPrIEcH2wgXyOLjQ9zoUFUE3ciSuXLmC3Nxcvra2ljE3N8fcuXOxb+dOfnpyMjNg2TIqVHuyuYIAzJ+P2tpa4YC7O1m0bRsuffKJUP7gASGdnahzcAAhBBzHgdNqMX3IEHhXVkKYPx979+7lu7q6hCVLlrDYv5+Ox1q37ukTFwTg1i3auI1hwC9bhlJnZyHZ25s02tuj1cxMCA0NJWZmZvD09IREqwUcHVHk6io0CwIZNHMmJEolHR/WQ2QkFa3/+AdtajdlCq3pZlnqUuimsrISu3fvxogRI/B4xNjZs8CxY8CKFbT++4cfqGPhyhXaZK2yEmfPnMGd+Hi8um4dFAxDbepNTbSBG0ADNSYmuB8QgPj4eDB//IFIqRSOu3bR90eNQp27OzZbWWHKlCkICAigr69aRffZ45a4dg27Cgs5Wzs7NiYmpvf8cnNph/ZJk3ob333zDa1N9/ICAFRUVOCnn36CIAiQy+UCIQQ6nY7odDrExsY+e10fPEjt88uW0XMCtY8fOnQINjY2nCAIcHNzY4cPH9596wRs3bq1rbm5OYPn+XwABIDQ1dV1E8CRJ393iIiIiPy7w37yySf/fx+DiIiIiMj/JWg0GueEhASPhISE2oSEBDepVJoUFhbmMmfOHLazs9Px4cOHL8TFxe1Vq9XtPZ9JSEgYOmjQoFFTp05V5uXljYyPjzePi4s7r1are96PsbGxmRAYGCjvuy9nZ2dERUURDw8PuLi4oKioiKuqqhL8/f3h5+dH7t27BwCwO32ab7x+XbimVHJubm40PdjZSYXlzJm9YollwXh7g1uxAifOnSM++/ZBr6aG2nQvX6ZWYjMzWnN76RIwfDgVJMuWPRZFLiYmxHrNGtywsiIRlZXokEiYi1IpH7pxI4H0n/RyO3ECOH+eZrFXrqSjqJyc6IitnByaGXdyoseqVMLMzIzcuHEDTU1NfHBw8DMt6z4+PuRyTg7s163DpaoqIefBA8HJyYnI09Npp+4hQ+gotLo62m09OJiKwDFjoLS3h+MnnwhJs2cLzuPGkVu3buH06dP8xYsXcfXqVdQfOoS2U6ew99Ej1NXVwdfXl4wbN46o1WpwHIe4y5eJw6uvwkqno93Vvb2pG6CqCrh2DXqVlWTwzp04JwhCgZ4ephYWEtf4eEw4fBiRkZEYOnQorL/6Sui6fBnWmzaRS5cu8YWFhXj11VdZlmWpoJZKe5uxCQIV199+S89h+XKA46BdsgQ7TE2Fm35+ePHOHRJZU4OhW7YQJycnWFpYgFm0iI4NW7AARZmZ5FxQEIK1WsjGjOmd2Q1Qm/aECTTTbGdHZ6XLZPSczM0fW8oVCgUKCwtRVVUlBAUFEfA8rQGfPp2OYDt9mq4biaR3drmFBawjI3F9xAhEjRpFBfzy5TQw01NGsHUr0NqKU01NKL5zB+PPnIH1kSOQ9ARvZs1ChokJ6uvrhcmTJ5PHPQUMDOh27tyhAQInJ+Tm5jIPHjwQQkND6Q9xHA3AeHjQ8/jkExpQAqjjoqEBcHDA9evXUVJSAl9fX37KlCmMv78/SU9Ph7OzM/y3bqWztmfOfHIR0tpzjYaWSnAcOjkOWVlZaGtrYwwMDEhOTg5JTk4WPD09ib6+Pvz9/aUmJiaOdnZ2Qc7OzkHOzs5BSqVyeF1d3arExMSO+Pj4NLVaLWaKRERE/iMQbeQiIiIiIv8rrF+/fpNMJluiUqm6Ghoa9FiW5ceOHSsNDAwkABATEyPhOM76xo0bBwGM7vPR6ubm5i5zc3P50qVL9Xbu3Lm0sbGxCsCX3e+rVCoV++T++uLg4IBly5Y9/pmOjg4UFhYCAG7eu8e0K5Voun2bMTQ0RJhSCcm4cdQK3V3/3Jfqhw8h0dODMjmZWnRPnqS12RMm0O/PPUdrac+epd3NVSqata6pgdn06RA6OwV9QSAme/aQE7t3C8P/qis5ALi40Jro4GBq2T5wgIpUQmiGdfZsYM8ecIcOIc3EhL+sUjEAMGfOnD/dtkqlwpQpU2B24gRulZaSQpYlZWVlGHjjBqT19WAnTqTNtQwNaY1tUBAwbhwapk5Fy9q1gqy1lehKSshXX30FlUolWFtbC1FRUYyxsTHY3bvBdnZi6tSp8PT0RI+4KysrQ1ZWFieXy9lTp04Jvh9+SLB+PRV848ZRQbxnD2BnB6lCgVy5HNrmZrLfyQme48ZxTgydpsYwDNwHDSI/A8KtfftQXl7OLF68mNZdAxC0WpBjx2hd94oV1KHw4Yf0frq4ANevg+d5HPr1V76LZYVVb7zBMjNnUht4QQG9xi+/TIMO9+4BH38Mux9+gPLiRS7v9Gl20LJl/efJRUVRi7+lJV0PQ4ZQW3d+PhX9r74KXUMDct5/Hw8ePMDixYvpBfnjD2DGDHTV1eHUqVOcf1cX27V8Oe969CiT8ve/c/bTprFcdjbKw8OxYOXK3v0tX06dF90BJ/zwAwBg4Esv8bK2NsZm+HAo+s6XZ1mETpuG8r/9DX0mftHzc3OjQY64OGDECCiVSo7n+d7n6ehRanM/cYIKb19f6naYO5e6Kz79FKishLS7IZ6NjQ0sLS0BAM7OznxNTQ2wZAmD1j8Zjb1yJbXey+WArS3sVq7E31atAk8IlIaGpKWlBZs3byaXLl3C+PHjYWhoCL+e0We9KOvq6nDo0CFNQ0PDJI1GExMbG9v1Z2tfRERE5N8FMbMtIiIiIvK/QlJS0raZM2eajR07Vj506FAmKiqKtbGx6Zd1dXFxYdPT060uXLiQplariwEgISGB6+joWDp06FCZVCqFu7u79Pr165FxcXG/qdXqRwkJCUY6nW5mWFiY/Nl7fhqJRAIfHx94enrCfNcu5Jqbo1EqRXFBAaxsbWERGQkEBYHjuP7iBMCxY8d4Nzc3wdvbmx67hwcVvDwP7N5NxdXDh3Qs03ffAfPn0wxxbS3I1KnQf/11Ej5yJPT09JCTkyNUVVWRzMxMoaKiglhYWKCmpgY1NTXQ6XQghFB7t709FdY9dbIffwywLBrs7SGXy0GUSrT4+OCXK1fAVlQIs/PySKNSKZR2zyr+s+tgZWUFdu5cxGVmQmJggFu3bqHh3j2UPHoEw/p6QZWVRbB0KXQ6HQpNTJD04AGPjRtJjbW1QLZtI17u7hipVCLylVeIj48PY25uDpVKBX0bGyhDQ2Hh6/tYaANAVlYWn5WVxUZFRSEmJobo6+tTIb9qFRVzXV1UOA4ZAkgkyMnJEYKDg/HiggVk0PLlDKKiaJZ6zBhwmzdD5uZGrl27htGjR8PdzQ15hw/ziZs2keYdOzDg6lWkKZWCnqsrkb7/PtjwcCqKu50K586d4woLC8lrr73GSqVSGlSwt6dlA6+9Rn/2zTep2A4MhGrcOIRaWDANR48KP2u1ZODAgWhvb0d5eTnMzc1pxtnTkwpgQuiXvj6gUECwtcXxxETcuX8f0WfP4kh9Pe4WFQkKX19i/v77SLx+HZmZmYxULodjaiopmDsXJU5OJOvqVcHsp5/ItbFjMXJ0n/jT2bPUDeDi0vtaSwssly8n983Nobd4MYwHDux9j2FQkZOD6wxDwvtuJzWVrtehQ4E9eyDExODwb78x9vb28PLyogGETz+lQSQzMxpAyM6m98zMjGblBw8GWlpQuG4dSu3t4eXtzdvY2DAAIAgCKSsr48JdXRk4OT3uWA6APhcXLtBtjx5N7/l77wGzZ0Ny+DCkkycDb78NWXs7qhsbcefOHVy7dg0dHR39mqb1oFQqMXjwYFlubq55a2vrDbVa/VR/BxEREZF/N0SxLSIiIiLyv0J8fHz1vXv3xnt7e0v19fX7ibAeGIaBkZGRrKCgIDIuLm6bWq3mEhISagGscnV11TcwMIBCoQAhhCkvLx8XHx8/XCKRfODi4mKQmpqK0tJSYmJiApVK9S8dE8MwMNBoUDtxolDZ2kqW/fwzsisqkOnuzqWlpeH3338nbm5u0Gq14DgOWq0Wf/zxB5k2bRpR9m0IJgg0e+rkRGtpMzOpkGltpYJl4EBa72pu3q+DuEKhQFpaGmlubiaPHj1CSkoKsrKycOvWLaSnpyM5ORk3btwQWlpbYbF2LWkYMwYqc3M0u7oiv6JCOHTiBMnKz0dTUxOflpYm6MzMhFmffcZInZ3hdP06aT19mqS0tQlewcHkWde7rq4O4HkMnzULQz78EO1yOUZ9/z3yXFwgDwwkeg0NuKinx/3222/M/UeP+NDycsarthaOKhUxf/NN6NfUgF2yhDZC63s9Dh8GkpKAUaP67U8qlZL09HRMmDAB5mZmdLwax9HrplZTy/3AgdSibWKCi1VVRD1iBDE2MaGZ/ZAQWgteUIDPi4uRl5cH69JSmO3di7u1tbz3mjVMI8fhWkgIymxtcc3MjFzr6MCVmzcRFRX1OHBy7do1ISUlhbzyyiuMQV/3QnU13YehIRWTQUHUCv7WWwAA8u23sAwLIw/s7PhLly6RjIwM5OfnCzk5OYJEIiHmQ4eCzcqiAnTVKoBl0dHRgfM3b/LZOh3xs7eH3aVLKAoKEhxv3SKW69bh+44OVFRXY9iwYRj9ww8wtLSE7RdfwG/IEBLe2EiYvXshe+ON/uOxRo+ms8DlvfGlxvZ27KqqQnhGBhynTQNxcup37XW+vig+dw7+kyf3PntnzlC7+pgxwIMHIMXFyNLp0NLSwoWFhjJ4+WVaQjBkSO+Gbt6kDQTt7Oi/zc3BGxmhcft2DHN0hMe8eUzPGr99+7Zw9+5d1nvlSii1WpDuDvIAaPmFvz8t1yCEPid+fjR4FRxMLefGxoCtLThDQ/CurtzwqCgmMSWFHzJkyDPXc1VVFVJTU3U8z3+iVqsbnvoBERERkX8zRLEtIiIiIvIUa9asmZmWlvZrUlLSjEuXLmWq1eqHf/WZ6OjonMuXLzdlZGSoWZZl7OzsmGf9wWxhYYHy8nJ5S0uLc1xc3MnY2FghOTl5ooODg2OPPdXOzo5hGMa4uLjYy8TERFZaWkqamppIdXU1MjIy4Ofnh35i+M/Q6UAiIuA2eTKJjoqCXlsbioYMgZZhGLlcjpqaGpKfn4+rV68iJSUFKSkpsLCwEIaFhxMkJ1NBmZ5Ouznv20fFgrk5FZlhYVS8yWRAczMVlT0WcycnoK0Nlu7u5MqVK5DJZBg3bhxMTU0xb948REREYNiwYaivr4ezszO5lZvLy0tLydlHj0hWfr6QkJdHnE+dQmBVFUmzsEBzczMACPPmzWOlMhng6AjpuHEwbm6G/WefkWyeh4Of32NxxvM8kpOTceDAAaSmpcHrgw9gGBkJdzs7SE6eRLG/PwpraxHn4YEHFRWMHsfh+aYmwcXOjjD79gEvvECbYx07RrP2f/xB7fLGxvS6lpfTLOgTTecMDAzQ2trKJe3fT8zMzYmpRgPExFALeVYWvU7BwcDs2aixsYFsyxb4fPklmLfeok3IDh6kwmzRIhh9+ik87twBEQQYW1ggRSolCeHhqPL25letWkV8//EPeG7cCLlcjvLycmRmZgoFBQXkxo0byM7OJrNnzyY2fWeBd3TQ+9K9f7z5JrWVh4b2Bg1+/RWYNw+DoqNJSEgIgoODoVarSWtrK0lOTuYLCwuFgHHjCDw80GRvjwO//CKYmpmRc+fOETs7O6js7fkzFhZEplCQeUePwlQmg8e6dRg7aRKcHB1pjXlFBV0zbm6AUomNhKC2oQFeXl5QKBSoqKiA8p13wBw4QO9DN83Nzbh/8iQGtbYKqtOnSb8GbgAazp9H8Oef4/7s2TAxNaUvpqTQbuAuLrTR35EjcBs3Dmn37pEImYyQ48epi6LPeDAkJtI1HB39+CVBTw/7qqvhamsLs88/B0aOBPT1YWdnR1QqFX41NkaWsTHP8zxxuHyZZrW/+KJfUz+EhFA7+fz5SLCzw7H4eC712jXheng4f0+hIIEpKfD57DOSFhWFmydPQjFgALHqrpvnOA7Z2dk4cuRIO8dxcwVBSEtISAhPSEjwSkhIqFSr1bq//mUgIiIi8v89Ys22iIiIiEg/NBqNAcuye6dOnSpvbGwULly4kKLRaEbGxsam/dVnP/roo80ajeb3hISEREtLS9uBfa2u3RBCMG3aNL2dO3fObGhokGs0mjNSqTT0yXm7oaGhTE5ODi+Xy+Hj40MSEhIwoLszuETyL/73dfs2HY80fz7t+H38OMZ3v9XR0UEKCwsFF2dnqB0cSOb+/WioqIBjbS3Bpk20oZSvL23y9PPP/buSnzhBRUlqKm3+tH8/bbqWkUGFaU4Ore01MsIKQ0OcqKjAVYBf/re/9fOsT++ezzx8+HAWzc0I8PPDZbmc+Pv7w/rNNwm++gq+zs5CsyDw8+fP71+3LpVC9eabqFSrgbffBl9cDGbmTFyVyXDl6lWhs7PzcaSDODrSTOnu3WBcXTGQZTH0+HFse+stgenqIp4ZGejQ1yf4+uuem0Rt1lIptbVfuQJs307rfgGa5e4OjDx5byeoVGznrl3YpdXCLD4exj0CvaqKitvFiwEHBxzbvh2V0dFwfPdduHR0UMuyTgfY2IA5exb2zz+POJVKKOvqIp0DB2KStzdyc3OF8ePHMxKZDJDJYG1lBWtra9y9e5eTSqVsQ0MD6uvrYW5uDse+Qu/AAVpzn5kJrF9PgwgHDgA1NShtbARfXAxnExOace/OMCsUCigUCgDAiBEj4OXlxezatQtgGOgmTkTptGnCyMxMsuOVVyCVSjFjxgx0dnYyDMPwqampzNdz5sArJESY+PHHNHAzYABdH3V1VHDv2gVs2IDlSUn47rvv8N1338HCwgIPHz6EmbEx/AIDhaG0GzcA4PjPP3NT/viDbbtyhcDNjc6Nd3B4PBJMGRODLcuXwzU7Gy49z9KAAb2N3uztAR8fmN68CSuZTLj7+usYeO4ckcifqM4YMYJ22O/Do0eP0EEImoKDBTQ1ERw4AEyeDKmjI0JDQxG0fj2OBAWRixcvImTxYshApwXY2dnBzc2td0P29oBUivK8PDg5ObE+Pj7geR48z8Ns6VLCyGRYynFEYmeHCxUVvO9XXzE6AN8dPNje3t6eo9VqlwDQk8lkhUql0lyhUPD19fX3NRqNT2xsrNg0TURE5N8OUWyLiIiIiDyJm6GhYae7u7scADE0NNQ/evToWY1G4xsbG/vgX/i80P31pygUCrz88st6cXFx06qrqydERkYqeoRND3K5HK+99hoDAE027JMAAAAgAElEQVRNTUhMTERnZycvCAI2b97MvPvuu48bZv0pPE/ty9bW1C4LUDtrSgoUdXX4UC4nOHsWyMrC6BdfxJ7GRuQ7OcFg40ZU1dXxw4cPf3YDsvR0msUGqKB5/nlq1f3b32hzq4AAapNtaoLRrl0IvngRRY2NDObPpxbkKVMAW9ve0U4AQAgUd+5g7Hvv9b62YAGmjB1LNjz3HKvVanvHd/XB1d8fB0eMQCPPQ/Ldd7x1WRnzwsqV5JpMJuTn5xO5XC5Y2NkRSCQ0K29rC68JE4DRo/HByJEk/eWXwdXX4+ewMDL45Elu8uTJVNSHhtKv7duBI0doZnr7dtq47c4dmqXuy8GDtBv4xYsoO3wY1YmJ2LRpE959913o6enR+eEPH6Krqwtbt27lGhoa2GFqNRyiomjTstBQICMDOmdnbPn9dzTqdGCbm4lUKhUiIiKIlZUVPDw8eq0SU6cCLS3Ye/y4rqamRjJv3jy4uLggNzcXR44cwf379+Hg4EAz2m++ibaGBjR6ecHC3h4Pw8ORl5qKggsXeL+EBOb82LHwbGrS2WVlsYkbNhAzMzPd4sWLJT229K6uLvz222/8wIEDhYaGBvbGjRvCNQ8PYmllhWlTp8LX3//xYRkaGoJwHN76/HP8sHIlwd69tIN9Zia992fP0mz/r78CO3fCxMQEq1evRk5ODn777TfwPI/w0aNRtWsX+dXMjJ89ezYDAL43b7KFLi5wUKmowA4NpdnjhQsBACamphhRXCzo37lD58ED4M+fB/H2fqzYhUWLUOThgUCFgim1tkZLSQmCzc3730dn56cCKY8ePaJLlGUJ/vY3mjF/+WVg9WpArQbb1YUZkZGkbcIEfCcIvMrWljRev46rV68Snufh6OiI+fPnU2v5yZNwnT0bBl5ecJ827an1rAKw5fPPaV34hg1gDx9Gy+LFrG9aWvztiIhYAGMmT56s9OoeSbZ27VoPAEoAbRqNxgi0+SIP4GJsbGzTUzv4H6DRaCSxsbFi1lxEROT/NaKNXERERESkHwkJCQGmpqbTg4KC5ABgbm4OnuellZWVoy5durRDrVbzGo2GJCUlvZaSkrI3KSnpb8nJyfMSExM/v3Llytssy74dGRlpEBAQwD7LRt6DpKsLbjY2koCQELmpTEbrogE6Z5phaJazowOQSCDneURHR8PV1ZVkpqSQTq0WYcHBkLEszcJyXO+Ge7ZDCM0kfvsttb02NNAM7YIFQGUlzVg7O9OxTO++C0REoIDj+MKWFnKvqEgoKipiWJblHR0d+59EURHNWn/wAf13cDBtQPXll9QK/eGHwMSJtDO2QgESHo7zenpck709PEaNIjqtFtJt22g2XC6nVnU9PSrQBwygIrwHY2MwhYV40NTE6w0aREx77MF9IITAwcEB1ysrec85cxjvkBCY3b4N2bFjKLCwIOo//iAmubmQXbxIM9UPHgB799Ju0wcOwLqhAS0aDXLz81FVVcUEBwf3D2IEBVGB3dYGzJkDKBQ0++niQoMYaWm0KzdAG4iFhsLM2Rn29va4efMmysrKBFdXV6IoKYF24kRs7OzkLSwsMGfOHMbDwwOSoiJg82badO7kSXSEhUGxbRu0hEDu6cmtWLGif911D998AwwZgjs1NaivryeNjY1ca2srCQwMJHV1dfyFP/4g5q+/jvNVVcgYPpz3/PhjktjYiCPm5rhRUYHm5mZhqIcHGXz+PGGXLxf88/LYAQMHEt/Fi5Gbm0uys7P54OBgBgB2797N8TzPTJ8+namoqMDvv/9OtDIZFO7u/PjYWIIHD+hILwCNjY24nZ9P0oODYdTVBd85c8Bs2EBLEBobqdBet466Ilavfnw6lpaWiI6ORmtrq1B24gSZcu4cLgcF4datW7yXpydze/duZAUGorqtDX5+fuBfeQVdgwdD0tUFSKXo6upC2tGjRNBqIR8+HFu3boXLgQP4meOQUVIiODg4EJWBASpra2F1+TI6PvsMod3H3A+lkjo5Rox4XJZgaWmJlJQU2Nra0gCGvT2t6a6rozO6164FcXCA1NISiIhARUUFFi1aREaMGAFTU1Okpqbieno6l5yUhLzjxwWr3FwyoK4OBgCtDz97lu4zJARYsABN5eXwUiqJyaVLIFu2IOyzz1jv5OShnh0dntHffiu1trUFIQRarRZJSUk8AE1CQsJgiURyw8HBYaqRkdHU1tbWlfHx8b+q1erGp0+SotFoJAkJCUZqtbrjidfdU1JS0jiO25CcnLwiISGhftiwYZl/th0RERGRP0MU2yIiIiIi/UhJSfl88ODBAX1FpqOjI3P37l2DlpaW+ujo6PTExMTnVSrV1ueff94mNDTU0MzMzDo/P185ffp0/alTp0qcnJyeLbSbm3tnXMfGAj/9RMWumxsVwixLxbBcTu3Zq1bRulVPTyA1FbqaGvhs3IgOpRL+P/5Iu0rPm0c7N2dm0s7SixdTMdAz2qq+nr7v7k7/mP/oo95tOjnRZlndeHt7k2HDhiEyMpIwDIPLly+TgIAA9Mu6//QTFcUREb2vSSQ0yzh3Lj2v+fOpiFarAZkM2dnZqHr0iLlSXIz01lZEbdpERZeeHg0IbNsGnDpFBXtFBTB2LK2ZtrUFJk2CZPNm0tTczNuo1c+MXhgbGyM4OJgMGDAAjJsbEBqKzPh4TDt9mhiEhMBk3DjaKdrVlYrl69dp9rKgAGTdOpjZ2iIpKQmCICAjIwM8z/NOTk69+9LTo8eVkkI7Sn/6KRVaYWHAtGlUoC1dSjO33ZSWluLOnTswMTER4uPjYezoSKovXxa0o0cTO3t75vDhw8g5cQJGhw/zhllZhH3xReDNN9ESGwudVIqAXbuQYWXFxF+/Dm9vb5od70tnJ+Dnh0GhocTOzg5tbW1MUlISCQkJgZ+PD3Gxt4f1+fNwTEsT2tzcBMc9exj/hQsRGRkJtVqNsLAwMsDbmzDLlsHRwYEYbtgAvTVrYGBrCx8fH5KUlMQkJCSgoaFBaGpqgpOTE/H29iYpKSlcdXU14+Pjg4iICGI6dCi1qCsUACGwtLQkoVu3Ih9AhYUF+EGDOJcJE2hTMQcHOgorI4Nez2nTAAuLfqfl7u5OzubmCo6bNpFh0dEkOzsbt3/6ifgXFMD4jTeQlZ2N5ORkJKal4f7PP8N77lykDBuGtGvXuCYHB6a6qwt37t9Hc0cHQm1toZwyBe08j0uXLpG2ujrO5sAB8sjICIESCSEjRz69mAgBfvmFBn8sLdHR0YEvvvgCWq0Wpqamgru7O10Xzs503X/+OfDSS0BKCsjatbB/7z0SIpcTuVIJZs4cDHB3R8jFi4javp2x//JLMnTlSlJtbAzdmDGw3LSJzpYvLqYlBuPHA+XlyFEqheSmJmI7diyMXF0hiYgAuXkT+g4OkDQ20ueYEFRXVyM3N7f0o48+2piWlnYgJibGfdy4cfKAgAB5cXGxtqGhIV2tVuf1nNq6devWp6SkfJiYmBidnJz8Oc/zGxiGeTc5OTny0qVLR9RqtU6j0RCZTHZt5MiRLi+++CJxc3PTy83NHXnx4sULarW6QqPR+KalpV1MSkp6Oy4uriw6Ojr/Wc+kiIiICCDayEVERET+69BoNMrY2Nj2P3lvhoGBwYTQ0NB+9mlCCKKiovSPHz++UKPR7JLJZN9OmjRJr6eDcl1dHQA6nufJUVrIz6fi9NNPqR25rIwKap6nrykU/S3J8+bR70uWAAB0Oh02ffQRr9Vqia2tLa/94gu2vr6ew9tv99YwV1fT74JAGzt99BG1aKvV9L133qEjp8zN6f7+BSIjIxEfH4/KykoYGRnRF3me1hZHRvb/YUKAq1fB5+ejY+tW5Lz5pqDYsUNoW70aWkdHcr+tjbGysuIZhmHa29uF1tZWom9uTo9HJqM13np6NNuZm0st2wcOAF5eQE0N3K5cQUVxMcOnpICxtaVC/JVXaEBCKqXibdYsOlpLJgOUSjR2dhKdsTEsVCqaibe1pXbm4cOp1b2khDbHUqnAAJg1axYOHDgArVaLy5cvM+Xl5cKkCROIkUJBrff379Na5x9/pBZolu2tQ37yngPIzc0FACxYsIBJT08Xjp85g0BTU1KZlYWy8nLBS6HAKI4j95qbmbMjRghTliwhnTodvv32WwCAasUKTDh2TGiQydD03HPE/Em788WLNKgyYgRcXFzAMAzS09MhtLeD+PrC/pVXgF9/hf6mTSQmNpZFtwX/qXr/iAjqTBg0iN5bAHp6enj77beRnZ2NM2fOEI7jCABh/fr1QmdnJ2ttbQ2GYWgHcVdXKvzNzYFLl4DBg6HX0oKXz57F/mnTcM/Ojhi99x6CamrovZs+ndZrX7pEP/vHH7QEoQ/GJia8YXg4q7h2DYsWLWLudXTAZfRosN3HZ25ujkmTJsHCwgJJ4eG4k5fHqfT0mJaWFm7uwYPszaFD+Rd27GBM3noLFkOHIlRPjzx69AhnVq9mBT095AwfLhgePAiXmTOpqH6SoCDg2jXwXl7YsWMHr9PpGAsLC+Tm5pLhw4f3Bj7c3WkJxR9/ALdu0evg6kqfQT8/Gjzy9YX+hAmARAI7iQRrV61CYGAgwgMC6FrS1wdef7133++9h8kAs2HDBuH+3r0wX72aKL77DkxGBn0/OZmWcOzdC0IIBEGQaTQaolQqWXl3Jl6n0+HBgwdSANd6NqvRaAZIpdJVEydOlDc3N8PS0hK2trZgWRaHDh2KKikp2QJgMYBwhUJhGRISQgghMOudZ64FAIVCsT0qKsrXysoKBw8e/KW7Xrzo6YsoIiIiAjy7Fk1ERERE5P9K1q9f/w2AtnXr1hVqNBqPvu9pNBqFVCr9YcaMGXpP1k8DgKOjIxiGGcQwTL2Xl5dJ34Zm7u7uAICff/6ZvnDvHhXWDQ00+5maSu3V+flAbS0VaB99REdB9TTlegY8zcihpaWFUalUKC8vZ8vLy9HS0sKeOXOmf114YiIVnHv3UlEcF0fn/ObmUkv3zZs0M9vTqOsvYBgGgwYNwrFjx3Dq1CmO53l6HocP9++y3IekPXuENo0GV9LTheLp0xkwDOP19ddkirc3GhsaUFZWhqamJvLNN99gx9q1QsWqVeDnzqXi95NPgMBAYMUKYM8emkEeO5ZmDTs7cf755/kGa2vg1VdpUMHQkIr8K1eAtWvpATg50ax6Xh5GxMejNTiYWug5jl6PV1+l53/iBBU4R45Q+25REdw+/hgfRURg1v37WLFpE0rz8oi+rS21jmdlUctzTs7jOdbgOJrJXr8eCA+nr504QS36AFpaWh7fn5CQEDJp0iT4FhcLLzo64r05c8jMe/eIaXAwBlRVCVY2NuT3uDih6OpVITo6GiNHjoSRuTl/YswYYmZmBpfyctoBvS8sS63n3djY2MABEH7YuRPCiBF0JjrDAF99BTyj1v0xn3xCz8PDo1/QgGEYBAYGYsGCBTDpPmcvLy8hODgYJiYm3IMHD4QNGzbgwoULeNjYSLvV+/vTjP/Jk5Bu3oz57e14bs8eJre6GklhYdDFxtJac6mU3tvu7txI6997cObMmWyRrS2uZ2fz0oYGeH/7LVLMzbnjx48L9DY7wdraGhKJBOolS7D0+HF2zqVLZNWqVax5aipG7NvHmBga0metu2u/RW0tZmVnQzZ/PkKHDyd/qNXIX7RI+GnrVn7Hjh3Crl27sHfvXuzfvx9xtbW4fuUKfvzxR9TU1DAsy8LLywtarRbJycm9B9rZSa/dw4d0jZSWUpeGry8Ncg0ZAhgb4+fffuO27tjBf/nllxAEAQqFAuZ2dvT3hKnpU+ePw4exqKqK6H3wAdk4bx46Z89G4enTNJAUFUWz6mfPYoBCAYlEYgwgpL29fXNqamozAKSnp/MMw6TGxsaWazQaZ41GYw+gked5juM4+Pv7w9HRETKZDCzL4rnnnlNKpdIX16xZs5IQMtHDw0PW48ypr69HV1eXAKA7oocBzs7OcHBwgCAIDIDWP19cIiIi/+2ImW0RERGR/yI4jntl1apVuHHjhnNKSsoGABP6vD3GwsJCam9v/8zPyuVyvPXWW4rOzs6nxm6xLAsnKyvO89Ah9l5gIKxOnIBhYSHN4tXW9h8tdPYszXoZGdHMs/B0L7XExETcvXsXjY2NfHt7O+Pq6srNmTOHJYTg119/Fe7evUssLCzoX8OlpTRz3dlJs7U//kgFPkCt3G+/Tf+Y37ePvvbll7SOu7ycinKWfWr/PUybNg0PHjzAzp07WVtbWwTm5aEzJgYVxcVoamrC/fv3+ejoaEalUqGkpATxCgVp3r1beJsQBsHBtBHaBx/AfOdO6E6eJGcjI2EnlwszbGxIw44d5La5Of/r88+TZUFBRAlQG+3UqVTAbNhAs8kvvgjGygr6AwcSsmmTgBUrCIyNqZ0doHb47iZZuH8fuHsX7cOHIyU0FLesrLCqvh4KfX0q3j/5hJ7z/fs0w5+RQWtvR44EfHzA2NnB7f33sZvjwEkkeHj7Nmx65jmnp9O6bbmcNkjbupUGT2bP7s2OfvghtUkHB2Pm+vVky/z5aLl1CypXVwQEBACHDxOwLPD3v1Obv7s7rg0aRG53dsK4sBAjz58nGVOnCiFDhpDQ0FDmj/p6nI+J4d0KClh88QWwc+fjbuF44w06jgw0KFN4+zbmfPYZaTMzo8clkdCs6V8REkJr9p+YGd6DnZ0d3njjDQAg0GoJ7t8HHB3Z6gsXcCMtjUtJSmIVX38tWH7/PcGPP9KAhoEBMGcO+Dlz8JObGzipFKVVVbizaxf/yiuv9Cp6CwtqoRYEGij69lvA0hIsy+LqiBEI4HkBhw4Bzz+PqtpawdbWlkRHR8O2b10/AKxZ01vrb2JCs8179lCrek9PgytXIF+6FCNnzQIAlBQX42FVFdTFxUzlpEngOK7nS5A4OwuyujpYWVkJXV1d7IwZMzBgwACUlpYiLy9PGDVqFH32NmwALl+mme3ISLoOup/35qFDhfvW1rg8darQUlXFDpsxA+fOnQMAPOwJkjAMcPQobbyXn0/XZlsbkJICY3NzBMTEwG/kSCSNGyekpqUJ7wEMWJbW658+DTJrFoYsXKiILyh4n+O4k7W1tRIAiI+P12m12tfXrFnzukwm+0oQBCIIwns6nW7SqVOntmu1WlczMzPd8uXLJQBt2Lho0SLlkSNH1ra0tJDBgwc/blpgZWWFqKgo+dWrV29qNJovpVKpEcMwuHXrFiQSSfpHH31UDQAajUYKwAZAeWxsLP/XC09EROS/AVFsi4iIiPyH0d1x1wdAVmxsbNv/5LMcxyn09fUREBBArly5Eq3RaJiePwylUulYT09P1T/7PMMwT8+33rULSE3FAwsLdnhBAU6fPIkaGxv8/Ycf6Pt9hbZWSwXFiRP03y+9RLPMq1YBGzdCp9MhLi4OKSkpAIAxY8aQkJAQsGyvIvby8iJlZWVCkKEhwQ8/0GZTgkCz2NbW/WqwYWfXr44YABWdK1ZQkdOTde+Tpe+hq6sLmzdv5ltaWhgAOH/4MCyOHsX+l14Cc+iQwLKs0NXVxWRkZMDHxwf5+fkIDQ3FuHHjCKKjabb3iy+ovXjlSngVFZGOS5cg7+oiktdeg2TPHkS7uDA3N2/m4uPjSczQoYzk4kXa0dzRkWb8d++mwYHx4+EzbBg5UlSEBeXlkHp7P9sOTwiEAQOQ6uWFjLAwOr7q5ZeBLVtowOHOHSpUyspoo7S+I566m3UxAPyWLUP577+DffJe/+MfNBt+/jwVqampQEsLHs987raO3zh9mqt3cmJZmUxomzEDKi8vgl9+oTXKNTXU0h8RAW7CBFQOG0ZvlYMDab56Fa1pafzx48dZQRDg6OgINzc39nBJCUKXLYPjqVNUYC9aBJw5A6hUyOM4lH3wgVBtYkL4RYsE94AAQnps/38Gx9FgS0UFHQ+3YwfNAPv60kzz6dPULXDzJnVMqFS05MHdnXa1LymB1f79iGlpYR9Nm8Z57NzJPg5gMAy1t48dC8myZXjr6FEUnDyJe/n5sB837mlHoVJJn4uWFqCiAjojI3z//ffC/N9/xwCWZcGywOrV4H//ndTV1SE+Ph4xMTGw7NsxPDgYaGqi+01MpI6SigrqlABop/jUVHqe3dTV10MVEcEP02rZgXZ2vUEMgKC2luDECfhOmULFejdubm5ITU0lDQ0NMDY0pNn5KVMAAF07d6KwtBTlly7xMWPGMAfee48XurrYUR0dxPWHH8B8/jkKrl7lCpqbWT09PQE9Y83eeovW+2dn0+/+/lR8A0BTExhHR9SsXy/odLr+127iRKCwEIEGBmyrkdH4CgODUaGhoQqdTgdBECRSqfQMx3GOYWFhCAwMxLZt274A8LVWq/0EwL7a2lrJl19+yevr6/MymYwJDQ1llixZ8szff9HR0RIHBwfzvLy8tRYWFhJLS0vs37+/taOjYzUAaDSacKlUeooQomIY5jyAKf98AYqIiPy3IDZIExEREfkPQqPR2Eul0luGhoYLdTrdG/Hx8Qf/WbfdJ0lNTV3o7OxsbGlpiczMzK7Ozs4rarW6TKPRWLEsuz0mJkah/1fZQEGg2auoKFrn2tEB8DyMnntO+FWpJG3d2cb09HTe3t6eGPUVPqdPUzEyoU9CXauF9v338X1TE5+YloaSkhJiY2PDDx48mI+KimKerAEvPXtWsL5yBY4XL5K2O3cgW7qUZnaDgx93T36MsTEVu9bW/euKpVKaAfT2pqLxpZfo+56eAGjN5zfffCMYGxuThQsXEpZlMbSsDBJ9ffi+9RYmTJhAIiIiSEhICHieR35+PrRaLebMmUNrgufMAUaPpgJnwABgzx5oT55Eib4+BlRX4zzH4WxlJSQSiaBWq5mLFy8Kl5OSiPTBA+G+qyspKytDaXk57/jcc6Snltvy7l2kmJnxqo0biXllJZgnm1tptVQsBgdjLwCj+noMO38e1i+/DNLURGuTHR1p/fbo0VTMDxkC9NakPsbc3BzZ2dm4du0a7t+/z9nY2DB6lZXA5Mn0WmVmAm++SS3oq1YBM2bQmvNuruXnk1SFggwaNIgkuLggYuNGgvZ2GhhpaQE2bgR++QWCoyPKAgJQU1ODhw8fInzFCkRt2sSEjx6NgoICvqSkhNy7dw9tbW3Ia2riIyZOJFizhtaya7WAVIo96enC7O3biY9MBuvjxwkbHEzXaE+n7IYGGty5dYuKuK1bqdh+/316HgoFteKXllLhd/YsrQn29aW1x3Z2tMfA8OF0vx98QAXytGnArFmwsbVlftZqYafRCIZr1xL8/e9AYSG9TjNmQOrhAUX3eLmrbW3wmT0bl4qK+KzkZHBxcYT18cGxkydxztSUv373Lu87eTJT4+KCkH/8gzA6HXD3LvD882hvbxcqKirw4MEDkp+fz0dERPRvlieXU2EcE0ODEZWV9DwDAuh63LaNPg/dXLhwAZGTJjFW9fXUAj5kSK+w1tOjwYapU6kD5fEu5EhNTUVdXR3n+8svDPLz0TZ5Mnbs2MFfSE8nXefPI2jzZmL01lu4lZMDU0tLMvTll0FWrAAUCvhNmsRwMpmQpdUSPX19zsbOjj6U+/fTxobjx9PgUo/tXyLBI319/F5TQ+bPnw+jJ4Mo4eGQ3rkD1717JQELFsgtfHzIpk2buPb2dpbneWNCCEpLSxETEwM/Pz9pY2NjcGdn5/jQ0FAJz/NgWZapqalhtFotycnJQXh4+NM1/d2YmJjAzc2NtbW1JbW1tUhPT29cvXr1co1GYySRSK5Pnz7dNCwsTHLjxg1lZGTkxmduRERE5L8OUWyLiIiI/AeRkpLyXXBwcPCcOXOUtbW1zMOHDyuio6NT/9XPJyQk2Ovp6YU5OzszSqVSWlhYODE+Pj5MKpV+GRkZqfL29v5zT/XGjfSP+M2bqW27oYGK5qAgICoKVlZWZNiwYXB3d4e1tTWMjIxw6tQp0tzcDA+P7vLwK1fAq9VILimBsbEx5HI5oKeH/JEj0XD8OBn7/PMkdMQIqNVq4uzs3F9l19UBH3wAi9JSktPaSsyvXMENlQq/u7tzwTNmMH86Ziw0lGbfniEq4e7eOyLM2ZkGDm7exD9++01ob28nb7zxBtHX14erqytMHz2C0cyZMOqTBZdIJHB1dcWQIUOQlZUlEEKIg4MDtab/+uvjRk4wMoLkxx9R7O2NfEtLXtfcTNRxcbCbM4cMcHZGREQE8fP3h9WaNeSCvT13Ky+PaWlpIaampijmONwlBMrycqhzc8lJDw/YuLjA0M6O2pUBKi4JAVpaQEJDcef8eWhlMkRkZMBw1SoaGOkRKgkJVMy88QYVhD1jyvrAsiwiIiKgUqmQkpLClN64IQTPm0cwYQLtqO3qSkW2nh61kY8aRYVZd6BFEASSm5uL0aNHIysri0RHR1MxuGgRDYxUVAAvvYRUlUrI02qJIJOBB+BZWAiTRYvAGBoiICCAJCYmAgC0Wi3s7e3hN3w4wbx5tG7+yBFg1y4YPHiAzIAAUqivD+byZd7o4EHCNDRQK3VmJl2fJSU069tznEOH0izqiy8CW7dC+Pxz1C9bBoWBAciWLRBmzEDJiRPC3WvXBNOoKCI1Nf3TcoOK4mIUpaXBrLKSWK1aBaKnR/cTFkYbnzk5gYuKwq76enRIpXjg5ASL4cOJyZ07sPvlF/IDz2P2unVws7OD/ahRDN/RAZ933iHKlBRq3X/hBcDJCba2tkxoaCiJiIhAQkICyc7O1qWnp/OlpaVk0KBBdPEPGULt40uX0nVnbEy73H/+OQ0U9OHq1auIiYmBPDiYfsbBgQaleuB5umbs7AAA586dE86cOUMAwMjICH537xJMm4YDKSlce3s7Wbx4Mbmv1QpCWRlJNzDg7967x3h6esLJyak3ELBiBQyioojl3r0YuHYt87OpqRDo5kawaBEV9ytX0jp2lQro6kLXsnP/ADwAACAASURBVGXYyLIY6O4uREVFPfsB9/CgdvyuLqC0FFpbW6aiokLgOI4IggBfX1/Oy8uLUSgU8Pb2lg4ZMkTu7OzM2NjYPF5fOp0OFhYWXFBQEMP+k7KSHjiOQ2pqKhsfHy+XyWTf+Pn5mUdEREgKCgpQWFiYEBkZeeAvNyIiIvJfgdggTUREROQ/BI1G4ywIwvSoqCgpANjb2yvkcnnI/2QbOp3uzN27d1sBwN/fn7zwwguWgiBMt7CwMLexsXm6i9TDh1RMCQK1ed67B1hZAefOUbvzE5kmhmFga2uL4OBgEEIIy7LIzMzEjRs3gIIC4MIFpPw/7H13VFTn2v1+zxR6G3pHepUmotiwoCKxYo29RI2JLdFYcr2IscQ0NSZqjLkmUWNvsUZsoCKIoBQBpStNpEsZZphzfn88gKAm97vfWt9aub81ey2WijNnzpzznrPOfvZ+9tPUhKtXr2Lbtm3YsWOHqq6uDvHx8ar+9+8L3Y4ff7MftaqKCH5ICNCrFzSKijDAwACFJ08i6NIl8ILAnTt3DmVlZcjJyQFAoUZHjx4VeJ5H0/79qNfXB8/zSElJwQ8//KDaunWrsHnzZnz11Vf884oKUlwjIsBfvozq+fMhb25mc0aNeqVy5eSQXd7Z+a3HlUtMhHV+Piv//XdSEn//nUgtADQ3k5U+JgbeMTEobGzkiqytUW1oiKrNmwW07bOhoSH09fTw/syZIgMDA9WLFy9w6tQp/tq1a3zKs2f8HpEIv0gkCI+JgUVJCamtSiV9xogR1C+9ejWwZg363LmDekNDNF279mZS+PjxRGbc3akHuj2o6y0ICAiAJmPo3qMHQ2oqWXzb4etL68PAgILu2ueOAzh58iQA6qWmZVRB88n37wdcXCDfswdpM2bw13182LzcXKw9fBhR69ah26+/dhB/rqAAy6ZPh2ZjI0afOQN/f3+GlStJpV22jALf6uvhpa/PvCsqeE25HDFiMbdXXx/CxIkUBBcbS/3oK1YAI0dSVkC3bq+U082bIR8xAvurqvgTu3ejzssL386eLWw5ehTlly+Di43lcjMyyCZfX//G8SmLi4N5r15oValwftw4nDl3jgIINDSoLz0uDuB56OjoQFtbm+/m5CRM27kToZMnI2T3bmZaWooVq1ZB9tNPcJg7l3lyHGzv3YO+XE5p87t2Ud/5vn1km3/+HNLycsybMQMDBw4UV1VViTMzM7uSUE9PWg9z5tA1K5fTTPm3QBAECh177z1SlBsaXv2noWFH2B0AFBYWQqlUwsbGRnhXqeSwYAHQrx9evHjBDRo0iDM0NMTY2bOZ8Nln8N2xg9NsaoL365+rrQ1TBwd0P3kSVxYtwoSoKAYLC2pzmDiREtp9fWmf8/KAu3chMIawsLA/qaS1ITyc1vk//gF3uRwKhYL5+fnx8+fPx7hx494+hvA1jBkzRtRlvvxfQE9PDzNmzNDs16/fP0aOHOkeERGhCQDp6ekNcrn8TPvroqOjJdHR0azt73rR0dEjo6OjZ0RHR/eOjo7+96xeDTXU+K+HumdbDTXUUOO/BCKRaIavry/X3jPdNgqpS0NydHS0o6am5n4AFq2trS9aW1sPATgcFRVV2/aStKqqqg6vtaOjI/r27cunpaWxQ4cOsZUrV9JYn+3biSDOmUP22uZm6mWtqCDC+c9/Ajdu0Ezp11RRnuexb98+VX19PTd79mx279494cqVK8xCSwvmvXohp7AQrq6ugpGRERITE0W7d+8WtLS0OLOEBMZ0dUmFdHAgsnTpEll8bW1JqTQ1BaZMgeHUqeghEoHneejr67OHDx8iIyMDHMeBMSY4OTmx7Oxs9tlnn6FHYiIqzMxQ7uYGDQ0NwcXFBWPGjGFyuRwXL17kfv75Z2HVqlWM53n8ZGQktHz0EZYEBjLDAQPIhmxrS0RxyBA6JqWlVHzIyCA17vBh4OOP4aenh/NBQdTXnJFBZKG+/lVv9fXrMDl1CsHDh8Np0ybcDwiA+5IlDO++Swrz4sWU7K2hgfnz54uePXsGNze3Dqa8adMmhKxZAxuxGOyLL2jbT58Cjo50bubOxcvgYOwMDISye3cAeNN2CxApKSigv3t5kRV80CAKunrtXMbHx2PMoUPQOHeOVNbOaA+hA0itNjAA1q2Dav166Ovrw8DAQHB1dWVSqVSoqKhgZnl5wLlzyJfJkNTcjKaQELw3ejT0TU3pe1y4QKqzhQVQUgIhLAzlgwfzWra2cG1p4RRWVvT/cXFE2nv2BJ48AefsDHtPT846Px/+d+7glpMTftu2TXh3zRrG/iSFPCkpia/IzBRC9+8XHZwyBWJ7e8z5/ntUGhpiSGgoE+nrw23tWrZ161ZhloYGQ3IyKb07dtAafPddID0dJt27o2jtWviHhAiMMaSkpPAAiERNmULf6d13gcOHwRhjxcXFrKioCI6OjsjMzISHhwe0dXS6tlVkZwP37pHlv76eUr2fPCH3RX4+cO0aLPz8YPL775hWW4vMUaMELFvG4OBALRFVVcDcuXRODQzoGn0LiVQqlbh69SofGRnJITiYxp6dOkVrEaA57MXFHa/X1tZmISEhCO3dm3EeHsC5cxAEARoaGkJjY2MHm+3dvz8gkWBVeDi1cLwF4hMn8FRHh2/54AMOU6ZQuNuUKdTPf/Ys0NoKWFlBmpGBbr/+ysfGxrIJEyb8JWM+XFamau7Th1n98gt8mpu5l05OgmVnpf416OrqwtzcnH/58iV8fX05CwuLv9r8G7C1tYWtrW3HPtXW1uIZJeYfBYCNGzdO4DjuoFgsfvHFF1/ki8XiHhYWFko9PT1ReXk539jY+DI6OjoiKirq4X/0wWqoocZ/FdRkWw011FDjvwQSieRdLy+vjqdma2trKJVKp+joaIOoqKi66OhopqGh8XtwcLCHsbExd/r0aVdtbe3eLS0t27Zu3Rovl8svMMYMtLW1Ve3bYIyhsbFRqK+v5zQUCmDwYPwydy5CnzyBvYsL2YTbR/3o6dHs6zFjKODqxg3q2501q8vc6fr6ejx//ly0YMECmJmZITQ0lGUmJ0O0dSu+W7VKaCgvR3BwMBs4cCAMDQ0FqVTKvLy8yFJ+9So9dC9dSv2mxsakZNbVUWqxuzsplW3YvXu3UFlZyQBg3rx5kMlk2L59O8vMzARAI5JCExMhODigae5cmJqaMsaYCADkcjnq6+sRHBzMAODhw4coLS1lS5YsgaGREYVjWVoCLi5UZBgyhMiPpycpyCNG0Hc3MgLi4/Hizh1BKyWFR36+COXlZHNWKEhVfPIEPxUUtBb7+Ii5oiJUBwRAQ1cXxkePdszFxqJFpER6ekLb0fGV9V4QgOJiyACh6tEjxlpbKcxs5UoqfIhEpHD37g2lnh4kublQtine9+/fR3h4eNeFZGtLx7Yd3t7kHBg5khT5TgFzMj09xISFoVlLC3OrqyGTyV69b9u2V6o5xxG527cPrXFxqK6uRlBQEAMozTszM1PlPXGiCBERsAoLg8rSEgOHDuXMzc3p/enppKqamZHSnpyM8pgYHDl4kIMg4PvZs9H07beQSCTCmq+/ZszVlQoM8+eT4rtkCcSffgrj+fMRdu4cbt+5w16OHw+JrS20Jk8G/PxQrVDg5s2bSE9PBwBuaGGhcHnSJN7Uz48bNWoUJxaLYfHll7CYMYO+i5sbTExM+BO3b3NzYmOZlpYWrYPGRuDOHWDQIEiSkuC8Zg2Ma2rY7t27MXbs2K5qZVgY8O23iNmxA01NTUwQBNy6dUs4c+YMa2xshI+PDz927NiuLRC7dpE74sQJUubLykh1bseHHwIAxIsWoebcOVSVlvJwdhZBpSIynpJChYH8ttHPnXrpOyMoKAgVFRWvRgF88QWF+g0YQETfyoqKavPmtW1GGwkJCcg7fx6N776LhrNngbNnwRjjVJ3XE0Cug4wMKhy05SB0ICkJyo8/hub06Zzx5s2v1tD+/RTgZ2TUUfRpPXoURbm5nNefKPPtSElJQUFBgWj83LlQpafD7tNPUTpr1l8qx7q6upg/fz6XnJyMuLg4JCQkYMGCBehYk/8h2s6hCIAtgMdSqXR+eHi41MDAwLqxsdHa1tYWurq6HYmDqampehcvXjwXHR1tFxUV9eZIBjXUUOP/C6jJthpqqKHGfwGio6ONRCJRt85juSQSCRwdHRUFBQW7oqOjN4vF4kWGhoYOXl5e3K5du+Dp6SlERkZyCoVC4/HjxwNLSkr6tLS08H379u2IsW5uboZ01y7RoIYGXB8yBOUNDcLzJ0/Yz+bmWLd0addeo+xs4OVL+vvChaRGffkljf+5fh1Ytw6tKhUePnwIkUiE6upqmJmZwcjICGPq6/m07t256uZm1qNHD1VISIgIAHr16vWKZTQ2UkK2lRX9hIdTGvfq1UQ8xo1747iMHz+e/fDDD1i9ejXaLaCRkZFISEjgx44dSy6AmTMBQYBuJ0Jz4sQJPjs7m2OM4eHt20LlyZPCY1NTburDh7zRjz9yGDiQ+q2//poU9oYGKjpoaZFy2N7X2aYgA0BRQYEq/NIlMZycKIW8neQMGAA0N0NPT48DAF4sxqO293UbORLeAwZQuNuyZXQMRo+mEK89e0jZLy5Gq5cXugcEMC97ewq68vSkAK9jx4hYKZWAnR1kjGHFihXYsmULlEolKisrX6U+t8PMjOzja9e+UrJDQsgGPnMm/Z+REfDgAdyHDoUsKQm7f/kFO3fuxNy5c2HT1sOLkBAK4/ryS/q3TEbna/16GEmlyMnJQa9evTB8+HC299tvRSo9PRTt2IG7VlYo8/MT9PX1GV6+pNC8kyfJ6j14MBVXPvsMFnV18P7wQ2RkZEDZ0oKpv/2GzJAQHm5uInh5UZq4SETHeuJEUulra6FasgRJHIfUujqYVlUhbM0atNTUIMPREUwkgszGBpPDw2Galsawcydr7zPvQI8eHfb82bNni/bs2cPHxMTwo0aNEmHjRnJ9GBlBGRiIMm1tNI0axZeYmHBid3few8ODa7+uGhoa0NLSghdbtsDi/ffh4OaGAicnPH36lAGAh4cH0tPTufT0dAQGBsJAXx8+NTUwPHSIer2dnChJf8wYUqw7W/gBUti7d0dDUxONDWvHmDFUFDp4kK4nS0sKtvP1pRFhbdDQ0ABj7BXJE4loUsCCBdQm4ulJ66AtiG7cuHGofvwYRuHhUFy5AomjIziOw86dO1v19fXffJ7cswfIyiJrOEBp+PPnA7dv43B0tGBvYcFzCoUImzZR0er0adq/ykoi4DwPxQ8/wE0mw7AdO/BcpULOpUsq6bNnjN+yhetlbQ1YW+P73btV9fX1ouHDh8PV1ZVyGIYPh8vWrUT416zpkqjeGTdu3Gi9ffu2WCQSQUdHh2+/Rv83MDAwgJGRkeaLFy+yo6OjJ0okkgBzc/OuqfGd4Ovri/Pnz5sCMAZQ+b/9XDXUUOPvDXVAmhpqqKHGfwFiY2PDrK2tRwYGBnaZ9+Tk5CStra11bG5unuvi4hI4duxYbZ7nkZSUBIVCAXd3dyYSiWBgYABvb2+Rh4eHWEdHh5TEsDBIpk2DuUqFZyUlKLO3xwN3d9YqlcLZ2RlxcXG8oaEhM2m3gj58SLbQdqXKzY1GC1VUoLGsDFWffIKDT5/ihVLJh4aGMi8vL3AcBwgC9BIT2SUDA8i1tDBq1Ciui725oYGswcuXE3Hcv5/IwbZtROQ0NYk0+PvTg7OHB4WlHTuGSnt71F26JIgbGpi5tzeQlAQjFxf4uLsziVxO5Pibb4hYTpsGnD+PupMncS0vjy08dAj9BgyAYXY2el64wHlv2gS7+nqWo1DgslyOVI5DhYkJnnfvLjwJD2cOI0eC5ecD33yDhuBgPCkqgpmZGWpqahAXEwPbrVu5xoAAFI4ejcLycjDGcO/ePVS6uODiiROqgspKka2tLT9r1ixWU1Ojqqqq4noEB8Ood2+ypicnk4I/fTp9/2HDSMU0MMDd/v0Rr6WFAfPng5s7lwhz//5ErLS16Xy2keXHT54gLS0NPXv2xLBhw9gbfaiMEQny8KD++3Y4OxOx+vlncirIZIC7O3T69IFKpcLTp0/x4MED2Nvbw8jIiIoCQ4d2SYDPevYM8RcvYlB8vOC1eTPTpl5l5OXlqZ4pFJz5iRPI6d4dNkFBzKe+HtxHHyHL0FA4YmnJ0hsb+daaGqHV2JgZbtwINmwYHM6ehfTGDVT6+AgDnz1j3ioVx/71Lyq8hIfTd+nXj5LCbW2BqVOhUVGBnqtXwz0wECJHR5wQBGS5uECQSmFUU4O+9+/DODUVzM0NnKMjEcrOoVjBweSo2LQJbNgwmJubs5iYGC4gIABNqamQfvIJUoKC8JOuLjKysmCSlcVeisVolUiY/uLF2FtZiTuJiXj48CEyMzP5svJyFc/zXPCNG0j184OTszOGDBmC0NBQ9OzZE/Hx8agpLYXPtm14UF+P8vnzYeThAYlEQtePWEzEdcKEN+4LcrkcDx48YH379n3FJgcPJsv5woV0/cyYQeeye3daX5GRwOzZKKBZ8YKfn98rgmljQwWflBQi+KWlpHIbGoIxBm1BAOfoCEloaHvLBpKSkngbG5s3bdhhYbTPdXWvZmhnZgITJ4L78ENme+wYF+vgoLL+4QdOY/hwev3y5TRnu7oamDULEqUSTs+f48F776nOlpRwhlVVzE1fn7tcXQ2/qVPR2tCAwowMLvTGDdwWi/kgY2MGiYTcGR4eVCwLCqLz+XbCzaWlpSFixAhEhoUxSVkZFQXkcmrp+PVX2pfgYCoKvffe27bRgVu3bgl6enrM2Nh4mK+vr7aXl9efquuCICA2NpYJghAdGhra+pcbVkMNNf5roVa21VBDDTX+CyCVSke7u7vrv/57bW1tREZGdpnVpauri0mTJuHIkSPs22+/BUDp0v/4xz/o4Tkqih56MzOBkhLoLVwI/0mToJuTg6KiImRmZqKoqAhKpZK7desWWltb4eXlBdy+Tf2nneHrixaRCC9GjUK2tzfeu3kT0sGDOfj6viIw9+5BXFICo379UFtYiMTERIwaNYoeaM+coQd7HR16kM3LI2JsZkZp515elDzd3EwP6zExHXOOFd98g1/KyvDutWusNjmZUrXDwkhJu3aNFM/qamDdOqClhchsQgK0ysogt7VF2YwZcJ0wAT5LljAA0AEAb2+c/eorvrmggFt54QLucRz/KCQEXEICe3nxIgyOHQMuXMCBH37gTTMyuOSICJSUlGDGkSNChr09SzU1FfQfPxYUCgW7desWs7KyUvXdtYsboVJx2levQiaTcS0tLXjy5IkIABR1daSUHjxI37mykooCUik6K66WlpbgeV749ttvMXfuXKbfbvV+/Jjs5CNHEiGQy2H34gUACrR63UIuCALKysqgFRwMI4XizYU2bRod9+7d6TwsXAgAGDx4MFpaWpCUlITm5ma0fQCRqDlzAAAFBQU4duwYhi5fDju5nOGnn8iVAGCWkZEIS5eCT0iAW0gI+C+/FK5oaTHBwUG4L5czY2NjWFlZcUJMDBJPnYLV+PE4GRsLeXExpsfHo8LWlmlUVJCq/8EH1It86BDth6srFShsbanvvLYWmtHR0Fy8GOa9eyMwMBBSqRQvXrzAnj17UGtggAE3byJVXx/DYmMhXr6cXBrLl3cQS+jqkqV+zRrY2dnBzsSELwsI4K5ERKBqxgzo5uWpJk+eLHJ1dcXjyEjEnTgBvYoKNGpqIqB3b4T98gu4d94Bpk7lAHClpaWIsbXlF6emcrJPP+0IaNPW1kbU4sVAVhb4/HyUjBqF2w8fIjY5GQCwbNky6I8cCaZQAHfvUtp4JxgZGVHIGUAOjNxcmqltYUHXy/37dJ0NG0avyc4m8skYXHfuREP7HO52MEbtEl9/TQWu1lbq27a3p/dOm0ZktBNEIlFH20IXSCSvktFlMprNnp0NvHgB8y1bcO/hQ9Q0Noq+GT8eUw0M4GxgQGtq5EhS2PPygMOHodmzJ/o8fSoKNjWF4ptvmLaZGT5UKnHRz0+V/uCByMjQEM1GRpAzRvcQXV26ppYupRT2DRuobeLzz+n7PHpE5PvcOTiWl2N2QAC4ceOgsLCAdMIE4MULKt64uHS0E4AxcpIolcD69bTNt6SWW1lZcUqlkp8+ffob9+rXwfN8+7mziI6OLlRbydVQ4/9PsI6btBpqqKGGGn9bbN26NTUyMrK785+kYb8N9fX14DgOP+7dy/splRjI8xwSE8mmO2YMPdg6O5Mtuk29ViqV+OKLL2BpaSm4uroiPj6eNTc3Y926deBnz0b5u+/i5osXMDAwwOPHj/mWlhZOEAS4WFqqJqSliTiZjB6odXTo4dbEhB7+AeQHBeHAgQPw9fERxrx8yXD4MBHkR4+IFHz4IanYQ4aQvfnhQwrBeg2lpaU4d+6cUFFRwXx8fFSpqakiAPj444+hra2NqqoqmJqa0oufPyfVfNEiIg0aGhAEAXv27BEqKioYAHzwwQcw6RTk9Gz6dNzlOCHP2pq9u2AB7O3t8d3OnULQ6dPsQXh4a70gcNLycm7W2bPI6t1b8MvMZFq7duGL2Fg4OTsjMjKy6w5XV9ODeSc1Pz09Hbd37YJlWRkcnz6Fgakp7A8douO1YAEVGZYs6bKZ58+fY8+ePZg0aRLc290FDg5kOS8sJCLg4QFMnoxzO3ciJScH/fv3V927d09kbGzMm5ubC8+fP2cVFRWcT3w8WjU1oTVvnmr48OFdWYNKRUWLfv1ILW8LeIuOjgYALF26FIaGhkSIrl9Hy9GjSExMFG7dusVUKhXWrVsH1thIZGfAAFJWvbzoOGRnU3iXgwNShg7Fubt30X7udHV1gfPncTImRpUhk4l8UlPhtmEDPNLS0Lh4MVQqFcouXoSHtTXtU2dVPj6eAtyWLgVaW6GaNg31pqYw+vbbLormlT/+EMy3bWMvAgNxp03xD+/VC4HNzRDduUPFDl9fsjGPHElr08sLvESC+pkzIT5yBNrGxmif/V5bW4udO3dCLBajtbUVPM9jyZIlMNq5k9wBEgn111+/TjswZQr1pLcVKJCbSz3Ry5bRNQkiYQ0NDdi2bVvHfg9tbIRFaqpgdvIk40QipKWlged5FBYWCgUFBWzAgAHo/d134BoaaNRXOzIySK3uNF+7HVU9eiBuzBjV2CFDRCgpIcW7HUePEnmXy4m8fvABFcZiYoDvv++ynb179/Le3t5cSEgIFcU0NF4Va5ycKP1+40ZqUXgLtm/f3jp06FCxp6cnuVoyMijAz8yM1qFSSfkImzfT9nx8qLccVDz6+uuvoVAoMH/+fLqOBYEKRgcP0v3lu+/I3WJoSP3wmZnkkgFQLxYjSyJBfHIynLp3pyJgZ0yeTMWJjz+miQWFhdRHn5ZG6/k1Nf/HH39EaWkpevbs2RoeHv5vBa24uDjFnTt3eEEQ6pRKZXhUVNSDf/ceNdRQ478Lahu5GmqoocbfHNHR0QFisXjliBEjJNzrY5z+AhoaGri1bBnvcekSV//8OTMICsJpT09Vnra24OHlxcHYGNi7l+b4fvwxwBgYY7h16xZmz57NmpubkZWVxSQSiaBSqVjWtWu4LpdDwXFoaGjg+/XrxyIiIlifPn3g17Mnx/r1I9vltGlEhHbsICXpH/8Avv8eRoaGcKuuhuyTTxhfUABtBwdKofb1BaKjyRbt7f2q1zksjAhIp8Cumpoa7N+/X7C0tMTkyZOZv78/Z2Njg4aGBv7GjRvCnTt3kJCQwB49eqRy+flnTrR2LUR79gCffEIbaLO+BgUFMX19ff7Jkyfs/v37AAAHW1ugpQUGe/bA4sMP2d3SUgQHB0NXVxeWVlbsZU0N3zcpSWQ2bx7rGxEB2dy5sP3lFybJz4dKWxs3OQ7vvvsuBb11hlhMx2T8eCJxggDza9fQPT0ddlpaMCwsxJ7hw6Ftakpjz/z8iDB069YRHnX79m2cOXMGvXv3VgUFBb0K1Fq2jBS4oCBS7//4Azh9GvX5+eixbRtitLU5XZlMMDc351paWjhjY2M2bdo0OPI8TMrL8XtDA2dlZQXjthnkz/ftg9aGDeAuXqRU6OJivLC2xlfffAPGGCQSCRISElBVVcXXuLqy5wMH4ty5c0JxcbHg5OTEKioqIAgCdGUy6AQEULFDEIiASSREWH7+GZg4EfcePVJVV1eztWvXso5jdukSPJVKrteiRfBevRpmc+dCvn49UqZORZyrqxB05QrTOnSIChKdlcW2c4fTp1FobY1/1dQI9wAWtHw5xN7epMwCqImKErSNjVnPHTuQnJwsKBQKlltcjHSFAnbz5kGvPfE+L48cA8ePAxs3gnl5QbN/f0h//x1swAAihWIxWlQqPL9xAw1iMXw9PeHQrRs8vLzAQkPp/CmVRODDwkglnTSJii4FBVTkUqnoWpkypeOrMMagoaEBT09PJCcno1+/frhRUgKdp0/ZH2lpuJGaiqqqKlVlZaXQ3NwsmDY1QXzkCFOsXQuztuu4A4sX0/rz8Xnj/pDg7IznPC/4p6dzuHyZiOWlS7SenJyA336jpPvMTOrfzs3tMt6tHaWnTzNNmQx2J07QNj75hAh6WBipwv7+1JP/lswFAEhISOBdXFw4k/R0OlarV5P9PDycLP5Tp9Losx9+oPvJr79SISAgAIwxmJubIzMzUxg8eDDjOI6+v5YWOT14nu4hCgXw6adUQBk0iIpSHh44mpjI33/0iKlEIsycORNvzNhOT6eWjvBwSoxPTSX3BMfRXHJnZzo2bfD390diYiKePn3K9enT583tvQZ7e3tRv379xMbGxro5OTlTr1+/fjs0NPTpX75JDTXU+K+C2kauhhpqqPE3QnR0dG+xWDxeJBIZKhSKXEEQ8iQSybfDhw/X7Jj5/O+gUAAJCcCmTbDR1WWxAQF4YWeHhJISABCZmJi8sjS9tBcSMQAAIABJREFU9x4pbQ0NaG1txb2sLGhoaEBPTw+nT59mEokEUqkUsdevY356OgYfPQpNHR0AeJP16+qSmrR2LQU0LV9OI6GCgoADB4DsbMh++QUCx0HjvfeIjAQHvzkHGiAi9f77RNQA1NXV4ejRo6rKykqRi4sLP27cOFH7g6yzszO6devGnTlzBhkZGfD29ORr/vhDtF9fHxg3DuOLi2EbE0P9q1lZaGpqQk5ODjw9PbmAgADcvXsX9+PiVANWrRKlBAQI18LDhZaEBM7V1VVlYWEhAtrG/CxaxKFHD5i4uJB9e/58+r4iEUqTkuD/22+o37IF+ps3dyU8EgmNT6usJHK1ahUwYgQ05s2Dxv37wLp1CC0t5RMSEoSePXuKYGND9u3mZpQFB+PUqVOqpqYm0cSJE+Hs7Nz16T0ggFRTQ0MiSadPA598AkMnJ4j09CBSKhHi5sYCwsK6Ht9+/WB59y569eqF48ePIzIyEiqVCg9u3oS7IAjmJSXMdvt2VI8Zg9vXroF5eUHgOCiVSowYMQK5ublcwfXr/MhvvuGe7tuHMWPGcHfv3sXjx4+F27dvs/r6en706NEcUlNpfbW2EtFsax9Q1tVB2L9f5DBiBLjUVCJ0U6eSw+HSJWiuXEm2/7FjsX/QIOEFzzPv/v2Z0Y0bdOzz88kF0FYkAACFWAzl1q043NiIXn36MMYYbj16JIRmZbF6MzM8Li4WXP74g3vx88+4dOkSeJ5nH330EXiex549e4S9e/eydevWgXN1pf14/30qOJSW0vmMj6dkcIWCzntVFQycnDDlxg0c69UL7gcOwKasDFxBAa3rOXPonCQmkkIOUPCYhQUVouzt6c/Zs996GYvFYohEIgwaNAiDBg2C6tIl9PvxR8ijomBobPxqHWzZgoKCAqFeLGa8IIDrvPaGDycL91vQUbBZu5b+fPGCguYKC0mhHjOG2jF4nlpPdHWpaARQGntyMrBwIYZu24b0tWt5LFrEtSeld4wPA+j6//FHoKaGyPNrEASB9uSTT4CPPqJj/d57r8Lyhgyha6i+nn7n4UHnYtw44Kuv4OjoCFNTU+HLL7+EUqlkgwcPRp8+faj33NqaVOjNm6mg5+REhZk2TJw4kfv+++/R2NiIw4cPq2bOnEnHNTaWCkOXLtH+eHuTS6NvX7KRr19PBRkjI7oPSKXAd9+B4zgEBwcjLi4OdXV1XRwzfwVPT09oaGjoHjly5GJ0dHT3qKioAgCIjo62lEqlGwC8A+CgQqH4RG03V0ON/y6olW011FBDjb8JNm3a9IFUKj0QEhLSz8XFJUAmk/UXiUThgwYNknXv3v0vZ8wCoIfL33+nUUgXLgDr1sFk6VLmMWQIBg8ZgpKSElRXV2PChAnMwMCAHrYZAwwNwUdGouiLL3BaRwdjx46FqakpbG1toVAo+JKSEk6voQEBSUnYIQi4efMmmpqaBBcXl7fv06BBRIjq66kfu7CQrK1yOV5GR2OvlRXKZTL4RkT8aUowAFIq794FevTAnj17VCYmJmzkyJGsd+/e3OsKP8dx0NPTw4MHD9Dt99/Z4GvXEN+/P+RSKR4+fIjclhZeLyiINVhZ4ccff0Rubq6QmJjIAAgm9fUs9fFjTsSYcMXamg0ZPpyNGDECgYGBXUcy6ejQw/6FC/TzySek2slk0PPyQmNuLkRHjiDL1hZ2HNd1xvCCBUS6vv+eFHyZjEj3118DdnawtLRk165d47p37w4tLS1AJsPj1FT8cv8+XF1dhalTp3JvTTWOiSESKxaT9X7hQvDff499yclI8faGtKUFoz7+GKIhQ8hO3A7GgHPn4Pzxx6ipqVHdjotj7hs3gk2Zwl6EhLBr167hSU6OcFVbm41/8gSKpiaUm5hg5qxZ8PLygo+PD7oHBTGNlhZ4fPABY4zBzs4O/fr1Y/n5+TA0NISzszODvj4lTDc1UQFo8WKgoQENNjaw2bABgevXg7t+nWaVz5pFVuO6OlovJSWAnx/sli5lzc3NqIqPF2p79OC77d7NsZcvoerRA1kaGiiVSpGamiqcvHuX5fTtq5rr58e5h4XBwcEBMbW1/P2GBtZ9+nSGe/eQMW8e0w0Kwo0bNzB16lSYmZlBU1MT1tbWLD0lBeYXLvCmTk4MK1bQOb5xgxTas2eJMI8eTa0Ns2YBffoAnp7IGzIEcU+fojwsjI/x92cZ+fl8bb9+vMX48ZzU0ZEIn68vnR9LS+oH/uknUl9zcui4FBQQwaupoXWjqYnaujqkpaWhb9tYPc7ZGeLDh6EZHEwW69xcss1//jlO6+gI95KSWFxcHGpra1+1GXAcqbM9eryxdAoLC1FTUyP4+/tzHet7zRpylvTvT5MHWlpoxvdXXwGhoaQ49+gB3LpFhHfyZJxydobcxoZ38/XlOmbKd4ZUSrZuuZwKbK8hISFB8Kmo4AyWLEG9vz+OHj0qWBYWgvP0ZGIPD7Dhw4kw19TQvS0igkIapVJg/34wjkNKQ4NQW1vLAYBIJBJ8MjIY/P2pkNH+mWvXkmvhtVT3+/fvw9zcXCgqKuL69u1LLQLZ2VRUGTKEXuTnR+dOKqWWlCFDSNlmjIoQDg50TX/+OVr79kV6Rgays7MRGBj4b9XtdshkMqhUKnFZWZmsX79+Z6Kjo23FYvGjwMDAoGHDhhkUFRX5tba2uvft2/f0/2iDaqihxt8CarKthhpqqPE3QHR0tBXHcRcWLlyo5e7uzmxsbODi4iLy9/fXMDMz+2ui3dJCPYknTtDDX1gY9T9bWQFt1l8A8Pb2xsOHD1X37t3jYmNjO1KleZ7HnpISVXK3btyciAg4+voCjEEmk8HT05PJZDIEWFjAzMMDRRYWQm1tLSsvL2eZmZkqAJxSqaQe3nYwRg/5Y8eSEvjBB6TU2thAKzMTrFcvpOflCYGBgax9396KzExgxw6UhIcjKSmJmz59OvsrpSgrPR2Gx45BtnIlThkaQimRQFtbm1+xYgV7qVKxx7GxwtNLl5jHqFGYMWMGMzY2RtyFCwhbtYrVmprivo8PWgE2ePBgGBsbv1L+ampIMfbxoaCpX38lsrNiBYV0OTmBaWjAMjQUub164dmRI4LXhg0MYWH0cA/QA/6vvxKpNDOjec2RkR0W1JMnT6KyshIPHjyg4+LpiZfLlsGsZ08hbNYs7q2uBp4nW6yW1qvfiUSo2LABBo8fI8/FBUqpFPmenkLgzJkM0dH0HbS1ydadlgYEBcHdx4fr6+3NTI8fZ90++wzufn7w8vJCcXEx8/P3h9uqVVBt3AgHBwfBa/x41lEgkUiIcGhrE9lvQ2xsLG9qaso56+vTOqispP7zuDhqWxg0CJfv3lVdcXNj/ceOZejVi8hre3iVnR0RGA8PNJ46hZTMTJ7LykLExYusIj+fk4eEwMjREUfLypD88iXM9u5Fga4uHzFxIjc0IIDTiIwky76+PgICAriikhKUSaV8iFzOuQwdilorKxQWFakePHjAtba2wogxWObmQpqUBOMbN5jBoEFUMPL0JNJpaEg9zN99R7bh9uRzV1cAgKGhIdLT0wV9fX1hxsyZnK6eHntUXi7cTEriWjQ0BKvBg5lYV5f6mHv0oIKLgQH1ne/YQQTezIzI6NGjwL17wOXLaI2Ph+7du7B1cqLX6ujQtT1zJinM9++TujxpEvwDAlhoaCiam5v5ly9fMp922/iTJ3TMp09/Y/kUFRV1Jdvt1y5AfeRhYVQs++UXUqY3b6ZzOHQo/bSlo+fQXHfe09Pzz3tcqqqop75fv9eWMI8HZ89yHjt24IK2Np9bXY28vDz2mONYXGUlLG1tYWxqSkUFMzNas5Mm0X56egIhIVBs2QLd2Fj2xNUVs+fMQW9fX8aGDycXhaPjqw+bOZNs7Z3uORzHwdvbGxUVFUJZWRnz8vSE7ty59N06p787OtLnmptT5sCTJ0S+xWIi866uVBj88UeoZs1C9rVrUGlrC3fv3mUeHh7Q/pNZ568jPz+ff/bs2ZX+/ftfvXPnzprAwMDQ4cOHS/T09ODi4iJJSEhw6d+//6b/0cbUUEONvwXUZFsNNdRQ42+A2NhYO21t7TmDBg2S/vtXd8KiRfRQaGtLttXISCLZb0F7P3ZraysDgNTUVNja2uLAgQMqTX199sFHHzGD3r3pwb4tmKy9J9IgPx/5MTH8LcY4gIKJGhsbuZycHKSmpuLp06cwNTWFXlYWKWD37pH1cvFiImNXr1KokVQKuzt30CQIwtm0NEgkEmbTWXHtDHt7ICwMxy5e5D28vARPT8+/LDpY3b8P15MncdvdHRVtidlSqZT169cP3bp1g/vly8z62TN4//OfAADTQ4fQrKPDrrm7I49UYyYIAgZ4eUFqaEiEQl+fRpMtWEC2eC0tsjBv3EjkZ+ZMIDAQuHgRwqpViLW357WamgT37ds5GBgQuT18mF63ZMkre/yoUWRJbYOZmRmsra1RVFTElEolHB0dkfz774J7aSnTnTTp7V84I4Me8j/9tMuvb2lo8A8Fgc3t3x/WPXvCPiiImRkZUYKytTURDo4jm2xAAJ2r/HxSL9uUSS0tLXh7e8POzg5MJMJNLS3e6tAhzszQsGv/r6srba9T32pdXR0rffSI9x85ksHKis7/hAnAkSNEoh0dcevWLdba2iqEhITQOeV5yg347DOA46AqKMDld97BSQMDiLy8mMuVK0xSW4vEXr2ge/kyfyw5mT3X04PAGAJTUjAsMJCThYWRutijBymNUikYY/Dy9GRuUimHkBDg889h0tSE3p9+yukoFEi9fp3vtnw508vPR9O6dYgrLob3pk0QrVr1StXU0iJVuqqK7OG2tkS2SkoAMzNwurowMjJit27d4oYOHQorKyv06NGDMzExQWJionD79m3Ws2dPUjgzM8lK/s471Ju8dCmtDSsrOq4TJlAYWHAwyjQ1UZOWJjiUlDBkZlKv+8WLpIZHR9P5mj69izvk4sWLTCKRwM/Pj35hYkLnyNb2DRfJG8p2Z/zxB5F/Q0Pq3XZ3p6JTbi4VIvT0OnrmCwoK0NjYyBsZGXFSqRRvLQzZ2JATZOrUDrIrl8tx6tQpvMzLg9LDA4ZDh7JHjx5BpVKxlceOoVxTE76TJkFTS4ve5+tLkwg2boTQqxeq6+tx/vp1/qxKxZoZQ8SZM+AOHBD05sxhLCqqq7MEIHXdzo5yDjrt45kzZ/i0tDSOMYaStDSV6+3bnMYHH3QZadfuYoBIROr24sXkGBg69NVrHB2BefNw+OefVe9v2MD1++orllVVhZKSEt7b2/vfO5MAZGVltZaUlGTfvHlTUyQSRYeFhem0Tx5ISEhQlZaWnu7bt++J/8m21FBDjb8H1D3baqihhhp/D2Q1NjZqtba2vv1h9W0QBODZM1LJCgspzOnbb+n3EyZQb2O3bvRw2AZdXV2uY3QTgIMHD8Ld3Z0bO3YsE4vFFABkbEwP9C4urz5LSwu6vr6csUQiVFVVMQcHB8HS0hKmpqbs999/R2N8PAr37RO06uuZgb4+uLNnSQmMiCDik5BAaeBnz4JNn45hly9zviUlwr9aW5GYmKjy9PQUhYSEdFWApFLwkyZB38uLeY0c+ecPqy0twJIl4D7/HKWXLiH311+hq6vLq1QqFhkZ2fE+6fffw6z9uNXVAbt3w2vHDpTp6iLsX//C+QED0C8pSdD+8Uf2MisLNc7OeFpcDE1ra/gVFoLxPETvvEPkq12he/KE/pTJ0CSXIzc3l1u+Zw89zDs5UZDTkiXUA3vwIJGGxYuBkJAuX8HMzAwJCQmCXC5nd+/eRVJSElT+/qyPpycdt7cVJNzcSC18DbqWloJxdTX42bPhm539qk/21i367p6etE/e3qQU/vEH7eufBFgBgI2bG3clIgLeN24QWRo/nv4jP7/LiDIA0I+PR4Wjo4CcHCKEkycTyV+6lOzh3bqhtLSUAWC//fab4OzszPzlcjSePInMe/cg4nn4pKejfNgwfuLkyZyrqyvqfv0Vd3v2BK+pCa3kZM5aIsFIiQStLi64/fnn8HznHfo+48dTf7GLC5H7nj2pp/2bb6j/duBAulamTUNAQgICVq7kNk+bBmtXV2GmQsFy/fwQo6UlDLe2Zl0YqIcHEdzQUCLKgkCOjc8+AxITER8fzxsaGjKO49irt3jAw8OD++KLL/h79+6xkN69GXfsGLUiREURyR42DPjmG7R+8gk2bdoEXV1djBgxAh4eHmi0tUXqkCHCgLbRdMjKIqfIkSP0AY2N1Dfs79+Re8AYE6qrq9n+/fshEonQ3NyMKWvWYP+sWeC6dROkUinfNnJKaGpq4uRyuWjv3r0qPT09bsqUKQzJyVQ8+uorKgwcPkykW0ODWgGOH6eCjL09Ef21a6EpFiMnJ0eck5MDe3t73tDQUMjOzhYNHToUAW2jxQSpFJgwAWzvXmDZMhQWFuLIkSNwePgQE2JjcWHDBn7eoEFc3759WWtrK7g+fVB09SoM2hP89fQg9OmD55GR0DxwQLj27BkeOzszPT09wc3HB1liMZr09DBj/35WuGcPHJcupWJYZ8hkVJiSy9HZ7i6VSjkA8H74EAIg+mPNGmG8vn7X+01MDB2X9nvpDz8Azc14lp6Of506BV1dXZWenh7jeZ49r6oSvczJgaGDAyYsWIAkbW3uprm5KjQ09N/6yXv37i2tq6ubqlAopvTs2VO/cyEyKSlJ3tLS8sW/24Yaaqjx94J69Jcaaqihxt8EGzdubPr444+1tDrbgv8MEyaQ2vTZZ6SUchz1BO/fTwrOtWukyEoklAjt5QXF8OH46rffoGx7YBwwYADMzMzg6OgIzc69ljU1RI7S04mEAZTM7OgIYeFC8Dzf0YcoLylB/IIF8Lp3DyLGsG/OHLTq6MBTKhVGHT/OxJ3JoCDQQ+uJE0QeTUygOnQIcTNmqB4LAqupqWHdunVj3bt3h76+PqysrJC1YAGeiETC6F272J8msaemkk39+nXs/vlnoaKigi1cuBDmnUdDtSMwkBLQDx3qCLnC+fPAgAF4vnkz9l6+DF4k6kxeIBaLBaVSyUxMTDB/4EBItm4la+1rEAQBGzZsgFFLC5Y0NRERsrEBvviCCg7V1USMUlK6vI/neRw4cEBVUVEhGjZsGG7fvo0XbbOyh8bFCTr6+gLbsIHz9vZGlx7y3FxKRp4374192b59O4zS0jDzn/+kz+6MK1fIAnvzJu3Lvn1vnRncGQ8ePMDly5eFNdOnM2zeTKR1wgRgzx4iNe02ZbkccpkMv86cifkzZ1Io3cmTrzZ06BDkp0/ja3d3dA8ORkNDg6q6uJjTzc1lvFyOgYWFONC/P7zMzflxixZx7eeB37gRpxQK/pFIxLUdbAwvLuaDDQw49OtHa2rMGCosNDWR3fmdd8g9sHs3eAcHpFpYwOPKFWju2QP06oVWKytcHDgQ2Y8fw7Omho84fpy7/MEHSGlqQmBgIOzs7MBxHI0cKytD8IkT0FuwoGNsFADgxQsIUVF4lJoqFC9dyoZPnPjGsSsoKMCJEyd4iydPuJEiEQy3biVXQp8+RJ4PHwYiIhB95AgsLCxQXl6OZcuWIScnB/fu3eMXvf8+h6IisrBHRlIWQlQUrWVXV7I1L1+O5y9f4uSpU4Kenh6ztbVFa2srVCqVEBQdzcrffx+1Tk7gOK7jp6WlBXK5HBKJBPFXr2L10qW0XnNyyLK9Zg1Z1Xv3pnvL0KGv0sifPqVC0rVrUB46hCPh4byKMe6ptjaEt+QwaGlpQaeoCEPT0/Hoo4+QmpoKCAL8U1KgtLDA6F27uhQZH06bxl+yteVWfvYZUlNT8eTJE1XL9esiuVgM/d69hSBLS+ZkZASuRw/KgjA3R+n+/agwMUHIsWPg5HI0LV4MVbduKCkpwfPnz1FZWQnh8WNBNyeHf+TtzZRKJadSqaBSqQAA448dQ52PDx9y5MibNxojI7qeZTIoFAqIxWI8WrYMuufOIX37dt7F1ZWrqqpCQ0OD4OTkxFzaC5UnTyKlrg6JSUmI1NeHyZYt+E8mSnTG0aNHm/Ly8k4rlcrZUVFRyujoaC+xWDyPMcYplcoTUVFRt/5XG1ZDDTX+T6Em22qooYYafxNs2bKlatGiRTKDTvOY34AgUHDPb79Ryq+3N5HjkBAije0P4YsWkRrV0EAPz2fOAE1NeFFaiuynNFmm15dfQuLt/YYyCYAIu74+BQV5epKFOjz8lW1SqSRb69KlUI0ejZtDh/LuQ4dy+/btAwD0i42FQiLBoHPnIJW+5owXBGDnTuoDXbYM2LEDwtatyLOzQ9rjx6qcnByRXC4HAOi+fIlJenqCTVTUm0/wzc1EqHbv7uifzcjIwMmTJ+Ht7Y2xY8fSg21DAymw3t6kOAsC9c36+5NN1sMDANlav/vuO0EmkwkSiYRNmjSJSaVSFBcXIzMzE3fv3oXVs2eYd/8+2MOHb+xOdVUVLn/4IYJEIrgMGEAkaO9eSmJfsoSKIuvXk01YIukYR3X69Gnh6dOnmDt3LtPtdC5aW1tRFBeH6itXEKunJzi4uQmjR4/mOvrcjx0jMvuauq1UKnHq1CkUFRYKKw4dYtySJW8ScpWKVDqepzTl9etpLvrq1WQR1tbukhL/22+/qZRKJZs5cyaHnBw69z17Up+qvj6FT61di/xx43Dg3DnYd+smzMrKYggMpITrNihaWnBx+nRYS6V80IEDHBgDli+H8vx5yB0doXXhAgoKChC/dStmJCaCpadTUJmnJ26XlPCxsbGckZGRqra2VgQAa9eupfV/+DCRwtmzqfdbU5PGaVVVAbW1KE5ORt3Ll4gdOBBaVlZCva4u41++5Gfu2sXVhIbCPCoKdy5eFHIkEtja2rLy8nJVc3OzAIA1NzdzCoWCGdbUYOrp07iwfj3EUqlKV1cX3t7eIgcjI9wZMwY9oqOhXVPzdoeAQoH4SZMEnb59mXe3bmCPH4OtXo3y8nKo9u+H8tEj/OriAj9/f6Snp3cQwL7PnvGDjx3jEBODysREcIMGQZaYSGnfEglUSiWKc3Mh27QJTTEx+Hn5cuGdkSOZl5fXq8/OyKA1/5bxXwCgXLwYtUePwrSigtRyNzcqwAwfTqnjLi70p48PEf7XkZUFmJpC6NMHhTIZ7gwapAoZMEBUZ2qKtLQ0iEQimJqa4nFmJu91/jyX7u2NOpkMA69dg8eIETBdvfqNTbZoaSH7t99g0rMnfvnlF3h5ecHb2xvd9u8H5+JC1/S2bUBCAorlcjx4/32kBARAqqkp8DzPnB49gkVJCeoNDZHXt6+gL5PxMpkMTnl5IrdvvkH53bswMDCARCJBRX4+TA4cQMXcuTh45AhsbGwwYsQIWFpaduxPXFwcUlJSVAqFgsnlcg4A9CUSYV51NdPdtKmLe+h1PH36FGkrVgguqaksf/t2PtzTk2u/9v8TtLS04OjRo00lJSVVjLEHgiCEBQcHS8ViMXf79m25UqnsERUVlfkfb1gNNdT4P4XaRq6GGmqo8feBwPM8AODZs2eora1FWVkZTE1NYWxsDC0tLZjOm0eEsbNaaGREltZz54hErlhBf/fzo4djf/8ONe7XjRsFsbExm2lpCcn331PgD8cRwerViwi8oSH9nDtHVtfKSnq/mxuRtBs3iFDMng0cPgzRyJEY3CbXdO/evTUnJ0fM8TxyvLww9C2WeF4QUDd9Osp794b9jRvQ9vFB69atsLazg3T+fFF1dTVfVlbGaWhoCK7m5rD57juGdeu6jgjjebKCm5qi84Orubk5qU7p6dDZtw+Ga9ei18GDQHo6qk6dgtGkSeBCQogAfvstEf7SUiAvD4klJWhsbGTjx49nDp3UYBsbG9jY2MDV1RXHf/lF2KmtzWqio6GpqYmIiAi0trZC2tSEvL17EZiWBuvt24EHD8guvHQp9UpraNBxfPKEEshTUkgtBJCbmytERkZyuq8VPcRiMZwGDYLTsWPwlMnYnqIi4dChQ8KECROYjo4OfYdORLa2thZxcXHCkydPmFwuh0qlYvlr1sDZxIQKNO2EIC+PCPqwYTSGasUK+v+iIiJbq1eTM+LZMzSGhqJk4kQUP30qMqipweOePeHm4UFhUUeOUIDYiBG0Lv74A7k6OrCysRFmREYy7N1Lx6AT/rhyBY9cXBBRWclh3z6y1Q8dCsmzZ5D89BMgFsPJyQlHzc2h6tMHYqWSjuH69chsamKtra2YMmWKSC6XY+/evXj69Cns7OyouCQI5Dj44w8iYytW0IdGRkI0ciS6bdqESyNGwPnmTTbu/n1kXbrEGW3bBtmRI2jR00OyvT2bPXlyO8nqIvXzPI+8vDzUpqTANDFRKAsKEqlUKhw8eBADBw5E3NChaIyNRfjVq1S8CgnpWsSKi4NFdTU71NSEit27IauuRoqJCZ4/fw49La3WvpWVIk87OzZw4EAEBwfj0dSpECuVUAQHc6U6Ojh+/Dhq9fQQtG0b+mhpoTkiAubm5ih69gy/nTwJDS8v3sLUFKveeYfDhAlUXGsrQOHSJQrk+/LLrheiszOwfj2Uq1bhZwMDrCwupvvBrl1UVGubs44+fSjs8MgRWsedRmcB6ChWsexsVF2+DLuDB5njiRPAsWPwb22lgpixMYYNG8ZBLMYAExPUDRgA3QMHoBEZ+frtAcXFxfhp9WpM9fFBeXk59PX1VaNHj6bzYWBA5P7zzylYztoalvHxODdiBFBRAZlMhmnTpkFTUxNcaSnw2WdghoYMAweK4OhIa2ThQth3KiZ1e/wYuHkTep9/joiICOHChQts7969AICpU6fiwYMHGD5nDmr27eOcvb2Zs7MzGhoaYGBgwMRyOV0DZ8/SsXsL7OzsoNq6lV26dAl6Dx+SA+P5c7rH/gfQ0NDA9OnTtYuKirSrqqps3d3dodNmlc/JyVEUFxfbAFCTbTXU+JtBHZCmhhpqqPE3QVxc3Cf6NZnTAAAgAElEQVRVVVXaV65cUSUlJXGZmZmoqalBUVGRkJKQwB7HxcFs1CgYzZlDamJnWFqSPXjQIOpbTU6mvkJT0w4bcVNTE27evs2UenpIAuC7fj00w8PJKq6pSWrluXOkhN++TdbirVuJ2H72GamdW7YQSXR0JEXZ27tL8JKHhwdX/fixYHznDsv29YVYKhXEYjGrqKhAQkKCcOvWLVy5coXdv38f+cXF/G2FgmUKAgpFIlhcvYrmy5dhZGzMBrz/PoaHhzO3Hj0YXF2JULeT7cZGUlUDAih0qROhbzpyBKKYGDyztMSE48fxB2N44OfHJ/v5sYwjRyCurxcsKysZDA0p9GzyZFKZPTxg4OGBrLIy2OnowMzN7Y3zY2hoiGCxmPl89x0qR4+Gvb094uLi0Hz1KmQ//ghVayv8+/eHhpYW9QVv2kQFjOPHyUo+eTIRoMuXaY7wqVNQ/uMfuC6TsWHDhuFPk9kNDCA9eRLeGzaw5ORk/ubNm9zLly9VtseOcZL9+8GPGoWbN2/yx44dY42NjczT0xPFxcXo378/fIYNgygjg4jn1KlEiq2tqVBx6RIpv7//Tirw5MnU1zp2LLBwIU6ePi28TExkeTo6vG5JCRt//DgaJk6E8Ucf0Wi3UaMo1GzZMiJg27fDTFsbyQkJ0Hv4EKZ5eQwREXS+amtxJyUFKTdvokevXiqX0aM5ZGVRMef8ebK06+iA53mcPHkSFdXVGPThhxQItns34OcHhVLJXpSV8YqsLGbV2AhldrYg379feHTnjmC5YQPT+OILWhfnz9M5bVf8Fy6E3sqVeFhYiHo3N+GdNWuY7pw5sOneHWzlSqCwENdVKuEFx7H84mL+QWoqn52dLSgUClhZWTGA2gmMjY0h69sXLoWFLGDZMnh4euLu3bsoLCwEz/MoE4kwYPduKly88w5ddy4u9O9Zs2B06BAGjBoFp9GjoYiIQE5uLm9nZ8fPmTdPbOXiwryuXoVGUxN0vb3hcOYMzBMScLlnT8SHhEAkk0EikcA0IwNpAGIKC9Hc3KwyMDDgioqKVB+vXCnyHT6cdYSChYSQk6K5mULddHTIoXL/Pp239jn3ERFQaGoiPikJvWfNwpPGRpi8/z5d07/+SoWoESNom9On033mtdFZHWAMRZWVSGFMCP7pJwYdHbp/XPt/7H1nVFVX1/Xc514uHS5FlCIgKKBSFBBUVLCggCXGHokmYEuMJkZjTYxBE3uLLbEn9hI1UbEiCKggYpciXUAEpHduOef7sbgUS568z/e83/uO8d05hkNzyzn37L3PyZ5rzTXXDXpeJSQALi7gDhyATl4exL/8QoZlb+CXn38W5m7cyIxXrcKjx495DQ0NkbOzM73p40NS9337KJCzdy8ULi64V1+v5HmeaWtrC3369GEcx4EZGoINH05Gdr/+SoqH7t0peHPtGl3X6dPUP/vLLwGOg4WFBfPz80NtbS0KCgqQnJyM4uJi9NHTg+uKFcysKZino6NDqhmJhFq2mZu3dT5vhadPn+LEiROor69H9z59eLuffybzxJ490Wyi+A/BGINUKoWFhUUbxVB2djZfVlYmiYqKivTz82v8xwdUQw01/tuhzmyroYYaavwvAcdxVWlpaSa2trZccHAw5HI5muq3GaZMQdWjR9imrw8vbW0MfdMwq2NHylJeuQKMG4fMvn2Rnp8Pr7g4GEdE4MmIEbh444Zga2uLiooKVFRUsK1bt8LZ2Vk5duxYEbp0IZk4z5PpWloaZQj/+INqt/PySFpeW0tZ27/pj83n5vK1pqYimSDg1q1biIiIgEgkgrm5OWtsbBQcHBwwhqS2nEwmQ21tLQwNDcHdu0cZuXPniNhPnUrZt+vX6ZwLF1LGUiSijK7KpEwupw3zpUsQXryASUkJRBoa2Dp/Pr1fVMS5ubkpDZ4/F0m8vBgvlwMZGeCAZuOxB5cv43J4OAakpMDpjz+ILB0/TsSzVQ29uH176A0ciMmTJwMyGQbFxSG3ro4Xams509GjIdbWJvntJ5/QFxQK6qe9aBGN2fPnRDwYAxwcUGNsDCYIkOfktHH0boP+/YGiIhjcvYvZs2eLXr16hYsXL7Jz6elw0NLik48cQXFxMYYMGQIPDw/k5OQgISEBRUVFSolEIsKwYTSu8+ZRPfOxY3RdABl+bdpE895KOZDw7Bmepaay3idOoJ+lJXf+/Hlhk6MjW9GnD9XH8zzNTUkJkclly4ABA6A/eTLGmpuzsro61L5+jdcWFrB59Aj1MTF4EByMuceOQSspSYRBg+g7dXXA/PlEgK5fx7rJk/HRoUNwDgjgER7OYc4cUnGYmaHnkyfItbbmusfFodzMTBj100+sxMiIJenpCYdEIvQbOxZujo4UbJoyhQJOPXtSFjErC69GjQJevxa0LCxa2pdxHDBlCh5ev85GXriADmIxl7xzJ+7fv4/CwkLB883+1HZ2VGedkACJtze+++47VFRUIDExEbdv30alXA5DV1dSTfA88N13VGM9eXILsQwMhO3Mmfjss89aBtzGhtbZvHnAwYPgzp3DnYgIGMlkyi+nTBFxHIf4a9dgu3kz9sycCQDo0aOHqKysDBzHtdQDchy1/RMEmp/GRqqtnjeP5io4mDKxgkCkGwC7dAmSykr8/OmnkOvpYQlAQZmFC1vM9QC6B/8FjIyMUF5ezlXU11M7wL/+onOFh1NJxY4dZFj39CnN/xv4448/eHljI5ffqRNe373Lv3z5Ei5vktGQEDJcZAxYvx51+/ahuk8fUT9NTfTp06ftg4njKANubw88ekQlE0OH0rjU19NYXbhANeqtUFtbq9TR0RHV1dVBUyLB08GDhT4c9+6n3uHD5AFx7x4pg96AWdOxBw4ciB49eoiqBAEGAJ170CAKdpWW0nX9mwgICNBJSUkJBpAAYPu/fSA11FDjPw412VZDDTXU+F8CkUj0cty4cZ26du3KOI4jw6CKCiIN27cjLipKUD55wt7bszUgAMW//IJTWVl8Dc9zVh078vufP+cGxMYKFffusUHBwULvKVOaN/hbtmwRnj17JnJycuK7d+9Or3McbfxtbGjjKBYTOdTXJ4nuu2o2W0GpVMLmzz9F2U01o3369GGRkZEYO3YsnJycAKDNflUikbRkaLy96Zw9exI5vX+fpM7W1pRhk8moHn3BAnIV/uEHckw+dQro0gWy8nIcNzERKgIDGZRK9OrVi09PT+cqKiowxNtbJEpNxRY/P5wD0Cc/XxjQ2MhSUlPh5uaGm7dvKwf4+4v6r1xJRKOmhtynJRLKBL98SSROT49+X2EhMHs2tDQ04JCfzz0cMEC4pqWFL775pq2Rm6cnbe7XN5kIp6fTv3fsAHr0QNHChbBdvRqa7u64df4832/o0Pe7J+3YAfj7w9zcHDNmzOAq/P1x+fp1KBQKzJ49m1Oti5MnTwIAcnJyRNXV1dBXKRKsrVvaw6lgZ0dk4dIlCjA0v2wHbW1t/unTp4KlpaVIKpW2zFtrafjDhzT+n3xC9dvR0WinUEB30CCcXrkSxa9f8zJPT453d8enn34K7e2teMCLFxQc6tgRGDUKkZaWkMtkMJgzB7a9e3PYto0+l5iI+O+/55Pkcq7Q1hap3buD4zi2PCAAZgEBMANY6a5dSr2ffhJh61Zar8HBpHwAiDQWFGBoeDg2bdrEXbp0CcOHD2/5HTNnYkFYGO4GBOD5o0do/O03wbyykvX46ae3uZVIRBnRwsLml6RSKSQSCQ+AO3v2rCIkJEQMX18idKqxjYtrOcbHHxPJAoiI/vorrefz54n4jR4NtGuHEsYgkUiaDbV619UBH32EDpaWePXqFfbs2aPKsL69ZhgjMltURCUf+fkkv9bTI2d2FQQB2nPmwHvAAMR06YKlixbR68eP0/2VkdH2uLa2RJpbt7xqBScnJ3Ac19angTFaWyNG0O8pLSWi+4aSQ6FQIDk5mRs7Zgykfn44d+sWp1AoME7lfK8ar2nTKOgHAJ07ozIzE588f456XV2UZmSgw6+/kkJDZSB45QqR4YQEarF24gSVxJibk9Fb6xZfTRg4cKBo79696N+/PxLv3IHnvHls5evXGD58ON4KwAA0vgcOUBb/DbRv3x5SqVR58+ZNUWRkJEQiEb799luwadPoA8+eUTDz00/JW0JlSvlfQGZmJkQiUZpSqTz4X/6yGmqo8d8KNdlWQw011PhfgLCwME4sFrtaWFi0davdsAG4fh310dGIf/KEMcbQu3fvdx5jb3y8wjshQTygUyfWdcECaGhocJWVlcj44APWt7gY+nv3ctDQILkwABMTE1RVVUGhULx9sKtXgZEjiUg9eUJZt5iYf0m2RQoFuvI87DduxJM9e5CTkwMAkMlkQmNjI9N8x8a2DTiOstbjxlGt86FDRPQDAymjuHEjSZ4HDiTS19RP+MFXXwmXjh5l+vr6/PLly0W1tbXQ19fnqqur8fr1a+iePw+sW4dPBg/G9WvXeNfvvuOONzQIOaamLDk5GdXV1aLm3sQiEZ3z+XP6761bgbNniQTNmEEEc80a2tDPnQsEB6NzdTW7e/Qov3btWiYWi3mpVAotLS1Of8oUOHl4oKvq+iQSoMlpHCByEtu/v3JHu3YiRVQU1+/334H9+9u0JgJA59bVpTFoMm6ShobiIwsLDkeOtPloYGAg0tPTkZGRgca6OuiravbnzaO2bm9CZTYVFNSc3TY1NYWpqSmrqKgQAEDV6/fx48dway0j/u03OraBAa3VHj0gNjGBwTff4NOQEPA8z+3ZswdFRUUtbZxkMsqs//orsGkTyj77DM/PnEHs4MHwGzxYaXzwoAi//kqBCYkE2LwZnUpKOM85c3Bw/ny+QiplSlDbukmTJjFxbS3scnKY3Y0byPzhB9gXF7cpLcCBA4BCAS2ZDK6urnxiYiJXXl7OjxgxgpNKpcDDhzh65oyQEx3NvLy8eK/CQmaemUlGXO9CQAAFUdzdm+uXVbWzVlZWLSeWSGhcjh8ng7r27Wm85HJSVIwbR++vW0cBmPBwCmjt3g2ACFRwcHBbm/gpUzChUyekp6fj0qVLqKurA2OM43m+5bkhCJRVHzCAFCKnT9Of0tK2ba+2b6cykOfP8ejXX4W+zs4tgaL+/WkdvokNG0iq/x5UVFSA53nI5fK33xQEyiBfu0Z13qtWUSArNBQAcPToUV4qlbLONTVMMyQE1qtX87m5udzr168hlUoh/uorIqOXLxPhrqoCJk1C2ZYteHHoEB67uaGnpydG6ejQPWphQcdXmcX99hupZXx9Kdixdi11CEhOprlqZSrXrl07BAYGCpcuXWIG+vrK+xs3ilhFBcLDw2FrawvTN3t4L1xIAaf3ZLe/+uorEUCu/ufPn8fTp0/h6upKb6oy/KtXU9Cld28KEvyrZ2Ur6FIJhqNEIrmyfv16Bc/zneRy+dzly5ef/8cHUUMNNf5b8O/1H1BDDTXUUOM/CrFY/J1UKhUZqWSbeXm0GV21CoiLg0KhgJ6eHiQSyTtbSMTExKCiokLscPQoXI8dYxpNZMPQ0BAenp7QDwqiYymV5Cze0AAvLy8GAPX19S0ZvCNHKOvj5katnQICWly8d+6kjNTfITkZml98AVmr9mIAcO7cObZ27VrU1NT8swHhOMqsnT1LxGTxYsqqHTlCGbInT6hWMjERFceOIWvNGjbZygpzO3QQcTEx0H/9Gnj6FPqFhbCTSsHi4sC6dYNlQwP86+u5B2PH8jZPnrBOWVkovXMHM+PiBI3sbCI+Q4cSWbG1pTGzsKCs4PXrlCUrKCDieusWZVAB6OvrY9asWdwXX3yB4OBgztrKCkEbNsBQT48/FR2NoqIiuq7+/YlQVVc3X2pBQYGo1sAAc2fPpgx6VdXb4ywWE/lftarltTNnKNv9Bry8vJCTk4P2BQW49M034OVyRNjZCVutrHCytJS/N3MmHj58iLq6OlRWVkLm7U0GY+Xlzce4fPkyXr9+zYYNGyYCAFdXV5iZmeHPP/9se7KICMpWqv69di1lZxMTm6aRg0wmUwLAkSNHyP2vqopkxCNG4MzZs9hrbg7BxkZYWlMD3y5dRBg4kAihQkGBF11dtO/WDYVPnqBAW5v7ZOdO5nfuHMvMzGQNwcGApSUchg/nMkePFo52746GN4NHGhokV7e0xEgXF27u3LkoLy/Hzz//jLS0NMh/+QUTXr5kYrEY2traguXatYyLiiJ5/dChRBJbQyIhmfrRo80vyWQyJpVKlf7+/gAoS/twwwbwhoY053/9RbLtZcto7aSmUg/6lBSqMX/0iAI4Q4YAgwahYP16MMZgozL/k8ko8NShA6RSKXr16oVhw4bB0tISgiBg1apV2LRpE3/h55/R4O4O5ZEjVNfcvz9l1efNa+kCoMKVK0BeHtKyslBXV4f+qrIMhYLugz593lpbGDu2pf75HahvWrc3b958+82PPyZjvUGDqEVh+/ZAXBzqamtx4sQJ5Ofnc8HBwUzTwwM4dQpjx47l7O3tlZcuXRLWf/891aCHhdGxGKNAhbk50i9f5vMGDhQWHD+OyrNnka0qyRCJ0FyuAFCwcPp0uvd8fChosHUr8PPPZArI86SuSU0FamvRs3t3Nn/+fNh37CgSHz6M/v37Q0tLS7h9+zZf3upeAUDPq6Iieh4UF79zbHiex/nzxH2lKnM0nqfnbFQUBQYYo4z89u0twb5/AIVCAYVCAXt7e5+goCBff39/a01Nze/+8QHUUEON/zaoybYaaqihxv8wVq1a9ZmOjs7iKVOm6Da/eOsWbcAYA0Qi6OvrQ1NTUymXy9lPP/2ES5cuNX+0rKwMt27dwrhx46Dl6EgZlqbsWBs4O1OG1MwMCA6GblP2iTHG5HfvUq30zZtEyM3MiEyosmA2NkQGmlpyvRcPHwJaWsjPz4euri5vbW0NExMTpertioqKfz4wsbHAyZO08WeMiIOfH3DnDhEHJyfA3BwGmpqw0tHhk27cEKqOH6fruH0bj2fMEPJGjRIU8+eTnDchATh9GpY7dyJg3jzOJyUFxiUlEBhDbmUl233gALLNzXHN1FS4deeOEocOUSb7ww8pY9W3L7m+h4VRxvDw4TY/lzEGQ0NDWFpaIsDfnzN1dsagKVM4X19f5YEDBwRV72yMHk3EoQkqWapgZNRsFAYLCyIHrRESQtnJprZQOHSInMXfAWc7O+W0/fvROT0da2fNwh1dXSbW0IBpp06s29mziDh9Gps2bcK2bduwcdMmXL12jVd88gkUCgWuXr2Khw8fIjg4GKrgD8dx8PDwEACgqqqq5UQnTxKJUuH+fcDYGAp/f5zavx9lTaqBWbNmoaSkhHs4dy6e/fUX/tq0CQ/XrcOzZ8/QwBgynJwEjb17m4M85bGxuBwRAf7bb4GXLyGXy3H43Dm0a9eOD58+XWFhYoJvS0qgFx0N8Dz0yspgf/YsEzgOT548eXtATEyoVtjGBsZGRvjiiy84CwsL5R9//IHo6GgoZTJIJBK+ViVPBojojxhBkuW8vLbHmzePAgZNxMrR0ZFVVFSI9u7di927d2PTmjUovXYNMh+fFiKmoYFmafzcuSSjHzyYlBqqrH8TQaw+dkwY4u4uNPdVz8+nddGqHVXv3r0xffp0uLm5wTYzEx/u2MGlFxXhaseOWJebiypLS6rTVgV25syh80ydSvdyeDgwdSoeP36s7NKlCxOJRCgqKoLs6VMKLL2rnRXHkYw8Kan5pYqKCuTk5KCoqAjHjh0TdHR0kJSUhKioqLbf/f57ygCLRHSMwEBg71689PYW7PfvFyZOnAgTExMyF4yLg46ODiZNmiSaX17OZu7ZA4WTU9usOmPAgQMoMTVl07ZvZ3pTpsDDy0s4fvw4Mt9zX+DZM5L1V1eTnL57dzrO8eMUQPDyorWybh0EW1v8/vvvvPGmTXBOToa3tzemTJnCXr16JezatQu//PJLc/cIALR2d+9+Sx6vgmpdBgcHw/rFC1IOMUYmlvr6LcaWr1/Tuho5kqTl/6JFb1FREU6cOAEAqKmp4Z2dnSEIAniez/7bL6qhhhr/T6CWkauhhhpq/A8iLCysp4aGxuapU6dqGxgYkHR2+nTKOn30UZvPVlRUiCZMmICKigo+MjKSNRmoCc+fPxfc3NzQqVMnkpyOGEFZ4U8/fVuOrKsLLF0K5OTAPCAA7i4uwnWZjLmvW4ciExMIjx+jQ4cO7/6xVVVE5M+/R5koCLSZ/fRTpKelKW1sbEQAMHXqVFFkZCQeP36M/fv3Y9myZe933gZos6lQkJS9qIiM327fJpJw5gxtZkeMAH78EcjKAnfsGNyCgri9e/fiQXk5VqxYAQC4mJvLFAoFOnfuDKmWlpKzshL59u0LnSVLAJkMYokEjWPGoCw/HxanTqHiwAH8VVaGSkdH1jEtDXru7uhqZATN8nIi+aosmFxO2aisLKBfP5LWt7ZOamggEnrsGMAY/Pz8RJWVlfyZM2eEzz77jMOePc0b8j/++APFTYQtJSUFvXr1ojmKiCCX4s2bifRZWRFR+vRTyh5/+y3JaU1MqI699RzMnw93QPTA3R1xffuiV69ewqBBg5hIJAIAho8/xsIrV1A+aBDE2tpoaGjAtXPn8DIiAn+uW8fXi0RccHAwrN4w4YuOjmaMMTS3KHvxgs7dShaPnBwox4/HT2lpGHztGpRLlkAxZ05zDa84KgoaN2+iq4UFHx4QwEykUmHmqlXc5ZkzcXLsWKF3dDTClyxhJU1y9+pu3VB2/jyMra2hoaHBzx48mEOnThxGjyalBUAZzqgo8IGBAIDs7GxBpdpog169KHiiVII7cAADBgwQnThxAlXTpil3PHwoYjzPhraumbe2Jpfq334jZUV+fguR0tCg4x07BsybB21tbUilUqGgoIABgH1aGjo0NkKrZ09Sh6h6Xask/6tW0XqysqJsqIlJi0lXt25I6taN+ael0XcBygRPnPj2vXLyJALq6hDJGNIdHPDVDz9AplTi0fr1iP3xRwRu2QJO1KRE79ABVTIZqqOj8URfX6g4fpxlZWVBoVCIGGN49uwZBEGApLER3r/+Cr/W0vTWyMoCADx69AiXLl1qIxm3tbXFhx9+iKqqKhw9ehQFBQX8R0OGcJyvL5maqVz+m4IXWVlZuOPtzT7y9oZEKqUxevKEZO9NCgC2dCmOVlejfuNGQSwWCyKRSJDJZJy5uTnT0tJCnYkJNMaOBSZPRrecHGa8fbuwWy5nBgYGSl1dXebr68s5OjqSuuHGDbqXs7MpWOfpSZnstWvJqE+V+V+5EidtbJR1lZXMy8wM3KlTwN270JkwAZ8VFor4Gzew96+/2JMnT+Dm5obmoIifH0nk166loForuLq6QnPGDD43Px/2n3/OsU6dKLO9axdlsbt0oWdHaSmNz/LllKGfNo3KFZYubfM8l8vluHz5srK0tJRDkxdGQUEBx/M8lEollEqlU1hY2DcALqn7b6uhxv8c1K2/1FBDDTX+hxAWFiaWSCQxw4cPN+vUqRO9+NlnJPv8/XfKuly+DGhooIgx3Lt3Dy4uLnB1dWX5+fl8UlISl5+fzwRB4IKDgzmRalNtbEwktayspdfum5BKwXXpAofvvmMdCgoQPX8+ZN98AwdHx3dusHmex75Ll4QOly+zW506KQsKCzktLS3o6uq2bDSzsoAtW1A2ezauXr3KjRo1SpWRR2ZmJgoKCiCRSDBgwICW77SGqtbUwYFqxL//nqS3Dx+SJDYwkLJAhw4RCU1LI6fhLl2g0bkztKdORebr10JxcTGztrbG/fv3BYVCwcrLy1Ggp8fp3LwJhy+/hGT+fEAiAZs6FV0MDXErNRVBQUEoLy+HiYkJKisrhdLSUu758+dwffAAYjs7iEeObDEGE4koQ7lnDwUezpwB/P1b3LwjIiCbNw+P/fxgbGKChoYG6OjosLi4OCYWi3E1Pp4Xli7FxexsPqu0lGtsbIQgCHB3d+dNTU1pYCws6HhLlxKZ7NGDCD3HUXZw+nTKdKvqPgHKwL5+DUydCi4gAMccHdFIBnSsuR4doFrQJUugnZsLzcBA6OrqwsXdnWXo6fFdHj/Gh+vWMaPWLtRNePLkCe/u7s7sVC2O6uspAODj0/Lfo0fjtJ8fymtq4Pz11zijrQ1pSQm8zp2DiZYWL6xYwbo6OaGdnh7ro6/PvObOZSJNTTisW8euDR7MOE1N5p6TI0hHjoTv0KHMKTwcSSYmeFFSAs36euY5ZQoaT52ChkxGNdN37xI56dwZ3BdfoFQmQx5jeFFUJDg7O7OHDx/i4sWLiIyMFLy9vRlnYoJCQ0M8qKrC1atXMWDAAGVgdbXIe/16JPTpw54+fars1atX2xugRw8K9BQVUc3y4ME0Fx06UBa2Wzdo6Oigd+/ezNnZGT1FInQ6eFAwKyhgmhYWpCipqiIp+PnzFCSYM4ek+0OG0PG2bCFpemUl7mdkILGgAD6pqRB36kTn2bCBAi2qebl6FTAwQMX06bhRVISknj3Rc/ZsmFtaQiwW4/G9e5iwZg1umZigU1M2WPHRR8i+fBmvjY3x3NycFTOGnj178k5OTiwrKwuhoaHw8/VF74kTEa2pCYm1Ndq/q3d0bS14ExOcMjAQHHv2ZCEhIXBxcYGLiwt8fX2ZpqYmDAwM4OrqioSEBNxLSOA9raw41uR+DgA1334L2fr1OFBbK3iNHw/rUaMYBg+m+Vy2jNb3wYMU3FuyBN18fNClSxfm6OjILCwsuKSkJAYA5eXlwhB/f1hOmcKQmQlMngw9xpjt8uWwtLbmxGIxu3btGu7evctX7dolVMfH47ilJc+lpeGmlhbv3KMHxwYMIJK7ciXg7g7B2BgPHjxAXGIi9/XXXzONgQNJ8r9xIwWXunQBW7QIHWtrcb6yUnCZOpVpTppEx2CMgjTa2kSeAVJATJwI9vHHkGZmslvV1ZwZf2MAACAASURBVKzYzo7vPHcua35m5ORQkO2DDyiIM24cEe0ffqC2fRERpGq4e5fIPGOQyWQ4c+YMx/M8EwQBGhoagq2tLe/q6sq1b98eOjo67aVSqV9paemM27dv6/br1+9t9zY11FDjvx1qsq2GGmqo8X+BsLAwi9u3b38THx+/PTY2dsiNGzdi/fz8at74jFF0dPTn0dHR/aKjo9P9/PzqACAmJmZh+/bthwcGBkoYY1R/6+pK2a/gYNq4JSUB6enQW7cOBk+fot7QEJY1NXAZPJjrP2gQJBKJ4OPjw4yNjdv+MMYoO96/f1uzKICyO4MHA99+C1ZejiKJRLC9fJnl9O4NR2fndxLhp0+f4klaGtr168fY06fc44YGPiEhASkpKXBycmISiYTk2y4uOJyayltYWKB3797NB9LV1UVxcTHKy8tRV1cHCwsL1NfXo6amBjo6OiSH7tWLyMfSpfS3Cg8ekKR+40b67d26EckrLqbNeF0doKODdlOmwP7HHyG5fJkdq62Fg4MD7+LiwlVWVgr19fWszNgYxr6+vEXfvgwKBRRnzqBmxQrc7dwZXl5e6NGjB5ycnFBQUMBev34N49pa9Pz5ZxxvaECPkBAUFRVBJBJBEAQc19ERSoqKWIapKXQuXUJ9x47Q7dABvFKJQ/HxysuOjlzq8+eIi4vD3bt3kZKSAqVSiezsbNjb2wt9b9/mzPz8OK9Jk5CYmAgtLS1UVlYiPz+fd3Bw4JrnMCSEDMgGDCDVw/jxNKcvX9IYeXlRrWdZGRkrSSTAkiXQnDwZA3x9kZ2dzefk5DBjY+MW4sQYzb+LC9VpGxmBMQYLMzPWfuNGxiZPflsRASAzM1N48OABS05OVpaXl3PW5eUQ2dq2tLT6/Xck19Qg3sAAGhoaQm1tLZx69ODNKyuFzvfvc+YxMazD7NngbG3p3CEhJNEPCECRkRHuKRQI3b4dpkVFzM7EhBn37g3dBQvgOWsWvDZuhK2XF4TiYuglJaFu2TJItmyh3xkURNlBDw9oFhcLmllZjOXkwHH4cLbv999RVVUFuVzO0tPThejsbOFRVhbrvWYNLObP5/v27y+ClRVEHh5wGzMGERER3MuXL5GUlIQOHTo0G59BT49q9bdupTZeYjER34NN5s/W1kBJCXSWLoX+6tWoDQzEbx4ezOXSJWgePEgkaulSyqy3b09zoKVF//7gAyJa9+9D+PxzPE5ORp85c2i+srPpPHp6ZAqYlwckJkL46ivszcxEhLc3PGfMwLhx42DeJDFnjKFrt27YKBajSBCEqvJyxlVVITYzk89ydGSBv/wCr5Ur0W/1ath7eLC6ujqkpqYiMDAQ2hoakJib446BgVJPX59rDgK2wrE//uCTeZ61GzKEHzV6NCcWi6Grq9tigNcETU1NuF+4wAoePOAeDBoE56Zny61btxATG4sXjMGkf382dOhQek6MGkXZ5pUriWTu2kWturS0oKmpCUNDQxgZGSE5ORm5ubloaGjAhAkTWLdu3ej7lpaAVAqsXg3prl1o37cv7D084O7uDmdtbeZ09ixTHjjAHDt25Exv3mQRenpcfHw87ty5IxTq66OboyPD/ft4UVCAc3fvYtiwYS018zU1pDJQGUROnAi9CRPw+tUrKPLyYB4ayrB4MQUKdu4kFcKGDaTQcXOj59vQocjr2hXRL17Ax8eHNZusvXxJRpTTplG5jupZLpORcV5wMJVq5OUBP/1Ea0ZHBxlFRUhKSkJQUBDGjx+Pfv36MRcXFw4ARCIRLC0t4eDgILKystJISkoyu3Hjxp7bt2//EBsb6xkVFRXv5+f39/p0NdRQ4z8CJvyLWhA11FBDDTXejZUrVw4XiUSnXF1dRd27d9fMyspSJCQkvJbL5e4rVqwoBICwsDAmkUiSO3fu3EksFiM5OVkQi8UxPM9raWtre06ZMkXHxMSEpJNr11ItZdMGTxAElJWVYceOHfjAzw9JBw6gu48Pety+TcRL1RInIIA2mm9sdjFzJm3uVe646elkKlZRQcQ+KYk2+QAit22D9bZtMFmyBEbTpr3VRzs3NxcHDx7Ep+3awebPP4Fr16BQKLBz505lRUWFyNvbWxiSns5qe/TA1shIfPvtt9S67A1s375dWVZW1uyw3LGwEKESCV330aOU0XqT7NfUEImUSIBffiE5708/0Xu7dtG/MzMBLS0I9+8j69EjaLq7w2LQIKybNQsybW0wxiAIAqytrYUPrl5l0txcXJ0/n9c4fJjpLF7Mevfu3ZzR56uqwI8Zg5ezZuFWbS0yc3Px5v8rzc3NhcmLF7OYPn2QNnSo4HL+PLN8+RJiABocB+7aNZw8eVLo3bs369evX/P32rhGNzaiEcDhw4d5nueZrq4uMjIy2MCBAzFgwIC2YxAfT9fPcTRvx46RhD00lDL/VlZUU/777y09tJtw69YtREZGYtKkSdDU1GwhEKdOUQAjOrqll3hiImXS36GIePXqFdLS0gSRSIT79+8j4OBBOH7wAVOZVlUvX47DNTUImDsX+vr62LVrF0aNGoWeHEfkOj6esuCMUTa6vJyydGlp2B8RwYvFYvbJJ58wCAI55nt50XV1707BgcpKYNo0XB0+XHk3MVEkCAL09fX5+fPnc8jIANasQf7Spfhr2zZ8IJPBysgIuZqaOAhAR1cXNjY2vI6OjtDZ1lbksHQpuIMHARXh2b4d/KxZWLVmTfP1mpmZ4fNWtfVt5iI4mDwAIiOp9jk+nrLfgwYBX3wBITwc+cOHCzn6+uhTU8PEU6ZQL/G/Q2QkaufPx19OThiVlAQ9ExMijx07EsHKz6da6pQUgDH8sm8fSkpKsHz58rbHOXEC+PprVD1/jvDwcGX3NWtEhpWVuLBwoRASEsJ0dXWB/fuhzM7GOTc3Pj0jgw0aNIh5e3sDe/cCvXrh98ePwfO84Ofnx4yMjCCVSsHzPA4cOKCsqanhJgYGMvPTp6mP+N9h0SKUWVpie5NXw2effYbTp08rzc3NRT0lEpj17UvXCZAXgVJJzySFgq7z++9pTD/4gHwTfH1x7sYNPG2SvNvb2/Mff/xxWyXCuXM0PwsWkFy/vJyekadO0bM1OxuYPh1CRASKiopQVFSEP//8E5988gls8/KQt3y5kOHpyQZu3EjHa2wkAztv7zanqaysxM6dO4XRo0ezbt26UdBvyxYKhjUZQ+LChTYt9U6ePInU1FTMnTsXzQHSH39suYfv3KHrbI2sLJqXNWuIgL98CYwYAXloKFbX1GD8+PHo1q3be6egvr4eW7ZsaQBwz9zcvJdMJuPLy8sLAEgEQRDL5fLg77///ubfT6Qaaqjx70JNttVQQw01/k2sXbv2xfjx463tW/VFjYqKUsTHx6fKZDKfFStWVIWFhQ3V1dX9MygoSKu6ulqwtrbmEhMTYWFhARcXF6plraujLIi/P9C3L2pqarB582YIgtBMEjmOA8/z+OKLL6jtDM+TmU5cHBGW6dNJerxpE2U5vbzIHGnGDCJTjY200RSLqX3UG4Q2JSUFV/bswcjz51EwcaLQ8/PPmX7HjhAEAenp6YiKihLKy8vx5ezZTCc+nohdU+1ueno6rvzxBz/41Cnuz6AgaBob8wsWLHivAWdmZib+3LULEpkMs5ydIYmPpzZD76oPBSirI5VSgKCsjKS4/v4UYABo81xTQxmlHTvoGhUK4MQJZPbpA+bvj1ITE2R98w1SU1KgKZNBWlaGOjMzYaa+PtOztaWMMUBj1thINdG//gpoaeHZs2c4c+ZM889ZunQpzdvr10TWGENWRgZyvvsO7Xr1EhSAEF5fz7Vv314xY8aMd3uj5OSQvLiykmTpAGJiYpRRUVEiMzMzeHp6Uv32m5gzh2SpgwdTZtvSktQQu3dTjeg7MtIAcPToUWVGRobI2NhYmDt3Lk2+qoY4MLCFdCYkkGw1Kqr5d70LMTEx/N0LF7ivFiyApEMHICMDaSEhwvMFC4SRo0dzALBnzx6lmZkZG/355xzWrqXgkK0tje+uXbQuIyJQcu8eGnr0wIOAAGHUjz8ynDhB8zFyJJEcPT0qr1AqaX2D6lXT09Nx9uxZhIaGwszMDKirA9erF65OmaJMUCpFhmVl6PnwIexycpD69ddK/6++ErVZY3/+SUGGYcPIoOrpU5QZGEAsFiMtLQ3h4eHN9f9t0NhIv6OmhkzyBgygWt0ZM0gKHhJCZC43FzHDh6MiNBQffPDBe8dShZe5uTh08CDGA7A7cgTc6NE0RvfuUZDi6FGSlDe1hNq4cSPv5+fHPD09297MCgU9F9zcgJQUNJiZobCyEh1dXCASiVBdXQ2xSISi0FAhGUC/PXuYqrUbXFyAsDDUDB2KgwcPKisqKkQ8z0MsFkOhUEBbWxtz5syBjkxGx09NbQnUtEZDA7Wy+uEHgONQW1uLjRs3wtnZGfn5+byNjQ1GT53K4fx5IqcJCWQa9+WXFFB4+ZJKaLZupWxyu3YonzMHj0eORKeLF2GUn4+IrVuVfXbv5swXLWJwdaVgYr9+9Htu3qSSk7t3qV5epaQAKKB09CiwZQvi4+OFq1evMqlUKsydO5dxHIf4n38W+KQk1rdPH/rOs2c0vyUlbS7x3r17uHTpEkbV1QlITYVtWBgzmjiRDBR37SJFTlVVm+daQ0MDNmzYgAEDBlCnhpoa+oyhIQUZBOHtYGNsLD2PoqOb5eO3T57kHQwNuepFiyCZMAF2YWFvf68V8vLyUFBQgJ49e0IkEiEvLw8SiQR1dXU4ffp0nVwuD/3+++9P/stFqoYaavyXoTZIU0MNNdT4NyEIgriurq6ZFAOAn5+fuKampsuTJ09erFu3LprjuGENDQ1a169fR0VFBdPU1ATP8yguLuZ79uxJu7CtW8HzPJ7q6uLunj0oLi6GtrY2/9FHH3EPHjxAbW2tctSoUaKGhgZy6wVoA9erV0vWOiODNr4FBZRtS0mhDE9uLmVEAwJoM/qOWlwA6Nq1K7ps2IDSJUvAffQRe3HlCrKDgxUpSqUYgGBlZYWQkBCSi0dFkZx73ToAQJcuXWA7aBCXdeGCEDB2LHN3d38v0eblchw5fBizDx2CaXAw2KxZwKxZfz/QH39MG2eAJJavXhFJXLyYXuvUibJ+z58DpaUor6vD2chIFBYWgsvLg15QELRFImG6szPjp03Dy8eP8SQlBYFLljCuXTsiMOPH0/gNHUok7Lffmk+vVDabqUNXVxfNtfHt2lEdrYMD7JYtg115OfDyJSs5c4YZTpokTFu27P3/j1W1MWsV8JZKpRwAFBcX4/Lly+8m2zt2tLRv8/GhYzg4UFbsPUQbAIKDg0Xr168XysrKWE5ODmxtbWlzHhxMpH/dOiK3Xl5EokpKKJv6DtTU1CA6KopbcOIEymbOxOnTp5Wd8/I4wdiY9fbxad7xu7m4iOKvXycjtchIMvwrKqLabiMjYPNmxAQHI2rnTng7O6P3p58y5OZStjQqijJ6gkDXu2BBm9+joaGBLl26QEdHB3v37m1+PWTSJAzR0xNlAEIFx7GnEyagXkODH3zypAi5uaT26NrU9fz6dQpaDRtGhIcxqIoxmlsztUZeHo1xnz50P02aREqL4cPJEM/cnIJBw4cDe/bgYU2NcLO4mHUoLMS2bdsU1lIpGz1mjAilpRRc+vZbui65HFi5Eh0cHeE+ebJg4ePDoKdHqoXffqMgmrMztQibPRuqNdLQ0MA5OTm1/Y3ffUfEbeFCyqQnJEArLg62rT6yefNmusb27dmMCxegoyJpjY3A48cAY9BjDHPnzhXxPI+ioiKkpaXh0aNHgqWlJa+joyOCjg7N6/vw9Cl5GTQFK3R1deHh4YHk5GSB4zju8ePHGF1RASxaRPLp27cpgNHQQAGkw4cpiLZmDQWmpFL8HhqqrKysFN0eMQIh/v7oY2Ulkjx4QM+3e/dIcn3gACkM9PSoZvrgQZrvcePoWVhZSX+aSh8qKythYGAg1NbWsps3b8LX1xdyd3f2qrZWQHw8g0xG8xAf3/b6iorgunIlkmxsBMWrV2goK2PKuDgyutuxg9QOn39OzyTVegOgpaUFNzc34ebNm8zHxwfiVavoufPXXzS3Y8a0LaEBKBgREwOcPg25Uok9JSUoLS3lYgC4+foqR+TliRATQ/Pe2puhFTp27IiOTT3hATKyUyEkJETn8OHDB3/66SdnhULx/YoVK9RZODXU+A9CXbOthhpqqPFvIioq6klmZubgnJwcsZOTk4ZYLAZjDI6OjmJHR0ctMzMzp759+4rr6uqE/Px8ZmxsDDs7O/j6+uLOnTusqKgI5eHhvG5NDYvt3p2/ff8+NDU1MWzYMDZ69GhmYGCAmzdvKrt06SKyt7en2ub3obiYyICDA204nZwowxsdTe+fP0/txA4dIkL5DnAcB21tbfyemwupmRk0EhI4y6oqPnjDBs7VzY01k0yplDZ/rbJ1ovXrYRoayszflD+3RmEhWM+e6OjvjxMuLkK6oyPr0KFDi7t1K8hkMty5cwc8z8MoLY3qllX1s927E8kZMwYQiZCeno4dhw6hy48/4kV8PEyHDsVjU1PIjI3R2NgIuaEh6vT0WP8PPwTr3RuGHh5wGDcOzMeH3MUdHIjceXoSkXqjv7CZmRlu3boFIyMjjB07FlKptKWuvbycHJY7d6ZSgC+/hOaYMai9eBE1jx+zDoMGvX88ZDIyumraILdv3549f/5cqKmpYQCQnZ2tbA7ItAbPE9GTyYikpKYSIfubTDQAeHh4sNu3b0OhULTIThkjkiiXUy08Y4C9PZGkN9yUVVizZg006uvhmJaG37W0BPtOnZjvxo2setEihWMrc7F2O3bA6ZdfwObNg8YPP1DWd98+oG9fKPX1kZ2WJpwvLWVdu3XDyJ9+gt6pU/RbKispK7hmDa1fc3Pq71xe3lLPCiKcDx8+FDp16sRsbW2FV69esUeM4WV6OgLCw+G3axfr7+uLzp6eTKSq7b98meZaKiU1SFAQneO77yjL2DQX25tcqRPi44WKZ89Y4549MA4Lg2jECMp2fvwxEbbPP6e537aNPBKys8mp3NgY3JgxzObYMd47MhIvfHxE42bM4DhTU2ob9/vvtH45jsa9Rw88DwzE1aIidqeqCjFOTvAYNgya48fT+nzwgAh6Xh4wbBgiIiPx6tUrDH3zXk5KovWRkEBkftq0NpnV+vp6PHr0CAYGBqiVSOA9bx4ku3ZRwOHTT2l8Wt3XjDHcvHlTiIuLYw0NDWzChAlc83Po7l3KGC9a1DarGhVFr8+f33zuxMRE4c6dO6yxsZEZVVUJn1y7Bt24OIa8PAoo2NrSPE+fThlxqZQCMs7OpNzIzkbnXr24Bw8eQMfQkC8RBOHOvXvstkgEZadOsBo6FKLgYLoP7t0js8AffmgJmhkaUrBx+XL6bd9+C8TE4HZZGev9119MoasrlD18yOQ3bvCpgsDktbV8z6+/5vDiBa3Z27cpGLVqFamQJkyA+O5d9Fi+nFkGBTH5zp0wmjkTEi8vmoOQEBr7Dh1aDASb4OjoyGJiYmBkZIQOHTvSOjA1peCoszOgMiB8EwcOoCwpCTFNzv7jxo3DgNBQjhs9mrLfX31Fz3YNjWb1wz+Bnp4eXF1dNTIyMjzlcvmXsbGxy2JjY7+KjIwM9/PzK/nXR1BDDTX+DmqyrYYaaqjxb8LX1zcrMjJye21trV1iYqKDnp6ehomJCTiOg56eHszNzWFoaIhu3boxjuPg5+cHT09P7N69GzzPQygsFDr99RcXq6ODTLEYoaGhzNfXl5mpWgABuHnzJry9vdu6Q1dVAYWFVKc8bx5tUhcupGzKzJm08R09mjIkKSlk6DR2LJCcTBnLwYMp6+PhQd9lrHmz3FRPrLxVXMxV6+mh+9277FZkJKLT03mPfv0YY4w2tHV1lAmzsKDN/cGDtLl8B3FGUhLJn4ODAUtLXGRMUVxRIaqsrMTLly+Ft2SwADIyMnDhwgU8fvwY/VavRqaTE56VlqK2thaGHTpALBJBuW8fWGAgduzYAQB48OABsioqBN1hw9jIxYvRPyoKHUeNwqPUVEgkEqFf//6seSMbFETy39hYqqm8cIHGbuVKkhTzPNXQ9+oFducObKqrEV9aivJz51ApCIKtnR1DTQ2ZktXV0SZ+yxYiWe3bIyczk7ffsYOL0teHSadO4Hke5eXlUCgU0FJloC9coHELDW2+7u7du7OsrCzU1NTAzMyMubi4tIwNz9N8x8SQvNbIiLJoz59TjeioUW3I6JsQi8UoKiriU1JSWGNjI9+5c2c6dteuUNU848MPqWZ7xQrauL/jeO3bt4e5lhaq+vVD78BA1ruigmlVV6Pjd9+1sDqeh3jIEPyRkyO4rFjBxGfOEJFYsQLF3bpha14enmhrs1m//grbPn2g7+RE6yM3l4IIPXqQodiyZSQvLioCRo6E0twcNVZWuHDhgnDt2jUmkUiYv78/PD09WWxsLACg0sAAIxhjmi4uRHZU6NqVxqm6mgIjBw7Q+g8MpGvt3r3ZAMtWVxc9u3fH4EWLmF5KinDe0ZFVT5mCLv37kwt+eTllgdeupXtp1Sq6tzQ0aBz374eejg7M3N2ZjpUVsxs+HDsMDNDx449h6OpKxFZbm36TgwPAGNpZWKBdu3ZITU2Fu7s77+zszJipKY2FoSFltocOhWL6dNwXBMHax0dwcnJqWR+HD1MQ4OlT+uwnn7QJwJSWlmL79u3Q09NThoaGcoMGDaISgMuX6Zni7U0mbK16eVdVVeH8+fNs8uTJ+PDDD1sM4wC69z08WkzDVBgxgtZq374AgNjYWP7mzZssVEeHDdy3D/GdO7PGujqmPXgwKtLTYTBnDgRBQPaiRdC2s4N43DiUlZVBLpdDaW+PVzEx0PX2xjaOg4Lj0L59e+Hly5esoaGBfWxlhceXLqHgzh04fPopWFAQ1bgPHUqqH09PCjqmpNC8X7tG90/37sCyZXD18UFZZiafzhjXta5OsImP51IcHJRTNm4UiR8+JJXH5s0UqLS3JxWNqysF/0aOBAoL0XD+PJILCtClTx8whYJIPkDKl169SHXyBmprayFfsQJ1hYVCkbs7a9euHdioUXSO92HYMDwyNUXnQ4cEpZOT4DFgANNWSfjd3Cijn5xMRN/a+u+P9QY0NTXh4eGh4eTkpNu7d2/t2tparaKiojw/P787//ggaqihxjuhrtlWQw011PgPICwsbLCWltYaAM5BQUFabUjSG1i1ahU4xrDAzg7FCQk4aWnJDx06lHNzc2vzOZ7nsXrVKnwREgLd1FRIDh+mjZ+TE20mf/iBNsoTJ7bZIAMgSWZ0NJFHI6Nm07Vm/Pwz1ZiqjMmysqjOsXdvQEsL+fn5kMlkSH3yRFl/+LDIMT0d3X75BZwqS7NhAxG03buJ9J8/T9Lm1sjPpz81NWTatGsXIJHgxx9/bJZmL1++HM/JyAk6OjoYM2YM2rVrh8OHDwsSiYQVFhbywsuXnNjSUqmtp8dqamqEmpoakbZcjil79+L09Oko09DAxIkT0blz5xZTttpaNPj7I9zSElnu7vyoMWOo125ryOVEktetI5JUW0tZurFjKYseFkbtvdasAQoKsKFrV8zYuhUNixejg7k5kcCMDNqA19dT9rFnTwBAYWEh4qOjYXPwIKrr6hAzZAhEGhqQyWRwcnISJk6c2LI+3qjTLCsrw/bt2zFkyBD4qMa7tJQ+5+1N8t0jRyi7rUJ4OAVXRo2i39XKlKk1UlJScOrUKQBoW49cXEzXffEiEbucHFoT78vML1tGc6tqwzZ4MG34AZICt2sHxMXh5tKlvJOdHdfhyRPULFuG2zzPx8fHc127dsWYMWMQP2yY4BUdzeR9+kA7JQV1fn7Y4uaGoKAg2NjYwOTwYeDhQ7BDh3B840bh5atXbOyZM0jw9kZqK1MoiUQCGxsbPj09nQMALy8vBEZGEtF5lzFZXR0Rr3XriKAOGEBzLhbTfbB6Na2N+/eBSZOQ+OWX6JKSgporV2AaGopcHx/UuLvDraoK3MCBFPS4eJH+XrWK5qJvX1r7iYnAyJGonzQJGhIJxEePUhazRw8KBsjlQP/+4Kursf74cdh27YqJEye+syuAMiQEspMnhSp7e5gtW8bYhAlEqHmeZPbdulFm+R3+B69evcKePXtgamrKf/7551yzUV9yMsngzcyAL75o/jzP81i1atXba6U1MjKoJ/bSpfQbXrygZw3HQRAE3PjrL95k9WrW2dub6QcGorqiApvT0lrWuyAAggBduRwfnjiB0+PHQ9K+Perr66FQKMBxHDiOg6SuDvViMTwSE2H50UdwOnoUJVu2wGrWLBTY2uKwvb2grK9nQ8eO5T09PVsuvqCAFAGLFlFgbc8eUhaoygAA7Nu3j3/9+jWbNGkSa+O+zvN0H/z0EwVmfH1pbaiwaxfwyy9I37ULF+7dE+afOMEwdSoFFgEqJ6isJKL+DlSPGIGb5ub8Aysrzs3SUvhg9mzGGhvfW3t94MABPi83l1tw9iz0Nm9+W26uwp07lNn+8UdgyZK3jN3+CRITE3Hjxo0/Fy9e/GFYWJgVAAsAz1esWFH5Xz6YGmr8fw412VZDDTXU+A8iLCzMS0ND41xAQIC5u7v7O3dNMpkM4itXwO3eTRv0NzaeCA8H4uKQExgIo+HD8cTDA0W2thjRoQNEX38NDSMjIod/h7Vrqf722DH6e+bMd3+O58mMqGNHIiYbNhAJOHECKpfpY8eO8ZKICIzq14+TVFWRiVFZGZGxHj2oNrOoiLKuANXY8jxtaM3MgOPH25wyJycHx44dg1wuR9euXfmUlBROtanmeR4GBgZCQ0ODMG3aNM64ogL4+GNwrWomeZ5HVlYWbEtKUHb1Kp717w8/P7+3+oPHx8UJVy9fZkt+/x2aCxe2bIIB2nR/+SVtpquqiGgeO0aEjIEmWwAAIABJREFU6w0ZZ1FRETIyMhAVFQWlUglDQ0N89dVXYNeu0XeLi2kMLl8mouXqCuXYsXjw4AFKkpOVngcPimIHD1am8bxIxhgCAwNb6rFHjaLMpsr5GEB2djYOHToEDw8PjBgxgpQBQ4bQ5y5fJtm4u/vbcykINO89ehBpVF1X6zGJj8fVq1cxbtw4dO/e/e21MHs2ESctLVJHXLjQYp7WGg8f0t+WlvT5rVtJEaDCtWvApk04ZGcn6A0ZwjyPHRPO2dszLXt73s7OjvPX1SUHcG1tyI4eBQCc+/BDpDXVt+rr6ytra2tFnXNyoFdcDHlIiJIxhidPnohcHj2CrVQK+5EjcSQ+Xqg1NWX19fX47LPP0L59eyQkJODatWtwZQx9jx+H8f374N4nq+V5mrNVq2jdurpS8MjHh4JZR44AISGQi8XYvW8fSk1NoaWtDalUypeWlnILN2+GBtBinpWSQkSX40hR0rUrSZk5Drt//ZWXNDRwXm5u6C6XE+lLTkbjo0doDAxE/ezZ0Kqqgt66dRDFxlIAoLaW7vURI5AZG4vYe/f4vvfvM5sVK5jmxIkU6Ll3jwIcjx5R8GPPnpZrUyiIjO/fjwgLC77g1CmuS1oadHbvhtuiRZTV/+gjIv0cR/JqJycia0ZGOJ6ZiczCQixZuvSd3QVw+zYFMxITKbM+fz5QXAw+MRFFs2cLF/38ENzYyHTmzGl2uBcEAefOneOfPn3Kzd+wAfmhoZCIxShRKgXtadNw7tw5ZmFhIYwcOZKlpaby/Tp04P5KTlb6fPaZyLCiAvFhYUL//HzGrV7dpgPDzp07UVJSAgMDA8HHx0fw8vLioFSSNP3nn0mxcfMmZYDXr4dy9GiUN7UevH79OgoKCpSff/55ixRAJqN1oalJQRcvL7rX7e1JWRQYCFhZISorCw3h4Xzg999zUBnNAfT8jIggkv8mYmNJGWBvj/LychzYvVvoLQiCz9Kl7/W72LZtm6Cnp8dCQ0NJVRQWRkGh963rX38lFcbIkbSuVb4X/wAxMTFCbGxsrkgkUgqCYKGnpyerqqqScBy3QyaTLV6xYgX/jw+mhhr/n0NNttVQQw01/osICwuzBjAEgBRAAoCnrSP+YWFho6ysrA5NmzbN8J0HqKwkieeWLbRR5jgy1pk7lzIR+flAQQGKlyzBb1u3wtzFRVAqlfzLly9FCoUCLi4uyjFjxogUCgWuXLmiZIwhKChIxBijzfnQoURwra2JQKrqHv8pLlwAdu2CcOkS6oOCcMPSEg86dsTEvn3hdOkSEblVq6jWcNIkCg7MmUNGPrW1VBO5dClJZd8TFNi/fz+fn5/fZmO5aNEilJSUID09HQMGDKDNfVIS1dOeO/f2QUpL6bxr1lDN5xuIi4vDtWvXYJWbi0FBQeikq0sb6FevKLN5/TpdK8cRSVBl3C5eJIf1K1d4Y2NjLiMjAyKRCBMmTEBhYSGiIyKwfPFiynRNmUJS1Z07aawXLwYcHBCuVCrr795lyqFDufLyct4/MpKzrK+H+OBBiFuT12vXiMy2Is+qDKRGYyNmFxdDunEjqQ/mz6f2Rf/A2RrbttFnb9ygoEgrk6Y1a9YI/v7+zNPT8+3vzZhBKomVK0m1EBDwtioCoOucO5dIh4pwAURkJBKa+wMHUBkejsg+fZDj66sMCgoSOVpZUXBn3z76jWvX0vdsbKBwc0NJWRn0nJygp6eHyspKPHr4EBXPn+NJcTF4vmV/P3fuXGROnMjbPXjARf38s9DXz49ZNMl1BUHA06dPce7cOXAKBRZGREBr3z6SDr8PHTrQelqyhNpNvWPd1tfXQyaTUT/pK1eQ8McffKO5Odd//Hi6v6qqaOyePSMSnJtLruC1tcCVK7gTECBcz8tjYrEYCxYsQHp6Om7evKksLy8XqfZiy5YuhUZdHRF+fX3KUmZkIN/NDTVhYdATiWDh7w/uyBFSEqjk7DIZKStevKBrSE2l9x0dUbpzJ4y6dcO2Tz5BiIcH9CMiwO3fT88IBwcioK9f0zEmT6bryM4GLl7Ei+xslEilcPf3BysvpyBEt25E3FqXGNTXEyldtQp8bS2iOY5nT5/Cfd8+zuBN1U3THBUVFcG4tBQSqZSI4cyZQMeOEORysAMHiNxGRgJr10LIyUF9eDgKra1x7vRpjLxwAVpXrsD6jcBYVVUVoqKikJSUhKVLl5I6IDSUAoGqZ+APPwCPH+O1UolDDg7Qd3BASUkJ5HI5Fi5cSN4YgkA1+OfPUwZ7504KZEycSL916FB6fkskiFi9Gn22boVuSgo9x1XIy6Pf/8knbS+e52ktrl7d3KavJCoKV377DRP37YPGO9aeXC5HbGwsEhIShCVLljC8eEH35r177yzdSU5Oho2NDcn+Fyyg60hIoIDDv/B2UJ0vJSUFBgYGsLGxAWMM1dXVOHr0aN3r16+/Wb58+S//8iBqqKEGADXZVkMNNdRoRlhYmCGAmhUrVijf9f7KlSuDNTQ0tgDQt7OzU+rq6mq8ePGioby8XEtDQyOroaHhFIBVHMctcHJy+n78+PFvO5odOPB/2HvPqKqu7nt47nMbvfcuYAGkKCJdUawotlgTNUVjiy0meUxiEoMaE02MSTR2o8beu2JBpClFEERAEWlSBUSudLjn/D8sqqJJfu+HdzxP7hzDoXLvPfecffbZ7LnmXGuRoiqT0UZsxQqyHn/yCVBcDKFnT9TV10Mmk0EkEqG6uhoqKioQi8W4dOmSkJSUxEQiETQ1NYWKigrGcRwkEgn+85//AAC4pCSykB86RMTxq6+IAHXRN/l1qKmpQXJyshAfH4/+58+javBgBDg4MLVWJfjaNdqwjhxJ35GWRqRp3jyylN65Q/bj17XyArBu3TrY2tpCX18ft27dgkKhwJw5c2BgYNB5s1leTupcx9zbjjh6lOyzAQGvvCSXy5GZmYmCggI8fPhQ+DQxkYmSktqLyWlptbsK9u8HNDUh9/FB9PffK5L19UXOrq7geV5hZ2cn6t27NwDgwr596Ld8OTBrFkzWrKHPR0fT57dvB9BiIV+8GMEJCRBlZxMh0tKi152ciIh1JLD79gGTJ3dqobRnyxao37yJifn54B4/JiXMzu6vHQ0v484dyk9v+XzkrVuIiYnBkiVLui6419hIQYeCApqXI0e29/huhSBQAau0NFINQ0LarapxcUQiPDwo4HLxIvjAQHCtub7u7uSKMDMjVXzPnvbCd3l5lMZw4QK9D4BQVwdoaGDt8uVolsnAcRzMzMwU48aNE23evBmShga4pqTAv6gIWufPd8qP3bx5MyoqKrBSTY3mUUv1/DY0NNA53LxJSn5+PhG82bMpkPBSYatOMDeHvKYG6aGh8PLyav95WdmrCmJdHbBzJxr8/ICPP8ZZExPke3igrq4OgYGBsLGxgWkLIe3KOp6QkIBr167B19dX8PDwYGoSCY1RfDyR+6++onzk336jQFdeHhG8xkY8ff4cW7cSL+I47tWe3J99RvPz11/pvjY1depYsCokBNYaGnh31ChyM6SkkJpcWkr2c2Njur6TJ4n8mZvjhViMo01N6D1pErwMDckp0mp119bubJPOyqLAjosLzYmDB6kbgJcX1Q0YPpzmXstneJ7HL59+ihGhoYhcuFCYt2DBKwPG8zw2bNjAm5mZwdXVlXOKjgaTy8lKDgDvvIOGzz/Hs3PnkHjrFmo1NVHs7S1oampi+vTp1HHhwAFy95w/T989ejTNj2fPUJ+cjOKAAGhaW8Pg+HEc2LED4qoqYco337BO9y8igtal+vrOBcvq6igA1lqQEEDzN98g/8ABqN++DeMuugC0Bo80NDSEZcuWtX/Jgwf0PR06OsjlcmzcuBFGRkbC/Pnz6b1Pn1Jw5cqV9lSk/wOuX7+uiI2NXf/VV199+X86gBJK/AuhbP2lhBJK/OsREhIyWCqV7hOJRCaCIOCHH34Ib2ho+GjlypWPWt+zZs2a6WpqatunTp2qZm5u3nFTLG1ubsaRI0d6ZWdnfwPgC6lUqhg0aFB7H6amJtrof/45kdGaGlKFOI5UzcBAIi86Orh+7Rp/69YtDgA8PT2FQYMGsVb75siRI9mTJ094uVzOKioqWL9+/SAWixEbG4sff/wRo5884a3s7TmNQ4fAWvqxSsPCXltVuiN4nkdWVhbi4+MVubm5Ih0dHd7X11fUb8kSsmdXV5MaKZFQsMDDgxSwkyeJlDQ1EYlUUXl9LmEL6uvr0djYiPHjx6O5uRlJSUlCTU0N27FjBwIDA9vylBljROILCmjj2xVGjiSF+SWiBQBaWlro168f+vbti1xnZ/ZEJhNs1q9n2LyZlNg9e9qv38oKD/bvx9n797H4wAHR4O++g0pwMACQDNTcDP7rr9FoYoKIgQPxQCzG9Oxs2NnZEWHrUBX68uXLvPrEiUx09CiDXE7ndfs25cP+/jv1sQ4Nbbdnr1hB7wkMpP9v3Yop33yDDfPn44i/P6b9+CPY8+f/nGgDpLpnZpJKam0N3tubH7B0KffayvZSKfD8OZ3r3btEvBITO1doVyho856VRSSvf39Sq8eMofkNkJWZMWDFCnAKBRXeMjAgV4G5OanhL8PamhS47t1JsfPwwN4jR4Snn37KBFVVgOfh6uqKMWPGiPbv3y+IRCLWJJPhnrc3AtTV21uirVkDqKujW7duqKioQOmMGTAuKqLc7D//pO86fJgKsm3ZQqTnzh0iQRoaRA6TkynY8PI4LVhApOvePdxLTeWjb95kPXr0YHqtKq+BAQVU9u2jsQcoiLJ4MWQKBYRZs2B8+rSgFhHBjDU14TBvHtT09bsk2Y2Njdi5cyeeP3+Ot956C52KoY0fTznhjo4UHAgOpoDa5MmApSXKR41C6cOHOB0eDsYYNDU14ezs3H5wQWgns15eFPBwc6OgSUvQCADc+vQR7t69y+RmZtDq1Yss562fl8uJ6E2cSPc6MRHYsQOaM2ZgnI4OLh06BMeUFGgtXEj3OzSUlHpXV5oPM2dS0K61gKKzMz3LIhEdqwtwHAcVe3v++JQp3EwdHQZLS1LhO1jcOY7Dhx9+yF2+fBmXLl3iE3JyOP9nz2Akl0NdXR01d+9ix/Hj4DU0eJMePZj/5cssQVUVk3/7jUEqpeJ558/TPWSMyPLCheTgkcmQmJ6O+KFDYVlRAW9fX2GSlRXb4OmJlJQUuHVsveXvD3h7ozQjA8eiovjAwEDO0cGB5kXr89GCZwsX4oiKCr58Q7s9QRDw4sULFhISgsmTJ8PBwYGKvZ040Ua2eZ7H4cOHBZlMhvLyclZQUAALCwtK51m0iNYfNzd6bv8P60lOTk61QqFICwkJYcoWYUoo8fegrEauhBJK/KuxatWqYRKJ5NykSZP0x40bx3l5eXESicT2yZMnM27cuLEzICCgDgBu3769esiQIb179uz5ysaY4ziIxWI+LS2NWVpacvPnz5doaGiQwhQTQ/nQc+bQ5njwYKoevWULbdxNTIgI3b0L/PwzcqysBJmODvP09ERcXJzw4MEDwdLSkt24cYN/8uQJy8zMZOPGjWMTJ05Ejx49YGZmhvv37/N2trbM9uef2RU1NZy5dw+RkZGIiopCmpYWIp8+RYNC0dytW7dXpOZnz54hOjqaP3nyJMvMzOSNjY1F06ZNg7+/P9cpqCCVEgkCaEPv6kqE5e5d2uyuXk35wV3ldb6EuLg4oby8nPfx8eEqKytx69YtBhC5lkgkiI2NFSIjI6Gtrc0Me/cmomdm1vXBZDJSE8vL24qTdcLu3YCJCeJv3ECPe/eY7LffiFBUVxOpKisDdHWx5+LFZvfdu7leGzfC8KuvIPbwIGJgZQVUVOBFbCwq1q9Hkq0t32PGDFZWXi7cuXOHFRYWCtKMDN4gOppDcDCys7MRFxeHDz74gInEYgo+zJxJYzd8OAVZWhRflJURQVq6lFTr9HSylfftC8HLC4OWLkWegYFwYexYlMlkrEePHl2Ssr+EigrAcXjSowdinj5lgYWFUDl5st2Z8DKsrEjJy8khkhEe3qY0A6AgxdKlNG9nzKD3KxQUeDl7lgjt+++Ton76NF3roUP0vpMnSU19HczNaRxGj0Zit26KxxUVXK+YGFirqQlvffEFc3Z2BmMM+vr6LDs7W9G7d2+urqFBuF5ZyXz69YPo+HEigqqq0LS2RmJiIu6npaFeXR0aR4+iTE0NcbduwW75crAFC2jOMEZukPBwsgkHBlIwyc2N7lGra0KhIBWW44D582Fpbc2qq6uF0NBQwdXVlRRRxihNxMPj1Z72HAfm6gqbqVPZvbt3hd4pKezwo0fgN2zAlaIiRaNEwll0CBidPn0aeXl5mDZtGnq87ExpaKCggokJFeLy86NUitpa4NAhXFi1SvBbupQ9nz8fjr16CU8KClhJSQlfVVXF9PT0oLZ1K1nVZ89uz7WfMYNIfMucKCgoQFhYGNzc3HgnJyeu09xrbKQ54ONDz/zVqxRMvHED/O7d+Hn0aFQaGCB74EDBY+5chnHjKMiko0NzasIEmvP+/pTnvmgR/VxXl+bNa+Y5z/O4cuUKEwQB9v7+MHJwaHdLdHCGqKiowNnZGb6+vkyQyfA8IoI/WljIkuLiBKPMTDitWsWCx4xhriNGsNOM8WaFhazbyZMMdXVUEG3XLiKoCgUQGIjaK1fw9OFDobiykqWWlkJwchJGL13KarZvZ9LiYmh+9hm7cuUK+vbtC2mrC4QxKPbswflHj4RaExPu3r17gnvPnkxSXEydGzpc4/PAQEFFVVWwHT26ywvft28fpFJpW2HJ9PR0qKqqQubjA9W5c1G0bh3Cb9zgryQn48WLF3jvvfdYRkYGr6enx8zNzekgcjkFMUaPpnvfVRrJX4DjOEleXt6o5ubmNbdv355+48aNfQEBAQ3/+EBKKPEvgpJsK6GEEv9afPfdd59KpdLtb7/9tqqtrS0YYxCLxbCysmLPnj1jz549c71x48aJgIAAPjw8vKeBgYG/ra1tl97ooqIilpWVhREjRjCjjAyykv70E5GNWbNISR0/nmyYb79NSpSGBm0s9fVJDcvIQJ6NDQIWLWK23bvDYMgQFhsfz5Lu3oVEImGZmZkwMDAQRowY0bYhk0gk8HZ0ZA7Hj0N68CCkzs5obGzkg4ODmYGBAcZ+/DESfH3xKC+P43keBgYGYIzh/v37OHv2LB8ZGckaGxv5YcOGcWPHjmUODg6QdVVMKjeX7NKtuYt2dkQ6mpuJWB06RAr3m3qBg/Je9+/fz4YPH86ZmppCXV0dXl5e8PDwQHx8PMrLy/HixQvW0NDASkpKBM+nTxns7bsu0tUKf3+yMnt702a+Iz76CFVpaYiysBCkU6eyU8nJzdFZWcLNxkauZu9e3vTTT9k5S0sht7RU5G9mBgNVVSJZDx60Ow6mTsUld3fFfX9/NueTTzhLS0t4eHiwqqoqZGRkMK3ycmZXVsaECRNw+PBh3sHBgevV0abZap01Nib1TqEgwnr4MOW85+bSNaSlkfI7ciRE3t5gWVkwWb2axd+7xwoKCuDh4dG+kf+HqK+vx67z5wWv0aPR3cKCIT+f8sS3biVl82Vyo61N59bQQHPYyak94FFTQ/blpCTKH28t0lRU1K52jxlD+eKt7YxGjSL76pvuYytMTaGYOBHHTpzgDDMyYJ2fDydXV6YzYkRbsEFLSwuenp7c1atXFRUVFRxjDH7Dh0M0dSpZZAcOhGZBASo8PfmioiL2pKgIzzgOfX75BQ95HsU//oi84mJYWlqC4zjk2ttDZfp0PM7NhVQqRXVNDSLEYnQbMABcdja5Q9auJWL43nsAKDhkb2/PCgsLhbt37/J9+/altcHfnwrZqau/SrhBz8CZhASW6uQEoakJdg8fQqW4mCu9exdqUin2X70qhIaGsrKyMtja2vKDBg3qfHN4no5//z4Fk3ie3CSbNoF/9108ksuRmpvL+h87Bsf+/WHt48O8J0yAfkAAS7t/nxe+/po9kMkElcWLmVZHN0hLITaMHIn7jx/j6NGjcHNzQ1BQENep+GBREVnP168nO/vFi0R0RSLA0RHn6+sVjdra7IMNG1hjr17MrmNFe8YoQNb6x8yM1sO1a9GwejVq1dUhi4ykSvBubrSOdpibjDFkZ2fzNTU1rKikRPCcN4/h+nV69v/zny5TV0zt7GB18iTrNW8ezMzMWE/GmMHEiW2vN/A8blRUMO8ePSAODaUA2MSJFBg6cABpY8fifEMDLNTUmMLbG6ZVVbzN6NGc9ejRaDpwAH9yHNwzM8FnZEDk6gojIyNUV1cjIiICetu2QSSVYtyWLSw8LIw5HzwIjbVrX8mzvn/8uGA+Zgyn10VtjebmZsTGxgoNDQ0MAAwMDMBxHF9cXMxHRkZy0dHR6LF3L5hCAbNJk9iECRNYfX09YmNjmaOjI6UoXL5MY3riBOWcR0e3r2//AKamppyfn5/E398fMTExagqFYm9AQEDlPzqIEkr8y6C0kSuhhBL/SqxevXquhoZGyAcffKCq3aGibSuCgoJU5HL54IKCgrCQkJDRAEqqqqoaAHTJJs1MTKBXUABrKyvabO/e3TlHVBBIObt0ifIdAdpkxsUREV+/HvjpJ3A3b7Lod99FUN++6H72LL44eBA1Z89CV1sbUdnZSE1NFQB03nzv2wckJ0NFRwe9dXXRu3dvDgBsLCwABwfM++QTxMbHIyYmBlFRUZBIJFBRUVE4OzuL3n//fUil0q4r5iQlUX62vT0FDvz8qEiXkREV0hoxgnKfV6yg65gyhVShjjmsL4HjODDGUFXV3kFGRUUFKioqWLJkCW7duiXExcUxJycnQSKR0AbawODNOYYcR0GLX3+lokXNzaSQXrgAzJoFzV9+AaZOxeWnTwFA3KNHD2HIkCGoqqrisubPR/O5c5gSHg7J6tXAo5bMgfJyIlUeHsCePchJS+MCAwPbxl0sFmP8+PFQKBR8Vng4x794Ae3YWNTU1GD48OFdn2er1fzmTSInyclEVvPyiKgcPkwWbmNjaiG1fz/UmppQX18PANDoqof5XyA+Ph7Jycm8mpoazM3N4eXlxYGxdofFnj0U+FEogJ4928mKSESvNTdTekDHZ0QuJ6X+xQtybSQnAzt3Up7r3r0UeNHSonkyaBC5Hv4OyW5BVlYWDh48CMvKSgRfuIC4PXug24EcdUTv3r1F0dHRYIxh3bp14HkeVlZW/PTYWE5SWoq3fvmFG6elhfz+/WG5cCEKXryAf0wMdrm6opHjEBUVBUEQMOj6deQDCH8pBeLO5ctYcPYsZJWVSPP1hW15OQw75GQzxqCrqwu5XN75xLZtI6t9a4X+DlBVVcWMGTOQkZHB+/j4cJJvv0ViYqLC6JtvRJqzZkE2ezazevoUdR4emDhx4qvscf9+sjmfOEH/d3QE3n8fvJMT7n/6qaD66BHelcsZt2YNvX7mDDhnZzj89BMcTp7kStTVhX3q6qzh8WNAWxtmZmaUKsIYIJEg6coVhObkwMbGBikpKaxHjx6wb+2lfekSqeGRkZRO4ur6SqDmkYYGFzBgANOTSBA4YQIF6BoaaO3ogKNHj/L5+fkcAEwKD8ed6mo8dHGBq5MTb+jsjNyrV4VRgweL0t3dhZghQ8AzBp7nWUNDA6dQKDBw4ED64iFDKPCgUNAz31UBQTU1GD1+DCNXVwpqdYC+vr5gIZUKD//8kxN8feFcVwdu4ULKif/xR1xLSVHIXF1F1rq6kE2ZAnh7c2hoAMaMgVH//pDExwv3rl9n/R48QGVjIzZs2CDU1dUxPT09wWjlStbD2poxxuBnaChINm9mP+/dy9t2784NGzYMampqOHnypFA6cCB796XzjoyMRHR0NGQyGZqampitrS2fnZ3NlZeXY8WKFZxYLIZcLsezZ89g9eWX6MEYw/r1gKcndHV1IRaLce/ePcHNzY2x8PD29J6gIEqbePy4y8KSf4UtW7YIFRUVDABbuXJl9j8+gBJK/MugLJCmhBJK/OsQEhKiJRaLi+bMmaNu+IZ2KDzP48KFCw1paWlPAUiCg4NNWgtltUEuJyJ96hRqFi2CUFxMpKjjBrSykgjrvXuU48jzlD/aitRUygtMTESMszPS09P5Dz/8kAPPkwLh4QHY2aHAxwdXHRyED6ZPZ2jtGR0aSnZoS8tONkoAZJUODSWVBsDhw4eRmZkJFRUVfPrppxC9XJW2sZHyUY8dozxfAwM67pgxbb1zAVDV5okTyQoaFkYFjQCyzJ87R9bSN1TM3rdvX7OFhYU4sDVH+SWEtLQcAwDG8zA0NMS8jz56s4W6spLOo6yMCPq+fRQs+PxzUtD09LBz504UFRVh5MiRQv/+/dsPVlEBzJ9PVai3bKGCU+vX03hKJGg4cAAb58wRli5dylRUVF756sbr13F//XrhvK8vCwgIwMCBA19/nh0RFgZMmkSk9PPPaX6EhNAmv2VsBEFATEwMHxYWxmlra/NLly59fdW5l1BWVoYdO3aA4zg0NjZi7ty5MOmq0JxCQfNz+nTKoe04L/bsIQKdk0MBFVNTUhwtLekeFxfT+LYqglIpKZ2VlUQ2/0ZaQUfU1tbizz//pErVenqCkYYGm5iWBtH27cCTJ6iTySCVStHQ0IDKykpoampi48aNAIDZs2ejvLwcZ86cgUgkgouLi6JPXZ3I4scfwaqqyJHh7U1W68WLkfP228gBEBUVhSkvXkAqk8FkxQowxsAYQ01NDdR69QKrrsbGpUvhHxWFZ2ZmQlVwMD9t2jSRWCxGVVUVNm/ejBkzZsDKyqr9QgShvfXW69qOvYQdO3YIxYWFzL64GO+kplLRRCsrciC0jmNqKs3vU6deed6frl4NtmkTdFauhCQggHLQra1JHRYEIrtyOepWrUKlhwcOv/MOqjU0IBaLIZFIeJFIBFdXVy725k28M2sWunXr1lacbVw3zJDQAAAgAElEQVS3boJjWBjDjz9ScGXOHAoevlQnAQCOHTumyMzMFDk4OAjjx49n3JYtNBc+/ZRqL3z4IQqLirBr1y4AgL29PUYOHAjNnBy8sLXFodOnFZqamszIyIhTy82FtKwM1nl50M7MRNHXX+NwTAwEjoOPjw8/ZMiQdnt7aChVHi8sfNWpceMGjV3v3hTI6tjdoK4OisOHEXHoEBL69cNbFy8KTFubWT9/jox338XZhgYsWbIEmn36UPBx/XpyKN26BQA4cOCAUFFRAV9fX6ZYuBCa9fXI++47PnDoUE46diyR2sxM8HfvolhLC2mPH/NPnjxBQUEBp6+vr6gvKhJ9sm4dWGNjp2dv27ZtQmlpKfPw8ODt7e05Ozs7fPfdd3B3d8eoUaNenUANDeRAOXUKcHHB5cuXER8Xh0WpqdD74Yf2NKDKyvbaAz///I/7cIeEhEAqlUIQhEdffvnl36+8qYQS/1IolW0llFDiXwepVLqxZ8+e3JuINkAqbHBwsMzBwcFSEAR0b92sAERkNTQo53PyZOC777AxPx+LBaFto1dfX487d+5A0dgIYcIEQaesjLlFRxN57QhnZwiPH0P4809oT5sGXioVWk6Aeu0CQH4+Hp0+DdMTJ5hi4ECI7tyhTdUPPxA57qraeE4ObSxbyPaUKVMQGhqKu3fv4sCBA0JAQACz1tWlAmTbt5Pa8dlnlMP5xRdoI/Qd8dNPdF7h4aQCz5rV/pqvLynKq1YRcXy58nELRCIRa25ufu24z5w5EzKZDGoA1B0dsf6TT3D69Gk4OzvD1NS0a4VXV5eIYn4+Kew7d9KG+IcfiLB8+y0GGRnBZsECrOc45rB6NTRramgT7uhI6tv27WSrvnKFNsgg8redMYzu1o29SrMJUnNzdA8KYqiqQlFRkQKtRdVeB0EgwjdjBn3vtGk0poJAhdQ2baJxtrAA8/WFn44OV2Zjw+dWVPxtos3zPI4dO6awtbVFr169WEFBgWBiYtL1eYlEFFypraW82fR0Ut8ZoyJi+flEYFJSiGz/+iuRmuBgCg7Mnk3uhtac/bNnKeDyD/LLX8jluJucDPGmTWg2MoKxQoG5u3czlpdH96e6GoKvL/b6+OCDP//ExmXLoNXcLDQAjGlqQhAENDQ0wNXVFc7OzsjNzcWLTz/l8quqhIglS4R3tLU5Nn063VtLS8DUFN3270e3PXswePBgukaZrD0NIioKKnfuAMePQ+jWDZ9bWQFbt+J5bCw7UVnJHT58mJ8xYwZ3/fp1hbm5ObOysup8bxij51xbm5T+v4GpU6eyjRs3IsvcHD9268bP9/PjNMaNI6vvvn2k3v7nP1RZvgPRFgQBmZmZONXcjAmBgYLhkCEMoaFk6b9zh+ZUUhKR5GPHoKKqimYnJ6FaVZV9qlCgXl8fVUFB3OHDh1G4fz8+P3oU4hZV3KNfPxgqFHi0Zg1EDQ2CUXU101VVpRzrLog2AIwePVpUWFiIq1ev4vfff1e8/fbbotTUVKgdPgzHFStw18IC2TExAqRSBpBLRM/CApg/H3oeHlj4zTevztPGRuDYMRSXlWHxr7/itrc3bvM8N2jQoPb+3yNG0DzNyQF+/JGe5VYYGRHhV1WledsKngc2b0ZtRgbyLC0x28MDlU1N7LaTE3/j9m2ux4ULmOjhIWhKJAw7dlDgoraWApFyOaClhalTp7JLly7h0qVLcJs6VfDkeebo6sqhro4KDS5aBEREgJs1C+bp6TC3s+MA4Pnz53jw4IEoPzcXu7/5RpgtErU9MLW1tSgrK2M2NjZ8UFAQBwANDQ3gOA5dObEA0PzNyqLfTZs3Y+TChcg/cwa1cXHQsbBA2wTV0aFznzqV5oaHxxs7R7TOsaysLMTFxfEAODc3NyQnJ3MhISEWK1euLHjjh5VQ4l8OJdlWQgkl/lUICQnxlMlk04KCglT/+t1kE+1EspubKZ+3Xz8qypOc3F5gqIMqIZfLcfjwYehdv46eJSVCwRdfIOLsWZiWl8O4pWDNrl27+NLSUk6hUEAQBKgEBMAqPh4BBQUcZs3qrAqKxfAfPx6bCgqEVBcXNqaoCL0OHCBLYHMzFXiaP79zzrSREeVTt4DjOAQFBcG4uRkVBw6w5Bs3BL1Hj5imtzcRpRUr6DNvQkICETCAPnPwIOUht8LGhja6ly6R+vX774BEgqdPn6K+vh4KhQIVFRUi3S5yWVvRrVs3+kdlJTB2LIYOH46IiAghNTWVAUDv3r2F4OBg9kr+8rvvkvI0ahTlW+rp0eb6zh3AwAD2EgmQlASP4mLsr6tD8PDhsATIXqmrS5v106eBRYsgxMYitbAQiXK5oG5uzvdet06E+Hga55eRlwfNuDgs+v13bNmyRVRSUgJjY+Oulfjycgp+fP01zaPffyeSvXAhBUZ69KAgTk0NEaTaWihCQ6FRWcm9fe8eWYdXrSKF0NaWLPulpTQfm5pQxvM4HhGBKrlc0NPTY+PHj+dUVFTQp6vicR0hkRAxXL2aFOzMTAq87N5NSn/fvrRB19entIcJEyiIERRE77l9m1IL/vOfV1tfdURWFj0neno0f06ehOKbb9C0fz/ClyzBuwkJKPbxwfD168Hc3GhsWgJLzMQEY+fMQa2qKpplMsz65RcGxvDLxx/jkz/+gKx/f8DICFxqKmz79gWCg1lZeTmuZ2ezX7S0hAnnzjHrhgayPm/b1u4oWLuWVORu3Ui1BCjFQxCAgoL2nI0FC6C7YAHGDRzInpaXs3VFRUJzc7NobBcujubmZmTNmYOYpCRheEEBs3gNMe0ILS0tvPvuu9i3bx9qGxu5BokEGrdvE0n8+WeqYL1/PzkRAOTn5yMiIoLPzs7mxGIxLGxtm3uGhIgxYgSprhUV1Ad91iy6lqtXAYkEV69caX48fjwnk8uh/uwZUxeLoW9ggKVyOarWrIF45cr2k5o0CTalpdA4fZrt2blTGDd4MFL8/GCyfLnQ6+V0lhaoqamhe/fu6NatG9uzZw/7/fffYWRkxCsUCtz49FNOuHsXi379laWOH48kDw+Fv78/LZwbNhBRbG5+1REhlQLTp8MmPx+Hp02DrKEB81JTIQ4OpkCEnh59hjEar9xcuubWZ9DOjpT+0lL6eStiYoDERGh+9RXGjBiBeC0tfuSvv3L2AFc5fjwa5XIYr1/PMGoU5bPv308BvG3baK156y2IxWJ4enoiPT1d6BUUxNCjBz0zp04B169TYKpbNyq61mHN0tHRgZeXF/rV1SH00CHW1NSE2tpaHDx4kOc4juN5HgMGDOjEghUKBUpKSt48ke7epYBY794YbmGBP2bNwtcdHTmMkcvBwoLWnKNH26vMd4FTp04pUlNTW3+5cUFBQejXrx+kUqllbGxs1vfff3+wsbFxpZJ0K6FE11CSbSWUUOJ/CiEhIZ4qKirfMsb4urq6EwAOrly5srHltX4SieTquHHjVLuyA78WrZu2ceNow9aqGr1UDIwxhr179zZLpVKutLSUE4vFGODiwjs4OHA9AwORlJSEu3I51LOz4e/sjMrKSm78+PGwtbWFVCptb7E1fTrDnTtECjooWGKxGG+99Ra7cOGC8GLhQlbU1CSY7drFEBlJBXBmzCDCOX06WXyzs9sUWuTl0Wa7rg7uR4+C9/PDcU1NtsncHIOCgwU3Nzem+rINvSNiYqiP75EjNBaNjURqX7bVA0TIpkwB5HJUDxuGc9OnKx4VFIjU1dV5xhgEQcArSmBXaGoCli1Df1dX9O/fn/E8j/j4eFy5coWJRCJh6NChTF1dnci/oSEp2xoaFADp0aN9o92xp7WzM4Y6O6Po4UOkr10LvX79oF5fT/d19Gjg+HFUW1sje/ZswSQxkck//5yf9eGHIkybRsrRvXvUE7gjxGJATw96enowNzdv3r59uxgAFi9ejE5BhchIGq++fdvPads2Coj89htteHv1IlfBjz+2BXEU06fj1vffg1u6FMYODridmgpbT08YGxhQwCclBdDRQd3y5ZA8eQKDoUPxdl4e0+rfn3Fbt9J7xo8n0mpmRrn3tbVENl8mNQYGdF8rK9uLe+3YQfMpNJQIREUFKe+//kqE286OLNqurvT+VjLz7bfUez0ujgI5aWl0jT4+VM9AXx/gecQOGYJINTUMHDgQNitXwqb1XObNaz8vLS0gMBAmS5eiTCpFv6QkIfncOURfu8ZkDQ3ga2tpDowaReM8cSLA8zDU18fXmzfjwpo1LPbePYX1tGkibNhA1759O/UQv3QJ+PJLGu+PP6YgR1paexDtJWjt3Yvay5cxw8aGXUlIEAoLC1lreklBQQHi4+MVqampIrFYDKeHD9mLjz8WcPz4X8r8lZWVSEhIaKvJoK+vTy9UV5MFevt2ZIrFMLCyQry3N+54esLM0pKbM2cOjI2NwXEc3cydOymlIj2d1o8zZ6jyvESC5uZmJCYmij08PCiI2Jqze/Mm1K9ehfq6dRQIeviQFOBvvwVsbWGgqoo5Y8eyB2fPIsrcHAFlZazXX/RqFovFmD17NsfzPEQiEScIAsLDwxEVFYWtX3yBqVOmwHvdOhGOHqX1pZWk7t9PtRe6gJWVFeZv3YqffvpJCNXUxEw1NYaKCnIAffklBUm8vGg9PH+e2pslJtI4JCZS9fbWQn8XL1JF95YUnz/eeQfM0JBzyM2FjY0NPbu6usAvv9CaMnAgpft8+y2tFydOACNHQnHkCC6WlAj2RkborlDQ/F+1is7lwgVKT7l0iQJSXY1Taipss7Nx/vx5pKenQ1dXl5mYmAg2NjZCxw4SUqkUGhoaSEtLg7OzM+zs7NpV/Y7w96dgmYMDJH36gHNyQss9aH9PUREFJOfNo98XU6a8Vt1uJdpBQUHo06dP23cGBgZKfXx8cOPGjXdSUlLe/uGHH840NDS8v3Llyvo3TgwllPiXQUm2lVBCif8ZhISE2IvF4huDBw9Wk0qlSEpKGlhSUrJuzZo1lwBoSySSYRMmTFD7q01iJxw4QGToxg0iQMbGROK6UKoEQYCXl5f42bNnvFgsxvDiYt5y7lwORkaQAVixfDme7NyJdJEIV69eRWNjI54/f45OxF9Dg9TVQ4doI7hvH5GSFlhZWWHe4MEsWV8f++/eZe8/fQqjAQNIQWlqIlu7XE4b7rVraeMXGkr5kiUllAu8aBE4xjCJ5xEbG4vY2Fj+2rVrorfeegsODg7gutp03b1LBLGVwEZGUu7u66zCUikKxoxBaHo6Jq5dK6rZvBnmI0f+bRt029iHhlKQAKTMe3l5QV9fH5cuXeJ///130bJlyyCePp2CCa6ulBN/9iwpey39ujuhoAAoL0fjw4fwSk6GjOdp09mC5g8/xEVfXzwLDhbeP3SILTl6VISJE+me1NbSpj48vHObMSsrausF4P333xfL5XJs3boVO3fuxJIlS6i6+7VrpPTfuEEq3JAhpL7l5bUfp6KCFKfsbBrvlhQCqVQKIyMj4fbduyz6zp22txs1N8PNzQ3eEyfi+fPn2DJ1Knx9fYXJPj4MRUWkgBYX08ZbIqFjXrlC1/Hnn0Q6nj2jObJoEY2DhQUR1vx8IioKBfXOHjSICGxoKF1vXV076XZ3p2Jy165RDnpBARHsc+eI5Lm7t7f7Skhov96jRyEIAhIeP4aqsTECWltsvYSa9HTIpk3DxpAQGBsb8+UpKVxweDgrfvgQdYMHA4whKzqa+ki7u9Pc7NuXyNXly+DkcgSvXo04f39RfUICVCIi6H6WlAAODkQ8wsKIcMTHEzHT0nrttJR16wbrBQuAzz/HtOPH2S9SqWBnZ8dMTEywe/duWFtbc5MnT6Y+31u3ouDwYfbbb78JY8aMYTavKUgll8uRlJSE9PR0BgBerUUGy8uBEyfQ3FIj4fB330Hr3Xcx0tsbg/buhez+fbIDd3xmLS0pqNHqMGhoAJYvB5yd8ejAAcz5+WdIv/sOWoaG5GaoqqJaBa3BlTVrqF3c22+3B9NWrYJ2aio8Y2NReu6ccO/ePcHf3/8vn2fGWBvJS0pKQlRUFACgtrkZ9x89guWGDRQUyMujYOb69e2F316D1NRU1NTUsJyaGtweNoz3dnDgcOAArUWTJ9P8PnGC+r/PnNneK3vUKCLhc+ZQCszmzTQvr18HJkyABWPIzMzE6ePH8bGfHwUcmpooTWfMGBqf0lIK1HAccPs2miMi8OzrryEdNIgNAcC8vOi56N6d1qPWuhSWlq+/oMWLcbamBrLcXAwfPpz38PBoHddOiytjDHPmzMHevXuFU6dOMZ7noampqZg9e7ZIrWPgV6EAduwA7+uL53Fx8Js7VxB1sKgDoPPasoUCh5s20b8XLgQAVFVV4cGDBzAyMsKzZ88AAO+//37nmgQtUFVVxahRo2SBgYHYtWtXcENDwyAAl994A5VQ4l8GJdlWQgkl/mcgFovneXh4SDw8PAAArq6u6oWFheqXLl16t7KyUpg9ezbT09P7+wcUBFK6ANo8nTlDxOXnnwEARUVFCA8PhyAIEAQBPM/DxcUFKioqHAQB6NGDQ79+RDgACIWF0K6vR0paGrS0tPjm5mYuMjISFhYWnTcyjNFG99EjqvZdU0MEBwByc8GNHg2XkycRlp/Pb9u2jfv444+hqaZG5MLdnYhDfj59VhCIfLq4vEKMOSoyBB8fH9Gff/7ZfOLECbGKiorg7OzMPD09SVmrr6cN7LZtnXtd79vXdZ54C0pKSrD/wAE4Dhum0Jk5U6SzYQMRmK4I8OswdSowdOgrP+7evTusrKw41W3bIBw+TJvF4cMpGGBjQ/csJaVz3nBhIZ3/rFmAgQF6zp2LzaamMDpyRKGiogJra2tOX19feD5pEqvV0sL8+fNpwzthAhHWpiYa38xM2nQ/fkyKLkBEISKiLRdfS0sLy5cvx7p164QLISEYFxnJRBcvUr7znj2kHH7yyavXa2RE5Dcigo45ciSRQhUVTJw4kUVHR/OmpqachYUFbty4gZycHFy9ehU9e/bEtm3bBG1tbfj5+TGIRO3Kua1t+5h3zGGdPJnmRmkpkWNjYyKo1dXk2ggLozHbu5dsuc7ONHYtm29s20bEKCuLjm9kRGO0bRu1vAKI3LeipV1WV6iqqoKurm6boltZWYnz588jJycHYrEYjDE0LV8OVVLMOS0HB8H6m29Y6FdfYe7+/bji5wcnJycilT//TCq6ri7de8YAQUBTQgLu7NuHZxkZCBIEIpinT1P+6t275Fj49VfK4f27+OEHyJYvx1vr1uHJRx/h8ujRUFdX59977702Emr+8ccwnj0b9Xv3soMHD8LX11cwMDBgTk5OyM/Px+3bt4Xnz58LpaWlnFQqhbm5uTBp0iSmoaGBpOho4OuvBX13d7a3vp4q/QPQcnYGc3OD7M8/KaBx8yaRpdbgSEYGuVxcXChwU1ND8/jyZdjfvo3bH3wgJOfmItjPj1mfOkW5vL/91n7t48ZR0GXcOPp/QwPNi5YaDJaWliw3N/eV6rqxsbFwcXHBvXv30K9fv06qa0snBAEA09fXV4jFYnh5eYmgpUVKdE0NEcCAAAo0vv8+BXI6BBpboaurC319fUVFRYWoqKiIHvDWntF79lBAKSWFyOPKlTTv16yh+x0VRfO+df4bGdGcKS6G2unTGPfwIcTTpgnCsmWMtXZfGDeO1v5Hj8gJIpWSldzXF/Xbt2PrnDlYvnw5BU2//JLOIz293Q7/5ZcU7HodAgLgoq2NQYcPQ01N7Y0BDE1NTSxatIgJgoC4uDhcuXJFlJKSAm9v7/Y37doFHD6MskOHcGL3bnyWmcng5ta5zZeZGQUi0tJI2f/5Z2DUKJy9d0+RnJwsAtDWClNXV1dhZWX15joUAF68eMEByAKAVatWTZdIJEMbGxuXrFy58vlffVYJJf6XoSTbSiihxP8MJBLJIFtb2067GnNzc7i4uODKlSssLCwMTU1NcHNzg6OjI+rr61FZWQme52HekkfdhqYmyke9cIFU4fJy2vgZGdGGtkcPZP3nP3haUwOn3r0Fxhisra0hk8lo8/fkCRGzDgSXe/EC2itWYPnixQDAhYSEoKGhAadPnxZUVVWZubm5MGrUKPoAY2RXPHaMSMC+fUS4ZTJg0yaIXVzwmUzGnVu5UsCJE6ytL+1nn1Hucb9+wPffk0pnZNQ5f7ELzJw5UxwSEgJfX1+EhYUhISEB7u7uwmhzc4bmZiJjHfHjj69WP2/BgQMH+JycHK5///7C8OHDaZO2eTMV0vLzo3P8O8Wzjh3rnA/eAb169mTX+/UTVKOimMe0aVCLiWkLamDiRCpMdPIkEeDGRtpQRkWRMssYBoBcAnl5eaLs7Gw+MjKSGRgYCIMSE5HcuzcqKioo2NBqT09Kos3pzZuknC5YQIqoWExj29DQ+QR5HvMGDGBXN25EZXo6DKTS9txniaTrzXf//mRZLSoiYi6TERm2soKhoSHGjx/fthGfOXMmNmzYgOrqamzatAkcx7E5c+a8WmH+TWCMbLWtFco75Pe3Ea333qO5Z2pK4wiQvXrXLlLHW4n84cP0918UHXwZjS3H1NHRUQAQNzQ0YPfu3WhqaoKJiQlcXFygLpfD+YcfwMLD2868ubkZKi4uQkRFBXNMT8exjz9WjD17VqQ6YwbqV6yAvF8/5Jqb82E+PkxVVRVVVVUMBgaYtX49BBUVyqevr6diWomJpGqLxXQf162j1mh+fn89hLq66D5kCLNMSkJceTlMra1fIUvis2cxcO1a6F29itjYWCEiIoKdO3euZSg10bNnT660tBSNjY0oLCxkv/zyCxjPo3d2Nszq6tjeloKAPXv2xMOHD1FQUIAjR45AW1tbWLp0KUNjIym2dXVUt0FFhe5lSgqdQEUFzd2LFyHR1saAP/5g3MCB/KEzZ1hzczPep+AVvVcQ0HzkCMQtSnlzdDTEH3xAx2p53luqwHci23/88Qf/5MkT7sqVKwCAmJgYvoWocUVFRUJjYyOTSCQMAKZOnSoyaG0Fx/M0DxMSaH5JJDSXGhooCHTtGv0sI6MtqGCRlISFCQmimw0NijoPD4Zx4xiOHCHimJlJqn5KCq1/1tYUFLK3p3HJzaW1y9ubvlehoH/37g2XwYPxwt0dV+vrcW7MGHz++ec0T2JjKdDxzjv0fiMjOt+hQ6GRlASdykocO3aMnzFjRntl9I5r4/Tpbd0neJ5HXFwcGGNQVVVFXV0dxG5uyG9uxhvTeF6ed4zh4cOHANC5HkNSErUYHD8e2lpa0NDQ4BtWr+ZUVVTAZs7sfJDKSnLpLFwILFyItCNHkNzYKDI1NcWcOXPA83yry+kvF5Xc3FyIRKKUlStXPlq1alWwmpradgMDA3F+fv4DAN//7QtTQon/QYi+/fbb/7/PQQkllFDi/zNCQkJEgiBsHDp0qPjlwlkaGhpQVVVFTU2NQi6Xs7i4OBYfH89HRUWxlJQU3LlzB1KpFJYdrX7nzlF+cn4+qRh795Ji6udH5Kq+HiV9+sBhyxbBOyKC2X33HbPW0mJMVZWUlcGDybrZkVQmJ5N11ccHAODq6gp3d3dERUWx6upqNDU1CU5OTkzSkYg5OZHK1NREm72wMFLlPvoI0NdHQUYGTAcNYio//ECFeGxsaEM5fDiRRE1N+r7kZLJRvgERERGYPn06q6qqUqioqHCWGzeyQkGAxY4dnW2q+flko16xokvSfO7cOWZvb49x48a1v6iuTtbpjAxSifz9/7ot1Jo1lEPcqlq14vZtGA4bBs/+/VnDBx/giFgM9V69YGho2F6UTFeXyP1vv1ERrEWLSOXtcL46OjqwsbFBnz59mFgsRlZWFht99izLdXJCMWOsZ8dq7Kam7TnEH35IRcFaq6JXVxNx69hCZ+ZMqGzciMylS6EZG4v7Zma81dixjL0mD7gNy5ZRUGDcOFL2U1KIPMyY8cp41dfXK1r7FAuCgOjo6L/fduyvkJNDwQ6ep3mcmEj3zdOTzqukhMbVyYkI9kv1C/4OBEHAiRMnhNraWixYsECUnJws7N69m2lrayuWLl3KeXh4wNLSEsYaGmDXrnUq4sRxHNzd3VnU06eKZDMzbsjly1xjXR1O+vryYbdvQ6StLegGBHByLS2UlZUxAwMDYcaMGezQoUN8VVUVVFRUmNaxY0RM1q2judFa5Tk8nFwLjNG8eU2P71Y809HBn01NvHtYGEZGRjLRe+91bp/m7AzMmQNjKyu4u7uzvn37wsbGBsHBwfDy8mJ2dnYwMjJCU1MTPD09MSggAMLOnbB78AAXxo6FtY0Nqqqq8Pbbb2Po0KHw8fFBTU0N8vLyWM61a0La06eC85YtjA0YQHnFOjpEsM+cISuztjYR2aAgmk/r18Nq2jTmP2kSkpKSBJlMxrp164asrCxELlsGg/nzsQkQ4qKjhaenTrEiS0ue8/JiOjo64HkeZ86cUfTu3Zvp6+uzEydOIDQ0lJfL5dzkyZPh5OQEPz8/5OTkCE1NTZyamprC3d2dGzRoUNv6O6yqiuH2bQoOqqvT+qRQEKmdMIFSFl68oGf3ww8pqNOzJ60FvXoBvXpBkZ2NJltbLiovj/kmJpLr4uRJso9Pn07jYGZGa8eyZRSwnD6dnBg1NfTdpqa0Ts+bB0ybBt1+/WDs5wfvgAAWHh4OHx8fiIqKaC355BOa4xIJpYH4+lIw7MEDGD57hsi6Oubk5AT1VldHRzBGqRijR2PPnj18VlYWKioqhJycHKGsrEwQi8V8//fe4/T/QS96AEhLS0NTUxP8/f3pB3I5rfGOjkCLs8DFxYVtVihgERQE3dOnKdgHoLq6GrEZGXxWURF/LjWVRT1+zHrv2wcVJydh8rJlrLX93d9FSy0ArRs3boSqqKhsGzNmjJmTk5MoLS3NNyYmZnxYWFhNREREWkBAgLLfsBL/OiiVbSWUUOK/HiEhITLG2BcKhUIlLy8P3bt3R0fCqoQiSe0AACAASURBVKOjgwGU/yoCgLy8PFRUVHAGBgawsrJCTk4Ojh49imfPnmH06NFkN8zMpJxhY2PalFVWtluHRSLg88+h9+gRQocNg9PIkWQZNDWljXqPHqQ0vZz7LBJ1yvVuLZ4VFBSkuHTpkqiyspLbsGEDvl68mMixpSWpMDo6ZJWuqaEcymnTiPhbWOBOfT2c+/VrJwoAEaLVq9uJ5dat7ZbnvwGO46AlFsO5thaHXrxA+h9/NH/wwQftvy+oiBvAcUhNTUVYWBg/ZcoUztTUFBkZGWCMdU36DA1pY/vbb6Q4X7rUmZS8jDNnXiXkzc10fQsWANeuoefSpYjNzxdOnz7NeJ6Hq6srmIcHjdGvv5I6BnQeny6gqamJ2tpayFNTIYSG8oIgvHpi6uq04Z4yhe71+PFkjV+2jPp7A2TnLSggUpCcjPGTJiHf0xPR+/dzN1evxqJFi/DGVIaVK2l8W+HhQdfb1ETzqcO8Hjx4sMjPzw+7du0SysrKmH4Xltt/jLAwmm+//EJKqZ8fVVIeM4aKpCUlEfHYv5/mpI8P3ZPffqMAzz9Q1k+cOMFnZmZyH330EUpKSnDx4kXWp08fDBkyRNSpboCxcee+yB3g5uYmunr5MvSfP8d1b28EJidzOt7e0N60ibFr1+BcUcFuDhiAhIQEtnv3bohEIq7wwAF0i4qCIj4eoqoqur6sLCpMN28ePS/OzlTQqvU8WgMeQUGvnENOTg7q6+vhefgwk166RLb7Z8/aC+kxRgGwlp7LWlpa0HopJ9zR0RGOjo4AAD4uDnaPH+PUhAkAAHV1dXz11VdtrgUxx2FUYCCMExPh/ssvbO1nn7HL27YhqPXcJk2igmonT9L3HjhA6iVjVAF+zx6grg5s7Vr4DhnC7ty5o6irqxMlJCTAREMDL+bOxbSxY5nx9OlM+OknXK6sFA4ePAgtLS2FhYWFqKamRvTixQv8/PPP0NPTEzw8PDBgwIBOtR7mzp1L/9mzh066rg4T331XtO6zz1CblAS11vmckdFepG3IEPrbyoqeMXNzUpEfPqQ5YGPT9t61tbXgW56TxshISCUSqpK/ZAkdo0X1RVMTkfXSUnJwFBS0F5DsIkUFILeFIAg4evQoP/ybbzjd4GBIfvsNALVzjLO3h2zkSP6Ru7vAq6rCJC1N5DV/Pm9oaNi1BXzjRiA1FRlLlqCkpISbP38+dHV1aXGWy4nMt9Y0+Jto6TMvmJub03F4nu713r20ZrRAQ0MD+vr6zVcPHBDN3bKFsTFjAENDpKWlIaqkhFu2dSu0z51DWXU19MzNESSVsk7V2v8mTExMMGbMGK2TJ0/era+vh7a2NkxNTfHJJ5+oPHr0yP3mzZvbq6qqPl27dm0cY4w1NjZ+u3LlyuJ//EVKKPFfCKWyrYQSSvzXISQkhEVFRS2Ki4v7PSIiYr0gCCG6urr97e3tpXFxcfzNmzeZgYEBjIyM0Nzc/ErBLx0dHZiamrb1K9XV1YW9vT2uXbuGB8nJcPv+ezBnZ1KHWqr4ojXPs0MbMIlEgsj4eDagtUDR4sX0Z98+qoQ7bx7lwtnakgq6fj1t+Nzc6AA8D2RkwLy8nOutrQ2j7duhIZfDbs8ecDdvkkKxbBkpb2PHkuK5fz8dz9kZmD8fsfHxgnOfPkxdXR15eXmQLFiA2qoqiGbObLcUm5jQRszDA5g797X5gxERERgwYADqd+4UDOPjOcsbN9Dd0xPh4eFcdXU1ampqUFJSAklSEuoGDkRofDxiYmKgpqbGoqOj8fjxYz4+Pp4FBwd3bpfWEWIxETQHB7qmwEBSirqCnh5tjFuJSXU1qVuXLlGF32XLAA0N9O7dm2VdvCj0Xb6c5QQEwNjFhSopq6gQ+R09mv79Bpw7dw4vXrxA4Dvv4JaeHuczZgy6bE/GGKnyOjpE5j09SXVTVSUlbfx4Uul27aKAAABtbW306tUL2dnZioiICE4kEsHIyKhTQKgNOjq08bazI4IhkVBawI4dwNKlZA/uMJ9FIhEMDQ1ZSkpKW3Xif0y6a2vJuqutTap1v35Ezo4do9zTI0eIRKurkxK4eDGRTgsLusbCQlIlv/+eggwdq7+/AXl5eYJIJGKenp74448/hG7durEJEya8Oi6PH9PYdtG33dLSEo/T0hTmZWXM79QppmtvD5WNG8H696dq07GxsFq2DE+ePBFGDB3KBjg4wOHcOWjfuwdpSAhYq1U8PJzIV1AQEb3Zs+k63nqLXo+OpnmblkYq6nvvoaCgAOHh4XxsbCxzdnaGg4sLg4sLBdqmTydLd6vLxtCQ5q+Pz5sDEteugX3xBS7PmQNTW1s8f/4cpaWlGDRoUPt7vL3B3b4Ni02bIPr0U0TExKCoqAgRERFtY2rs7EyKvIoKcPUqam/eBMdxiO/VC+dqa+G2bx9w4QLO6OigqqGBKygowIQJExAEQG/uXOhUVUFcXAzJggVwcHTkvLy8UFxcjNTUVCYIAiorK+Hs7Izp06ezbt26UVhv505am06dIsK/aBE9pxoaQGAgIjkOOQBUR4yA1ezZdC0d84hbceIEpWqMG0dKrY0N+H79wDiuzT2Sm5uL58+fw8/PT9G9e/dXSW5TE1m9ly8nF8b16xQ4WreOnqvoaFLPHRxe+ShjDFVVVRCePmWxRkbov349wsLDcerUKSEyMpLl5OQg6PFjZuruzskGD+ZMT51CVlkZu5aTw0dGRsLR0bFzZ4fhw8G/8w4OXrrEOzk58S4uLu3nK5NRTvdfuXw64NSpU4pTp05xdXV1bNiwYfS8f/UVVdf/8stX3EaOjo7c1Vu3mPDJJyjKyxPK//wT4cXFzNPXFz2ys2E2aBC6Dx4M1b596RkWieh3zz+EkZERXFxcEB8fDz8/P6ioqIDjOBgaGqJv375SPT09EzMzs35isbjP8+fP1f39/S/+4y9RQon/QijJthJKKPFfh6ioqJna2tq/BAcHWw8aNEhl6NChnJeXl9TBwQG+vr5MJpMhPDxc0NPTY1u3bkVCQgLS0tJ4CwsLpq6u3qU9TkNDA052dtD87jskzZmD7qWltBlrVVvU1EgtHjOmbQMtkUgQGRkJb29vIrYyGW3Qp0yhTaYgUM7rqFFkw92+nUhyUhIdy9CQWnWVlOCJi4tQkp7O1GfMgHlUFJiVFdgXXxCZsbIixXPBAlJuBg2ijayNDeymToWQkMD+ePpUSE1IgGpKCuIaG3ExLY1lZ2crampqBCsrKw56emQlHTjwtfnSkZGRGDBgABQbNrBKU1NmPmYMZDIZzM3NkZCQwOfl5Qn5+fmC3dq17GZFBV4YGysmT57MBQYG4vbt25DL5czT0xM+LTb514IxIk99+5JN1Nf31VxfQWhvWcVxpLICpHQ1NtK4icXA5s3goqLg/sknLDcigo9RU4P9uHFM1cCA3qupSZ99QzXg9PR0JCUlYdiwYSiMj4f+jBlw61hwqCtwHAUufHzonp48ScGYQ4fI4v/SGKurq8PT05MrLS0VEhISWGJiIp49e8YXFhby1tbWXGtLNJ7n8WLbNly/f1+4nJeH58+fC+Xl5Uyjf3+oaGgQOWCsE2HT0tJCZGQknj59itTUVKSkpKB///5/bQN9+JDs4ps2EUGcOZOIfk4OcP8+3Z/PPqP3xce3q4Zvv01BD4mEzmXoUHJzhIVRYbdz5yiw8xeW+aamJpaSksJLpVJkZWXhvffeY13mnGto0HPYRTVklJejZ2Iit83MjGloaQmm7u6MffABBQrCwoDvvwdTKODs48N03d2hsm0bGm/exM8iEZoUCt7W1pYxxuieffQRBabs7Oj5bFGWAVDAw8wM9ZWVeJCayu/LyWGus2ahUiIRPN99l3l7e7cPdu/eRMhTUijYNmIEzRdPT0pJGTCg62fwwQMKqqxfD9cRI+Do6Ag7OzskJSXBUlsbej170noyYQKp7BwHiMVwc3ODlpYW5HI5ioqKWEZGBiIiIhAREYEyuRw3ZDI+TCxmtefOwfXIEdy1sIBRTAzODxgAq4wMDNXRwaDPP4elSEQBsJISuo/r17edp0gkgqOjI/Pz88MgX1/4VVai59ixtEbt2EEFxyZOJELbty8FFLt3p7ni4YHs3FxcuHcPmlpaePz4sVBYWAh7e3vWZeuqq1dp7rRU44+9exfHeR61Ghq8bVgYg4EBpObmSE9PR35+Pufj49MeWKyvJ1fS228T2f7pp/Ye8Vu3tre0GzeO8sELCtpb1rWAMYZez5+jx6xZyJo0SZFbXMwnJiZyTU1NLDg4GMOGDYPeggX/j73vDovq3L5e75lC700BkSoKSFXEhlhiN7FrNPZertGoid4k10ua0Zho1BiNGo2JGo0xFhQ7RZqACtgAKdKl9zbMOef7YzMUBWPu9/ue3833zHoen3szzJw55T1n9tpr7b1h6O0Na6kUpn5+cLl2DUYrV7KSkhJER0eLycnJLC4ujk9PT+f4khJYDBiApGHDMH3OHK5d8vfSJUqgqrqWvwYiIyNRXV3NACAlJUUc7OjIYGZGa66DLvoymQwVFRXi06dPBfMrV0SH06e5pEGDxNmzZzN4e1OPCFXJjLMzlYcsWvR6fTVegIaGBu7fvy/IZDLY2Ni0bIDjOJibm6Nbt2549uxZ0/Pnz0P8/f1v/uUvUEONvyHUNnI11FDjbweO43r36tVLozP1tE+fPrh69SpLTEyEiYmJMGLECC4pKYkdPnwYPM9j8uTJoqur60uRhNF336FOT0+8+uwZKzI0VM7/4IPWZ6SNDRHCe/dI2aT9gEwmE6urq5mJkRGpYocPk/0RoGAlNZX+/4QJVOv84Ydkie3dm4LA69eBsjI8e/BAGH7zpmS7tzfCR4+GRffumFxaioqqKjjk5JDNddUq4OuvyU7ePCbpt9WrRUVFBRuiVDLvTz8Fq6xEP6USBbW1CA4OlsTHxwsDBw6kwPyDD0gB6dKlZcxLu+MvLQV7+208WrNGLKupYb7Nr9vb22PVqlWtEaK7O2b6+QEaGi3MaOPGjXjw4AEuX74Md3d3WLzYUK0j9O1Lxz9qFBG9ZctaA7yyMkoMqIJoW1si4Ldu0eiejz6i42lspM/o68P1yhXu9v79wuHDh9n69euJbPr6UvLj+PEWwv38+XNERUWJkyZNYllZWTh//rw4atQolJWVieVOTlx9UREGi+Kfk1WJhNRgS0sK2n19Sb1MTGw/GqwNZsyYwQDgypUrKCkp4VJTU4W4uDiYmZnxNTU1kvLycsiHDBFHOzqybj17Ii4uTnjy5IkQwxhbvXo1k65ZQ52zIyJatslxHDZs2ABtbW2kpKTgjz/+wKeffgo7OzvxnXfeYe2Ce1Eklc/Hh5I2a9YQqZJISJFPS6PzundvyxrDkSNEwBctov8+dIhsyidOtG7X1ZW2e+kSJYYGDqR13rNnh64Cnueho6ODuro6Ljg4GC4uLnix10KbN5PduKOGZSEh0PnlF0w/eRJnz55l5ubm1Oxr9WoiewcOEMn65hsiXvr60NXTg72DgxgfH8+6d++OHg4OVBagqrcdOpTO7/797WZ919TU4FhsrNjQty8zMzaGOH8+Rv7jHxwuXCCbeUJC634ZGZHyn5hIa1lXl5JDR44QUba3b38cT54Qyfnkk1biA8Dy118x78wZ/ASgx7hxGKupCYOuXdt91NDQUDVVALm5uThz5gzq6+vBcRyePXuGuro6bvXq1dDU1ISmXI7FcXFQhoVhes+e0L5xA+zxY1J89fTIybB+PfD++7RxhYL2zcMDmDkT0v79SbWeP5+eZ2PH0vMIoDXSsuOtkwsaGhqgr68PmUwm8jzPxo8fz+Lj44Vdu3aB4zhRQ0NDHDx4sMS7uZ4Y/ftTMgfA06dPcf36dQyaMAHRkZGcQ3w8Gs6cEc+OH8+0DQxQV1cHnudpPy9fpiRLnz70nHRzo8SErS0llP7xD9pnY2NKDi1ZQmtCoaB+F6r7RKkEevZE+f79yMvIkDSVlMDDwwOJiYm4cOECOI7DkiVL0GXpUiL3V65A6uMD14oK9FiwgEVFRYkAIJFIJFlZWUJMeDjnrKeHOfPnv5xcqKxs7fD/mpg5cyYXExOD2NhY+NnZ0Xo9fbr1d6cDNPfPkABAUWEh1qSkMKSm0rX77beWSQro3RsIDKT1sGnTX9ovgBrA8TzP3bx5E+7u7i+VSwBAcnJyk1KpvPuXN66GGn9TMPE/qM1QQw011PjfRGBgYA+ZTJawbNkyrc4ss6dOneJLS0sxZMgQiaura8vrsbGxuHnzJmxtbZUKhYLV1dUxPz8/0Ss/XwItLfAeHihfvx41UVGwVRFlFfLyyB5661YLKdyxY4fQv39/rigtjff5+muu6eRJ5qD6PlGkoFVHhwLowkIiI0eOUE3sjh2kJOnoACdOIP3pU5iYmSE3N1c4f/48p1QqAQBb5s6lQEpbmwLB77+nYH7mzNZ9i4+nfXv8GKK9PRonTULGvHm4deWKuHrTplbWqBqn07bzdDPOTpmCiTo6uD57tlBcXMy909xBtx1++40s8seOdXjeL1y4wOfl5WHFihWvX7xbWkrBnaoJmZYWcOoU1auHhpLiV15OCqulJdnCfX1JEXqhW7lSqcTWrVvh7u7Ojxw5UqKlpUXH7OHRUqMZGxuL4OBgWFlZCYWFhdzQoUNFDw8Ptnv3bqz79lt8P2cOYGmJNWvW/Hl379RUIiknThCxsrCgGd+RkWT7VzUv6gSCIODZs2eIjIxEXl4eFi1aBBO5HFyPHnTcRkYQBAHffvutYGFhwYb6+rKuhYV0HiwtyU3xAhQKBRISEhAcHIxu3boJCxcu5FBRQaUEt29TXefJk1SrrLJsFxYS8fb3J+KhssGWl5OTQy5vfe/du7QGOnPGpafTe957j7pA//gjYGcHQRRx48YNPjk5mVVVVXEymUzU09MTdHV1JW+++SYMO7IUA5TIcHMDKl6YIFRRQUpeM0nauXMnb2Jiwk2YMIEZGRmRo+GXXyg54+ND90gbXL9+HY8fPxZXzp/PZD/9RMff9ju/+YaIsZcXeJ7HZ599BgDYvHlz+8RAURHVd7/1Fl3/gweJhHIc/QsOJgIbGkpui4qK9vbp/HxaPzY2pBADlJz7978BQUBdQgK+KSwEz/NYvnz56yWy2kDsKHEkCEQUx4yhfgMWFrR+TU3pPhsxggjr3buUIHz+nBIXvXq1KM6viyNHjiA7OxtyuVw0MDDAkiVLGMdxSE9PhyiKqKqqwvXr19G1a1doa2vD9ehRdJs1CwZz5uDixYu4d+8eFixYgNu3b/NpaWkSJgiY/csvqDIzw4VRozA2JUV0i4tjdV98AS0bG2j3709JuJgYOjZPT7o2SUl0LTIziXgDRHS/+IISI6tW0Rp/6y1KBH70EUpKSvD06VP0798f27dvF+vr65lUKsXw4cPh5+FB9x/HkXU+Pp621RGuXSPHxF/ondERGhsbceTIERQWFoIJAv61cCH9HnXwPH8lRo6ka7ljByXHPv20NbGZn0/r/vDh1nr610Bubi6OHz8uNjQ0MG9vb37cuHGSF0u4AKieTbmbN29+xfBxNdT4/wdqG7kaaqjxt0NAQEBpWFhYeUpKylBvb29ZR4TIzc2N69u3L2dubt7udSsrK3Ach4KCAi4vL4+rqalhdoBgvHUrd7apSUisqRGsBg/mgkQRYQ8eiCYmJqxlTI2eHgWfUimCU1Nx+vRpCILASmJj4VBUxPKWLWPB16/D1NQUZmVlYFpapAZWVFCTHFUTtcOHKSAPCCD1oLku1NjEBJqamjA3N2cymQzp6ekwrKsTvRcuZPWLFkHevz8phcnJpC6qZhffvUtKzfr1AGO4aGGBCyUl4C9cwKz9+9lRCwvx4a1bYszDh0KcTCbEV1eLko8+wm+VlfydO3eEmKgoQf8f/2AJrq6sz759yMrKwuPHj5mZmRnMXrR3375Nx9FJcyEzMzMuPDyc8/8rAbm2NqnYV66QmjhpEgWlY8eSBX/7dgomCwuJAKxeTaS8A9LBcRwcHR1x+/ZtMSQkhJNKpaLeyJGMmz8fiVpaiExN5WNjY7lBgwbh0aNHTEdHB35+fuzixYuCpqam2G/pUlZqZoZn2dlwdnaG3qus0JmZpGD370/7Pnw4kbTTp0nh9vQkxb6khNTdDmq0GWMwMjJCdnY2tLW1+X79+nFMU5OO39ER0NAAYwy2trYsLCyMZeTmir5TpjC88w7Vtrbp0K2CRCKBlZUVtLS0UBUXJ7h7eXEtdtF//5tqiU1NW4Pr8HBSuCoqaA21JZKzZhFJnDSp9TVLS5o57Ozc/r0qGBtTIO/kRDPDU1KAoCAkAwhNSmIjRozgRo8ejeHDh7O+fftyHh4eNKO4M+jrd6yyvfEGqenN3cK1tLS4xMREGBsbs64pKZRgCQggQtFB53w7OzvcuXNHrE5PFxyDg7l251Jfn5To/fuBkSPxKDUVGRkZ4qZNm9hLNeU6OnRfArQWBg2iTvrLlxOBNzWl5JiHB9nxBw6k9zs7k735s8/onA4YQKRn9Ggiiu7uQN++kPn4wNvbG/Hx8WCMwdLSsuN6/47Q1ARWVUUJkKoqun8jIohsHzxIpL9fv9YyiLo6+t/CQrp+AQFExhgjxfg16/EBICUlBT/++KNYU1MDnueZIAhs7dq1TCaTgeM4mJiYwNTUFFZWVnByckJ9fb1YX18vSKOiuNtNTUiurERZWZlQU1PDevfujSFDhnCGhoZCcmoqK+3aFX3q6tA7MlKsBNilgADcUSrFiKdPWUREBMxWr0Z9VBTKV6+GIWO0tkeNovv0+nVa17Ro6HqdOUMzyd3c6NjffhswNIS2tja6deuGuro6REdHM57noa+vj3HjxkGmpUXn7Z13yJGQmkqOhY46kru7U7L2xc72Hh5UrqQaofcKPHjwACdOnEB5eTm6deuGfqdPQyc6Glpb/4PJWu+8Q4mW8+fp/qyupmMH6HeuRw9KTqia/L0GfvzxR16hUHALFy5Enz59uM6cQc+ePUNaWlpNaGgowsLCygICAkr/+gGoocbfB2qyrYYaavwtERISEi+K4gh9fX3bri/YKv8M165dE/Ly8hhjDK4ODkLD1atcvI0NU3p7M+mVK1y/rCzctrZGXV0ds7e3R8v2GcOjoiI8/uMPMUkqZT179hTeeOMN5h4TA7d795jTF1+gurpaTDp1inkvXw4uK4vGgKmUol27qBY2IoLUTx0dasQ0duxLVtukpCQhPz+f8aLIsqytcS0rC06ff44fBYGPUypFYdgwrltBAQV3kyaR/Xf0aADAw9RU6JiYCGM3bmSJvr4wsbFho1euZOZWVpzxkCFcd01NrtepU8xw+XLOzsWF6yWXczY3bjCbbdtgZG4OqVTKMjIy8OzZM97X17e9NFFd3eEYKoAUtMLCQqSkpAgDBw78awV/EgnZIc3NiaCkppIiuncvqenm5jTvfMWKP92Uvr4++vfvz5mbmyMoKIjdvXcP9QqFYH7zJvL79mUzZsxgrq6usLOza2ksJZVKhaVLl0q4jRvRY9MmZObkiA8ePEDfvn1Zh0GjQkFEZd48IpdpadTA7MQJsgy7ulKzIgMDCtzPnycnQn7+S3WVSqUSV65cEZ2cnJitrS19WVMTJWWaG2Pp6ekhOzsbGhoacHNzY2zaNEpQ3LtH5Q1tSa9SCaSkoPrGDXju3s1p+fsTiVPNkm57PJcvk210+PDWOuC2sLEhgqAac6bC+vVUjtB2PFpbMEZ/mzuXCMndu2DdukHz8mXms3QptDpTsTtCSQkRomXL2r8+cyat+WYVvkuXLkhOSoLL6dPMyN2drtG771Li4pNP6Bo03yMAkJOTg8TERFgoFKJjUhL3kjpob0/EMzwcwZWVkMvl6Nu376vXtY0NJVUCAojMmJgQ2V66lBTwGTPouo4eTfb4oCBSRMeNI5Kzdy85C8aObWcLlsvlEEURd8PDkX77tujt5sYQGUnPEqUSLXOmT54kxwfP03X7978pKfXbb+BLS3H3+HGgsRGZcjkMdXUh9fIiEjp0KNnFLS2JAOrq0vkLDaUE17FjRFiLiiih1NhIx/aKUovCwkIkJSUxnueZjY2N6OfnJ9jY2HTYsVtXVxcODg7Mzc2NswoJgWTKFNQyJlhaWrKAgABm32y7r0tLYwZHjoDjeQjV1aKemxtzTUqC79mzGBQQwNx79kTflBTUjxqFiN69xcjQUCaLiBCzJk1iWlpa0Bk0iM512wZmMhkpvceO0T28a1e7/gCCIGDPnj1CfX09A8gW7+joSE4Mc3N6dg8aRPdSWdlLNeAA6NouXUrnrC28vAA/v44bxbVBRUUFjh49CmdnZyxbtgxuvXoh58QJ3PXz490DAjrugv4qqGaMDx9OSZSUFHIMqWBqSkkga+uOeyW0wd27d3H8+HGhpqZGMn/+fFi3mbjREUxMTKCrq6unr68fUFZWtiwiIuLNmzdvhgQEBJT/5eNQQ42/AdQ122qoocbfFTqCIHTrsMHOn6B///7ctWvX+KqqKonl3r1cTysrSHbvxs5duzDNxAQSqRRDhw4VQ0JCREdHRy48PByMMVRXV+NhdjbGJCUxmakpdDw8mK1cTsEZANy+jQkffcT+WLhQfNqtG+spimDa2qS+5eS0vo/nKUju04eCrLQ0Cqy7dGnZx3HjxnElRUVK/927pdzBgzh66RKKjIxQVVUlESQSFBQV8Th9WoIbN8jC2gaiKEJXV1fU19fHAFUtXn4+bBmD7cGDRDyKi+EaGkrdukeMAO7ehU12NlBSgu6CgEnFxbiiry9i2zZSaRcvJotrQgJZJX/7jQLHefOAK1dQXFYmplVUMGltLXx9fCiI09QkRTo0lNTr5vnkGDaMaqhra2m7UVFEWEtLSXFzcSFbOUAB37NnHdql/wwuLi4YOHAgHxkZKUnt21fs7enJTbOwgLTZqWBjY4P5yiMmQQAAIABJREFU8+cjJCQEQ4cOJctjWBggCJgzZw7btm0bTp06JY4fP57pvkg2Z86kfTp5kpSwxkYivEeOkA21S5fWuu2rV4k8JyZSYF1dTRbnZtJ99OhRXk9Pj7VLbPA8kaZ//7uF0Li7u+PcuXPs008/xeLFiyHT0IDJ5s2oc3VFxNSpgolUyrzd3Zl01izU5OfjwowZeOP332HcUa1zWRklLtatowRBRw3kFiwg10VHDe927243xq5TyGREYsLDYbxzJyzz85E3bBikX3wBK1XjsD+DXE5KmwqiSOUDBw+Sktzmdd+ffhK7x8WxQxoaGL11KzgAhnV1gJ0dWH09NAQBHMfh3r17uHLlCvz8/MQAc3NJp6RxyhSUvPsuNOrr0XX+fCaKIvLz86Gvr4/MzEwkJyfzEydOlLxUb85xrfusqhP+5z8pwebuThZ+1Yzy6mo6liVLaL0fPEj3wOHDdO/NmAH88gv8m5rgKggoys1lNXZ20Dl7FszCgpIaOjp0DXv3psSVvT3d14aGtH5WrEBDbS0u19W17hMAVlgIu/JywbC0FCOnT+c01q2jpERUFDURGzKE1m5CAt2fDQ2U4FF1zO/ZkxJKlZVE2O3sAFNTRD98iJiYGAEA16VLF2RnZzMLC4vXIoXS58/hNWAAvAwMWt+flwf8/DPs8/NhMWIEnvn64k5ODgvOycEHX34JzUOHgHv3YCSTASUlMDp1Cj1kMlZ28CBk//wnO2htLYSGhXErfHygu2YNfg8M5CsrK9myZctIgZVISO2Ni6PExdSpLSq+IAhoaGjgJBIJ1YcDrR3/TU3pcxs30rNs40ZKML2I5nGR+P339q+npdHvQCcQRRFxcXEIDg6GqakpP2XKFAnS08GNHYuIyZOxbtOm1y/X6WifCgqoVOTzz+m+Ut0HWlp0PEVF9CzqpJxGZfM3MDDgJk2aBKtX1I2roKWlhX7UVV5jzJgxuHPnTt/w8PCQwMBAjy1btqgJtxr/30GtbKuhhhp/OwQGBjrK5fKIpqYmh+TkZOTn54uurq4dK5AdwNzcHP379+cKLlzgqziO8zxwABoGBnielsanlZRwjdOmie7u7iwsLIzFxMSgoaFBKCkpEQRBEDR1dcV+XbtyQwYPhv3o0Qw9elAwWlpKatDOnehpbs4StLQEvqIC2b17sy6ffALWdowUYxRAl5WRvVwQiIT16kXW4WYkh4cLHrdvc+afforS8nLeJDycs1u1Snz67BnTtLXlZObmMJs1CzkzZyI0MlJ8+PAha2pqQnFxMeRyuejs7NwarMpkpGqqZlRLpfRd8fFEMOfPp7pWmYysp8ePI83GRvDQ1uagpUW1x42NpDi7u5P6PGwY4OKCOltbnMrJYc5vv40uI0bA8c03mbRPHwrUe/Wiuj9vbyICzs5EQnr0oH1xcSFrvasrBexNTaTUHzlC+1hfT8qchcVfGo+jgr29PdevXz9UVlfjWVgYkx0+jMeurjAxNYVcLgdjDPb29q01rWvXApqa4CQSSCQSxMXFsefPn4seHh7tF5ezM6mPRkak+P3wQ2uHdC8vUokXLWoNUiUSIuBr11Ig27MnUF6OG01NSEtJ4eYtWMDaWda7diUlt7a2xfVgYWGBIUOGIDc3lw8NDeXuJyQgwtYWaRKJ6BIUxOx37WI3k5Nx08cHN9zcYG1nh17u7i/XQmdmkqIqCMDs2R2PXhNFugZTppB69yJSUqiRWgc29pfAGGBrCzZsGLRTUpBkZiYYf/stKz5yRPy5spKlZ2ejR48e6DRxJpeT8tjW/VFcTI26VJ/ZvRsYMQJ/TJ3KQt94A2UGBrh//z7u3buHqKgoRFVU4G5REXDypFhua8suXLiAKVOmwNfXl7EnT+h4VA3h2kJDA9HJyaLOo0fMys8P4YmJ4rVr11h0dDTS0tJQVlbGJSQkiJGRkczc3LzjGera2nQORowg1frcOUrSZGTQ2lmzhghsTg5di4oKSmSYmdE91K8fWebnz4d09mwcra3F7bIyhFlZQW/2bFj6+hJhc3Skz1la0hrT0mqnPFdVVSEhIQGbN2+GlZUV0tPTYWxsDB0dHaZz8SKrrq0VrSZPZnBxoW00NgLffUc12nZ2tP2ePcnyvGwZkXFHR1rX5eVEVNPSgD/+gGTnTnTJyWETBgyAn4MDChIT8TQ/n0XExYmeXl6s02Z4SiX98/NrbTCZmEjX2sMDCAyEPCAA5UoloqKiAAADhg2DzNGRnqVr19J92dygTcvLCxrr1qH/oEEsPT1duB0fz3RLSlDevz97/vw569atGzNUjfEzMaHRV0lJVAdtYQGYm4PjOPj7+8Pf3x/Z2dl8VVUV9+jRI7Ffv370m9PURK6bd9+le6uoqNWSrUJgIHD/PiW3VKitpXO7eXOnSaerV6+KoaGhbNSoUZg8eTK9KToaCg0NhGtr4+nTp4Krq+vLpQ2vC8aoxGLnTnqW+fm1/s3Jie5xTc1OR4HJ5XI0l25gzJgxL43Z/DNwHIdu3bqx6upqrdLS0kGDBg06+p8diBpq/PdCrWyroYYafwsEBgYyAD5yuXyNVCqdNnToUDljjH/8+LGksLAQ3377rTB+/HjOwcEBSqWy887GKiQnY3psrKRq2zZImwPkt4uLJc/Pnxfjhg9nhw8fFjmOY3369BHGjBnTPoKoraXAb+hQUoCmTiV78NWrQN++YOvXw83IiIuMjERqcLCYlZUljhs3jmsJiBgjhUNPj7ZlbY3iw4dx4sYNuO3ciXhfXzRpaMCkqEgasn073pLJMGXKFAl27UKDkxO7HBKCgoICnJdI0F0qxaVvvoHYu7cok8lw7tw5ZmBgAMc2pB0ghUQURQqGdHVJrejRg4jUoUMU2D9/3vL+NFtbVEdHU9MgFTw96Xj/+c/Wuj4AT4uLUWtnx/eaNKlV/mjbCKgjG2LbxjvJyUTmd+wgxfzjj2n/6uroPI0aRaRw9+7WhkR/AVpaWhg/fjyHceOQ/PnnYvoff7DbkZHo06cPRo0a1fpGQSBi1KxeDRw4ELGxsXzXrl1bj6uigpIS0dGtJNTMrHUsFkCB9qFDlFD5/ff2+6uri9LSUoR+8gmynz0Tev3wA7cxNBSSjz6i72/73nXrSPX85Zd2x/POO+9IwPNAVRXEf/0L7Nw5BgsL1K9dizIbGxRnZcHExASiKOLEiRNYuXJlK+E+fJiIRVDQq+2hQUE0L74ze6u1NSn6fwXm5tA8cgTDrl3jGhUKlDDGhly5ggrG8HNWFpb8858df66oiPa1sZHWxJYt5HzgOLI6nzsHZGSAHzwYhc32YI7j8M4777Q0n+N5HvZlZaJrUBDb07z2amtrBQBcnVQKuY1NhwFRSUkJYpRK5imToWDHDlRMmyasWLFCYmBgAKlUCqVSicTERJaamorjx49jxYoVeLFPBGpriYjevk1W7aIiIjK1tXQ9cnPJ6VJWRv0JBg8mxb4DR4FMFOHv7y9ev36dAUBQUBB8fHxe6/QbGxuD4zikpqbCxcUF76s6jgMoPX8eoZWVrGtuLtmAhw6l5FdICJHBjRtfXi9yOZFwOzu6J5pxLz4eN3V0MEhPT9A2NeXw/Dmm8zxq9u/H4549mbShgdaVXE7E3dKSFGKOIwV71y5qUJaQQI34xo+n8ps2z/TS0tYy3++++07YKJFwLQmCGTOoweKTJ7RWUlIAALNnz+bS0tLQ09oaPpMns8PHjiErKwu23buTi+TTT2mDy5fTffvzz5Rs8vVt+a65c+dKRFHEF198wY4dOyYMHTqUs7GxoWfY8+f0rNq3r33zSoDU8mY3gQoKmQyS6upOmzDm5eXh3r17bPr06eilmge+bh0waBA0v/oK0x4/xs2bN7ldu3Zh9OjRaOnm/rrYsoUaOc6ZQ84I1XjFtggMJCeUat78C7Czs4O1tTWfl5cn4Xm+84TZK8AYw4gRI+R37979k5mRaqjx94Ra2VZDDTX+6xEYGKijoaERrKmp+ZGfn5/XpEmT5Pb29sza2prz8vKCl5cXUyqVuHr1Krtz547w8OFDvm/fvp0zsro6IDUVnJsbtMaMaX29Z0+k2NmxqEePYCMIgpOnJ1fz8KHgIopcNoD8zz8H09CAloYG2P79FISWlJDKc+4cBZw7dwJmZtDT04OLiwvc3d3ZzZs3xZs3b7KkpCQxLi5OjI2NFWOTk0Vx61Y0ffkljiqVYlJRkcBJpWxaWhrr7+yM/itXwnPePFQOHw4bT0/aP3NzcC4ueJKaign79qFreTlSvbwQMH06hs2ezfLz84Wqqiro6uqy1NRUThRFIT8/HwqFAhcuXBCvXr3KXF1dkblhg8iSk5n28eNkldy7l1SqNsjPz0dOTo7YYm0uKCAle+NGUoB27ULtwIGIjItDWFgY3NzcmJOT01+r087NJeLv60tW8SNHSNW2sqL9ycqirsETJ1IAHhlJRP8f//iPZsCCMZhyHPO6cgWaM2aIoRERjOd5qOpBIQhEMNrU9TLGuNDQUDDGRBsbG1aQno66wkKI48dDEASIokjBsodH+yZcXbuSynXrFhAQgIaGBoSEhODMmTNCVFQUq2lsxMTp01mfxYshmTSptbbXza3VfuzkRNtpWxddWUn/+vQBamvBVq2ic7J0KQotLKDz8cfosWwZJk6bBk9PT2RlZQl37tyBRk0NM9q/H1I/P7Ir29l1fp6qqkjlnTevc7JtZESESCLpuCHUq+DgAOmIEdA3NESXuDjoSKXIr6+H2/PnlNB5kXxoaRHh6tqVFODvvqN7judJcWu2qXNz5mBIQADs7e1x//59PH78GO7u7pg6dSqys7N503794HbgAIu8fh2CRIKnT58ypVKJ9O3bURIdjSO5ubCxsYGRkRHy8/Nx4MABREZG0tzl2bPhX1AAn/79OR13d0gkEjDGWprRubu7o6CgQAgLC2NyuRyG2tqQFxS0jvPy9KQE0+efE5H98UciO1VVpGj/+iutnR07SFFljO63pUspodCcqFMoFDh27BgDqM5ZoVDAysoKnU1laIva2lpERkZi/PjxL5EiTV1dPDY0REhsLLp37w4DAwNSNEeMIGV91Ch6BjT3EOgMhw4d4uPv3uWa5HJM2biRydzdAXd3cGPH4my3buJDTU2mMDWFg7c3cOcOqdaJiXQ+9uyhJM/9+3SvL15M7hBf35fWRGhoqFBeXs4kjGHOgwdMf8ECWteurrSvHEfPjOXLW2qoJRIJzMzMwBYtAiZMwJOCAkiVSsEpKorhu+/alyq4uBC5/+EHSqo1JxoqKytV9cnQ0dHhoqKi4OjoSI0UbWyodtvcnO6Jts0ls7IAd3dE+PsLkdHRLCYmRszevp01bduG4zU14pMnT8Ta2lrGcRz09fXR0NCAM2fOiBoaGq3J3poaSgi8+y5gaAgzMzP4+vpCFEVcu3YNFRUVePz4Maqrq6GnpweNzkpvLlyg83viBO3jkiV0Db7/nsh12znwqn4lZ8506PxQKBS4ceMGx/M8hg4d+ufjEjtBdXU14uPja/39/f+Dbm9qqPHfDbWyrYYaavzXQy6X77O3tx84ZcoUjY5UAA0NDQQEBDATExMxMzMTSUlJUqVS2XGWvbSULM3Pn5Oa9K9/Eck7fhwYMQI+c+fCZ9Ei4M03Jcl79qAuOJhrOnECx2fMwOITJ1B68SJ+HzwYS/Pz0TwwmYLRjRtf7jQLwMDAAO+++y5XXl6OvLw8BqAlGuFcXJC2d69obWXFOfXoAUNDQ0g3bQIaGiDz9cW9N94Q6q2tW5MGS5eCi4vD22+/jYsJCeLc1asZ3N2pWVVjI/RNTBjP86yoqAhWVlYIDw/nAEBTUxMNDQ0MAPbs2YPJ4eGs3NBQMGWMQ9++FJAWFbWzC4uiCEEQWmdDdulCHZJ1dSngPnsWD3fsEO6YmHA9evTAmDFj2kVZSqUS2dnZ4Hke7eahC0KrRfmf/6TrcOQIqdaMtY7ymjmzVUlzdSVlcP58stPeu0d1kefPt7PdvxYGDQL++APWSiWTy+VobGxs/Zso0tpoAz8/P8THxyM0NJTVbtsGjjE8GjZMqPnmG04ikUAmk2HChAlw0dIiUqSyOnMc2fUPHkTpgQPYX1ICMzMzfuDAgZLMzEzk5ORAR0cHnFTaSqavXaPjX7eO7P23b9OooidPKCly9CjVy8fEkHKnSsI043p4uDChooIzbXOPzJw5k7tx+TI0N2wQE+RydkNDA2s3bcIr6bFMRmrgi53oX8Tnn5MC29Ya+woolUqIokidtI2M0DhyJMLi40UuMpJ5FxaSkvnhh3Sd29pWlUq6z6ysqCwhPp7s2I8fkx27Z88WRwBjDDrN5L+pqQmXL1+GoaEh5s2bRyelthb//OorFIaHIzIvT8zJyWHmHIeK5qTCzz///NJ+Ozs7Kwf5+0vh5kbr0srqZZswgLfffptL2rcPed98I2bl56OXjw9jqm7o+/aRUu3pSWRJVdtaWUl/V815/uMPev35c3JzyGSU1OjdGzhyBOy778AplRCkUtTU1AAATpw4geXLl8Pc3Bz19fUoKSmBlpYWtLW1W84FQAk0mUwmampqtmdEogju448x7epVhCYl4eTJk/jggw9QVVWF5ORkmJmZ4fLEiRgqioLLhg1c6bhx+CEmBqIowsbGhm92ErHa2lrk5+dLAGDTpk0vkb0333qLfZ2WhntNTeIbAQGsHXmLjKSkyttvky3bx4fuJScnInoKBV3vZqfN9OnTue3bt8M+ORkGGRl0f6iO1cmJur7n5lICq6CglTQClKwxN0e/fv2Q8v77XFNqKmQdjc4aMoTugU8+AWprUTNgAA4fPiyamJjggw8+YFKpFDt27MDFixfFpUuXMqSlURnAoUPkcmq7ho2MAAcHhNy6xcm0tWFtbc0cfX1hoKuLIUOGsJycHJaYmMjfvn1b0tTUBF1dXWhoaIhz586ldfvgAY1eu3OnnfuFMYYhQ4ZAJpPh+vXrMDAwwIMHDxAcHAx/f38MbZscqa8n58DEidSNPTqa1tfVq0Ska2vJRbNwYXsXQ0AAjWFcvrxdXxEAOHbsmKBQKDhvb2+eMfYf14/fvn1bwXHcqf/082qo8d8MNdlWQw01/tcRGBg4nDE2VBTF81u2bIl74W8DNDQ0pk2YMKFDot0WvXv3Zr1792aJiYlIT0+Hc1tFsKSELMrz51PgZmVFtcqrVlEAnZZGSsrevRTMFBejPDoa96dNE+sjI8UeGRlc6nvvib22bmXTLlyAIioKGjo6pAZcudKqwq5dS8pAm1pWqVSKDsdoATh+7x7Gbd0Kw9jY1tFQWlqAjg6UMpmom55O5FYiIet2ZSX0pkzB85EjWctYFhsb4MoV+B84wDHGeJ7n2ZAhQzilUom8vDxcvHiR53leMjQlBbJHj3B26lQwxjjZ1q1wc3PjJwQFSRASQvbLZkiaa5Zb0L8/qU8gIr7HwQGTd+zglv/+Owyp2Q0UCgWCgoKQm5urrKmpkUqlUjQ2NmLYsGEYqBpt4+ND52fnTiJN/v6U9Gg7WgqgBIa2dut/a2oSsdq3j8iJvz8Fwh9/TPs2duwr10Y7bNkCEy8vsKlTMbqNio3KSlIi58xp9/bVq1cjIyMDLDkZVn36YPT69VxFRQV4nkdGRgbOnTsH6aZNYg9RbCExKSkpuHPnjqhRVSV4Hj0qGbZpk9B/zhwJAAwaNAj79+8Xnjx5wrp06dJKfFSNkj78kAh2XR3Vgm7ZQg3nduygQNnS8qXmZLm5ucgvLeV0ExIooJ4wAfj9d0h/+gmj9+wBbt9mu3/6SdBSKrnOFK+CggI8CwsTfZYuZclXr8Kwvh42r7Kar13bYeM6nudRUFCA6upqyGQy6OnpoaCgABcvXoQgCHBxcRElEgkePHjAwHHMe8kS2GVkUALhyBEK9nv1Ios1x1FTrq+/pm7b6emkNkZEEAnooLmUkZERunXrhpycnBbCvXLlSvqjjg7Ykyfo4uCAKc2Jryo9PYTExQmOjo4sNzcXMpmM1dTUYMOGDdCmNUixkrExrY+9e8nura9PCZrUVFKn33sP7i4ucFmxgh24fFk0WruWJhkIAo0Qe/SIknptx2cFBJAF+fhxUnBVz6wuXSgZBRBxrK4GCgsh/+YbfPzoEYo++QTJcXGQfvklrl+/jv3793d4iYyMjERjY2Nx8uTJ3LNnzwQTE5OXpceKCkqiGRtDqVSioaEBhw8fFgoKCjg9PT2+vr6eM7S0FH97/pybyHGi5cyZzG3zZrH3hAnsXmKiRBAE1NXVobC5rMDIyKhDVfXSpUsCAG7w4MGt+9DYSCRw3Djg0iVa5x9+SMm3vDzq8O3oSCrslSvU1M/CAhr//jfWPniAlLQ0ZO/ZA9cXR/TFx1Py0MeHniWffUbJC4B+B5qa4NitG1KnTRN3p6aymQUFHTf3cnEBduxA3Zw5uNajh2js5yfMmzdPolQqceDAAdTV1WHBggWsZX0sXEjrorCQkgaqZ7qZGcT169H14UOMXLyY7quGBkBDA3aMoW/fvgAgUSqVOHr0KKRSKWbNmsW1lEMdOEC/C52U0AwYMADm5uawtLSEVCpFWFiYEB4ezhUVFYnTpk1j3IcfktK/eDGVK7R1rOTk0DNFT49+B4uLaRyganxjly707D99uv0serrWXEVFBcaPH/8fEe3Y2FhldHR0fV1dXbFCoehgvp8aavz9oSbbaqihxv8qAgMDLSQSSZCvr69mQkLCuq1bt55TKBQrtmzZUhUYGGgHIHLChAlQKBRoaGiAUdtGY51g4MCBwsWLF+Hk5MRxokgk6tw5UgaXLAEmTya16IMPqFP4xYsUYOzb17qR58/h8/33uOngwOknJaHW3h73NTSY3NsbXosXQzpmDCkpCgUpr0eOUDBkYkIB+K1bFCxfvEivdRIkNRgYQJDLqZ5PVefc1AQwhvSBA8U3du4EHxoKycmT1LxHSwuwtUVT24Y4a9YAjx+DXb8O/5EjW4IeuVwOOzs7rFmzRrJ92zbBxcGBewSIAJimpibq6+tJZdTXJzVm2LCO1cyGBrK2Ntut6+vrUV5ZCcXOnTBcvx7KkBBcDg5GcnKyYGJiAn9/f6mVlRXMzMzwzTffiBrl5QwWFhTI/fwzHev9+0Qk2yrLbbFgAal5e/a0vsYYJUcGDiSF9/x5SqKkpREJy8trDRBfBX19yCdNQp+UFKSnp7cq7wYGtE46gP3Zs0T+mhMAqhpoExMTmJmZgX/jDVY+ZgyMPD3x+PFjnDt3Dl5eXqKhk5NEx90dPSIjOUya1DJCq3v37lxWVhYP4OUg1dSU6nZ5ntZOQwM1LmpooHXa3PxJBUEQEBQUJPbo0QOampoMVlakBv/+O6l7P/2EzLIylJeXc++8885Ljg+VDTUmJgZmSqVYOWYM7ly7xgAiTu7u7kJAR+OFHB2plOLw4XYvnzhxQsjIyOB0dXV5AKympoaTSqUYO3Ys9PX1ERQUJDLG4ODgwMaOHdvaVGzxYnKHjBhBTeZmzqTzsHgxOVKys+leW7iQCG4HEEUR9+7dQ05OTstrlpaWAoDW/XdwoGTE/PnAlCnQT0vDW/36cZg8GQDw66+/AgAvlUpfvjbDhhH53bqVyJVSSY3OvLzInmtrCykA/exs4dC+fZLNp09DevgwXfdFizqeU81x5GoID6dO5C9CLm8dGZWbCwCIzc0Fp6uLsV5e6DtsGM6//76YU1XFZE1N8Jg5E1lZWRg4cCDS0tJYbGwsCwkJEbOzs5m5ufnLZPvZsxYi6u/vD9Vc7ClTpsDQ0FACACkpKezXX39FXI8eYr/gYDbh4EGGu3dhu20byjQ1cerUKfA8jyFDhojNz+h231NbW4vk5GQOQOvs+l9+IZW/sJCcNRIJKdyzZ1MS5b33KMGmp0dlA8uW0bP1xg2gogLijh2wqq+H0tiYngn29vQM37OHOoSrEizBwfS5wEB6rhQX03lcvBhjP/6YNXl64tSpU+KKFSuYVtuRYCA3xu+RkXy2v79kztWrzHzwYAl4HtXV1Xje3OOinYV/1CgipyEhlIhtS05Xr0ZfNzfUqcYXenrSb9EXX7S8RSqVYvHixe2vz86d5B55VekH0K5Px7Bhw7j6+nrB/R//4Eo2bYL51q10XlQOirYwMGjfQ2P7dlK9IyJaX+vRg/okeHiQ4t+McePGYdu2baioqHit3+a2qKiowLVr13ie51cACN6yZUvZX9qAGmr8TcBEUfzzd6mhhhpq/D/CJ598stLJyWn722+/raNQKHD58uWGR48eNQmC8D5jbByA8V27dhVyc3M5AJg8eTLc3NxeWRsmCAIOHjwolJWVsaU//8wEExOUHTrUqnRv305BzvPnpJzcuEG1k8nJZJczMgKGDYO4eDGuLFuGe2lp8I6Jgde9e6jy8BB0t27lulZUgB09SmrwoEEUTDs7k/Kydi115z1wgAhl795kf962jchlG1Xhxx9/hKOjI/zT0kjV6d6dLIP29rgaEcHfu3NH4mlqijFlZZQs+PBDNM6ahe3bt+Pjjz9uPegbNyi4fKGREADg5EkUvP8+xJgYWDarN59++ikEQYCJiYno7e0N8ZdfxCoXF6G22SZYXl4uKS8vx/DhwwUfhUKCHj0AExM0NDTg0KFDfHV1tcTX1xd90tKQGhQkxI8dy/z8/Jinp2fLtRF37cL9Eyeg2L0bfomJlOjgOJo/7ONDalNnKC8nVejFkVsq1NQAX35JyYdx46jm8OOPqU66pISSA69CQwMSZ80SSidM4IapLKSlpaRqX77c/r2ZmUT6kpM73Z87w4aJVl99xax9fPD999/zLi4u3JAhQ1oX6ddfU4C/cycA4Pjx42CM8bNmzepYEWpqIuKZlkaKr5sbKVPffkuvXb5Mx81xePr0Kc6ePSuuW7eOujzzPCUdDAxIEb11Cw08jwMHDggVFRXcxo0bVYotRFFEcnIyTp8+jS7GtFuIAAAgAElEQVTV1VhWW0uEhTFcu3YNmZmZeP78OT766KOXGzkpFEQssrKIEDUjIiICMTExwoYNGziA7sfXalqoOu7t22kNGxlR0D9qFN1ns2aRy6MjwtCMqqoq7Gw+xwsWLOhcmd+6lUh93750v44c2eKOuHTpEuLj4wEAOjo66N+/v+js7MxMRZEISFYWJdKWLCESZGPz0v4IJSX48ocfMO76dbG3ri7jDhx4KUnS/gMCJVNOniRC+CdNAAMDAwEAH374IaSxsXQ/ffEFhN9+A/f4MVl+V6wAPDyQkJCAS5cuQS6XC+vWreNeKq85dIgSVVu2vPI7FQpF6zXkeUow6uri4q+/4p6nJ+wdHIQ5c+Z0uOM8z2P//v0oKSnBvNu3YevjQ8nOkpLWWum7d+m6nDlD/11cTNf7xAm0uHgAcheYmOB7HR1BKpOxRYsWMS4jgxRyQ0NSso2M6Dlz8yYl9nbtorIMMzM6zooKIvc9ekAQBBw4cIA3MjLCjBkzJC3PL1HE0aNHhaqqKnHJkiUSbbmcng+jRwMzZmDn998LVVVVHAD4+fnxo0aNohvk8WNK2p0/T+tXdS1v3cKlZ8/4MpmMmzNnDkNBAT2/XjVj++FDeg5ERr48o7szVFRQImngQKTfvInCqioMuHWrfTf/thgzhp5PbW3vogg8fdq+jj0khH4nx49vqaEPDQ1FWFgYNm/e/Hr3dxtUVVXh0KFDdTzPi42NjTKJRLJt8+bN//pLG1FDjb8B/lpLVzXUUEON/2FoaGj8o2/fvjoAKbETJ07UXLRokZ6FhcXXjo6Ow+bOnYvc3NyWAPHs2bNoamp65Ta5+nos/eknbn63buzWwoX86alTxdOnT0OhUNAbkpNJ7R40iGy5lZUUbNbUEOFSKAB7eyR+8w1inzyB75MnguuQIaj5/HPo37vHnfzpJ+TZ2NA85dGjibhfuULB59y5NFe6oYGCQqmUlJVVqyjwsrUlhS48nDqYg4I6XL1KAY8oUjATHo5Ro0ZJ3H18+CZBoAAuPR3YtQuS8nJwqmNRYcQIUtFf7BDd1ATY2CDBz08Q2iRXra2tlfb29rxEIuHv3LkjVHh7c4O+/FKqqVRKZTKZ1NjYmOnq6rLr165J6saPR01YGADg1q1b4Hme8/X1FSMiIvBDejp0y8rYAjc35uXlRUR73jwgJwfMyQld3ngD4eHh2Flby8PLizpcBwe/mmgDZKu9erXzv+vq0jZ69WqdXZuRQQqZszN1TX9VMllTE0b29pzxjz+2vqm+nrbRFllZFFjm5HRKtAVBAGtsZPX5+fj+++/58vJyiaenZ/ts0MqVtK8nTiA0NBRpaWkoLy/niouLO96/c+foe1esaA2C166l/SspIcXs/Hng8WPUpqdDU1OTl8vlREzef5+s1idPkuLa1ARNqRSrVq3iTExM+O+++67la1RE29bWVlzm5UX1rc1kY+TIkXhTNae9I8jltD/N3dtV8PT0RGNjI6dSlzmOe/1AXCYjG3FAACWQhg2jIB+getXbt+l++/JLIiChoaTMNuPp06ci7Zoc+s1zzDvE5s1kgQ8KImdHG8V5zJgxGNPcOJFvaEDOnj3s+bBhqNi1i+5DDw9K1IWE0Jp4kRjX1IDr1g2b3nwTppmZLFPVcftV4Dgi3J9/3l5R7AQDmjuVJyUlUS24hgYQGEhEWxRpvfI8cOIEPOfOxYcffoiNs2dz0o5IvJtbu6aAnaHdNZRIyHHg4YGB5eVwfvIEFoLQ6WclPI83b90Sdaqr8VBXl55xxsbtyZy2No34UsHMDNi+HQ2HDkEoKqLXioqIPM+cie62tmJ5eTkEQSCXRe/eNGv8/n3g7FlKdH79NZ2H336j7xw6lK5F794tddwcx2HevHmSZ8+ecVFRUS0HkZmZiaKiIrZq1SqJtrY2PctPniRnw7ffYt7EiZzKlREbG9uaiYqMJJK/ejU951W4cgXDDx6U5Obm0j7v20dkuzNkZtJvRHT06xHtoiJaz0VFdF8kJ+O0tzdujhnTOdFuaCDLfduSKxV8fWl7KgwdSgnfNu4f3eZnolKp/PP9ewH6+vp47733tDdu3Kizbt06OWNsQ2BgoMdf3pAaavyXQ20jV0MNNf7HERgY2B2ALoAnW7ZseSkCCwwMNATgCaCrtra2jd0L9rguXbpg6dKlLQW7q1atgq6uLi5fvoyysjKhvLyck0ql0NTUhEwmaw0CKypIOdyyBWzYMHQdMwbTTEwkALBnzx7x1q1bbPTo0RQ0WFtTYJudTcGpanzNV18BzQRDoVBg/O3boo9MxmH3biAiAnl376L+yBEY6ukB69ahdsQIZBsbC/UxMXDZtInT1NRsJYEqC7TKoufsTMTAwIBUMQMDaPfvj27HjlHTHg0NCrDOniWL4bFj8Dp0iMtzcGhtyLRmDSQ//oiVe/dCmD8f3LNnpLrK5a2qzu3bFHSGhJAamJGBR/36wbVNMLxgwYKXn/9hYRjftWuLTbCurg4RERE4tnChaAEw47AwJCQkoG/fvuLw4cM5iUQCR0dHWE+axHDqFDUxe+89Sl7k5ADjxsFy3DjM3LcPv5SWSrBhAwW8r4PkZAoE/wyTJ9O5On2alLIPPiBCZG5O6u6AAcC2bcjLy0NhYSHKy8vF+vp6oampCVW2tpx2Xh7zTEujYL1rVwps22LZMiIFJ050+PVVVVW4dOkSeldWIubcOXSfMgVz5sxpCUJboKUFjBiBxs8/RwbHwd7fH0VFRSwoKEhYsGBBewZ0/DgR6nnziHi1JUiMkWqfkUHrdvFi2IaEiPfXreNw+XJrTburK71/924iHGfPQhoejoULF0q++uorCIIAjuNQVVUFAJg3YQJDaWlrXWszzp49y/v5+XESiaRjK8nHH9M+7d7d8pKuri4GDBgg/PTTT5yWlpYwa9YsrmvbBlWvg759qcv45ct0TRYsAKZPB775hsYQGRiQyhYaSuSneS61hoaGOPLRI/bU0BAHt27Fxs8+a62bfRFHjlCSrWvXdokZjufhKwjw1dICMjLQCOCktzfOM4Z333239dru2UPnS5UUEgQ61+vXA3Fx4NavR/H27Tj/5Alcz5yBRCIRRowYwem9WF/ceuKorruysn3N7AtISUlpmTHd4Vxvxqj+GaD7QHVs/fqR4vnuu1R3/vHHRJo/+ogs1v8BFA4O+G7IEFhnZ8Nv714GI6OW5ye9QQGEhUHh5QWdhw+Z3NISNarygBexeTOduzbI7NULoWfOYNCAAai2thbkVVXc72+9BZAjSFJfX4/MzExoaGigS5cukB8/TgT1ww9pA6qGXpGRdB4+/piud3Ex9UX49ltg0yZo9+6NKVOmsDNnzjBvb29oaWlBEATIZDLhpXKCvXtRsX49ijdvRlO3bjC0tsa7bUf/LVlC/377jcoDVI3gMjKgUV4OhULB0h8/htPBg692E8yZQ46okSNffRFqauj5//XX1Btg7FigoABNUikUX3wBmxf6O7RDXh41CX3RtcIYnUdtbWqcpmo+99FHRLgnTQI4DjKZDBzH/Ucjv9pCR0cHgwYN0oyIiFgPYO7/1cbUUOO/DOrRX2qoocb/KLZu3bpbIpEc1dTUXAhgza1bty4HBAS0yHeBgYF+Uqn0vpmZ2QwdHZ0J48eP1/2zsTXa2tqQSqUQRRHx8fEsNjYWsbGxiIqKQmpqKoSmJsFaFBkePSLr9qxZpNS0abBVXFwsJiUlYZCzM8OIEWTBraykmktbWwrCIiKoFnTAAIDjYLBlCx4ALHn8eL5n//4c3ngDZWVlSEpKQncHB8DYGD9ER6PQxUWsz8hAcnAwcxs/ntTtMWMo4Lt5k+zhKmhqUiAzZw4wdSqehoTANSgImrNn02sbNrTaSRsb8dDbW8x0c2Nuq1ZRzWdAANjgwdhfVATv4cMhXbCAulNbWlIHaV9fUulEkUiiuzvQuzeio6PFXr16McNXWRZHjSIyM3p0SyDlsHkztMzNWUhpKbKyssDzPEaPHs3u378vhoaGsuLYWNG2Z0+mFRVFQdiGDVRr260bbbOhAbpjxyLdzg65bm6is4fH682GGTu2wznDHcLIiIhETQ3ZRa2tITo4QOzVCzVWVriycydkX36JaGNjZYNCwfE8zzHGOImWFnvTyAjSoCBaD48e0XeqAv6yMlpLU6a8HIwCOHjwIH/r1i1OoVBAy8FB6PfOO8xnyBCuUxXXyAgpmZmQPnkiTFy7lj3MzBScnZ257m3reKuricC99x65Eiwtqdv2i2CM/r31Fm5pa8Pvq684/SNHwM6coeuur99K0t3ciHD26AGJTIaY2FjR1taWGRgYICgoCEqlEoNOnCClvE1jPwB4/PixmJOTw6ytrZmBgcHL+2FsTOfGy6vdy7a2tszJyQlKpRJRUVFCywi5vwKJhMjK+PGUDDt+nIictjbduzNmEBGZP5+cHba2MHd1ZbKsLOhHRUG7thb8rl3QCw9HfUgINFSqfWkp2d7Hjycl9dEjarJXWUmk88MPW2fQL1wI6dy5sB82DFGxsYiOjoanpyc0NTXpPlMqqYP40KG03UWLKCmwcyfg7Y0uS5YgOTlZyMzMZEVFRezhw4fo3r07OiXcMhlw4waEefOgXLkSkg4SBbq6uoiMjAQAPHr0CLa2tujw2gC0DlQW7LVryc3z9Ck5A1aupCTGpUtESnNz6RmioUFqZ2EhPYtycmgtFRaS+0Mmo3IYiQQSmQy8KOJRTQ2cJ01ihpqadA+qZlP/8APwr38hbvBgnNDRQYO2NsrLy9HQ0CBER0cLoaGh7NatW6y7pSUMPv+cyGez2isIAvbt24dKAwMYlJWhZ2UlkwUGos+bb8LCwgJJSUmiKIosKSkJCQkJuHv3rtinpoZJeb5dXTEAeg7PmEGjsz76iOz/ZmbkXNHSAvbtg0l9PZI1Nfm79+6xHj16MKlUirt377JBgwa1f2Yxhhi5HAXh4fB58ADOs2eLpvb27d/z4AE9R93cWps9vvkmmK8vCuVy4VluLjx+/pl1Wi5w6xat64kT/3zMoZ0dEfuhQ8kJs2EDIJejqakJkZGRMDAwEL28vDreSFwcJZwHDXr5bzIZ3XOjR5NbBqDnUWYmObkCAsAYQ1xcHIYMGQLuT0of/gwZGRlCTk7OvcGDB18IDAzUjomJCQoLC9sbEhJyLSAgoOD/auNqqPG/CLWyrYYaavyP4ZNPPhmro6OzaOXKlRpaWloaFy9e1E5MTFwIYAMABAYGSuRy+Yk333xT11WlvP0FuLi4ICYmRszJyWGDBw9GcXExnj59CoPlyzno6JACdudOh58dHRfHWZ87h4y33oKVRILiqipYL19OlriJEymYPHCAFMw33gC0taFra4sxUVHY6+7O4ZNPAAC2trYYMWIEzpw5I47+/XfWZ+NGGDk7c+Vnz4q9s7JEqBoDMUZ207o6ClBFsX137eb3VFtb4+YXX4iTrawYhg8nlSwri+bumpmh6upV8CUlFMBu20bHaGcHhYUFtu/fj1m//gpHGxuy+33+OdX39u9PKn9CQtsu36LwCpsnAFIvqqtJWZ8+nV4zN4fr1Klw9fLCzz//LGRkZHAHDx6Edk0Nmzx3LozGjWNPr19Hvz/+oMZTT56Qqp+dTUTozh1IysowoaQE+/btYwqFQpwyZcqfE+6pU6lj/Lff/ulbAVBgOG0axJ49UfTZZ0j57DOEDh0KxnFwNTYWnBwdOc/Vq6X4+muy9KuUmqYmsntWVND3qYj2s2dEUtLS2o1EawttbW2uW7duwty5czn29tscevUid8Qr8HtVFeZ06cLh22+hMDcXo6OjkZmZyc+ePVtSnp4O0507SYUzNSXCQs26OgTP86j6+WcMs7RkwZ6e0PzmG5jb2pIqbGNDzeiamsjxsGQJsGgRuMxMdJkzR4yPjxcyMzMlhYWFGDF8ONVBd2C7nzt3ruTXX39FcHCwsHTpUo7neRQVFSEqKgoPHz7ElMmTRbfsbAaeb5eQYIzB0tIS2traLCEhQZKSktJ+OsDroLaW6rdv3KBxTozRMc2dS2Tw/n1S8qXSdqTSaNs21ObmQkhNxW+RkdBqaIBVbi5mNjURUfjjD+D6dSImT58SofjjD0pqJCZSqUN+Pt2LU6cC//oX9P38sHTqVGQvWIDKt96CYUkJEap58+h5ceAAEdW0NCKw8+ZRAoAxLF++nAPIavvFF1/gyZMngqWlZafMpKBfP/y0YAGM165FpZkZ6uRyODk58dOnT5cIgoCcnBz4+fkhJiYGSqUSR44cwZY/qbcGQM84DQ1ao0lJ9JqJCSnyhw/TPX/hAj1nQkNpPajI1eDBRNDlckoE/fIL2bPNzeFXVIQqPT2Yfv893Sv6+kTeBw0i0rhmDfwYQ0V1tXjnzh1mamqK4uJizsLCAp6ennj69Kl46fvvmduyZbxuVpakISUF9fX1uH37NgDgA6kUmrW1rf0U/g973x0W1dl9u94zhTZUKdK7AoKAAmIEscSOPZZYY0uwxJIYTdWg0WCiiSXGllhQoyYqdlGDAhZERCliA0QsSBkpAtJmzrl/bIaOJb989/fd5856Hh9kmHLKe87stdfae7/7LszNzeHr68uKi4tRUFAAuVyOzNhYHNXV5UfPnl1/bKur6Z555gwlVFXNEA8epMc3baLf8/KAK1cw7f33RcmbNwvhz58jcPBgNO1rlJeXh9zcXJS/fIkbPj6QFBYK/TduZLC1bTyG0MOD3Cd795LarJow0L07+PBwwf3qVa6uw3pTHDxI48YuXWqdaD95Qt8vH31E3w0ZGUTOG9jSVR3hra2tW7/fVlY2HvPVFO+/T8kCQajflvfeI9fF3Ll4+vQpAODp06ewban53xuC53lcuXJFUVNTsyo0NFRXKpWednR07GxmZqZ55cqVuQA++MdvroYa/8tQk2011FDjX4OGhsbMXr16aas6umZmZr5UKpUNir7wnpGRkYlbw0Ysb4nAwEB269Yt9AgMBBcUhAdBQTg0aBBcli9v/uTiYgr49u9HlYsLMpydcT8iAt0nT+ZjLl3iXCMilEPOnBGJVYTUzY26Pv/0E6kFQ4fixdKlKDtxgp0+fRr9+vUDx3Hw9/eHn58fY1u2gDk6IiwyUuixZAm7mJWlzP/yS86ia1c+ODhYVPjuu7h79y66fvIJuLg4Ir9NYGJigmwVSV+4kIiRmxuRLQCMMWpkKZUSGawlifPmzcOmTZuEoqIiBicnslOPGEGBcW4uBdanTlGDnf37IenUiYj06/DVV1S3Kwhkx543ry5AnThxInfvzh3kZmYicPhwcPPn43liIiJ//RVdLCxI6du9m5IX3t5Qdd9+UVqKX2s7vYvF4jdTtj/6qFHTrZaQl5eH9PR02NvbIzExEbm5ufzz5885bVdXfoyLC/dOTAwUe/dC09ycgu+iIkqmdO5MAaa1NTkNQkJoW//8k8gCz9PfoqJaJdoAMGrUKPbDDz+wgoICmJqYvFaBqqyshCAIuOLvD/uMDAxgTHTfwwPXrl0THTx4EE4//ICy9u35tjIZp1lbz9+wS3BFRQVu3boFZ2dnGMhkuLRundLpp59EF8aN41O9vLhBlpa0DdeukcofFUWk7/59Wg8//wzcuoWAsjJu36VLEEQiaGlpodumTUQOPm8+eYfjOAwYMAAbNmzgVq5cWdcvQdUsLSU1VXA/epShf3+6dprAwMAAwcHBwsGDB5mPjw/69ev3ymNUB0GgpEN6ej2BGDOG1taECfT3L7+kdbZqVaPRbxoaGnB0dISFhQU0NTURGxuLe66udJ4Buk54nhTbzExSBa2tyYY7fTqp9RkZZFMHyGpbVIQCjoOZXI62RkbkJklIoNr5O3co0ZCdTeUgOjpUa95E6SsqKoIgCMjJyUFlZSWp401QWlqK8PBwuHXtyg/89VeuMjcXpyZMwJMnT9iWLVuEFy9eMMaYYGBgIPTq1Ys7f/48/kniEgD1eJDL6TjExlJy5+ZNOsZJSXS9qOzYAK2lpv+vrsaT5GSkHTmC3hIJkXKZDLhxg5JaHAcwhhcvXiAxMZEFBwejc5OElIeHB3sYHY2ilBTukqGhQiqVMk1NTRFjDHqFhXhUXc23272bg7MzJcOuXCHlWiKBgYEBDAwM4OzsDJuQEFakVLKNAD9z5kyaRNG/P91LDxxofH2amTVuTDZlCjBlCrjbt+GtqcmevnihLJk3T9S+Z0+2evVqvry8nJNIJBAEAXp6enxhYSGnp6cHrR49eGhqijBhAiUgGhLutm0pcVNaSmpzu3ZA9+5wdXHh4mNi4BYcjGYr4MkTOg/79lHSoin27KFGclOmUHKorIzWWsNa91owxqCnpyfEx8czf3//5qUtAN2v58xp/rgKHEf75O1N180339CxO3wYWLcOFw0NlV26dBFZvcqq/oaQSCS8QqF4XyKRTHJzc7MYPHiwZlZWFgRBCAwNDR0tkUi6cRxnXF1dvWvJkiVn/8cfqIYa/5egJttqqKHGvwbGGFNZyQRBQFVVlZgx1tAj7uzo6Kj9qk7ir4OzuTmc160D+vdH8aBBOFlZKXgGBvJoWFf33XdENM6fp8y8tTV03N3RtXt39JHJoD9yJOe7ezeOxcSwrSYmvNXx45yxsTFK7t1Dz/v3oTlmDKlphw7BaMcOTNy1CxFHjghlZWVsVG1Qw3Ec2U81NMCfPctuJCUJbVJT2aRDh9hPYrFo4MCBiIiI4J8+fcpdtrHhZ82bx8nS08myOX8+AFIDUlNT4erqWr+D7dsT0awlEBzHkboSFETjimpJuKamJjQ0NASlUskePnwIOzs7IhBaWhRgentToNSuHWBtDaVEAptevUiVWLSICEZISPNOuKrP5ziydAYEkGIOAEuXov3Bg2iflkZKkJERtF6+RMeUFCJ4XbuS+rR6NSn6338PAI0a2qnGbD1+/BjWKqt5S7Cza7XWNiEhAVeuXBGKi4uZTCYToqKimLW1NW9gYICCggLM+uorTiIWA/HxEPfuTapjQADZzVUJj1696FgdO0aKqLY2HZMNG+jci0TNRlo1hVQqhZWVlfD3338L48aO5erU8hZQXV2NzMxMAEDnLl2A4GA4zpgBR09PZJuZ8Tq7d3O3OnfGcwcHlK5ahamdO4OFhMCK4yCXy3HixAlkZ2fToZFIMDwqCnIzM5HHlSvIP32aQ15efd0kx1GgPmQIqa1t2pBCGRICLFoEy3ffxcjiYhwcMwYLFy6k0UOvIMGGhoaYOXMmUlJS4ODgAFtbW3Ach9jYWNy/f1/AgQP1ZQMtwNPTk+nr62PXrl3w9vZGYWEhzp49q+zWrZuoKfGqw5491D8hMbHx4xoaRJzOnSOldcQICvyVSlKSGyh7Wlpa6NmzJ2JjY9GuYSMu1TGytaV/aWmUfJFK6zthDx9e7wr5/XfwPI+/167l/ffs4exsbOhYhoSQc2DZMiIsCxYQERwzhu49nTrVXa8AJdYmTZqEAwcOsOzs7EZKf0VFBa5evYrLly/DwsJCMWTIEDH69YPs5UuMfvAAlUOGcBcuXICNjQ06dOjAADClUonMzEz+6dOnqK6ubr18oSWUl1N/g4gIIoXDhhF5W7mSnALduhHZrqmh9ZOcTLZrbe1GSYSynBxUzZ4tKAYMYOlFRehkZ0dJwW3biAB26AB8+SVOSyRQKBTwblJuoIKdqyvsxo1j3v7+dXFp8apVUO7fj83Tp3OzTE1hqEoYffABqb5LlzYi0GU7dghXo6MhLyjgoseN4y0KCzn5iBG4p6mJ9ysq6jrwA6D17utLiUlVXTdAic7wcPQvKxM9mTwZ+hER8JwzhzO3sYHc3x+GRkaQyWScIAiqqQv0vfPnn3QMt22rd7eIRNREU2XznjABOQYGfMGpU6zS0FCQTp7c+IuwspLcBZMm1SeGVPj9dypNEoupIeT9++Q+aK0fQS1CQkLY5s2bsXv3bn7mzJnN3RQ1NY2b1LWGn35q7NpxcADS02FnaspJ3N2bTyp4S9Q2qdOKj4//wsrKSurl5cUYY7C3t4eXl5fl8+fPf7OxsZGJRCIWFRU1KjQ0VLOlfjBqqPHfCDXZVkMNNf41VFVV3SooKBgAgGOMYdy4cZoHDhwIDwsLG1dVVTVRKpW6ymSyf1bYpVRS0yyJhNSX8nJc7NgRkqdP0SMoSISKCgqUtm6lQKpzZwrEGigzlrVjryAWQ6OiAu8tXswl7N0rpGdnK1+eOCEadPAgahQKPLt5E/aLFhEx3bkTtsuWoYuTE3+vvJyh4RSHvn2B2bMxbNgwpKens8CxY9lJhYLvM2AAE4lEzNvbm8vPzwcnkXC7oqLgeecO/G/ehLiWbB8+fFhpYWHBBQUF1b/n6NGkWtQHUUypUBCRKCkhxa12PxhjwsWLF4WXL18yMzMzZciZMyI8eEBWekGoV6V/+gn4+WdkREfDxcqKFLhz5+o/S1ubmkWpCPOsWaRuHzpEyq+3N9U5fvQRvbaggGzry5aBT01Fh9RUqtf+5ReyHE6eTO91+zYwfTpEIhGkUilsbW1x6NAh3LhxA5mZmdDW1hYcHByYXC5XmpubcwYGBuzZs2do164dLD78EDo2NlBu3w59fX3wPI9Hjx7h5MmTSrlcLurYsSMGDhwoODs7s6qqKmhoaHA1NTVYuXIlfvzxR1hbW8PKygo94+Ko7rp3b9pX1SzYv/8mxemPP0itSUggpfTgQbLAv+GYnSFDhrANGzawF8nJ0LO0pGZtLeDw4cO4d+8eLCws4KKqwf7sM+DMGYR4enJ8aSm4pUvx0tCQW7t2LR5u3MgXSqWc3nvv4ezZs0J2djYzNTXFAB0dXLp0SbguCMxx4UIY2dpCR0dHcHBw4DmOax7xqpT5jAwqZ1i/HlpZWSj58Uf0f/AA3ObNtA5eE3CbmJigd8PeAwDMzMxw8eJFUdHff8Pw7NlWZ5QDQHJyslJDQ4O7desWi34m5XEAACAASURBVIuLg7u7uygqKgrnz5/nu3TpwnVv2Azs5UsiFl26tEwmOI6u8Q4dqKnWgAHA9u1ExE6ebNbh2djYGKWlpY1nbTdEmzakwrY047oWZ86cgUQiYT4+PvUPHjhAn3vmDCm5GRmkxuvokC1/5kwi3TxfNyPZ3t4ejLG6Oe21Y6eQn58PsVgMZ2dn5ZgxYyg209AgAhYcDM29e+s6pKsgEokwatQobvXq1QgLC8OSJW84NamwkBIxMTFEoAFKNOzeTZ/5/DkdU9pASnzY2VEi7e5dsvD/8ANgY4OaTp2g/+wZ83Z2hvG0aZRc6NmT9psxYOlS3BSJ+Ac3bnDahoYCx3HNM61lZXTdNSSY16/DoFMnZO/aBcXVq43t3Js2kVIcH1/vppg2De2dnFj7GTNQmZmJ4iNHuMNdu6Lg+XMAwJo1a2BmZoapU6fWJ6WmTSObuaqZXAPklZTgSGCgUObtzfpmZfF2Z85w1qpJChMngjUtCbKxoXtq//50v1U1gFu5kn4/cwbC++/D+PhxTu/xY4zLykJNZSU01q6tf4/iYuDjjylZo0JJCb3nzZt0PXTvTsTdz+/1tdyghJOdnZ1w69YtLjo6Gj1UzdoASrgEBjZONrSGnj3p3HfqROUTWlqUYPrlF0FTIvnn2fMGMDU1xeDBgzUaPlbrrNEAoAEA4eHh5VKp9Mjnn3+uJtpq/D8DNdlWQw01/hXU1mOPd3R0rAtora2tMXfuXO1jx44NvH///iOxWKz9jyyPgkDE5PRpInC1nWXLS0vhcOgQe/HVVzAqKAC3fDkpMk0DoabvZWQE8DzY6NHwCwhgfgUFIpiYAOvXI62mRjh64ADr1asX/OPjge7dUTVoECx37hS9/PHHxu81YgTg6Ag3Nze4ubkhJycH6RYW3IgpU6A4dQqPHz8WGGOsvLwcFhYWfKavL0txc+NnnT4t4j/+GMUTJ4rGjx9fN6IoPT0d13/6STl8+HCR5pw54HV1cfnyZTClEonTp/OdBg7kWIP50YwxJpVKmZ2dHX/nzh1RtaUlpA8e1I95WbSIiFTXrjA2NhZOnTsnaL/3HrPx8iLlHyCiWVBAVtIPPiBXQE0NEevqamqEJBaTynj1KgVcT59Scyk3N/A8jyQfH7SLiKD62atX6X2vXqVawv79IaqpgSAI6E4duIXMzEwGAKampkJeXh60tbW5x48fC8nJyYznedy9exfGY8cKRcXFTLl2LRhjDYNtUe385LoAT1WbKBaLMWfOHOTl5SEjI4OPi4vjXrx4IQw9fpzh++9pDc2fT02LOI46Wo8bRwRBJqOAf9cu2j8VCXkNjIyMIJFIUO3u3upsb4VCgXv37sHJyQnjG3b77tqVCM/vv4PbsAGwtIQ2gC+//BJYsIDbrFCoZkazGTNmwOLCBWDrVtisW8fyzMyg6vBdUVEBc3PzVwe8Mhn9W7QIrFs3vNOxY33iITb2zdStJnB2doZEIsGB5GRMEovR8KrjeR4xMTHIzMxEYWEhX1lZKaptcIjx48fDzs4OSqUSGzdu5KKjo9GIbIeE0LbWlh60CisrIohHj9LzO3em1/bpQ03mGENeXh6Ki4thZ2fXepJPR4cU6YqKVkcx5eTkCJ6enqyResxxdB3s2UOW7MjI+rr18HAiM5GRpMCHhgLOzlA6OEChULDz589DJpMhOztbeP78ORs4cCA6d+6MZgkTfX26lkxNKdHVpC5WR0cHvr6+SEhIQFRUFHr27Pn6RlULFpB62nSNd+tGCra/P6mmlpa0j6ru4rt2UUKirIySbyNGQMfEBNE+PvDt0QNWVlaUeDA0rO+JMGYMji9Zwn26di3OjBjR8vbExhLhU5HHw4fJLXDyJCzNzKCVnMyfO3cOo0aN4jiOo3O0bh0lO8eMoXuVtzfd90aOhObYsWiblIRZQF3X/bS0NBw8eBBhYWF499134e/vTwnCVkZiVVdXo6SkhE2cOBHHjx8XLo0bJ4wsK2M2p0+DpaWRam9lVZdEAUBJm4sXqdni9Om0bRxHiUktLbCMDJz49lulwthY9ERfH8OHDat/7ZYtVIKQnFx/HM6do7X99Cmt53v3KCH4qnFhTVBRUYF79+4xHR0dxMTEwMDAAF6qhOCDB3Q/f9PGZu3a0XdE7TG7a2AAx+RkzrnWcfN/AyUlJcqqqqrWs2JqqPFfCHU3cjXUUON/jNDQUF2xWLy0TZs2Qb1795Y2tImLRCK4urqK27Vrpx0YGChpsW7sVQgKooAgNJTqI3m+rqa2fffuuGZkhBQLC2RVVyudRozgXtbU4MaNG4iLi1MePHiQ8/T0hFbD4KSmhtRvIyNSEXbtIlIqlwMbNsDU25tZWlrixIkTeF5aKrSfNYsVffUVzowfzw/bs4exx49pmxijIEVHp45kHT9+HNp6eoJtTQ3bc+MGXspkgrm5OS+Xy7nBgwczQ0ND9vDhQ9536FDuzrNnSJNKEaStjSpjYyQnJ+PEiRPotnUry3z5kreVSjmWnIwkU1Ohr0TCniUlQef2bXZZLOYrqqpYUVERHjx4wIqLi+FUWCgM2riR6YaFgX37LW7fvYu8vDyYuriQArJ4MTxDQ7myly9ZZGQkysvLlXZ2dhS4mptTTZ6RER3fDh1I6TlzhhITUikpK6ampKyEhFBgrKMDrFkDbtMmHPfxQfeIiMZKi5UVKXtZWZAMGoSHBgYInDAB/v7+LCgoCN27d4e3tzfz9fVlXl5ezM/PjwUFBaFHjx7o0aMH/BITWRdtbbhOnAhHR0f4+Pigd+/e6N69O1rrXs8Yg7a2NkxMTNC+fXvm7OyMM2fOMJmuLszHjCHLfFgYESJnZ/qZkkJKn1hMNbs8T/v5hn0Fnj17hps3b6K3oSFEjx61WLeclZWFlJQUTJgwofFaVCjIol9aSs2OVM2bnj4F9u1Duw0b8PTpU8HFyAidNmxgGD4c+OADMGfnRt2snzx5wjIzM9G1a9fGhLu8nBwKBgak7HfpQm6FGTPIgpuWRgF8cHDLHc9fA8YY0tLSlLmMcfnl5cgqKVEaWVhwOjo6+PXXX/k7d+4wqVQKNzc3JpVKhZCQEBYYGAjDWpLPcRxsbW1x69YtVFVVwcHBQXVQaRtfNSe7IVxcqIt8RQUlkS5epMd0dFBcXY3ExEQUFRXh0qVLQkJCAuRyOd++fXsOAIqLi3E1IQFtly+HWCJplXgkJydDR0dHcHR0pGM8aRI5X3x8qGHYb781L8mQSmkdjR0LmJmhsmdP3PvtNzy0t0deURGe5eaioqKCLV68GLa2tmi1vMbAgBJB779PCnyTMUvOzs4QiUTKmJgYLjY2FikpKcqkpCQhPj5eiI+PF4yMjLg2bdrQ9fzJJ3S+x4xpWRlt25YIvoMDXR9SKSXdrl0DTE1R3q8f9hYUCKeCg9kVCwvU7NsHx0ePYL9oEdjIkXQf+eqrOrJdVlaGwuJiXNXXB/P3h6dYzJopqSUlNB7LzIzOn60tWfidnFTj6VhycjLr0qULJCqnA8eR2+fCBSKoKSl0rwsOpiRoLVTH1NTUFD169FB1xxfeeecdxlTqbGkp9SxoAENDQ8jlcmV8fDzmzZsnellRwc5euSIku7jA49NPmWTPHvr+GD2avp8auKYwZgy5LJ49oyTA1Kl0Xzl9GhqRkczh2DHW8cULSIcOpeNbVUUN+SZMoPumIJDTaM4csrn37EnHVCqlREfDuvBWUFvGhaSkJGRmZmLBggWIj4/H7du3kZycDJFIBNP0dIhyc+tcDDzPIzIyEoaGho0t9/UHk5T15cuR8NNP/JHCQibz8BBcSksZ8/Vtti7/E5BKpdKsrKze58+f39GjR4+q//gHqqHGvwA12VZDDTX+MUJDQ3tevXo1ShCEFVZWVp1Hjx4ta6npD2MMMpmsPlB6HV6+BD78kIiLuTkFtTIZqSrBwVRr6OcHUXAwbLy9kcMYnjx5guzsbNy9exfXr19ncrmcMzExEQICAlijIJbnySa8ciWpWRkZpEwtXlwXfBoaGsLV1RXR0dG4mJbGUtu3Fz46dIgTzZ9PdYwaGkQ+V6+m19faay0tLXH+/HmWaGwsONy+zfoOHMj8Bg/mAgICYGRkhPz8fDx8+JC/n5WFKxUVzCw3F36LF+Oko6MyLjGR6927N4yvXEECz3OaH3wAE3t75D1/zreNieFMcnLQ9swZdsHXl2VmZSlTU1NZaWkps7GxQZCrK6s8dQr6qan4S0tLiIuLY7du3UJAQAA4Cwva38hIOM6YARcXF1y4cAHXrl2DtbU102tIaBQKqhX/4AOyLUZHU+Opx4+pK/aYMURqLl+mgM/VFWWdOuHa06fo3nTUDp14oG1bVPv5IfbRI3TdsQNo3x7MzKx1YqHCmTMQ8zx0BwyAiYkJDAwMoKGh8VbzXGUyGdq0aYOTJ0+iTZs2MPHxoQB9yxY6/87OZElNSqJANy+vfma5p+cbBY/p6el48uSJECASMaSnU2KiCQoKCnDr1i307du3fr8FgRS8Pn0okTR9Oll17e1p3FVFBTSCg+FtZsaco6IY2rShwL6FEU+Ghoa4c+QI8/XwAHv5kvYhJITI2b59dD7j4uh6evfd+rVuZ0e13B99RLbcgIDX1oA2RefOnbnu3bvD+ocfkFtUxF0uLeWjoqJYaWkp+/TTTxEQEAAnJyd4enoykUjU7LzLZDIYGBjg3LlzcG3XDjpDhlD5Q0PF8E0gkxGxNTenxnr79wMnTkDRvj1eaGlBLpeD53nG8zzLycnhkpKSEB8fz8fExLDs7GyYHDiAtgsXUsKpBaSlpTFNTU3B2dmZ5pGvX08OialT6diqXAItQUMDNVIpVhUVIc/cHCNycjA0KQmZFhbKd959l7N7A/IEDw9aKxxHymKTpICtrS0XFBQEd3d36OrqcqamppylpSWno6PDRUVFCRoaGqxg+3YoYmPBz50LzdZGhQFUCrFuHblchg0D1q0Dv3QpfpFI+CgvL9Z97Fjm6OiIO3fu4LGVFbqvXw+Zjg74rVtR/vffyLh3T2CrVrFIQ0NlZGQkV15eLvgPHcoG9u7N4OVFpLjh+Z0zh1wJjx9TA7ShQ+vIb05ODo4fP44+ffoIDqrxWtXVRMrd3elaXruWmtT99NNrHRpWVlaIiYlhGhoa1DMiIYHWjJsbampqcPfuXZiYmIAxhvbt23OxsbHM1NQUfn5+sLGxYXFxcay4pIR3mz2bYdYsUoc//piSuJqapG5LpXTfPHSI7qHdulEyZutW6PXqxaoKClCVno7M/v3R1tCQ7qnjx9NxOXyYVPOzZ8mhFRND7hvV+jp4kKzcr0ha19TU4OjRo8qIiAguMzMTJiYmCn9/fy4wMBCOjo6Ii4tDeno6+MhIocrPj1VbWuLAgQPK6OhoPHz4kN28eRPV1dX1ya+muHcPyZmZcBo9mvWZNImx8+eptKCl8WH/MoyMjFhcXJymUqncGxMTU9GjR4+a179KDTX+d6G2kauhhhr/CMuXLx+poaGxe/jw4VqOjo4Qi8VvF6W3BJ4nItehA1kmnz+nGcdmZlSbOXkyWfRUGXZQt+NRo0bh0qVLXJTKGg3A19dX2b9/f1EzS2VJCQXKM2eSgjtxYosBmrGxMWbOnMl27dolCHp6gmj/foY1a4gIrF4N5OeTEtrAVmpkZIQvvvgCANjdLl0E5aZNDL161RFEa2trlJSUiIuKimBpaSnkikTs2qlTvL1IJPL47ju0mzsXoUFBDACORUZCMy6ON5PLRfr798OkbVuWX1CAWRYWACB6+PAhwsPD0XP9esFw/Xp2ZsYMvqS0lJVnZ7M+ffrgwoULvFgspp2fNo1G0Bw/DpPBgzF37lxu9+7d2LFjB/z9/fmePXtyIpGImoV9+y0FoHv2ECHs0IHIaHAwJUE++YT2PywMiI2FcPIknO7epS66tR1p8/PzkZ6eDqVSiZqaGlRUVPClGhocLC1JMReLiay/isy+af3pa9ChQwecPHoUqSkpcPvlF1KGVqygdRURQRbZc+eIkA4ZQg6H0aOJuJ04QWPMXgETExOUl5czxbBhELfS/CkiIgKMscb23m3byCJ64AARpz/+IHJ84gSpg6NHE4nYsoWSQg3J3PHjRLxOnAA2b0bkxIkY8+efYMbGZO/19qZr6dCh+tds3tx8w0pKSD2uqqJjMGMGNch7S3AcB6Off4YsPR3yrCwOACZOnNhy9+MW4O7ujujoaOF+VBQzlUpphNw/Rd++lFRQKlG1cyeurFkDk8pKlAwbhpKXL3lfX19OLBYjKioKJiYmzMjICObm5kJUZSUz/eknmLfytk+ePBHatm3LJW7eDG+xGFxkJCUpfv/9lQkKlZ0+NjYWnESCyd99Bz1dXeDSJVTv3y/qMGkSqZbTp7c4y70RPDwoWWJuTvb5JmCMwdjYGMYNmrIJggBdXV2kb9kCt5wc4eL77wsPw8OZjo4Ob2dnJ+rVqxcAcl/k5ubi9u3b/Mj+/Tkr1XW+YQOqP/sMYQoFhMJCbubMmdDU1FSVN2Ds2LEwre0LcGzwYCV34YLomVjMXNu3B8/zogUrVgB//smkbm50387IoGTI5s2UUFUoyIFRVET7FR7eyFXy9PFj6JaUwMbammHECLoP6etTwujCBerDkJND6zYnh0j7uXOvVH81NDRQUVFBv4SGEmGuqcHt27dx5MgR2Nra8r179+YqKytRXV1dN8N8x44dAAA9PT2qadHUJGfDxYtUWz98OH0vXLhA97awMLKGz51L5zgoCKLUVBiuWIGVaWkQR0bCXKGAaY8epIzv3k2kHKDmbTt2NHZ3dOlCvQFUjT9bSFgeP35ccePGDbGBgQE++eQTSKVSSKXSuhuttbU1PvvsM4jFYjwYNUq4du8ey/rtN9jY2LAhQ4ZwNjY2KCoqwq5du1BWVobS0lJBLpfzPj4+okBVHfqHH0IwNWVGn32G/EOHYDZ7NiX45s59dQnXv4D4+HheEIQTAKwB3Pnuu+8+/Prrr9W2cjX+q6Em22qoocZbY9myZWMkEsmOyZMna6nqRv/HUCqpE/TatZQlnzKFalqLikg5UgXgRkYtvjw1NVVpbm4uKi4uRkBAALy9veuJdlUV2SC7daP3LywkYrl4Mam+xcUU2Hh4ELGPiwP69YNWWhomCgLbnJvLsk+cgO3IkdQ8LCiIgp2FC6kLc4PGShzH4cCBA0LWkCFs2sSJpNbUdmo2NDTEuHHjUFRUhNOnT7O+ffvi7NmznGZVFSbq6gIiEeatX4+L77yDGz4+ONexI8Z5eUHXxwc4dQoWI0ZQEgI073vJZ58B168zrF+PsR06cE9mzYKJiQl+++03wd3dnQPI5mxmZoZjz54p3P74g3Nyd+dOpKYKT548YQBw/fp1Lv3gQYTcvg22YQNZmF1dyZJrb08ElDEKJtu0IcX7xg3a2YwMKHR14X/pEiVD5s0DwsOR5u8vXHv8mJmYmEAkEkEsFnOdu3ShRAlA6m9VFQWlrWHcOCL7+/b9s/WUn09k8vlzzA0Lw7H583n07s2hTRs6H9eukfX0r7/I7vn119Q0zcSEXAuffkrJnvJyvKrLeGFhIQRBALt5kxqE7dnT7DkWFhaQy+X1M9hTU0lV//nneoXKyoqSQMOHE+ny9KSAe9o0ItoLFpDd9bff6No4fZqs0gMGQENDA5tmz8bEiRPhIBbTWn4TSCREcDQ0yIqrasZ08OArlbMWYWQEn2PHcN7DAyNGjGhdFWsFBmVlvPGNGyKcOfN2n9sCqjQ0EOXjI6SXlbEPT5yAlkyGXoIAjBnDqWYKB5AKxwBAqVSyszo6wtU//0SwSMQkqnVaC7lcDoVCwa5cuYIhhw/jpa0tZL/8Qsk6lX24CV68eIHLly/j2rVrAMj1Mn369PonBAbCVanEHokEdnl5vLObG2fr6wvxli2vXG84cIASQnJ5o07nrYExhnfc3dk7168DW7Ywb39/lpWVhaKiIlFqaqpy7dq1olrnES/T1hassrNFZh4eOLl/Pz8oJ4dT9u6N+NhYwXDIEEyePJnp6emB53lIpVIolcr68WWCAL9ff+WerViB4Pfeq08stW1Liu3331Ni4tEjuqaXLyeClpNDNulFi2hte3nR/fiLL4AZM2D322/osHcvDnTowH/QsSPHVOMZR46sf/8vvyR30erVdN+wtye1WalsVvcfGRmprKmpEaWnp/NmZmacs7MzpAEBwMqVOJKdDdU9648//hBEIhHv5eXFmZubs7S0NACURHJwcGicFZFI6N/ff9P+REZSj4t162h7zp6lxMKHHwK//gpJSQmWeHvjert2fPHFi5zpvn10z+3cma79QYPIVdBSd/kPP6R9NzamxFIDZGdnIyUlRTxt2jRYWVm1mrnR1tYGBAEu3bpxLlOnQmjTBoyxukygubk5pkyZgi1btsDY2BhOTk6i5ORkRWBgYB1nGDRwIJ4vWoQ/1q3DyIULcX/WLL7dpElc5Q8/vPW1/zbgOA6MsS4SiaRfu3bt8ODBgyEA1GRbjf9q/LOuwGqoocb/t1ixYsUCLS2t7VOmTPn3iHZwMNnuZs8mO97GjRRQPHlCwUdrSldNDZGh0lK8m5LClTx9ig5Xr0Jr9mzI5XLAzg6Zs2YJ577/Xolhw2hu7pYt9FoDA7IN37tHxC85mf7/6BEpiXl5QFQUdE6fRveOHVEVHi7g/n3qgnzkCJHerCxSFysr6zYpMTER2dnZ7MPZs2Hy559kiWzQSdfR0RGdO3cGz/Pw8vJCQEAAJsyejbaJiYBIBP2XL2Ho54fAwEDMCA3ldG/coPrJ9u1J1VLh8GEia8eOAfb2EI0cCVtbW4SHhwvl5eWspKQEa9asUW7duhV//fWXkKqpKU51cuIehIVBLAisTZs2/KJFi/DuO+9Ai+dR9egRKufNw+4jR/DwzBk65hxHbgJ/fyLhCQmNa3udnCBwHP748ENSZWQy4MULiBQKTN61C1MvX8bk0aMxvm9fDGpIYE6dosD6xx8pKK6ubn5uP/2Ukhlvg8JCClKrqiiAX7EC8PAAHx2NexIJl+zkRLWhKsTFkaI8bBgR3cePSakCKKlSU0MOiqKiVj9SX18fEokEIlNTIq1NUFBQgKysLNTU1BDRzs+nRm3l5c0dFd7elOz5+29agz4+pI4BVFf53nv0f7mciLi7O56NG4f79+8DwFtZ7AGQiyQrq/53xijA/ydjfAwNIZw9C8bzcPwHynSftDSu+vp1hIaGIkc1Y/wfIC8vD2vWrMHt27eFHuPHQ+vGDXIB3LtH51k12qsBRCIRunXrxlzT03Ft5UocbPKco0ePKl1cXPgl7u64P26c8MzQUMA337RKtBMTE/Hzzz/j2rVrcHFxEb755pvGRLsWPXr0wITvvsMza2t2dOhQPLGyonM/fDglg1qCaoybmxuNv3odSkvJpn3wYF0/AXt7e3Tq1AmTJ08WzZs3D7Nnz8a8s2e5aadPi0b88AMenDqF5Lt3OejoYF/v3kqRm5swZ9KkupITjuOwePFimJiYCDt37oRCoQAyM6FTWcnOZWQIsbGx9Z8fEEBraulSsnoDRKx//ZXW8ciRlEA4cIDWeqdOpArL5YBUCpNffkHMwYN49OQJ92jKlGY9EbKvXkUOgON6eoLSy4uuGaWS7rv9+tG16+RExwFASUkJ3NzcwBgTTp06xa9ZswYX168X4pydYWRkhIKCAgQHB3OLFy9mCxcuFA0dOpQBQHl5OQAgIiJCeOX6tLAgS/jx43SOfH2pAdyiRVS64uIC9O4NtmIFbAoKOL2kJFQPGUJJriFDKAnXGtEG6NpcuZKcSg3uS/Hx8fzevXsRFBSEN5p5XVxM34VEtJv92czMDIsXL0ZISAgzo7Kfxk+SStHm/n3YOTgIV+bOxaX8fK7ozh3ELlvWuHP8v4xu3bpxY8eOtfroo49kfn5+4HneMzQ0VM1l1PivhlrZVkMNNd4IoaGhtmKx+AtNTc2J06ZN0zZo2gzobVFZSepwSAgFZCtXUpB05069dS45mX46O5Pte9kyUkN//pn+5uhIQcqSJXCOjWXdQ0Px6NEjPG/TBg+uXVMmjhsnuqtUMpGOjqhPWhqRq2fPyA6toUH1umlppGTu3Fm/bSpiSF/myPzzT6F0/nyh3YwZFHAMH061zT/8QNswahTViOro4NatW4KjoyOMjIwYPvmE6vGagDEGkUiEmpoa9OzZs/4PWlpgYWEIGDqUrIKVlUTA4uKoAy1Z1AkJCaSmhIVR3Wj79qiurkZ+fj7z8fFRVlRUoH///iKFQoHo6GheV1cX5b16ieTbtws6BQWCNDgY0j174PP11zBZtEj4ycaGaWpo4IPFixE5bhxvbG/PyQoLybL5+DFtw4QJzfaF5xtMYHFxAXbuRNW5c0gYNw6D/f1JCR8zpj7JAVBixdGRyE9YGAXWDx40rvmTyWg9vAnmzSO7u7MzqUrTp9P6qCWf2p06oUd5uXDmzBnB09OTArOiIlKS9uwhB4W7O1nJtbVJVWaMHAyjRlFgqqFRZ5FUdTgGgMjISEFbWxtwcmJ1434aQBWkW1pakmU2M5PWfXBw4ydWVxN5srWlRJCDQ33zJ6CZigVQl/Nt27ZBEAQMHDiQt7GxeW3QWVlZif3790NDQwPv3LuHtgMGoC5FoKdHyZsbN+rr9t+QeEdcvozMGTMwcfDglpsrvQp37sBs9WpmpKWFmxERQnh4OKZNm8ZM3rAjvApKpRKnTp1S2tjYsAkTJtQfixUrqPb11i0isefOkQraoMmcnp4eNM+fZwe+/x6S+/frXAg8z6OgoIB719qaYcIE9HN0ZOe9vASnESPQlKIUFxcjKSlJiImJYRzHYezYsXB2dn5lcwKZTIYpU6awffv24ZJSqbQbMECE588p0TN4MBHG2bMbW4Z1dalEwMODyiJe5UKYM4fWtqr5XkMUFUHX1ZWSLitXAtbWJduD8AAAIABJREFUYGIxHHv0QM3ly6iurka2trYoePp0MA8P+kxfXwBEuEeOHMk2btyIlJQUdMrLg+6lSxhSXs6OHDkCPz+/5utApdhnZ9M1P2AAkeDMTNqHX34h55JMRo6TWiQlJwt9+/ZlNrWuhIY4GBEBCxcX3L9xg9V4ePAjTpzgkJZGtm3G6P3HjkW1hgZyAgIEC5mMs/v11zp1Oj09HaeOHeNHLVokip86lXd1dWX6+vrNzpmfnx/8/Pxw5MgRdvDgQeHjjz9uxj8bQTV3PjWVElp791Ly8ulTSgrOmAFTe3uUAsKOjh1Zl+xsvuP9+xzX8D7ZGtzcKGn47bcoW74cO8PDhbKyMjZy5MhGs9tfiRs3Xjs+TDXlwd7eHn///bfo0KFD6Nq1KyyolAkAMLiqipU/eoTsrl35mKAgbrStLRjP/7OE3RtANXsbqJsEYVRVVeUG4NZ/5APVUONfgJpsq6GGGs0QGhqqD6C3RCLpIxaLfRUKhbNEItHw9vZmgYGB0rfuKN4QCgXV/f7wAxGjoqL6ESfXr1MQNm4cBZGff042102b6HGFgqzgKuvigwd1hEqRnIzoNWv4AQsWcFevXlUUZWSIKjU0oKOjA2trayW2bhVh5cp6NRUg8mhqSpbpL76gvzVRFO7cuYPs7Gx89NFH9cG7hwftQ1JS/fifqVOBAwfQu3dvtmvXLly+fBmdO3eGprk5WQRTUxvVdopEIsTExDRWfAEi/RxHSquZGR2TmhqyBn/yCQVq33xDwaQgkOJdS4JfvHgBqVQqDBw4sC7Syc/Px8uXL7mAgACWnJzM3x08GLYnTnCTCgvpdY8ewfbXX9nk8+dxITJSWT54MFfo5IT7/frBuawMunfu1B/7hw+pqVYD8DwPxhgUCgWys7ORmJiofPTokcjc3r5eiU9Pp5/ffUek3dub6l2XLCELfnw8WaP37SNli+OodvzlS1oXjT+Qztfly/QeN2/SemjbltTsVtS+bt26satXr7KLFy8iUEODlK+UlPrxR76+1DVaqSSCHR5O5Przz4msXboEHDuGjOxs7N27F7q6uvz06dO5vLw8Nm7cOFKsT54kktoAdrXHq6CggN4nJaVxLbUKw4fT+dy0iY5FmzZU375iBW3jF180G1MUFhYGQRAwc+ZMmJqavpJoJyQk4NSpU40e0yopwbOYGNTk56O0tBSjR4+Gs7MzKe5Dh75RwFxZWYmYmBikpKRg6o0bsNbXr3cHvAkUCjruc+dCMm0aJkyYwMLDw/mDBw8KM2fOfGPF6tGjR6qaWlFIw3nNKgQF0fn56y/qtdC3LzksGtTCl5aWYu7ataj+8cc6BnL9+nXoVVXB0sIC+PFHyHJycI8xrF27FmVlZejZsyfv7+/PnT59Gjdu3IBIJGKDBg1Cp06dXj+CqxZlZWV48OABFAqFCFIp3UsEgWroExLIfZGRQcRZ1Yk/MJDU4KVLKSHTgqsCYWG0lvv1a/z4unVkbT5xguzdtraN+ieo6pnXrVvHM8Y4fTs7ujdqahJ5rU1SGBsbw8PDQzh99CizPXQIbY4ehZubG27evKncsGEDN2/ePNaoYaYg0GeuW0f78tlnlOC5fJmcMV9+2WIfB6VSyVxdXZspsMXFxbDMyUG3ykqYdOuGy5cvc4/s7THi7l3oR0ZCr39/CDo6uBYcjNi1a9HZ3p7vNmaMSFOppHtQYiKcnZ0x79NPRbh6FfPHj+de15gvODgYq1atYunp6WjXWjM2pZLuB9XV9LO4mKzt77xD52nNGuDlS7BNm6DXtSsb99dfuJ2ezsUGB6PHKz+dwPM8yocPh+zYMTw/dgzl5eVYuHAhe2tny8SJb/Q0ExMT9OzZU5GSksLt3LmTmzVrFm7evAkDAwNck8mU8qFDRYMkEub6ww/Q+PhjSjw3HHH4HwJjDObm5nx6eroX1GRbjf9iqMm2GmqoAQAIDQ0VAxikqan5kUgk6mVpaVnVrl07PTMzM5iYmEBPT+/1HaTfBPPnk018xgyqQ5XLiYT27k3E19ubFCjGqC5VhZMn6/+vUmkaBBcRERGCgYEBPDw80LFjR7FK9XuZkYEBhYVEtPv0aTzixdiYiH1ODjW5KS4mYtvAHlpdXY2amhpWVdVkykinTvRzwwayQk6aBMycCauNGxEcHIzo6GhlTEyMaMyoUXDs2ZPeu4FSN3nyZOzcuROurq6Na9xu3qSfqoRAWBht8+bNZC1OSSH1MTS0XjWpDbTv3LkjyGQyHkAdS1J18o6JiYGLi4swaNAgEXfzJsRhYWS3XroUWLoUloxhQkmJCE+fYk7nziwzPBwbDx2CePVqnud5jLtyhbN6+JASDA0gCAIEQcDmzZuF0tJSODg4cEFBQfD09Kx/kirg/u47+llcTKqyUllPKK5cISI+fjyNZtu7t151qawklbd/fzo3EyaQ2rd6NQXrq1bhdeA4Du7u7kg9e5YPnDuXw44djecMKxREKORyUkH9/GgbPD1pnWpqQvH0KY4dOcK3a9dOUCqVbN26dQAAJycnel1LHdkBjBgxAue2b0cBAJOGypUgUOB97x7Zam1sqJY1N5d+Hz+e1uyFC7Qdy5aRK6CW7GhpaaGsrKxOgQKApKQkREdH8z179uRqamrg5uaGiIgIZGRk1D2nU6dOcHJygmluLv4UBBTV2lH/+OMPANSxWd/TE6Yffgjv48dxeccO2NnZwdraGqdPn8a9e/fQtm1bQaFQsNzc3Lr3Nf/mG1pTb4PKSlI0a10NIpEIQ4cO5TZs2IAjR45gWMNZxE0gCALkcjlkMhnOnz8vmJmZoV+/fsyslXnn0Namsgg3N1L27OyoXOGbbwDGwBjD+f79MVI1VxrAkydPMHLvXogPHAAmT4boiy8wp6yMbdy4UeB5nkVFRdU1ZtTT0+MXLFjwVpbWy5cv4++//wYABDZ0RjBGtuIhQ2h9xMSQArxkCZU4dOhARFqhoHUkCI1VyqNH6V4xYwbdXwWBlORvviErtqEhPX/KlBa3y8PDQ+B5nktPTxeqqqqY5pgx9cmk2Ng6lXrEiBEs3doaV69fx/U9e2Bubi64uLhwcrkc27ZtE/T09DB5/HiGzz+nDuK//kqJwh496PMjI+m6PnWK+mnMn9/ovr5lyxZBqVSyygalOkqlEmVlZdi5cyccKipg5ukJy169oFQqkZ2djZNKJd9vzhwubvhwXu7lhezsbK5Pnz7wmj9fJJVKqVSoXz9K6o0YQc6bP/9sXFLRFJWVQHExxNXV8JHLlclLlogsRo+G7Plz4P59WvdiMd2Ptm+n+9SYMXSv43lKkvToQTPTASLeRkbAqFHQDQpCycKF/NWLF7mOnp4waqEviVwuR2FhIZ49eybExcUxpVIJbV9fYeznnzPnr7/mxWLx20nJt24BLTgFWkPXrl3FXbt2RWhoKNatWwfGGCQSCby9vdmQIUNg3rkzg5kZNUm7ceP1jot/CVZWVroPHjzwAtC8WYYaavyXgP0nayvUUEON/zcQGhoqkUqlZ/X19X38/Pxk7u7uaGmE1/8YCgUFgTExpGI0nbda9zQFOI5DXFwcfH19IW2tfq0Wly5dQmxsLCZPnozHjx/j9u3bgoeHB+vQoQNOzJwpDExIYDINDSJzr9ovnqdtWr6cVNNa/Pzzz0Lfvn1Zhw4dmr8mKYkCKGNjIuxOTnVEOTY2FpcvX4Znx458/8uXOW7BgkYE79SpU8qMjAw2a9Ysrk6VmDGDFOExY+j39u3JNl9SQuRvzRpSHq9fp2A7KYkUYAsLRHzyiWBkZcWCZs0i1ejrr4Fr16DcsQNFW7eijWr01P799B4cRwG8nR3VUC9aREpu376Alxeqq6vx9OlTXLlyBcV5ecrZH38satp5OT8/H5s2bYKWlpYwd+5c9tbrpqyMyHbHjtRt19MT6NWLFF59fdrfuDgK0lNSgIICaib2hqqhCo8ePcKBDRsQsn07dA8daly7TU+gxItKGc/LoxrpSZPI9i2T4UWPHsh4+RIu0dHQ0tJCeno6MjMzlf379xex58/p+dHRjd5WEAT89sknGLxvH3Sjo6GjqnnPzaVAPCyMSilUtaiXLtE6rO22X4fSUlK3BYHOz7vvYtmaNRAEAZ999hm0tbVbVK8bYtasWTAyMoJIpVgPHw78+CMER0fU1NTg+++/b/R8cXU1HDIzcd/FBWKeh6KB0t2+fXvh3r17DKDEkZ2dHa3Dr7+mEUxvgowM2s+7d5vN1I6PjxciIyMZAMycOZNs3k3WVlJSEo7WOh80NDSEWbNmNR5l9ypUVRHJ37aNSOjChXihq4uNP/6I90UiROjr8zzPc8KzZxhtYACb589pLTZYd9XV1bhy5QpiY2PRt29f+LcwY/1VqKysxOrVqxEUFIR27dqh1SSBCqWllBAqK6NrRU+PiOrgwaScbthAz0tIILt8v3703J9+IqfGggWkmjeZKf0qrFq1Cp06dUKfPn1oXf74IyUsTEzqnQ+jRiHJ3x+3zMz4qqoq5Ofns+rqatZWLofP5cvwnDwZYo6j7WmqBg8eTOUny5cTCbexoWRC7X0mNDQUAPDll1+ivLwc27dvJ2W3tjzD69YteIwcCYexYxu9beaePUjfswdPvLx4p/79uZSUFGVxcbFo/vz5aLRGli2jsqG7d0np3b6dnAK6ukSYr18n51X37pSgMDGBcPUqoioq4Dd9OvTE4vrpCjIZEWhd3ZbvT8uW0XO6dyfH1jffUALj/n0IFRX4fvt2DB8+HK5NJiDwPI/ly5dDJpMpZTIZunXrJnJzc8Pdu3ehf+UKLI8fb+4Aeh2+/JLuV6qk8RsiNTUVx44dw+TJk2FpaVmfgOd52ufHjylpYmlJTqz/MCIjIxXXrl2LFQRh7tKlS9P+4x+ohhr/AGqyrYYaaiA0NHR827Ztt0yfPl1H9B+qtQJAKpKREX0pu7hQsN8EeXl52LFjh1BdXc0EQYCTk5Mwfvz4ViV1nuexdu1avlu3blyXLl2wbNkyeHl54fGVKxh09qxwdOJE5iCTKQcnJ4vwyy+v38YXLyjQ++wzCqLWrsWqVasgFot5b29vrnv37s0bUd24QXbMqVOB27cpCJ4xAwApEtu2bRM+PnyYyWbNaqQkVVVVYe/evXxhYSEWLlxI0dk335BVvn9/IlYBAaRku7mRJbFTJwqm9fRIKdbTIzWhrAzpLi4oMjDA/WnT+Pd//53LWLIE5Q8ewPDSJfzdvTumpqVB9PAh1SkvW0aKvqkpfcbcuWRvblA3XVNTg1OnTvFpaWncuHHjYFdTQ9v28CGgrY0XL15gx44dSsYYN3LkSGbZSsOoN4IgEFFTKoHERGoUBVDgNnfuK2sL3wT3r17F4YgIOD1/zg/bvJlrdg6zssjO2rAuXamkjvVVVcC8ebiRmQn5H38IfUNDWVM7PYqLiTDfvdvo4buJibi5ejW0Kiow7MgRevDZM0oozJlDCn1DrF9PqlcrVmw+LQ3c0aNAeTlO3r8vJLq6Ms/OnVFeXq5MT08XAYCDg4MglUoFjuO427dvw8bGRujXrx8zNzdv7E7p04fGn9UqUPn5+TA2NoZcLq8bI1VRUYHK2bPBnz2Lw0uXYtiwYdDQ0IC+vj4qKiqgpaVV/36CQKQvOrpZuUGLuHOHnjtzZot/fvz4MbZv3173u6enJ/z8/GBmZgZBELB+/XrBw8ODOTs7Q09Pr0VF8LUoLSUXw7Nn4FNT8depU/zQefO4HxcuhENpKcZu2QLRiBFk5f4XERkZiYSEBMhkMsyZMweSt5xvjn37iPiGh1OzRC8vIo3FxZQccnKiUgBLS1LDDx586wSVIAhYvnw5fH19MWDAgPo/fPQRXRM7d1KCxdWVCKqqj0d4OCp+/RWn33kHullZQp/du9kbqZzV1ZSo9PWlJJdUitOnT+P+/ftCQEAAzpw5wwwNDXkXFxfOysoKenp6KJs5E9fbtBFsp01jVlZWuHHjBh4/fqyUy+Wi9nfuwO3OHcQOH46OvXohNjYW77//fuMGfoJA17mzM93Xhg+n4+joSM3OJBLaryZz1MPCwgRzc3M2YcIEvNF3ZmUlkXCplBwwBQXUWXz6dLoPe3ri0KhRgu7w4axvk/4MSqUSK1euxIIFC5qP0+N5YNcuWsetzIhvBp6npOa1a9QL5C1x9uxZJCQkYM6cOXVj0QDQ99SkSVQ2NGQIOSDedl2/JZ48eYLff/8dYrG4ijG2u6amJmzp0qWZ/9EPVUONt4TaRq6GGmoAgJauri77jxJtQSAVddIksnOGhFAwWBsA8jyPI0eOCHfu3GH+/v58YGCgqKSkBL/99htLTU2FRwtqzKNHjxAeHg5jY2N4eXkBALS1tflnz56xwG7dmFZ0NHzkcqWbkdGbEW2gXmEbPJiswc+e4WMDA9zy9uYSEhL469evs5CQkMYKWqdORHzDw+uahMHHBwoPD9y8eROCILBH27fDzciIgqxadVtDQwOjR4/m1qxZg7KyMgqkli+vf98XL6gus00bUrfnzKHjNns2qb4hIaTUT50KADg6fbrg4eHBMq9e5cIGD4Y4NVVo06YN/EpL2cDvvkPqiRPwCg4GTp8GP2gQ2I4dYLt2EekRBKqFboA7d+7g3r17CAkJqScyYWHIzMnB2XPnlIWFhSJHR0eMHDmSvTVZOHeOFOqYGFJA+vQhtdfAgIjv/Pl0LiIiaB9HjGhs+34bKJVo99FHGN2pE3bb2XHBCkXzhElGBtl1G0IkIqv68ePA4sXIs7TE8+7deQwYIMLMmY2DWz09Uj4b2nkFAUYrVqBNWRniunXDo5Ur+fHXr3NntLTQ8bvv4K4aX9QQf/1F6l4Dss3zPORyOWpqarD98GFYWlpCnJHBtykv5ybv2oXYrCxk2duLho4YAUdHR0ilUqahocEAUk81NTWbZypqaohMNGhipZqXrPoJ0Jgg7Y0bEb1nD/QFQWkqlYpQG2BrNQ3UGaNyAFVd8avw++90P1i5stWnWFtbw9bWVsjOzmaMMeTm5vLbtm2rY4y6urpCnz59/mdZGF1dKsfYswdcQABG+/tzx7Zvh+zJE4zv2pUcDg1G+/1TVFZWYu3atUJNTQ1TNRV8//33W6/7fR1GjSL3S3Iy9TyoqCBl1saG+h0cOULXdb9+RMbfEvn5+Th37hwvlUpZs2P8zTfkjikvJ1fPqVNEWFeupASlQgGtr75CoJ8ftv72G3MpKoJ1a2R77VpqUrltGxHRTZuIzPM88MEH6NevH1JTU4UTJ05wXbp0Qf/+/RtlDAzefRdPZDIhMTFROH/+PGdra6u0tbVFx44dcR7AcBcXdPTyQnRlJc8Y4xoR7cePSd39+GNKBOfmkrPn/PnmzpcmsLe3Z5mZmYiNjeV79uz56ixGWhqVB127Rtfd+PHU1LE2IatS0B/s3MkmffEF9SJp0HcgJycHEolEkMlkzdc6x1EJ1pIlZA13d3/lpgCgKR/e3v+IaANA3759kZiYKFRUVLBGZHvIEEpY6OjQvfDDD998DOE/wM2bN3Hs2DEAwMyZMzXi4+MnJSUlTQgLC4upqqo6AOD20qVL4/9jG6CGGm8I0bfffvu/vQ1qqKHG/zJiYmKySktLQ3ie19bX128eRP8b+P13Uly8vOrnNRcX11ka//jjD2Vubi774IMPmLu7OycSiaCjowMDAwMcP34cgiDA1ta20Vvu2rVL6e7ujjFjxnAqsvcsKYn1W7qUVU+dCpulS5l2eDgnvnsXmqNGvd32OjmRtfniRUi++QaWK1bA18ODFZWV8SdOnOCabY+xMZHBb78FVq2CcvVq7IuP5x8rFBgyZAhzcXMjknzwIDWEqoVUKoVcLuejo6N5e3t7Tuedd3A7IkI4VFbGe8XEcKKffybbpqMjKb+dO5NlkOOA0aPJehkQAEgkSE9P53me58rKyngfHx82acgQ1klLi5kFBEA4cQKRL1/iSUiIUPjoEfI3bGAF5eV4NGGCsnzuXE4rPR3szh1wVlZEggEkJycLFRUVXFVVFa5duyYcO3aMpYhESuOvvuKkrq5My8aG9e3bl3ttw7yKCiKu+/eTFTonh8h0WhoFn506UTLB0JBcAkeOUDdf1T5HRpKSZ2JCj7+Nyl1dDeHhQxS6uyPawkIoLCpinTp1ar7GU1JQKpfjSG6uajRQ/d/atwe6dcOLVavgY2rKac+fT9tRVlafAFDV2E6bVq/mPHkCnagoOOzbh/zoaLCCAqadnIzrPj4o09ODu7t7XROtwsJCXLt2DXazZ1M9bm0ygOd5JCYmYt++fXjw4AEqKirw8uVLFHIcy7GyQqaDAwIuX0ZfQYDdO+9Aw9YW4gaJj1abJpWXU3KhYTf81iCRoEomg9X8+czowgX2yuZH69dTgN2Ca6URtm4lt0bnzq98WlJSEkpKStiUKVPQs2dPZmVlBYVCgaqqKuHDDz/kXldi8kbgOLov9egB9tNPcMnNRdfNm4lIHj1KCQQnJ3KUZGZSQ7H164kkFRWRTbtnTyrFSEsjwjNhAq3r27dR+tFH2Pz8Od/j8GHOTC6HyNsb07ZtQ9tx48BOnSJiNX06uR0yMihh5+BAx3DtWiItc+fSdZmTQ0kSGxuqn16zhu6lf/5JJDg/n0jdqlXkzsnJIZfI/PlkXw4OpuSRasKDKkGiclR88QX4Bw+w4/BheCQkCL2HDuV0c3MpKebsTHXX2trUoM3Tk/pqJCTQffzyZSL8I0YA7dtDRyaDRCLhjx49ylRJhWbnSy6ne0PXrvQ7Y5To3LoVKCgA8/DAxbg4KJVKNnXq1GZ9Q8TvvQe7tWuZX/fuLDAwEB07duTatWvH2dra4urVq4LQrZvwf9h776iqru0LeO5zuZfeUaqgVFFBUUABC4ho7L0mxtiiMZaYaExe8gtBExNbjMaoJM/YW2wxKlERpAjYKAKK0ou0S+/t3nO+PxaXjjF5ed83xvuYYzBMbjn3nH323mfNteZaq9+lSyw7MJAZTJ9OBQDj44nQz51LhNPVlRwuvr5ULXz6dNpbX9FxIy8vD9nZ2cjKymLW1tZ4ZfqCjw/tfVevUvR3375OBcRkGhoIDg/H6JEjody7N6UNJSZCbmmJQ4cOwdLSUhg0aFDXG5+2Np3rzZt0D//Maf7yZWvXhb8BnucRGhrKRo8e3a5eBAByjDo7k6M4J4fmsaIy+z8MPT093GtO+yktLRVmzZql5O7uriSXyy1UVFQmVFRULL579+4OT0/PHglvD/4/RU9kuwc96AF8fX3L/Pz8vKKioj67d+/eZIlEwhkaGgru7u4a1tbW//kPVFWRzLptPumKFfTaggUAY8jPz2fTpk3r1O7HwcEBqqqquHjxItTU1ODcHPG7ceMG5HI55+3tzdpW/U2rqOCNra2558+eyV8+fix6YGsLmVyOLwTh7xV4mzSJ/oqLwfr1w9TISJGDgwPOnTuHvLw8ft68eVzL7zs6ElFctQqPdXWFyefPc2rx8VBRRPr27yfjUpHf1oxp06ZxZ8+e5Y8cOQLHd98VcioqIJfLuWsiET/rm284JCWRQVheThWqp04lYpeTQ0ZwRQVw9Sqs6+q4dI7Dxx9/TAefO5ci6UFB0PbywpQFC3Dr8WNBJymJs+d5FFpZIdrMjA1dv1445eYmaBYWcq5//MFbBQVx8g8/RGpqqlBRUaEogsUWL16MCxcuiCSNjShJTGQlSko4c+aMfN26dZ2tu8uX6b5PmUJV1T/5hMhdYiKde0gIGe8qKiRF9fMj8nrlCkWJ/f0pQuLuToQ7Pp5IXFgY5Va/jkwZABYuRHlBAQ6MHw8lJSXGGINUKu0kOeYzMhCYmio8FwR26NAhuZubm2jIkCG4e/cuL5VK2dChQ9mNqVPxiY4OEQ5ra5Li//xza2E0MzNyHgD0mU8+AR4+BDt3DuNPnRLOTp/Ors6aBQDIzMxEQEAAHBwcEBQUxOfm5nIAMGrKFHA5OSisqEBsbCzi4+OFuro6BlD1ZX19fZSUlMDIyEjw8PBgvXr1QumKFdDMz6d804AAcr54eb3aKSGVvl6f5mZYWVlhx9y5bNOHH0L54UO6/q5k215eXfdNb4tjx2hs/uQeCoKArKwsBgAmJibYvn075M3t4AwMDFBbW9tZVvt30NREpPn33yniuGULKRRkMpqzb71FpPXnn2mtjR5NBQsHDSKyFhBAcyElhUgMY0BFBUqlUvxx6ZJ8QHGxaPDgwRjQ1IRSiQTRpaV4amEhDNfTY3B2bo1wfvIJEWkDA0r16NOHSLaianRcHK0JFRWSJTNGsv3gYOpSYGhIeei3b9Pr6em019TXE7E2MaHzVlIiR8GSJXQNRUXkfFFVBTQ0EBMfz6soKXEu+vq0S0VF0W/4+BAhdXamOS8IFMn08SHHyY8/dhpaNzc37sGDB8Lhw4cZAMydOxcDBgxo/YC3d2fSxxit/7VrgepqSHhe4MVi1qm6u6IyetvoahssWbKEHT16lNls2QLtOXMwuKiInANVVZQ7rq4OfvlycO+8Q/MZoHF78YLGMy2NHH5dwNLSElFRUbC2tsbx48excuXKdooQADT2R4/SnpaeTqqu0FBKxfm//yMnCihd4vbt27y6ujrUV6/mwHE0177/HkJ0NDQaGqCkpPTqh9eYMaQW+ve/u03LaEFcHCk2/iaysrLA83z3tVROn6Zxy8mh1CRX1/9KK7Dy8nIA1FbRy8uLAVRg0cvLS9zY2CjesWOHDIAKgNp//Md70IO/gJ6c7R70oAft4OfnJwLQD4CHRCL52sbGRm/69Omqf1km3BbPnpGB2LbqNs9T+69x4wBnZ3z11Vd4//33odshN06B8PBwxMTE8OvWreM4jsM333yDxYsXw6w5CouGBsDcHLKAAKSqqyM9JQXj5s5F6v79uFJUhA0bNnQyzBsaGhAXFwdXV9fXI+JxcUSolyxB9YQJ+KW0VNDW1uaXLFnSYkkHowfUAAAgAElEQVTk5+ejIDwcpuvX4+WXXwpDz51juHq11SC8do2cDJGR7Q4tl8uxc+dOmD97hjcWL0Zpv354uXat4PXTTwx6emTwjh9PEaUFCzrn+s6ejQKJBDccHeXLy8tFMDKivO9hw4gEmJqSkezkRIZcYiKRrbffpkhabCxCExMRc/MmhoeEoF5FBUnDhwslamotzwkzMzMUFhbCwcEBBc+e8f0AoaRvX5EkPV2WX1YmkotEWHHgAOOCgqC8axcZq5WVlKfu7k4GWMcKuMXFFKGbNKm1cjJAxqihIRncCshk1CIoN5cK2S1b1n0eKs9TlFxdHf8ODORL6+u5KVOmtDf0Wz7KI3bqVKSbmmLy998jKChIHhMTIxozZgyioqJga2srT05OFjU2NmLdunXQe/6cCI1USuSM5ylvddcuOiddXXImDR9OBZC++w4yiQQ7Tp6ETCYDAIjFYjQ1NbX8CwBqSkp4+/x5FBw/jqvNao62UFFRwaJFi6CnpwfGWOc+xoJAlcujoylS9957rRHDjggLI3J0/nzX73eBXbt28fPnz+fMZ8+mdduhoFoLfvmFVCxdKWRKSigKGhn5p2S7uroae/bsaffa+vXrIZVKcfPmTaipqQkmJiZs4sSJr91mq+UcAgIoN9jXlyK/27ZRKsi335JaRFeXXjt3jhxoil7rfwHbt2+HlZUVP2vWLK7t/llbW4tdu3Zh2LBh8PT0hIaGBnJycmBqavrn16FIUzAxofGfPJlSHY4fJ+KrrExR4VOniNj9RQdjRUUFfvjhh5Zieq9EdDRJ1e3tacwOHUJXrbOqqqpQVlaGixcvCp6enmxo26Jcv/5Kzo02lcCrq6vx22+/8YX5+cLI69dFNRIJBh89Cn3FXq9AURE57V6hWjpw4IDQV0eHpSYkYP3+/SgbPhxhGzagpKREaGho4BtTU0XKDQ3QHzaMn798eevgf/EF3ff4+E7H5HkeO3bsENzd3TFq1Ch29uxZoaamBitWrGjvELhwgaT+Dx7QfTp9mhwT8fHkSGl+1u3evRtisRhLly5tFyHPzsqCYVYWlGbPxqEFC7Bm375Xz4+8PCL306e/Wk6+di3tTa/Z+qsjDh06JJdIJGx52/HqiKwsUnZMn04OuL/5W6/CiRMn+IyMDG7YsGHo378/2gYGKisr8cMPPzTK5fIVgiCc8vX17SE7Pfj/DD0y8h70oAft4OnpKXh6epZ6eno+CQ4OPlxRUTEkNTXVfNCgQeK/ldNdXExEavHi9rI8xsi7npwMuLggKipKcHBwYN1FqgwMDBAXF4ewsDAhOzsbUqmUTZ48mYyPhgYyMo2NwY0fD4NevZCfl4cLxsZIaiYyhoaGLdEwhUc+MzMTV65cgZ6e3p9XAgaI4DEGPHwIib09LCws2POwMAwdP54BQHR0NC5cuIBiQeD5xYt5t927OTZuHEVp5s+n7xoZ0TX7+LQjihzHYdiwYXA8fhwajY3QnD4dRmvXsjwvL+goyEt2NkVDRo7sbETPnw9+/HhknDzJDfzlFzCplCJwn3xCkbmRI4n0NisJkJpKPW8dHUn6rqGBvn37wtjaGkVOTkjPz8eE69cZzxga7ez4+vp6VllZCU11dQzo3x/9v/ySOQQEcIkTJ2LM1q3ccG1t5lRbyxqzspAqlQpGEyeS3HjnToq69OvXOQKVmEhS2a+/ptzJtvNLJKLoU9uK9RxHzgITEyLS/v5EmLqKbO3aRdf12Wd4kZnJ19fXY+rUqZ2Yx5EjR2Q3b97kdGpqBJeVK5mepSV0dXW5hIQEITMzk02bNg2enp6ch4cHbGxsoK+vD87CgmTHUinJ33/8EZgxg6JKQ4aQ8+Ctt2iMLS2BqVPB6eigqKhILpVKOQDo06cPL5PJ2KZNmzBs2DA4OTlBUlcnS6qq4h5XVkJLS6ul37EC6urqqK+vF3R0dJiBgUHna1ZEOkeMIGN+zx4iI4MGdZbESqUUmf4LUtInT57wWlpanKmvL81ff38izB1J//r15FTpqIoRBIoafvYZVWP+E0gkEowcORLh4eEAqOq5sbExDAwMMGDAAAQGBrK8vDw8ePBAGDRoUNeV8GUykjlra9MavHCBigKeOUPR2MpKchwoK5MKo64O2LSJ1sXQofReXR0RBh0dul5Hx9car4iICGHOnDmcZnOrNgXEYjGsrKwQEBCAqKgoPH/+HPfu3UNcXByvpKTUfaHBS5dIar5pE63nCRMo0n3pEkWrm2s3YNgwIlNvv017719ICzp//rxcS0sLbm5ur2bpVVV07G3baI7n5dFYFhbSHGyzPykK6qWlpQm5ubmCk5MTq6mpQV5eHjIFAQ1eXtCysUFtbS1Onjwp3Llzh+nr6/Pe48aJes2bh74ZGdCJjqaIcFun76NHJM2eOrXLU6yvq8OjCxfYgr17kaepicdublAvKxOq+vXjTe3smLGxMfP87Tc4mZiwGw0NbOTIka1k1tOT9qaUFHL6NV9PfX09Tp8+zXMcx2bNmsVEIhEsLS1ZSEgI0tPTBbFYzGQyGWovXULj8OFQ7dePUmfEYko34DhydBkZAVpaSEtLQ3R0dEvFfQXu3LmD369dw/2XL5HYpw8q9PTguG4dmoyMoNyFwxAARfkLCih6P2pUlz3LAZDqZvHiv9Wa6+DBg/KamhrR8uXLX12nQ0eHlD4zZpBqxMzs1Z1A/gZsbW2Znp4eXrx4IURGRrL09HSZk5MTB9Ccs7a2FqWnp48XBMEnKCjokqen559IbnrQg/8Oesh2D3rQg27h6enZFBwc/GtDQ4NdbGysjampqVi7G8letyguphxtb+/O75mbA3v2IFRVFbklJczb27tbz71EIoGLiwvr1asXy87O5nv37i04OjrSh42NKY9y0SKAMZQePAijlSvxwNMTEokEDQ0NsLKywsWLF4WwsDD24MEDIT4+XoiJiWEikQjPnz+HkpKSvE+fPlxNTQ1EItGrIwjjxgE2NuB27oT5xYtMed06lJeW4tz585g5cyYmTZrE+tnacszHh/Lz3nqLok9TpxIx8fCgNlvjx7cj3GKxGKI33wTGjoWIMVyzsRGeZ2TA6ZdfGNauJUN74MDWPuMdoLx0KSRJSYj74ANYKXo3GxqSTNbJib6nMIJzciii9+23dD3NpFZHRwf9LC0xaMIEZPfqBVOZDKMfPWL1OTkoMDXFxq+/RnxeHjLMzWH08iUkWVlCpZ4e1CwtWb6hofBg3jz21NCQH75qFQdz83ZGt6Jlj4TjKMK/eTNFfjsYj7W1tajy90d1WRnUhg/vrDrQ06OIrZkZOR8aG4nkKsYyNJTaOS1ejJjUVCEqKoqTy+VsdId2Wk+fPsX9+/e5mTNnwu3ECaa5bBmgrY3S0lI8fvyYCYKAyZMnQyKRgDEGLS2t1nmhyE00M6PoXlkZKQQqK4nQPX1K0cY2jpH09HSB53luzpw58PT0ZO7u7uA4DsrKylBXV0ffjAxu4K1b4N96i3/27FnLRS9ZsgR6enooKyvj09LSuNjYWDg4OHSObCvAcaRkmD2biKKvL42RqmprjnlMDK1NF5euj9EFsrOzucrKSvmAgQNpEN59lxwfHaNojo50Tzue308/UcuhDz547Ygrx3EIDQ0FANja2qJXm+KCI0aMwMCBA5GYmMgiIiJQUlIi2JmYMFZXR8qI1FSaZ4cOkeOpVy+Kgg4ZQvN/xAi6R3PnkrOnqIiKHC5fTk6K8HAaw8mTyfkTGEhredUqksK/Ilonk8lw79495uLi0mUNDG1tbZiamsLQ0FCQyWT8pEmTOC0tLdy+fZuZmpq2jyq7ulLUetkyug5zc5p3hYVE/r/9lop8tSUzvXoRIT54kHKoX3O8MzIyhJycHM7a2rpbxycAaolVUEDjwXE0z/v2pbZd9++T46LDb+rq6rLQ0FAWFhaGyMhIJCQkoLSwkNc4fpwdz83F/fv30dDQwJSUlITVq1dzenp60NDSgrK3N62nX3+l54iCRD5/Tuutq2JmAQFQmj4d0cOHy/VWruTcPvkEw6ZPh5GFBbM7cYKzWLGCmdvaMo3cXCZbswZR0dF49uwZ/+zZM5aYmIjEp0+RWVQEy1WrwGpqwEaNQmNjI3bu3Imqqiq2fPlyplh/EokEw4cPZy9evEB8fDwfFxMD7w0bWO716+DLyyF+/32IDh9udRTMnk11IAYMQF1dHWJiYmBhYQGFA43neZw6dQqjRo3C2LFjYTJkCBydnJCdkMBH5eSwxidPYKqs3FJbox0GDiQnqrp6lyoDNDWRWmPu3O7JeDf47bffhPT0dG7VqlWvzlFXwNGRUqd++onmyF9sjfdnEIvFMDY2hpWVFXvw4AFqamq4tnu8pqYmnJ2dJSUlJUZlZWWTg4ODT3t6ejb9oyfRgx68Bnpk5D3oQQ9eC1u3bl2gpKR0wNTUVMXHx0fdxMTkz7+UnU1GbERE9y1A/Pxw59Ejod++fcyqm/y4bvHyJRHt+HiK0CmIUHU1Xp4/jxRLS7x48UJeXFwsUlZW5k1MTISFCxeKUlJSUFFRASMjIygrK8Pf3x/jxo2Ta2hoiK5cuQI1NTWoqKhAVVVVWL58OetOYi4IAs6dPi3H3bvc5IsXWfDBg/IZb77ZPvxfWUkGso0NGRsffkgRvkGDSOrZsc/phg1EYHR0ULxvn3Bk3jy2euNGaKupUb72lCmdT6S4GKitRemXX0Ly66/IffwYdhxHx87Lo+jyiRN0PyoqKMqXnEw5qv37tz9meTn9mZqSsRYZidSJEwWTtDRW8vnn6FNXh6LcXFRFRCDbxATuUVHY8fHH0NDXR2VlJQBAT09Pvm7dOlFERATkcjlGjx6N2tpa7N+/XxCVlbF5DQ2wKC4mwqKkhLq6OkRGRvK1tbXCmDFjRPv374f7o0dCvoYGG7F1a0urHplMhszMTPTt27c1Z7C0lCJG2dkUZcvJIafL/fuAiQlOnjyJ9PR0rF27FvodqmTv2bNHqK6uZm8uWABrFxegtBT5RUXIyMiAq6srRCLR66UXZGdTNDskhCLKv/5KhK6D7Pj27duCVCplb7VtL9YWL18CT58i3thYuHHjBvP09BRcXV3bdQlQtLoRi8XYtGnTn/agB0DKjwsXSEmgpUXFnx4+pFz5FSv+/PvNiImJQUREBL9u3bpWD5EgkPx/+3aKGAPk7Dh0iIz6tudQWEjz7y/0eQZae7kvWrSICly1RXExcOECCoyNke/nB9vkZMSeOIGR4eG097i7t+4L2dmUCpKeTlHR7dvJSQdQznJUFJEUNzfas774gqKAHbFiBa2f06fJqfPwYSfHwp07dxAfHy9s3Lix2/2jI/Ly8nD06FGsWbMGOjk5kI8ejYRbt+BUV0fzqaM6obyc9r0ffqA6CB0hCORU+fhjmpOvUSVeEAQEBwfzDx48YLNnz2Z2dnZdf3DdOqql0FER1NREa3DNGmD2bDQsXozH0dGorq4WYmNjmbOzMzw8PHDs2DFUVFQIH7//PmN9+6IuOxsyuRzq6urYvXs3+vbtK8ycOZO1zG+5nEhbZCTtGyIROUp0ddtf+7Fj5ATw9gZycnCkrEyurq7O5s+fz7Xch23bSHVx8SI5TsaPR25uLjIzM9HY2IimpibhxYsXrKamBuqVlahlDL05Tt5gaipSVlbm3377ba5blVdWFiAI4HfsQOn9+wh2deUn//Ybp56R0dn5BKCkpAQHDhzA5s2bW5xnL168wLlz5/Dee+91ygMvKChA9rx5MMrPF2K2bmXjfXyg1lHl0tBA8/7UKZL4t0VxMeX1/41A2+XLl5GQkAAAsLCw4BctWvTnBQoDAuh+XL9Oc/AvEvzXxeHDh3krKyvOx8en03s8z+PixYv1aWlpJz/99NN3/ysn0IMevAJ/reliD3rQg//f4osvvjjX1NRkmp2d/fGxY8dq0tPT//xLUVFEKl8hN/vd0pJ3jItjFh2Ly7wOvLyosJaTU6tBvWYNcOECzJYvh5eXF1avXi36/PPPsXnzZu7NN98UcRwHOzs7uLq6wtzcHOrq6gAAxpjoypUrsLCwQG1tLUQikZCbm8tOnTrFd+WUrKiogCAImDF7tmjwxo3sjwkTYNG/vwjLl5OxqYCWFlXSVlcnY3/fPjrXxESK/DQ0tD/wgAFEzB0coL9pE1u/bx+CN27k5fb2FBHvCp6ekL/7Lk706YPKGTNgt3gxSbArK1v7cScmUuTOzIzuS10dyZ4V/WUVbahmz6aIs1xOEbGQEPR1d2cNKirQ3rULqKmBwddfQ3bjBqxOnYJw+zZWvP8+KisrwRiDrq4ujIyMRAAQFRXF3717F5WVlfjxxx95i9paNiYiAsqCQLnXzYbXnTt35LGxsSwmJkZUV1cHuVwOoaEB6jo6giLCFxkZKd+7dy9Onz6Nb775Bnv37qX8Zz09Mp7ffBNyHx9UPHuGkl9/Ra4gQCqVQjFPu6oFMGrUKAYALxMTga1bUVlbi2PHjiEwMBA5OTmvR7QFga5DRYX+OzeXHEDDhxMxyMtrKUYkkUiYIj+7S9y7B+TlwdHRkX366adwc3Pr1I5PUaOgqakJFy5cEBRtpF4JZWWKQH74Ic2LTZvoHj94QIT7NWFra4vy8nJOUaQMAOWrp6RQHrQCvXsToW2LpUspn/QvEm25XI5zzaRdSyIhNYZUSkR66FAiN6GhMNLWhr6/Pw7961+o1dSkKt4jR9Jay8oiR8Ybb7QWQTt9upVoA6Q8uXChNcd9xAiSwHZcnwCtm7AwWs8ZGXR/V62iSHgznj59Kvfw8Hhtog0ACQkJwoIrV1A1bx6/PyhI+G3CBPx+8yZeWlt3Jtru7iTj//HHrok2QPvL0KF03fv2vda9ZozB29ubmzRpEi5evIh9+/bJz5w5g4CAAFRXV9OH4uLo2rtKZRCLSVb+ySeQP3qE85s24dGNG0JGRgYaGhrAGIOqqirc3d3R0NDAZMrKYCUlUFNXh5aWFkQiEZYtW4aamhph165d2LFjh3D48GE+LiGBlBRvvEGpMDxPygXFegoLo3sRFkbyaBcXYNYszJ49W5SWlsYlx8TQ2gwKojH55htyXhYVAaAiWx4eHvDy8sL48ePZlClTIBKJ+HXbt2PDjBlY9OOPosaMDEyaNKl7og1QxNjeHlyfPjA4dw4jVq7kLk+YgNBHj1oXzRtv0B4MdK7mDbREjZOSkuQdnz1GRkYYEBCAikuXmFZgoCC3tERjx2JnysqU3uLr2zo+CkRGkiLgb2DGjBl4++23sXTpUhQXF+P333/vdH6doEghy84mJ+h/CYwxpKWlwd/fn1e0A1OA4zh4eXmpNDU1LfPz8+spDN2D/9fRIyPvQQ968Nrw9PSUjxkz5lFwcPDDtLS0OW5ubuJujcmiItQ9eoTcFStQUloKHR2dduSlvr4e586dE56lp3MuHh7QLCkhCdzr4OlTMoLXrqW8xbbnoCD4r1lFXSKR4MWLF/yTJ0+YioqKsHbtWubp6QkXFxemr6+PyMhIFhoaivDwcISEhCAyMhJPnjzB3bt3ERYWhoiICDxLSkKJoSGc7O1hcPEiyRrLyymCzHEk6XRyoshMRESr/HjOHDIQx41rPSFnZ4pGPH8ONmoUioyM8LCsjGnV1Ai93n+//WD7+wNFRUiIjRWExERWaWkpH/7llxz69KExaB6XwsJCBIaFQb9PH6ivWUMRazs7ijiUlVEU6OxZOg87OzKM9u6lczYyAjd+PG57eeGWoSHc5XKw8nLo29tDu08fKPXtC/UZM6A2YgTmrl2Le/fuob6+HrW1tcjOzmZyuRyPHz/G4NJSYWhYGPJdXVnamDFCYGAgb2VlxampqSEiIkKwtbXlSkpK+KioKCaRSATLFy+Yy9SpTLdZenjp0iXWv39/pqmpicrKSjQ0NMDc3Bw8zyO/oAB1ysoQ7duHnORkZEdGIiohAbFSKWQyGSwsLGBra4szZ87IEhISBHt7e+7mzZuIiooCz/OYPmAAVCMiEGtsjOTkZADAmDFjcOfOHXlERIRgZmbGKZwy7RAVRXLl4mKau9u3U/7506dEap2cKMK7bBmwaRNU16xBqUjE240bx7qU9e7fT4So7XzogOPHj6O8vBxjxoxBYmIiq6yshK21Nd3rkBA6Fz09yjf18aHo36pV9GdvT5HAqVOJMMbHE/G4epUcFosXUyRKT6/LKJxEIkFUVJRgZ2fXKjHmOCLSEgk5v6ZNIzmxmxs5IVRVyQkRHU0R4Ve0U2oLQRDwPDgYwXfvYvCRIzDLy8NAsZjW0NChNLZLlxJ5nzMH6NcPYl1dhEdFYcmSJbTX5OUBT55AvnAh7kul+NXdnY80NBR09fTadz0oLydHx+eft143x1HE2Nq6+/xyc/PWYmpLllA6wdChqLt0CXfLyzlvb+/Xq5ZeXg7BwgKXtLVZibo6UiwtYe3mxrzXr0dNTQ3CwsIEqVTKNzU1cb169aJri46myOz333dfJBCgeeHuTpX+fX1pv3xF7mx+fj6KiooglUohFosxdOhQrqGhQZ6Tk8PCw8Ohp6fHetXWAh4eyBWJ8OTJE0gkEqipqaG+vh5PnjxBfHw8shjDqfJyDI2LE+Y9fMhcdu9myTk58qSkJM7Ozg6nT5+GkpISvLy8aK2MGdMSJVdXV4eTkxNzcnKCvb09CwsLYykpKRjj6Un7miCQ6mDSJFKSNDXRnFY4FlRVSdny229QKS2F89atUPnlF0hsbcHdv09EXVeX8vklEnIO+fmRg6x5nVdWVuLRo0fM09MTSqamUPL2xvCZM6GhrEzf6QoJCeQIkkhoXwCgvXQp8t5/HxFRUZyrqyvEYjEpIUaOBHr1gkQiQV5envz27dusf//+DADu37+PyspKJCcncwMHDkTHvUcikaB3797I0dVlwYwhLjWVd96yhcHDozVNxMKCzuWPP9q397t6lfaBP2m51/VUImeqtrY2Bg0axG7dusWUlZVhamr6ao8SY3QeWVm0L7xOfZS/CEtLS1ZaWsqnpqZyBQUFcHd3R1unyK1bt+qKi4tvCYJwxvNPeqj3oAf/NHrIdg960IO/jNDQ0EK5XP6Zu7t7917+L75A0h9/4Fx1NeLj4/Ho0SPBw8Oj5aG8Y8cO1NfXs6VLl8LIwICIwtixr5db+NZblDOoKPalwJ49FLHrTvrYDfLz81lhYSF69eoFQ0NDpogsGBoawtnZGSoqKnj58iU0NDSgpaWFoqIieHl5wdvbGzY2NkhPTxdmzpzJ+js4UFGi3r3JqH34kKqxAkQyhgwhAnLwIJ3j8uVkCInFrQbzmDFU8OjYMRTExwtBUikbVFQkyL7+un3hJEEgkpGQgCsLFgiWOTnMpW9fjps7t10ObU5ODo4dOyZUVVWxuoMHYXzkCJRnzaIIYXk5GeAff0zGqaoqFY4aPJiIvKcnRdqNjWFpaYnw6Gj0WbkSuklJZOx6eQF6emBxcTCbNg0iExMYUe43n5GRwQ8ePBgTvLxY38hIwTU9nUtfsgR36+pYSUkJq6mp4erq6tC/f3+EhISwIUOGsJkzZ7KRI0dizJgx7GlMjPxxZSUzdXBg6urqSEhIEHJyclhpaSksLS354uJi9uzZM0RHR6MyLEyeGh2N+DfekI87eZIz79ULLCyM137xguXr6aG4uhqZmZnIycnhJBIJCwwMZLm5uTA0NOSrq6tZQ0YGbwgw/enT8fDhQ6ipqfH37t1j+fn5XENDA5eamioMGjSotSBQUBA5ehwcqMK6opK6pydF7L/6ilIHvLzIkfHRR4BcjqqLFyFVU4NNVhbDnDlE0G7dIuO/Vy9SLowb114JUltLsmxLS+DYMZTfugXxiBH8tC1bmJa3N1984QKzWbIE3P/9H0l78/OJcJw7R+kB5uZ0P21sKFfawoLIV1gYcOQInaudHb02eDBdi4YGyarnzaNo1JMnREx0dBATEyM3MDDgjNoWrgNoXkdEtOaCrlpF6okhQ2ie+vm9ut+uIFBKwMGDQFkZZN9/D7GvLyKMjKBXVgbzlSuhu3w5jZmZGTmMOkRW5XI5wsPDMdDKCupbtwKbNyN5zhycVFJCo6Oj3H3UKJFUKmVpaWmCq6urouIVjdOaNUT62kIqpZSOP0mZiYyMxDkLCz5z5EiY+Pszze++Q9jIkZjEGJjCEdIVPvsM/IEDuKypKaRLpazYykqYtXkz85g+ndnY2EAsFqO5PzUrLy/nHj9+zOscOSIY/PILY4sWkWOjm/oN7cAYXVtiIo2xhUWXhDs9PR1Hjx7Fs2fPkJeXJxQXF3N1dXX8/PnzRc7Ozowxxu5euyYM/PprFu3kJL969y5XVVUl3Lt3j0VGRiI8PBwpKSkoKioSysrKBAcHBzb2yy8ZN3Ei5FevwvbuXS5eWxv34+IAAG+++SYpToqKqKBXhzxgZWVlFBUVISEhAWvXrqXcd8ZoT0pMpI4MJ07Q3qWmRvP47FlSFg0YQKRuyBBc1NdHgJMT3N57D0oTJ9L+GBREsubx42mNBQTQ8+eDD4C8POSZmCApKQljFG39TExIGbJvH6WMdMTvv9P6nziR2sBZWJBDRCrFS2dnlJeXCyNHjiSlw4QJtN6b54W5uTkXERHBHj16hEePHgk5OTnM3NxccHJygp2dXZfqiKamJpw6dQo1mpqokcmYu6kpREOG0L6upkYOM3t7SiEaMqS10GRaGtUA+DtKsg73pqmpiT158qR1LXUHxuhZbWVFz+e33361g+hvQFVVFZaWluzevXvw8vKCZdvOJwAaGhq47OzsPoyxRcHBwdc9PT0r/tET6EEPXoEeOUUPevA/DD8/v94AZonF4sFyubyI5/lwAOG+vr71/+Ghp/fp06dOLBZrdvluZSUS3dwQoqyMdevWIT4+HqGhoe0eyPb29nj58qWgp6fHYGREJC8pqVOxrHZQyBdv3uz8sC4ooNzdlSv/coueSZMmIT09XV5QUCA6cnLKheMAACAASURBVOQIHBwc5LNmzRIBgIaGBkaPHo22hVeSkpJgZ2fXUizL3t6+s7Hx4AEZ8nv2kCEXFERkRkmJSPj8+STn272bIqEXLtD3rl2jfMScHCjNmIGROjro27s3aylkJQhEyMeMAfbsgfydd9DbxITL27MHugMHoq25eisggI+JjuY2HjzIVA4exEWpVJBWVzOtYcMoiqmItr/7LrUDs7EhsjRgABmwixaRcZaYCImxMQRBQEZGBt9v3ToOU6bQtRkZkXGZkQHcugWrCRNgZWVFA5OXB5w7B+OaGoajR2HV1MT0z54VioqKGAAkJiYiMTERKioq6N+/PwBAqVla/kZJiShCTU04c+aM0NyajTU1NQlyuZyJm3vuvvfee9DX0wP69xdh9Wpg40b63alTUS6TCU0nTggzHj1iMUZGyGwmsObm5kJhYSEDgHfeeYerqalBypYtLDY7G2mXLsHQ0JBfvXo1FxkZicDAQJiZmQkFBQVCWloac6isBGJj6X4pJK0iEUWTv/2WomJRUdTqTlWVyKeHB31GJELWZ58hOy6Ox4oVIjg6UnTN358Mc0WbroEDKZ/YxISipq6uRFZLS1Hx7Bm4vDyMGjWKw8qVGDxlCnctJwdJ/fuj7+nTQv8vv2TDFBGra9daJ4KiFkJJCRGNoUOJSLu5ESk/cIDmVVMTSXMBIu2zZtE1ffEFEfADB7Do+++Vnikr89DW5lBX15oTqqREUfTERCKvP/1EBn9SEpHWjuS8srJ1TXz5JbVSi4gA4uPx0sBAOKmtzRrXrcNQZ2e479r157npgoDs2FjhzZMnmUpCAq4PH46ERYuA0FCMnzlTGDZsmAgA7t27x/fp06d184iOJkdGV8Wb+vUj6fsrooCVlZUICwsT7OzsOLFYzJ8cORJNlpZs/rRpYC4utEZmzybip6JC6Rl2dsD165BPmIAz2dnIfPaMzd65E5P69+/US5rjOHg0p4/k5ORwN7duhcHTp4JRXh77K/n2YIzUCwcOUBrBnj2dyG1eXh4AYPXq1dDX1+fq6+uxe/duLisrCxYWFvDw8IBRQQHLCAzE3ZQU0fjx4+Hq6spkMhkCAgJgbGyMpqYmuLu7MwCte6KVFe4YGkI3IwNaOjror6qKyd9911ps8K23uq2YrqGhAZFIRNJquZxI+a1blPsPkOPl0CFy5OjotPSubq5sjrKkJKGhoYHNmjNHUFFRoXM6e5YcjQoSa2lJrwE0H8ViWDx/jgWnTuF2nz4Yv3QpvbdrF80XRd0LBTIyyPk7YACpN0xNydEklQK//IIn338vHz16tKiFNGtr07qbPLn5f7Uxa9YsqKmp4dSpU4zjOEybNo11rGLfFhKJBGPHjkVwcDAEkQjVn35KjovPP6dCft9/T6T76FFySpw5Q9XK798nx9o/gPz8/BZFQ5edADqiqIie3ydOtFbO/wchkUgwdepU3Llzhx89enS7hTRkyBDR4MGD1cPDw/tHRESE+/n5ufj6+kr/8ZPoQQ+6QE+BtB704H8Ufn5+A5SUlB7Y2tqKTE1NVWtra+Wpqak1xcXFEolEElFfXx8kCMJzAHkA5ACaAJQBKPD19X1li4wdO3Y8nDx5ssug5ghqY2Mjbt68iWHDhsHU1BQVy5bheWIi8j79FDNnzkR6ejpOnjwJd3d3KAqY8DwPf39/eWNjIzdu3Dg2MDqajBZf3+5/+F//IuP9ypX2r1dVEbFtKw39G8jPz8dPP/2EmTNn8i2Vzv9TpKcTqVi0iAzczz+nSOHdu2QkHj9OJGPzZvr8zZtk0D1/jhve3vyLFy84a0dHPkMqFfRlMsxYtEikZGeHWpEIRz/9VK6VmSkqNDKCpra2UF1dzYZaW6NOLofhmTP8oLAwriwiAn2vXIFMTw8JP/8My/x8aM+cSUZpeTmRa6m0c+Ga6mo0XLmCY7W18nF79ojqlJXx+9SpWBMcDJ3ff6exrqkhIjZ8OPVrVlOjojwAkc7AQDKA161rMWxDQkIEheNFXV2dnzhxIqenpwfjjlLdqVPBr16NX/LzZbW1tczY2Fg0btw41DbnVctkMmiVlcFTVVVw2rKFdZQnnzx5Uq6joyPSyMqCyunTUJbJ8MekSZC1ITNbtmwhI3HPHjwtKsJFVVW4uLhg0qRJACjVQSKR4PLGjXKngQNFVkePUlurhQtbfygxke5tbGz71mXZ2URsX7xokSHHxsYiIiJCtnbt2s6O7qIikl/b25MEfMoUkjC3kZAeOXJE0NPTw8yZM1uITFNTE7Zv397yGd/u1s/NmxRpT08nZ8DRo1S0CKDI3s6dRBguX+5eJltVhfQPP0SEnZ18cXm5COfOETkfMwbYuJEie1lZrfN9yxaK4i1YQONgbk7zTiKhqOxPP9FndHWpeJeZGSorK7F3715YWFhg0aJFr1cALiwM8n/9CycGDICZXI5Ru3dj5w8/QBAEfPTRR+2k3Ldu3eIfP37MffbZZ7TOtm2jfspd4flzitA3V0NXID09HRoaGtDR0cFPP/0kNzU1xcyZMztLfGJi6B46OpIz0MWF7sPBg8DSpYjPy8OV5r2s2/umQHY2MHUqHu/YAZ0NG2AVGgrW0YHxOhAEakU1ejQR1zaELjQ0FCEhIViyZAn6NvdAP3/+PNLT07F69Wr8/vvvwuC9e9nDKVOQLxZj2rRpcHJyeq2fvX//Pm7duoXV7u4w/PJLYPVqItmMkTNj3jyaC23B8wDH4eG4cYI1zzO94GD6ztat5AhpaKDvmZmRA/PxYwBUSPH06dPIzMyEqqoq6urq4OrqKvj4+DAlJSVaq5qaXaYa8TyPp0+fIujcOQwLD0f9+vWCz+7dDNOnUxtFQaDf3rqVVBaKKv+jRtHaUVSR37WLHEiBgfj6668xd+5chUqB1oiRUZdO4YaGhpbuB6++jQK2bt0KZWVlLFq0CObm5m3fJEfdxYv0vDx6lCTu331HypnAwH+kUFlcXByuXr0KANiwYQN0XidFJCmJ1v3GjbQf/MPgeR779u0TnJycWHdy8ZCQkKbIyMjKpqYmd19f3+R//CR60IMO6Ils96AH/6NQVlb+0t3dXXX06NEKI1A0btw4rdraWqSmpnrn5+ePLiwsrKusrBQEQQDP86y+vl6poaFBZceOHYkNDQ1bBUG47Ovr284j5+fnN0BZWXmQfZsqp1lZWYiNjcWTJ0/AGMOYlBTkzZkjTJ84kQGApaUlfHx8hMDAQFZdXY2ZM2eC4zisWrVKdPnyZdy8eVMYuHEjw8CBZNx2NCIfPybD4dtvu77YTz8lwhob+7fHKyEhAZcvX8bo0aPljo6Of6OheDewtKS/tDQqyrRmDZGqsWOJ7GzYQFGVjz5qjYInJwNPn8ItIIAztrDANRUVboK3N5y9vRF/+jRC3n8fq+/exTQvL5G0sRFvDRgA1eRklqisDDtXV0TPn89ruboyJZkMfZcuBZYtA5PLYVxYSAWV5s5tPb/IyK4NLw0NBOnrQ15VxamHheGUvz/c7O2hk55OETpFH+kHD0hSLQgkmS8rI0KRkEAG/RtvtDvsmDFjmKqqKm7evImxY8dyA7vL03/nHXAODlgxeXK7k9PV1cW6detw48YNYdjOndAzM+tEtAHA3t6eCwgIgL6+Pu/5ww/M6Nkz9u7mzQhdtEjItbDAipUrW/sy29hgoI8P7AcNatfyTSUnB7hwAUPCwji5gwPNsY6KirQ0kqIqiLatLRniCxZQdJjngdhYFBgbIzg4GJqaml1b0RxHhbbq6+l3li8nyWqbonj19fW8ubl5u7kpFouxceNG7N27t+txBIDGRtTMmIEmbW0U+PvDgDG0E2CrqZGTKzKSfu/Eic5VjAFAUxPqX32FnCNHRMK2bWBffUX3/Y03aE5HRpIjIieHiH1MDEXS/viD3jtyhMbH2Znk6jNndvqJxMREaGpq8m+//Tb3yvZ7gkA57o8eAe+9hwgHB2T37o2aXr3kD378UcQYgyAIOHLkCL9hw4aWAw0cOJC7f/8+kpOTobp8OXK9vGCSnQ0zM7PO7f5sbUlxUFXVQkoPHz4sk0qlSkpKSuB5HnK5XLSou8JPQ4fS/dy1i8aloADQ0UFVUBC+P3AAfDOhEolE4Hn+1e0GtbQAFxc4+/vj24ULMbuqCjZ/g2zLeR7CV19B6YsvaK/ZsaPFoePi4oKQkBAcP34cb7/9Nvr164eioiKhsbGR7d+/HyZSKdOqrUW9vj6G2di8NtEGAGdnZ9y6dQs38/Kw5MYNUoM4OxMBvHq1NVLc2EjOv969acyTk1EyfLggU1Ji7gA583x8iNR+/jntlUZGwA8/gC8uRtVnn+HfNjZCdU0NA4CPP/4YGRkZuHTpkpCcnIxxvXqx/tu2QfT0KXieR0FBAdLT04XExEShtLSUA8iBJVJVhUdICDiOY9DQoNSO48dpzn3/PTnSMjMp5cfOjvZABdHmeYrczp8PAJBIJPyTJ0+Yra0t3fCrV2l/6ELK3VXBtK7w4MEDHgDX1NTUnmgD5MD46iva558/pzF74w1y8vTt+58T7cZGoKEBQ0xMoD1yJAJ//RVqycn0u1IpzXN9ffpcQQE52vr1ozFMSaFn0JEjpHxSV3/ttnSvA47j4ODgwDIyMtAd2R4zZow4ISFBtbS0dCCAHrLdg/86esh2D3rwP4ht27bNUVFRmeLi4tKJNKqpqcHR0RGOjo5iAJ3KhMvlcqSkpDjeunXreF1d3RwAC9u+r6ys/IWbm5ukqakJ9+7dQ2hoKBQKmc8++wxVH30EYe1ajJo/v90T1N3dnZmamuLYsWMwMDDAqFGjwHEczMzMUFRUJIDjGHbuBA4f7tyWJC+PSE1XEAQiJc1tp/4uGhsbFQV7/jmi3RZWVmR0yGRkZB04QEbb0aNkeJWUUJR4/34iK5mZeHn1Kv9YT4/7GICqgQFgZ4fBGRmw2LQJatu3w+bMGdiMG0eFuX79FYMmTwYWLMAIMzMOenoUhR0/Hnj5EqKKCiQ8e8Y3qatzk9qeV20tRVEVUc5m8DyP5ORk3t3dnTMyMoKWlpYQlZTEvE6epCI/+/eTc6OggCKD06dTxGLePCpWtHdvq3y5DRhjcHFxwc2bN1FXV9f9eN29S1LqLqIfWgAWZmUxhIa2i8y1hbOzM3N2dgYUXTecnRGurAxXPz+Yr1rFUFHRGjW+dg1YvryV7NTVkcw+MxO4fBl3NDV55yFDRJ2I9osXlAO7e3frazt2UKQfoOMfPgx8/TXCdu9GdXU11q1b1/X8+ugjipAdOkRjaWVFZOLxY+DNNwEDAygrK4uKiooEtJXogqoXL1q0CGfOnMGBAwfkADBixAiRs7MzcPgwUq2tcfajjzD+zh3cjo6GVVoazG1tMbLtQRgjon38OEWlDx2i6+hgCBsaGkIQBFRWVkJbW5ve37KFCKmDAykZGhtbi4qpqlIEUSwmmfq1a+R0Sk2lHuuurjTfZTLgvffAffQRXPT0OM7NjT7j4ECEVSKhNIeqKiIPgwdTdemZMwF3d2SmpQnIyGD6+vqYP38+4uLi+IcPH3LW1tbtbpqZmRlEIhFSPvwQFcOHg3N1lQefOiVqamqCWCyGRCLhJRIJr6GhIdLT02Mjjh5FakoKMvv1Q3p6OjiOU9q4cSOkUimKi4vx+PFjISoqSpg8eXJnpvzLLxRhzM6m6zh+HMjLg8rmzfg4NhZJXl4YcPEiJK+QCwMgx8TmzXSM+HiI1NWFkpISpmiDVlpaiuTkZCEzM5OfMWOGqCtJb11dHW7duiU8efKEAYCFtTW/MC2NUw4KominmhrU1NTw4Ycf4rvvvkNQUBDefvttlJSUMB8fHwQGBmK6tzd6L1uG9X+zuBbQTCZ1dOjv44+pSN+lS7R/HzvW2hv+zh1Sx1hY4KFEwtna2qJF/Hz2LBV809Ag0uvpCaiqIvinn2B//Tpqli9n82fNgllz5Lpfv3748MMPubt37yL1/Hk+39KSyzt1Sv7y5UsRx3GCjo4Ob2dnJzIxMYFMJoNcLseVK1dw+fJl+Zw5c0QtDsPGZsGXosBYVRVdR0ZG+8J/R47QHG1WREydOpU7f/48/v3vf2PZsmVg+/eDeXj8R3nTenp6HABMmDCh6w9wHKUJ1dZSasqQIbTHuLkRAZfJaJwzM2ltiUS0zp48IWeqgQFd34MHRM779qX1+8cftJbHjUNeQQEaY2Lgra4uSKytGdTU6HkgEpFzSFmZHCHGxiTzV1OjdA1/f3pu375N+4yvLxWM+wcQHR0tPHjwgHU7LgDKy8tRUVHBAFwDgG3btk2WSCTvNjQ0+H/xxRcB/8iJ9KAHbdBDtnvQg/8x+Pn56YnF4iOLFi1SVe0mD+5VEIlE6N+/P6ysrNT3798/zc/Pz8vX1/du87F7KykpTXd1dRXt3r0bcrkcOjo6UFNTg6WlJbjGRmhfvkw5Yl3AwsICEydOxB9//IHQ0FD069ePl0gkXO/evYmtu7pSVFSRE/foERnyd+5030fW1pbIZtto7V9EcHAw4uLiYGxsLAfw3yHbCigpkfRYV5euraGBjBofH5K1BgVRFWQDAzgeOMA5urvTZ2/eBJ48gSg0FPo//0zjsWEDRSx69aJI4siRRK7bFo4qKiLytGwZbD/5hDt16hRUVFTg6OgIAwODVjJbU9PaTkcuR/Dvv/ODkpLYgCFDgOXLsSwtjd0wMYF0+HDB1M6OgePovFeupAhVYyNFchRGslxO90+Ra94GHMeBMYZue/gCdM3dyRJPnKDx2LTpL0VFpEpKQvS77+IDnqfvLltGCoOYGFJHpKaSE6G6miJVbm6AsjKE5ihmJ/j6di76NWIEOYYURbVWr0bCkCEoPHUKjoMGdS+Lnju3vXNi0CBKl9ixA/JZs1D40UcozM/HpEmTurxgS0tLzJo1C01NTaLc3FzcuHEDqSkpmHf4MCp79+ZnmZhwaufOYVJpKa/k78+9ePkScXFxiIyMFEQiEZYsWUKR/gEDWgv7eXjQHGuWYfM8j1u3bkEmk6E2NxfaO3dS9fVJk8h4v3OHIvI3bpCs9sgRIhRt75GiD64gUKEpQWhtj9anDwqMjcHxPFIfP4a1VEoG+uXLNI83bKBx0tEh59DOneB5HpEREfzLly85kUiE4uJidvjwYaiqqmLlypWd+hQDgC2AgYmJMPruO6jY2ooU11ZdXY2ysjKuvLycKysrE4qKiuQZWlpc6fPnTGJnJ3dychL5+PhARUUFmpqasLKygoaGBrt27Rrz9PRsrRp94wYRsKlTaf6LxTRXLC0BR0eIfHwQu3o1X52SwpU5OQmGcjlDXFz7PGAFBIFy+l++JLK9Zg1qt29nRc0tq9LS0nCKUjeYqqoqd+HCBX7evHlc2wjpuXPn+BcvXnAAmImJCYYNG4br169z30okmLNvH/qdP4+iL75Ak0iE2NhYHgA3ceJEVDTnRYeEhEDc2Aj9ffta00T+AgoLC3H48GEAwDhFpf2KCro2b2+SFAsCOa0uX24tEDh0aEt3gCFDhtAcGzqUyLZCEXPuHIp270bW3r2ImDwZEe++i80rVkDN1paIYzPh5jgO3h4ewLFj3PMdO9CYnS0aN24cTExMGDrs+YmJiRCLxcjLzBTh0SNKuzAzI4XC48f077RpVBRt7NjO+5SuLjk6m9G/f3+4u7sjMjIS27Ztg2T5ciwyMIAF/iMIYrGYZWRkwNXVtftPqalRJDkpia7j2TNyUFpYELlOSaExNTCgZ5OxMRFrfX36/5Ej6T0NDSLh27YBqqqora3Fz7t2QTJwILZs2cL+tOCZXE4Oih076L58+CE52VatIqfkmDHk9FWkU/0N8DyPmzdvshkzZqBbxRQAHR0d9OrViy8sLNzm5+eXIBaL/UeNGqVx7949n+3bt+9qamr6sqOirwc9+E/QQ7Z70IP/MYjF4s8HDRokaVe5+u8dB15eXmqBgYGfALjb/PJQQ0PDhvz8fBVFr93169e35pf98Qd5wl8hb3RxcYGGhgaamppw9+5dVlVVBRcXF3pSGxqSsR8VRSTS2JiiCN09yBmjKHhzoZm/i+joaL62tpbz8PDgYmNj+cDAQK6pqQnLly9Hp6rL/wQURGz4cDIgFy4k4+zsWXJU2NiQBPmtt0j69+23FD329ydj7/Ztigb98AONj51d5wJDMhkZVikpFG3W1oZFbS2WFhYKuXv3IkosZn0yM9F72jSYJCeT0+LQISJIFhaoLyhgIy0tmYaaGjB2LLRnzwafkoLQgQPZoo0b27eGmjqV/hUEqsabl0ekKyGBcrmHD6dIR/N9zM/PhyAIKCkpIcLfFZqLNbUDzxNpWbeOcn//ohzSyMgIubm5PNavFyE5ma41LIycFVu3Ul9le3si4W3ICsdxncl2Xh5JYDsSpBMnKHIbH9/yUt+BAzEjJAQVoaHwZ4z39PTk2jkaMjKI8HeYx7wgINbHBw9LSoShX3/NZvfpw5uUl3NdVccWiURwaO5h7eTkBLc7dxAXHs7vXrQIQ+/f5zRfvICpri769evHNTk7435MDK5evYphw4YhOjqapaamQlGDAWIxSV2/+YYI0DvvIL+hASd++02YefYsPKZOZVqmpjRf/fwoxUMxHz77jIzqb76h9fzttzQPT5xof8KMtY6djg4wYAAEQcBTFxfIZDIYurrCWqEQUPSAFwSqZPzyJX23shJXAgORmJjIzZ07FwYGBsjKyuLs7e2hoaHR9aYhl2O2hQV+njABWo8fY1FzHi3HcdDS0oKWlhYsqP82kTBHRzr3efO6dMLZ29vjzp078u+//1707tKl6KWhQWRbW7t9H+9Dh4h4a2iAE4ng7O/Pbdu2DeA4pl1WBpURI0jd8ttvrQWsfviBnBD79hHR/v77FiJaWVmJf//733xubi5nbm4uvPnmm4znebZ79272bXO6zZQpUxAUFIS6ujpu9OjRKCwsxIIFCwAAQ4cOxZMnT3CJ5+EUEwN+82YkODpCLhZzU6ZMgbGxMTiOa8kl55OSwDU2vrqifDf45ZdfAADLFi6EwfHjdE0mJq3F6YKCaC11sRcUFxdDJBLB3t6e9jRv7/YV2GfPhjwlBWWmppjWv79gOXEiU9PWprQFa2si8u+8Q0qIX38FmprQf9Ag9G/TsQGCQE7PO3eAFy/w/MkTYVp0NNOsribHm7o6PZe++oruw8OH1D3j3j3Kx2+L+/fJkbB8ebuXR40ahfT0dKirq8N70yYE1dfDws/vL48lAKSmpuLSpUusqamp1XnxKkREEKFevJhIbYdz+6vgeR7XmgswLl269NXpDwDdN5GIyPTKlXS/R40iR520uUbZ55/T8+LoUXpmbdz4l2uwMMYgk8kQHx//SrLNGMOcOXPUb9++va6xsVHu7u6uYWNjAwcHB9Vjx45tqqysrAfwjeLzfn5+ygDsAeT7+voWdnvgHvSgG/SQ7R704H8MHMfNd3FxeY3SoH+OAQMGICAgYIyfn5+Wr69vJQBJcXGxxtmzZ2FkZIQJEya0Eu2CAooOdpAjdwRjDAOaDevi4mLcu3evfTVvZ2cile++S3K3H3/s+kC5uVT59fr19oWp/gbWrVvHXblyRbh79y5rbGxkOjo6UFJS4v39/bkPPviA5LL/Dbz5Jv177hwZHYJAhp22NkmynZ0pAuDjQwZIcjJFGydOJOMxKoqIx927FE2dOZPIaFERGTVffUXEx9aWvt+3L0zMzJiJqyvqHBxw+ocfMHzkSJg4O1Oxn6lTgWnTkJOTg7jjx9no9esRkZAAeZ8+ePDgAV9bW8uJxWIU1dSgVxd9mHHsGJHq2bOp4JZUSkTy22/JGXDjBgCgV69eMDU1xePHj7uPbtfXUzSkLeLiKNq7eXP3RbxegaFDh7KQkBBRxp076BceToRQR4ci9J9+CunQocgqLISzRNJOp81xHJN3PJd336WozyeftH/94487FXrS1NRE+bVreHL3rlz1/n1RQHU1b2dn12qh3r5Nxntzb16AKt5fvXpV4HmeGQ8YINhs2cJ0//iDQ2AgObXWrOm2gjNjDAbV1Rg7diw3KCMDYXPm8BEpKRyflAQ3NzeI9+zBO7//DnmfPlBTU2Pp6elCVlYWG9SWgIjFgLo6pIWFUPb0hEFeHobfvs3bGBmJmLU1zdH0dPqsogBZZiZFvqKiWs/N2bn7PtUdUFVVBZlMBsYYhiuIdluEhhIpU1OjOT99OlRWrQJAexWALiPZ7XDwIERRUSi0sUFhSsqfn5SODpHC5mJdHcFxHDZs2CC6cumSIHJ0ZA0rVkD54MGuj9PFmgnneYRra0Nv4UJ+6e3bnMYPP1A6iY0NESR9fbrWP/4gYgpARUVFSE1NZRKJhNu8eTPU1NQYAJSUlEAxT8VisXD9+nWmq6vLz5o1i7PuohjY4MGDYWVlBSXGwG3ejKkZGeBOnepSLcL961/d18zoDnI5kJ+PxYIA+YkT6DN8OO1hEyeSY0sBQaB9oqamXUFAAAgMDKSodmoqOXc61CYIf/ddRERHY7mTE3r5+7OWVouDB9NxU1KoEGR5OY3p119TvnhEBDm4DA3J4WZiQvUmHB1h4OHBgrW1scTPrzUPuy02bqS53lVRu/37u0x9kclk0NTUFFJSUphVv34Y+R84iIODg/nGxkYOAH7++Wfhk08+6V7ec/06kevkZHIkOjiQQ6ft+P8J7t+/j5CQEMHb25sFBLRXWYeFhQnz5s3r/vcVyoydO8lJKhLRfVi4kBzJ4eFEvH186C8tjZwZ+flUEPWDD1pVDH8Cxhh69+7NS6VSrrq6+pU97vX19bFw4cJ2k01TUxNvvfWW2oEDB/z8/Py+8/X1bQAAZWXlUIlEMqiurk7p22+/DWpoaPiw+SvNGyC8AGT2FFvrQXfoIds96MH/GBhj1U1NTf/IsVRUVGBqatqYnZ09BcAZxtgEmUwmmj17Nuw7FlBijIwxM7PXOvaprv2GcwAAIABJREFUU6fkeXl5bMmSJcyibQTIxYVIx/z5XRqnLSgrIwP2PyTaAF3nwoULWWJiIlJTU2Fvbw9VVVXu1KlTr12w5j/CtGkU1Tx3rqV1DSZPJuOovp7yggcOpBy8b76hCNDWrZQzPGAAkWlFJetDhyjiGx5OBG7IkPa/1SzrVgVQ3r+/UK6szDBuHEnbm43s4uJiaGlp8dHR0VxUVBT09PR4Ly8v5vT/sHfdUVGd23d/984MVboC0lGw0xVRQQR7L7FFfcaWaKIxiTE9jzdRY2JMYkxiEktiSezGFjtgQWwoCEiVoiAKCEiHaff+/jgMvfney/vlvcVey2WCw8yde7/v3rPP2WcfT0/s3r0bBw4cEBctWsSatCnExpKEcto0kmBnZxP5HDGCEgvnzgFLl0ISHY2ysjKx1jCopXNSX1Xw6af0PrGxz3fN8/Io4B4yBHpeXpg8Y4YQeeqU6HT1Ko9p06g6tXEj0KcPLu7dKyQrFNzp06cxYsQIzaBBg3iAyHaDyra2gt/c+BrGKHj89dcGlU07JyfMsbfnq995B6eGD+dOnjwJKysr9O/fnxQML72Ep0+fQhAExMfH49q1axg9ejSrkYgSy5s3j4LQ334jmfvixXSe6+Pjj6lPc8cOcFVVsHJywoyICC7ZywudO3em9WRoCJ3u3WvPo7+/Pws7dkwcN3Ysw86dVAE8cwY4cQI3nJ2FrLFjuVmxsWLg48c85s1rWWmyaRNd//rrYvhwSv7s2NFmRU1737K1tW3Slw6AvA1++IGSSsOGAe+/D5kgwOHOnbYNxgBSI1hZAWvWYFhODiIjIyGKYuvOz5aWRAzKypqXeSsUwMsvY8q777LdL70kSPr2xWxR5Jq8p4FBE7WEg4MDHj58CHd3dxgYGGATx+Gt5cuhv2IF9bbfv0+kTk+PEhg16N69u5iTkyO+/PLLXP3+bK3se9y4cfDx8dEeQKsnpZaQbN5M1zwkhKqM9ZNZCQl0DZvxYGiC0lIyXvP0pD3s5QWzpUuxLz0d493dYdnIMBEA7Znr16kneMmS2h/n5uYCAM26vnCB9mojRMTGitNCQ9H5b39jsLYmAq01FWSMyGZxMd0/nzyhyu6cOUS2e/emBKN2NBtAPiRhYdC1tYVxc0T7++/JNG/hwqZJiYICCJs2IS4jA4VhYWCMQV9fH9evX9eUl5fzPM+zCRMmoO/y5ZCZmrZ9LlvAqFGjuLNnzyI3NxcKhaL5xRsZSdd0715aQ8nJ9P+lpUS8n4NsV1ZWQqFQ1BJta2trTJ48Gfr6+uB5vuXNk5lJ8cDevZQ4WraMRkwClDhavpySZ/7+db/TrRvt8bIy2qv799N1MjOjFqk22oa6devGXb9+HWlpaZSkeU6YmprC1dVVnZmZeVMul28EsFdXV7dqxIgRBr169cL58+eDExIS7jDGxKqqKl0AgomJiaqsrKxSLpc7hoSEVLb03nK53BHAEADhISEhzUi3OvC/ig6y3YEO/I9BrVYfun379lv29vbP37DdDAICAjrt379/6/r168fzPD951qxZ6NY46MrMJJOapKR2v29RURE3YsQIph0xA4ACARsbqpD+9lvLv3zpEj18tbNR/03o27cv+vbtC1EU8fXXX4u+vr6irq7uv2cEmCiSUVJkJEnkV60i0jJjBknIV6ygwN3cnOSkQ4YQOXFyosqgri4F6/Pm1b1nc26rXbsSKXV3bxC4NofAwECcO3cOpqam6P3kCbgXXwQuXkRcXJzg6OgoGhgYwMTERFi6dGntOZgzZw62bdsm7t69Wxw2bBjn4uJSR1Y2bWr4AXZ2VKGPjaVq0A8/UIXD2Bgzv/mG5c+fT47lzaBq40YIvXrBQGvUs2cPzX1ui2hXVRFRGDAAuHgR4nffQThzBk+OHYPm5ZfxzM6OS09IwDMfH5jOnk1EYvFi4PPPMeLuXa74rbdQpFSKFy5c4JOSkoRhw4ZxjLGGZHvMmLpqfXNwcWlalQcAnocsMxPSsDDkHzuGaHt7XA0LE1Z+9hmnTEzEFm0gCiA4OFgcMGBA08jS2pr6zVNTqYJua0vnnefrxhIZGNQpJWoIS0/t7z9+TOuC50nOWVCAuxUVwpvr1nEYN46SM1rjusuXUbx7N1eZmwuLNWsYli6l3xs5kki1FqJICoxXXqGWhsa4fZsSGm2QbYVCAQBQKpUCmvNOePKkYbA9Zw58Ll+G14kTiNqyBb7Ll7f85oJAyptBgyA4OeHaoUNQKpWorq5Gm94WoaF0rWfMaPjztDS6D+XnA8bGmPDhh9wPP/yAo0ePwtTUFB4eHjT7GKBxT/XW0NGjR/Hw4UMAwGQiPty9e/fEL775hk2fPBm9y8qo/eKDD2jv79tHRGXlSkxLS+OQm0sS599/R4WrK3ZGRKCgpARgDG6Npc3tgURC96WICFJNbN9e92+PH9cl8ZrDs2cktZdKaa+XlpKiISwMsLWFPoDKuDgx6s4dNn78+ObfIziYznO9e9adO3cEQ0NDZpKdzTBxYhNfjsePH0OlUjELlYrWxpYtJME/fZoSkqmp1Maybx/5Chw8SMqLIUPQ0ozy8PBw6Onp1crtG+CPPyh5uGBB86Rv9WrcTUwUz02ezCwtLTWiKKKiooLz8/Pj3NzcoK9NHstkpNKpUWM8D0RRxM6dOyGTyWBjYyN06dKl4UxzgO6XKhWtTY6jtevpSWowbUKqBaVGcwgICKhttXrzzTdp5nl7MGQI7bnlyynRqk0caWFmRgnZ4uKmfe+dOtG1AshY8J13KKF8+zYl3Vp4DlRXVwuGhoZc73/i3Goxffp0vXv37rlfuXLlp2fPng1UKBT5hYWFkEgkGDt2rM7YsWN1AEoOajQa6Orqyg4ePMgyMjLOyeXyOSEhIVmN31Mul5vzPB/n4ODAsrKymFwuHxwSEhLb1rHI5XJLAH4hISEtzCds9nckAMYBCA0JCal4jq/egT8JHWS7Ax34H4Nard6YlJT0Vmlpafsfiq2gW7dueOmllwyysrJmu7q6wtzcvOmLMjOJBLZWiW4EGxsbITMzk/fy8qIfFBXRw/f4cXpIh4dTD1dNH2oDfP89Bftbt/6T36plPH36FGFhYaJCoWBZWVnIz89vW5qqRWUlHbOzMwV5MTFEiKZOJVLZsyd9Lx8f+o7W1mSwk5BAgawoUpIhMpKqOHv3EhHv2bPtzwZIHql1dHd2brEKUF5eDrVaDR8fH6ajoyMeP36cnS8txSilUswNDWUPHjzg/P39oaOjg7CwsAYRmUwmw4IFC7jNmzeL+2qSHa+99hosRJGqEs+eNQyEOnWi6qu3N/Dtt6heuhQ5mZlI6d0bljY2dJ6iosh0qOZ4U1JSYJGRgT0//wy33FzBztGRs4uOhm5zpOjJEyK+33xDVVSOI1m2hwf2//KLkCKRcJIpUyDbt0+QyWSCdVISN+nhQ0765Zd1gZ+/P6rfew8XBg2CXna2EDhhAnf+6lU8evSIO3/+vFheXl6XFMrLo2C2tWuybRuthWbAdeqE0d26gT9wAPEnT+Lc2bPcFW9vpJ85AxsbGyEoKIizt7eHRCJpvYTj6krV81On6tbW/fskiQWo8paUVFcRFQQi0XfvkvR98WKSJ+fm4rGTE5cZFYXu2lFO9UY6+fr6Yv/+/SiTydBJS1Zu3iRSpFW3hIWR1NrBofkAfswY+qNQNDtbWIuuXbuixvmaT09Pb5jUCwigylj9GecA9AcOxLcLF8KhtBS+L79M9watwVZ9PHlCJGfRIqjVaigUCrzwwgttE22A1CAxMQ3J9rlzpEhITKSKMABTADNmzMDZs2fFhIQExhirGz/00ksNiEZGjQTfx8entoq/YsUKljVqlJhgasp679pFKoU33qB7RkICfTcXFzr/t26RSuHwYSSXl4sD9fXRJzmZyb76CtyyZXS9P/uM1oefHyVl9PRa9zmQSqn1REu4N28m48OvvqLWHi00GupL3rWL7lFbtpBHwaxZdI0a3XdKS0tRVFTE/BqrMOpj2jR6jlRXQy2R4MqVK7h9+zZnbGwsYsYMItqffNLgV7TTDAwuXqRry3H0unnzyKgwL496quPiKDGxbRudFzc3ukdqe+NrcOXKFYiiCHt7e8He3r7hQs7JoWTp3bvNzucuKSlBhCAIqYMHc8uWLYOJiUnLWcHs7Gb709uDmjUlXrp0ifXv359zd3dv+IJ33qGkQGJiw2Ts5MmU0FixgvZ+bm6bLV9aSCQSrFq1ilu/fj3y8/PbjitOnqSkXUICkWi1uq71oz5mz6b71dq1DSc6NMb8+eTVkJhI6+3JE3qeeHo2IeklJSWsvLwcmZmZrRtwtgKO4+Dm5gYHBwf98PDwhZWVlZp+zcQhUqmUJnMAmDZtmn5ERMTAyMjI1M8++6xYo9EYSiSSRxqNxlwUxfdkMpl3v379dMePHy+9dOmSGBkZ+cGnn35ayPP8GI1G84dKpYoDcDEkJCRN+/5yudxOJpNdVKvVTnK5fExISMj5dn6FvgCOSaXSR3K5PCAkJCTzec9BTZ+6O4D4kJCQFseGyOVyIwDlISEhzTiIdkAL/h+NR+x0oAMd+K9GYGBgdWRkpBPHcW5OTk7/lqqskZER7Ki/s+k/ZmcTQXzOfr6YmBjR2NiYegkFgWR8vr4UEPA8/Sw5mR6q9ZGXR5WF8eP/rfM5ATKe2blzJ/T19TF69Gh29+5dlpKSIlpYWDBdXV16sBYWEmm4fp3IvocHSVqvXqWeww0biPgUF1OFcdQoIhpz5lDQO2sWVa+9vChw5vk6ghIVRRVpf3/6dysrItvOznR+2upTXrWKguO5c1s9N99++60QERHBjIyM4OHhwQICAuA2aBDu6uoKymvXRP3evZmPjw8zNTXFlStX0L9//9qgAqAgY8iQISwgIAARERGwsLBA165dwbp1aypbB+j7BQTgMWOoHjcON/LzhfLx44XRCxZwyMwkCfuIEWR2N3Ag9h85IlRVVbFsOzsMunCB3TMwwO8pKbh74QKyf/tNOBofz1zmzoUiJwf3jYzA1qzBlZ494fL221Q9ZQyYNAm/nz/PJk6ciBkzZmDw4MFs4MCBXJ+4OGYVGwvZokV1xIMxlEskYGfPYhjPM6cbN+C+fj369OmDq1evMpVKhZKSEvj27w+2YweRkNZI2uLF9JrmZOYAeEtLcCtXwtLCAtU//YTk7t3xFICnpyfz9PRsWw6thUxG8thBg8hYyMeHiICBASV4goKIVPj7U6X/6lVaS1lZFOiOGgXVmDG4ceMG7Lp3h3Wj3urs7GyEhoZCoVAgODiYFAxDh1JCJSqKEmTR0bRX//GP1pNtubm0hlesoEptC9i3bx/UajWGDh3akAiXlhJ5aCTtjYuLw70HD9B/0CDYnDpFlXUrq4YJnwcPaB/+/jugq4sDBw4IRUVFLDg4uH1k28WF3sPXl95D25e/aFGT8U1mZmYYMGAAy8jIQHp6OgRB0Njb2XHw8AD+8Q88yc3Fpk2boFAoMHz4cDE4OLhuo2o0KF+zhpm5uMDs9deJ3N+6RWRk9266p0yaRNd46FDAyAjJ7u44VV3NJn75JdNdsACse3daF6amdGx79lCV89AhcnMfMoTuQXFxpII4dIh8FrRVdz09UhdFRFCPfJ8+lMQbOpSSCmVlRGhzcugeNWkSnZfJkynZ1sJ9p6SkRHPz5k2uS5cuzZsichw0P/6ItGfPsPXCBVRUVAgajYapFApWPX++0G3xYtb4vZ8+fYp79+4hkDFwnp50frS97QMGUFKpf39SFbi60nXjeVIiTJ0KiCJKKyuxfft2TWRkJJeWlgZ3d3ehR48ezNLSsu7DRJGI3dmz1E9f7z6sVqtx48YNPFm2TOxkb8/83n6btZmcXbOG9uw/4TsBAHl5eSwtLQ3FxcWoGW9Iz6PoaOCFFyjZ0JgQ5+XRubCzowrx8OG10vn2gDGGrKws8fLly2zw4MHgW1MYzZlDiRst2f/uO/rTWE2go0Nk+8ABes619p6M0XqeM4e+x3vvUUvV1KmU2DQ0RHp6Oi5fvsyMjIzQooLiOaCrq4tevXpJ3dzcZM3GPvXAcRwcHR05X19fSd++fQ0HDRok6969u4WNjY1+RkbGQEEQTL28vLpaW1sjKiqqKi8vr4+Dg4P3xIkTzWQymYexsfHIZ8+eLbl48eLuwMDAMrlcbgHgiSAIZv369VMXFRUV+/v7nwWAdevWvXDt2rVPw8LCrgYGBjaYebpmzZr5Uql0D2NM18vLy6igoCBgyJAhz1WV+OSTT4ZKpdJbUql0KQDu4sWLcZcvX1YEBgY2cGiXy+VjANyTSqWjIiMj3758+fL6yMjIV8LDw68FBgbmPM9n/q+jo7LdgQ78D0KpVH4fHR09a9iwYZJW+xH/Hfj6awrmngMqlQo5OTm8m5sbVQr69aOsdX2nW19fMrPRVrwBqsw5OZGU7F+QibWEiooKGBkZaRbMncvj4UO82akT9lVWCuWTJ/PF5uai3rvvMrzxBgWtokjVEp4nR2Y3N5JwL1hAb1bfJba+e25rOHyYvp8WgwZRlTskhExtlixp6n4LULCxdClVwloJeLVQKpVcUFAQzp49i9LSUgwcOBAGBgYYZ2rK47ffGlQZZDKZUFBQwNk140LMcRx69+6tOX36NB/7009Y+M47LTaIiqKI48nJouuiRWyGgwNXa6o1YQL9efKEAuXZs7HYxYUriItD14cP8dvcuXjr998xdPly5Bw+LPa4fJmr+PJLZOvqChdzc7nyW7fQ/ZtvkB4XB3AcpFIp/Pz8wHEcXFxcNNevX+c9PT0poNuzh6o5K1Y0OT7jqVOh/8UXKPngA+gKAkzj42Hq74/evXuLnTp1Yjdv3kT299+L9r/9xuqP9WkWGzZQNac1GBhAs3kzhp09C/dt27D72TPh0qVLXEFBAaZp3bfbg+3baf1lZdF/r1hBVdcvvqCE1M6dJOUMDSWVwbp1tE5qgsdbt25BKpVqPD09G0S66enp2LdvH2xsbDSzZs3iG9xHZs2ifbtxI/XSHjnStqrFyoqqwy2Nc6uBr68vLl26hC1btsDa2lrw9PTkvIqLycOh0WSA/Px8nDx5Enp6emL/4GCGoCCq5ru7k5GetrK1dy8lPmrMt7Kzs1n37t3rJN5tQV+fxjwNGlTXky+TtTonecqUKTh27BjCwsJ4F0dHdNHVxYkTJ3D37l0AwKJFi2Bra1t7UtU7diA+IwM3p04VX3Z0ZNi8mcyixo6le52tLZ33ESMokdOrF6qrq3HmzBlNQEAAp6ury2qTGNrpAEBDF/gvvqC/V6+m71FWRtfEyoqu4fnzRLAXLKBEDc8TMZs/n47D1JTW2P799PN2QkdHB1OnTuVjYmLEI0eOsLfeegv1e81TUlIQEREhGogiK4mOxrhly+Dh4cEpUlMhcXfH52+8wcUmJgoLFizg6iur0qKixJ4JCUxdVQVJ//4ot7KC/qVLKN2zB5Lff4ehVgVhZUVEWZtcCwkBBAGiqSlOzpwpFtjY8ACpdiZMmMA1SXZFRlKrxuDBTQzcDh8+jJSUFMx/8IA5rF4N1h7PkkOHKDHaioFXS3j8+DESEhIAoG56gChSUk1PD7UGcfWhUFASVKsscHOj/ZGR0fzrm4EgCJg1axb79ttvhRs3bjB/f/+mD5l16+hZHhVV9wwSBFo7QUHNv/GQIZT4+eWXOr+StiCV0jksLaX7+RdfAF9/jZi4OA0AfmELSc7/BHR0dGp9XoyMjGBvbw+O47pkZWUZa31uMjMzAeBDtVr9kZ2dnZGdnZ0MgOz8+fOKO3fu3F67du2vANIBem5aWlpKU1JSZgB4Qy6XW0il0u9dXV0tkpOTh37++edJarV6l1qt3g6gL8/z2+bNmye1sbFBVFSUyBirbuuY5XJ5J5lMdpsxxisUimUcx73p7Oxs4OrqKjt58uRHAD7ieT5ELpeflslkPzPGbisUisUAHnMcp9LR0elfXl6ufX50AqD8957V/350kO0OdOB/E3dVKlXJ48ePDf7VEWCtIi+Pgthm3FfrQxRFpKamIjIyUsPzPB4/fswbGxtr+vXrx8PenrLUr77a8Je6dKEKWHR0HXHV06M+vHaasLWKkhKSvvbuTcF4VBRMFy/GvDVreKSkAO7u4MLDMWftWj61rAxXHj3CJH9/5J87hye5ubC3t4e19pibM/15Xmg0lGAYOLDhz6VSMgc7epR61cPD6VzVr4r88gv97ezcZh+eWq2GSqXCgAEDoKOjI0ZGRiIiIoKZmZlpZs+axZulplLwxhiKi4uhUqm41kjJ1KlT+YSEBAy9dAkKQYBezZgfLUpKShAWFqbJzs7mKioqmP+HH1KlbNky6u/UOvpaW9P1AKCcMwddc3Jg+eQJnikUotLVFeZdujCzr79m+PpryACYvv02p3f/PsrKykQvLy+WkZGBM2fOCGq1mt25c0drHMQzxpAUG4tednYNjXhAI8iOHTsmDBkyhHNwcEBSjx6YkJZWN2N65kxMX72aAUBCfLx4ODOTebz2mmAeHw93d/eWT7SxMVXRvvyy1evBrV6Nb/LysMTFBW/27Mmt2bIF9+7dQ79+/dCtW7fWq0cAVRdnz6ZAmjFKxuzdS/82bBiRzZSUumMCqPpXcz1FUURYWBheeOGFBh8kCAKOHz8u+Pv7Y+jQoc0fhKEh7cPlyykR5OraKvkEQLLz4GBg61Y81tNDTk4OioqKEBcXh8mTJ6N79+4ICAiAs7Mznj17hqSkJJw6dQpef/xB6pBGSrzjx48DAN555x2K7Bmj/fzqq0SsYmJINWFiQsmoGujq6opFRUWMFRdTldPUlCrIjBHBvHOHiMlPP1FF/p13iHBpZePtSGCamJjU9mT/8vPP6D52LB7UuJ+//fbbdTO5a5D066+i+OwZm1NZybilSylp9t57tD/09amCV1REFdvLl3ExOxtXqEWAb9a5vREEQQCnvWfUr/ppK6NTphAxUijoGt29S5MORJESNq6ulBDLzSUVU0AAkaiCApoJ3w6FgIeHBztx4gSuXbuGgQMH4sGDBwgLC0NRURH69+8vDFm5kjd89VVwNeoYHRcX4MwZTLG0xMGDB7ns7GyYm5nRcW3YgDEZGeykRILdRUXQsbGB5PZtpG3YAAgCPJVKPFm6FMXOzuLs+Hhmv3YtiouLkZycDI1GA0dHR5ycMwd5lpZsRpcuMJ8yBUePHxe++OILNnz4cOZdo6hS7dsHfu5c3Fq2DJylZS3BTU5OhrOzM1JTU+EnlcI+PBysjedgLbQu/s8JlUqF7du3QxRFjB07lswVp02jxMeuXS3/4oULlITLrKcmvniR1norZFutVuPKlStISUkR8vPzOcYYpFIpFx4eDnNzczTpi87KontC/f3x9de0B7XPqMZ44QXag7t3U6vF81T7jYyAF1+k+EAUMflvf+Ptra3BXn65eTPD/wcwxuDh4QEPD4/a/hl9fX1RqVS+wPN8g4fDiBEjdHr16mWdkpLyVnZ2dpWfnx969OgBURQRHR1t/NlnnyXLZDKJl5eX6ciRI7msrCyToqIiv7Nnz3pwHDdPEITBgiCItra2CA8PV9y4caNKpVK1btxCmOPo6Gjbr18//QsXLvyuo6MjODo6MhcXF3h7e6s7deokuX79ugOA4xKJxNrExKRbYWHhSKVSuQNAjIuLi3dycrJSpVIVq9XqcSEhIXfrv7lcLufqy8zlcrkXx3FXBEF4MSQk5MS/eIr/K8BEsWNuewc68L+Izz77bGdQUND8GjfjPwfLlxOZ2Ly5xZdoNBocPXpUk56ezrm5uTGO49CjRw84pqWRHLJr15YfsJmZFEzcuUNuxj/+SFXt9kAUSeKup0eE/exZMhsaPpxIyKRJwM8/01zgrCygtBQnrK01VTk5bObKlVzjgHrjxo2CiYkJl5OTAwsLC01JSQlvaWmp6datG9e3b1/W4rzo9iIsjEh1WFjLr8nNpd5kAwM6L927U9Xp9dcpaG7H3OlHjx5hx44d+Pvf/15rbKZUKnHw4EHNw4cP+fGVleh57hz4GzewadMmwcXFRZw0aVKrrO/p06fYtm2bOGjQIFbbowrqqdy7d6+gUCiYt7c38/T0hEx7rYuLiWzv2kUBmTY4evAAePddKK9dQ4KTk+ahmxs3LiKCSTmOekNTUqga0kzfJECkIj09Hbq6utDV1UXM3LnwT0iA3qNH2LVrl1BcXMzc3NzQtWtXdvToUUilUigUCqhUKvSsrBRm8jyHtWuJWCiVRGiHDEHUSy9pLMPD+V9qTL5mzpyJni31bQsCJT7u3KGWgZYglyP51Cmxq1LJjPr3B7Ztw88//yxkZ2dzJiYmWLFiRcuS8tBQWgNPn9L+eestqlx/8AH1TL7wQvO/t2kTyUzNzaFSqbB+/Xq8/fbbDVpELl++jMjISKxevbpB+0ADjB9PxO/DD0kiqlSSbLTG7b4lqJcswaW+fTU3y8t5mUwmVFVVcfXjkPrrMiYmBqdPn8aHH35YmwDSIicnB9u3b4eTkxP+9re/Nf2gjAxaJ8XF1O4RHk7HOHgwMjZvFrOsrNhQhQKM56kXeOdO6jkfPJgqyoMGEbk1MCApdXAwJYi2b6cK3tChrX5PAMjKysIvv/wCWVUVZh48COfmSFZ+Pqq++AJ7i4pgYmKCaRMn0nunpJCC5swZ+t5qNR3TTz/h8M2bYvBHH7Hin35C1/HjIZVKqWc4IgJFRUUICgqCvb097t+/j8TExNpqeufOnTWvvvpq83v53j3aiz17UmLh0SMi1Hp6lOhTqUg1sH49KXpsbOi4fv2VkhA7dtD/b98O9bFjqPDwgI6bG3TNzGqvmyAI+PTTT2vHk2nn17/00ks021yjoXvz1atkapaWVjv6MeKPP8TsvXuZbVGR6FFczGSLFyPXzw+7Dh8GAEy9eBGuTk54+P77kEgksEhOhvjIQZZNAAAgAElEQVTtt8j48EPYTZ6MgwsWiAWdOjFzc3NBFEWUlJRwarUa0Gjw8TffgDt1CuLgwTh58iSSkpLE1atXs8InT3D5/fdFfVFExcSJQn5+PisqKuIEQYBMJgNjTFRUV7PV+/dD/5NPmhrotQRbWzqXnTq17/WgvvetW7eiooL8rj6cNQsSCwt6rnl6tq70Uirp2dE4GSCKtK5q9rhSqcS9e/dQUlKCqKgoUalUMgsLC8HZ2RkDBgzgRFFESkoKkpKSMG7cuDovk4ULaSb25583/ez16+le3cjcrgFWraLj++CDdo/5aowTJ05o4m/c4K2qq8WXSksZ//AhrWcrq3Ybwf2nUFJSgqSkJPTr169J0q0laAsWKpUKffr0aTBBISYmBidOnADP86rx48dLPTw8sHHjxvKKiooQACKAgwBsAAwGsB3kEdEFQA4ANWPsO39//yXDhg1rNouYnJyMY8eOJSoUiqkcx8W+8sorOtXV1bhz506Vubm5jo+PD6ejo4MjR46okpKS3g0JCamdzyeXyycDOCqRSN788MMPN8nlcl+e589rNBojAGNCQkLO/lMn8b8MHZXtDnTgfwByuZwD0B2AL8/z/aRSqZ0gCMPb1Y/4z0IQqBI7cmSLLykvL8e2bdtEtVrNli1bxhoYqyxYQMHjunUtf4aTEwW5R44QSW4sVdWaPmVlEfmYMYOqWmo1/f3225TRl0gooJBKiRj07k0kSDvnugbVBw9Cam/fhGjToTixhIQEuLu7i5MnT+ZLS0tx+/ZtPiUlRXPt2jXe0NBQcHd3Z87Ozqy0tBSxsbEaURQRFBTEN+6FbRaRkVS9aw1WVkTIr10jKbCtLZkkeXq2i2gDJHOTSCQNHtYymQxz587lExISxPizZ4VnJib8tQ0bRDMzM0yYMKHNOVudGcObe/awr0URdnZ2MDIyQmRkpCYhIYE3MTERlyxZwskaJ1RMTIjYXLxITuVbttB13LgRWLoUsv374eHuzicWF+PTqVOxevp06Gs0REISEqgaYmFBgZ5EQpU3xrTycVoD8fF4EBgoWsyYwYzT0/HgwQPOy8sLaWlpwp07d0QnJydx5syZvCAIiI2Nha2NDYc33ySy5eJChmNLlgC7dqH/rFl8amAg8PAh7OzsxMzMTHbgwAE0687LcZQ0aARBEPD48WPY2NiAMQbB0RFJTk7McP16GNnZAREReOmll7jk5GQcOnQIa9asgaenpyCRSDhvb29YanssL1+mfZGYSJ+lUNDx2thQH6RSSeencaW5qopcpWtk8NpRUYmJibW9nyUlJbhx44Y4bNgw1izRViqp5/T77yl4Z4xIYVgYVa/u36dqUzPIzc3FVjs7uDx+jFemTYPFgAG1UfC2bdvw+PFjfPLJJ3jjjTegUqkQGhqKaXv3ojgvD6qPP6bRZTXIzs4GAEydOrXpBwkC7W9bW0qa/Por3RtcXCD06oVj6enMb+JEsPpO+MOGNXvMtVi1ihIM9vaUXCwvp/tXK6oPe3t7WFlZiUUPH7KSlipt0dHgjh3DiOpqmOzZU0fi9fUpgaLdpxIJ8OabqNy7FwkGBkzX3x/qiAjNmbt3WYFUyjHG4OjoqDEzM+P27NnDXFxckFQzHcLBwUEMDAxke/fu5eVyOQYMGIAxY8bQHvnkE5LibthA62fMmDoDx5AQkupu2EBVUMYgVFYCly8jw8cHXQYMgNHp03SMvr44vXkz0r7+GgNu3kTx+fOwKCiAl0oFbssW4LvvwHl4YJCzszr18mVJlYMDSlUqfPzxx3UJJZ6vG1XVrRt950ePgM8/x+Bnz9jTnj2RLJXia4UCyMmB0fnzteZyetu3Q+LkBFetGsTZGQgNhadEgoqHDxGUnc2cnZ0hk8k4jUaDtWvXgjEGTiZDVkwMHJ2dwVauxNgPP0RMTAxbt3YtXt+4EaNNTJjBw4dgjPEAVXsrKiqgp6eH8PBwIToqis9ZvRourZHJxpg7t4lvgSiKUKvVLSa3OI6rJdoSngcXFESKi5Ur2/68VasoudyYbI8aRX3be/YAAHbv3i3k5ORw+vr66N+/P3x8fNCpU6cGTHXgwIEYqFVfiSIlSIYPb9gCpsXZs6TUamtvLVlC6pi1a+n6/xOtb/b29lxMTAy6+PkxfswYavt4/JjGz3300XONOvuzYWxsXHcO2wnGWIuGb+7u7jh37lyVQqF4/fTp05udnZ31unXrxrKzs/9hamqqm5WVNRdAT2dnZy4tLe1jQRBMDQwMyhUKBcdxXIGxsbFZ3759Wzzprq6u4DjOkTEW7erqqjEzM4NEIkH9iTc1pnRqALdq3ND1JBLJiwB+rHnJx3K5fDOAERKJRF8ikVQrFIq85zoJ/8XoINsd6MB/IeRyOQONSjZjjE2VSqUhUqlUx8bGRrS1tTU0MDCAubk5Gsyv/nfj5ZdJlltDAPLy8nDu3DlNQUEBJ5FIRH19fTE/P5+3sbER5s+fX0fYTpwgctDYmbQlvPgiEfMpU8iQ6PPPiZh++SU95CdOpODw9m0KFOfPpyz7wIHkDN4YjaTE9VFUVMQ167YOYMqUKSwgIACdO3dmAPVjBQUFISgoiFer1bh9+zYXGxuruXbtGieTyQQnJydepVKJv/zyC2QymWhqairUjAphBgYGop2dHWdtbc0cHBxgoKNDgeUbb7R9Phgjies335Ahm0RCvZftnAdubm4Oxhiys7PRuA+7T58+rE+fPrxi3Dh0PXWKdZ45k7XLrEuhgJ6/P/r07YvDhw9DrVaja9eubPHixbC0tGyZrEskJA3OyiIpNM8TkakJzlhxMSYlJ+MrV1dsOnYMPj4+wohPP6UZxpmZZOJUUkLVVY2GAs/qavr9X34Bdu0C/+67YmhhIVT797NRo0Zpg5wGX4rjOHhqHbhfeIESQDt3EoE9eJAMABlD93XrgDVrkJOTw7Rk79SpU+LYsWOZcWMytWABEaaayhxA1ZfY2Fi+W7duGh8PD748OlqM792bTXF2JhI7bRq4PXvQe9QomJmZiUVFRSw2NpZjjCEqKgr29vbiPD8/Jpk4kRICNjZE+Hr2JJWGFh9+SJ/dOMBMSqK9UXNNLSwsMHbsWOH8+fNcWFiYyBgTFQoF5+npKQwcOLD567ZzJ+21jz9uGBQHB9PxXL5MPeJyeRPFyqVLlzTm5ub8rJMneaYdz1YDMzMzPH78uOYjdqK6uhrGxsZIGjhQc0ul4h788ANjjMHGxkaYPn069/DhQxgbGwuGhoYNF6gg0DlxdyfTKEGgoP/aNaCoCJWurijr1AlGrSkOGkOhIOn09OnUe6olOuvWUe9oM606giDgxx9/FJ4+fcrpaTRgzakI169HhYMD9gYGAoxhUX3y/9ZbTZMWs2ej4MgR+HXpIvT57jsu79Ytftzrr4Pt2gUJ9cTyAGBpaSmcPXuW69Wrlzh58mQmk8lqL5RUqYRzTg71f/ftSwmZbdvI7CwwkOTqy5ZRVdvXl/aVkRHtgx9/RMH27TDOyMBVS0v0OH8eJnK5aL92LdsaFyeUmptzCxYsAMdxELOzcSY0FH0XL0ZpVRW6BAYCSiWGGhhI+icm4npmJrrm5YG7c4fuZadO0XpITaXE2xtvkLJg4kTg/ffBeXvDUqWCZY8ezPW337Dvzh1UV1cz37g4JAQECNflcq7r8eMo+PFH2EdFEaFdvBjo2xcGw4ej58aN1Ibj5oabt25pmFrNixIJXn/9dUqWqdXA9euQFBbitddeA5eWBllKCvTWrm2wziUSCbR7ffTo0XzfZcvEynffbWLe1iq0CeB6OH/+vObGjRv80KFDhcDAwCY3XUNDQ9jZ2Ag+mzdz3X/4AdzKle2vjD950nx19+uvG7iiOzg4cEVFRRgxYgTMzc1Zm8n66dPpvnWiBSXwe+8R0W8LPXuSz4QgkAHpP6ESc3d3Z3/88Qeqq6vp3NYY4NWOaJw8mVQT69a1OzH93wKO42BgYKBSKBR3eZ6/dP/+/TFTpkwxAIDi4mJ8++23nmPGjBF8fHz4Q4cO6ebn54uvvfaaYVFREYqLi+2dnJzQmrcPx3EYPny4jiiK8Pb2bva5oFaroVQqJXp6el9WVVX5AoAoitU2Njaa/Px8JgiCqY6Ozi21Wu0SEBAgCQsLYwD05HK5KYCKkJCQ/+k+7/+tFdeBDvyPQi6XD5RIJJOlUukQURSteJ63FkVRJpFI1E5OThp/f/8/tze7HtRqNU78/rvGLyyMP2drK+ru3y+UlZUhPz+fd3V15caNG8cqKytZWVkZ/P390aNHj4Y358hIqrC1pxKgVpPUOD6egrChQ0nWOWkSST7j4+tcV+fPp79bkBe3B0+fPmUtye45jmtQWasPiUSizfhrv6v2b6ZWq5GamsoKCgp4mUwGmUyGwsJCZGdni3FxcZqKigq+d36+0OfaNVY2cqTg4+PTeiVZFKl6qFLVGVSNHk0yTje3NqsCgiBAFEVIWgk4dGJi4Prjj+0LlAC6Jlu2YALJQoX79+9zhYWF7XN6Bihgk0jqrm8N1ImJSP/sM0zp0gWnS0rE69evcwEBAWSu5ORUZyYXHk4O2Tdu0Izdc+doPNKAAVgQGMidPHFCLLG0bF81YezYuiq7vT0Z/pw9CxQUgHv/fUyfPh2VlZUIDQ0VFQoFS01NZSYmJujcuTO8vLzqqnSzZjWoXimVSiQmJvLBwcHIzs7mkjZt0gScO8fPuXSJXiCTUQJBVxe4dg0rVqxgAEn4Dhw4ACcnJ5gdPMg+S0/H327fhnViIqR2dnTdG1eV0tObD65VKjQ2d/Px8eF69eqF8vJyduPGDZaYmIiRI0fyzQZfe/aQimLmzOZbP3r2pLYQLVn65JPawFmj0SA9PZ1fuHAh2Kuv0jqtJw0PCgqCmZkZiouLodFoYG5ujqE8D87Pj4e7O9RqNWJiYnD58mV89dVX4HkeJiYmdV9SFEn1MWwYJeT8/KifeM4cUr0MGAD84x+oOHYM3e7fr60StgtPn1KVrP452bCBlAVbttD6ffvtBu7Oa2hGMAcAMoUCxo1NJKuqgO+/h7RTJ7AhQzD8gw/q1o4o0r2tZr2eOHFCk5mZKRoYGPDSzp3xwrVrnIG1NWymTCGFiJMTJRNqRln5+vpyNX3cdMAKBVBaioVhYdC/fRv8mTOUIBgxoo54iCLtoadPaT+dOkU/l0jou929i7tLl8LyyhU8MzPDqNWrIfvqK+zW12fjV67ERMY4mx07oGthAejrQyaT4fz58/i8ZjyjkZERqqqqoLp7Fxg+HLa2tqJHnz4Mjx9Tki0hgUihtrL98CGRtfh4+l5ffEHHFRcH608/xeTRo/FHZSWG3b2L0QcOcA9XrcIzc3PcLCjQ2Gdm8njyhK6/KNI9JSaGErzV1ei3bBnvXVCAI3PninrTpjEMHEjncOxYQKmEhbMzJe1SUhoaVjZGdTUqjYzYTUEQXRvPum4NdnZ19zwA8fHxiI6O5vv27YvIyEiuuroajo6O2ooi/U5lJXp07y7KlEooGYN+e4m2Ukn3iOaUFX36UHLTxQUYMwbBwcEQRRGhoaGCSqViTk5OwqxZs5o+jyoriRSvW9fyZIFHj+i+2V7H80GDaE988ME/NdJTFEVoNJqGsmzG6vxedHXp2XDmDB3X6tXPZfL3V4dMJuOlUunv1dXVdqmpqdXe3t66AHlHfPTRR0yrzCgtLYWenp4IgJmZmcGs0XSHluDl5dVqXGJiYoJ58+ZJVSqVb1JSkhATE8MFBgZKhgwZwgMUd2zbtq1nbm6uQWpqKjiOK+U4brooissAVMvlcueQkJCif+kk/IXRQbY70IG/MORyeScdHZ2ThoaGPh4eHro2NjZ8p06dYGJiAn19fTDG/uN7OCYmRjQ8cIAr3LwZPTt3ZgUFBXzXrl0xadIkdOnSpeWA4/ffKdj59tv2fVBKCvW8hobSw/fmTSI+I0dS4Hv//nONLmkLCoUCoijCo7nRVf8CJBJJUyMZAgPAK5VKpBw8yMVVViLvxg3O29u75SyzKBJZksmojxKgSuyYMZS59/QkyVwrc1CfPn0KAE3GPDXArFn0vhpN6+NYtBg2DBg4ENwPP2DKlCmcUqnEr7/+im+//RYvvPBC6/NOCwvp2H/7jcjt1KkQAgLwe79+4v379/G3X39lxgUF6LNvH4YGBjZwMa4FY+RYP3Ysmfbt20d9y7Gx4G7ehO/+/Siwt6eETe/eVC0zNGw+MaGjQy0I4eFk2HPqFK3d0lLg3j26liYmcHNzY8+ePUNcXByuXbsGAIiIiMCKFSsokREYSCZTIJf7L7/8EgYGBsKgQYM4juMYJk/mkZ0N8/qzpA0MaM2PGUO94oaG0M73DvLxgcmKFch0dMQve/di8datkE2eDP0NG5r2/X36aZ38tz5SU4l4NYKBgQEMDAwwadIkpKamCrGxsax///4NT86DByTzPH68dfMhIyMai3XpEhHdv/8dGDwYmZmZkMlkgrW1NbGHlSvpu169CgAwNTXFsMZyU20v9u7dkEgk6N+/P/r378/FxMSgsLBQjIyMZEePHsWkQYPA8TxV2CZMqHPs19WlNQzQveKHH9D55k14ff89cj78EEfmzdMMnzSJb6JKaAwTE/o+jdG7N+23zz+nRE9pKTB3Lvb8+qsGAO/u7o7Y2FhU6enhRr9+qKVtx49TMmfzZlTY2iLnzBno6emhtLSUqqzJyUQua/ZocnIy8/Hx4SUSCboGBsIgJYXeY8oU2nvJyaSk2LGD5N8AVQnj4+le+eWXwOjR6Pz++9gQGgqT2FgMGDAA3lqirVLRev3gA2pVaZy4ZQzi4sW4tWgRcqdPx5CrV+E3YQL0CgrwUkIC0ufNg2tZGXSvXCHlQ2AguowbBycnJzE3N5ctWLAAp0+f1pibm/N37tzBG2+8AWNj47r1FRtLjtTnztG53rKF1s3Tp7RPO3em63fkCL0+IQEmRUUo+f57XP/tNwQaGsLhp59w6dQpKI8c4Xd7eoqTQkIYZs2CcX4+EfhZs2oTsoVubtj9888Y5OPDpPb21F6Un0/XTyYjxZAg0LNKLm/xXqHesQM3Zs1CxfPKnktLa4l2Tk4OTp48iUGDBiEwMBDx8fE4fvw4bt26hfHjx8PLy4sq/N27w+/cOf6rxYuhuHgRboWFwoQJE9qWHR08SOqhtLTm//3ePdojY8aA4ziMHDkSw4cP50JDQ3H9+nX+wYMHtfegWrz8Mt0PavZuE4gi3dM/+IAqzO3BmjVE/Pfto/d+TiJ87949kTHW8tg1b2/6k5dHe/XRI+onnzGDVDn/5Zg7d65Bbm6uQZcuXaCvr9/gIVk/nsjLy4OXl9ef0sSuXSfdu3fnAgICYGxsXBufchyHBQsWGBQWFsLCwgKRkZH6SqXytaKiIj4lJUUHgB2ADrLdgQ504D8LuVzOdHR0jvbs2XPghAkTdNp0Jv4PICsrC7eOHmULoqOh/8MPbbsP14dM1j6nUVEkcqN1IN6zhyrcz57Rg1IrmX7lFSIOJ08SUZs9u33EsAVIJBL8fxhGyjgO/T75BA5//IFN+/czURSbJ9uiSARMoSCZcH0YGFDWfutWClKnTGnRwCkmJgbGxsa1vY7NgjGSlu7eTYFJW7hyhY5P+51kMixcuJC7fv26eOTIEfbKK6+gWXl+XBxdt4sXa9eScOAAzv/jH4L91q2c7zffwGroUPCpqRjv4sJalU0ePUpB+vnzdePXXF0BAHeMjISKoiK+r0JB/84YrauhQ+mPtXXD4K5vXzqXz54RKdy/n1oVXnmFqs/nz0Mmk8HS0hIjRoyAlZUV4uLikJaWhrt371L/85MnEPz9sf3bb4UnubkcAKxYsaJurFBQUPMOvd7eRJBqvAh0e/fG2LAw8ZGLC2yLi5n/4MFIsbPDrmXLoNJogI0bERwcjP79+0MikYDjODBnZzr2xlAo2nTy79mzJxcRESH2r290lpxM43wiItq35xkjEqinRwTp/n3kduvW0OzttdcarJlmUX9sVT3USP6ZiYkJorduhfqVVyALDaWEXH0YG1MVq14FnfP1BfvlFxT+/rsQtGoVH33uHIb99hsAqo6VlZWhU6dOtXtQEATg4kVwP/xAewxkMqivr09VIT09IjMJCcDnn0OZloYchYKHnh4qKyuFJUuWcLkXL6LboUN1x/XVV0BCAkr37kVOjQ/FTz/9BFEUYWxsLA69dYtZm5rCasIEJCUlQa1WM0tLS/TRmkdVVBDBDw6m5EbPnnS/lErp5xMmkIRaEGjtbt8OODqCE0U45OeLeXl57PTp09DT00Pv7t3pdUOGUDJm06Ymvb2VkyZh/7VryLOywvxFi+AQEkJJv9RUmJaWwmfkSHJzDwqiJGl6OjBsGOaOHMmEjz6CxNAQ8+fP5zfWjBQsLy8nKfbf/073rsmTaR9KpVRxXL2a7vOGhuTnoNFAc/EiKqRSCBYW0PnjDxwLCBA4juMuX74MDw8PmJiYYJCuLvj9+3Fk5UpRmZXFsvbvR5yFhfBiZCQnMzUFq0neVNS031h361ZnyNWnD63ZxETaf7Gx0PzxBxSvvw4WE4Pi3buRW10NJ1dXREVFCQlXrnCLNm1C7tKlGDthQu29VKlUQqVSQU9PD4yxpvdyrbqkpu0jJydHBIDAwEAGAP369UO/fv3wxRdfCNG3b3PKzz4TTUNCWI/Tp3Hy8WOxoqKCAUBqamqze6MJgoJIBdYStm6l6rdSCchkEAQBmzZtEiQSCRs8eHDDBHpWFt2zt29v/TOVSkqUPU+fNMfRc2DWLLrfPCfZjoqKgqmpad3c8ZZgaUkVeZWKVHa//06V+ooKIt5/MTO19sLAwADd6iduW4CXlxdu374NLy8vtDkP/p8EYwwmzYx4lMlktUn+wMBAHQAoLCyERCLRJCcnX167du2Cjz766OifclD/z+gg2x3owF8X/jo6On8Zog0A+/fvh7ejI/RPn24/0T55kipt1683HDnTHB48oABs5EjKimvnU0ulRIiOHKFeUYD+WxRJerhyJT3YQ0PJGKmVvuyWEBUVJejq6jKO457fneVfQUIC0KULUkpK0CLRBuh737lDMrjmIJUSgblxg15bXk4BdL2qXXV1NaKjozFjxoy2v+OECW3PTgao6vLBB+QU3wgDBw5kWVlZmi1btvCrVq1q4HiNtDS6br//Xke0BQG7Tp0Sypyd2Su6utB5912q4EVGAuPGURDWnIGQQkHBWU3va2NwPM80UikFgEuW0Lrx9qYK08GDJMF95x2S5Pv701oSRapMHz9eF5Bv2ULHoE0E1ezLfv36oUePHti2bZt48eJFFh0djUmTJmF3SAi48nJu/PjxcHFxqXNiVygoYdRMQCkIArKePkXZ66+LfTMymBgTA6sHD5hhTbLCzdsbXQcNwszZs/HgwQOcOnUK4eHhCKvnYu/euzeyMjJQtm4dhg0bJg4aNIhOypEjTavd9RAeHo74+HhMnjy57iSKIvW/zpv3fMk1gGTQdnbAhg3ofeEConx86ti1qyv1fi9bRqZtjbF8OSXX6pPU+sjMhPd33+GClRX2vfgi5jc3fx4gxcOdO0RIa6BjaooUmYx7OnMmBpeUiOIHHzA2dy52R0UJNSZ64vDhw5lMJsPPP/8s6MbFcXOCglBdWYmrV6/iOo3cwoABA4RRo0ZRAqVPH2DHDjwODcXC+fNx09cXw1at4gwNDdHVzY0qtsXFRMyDg/Ho+++x89ixWmduDw8PsUePHiwjI0OsyssTT5aVcWZHjiA5ORk6Ojqsa9eudd/J3Z2IYUICyeU1GvIu+OGHWrMrHDxY5xheA8YYXnzxRQYAYWFhCA8NFXvPn88wfTqRnDlzqPrbaA8l5+QgKCwMBlVVEObPr/NW2L2b9mXXrmRodugQtfj06EFKl8OHwc2aRfty4kTo6ekJ/aytOZt336XKvURC66NHD+DTT6FSqZBy7x45Lo8YgQfLlyMvOBgqlQrXAgKgqaiAbU6O6JmdjYKyMrZy0yZc8/CAet48gDHIGAPc3DBjxgyueu5cqBMTkZWYyO0yNUWvp0/Brl6FKIoIDw+HRCKpS16AEi3lu3aBrVqFmAMHYGFkBJ1r18TbvXsz1r+/ULpjB5uwdy87HxSEHF9fTJk0CZUvvADVH3/g9OnT6Nq1K86dO1dLgmU1xNXBwUEzdepUvvbep9FAvHoVUbdu4erVq2JZWRkDakaz1SN6FhYWYll0NHpcuMAeL12K8gED8OTuXUgkEgQHB8PGxqZ9rPD48bZHU/r40DN340ZkZWWhrKyMa2Bcp8XWrXSvbO05Lgi0Bn766fmJ69tvEwn+8kuSlbei0KoPURTx6NEjNrQdEwJqIZWSAgeghNz339NowshIUjS159n3X4jRo0cjNzdXvHjxojBz5sz/98DS3Nwczs7OfEJCgrFUKv1rzGv7E8D/o9Hcyg50oAN/DVy5cmWup6fncBcXl79EqjU7Oxvp589j5i+/gK1a1XKvVmMYGRG50Mobm4O2mn3oEL3uxRebmqT07k1EYeTIur5exsgk5t13qWq+YQNVHoODKViv35PYBo4fPy56e3tzf6qpXHO4cgWHLSw00Q8ecMOGDRPt7e2bssX0dKoYffxx2wGIrS311J4+TQG3uXltT294eDiUSqUwatSotsm2sTGd3/Bwku+3hLQ0qnQsXtzknxhj6Nu3LxcfHy/IZDJW6ysQHk4mQYcONSCcFy5cQEZGBluweDEznDSJiMSoUeQsnJxMVfDGkukffqDvq5392wzS09PFyspK5qYlZIzR+rKxofW0cCGdp8JCqq7s20eJm++/p95jbV8bxxFpevllkiHWG3PG8zx8fHzYlStXxLKyMhYVFYWhZ89iSmAg7EaPhk59A7vUVDrmetX+W7du4eDBg8KFCxdYfHw8Eq2tmVVamnjv9GkxZt480W/tWgaFAmzNGhjUXA8TE8OYzhcAACAASURBVBMMGDAAQ4cOhbm5OUpKSlBeXg7J3buY98sviPTzQ0ZGBvPz84OE5yH+9hvShw5F5pMnaEDeanDkyBFhwoQJrLbtQaEg9/6QELoO/wyMjIChQ6FSKGD/1Vec6Ysv1gWyhYW075tzL7e1JYLcuFojilR5DA8Hy8qC8fz5uJGdjfj4eMHT05M1SUz270/rt16SRk9PD5GRkVAaGUFn6FCWEhoKx6+/RpSuLgsYOxb3UlKEsLAwLjY2VlQoFDB7+lSMy8pip2muuxAUFMT8/PwQFhYGpVLJag2GeB6no6IQZWSEQVZWotXVqwyOjrR+HB3JPO7zz6GSy/HThQvo06cPnJ2d4eDggDFjxjALCwu4WFgwu1WrWKevv0b2o0fqgoICztnZWUhLSxPOnTvH9ejRg5JW1tZkGPngASldPD2pv1hfn6rBjYh2Y5jo6OBmeDjzGD0assmTSQmxfHmzrTnW1taoKC5GZVISHnt5wbZnT9oL7u503338mKqnCxdS0sHVlSr+3t5EzO7fBw4fhvOBAyy1pASGqaniWYkELm+8wSQ1xoQPHjxAbGwsQkNDkZ+fr852cRHSunQRC8vKWHl5ObOwsMDs2bMxZOpUZr1kCSurrGRXAbjPng37+Hjai+PHkyR93TpIPvkEtp6eCAwMRKFMJri99x6LqKhAXBEpVa2srKCdpQ0AZ/fvF48nJrIkV1c8EQQxKSNDLLKxEWf06cP1W7KEec2cyXT8/CCxscG4/fuZ6Zo1MHz3XRhYWyMxMRG3bt1CYWEh/Pz8MG3atNqe66SkJJaTkyP06dOHA4CcvDwc6dpVSEhIQI8ePaCnp4cRI0Y0lD/fuQOXJUu4CF9fmK1bh/jMTOH06dOsoqKCSSQScfbs2U0NGVvC9Ol0blrrPR85kgiyVAqO43Djxg34+vrWOaNHRpKkfv36ZpMxDaAdufXWW89Ptrt0oWMZN472fTv7iRljiI6OhrGxMU2ieF50705rV6OhpHFUFD1nZLLnGs/23wKFQsEyMjLQpFXo/wEJCQniqVOnSgRB8P74448v/H8fz5+Fjsp2Bzrw18V/vsraDKqqqnDixAlNWloaP6x3b7A+fYhwtIWbN6k39tGjugxyc9Bo6EHHcVQBb6mXWCKh6uNrr7Vc7dI6MmdlUYVXIiESZmNDlbl6KCwsxNOnTyGRSJCUlITS0tI6MvafgkYDcedOFPTqxQ2dPh0DBw5ser0//xw4cIBkmu11UdXRIXOzs2epbzMsDHj7bTx48ACGhobtX1NRUSQnb2GUEwAiMjWVvpbg5OQk3rx5U+Pt7c1zV65QhXX7duoPrcG+ffs0mZmZ/KxZs2CqHafk5EQ9k7t3U9/l4MF0Xc3MaN1ER9OxDRjQagDIcRwTRZHkjTExVJl79oxUF3PmEMm4f58k5qdP11XRu3QhQnP4MPWNAhSAHTtGMtczZ4AxY1BUVISIiAhUVVVBrVaz5cuXY+vWrSIvkUDS3B5etYoqk++8g/j4ePzxxx8ix3HM39+fubu7QyKRICEhQYPISOYXHc0FODgQ8a8xwGr+MpD8FABVhxYtQoivL7766ivhxIkTzNvVlT3S08PFkycBxhoQDS14nheLi4vrWgxCQui8rFjR6vVtE/r66DRvHo5fuACn0aPBDhygBEq/fqQSiI0l4qZFbCwF7RMnNn2vhQvpGsbEAC+9hN5qNeKSk8X09HTu0qVLGNl4FGFlJfX+1kui6erqwsvLC9HR0UhITIRRcDC+6NcPw65fx4Aff8SADz7gy7t1w5UrV9iQIUOYwfr1UJiYoOill2Bra1vLIAIDA1loaChu3boFtVoNoEZ23rkzKiZNoj07fjz1un/0EbUnlJbiwYMHUCgUsLGxaSp7vXEDcHWFa8+ecO3ZUxIVFYXQ0FCOMSba2tqKx48dExYOGsTj11+pmp2XVzcfGyBy1Y52GL2xYxEklUJv3bq6xE8z6hQtOi1ejEfnz+NWaCh8tfJgjiN/AwcHUo306UPfd/hw2pfz5pEaZOpUIDcXTxmDZV4eBI5jVTExuHDmDJxcXCCKIo4ePQojIyNNz549MW3aNLrRLVpEya5mkn3V1dWaJzY2fPrDh0KPHj04vPoqtV5cu0brv979YOSoURzeeAN2V6/iqbMzzMzMhHnz5tUxQY0GgXI543r0EEcdP85A65/e4Ntvyaht1y5I/P3RC6gzAYyOhtevv+KBm5smURR5iURSu/60MlqpVMoOHDjA79y5U5RKpUJuYiL/2nffccKTJw2VPgBVhRMSAAcH8EuXolffvjh/4QJUKhVnYGAgBgQEMA8Pj+eLCTIz235Nt25EjidPxuG0NNHKygq6urp1n/PkCa2ztnrTRZGIeWTkP9fSpadHrvi9etE6jopq9zPP0tJSU15e/q9Vam1sKLbQaCjWOHSInm1VVf+S8epfDd26dcO5c+e4ioqKds/5/rMQGhparlKpVgJI+X89kD8ZHZXtDnTgL4pLly5Jy8rKXhgwYICstbEMfyZu374t7ty5k1VXV+P1oCDm/NNP5IDd1vFUVVHfn5NT61XRS5eoT+q990hO2haJt7Ul4uno2HrvqbExsHQpBYNnzlAV3sWFKt5z50LgOGwkyZwmOTlZ1Gg0bNSoUcy2jX7WfzvKy/EoMRFXjYzYqFGjmj74tAR71apW5/m2iO7d6Vzdvg2cOAG+WzckPX3aPKlvDp6eVLFWKFoOevz8yFyoFem+o6Mjd+vWLVaxYwdsr1xh/N//TtLRGpw+fVq8d+8et2zZMjRx1XdxoQA+IICqeAMHErH45hvq7XznHQp+AQpWU1Io2ZKbC+WmTQg7f16w27GD8927FwgOBrdyJZjq/9j77qioru7t59wZht6liQIiXXq1IQRF7BobxJ4YY0kxiSaaLKMxplli3hTjaywxlmhijb3TxEIRBATpRXrvdebe74/NUAQR86b9vuWzFksZZubee+655+xn72fv3UpjI5ORcffSS5S+YGZG/7e3pwJx779PY3DwIEnKPT1pTqmoENFZsABwdMTJmBgkJSVBT09P5ufnx5mYmGDkyJFswMsvM2Zj093wtLICxozBheBgXL9+HZaWlmzhwoUYNGgQk0gkEM+bB6NHj7h+P//MxNu3g5s3j8h+H3LyANDx9u4FzM1hYGXFwsPDURwWBpfsbJbp6CgMHz6cPV706OHDh0hPT+eSk5OZlZUV1M+eJfnpggV9UrFIpVIwxiAIApqbm7tVuy8sLMTNwkLBe/t2hmnTaD45OQFFRTTeb77ZcZyffqLnXF4gDaA2RVFRlI//zjvt9R84joOamhqLj49HXl4eBEHAoM5RvBkzKFrr6trlfPT09BAZGQme5zFjxgxUVFdj4tat4HR0gJwcSPbuhWVgIBT79QM3YAAU3N2h8diYDRgwAM7Ozrh9+zZV+ed56JaVoVlREQYZGYLZo0cMhw9TC63SUpJq29pCe+BA9OvXD2fOnIG9vX1X0pWfT3Ow7XnX0tJCZGQkBhobs/H5+cxwxw5Ox84OSbm5sj12dpxHdDS4adPA2pRAgqEhSisq8PWdOygvLxfMzc1Zl3tRVYVru3YJ11VUWM7IkbKhAwZwcHAg6ewTejwDQLMgQPLFF/ALCcF3zc2IefhQMDQ0ZKpqauCsrclxlpxMTpLCwo7Un02bgDFjIAsKws7KStguX44hPj7o/9tvqMzKkpVHRgr3a2p4VS0tYcWKFSJ5BBgA5ZubmXWkE3WCjY0NV7N/vzDi2DFO2deXnnlBIMfGBx/Q/Pr0U8pZZgyPTExQf+QIVIcOFbKKi7nMzExBUVGRqaurQ4ExRCQn8zE2Npyrh0fXXtceHuSkKyykNaOpiRwne/cCgwaB5ecjuqwMY06eZOP9/KDwmBNLV1cXzs7OuHHjBlRVVYXAoCBOXVsbCp1UMe3473+pLsSHH0LB1xfm5uYICwuDqakpVqxYwYyNjfFMKWW7dpGjW96pozf89hvQvz9CSkp4DQ0Nzt7eHsKJE5AuWgRu+3awvhQ6O3+e8qHle+8fwdix5CDOzYW0f39El5W1dyhQVFTs8frbnDWcpqYm/hSHOceRkmfJEjqXpUsp0q2t3XtxyP8jUFFRQUxMjExDQ4PrtVDq34CEhARpbW3tbAUFhfk3btw47uvrW/uPntBfhOdk+zme41+K0NDQbEEQAmpra40sLCx6bsXzFyMiIkLQ0tJiS5cuZQpHjpABM3587x8qLCTys3w5GSo9obycoq5ffUWGtatr3zzhIhGRtLNn23sxPxWjR9N5lJVRJHTWLLCFC8Hl5AgW8+dzM2fO5FxdXVm/P9Db83/GBx8gq6kJKaqqqKqqkpmZmXHtcuN9+4CPPwa/bh1qVVVRV1fXPRLSF6ioAN7ekBoYQPb229BoaMCAvuRsA+RU+eQTinAuXtzzewYPJjLRw/iVlpbi7t27QlpaGgbdvg2uqIjdsrPjHWfO7HL8yMhIoampiY0ePbrnnHWxmCIdWlok8b56lSJq8gj1kSNkVDo7kxwyLw9xCgpIvnoVmWpqLM3MDHfd3HAjNxehlpZ4aGQEUx8fqL7wAn2npmYH0airo2sKCqJ5bGxM5HjXLop0i8X0fi0tIqNqarBVUMDt3FxMmzaN60L0zp0jJ8T773e8dukSRfanT0dhYSGys7NRVlYGKysrKhollZLTICKCyOjGjZRC4eVFRaT66hB65x3A3R3abm7w9vZmbk1NTLuqCsKkSSwmJkbw8vJiTU1NKCwsREpKCn/mzBkmEol4RUVFVnTqFEx27RJagoKY0uNtxTpBKpVi//79OHPmDMLDwxEaGoqwsDBERESgvLxcuH79OjMxMYGSkhIOHTokDBw4EEM8PBhmziQynZhIaoX167vmSHp7dxDt8nIiO//9L7135MhupFBbWxs2NjaIiYlBTk4O8vPzBUdHR5pIr7/ejWgDZKDLq8iPHz8eHh4elKM6eDCRqowMKgaVnU33fsAAIn1SKTlp8vOBrVuhEBAArXffhV1uLjQCA4U5a9awZEtLDDE2hl5UFENlJRXdqqujyO/t22AXL0JfXx/hubkY4uiILpLgN98kBYOhISAIKIqKgv6RI3A5cwaP9PURYWyMga+8gkslJahpbmZV9fWQ7tqFi0pKKCwsxBl1dUQpK8PcygpJSUksJiYGNTU1vIGBAROJRJBNnQqEhbGsSZPwxrRpHMaOJTLxlHz88xcu8AkSCbPatw/q1tZ49OgRu3XrFiorKwWRSMRSFRSEAY6OjH34IakOEhIoMqitDQQFgVNSQnhkJCZPmQKJtTVUX30VgywtOcvSUs6jqorz4DiOubp2LaI5cyY5YJSVu+4PjY3Axx/jQV2dUGZoyAwPH0apvz80Vq2i51VLiyKx69cDs2Yht6wMGRkZULp0CV79+jG7JUuQl5eH+Ph4oXTHDma0ahV+GzWKmZib82FhYfywYcM6mCJj5Ohat47ScQoLyQnw+ut0riNG4EZiomDVvz8zktcQOXOGnHRt16KoqAhvb2/m7OzMKSsr01h3Xi+vXKF0jbVriai27QHp6el48OABfH19YdhJBdRnyGQ0j9qk+r1i6lSgf39cvXGDq6ypQUVJCX8vORmlZWWsesiQHtNOukAQSB0xYcKz13Z4/KvefBMJCxciee9eRIrFfGZmJn/37l3u5s2bSEtL469fv87c3NzaHXqMMURFRUFPT+9JnT/+GCQSChjMmUNKnDFjSPk0dSrtA/9QEOTPQGlpKZecnMwbGRmxxsZGqKmp/SPn4ezsLHFzc0NhYaFyZWVlga+v792nf+r/Hp6T7ed4jn8pfH19cf369d/LysqCJBKJRmf54t+B+vp6XLlyhVlZWWGQ3Ev9+uu9e6wzMymiPW3ak6uJPnhAhVB4noh2T1Wqe4OeHhkz2tp9Jx4A5Y7KvfMVFchVVORrqqo4i1mzyMD9J6qQvvsu+m3eDF1ra0RGRnI6OjooLy+HVmQkRObmKA0IwPenTuHmzZuIi4uD9x8o/AYAYAxf//IL/8DAgE1wcGCSb78l4tKXDdbSksheT1HV2lqKZvWQjy8IAnbv3i2UlpbC5PJlaGdk4LaVFSvS1OxWyMbOzo6Fh4fDwcGh597cPN+RG7p9OxnyS5ZQpNPGho4/ZQpFSFetAmbMQHZrK4KlUtSpq6NRRQW2Hh6CtY0Ny8nJQX19PaKiohAbG8s7ODgwSWcDXyqlOdY5R1lDg6S20dEULTM2RnsubkQEREuX4r6TE38nLo6Zmpp2VGKV54R3jtZfvUrR3IkTMXDgQBgaGuLBgweIi4vDgA0bBG7XLtb6ySdQPHKEVAOmpkQu33yT5ntkZPe+2j1h2TIyEOWIjAQ8PGDs44Pw8HAhLi4OoaGhLC4uDmlpaSwwMBBTpkxhw1VUYGVigismJsKVlBRmZ2cHZWVlVFdX49atWyguLsa1a9egrq6OHTt2oKampv0Q8grtWlpaQnl5OSorK1lMTAzCw8MhCAJeffVVxhgj8uTvT9XNv/qK7p2tLZHiU6fo3Jcto+J7zs503Z9+Su95AtTU1GBlZYV79+6hoqKC+cojh6tXA/Hx4IcO7VKAMDw8HLm5uQAAT0/Prjn1CgpEeC0sSEFx+jTds/nzicjp6dHP1q24MGAAcoqL4b5kCRynTGHso4/gPnky9E6cYEhOpnzqjRtpDr/6Kq2Npqao/fJLDA4OhqmVFVhLC5Gi1lYq5jZ/PnDiBKSbNiEsJAQtra1o/OADKPj4ILm6Grdv30Z9fT1TU1MTgj76iNWWlKCxvBxJNTWCY2GhMH30aOY+fTpGjRoFnufx8OFD/t65c9yjvXtxyc4OcY6OmDljBrQEgZ7vJ9Q66AwlJSVWevYstL75BkPOnYPn7t1ISUsTMjMzWWJiIvKSkpjhzp3QamgAx/OkBpkyhQjJsWPAN9/AMCEBajExUJRISC1jakoOKxcXUo4sWUJzVlm5I4IoJ4rDhtG/UikR3zt3YFBezkp1dYWLkyfzd+rqOF1dXejr66OxsRHlMhnU1qxB46NHELm746KKCrTGjhXsvL2ZhrU1nJyd2fDhw5mBWIzcfv1gPWMG/Pz8WEhICKejo9O1UrOaGpHjykq6ns8+67JfNDU3s6tlZRi+Zg04qZTI+NCh9D4bm66KrcxMcrqsX9/24SaSKhcWUvpKp7UoLCxMKCkpYVlZWYK3t/ezM7vMTIrsd57bPUAQBJSVlUHF0RFNMhkGKypi1CefsJhp0/ghS5Zw8gJwvfZkPn6c7t+6dc98mp1RVF6OyJwcPqqyko3Nzobf0qVs2PTp3MiRI+Ho6IiKigpWWloqqKmpdSkaqKuri4iICGHEiBHsTw9MSCQ0J5csofXo4EG6f+7uZL/8H6xgXlxcjAcPHrDk5GQhOjqaPXHv/YvBcRwUFRXB87w4KytL58aNG7WhoaHZvr6+rX/7yfyFeE62n+M5/sXw9fVtunHjxtXc3Nwlw4cPF3erDvonQiqVIiMjA3l5eYiOjhZOnz7NtLW1pTNmzODY6tUUYfL3f/IXyI2nCRO65l/KUVNDRCk6mqSgj+VQ9xkcR170vLyej9MXuLqiRF2dS0pNlbmPGsVh2DAyjh496ih69Vd7rZOSAE9PcCNGoLq6GvHx8SgsLETt8eOw/uUXKCxciLMPH/IVFRVMQUEBc+bM6bGdRm/Iz8/HxYsXcfbsWQBgK9etg/KwYTSGCxeSka2v3/u1amhQdO/qVTIuOuPmTYp4r17d5WWe57Fnzx6htrYW70gkzMzMjOmvXs1cZsyAr69vt+g1x3GIjIyUNTQ0cAUFBQIApqGhAVZdTZEtDw/KZZXJSKZ6/DjdL39/cgL060fX0Sn6FRkZiZKSEqxbtw4vvPACbG1tmUQiwb1799rf09zczHJzc+Hs7EzndPw4FQH6/POex2LIECIntbWUQ+3tTQR/4kRYtLay+IwMVLa2dkRVJRKKWmlodBT7UVZuLzLEGIOenh5GjRyJ/IQElPfvzwafOYMzWVlwPHGiqzNJQYHGe/Jkilo/LZdx5UqKoMvzns+dA0xNwQYPhrW1NauoqGDjxo3DxIkTMXLkSCIXjY3A+PFQcHSE3YoVLD09XSgtLRV4nmd79+5Fbm4uMjIy2ucrALz11lsYN24cRo0ahVGjRsHNzQ0uLi7My8uLubq6oqamBvPmzcMLL7zAuqxfHEfjZ2FBRuzEieQMs7WldeTjj4lgzZ/f5z64BQUFSExMhJGRUUdOekoKMuvrsTcykr9+/Tq7e/euUFJSQlW/GxuZuro6fHx8elZUKCvTPdi7l5QUw4Z1RJ4NDIClS3H//n2k8jzcZ8yAqooKzaEPPuh4Ll58kRwdUVFUzM/QEAgIQLqzM843NMDD2Bii0FDKuz56FEhPh+zrrxGtqChck0pZio0Npm3eDGsXFxgZGcHe3h4JCQm8sbGxMHXqVE7XwAD69fWwiYvDyM2bmcWhQ0yprW82Ywympqbw9PTkBvzwA/Ti4xHv6QkTU1PBbN8+KF2/zsqXL+81qiUIApqamtDU1ISw9HQElJdD7OwM+PvD2cODPXjwgB/Vvz8bc+oUJDk5YCEhkAwfTg6KHTtIFWNiAqio4IyzM2+kpsa0ZDKqBbB/P5EWxkixNHcuzfFffqFce6mU2mSNHk3z5coVWrPq6oCPPoLym29i0Pz5bOiwYVxcXJwsNjaWq6iokJ06dYqLjY2FpaUlvtu/H0wQ4LF6NbxMTRlWraL1wsoKCAiAyuTJMFy0CEZGRhCJRLh3755QUFAgODs703y9d48k6m5ulEZy6FBH+klbmomxhgbir1yBg48PJKam5JjjOFoz09I62lbKHXRyoj1/Ps2J//ynR8VYdXU1MjIyGGOMjXqCQyQ/Px8pKSlQVVVFbW0tkpKSoKKigrt37sBg4kTsr6xEdmMjb2dnxxobG5GVlQUdHZ0u8/3hw4fYv38/oi0tUaWpCVVfX8Fkzhw2PCiIMzQ0hLKycnvKwxMJWXY2jY+9/RPnUl+we/duyOrr2SvBwVD98ENa/wcMAGMMysrKsLa2hoKCAgsJCYGHh0e7rFxHRwfBwcHMwcHhjynA+gKRiJ77kSPJgaqvT4ojBYUnq/j+pTA1NcXQoUNhaGjIHjx4AGdn538sug2Qs+TevXt6LS0tgRzHiX18fK79YyfzF+B5gbTneI5/OTZs2JD8+eefN9bW1io9K9l6FiQnJ+PkyZNQU1OTqampcfPmzYOZmZkYNTUkM+ytQMilS2QQFRX1XC07OJgKTjU0kCH6v1b4DAigH2fn3nPCe0F5eTnPaWtTZWmACuFoaFDP6MmTqU1PU9Nf1wLk2DHKA5swoT1X2yA6GnWamrj3xhtQrqlBSkpKOzu5deuWzNTU9Klae0EQIAgCTpw4wSclJbV/fsWKFR2tp6ZPp4hSYCAZR++807v0LyUFuHyZ5kFnjB5NEZnH0NzcjKKiIuYaE4N7ZWWC7J13mJeZGXrrsu7t7S2KjIyUqSoocFHnz8Pb1hZDV6ygfPATJ0hW7+REBu7atRQNkreBewx1dXVISEiAg4NDlxw/Y2NjLF68GHv37gUATJ48GWfPnsWmTZvAcRzGVVbCyda21/OEpyc5jq5dIyN57lzUGRoi64cf+NmZmZxs+fKurO2HH8gYGzyYDNKAAPq3E/HkPvkE837+GcjORqYgIP9JTjUfH5Ivl5cT4WjrI94jJkwg8iyHllZ7ReJ+/fpheqccTI7j6L1nzhBZbCu2NXLkSHb48GEWGxsLIyMjWWBgoEgueT5+/Di8vb3bi9n15AjU0NDArFmznnyOADlN9uyhPOXiYup3+9FHdB5GRs+0Vsg7CRQXF+P777+XCYLAWqqrGZqbmf+kSZy1tTVSUlJYQkKCrKWlRQSQkdcj0f78c4pg/fgjFQmU925/zCAdOXIkkpKSEPXbb7yDpiY38NgxSmkYMYLe8Pvv5BwsK6Mq9G3jZWxsjFpNTeR5eWFQYCDdV1NTQCxGnY4OcpqamJuNDWa89BI0OkmONTU1sXr16q6DPXkyOaESEyn3tjOys4GdOzHg6FFU1tfD6NQpoaysTCjJy2MXbGzAnTolLFu2rH0AysrKsGfPHqipqQk6OjpCTk4O19LSQn9UUoLie+8RYYyNhXjkSLzZ3Mzho49Qa22NH5Yuxfu7d5PUdtUqUibo69M9njULNvPnw/DCBRoLxijan5tL511RQZHY33+n74+LIwcEx1HE39qaip/NmdNR9bwTVq5cKUpPT0dkZKRIQUEBra2tOHPmjACOY7qffw4rExNylL39Nu1TNTW03nf+np9/xmsvvMCu/PabUN+vH6SpqdD99FN6dsaOJeeLigoR46YmWhfr69G6YwfmHzqElEWL4BYURHvK9Ok0nz/5hLpkHD1Ke8ulS1SB/5NPKArcC8GxsrJily9fxugenE2JiYlITk5GUlISAOqGIAgCFegDAEFA7GuvoUZTE6UpKdyePXv4goICjjEGBQUFaGlpCQ0NDWz06NG4desWVFRUoJ+RgbmHD0O0aBFDp9xzd3d3JCYm8sHBwcL06dO770HXrtG9+x8LKdbW1qKmpgaTXn4Z4qtXaW/y8yMHTKfcYk9PT0RFRcn27dvHhg4dyjk6OrY/w/Hx8fDz8/ufzuOpYIzmA0D3tbKSHIYSCTlr+9iy7J+GkpISwsPD4ezszBsYGPyj4XmJRILXX39dcuLECVl2dvY/m0j+F+A52X6O5/iXY+PGjRocx6n9Zd7aNvTv3x9isRhisViwt7fvKKD05ptkfHt6PvnDr75KxGPmzK6vy2RU2Cg1lYzV5cv/nJMVieiYP/1E0fJnhCAIuH//PjdjxoyOF+WeaUtLqt7NcUTkZ8+mIj+lpU+ulP7sJ0CGdZv8mud5GJaWYsKtW6jYsgXXq6r4utBQwcPDg8vPqwCwEwAAIABJREFUzxc4juMyMzNFMTExMDc376jW3QOuXr3K3759m2OMcQCwfv36nsmEigoZtt99R9EVT88n5+OvWEFEu6qqqyTypZcocvrSS13erqykhA+VldHs4YGcYcPY75GRggfPs96UGZ6amvB84w1Rk7s7shobMfjTT4lEaGiQVD00lOaiigpw+DBFlp5Ats+cOQMAqK2t7ais3YYBAwZg/fr1aGhogFgsBsdxSE9PF1T27WOR5ua4AGBCVBQ8eotUaGhQocDMTMhmzMAdU1OhbOlSOLu5QSyXecsdG1FRHZ9TV6donzznmOeJVLzzDs3jmzehtnw5Wn/8sVvf3XaoqBCZi4ykiv9PwogRpJ4AaL59/HEHYewJy5bR8zp7dvtL5ubmEIlEmDlzJmxsbLoY2TMff9b/KHieHAfffUeRY0GgCODRo8+sLlFSUsKcOXPwyy+/wNraWqSkpASnQ4eglpgIbtMmAICzszOcnZ1FAPDrr78iLS0NW7duFRQUFASpVArnnBxOWUMD2Xp6sF+7FvbXr0PUS9skIz09LB06FK2LF3P3nZ0x8Ny5jj+++SY5pKZNozoTCQn03GdnI9nGBhzHUUFAxiiCfuoU4OCAi8ePC0opKRiSl8dEv/5Kc9/fnyK/OjrdZascR45HudQ+P58cBACtZQ8fAhIJtBUVsfillxgCAhh/+DCuHDiAmb6+7YPM8zyOHDkiqKqqMkNDQ9bU1MQCAwNhYmKC6OhoWkfu3iVCf+IEEd/SUmD6dFwaOhRaFRU8P28ex61cCWZqSuv0pk107bm5KJg6la8fPpzzr60laf22bXSvO6ccvf02tRMsKKBo8qlTdKzQUPp95Ej6KSqidamlhboVuLvDYscOREZF8TPPn+eOzpsHp5AQ2Hp5QcvJicjtzZv07A0dSn3CIyLo35MnKYL93XdQefttTFu1irtVXMw/unaNCzxyhJ7P3Fwax08/pfM0NAQuX0ZeXh72amrCaPNm/lUXFw4HDlCtB8bImaqgQMTcxoaKKi5bRp83Nu7onvEEqKqqQl1dHQ0NDd3+FhYWxpeWlrZPBHm/9nnz5kFVVRX8gQPIuHYNN/z90draiqKiIm7cuHFwd3dHSEgISkpKmJqaGkJCQmQtTU3sFZGI0z1wgJ6/5uZuxxs7diy3f/9+FBUVdc8f3737j6vMOiEpKQkSiUSwtLNjOHaMcv+nTqV0k07rEgAsX75cdPbsWVy7do0PCQlhrlSXgaWlpcHX17fntfOvgFztZW1NTsMffqB6FLa2pNCIiSEHW+eUnn8J8vLyUFJSghkzZvwrdPCKiorIzs5mUqm05Z8+lz8bz8n2czzHvxgbN25UlEgkJ+3s7GQSieTJpWL/BOjq6uK1115Dbm6u+PLly1BUVIS7mxvJVZ9UHGvfPoq25eZ2NwBjYqidyNq19Pk/Uk27NwQGUpQgPLzXStg9ITk5GQAEc3Pz7tY8x3UQ7/v3Scp4/TpFKurqyCizt39qHlyvuH2bDNZFiwAAJsnJWBoQALz7LrTNzTEY6DyYDAB27NghO3funMjPzw8ODg7Q1NSU52PC0tIS5eXl0NDQQEZGBtoieJg9e3bPRLvzta5cSffqyy/JGPT37zmPfuVKIm83bnS8Zm3dPX9YEIDDhyF++BDiL7+Eja4ujkdEsOLiYnSrfFpVRUa1hgaRhcxM/BQYCI3+/WW2iooi6OnR8a5cIceAPI+zvJwiUxkZPeaSBwUF4YsvvkB2djZrbm7umo8LKqgjVxM4OzvD2d6eCe+9hz1tudUXLlyAi4tLt4rajyNPIsEFf3/errkZsyIjOZG3NzmA4uKoZRtABtdPP5Gk9JtvqNCbHAcPEmHIz28fB30AYrFYyM7OZuZPMtC+/ZYi0XfvkkOoJ0dcQgI5QkpL6fvt7bv3KJfj1Cmq7G5h0YXgchyHYcOG4cSJE1BSUhIYY1BXV+c1NDQgk8mEpqYmVl9fz1paWpi1tTXv5eUl0pO3SOsNra0Uub52jQhUaCgVHwoMpHvNGBVEi4kh8vUM0tSMjAwAgJ+fH6karKxoXveAxsZGyGQy2NnZQUUigZauLmewbh2qJBK+fsUKFpqUhNzERGY5bBhsevqCuDjg44+RZmzMxwUFcT4LFvDo/OxKpR1jrq0NobERDx894o1v3OCueHpi6tSppDbJyyOH1cWLJPXftg33GxrYfQCaHCe8vXo1w8WLNGaXLhHZdHYGHB07FCmjRnXUwLCwoIj2smVEVDuv3/fuAQYGSKiogEwmw/Xr1wVlZWVmamoKmUyGiooKNmPGDNg/NuZDhw6l/5iaEnFMTSVVy+zZwMsvI+3zz6GmpsZdWrQINebmwqi33mLGr7xC87SpCSgpgbqaGlfc1ETnFhxM80Asbt87eJ5HQ0MDalVV0aSgABOehygpieZ4fj4R9+BgcgoVFtI8MTGh85o4ETAywkArK64uNBR6enqw1dNjWlIpHefiRXr2rl2jKHpFBalmAgMpegpQihOAqqoqBA8axFnU1ws4dIihoYGcA4GBtOaFhrYrF/r16weO49CvXz+BMdaRWw5QZB6g516O33+nOb1qFd3zTZueqBoTBAG1tbVdq+sDiIqKgqmpKVdaWorZs2dDKpXCzMwMampqHeu9lhb6+/rC/f33UV1djbNnz/IXL17kgoODBScnJwQFBckfdBFiYmiteP11WpOyssgJ1mlP79+/P2xsbPhdu3Zx8+bNw2D5upuRQQ71/6EoWk1NDQoKChAcHAyZTEbnVVtL9RyuXaN5PHkyKQvawHEcpk6dCp7nuYiICDx8+JBXV1cXKioquG3btgkrVqxgf7ksmudpbsfHk61z+jQFAeLiaG8DqGaDkRE5Ew0MaO9PTaV94eRJSqUYMoScxw0Nf2tf78zMTOjr68t0dHT+t5Zpz4DKykqoqqqiS52UTrC2thYSExMXf/rpp6J169b14h3+v4XnOdvP8Rz/QmzcuFEUGho6WSKRnDQzM7OfNm2a8t/hqVVVVUX//v1hZGSEM2fOYPC+fUh0c8PAJ1X+XrWKSGfnqHdLC20kR4+SUeHv32WT/NPAGJG0Xbu6R9Sfgl9//ZV3c3PjHjdiukEioZwxCwvaTBUUiBSUlZFH+9Kljv62z4L//pdI4qhRZPRNn065nT30PpbD09OTq62txa1bt3D37l00NTXJrl69ykVHR+PmzZuIiYmRV1cWFixYwOLj42UaGhowMzNjMpkMjY2NT9zg0L8/GVv37pEDRVW1uyfe1ZUMTHlUo7WVzrezlJnniQimp1MESFcXhw8fltXW1jI/P7+OFkRXrlA0a9YsItNLl6Jm2TLsPXGCr2poYHPmzKGqvY2NFNlYtarrcRijc/7pJyKJnZCUlITTp0+jtbUVtra2wpAhQ3ovmMPzwK1bYNu2wWniRNjb2yMrK0u4evUqy87Oljo7Oz/xwdu9e7dssLs757d0KcclJNA1HzhAz8OFC0QSBIEIxdChlKs9bx7ljB44QPf81CkyJDsV+6mvr2ehoaEYNmxYz84Sxmgujh9PEsae2gj170+RUA0NIvIGBj1Hn06eJMfAa6/12NbG3Nwc7u7uMDQ0ZJaWlkxZWZmTyWSciooKp6+vz1lZWTEbGxuWkpKCsLAwJhKJ+IEDB3Y96ZYWIv+hocDWrWRsRkeTo27OHPqxtqbxCAig5+2ll6jjgJUVjU9REb2/l3tZXV2NEydOwNzcHM7OzvRiaSmlQDymzMnKykJYWBhEIhFeffVVNmjMGGY0cCDUv/kGekFBbPDgwcxLTY0N3LkTe3V0EB0dLUtLS2MNDQ1MW0EBkq1bAQ0NNBsb46BUyibOnQsHB4eOk0tMpJxcedsqKyvkNzTgUH09S1ZVhR3HyXzmzqW5VV5OjqyZM4HYWFjOn88Gu7tDWVkZGZmZrEhJibdbuZIxDw9yLJqYkBplyxYy5E+f7qjD8MYbFMV99IgisbNmdYzZuXP0bG7ZAn19fQwcOBDp6eksPj4eXl5eEIvFKCsrE6KiogQHBwf2uJMKMhlFo5cvJ/lzURGRBZCzNjU1VRZw9SonMTBgZwsKEBISAtNZs6ApkwGbN8N471521cZGiC0okF01N2d3L1xgbtOmYWddHa7euYOQkBDcCwtDyeXLsNy0CQpXrkDh+HFSsJib03zfto3UArNnk7NJQ4Oub8sWYPx4PCotRbiqKqqkUtS6u8uGLFnCta/be/bQdxkZ0Rp+8yY5cjo5a5ubm/HDDz9Aq6QEo/btY7UjRgBvvQWlTZvIYWxkRPO5rVifWCxGVVUV4uPjORcXFyj11ibv229pv5TJaG4XFNA8Aajg3mPrc3NzM27duoXU1FR4eXmB4zg0NDTg559/RkFBAcRiMV544QWYmppCUVGx61rRtqYrKChATU0Nbm5uzNPTExoaGiw0NJR5e3uD1dWRJH/JElpH5Yobc3MigJ3aMwKAra0tu3PnjjBgwABmaGiIiooKiObOhay6GuKe1qA+oLCwEDt27EBycjJUVFRkEyZM4AwMDGicVVXp+Q8L6yiW+BjkdQnc3NzYsGHD2PDhw1FSUsKfO3eOs7Gx+fP7SOfnk33j5kbqhMJCmj9iMdVycHenZ7mhgVK1Vq5Ee6vDSZNoTOvq6O8+PkTGJRLaE0xN6X5s20Y/c+fS57W1SVF28ya950+qJ1NeXo7MzEx4eXn95WXVW1tbsWPHDmloaCgnkUigpqaG69ev80ZGRl3WmcGDB3OMMWRnZzuHhoZe8/X1ffRXn9vfASYIwj99Ds/xHM/Rho0bN9opKSl9JpPJXtDS0uJ8fHzU7ezseo9O/kWIDw4WBr74Itu9ZAm0raz4lpYWwd3dXeTl5UWScX19MpI7o66ODMbBg2mz+KurW0qlZEC5uFChkj6A53ls3rwZ8+fPxx/uqy0IRKaWLaOo/v79HcWe+vLZffs6qlvfukUS7T5UZa+oqMCePXv4xsZGTldXF+Xl5XBxcZFVVFSgoKBA5Ofn1x6BysrKwpEjR/Daa6/h+PHjfHFxMRcQECB4eXn1Tj5DQ6moT3o6GaadjZVffiHysnIlOVNWrCAjCCDSevYsSbz37AE0NCCVSrFt2zZ4eHhgtIUF5fW5uJDBEBUFWFigoKgIFy5c4AsKCjhzc3PZlClTRBoaGmTIv/kmtcQxMOh+niEhdG6d5N5SqRRffvklBg4cyI8dO7ZvPUQPHKCoVWpqe4E1QRAQFRWFS5cuwdHRkZ82bVo3wi0n9e+9915HX966OiICI0cSwfvmG/o9LY2MJTU1eiZu3KCcv3v3KDr5GHiex5dffomXX365uxqgMxobyXi/ebPnitILF9I9fPCAiNeaNV3/Hh1NYygW99jL+FmRn5+P/fv3Y9GiRTBubqa5NGgQRVYHDiSpfEsLGaSPqwZef51UKuPHk3x03z6SVn/2GTl68vNprkVHk4Fqb9+tXeB//vMfobq6mi1durRD6nrpEjnK5JJ6UCTt66+/BmQyvPXoEbR37qRjenh0Pa/t24GqKtSvWYNHjx4hOzubL4mOhutvv3GNmppC/JQpvLOfn+jcuXOYO3cuLDo//4aGEDZvRtPs2VBWVkZLYyNgbY3/zJsHs/R0jIuMRFVICESxsehvaAgmj4gmJBD5biMvaWlpOHHiBIYOHQrfxwmNVErrz8WLRB4jIymqVlBA0fLOVfAFgRwO777bJe2D53ls376d9/HxYR4eHozneZw+fVqWmZnJ3n77ba7dQZaZSeT2wQNa/9sKjD1emVv+nceOHcPDhw/RHiXfuRPlly7htI2N4Dl3LssvKEB0dDQGP3iAgO+/h9qpUxDp6EB08iTg5IQ76eky/bIykfmxY/T9mzeTY+nLL+la3n2X1s67dzsi197e4N98E03h4fjv4sVYmZUF0Zo1RB4TE2ndCgsjshIfT/Uf5DnjHAdBEHDkyBFe8d49YYqnpyj73j2hKjSUDczOxv6VK/nZs2dz5iYmdG9OngT09SGVSrFlyxZh8uTJzOFp9UPGjSMyde4cpXTIndhz59I53LlDzt229bm8vBzff/89AGDhwoUoLy/HubY0hQEDBvDz58/nenSg8jylGyQkdFMe8TyPL774AlKpFKo1NVh08SIebN8OUxsbmJiYkPy6svKJSrSTJ08iISEBEokEMqkUFklJSLewwIDBg/kZM2Zw6s8YmT127BiflJTEffDBB92dwT/9RGvC++9TBH3ChD4TzX379vF5eXmcgYEBP3/+fO4Pp+G1tlL0+pVXyHmWl0dF8q5fp2fO2Ljr/L98mVRMMhndU7lSpy+Qyei7YmNpXk6bRs4yuXNr7Fg65vTp9N7z54mwf/MNOUrPnaM9s66O1E5PCdC0tLRg+/btgqWlJT9t2jTRM/VwfwbIZDIEBwcjIiICAODm5oZ79+5Bzj+NjY2biouLFe3t7fmSkhJ4enqKTp8+DQAtGzZs+B8khP8ePJeRP8dz/EuwadOmhQoKCjtHjhypaGtry/XaZuNvgKNUyqqjo+FXVSXU1tayiIgILjExUebl5SVCcTFt6HJIpSSL3rePCgt5ePw9PSjFYoq0nj1LhmQfjhkcHMyLRCLWv3//P36CjJFs8VGb03XvXnpNLKb8tc8+e/Jno6PpfBcupI1xw4Y+tz/T0dHB+++/z3333Xey8vJykbq6Oj9lypQed0g9PT25NxlGRkbC6NGjERYWJsTFxQmBgYHcE/O+fXwogrBnD0U8Nm7sIGKVlZQ/DVBkSW4symQUaYuOJkLeZqDHx8XBMTJS8Fi0iGH9eiJM58+TxI4xPHz4ECdOnIC9vT3z8fGBpaVlx7W89VZH1eee4OtLY3jvHhmVoMJoPM9j4cKFfZOB1NVRP21//y7EjTEGFxcXXLx4EQkJCdygQYNga2sLsViMpKQkhIaGyqqrq0Xjxo3jFRQUOo6lpkZOmG+/JQdKYyMZZTNnUmTbyIiiGF99RTnUT+gJy3EcTE1N+WvXrgnz589/sgWkrExjKc9rfFxuXVhIxK24mCJnnVFURPf3s8/IiP1fUVMD46YmjLtzR+AuXmTt0lwXF5oXT0sj+e47ml9KSmQkKinRGvPrryTzlRelOn6cHAzm5h31FBQVAcYwaNAgFhcXB93Oz9O4cfTTCRKJBEwmw5KXX4b2tGlk2HaW/8rR1AS8+SZUVVVhY2EBm82bOSQlQXrzJgqqqphxUhK7cuUKAEBFRaVLazFpaiq++fFH1G3ZAgcHB1laWprIzskJPM9j+Fdf4dK338ryDh1iw86f59SVlCA7eRLl5eUYHBcHdvp0O9m2tLSErq4uX1JS0n1Oi8U0Dq+/Tr+Xl5MD7OpVKjT34EFHAbBdu2huPjYPOI5DQEAAd/78eTg6OkJRUREDBw7kEhISWHl5OQwMDOh7x4yheZSVRdW0xWIiIhcuUMSubbx4Cwsc37YNhUVFvI6ODrOysmIAUBkUhNBr1zB91y6mEReH4vfeg0wmQ56rK6QXLkDyxhvAhg0Qpk4Ftm1D4+TJiB8/HuZywuDlRXJlgNbajRspzSA/n/7fVguBO3QIR/fsEZSKihgnCBTlv3qVIr337gEREWh2cECrnx8ejR3LD0xP52Tu7sgbOpS/bGHBBsXFYUpRkUgUFATL2bMZWlogZGWh/9277ODBg3BxcRGmODszpKcD+vqIjY1Fa2srs3yas+rXX2nuqqkRQevsRDt8mJ7VL7+k1KWdOwEjo/b+6z4+PpBKpUhLSwMALF++HPr6+k9e43ieCGEPLQI5jsOKWbOgPnEifn/7bf7HefM47awsPuTuXW7AgAH84sWLOWhp0Xp1/Hi39ppjx46FlZUVVFRUMGjpUuS+9BL8Jk3CoUOHWGhoKD9p0qRnkuDZ2dlxSUlJiIiIwAuPK+i8vWk/tbKi/UBNjfanPmD+/PlcaWkpzp49y/bv3y9bsWJF35ikVEpz2sODHDxHj3Y4rqysSAG2YgW9t6cWjI8e0ftqakj9sGtXR57+0yDfg1xd6QegQqpyyKXpO3dSZJznac3X1iZiv28f2RTDhlEE/aefAEdHVJ04gdLISKhHRSFu1iyYlZaCHzgQ2Q0N8PT0ZOHh4SIlJSVh4sSJf7rRxvM89u7di0IqpOoMwC42NnYXx3H/FYlEfEtLy5r8/PwtAJrj4+PVeJ7/4PTp01BUVLzQ3Nz8/42M/DnZfo7n+Bfg008/Xa6kpLRt0aJFyn3KefyrkZoKvPceNENC4G5hwRITEyEWi4X5paUiLFnSUXxH/t6YGKpkfOhQh8z478KkSZTXlZXVRfosCAIqKyuhra0NxhiSkpL427dvC0VFRaLp06f/uQVUbt6kf69coZwsgCT0M2Z0zydnjAjI/PkUifoD0f8lS5aItm3bhs59Rh+HRCKBqqoqL5PJ2Jw5c0Rqamrw9PTktm/fjtjYWMHPz+/JG6umJkm3s7JI9jx5MkUXXn+dCGppKW34AQEUQblwga57/34ywhMSUBsRgUsFBVh98yaTFBdTVedOY3D//n2cPn0aw4cP58eMGcO1R9t5no7zzjtk8D0GnufR0tJCck1lZYrM8jwepqbi6tWrgr6+voCuOe9PxuTJ5KSR51d3gkgkgr29PRITE3H58mXcvHmTb2hoQENDA9evXz/urbfegpqaWvfj6OqS8b97N0XSmpuJbCYkUFQ3KIiI+FP60b744ovcV199haampt6lqVpaRDg4jqJ8nRUeV66QI+TQoa6t9mpqKNq+c2ePY9wn8DxFB0tLySB8+BBYuxYmTk7sZFUVqlRVYWxsLHvJweHpEZN794hIHTxIv8+cScbumjV0DIAcQO+9R/MRICdGWho5CyIjUbx6NZLv3gUUFTuUBgAReBcXyhNug9KDB1izeTO28jzW3b/f8znl5VGUWFOTcm+zsoi0f/89xKqqMNHQgImJCTd06FB888032L17N+bMmQNLS0vA3h7cjz9ioIkJn5GRwcrKymBsbCxzcHMT2RkaYsDAgZi9cKEI06bh2xUrZFdqa0X49lsAwMLJk8HX10O7be1qaWlBeXk5116wsjdoaxNZu3+fiPCkSaRs0NQk59mCBT1+zMHBASEhIbLw8HA2ZswYrra2lonFYsFAXZ3h9ddpnG/cIPIuz80NDqa5LAiU+wwAUimS3dyQnpkJX19f5uTk1N7DPjMzE0kuLrAzMYF2WRlkiYnQKi+HY1gYsuvrkf3xxxjk6gq2aBEy+/dHvLq6qH9rp1a7Pj50L3NzSUavoUHOvvR0mnstLVSR3cICnFjMG8XEiNK8vWH58stgOTn0jMhk4BcswN0BA1A4axZvl5rKjrz8Mj/hxx85LjGRm1VTg4Evv8xgZEREydER2L4dbMwY6GVmsqysLMTGxjI1U1Pe7/JlDsOHtxcm27lzJ955552e78vDh7SeBQTQ7/ICnJ3XACMjUmz88ANFL7dtQ8WKFWCMITQ0FABJ1seMGSPo6en1ToquX6fnvidIpdAeNAiYMQMzli/n2qKf3MWLFxEZGcm1tLRQhNnZuWs3gzaoqamRUqGxERCJYDp9OqClhdGjR7PTp08zRUVFqb+/f5+4RVlZGU6cOAFFRUVhxIgR3a/JwoIcEHfukGM1Pb3PZFtBQQH9+/fHhAkT2IEDB0RSqbTnGhxyde9XX9Fa9sortM7s2UOt+9atoz3tP//p03Fx9SrVqRk7lgrwffMNEfCeiPkfRWfFyo4d9K+pKQUAANprWlsBnsc9Pz/h+unTbEheHj8oIYGV+PjwHmvWiFKdnAR+9GjZiPXrxcVbtvBOoaEMly51FEwNCKDnTBA60gv+AEJCQmSFhYUiAC9t2LDhPoD7AI50esvaxz7y4caNGxXWrl37/1Wf7edk+zme4x/Gpk2b5kskkq8WL16s/E9Hs9uhpEQRai0t5Ofn4+zZsxgzZgwrTUiAkaoqMRmeJ5K7fz9FBo8e/dNPo6KiAnv37oVEIhFaWlqYiooKDwATJkzgBg0ahIqKClRWViK2ogJOr7wiWAYHM6lMhvr6euTl5eH48ePQ0NAQDA0NkZmZyTk6OuLFF1/EXzbOY8d2tAQpKCCD59YtkqMeP05jtnw5RZdmzPjDMnslJSWsI0PtiaRSIpF0axF0//59CIIAW1vbvnmwBw2igj6HD1NO77p1FIkvKSEvvL09Gd1JSSTH/vRT8uKfOwfZhQvA+PHgc3ORXVSE5pQUWLflABYWFuL8+fMYO3Yshg0b1vUafvqJCL2razelAs/zOHjwILKzsyGRSATbcePgMXcu09mxA+eam/lBgwYxf3//vntRVq9+ItnkOA4vvvgixowZg8OHD6O0tJTjOA5Dhw6Fv79/r5XVAVDUePJkyiWdOpVkkPHxROJ6cZJUVlYiKysLjo6OEIvFQk1NDeuVbAMkGTx/ngzSoqIOKfScOWQsqah0RDQFgQirsTEZVc8CmYwidMHBFG3+5ReSr69eTd+nqgq9CRMQWFWF0tJSnD59mjt27JgsKCiod7ZdW9tVvj11avf88ZUriew+fEhpKhYW9BMQANy4gbLGRizbsQOZDg7krNHSou9UVycpvyDQ+J87B3z4IULWr4dMKkVxcTFFbx9HXR3N78hIus4tW7rlvNfW1uLbNpLs5+dHhax4HnjxRXCurlAuL0dLSwsbM2aMyNzcnMZJTu7t7YG8PKwQiUQHTUzQ0NDAGxgY4OezZ7mgI0dwLCYGhQMGQEVFBTzPw8XF5en359Qpaj0kN+zv3CFDf8ECUgH0IqX19fUVnTx5sl3qqV9UxA6++64wJTKSaYpEFOHsTPhHjAC+/BIlEydCpa6OevRKpQgbORJOdnbC8OHD2x/euro6BAcHw9XVFTYTJgC//IKxixbBV10dzWvW4LiuLoTbt2G0Ywcq7ez4lkWLmOj2bVbemTAyRuqNzExyBMphYUHS8IYGctD8/DMWLFjVGJj1AAAgAElEQVQgSti9W8i/eJHdUFER5syZwy6mpMhqDxwQ6X71FdJSU/nVbm4cN3s2HE6eZPjuOxhHRJBzRSLpqBI+b177+qCoqAgTExPZlClTRAc3b+ZsLl2CyltvYejQoQgPDweALsqGdrS20lxOSOjo5GBhAemdO4iNikJ4eDhqa2sRFBRE6+PbbwPFxZCVlyNm7VrBesAAVj9kCB7l50NfX58fMWLE09e333+nCO3UqV1fr64mAhUZSSS2E0aNGoXIyEh88cUXcHJywrSdO8mJ/iTs3UtOvLZrcnJyQkhIiHD//n2xv7//U08RAHJycqCgoCCsXbv2yftRaiqloxw8SHvmiBHPVCelsrKSl0qlXFZWFtrVB8nJtJZVV1NKxcmTdN9NTSkinJLS5+/vguLijlQpHx9aC7y96X688cYf+84/ijaCfM7UlE2ZMgXy2iO2gAgLFmAYwNDaKsbw4Xhp6FAOv//e4ZD88Udaz5OTyWH366/dqsH3BYIgIDw8XL7299I6oys2bNjw/xXRBp4XSHuO5/jHsHHjRvHt27e/V1RUXLdo0SKVfp16qf6jiI2lXKGvvwYYQ2pqqqBy+DCz/c9/sNvCAvn9+ws2yspMtH59RwuuHvqAPitSUlLQ2toKqVSK6urq9sImoaGhaGpqYoIgQF1dnTU0NLD09HRBU1OT7du3D/Hx8SjV0YFbcDBTHzMGF6KihNOnT7OkpCQ4OzvDwsKCPXr0SJgyZQobNmwYlP/qPHI5Zs0iozchgaJijJGhkJFBBrGRERn/ycm06RcWUtQmJ4cM/epqkm+Wl5Ph1NREUdKWlo5crGeMzl+8eBFVVVXw8vLqe+EYFRWKlvI83esPPqAo6SuvkNF25w4ZKKNGUR7lsGHAnDlonTULERERiIiIwP3795GYmIiYmBi+vLycXblyBSoqKgLP8+zEiRNobW2l6rZ796KptRVVixcjv6oK9fX1iI6OhqGhoTwfWNbQ0MACAwOZi4sLu3PnDkw//pjpHTqEEA8PtujVV1mfrqu6msj8+vW9SvgZY1BSUsLDhw9lFhYWXH5+PvLy8mBtbY0+5Samp9PYbdxIRve5cxQN/PpruneamvQMdYr8njlzBrdu3cLt27fR2trK7Ozsem311g4rKzLoioo6ci7NzIiA1NeTQ0wsJqI8ahQ5T542f1payFmUkkJy7atXidTI28S98grNaV3dLsWdlJSUoKurCzMzM3b16lUuJycHdnZ2eGKEW1OTxkUOJyeSRRobk+MPIANWRaWj1/iYMfQ6Y4C5OcR6etjV3IwKDQ24x8dTRNfLiyJwQ4eSYysyEjh7FnFDhuBGcjIUWlow3M4OShUVJNEMCyPjvriYjMuEBHIqfPJJj4qdb7/9FlKpFAAwZcoUqKiokGz0jTcAZWUcPXqUAUBhYSHv4eHBMHw4nbe8INCsWeCOHYNuUBBiExPZkiVL2KhRo6B2/ToEXV3kampCKpVCKpWisbERNjY2kMlkPatyeJ6Kz/n6dpBtJSV6Vk1NqVhfcTFF73qIsh0/fhwNDQ3Q1tbGMmtrDP/2WxjHx7Nf3n4b99zdhYjbt1l0dLTg6upKjiaRCHBwQJGjI7itW/Edzwuq69dj6Nmz7KK5OfPw8GiPJBYVFSEyMhKtNTUyj6IiDqdPgzk5QSyRQHHNGjgWF8Pa2xsYOxbmn37KUktK2IgtW9AvO1swfO21DiJmaEhzra2fepfLF4lQO28eRNOmofLrr5FubMyKFy1CQUEBu3XrFhoaGhjP82iprhYCVVU51UmTwLZuJTXIhg1Utd3bm+oL3LpFz+i2bch49AhHjhxBZmamIJFImLe3NzOwsEBScrJw6d49FhEfzzc3NzOpVIrS0lLe2tq6qyNu1SqKPsqlxwBgYIDYGzf4axUV0NHRYTU1NUhLS6Ne1/r64NTVcVlREQWamsK8CxeYa3U1XDduhH1PRet6woQJJHfujIwMGj9Pzy51LuSQSCRwc3ODRCLB3bt3MWr4cDAbG/qex51RaWnkNF65sstzf//+fb6yspLjOI43NTXt1aHb0tKC33//XdDW1uZdXFyevBANHkzrg5kZrW3BwR0KgT4gISFBqKysFCaZmDC8/TaRx9GjaQ8IDKR10c2N1oje2j32BTExpPQaPpzWe7ns/cABev3PjG73ETk5ObIHDx5w7u7u3ddfkYicLxxHSg55Ks2yZVQfY/NmGvsFC3p1EPcEnudRU1ODux3tKbf6+vrW/s8X9H8UzwukPcdz/APYuHGjoaKi4u+Ghob2gYGBKn8bAewLjh6lzbSth7EglaIgPh6N585BfelSJKxdK4iKiqBsbMzyx45FTX290NDQwLu6uoqG9ZT32AdkZ2fj559/BmMMIpEIjDEwxuDm5sbfvXuXe+ONN9oJh1Qqxb59+2SFhYUiFRUV3snJiRs0aBAuf/MNRoWF4fSMGbCxtUVqaiqUlZX5uro6TldXVzZ8+HCRs7Nzu6GalpaGI0eOwMrK6umRt8chCLRZ19cTkVZSIslYVRVt3L/+Sh5uJyd6n6kpyQNrauizixaREZ+TQwaEjQ0Zy3l5ZBTJ2zmlpZHE1seHjpGTQxLB0aPJcEpPp99nzyZ5Z00NqQ38/WkTraqiz3t4oFFDA2EhIRhQVobBEyZAydycDILcXNpI9fRo8y0oIKJmYEDnWltL56KtTaR/wwYiJLm5FDkdN46i9qdOdRsmmUyGtLQ0REdHCxkZGe3Gl7u7O6Kjo2Fqasrn5ORwqqqqeM3fH61Tp+Lo1Kko09FpN9SlUmn7vHB1deXHjBnDdZEJNzSgtqQEtRyH/vI2QE9Dbi4ZEnIJ3lMgL1SkrKwsTJ48mdm2VSLuEUlJJKkfMAD48ENyTvz0E0U19fRoHHftovvz8CEV8PrxR5Lvubjgto0NUm7dEiwnTICmjg6ztbV9MkntCQsXUjTzxg1y1Jw5Q+qT0FAqNnf4MM2RJzklcnLIoDUwIEJuZ0dRXXnk8Cnt0B7H3r17kZeXh379+mH27NnoliYTF0eGbn1914JnXl40v1au7Pr+5maK2nz9NTmsOhVp3LRpE3iex0cffQQuOZnmbUAAPSd6evQ8GhjgWmkpBqWlway2FqIFC4iw6+kRGXd2pp+FC8nZUFVFjq4exuvo0aMyBQUFUU5OjlBbW8uUBQErv/4aN3fuFCLS09vtK21tbf6tt97ikJhI5CUsjJ7/hARgzBi0vPMONj94gDVr1pCEt6aG5oanJwRBwLFjx/jk5OR2QqKvr8/7+/tzZmZmqK6uphz1tDRSN6xc2aEIkVe79vOj+/bf/1Lhyuho+r1Ta6Svv/5acDYxwQt79zJMmgTo6SGtqQm/ZGZ2uWaO48DzPFRVVQVzMzPm+fbbqPT0FESffsrEYjH0dHRw5LffZIqKimzkyJFcSkoKz3Ec1/LTT3CJiYHSRx9Bd+RISKytad2YNYvm2uXLJNtmDNHR0Th/9iwUGMPkvDxw5eWInjYNzU1N/MwNG7hTS5dKqzQ0mEwmYzKZjPFSKfO7cAHBAQHoX14OzYIC1KmooEZdHUvGj0ehlRVqVqzgC4yNmXJjIyxv32ZHAwOh1doqqJmZMXt/f9jq69N5fP45sH07ZCIRwn/9FTdv3oRIJIKdnR0ePXrEv/HGG3Qf1q1Di44OEn19cfbs2fbxWbVqFdpbTvE8hKQkJOfkIDg7mzc2NkZubi7Ta2jg3fbvF2mHh0NPTw88z+PAgQOyR48eiQYNGiQYGBiw27dvIygoCFaDB9PzvHYtrR3bt/euiGpqor2hoKBDydDURNWur19/aiFRQRCwZcsWTJs2DdYmJj0fS5620kPxxu3btwu1tbVs7dq13VoudkZBQQF++uknrFy5Ek9t0bVsGZHBBQtongQE9F4DormZ1hU3NzRYWSFy4ED4btpEa9/WraR2+Stqypw4QWvTlCm05isokDri2jVy4Kxa9eR19y8Cz/PYtGkTZs+ejV73LTkEgfYwDw9aB11dad94Rnz33XfNFRUVimKxOEUqlY7csGFD2R84/f9v8FxG/hzP8Tfjk08+maugoPCDp6eniq+vr/jvaOnVZzx8SJt0G9HG+fNgCxfCuKSECODrr8NATY3FL10qZAJ8a1OTYGJiwikrK4tu3LgBqVSKxMREfuLEiZxJG/G5du0aYmNj+cDAwPbXHkdCQoKgoKDA/Pz8ICfEoaGhuHXrFmdubi7T1tZut8LFYjFee+010Z07d+Do6NheZdRg0yaobtwIKw8PKI4fj9u3b/Otra0QBAGhoaGis2fP4urVq8KIESMYz/MIDQ2FgYEBSkpKunocpVIybh49og09Lo5ef/CAvL0jRxL5KC2liFdqKhUQEwSSNFpakhTQwICMkrfeIhnZ5s0UZVq+nAjClSvkWXd3/2P3ShBICtfcTES9LUcL775LhrQgEFmYNAlQUsL18+dRYGyMKnV1QE2NH2JiwqGpib5DHj1saaFrrKmh/zc1kREvk5FXnjEysNqKQqGxkZwJixZR1OyxCIhIJIKNjQ2sra1ZRUUFVFVVIZPJoKqqigkTJiAvL487ePAgZlhbI3npUtwMCsLM5cthZGQEsVgMjuNw9OhRPjU1lVuzZg3EYnH3h0VFBerHjkH9ww/pvJ/muLpwge5tH4k2AGhoaMDIyIivrq6Gjo5OdyutrIzyeuVFdXR0SGYZFUXjN3s2RRzlBt4nn3R8dtMmulcvvAA0N8O5qQmGv/7K9CQSqJWVkcrhiy8oAufh8fToyP79dF8iIugerlxJ3y132kyY0NXgq6mhe7hjB5HsV18lZ4SlJa0H/6NxuHjxYqSnp+O3337DDz/8AA8PD37cuHFc+7rn7EzyxccdCufP92xQyw340lKKqnXqB8y3FW3kOI6cBFeuUAT+hx/ICfbZZ4CODmqCg2W/ZGSILIcM4YOCgnpegDdtojWvshIwN0ft6dO4Vl0tU1dX53x8fJhIJEJGRoZIJpNh4cKFLCIiAnVVVfjy3XeBtDQmkUiEgIAAZm5u3pHbb2VFEXo1NYrkKyoCN25AEhUFnaws2cmTJ9m4ceM4rYICksJHRoIxhokTJ3Le3t4oKyuDuro6EhMTcfToUTDGIJPJYGZmxs+PieGYl1fHHIuNparF9+51OEiWLaP7e+4cpYQcPQrY24PneSimpTEPgGTutrbAihUYJJXihVu3oK6ujvPnz2Px4sW4dOkSRCKRoK+vj6KiIln41q0Y3a+fSD8ighw148fj1VdfFZ06dUo4f/68TCstTTQsMlJodnLC5YAAVpyXh3mLFwuDJ09m7fdcWZnm9xtvoGXVKtw/dw4qWloQ9PXRoKQk09DUZCZGRpwyx3FwcsJYHR3x/2PvvcOiOtfu4fXsPQND71UEpSkoIBhBsYG9a4wlGnuPJXqMHhNjwsEkR0+SY4yJURNLjLHEaGyxYwMVaSLSm1TpUpU6s/f3x80wdNGTvN95fy/ruuYyGWbP7PKUe6271U2cCJlMBplUCpmaGjR++w0D3n4b6NkTP23YgBJBwOhLl8Dv24ejH3yAt2trGSwthVJvb07x9ddYZmCA6g8+YLJ//Qu/JCbC4tdf8cPmzcLEggKuaMQIRbiZGT/oo48wddUqhfPs2XxiYiIaC4bw9YXawYPwWL8eV65cEevq6pijo6OopaVFnxFF1LzxBh5OnCjelsng5ubGlZSUUL6zQsFb5udDuz6ajeM4zJs3j9+xYweysrJYbm4ubGxsBEdHRxo33boRSTx8mISSmBiq+9Ca7VBXR/uPkmgfO0Yt9ZT72Uvw/PlzVFdXQ18Z8t6zJ4lQurr0/+HhNK5SUloce+7cOVRUVDCe59sl2gBgYWEBU1NT8bvvvsOMGTOYbfNWk40xYgSJhQYGdC6lpS2Ljj1/TmlMU6cS6T15EkhOxqPZs/Ggrg4+Q4a03rHhz8TDh6q8aXNzWoOV53/6NK1prxGO/boQBAHBwcHgeb5Fr/ZWUVJC4nBEBJ37xx+TjfKKqK2tRXFxsTqADXK5fKefn18bBQT+76DTs92JTvwP4vPPP39fQ0Nj68yZMzW7NC5y8d+COXOoyNjWrWTMGhmRIltZSQVeDhwgpbOVdiMXL15URERE8K6urmJsbCyTyWQKiUTCqqqquJqaGmhrawvvv/8+BwD37t1TSCQS5uXlxQFAYmKieOrUKTZp0iQ0bp8iCMKrFTK7d4+MxXffbTA6nz59inPnzolvTpjAnj95gvMXL6JrVhbUq6qg5eYmmsTEwM3GhqG6mgjjoEF0nba29D0JCeSlNDUlA8bWVlWYqy389huRLh0dqgb8+eeqHNQbN+g7IyPpb6tWtXo//2xkZWUhLCwMMTExEEURn3zyyeu3lLt3j8LeDQyo+uk771Ce4/DhZNCYmdG1tRN6lpKSghMnTmDwwIEYumEDIkeNEv/Q1GSCIMDW1laYNWsWJ5FIUFNTA47jmha9ao7kZPL8HD7cxFvXKt59lwzHVoqitYeQkBDhypUrXO/evfHWW2/Rm/v2kZdy1SryGq5d27JX+pIlRPiUx3QA+/btE9SkUiz08OAQHEyenPffp/tqaUkk/sMPSQAyNycCzXEqoiWKFNq/fDk9h4wMOtd9++j8oqNpDMbEkGE1bx6N/VGjyLj+izoJHD9+HElJSdDR0RHWr19PE3vyZCpG1NzjrVDQXLt9m2oHtIFno0ZBXlqK4M2bERUVhV69eonT3NwYjhwhIj59OlV+P36cyPekSUhOTsavv/6K9957D7pKEtEcaWl0j8aOBaKjcb+kBIYrVyLTygoaH30EnufF69evM6lUig8++ABcXh7qunXDlxs2oK6eaHAch3fffRdNUoS2bSOSvX696r3Ro1Hj6YmvtbRQU1ODTz7+GOzzz2lMtzHuy8vLUVtbCwA48OWXmP/LL7jo5wc9Q0NMGT8ekuRkIDkZD+vvnYeyurESd+4A9vYQly9HWFWV0OfuXU7yySfgPvywCYkrLi7Gt99+C5lMhk3NW8cp0aULCTvKDgETJtC9S06mFIoNGwBbWwgjRuDTv/0NEy5cQE8AWg8ekFCloUG5v48eAWZmyJgyRTRISGC/zJmDJVpaUJs/nwTL/fuJ0Ny+TQQyJoZCXwsLSUAIDQXefReKgQNxdsAA5FhaQsZx6Nuzp+Dx8ccckpJICFOiqgoQBNQVFuLFiRPInTwZBmPGINvFRXB94w1ObedOmhO//orcvDwcPnwYH3xQX89JFClyZeNGRGVlISYmRkxJSWHm5ubo3bu36OLgwOInTsTD6dOF8TNmtBSax4+nrgV2dgCA/Px87N27F9ra2hgxYoTg6urKtbo+R0XRXuzvT9eirDavRGwsiSs9etAcsrGhfOfmlb7bwJEjRxQVFRVoqN79t7+RMKNMt4mPp/s+fXqT4zIzM3Ho0CEAJLB1pK1mZWUlvvzyS+jp6Ynr1q1rf9H55RfaW0pKKHJo1Cjakz//nKJ4bt6kc/L3pzWufgz/8ccfSExMFN9///2/tj2KXE52Qloajef8fLKnrl+nv794QVEF9++rhIu/EGFhYbhz544AgI0cORJubm7tX39YGM2/zz6jPPapU0ng+fXXV05XE0URW+sFZZ7n73Icl7V58+bZLzns/2l0erY70Yn/IWzdunWMurr6p4sXL9bQa178578BT5+SktmlCxlK7u6kXicl0YZx+nS7hUnGjRvHjx49GhKJhI0cORKxsbFcenq6MGrUKHzzzTcAwPLz88EYQ0BAAA8AZmZmOHv2rGhtbQ3GWIsNukNEWxDIG1tZSV7eCxcox1RPD3jjDXQ5cQIrk5MZ4uOB0lJM1tcXZLm57LmlJQvKzWUOgweTd9nenjZJPb2mhvCrtEUKCCBy1bs3GYGffqoqiqOEszMZht98Q4R9wADyWNYXWvqr0LVrV1hYWCC6vk3Wa0GhoFDcSZNorEildH/mzqV7aGVFHpTERArH1NOjTdvBgYxKU1PU1NTgzJkzYkpKChs7ZAj6HjoEHD8Od0dHZltWhpCQEAQHB3O1tbWQSCQv9ZAAoO//8ksibtevt20cBATQ517DW5uWlgYmCBhpYUFq/9ixFKbt60tGlLd3y4OKilT9Ul8BkyZN4g4cOIC8ceNg/u679KYyTL+sjIwiOzsyQIuKaA58+ilFTBgYkOfn5k3yFvftS+9PmUKfz8ykkPVBg0h4MDBot3DWn4WYmBikpaVhxowZOHnyJOfv748R3t4YWFrawuMml8uRmpoKhZsbHh46JHA9ezI1NTVWUlIi2NjYcPn5+WJOTg7kcjnT69cPttra8qrUVGaVkcHrBAez2p49oTZ4MF1zTQ0JhIJARDA4GKEREaKxsTF0dXXbNkCjo4kYjx0LuLigV1kZAp2cUC6T4cWJE7BNT4fVm2+irLxcwXEcDzMzlPz0E7qBKm9z9X2bm6QAVFfTvNfVbbrGfPst4h89gpiUhLfffhuM44hIXL2qaqvVDI1FgpXl5Tg3cSKe5uUhOzcX9l99BT1nZ5xzdRXLYmKYlpaWGBAQINrZ2YlDhw7la2trobC1RZeKCtQ8fIhqW1tOMmsWuLffbpg79eHrisTERF5fX1+sqKhgWVlZ6NpaZEVamkowLCgg4W33borqKS6m8TptGuqOH4duQABS7ewgMTCAm5oaiUdr1pBgVV8EziY0lGVnZKDu669RVFgIy9hYItn29rQfLVxIURiLF9N+deWKqriTjQ0qExIQu3MnZs2apSyKxcHZmdaj2bNV7Qw1NIDPPoNUQwP67u7Qt7AAMjJgriw8uXo1kaTu3WGyfTtqa2vRUNWaMfIi370Lt0mT4ObmxqKjoxESEiKGnznDtK9dQ8C0aZg3cybX6j3T0aFUo3qybWZmhlWrVuH06dMIDQ0V3ZoV42uAmxsJthkZtA4vWECEWEnM339fRbRXr6b1uA3xrKqqCsnJyXB2dkZVVRVKS0uRlZXFb9iwQfWhL7+k3xs9mupz7N6t6hrQCNbW1jA3Nxfy8/O5u3fvdig1SykmdO/e/eVE+OJFErGnTqU9ftMmSpGZPJnmqJYWRS01giiKiIiIQO/evf/6PqSPH1MNC2VklbExje3ycprvWlp07xYupPSJv7A16h9//IHIyEh4e3vD19e3/WKeokiFB99/nwSVuDg61+3bqc5AB4m2svuLjo5OE2FcoVAMAtBOL9T/G+gk253oxP8A/P39e0il0hMzZ8787yTaAHnKbG1p4/LyIu/B3LlUAOnYsZduDoyxhjxbLS0teHp6Mk9PTx4Ali1bhsjISHH//v0MAExMTBTV1dU4fPgwb2ZmxoqKioS5c+eyJoWgRJGMmYwMMhgMDckbDNCGUFJCBW22baO8Yn9/MsRsbOjzAwYQsd20iQzX+vwy+3pDqqioCAX79iG2Rw+Y9O0Lzf+EcJw7R2LEpUtExD7/vCXJVsLCgjwPSUlkXB48SATqwQO6RkfH1z+Pl6DxpiuXy9v3FreGujrK/9y9m8i2REI5oMrIh8WLiUSMGUOGXmQkebeioiB++y0qY2Jwp3dvsVJXl0n19cG2bEG8vj6eZmRghKMj9PT0UFFRAUdHR4Wmpuar5dGXlpK3LiGh9f7V5eX0bAIDW3qD2kNtLVBbi0E//8yNDg6GbOZMIthTprT9jJXYvJk8Ba+Scw3AyMgIPM+LT58+ZebNC3Pp6ana33z/vep9T0/69+5dCsktL6dnlZ9PbW369aP5UllJ/6+h8T9GtPPz83H+/HlMnjwZTk5OWLx4MQ4cOIDAGzcQMGwY8Pnnysr0kMvl2L59OxQKBd6YM0fom5TExamrKxhjcHR05B89eiRaW1sLs2fP5vX19aGpqQme5yVYuxaKkydxbfBgHNLWxnJlJEFuLpGFjz8m4icIUDt6VMiXSPjTp08rJk2axDeeB7m5uXjy5AmcPTxg0ChXUSKRoGTiRKSlpWG6VAqb+/dZRGGh6K2ry+H5c2DZMpgeOYLZPI9bt27h4cOHTb1pNTXUDm71avLUx8WpxqmjI6oXLsSsLl3QTdkOqriYPHhtkO0G5OVBJysLc+qLMIXcvy9oXLvGXTczg7a2NkaNGgUHBweWnp7OTp48ibi4OACAXk0Nxp88ia6FhTD87DORO3+eYfFiEqvq70d8fDw/f/58dOvWjR08eBDR0dFC165dW1rfamok3ty7R+NsxgwSoubNIy9ZPREvWr9eHMcYOz1vHjxnz25o44fERBQ/eIDCmhrUt9dDZWUlKoyNET5sGCZNmkTnVFhI5K+srKHGAUaPplzc48dp/U1Ph46tLVxdXcVjx44xAwMDhb29PT9o0CDoKtNuKirIQ2tnR2JVXByd++TJtK80JkwhIYBMBklCAvQUCrG0tJQ1RCpMmNBkbru4uMDFxYXdWLlSUK+p4eSiiDb3+/ffb7F+GBsbw8fHB2fPnm1/wWCMQssDA+l8hw6lc9m4kSpry+W01pWUtLlv5+Tk4NSpU2JpaSk7c+YMpFIpBEGAvb29Qk1NTfX7VVVEcNPTySvbTij622+/zZ07dw6JiYl8TU1N20KpIACMQaOgAMOqqoSbjx5xvjdvQnf0aLqWnj1JFFy3jkTD6Ggi2JWVtHavWEHX9ZI2YMpaH22lr/2pyMoikUMJpdD57JnKkz1oEF3H8eNNak38WRAEAYGBgXj06BGWLl0Kc3Pz9plycTGJ4oaGZDcNH05EOz+f5kWfPm0eWllZiWfPnjWIbw8fPsQff/zR5DOMsWmiKF7YsmVL7X9+df+70Um2O9GJvxj+/v6aUqn0xpgxY3Q71Cv1/w8IAi2sM2cS2Vu3jop17dpFXtr/EBYWFrCwsOCMjY3FxMRE9jQ1lV81fToCbt/GaJkMmrW1HFJTyRgtKSGCk59PYWOHD5MIsHw5EYguXUjR19Qko1WZ+9gYw4erWgO1AWNjY7i6uirCwrxdOLAAACAASURBVML4sLAwuLm5iVOmTHk1ufnECSI3x4+T0Zef3zFi5etLJPXoUcqPBEhFfvxY1bP7LwDHcVi1ahV2796NkJAQDBo0qOMHp6SQMLBrV0svvKYmhTfK5US0fXxIvHF3b/BWZYwcievffgtfmYyN0dOD4uxZlBcVidk2NuKTCxe4w4mJmL96NTIzM4WBAwe+GjsFyHtZW0sF5ppDFMmrmJTUbvXxBsjlRNqLikhEWLMGWosW4bCjI2xTUjCpeb5ga0hJoXN5FWKPhnZ3Ym1tLaurqxMBdGxMKosVTZ9Or6wsulYHB5rL8+eTl6KsjESQ774jw2rKFDpXQaBCZXp6NK9EkeadgcF/7IW5cOGCqK+vz3rV3wsrKyv4+flB0NbG/bVrcUNNDdeuXYOXlxckEgkYY/D29hZHentzWLECTgkJvLK/89ChQxkA1fh48YLEwKAg8HfvouLbbzH0t99QtnEjER1jY1WrKMaAnTsx/Zdf+IMff4yYmBh+4MCBUAoaNTU1OHToEPT09ISAgABuzfnzqNm5E2YDB+Ly5ctCRkYGp6GhAeuVK6G1eTM25eYyODlRCHxuLsDziI6Oxt27d9GjRw+h4TxFkby32tpU7yE1lYTMvXsBDw88f/4csT16oF/jsblmjap/dXt4/JjWDkNDIDcXXps3c7hyBT2oWn7Dg3NwcMDGjRvBA+BXrIB4/DieBQejpmtX9DYyYli0iASrzZuBmBiwkyfB8zyMjY2RnJyMvLw8tGirV1VFnvpNm2iuAEQg7ezIE80YEe2tWwEHByT94x9CYHw8LwVw9OjRhh7VUl9fqOfni5I7d0R1dXXIZDJRJpPBLjmZ7xoSQuv9e++p0pv69SMxdfp0WuP/+IPW3a1bae339sawFStY4aRJsAwN5XVOnsSllSvxtpWVKtXE25vGtzI6RBSJiDev+TB+PL1u38aq4cNZWVmZav1TitMxMao5Eh4O36VLuU/rx6sy1L8F7tyh9Wrz5iZvBwcHKwRB4FttI9Yc+vr02r6dRJKdO4lwb9sGXL5MKSJyeavdK3755RfI5XK2ZcsWCILQuAd107VXR4fWjKdPaX1oq5d4ZSX05HJ069YNpseOoS43F+p5eXSvjh0jwm5nR2KJRAI8eYLMPXtgd+QId3P5ctQ+fUr7u6YmhV8rFCSQbd5MzyYlhfKw16yhter5c1rHXtISr1evXmJwcDD69ev313q3CwoaohQakJdHQo4yDYbnSWzbupXshY7sRR3E+fPnxcjISAYAs2fPRguRtjkePKA9LiWF6vSEhKjG8KNH5JVvRVipq6trqMEBAH5+fsjPz0dSUhIA1AJ4D8BeABBFsczPz+//PNEGOsl2Jzrxl0MqlX5kZ2dn6OHh8deHMr0u3nmHFtf4eNocExLIGO9Ie6PGUCjIGKquJoNSQ4NClMrKAC8veB47xjxiY/FQVxeKqCh49ugBzbw8IgTu7kSYzMxo09LUJKOocbjlmDEdO4+tW2mjvny53Y+NGzeO79+/P6qrq3HkyBHGGMPEiRNfHr4eHk4GbkEB9VNOT3+1ezVqFBlIGRmqNjbff0/G3ldfkTd4796/JNQsJCQEjDEEBQXBwcGh9R7DrSE8nDbhtnKepVJ6rV1LRlNEBD1DJyeIoogLly6JL8zNmfnq1eCCgsAFBcFo1y5mdPMms8nPR8KJE4g+dQp9OY5zGTyYDPlXrdKfk0Ni0cWLTcP/v/qKhIL4+LaPFUXybikrXCsUqvQJW1voCQLKYmMRGRkJLy+vl9+32FjyGrxiyHp0dDQqKyvZhg0b0FBs6VVQXk6VuuPjyfNlZEQi2rZt5MlITaVrXLKErvn5cyKKyldhIZGAo0cpPHjWLHpfXZ2Ia/fudI8VCvoeK6tWQw3T09ORlJSEN954Ax4eHuzmzZsthAMuKQmDzM3Rp7ISu3fvxvbt26GnpwdRFOHs7MygpUXGaj1xaYFHj2je6OkBwcFIzsxEpba2WGhiwmQlJdDjecrjPXWKep4DRLzXrEGva9eQlZGBmpqahq/LysoCAKxatYrbt2+fWFRezu4dPoyC+/ehUCi4ZcuWNX3uFhYkapSXUwSDnx+S3dxgb2+vmDFjBpEWhYLWoxEjSNhQ5tNu2UJjPCcH10NCkG1jA37cOBqnixZR0cJBg2getVL1GQAdf/CgquBeUBB5PNtYi9QSE+m59uoFtnIljN3cmq4x+vrUDu/IESA7G743bqBi7lxcunRJMWDAANbg1T56lEjcpEnUq33xYhJKLSxI/EhMJBHUwYG6F8TGAh4e8J0wgS8+fVqMjY1la9asgY6ODpG8jAzAyYkhK4vBwIDWxIAA3LxzR2GQk8M3tB3ct4+8qzxPY5cxitYYPJjOPzyc/q2rQ87OnWJpfDwbbGQE7awsDBgxgu7nkiU0J168oFz+CRNovejRg/a9tuDpibAxYxRGdna80eHDtGdaW5OYkJysikhauhTcrFmwtrFBZmYm9uzZg82bN7fsKNCjh6r4ZiOUlpbC09NTZEqmLZeTiJuXR2MpNZWe0+nT9LchQ4iEPn1KooFCQQKMQkHkOC+P1uqjR+kzTk7AzJnQVFcXl//zn4zbuBHcd98R+fr1V1r75s6l6LaVK0lQPnGCwtV9fCgVZ9w4ElhkMppXBQUUFcBxuOXtjVXh4RDT0mheKoXwFStU5LKiAtDSQqyPj/hQV5e9NXkyjP38VDdh2zb6VxlB8PbbJI6sXElzY88eWrd+/rkjZJs9fvy43c/8KTh0iO5xY1hb0/1SFk0DaH4OHkxjuZnQ8rooLi5GTEwMpk2bhq5du7Zdh0KJn3+m+hVTp1L0U+M6HQoF2YCt1DQpKyvDgQMHql68eCEHoAMA/v7+jT+i5ufnt8/f3/8MgF5+fn63/pQL/H8AnWS7E534C+Hv768tlUpXDx8+/L+ot1czVFSQQnz1KhnoGzaQ8dQYStU/LY2MSxMT8lyIIh1TXEyb/rZtRLSVId329mQY2NuTh/zjj1GtpYXLP/+MW/UVsDkjI0EQBMyzsuIs/tM+l0oMGEAeB2WbrDbA83xDK6I333wTJ0+exNChQxsqsZaVlUFDQ4Pa8QBkRM6dS17cQ4deX52WSMhDEBTUtGesujptxAUFZEwoFC8PVX5F5OXlwdnZWUhOTub++OMPwdHRkXN1dW073BEgz82YMWT0/PZb+yRYSXI/+4y8SImJyMnJQXFxMQOA9B9+QG+plO6fmRng5QV9ALaZmQg6dkzhXVDAaezezWBuTka1vr6qcNfLCslZWZHRrzS+ARqjs2e33Zs1LIyM9C5daPwqRRplIaV6dZ/jOEybNg2nTp1CXl5e+2S7sJCK7P34Y/vn2wqUoZeSV2yxBYC8E+npNHYOHoSooYE7t2+LLhs3MqOHD8lA19Ym4rdtG3kIdXTo5eiIJt40ZZhjZSWNx7IyCucsL6ffuH6d5tf48WT0m5jQ59zdkWdqisDLl8UqTU324N49iBwHxhh7/PgxevXqRcRj40Y6D0tLaGtrY/ny5bh7965QW1uLoUOHcg0FJNPS6DciIlTXmZ9P3syrVynXtz6c9NixY4ChIevi54cuERG0jn3zDd2PegiCAIHj4LRgAV64uuIPU1PIZDKhvoIu5+joKADgli5dyu6YmOBNBwdcePRI9PDwYK0+8wsXyOsaEADIZND46iuhZ2wsnz1oEKy6diVyr6ZGxLTx+J00iQS1bdswcNcuxMbG4unTp+ji50fh1xIJEZxG594Cqal03x0dyXuorU3zqjlEkYjYiBEq8tbW+NLRAVauhJieDvOcHHChodB/+hQ6zs6cOGYM2LlztB7wPM2NjAw6x02bgLNnaa13c6P9oFcvGiNz59JcVyigoaHBRFFEQUEBDDQ1KaXh73+nNIjRo2kufvghnmtq4r6lJe++YQOl6airU44uz1Ous5cXzbPWIJVC1r8/EzMzhZ7+/ioliLxvFD1lZkaV/ZXCRF0d/TtoEIlknp7kQVWmWWhq4uG4cfDR16fnc/s23evvv1eR7cxMGgeGhvCMi0NmZiYEQcCBAwfEZY37hQMUSRYURGQ4NpY8jLa2eDszk4+/fh3Cjh3gRJH2sfPn6VwWLaJr1tWl9y0tSfzatYvaPS5cSOvzvXskgCmv6YsvKLUJoGdkaAjTa9fEK8OHs4kSCdXbUO4zlpb0/TU1RM4BCs9XKGgtAKhKv54ePeuDB+m9+kJgGysr8bVEgknGxlTsVDkeR41SXbuWFmpraxEaGsoAUIX29mBvT3NcIqE9ODWVBMRTp2hdamfvUkYTVlZW/mepYu0hLU0VadcYI0e2PkbfeovWpYiIlgU1XxGVlZU4ePCg4ObmBmdnZ9ZuNERxMa1H48dTYc2PPlLNZSUCAmgdaeWZxMbGoqKiQgOAEYDpAMIYY1+JoqisvncVAPz8/AoAdCAs5/8OOsl2JzrxF0IqlW5zdHSUNqlG+9+GkSNpo1AqmUp1PyCAjBKlcebrSx4POzvy5lZWErmZNo2Ot7Eho+olJEEbwIcffgipVIrKykqcO3eOS05ORkpKCiza8uC8KpStf86eJeW2Ax7iHj16QEdHR4yKimJDhw5Famoqjh49Cp7n0Ts7W+EdGMjLSkuR6+oK29xcSBpXtX0d2NvT/VR6aJTw8qLX7t3k/U5N/VOrlZeXlwsODg5cWVmZIjs7m8/OzkZ5eTmioqJgbGwszJo1i9Np7hl7+20yEPz96bzbyeVqwJYtJNwcPw7TXbvgvGQJ8nJyYPPttxS62Yy4mFpb460PPmgaHpyaSt6fjRtprLm4qAyBxm2OGmPBAvKwnT5NBv/w4eTNapwjl5tLlY2HDKG+tRYWNKYnTmz1O2tqahAUFCTeu3ePqampiZaWlu0PqOxsMg47GjVQj+LiYly/fh1vvfVWxwrDKaH0wu/ZQ9fx5Zf0PrW9Y3cAjPvHP4R+69dzGDSInqGxMRn5Dg6AmhoCAwPFW7duMQBYsWIFRFGkUERNTfLGAESilJg2jf6tqyOSXVsL3L2LisJC3L5xA27l5cy1uhpyXV2IhoaIra0VpXPnskgnJ9Fp8WKmFR2N6i5dIPbtC3UDA+jr62PChAktXeQuLvS8FQqa16dPk9BnaUliTiPhh+d5KBQKjBw5UnW+jAGxsRBFETExMTh//jwUCgWMVq8WzHv35tRyc0VjY2POzMwMtra2MDU15QASV3yTk4ETJzD35Mm2n/esWURi673vfUaO5LKio3Hqxx+x/sYNSFatAsaMgUzZXq8xli0DampgWlaGAZ6e+CksDFOOHxd7JSYy9OpFYdIPH9Izag5RpON//pnW6E8+oX7tzcdvWRkR+/x8ihLQ0Wl3jRZFEWlpabh8+bJYsXAhNgYHs1k//MDfDQ1FqVwOg8rKBs9jbW0tbt26JZpVVjIzZ2eYm5iAiSLg54eyefOQvm0bsoyNBe3AQOh+9x2s8vM5WwcHaEdGQpqbS2HPu3YRidy6VZUCsnIl5KWlYLW1MHjnHfJq/vvfqpPs1o3SihSKNtN26urqIJfLuatXr4qjR49uelMMDIj8KXsvf/ghjTNRpHnbtSutUevXk7j0xRdAv36oq6sTmbs7CRcFBbQOrltHAkHv3kTQIyMBxtCrVy84OTlh165ditzsbP7u2bNifw8PJvn4YxJnPv6YxKLcXCLSxsZA794wHzQIAXV14nEjI7yzZg1r6PjQHPn5tK59/TXtE0OGECmuq1OFKjf2FivrEtTnFY8YO5b7NiUFI2tqIGscHfDVV6pjNm6kNfHcOar+rqwL0djr2aymgKamJvT09BT379/nk5OTFd27d+fd3NyaRIsdPXpULCgoaHgmmZmZbedVf/EFebSVnS3u36drl8lI5Nizh6r2t4H8/HwAVIvBrnmY95+F9HTaI5vDwICirZrv8bq61FP+wgX691VrpzTCgQMHFFZWVmzs2LGtV69XIimJ9jpXV1oDJk2icdJ8/pw6RSS8FTg4OOA6iSrv+vn5fe7v72/SiGgDwPhWD+xEJ9nuRCf+KmzdunWWTCZbPG7cuP9er/aePeQN09QkBXbbNlI+ZTIy0hYuJA+fhQWFwzbO12qsVL8ilJ5iLS0tZGZmwsLCAv3+LK+2EsOGkdcyOpo2mJeA4zgMHjwYly5dgrq6Ou7evStM1NCA648/cs9sbLhSPT0xdOlSoVxdnTt/8KBoYGDAWVhYYNyrVCtvjN69SbBIT2+9tdGqVUQUMzMpjE9ZOOk/wIMHD/DixQvO3d0d/fv35+Pi4pCXlyeEh4dzCoUCubm53Pnz58V33nlHtWtXV1OIoZYWbdgDBxIR7ghkMuDNNyHNzMRoDw8U/fOf+Hn6dHHVwIEvVz+0tOi5ubqSJ6m8nIzxhAQyItXVSQDq1o2M4759yaCRSIiQp6aSl6pfPxoLABnsDx6QgazMaT537qWnUllZiXv37jEAcHZ2ZibN21Q1RnU1GV7KHquvgJKSEkil0pd7ehojK4vux+DBJNA0qugviiI4joOXlxeuBQdz7ubmkJSXk+eue3e6Z15eyNi0Cbdu3WKGhoYoLi7G3r17AQATJ05EYWGhoK6uzrm5ucGgteJIUinQpQuePn2KCy9e4FlVFazHjxdd58xhjDFIBQF49gzu1dWsKjgYd44eZQEHDqBP164oPnwYht98g1IdHeR37QqLggJIzczEritXsqcZGbD39gZnaQn5Rx9B8vvvJAA6OpJXq5VqzTzPi2ZmZkx57SWGhih++22h6/37XMnq1fj9998BADKZTCiqrubm2NlBb+5choKC1kOv33qrZR5mY/z+O/U1P3++4S2jN9/EgeRk+F65Akl4OH66fRvlkZGo1dODQqEAz/Po168fDA0NERQUJLq4uLBh/v5wMTBASv/+CDx7ljmfPg0WEUFez337Wu91GxBAAme3bnQOYWEtcyyvX1d5/qZN61AUTkREBG6cPInV+/cz9fh48DdugO/dGwUlJag4cQIGO3YAW7eiqroahw4dEgsLC9nEc+dwztMTfR88QEl8PPrs24dfnz9HnZWVaFNZySS9e7OaFy/Qe+NGhEql8Hn0CCmVlciOjoaV0nt64gSFcy9cCAAQzp3DyCtXkP3pp7BqHpXCcUQCMzIob7oRRFFEYmIifv31V+jq6uLx48ctyTZAx/72GxWw0tNT7RFKD/CSJSoC9fgxYGQE7YwM3sHHh9aWzEx63bhBz8DQkGp3mJhQtFhqKrgvvoClj484a9s2vNDSYg937oSnlhaRLUNDWofWr6d1y9wc4DhER0cjPS6OOTo6KmBk1FJJyM8nUj1qFP2WiYmqNklOjqqA4rVr9GpjnzasF4ujo6Nb7r21tSQm6OqS15vjSBSprGwaYdIGpk+fzp88eVIRHx/PR0dHo3v37g3RYtXV1UhJSWFqamrQ0NAQZ8yYwdok2sqUmPnzm77v7k4Ee9kyGi+bNrUqkhYUFODIkSOQSqUd6zP9usjMVIW8N4ahoSq/vPn6MmkSiS3377+00FtbCA8Px4sXL/glS5a0n/q2ezfNr8OHSZA7eZJqBjQX8ePiaI1Q1pGph1wuR2RkZGNnyGcAPvfz8yv09/f/O4AvAFQD0AVQ8loX8/84Osl2JzrxF8Df37+3VCrdP3/+fI2/LHTpP4VCQR4ZOzsKafrqKzJgt2yh8Nf8fFKyLSyI+M2bR0QrK4tyu8rLyYuWmEiG+2t6X/X19QUnJyeuVc/PfwKOo9DjX34hJb4D3m2lZy9+zx4siIpihs+eMW7KFJh+/DEzNTWFA8DL5XIEBQUJxcXFYlhYGEtPT1e88847vJ6eHvLz8yEIQsc89IyRl+HwYcodbA09e5JY8Ouv9Ax0dV+5srUScrkcN27cwIQJE6D0XPchDzU3YsQIREVF4Y8//sCECRNUN0qZ4/fkCRFYR0ciAK+SS66pCXz4IbSHDIH02TNoPHvGcrOzYdGBPqxNoKurKrg2axaFxMXFkWdo1y4VibayohDjgADKkf3oI8oXnDKFDJ+pUynNYODADv+00pAZPnz4y4vKnTtH3/0a0SzPnz9HXV1dx/vLX7hAXnQPD5qfjcZGbW0tLl++LEilUjZkyBCWlJSkuLl8OUYVFvKYOhVYvx5iSAgO798vdF20iJsHiN1v3GCXL19GaGho/ddfgFQq5erq6pCamorFzdNLGiEiIgL5+fnw9fXF4MGDVeGMHNfQQ1uja1dY9OgB4ylTxEojI5Tu2MHcfX3RtaQE1nl5KAkLQ+Jvv7HoI0egX1SE/N9+g2N8PNSqqmBQXk4pCitXtkq066+ZMcbElJQUdurUKQiCgDcsLJCxYgUSfvwRHxw8iJ3vvINl773H7d27F1UWFtALCyNDWBBa5p53706G6tSprbfAqU+DaIyS4mIMDAiAx5w5kJ84gQkAugwejLLx4xE9cyZycnKQkpIi1NXVsYqKChYcHIxHzs5wi4iATn6+kKyjw2WsWYNuHEdikrFx6+cWH09r9d695I1asED1N1GkMOepU8krfPVqm88NAHlDpVJgwQK4PX2KiwMHonTBAnTR1W2ICOmbnIzbiYmiRUgIy1+9GlqCIL6wtcXyt96Cxr59iGNMdBo1ih187z1RvmkTW5iaCu316xl69yZi/PnngLMzJowYgYP//rfc7sYNSa9Tp2i++PiQ91ZZ/CozE4a7dqFozBgxOCEBrmpq7Pnz5zA2NkZ1dTUqKysVbv7+3HMdHfHOjBliTU0Nq6mpYXV1dUwul0MURQAkksnlci4iIkLs27cvDcgnT+i+3LtHwt0XX7Td0lJbm/795RcAQPE//sESFy9Gbx0diozx9qb1xcmJ1qDCQhI83n+fxqqxMWZMny5J6d8fR8+cwXR7ewo7j4ykvXfuXJqz9vaUA+7vj/LychgZGQkNOf+Nn1FZGe3VDx7QfFeGcDc+3/79ieQtX07rYJ8+bdY86NWrlxgSEoK+ffs2bQ81ezZ572/fpmcDkHDZweKdZmZmWLNmDf/999+LhYWFTEm0CwsLG4Q8fX194d13321/kcvLo/Wt+Z4XH68KHV+1ilIo3nmnxeFHjhxBXV0deJ7v2Hr6unj8uNXfB0D704MHFEHYGIyRSDp5MolMr5GOdvPmTdHd3Z1ptJXWpezS4uBA4+74cdoLt2xp3V6LiqLx0szTfu3aNSEsLIwDIIC6uTQswH5+fl8C+PKVT/7/GDrJdic68SfD39+/m1QqvTZ+/HiNDhef+p9GeTnl0125QrliAJGYqCgyHufNU+VaKcPcyspUxcqePCGPAECfP3yYPI2rVpHq/9VXtMHPm0f9UL29W1YMr4ednZ148+ZN5OTkiDNnzvxzK4JNmULe+7a8x80wT18f5cHBgv2NGxzn68sQHt4i5FIikcDX15cHAFdXVxw7doxPSkrC3bt3FeXl5byOjo6wfv36ju3sixaR52DTprbzoF1ciHBHRpJ3KjCQ8otfEV9//bWgra3NtdW/Vdm2o0k7MDMzUsGVxHHLFvIAzZjR8R8WBGDDBnD//jdkbm7I//RTGHt6UuseZXum14GhIRmAAIXbFhWR0ZOZSUbFiRP0t6+/pt95663XbnN17949EQB78OCB4ODgwLU5rxUKmkNfvrrtcfnyZfHhw4dsyJAhAvcyy1AUSaBJTaVn0gpZuH37thATE8PNmjULMpkMs2bN4vfs2QO9W7dEBwsLVnT3rngtNZWVSKXcsPnzYV1WxvDiBcYaGWGsnx8EQUB8fDx69eqFzz77DIIgtFsZvbq6WgTA+vbt224VZTc3NyA6mqGqCg71JJwzMICBgQEMnJxgPXs2ysrKoK6ujn9/9RVKdHTgxXEiIiIYiotVvYPXrSOxr74AXUxMDADg6dOn7Gh9oSIjIyPFCE9PnvP2Rs6jR/IwFxfOedAgZlBSwtTV1RUFBQW8uasrkbzHj8nb0xg8T2Hry5e3vMfJyTRnG4dciiK433+HUXExYmxs4GVlBW0AiI6Gfm0tBu/dS+vh9esNz7esrAy5ublw+PvfwQ8Zwin8/cGNH0/rwtmzFOL83nvkCVMiLo4Kv40cSfmXb76pWqdyckhMWriQvGYuLq0/CEEgcqimRoJqcjKwbh1CoqJEpKczg88+a7ImOTg4oMvOnSw9PR0v/v1vcfTly0w9PBzc48fAkiWYs2EDw+3beM/dncHeXtUXPjpaJc4NHw6IIqokEhbn5YX+69cTIbx/n4pF5efTvO7TBwgLg0NqKnty9aqQlpYmZGVl8erq6qK5uTmTyWT847Vroa6vz1wMDKCjo9Pw0tPTg0QiQXh4OGJjY5Geno6EhATWt29fijoxNSVPooYGpUk5O7fbXk0QhIb/NrSyEkt69GCoqSHBr2tX1RqmpkbPIDdX5cWsF2LsDQygceWK+Ntvv7FNGzZAtmcPCRBKEhkSQjnTx4/DvU8f3Hr2jGuSYyyKtO76+BBpamt+JSaqIjGUOeFffEHrUSvH9O/fnx04cAA//fQTFi1aRMTM1pa8n809sWvWqNLKOlhPQl9fXywsLGz44XPnzkEQBGzatAkSiaT9Ne78eSKG9UULm0BPD1i6FIKpKZ66usLq55/BZs1qIkgpFIqGdUhdXV1JEv98vHhBe80XX7T+9/HjKYqiNZiZkQ3w1VcUbfQKInZqaipqa2uZVzOxrwEVFTTfZs6ktWD7dopK2LChdVvs+XMS7Xbvbvb2c4SFhXE8z4cqFIqhAGr8/PzEDp9oJwB0ku1OdOKV4O/vbwpgDMdxPSUSiSnHcZqMMX0A3evq6qxFUeQlEok4bNgwqZub239n9XFRpMV2wwZS5I2NqSBUv36UF+XuTuG2hw/Txjp3LqmuBgaqMPIPP1R9X2kp/VtQoKoiWlJCIcSiSJtNQgIt5IcOkfG1Zg2F9k6ejJEFBbz1jBk4feYMnj17BqM/sR0GGKONSDfqWwAAIABJREFUbMWK9r07168D330H86gomE+cyCE3t0NVpLt27QpDQ0Px+vXrzNjYmJswYQJOnjzJJSQkoGfPnqiursaePXsUJiYmmD17Nt+CQ2lpUV7cjRsv76fr5kbPzNCw3fDA1nD+/HmxsrKSW7JkSZtEyNjYGEVFRfjyyy+xbNkyWFy8SMba4cOqD5WX05h4FezYQce5uYGpqcHe1VXxYNEifrCjI3mMPDxa74v9qjA0JKMmPZ08Msr8bju79kOBOwBLS0tmbGwsFhUVcXv37oWvry+GDBnS8oOXL5MI9Yr5/NXV1Xj48CGbO3curK2t2zcKY2NpPH/2GXnQ2qg8GxMTw7m4uMC2PszWyMgI7733HiJdXdnVM2dEJpdj/rFjwKlT0FHm4P/+OxHLvDxwPA9lqy5ra2ukpaWxc+fOwdHREYIg4NSpUwAAd3d30dfXl8XHxzOAUkPaxa+/kljXeA1pBIlEAiMjIwg1NfC5eRNmrq6i2UcfMcTE0HM0MKDic2fO0Hrj6QksWwb1+rxmFxcXmJub4+bNm3j27BmviIwE17075i5cKMHChRQKa2QEsw0bWEhIiOjq6sqwYQON0dYQGdl6b+EffiCPVVCQ6r09e2CSn49LixcL6aGhnJmTExVoUnpIZ82icOGyMlpLd+2Cnp6eqjjh3r3gc3PJozdwIBVeGjCgZZG03Fwi2kuWUJukd9+l98+epeswNqZ1tzWiff8+feeUKfTZ27dJIOraFXXm5rhx7hwbMGBAq8WkNDU14WxjA+dp0xj27CGv3JUr9MctW+i8EhOpCnNYGL0/cSJFody/TwT3p5+wrGtXHhs34umCBeh2+DAR1b//nUSzigqKSuE4ODg4wMHBgaPTvo+YmBhxwYIFtIBlZJA3/1brBY/79OmDixcvAiBi8rufnzDhX//ijn71laBwcQEOHoTzjRvoEhHByfr1a1LwMCoqCmfPnm36haIIh6Qk5hAURILGiRPkAZbJyPu6fDmdUxui7oIFC9iePXsQevq0YgjP803W+/o9r3b3bkQYGQmaQ4cynucZsrPJC//11xTFYmvbPiFTphUosWIFedyvXKGCas2gLEJorq1NnvPcXBKVWlu/ysvpmhcvJluhA6iqqmJWVla4e/eumJycLD59+pSztrYWZDLZy8XEkSNpX24rksvHB0Hx8eLt+HjmamEhjIuJ4XhnZ1RWVkJXVxeMMVRUVAAAFi1a1CGiXVlZidOnT8PY2Bi9evXqWH/ux49J9GvrPD08aE60hUWLVONYme7UAQQEBMDR0VHQ19dvem2iSJ7+L76g565Q0Bg6cIDWhbbO8/p1eu7NWoZdu3YNAKBQKKb6+flVd/gEO9EEnWS7E53oAPz9/dUkEsmHEolkU/fu3RVdunTRkslkTCKRQF1dHTo6OjA2Nm5o7/Gnh0T/mZgxg0i2sl3MTz81FE0BQIvtl1+S4XDoEG2wpaXkUWqvYrWpqcrjqcx7A1QVTGfPVhVYUVOjV3Ex8O67sC8uxozr11Hz449kYE6eTJ4rd3cyzpcvf/3r7d2bjJBLl5q2gwKImM2aRQRmxw4KyXyFIm0ymQxLly5lqampsLW1ZTKZDCNHjsT58+eFtLQ0ztTUFOXl5TwAcffu3cK8efO4FlW/7ezIW/0yss1xZFTn5Kiqznp7v/QcY2JiEB0dzRYvXtx6zi2I4CxevBg7duxAXV0dfvjhB3jl5gpeNjZcwxGCQMLFq6QL7N9PoeeLFwNqaggJCRHj4uL4VG1tZERFiR47d0KD58XugYFcW5EPL8WLF2RISCTkHejfn8iDMnQyPr5FDtqrIj4+XlFUVMSbmJiICoVC7NOnT0vjrT50FfPmvZKHQi6X4/vvvxcsLCzaJ9qCQGGcu3YRUW0nzy87OxsVFRUY1sx409XVxdBJk4C6OoaffybCV1BABEdHh4yyiRPJw+jjQ0TSzAzDhw/HTz/9hKioKDxq1q4oMjKSRUZGAgBmdCTiobiYXu2hvBwF//gHpIzBZONGBgsLIqyurlQwb8MGej18CPzwA2q//RZd7t3DPB8f2MycCa5nT/Tv3x8AwIWFNS0wpqmJpKAgpFy8yI3Zv5+q7NvY0POztyfPTuMc4a1bae59/bXqPVGkNbKR1xNhYeQFP3YMI+Rybv/+/bh165awcOFC1TPt3p1eeXkUUVRVRd4mLy8ibd7e5F2fM4fW3UuXaN1UVtFW3r/ly0k81NOjtVJ5nlu3kqHdPK82NpZ+8403KHw5Lo6iP5Se6/r1PzQ0VATARowYQe+npVH0hJcXrU+LF9N6/vXXFIY9YwZ974wZtC5cv073UFOTRApAFWEC0PVyHG5fv46obdvwvo0NfdePP1Kht7w8qoL93nu0bisruYPqfMjlcpWH0sSEhL/WQuxBKRBKjOI4yH19uTgrKzg4OTV8uFxDAxEmJtC7ehVz5sxBYmIiHj9+jIyMDFEikTAnJydxipMT4/bvp3UlLY3u1VtvkTCwbh3dky5daJ+aM4eKXirFlUYwNTWFjDHRftMm/ufly8V3BIE1bgd26dIlRI4di759+4prd+/m+KtXaTxNnEjktiPr7vbtTQt1SaUUEfH55ySA1ItnSrx48QJMEDB85UoSgOpDvFuFnR2FSnft+vLzqIeRkRGioqKQnZ3NUB8VY2dn93Li+9lnRFDrw/ebQxAEJHp4IDkykr2pq4sCgGX87W/4bdgwyOVy9OnTB0qP76uEkD979gxPnjxBcXExQkNDMWTIEPj6+rZ/UGEh4OKCyspK3Lp1CzY2Nk1rbmho0FyYM6f14yUSKpa3dCkJbB3YB2/duoW8vDz4+Pg0vbDiYhLir16ll7Exzatu3Wg/bGtfEkUqVnjsWIs/mZmZidHR0QxAzktPrBNtopNsd6ITL4G/v7+WmppakKWlZY9JkyZptEVY/tdg2LCmrZEMDIhMN/eQ2dhQqGphIRXpGTOGNm0Pj9drR2VurlJNG1eWLS8HD8DkwAF2dNcuDI6Ohku3bqT2JyVRvvXy5WT86+iQwv/GG2TU8DxtLqtXk5HW2kbFceRJP32a1H3GyCP10UdklK5dS9/5mhXjZTJZgwcQALp3747Lly9zyrxXOzs7xdSpU/mTJ0/i8OHD4sqVK1mTtk79+5Oh9uxZx/K2LC3pvogiebe/+07V47UV3L59WzF48GDOysqqXQYok8nw9ttvIzExEbrHjiHM0JCL1NDARrmc2lAFBJAh9zKipERkJD2jgwcbPIMODg7s1q1bcHR0VERHR/NpkydDpqYmbly3joyreo9Wh5CZSWLJ7t0UUjt7NuV/Nm6lUllJgk1s7H/k3R49ejSflJSEoqIiBoBdu3ZNnDZtWtP7GRFB1Y0fPGh4q7a2FiEhIXB0dGzwnMnlcty8eRN9+/bFqVOnhBcvXnA8z7PZs2e3/Xxqa8moLy+nsP6XGGTl9V7aNtuHTZ1KESbKSsOHD9NLIiED3dSURKj6AnJdxo/HRx99hNraWmzbtg16enriwoULmZ6eHqKioiCRSHDq1KmGHtlthpHX1ZEnp73zz8qC+OmnCCsogM7HH8NI2RpPR4dIR3R0Q/qAws0NSWvX4uyZM/D08JAPz82VwMsLsLcHt2ABhVfPnk0EtpG37gXPA4IAi9xcEhqU1YI//FCVmqDE4MGUotAY8+bRMUoycPIkvS5eBGQydAGwevVqfPfdd1xtba2qdaAS5uYksIki5aBPm0brkVRKIsqsWRQ+e/s2eYTDw8lTLZHQWuHuTnM/Pp5CkKdOpXN5+FBV6KumhnJCt24lQqsMK33xolWjO/XgQTEhNJR5OziA8/am3OD9+ykqKTCQSP3QobQvrFhBBykjBZSezsYeT6XA1Zh41s9tLy8vpF+8KIZPnsxcu3WDuq9vUzFjxw56FkFBdK969YKamhoUCoXqM5qaFN1QXt5kP0pPT8fZs2eFiooKbtq0adB/8gSW8+aBvf9+032vuhriihWInTEDaWlp2LZtG9TV1UU7W1th/IgRvNa2bVD77jt2aOxYeFZVwcXTkwg2QCR//nx6TkqPsZYWzZeDB0ksaAXj6+qQZ2mJNMZYeHi46OXlxehUqhEWFgbtmhpkBwTwefn5YhdtbSYyBvbxx61+VwtUVdEYb743OziQl/jBA1oDZTKUlpai5NkzlCxZAlm/fpDevPnyIqJSKa2z//wnCX4dwJQpU5i9vT3OnDkDnudhbm6OHo2F/bbg4NCqQFpeXo5Tp04Jubm5nEwmE8elp4t2ZWVc/Ny5gsW//sWLVVWQamqioKAA+/fvB8/z2LJlC548eYKQkBAIggAfH582W4BZWFhAJpOJpaWlDEBDW1BBEFBaWtpQVK4JkpIQr62NP3bvFvT09PDo0SNOXV0dDkqBz9YWuHmzTVEIAAk4q1fTnPv73196e3r27InAwMAmKQ54+pQKxnl4EMGOiaH5evs2rbnt4dYtWhuUFd8bwcTERLlYaAFopw9hJ9oD/4+2CvN0ohOdAAAEBwcfsLe3Hzpr1iyNNgtR/G/A5ctkIOzZ07Ql0cSJZMS15SXV0qK2InPnUr7YN9/QRqijQ96YPwkyAwNUamjIr127xlksWgQjZ2fyGKxdSx+YMYM801paRJ7GjydD88cfiYj06kUhXRMmkIq/dCkQHEw5gePHk2obG6vyiBkZEQGfMuW1c3lbQ3V1NTIzM4X+/fsLrq6unJOTE2dsbAxbW1uWkJAg3rlzR9TS0mLGxsakuEsktCHWh1p3CMr8wPv3aaPW0GjzGm7evMm8vb1ZR0QiAwMDONjbw2DdOqSbm+OZnh50dXVhaWlJY2b06I7liz9+TCLIli1NqgVraGhg0KBBcHJy4vr27QsrKys8iopigzZuBGdpSQbJqVPthynGxlJonKcnhd87O5MINGdOS2NBKqXnbWrasv3KK0AmkyE8PBxaWlqoqqpCYWEhMzExQXV1dcPfhZ9+wvM+fbD9+nUEBweL3bp1Y99++y3S0tIQHh6OtLQ0xMfH4/Lly0hPT0dkZCTKy8uZoaGhOG7cuLYrnAcGEol/6622C9s0g7GxMe7cuYOqqqrWjVvGyFs5YADlyj5+TIKNUnBSFucSBCITNjaAszN4noePjw/69+/PlNE75ubmkEgkCA0NxcOHD3Hnzh2YmZmh1ev55hsiKMraD80RFQVcuADB2hqXLC2FrJwcpqWlhcuXLwvZ2dlCj7VrOfzxB503gJycHPzyyy8Y4O2N4cuWcXjzTSKvgkAE9f33yVtqYkLXW+9JtLCwQPCDByK/dClz8PYmb6yREYVzhoYSeV2yhO6TjQ0Jjo1ztu3saK5aW5Pnd8MGEpYaER2ZTIbAwED4+Pi07VljjM538GCKMtiyhYhsv35U06CsjMJBlZX55XIiwO+8Q8/lxQvyAtbUkDDatSu1sgoIoPmxbh2tb/PmqYhiXR2RVImE5s+bb6Ju7lzUzJzJtBmD9dKlKOc4yEaMADdzJoomTYKmpiaYt3fD9dXV1aG2tpbqO2RmNnj3GiM9PR3nzp0TtbS0mCAIEAQBz58/R0VWFl5s3AiD8nKWxnEw//RTaDXPO9XWJnIfFkZj38EBJVpaSE5OFvr376+6mVOnkvDYqPjU77//LhYUFHCzR42C7Y4dMNi0Cezvf285b7KzwQID0WP7dnTr1g0jnJ3hq6nJnHbu5Ey1tKDfqxd01q1DaY8euFpZif6jR5N4JYqUTxwfT+O4cVjuwIGUQqWv3zLnuawMpmfOMHHHDsRSq0umTEU58tNP8qqqKrbswAGmU1GBo2PHsqxhwxQmkyZxiRcuQHPKlJdHy5WX0xh2d2/5N1dXEsyLi1Hn4oId27cjISwMg27cgO7cubBuXryrLezbR/d71aqOfR5AYGCgWFlZCalUKpSWlnJ1dXWKqqoq7sqVK4o7d+4whULBCgsLYWhoSPd33z4aS8roika4cuUKkpKS2OLFizF69GhmMmMG4+fNg7GhIZdVWio4BwWxxO7dYWRkJPTq1YsVFBQgMDAQsbGxkEgkitjYWK6goACu9cLCgwcPEBQUJHbv3p0BwK5duwQdHR1MmjSJ1dXVKYKCgrjY2FhFSkoKu3LlCjMwMIBEIsGVK1eImKurA8uX47CxMSoZY2vXrmWlpaVCeno6y87ORlhYGGzs7aEeFUXrRzNBv7S0FHK5nMQ4ExOKDDE3f2nLyPT0dMTHx8PX1xeaGhoktB0/ToR93jwi3nl5FEXTkdD0CxdozWklBSIoKKg6Pz9fwvO8w5AhQ357+Zd1ojV0ku1OdKId+Pv76wH4eeHCherS/6AX4n8Fnj0jo615FeZ33+1QODIkEvJuDxlCxvm//kVqeq9er10huzm6devGVVZWCrdv38bjx4+FoqIi5uDgQAxJKlXlUY8dSwTT2ZmINkAGwJgxqmsdO5ZCqYKCKKdu2zZSmD08SHBYuLD9sPjXhKamJt544w1mbW3NmZubN+Rjqquro6ioiGVnZ7O4uDiEhYWJJSUlzM7ODlzPnsDf/ob8yZMRHBwMGxubl4e+MUbCgrk5EW41tRaVkQEgODhYtLGxYaZtVKRtgcRE3B8+HFH1+W79+vWjPPojR8jj8LIIgOpqMpQnTmx3XKmrq6OiogLx8fEYOmYMGSPp6dTSZdYsMmqVBrIoUgjn5ctESiorKe93+3YiK9bWbRNpiYS8gMHBLw/VbwOMMQwYMAD9+/eHl5cXQkNDERMTg0ePHiEiIkKMOnmSSa5fx2EbG4AxKBQKlpGRgaqqKtja2opdunRBcnIyKy4uBsdxWLlyJYyNjZGUlITBgwezVlt9VVeTKLRmDZGwESM67PVnjOHp06dibGws69OnT+uGulRK8zcvj4janDkU4aJM9QDo99aupTk+bBillrQSvq6hoQEPDw/U1dUJubm5TEdHB/bKdkSN4elJXtzWImOuX6cxNnAguNmzMcDbm2VnZytiY2NFmUzGJyUlcf28vCD95BMq/mRhAW1tbQQHB0NHR0d0cnKiASCTUbTI1Kk0L65eBQoKkBMYiJrVq8VLz56xq9HRQnV1NZednQ0PDw+oOzuTIBcWRgbns2cqY7+qikJx33uP7sfhw7Te+fhQmPeqVTS2mkUG1dTU4P79+y8PQ2WMvnfYMJq/tbV0Lv/+N43xU6eIPDNGc+DoUbr+3bupt3ZkJAkDY8cSsb53j1IDxowhUn7+PAkGmzfTc541i953cSHBoE8f3MzNFc+ZmbEkGxvEPHmCZC0txd3wcC4qKkq4e/cuKy4uFnr06MFqa2tx6tQpxYULF7j79+8jKytLcCkpYSw2Fhg9Gk+ePMHvv/8u3Lp1i4WFhaG0tJRFR0cjLCwMEXfvovzoUVh99hnCLS3xqGdPMdvcnA0bORJt7q29e1OkQXIy1HfuRIK+vvhG49BZd3dV9e166OnpsbS0NCErNBTmV6+yOHd30ap7d9Yi2iI5GRg2DJpqajCZNw/qSo+0pyfw5puQu7ggNDsbtbW1yMzMhLu7O3lEf/iBoiu++aZlf2RNTSI9N260zJH+5htAXx86U6YgPj5esLa2FpydnTnk5MBu+nSuQFubPZg4UUxxdRVramqYmZkZ6zZnDgvR0xMTL15EHc8zi+7d244aiYsjEaVxIb16VFRUQH30aAh+fpBYWsJ7/Xo86NsXId7eyCgvR0JCgtijRw/WIgKjOXx9aYx2797uWlRUVIQHDx4gISEBsbGxbPHixWz48OGcra0t4uLikJiYqLCwsJB0796dxcXFKRISEpCTkyO49O7NYf58CP3740BgoCI+Ph49e/ZkPM83eHHj4+NRXV2tcHZ2phP49VdoT56MbkePMvM9e+C6bRsG+PoyW1tbeHp6wtXVFb6+vigqKhKzsrI4uVwuhIaGKkpKSrjHjx+LT58+ZSEhIXjw4AHMzMzExYsXc8bGxrCxseGCg4Ohra3NCgsLmbu7O27duoWIiAgUFxcjKSkJBnl5UMvKQkLfvoJCoWADBw5ERESEkJqayikUCkEmk4kBAQFw5HmmZWTUEF0VEhKCa9eu4dq1a0hISKCQdy0tWvPDw+keMwZBEFBVVdVkfsTFxeH06dPw8vJSuFpZcbh1izzZn39O8yUwkNaBVas6VtclJ4fWlOXLW+33HRQUVF1RUfGuKIrbfXx8al7+hZ1oDZ1h5J3oRPswVFNTq5PJZP97mXZdHRmGP/9MHpjm2L6djMeNGzv2fba29PL0JAXVxYW8MWvW/Cmke8SIEZyxsTFyc3P5yMhIqKurt8g9bRUcpwpP3baNvFuJiWQIX72q6heanEzqvL5+q7l1fzYSExNx8+ZNRVlZGaempoaJEycyc3NznD17VkxISGCRkZGws7UVe9rbs/itW8VUMzOWkpKCKVOmdKyFmNLDra5OOXc+Pk28cDKZTFR6YF+K27eBKVMwND8fIaGhoiAI7M6dO6KDgwPDDz+QwNFWmxyACjn94x801jpQ9ExPTw+s3qjgOI7GVHw8Pbfhw8l47NmTCFpeHo3RYcNoTIeHt13BvTk++aRp3u5rQGnkamhoYP78+UhMTES/fv3w3XffMY+ICOSYmwOMYcOGDSgqKkJoaKjC2tqaHzRoEDMwMECfPn3QrVu3hroORkZGKCgoEOLi4pinp+f/x957h0Vxtm3j5z2zsJSFRXqRolhQQUQUCzbsNbEbe0vUJzHGxDwxxhiDMfHRdGONJUZN7LFhJSqKAgIKKGJBQEBFQJC+y+7OzO+Pi6W5CJjn/d7fd3ycx7EHCrs7M/fcc99XOa/zqmlBp6bSHFYqKdP6Gs/VxIkT2ffff4/Nmzdj6dKlhoM3n3xClN2DB4ka+sMPVfXbeug/t3QpBVoSEijjWksgztLSEiNHjuRu3LiBqKgoDKndG/nFCwrs7dv38nns2kVzZ9q0GsHASZMm8QBlsLdv345ilQpma9ZUOlccx8HNzU1UqVQveyCRkfSdeXkAY7i/dKnUQi5nXZ48wRsbN3J5I0ag6MMPYWlpWRWEmT+fMsDffEM0ZmdnWte2bKH1hOfJkfLyojrITZtIVM4AXV+lUoFvzH0zMaHsUlEROZG2tlR+8ddf5MRdvkwZXFEkh3/WLHKW58+n8VMq6f6tWkUOqL4H8e+/k8M6dSqVgVhakmOmR+vWeLRtGwOAuXPnwtbWFiYmJvydO3eQnZ3NdezYEdu3b2dXrlyRBEFAVlYWe//998HzPL7//ntuo0qFls2aCVk7drDHjx9zfn5+XJs2bSBJErXJEwRoK+jHchcX4P59jDYzw549e1hRaipycnLgri8VAJCfn4+nT5+C53moVCqoVCqUmZlJtjyP5vfvczXEIe3siAHw1VeVn29ZWIiPfv6Zw/37uDNrFkIPH2at2reHvb09MQBkMqojnzOHxnXHDho/T0+6vy1aQJIkREZG4sqVK7CzsxPd3NyYUqlkyMqiuRASYtAxAUBsijNnKOChD3A+e0ZzKSgIOp0OGo1G0kRG8rpt2yA7ehTqVauQnJWFRQsWsGbNmunnMgOAGQMGsPx27ZBy5QourFkjDhgwgDPocBcVvRTwSU9PR1RUlHjv3j3OpLwc3dRq+PzrX5Bv2oSPp0yBTqejvuoXLrCNGzdKH3744asd7tOnyak7c4b2UwPIzs7Gjh074OjoKPA8jzfeeIOztbVlAAmyzZw5k0M1ZfA+ffrwGRkZ2L9/P5Us3LqF69HRKLx/n5PJZNJPP/0k2traIj8/H5IkMXd3d7EysAYQ46ddO8DODtyuXbBOSal0ao2MjCp1Sjw9PfnS0lLBycmJl8vl3PXr10WO4zB58mS2f/9+aLVajBgxonJszc3NoVAoJKVSyQRBEIcNG8b169cPxsbGSEtLw/nz58U7p04hGeDy8/O5Dz/8EDzPo7CwkAHApEmTOCsrK2zevFm6lpyM4SoVYq5eRVpaGjIyMtC9e3eYm5sjJSVFKigooPZoI0eieNYsxK9cicDgYGzZskXIzc3l5XI5dDodeJ6XBEFgCoUC/ZRK/vns2VC3bQvno0cRn5AgPli9mms9bJhkFxzMLly+LNglJcHd3Z339vauO0hz+TKtZQb2UkmS8PTpUwWAv1auXFmHgmQTGoImZ7sJTXg10jUaDa9SqfB/LYW8uJgW07qETRSKhtfJVkfr1vQ6c4ZoTJ98QpHVCRP+kRMrk8nQpWIjd3Z2RkhICDQaDYbqs9b14flzCiroVTk7dybnWi/go1CQsXTkCGWEDNHuXhOiKCIqKgrNmzdHdnY2IiMjhZKSEj4wMJBr27Ytc3BwqNz03nnnHa6wsBAhISGiWq1mZa6ukk1SEpMHBSE1NRUnT54Ug4KCuKioKLi7u1fWmxmEXuDp4kUKJnz3XWWm18vLi4+NjRW7dOny6pssCJS1vH8fnFyOTz/9lMXExODixYtMkiSw69frH4APPiADs4GCZAqFAlqttopKB9B5t2hBbWgWLCAnon9/Ml6zsshha0jdX3Xoe8QuXmzY2WskXFxc4OLiAlEU0VqplJ63aCFl+/iw6RMnMnNzc5ibm8Pd3b2Gp+VpoGbczc2Nu3nzJjIyMkj5VpIoUKHTEbV4+vTXpr4bGRlhwYIF+Pnnn3Hu3DkMM6BGDMbIaF64kITH1qyh/x88+HJJg955XrmS5ll4+Cup+fn5+TVrHDMzqd6/+vsliWijd+5QVrGOgEhUVBQkSaK6d56nc7x9G+XGxsjIyOCGDx+Ol2qjP/6YSk8qjtd1xQr2i5WVZGJkxN5Sq0UnUeScfvyRrmXHDspEJiTQWrh0KQmY6bUYYmLonvTtS87ruXPUUufixTrZMY12tvWwtKSstSTROjV5MgWg4uLouDxP1PXBg4lOv3gxZVvHj6dzU6noPrZuTWv+tWv1HvLNN9/E7t27sWPHDvj7+2PkyJHo0KFDpRbFuHHj2LFjx8Ty8nLurbfOxob5AAAgAElEQVTeYnq2zty5c1F47hwc167lw9asgYODA0ZWZ49ERAAhIZCXl1MQoJqeQo8ePfD06VNp7969bNasWWCMwd7eHr/88gsAQKlU6ioESDlTU1OueMoUeMlklCGOjKQAGkABj+Bgum85OUC7drjctasYs2EDysvLufaAZBcZyfDGGxS8OHyY9gR9UMfVFZonT1Bmbw9JkiCKIkpLSxEWFgY3Nzdp8ODBnCiKyD1/HorPP8edn36CLi0NwsOHEASh8iWKYuVPHDkCrzVrpJDp00VBFFmXsDAmKy9nlx89gt2TJ7BSq3mtKCK6uBidNBocEgShddu2/EvimRWwTkxE5q1bED7/nIu+fFl0eucdTq1W48SJE2jbtq0watQoHnl5eNGyJfZv3izl5uYyjuMgCAI8PDzYgP790fb2bTQrKMDVdu0kcedO5tatG1q1bo0ePXqga9eu+Prrr9mZM2fwpl5wzxC6dqVxq9Zx4Pnz5wgLC0Pr1q3h7u6O3377TerevbvYv3//Bk9+uVwOk/x8IDAQRVevIjMzE1qtljk6OooeHh58bm6u4OLigkGDBnEcx9X8XpmM6PNDhxLbbvNmCjjXChq0bNkSLVu2rPysXuQyLy8PkiShe/fuNUpfOI5D165dxUuXLvH29vZgjFXWeleq5O/ZA9HODoMGDKjMPvft25ddu3ZNNDc35xhj6N+/P9Lu3BET1q7lLgweDDc3N0yaNKmS+RMSEiLu3LkTixcv5s+cOYNnzZpJHc+cYY/Hj0d+fj4/ePBg3L9/X+zatStnaWnJLl64ANtDhxCRkCDmK5Xsjrk58zt1Sio/fZobcPw49puasnwbG3h4eHDFxcUsJCREOnr0KPPx8ZE6duzIauxDWi09R7GxBu9LxTVr1Gp1GoD/y8WK/nfBJL2CahOa0ASDWLt2bcq0adNaurxGb+P/dXz6KTkms2f/zx8rKYmMxMBAMqpnz25Q+6z6cPr0aTE5OZkbM2bMq1txxMcT9TIjgzaPadMoy1o9O1xcTIZWRATVe6ekkHG6aNE/rtvW6XTYtWuX+OzZM04QBBgZGWHgwIHw9vauU5ClBrKzofrPfyD/6iv8uHWrVFZWxiRJgpWVFV68eAEAePvtt1HvPJQkEoZq2RKPFi3CiRMnRJ1Ox31UV52sHlOnksG6Z0/lr3bs2IHHjx+jR3q61Cc+nuUcOwZXV1fDUfL//Icyaj17NlixvCKjIX7yySdc5bn/+CMpo0ZEkFG9aBEZeR9//FI2tVFITiajvA6F29dBRkYGEhcsQGtzc7HFvn1cnYJkdUAQBGzcuBEODg6YNGgQ1c6dP0/qtK9iEDQCV69eFcPDw9mHH37I6qz7TEoiAaU5c8hhKy8nR7+uAKMkEevg3/8mFkK16z59+rQUExPDAGDRokVVCvgqVc3vKy8nQ695c3Io6yhPKCsrw3fffQeZTIbPPvuMfjlrFrB4MY49eiQkJibyeuEsuVwu8TwvDVCrWef58xns7Go493l5eTh58iTS09Px8ccfwzw7m5w3X1/KBI8ZQzXS+/YRe2fkSNJ+MDGhdXTnTqLSmpvTWBmi/1fg4cOHOH78uLRkyZKGRUtKSoidkpxMGb6xYymrPnIkOaoREfS+d9+lwJGJCQUJjhwhpzsggGq4ra0bzvioBkmSsGHDBkmj0bAlS5a89Hd93fVLc/zJE6IuV1dAv3+fsuzt2pEWgF6Y0gAOHz6MsrIyIS0trdIRUiqV0uLFiw1/oKiIAiGLFtF6360bzceICAoI5eXh23XrpAk7drB9b72FnhER6MpxMAsLI5X96vWwx48D+/ZBdfw41n36KYyNjaVqaxvjOE4CIDFRxOStW7nQvn2R0aIFHB0dRSMjI4njOHAcJ/E8zyp+guM4FD1/Lht34gQKVq+GkVIJ688/h/bzzyF3cYHRmDFgHTqg/PvvsXv3btHR0ZFr2bIlTp06JWo0Gi4oKEgIDAw06KiWzZmDmwUFYkT37uA4TnJ2duZTU1MxadIkPFu6VMoF2JOgIIwdOxbZ2dkwMTFB+5MnaY5ERwOSBFGrxa2hQ5FiZYUeGzaQHgeq1vopU6ZUiXtVQKPR4OzZs9A9f443LlyAbMgQYMYMiKKIb7/9VnJ1dZUePXrE8TwvtWzZUpowYUKjovfZ2dnYsmkT2gmCmGxqypmbmwtGRkZcSUmJJAgCJ0kS5s+fD9u6Spg0GnpeDhygvWPWrIZpi4Dm9cWLF8WYmBjO1NRU6Nu3L+9XLQBfWFgIY2Njw8mWyZOpxKeWyvtLePwY5UFB2LJwoTRw4EBWXVBVEASsX79ekslkyM/PZ15eXmh/6JD0pKSEJQ0ejEWLFlU9c4WFuDB9uqR8+pSlTpyIR4CktLQUOpw+zd/w8GB2zs5i20GDuPj4eHHu3LkcQOtnamoqjh07hmbNmkm2trbikydPeB8fHyEwOZmXFRbCuI42jAAQGxurb6HHNfXXfn00Zbab0IR6wBgr0mg0/7XvEwQBBw8eFJVKpRQYGMibmZnVXbP2T1FQQFS7VyE4mIzLe/f+2bHatydnOyWFWoYtXEh1u/r66teEg4MDYmJisHfv3ipjWw9JIgXgkhIyQvXtS3btMtzv1MKCMkIxMWTE5uWRmve6dSS+Vr1etZE4duwYnjx5wi1ZsgSMMXAc1zg2hIMDTPPygNhYDBkyRHr27Jnk4+PDWVpa4urVq4iIiMCBAweE0aNH8y2riY7VRlFxMbh//Qs3zpxB0i+/oH2XLuhhoI7vJSxa9BIddtKkSXjw4AEidu5k6ubNEffbb+jcubM4atSomsbUvn0U4Jg/v1Gtwezt7aFWqzn1s2cw+egjmidz5lAN2fHjJAZ19Cg52wEBJPTz228N/v4aaN2aHO2dO8kYex02Ry0c2LdPnGBuzjy++YYzRCWuDxU0S4R/9RXUO3fCZOFCOr/XbYNmAD179uQuXLiA8PBwDKpLCMnBgVggPj4kohgSQgyVw4cNiyAyRnXF5eX073XriD7L84iLi6v0Viqd+4ICYjzk5FD5Rn4+ZWNlMmKXGHhOysvLceTIESE1NZWXyWQYPXp05d+EdeuQ9dlnSGrZkn///fdhYWGB0tJSPHz4kMnj45n9J59gg04ntu3VixswYAA0Gg12794t5uTkcMuWLcNPP/0k3bx5k/Xu3ZucbYC0DyIjqdYxLIzWhdGjqW4+OpqCce7udH82baq3LEGtVsOqsFBCaiqDrS2tSxMnkt7F9u1US60XqZs0idafkyeJph4aSs5DcTEFD6ujXTvK3CUn098TE+l833uPAiV2diRYptGQs755M4353Ll0HR07UkYLoOcqJwewsQFzckJ+Xh4DY1i9ejU+++yzGqUHFY7lyxfq4kLH1mqp5nTz5qrAzaBB9c7lwYMHY8uWLUx/DKVSKc2cObPuAIWlJQVM//1vug8ffUTPcng4re3jx2Pk2rWMP3NGmjtlCgtp00YSPDxYf+Bl4amKLO7dx48x0NoagatXM0RGVhd1ZHjxgmHECODCBfRjDLt374YgCGz+/PmvXkDGjYNy/HiiNAcFwWTAAJprFy4AHAc5AG9vb3bp0iXpyZMn4vjx4/lLly4hJiaGC6ytq1IBs5070Qvgeo0bR/vbxx/j3Llz+PPPP9HaxIQNmTcPNhXsJ5fSUtoPhw6tEpxkDJyxMTrt3QtHPz/s37gRPv37o2vXrnByctI9fvxYdvbs2RrO9sOHD3Ho0CHJwcFBUhUUMPWFC0zx+DEwYwbu3r0LmUyGyZMnc/n5+UhKSmL+/v6NpuLYxcfjg99+Q9yuXdzoXr1gbGysDzawkpIS/PLLL1JZWVnd32tsTGtWeDhd77RpxDppACuI4zgMHDiQ69OnDw4fPsyfOXMGBQUFcHJygpeXF+piGyAvj+yIBpRLwdkZ8gkT8MHs2aw21Z/neUyYMIHdunVLMjMzE729veE1ZgznFRCAoWPHVu3JUVHAV1+h26xZ7MdbtyCqVADA3J884Xo9ecKUCxeiw8CBHMdx8Pf3r5ybZmZm8Pb2RmhoqPj8+XNOo9EwFxcX3IiN5eRnziDR2xvz9B1HDODhw4fgOO7WihUrmhztf4AmZ7sJTagffEP7NNZGeXk5CgsLUVxcjOjoaMnS0lKKjY2trFmKiYkBQL0MFyxY8Hp8UUOIiyPHd9u2+jecd9+lCO1/C56epJCrUpER9uWXFHGuoy6oPigUCk4mk8Hb21tCRR0b1GrKwFpaUkaK56nFz0cf1VC/NohvvyUjdMgQop+tXEkG9bp1tHHq6YmNhJGREdq1a6dTKBSvv66+/Tbw7bfwPnmSqy6a1atXL+h0OtHIyIjbt29fDRqaHs+fP8fevXuFwsJCHgBaBwSIM0JDOfP//Ier9/6++SZlpmvRvxUKBTp37ox2ZWVIk8sRd/o0bt68yRUWFkqTJ0+mPrGnT1f1IW5kW7znJ09iyp49kPr1I4Nw9GhiHJw9S5ndGzeqnOLQUMqi7dlD2a1GKOJWQhAoa9yhg0Exucbg9OnTaHv1KufQps3r14NrtfD4808UKJXiOXNzvDlqFJednQ2O42BhYVG/AnEDoF+7oqKi6na2bWyo7lWtJrr08OHkDN6+TZRtQ2uIXE7rRlYWOVhTpgBOTliyZAm+/fZbiKKIZ8+eoUWLFuTsJSbST72wXc+eVHpScX6pqamIiooSOnXqxDs5OaG4uBiPHj3i3nvvPWzevBmO+raBABIzM2F97hwm/vJLpTFsYWEBv9atARsb5P31F/ooldy5c+cQGRmJCgYfBwA5OTkwN8S4GT6cXoJAAR4LC3KM/Pxobm/dSmUNS5YQi0av/F1YSNnvoUOpfVduLrBoETQnT2Lk779zuHuX6uFDQshJtrGh50ySiPbq6kqOk55i36sXrUkA1VjrBdGaNydmR2goBS8uXaL3H6omEDx+PP1UqcgZMDOjNbKkhMZeL0wXF0f3TaGgkpPSUqB/f3xy4QKuyeVokZ4OFhtLFPUTJ6jeuFMnElPy96dSHYWCstaCQEEsMzNaW0eNous1M6N7LUm07j9/TsKGMhn9VCqBuDhY8jw+6dKFu7hjh8RKSlif3r0Zv307jcvZszQn+/enYHBhIY2TUlnVfULf933VKhJKbNsW7XQ6ICSEwdoa9hkZLCYmBl27doVFbYVwACne3rjr7o7uJ07Q9bZsSU7xwIF0n77/ntbHli3RApUt3ZhWqzUcJBdFCpAYGVEg4+pVejZWraI1raiIxsbYGAEBAczY2Bg3btzg9uzZAwcHB3HixIn1Gxv/+hfNzxcvMCQoCObm5vA6eRI21csW3n+fAiE7d778eWdnWIeG4q358/GbiYkUGRnJUOEPtKgVpD5z5ozQqVMnftiwYSwxMRFnb96UPAYNYp0rKPM8z4MxBhsbG/Su3lqtEeBsbGD18ccIMqDN8ttvvwnOzs6ca339vQWBAoChoTT/791rcDkTQH3cTUxMBK1Wy1+5cgUuLi5iq1at6mYr3b1LAeqGlPno97D09JdU+wGgefPmqGjNWfll/MGDxC66fJkYLGVlwPvvQzF0KGZ1746U+/fRddMmHLG35xARAZ96glrTp0/nJEmCnZ0dncytW+zprVu4ZG+Pw4cPS6NGjWKG1kUPDw/x/v37HYODg5UrV64srP9im2AITc52E5pQDyRJsqhXpdMA7t69i4MHD1b/FQPAnJ2d0b17dzRv3hw3b97E1atXkZ2dzdRq9X/FuAZAWQ+druH1nhERr+zV/FowNaWNPjOTMs137xK1vE+fRmXucnJyRJ1Ox8XHx7Os2FhhmKcn73b6NCn09u9Pxt/27fU72XqYmdHYnD5NhiFAmZJffiElz/79KQvVyPEwNTWtFEd5bfToQUGJ1NSXWmYNGzaMA4Di4mLh8OHDvJ2dndivXz/O09MTRUVF2LRpE3x9fVn//v1hbGwMuVzOYcoUMk4PHiSj9JtvXj5meTllwV4hxmb6xhtov28fBg8ejPDwcKSkpLCIiAi4A1AsWYK7c+aIPSZMaFhIShTJmL13D7lt2iC/c2epVUAAQ0YGjbu9PYnw1KYBWlvT6949yqTPmkX3sTGK8jxPDrsgGBQUagwS4uOlBXfvMtNVq17vC8rKiAng5IS2ixdz5/btQ+qPP4qlpaWVZQiffvpp/ar0DUSNnqwv/5Hm3Pz5RJH+4QfKrH77LQXPvvmmxlpSUFCAP/74QwTA5eXlQfH++yjZtg2f/PwzLk6aBNHZGXZ2dpUtdbBgAZUBXL9Oa03PnnT/qiElJUVKTk7ms7OzBZVKxWu1WpiYmODQoUOCs7MzU6lU3Pfffy8FBgbC0tKShY4ZI83x9a35vL33HiBJsNm9GzYA2rdvj4SEBIiiCE9PT2zYsAEAUUPrLMfgecrIPnhA8/HsWQomSBJll0aOJGc4MJBoySoVZf9Hj6ZgXbNmQO/eUD19ihOffy6888475AFduVJ1DH3msi4NBpWKGDt791LGXaWic/L0pHHUasmZVioNC3WZmpJzDtQsvejcmX4OH171uwEDoNVq8eLFC7wYOxbXDxzAPS8vvD16NExatKAgGGP0vKSk0Dqxfj05/B9+SEHK69fJKbC3pzFZtYoc9CNHiKLfvz8FWwoKyOHUt1S7e5eec1FE15492dXwcOnorVvMRKGQBvTowUzHjqX56OpKzBYTE9o7jIzI2Z8+HTh2jI53/Dg5xebmxNYqKAAmTcKQ8HBYpaUh190dJ6KjJYtOncQ3ZszgAaLO7927F2jTBsPfeIOCwk+fEsPAxoaCLEFBNYLRNjY24Hke+fn5sNNowO7cAWvRgpgQWVm0H0VEELXe0pLuBcfRfEpIoMDGs2fAmDHgd+6Ev4sLWs2YwWJCQsTnFhackbMzBQ1atKC9ytD91SvlBwUBNjbodfgwreNubrSnDRpE+9srNAOMfXzgOG4clr14weLGj8eJEyfQsWNHcfjw4TUWnA4dOvDR0dEYNmwYvL294fH0KZMWLcLOqChkOTujQ4cOIoDXV0Y9ebKqlMsAPDw8+NTUVOnatWuwsLBAx44dDZcx8Tzdc46j/eHYsUY52wAwZswY3s3NDadOncKTJ0+4r7/+GkuWLIHCkAZNZqbBvtR1wtaWgkUNRevWNJ8DA+m5Xb688niuDg5wLSqCpNEg29ERDzMzDXd/qHH4WhT82Fg4DxsGPysrKSEhgX333XdYsGAB6WJUoLy8HImJiWoAZgDsg4ODBQDiypUryxp+IU0Ammq2m9CEerFmzZrc+fPn29YQ+2kAgoODAQAffPABMjMz4eLigpKSkhp1x1lZWdixYwcAwM/PDyNGjPhnJyuKtGmtWFFvr8ZKXL9O9Mb09H927Pqgz6b85z9kJL3zToM/qo6JAWdtDbFLF6gkCRoLCzxXKHBj/Hix3zvvcK+s5TaEhAQyVmtH0iWJqGiffEKZvkYEBg4dOoSioqLKWqnXxm+/kTFayxnR48WLF9i1axeKiooqBZgEQYC1tbW0cOHCl1vcAGQIr19PRoggVDEMCgrISJ0x49WBmfJyMvo4DhqNBr/++qukTU9nHW/exD0vLzyvUN319/cHYwyurq6VvUwrkZ9PTnbfvpQt690bj/z9Efbdd9Kshw8Zhg8nBfL66t/0OHCAHI+4uPrbkdXGkCFEPTx5snGfA3Djxg2EhITAIy0NHTw8pC5fftm4AIskkROyYgWNfevWAGMoKSnB/fv34erqClNTU6xfvx7NmjUT3n333X8s8f/o0SP8/vvvWLZsGYmIlZRUiQbOnElzzdqanMgWLSgTV14OZGVBXLcOjxwdcb99e8iNjKCWJDy1s0OhUolSc3NIFXWqoijCLS0Nuo4dpYkAU/bsSTX8kkTO9fz5tNaMHVujL7IehYWFWL9+PSwtLQWe5yWNRsNNnTqV+/XXXzFu3DicO3dO4nkearVa0mg0XFDr1gjUt74yM6MM6KlT9EwbYFiEhoYioqLuWS6XY8mSJTASRXKu7t2j4Me+fXTd/ftTcOrOHQp6XblCzt1XXzWYBXTx4kVkZGQIs2bNatz9S02lNUAUyZkdPZocTYWCWELr19M5LVtG9zE1tV61eq1WC309sVqtRkxMjJSbmysqlUq+tLQUSUlJKC+nrj7GxsbQarUYMmQItSSqC3p9hR49yOGOiKAg2bVrxAKoFaDOyMhAdHQ09PX1zs7OkCSpUpSs4iU9fvwYmZmZbPny5a8WmLtyhXQmHj6kDLexMWXp8/IoSNGsGVBUBF1CAkJ//VWUXrzgHAsKpOcKBQu6dw9cnz642qqVkH3nDq9ycRFnrl3LITKSrmHfPppXs2dT0OLyZWISJCWhrEMHPPz4Y2iNjcEkCZwowmv9epjcu0c1/N26ARwHSZLw5MkTFFe0ULSxsUFl+0VRpHn28GGl05a8dy/upaUhyMYGirIyKr8KDqaA4vLlpKvg7U3OerNm9GxlZdHa9+QJBba3bKHyjBEjGqb58OJFpT7Bs06dsHXrVgBA586dpdatW7MTJ05IeqX/gQMHwtLSEt5btoDduwfVsmVgvXr98wTBtGm05lRTlK8OURRx8OBBqaSkBE+ePGGurq7SnDlz6l5zO3WiEoa//6b9xlA5WT1QqVRYt24dZDIZli1bZjjguXYt7WXduzfsS3ftIgbd+vUNe39CAq3NHTpQXbg+cFBYSE744sXA6NH44YcfEBAQQMr/DUVJCTF2EhMBuRx5eXnYsGEDWrRoIc2YMaNybG/fvo2//vqrxkc5jvt7xYoVDWzM3gQ9mjLbTWhCPWCMCa/MChlAdHQ0AGD06NGwsrKCVQV9r7bDnpycDAcHB8HX15c/c+YM3NzcJB8fn9fPjubkkBHSmA2wW7f/eUcboMXdz4+yFFFRtGFMnUo/DTm0kkRG8PXrMPn+e6BvX0glJXjh7S39/fbbUrdBg7i0ffs46dKlV9f4GYKXF9E1//ijZgabMXKwIyKoxnH3btpUGxDB7tWrF3bu3Mnl5ubWUDVtNLp1IydQFA3WFTdr1gzTp0/Hrl27UFpaioEDB6JTp04wNzevewz69qXXrl1k1Ny+TQ7K8eNEk5w5s+7zOXCAall/+gkAGeML332XSYMHo7hfP/ReuhSbNm1CYWEhblSIJMXGxsLFxQXW1tZgiYn0HcOGEb2yeXP6rqwsOH/7LXqfO8dw7BhlBRujvD1pEolWaTREt9261WCW++DBg2JqaiqzsbFBQEAA8/X1pfvaSMq7HpcuXRLNTE25sSoV5H5+jZt3hYVUB52YSFTlanNPoVDAv5pa87Rp07B3717+9OnTGDx4cJ01dQ2BXC6HWUkJSleuhPGMGVR6odGQQfrDD6R8npZGjBjGKBjh5oayn3/GL0ZG0rzNm1nzjh2Rz/OSXWYmG6BWw9jICC9KS9EsJwesWTNg7FhIffqAOTszBAfTtXbuTM7CrFlk4L39dg1F6upQKpUYO3asVFZWxvM8j9OnT+Pp06fw8fERjxw5wllZWYnvvfcez3Fc1ZiHhwOPH1OdcpcuVKdp6L5KErTZ2XB79AitjY3RrmtXGI0YQc/BF1+Q0+HuThRxmYwM3agoMnTlcspa5uXRPOvVi5xefXaxDpSXl8PY2Lhx2Yxz58jBd3OjANiIEfTslJdXZUjfe48ccFNTuvbiYsqUGnCuSktL8dtvv0l5eXkMoHZGGo0Gcrlcsra25pOTk6FWq9GpUycMGDAA58+fR3Z2tqhWq7mzZ88iOzsbbxjSe1CpaE04cYLW79Wrgb17kXPlCkofPIDZ48ewOnQIt/r0QUR0tFhWVsY0Gg2Ty+Xw9vYW4uPj+ZSUFDg4OEiMoK8JZ/o2hdevX4ejoyMM6lOIIj3ra9fS/WnblgI6hw7RWKxbR/Ng0SLI+vTBsD599Asp+/GHH5Do7Q37nBzoHj/m3/Lygkl5OYcBA2ht0rOLmjcnuvaZM3R9W7YA06bB7M038WTkSN0tQZDJnZyk0tJS9qy0FMaenoBKBe7KFXTr1g06nQ4XVqyAW06OGN6tG2dsbIwBAwZUXkJlUNTeHkwQcMrSEiOXL4dCzz4AaL7l59N4t2hB1PHwcJoj1tZUo//0KQWxNBp65tasofNvCJo1A/z8IG3ciBtPnwrm5uZs7Nix3J49e1hSUhJMTEyYIAiwsbGRrl+/juLiYhauUEg9e/ZknWSyxtkZhvDgAZW6veJ7OI7DyJEj2d69ewUAvFKpfPWau2IF7eEtWlAw+TWgDwgZGRlJKSkpLCsrCz179qxagwWBAk0LFzb8S/386D7VB0mi0oN16yhYkJ1NwaPVq6lERaWibPeoUZAkCcXFxcjOzsbevXvFadOmNSzQ/+OP5KxX2F02NjaYN28efv31VxYWFoZevXpBJpPBw8MD48ePR3Jysi4hIUEGAKIovt3wi26CHk3OdhOa8AoEBwfLOI6zNlTrZQgajQZarRapqakAAN/a7XNqoSLjzQICAlBaWoq//vqLOTs7w8bGpvEnu2cPGWm3bjXuc5JEjk5CQqPErV4bPj706tKFjCW9WM+ECXT88nIyzMeMIUMjKIg2mWfPwHJy4NSsGZsOsNLSUgDA0KFDGx+ckMspm5+cbJguznGUKTh1iiiKAQGUja9F6UtOTsbp06cFnU7H7OzsmEwmY5s2bYKVlRVmzpxZGWRpFNq3J/GfN9+sMztha2uLIUOG4K+//kJJSYnhGlRDmDGDjEiViqj3//oXXVd9qK6mLgjA6tVgq1fDsls3gDEsXrwYT58+hUKhAMdx2LJlixjy4YecnYMDhubmgrOyIidLT7394w9g/36Iw4bhj6lT0SklBW80NKNdHZ06ARV0umt79yIlJ0fgWrXibWxsUFpaitzcXLG0tJQbM2YMsrKycOrUKZSUlCAwMJAMmrffpoxRraDG5cuXkZ2dXVk/qdPpUFhYCBsbG+h0OjrewDYAACAASURBVDbMzw8WklRVhtAQFBdTwKF3bwpw1EMPd3d3x5gxY3DhwgXpxx9/ZCNHjkS7xtAiy8sBnkf5vHnQpKbCxtMT8pwcet4uXqyay337kvPSsSM53z16UI1gQgL2r14NY1tbZvLtt/CJjQXWrWPVheCs1WrKrl2/DpSUgD18SDWTZ8+SMzhwIDkHzs7kNJ47R4ybZ8/IUXzzTcr2eHgASiU6tGnDYGSE5ORk8Dwv+fr6Mj8/P65CHO3lNOeyZRQsmD+f5ra7O2X+CguJRpudTTTl06cxJC0NCR4eeC5J0tGoKDbwvffgMWJElQBRdDRdR3w8UZ8vXyaDd/JkyjBv3UqBiMGD6Tj1Dn85Glx+pNOR4xQfT6yCHj0oQ+npSWPXogWJoAF034yNaQ4xRgEBtRrIzobE87h//z4uXryIgoICaGltZW+//TYEQcDTp0/h7u4OJyenyslXvWXa2LFjAYCLjIwUz58/z8XFxb3sbMfFUbBm40a610ZGQFoaNDt3Is3TEwqVCic3b8bEPXtw5+5ddBk6lLn36sVSUlLQrl072Nvb81ZWVlJ4eDibO3euwbU7PDwcoaGhFbd4Wc1xFEV6hlasoLECiK69cCEFTYKCaEyWL6d5rlDQHAeQmJgoFRUXM6+AAOj3XBO9NoYoUpb5+HGaQ0+e0O9+/JGo6Xl5wA8/QHf2LKLlcllAQAAGDhzIjh49iuzs7MrTe/ToEe7evQutViu5FxayIKWS81u0CAcPHhRiY2MlAKjOKNVqtUylUvH6LH8N8HyVwKmeveXrS471zZs0x2/epMzn8ePknL/9Nu2XoaGkeTFq1Ku1Unr0QMndu+CPHuUX/PknFBYWeP/992FlZVU9o8sqzhVPN21i4k8/oSw8nBTeXxeCQEymH3+s0hqoA1u2bBFLS0v5Tp06oS7xuEqMG0fBVFGk9SEiotHlQvpAiEqlYocOHYJCoZCuX78uBQUFcV26dCFbSV/i0FC0aEGJhle0S0R5OTETs7IoKCuX03qdlES0+C+/pLaEX39N5wmga9euupiYGBkALjw8vP66+fJySq7Mm1fj105OTrCxsRGuXr3KR0VFoU+fPvDx8YEkSbh3755+0TdbuXKlquEX3QQ9mpztJjThFWCMzba3ty+Xy+UNkgtfs2ZN5b+trKxEVIjy1IX8/Hyhffv2PFAlllan+mV92LGDjI8Kw6LBYIycSZ3u/4yzrYefH7B/PxkLv/9OlDq9uviECbQpZGTQJvX33y+12cnOzoapqano4ODwerTtxYspc9S3r+G+4IxRfaafH21yHEcOo48P1q9fL/E8LxUWFnKBgYGcvb09S0tLEwGIPM9zDx8+ZHFxcVJQUNDrsRTGj6dMSkU22RDatGmDzp07S9HR0SwhIUEcM2YMV7tly0vgOHKAEhKIgp2URMd5FUaMoCyyHsHBVbWJ1YwGZ2dnMqAOH8aioUO5sp9+QmiPHoj44AOpV69eDGo10WNTU6kecvdumDRrhtmZmfjjjz+g1WqlcePGNX68LC1xePJk6VlaGuZt2cKnjhsn3ejZUzQ1NYWvry/fsWNHWFhYoG3btmjRogX++OMPxMXFCR6OjnzHQYPw9Pp15L94AX9/fyiVSiQlJeHy5cuQJIn7+eefBVEUWVFREUfDx0EURab8/nsJ06ezBimaCwIxCk6dome0EQ5z+/bt0bx5cxYWFobDhw9DqVQKixYtqptbm5hIgZS0NGr798cfSLO2lq7rdCzTzQ1mFSUrL4Hj6J7qjdLmzQEXFwyZNw+aTp1geuoUBciCgsjA1wcfTUzo+axN1fzqK+DRI7rm8HAyGM3NiWI8ciQZjhcv0tx64w2ifbZtCyxZgtI//oDp0qX4V24u4959lxyrRYvIad68mRSo//iDxnXBAsqMRUSQAxwcTHXFjo7094AAyq4PHw7e3h6dAYiiyI4cOYI9t25hsIOD2M3CgsPXX5Mh3L8/OSvz51Ng4NQpclzs7at6u3/xBQUD3dzoGuqoldRoNDA1Na1/PufkkPHs6UkOpL09ZfxKS8nZV6nI4a5eKnP0KHDlCkotLVEsipArldLtrVtZ+bFjiOjVC3K5HDKZDK1bt0avXr3gVKHHYKjcxlBAoHPnztz58+cBGi9yukSRAibu7rR2VuvckFBUhPBp0zDgrbfQzs0NrbdtQ/m9e5ilVAJt2jAMGoTmGzdWrhe+vr7swoULyMvLMxhYzsrKAkBsj40bN2L+/PkoKyuDpaUltPn5MO/c+eU2gPPnUy36unU0V7dto/n2/ffAvXs4bGkp3HnwgPfy8sKYMWNevu4DByhT/+efFBCKiKDrHDmS7kVYGFBUBNnEiXB9913B09OTNzIywsSJE2t8TXR0NKKiosR27dpxATNnAkolrADMmzfP4HOrp+rOnDmz7taWOh0xLXr1ovVj7FjKdP71F9GSAwPJgerTh+bQoEEkSPfnn5Td1bc8mzvXYMlBXu/ecNy2TVIcP84wbdpLDDw9jIyM4D5mDJITEsRTKhU3ID+/zvfWC46joFwDgqxmZmZSaWkpbG1tJVtb2/qfqehoClJ0705dMgwIr70K5ubmWL58OR4/fgxra2uYmZmxo0ePslOnTqF9+/Ywe/as8W0oLS1p333+3HCHmFu3aG3r25f2f/38NDKiYOTJkxRAqjXfhg8fLhs+fDhCQ0Px4MGD+p3tK1eodMlAmeHChQv5srIy7N+/H2FhYQgNDQXP8xAEATKZ7M7y5cubHO3XRJOz3YQm1IHg4OA+xsbGP48ePbpBEto//PADAMDT0xPdu3eHh4fHKy3xoqIiFBUV8R4eHigrK4NKpcKgQYNq1KlVzzrUiWfPaNO9ePH12xlt3/5faYX0WvDxIUrg1q1Eob5/n2pHP/mEftepk8GPKRQKaDSa1z9pIyPKav/550tR3hpwcaEs06lTROvq3RtFBQVMkCQGAB06dGC2trZo165d5bkIggCe51+/HGDyZNpcXyHiJZfLMWrUKNa/f39s3LiRu3TpktS6deuGHbNjR8rqFxbS+J4+bZgqL0lk/MfFkbOxaxcZfAEBNbP8evGkAweAGzdgvGMHjDMz4ZWYiOPHjjGftDQojxyhzO7MmTUcFFdXV7zzzjvYsmULS0tLe0kNF6BAVHh4uODk5MSNGjWKVResOXPmjPTw4UM2d+5cyOfNQzudjrVbsYJHt26U5agGd3d3LF68GPHx8Xx6erpw0suL9VqyhBWMHy/9euMGJ4oizMzM0KdPH8nDw4O9ePGCLy4uhre3N27cuCHZ2Niw1ra2sDh0iNU2egwiI4OMqLNnyZCqpqjdUFhaWuKNN95Aq1atcOjQIb6srKxm3/a4OHJCv/mGnNcpU4gOfe0a4OQEzy5d2IFvvoGFhUWVmn91FBSQQxoXV/P3jCFu7VoxMzaWm3roECxHj6as2qVL5IC8ar0QRRLlu3+fnF6OI7rr339TwGX16qr3PnlS+U/NhAn47ptvYBYUBAczM2mGqSnDV19RoE2no7mnUFDGR8/OUKnIGZo8GQgMhGhpiVPnzgEAMjMzxS4vXnCZt27B29sbbdu2BcdxmDBuHB789RfylyzhCouLody1i9YhQSBq+ejRVcZ0WhoFBKrT+BUKcsSaN6+z3EOj0aBeyuv16+Q4e3tTwIHn6Zn74gtyEJRKGsPg4JeOIe7cifL0dGx7+220ychgIz/5BDpbW3Q7cACWr+sAVUAul2Pq1Kk4ePCgVFZWRs/b2rV0//buJcp2BXQ6HU6dOiXNjIpiLs2bA/Pnw/j4cRh7e9M43r5NDKUvvyQnKCSkcv5eunQJ4w1kNfv37w9TU1OYmZnh6tWrOHbsGJKTk9H1+nU4ZWWhZVgYlNWfAYAc+W7dqAZZr/gcGEhMoQcPMGzWLF7t74+HMhmOHDkiWVlZMV9fXzhbW9MzYGtLjh9jFFzMz6c17uJFICUFSatWwebPP3Ht3Xelx3I57z56NAV7agl7BVDWnG7W5s20v4SH1znWGo0GRkZGsLW1fbk2+OBBcq6zsmh+5+ZSttrKioLRCQl0fePHkw7E5s0U+NqwgZhZw4bR3vbrr3QOrVuTEOjChRTEqljDbRwdcbpHD9YxIwPcgwevFgdt3hytsrM5bWYmLm3ahHGff173e+tCXh6tJbdvN+jtCxYs4NeuXSsVFBQYXsNqY8MGeo4ePaLgQ9++9eoa1IaeRq3HiBEjkJSUhEuXLmHEo0evJygrihRIq+5sSxIJBU6ZQoGhIUNqfiYvj57/jh0NMhQkSUJ6ejri4+PhXSshYRD37r2yxamZmRnmzJkDAEhJScHjx4+liIiI2xqNpmuDrrEJBsF/+eWX/9vn0IQm/P8OwcHBPXmeD500aZJpQ8S38vLycPXqVchkMixcuBDW1tavVBEWRRF79+4V7e3txYCAAI7neTx+/FhMTExEeHg4CwsLQ1hYGK5evQqO4wR3d/e6v2z7dqIczZ7duJrX6rC1pYyFgbYU/yOQJMroTJ9OdLeAADIoJIkiup07EyWwTx86NwNjaWRkhCtXrqBZs2Y12gI1Ct7eZFS1a/fqsWOMNtdOnYDsbPT5+2+0nzwZDwsKxOjoaNauXbsaDtA/VpCWyWg8ioqqMmp1wNjYGI8ePRKeP3/O+fv711/bW1REWbR336WIeWYmGSNpaYYj7kOH0jhduUJG2/TpVcrlT5+S0depExms69bRPFQqAcbgUF6O5l9/jdzYWDyZNw8O06eDGchkVfSal06fPo1evXpV3ghRFHH06FEpJiaG9e/fn8vLyxPPnj3LJSYmChqNhrt06ZKUnJzM5syZQyqqJiZEeU9PpwyCmxvNnWqaAEZGRnB1dYWPjw8XEBDAHA8cYD5TpjCPPn3QpUsXDB06FB4eHszKygpOTk5wd3eHqakpPD09mZOTE+Tr1xPNv751ITqanCY7O3JUDLEnGgE7Ozs8fPhQTLt2jXm3b08OYGIiXe+zZ3QPly0j5oKZWWX2+f79+0hKSsLYsWPZS4q0AGWdS0rIMK+FNh07svtZWaLrsmXscUyMmPqvfzGb6GjIQkIMipwBIIrruXPkSG7dSkGGiAiiX8+bRxmVrl1p3tQS4NIJAmJv3ICK51HAcSwqJkYy69yZOXXoQOtAYCDdY39/MkJNTMjRuH6d6ORmZki6dw8XLlxAVlYWysvL2YMHD9CsWTMhIiKCe5GXJ7ZNSWHYvRs2jx7hllIpZX/0EVoGBjLwPNHao6LIMdQ/zyEhlJWvvTb26EHOgp8fPUu1gqI3btyAra0tq26w18DWrVQPPGkSHVe/ZkRE0PM5aVJVOUtCwktG+H13d+wuLoaVkRHmrFkD4z/+gMmQIZDzPNV1T536jwKoCoUCWVlZYsK2bZzXl1/C6KuvKHtcrSZerVZjw4YNoo2NDXr17cs4IyMqD3rrLXJuYmNpbbWwoACbIAAtWoBbtgy3mjWDwtoaHQxkNs3MzNC2bVu0bNkSxcXF0p07dxgAtHr4EE9cXGAXFIS8vDzEx8eL7u7uVaKQPXoAajW0koTNBw5I165dg4O7O2vm4wNjb2/4qFToDCCupES6l57OCgoK4Lt8OQU29bW+eugV1T098duJE7oYjYZzGDgQXIsWmLJ2LTMKCgJbuJDu19WrhoPCCgU5v6/I3sbHxyM/Px/u7u7IysrCk0uXoPn8c1w2M0Oz5ctxOyUFpywthag+fcSIqCjx2o0buHjhArPasEFyMDNj+Pe/aazlcgqyzZwJKScH2itXwEpLwXr3pjk6blyVkKS9PQU9f/8deOMNGBcUIPLZM6kkMVFyjotjRv37G1ZBBwDGwNavhznPI1mrRUhamnjr1i2mUCgarlVSWEh76tChDXo7YwzR0dGir68v3+C9/oMPiDGi3wMaIhj3ChgZGUGhUODatWvodvYsyseNg3FDRWj10OloX9LPlfx8qrE/eZJetc/x0CFiJXzyCTB7NqS33sIRjUYUzM2Zg4MDysrKsGnTJikuLg6+vr5smIE1vAYePybW0cqVDVobjIyMsG/fPiYIwkYAEf369RMad8FN0KPJ2W5CE2ph1apVoxljp0VRNL59+zYqhEJgb2+PtLQ0KJVKMMagVqvBcRx+//13KTQ0lAHAlClT6qVViaKInTt3CmVlZdxbb73FGRsbgzEGX19f1rNnT+bm5oYePXogICAArVu3xrlz55idnZ1hQ3nPHqpVnDv3n2WmZ8wgI6mR0d/XwqlT5JAFBJDBfesWUcjVatp4goMpiu/oSEIwa9eSIVtrI+J5HmZmZggJCcHt27fBGKu7nU9dsLSkGih39/qdJ4CcyPbtATMzmB85gu5eXiwsNxfOLi41Wmb8V6DT0bh0rT+grFQquTt37iAsLAypqak6Pz+/uieDTkdzZciQKlp5aSkdp1MnMvT1OHOGjAOtliLys2dTduT5c8rWvvMOZSiCg4nepnf009OJbbFiBcyXLcPJFi2km+nprEePHgaDAdeuXUNERISkVqu5uLg4ISIiAuHh4dKVK1eYKIrSlClTWJs2beDj48P5+flBEAQWGxsLS0tLafz48eylsQ8IoMzNmjVkdL3/ft3BlOnTAYUCVs+fw7Ke9il49oycx1mz6q7XKy2l8VCpaEwMCUw1BhoN0VtVKrQ9eZK1Wr8e13x94eLmhlhvbzi/8QbYsGHkHNa6xnPnzknnz59nADBo0CDIawsRlpURtf3TT+scH68OHVhMq1Z47OjIuM2bpUtOTvC/fJnU42sHENRqojomJBCNl+Mow67TkaOsf7+jI/0/KoqyUBVGokwmQ0BAADw8PDBq1ChotVp2+/ZtoUuXLjXn89SpVH7y5ZeU8fHwoGM1awZ7e3sUFRVBrVZL//73v1lAQAD8fHw4t5gYOHz1Fbv+5IkUp1CId3v04JIkiQ0ZMoQpcnIoi7hgATmU1dfSNWvI8dUrSVeHgwPRzzt3prWs2tyOjY0VHR0d2UvBWq2WsqF2duTwVFftf/6c1qNRo6padxkZ0XNZqzWfjaMjirZvx/CDB2G6dCllxtPTafwHD6ZgSElJo9osVgfPcbDNzubEv/7C0zZt4DFrVo3re/jwITZv3gxra2vMnTuX41u2pMBp7950zPDwKpX7jh3JSe/WjYJ927Yh3cVFaMdxnIOv7yv3nbZt27IXjx5hwIYN+HvQIGS7uSEhIQGJiYl49OgRMzY2llxdXVl6ejrSMjKQvWULcn/7DXHNmzNTU1PJ1taWubi4AB4eYD16wDg0FC22bWNWWVno1b07TJYtgzh9OlTl5SguLsaLFy+Qk5ODImNjZMhk2Hn0KPLz8zlnZ2ep0MJCyFGp8LB5czwoL0fB5s3M9uRJGJWWErX7xYua2Ue5nLLQrxBj9PT0RGRkpOTy2WcsPS1NeqJWS63CwvCwVy8xd+RIiXXvzrk0b855enpyXl5eXMeOHZnlsWPgtFrm8tNPdIziYuCjj4C5cyEWFEBydsZGX19E2dlJnfbuZUZffknzWi6n9drVFamdO+OuRiM+z8gQnf79b66LpyfLtbaWSs+cYaZubpC/yjnt0gVG06ejTb9+aNayJYuNjcWdO3dw584d0dzcnHEcV5N9Ux1JSeTkN9L3uHz5MnNxcWHW1tYN00Lo04eCe15etCbXV2LVADg5OUFMSxNkR45wm01M4ObmhmaNEdrMzqYyqp49KRgbEUFB6h9+ePk5LS6GOG4cQhUKobBlSwaZjGUaG0u227Zxx2Qy5OTmSqdOnWIKhUL64IMP6i8hA2hfGjy4TpHK2tDpdIiPj9dqtdqBAD7r16/fa/a5bEJT668mNAFAcHCwA4CexsbG0+Ry+dAJEyaY7du3DyqVCnK5XCovL6+0RO3s7JCbm1vj887OzujTpw/a1pOFFEUR27ZtE3Q6HTd79mxW54ZUDWFhYYiMjMSMGTNgYWGBsrIylJWV4UVSEvxmzwZu3gTn6vp6F67Hn3+Sw1mf+MjrQqulLN/9+2RoJidTlPX77ykbsXVr3X2yr10jJ+fHH6nOeNSoGsZZbm4uYmNjERMTg7Zt2yIvL0/w9/fnX9m2pjoOHKDMS0XvWY1Gg6Kiopf7UtbGixd4PG0aSlJSkLZmDYaNGdOw4zUUWi1F/vftM2zo10JoaKjw+PFjPiMjAwEBARgyZAhyc3NrBgH+/ptokYb6bT97Rkbhe++R4I6HB0XUc3LI6dfXL06fTs5URAQ5aNUz1YWF5LSkpVFWaN48wNgY9+/fx/79+zFp0iR41TLiRFHEt99+K3Xv3p1xHAeFQgGdTgdnZ2eYmJiQovnrMjYAyr7HxxN9+fffa4q96aHvR3727Ku/KzycHNTaVD89bt2i92Rk0Bg2UFjxJUgSifyEhFCv6/HjyYnq1w9Hzp8XE+/d48zMzFBWRu1Ovby8YGpqitTUVKG8vJyXy+WSi4sLS0pKAgD07dsX/Qz1dA4Npefx7t36z0mlQnnv3jjTvDl8vvsOnosX01zRZ1NKSyn4V1hIhn/1zFhdokDHj9OcXL+eShRmzqzh6MbHx+Ps2bPS1KlTmaura1XrnKVLyXjWO3+rVtHxli0DAGzfvl1QqVTc+7NnM4SHA3v2QDQ3R2z//pKVvz9LT08X79y5wwYNGoQOHTowvPkmObcbNtQ8z0ePaHxOnHj12GzdSs/Uo0eVn9+8ebPg7+/PB1SnbD5/Ttf45pv0HNWmhV6/DsTE1FQ5HjGCxMhqZcjLyspw/MgRyfjkSTZu/34at7w8csy/+aZqzU1NfT0tjtmzob19G2tGjIDEcVi5cmWNP69evRrm5uZYtGgRlT6JItC2LcQZM8CZm1Mg5eJFosOXldH6PmoUrTH+/vjphx8wd+NGWIwZQwGNV51jZCRUX32FhBUrENCtGwoLCxEVFYX4+HhMmDABrVq1wvr16yWdTieZarVS+7w8PtPVFeVmZmJWVhbHVbTiAgCZSgVWXo63t29HrqMjTo0Zg2K5HDzPg+d5ied5SSaTSY5ZWeh5+jR3aNYsKBQKZmdnBzMzMygUCigUClhYWODUr79idmkplIsW0Vp34wY5vvo5/O23NKfq6vZx8SKwaBGCJ0xAv4sXUejrK/JDhkgjRoyoO/qwfz9yjx7F0Q4dhHlffEHvKyigEovERNy4cQPXTpwQ5y1bxu3evRvNra3F4Z6eXKGVFdI3bpSEqVMZYwwhISFwc3NDWloafLy8xILMTEwrKOBUmzdDplTCvHdvWv8NZeXnzyeWQkYGcP48RFFEcnIy4uPjxXv37nEA0KVLFwwaNOhlx/iXX+i6jx6t+34bwPnz55GQkCCq1Wpu+PDhor+/f/3ZhQsXqvRW1q+nQMM/xd9/Q5OQgEve3kJUVBQ/ePBg9OjRo2GfTUmhNb1DB5oXc+caFtlctQoqAN9ptZCMjGBmZiYKgsAgSZibmcmKra1xrW1b2Nvbo1+/fg0LPjx/TsHxP/98tWBeLaSnp2PXrl1gjD394osvGpnNaIIeTc52E/6fRXBwsLVcLl8nSdJIURStXFxcylu1amUREBDAai9e165dQ2RkpOTj48OioqLA8zwGDBiANm3awMjICJYNVLvcuXOnqFar2Zw5c1hD+1OKoojt27dXKp7KZDLJ5dkzqcTEhOWbmDCR5+Hq6iq6uroyrVYreXh4cC4uLg0+JwBkzHftSkbsfxMpKeRQDx9ORmTHjuT8fP45bXynTjXMIZEkMkQ3baKorKcnGfnVnO4jR44IGo0G1tbWfFxcnPTpp582zEMTRbr2EyfwlDHs3btXUqlUTKlUCoIgMLVazTHGJLlcLnXt2pVzcnKCq6srzp07h6TERPhptfDZuRMOP/wA2dix/93a9+XLyWl9770GXoqII0eOSElJSZXX3rx5c0yePJkyDdu2kUFYlyiaTkesgilT6J5ptWQctWxJ9YBDh1Jk3MvrZad1+3aimvftS5+tlYm7du0awsLC8OGHH9bIeoSFheHWrVvS+++/b7hH+H8DOTkUqNmwgRzL2oEYQaC5pP9pCCUlJNz3yy+GRbG2baPa96VLG957VY/SUnKKBIEEuubNI0eysJCcvWrnpNVqERcXh+fPn4tWVlacXrUZAHr06IHIyMjK/+v7X7/11lsvBwIFgZ6rRrQUe/HsGXZ89x0mxMVJ7h9+yLB5M9Ef9e293NzI0av+DISGEq04L6/uL372jNaGxEQ6nwp2UEWPXajVasyaOJHGf+RIUtGvjjt3aF5Pn47Mx4+xb9Mm+Dx8CEdRhFRaKt319hbdR4/mX+pFe/Ei0bR//dVwEOb4cQocLV786oHR6Wh98ven9cTMDBs2bNAFBgbK/Pz86D3nz9PzvGMH0LEj1Gp1zR7FV68SA+PGjarxKy+n7HlWVg0DWRAEfPPNNxBFEZ729uK0HTs4nD9P41ZaSlluvfBk27YUbKrWduqViIykMoDBg4FOnbD599/FFy9ecB9//HGlUX/8+HEkJCTgw4ULYfH4MRAbi9RNmySWl8du+fig/5dfwsLXt6plmVZL679WS2v4rVs40LEjAocMQfNhw8ghSkykwGrtNeDrr+l5qnX+Bw4cwL179yCTyWBiYiKVlpaySZMm0Tz/6Sfg1i3ofv0VBQUF4DgOjDEwQYBlmzZQ//wzHnXogJLgYGQrFOKI4cM5bsqUmscuKqKATmZmnYGAVatWYeDAgVJPHx+Gn34ixsbz5xQ4Cgl5OZtaWlpVkjBxIs3j33/HRrlcLCkt5TiOg1arxWeffWb43ly5AsTGoqhnT/x47hzGjRtHdboqFfD117g0YIAQGRnJjxo1Cj6rViEpLU1S//orc3Fxwa1ly0S/M2e4fZ99pisvL+c8PT2lMWPG8GvXroVGowHHcdDpdDA1MREXtGrFWX7yCc3H7Gz6/i++oCw9x9EczsurYvFUg06nQ3R0tBQVFcWKi4thb28vzJ07JO/Q7wAAIABJREFUlzc2Nibn3MSEWB2NWOvVajXu37+P0NBQsbS0lBs4cKAQGBhYPw0vJ4dKm1q3pnvz5psNPmadOHCAGDojRuDKlSvipUuXuKVLlzas33heHq2RU6ZQ2UJt1oskUbBm8mSUr1yJK4WFuHnzJoKCglAZtMvMpGDHrl0NCsJXYu9eeh7nzm34ZwAUFBRgw4YNKkEQnFauXFnYqA83oRJNznYT/p9EcHBwOyMjo3BfX19F165d5XZ2dv8se9YAHDhwQHr69CnmzZvHGtyqqS54e5NTtHw5ioqKEBISIul0OiaKIrKzsyWNRsP8/f3h5+dXqUT7fxQnThCNeM4cii5PnkxG04ULZGRMnvyyw9NQXLxIjs2IEbRx13JwHzx4gH379kEul0scx0k9e/bkXjKya2PZMuQJArZaWSEwMFDs0KEDl52dDTMzM8hkMgiCgNzcXOnmzZtSUVERKysrY7a2tuLIkSM5Nzc37Fm37v9j773Dojq3qPH1njND7x0EKSIIYgcrCmJQI4q9m9hbNPHGaEyuscVyNdFEjS2xRWMLKlZExYqCKCKI9KKioDTpdZg55/fHZigKiuXe7/d9cT0PDzrMnDnlLXvttYvQef9+znT2bOgPGNC4Sv+2UCphZ868FYnPz89HVlYWEhIS8OjRI2hoaChmOjjwaN++af2lBaE2n1tTk3JTb94EJkyobySJIkUrzJpF4egTJry2x+uGDRuEAQMGcHVbWP38889C//79uTb/i3oBKSlUUfvgwVerycbEkFGfl9ewcR0ZSeTl5QrxOTlExkaNIsLYQIG3BpGdTccbOpSML6mUyHpExFsV9MnLy4OWlpZSmQNAxiljDJs3b4ZMJkO3bt3Qtm3b+tEa//43kcnY2CZ9T2xsLE6ePIk2Dg6Kfps386pr1lAdg8GDiTwMGFA/91iJkhLIIyIQVFaGwsJCCIKAnJwcRbVnRXylurqdHZ56eAgXevTA0KFDuT9+/x2j09Nhp2zNVX2N2dnZiImJgaenJ9VI8PFB8dy5eLBzJ4xiYiAMHoxHzZsrtNq35+RyObtx4wakUil4nhd8fX25Vvb2lHtvaUm5rg3h0CH6e1MrD0+cSFE7oaHYtGmTvE+fPhIXR0ci8/fuAYsXI1dHB6dOnRLS09O5Fi1aiNbW1ujRvTvDokVgvXqB1U07yM2lceLsXO9rBEHA6tWr4eDggJEjR4KbNIlUfeW8Skqicz53jubwjBlEdF4XUq409Pv0IQWsumjkixcvsGXLFnTv3h19+vTBjQsXEHP2LMZlZ0O/vBy39fWFnPJy9tTcnA02MEDOuXMwOnkSzcrKaD415GTJzcXROXMwzNISfPPmtL45OVG4/vr1ROYMDYmcu7qSI++ldJqdO3cqnj17xgOAr68vdHV1YWNjQ2OhoIBIxZEjNK/kcpqj69bRfai+TwEBAcgICECfsDDRfO5cpuHtTWkBSkRH0/pXN7WmDo4fP46YmBgsXbq01n7YsIHWCH9/cu4p24ddv04OqaIiciq0bw9oaSErKws7d+5E165dxdTUVNHX15drcM+OjyeH55AhgJcX7t69iwsXLsDMzExhrKIC161buX2DB2Pq1KnMxMQERYGB+PPiRfSaOBHnzp2DlZWVMHbsWE4SGUlpGHFxgESinI84ePCgWFZWxhYtWgSpVEr5whcukLPvyBFao4YPp/oEPXrQnpCVRa83kIIlCAIKCwtx9OhRobCwEAMHDuScZsyglItNmxofhw1g//79ePToEbS0tPDFF19A/S2UWchkFCW0fz9FXb1rfRclvvmG7BdXV4iiiFWrVqF///5wa0K6F7ZsoZxpDQ26/ykptbUgBIHsJQ0NckhVIzQ0VIyIiBC+/PLL2nXy7FnaJ16KNmkUcjk5HQYPblL197oQRRFr1qyRy+XyvsuWLbv6Vh/+iBp8rEb+Ef84rFixwk4qld708fHRb9eu3X+XYVcjICBATEtLYzNmzGh6T+SGUFFBKsWdOzVKjI6ODsaNG1f3OlhISAjCw8OF8PBwrkOHDoKhoSEXGxsrenl5MfuGVLnly8kI2bPn3c+tspLIg7ExVSx2dSWDYvVqWuhXriQj7m1bZrwMLy8iTDExZHycPUse+A4dgGplgE6nkuno6LDLly8jJCREdHFxYR4eHggICICPjw/qVrTGd98h7ZtvYOXoqPDw8OABvBJGbmNjw9zc3BgApSpVwyrGL1jAHbOxUegcOsT3OHcOOt9+S9f5viq3kRHdx1u33irEX19fH/r6+mjVqhUyMjKwZ/duHqtXU7jm+PFvPgDHkTL99CkZRzY2pOZ27Ejhodu3kwE2axZFKhw+TM9DmWfaCCQSifj06dOaftFFRUUoKyvj7D6Uc+JNsLcnA1pVlfK6//OfWsXMxYXUx4aItkJB0QUnT9Z/PTWVejD7+NDP61RimYzu6+zZ9Hv0aJrHn35KoedKIvSWbWoaqhGhpqaGoqIiGBgYID09HTdu3MDNmzfx3Xff4cSJE+jXrx/0lixp2lioRkREBJydnUXfoUN5jBhBxvaECaS6KlWjhqpyq6ggeO9eMb55c6aoToPp2LEjX15ejpSUlFe/KCoKN/38xOZ+fnzpjz/CrE8fGJuaAgcPoqS8HGfPnkViYmLN22NiYgQPOzvORUMDGD0a5a6ueP7LL4LHwIFcqzq9uXv06IHi4mI8fvyYJS5dCqt796CZmNi4U0MU6Xn/9FOT7xG2b6e83adPAZmMqQgChYs3bw5s2oTcigrs2rULtra2bPr06Thy5IiYlpbGiatWoVBVFckWFvBNSYG2tjYyMzOhf/iwaJWVxdjevfW+huM46Orqijo6OozjOCISf/1F469lS3KCPH1KY1pTk8LbY2LISbl9+6uqYnk5zWVRpPld557o6OjAxsBALPz9d3Zu40ZYZGejO4BrrVqJaa1aoVhFhfPx8YFHy5bQunMH+adPo6SkBKWiCDZ3LuRFRa9GWRkZIb51ayi++44ekKtrrYM2JoauJSKCziMqiki3XF5vfsnl8pp/m5ub1y+SqadH0VMLF1Iod3Expb0UFtYQ7dTUVNy9excwNcWhgQPZJA0NaEyaRA6Tzz+n4/j7075Wp6VnXTg5OSEmJqb+i1OnkpPh6FG6DlEkJdTGhqI+5HJyNFenWTx48AAGBgZiz5492SeffNKwPZKVRSkuPXvWrA/29vYICAgAz/N8qVwu5pqZsQkTJsCkWu2MNzAQJKLI6QwfDp9jx9BOWcfDyak2cubBA3Bt2sDU1BTz589nmzdvVpw8eRIjRozg2ZAhVOE8Pb027ejYMUoJmDSJyKKLC839SZNeOWWO46Cvr49JkyZxN2/ehP+xY5i/cyfUm9Cj/mVUVFQIADiJRCKGh4eLPXv25JosjuTlkSNq6lRy2rxL9fTaE6FnunYtAChr7YjXr18X3dzcXr/ZR0dTeohy/X/6lCLFtm8ne+bFC5q7yrFXDVEURZlMVv9ivbzI+X3tGtBQelBD3/3w4VsTbYAiqeRyuYTn+QsrVqyQMsZSly5d+obiJh/xMj6S7Y/4R2HFihVqUqk02NvbW+9/RbQDAwMRExPDJk+eDD09vfc72E8/0SYeHf3at/Xo0QM9evTg7t69i5s3byItLU3BcRx/8OBBDBw4EO3bt6/XYgzdu0N0dARE8e0V/uxs2jAGDiSFYtEiytlcsoRCbm1tSdGu0y7mfZGXn48/g4JQ0awZXK2tFb0XLuSlqqrA77+jtZMTrL/5BvHx8QgKCkKzZs3EXr16sRMnTiAiIgKiKCIhIQEtWrSAo6MjOnXqhEJBgHpcHNpYWDRJTnw5ZIzneYwePZqPsLcXDx04gKm3bjHpyZOkprxG6W0SunShjfId8+kfP34s6nKciJQUrkm5m5s2UYh/UBAZFyYm1J6pqoqM9OJicmxs2lTbpsjOjgzBESPI8RESQmO0b18y4r28gOXL0XfmTP78nj3oZm8PbTs7aGlpQSqVIjw8XPT09PyfzMcaZV9JNENCSDnU16cx2q8fGWd1jZgTJygcVxm2p1BQ+OS5cxRp0VjKRmwskey4ODLyjh0jB5SzMxlYb0ms3wZRUVFieno609XVFQsLC5koiti4caNYXl7OHM6cQYd27YiMVKOiogJyubzGCZWcnIyAgADR0dGRJSUlKQoKCnhl9MG169eRGBKimP7gAc9t2UKkaM4cUmTqrB+JiYn4+++/Mf/0adb5wAHIOndGZGSk2KtXL1ZcXIzU1NRXW/no6KBn//58aH6+QiU6mveIjYX2ixeIf/ECfkeO1LzNzc0NvS0s8HTXLlbs7y9eU1Njpb16Ia5HDyzo3/8Vw1dFRQWGhoYwrKhgVm5uOKuhAf3Ll9G3b9+Gb+Djx6QQv8381dCgn06d4K1QsOZ//03ORm9viIxh3++/w8HBQRw2bBgDgPnz53Py7Gwodu0Cf+0aikNChGPHjrGqqiomCAIc09KYpoYGBjawLhcVFbHY2FjUVCAOCKBQ7c2b6f9SKSnEn31G1yKVknNIJqN/Kx0jUVE0p11cKIVBuS88fQpERkKalISJv/3G8kaNwvGCAsS2aQNVMzOMGzeOeUgkkEgk0NbWpvPr0weP3dzE+/v2MfPcXAgSCdJ//RX9+/eHhoYGFApFTYFRgNRPqKnR2tajB6l7n35K13LgAJ3Hli20n1y9SlEDlpbAggVwMTDgXNeuxU/ffQfJihV0DQcOEIH57jtSxnfsoDGZmkqOtDqRnHU7RvBqakg0MhKttmxhePiQHGGrV1PU1Gv22lYODtDNy0PCvn1wun2bIhG6dqV7p6FBdVCeP6c5UVJC/751ixTMhQsBdXV0f/AAsXp64qYlS5hzr17ygQMH1rfLy8rIYWBhAYwcibS0NBw9elTU1dUVOI7j+/fvDzMtLYY1a+qp8rm5uaJK8+Ywc3aGXd2WUFpalFKTnk4Ox4yMmrSNadOm8b/88gt+/PFHDB48GO137iQV196e1r6xY8nB5u9P69msWRTZ88knVHuhAaioqKC3pyfajRuHCIkE7tV1Fd4G+vr6QnFxMdq1a8ddv36dGRsbo2501GthZkbnmJNDDp2KChpz74L794kw16lHYW1tzZKSkl4fIlxSQuNC2UITIAdccjLNQ1NTegaPHtX7WFVVFW7evMl98skn9Y+noUF7a0QEHfdN1/Pnn43XGXkDVFRUsGzZMigUCmlwcDCCg4NbrFq1aoFCodiwbNmyj6HRTcRHsv0R/yjwPP+FlZWV/hu9kB8IgYGBuH//PqZMmVLjcX5nREcTCfjXv5qc7+Tq6oq6lXz9/f1x8eJF8c6dO5gxYwbjeR4xMTFIzs9XPI2P54VNm0Q3NzdWUVEBmUwmPH36FNra2uLo0aP5V9pZRUaSqnnuHHnrra2pkM+8eWToxMZS4aCmeF7fEjzPo7i4GLq6umJaeTn7ycsL7oCi7dq1vE5lJbSmToVbt27Q1NTEyZMnmb29PRYuXIi8vDxoamoiODhY8eTJEwQGBvKBgYHgeR727duLXSws3ovwderUiSUmJgqHKyrEz42NOfj70yY6ceK7t2Xr3JkiAoYPbziv9A2QREaymatWsbrE6rWIiCBjwtOTSOSDB0T4s7MpHDYtjQzjIUNIQVKiqIh+r19PagJARpi9PRmh167BaeNGcBcviuzaNYbwcFQYGKDV+PFiZxUVhqFDidReuEDEt3Pn1+dQvy/mzaPfPj70HadO0TNyda1/Xco2dUuW0P+Tk6nYmoUFGZgvE+1798jIX7uWDKKpU8lA8/Sknu1NrAT7rqioqEBgYCCio6PZuHHj0LJlSyaXy5GSkgKe51lMTIyQGx7OvRxSvGvXLsWLFy94NTU10d7eniUmJsLCwoJFRkaiY8eOrHXr1jA1NWXHjx8Xk+Pi2Ij9+/kTPXpAceaM2Pv6dWZsZUVjpW1byOVy3Lx5E2FhYRBFEeo5OTXOPW9vbwaQEaempoZ169aJenp6wsSJE3mlE8vy2TOMKi3l43/6CeeTk4UJAQHcmUuXxKESCbuvpobnWVkw/+EHsObN4TBzJsPKlcgTBIgDBqAwKwvFxcUNVwnesAHYsQMmsbHwyMvDvn37UFBQII4aNerVyXnr1rvVsBAEYO1aGH/2GVeyeDHUvL0BjkNZaSnKy8sxZMgQVve9kl9+geTqVcDaGuPqtHgsKChA4pgxCDE3x+nTpzH4pXxTNzc3hIWF1b6wbx99d0YGjTOAimYlJlKEjJsbOT1PniSidPcuzctRo6jA3IIFREh++426ELi7U751+/bAw4cw4Hl8mp6OgwcPonPnzo3uZz5PnzKfQYPIwG/XDn/Z2gohISFMIpGI+fn5XHVFfFFHR0eQSCQ0KJREKCaGvlPZx9zNjQpoLltWG1p74wZgaIiEvXsVz3x9JQYGBtDu0YPINkCpHEZGpAZXVlLEyKNHdN0nTtC/27SB7bx5WDRuHFT+8x+c9PISM4KDBUydyqN/fzqfOXOoH/yxY+QAMDQk4pudTcT98mVwjo7wvH1bNBBFBm9vckJaW5OCfvw4VZm+epVCwFu1ougugPZOQQCCg1GRmAitXr3EaVu3ovT+fR4qKuS0njeP1qPjxynK6OuvUVxcjMOHD6Njx45MKpXynTp1IkW/pOSVdYjjOKZhZgaNM2coYq137/opLpaWFIWhokJRKZs3Q8PaGvPnz0dISAgCAgIQAGCapSVMXVzIUbBuHa1pUinVTzh3jtbA0aPpmMOHk5PipSKYrKoKMh8f4WpZGSe9fRtNLl5ajZEjR9ZwlaioKIWfnx+/YMGCpkcJKhSUCuDlRcT0LSJ66uHp01fqdRgYGEChUDS+uefk0H6Sm/vq/l1WRgT+jz/o2Rw+THvL0aNAHacU39Ae6OVF4+TsWXJyN4akJBp/bxm6/+phkhAcHAwAUCgUPwPYAqDivQ76D8JHsv0R/yhIJJKvPT09356xvAOqq2di8uTJ70+009MpLDkysuk5oQ1g2LBhEASBrV69GqtWrYK1tbWYkZHB+iYnc4POnsW5PXsQFxcnSCQSjjHG2djY4N69e+LTp09hbW1NxCMwkMIUlSrmli1ErpYsIaJkZUXKwrvmZDcBampqaNWqlZCeno7p06dzeXl5CAgIYHdUVIRmMTGcwZ9/Cs7Tp3Mp7u5ilYUFy8vLg5GRUU1oeL9+/XiAPMeMMWpHJYoMHTqQulm3Hc9bwsvLi9u5cydihw1Da1VVIl4zZ1Ie3zuQZRgZUShocHCT+5LWhWn//jgUFydOVlV9Pdtv145I/f799V9v3ZoMzl69yEhR5rk2Bk3N2rZY335b+3pUFAAg8vvvcauiQjA5d44rHz5c7P/vfzON3Nzaqua7d9Px3dzIqEtIIOKzdy+Fza1eTYS4b19SlXv3fr9w/YAAMla//54cCQcPkhJ98CBdb0gIGfgzZ1KEweDBRFbGjiVjODOTznP8eFIZHB2JMDBG8/a/XAuiLuRyOfbu3Yvs7Gzo6uoKLVu25ABqqdWqVStkZmYi9+JFLrljR3h/9VW9z1ZUVLBx48ZBFEV25coVwc3NTfT29lZaeRxAxeyy791jCy5cAHfsGB5lZyvCgoN5E2treB49SqTNwwObnjxBSUkJPDw80KlTJ/D//jeN/To5howxzJkzh+Xl5eH8+fPs77//VkycOJFHeTkwYwbKZ89GBMcJRRIJt23wYAz49FO0nT4dbc3MIM/MRPbkyVCbMaOmcJgBAMydi8+UUQp1IQgUhtu7N6mVKiowMzPDzJkzsXfvXuzatUsxZcqU+g7FR4+IcL4NCgrIcA4Jwb4vvxS/XrOGQVcX+PxzPHv2DKqqqgJX90v27ydS2EA3CT0dHXRJS8PDAQMQFRUFW1tbsU2bNjVFBB0dHREWFoaYmBgqkqWqSuRu3z6aa8pxp6JCY3nrVhrL/fqR0jt7NqnZv/xCRGDhQiKJJibklKhb7b0alpaWEARBDAoKYt0bi1SaP58I5+bNgLo6PjMwUF4vS0tLw7Fjx8SSkhI2bMAAnouOpnsQGEihytOmUdTH5s30DBMTac7PnUvOvAEDAFtbGoclJZLc9u0hy8tDhp0dalJRfvyR6hGsX0/tzz7/nBT/adPoWgEIy5eDa98eagBK4+OR0Lw5GxkYyOedPAmD+/dp3dq/n9aEa9dIzXVyInLaqhXtd7NnA66uuGZgIPTu3Zs3rVvpetYs2qc7dKCx17UrEe1Bg+h1ZVHQQYPgl5GhyMrK4s/u3i0O69eP4dmzWuLs7EyRCHPmQHRzQ76Kitjd3l7s1aEDBzMzmlMKBT3jL7+s9xh4nqfIAYAcHwoFre91oaZGDgm5nNbsykpoaGjAu08f9PrpJ6TI5UgYNUowXbaMw5Yt9XOEKyooNN/Kiu6TIFA6gpEROQgqKymfu6ICmDcP5lu3ct5RUcL169fh5ubGveK8byL+9a9/8WvWrEFZWVnTyTbPk22iUJAzqrHuCG9CdnZtjnU1tLS0UFFRwR4/fgybhqJgBg2isfNSKghEkdpCduhAcxMgh0BsLKCiguKhQ3GmY0dBznGcsuvEK1i4kHLoPTwofa8hHD9OY/U99yE/Pz8AAGOssyiKj5ctW/aRaL8FPpLtj/hHQaFQ6L9VcY13RFhYGCIiIjB58uT377+cnU2GR1wcqWnvCY7jMGvWLFy/fh2CIDBfX1+0adWKQS6Hr7o6w0thnXl5eeKhXbuYY0WF0DIrC62ePOGkGzeSxzQri6pzTpxIhltY2CuLfnl5OTIyMpCTkwNVVVWUlZUJT548EaytrfmcnBwhKyuLdevWjbVt27ZJu0FmZib++OMP6OrqitOmTeMB8i5/9tlnHACUlJQgJjqaK3FxgVt0NLMNDUVQZqY4cvNmJqnbjgigQjBKMEbGwcmT70W2zczM4OPjg/Pnzytaf/MNj//8h7zXbm6klAwZ8vYb3+LFZOi8Ldn+9ltYFhXhefPmrMYofxlhYUReq4lSPSQkkNEZHU3v2b278WJSTYSvry/75ZdfWEZGBiavXMk0LSxIidu1i95QvakDIONfU5MMTmWOZmoqheAVFlLqQmEhGR23b9O1DBlCxLh7d/LmL11KhrqGRuM51VpaFNp54kRtm7mcHCLQxcWkfv/wAxlqwcFEWmJiiFgdPUpq99Ch9KMMt/w/gEuXLikKCgq4yZMns+bNm9ezZs+dOyeEh4dzs/39oftSjmVWVhYqKys5W1tbSCQSODg41PtsYGCgGBcXJ7DsbH7qo0eQLFoEtGsHb4APDQ3FdS8vdN67FxoXL0IMDoYwZow4bNgwVlP0ztGxwVBHlWrS26dPH+7woUMCVq6sqUmxd9cuBYqKuFmzZsE4Kgrc0aMMvr6AgwMkiYmw2LixNjpBiT59yAl482Z9B8zq1aQAXbtWr6q3np4eZs6cyfbv38+2bNmimDVrFlVNlsspbeJtchwTEqiOAWPAyZOQr1uHwrt3YWBhARw9imRNTcHExKR24ivX9e+/bzh6Qy4H9u7F6M6dERoaihMnTrBTp05h+vTpMDMzqwnHbqZUsQEqZtVQoaZBg6jegr09OamGDiVl+6+/aA7NnUvO0VatEHTpEmylUtg3MleUJCk3N7fh9ohmZhTVcfcu1SSoA+vmzfFNnz7s1tq1Yv758yzOygouI0YQuVfeg8JCmsvLl1MUyPLlpPitW0ckdvVqiKIIuVyOTp06wcrKCrZ1HdC5uZQCk5pKzpUJE8jR8tNPwFdfId/WFlvi4iBJShI9PDxYxvz5qIqLw51//QspKSlYIgjgtm8nNTIjgwja+fNE3u3sXmmbVFlZydVLD/P3JwLUvTvdA44joq2lRWvRzp315sKUKVP4O3fu4Pr16yw0Olrw8PDgsGQJOQB79yayOmcOsnNyUJCaiu5Pn3KYM4eeYUAAOfTmzaN7mJtLa5a5OTiOYzVkOziYyF18fG0RPSVUVek4okjj0dgYOHsW/MiRuJacDGsrK1rPliyh9/n40OdGj6bUoYgIGssmJqR0A7TfFRTQ55YuJWeHqiq6dOnCBQUFYd26dWLHjh1Zv3cIbc7Ly6txkr8VunWjfSEigiIF3qV2zO+/E2lvAMpaMfVQWkrvfzl9KzycokyOHq1v0xkako1QWoqs7GxRJyOD62Vqiq4v2S01sLAg5+GaNeRIevmeFBXR+HnPFL7w8PCafy9dujT8NW/9iEbwPwml/YiP+P8LOI5bvnPnzvKkpKT/2nckJCTgypUrGDNmTP2iLe+K3r1pMf0ARFsJY2NjjBgxAqNGjUKbNm2IUDQUZpydjTEeHty3f/8Nj9u3ubBOndjWwYPFLDU1KJYuRXn79uKLX39F3L59uLRunbh2925s2bJF8Pf3VwQEBAjBwcHi6dOnxYMHDyIqKkoRHBysuH37NrS1tSWRkZFCcXEx43meu3PnjtDUcz98+LDo5OQkzps3j9fV1X3l71paWujavTucv/oK5jt3os2sWehx8SJL37aNQt5e14Fh/nzasPLzm3o6DcLU1BSlpaV8QEAAtv7xh+KxkRGFfJ0/T5viS33a3wh7ezIQkpPf7nNubpB4e6Nbt27i+fPnxZKSkvp/F0VSak+coN9KozElhSIW1q2jMNKzZymM8/p1KuQiNPlxvYKgoCCRMYa+ffsKFm8a09raZKy6uNS2LNmzh/JQ9fRISVFRISVLSdadnGrzzA8epNfGjat1JNjZERm7f7+2sE9ICBnVixYRaTp3jo537RoZaAApDgoFqVqrV5PyuWwZGbva2vS+941geQ/cvXtXvH37Nj9q1CjWvIHqwJGRkRwA7Jg1CwplSHw1QkJCYGJiIkgaIFjJycm4c+cO87Gx4b8KD4fuwoUUQlqNiRMnAgB+LivD6S5dcGDwYAzbtYs51yUlkyc33E+2Ghb6+jBIT+cKExKIFKmooKSoiA1NUIT0AAAgAElEQVS3tmamEyaACwsjVXPpUiJPK1fSM8vNpXXx+XM6kLk5PSNl0arCQhobkyfT3G/A0aqhoYGpU6dyBgYGbPPmzUJRURGN/2vXSKVrCpQh523bQr54MU6dPg2ZTMZU9fTI4J09G7khIczZ2bnWGl6/nlIOHBwaPuahQ8DateA4Du7u7vj+++8hCAL27dsHuVyOgoICiKKIeqqXREJrRcuWFKJaFyoqZJifPk1K1/jxFIpqYQGEh6PwxAnc2LoVcWfP4uLGjQgPDW1wrfz666+Zmpoatm7diga72RgZkcNSQ6OWVObkUHTI4MHAwoXoNn06w8KFwvHWrbEyMREVdYnK+vVEjBwc6PkpsWgRzbcdO+ARGQkDNTWRMYa2bdsS8Xr8mJRgZerLixe0PgBA//7I7NIFt//4A+eWLxfMzc0FFxcXMSgoCHFxcfD09BRtbW3RUqEQuCNH6N6vWkXjKi6OxtO4cUBcHHJzc3Hq1CmcPn0aV69eRUVFBavZ50+fpnWyVStAKkWxtjayHR1JYZ4/n1JVqotrAYBMJsPGjRvFy5cvQ0tLS7SzsyObvF8/Csnu3p3C0B0dETdrlnBm6FB2cOJE4dnvv5P63q0bOUnU1enZp6bSfdPUhObTp8wgKal2rd65s/EaEUuXkup98CA5Cq5fR0GHDnihrY3u3btz0NMjZ2RsLDkjleNp+nQqHnf+fP3j/fgjsHkz5HfvomrnTpz89luknz4NoaICCxcuhJWVFUtISFA0fDKNIz8/H9u2bQNjrGFHz5vAGO0DynoAb4OEBKrf8JJNp6+vDwsLCyH55b3Zz4+cwjo6r647S5eSM61ZswYd7+Uch4N9+zLO1xc9i4uRO368+NPSpcgKb4Dnjh9PEVc3b776t1u3aFy8hx1aVFSEc+REGQ/gAxi0/0zwy5cv/z99Dh/xEf8z9OzZM/TKlSuRjx49GtK9e/cmVIt6OyQnJ+P48eMYOHCg2KpVq/eL2xFFMlKGDycv9ofs3/wyiotJQVD2c372rLaQi4UFuP/8Bxpz5qCDqytrNm8eM1ixAiEAYrp0EcMGDBCTCguF0tJSeHp6MnNzc/bkyRP27NkzpKSkcDk5OczNzQ3jxo3junbtynXv3p05Ojqic+fOXLt27ZipqSlCQ0M5d3f3N3qr5XI5Ll26xGbNmtW0nsyMAU5O2AcobNXUOMPAQNo0AQp/e/kYHFerFjSllUcjUFdXR0VFhSInJ0c0NzfngoODxR4DBzL4+NB9/eEHyuE1MGiays3zZHgWFLySD9coFi8mlbdrVzRr1ozFxMQgPj6etW/fHkyhoBA2Ly9SLJQKZGkpKSTh4XR/Fiwgsg3QvdHUJIUsI4Pysd8CMpkMfn5+YkpKCpsxYwZatmz54eKrtbRqie4nnxCBMDauDa0cN44IF2Nk/Li7U1TGzZukhM6eTZEaXl5E1m/dIvK8dCmRlhYtyKg1NCRlZ8YMUms1NP6787KJKCsrw9GjR9GnTx/WWAu1Fi1aoN+cOZA7OCjuFRWhTZs2TC6XY+3ateLz58+Zs7OzYG9v/8rF6OvrI/7YMXTLz4fG0KGv9DvW09ODjo4OkpKTkWlggA5XrsAhLQ2qhYW1BNvPj55LQznQpaXghg6FSXm5+HevXkKXPn044coVyPbtY8537oBbu5ZUNFvb+vdaW5tUOTU1em6LFpEaPW0arZs2NpTCcfs2PfvXpHDwPA8XFxeWm5srnD9/nrW9c4eptmvXoCKUnZ2NrVu3QkdHB9pSKUInThR0Q0LYg1GjcCYvT7xy5QoyMzNZz5494eDgQOe3YAHuxsWJzjdvMv1+/YjExcbS/GqsaOHduzSGqwsj8jwPc3NzJCYmimFhYUyhUCAzMxPGxsawrJvWoa1NpMvNjf4tk5FzaNcuIrs3b5La9fQp0LUrBB0dlKxfj+SsLKQXFcGzqEh0v3WLBZSWosOIEZBfvQqJsTGtWZqauLNhg2h89y5zGTMGFnFxtGZratJ+xfP0nV5etPaEh5MjLyCA1q8JE0jJt7ODpZMTKywsxPPnzxEWFoaioiLRQU+P4bvv6LlJpUQm1dQoxQUgZ0qPHmBnz8KS51l4RARup6UhNiJCvBUXp5A9fsztzcxE8I0bKD5yBDlZWbhcUSEGBQXhTnExk+bmom9mJnP19WVOn3zCevbsiV5du8KG55nx11/D+OpVFmxtLdh36sS48+dJ9V+5ktK3Zs6E4uhRHAoLA2dqqigrK0N8fDzT1NSEu7s7rR+CQOuDvT3y8vJwevVqUe/0afb7ixdQiKJoO306U2zYAFhagjk4oLS0FGFhYUwQBEilUqFv27Yce/aM9vuICFLy3dwAjoPNmDHM2toaJaWlwvm7dzktExOYz5pFYfLJybSGjx9PToaHDyEpKED7P/8Ed/EipcfY2JADledpHczKojXO3JxU9A4daHx06wYsXAgNPT1ESaWCRCKBtbU1g7k5RZ2cP09rgHIttbCA4OUFGBoiNDRU/Ouvv1hlZSUqKiqQ9eWXYpVMxoKsrdF1yRJEXryIRxYWkOfnI7uoiHXt2pU1NZxcEARcvnwZz549w8yZM+t3E2kqBg6ksZSURERXmbrUFNy7R3uicj+sg2vXrsHZ2ZnVRJkoFOQg9vWtH+l37Bjty/7+JKI0AqlUihcvXsijoqK4+ObNhTAnJ848JQWdFi+GZMwYmm/KSBCOo+cxbRpFEihVcFGkdAwfH0rreEds375dUVlZyQFw5jgu1cPDI/6dD/YPxscw8o/4J8LAyMjog1dRTE5OxtGjRzFgwACxqSHRr8WaNZRr9bZq5rvAxIS898XFFIqoDJU9d47+lpsLzJwJ7vhxNB80CBWLF0PL0hIenTo1mHtV3SKLZWdnIyUlBY3m94HatlS3l6gf1t0A4uPjoaWlpeA47q2qZlXJ5ajs3p02pLQ0ukaA7q+hYX3CO3EivUcQ3plISaVSDBgwgAfIQRAVFcVkMhlUVFTIwB4xgoicoyOdU1NSDVxcaqu+v6loWFUVGbmTJwOgcN1Ro0axLVu2IODUKQwaMoSM/LIy8nzL5fT+DRuoMFHv3o2f07VrZEDk5dVUsX0dIiMjoaqqiuvXryt4nmczZsxgDbWr+q+D48gA8fIiYyUnh+6pQkHKwIABpDyZmJASUVBAn2vZkgzVGTP+p/nXb4IoivD391cUFxcjLS2N5ziOva7wUDMLC2DJElh37szfOnkSBw4cEMzNzTmJRMI+++wzWFpaNjionh49Cs+bN6H9yy+vEG0lOnbsiI4dO+LRo0dQnToVGi9eENEaM4ZUwiFDGg7bDAkhFXnBAhj36MFaTp3KR1+8KOro67MnzZtjla0tBjGGjo3NQ56vdagUFxOxfPSIakbo6VH48ZdfNqnIHsdx8PX15XV1dYV7a9bAZv581lwQkJubiwcPHqCyshIKhQL37t0DAFzZvRtpKSkwys3ltnt5QZ6cjGbNmmHChAmsWbNm9RyHpeXlUCQnc9YnTpCx/fnnpP69LufUwIDITx04OjpiwYIFbOXKlYiPJ5u3wQ4XmzaR86F5cwp9nTmTyLaBARGyv/6i/6eloVIux5+ff44eLi4YfOIEpOfOMUgk6B4ZKf7t7Czkpqfz0pAQ0dHAQDQqKeHadezIkmNixKvnzzPXBw/I+WdnRxEnP/xARPnaNZorw4aRAuvkVJunXAfe3t5ISkqCQqHA/fv32YB798Bt21arBE6YUFu9WQldXWDDBhifOAG306ehWlUlOh04wKIvXpRo9O+Pmbq6KCkpgfT2bVxo1gzWFhail5cXZ2BggE2bNmGIqyukkZFATg54bW1SfC0soL5uHXg9PVgPH87Ji4rABwTQtfn60vnY2SFGXR39Fi+Gdb9+vLxvX6xevRpz5syh6IWvvybH+JQpAAB/f3+Fc7NmrJWhIdPS0hILCwvxR0qKwlFbm3cbORInNm0StExMOI7j8N133+G3337jCiZOhIFEQpE7+vqk1J87B9jagjEGa2trWFtb8zzPiw8ePGAdO3ake3L9On13u3ak2goCHgcGIprnRW3GhDabN/NahYVgy5eTU71DB8qTV1ente3l/fnKFQDAcDc3LvfkSfH5oUMwNzcHZsyAuH49io8dg3TQICRbWsLst9+QcfAgTg8ZAlSnoN26dQsqPC9q9O8vzp06lS0xMwOWLEFqaCjkP/8s9L90iduwYAHLz82FcRNU13v37uHMmTMAAB8fH9HMzOzdF+KxY+l5yeW1BeuagqdPG1WIzczMhCdPnvBubm50XBMT2kt69qx9U3k5Of6GDatXzbwxDB8+XNK3b19cvHhR5HleIff05PfY2Sm+sLDg0bw5RXnMnk1vNjEhx8zKlbVt6sLDSVioew7vABMTExnHcar5+flOEomkD4Dj73XAfyg+ku2P+EdhxYoVllKpdIunp+c7uEUbR2JiIo4fP44BAwaI7du3f3+LvKiIwrnGjPkAZ/cGyGSk5Do5kSE2fz6Fwqmrk5d88mQiJCEhZDj37Qs1AE3RfU1MTN5YHI7jOEgkEmRlZdVXaF6CKIq4ceOG0KZNm7cuT92tWzc+ICAAFhYWMLSzo40wOpoIlIUFkW9lOGfr1qTwq6i8tXrbECQSCbS0tMTExMRa1dHGhoy89esp9NDE5BXD+hW4uJBHPinp1by7upDLSRWJi6v3skKhgLSiAp9Wh0PuHzJEXnHpEjf6+XNOd8cOet5nzjReaEUJMzNSwJs1I2//a86lsLAQgYGBAADGGP/VV1+9X5/5pqCykn4nJNC9XbKEwjLd3ChfdeRIus4nT+iH50n569aNVJ7Bgyk3vV8/MjoLC0mF7NuXSIyz83/3/JuIGzduCKmpqczW1pYDgAkTJrz+AwsXAkuWoKW2NkapqMDPz49LTU2FkZERrBoo0AUAlZcvI+b4ceh8+qnA9+nzRs9TTe6spSXls58/T6rmoEFU5XrhwtriZfn5FMa/bBlQVQVVd3f0HDEC1zQ0hIdSKbO1tRXTIiL4wMBAtG/fHg0qYJmZNNZ9fGgdq656j6goUnNMTd/KQcIYg2fnzlyaRIKDwcGQREejXBmKXOc9Ts+f49OqKtw1NBSbbd/OZmlqQltbG1KptMEvi42NFUvt7AQ+PZ3H4cO0vnfr9vqT2bCBake8NB85jsPEiRNx6tQpsaqqijm8HIaelUVpH/7+FA1w9Wp9BW/3bpoTGzYA06YhzssLw0+cgIGTE6QaGrR+KBTo0KED69ChAy8IAjIyMlhycjJuxsUJp1684HgfH6ahoSGyX35hePKEnveePbSPzJpFIeOamvQMHj2i/NQ5c16pmK2hoQFPT0+Eh4fLkZYmibx2DbHa2qL4+DFTUVGBrlQKneBglBcWokuXLvX6dUsGD0b6gQPwbN+e8bm56ODoWEuGRBGwtMSMVasAY+OagaOroyNEy+Vcp4sXqbjXTz/R+HN2BtLTYbFhAx5qayNXoYCoqgrL4mJyjnp6AmVlCImKUnRdsICzTkhgD5OSRBUVFURGRLDuHAfp6NH1eiTn5+czi169uJCsLJHjOKFfv378+vXr+YnHjkH+9df4ZNs2dmbKFGH8+PGc6sOHGFFVhV2enuJXM2cyNQ8POj+A1uX9++m+groNREVFsZqWb4JAdkL1eCouLkZ+fj5EfX08HDiQZWdn80FubjCrqlLMVFGhvTM1la55zZraKu4NwGzdOlw7eRKX16+HBs+j2MgIeoWF6LpqFW5eu4YcU1PMevAALUtKYG9vj+HDh9fs59yAAQxeXqwuQe3avTtw4gSHvDws09Oj8XDpEhWQew2URHv+/PnQ1tZ+P/vq4kVaJ4KDSdFvqkIeEUE2WQMwMzPj79y5I0IUGSQSKkpWt7jib7+R0ys6+q0c+Nra2hg+fDgPUC2aX3/9lZcxBpUrV8g+WbyY9rD9+2mNTU+nOgmdO9P6u3hxk7+rMYwdO1Z9w4YNco7jCmUy2dv3bfsIAB/J9kf8w8AYG+Xs7KzaYNXId8QHJ9rp6RQSFhVF4av/LYgiVcj8+Wcq1DFlCpEOAwMyBufNI1W9pIRU2JerVH9AGBgYKBITEzlLS8tG719iYiKKioqY1zv0Ju7evTuysrKEP/74gxs+fDhatmwJ1qEDXffff5NxuHYt3QN3dyIBoaEfhGwDgIuLixgWFia2adOmdqdVVaXNMDycDODHj0nReJ3qu2gREb4dOxp/z8GDZEynpdUSDUGAyYUL8Bw0SDyYn4/KoCCmkpgocUlPF3N27hR1f/6ZwdOz6cREU5PIrLLNTQO58w8fPoSfn5/Ytm1boU+fPjxA4fUfBAoFkQo/P/Luf/MNvfb99+SU2LWLxveNG/T65MmkkLVvT58DyCBRYtgw+i2TkQPG3JycCJs3U86vhQUZpT/9RK2S+vZtvNja/wBFRUUICQnhBg8eDOemkP+CAnIwLF8OjuPg5OSkTHWAr6/vK8feu3evwiQujmt95w6T9usntPrmm7cP8di6lZ7HokXklDl9msL5dXWJ4BQU0Hpz+jQRiitXoGlgAB+gxpnWs0sXnPjxR6xavhzuERGik4sLM582jcj1ypWkih84QI4RU1NyAI0eTU5BY2N63xdfUDTIa29PAYKDg5GQkCCqZmQw18JCVKmqQoXj0LlzZ1hZWcHOzg4x9+8LL378kZOKIrSWLIFnjx5NmjDR0dFC69atecTHk/Kkq/v6isiCQPeqkfXfxsYG48aNY9u2bYOy0wLKy+l+ZmaSmnXlCq0xxcX1yfawYUTMqqu2W0yejMw+fUQLxhg4jgjYrl1UCVxbGxzHwcrKClZWVszLy4ulpabiwbx5SG3RghUuWADdffuoQNayZRS9wHEUJbVuHZH+li3p+efm0vo2Zw7Nn2qoq6ujqqqKzerQAem6urB1dWUymQyVlZWC2Y0botWlS9y28eNZXFycMG/evJpxyD18iP4BAYieNQtuWlq05n3+OV17YSHljivzeouLgaQkDD95klPk55MzWRCIFDk4UITPwIGAszNMR4wQw0xMxJgDB7hFTk5Q27gR+PVXCF5eKFi8mLefOxd49gzaX30FJ01Nxm7eRFZaGo5OmaKoXLuWU1dXF6RSKSorK/nLBw6g4+PH4uyNG3mO48DzPC6HhwttV67kLLt2ZdOyshisrIAVK2AdHs4Mhw4VUhcsYK0XL65dUxMSiKhVk+1CymMX27VrR4MnKgrC99/j8MKFQk5ODissLGTq6upCeXk5p6WlJWhpaXElJSUo09amAoN6ejS2duwgErh9O63jLi60jtbZfyReXrAAmNOiRbAqKECWvz9kMhlau7hALTIS2hMmwHTqVCAzE+NfLoL46acUFdAQlN9x/TqlJU2aRNFYyhobdVBU3VJyzJgx0G4gOuKtIZWSLbNxI61Bypocr0N+PkUCKKuGv4T27dvj5s2bLLVzZ6GFoyNXLydc2Xpvz573SjlSU1ODIAjw9/dHp06d0NLeniLkgoLIZlyzhiKPjh+n+9usGRU1fQ+IoogLFy6gtLRUAsB+2bJlhW/80Ec0iI9k+yP+UVBRUWlrYWHxalncd4SSaPv4+NRufu+Dykoy9I8ff688m9eitJSU3NxcMoQPHaKN9ttvySgJDKTCJ46O9LfXqagfCA4ODnxkZKRobW0N+5f6WIqiiOjoaAQEBMDb25s1VMSpKSgsLBRkMhl3+PBhAMDixYup5de4caTmGBiQkbhtGxkdsbEUXqxsKfOOyM/PR2xsLFdeXt5w6oKbG93/q1cpbHLmzMZVbjc3au3TCMFFZSUZnMOG1Tfk09KA+fPRLSmJCX36CNp+fmJLGxuu2N2d/dm2rbjoNfljjcLamtSzadMoAqLO91XnEIvu7u7o0aMH/9aVYwFy+Jw9S+HdBw5QCN6ePfScFi2ie+HnR9EYvXoRYbO0pPBwZQ9pZf/XpkSIFBRQVMeQIZQzuXNnbZEpxihMc+BASjPYto2uvbF82/8iRFHEkSNHBCsrKzg7OzfNelMoyOirA0EQIIriK6p2WFgYrB484N3t7KA1YgTaenu/u4VoaUmkZv16GoMqKlQx/vRpcnbk5tJa1K4dVe1NSqLx37MnMGsWdBnDxDNnUHH5MnKfPkVsTIxobmXFsHs3GZLq6rXqzY8/0tjfuJGIbF4epUR8/TUpvM2bo0gUwXFcTb5nYGCg+ODBA7GqqoqTU7V71js5GYZt2wrLli2rf925ueh88SKXpKUlxnh5MWUu9etQXl6Oq1evKrKysvihQ4cS8V+wgM5z3z46x6+/fvWD16+T+viaeZNTXWTx1unTikFaWjx++IHuxb/+VUsyf/iBlLy6VcH19Un9z8gA+vfHHS8vceCRIwzLltF8uXaN1rzduym8uHNnup979gDXr8N61y7o37+PXCMj/OnkJMzLyuLAGBn+p07R/EhOprzwtWup/seePeSc8vQkJW7dOuDxY8SNG4eIhw8hZmVxKpcvo8Xu3WhRq+Rz6NED+PJLjGYM/v7+TBAEcCUl5AjevRs/f/89/tWtG6mTvXvTvQ0IoPUvPZ3m9O+/1xQ6TO3eXchq04azkkjo76NGkUPVzo5C60eNQssVK1hQUBAMDAwUKj168DAzQ6KODm5OmoT+/fqJKioqLM/EBKabN7OBp0+jIjQUf/Tvj/Lycn7mzJnIyMjgy8vLYW9vj6BffhFLnz/H77//rujVqxf/+eefIygoCPfv3xdH9eoltrh0icPdu8D582AAPG7d4vL37hXlK1awjLQ0vHjxAhkKhWC5Zw/XVqEAz/MwNjYGx3HMz88PrVq1gktSEjKsrZGSksJ5eHjAxcUFRkZGnFwuh0Qi4QBAXlUFbvRoDqqqND4AWkMXLaIK5ceOkXJrZkZ7z6hRRJa1tKCvr4+QiRPlnWbNkphcvkxE9eBBOISGkiqtDLXfsoWcKQDN6YULG2xpVw+dOtHvhQtp3c7OpgJmkZGAmhri4uIQHR0NU1NT4eUOCe+FgQNpj7h6tWlk+8GD16YRGRoaYu7cuTianc0ZDhmCmsSOZcvIlouMbFLo+OsgkUgwduxYHD16FImJiWjZsiXc3d3R/NtvaS4/eUKRMH//TUVYO3Z8fZrKG5CXlwd/f395RkaGRCKRzFcoFDtWrFixcdmyZaHvdSH/UHwk2x/xjwJjzEqtgRY074IPTrQB2mj69QNWrPggh6uHu3dJVZk6lZTz4cNrN8PCQiJ8lZWkgmzaRAt1RASFghoZkXKUkUHGU4cOpHhLpWREm5rST1YW/Tg6krGlqUmbuYUF/T03F8jPh8LUFHKZDKoGBkBSEnrr60OqULCIlSth0LUrDGxsIAIIT0wUMkJDOVFVVRzVvz+zLyggAiaR0Hfr6tL5VIcNoqSEjHuZjAxxXV36zrIyyO/d48e6ukJdQwOyFy/A37lDeYQlJWSUOTjUhkhPmkSe4S++IEPxLSvBx8fHIyUlRfHw4UOupKSENWvWTPDx8WncWFBXJ1JpakoGwDffUDXcl5VgXV0aIxERDVeW9fYmA+bXX+n/Dx4QGQ4JATIzwX77De6XLnGYMgX45BOUyWSo2LqVlZSUvFvBmWHDSC2uqiJCp66OhIQEHD16FC1bthReS7SrqiiUr21bGpt79xJ5dnCgolBz5pBR3q4d5Z4qFPTMk5LoPnEczRWAjEMllET7bVBVRfd/xoxaR0VDxpWqKjmgbt2i3L+hQ8lZ8z8skiaTyZCZmcn9+9//btoHQkJISSwurneeHTp0EMPCwtiOHTvks2bNqrEF1CMi4BAZKZpMn87wmhzwJuPwYXLGWFrSvDQ0pPlkZEQOG19fImlxceTc0tIiEtC7N2BmBjZzJtQBqP30EwvZtg1VV6+ie/fu0H15buTlkcH555/0fE6cIKfI9Omk+ty/jz9HjBDzGWOdOnWCKIq4f/8+69+/P4uMjBQGDhzIGRkZQTpuHPDVV/Uf6O3bRCYGDcINMzPBvlWrN6aylJeXY/v27aKqqiqmTZsGw8BAcgwpVfbSUsoDbQihobR+vgbO+vro9fCh0CIigkfbtqQevzyHly0jYl9VVd/YP3WKnoe3N/RnzmR/amhggpcXVJUkqVMnem7dutGa4+dHz8vFBVBRQaK/P9LOnYOhpqZYb+wPHkzrvZERzdv+/UmZ7dOHIiuqHQv3/PxQceYM7hw7htF+fnhiackqPDyg/nIKi44OMHw4HPz9YWBgIG7fvl2YM2wYj4QEXDhxQhTqLi7K1Jxjx+geiyJFh1VV0V6npYXOGzZwpxYsEBEWxpCaSvdBJqPxefIkdShQU0Pl2bPo1q0bzwUGAoaGiM7Kgnp+PtLWrmUBbduC53lYlpeLn50+zTQdHNAlJ0e8pK3NNDU10a5Ov+1h3t6sNCmJie3bIzAwUPz222/ZlClTuDNnzuCWlZXQIjKS1NKiIkBHBy0ePsQNGxusO3AAcrkc2traCjMzM95o2DBc79NHMJ43jxNFEVVVVUhLSxMePXrEMu7fZ/EtWsDb27tebZS6TmmJTEZrqJIM14WTE0UFiCLNwV9/JSfJ+vWAnh6M+vWDgjHac9XVSQwAKPd7zBiKgmjThvZLgGyJCxcazYeuqKhAXRvs9u3buHHjhiAIAtO+eVPhambGXfvtN6738eNItrFBkpMTpk+fzr2Tw7YxtG5NtsnVq+T4a0yBVyI39/XpQ/fuwXDUKOgsXao4l5PDjZPJGI4fp3H3xRfvTbSVcHBwgIeHh3D58mWuoqJC2Lt3L9e6dWuMGDGC7KLc3NqOIYcOvdN3ZGRkYPfu3cpOAxIACrlc/gsAcBxnBuAdeqZ9xEey/RH/CKxYsUICYJKamlq3V3Lc3gH/FaItl1O49nv2RKwHUSSD8+pVUgP69iWSNmBAbf/Q8HBS8MzNyVj95hsiM6WltWRDFOn9VVVkBMpk9KNQ0OaqoUHEtSp5Sv8AACAASURBVLCQwhhNTIi4y2R0TMYAqRTis2fIf/AAIc+eQVtLS/T09WV48ABcs2bo1aIFIhMSEH/kCDp4eyMhNVXIKC3lvNTVoWthwWrCE1+8oDxbPT0y6u7epfPq0oU848XF9GNiQkZ9VBSQnQ2r8nIYq6tDX0ODQrafPydjPyaGDMT+/Yl4iyK1z8nNJVK7axcVWWrZkhwJly8TOa9L8F5CUFCQoKGhwXfp0gWdO3dGg1XkGkKnTmT87NlD4eynT9caMUrY2lJF4YbI9s8/kxENUOibjQ0pgBER9Fw//ZQUteqw0uRbt8BxHBVue1fY2ZEaXFEBnD+P2NhY6OjoYMTw4TyLi6Nw3qwsyhHdto1ypkWRFKdp0+i3qipdpyDQNVtaEnG4f5++o26/Y6Wx96Hw9CkR7L17iUwAdC06OjSeXi5AxXHkDDA3JyVcX5+e2XtGQDQVYrU62+QIjx49iPC8NARdXV2ZtrY2goKCJOvWrRN1dHTQ9tEjppWYiIhhw0SbLl0+3LoWH1/bhun0acpd7NiRHDXPn9P6UJfYN9Cn3MjICJ6enmJ8fLx4584dzsbGRhg9ejSnVl3tG4MG0by4erX2mWlpkdG5aRNQUoKOT58yjchIpJqZKeKzsnhRFOHq6gpXV1e6Ofn5dE51w7d376YxMnkyZO7uyPnlF967bl/nBpCTk4M///wThoaGwpQpU3hkZdE68ttvtc9Bmf85YwYpvuPG1R5g2LDGCxQWFwPr1qEoIwNFxcXc86lTxeaDBzf8rKRSWhtbtiQnlXKe//lnzVt69eqF7OxsXHj+HIOWLQMbN47WmH//m8L0790jx0edInPKvrv5+fl8Tk4OjI2NaW/p0qWWZIWE0D7ToQOtD8nJuJ+aiodZWYhJTET/X3/FnLZtUeniAodvv0WSRALHvn2hdugQEXyptKYNI5eVhc969OBkPXtic0GBIP38c7EgKYn/bPhwaN27R6poVBTtZWPH0r179IgU/VOnyKFnb49z+/cjJztbwJw5fM19aN+eVO7yckBNDSUlJXBwcGC3b99WdA0N5dGuHUrMzYUW6emcrkwmev7xB9NXUcHRb78VUgcM4Oy/+op1Cwxk7VJSoP6SI19VIoGquTnc3d0RExMj/vXXX+Jnn33GKc6eVQwNDuZx6BCd34QJwI4dYNevo9uqVQxPnuDq1asYO3Ysb25ujqqCAiRUVXGXL19WlJeX8+7u7vDw8OBKiotR7O4OlT590LWxvOdDhyhUPDj49WlCjNE6u2sXrcPXrwPBwbBYuhQTVVUlyMqi57l+PZ3zpEk0j4cMIXX48GEaxw8e0HrTwHetW7cOFRUV0NHRESQSiSgIAioqKngfHx9OT08PRUVFkgIvL9g9fy46OjuzjqNHI1MigcX27W9XzKwpyMmhc92z581kOz6+8bZpAM2v2bPRz8eH37FjB8q++w4akZEUJfiBxB0l3N3dOXd3d+Tn53NbNmzA89hYEampDJs3k4I+dSqd79WrFG3ShOKQSuTm5mKXsoUmAFNTU0VWVpbyAAWCIAz4oBfzD8JHsv0R/09gxYoVHKgHoBUAfQA2EomktVQqdRJFsQXHcc1MTU0rBw0apP6+yvZ/hWhv2UIFLaKjP8jh8OIFecydnMiQGDuWyJu7OynRx47RBvzttxQm2KNHrQoyahR9Lj6eyNr7ojqU99GjR7iQmSkU2Noy/Y4dWVVVleg5diyra1h3GDkSGzduVFwqLOQt3d0xfPhw6DZUbbcuGvLW18XEiaioqEDYunWsz+LFTc+1LS6m+zR5MjkeVq+mjSs0lO7hqFFkFB44QMbpTz8B+/cj5tIlsSQ/n5s0aVK9gj5NhoYGMHcu3bdBg0g9/eKL2mq+Hh5kCH/5ZW2xKZmMQm+PHyfS6udHOX4XL9Iz/PlnMqJeahsmkUhExhh7Z7Kdn08Ol+XLSVV6+BCDbtxAzsmT2FJZKc5dtYpJ1q+vbd1TVUWKva4uRVU8elR7LKUx09TWZh8COTmkzmzYUEu0ASL/oaGvEu26sLMjNWzPHrq38+bRs/kvVyxvsLdxYzh7lnIzz5595U+Ghobo1q0bdHV1kZCQwHKCgtAsMBBhkyYhUS7nfKuq3tgd4I0YOZIU5+PHab3JzKQ51K8fKW1vAcYYPDw8WK9evdj9+/dx8eJF7siRI+KkiRMZEhNrDWYDg/rElTEKay0qgp2BAXLu3cMAJyfeS00NOS8r97Gx5GhRUSHC8fXXpIBNnw7Y2uLvv/5SSKVSZmlp2ajzTBRFHDt2TLC1tWUjRowgQ/XGDSILDbVls7R8tS3Z9OnkIKhbv0Gpvq5bB6xZg2u2tohKT0dXNTWWkpKCFi1aNNw60cmJDHDldSnRrBk5Pjp1wogRI7AiNhZ5JiaY2KsX2MyZtDfs2aO8qHqHdHV1FQMDA5kgCLV5tIsWQTQ2hkIux5MnT6ClooLHO3bgooUFpFKp2GndOpg9esTiRozAyDFj0Kp6nkvT0lC5Zg3uyWRi+vXr6PfiBZO4udH3T5hA9+7FC2ja2ICNHw+fNm04dT8/lIeFwbxZMwob79SJnvkXX5ATbPx4WmcUinpO0aKyMkEul3NZWVkwffiQnq+zM6m5AQGQy+XYtm2bsigeX/bbb9AwMkLO+vXotXcvEhMThXPbtnFT7t1jdhUVfOpXXwn25uaM8/WF1sKF5Mj8/PNah0qzZoCXFxhjGD9+PPfXX38Ja1evhqqWFm/dsSNatWoF9YQEcq5MnAh06QKpjQ208/MhkUhQWVkJQRAgnToV3o8ewbtVK37z5s2isu+llooKtCZOhPnUqQCAqqoqyGQyCIIAhUIBQaGASnIyqiZPRkVmJhhjUFFRQU5OTs1YkUqlUOaTK33Cf/75JxQKBUTGgPnz0fbRI2FISgqHb76hNd/VlfYWU1Ny3O7bR84OZXu/1NQGSV6LFi0QGxuLoqKimvkze/bshoqoMgwfDgCwOHGCSCRAe8zYsU0vavY6uLlRusORI69PF1PWtpk7t+G/eXlRusY330CIj0ffM2cg270bGi1afHCiDYDskps3kVpQgC+3bYPO998zeHlRfQoTE4pelMvpGc2eTXZlE2FoaIgvvvgCurq6kMlkYIzxR44cKU9PT1cH0H/ZsmUlH/6C/hn4SLY/4v9qrFixwlhFReVHiUQyjud5qZaWlkxdXR0GBgZSExMTDX19fejp6cHIyAgqKirvHcuTnJwMPz8/+Pr6fjiiLYq0Sb2Uq/xOiI4mpfDzz2tz0kaOpFBr5Yaork4b5N27tWRNiR07aNG+dIlI2uTJ5M0eOfKNX52fn4979+6JZWVlgoGBAa+jo4OKigpwHIesrCwxKiqKtW/fnps2bRrKysqwdetWdvbsWaF3795c3QrVc+fO5dPT02FjY/PBYnOfPn0KiUTSdDUQIGVl507y4Gtq1uZ2LV9e+56ICFLPY2KAqioIggD9iRPZzDZtoLN0KRHH0FAidc+fk+e/qTA2JnVh9Woq1NW7N0U9SCS1xrjyXMrKyLteWUlOlk8++f/Ye++wKM7uffx+ZpZdOgiigDRFQRBEQVBRiRV7r4nGrrFGTXtjNFFM7LGjiRq7scRYgx0L9kJRQVAEAZHekbbLzszvj8NSFBSNeX95vx/v6+ISWZidnXnmPOc+5T507w4fJqXXr76qlgQ6OzuzU6dOISwsDC1btnz1HESRAg2SRMcdNowyJOvWkQPk6EhBgWnTqMXA0xPyL76Axc6daCWTsSVffQWtrCwMq1sX9ppsWnXvUw1yc3OxefNmDB48+JU+/veC69fpvE+ffrVN4K+/qu+Jrw7jx1O57d27lKlfuPDNvYp/A9HR0eB5XkLZmJ3XwtS0qhDcS2CMoVmzZmgWGQkhLw+bxo9HNqgEdd26dRg6dChsbGyqJ3E1IT+fHNnt2+la1K9PIj6hoUSepk+nCph9+yi7/ZZgjKFFixYIDQ0VLYKCOKSk0P3SoFcvco4riXCp1Grc7dIFFmfOQLe0VEp7/Jg12rULpllZ9NxoiEFUFAUBkpOJsM2eTccpK1k3MzNjSUlJ1DtcQ7FKYmIi8vLy2GeffUYX7exZsr1//FH9B/rhB3rOPD0p6NOhA52ThpgnJlKW6uJFWlcPHgDa2uiYn4+U/fuFhw8f4tatW/yXX35ZfSsIxxEZGDeO3ktDKpYvr0Iw+vXrhxMnTiClY0dYbtpEz7qdHdmQGTPIfpWhVatWLCYmBjExMUhLSoLN7Nlgu3bhzO3b6jtHj8q0tLTQwMQEerm5aDd0qOTi4sKE4cOhNW8evhg8GDqacV6pqYBcDsXQoRhjYsI2ANKjgADxy5MnOajVZHMPHqTrs2QJdFNTYR8Xh9xu3XDK2BgTP/64igK4BiLHoWjWLGh3745if38onJwgl8vRp08f7urVq8LujRt5HblcnNi4Madtbk5kV6lE8b17UKvVsLGxEZ89e8bJXFyQdfQoSktLub179+KrDRt4ea9eeB4Xhwt9+6KvjQ0tgrp1ySZu2UItYD/8QGvq2TMKdA0cCENDQ0xr1oyTBg3Cbz/9hBOGhjBJT4etjQ2R09hYWnMArK2toaurK+3atYsNHz4cTe/epSqajAy0b99eunDhguTj48Ph99+p+gY0bWLJkiUA6PlVKJX4ZPt2HBw5UnqRmQlp926UlJQwmUwGSZKgp6cnKJVKThAExhiTtLS0JEmSIIoi4zgOs2fPZpo9s7wyq7iY1uKPPxLJ9PSk7HC/fmTXe/emwH0N2dRBgwahY8eO0NXVhVwux86dO8VDhw6x6dOn12xgBg6kL1EkXYbWrSloZGxc4yiuWiMtjdbPzJmk9VAd7t+n/bSm/cDdvbzEPHPVKslUkpixo+P7FdAsLKSgvqsr1KtWQZaairjJk3F7yhRp+tdfv3rtZDIKesydS7aslm1wjDGqUgGNC83Pz8fz5891GGPfS5L0njJB/zfxgWx/wP8sFi1a1EdLS+t3Nzc37TZt2sjLZve+J7nj6hEXFwdRFGFoaPh+iHZ2NpWxXb9OZczvAkmiDV2ppCxAnTpUDvbzzxS5HT+eyumWLSOS5uxcc/bNxIQyfFFRtGnWrUuGOiaGnOgy51iSpCoOeHh4OP766y+YmZnB0NCQT0lJEYuLi0W5XA5RFJm2tjY3duxYWJYZfUNDQ0yYMIEdOnRI3LVrlzht2rRyz1Umk+F9qsWrVCocO3ZMbE+jOGpP4LW1KZtUUvJq77QGGhLo5QXs34/szEzsmj4d3337LUWXu3Qhp2DTJiJ116/TvfnmG3JQfvyRxExqmumtUJCDERhIfaeTJpGT0707OV+CQPf46lXajHfsICf63j06frdur42u6+npQUcux9VLl9AyOJgc/dRU2qTPnqVKCFNTKhE8e5acDk9PIu8AOSuadXDsGK0bR0dwSiWeHDggGhsbs/r167NLly4J9vb2bzWyLTY2VjPmRmjcuPFbj3t7La5fp/LgvXtfcUSKi4uh3rsXBlZWlKmuDZyciCDl5xM5sbSkANV7znKr1WqcOnUKrVq1evOBy/pP8cMPr/+9iAjg55/BHzmCmQ0a4OnTp3jx4gWOHTuGnTt3omnTpmL//v25N1YEabLof/1F5Kxly4qMbV4eOcxNm1JJ77BhZGtu3HjnthltbW1OKyEBorV11Yf62rWKVgqQbTpx4gT1wNapI5m7ubEOubnUp9qiBVVdzJxJxG737oqM6vffU4Cr0j3s3r07FxoaCn9/f2n27NnV3oP4+HjJ1NRU4jiOoaiICMqXX9ZsQwB69vv0obaWqCi6dnl5VPFUWkqkeMUKylyVwdDQEJ999hkviiJ+/PFHHDhwABMnTqz++IzR8UJCKgh2v37UEjNwIACgZcuWuHnzprhDJuPGu7vDwseHnvMpU4C6dfHs2TM8evRISkpKEjMzM3lBECRJktiBzZvxZVERZPr6ePLkCd+9e3dcvXpV9HRz45yzs4FOneg6mZmR2vSgQRRI8POjcuSydiAOQJtmzdijPXtYuiCgnka/oU4dssMODrTGDA0hc3VFRkwM1m7cKDVp0kTs1asXTx+T3urIkSNCZGQk37+oCI+WL4eyRw9h9OjRvKmpKQYMGMBnrloFRERwiWZmuNq5s9g5KYmzUyqhe+QItOvWlUxNTZlVgwZC5Llz/JkLF2BlayvY2NjwUXfviu7nz3P+kyfDwNhYsrS0rFgDhoaUSZw6lfQC+vWje5edTa8nJ4O1bw928CDEmBjR1dgYths3cpAkuiYrV1KWtU0b1G3aFHPmzGEbN24UCwsLOYwcWU7E3dzcuL/++gt79+4VR12/zmnaL3ieR9euXYWQkBBOpVJJ07y9Od2kJExdtozVYIcq21SG2gTvNNoivXpRy9X69XT/jh+ne7RzJwUNBKFaws1xHCnnl8He3p67ffv2G9+27I8rNA5Gj6bg7oULtF+9K+m2tKRqliVLat6DExKqH022bRuVy69aRW1pbdsieeZMJPv4oFENRDs+Ph4GBgYwrTwd4HXYtQs4fRoZ06ZB+uUX6WbHjtI9FxdOt00bFMXHo8/gwTXfs48/rgiE3Lr1TuQ/rWxyhyRJpxYsWFD8hl//gNeAX1g5S/MBH/AvhZ+fn35QUFCToKAg3aCgoIKgoCBfuVx++NNPP9Vv1aoV/95GCr0B9vb2CA4OFrOzs2Fpacn+9szg0lJyhHq9QyuMUknZIx0dKofz9ianDiCn5M8/qe/4o4+I0Dk6krP2JgLQrRs5ZYaGZLBtbCCsWIGSVauwRy4XTp88yV26fBnXrl3DtWvXcP36dURHR0t9+/ZlPXv2ZC4uLvDw8GBt2rThPD09OS8vL87d3Z29PLZDT08P7u7u3NmzZ5m3tzf4t+gtehvExMQgLCyMeXt7s1pvchpkZJBDXov7LAgCtm3bJjVo0EBybd6cgecp0s8YqWWXlfqhYUPavDMyKJr+2WdEoA8fplLIPn0qZvBmZlJEvVGjip7UXbvIKdfSogj/+fN0nN27KRM+fz6RBze3qhvsyZPk+OfkUMmitzcwdSpc9+xB0uDBcF27lpzghg3puK1bUz/pxIlUtqcRjatXjwgTY6+uJTMzIC4Ogo0NAp2d2eSZM5mFhQWuXbvG+fi8na6KtrY27ty5g9zcXM7b27vGTOJbY9s2em5Wraq2muTu3bv4/cUL1Pv00/Iof63AcRR0sbCgDNfZs9Qj/57OOyYmBhs3boQoihg9evSbr8eiReT0lznpr0CSKNiwbBmpNZdlburUqQNzc3N07NgRrVq1wtWrV8Vbt25xbm5ur/T2FxYWIvfzz6EnlxMpevGCCGrr1hWiQM+fU0nvokW0jrp1o6oIHR1ah+3bv72znJAA+Rdf4MagQWhbeZbs3btU2VNp1NfOnTsltVrNJEnC3LlzmVP//qijVlOgy8eHzictjUTQrl8nsquZ0PDS+maMwdzcHMHBwYznedi+NDFCqVTi2LFjaN68OWdra0vPZWgoZfPfhI4d6Z64ulJ/9f79VDUyfDgFwWqwQbGxsQgPD4eJiQmcnJxqrt7RKPLn51OgIziYnmnNngHA09OT5efnIyA2Fi309KAdHAy0bw+xQQOk9OyJkIYNmZ2dHefh4YFBgwaxiEOHYAcguEcP8fzFi0ylUqFfv34sJCREysrIYM1LS8G9PD6xXTuyYw0bUpCwTRvSDsnJgeXYsbBOTMQ1lQoNS0sh++EHuoatWlH1jp0d4OMD+dq1aHf4MPLatWM4dIj7KzQUCdnZgma0YmhoqNSgQQOu09q1KHn4EEVPn7LGnTszAEiMj8e9y5fhmJuLup06IbVtWxYYGAj7Pn1gdPgw7GfNYmcvXGDmMhlr6uDAHjAmTps2jW9oZQXLkBCm4+UFh+nTcffuXRYdHS1ZW1tX7G1yOZGbffvIFnt50T6qpUX3dfp05B4/jsZr1rBnVlaSs4kJw8SJFPCYN48qF7Zto7UglyMsLEwyMzNjVtbWROL19cE1aoSioiIxIiKCS0tKgti3LzLy85GUlATGGGdubs6s1q1jT2JipMZ79tREtP8+1Gran+7do3MLCCB/5t49Cu5HRVGQWKmk9aatXeV5ys7OxtGjR9GvX7/qyshfj4EDqX3syRMKcn79NR37XWytiQk9A3FxZK9fxs2btN++rNPw6BEF4j08gGvXICoUOKxSsTZt2sCikq5IQUEBAgMDERAQINy5c4eLjIxE69atERoaitDQUNHW1pbxPE+BosJCClaMHk3HLi5Gab162PzsGRI6dmS2H33EevTsCZ7noaenJ3br1u31N7djR2o509PDu4hdJiYm4smTJ0mSJH3fsWNH9Vsf4APK8SGz/QH/avj5+dVRKBS7eJ7vrqurq1Sr1bxSqZQrFIrSoUOH6rw8tua/gcGDB3MXLlwQt27dyvT09CR7e3vRzs6Ob9y4Md6qH3zAAHL0/vOftzuBpCSKxgYFkVPWtStlDOLiKOtob09Zz3Hjqs5XrSUkbW0ISiWkb75BztatePDggRhSpw7Hjxoltnnxgh+7YgXE9HSUqNVQKpVQKpUwMTFh79ILL5PJIJfLpby8PFY54v0+4ejoiE6dOuHQoUOYMGEC6tckPFQd0tPp6w3OgCRJOHPmjCiKovTJJ5+8PmqgCaxYW1NQA6Csd9k8UXAc9Wfv2UNZ6qQkUvnt2ZOyTLNm0UZ/+TKVKbZpQwQnNJQEmFxcKLP49dfAqVOUMcnOpgzn0KFE6N3difhs2oSTx45JhUol04iRqdVqxPTsCSk+Ho6Ojm9RClCGRo1wafFiGOnri/pyOadbvz44jpNCQkLg4eFRa8/v2bNnMDIyEvLy8viUlBRYVcpWvhMkifqGS0ooI/Oy8FwZciIihDlr1vD+MhmysrIka2tr1qBBg9r3L9vaUvvBw4d0nRcvfuOc5zchLS0Nv//+Oxo1aiR16tTpzePvRJGqKWrq75YkciIPH6YRZjUEuvT19TFr1ix+3bp14vr16zl7e3uhTZs2fF5aGswPHsRvdeqg940bKLa3h81XX4FVlwEqy9aVO9oFBbSOExIoC65SEekfNar2FyQxESgshNnLYnmpqVQ2CWpr2blzp1hSUsJ9WYlQymQyel4aNiRSVK8ekQVLSwqAnTxZ7fMuiiKCgoLE4uJiDgAuXryIe/fuSUOHDmXmZcGCR48egTEm+vj48OVq+9u21e4zCQKRz4ICIir5+URcXm71eQmNGzfGqFGjsHfvXmzfvl2aOnVqzc/Yli2U7bp4kUhv2bWqjD59+iA8PBy/t2uHwTk5MG7ZEsGjRok2eXncdE1LTRnaP3smGYeHs+MNG3IDBw6EjY0Nk8vlmDlzJrd65UpJ+uUXBj+/qr3iVlYUeNHsTUZG9Ew2aACue3cYpKcj3t4ey93d8b2nJwWVnJyomqdTJyrt37EDXG4uMtavFzxzcni39HREbdvGB23diuDWrVGgUPBDy3q1HbS0YH7oEFtuagq+pASzV6+G5ciRorarK4cvv0RvZ2cUFhYK2/bv57uam0ueS5eyeq1aSYWnT8PgyROqwigupsDpxInAzJmwWLwYffv2xdmzZ5GYmFiFXIHn6fPNnk3r++ZNCmx26gTMnIlLcrmY1b07p+vuzj3r0AE2Gu0Cxshm7N9P9n/5cshkMkmlUtHroliuUN+rVy/O1cQE4vbtOOzlJfEymaTpuVbl53MDkpNxy9dXwPv08SWJ9hhDQwrC6uvTPsMYBXy/+ILWrJ0dBY+NjSmgs20b7W+CQH9bpw7QqRNuRUdDrVbjypUr0uHDh5mdnZ1YUFAAb29vrrS0FCYmJq9vH+I4qnbIyCAi37gx+TyVg2+1AcfR+Z48SdWAL+uXHDtGgVkNiopoHWzfTlVBzZsDly8joWVLFO/eDXd3d4iiiKNHj4rPnz+XXrx4wVtZWQmdO3fmAUhnzpxhK1asQNl95WLPn4dKLkf/8+dhnZwM5bVrMB4xgsTN9PRw9eJFEaGhGD9+fHk1f2fSN3nz1szzdM/at6fPWFmXpBpER0dDEAQEBgaWZmdnaza8BgC8AVys1fX8gGrB3kps5QM+4L+MZcuWHWzatOnAHj16aGnInEqlgpaW1tv1Ef4DUKvVCAsLQ3R0NNLT04XCwkLe0tJScHNz4xs1aoQ6b3CS8OOPtJFr+tfehIgI2oR9fCh6vngxZTlTUymj5OpKZcuvUUUWRREZGRlITk7Gs2fPhKysLJSUlDCVSsVKS0tZaWkp1Go1eFGEbWIiSnV1JZWLi9ixY0fe0dGRNvwrVyhi+vHHtLG9wYC/CatXrxY8PT359u3b/6P3dOPGjaKzszPX6W1mSm/eTE7eG8rag4KCxNu3b7Px48f/M0GDrVtpU7ezo+v9+DERmCdP6PU9e0iky8aGMkHz5pHzY2xM0XJb22orGjQlqPXr10e9evUQHx8vFBcX83K5XCoqKmI+Pj54q+sF4OnTpzh48KA0ITWV1TtzBnj8GMePH0dkZCS+/fbbN95jURRx6tQpISQkhDcwMJBatGjBbt26hU8++eTvtRd89x05St99V2PwRKVSYeOiRRhaWIjMMWNw6dIlIT8/n2/QoIHk5OSEdu3avd0CjYggwZoBA+iZeZtMeRlEUcTu3bsllUolTp48uXblH23bkp1YvvzV1ySJnMcLFygbVYuKkvj4eERGRiLz8WNBjI3leAMDoedvv8m2jxsHqxYt8OzZM8nCwkKytrbmVCoVkpOTpczMTObesiWaR0ai3pAhVYMbgkC2REsLePAAwoABuLdsGeq2bg1zc3NwHIe4uDio1Wo4OTlVrBlRJFK+ciV2BQaKSUlJnJ6enqCtrc23atUKHq6u5c7ymTNnoClRnTt3bvWK+3FxlDm2sKAKoPv3yXk/deqVXw0ICBDCwsJ4S0tL2NjYQEdHBxkZGYiKisLEiRNRr149mHnDQQAAIABJREFUnDhxQigqKuJHDB1KAZbly+m5fRNiY8mWP31KAbHoaCr9vH2bgmQ9e1aUk9eArKws+Pv7Y/jw4eXiY69AqSQyr1AQWfr+e8pO/vJLlV/7888/8fDhQwDAyAsXwPM8csaPh7uHR8UIpMxM3IqJwdmzZ2FoaCjOmTOnivO/atUqceyFC5zp+vVVK0hKS+nL3p7OJyaGAsbW1hR00dfH+cBA3Lp1C19//TUFsIuLqXc+IKBifGWTJkhMTMT27dvx9ddfQ7p5E7IjR1D68cfQnTAB3KefUuWEkVHFVAwjI8o279lTIVpXFrjKyMjA3o0bpZGbNrE/pk8XR4wcyZUmJOD30FDxK3d3Dvv3kx2uZL/27NkjPX36lLm4uAiDPTx4mJhQT/2qVZSNX7CA1vr06RRY79UL+w4dEtLT0yFJEoqLi3lnZ2ehT58+fHkALS8P2LgR19LTxQt16nAdOnTQkKuqpc6PHlGgrPIIwKtXkbp4MU4OGSJNmDjx72+mgkD7fVERvVdsLAV0c3IogJCSQuthxAha72FhFCgC6Lw0wpiSVBG4/uMPIDsb+XI5CqKjIUgSklq0QEmDBsjQ08PTtDRJLpeL+fn5vI+Pj9CpU6fa2byEBAoEHT1K/fMREW/3WdesIRtVecpIUhI9e5WFa+PjKfOsmSwQEQF8/DFSUlKwdetWfPTRR4iLi0Nubq7Url075uTkVK6lUFxcjJ07d0L24AGGNG8OWf364GbPRtY33+B8QoKYravLqeRyuLu7iz169OAYYzh79qwUGhqKuXPnvvv9nDCBggmayQ/VID09Hb+8ZAcALAaQD2DdggULlO/8/h/wIbP9Af9e+Pn5MYVC0dbT01Orctb0b40peo+QyWTw9PSEp6cnAPD5+fm4efMmHxQUJAYEBHBjx459pcwQAHDoEDlW1SgEvwJRpNmXERFEor7/nghgSgptZOPH01fDhtWWUEmShNTUVISHh4sxMTHIzs7mtLS0JF1dXaFevXqyRo0aQU9PD3p6etDX14eenh4MDAzoGl+5AixezLB0KV+u/slxFeWOL14QmXvwgBylNwUXakCXLl34U6dOSYaGhqzyjNL3DWNjYy4/P1/E2/Rt37pFZWavIXmlpaW4ffs217t3b/xT2XlMmlTxfVQUOaXduhHZnjSJ+jktLcnByc6u6jy/huCVlcVKqampTF9fX+jZsydvaWkJIyMjdvr0aTE2NpZ10vRcvgFqtRrbtm0TsrKy+A4dOkhmrVoxTJgAlJaib+/eiIiIQGJiYkUm5yUUFBSA4zgEBASIUVFRPAC8ePGC+fj4QCaT4ffff0fXrl0lLy8v9lZBGUGgaoD27amq4DUK8RcvXpQUubnMatkyWOnro0WLFnxiYiKuXLnCAgMD4enp+Xb2x8UFwu7dYAsWgFu7lrJWtVThLi0tRXR0NCIjI8Xk5GQ2Y8aM2vdZbN5coV5fGaJImeasLPq3lq0bdgYGsOvSBdixg0dBAXD6tCz+s89gfuUK+vTpA7lczgICAlh8fDxycnLEOnXqcCqVCk+OHEHDy5eR17MnUq9eFWJjY2Fubs7srKw4+zZtEPDVV6Lo6orHI0ZwjY4eRfHWrdLejh2ZXC6HIAgSAERFRYmDBg3iGWNQZ2VBGR0NbYUCo0eP5hISEpCSksKnpaWJp06d4lw++QSKmTORM3IkBEGQGGOsd+/eNd8zW1sKUoaHk7O+ZQupLE+aRN8zhsLCQty4cQMhISH8p59+ikYvEV65XC799ttv7OOPP0ZkZCTfv39/aiHQ0XlzILKwkLKC27cT6V+0iOypjw8RYltbIowrVlCGMD6e+tzbtHnF3puamqJx48biwYMHuf/85z/VV1kpFFQqP3Ei8OQJVO7uyE5KwstF/HFxcSIArlu3bmi8YAGwYgUazphBmdWgICpJbdIEHr/+imRXV4SHh3O5ubkwrqTe7+TkxF0PCUH7lBSYaMj2ixfUj75uHfX3Ll1KwosPH1K2vWzNdujQAc+fP5dWr17NjIyMxGHDhnFmgYH03pGREO/cQUH9+rC2toa+vr4QGRnJt+rSBejSBQoAOHoUoRcuoGTSJLHR/ftceL9+Qrfdu3lYWFBVRGAgBQDKCO7u3buF7Oxs5DPGn+jXD6OMjDjjffuQrVCgxa1bDJmZFMwEyJbcvg1cvIiR9+6xjJs3cTs/n4LP/fqRvXn0iKpa5s0je+3tTZ9t9mx8snIlr9EyyM3Nxbp163gbGxu4a8QCyyZcOKanc4oVK+Cq0TQ4cIBU9csE0XD4MFWyVcbhwyi2sfn7WhGBgfR+48eTyOHIkURGDQzo2JJEBO7pUwr2r1tHa/iXXyiTmp9P1wAgP4UxEkqsX79c+M9QFGGYnAxERsL61i0KhmhpAeHhDA4O/FUzM4SfOMF3at++dnOqNb7W4MGkNSNJtNZ27KidKKe/P137IUMqnq3o6IoRfQBlvvv0Ib2BoUPJhyubplK3bl20bNlSCA8P53iel0aNGsVV8QcSE5H93XfgdXXR9fJlGLx4AdmOHUDfvtDT0sKEMr8kLi4O+/fvZ5GRkdDW1kZmZiarX7++Gn+Hr23cSAGuhQvp3KtBdTZSS0vL7Lvvvpv/zu/7AeX4QLY/4F8LjuMmGBoamli877m6/xAMDQ3RvXt3WFpackeOHKmRVEBPr1yVWpIkCIIApVIJlUpVPrZDLklQKJXQnTABLD8fbP162liDgsiJa9WKspvVGEhJkpCWlobw8HDxwYMHTKVSoV69enB3d+ecnZ014m5vfvZ9fKjMOTX11d5WxiqCBePHV4jfvAPc3NyQlJTEQkNDBTc3t3+kcVsURaSlpUlv3RtmY1MxH7gMarUap0+fFlJSUqCvry89f/5cpqurKzRr1uyfaTrXQJJovvfnn1MgJDKSxNY8PckRCg0lR3LtWvp5mzbl82VrAmMMkydPZmWzm6ucf9u2bblff/0Vly5demN2u6CgADt27BB1dXXZ7NmzoaurS95K8+aApye4du3A6tWrsc0iJCREOnnyJAOAunXrSjNnzqTRM5IEmUwGHx8fWFlZ4Y8//kBhYaHYuXPn2gVMBIH6162sKLv8mlYHQRBw+/Zt9rWGKK1cCYCUgUeOHAk/Pz8EBQWhW7dub3xblUqFo0ePCnK5nD179ozlKxTM0ddX/OjbbzkDKys87tkT+lZWaNiw4Ss9toIgIDw8HEFBQZJKpZL09PSkadOmcbUeIzdrFvUzvtxjKEkk/qXJZNZG50Ktpi87OwoUVCLodnZ2VSoNhgwZovmWA+iZe+jvj8jnzxF24AAA8G3atEFMTIzw8OFDofmkSZzK2prLyciQ2nTtis52dsDQoczG1xcpggBPLy9WVFQEf39/bunSpehz7ZoUZWHBHvXti06RkfDx8al8Dpy2trb6aEyMrImtLYofPkRwcDADgICAANjZ2b0qShQaSrbr3DmyyUZGtF4HDKCe4rVrIc6ahR07dkg5OTnMx8fnFaINAL1792YmJib4/fffoaWlJTnq6zNcuEDZ09f1kF68SL3KxsZEVDSOed++1Fo0ZQppbaxYQToMc+fS89+/PwXbGjcm8lPJpo0cOZLz9/cXDhw4wJmamrKioiL07du3XGwwISEB+bm5klmzZix7+3Ypv6QEQmwss9m9W8xVq0V9fX0MGzZM5uHhwV29erViQsE331AGWpKIoDIGREZCy8ICpkFBEgCWnJxchWz7+voifN8+6drGjczJ3ByW2dnQc3MjsrJsGX02U1OyVampFAT76CNg5Upoe3nBx8eH7d27F9nZ2dzWrVvRv1MnNB46FMlHj+LE9euiz0cfcQG9e0PbyIidPXsWurq6cC7LugcmJ+NWXh5azpzJ7bx2DTaMSSgpIUJ37x4RfUEAJkyAWFyMhIQEvmPHjsjOzkbnYcNg4OdHmX9ra7jcucOwdCkFuA8coGC5QgEsWQIuIQG8tjbsz5/ngz/9VGw1fz4Hc/OKZ2vYMAoo2NtTECUlhdbE7NnAtGlIKNtbXtYzQUgILo8di36ZmVCUlpLd6tWravDm4sUKxX2NcvvChXgeH08k8W3w4gXdg5Ur6XObmxMpdnYmn6MyVCoKQsfEULBIT48y96NHU8B30SLqo27YkD7799/TPX4ZHEd22cqq4nOoVLSHnT8Pr8JCICQE0oQJYElJlD1v0YJ8jRoqtQBQcFyjXu7rS6KMa9dSRcf81/DGmBjSlsjKqghQP39eVU8iIIBaL2bPpgy9g0P5S1paWujbt69mD2WQJFpjX3xB13bOHNSTy9HUywu7LS3xn//8B7JqgggNGzbEt99+y06cOIH4+HhxxIgRnKOj49/jatraKNcG6NKFfLuXoFmDHMddFUXxWwBrSktLd/2t9/2Acnwg2x/wr4Sfn5+tTCZbO3jwYL33Joz0X4K2tja0tbVJ1VitRk5ODnJycpCbkgKnr76STo8aJaXzPFe6eDEEQQAAzYxLSUephFFurtT9zz+5R/b2yNfTg7YgoPGIEbjTti3iGjdGqbY25NHRkiIhQVIoFJK2tjZ0dXWho6PDGGMsKioKSqUSZmZm6Nq1K3N1dQXHce8W6p4+nTb4I0dqztKcO0cb2+bNRPo0Ee23gI+PD9auXcsXFRVB9+WZs+8B9+/fh1qtZm9dBuzr+4rDfP/+fURFRTFvb28uPz8fzZs3h4uLyz9PtLt3J0d3/vwKdeHMTCIM48bRpg5Qb1ZuLpWwfv899Sdr1MmbNHnl0Iyxaku7jY2NMXLkSOzatQslJSXo0qVLleh3QUEBzpw5g4SEBKGoqIi3trbG8OHDuVfECrdvB2xtwa1fL+3atYv17NlTcnFxKX/DzMxMBAQEsA4dOqBjx46vkH4NGjVqhHHjxrHt27fj7t27aNKkidClSxfeqKaRLNnZlKEcN46U/mupxpp+/jzsqqlI8fDwwI0bN2Bvb18t6dJAkiSsXr0apaWlvKmpqVi/fn2pf//+LCwsjNspCGh/8SKyUlNFpVyOg3Z2nImJieji4gIvLy8uPz8ff/zxh1RSUiK1aNGC69y5M3tr+/f48auOqFpNirsuLkQAakO0f/+diHtaGpVbv2XVBieKcNmzB+LPP6Nzq1ZYvXo12rRpA19f34r7O20aMHkyQ4sW9P/gYNisXAmbmBigdWvo6+tj2rRp7NSpU9A7dIi1HTQIxTIZLl26BBcXFygUCty5cwf6+vpo5egou5aXh4DQUGiEK3v16oVz587h+vXrMDQ0hFKpFHx9fXmWnk5Ed84cIquSRARl82b62fz5wKFDiF28WCrW1pbmzZv32vvQtm1bxMfHC1paWjz3ww+UzatJVDEjg8impSURqJfHASYkVHw/cyY59vv2UXbR2JjuB0B9syEh9HXvXvmkiLFjx/IHDx6UQkNDAVAfOQAYGBhIpqamUmJSEpfXoQNG+/uzcyNHisP27GEBz55xiW5uHMdxWL58uVTWXshycnJQ/jxv2UL2pHVrIpv5+YAgoEWLFiwqKkoKDw9nGrILUOVXy65dmfaJE9KZAwfEyT/9xN91d4fM2lp0/fxzjvfwAGvYkD5PnTp0D1xcKLBz9y6MywiPKIoYMGAA/jxyBEbjxiHv8mVY1qvHmjGG9AYNUMfDg7t48SKyNarfABo0aABRFKEXFyf2vniRud6+LYOpKRF9Q0PqJR41CpAkcPXqoWPv3iJna4v+UVEcevemsna1Grq//YZDQ4dK49q1Y6UmJijo1An13NzAjI2pB/vTT2Fsa4vo0FBcuHCBM1KrUU+lgpGODpVaR0RUKOM3b077aHExEbbgYNgbGsItLg4nTpzAzJkzK+yrjg6iGzeG1ty5SLh5U3o8ZQrqmJjA8+FDBpmMgsDffFORsb1+naqczM2RdP26JIrim/e558+pPHz58oqWEk9PCja9rt1l+HCqcLh0qbyHvFzgE6D9aP36CgX+mzeJdNamnUIup8CEvT2uXbiACJlM6jBtGkNwMF3Le/eoCuLGDSKMnp4UdGrQ4FWRRY6jQFXZ9YQoUnZ36lTK9CoUVX+fMbIJv/5K5eMAVZL06kX2784dygpPn05fGptVGYJAAZXISLo/S5ZQwKJPH6BNG8hat8alRYugr68vaWtr13iPOI7DgAEDgLepwnsNCgoKAHd3pPv6SrmzZrGAPn0wfNQoOJa1MKpUKixduhQAIIri5QULFtwA8PaKah9QIz6Q7Q/4V0KhUOzw9vbWfisxq38J7O3toaenJ6xevZrnOA5yuVxUKBSimVotczA0ZE07dGAdraxgaGgIfX192mBVKmDlSoadO4GvvmJo2hSW3boBenoQ27ZFkaMjugoCRFFESUkJCgoKWEFBASssLERRURGKioqQl5cHtVqNTp06wc3N7d0JdmXI5SSCcunS60siOY6cQSsr2nBGj6YNrVKm43XQ19eHkZGRcO7cOfTv359/373bRUVFKC4ufnsiHx1NpE2jDg6KAHMch7JRYv8sVCoqRfTyoqj0+vX0vbc3cOIEfZ05U9U54nnKGK1YQU7906fkjO3bR45A06aU+ayFeJ61tTWGDh2KK1euiD///DPn6+uLVq1aIS0tDTt27JAsLCwkb29vztPTEzKZrHrHwNUVUKnwnxUr2NYRI3Dd0JC5VFpLhoaGsLOzE69fv86JooiuL6sXV0L9+vXx5ZdfstTUVJw9exb+/v7geR5yuVyyt7cXO3TowJuYmFCp5fLl5BBrFOHfAFEUUT8lBTqffkr9nC+hT58+ePz4sRQYGIjJkydXe8AXL14gJiYGSqUSZb2W5dfEzs4Oqt698WLWLJhGR3PYsAEvFAo8aNqUC71/X7hy5QoAwNnZGQMGDODeKcj4+DGJEb38eadOJbI8YsRrs/sAyLls3pwE9oKCaD3Vgmi/Mns6MBCsYUO4lY1PY4whIiICVlZWFVU/ubnk0GocV8ZIwO+774iEmZnBMDAQIx4+pH5QAGMB+Pv7Y8uWLXBwcEB4eDgAoGFcHAZcuoT4jh0FmUzGunfvzrm6uiIhIUF49OgREwSBqVQq3uXJEzTw96d7rHluLl6ka7dsGRHusDC8aN4c7Msv2bC1a2sV8Hjx4gXTDQvDJVNTFFlbwzIsDK6urhWVC6JII5K2bSOi169fxWi0yhg0iMTtzM2JsK9eTSW7xsa0ljXn8ttvRB4SEihbde0awPPQt7LChAkTmJ+fH1xcXNCiRQsIgiA5ODgwlOn0MMaAsDB83L49h6++gmdqKhplZSE5OVmIj4/nMzIyoK+vj1eqypo1o89RXEwZwClTYJSYCOWnn6LzrFkIfvJEbPXxxxwWLKDsu44OnEJDmdOkSbzk6opGkyZha3o6M27VCrt//BEwMIBZdDQy/fxgbmEhshYtULhvHzd83TokmpmB79cPAwYMgIuLCywsLJCZlITG3btDfeUKk8+Yge6RkcCGDZBNmoQzZ87AxMQEDg4OCAsLkwxEkbVo1YrLWr8ejx89guOjR0Qk9+6lXmrNdIjkZNR78oS79dtvaBsWBo7nKWgcGQk5z2NAbi574uwsPXV1lZ5oa3NDfvkFNpUqOvKysqBUKiFJEvbt2wcnJydh2LBhPPLyKND4MqnT0Smfua6/fj0GREXhRysrBH32GZwXLcKLwkLk5+dDLZdj/4oV8Dx5khWdO4dbRkbwPHeOjhETQ+uoe3eqoDAxoT2a51FSUiKam5tXH/yNjKT9olkz0gL45BMKqK1eTZUdr9vjU1OpuqVXLwpISRKtT6WSbIpmhKKXF1VnnDhBa/zwYSKemzdXlHrXAvHx8VJhYSGDlhbtv5X2YLx4QYQ7OZnsgp8fXZMvvqBrYWtLWjia9rfPPqN/nz4lXQYtLapSaN26agXQmjX0bGoCQP7+VGWyYQNlx0+fJmHSyvZQraZr2rYtHc/enu775s10LSrZ4rI51tJbT0X5G1Cr1VhVJvDGN27MJgUGov+xYzggk2HBggUAgGvXrqkAyBljJyVJWv1fO7n/Q/hAtj/gX4dFixaN1NfXb+3t7f3PZgv/IXAch5f6Kzl88w2HDh2AmzdRPtVXkojIjR9PJU5ubhSpvXePyq/c3AC5HBwA/f/+x6jA55+T4MvBgxTVrgn6+uQMZmVRJFhHh6LQTk61GskxZswYfuPGjZKDgwMqZ0jeB8LCwoSyfvC3W1PGxlR6CMrAymQy3L9/X9LW1v7nlSWDgigT/egRZWjr1aP1onHee/emrJihIX0/cCCVilUGx1HkXzNr+fp1EpC5coWc9QEDqPfsNUERR0dHODo6co8fP8aRI0cQEREhamtrc7a2ttKIESO4WgVG5HKwgABoxcQgMSEBp0+flrp06cKePn0Ke3t79O/fn1u3bh3yNYrsrz2UHDY2Npg0aRKvVquRkZGBnJwcdvv2bfbLL7/A09BQ6BgYyMunTKl+lEsNCA8PByeK0H2N6rmzszO7c+cOoqKiRCcnp1cW9f79+6WUlBTWqFGjaoMxcrmcypnbtgWaNYNBWBjazZ+Pdj/9xMfb2oLjONjY2LxbpEmpJJtx/36F6GJpKWVAhw6lbF1NvcspKdSreOIE9Qi7u1MwphZO4dOnT3H48GEUFRWhXr16UsuWLZmXlxc4lYpKSstgZWUlnj9/njMwMBC++OILeg737SMnPiOjgvhaWdEYu1mzKPvm7U0ObSVMmzYNx44dKyfa3bt3h1wuh/7WrZitpVXlGR8yZEj5/0NWrMC127fh/dNPqG9khPKrER5OGeJp04ApUyBt3ozCBQuk6CFDpF5793JwcXljr33XNm04fssW6V67dihSqcTgEyf4U6dOYd68eVSFMm0akett26h3tSbk5latwjAwIMe9Rw8iEZUJkZYWkYX0dPqbHj0AxiAeOQKTzEw4OjrC3t4eqDRDufx53b+fqpIuX4blTz/B8uJFuLq68iUlJdizZw8kSRLT09M5MzMzcBphwaNHqeLh2TMiert2AY0aYWBODst4+hTXcnI46+Bg1C+7Lxgzhtblhg1gwcEwBaC3YYO4e/duXiaToXOnTnAfOBCh06fjvihy6enp4HkeOyZOhKhWo9edOzD+80+U3LqFunXrkibG9OmQabQGysrZrc3NwRjDiRMnIEkS+gQGYlhKCmQLF+Lhjh1S8JkzouOtW3xYSgoMrl9HoxUrKlKGBgZwdHfHlZYthfUODtzkoiKmO2kSMGcOuK++Qv25c1E/N5c1s7Vlfn5+EF+6XadOnUJ6ejrMzc2F7OxsvnxCym+/kV2tTqFfg88/Bz7/HJ6bNkktN29m5xiDWKeOUGxpCQcHB3748OHgeB5NvvoKzz/7jAIqABHszp3p2i5cSH3JZddEEASmrvy8xMXR7zdpQgTT2ppGvl29WrM9qA4TJ5I92LGD/ADNGk1LowBG5WPp6FBguG9f6qG2tqaM8ahR1ZeUVwMLCwv2/PlzlJaWvjoJwsCA1p8GEybQmnzyhMrQg4Iom9y1KwUHdHTo+W3RgsYDAtRKk5pKlU9PnlBrnpcXBRLGjKGAzKhRpCj/0Uc0StXCgq6BKNJzoFGI37OHkgs3blS0ddQw7aK0tBQWFhb/NXXfyiNVBZkMumvXwnHTJkzr1w8AkJqaiqtXr8oBQJKkUQsWLMj9b53b/yV8INsf8K/CokWL+svl8q0jR47UeeN4m/8VSBJFVCtvDmfP0ibp4EBkavp0Ms4dO77TuK5/HJ6edI5DhrxZVMnUlDYdgMjOF19QKeQbYGhoqKkCeK9Blvz8fGRlZfHdK1//2qJRIyAyEjk5Odi4cSM4joOBgYH4xvFefweCQBmwefNo7Mjy5eQEJCZWHWvi50eR/d9+I8eiNsJb7drRlyiS45CbS46Tlhb1q8lk5AxVQ6AdHR0xdepUrFu3juM4DuPHj68d0dbAxwdjSkqgnjMHS2fPZk+fPkVmZiYYY9BMxYiKikJBQUG5euubIJPJYGFhAQsLCzg7O3P5QUF45O/PHZfL4evtjRoKzF/BixcvEBAQgPotW6oNpk6t0fD07NkTERERuHPnDpycnF553dXVlaWkpKB9+/ZvFlLT9Kj6+QFXr8Lu3r2KDMy7QKEg0qrp/xRFIngODtQOUZ09PX6ciObs2XTf5XJg+HCkp6fj5I4d4DgOzs7O8PDwwOXLl3H9+nXo6OhAqVSC53lJEATGGENpaSkaNmwIxph09uxZdvXkSUy5dAkGx46Vv1VqairH8zyaNm1addF88gnZvj17qp7bZ5/Ra4sWUbaoEjiOQ5MmTcrJNsdxcN+0iZzpGTOqvz7+/vD46y9kTpwo7YuNhXLZMubg4CB5e3szm5fsU1SHDrgdEYHRABHtMWNIyOw1e1IjUQQmTGC2U6cCAH/z5k1cOHkSD3/6CU2OH8cRZ2dJ2bat9KmZ2etDjxermbBTpw4R4w4dyOmv1CsKoEJA6uxZQKVC4MqV0uQtWxg/dy4JdDVpUv25//EH2QB9fQpoyGTQ1tZGnz59sGXLFu7XX3+Fq709Bq1fT6Tq2jUi/JJEIlrTpgGLF8OmY0eUpqXhjo4O8vLzUV+jcxEdTSRk8mQiLcePY5K/Pw+gQr/hxAnkx8UhMyYG3bp1g6urK3iex40bN1Bia4ubAQFo9/w5LIOCiFTNn0/B6BkzKJAbFITYadPQPiEBTRwcoO3gAKNjxxhTkniyp6cnCzt3jn+kq4sbx47BKz9f2h4UJDWIjeVSUlIkpVIpOTo6cp06deKfz5ghqTdvpv7q0aMpS7p8OVUaJCRAoVBAoVBArVZDpVKhtLQUpaWlaN68udStWzfNnkD/lpYS2asFekybxjB1Kj4BgB49eDx4QCXnWVnA7Nm49/gxWj55Qvd++XIKrvbrV6G6XaktRM5x4IuK6JkODKRzLy2lCqMyMa+3wv37VA79669UFTV9Ou1HGixeTOvP37/iZ336VASqbW0D7J+KAAAgAElEQVTp99PSqKLq8uXXilSWX5MePRAaGoq7d+/CWyMSVxMYo/epnDmfPZuC/ZmZlPX+6y8KqPXtS6Xds2dT4OrAAfp8CQm0n8rl9HcxMRS0HDGCbNP160TUp06lvXL/fvJvZsx4K7ttbm4uPaMA/n+FcL+8R6+OiUE3SYJDly74cfp0iFXtQgaAWs64/IC3wf8jbOYD/l/AokWLhmtpae0YPXq0zv9i+Xi1uHWLsgF//kkk6vPPyWFxcSHS6u5OpYv/dhE4Hx/aXPbtow2ztoiJoY14xgyKIv/5JwAq6y4oKICJiQlkMhmePHmCBg0aQKlU8u9T0Ts+Ph6HDh2SzM3NUadOHQaQANXly5elkJAQ5uHhAZ7npbi4OLFDhw5848aNUVxcjNu3b0utW7dmXHExtJ4/R1ZWFgBg2LBhcHR0/OeI9qZNVMoWEkIEqbCQIuUHD76qLu3oWOFcTJ1KzvjDh7XLHHBcRcbF15fUdUtKiNiYmVFfr63tK8Tb2NgY48ePh4GBQRUxpNqC69oV8iNH4FFUhLDQUPA8jwkTJuDOnTuIiYkRCwoKuJSUFDSpprf8jbh0CYZ79sCka1d2PjMTlmWjX9LS0kSVSiUVFBRALpdLcrmcMcYkQRBgZmbGeXt7c48fP4YkSZg8f74MurpErmqAs7OzFBwczK1bt06aNm0a09LSQnx8POLi4nDt2jU4ODig4cviZK9D585UfrhwIX1//nzN/b414flzUlt//Jj+n59P2avPPiOHv3KATJJohFGPHuQMZ2QQ2dqypfxXdu3aJZaUlHCaz3bu3Dno6OhIABjP8+jZs6ekVqvZ6dOnAQBeXl7oSVUE3P379xG0bRuCtbTAR0YiLy8Pjx49kkpLS1mjRo3g4eFRlWsePVp9OXVxMQWQVq6kz3fuHMBxEAQBpaWlOHLkCADAxsZGcnd3Z2jS5BVSDoBI5LJl5BgPHozuFhasO8g27Nmzhz1+/BhzVq1C+ubNaPzxx4iLi8OpU6fgPnAg4+VyEir74QfK7O3YUX1Lwr17tGZu3iz/UVvGYJSaKhbfu8et6dIFcnNzJissxLJly6Tp06ezarUGoqKo6iG3muSSkRH11fbtS2ukJrFHuRx3AcYOH0Y3S0t6vtu1o+uYl0dZRg02bqT1kJdHLStljreFhQXMzc0lmyNHWIeVK1HQpw90r11DbkEBUiMj0UhXF9pmZnQO/foBa9YgVamEqKMDWZcuFdMTNGv5m2/o+hkbQ1uhAM6dQ0G7dnj06BGCz58XvP/4gx+wbx8qt5d06dIFkiRhUUICml24AMslS+gacxyVJjdqREQIgJOdHRIfPBDjQkI4eX6+4D5lCq+xl3K5HCO7dcOL6Gh84uoKnbp1WYRMJmZmZgqNGjXiiouLudvnziFeECSFj4/UqmlThosXqV8+OprKiGfNAp49w9SlS/FLTg6UurrgOK68baLZyzY3LY36oavr660JmnV19izZ4sBAeobv30dqx47o8OefEubPZxg1ip6Fp0+pfFtHh8qqHzwAMjLQY+VKFu7sTO0I48ZRS0g1gli1giAQoW/ShO51UhKtw8qYNOnVyiiOo71o9+4KFfe+fYnwjxpFqt4vB4xeQlpaGgRBqH6iS23AceXK5+jUic6zqIhsZHY2CSQuXEjP2+TJVKWhCQqtWUMiemU2A7/9Rs/ehAmUATczq3Y8YG1gYmLCIiMjoVKp/pHJOrm5uUhISECzZs3KW1jmzJmDNWvWlP9OYNeucHnwAM0fPEi55+4+EEAigMOMMeG9n9AHAPgwZ/sD/iVYtGhRD4VCcXjMmDG65i8LXfwvY+NGEh/R0iLHNjOTNsBOncjB/l8Sf0tLo8i4v3/FrNXaIioKOffvI93eHrkzZuBMjx4Ax5VvBqIoQiaTSYwxZmBgIKnVanHcuHG8vr4+Xu6XfP78OSwtLav8/OV+UUEQcP78edy5cweOjo5i//79OY1o3a+//ioJgiAZGhpyKSkpUCgUklKpZAAwdepU3L17VwgODuYBQLegAE6xsQhxc4NCoQDHcZKvry9r8TZOVG1w5gyRHlNTItUdOtD1trOjgEV1JWm3bpHjrHlt+HByhCqRpreGKJIjsm0blRTPn0/R/AEDaidw8wYkJCQgKSkJFt9/DzEuDvUfPKiSxX6brHYVHDtGpbQODkh3dkZAQAAEQRDL1gVXr1491KlTB0qlsry/kud53Lt3D6WlpWCMoXPnzmhvb09ZlzeQ3czMTGzcuBEAMG/ePKxZswZFRUXw9PSEr6/vKwrjtUZCAmVzhw0jovym/moNcnMpUPPdd+R4L11Ka+HrrytsjChSX+dHH5ETumYNEe6XsHfvXik2Npb5+vqiTZs2CA8PR0REhNilSxeuchBUEARERUWhpKQELVq0KP/MSqUSyp49EdKrl3ilsJADAIVCAaVSCZlMBj09PWH27NlVA1aLFxNhPXSIznPkSPoMdnZElm/coFab1q2x9/RpKTY2lgFECidPnkzHePiQ7NLLZHjlSnpWtm+vQhSePXuG3bt3w9TUFFanTsFo+nSI2toaETxp6NCh1KtdWEg2W60mgrBsWdXjiyKVpBoaUiAyK4uCkjExkHx98WdRkVhcXMw+/fRTdv36denChQvsk08+qT6gVFRELR7V3JdyPHlC1+f48RqDtEuXLoWHhwd8fX2JTKtUNGd37Fh6ThISiEBxHJUAz5xJBEKjYv3iBTK3bQO/ZAnue3khqFUrmCcnwywzE0+9vcXP583jEpcvh6CvD+XJk2JjW1tOXqcONhkZSW7u7sxHQ+569KCg3oUL9LkYA2JiUNq8OVbOmgWFqank7eUFz3HjmCwkpNp2lk2bNokAOBMTE7g1agQnX186VoMGlPHnOCrLX7IE6ps3cVgQJO9Fi5h15aBCRAQJXG3bRvaxEjkWRREPPTykRnI50yuby45nz+g9Ro0CAOTl5eHIH38IDY4e5W96eaHDjRtI+ugj4UWdOkwo01IRRZGJoghJkuAQFsasY2JwduBASJIEURShVqup1UFfX8zNzS3frBQKhShJEqv89wAJLeoWFKBYRwezf/4ZyZ07iw4vXnCYMoX2iy+/JNvcvDkRaycnYMcOPA4PFw/cvMlp+nHfGZcu0bq4c4eCNd7e5M9URkkJrfnqxggWFVFyYfPmqq+dPQt8+y0FeF9DNn/77TchJyeH//rrr//e53gTsrJofnh2NmWwnz4lW6QRf3v4kPq1Bw8mbQRBeLUP/y1QUFCAQ4cOiRkZGeyLL75g76uCMzMzE1u2bEFpaWmVn3/33XeIj4/Hvn37ND/yYoxt/mHBgjEAjgGYBkk6W90x/fz8zOVyub+WllZhcXFxqiiKmxcsWPD0vZzw/zF8yGx/wL8CCoViad++ff/fIdqiSJkmpZIyhDExtMH07Vt7B/rfhvr1qY8pN5ccuFqUEIeEhKBly5bgnJyw+fhxyezSJeabnAw9fX18OXw48o2MwPM89PT0kJSUxAoLC5GWlsauXbvGb9iwAXK5XGrVqhWLiooSzc3NudjYWE1vqHpqWbnvhg0bhOzsbJ7neYwZMwbW1tZYsmQJRJG667p06cKdP38eT548kYqLi5m+vr44Z86cyp4B27Nnj/D06VN+27ZtkMvlfPv27REWFibqAuh+9SrXbts2REdH49GjR+z48eMoKCiAg4MDDh8+LDg6OvKdO3d+t2ual0drRdMfpqkauHCBAjKxsRXCMy9j4UIiZqNH0/9376bMlFL57s4Ax5H4y3/+Q9kojQDN9u20bk1N6T0tLd9plmtQUJAYFxfH1XFzQwMfH2Gwnl4VD+2diPaff1KmZ8oUwN0d9QCMHz8eqIWSq66uLoKCgtClSxe0k8spy1p5rmoNqFu3Lnr16oVTp07h6tWrcHZ2RnBwMARBeHeiDZCt+OsvUn0+fJgcaM383ZqQkUHO7nffEZH6/HMi6wMH0j1SqSjTHRhIDnpcHGVQq0FAQIAUGxvL9PX14eHhAcYYmjdvjubNm79yLXmer5KJ1ECRmQlFVhY6zZjBJRw4IDo5OaF169acJEk4evSokJyc/Oobd+5ckXEtLKQeck3AQyajzNzUqSheuBDJQ4YwALCyspKaN29Oi/D5c8pQ5eVVHDMvj8jlwoVEGsrsrkqlQkREBP766y+4u7uLvV1cuD+fPRNDb9/mAMDAwEAcPnx4xefViJSNHUvkwN+/aqn6li30nEyfTtU/J09Sn+jYsWAGBhhaaR2WlJQwAEhMTKyWbKvT08Fpab1+4TZpQoGImTOJyFTTdiRJEjIyMug/jJE9GDSIMnUKBdmWzz+nTJ6vL5UIk/oxXX8vL9TNzASOHUO7WbOQWrcuTG/dglt+PgZ88QW3oqhIEouK0PnAAWaSnc35u7piZGYm2h07xk4XFCA+Ph5ZWVlCBz8/vpWXF7VQ3bkD0dMTYXl5OPmf/2Dw4MFo5uvLsG4dBRA05/sSOnTowJKSksSkpCTu6r17otP+/RwaNyYbVK8eEaM7dwBbW7AHD6BITX01Y3jzJvUo29tXDRT//DM4XV08mD0bR+Pi4Pznn4KLiwtvEB0NXiZDaWIiOI5DQkICUjMzucZz58K7uBit/vgDFgYGfLGjIzhjY8gUCshkMvA8D57noXBxgUwmw5iy7CLP8yguLsbz58+hra3NyeVynD9/XrCwsOC9vLw4bW1tKBQKyOXy8ukQmvJfjuOAjz9Gk6QkDkeOlGfz0aIFZbOdnChbW7a+HS0sONy8+apg4dsgNZUyv/PnEwkNCqq++uTJEzqH6lrLdHVpzc2dW6EKDtBacHOjIMzBg9VOyIiLi0NycjI/uxbtZ38blbUpjIwoUHbrVsXrU6ZQEKpxY2or6NiRKs/27qUWnOPHga++okSEgwPZVxeXGhMp+vr6GDt2LLdhwwbpjz/+kEaMGPH2EyeqQWRkpFRaWlp5Uy4GoLNkyRK0LROX43n+mSAIoZIkjfFbuFB/np9fqEySVgGolmwzxmaLojjIw8ODJSYmivHx8XOWLFmSK4qirkwmu6dUKr8EELJgwQJ1dX//ARX4kNn+gP/f4efn10ShUNz/+uuvdfg39QP/r2DePOoDMjOj0jdzc8o8mpvXegTRvxKSRI7alCkVm34l5OfnY8OGDbC1tUVBQYGUlpbG7O3twXEcnjx5gu+++47ETgSBMrhBQdQP/hK2bt0qJScns8aNG4tZWVkQRZEzMzMT9PT0+Pv378PJyUkaNmwYO3fuHEJCQjB58mT4+/vD2dkZkZGRAKifV61WQ0tLCzzPS87OzszGxgb29vbVkjpRFHH27Fnk5+ejf//+1FOoVhMBSkwEOK5KRpMxBhcXFykiIoKNHDlSI0RUe8THA926EdEeObKCvBYXU8bq8uXXlyFevEgZmsotFyNHEiHYsePtzqU2CAsjMtqmDa3tfv0qSghrSbyTk5OxdetWdOrUCT4+PuTAfPvtq8JutYEkUUZRFMmBe8vSc1EU8eOPP0KhUGDixImoe/w4Zcj/+qvWf79582akp6eX/2zy5MmvKji/Kw4dIjL1/fdEJGvKAh0+TJnhkyfp/AsLiVQLAtmali0pk71zJ92n19yr1atXi82bN+depwj/Rly6RGXp1TzXP/30E8aPHw/L6gJIcXFEWCdOJCf9JSQ9f46Dq1fD/f595HTsKPSfN48vd1JVKtK+0FRfFBdT8OnhQ8rgV9pXfv/9d8TExMDW1lYcM2YMx/z8gOPHsWH8eKmoqAhDhw5lNY5269OHnttVq2jNKZX0jP7+O2XQW7So6Al9Cenp6di2bRvUajW+/PLLVyYj3L9/H4nffw+vW7dwZdMmcciQIVxSUhIiIiLQrl07aGtr4+LFi2jYsCER9cOHqZT1558pw1sJS5YskXieZzNmzCgfg1YFgkB9vL/+Sv2nT5+S0FrbtkQSQkKIOFy6BEyYgFOtW4t3U1K44cOHo2nTphAEAXl5efhl0yYYGBgI6vR0vpDn4RwVBdcHD3BoxAgY5Odj8i+/YM0PP0jDMjOZfWoqLk+ejKCgIBgZGWH06NEwuXeP7MnUqRXr19iYRnPl5NAaOnYMyMtDcGamVDclBXaDBzOsXk2EECAS3bYtwPMIDg7GzQsXpBkXLzK2Y0dFoHL+fArg9OtHc8rLArFYsgQwMsL+uv8fe98dFtXZvH0/5yxLRzqCIKAiIKJSBLFgQY29ayyxG2uiiRqNiZFgNDFRoyaxxZLYiBolQY3R2BFsWBCliEiTDgLSy+55vj+GZUEBNW/yS973c65rLwV2z57zlHlm7rlnxpTHxcUxBwcHnpWVJfU6fJgJCgU7/eabnHOOqqoqwcDAQHrnnXfqekX9+tHa+u23uuPr5UUAZSPVvbdv365s166dqHKEUFlJDLht28iBFkUCPFWMlcxMirD26EF2RVZWvXuZc46VK1fik08++XPO9k8/UdpEeDjtncBAcqrrk/x8OscbsmlU6S3R0c8766dPU6T8wIHnUqS+/vprZZs2bYR+/fr93xQSy8+n8XRzo4i2tzc51h070jymp9OYcE4sFxsbGp+UFKpjM3gwgVeMUTHKlBQCzkWRAIWRI2vWGq5fB0aORE5ODrZs3Qo9PT0sWLDgPwNpq6Wqqgrr1q3jlZWVDIArgGJBED6QJKleBFmoqsLy1asvM+AyOK8pCBMQEMAAzAPwbb9+/aq8vb01ACAnJwcFBQUwMjLCw4cP+dmzZyFJEmOMreCcB/n7+0f9xw/xPyqvne3X8o9LQEDAQjc3t8+HDBny57k5/yY5d44O+dBQKihy8yYdmn370mHq6UkHj7s7GRr/bZHuq1cpmvD2288dskeOHEFUFOlbfX19vPHGGzh16hSKi4vVDpZKCguJejlsGBknH31U86fS0lLs3btXys/PFwYOHCipImuZmZnYvn07AKBr165IT0+HmZmZ1K9fP+Ho0aOK+Ph4mZ6eHsaMGQNTU9N6+0e/smzfTlHnWkarUqlEZWUltLW1cfLkSWV4eLhYHU3hvr6+rEuXLg1fLy6OjMsdO2gMajvUa9eSs9GYAaOSKVPIGKptaN+5Q8yDnj3/zJO+nHBO85+cTIZwXh5FLzQ0AA+PBp25goIC/Pjjj9zKyoqPGTOGrEBV0SctrVeLlHNORuCZM+RoNlD5tSFRKBRYvXo1AKgBoD8hnHNs3boVeXl5mDBhwqvlar+MZGVR5CQ0lCjhqhxElVRV0bilpxNo06sXGXlpaWToX7lChrmx8UulrAQEBKB169YY92cKKQE0LxMmUNS1QwdyRnNzyehu0gQ/f/MNfH18YKGqnt28OT3blSt0z7m59Lk7d4DoaJTZ2yM0NJRbWlqy4OBgKJVKjImI4E4AQ3XONgAy2KsjgHj0iIDAHTsoglZrXQUFBUn37t0ThgwZwt3c3Gr+oKisxOdr1mDBggVosG87QIb4okU03h98QAXG2rUjh3ftWnLAG9i3v//+O79x4wYzNTXlMpmMWVlZQVNTU4qMjBQsLS0RHx8PHx8fREREoKysDIIg1LBzahcRlMvlfOzYsczezo6c/vJyKkJZ7cyo1raOjg4vLS1lxsbGfPbs2azBNV5SQo6esTH93LUrRe91dOoAWF9//TWcnZ3RvXt37Ny5U8rPzxcA4JMJE4C2bbH3q6+QXlQEu/v30e3aNRgdPQpFUBAO2dhAfPoUg62tsT8tTVJyLixYsACatdk3Rkakt7p3p/WTkUGATe/e5OiZmmL35cuSt6en4OLrSwBBXBxVrl+3jiLWEycife5c7NyxA2N++YVfGTAAfrNmMVtbW3KYrl2jOXNwIGeqUyfKRwate1EUsXz5crqf4mICWqtp7WvWrOG9evViXrULgwE0bk+fEk3d35/A46dPCUS9dq3+iO/t20BVFQ5dvMh7nT7NzD79lFJ2Nm+mFJB+/cg5GzKEgIQBA9Q6dv9+2lP9+1OKxe7dz32Hytn+UzTyixfpmUxMiDVgbU3z8gyYUyNvvEF2zaJFDV8zKoqAAj+/5/+WmEjPun9/DUB1584dHDt2DKNGjXo+F/6vFlVHGBsbWv8WFjQ/jo6km0SR5tTamnLjHz4kvbpgAa2fxgD22FgC/ZycSB8uWkQsgI8+Ih3l4ID7+vo43asX5l+6BI0DB4jdERFBoGNmJunuVwhA/fDDD6guvva2v7//zoCAAF8Al555Wz8AFwAoAHT56LPP8kWlMvzwuHE/Jrq69pIkSQOAppGRUZNhw4bp1guMVsvevXt5YmIis7e3r0xMTJTL5fL4ysrKkf7+/pEvfdP/n8h/cYjttfwPiYL/r6A+ly+T4RMURC29WrcmmmFhISnrTp0I5e3enaLDnTqRY960KTlYfftSVPPfnMvt40O0vY8+qqGHSZKE1NTUGkd7xYoVNY6ui4tL/cVAVBVJJ06kw+zBA4qkzJ4NHR0dzJ49W9i8eTMiIiJ4u+qIVe2WJtevX4coitzS0pIDwMiRI/8efXbxIhkVtZxtURShXV0BdsCAAWK/fv2Qn5+P3377jd24cUNKT08XBg8erK64C9ChfuECGTJdu9JhXtswlySKPnp7v7igDeeEmH/7bd3fu7lRZHbXLnUf2b9aGKPvcXOjKFFEBK1XVW7dhx/S3Hp4oKS0FDdv3gTnnIeFhbGWLVuqHW2A1nt6OkXoHzxouOhTbVEoiEYLUKTnFQq13bp1C/fu3YOKyjxhwgS1o21jQ2P3Cvn4OTk5NXTdZq/o8L+UWFgQqKWrS4ba2bNkuKnWzbvvEvi1eDGBeNnZtD+vXKGCPvVUTG9IKisrAQDdq/ti10hVFUV+FAq6/tOn5BSUlFCk5vx5dRTy7l3SDe+/T86AhwftHwCKUaOgn5EBjadPScdZWJDeW76cwKFly8hh6dGDnHMvLyiCgpB24ABTZGfDYuRI5YwZM0QADGVl5Ix88AGBNeHhpF89PChKO2VKvfNYUFCANm3aSG5ubuo1OGgQsGIFOOf47rvvsGzZsoYjglpa5BSp8jcNDAgQuX37haCpo6Mju3PnDnJzcxlAwCGqKebx8fGwsrKC37176Jmfj6g334RCoYCLiwu0tbVr8n4551i9ejULCQkhYGfhQmTPnInsefMQ2bMnNHV1ERcXxy0sLPD222+zCxcuICwsjDUKOiYmknPdrBnpqEmTKGoeF0dRVkNDKBQKFBcXQxRFrF27FmZmZqxLly4ICwtDpo4OrGJj0T4/Hzlnz0rN580TThsaYur48RC//hot5XIpLCxMyFi3Dg7W1kLfw4fJ0a6oIKdiyRIqThUVRetGUxOo7sl+7NgxyGQyaKen8zylUujYrBkxFp4+JR3g4EBgTX4+MHUqrM6dw5LAQCT+8gur2r6dG48dS1Wkr1yheUpIIKdq8+Y6BbpMTEwkb29v9aTPmEHjMGAAAEBfX5/n5eUBz1aQlsvJIbKxoTXMmDptobycgECVjlu+nPbCwoWk+z09WbGVFcyaNaOo6erVtK/PnlVff+xYOhfS0+mZOScQZPRoeoazZ+t2OQHwp02pbduIEXXsGDnYrVsT+NWQow3QPnhRl4+qKqpz0K3b8+wce3va8/v2EQDdvDkqqqvIvzJT7FXl0iXSE4MHE7CTm0ssRA8PskcMDUmf+ftT1PqddwiIMDKitRcXp67OX19BXycn9f9VReKcndXtU2/fRtX161wrPFzSGDRIhKEh3dNvv9G+6NCBgKfx4+l14QKB01lZlOZ14QIFbGqBg56enjwlJYUJgvAhgJ0Akmrfkkwmm//xxx+fDggIEAFYAZB9FRCwy/PqVVmvhw9npsydKzytqEDTpk3h7Oz8QmbEW2+9xb744gsMGjRIrqGhgbi4uBYnTpy4GxAQYOnv75/5qlPyvyyvne3X8m+QC/Hx8f/9OR9379LBe+CAGvH096cDVNWaQ5ULFBFBB+e2bWRklpUR2nn4sLpKtJERFSZxdq4/X+qflDFjCIH/9FNARwebN29W5uXlie3atZOGDh36XEuoRqtujhxJ/+7aRfm3s2fTYW9oCC8vL1y+fFngnIMxhmbNmmHChAmQy+UQBAHZ2dmsQ4cOf2/ugYEBGQyNiCAI1D8ZUBYWForR0dGIjo5Wo/Pl5XSA5ufTYdqjR90LTJ9O4EzUS7KwJIkKu9S3Lh49IgPz73K2a4vK8QaIFpibS1TzX34Bpk+H4tw5PFQokGZtzTw9PTFw4MDnT28rKzJGzMxeXAugrIyi6UlJ1Mf1Fat2nzhxAgBgbGwMBwcHRatWrdRn4Icf1jWQXiD37t3DrVu3OKoN8PT0dNjZ2b3S/byUMEZGV2oqRfHmzaP2P4aGZKBmZ1NO8apVNJZOTvSZZ9kVSiVF7LKzaZxzc0k3Xb8OyGQQLCzQ7eJFGN25Q4Z2SQl9x4YNdK1hw6j2hCpao6tLjouTEzlqrVrRtRYsoFxguZzuoxqEESQJWYIgbUtLE1xv3ZIGHz8u4O5dMhgvX6b9X1VFoMu6dUB+PvRFEd3On0flnTvwHj5chLs7RZC7dSPA8vffCVzYsIEM37feotSCBgqMKZVK4ble7oaGkJmbY8yYMTh8+PCL50NZXbC3vJzud8YM0ofLlpGBXKsNU20pKSmBJEmYPn06rK2tUVZWBoVCAf3aFNoff4QoCHi2ACNjrKZfrre3N65fv45Vq1ZBV1dXWdSsmTgkOBg2eno80tGRDx8+XHB0dARjDK1atUJ4eDj/8ccfuapVXx3dXFpKkXknJ3KwPT0JPJHLCTwoLAS6dIHM3x8ubdoor169KrZv3x6DBw9mWVlZCAsLw6+//irNnTtXcHvrLbi99Zbww8OHklaXLhDu3BGwYAF679kj5Do6KoVOncQh5uZ03WvXgDZtwGNiAA8PsLQ0Ak9SU4G2bVEZHY2jR4/C0NAQfn5+uHnzJisoKAALDEQO5zAzM6P3x+igGykAACAASURBVMQQcJmTQ2fGvXvQWrYMt2/elIb9+qug5+BA83TlCjk5779P+2fevOfmp07kX0urjhNjaGjIbt++zUxNTSGKIlq0aIEmgqAGWp48If21aBFFyzU1iV784AGBMj16ED3byqoGfFIGBiJ2wgTJvmXLhj2a4GDa46mpdatf+/rSXi4rI3ZPnz7PfXTr1q1KQRDg6ekpenh4NPgVkCQCLfv0IcdToaDIbmpq4/o1O5v2wouqhXfoQKDs06c0Rs/K0KEEus2cCaxbh7i4OBgaGtYFqv9KOXSI9Ji+PrFpYmJIdzg7E4CxeTPp2169aC5zcmh8QkLoTDUxIfDgp58InPj+e0ob/OCD5zuGNCb6+kgsLJQEQ0MGahdIwKqqkn9mJunpoiK6vrEx6eTcXHURya++QoUggM2Zg13+/grXoCBZN01NxS0fnw/B2Nw2o0ffjnN2PilJ0nauoVGhUCjCAgICeoAi25DJZAo7OztFj3nzZJo9esB87Vpacy8pgiDAzMxMunLlCgYNGiS4ubkJJ06cAGPsckBAQGt/f///jSDaXyCvne3X8m+Q6LKyMo3y8vK/T8H+3fLkCSm/Tz+lQ1YlkyaRQr50idBrlXTooG7To6LwjR5NDteMGXQIxMWRAa2jQw5NZSUh6AMG0Pv/Cor0nxVLS3IaFy1C3mefIS8vT3z33XdhbGz850Py06fTKzm5BlX38PDAuXPnkJCQgJYtW9YYkCqxtrb+K56mcTE3pwPuJXKCx44dK8bGxuL06dMoLS1F2KVLsJk5EwatWhHlrwFDHBUVrzafN2+S8ZiU9Pzf3nuPDLT09IaLq/0dwhgZUzNnksHw5Al4RAQ8T5yAXWIizKKiyKhv0eL5Zx05koyWTZsaBhyePiUnUxSJIvwK7I/S0lJs3LgRAODj4yP16dNHYIypz7/YWIpAvaT+qaysVLWeYgMHDqxTjftvE2trcqoTEwmQUyhoTObOpbHIzaXaAqrWNomJ5EyFhdG8eHpSNCQtjXRMaSkZjpmZgIMDSnR0kNasGeI8POA6fDgEDQ1ytleterm1KUn0XcuX11ugTxAETP7lFyGxpIQfc3ER7s+YgbbXr1NUs7iY3rRsGTnQFy4AP/2Ehz4+CNTWppZdDx6g09ix5NRv3Uo5kGlpFCXMziZ9tHYtRaaeuzUqFuXh4YFTp04hKiqKQLCKCnLyNTVhU1wMfX19HDp0iI8bN+75B05Pp/x5ExMavy+/VEe22rWjvT1pEuU7R0URcDp0aA3z4vr161ypVDKVztKuTxdMmdLoED9+/Bjh4eFo0qQJSkpK4O3tLWpra6PFwoUwmDiRdevZk9UGjOzs7LBo0SL25Zdfss8++wxGRkaYNm0a1axYtoyimH5+xKhRMTMyMgjsiKxmgq5bBxgaYuTOneKI5GSwxYsBzmFlZYU+ffrwM2fO0EacPRvw9kbZmTOwtLQUigMDoV9cDKxejZZWVuyhnx/alZTQefjxx5BiYvDtmDFKM1tb8dGmTRg1ahScnZ2R8eWX2LN3L7S1tPj06dOZnp4e7OzscGbNGkk7OFj4ddAgvL1wYU2lcAC03pKTgVOnkDJrFlICAwXDQYPAPv6YcmxjYmiuo6IoReHJE1Tp6uJAYCD39vGBJEmoUy9myRJyODkH4uMxxNychVy9KpWsWwfbmzeFFAMDOPXoAQ0VDXnIENK1hYUEHAYGko4+elR9zWd0sYaGBioqKnhSUhJiYmIkhULBBw4cSHUIFApirPTuTXPzrK5zdSVWmYEBOYL379dEjQVBwMiRI1FaWirevXsXt27dUnp4eDQMSH//PQGlU6YQwG9sTGfV8OGNrkX8+isVZlQ5io1Jt250vcuX69cl3t7AtGmovHwZRVFRKDAzQ35+fg1Ir9Ktmv9BFXDcvk179+JFGlsbG9JtK1eS8z19Ojmze/eSLVdaSmBkQAC9PviA1tidO3ROBAaSnfLzz3T9li1JByQkvDT1u2fPnuI333yDixcvosezADxAY2VgoC6CWhskqmZnJUVE4PLo0cguKpIVGBpCIYoyXaWyF4BuvpcuLWyRmGjuGhHxy9pPP80cvWePVbKdHb/v7S3NLSwU5MuXy5gkyVBaSvps2zba9w10HqmsrMTu3bsVlZWVYIxBLpczfX19MTIyEoMGDYIgCNDW1kZZWVkrAJoAyl9qIP4/kNfO9mv5NwjnnP95+tM/LXl5FNXet08d5astzZtTTlJDUTtVO42UFHLorKzIWenalejoCgWhzLGxRHM9fZoqRtvYkIHbty8ZBv/XQIWlJZCRgeK9ewFQSy5jVd7ffyK2tuQo6OtD6NkT3Tp35ocPH2azZs36a67/qiKK5Mi8hMjlcqre3KYNUmbMkB4C/Iy1tdj/88+hU59xPWgQGVT797/aPZmYEDDTkKxZQ2vl5s1Xu+5fJYwBpqbInjQJwRoa6GNjA+f0dDJEhg6l5162jCIBKkNy7FiKcNS3T7KyiELHGBk+r5hmsW3bNqmqqkpo3749r3a0675hwwbKxzt//qWu9+jRI2hqavIPP/zw70G8ysuJoeDiQg5kq1ZksHboQNFjpZL+DlDk5fJlGrfOnckIfvNN0hvFxWQU6uiQIV2rLsKz0gSAVlERfnv4EDfOnOETJ05krwR+RkZSWsOzeesxMeQYHTgAjB6NkOhoViCXo/DKFZ6xaxcTwsNR01LszBkCY6ytIc2fD1liIgBAR0dH6tS5s4DOnel9CxYQOKkqYqhU1rRCys7ORmVlJa5fv84TExN5RUWFoCqUKAgCr6qqYqdPnyZne906cv7i41FZWQmZTFZTMbxGkpLIKJ89m+Zh/nx6Hh8fcsp+/52ebe5cinbFx1OU9ccfgYwMZDKGx0FByO3Vi3l16VLDhKhX2rUjfb5uXb1/Pn78OBdFkdVbpfnIEQJiLSzqsBrkcjlmzZqFyMhIxMTESBs3bhQcsrL46O++Y+nu7tw0KIhp1Y7ITZpEe3HLFgLBqH864OQE9ttvdBYNHAh89hna9e7Nzpw5g3379kkTJ04UEBKCnmfOsBOurmg3bRoyzp9H67VrYfT++7Bdvpz06BdfIOHcORjb2sJ+8GBxwN692LZtGz927BiLi4tTRmRmij3v3kW3hATGli4FAOg9eoTh6elC6vbtKLhyhWPaNIZ9++o+v5cXEBmJrI0bMenQIa4ZGckgCOQcAcDRo+CrV6O4Rw/cdndHVtOmeDM4mO2dPBk9w8KYw44d5JC1b09nbbduxFwQReiPHImBnToJ0NJCzrhxCLp1ixtMm8ZsazNZtmyhtT5/PjmuQUG0TjdurPfcLykpQXJyshgTE8Otra2FxMRE9OnThwIOW7fS5+LjaT08K4zR+isspL2/ezedB9XOqapLQGVlJWJjY+tXlhUVBIwuXkzrubKSfhcXV38E+lmZNq3eQob1iqsr7dP4+AZB66rhw3F+0SLuFxqKq35+/ODBg0Lt4pP6+vp87NixrLEc4nqluJh0Y//+FAwJC6P9OW8ePfdXXxEzpVs30p1lZfS5NWvIFps3j5xumYzW/ejR9L6UFLXt1qIF7b/Hj2kPp6WRnebt3eit6ejowMzMjIeGhjI7O7s/xYxy7NABjjt34tGjRygdMQJBQUEQBMEGnLvuXL16s0Kh8EyYMweDPT2tmhobw6FtW9bH1pZh8mQCFDZupGh2QgI924kTNK+HDtX5nvLycmzdulXS1tYWe/bsyZRKJUpKShARESGhVseF7t27V506dUpDLpefB9D5lR/of1ReO9uv5d8gfQwNDSu1tbX/+wqkcU6R7PbtyWCvT/z8yAnfsqVe+lqNNG9OETuADkELC0JgBw8mJW5urjZ8srMpAnH+PBnQTk6E7hsaUqERLy86MP/O6LcoAt99B9Pz56GZk/Oni0zVK6oDtUsXdJkzR8j86ivpzpYt8Fu+/P8+mb1rV3XxoJeRyEhAWxvNHz8WrD/8EFvPnePrd+9mPj4+6NGjB6H0nNNr8OAX52fXJ1VVRKNtSD74gK79DwrnHBcuXJAsLS2FTlOmqPO/fv6Z0ii++ILqF8ybR+u2Y0eKEA0YQAZZdfEiJCWRw2lvT3mNryhJSUkoKioSmjZtyocNG1b/hqguuveycv/+faWxsfGfT1+oqCCAzsuLvptz2vPW1mTI3bxJEZfMTDKCDAxojIYMIRBl9mwqjlVYSKyZykqifI4fT86jKoodEkL5oyYmREUcOpT0UVISgYNFReQkhoQAbm4YPXo0srKysG3bNpaZmflqxt9PP9Wl4X/6KQECX31F99WiBZQODpCmT0f/+Hj++L33cCE2Fo6XL2OUig20di0Z8V264HqfPvzJwYOsla2tJHTr9vy8WVkR2HjvHn3m9m38unq10vOHH8QzfftC2aULHzBggCCXy2FlZQVJklBeXs7KysrUlcAXLKiJkF66dAkFBQUYPXo0/S05mcbw6VOaFwcHGleA9L0qTaN/fzK0KypIX3frRr+/cAEAEO7vLzWVJKFVq1bK/pMni5g9m+Y6K4vWfG0dffx4w+wXAAYGBqygoKD+P8rltHYGDiRwQHUfAMzNzdG7d2/07tlTKG/eHKykhEW/9x6CjIwY//prtG3bVho0aJBQEznU0CBwKzlZ3bbJyEgdTT5yBNDWhtbbb2NGeDj2Tp8uoKgIKCuDM2PMeepUpAcH435UlNIyMlJ82qEDipKSUGJoyM/GxPD0tDSh1YAB6ObvD5lMBl9fX3b8+HGkpKRg9uzZsBBFApUAWqPTpgHffANFs2bQzM9nyocPITYwTlKzZigwMUGzAweIefD55+Tkz54NBmDzRx9h/Pjx8DY3h6BUYiLnqEpKgpYk0VyMGkVRPmdn+tfKihyP6lximaEhiiIiWFl5raBdSAgB4KtWUf4vQDTk774DUlIQU1qK4uJiMMagoaEBDQ0NlJaWom3btsqRI0eKABVpO7VoEXeIiYHD778z+ezZjZ/fs2YRuDZjBjl2KSn0rLVEoVBAFMXnL6JU0lqprKTorKYmgb4GBqhTeLAx6diRQAFT0xe/VxDo+w4caLCY2sGDB5W59vasl7e3oIyLY9fi4ni/sWNZQkKC0t7eXkxLS8MPP/yA2bNnw8jICKmpqTAyMoKenl7DhVDPniXdc/UqAVArVhBYO2ECgfoKBenRuDhaZ6NHE9sDoLlftYoi8n36UGrU7dsEXKrqjKiKkV66RA52v34UGd+xgyLoM2fSPvzuu3pvT1NTE3PnzmV79uyR9uzZI/j6+qLnM8VNMzIyYGJi0ngqHtQ57kFBQeCcawKAQqF4rKmpqRg1daqMMVY3Yl2tn7BhA72qqgjsfviQnO+iIloTmzYBnTrh9OnT0NLSwttvv81qs0CioqK4k5OTEoAIAN7e3hrXrl2TCgoKfAICAjwB3PH391c2evP/H8hrZ/u1/OOiqan5YefOnV8h2eVfIgoFHUybNpHibUy0tEih+fi8uG8uoO55qlCoI1MuLnRgfPcd0dbbt6fX+++TIZCQQAr/hx8oqtK+vbqi6pAhRBP8D6PfpaWlqKqqQn5+Pvbs2QMLCwve8cABvKGrC6fPP//rPftVqwAAnZKThZKwMI7lyynq938Zxb95k1DtF80b50SJXbWKDrJz5yAAmOfkxFJSUnDw4EFcvXoV3bt3h++qVTQff7ZF16FDZAg35HDr6JAxP2kSHf7/gKSmpiIzM1NYtGhR3UIrzs70GjeOIkGMERUxL4+MtzfeUOfc3r9P0U5vbzWV7iUlJiYGwcHBvKKigmlqamLy5Mn1r09JIqf34kW1odyIpKSkID4+XhxQXTypXlEoyNDv1o2opFFRZOQ5OpLTa29PoEJBgbq/sCBQRNPBge5H1e9761aKJiYlUaeDHTvoun5+RFVWpRLs2kXfU13oDGVlVPAHoLWgVBJN8t136XcZGeq+1KNGEbuiqAgWc+ZA54MPuHL+fKBrV4YlS8hxXrBATQ9/tlYA57RHunShqMiiRUSrdHEhBozKwFYqYcY5N5PLWWRBAbe1s+MDBgxQz0tBQc09mXh7s8TQUD6hokKojxpeE8lfuJBAGMbQYfhwMcfVlY+ZMYPpfP+9wKKiiHpfbYw/1/Jv8+aa6JyFhQWPi4uDflUVQ2KimnXybGTy++/pOWv3t/bwoAj3jBk0B7WK9hn37Suc1tDA8unTRfTuTbo6PJzo9j/9RPrdxYWioRcu1B/JrBYjIyOeSyyb+teynh7poEWLiDVSO/e7tBT45BNolZQAa9ei7dtvo40koVo3sTVr1qBt27a8oKCAN23aVBp45IgMxcXqqGltqb5u5JIliNi5E5O9vAhQ/uwzGtMzZ1C8bBnu370ruu/dC9nIkdzs0CE0efddNuPrr1mVnR0qIiKgv2oV0LMnmhkbY1RgIHfcv1/EoUMELg8cSOOor09Ok6MjmksSDKyslF+NHSuOjItD6+oiZ2lpaQgKClJqamqiqrJSNB4/XnI5dUrE6dM0Jm++SSyEsjLoOTpKubm5QvPmzQEAckDdouvMGQJXV64k0FShICfLyorO2bAwxH37LQYePw7rQYPoMw8eEKNj/vy6+cvVlOX7Q4bwvKdPWdS4cRLnHEqlEkqlklVUVLDaFfFHjxgB2e+/s6KbN1FeUQG5qoBoQyKTEUVeVRAzN5ecvlpngkKh4M8520+e0H7//HN1Ff/MzFfK1QXntL8dHV/+M4aGqAwOxn0LC7QZNaomZbCwsBAKhQJpaWmiu7s75H37wuXwYbgcPcrQpAm8x41TeXbsxx9/VGzZskUmCEINS8XOzk45adKkusDnhg2Up//991Q358MPaS3t3Uushfx8AslWrKCzaMIE0snVgAoAAti0tAh4MjUFTp2iMys3lyLZiYmkj0+eJHbBvHk0nubmFFhJS6P9bGVFnysuJoCznoKeEydOFI4ePYqQkBB4eHjAwMAACoUCGzZs4KWlpUwul/OWLVsyHx8f2NjYPPf5pKQkXL58uaaArCAI4atXr/6aMRZcVVVVef78edHPz69x+0yVEjNnDj3TypV0NmlrI2X8eHiEhaHDpUuCqFTW0OQfPnyIJ0+eiBMmTKhzKUdHR+E6gWXhoijGBwQEzAdwBoAGAKW/v39lo/fyPyivne3X8o9KQECAq6ampne7BnJE/tXyySeUn3fixItprTo6hNTn5LzQWZQkCTWFbGQydbXPW7fIWL53j4zt3FxS8q1a0cGncmJ696bDMDtbXSxrxQqKfLVsSUa4tzc5/k2avFL0e/369TXtaLS0tNC6dWtW/N57UqeSEoFlZREy+jdI8pIlCA8PR+ugIDJoqTLs/40YGlIEtjHZtIkcn2PHCAl/pn1Q8+bNsWTJEsTcuYPff/mF+65Zw/6jsZo2ra5hUJ/Y2Kgpcf+AWFtbw8LCgq9fv57NmzcPpvVFQFTVsi9coPV8/jwd+lpaRD20siIn+803CUwyMaHoywvWrCRJOHz4MHR1ddmkSZNgZGTUcD2IsjJycBtxtFWt3mQyGQ4cOADvjh3RPjubHJirVynSt3Ur7as+fSiiMXQoRQlKStQ5yRs2EGjTtClFDgByulQyfvzzX/7bb/SeoUMJnCgooAiyrS1FI1q1oj1hYUEOtarwWVkZvZKSCGy7epUYGirHd8kS9Xeo6JolJYjbtAlViYlMr2NH0hecE3g3ezbl9G3aRMZ59+7kkL73nrqv9LJlxKhp0eL5Am3btqFw3Tp+f8YMuHz6KWbY2z8/iXv21MytiYkJku3sGOzsyHA2Na3LMJEkMpj79SNj2dQUOhs3gr33HpM7O4NNnUqRqMuX1Tnez1LcVQUpHRzQsW1bFn/oECp79SIH/s6d+nubb9hAxfmelf79gXv3UBUaiuSrV3HDxQWPHz8GYwxKpRLl5eXQUjljrVurC0MeP07AR3AwpQ/170/j7uNDjqaxMTl9dnYoyspiFeXljedb2dkROLJ6Nc1H+/YE1tjZEXvq/n1yJED5vXZ2dli8eDHbsWMHoqKimJaWFktNTRUiIiLgYGQkjf7qK4F5eNB1npE27u4I7dABO27dwuBNmyR3Dw8BEycCZ8+iNYBlXbui1NQUzTdsEEuDgqCTlgYGQJ6VBbmPDzl+UVHQatkSto8esZoUkcxMWsNPn9IarnbqBEHAlA0bxMSZMxH4889wdXXlLVu2ZMePH+cdOnQQtLW1Yfvuu9zSwkKs6du9fz/tjeoe4k0KC1WARfUyktRg4KefomrIEKS1bg3rOXMg69AB2LwZkiSh6NNPIUkSKu7dg31BAcoUChQtWwajAweQtmABioqLUXH9OiRJUjnUkCQJj9q0wfBr1+A7frzw7LkAFf128WK0CQkBbtxAQFkZPEJC6hbOq0eaNWuGVr6+tH6MjMj5mz+fwIEWLVBZWYmysrK6aQs5OQQOdOxI654xyjH/+GMC5F4gQUFBKC4uhlFhIdzfegvNqiuVFxcXo6SkRJ0OUo8kZWbiQsuWkO7cwamUFD558mQWGBgolZaWCjKZDJxzddu9MWNIH4eE0NqvBkOmTJkikyQJmZmZMDc3Z+Xl5Vi/fr1YUVFB+dx795JuNDYm3XT1KjGoXFzoObW0SF8VFlLk+p13SBc7ONBaq52n7u6uBhe//15d3NbUlPaTqSldZ8AA0vMuLmRjlZaSfbZjBznoBQWkc6OiSJdfukTnt6oQLmhd29jYIDo6GmfPnsXjx4+VBQUFop6eHps9ezYKCgrYyZMnsXv3bhgYGEhvvPGGUFlZifz8fOjp6eHcuXPc0tISRUVFDACUSuVH1dedIknSwNDQ0PMWFhY16QXPyc6dNB5jx9aASvz335EydCiOBwVJzRQKoevHH8PM2Jie+4cfgG7dkJuQAENDQ0lXV7eOAdyvXz/4+vpCJpMhKCjI5sGDBycZY1Wccw2ZTJa3evXqDz7++OPdL1xw/0Pyus/2a/nHJCAggGlqal7w9fXt1rlz539xr6t6JCiIDgAzs8ZbYzwrAweSIVWfsQbgyZMn2L59O5RKJdzc3JQDBw4U66VIqdDFAQPI2F65knKMVq6s30AEyGhJSyMD9MgRUppaWvSvoSE5Cba2DX5eoVDgiy++wJIlS6BQKKBQKNSH44EDdMj//PPfQl0vLS3F+vXrsXjxYmgnJ9Ph6OtLSHGLFn/599WRmBhCw+srGBMaSuP6+DFF8FQ0/wakatgwPI6MRNqePehWi+b5yrJsGTkzquhKQ1JQQId8Y32//2KRJAkhISHc2dmZxcbG4uLFiy/fM1WhIHq1ri454ozRXI8eTTRJSSLn0cyM2B/BweQMvv02AUvNmwOSBElHBxtPnYK7s7PUY+hQAebmDa/LsrL6+3yfOkW04fR0PJo0iR8YMYJN+OknKHV0eOtr1xhMTcmRy8oipsHOnfSzrS3dx38qp07Rft63j5ySamcOpaXkZDs7k+G2cCFF4+7cobHYvJlojip2QG4uUSU//5wc5HbtKBJXWFgHFJIkCcHBwTwmJoYNHjyYu7q6NswEEAQyHO3siKFhbU3G8bZtBAo8O5Z5eUBeHq58+63yhr09mzFjhvBclBmgKHPr1sCaNUhNTUVgYKC0ZMkSAQEBBFh8/jlFms6epWf49Vf6rvLympZRG9esUc7esUPU2raNdFpurpqe/sUXBEbWLq4F0Bh/+SX2DRiAMgAzVdTpZyU5mWjejbSoKzp+HOXTpmH7rFlQamjAyspKOWHCBFHnRR0lFApa6127UnRbqaR55pzWOWN4qK0NRXIyd9bVZTWRRQsLck6bNqU1oaFBcxEVRfrJxIQiVaJI51Yj51V4eDhOnjyJtm3b8srKShYXFwfrJk14X1dXZqOtDXTujLy8vBrgKTo6moeGhjIjQ0OIkZEYZ2UF/bAwihBzThXHjY1R/PgxyktLoRwxAvoxMdA9fVq9Tn7/HVnZ2fjxxx/50qVLGRQKYNgw8F278PDRI1TExMBi8GCYV485P3MGJe3b496jRzhPNRY455y907YtDE1NCYyzsaHn3LuXXtHRdPbl5CB+7lzpaMeOTENDgysUCl5RUSFqampKI8zMhD/y8pQ5xcUiAGiVlWH4W2/h4OHDgEwGmUwGxhhXKpVMFEUwpZL3//lnpiNJ/PSsWXzqRx8JEX36SPGdOnHXixfZnREjuCAIEEURfn5+YrMZM4harmqRCBDIkpVF+7CoCGjXDr/++ivy8vKeo0bXttWfPn0KAwMD5fRp00TMmkWsExcXcgi1tMBbtcJnX39d05996NChaK+nR6DUypVq9kR2NunS7Oz621fVkqSkJBw4cABeXl68+ebNqEpKYlHLlvE33niDbd++nVdUVDBLS0upR48eQrNmzaCjo4PHjx8jKChIaWdnh44dO4p7v/kGS48exaXPPpNi8vN5Xl6e+NZbb0FLSwuRkZHSjRs3BE9PT/Tp04cAkEOHKGq/e3cNQFRbQkJCcOPGDSweNIh0rpcX7XFbW/V5HRREQNHMmfS74mJysvv1I515/TpFpeVy0l8qmTqV9ISfnzpta8cONYtx1Sq6r4QE+vn2bTqHvviCbKqFC0nnjBqlZgIFBJAuXrqU9rpCUQOw5ufn48iRIzwnJ4d5enrC1NQUtra2qi4n4JyjsrISJ0+elKKiogSlUgkDAwMuiiIvKSkRJk6cCGtra+zatUuRmppaE0gVRTFVqVR+A+CrQYMGwd3dve7aio2lcfjkExQfPYp7paXITU9H74kTcXzCBN7B15c5LF0KduMGzUFKCoGA69eD79iB1e+8gw8cHKA5dGiDKTCcc5SWlqKgoABlZWU4dOhQpSAI+wBIgiB0USgUxQqFop+/v39+o4vwv1heO9uv5R+TgICAN42MjHbNmzdPV3zJ6o3/Cjl7lg7M48frPQAAMlxTUlKgVCrRtGlT6KpaaFQf+HB2rrdi78WLF/mDIwWPOAAAIABJREFUBw/4mDFjhB07dkg+Pj7o1q3bi4GIBw8IDQ4Pp8PU0ZH6VzcmnNP9ZGaqaclOThQx1denQ8bJCTAywtOnT2uqOfv7+z9/LYWComtr1vxt0e1Vq1Zh7ty5VCStooIOth071P0mX6ZH85+RS5eIvbB2rfp3qqIrbm4UIZw5s/FrFBTQHDk748y5c4hKTla+9957dRZ9ZWUlqqqq1GulMRk+nOZ73LjG33fxIjmqWVkNsi+Sk5ORl5eH5s2b1xzs9UlmZibS0tJgY2NTY/jWJ0+ePMF3tXLUtLW1yWFqTCoqyLnYtImiCLdvk7FjakqA0smTFD01NSWAQakkQz0hgZ7NwoJAEX194Pp1lObl4fjTp+ieksKblpQw+PnROndxIaaHnZ26R/iGDbR+s7LI6IqKovdYW1PKRps2iB0zhj9cuZK1Ki2FY8eOEP7OHrCPHhHgkJxMhvC5c+RAqww2B4eaKsnw9CSnWZIIgDExIRpgTg453M/OU2wsPatMRgaoqvewpSViCgpw+PBhTJ06Fc1fBSzYs4dYN8uWEYPGwoJYPyr99sUXZMQmJ6OiogLBwcFSQkICe++9954vwHb+PDmKXl7IyMjA999/j2XLlkEuihSpdXEhhzw4mPp5q3JUExOJMh8Sgv3790M/OloaWlgoYMkSNfAhSbS+FAq6zrZtZGT6+ABz5kCpr48NFy9CQ0NDuWDBgvoPpAEDyEifP7/RIXmcnIzYWbOgWVaGC716wcDAQNmuXTvhhVTOY8doPJcuJcfhGdm4cSN3adWK9fH0pLkrK6O9EB1NIFTz5gQuxMfTnJSXU9RNLqd1smAB3X+XLrQWPD3rGMgpKSn44YcfsGDBAhgaGiIjIwNHjhxBswsX0C80FGmXLuHQ0aNgjFHbMh0dqU9pqeB44QLuVVXxMpmMdQkJAbtzh6KS6en0TKtXI27fPsnu+HEheOhQDD94ELLKShr/lBQURUZii4EBX7pwIYNcjrIBA3CoY0eeraeHVrGxgJERH7F+vYCjRxH8+++IsLGBhoYG9PX1le+++664Y8cO/ub69cxg8mRahypZtIgAFzc3cpj++AOKdesQ+cMPuP/gAS8sLGRTp07FoY8+wujdu7F1zhzYuLlxV1dXdvToUXQNCUGrx4+he+XKc8ycmE2bkH/5Mu988CCDTEbrS6kk523hQlqf48cT22XBApqPw4eJ0qwCPN58k5gv1S0JX1YuXLiAxMRE5bRp00Rs3Ur6QJV2MmUKIEnY27u3MjExURQEAaYZGejRqxecS0rUqSW5uaTjHj1SV6J/RlJTUxEcHKxs166dWFxcjEePHvF33nmHITER+U+eYN+VK7ywsJC5ubkpvby8xDt37vCrV68yADA0NERRURFcXFykmJgYoaqqCqamppj3+DGyPT2xNSoKGhoamDx5MppVf398fDyCg4N5cXExGzFiBFxdXWkcjxwhx/WZ1oz79uzh1nFx6Ll7N0NYGDGhVq0iUNLVlWwSW1t1qyx3d3K0VT2zbWwoqi+X0/lYu6NEcDCtG5X+CAykn1VsLIWC7CdLS3WAIiGB5nP7dooUL19O7/nySzWrRpLoXJ46lebt4kXS94MH1/3+RqSkpATVzna9f9++fTvPyclhfn5++OOPPyAIwiVJks7IZLJZNjY2Zh07dtRydHSEkJ4OTJ0KZadOuMw5v6Kjw7S0tHiTJk24p5kZazd+PGN379L6SEsj4ODoUXWAo7gY3377rXLC+vVi+fvvQ+HgAKu8PMhmzWo06JKbm4uHDx/W/P/27dsAMNbf3/9Qgx/6L5fXzvZr+UckICDASENDI2HixImG9eWg/GslNJSiwDo6jUZUT5w4obx//76goaEhlZWViaamptLw4cMFCwsLMj6++IKokc8opH379inNzMzEfv364fHjx9i7dy/GjBkDh5doO1Ujn3xCir1XL1KOL5mLCoCcyMREMt5+/x1ZWVkosrBA/uPHvMjUlLVZsgRN27Wj6MmzUlRE/URV1dL/Q8nKysLFixeRn58vmZmZCbGxsRg/fjzsn6WCdulC0aoVK2g8X7FS9QvlwQOKfFXnjyM6msZ1zRpiKrwMULRoEa2d69fx8OFDBAYGQiaToVOnTmjZsiUkScLRo0dRWloKAwMDPn78eGZubo7S0lJUVFSgvLwcUVFRMDIygoeHB1hqKh3yL3M4Z2UB5ubgoOhESUkJHB0dwTnH+fPnlTdv3hT19fWVBQUF4ooVK56LqJSXl2PHjh28qKiIGRgYKAsKCkRDQ0Our6/PfX19BdV8pKam4u7du7h79y4sLCwUI0eOlOXl5UFfX5/64tYnqiiprS0ZiZ98QgWZVJTImzfJKAHU+dtnzxIQNHly3bzZWvLHH3/g6tWrMDY2xrvvvkvfU1hIxs3Dh7RW5XJqXbNlC9EOHz8mh1ySiC1SLbGxsfzIkSNs5syZjYIM/7FUVdF9eXuTEb5qFTlTfn50nyqjb/9+0kGDBpERGh5OxlpqKhmIH32kzgffuZM+Wx+r4MkTGr+hQwFRROW+fbgzfDiPGTaMT5o9WxBeZh/l5BAt89tvaQzLyggAO3mSqNvR0XT/KsMe5ND9+OOPWLx4MZ6L9kZEoCw1FRVdu2LTpk0QBAFLly6l4kClpTTn5eWU2147X7SkBBg5Eo+++w77DxyAk5OT9GavXgL69CEnfeDAut9z/TpF9zQ0gCNHwLt3x+effw6FQoHWrVtze3t7lpeXh6KiIgwePBg6OjqIjY1FxscfS8rJk4XeQ4Y0Oixbt26Fxq1bGJeeDnbgAK7evo3Q0FDo6upK7u7ugouLS/2U2wcPyGmyssKz1bazs7OxdetWLFq06Pnc8/rk5ElaR5zTWDFG8z1lCunnRYtojr76ioCdsDBUvvUWDunpof/8+TDNzAR690axQoFjx48j/f59mKemosW4cejq5QXpxAkIn3xC45iaCkkQcKO4GDrdu6Pdrl00J92709oMDkbJ3bsQZ85E+A8/SN2+/VbA9u3EsoiIQMU772Cfl5c0/bvvhMA1axBfVAR3d3cMHDgQyuHDca2wELZ79sBi+XLcjI9Hi6NHYWlpSXt1xAhc19XlmaNG8aHDh6sXrUJB4MqUKfTcW7cSaHX/PhAZics5OXiQmMhnTJzIygsLkXLzJiqsrREREaFMSEgQAaCPoyM6lZdDeLYl2/79eLJlC359803l9IaAGYD2goMDgXsODnS+OjiQvhsxguoniOIrs8HOnz+PlJQU5ZQpU0RUVhLL7tYt5CsUuHHxomQZFSUk6Ogo7xYViWbZ2Rh36hRO+frCZNw49O3bl/atqSnpO0vLer9DoVBgzZo1aN++PY+OjmYVFRUYOnQob+/qytCnDxAUhFINDRQXF6tZB5wjLi4OBw8eBAC0aNFCOXHiRLG4uBiHDx/mOjo60tgRI0Q+aRIeLV8OGweH59p5cc6xcuVKdOnSBb1796ZfBgdTakRoqLr3t78/Kg4dwtrRo+Hn48M9u3dnGkuXEttl0iSqHyCKlELTqROB846OZCuo9v6kSRSU4JzYebVl6lQCtFVstYIC+qwqzUklbdpQSshnn9HPRUX02ZEjaY5v3CAm4xtv0H5UpQeUltIadXMjZsH27VSDwtiY2Bl/gXDOkZKSgpCQkJKEhARdABBF8YgkSf2cbG3lo+zs5PzuXTz64QceMXgw9507V2haO1gyciSdxbdv0xj5+9M4JiYSA0cQcPr0aVy7dg3gHO0iIuB+5w7+WLZMGnX8uKAXEAANVVHJBuTx48fYvbuGUd7M398//S95+H+ZvM7Zfi3/iMjl8k1t27bV/q9ytHNyCF399lt1HnUDEh0dLY4ZMwYtWrQQFQoFTpw4wXbt2kUobt++FMEpLa05OJ48eYLw8HApJSVF7F5d4MTGxgb9+vXDzz//jOHDh8NZhai+SFRKPzubcod0delwcXMjB5HzBh3S23FxiI6Ohp+fHyxGj8a2lSuhW1SEdk2bSn5lZaK4YwcdUubmhAwPGkSHjb4+vbp2pSh5Y1XXX0JOnjyJ8PBwuLi4SI6OjkJSUpLEORfqzfsNC6N/Fy2icVW1evmrRF+frhkfT21Stm6liGevXi/+bEoKgSvr1pFhCMDBwQHLli1DcnIyAgMDERoaCsYYOnXqhB49euDgwYN827Zt9VpfWlpavKSkROo+daqIU6eIcvsiycgAunZF5b172Lt3bw21UBRF6OnpYfLkycjMzBRDQ0M5q+Vpp6WlITc3F2FhYZIgCPzDDz8UBUEQ8/LyEBERwWJjY9nevXvRvHlznpmZyTjnqKqqAgBMnTpVJggCDOspBlMjqtoDSUkEPKmiK3I5OYvTppFj3KsXGadt29Jr1iyi4r3xBrEKHBzqtNxTKBR0+ANkWAK03lX34uVF8/nll5TSsWsXGeSqVi/PGNY5OTlcLpfD3Nz87yvtr+rzOnw4OScaGhR5PnCAxkYlW7dSUa2QEPq5Vy9yJhcvJmBi1iyin3/zDX02K4uMaVX0v7aogIpqKr48IwNeycksLDsbWR98AEtra8rfa0y2bCGHSpVLra1NIEnHjqQLtLTo2WoxgDQ1NcEYw/379+FVHb29e/cuDw8Pl7xOnRK0IiPZTxMmoEmTJnXZHzo6pNu6dSMWQ21nW1cXOHQIofv3cwBszJgxAhgjlsStW2TwqiLFlZW0pwcOJFDS1BSMMfTq1YunpKTw/Px8nD59mlX3i0VcXBz09fUl9+BgQezYUQi5cweR8fF82LBhrEU9gGtKSgqys7Nh5u7OdXfuZJg2Db0Yg9WiRUhLT8eVK1dw+fJlmJiYoFevXmjVqpW60rCmJlVGXr2a5rOWofrHH39wURRZXFwc3Bsr1sg5ATW7dtF6GTeO9tiGDVTYLD+fnLu7d9U5uxkZAOdI09ODJIpI/PlnyfTsWQGurtDr3x/jzc2h2L0bYufOYIcPA+7uEPr0oT21cSOQnAwhIACJrVpJJi4uQjtvb3JKNm4kYCM2FoWPHuFW797IKixkbVu3hiSXQz8gAPL4eFR07w4hO1uoaNIE5dHRcOnXD4OruykIwcGI27VLurx3r6Dn7g7J3V3qYmkpID0dkr4+YuVyfqtpUza6a9e6+zMmhhxr1TqfM4fqDlSDELbvvYe7FhZKzJol0yoqQuvqnthBQUEiADDGcObBA7QeNAimKhYVQHqrqAgPZ82CUK3TG5RBgwgE09OjCPtvv9EeCQ2lNTluHI3RgAG0rl/S6VbVdQGACs6RPXw4kubO5edbtWJNmzYVcvX1+ZA1a8Q35s7FcS0tfuWtt9Bj9my2b98+ZGdnSxOWLhXY0qXA/PkqMFXJGMPw4cNFS0tLCIKApKQkCIKAwYMHs+7duyM5ORlt27ZlNawZfX3oCEIdwIwxBkdHRwwbNgwpKSn83r174q1bt+Dh4YFp06YxVFesZhoaaHX9urow3TPCGKOotkqGDiV9OGYMnRO9ewN9+kBz0CCM0NVF5oIF4KNHo8jODul//AEHJyfExcbiSGAgBpmbS+1sbAShqIh0Z0AAsdS2b6fxnjWrfjZeRQUBtCpRFeu7eZN0m0oOHaJzWAUc6+sTgyE+ngon7t1Le2zNGvrX3Z10pmrcoqLIed2+nRgRZmbEuCosBIyMoGqLKwgCioqKcPv2bXh5eUG7ka4FANUZ2b17N09PT2e9e/fWNjExUYaHh4tKpXIUOEeLzZsLM62sNCK9vLjg4CCMWLGCyZ4F73fupPPw00/ptXKlujvAokXAnDlwcnLCtWvXsPiDD1BYWAhlcTHs7t4VcpKT+a0VK5ibjw9MfvqJ9mI9dmdsbGwVqHAaZDLZ559//rlJVVXVFH9//yeNPuB/mYiffvrpP30Pr+X/MwkICOgkl8vXjB8/Xvu5zf1vlbQ0Mtrmz6+X3ves3L9/X6qqqmIODg4QBAFOTk4sKysLCQkJSlc3NwF+fkTv7dwZtx494gcPHmSSJPFhw4Yx21oVTa2srGBoaIhgqhTKDQ0NmcpYfaHo6hJlkDEyKtu1Iwfc0ZFoVE+f0sFffa0bN27gt99+Q35+PiIiIpCSkoL8ggLMXbIELoMGCUL//mQ8dO1KUbacHIqs3b5NlXjDwuggycig737Vfpi1JCQkBPb29nzEiBGCvb093NzcmK+v73MoeB3p3p2i3Do6ZEiPHfvStKxGpahInTNWVUVjUCvy2aioCvRMm1bnoBFFESYmJnByckK7du0QFRUFKysrNG3aFF5eXszX1xcdOnRA27ZtYW1tDUNDQ2hrayM3N5fp6+tzZxMTAUOHvtzzmZoC6emQ9eyJR8nJyg4dOghDhgxBly5d4OvrKzRp0gRpaWmIiopimpqa3MbGhgHAt99+i+joaDg6OrIJEybURDq1tbVhb28PDw8PpKamIikpiSmVSsyZMweurq7Q0tJCq8bGZ9EiGs9Zsyji4OxMSL4k0cH+3nu0x95+myKyz/aQ19CguZ4wgSiQJ04Qe6O4uCaCd+nSJQwfPhxt2rSpu1cKC8lhy88nA+vBA/qZMTKikpLIkKv+jFKpxMWLF3lZWRk6d+781zvbGRnkBHh5EWgwdaq6p2tSEhmZtWsorF1Lxv7ChfSzTEbj2aIF7UdLS6Iyzp1L1ZS//pqeb+ZMet6GnDTGAH19sHffhaijwx4fOQJ9PT3oenkRHXPkSHIYns31S0+nfVbb8AsPp6jszz8TC0GpJFBDUxNVVVXYs2cPLy8vZx4eHjXU3BMnTvDMzEzxoYEBe+DkBCMrKz506FChSe2CUsnJ5CDOm0eOpJNTXZp8r15oWlHBbhkZoVOnTtRir1kzyod9/33619KS5rdtW2IAPHlC4+XuDhsbG9a2bVvm6enJevTogS5duqBHjx6oqKhQaGtpsQ4//siyevZEh4EDce/ePRYZGQm5XF6nOnBCQgL27NkDAFi8eDFhV87OYE+fwszLCy3atmXdfH1hZGSEhIQEHhMTw65fvy61b9+eyeVyMuiHDqUx/O47WuPVY/7LL78wzjkePXqETp06od7UK6WSgJaVK0kft21LzqaKMdK/P83j11+Tc3f7Np0LFhaAXI4rmpo8uqyM6Xp5McevvwYzMqI9OXgwBH19sCdPCPwZP5725/vv0xieOQO4uyOhSROmGRiICltbbrx0KROqqsiRPHYM+gEB0Pf1xQVRZDdNTXErJgbpyclo06wZNH/6CdZ37+LkqFHKQgsLNm7fPiZzd6czpKoK7QcOZGWamtLA/ftZ559+YmJlJeDkhD/i4qSIbt0w6u232XOsk3PnaH/Uro3BGDk3SiWyFyxAzP373Kd/fwETJtTUL4iOjoZcLucymYxVVlaia+fO0Fy3jiKgGRnAsmX4ubRUeZNzwcTEhLdr104A57TWnz6ldTphAgEb779PZ8CMGaSr/PyIgfXgAdkSixZRmpKXFwGLfn7kgB87RuvyGdq0ShITExEVFSXcuHGDX758mT2pqlK2KSwU+q5ahc6dO6Ndp05M09AQGt98A92RI9lFhYL17dsXLi1b4uxvv7Eod3fecelSBsawZcsWpb6+vqBUKlloaCi7ffu2BICdOnWK9+jRg9vY2DBNTU1YWFiQLlW1C2ykyGvTpk3h6OjIdHR0cOLECVRUVNQ9Ezp2JNCrRYvnAAbGGBITE6U7d+7wjh070h7inBhzHTtS6siUKcSie/oUZj/9BKPcXBbZpg0/P3SoFBEbi+vXr8Nl4ULmkJbG/+jeHa4rVzIxJwdChw70nStW0Pxs2ULR5fq6etjZkRNdGzAePJj0Xu15sbCg88TSkvS3vr6aRWJgQOdVbi7ZAOXllH6oorlratIaNTMj4NTamq4xaBDd45gx2HHwIP/t1ClWWlrKf/31V5aYmIiwsDCUlZXB1ta2Rg9UVFTg0aNHKC8vx82bN6UbN25ISUlJAgAkJCSw9PR0wdrampeWljK9vDxYZWRonuvUqbx7YKD8aocO8H6W/QOQXi8vp4h2URHp061byX5MTIRy2DCERUVh9LFj0L97F/oKBQwWLEBLHx+YlJczs8hI3MvIQPPbt8G6d3++QCUAURRFpVJZbm5uXlVeXu5RWlrqCOCzHj16/E9VLH/tbL+W/3O5du1aYO/evVu9Ul7gPymcUxRNLif68EuIlpYWu3HjhtLHx6fGwzI3N8fZs2cFLy8vyDQ1Sek2aYJLOTmStbW1MG7cOFZfJNDCwgKWlpa4fPmyFBoaKjx8+FBydXWt0+tQkiSUlpZCFEXUS/9s3pwUpaUlPYu9PdC9O0pOnsS3yclSSmCgdC0jQ9DU1MTMmTNx48YNSJLEBw4cCFtb27qnoaYmOXBeXmSE9+5NDpOpKTk+oaFUvTgpiQq2mJnRwdyYo/yMnDp1ijs5OT3/3Y2JTEbPl51NharGjqVIt63tn6eWK5X0jE+eEAWxX7+Xo43fv08Uwc8+e75tTi3R09NDkyZN0LJlS5w5cwaXL19GWloa2rdvD21t7ZqcrGPHjqGkpAQjR45EO3t7QYqJQZaDA6KiopCXl4fHjx/DxMQE9YJXgkBGyq5duEv5WEKbNm3q9O1s1qwZ7t27h/v377PmzZtDQ0MDYWFhaNKkiTR58mRWH7jDGMONGzcURUVFgoGBAbp16wZjY+Oafp91pLycxs7bm9aDpycBFrUP34QEGuP582mMly4lxD8zs06Er0bkcmJV9OlDa/vAAWDlSuRYWuJeUhLcfHwov18lISEU9S0rI2MxLo4MrT596O8+PvT/4cPJoOrRA8nJyQgJCWFz5sxhL4okvJJUVCD/yBFc/uYb5GVk8N+1tJBnY4MW9vZUpb59e4rC1Xa0FQraY998ozZQRZHWeYsWNK729gR8ubmRQbp+PdHje/QgI/677+g6jaTAWFpaIlJHB6cLC/EkJ0dpY2AgyIcMId0XGEjXyc6m3GIzM6I+1pbISNr3b71FNM1Dh8jxa9YMMDXFxYsXmSiKfMCAATU6zNLSksXGxnLzhATW+/x5ZPj6sq5du9bVZf7+VJ9h8WIy1JOS6JoqWqmvL0o8PXEzNhbh4eE8MzOTPX36FMfCwmDWsyeMKisp2mliQiwCQaCczS++oHlvoIBZy5YtBWelkuWNHImTiYl8yJAhzM/PD7q6uvz06dPMwMCAKM0gRk5eXh40NDTg6+tLFzAxobU1Zgxw/DjY6NFo2rQpOnXqxDp37ozo6Gikp6ezNqqK7uPHE8jUrRt9tvr5wsLCwBiDrq4uQkJCcO3aNSkiIgLFxcXM3t6e1vWbb6Lq3Dn8Ons2fo6MxM3ISEm7Vy9mqaLOammRU7l8OTl/y5aR892kCdCkCVq3bs0yMzMVUVFRQkZGBhXJk8vJgdDWptfNm5TvPmMGrYFHj2gdTJgA0/Hj0SwuDiWFhYhPSOBN/x977x0W1Z2Fj7+fO0Nn6L1LF5QOAoIIdrFj75pY10SjSTa7KcZsuppidBONpqgxUaNGwS5SBaSDdATpINI7M8y9vz+OgAWM2d3f9/vdfXKeh2eUGebe+2nnvKe858gRlunnB+Px48GOH4dkwQKYhYYiZPJkaN24wXv+8gtTDg9H18qVyLl5E47vvstNjY1l4ro6chQ8zMjgxGLYbtjAlAMDIepnnN64EadqatiyZcuYgYEBmpqaUFRUhJycHBQVFfFqu3cjwdeXz7h3D9nZ2fKsrCw+PT1dSO3oEFIsLOCydi0mnj3LsUmT6Hzctw/w9kbcL7/A19KSGTo7w8HODjajR9NejIwkp7uODsrv3GGTZ89mvtXVHLdoETkOw8IIWAUG0nj5+9PZ/8IL9PuQENq//evOwYHA9vTpBNAPHCB94eU1OK5OTjQOXl6kg6KigNGj0d3djfLycnlYWBg3btw4+IaGcjrR0VC0tibw98UXBPRsbKARGYkkCwu+prYWii++yLxTU1E4dSq8vL3ZyZMnhfr6em7jxo3M19eXBQYGUkT/+nVmbGwszJw5k3vq7F+3jvRbf4r3M8TExATFxcW4d++eEPBo5oGGBjkZDQyGdFzb29uzyMhIpqSkBHN1dVpz27bReG3bRmeblxeRZCYmQvntt2H+zjvM08+P83NzYxqdnUxj4kTYrFjBXDmOHbSwELxPnmRidXWyUdato7N13z7aY0MFUD75hJzrj7Lwy+U016Ghg+ngAO1RH5+nGftdXMjuWb2a/nbBAjoLGhooUqynR45IgM5yFxeyYdTUSA+dOQPHTz9lyl1dSFZXZyqqqpBKpVBSUhIqKipYfHw8xo8fj8LCQhw/fly4c+cO7ty5g9raWrS1tYmUlZV5QRCYWCwGx3FoaWlhC7S1MWb/fpyZPx+GdXWcWmMjl+XpiZqaGujq6j7Ngn/qFNlRSUk0bl9+SfrDzQ33MjNRr6gIt3feAVu9mkhrt2yhcZg1C0qtrdC4fBmdurpQ/fLLITM3tLS04OTkJNbW1lZITEzsEwTBc+fOnZXPXFj/hfIn2P5T/o/Krl27vBQVFf8+d+5cheeqCfy/LT09lAb0yiukTJ8zzau9vR35+fnCoyzrqqqqyMjIkCsoKDAzMzOGsDCgoQF91dWsqLOT9/T0HHZAdHR0MGbMGC4gIADx8fH8/fv3hZEjR3LNzc24ePGi/LfffuOSkpKQmpoKDw+PoUFXv/RHnF98EZfkcui3tQlz9u8X2X/xBSbJ5dAAELRkCfz8/JihoeHvPzBj5P21tCSP7Jo1g62UOI6M/6tXKTPg/HkyCrW1ybgdYjxzc3ORk5PDli5dyv6lNaKpSYYtQIrN0ZE81H+kLq6rixRjRgYpl9Onaf5/j1G4X2pqCGDMn/9c15VIJBg7dixUVVWF5ORk5uLiMpAmJpFIoKamhoqKCj43N5eVR0RgxA8/4AdVVeHu3bssPz8fVVVVQmVlJe/i4jL0gMlkwLx5yPf3F9QNDAbqrB+V9vZ2VFaz3VUeAAAgAElEQVRWIjs7Gx0dHaivr8fSpUuZ5tPtagYkMTFR6O7u5tavXz90HWl6OqXQT59ODpDgYEqtexLsZWYS+P3880FnxoULg8Ry/fM5lHAcYGCAag8PZOvooPWbb+BdXy+YSSRMrKVFa/ODD8jIWr6cwGJiIhk5Q5EISiSAgQHkxsZIzc5GT0+PMG7cuH8rql1cXIy2tjYkJiZihKkp5PPm4cG5c7gUGIjq0aOZkkTCKhMS2OjAQCj6+DwWXR+Qzz+nmuV+boJ+efttAqCffUZ7b+NGMtZnzSIDv7qa/n3wIGUUeHhQVOfh3urp6UFNTQ2OHz/OX7t2jd26dQv19fWQy+VQ09JiV3t7WXFpKZ+jpye0Ozqy7MJCue24cVwHx4FbvBiilhYymn/+mcZzz57Bdc8YAdn2duDYMXAiEXxXrEBiUhK7deuWUFJSAi0tLZaUlMS3tLQwe21t3qi8nIs1MEBWVpagp6fHdHV1ydhesYJqHkUiigpdvUrgIzAQja2tuHXtGvD99yizsMC4ceNQWVkpZGVlsa6uLtytqoLXvn0QpaTQGPanoCspkZOnsPCxVjxPyaJFkJiaolhNTSguLmYuLi4wNTVlMTExKCoqwqhRo6CqqorY2Fh0d3djwYIFT7e662dMrq0lcErs1mhpaUFtba2gpqbG7t+/DwNNTVqz5uYUIV2zBhCJEBUVBRMTE2Hz5s3MysoKampqTEVFBUlJSayzqgr2n38OPjERe+bNg8zICKGhoVBRVWVNFy7AeNo03KmsRPZXX/HGe/YwxTVraA+uXEn3FBgI3LmDRl9f3EpI4Hp7e8HzPCsqKkJ2dvYAF0N9bCw6GEO5WIz6WbMgVlKCSmEh5EeOoPb8eZwMCxNifXxY8GefsWwNDZw1MmJFxcXwfO01KE2ZArS0QCc2FkpWVkgTi4Xyzk5Wo68vFJeWIl1LC7PDwhhUVQlI+fmRc+7WLYrKf/UVgasPPqCz2NoaZWVl8tjYWC4uLg7p6emoqanpk8lkHKurY/q5uSzG0pJrbm2FmZmZSENDg9PW1uZMOI4ztLFh5ufOMcXWVrAJEwjwrVoFbN0KhQ8+gEdCAqw//BBmo0eTg6K7m5zUD1OGJdeuMTUnJ6gGB1M02saGQEZQEOkfDw9AWRltbW2oqKjAvXv3EB4ezickJAiJiYmoioqC2gcfsB8AIenePdyWyYT7jAmxUqkwYtEilqinJ4S7uQnJ+flCpaEh4ltb0X7qFJSPHWNHFRR4vzVrWC9jnNv06VC7fZvuv66OnGB6ekTCtm0b+EmTkPr990KVoSF7UFXFVFeuxB1ra0GqoIDKykpWWlrKli9fDu2HhG0PW1Axb29veHt7D61/lZXJcfOoI/MZoqSkhIKCAhb4ZIZBf2BhiFIoBQUFZGdnC26//MJ0du4kp+GWLWQ3SKXkmNi7lwB7RMRjnBQsLAyGSUnUw/3ttyGKiUFDUxMzOXwYKg4OpIvMzckJOXfu45kPj0pREX3uUb4cJSVyOFlaPl1XbW1NZ/TmzXQG94tEQuvW0JAchkFBtL4dHYkp/eZNAqn9NhtjdD6PGgU0NaG7sxOZ2toIiInBrKws+H/7LcYGBbGcnBz09vZCT08Pp06dgqenJ9PQ0GAtLS1Cd3c3N2LECLmnp6dowoQJmDx5Mtrb24Xme/eYJD4exm+9BdMxYzD6yy+507NnQ85xMDU15a9fv84qKirkLi4uHFJS6Iy1siKn9549pE8uXqQzXUkJyidP4rfp0xF4/jyVl6xZM6ibPv4Y8vfeQ5qXF8yPHoXi7xDnHj58WNrb2/vOzp07zzzzg/+l8l+Sw/un/C/Irl27vBQUFK5MmTLlvyd9/I03yOu8evUfAmvd3d0Qi8VPsQ8GBASIrl69iurqavmoUaNEdpmZsNm/nyVs2/Zc38txHNasWSM6cOCAcPjwYf7+/fucqakpW79+PTo6OvDTTz+htbV1+J7CANra2nDmzBleKpUKPT09ohHOzhC1t8OUMfKsGxmBOTgQ4/rnnz8/wBy8SUpZ/ewz8or294mtrSXwFR5OqYjd3aSI9PUJXNjYIOnOHURGRmLGjBmCWCxmPT09SElJ4XV0dODs7PzHkDdjdE2Oo6iqsTH1h3yWyOWkVMLCSAGuXEmeb5mMUsV+j/gtK4uAYXY2EVP9QfH29maXL19GTk7OYGSMfg9PT08uPz8fI4yNofrKK/ibvT2TSqXgeR59fX1s7969ou+//55fuXIl91SKqURCxtjhwwN90p+UiRMnYuLEiYiPj0d0dDQA4MaNG8R4O4y4uLiICgoK5Do6Oo9/5swZMgAbGig9EqD6r+HknXfIYJ0zZ/B3X31F8/Ec/V8BYlWPrKgAgoPx1qpVTHTnDkUhsrLIYbJp02D95ubNFCVxc3v6i0JD0dvbi2ZLS6jb2MB3375/CWi3tbVBLpdDSUkJJ06cgIJUitCICJQqKgqF69ez9OpqgDHMmTMHNjY26FZTQ75IBM89e4b+wgkTKCL55Dk0Zw5FeeVyAmc1NfRcp08TOH3jDUojb2qilNa2NiAgALyJCT7y8EBfXx8AgOM4bsWKFVBRUYGenl5/eiIrKytDUVER16GtjfzGRqGvuVlUamuLWHNzjH37bTj09FDbv7i4x42tR2XuXIra7NkD8b17UJVKoWlpyXR0dOQnTpwQyWQybuXKlRhhbi4S5s9HSF0d0tPTceLECah0duLl/fuR/+23yJRKeSMjIzZt2jSGv/4VLevWIf6VV+R3zMw4654eeEVGsh5nZ1y/fp1t376dSSQS3IuNRVxkpHDOy4t5LlgAW0GgsVmwgO7t1VcJaK9cOXRbLEHAbU9PPra2ltPQ1X2srnHt2rVISUmRf/PNNyJdXV20tbUJU6dOZY5Dpf9qatKZEhpKBvuvvwIAHBwcWHx8PPvtt9/Q09ODNFVVBFdUwHj2bIh9fICqKrQ9XLcODg6MMQYLC4t+xnjmqqUFlblzUWJmJlx44QWo6uiwVatWQV1dHU5OTuibOROffv45+hiD84wZ3ElVVUw9fx4mixZRxFVLi5xdd++iasYMhPT0oOaNNwSRru5ACcbDV2ackwO9ujqh8swZoVcqZXX19Sxi9GgEamkhLTSUnxcTw4xFIog3b8b8xYtZ6OTJaJw7Fz0PHiALkBc4OCDk8GFmOXs2N76ggPvy3j1Y1dUxxhg8PT1pnIKD6bWri+bG2XlwXlxdSYc8lGXLlokOHz6M2tpa+Pn58RMnThRzHIdrH34opBsaMmV1dd7Pz0/w8PAQQRBID6mr03eUlgI3bkB++DDuTpyI63/7mxyHDgntM2eKNcLCYA/Q+SWXU8aIqipd39AQBS4ugnd6OkNuLmXBpKURaFNQIPAokSAnJwdnzpyBRCKR8zwPQ0NDztfXlykoKICNHQujixcxOSSECQ/3mTBxImwANJuZwbq7mznn5jKuuRl3165FbkICvP/+d9QIAsbwPFe9dStK6+qIgPDll8kxcfYs1aOPHAnk5ED+4ouIFIvld2fO5La+8QaDXI7Ow4fBnJzYpatXIU1NxfKlS2HG8wTSlZUHUqCH7YjR2kqZa7/XcvIRcXJywsWLF1FcXPw40eusWQR8TUwe49yAXA4sWYJgjkOyuztvu2MHh5gY2jtKSlSWNXUqOWNXrx4EqbW1VCLw3XcUGf7hB+Cf/0Th0qWC1507TPvgQQLqBw/S55cupTnr72jwpBgZDU0E6+tLxGdJSU+fdcuXD536b2BA+0wup6yDd96hZ7a3J8eor+/jLN/9smwZJEuWoGXTJqh1dIAtXgzFU6dQ/9ZbUJ86Fc1WVvj111/h6emJxsZGvri4mJs9ezZnZ2cHVVXVx3Ry6LhxbNwHH+CcnR3uVFfzjj/8wPF2dnzIjBkYM2YMV1hYyCoqKoTK8nIOzc3k3HzpJcrOMDCgZ3N3p7MeAO7dg2jZMgiCgN6sLCjPm0e8BtevE1eNqSnOrV8vH5+dLVL/ne4dly5dkra1tSkC+PWZH/wvlv8SxPOn/DfJrl27rJSVlb/ged6f53kVxlifXC5XVVBQwJw5cxQH0uX+X5fvvydvqonJ86UOPyL9JFRPiqenJ9TV1ZGdnS06e/asAEFgKvPnC4tHjnzuC2hoaGDDhg0sKSmJzZkzB3p6ehwA/PTTT3J/f3/RkCy3j0hhYSEqKio4NTU1SCQS3t/fnxtQGj//TK/l5QQY+9N5zc1pLJ5XPDwGW07p6JBCNDenn9mz6TNNTQQMTp4EvvoKbWpqECUlYdOoUVBvbGSZFy8K51NSmEgs5sRi8fP1aX5S+j3zX39N7Lj5+VT3Osh+OSiVlRQFDA8nZdpfFwuQ0vm9KLtcTspyxYo/lDL/qDwkSuOjoqI4BweHxxiLOY6jMTh6lCKzX3/9WCr48uXLcfLkSRYbG8sHBwc/fbNdXZi/fbsocahnf0QCAgJgZWWFkydPCnZ2ds9cl3K5HB0dHVxDQwNF8m7eJO/3vn0EsLZtG+zpOpwkJFB68pNOnVGjBiOlBQXDMo/3i5eXF6Kjo4XAwEBBZGzMoaCAUgV37CDgnp1NwFRfn675jIi9oqIirv/lL2huaID+qVNyT1dX0R+t/z969KjQ2NjIlMRiYXxGBivV1katkRGyXV1Zd00NwBhUVFRgm5cHGBjg7KefClUdHVDNz2dPkSG2tw8aeE/KhAkUbTh7lhxkK1ZQut/162RMmphQbeJvv9F+6+0FX12Nb11dIamrwyJ3d2gsWQJlZeUhuSCsrKxgZWXV/1+GS5cALy9UrViBX2JjsUMigbqTExlkf/0r1VMXFDzeKg8gZ8oXXwD792N1YyPO1NXBcuNG0dixY5GamipYWFgw1NWBBQYisK4OAQEBLPnWLURHROCfmzah/e5d2NraIjk5mSUnJ9N3mprixQsXRE5z58Lq3Xchf/ttrHvwAHfv3qW90dODEevXw3jNGvaJkxNUFBVha2ZGa8LEhDgelJQI1P34I63XR0Qul6Ni4kShzNCQ03V2FiorK9kAKASRWJqbm4ssLCyEixcvMgBsqKyRx+T8eXJ8HTwIhIbCzMwMW7duhaamJrKysnDj+HEoHjuGDwCoGBkJC2fOZNEPS1FcXFwe/678fJhv3Youd3d8PWYM4xnDtodAu196m5uh+PXXMNTWFubMmcMuKyoKba+9xpqOHMH1WbP4NWvWcFpaWoCLC+p37AAOHYK7RMKMzp4lUPAo98bRo8CqVcywq4uhuBiorASvo4PTL76IVfPmcSaxsQR6SkoADQ2o+PrCrLcXDfPnQ7+jQ2SRkoLM7duFti++gMmuXZBYWAh9PT3CwjFjODUlJYpYpqWR/rh1i0iqwsMJPHV2EiBcsIAcRlevghMErF+/HvX19Th69CiamprkCxcuFBlmZKCV49Db28sMDQ05HDpEacElJQQsNTWB4mLc270bFVIpyj78EL0uLqKQkBBcuHAB586d4xUUFISRmZnwvXxZJP/yS+iFhhKpWXo6PBIT2fXvvxckqqpMo6gIyhUVcCovB5ecDNy+DWZjA3VlZTjJ5fyCsDARXF3J2fvo/tq3D06MPd0poJ80rKiIzi0NDahduACX5csHUo7TOQ64ckWuaGMjwqxZdK71l9pUVKCtowO59+4JnJcXVq9dy7i//x3Q0IBEKkVXbS18VVTQGhsLM1VVciD39NC61NQkZ4dMRtkexsZ0f2pqpBuvXKExnDqV5kIkIhCpp0fn9xB6j+M4uLi4yC9fvoyn9ElAwGBLrd5eSq3evh291tbQ+uknFvjRRwy7d9O1e3rI2eHsTKUzBw48fqF33yWn49WrdNZ9+CFgZ4fymTOFAktLNqenR1B55RXWUlAAe3t7cF9/TVHb4aSwkHTOk+ny1tYE2ktKnk6B19cnXoRx48hhv3Xr4HuKirTnW1pojvfuJafF5s1UC75oETkAli59LGugra0NtSYmuP7GG8L6xYsZFi0Cp6wMpd5ezMjJESwkEpY1dizS0tK4DRs2wGioCDLPAxkZkPj5YdZbbyEzIQG2dXWC2V/+wvWTV0ZGRgomYjE3MyqK5j87e7CMads2cuS2tAx+54gREF59FTpHjvDX3n2XmzV7NumZ06fJzvvlF9hERoqS29qEaaqqw4aqUlNThczMzAcAXP/XSNEelT/B9p/yH5Vdu3ZZicXibD8/P1UnJyeRiooK+vr6oKysDEVFxecj9vp/QSIi6OCYMeO5I7tNTU24fv26vKOjA62trdxwNZ4ODg5wcHCAVCplFRUVsOY4xoWEUE3pc9ax6+joYPr06Y/9TktLi9XX18vxkPFzOPH29salS5cgEomwYcOGoRGkpSUBOoDSnyQS8lZOnkwG/7NYpvvlhRcomvXDD0PXROno0M9D46KhoACJPI+qlhaYvvsuFHkeUyQSwVRFhZWIRAJKSqie9Xn6UD8p/QZwVBRF1AGKbvQTRn31FYGDrCyK0D25TouLSTEP56EtLSWD4d49ij7+GzJlyhTu/v37/IkTJ9iGDRvYU+2ReH7I1iA2NjaYOnUqCw8PZ0FBQU/X7mto4Na6dXJBReV3HTtmZmbYsWPHMzdrW1sbamtr0dXRwX74+GOMGTlSCNixg7HiYiL9eR4pLiYQmZr6dHu6gwdpHcpk9PM7Ul1dDZlMxsaOGsUwfz4ZShcukJPktdco6nPtGqWhvvUW/e7RmuhHpLa2FqU8DwUDA6z69lsar08+eeb1eZ5Hfn4+7OzsUFhYCCUlJWbS2IhQf39mHBcH/1degYKLC7wbG9HV1YWSkhJeWVkZWLuWw/btWLxxI4uOjsapU6cQHBz8WGYDsrPJGTRUxgrHEdi+coUMt7FjyZlgYUElHC+8QM6usDBg40bIKirw8cqVGJOcjAlNTRBlZDyTV+AxaWmhjJVz5+Arl6Pz5En+874+bsuVK9DuzxJQV6efvj6Ksp06RZF2xsgY37ED7YcPw/rnn6F//jy033wTkyZNorVmZETzA3I8jTl0CJ41Nbj08suCr68vMzAw4EpKSnD8+HHo6OjA2tqaN1q3jhO99x6Qmwtu82aYLF0Kk02baMzq6oBz56Bgbw+8/z5KSkooovbttzRelZVkuAcGUh1ye/tjtZjhFy7IPcrLRVM/+ACa/v6M5/khOTH6meoXLlz4++3h+onbIiOpTOWbbwZY+93c3ODm5oZuf38Y1dYKhqamYDduCEaxsYzNnctraGgMXjwjg1JSX30Vqm+/DZPvvkNdXR1+/PFHNDc3AwC0tbWFsD17mGj1aqFeJmPvv/8+Fi5cyFq//BKiu3dhUV3Njh8/jo0bN6K4uBiZ5eV8V2AgZ2tkRHovOZkcptOm0XpqaSFypx9/JP3497/D0dAQOb/+Kvx04YLw2pUrHDiO6pUdHIgNfsQI6NnZQW/VKqCiAha6uuzukiWoP3oUa7q7WbWCAiuSSGAaFgb9lhawmTPpnLW3p0wJQ0MCfTExxJ+yd+8gw76tLXDsGAz8/bFlxgxuf0QEf+XyZfm0ggKR5a+/on3VKuQlJAiyt95iFj/+CA4YcLL1NTUBpaV4MGMG5ovF6F29Gjq6urC1tUXL/fuckJUF3Z9+QruSEqpUVKCnqUkO2G3bcOfAAciqq5lmbCwuOjsD6uqIrK0VZEZGkIeGsnEmJqiIjoa2rq6AS5fIWbt8OZ1jVlYUqb91ixwLlpa0V3p66JUxctxKpcALL0BWWgoGCPjlF4a6OkAuh6OHB9SPHRMVJScL9uvXM7z2GkW5Fy0CcnLQkZKC+IAAtmPXLhHX3EyO7YdlW0e/+YZ/oKHB6W/e3Dd648ZBDPDpp3Td7m7KKmhtpflubycAJZXSOd3P8u/vT061vDyqLVdSogyJBw/I0aqhgb72drSVlWHkyJGihjt3IL98GSKep7lVUaH5jYykvTd2LH1vURFurl3LO0VEMEsdHYbZsyl67+BAY9Z/BtrYkN62tKSo8P79tLdOnCDwev06EByMad99x4Xs2YNT06fzNefPc3K5nK29dUswWraMDceGDoAyUIY6bxmj8zYhYXii1I0bH2csf1T6Gfxra4mp38eHwGlsLDGl79tH+/phhkdpaSkAoLa2lt0pL8foy5ehJ5ViyVtvgfvhByZdtAj5ly5hW20tNIfLaNy9mxwRX38NLQDj6+s5SKUDXSI6OzpgfuUKN2bLFijPmUMZYY9G9bW1aQ3/85+Ajw94nselS5eQlZWFvr4+zsDJifStigrpo9OnAT09uO3ejbiFC5GVlQW3IbLIysrKcO3atQ6ZTBb8vwy0gT/B9p/yHxYlJaV3vb29VcaNG/fHQsFPiFwuR1FREQwMDKCrqwue55GXlwfGGBwdHQcYGHmeR0pKipCRkSEIggBDQ0MhICBA9G/1w710iQ6h8+fJU/kc0tnZiW+//RampqacnZ0dU1dXh4uLyzP3l6Ki4iBD57ffPn8v7GFk/vz53FdffSVcv36dDw4O5oZL1e/o6ICOjk5/mvvvez/6I7ytrVTvqqlJ0TR7ezp8pdKhI7liMUWM4uOfi8HdwtYWPkuXQldXFxoaGjA0NGR8QwNO/eMf/Jj79xkOHiSDRE2NXufMISWvp/f8Kf7BwfTT2krK/fXXCfB9/DE921BppAAp9ieJQ/qltpbAfH/K/H9AwsLCuL179+Kzzz7DW0+C96Cgp/sGPxSzh8pz//79mDt3LsRiMUQiEW7duiXX0tJi5aNGYerevTQG/2Yvz5ibN1FcWIh5Z85Au6UFR9atY3Z5eTB6zj2Dnh6au/j4x1pDDUhvLynugAAy6J5R81VRUYGTP/0kzKyvF7jt2zns2EHj1L8uGKMxCw4mo0BTk4Dg9u0Eth6JmguCgOTkZLlIJBIJAAp//BE+Xl6UHTF//rBnwoEDB+RNTU0iZWVloae7m03MzhZ8CgqYwo4dwKJFUHh4L7q6utDV1YX54cMcpk+nZwMp44kTJyI9PR1RUVFISUnhQ0NDOUdHR7r/1tbhHX8//EDrs7ubQHVEBDkKX3qJgK6nJ33HP/+J1H/+EwFnzyLP2RmTOzsJ3J44QQZZevqz99LevTQGEgmUjxxB6M2bXOrSpbhZUoKwfmNq0SJ6lcvJ6LS2pqyYpCQCF5WVKHV0RGVQkHycoqII27eTkamhQZGy+noy9hUUgDffhFhdHbNMTQduysbGBgYGBn2enp5iHx8fAp+vvUbPOm0a7Wuep3KODRuAyZMHvI/t7e30jxEjIAQEQFi1Cm02Nmg3MoJSTw9av/sOdR4eaGtrk7e3twvdV6+K83bswNSHJHDDcUjExsYKbm5uwsiRI5+Z/sLzPJqamlBdXQ2DL76AsURC6euvv/5YGySVmBhscHVlmDMHmDQJlteuAY9mIJ06ReUB7703EI13cXFBVVUV2h62K3rYHoip2tnhle3bGbS0sGfPHpw6dQo7d+4E7O3hNmMGOx4UhM8//1xQV1dnXV1d3NatWwn8e3kROFy/nlLe9+2jtGpFRZpbd3dALIZSZiYkpaWwuXePYcECWs8bNhAYnjKFsilkMgIWublAdzdsJ09Go1yOxiNHhAJvb9Y8bpz8QlWVaJy/P4IlEiJuGzuWQMmXX9L6KC6mFOL+EiQ/P9JDISHA4cNQ/vRTTL95k+tdtAhMJoOWnR00li1j+Y2Nwqlr13ipVMrJIyOhrKzMi0QiSKVSTrZyJSYEB/Nqly5xanl5QGAgJIIAyeuv0zX27MGF+HjeysiI5jUiAjAywlglJXLcHjqErk2bEB0XhwkTJjAFBQXIZDLEx8fzruvWcf7+/oN2UGMjRUM7OyljICWFzrj8fMoeSE6m7BAXl8HWbOrqUDAyQouxsYCKCoboaKCsDCrOztC9cgXfHD/ONo4ZA91btyirZN8+ICcHJqdPY0Z9ParWrYPF1q3k3OnoQKOKCtpGjOBsbGywdOnSpw0EkWjQWXb3Ls27uzu9nj1Le3PLFtrTiYk0P/12hiDQ89TXo7u1FScOHxYkjEG1sZH1pqUh0NMTort3ibXd0ZGAWXk5nV0A/TsiAtDXR0dGhvDbrFls88GDUBgzhkD1kynW771H93bmDDnWxGJyKmzbRpwO6uoDpRpKY8dixSefiADg22+/lUulUm5Yx3m/XL1KzzRcRt3evRThH8puW7p0sPPLrVtPl59Nm0b6LziYbKMvv6T7/8c/6PPff0/rfe5cuLi4oKKiAllZWYiJiUFeXh78/f1h/umn4F96Cac//lhY8uOPTMPMjErdzp6lDID+DJy2Nso4689o6+mhNdjf4rKzEzU//ADX7Gyci4jAxq++evxee3pIlxoaAhcuoCUsDD/GxckFQeDmzZvHTp8+DUt9fdJNf/kLrXErK+DiRbAxYxC8YAGLiIgQqqqqhNDQ0AHCvdbWVvzyyy89fX19K3fu3Fn87Mn475c/CdL+lP+Y7Nq1i3EcdyQ0NFTtyWicVCpFQUEBkpOTUVJSAk1NzeHrggBcvHhRHhcXx9LS0pivry8SExOFyMhIlJaWCtHR0ez27dtCbGwsi4uLQ01NjeDu7s6ZmZmxiooKLjs7m/fx8XnuEPqDBw9w7do1eWtrK8fV10N9zRqwGTOGPWQbGhqQmZmJu3fvorW1FTKZDOfPn+dVVFT41atXc5aWlujvVfncYmNDB7dUOjTz8nOIoqIibGxsWHR0NJ+YmMgsLCyYxiOA6tixY/KLFy9y8fHx6O7uxujRowVbW9vnTzVQViZWY8aIvdXNjQDAiBEUUbt/nw7cR1Pu3d3JiMjJGb7t0EPhOA5mZmbQ1dUdSINMvnMHt6urmedf/8qEiRMhDwqC4ujRpCyuXycle+YM/buxkQx0ieT3W2IVFBAhzt27pOD19OjZhpOrV0mRPTk3zc0U8Vm8+LkcCs8rioqKUFVVRVFREfT09B6Plq1bR8/6JAs0qF97Tk4Ourq6kDXDOo8AACAASURBVJGRgZycHCE9PZ0xxlBZWSk0trSIAjIzoeTvP9jX+l8RmQy2gYHoMDHhb3l4sJpZs4RXduxgkj8C4NesIWNo06ah3586lealqAh94eHgnkGS9st778GwpIRN7Ohg7OOPydh4EjTeuEEMygcOkEG7Zg0ZIVeukGGrqIhajkNFRQWioqI4QRDA8zxaOzt5b39/hh070Nnaim5XV1RWViIlJQWnTp1CXFwcoqKi0N3dzTk7OcHyzBk2KyEBjseOMdGWLWSkDAVgDx2i956Irri6usLDwwO3bt1iubm5GO/tTcapre3QNeYAAdk1a8jwCw6mcW1uJsB98CClQioqouTePZxLTMTou3cxSSaDwi+/UJuuvXvJQAwIoIjpQ3btx6SwkBxLrq7E4P3WW8CWLSivqEBRURECAwMfP/P6I5zKynRPAQE0n2ZmqNDXl0NbW2Tv5kYOqo8+IiIiY2MC80FBBKb+8pchHTEJCQm8g4MDN7AvdHVp39fW0t5OTqZo2SN72s7ODunp6UhMTBRu3rzJbhUX47alJbS++QZFaWl8gUTC69y4wXKNjSEHOIm6Ojf9yBGMmDoVCo/2/H1Curq6cPnyZWZkZMTq6uoQFRUlFBUVsTNnzuDBgwd8a2srKy8vx+3bt/kzZ86wlJQUFBYWIjMzEypaWrxpURH1LvbzG1wnt2+TkRsYSOD2yBECd9OnU9nNypUEPJcvH7gPU1NTGBgYwNDQEOXl5XjhhRdgZWUFlZUraQ4Yg0QiQX5+PszNzaFjbQ3O2Rk2vr7obm9HcU0NA4Ds7Gzo6OhAX1+fzvI5c2htbN5M56yVFYGQkycBMzP0XbyIqowMZi6VMiN7e1pzMhk5LpOTaU5mzyZAIZeTk8DaGqoBAVAcO5ZVRkUJTjo6nCgjA17nz0Pdy4vm8scfiZi0qIj2bXs7ZR2FheF+UxNOi0Tyasagn5LCVDo6cMXQUP4gIoILio8Ha2oCNm4EmzABNuPHs7FjxzINDQ0UFhair6+PLVu2jPX9/DNWX7gA6y++YGCMxrevj5zt7u70rOPHI6GvTzAyMmJmZmbglZTQZm+P8+rqyOd53J00Ccr5+Zj56acw3bkThmZmMDQ0hLOzM1NTU0NrayskEgll9FVUkDPr669pDyUno09bG3fWrIG6vj4U33yTHBv9DuHFi4F581BiZITbYrF8zD/+wcHFBS1XryK3uBitAEQ2NvKC/Hy4TJ7MEBpKeyg/H9i/H1cZE5TT0gTjmTNZiasrKu7f5+MbG9kDiQQuLi6ClZXV04dSby+ttdpayM+exd3btwW5mxtTGzWKosXV1ZROrKZGoLapaTCCyxjpXU1NZFZUIKOmhvXo67N2Bwd56Ouvc6aTJxPh3ZIlRLA1dixdSxDIcT9rFkV9ra3hZGXFGe3cifsFBdB/5x02FIkaMjOJcGzfPhqr/lZv779Payw8HL3OzuAOHQI7cmTgz+ovXGB3nZ35UQsWPNtAi4mhdpJDsa5LJHTWWloO7wBVUaG1P23a0GWIYjGNhaUl7Q1LSzrHLCxofAoLiVSyrw/dVlb92VJCVVUVy8jIwPjx49HOGC7ducMmfPIJxGvX0hhGR5Md6exMZYGbNpGe6w8E/PQTOXpefJHsmnnzoPvmm+jbvBlRBQXw9/eHIAj45Zdf+OLiYmbMGHr37kWivT3yDQ2FyLQ0ZuvqKixZsoQTiUTIjI2F68WLgr6PD4NMRs6C0FDi+/n8cxhaWKC5uZmlp6ez/paMMpkM3333XVd3d/f7b7/99rfPnIf/EfkTbP8p/zGJiYmxUlBQeGXSpEliQRBQWVmJqKgohIeH8zExMayyslJQUFDgOzs7haioKK6zs1Ows7MbEuzl5eUJHMdBJpPxCQkJXHl5ORYvXsxCQ0OZq6srrKys+hkzMXHiRGZpaQkzMzM4Ojri5s2bzM/Pb+g+pE9IQ0MDTpw4IfT09HBdmZnyBxcv4oqPD0vv7eWbm5sFLS2tgTTerq4uXL9+XR4REcG1tbXxHR0drKioSJ6VlQUlJSVhzZo1on+LYV1XlyIyT7aP+AMikUjg6+vLdXR0sGvXrsHV1RVKSkq4efMmsrKyOA8PDyxZsgQTJkzAcGP/XKKvT4axnh4ZYiYmlDp2/jzV1J09S5FDxggAv/oqRTv+YO27iYkJ8vLy5Onp6UhOTmZxiYkY6ecH9cDAwUiljw8Z5NHRFPVITyclExtLkQKOG0w97+khBRoQQIBr61YyUEJCKMJ6+TIZBE9KXh4B/EcBbr+H/oUXSFH+J0UQYMwYGn/8EU0qKhh5/Dg9k5UVefSzssh5oaY2YEgDwLlz5+RWVlbcunXrEBgYiHHjxrHAwEB4eHgwX19fbty4cVDauJGAmL7+H2NoB2h+Z84EXn4Z3PjxcNi0iZnZ2SEpOZk1NTXx5ubmTHGY1OzHRC5Hi6IipIsWQWk40rmXXgI8PdFjY4Ov8vJQWFMjiMVipqSkhNraWty5cwccY5DExEDl0CGou7vD/PDh4Wux8/LISOpPz1ZSIjA2bhzQ0oL66Gi0v/oq0ktL0aylBWtbW3h5eaGnp4dF3rwpj7Oxwa3eXmb86quIqq1FTV8fFi9ejKCgIGhpafG2NTXCOF1dZq+iAvWXXqJ9PFRa38KFlL68d+9TQBsYdLSYmJggJycHJfn5cr2+Pk7l9dchGs5BWVtLkaagIFrXFhaD/X1TUqi8wdsb9+/fR25eHopGjIDT4sXQeOcdigqPHk2RkOPHKeKxY8fTDqsDByi7QCKhtb94McBxsLW1RWJiIhoaGgRnZ+ehF5SCAv0tx6F7yxZcKy1lHtnZTP+HHyj9MyKCDL+ODsry0dOj83DChMe/h+eB9nZkRkYK7jzPSerr6dni4sigvHCBzh6ZjEBiYyNFzB8CTUVFRVRWVjK5XA6JRCLf8eabnIm/Pxy++YaN/vprzjA6mrlPncpGz5oFexsbKHl4QCE09Jn7pK+vDwkJCaitrUVNTY3Q0tLCZDKZoKyszBQUFFBaWso/ePBAUFVVFZmammLt2rUICAiAmZkZLly4wExffBE6wcHkyPP2prN0zBg6t/prSj08CLx+8glFqMLDh3QOXrlyRZ6dnc2ZmZnxbm5udNOqqmTwm5jA0NAQRUVFSE5ORmBgINiIEVA6ehR2Fy+yoAMHYGhigqysLOTm5sLQ0JAAN0D3cvAgzcmpU+TI8PIC3n8fXGgo2kaPRkRXF6R1dXwUx/Gqc+ZwetOnE+CoqyMAW15OYPLFF9Fw/Tqgqws1a2toZmczkw8/RJWJCZIdHZErEvEFvb2sRE2NVykqYoKnJ5R8fMDMzIA5c1BZWIgff/5ZsB45EqI7d7gHggDdn3+GTXIy5+DqCtGrr5K++fRT2n+2tuB5Hrdv3xaam5vZ4sWLYW1tjZjMTH7ElClM3ceHoqavvUaZWuPH031v2wb4+SE1NXUAbEdcuQJRUhKYvz/fp67Oy2QyvlUs5tva27nGkSPRlpiI5LIy4ezZsyw7O1tIuX2b+RcWQnToEKpTU1HX3o4HK1eiMycHt9au5c+5uaElL4/Z/POfKGlokKuNH889eX6Wl5ejrq5O0NDQ4PKamoRomYxlubvD5cQJ2N+7x3VmZDCrX38Ft307HnR0oHfHDpyqqpKXSaVc2N69rKmmBscAtJqZCS6GhszJzQ3RKSkoKSkRuru7Gc/z0GpsHKhxxu7dwMSJqPv5Zwi1teyItTWKjIz41pgYZtzUBPH06eQAWraMdPyWLUTw9UiENzY2VlBVVeVfeOEFLiEhgYlEose5DBITSXeHhVHWwrRppL9XrKD2eCdPQjkigh3t7GTpWVnyMWPGPG5YCQLpIU1NKoERBMhDQ3GnthY/3b6Nku5u3LCxge5nnyFFQ0OwmD17gNjQdMkS1mFoKFjOmPFsY83YmJ5vOA6c9nYCsqtWDf0+YwTUT54knT2Us1hRkWyUmzfpc/Pn015TUCDHqokJ5Dk50P/tN/j+9a/oBVhlZSU4jkNWVhbv6+vLkpKS0K2q2mfn5cVh/Hg6y1VUaP0fOkTj2/88AOmEt94i0G1rC0yahK5Ro5BXWCiUlpYyGxsbfPfddzwAxnEcHxMdzXUBqNLWluvX1grzrl7lnPbs4UQiEVQVFBAwcyYq1NRY7auvwkhLC8zLi4IYubnA/Pm4fOUKcnNzsXr1aujo6EAQBJw5c6a7rq7ukkwm2zr+WUGO/yH5E2z/Kf8xiYmJmcUYCyssLBSuX7+O3NxcpqioKJ80aZIoNDQUAQEBbPTo0ZyrqytnZ2eHS5cuMR8fHzDGUFBQgJycHNTV1UFXVxfW1tbcjRs32IoVKzhjY2PMnj2b9bdSUVZWhra29kBLpEdFQUEBmZmZ8qKiIujo6DANDY2n6sRbWlqQkJAgXLhwAYmJiczU1FRYtXIlc9mxg3OcMoWN2rEDIpGIFRcXCzExMay4uJgvLy8XIiIiWG9vr7BixQouKCiIubu7w9fXlxs7dizz9PTk/u1WZg4OBIL27/99UqnfERsbG9y9e1eekZEBJycnVl1djZqaGrS0tPDFxcWCl5fXY4MilUrR0dEBJSWlP15X3596vXo1Kc+KCjJ0X3yRjF/GKJ303Lk/HLVnjMHHx4cbO3YsCwwMRGxsLJqbmweJghgjwGlmRt7g6dPJUHVwIHAcF0cRipgYAgtvvEFZBHv3Drb6ecgeiytXSEHMnEk12I+mlD/02A+ksvX20t+PH0+p7P+K9PXR9RQUCMTu20eRLGdnoKgITC6H8okTMF+2DJoaGnSdoCCK9ri40P/XrydimHXrgCtXUCCTCWUVFSwvLw8jRox4ut4bIEPF25vG7HlJ59auJWN58mQCAC4u9PeMQUtLC7a2toiPj+djY2O5W7duQRAESKVS6OjoPL2eWloAJycc0dAQooqKWHV1NSQSyUDdakdHBz7//HPB4KWXkFJVxcstLbngLVtQMmmSkJGTw+Lj41FcXCwIZWWC5M03WTnHIdnWFmZLlsDkUTKnRyU8nAzv/tS5R0VREc1GRvg6NRWW06fzDjExMC4sZO0PHiD7/n3Ut7cjNDSU8/TyYkHjx0M/NRU+8+fDf8UKaGlrQ6mjA6ZKSsxs61YmHj8ebPPmoZ0v/Szwzc2DDqJniI6ODkxMTOD5zjtclq2tEPOs9oDW1uT4SU4mI1ZPj5xNZWXkUNqyBZg6Ffq2thAEAeUVFWA6OrxtfT1jiYm0P4uKKH03KYn21NSptJ/U1CjKcvw4rdPduyki83BeFRUVkZ+fj7KyMmZnZweNIbIbZDIZCgoKcO3aNf7ytWtMJBbzU955hxNt2EB76b33COAfOUJr+upV2pdpaeRY2r+f9m5+PhAVBYWYGM6spARiZWWaV7mcAHZ/qcHEieTICAmhiKWuLtoOH8bFrCxIjIzARCLe1NRUcHZ25qCvT/tn3jwau4MHaZ3MmUPz+IyoNkCR4MLCQowePRrr169nQUFB8PX1Zb6+vnB1dWVjxozhvL29OWdn54ESKJFIBF1dXdTX1yMyMhIBwcHgensJGBsbk1Nw/nyaF44jx9DKleQMjIp6nL35EcnLyxOkUik2btw4uE5mzaJ9/tDROXLkSKSkpAiNjY28o6MjBz8/oKcHzMgI+ra2MDY2Rk5ODnJzc1FUVITRo0dDdO0ajX92NiCXQwDAjI2BsWPR/uabuGtsDEUlJQR89hmr1NDgijiO92xtZdiyhdaUiwsB9e3bcTo8HOabNqHs3DmkXLkCzZgYJPr6QrO1FYZ9fZh67Bhr2rYNvFgMnDvHTmlqYoSjIxQUFFAbGAjh669h0duLUS+/zBlu2YJuqVRQbW9noldfhcr58wQYp0+ncXzzTaRYWODEuXN8V1cXW7FiBTM3NwcAJMfFCQ5OTkzyMGoqv30b1c3NQl9UFKvOyxNKEhOFiy0tfENzs6isrEy4ffu2UFVVxfz/8Q/YMcaSa2tRX1+PhuZmcYWJCdpyc4VJr73GIgwMmK26OtYbGTHnzz5DSWenUDhmDM4xxu5pa/OK588LbsePs/t2dixk0yYWJJVCKTsb4XPnCprr1nHy06cF1ZUrWf+5WVpairy8PC4nJwddHR3Cyu++YyMPHMDPvb1o6+yEc2EhygMDkdLTg0uXLsG0uVnQ09LiCtXUYN3ailxFRUx0dkbw5s3MZOVKaJmbo8XenhUVFDDl8HBUX7sGh8pKcG1tlJWQkQGUleEXd3chftQotnTZMqiqqrK28+ehoqsL7UmTaF2JxQTqzp8nJ9nDUpWysjLExcWx5cuXcxKJBKampuzy5cuwt7enjLWcHALWTU2ks8eNozNn507KcLGyAvr6INbTg763N1JTU7mYmBiMHDmS/j4hAQgMRPhHHwm/SCTs7vXrfNnRo8JvgYGsqa8PG44fh+Px4/CprIT+li1I1dfno6KjWXV1NbtfV4dIfX1eNSQEdnZ2zzbYTp8mp0BIyNDvGxgQf8WSJTQOw0l/J5PhQGU/KJ87l4IAPD9oJxkbI00mQ2JcHLR37UKNqipqxWIIAEQiEcaOHctMTExw48YNbuTIkVC1sABzdiaH3WefkZ0TGUnRflVVGrvgYOqU0twMvPgimg0M8Pnnn6OiooK5uroiMjJScHV1FRYuXMi5urpyXiIRHBIT4fnxx9yIwEBOHBVFNtKdO8AXX4D5+aFy48a+qKQkrvnmTdzz8IBhZyeUQkJwPDpaXlRUxK1evXpAN6elpQmpqamVUql00s6dO3+fjOV/RP4E23/Kf0xiYmLCeJ4f7+rqismTJ7MpU6bAzc2N09XVfSrKLJFIUFpaKr958yZLSEhgubm5KCsrw71795CUlIS6ujp5a2srFxQUBAsLi+eKUveLm5sbV1JSglu3biE2NpYVFRXJW1tbWX19Pbtx44Y8MjKS6+np4X18fLj58+fDdcQIxr75hg6kqVOhqKgIMzMzeHh4MB8fHzQ2NrKOjg42e/ZshISEcM9Kf/+3pa+P6nb6SY3+DXFxceGys7P569evc5WVlZg2bRpmz57NLl++zEaOHInS0lKcPXuWv3HjBouLi8Pt27fR19fHW1tb/+sRb5GI0pVef50O9/37CXyPG0eRMBUVAgJdXWRky+UENp8T4BcXF+P+/ftwc3PDsBFUBQWK2np6khKzs6PI2dy5VHMoFtP/r1whw0JJiQBFUBApkaQkAtJvvDHIQJ6aStG8yZNJiTk6knHwe4R2/cQ3eXkEJLy8yMCIi6P72LyZQHNrKzkHpk0jpfywzcYZNTW+urdXcFm+nMHJie5HLCaHgbo6ecsXLqQI3vr1sHN35zpzc5lyejpT8PbGkMz0/fXLPj7PXmPNzeS1nzGDgLaLC/0MAT4kEgn8/Pw4ZWXl/r7BfWlpaVxycjJ4npdbWlpyj35vT08PbqqpsalTp0IqlfKRkZHMyMgIEomkv+aRjevuZqW6uogrL2cjSkrg//bbbOy0afAdMwZBTU3MpaqKNZubI1wiQYeyMry9vaE7HGP52rWUNu/t/diveZ6HTCZDREQEGhobsfDll5n2Cy8wkbk5pJGRMKirg9W9e/CaNQvaNjZQUlaGOCwMnIUFWL+T5dVXaVz27qXX4WTyZAJK//jH7wJtgBxNulpaUNm+HWp2dizTwEDw9fUd3kBcuZKMw3HjCHRaWtI+3LyZMjJefhlYvhyWlpZob29HekYGi1VSgs/s2VBYu5ZStl96iQD7rl20j5YsoQjOm28SaPPwGDQEOzpozYrFcC4uRlpNDWR37sA2Jgbc+PGQb9uG+2lpuJmXJ+jOmcMiBUGwuHiRmxkejqDTpzmxickg38HBg9Qv/IcfKBLa2Ulg186Ooi/+/hRFWrAA0kmT8N2DBxi3fz9EgYG0f+RyIi3y9KRnDgujlNSXXwZCQyFvaUHL1auoU1fn137/PfMrK2POW7dy/UQ+0NamPf/117Qv+voI5G/c+LvcBvn5+fKKigrOy8sLpn+wNMPKygoJCQmIi4uD1rRpgpGTE4OzM82hgcEgS3NQEN3TmDHkGBhGHzY0NKCmpgb+/v6DB2p4OK2Fh3pLQUEBjo6OLDw8nLO1tYWGlhbNa0gIIJNBb/p0BAUFIT8/H/fv30dNZCRc339/oN+3cOUKjojFqGxpQX5hIW9y6BD7TV8fSnfvIjokBC3GxggNDWXiAwfA0tORVlUlHKmsZPHx8UhJSeE779xhLSNGIKS7Gzb6+tDNyoJmRARGt7TAIjUVot9+wwh/f9gVFDCRoSGSxWLk5eUJbNcuVmpsjHIbG9ikprLEtDRI2ttRbGvL0leu5HPV1QWDtDSkKivzelu2cJn37vHHpkzBmA8/ZO76+mzCzp3sUZb29s8+E0Z+9x0TvfIKcOgQusePR0ZBAVP4+9/B2doy0zt3mFJYGOf+2WeQ29nBKzSUc3BwEOyUlZk8OBjpAQGYOHUqFxQUhClTpsA7OJiJjIygduUKnCMiUC+RQPejj5DA84JOYiIma2oy7+RkZpeUxEQARuzbB4mlJeDpCW7dOrg3N3PSmTNxtaEBGmpqTOfmTcDdHdnZ2UJtbS1bt24dQiZOZArZ2dCYNw++ISGwyc1FW1cXb3j9OrtuYoIZc+cKo6dOZTq6uqhtb0edhwcvdnfnvXbu5JJ0dfGTra2Q1trK3G7c4I39/IRJV6+yu7q6uGBhgYCwMDAlJXK6LVyI2MpKwdfPD56enszc3Bwqr78Ozffeg8qjDk2Oo31qbQ14eaFx5Egcv3EDVlZWcm9vbw4AtLS00N3dzV+5cgWyPXsg+fRTiMeNY6LRo+nM6u0lm+H77ym1fONGAuPt7dALCoK1oyMyMzPBGINdP1mrsTHqvvuOqXZ1YXpLC3NqamJO77wDf3d3KHp6QsHICApvvAHR+vWw8fDg2traWFNTk3zMyy9zqk5OQuD69b+fhdjQQM83XPmbWEz2RVLSsx3uxsak09eupTP10fZnj4pIRA7CrCw6j/T1AY6DnqEhfisqQoG9PUKuXIG/ggJcN23C1NmzGWMMurq6KCsrk0dHR3MdHR1yOzs7DsnJlD316qvkIJswgaLof/0rOUcSEoAZM9DDGA4dOgSZTAa5XI7a2lrMmTOH+fv7D9RWizMywGVkkP0mEtHzxMcTN4eaGvDNNzCzteX8eR6W33yDdD093uijj9iXqqpoaW/nNmzYMFAGV1ZWht9++61DJpMF7dy58/l6ev6PCBuqPdGf8qf8K7Jr1y4/LS2tq1u3bh2GSepxaWtrQ1FREdVFtrYKioqKLDc3Fw8ePICnpye8hwMLf0AaGhqQnZ2Ne/fu8VKpVDA3NxeFhITgsYjfpk1EInH8+B9Pq/3/Q3p7SfG8+OK/DbgBihRyHDfwzIcOHeprbGwUKygoyD09PUVOTk7Q19fH1atXUVZWJgQFBTFNTc0/bDg+U+rqyOCztiajXU2Nosfh4aSwTE1JucXH0/smJgTIc3KolvFhVkNfWRnOFxSA19DAgsWLCQgYG5MCVlSk+dPUHGTFLC0l5VJZSSC5n8its5MM/dOniT1VIgGqqmi8582j+7G3p4jQjz+SoXvoENW3urlRdO/R7IOGBookjh9P79XWkqKbMoUi0bq6FGH/6it6Rlvb54oq19XV4fDhw9i+fTuGjFI/IZ0dHYhZvBj+TU3QCg8nY+bdd58CmQBo3N944+matORkiiDOmUOg5eTJ3227NZQ0NTXh9OnTqK+vR0BAAFxdXaFz7BgqMzNx0tmZNzU1FZYsWSICgJs3b/alpqaKZTLZQN/nVStWwGrECPCCgL78fCgqKNC4vfACRQt27waMjXHr1i2kpaUJMplM2LFjx9MWVE4OrTsVlaf296FDh/pqa2vFIpEIPj4+8smTJw+gmN7eXogbGyGKiSECMWdnSnN0cqLU7FWrBhlwg4KGH4i2Nlo/lZVkQA0XfR9KCgoAbW2UtrXh1JkzwhtvvDH8ARUeTuMyceIgQDx3jvbBrFm09gRhoB3NJ598ImhoaLCNGzaAvf46ZYOsWkXOoXXriDSwp4cMQJ6n9dvUROPf1kbA1syM9oSzM+JeeYWvSU3lxmZlIfn99+V6+/eLmgwNkWdtDa+UFNwNChIkIhEWjR/PFH19aUz6z7eamkGivM5OGs9btyi19/btx8BlYWEhIiIihAG2/MxMmmMtrcH9+be/AaDSn2vXrsnz8/NFYrGY9/Pz4wLc3cl5JJfTmRAQQE6QpiYC/T4+5Kw4dGjoffOIyGQy7N27F7NmzYKRkRFSUlLQ2NiI8vJywd7eXggLC/vddKempiYcPHgQUqkUW7duhVZ8PKWv/vQTzcmBA7Ruz52jc+bdd+m9IXRVX18fdu/eDRcXFyE0NJQ+YGNDTozAwMc+98EHH8DGxgbL++u+c3PJ0J8+faDbRH1VFY5+8QVUGBM2fPQRE/f0oOurr3AtLQ2TWluR/Pe/o7u7GybGxhg1fjzkGho48+GH8vLKSpF5ZaXQKZczRZkM1TY2EPM8PFNSYF5SgswxY6AYEsLPW7GCQ0sLjf+8eTTmcXE078uXA8eOoe+119B05Aj0//Y3lDo6CuKffoJKQwO7FhYGq8mTIejpgeM4cD09gKoqOI6DcnMz1O7fR+ORI7BwdISxmhplCnh7D3Qg2LN7N78gLIyzNDICHBzQGxKCkowMOKWlDTIxt7cje+ZMQSksjDkIAq2b3bsRe/Agou7fx4SJE6FYWwvt1FSYHj2KjBEj4GxjA0RHC729vYxjDOEzZ2JEaSlqLCwwLjZW6OY4dmX6dDhyHO8dH8817dyJiPx8YeVHH7FrkyahaPRouGdmwj0tDT9v2cJLe3s5L+//j73vjorqXL/e7znD0AWkCQiCNFFAERARe0PsvXejxm40sV1LTIwmllhjbMnVWKNiQ0WBgCIqVVHABigIUqR3mHLO98fDgAWjKff+Es8qUAAAIABJREFU7r2fey0WiTPMnHPe9uz9NE/0Vp03u3fTudK8OZStW+N4u3awHzYMHl99BX7RIhrvHTto7WppAT//jCvnzqHk2DF4VVXBKioKXIcOtHc1bYrAy5eReP06Zu/ZA27/fmiOHImoqChcuXIFEydOhI2NDQoTE/FiwgS4xMa+U+hRbt+OM0lJAt+mDesxbhzTeyWdR1lejqyVK1Fob4+ia9dEs7Q0Znb5MhoVFVEky4wZdG5Pn077pOo7aguBPt25E6GbN2PCwYNIvnoVLby9kdSrlygWFbE2MTH0/mHDyFZYvJiEdHt7qHK9KysrcfvWLUFr7VrO/dQpSH+n2GYdrl2j/ffTT9/9nsRESo+7fv39tprKHlBFBtRCLpcjKChIbNOmDbOwsKgPkafIDoAxPH36FIcPHwanVGICAOv79ynN4ZUioPn5+di7dy/6FhYKdlVVnO6uXbT3l5VRPR1HR9rHp0wB5s+H0KULDk+digo7O2VzW1s+KioKDbYOy8igaDSVyL5uHe1Jn35KIqzqvmtqgMxMFAUFITEwUIzw9oaWlpZQXl7O9+3bV7S1tWU//vhjVU1NzZDVq1dfff8A/G/hI9n+iL8Fa9eu7SSVSo97e3sbde3a9c81GQYQGxuLkJAQkTEGFxcXWFpasps3b6KsrEzo06cPZ2tr+0Gk44MgCFRNc9Agar3wIbmm/w4IAnkw5s0jsvRvQmlpKY4cOaKUyWRiVVWVpFevXm+Fm/8pCAJVds3JIS/Wm4qyKFJBj5oaMnhFkUhDdTVt9OrqdKDUeohv3rsn5FZWcmoyGTpraUFqYADBxATaHEdGm1RKxOjECfobNzfyjEmlVOFTXZ3UaqmUjFfG6GCuqKDXVf2ZNTTI0FfljZ4+TfOFepySJ9PTs77Yy759RGji4+kzR4+m+/qL8/XQoUNCbm4ulixZ8l7Dvbq6Glu3bkXv3r3h7uBAPap9fUlUiIuj3LCqKhIXVq0ib1ZtixH89hs9l23bKGz4xInf/a6kpCQ8ePAA3t7edVXQ34QgCDhw4ICQm5vLSaVSLHrxApcyM2G9ejWFp75huJWVlSE5ORk2NjbQt7cHO3CASP/KlZSLn5dHz1g1fgAuXryofPHiBV9UVPQ2GS0sJLJ45cprXueUlBQ8ePAADx48gEwmQ6tWrZRDhgx5t7dDEEh0URGgigoidVOn0pz29393mGC/fjSnL1/+3efZIJo1A4yNURYWhu3bt2PFihXvLryYmEiVxfPziRgDNH/37cPVYcOUQkYG+qSl8eyTTwBbW2zevFmsqKhgn376KUxNTEgs+vprysmtrqbxX7aMDFmeJ2Px888pUkVVj+GtS0hEQECAaG1tDTc3N3by5En06tULsbGxMDQ0xOjRoxu+/v79ieC5udGa6tyZDMT9+8mgCw6uE7eCgoKQk5OjnDhxIo/Hj2ke1xaTQnQ0iRlNm6KwsBCHDh0StbS0RD8/P87q96JQcnPJuHZxoXHMy6N/37+fxDLVXmRmRjUpRBE4fx4v9+9HoxkzIPA89k+fjnk7duBGp06wl8vR+M4dXF60SDl4zx6es7SkHtVt21JEytGj9cayqSnEEycQOGSIYBMSwjmZmJABP2oUfc+IEVSvgedJoGjfHjhw4J3F8q5duybGxMSIX3zxxTv3CxXZ7tOnj+jl5VU/kA8fEmn57Te617lzIX/4EHuHDBFFUcScOXNY1ZAhOG5igp5Dh8Laz6/+QxMSSJTKyiLPWWoqhM8+Q7m1NTRlMuCHH5C3ZQsatW6NuIwMXLt2DSaMKdrfusWnT5rE+sbGQjp3LolFT5/SmC5fTtEGmzbR2P74I5CdjaLu3XHCzEzU1tYWJ06cSPdpaEgkc9y4uks6Pnu2MCAzk9PZsIHGUUeHxEM1NZydPFnorq7O6U2fDty4gVI3N+wPC8NiVY5rLXbv3i20a9eO88jPpzD+pUsh69YNsqwsVBkYiMXm5jBPSGCVmpq46ucHD3V10XHmTKY0MwNzdUXZgQPQ6t8fpUlJMBo3DtVr10Lh54cb4eHQOnRIeKqryxnm5qLxxIkwdXSEtKYGOs2bQyaTQYiJgcGoUci+exe2LVrQBY0bR1FajRvjhbk5jpw/j6VLl9Iz++wzWqPffkupGfr6wNWruGtkhKZz50K3uBjcggWQLlhAwlZKCjBiBBQ//4xt58+jgjHwPA+lUgkrKyth9OjRnKamJnLPncPNX35BSvv2QseOHbkOtbVLBEHA1q1bhfLyck5HR0cp5ObyC48cgdrevUQYVZg/n/alzz9H9cmTOMFxolVMDJOOGYOOABFAUaTzu1u3ehuhpIRSYaKiEBkbKyaIIitv2VKsrKxkzR8+RJ/gYBg8f07vX7WKukscOkSCzfnzAGNISkrC2bNn4ZGYKLgsWMBZeHu/cxt4DcePk9d6+/bff9+ZM2QjvKtWyKsoK6Mz4+jRuvPr66+/hqBKMQLQtm1b9OvdG1xcHIm6nTsDbm64d++eeO7cOQYAzWtqxPFJSYwNHUpneG1qRGZyMjS7dMHZoUNh0qWLYmBoqATBwRSVFxFB39m/P5SBgVCOHYvz+/eLIzZuZMoOHSD88APUXm33pcIPP9Ce/913FEHn6Un2Q0TEax1axIEDkeDuLmgfOcI93LIFffv3B8dxuHHjBm7evCnW1NQwnuc3rFy5csX7H9T/Hj62/vqIv4x169ZN1dDQ2Dlw4EAtJyenv/RZzs7OSEtLE5KSkviYmBjExMQAAHR0dLgzZ84AANTV1UUA6N27N2v7ngrXv4tz54hAffLJfw7RBujguHiRDp+Cgj/lVfwzUFWJFASBqaur4/r16/D4k5XRX8Nnn9Hhd+dOw6+r+u+qq78dsvlmWG6/fvABuDt37iAgIACqTzQxMcGsWbNI1Z4+nfK+IiMpFLOykoxUmYwO76Ii+i65nAyA7GwiUxoaFL5laEjkW6EgAeDMGaj6m2LXLgoh//nn+rYn9+5RrtmsWeT5EEUyeMaPJ6Jla0thuV26kOftyRMyGsPDSUzx8CDD+pNPiEjev0+H444dwOrV6OvgwB3MzyfS8fnn5IF69oy8rCdO0N/XkiWN3r1hUlUlqqWmMri7U+5pkyZkWIWGEvls2ZLU6cmT6cCMjKQDdNy41wuqvAdnzpyBIAh48uQJ7OzsxB49erCamhqYmppCVYyG4zjMmDGDqywvx50+fZCwejUSo6Mx+B1EQVdXF3Vr+soVIrYFBWRM/PYbeejeiAhITEzka2pq4Ofn9zb709Wl8XtjHl26dEkpCAKvqakpmpqaisOGDXt/nsrw4VR0RleXvLp375Jhd/YseUNv3ybPigqpqfXRE3+2nsMnnwAODqioqIBSqURmZibeSRo5jsbYyYnmIGMQPT1xZ+tWMfnSJb6oSRMYymSCxy+/cNyXX8LBwQF3796lsWKMjDp7e5qbYWGU0nDsGL0WHU0CzLZtNA/fEQHk7OwMZ2dnBgAHDhwQRFHkgoKCAJAHd8uWLVi4cGG9UbdtG4lTv/5K0S4jR1IUS+fOVJho5UqKOBkxgkQwMzNkZWXBxMSEExISwF29CsyciWQrK7E4KgqeX3/N4O+PZ8+e4dixY2jRogXe612urKS5pSo+tmIFee6Dg2neJCfTOjcwoLVubk57wezZCLh3T8HGjpX08PLCp6NHg7O1RZdevYCyMrw4cQISW1v2m7e3qNmokWjL85yZpiYR7iZNiDT7+gLp6WA2NnDr0oUrvHIF6Tk5aJKXB3WVIyQpiZ7B3btEYEJDaU65uLzlZZTJZIiIiGBDhw6tH6DevYm0qkQ1oG59hoaGMq9XC0I6OZGXMTeXiNrYsVCzsMB4PT22e/durFu3DsPNzGBkYACTESOovZFKTDQ2prkhCPQ8U1LArV6NRubmtM/p6MCc54HkZHQeNQp2kZHQHzpUopGaiqDWreHr70/XWtu6DMHBtF/OnUt75ebNwPLlKHJ3x8H9+yEvK2PFxcX113737ltRI8+aNuWUGzaQ0JiTQ9exeTMwejR4hQJiQQGEQYPAWVoix80NNW+2sJTJYJKWhkaMkY2grg507gypVAppeTl0DA2Z8YEDgJsbtOPi0C80FAW7duGXTp2EMdHRnHTtWuj7+QFHjsBo5Upg82ZojBgBAPDr2xfo25dzefgQ+UOHipU//QQbLy8mOXeuXrDt3x9hc+Yoo8+f5xb++itTX7qURBrGgCFD0OjqVchkMrrW5s2JYI4cSVEhU6bQWF66hMcuLsrQpUv5ipISLO/Zk8Sa5s2JJI4eDUmbNhCCg8Xp48czAwMDiKIILS2tunVjmp6OAcOG4Z8VFVxwcDAePXqEwsJCQS6XMw0NDfbJJ58gLy+Pj4+PFw8OGMDGWltDOyCAPMtz5tS3Nl2+HBp9+mBybi6riI7GgceP0WbVKugwRvfk6EhzICiI9jM9PdpvFy5E62bN2NURI/D51KmMO3wYyM6GtEcPOsNUtSYKClDh5IRbOjoiHxbGcnNzlcnJybxts2bos2MHh3/843e3gtfQsSPe2x4MoPk+dSoJru8Dz5PNUVZWZ9c1bdpUeP78ed2zvnPnDkpLS8XBgwcz8do16EyZAsTGwsXFhZ07dw4A8FRdnWXt2gWLS5doPnfvDri7o+n8+UBkJKZ6eOB8drakbN066G7cSF7oVq0AKyvIGMMljhPS160T548fz2P1avBqauB//ZXE1ujo14UDY2Mau1OnqAbP9Om0H23cSO+vRW52tlgZFsas+/aF7cCBdf/eqVMnaGpq4tKlS1Aqld+8/yH9b+Ij2f6Iv4S1a9dqSySSHVOmTNH6S72ta6GhoYHhw4fzL1++RF5eHqytrYWJEydyBQUFiImJEWv7brNDhw4hICAA9+/fx+jRo6GhoYHq6mpoaGhAEIT3t906frw+l/cv9hv+l8DUlMhbbi6RhX8DHj16hIqKCt7Pzw85OTmCo6Mj9/jxY8TGxirbtGnDvbPS8O+hpoaI3cuXf+u1tm3bFs7Ozjh27BjS09PxMieHFNihQ4mYTZxYL1L82Rz7/HwKW376lLw15eXkeduyhYQDHR0y4gSBQpRjYui7q6rqC6mtXk3fr6tLokOzZmS0DRtGr7u70zyUSMhQlErp85RKej09HRJTU6jX1KDit9+gPWsWGVI3bxLZ/u47ysl1dQVmz0bB5ctof/kyszxzhowuV1c6FM3MyFuWk0MGzb59ZNQuXUrfs2wZGURr1pCBceAAefIOHqS83enTUa2jg5RVq8Sw7t3h8OAB2srlzPGrr9Dk8mVcfPhQPHr7Nmuan48ndnZowZjYvFUrVmFigsfh4UpZVhY//NEj7Lt5E919fQUA72efDx4QaVyzhrx4TZoQGXgDM2fOxI4dO3DlyhW0a9cOMTExCAoKQl9dXbjt2EGtwxQKBAUFoaqqChUVFSgpKeFHjBgBJycnhvf1ms/MJAOjQwci1LUhtsjLI0FkxgzyqsybR95rVeG9778nr8ylS++91Qaxfj1996pV4Gs9rfn5+e8k25XNmuHJL7+gJjYWhoGBsPXzQ1p6Oh5KJGx4Sgq01q/H/j17oBkRIcq2bEEqzwsAeKbq4b1+PV2vqm/9pEn07B0cyNCqqKD0isWL6d7eg5qaGhgZGUFDQ0N0dHRkbm5u2LFjh7hp0yY2evhwmADQycyEYuhQCGpqkIoiedXfRNOmJBIBEJ2dwTw98cDIiLXy90fagAGCrHVrduvYMaZdVoYW4eEIvnRJLCoqYjzPi8OGDWt4bAsLyfDt04eMya+/pjXh7k4/WVn0OzOTcsD37qXn8qpoY2qKwthYbsSaNWimqho+f37dyxbt2sFcFLlHPj6IiIjA87AwjF2xgtZT8+bk3VJhyRKYAQgtL0d2QgJ6nD4tOnfowPjVq8F9/z0JN5s20TVnZpKXHKCQ/lcgkUhgZmYm+Pv7c40aNaKIk7Zt3+7zC6oJ0GBk48KFNO47d9J8btYM+gBWrFiByMhIMXLfPuZ58SLyjIxw4csvUaavL/r4+LDOX3wB1rEjeVQ//ZTI8tKl5BVbsYL2wBYtAHNzsMBAWCxdCoSHQ2zcGIr16yFRU6N1dOECCYhPnpBXcds2OkNCQyGKIk7++KMwb+VKLmDdOqWM9lkegwfT976yNqqrq6FQKKCrq0tk5PZtEu7c3IBdu/Bw1ixWFR8PIwcHlA4YICRfucI5VFaKOHiQobCQxNk9e9DG0JBh6FASKYcNozB+TU169rm5tH8/eQJYWsKgpASaY8awjIwMlnfuHCw++YQEN9U4RUbSXnb4MJ3vGhpo3KwZGsXHs73ffCO0PHKEvksup7OA49B10yY+++efBfny5Ux9wgQilzk5wKVLCImOFt1ragQAPAYNogiRjRtpvSYkAHFxqG7SBE1v3uRHJiaC8/Ki+iOBgRQqX1hIUQjLloFXU2NNuncHd/YsnWX//CeJbj16AJGRUPvHPzDZ3BwvzM2RoqmJdu3bc8Z2dtDT14eGhgYsLCzQokULtvH5c2gaG5OAbGNDAlGzZmTHZGfTeoqLg/asWajauBFlZWV1rT7rChwWF1Me96FDwPDhCNq/H7Hp6ZjMGLQ9PWm/2rOH9qY7d6gjCgBMn45KLy/ckskYX1AAFxcX3tPTEz6urrRu/ohTJTKyvijk76FbN1qbcnl9+sG7oKVFzzQmBti3Dxnjx+P58+ecRCLB8uXLsXHjRrGmpoalpKSwzZs3AwDWREUBkyeDs7PDjBkz8PTZM4SEhODIsWMU0ZCYCNmlS+BHjAAvioC5ObiBA1GspSVmVVQwx19+obNr6FCIDg44l5srpimVbNq0aVTUNy2NztlDh+hM09IiYr5sGdkYCgU5Inbtogifw4dpDtaKqACQFBoq3vb2xrhWrZhmbei/CllZWQgKCqoC0H7NmjUVHz4A/1v4SLY/4q+iq5GRkfLvINqvYvbs2ZDJZFBTU+MYYzAyMnrNc7VmzRqkp6fj4MGD2LZtG0RRFGUyGZNIJFAoFOA4DkuXLm24iNaLF3SonzlT33vwPxEbN5JH+NXcxn8hXF1dERkZqQwODubnzJnDaWtr4+jRo4qXL19Kzp07Bzs7O6i/qfz/HlT51jk5H6YQ/0FIpVLY29sj99EjDDM1RfmePZB27w5p7SH1p7F6NRkqZ88S6dTXf32e3LlDXi4np9erkKp6gWpq1vce9fGpf336dPptZFSf/1SbPwuADHqAwqYHD6b/PnQIWUlJKMrPx8m5czGladN6ggyQNweUl39z/34h9vx5znL+fMFx7Fgis2Vl9e+tqaHfISE0p3R0yHPJcSQE2NnRPVhZkdFWUEDGfbt2qDEwwOlTpwQvbW02bMQIpr5vH/SkUkjMzICwMIyeO5cTSkrAbd2Kl8uXQzJqFCv49VfE9+8vjtq7l7/Tpg2SW7bE0k2bgEWLONHeHkxVzGryZDJWHj0i4yY6mp5bXByJJ199Rca3uTkZTG+QTQMDA/Ts2RMhISEAgPj4eEFHR4eLf/gQBsOHIyc6GmFhYaKuri6USmWdN+zkyZMYMGAA3hkdU1JCntUZM8jInjz59XWoImmLF5NndvBgCkX+8kuKYvjhh4Y/90Nx8SIZQnv3IikpSQTAlCoR5g08ePAAp06dwoSjRyEqFMg2N0eegQFMTEyQameHYamp0MzJwbzPPuP8X75U2p45w7eaO5f3GTwY2mvWkPF04AB5JnV1yVvy9CkRpTFjaM7fvUv75qxZ5DXbs6e+BkIDcHd3Z+Hh4cKECRM4VYXy5cuXs6OHDwtcr15cioEBIj/9VMi9e5fD3bsYeeoUjHV1Rf0bN+ra9NSB5yEIAmIcHYVqiYQbePYsXsyciReWllxNZqbYunVr0d3RkT0ZMAC5Dx6IxcXFTCaTsdLS0vrq6DU1JCCpokYOHaKIFG/vt+8jMZHG/cULEqUqK8noHjSo7i1paWmQy+Xc79UVYYzByckJGrm5zLJnTzy3tITVmDENvre4uBju9vZo/t13yCkpYesnTcKoDRvEFnPmMBw+THmhPE97hp4eEYclS2jt1rb14TgOU6ZM4Q4ePCjcvn2bGzFiBBHnBkiAKIqQy99RDHjMGIr6ekPcat++PWvv6AixZ0+U/PwzxujqIjI3F7GxseKTIUOEjt278zZXrkCjupr2z/nzSbTJzyeirdojrayIzOrqonDQIFg1aQK5RALZN99Ae9062nNfviRj394eVb6+SAkPR2xqqlBaUQHcuoUhbm48JJK6KI43vdrBwcEwNDQUOY4ju0Fbm34iIoCsLHzeuzfjBAFPnJ3FmshI5p6eDkNtbQYfHxIorKyAxYsRdvCgUF5ezmnn5YluTZtynipPvq0tEUoPD1QdPYqa8nLczM9HgpqaaNusmWBw7BiPrVtp3bRpQ/MoL49ExKws2psXLwbCw8E9foyBR45wWX5+sOZ5EopXrAB69gRr3hyeT55wJ0eOFIdERzODL74gMXbKFOgHBIhekZG8+PXXYH36kBikq0s9jm/fhrB/P7Ld3cXkbt2YT3AwfefevUQkZ8ygM2DGDNTo6qI6OBhsxgwS+lu3JtFw5EgivgCQnw9pQQEQGSkWWlgwu5gYyDgOqW3bwkRdHfqjRqH8xg20zs4WOX9/hrIyEoP376f95JdfaJ+3tKzr9CGK4uuOEYmEOhGEhOBFkyZo8sUXOJWWpnyho8NNmzaNmUZGkhd55kxaw3v2UOqMhwcJB1ZWMP78c0zOysLZs2fFJ0+eiBPGjePUXV1RdvIkdN9VWbwhaGt/WKtSQ0MSpgIC6Lz6EKSkAFFRiG3RQgTAXF1dwXEcevTowS6/km6kq6tLe9OKFcDOnTArLYVBy5YICQlBo0aNRAAMzs6Iu3oV7eRypHXsCHNBgDQ7G/pWVkLR7dsMn33G4f59YNYsyJyc8LC8nC3btInOeyMj2s+TkuisWbWK1lJ6OkXxPH1KY2ZiQnUUZs2iC+vYkWyklBS8bNQIRV9/zcYUFUGzuJjGphaVlZU4cuRIlUKhGLdmzZqED372/4P4mLP9EX8aa9eu1ZJKpYn9+/e3cXlPe5R/FW7cuIHQ0FBYW1vDy8sLJSUlkMlkCA0NhZ+fH9q1a/f6H8TF0cbSr9+/LTz7L+HhQ8rPunfvdw3bvwv37t1DQEAAZs6cCWNjY1y7dk1MTU1FcXGx0K9fP76FKm/sfVAo6KC6ffv13tR/N779FiXr12PXvHlQSiTQ1dfHZ5999sc/RxTJwDpxgkhmSQmF0f4e+vUjg/fs2T937X8A3333Hdq3b48uDRTiKioqwt69e6Grq6scPnw4/8FFBauq6sUCUaTDtEsXMgZXrCCCAUBQU8PPP/+M8vJyzJo1648JLgDQsycqra3xg4ODICsp4RRqauhoZSX28PNj0NWlvOI2beiA37KFDBd3dzJOBw4kDwtAnlWl8q1IFFEUkZ6ejkOHDkFNTQ2ampqwuXULTnZ2uNC4sVhZWcn69++PNm3a4MGDB2JERARGjBjB9u7dC4VCgcWLF+PVKsUQBDJGk5MpgmD9+gY9g69BEMio3r2bQpFNTcl4aajP9oegoIAMcxsbZBcW4uzZsygsLMS4cePwWr/aWgQHB4u3bt1i84yNoV5djWMPHyKrNo9eU1NTXDJkCFMV1goICIDF6tVoqasLjf79SUiaMOHt/WXFClq/J0+SsJCdTeRr+3Yaq5cvyYh+xz1WVFRgx44daNKkiThlyhQiPCEhgFwOQVsbj/T08OLlS1hYWMDCwgKFyckIP3lS+dLYmPf19YWLi0td2ziZTIZdu3YJrKaGm3v0KDiZDPyAATRfVFi5kozYEydQWVmJHTt2AKKI6a1bw9DDgwzyjh3pmjU1X2/t9yaUSpr/qpzEhASKIFm0CBgyBIIg4Pvvvxc8PDzQtWvXD8oROLVtG5SRkRh5+DC4N8ivQqHA9g0bxBaRkcz97l34z5wpwsgIUzZsYFr375Pg5OxMaQUq9OtHY7B7N/24uhLxBrBnzx6xuLgYy5YtY7C0JFK3cOFr3/n999+jrKwMa9asef1Cb92iOawqELdhw9utKP39iVw0bw6sXo2aqio8at8el4cPR5OUFHC2tqLn1ausUX6+GDN9umCYmQmv48d59aQkoGNHVBw7hvSSEhjJZNAdNAg3O3TAzc6d4fD0KXwvXoSwYAGMRo8GHB1RWlqKcytWYOjhw7i9cSO8x42DTmEhXd+6dfVk9o3Uho0bNwpDhgzh7O3tKSqpuprmc61HHRYWRPhPnyZRe8mSBosXPnv2TNVDHRkZGWKHDh2YIAho6u8PzsAAXE0NkiIjEenlBaVEAk4uh6OGhrJdx4689dChRGAyMoiYHz9OHsHaiAEwhrzsbBw/dEhwiotj7tOns8bl5fX5+l27Aj/+COWSJTijpob2XbvC8ptv6H6SkvDP4GDl8+fPeX19fXHw4MGsWbNm9H2VlcCMGTh26pTSIyCAs9y7l2nyPGBujoKMDFQVFMC0USOoJScDY8fi+fPnOHHiBJYsWUJCU2wsReSoqQEtW+K8i4siMzOT8w4PR0jr1pyntzduhIfDSEcHtpqayoqEBC5HImEu9+6heW6uaOHry5CURKJQQADZA/36UdrE7t1k17Rvj5vq6nDauBGNR42CEBCAOytXCk2jojjDggJUamsjztMTlV26KPvu2cNzN25Quo6ODkVN2NuTUJybS2fFoEHkMa7dj0RRxNGjR8WM1FTWMi4OxkuWoINKBP8Q5OTQs3izgGhDUHWZ+O23Dy6ym5qaiofz5+OZjQ16zZ2LFi1aQBAEJCcn40RtvZQ+ffrg1TQPxZw5eHHxIo6MHw9eV1ccNmwYCwgIUJaXlPCNSkqgV1ICp2bN0P7LL5GdnY0La9eiQ2Ii9I4do4ioadOQ2aIF4qqrhScKBXM0NWWmUVEoGTcOPnFx0DZ+bomMAAAgAElEQVQ2pj2mdWtyRk2dSmtFTY2ex6tOhwULoNDSwk4TE9FVXR09iosZ3Nzq7Kba51/5/PnzAytWrFiA/8/xsfXXR/wprF271kAqlZ6zt7d36tq1q9of7s38N8HKygrm5ubo3LkzTE1N6wo1xcfHIyUlBa/1fRUEChtUGV3/DTA0JAPSze3fEu5+6NAhsWfPnszKygpyuRx37txBZWWlwPM89PX16zxUb3meXoUoUs60ufn7CeufgShSXun588DgwXg5ciSSCwtRI5ejV69e7+613BCCg8mTPWwYGUReXuR1fld7jlfh60uHf3o6Xct7Khf/FZibm+PChQu4f/++4O7uzhhjyMnJURWBgoGBgTBr1iz+NdL4PlhYEIlSGaq9e1OIuSocrn9/wNgYmZcuIdrKCuN694b+H61Q/+gRMHky1IYPh7ePD2vh4oL8ggIUKJVi2y5dWF2Runv3aH2eO0fP9auv6vu2q+ba4sWUr9q3L/z9/ZW//fYbs7OzYxcuXFD+9ttvnLq6OnR1dWFkZKTsEBzMTFxd2RMDA9HLy0ts37494zgOpqamzNPTk2lpacHHxwc3b95EdHS02Lp1a6aurk7XMHkyiS4bN5Kn4vcK3FVXkzjw/fdE4GxtyagUBJqnpqZ/OI1BEASULVwIyezZOGVvr7waHMzp6OhALpeL1tbW7FUxRaFQICUlBdevX2dyuRwuvXvDKCMDTmfPotm8ecitqBCGDRvG6VlaUuhoaiqaHzuGyKZNYR0dDfUNG+h5N7Sev/6ajCtrayilUmQqFEjR1RVzqquZ/g8/4HmLFhC3bsVDExO8KC5Gbm4uoqOjcerUKURGRiIiIgL6+vrKAQMGcLq6uuRZ7tcP8PQEGzgQxk2awNbWFsbGxtB4/hwGQUFos2YNp6amhpCQECE+Ph7Gxsbs+PHjwtWrV5mNVCp+cukS40+eBLdqFRUm/Pxz8lAPG0bEyd0dsLWFWno6mhcXw+DGDVHrxx9ZnKYmrHfuBJs0iQj07/XFBciInzaNPIFt2tA4qgo+zZuHUIkEL0tLMWrUKO69Z9++fcDSpQgXRQwMCYH2F1+89Za8zEyYLVvG2j96hEY5OWjXrRtr164dU1u0iK61UycK49TWJo8UQOS6spKEkqIiImAvXwLLluGeKLKOgwczM3NzIm4dO77l3RYEAU+fPoWXl9frhZFOn6bP7duXyNKDB/T3r97n+fM0Z3r1AqytISkuhumBA+i4fTvaTJyIYoC9aNwYGuXlTDZgAJdmYMCFNm+OvMBAQf/BAxyuqGD95s7FWW1t4VGrVvBbsYJ1kcvhfPcuoh0chDNNmrCYp0/h7e2NiIgIIYfnRZ8FC5itry+kW7fSPrV2LREDe3vK6X9FELt37x7KIyLQLTWVMS8v8vxraNB9tGpFz87BgUhF165EtktLKVc6O5s+s/Z+09LSlE+ePBEzMjK4mpoaVlJSokxNTUV+QYFoHBiI056eoom2tjjp/n1W3LUruh4/LrrGxHAyf39krV4tGs6axdjy5SSEzJkDnDqF4rlzcfvwYTxu1Aha+vq4k5DAOsybxyy9vMgh0K4drctp0wBnZwQbGCgTlUrWLzqasU2byHtoZQU3T0+uc+fOiIyMZE+ePKFid1evAtOmoXLtWkivX+esIyKYpqUlzYOdO5EaEiKe1tVl+adOwXj/fjHQ2ppFRUWJRtnZaKOtzfDJJ3R+V1YCzs4Qli6Fv78/5+Puzpy3bGGVPj5o07s3EpOSRB0DA/QdN46z7NqVefXvD8Pbt9FIJmOSnTuJUHfqRPt7RgbZL6mpNFYdOwL29rhYXAyXvn0hOjjg57t3lWXGxsxp/HgmSUqCdM0a2C1YAIcePThmaEhRJqtX0x7i6kqeckNDEkm+/prG8pVK39HR0cqYmBiuf1AQnnXtKmqamjLbPxJhFxdHotOHeKutrcn7a2LywQJrQECA6O7vzzy7dYNVbSTb/fv3cfr06br3eHt7IzU1VTx79ixsbGxYcvPmiMnJgbtUCtnz5+xGZiZkMhnXJzAQbcaORXs9PdgcPAg2ezZ009LgfOECrk2aJISGhrK2bdtC/cEDNGrbFg5TpjBNPT1WqFAoFW5uLC0tTYxmTGg/ezYJh+7uJDI3bkw1E4YOJfvmq6/qKvqLzs64kpamLFNTE0edO8chOZmiDGoRGRmpvH///nO5XD60a9euDYdk/X+Ej2T7I/4Q1q5d63n79u2NjLH9rVq1sho0aJDme/Oj/4VQ9Rl8taqxvr4+2rVrh1u3bkFPTw/NmjUjVW7LFlJVX2mB8h8PxoiwLl1KG/nf2Y6rAaSmpirj4uK4W7du4datW1AoFMLYsWP5hIQEMSEhgYuOjkZsbCw8PT0bJtyiSD+tW5Ox9nfPjeRkMoCPHqVx7N4depaWsLOzQ0xMDIYPH/5hPdmXLCHj08iIiN6AAZSbpsrH/RBoadGYXLtGeUyTJtV79P9mGBgYIDU1FS9fvmRWVlY4e/ascOvWLfbgwQPBxsYGEyZMeL/R/yaGDaOogzfHUZVnV10NxMUhqFMndHvwAM3mzydvZ3IyGU7vu8+HDymfbepUQE8PjDHo6OjA2NgY4aGhzD0hAVJzc8oNy8uj8Nhp0+ojTnR0KKz55k0yiPX0kF9ZiXJbW1y9epUzMTERQ0NDWVlZGWvfvj3Lzs4WevXqxXpqaHCNFi1i0p494eHhwaysrBp8MBzHoVOnTnj8+DEexseztr/8Uh9avHDhu/P75HLyePz4I63LHj2I8Pj6EjFr3pxEDFWefsuWf6jdV3h4uBAVHc0glYpJtrbilClTuPj4eMHY2BidO3euC7EuKSnBvn37xNjYWCaXy/HJJ5+gqakpMGkS1KysYNipEzxHjmR6OjoUmpmZCYSGgv/8czxv3Rr3GjcWXfbuZZg8uWFvjJcXMGkS8vbtQ8ny5ThcVYXHHMc67t6N9LZtkdiypaiRkgLhzh08VCiUqfn5YklJidCqVStOX18fOTk5WLx4MddIXZ2eT1YWPV83t7e/KzSU1vTkybCwsIC3tzcrLCxkQUFBYnl5OWekp4fJZ84wbulSui5VQSVjY8rzNDenkFKlktZk9+7Q1dND01272PM+fXDl/n0UKxTKFi1afPiG9OgRhRKronm0ten7YmPxICIC+RIJi3v8WLh//75obW3N3tklQ0MD0NfHo5QUmJSW4rREIj58+JDduHFDCA0NRcS1a6j5+WfmlJ0tapw8yfhmzer/tmtXiujw8SECNmUKGb46OiQAVFRQKsiiRfQeCwvIi4pwVyYTu3/5JVNLTCTSxPMNnh3x8fHgeV6wsbGhCeDvT5+jagfm7Ex74ujRFFqu2itUfcifPCEBW1sbzwcMQFF2NrKqqyGtrhabLVvGZJcuIdjQEKy8XJi1ZQszadKEGT56xCx9fKChpwf3f/yDuTo4MMnixeDatgWnpgabxETWfN06REdHIyoqCs+ePWMVFRXMuV8/aCkUtCd07EgVtyUS2j9MTIjUlZQAQ4Yg8elToRXAjJKTGUaOpPXcowft9y1b1hek9PWl13btovu+dg346SdKLThxAnBxQVBYmJifn88bGhqiefPmGD16NOfj48Na+/gwo7t3WYeNG5ltr16MzZ8PrZoaRHTqJHrJ5axq8mScLisT71y5whwFAec0NATTTZvYqevXlaGampzz8+d4mZ6OuIcPUaWujsePH8O2tBS6Fy+SIHDqFHD+PIpSU+Gfm8t9Ym/PdOPiSHQcNYo843Z2KCsrw/Xr1+Hj4yNYJSRwEASgb1+8sLTEVYUCLj4+OHTokPKuqysaKZWsuKSEjXj8GA4BAYhs3ZqVnDgBq8RE5hwdzWJkMkR6eCj50FBmMnw4w8yZKCoqwp07dzBmwgSoff457N3coL1vH1rPns2Cg4PZrVu3kBQSAoPNmyH064fGcjlYjx40PvfukXNjxoz61CAvLxKgTE1xpbwcxZWVuPrsmWhiYYHRly9zWh07QrJ1K/jhwynSysGBiHVMDEWXqDpfNG9ORSsvXKBQ5507XxNcIiMjoSmTMb/QUMjnzEF4ZCRzdXWFxodGG6kKqH6IvcgYpf2cOPF6FfY38PjxY6SkpODKlSvIyclhbbduRZO+fWn+eXkhMTERGRkZde+/f/8+kpOTWWVlJbt375747Nkz5DdqxCTPnqFvYCA0ZsxAem4u+iQkoOnUqZAOGAA2ZQp5ow8ehMTFBa6zZ7OYmBjBxsaGGQweDFhbg0kkMDc3R8uWLTl7e3tmb2/PIiIiOF01NZgpFLS+9+whYePTT2nPLi+ntebuDhgZIerJE7HN7NlcuzFjOLUffiDh090dAJCRkYHz589XyOXyDmvWrCn4sAf+v42POdsf8UFYu3atu4aGxg4tLa023t7eGq6urnVezv9EhIWFKQHw9ioP5erVtCH+N4SONwR1dfJMvRkW/zdj0qRJktLSUujo6KhyqXgAmD59Ol9QUABjY2N8//33Qn5+PtdgH+4pUygENiDg770wpZIEk+7dKQz04MHXXjY2NkajRo2U586dY4MHD+YabGGRm0u5bN9+S/ld+flkzKpypf8sxo2jn6AgKsz29OlfbvfVEFTr7cSJE7C1tWUTJ06EVCr9c2rGtGnkHVEVcmsI2trA+fOoOHgQpXFxIhYtYqiqomfm50e5eMHBZGy/CbmciMrFi68b+Y8fwyIyEiZ5eSjdvx9o0wY6Fy68+xpatgQEAYIgIFouF8sCA9mt0lKYmZkpJk2aJAEAQRBYbb4bh/Jy8koFBJCR9x4wAK3v3kViVRWKGzeG/nffkRHbEPLzydMRF0fzsGVLMj7e9JLev0/EpLCwPk/zzJn3ekiqq6tx4MABSB484MY9fgzd0FDWWkuLP3LkiMBxHBs7dix7dV6fOHFC0NfXZwvfCA/G6dN0D0+eUA68hgaFr06aRITbxgZmcjmSTE3JWN2wgUSUV7FqFaCjgwtOTsq7enp8y379MFVTE0Lv3ohPTBT7duvG2g4cyPDrr4CXF9o+eCDBsmV1tQgKCgqQkJAARVoapJqaRIbGjXt3AaERI+inFhzHwc/PD127dmXHN26E78aNuPjFF0ofb2/+tR28bVsihGvWkJBqa0thn8+e1Ql9zq6uCL12Tbx37x7fr1+/htvb1KKiogJyuRxaWlqomTkTGmlpSE9JQU5ODvLz85WFhYUotrDgpACbffYscufP5y7L5crdu3dDXV0dXl5eyi5dutSrUPv3E3H65BNUxcTgio8P9LS0YGhoKFhbW3M2VlZovH49NMLCwO7dY2/NvU8/ra/v0LQppawMHUrh+JqadO8LFtA81NKCqKuLnzQ1hRpTU0jPnWPIziZyVlhI88LSkoQJjoOVlRV4nkd4eDjXrVs3ijyYN49InqNj/TW0akXEPj6eiBJAe8A335DotHUrlNOno/z2bVi8eIFcNzfByMKCs3F3h1JXF+rt2oHnOC6kWzf4urpCGhoKKz09Eu2WLaO96OpVEqeUSqC0FJYGBli1ahXS09Nx+fJlFBQUkIhqYEDrr7KSRICyMlpvq1ZRelhCAoTevZFUXMyc589ndQW3Xh3zwkJaB8bGtDaysylsunt3yOfNQ1yHDtC8exeW/v4oKi+Hxb17vGhqKlaoq4t3797lWrRoATs7O/L6m5nRGjp1CvjpJzQ9fRqDtmzhUn/6CXbDh2MJwIXs2iXeuHePPX78mEtJScHUH37gs8zNcWXYMLjeugW/S5dwZ8wYMaOqij3asQNs6lSYWVuTkHL8ONSzstBx+3Y8DguDaVAQpSDMnl2Xp//s2TMAgJeXF4+hQ+l6ACgMDdE5KAhXU1Oh7N6d6xkejoe2tspyV1fW3tCQe/7gAVpNmYKeubl4vGIFDH74AS/u3xf7fvUVf83aGndkMtE4MJAplUpIpVIlam0BJCUBP/0EncWLsWDBAmS/eAGLuXPx/OVLMT4ggBkKAnQVClqPGRlEhvv1o+tSKCgtZfZs4PBhWGtrwyI7W7CfOJG18fDguJs3aU4zRnPDyIjO+82bXxd3z52jaCINDUrXOX+ezrLa1pX5LVsiOTmZ69GsGZCSgnaMsWd5ecodO3bwoihi1apV7y+iy3EkEH4ohg0jL7CqhsArKC8vx/3798Xg4GCmra0tVlRUsBkzZsDMzIyiUb79Fhg1Coav2KczZsxATU0NLl68KHbp0oW5uLiw0tJSHDp0CE+cnJDt7Ay/06fFvsnJzDA29vU5PnEi1UtYvBgAoKWlJYaFhQk28fEcrl8nEeQV6OnpwdLSEtqzZonQ1ycBdupUsudUz71RI0rZEEUomjVDtaMjw8SJ0CwooOuvFeiqq6vx66+/VioUivFr1qxJ+/AH+L+Nj2T7I34Xa9eulUql0q3q6upTevbsqdmmTZsP8xz+HyM9PZ1v3LixaGZszLBqFbWGsLL64Hya/zhs3UrGc0jIh+UQ/QU0JKJIpVKYmZlBEATI5XLu8ePHor+/P0aMGMHMag99iCIZ7eXlf+8FxcbWh/bevfvO/NkpU6bwe/bsEW/evImur/Y8vnGDQkydneng//prKgb1d6NnT/Jwq6mR8b9y5furk34gVC22AGDhwoXQ0dH5axO5oOCDw5sHDhyInenprO3q1WBPntDz37GDjO0tW6iATkAAGeWqML3p08mwOnKE5sXJkxTtsHkzUFQEswkTsL9JEyAsDH01NeH5rhD8SZNQnZSEsPXrxfsSCRYkJqJ82DD0mzy57ux6y2iKiPiwgnxPnwJ5efC4e5fdc3HBERsbYa6NzesfVlFB6+6f/6R8vE2bqGiQqvp0Q+jUqb76/pAh1DLriy+IrM+Y0eCfFBUVISwsDKWlpRhvagrdO3eQ8+gRDoaEiNra2mzKlCmvEW1BEJCTk8P9o6FWNv7+REh++60u/Fh27BguBAQIplpaTP/UKdF4+nSuurqaRdjZwWH7dqh37YrgrCzBwcGBe/TokdImJoZL19FhSdXVfIcOHdClSxdIt25FzYwZeDhunNhXT4/h1i3yBFZWkqEZEACUlyPHxgYHDx6Ek56eQtqrlwQbNvx+r9rnz4lQFhS8FWWhKZNhaloaKr/9FqUcx/bs2QN3d3elr68vVVG/coWITtu2JK6kpREhe2Ofd3d3ZyEhIVi/fj0GDx4MOzs7pKWlIS8vT6iuruY0NTWRnp4uZmRkMKFW3NEtLsane/fCf9UqsbGRkaivr8/b2NjAzMwMVlZW0BwzBtYVFZidlMQrFi9G2M2buHnzJv9aXYWQEKCsDKK3N1olJkLZrJnYedw4qoAviuQ9KimhtdSQyOPrW1eJHQARioULSTg5eZL+PyKCyMfYsSgvL0dubi43dOhQSFxc6LkmJNC+aWNDkSwWFrQuS0vRu3dvBAYG4qcDB4Spbdpw7NGjt9OVOI723s2baS/9/PP6Nk2TJwMKBfhZs1DVtCm0161DuxcvOO7sWYDnwc+bB8eJE6E0M4NWQQE05s8nUv3zz5S2MmsWncs6OrRHe3pSvQTQujY1NUV+fj6mTJkCA5mMBOdjx4icq8ZYlWOurw9wHG56e0MRHY2GalcUFRSABQUhY+9elNy5A4lEAoN9++Dw8iVYdTVe5OTg6tWraNKkifLWqFEQBQEds7K4jllZTGPqVHZx2zaxpqqKQaEgkl9VRftDSQlgYgJeVxdl+vpILioS7Go7LvS0tGRFLVrAccwYHD9+HBcmT0aPiRMxrXFjXLO1FRjA9Y2NZQWnTqGsSxf8lJyMPkZGMBg5Evl5eWjy9Cm6nD2Li9OmgVNF27i6Ivunn3DCykpZXV3NOzs7Q624mEQHV1cUFhbi5MmTGNOqldi3UyemPW4cw8aNsF6/ns/IzMTDjRsF9t13nHF5ObjoaDjZ2wMlJTDds4cpd+6ErbY28vLykJubq0xNTeUNDQ3rF5SnJ+1p8fFotHs3Gt28CUgkaNmlC7vYrJnQODERHq6uXJy/PzT09WGtUEAUBJiEh9OYxccTCTx2DDWrVsH56VNObfhwpFtYiKETJoiDtLU5I4D2ytmzaaznzXv9LM3MpPoRBgYkaKnWzogRyOV5XP7HP9DKxUXRbt48Cbp1A8zMMGjQIP7+/fu4evUq7ty58/62phUVdEZ8KKysSMC+fBlV3bujsrISWVlZiIiIEAsKCphEIkHv3r0hlUpZRESE0szMjAxpExMKkS8oQKuMDGS2bQtdXV2o7Kq5c+fWPfsbN26gsHY/6NCzJ57b2LB2c+dC7NkT7OzZ+pxqU1OKyCkrAxo3xoQJE/ht27ZBWL0aXANFEat8faGvoQFdf3+GnBzaW+bMabB3eGVVFY6MHQun1q3RuKSExtLbG5gzB6Io4tKlS9VyufzU6tWrf0dF//8PH8n2R7wTa9eutZVKpeesrKxshw4dqqn5vjy3/yDkUascJpw8Ce7mTSKB/wUiwTvBGG38s2aRR+BvInF/FPn5+VAoFIiJiUF1dTWrrC2ihRs3KLTt+fO/r3J6SgoZViNHksrt7Py7b9fX10eXLl3YjRs3iGz7+xMJOHCAjLkePSj/8F8FjqOcsfx8IgATJ5IR8BefR3l5OY4ePQqFQgEnJyf8obzshpCeTh6y9whPMpkMEokEJSUl0NDQoGJVjo5kvANEtJYvJ2K5Zg0Ry0WLyCjv25c8aPv2Ue63yvNYK3IMAjBo8GB8++23YlBQEHNzc2swLSEzMxO506ejVV4evKKimMaAARji4NDw/I+KoiI5r4ThvRMHDlBLoTNnwN28icZnzyIxMZF7+fIlTIyNKbfw9Gny3HXuTELKtm0flhbBWH2I6pgx9Mx++onm3oQJVKW8ltBkZGSgoKAA58+fB2MMI0eOhFVpKbBoEdIfPEBNTQ2bOnUqVaV9AxzHobS0FI3f7KhgZ0eeXQMDYPlylFVXQ7CwQNaqVeAGDWJOs2ezU0olRKkU0ampwpNmzbhWn32GB336cJmZmYpmjPGakyczW0dH9La1rRffli7Fyw4d0H35coYJE2g8hw8n8uTiUlfpOFZHRxhUVMQ5LVsmQffu9Z7Zd6FJEwoTfXP809PJu7JhA7TatcN4gMvLy8Pu3bt5Jz09mHz7LTQ6dwabOpVCVKdNo9SHHTvob3/8sS4VwNvbGyEhIWCM4fLly6JMJlNNfk5HR0fQ0dFhEomEDRkyBA4ODigrK4OBgQGE4cOxtH17Bp5/e7F4eJDQtH07JE+fotuGDYiMjERNTQ0VEUxLoyr1ACCKyDcyAmvRgikUCgQHBYmamzcztzt3kDd7NkLV1IS+mZmcquZIHVauJPIZF1f/b0OHksCxciUVCNPWpnt2dYWuszOaNm2KM2fOoFWrViREjRlDEVFubrR2s7Op4v8XX6Ddjz9CR1MTGSdOcNi8mbyW70L37nT+LFhA66+2JSCMjQGOQwtnZyR06AD9Tp3QXF+f1vxXXwELF0K4fx+FgoCS48dhr6cHyf79ZKB/8w1FKT15Qnnndnb1kSLFxdC4fRt9Q0NF0wMHGBijsOLSUrrvvn0p9eCXX6iQYu3avH37tuDn5/daWo0oioiNjUXy9u3of/EiglasEHT09FhlZSVKS0vZxEOHkN28uRjdvz/09fWFmTNn1hsLc+bQ77AwtL1+HZUdO1KkXHw8eTIHDaKopoAAxPbqhd+srbHo4EEOOjr0jK5fh4GdHQwcHDBhwgQcPnwY6ufOwTQ8HKMCAzkAKElLg0ZNDawGDEC2XC7cvnKF9bl4kd0uKhK7BQSw+76+EFu0qO9MIgjQOXYMpbNn8xKJBMnJybj244/QqqhAYJ8+4HkeHh4eSpsVK3iEhwNXr0Lw90fixIl4YGEBw5oazn7LFui2bFmX5gCeB7ZsAX/0KNoAgKcnQ1QUf19NTTA6e5arqy6/dStFIGRk0DpbuLDO8zwyKop7ZmuLW3Z2SHvyRFR/9kyozMvjru3fz9asWUMpQX36ADt3Qpw9GwWOjjjcvLnSqVEj5pCRwfnu3cvOP30K+169YHLlCoyiolDQvbv4PDwcpqamzMnJiSJT2raluhhbt9JY1CJp4EBcuHABEwIDYVBeLkFAQF0EgIaGBtq1a4fq6moxJiZG8PDw+H2D0Nq6ruDgB6OyEuLBg/g5LU3Mz89nGhoaop2dHRs1ahQaN27MAKonUFZWxoeFhaFbt271f3vgAKQnT2LAq2v9DfTr1w8dOnRARESEcPXqVQ4AkqdORau4OLTv0QOaAQEkWu7aRVFl27YBYWGQSCTgeR7JKSlwDA+nNDqAxMqePVHl5oas4mLcDAhQDs/I4DFyZH3ngFcgCAJ+/fVXgbOzEzv5+vL47DMSJWo96PHx8eKTJ0+yZTLZ3D/24P738ZFsf8RbWLt2rZpUKt2lpqY2sUuXLmre3t78/1UBtD8LqVQK14gI/JyRge7btsFKTe2/f7L7+ZEXIz29vkjOvxkmJiaYOHEiLCwsmL+/v9Lf359fuHAhpO7u5PX7O4i2KmR82zYyKm1t6/Mm34HHjx/j0qVLyqqSEt5WLhdRU0OFXlxdKU/03wkjIyJWCgWFfv74IxHRP4GKigps2bIF6urq4ogRI1jLli3/+vWpisu80sboTZSWlmLnzp2QSCSiKIqwsbGhXq6vIjSUfh8/TgLDqlWUN/fNN3TQr1xJUQ6TJ1P+awPo168fO3PmDMrLy6H/Sr68KIr45z//KWRlZXFO8+fDfeRIMra3bydP+Vdfvf1hhob0+u8JUQcP0r3v3UuhjbWGmImJiaBZWcmdW7kSM0pLSejZvp08iKrIjT8CG5vXPYTGxkQuAgNpXs+bh6dFRTh8+DDU1NREExMTcdq0aZw0IYFCdA8fhtewYUhLSxOvXLkiTJw48bVnz3EcLCwslFFRUbzfq0UIVdWpFy0CvLygdHPDyZ9/Fq29vDB33jyO8/HBC0tLsU1ICAhPpyYAACAASURBVLvWvz8WLVrE+Z86BVZcLI5ijDkuXCjB+PFkeI8e/dZtyS0sUKSvL9YcOcLUL1wg47ljR/IuFRcDjo5wsLKC1YsXRIgaqJz+FvbtI1LyKrKyiKiOH/9a6owxz6P98+dKjcGD+fO+vjBwdBR6d+jAMQBKNTXIW7TAPT8/pVVhIW+WkkKCj74+qqurAQDjx4+HtbU1EwQBiYmJsLa2hr6+/lsKikFtpXLu4UMSOSdMaPja9fXJC/TiBSRdu8KmbVshPT2dc7CxIc9bZCTQujUYY3DLycGJigrc/eYbtLtzh7Vp0QLpcjkCdXSgyMvjoqKi8BbZ3rnzbYGH52ntrlpVLyhu2lQ379u3b4/Tp08jNzeXvGM9etSfFzxPe1LTpiQGAbBeuxZaz56h/NtvofvLLzQWDeW1tm1L4z1uHLW343n6Ti0twM8POpWVaG5iIiYtW8aaCwK1fZJKgcBAqC1ahFYzZ+KcvT2Knz8XvTU1Gby9KRrm4EEibjdvkng3ZgztF82bg7t7Fzaamix26FClz+ef87C0rL+e0lLaVxcupDny1Vd1e5qRkREUCgWysrJQWFiI27dviy9zc1lTHx80+v57LDYxee2hVowYAbG0lHnJ5TCztGyYhHXrBoHnme6VKySoeXhQlMChQyRsrViBB7GxaOvuLqr5+TFs2kQigK5uXTX36upqSCQSNO7Vq35cr15FaVAQgmbPFqcVFTHf69c53zlzgNJSLCwrYy9cXHDO3h6DDx5EcXw8ajZuhKGTE3KPHgV3/TrcPD0FIyMj5qJUsnRHRyApCR4eHvD19aX7GDMGyMuDYtIkyCsqMDA0FJKMDEjbt6d7+O47ErxyciBGRSHexkaoYgyOTk6coZ0dUmJixFJnZ5h36kTzJieHxMq2bek5VFXRXAgMhPXYsbDOzaU5CTAcPMgXFhQgVkeH2lT5+NA4u7pC9PbGZ87OkA4fzkNVFKy8HD0mT4burFnQLC7Gb4sWodzCgiEtDQkJCcqLFy/ynrq6ys7ffsvnjhmDphyHKlFESVYWbt26pUxOTub8/PzYZS0tcejOnUzb2JiiHl6JZHN1dWU3btzgZTJZw61hVcjLI4/6w4fvfIuqm1NdV6cBAxD59KlYk5uL5StXQiqVvmU4W1lZwdnZWQgPD+c0NDTg7e1NLyxdSiQ4Kor2jnekaRoYGGDAgAGcr68vTv70EyzCwhDerRtkxcXwDQ8nx1J0NIldwcGAQgEtLS0YGxvj8q+/wu7kSfBLlpBtNXYsEBKCxt9+i4n5+Xjcvz9/y8MDsXFxKAoJwZgxY+DwCum+fv26UJCbi89ycnisW0dzv3t3oE8fFBcXIzAwsFoulw9cs2bN3xze+N+P/3r+8RF/L9auXctJpdJfzc3N+4wYMULjnUVf/sMxu18/aOzZgwudOomHz55lqvZMvXv3rt/c/hthYEC5oseOvd7D+d8ILS0tHDlyRHj58iUHhQKSFi1o0x037q9/uCBQf08HBwp1/ECR59qpU2J5TQ0/JTUVZnfvMqxfT56S/0tIJBR+2bo1PR8PDzIu/wDy8/MBAEuWLGF/WyHCjAzy9DeA7OxsBAYGCrm5uez/sffeYVGda/fwevYeytCGXqQKCCICIiKKBQR7j7130WOLJsbEmIRDosnRxB5bVFRsiF2xoyg2FFHBigKCghRpUgeY2fv742YAFdRz3vc9v+SL67q4TCh79n72U+617mZlZSWamJiIcXFxfFJSEr9+/XqhTZs23Dvt9EaOJGJWXk4Kt0JB5JvjyBB++ZIiE2ramdSHm5sbDh06hMePH1M/Yk1NKJVK7N69W8jLy2PffPMNebyXLSMCP2FCw57rzZvpc95uY6RCYiIZicnJ5K1Q1XIQBCAuDp2uXePcU1JwTaEQMXcug5/f/yzl5PvvqUZAfairA7/8gqh165ReXl78NT8/eA8dit69e1NYMUDksG9fmjMAunXrxtavX8/n5OS8ExbbuXNnft++fejSpUtd0Z/KSirS9tNPgKkpMszMkCGXM+vPPycv56+/wlJLixn++iva/vwzsHAhBvfrB7RsyXDgAIVnrljR6GPJZDLc69aNPSwsFGYNG8ahqIgIpYEBUFgIhZYWcgwMOBOOg2Zc3IfJtigSUezWra6exosXZHgOHVonUlVVUbjw+vXo0bQpL2RkwC8vD6GhoVxsfDzMzMwEDQ0NTtquHXhjY/60uTkmbtoEm/h44NAh3Hn6FBzHwcrKChzHgeM4tGrV6sPvMS2trj99Y5BIKIIlKAg+R45wCUePKp2++opHdnZdGKZcDpviYkyaOxfFv/wC67Q0qEml0L14EcY5Odi8eTMV8nwbOTnkSY6IePP7+vokqKxeTWPcoQOJaKtWodLQUATAioqKiGwPG9ZwVFcN0VCXSJDi4AB1iQS6u3fT3nv6NOVn9+377rM6OlJaxZIl5NXKz6ev4cOR2KSJ4PivfxF5OnCA7js7G9DWBispQeLy5XDnOAY7OyJp48eTt/rBAxI1ZDJamwMHEhGws8Ph7duVNjY27A2iDZDIFxJC637/fmDpUlRbW6OiooLbtWuXWFlZyTQ0NASpVCro6+tz/rdvM53Q0LpewfWg3aIF7GfOhD3PU5RAfVRVUdTWjh2I79NHsG3XjjOztCShoUcPIv25ucAPP6D38+fY36MHvHV1oT9jBhWMcnevnQdPnjyBvb29UqdNGx5ubuQpdnGB5pw5yLlxg101MBA69O3L4dQpSg04cACWPj4IVldHTLNmwt0dO9iVtWsZNDUxevt2DBk8WGjeqxfHTp5EZUkJ9j14AEtLS2VAQADPRJFSEEpLgepqqEulyBw1Ski5cYPTu3ABPWvyb8UZM3B7+XIhPS0NRcnJnG1iIrvv7c1uxsaK9vb2QopSyTdt27YuPUpfnwRDV1dAFPE0Lg78okWQTJ0K0+vXEXnsGIqKikQzMzPW+fhxnDMxqR9JQuPh7Q1h5EicDwhAgKEhNF68oHoCOjqwGzWKzhRdXfT78UcaX09PYMcOPjMzE9eOHOFe6uuLry5dYtvGjgW/ciXU1dUFfX19zJ49m+no6EC/uJgV8jw0e/WCzrNndB5lZgIeHtDX14exsbFw+fJlLjAw8N11UYMzly4pPYqK+E0hIY3+TkPoduYMG2Bn1yiRNzAwwIABA7iEhAScPXv2TXuUMTrjxo+nM/M9UFdXxygPD1QuXYprEgmeu7jQXFUq6XlNTEhos7cHTp9GYGAgdmVlISE4GK2trBC/b5+YtmmTUP7gAZQJCcwtMpLLMjbGXTMzKGtsJ3NVzQMA6TdvwmzOHG6Gmxv4uXPJCbJgARAbC0EQcODAgXJRFJcGBwff/7cG7G+CT2T7E96AmpraSmNj4x6jRo2Svq+QzJ8a589DlpICPHiAoTo6rLq6Gg8fPkRmZqYYExMDNzc39j8Oxf1/BW1tMnRsbYko/JcrwcfFxeHMmTMAwLVv3x5+nTuDk8mIIP9P8Po1GZW9epGny8Pjo8iOUF6OqCtXMPbnn9n1IUNE61272J8qL1+VO37kCOW5LV78b70zVaX9xYsXw8jISDlz5sz/WS6Evz95Mur17gSosvXmzZuFyspKrmXLlqK7uzvn6enJeJ6Hh4cHXr58ifLycu7s2bMwMTF5s9dzcTF5p06fJiVdJiMCEBxMKQ9VVWRIKxQUFjxkCHl8auDp6SmeOXOGnTlzBmpqahBFETzPs+nTp9dW3sbgwWQ09O9PnpG38fQp3jHGARIAVD1YFyyg8QfIQE9PJ4NGQwNYuhQ6kybhYVgYS7p7VznI3p63sbH5z8f56lX6zPr5tgCeJifjan4+rxwyROxlYcGM6hM5QSAjc8WKWk+koaEhRFHEzp07MWPGDNQXPx0dHWFtba0MDw9nw4YM4bT69SPysWULeTx5Hjbt22Osjw/27dsHCwsLuNW0PJS2bUvGGGMk3o0eTWR35kwizm8VIFTByMgI/fr1Yzt37mTP/fxgk5gIVFai7OxZ3MjOFkRXV65g8GCh08qVHLZuJbI4a1bja5kxeg8qZGZSSkr79nVEOyqKai1YWNDzubiAA2BhYYGFCxfi1atXuHPnDhcfHw9F8+YYdueO6LZwIdt25gzcHB1hsXWrMicvj9cwM3u/J6shLF5c22/+gxg/HlerqpT9v/mGF5csAasv9lVXA5s2wfj4cRjb2VHI/S+/gOd5KJXUFefEiRPv5pGqqxNZbQhNm1JIeXAwEBaG6oUL8SQ6GpEaGqxPnz5wcXGh3+vdm9ZOWNi71xBFvPb1xa3KSrTp3p0EDlGk/PvoaCJYkZEkfqi87osXEzleuJA8tosWETlwdYX2tGlMumcPxBs3wC5cQNHatSgzMoKlry8exsejeXIyyouKUHzqFPS2baM5n5tLotymTTQXf/yxNpJJoVAgLy+P7/s26QcoP3baNPpvJydg+XIUeHujrbe3aDBnDmvRogX09PQ41OROF5ma4qRcjn4lJQ2mZWDUKPIIqiAIdZEbNXn1VTKZaL94MeXZ/vOfNC9TU4ns//orjB89gvmLFyw1LAy2r17ByMqK9v6yMmDAAPRZsgQlZWV8hSBAunEjCUgcB5MrV9A3MhJ2mzZxyM+n3NvCQhQdO4b9Bw7AqkcPsZjnkdq6Nft882bI9+2Duro6ZA4OHBgDduyAJCAAyMnBpEmTeC43l8bG1ZVEzgMHIJ87F65jxnBaqam4PGgQcjIzcXjxYvQ/ehQx1tbwKSvjWpWXw8bYmHW6fRuxX3zBsrKyeOTno+zUKVRs3Yqy2bOhNWUKtL77jkSyI0eQvG6d2OP4cbZREPBKUxP6+vqijY2N+PLlS/FRbi6X5uDAqqqqsHbtWrGqqoqVlpZiVloaXm/bJt4cMoSl5eSgf2AgHowbh6pWrdB50SJUjBgBs+Bg2gcfPyZR4osvYPnrrxh67x5D797Q7dAB302fDjEjAxKJ5I1D1U4ux4rJkwVpaio3ffp0sF27qNZAZiYgkaBNmzbclStXlIGBgbxCocCxY8cgCAKaNGkCnudx7do1UVFaynX67jssUs0xvFkfpCHxW6lUYlNODiYpG+90VVJSgtDQUBEAa9mypaCan7W4f5/EscREEibeA87XF9K0NPhGRwvCjh0iSkp4PHpE5/DixcDmzaj+4gtcPn8eD9PT4fHsGSw2bcJ1Z2dEXbjAHBwceIlEAllEBORlZUho3x4TJk5Eeno6oqKi8PTpU3iVl0MREoJLTk7w7dkTWiEhtC/cv0/h6owh6ty5qry8vASFQrHkvTf8N8Ynsv0Jtfjxxx9H6+joTBkzZozWX5ZoK5UU+hMcXOu9U1NTg4eHB9zd3dnLly/FNWvW4Nu3q+/+laDKnZPJqArkfxHx8fFKa2tr/sWLFzD/6isxQSbDk6AgpSI8nOno6MDCwoJv06bN+/tw14cgULioypOoas/yMX93+jSEIUNw/euv4XDtGgJbtfoTsey3UBNZgfHjSXU+ceKj/kxHRwdffvklCgoKsGXLFv7QoUPioEGD/rPnFEXAyQlVtra4cfkyHj9+LLZp04Z5enri2rVrMDAwwIQJE8C/VQHR0tISlpaWyMvLw5UrV+pyhEWRnqusjDzGO3e+We0/OZlIxrFj9K9cTl5QT0+cDwlReu7bxxs+fIj+/fuz/Px8GBoaCq6urhzP87C2tmZvzCEHB/Iw/v47hdbeu1fnnY+MJJL4tmcwLo6MtPnziRxoaNC4P3xIBpy3N+VPu7oCjIEHMGPGDBw6dAhhYWEwMjISAgICOOf6lZk/Fr6+lNNeg/z8fJw+fVqZlpbG9+jRA+3ataNCNNOmAbdvkyc6M5OM/W3bKESzBjo6OiguLsb27duFGao+qDUYNGgQv3z5cixfuhSTdXVh3qIFigoKYKimBmzYAJaZCfuZM+Hg4KC8fv065+bmRnNn6FCq4Pv6NeWeiiJ59I4do9DYgQNJ8NLWJpGosrI2nNs+Lw8DL19GQfPmsAkIACwsoK6tjQdTp3IdfvwRga1bcxg+nPJwhw6ldz93bsMiU/v2FBXx+eckgISE0LPPng2kpiJv5kwkaGoKeWZmYoaaGoTISCYeP84YY6IoirCyshL9/Pz427dvQ0tLSywrK2PmDx8yg4wMjBo1Cjk5OSgOD+c6792L9MmT//33qFCQKPHsGRGs96C8vBwZOTm8JCoKbPt2mqeDB9N4RkcTiTQ1JWHo4cPasG9ra2uoqamhW70ewbUwMqI2PjXIyMiAtrY2OI6DIAhI09WFmp8ftEaPxn5fX3S+fVuYGBjIWdcn7SdPNpzek5MD9O4NZUQE5Lt2ISUlBa1VocGqcyU1ld5beTnVLejWjUQrOzu6Lz8/IuFJScD9+2g9YgRXaGcnXjQ3R6mXF1OEhsLq+XO81NAQ3W7dYk6iiMvt26OA52E5fjwcT5+ua7E3ezatWU9PKo61ejXu3LkDbW1t0cLC4t0978IFKp61dCkA4FFWFqJ698ZwLS1mmp//ZhjuH39A38AAQo8eytWrV/OTJk1Ck7fb8XXoQOO0cSN9/pEjlMYwbBgR6w0bIHFywp01a+DXvDmdwxs2kCdSJqP7mT0bnwHYpKWFZ8bGwuBlyzh8/jntQ02aQO2zz3AiLEypzRgGHTrEIyiIKkObmODEyJGira0ta5eaCvu5c4HVq5EeEIDekyej+OxZdmrMGGZoZycqZ8xgJnI5CZeRkSRWzJ4N0dMT7NdfoVy2DJyfH4kskyfj8qJFgqSigvFjxzKN0lI8nT0bRZcuQSsoCC28vXH822+FebNmcTh4kPL5t24FMjPReeZMlAYE4NmpU6h0dkborFmKsqoqrmL5cq6FQiGqt2sn2P7jHyze3Z3rHhSEGZ6eECnlhjHGGPLzgfv30W7pUmTn5CA+Pl58HhnJellZ4fW5c5DHx7Ox58/jZv/+yLWxgW1ioqCIi8O+uXPFl9nZ/NdyOTTt7UkoSkyk+eblReN19iyMPT1JIHl7bsfFAePGYdydO9y6P/7Aq1evYDp2LO1FT58Co0ejxfHjiCwq4kNCQqCurg49PT2RMYbk5GSmo6OjdHBw4Pp26MC4oUPrcvY/gJrK4XhlZga5jg40o6Op9eVbqKiowOvXr9lXX30FLS2tdzdFnieBzceHhMj37TuOjsCGDXBxc+Ny7t2D4qefINHRIafFiRPAkyfYmp8vtHj2jE1++pRp6umhZNw4oGVLLBo1ChzHIW3LFrxQKkXf7dtZKyMjaGtrw6pJE3R49Aj44w8I/fvjqrW1ILRuDccpUziIIu1rY8cC3t5ISEgQb926lV9dXT0gODj4b99PuzF8ItufgJCQEI7juOkSieS3UaNG/aUKob2BlBQ6/G7ebDBMljGGNm3asKNHj+KD+Tp/dowdS4bGfxm2trb8zZs3oaOjI7h89x334NUryGQyiUQiQUlJCa5duybcvXsX06ZN+3DfZ7mcQgHXrSNDZ9u2j7uJTp3I87FpEw4GB8PL1VXp0KrVX6P63ZIlREKfPSOvxkeE3kulUlhaWsLKykosKCjAgQMHxGbNmrGXL1/i0aNHgq+vL9fuIyILhIsXcSQwEPc2bYK+vr7YtGlTdvLkScTGxioLCwv5gIAArqFOA2lpabhy5YoyKyuLt7W1FWQyGVernickUHuixnJa1dSAwYMRZ2OD9OPH4R8QAENLS/CHD/MsK4vy4nr1QqvAQETq6HAaGhqKnj17NnwuPXtG6RNL6onnVVVUpEnV7gogw//bb0nZ/+c/ySC5cYOMJi8v8tLNnNlgezapVIrRo0fzxcXFiI2NxeHDh8EYg6ampiCKIlNTUxN79erF2b+vZRpABlNMDHm2/f0REREhVFZW8kFBQTAxMaHfMTcngeL+fZoHQUFEODU03rhUdXW1CIC9evWKKy8vf8O7raOjg3mPH+NWdjYiBgxAVWioIJfLudZPnwq97t3j+Jqigk2aNOGfPXsmQBWubmVFZPjQIfKmb9lCRbgkEhK8Fi4kksUYRQaUllLuaXU1UhMT8cTEBI6mplSBVyIBb2WFcnNzWIWGUoErFxci6927k5e9uJjeydt77vff0/vJygIuXaK/GTmS0gL27sWjdu2Q7eSE9p07820ZA2MMPM9DFEUGAAcPHhR37doFd3d3oW/fvmS4fv01UFKCZoaGaNasGdCxI0sZMwbNFi/G/dGjhZa7dnEfHf2iIl9vvZOGcP36dbRLS1Pq3bnDY+VKipoYNow89ZcuUcSAuzuN7VtitkKhgFtjheSsrVH60084qqamTE5O5hljUFNTA2NMrKqqYrra2qKvpibGpaczi6lTOaxeTQKTCqdP03O83Xru+XOgVSuYOjhAV1cX169fF1u3bv3mwNjb19VJmDmTPPJbtpBAumEDRR1cvUqChKUlWGIiDJydWauEBFHn4kWIbm7g7eyQ7eXFHowfL7qPH8+8qqqwe/duXExLw5gXL9C0adM6L6G3NwlPOjpAeDhuZmUJLjSH331hpaW1Vf+zsrIQEREBqaWleKKqChNDQxmeP68LGb9wAejUCWNmzuQ3bdokPHr0iDVp0qTummVlJCjs2UOi0+efE5FdsoTmZ+fOQOfOeL19O4xKS4G8PAg7d4JTdVHw9aW5HBsLtGunKioogjEqCHbhAq1vLS0MmDqVX7VqFbr07AmDggLg1i1kr14N19u32Z2qKuTGxaGbkxNcIiLg4e8P5bNniP7hB/Q8cUJs0bQpwy+/0OcEBZGImJ0NvHgBiYMDPDMyxNcPHjDjoUOBadNQUlKCjOfPOYesLJR89pnYxtCQeUyahJzsbKgdPw7D5s3R89dfOSQlkTg0cCAJa2lpwPPnYDt3IqFLF+WYVav4NjV8Qf7bb8gXBJbk4MA7LFkC2/nzwXfrBjRvDrZlC4kQAKWD8DzAGMzNzdGnTx/u/k8/wSIjAwaLFpHYU1EBe1VtiDFjONy6heZBQViclYXcPXtgM3gwpSIsX079q6OiaF9q04aEwIZS6by8gLg4GFtYQFtbWygoKOBMTU2pDkHTpkBgIKQmJphsb49Sb28YGxvDwMCAXbhwQXj06JEwa9YsOgQrKz+qfSQAJCUliQcOHGBqampCQEAAp//wIe0bDZBtU1NTaGpqIjc3F3aNdbUwN6caGBIJrdPGoqzmzQPatoX5unWI9fMT8jQ0uECAnnXLFpQeO4aJv/7KqbduDWZnB+zbB73SUrRXRUU+eQKbx49xxdoaFlVVcOQ4ErRdXck+6dkTl7W1hZstWmDehAm0SLOzKQWkZ0/k5ubixIkTFdXV1d2Dg4NffdRg/U3xiWz/zRESEmKqoaFxSCaTtRo8eLDU9APq/Z8aS5ZQ+NV7QsRdXV1x+fJlcdmyZaxz587o3Lnzf/EG/xfh60se4H/8g4ye/xJ69eoFz/x8mK5axXFxcXBjDPVNREEQuKVLl4rPnj2Dra1t423i/vlPysm+d4+I2oc84bm5ZMA/e0bGn6MjqhQKPJbLMcHN7a9BtIG64kT79xMZHDmSCM1HGP/9+/dnW7ZsQWZmJh7UVQ3mHj16JLZr1+79FxBFVPfujeqgIHHMnDnM3t6eMcbQvn17JCQksEePHolOTk7vXEMQBERERMDW1pbv1KkT2np7cwgOJkHrp5/IAP9Alf/S0lKcOnUKBgYGWGdhAWdBQJqvL0r19PB482Zh9r17nI62NtpIpfBZuVKCgoIGe5WiTx/62rCBCMSQIZQr+uABrfvSUvJqxMYSqblxg8hueDh5Zc+de7Pn93ugp6eH7t27c35+fli9ejVMTU05Dw8P3L17VwwPD4eOjo44bdo0pvE+EnbtGkXa+PujuLiYk8vlkL3dSkVPjzwY0dFETLt1IyO/Hqqrq5mPj4+Yk5Mjrl+/Hi4uLlyvXr1qCYqOhwfU3N2VUqmU6969O+fk5ITfV69moTIZZDyvzP39dy4/P59pamqyN0TGuXMpVHv7dhrDu3cp1LhXLyIfXl4NPtbJuDjYqauj1e+/kzgQGAjuyy/RQi5Xytet43dVV4vNPTxYm4ICEtJKSmjOZ2TQ2lW1nLt+nUiagQGRwxYtyFP62WeUlnL2LHKOHIGOmprYmLgxZ84ctmXLFuTUz49XVc2Pjq7tYGDv44Pjjo5wP3yYw7NntSLBR8Hdne61oX7yAK5evYq0tDQkJyejc0oKXyKRQHfSJKozUFREYciZmXRfa9e+U+xRqVRCFEXExcVBW1sb2traMDIyqi3ydX/ePCHq6VPOxMlJ5Q2rfdLaf6dMof20ooI+48yZ2gJouH6dRI76ZPvoUSKXW7cCoKJqiYmJIhoitaoxVYX1V1aSUGVtTd768nKaLzk5gJsbWKtWMGjdmmHQoNqKxpb0xQBATVMT06dPR2hoqLhr1y6mqamJcePG1bY6wvz5wIkTqB4yBAEtWnD21669czslJSWo7tgRUXI55GFhyoyMDN7FxUXIy8tjz1+9Ylixgtb/li20vkJD6Z4VCuiUlTH+/n0Rly4xdO9OZ9GdOyQa3L5NH+DhQR7FKVNIZKoBz3Fw+fFH3FFXFyN79WKDtbXRokULIkdjxgDx8VC4uCA9PV0cMGBA3ca4bRsQFARBELB//36FKIoSlpND1zY0RLKREXxv3ULPbduQ/tlnyL1xA2WJiVg/dy7UDA2Voqkpd7h7d+ZsYQH+yRMSBfLyyOYpLSXRsWtX6M6fL+5r1Yp9JpVCWlgIpVIJA2dnsfr5cyZ3cWGZrq6w9vSEmZcXcOoUKgoL8So/X2nbti2PNWtIIDIyoiiDn3/GXYkEfceO5WvXJQDN+/dhmZEBy0mTsHHZMmUrT08e6uqU/qKuXrd3HzpEYg1AbQgjI3G0b19MVwkgPXrQ18GD5LneuhX49ltwGzagpUwG8xUrKILi9Ws6Y0aNItGiVy/aIwcMAD7/HMJnn6G8vBzqMtXtnwAAIABJREFU6upQT0+nPTQ5GXK5HGVlZZx1/fQiTU0Kfy8uhtVXX9F+17w5EhMTxbi4OG7ixIl1vyuR0Bz6AO7cuSMeO3aMOTs7Y8SIEbQpt2pF+2dxcYOFzmxsbIRz586xqVOnNn5ua2hQkdGICBJG3/1g2i/lclT+8QfShw/nAlT2uyiievZsHDY1FeyHDkWHWbM43L5NIlJCAr3j7duBo0fBeXnBTlOTpc2fL1pPmsQ0YmJozwoNRXp6Oq7u3s2NHz+eohWfPCEx88QJKEURERERZUql8vNPedofxiey/TdGSEiIjrq6eoynp6dDt27dJP9rBZj+26ispMJM//xnw3mb9aCmpoYpU6awkydPitHR0Sw+Pl6cOnXqXzOHWxWemJFRl0/3X4B5x470mQ0QRI7j4O7uLu7bt48JggA7Ozuln58fX1tld/t2Mgw6d6bw0Q9FUXz/PXmC9+6lNh86OrUhtpxCAYDCMP9yGDqUyOLt22Q03LtHpOM9MDExwcKFCwFQWy5BELBt2zahSZMmbyzc8vJy3LlzB1paWvDw8EB8fDzu3r0rvA4J4b6YP/+NQmsmJibo2rUr17WR3u0KhQIVFRXQUFcX2hUUcOjYkd7JrFlElD4C1dXV4DgO06ZNw927d3Hq1Cl0699fcPn6ay5+zRpu+RdfwLNpU6VBQgLTvXGDQ3IyveP588kLyPN1IciRkfR9VQ53r14U6lpZSXPKzIxCs7/8ksjFpEnv9mj9N6ChoQErKyshJSWFGzlyJFq0aMEpFAqsWLECt2/fFtu3b9+4sbRnDyorK3Fs/35lZWUl37Zt24ajaXieogMOH6ZQ3XoQBAGMMdy4cYN98803LCkpCVeuXBFXr14tTNfQ4KWHD4M7dw6dAL4TqKDepk2blJUKBT/90CFUGxryV3/7TRw7diyOHj0qhoWFCVOmTOFRVkaGV2UlEdMjR5AllwPu7uLLHj1Yq40bwW/e/O69hoej7YEDoiQkhLElS8ggnDEDcHJCXxsbXt61K5olJrKs1asFVFXRS1u0iLw8qt6tv/1GQuH27eTR7tqVyG9eHo3B5s21fdJrq/w2Ap7nYW9vLyYnJ9d9UyIhwllP2GCMIXDyZGzT18c/fv8dpunp9PkN5e6+jTNn6L4aJ9uCpaUl62tvz1536ABJfW/bvXs0J2/dopzzBroqFBUVAQAeP36srKqqYpWVlaykpIRpamqKVVVVrEleHhvdtSss+vVr/ICWySgiYf16ItxbtpDBrK1N++bbWLPmjQ4JFhYWuHr16scZABIJ7QFVVUSAVJWevb2JWFy+TN6xmzeJ4DZwNjHGMHnyZJaSkoLw8HD88ccfmDlzJoxrUoiEXr1wZMAABPTuDY3582lN1wgnOTk52LJlC9qfP492T5/i1urV/Ny5c6GlpcUtXrwYHTp0EGFry8Bx1N9+9mwai/XrAR8fdDQ1hWLBAsqNlslIpDAyonV49iwRuh49qCjaixdEuGrOOpP0dJzv2BFP9PRY7169xEOHDjFHR0cqutepE7jTp5H6zTdQb9YMDjVzGN26Afb2yHr6FHuOHVOWlpZKACD34UPo13SXOF9YCPVDh9D2m2/QrEMHlHbtitKxYyEIAniCUFpayq66uKBzu3YUxaemRhExGhokJI4YAUO5nOUdPozN9dauWXY2c5FIcPv2bTx48EBYYGzMYds24NEjxHl5YeDlyzz69KE9tqKCzvaMDHpPmZnCk9GjubZpaSTIcRy12ho5EigpQXlFRe07w8CBFNJ/5Aitcbmcxh0AkpKgVCigFITaSv+oqiIxdMgQ+v8hQwAvLzBBgMvZs3hhbS1a/fgj09ixg4SMHj1ov5o2jbqy6OgA27YhLCxMTE9PZzzPw7qiQuHp5cVnnT/PVMW9CgsLoa0S+FTQ06MQbTU1iJ07I8PWFp2nTasTfAB61ry8unZrDSAmJgbR0dGsffv26F5TcR4A7Su//05C3RdfvPN3Xl5e3J49exASEgILCwsxKCio4XPkxx9pDhcW0lytb6MvWwYolVD06IH1w4YhYNQoqv9QWIhyNTW8On0ahkOHovXmzRyyskigyc+na/bvT/tCXh7QpQs6/vwznubkYMeTJ+KY7duZlpYWysvLERERgfbt28NSJVJv2kReb47DyePH5aWlpdcEQQht8N4/4Q18Itt/U4SEhDB1dfVtTk5Ott27d5f81Vp7vYH9+8ngtrD4KA+hVCrFoEGDGM/zSEhIYMuXL0dwY1WM/8zQ1qYDMDycDKf/68rxlZXkUQ8Pf2+lzD59+nB9+vRBbm4uLl++zIeFhaGlrq5Sz9yc896wgWl9+SWYKtSsIYgieSL37SMDUhXKNW7cG7/2/PlzSKXSBguV/CXAGOUnLl5MB+nGjeRJ+QiPm4q0NW/enIuJiUGzZs2g8v5FRESI6enpTEdHRzx16hTTUFMTpy9fzrGDB//tsVJXV8dnzs5I2buXEyoqwG3e/MF+529DV1cXEolETEtLY23btkXr1q1rC9rMnDkTjDEYGRmRF2jNGhqXDh3IMJg9m0Ktnzwho7J3bzKIXVwob3/tWjI45XISLVJTycN29+7HEamPwLBhw7h//etfOHbsGPr37w+JRAIXFxd269YtsW3btkhISICNjQ309fXfrFVw9y7QpQuSvvyS5zgOGRkZ7xbDUSEujoQFaplTizNnzqC6uhr+/v6ihoYGc3d3h6urKzsRGckOHj8OG3NzQXLtGtemTRtERUWJd+/eZR4eHujatSs0GIPm06fo2bMnA4AhQ4ZwG9avFy9OmaL0T0zkYWpKkQIJCYCWFiL37lUOYIy/YGWFnBs3UBgSIsDRkXl4eDBtpRJNLSyA7GxIjYzEe5mZYms/P3qWCxcAHR0wW1tIpVKkpqYqq3r04DB+PK3luDgKd5RKgYkTKcz/4EHyhPXrR0bgmDFE+rt2fWcP/9DZlJSUJPr4+Lw5rv36UW5xvdZZbm5uuH37tnBFTY0bpKNDobKGhh+OdvjiizfDst+CpaUl09bWFrymT+fxr3/VCYjp6ZSru2RJXbX16mryBNcTAgoKCqCjoyNOnTq11hOam5sLURTZzp07xc6xsYy7fx9XDAxgaGiIRtv/NW9Oa+DmTRJSCgrojPjyS/pvVZrOiRNEiOqtD0tLS1RUVEAQhHf3iNJSWns//kgEoqCAnkNVObm8nDyQTk4kYKSnE1nbt4/EMV9fEhtmzaJ3YW9f+/wODg5YtGgRduzYIa5bt4717dsXXjURFRk+PspLx47xvaKiRI3qasZt3AhBEHDkyBGhefPmzH/4cMZlZsLGzw8AhZIrlUrY29szUaEAKyig+66spD3BwABIT8fBzZvFFk2aiA6TJr35oKNGkTCwbx/Qvz/EpCSkbdsGm5UrwUdFAf7+8Nm6lV83diyaubgIbdq04aKiorB06VIIggAtLS1hzLhxXPHs2aJny5Z1wpqaGvKCgnDXwEA0GTyYCwoKQmxsrBAXFSU6tmzJcwBkMplSoqHBIysLuHoV7sHBeDZpEqxsbQU7FxehU6dOktUrV4pCWhrDmTO0P/7zn3XVp9u0AXge7gCTSqXYv38/tLS0RIVCIco1NblKbW0MGTIEhw8f5vDLL/ReZsyA+ZUr2GtrK345bhxDbi6R48ePKXKoXTu83rwZ/jXt67BmDUVofPYZRQ8xhsrjx3mj+nU6evUicU2hoLF//BiYNQtpu3fjlrGx0unoUf5qTAxaBgdD/fVriB06QAcgj/jRo8C4cZA/fIjUpk3hIZeLsQoFS7t4Uej6/Dkn7dIFhosW0TMnJtLesnYtXnl5YfTo0TDR0UHJ6tWS+LFjlSWvXuH+/fucRCJhBo2J2BoaKCsrQ7ylpZCnq8sCNTRonqi6PjBGhLYRPHz4ENHR0WjWrJnQvXv3d/f1oCASBRpAbm5ubRRJeXl54xElHEdrxd6e9s7vv6/72d69QFgYuF27UN6hA6KjowUxLY1ruX49/pg/H9ZLlyoHDRpEbXvt7ant5NOndJ2lS0nQPneORPP9++HAGLuyY4dy3bp1/PDhwxEdHS0YGBiIXbp0oX0pIoLevY8PUlNTce/evbLq6uohwcHB71dEPwHAJ7L9d0YfqVTaq1+/fpp/aaK9eDHlxRw+/G95rxhj6NGjBxISEmBkZNT4ZvdnB8+TsXrv3n+nWJqPz7uFqBqBqakpBg8ahKzoaOiNHMnfGDUKawYOFNubmor+DZGOqCh6ltBQCo/iuLpq3g0gJSUFpqamSrzd//mvBI4jg/X1a3p/Xl5kOH3kmuzUqROuX7+O6Oho2NvbQy6XIysrCyYmJuL06dNZVlYWTI2NmZpS+U4F8g/i9WsgNhZu27ah2MpK/NndnUnPnBH6qqn9W0XDJBIJpFIpkpKSlE5OTnx9Qmr8djE8VUj6qVP0b2oqGfMcR0ZQ9+5EfoyNKf9u7FgKk505kyIlHB3/1yv0SyQS+Pv74/z58+jfvz8SEhJw7949sbq6mi1WVTevgaOjo9Lc3JwzMTFhRnp6SA4MFB0dHJhHq1aIiIjgYmJiGk5dmTmTSM1bZPvFixdKXV1d3sbGpnZC8Kmp6D9/PvfyxAkkZWayq5cu4eLFi1BXV8fEiRNhYWFBg9inD3mdlUqA56FVVIRpKSlMsnMnnz9hAow2baIL7t8PxeXLyM/L4/moKIzX1UXS3bui3b17bD/AkpOTMWvNGqQHBKDou+8Qr6eH7LS0ukE2M3uDPDo6OrJz586xbdu2CYMGDeJk16/TD86dI2+hkRF5rlUk4auvqMXUWyH5paWlyMvLe7f3dD0olUoUFBS8Ox+1tIgwPHgA1MuFzsjI4AYPHlznkd24kYz89xFuiYTIxsaNVNDtLXTq1Int2rmT9756FZYqb2ZSEr3TvDyqSzBjBn3/iy9ILLpfF3X5+vVraGpqvnEGqdK5vL29hYsyGTiOg+LBA5aXl8c1SrYBEieTkihH/IcfKDS6Tx8K8waoZ/C0aZSfX49sq6urQ1V3QyaTkdB19y7l3xoZ0bXs7GqLCda239u7lwzwefOo+KO1NZ3Jd+/S7w0aRCKKTEYto8aPJzIeHk7G+5AhgIsLRo0YwdZv3Ij4+Hh4eXmB4zjMmzePL548GZHffSfol5ay9q6uXHK/figyMGDjx49nXHb2G9FAlZWV0NTUFHdu386+OXsWGnp6RJyNjemZz58HyspgZmbGlZeXv1vIKTCwrnf0ggVYL5MJeSdPcjpVVeI0MzOm4+cH2evX0DcxEVXpILNnz8azZ8/g4OCAM2fOsJ3Hj4t+vXoxsy1byPtbU0zykY8PKlJS2ODBg6GtrY0OHTpwpzdswI1790TrgADm7OzM371zR9nazIzHmjXgu3aF46tXcPzxRw6Ojhz69sWUmzfZqZkzBeTlcdi7l0STb7+ltTN6NHnoJRI0a9YMgwcPRmRkpDB//nweycnA5MkQmzeHIAionj8fkiFDoJw1C34bNuDhw4esesMGqH31Fd3zmjXA7duoTk/HTUdHzm/TJnpn8fEkeL98CQQEQFlZCQNPT8hWraK0j3btqG3b1q0UUaRUkuhlZoa8tWuFMk1Nfujx4zjq4SGmdusmtIiM5HDjBvOuqRCO3bvpvJfLUTJxomA9aRJnNnMm1OLjkRsTgyuBgQi6fRsan38OoXdv3D9/Ho4xMRCbN2fl5eWQ3bkD2bFjsPrllw/aA0VFRbh586bw4MEDpuXvj8mTJzPJiBG0TlRnD71gij7T13/nGgU1nSaePn3KvXjx4t0IO19f2vPi4ijqox7c3d3Z9evXUVZWBqlU+uEDKyaG9qiKChLzVHU+QkPB/fYb2uzfr7TavJk/PGIELqxfLwzv04erPQcA2of27aN7ysggweyLLyikvmYucwAmTpzIR0VFYRsJc5yWlpawevVqhY5cjkGrVklipk9H2fPnSE9Pr1IoFGOCg4OLP3jvnwDgE9n+20JTU/MfnTt31v5LFwl79owO+0mTPqqAzbt//gwcx2Hs2LF/TaKtwo4d9G9WFnn3/y8wdSp5m9ev//i/uXEDmD4dFseOATduIMDODkkbNohKpfLN8V64kMKaZLI6kvnbbw1esqioCI8fP4a2tjaSkpKQn5/PHz58WNm1a1e+wXYuNSgpKUFhYSH09fVRVFQEAwODhtu//L+CTEbzGSDjNDj4o4qnZWdnQ6lUori4WAgJCeF4nodUKmXTpk0Dx3EU/rVpExn+H5ujCpDna/duYPBgsN270cHIiLkWFeHUqVPswIED0NPTU/bt25dv2kAf5fDwcOHp06ecnp6e0sTEhM/KyhIEQeCaN2/+74si9vY0v5VK8pjk5FAemyhS4ZiSktp2Lv+X8PHxwfnz5yEIAoqKilBdXc0A8pb27dsXCoUCKSkpePHiBf/ixQs8ePBAIZfLOZmhIecvk8HZxQXe3t5icnIya5Bsh4bW9f6uh9atW/MnTpxAYmKi0LRpU6oEq60NTJgAYzs7HDp7VhAEgff19RV9fHxY/eJpeP2avJzJyRSG2KcPuAULEDZuHLLMzdElJgba2trQr6hAyW+/ic7NmsGoe3eGlBSYrl/PEBmJ73bvhvzbb5Hbsyd2nzsH7QsXlLa2tnyvXr3qPqek5I32WC1atOCqqqrw7NkzbN68WZg/fz4Zk0FB9AtyOQlLgkBVuTU1yQj09ibinpoKzJ6N+59/DkcdHVh98QWHo0cpmkEmI1HVwABgDBzHoUmTJsK+ffve8AyDMfLivNXr3MzMTJmZmck3b96cokhatCDD9dYt8go3hgEDGo2UsGEM8//1L6yurISoowNlcTEWhIWBX7SICFxNGzcAtK9lZdHcvXcP6NMHUqkUb/Qhrgc/Pz/eT0MDWLwYJXv3Yu3atY3fowrTp1NBqZwcIsWtW9OYV1XRuF29Wue9qwc1NTWBffklhyVLyBu/axcZ5V27ksgQEAC8XdE9JYWiTZYvpzSOyEgiZiNGUNhvYSER6u7dqVjclSu0ljMzSSyrqACmToVaQgJspkwRjQ4eZFEZGcquwcF8dEwM0tLSMHbFCj7xiy8gvHyJl0+fgnNyYrGxsfA/fJiI0OXLAAA7bW18/eABu56RIeYtWcIsVdFT/fqR1z02FggPx8vOnUUB4KP09BAQEECe/KlTyWtoZQUcOIAX5eUQysq4OeHhuDRnjiCPjORTHRxE91On2KAFC1jY5cusU6dO0NXVRcuaSJ/+/fuzPXv2KKPS03l/AwMc/PxzpHt6CoIgiNX6+tyAkhJoq6szANDS0kIfCws8qK7G1q1bYWNjA6vjx3l89x0JUd26USTB0qUkYISE4O7z5+Lr7GzyRH71FUUYlZfTHLawoHoIX34JANDU1ERpaSkvCAI4PT2gvByMMVjxvKB48IA7Nnmy4H7nDpd8/jwYgKpRo6CWmUm2lFQKTJ2KvOBgGDZpQjUCXFzo69Ej+qzJk/H00CEo7t9HQUEBNK9ehfatW2CRkUTi9u2j+TZ+PGBpCb3KSvGliYn4OimJjTQ3Z/j+e16Rno7bTZvSPrBqFRG/o0dx+vhxwSctjeHWLaiHhcF39GhOcHPDMV1dbLl/H152dkLV7dscUlLEmNBQcVDLlpxDs2a0fzbUIq4B7N27VxAEgfPx8UG7du0oterAAVonv/9OJPnIERKaGkll6dixI9zc3LBq1So8ePCgjmwXFdFZ7ulJ8/PMGbr2q1d0PSsraGtro6xGAHunjkdDsLKifcPenvYOdXXaN9etAxYuRLdBg/j7p0+LnTt3Rmdvbw6XLpHgOH483c/y5eTM8PCgqICNG0kkayCNr02bNrha016zvLycE5RKzquwECkrVkDP3Bx3Y2IgkUge/fDDD6c/arA/AcAnsv13hryysrLxsMY/O1RtKu7cebfC7UeiSZMmEAQB9+/fR4eGqlr+VWBsTIfVgQNkePxfRCo0aVKbR/lBpKaSZ8XRkQxsK6vae6qurhbv3r3L+djbQ2f7dmr18+IFGV9DhryjANdHQUEB1q5dC0NDQ0EURUGhULDWrVvzr169YuvWrcP48ePfzLmqvZ1UhIeHQyKRCFVVVZyGhoZQWVnJ+fv7Kzt27Pjn8Yqr3tuyZXRQnztHY95IcajExEQcOXIE2traQnl5OdevXz/o6urC0NCwrjCdQkHEvXv3jwurfvSIvCWjRlEhlJoQfgbAwMAAI0eOZDk5OXj06BG3a9cudOvWDW3btq0NPc3MzERSUhLn5eUlmpiY8C9evFB27tyZa926dePF8upDoSAD/OBBIlUFBWT0f/01iTepqeRlO3aMxkZfn0jTsGFkoP4ftCxU9bkGqCaBn58fOnXqBEEQasPG1dXV4ebmVr+iNP3A35/eY+/ecHZ2Znfu3MHGjRsVQUFBdTUyfvqJjCaV2FIP0dHRgoeHB9e7d28OlZUU+bBpE7BoEU4fO4b8/Hx+wYIFkEql7y56Hx8y2FU5u5aW0OzfH56lpXAqK0N0dDTU1NRQXVWFXgB7JZWias0aqAMUtn/3LviKCmi7usLaxATK06ehpaXF6+rq1q4zURRRlJUFSXY2dEHhz9u2bYNSqUR1dTVnbm6uwNtnzMuX5O1R9eGOjiby7e1NBmlKCuTGxijR0UGndu2gqa3NcPAgkbhbt8hYXLYMmDkTzNkZg4cP55LXrUORpib0RZHChvv1o/kyfjyFi9d4pnx8fLijR4/C29ubKkb7+hIBnDOH1lxjVX/nzqXrNAQLC6hv3Ih5w4fj3M6daLViBZiaGoki9Yk2TSDyUC1bRtFYffrAwcEBpaWlTKlUNrxGDAwAKyswxqBQKLBs2TKB4zhx2LBhDfeC5zjy5CYnUwFNGxsaOx8f+t7pt2zkXbuAixeh4e4uSI8c4eDqSh7Oly+JaDTWprCykt7HggXkwf3xRxJjQ0KIqISG0vNfvUrn0vjxRNSmTydiNHcuXad7dyA7G58xxgoyMxEfG8vHdugAu5wcpAQG4n58PJoNGACNadPguWoVOm7Zgke3bws4c4bmVXk58PPPwIgRUNjaItrKig21saH9VBRpLuTmAuPGIb9PH7iOHcs6P3iAXdraQkJCgvjlpEm8GBuLC7duIWHHDmHK+vVcmJYWmrRsKUq6dmUDBw/mkw0McDgnh7W8eRNNqqsxMygIWRUV0F21qt6wc7WdDLIGD4brqFFI9fLi9Pr3hyAIcLxwoa4QIgCNVq3g6ePDLp0/j5y0NAy/cgWFSiX0RREsN5ciCXx9aV5qacFs3TrmfOwYQ1ISrRU3NxKB7O0ppHzPHmDPHhT07Int27fDy8tL4DiOg55ebbE8KyMjbJwwAcWpqVxhnz5ovXIlPr93Dw+OHcNLe3tU3LgB56IimJiYINfKCt6XLtVGxgCgTiB//AGIItKdnPBaRwc7TU2VxcXF/KwZM2D09ddEnquraS+eNw8QBDhZWPDtS0pwbsECjDMwALS1kfL772iyYAF5y0+dAnx9EVVZiWdOTqyvgwPDb7/RZ9vZgUtJgbWGBvKaNYPOwoWc044dUC8sZJgzh+HYMYpOed8arYf4+HixoKCAmzdv3hudHQAQgQ0MpCgjuZz2b0Ggn0VEkOCUkwOsXo3KtWuRM3AgAqur0XLOHPrb69dpvv/6K625xESybzIySNCprgZCQlC6ZAk6GRrC28sLuhcv0js+fJhEIV9fqvhtb09ntsousLCgfdPEhGxfX1+KYpkwAdy5c2BKJWt19ixF8+zZQ4R69myyv5yd69qPAnTmnDhBa7VeTYLS0lJs2rQJHMfh+++/R1paGl4FB4sm169jr44Os6isFCQSSalCoQj84EB/whtgHypA8gn//0RISEhvS0vLvVOmTHm3VOKfHQoFVXtctoxU9f8QxcXFWLlyJfT19ZX/+Mc/+L+0l1+hoNC8ESP+d718UVGU67d794d/VxCo+ubEiWRo7tz5DvEXrl3DgT17hAw1Ne6LkyfJs/UxJAzAy5cvsXnzZnz//ffv5BWeO3cOt27dwrhx42BpaYmCggKcOXNGWVVVhczMTD4wMBA+9cKoVQS8bdu2YklJidCxY0fe5CMLfv3XMGgQHbbbt78xjjk5OcjNzcXx48dRXV0NfX19MTAwkLm6ur6b39pQVe+G8Po1vevQUPJuDBz4wffy6NEjREREQE1NDU5OThg0aBB2794tVlRUoNGCL/VRXU2GS2QkGd/h4RRie+QI8MsvZCD27EkE4dAhKjYjk9G4qEKuIyOJPGzZQkXniopItHm7h+5HQBRFKJVKPH78GK9evYK6ujocHBywbds2VFVVwd3dHZ/VKyr1UVAZnTV4/fo1du7cKRobG4u1lWvnzqXK5TdvvvPnYWFhUFNTU44cMYJHdTWF6H73HaCujszMTOzcuVNs37696KfKn1ahsJA8Xb/8Qh6mV6/oMzw8ACsrVFVV4cHjx3B1dcWTJ0+Qtn+/2DYsjKVOnYp2gYHkITp6lD7v5ElcunRJvHjxYu07NTMzEwwNDblXr14JRvHxrEBfn7kPGoS4uDjRysoKXbp0YYmJiQh4uzaDKNK1+/cnQi+VAtu2QTQ0xI5Jk5TlEokInkdhYaFEoVDghx9+aDhnWxDIiJXLAZ5H7Jo1QrZMhoHGxhweP6Yc8G+/pfDLpk3JA5uVBTRpgpUrV4pWVlZs6NChddcrLaXKxCkpZCi/nYpw4ABF4Dx9+ub3Y2JI+PntN6CiAuUuLnjYpAnaREV9XA2NI0eAEyew1NFRnDhxImu0G4hSCXAc8gsKUFlZidu3bwvPnz8XZ8yY0fgiTUoisdPGhgjSnTtE9C0saE5aWVEP7mvXgJUrseWrr8TeJ06wJtOnN1oM7g0cO0YezN27KbrB0ZHEMC0tWq8pKUSsVdWYVa219PUpIm3ixAY9kenp6Xj6+DEq4uLwsLgYPc6ehYZcjietWqHdlSuitLyXKlplAAAgAElEQVScFS9YAKuHD0lo9/YGLl/G2cBAMe7lS6ZQKDBv3jxV+y06Y5o3R1FxMbZt2yYayWTCOGNjvqRFC1ydMwfdZs7EvqIiMSs7G518fZl2aipMu3eHiYEBjYNSCTEsDL+Fh8PCwkI5ZswY/kZ4OOKuXsU0mQxqDg70LG/j6VMSLWNjaS998oTGoqZwF775BhgzBsU2NriwahVyRFGZLQj8lM2bcWvIEAQGB0Nnzx6gvBwVLi6IvHAB3ocPw6pFCxTv3g3tlBQULV+O8l9+QdOmTek5g4JQtXUrfjt4EEOHDqXWd4JAhHbHDuQOHIgkNTXBbvVqztLAAFxgIDB8OMSdO3Fv4kRcU1dXlpSUMFEUIS8r43xiY9Fj4kSyK1Tv0N8fkEoRMXOmQkNDQ9KnTx/88ssv+Oabb6AWFUX2h6rNnbo6kc8tW5D0/Llof/kyU7OwAJ4/x2t/fxy1ssIra2uRMzERnJ2deetvv0WTSZNgNGcOfdaYMRR5Eh6OuOnToeXkJLquX8/g7ExzWxAoRaO0lJ7/fdEpAF68eIHQ0FAMHDgQHh4eb/5QRapXrCByeu8eRVNpa9P56OBAxRtNTCB++y1+7tABjgkJ8AkMhN20afT7zZvX7feCQPffuzetjZr2nBfPn1fGnzrF9+7bFy5SKQncXbrQfq2uTpEoYWEkHkdEkE0VGkqCUqtWtMbCwkg8a9GCPOf37yNu7FjhSbNmTNqpkxAYGMh/0GNeUABs3Ajxm29w7PhxMSMjA3l5eaxGNKIaMDk5tWLlc6lUFV4eFBwc3ED1zE94Hz55tv9mCAkJkQBoAaAtx3F/vfDpxERSz2/f/o892iro6empirOwQ4cOYYTqQPkrQiIhBd/ZmbwI/wHZaBCiSC1QPgS5nPKsvb2pYN3bhP/UKaBXL3Br1qA7z3Mb3dzIGPw3EBMTo3R2duYamrfdunUDz/PYvn07HBwclGlpabyVlRVnZWXFvLy8akP9VLC3t4eDg4MyJSUFEomEDw8PF2fPnv3nWg+HDtH4L1xI8/7kSWRkZGDbtm0QBAH+/v5ix44dGcdxrEFCUlVFKvitWw2GKAOg6ycnU964tzdw/PhH5zy7uLjghx9+wIMHD3Dw4EE8evQIgiAwZ2fnd/Poq6qoWE6NlxcjR5K3af9+ejYHBzI+ly0jQy0igshRaiqFJU6aRAajypDR0yPP2vXrZGANGEDzaf9+Ikb37pEnwdu7UbHh1atXCA8PF+VyOdTU1Gq9iypoaGiI0dHRTKlUwsvLC30/MkTxDbx4QXnlNWF5MpkMw4cPZ3/88Qe7dOkSFMXFaKqhAfuaUFgVHj58iDt37iA/Px/m5ua0tlWe6hpYWlrCz8+PnT17lt2/f18YNWoUZ6CvTx6M69fJ2BNFqtprbAwsXAjlhAm4mpsr+Pz+O3dv3jxBMmsW03n9Gi8mTRJ7PX/O9IOD6T2kpxNpGjECJWvW4GJhIWvTpg1KSkpQUFAADQ0N9uTJEzDGuL4ZGWJ1ixaIvHVL2aJFC75bt27gOO5dog3U7RNTphD5CAsDEhMhnDuHwPnzeSOpFA937oSSMVjVeHMbBMe9UTviWfv2YnFxMXl0jx0jQWLBAhI7pk4lg9HDA7hyBQPbtGF7LlzAzZs30bZtW7qAjg6JNZMnE0mysXkzxLJfPyIPbyMnhwSe7Gxg6FCUCoKo2acP++hilVpagL4+NDU1hdycHL5Rsq2lBdy8CaMagiCTybhVq1Zhz5498PX1bbhfr7MzeRZ//ZXCxs+epbDkmBgiMI6OdFb07g3ExsJAT4/dGD9e+VnPnh+nft66VVe0UleXIjPy84mcDBpE3tZx42gf4zh6hilTSDjQ0qI5NmwYrd3Bg8kzCMDW1ha2trYQu3dHYEUFXgYFYffu3eji7Q2zSZMYpFLojR9Pn6emRjnG06YhOywMCoUCPM9j5cqVUFdXF3meF0fHxHAmv/+OTfv2iYaGhhg0bBgPHR3o3L8P/5gYlN69i9Rp09iM2bNhKJfTfBk1iubM9OnAqlVgsbEICgrCqlWr+C1btlD6jrExyqVSyG7cIGIkkdQRaYD23GXLyKO/cycJEkOHUkFCgAicqSn0AAzctw84coSvMjUF++03KNPTsWXnTswdNw5o2RJlTZuKeZ074+LkyayE54WCtWs554cPYZaTg7j9+4X58+dzO27cEEo8PbnB336LNoMGiWfPnhWaNWvGg+MoSqGqCko3NyQBYidra4p6sLMDGAM7fhzuVlZwr9m3ly1bJogch2QPD5isXSs+vnNHWergwNna2nKe7dqBb94ciuvXedsRI5CRkQENDQ2oqanRu750icRPlX3WoQPg64tif3+W7uYGx5r2aroTJmDAnDnglUqWGxLCl4WEwNjUVDDS16cD6Px5WuM//QSUlMCF55EdG8sqb92CxvPndC6Ym9P5deYMCYPvQU5ODnbv3g0PDw/Rw92d4fVrus+AAPr7f/2LPNAxMbRfzpxJ59Xz57SnXLhQG2XGTp6EIiQE+f7+RLSBN2pDQKmkObR8OYkte/bUkm19IyNWLpNB296e9hmVE2DFirq/HzaM/p0xg/YWPT0SdDQ1a3vLQyKh87p1ayApCV6TJnESnkdCYSG3fv16DBs2rK4ifkMwNETxrFnI7NZNNCstZa9nzsSIESNQW/BOEGj+BwYCs2Yh49o1QV1d/VZVVdWW9w70JzSIT2T7b4KQkBDG8/w8iUQSLJVKOVNTUzEwMPBPlLT6kVixgja8/yUvtEQiAcdx4kflzfzZIZPRYZ6d/T8n23I5FTPavp0MicZQWkp5YmPH0qHo719HtBUKOgjt7Ohad+8C4eE4tXcvLKqqBACcKIq1BrVcLse5c+eUjx494u3s7JS9e/fm67dkk8vlMDAwaJQQBwQEwNPTE1FRUXyXLl3g4+PzXvI8fPhwHgDu3buH8+fPf9y41OD58+cICwuDVCoVJk2axDVa8fR/CsZIwb9xAygsxOt9+2BqaipMmDCB09DQeL84oKZGRl5jRPvhQwo109KikN4G2hJ9+PYYWrZsCZlMhtDQUEAU0crFhcf588DFi2RwjBlDHrUhQ2huAuQRUxGat1M4SkspFO7rr6m4U1xcwwLAvn00v1RwdiaSJZcTWV+4kD5fW5vIUk37l4qKCkRERAjp6emcnp6eOGTIEK64uBimpqaQyWQN9TH+z2FuTmJVvQiDmnZr4sWLF1n75GTBbutW7qq1teAdFMTt3r1byMzM5Hieh6urq1BeXo727drxKCkho+cttG/fHl5eXggPD2c7Nm3C3MRE8pQoFCQ4qGof/PEHBFdX7DIxEYrU1UXja9fQmuM4bXNzyE6eRFeAKfX1IRob1xEjAHkmJigLCcH/x953h0V1bl+v95xhZuhIB7EAUkRRELCBomLB3tDYayxRY43RJN4YTVETTTQx8cYaazS2oCIqEgEFRLAgoiKiSJPepc6c8/2xGQYElJR7fzf5XM/jI2WYOec9b9l77bX3Nnz3XQwaNKhutXVWXV2N7du3o6KwEA6tW2Pp+PGvdtIqKsjgPHOGIn3t21MO47VrqBw8GM+++w6tqqrgLopEmmza1KwhrqioQEZGBu/+7JkIb28ymjdtohoTQ4eqX5iSAujpoe2AAZiYnIxQCwu4m5iAU9UdMDGha9u6ldQSBw+q29vJZNTf3ddX3b/61i363seHHFaZDKc++EC0trFh7SoqIJPJXltJHQMHItneHm4LF/Ktv/uu/nyui/v365EL2traGDlypHjnzh2cOHFCrM2LfxmjRlHth7IyIrymTiUFhUJB+3q7drV1EQyvXMGTJ0+aM+TqPNa6DsbRo3RehIXRXF+7lvaAZcvoedQ40+B5Gi9RJALj++/p9dnZtFfUFE1kjEFTUxPXrl0TbG1t0XvIEPU9rl5NkT49PXouK1Zg2rRpLDo6GufPn8fKlSsRHx/PcnJyRH7bNuzdvBnGrq7irFmzONUzKTE0ROGdOwjYsgXvfvcd9MeNozNUW5vm0IkTRCj4+UEoL8fzyEhwHIf8/HwMGzZMLCgoEHfGxGDZ9u2cZM8eIhcePSLiRvXcvbwo8n/iBBEQlpZUhEsioTn2ww9EUKxYAZw4ASnPA1ev4vGlSxhx+DCNye7dwLp1bNIPP+CndevEnp6eXOfOnZF9+jS0tbURdvMmt2HDBhgaGrICY2PEPn6MNl98wa6PH8+np6dT3Q6JBLh/H3x6OrLs7WmdqnLoV62i/XbnToDjIAgCqqqquCVLlkCpVEJ59CgzTU2VnC0tRXR0NKpKSpRtdu3iRj98yMrmzcPD9HRopaQgfuJEIWfuXM710SPoWFio2VaFAli9GkaAEL9qFdeuRw/AygqCUomCdetETRsbZtO3L1X8HzOGw8GDlJOup0f7l4kJ4OiIkh49EJORAes1a4hE3bOH5kqdVmVNTtf0dCQsWCBa+/iwUQcPMqxZQw52UBClSk2bRutER4f2ABW2bKHr/+orIooiIuicAdCpUydleno6Q2OpmNeuqe2wIUNoHtW0EOvcuTPn7++PjIwMyGQymDVSP6EWGhrqdrbDh5MKyt2diGV7e9rTUlMBhQJcVBRcBQGuCQksTSZDUFqaeJvnUeHkJGi0aCFqamoyLS0tTi6XM9XedPXqVVjb2mLwgAHoXlfpAxCJ6OQEzJ+PkpIShISEVFZXV097U338j+GNs/3/CTQ0ND7R09N7b/z48VpNsuf/yygtpajFl1++vlXL70RZWVm9iNbfGhs3EiGRnk6b8x+FKmepqT7YgkBMb6tWJFm1s1MX3cnPJ6Nl5UqSJsfFkRSqBrm5uaKRkRF369YtREREiPn5+UwqlaK6uhpGRkYYPXo0goODsX37dowdOxYcx4ExhoyMDF5QSb2aQIsWLTDu5UPjNWjTpg3Ky8sRFRUldu3atfEoMV03jh49qqyqqkJ1dTUHgDk6Oop79uwRmjR2m4n09HRoaGjA1NQUgiAgJycHRkZGkEgkiHvxApcyMoSJx49z9hs3ImDpUk7WnIKAy5fXbxWiQmEhGTHt2xMrPnVqs6X89aBUkgFx6BBa5eVhrpUVJBs3inFhYczG0xNShYKMocOHyTBoTsT8l1/IuFy8mAqjvaryvSonMDi4XjVsyOVkPE2bRpG748dpPiclAbNn46egILG8vJxNmjQJNjY23H+0dZyWFt1HSYlaTgugW7durFu3bkByMneWMdzKy+OCN26Eqakppk6dCmNjY2hra1OxqlmzKNezCUhEEQaXLolv+fszfP45Gf2VlfTLmrksduuG+wEBYmFODt6ZOZOXBgbS2hw5EujWDYZ+fjRfUlIgrFqFexMnIjwyUigoKOB6+fkJ73p7cy+rVbKzs1FWVoY2q1YxtG//+rGIiyNDte4ztbQEdu2Com9fhPj6wmvKFCJlGCNjtaqqyZoFKmT9+itswsMhzcxkZzt3ViZdv46i4mJ+CGPwmDuX7mvq1Nq8bRYUBGlCAoTt2yEsXQouMZGcUWtrckoWL6b/MzJIfq+q/l1Wpo4qART1cXGhuWVmBixbhj7m5tyZM2eE6OhoTi6Xi0OGDGGvrB4O4NixYxCcnaHr4yN0rqzkkJbWsD7G7duk9FD1LQbQsWNHZmdnhy+//LLhhpWeTtHi9etpPuzcSetES4scFaWSiKk6MDMzw+3bt5u3GFJTaWzq1shQVXNWgeNIxRASQoThgQP130PV4s/Tk5zUQ4foX3Y2nfVt2qCsrAxpaWnc+++/X/9v586lAk+DBqlltdOmwdXVFefPn4cgCPCg+h+cmJaGsbm5aNGiBVd3by/w9kaGsTGyfX2ROX069E1NKfr/8cc0Tm+9BdTkyT/w8hINsrOZyUcfCZMnT+Z0dXWZIAjs6dOnwoEDB4RZ8+ZxGDmSVBELFlAk08aG9qjPPyci2tWVxjw8nIhNb2/aiwcOJIJi+XLaq4YOhSkgZOjqcklGRmLR9euirVzOVXt7C0uWLq19PlZXrgDDhuHdd9+FhoYGdHR0WHV1NYTqamQdPIhup08jytkZY8aMoc8LDIQ8Oxu6pqYUTQ0JIcLz11/pWUycCHz8MRR2dhAEAXp6epSutXAhsGgR+nCcEGBoiKGbN/NcQADg6QnN1q3hYWMDo4sXoR8ZyT0NCsL3np6YZ2gII4CUJTUOq0V0NJdQVQW0aoWioiL89NNPgrh4MZsxYwZ9/tSplLLm6EhzwNeXnrGuLrB+PbTOnIHQtSuYvj45yUuW0HgtXUrniwqJiZRetHgxqVk8PSGMHQvzBw+Yx/r1RI61aEF7z8v1C+pCFClHvaCAUlIEgdQYy5cDP/4IDw8P/u7duwgICBCHDh2qnliZmfS6oCA1campSQqMOj23L168CKlUKmpoaIja2trCvHnzJE2eRaJIJOrdu2SX3b+v7j2fm0skgWpvSEmB1c2bmGxgwAq/+gqyS5f49B49oKisRKqDA3IlEjFfX1+pEARWUVHBF/bsKcj8/Hh4eJD96OND5MuYMaTCkEhw4cKFcgA71q5d+/vkiG9QC/6TTz75v76GN/gPY926dS4aGhp758yZo2VY04rib4djx2iDmTnzLy8AJpVK2ZUrV+Dl5fX37dlcFxERVPxCJUX6vVi9miJOH37YeP63Ukn5RRs30oY/YwZt9tXV5LS1bElO+PLlZHi89LyKi4tZbGws0tLSBEdHR27SpEmwt7dHnz594OnpyRkZGcHd3Z2Li4tTREdHc48fP1Y+fPhQBMC8vLxYY0XQ/gxkMhnMzc3ZhQsXxOzsbKF9+/aNToLg4GA8efKE8/Pz4wwNDWFjYyN4enryYWFhjVeYbibCwsLg7++PW7duISoqSggJCWF37txBeHg4MjIycPPmTbGqqoq7W1GBMDc3+Do6ihajRjFMmFAbrW2AsjKS0c6bp67ULwhkAGzdSkby+PFUZKU5c768nJyK/fvp0A8MJCPH1pa+1tOD7syZ+FUUxWcdOwrdV63i2IABdH3Gxq9es6JIxtG8eVT5eORIMkQbabdSD1padJ92dvUc2Xpo04Z6v7Zti6qHD5H70UcozctjEwYPZqbu7mB/hGT4vRg0iKKgL5NfCgXQvj3azJwJ+zFjMGDAAPTo0YMZGBhQj15BIAWPm1vT6oQ7d5C8bBnkDx8yrU6dILi7Q9qvX4Nq01UtWuD47dtsYUQE01izhsbOx4eUMP36Afr6KCwsRHhMjCDdtIndLSgQLHv14qZMmQLrykqGgwcpz7rOc5TL5YiMjITznTuoYgyck1Ntga/nz5+jrKwMtcoUVYTuxx/rzwV7e1zPysLNuDgUKBToNWIEOSnx8WRIDhtGpFBj95+YCKSmwmDZMsDLC2ccHMA7OnI9e/bkEhISkJKSInp5ejL07KmOUAMAY9AzMUFcbq4ya/x4zq57d4ryhoeT45OeTs/s6lUy2AcPprk4bBgZ6NraNGfHj6eoalwcyfatrWFsbAxPT0/VfsDOnz+Ptm3bvrLicGpqKrKLizFu1SrGvvyS9s0lS+q/6OOPaa7X9JSui7CwMISHh+PatWvQi42F+dat5CgmJxPxOmQIjbmPD0WDP/mkVj5cFzKZDKGhoSwmJgY9evR4ZVS+8OxZpMbEYFdyMtLT05GRkQFoaMCwpIQk6qq0HY4jh8XIiBz/wYMbJ/aMjChv1dmZxnPNGqBVK3AvXiAyORkWFhYN2wTu3w/x7l08LiwUH8bHi7djYsS7JSWssLAQnTt3hnbN3sicnaHdsiW4Ojm6ZWVlCLx7F10+/xzuvXvDauRIcIyRMuunn8gZ69kT5amp+HrPHvFB27asz7598PT2ZiqikzEGe3t7FhoaypRKJdp06EAqlsxMIjQOHSIn0MiI9s7vv6evDx2iZyGKRCbduUPKpSVL6O8mTcILCwuEderEZNnZ4oTt27msb79Ft8uXGfPyovcASLmzaBE0W7asVVHwPA+JVIpdp06hS3y82MPFhfEuLhQJ7dMHbOJERMbEwD0rC/z69UQC9O2L8hMnENGpEzS2bEFiaCieGBjArVs3da/wXr1g/O23LE5Xl7VwcoLhv/9NJFS/fuADAmC8ezd0334bduPHI8HGBj39/MAUCirAdeQIhIMHsUFDAx7e3tDT08OePXuUpqam3NSpU5mOjg4RpKNH03zx9SXFQ48etP/Z2ADJyZDHx8M0JQXacXFgSiWt1+xsUqBMnEj7GMdRutL580Tade6MslGjsDUoCPddXMReo0Yxzty8ecVCAVLGqOoctG9PUnUtLcDBAboyGcycnHD16lXGcZy6Gvm8ebRW63ZskMupUOXs2WCMQRRFhaamJlMoFGjXrp2YnZ3NV1ZWssa6ewCgNXvnDpFKycl0r507U7S9RQtywFUpVvr6QPv24K2toT15MuQLFsCkXTuYaWrCvnNndNy8mXn89hvXbfp0zqOgAI8ePWJJRUVwNDNj6NOH9rqzZ+l+vb3x7NkzhISEFFdXV4/s06dPdfMG7g1expvI9v8HkMlkn3p5ecn+p1od/R689x7J4X7++S/voZuZmYnQ0FBwNfKpfwSWL6dcwoCA+jLK5uLRo/oRirr44AOKaF+6BCxapJbznzlDkcTCQoogvkJWPWDAAPTt2xccAQAaVgUF8M4776j2p/+4R2RnZ4f58+dz27Ztw8iRIxtUBQ4PD8etW7fg7u4u2NnZcXZ2dgwAX15eDgAIDAzEoEGDfhdZk5SUhIsXLypzcnL4yZMnw8TEBMnJyZydnR20tLTw/PlzhIaGKq2trTFy5EheJWE0NjBgSEqiCsBnz9IzfvlzGSOnWIXycjLAJRKS371K9l5WRgbjmTNk/O7cSbK4CxdIgjpoEJFe8+bR+9QppvS4vJyztbQUmz0O0dF0ncHBZGD7+Pw+Mm3AAHL6jx9/5cueV1ZiZ1kZMHYs5ujrQ7ZnD83V3FySmqsM2P8EDh1qXCGSnAwoFJDb2aFBVenr18lYfPxYLb+ti8JCGrN9+9Dm4kX8tGKFMsTcHOVpabxnWJhob2/PzFX5o7/8Ao2jR2Gkp4enBgaw27ePpIjXrkEAkJSYiIiICCEtLY0zMTERrU+dgl9WFofWrWm+DB1KZEhxcT0FgVQqRb9+/YSoTZu4vMhIpKSlwdHRUfDz8+P27NkDURQxffp0urdNm8ggfPnZymQQzp1Dp5YtBatFixgAhgEDyOnp0YOipV27kqO7bBnNj4oKet779tF+dPs22lZXA19+CTc3N7i4uODu3btKAwMDHi1a1PY6bjj8ybypKmc7JobeNyKCjP70dIrCHzpE0aSgIJLA9+tHEW8TEyKGzM1pfTQyZ7t3747g4GB1oa4moDqXz5w5I/RdtIjTX7aMiITAQHL2AZIhNwKJRILVq1dDeeYMrt+9KxampTHcv0/5uDt3EtF2/z6N27Bh6oJkHTpQxFLV/7vmOnR0dFBaWoova8bS29ubcnHrICYmRinfuJGP7dMHmpqayMnJERMTE1lkZCQGPnyIHsXFtNeoL5Ki1wEB5KysW9f0GtfWJpJw5Urg4EFItm/HaC8v8cGWLaLDv//N1dvnbGxw28ZGeODgwMYdOMDl8rwY27atcsyYMfWLXa5cSXNIBaUSyq5dUTZkiGjaoQOTSCREbGlqkqw+OZmI47w8KP38MKusjGl88QV0V62iewgLq02B0dbWxvjx43HkyBHY2tqSZFtFjJw4QRLx5ctpLSsUdK6uWEHPNymJFCj/+hc5i1evkuMYFwfh4UPW5vRpceqXX3JYuRIeFhZEYIwcSTnRHEdro4mcXJ1WrYTCefM4Kc8T0XfzJrB1K6Q9eqC3iYnw7+HDxdnt2vF3IyPx9OlTdExNRY6pqZg+e7bQ7do1/h1/f8gmTUKtYkVXF2zTJgz080P+oEFEoCQlUd656pyRSHD/1CmUxcUJnELBYf9+Uj+0aoXg334DAFy5ckX89ddfGQC+qKgI169fR58+fepfvL29OlJdpwCu4tkz/Lx1K0aeOwdrKyt6BgcOEGFhbk5kTp8+RPy/+y4AoKJ7dxz46Sexurqa6ejoCDExMbyLiwukUunrz+rCQlpDqnannTqRwzxrFmBqCjZkCNpPmABx0iT4+/uLycnJbLi9PXQ//JBUMnXRrRvNgwcPgPbt0bdv31rfq7Kykk9PTxdevHjR+KLIzqZz98cfiaApKFDneZub0xqOiqJn3Bg4js5xVcrH3bv0XNLSIE9IwIhWrVj54sUod3WFprU13eeWLcDKlVAqlfD3939RXV39ztq1a5swCt+gOXjjbP/DsX79+vGampo+Xbt2/XuGbBMTyblrKsr6B1FWVoZff/1VePr0Kde6dWtx3rx57G9djbwuGKNDcP582oCbmzYQGkqb+qlTDX93/DgZ/Z07k+GgMrp79aLN+dtv1dWUm5G/LPkrK6b/Raiurgar6d37MgoLCwEAL0e9NTU14efnJ54+fZrl5OQop02b1ixioKKiAidPnhTs7e15fX19wdbWlmOM1auQamFhgQkTJtR7v1ojcvVqcqDfeYcihnWrBxcXkzPw7Bk9s337SDK8Zg0d0qr7E0UyCu/dI/LEz08dff3pJ5KKurvTwWtkRMbo3r1N3lN+fj4YY/Dw8Hi9t5yYSE7L6tVkGKl6xTcT1dXVkEgkYLa2EKOjUfr4MbRtbMBxHJ4/f474+HixpKQEI0aMYFlZWfj555+hoaGBDz/8kN5g6VIqsLR/P0lSBwwgGd5LhfT+EsjlZAhu21b/58+ekSH8slReqaRozuefN+5onz1L7xUbCzx6BE5HB7M0NGrrD0RGRgq3z5/n50skkJiaovT+fZR264Z+Eybg0LFj6G1iApfr14F338UPSUmiguNgb2/PjRs3DlpaWjTftm4lxyg8nCKREyfSfvLzz/UupXv37hw6dwZcXJDXqxf279/PtmzZokjmhq0AACAASURBVGSM8W5ubsKpU6ewaNgwTjJjhjr38CXcdXPDTGNjzrFnT/UPNTQoOnXwIN2vuzs5QcuWUZX0wECa8zXrITM1FVKpVHR2dmaFhYV4+vQpP23aNHL6Fi6kIlV1oCJWax1hldyzf38iYGQyWg8jRpDz/f77RD59+y05YtbW5Bw9f94kAfzw4UMIgvDaPrrDhg2DnZ0djh07xj19+hTz58+HZkQE3afK2T54kNIxXpa9RkdD1qkThPXr0ePxYxazezeRI9radA/r15Mz9/IZ8PXX5EhduUJru1MnxMXFobKyEv369UNcXBzCw8MRHh6OJUuWwKBGZVJZWYmLv/7KTzMxweQvv1Tl9jMAuHXrFs4pldD18UHHsrL61dg1NGg+BQaSc/Ddd68+zxkj8nbSJNjExbHqmTNZ5ahRkH3yCREFMhlKnZ1xo1Ur1mfiRCY1NoZlUhKzfPaMb3Cvzs5qdQ8AFBVBbNUKWXI5++KLL7BmzRpwnTpRtPP2bXrN5MmoOHIE22bNgkPr1ko/d3cepaV07s2fT8oLf3/g0CG07dsX/WxthZO7d2PBRx9xEomE7v2332gt+/jQ+G/eTJHkI0fIUczMpHPV35/IiPT02naG9qWlcN+xgymGDYNEVWiwVSsau48/JrIzJaVJ0kKpVELm7k65zF98QY5+q1ZAUhJcAwK4S6tW4bsHD8Sq2FjG8zxKu3dH/0ePmM2+fTzmzqUzYckSCnKMGkWKjs6dwfr1Ews2bGBXvLyEvpcvc/Dzo+tmDLC1Rdb778Pc0FDEpk3UCSEpCbCwgPLCBQEAV1RUxIYNG4aqqipcunTp9QGOxYsRYmUlZt++zUafOAHlZ5/BcPp0IkVu3FD3wp41i+ZUnRovANVWycrKYgsWLEBSUhIXFRUlXrp0iXEcBy0tLeXEiRP5JpVyBQV0btaFgwOwfz8Uo0bh1iefoJOXF9pv3w6ZRMKO3r+Povffx+OFC9F51ar6idyM0Tg9eoS6KTf79u0TUlJSOA0NDS4zMxPFxcVCXl4ec3NzY126dIF8505a84GB9Af37tF8+fhj9Xtv3Ehnft0Wba+DiQn9c3WFDoAwW1tl1a1bbJSFBYeqKrLzAFy5cgUlJSXpABpn+96g2fjfs3jf4C/BunXrpIyx+VKpdOPUqVM1/5aO5N695DRGR/+xnNImoGr5JZFIuHnz5sHY2Ph/qwr1XwEHB3KeKyqa/zeq4kp1kZVF0et168hQmDCBDExTU3JWvvqKcqx4vn7e1N8QaWlpkEgkuH37di3zXVZWhoSEBOXjx495AI1W/XV0dGQeHh5ieHg4//333ysWLlzY6L4aHR2NS5cuoU2bNkhNTRWtra3FkSNHgjH2x4gwTU1yQCQSchRnzqRqynp6JDkLDaXISVoaOZVaWuQwhITQ66ZMIefS15eiA1IpGRdWVur3/B0IDg4WGGOcQ00BmUZRWUnrOiCAHN4rVxp3KJtAREQErl+/jpKSEuoRXV0NvVGjoLdxI4o7dICDg4MYHR3NdHV1xZKSEu7u3bu1f9ulS5f6b2ZtTfI8UayV+OFf/6I1M3x4fQP9z0Amo3H/+uv6+9jYsSQRr1ucr6iInAN//wYOIjIyaP3Z2xP5mJPTgNhyfvYMzufO8aft7JSx16/zMZ0744WtrVBVVcUt1NeHm5sbQkNDcbm6GgYffqgcoqfHOxw+DLZ0af2ik0uWkJMQEkLy3u7dyfFLSWnYi7qmN7qRkREWLlzIkpKSeFNTU+jp6XEJCQli1syZaDl4MF3zS0hJSUFOixbgi4ooX79u6oupKTnXixbRPOnalaK0urq1RYpUiIqKUrZt25bjeR6Pa4qMRUZGiq3272echUVtWx8VkaaSSKenp4t4uRCe6rknJJBjffIk7YMXL5JsfOFCImdCQl6ptLp586bI8zx7/vw5LF9TsNLBwQHvvfcevvrqKxw4cADz5s2j53/0KJFlGzfWVymVlJDD178/4OyMm3v2IOeTT+A7ZkxDFYW9fcNnpiry9vHHNN9//hklJSUwNDQUevXqxan6yH/66afYtm0bFi9ejKioKMTHx6OtQqFsNXw4/3LF9S5duuD8+fPQmj0b1bNnQ+ODD+p/pkxGe83Zs+RwTp78+nNdIoHU1RXxq1YpE4uKuDGbNzOkpeHZ++/jyY4dwviEBK7Fl1/SGfTbb+QIv3gBbN+ufo4rVpAz+9ln5PCcOAG9wEDMSk1F2OrViA0NheuECWRnqLB1K5InT4aWkxOGr1zJQyYj2bQokmO3dy/tp+7ugFKJbjducO1OnkRwcbEwKDmZQ7duRBZUVVEK3KNHRHxXVJCTU1JC+82KFVQ3IzgYj0xMYM5xkERHIywqSij59FNxVr9+9Qdo2jRaRxcvNl4dvwZ6enri8+fPiSgSRVKW1BSe5F68gGLzZigUCubs7IzKykrlM6WST3n0COcWL4b37NlE+gYE0L64Zw+tt7Fj0Xb7diZ4ecFi82YO9va0f3bsSPc1diyeCYJoc+4cX6WpCem2bYCFBbKysiCRSGoXSllZGTp06IDMzEwhPDyci42NFbq7unItfvkFl8zNhdEpKVyrkBDg8WNUPn2KlIIC9szODo+io7G4QwdSWrz9NqUnbd1KEeNWrYhU2Ly5HgFhV5N+Ul1dje7du7PuNRXBS0pKEBUVxfbu3Qs9PT1lv379+A4dOtQfRBsbem51UFpaiqMhIcrqoUP5scuXY/vUqWj/9CnMiopE444dkdSjB7utqSkGf/212L9/f87FxUX9xyNH0pgqlRA5DqmpqUhJSeFWrFiBwsJCasGYnAxBEHD58mUIOTmiV3w8w5Yt6vewtqb2Y/UfNv2zsiKC9uVzoxlobWvLX0pKUqJNGyJILC3x4sULhFMXjSlviqL9ebzJ2f4HYt26de01NDRut2zZcshbb72l9cpqh/+rULVsmTWroSTnTyIjIwNxcXFYs2ZNo/LlfwwMDEiKqa1NxVmaQlUVRW5Wr6aiGCpER5OB5+FBhel+/plkW1Om0Obu4UFG3O9wlv6XYWxsjJSUFOHx48dCUlKS8OTJEyEjI0PU0tLiS0pKIAgCJBKJ0KZNmwbkTMuWLZmDgwNiY2O52NhYISEhgaWnp4MxBh0dHRw7dkyMiopigiDAxsZG6NGjB9e3b1/utdWKXweVsc8YPeOsLJKK1hjQUCpJfbBwIT3n9HR6Xv37U9Rvxgxisfv1I7mtgcEfStXIycnB+fPnmbW1dcP+pQAZfJs3k4GpKljl7NxstYooijhx4oQYFRXFtLS0MLOmr+2LFy8wztkZPQIDEW5tLT5//hzjx49nw4cPZz179oSenh58fHxgZmaGiIgIJCcnK01NTbl6KTWM0TqZPZsM0rNnKaqakUG5en82/UYmI2Kh7riKIj2HdevUefcKBUUAdXRo3anmhkJB6+6TTyiy1qYN7Y2qKHxuLjnyISH0dbduMJsyhct2c8PASZPQt29flpSUpLx06RKXmpoKU1NTcdq0aaxP376csYUF2M2b9HkvXqgdFMbI+Z4yhQxZJyfKFw4LI+egLnie9mhTU0gkEpiYmEBLSwuMMQQHBLCuTk7QXr680Wf9ww8/iF27doWdvj7DkSMUQa87RgoFRfEfPCAlR79+tXmg2L4dGDYM9x89wvXr19mECROYpqYmLC0t4eTkhOjoaOCzz1hQbCzO371LznerVszAwACMMZiamiIsLIz16NGjQdpI7RjwPI2zpyeNwfr1tLYmTCAH3tOT5s2SJeQYjx4N9O4NODrCRk+PmW/ZgiMlJXCJj4csJQUVVlao3rcPGtbWRDw9ekRS0OpqPM/Oxp07d9C1a1f1HqOhQa8bO5acNH19Ij3GjiWHY8IEYMQIpAMILS9Hrz59GipzvvqK9oCXc54BilqOHQt88QWMv/4aITY26NWrF6PbZ8jPz0d2djaioqKQnZ0Nc3NzYbKFBc8YI6LoJbRt2xYhSUlIKC5Gmz590KCQo0RCDveVKxSt69sXYAwFBQU4ePCgWFJSIpqYmLD4+Hjo6+vXStiNjI25y5GRLLZdOzzU00Pu1avolpLCWowaBc7Dg+atyk7Iz6c0DFVKyvDh9DlSKSl2fv0VKf364eDBg5h8+DAsr12D5PBhSiepwf4TJ4QIbW22xN8fckPD2kgfGCNnp1Mnmg+urkDHjmCDBkGYOROBt28zIzMzGOvrE8m5fj2tq7AwIrOTkuhag4PJ8fL3B4KDce+tt+B/5AjigoLQftUqaD1/LnqvXs1LKiuJRDA1JSXFjRskrZ46lUhvb2+qF7BiBV3/sGHA/fso1dNjPRYsYNJu3UiNERZG5Ny8eWCGhrC3t4erqyvCwsIET09PfuDgwdDOykJRYiLQsSP17eZ5mt8mJnQNQUFgZ84gztVVaXL/PifftImIqF27SHL9zjswPnKEdTx4EDuGDUNyq1bC9evXxZCQEJaSkgJ3d3dkZGRg8ltvQfv2bbT39WWe+/fDOi4OIRoa6P7jj+yetTWTeXujzapVgKEhDldXI5njMPedd2DTrh2t06+/pjk0bRqNoY8PEc0tWxKBM2xYLeF05swZZGZmwsfHp146hEwmg42NDevUqRM4jmOXLl1iRkZG0NfXV+8FQUG0Ly5aBIDOuJMnT0KpVLJchYIJAwYI00+fZobffINKMzM2YONG1ragAN0OHGByLS0EBgaysLAwdc0GAwNg82YUKhT4d3CweOPGDSaTycR+/foxPT09WFtbw9XVlXXv3p2V7tghdN+zh6s8fhyyGkWQUqkEFi0iZtDdveFa7taN8sQZ+911jYKDg5VtSkv5dvPn01klkyE4OLgqJydn75o1a378XW/2Bo3iTWT7H4Z169Y5amhoXB08eLChq6vr3zNiGxFBEYSYmL80oq2CRCKBVCoVBUFg/4iCaE2BMTIu5HIyDJu61ydP6GDZsIG+j4oi5/uXX0hSPmcOyd28vNRVxVW9Jf9BkEgkmDJlSpMTYt++feKLJnLZZTIZrKysMG/ePERFRbHq6mohNzdXvHPnDq9QKGBiYiLOmDGDtWjRAnp6eg0/o7KSjLC4OIpWmpmpWfvgYJIMr1xJRsXYsXTYenqS8bZsGUW5Zs4k41yhIGm0kxNFfAYOJOf6dQXH/gBEUURISIiQlZXFmZmZISUlpf4LlEqaR3fv0j1dvPja6tJ1ER0dLd64cUOUSqUsKyuLjRkzBs41uWe+vr7w9fUlpywhAauWLGF1o3pSqVRVkRhmZmZwdHREYGAgv3fvXnTs2FE5YsQIvh7ZwXHkQLi5qXMpe/emvD2JhByWP0qO+PmR0bpjB32/aBFJ6Zcto+9FkdbXqFFEeqlQYyAjIIBy9mbMUP/u8mVq3bZiBb1u7lyS2AIwBNCzTjR1+vTp/MaNGzFgwID6Mn8zM0pDuHaNPjsxUR0tb9GCrldHh3JPzc2JiBg8uH5rwcBAkka+FBligoAZ+/dD/8iRJgk5uVxOxu2YMeQMqiLnOTk0zwcNorxpfX2SdQYGkkO+fDkgk0FZVITYLVvQb+5cVrf9nqmpKd59910mnD8PN19fVAwYgMjISOHgwYO8lpaWMHjwYE5bWxuiKDae0iKKRF4kJZGzdPYs8NFH5OQolUREvvMOyVj19ennFRW0z3p5AcbG0C0thbOdHZ526SImfvklq5JKERYTgzm7d+N2fr6ym4EBL/n4YyJIHB3BDAyg36sXeo0ezeHsWfqMfftI2uzkRISDqSmdj6p5WnPtzuXlCAwMRFRUFDxfbqUXHv5qwogx5IwYgaiHDyFTKGivr+noMGbMGIwaNQrp6emqAlAcBg6kCFojaN26Nbq+/z5ezJ+PHz//HO26dxdGjhxZv+K/TEYR6ClTgGvX8KJLF+zZswdlZWWssLAQV69ehSAI8PLyEn18fBhAKTW9e/cWwsLCuBadOgk9ly7ljB49op7yXbqQw/XNN5SSsncvRbF1dOiZHT1Ka3vECGDmTMR6egotvLw452nTFAaRkRIsW4aysjKcOHVKSElJ4bS1tVFcXMwtWb4cmuPGUYpNXUVHQABJwB8+rNcuUUdfH2OmTcPPP/+MmWPHwsLCQj1fgoPpeS5cSOu0tJT+6NAhoKoKlnFxGHXyJFqnpSHj4kU4TJ/Oxb/3HvguXdDxyhWKWl64QKknKsXSvXs054yN1RXzp08HWreG7OlT9qRLF7HTjRsMlpa0z+joEFk1ejQs58+vvZ+aSubQmTgRnYODxeB791irVq1qo8JwciKiLywMmDwZlubm3GU/P/R8/30oRFF8Zm0tuq1ezT3etQtlOjrw/+AD5Esk4HJzYW1tzQ3t0wcVPI/Wa9bgRVUVxIgIWtvJyZCMGwdLLS22wNsbt/r3R1FgIHqOH4/isjLoCAIyMzPF8ePHq+tP+PvT/U+bRuTJ7Nm09zBG+4+7O503M2YgNScHd+7cQefOnZsMqhgYGMDLy4sVFBQoAwMDufLyciaVSoUuXbpw/Wxtcb93b/He0aOsvLwcWVlZoiiKbPLkyaymxgaHAQNgPGcOjP38SPk3ZAgQGYkuhw6xmM6dhaz8fO7gwYPw8PAQ+vfvz5UNGICi3buhMXYs1lA3gIYHSnIyXFu35n4xMRHTvvuOaWpqQl9fX5mfn8/73rwJh8GD0WiPGE9PshN8fIhUb6Yyq6ysDElJSfzg4cOJsNLTQ3FxMW7duqVUKBRrm/Umb/BavHG2/0H47LPPZmtoaHzn6+sr+9s62gAZf25u/xFHGwCsrKygoaGBgIAADB069J9RgbwpdOtGEaDNmynv72Vs3UrSzbt3KZIXF0dS1vbt6YDdsaNWKoeBA//rl/+/hOzsbAwcOFA9WZKTyVl58YIMgLFjoXP+PHzkcoYRIxjGjkXlV1+Be/gQGt9+y2H+fHJo2rcnyZumJhnzP/5I/7KyyIgYOpSMxC1byDhLSyNnGqDos6YmKQvGjSOnYM4ckiUCFJFNSyPHe+7c//iYKBQKhIWFcQAZbbWGviiSY8TzFGH/6CN1UZcmUJuHXePQ3rx5Uzx//jxzcHBgFRUVWLhwIRrtZ84YRclXraIoThPQ0dHBuHHjUFRUhB9//JEFBgYqhwwZ0vgmI5ORmuOzz8ipPXaMnBZLSxr336uIWbCgvsMTGVlfAl5dTQ6kao0VF9O9bNig7l+sp0dr8/vvaa44OtJc6dyZIvOvgFKpRHV1NbS1tSEIQsM9z8uLnAhNTcoNnjyZft61K0WS796lgk/jxpEjXNfZFkXaH15C3p07KNLVRdizZ+jv4ICXW05WVFSguroaurq6DFIpraEzZ6hWwNdfk6pp0iQyqrOy6ExIS6M1UlM7IHnZMnHYuXNMd+nSRglF7swZSHkeUo7DoEGD+P79++PWrVvM398fAMDzfP2xuHuXUi7GjiW57sCBVOtg8WJykLy96TOSk+nr3btpjvfqpX4PFWkJAMePYwTALmlpwdjYGDNbtkTVkiW4cfQou1FZKdrs2QObuDjWMjoaWY8eoTI4mBwze3siZ6ysaGx79KDPXLyY8npfKoylqakJlYS+nrMtihTZVTkrjaC6uhp7z5+HvqcnFtvbM0yeTJLXmrSCepWWMzKIXGgsslYDpxq5tYWhIfbdu8fdu3cPffv2hVwuR05OjpidnS2UlJSw1jNnwmvrVu6pXC4KnTujZ8+e4u3bt/Huu++yI0eOKEtLS7mMjAyY1igmPD09uSdPnggaGhrUkeLmTYqQ37pFZMiaNbSOVEqDuDiSQOflqSPcvXqBnzOHS7e0RNdDh/jiqVOhd/Ysju/fL2RlZXFubm549OiRyBhjurq6tK/o6pKjvnOnutDaggX0XGrmkQrW1tbo3r27cPjwYSxdupTytyUSIo1U0v3Ll4loiI+ntXTkCAy9vJDy7bdIt7LC8WvXwObOFW1tbfHgwQOm3LaN1EKPH9NcGzqUyPDsbHXRqxp5NDp0AL75BpqA+MLUlJQPqhou1tY0Xg8fAvv2IT4+XjS0tuZqUxzatYOlpiYzT0wUT5WXY9WqVWobsm9fIkhiY6H46iux/8mTrKRzZ5iHhjLJe+8xsVUrOB85AiQmoruJCYQVK8BNn87h+HHaSwoKqAJ+bq4QLZezHvn59N51Knbb2dnhwoULOHbsGB4/fqyq2s1q05LCw4lc2LpVrdLYsYMi2oaGdJ8bNhBJNnw4qj77DIwxjBo1qsm5CpCCY8SIEbwoikhISMCzZ8+4e/fuKcWcHD5dW5tpMqbU19fnU1NT2fLly9UdFgA6y728aM84epRyyeVy5MTGiuXm5uzDqVNR0KIFDhw4gBs3boBXKtHV0FDwcnRsvOVkYCDw3nuwvHIFs0xNWV5eHnJzc5GRkcFbmpjgdnw8Wvbq1bizDdB82LHjd6VAhYaGKrukpcFg/XoeJ0+qflbJGNu1du3a581+ozd4Jd7IyP8BWLduHQsPD/9ILpd/MXv2bC1bW9u/p6Odn0/G3LffUiTuPwTGGFq3bs2uXbsmPnr0SHBxcfkHe9sgo/7aNTKg6kbmRJGiZi4udGANHUoG98yZdGhHRZFzOGTIfyQq+l/FixcU9c3IoOidpSWRDxIJHUzz5pEE9N//Judw0CDUtgwqLwc6dkRkr15iry1bmHTnTnIEWrcmR6eggIyvZcvooMvOpvm7fz8kQ4aA19Sk19S0WIK7O423jw+1NOrXj6LWjJFB068fXet779H/rq70HAC6LlX/1v796W9URjlAxr+PT/3+t/9B8DyP4uJiMTMzk61cuRLt2rUjpcTu3RSJ6dOH7k11fTXIyspCTk4OZDIZoqOjERkZqTx9+jT34MEDMSsrC1KplB0/fpzp6+uLb7/9NnNxcYFmUz3fAXqOH31Eka3XkGdyuRw2Njbs7NmzXK9evV5NtvE8PaNRo8gBOnyYnourqzqq1BxYWFDUwcGB5qKrKzlzHEdR7m3bKK9SJqM5+P339Iz79CEyJTqa7s/CgtblrFnkPLi6qlu+vAI8z0MQBOXFixe5/Px8tG+sL7aVFT27iRPJQFaRAz17UmTP2Znm/MiR5PCqDLqqKvp53QJohYUonDtXPDJoEBM5DtevX8fVq1dRWloq1lTyx86dO5UmJibo378/45KS6DNKS2k+Dx1Kjo2KcFVF1z086Gt3d6B3b0Rcvy4alpczvfR0kj+6utYvOOfqSq+vkQFzHIeWLVsyFxcX3LlzB1Xl5fB2dycndu9e+tuSElI0LF9OTgbP0zobMYIM6smTaT/s0IH2CjOz+uRDI7C1tYWFhQV0dHSgq6sLV1dXlpWVxXJzc9nNmzcRFRUFSysrJD19ilbu7jA0NSUHwtGR5sPTp+RgtWvXZAXqK1euoLi4GPVk8aWl5BCOHt3ktSUnJ+P+/fvi0qVLGWdtrVZKODnReVy3wFtEBJE+dat7N4YJE6Dl5IR7SUlCZWUlKygoENLS0pRFRUW8lZUVZ29vz1JSUsTrcrno8ttvXI/Zs5lT374sLy9PvHz5sjhq1Cg+KipKuHHjBpeYmCjq6uqy4uJiWFtbs6tXr6Jnz56MOTiQukAmI1l3375EUlVVqatUHz9OpOO0aUSIzJ8P07ffBjd5Ml4EBOBwVRWepKayp0+fsilTpsDd3R1du3ZloaGh6Nq1K7W+MjAgsqK8nMgofX1SYkya1CjB06ZNG/bw4UMxLi5ObBD0qKggKfisWbSu3d3JIX72DEYHD0J/4UJI5XJBQ0ND9PPz42JjY5EZGyu4m5kxfPop3YePDxUFKyqiPWT4cCKqLl4EvvgCZcOG4aBCwTw/+IDptW5NCp29e+mMGzwY8PBAXl4e7j5+zEZYW0MnIICILG1tSNu3R8ujR9lVS0sml8thaWCAK1ev4tH16yjv3Rv6jo6wGDyYyQcOhP6+feDz8qB74gSkvr5g1tZgNWcj27iRzrHBg2n/l0gAHx9EZmVBU1OT2TYyh2UyGR4/fiw8ffqUAUDv3r3FNm3aCDY2NhxycohEGTiQ9gAVUlNprapScRgjp/7xY+gbG+PWkydwcHFpVrogYwzGxsYoLCxEXFwcZ3nuHIbdugWXHTu49u3bo3fv3g3TIgSBzv05cyiibm8PwdoaW54/Z2Pd3ZmJnx+0+/dH93HjmLOzM/r4+MDu7l1mrjrX6yIujtbr6NG1SgWtGpLO2toahUFBsN69GzuVSjx48EChra3NGRkZ1W/Rp4rwr1xJtsiECa+854qKCpw6dYobq6XFydu0Aby8kJmZiQsXLlQoFIoxffr0KXvtwL1Bs/Amsv03x7p162RSqXS3trb2mBkzZmi9rs3I/zTOnSPjtbnVs/8EWrZsiUmTJrEdO3bwx48fF8eNG/f3JCiaAzs7ihTNnUsbsERCOW3FxZQH5utLkbrLl0mGlpRE0e6X5Yj/bWRl0WFmYUGO8ciRFNn69VcyMtavJyN/+nTKqwwNpd/t3EkGTefOVMho3ToyuIuL6f4DAykqsns3GS9+fiTJE0UycFUVvydOJOfIygrYuxey5GTh6dq1XEdV0ROVpB4geR9AKgIVzp9Xf62KQNTtvfmaaOTvhihSBHb27L/2fV+DxMREBgCbN23CmKAg0SwlheXs3An7pUvBSaUQBAEZGRmIiooSU1NToVQqoWpzIopUd8Xa2ppNnz4dt27dYomJicLNmzcZx3FYsGBB89alrS3tH3FxRB69Bg8ePICFhYXA83zziDbGyKn64QcyiH7+mXJhx48n52Po0Fcrcaqq6LXFxSQT//FH+lllJRneEgk5VHv3EjFWXk5Rua1baY4OG0aOt4cHORV/AP369eOLioqE4uLipu/Z3p4kiFlZFLn19ydnY+lSMuKOHCGFQni4ugJ+fDw5I3Xn8/790NfWZnZOTsohQ4bwJSUlCAsLE3Nzc2ufJ2MMyqIiJmRkEElibk7OjKdn4/3jR42i/WrECPq+uzM4SgAAIABJREFURQtU9unDLrRpg9mOjhQVv3SJotMjR5Lzv3IlkQUvPQvd7Gz4Pn8Ow19+oc9r356uoWNHNbH1Ms6erb/mvb1pXY8dS+v6/fdfOf51IZfL4efnV/v9rl27EBoaCqlUisjISLRs2RJyuZyIleBgGneOI1Kgb98mCRZBEJCXl4faCsvV1a8lhPT19aFQKFh6ejq1rlLN9RUraO89eZLGUyKhedFUz/e6UCohODhAY9Uq9t5770Eul3NA/QLNHh4e9P3HH5MT9fAhhr/zDrdlyxZkZ2dj8eLFfHFxMY4fPy4eOXKEMcbw1ltvgTHGnj17Buu8PIoQP3xIbyiVEhGiauN14wZd74wZRIA+eULR4UmT0PKjj1Bx9Spzi4gQq6qqBH19fbRu3ZoDiJCRSqViQUEBU/XqxqhRtBbefpsceTMzmmtTp5KkvI7DU/P3ePLkCVdYWFhbyR0AzaFly+ic2bCB9o0PPgC++goaQUHA4sXoOWdO7ThpPn2KacHBHCwt6xdSBGgOXL1K5114ODBmDDKOHcOuXbvQsk0bwcrKit5n2zZ1oVQLC6C6GtIffgB69hS1Fi1iOHGC5u+aNUCPHtDt3h3Tu3XDkd9+g+OgQajs2RM3PD0hiYyENDQUIxwd4eDvT9evUuJMnEj7n4YGzdOrVxudFh4eHuzcuXMY2IRKbsKECVxhYSGMjY2hoaHBAPAoLqYx/+67hvUi4uIa7ruMAevXgz9yBGN+/RUXjI2FycuXNzug0rJlSzg7OwseM2dy8jpFIxutr7JnD9lN5ubAhQsQd+3CLRcXged5TsfdnZ6LkRE4Pz8YfvYZOdFjxpAdUxc3btDa3rGjyfo6d7Ky0HbaNHHRokUsPDycBQQEIDAwUBgzZgzXoD/3+PFExrwGt2/fFh2fPxcMxo3j4e0NURRx9uzZcqVSuWbt2rW5r32DN2g23jjbf2OsW7fOViqVnmrdurWdn5+fZgPW7e8ClRR2+nTKUfuzRaOaCVNTU8ydOxc7d+5kjUor/0kwMKAK1devk+xp3z6KPG7eTJu/Snr0mn7FTaKsjA5ZjlMXFomIoEiynx9FAydOpCj7rFkk9Xr/fYpW7dlDkd6dO2kujB9P76eKrp0+TRLhjh0p4nLpEjnb1dUk5WOMHBGJhAxglTz3l18oAqClRa/jOJIFf/YZ/T40VH39qh6Vdash1/TqBEAG/rffQqmr26C9yP8MVMW92rX7r3xcTk4OMjIyUJ2TA8+YGHjK5Xg6bhy7KJeLT2JimDIqqva1UqlU1NHRETp06MDr6enBzMwMOjo6CAgIgKenJ9q1a8cBtdXeubCwMISHhyM3N/e1lZxrER1NhXwaS5d4CYwxVFVV/bGNRkeH9qtp08hB3riRotJffUXOamPOjZ4ePR+JhJwzV1ciB95+mwyvXbtoPfTtS7l/e/eSEZedTakEfxExw3Ecy87OFvCS41MPPE/rplUrdW9gbW1ac8nJZPTOn08RNg0Ncr7qSuIzMgBXV2jPnIkJenp8eXk5YmNjlTk5OZyWlhZV/xYETPX15avd3ZEWHw+bgAB1yoG/PzlRL+PRIyIpVM42qBPAiXv3cNPCAm4JCfQctm+nXNijR4m41dQkYuPiRXKOa0iLvIEDxZBRo9jCgQNfnyJz9ixJ/7/4ov7PDQ3pmmuiilix4g9VsZ8zZw6KiooQEhKC+Ph4cdOmTWyipqZof/YsQ3CwulJ8XBzNcZUkuQbHjx8XATAPDw/Ua2X0+DHt+6+AsbExevbsKfz000/cBx98QOcgxxHBolBQhJ8xcjiPHyfVxWtQoaGBhyNHQk8qFeRy+avzwRijMVy1CunnzqGqqqq2xaGenh5mz57NCYKA3bt3ixkZGUwURSIiLCyIBHgZHEdz08eH1D+9e9PPP/yQ0jF69QKcnCCXy9GvXz+GOnmzgiBg586dSo7jeMOX+7OPHEmR1CdP1G3ifvqpgb2SnZ2NJ0+ecCtWrKgvORZFUgr4+hKBMXQozeeZM4kQCAqi1/n7A1VVqD54EFrGxkj55BM41CFmkJdH5+ePP1KRS2NjckYZAzIywBjDrFmzuNrX+viQ1F6F1q0hO34cWT/8wErOn4fu7Nlke61eTSoKNze0vXgREyZNwsPHjwXv0lLOd8kSoE0bJLq4ICklRXSIiGBgjM7myEiyJVaufOVjpo9uDYVCgeLi4kb7z2tqajZUML3zDu1FjTmh9+41msICgJQH169DePyY4fnzZqu9LCwsMGbMGA4nT9J+t2JF4y+8do2egaqoo68vijIzUbZ7Nzf5gw/U61CpJPvm/n3ai9zciBDv0IHI/NhYtW3SyD0mJiYiLCxMsLt0iZX36CEaGRmxESNG8EqlEhcuXOACAwOVCxYsqL/GPDxovrm5kY3XCEErCALCw8Ox4NIlHu7ugLc3kpOTkZubmyeK4g/NGqw3aDbeONt/Q6xbt47jeX6phobGp97e3vIePXr8+arG/5dISKB/rq7/NUdbBYlEAplMJnIc9zcewGaA4+hQ/O03cijffZeiwIcP0//u7mSUubnR7zZsoNefPEkH8KRJ6sq7LVtSJHfDBiqg9uABOXhz55Kcevx4kmJevkyHoZ8fGSgFBRQlVslNBwygCB5AOU81Ujakp9PP6va4zMhQf60yjD/9VP0zlQFoYKCOvNRtD/Qn51VoaChKS0slpqamUCqViI6OFmNiYkSpVIohQ4ZwVi/JpP9PcPQoERF/lDBpJl68eIGYmBhcDQpC97g40b11a9ZWSwuaO3fCydwcTgATBAEPHjyARCJBQUEB3N3dmUQiaWB0T2+iTUnv3r1RWlqKAwcO4L333mteX/Zx46gqbjNw//59QUdH589NCpmMZL4//URFrrZuJcnmypXkfHbtWn/erV1Lf7N7N0U8tLXJwDI2JoNy6lRyjoqLKXrcp089x/KvgKamplhZWcklJSWhMSlnvXs7dIicfSsrKo40ezY5RcuWEXlw4gQZmgkJdA99+tDf/utf5Kz37o2CggKcOXNGzM/P5xwcHFjv3r3p3t95BzpHjiBwzRo8kUrFBRxHIzV+PBmkT540LKTn50fS1DrS3Q4dOuDEiRN4+PAh3NzcaE9YtIgUHsOH07VNm0ZOcUICjXdkJGBhgQ55eexKRgYyMjJeT+iUlKiLWr0Mc3Oae//6F5EEf3D96evrY+TIkRg+fDj7ceFCvLh+ncHfv35LNlWxrzpQKpW4f/8+Gzp0KNxfzqUuL1dX0n4FeJ5nCoUCBQUFMDIyUv9CpbjgOIoqpqc3yP+Oi4tDRESE4O3tzSmVSsTExAipqamcuZWVODQzs3kMtrExKr/5BspOnTByxIgGLRY5joMoiuz58+eorKzEo0ePUGJkhDaDB+OV1IaLCzlFSqW6intFhbr7Qh15sSAI2LZtm6Cjo8MWL17ceNrKokXklH7/PRFiAwfSex05ApibIyEhAUePHoWlpaVCR0en/qY1fz6RuuXlpL54+pRUV6NHEyk0ZAiRG1OmANHRKBgwAOnGxqhWFWGLj6fxX7uW1Br+/ijOyoKWgwPy7Oxg1rkzCgoKoKmpKdQmBJeW0rn60pyRtm8PHZlMKX7wAS+6uoI5OpJT9uQJzeMjR2ATFwebadM4PHhAZNX8+bA4ehQxPj4MISHkwJ86RUTgiRNEBKkUXE0gIiKCPr+5rWh37aJ9p6nilN9+26D9IQBs2LABUqkUFWZmmF5ayjBrFgUYXlG7oAFSU0nl1xgqK2kPPHSolnx/+vQpjmVmoquDg9I6OpqHrS3ZIDxPBKUo0hpydaX94vRp+v+dd4gIGjaswcdkZGTg2LFj6N69O3NPSmKsjtKL53kYGBhAJbtvAMYokNJYyhCAq1evirq5uaJWSAiDhQVEUURQUNCL6urqj9euXVvd/IF6g+bgjbP9N8K6des4AM5SqXSnkZFRh7Fjx2rVOxj/jtiyhTahsLD/uqMNUDXK6upqlpeXh7/1WAoCsdhxcXT4/fQTRZ1696aNfNo0OhzXr6fxzswkQ3nfPmLUv/6aNmU3NyqiUlREz6NG5gsvL3IspFKK4KjaGb39Nv2+rjOsqtj98cfqn506pf5aFXkcMED9s7o5+v+DPeFTU1NFuVzOzp8/j5ycHEgkElEqlXLPnz/HvXv3hJiYGLGgoADDhg3jVVGZ/zq0tcmY/A8j/u5dPP/3vzFQoRDcraw4bvHiBtJSjuPQoG/p74Svry9u3ryJioqK+hGipqCjQ0b1li1NRyNqYGZmxiUmJuKbb74RW7VqJY4YMYJLT0+Hubk5ioqKkJiYKLq4uDDd5rb9MjYmRy4/nyKw33xDa2ThQsonlcuJZIqPVxc2Gj2a5JaGhiRL5XmScv6ZquevQe/evTme53Ho0CEsWLAAr52rpqYUgW/fnhztvn3JcNy8mSS6qv2husY2KyqiOVjTo/3EiROCQqFgc+bMYTohIZR3PmcO7UkdOsBaIsGd06fxWY3aRBAETEhIEB1u3mT1yDSAnPApU4jUq9MirG3btkIFyWTJozAzI8fKx4eih3p6pD54yeGQ11RIj46OxsjGIqQqKJUU1a3TGqoBtLRI2XDnDo3Rvn2UO/oHwF2/jv5BQTg/bBhcWrWqX664qoocvISEWvlsXl4egEb6yAO0JpqRDtStWzcWFhaG8+fPY+rUqS9dEEfPt7KSPj8zE3lFRbiZno579+8LZWVlnJ2dHTt27Bg0NTVhY2MjLlq0CAalpQwuLrQuXhPtF0URv/j7KytnzGCzNDQ4BAfT86uDoqIiITMzk9PT08OdO3cUj7Kz2aRt2/gz+/YpR48ezTcg5PLzae2Fh1Muvqp2RFkZ/e/kRITY7t2Apib27t+vVCqV/IwZM+q1iWqATz8laXZsLJ2XLVsSQWZuDktLS3Ach/Hjxze0rT/5hCLQKsJt2DBStajmpUJBRHdRERAVBWN/f8z78EOce/YMhenpsBIEse3y5QznzqFCWxuJiYm4fPmyWLliBRMDAjBeRwc3btygHGcVlEp1B4SX0GvZMv4wz4t9T5+Go5cXTbN9+2htpqWRvP3QIXI6334b+PJLPFMoxC6BgUBMDIOxMc1BMzPa/7Zsoft6BTHq5eWF2NhYZGVloU3d2gqNYf9+ZO/fL/42aZKgU1aG3r178zo6OoiNjUVOTo6yv48Pz0aMAHJyUF1VhfLycmRmZoIxBgMDA7GqqopNnToVVq1bE0kVF0dj3FxifOnSpn/36acUqT51ClVVVfj111+FBw8ecL169UK/1at5HDxI++XkyeqzkTEi+rKyKMgREEDO9+efN5kapFAooKGhgf7e3gxBQfXrYoCK/V67dq3pw+Kjjyjg8dZbyDh8GJcuX4aenh7y8/ORlZzM5u/ezY5WVSk1vb15uVyOvLy8bFEUDzRvgN7g9+CNs/0/gHXr1rVhjI3mOK6tUqlMApAAIBlADgAjAAM0NTUn8DzvoaWlpfTw8JB7enpK/vayZ1UBkx9++D9xtAGqoCyXyxEREYHhw4f/n1xDo6iqIsm0sTHJytq1I4PnwAGSZH/2GRUU+uEH2qinT6dc48OHKapz7x4Zyvr6JK1u356i1/fvkzRq5Upyam1s6G/MzclZ37SJHIETJ8joX7CAjJYFC9TXpmr7ZWKizm/+h2P8+PHs+++/R1paGnx9fdGhQwduy5YtsLW1hVKp5O7fvw9TU1MEBgYqp06dWttSSqFQICcnB4IgQEdHB/n5+cjIyIBUKoWzs3Otwf+X4MmT2p6gfzVEUcSjR4+Q//ixaLJsGWtpaCi2PHOG+0/WV+A4Dtra2sp79+7x3VXVdl8HJ6dmOdtjxozBw4cPUV1dzYKCgsQNGzbURs9FUYSOjg5CQ0OxePHiRuWOTcLQkCoDHz5MfXDff5/W6ddfk/TXyooiWHPm0Fq+cIEcpylT/itFCPPy8nDt2jUAgKpK9Gvh6UkOwKpVRJRdvUr7TWoq3a8qh1gUSdY6ezagp4ecnBzk5ORwU52doZOYSKoac3OST9b0CHd0dMSqVatYTk4OgoKCkJSUhKCWLRlvYIB2CkVDw33EiHrRyIKCAqSnp3PdGqt036ED7Zs9ezZaNK+6hiBwbSJHshZHjpDhXePUNglV9HfBAvp38CBF+H8PHjwArlyBuHo1CtPTa43tWtjYUH5tfn7t3qtytquqqhruJwkJRO68BjKZDBYWFmLNvtXwMC4oIBLlk0/w/Y4dynGffsq3cnISMmfMEMeNGwe5XM7u3r0LJycnaGhoEAtgYEBKjaqq1zrbxcXFSElJ4Vd++CG4ggKaQ8+eUcpRDebPn8/l5ubChhQPEigUSHV3x8N79/j8/PwG1e6hVNJz27+f5l5ISP3f37pFEeZDh1Cxbx+kHTvyC9asebWjDdBcX7GCiBwXF3rO0dHA7duQODpCEARER0fD0tISlpaWuHjxomi5d69o+uIFM50xg7WYMeP/sffdcVFc3fvPndmlFwEpothQUUCwoWDH3qPGGEuUmFiisbcUNYrlG5NoXl87mtgL9oqKYkFUjIqKgiJFEVSa9Lrs7tzfH4dlQWkaTXl/Pp8PH2B3Z2fmzr3nnvOcRh5PBwcy1CWJ9nNN321LS0CthtCxI3QtLNDp8mUIZmY41qcPn9ivH1Mqldi4YQOXJIlbWFhg5OTJTFGrFvbGxOB57drCV199hZycHMjlcsjbtEH4lCm4ZWcn9erVSyjKhQZAucm2bduy+9u3w37NGsgHDCA9oUkTbXeEOnVojS9bBjRsiIZXrjDFw4dIlSRYlPT8a+pRXLpUbnHbwsJC+Pn5AUDlKXtBQcgyMsIBNzdmV6uWmJCQIP3nP/+Bubk5Lygo4Jxz4cG9e3yEjg7b+OOPYIxBEAQYGBioFQqFYGZmhtzcXO16GDaMZPDixUQeliz6Vx5GjCCStKjzQTEUCiJxirp9nDp1SoqKihKGDRuG4srpo0bRvNi+neawJkpHT4/kl1xO9RgOH6bIwXJga2sLpVKJ3NOnYbh4MbXDLQFTU1Oo1eqKledatYAGDXDu7Fk8jY+Hk5OT2s7Ojn3co4egX6sWGrVuLd67d0/99OlTEcC0hQsXlhOX/wF/Bh+M7b8ZS5cuHSOXy9c3btyYW1lZ6aelpRUkJSUpsrKyhIKCAl25XK6qX78+mjRpYlC3bl0YllU45t8If39iQa9dq7R68PuGnZ2dlJqaWip3652DcwpfCwsjj0dkJHmQ584lwezuTpvvggXEmq5ZQ8qAxks2ejSFAxoakgI6ejQpE/Xr03eamhJhoWFjS+bVTZlCG/rWrZTHDNBmamlJzPXUqfRaXh4p1c7OpPRFRFCP1+hoCqNs3pyu8+VLqr7r4UE51f/WWgFvAB0dHYwcORK///47IiIi1G3atBEbNmyoTklJ4QUFBWLv3r2Zvb09Nm7cKKxbt07S09PjWVlZQm5uLtPV1eWMMa5UKgW5XC6ZmJggOzsbt27dYnp6epJSqYSnp6fYsCqFh8pDWhoRV9988+5uugRC/f2hnjcPFtnZTPXf/6Jmr17sryDIXFxcxKtXr3J3d/eqnaxXL/LmlRWGXAKCIFCLIgBNmzYV0tLSUL16ddy/fx+MMTg7O7P169er169fL1pbW0tDhgwRquzlBuj8nFM9gtmzyaO9Zg2tJ4DWfG4uEVua3sl9+pBHydLyna+pgoICxMTE4ODBgxAEAQMHDizuV14lmJrSPSmVdJ21a5Msio6muaevT+NtZVUc+n7z1Cm1LDNTrDZqFI516YL2mzeXGT0kCAKsra2RmZmp7tKli5CRkcEUCxbQd73qZZ0wgdJeQOHTFy9ehL6+fnE/5tewbl25BcI0hFilxtVnn71ZUbpPPiGDo0MH8maWzLctB5IkQfnoEVKmTkVq1654aGQEW1tbSS6Xv745jh1LIahFyn5eXh709PS4np7e62MQH19uj/NX0bZtW3b48GEUFBSUNtoPHyav/bVrAGNQqVTw8/aWOri7C6OuXgVzdwdCQ6k91avYtYuMmzNnyj2vWq2Gn58fNzMzk3R0dERYW1NKxvXr9KyLCtaZmJiUIr6SEhOR4e0NjxUrJEtLy9LjtHQp7Vk//khjUBahYm5OqQ+dO+O2jg6yY2Kg36MHrcmNG0mOlBdRwxjd2+HDVNxqyRKgdm3orV0LT09PKTw8HLdv3xby8/NhZWWFHubmgs6tW7i5dy/448eSZ3S0AMbo2MRE2tO3bqV1f+oUfW+tWjDYuhW1mzSBNGkS7AoLsWfGDCm1bl0hNy+PzZs3r/h5y7y90ap2bSTevo2NJYw3sy++gGRoqNZTKMQtW7ZArVbD0tJSGjVqlGBkZISnjx5h+OXLCK1bF9VVKtSNjaWolPbtyahctYr0grp1AZkMutev42xwsPqBr68wceJEVvw8GKNIhDlz6LNl1A3Ztm2bmnMuDBkyhNm94qEtCVVUFNiiRfijVStJaNSIDxgwQLx7965w4sQJNG7cmHl6ejIACDx7FsLMmRj/+efw9fWV8vLyBMYYWrRowf744w9IkoTff/8d3333HX3xzJlaIrRXrwr3BwAU9v8q2cc5RSRMn454xrDF2xsABHNzc7WDg0PpNKlRo+hcq1fTuW1sSJfbto3mtbU1rQ21utzims+ePSMiwcSkFPGkgampKXR1dfmFCxdYly5doFKpsGrVKkmlUjEdHR0pNzdXlCQJug0botmhQ2ispye1WbhQRGwsyenr19FCTw9yuVxMTEy8p1AoTlY8KB/wtvhgbP9N8Pb2ZjKZ7HtdXd3vx4wZo1ddqxDoFf1o8M+LqX0XOHCAGPp/gHc+MzMTb9UuLSuLWMrr18mQdnamzalXL8rH2rqV7tPdnRjspUsp33HePPI23L9PgtbFhQxpV1fa5J2caGw0xkxwsPacmsq6JTerqnjEoqPpvCNGkGIsl9NGb29PRsCQIVRAxMCAGPu+fek4TehkYiIpLQCFjG/fTteVkEDem/h4Ui41bTns7Cpth/Nvg5WVFTw8PHD16lWRc44RI0a8tkNOnz6d3bx5k6nValhbW6NmzZooyg3WzC8BKG65wc3MzERRFLF//35MmzatauHSZUFHhwpDVaEN1BshPh7SqlVIiouDVLcub+nry975OSpAx44dNa3BJA8Pj8qFhaZAn1r9ejGrcg8RoJG/JQ3QSZMmiRERETh97BhW//wz6tnYqBs/egSrGTNE06NHYaxUkoerRQsKq87MJMMsJ4eMQisrMrZv36b/W7cmY1sQKO9z5kxSaI8epWvu04dkwKRJZASMGEEes9WraX3t20frdMYMWlv79tHfkZH0/Bs1IlLtFcUtMTERPj4+xf/PmDHj7eaZTEbfbWtL16hQ0H1160byTVPgUlcX8PNDjx9+EG0XL8aaqVMBPT3eXV+/XBmblJSEjIwMsWXLlvD391cne3iITsHBrxvbMhn4ggW4rKuLP2JikJ+fj379+pUvu5cuJVlaRgi4aZF36+jRo5g4cWLZxxcUUMj6G+Zhq4yNETZ/PtiWLXjs5yfFubry+vb2YqNGjVCzZk1cvnyZR0VFSUqlEjo6OlA+fy7aR0RA1NfHbYWCPNKAkJKS8nqoP+cUvTF2LCAIuH//Pjc3Ny97DIyMaH+pAho1agQrKyu+bt06PmvWLO1ai4ggUrhoPxo9erR48eJFHDh1ivd2d2fNfviB1tuAAUSglyxENXgwkTEV4OTJk1JcXBwbP368duI2aED3qemd/cknrx33ODYWzaKj0bRrV+E14q+ggGRhzZqU51xByHJSUhIuJCZi+NixYN7etFfu30+RJ6tWEank6vq6rmJuTvfXty+th1GjwHJy0LFjR6Fjx44oLCzE48eP4aBSMaajA2zahHaffIJroaEswM8P3VxcSF4sXEiyYMECihpZtIj+LyGLhF270KegQFA4OeFRzZo40aULzp07h+6aVKyvvkKzjRvh0rMnchwcYGBgAJmPD8mDKVOKx1WlUmHz5s18508/wfPwYdRt0ABHBw1Cy7g4ZP/xBzKfPYO4fz+gUkEnLg46jNF69vKiPX/VKrT28BBvFxSgoKCgdNRP/fokw+7epb8FARkZGQgPD+chISE8IyNDFAQBx48f5wYGBtzY2JgbGxvD1NRUNDQ0hJGRERRJSYhevx55tWohw9YWX44aJWo84l27dkXbEp0FujZvDvz0E25164bMzEzhs88+Q3p6unj+/HkYGRnxrKwsplQqoVKptDU/2ralvPkhQ0j/qagWiJERPeOSePmScsS7dkVyeLgagEjTrUB4jaQCKIxcpaLowrp1Kf3Ay4sM70aN6DMVdNDIzMyEoaEh13/yhL2aVgEQYejh4YGLFy8iPDxcbWJiIuTm5grjxo1DXl6euHv3bgCAQqGAgYEBd2jUiCbx48e0H+npQZIkBAQE5CoUilkLFy7k5Q/IB/wZfDC2/wZ4e3vLdHR0fjMyMvrEy8vr392u603x4gWF9Bw58uYhdu8J2dnZrE6dOrQxPX6srV7r50d5lXv2kMKxdCkV3fn4Y1I81q+ndjO//ELeg19/JQNcFEkBNzCgfMErV+he9fS0ni1AG5o9b572tfcxJpmZQL16ZDCXRKNGpNDs3Uueq9Gjy/8OGxttcRE3N22lbqWSPAhXrxKB8OwZkQ83bhBzW6cOKXvPn9PvZs3oXP/SCI3CwkKoVCpwzstsBaKjo4N2VciR1NPTw4gRI4q/4N69e+pjx44JNjY26i5dusjS0tIgCALMyij+UiZ++IE8oh06VPpRTbstxhgyMzOhVqvxWuXdjAxg9Wq8NDLCndBQhLq7Y+aiRaxC5eQ9QEdHB8OGDWN79uxhBgYGkqura+UG95gxtM44Lz89JTOTDFhnZ6qC7+hIc3XcOPI8rF4NXLyIxsHBaNyqlZA7YwbuFRaK9fbswSZ9fbjevYuurVpBLoqV3EeOAAAgAElEQVRUSdjBgdZuZCSt/wsXtOfy9SUjZONGUqwNDOhzERFkiCxcSD+ANlRZoaBid5qKy8nJJJ9MTbX94o8coXW4YAEpdDt30hrdsYP+/+47IDYWsokT0V2hULfdu1fEt9+SwvjsGY3R8OGUVmJhoe2rXREYI4NaqaRrU6kotNHQkGTm9u1kmB4+DNn583CuWxfHli2DgVzOjx8/joEDB7KyUiceP34MCwsLycDAQJDJZOyZhwcZcJGRWqWUJgSSfv4ZV+7cgWBoyGvWrMkrnBOtWlVoaLVq1YrfunWLlVcdGYmJJNOrOO9jY2Nx9uxZKS0tjRkYGMBx/HjebdMmoSA4GMeNjNSPHj0ScnNzmaGhIfP09BQNDAxQkJwMh0ePwFq1gv6CBeheUIBjx44hIiIC69dTUeDRo0ejuL1Ps2YUbpyTgywA8fHxrNwUqLQ0bV59JRBFEcOHD2erVq2iRcM57XXffVeqt7GZmRkGDx6MK1euMP+rV1Hvq69gqmktZWpK3rqmTWncbW3Jq3zlSnEef0nk5+fj7t27wtSpU1+XdQ0bklx7+JA8vYMHl3rbtUULrPz2W0zNzgYAhIaGwqZ6dTRcsYKIturVKfqqEkI/NTUVjDEKT2eMZIGjI4VGc07GtJ0d8PPPZIDVrKk92Nycing9e0bFztavp5xckOxq7OBA68rYGHj+HPIzZ1B9715mNH06z507lxkePEhFSAcMoND5uXPLTc2S6elB9ugRItavlwZs2SJYhoQQ0aWRcWfOQMjPh4nmWaWlvVb/RCZJmJCbK4a6uMA4JAS1Y2ORYmOD5k2bImLWLGw4fx69vvkGYU5OsH3+HGZbt/L6kZEIHjeOiTt2oHlkJMyXLYNZ9+7QK2tcv/wSyt69ERMVxS+YmvL09HTBzMxMatmypdi6dWvk5ubi5cuXLC0tjWVkZCArKwuxsbEoKChQKQsK0GPHDrGZvT1LnTgRrVu3FhhjOHDggNrU1BRt27YtzSIaGgIffYRWrVrh6tWruH37NgYNGqQpEsiePXsGtVr9enHNKVNIpk6eTJGAmgJ0r2LaNKp5s2wZ/R8fT6/t2wfI5WjZsqXYsmVLpKamYvv27eznn38GYwxdunQhHeCPP2geenmR3hgXRyHdmr2WMe282rGjTBnTsGFDHD16lPGdO8HKKXTm7u7O5HI5VCqVGBYWxgEq/NugQQPMmjUL//3vf/Hxxx+j8cKFrDjVx8OD9j3QulEoFFEAzpd5gg94J/hgbP/F8Pb2bqurq7vBxsamwfDhww3+te263gacU6/IFi3+GkNbrSYFNTGR2NZevUj4NWxIzPmyZVBPmYLOvr6swdKlZBC2a0fCsVs3CvPu1IkUWE07nB9/JKOxenUK2WWMFB8N1pfomKAJqyu5Of8dGDeOFICSra40YIzCAwEKlfzoo4oLg7wKuZw2j5LKnqZIV0YGbVBqNRkRvr5kYISGkmJuaEhj2aIFKe21axMp8A+trK9SqXD79m3eq1cvvOvq9bq6uoiOjmbR0dGyGzduQJIkqFQqzJgxo2o5w7m5NFcrgFKpxPr165GRkQG5XA5BEKBQKAAAffv2RUpKCo8OD2d6MTH4KCiIm/TowR64uuJahw4QRbFqFcHfA+rVq4fevXvj2LFjQlFOaPkfVqtJoRk8mLzNwcFEItWtS15YFxciyAICyOscF0dtnZRKkgv16tGc/uwzbR/pjAwYymSI27dPHb58uVDw/Dlz/v13yDWFdmbM0J6/LBLpwQMiozgnA3XZMsoV3bqVPEBDh9J3lGzvpaurlR8lCZTff9f+rWmtVhRWDYAMQ0GgtWdqCggCIs3MJIWJiQilkmRhXh5dz7p1ZGx3705z5+uvqfiWvz+F/QYEEGkwahStaU0xqWnTyNhfuZKMqpEj6Rq++45Igh9+KCbmZABmzpyJxMRE4dq1a9Kvv/7KLC0tpT59+gg1S8jFyMhIycrKigOAjY2NEBMTI0lZWYKwdSvJ3BLIe/wYvU6fhsv162z16tX86tWrUqdOnQRASyTFxcXh0qVLapOrV0W9tDS1pa6uEB8fz/v37y+UnMd9+vRht27dgo+PD58zZ87ra9rK6vX+xhXg6dOnSEhIEDRh+oIgMPToAeOAAHx59qyIpUtRoKcHXV1dIusUCiI9bGzIiwsi4j799FO8fPkS69evB+ccQUFBWmMbIAI4ORnp330HSZIQFhbGmzVr9vr1W1pWueVRcHAwP3v2LCtqiwcdTYpAOV639u3bIzg4WNq9e7fQt29f1CnKx8Xq1VQ4zNubPMwhIXSPZRjbkZGR0NPT42ZmZmXLUycn2hu8vcmjWCKc38DAAHOXLcMOQeApjDEjIyOuFxnJbC9fhkH9+mCPH2sjtCpAo0aNoFKpXjfMNORySAiRXfv3k2G9eTP937MnGbPOznSf58+/3hYtO5siW3bsIDkwYwYajRiBaAcHHuvjAyeAoXZtMrKrQkDLZPh4wgThJiClnjvHbO/fZ4ImJPjoUZpPGRlE8s+YUToM/uVLYOdOCPv2ofmoUYCzM6LUau46YgTjHTqgiSCgSVGnj2YpKVDn5iJl9mymN3s2jJ2ceHRyshSSny9KQ4ei8+nTSO/QASYXLwI2NsjOzkZ4eDi/c+cOVzdpIgzcuhVNN20S2rRtCx0dnWIjuVq1aqX7jpe4Mxw8SLKjf380LHoOISEh/PHjx8LUqVNfnx9pacU5zH369MG+fftw5swZ9O3bF4wxVNghpHZtIoHGj6c887KIg9OnSxvA+/fTHHxl/7GwsMD48ePx5MkTpCQnS6nLlwtqpRLi/PkkNwcMoP3o9GmKsDl4UPtcPD0pHeHJkzL71584cYLbWFtz+PgI5a1DQRDgVkSwODo6slWrVmHnzp2YNWsWQkJCoFKptNX9o6KI3CmK6ikoKIC/v3++QqEY98Gr/X7xwdj+C7F06dKJenp6K3r06GHg6ur6v93X+VWo1cSoTZv2emjgmyAjg7xS5ubEdjdvTgqkry9VnPXyok1rxgxSJOfOJaF84gRt9rGxWi9t//4QGjfG3YEDeU69eqyzgUExKw1A22KqZG5jyQra/xbs3VtpKB8A8rQ3bEiC38rqz3ufq1XThriX3CgkiYyC27dJacnIoA3v7FkiO/T0KLw9OZlC8B0dSXl41fv6F+HRo0e4ceMGnj59CplMxiotqPQWGDNmjBgVFYWMjAxUr14dTk5O+O9//4tTp07xoUOHsgplhUpFDHyJisoqlQoZGRnQ1dWFvr4+RFHE5s2beX5+Pjp27Mjq1auHp0+fwsLCAtevX1f7nz4tmufm4qNDh5BmYIBNgwYx0cAAeo8fq5s0aSL26dPnnd/zm0Aul2t6Y5OxnZNDRmN+PuVLfvstEWmnThFpFh5OMqewkIgzW1uKtLhwgeZTu3Zab/KJE9oTaSr3liADQ0JDce/ePSQnJ4sFBQWwtLSUatWqVbnwfviQvCj+/hTKrEGPHtrCYmo1zfWHD0lxr1nzzdrTvAoNeWtpCfTrR4aag4PQuXNnIgiPHKH3HR2JhANIAQNoHl25QsSEKNJ1ab7LyIiKZAUE0HGrV1P447lzRKABdE+v9H8GAGNjYxgbG6Nhw4ZCcnIy7ty5g23btmH48OGaYldITk5G586dRYBC+W/evIn1eXncUS6He3o6MyjyenLOcfHhQ3QrKhw2cOBAwdfXF4mJiWpra2sWExPDXrx4wRhjcHBwELsaGCA8NpZdunSJ5+bmCp6enqWUfcYYZs2ahZUrV7L79++XzmMvLCT59egRkTBVgJubGy5duqQxtOlFAwPK84yOBgYNgt65c0QoalIdkpPLLBJavXp1/PDDD/jpp5/w5MmT0icaOhTYtQua3NeYmJiyjdW1a7Ut2SpBvXr1GADMnTsXOjdukPF48WKF5KeXl5cQEBDAd+7cyXr27Mnd3NwYzp6lN1eupJ+oKCKzyih4l5KSIkmSxCRJKl8fcnWlwlZXr5IxqSHBAAgzZ6J+06asrb09HLOz2XNDQ2yoXVuqHhUl5B06JDk4OLBmzZqxijqNaMa2zCJrAN2/tTWt5TFjaO9asIAiYGbNIq91q1YUoSaT0R4aFERrsUYNWs9nzpDBffMm5NOno+7HHwsH7t1DQE4Ob9WmDWspiqhaZj3N2XZTpgjLc3PR9N49WM+fT0Sdvj6lFrx8ScTZhAna4nhTp5JH9vhxImosLYGpU/FwwwYp+dIl0e/SJfzwww9EAC1fDnTtCvHGDdicOgXExqIdY6wdIHLOERcXh22FhfhEJsOjvXuRHhTELzRvzkzMzCRnZ2ex7ZdfQqdvX1Zr8+ZKCeBibNhAxuiuXcVzJDExEf7+/uyTTz6BQYmiiMVITaWIApAH+NNPP8WRI0ekqKgo1rFjR+bg4FBxusyECZSeoOmw8Gq6xTff0PudOxOx2aKFNqLvFRhxjqbnzoFbWQlBmZk4ZGcHZzMzOJaMFuzdm+Rqv36k62iiDpYtI1Lo+vVShnxhYSFiYmIw2thYYPPnV1j3QANTU1N88skniI+P5wCYo6MjLl26hMxNm6AXF6f1ohel1Vy7dk0F4MTChQtvVfjFH/Cn8cHY/ouwePHigTo6OivHjh2r/69uMfU24JwUsvx86h9bEpJEinNoKLHYV65Qwa+vv6aCED17kvH1009UFXLKFNpwT54kwaynRxtHzZokyGbPJqWvaVNSbjQCTdOGautW7bnHjgUD0Oajj5i/v7/UmbH/LfZDkshztmMHGWOVQZMX17UrjeH76tcsCKTEdumifU3TYzIvjzZQpZKe8YUL5H0MCqLfrVoRK+3sTMqwg0Nx8Zb3AU3f1JYtW0p16tQRHB0dq94j9A1gZGT0WlXkESNGYOPGjSwwMLDcqtF5eXkI9fGR7LdsYXEODiwhIUEdGRkp6uvrIyUlpdRn5XI5mz59erHiomG7nfPzRVy6BLi5MezeDZNGjdA0MBBOTk5o0KBB2ZVb/iqoVCi8dAmXL1zgn1pbw7B3b4YLF+i5d+tGBjPn9PyXL6ccS1EkAkcQSFH+8UfyJshkVeo5XBLnz5/HjRs34ODgoO7bt69YVFW+anLC2Ji87K8aK25uRGb98gsVFNJ4bsePJ4VLk9f9DiI8GGOwt7fnZ86cYdHR0eqRI0eW/zxlMm1vek0NCYDSYzS4fJl+a0iJ48dJ1k6ZQspqRaH7oLoHPXv2FERRVO/evVuUJAlNmzZFYWGhoAkj1tPTw6RJk4Rnz56Bf/QRD3j0iBtPnMg6deqExMREPKtTBzUnTQJevoS9vT0cHBzUCoVCePDggZSRkcHatGmD5s2bU65zw4bwqFZNaFq9OlauXImnT58iJSUFGRkZSEtL4+bm5kzjYb93715pY1tHh/atKhraALB//36pXr16XBCE0uMsCJSHO2QIyTAfH0pZYoyKJlUwZqampigoKMD169chk8nQtGlT6Do7A02aQAgLw7Bhw+Dr61t2v3Bj4yqTN9bW1pDJZMjMzITeokW0H1QyB4vqWLDExETu7+/P7OzsYKM536xZFCGiVBLR06ABjWdBQXHRNk9PT+Hq1atlV1IvCWdn0hPmzqXnUTRPZV26oIuHBxEa7dqhZkgIZh84IKR8/jmio6OFsLAw6caNG8zLy6vcXur29vaws7Pju3btYsOGDYONjU35hr+maNrZs6TPbNtG3uxdu6h6emEh3W9mJhF8vXpRdI2nJz3/2rWB6dOh37Qpmt65I92/f5/dvn1bunDhgiCXy7mJiYk0ZMgQsUyjH8DLly+xbt06Tb0BbjpkCMPw4ZRu0asXkTbu7jR3/f2J7AgLo+fo5kak9c2bRKgHBcGtWTNRb/x43GnWDKELFnCX1auZsHo13QdA5NzWrUQKffUVeLVqOHv2LGeiyA4zBruMDHWfhASxxdix0GnaVCyuJdOxI8mIc+e0TovycO0apRysWkXXB8ox3rNnD3d1dWXlFg5t0IDWThEaNmyI2bNnCydOnODnz5+X/P39hWHDhhUTemXC3JzSgFatojSfknt7RgaNgyRRyPnkya8XSkxMJEKrf3/g1i2wuXPRITAQhw8fxhl/f9SuU6e0wd+jBz2LHj20BreNDckEX99iR1ReXh5+//13tbm5Oatlbs7KLPBXDhwdHeHo6MgAIHvfPhhlZUFtakr7oiAUG9r5+fm4fv26SqlUvp+qqh9QCoxXMZ/nA94e3t7eTeRy+a3PP//coDyB/z+LvDzyhERE0EZw6RJ5WoODScj06EFhiD/+SKzbH39QuJWPD7GNnp4kiBMTKXfvPRg6mZmZWLduHQYNGoQm5eTF/Gvh40OkxZsUtVKrSSE6fJi8WZpq5X8nOKeQvIgIKsqmp0fG+OXLxApnZJBCp6dHilmTJuSZr1HjTxktWVlZ+M9//oMRI0bgT1ULf0tcv34dAQEBqFGjBh86dCgzMjJCQUEBRFHExYsXpTt37gg1Cwslq4QE4W7dulwURW5hYSGkpaWpmzdvLiYkJAAALC0t0bJly9LVoB88oDoEI0dSGPqQIX9vwcLkZJINXl7U6zU6GtizB/lOTrjQty/vM3EiY5cuUXSMKJZbwRVDh5IXYcwYUpQWLKAw1MBA8gCV5SUpB4sXL8bQoUPRuLy8vrIQE0NyKzy8/Dzo8HAimKKiShNFOTnkgdqwgTwZ5VTSfhPExMTgyJEj4JxLc+bMeTcPuLCQ1pmtLcl1R0eKHHJ1fT2UtgxwzpGTk4Pw8HB+9uxZVqtWLemLL754/dqOHEHq2bPY0agRt7S05AYGBvzp06fCjCtXGNq2rbTFG+bNA2xtIU2ciH379vHnz59zQRC4rq4ujIyMxOTkZKmgoECQJAkAsFAT7QAQgTB2bJUJmiNHjvCoqCg2ceJEVFi5/soViiKqW5eqFVeSK5+UlIR9+/ZxQRAklUqFwsJC0cHBQWp96BCrYWrKCleuxI9FhM2YMWNQu3ZtOlClolSKuXORlJKC6OhopKam4sWLF1J2draQn58PU1NTrlAomImJiSSKIl68eCF8r6cH+YgRr/X0Bahq+rVr1/D8+XPo6+sjOjparVAoRBcXF+nOnTtC+/bteefOnbUCNzOTxvDgQZLDkkRro8gYVPXogXWOjhgzaxZM9PUrn++PH2sLerZrRyT7sWOkH3TqRFEtr6zVs2fPIjQ0FNOmTSuXKC0sLMTatWul7OxsQRRFzC8K6a8SVCo6/5w55F22tCTZ6udHY9i3LxnZw4aV23s9Ly8PCQkJuHfvnvTw4UP29ddfs+fPn+PixYvqgoICqNVqpqOjwznnQm5uLgPn8PDwQFcNYc057YWNG5M8v3+fZOi8eTSmv/xSnLKS9ttvEH18UBgaytcPH876njiB+61bc9HREe5r17L60dFgcXEQHz6k1BFdXXKEnDuHp8eP49G5c1yYPp21b9+eCBLOiSAcP55SSTRpZKmp5B1eurR8wicigkiIefNK9YMPCgrid+7c4VOnTi1fXgUE0Pwux+N74cIFBAUFoXnz5uq+ffuKYnn7BUB7j6cnef01612pJNl8/z7do4uLVpcIDyfC4vRp2ocWLiwVDfXy5Uv4+flJiYmJbPjw4ax4TYJSXF7u3IkWV66QjqWvT9EIa9bQeBkYwNfXF9nZ2dzLy4vpXL5MEZxv4qTLzARMTaFo2BD+jRvjTqtWmDx5cqn9PzAwUB0cHHzw22+/HVb1L/6At8UHz/Z7hre3t6irq3ugW7du+v9fGdpqNW1CQ4dSXk1eHhlCPXqQISSXk1Jqbk4KflF7j1JhR7Nna/9+B0pneTA1NUW/fv34kSNH2MOHD+Hu7l4uC/6vwaNHFF67a9ebHyuKZKhmZtIzkiTaZP7OXGrGiPVu3Vr7miZkVakkJSs3l0If79+njf70aSrUNmYMGVgODjQPW7QgAqcK9RJMTExQs2ZN9d69e8X58+f/5akf7u7uMDY2xsGDB9mhQ4e4s7Mz8/Pzg1wuhyRJQv/+/eH6++8Chg1Dz06dSlY9L1+zePGCFLPTpym6oFu3v659m6aQkqatz3/+Q56qvn1JGT18mLwEw4fTM69eHb8vWKB2cnJizM2NlSzUVC5cXEg5BEi2aArcTJ5MoYNVrEkgSVJx/9YqQ6kkhXrGjIqNKCcnMhyePi2tgBsZUY4557QOx48nEvJP9OC2t7dHhw4dEBIS8m6YdU1RxObNSW4XtVDDoUOUplNJ2zWAPO7GxsZwd3dnzs7O0NXVLXuQBw2CRUwMvnB3ZxsCA5mmxgC+/bZqxGtBAZCTA0EQMHz48LLaOwqZmZnYtWsXXr58idDQUO7q6kqfefSoyjIvJycHYWFh7Ouvv67Y0AZIsR45kjxZPj5EGFRwHmtraxTlrIoAFTQ6evSoEGVtjdlffQUduRzt27dXX7lyRSw1V7OygLt3kZOXhx07dkBXV5crlUrJyMhIbNGiBTjnMDAwYLa2tnjy5ImQnZ0NvcuXITx8SJFlKPlVWThz5gzi4uIkURRZgwYNpJycHHh6eorOzs7w9/cXdHV10bhxY7qR1FQyXC5fJmN71SoyimNiSEbn5gKcQ2ZgAONataTH8+YJzQICSDZ9+ikRUSNHUmRT167a8alfn4y58eMprDwwkNbQoEFUYKyMnsU9evRAeHi4+v79+2LLli3LHGO5XA4vLy8hLi4Ox48fx9MnT1Cnbl16Vk+fkudx927aQ+rUIQ/7ihUU/RUURO89eEDX9fw5EcEamJrSfSxcqCUACwoofcXDA3Bzg0FICOyvXoV9nz5C02fPeNThw2h5+zZsnJ1FlYcHdKOjYXD/PrIbNkSBkREyJQn1ly0jOdKrF43rvXsUMRMXR9c9cyZFtc2fTz/79yPTzg5X165FvoWFVN3BAQ11dRHfqhUbVq0a0z90CNl79+LU4sXo5uCAp0FBcNi8GYKrKxAdDZVKhSeBgbyhWs3qdelCcnr9ehqPFi2oOGJeHpH7CxaQXG/Zku67RDeEYqSm0th+9VUpQxsAMjIyJGtr64qjqoyMysxz1qBLly5gjOHy5cuik5MT7DVpMWXByopkyvnzRNBbWVFU0i+/UFTlmTM0B4ODiUzp3x+YOLFcYrF69eoYNWqUJsUCderUkQYPHixkZ2dj165dXKlUsuTatdGyd28Y79+PqKQkFF67xqWpU1nTtWuRlpambtCgAdPR0WH49luKoKiqsR0bS6TL8+fQjYpC7bt3cefYMTx48EDq0KGDABC5FBwcXKhQKLyr9qUf8Gfxwdh+zxBFcaalpWXdli1b/jOrPr0PZGVRaOG8eeSpMjam0O+oKAr13LOnWJH+p8DFxYXp6enh7t276m3btokmJiZqOzs7sWvXrm/fjunvREoKER5/BpMn0+9582izCQn589f1PiCXaw0WjZEFkGKTm0sbelwcfe7IEVIK2rWj+airS8aCnR1tUPr69F1FSuu1a9fw4sULsVatWhL7m9IMnJycYGNjgy1btsDPzw+iKKJ3795wcnIiT42fnzb/tiJkZ1PBuo8/Js/Q9u3v76ILC2m+uLlpc6NHj6bWK5s2ERGXmkpG5ZEjFLViaKgt4ldkrEmShMzMTLFCRakkgoNpLKytX3/vwQOSO59/TnNh8+ZyvyYpKQlbt27l1apVQ/Xq1asmu9PTaR6FhZUumlYecnPJi3L9OhGQGsjlZKCkp9M6vn6dPHhOTm8cecA5h0qlgoWFBdLT0/98SgDndB3161MdhuHDte9ZWxMp0Lgxpe2MGVOlr6xUvioUMD14EJ2GDMGlS5f4tGnT6Hloig1VlELy9deVGsympqb4+uuv4e3tjaNHjzJXV1cyhjS5x1WAprBWpYb2sWMUev/99zQPd+0iA/GLL6pMeLm6usLPzw+5RkZUp2HtWly5ckWUy+WlikLlJSZCnZiIPbt3c3Nzc/7ll18KKIeEq1u3LvD4MXaePYtna9agTolnkpWVhY0bN0q2trbo0aOH4OTkhFfdhOHh4bxv377MxtKSquIrFFT/ZNMmKioVFUXk78WLZKBqxunoUfRNShI2btyIyD591EMBES1akAyOi6P704SPp6ZqixoePUpEvlxOzzg4WFsXhHMiig0M6NjLl9GpUycx/pdf0KRvX8g/+gh85EikzpwJvbg46KxYgS3jxvEBv/7KJEtLGLdvj5oNGyJh/37UePiQwqijo0le9u5NRlhKCskwe3sKJweoZVezZmSMaToD9OlD5NSuXWR0iyIZ24JQNonNGOwliYExMEFAqSoljMGcMXDOcXjdOp7w3XesS8lUrJIYPZquT9NerEsXoFYtxDZtKhkYGwv92rQRWGAgje/33wMHD0JKT0eugQG6r1yJuytW4PLZs3zg8eMwycpiNbp1Q0hICG61bcvbT5/OUFhI91KtGkUlPntGhqdSScb3smVkcI8ZQwRqQACRuhoUFmrD6pcsee3yc3JyuKY1X7l4NRWtDHh6eiIsLEx6/Pgx7O3tKxaeo0ZR7YxevWiebdxI8nzRIto716+nqKPly4mIqyRaUBAE9OjRg7m5ueHEiRP816J0nHbt2nFLS0skJSayoHXrULtPH5zv3h1NuneHwaNHfPX//R/LF0Wxe/fuNEfGjy+t15SH//yHSJ7z5ymtoMg4d3FxwbFjx3DhwgUhLCxMql+/PtLT0xmA8wsXLnxY+Rd/wLvAB2P7PcLb27uVXC73HjRokH5ZbYL+5xAURCzllStkUJdsu6GrS6ynKNLmUlj4XkLC/wwaNWqERo0aibm5uQgLCxMfPXqkXrNmjfD555+zGlWs6PqPwLlzRHbs3ftuvu+776gwXHo65XtVloP1T4KhobaVC4DiXpWSRKFjyclUqT4+nuanry/lkC1YABQUwCQjA61TU3mvQYME5ORUrT3Se4CFhQVmzZrFrly5And3d204ZGYmGavOzuUfLEmk7KxdSxIJg5IAACAASURBVMrf1avvtthceDj9DBxIoXiff05re9Eikgk2NqRoNmhAXh9Nbuann9LvcvLRsrKy8Ntvv0nm5uaoUkEygBSTESNozr4KjQz++msiBF++JOP+448hSRKePXuGwMBAtVqtFhISEtCyZUvevXt3oUqyOz6eFLPTp8sNFX0NRkZkJGjypF+FmRk9W85pjJo3p7oVlVwP5xwKhQK5ubk4ePCglJiYWDx2qampeOuaIVlZpGBOm0ZKqXcZThFdXSLmTExoTlRFSawMY8cCkZHIT0vjtra22mJJYWHkRS/ZGuxV7NhB41UyPLwc2NnZIT4+HocOHeKDv/uOsU8+0UZFVAI9qjAuJSUlCeVWQQ4KInnz9de0FgAiNGfP1kZ1VFFPUCqVlLc7ZgzJLWgrsQPkuTqyZQuvL0lMX1+fDxs2rOL1I0nAmDGoZ2zMo/PyWI3CQty6dQvZ2dkIDQ3ljRs3Rv/+/ctcC5IkoaCggJk+fkxGplpNMmDsWO2HZDKaxzY2ZJzq6xe/FRwcrJbJZGI1Ta/wb0qkkObm0u+ePWn+AeSB3L+fcpOXLKE1l51N6z4xkda0pSU9e1NTYNIktHj2DLoJCdKdTZuEi1FRGBwbi6unT0u8sJA1qlOHu7i4CHa+vrCzsEBhUhJWGBujs60tagwerG3NWZJ80Xiuu3fXFk0dNIii+C5fpho0mzaRgbl9O3mdU1OJIKikvgirKNwZQEJCArKzs9G5osJ3P/9MBmFgIF3TsmVAbi7+cHVlCQkJuHLvHpi5OfiBAzDq0IEPUCp5VkqKcGflSrjfvo1748dL6tRU4WCfPoAkodqqVXiZmQlHR0eq5i+TEWkE0N5jbk4ElYMDefHr1SNC296edMLNm8n7bW5O8+DqVXp+48eXefk5OTmswmriAIWuX75M414BqlevLiQnJ1fN89CkCc2vFy+IIMnIoPuSJJpn48a9WUoeqFXe6NGjxePHj/M7d+6wzp07C4wxODs7Q/L0hHLtWjSPjYU4fjzDN9+gdbVqCHZy4rVq1WLQRHtUJBfmzaNIEE9PSusBiMQogiAIGD16NCwtLREaGipERETwpKQkplQqv3+jG/mAP4UPOdvvCd7e3qJcLn82cOBAG0eNov+/ig0baMPv04eE+4QJFQuHP/6gDerly3+cwf0qfHx8pLp16wo9y6iy+49F584Ubv3zz+/2e/fsKe7d+09tz/VOUFAAVXw8zm/Zos58+VJwuHuXuTo5kcF+7RptvP36kUHRqBEZGA4O761IW4XYvp3CEQ8eLPv9o0cpDG75crret62krlSSkrFxIymy9vakOG7YQIb89evktVq3jsiYP5nfnp6eDh8fH+7g4CD1799frHLbMU00RyUKKwDg99+RuXgxts+apVIoFIJKpRKsrKwklUoFa2trPmDAALFKIeSFhaREnjtXun1XVdGpE4W2V6Q0atrn/fQTefZ8fUvdI+cc+fn5+OWXX4pfk8vlsLCwkMaNGycEBgbi6tWrnHPOOnXqBHd3dwiCgHPnzqk556xatWrMw8ODVUgsODsTiRcZSc+6opZ0jx/TfZ04UW7rqDeBNHAgzhkact1x41ixkZGbSwRgRUr5iRM0dpo0pQqgUqmwe/duxMbGQj83F9NmzIDuG5Csy5cv515eXmUTs/fuUQX3YcNKe/gAkifXr2urd1eBrFmyZAkkSYKoVMIjOBhXOnSAc9Om+LgoXzYwMFCdsGcP+8TWVhCrQDTg7l0gLw8xVlbYtXs3ZDIZqlWrJpmYmPD69euztm3blk86qdW44eEBJ0NDGK5bRwZLeZ/94guaRzNnFr+0bds2tZWV1Zt3PLhzhwy4sWPJ66gxSkoYGm+KsLAw+Pv7S7q6uuzLL79k+iVIgSph+nSak99+SyHUAwbQWg0PJ8KnRQsiWyrz2laAmzdv4ubNm+pJkyZVLOSmTye59MMP4BER4K6uWP7NN5g9fz5kMhlUKhUEQcDdu3cR9+ABnFq1QoM9e8CWL8cfx44hX18f165dw4TVqxFlb4+A3r3RvHlzqWvXrkKZ1cFVKiqmOGMGPef0dAq1vnuXPN+DBtHesGYNrcuTJ8vV/3x8fCQ9PT3m5eVVvkB6+JCi1kpUpy8La9euVdepVUvo360bQ1ISRV2oVBT1EB1Nc0YuJxIoOJjkaloaEToWFvTa2rX03OrUIXnyBnU/NEhMTMSOHTv43LlzS98T55SSkJ9P82XxYiLeACL909Pp/K/i/HlyHgwbRpFqQ4ZU6ToCAgKUN2/e3Pvdd995vfFNfMBb44Nn+/2ho7GxseH/tKHt60s9YCMjKaywSZPS4ZDloU0bYj/lcuC//6VCNP/QNmhKpRI5OTl/92VUHVlZVITufWDECBLsoaG0kZ8798Ys778CenpYc/KkJFWvLrr17g2rxYspj4tzYrrj4miTT0igAi8bNpDHdto0Ci+0sCCFuXZt+m1u/v7ICSsreiav4sYNClk1MyOvRlVbsOTn031ZWpLS9NlnFH49Zw69HhxMynKPHuStcHfXRgsAr+V6vg1UKhW2bdsmOTg48EGDBlU99LlrV1LoNOkPleHLL7EzP19t9PSpbNJvv0EVGws9U9M3E0QBASQDnzyhMXsbDBhQdth7SYgiPespU8jbHRcHHDkCxcSJiH78GH5+fsgvCmft3bs3CgoK0JE6EAgAhVN6enqyo0ePqq9duyYEBgYySZKgq6sr2NnZsZs3b+LKlSswNjbmY8aMYaUqQz9/ToTC7t1E6nzxRcWGNkD7gZ8fGcLvwMP9rFcv6J48qbknwv79RACeO1f+gTo6FP1RBchkMnh5eSH1p58QdOcOrjx4gK5vYGyrVCpWZkXtJ0+0HrhXDW2A9r62bWmchw4lJbqSCJqhQ4fi0KFDUHIOl8ePEWNvj5gSBsDz58+ZSY0agliVcffzI+Pw6lXYm5hg9uzZ4JxXXnVfqQSOHYPql1/w1MGB602cyFwq0nfs7LT9qUsY2y4uLsL58+elPn36VH3tHTtGxJ6ZGckouZwMobeESqXCunXr1AUFBWKnTp2Ym5sbq7CgVlnYtYsIbrmcCJSgIC3R5OREkWYvXlBqzZgxZUffVAHPnz9XGxoaVr6hzJxJ5A2ASwkJ6kdTpwo9P/qI6QwZAixcCJ2i/PVWrVqh1ZgxNI5jxwJz5sBDJgOWLUPHRYvwW3Y2Wn/8McbWqoXdu3fj4cOHGDx4MOzt7VGKgJHJKNwfoKJoL17QfQ8aROTsjRukKxob095R0tDWhNanpQGCgI8//lg48f332MsYhnftSsd5emqLzjk6Uh66KNK+XFhIRum1a+RV19enPfrYMfS0tBRzNAVVg4LIKVS/Ph0TF0cEtJkZ7du9e5MxHhlJhHHfvhQZcOcOfX7fPpLBmZkkB52cqObCxo0UzVFORf1Hjx7x0NDQsp8ZYxTd4u1NsqxnT4rY0IThv1pcTqmkdTRwIJEFvr7lTgGFQoFz585xtVrNZTIZ55yzO3fuCJIkVYGB+4B3Cs75h5/38LNo0aLvTp8+reT/i4iI4PzFC87r1OH82rW3/57nzzmvWZPz5OR3dmnvGgkJCXzZsmX83r17f/elVI6kJM51dN7/eD5/zvlXX3EuSZynpr7fc/0NyM/P50uWLOHp6elVP0ippHG5epXzkyc5/+YbzqdO5fzXXzlv25bzNm0437mT8+3bOQ8M5Dw0lPO8vD9/sXPmcJ6QoP0/IoLzK1c4b9aM8x076BmVh8BAzsPDOb9zh3NPT84fPeK8Z086VqHg3MuL85AQzjMz6ecvwvbt26W1a9dKKpXqtffUajXfs2ePFBwc/PqBW7fS/bwBFAoFX7p4MU/bsIFeWLGC89zcqh0sSZzb2nK+f/8bnbNMBARwfvZslT++Z948nmhjw9dNnco3zJunDg4O5mq1usrHq9VqHhYWxuPi4jjnnEdGRvIbN27wdevWKZcsWcIDAgK0g9+1K+fDhnH+xRc0f98Ex49zbmfH+ZuspTLg7++vvtenD+f372tfTE2l51URVqzgfNasNzvZ/Pk8bNgw7uPjU/UB5Zz//PPP6tf2iZQUzj/7jPPff6/al2Rlce7szPnhw1U+rzIkhP9nwQK+aNGi4tdOnjypvjFqFOclXisT6emc371LMuNNcOUK57/9xvnw4Xz38uWqHTt2SAqFouJjgoI4P3WK8y1bOD99uvjl/Px8vmjRIv7kyZOqn3/sWM5HjeJ88mTOo6Le7NrLwNmzZ/m6devUSuVbqmwPH3JuZcV5Wpr2tfbtSfYHBJT+7JMnnG/bxvm333J+4cIbn2rz5s3qtWvX8jJl4KvYtYvzH37gBw4cUB04cIBztZrW8osXtAdp5LqfH+cdOnA+YQL9Hx/PeePGnKek8KtXr0ohHTtK/Px5zjnn58+f50uXLuVHjhypfLAyMjhfuJDOK5NxDnDevTu9p6ND+uP69ZwbGdFrLVrQc+Wcq3V0+K/TpnH1mjWcOzrS+926cb50Ke1PcjnnTZrQWO7fz/m+fbTnBQeTnIiM5Dw2lu/w8ZEOHTjw+mZSFsLDOf/0U84PHqTjjx2j69m1S7uXaubI5s00j5VKzs3MaD//9lvOW7ak96dO5fzSJc6VSr5z0SL1ypUreWBgYPnnliQ63tubcycnWpuNGnEeG6v9zJIlnNvb09+VyHtJkvjWrVv5okWLuI+PD9+7dy9fsmQJX7JkyS3+D7CR/n/7+eDZfn/IUCgUSvwvRQ8olRTO8ssvFCb45Mmf89jZ2lJ4UUEB5bAdPVpx7unfABsbG/Tr1w+nTp3iTZs2/WfHTltZUSGat/WwVRW2tuTNDQ+ncPVnz4gZ/h9AYWEh1q9fL9WpU4dXq1at6q4NmYzGRZMz1bev9r0JE+i5pKdri7XNnk0ekE8+obD8oj6xMDcnZr569crD0nNyyMPj7U0hxpcvU57g6NHA7du0Njmnz/3+O13T+fMUihwVRSF+n35KjHzv3nTO06e1a3rbtjcZuncJ1qBBA16WZ2nPnj3SixcvhMjISKSkpKBv375ULfzwYRq/Nwxf19HRgXn16upt+fnCl2lpzEQTBl9ZMbKNG8lLGR//bqJyTp4kz6Ym/7MCHDt2DJFyOSInTEBdMzN4LV8uoH59ijKoIgRBgFMJr6emrZ2bm5usqDicYJ2RwZ11dBiOHaNQ0HXrqEL0m6B/f0q1ePGC5mFleZjlICMjQ6plbi5g0yYKxwZorRgb01xv0aLsA4cO1RawqgpUKsDbG6r795Fw9KhQXo475xzPnj2Dnp4eRFGEsbEx3NzchJMnT8LQ0BCFhYUwlCTYHTpEYzB0aNXOb2xMVd2XLSNPW4cOle+xooiRGzdi/aRJCAwMVHfq1ElUKpUcSUkc1KGgbEgSyYrOnUt5mivE06cUzXHxIs2FPXvwYsUK1qFRI1ZeW61imJmRR87Pj1IAisJ/4+LiIAgCKi2IBZAO4uVFaTEbNpBMTU+v2rVXgNzcXFSvXp1XOV2lJCIi6FnFx5f21urrkyx/tQBg3br0c/QoeUW3bKF9o5ze2q9i8ODBws2bN6WgoCDh1q1b6p49e4rltqV0dwd++AHVVq8W45OTSVZparkMH07Xt3UrjeFvv2n3r1q1isO03ceOZWE6OkiOjIRVly7o0qULLCwsEBAQIHLOtd7tpCSqo9C1K/XZHjWKIqp8fChv28iIPL+a3tjR0eS1bdOG9kigVBHWgBMnJOHhQy5MniwWRyuVjGLZs4fSt5o2LXesrl+/jmepqWzi0KGV7+WaDhlTptBeMneuttXcqVPaNmaaZ1yyJkFaGv2eMYP2dM1rSiVw7x6GLVkipD5/Dutly8gTvX49RR2MHKn1XDNGxebmzKF9YO9eWiO1a1NR0379aByL+mRXtO+kpaUhMDAQT58+RYcOHdClSxcUFBTg119/LVAqlZ9WOhYf8M7xv2MI/vMQmZCQoATwhkk//1B06kThOxs2UI/Qd9kqSE+PQnIaNSIB/w/rdd24cWOcPHmSXbhwAeVW//y7sWgRbVQnTvx153RyopByExMK6Vq48F8dVp6Tk4MdO3ZIZmZmGD58+J+v3KyBgcHrvXo/+4yU3fR0Iq3i40lpS08npWv1agpdnzSJiv/UqEFhknXq0Hhretbu30/KzM6dRIL98AMZyb17k4KfkUEG+YEDlEPo6UkKFmNElmgwZ847u90/i6SkJMnDw6OUJiFJEo4cOcJjY2OFSZMmISEhgZ87dw67du3iAwcOFIx/+gls+PAqt/UCgIyMDOzfv1/KysoSlEolW7V2LTx8fKRuzs4Cc3SkED5NWOSr8PUlGagpdPVnsXIlESNJSRWGlGdlZeHu3btwcXGBKIpSfHw8cO+eAM5JeXZxeevwVA2sra0xcOBAljNmDH8hSbyGkRFjS5eSkfU25KqDA8mHEyfIaH+L7zA2NhbC27WDY5MmVB9EEzIdEkLzuDxj28+PPlNB5flSmDEDCAqC69278PPzw9q1azF16lTk5OSgsLAQ9vb2SExMxO7du7lSqQTnnEmSBJVKBR0dHTDGpIMHDzIdUZQ89+wRU+vVk1znzRPe6I4bNSLD5+OPad2uWlVhDQLByQn5NWrA1sAAsbGxrFOnTjA0NGRxpqbMrbxxAUiuuLgQ6VYZOKeUrytXqEjU5s3FRc4YY9yksrQCgOpLhIQQ0bdzJ5FLNWsiIyMD1apVU5uZmVUucy9dolQdY2MtefMOdJFmzZphz549YkJCAt64GOrgwURavpobv2MHEaZPnpR93MCBVA1882Z6zosXk3FcyfowNzdHz549hY4dO+L69evs4MGDMDc3V02YMOF1fd7eHjhxAjaHD/MwIyMuSZJQXIfi0iXaQ1atonnfoQMVN9SEeteoAdSsCVWPHjhubIyB9vawKipuZ5yfD92nT5mqsBDyZs2IpD1yhAz3e/fIEHVzo7k8bhzJpj17yLFy4wbpDmX0cddAkiQEBwcLXl7lpBVfvkyh4BUY2pIkITAwkA8cOJBVq6x1YkwMER+//EIpHQARULm5NA7DhlEhss2byUAu7/usrLSkyc6dAID8/HysXLQI31taEuFdWEjv+/hQyPjhwxQ2npBA19CsGaVrLV5MslOSqBaIoyMR4hV08cnMzERCQgIOHToEtVoNDw+PYp31jz/+UAuCcHLhwoUxFQ/GB7wPfCiQ9p7g7e1tKIpi+jfffCOX/1sNkBcviEVbsYI2tmbN/lS/10rBOQmSzZtpA/sH4cGDBzhz5ox65syZ784Ie5d48IBY5ap6UN4l0tJog/L3/1N5c38HwsPDERAQoAYg5ObmMktLS2nEiBGCoaHh33thSiUZ34WFpJxmZVH1V0Egr0FYGHmpk5Mpj2zYMMq77N2bWPMVK0iZNjd/v2v2HePkyZPqkJAQcf78+dB4tvPy8uDr64u0tDR8/vnnqF6kbGRkZGD79u08Iy2NyXV1+bBPP2X1X2kRVqS0ITo6GoIg8Pbt2zNTU1Ps3r1bnZaWJjZu3FiytrYWZDIZHj16xJOTk1mHDh3QXleXvOSXL5OCqFlX8+cTGfimHt6qYMsWUrBeiRg6cOCAFB8fDw8PD+H8+fOwtbWVhg8fLty5c4ffvn1bmjx5Mg3U0aMUZdKuHa3Jkrn0b4K7d4F9+5Dz/fdYv2aN9NXOnYLx/v1gryi2pbxalYFzIpQePCDF8Q0LRPn7+/PExETmdeoU5VxqlPCsLPru8r5v505S7tesqdqJCgrICLS3hyRJWLJkCbp27YrAwECoVCq0atUK9+/fh6OjI/r161fcg12SJG0/dkkCFi9GYb16WPX8OW/dti3v3Lnzm4c/5OeTEfb4MTB1asUdBMLDEXfhArampWHIkCE4ePAgRsXEoP6ECa/1MAZAhUy//prI0opyk3lR5eg5c8gQaN36tQJkK1askOrUqYNPPvmk1D2qVCqIolj2HClqF6VevRq//vorWrRoAWdnZ1hXVLvg4EFaFwMHUmVwX9/iKuqVtYCqCk6fPo2IiAg+bdo0VqXCiJJEUUlmZiRjX73PwYOJzFixgpwIFRiXuH+fKqmvWgX83/+9Ts5WAIVCgTVr1vCGDRvio48+en2wo6Oh+vJLrPDwQKePPoLHq0UcX7wgo9zIiGrpTJtGc2LLFsDdHYGffSYp4+PRbe9eAWZmkFJS8KRpU5iYmXHLq1cZFiygufRqbjFAkSLu7jRnDh0iwm39ejLqK8GmTZu4hYUF0xT9K4Xhw4ns2bKlzGMlSYKPj4+6sLBQmDx5csX595cu0bivWVP6GSkUFJVy5gztu2o1eZv9/ck4rmLXifj4ePj6+kpz5swpe1IVFlLESMOGRJa7upZeZ8+fE5GTl0d1M4YOpXxuS0vSAUaOpGsTRVy6dIkHBgayxo0bqz/99NPim1YoFPj1118LCgsLXRcuXBhZpQv/gHeKD57t94SFCxfm/vjjj1k5OTkWZv+2ENubN8ljtmAB0KoVKZd/hfHBGClkBgbEtA4YQJ64fwDs7OyQm5srxsbGUk/SfxKmTCH2+O8wtAFSAiMiSFGtW5eUolat/p5reQNIkoSjR4+idevWokKh4C1atICtre0/o1KfXK5l7YsK2WD8eG1YeEwMrVG1mtZIRoa2OEubNvT7facTFCE1NRWGhoYoszhUJVCpVJDJZBpFV11QUCCKoohffvmFu7m5sRo1auDcuXOSIAjMy8uLVS/B6lerVg3Tpk1j6hYtEOvmht179sDY2FgtSRJkMhkkSWLZ2dmCkZERb9q0KcvKypJ8fX1FlUoFV1dXfPHFFzA0NCx+3u3atWOXL1/GtWvXJPeZM6nFzc2b1DVh6FBSipTKdzNoZcHLixStV5T2iIgIQZIknD17Fh07doSnp6cAADExMVxfX1+rRWoqbu/fT8ZReDgZBFXxOpZEaCjw+DGM5HJMOXVK2NGjB5SBgbzu06dIT0+X3NzcxNDQUCkiIkIwNjbmJiYmkqurq+Dm5sYA4OXLl9DT0yvdP1tTJXrqVCoktWvXG11SnTp12K1bt7C/WjXJ7NQppOrqCi4uLnBs0ICU/EePyp7vXbpUPVIqMpIMgVWrAEAT2szPnz/PHB0dpVatWgnHjh1TN2nSRBwwYECpQ0sZZ4sWAQoFdIYORfW9e5Gfn/926Uf6+hQBs2IFecSCgspPLUlIgNXWrcBHH+HEiRPc09OT1U9JKZ+EePiQPHcVGSFPnpAhsn8/jUs5nQx69eolHDp0CBEREcjMzOR37tzheXl5LCcnh4miiPr166tbt24t1jExgaxbN/J67t6NfDc3KIsiBq5fv47g4GAYGxurGjRoIOvdu3fpMVUqgW+/hXrdOjx48ACN9+6FfPhwIg3ekTOjZ8+eCAsL4w8fPmROVSkst3EjhbM/fVq2N3rqVHBjY8T37o3oyEieGBSkliSJ6ejocJlMxjp16iQWpyg0bUre3hcvSOZcvFi1QoQAdHV1MXr0aLZ161ZkZ2ejsLCQc865k5OTYGVlBWNTU+T06gW9pCQ8e/aMAyh9sba22g4mFy8SodilC6BSIebXX3E7PJyNGz+e4cwZIDsbd69cgd/w4fj+++/pe8rokw2AdLjTpyklYsAAGqMePcps/1YWGGNcJpOVvXb27qW9sBzcv38fubm5wpQpUyo2tGNiSCYdPvw6GaKrS4ZuSgpFG4kirce8PDJyXVzIe18JUlNToaenJ6GoWOVr0NHRpj8tXkx61PTplFJZvz49n3HjtOkwnNOcDwkhWTNyJOkI9vbo6OvLmg8ciFNz5zLluXOQnzsH/Pwzbty4oWaM+X8wtP8+fDC23y/+XWEDt26RgLl5kwSMoSFt9H8lNOGBqam0wapUf09LpVdgbGyMzp07q3fv3i327dsXzd5BO5t3hqioCjeevwx6ehR226QJVe90df3HVpkHgJCQEBgYGPDu3bszvKqA/FPBGG32NWpQiJtCQb+XLKEQuL+wJVtWVhYOHjwoPX/+XBBFEdbW1rx3797MVpP3VwbOnz+P9PR0ZGZm8tTUVF5QUCDI5XLOOWf9+vUTRVGEjY0NHj58iODgYDDGeM2aNfnAgQOF8trwiD/9BHsHB/a1kRGePXsmiqKIrKKevA0aNIClpaVmUESVSgWFQgFDQ8PXNDDGGNq3b4+bN28K165do8rXGkWya1fK6dP0/X0fEEVSrJo1I2+sjg5CQ0MhSRK6d+/O27ZtW+rh1q5dG7du3XpdiRs6lEKQ792jMNXDh6tGft26RSHoZ86Q4b9iBfTbtcO4//s/BJw/z+7evSupVCrx8ePHACD06dPn/7H33WFRXevXa58zDEOXIkoXQQVsiIrS7L23WKKxxRJNNFGjqVclub9UvSaxRI1eK4m9t9hQsWABFQVBkCoiSJMZYJiZc873xytNumJi7ud6Hp7RKafus/db1rteKBQKdufOHf7YsWNITU0V7OzsuGPHjjG5XA6FQoGOHTuiRYsWaFjsCO/eTUq+GzfSPmo5t7u5uWH27NlQKpWcZtIkPAgOxg2NRvTw8OAwYwYFoCpzts+fpzrPTZtq3klSEhnfZTBt2jSWl5dXEoT76KOPqmc2/fILlRYMGQIYGCA9Pb3mfsHVgeNoTh05kjQCfvyxcgO/Rw8o/vMfLPHzA/T0aJx8913llNPJk8lAr0ofQKUiVefly6lO9Pjxag+xVatWuHv3Lg4ePCjJZDLJ29ubs7S0hFwux5kzZ8S4uDg+KSkJdjY2wjtt2vAYPBixjOH2xYuwHDMGPosWSS1btmTGxsY4deqU7MaNG2jYsCG8vb0hiiLuXb8Obt06Mfnbb1FQUMDd2bMH0zUa2Pj60liqLuNfB3AcB2dnZy4yMlJo2bJl9fc5Nxd4913KfD6bc0VRhFKpRGZmJgoKClAUGyulXbiAHDMzqcuVK5z6669lAOmC5OXliWvXroW/+F8h1wAAIABJREFUv7/YoUMHYlJxHD0T+fn0HA4aRNlgK6sa53Vra2uMGTMGwcHBgrOzM69Wq/Hnn3+WnJctx4kTgoK4rWZmLKdXL1RIAAUGkhOckEDBlfR0YNw47E1KQudevZhxw4bkkE+YgFb9+yP2448lnucrPyhRJPtNkihj3qtXaUBEoSAHcerUGltVubq64tatWwKA8vfiiy9obqumZM7IyAjPAhtV7+DwYQoEXLtWpYo4wsJKad/FmDGDnO1ffqEMew0t6zIzMyVTU9PaMSLv3iUdhR07qLb9228p+Fe2PGr3bnp1cyvVhrlxAygqAscYuPnzoTUzw43VqwWfiAhe9803uHz5sqaoqOjLWh3DG7wS/P1ezP8wRFFUMMaQl5cHrVYLrVYLKysrvJAIx6vEo0cUuRs9mrKk8+ZRrejfia1b6dXHhyLqa9b8vccDICAggDc3N8ehQ4fg4OBQqXDOXwpRJMO4FpSsvwyzZ9Nx9e9PbVAmTvy7j6hKRERESK1atfpnONllsWABMUDOnycjITWVHAqVqsaWQS+LvLw83LhxA2FhYWJBQQFnbm7O5s2bh+zsbNy6dQtbt26VPvroo3ItkNRqNR4/foyoqCjcvn0bzs7OgqOjI9e5c2euWbNmyM3NZUZGRihL3ff392f+/v4ABUGqNlTWrqV6XUdHWIDqGauDTCardv7leR7W1taCUqks28Sa5sSEBDK83n2XSl1eIJNfIxo1ImdbpQIsLJCZmQkAeN7RBgB/f3/u/PnzSE9Pr0i/5XmaN3ftIudv+HAy2KpqAVecLWnRgn67bRsZ/0uXguN59OnTB3369OHy8vKQkZEBExOTkn22bt0a9+7dw4EDB7jbt28zuVwuDR48mIWFhSEsLEy4cOEC7+bmJgwfPpxnenpEV127loKp771X60tjbm4OAwMDXJHJMCQrC7YTJ1KQ4d13ifru7FzxRy1bVjSWq0L37hUcUGNj4/IZ+uqwYwdljP/1r5LSDa1W+0KMjwpwcSF68dy5dE+fz8JxHFGAjxwpDZAbGlacD1JT6RirEtM7epSYSZ07U411ZdTgSjCW2g+WBC2VSiVWr14NV1dXTl9fX0xJSeGSHz7kN3XpIir37GFmd++ynm3aoOHatVgfESHevn2bGz16NIuIiEDjxo2l4OBgycvLiztx4oTQ8PvvuUZaLfeke3epUK0WLbKyuEJRRFRqKhwuXEBWQQFuxcYKjRo14jw9PeveG7sM/Pz8sHHjRv7y5cswMTGBRqNBdna29OTJEzEnJ4cVFRUxhShKk7/6irv84YdCZsuWfG5uLpRKpVRYWMhkMhn09fUFmUwmeYWF8d3u3mVGBw4wbu5cNCnvlHGpqanYtWuXFBISAkdHR2Hs2LG8XC6nJMcvv1AgYdw4mhNWrKi2HEij0SA/Px8+Pj68Wq3G1atXJWNjY6l79+6cp6cnOI7jxJQUtFCrpSNHjkjvvPNO+QDdpk00X8yYQXXwx45B+PJLFDEGLy8vGgvvvAMMHQplixbw27+fnN7K6uU//pgCfMuW0bz5PLti9GgKEtd8L7gLFy7g3r17cC/LThk1qkYxSEeiYIs//vgj16NHD6lDhw7l58/i0pIjR6qfx2fOpM/XrSv/fs+edD8iI0k4cvbsKgMiaWlporW1dc3O9r17pK/y+edU6z5tGpXJ1KbkRiYrCVyafPEFus6ezaU/egTx3j1ERUUBQMSSJUvu1ryhN3hVeM28vv8dBAYGyhljhhEREQgODgbP85DJZBLP82z69OmoUbDhr4AoUkRs4EAy3GNjq6eV/R3Yvp3oRlevkkFYB8XdV4GWLVsiPj5eWLduHd++fXupb9++f5+zFhxMWYrHj1+vDDLHUZ2hgQEZhyNH1r7P818ElUqFjIwM+FVWz/g6IyeHHKGyxoqLC1F/IyJoPFy6VCNFr7ZQqVQIDg5Gfn4+Hj16BKVSCTMzMykgIADe3t7gONJ+MjY2hoODA3vw4IF05MgReHh44NKlS8IzA5WTy+WwtLQUhg0bxrm7u5ebZKxrqcJbKfbvJyfR2/vlTvQZVCoVEhISeKdi7YFFi4i+e+UK/T8pica2TEaGUF0p2jWB48jwXbcOQYzp4h4/lrVq1apidgcUGPD09BT37duHWbNmVT4BdO5M83zbtnTsV68SK6Js3e2dO5RFi4qibhOPHpFxv2NHhXFkamqKysSw3N3d0aJFC5aamgoAzMHBAa2oswRfUFCAlStXcn/88YfYvXt3LjU1FbZBQbA1N6cgah0Cu0qlEqEdOqCLvz8FJIyNSd0/MJACCs9DFGl+rAmRkUTFzM9/sTXw6FESOJo/v1TRGVTX3rRp07pvrzL4+pKGgJ8fZabnzi1v4AcEkK4DQIGMdu1KmWIAzRGbNxND43nHIDqa6MPDhlEApLgU5QXxjDUkjho1igPA5eXl4eLFi+gybRqn6tULZnl5MEhNBY4cwRy1mt8WEyPt3LkTkiRh/PjxbPny5ezAgQOCxeXLLG34cHSaMQNNjI0ZABaxbBkepqeL4SdPin2uXOHv5OQwdb9+fHJyshAcHMx7e3uLHh4eXE5ODmJiYoQHDx7wOp0OoiiC53no6elJZmZmkqurK5ednY2cnBzJ3d2dFRYWIjw8XDQxMSlmcUg8zzMTExPe0tKSb968OUyMjRF5+zZ2jhyJZJmMR0wMGjdujBEjRjA7O7viwAoNILWamHnGxlRr++hRubFhZ2eHefPm8RqNBr/99hs2bdokTpo0iSsJzpiZkdO6bRu95uVRbfSzLLEoikhISEBYWJgQGxvLKxQKQRAEHoDk7u7O9e/fv1xgkVu1Cl1HjWIbjI0r0uD69SthQeicnHCtWTNYXrqErhkZOLVlC/zv3kXDwYMBjkP8zz/D/MMPiWFx/nxJJxJ1Xh5SDxxArpUVWjZrBkWTJuU0XHQ6HTiOA1dcc/z229Vm7JVKJXieRzmmVEoKBT1r0PSRy+X4+OOPubNnz+LYsWOsffv2pB0gSSTyu3cvJSlqKkGYO5e0TypD+/bEpvnqK+oCMm1apXNHVlYWVyMTcs8eEiDctKlUdNPNja5fkyZU4tK8efXbAACtFjlHjkB58SKiZs1CR7kcISEhKrVa/e+af/wGrxJvnO1Xh6aSJHEhISHo0aMH/Pz8wHEc27Ztm3jgwAG88847XLW1JK8aixeToRoeTotwNdTPvxXFgkf/939kNO3d+7ceDmMMQ4YM4Zs3b46dO3cyPz+/2mc+6hOFhRRdTU//S6nDtUaxkZefX9oC4zUSCgwLC4OBgQHc3Nz+7kOpPR48oExdsRrv82jalLJzenq1qomrCXv37hXu3r3Ly2QyGBsbQ6fTYdSoUWjZsmWltHvGGDp37ixcv36dS05OZq6urhgwYABnbm4OQxoP9TvhFRSQWE09QqFQwNTUFOfOnUNhaqrUZ+5cxpUV6HFyomCGKJLTeuIEOTn1iKDffxd7L1nC6QcEyEZ//TWeD06URc+ePblly5YhJSUFDlUJMD3LUAMAPviA6nBv3iTDUKMhx3vBAsqoPX1KhvC+fbUWACrdDVfpMRgaGmLkyJFsx44dLDY2tuR9K0EQ3169mtsXHS2lWVszIyMjged5rqCgAIwxcBwnGRkZSQ0aNOCcnJyYn58fMjIyJI1CwaIXL0bLDz4gcaxu3SjzJkkV58KHDynAUBM8PCjb9SJrckQEqYd/+WWF1nP6+vpSZmYm7O3t62eS1tOjNkQ//EAG+uDBpZm53r0pAJGURM/+n39S9rEY339P80fZa5SXR/f6wAGi9g4cWC8B96SkJNHBwaEkAGRqaooBAwYAd+7A2MystHzg2DFg7VqM37+fHTx0CDY2NtBoNNDT04M8MRGd9u7ljk6ZIoIcbQBAm1OngMWLuS5+fhxMTODRtm1xmQSfmJiIo0ePSuHh4aJcLpesrKw4Hx8fPHnyBD4+Prh//z4uX76Mx48fcyqVCvb29oKTkxN/8+ZNUV9fX+rZsyfftm1b8FUZZ9Onwzo6mq3q0wejRoxAtbXdWVkUZI6Lo/tw4wbNHc9BLpdj1qxZ/Pr164Xt27eL7777LlciKqdQUL1ufDw9lydOAJaWyHF3x+bNmyWtVis5OTlx06ZNQ6NGjfjU1FRs2bKFyWQyVBB5k8lg0KgR2ly5wq1Zs0biOE4cNGgQ/+TJE5glJUF9/jxCY2Lw6NEjAIBxx46Sz+PH6Lx+PdI4jgXt2ydqNBpma2vLcvz82Ixt23B06lREtWyJlu3bSxYrV7LWd+7gjp8f7tjbo/D6dVF59izn5eUl3b59m6lUKlhaWuKD99+naxEVReOxCuTm5kKhUIhmZmalJ3L8OAXoaiGgGx8fj2vXrqFPnz4iY4yDKFKt94MH9OzUxh5p0YJYTN26Vf65oyOVWxw4QIGQZcvKtXp7VlrAnCtj3RQjKIgYAsUlKMXo358CGatXA0lJyFuzBvHx8VWXMKrVeDhtmpQRFcUOjx6NSYMGISUlBXl5eUoAx2o+2Td4lXijRv6KEBgYOB/A8jZt2ojDhw8vmSw0Gg1WrVolKJVKvm/fvmLnzp3/upSkRkPGSf/+pXTB6lqDvI74+WeizB458ncfCb755htMmDChmLL018LPjwIRxXT71x2dOtFfcY/cvxmrVq0SPDw8uB49eryGkYpKUKxdkJJSvaotQPWWgweT0f0CgaDc3FycPXsWkZGRaNOmDfr16wf9+mz1V19o1YooiYsX1+tmNRoN0j7+GA22bMHPCxZgcVXbT0oi5/vjj0kh/jnRrBdBVlYWVq1ahbdHj4YyKwue7dqBq0acMi4uDjt27MCnn35a+/KkwkLKWn//PdGKo6JKx9SUKWQAf/zxS5+LRqMBx3EICgpCcnIyvLy8BB8fHz4oKEhydnZmFhYWomPDhpzswQMYxsQguWdPpKenS4aGhpKLiwtXUFCA8+fPIzExEQBgaGgoFRQUMBMTE/iJotTJ2ZmhOBAyYwZpRcybV/4gcnMpo+jhUf3BzppF9fnVtNWpFNHRZAxPn06CSc/h+PHjuH//vjRp0qSa2w/VBaJIx5yVRS2VimtTZ8+m1kNDh1KGujjQ8OuvVDvr6lraNjAmhu53ly5UG1pPwX+NRoPly5dj6NCh8Hj+uoeFUTZ0/nwSBVu6lBh1H35YEhy8e/cuTu3ahQaZmSjS00Pr0aNLGUi5udTH+NgxcpZ++IEy+JXUn0dGRmLPnj0AAEtLSyknJ4fJ5XL4+vpKnTp1qrkv+POQJODKFUhGRrikVEpnzpxhb731VsVzLIZaTfPwyZMUxCospABdFdDpdFi2bJk0bNgwVlkQ+GluLjLOnIHZ//2fdMfamuVMniyOGju2gv2YlJSEffv2Sba2thgzZkz59S0tDemnTuGWiYmYq9EgOjqas7CwED0uXoTjo0cs/IMPJH19fda7d29WUtbTvz9UBgZ4HBCAnQUF0Ol0AACz7Gy8/ccfeGxtjVwHB8l26VLm+t13KOreHSEeHsjPz0diYiL09PSEgoICVlhYyHEcBy8vL/S3tKQ5p5rrER0djcOHD1dU8a4sqFYGRUVFiIuLw6lTp6TmzZtjwIABDJJE3TqePCH6eG0Ff4uKqId6enr1v9FqiV1jbU3j89l5hYSEiCEhIdznn39e+e+WLyemyX/+U3FdHzGC7LxJk6C7fx8/BgdLGp2Ode3aFf7+/uXne5UKOHkSl37+GcLixejSsycSEhJw8OBBXV5e3oLFixe/HobX/8d4k9l+BQgMDOT09PQ+GDRoENq0aVNuopDL5Zg/fz6/ceNGxMbGovNfQYvOyyND4quvqM7E05MM1X8ievakRVkQiD5YXcTwFcPOzk7cuXMn9PT04OrqygYMGFC7liH1gaNHS9Up/wnYto2Mo9BQcgD/5vFXVFQEOzu7f4ajDZBhOm5c7RxLX1+ikyoUlEmpA1U+JSUFmzZtgiRJaNasmdCvXz/+tXS0AXIY69oTtyaIIuQ3b8JpxQqE9+oF6eZNHDp0CObm5igsLIS9vT08PDwgiiKuP34MV2Nj6KWn4za1FxMdXF25x3l5UufOnZmrq2sJ1ZPjOKhUKrSuoi9sWFgYUlJScOfOHRgYGMDVzQ2sd29y5jdurPJwBUGAIAgVs1jVoVjl+uuvKXPy2WdkiF68SFTsfv3qetXKYdu2bWJaWhoKCws5IyMj5OfnY+DAgWKHDh14AJgzZ07xc0cHnZMD/Oc/aDV1Kp5pKLDCwkLcv38fDx8+hJubG3r16oXs7GxmampaXCvOMGwY1Zd26kRrmihWPJiwMKqBrC67XVRE516Wcl0bqFTkZH/+eaWONgB07doV4eHh7Oeff8a0adNgZ2cHQRDw0qw2jiMH+vRpqsWfP58c6fnzKajOGNWbAsRe+/VXqnVljDLxW7aQgxAUVMoeqydkZGRAo9FU7oTGxZHzOX8+sSnat6dSjIkTS4SfHj58KI46fJgzaN4c2p9+Kt/3+t49anNYnJW0sKhSp+LChQuCi4sL36VLFzg4OLCcnByYmpqiSoXr6pCUREGJO3fATE1hERVVUj5TJRQKYuQVFpLd9eOPlIGvrNwBpCfRs2dPtnfvXnTs2FFycnJijRo1KlZrF+/cucMZGRkJjWbO5Hq5u6PhrFkcCgupdKiM8+nk5ITJkyeztWvX4saNG+hQVhzRxgaNgoPRt317Tnr/fQiCAJlMxmH8eCAzE82aNy9/bWJjgfx8GH/3HVw3bED39HQpe+JE1q1bN2RmZsLa2RnW335LtKYrV4AJE6A/ciR6lT81PigoSFIoFELnzp35LVu2oIWREZouXUpBkyrg4OAASZK4Y8eOESvi2jUaw0lJ5b4niiLi4+Nx9uxZISsri9NqtYznebRo0ULs0qULD52OHGFXV1o/69JZR1+fgnU1/UZPj+bT/fuB+fORNWMGdkVHi9nZ2dyYMWMqfl+rpY4RgweTKF5lQb5nJSkFhoa4969/Sf0NDSH79lucPHlSCgkJYUZGRsK4ceN4GxMTiLNmocDQEKd79EDTR4+wfPlyUaVScSAfr/L+aG/wl+KNs/0KwPP8HAsLC+uqDCsAeOutt7By5UouMzMTVnWNptcWT57Q4lZYSLTB/HyKwv+T0aoV/f33v2QgZmT8bYfy9ttvc7GxscjKysLVq1cRGxsrNm7cmHvrrbdenQieWk3nf/Ik0Yb/KSiuN/r6a8rGHDz4tx3K8ePHJZVKxdckpvVa4Y8/6na//fwosDFwIBkLNTgSsbGxOHPmjJiVlcV5eXlJ3bt3Z5Updr82WLqUggr1HbQ5cIAygunpaDtwIB5xHOLj46WUlBTJ0NBQunLlSsk14XkeJ06cgH7LlrCxsRH7bt7MxIQEPPrqKykoKIgZGhqioKCg5LuCICAtLQ29evXC06dPSxSBV6xYIebl5XEAGZjvvPMO1Rf++muNVEdXV1dwHIfU1NSqaeSVITeXxCc3byZhre+/p/Eybx6tF3XM+omiiCdPnuD8+fOIj4/nxo0bBxMTE1y4cEFo1KgRih3tSuHtTevUH38AZmZ42L49du3aBVEURX9/f67rM72HCoKUHTsSBbpTJ3IsDxyomPUqzqBVB5mM6tbrAqWSssI//VTamq8SGBoaYs6cOdi3bx82bNgAX19f8fLly5ytra3YunVrrlWrVi9ehsRx1EopJ4dqrUNDyXFu1YoyxdnZlJF7+pScGkmiwIqzM1270aNfidaHqakpGGPQaDSokD0eM4b+AKrZPXgQ6NuXKLhPnwJmZsi/elXKnDoV7WbOrDgO794tLxQWFkZzWyVJC0mSYG5uLjo6OnJAzeKJ1UKhICaBqSlSU1Oxf/9+DBo0qHpWW3w8tdB6+22q6S0spDaC1aBt27Y4efIk7t69i8jISKGwsJBnjEnW1taYPn06ygltbdtGzt1//0vPQJm50NzcHCNGjMDevXvh4eFRXMZDmDcPCAsDe9YiEQCN/0WLKgalDh6k77dtC3z+OfI//ljqtHUrM+7VC8YAqXJ/9x3wySf0/JahyWdlZeHevXvIyclBQkICGz16NG9nZ4fBgwfj0O7d0pysLMaLYpVj0MjICOPHj8emTZvQqlUr2Ds4oGD+fMTdvo20tDRkZmYiNzdXUCqVvJ6eHtzc3DB8+HB2+/ZtKSIiQho1ahRdqylTKGj5zjsvJmq5aBEF9b6sQcybMWDECDxJT8fVVavQtk0brsNnn1V8BgSBmCQ6Hc3BVa3Nt2/jcW4utuzeLXVq107y1+k4WatWaNWqFSsqKsLFixfZzlWr0C4sTMoyMmL33d1hZWYGMzMzoV27dnxMTIw2Ojr65y+++EJV95N+g/rGGxp5PSMwMLCnQqE4NH36dMOaJvc9e/aIjx49YnPmzGGsPutuCwtpYR01iiay14By/UqgVJLzOX8+GY1/Yw28VqvF1atXpeDgYMYYQ7NmzYQxY8bU/wEJAjEUlix5vUTR6gJJIgGmO3dq15KnHvHgwQP88ccfmDp1KqprUfXaYPt2yo7cu/div1eryYhdtowyK1Vg/fr1olar5Tp27Ch5e3u//hn/sWOJrle2nvploNORcztnDmUGq3A2dTodCgsL8fTpU9jZ2ZW8z4qpuU+fAnFxSJ89G2sHDYKBgQH69euH1q1b48yZM7hUxhi1tbWFUqmUlEolmzNnTuXOwI4dFFj7b+XJifv372Pfvn3SokWLas+qyc0lZ2z9+tLMzbFjlL2Ry4ma27MnGZe1yPaGhoaWtBlycnISAwICOJcXyZZu2ABs2oSv+/aFwsAACxcurP77ajU52AMHElumTRsqqynbDzonhxyAQYOq3o6lJV3foUNrf6yDBxPNc8qUWn19z549iIyMhLGxsTh69GguKioKsbGxgkql4seNGwenMkJSL4ScHLoOn3xC46VxYxJvOnKEAp0dOlCtvqcnsRrqW9jvOSxbtkzq3r07a19ZIMLWlrKUVlb0l5BA7LvQUKpHnjwZD7ZvR4dez+VHJYky4F98QeJRAM2NLVuW9pgvg7CwMBw9ehT/+te/8FL21dtvA4MGIbtfP9y4cUO6du0ac3Nzw6jqWlfpdBREOH+eShgiIuj99HSiGldxPPv370dGRoY4c+bM2i/u69cTDfngQaitrLDvzz91rq6u8Pb2lgUFBQmSJLEJEyaU394nnwB2diQABtA9+Oij8kFwtZoywrNnlwSsNq9dK/hFRvLN8vJoHK1bBzRsCO22bXjs5IRjQ4aIOWZmTCaTSWq1mrO2thZkMhn8/Pz45s2bl9yHPXv2COzYMebg7c15TJhQZcDp1q1bOHr0KHieR6vQUNzx8oJRgwaihYWFZG1tzaysrDhnZ+dyrczS09OxceNGLProI8jefZeOs0+fF9eM2bCBnPQJE2r8alRUFA4cOICBVlZoe+kStQUrm9nOyCgdwz4+VbY+zM3NRZG3N254eMB0wQLR39+fY2fPEhOpuPRTo4HS1xepXl6iy6pVnF6ZNauoqAjLly9Xa7XaFkuWLEl+sRN/g/rEm8x2PSAwMJABaMnz/HCZTLZo1KhRNTraADBixAhu2bJl4pYtW6TBgwdzL91KSpJo0WrRgjIVBw7UOUPxj4KJCVHJHz+mxUsQ/jaHW09PD/7+/szPzw8PHz7E1q1b+dTU1HIG+Uvj+nUyngID62+bfwcYK+0Vq9FQcKg27S3qARcvXoSNjY1ka2v7+juUABn1L/MMKxRkSJ05Q4JilThPaWlpyM7OZj4+PoK3t/frm80uRkYG0V/r81m/c4fq52bMqLydzTPIZDKYmJjApDLqKseRMaSnB2WjRmjcuLE4MyCAK64X9vf3h7W1NRo2bIikpCQpJCSE2drassGDB1eddXN2rtJIVKlUOHjwILy9vetWvrJ/Pzln+vpEZ/ziC3JUp02jz3v0oPZSo0aR4F6/fpQ1q8RByMzMxKlTp9CwYUO88847MDExefEI4LRpOGBuruu5fLnM7N13a/6+QlHa8m7aNKrFfN5pTUyk96tztoODS523miAIJCQ2Z07VfaorwYABA+Du7g53d3euWEiub9++/KVLlxAUFISBAwdKbdu2ffE5ydycMp0ffEBBkpUrSeBuzhxqGZafTxnIv4jNw3EcMqpinX3yCQVHFAqyVziO1oCffwZWr0bw+PGiQ1lRrGLExJDgXdl71bp1laUkV65cEfz9/bmXymRIEmBujhi5HHvXroW1tTVGjRpVqbBmeno6jhw5Au+EBKn1jh0Mn3xCgl5yOQVNx48nx3vXLgpmPYfExERERkaiZ8+edXuGZswgR3DzZhQEBSHfw0N2JilJY25uDm9vb37Pnj0VM2ojRhCNv9jZdnCgtaYswsLIGXzmaCuVSjzOzeU13brR7yIjgQEDkPHoEYKmTUNnKytp6rp1nGrBAmQOHMgcHR2hr69f6SQ9YsQIPu3CBWTu2yf+lJnJGRgYCLa2tnyHDh3g4uKCc+fOITw8XJQkievevbvkzPOs8Q8/YODu3WB6etVeH0tLSwhFRRCnTiWHtl+/l1srxo+nUoxqoNVqcezYMSEqKoobPHgwa926Nc2doaHE5vviC+pWsHcvve/vX+l8KooiTp48ifDwcDT/6iuhR69evIWVFZ3vtWu0vYMHSbvl449hsm4d3Nq3r3A9rl+/LnIcd+KNo/364I2z/RIIDAxsI5fL5+rp6Y2Uy+V6LVq0kHl5eenX1sHiOA4fffQRt2LFCmzcuBHvvfcejIyMXqye6/RpMjhu3SJaYE2CMP8raNmSDK6kJDIIk5NLWlH8HWCMwcHBAQEBAcKWLVv4gQMHom3btvWz8bg4agHxv4CAAPr78UcyDJOSXrmqemRkJBITE9G7d28R9a2MXd8QBMrQ/fe/RPesJZ4JMEre3t5MFEX4+/uDc3cnY+HqVaJrXrpUzqHctWuXYG9vz3Xp0uV9aMHWAAAgAElEQVT1vibF6NKFrslXX738ttRqmjc3bCDHrB5wOCUF4R07Yri9PQcvL8pmmZlBoVCgzbPaXhsbG1YrvY5Onci52L2bMvnPIEkS9u3bJ5qZmaFHjx61N84fPaLaxZ076f9Hj9JzOHly6XfMzYmWPWECGXZnz1Igol8/iKNHo6CgACdOnMC9e/cgiiLatWsn9uvXj6uz4NRzyM7Oxu27d2XzWreGaW2ZHDNnUqYeoFr0BQvKs2Xs7at3tJcvJxp7bY998WIq5+jRo07zlaGhYTnlapVKBY1Gg06dOsHCwgIHDhxAQkKCMGTIEP6FdT9cXIihMHAgBdcAetbXrKkyWPKq4OHhweLi4iptWYcxY4iV1qABrd1Ll1IGuLAQCA7GYxcXOFV2DcLCKMtcFvfuURa5OHhbBoIgsJcq5woNBdatw8WpU8ULISFchw4d0KdPn0ovYnBwMEJCQiArLASOHGHZBgYwHjOGKMQhIURh7tGD7stzivXP2g3i8OHDkoeHR3G3h7rB0BCYPRsJT5+KjUNDd/qFhg4+8uSJPI+C2BW316kTBWH27iV2EM9TieGECaVU66NHy7VUTElJgahUwsDWljLFaWnAzZto8M47UEZHo5mvL9P77TeYx8XBXC6vNmjJcRzs5s+HXUIC5+bri4SEBD46OlrYv38/r1arYWlpKfbr14/z8PAoaS+JrKxajWE+JweT9+3Dro4d0apLF3i+bFD2yROae1NTq9z/tm3bxKdPn7LZs2czs+LEQbNmNJcuXUqCk+HhdM19fSvdxunTpxEWFiY9o88zp7FjefB86bz/wQf0b6WS6v4XLaqyhCUiIkJVVFT0RhTtNcIbZ/sFEBgYqJDL5Sv09fUnde7cWd66dWv+RbPScrkcw4YNw759+6QVK1YwCwsLady4cazWddzffkvZwfHjyXFp0KB8TdP/L3ByIlEoc3OiEk6Y8LfSrLt06cLr6+vj6NGjUKlUL9/P+eJFWhTHjaufA3xdsHAhGV9paSQE9a9/vbJd3b9/Hw0bNhR9fX3rxamstCaxvsBx5CTUsSb5wIEDUCqVLDQ0VCoqKmIxMTGwtLREVFQUGhkaYryPD1Li42EqlyNfkrB7925Jo9Hwjo6OAmPsn+Fsnz9fpShSnaFSkZNdVPRi9XzPQafT4ebNm+jdu7fUxteXITeXjE4/P6Ly+/jUfaPZ2ZS9GjCgRKgnIiICaWlpmDdvXt0muYULiV4cEAAcPkxCWRs3Vj5XGhmRcyOKKAwJwcPNm6H/2We44u2N7LZtpZ49ezJ3d3eYm5u/9ER7+/Zt6eDBg6xRo0aC8b/+xSM/nzLrK1dWL4Ln7U3nYmJClOTQ0PJ124JQfVbqyhWi9dbUvk2SKEPVpAmttS/oxGVkZGDdunUQy4i5yWQyyGQy3L59mzc1NZVeuEPCzZs0jn/6idhtAAWSBg6ksqNu3ajc6tNP6XwMDV/JGllUVITo6GjJxsam8vMYM4aCIEFBNL/17k0Bk759gT/+gDRlSgndWK1WIzQ0FLGxsdJ4lYoZ+vuX31ZODmXHAVy/fh2PHz9Gu3btYGtri/z8fM61bCulOkJKSkJqeroUcvEic3d3x40bN6BQKMQuXbpUuGiXLl2C37lz6BYWhosTJwqHTE35bnfu0LofEEDMArmcAgMffUTtpABcvnwZp06dgoGBAbp06SL5+vpyLyOyGuPkVPAwJye4/fXrGT3PnesS6ebWLr1dOwmVOdx6etQdpJg9NXUqjQuAkhZHjxJN/xncGMO8FSsQe/062VetWmHH6dOC2w8/8F2MjZE/aRKsoqJo/HXpQkGvHj2qPtgmTYAhQ6C/axfc3Nzg5ubGS5JUvK6Wtj8TRSo9CAmpEKiogMxMsP/7P9i//z54U1MhITGR9yxbVvIicHQkZkIVKugJCQlISUnhZsyYAbPnGXpWVtSl4uhR+n8Vx3/kyBEpMjKSjRgxgrm6utL47927/PdNTEhP5MwZCjxVkdRLS0tDTk4OAFx5gbN9g1eEN852HREYGGgtl8vPNmnSpOmwYcMMDF6yly0AtGjRAgsWLGBZWVm4fPky1q9fjz59+kjt27evnAElihSFHDyYMjMuLpSteImF5X8C3bsTVXbhQop0u7v/rYfTsWNHGBoaYv/+/XBzc6so8FMXTJpEi/ScOfV3gK8LHB3JgTpwgJR9Oa7eszA6nQ7Jycl1ugeiKJaoPGs0Gjx+/Bj29vbIzs7G5s2bxfz8fG7JkiX1epwAqP4uN5ei4HXEw4cPRU9PT27o0KEsISEBoaGhQmJiIqenp8ceKZX4ycZGcvrhBzZ81y5s/vhjdO3bV3Jzc2MNGjT4Zzjas2fTc14my/tCKCwkKt/u3RTIqgdoNBosW7YMhoaG8PHxoQGsUNB8XTwf3bhBrKO6KF87O1OpjFZb8ta9e/dgYmJSt2BPTg7RPz08qLQgJ4eetyqoxSqVCmFhYUhOTpaSk5OZSbt2Ut9hw9jos2fBOI4hNJSMyZdkEomiiGPHjjELCwu89957NA6NjcmhvXq10nrccujbl7LvX3xBvaUzMgBSLKf1oCpnWxCo325tcPAg0ZjnzKm7ankZ7Nu3T7KyssLIkSOZiYkJ1Go1MjMzIQgCCwkJkYyMjOo28WVnU7b9l1/Ioba1BdauJVErc3O6P+npVDrg6EgB6UWLqP2nVkuGe58+ROG2sKBrVaxa/oLQ6XRQKpVs6tSplW/k3LnS7dvZ0b3u1YtYeaNGwSQzE7du3UJISIigVCp5KysrMSc9ndOFhVWskffwQI5cjo3LlokAmIWFhbRx40auadOmkCSpvJJ5HSB9+inOeHgIYV27cu9OmcKsra3RsWNHbNq0iXNzc4O1tXXpl7OzMc7cHI80GohDh6LrN9/wT/78U7hy5QrTarWcv78/ZIaGEAwNkWdsLDGOY4fXr5eYoSGSkpLY2LFj0YKCIy8d+TA1NdUrMjH58reZM5vKtNqC4Xv3wuDKFba1QYOifJ1ON3HiRKOSll4+PvT8JyWRU3frFjnZLVpQwmLp0tL7FBkJztoasS1awMjYmAI4lpZ4cPw4b75wIXrl54NfvJjqxwMCiCn04AGNuersUk9PoqM/o+Uzxiq2mRQEGrM16UDEx5PtN306WL9+kO/di/j4eDEuLq4k6HLjxg0olUoEBASA5/na1/Jfv06BrP79K3xkaGgIxhhycnIqjrd9+4idtnQpXYvffwcGDkS2hQU2bdok6nQ6xhiTeJ5nU6ZMKT+uevasKO4YHg5p4EAk63SQVVGmGB4erhFF8T9LlixR1+7k3uCvwBtnuw4IDAy0kMvlVzp27OjYs2dPWX2KmsnlctjY2GDkyJEsNjYW+/btw9WrV0U/Pz/m6elJOxJFMj6cnEjMx8/vn1+/W98wNCTjQhRJFGbFipqzFq8IHMcVCyNJcXFxrLaOXmJiInieL1UX1mqpBcc/VRCtNujalaK116+TsRcVVbcWHTVg69atolqt5vz9/Wu8iLdv38axY8ckrVbLrK2t4eLigmvXroHjuJKMlE6n4+pV1LAsGjR4oXt99uxZaDQaLuDZeHd2doazszMPAL///ruYmJjI9ezZk7nOnAn9d9/Fx+3aQe/WLe6vquOsF8hkL193KkmUbe7cmYSk6gGJiYnYtWuXpNVq2ezZs8sbcRxH2R6AMhMBATQv1QUcR8ba8eOINDZGXFwcxowZU/tBIkkkAPbhh1Tn+t13RCV9Rr+9ffs2Tp06JTLGoNVqGWMMoijCxMQEtra24pgxY3gXFxcK/g4dSkyUU6coU5qcTNnSli3r7KQ9ffoUq1atgk6nK9+iiDESh4uOpv3t3Vt1Nvmzz2g9zMuj0gJLS9IsAciZ27y58t/17UtZ7d9/r/4gDx4kp/SXX14qsJCWlob09HQ2ceLEEqPawMCgRNzJzc2t9hfv88/p2CdMAC5fJobGoUP02c8/0zj75BNyUE6cIAbDiRNURmBgQPR7f38aV/360TN1+TLw73/T/Nu8OTEpFi6kfX37La2pokjjsBoUOzFiZa3YAFIU/+EHqjHX6SjIKpPRsaxejY4zZrBrLi5Cly5d+ObNm8PIyIjb8MEH0C8oqMjaKyjAtYsXBdcBA7jBgwcznufZpUuXhJiYGAwYMIAxxuo8kYpZWcjfulWKnjqVzfz445L+6Pb29jA1NdXFxsbKTE1NoVAooNPpED9jBqxPnYLmo48EvXnzeJiYYOjQoXxERARCQ0PFy5cvc1ZWVjAZORKNhw9nnKWlZJubixytVpw+fTpfzsF6SZiZmemLoujIGJsrKRQH9o8f722SkdGkz3/+I1eamSlXPXxo8P5nn3ElgmQNGhBr5uRJqgOOjyf7MienROFdTE6G5O2Np05OuNmxI1KaNkWfBw9gkpEBhUKBPI4T+YkTuZKxdOgQlbwNGULPzL59VWekf/oJuHCh+pPau7dmpmJMDDF1hg0raV04ePBgPjg4WAwKCsLYsWMREhIipKam8gAQEhICoJRVIpfLJblcLunr60v6+vpQKBSSgYEBUygUvL6+PlpcugTt48fIc3aGvr4+5HJ5yWuDBg1gZGSEixcvlra7KxaBdXcnxqm7O9mjf/4JfPklYtq1g3nDhhg4cCArLCxkDg4OJeWjkiThwoULovuUKZzZTz9Bf8iQktPMOn0aa9evB/f775JGo2GOjo6YPHlyyXqj0+lw+/ZtURTFbdVf1Df4q/HG2a4lAgMD5fr6+qc8PT3t69vRfh7NmjXDggULWEhICDt8+DDMGzSAlJ8P6927Ybh1K9UpRUW9sv3/T6B44m/ZkozBGgyEVwm5XI4bN25InTp1qnHQHD58GOHh4ZDJZPjss8/A3blDNT65uf/bznYx2rUjw93AgCih9dCHPj4+HqmpqdyUKVNgb29f4/fPnj0r+Pj4cB4eHjhy5Ih4+fJlztzcXJozZw5LTk5GYWEhLly4IOrr69fvDVGryUhev77OomhRUVEIDQ3FpEmTKhXb6tKlCxcbGwtPT0/KHPj7gz97lgzrtLRq6+teG8TGktH/MkEYUaRM8YYN9doGMSwsDIWFhezzzz+HXnWqt9eu0evixXQ+f/xRux3o6ZGwkrs7wg8eFOVyed0Uv1UqoEMH5HXpggcbNgAPH8Lqiy9QnDc5c+aM6ObmxllbW8PGxgYRERFis2bNuGbNmqHS8gIbG1LVLSoip3vmTGIbuLkR86AW40mSJNy4cQOmpqbipEmTONPKFLKdnSnYmJBQtcHO8+TA/fEH1TVmZZV+lpdH5RipqRV/FxRE9Y/VITGRHNBdu8iJf0GkpKRg+/btko+Pj+Ts7Fz3eUOSaLz07UsBCAuLUjXv5zP3QUHkLIeE0LXZs4fuyfXrdD6HD9Mcu3Il3a8GDcgpd3Gh2mKAHHOZjPar1dLrt9/SvY6JIXbE4sUUsNm5k1hXPA/I5dBqtRBFEQ2qKmfjeVLtVyopyLp5c2m5jJUV3HNzmXvbtjw8PZ+dugSXe/fAvfdehU2lAUjTavmubduWOCt+fn78i5Zt6S5cwLmTJ8Vb8+fjvffe455Xye7WrZvsxIkT0tmzZ1mH1FTRLTWVi+I4mPj4SO5z5/LFwQA9PT20b98e7du35xITE3HlyhWxzaefci2vXQPeeovB1RWIiXlpNpFarUZERATy8vIQHR0tZmVlcQAgSVI3QRCSATTJsbTE+R49Js9Ys8Y9ycXlfSkoyAQzZlBAq1MnspFSU6m2v0EDGieWloCNDe59+aWUEBODBh06sHQPDynFzo41btxYCg4OZoIgQF9fX2rVqhUHmYx0H7y9yV7ZsIHWsiVLyIE3MiLWxfMQBAqMDRhQ9Zq3cCFl2qsKSty8SWN5xIhy+gxyuRx9+/bloqKipB07drCmTZvC3NxcsrKyYg0aNICxsTE8PDygUqlQUFDA8vPzWWFhIQoKCqBWq6FUKpGZmQmNRiNG9O0rcSqVpD5xggmCAFEUmSAI7NkrRFGEKIqSJEnkGWzZQvbL2LHlz7tvX2glCcrt2yVPW1vWaOrUko8kSUJycjKCg4PFJ0+e4MmAAZIyLk6aLElcSmIirDt3xq4RI6Br3BgLP/yQbd++XUpOTmYxMTElgn1xcXHgeT7y888/j6/DMHqDvwBvWn/VAoGBgUwul293cHAYNn78eMNX6Wg/j31794q+H37IZVhZ4fiwYXBr21Y3ZNiwV+rs/8/BwYGMgQUL/vJdnz9/Xjh//jz/jNImTJ06la9MtEWj0eDXX38VJEnihg8fzvbv3y9ZW1sLb7/9tgy3bqHY8Kgr1Go1FPVQi/qXo1jwLiam8kW6ljh8+LB0584d1qdPH6lDhw41PjSxsbHYvXs3Fi5cWOI0qVQqCIJQrh4rPDwcJ0+elD744AP2wn1yQXTgM2fOSCNGjGC2RUVUD3ruXJ2d7V9//VVwc3Nj3bt3r9SQF0URP//8s6DRaPi5c+eipPylqIhotytXUrbpdYabG2kWvCh1v1iNfft2MoJeRjzpOVy9ehXBwcH49NNPa/eD69cp+zNyJGU7nlcCrgqzZqEgIAAbs7IkmUwmTp8+vdL5pByePgVGjULKTz/h6tdfwyc8HGE//ijdjYxkPM9LWq2WiaKIadOmvVw7vLNnyRmTy6m11IwZ1dbWHzlyRIiIiOD79++PdjXVVe7ZU1pjXhlu3ybnzdeXqMarVtG+8/PJQX2+VODMGRKbnDWr6n1GR9M5HDjw0myK7777TrKzs2MTJkyoPXX18WNiXnTpQvWt69ZRgOijj6oeu8935NDp6LX4+5JE2501i54Fb2+6rmfOkIjT1Kk1i6uKIgU2OnemvtFz59K979oVkokJNgwaJPVduxaOe/YwmJpScqB///KsB0mie/bbb3RO48ZRJnTcOAqc6HQla979+/eRO3EivLdupSBCGajnzsXl2FgpfuRIacyYMVylXQJqgczMTNy+fVty/PBDprKwkNxPnmTVrZsFSiXCFyxA47NnEefqCs8ff0Tj1q1r3pFKRQ7h0qVUQ/8SSExMxPbt22FkZCSZmZmJSqWSy83NZTKZ7KZOpyv7QH2xZMmSbwIDA/u55OTsfzs+XsFNmECZ1qZNSQBs7FgKbHl4UNC3bVvA1xfaxo2R3KIFmh4+DFbb9nTR0cSGmDqVEh6ursQwOXKkcmZIsTBbVR1JqqiVBkBsjKgoCg4MH17pVzQaDfLy8mBlZYXY2Fj8/ozJYmxsLCxYsKB2AY/QUBLlTK5c3FupVGL16tWSr5eX1GXXLg6DBlGA67lzUqlUWLdunWSdkSG2Pn2aR+fOkvjuu8zIyAgJCQlSeHg4a9KkiTRixAimv3AhfjU0FLNNTDieMalrRIR0pUMHrrCoCF9++SU0Gg2+++47jBkzBi1atIAoili9enVhTk7OR4sXL15fq/N6g78M/x+kyl4eenp6X5qZmQ0bPXr0X+doJycDHTpghL091/jQIbS5dg1Dx47F7Tt33rAR6oq7d6mG7ccfKWL/F+HmzZvSpUuX+G7dumHRokXgOI6tXLlSVKlU5b736NEj/PLLL6KBgQE3a9Ys5uTkhFGjRjGXNWtkedOmvZCjHRoaitWrV0vff/89bt26VV+n9JcgPj4eF5KSyAm0sCBqWFpayec6nQ5paWk4fPgwYmJiIIoiMjIyoNFoym3n3LlzuHfvHps8eTIqc7TVajViY2MRExNT8l5WVhZ0Ol257KSxsXEF4ZN27drB1dVVCgoKEl70PMPDw7Fr1y5wHMfC3n8fO3bsEDQv4GgDgEqlYk5OTlXO5xzHYd68ebxcLhfKni/09SkTGBxMjvfrjPBwMuJeFM2bE914woR6dbQB4MaNG4JWq0Wtg9cdO5JjcfQo1aHXFubmMBQEzJgxg8nlcrZq1SpRra6hNO/SJUChwJ9Hjgg+ubmS3dGjGDJ0KBs8eDC6d+/OPvzwQ3z++ecv33e+Rw/Kfk6bRmO4UyeiCicllfva06dPsXXrVjE8PJyfPHlyzY42QE5hXh45z5WhbVuiyF+9SvP9lWfaQFX1x42MJEO9KqjVJOS1YkW9tMsSRZHFx8dDW6buvlLcvEnOyrVr5GCr1RQIW7WK2D4ff1z92G3Rgr4P0DUwNi7fP5wxYiXs30/bvHiRHPSMDNqnVkvBnw8/rHofHEdBQRcXusdXr1Lm8to1PN20CU+yslijQYMYjI2pS8rcubRfd3ei/Ccn0/hYurSUXdK6NQn3ARRcnTgRSEtDbm4uDm/ejIaurpXW7CqaNUPXkSOZnp6etGbNGsTH1z6hl5+fj6tXr2L16tXi+rVrod23T1Jv24a2wcGVOtoPHz7EmjVrcHfQIEnesyeeGBkJImNI7tQJ1mWU5quFsTEFU6dMoUDOC0Kj0WDPnj1i27ZthXnz5rGpU6fyrVu3luRyeahOpxvHGPsUgHzJkiXsmaPNFArFkjZTpyq4ffsocDlhAo2Vhg0pOKZQ0N/27UQFHzoUaStWYOegQUgQ6rDMubmROFpQEAmeFgv3hoZSwPN5mJuTTkll8PGhObsyhISQuKOjY5WONkAZ7mLB4WbNmmHx4sWYOHEiNBoN97zNUCXataN5uor53cTEBJNHjGDZGzZw2SoVBZcqCR7cv38foihK73z/PW+3aROUkgS2YIF04tAhITIyUhw3bhzefvttplAowI4exWAfH659s2b4JDWV+e7YwS1YuBBffvklvvrqK3z77beQJAnNnwWg4uLikJ+fnypJ0obandQb/JV447jVgMDAwBZ6enqfTZgwweCVKQ+XxdmzFDX+z38o6+HlVdJn1dzcHDKZDKIoVtoerLCwECqVCg2LF61nkCQJjDEUFRVBpVLB3NwctVW8lCQJWq321aku/xUonvTi4sgoqC5SWg9Qq9W4d+8ejhw5woYMGVLS+mvKlCnc/v37xZUrV0oODg4sJydHKCwsZBqNhuvcuTO6du3Kip08e3t7PB0yRDx18ybT7tjBioqKxIEDB3LPq9TrdDpcunSpRIwtIyMD8fHxOH36NDw8PKTMzEz25MmTV3au9YmCggIcP34cd+/eBQA4OjqiiY1NKY1Ro8Gl69dx/vx5CIIAKysrKSoqimm1WgiCAMYYOI6DTCaTnmXsuLFjx8LW1hZ3797F6dOnhQYNGnA5OTmiVqtlWq2W0z3L/NjY2IDneSkjIwOtWrWqsTUYYwwBAQHcf//7X+h0OtS2xYxOp4NGo0FaWhqOHz+O3r17w9fXF7qgIFyPjcW3334LGxsbYcaMGbWmGObl5aGoqKjkXKqDm5sbd/z4cTRu3BiNi+uVPT0p03ruHAnRXLpUZW/nvw2jRpEoz/MCSbVBQgJlCE+ceCUtEUVRRF5eHj9z5szaZy2LMXw4/aWnk8N49y5Rg6vCN98ADx5A/84dTJ48mQsKChJ37NghTp48ufIJPS0NuqwsHBoyROr13Xe85ZYtJXTs1rXJxL0IHBxISGzmTHJW58+ne9e0KdC1K86dO4eEhATuvffeQ6NiIbOa4OhIDuJHH1HG+uuvK35nzBgSoLt4kTKIAM0b69YRpbUYkkTHV9W9ysykbPiBA5TtqwdMnz4dv/32m7RhwwbMnj27/I4fPSKH4uuv6Th79iTqenY2OT91aR25axcEe3vwAIR27XBnwAAppnt35hkTg/j4eNHFxYVr3rw5nbuFBdHjL12iftANGlAwQ60uzYj360djzsur5n1zHO6lpYE3M5P0v/mGztHFBZg+nT7fsYPu3apVtP3jx0t/+9lnpaVxFhbkzKen41hUlNBKEJiztTVXaa9kNzfwenqY1K0bf/nyZezYsQOdOnUSu3fvXq2q961bt6Rjx44xExMToW3btrxvYSFkU6dyWLasQk/mXbt26eLj43lBqWS2oohLLi4srbAQLW/e5PkdOzDB3b3W9hQA0gBITKSs/ujRNOc2a0aU+suXKct8+jQFPz/8kMZ98+aUHU5JATp3xoUtW6RmGg0Gdu/OIz0daNAAMTExOo1GE7FkyZIYAN+X3SXP8/9Wq9WdPTw8KFjTpg3d+6QkUr2eMYPmnuBgul9LlwKLF8Nx1ixov/oK27ZtQ53EQM3MiEq+dSvVg//6KwVU/Pzo/MuuL3Z2NE9Upj80Zw4FdJ7H8eNUVjF1Km2zDmCMwdnZGQYGBrh3717tWrPq69M9SEysfE54+BCNP/kEnr6+WKvRYEp2djmxNJ1Oh+3bt4uPHj3ibG1tGQA0bNMGDX/8kWHJErQTRR7vvVeeCRQTAweZDA7XrlFQ5Nl8FRUVBUmSoFAoMGPGjJI1JzQ0VFVUVPTdkiVLqhBLeIO/E2+c7RrAGBvcunXryuvJ6hN//kmLTHIyOYRGRrQAlUGjRo0gk8nEuLg4rkVxa49niI6Oxs5nvVOnTJkCR0dHSJKEXbt2FURHRxsyxiTGmE4mkz0FYDpz5kx52drO7OzsEke9mGJ669YtHDx4UALAHB0dNUqlUs0Yg52dnV7Tpk0N3N3dKypHvs5Yt45eR46kiau2SrTVICEhAffv3xfz8vKQkZEBpVLJPQtOSCNHjmQeZYx7nucxcuRILi4uDnfv3hUcHBx4KysrNGnSBEZGRuVX60WL0HL+fE4aMAC3bt0SDAwM2IYNG6T+/fuz+Ph4KTk5WdRqtUyn0zFJktilS5cAoMTxe+utt9CkSRPu7t27CPibBOLqgry8PKxZs0Zq2LChNHHiRO78+fPYsmULTE1NhUbjx/Pa48cxes4cPBo3DiMXLSpWb2WiKCIpKQk2NjbQ19dHUVERCgoKWE5ODtu5cyd2794tmZqaijk5OXy3bt343NxcsUmTJszFxYUzNDSEpaUlkpOTcfHiRcHKyor179+fa9y4ca0c3UaNGkEQBCiVyhKRo+rw4MED7N69WyoqKmKMMXh6egqdWrbkcfw4ZJcuwQfg7VNSsHXrVl6tVmU1GnoAACAASURBVCM6OhpmZmZwcnKq0pjLysrCpk2bRGdnZ9jb29do8fXv3589efJE+vPPP6WJEyeWF3lr04ZUgXm+lHL9usDLq85t0EowcCBRVL/7rn6PCWT4HDhwQFIoFOz5IGed0LAh0eOtrKiG9a23qq5N37gROHsWfGgoBg4cyK1duxYnTpwoqTX08vJC8+bNER8fD2H8eOkpwMxbt0bDoUOheL510quEXE4iXWo1so8eReaKFaL+woXsqbs76zJ+fO0d7bIYMYKMdVGsqGGxaBFdwz//JGfh1i1yLMaPLx9gLVYHTkmpuH2Nhuq+P/us3hxtAGjYsCEsLS1Z3rM2VQCIbeXjQ+P60CGqgb56tfTzap6/jIwMmJubIzU1Fbdu3RIaNmzIW23aJN4xMkKkhQXXtGlT4cmTJ5ypjQ1rtXOnFHv+PCK8vNi1a9ewcOFCGBZvm+NIsM/fn7Kuokhq5u+/T9esUSNyhn79lRIBu3dXe54qlQrGxsaVByvXrCEna/lyCirExVEgVU+P9tWpE3WlaN8emDMHBX37IrVXL37048f0DFeGpCSar7p1g6+vL1xcXLBt2zYkJiaKY8aMqVBzrVarcejQIeHBgwdcz5490alTJx7371OA6P59Yg+UgU6nQ1xcnMzX1xc+a9ZAPy0N2g0boB02DOzAARg8Z4dVCUGgevjwcLq+Dg50niEhpcJ2ggC4u+OJmRnu6nSwVygge/gQTlotuOIa96tXcToiQmj6+++8vb4+4wICSBSwWTP437wpN8vKmoGlS3cDWAHgMYD/E4Fxvr16tbP39JRk33zD8Pbb5Kza2BDDpnt3Ks+4c4fsz6tXKUD5zD5s3749Hj58KKKuTFgjI7rP27fTc7t8OZ3/7t0UUCh+fv39SSgtK6u8LsKZM+SgP89ouHiR5sBPPqm0v3ptYWdnJ8bHx/O1crYBsh8DAoAvvyz//r17NJYDAtBk3jx0OHUK27dvx3vvvQcTExPk5OTg4sWLSE9PZ4wxjBs3rnTRNTKimvWDBylw8OOPxGgBqD3e/v3ECClTBuPo6AhTU1MxPz+f2717t9iuXTvO3t4eKSkpHIAdL3xB3uCV4k3Ndg34/vvvTw8YMKDnK8sC3LtHUc3WrSlqP3NmtV8/f/48wsLCpLlz57LibFpiYiKCgoLydTrdDQBdmzdvXhAQEGCoUqmwb9++J1qt1hmABoCwZMkS8d///vdngiB8Y2lpmefs7Gzw+PHjwsePH3MymSxNo9E4tWnTRhoyZIj+5s2b85OTkxfq6+vbSJJkpNFotgOQA/BSKBSj9fX1vSdNmmSor69funj/E5CcTIJjpqbAw4c02dcBOp0O0dHRuHjxopiTk8PZ29tDFEU4OzujSZMmMDQ0RK37pFcGQSDnokxmRZIknD17Vrx9+zaztLQUvby8eCMjoxIV+5SUFPA8Dzs7uxLHTKVSYfny5ZgwYQLqJKZUC4iiiDt37kAul6NFixZ1i+xXgpUrV4rW1tYYPXo0xxiDJEnIzMzEzZs3xaKiIsnQ0JB3fvIEDoMHQ+/gQap7qyEAplarkZ2djd9++w329vbiu+++W+9lM6tXrxadnZ25AQMGVPu9+Ph47NixA926dZO8vLyYRqOBqakpZTeWLaPa9GfYsWOHEBsbyxsYGEiCILBnARTR0NAQnp6eXPPmzWFgYIAdO3aIGRkZnLu7uzB8+HC+tvdArVZjxYoVkqenp9SnTx+uAkvmwAHKdKSmvh4Z7suXyeCqq3NWTG9t0oQciXpGeHg4jh49Ci8vL3Tr1g1G9aGeL0lU47hmDWVXK4NOV5p5VChw6dIl4f79+zA1NYVcLmeRkZGcIAgwzM1F08aNxV4aDWeUmlouO/JX4tGjR9i2bRsEnQ7tra3R7o8/YK1QECW6VauqhY+qgiRRbfYHH5AjXRazZxONNSmJDFeOo0zxsWOlom2FhUBERMWMWX4+MSemTKm0xc/L4ocffhCHNGzIua1YQfXKixZR3fPIkbXeRm5uLvbs2SOmpqZy/v7+Uk5OjhQZGckBwORDh6AeOBCNFyzAuXPn0KhRI3Q+eJCcm/HjgcBA/Pzzz4KHhwfr3bt35ZOFKFLG88YNsklsbCjzefkyBS9mz6bxuXMn1fw+h7t370qHDh1iH3zwAUoSFL//Tvfg7FlyMovX24ICyuT98gs59zk5JTW9oigiwdMTqoED0TYhgZyryp6vX36h17lzS97S6XTYtm2bmJGRwY0ePRrOz9ZQQRCwceNGsaioCCWCfKJITs2//03OznOIiYnBrWXLMLpzZ7CxY+k6BAZS5rk2WiiPH5Nw2LhxNAbVaqJX6+uXtmu7fx+wtUVeXh52794tpqWlcXp6ejA3NxeePn3KazQaNGjQQMzPz4dOp+M4jsPs2bPxfAIoOS4OO9ev1y388UcTAG4A5PtGjLDTymT78kxNMdrXF2bp6cSk+flnCuZ07kzOcH4+ObeSRJniFSvI4fb3x4W4OMTExAjTp09/cUG3Bw9ov598Qg74W29R0KsYU6b8P/auO6yKa/uuM3MLHRERBEUUO2AvKIqoiBoNdkJsMbEk+iwx3eczeI15lvjUJEYTjabZjb0rFrCLSrOg0hRBQEA6l1vm/P7YXkE6iiW/ZH0fn4q3zJw5c+asvddem+bFhAlFv/P1JTWMoZMDQM7m587RZzxnq9tz584hIiJC/6TdYGXIyKD5WvyZeOlSkSKlWEDo119/lTIyMgQLCwspJSVFEEWRW1lZMSMjI/0777xT9vetX0+KnO7daf/38cdU3nH79tNBuMdQq9U4c+YMbty4oc/KyhJlMtn22bNn+1VvFP7By8I/ZLsCqFQqH6VSuXvGjBnGNU4mNRp6oA0dSq0PDNKuKmD58uX6Jk2aCI9bW2DJkiVSYWHhHgBzBUEYKUlSniAIC2QyWSaAr2bPnr2yxHkJAOoDaAigO4BrAIICAgKyVSrVKAAbjYyMuCRJ0RqNpnV5/foWLVoUWlhY2NbMzEz90UcfGf3lTNseZ0Gkc+eQnp6OWrVqlekkbKgJtrCwQGxsLI4dO8bVajUaNWrEhwwZItSoAZlaTQGYqtQxVoLCwkIsWrQIvr6+VauLrAZiYmKwYcMGAIC1tbU0ceLEZx6HK1eu4MiRI5g1axYq7VvPOcmep06tNDBlwK5du3QuLi5is2bNanyCXrt2DXv37uXvvvsuK6+na1JSEtavXw8PDw9dr169itRE4eEkES2jrCEzMxMWFhZgjCE7Oxv3799HUlISYmJipIyMDEGv16NRo0bSkCFDSmVvqoKUlBRs2LBBYowxIyMjrtfr4eHhgfbt29Mm/NIl2mxfvVptmV6Nw82NyEJVzccM8PUlsmBQtNQgDEY7zs7Okr+/v1DVMoJqoXt3GvvFi0v/39GjVBsdH18qwytJEmKio9Fw4kQohg6lTPmxY9UntTWA+Ph4/Pbbb2jVqhWGDx9eFJS7eZOymOHhtGkeMqTyPrrFsW0byWzffvvpGuaoKAqkGvweuncnghcZSTJpjYbcjZcte1ourNVSMCIjgzbPNfUsy8oiSW27djhtaQnMmIEeN29S5vwZApQXL17E4cOH4eDgAAsLC96gQQN29OhRiFotpn/6aSlvCUgSnYskAQUFSMnLw2Mpe+VqnD//pLm3ejVlVXv1orVq2TIix3Pm0PULDHyyhkVERPBdu3axMWPGwNnRkb67WTMa27Ky01OmUEbfzo5IxSefAPv2ISQkhF86cIBPsbQUhPR0IsNlYds2uqZl1OWfPXsWQUFBcHd3l7y8vITz589Lp06dEj7//HMq++Gc5okk0TpRBhISEpDo7w/39u3pfAcPJjl8ZaqHCxeobnncOJKKT55cZOzo718U1PjuO6BOHWiGDsXy777jzs7O3NvbWzA4uXPO8ejRI1y/fp3b2dkxGxsbmJiYlFnWt2/fPl1YWNjeuXPnPoneLFiw4Eu9Xq+aOnVqUXlhcjKpP8zM6BidnOjamJlRQDMri4j4ypVAaioe2tsjKjwcyrFj9Z379RPRoMGz3R+JiXS/du5M423IfAN0PIWFtGYXR/Fn46VLFKBat656a0U5iImJwe7du6tuknb2LNWgnzhB/969m4i3UkmKsGLIz8/Hr7/+KjVp0kTo3r07TExMsGLFCn2XLl3Erl27lv8dp0/T/fb22xQAGTKEki8VBHIlScKSJUvUhYWF3QMCAq5U6Vz+wUvHPzLyMqBSqYzkcvl8pVI57a233qp5ov3xx2SMc/06PWDKc2EsB6NGjRLXrVuH0NBQzjlnAASZTNZ8zpw51wFcf3wO/5s9e3aZtRuPazruPf45XeK/wxljV3Q6naVOp+tRHtEGgMLCQi8AdTQazaX09HSj58rmvmBwznH37l3o9XqcOXNG/+jRI0FtbMys33tPqvXee0Kn06exbtIk9OjRA1qtFmZmZkhOTpbCw8MFIyMjrtFooNPpmLGxsdS+fXuhe/fuUCgUNR9d2LCBJI737z/3RymVSpibmyM2NrbGyfafjyX4H3/8MX755Re2ePFi9OvXD+7P0KrLQFQqJdoAPXjDwujPwYMpA1VGS5jiGDp06Atb51xdXXH27FmcOnUKb7/9dpmvCQoKQsuWLfVPEW2tlojUyZNlSuGKt82xtLSEpaUlXFxc0LdvX0Gn0+HRo0eoU6fOM/f6trW1xZQpU4TQ0FCIoshOnTqFffv2ob2hNrNzZ3KPHT+ezOleZYbbcL2riiNH6P7Zs+eFZHIfPnyI3bt3AwD8/PxeDNEGKKNjaMUTG0s1yQZ4e5MZWRnBckEQ0LRhQ6oB3biRSG15bZheIDIyMrBx40Z07doVPj4+T/9ny5b08/AhEe+xY4l0z59PfX4ru25+fkQKmjal69y6Nf2+eXOS57ZtSxns7t3p/w2b1Tt36J4rTrQlicZSEGjtfd45ExFBZQGXLlGQKDsb+O472BkZYdPBg6jr74/mz6gEatasGQ4fPgwfHx/s2LFDSkpKQpcuXcTeH34IhVpNKpniMMjEMzMBR0fYHjgAY2NjfXR0tNipMgnuiBGUdb98mUj31auUkTR09Zg1i/YvOh3tYc6fR0RICJo1aQJnvZ5KIm7doqxmeWO6ejVlO1evpkyqToeC/HwEBgYy3+HDmTB2LLWFKg9aLZHtMuDh4QFRFHHkyBHB0dERoaGhrHPnzkX+Gv/+NzmxP/YIKYVPPoEsIgJHfHzQxsMDxn37Eokuj/hwTvs6S0siqjt3kuS4ZNu5uXMBIyPcv38fd9zc0NPbG2G9enErPz8+fPjwp9Z0xhhq166NHj16VDopQ0NDRc75IQBQqVRvAWgHwAEAsiMiYHP3LhG4336j9WPcODIwNDEhL4sdO552e39sTGZz6xbEPXuw4+JF0fW//4WJmRkFTxISyCm/qi7lDg5EVEeNovXs2DFSM8yeTc7sw4fTtZbJSKmQmkqBDc5pHdu7l67XMzrOl4StrS3y8vLEKnuuODgU+WkcP07lAF9/TetNCZiYmGDq1KlPbvL4+HhkZWWJCoWCAyj7WkoSrWcTJtCa+OGH9Pmff17hYcXExIAxFv8P0X698Q/ZLgGVStVToVBsbNiwodWbb75p/KytJEqhsJAeXr6+JJmZPp0e+NUk2gAtEg4ODrnx8fFaAGsUCsVAjUbz1E7/WU0SAgICrgMorQ8r+7VZALIWL168fdWqVRNr1aqV16dPHzMXF5fXyuWec461a9dKGRkZTBRF7uTkJHTo0IFZWlri9u3bgrOTExydnNCrVy9cCwzUw85OLCws1CuVSjZixAjk5eWxtm3bGrIyL+7c9HrKVj2LCVQ5sLa2RmpqavkL/DOCMcYBsNzcXLz11lvs6tWrOH78OHJycuDh4VGtsoKbN2/yx9nZqh2jYTMyYAD1CE1JoVqvF0V6KoBer0dycjIrK8gQFRWFhw8fIj4+HqNHjy7a3WdnUzQ8La1qUsQSkMlkpUwQnwUmJibw8PCAWq3GkSNHUGqtM/QnTkggufvChc/9ndVGr160CZs2rervuXiRjru4JLEGsWbNGuh0OowdO/bFGkcaZLqLFpF08q23imqVBYGeJUOGkEy4+DzKyyOS2bw5BaJeAdEGgM2bN3MTExPu4+NT/pppY0M/wcFEVL76iu5lb28iiRUF4CwtKdMlSaQIMjKiteHdd+nZ6vdYUenvT5v8+vWJVEREPP05J0+SC/jGjc++hsTHU6brww9JrTZtGmV/Hzx4QnidJQk4eBC3b99GSc+VquCxAzU3MTGBvb09mzVrVtGacuJE+QGxMWNIjuvpCY1Gg5ycHNHR0bFqX8oYBQP37SPC3rs3EYKtW2k869en1x07Bri5wWP4cGafnk5r2++/U8a6MjRtSnJma2tg3z5c2r1bsrS05C5ubiJq1yaSUx7s7SmoUgKSJGHVqlX6zMxMcdCgQVwul7OcnBz07NmTXpCfTwqHsgK1Oh3JeXv0wMmHDzHIzQ3G69dTILwsoq3RUG1t+/Y09xQKWn80mtKdJQ4fpgDFjBlYP38+zMzMIH35pdTywAHWZOjQZw6eAkDdunVzU1JSEgGA6fVbjNRqdDt3Do1iYxF640YhMzUVG0+YIMO0aU8HP3Jzad9hCFiVRPPmqP3ZZ0hbuJBfGj2aeTVvTjXKkZG0Z/n2WwpuzZlDn+XmVspk7glkMrrPvv2W1imFguTT771H7z13jgj86NE03zgnV/3160lpUVP7cQCmpqYQRRHx8fFoUhVJupMTBU8CAkjZsGYNGTfq9UUKlitXaI7UqkVBXw8P4Pp1pC1fzn3mzOEdFi4U0KkT7Vv+9z9av9aupXkRHU332rBhNH8kqUolJpcuXcpTq9Xl2Ln/g9cF/5Dtx1CpVGYKheJ7Y2Pjt3x9fY0NTeKfG8nJtAh99x0tSH37FhkgPCMKCgrQr18/s59IHnlv9uzZL6igvGr4/PPPP1CpVDMePXo09sSJE8tdXFxqbkUsAb1ej1OnTkkFBQV8wIABYlmu7AZoNBo8fPgQN2/eRFpaGvviiy+YIAhPPc0aNGhAf/HxQZfAQHRZtEhEVhYgCM9en/SscHOjDfWz9hEuAxYWFlJ8fLywbt06DBs2rEpGXlXBJ598wvbv348NGzbwGTNmsP79+6Np06bYu3cvv3LlCiZMmFBlw6jMzEw8k8TbsFHq1Ys2ajVgeFddiKKI3r1785MnT0p169YVT548ydPS0vSP3cGF2rVrS3379mWOjo5F5zdmDG3EXmIbuvJw584dbNmyBU2aNJH8/PxKkyKlkjIMp07RJvRlBzTeeadsN9qysH07EbZff32hh9SuXTuEhoaicePGL/R7nsAgnz9wgJQGqam0WTY1pQ3r/ftP1y9eu0bkc+TIClvivCgUFhYiODhYSktLEz7++OOq3dcyGSlVfH1JrhkfT4GEnj2JFJVHJKdMocxrmza0Ube2JqI7dSoFh/bvJxKhVNKY1K5NZMdAEnftoo387t3lE4SKMGUK3c/5+UQuZ86kTbOBzBQjW1FRUQBQtY19CWg0Gmzbtk3Ky8vD9OnTn1ZTLF1Km36/cso1332XyPCKFZApFLC3t9evXbtWbN68udSvX79S5q95eXmIjY1FTk4OLCws0LBhQwrE2djQWIeEEEnIyiIJuJMTjXFiIgonTpTifv1VkF2/jiYjR1KGTiYjIlJe0GfiRFI2PM60u96/L5gayP39+0RqvL0peFQSYWGkjiiR/Q4NDUVhYaHw+eefQy6Xs4SEBOh0OpacnAxHY2NSVZw//8SZ/ym8+SbNhf37gZMnecPZs4HDh1kp6fijR0SKfvmFnLcXLCACamjvVlYg7upVZF27hp/UakmpVAozZsyATCYTcO8eZZUfPSp7jKqA9IQE82axsQclQXjnS863ArgD4H567drnQ5s3N7siimcb3rmT59uypekTc9x160iBUYVSG845JM7B69UDs7cnUgxQAOb0aQrKrlxJXipNmhS17axT5+n7Vy4ndYS9PWW109IoKPPzz0Qwo6MpgD5oEHlXHD5MmeQaVikxxmBkZMRZ8QiHXk/Bm9xcCsC5utJ3h4ZSIK1rV7q+y5fTWjVuHClUjxyh+TRuHL1m0iQ6bkdHWqMFAXqAyHf79jSX//Mf+nuXLnRutWoVqSBWrKDAQiVrRU5ODuLj4/8xRvsL4B+yDUClUnWRy+U7mzVrVnvgwIFGNVKDGxtLi4aJCdWBabVltyupJgy1sjY2NmqZTFag0+m2Pf/BPj8CAgI0X331VT1HR8ca05sWFBQgIiIC9+/f1+v1etStW1e4efMm8vPzIUkSe/jwofTYPRoZGRnIysqCnZ0drl27xoODg5Gbm8uMjIy4paUlHzp0aIWtQADQAz02lq7VjBn04HiZ8tkdO4p6jdYQvL29hVatWuH06dN85cqVrHbt2no/Pz/xeTOjgiDA19cX3333nbR//34MGzZMdHZ2xsyZM9nOnTulNWvWMIVCIY0fP16QyWSwtLQsZaImSRICAwORlpbGBhfvBVtdHDxID0jDBm3s2Oc6t+qiXbt27NSpU+KaNWvQokUL7uXlJbOysoKdnR3kcvnTJ63VEiks0Wv9VeHatWuQJAmjRo0qP6vi7k4biUOHSOZ75syzEZPqYudO2pyUtSEuC9bWFWfBagjXr1+XHmcGX66CZ8AAIocArU8LF1JG8/TpohrIpCSSiTZqREGo5zQurA4ePHiA48ePSzExMYJcLhf8/PxQbT8Bxigr3707SUv376dN/ZgxRKjKqpXt2JECb5wXuegLAhmlaTSAjw/NV4WC1ggD0Y6NJXn0oUPVm88HDhDBPXmyaGPerx8F0itAixYt0LBhQ2zbtg3vv/9+Ueu9KiA4OFhKTU3F5MmTS3tj3L9fuQHg4xZTgokJJk2aJGZnZ2PLli348ccfMX36dJw/f1537do1IScnR5AkCaampnq5XM4yMzMFSSKRnL+/P2Xku3enMf/pJyIHc+aQXLxRI7TYvl041aWLdOXoUf5xWpoICwtaj2NjKYhy+DBdj5LzUpLoNUuW4I+4OAyqVavIRO7mTaoLL4tsW1mVmdlOS0uDtbW1JJfLRYCC6l5eXvo/fvtN9OzeHT1++aX0uhISQmRr7Vr63OvX0SAqCvvd3dkQKys8CRUkJtJ4t2pFhCg8nGqIBwyoMJMvFRbiiLMzrkgSPN3dmbu7e5F8ed48InQhIdVz2E5MBE6dQh6A6d9/j0dWVrcFzkMAHAfniQBgDWAuAJVKZZKQkPDxpk2bvpg2bRql6AsKqtwOceDAgWzv3r0wNjbmXbt2LXpYODoWGRX26lXUqz04mFQkn35K82XCBLpHO3YkFcqoUfT3IUOoft3ZmeZI3770jJQkCu5s3/58RNvQR75WLQo6KZV0nTZvhmN6OmovXkzXcP16Cu65u1PN9J9/UjAtM5Ous4UFBQASEohAe3qSg/qyZUUK1ccBNQBk4vgYp9PSeCO1WsKsWUUT//Fc4ZwjNTUVsowMWNeqRd/Rvz+tf5UgLCxMEkVx15w5c3KefYD+wcvA35psq1Sq5gqFYq5SqRzm6+tr3Kome7DOnEkP/a1bq+12XREuX76czxj78uHDh7kAzgYEBKTV2Ic/JxQKxXg3N7cacQu7f/8+Nm/eDIVCobe1tRUUCgWLjo7m9evXZwMHDmQ6nQ5bt26V1q9fD29vbwQGBkKhUEhqtVoQRRH9+/dn9vb2sLW1ZaiOhNrGhtzKr1yhh4ZM9uIdfDMzaZE/c6ZGZVIAYG5ujubNm6N58+YsLy8Pe/fuZatWrYKnpyd69er13J9vaWmJyMhIsX///jAxMYEgCBg+fLiQkJCAa9eu8R9//BGSJEGhUPDatWvzhg0bCp07d0ZmZiY2btwIU1NT7ufnV665WJVgbEw/16/TxsxglvOSDPvMzMzw8ccfIzMzE/b29uWzm8hIegCnpT3d4uQV4Pr16zh16hRPS0tjjo6OT0f3y0PHjrRRZowIxgtw+H4KS5aQBPjDDyt+3fbtlFUKD6csywuEJEnQ6XSsS5cuL98NUhDoWZKRQURPq6XypH/9i+b8Z59RHe2tW0Qgi7V2fNHIysrCmjVrYGVlBRsbG5iZmUktW7Z8PqZfvz6R6LfeIgnvtm20afbxKe3UPmsWSVO3biUVxqef0kb/3j2aH926UY2lwVfh0iXa/F+6VLFUHSBCYmxMmcfPP6dNtmHtrIaKQhAEmJqawsbGhtvZ2VV5/nDOkZCQwBwdHVmp4EV2NslRKwoWKBQkZ7ewoPs2ORkWdnaYPHmysHr1av2RI0dYeHi4rHfv3mjZsiUYY7h48aIYEhLy1LE/5cliZEQKgu7dKfs3YgQ9KyMi0K1bNyEoKAi5ggAzgOqC9XoyWRsxgurmo6PpMwzE0tiYMtR37+L99u35lbt3eRMPDwH16lFmftUqen/J52OzZmUGqGvXro3o6OinxrhHjx6i/cyZSD9wgGqvS2LePMrS//ADrSULFqDTJ5+woAsXkJubS2T7+HGaW1OnkhfC9OlECqdOLX/8AaxevVrXeN8+WZerV9EuLAylrn+DBhSIcHGh7yhP0s05EbwtW0iNMWAAkJuLEEFQh8yYseLTBQtml/1GICAgoEClUi1MT09X6XQ6yObMoeM3BJ8qQZs2bXDkyBEpLy+v4vtaLifCOGgQ/btjRyKoV6+SsaOvLxHbbt0oaLh/P/390SMi2lOm0H2+cyfdX2UFDCWJAouiSONx6RKtCcuW0Zo4ejRljnv3pvXQUCaydGmR3N/CAmq1Gnl9+sDqnXcoi5yUVKRIMAQQivtNLFhAaqvPPqMA9K+/kifBJ5/QOjxkyFP3IuccgYGByM7OFry8vJ46kcTERJw7d06flJQkZGZmMgD4YOhQ2HAOdu0aWMlOCyXAOcelS5cKCgsLv63whf/gtcDfkmyr5GSskQAAIABJREFUVKouSqVSpVAoPN3d3eVdunSR1bgJ2p49NZ5ViIiIQHR0tJZz/mtAQEB6jX54DYBzfjooKMjh3LlzuvT0dN6jRw9zFxeXavfizs7Oxm+//YYuXbrA29u7+C7iyQNKoVBg7NixQmRkJPbv388dHR2lMWPGiCVf90xwdKSIak4ObVhDQp67zUSFYIw2cDVMtEvC1NQUb7/9trBlyxYEBwfDzs4OLVu2BEBj/iy95JOTk8XHfz6R1TLG4OjoCEdHR9Hb2xsymQxJSUksJiaG3bhxQwoJCREUCgVv2LChNGbMmCq3rKoUBrntV1/RxuVx7/GXARMTk4rr1DmnMoHjx4taEL1CnD59WsrLyxO6d++OBg0aVO1+sbGhtjcbNtCG8/79F5fh1utpQ1xZtwytlohP8f7FxcA5h16vr5oBTiVQq9X46aefIJPJWMOqmgK9CNSuTYEbgCSa33xDG8CbN2lTO2tWue7KLwLJycn46aefULt2bf306dPFo0ePIi4urua+wMqKSIFGQwQnJISy+l98QUFKA1l+/336e04OEYjBg2kj/s03pHjYupUyRsbGpNDYsqV8on3jBn3G77/TnH/4kDbyHh50PF9++UynkpiYKHXs2LFaz6fw8HCekJDARo4cWfo/33yTHKS3VSJwO3mSFA+G2tKdOwEAQ4YMEdeuXQuAyJThGRAZGcldXV3ZgAEDwDl/uq2dJJH8eMIEUhu8+y6N76JFwOjREH/7Ddbp6bh58yaemLCJIj3fDIqeefPoWDZvpr9/8QVdizlzoNBomCYlhUkxMRC8vem9ERFEbKZPf/q8HjwobUAGwMrKCpmZmUJkZCRcXV3BGENkZCRuN2+OHiXrtOfMoWu6fz89i2/cIHm6vz9SmzYFO3MGVu+9R+3kTp6kOXfxIhGsI0cqHnc8MceSdV2+HBb5+eVnv+vWpeDBt9/S+Bqg0VAg+dw5yhZ37Uoy6wULyMhSEHBpyRKpQJJ+q+xYZDLZl8bGxoVieroxdu6sdoeHAQMGCHv27EH9+vVR5VJLa2v6aduWarO1WjrXyEhS6mzfTmO5di2Nr5UVEWV3d1KiKBSUZR4/npQoly/TtTK06GvWjMamc2cyazMzozKB//6X1AtWVkWB9+DgouNq0wYpS5eisFWrqrubN25Mr71/n66DQVY+fTol1/z9qVxl3TrAyAgFMhkuPA7sfPfdd5DJZBAEAZIkgXOOJk2aiJ07d0aXLl1w4fPPcWHuXNzo2BHj163DlUGD0L8C87a4uDhotdpUACFlvuAfvFb4f0e2VSqVLciFMQvAzYCAgMzHvzcHMNTIyGiWqalpsx49ehi3a9eOvTCDmxom2jqdDnv27NFLktT/dSTaAFBYWPh+QkLCNc55GoD7x44dm3fo0KFODRs21Pbu3dvM8CCvSFrIOcemTZukBg0awNvbu9JBdHNzg5ubGwNQ87t+c3Oqx3J2pkV/4MCal2WePEkbhu+/r9nPrQC+vr44efKktGPHDsHMzEwnSRLLyckRlUolLywsZJMnT0Zlmebw8HAcP36cA2BvvvlmuZJIw/1Vv3591K9fHz179hRSU1ORlJTEWrZsWXNEuzjef5+yLbm5FNUuwy30paNNG8rSVZIBeVmwtLQUdDod79OnT/UDU6NGkaTT0K+4mFyuxtClC9UcV+TEevw4ZcrS0kqZoWVmZiIsLAxXrlzharWaffTRR1Vzu68A69ev55mZmez999+vdgDxhSEoiDaW7dqRhHHECKob1GpfSglMeno6duzYwQGw6dOniwARnevXr9d85l+hoMyXtzdtsv/3P8pY2doSiba3pxZLI0fSOj1zJhGxXbsoUxgeTlnVESOoPrQkWYiNJXXR2LFkzrZyJW3oR42i/zdk6p4DBQUFpXxDKoJer8fx48eZo6Nj2UqBQ4fKlFGXgpMTjY/Bffox6tWrhzFjxiA0NPSpYKtcLpccHBzEUgFEnY7q6efNI6I/d27R/82eDXz+OcRt2zB4zx6k1qtHQZ+yMqeGGuH0dApofPEFkemBAyH+9BPYxImS7qefBMU339DrZs0iBZjBINAASSJJ75N/Srh48SKuXbvGTU1N2c7HQYU69+/Ddvx4iEePom6bNvRizimop1RSgJ0xmh/ffEPzy9UVDUaPhru1NY8eOZK1+eQT6E6dQuHKldDr9Xh09y50Oh2cq0DUTNLTYfbNNzTvKsLhw5R9nTGDZMTm5jSutWuTM7W3d6k2sZxzFBQUGANopVKpHjw2ri0TgiA0Vz54oGCRkeRdUE31l5ubG86dOyfFxsaiRYsWz/bwlsvpPAztssaOJQVKYSGRbIDuzZ07KQnRuTM9Yxo2pFpppbKonZ8BEyfSn66uRb+rgteHgfhWGaJIme74+KJ5LZcXeWN4eJBr+oMHgL8/TN59F7MGD8bvgYFcMDGRhg8fLup0OsjlclhYWDxlsNktKQno2hWDvbyQmJ6O61FRPCY+Hj4+PmjZsmWpCxUSEpKv0WhWBAQE/NO/+S+A/1dke+HChR/LZLIFtra2hWq1GpmZmSZff/11oSiKOaIo1nZ0dNR26NDBrGXLlqXqR19nqNVq7Nixo0AmkwVqNJrS3e1fEwQEBGgA/K/Yr46rVKq6sbGxQ+/evbtcr9cr5XK5fvbs2WXuArVaLW7fvo20tDTh3//+98s56MowZAg90CdNooxeiX6Kz41z5ygjZdjQvQSYmJhg4MCBgqenJxISEmQajQZNmjTBw4cP2enTp6Xz588Lw4YNq/Az0tPTkZOTwwDgwoULRS2jqoC6deui7ovs+Vu3Lj38fv+dyFpS0kuTlJeLr79+9f2qH2Pbtm1SXFycMHLkyGcbFEEg+ee2bbQpTEys+Qz3okVPt6EpiYQEymgfOFDmd584cQKRkZHw9vZmFy9e5N999x1zcHDQe3p6Vt2JuQQEQWDNmzeX7OzsXt3DQ6+n9cLZmTbhNjaUGTI4bB86RCTC2JgCEP360et++41klDduUFZoxAjKFDVsSL//4gsimgZjtYMHaZNrakpZ3caNi1zQnxyKHiEhIcjOzsa7jzsoZGdnIzAwkL9RUbum5wVjFFzYsIHkyIcPk0T84UNSXKxeTdlvNzci4WlpFCgdN47Gb9asIqLNOQXnPv2UAhfr19Pr0tKK1owKetxWFyYmJjwlJQWohvoqNzcX/fr1Kz3nli+nZ5OhFVdFaNOG1sLcXMrUm5k9yc43bty4lNlfz549xSNHjkChUBSt7WvX0nsTEiizV9aaKgjYZ2qquzV2rGz6oEFEWocPJ1Je1hphbU0tvwCam0ol0KoVvLZtE/KNjLgiOprB1JRI+dy5dG18fYvKcIyNnzIiW7FihV6r1YrNmjWDUqnUN23alCkUCmHfsWPweeMNtDIQbQDo04cynwbiHxdH0mC5nMbm5ElkduqEbqtXs4P+/jzt999ZRGwsz166lImiCIVCwQsLC9mHH35YupNDMWRlZcEqOZkIWGUwN6d1NS6Ozm3YMFrjbG3LfQtjDH369NEdP358h+FXJV+jUqmMAIxijMk8jx4lQ7Zn3MuYmJgIISEhyMjI0I8cOVLU6/VQKBTVVw9JEs3H4GDa/yQlkYQ8IYFI844d5EcxZcoLC5gLggCtwdCuqjA3p/WlrPJQM7OivVxQEJCdDbP//Q8fHD7MVvv5ieq9e9Fg/PjSrQdXrqRAjLExsG8fHPr2xadDhrDNmzfj1KlTUkmynZ+fj+joaIFz/kc1T/kfvCL8vyHbKpVKALDU3t5em5qaaiQIgmRvb18giqLC1dW1nouLC4yMjF6TdET1cOHCBcTFxYXp9fqRf7UoVkBAQCqAn1Qq1VEAwywtLecDkOv1enDOIZPJoFarsXbtWp6ZmcmUSqXk7OzMhVfhBl4eBIEelIzRpu2DDyiq/7y4do0kbK8I5ubmKO5TYGZmhtzcXCEwMFCPSpQCvXv3RkFBAb98+TKrMef+msa4cSTrunOHNtM7drx8N+1Nm4gIFJevvUKEh4cjLi5OmDJlyvM70/v5kVT39m0iPl9/XTMHuWoV1fmVR4qjoohsPXxYKquu0+kQGhqKu3fvQiaTwcPDA66uruzixYt49OiR+Ntvv8HU1JQ3bdqUv/nmm9Uizaamprh165YQFxeHRmUZddU04uJo4+ngQJnE3r1JWn/2LGVrg4Np7O3siFRHRdGfgkDSU72eMtxNmtCPXF7UisrTk7JDjFFGiXOSZ6alUfZ44ULa5BYW0t+Dg2lzbmkJbNgAadAgHHN35/qcHDbz7l1mMnUqMH48bmdnS027d0ebX34RMGECZR0vXqT63iNHyOCsYUPaWDdoQETpeQJhTZrQZ+fk0OcbgggdOtB5FBbS98ybR1ndIUOI/G3aVCRhTUqi8544sShD9oKCc7a2tsLDhw95Xl7e09LsciCKIlxdXfnOnTtZTEwM3nzzzaJEAefVU1pNmUIk1cvrqex2WejQoQOysrKwb98+tLl8GaIg0L3evn2lYxMVFSVz9fAAa94cOHUKujt3UOjpiRxHR9iuWQNWnJg+ekRy8vh4ug4zZz7pJ2+kVrOCGTNg3L07Xa9336VOHatWURa8Vy8i3snJwPffI3jxYoz4/nvRwcMDoq8vw8yZYuH06Uj38YGvuTnsQkIo4FC3LikXWrQgWXNYGAVsDGqHf/2LjmvxYlgtXoxLDg64fucOE0UR7u7urMvjjKmZmRn74Ycf9D///LPg7+9fpu9IRkYG9uzZg97DhumEoUMrfvgYSma6dyfTsuHDKeBVBXh4eMhPnz4t6XS6yHJe8gGA5dYpKTji56eJadJEZxUUZNKkSRM4VNNUcvTo0Xj06BE2bdqE77//Hvn5+VAoFOjWrZvUuXPn0gZ+JWEwMezQgc5z3TrKbBsUjwavCUP5RkIC3cuXL9eIT0h6ejoyMzNRr149CILA9Xo9y8jIgCAIqFWVNomdOpH6YNasil9Xty79rF4NITkZ7idPSrq5c4Wcs2dhPmIEeSh4eNCa/u23pMoBKOAzYAAEQYCHhwe2b99e6iYPDw/noigemDNnzrPb1/+Dlwpx3rx5r/oYagReXl78woULtfLz80O0Wu1IvV6/Ojs7+3xWVlZIfHx8+9jYWFhbWyuqdDO9Rrhy5Yp0/PhxHef8XwEBATdf9fE8K7y8vDKDgoKitVrteM65+caNG6XIyEjdvXv3Ck+cOCHTarV81qxZrGfPnszNze31kx0YNhjXrlFEHCjq6/os4Jwe7C4uZbusviJYWFggODhYaNmyZYWbwYsXLyI4OJiNGDECXaramulVQBRpM3fqFG3ECwtfrsO8iQkRitdgjHJzc7F161bu5OQkdezYsWbuMZmMMlK//kp1wzVBUubOpU1Ku3al/+/QIarlK6N/dE5ODpYtW4Zbt25BkiTu4uLCWrRoASMjIzg7O8PV1RWtWrVCfn4+Cw0NZUFBQbhz5w4aNWpUJYm5s7Mzzp8/j7i4OHTu3Lnm1FE6HbnkPnhA0sjp04kgzpxJJHf0aDJF8/CgetG9e6lG3d6eMiFKJbmS/+tfNL/Pn6eNrCjSXLe1pT/t7UktIJPRhr5+fSLygwZRtqZvXyJUZma08WvXjjaWs2bRWE+bBowfj4jEROzPz0duixZ8+LRpTNmoEdC2LTQJCbiYnMz6jBnDjC9epBrN69epVvqddyjj8+gRrZv+/rT+/ec/RHi++IKOLzubggTvvkvE7rffqJSnf386DhMTOsctWygrGRlJm/GGDWksZs6ketbduymb6+JCpTqpqZTpXrqUJOXW1pTF9/Sk4zK0gHzBcHR0xJUrV/iZM2eYi4tLleZd/fr1mampKS5fvgzOOZycnCjT27VrUfulqsDbm8ipuzsFTZKTqea/HGRGRSEmPh49Da3m+vWja1QB8vPzcebMGaSmpvLTp0+z82FhCI6ORrSjo16MimK3T53iNqtXM+Wff9Kxt2xJ8vBOnQCZDNzdHfffegt5oogrzs7c6eBBJvr7U1a9WzcKHn32GWU+Z80C7OwQXVCA3U2aSDFaLdq+9RazGDAAqF8fegC7YmKkhteuoZ63N8Mbb9D68csvNH6ZmXSf/PAD1ePn5NC9MmMG3VNNmwKurnDo1g03btyQ3NzcuLe3N1MoFE/kvx07dhQePnyIw4cPszZt2sBANOPj43HmzBmcOHGCd1QqJa8PP5Th008rDo688QbN5+++o3/n5lbZKTwjIwPnz58H59zBy8urVEImKCjoisO9e0kT1q3zylEo3r9qZHTq3r17UWFhYR0cHR1l1dkXM8ZgYmKCTp06CQqFAv3794eDgwPOnz8vBQYGCowxycnJqfSDQKulZ4WLC93/b79NgTG5nNaEkqhThwKunNO64ONDwTCttsrjUhycc+zbt0994MAB/c2bN+POnj1rWVBQIKakpOiDgoKEixcvonv37qXWdY1Gg4KCgiLJt4UFrbH29riZlQWlUomCggJs27YNeXl5Re1ki8PMDPZubiyqUydpc2oqkw4f5nV//JHx+vUhHjhA869ePTo3Q226XA4jIyMEBQXB09MTBg9Tzjl27NiRl5eXN8PLy+tetQfiH7wSMF6ZAc3/A6hUKjljbKxcLl9obW1t7O3tbf7SeqQ+Jw4dOqQPCQlZ/eWXX06v/NWvP1Qq1RsADgiC8AUAC0mS7oqi+Jler3f28vKSevbs+foR7bLg5kaRdcODsbqQJFpYX5faz2LYvXu3PjU1lU2ePLncaxEZGYmdO3eiXr16Uvfu3QUnJ6eKDcJeB9y4QRvNu3fJNOVFQpJoc7tmzYs116sGQkNDceDAAcyYMeOZzPAqxZ9/Un/Q4OBn9zbIzyfSVBZpT08nOXNk5FNZ7/T0dMTGxvKDBw8yQzagdyWu5CkpKdi9ezdPT09nOp0Ofn5+1NYIQEXG7Ddu3MD27duhVCrh4uICtVrNhw8fzqpEvCWJzmHXLiKy+/dTtjU4mJQy/v6UeY2KorXFcI00GpIMr1hB575vH8nsJ04kwswYvffWLfpcBwf6zBoySZMkCVFRURBFESYmJli/fj2aNWuGtw3O3o+xcuVKva2tLUaOHFl1VZJeT9fczKyo/loUqdXTgAF0HrGxlJWdMYOCAUZGtDn94Qci6w8fEoHq2pVeo9PReG3cSKTdYKDFOY23r29Rq55XgLy8PCxbtgyenp5PbaIrw/Lly7mLiwvz8fGh7G9yMklVqwND7+IJE2h+/P572a/T66G2tET42LFSl9Wrq3wza7Va/Pe//0Wb5s3h6ekJYd06GKWkwOjtt8HffRcxXbtCCgmBc5cuSJfLcaxxY32KQiE0btxYMjc3F0NDQ3m3gwcRPmCAngkC3vnmG5mxuzsFuKdPJxISGwv89BPi4uIQ+Z//8Prx8Uz44Qe4uLhAXiyQGrNmDZK3b+ddDh1iMpmMavzd3CirbWxM99kff5Ak/vr1otZ6/v4UtCmWgd+6dSvX6XTS6NGjy5zbK1eu1FtbWwvt2rVjgiBg+/btaNCggT4rK0sYP3IkM4+NLd/borCQAj+HDtEcbtiQ7glXVzquKgRkjh49qgsJCfl+zpw5H5X5AsZEAEoADcH5TQBQqVQyURTPd+vWrWPPnj0hPmcp0JUrV7B//34MHjwYbdu2pV8a6qFHj6bnbnAwlX48qxru88+JdFtb07X73/8qf89jpKWlYc2aNRlarbZRQEBAtkqlcgbwAWMsn3O+TaFQbO7Xr59byZK4hQsXcp1Ox8aOHQsnJydIkoS7X32F2zdvSheK+ShYWVkhOzsbMpnsyfm3a9cOVlZWuHr1KtLT03Hnzh19bm6u2KlTJ9yKiuJjVCqWZ2nJE9u145bm5oKdRgPLxo0hFFOJff3115g5c+YTr6N79+5h48aNiRqNpsFfTen6d8bfgmwboFKp5ACGymSyX99//33jOhVEdV8XBAcH4+TJkwAgBgQEVMPJ4fWFSqVSBgQEFALAwoULV2k0mikAMHHixGpLml4ZDBmaPXuIMBsMMqqCvXspc5Sc/OKO7zkQHx+PzZs389mzZ5e7C9RoNFCr1Th48KB0//595OXlCXPmzKkR1+cXipAQymasWkVmZS/Ku0GrJeOXtWtfuMt8VREXF4fNmzfj7bfffjEy6ORkkmL++99k2vQsta6uriRPL+n2/NVXVJtqZAQIArKysrB9+3YkJSXB8AxzdXXF4MGDqz0Hg4ODcebMGWi1WtjY2GBqGSZ2kiTh6tWriIiIQEJCAgRBgEKhAGMMcrlcmj59uvDke/V6kk/HxFAAYtIkOqfMTMryLlpEZMfBgQhgq1al56FWS4qMQYMooz1mDGXefvutyPV2/Hgy/Soe5EpMJPIdHExrUg0E80JCQhAYGMjlcjnXaDSCUqmUJk2aJJQM2CxatIj7+/szJyen5/7O54IkUSZMo6GN/YAB5HZc3MzrFeLnn3+WEhMTBQDw9vaWPDw8qrQIBQYG8tu3b0tTp04VnxiDVXdtGTKEAhTt2pVdQ52URON19Ci+W7VKch84UOhcmZTZULKwahVgZYVHaWnA8uU41LcvWubmSu26dhXw4Yd0XZRKrFixQq979EjsERjInfV6lj1jBsJycpAhiryzoyNzmzQJ7PZtmrtJSVQCtGsXSf0LC6l0onlz/LFvHxpcusR7ShJjP/xQ+lRmzsTD0FCpTXCwAM5J2TRkCGWQT52iQKi/Pyk9/v1vIoM6HSkdGKPa6ceBkIULF3IPDw/WsWPHMoPKKSkp2LNnjz4rK4sVFhYKDRo04O+88w6DJNG9u3p1+QGerl1J3Vayjdzs2WRs9/77pd4iSdKTDGxeXh6WLl0KANMCAgJKDwQAMPYDgCbg/EnPPJVKZQYgBwBMTEzUU6dONapKaUNJPHjwAEFBQYiNjcXw4cMpaMk5XS9XV1rz7O0pU11T7QgPHiT5+Y4d1CJywoRK22lGR0dj586dkZ999lmZfdVUKpW/jY3Nzx988IGpYWwlScJXX32FVq1a8YSEBK5QKHhmZqbYMDOTd2WMO379tcAYg0wmA2MMmZmZOHv2rJSZmcmjo6NFAJDJZLC0tJTMzc15mzZtRGdnZ6rxj4mBRqlEkk6HpKQk5B04oO+6YoV4tmdPJAwbJvXs2VNo2rQplixZIo0ZM0awf6wq+fPPPwtu3rw5b+7cuUtqZjD/wcvAa74zrlkEBARoAWxbsmTJtLS0tB5/BbJdbENTC0DGKzyUGoOBaD+GBwDIZLLsxMREMwcHh79GZtuQGb1yhTYaQ4ZUXUI7cCBlXV5jaLVaduvWLQBA06ZNERERgZycHOTk5CAqKkqvVqtFrVYLIyMjplAomJ2dHRdF8RW7kFUBnTrRJmDxYpKmvois87p1RJ62bKn5z34ONGrUCC1atOAbNmxgAwcO5O3bt6/Z62VnRxv5H3+k+u27d6sfzNi6tXRrHI0G2LgR2v798Vt4OE9KSnoSJO7QoQM6d+78XIZ7np6ecHd3x7Jly/Dw4UP88ccf+s6dO4tNmzZ94la7dOlSSS6XswYNGnBXV1cMHTpUEAQBmrg4bNu7FwdGj+YdHjxg9TdtojpLX18iLfn59CUbN9KG09SUzrE8aLVU/x4QQLXZs2dTJmf9eiLn7drR5vv2bfqz5Mb/zh0iETodZYoXL37mcTHgwoULvFu3bqxnz56G+VLqom7evJkDYPWr2LP3hUIQSFru4UEkzdubghY+PlSb+wrNUVNSUpCWlsZGjBiBsLAwREVFwaOKxokmJiZMbyC1t25RnWd1sWsXyexFkQhKrVqUHczNpczqoEE0XrVro9DMjJ07dw6dOnUqyr4b1BlbtlCWeP9+mtvnz1Mg09cXVh98AEybBtfoaBw7doy3K2HgNm3aNDE0NBRt5sxhCpkMdQIC0HjnTmD3bga5nNYNA+ztgY8+orKA33+n9XvdOuTUrYtYuRw9+vRhrLDwqc+HJAELFiBnzBicatxYatOnj4B+/SjwtXs31QI3bUqKiBMnKGhlZkbzQqGgOv6MDLp/vb2Bjz9Gr1692NGjR5GcnKz38/MrFaWwtbXF5MmTRYA8doKCgmjAHj4kNUpZQZHoaApK/vFH2Y7t778P/PgjMtLTodFqwTmHkZERoqOjcfDgQdStW1dq1KgRrl+/LigUinMajabs1l908X4H8FR/xICAgFyVSiUDIGq12tjU1FSH6gZhb9y4gV27dsHJyUkaN26cUN/BgVqS/fEHKQe2bKFygZq+5954g360Wnre9utH97yxcblleffu3eNqtfpEBZ+6LT09fc2ZM2f0np6eIoAnmeqBAweyZcuWsWbNmul79+4NE8YYfH0Z9PqnlAe1atXCwIEDBQA4fvy4lJeXx3r06MGsrKyeHoD164F586CIi4OTKMKJfivi+nV0NDMDrlzBpk2b4ODgICkUCh4fH4969eohLy8Pt27dYpIkVWJr/w9eN/ytyDYAqFQqplQqG78QGeULgJubG8LDwzVJSUknALR91cdT05g9e3YbAFCpVD1Pnz59oEmTJqa1ayr6+TJgaE3y8ce0uT1R0VoOqqts0aJ0v9DXCE5OTvD09NRv2bJFVCqV0Ov1MDIy4sbGxlwQBHTv3l08ffq0NGjQIIExxo4fP86HDh3KqiqHfOWoU6eo1YirK5GbtjV4a929+/KN2KqIN954g928eRN16tR5cRdr8mTaFGdn04bL3b1q7wsIIKmli0vR70aNonreqCicPnGCp6WlYdCgQYiKioKPjw9qKmCqUCgwbtw4HD58GJxztmvXLm5vb89HjRolAIA+K0uY0L49rPv1Y5g5k+S7vXpB8eWXGP3FF0KCiwtuZWZi+x9/SA0XLGBDxo8nafngwZV/uVZLATtvb5KQzplDDsQNGhSR74sXKdvv7U01x5s3U7a2JOrUKXK3TUwk8t206TONSVZWFgIDA/U2QgpxAAAgAElEQVSZmZlim+IuziWwY8cOnpCQwCZMmPDqlS35+SQhnz2bAqFr1pAE3dWViNS9e5QtfEU4fPgwb9KkCVq1agVBELBjxw7h/v37qEqQIjw8nNepU0eATFYUxKkuLl8ucmgfNIjItl5PionVqyn7+1iaO3nyZPbbggXIWbIEFiNGkEQ/LIwk/hcv0nPso49IMWBp+VRwsbCwEKdPn+ampqal1hmZTFbUfxsg1cqUKRSE+uQTIu9+fkUErUMHSBERkNq3R46FBbb6+qLFxYtoPXWq3tHMTMSNG09/wd27wC+/QOnlBVZQwDBqFJWdjBpFa9GGDaT8eOstul8SEqgnswGMUZZUpSKCfvky3Js2RWpqKhITEytdN1u3bo0jR44gLy8PppxTp4Cyno2TJlFXgfLagTk6QvPoEbZ9+SWyGzaUAECr1QqiKHJPT08GQAgLC9PnUu/ybgBy5s+fP5Jzvp0xduDLL78cBMYUAMIA+ILz6DK+pT6AeO1jMp+amgobG5sqlTZIkoR9+/bx/v37o0Pr1gJcXWmt9venshjGqBzmRUIup8ATQD4esbFklHjrVimpekpKSgHn/E55HxUQECCpVCr/kydPHmjRogXq1q2LzMxMKBQKbmJiwv5DbcmKAi1mZtRNxuDhUwJ9+vQpO8KQnU0dH3r0KFKXfPrpE/NIa8bQr18/IT4+Hg8ePBDc3d2l4OBgfubMGSaXy7kgCFsCAgL+XyTe/k54PXeELxa+JiYmtcrrC/y6ISQkRHf37l25KIp7X/WxvGAEq9XqeatXr57fv39/ow4dOvxFmNtjfPwx1QTn5hLp7tGj7Nc1aUIb6tccXl5eYs+ePQFQdNfc3PxJf9jExETY29tLu3fvFmxsbPjYsWOZdSUSrtcOjJEkedgwuh537z7/ddFqyblZpXr1rcbKQVpaGvR6PZ61/VWVIAgk1f/1V8p0JyRUbTzu3CFTKwMMfXA5h1qtxtmzZ9mAAQPQvn37arWaqyrs7e3x3nvvAdeuCRrGsDo8nD1YsQKihwc8rl2D1cmTFAho25buYx8fYMgQMMbgCMBBr4f97dvCkSNH+Pfff6/39PQU27Rp85Thzvnz5yFJEkxMTFDf1hY2ly8Dn36KmK+/xq3WrbnzhAmsucH854cfiFQbamw5p/XFz48Id1lwdaVMT2YmGa4tWAAcPVptF9+YmBhs27YNtra2bNq0aeW69Obn5+PatWts2LBhsLGxqdZ3vBBcvkykbdYs2rzev09jIpdTFnbBAjLlGj78lRxeQUEBZDIZZ4yxli1bomnTpti0aRMmTJiAytZQxhivo9cL6NmTNuvPgg4dqH8x51Rm0LcvBXGCgyk7uGYNEe433oClgwPqGxvzwshIoF8/hiVLqKa+Th0irOVAo9Fg1apVAMCnT59etZSmvT0FoBs3JgLq4gKMGwfps8/wIDkZgWfOwMbdHf2OHUOvTp2kZk5OAlOrRVhZUb2zAWfOUDu8mBjUd3RE+5YtBQwYAMyfT+e8cSOdQ/HSjcOHyzaw7NKFfr78Elnr1uHaO+/A8403Kl3IEhMTIZfLYSyX01wLDiYVgAF79lAZ2uHDFZd5CAJ0nTujVnAwPvjss+Lj+OQYevXqJep0OoSHh+PAgQMwNzf/Izs7G5zzNSqVSphRq5alVWbmEQAx5XyLFwCIonh627ZtjTnnShMTE9OpU6cay+VypKam4tGjR7h375508eJFJpfLCywsLHTt27c3S05OFlxv3UL71asZoqNJzTRgQI22yqsWfvyRrvGdO6QASkmh5465OTjniIuLEwH8WdFHBAQEHPzqq6+iVq9e3WLu3LlQq9XlBxBnzqSgUzlku0xoNNRpY8ECWsdv3SKi/csvT2TwsbGx2LVrF8/NzWVKpZL37dtX6NOnD27fvo2tW7cyAD9W/Qv/weuCvx3ZVigUy/r27Wv6svps6/V6CIJQZRMUAwoLC3Hs2LHCq1evyjnnG3Q6XcALOsTXAgEBAVylUt1hjB0+e/Zsvw4dOrzmblslYG9PP6tXk+NtTBnPtq+/psj9a2iKVhYMc9ayWK2ZwRhNoVDIOOfIyMhgrzyb9TyYP58ygc2a0XV7551n/6wbN4p6Fhfr/fq6QJIkHDx4kDdq1EhCJa3dagTjx1M2KSyM5Kvz55f/2pQUkh4aIv19+lDrtseS65S7d8EYKzLeeV4YiPzWrSTFNjam+3PaNODAAShSU+E0ZgyuxsYiTiZD888+0wkDBtBEL8fcRxRFPCZQ7OjRozhy5AgOHjyIxo0b8759+7LU1FQcPXwY9nZ2ep/vvhPyNBoWNHmyZPXuu8LF27ehtbVlsvR0NI+MpM3Yu+8SqXFyouPt25cyRipVxec2dy5t8C9fpux2Rka1yHZYWBg/cOAA69u3Lzp37lzhgzIkJAQKhQIuxdUIrwJHjwIffkhZREPP8du3KZNZPLBua0vz7BWR7fz8fKl169ZP7r233noL69at45cuXeIDBgyocKzNzc257e+/cyxZwp6cY3UhCBSwcXenzG5GBgVlFi0ipc/166TGWLAAmtatcfPnn1kbf3/YODs/+QhDBrROnTqlTLVSU1Px+++/Q6vV8k8//VSo8rMhJITm/J499O+tW4GgIES88w4epKfjfvv2cP3vf7n41Vesubu7gMmTqW533ryns/wffkhBlsWLkfXtt3CdOJGhYUO6tw8epPP393/6u5cvJ8O98taW+fOR07s32i5dimapqQxdulTY0eLUqVNS+/btIYiigD17nibaAJFsa2twhQI6rRZqtRqhoaE8KSlJ0mq1zN7eXqhbty70ej2a9+kDrzlziKSV80yRyWTo0KEDOlAvaqMtW7Zob926tcctPBx3mjWL7nzxYkXSli0AAv/zn/8kAoBKpbLXaDSJBQUFuHz5si4wMFCSyWRxGo2mOQDo9XoXtVrtbO/ndyDP2VnZbP58xgYPJiXXiBEVfM1LAmP0LM/MpH2WszPg5wftvHnQajTKgHnzUir7CEmSeoiieGn58uV2ubm5xgqFouzNu6NjUeCqqvt7QSAlx4gRtO/49VfqulAs0BYXF4e8vDwGAEZGRhwAEwQB5ubmUCgUDzQazaWqfdk/eJ3wF94lPxs454rY2Fhtw4YN5eW5J2s0Gmi1WmRnZ8PMzIzMDJ4BBQUFWLZsmc7Ozq5w/PjxpsUfTMUNLkriwYMH2L59e35ubm4w53wmgDt/B9dBhUKxRKPRNPP09MShQ4d0Pj4+sud1yHzpmDKFaq0iIijyefw4LbCZmWSWNWkStTX6i0Iul0Mul6NPnz5o27ZtUTuMvzIEgSLMlpZEZAYNogxQdbBvH/WvNUjaahgajQYAnmu8jxw5gpycHPj7+7+8m0qhoLZLZ89WvCnp2ZMi/QYi6etL7akeQ6lUQpIk6HS66kuVc3KoNvKXX2hDfeEC1cwFBtLv+vYlM7v//peyDm+9hQcPHiDi558htWkDPz8/tGzZsspfKpPJ8MYbb4j9+/dHYmIiTp8+La1dtUq0i4/HZ2fPwnjFChHffovsJk0ghIXxFLVaP9rDQ9y/c6dkn5wsPDFQ69evaLx0OsoajRpV+QHMn0/ECSDH8g4dKADk51fh23Q6HY4ePSpdvXpVGDFiBFpUwTG4U6dOOHv2LI+Li2POxQjZS4MkUWCzWTNae4vPjeDg0vNt0iRSs8ydS3PtJdZvHz58GPn5+WJJVYmtrS2LjIxkffv2rXBuN2/eXDzcp4/UesaM55PNLF1K5DYvj9quLVlCEtwPPiC/gdq1gZUrkbV0Kazv3YPVgQPg06cj8eRJRD16xOMyMpCSksI453B3d9d7e3uLhsBsaGgo8vLyMG3atOoFYfPzn26/1ro10Lo1wu7elXrGxAjGpqa8bVAQw9SpdB/s3UvP0/v3KZMqSSTpPXuW3vveezA3MUGitTVqdepEdcNltUmTJFKiVbKu1u3WDXfc3Hj3y5cpi2tkRKS+DBgZGfGLFy+K7X/5BVaffoontHzRIsq6rluHP/74Qx+/YIEoSRJkMhlq167NHRwcxOzsbFy9elXSaDSCTqdDnTp10M3ODnb799O8rQRqtRpxcXESADRITEx0iYz8o6LXP/bPMRBtHwD7AOrwcOLECUmSJJfZs2dHL1iw4HSd+/e7v//TT4cZ0OVRrVofR7i5rezZtWvZRnuvGoaERmgowDmEn3/GzBUr8JUktZ87f/7Vit4aEBCQplKpmuTm5noAcJQk6ee0tDSjUiVLjRpRoDYtjdQUleH776nU4uxZKq/y86NnUIn9YHh4ODjn8PHxgYmJyZMF6vLly2q9Xv/z34EL/H/E345sa7Va97CwsGUZGRlvjho1yrgkmYuLi8PGjRu1jDGNKIoZnHOb9957z8jU1BQajQbGxsalemNKkoSwsDCkpKRovby85EZGRoiLi0NERASXyWQXU1NTtRcuXPA0uI5GRETod+/ezdq1a6d58803SzUY3LVrV05mZuZ/OOff/51uLI1G01upVO46fPiwvU6nE2rXrm3bpUuXv4ZhWnEIArXtadmS/n7vHj3M4+Nf9ZE9N1q0aIFevXohMDAQarX6iZHIXx4Gn4DISHqItm9fPSn4559TcKUM59hnxdmzZxEUFASZTCZptVpBkiQYGRlJjDFIkgRTU1PesmVLsVmzZrC3t6+w73NKSgrCwsJ4//792Uv3q+jXj37++INI7okTpcc2KIhMhLy8iBCXMH+Sy+UQRRHBwcHw8fEp+3uys+knOZnMlN57jwjY3btFG5wOHYjITppESpTAwKL3F+shfPnyZUiShHHjxj2zc7vAGBrUq4dRhw+L0pUriP/pJxiPG0eBBcZgAWBY48Z0/6SnY8CaNYJxgwYUoCs+Ph99RFna/fur9sVmZtQmzNAbe9EiOvdKMjA7d+6UoqKihMGDB1eJaAOAiYkJLCwspIyMDPGVkO1Fi4h0xcaW9sHYtKlsEzFzczID69aNiNtLwPnz5xEWFoaJEyeiZAlb//79ceXKlUoDSWa7d/PBu3YJ/NNP8cxsOzKSSl2uXiVynZlJ/gCff06GU05OFNhRq2FTty5a5+byzBUr2NqCAkxcsgSW7u5M3bIl5nz3HeIuX0bC9Oli/uLFMA0MhN7PD7KmTeFoZcWtFy0i2fnNmzTvXFyI2Ja1RiUnk+HoypVP/TowMBAJlpZC6ooV8Kpbl2HBAvJ1+OgjuraDBlEWfsQIWldmzKDezUOGAN9+C11EBDZs3YrZEyaUv9Hdu5ey42FhFQ7b/v37kWlkxPL/+AMWEREUvPrhB3ISL4HRo0eLp06ehPbnn/HTr7/C/t49dGzdGnZ16qDA0hK/rFiB7OxsccKECUhJSYGDgwNsbW2LD4wgSRIyMzOxdu1afrpHD9bu/Pkqke2EhASIohg5V6XaKnC+HpxXubZXqVR2LywsVAiCcGzTpk2ddDod1Y4w9uOnCkXk+a5dpzKgN4DcHz/7rJEkSXzDhg15ycnJQtOmTYVevXoZWb3olprVxeNnnWzyZFxOTi4QOfcCYzsAjAbn58p72+POP6cBYPHixeOTkpK8S5FtxihgdfYszbnK0KoV9YjPyiI/ifHjy0y8NG3alCclJfGuXbs+mRM5OTm4du0a1+v131fhrP/Ba4i/HdkOCAhIVKlU4xMTEw8sXbrUvVOnTopu3bqJSqUSjDEcPHgwT6/XTwgICNgKAF9//fUHa9euXcYY0zPGNHq93sLW1lYzadIkE8YY9Ho9fv/997yUlJRber3eJC8vr8Xdu3fztVptkk6n26HX69cBsDp79uzJjh07miiVShw/fjyfcz4/LCxs8cCBA5/aJCckJODRo0cC5/ynvxPRBujaAOgMUBuG27dvr+nSpcvr0TepunByolrLqCiqGZTJyPioVy96sLdvT06lWi1t+u7cISmRnR1F+evUoffI5a9d/W/Xrl1x584dnDx5Ujx58iTatm0rDR48+K8XFCkLfz4u6fL0pCwPtVQpH2o1Xbvr12v8OgU+JoK+vr6CtbU1srKyoNVqBcYYTExMEBMTI926dUu6cOGCoNVqoVQquY2NDTMEQdzc3KDT6bBu3Tr9w4cPxdatW/M2bdq8usnUsydJViWJxs1Q2zdlCjlHDxlC5KkMcmttbY1x48Zhw4YNcLS3Rwu9njbop06R5PTnn0nePXIkya9r1yalwv799DpBoCx2FTFgwABERETgxIkTGD9+fPV60Op0RDImTKBzmzABwtdfo3HxzJ0BajX4ihW49H/sXXd4FOX6Pd/MbnohCaSRAgklIYQSegcFlN6kKogICIj9Cnop64ACYkG5eAFBQCkC0ov0YugBAoGQhASSkN572zIzvz/eLNn0AijcX87z8CRsZmdmZ7/55jvve97zXr6M8E6d4PvRR3AyHEcaDQUgapLRNoSpKZ0HQLXlAMmGd+2qcJxmZmbi0aNHXKtWrVCVGVpFsLGx4ePi4sROnTr9fYG3ixeBCxdINvz+++U/U3AwLW5bty7/XiMjynqHhlKG19Cs6xnh5s2bctu2beHo6Fju4mdmZgKgoH1FkGUZsiwj2cyM6dzdcWnzZmnSpEmciUm5OH3VCAqifsd791JHjDFjqLxj8WKS13t7l2x74gQAoPv27SwhIQGdwsKQc+MGOjVtik6SBEyfDo+WLXGtQwfxklrNehYUcI/S0+VIKyvWysOD4ddfyTx0xQpyLz96lMbkzp1030+fTkHoTz6huledjuTWgkC/37yJS3fuwN3aGn6+vpRB/OUXel6OH0/BJ0dHepbKMgXWzM1J1hsSAixfDrPi8r3w8HC00vsglIWTE80XVSA5ORkhISFwc3OTHR0dGRwdiSQplXSP//BDKbdxjuPwko8PEBWF7nfugP/0U5mFh7Pl06eD53nIsoxp06ahcePGlbY65TgOtra2mD9/PivKzSWSVgOzQ41GgwZpaTJHisgNVW5cBp999tliAIsBINHJqYlNZuZhjZGRCOCKsUYT2/f8+bsA7gKAdsmS07IsfxIfH39Cq9XOv3fv3owHDx58MGvWLJO/I5hba4WTUgnfuXNNAzZuXJro6LjCKSnpDhhbDMAesjy3srcJgmClUCj8lJWVDYwYAaxfXzXZ1uno2bdpE/3erRvddxW4pkuShIiICBgSbQC4du2aluO4HSqVKrVmH7gezxv+35FtAFCpVIUAXhIEofW1a9cWXLx4cawsy5yVlVVhQUGBFsBu/bYLFixYBwNDguXLl6/jeX6K/v+XLl0Sk5OTA9VqdT+lUjknPDx8gVarfV2lUp0xPOaKFSv279+/f4y3t7dJbm6uGYC7HMdpjx07xkRRFAGwrKwsXVRUlIWRkdFPZdpj/b+CIAiNlErla1qtVilJEkJCQhATE6Pt2rWr8oVyKgeIAPzwA7XgcXGhyTY3l6KbV66QxNbRkSRGpqa04LhwgTIu33xDctB168gh1sWFFo9hYUQqdu+mB/6779JixsODiEVKCpHFwEBagHTsSLWb9vZ0jGLTkLrKv06dOoWoqCgAgK2tLW7fvs3FxMSIs2fP5l/o+m1DfPcdLfDu3aNrVZmh2I8/UmbtQUVGr08GX19fOTw8HKIoMicnJzg5OZX6u4eHBzdgwIDHi/HY2FgWGRmJvLw8/s8//5Stra3ZwYMHRSMjI/bee+/B2tr6nw2IuLnRovHbbykQ9fAhjUUjI8pq7dhBdZWGSE2l+6FXL7guWoQZYWG4lJIiefn7c3j/fQpKjRpFxDo9vURGXMN2SpVBoVBg1qxZWLNmDY4cOVKzYJJOR4ZwO3aQfPGLL+g+riwIc/UqkJGBwqAgXGjRApyzM5p6eJT8/cABUks8fFh7d/tPPqE6+IICGsc9e1JNbGxsubGcl5eH1atXAwA8DI9fQ9jY2CAj4280x9VLhh88KN/6TI+AADJJquzam5lRqc+KFUTOnrEUtrCwUG7SpEmFY6hRo0awsLAQo6Oj+bKkMCkpCevXr4elVgvb7Gy52b//zVIuXmT37t3T1+jWDOnp9CxYtYoW+Wo19bBOS6NgzoED9P8KzJ6cnZ3hbKD6AMc99i0Y8eWX/Pr166W7a9eik0rFUi5cwIy33yYSCpDCRI+sLBrH2dn0TASI4HfqRF4E/fsDWi3yjh+HdulSYMYMjFu9GsrAQJovHB2pTGfwYNrHyJElLQYvXKDnpK0tjXmdDlAooFQqcerUqcrJdno6kfcqcP/+fYiiiNzcXFZYWEjKxnbtKKvJcRTAGjKEiLse/fsDkyfDb8wYYPVqJkkSPnB1hZWVFZYtW4agoKAaOdADgImlJd2/0dHVkm3L/HwoCwudADSFLOtqdABDMPYBAB8n4CMAp0zU6jTIcrmWYosXLz4uCIJCq9VKxUmhz5YuXdr31KlTHcaMGfNMFwHJyclYt24dGjdurO7atauxra0tnJycqvRF0vsMMI5T/DxrVrhKpcoDYzcAOIExc9CafzxkOa/MW50UCoWpt2EgyhA9etC9o1ZX7sWTmUlzriTRmJk/v9L2ZFlZWcjNzWWuBoFZjUaD69evixqNZnmlH7Aezz3+R1bGdYNKpQoGMFEQhGkAdDk5OX0BJFaVUeZ5voO7u7tpWFgYHBwccOHCBa1Op5uiUqlEAP8p/lcOarV6RnR0tNWDBw/6yrI8WKVSXRQEYcCNGzc6AlCDHCazAez//PPP85/2Z32RYGRktEuj0fSLjY3F8uXLtTzP39NoNOY2NjbNu1Ug23ruMXcu/btxgx7s9+/TosNQwmhQn4p58+jnnDkUyZekElmzVkt1ah4e9Ht2NslGbW2JeKemUgTcy4sW/Wo1HXvzZnpPTAw9tD/9lFo0WVkRKThwgDK58fEkxZ01i9rBNGpEC4fAwMfZeM/wcNzNzkYHS0t07tgRt5KScP38eV6Tng6FpSU9dJ7HOq7aQJ/tmjSJTISOHy+/TVISPTg/+uiZnMLo0aOZv78/jh49CltbW7hWlBkFGdkxxuDu7g53d3dIkoTY2Fhs3rwZnp6eGDt2LGf8PJnyffwxLerT06nW/fXXSR4aFERBjilTSDLZti0Flw4dIqObYcOQ368fIjMyZNy8+cxP087ODs2aNUNYWBiXkZEhv/XWW+VXczod3TMTJxJhGDGCsnZ2dkQKKoIkUZbjwAFI8+fj2GuvQRcRIX/28ccl+y8qoszs6tV1byP38ssksf3iCyKX58/TnPLhh6VM3gwXqefOnUOrVq1Qm6ypsbExsrOzuaKiolq9Lzs7G/v27ZMbNGggjxw5kquRiei775Jk/NixqrfTaEgxVBXeeovq9S9epOBkLZUpBQUFqMz3pfzpaLiy5WeGsLOz4+/cuSN7e3uXaqGol+W+ZWwMm2PHGP7zH0RFRcmPHj2SOnToULNJVqcD3niDMmqLF9NrxsYUsDh1ilqB7d1LNdO1cVYGlRF88MEH3MqVK2VJkphOVwW/03/+hg1LJNFXr5JR4YQJFHwGsN/MTI6cMYMNGzYMRgsW0PgXRZor7O1pXtDpyGfhxx/J8M3Ojp5bW7fS83HCBOD8eQwFoIiMROTOnbLHF18wHD9O5RV9+lBwYcECehb26kVqNMYoKMEY0tPTERoainPnzgGg8bpy5Up4enpKvXr14tzd3SnQmpFBz84PP6RnLUBBhvXr6f67fRscAL2fvyzLSEtLq9V1RqdOtO9+/aqcD5wEAV0ePXIUvviCVeaoW9xbeyzHceKiRYt2gzELANsAqAAkASiCLOcD+LiqUype8z6GJEkfxcfHHwfwTFPbQUFBWp7nN8bHx8/au3cvlEplOgArY2PjInt7e+bq6mpRVFQkpqWlFWq1WtHa2lqZmZkpJicn5+h0ulkAjgIAZJkiu4zZA5AAFIGxDwHcgCxfLD5cuiiKlQdaTU0pwXHvHq2PymLLFiLbn39Oa4mffqrShNXW1hZNmzaVNm/ezNna2ort27fnzczMwHHcTZVKVZmjfD1eAPy/Jtt6FGe6AeBUddvKsnz2xo0bNjdv3kxXq9UdjYyM7i9YsCC6hscYXua1CyiuC6lHCXQ63ToTExOToqKiOTqdLnXBggXxK1asOJuQkNCksLBQaWRkBK1WW6uF3XOBVq1osahQ1Lz/rV6+ZBgF1/eJHTWq5LUWLcq/d9++kt/L1ibKMi2yCguJGHfoQBnc5GTKjDs7k2Mtx5WQ/IIC4PZtuCUkoKmdHez27cP9gwfhN24cGp0+jXMPHqBrQABseB7czz9T1sjFhRZHoaFkCLJzJ33+994jia+HBwUKkpIowxIYSA+wrl0pKODoWLJIa9CA3vt3yup37KCF3S+/UCZMXwNaWEhy5xs3SveFfsroXWzqs3XrVgwZMqRGMnCO4zBr1iwmyzL4ujgMFhWVBEsiImjcPnxIUfk2bUhR0bMnbfPbb7RY3b2brtOkSUQ2P/uMFBwrVlDmZ948+p4/+4zUGwcP0u+//ELH+eEH2o7j6P3e3sC//00Z62nTKPvdti1YTAwKtm792yI5AwcOxG+//YaYmBgmimKJnFyno2z8N9/QuPjkE8q0VXe5//qLrsV//gN5xAhsOXxYSk1NZa+++mrJ9xoXR4vry5dr1qe7Mhw/TsRGD44jufA335RcdwDm5ub46KOPcO/ePZw8eRKXL1/GSy+9VOPD9OjRA9euXcOZM2ekIUOG1Fg9ERAQIMXHx3MxMTHMw8MDrVq1QqVyzYQEmn8mTSqZDypDWhq5uE+fXv1JODjQNZ4yhVQENcSuXbvksLAwNmvWLDg4OFS7PcdxcmFhYaX37qBBg7B+/Xp2586dUjJ+vVcBmzuX7gfaltuwYYN89epVqWvXrtVf76QkIrdl5dKSRN4FO3YQSQWI1NZyyuA4Dh4eHrh4kfjJqVOnpAEDBtRsHLz+ermX2rRpwyIjI+Hj41MiFeb5kp7NnTpREO7jj+hAT5AAACAASURBVGne8PenFnlz5lAGfPJkSEolAnv3xoWzZ+HQrRsK4+OZRpbh1a0bzWPZ2RS0mT2bnOwPHqTr89//AhoNpG7dEHv2LJKbN8c7qamwb9cOucOGIf7IEQSfOMFFJSXJ7j17Mnh60lxw9CgFD1Uqut4mJnSfVXAtlUql3KJFi9o9xFq0oDnw6tXSgXlDJCRAuWMHAnbsUCMlRfPll18GSJJ0kjE2B8AGSZJUjLFeSqVyu1artW8VGhoHxl4FMAOU6MmCLO+q1XmVhq25ufkzL328d++eWhTFzSDJu0ar1eYDaKXVajV5eXldoqOjvSVJUgOIApAO6iWeCOC8SqXKLrdDWU4BMAwAwFgnAIVgLBzAK8qvvmrn7u6uA1B5tNrTk9ZNFeHqVQreGBnRs7C6ACCAKVOmcLm5uQgKCuICAwOl9PR0DkA5dUE9XizUk+1aYv78+fMBzAcAQRAaq9XqrH/4lP7nsGjRot0wkPIDgFqtHhceHv5dVFTUuAYNGiAhIcH4o48+YnV1iv9HYGZGC4SoKCItycmPDTz+djBGi1b9wlXf3sswe2oYgdX3DX/1VSgAeAQF4YCVFdq3by+1HTyY85g/Hzu+/BI3OnfG3DlzYNegAfUolWUi6gkJJKXiOFroNGhAhN7cnEhZXBxFgPfupWy8iQll45s0ofdGRVEGeepUumbLltG2nTrR33NyaOG0aRMRjFdfJSLs50eLb42Gslf37tHfXVzoPBwdS3puV5b9VSgoo2JhQYvRggL6PSqqdFuhuiAnp6S+NjKSiPvdu3SdfHyAX35B7zFj0CgrC1Gffcbi33tPNzguTiHZ24PTmwNt3Eiy2Z076Zq9/Ta4zp2JQPj6khnLli1UJ3b+PAVWJkyg79fNjeSdH3xAUsyQEKBxYyLCQ4eSFFetpoXCo0e0MF20iByM3dzo2J9+SrWTWi2Robi4Eomlnjx5eJSM9eHDabx9/DGRdSsrCr6oVHRuGg2RpW+/pYzAkSN0bVq3RuPZs6HMzobs5wcWGEjZrvR0GheXL9M1M2hV96Ro1KgRevTogRMnTuC3TZvw1muv0fn7+dH1HTCAsmrVtZLKzaXr/+ABfc7OnQFZRkFBAeN5Xm7Tpg0tvgsK6N5YubJSt+Maw8WFznX16pIA3eLF9B3//jtl44thZWUF8+IaekmSZKDmHlwmJiZwdXVFVlbtHoWpqalo3rw58vLycODAARw4cABt2rSRR40aVf7Y48fTonbLlup3fOIEjfuaKAKUSvJpMDYm1U8N+8/ry2gKCwur2ZLQsGFD9vDhQ9Hb27tCJuvg4AB3d3dcvny5VEBNrVajaVgYLF9+GXo1R8OGDeHg4MCCg4PRtWvXqg/800+Ufb11q3yQUqkkcyf9HDZwIP1uKP2uIcaNG8dWrlwpFRYWcpcvX+batGlTfRBi61bqV19GrZaRkQEzM7PKAy86Hc11JiZEkP/4g4ImxfOOdtAgXHv5ZZwpzrIbN2okZiuV/L7AQPy7OGDxGEeOkKpLjwkTcD84GMd27pSdRozAqGnTmNHNm0BODqxtbGDduDHsLS0RfPQo0wYHQzl4MGXH4+MpEPvbbxQktrOj61nGzHH16tWiWq3mbW1tIcty7VrCLl9O83tFZDs2lubqGzcweeZM8+KsfMeioiI/pVKpCA4O/ig7O7ufkuNazwoONssaOBB3Q0PTAeQAkCDLT9DzksBxXC93d/ea9xisArIsQ6vVQqlUlrpGubm5KCgoYABuFpuY6XG3+OeTtQORZYr+MDZKrVQuVfC8/Wg7O9NKzf0ACvzMm1c6oSFJ1BXinXfoOTtoEJmE1hCWlpbo2bMna968OduwYUO+KIr1ZPsFRz3ZfgIUG3rV42+ASqVKA/Dm119/nZGent5UqVR2zs/Pd3qhyLYeTZsSQTAxoQX76tW0wH6BcPLkSalDhw6cvjdsXFwcAHIrt9O3wTBcbOlJ/NChJa9VlNnfbRBjqch1etgwyiozRtlvc3OSzqek0DH69aOHol7KrtORbD8piRbr27bRdW/ThjIiAwbQgjQri+rmly0jstmhAxHzsWMp48rzJBPs35+ykw0b0jFHjiQ5Y/fu9C8ykohqWBhlSzw8qFa2eXPK3Ofn0zXIzCxZ6P72GxHu11+n81i8mMj81q0kAfbzgzfPwzMlBd8FBSlcjh5FkpkZwmJi5HcSEpixRkOfSd9L2ceHSINSSedvbEzyUL064rvviHzZ2RH5NjKi1/QS0PT0kuutbyG1wcBrx7C9mf73ZctKXjOU3G/bRj9nzSp5bXlx6dnq1ZRhnTyZgjL67GVeXkmNG8fRdQOAhQshd+kCrF2LrKZNYQNQYCImhsj22LG04LWyokxeXh4tdoyM6LsdO5aCBgoFfe4336TPamlZZeufrh07ovG5c7CYNw+FvXrB9P33aRzWVFmTl0ey1e7dKRNXTAIZY5gzZw779ttv2S+//AJeFOVhKhW71LUr5KlTMaqa3dYIpqZ0f+jJNs/Ta19+SSRH/zqIxAElbeZqA0tLSyk4OJg/c+YMvLy8KjV+AsgEKD4+Hjk5OczR0RHjx4+HTqfDDz/8IEdFRZVmH4sXk+T9xInK67PLIjCwdqUdTZuWGIVduVJtT/KDBw/KarWaAUATg+tXFRwcHJCYmFgls/L29kaxh8tjBYWxsTEybG2hGT8ehvn8/Px8qWXLllUztawsKgPatq1yNZCbG80XX31FwS3D2uxawtnZmXv4kJSueXl51ZPt3bvp3jYg25IkITg4WPbz82MVdleQJJo3Hj2i+nOOIyLTqRP97fPPwYkilLm5mODigpZbtwIWFvzWrVvlyMhItmrVKmngwIGct7c3uMzMcmMqOTkZew8dQs+BA9GrVy+S9Bt+xx06IMTfH/ccHaU+c+ZwYIwUWwcPkmx8+nRSvHz3HQV7ly2jc3vlFcRlZCAnJYWHUomdO3fi9ddfR7NmzWp+gT08KGgXH0/PKMNr4uBAQYMmTcADsLe3h729PQeAA4CcK1eY2/79nTVr1qBBcDDSZBmhfn4pw3bu/LDmJ1AxBEFojWKy27KSWuSKkJSUhHPnzuXHxMQwnU6nsLKyKtJoNJxarVYCYDqdjm/QoIG6a9eupl5eXszKygrGxsbQ6XTmHMddW7JkyYrFixfvfYLzdgagUKlUMeX+KMv7Vy5d6utsY7PAZPJkCnJ06ULrjbLzkJ8ffQdJSSWBq+vXKfDfpw89x2tpOqnH3bt3dRzHbVi4cGHtJ+V6PFeoJ9v1eKEwf/78jwRBMOI47lZoaKiDo6Pji+mCbWZGWc2HD2miripy+hxCluVSi6mdO3cCAMLDw3Hp0iX0eEKDqkqhUJQ4v+rJuuEC0VCWqF/EGZJ2wyyKXjaqJ4I6HWW/JYmyq0lJtKixsKAseMOGJTJLnqdtANrGyorqIgEidcUOw9iwgbJsVlaUeQVK5NIAkUs99JnB/ftLXouNpZ/t28NoyBA4bNok7h8wgLe0tJQLcnJY0I8/orODAy22+vShbT82KLXTE9vWrUucmfXkFaCABVAtwXjqUKspSz59OklZMzMpw2hmRtc5LIy2Gz6c/lZQAEybBsWff8I+JQUpRkZEtg0zVfHFsU+tlkg4QKUW+mtdWEjj5+ZNWqi/+SYFW7y8gO3baVxFRFAwZc8eeq1VK8DdHa4//ICzV69KISdPsqnTpjGLmhDt7GzKUD94QIvxCmruOY7DgAEDxIf378t25ua8OG8egnNzYfzgQa2yy5Vi166SMaRHs2ZUYhIZSfdOcaAhJiZGNjIywuDBg2t93JEjR/IuLi4ICAgQr127xru4uIgDBw7kDdtcpaenIywsDNevX5fz8/OZg4ODPHDgQAaQMVB+fj7z9vYWAfDQ6YggJiSQGVVNiXZGBgU3amg+9RijRtG4UavL3Qt3797F/fv3kZSUJBUUFHBFRUWsS5cuuHPnjiRJEqfT6WBUTZ/muLg4qTLPBT3S09PB83yp7iRcYSHcsrPlBzNmMF+DbYuKitCsWbPKvye92dn+/aXq8yvEp5/SHNamDbB2LWK0WhQMHCh7eXnVahx069YNdnZ2uHv3Lo4dOybOnTu3cj16ZiZ5MZQJAnz99deyJEmsQkd8WaYAqb9/6Tm0QweaS5RKwNERXHIy7g0bJvfbsoXlt20L8+PHMX7cOBYbF4dt27Zxe4o7TjTXaiWTLl24oRoNdu3aJSYnJ7P8/HzOxsYGPXv2ZJVlnYuKipCWlsYxnY6UNfqgwUcfkfGVu3tJ94DPPydVQX4+bM6dw7v79yNpzBgUWFjI1377TfZcvJhjNfVkMDen44SHlybbc+fSuC9+BpfCt98CXl7wtLFh1oyJDb29efz+O0zi4iBv316DOrYaIR0ArKysdK6urjX6MJGRkdi5c2eBTqf7XJblgwAKMzIyPIr3lQPiJkmZmZmvnTt3bsqpU6f6NWnSBJ07dzYDAEmSOgLYIwhCs7rUMguC0ABAvFKpvACgggbsgCRJK5PT0l66duhQn65du1I505079PwwNETjOPrb/ftEtgMCaA2hVNJ6oY5EW5Zl3L59W63VaqvslV6PFwP1ZLseLxQEQeAVCkWshYWFmaen54vDTiuClRVlN3U6ykAcOvTELsp/FyRJYtYGct1p06YhLS0NsbGxOH36NAoKCjBgwIB/8AzrAIWipN82UELiBw4ksnfoEGUE27Qhovbnn/Rg9fIiae6CBbR47NixZB+GhkP6h66+5VUdMHXqVP7WrVswNTVlf/zxB86fPw9PT0/Y2dnVeZ//CG7douvl7k5ku7JAk2HtcG4uAKDx9u1Sg5gYekOzZiRrHzGClAjdu9MiRy/BfvXVkvfr+1S7u5cYNAUG0k9ZpgygnR2R0wsXaGHl5ERSfTc39Dt5krvQrh0yX3tNtkhIYLhzh7IdH39MgY5p02hM7NtHBl7DhhGB27ChynKR9u3b8+2XLKGM844dMP7mGxQUFDCNRlMtiasWBQVEACIiSi/QW7QgInD6NLBsGY4dO6YLDAxUjK5BL9/K0LFjR3Ts2JEvKirCwYMHuU2bNsHNzU2yt7dnJiYmuHjxIrO1tZV8fHy4l19+GZwBq0xJSQEAcBzHJSUmwvHll0mtsHFj7U7i0SO6P2tKzg0xbhzQvTsetmuHXS4usq2trWhhYYGHDx8qAKB58+Zo1KgRWrRoIXt4eLCAgABuxYoVEEURFhYW4jvvvMNXZJgWHByM1NRUrpe+FKcShIWFSUqlkrtz547Utm1bDgDk06fR9+BBllRG/mxubo7o6OiK3eM1GlJszJpVPdEG6Dpv2ICC9HSknziBsPx8XMnMZPPmzUNVpm5l4enpiSZNmiAgIAAWFhZVP5vbtycFkIHi5erVq5AkiX344YePSxpKYd8+YO1aItaG84VCQVnkfv2AhQvBOA7T3n2X5bm44M/WrTFmzhwY+frCc948LFy4EEVFRXjw4AGavfoqd/Ctt+RvoqKYTqfjR44cCVdXV1haWqLCrDqAs2fPIsDfH7737lEgY8cOCtr27Utz//Tp5Muih5ERzREAzF96CeZr18Lm8mVobtxgcX/9xSRvb/Ddu1MZTYMGpL6qSlresiVl9vv0KbkGAweWDuRlZlILtZUraZ61tYXvnDkKzJnzeBNHR0doNBoXQRAsVSpVbuUHrBqCIDQ2MjL6TqPRYOzYsTXiE6IoYs+ePYVarXaoSqU6Z/Cnitpa7QawWxAEs4cPH86Mi4ubqlAonAE80ul06wBEV3FuzQCkq1SqzAr+PBQAOI6LreBvAACVSlW0ZMmS449iYjp37dbNFJs30zMoJoa+h9hYel4wRqVLwcEUrB81ip5DV6/Suq6OiI+Ph06nywJwq847qcdzg3qyXY8XDTLHcTmtWrWycqthfd1zD4WCpEbdupEE9+WXS2pdn0M8ePAAarWaPXjwQNIHPBo1aoRGjRrBy8sLGo0GoaGh4oABA15wS3IDvPUWEbKdO2mR88knRLzj4+lhO20ayRkFgQjWM6rF5zjuccufRYsWYfPmzdJ///tfbvTo0fB5hkZtTx36bBBA9ct//UWkdMaMyt/D8wgMDJQvu7hw/MSJcABIHu7rS0GP8eNJMv3ZZ0Qy9O2HqquLlGXKQGzaRDLvX36he7FTJ3KqLsa5gwehuHQJFsuXM+zfT9mLhg2Bs2dJNXDqFGW6vv6azicxEfj+++rHQmYmLdiLiZG5uTkKCgoQFRVVK1lmhTAzowV3mbZxYIzKJ+7fR8qtW7h586bi7bffLtderi4wMTHB+PHjWV5eHg4dOsQiIiJkWZbl0aNH815eXhWymFatWmHQoEEIX7OGHXrwQJ65ejVD7woTTlVj925SI9QR0o8/4tY330DXqBFLTk5WJCcnw8TEBJMnT4azs7P+3BkATJkyBRYWFjAyMsLu3bvZwYMH5YkTJ5YabFu2bJEePXrEDRo0CJW2DyrGpEmTuC1btuDixYslmd3hw7Hv+++luN27OQsLC3nGjBnMwsICOp1OsrW1LX8tZZkIX/PmFISqIeTAQETv3CmfHDYMlmZmcoOMDOzfv1+eNGlSreZwvYx86NChVd90hw6Va38UEBAgde/enauQaN+5Q4GXvXvLB+bataOscseOFFw7fhyYOxdmDg540KIFNNu3w8TLC/j5Z/DOzjCfMgVtfX2BJUswafp0lpScDFEUqyx9AICM+HgUfP89Oup06NO4Mcm6Deu9NRoK6OnVRBVBqQT69EFS06a4nZeHtD59YHvrFqy++Ua2fviQSUolLE1M5Lx+/dBm1ixmbG9f+v0dO1JA6f59Cij26UPXxMWFjl1QUOLTkZND6pwKoFAo0KRJk6LIyMhxAH6pcCMDCIKgBMBUKpWm+P9ePM9/o1Aoujs4ONi88sor1V4/PUJCQiDL8r0yRLtKqFSqAgA/FP+rEkuXLh0C4AMAAziOyxQEwU+lUkWX2UxpY2Ojy8/P7y8IAl/WWV0PWZaD8/PzNUBxFYelJf0LDKS5v1MnGnNvv03fQ/v2NOfOmfNERBsAAgICCnU63dqquiPV48VBPdmuxwsFlUolCYLww+XLl9dERUVly7IMX19fs27duilrZTbyvEHfJmjGDJJ+VdP785/Ejh07YGdnBx8fn3KLPVEUERkZKTdp0uR/g2hHRFAm4ZtvaEGjX+j16kUE/LffKMt99y4Rtp9/pkVuair9/xn2Hec4Dm+//Tb366+/yufOnYOPj8+LcQMUFhLBNMw6BwZSJqAKsl1YWIjDhw+zdu3albhl601pXFxKpPvW1iTxCw+nxWlYGEmmtdrSmXJRJFI8dy4FSUaOpPo7c3MyXfL1pdrLoCDgwAH0HToUmldfRUyjRnKDQ4cYmz+fSgaUSiJ4t29TxnjIEMqyX7xIEvKdO4F//avibOuqVUR6IyIeBwXc3NyQmpoKq6cUsJEYQ9HIkYj/7js0N/RJcHKCePcutCNHotOqVU+FaBvCwsICkyZNYqihHL5z+/ZodeAAHrZoQd4ItYVWS+qE0NDav7cYt3keyfb2+GT7dvwwfjz6Dh4s9+jRo8LzN6zXHjVqFLdhwwZs3LgRoiiKsiwza2trLj4+nps4cSJaVNQtogwcHBwwdepUrFu3jh08eFAcbG/P60aMgOmPP3ItW7ZEaGgoi4qKgq+vL5o0acKfP39eLiwsZPrWf4wxcAUFcC0sRGLXrpACA8FxHFq2bFkuQ/3o0SNERkbiypUr1NXD01NmTk54d/RoZjRhAstzdMSPoojo6Oga16XLsoxbt27J7u7urMpA+PDhFKgsY0iZk5PDVVjnffYslYucOlVSQmQIExNSaaxfT6R78WIgLQ2RvXrJ761ezYxWryZSlJhI5RySRDLg4hZfjtWZXGZnA/fvg5s0Ca4uLvDcvBmmFZkXxseTUq0GJMvNzQ2NGzcWc/PyuM7z57O4uDjm2r490qKikLFlC0wPHmQ52dlolJ1N6p0+fSigZ2lJvhMbNlDAt2lTItSzZhHxt7EhhcaF6pvcdOnSxSIuLm6xIAi/F5PZxxAEwUahUHyoVCp7ybLswhhrJssyEwShn0qlOs8Y+7coikMBQJKkAmdn5xpLSQIDA3OLioq+r+n2tYEgCKY8z2/z9PS07NKlC+Li4qz8/f2DBEFwV6lUhg6Ot9Vqtcba2to8LS1tKioIOAiCYMTz/FvOzs7l5R36wNn69VR2EhZGppN795b48TwBCgoKEBoaykRRXP9EO6rHc4N6sl2PFxF/ANAkJiY+AmCenp7+Y2hoqO3kyZPNn1h2+U/j0SMidK+/TnKk1177p8+oHGRZxoQJEx4bKhmisLAQmZmZLDc3F7GxseLw4cP56moVn1vIMknxrK1JpmcYzGGMagW//prqfp2dqY579mxa7Gg0FPk+dKikZc0zQEhICLKyshjP8y9O9PvLLymbbXivfvQRXe+cnAozwaIoYtWqVbCxsam+vZRhRu/CBfpuvv+eCHefPrTwzsggkjxhAnUFGDKE5OVvvkmy2pAQCgr070/ZCsaA7Gxk7N8vXYuI4PL/+190NzenbfXw8SGH74YNSzLuSUnAmTPkVjtnTumsa1oa3eP9+z8eWzqdDjdv3oSLi0udyW9BQQHi4uIQExMjR0ZGSjnR0fy4W7fw+7ZtWCwIpbY9ptGIFq1aca907PjPBWrUapLcf/st1n7+udSjT5+6lQdduUKlCU/gP5CWloa0hg2R7+eHf73+OoxbtarRdbGzs8PkyZNx79492dLSks/KykJAQAD69OlTqzZPDg4OePfdd7Fu3To+TpLg0q0bYmJjYWdnJ/fv35/5+lLl9tChQ9mePXvku3fvirIsQ5ZluN+6Bb9jx7i98+eLcng4AyDn5OTw8fHx0pAhQ3iA6o0PHTokhoeH87a2tpJWq+WcnZ3Rvn175rt5M7hp04Bdu2Bhb49mBw7g119/ha2tLSZPnowG1Zh4ZmRkICIigs2oSp0iikR2K6ipVygUsomJSelrFRxMUu0tWyom2nr06EH38fDhwI4deHjmDP7s3Jm9d/MmWFYW3Ztjx1IQ29eXsv/GxmTOWJmKLC2NAnaLFkFs3x7rJkxAr0GDJIumTSsenytWkAx83rzKz9MA06dPfxyQ1n+vDo6OQLdubOXKldLrEydyKCgocTrPzKTzT0yk50paGilx+vWjQGEtSy6aN28OExOTRhqNZiCAxyl6QRB6KRSKQ97e3iZNmjQxsbGxgbu7O/z9/XHhwoUhAM7LsjwNwDQAVvHx8ek3b95ER8PyqUogyzISExMVAC7V6mRrCGNj41O2trYmEyZM4Blj8PDw4DMyMpShoaG7BEEYrVKp8gFApVLdWrZsmbZ///7We/bsWS0IwhWVShVSZndjRVEcXaX/jL63tkZD43PVqicm2gAQGBgo8Tx/aMGCBbVsyF6P5xX1ZLseLxxUKlUKgMf2yIIgHI2Pj8/Pz89/8hrHfxr6zKmLCy3+8/IoI/acmacFBwdLffv2LXdSlpaWWLx4MXJycrB9+3b+zJkzmDp1KtLS0pCSkgIzM7MaZ0r+UVy+TNLec+cqX7y3aEFE+/z5EkMcgOq49Pto0YIIXEEBZUufMg4dOiSr1Wq9sdTzP5/rdFTreOJE+b99+y31uS1uq1QWWq0WkyZN4hS1UQu0a0c/v/uOfmo0JP9MSqLSDVdX+g4Zo1o8PU6dop8GWS/O1BSTJk3irl27huPHj6NJkyZwLuvePHFiacM+R0c6dlYWZfK9vMjZ9tYtagUTElLK7VjvBF6ThWtZ3L59Wz5z5gwKCwuZqampaGtry7Vs2ZL3fe01xI8fD7u9pY17i4qKcPfuXX7oypVk7vf++39/cC87m2rVnZxQYGWFQq2We/Tokdi9e/faK2MCAys0oasNcnJyAMaQsWQJ7HfsoExhDZ3NGzdujMaNGz8mi71794a5uXmtgxgNGzaEU6NGumYnT3KtN27kRlCmtNR+OI7DuHHjGAC6TrJM6ptVqzBnzJjHN8j169dx8uRJXqvVyrGxscjOzma2trbsnXfeQXBwMJefny9NmTKFyucFgcilqyugUmG8pyfuvfYajh8/Lh8+fFiaPHlyld/J8ePHJUtLS9nBwaHy7S5epAxsBfewqampnJCQwB7Xod+8CSxdSv+qqz1/5x26l8zMEDt2LB599x36/fgj2KpVJKn//XcaH1otEdSvv6bnaseO1KJp+PCSYGpREbBuHcnRP/gA2LABtzMzoT5yBM7OzlxOTg5MTEzKrzVat6ag0RNCo9FAq9Vylvogb8uWpMiJiyPllCyTsWpcHCmr6qjkYIyhZ8+eJqdPn14qCMJpAK2MjY0/NjIyGjFq1CgTr+JrLssy9u7dW3Tv3j0TAFkAoFKpdAAgCEI3ADXqNQ9QIFCn08kAKq2VfhIwxmz69etnYqhyHDJkiKkkSb3DwsJCBUHoq1KpIpctWzZTq9VaN2vWDAMHDjQ9derUXkEQ2qhUKi0ACILAKZXKr1566SXR0tKy+rno9m0KvBq2S30C3L59O0+tVq99Kjurx3OB539xVo96VA+mUCiit2/f7jR16lTzhIQEXLhwITc1NdV4xowZRi+ceRRAiwGAFgN+fvSQfQ4gSRJsbW3x119/cZ06darQyIYxBkmSkJqaCp7n8fXXX0s6nY4zNTWVCwsLmYmJieTi4sJlZGSI1tbW8qRJk56veSg7m+SAM2dWnyUbPpzat3l6PjbCeYzibAUWLCAzlbg4kj1fuPDUWr3pFxX29vYKw5ZBzy0uXaIMUEWkaM4cUgVUgJCQEBgbG8sNGzZ8sgyskRFlnjiuxMiulsGfLl264Pr16/KVK1fkMWPGUMBJp6OF9ubNFfdeb9CAAjf+/vQZ+/al4EujRtDpdAgKCoK/vz/y8/MBAC61cNPWaDTYtm2bFB8fzw0ePBht27aFQYMEjQAAIABJREFUQqEoNRASVSpM2raNiAWIaP/nP/+BtbU19X7+9FOS1P+dZDspiYjE5cvAr79Ck5UFWZYxcuTI2g9iUaSAVh2CFIbo1asX7t27h9jYWMmra1euLj2n9aiw9riGeLtTJwW++opUEtUhP58CRps2lSOlnTp1QqNGjXDy5EnZx8eH8/DwgJubG8dxHAoLC6HT6UpabFlbU/nLyJE0d2k08PHxwe3bt1lMTEyV34kkSXjw4AE3x8CEqxzS06lcKioKKFuLDOop/lh+fv8+BbxmzSqZRytBbGwsEo8ele3++AN7IyOZNjsb/dRq+AJ0v8+ZQzL0yEgixCkplOV+8IAk2GvW0O/6+3fvXiKxY8c+NhX0X7VKBsB+Kx4Ppqam8rx580rmosBAGs91CCQXFRVhy5YtupycHE6r1XI6nQ4eHh5E8vLzqTSiQQMK6qakUEDE2pqeI7t2kWnX9Om1Pi4AdOjQgcXHxzcLDg5OUSgUrFu3bspOnTo9NvqTZRn+/v5iWFhYDsdxgZIkPXb+FgThDQBbAeDw4cM5SqVSbty4samnp6dRs2bNKnwWaTQa8DxfsHDhwmeixCoqKlp+8uTJdR4eHub64yuVSowePdrk2rVrzqdPn74rCML3ABYC5IreoUMHdu/ePbf4+PgvACwAAIVC8YmdnV3Dzp07Vz8XhYUB166R+uApID8/H1lZWUZ4Rtn/evwzeL4WufWoRx2gUqnUgiC0yM7OXr9ly5a3c3Jy8rVa7WyO49qeO3fu3ddee60O1rTPCY4cIVnSqVMkdzNs2/QMcODAATk6OlpUKBSws7Pj+/bty2JjY9G4cWMoFAps2bJFFkWRDRo0qEqnWhsbG8yePRvZ2dnIzMzknJ2d4eLiwnQ6Hc6ePYucnBzJ09OTCwgIYCEhIWj1BKZGTxV79hAZCwqqWraoh5kZRbT/+ouyqGXqEAFQdrtFCyLxgweTTHr2bMpWjB37RKc7Y8YMduHCBfmvv/5iLVq0KJ9pfd5w4EDl9bjm5nTd588v18YmPT0dT0y09Th2jGodn4CYjRo1im3cuJF5e3vT2M3MJHmsoZt9WZiakkvtlCkkDVWpkHjgALaEhqI4UCIPGjSIubq6PnaXP3PmjJidnY0RI0bwlQVSDhw4IObk5LD58+dXquzx/PJL/MJxSBMEvPHGG7h165ZsbGwszZkzh3Y6bBgRu6lTSbL7LCFJtDB97z3g/HkUNG2KbT//LGZnZzMAXJ0CRvfv0/daxrW7tjAxMQHHcXBycuLQujXJk998kwhZTeaDp4X27UsrLSqDLJNfhJdXOcMxPZo0aYKZM2eWUiHpdDpcv34dDg4OEvTZcYDk1kolZbkLCoCMDFhYWMDY2BiSJFXq0i3LxJ20Wm3l52prS2SxOAiRlZVFRDkxESkpKdDpdJyLiwuVUv34IxmFGvo6VIBr166Jx48f581EkXX09JSdnZ3RtEcPdG/blhzKfX1pPnn3XaqlfuMN2jfP033Ypg3VPs+dS8qA11+nubxMCUe7du2Yv78/3nzzTXAch+3bt+PQoUNgjIHnebTcvBm8KCLi1ClwHPf4n/7vHMfBx8ennA/DpUuXcOHCBdnZ2Znr06cPFxMTIyuVStYvL49nI0ZQGdOff1LAYOVKkox/+y11tnB0pIzqnj00Rnm+1go4juMwcuRIE33btrKqoZCQEFy+fDlRFMUeFfSjvsJxXI5CoQhMTU39EUB+YmKi3927dyfJsty8S5cuym7duilMDGTVoiiCMVahGdlTwvbc3NxpZ8+e7TFgwIBSk2GXLl14Z2dns02bNi3Uv3bs2DHMnTsXfn5+ZtHR0f8WBGGfSqW6qVAopvTs2dO8svFeCl98QWP1KakPHzx4ACMjo4vz5s2r4maqx4uGerJdj/8JqFQqWRCEJbm5ubwkSYdUKtVBQRACIyIi5iYlJVVvgvK8Qn/eBw7Q4ucZku3k5GQEBQWxUaNGKXQ6HW7duiX9/PPPzMjICJIkQRRF2Nvb46233oJxRaSyDOzt7WFfJoOhUCgwcODAx08lnuflgwcP4urVq8za2hqenp7w8PB4auZQtcKNG7Sg/u232i2shw6l72fPntJ9vsvC2rpEsWBlReQrOJgyF8uXV++aXQFsbW0xYsQIFh0dLYaFhfHPNdnOy6MFoWHf87JwcCACUQZNmzbF5cuXceXKFXQz7JVeF1hZPZFjNUCSYY7jkJGRQQGC6OiKpfGGyM2lDOwffwC9eyN582bkLV+OfiNHovOCBeCsrcsNgIsXL/IA3Us9e/asYJe5ePToEd+vX78qS2hMrK3xRqtWuP7HH9gGoEGDBvLEiRNLs9q2bWk8RkSU9LB/FggPJ9PB118H2rfHX8eOITExkR84cCBatGhRt1KgbdtK+tw/AWJiYmBhYSG3bt2avgsrK6qRPXOGMr5/Bx49ou8iNbX6rhSLF1OWeNOmWh1Cq9WC53kkJyfz8fHxJU7S7u5k+rRrF5U/tG2LQWvX4ptvvkFISAhat25d4f54noeRkRHCwsIqDvhFRUHs0gUbFi0ScwoLmUaj4QDA0tJSbtCggdSoUSPWpUsXjktLo3Z0bdoAY8ZU+RlCQkLkEydO8A4ODpg5cya4V15haN+eAgYaDfkw9OpFBmMWFkDXrhTEe+01KveIjqZM9uHD1LLpp5/oOn75JRk16ktQAAQFBYmenp68m5sbCgoK0LJlS6moqEiWZZlJkiSHdOiA/AYNmCYxUf8aZFmGJEmQJAnZ2dl8TEwMxhsYn0ZEROD06dMYOHAgurRvz3GpqfD+7TcGNzc658GD6fs4eJA8Jl55hc7xv/+lZ9WCBVRzPnkyBQp27yaVSA07mRQWFuLhw4fQaDRQKpXQ6XSwt7eH0uD9iYmJ0Ol05ysg2ijub21d5uVTAL4WBKHltWvXlly9enVYt27dlD179lQoFApYWFhAq9XaCILAnoXLdvE6cML169cjWrdubVTW98LV1RWzZ89GTEwMcnJyZEtLS/z000+F6enpZjzPZ4miqAMAURSDs7KyKh7shrh2jQIdhi0+nxDXrl3LKyws3FD9lvV4kVBPtuvxPwOVShUH4G2D/4cKgvDd9u3bP/nkk09q3jD0ecRPP9HPJUuo3nP//qey27t37yIiIgJ5eXlyXFwc8/Pz07Vp00YBAH5+fpxarYaxsTESExORkZGBVq1asafp+t6nTx9mb2+PuLg4KSUlBWfPnmVHjx5lCoVC4nkeSqUSgwcP5po/y8U/QDV6a9eS1Leq7GRlWLmSsmBjxtTMIEVPuo8fJ7LGGGXPJk8mUl5LdO3alT9+/Diio6Plhg0bSi+//DL/JDLWZ4LvviNiUNVi0NOTDJFu3CiVeXZ3d8eECROwszjj/USE+/x5us5PCKVSKdva2jJs3EgEacSIqt/Qvz+Zs61ZA1EUsT0zEy0WLcJQDw8qL/joo3JKB2NjY6jValy8eBFeXl7lTAn/+OMP0c7Ojvn5+VWbVrFWKNC/ZUt0/eSTivsgm5nR4nHJEnLaf9reCiEhZPB0+TJJeovnkY4dOyIgIABhYWFSt27dap8e0rduq+761wANGjRAbm4uW79+vTxlyhRmampKGfMrVyig9ncQbgcHcpuujjQVFdFYrkOJkampKcaNG4fff/+9fL2tWk2Z4Fu3ADc3GDGGxo0bS3/++Sfn4+ODyub/Vq1ayXFxceX+qNFocPjUKdmsSxfm6eXFWrVqxdnY2MDU1BTFDxMK+uTkAF99RXXyn31W7We4dOkSzMzMMEvfq9vBgeTcPj4kH582jebUH4q7RZ07R1nrvn2pjKVvXyLdJ09Sic+CBXT8vXvps+/ZA/zrX5CsrKDRaFiXLl3AcRwsLCwwevTokkBVZCQFPUJDK+0+sXXrVjkqKgonTpxg6enpyMzMFPMyMrjuzZrJXY8d49jYsSQL79qV/D8MS9+SkkhxsmIFlSqdO1e+3Obddykbn5lJnRCqMOXUaDS4ceMGLl68WAjgiizLmYwxS1mWm2m1WldnZ2f1mDFjLKytrXH37l0Nz/O17mGlUqnuAxgvCELzq1ev/icoKKjXuHHjzJycnGBlZaXOysrqBcC/tvut4bFTli5d+tmJEye+mTp1aqmHYE5ODnbv3l2Qk5OTJ4riFUmSRgBYBeD7hQsXZui302q1N9LS0kYCqPxhrtORIdqkSaXNPp8Aubm5SElJ4QHseyo7rMdzg3qyXY//WQiCYA9goaur64vj1FwdRo6EzsMDuampyLt/H+ejoiRZlvHqq69ykZGRSE5ORuvWrdG0adNKJX95eXl4+PAhgoOD5djYWObi4iJZWlpiypQpzMXFpdScoM9gOzk5PfXWQABlun19feHr6/v4ZNPS0pCYmMhZWVnh1q1b2L17N2xsbMRx48bxFTmgPxEkiUj2Sy9RhrouRBug923cSDWGtZHhvvoq/dNoyMG6TRtaaFlYUFajhujSpQsiIyPFnJwc3Llzh79//z4+/fTT2n+OZwVRpOzjuRq0Vg0MpMVidjZlwovh4eGBsWPHYvfu3XB3d6+bZL6oiDKGT1gzn5eXB47joL5zh2SdVQWgZJkkwevWAT4+EEURq1evlkVRlHsMHcrBxoYWbZGRVF7w8cdA8+b6fvYAADMzM3nt2rWsT58+crNmzZiTkxNyc3ORmJjIv/POO5Xe66Xw9tvAxImwEKtQcfI83RPz51N282khNpZk6qNG0dg2uF4ZGbTGHTx4cN10mA8fUt1yDU2aqkJubi5kWUZSUhIT9deJ50lV9OGHlG181iacX31VvRz+2jXKZvr710kRA5D029jYWFYoFKV34OREddU8T/LqVq3w2ptvcmvWrJG/++47DBkyhFXUM9zZ2ZmFhobKeXl5zKLY6+Lq1au4duSI3OvePbhv2QI7O7uKv+OiIiK31tY09qpBQUEBEhISWH/DkpSlSykDrMf48cC+fRQo0QfnevWin7GxRJC3bSMi26ULzd95efQ9Z2VRwGPRIhzneUnXqBFXaVeN/Hzy96jCuLGwsFAGwMVHRYmt0tKYRfPmfIvVq6GcMIGxESPo3nR3p9IKQ8TGAp9+SoG43r2J0JuaksmjIczMSNlx6hT9vH+/wjkuLi4OO3bsKJRl+VJRUdEKlUp1xvDvgiBYJiQkCBs3bpwzbdo045ycHCWAOru+qVSqCEEQBmk0mjc2b9783+7du5t4e3ubBQQEDMIzItsAIEnSpoSEhGVlVY2XLl3SZGVlbRVF8T2VSqUVBMFCpVLllX0/z/OudnZ2VUfNY2Jo3DwFUzw9rl+/ruN5/uDChQt1T22n9XguwOQKJHv1qMf/AgRBYADGKZXKjW3atDGWJEnOzMwUJ0yYYFoTGfTfjdTUVL1hR7naqeTkZJw/fx7p6elidnY21/H8edbp2jWc3bRJ5DgOQUFBvImJCdzc3MTIyEhep9PB1NRUsrS0lE1MTPjs7GxRq9UyURSZKIrM3NxctrCwkEeOHMk9dQL7lFFYWIg9e/ZI0dHR3Pvvvw/rOmR+K8WaNbQg27uXMipPArWaFksjRpCEsa6YOZMWVf7+9ECvBekGgM2bN0sxMTGcl5eXPH78+Oej9/bJk7QoHTWqZtsXFVWqEPjzzz/FR48eYfbs2bUv7s3MJMI/enSt36qHv78/Ll68iGZubuLYd9/l2ZEjJYv4ivDBB+TCfPMmcnNzcfnyZfn27duYP39+6e9Gp6MF/4YNwKZNSLC0xIatW+Hs7Iy3334bFy5cQFhYmJiZmcnJssx4noejo6NuypQpNQ+a9+9PnQ6qCgjpdCT1joykANSTYtcuyrwlJpbL1p45c0a+fv06PDw8pHHjxtXN3e/CBZK/z579RKcpSRKuXLmC06dPY/z48fAq64CdnU1KlFGjnh3hTkgghUxERJXkDR98QK73NSCmleHOnTvYv38/Jk+eDI+y5K2ggLK/s2dTi8Pi+/bEiRO4evUqPiF1RKm3SJKEffv2ibGxsZg0aRK/a9cusaioiJ9gagrXrVvBAgIqPhGdDlCpiOj+8EONggc6nQ5r166Vs7OzWc+ePdG7d29wp0/TuN6xo2TDS5dIFbZ9e8l+r1wh9UZiItW5jx5NKppu3SjI98svJR1AEhKQPGEC0nJz4XnhAkwqMswUBFLKlL2Geogijn/yiezUogVre/YsHXf/fprjqupHHh1NCqhXXilRVCxYQEZz69ZV/r7CQrp/582jAEZxSVRBQQE2btxYmJmZ+bFKpapiB8CSJUvUvr6+Rnfu3AEAe5VKlVrV9jWBIAiuxsbGW9VqdZ/ilyYAOKhSqYqedN8V4auvvvqhV69e7/fu3fvxgNq8eXNOTEzM2yqVak9V7125cmXAsGHDOlUUVAJAz5GXX6a1w1NSAImiiG+++aZQrVZ3rKANWT1ecNRntuvxP4vimqBdgiDcCQwMHKFQKLxEURweHh5u6luNw+nfCZ1Oh4MHD8phYWHM1NRUPnPmDDM1NRVNTU2Rl5cHtVrNS5IEHx8fsX379ry7uzucP/8ccm4uRmdm8li+HAO+/RYcx8HU1JSXZRlFRUVITU3lEhISUFhYCBsbG97W1vax0Y2TkxNDmXYyzytMTU3xxhtvcEuWLEFoaCi6du365DstKKB2MYsXk2S2FpJrSZJw/PhxJCQkSFqtFrm5uczY2Bj29vZs/Icfgtu+nRZtZqV9+XQ6He7duwcfH59ywZRS+Plnyi6Gh1PdYGwsZQNr2L9z4sSJ3Ndff42iomeyhqkbDh+mrGBNkZ5Oi98HD8oRm27duvG3b99GZmYmbGobIAkIIPJXB7IdGRmJP//8U87NzWUTJ05E08aNefTvX7H7uB76Re+iRZBlGZs3b0ZOTg57o6L6YoWClBEzZgDvvw/nqCh4du8u2bm4cBzHoU+fPujTpw8PkANzamoq/Pz8avcM37y5+nGkUFBWc9EiMt5q1qxWh3iMyEgiOZ99Rv3NyxBtjUaDy5cvs549e6Jv3751t9FfuvSxy/qTIDw8HKdPn4aXl5fk5eVVPgNraUmBkLt3qa73WcDBodLWdwBIITJ9OpGuun4vALKzs3HkyBFwHFexQsTMjAKR/fqR6iY2FnB1Rbdu3RAUFCStWbOGmzlzJhQKBbRaLURRhCRJMDEx4XJzc9m6devQtWtX1q9nTxjJcvmMrR6yTHXSFhZEuGuYpVcoFHjvvfdYWFgYdu3aBUtLS3To0IHqmw3RtSvd8/7+NAYB6lEdEUFE19iYSjgKC+k+/de/AEdHSKKIK/v2IXvjRjFkwADO7uFD1rRHD+Dzz6lUSD+W8/OphWBFn+/KFaoRnzwZ7Y8cYRlvvEGZ9JrM49HRVJvdu3fp0oW5c0upfSqEqSl5Ljg60ncXHw9No0bYuXNnUW5u7l4A66s/Acy/c+fOKo7jvly0aNETE20AUKlUsYIg9GOMDZFl+TCAnQAgCALwlAi9IXQ63bnw8PC3evfu/dgAplmzZlZxcXHzAVRJtkVRDMvOzu5U6QYBAVSG8BRLbcLCwsAYC6kn2v+bqCfb9fifh0qlCgUQCgCCIHQ+ePDg+cOHDxt99tlnfI3kl88AGo0GwcHBMDIywtGjR6UGDRpg5syZrGHDhiwjIwMpKSl8dnY2LCws4O7uDnNzc3AcV+opyywtH5szmZubE0EDtYMyNTWFm5tbSSuVFxyMMfj5+ckXL15E586d2RN9b5JEmWxRpIdlDQxlJEnCw4cPcefOHURERMhqtZr5+vrC3Nyc5eXlMRsbG9y+fRvfxsZKo+Ljuea//FJuAbZ69WopNzeXMzMzQ7U16BxHWZeUFFrkN2tG9bzLllW7IE1NpTVLdnY202g0/3zv+YwMkqZW5kJeERo3JlJRPKYNYWNjg9atW0vbt2+X58yZU7t7OCWlzguk4OBgpKenszFjxqDpiRNUgx4RUfkbjh4leeiDB4CFBe6HhSEzMxMffvhh1eoMngd++gmZ+/fDdsMGzvnMGQnt2nGGDsmurq6oVNZaFVxdyX15zBhyRq8MLVtS0Cc7mzKPteltDhCJ8venzKJCUWFA4tatW5AkCd7e3pXWAVeLuDgiPJ071+39BrAtLiF55ZVXKh5QHEeBGq0WuHOHSj6eJnJy6Do9ekTO2RXB35+I2BPM63fv3pX/+usvxhiTP//8c1Zp4G/CBKp7vnmTiMXq1bCyssK8efO433//XVyzZg0vyzJ4nn/sRm5vbw9zc3MpLy+Pa9GiBWe0YgVlcYODKz7GypX0eb79tk5qAS8vL/Ts2RMnTpyQO/z73wwKBQUrmjalDXie2n2dOUPElTHKLEsSGYr17Emu5Lt2EdEGgDt3cO6TT6TYPXu4N3bt4v3Cw2F94ABMW7Ykoq7R0D79/EjNdPhwSflRQgKVhLRoQedhbg54eeH4okWwsrKSmwKsWqodHU0Zem9vqinXIzGRxlxCQvUXxswM+PVXOocuXRC2YgUSExOzdDrdjJoYk8myHAMAkiR9X/3Bao7iYx8BwARBaAvgIwBvAngWztsXkpOTTWRZNmyRCUmSOgqC4KxSqSq9kBzHeVVq0hoRQYGQAwee6slevnw5t6ioaOVT3Wk9nhvUk+16/L+CSqUKWLZs2T5ZlseVJa/PEhqNBn/88YcUHR3NKRQKWaPRMDMzMxkAevTogR49enD6B4KdnR1q3Bu8Z08y8Ll+nSL0SUm1Xxi/IBgwYACLjIyU16xZI/Xv35+rU7uwhASSRG7aRLVtNVjk7927VwwPD+ckSWIuLi5Sx44d8fLLLzPGWKlFeb9+/XD//n3uz4wMDAwKgrfBou/s2bPQ6XScpaUlQkJC5ObNm9eMXehd0S9fpnPduJEWqOHhlZ67q6sr2rVrJ9++fZstX74czZo1k8aOHcv9Y6R740YibDV0yX2Mzz+n7NpHH5X70yuvvMKtW7dOXrNmjTh9+vTHfWGrhakpMHEiMjMzsXHjRgwfPhwtK2mZVBbDhw9HZmYm9u7di9azZpXraVwKoaHkA3Dx4uNe7QEBAaKnpydf0zKIrHbtcKt9eziGhXHw9yfp4owZ1We2qkNqKpHU6tCpE9Ujtm9fu8zxd9+R4/rVq1QPWwkKCgoAALt27RI/+OCDun2ou3cpePAUgqb6oE2VihA7OyK8b79NgYQK+kXXGRYWRNwqI9rbttE9dOpUned4WZZx9OhRplarMXPmzMqJNkDzS24u3YeGpA/AxIkTeZ1O9/iaZWRkwMjICFZWVgwAW7NmjXzgwAH24b/+BVaZS/zmzUQKv/qqVsqisp/H0tKypKdzQQE9C/VkGyC576FDNFbatKHjJiRQNjsoiDoTfP01sHAhjSN7ezzq149rc++epLCx4RwdHCigo/edsLen4M6bb1ImvU0bCqxt2ULjnjF6Ls+Y8fgUOnbsiD179rDc3FxMmTKl8g/06BGpQZycSHFliMxMup9qM482bQrdmTO4ePKkZsSOHXGtQ0KUqIFsmzH2b1mWoVKpMmt+sNpBpVIFAZha/O9Z7D9j2bJlhTk5OUb6Obdly5aws7Mrys7OXiwIwuyKAg+CILQA0KnSYObSpbTWeopGsQkJCUhNTdUBeLoMvh7PDfgvvvjinz6HetTjb8X58+e3tWnTxrh58+Z1z6hUA51Oh1WrVknnz59ngYGB4l9//cUZGRnJr7/+Omvbti3r168fevfuzbp3787c3Nye3OHbyYki4d7e1ILoCSSGzysUCgV8fHxY3v+xd+VxUZXv99x7YWRV2VREQUVQ3AAV3DJxDSnJvb6lmWlqpbmmaepIZmalWZmWS5jlkprijrjhLqioKCLmAiIi+w7DzNz7/v54GBbZZmDc+s35fOaDztz7zt3nfZ7nPOfk5rLQ0FDu0aNHcHV1hda+vMnJJBwjCFQl1uKYHz58GDdv3uTfeustbtCgQfDw8OBatGhR4fniOA62trZg5ubqR8HBfPbBg1Kjt97iDgUHs7CwMG7QoEHs3r17cHBw4JydnXXbeXNzerm5UdWxRQvykPX0pGrlE2jdujXXrFkzZGRk4P79+5yTk1Nx5e6ZgjGi669cqbtPsSTReRoypFw/vZGRETp16sTFx8ezkJAQrkWLFpylNuP/+ivQpAnWHD4s5eXlcXFxcTopm7du3RpNJk+GuZMTjCsTxklOpkC1f386R6AALjg4mB84cKDW56F+/fqIuH6d1Rs0iGvu7g5s3UqT+u7dy7Uo6IQhQ6iCqs0YvXpRJdfenhIVVSE3l6jjTZpQD2wV7I3ff/9dvH79Og8AKpWKt7CwqJkA47ZtVEUsHVzVEGZmZjh58iQsLS3RrCr2g5MTVTUtLeme1Bc7ato0EvWq6LwoFPTZqFE13tf8/Hz8+OOPUKvVmDBhgnZ2mCNH0nNy0iQ6t6W0CTQ+0hzHwczMrIwdpKWlJWeybh1L2rYNZqNHc+WSYRs3kuPAhAm1ErY7ffo0jhw5Ajc3N8nNzY1HixZ0rZbWIeE4euZv3EgaBG++Sc+Ba9cQJJPhmKOj5L1iBYfHj4HXXkNySgru3LmDh1ZWrNP69RwuX6bf1O+/p2eSKBLDaNw4qpgbGVFf9fDh1N8+YEC5hIlCoUB0dDQ++eSTynfmwQOipNva0vF+EgcP0vOwGnFHSZIQHx+P8PBwyGQynLp5syD53r2IN3bvZjxjcQgIuItFiyqtbgcEBJgA+AXAWR8fH9085V4wnD9/friTk1Pj0s/ctm3bGkVFRbVhjPU9duxYkI+PT2Hpdc6ePbuoXbt2nd3d3cv/yJ89S/ff8OF6K2owxrB9+/a87Ozs2QsXLqxE2MCAlx3/zRKYAQZUAUEQfr1y5cp0Ly+vp6KwDQDLly9noijyY8aMQXJystC0aVPY2to+Pc46zwP+/vSDPWIE/a2l4vKLCEtLS/j5+fGenp4ICgpiK1asgJeXF9e3Op/La9doohoYSIKOaC51AAAgAElEQVQ21UCSJKxdu1bKyMjgx44dq5NPe7du3YzSfv8dGf3786tmzECGtTX3yiuvoGHDhlx+fj4GVOUzXR3MzGjCKElU5XB1pUlgcjJVvEuhWbNmSEhIAAA8Nxuw/fupYlQT5XBjY6pAqSpmGBobG2PkyJHC6dOnpY0bN3K+vr7o2LFjlUOqWrbElvBwFAD8//73P+zevRshISFanxNFQQGy6tUDX5nmQ1wcYG4OdvYs7nAczv3xh2hjY8PFx8dzFhYWzNnZWetnAMdx6NKlC3flyhWxT58+AtatI+aKjw/1vy9dWrMqN2OUjNu9m8aqCg4OFLz4+pLNVFUB97hxQFISLVcFioIBYeTIkWjRogXu3LmDvXv3subNm3M69eCrVMTwmDFD+3W0QGZmZvULvfIKBVVt2xJtuLZQKEhEsKJe8IQEovRfu1bjJAtjDMHBwSw/P5+bPn06KqXIVoTPP6dESlUigE/Azc0NbgMGcFfCw6W1a9dygiBArVZj2rRpMAsJoarytGl0fdUCN27ckDp06MAPGTKEboR794i19CTFd8AAEko7fx6YMgXqnBwcvHpVunXrFl+oVPIpoaGo5+ODUw8fSmeLnDFcXFwomdK0KamT5+dT4D5/Po3522/U0+7oWK3tXJMmTaBWq1FQUADTiu6h+Hgaz8mJEhCg+6S4RUaSqPpegV5JYmIioqOjERcXxzIyMqSCggJBEATY2dmJ4eHhglqtNoWR0XAjSXoMwBjAHXDcZ2Dsn0o2d+YTf19aiKJ4OTk5uVPLUsUHc3NzfPLJJ2YHDhzoduPGjbsBAQFvyuXys6VWU8tkMiWetP0SRbrXR4+mXn894d9//0VKSkqaJEkb9DaoAS8cDJVtA/7foWfPnocvXLjQ586dO41atWplZGJiArWanBZqU2HOzMxEUFAQQkJCRLVazc+ZMwf16tWDvb09tKa51hYa6xRBoIlA37418m1+0WFpaYnOnTtzjRo14k6ePMkuX74seXl58RWev7AwqkK1a0fHQwvs2rULWVlZmDRpEleTirCZuTmsBg6E99KleGXtWji3bo3ly5fD2dmZdejQofZ0Co4j1WIzM5qoSRLRGcePp4pqEWW8Tp06uHPnDgDA1dW11l+rM1asoCpgTZkWkkTU3d69qWL6BDiOg5OTE9ewYUPs3bsXVlZWaFAZtVethmLIEBzy8MDEjz6Co6MjnJycEBwcDDs7u3Ie1uWQnQ1hwQL85eGB2Nxc0cjIiM/NzYVSqYSJiQkexsfDondv8ByHY+bm0tGjR2Fvb8/du3cPOTk5/IABA9CoUSOtz31oaKh0/vx5TiaTMS8vLx4cR9fx229T/+b+/dRT3aqVbpRGjiMapLu7dpRUR0fqO3dxqdgab/16ou5+8QVdf9VUejmOQ2RkpKhUKln79u15Ozs73L9/n12+fBmdO3fWnuUTFES2X094k9cGFy9eZDzPw8PDo/qN6NOHgrHGjWuvTi6KpDBe0SR+2jRqw6ihj7goili+fDkSEhK4jh07ooOuveZ9+xKNPDOTgm5t3Ctu3gQ6d4b96NGchYUFoqKiIEkSCkNCmMv16xw3ZAglKmoBxhhOnDjBOTs7o7mm2m9hQdXhDz4ouzDHESNk2jRk9+mDv48dkzxPneJelcu5i9evI5fjcM7EBP5BQZxjy5boP38+OnfuTNdA/fq0/+fP07EwNaU+72++ofth9Gj6zbWzq5BlRF/PITw8XHJycuLqP5kET0ggG7e2bYFx46BWq3H48GHs2LEDZ8+exbVr18SI8HA0GzmSy3d1xaVLl3Dy5El24sQJ6fjx4/zVq1ehUqlEJycnrlOnTny/fv3Qt29fdOzYkU9PT5dSU1MPLly4cC0WLQIWLRIREHAPwF0EBLgiICAJixYVewEGBAQYAQgFALlc/iFechw/ftzB3Ny8j5ubW5kHHc/zaNWqlVHDhg3NYmJi3jl16pTliRMnxJMnT7bief6rvn37WpQ7Tzdv0vNXw/bQAyRJwubNm/Py8vLGyeXyW3oZ1IAXEobKtgH/L1FYWDiisLAw6c8//2RDhgzhNmygpOKMGTOgFR21FCRJwvr168XU1FTB1taW9e/fX3BxcdGe3qxv8DwFKU2aUH9Zfn7taKcvKDiOg4uLC6ZOncr99NNP/Pnz59GjR4+yC124QJWC33/Xynbq8uXLOH36tJSXl8ePGTOG07UirFQqkZGRATMzM6gsLWE9dCiUP/+MfW5uIgDB29tb/30Lb71Ffx89InEdQaAKfqdOxQGkl1flwqpPDcnJRKPu06fmY/A8BZXVVKxdXV0xePBgBAUFISUlBb169SrnPX376FGYyGR4xceHNWjQgAOov7158+ZiVFSUUM7q6UlERMA4PBwzQkNx8NAh4fjx46IoilAqlTzLyeEaFRRI2YMH867u7rh84QI/ceJE2BGdlPv6669ZWFgY16FDB60SenFxcTh58iTfr18/eHl5lX2QWFsTNf/sWQpwrawo4KtOdK80mjenSvRff2lHg162jIK9994jcTWAKuSiSOJXTZro1Cbw2muvCdu2bYOvry/MzMzQrVs3fsuWLcWJC61w9CglHvQIU1NTVrduXQag+oe3gwNV19u1o4ReTfu31Wo6h2FhxW0Hxfj5ZzrHtVQ9zs/PR9u2bTGoJp7ADRuSMv3atZRMWbKk+nXGj6dWgp9+gqenJzw9PXF7yxbEBQZyp/r0EXt5etb6xzEqKgqFhYXo3bt3yZsODnSNJiWVp6c7OiLJ3R2F77/P+rZqhSY5OVzB3bvFVHh07CiZDBnCu06bRhXk0aPLru/jQ8nDnj0p8AIokX3xIt0HRRoElcHc3JwlJSWVaVGIu3gRDX74ARmdOuG0qSlSVq0Sc3NzeXNzc4waNYozNzfH3bt3BWnqVFw3NcW5fv1gZ2cnNmvWjOvUqZPg4OCAevXqgeO4csczPz8fUVFRSlEUp5b5gLF9RQ+hCwDCAZRW8hwIAIIgLK1yZ14e3ExMTFQXFhZW+KGTkxPGjBljcunSpRn379+fIggC69Spk2njxo2hVCpLFszKgtHkyRBXrACrhGlVGaqyV46MjGQFBQW3QKJxBvyHYQi2Dfh/CblcnhwQEDAoMzNz+4YNG0wBgOf5xOPHjzf08/Pjjaup+KSmpiIwMFCytbXlk5OTmSAIQv/+/dG2bdvy/WnPAyYm5DcqSTQh2LevesroSwojIyO4uLiw0NBQLjU1FT4+PrC0tAS/YQPZR23bRiI4FUCtVuPAgQO4c+eOqFQqeUEQOAcHB4wdO1ZnP++bN29Ku3bt4jmOK2ZKONnZSV5//cXnpacLY6dOfbrq8I0bk3gTgJS1a3HVwgJXPDzglJyMyMhI9O/f/+l9d0XYvJkElnQVRnsSPXvS5H3uXKCKXvc2bdrAxMQEe/bsYefPn+fatGkDPz8/yGQypKenI/jIEdT38cHbr7xSJtr19vYWduzYwVCVFV5kJFWQw8JgAmAoWYcJACXbxGHDYFxYyJ9fsAAhISGwsbFhdnZ2xeP17duXCw0NZTt37pRGjBhRZaCRnZ2NLVu2oF+/fuWTR6XRowd5hu/ZQy0E48ZRQKxNks/CgkTcUlO1DxJHj6beVU2w3acPVeNWrdJu/VLQqPHHx8ejVatWePjwIQPAaS3ip1DQ9d6li87fXRmuXLmCtLQ0vtqWlNJo1oxcB3JyqLJZk4qXkREJID4ZaB8/TorSo0bVShRPk3RKTU2t8Rh4/XWq8M7UglnMWPlWgthYuO7eDbsPPsBPt28LTe7eRUFBAURRrN4KsRIkJCRIAPhyjgsXLlCg7e9fZvnDISGIU6sxuHdvNFi2jIetLczy8tCxsFC6ePEi7+npyTBwICl4BwQQa6HIIlClUqFw40aYz50L7vx58leeMoVEy6KiKLlZWEj35B9/lGPyZGdnQxAEIT4+XuzSpYsAANt+/ZW5r1rF7e/QAbcVCrQ3NRW9vb0FBwcH2NvbF583W1tbpLRsCeNBg+AzcmSFgXVFOH/+vJrn+e1yufxuuQ9Jors7AFNw3EoAkWDsdwCpgiA8FkXxDy1Pw4uO2xkZGXW/++67Mm9WEAAbF71w7NgxHDt2rMyH9g8fwk0UcfzAAdLNqCVKJVw5URQnaaMQb8DLDUOwbcD/W8jl8v0BAQHmAOwBZEuSZHXz5s2Nt27demXq1Kmyqiosu3btEi0sLITExET069eP8/DweP4WSxWB58kT0s2NesKGDdOOBviSwdfXl2/YsCGLjo6WVq5cKTgmJ8N/zx48+vFHtBo0CJJCUa5iplAosHr1asnU1JR79dVXBUEQ0K5dO8hkshr11hcWFnIcx2H06NE4deoUevfujT179rDYjh3F/8XGCkY1sWqqAYKDgxHm6wtLS0u8rVbDYfNmqH79Fbh1q2oFbX2CMWDnTkp06APp6dQPXY2wXIsWLTB9+nQuPj4eu3fvZps2bZJGjRolrFu3jnmnp8O7Xr1yAd2///4rymSyqn3nx48n66Dvvy/7viSBP3UK/Nq1QL166CaTwcbGBnv37i0TvHfp0gXt27fnvv/+eyE1NRW2traQJAlZWVngeR4WFhbgOA7R0dE4ceIE7O3tpR49elR/HXIc+fB26kTH59VXKVCozmaN54ErV4garC2GDyeNgLfeot7FL74gH/gagOd52NjYSLdu3WKtWrUSJEniGjRowHie1y5avXqVtl2PLTIHDx6Ek5MTqmU4PIkZM6jym5dHlU9dMWECeTyXRng4JUwPHCgnDqgrcnJyAACFhYVVJ5SqQteulKBp144SOlVVt7t1o33SULkTEui+mTIFVq++Crft29lff/1VvB2hoaH44IMPdGaU9evXj4+KisKOHTvYu+++W7JfI0eW0YhQq9X4448/pPT0dH7U3LloMG4ch88/p777tWvhtW8ff/HiRfTs2ZOC2I8/JlbOL78A1tZIbd0aZ+bNY9ccHTmHyZPh8uiR2Ov+fQEpKcQMWbaMvsjYmJK6devi1q1bOHnypGRnZ8f8/f2F3bt3IzExEYmJicK9e/cg5eZi5JYtnOWCBXhj5Eio1WpYWlpWHESnpcFu8GC6/7SEUqlEWFiYWqVSLap0IcZEALnguBgADuC4+nIgDIw9HSGb54NkSZKkefPm8TVJ6AAgHYC33gIuXEAPPbIV9+/fXxgZGRk4f/78S3ob1IAXFoZg24D/1yjKKGr8FnMDAgLGAojLz8+HiYkJRFHE4cOHERsbW/jhhx/Wyc3NRXBwMFJSUoSpU6fCosjW54WGmxv9Xb2aqlha0KlfNhgbG8Pb25vz7txZYJ9+ivRXXkGUnx/O37iB3d98A57nIZPJmCAIkpOTkyCKIu7fv88aNGiA0aNHc9UxGbSBh4cHl5mZKW3ZsoWbOHEiZ2VlhY8//liAJNGkbO/eGvdd6oLk5GQAgJ+fHxxbtwY++wyCWk0T5j//pP5nMzP9qShXhH/+ITqnvhIMu3aRZZWWLRFNmjSBubk5s7CwEL799ls4ODiwHl5evOwJcSLGGO7cuSNobPgqRGYmVeoqSr5t2ECT7Zs3AZkMN2/eRFBQEBwcHCQAZQ6wmZkZ2rdvz9asWcOZmZmxgoKCYr94DRPCxMSEFRQUcIMGDdLt5DRtSq8FC4CICAqk33uvaqXns2fJ2istTfuKrLMz9Uo3a1YSZOiI69ev48iRI6IoinxmZiYDAJlMBoVCoX0wePt2pWyVmuDChQtQq9V49dVXa6bbMWQIsGYNJZl0WZ8xouGXvi4VCrKVksur9kLXAnl5ediwYQMzNTXF8OHDa9fCkpREQWhV1lUACTdqWDQKBQXd779PiSAAI0eO5DTVaKVSiY0bN7I//vgD7733HqeLcJsgCOA4Dqonab116lA/9ebNSE9PR2BgoFSvXj18/PHHJBTp6krJDLkcyM+HRpSvsLAQRW0hePC//8E+MhKyyZNxq107qf/hw3y36Ghs+vNPFhoaKvA8L/Xs3p1H06bA+PF4sH07CszM0PLXX3Fj/340GTcOzdas4W/cvy8tWbIEpqam0vDhw/mWLVsi6cEDWAwZglO+vtKb771Xsc5IaezcSS0TOgTbN27cgCAI5+bNm3e/2oUZWwMA4LgtABoBqEXfz4sFuVzOvv7665z8/Px6OokClsaPP9aaXfIkYmNjERkZWaBSqRbqbVADXmhwVfUTGGDA/zcEBAQIxsbGe1Qq1esNGzbMVSqVXFZWliRJkqWxsTFUKhXs7e0ld3d3pqGDvXTo25d+uD/66HlviX6hUpGQ0+TJ5HdqbU0UX1GEKIrIzMxEUlISgoKC0LRpU6lHjx68q6urXu3fGGNYsWIFioL4kg+uXaPJ+FdfPRNmwYYNG6SHDx/yCxcuLNk/pZImDO+8Q6Jq587pHhxoi/ffpwlKdRVWXeDsTOMuWFDtorGxsdiyZQvs7e3ZgwcPuIULF4L75hsKLtu1K14uNTUVv/zyC8aMGVOx3dPdu1S9jYsrLw527BgFQ/n5gI0N7ty5g+3bt2PQoEFoX5laOYDc3Fw8ePAADRo0KO6plyQJarUaaWlpWLt2Ld55551iqrXOUKup8vj330BwMPWxVjRRlCS6Bnr0qP4akCQ6nxMn0vHYvJkq6jqqzGdkZGD16tXw9vZG3bp14erqCisrK8TExGBbEQuiT58+6FmV8jVjRF8/dUpv99LSpUtZjx49uB49etRcayM/n5Jpv/+ufZIpK6tsdb6wkDQKPD3J3q+WWLFiBfLy8vDFF1+U0zCoEZTKYj0IdO5c/vPly6kK2KQJBdrjxtGrCt0GSZLw559/SklJSfzo0aPRqFEjrZ/JEREROHr0qDRr1iy+eP/u3gVeew039+7Fnj17mLu7u+Tr6ysUf15YSNv09ttAdjakrl2xYtcu5OXllRnbIisL7wcGIqVJE6lFUBAvc3ZGfn4+vvvuOwiCgE8++QRWlpYo8PCAdO8e9rz9Nu47OwNqNd6Pi4PDunWQANyKiUGLFi2IWaVQAP37I7h7d3B9+kivvfZa9Sfl3j0gO1snFsmaNWtykpOT35HL5dr3AnOcKQA3AKYArMHYPq3XfYGxbNmyuDFjxjjq4ihSjDNnaE7xv//pTYFckiT8/PPPeVlZWaMXLly4Wy+DGvDCw1DZNsCAUpDL5WJAQMBwAF2TkpKsAOSA1DlVADB27Fg4Ojo+xZLgM8A771CV4eFDqnS/iPR3XZGfT5OnIUOoClA0WeN5HjzPw9jYGI0aNUKjRo3g7u4OPFF11Bc0lRa7J3xW4e5OPZkXLpB111PGwIED+XXr1iEvL6+EfaE5z5s3k5r1nTvkBx0TU3Nxp4rw8CFRNHv10t+YAAkRaUmptbGxgUqlwoMHDzhfX1+avF+7RsF2KTx48AAA8Oeff8LCwkJq2LAhl5aWJpqbm/M9unfnW7m6UlD9ZKB99y5NwMLCwJo1w9UrV1hwcDDXsWPHKgNtALCwsECbJ6qyRcwLaIR8kpOTax5sGxlR1W7GDGDWLLJZ+vLL8iJbPE9BeEAAUJUrSX4+XTscR8rw9eoRNXjyZGIc6ABBEMDzPFMqlZK3t7egCaqaN2+OXr16sTNnznDHjx9HRESEOHXq1Iqj3vBw0p/QU6CdmJgIpVLJdenSpXailmZmFGSGh2sfbDs6EutFc11Om0bV46Je4dogMTEROTk5mDx5sn4CbYD6/D/9lBJeTwbbkkTsqf796TiMHUvPgdICZhWA53mMGTOGDw4Oxvr162FiYoKxY8dW7w4AwNnZGfv27eMPHDhQIv7WogXCx43DqW3b4DdsGNzd3cue1AULyEJvzhygSRPw9+7Bb+hQhISEiMOGDRPs7e1hZGQE6do1cBERsFEoeIwZAxw8CHXRENbW1mzjxo3o2rUrl+jqCushQ9jQqVO5zBUrYPv55zCqWxfYswf8Z5+hTUwM3Ts5OcDQobgyaBCuiCJGt21b/UkpLKTfi7CwahfVICkpCRkZGWoAwVqvBACMFQCIAMeNBTAdHHcAAKtS4evlgFqSJN3XEkVSiX//fb1afZ06dUpdUFBwhTEWVP3SBvxnwBgzvAwvw6ua15dffjn3xx9/LCgsLGQqlYr9J9CtG2Pvvfe8t6L2SEtj7PZtxqZNY6yw8Lluyo0bN9iiRYvY/fv3y3+Ym8uYjw9jkZFPfTtu3brFlixZUv2Chw/T3169GPv2W/18+Q8/MPb11/oZ60mMHUvja4HCwkKWlZXFJEliTJIY27iRsby8Msvk5eWxx48fs4KCArZ+/Xpx48aN4qVLl9jhw4fFsB492I3OnaXHjx+XHfj8ecYSExlLTWWMMRYaGqpeunSpFB4ezkRRrPUuHjp0iH355Ze1HocxxpgoMrZpE2N//cXY/PmMZWWV/fzgQcYGDKh8/dxcxuzsGAsOLvu+UsnYjRuMhYbqvEmpqals0aJFLD4+vtxnMTExbNGiRWzRokVsy5YtFR/M3bvLb08tEBQUxNauXSvpbcDjx7W+RtmDB3SOGGPs2DHGbt5kLDu7Vl+vUChYWloau3r1Klu0aBELrcE5qhL79jGmUJRstwaZmfRXFBmbOJGxXbsYU6t1Hn7Tpk3Sxo0btbqRAgMDxe+//1589OgRY4wxlUrF1q1bp77p7s5SN2+ueKXISDpHgYGMffVV+W0MD2fMzY0xze+8Ws3YK68wNnIky0pOZosWLWIZGRls586d7Oeff1bf7dZNEnftYiw/n7HOnWlcxuhZExhIz57kZMaGDmWKf/5hS5Ysqfj3oSLcukW/0zpg3759iq+++uprVps5D8AxYAwDTjDApFZjPefXsmXLbsbFxel0DBljjF28yFhQEJ0/PeHRo0dsyZIl2YsWLXLQZtsNr//Oy1DZNsAALSBJ0rcZGRlfL1++XJIkSZowYYJRuerly4bjx+nv1q1UvRo37vluT02QkkLUzdGjSbTpOWPPnj0AUDEl2dycqoF//0199DUVbNECTZs2hUqlglqtrlrpd8AA+jt9OlXjjh+nfsfDh2tOLw8NBVaurNm61aFzZ61tkGQyWYloYUICVXvHjCmzjJmZGTTuAePGjStdaeLFwkJEXL4srV+/XuA4DmZmZqIxgDFyuXBmyBApqXdvzsvLiztz5owwevRovSrN660SyfN0b6SmkqjYli10nv38Svy2X3uN/JOf1J/44w+iBAcFle8dNjamqviMGXQ96+CjHhsbi7p167LGjRuXu8BcXV0xe/ZsPHz4EDt37uTT09NRxueeMVJg1+OzqkePHvjll184URT1Y9doZUWWXRMnlu3FfhJjx5KQWNOmROefPp2OtY5CYaWRlJSE3377DYxRMdLR0ZF16tRJv30iGlbB++/TfgJUtXVwoP7zP/4gpoyfX436XF9//XVu1apVXHBwsDRgwAC+qnshKSmJ79q1K+zt7Yv7s+vXr885jxsHWWV6BTIZVeF79yZ19VatiOUDEE2+bVtif2mem4IAHDoEdO0Kk3ffBbp3h7m5OYaRKr8AFxd6JpmaUgVakkgkbsECOkaaZ/7q1ditVLImTZpIzZo10+7AZGQAu7VnGqtUKkRGRjK1Wv2b1itVBMYYOG4PgKYAeHBcGzB2s1ZjPj9kKBQK3dbIySG2j1yutzYrURSxZ8+ePFEUZ8nl8gS9DGrAS4OXmw5rgAHPDpKxsfG3oihOV6vVm0NCQvKqX+UFh4kJve7eJSrxy4aYGAqipk0DJk163luD/Px8WFhYwMbGpnLO2rBhJLilL5XuSmBiYgJjY2Ncu3ZNuxXefJO8rBs0KOkNHD2abG10wdatNNGspS9wpfj4Y/KIjojQbb1793QKCDFqFAR3d3gtWiTMmzcPEydOxMDWrYXelpbCiVWrpLDmzfmsrCxu165dcHR0lPQZaBcUFDBjY2P9UjdtbYkS6edHom4TJ1JgAdC/Bw8uu3xyMvD55+TbXplIV+vWZDmmVFKfuBZ48OABMjIykJ2dzWmE/J6EqakpXFxc0LZtW+m3335j6tJja6yWqqHq6wJbW1sYGxsjIUFP818PD1L+X7eOkhyVISeHAusHD+j8BAbStV0LJCUlgTGGuXPnYubMmRg7diyndxFPCwvqKX/nnbLvHT9O97+tLamr15B6a21tjTfffBMXL17kv/32Wzx69KjSZTt16sSuX78uRkRE4LfffmNt2rRhY8eO5WWvvEL9thVh505g8WLAyQnw9aUWEZUKuHSJ2iSMjYH588vv87FjyEtIwJtBQYiPjy/5LCeHWnMASm4ZGZX0+EZE0NiTJkHy80PsvXvo06eP9hmIOXOA69e1XvzBgwcwMjL6Vy6Xx2m9UmVgLBOMfQXAG8BJcJwee42eKVIKCgp0WyMujmwFvbz0sgGMMezZs0eRkZFxVpKk9XoZ1ICXCoZg2wADtIBcLmfz5s2bM3/+/J9kMllPc3Nz82XLlimWLFmiDA4OFsspor5MmD+fJs1z5lCP3cuA9HTyUY2MJKuXpyHypSPOnj2LjIwMvPXWW1U/V+fNA77+mqyCnhJ4nkfnzp2l/fv349atW9qv2K4dXQtqNYnycByJbG3dqt36u3frbYJSKRYs0N1iydKSepO1QW4uee0WBSkcx8HGxgatvv8ebcLDMWj0aP7zzz+HtbU1A1D9+dYBQUFBUmRkJOfk5FSDJkMt4OhIiZ4ZM+g8/fADXY+//EKfJycTe0AUKZFVnQ1W37703KjKCqoUtm7dys6fPw/Tqiq+RfDz8+OVSiW3Zs0asbjnMjCQ+pn1eL+npaVBpVKRUrW+YGxM1erFiyv+PCEB2L6dEkDvvgvs20fJrlpCk5hYvnw51qxZw2rUq6oNTp4kK8kjR+h+8fGhxExUFGln1DLAd3d3xxdffAFLS0vs2rWr0sRThw4duLS0NGH//v2wtrbGwIEDSQitTp3yXt8azJxJxxug69fZmaTHK9EAACAASURBVFwaOnemdSpzpmjYEP8MGMCaJCXB6euvS94XBOD8+bLLfvopVcj796dE3+LFyHnlFbz5zz9wcHDQ7iAwRgkNHx/tlgclW9Rq9VmtV9BuO0IBtAF5cm8Gx+mvgfkZQBTFFJ0q23FxlBR/0o6vFoiIiGAxMTEJSqVyqFwuf0o3pQEvMgzBtgEG6AhJkoyvXbsGhULxiVqtnh0WFiZER0c/782qPWbOJLXs/PzKqwIvAvbvJxG0Aweqt6F5hvD09AQArF69GqGVTfQAUnBeu7Z89UTP6Ny5Mw8A8fHxuv+4GxsDe/aQvVJEBB1rUSSKaGUT+NhYEvMpsvh5ati+nUSlmA7F36NHtauGP3xIy549WyKKlptLAWpQENiaNUhPT8e///6L2NhY7s0338STvt01AWMMISEhuHbtGj9ixAi89dZbT8/pQCajIHrvXhLKCw2l63HzZgpSunQB7Oy0t4b75ReqZmoxoeV5nrVp00acPXs2qlMHNjIywoQJE5CRkSFEaM5ddHSt7bCexLZt20QXFxfJ+kkRvNoiMBCYO5eupyfh5QWsWkX05TlzKPmhB7i4uMDPzw/vvPMOGGPc4sWLsXTpUqb33yeOo+dDYCBV75VKqmyvXEnVYT3g6tWryM7OZi1btqzwgaNSqXDmzBkRAJycnCBJUskDoVUrCpY07I3SOHwYmDqV/t28OQkvjhtHlPgOHap8rjgOHMjtHTiQ2GBz5tCbr71Gv5ulkZxMx2bGDGDjRqR88w0OtmsHYdUqjktK0u4AbNxI4oY6tBulp6cXqlQqHbKrWoKxFACZAGxBntx6zEw9XahUqsc6VbY3bya7Oj2JoqWmpuLw4cMFSqVykFwuf/kZkQbUCIZg2wADdIRarR4pCMIyAH/VqVNnHADts9UvMho0oCz6xo3Ua/YiIieHaOP16ulGC34GsLW1xaxZswDQ5K9KdOxI9MKKJuJ6QnAwidHa2dnx0dHRiIurIbNw3jwKbh8+pAlsSgopexcpZxdj92767Cn2ogOgIHDWLJrkaot69bSjHgcGkn1Rafz0EwURZmYIPXlSXL16Nfbs2YMhQ4ZUqzyuDRhjOHnypHT+/Hn06dOnnFL5U4OTE/Ul2toSU2DUKGIz/PKLbufQ0ZFaB159tdqA28fHh79x44aQkpKi1dD29vbw8/OTDhw4gNToaPJIbttW+22rBgcPHmS5ubnC8OHDq/c71hWmpsTA+fBDqpaVRkwM+ZtPnAi8/rreKvWWlpbw8vKCk5MTPvvsMwwcOJA1b94c27dvx7Jly3DkyBG9fA8A8jr/9VeqcosiPSf0pBCfn5+P4OBg5ubmhtdee61c4qmgoADr1q1jDx8+xPTp0zFw4EAkJyfzixcvhiiKVG3+9lvg9Onyg8tk1D6lwSefEJ3c1pY0DdzcKAA/caKkl7sIsbGxUma7duA//hg4eJCeDYJAWhCawD45mX5DBQH44gvAwQGJu3ez3llZcO3alWzTfvqp+oNw7JjO10VeXp4KQJpOK2kLxrLA2GsACgDcA8d1eSrfo2cwxtLz8/MryLpUgDNnSn5f9AC1Wo1t27bliaI4Uy6X/wcqMgbUFAaBNAMM0BFyufwCgAsAEBAQMEoQhOWnT5/u5eHhYRwbGyvl5uaqPDw86jRp0uQ5b2kN8fHH1AMdFUWZ+UOHtK9yPU189x1VGaOi9GrFoU9oBJaqFc8zM6MJ2qlTQM+eT2V/evXqhX///Rd79uwBx3FgjGHBggVIT09HQkICGjduDFtbW+19xp2cAE1VxteXAqwffqCkgbk5cPPmU6/WF2PMGAoMtUVoKFFGq0J8PE2O580reW/9ehKumjED2bm5uHDhgjB8+HC4urrqRcQsPT0dly9fxrlz5/hBgwahox6oxFVCY2czeTIJ4W3aRD3Dbm4UWIweTRVKXffN05Ned+6U8TF/El5eXjh48CA2bNiAzz//XMuhPfkDBw7g7rZtsBZF8JXRfKtATEwMQkJCREmSwBhjarWad3V15a9fv86NGzdOL+yECtGmDbVhKBTUNmJuTonMLl0oOTFu3FNrgeE4Dt7e3py3tzdu3ryJc+fOsXPnznHnzp2DsbExXn/9dY0NYsXQtJIIAiXaeJ72ISGBKschIZT4ZIyq23qqaANAYWEhVCoV9+jRIykqKgrt2rUrPkg5OTkIDAxkpqambNKkSQLP86hbty7at2+P+Ph4xvM8Ldu1KwW+T6J3bwp4NZg6le5xDby9KYH03nv0vFu3jpJt06fD08ODDz15EurBg2GclUXBeb161H6Vm0v314YNdKw0lW8A+/z9uamffkq08DfeoPvv+vXKE4BKJf32VnV+KkBRdV87AYWagrFEcNxIAFHguAkA/gBjhdWt9hyRUZSEqPomZ4zaPiZOrJGwX/nhGA4cOFCYm5t7RpKk2gnWGfDSwxBsG2BALSCXyyMDAgIm3rp161BMTAynVqv3qdXq1GvXrs3r1KmTSa9evYy06U984cDz9HJ0pL8KRdlqwLMEYySMdOwYsGzZCxtoA8CuXbsgCAK0EiXy8gJ27KB+6KfAJHBwcEDLli1FKysroU+fPli2bBk2bdqE5ORkKBQKyGQyJooiZ2dnJzZr1oxzc3PjGzZsWBx4xMTE4NSpU6xp06aM4zje1NQUPXr0QHJyMh7//jvatmoF2bZtNFH98UdSYa6uoq8vtG9PfsY//wxMmVL1sowRxb0qmrAk0QT8559JgRsgwaTFi4GhQ3EvMxM7d+5Eo0aNpNatW+st83Tu3Dnp8uXLvLW1tdixY0f9U8fVaqpSL15M3sKrVlECwc+PgoEhQ4B+/UhF+bPPSCivTx8KCj78UPtAkOOoh/fDD8kz2t+/0kWNjIxQWFiI48ePM1EUuW7dulV5v2j0MKSjR3HIzU3qr1TyugbHYWFhUCgUgq+vL4yMjJCdnY3Lly9L/v7+XKNGjZ6u4EOrViSYBdC9zvP0HDt8mJ6v+gRjRD2WJAo0s7IAOzu0iYtDm4YNuURLS9w9cAB3eB45Bw8iz9UV5u+8Q0GjJAGNGpG4W//+JJDXsCHpS+zcScynJk2oIm9pSfeUhnK9eTNViPWh5g7AysoKU6ZMwdatW/kTJ06wdkUJnMzMTPz+++/M1taWjRo1qoxSefv27XHjxg3uyy+/RPPmzcUB/v6C9e3b5SOsHTuA778ndg5AgnbvvEP7C1DiCQD++Yf+xsdTkiQrC52WLEHj69exycJCGufpyaNnT6KQDx1Kav+nTtGyn31W/HVJRQlKUzMzSmTHxlIfd7duVD2vSOMiJISSYS9qOxdjJ4uo5BMBFAL44zlvUVXIKSwsFKtd6vRpaqXr318vX3rhwgXx5s2bCUqlcoRcLn/ZvcoNqCUMwbYBBtQScrn8HoBWpd/76quvGoWFhX3aunXrim2gXga4uVFWPyKCqq8ZGUTBe5aQJKqyq1RUIXqBoVar8e+//6KlLvT2Tz6hSsjjxzTR1SNSU1Px4MEDwdvbGyZFiRINlXzmzJmwsLDg0tLSEBUVJdy8eROXL18GYwzu7u6Ii4uTUlJSeFNTUzDG+MTERADAiRMnAFDAFBERwfz8/Dj76GhIH3wA6cQJ3LS1ZYnu7iwzM5P16NFDSElJgYeHh/bVc10QHU3XRHXBdkEBLVO/fuXLMEbUUU2yYMsWuuZv3wbq1EHQxo2wsbHBmDFj9BZoP378GA4ODvzly5chSZL+qCNHjpBAlbMz2TElJVG1uWlTCopLU2Ozskqsj4KDKejesIGC8rZtKalRt6723923L1Foqwi2P/vsM/z44484ffo0B1D7hUbvoCKYmJhg4aefQgoNxXfOznzCxo0YP368TswCtVoNOzs7qX379sUrdenS5dnRddatIzHHo0epGjxwIB13DUSRWmQ4jirHxsZEQU9OJur8sWP0/H3lFQrQ7O2JSZKWBowYQcF7aipVY8+dowDyyhWisn/4IQWOlpaw9/GBvYcHXvH0xOm//pLOCwLfp0UL8GPGUGW6YUPalvr1KTDU3LcjR5bdn/x8Gn/ePGD8eDrfgYHEIKlXTy+HzNraGjzPSxYWFhxAz7PAwEA4OTmxkSNHljt3Li4umDVrFlJTU7Fjxw5u97lzGBYZyRqMGVP24dOvX1m6+5o1VSc9mjYtFmTk169H1PffM9O0NNrvUaOI0bFqFWmJTJpEVetSOHPmDJydnUVBEAT07EnPlfHjARcXEmULCSmxYNSgceMa9fHXqVOHB/Bs+qkZywPHeQMAOO4CgGVgTHufsmcHSxMTk6qzQLm5xNKaPVsvTJO7d+/ixIkTOSqVqrdcLs+p9YAGvPQwBNsGGKBnBAQEGAH4tFu3bi9voF0aHTtSFVEmo0nHtGnPhlauVFIFoGnTl8ID/ODBgwwAV+S/qh2cnKji+uuvevX0zMrKwtatW1nLli3h4uLCAcDbb7+Nbdu2geO44kqijY0NXn31VbxaJGp248YNXLx4ES1btuTHjx8PmUxWvEGZmZkIDw+Hu7s7TExMsHXrVrZ27VrO29paQr163IN33+VkZmbwXroUzVNShE3vvgvGGFJTU6VevXrpXI2sFmPGlNDJqwoINd6/b79d8ef79wMffUR0aoCosrNmAbt20bUHYMCAAdi/f38JRbWWuHz5Mvbv31/8f2dn55qPyxhRUidPpsraqlVEX166lIJoa2uqYJeGJFE1bulS6tEuKKB/Hz9O6/z+Ox2D2bNJHK+qREVpvP02MQQWLgS+/LLCRWQyGT777DOsWrVKTEtLE+7evSu6uLgIVVW3uXPnIAwYgPfGjMFff/2FvXv3wt/fX+uA29HREXfv3tVuH54GLCzoGTp9Ov171CiqWsbGEpvgo4/oPK1YQV7bTZtSq0leHlWR4+KI0VO/PgXfTk4lFOOWLSk5VLcusUsqeoYMHFjuLV4m484ePYroAwfEKVOm6FaSnjePEgjXr9M+2NrS9vbtS8JpetIw6devH//333/j0qVLOHbsGNzc3Ji/v3+lJ93MzAyOjo6YOXMmH7xvH24lJaEBY8XH5OzZs+Kt48d5H7UaTv37c0ZGRkSR79BBuw1q0ACXrawwaNAgHvPnE3PkwAGq/EdH0+/UiBF0HwwaBInncefOHTZs2LCyx3ftWuDRI0psTZlCydbSSYrVq+l+rABpaWl48OABsrOzUadOHTRp0gSaljUTExNjADpkx2oJxqhizHEbAOSA41oCuF/8/osBa3Nz86p7T+LjSa+mW7daf1lOTg527NhRoFKpBsvl8ge1HtCA/wQMwbYBBugZcrlc/fXXX08LCwv7WpIk3tfX9znxr/WItm1pcvDDD1Q90ZMQTqWQJPL+tbEhW5aXAA4ODlxkZCQePnyIZs2awUhbkalPP6Uf+jFjau2zC9CE8sSJE0KzZs3YsGHDiiemrVq1giAIEEURUVFRaFuB0FS7du3QrpKe2/r162NAqQrMpEmT+Fu3biF/8WLewsoKXb/6ClZWVhwGDODw77+Y5+0NlY8P1qvVLCIiAkOHDoWLi0ut968MNIrPjx9XvkxyctX0di8vosFzHAWbGtp5UXJAkiTExcVJjDG9lefNzMyK/92mTRv4+flpv/KjR1S9HD4caNGC7sePPyaFcUmiYEeDyvY7LY2qkxpdCVNTqrzOmkUaDYwR1TY8nGjnZmYUEGqTDLK3J/Go11+n3uRKMGnSJOHGjRvYs2ePcPfuXfj7+8NNQ+F9Ejk5QPfuaNy4Mf73v/9h06ZNuH79Orp27Yq+fftWG3QzxqBWq5+9P2BCAtGTFy0i5gRADBZHR0qIeHpSNXns2JJjq6Gcl0Zp+6eK+nhr8Dzu0aMHl5aWhitXrgiSJGnPFMjJoWtt3Tq6/jiOqrRr11KFtm1bomr361fr5KGLiwvc3Nxw4MABtG3bFv7+/loP6NW9OxLnzuXWjx8Pi/79mb+/P3fixAmhv0oF21Wr8IOFBfr16weP8ePBXbpUlmlQCYKDg6FUKrm0tCINMiMjOjeenpSErlOH2CAyGbBzJxQff4zGkyfD+dQpYiJoGB88T/eehQVVtxkjKv6779I1c/gwtWWUgiRJCAkJUV6+fLnQyMjoiFKpvG1kZGQH4I3WrVvXe/PNN03Mzc2NBUHQL0VKGzC2DgDAcRcBXAfwwTPfhkrA87ytmZlZ5Zne+Hiaa4SH1/q7JEnC9u3b80VRXCGXy0/WekAD/jN4AVSPDDDgv4d58+b9KEnS61evXn3+BtD6QuPG9MNkYUHVlZiYp/M9SUlUbZw+nSpsLwk6duyI1q1bi1u3bsUPP/wgZWsr4MXz1Ms5YgRVSmqBlJQUHDt2TPDz88OTPY0AMHHiRADAwYMHa/U9GrR2cUFHR0e4zpkDKysretPVlQItU1MYDxiAj774Qhh27hxuzJ+PzMxMvXxvMUaNIpuu6lCZRdyHHxLteuhQ+v/8+dSrXRRonzx5Uly5ciWLjo7G8OHD9XYvNy9KqhgbG7P+/ftXHegwRq8BA6hKf+RISdX40CHa5gYNKNjRJmBasoSqcSEhVDXV4LffaP1Ll4ALF+g9b2+qvO7YQbRhbc6fhQUFCw8flgjqVQAjIyN4eHigbdu2UCgU2LFjR8ULKpUlKtEAmjZtilmzZqFnz56IiIjAsmXL2N9//11lJS0yMhKpqanP7lm8eDG1iCgUFJCZmJAY3erVRAOfMoWE6qZMoaTHc0JzXZN76ekU2J8/T0JjNAjtV1gYORKsW0fX5Jo1uokYVoJ69erB3NycDR8+XKf1bGxs4NixI9opFIiJieFWrlwJExMTqcuiRaiXlAQfHx8cO3ZM+iEggN1QKsGqsRKMiYlBWFgYunTpwoodA9LTKVF34QLQqxf1rMvlgL8/ojt2xIbJkzFszhyOu3qV2jSuXqVkyZkz9Pvp7k69wrNmUVVcqaR7MCKiXP97eHg4u3LlSoxarW42Z86cYQsWLJg7d+7c8Uql0uXWrVuX9uzZk29jY8PJZDIPnQ6UftEDwDxw3BRwnA4Ur6cHY2PjhiZV6c0cPUrCkHpofzh58qQ6JSXlulqtXlTrwQz4T8EQbBtgwFOCkZHR0I4dOz7jJudnAJmMBGWcnctb2tQWjFH2PzSUhEpqoDz8vMBxHIYPHy5MnjwZ+fn5fHx8vPYrOzhQwPfddzX+/iNHjohr166Fi4uLWJmqtUYlvTqPY62xcydNOCvqebSwICaEkRFaurkBjCE1LIwUvyvz6tYVxsbUbzd4cOXLnDlD1eCKYGoKNGtG1OmffyYadpGKcFxcHM6cOSP07NmTmzFjBq/PqnxWVhYAYN68eVz9JynakgT8/Tf1zs6cSRNyjqP7DSAGRGQk/btVK92t1iIjiW78JB4/pgrlsGFlg2oXF6r4X7tG4nHXr1f/HTY2VCn66KNqFx0+fDhatmzJjCu71yMjSbOhQYPit+rUqQMfHx+NvRUXGxtb5VzGqyIRKn3j7l1SGA8Lo/3v1o3OWWAgBdru7rQfkkT3xunTFJDOn0+idI8e6eYdXwnWr1+PgIAA/Pbbb9JXX32F7777DpcuXWIKhaJcQKlhu9y+fbv6gUWRqO7ffUd0cXv7ks8OHSLRPXNzShp26EDCal9+Wc4+S1ekp6dLjRs3rlGipO5XX6Hrp5/C19cXTZs2xQcffMAjKgrw8YGXlxdmDBvGjwsM5A4eOiR99913UFRiXXfv3j3s2LEDPj4+oq+vL1fsNrFtG1mfhYQQU6EULl26xJp17SqZ1a1LVl+rV1PCKCCg5Lp47TWyUIyLoz7uEycoibFvX7ltOHnypEKpVL4vl8vTS78vl8tzlErlgocPH4o2NjZgjLWuybHSCxhTgrHHAMwA+IHj6jwdwQ7twfN8w9JMojK4cIEYJ198UevvuX//Ps6fP59bWFg4WC6Xv0g0egNeABho5AYY8JSgVquDrl+//oGPj4/5U7OWeR7geaJFShIJL+3apR8FzwsXgL17abyX2Lc8IiKCmZubo23btrpNMiZNoolsdHSJIq6WePjwIS5evChMmjQJNjY2lfZfKhQK1KtXD/fu3dNp/EqhoXFXh2++QWZgIDu7eTPX5PZtmHz1FdGTR4yofUVBM+kv1ZtZBm5u5am3ajUFAt99R9TPuXOBf/8tI7YWFBTEvL29uacRqGkqLenp6bDWqKQvW0b31pQpFKR6e5e1JVqzpnZfevEi9Yhu21Z5X+9HH1Hiws2NRLk0SRSOI6qzlRUF4itW0HZVNY9evJgC5YgI0n2oAkZGRlz9+vUlVFQAOHiwjI1SafA8D2dnZ4iiyP3+++9SnTp1uKSkJKl+/foCAEiSJPn7+/PR0dFS8+bNOQD6m/hrGAdTpxLjYOdOCjjbtClPn//4YxLGmjmTPnd0pGPXuDEFYocO0fX33nsUjPXoUePN6t69O3bs2IEOHTrwZmZmKCwsxMmTJ6UDBw4IvXv3LtZnAABjY2NorLOqxauvUmJKwzwqjUGDytpsrV1LLKh58+jcffQR0cp1wNmzZ6XLly9z2dnZvJGREUtKSuIaNmyo0xhITASWLkWXoCB00ZyTzEzaDwA8x6GeuzumTZvGL62kRxoAQkJCRHd3d/Tq1avk2RoXR/fKBx8QcyE3t/gjtVqNhw8fok+fPmWv5zp1ShKD33xDr4ICoHt3CrLHjSOdkieSh0UOEqYAKqOKtLCysuLMzMwgiqJ+FOpqA8aWAQA47lcALcBxr1VLHXh6aFCpI8yiRXT+apkPyM3Nxfbt2wvUavUIuVxeRU+TAf9fYahsG2DA08MJlUoV9Oeff+YnJSVBXUuK8AsHnqcJVf/+NKmKiqr5WIwBCxZQP+NLHGgDgEql4vLy8riTJ3Vs2bK2JnGpn37SucKVnZ0NIyMjyaYKr9sDBw5g2bJlyMrKQpcuXWqfeb91iyqdWgYGY8eO5Qq6d8eygQPxzZIlyJXLwc6epcpXbRgSDRpQgkbTE/skli0r39N6/ToFnSoVBdoLFgDbtxd/nJ2djdzcXK5vdd7cNUS9Bw9QPy0Nx3/7jajcGms9MzP6m55O9FxHx5K+6toiIoISZJVNLHv2pODPyIgC6y1byi8zeDCJd505Q4mWgoLKv08mo33TBBCVIDU1FXfu3IGtrW35+Ygk0bpVKPxbWlrio48+QoMGDZCVlYXu3bsL8fHxUCqVLDc3l1+7di0eP37Mm5mZ6SfQVihIpb5VK6LLDx5M15iNDfVnl6bmA5R02LGDlMT//rv8/SII5L3csydZACqV1N6wZ0+NKt1ubm6QyWSwt7eHu7s7vL29MXPmTMHf3x9nzpxBbGxs8bJ3796FSqVCg1KsgUrx228k0DZhQsU2kBculHisc1xJX3piIrUD/fUXVXG1AGMMR48e5QVB4KZPnw43Nzdp3bp12Lt3r26UmA4d6HyUPo5NmxLVG6Dfm99+Q1RUFMzNzVGnyFayyOcbAFUsk5KSBC8vr7JJzHfeoQSY5li89x7dtyDXhvr16zMHbX7LTE1JiPDhQ2JAjBtXzqbw/v37qFOnzl9yuTyhklFyCgoKJAsLC6jVaouAgIAXhRL2BYCfALgWK5c/Y4ii2Ny6ItvHI0co+aVje8KTYIxhx44d+aIo/rRw4cKjtRrMgP8sDMG2AQY8JcjlclZYWDgmOTk56Ndff8VPP/1U+PySu08JGgrs5ctVi1RVhb//Bnx9qYL16af627bnBB8fH/j6+uLUqVP44YcfWL4uPZmDB9OENTRUp+88dOgQ8/DwqPJ5fuPGDQYACxcuhK+vb+0Ncffto35THWjMkyZNwoQJEzBkxAgsnzABCR06UM+jRsG9Mrp3dYiNJaGiJ4O/zEwKZkpTtWNjaZIdEwOEhUE6fBjR//6L0rP4x48fw9TUVNTFXqpaREQU944r/vc/eFy9Cr5lS1KfrlOHKqSffKK/79NAraYJ/JAhVHGsDKNHl7AEZs6kICkjo/xyzZpRoqJTJwocT5+ufMxWrSiwUSqJhlwBDh48yNRqNTpUpAgdG0tV9moSDlZWVnjjjTf4jz/+mOvatSvmz5+PSZMmcdbW1kwURTRp0gRvvvlmlWNUibw8urbef59stRwdqUXC15co1d5VxBGFhSX9tx98QD3xFYHnSYOgVy8S7du9m54DR47otKkcx6F9+/ZSSEhImcDU09MTHh4e7I8//sCpU6cQFxdXzLBYv3595QMeOUJCaPfv0/0+YkTFy3l6kkVY6d+4efOoNSMvj5JZ33yjVWKN4zj4+fmxzMxMpKen48033xTefvttXLt2jZd0aUExNaWk1c2bJe/duUPHGAC+/Rbo0QMuLi6QJElasmQJVq5cyb799lssXboUf/31l3js2DEIglC29aaggNpONEE7QEmhoh71qKgosWvXrto/PL79llg2gYHE+HkCiYmJBYWFhZVcOLSn+fn5Mp7nYWpqqgCgpwxdLcFYGhjbD6A7gJ3guGfKpg0ICDBXq9X1y7Xq5OeTa4Opaa2dVU6dOqV+/PjxLZVKNb9WAxnwn4Yh2DbAgKcIuVwuKpXK5UZGRln29vb/3T6ew4dp0tm5MwkwaYvMTKqIfPLJS9WfXRVMTEzQpUsXDB06FNnZ2dz9+/e1X9nIiIKY7dvJA1kLpKWlITc3l+vTp0+Vy7Vv354DgJUrV0KsJPDRGkolVa+q8FOuDPb29mjVqhU8PT2lTZs2Ieurr6i/Ny6OehkTE2l8XdCiBQWGT9IFFQqa4Jeq5rIPP4Q4YwaU8+fjUno6vhk2DP/s24c//vhDevToEQ4ePCgGBQVpryZfEXJzS4Kqli1pEm1iQkEHgNTgYIT27YvWbdvSffM02xrv3KFKfnVU4W+/pRdAwb+1NSXCKoJMRtT8gABKkPz5Z6XBNAYPpqCrEpruyJEjOZ7nsW3bNpS7V0JDa1TdFYqCWycnJ1b0/8p7wiuDWk0V7Dt36Po6TtoUAQAAIABJREFUfpz6q8+do3P5+uvVn7fQULJBGzuW/m9nR8e2KvA8VY83biTl79mzSVhLI1qnBby8vPjExMRy8zs/Pz/O2dlZffr0aWzcuBEbNmwAABQWFlYewXbvTudv8WKq3Fe2z2Zm1FqwaVPZ942MKMnk4kLX1ObN1ONc/T5wbdq0EU+cOCEBgLOzM0xMTMpY5mmFnBwSdNOgefOSgHbSJGDrVlhYWGDWrFn8uHHj4OPjw82ZMwczZsxAUlISl5CQgLfeeqtk/aws2hcbm7KJxjNngGbNoFAokJubK7i6ula/batXk/Dh3LmUyKkkkfDo0SMlgCtVjLQrMzPTJC8vD3Xr1lUDaFb9lz9DMBYIwBVAP3BcEDiumptAb/C0trbOL5c0vXOHko+vvFKrwePi4nD27Nl8pVLpL5fL/2PURQP0CUOwbYABTx8dbGxsTN5++22z56wV8vTx/fdURbt6tWwPX0X47juiTi5fXqOg7UWHhtLtrBG20hZdu1KFsTJadBFu3bqFVatWSatXr4a5uXmlk+Xs7Gzs3LkTyqIAVmuV9Kpw4ACpUxf5UNcE/v7+vLGxsXT48GEGniebqsRE2ndPz5JeZW2hUFAFtHSSIjwcOHgQKSkpiI6OxsXQUKzq14/96OCAnF9/RWRUlPj6G29g9uzZqFOnDrdu3TrExMRwnTt3hp+fn27Vf1Ekf92UFLq2x4yh91evpoldmzbAP/8AADQ9hFV5S+sFy5cDqal0HKpS5AWomj11asn/33qL+umL6LQVYtAgChaCgqgvt7LA+NtvS8TBnoBarYamWlmO+bNhAwW1NYSlpSUPAMbGxpzWFVHNs+vdd0m00NmZWAmvv06Jk4ooqRWBMaKoHj9e8t7cuVpZTBXD35+SNubmZC21eHHFbIMnoKFDVyT4NWrUKKMvvvgCcrkcrVu3Bs/z6NSpU/m5IGMk8hYeTtf2Z59Vv+1xcdSj/WTiZdgw+m3YuJESGMePEwW7mqRfw4YNhcdFjCmO49CoUSNcuXIFSVWo3JfDhAn0TNVAkkqerWFhxdRvnudhb28PDw8PyGQyWFhYoEOHDnzDhg2lMuKIBQXAjBnlbfV69QIWLcKlS5dgZWUlmZubV75NBQXErDEzo5YBTTtWUfKjNBhjyMjIMAVwo4q95AA67zY2NkZ40YJtAGBMAUoYpALgwXGNn8G3dnR0dCwrmJOQQMmv116r1cAKhQJ///13gVqtfrsKer8BBgAwBNsGGPAscDs1NVXxzTffqB/VlCb7ssDHhyi6s2fT5KwiMEaTHQcH6mXUJ1X3BcL58+clU1NTVqXtSGWYPZuCn0p6XePi4rBr1y60adOGnzBhAmbNmsVXVIldv349++GHHxAVFYVr164BAKZOnVpc+asx1q+nSWItMWLECD46OprLK6r4FtO9jx0jO5y//6ZgR5tAqV49YkiU3rfCQtw3Nmbr1q3DiZ07xQ4DB8L31i1umo8P1FFR+GDRIsHd3R0ymQzvvPMON2fOHEydOpXv06cPWlbRJwyA1LkBClD9/Oh7MzPJwzogoETDYMCAMjT28PBwtmrVKgCA7dP0qxdFqiJqqxWRnV1W+Kp1axL9OlpNG6KVFZ2n2bMpeVZR1bJlS0pE9OhBSZFSsLCwQM+ePQGg7HUZE0PLV+L7rg06duwIf39/dufOHfz888+VL5iYSD3ScXFEDY+IoMDn+nWq5NZER4Ixaq3x9S15b+pU7azqSkMQqJJ66hQwcSIJ2fn7V3lejxw5ItrZ2amre/YMHDgQkiShTp06eFxRG1Dv3uQDfuUKsZaqQ7t2tM8VPdM5jiwOX3+d1NcVCkqopaaWW5QxhpSUFBQUFMDCwqI4AzNkyBAYGxtj48aN2tMdUlPLCB8iP7+EwfHnn5QoqgQqlQoymazkuw4eJOXqGTPKLSstWIBbnTvjzJkzrHv37lVn1QcMIJbE++8TLX/iRDo2FSTjk5KSwHFcPoCqsiyFRkZG98+ePauqX7++KYAXU/iEsSQwNh6AJ4Ar4LiaZ2u1gEwm62FjY1O2in71KiXRSqvp1wBHjhwpFEVxx8KFCw/VaiAD/l/AoEZugAFPGXK5/FxAQIC1JElLN2zYMGv+/Pn8f77CHRxMf3/8kSY7ixeXfDZrFlExT5/W3bboJUJeXh6nUCg4tVpdjpIsSRI4jkNCQgIePHiAq1evSo0aNUKrVq14R0dHWFpa0mRs+XLqLSu6XiRJwtmzZ9np06c5Ly8v1qdPnyovpKSkJA6gALtekep3ra+9qCgKIIoCpNqgWbNmMDU1ZceOHWP+/v58RkYG/vnnHzZ06FDO2tqaKqeaY+fpSSrYGn/fijB7NrByJU3ieR6FsbEIVSi4UaNGwbFJEwFdu8IlIAAoLETDCuzPqk2M/PQTBR+aPt1Hj6iCranQaa77KnDv3j0GgJs+fToqtaSpLcLC6HX5svYU9du3SWhu1qyS98aOpSrkwIFVr2tkRAH1F1+QsJqtLdHMSwfPrVtTv/e1a2XUui9evIjTp0+D53m67ks+oERLLa9XT09Pbu/eveU93kWRgq3u3YmJcPs20b4fPqz9c+n+fdr/x4/LjjVpEol21QQyGYkBHj1KAfuvvxK1/MsvieZeCgkJCXzXrl2rPXB169aFl5cXwsLCxCNHjghvvPEG69ChA4dNmygoWbGCWlp69CD6tTa4cYOUy1NSynlFw9WVEgevvkp/e/Wia+bDD4uD+Xv37uHo0aNITEwE8H/snXdYFPf2xt/vDOwubekqKMUKAiqW2DXYxd69dk03ahKTGDWJ7l2TGFN+MYkaW6561diw9wY2RCJYAFFBEFBpSpO2sGXm98cRLLQFl0hy9/M8PCK7Ozu7OzP7Pee85z1Aq1atSjdiaWmJUaNG4dChQyL0dZZv04a+g0qmFTg4PJ1t/owpYnl4eHggLCyMT0lJgbOzM/V+O5ctyB49elSMO38e1kVFrNeECfD19S1/3374gZJT27Y99SEoLCQvhQpUQrdv34YgCPsVCkWFCQaFQqFWKpVdQ0JCYlxcXCwBVCJHqQOIYggYex1AHhj7HsBCiKJB2+yUSqUpz/PDn/ssrlyh86ac0WrVITk5GVFRUUUajeajl91PI/8b/HNXukaM1CEUCoWgVCojbGxsNI8fP5ampqbC1dUVlUrN/s6UVDZsbGhRKwhU9du+nYyYFIp/dKANAAMGDGC//fYbvvnmG7i6ugoeHh6clZUVVCoVzpw5UyrxtLS0FNq1a8fdv39fPH78uK6wsJCXSCRwc3HRtU1L47BoERouWMAKCwuxefNmiKIoTpw4kbm7u1e52Bw5ciQCAgIQFBSEUaNGGeaFHTxIC8aXrY4/wcLCQrCzs+NFUcSNGzeQnJzMNm3aBGdnZ92DBw84Dw8PuFy/jlZjxjC+RQtaLMXFkSz1RRijgLh3b6BtW+RfvAgLV1eta0CACfbvJ6nmhQv6BXAZGXQcJyRQ3/G9e2TYpdPRgr2oiLZTiQN8eQwePJiLiYnB9evXnxvDZFC2bqX9rE6gOnEiMGHC838bMoQMsi5e1M913t+fEjGjRlHlde/ep14MJZXNL76ga8GAAVi1apUuIyODl0gkGD9+PJ5zDVapyCjMADg7O4spKSls69atwlAnJ8760iWqJv7wA8n81617+l4Z4rrk4kIB1YvJlNTUp4FeTbG0JAlsz560rfx8MlabPx9o1QoAoFKpmL7fLYMGDQIAfufOnTh+/Dhr3bo1tRzI5TSObNs2kn/ri5cXsH59xYolxkj9kJBAM6pHjADWroU6NBRRXbrg6NGjsLCwEGfNmsXKU35kZWXB1NRU/8q2XE7y/Xv3SPqt01HAnJ5OCUOFosKxZE5OTuB5HhzHUQW8ZLTbM5w9exaRkZFsVlYWLKOiwH7+uexJV1RE58P586S+KQmsRZGSPUuXVui4n5OTo9LpdFeqepkKhSLtm2++WXf37t1PAdR9CZ0o3gZjDgD8APiAsRsGDrgn29jY6J5LaC5dSu0hL4FOp8PevXsLtFrtHIVCUXVPhxEjMAbbRoz8laRmZ2dLV69erdbpdOmNGze2mzRp0j802n5CSd/qvHm0oG3XjqRz/9QkwzMcOXJEBMDefvtthIaGctevX9epVCrwPM86duzIWVlZoWHDhnBycipZlTIAvCAIiIyMRFJSEh/fuzfaf/89fmMMGpkM3t7ewogRI/TW3UdFRYkAmHM51ZgaoVJRcEkLdIPg6+vLnzt3DuHh4WJeXh4bNWoUYmNjRcYY//rrr+PmzZu4deuWEGRmhrflcmbl4AA8egRtfj5MPvyQKtklFVGOIynwkz7jtNatkWdlxaFVKwoaCwoqD0CvXiU5sVJJrtMzZ1Kv7KJFtDD+6aen961BxVWr1WLbtm2iVCqFp6en4eUtajXw9dckk61u+0J8PFXrMzOf/3vXrhRs6Tv7mTGSn585Q+Z07ds/f7x4e9Oid8AAZGdn8+3bt0f//v0hkTzTWvn4MSVNrl6t3muogOnjxrGcuXNxKj2d+zMuDl2kUli97LjCiggOpmTHmjVlb9u9m6r1+phnVUXJyKjCQgoAd+8GYmIgNG0KjUaD6p7z3bt3R9zt20gbPFhssGcPw9ixpKoZPPh5N/+q4DhqJ/rsM0pmVLDv2mbNkPL22wIzM+OiR48WuH37uPo7dohdFy9mffr3r/DcyMzMFPLz8/nCwkL9lSGXLtH56+ZG+9e9OyWAe/Qo23v9DPfu3YNOp0MDBwdg9mwyf3sm2L5+/TpCQkIwdepUWFXkmJ+XR+fVrFllK6rFxdQSUMkM8nv37mkAXNbnZWq12jUAPgXwkhmdvwhRzADQEYzZAUgEY2Mhivo7AVaCiYnJTGdn56cXwSNHaD3yEh4QAHD48GFVQUFBiCiKW192H43872AMto0Y+es4y/P8Ao1GEyeK4pm4uLjMW7duwcbGBvv37y9o1qyZSd++faX/SIn5N99QNcHSkhY4/v60eGnQoHadmF8hSUlJzN3dHc7OziVVZb1KwRzHwdfXF76+vvQHT0988uefYAsWgKvmLKo+ffqw27dvQ/VkJJZarYapqWnNpeQnTpCs+J13avb4cujWrRvq16+PnJwc5uvrCxMTk1LndAB4jaqb3M8//yysW7eOtW7dGklOTkK2UslNP39eNMnKYraHD1MQVyLRdXYGVq6E16+/Qt20KVdw8iQsDh9+3glaEMgIq0EDGi+1Zg0tfv/8k25PTHxa5TTQ671z5w7S0tLY/Pnzq5as14Tz50l5sGhR9c+rhg2puv0igwdTVe7+ff0N8XieAgiOI0m/pyc91tSUnqNLFzxcsACmtrYYOHBgWef3/fspYHvZCQWHDwObN8N00yY4pqWhz+zZWBMWBsdhw9C2trwisrMr9hg4cKBqV/jqYm5ObuEA8MMPEOfNg3z4cNhmZlZLddGwYUNMGTQIunXrWPDZs0J3OzsOBw5U3bNfHjxPx+FXX1WY9LkeGYnjAwdy9c3MhN4bNjB+7ly4q9UM335LkvIKjOg6d+7MXb16FcuXL8ecOXMg1+f9HDnyaUKOMXJVF0VK/FZiYBkdHQ0PDw8dYmJ4bWQk8hhDxp07EAQBaWlpOHv2LGxsbCjov3uXevKfDah/+43UIcuXU5/2sxQVkUv7+vXUIlAB+fn5UgB6jbRQKBTxSqUSqCujv/RFFLPA2GcA7oGxfgBOl3VL1B+lUsmkUqljhw4d6CQvGdW2cOFLKbLi4+MRHR1dpNFoRlUm6zdi5EWMwbYRI38RTy7O35X8X6lUvnn8+PEVRUVFTK1Wf5aZmbm0RYsWUrdKMu1/WySSp+7Sw4eTodLixRR8d+lCQZKn56vdRwMS+mRMz5QpU15+Y6+9Bn7PHurP7Nq1Wg91cHCAVCrF+fPnkZ6eLsbExDBnZ2fxrbfeYjUKuHfsILd5A1OlGRmA9957j7ty5Qqio6MFBwcHbvDHH2O7iwuyN23C+6dPQ+XuDvtly2Bx9y4Fa2ZmgLs73JOSoON5OgbValpoL11KC+PgYKpufvgh9ZA6Oz9dFNdCm0PJyLWsrKxqVx6r5PffKcC9erVmpoMyGcnsi4qeD5AsLUmCu3t39R3ie/em/VEqgdu3SYXg7Aw4OEB64AC8Bw3SmZiYlF39JiSQqqAm5OVREPOvf1Gw7ukJSKUQjxzBrpUrRW9vb9a2bduabbsqoqLI+Xro0PJvHzOGVD5DhtTO88+bh2s9eoj1V69m3KRJ5Pvw+uv6Vaa3bYNr27a4c/Yszu3cyXU8eBCSZ+ayi6KI8+fP61JSUpCVlcUJgiB2795dbNu2bdnPz8aGDO5enHv/zLauXLkieHl7c6NGjeJQUED90P/+N/VY9+5NyoZy2iwcHR3x/vvvY/fu3Vi+fDkWLVqEKnOQtra07QMH6P/+/kBkJH3v5OdXeL44OTkhYeNGvnD2bKyYNQtFHAeJRCLyPA+NRsMcHR1FQRCwYcMGNJbJhD75+by5Wg2JiQklp37+mV7DsyZ5JURG0nlVSbJAEARoNBopgKzKX2AZDNN/8VciitufjAT7FcBaAD/XdFOMsfctLS0dG5WoDSIj6XpSMl+9BhQWFiIgIECl0WhGKhSK/BpvyMj/JP9MG2AjRv4ebMrNzZ2qVquHA9iu0+msbW1tX/U+1T716pE77Lp1FLjJ5STV7dOH5H7lueL+zTh9+jS8vLyqXgTqg1xOPbA3bujvLP0M/fr1AwCkpqaiZ8+eSElJYQEBAeK5c+fKHQ1UIZGRVD3u06fa+2AIZDIZunXrhnfeeYcbNWoUnJyc8MYbbzBHR0fhj9Gjxc0eHjj4ySdQDxsGdO4M3YIFyEtJEXe++aZg2akT8P77VNU4eZL6NX/9lYIjgIyrDB38loOPjw/atWunXb9+fWlCxiAUFlJAm539cu7+H39MQfGLTJ78tF9dT1JSUnDu3Dl6zxUKCjpiYigRYmWF9BUr8Dg/n8ejR88/8PFj6m2uzlg5USTn9VatKKnStCn1e48aRQZiPA+VSoWsrCzWpUsX/bdbXT75pFyn6lJataJZ27WEKIo4duoUEwcMEHDyJBnRtW1LAX5VhcItW4CDB9G8eXP4xceLV3x9BfEZM7fr16+L58+f53U6He/m5sZMTU258PBwtmbNGnHz5s26whd70dPSKOjOyyvzVGlpaUhPT+cGlbQXKBSkxvDxoTaQI0eoZWPtWkqQvYCjo2Opg71euLiQ4V6J4mDXLqr6X71aaVKt82uvocjDAwemThVGTZ6MRYsWYd68eczW1lasV6+e7v3332fvv/8+a9++PRMbNOA3DB2q+++XX0Lr6UktK7dvl++mr1KRb8GlS5UqUBhjMDU1LQZQX/8XCw2Al2tMflWIYjGANgBWg7GdYKza/UpKpbKDKIorR48ebcFxHF1LFi4s9TOoKYcPH1aJorhJoVCce6kNGfmfxFjZNmLkFaFQKAQAewBAqVRyJiYmuQEBAabm5uacg4MD16dPH1ODBGt1FcZoIeLjQxW1a9eor/b33+lv9vbAuHHV7z2tA3h6euLevXsCDJXQ7NOH1AB2dlQdqwbt27dH+/btgSfuvc7Ozti5cye7desWzp49i9GjR8PNzQ1WVlYQRRGiKJafJNi/n8al1KFj0tLSEu+//37pDiUkJODHVq3gtmSJMOHUKc4KYJNnzmTcjRsU9PE8JS1eIUOHDjWJi4sTCwsLn1tlHzt2DMnJycjMzBT79evH2rVrp98Gw8IoyE5IePlqfEBA+f3ELi40Wm3NGmjffRc3btzAgwcPoNPp0KtXL+Tk5ODUqVOws7ND165dER0djeDg4NJjqUePHtQ2cvkyNF9+iUMnTwo3HBy4aZmZAmbN4p5zhb53j4LlZ53Jy0OrpUCsRw+aCe7nR5VciaRc87yS2ebbtm0T582bZ/jeFVGkNovKEhJTppRxDjckxcXFEAQB3t7eHEoSt6GhlOQoGYv39df0WZZQVERS8WNPJhhlZ+M1lYptsrdHyr59pR4RBw8eZI0aNcLkyZMBAF999VVpW0txcTH3ww8/YMGCBaUzvtGgATmal/M5RkVFCXZ2dpDJZE8vJlIp8N13FIhqtWSmt3gxJcKWL39+n0Hni42NjaBXa42DA6lysrPpe+XIEVJsHD9Oz1EB3MKFeDMjA9i0iQMombFnzx5dQUEB++CDD3iA2n56lUxIsLDgT02fjrC2bcV2TZowaUW79vXXNL/8RWn5CzDGYGZmptFoNA6owPRMqVQ6AihSKBR5SqWS43k+U6fTNVAqlbxCoTCow/dfgihSdoWxSAD1n5ioZeorK+d5/pMnXij0h7t3yTPiJc67qKgoxMfHP1Kr1Z9WfW8jRspiDLaNGKkDPHEr7/PgwYMuPM83jo2N/Sg/P1/o2LEj17AmM17/bvA89el16ECLvzt3KLj08Hg62qZjx+d7busomZmZiI6OhkFVCoxRdWr5ckpMvLDwrA4eHh5YvHgxvv76a+h0Opw8eVJXWFjIe3p64u7du9BqtfDy8tL5+/vzpQvnwkIyEnpSJa+rNBZFfOjmhojiYu5es2Zi/a5dmaW7O42cqkNIJBLxWR1/dnY2Ll++jJYtW+rkcjl/6NAh3L9/Hz179qz6OPriC5LDVrFw14vdu2m0WnmGTePHI3PQIKx89Oi5atz169dLf3/w4AEiIyMBABMnTgRjDLt27UJ4eLjQoEEDLjMzU1fcuzff4vFjtuDiRUhWruSQnEzu8iWtBJs2VTxbWxTpWPzjD+pTvn+fqslDh1JQ17FjhS+NMQYPDw/ExMSw5ORkGPS6KoqAqyuwahUZXlXEokXU/1/NhJm+hIWFwcTEBPbP9mrXr08/LVqQrPnOHTJwW7CAAuIDB8jJvETafvIkJEOHYvTAgWz9+vW4c+cOGGNgjGHGjBmlmzU3NxcsLCwwefJkbtOmTYKHhwcvebH32MmJtnvwYGmSLjMzE9cCAzkbBwcgOZlumzKFzNhyc8klvkULUgh88w31QI8aRe+tpyfS0tJw4MABobCwkJs1a5b+mb+NGynoGjuWAu+WLUnlUkmwjcaN6bmfcOrUKSEpKYnNnj27bIy/ZQvg4IDOs2djc1CQELxiBT9p0qSy7SJFRXTMViCxf5EnMaZlRbczxn4QRXHakiVLMiUSSaSDg4NVYWFhfk5OTlcAF/R6krqIKH4DAGAsBEAcgKlVPUSpVDY1NTUdUqqYiIykFqGwsBrvRk5ODg4fPqxSq9UjFQrF38N4zkidwxhsGzFSR1AoFOEAwgFAqVQeiYyM/CAuLq7/vHnz/n6l3ZdBJiPJV6tWVKk6fBg4epSqL7m5ZLDUvn2dqrA+y+7duwVXV1duwoQJht3BFi1ogfj997QIfUk+++wzAIBEIuEDAwPFqKgowd/fnxcEASdOnOCkUqk4YMAAxnEcKQ6Cgp66y9c1ioqoyrlkCSweP0bXffuAN99k0GrL9iDXAVxdXcULFy4gJydH8Pf357Zu3apr3bo1GzlyJK9Wq2FhYYGoqCjcvHlTnDdvHitjIAbQ69q2jYIRQyWh4uIqNIwKzctDQteuGGZlBe85cyCKIniex+3bt+Hp6VlqcqbT6aDRaEoN4GbNmoVbt26xpKQkXevWrTlXV1e4NGrE+NWrqeKanU1tJIcO0ezh/fupv/ZZkpOpOtm0KbWezJhBsnQzs/JN3SqgXbt2iImJQXh4uGGD7ZKRc+X15j7LBx9QT3ItkZeXJzRo0ACNGjUqe+1p0IDc4dPTaX9PnaLPe8YMUkUA9O/Ro8D338Pe3h5NmjQRd+7cyVxdXQU3N7fnAsxPPvmk9D9yuZyzl8lEducOQ4sWwOrVlACysqIKbng4sHkzxOBgbJ04Ufzkp5+YsGgRtXFs2/Y0WWJmRgH66tWUSBk+nBzAPT2BSZOQq1Bg040baOTqihkzZlRvTn2vXk+NyJKT6d+33qr4/m+/TceltzcAIDQ0VLxy5Qp766232HPPGxxMCeG9ewFbW1ipVJg1axZ/8uRJbNiwAQ4ODsL48eO50qTZ22/Tsbx8uV677e7ubhIVFdUBQEh5t4ui+AaAFFEUF6rV6l48zxfK5XLz/Pz84fg7B9tP8QfgBMaGA8iDKAaVdyelUmkukUhO9O7d27x+/Seq+99+I0VNDQ1BBUFAQEBAgSAI3yoUCsOMRjDyPwl7CcM/I0aM1CJKpfJjqVT63bhx40ya1KL08G9DVhYFfGvWULDt4UGGYXXIWE0URXz99dfo3r37U2mhISmRzW7fXqty1MTERGzduhU6nQ6TJk1Cs59+ogWxv3+tPWeN0Wqp6v7RR8Cnnz6/sGrfnsbNrV//6vavAu7fv4/t27eXOsVPnToVjRs3Lr1dp9Nh8+bNwsOHD9k777zDylS4166ln7Awg808h1ZLY9OeSK4BWnBu3rwZSUlJaBobi4mWluDWrjXM8yUkUKXXzo4Sa/n5FIC98w4FhWo1Bd9KJd33/n0632uYaAsPD8eRI0cwb9686gVqlaFWk4x9yxaSJlfG99+TAqFk0oCBWblypZCXl8e99dZbcKyqN7y4mALcZs2oumxjQ5XerCwKUAAU5uZiy4IFyLGywsBWrdDmzh3qr37rLbr+DB0KjBqFM6NGweXWLbFZeDhDaiopYEaOpOvF+vVUHba0RNKtW9h66xbmf/YZTKpymhdF8gkYNowSAm+8gfShQxHXoIHYbc0aVt359oiNJdXE0qWUtCnp3d9azgSnrCxK5gQHAzY2uHTpknj27FlMmDCBuZeoZEQRePAAuHCBpMpffklV/FatgG+/BUDTHwICAoSkpCRu0KBBgq+vL4dffiGHfz1MIQHg5s2bOHToUOj8+fOrNBtQKpUNAAwEsBEAFArFP2fUB2MKAN0B9H9RUq5UKplEItncrFmzMWPGjJExxih5l51Nc7VreH38889P4TxvAAAgAElEQVQ/haCgoAi1Wv3a31KSb6TOUDdLQ0aMGAGAlcXFxVsOHz6sn97sn05Jv/Lp0zSyRaulxWHfvlRVyn/1BqGMMcjlcsOaXz2LRALs2UPBSDXMqqqLu7s7Zj5xg765bZvwKDcXKj+/Wnu+GhEbSwvbBw9opvO8eWUrGIcOkTy1DuLi4oIPPvgA8+fPx6effvpcoA0APM9j+vTpnIODg3jixAnx1KlTTyXb+/ZRRTckxHCBNkBu4y+MOYuOjkZSUhJGjRqFIb/+Cs7FhUamGYLGjUm6/vnnVBVesIBkzhoNVRS3bCFn8eRkCgy9vGocaKvValy4cAFSqVQ0WKAN0Hvx+DGN4KqKQ4fIdbuWGDJkCKdWq3H27NnK75ifT9ePvDyS2h45QompyZPpNj8/oFs3mGdl4e0DB/CWuzvamJlRYqe4mEzXfH3JwX/7duhefx17e/ZkuuRkOgdPn6Ye8SZNKAjt3h2wtMQVQJDJZEKVgTZA22ncmI7x7Gzgp59QOGcONBkZLHrmTLHU3FBfLC3pOiGK5FQ/fDh5gryISkWfU2QkBLkc586dE86ePYtJkyY9DbQBqlAPGwZMmECvESAV1pNAGwAkEgkmTZrE9evXD8ePH+cwdy45YusZaAM0qUGtVrdXKpVVHmAKhSIN/4xqdllEUQmgP4AlYOwAGOMACrQZY/M4jhs7dOhQCrTVauD//o9aO2p4fczLy0NgYGCxWq2eZAy0jbwsxmDbiJE6ikKhUJuYmGgKCgqk2dnZr3p36hYtW1JQMGcOBVMZGVRtWbyYTIoyMl7Zrnl5eQkODg4VDNo1AI0akWGanjLEmmJvb4958+ahybVr7HJhoRgUFFQ3FhxhYbSgtbOjRbOLS/mmXgA5jM+c+UqPh8qQyWSQyWSwsLAo93bGGPz9/bmYmBgWEhKCsLAwAWlp1NealmZ4efzEiVSlfYa9e/cCoOSAjb099f8uXWq457S2JsmxtzfNCf/Pfyjgu3+fgnBra4PI5LOzs5GbmwtLS0vDyfny8ujYCgrSLwlw/Dj1DNcS7u7ueOutt3Dz5k1kVHbMv/02JTekUjLVmzaN3LMtLYH//pf+n5UFnD4NbvVq6gEfMoQk5mZmdN0dMoQSDB07ou+QIdDpdMjKKmdClYUFkJkJuLggMTGRc3V1rd6608qK5nV36YLGq1fD6403cFunY5offqBrvb7qTGdn+n4oKqIguXHj8kcprlgB8fffEX3zJn7++WcxLCwMkydPZq6urnT7wYOkrlqwgCrfzyb4tm6l4PsF7O3tocvLg3b//mq70UskEvA8rwNQ/kXiGZRKpQzU3wxTU9P3qvVEfweoov07gEAADqf69fOXSCS7LCwsFOPHj5eWtK4gOJgSIDVMEIuiiN27dxcCWK5QKG4ZZueN/C9jDLaNGKnDaLXaxVqtdvuZM2f+ttXtoqIifY1Eqw9jJCtdsoR6+3r3pgXw8OG06Ll5kxaRfyEtW7bkMjIyavfaOm0aVabu3q3VpzEXBPiMGMFMR45kkZGRfLVGhRmatDQKwE6eBG7dIlfhL76ounKRkEAO139TnJ2dsfiJiZPtgwd0fN+9S1VFQ9OoUZngpcTgKTU1lf4waRI5V5cz0qlGTJ9Ofb29e1Pwt2kTVVpr2GdZEfXr10enTp2QmZnJRUREGGajGzYAo0frf/+BA8mcqxZp0KABAOD8+fPlX3Q1GrpW7tlD/09Lo8rxlSs0jiokhNQGgwbRT3Iy9VA/eECtO19/TVL/06efM/kSBAHFxcXl75SpKYRWrVD88OHz5m3Vwd8fOHIEBVevwj0xEdm+vnSc/Pqr/tf4336jY3faNEp6PDNHHADE9HTE9+2L34cMEY8cPSp07NiRffrpp5yLiwudF8XFZLSWm0vV6ReTZC4u5fbkN2nSBJ2Li4VDy5bpUAOvAGtrazWAllXdT6FQFAFoDcDl888/N1CvR91BqVSaLlEq+363bFnP4yNGRHYNDT3SqnnzYe+99555qeogPf1pVbuGhIaGCmlpafEajUZhmD038r8O/+8XjUiMGDFSZ/Dz8ys4e/ZsZn5+/tSuXbuaMAMvQA1NUVERCgsLIZVKodVqsWfPHu3+/fs5FxcX2NnZ1e6T8zy5Tnt5UUBgbU0LykOHSOqZkUGyxlp+D+Pj43H//n1dly5dai/gtrIiue3Jk1Tlrq3XdOQIcOYMhHHjcP36dSQkJIi+vr7sLx1JVyKXb9kSMDWlisXIkfq/5unTabZ7HTXU0wfGGNq1bQuXqVPZ7bg4sbBLF5aQkIAGDRrAoNeEoCDqyX2PimLZ2dlISkpCVlYW+vXrR6ZnpqYkIT506Gnfa3XJzQV++QXo1o2OsRYtSNL71ltUcfz8czJPM7D7vZubGy5cuACZTAbPl/V6EEWgc2dSTugrVU1Kosc0avRyz10JHMfB1NQUoaGhrFWrVs/3psfF0TVy7lyUjgbr1QtISSGVCGNUuXZzo+tlvXokGf/wQ+rp7tmTkjy3bpGzuZcXyfzDw3EZgLtKBQdv77LnmokJsoKDEWFhAY927WBmZlY6iq0yRFGESqWCKIpQq9UoFgQ4DhmCoPBwmD5+jIaCQJXqqCja16oC+cREmrX9ySckc+/dm45nAI8ePcLjHj3E5EuXYD52LJs8eTJzc3Ojx+l01K4UFUXJoG7dyt++uzvd9uI5eeECXL7+mu1v0IBr6OZW7e/CnJwcPjU1NbVnz55nqrqvn5/fQz8/v9xqPUEdR6lU+gQHB882MTHZ7ezsPKhbt26+rqNGWdp/+inz4DhesmQJKS04jtqLLCyqNiusgLS0NOzbt69Qo9G8rlAoMg38Uoz8j2I0SDNipI6jVCqtJRJJqJ2dneuMGTPMy4xXqQNER0fj8uXLeWlpaWZqtdrE09MT9+/fLy4qKsrU6XTOADBlyhS8EqO39HQgPp4kfoGBNMu1eXNaKNZCkLp9+3bExsZCoajlpLhaDfzwA1V89J3JXF0WLwa6dUNhjx7YvXu3mJCQwAYNGiS89tprf03kGhdHC+JDhyhRUtXs5fKIiqKgMC+v1hMttUZhIYTQUOQ0bowVmzeDfxLcjR8/Hs0NWeFWqSgx9STIWLdunZiamsoA4JNPPoFliQHY/fvA++/TCC65XP/t5+RQpdTSkiSed+6QjPn6daqitmxJz71+/dPJA3PnGmasGWhM2YEDB/DBBx+8/Gi+oUNJmlwds7gLFyiZUIvBNgBoNBp89913sLW1xaxZs+iPokjHf1gY8NprFEBevUpBqqtr+efGjz/SyK0bN8pWcUu2GRQE5OQg7O5d1FuxAnZvvw0rjYZk6EuXAllZOH7rli48PJx3i4lBVvv2uryCAr5t27YYOHBg6bFcdtMifvzxx1IDwZKZ7c2aNRNjY2OZp6srxhcV0RxxFxe6HnbvXvnotZQU+g64fZuOubVrSz+Lndu36yzPneMH/PgjTJ41ugsKAho2BK5dowDOxqbi7Scl0XXqWT8NUaRrT2oqTiQm4sqVKxg0aJDo6+ur98Xo3r17+OOPP5LVarWrQqGovRalOohSqXQHkODr61vUuXNnWanTeAkPHtCEgh07KEH3xhvkQ1DeBIcqyMvLw5o1a1QqlWrG4sWLdxpi/40YAYyVbSNG6jx+fn7FQUFBa1Uq1ScuLi6yWq8QV5PExETs3LmzKCcn5wudTrcDwNePHz/2Kioq+j9RFCcCWALAPT4+3qdr165/fXnR0pIWY4MHUz9qWBj13UVGkmTS1fWl5la/iI2NDa5du4aGDRvWXDKpDzxPVZoNG6i6begkzLVrVHX87DOYmpqiTZs2LCkpCXfv3hU7duxYu1FrYCAF+pMnU7WoRGJcExwdycHdze1vGWwXFRXhcv/+YJs2YYulJTRaLaytrQWJRKJzd3fn6tWrV3rf69ev6xITE1nDhg1ZjSreKhUwYkTpSKQ///wThYWFbMSIEXB9VpZpbU3Hm0YDvWSxhYVUQZw4kfqW33+f3ONLKqCZmXQefvopBdZyOVVY8/IoIG/RomaJFgCHDx/WXb58Gebm5iwgIAA9evSAl5dXjbb1HC1bAh060KgqfZk9m15XLbmRl8DzPLy8vBASEgIzMzNqBWjXjnrhS3rGv/iCqtOffVbxedG1K52DBw5Q4Nyv3/M+AYxRcOnlhYbduuGgvb1408pKbN22LWOpqfS5DR6Mx8HBnAXHYXRAALp8+CHn0rEjjp0+jfPnz6N79+7gOA7p6ekIDAzUhYSEICcnh+3atQsqlQpz585FmzZtUK9ePfj5+SEmJoY1aNAAo8aPB9+5M+3jli1Aaiq9txcukHqgvCA+I4PGJiYmUnvJtGl0LOfno8mwYdz57t2Ftr17s9IEQF4eVbQ7d6Z2gao8EuRyUgE8W7X//Xf628KFaNasGerVq4dDhw4xBweHqh3jSzcrx7Vr17iioqJwPz+/eL0e9DdHqVS2Cw4O/lQUxZ2NGzcuGD9+vLlleW7/cjnNab9xg1QaixcDHTtW+/nUajU2btxYWFBQsGzRokW/GeAlGDFSijHYNmLkb4Cfn59w8eLFDk5OTl4GnQ/7koSEhKj37t3LSySS/3755Zdf+Pn5Rfr5+aX26NFjo5+fX5ifnx/8/PzEc+fOyTUazWi/V+1obWFBC+TBgymIS06myll2Ni3EbG2rHt9TBZcuXUJycjL69++PWlchuLtT76RcThUzQ7J8OVXAnlTNtVotTpw4ITLGOGtra1y7dk0QBIGVl1AokX+a6uM6/CxXrlAPaUoKVUH9/QEfn5d7HYzRNk+cqD0FgAHR6XTYu3evYGtryxhjuPrLL8IZJyeW5e8vtO3cmbVt2xYeHh4sIiKCOTs7M41Gg9jYWOzbt0+MiIjg4uLiWPv27SGtSXKC5ykAmzkTYAz37t1jDx8+RK9evcqauOXlUW/kqFGVS/QFgczsOnUC5s+n4O1ZVCrqCf70Uwq6jx+ncxSg469nT0q2hIdTNbkaaLVa7Ny5k8vOzmYxMTEiY4yNHj365c/LOXPoWGrbtnqPa9iQHmfA5F5FyGQyREREiDzPMw8PDwqKBwyga2BYGFWAp0yp2kXd3JyUQKGh5Fh+5UqFYwebN2/Ozp0/L97IzxfTvb1FxxYtmNknn+BocTH4oiLRp317hsGDYdutG5rExyOlXj10vHMHUQ8fYsexYzAxNWUymYwlJiYKXbt25SZOnAiZTAZLS0s0bNgQVlZW8PX1hY+Pz9OKuKUleXQ4OpIigudphniHDmWv5TY2dC1YvZqSPSWfQ3IyTHJzEe3lJUZGRqJt48YMgwbR6/7xx9JZ21XCGLVHuLg8rYBnZFCy74mJo4ODAwoLCxERESG0a9dOr6QYYwxSqVSSkJDQPigo6Hc/P7+6YVZZCyiVSqvQ0NAtEolkWfv27bsMGzaM79Spk6TK1iVRpID7+++r7UAuiiL27NmjSktLO6jRaD585esUI/84qq+zMGLEyF+OUqnsaWpqOqi0h6wOkJGRgVOnTkkAbCkuLv6yirtfBICAgADVmDFjzF557zljVEl75x1y5j1/nhaTcXEkLxw2jCoZNZCiXb9+He3atUO5Wfja4McfyZQnMJBkrYYgL4/MkIYPf+7PgiCwvLw8HDt2TJDL5dyVK1cwc+ZMWFhYgOd5qFQqxMTEICIiAvfu3YOdnR2mTJkCm8qklyXPZ2FB/dVjxlDfsCFdmy9fpgX4m28abpsGRhAE3LhxA8HBweKjR4+4u3fvQp6YiKlbtnBeoaGw8/YuXW2uXr1a0Gg03OnTp2FmZqaTSqVwc3PjXV1dERMTA6saVoFhYkI+AFotIJHAyckJN27cgIODQ9n7dupE51FCQvmjjA4fpopefDxJ+StKBp0+TUqTr74CoqPJ6fndd5/ezhjt040bwLZtFKzq2W8dFxcHgAyqpkyZYpiLjiiSDLkmI4VCQqjC7+JikF2pjMjISKhUKrH/48cMnTvT9Q2gc2H4cFKuPDFTqxILC2DFCvp8PvqIqtxDhpSpiJubm2P27NnchQsXcPv2bURGRmLcuHEwd3DA7ZwchkWLSGFy7BiYWg2zHTvEqIAAFtW0KWbfugVrjmM4ehTYv5+Hs7P+PgtSKSXmPDxIPeDjQ7O5Fy6kQLcExuh4bNaM2iUKC+kzOXkS7JdfMDQzk9u9eDFSOnaEs48PtZ9UN2m1dy9VVt3cyJDNxoYUHc/w+uuvIywsjFu2bBkWLFiAU6dO6a5cucJzHAdBENQ+Pj7MycnJ1MLCAvXr14dUKkWrVq1w7ty5Fjqd7qpSqWz9T5STK5VKW4lEcqxp06a+w4cPl+qdFPvxRzqvjhyp0fNevXpVvHv3bqparX5DoVAYe2uNGBxjsG3EyN8AmUz2Vd++fc3L9Cu9QqysrGBjY6MpLCyULVy4MK2y+yoUinilUml98+bNx5mZmeUv3l8VjNHs09dfpyrb1askSzxwgBb23btTBauKRVdRUREuXLggiqKIwYMH/3XZBImEpJELF9LYHkNw8iQQEfFccGpiYoIvvvgCmZmZsLe35wBg165dwooVKzhBEODl5SXk5ORw6enpJeOVWFZWFgRBgFarhVqtRl5eHurXr4+HDx9i3bp1GDVqFLwsLakKtXcvPWdtGJnNmUM/dZTCwkIcP35ciI2NZS1btoS1tbXO9O5drt6AAcx88WKYvxAUTZgwgYuOjkbbtm1hbm5eGvUVFRUhIiICOTk5VSc4KsLPD9i4EY88PREYGFi63XJnU//73yRDfjIeDADwwQdA06aUxFqyhP5WmeqiWzfg0iX6fcQI+nkRW1sKmhYuJPfpkJAqjxNBEHD48GE4OztjrCETN1euUOKmJpw8CTg4lD9yysBotVrIZDJR1qsX9b8DZGzm6UnvdzmBtlarhckzCcaIiAhcv35d5DgOPM+zR48e6Tp+/z3fxdeXqtyrVpUxyZPJZOjXrx/69OmDffv2YevWrQDo+gHG6DNs2hQutrYY6uHBLl68iEE9esCa4yihUlhICZhHj+h6fP06VYtv3yb5fWUJxSZNaDzZmjV0LVm+nJIKc+Y8TQzY21Mw3r07/X/fPur3BmAfG4upO3Zgl7U1+i9cCKeajNa7fPnp7/v2lZvgk8lkePfdd/Hbb79h1apVYl5eHq/RaABgJ4DlERER/W/evNmMMeYpCEJTnU5nJgiClOf5AgDXAPzjAsIngfafbdq0cRs4cGDVlexn+fNPUmnUgPT0dJw4cUKl0WgGKxSKv+3UFyN1G6NBmhEjdZwlS5YMNTMz2/nRRx+ZVVuWW8sEBwcjMDBQp1Ao9ErcffvttysdHBxmjBo1yrxW+5kNQVYWcPYs8Pgx9d116ECBhZNT6UJfp9MhNjYWGRkZCAoKgkwmE8ePH89Kx5D8Veh0T8ed/OtfL7+95ctp9nGfPlXetbCwEPHx8di3bx/MzMzECRMmsEaNGmH37t1CdHR0mRVTv379EBkZKVpeuMB6nT2L+9u2ISUoSByhUNSuy3n//kCrVvQ+1REePXqEPXv2CBkZGZxOp8PYsWOpn1irpQBVqSTDHz2JiIjAgQMHMGbMGLRs2bJmTuXbtwO9euF6WhoOHDiALl26CP379y//g9FqSYEwdy49btUqCobd3Mgxvuodps8lOfmpisTNjRJeFV0fVCqqrtrbU5KpnNeYnJyMY8eOCcnJydzChQsN185x/z5VT1NTy0jBS1oneJ6HRqOBubk5GGPPfwYlveu1fB1XqVQ4sG+frteSJXz9ffuov1yloiTIihVlxpUJgoCAgADh9u3bnKOjo+6NN97gZTIZli1bJjZv3hwWFhZiWFgY99prr+Hq1atwdnZGz9u34d62LThHR0oeVBCYrl69Wnz48CFbvHgxvReiSMHzO+9ULWF/+JCuwX36ULuClRUlcQICKIC2sSGDt/LM7uLigF27KBHk4kLjzHiejrVt2+hztLN7mgj66CNg7VrkPnyI0zdv6m7dusWbmJiIVlZWgkajAcdxooWFhYm5uTlGjBgBWUWB+Jtv0rE5dCjJzyvxWElMTMR///tfmJqaXtRoNBMUCsX98u6nVCoZADOFQlFY+RtW93iy7xYKhSK/kvvYcRx3w9vb22HkyJGmel+37t6lUXTr1tVIhaZWq/Hbb78V5uXlzVy0aNHmam/AiBE9MVa2jRip4zDG/H19fWV1LdAGgGbNmiEwMJBXKpWtFApFVFX3V6vVHz18+DB75cqVX0qlUo2lpWVRz549LVu3bl33nKvs7GiBB9DC6cgRGklkbQ10745CV1cExMQgMTERAODl5YWxY8e+mtfB8ySjXLCA/n2ZntCICOqbnTtXr7ubm5ujVatWaNmyJUxMTEpf/5gxYzg3Nzc4Ojri7t27yM7Ohk6nQ9Kvv8IaYBI3N0S0bo2wM2cAjmNNo6Lg7e39XGXNoCxc+JfIdytCFEUUFxejoKAAWq0W9evXR05ODtLT07nJkydDKpWiUaNGNMro3j2q3lZzf/fv3w8ACAgIwPjx42s23srMDHj4EPInLuOVzozPz6dAKDmZZMr371PQoi9yOfWIP/uZjx9fuZGdmRkFXJs30/vk6FgmaNuwYQM4juOmTZtmkEBbFEVSCzRsCGRlQcNxgFqNtLQ0pKSkICMjQ3f79m1OpVIxgMZvMcZQv359YfLkyVxp//zIkXRNeVYmb2Di4+Pxxx9/AFot30YuF+s3bMjyk5Nhnp+P1F27UL9zZ5gAOHbsGCIiIkS5XC7m5+czc3Nz9t577+HUqVNszZo1uhkzZvAA0KFDB+bm5saio6MFe3t7burUqbhx44a459Ej1tzUFAMWL0ZGcTEerlkDDw8PxMfHw93dHUePHhXT09OF9u3b80FBQbh8+TI6duyIY8eOwe+nn2DeujX14ldGvXo0Eg4Azp2jf1NSKAiXy8kDIDiYzpXvvgMmTCA1klxOUvEFCygBGRhIVe+LF+mYXbyYAnUTE+Cnn0gmb2YGWFlBbmeHUZ6evCAIuHfvHktNTeUtLCwgCAJycnIQHx8vrF69GrNmzeLKPbaGDHlqBrh/f4XBdlFREQ4ePFhoYmLyzeeff760srfhibT5bxdoP8EfwJHvvvvuRFFR0T4AVwDcB/AIgCPHcRNMTU3/7eTkZDFs2LDqjTc9d46SSDX4zniSYFKpVKqDxkDbSG1jrGwbMVLHWbZs2Zn+/fv7tauj5k6//vqrLjs7mwEw0bffacmSJdNEUdQAaMjz/LdffvllDRogXw1F167h6Nq1aB4cDOvcXOQtWADvkSOr50pcW+zfT4Y8T9yka8RHH1FVuxoVVb1ISgIcHPCwb1/csrISm2/axGxsbFBcXIxff/0VANCiRQuMGzeuwnFAL83ateRAXBN5qJ6o1WqEh4eLWVlZuHXrFuvfvz+0Wi1CQkLErKwsBpCktl69esKjR4+4li1b6kaOHPn0BU+YQJ9hDaTKcXFxCAwMFNPS0tjgwYNx48YNYcSIEVy1JOXDh5NcV6lETEwMdu3ahX/961/PjxjLyaH38OefaeSOuzuwcmX1RloVFNCx9ttvz1d6U1Io8NFnNNeCBSTNDgkp/UwzMzOxcuVKdOzYEf7+/vrvTwWIooj9+/cLbNMmruflyzjx009CbGwsx3EcZDKZYGlpKdrY2PBeXl7w9vaGVqtFVlYWkpOTcfbsWaGoqIiTSqXgOE7sfOkSOsyezWS9er30fpWgVqvx559/wtPTE7GxseKZM2fYv3JzERMdjfCOHdGrVy80mj4dBVZWODJhAgRBgLm5uU6j0fDDhg1DVlYW7O3t0bx5czDGoNPp8Ouvv4r5+fmsdevWumHDhvGMMWzcuBGNGjUS+/XrxwA61rZv3w6e49DUwkJssWULy5PLcWXIEDE3P581a9ZMkMlkXHJyss7Hx4e/cOECeJ4Hz/Mkb5dIhHdnzuRfWs2i1QJZWdBt2oT75uZwj40lv4CTJ6mK378/Xc+++grYuZNGQK5fTxXRffvo97t39Z7moNPp8McffwjZ2dnirFmz+DLJQa2WkkAFBaSkqYB9+/YV3bp1a5dGo5n+T+4TViqVr1tbWx99/fXXzePj4wtTU1O1+fn5ErVaLTM1NS1u0qSJtmfPnhbO1fUbUSqBSZNIrVEDBU9gYKDm8uXLV9Rq9esKhUJd7Q0YMVINjMG2ESN1nGXLlgV37dq1c8+ePetkQBoWFoajR48CgKlCodBW57FKpdKVMXZ3/vz5fI3ck18B58+fx9mzZ/HxRx/B/N49cPv2kWSxxGV41KjqzR42JJmZFIDMmlWz8UJ5eVQxnDSp8nmy1UEUKXj08KDArJyZyeHh4cjNzUVYWJgoiiJr0aKFzt/fny8oKICdnR0MJi93cyO5cy32zG7cuFG4d+9e6Q7LZDKxqKiINWrUSDd+/Hg+NTUV9evXR0hICHx8fKiaXUJMDB1DVlblzzWuArVajW+//Ra9e/fGnTt3hPT0dE4mk4lz5sxheisGcnPJn0AqRW5uLpYvXw4AWLx4MZgoUguFqytVZ7/4gh7z66/kHP5C/26lBAWRA/nVq8//vUsX+vnpp6q3IQjUI5uTQxMFJkzAlStXcPjwYfTu3RstW7bUyx8iJycHcrkcHMchMTERxcXFyM7OFpOTk4W4uDie4zjh7eHDuej168UrLi7C9OnTeXNzc71UGIIgICEhAYIg4PaqVcIjCwvm2Lmz0K9fP75CKbIeZGZmwtzcHDt37hTu37/PCYIAMzMzYdy4cZz7jh04HxQknuvWjTk/fIg+48bB0dcXFtbWSElJwYMHD+Dj41N+Hz6ovSExMREdOnQolcEHBwcjKipKmDlzZumx/fjxY5iZmUGtVuPi//2frl1cHO/4xRfQSaXgGzdGWloatm7dKmq1Wjg5ObG8vDxx1qxZTK1WQ+PggIyvvoL7Rx8hJiYGR48e1QmCAAs+/tkAACAASURBVI1Gw/Xp04e5urpCKpWisLAQpqamFY7JEgQBgYGBuHLlilhcXMzGjh0LrwYNnnpZWFtT8mbLFkpCfvQRUFxMx/HOnUD9+nS95jiqkmo0pJa4d4/OhWbNKAEkk1EfOQBto0YI2bBBREaG2G3mTI5PSaHrZUwMVdFPn35q+lgOWVlZWL16dYFWq3VRKBTZNT4I/gYolUpznucz586dK3t2ooEgCDW/rqelkddDSAh9ftXk/v372LJlS65Go/FUKBSpNdsJI0b0xxhsGzFSx/n2228P+/r69vH396+9clwNEEURQUFBuuDgYB7AmwqFYkNNtrNs2bIzHMd1ASDOmDFDpu/s0VfF4cOHhcTERHH27NlPkx9qNRAbCxw6BPznP8DHHwNeXuRKW1VfoqHZu5ekkpMnV99sLCCAgpcffjDMvhw7RpLfmBiSR+vRp/9k4S0UFBRwOh1NuBk+fDh8nyQPBEFAbGwsXF1dKwwWXiUXLlxAUFAQAGDu3LmlcuwqOXaMHJTj42sUaAP03qxdu1b36NEj3tzcXJgyZQq3Zs0a1KtXT9TpdBg+fDhzqUqavmIFmVGtWgWtVotffvlFyM/P52b07AnXoUNJwqtWP5+MyckhT4PISP2P94QECtpfVDFkZNA2qvPZnjkDvPcedBcu4OtVq8CZmJQu5lu2bCna29szd3d3WFtb486dO4iLi4OlpSViYmKEJk2acNHR0bC1tRWcnZ252NhYcByH4uJiyOVy+Pv7w/P8eUpATJum/z6VgzB6NOLc3XHUxUVs0aKFMGjQoEoTqAkJCTh69Cg4jhP69u3LSaVSmJmZ4dKlS7pr167xAGBubi7MmTOHu3HjBny9vGCyZAnw7bdITUtD+vz5oldwMJPcuVMz9/RnCAoKQkREhDh37tzKy4jr1gHffkvjxRwcIIoiwsPDxaNHj7LOnTtjwIABAICwH37Qnc7P5yGTiaIosm7duon29vYsOztbOH/+PMdxHHQ6HXQ6HXiex8iRI+H9wgiukJAQBAcHC2ZmZszf35/FxMTo0tLS2Jtvvvn8hU8UyUmd56klKD6efu/e/emMZo6ja5SFBSVOg4LIr2P0aEqmWlnRsW9pCbRpA92VK7gRHCzec3cXB7drx3EODsCDB3S7t3els56vXLmC06dP75s/f/6oGn4cfyu+++67g/369RtqEHXexYt0jRgypEbHtFqtxooVKwoLCgqmLF68eG/VjzBi5OUx9mwbMVKHUSqVnImJSc9OnTq9skBbEISyRj8gc5cngXZrffq1K6K4uLg/gM6MsVPFxcUvube1R25uLnbs2CGkp6dzY8aMef5GiYQcbn18gHnzSM597BgtDDIzaZRYz56147T9IiNH0vgsR0fq364OubnVnmVcLuvWUQA2bhw5A1tY6B1Aenh4oHnz5ty1a9cgl8sRHByMAwcO4MiRI7CzsxMfPnzIGGPgOA5jxoypXl9yVBS5XcfH1/CFlSUtLQ1Xr17VNW7cmGvevDkLCgqCXC7XtW/fntc70L53j1zAL1yocaANUK/wzJkzebVaDYlEwuU+caF++PAhA4CLFy+id+/e2LZtm9irVy9mb2//fGUdoHaIzEwAJHd/99EjLu7YMdh+/DHJbssLhG1saIZzQIB+Aen168CgQUBiYtkF8+3bZIr1ZVXTBJ+hVy/g5k3kr1iBCdu24cLMmcKDvDyuf//+iIuLE+7evYvLly/zGo0G1tbWQqNGjZCYmMh4nucePHgAALC3t4dardZNnz6df/ToEQ4cOICRI0fC3d2dXP4NMMqPW7gQLRo0AFdUxHbv3s0PHDiw3OpeQUEBdDodQkNDdRkZGby5uTm3Y8cOmJiYiBqNhsnlctapUyc4ODjAy8uLk8lk6NChAwWNhw4BS5bAKT0dTqtWMRQUvFSgnZKSgosXLyI2NhbDhg2r+gHvvAMMHkxz0devB1u3Dh06dGDm5uZo9syIuNfmzePbfvghHo4dyxw6doREIin5guF6PBnXJQgCsrOzER0dLezevZsLDg4We/fuzQoLC3H69GkBABs0aBDn7e0Nxhjs7e35VatWPa2aarX0noSHUxKybVvav759KaCujCqMJvmBA9FSrWZ/btokrtVqde8OH663JD49PV1TVFR0uep7/jMoKiraf+fOnd7t2rWr+cWthPnz6fv0hbGU+iCKIg4dOlSkVqsPGQNtI38lxmDbiJG6zUitVmtlMDfdGvDVV1/B1NRUmDt3Lrdt27YiR0dHcdiwYWbPzJEuepntKxQKDYALS5cuzcrLy3MCgOLiYtQlWbkgCFi+fDlMTU25efPmVexEC5BZy5gx9JOdTQHUihUkX37tNZK/eXrWqM9MLxijfsQvviDzKn2PnZs3qX+xZB5vTbh4kYyI7t4leZ+7O/1UE47j0L59ewBA06ZNkZGRgZSUFMTExDAPDw84OjoiLCwMO3fuBMdxGD58uCiVSpmbm1vln03LlhQMiqJB3v+ioiKsXbsWtra27OrVq4zjOFhYWAhz587VP7rJz6eZ1Rs3AgMHvvQ+ASg1BZPL5ejcuTNCn3ymMTExiImJAQBWYqamUCgAUNLgzJkzOkvGMGTcOF49dizY0qW4zJhQ4O3NtbG0BPr1q/hJ//1vcpnW6aoO7ho0oGOtvGMzJYXGa1UXnodk2jQ82rMHg19/nasnkYBr1QqdOnUq3RlRFMEYK42IUlJSsG3bNrFJkybChAkTeI7jIIoijh8/Lnh7e3Pu7u6UoPnqqxqZMJXhyBGgUyc0GzgQjDExPj6eJSQkiD4+PqykZzUpKQlbtmwBYwxNmjSBVCpFjx494OvrWxqQcuVFdQEBdL5HR5OSZOBAOh+bNq3x7p46dQp//vknGjVqJEydOpVzcXHR76Rp2JCMwQ4eBDIywKKj4d2zZ5m7mdy5A+e8vAqvURzHwd7eHj179uS8vLxw6tQpISAggOd5Hr1792bt2rVjz/o72NrawlouFy+vX886d+lC/gcODjRNYsAASmjNm1d1oK0nEokE06ZN49auXSvu2LFDnDhxol7vT2FhoQCg7szxrH3OJCQkcE/Ov5ptQa2mpNfJkzVWi4WHhwuxsbGparW67Dw2I0ZqEWOwbcRI3SYUoODT0gCVleqi1VILtkaj0Xz//fdSxljBgwcP7FUqlfr27dsSxliqKIp3DfR0n+/atWujXC5X5ebmmrm7uxcOGTLE3M7OruZf0AYiNZXauj7++OPKg7kXsbWlLPywYcCdOyR3DQwEPvmE5gbPmWOQilkZWrakqvaXXwLff6/fY9avL3cmrF4UFFD/7MiRFDQuW1az7ZQDx3GoV68e6tWrVyolB4CWLVvi8ePH2LFjB/bt21cS6IpNmzbFgwcPMHnyZGb9oiu7iQnw3nsQQ0Px+40boqurK+vTp0+NHdBv3LgBAGjWrBnXt29fnD59Gt27d9dfvqBS0SLyyBGDBQAvMmDAAAwYMABLly7Fk1m+8Pf3R0FBAS5duoSsrCzcvHkTgYGBkKhUvN+ZM9A+fox4xhD644+47+zM9Xz77arPQQcHqoifPFm5oiIvj1oLdu8u//Zx4546UFcTM1tbpHzwgaDev59rsGMHKUyekR6/+BqcnZ3x6aefMgClEVtWVhbu37/PDS1RePTpQ+PiajjH9zkiIshhG4BUKhXOnTvHp6SksMuXL0MqlcLa2lp49OgR1717d3Ts2BE7d+5k9evXFzp06MBVeYx+9RWpSfr2pecIDn6pQFsQBISEhKB///5o37599SU5ZmZkfpeQQB4Sn31W9j08epTGoWm1VSYzHBwcMGHCBL64uBgcx8HU1PT5DzMpCTh1Cv9KSmJZJ0+KqF+f4dgxalXYuJGSQFFRBm/rkUqlmDp1Klu3bh3Cw8NJYVAFcrncBECWQXekDqNQKBKWLVuWlpiY2Lhxyai16rJhA/3MmFGjhycnJ+PUqVOFGo1mgEKhKKjZThgxUjOMwbYRI3WbFI7jik1NTV9JmVelUpX86gmgvyiKOwA4xsbGjmWMCaIo/q5QKHSGeK7PP/98k1Kp/DM3N7cpgOj79++HrFy50nzw4MF6LWBqC7Vajf/+97+oV6+eIJPJaq4Db96cfkSRqiwHDpARmY8PSczbtKGKn6GYMIGqWzExZE5WGbm5JDufOLH6z3PqFEkuY2OpKllbo7tewMTEBPb29njzzTdx7tw5dOrUCYGBgUhOThby8/O5vXv3CuPGjeMsXpBla374AapDh5AyYQJLSUlBaGgoTE1NYWNjIwqCACcnJ2HQoEG8mZlZpc+fnZ2NI0eOACCTQGdnZwwaNKh6L2LECOpj37ateo+rATNmzEBCQgKkUinatm0LAIiKitKtWLGCl6hUkBcXw1GtFj1jYtjR0aNx3dW19LGRkZHoVZWDNmM0tisvr/L73bxJio+KlCvFxVQVzc6usOJZWYUsKysL9fv1o8BOpyNDrB9/1Ou41Gg02Lx5s8gYY2ZmZnSuJiYaLkDbuLH0NTVq1IiPj48Hx3H44IMPkJWVhTt37nDDhw9H/SemTzNmzKj6elNQANy6Rf3yWi1dYz77DJg586V2leM4vPbaa+KZM2fYqVOnIIoixowZU6ZvukoaNyZ1T3Ex9UgvWfL8yK+S/f3wQ70295ziKSuLnMfr16eAfvBgmH78MfbY2bHPR4yg+4gi8McfNF2hljwebGxs0LRpU929e/d4fb6rNBqNAOB/ygFbFMVTaWlp79Qo2A4MBMaOpe/LGny/FBUVYceOHYVarXa6QqG4U/0dMGLk5TAG20aM1GEUCoW4dOnSrISEhHpt2rT5y93IT58+XcTz/L0vv/wyEcC6J3/OBWC40uUzKBSKWwBuAYBSqWwIYNyRI0e2N27cGPZ6mGvVBrdv3xZFUWTvvvuuYRquGaMF5qefUiBw6RIZ70yfTovBxYupImVl9XLPY2kJbNpEI1I2bHh+vNKLHD1Kcubq9Av/8ANVilasoHmnr+jzkclkpaZLo0ePZgB4tVqNLVu2CCtXrsTYsWPRpEkTAMCDBw/wh709TN5+Wzd6wADex8cHN2/eRFhYGERRZK6uroiKimLff/89mjRpgokTJ5YZQ/bEMfm5XlsHBwedp6dn9c7Phw/pPXyyb7WNk5MTnF4YT/f+W2/xj1UqFHfsiBwbG9EzLIz9x8dH67NliwlcXCCVyWBpaSna2tqy7Oxs2FY1jqtnT6B9e3pNFQVlHAecP1/xNqRSUmOUY96q0Whw9epVnD59GiYmJtDpdBgyZAh0Oh3c3d1ha2sLuVwuZmRk0L5kZlJwHxpKBm5VqFI0Gg1yc3PZO++8A8viYmqBiI01XJA2Ywbt10cfYcSIEThy5AhcXFwgl8shl8upP7y6rF5NHgkREdT7f/IkXV8MwKBBg9jAgQMhCAJWrVqlS0hI4Ly9vasvMyox05s2jRIDlpY0LUEioetfdebJa7WkBDl8mBKVSUmkhIiNBTgOllottDod1Go1JEuXkoP46dPV3uXq4uDgwCIjIwUAVX5PSKVSnjH28v3LfyN4nq9fI0PLzEwy+zx4kNqwqokoijh48KBKrVYHLF68eE/1d8CIkZfHGGwbMVLHEQRBSEtL49q0afOXPee1a9d0Bw8e5AHIAPz2lz3xMygUCkGpVO7jef7g1q1bB86aNUtSU7nvy3DhwgWxY8eOzGDjp57FxITk5ACZCl2+TIFxXh4tyhs0oFFZNTU4atqURtf85z/Ae++Vfx9RpB99jdE2byajoXr1aN+sremnDiGRSPDmm2/yJ06cELdv384sLCxECwsL4dGjR7y7uzsmnjzJw8QE8PGBl5cXvLy8Sh/r5+fHrVmzRrx79y77+uuv0a9fP/j6+iI0NFS8desWy8jIAECV9YYNGwqTJk3izMzM/p+9M4+P4X7j+GdmN5tLEnKISIgjJEjcQtw3pe46WrRUHS3V+lWpK9stqtRR2mpLqSpp3LeWiMSZkLiTEEfIidznZq+Z+f3xJAQ5dpMNofN+vbxIdvY7M7s7az7P8XkMe4P++IOCKsUZhL0sNBpI7exgFx4OREejtlTKAEDPPn2krp9+ipPdu6Njz56cjY2NJCAggN+wYQP74YcfwtTUFCEhIXx0dDSsra0xbtw4ViqVQhAEcDwP6YQJdGNcnNi+du2pu3NpXgI+PlQSXZDhTUpKwu3btwurELjBgwdLZDIZwsLC+P3797MMw6BatWrChx9+yPA8D5PCwJKdHXDsGFV3tGhBvgmllOvnFGTlQ0JC+OFvv83iu+8oy24s3n6b/BpAn58h5TB5eobYWArazZpF1SVKJQlRI8KyLFiWRZcuXSSHDh2CUqlE9erV0aFDB/2d9guZPJn+HjyYgnsnT1KZ98SJVFVTmifA5cvAnj2UKT92jAyyRo9+IYgolUrBsixyU1Jgq9PRKMaXgLe3NxsaGoqoqKhnvk+Kw9HRkTUzM+v0Ug6s6iAxuB0sLQ14+BC4cqXcVV8RERG4d+9eskaj+aRcC4iIGAFRbIuIVGEUCoUtAMe6deu+1KblK1eucAzD/C0Igh+A4Je57+fQcBx3ITMzc3BOTk7ZmbVKoH79+nx4eDijUqkwaNCgynsfzMxIYBdm5G7fJgfdzz8H5PKnbueGiH6GoazA0aNAYiIZFz1PVBRlEp+fd/w8Dx6QU/WyZbR9BccgvQz69evHeHp6Ij09nUlPT5e0adOG+r4zM0vsZ2VZFp988gnD8zwOHz6MgIAABAQEAAADAF5eXkhNTcXDhw+RkpLC/Pzzz/zs2bP1f1MyMkh0tW79aoT2n3/SXOxLl8iluYgo5nkeGZmZ2D5/PgSJBO7u7hJHR0e0aNGC3bt3r7BhwwZGEATUrFlT6Ny5s+T48ePC0qVLMXfuXAQHB3MXLlyQjBk9Gu4rVwKJiUgQBFhYWMC2ULC6ugJ+fshRqxF29iy8vb2L96J4/30SX/PnIygoiAsJCZHY2tryAwcOZL28vJ68aO7u7qxKpYJUKoW/vz//448/SjiOkwwvKrAYhgSury9Vbhw+TK//c2RnZ+P3338HAOReuiTgzBmgYMa40XB2ppnOxiA/n87r8mXq+1+9mvrmK4HAwECEhoY+cWxPTEzEjz/+KGnbti28vb0N/14+cICy8AsW0PeZjU2xlQzIywOWL6fee4WCMuADBtC87FIYERjI83v3smV+pxkRMzMzyGQyIS8vr8z/I5ycnMBxnNfLOK6qAs/zXraGBq5Gj6ZgzOZyTRWFUqnEkSNH8jUazSi5XK4s1yIiIkZAFNsiIlWbbvb29qomTZpUujtaWFgYd/ToUUmNGjXUGRkZpgA+k8vlr9rExRbA0hEjRrwSoQ0AAwYMkDZt2hR///03PDw80MhIJZqlYmdH2T0fHzI58/MDfv6ZyloZhnp99R151bAh3cj++Scwf/6Ljxf2M5aUdRAE6rkcNowykzdvlvu0XgXOzs5wfj7I8Mkn5NrM8yUGL1iWxeDBgzFgwAAkJCSgbt26T0rH7927h23btkGj0TBmZma83gfz6BFlWAMD6e+XyUcfkXFZr15P+x6fyz4nJCTgyJEjmLJxIxJnzhTs7e2ffCiGDx/O6HQ6ZGdnw9bWVgIAnp6ezE8//cStXLlSwnGcxNXVFXv37cMQQeBtv/+e3VSjBtzd3fnRo0ez106fRp1JkwT1sWPMPzt38snJyUxkZCSmT5/+YtXIlStPMpa3bt1iHBwc+MmTJxf7RhUaFo4bN06iVCqRn59ffMvJ2LHA48fAokXkLTBlCgAgNzcXV65cwfnz51GjRg1u1KhREvPAQEml9NH/+SeVs3t6Vmyd/Hwqt3/0iEZ9LVhA2XtDzBsNIDQ0FEOHDi3s15YAwM2bNxEUFMSHhYWxUqlUMDU15WvXri2pX78+nJ2dUa1aNVy+fBk3btzgtVqtUNCOwbRp04ZGe7m6Uh/u6tXUx/34MfVgCwKVlm/fDjRvTsGJHTvI9LEseB64fx+mw4axBx4+5CYVMb6rbOLj46FWq59MUSgNOzs78Dxvo1AoHOVy+eOXcHivFIVCYQrAtdCLQC9u3CDTTkNaDIogCAL279+vFAThD7lc/p8ZsyZSNRHFtohIFUYmk43z8vJ6KTbkDg4OEgDIyMhYCmBHFRDaAKAGgMePHwtNmzatnFJuPahXrx5at27N7dmzhx04cCDj5fUSkxI2Nk/NjuLjqf9wyhSgY0e6ce/YESgYG1QikycDX31FjuhFgwXZ2WRcVCA8XmDpUioJPneO5h87OBjnnKoCHTpQxr+wjL8EpFLpC720RXsPmzVrpt+HMj+f3sstWyoutvQlO5vGwK1YQVUJ5uaAiwsJz2LIysoCAGS0b4+2Awcyz2fepVIpimanZDIZPvnkE0lycjJsbW1RrVo1JCcn45+NGwX7yEjOzNpacvfuXXbz5s3QXb0KOysrZvO2bZBKpezcuXOxZs0a/rfffoOjoyPfp08fiVWhT8GePZR5//57jB49mv3xxx+hVCpRVs+nhYVF6ds4OlK/eG4uBay+/x6PWRYnT56El5eXMHz4cAni48lYcPRoPV5gA1m0yDgtFwMH0rXo60tBsK5dK01oZ2dnQ6fTPTMjG6BpAE2aNGF1Oh2SkpKYmJgYSWRkpPDo0SPk5eUxhc73Xbp0YTMzM3Hjxg0AwPnz54UuXbpQEKdlS2pL8fcnQ0cXFwoE7dxJhmcdOxpWyTNpEhAdjbqnTyPjhx8kfn5+/DvvvMO+jNGZiYmJsLGx4VmWLVPgMwyD+vXra+7cufMWgC2VfnCvHjOWZbVardbEpDTvkEJ27aIRbVFR5TbcvHz5shAbG5uk0Wi+KNcCIiJGhBGKK90RERGpEixbtsy/U6dOo7p27Wr08uX169fna7VaDgAEQZBkZWWZy2SyWxqNpqtcLk8x9v7Ki0KhaAngCgB4eHho3n77bZlOp4MgCEhNTX3hJrCy4Hke27dvR2pqKj9r1qxXo/qLcvYsiWATE+DqVZoh+8EHJZcmBwZSOd6mTU9vzLdvJxG9ePHT7QSBfvb2prJbjYayoW8aanXJjtil8OjRI8TGxqJatWrYvXs3JBIJFi5cWPqTBIGcmLt1A779tpwHbADp6ZTp9PIiw7KwsDLLlxMSErBp0yYAgH1KCibOmAELfasnimPqVPA9e+KWlxcuXLggNIuPF7znzWNPnDqF6tWro23btkhJScH58+eRnJzMJycnsx4eHlyPHj0ktidOABcugFuxAidOnEB0dDQ3c+ZM42UpBYGCT87OwPjxUKxbBwD45JNP4DBxIlV5HDpktN09YeFCek8qIuQFgVoR4uIoKPDPP+ShUEksX75csLS0xPTp0xlDem5VKhVYlkVWVhY2btyInj17wtvb+6mxoCAA//5LAYOQEBLaHTpQFtvQ6zI7m8YqNmtGvb3VqiErKwu7du3iU1JSmEaNGqFv376MwT3mBvDHH39wzs7Okr59++q1/eXLlxEQEHBo7ty5gyvtoKoACoXCRiaThTdv3txl4MCBZUeEEhLo/53k5LKnaJRARkYGfvnll3ytVttWLpdHlWsREREjIma2RUSqMBqN5k5SUpIaZFRmNAr6Tc0BvAvgMei7gNNoNEFyubxKReDkcvlVhULRAUCNW7du7b137x4AqLVarSkAzJ49G8+Pd6oMWJZFnz59sGHDBlan05V7NrPR6NyZ/qjVVHa7fz+ZBnXpQpmuli0pk1lIr17Atm0k0nv3pptdmYwyfIWcPElrXrhAWfMysr6vNbGxwMyZdMNvADt27BAyMzMZe3t7eHp6IjExUUBBP3eJ5OcD8+bR+1KZZGaSqF60CIiIIJf46Ogyn7Zr1y4hKirqyTmMj42FxR9/UL9seRk/HuzRo2g6ahSa6nQMvv6awbx56N2795NNHBwcCk3C2NTUVBw8eJD5+eefUbNmTaHftGlMwObNfGpqKjNgwADjlgMzDJ2bRgPB3R3tGzdG6vjxcHBwoH5iTSVNZXr0qOwqlNI4e5au8atXKVBw6lS5BYk+xMfHQ6PRMOPHjy97zvpzREZGcuHh4UxKSgrbuHFjoUOHDgweP6YWmaZNafrC22/TiLa8PPKUYBjDMtmFfPUVBQ0DA5+0w9jY2OCjjz5i4+PjERwczK1bt07SvHlzYfDgwZXiu5GVlcW2b99e7+0bNWqEf/75p7dCobCWy+XZlXFMVQGJRDKrUaNGLgMGDCj7HiY7mwKSP/ygv2HncxS4jysFQfhWFNoiVQVRbIuIVGFMTU0969SpY1ShnZeXh61bt6oZhpnm6+vrb8y1Kwu5XH4BABQKRSedTjdNEITNAC6bmJjEBQQE2LZo0UJmZ2dnuDuugTg6OsLOzk44ePAgM/wludyWiakpZYQ6dACyskgwR0dTxsjNDZg7l/reJBIaEeTtTW6+yclUXnzxIgn2qCgqLz592uiOxlWSWrXotROEkvvViyE7O5txdXVFWloaIiIi4OPjU7rYXrEC+Osv6kGsbNzcgFWrqL9fTw4fPsxHRUWxAJVhv/vuu7AePbp0t3B96NyZvAaOHqVAz6FDpY6fs7e3h0ajQZ06dXir7Gy4NGnCSDZsEObOnctWWvuITIa7P/2Eh3v2oGtsLPUFy+Xk7F0ZfPttuaopntCpE1Wj9O9P467kcuMdWzGcPn0azZo142rXrq13sIPneZw8eZK7cOGCpEmTJpjQuDFMGzZksGIF8N139L3z8890LhYW1Jf9zjs06vCPPww7wJgY6H7+Gf7NmvH5zZvDws+PHTJkyDOme3Xq1MH48eMlt27dwt69e5nBgysnkaxWq5lizf5KwMrKCu7u7oiOjv4SwKJKOahXjEKhaMGy7Hxvb2+TMoM1HEdj3X75haZclJOoqCgkJSUl63S6FeVeRETEyIhiW0SkCiMIQiMbI/T4RUdHC4GBgflKpZLJy8szZrqzsgAAIABJREFUl0ql//j6+m6p+BG+XORy+RUAUwt/VigU7aOiov66du1a16ZNm6pHjhxZgTvZsmEYBlZWVrxUKn1F85rKwMaGejgBGq8THEwZ1erVSfA0aEBmSl98QX3DY8aQu/hvv5Hb+IMHFRMDrxPW1sC+fZQN1sN8b8eOHVxMTIyE53lkZmbys2bNYnU6HWQyWclKUK0mYdS9uxEP/DlCQigLlJxMAZOaNQ16ekxMDAMAY8eOfdqSERQEXL8OfPZZxY5t4EDg119pnatXS91Uo9EgJSWFnTBhAuo4OwONG+PDfv0khgRCDIXnefiFh8O5Qwddw4AAKSwtK3SjXyb/+x9dd199Zfhzmzena7lVK+ppr8SMNkBl4Hfv3sWkSZMM+q67d+8eYvbskbydmYmaY8fCtH17YO1a8p34/HPqwX1+xNf//mfw53bjxo2c2549rOPDh7g3bBjbq3dvBAYGYsuWLfyMGTNeuCZv3brFOTs7M9BjDrah6HQ6qFQq1DawaqFHjx7m0dHRsxQKxZoq4pFSIRQKBQPAEUBDExOTEQBmdevWja9bt27ZT546lcZ8VSDQm5ubi0OHDuVrNJpxcrlcW+6FRESMjCi2RUSqMBzH5dy4cYPz9PQst7jLycnBrl27dBzHLQAQBsBSp9MFGe8oXx1yuTwOQLdly5YdrFWr1sCXsU9bW1vm3r17HF6i0225sLWlGbPDh9MYsQcPgPXrqby4WTPKuPr6kigfNYqyu/8VoV3IF19Qlj+q+GrDnJwchIaGCiYmJsytW7ck7733HpycnFCtWjUWIIOwErl3j7J31649mRdtVObMob+/+4768FnWYMECAL1792Z27dqFS5cuCW5ubqRsb90Cjh+vuNju25eqLKRSoJSsX1xcHLZs2QJBEFCn0H04K4v6kl1dK3YMpcAwDKRSKQYNGiRFeDhVBUyfTm0WkyYZf4dDhhQ/fk8fPvuM+rN/+om8GioZMzMzmJiYQG9fn7w84KOP0HDKFKRpNLzZ5cvs71u34t2zZ+FW2gSHzZupR7djR72P7eQPPwj9162TRK5aBcbdHZ/WqgVbW1tcv35dSElJYW/evIkmz7mXF4yOrJQSidDQUN7KykowNAhrZ2cHT09PaURExFwAcyvj2F4GCoWinamp6SKJRNJbIpEwNjY2agcHB5MBAwbA0tKy7Nc8L48Cv/o4zpeAIAjYu3evkuf5H+VyeeVfICIiBiCKbRGRKgzHcatu3769SxAEvXvmtFotBEGATCYrNPVSsyy7fuHChT9U8uG+EhQKRSuGYd7S2xW6gtSvX5+9du3ay9iV8WjcmP706UOi++uvyURr61Yal2NpSSXI/zUWLya37iKo1WpkZGTg0qVLfHh4OGttbS2YmZkJrq6uQqNGjfS7mdZqKdixYoVxhTbPU4/rr79ShtPEhEQ29T2Xi6iCQEN0dDTz5Hvm448p01RRTEwoQ16K+/qlS5dw+PBhSCSSwv5tYskSaoEwstgWBAE3b95EnTp1EBISwut0Olb7+DH5FNSsSeOoTp+mHmInp/L1EJeEhUWppfTFsmkTiWyFgka3leHIbkwcHByEmJgYpk5x45cEgcp+f/6ZvCAuXgTMzMDWrIkOmzaxgiCg5/nznP+OHZKJEye+OH6vkHPn6LtJXzQapF+5AmmPHuhfWMVTQJ8+fRg/Pz8cPHhQaNKkyTP/YbZu3Vry77//Gj1Imp2djZMnT7Ljxo0r1/O7dOliGhERMUOhUKyVy+VJxjw2Q1iyZMkwlmXrzZ8/X+/h8t98883bJiYmv5ibm9t17tzZrEWLFkyBf4r+rW/h4RTwvXWrQjPoIyIikJiY+Eir1b6RJfkirzei2BYRqdrsZRhGFRkZaVZgxoTMzEw4OjrC3t7+yUaCIOD+/fvIzc3F4cOHtVqt1uTzzz/HwYMHheTkZAiCsOAVnkOlwjBMD0EQpKdPnxaGDh1aeTWnBTg6OkKn01X2bioHhgHq16d5v7a2lFFYs4YMvHr0oJuev/6i2dyWlhXv263qVKtGgtvTExg2DPfv38fevXuRm5sLAKyrqyvef/99w3qGOY4y2tOm0fxyY5CeTpnmMWOo1DI2lkYlGYHCmdR9+vR5GtC7eJEqIhISKrZ4RAQQEEBeAPn5zxr2FXD+/Hmhdu3azIgRI54ZK1ZZPe5KpRK7du0CQD3qH4wdCxeAKhCAp9Ugw4fTMR89alBPf6ns2kXCsl07/Z/j6UnvR58+NOPeGKPD9CQ3N1eQSqVPT16nA+7epbFjTZoA69ZRb379+vQaFem5ZhgGnTp1kmRlZXF///03M23aNPaFnubUVFpDT4NL/swZ5A8dirgZM5j+X375wuNubm4YO3Ys/Pz8mLCwMLQr8jq7u7vjwIEDkoSEBLi4uBj4SpRMXl4epFIpduzYITg4OAitW7dmW7ZsCX2/M2xtbdGqVSvptWvXlgKYaLQDMwCFQmEFYC/HcQBQpthWKBS2pqamv5ibm789bNgwi4YNGxpsoAfgaVByzZoKCW2lUokjR47kazSa98TycZGqyKsfXyMiIlIicrmcB5AbEhKCsLAw/Pnnn9mHDx8+8+uvv+avXLkyPyAggHvw4AGCgoIEf3//pH/++eemTqcbIZVKQ3744QfExcWdEgShs1wuz3/V51JZ+Pr6rgYwICIiQnXv3j39yx7LiZ2dHaRSKZKSXlkSwjisWQO8/z4ZEw0aRDe9KSl0Mz9/PgluQaDS2sTEV320lUdaGpCWhrS0NGzduhU6nQ6NGzfmGYZBz5499b5pfgLPA++9R6X5FSU9nQTJiROU2QTIAM+Ic7rv3LnDA8Az2cuGDWmMXEXx8ADOn6ds7E8/FbuJg4MDkpKSkJeX9+wDy5dTSbeRsbS0RIcOHXiGYTBy5Ei23qlTJBif5++/qVT/4EE6B2OgUAAffaTftjodMGAAfZ5Wrybn8ZcotO/cuQOlUsm6N2xImeuHDyl49M475Ch+7Bh9xtu2pcqKEsRWnz59JCqVir19+/aLD37wATBjhn4HFB+Pf8LDcaJvX3z0xRcozoyMYRiEh4fzhZVdRZFKpWjatCkXHBxs1P8gnJycMG/ePEyYMIGpV68eExwcLKxYsUKIiIjQe40ePXrIJBLJ6CVLlkw25rHpi1Qq/bxhw4YqmUymWbx48bTStlUoFO1MTExueHp6Dp05c6aFm5tb+YQ2x5HzeHh4hWfa//vvvypBELYWGqmKiFQ1RLEtIlLFYRhmSVpa2tHAwMAHWq122Ny5c7tyHGeTl5c3MCQkZLGfn1/amTNnGK1W23vu3LlNfX19D+l0uk4A2AULFvSQy+Xhr/ocKhu5XP4Pz/Ojtm/frvnuu+84nucrbV8Mw8DOzk7w938tjNxLp2VLGkf18880isfFhQS4pSWVmOfnUzYuNBTYvZvGhHEcCYA3gKtXr+LS+PE4U78+fvnlF9SsWZP/8MMP8e6777K+vr7Qy9inKPPnU0b0889L7VEuk8LXt2tXYOlSEjU3b5Z/vVJwcHBgAWDTpk1PA1V2djQPuiLvc04OueBXrw5MnEjl0MWs17VrV4ZlWWRmZj77QOPGRg0qFJKbm4vQ0FBWEATExMRQb3ZxQtDUlKo9Hj2ia8MY48B+/ZVM+fRBpaJrbehQKnEvre/ZSPA8j507d/JLly7FlQUL8H5cnGDPMNR2EhsL/P47VSsA1MagRyBKEARwHFd8GfnKleQ5UBbBwRC8vXE3OVnw+PrrUqdOZGZmMgBlO5+nTZs2koSEBBj7/weGYeDk5ITevXszs2bNYgYMGMAcPHgQ+/btE/TZl7m5OUaOHGnOcdyGb775JlehUFT+LMsimJiYDPT29jabMmWKzMrKatWyZcsOKBSKZkW3USgUzNKlS2fKZLJTgwcPrv3222/LTCvi8aHRAD170vi3ChAdHY1bt27lajSaF0sdRESqCExlZ4FEREQqF4VCIQNgKpfLc171sbxqFi9evIDn+SW+vr7li7bryePHj/Hrr7+ib9++8PHxqbT9vDQWFHQZLF1KplRt2tCIsDlzADMzoH17muW9Ywe5lzs4ALNnk4i6dYtEYSW+3sYmNzcXO3fu5OPj49nqNjbcJ3PnSu5v2IDGFcnm8jzNOndwqNh88sxM6hWOjaXSSjOjTv57gRMnTuDcuXPo168fzUIuxNmZRF55S27j4ymbXTir+/BhcmcfMeKZzVavXs25u7uzb731FvNMFYFOBzx+XH5DsRLQ6XQICAgQwsPDmRH//CM0bdKEwaZNpT9Jrab++9hYEszScnbgzZlDPehlZex/+YX6xzt1osqGSgg6FCU0NBSxsbFIi4jgB/r7sylz5qBxQgKsc3Ioq15OBEHADz/8IKjVarz//vvMM27dy5bRFIBpLyZSU1JSEB4ejvT0dLRNT4fjoEHIjYjA5suX0ahRI65r166S2rVrF/sdHxwczIWHh0vy8vJgbW3N1ahRA++//76EZVkIgoDVq1cLPXr0YFq3bl3u89KH5ORk7Ny5U1Cr1cK4ceNYxyLeDTzPIzc3Fw8fPkRycrIQGxubGxcXJ2VZ9rhGozktCMIauVz+0m7Oly1b9mjy5MmOheP3QkJCdKGhoVoADziOC9JqtQ9NTU3ftbCwqDd+/HiLGnpMbyiVVauoV3/v3gotk5qaio0bN+ZrNJpecrk8pGIHJSJSeYg92yIirzlyuVwDwAhpl9cflmUb1K1bV8cwTKV+txXeONWqVasyd/Py8PEhMQ0AdetSOfmuXcA331DvbGAg/b4wC3X5Mhk9Xb1KmfCbN+mmvGlTKqGuwuTl5WH9+vWwsbHB3LlzYWZmJkGvXmhcASdcXLtGI9ciIspvYPXDD9QffPw4cOZMuZzFDeXUqVM4V+Bs3apVq2eVy+HDFTuGPXuAhQuf/mxrSxUUw4YBLIsDBw4I0dHRAgBJly5dXizXv3KFXMGzssp/DMUglUrx1ltvMQ0aNMCFpCS4DR6MMp0JTE0psLR0KZVTm5qW77WZOVM/H4TLl6lMe/DgEsvvK8rNmzdxbO9ePlutZgcePYoeOTlCup8f65KXB9ehQyloVEEyMjKQnZ3NdOvWDQkJCc+OxkpLK9Y8MDAwECEhIXB0dOSbZWfDackS9s/YWGRVrw4LCwvu9u3bktu3b+Ott96Ct7f3C8/v3r27pE2bNli9ejVq1aoluX37NhYvXgwfHx/07dsX3t7eOH/+PNe6detKnSZRs2ZNfPLJJ8yhQ4ewceNGjBo1CnFxcdpbt27lZ2ZmmjEMozUxMbmm1Wov6XS68wDOyuXyCpoklA+dTle9sCxfJpOhW7du0i5dukjv37/f5PHjx02USiXv7OzMuru7G95WUxymphV2+1er1di2bZuS47hZotAWqeqIYltEROSNQafT8S+jl5rneTAMAwcj3JBWCd5+m5yO+/aljFObNuTKPGQIiaZDh4BFi8ioqU0bEt4AZWAfP6Z/cxyQkUFznydOJOFoYkI37ZWcnTWE+Ph4MAzDT548+anxma0tCUN9SlqfRxDo/GbOLJ/QHjcO+PRTKqksFB9t2xq+Tjlwc3PDmTNnwHEckpOTn+3b3rSJjq1DB8MXjo6mTHDRflwfHwrOPHgAvl49XL16lfH29mZ69epV/Ai1Vq0os15JNN65E6dNTJj4xo3RUJ8nuLhQxnnTJip/Dg423GleoaBM/ddfF/+4Ukml2rNnA7NmGebSrS8PHwIPH+La3r3c9LVrJVn+/jDr3h3VHB2Zms2a0fVtJGrUqIG+ffsK165dEzIyMlitVotOnTpR9czs2UAxwcpz585h1MiR8Nizh8WkScAHH+BTe3vk5OTg4cOHEpVKhX379kGlUpW4XysrK/j6+kKn0+HkyZMIDQ1FWFgYunbtitatWzOnT5+WpKWlPTEHrAwEQcChQ4fyIyIiBEEQLP7++2+tTCbbotFo/gAQXZXmavM8b2LynEs+y7Jo2LAhGjZsCBir5TQ5mXwIjh+n79xyIggCdu/enZ+fn79z4cKFG4xybCIilYgotkVERN4IFApFIwAfDR8+vNL3JQgCTE1NcfnyZXTt2rXS9/dSkEjI0KpoJlEmI9fr7Gwq+fz4Y3q8Xz8yVCvKsmX0t1pNwtXJiUqG09KAoCASsjNnAkVc9F8FpqamUCqVbEpKCoqWduLgQToHQ8rhdTqaD7xyJfVp60tGBs08X7CASsXz86lUv3lz/dcwAs7Ozpg0aRI2bNjwosN+QgL1K5cHFxcgMvLZcmuGof7tVatwoHdvmJubo0+fPpCWVJItlQJ+fpQBq4RZ23xAAMzr10exY61KY9Ikep/i4uj4Zs3S/7nDh5duchYSQoJ840YamWas1oycHLpeZ8yA8O67uJ2airsjR0oyQ0LgUIkl6gzDwMfHh/Hx8WEiIiJw5MgR3sfHh2UnTgTc3ekaKIJSqQTLsnDOyXn63hd8X1hZWcHKygoA0FyP64RhGJiYmKBfv37o1asXfvvtN2HVqlWMqakpLwgCe/78eQwaNMio58txHFJTU8HzPK5fv66NioqK0el07QEoX2ZZuCEoFAopAJZlWSQmJuL69et48OABl5OTw0gkEkydOvVFF/nyEh9PTvwVENoAEBwcrI2Li4vWaDRGmE8oIlL5iGJbRETkTcEDAKKjo3U6nU7arFmzsrYvNxKJBIMHD8bevXvRtm1bWLzE2beVyrp1NNpnyBDqp5MUVFpaW5PQnjyZHIkvX6byVn9/4OzZZ9cwNaWMKEDZbbWaBHdwMLmeb9xIZdf+/vRYRUx2DCQrKwsXL17kLC0tGRsbm6fZGmdnICqKstSGoFZTX20x5azFkp4OJCWRA/zmzTRH+jnB8TLZvHkzHx8fz7q6uvKurq7PZq/8/ctnkJaTAzRo8MTQ7cGDB/jzzz9ha2vLd2rfnm31ww+4bWWF4ZMmlSy0C/n3X8r4G1tsx8QgZtMmJO3bx0ulUsOzdu3akVfBli1UFVK/vn593DodjTsqjn/+IUO5mzfpdTeG0F62jK7hgQPBHzyIbTodl9Svn0St0WD06NFw8PCo+D70pFmzZjh16hSzc+dOfoyfH1tctYuZVIoRO3Ygv3VrWEVEGG3GuVQqxfTp05m4uDio1WrWz88PTk5ORllbp9Ph8uXLwpkzZ5R5eXlmUqk0g2VZFcMwdzUazWi5XJ5X9iqvFE4qlQrfUVUP4+zszLVo0UJSt25d7N69Wzh//jz69u1b8b1Mm0bXyi+/VGiZqKgohISE5Gi12rcKWuhERKo8otgWERF5UzgCYGhkZOSkK1euDLp06RLHsmy+iYmJ6eDBg03Mi5nxWxE8PDzg4uLCb9++nZk8efLr4w5WFo6O5ISckfFiFloqJZfyu3fJKEqtBg4coP7SOXOAevVeXM/UFKhdGzh9mn7WaEhs5uZSGenGjVQ2ffcu0L9/uUQGz/OIjY3F3bt3hbS0NK527dpM165dn/RkZmdnY9u2bUhJSQEAyZQpU2D2/M2+ry8JnrAw/XY6axaVya8pcywtnWu1ahSs0GioLP/ePf1PsJIwMzNjARQ/S3zKFAqyGNozrFQCX3315LNTuG56ejr774kTYN9/X+jk78/YFjMn+QUuXjRs3/qQng40bYo6N2/CxsYGy5cvF9q2bSv06dPHMGXn4UF95UFB5Fuwa1fZZnKFffDduj37e0GgNbRaKsEvrxcEx5FYHzuWPmNaLWBnh4e2tjj+ySdISEiQdPLxEdLT0+Hk5PRSv7MYhsG7777L3B4wgOHu3YOk0JSxyLHHhIbCRKOB0Lix0YR2USQSCe7fv88zDMPWK+67ykA0Gg02btyozMnJCVOr1V8BCJs/fz5X4YVfInK5XPj2229zR40aZdWgQQMwDPPke7N27dp8QkICC6BinxW1mr7f3d0rtMy9e/ewf//+XK1W20cul5ez7EZE5OUjupGLiIi8cXz77be/CILgodPp/gGwfOjQoWjRooXR95Oamorff/8dzZo1M3pJ4itn+3bgwYOnTuXFER5O5cIzZ5Ioa9iQymT1rSpISaFS9T17yHzqzh0SHePGAf37Q6VSged5mJqaQlKQZec4DnFxcUhISBAiIyOF1NRUlud5mJmZ8Q4ODoyZmRmTkJAgfPnllwzHcbh27RqOHj0KAGjTpg1atmxZfFYrJoacwPV1KV66lLLa3buXvh3Pk2g9cIBGSVWCiCgPPM/jwoULOH78OMaMGQP352+ET52iY+/Rw7CF588HZs9GJsvCz8+Py8zMlFSvXh0ajUZQU5UDM+7oUdQ+cQJMWaLy44/pGH77zbBjKIvMTKB6dRw+fFh36dIlqZ2dHTdjxozyGWYJAvDtt2QMmJxM7QAl8fgxBayK9gpnZlKW3NOT3M4Nrci5f5+CXF27UoDj119pasCUKYCZGYKCgnD69GnY29vzrVu35n18fF5ZkoXneYQNHy4kWFsDQ4YwgwYNglKpxKXTp9Fkxgyc7tgR7PDhGGWMGfXPceHCBT4wMJCtWbMm37t3b6OI7SNHjqivX79+TKPRDK2qZeL6sGLFissjRoxoVdCf/YTMzEysXbsWtra2nJWVlcTW1hZeXl6oX7++/osHBFAwduXKCh1jbGwstm/fnqfVavvJ5fJzFVpMROQlI4ptERGRN5pvvvkm08bGxuqzzz6rFJUTFhaGgIAAzJ8/vzKWf3Xs20c37WXNExcEEt2//UbzuJ2dqfw3MdHwsVE8D+6zz3DWyornHj1iWu3fz2yaPh2WaWkw8fDgBXNzISUlRWJiYsLb2NigcePGbKtWrWBubv7EZEulUmH58uWwsrISLC0t8ejRI6ZLly7o2bOnfufs7Fx6WfiZM8C8eZSpL004HzwIfPYZiaG4uKemclUAlUqFU6dOITQ0FG5ubny/fv1Y++erGO7epfnTAwbovS4fEwPOxwdbFQrhUVoaY2tri5SUFHzwwQewt7fH7du3cfDgQUxt0AC1nJ2pRLw0jh8nsd2/fznOshj++YeMuSIjAQAbN27ks7Ky2MmTJ8OmtF5qfYiKop7sn34iF/XimDOH+lW/+urp76ZNo5aC7dvJlFAfQkKoYuTxY8piX79OgauGDSmwU4T169fzPM+zM4qa1b0qbtyAYGKCuxIJzp07x8fGxrKm+fmoZmWFLuHhOOjpiRqOjsKMGTOMmnVXKpVYs2YNxo8fj7pGug7z8vKwZs0aFcdxteVyeYZRFn1FLF269Pt27dp91rdvX5PnH0tKSkJycjLS09Nx5swZAICLi4tgbm7O1KhRA126dEGpPd2zZ1NVT0mmgHqQmJiIrVu3KrVa7WBfX9/Aci8kIvKKkHxdgQtAREREpKpz7tw5H6VS6ZGenq5zc3NjCzOkxuLx48eIi4vjO3bs+OaUkgNAkyZkcDZzJpW1ltTjyDAkUAcNoozduXMkAt55h0Qpw+hfGs4wuO/ujoNRUUyD/v0Z1z590G3GDLRZuBC1HzxgzLp2Zd++fh09vvySadOpE1OvXj2YmZmh6HsqlUrRvHlzODo6MhzHMYMGDdLLUAkA8OWXVHpb2pzsO3coO1lSxnf6dNpm5EjqNW7WrHRTrFfA2rVrhZiYGMbExATTp09nivUc2L+fxpHpMaJHpVJBp9PhdEAA/F1d4dKggTBixAimW7du6NatG6pXrw6ZTAYnJyd0794d1UxNaf72oEFUil8Sjo7U52yo63dJWFiQ0V+B23t2djaTl5cndOrUqeLXroMDeR3Y2lKQpXfvF/0ILl0iMdyuHf2cmEiftZEjaRJAacTHU5VJnz4k6nU6cv2fPZvOycnphf0lJSXh7NmzTI8ePZ4du/WqGDkSzOPHsHv3XbRs2ZKxNzMT+svlTKeBA5E8dixu3r6N0aNHM9WrVzfqbnNzcxEWFobIyEghKCiIiY+P55o3b16h4Gt4eLgQHx+/f+HChX8Z6zhfFUFBQckZGRkTfHx8ZM/PLreyskKtWrVQv359eHl5oVatWrCysmIkEgn/6NEjPjAwkM3NzUXDhg2fnXuu09F1sGiRQQG750lJScGWLVvytVrtaF9f32PlXkhE5BUi9myLiIi80cyfP3+IQqGYfePGje+bN28ONzc3o65/48YNrmHDhpU6s/WVwTDkRH7pUtnl1QwDjBlDQmDXLvr3b78BixcDq1bpPX/70qVLfN26ddGtX78nN8Ps1atw4jg43btHhmIqFZWbW1kBP/5IPdFFMnq2trawtbWFwSZ5hw+X/JhGQ0Jn06YXM5cqFY252rCBghR165Kwq4Ry2IoSFxeH3NxcBgDs7e15lDTWZ/RoGgdXBtnZ2VizZg1M1Gp8tnYtUr/9VnjnnXdKFzIeHlT1EBtL/y6JM2eA8eOB1NQyj6NMDhyg92nqUwNjrVbLK5VK4wXJ6tQhZ3m1GjhyhOZkFw1kjB791HTw4UPafvBgCmyUxIQJVJru5UVtHXl5dD2WQVxcHP744w84OjoKbdq0efWBQEGgipdCQXbvHjydnRls3Aj07g3nDEoOp6enw9XIhng1atRA165dOaVSiQ4dOkjWrl0ruXHjBry8vMq1niAIuHjxYp5arf7RqAf66riu0+kyk5KSLJ2dnUvcyM7Orui4NBagz9mWLVtw7do14eOPP2aeVIiEhVGlk7U14uLikJ+fDycnJ1g/V3lRGnl5eYUZ7em+vr6lfDmLiFRtRLEtIiLyRqNQKLwAfD906FA0aNDAqGsLgoBHjx5J2hVmqt5E/vyTxM6nnwJr15bdcyyTUWnrW29ReXnt2tST+t135EL+998lPvXx48e4c+cO+15xwlwiobnDJ0/SzxMmUCDg7l0q+z50iG7ks7KAgQPLd66CQCLw2DHqoS1KWhpl+IsKgfR0uqHs359mSCckPDtbugri4uKCZs2aITIyEu3atStZhKWlkdA9darETfbv389fu3aNdXR0FJzMzJjzQ4dac3fgAAAgAElEQVQKXUeN0k/YTZ9OmdkzZ0qufOjTh8rwjUFoKAVlRo9+8iuO49isrCxkZWVVvIy8EHNz+owXGLHhp5/IhRkgMz1LSwpA5eUBJ07Q6LhCBIECAqdOkZleVBT1Y7u7A507lx4Meo6zBVMCxowZwzyfrXwljB9P57JkCQWuevakcvqPPwZAAbK33noLnpU0iqxLly4SAEhOToYgCBV6vzMyMqBUKrUAzpa58WtAgUnawZiYmGnOzs4GfVjq1q2LqVOnIiQkRFi/fj3j7e2NXo8f00SCCxdw+epVHDlyBBYWFoJSqWQYhoFMJhNMTEx4U1NTVKtWTVKjRg3Y29vD0dERtWvXhpmZGXJzc7F161alWq1ev2jRoi2VdOoiIi8FUWyLiIi86dwCgP379+PYsWOqNm3asL169ZIZY+Hk5GRoNBqji/gqh4UFCYD796kvVB9sbak0tnlz6l9ev55MnHJzgYkTIWzejMu3byMzMxNpaWlcYmIim5eXx3h6egr169cv+4avaL9vXByV0C5fTu7QffpQ6eJ33wFt2hhUxo5vvnmxZP6zz2idHTvo5/R0wMwM+P13YOdO2ld4uH77eMWwLAulUglPT0+hZcuWJb8wFhYk/EohtSDj3LFjR6b5unXA6tWM3iXfbm7kzB0cXHJJvlQKfPQRlbNXZFRTfDwZ2j0XKGratCkuXryIffv2YcKECeVf/3kYhloqfv+dqi/27yfB/c47FIyqV4+ug7Q0EtihoZS9rlOH5tp/+inw88/l7nVNTEzE/fv34erqalAmsVLp0YOqGAICqHXg/HlqPymAYRh46ztCrwI4ODjAw8NDOHPmDDN27NhyrZGTkwOpVJo4f/78N8b0SKvVnrh3797YLl26GPyBcXR0xNChQ9kWLVrgwJ49QttlyxjVDz8gRhAQFBSEMWPGoFGjRowgCMjPz0d2djaTlZUlyc7ORmZmppCens5fv36dyc7OZvLz8xlBEGBiYsIDWKXVauWVcLoiIi+VqmGLKiIiIlJJyOVyLSiw2DA/P7/fuXPnhAsXLuiMsfaDBw9gbW3NvTBG6k3DwoJMmFJTye3YEGrVImfk3btJXKxbB9y6hai7dxG3apWQcfUqpFKppG/fvsznn3+OoUOHGp6Gs7Ymse3rS2Kb40gcy2T0uwYNyGgrOJgyiqUxZszTMWUAiaHUVAoyFM6dbtmSzmPOnNdGZBeyf/9+PiEhAR4eHqVnPO3tKUPLlTzJqH///iwAXNy/HwgMfNZluywYhly8C+Zxl0hqKjl2VwQfH2o3eA5XV1d0794dKSkp5Rgorge9e1OwaeVKcmnPyCBztp07gS++oH//8gt5IxRmtJcvpzaEwYPLNQbv2LFj2LRpE7y9vbkJEybghZFur4KgIKBFC3ofPv2Uxt6VUq5cmTAMg6ZNmzKJiYnlfs/T0tIgCEKUMY+rCnA2MTHRjOfLfynUNzXFp3Z2zFV/f35jbCwCAgLw3nvvoVGjRgDotbewsECtWrXg7u6Odu3aoU+fPszo0aMlU6dOZb/88ktm5syZkEgkPMdxP86fP9/3dXZ5FxEpRMxsi4iIvPHI5XIOQAyAmCVLloQFBgZ2bl/aiB49MTc3h1qtZnierxo3tZXNo0fUlzx1quFCwM2NHJfPnYMyLg7pc+ag74MHjGXLlkC/fpQtLs3V1hDMzUm0AECjRk/Lyj/6CPjgA8pE//UXsGwZbVuU+/fJFGzoUOox3b2bjvvePRL1qalARMQLrs/FodFonrikVxXu3r0LnU6nXz97t26UiSyhr9rFxQUsy8JUpaJyfkPNB+vXJ4HZowf1uhdHYCAFPCrCjRslmtR5e3vjzJkzlXfxmpuT2MzMpKBPbi71c//xB71ePXtS5UdWFgVz4uIo0KHT0XNlMr2utdOnTyMoKAgAMGjQIKF169ZVx0fim28oGNavH70XpZnivQQyMjKE/Px89v79+4aNsSpApVKB4zgjGAlUHeRyefKyZcuyMjIyHOwMCZoVZfFiSFJT0W3XLraltzfy8vIMMubLzs7G5s2blQzD+C5cuHBV+Q5CRKTqIYptERGR/xQcxy3kOC7YGGt5enri1KlTzL///osBFXBcfW0YMoQybhs3Ug9p06YGPV2j0SAM4E/VqcN6u7nxlrdusbh8mXpZf/uNyn05znDRVhrm5kCHDvTvu3dJuF26BERH001/x47Uc/3ll5T19vQk4Q+Q8L5zh+Z+b9tGPbNmZvTnOeLj4xEXFwcLCwukpaUhIiKCy8rKkpiamgrNmjVj+vXr98qEN8/z0Ol0kMlkMDMzY6ysrAQAZSu4ZcvKdAKXKpUYtn49+KlTwRo6VsnUlD5TKSkli+0BAygLunGjYWsDZFbm4QGcPUuO3cWQnp4OrpTsvVHgOPrc5ebSz35+1FIhk9HnnmGo9WHbNnIoHzmSKkmcnKgEvXZt6m/396c13n6b/AE8PYFjx/BIEJBgbY2h6elo5O4Oi+vXGQQHk6nauXMk3jt0oFFulpbU4qFSUTl7fDwFuerWJQ8ES0v6fDMM/Vsqrdj1qNGQB8KYMeR38IqFNgA4OTkxABlwlYeCCQj2ZW/5eiGRSOIyMzPLJ7b37KG2nQJDQBsbG4P64nNycrBp0yZlfn7+kgULFohCW+SNQhTbIiIi/zVuAyRAKpqNZlkWQ4cOZf766y907ty56vRHViYMQyZld+4A33+v99Nu376NnTt3wtzcXHhv7FjUq1ePhVZLwnfaNDJKio0l0XX5cuku1RU9/rZtaeYyQD2x1asDFy+S8Lt0icbV7NpF84zd3an8FwC6d39hudzcXOzevZtPSkpi7ezseI1GA0tLS6FTp06SFi1aID4+ngkKCuLXrVuHmTNnskXngV+5cgXXrl3jcwtEmEwm46tVqyZ1dXVFr169jHK6PM9j6dKlkEqlwrx58xiNRiN06NBBvw8+wwDJySUKVQAQWBaHBgzAYDs7WJbnAIcMoVLrsDASgc/z9dd6VREUC8tSNUOdOiVuEhoaypubmzPQJ/hQHubMocz1rVvUe75/P/Wq79xJ/dmjRz89vnffffH5a9aQuzlA1RYaDYnfjAzqB2/dGud27uQdPDyYFq6uDDiOBI9MRq8bywI5OSTSY2NpHRMTCii1aUMzvjmOTA0LzQdzciiz3rcv9bqnp1OlSGHQIjWV9jFwIImsvDyaDhARQaI9Pp4e9/Gh4Jy9PRATUykvb3moVasWAJQ7yFK9enUwDFNJX1CvDkEQYrKzs9sY/MRLl4D//Y+u4XIYz+Xm5mLz5s3K/Pz87xcsWLDM4AVERKo4otgWERH5r2EilUo1DMMYJc1Yp04deHh4cP7+/pgyZUrVKd2sTP7+mwTAmjVkHqZH0CIyMpJ3c3Njx4wZ8/Q1MjGhjNvFi7TWxo3kUm1vT5kwNzdyL65Mis43TkwkkXDzJomNn3+m8V3Xr5O5VRF4nkd0dDT27dsHFxcX4dNPP4WVldULL0TDhg1Rv3599q+//uJ//PFHvlq1asjOzoZKpWJr1KjBe3l5Ma6urgzDMMjMzGQTExOFs2fPMo0aNUJdQzPFxbB27Vqe53lWo9EwCoUCANgmJWWRn2fTJhLb7u7FPhwXF4fhu3ej7qFDsLAsl9Sm7OmQIcC+fcXP9K5bl1zsDQ2+pKZSXz2dc7Hk5eUhMjKSNao5WiGtW9O18dVXJLgjIkiwHj1KLuRZWRTQOXmSROuaNZRFfh6GeVpJUbSiokAwwsUFd8+eZRs6OAjFzjMu2i5TTLAII0Y8/ff48S8+/sknNHtepyNBzjAkxlUqElb165PYdnIiIW5jQ380GioZz8kxrJf/JXDnzh0wDAMXF5dyPb927dpQq9UeCoVCJpfLNUY+vFcGz/Pp6sLAjr7cukXVL+HhNGveQLKysgqF9qr58+d/bfACIiKvAaLYFhER+a8h6HQ6WUJCAuqUkvEyhH79+knWrVuHqKgoNDWwtPq1hGGov/Snn6gMW4/+9zt37rAdCsu5n0cmA+bOBR4/pv7oX38l0dutG/VKz5lDvdOVPcKoUDC2a0elzYUl5YmJ4DkOGjc3BL71Fhfj6gqdVstkcxw7cOBAoW3btqUGWViWxdixY9krV65Ao9HA2dkZtWvXhkwme0acu7q6IiMjQ6hWrRpft25dowRulEol27p1a75Jkybs9u3bC05TT2F84MCLPe1FuBYQgHZZWTCtaFBgyRLKjI0d+2KJfkAAIJcbnhkND6fnfvNNiZvcvHkTUqnUoL7SUrl/n8Tp0aPArFlUGl6YrU9KArp0AWbPBo4fpwoOmYyCGSNGkHFYXByVmBuYHbSwsOBiYmIkarUapqamxjmXopiY0J/Cz0LRCoRC0Q88bQVIS6Py+HnzSJRLJHRdDxliPF+GcpKSkoJDhw4BAMrbm2xmZoaaNWuqHj161BfAGzP/WavVphkktnkeGDSIPrszZxq8v5SUFGzZskWp0WgWLViwYLXBC4iIvCb8Bxx9RERERJ4il8vjAazdvHkz/vrrr5xTp04hPDwcoaGhiI+Ph0ZjeKLC0tISvXv3Fo4cOcJXxM31tcLCgvqenZ1JlJUBwzBCmaLG0ZFE18SJ1A++aBE5iBeaRK1bRxnLykQQKKseFkaZ9s8+w9nRo7FixQohyNtbMPH2xrC8PMmny5ezCxcsQNtr1xhERpa5rFQqRbt27dCpUyfUq1ev2P7tnJwcnDt37tnsfwUIDQ2FRCJB3759WTc3N4wbNw7Tp0/Xf4E1a0gwlUA7Ozv8/tFHyM7Pr9iBWlhQSfS+fS8+Nm6c4UI7I4N6okNCSt2sXr160Gq1uHfvnmHrP8+5cxQMsrenPnSNhrLERYVofDz5AowdS5UgAweSE/n9+1RevmABlYnHxFDpdZT+ZtfTp0+XMAwj+Pv7V3LzeRnwPF2f9epRv/zYsVSdIpFQVt/f/5UeHgBs2bIFANCtW7dSt9PpdCjtu7xt27bWpqamcxQKRRUYYm4cBEHIUavV+k3q4HmqXDh+vFxCOykpCZs2bcpXqVSfiEJb5E1HzGyLiIj8F5kPIDMmJuZubGxsSxMTE1ee5501Go0PAFSrVk3Xt29f1svLS++AZJs2bZiLFy8KBVkTVXx8PJ+WlmZhb2+v+vDDD83MS8kQvrZIpVQe+8knZGRVivmRtbW1EBMTw7i5uZW9rrMzZQAnTQImT6aMWkQElXV7e1NpqpUViRtjc/s2lf5euAA4OSFHq8X5ixcxYtQoxu2rr8AwDAnhOXPo/P38SFwxDPWenzhBr0M5svDJyckwMzPjnJ2dKyS2lUolrl69ioCAALi4uAimpqYMQCXtBlGv3lOzuOfJyoLVxIlo9d13fI0aNSoeuH/vPRKagvDia9e6NQVd9O3dnjqVMsbBwaVuZluQoXUoR/krACpvb9aMggSpqdRqsH9/8dvOmQOsXUvPKWTLFhLbMhllwefPp3L5Dh1oPYWCggYlzSEvgGVZdOzYkQkJCZFERkaiadOmKHWsW2UxfToFOM6doz71iAg6R4YBrl4lgbZlC1AZZft6oNORjuzTpw9atGgBQRBeeJ3S09Nx+PDhvAcPHpixLMvb2toqXVxcTKtXr24mLSjz53keGo0GarW6CwBeoVCMkcvlO172+VQCMqm0uF6GYpg3j1z2L1wweCdRUVHYv3+/UqfTvevr63vQ4AVERF4zRLEtIiLyn0MulysBfF3w47aijykUiu65ubkD9+7dO7tmzZpwLMONuZACszR2y5Yt0Ol0ZgAmALBIS0tbeffuXXh5eRnxDKoQ/fuTYAgNBVxcqIfzObKzs5GcnMyOHTvWsLVr1Hja1/rXX5T5YxianR0bS/Ow1WrKJhqLzEwqd2UYYNcuBE6bhulHj8LywgWgoPwUwNNy2MBA+vv+fTL6kslIHHp7U5l9UBDNW9ZD/GRlZUEqlZZ7zhXP8zh58iTCwsKg0WjAsix8fHzKr7qGDKHy52I4cPy4kD92LPqNGmWcCrmWLUmQOjlRa0IhDEMzzlUq/cX29u0UkCkDlmUhk8kQFRWFzp0763+sWi0FWnr0AP78k+Zol4YgUJVE8+YUVFizhs7L2pp+16gRZbPv3aNgzZw5lA0OCAB+/52ug/x8MhwrgSZNmuDSpUvcnj17JLdu3eJHjBjx8ioX//6bjnflSurtHjQI6NQJmDKFyukByuanpNC59e5N3xUvmaNHj/JarZYNDg7OOHnypKmpqSk6d+5sZm1tze7evRvdunUTwsLCVCqVaqUgCN9xHGeakpLSPCUlpRXLsk4SicQKAM/zvJbn+XwAaSBjvX9f+slUAlKp1FavNoSYGODzz6kKyYCgTsH3k/bixYtZWq22n1wuv1yBwxUReW1ghIrOrxQRERF5wygoDeQB4OOPP0bNmjX1fq6fn59w//792AULFtQHgOXLl/+gUqk+mzVr1pvtVj5mDN1o7979zK9v374Nf39/2NnZcdOnTy9/xpbjaPRWYiK5386eTaKnfXsymzLWWK2vvyZTtILe+2teXvBMTIRk+XJyttbn5jIpiYIAPE+Oz9HRVGqv01EVQDHwPI/169cLHh4eTO/evct16Nu2bXtSEu3l5SUMGzaMqVCG09+fhOHz2StBQIKrK3R//IF6RnJNB0DC9fFjEmRFUalIsOpTHdKjB7UhvP++Xrtcv369kJ+fL3zxxRf6idPjxymDnZlJ768+icDffqOKiVWrqC/bxeVZU8HYWPILSE2l/nSeJ7fywvdu927yNDh+nKooSjG4S0hIwKZNm/Dll1/ComAMU6VReP/4v/9R8GnxYvr577/JeHDhQro2/fyePqew1DwkhII5L4n4+Hhs3boVPM+v5nl+Duj7vR+Afwq3MTExuajVahfL5fI3pg/bEFasWHFiwIABvTw9PUveKDgY+OADCgo1aqT32vn5+di5c6fy4cOHkWq1eqBcLk+p+BGLiLweiJltERERkeeQy+WCQqGwB/C/X3/9dV6vXr3Qvn17Rp8Ku/T0dC2AxMKfVSrVLIlE4rNmzRrvd999F40bN67EI3+F+PmRwDx0CLndukGl0aB69ep4+PChUKtWLaHCTu0SCd2cq1RPy8z79aPMskwGtGoFfPEF9fiWF52OBP2UKQDIbfvo4MHwSkwkc6vOnUnETZ1a+jpFe9MzM5/+nZtLzs0eHpS1rFXryZim69evIz8/X+jZs2e51XFeXh68vLz4YcOGsRVT2QX07l2sk3TosWNwlMng2K5dhXfxDB98QJnfmzefFZRdupCTtj6j5oYMofdJT9q1a8ccP3687Ndq0iT6nP38MwlFltXLhR8AZdkLS9W7dqXPWFFB4+pKArRLFypD53n6DM6eTU7w77xDme+HD6nXe+pUalkoJvjg4uICMzMz4dixYxg2bFjl1pK3b0/l72vW0M9ZWfQ+BQaSidq8eXQuRWFZenzhQjonI5a75+fnY8WKFejUqZOud+/eT76sY2Ji4O/vr9TpdJPlcnkR5Y9/FQoFC6CBXC6vYOP+641CoWCkUql3qaahO3aQ2N60ySChnZycjG3btinVavVWjUYzUy6Xayt+xCIirw+i2BYREREpBrlcnqZQKHwFQbhy5syZeSdOnGjdqlUrZdu2bS2srKxgZWX1wnNCQkKQlpYmA/BOkXUEhULhA2BuRETEosaNG78xzdu5ubmIiYnBo0ePkJmZyZkzDNNt2jT2WP/+uOvpKXAcx3Acx9SvX994rnFmZiSo3n4bWLaMsmrffkvio0cPyggGBZH5lKFkZwMrVgC1a+PkyZM4c+YMmrRooWMHD5aiaVPaT8uWxfcVl0VhtlarJaHh4vIkW5l75QpS581D308/ZSsy+10QBP7evXus0fp1zcyAY8dIUBVw48YNPPD3R81du2BRGZUa3brRzXzR0uy//9avhPzrryngUsy1WRLm5uaQyWQ8z/MvvvYaDTBsGLUwDBtGgQeWpT5tQ/D2prJqgMR2cVUY9vbAmTPUJx8fT9uNGUOtElZW9BxXV6rqyM0F3nqLXpP9+18Q/Z06dWICAwPh7u5eOdMRDh4EevUi07NWrZ7+PjmZro9Ct/LVq4uv5CicL144t7u0TGopFLZKFAZBb9++DQDIzMx8cm9769YtYffu3TqO44bJ5fLjz68hl8sFAP9poV2Am4mJCWtTnBO+SkWu/i4uFAAywPuhsD+b47iPFy1atNWIxysi8toglpGLiIiI6IFCoWgMIBogZ+0+ffow7du3B8uyUKlU2LBhA5eRkSExNTX94quvvnrBXXXJkiUrrK2t/zdhwgTJ615OLggCzp8/LwQFBTGWlpachYUFa2try2i1WjSuVQueLVvC7PZtZDVpAkEQYG1tjYqIyFJJTiYxdPcuCYBCM6ZvviGH54ULKXusD99+S2OLVq3CkiVL4OTkhA8++ICi0j4+wObNlLVu1w44dQowwui4/NRUHAkMFLp+/jkTP2cOWjVoAPb336k/XE9RHx0djT179gharZZp3749179/f+PMe8/NJeGkVgMMg7S0NGxYuRJf/P47ZJcvG+X8XyAtDThyhDK4hVn169eBK1co810SOTlAixY0s90A4zyNRoNly5ZhxowZT0dBJSXR+K5Jk4Dhwymjro+xX3HExlIQ6M4dqs4o9DYoqWc5MJDE6N27ZLTXsydVU0yb9uxnITWVHPMzMsig7fvvSaiDWhIWL14MlmWxaNGi8h13SQgCia1ly0gwFxIcTL4FEyc+/Z2nJ1W8NG9e/FqjR1MQ4a+/DDoEnudx+vRp7syZM7xMJtPVqVNHV69evWqhoaGcUqmMZlnWrlu3bvYtW7aUrlu3Ll+j0QyQy+XBBp/rfwiFQvGBh4fHT6NHj352NtvDhxS4lEgokGXA6LaLFy/yJ06cyNBqtX3F/myR/zLi6C8RERERPZDL5bcBeAAwEwTBNyAgQLl48WI8evQIWVlZyMjIkABoVJzQBgCO435SKpXBa9eu1fj7+yvLM2KsKnDnzh2sXr1aOHv2LN555x3MmjVLMnXqVGbkyJF477330LZnT5hFRgIDB8JGKkX16tUrT2gDQM2adBP46adUBv7bb5R5TEujXleOIwGWm1v2WikplDEEORanp6fzK1euxMnTp3n14cOUaZw1C/jwwxfnQZeTa3fv4k5MDCL9/BDM89hx+rSQ7+GBGzduQFuzJjK/+Qa6tDTKcJYQHA8MDOS1Wi3zv//9D/369TOO0AboxlqleiLyzM3NoZHJcGbRIr5ShDZAAvvmzWfHgF24QNnu0lAqybjJQIf6+Ph4mJiY0Ozx5GTqGQ8JIXM7hqHjKK/QBigrvXo1iRWAxnwVNdp7nl69gPPnqd0gI4P65q9epeBOkRnISgsLXLSzw4bHj/lT6ek4fOCAkD9qFPjLl8GyLFxcXIzrSK7RUDDj5k0ycisqtAFyGS86Ao/nyXm9JKEN0Ln9+SeJbQO+D8PDw3Hu3LkHPM83V6lUPnfu3JkWHBx8NicnR8px3AitVrsoNDSU27t3r0oQhI2i0C4bU1PT3vXq1XtWSZ87R0Zo7duTKaWeQlur1eLgwYPqwMDAJK1W6y0KbZH/OmJmW0RERKQcKBQKlmGYPwVBKNokbCKXy0udU6pQKKxlMtkBHx+fTt27dy95VlYV5MGDB/Dz80O3bt3g4+NTuohOS6Nso4UFCeKXRUQEOZjfvUsivFUrulns2JEMqjSa4o22cnKo99TX95lf37lzB//++y+nzMuTzNi////snXdYFNf3xt87u0tHEAVEFDsoFuxGsQQVC3aNNSrGqDFqTKJJ1BhdNlFjirHEEo3d2BV7SVTQIGLBhgWxIEGlSocFtsz9/XFAOiwKSX7fzOd59hFmZ+7cmR2Qc8973gPz4cNp3GPHKLsXGPhGdafHjx8X09PThVGjRiE9PR07d+4UY2NjBQsLC24RE8NSZTLUjInBOwcOYM+qVWL9XbuYun590XTsWJlWq8WlS5eg11N75c8++4yCxoqkYUMKitq2BThHUq1auDhjhn7AvHkVF9QX5u5d4OBBUiXIDDjNwYNkXpeYWK7PQq/XY/Xq1bxe7dps4NChFEx6eFBAXFEsWUI15F270vfnz1OtfuPGpR83bx49Y3fu0DW9/z79TO3ejXS9HqtWrYKpqSm3sbGBQqGAXKcTa2zcKNObmiKjQQMxy8xMuGdpCaVS+ebX8PQpdRmYO5de1tYF3w8MJPm4qWmepN3fn3qNP39e+th6PdXnL1lCtellkJWVhVOnTiEkJASCIGxcsGDBZABQqVRyAEsArACwBsBgY2Pjo9nZ2e8qlUoDVtr+2yxdujTS29u7toODA23YupUWn9q2JXWFgcTGxmLv3r1qtVp9Njs7e7xSqUypnBlLSPz/QarZlpCQkHgNlEqlCGCcSqVaBaAqAAUAvQGHZmm12jbPnj3T5Rzzr0ev18PX11f/8OFDWYcOHeCeW39aGtWqUX3fkyeUlf27aNaMMttnz1JWZsqUvLZL69aRvDwmpuhx589TgF6IRo0aoVGjRrKgoCDsjI5GyydP9K0HDpTJt24lMyhRNCwgLIQoiggODsbdu3cFz5yaaAsLC0yZMkUAAMbYq8Xw6OhobHnrLdFMLuctatUSnltaCpeOHuXv/Por4xs3ooqZGRyaNq34QBugWuWcGtw7QUHgNjZwHjiw8gJtgD7D1atJmTByJGXXGzSgWubiFniGDiWZfzkXPVJTU6GOiWED5s0jx/irVyu2jRxAHgJt2uR9n5hIjuRlBdtLllC9882bZJL266/A6dPA99/jjEajb+jmhhEjRuT/HGR83DhER0cja9kyofr27Xjq7Y2DPj7cc9Ys9tqlKzEx5Mz/9CmwdGnR91NSqCXf2bO0WJFLgwb0s1YWMhlly/V6atlWSnvAS5cu6fz9/fVyufyWXC731+l0r1ZFchY5v0YHUHgAACAASURBVAAAlUq1kDHmlJ2dPTinJluiFFQqVVWZTGZvb29P6pn58+lnbsYMoH59g8bgnOPq1aviuXPnsvR6/XRRFLdJ915CgpAy2xISEhJ/IyqVqhmAO23btoWbmxscHBwge41g7e/k5cuXWLNmDaZOnWpw33EA9Ad0QgLJs8trKlURcE5Z7txAoE8fatXk4AA4OZH5V26AEBZGdbAlLCSIooiAgADYT5qEGlott376lIEx4MMPSV5piFN2Pu7cuQNfX1+4urry4cOHlzs1rktJwaWpU/FiwAD96M2b6QH64w9g0SKSupfDJKxUbt4kZYKjI6JXrIBvYiKmqVQVK1EujosXgQULKEMKAB9/TIZcheX7CxZQO7jNm8s3/oYNUG/bhjWDB4ufd+smoH37ipl3fjIzSd5uY5O3ELB4MXD/PgWWhuDhQbXYW7YAACKWL4fFkiUwDQ6GeZ06JR8nikg8dQra997DI29vvPXFF7jz/Dns7Owgk8lgZWUF09JaqT1/TkHX1q1kHFiccRZAUvf09KL1+8HBVI9dmow8P+HhpEAJC8szWCvwdjj27NmTpNVqOymVygeGDSphCCqVqr+jo+POSUOHVsHEiaRI6NXL4N8hGo0G+/fvV0dGRkZoNJpBSqWy6KqlhMR/GJmPj88/PQcJCQmJ/wwXLlxIEARhwIsXLxxu3ryJ5ORkTZMmTf6xaFsURezevVt/7do1npKSIgiCAJlMBkEQXsnEAwMDxZSUFHTv3r18HaUEgYKj9u1JTt62bSVdRQkwRkF+9+6UqVm8mDJ0PXsC9vb075dfktHU119TVi23RVMhVq9ezUNDQ5nQsqXYqH59wSg6mrKTxsZ0jfldmQ3g8uXLYkZGBiZNmvRaUatgYgIjd3f4nz8vJA0YoHf+8kuBpaeTO/vw4VTjvHUrMGDA67mn5zJlCmVjW7eG+bBhCGzSBNZOTrAt4T5VGLVq0b1Vq+nrpk3JMKxw5lkUSZbdvLlh486cSc+iiwvuR0fjkaUlOo8cWTkrB5s3kxQ+v2lYu3a06GNozf+oUfScBgRAV7s2fg0MFGt88QWr/e23FKCWlNFnDKbOzrjm4oKr8fFoNm4ckoKCsCc1Fffu3eMXLlxgwcHB/OHDh+KDBw+4TqcTYmJiYGlpCTkAlpZG9dRjxlAP8OJYt47k+19+WXQOU6fSIkivXoZdZ9WqVJ6RlEQ164Xq5Pfs2ZOWlpY2RalUXjBsQAlDuXz5srJzzZptHBYtYhg5kuT8Bj6farUaW7duVcfGxh7VaDS9lUrly0qeroTE/zskGbmEhITE34hSqdQDaA0AKpVqVVhYWDG9cf4+Ll68KEZERMjatGmDW7duISgoCGJOb9wOHTrAysoKN2/eFDw8PF7P6Ewmoz+eq1en4MBAWWKFYm5OxlOtWpFcvFUrChAyM2k+nFMGfv16qtctFDj4+fnxxMRE1rRpU/3Qd96RYft2Ctpq1CBDNb2eFhJWrDCox7NOp8PDhw+FZs2avZG0zMHBAR988AFWr14te/vtt1El11wMoIUAuZxajVlbU7smZ2dy2u7QwfCTfPcdZTUVCgRv3Ii0W7eg0WggimLlGt8JAn0O+/fTfPv2pR7q8+bl7fPrr1QL3bNn6WNpNLT48OGHFLynpCDUzg6Hq1XD+BEjKi9F37QplVLk5/RpUkFcumTYGLlBzwcf4KqXF6/WsiWad+pEwegnn5CZW4sWJWYhPQYOhHufPtBNmYLWMTFodegQ2P37LGDMGP2LrCyZtbW1LD09Hf7+/nq1Wi1zvXEDnmfOIOPhQ9jnqgpKomdPqtUuLtjfsaP8CzwyGXDkCLB2LS1I5CMuLs4CwNnyDShRFiqVyrLFvXvDWhw5wrB9e7n6Z6ekpGDLli1qtVq9QavVzpJk4xISxSMF2xISEhL/HB+6urrKHj58iNTUVJiZmeH48eNZCoVCMW3aNJlxRdePFiIsLAyBgYFs7NixcHJyQu/evV+9d+bMGTE8PBzZ2dliw4YN5W3y152WF0dHcibu3p2Cwb/TMC0/NjZU39u5MxmhRUZSbWmNGoBSCWzcSPudOgX06IGUzExs2bJFzMjIEHr06IHOnTuTAqFrV8qAd+kCJCdTMDt4sGG9oAH89ddfUKvV6N279xsHeubm5pDJZHj58iUK1OV6eeV9fe0a9WjeuJFqoR89onrMMWPIOK40Hj2iAP2nn9D6t99wycpKf+TIEVlMTIzYp0+fyu1oMngwKRGSkkhOXvj+bt9O2W4Xl+KP1+lIDi2TkXJh7NhXn3Hs+fPc1NSU16tXr/Ku4cIFYPr0gtuaNSv7nhfGxATPjhzBnwcOsA8tLEhdYm9P/cfnz6ee5OvW0XOcj/Pnz+sVCgXc3d1lRjntxlijRoj38cENPz9Zz3PnoP7yS/R5910gMlKGK1cgzpqF3XPnii7Pnwv2hcbLJTIyEsZjxuCei4uY2Lev8Nbz56hRo8arftcAgIEDqe1XeWvFp0+nrPjZs0CDBsisUQN3794FAAYg4euvv167cOHC6WWMImEIjMmHNW++U6hXT5Dv2kUmeAby4sUL7Ny5U63Var+eP3/+d5U4SwmJ//dIwbaEhITEP4RMJnt88+bNxiEhIamCIDzXarWuMpnsRmZmZqfw8HA0adKkUs8fGBiob968uczJyanIe56enrlBSMUEI25u5FpsZkamSiXVgP4d2NkBv/xC9ch79lBQ1Lo1zQ8AJk8Gfv4ZGY0bIy0hQTCztsZbb72Vd3zdulTv3b17Xub+q6+Ahw8pwx0YWKrRVv369WFubo4TJ05gwIABb3QpJiYm8PT0xO7du2Fvb6+3tLREeHi4rG7dutzd3Z3Vrl0bzNWVdv74Y3oBVJ+u1QLHj5P0/MEDantlb1/QhCw0lFpPDRgAebt2+KRDB9nNmzdx4sQJwdnZGfXq1au8+u3cmuI1a8hkLDSU5gqQEiEgoPjjcr1opkwhN+9r16hfcD4CAwNZ3bp1Ky+rnZAArFoFfP55we21ahlex5yDKIrYf+aM6OHsDKuZMwV06kSeAwDV6B88SH3B69cn074cLly4IAMAhUIBJycnmJmZISY2FgdsbeFWvz6E588Re/Ys52lpjN26BTx8CGH4cNj06SOcOnUKkZGR4ttvvy3IZDJEREQgKioK5ubm8Pfzw1C9HqYeHoJWq9Vv3rxZZmxszD/99FNmZGRE9z8rq6hruaHIZMCyZdDWqYM1DRtm6nS6QJlMdlev11/lnJeRbpcwCMYEkbF9qVZWnvV++EFRnkD78ePH2Ldvn1qn0727cOHCw5U4SwmJ/wkkgzQJCQmJfwiVSiUD4ArgsVKpzFSpVMZKpTJ78eLF39SrV2/2yJEjTSvLPE2tVuOHH35A7969CwaSlc2nn1KQFBz8952zLL75hjKgMTEUIDk5kQR23Dho7t3DTyNG8KZNm/IePXoIZmZmdExyMtCvH+2fm/XXaKjf9w8/lJnRu3LlCgIDA/WzZs2qkA/42bNniIiIQGJiIszMzHDv3j29Wq2WabVaDB8+HK65AXdh4uMpUPP2Jglp794UwK1dSxJoIyPKhg8cmBfgAfj+++95ZmYmGzJkCFqUM3gsF4mJpESYPp36bW/fnmc6dudO8bJXR0dy8x45kuZfjNz93LlzuHjxIgYMGIDWrVtX/Lx1OlrQKJwdTk2lBZmwMINl1qdPn0ZYWBifMWMGk6Wn0xjh4UC3bnk7nT0LzJkD7NyJCBMTnDp1SkxMTBScnJzw9OlTGBkZca1Wy0RRRJcuXdC9e3dER0fjr8GD0T4jA4KtLWX9MzOBZs2wfv16Hh8fzxhj4JzD3Nyc29nZ6SPu35cPv3oVdfbvh3FOMM05x44dO/QJCQl4//33ZVXkcmqlVx4zxcKIIv744w+N0U8/XX77zJm3If2xWnEwVgfA7t+9vG7FjxzpPXb8eDNDD33y5An27t2bodVqeyuVysBKnKWExP8OnHPpJb2kl/SSXv+il4+Pj9m3334bqFKpxMDAQJ1er+cVSXx8PP/xxx/FFStW6GNjYyt07DLJzuY8JITz+Pi/97wlceMG5wMGcK7Xc37/Pudt23I+Zw7nUVG0LS6OJ127xjNNTfmSr77iAQEBeR/GxYuc+/pyXrs255mZeWNu3Mj5u++Wetrw8HD+3XffVewHWwi9Xs+XLVsmXrx40dAD6Dru3ePcxYU+q+bNOQc4v3CB88uXOb9yhXNR5C+eP+crVqzQ+/j48DNnzujVanXlXcixY5yvX19wW2Rkwe9v3OC8RQu6hoAAzrXaUod8+fIl9/Hx4UuXLhUreLbEmDGcL1lSdLsocr5/P83TABITE/nixYt5ZP7r3b2b81q1il5jQgIXFyzgl3v2FA/u2cPj4uJKHjgigocFBfFtY8fyp5s20bwOHuS8USPOb9/ma778Uty4cSPX6XQ8/+8f0d+fi+3a0f750Ol0fOPGjeKiRYvo+W/VyqDrKwlRFPmPS5ak6wThBQd68n/B7+X/iRfQhAM3E6ytuy5atCglJibG4M/kyZMnfPHixRk+Pj6d//HrkF7S6//Rq3LrrSQkJCQkyo1SqVTPnTvXnXPe+cyZM7KEhIQKGzs0NBS//vorGjZsiI8//liw+7vrp42MqG61Y0cy3/qnuXCB6pkFAWjShLKn9etTT+Hly4HMTFi3bg2TXbswYsIE2E6ZImDPHjq2QwfAxwfo35+uK5dWrYpmNAuRnZ2NzMxMISsrq9IuTafTITs72/B2bbnu8Y0bUyaZc+CvvygDW60aZbinTAFCQlCzSxd8nJAgzLS0RK25c7Fp/nwcGTQIu0ePxr6tW7HBx4df/PNPnmu290bUq0dGaVWrAjduUElCrVr03ubNVBLg4kImagBlwuWlV8mFh4dDEAS8//77lSMld3EpmHnOhTFg715y6jaA3bt365s1a6avnb+11qhRJPsPCADu3Xu1WbS2xummTblZXBwbbGcH21Iy5+LAgdB89RV/2rAhrltZiWCM/AyuXwfs7TFh40ZW/+BByIA8I7wnT8CqVwe7cqVIVl4mk8HExITrdDrcsbcns8A34NmzZ9AAyc9q13YCEAjGRrzRgP91GJODsRkANgJwXzt7ds8GDRrIDf3dkNN6Ta3VavsolcqLlTpXCYn/MaSabQkJCYl/Lw8AICQkBN27dy+1NjY5ORkxMTHIyspCtWrVUL169SJ9dE+dOqW7deuWzNPTk7Vt27aSGyWXAmPAsWMkBf6n67ebNyejsFwEgQLKlBTqhXzgAPUR/vhj1LO0xAUHB9ja28MmMBA4c4ZqtQGqJR4/nmq/c19jxpAs29u7yGkb5cifAwIC4OnpWSmXFhYWBhMTEzQs1EapRBITqT67dWuSwvfqRZLy8+fJWXvHjrx9g4MBzlE1OhpVjYwERy8vaD/7DFHPnuHhzZsYffQouxkUxJ9bW3On6GiGvXvJpMzdnWrdHzyg81hZlS2nbtqU7mHr1vTMDBsG/PYbMGIEmbeJInkBLF1q8L158OCB2KBBA1a9evWK/zlITaUFi5KM0O7epXkX7k1diKtXryI9PV3Wq7j2WebmwP79EB8/htrXFwkJCfD39xf/+usvQbFsGZo/eEAlDkuXFjSQGzsWmD4dD7ZswcFjx9iUKVNQo0aNvMSLpSVgaYmtM2eK9atWFTB8OP1M7N5Nbb6aN6dxi8HT01OoUaMGnmzejLC6dfUd6tWTOTg4FDROM5AHDx6IOp1ue92ICD0YawRgDRg7Ds7V5R7svw5jNgDeB2ALoNfXKlV/Y4Xis759+5bSaD2P27dv8xMnTqi1Wm0/pVJZglGChIRESUjBtoSEhMS/FKVSmahSqVyvXLly3srKqlqbNm1k6enpuHfvHvfz8xOrVq2qr1evntH9+/ez0tLSjEGOvVAoFHFarda2WrVqam9vb3MTExMEBgbi+vXr8unTp6Nq1ar/8JWBgpHISMDVlQK3unUr9XQxMTE4ceKE2LJlS6FFixZQKBRk4rRgAdUsF8bKCpg2jUzDfH2BwYMhfPIJ0qdO5esvX2bvMobajx6B+fhQUJqeDnh4UECYS9u2FAQWgyAIMDExwcOHDyst2L5//z5q164tAii9LvzhQ8DBgeqcAwOpnVRoKPWE3rMHyM4ueoyNDf1brRrQrBksAcDXFzYAmgHAypXAhQvsj/Pn+aQJEyiIs7Gh+3TnDvD993R/f/mFgs8bN8iYbuBAOu+1a/S1Xp937MWLQM2atMDh5ETPTO5iRzmIiIhAeHi48HGuWVxFc+wYtSUbUUIyNiAgz/ytBLKysuDn58cHDhzITErqebxmDfZs3qy3GD1aFtasGSzr1MGcOXNgYmJCbblsbclNPjSUFn1MTUm5oNfj3pMnxQ756NEjHDt2TJ+m08mys7PRZ/Vq4I8/gMOHqX1fKffMzs4OPXr0gH7ePNyUy9mOHTug1+sxbNiwkj0DSuDx48cZoihST23OQ8BYDQC1wVgdcC712jYUxqoB2AXgDIC5Kh+f+gq5fIu3t7epVRmLnGJO3fyNGzcStFptL6VSeffvmLKExP8akkGahISExL8clUrVSi6X/ymTyUx1Op1GJpO91Gg0BwAYKRQKF61W+zOAU0qlUpvvmIYKhWK5VqvtD1Bw16JFC3h5eVGg+W/hjz/IQVmnKzEwrQh+/fVXMTs7W1Cr1WJmZqbw4Ycfwk4USRq9aFHZA6jVZH724gVCJ0zA0fBwXs3Ojo8dOFAwadCApOO//krBedeuecelpVHP4AMHKKDNx59//olLly7xuXPnVnh2VRRFLFu2TBw0aJDg7Oxc/E7R0WRiZWsL/PwzMHp0Xpb58WNg2TJqkbZgQV5bNANRq9VYuXIl9/T0RKkqCq2Wsry2tsC+fZTt1WrJCG32bOol/fAhqQjc3HIvrvw9nEGy+uPHj+ujo6NlycnJfN68eZWj7khIILO9pk2Lf3/QIGrN9sEHJQ6xdetWvUwmw9ixY2UlKVpSUlKwetUqfHL4MMxHjCCDtMJcu0a9yQMDgRMnSFUA4M6dOzh58iTPyspiHTp0EPv06SPcuXMHvr6+8PDwEDt16iTIZDJS0yQkAC4u0A0cCPbRR5Dt2UMZ8ubNi5885/QSBBw7doxHRUWJH3zwgcFGgGq1GsuWLdOIomitVCozX73B2AIAA8F5O0PH+k/DWBcAXwJYAM6DVSqVYGxsfK1bt24tO3bsWGoZaXJyMvbt25eRmJh4Kzs7e6BSqUz8eyYtIfG/h5TZlpCQkPiXo1Qqb6pUqq56vd6Lc/79V199pTXgmMcqlWoIgBYAqsnl8l63bt367N69e9keHh7Gbm5ukMvlkMvleTWZ/wS9elHt9t69VC9aCW2kkpKSEBUVJbz33ntwcnISFi1ahKCgILH77duCZdu2hg1iZgZs2gTcuoUmv/0GF72eHX35ku8ExPeSkwXhyhXKZGdmUmYzt37YwoJkvBkZBYZLT0+Hv78/3qh/eTHodDpcuXIFwcHBorm5Oatfv37BHUSRMtXHj5MsODGRgu78NednzpDaYN06Wjy4fbvc8wgICICtrS1v27Zt6Q+XQkE18wAwYULe9j59cgeiwC02luaY23Yt9/1ysHPnTjE6Olqwt7fHoEGDKq+MYsQIWrwoicGDqb97CYSFhSEqKko2Y8aMUktHLl68CD3nYP7+uH7rFkzGj0ewhwevWasWe6WWaNeOgvDBgwu0BYuIiBCzsrKEmjVriq6urgIAaLVaGBsbo2vXrgU+s0xBwP2PPhKPA4L1mTPoc/u22CA1VZCPH0+qg2bN8nbWaGjB5NkzwMgIbm5uLCQkRHbixAn069evtLuWf24wNja+/MUXX2QWeIPzb8DYEjDWF8ANcB5r0ID/NRiTAXAH8D2AMeA8HADkcrmqWrVqLm+99VapP5N3797FsWPHMkVRXKTT6b5XKpUVYLwgIfHfRQq2JSQkJP4foFQqbwK4Wc5jdABu5Hx7RqVSfaHT6br7+fntOnfunBXnXBBFUc4YgyAIekEQdDKZTJTJZGJGRoZF9+7d0aVLlwq/liJ89BGZk2k0pfanfh20Wi327NnDq1Spgpo1azIAGDt2LPz8/JCwdStC3n9f5z54sOH/F7ZsCbi5QTh7Fv0fPxYerFmDGFtb1KxWjeavUNDCgacnmXQxRiZeV65QpvbwYdy9dw9HjhxBjRo19F5eXhXW200URSxfvlzknAtt27ZlnTt3Zq/qZUWRam9bt6YAbM0ayq7KZPTKz759JNcGSHpcjh68uWRmZsLCwqJipHOMkTlaRga1+nr5kuTnU6eWa5iIiAjBzs6Ov/feexUyrWLR6YC4uDwDt+Jo3JhKGIpBFEUcPXpU7NGjB6tSpUqpCwJxcXEi51z4ccUKWAJ47/ffkdK2Lc5cupQbNNOOnp4k388XuNvY2AgA4OrqypxyWro5OjoiOzsbL168gKOjIzjnePHNN9Ds2IErn37KB7u749GjR/jD1BRZWVkYd+oUamzZQiUAaWlkLJiVRS3xchZvnJycMHLkSOzcuRPt27eHra1tmbcwLi6OZ2dnF99WinM9GPsSwHkAC8oc7L8G1WdPB+ACwB2c6wFApVLNlslkXw4fPlwoaQEnOzsbJ06cyAoLC0vQaDSDlErl9b9v4hIS/7tIwbaEhITEfwSlUskBnAPwyoJWpVIxzrlcr9eb6PV6Y61WawzAhDF2xM/Pr6m7u3vlZ77NzKg+9623KNP99dcVNnRoaCji4uLY7NmzXxk11a1bFxNHjBAe6vW4kZYmuJd3UMYg9uiBVbdu6as3ayYbtWwZBaXr1wPt2wMNG1KWNjHxVT34MwAsPp4fWrYMKVlZzMvLC61bt67QJupXrlyBQqFg06dPh0KhyPuLOjycFgkePQIOHqRMslxeMJudy9GjJKvPdanPDXTLSUxMjNikSZOKeXBOniTZckICMH8+ZbsfPaJ6//yS/VIIDAwUAQhDhw6tXGPAqChSNpTWZ93fH4iIoBr/Qhw+fJhbWFigXbt2Zc6zX79+QmZmJhwdHenZnj8fnf76i7F58/gtOzt07do1b4zgYDLwCw0FALi7u+Pu3bv6qKgohhyvh6CgIMjlctjZ2SElJQWHDx8WzR89Ym3HjGHTpk2TAYAbSfmFJUuW4GbHjmLf+fMF3L1Ln4+PDzn0f/ZZgXk2yMni37hxA7179y7rspCQkKAWRTG8lF3eBiCCsTEAdkOqhyQYcwXgCfo8x+Xel2+++WaCsbHxIhsbG2zYsEEcNmyY0KCQsuLp06c4dOiQOjs7+5BGo5mqVCrT//4LkJD430QKtiUkJCT+w+QE4NqcV1rudpVK1dLIyOhZdHR0DUdHx79nMitXUmZbpyuzdZMhcM5x8eJF1KpVCxYWFgXfPHUKdU+cQErbtsKDBw/QuHHjco19//59aEVR5vXTTzAaMYJqjpcupblrtbR4MHkyBbrh4Th49y5X9+/PRj56hHq1akHIb6RWQcTHx0OhUECr1VJd/scfk+v3qVMURNvb06skEhOpjtjfP28/hQKwti73XKytrYWXL1/qUZY5myH06kXSdsaAd96hdlebNlEQfuECmaSVUX5w//595uDgYHgbtNfl22/pGS6t9ZW3N2WaCxEdHY3Q0FA2adIkZsgCV5G2fTIZoFajTWAgC3BxQXp6et5z36IFtQzLVTgASE1NZd26dXt1ohYtWuD27dvYtm0bj42NZf2vXGGuc+cyRTGu6nK5XKxVq5YAY2OgTRuqCRdFUkHY21Ppgbk5AIAxhpEjR8LX1xdGRkbwKGaRIT9JSUk6AH+VuANlt80A/AjgIYDgMm/W/yNUKpWDQqE4JwhC3Ny5c9826CDG2gL4AoAvOF+ZM45MoVD8YGpq+oG3t7dJ9erVcfnyZb537140atSIDxs2jDHGcO7cOe3Vq1dTdDrd+wsXLjxaeVcmIfHfROqzLSEhISFRhBwJ+s4tW7bo//jjD2QUqjmuFDp0oKxr7dpAWFi5DuWc49y5c+Lhw4f10dHRuHbtGn744QckJyfz/v37Fz0gMxNGw4ZhwIABOHjwIK5cuQK9Xm/QuXbs2CEePXoUwzIyxOpz5wJ+ftSK6sYNkgg3bkwtrlq1oiBx+nS4NmkCGxsb3qBrVwiV1Fvby8sL2rQ0aBs3pvr3MWOAxYspuMpXr1ssnANJSXnXkIsgkAy+nDRq1Ah3796VxcfHQ6fTlfv4V2zfTm3Vcmvbra1Jon3pEtVGnz9Pcy6hn7coilixYgWioqLYO++88/rzMJTRo4EPPyx9n6dPAZWqyOZ9+/bp27VrJ77RgkCzZtDcvw/n6GjuN2kST0hIoO3GxpRxzu0RT7C4uLhX39SvXx+MMbx48YL16NYNbk+fMoWlZbGnsbCwEH19fbFy5Uq+etUq/nj3bhHTpwNffEH3oGNHaiOX89k3btwYo0aNQnBwML799lt++PDhEnuwp6SkyFBasA0gpwVYbQAPwNhgw27Ovx+VSlVToVDcaNKkiTPnvAQHunwwJgNj7wMYB0AFzveoVCpzlUo13tjYOMzBwWHKtGnTzGxtbcEYQ8eOHdmUKVMQExODrVu3iiEhIbh27dpzrVbrKgXaEhKVgxRsS0hISEgUy7x58z4TRdErKChI8+OPP+LmzZuVr9isWpUCRFvbEgOowmRmZsLX1xeXLl0Sbt++LduwYQMCAwP13bp1w8yZM1mR4IVz4PJlYPBguLm5YfDgwTh37hzOnj1b6glfvnyJlStXIioqShg7ejQaqtUCunXLy6qamVEGOSGBelK3akXbQ0NRPSSE91+0iIlDh5JMe9gwkvZWFIcOQT5kCLr26sWuOTvjKUCLF4YawK1YAQwdCtSoUXC7QkHbyvm52+S0Blu7di0WL14MPz8/xMfHl2sMANSqqnr1gts8PYH4eAq8z5wB9u8nE7BiFksYV/yNugAAIABJREFUYzAxMYEgCK/mVGkkJ1MteUnu73mTIlO6fPj7+0On0wkeHh5v/HeZRZUq6NW8OWtx+TJbvXo11q1bJ166dAni/fvkG8A5YmNjoVarWW6/91xq1KihNxIE3v75cwqWS3AcnzZtmnzOnDnoamLCGt66xbBypfB0+HByrv/mG+oysHo1OaHfJKuJ+vXrY/bs2Rg/fjy7ffs221Mw8H9FRkaGKYDnZV4o1SO3BPALGCumJuL/FyqVqoqRkVFgly5dqr/11lsyxljpPcUZswYwCkBXAItUPj73lyxZMlcul8fWq1dvzZAhQxpMmDDB3KxQl4fq1atj4sSJLDk5mR09elSv0WhGKJXK1/jhlJCQMARJRi4hISEhUSILFy78Q6VSNQIw+ejRo1/9/vvvmc2aNTP18PCAeY5MtMKZOJEycMuWkbFYCZJanU6HnTt3IiIiAgDQq1cvuLm5QavVwsrKqmT5cm7v4RyJbdOmTWFra4v169cLERER4uDBg4XCAXpycjLWr1+P6tWr4wOFAibDh1NbpcLyZUEA+venXtITJ5KkvGdPNP/oI+FSvXowSkmB3ZMnVBOdm3V8EyZPBoYPJzVAq1Zo3bo1Vg8ezGWpqcxgWzOtFhgwgILYwtfDGLWq0uvLJe2vV68elEolAODw4cMICgpCQEAA7O3tuV6vx9ixY1mVKlVKddvG5ctUk124X3WLFoBSSQsJnTtTHfe0aZThdnMrUIvOGIODg4NoZmZW+cmFmzfpuSrLUb9DB2DVqlffpqSkIDAwEKNGjWIV1ZbPbOZMpL39Nrp8/DHC69cXgtRqflUm4+5HjwpGISHIylFXFO61PGXKFBl27KDM+/DhJZ8gKgomx4+j1cWLwIgROODlhXv37mF2nz4kXa9Rg1rhHT0KnDtHpnsffAChbl04OjrCw8OD+/v7s9DQUDRp0gR37tzB5cuXxdTUVMjlcplWqzVspY3zi2DMEUBDMGYFzq++7j37p5HL5bMbNGhg36VLF3lCQgJEUTRXqVRCsW7gjDUBMAW0KPGeysfH2MjIaL+1tXXfESNGmFWrVq3Uc5mZmcHBwSH76dOnfy5YsOB/SoYvIfFvQ+qzLSEhISFhECqVioFaygQAwLhx41CktVRFkZ4OrF4N/aefYv2mTdzCwkJs1KiRrG7duhBFETqdDlu3bgUA9OvXD61btzbcyC0ykloTuRe0RouNjcUvv/yCUaNGwcXF5dX2iIgI7NmzB9nZ2Zg2Zgxsg4MpUB8woPjxNRrg0CGSO798CTg5AV9+id9NTdG4XTvU+fZb6h3NOTBlCuDrSxlkQ3n2jEzkNmwApk8HhgyhQDmHFStWiM7OzszLy8swM7Bevaied+LE4t8fO5ZqpN/QKd7Pzw9Pnz7F8+fPIQgCXFxcxD59+ghVqlQB57xo4N21K6kDVq4sOtjZs3T9+/blbZs+nRYOVq4ETE0hiiLCw8Nx8OBB9OzZs8LbrBUhLo6UGWV9lklJ5Ar/9CkA4JdfftEnJibKrK2t+cSJE5mJiUmFTSl2+HBY16kDtmQJ/vzzTzHu4EFhwK5d2Ojjw+3s7PiYMWMKulMnJtLnnJ0NlKQE+P57ylwvWEDyfgsL/PDDD6KNjQ0mTpxY1O06M5PUB3fukGJl9mxwQcCuXbvw+PFjNG7cGBEREdzR0REKhYI9efIk7MsvvyyfiQJjywG4gfPu5btD/w5UKlUVuVweNXXqVPNq1aqBc461a9emJyQkzOWcr83x1iAY6wegLoAUlY/PXgADjYyMfmrYsKHt4MGDTQ1ZsPHz89NeuXLliUajaV2gl7mEhESFIwXbEhISEhLlQqVSVQXwefXq1T+ZMGGCaaVluAHca9oUiba2SJ8zB0+fPtWnpaUJGo2GMcag1+sxf/78Vy7jBjNmDLUnevfdAptXrFjBMzIy2Pz58wEAarUasbGx+O2332BmZoZPbGwg8/GhQLmswHPtWmDLFlo0CA0F5xxHJ09Gq8hIOPXoAXTpAixZQu2s9uwp3bgsl8OHyQTL1RV4/30yPjM1LbLbmjVrRHNzczZhwoSyg22dDvj8czIZKykb1r491aUXNpl7TZKSkqBQKLBt2zYxLS1NqFq1qhgTEyMU+CxFkTLEnBevbBBFkkSPHEku9gAF2j/+SBnvt97Co5gY7Nq1CzVr1tS///77skp31W/fHpg7l+T4pcE5yd6XLsWtkBCcPn0aH330EXbu3KkXRZFNmjRJKPczXRqZmVRH/v33tBiwZw8toBSXgffwIHVAYYM3zilDvWgRmeh16VKgvdmqVavEpKQkoUWLFuKQIUOKv9EvX5Jjv68vsGsXeKNG+Pqbb169PWHCBNy+fTvr1q1bXy1cuHBZua+TMTmAvgD+BOcp5T7+H2TRokVfOjs7zx8xYsQrzXdcXBz27NmToVaro7Kzs7+u8/Tp/gnbto0A4B7auPEp37Fj3RljU6pXry506dLFsnHjxqUrRXIIDQ3FoUOH4rRabXOlUhlX5gESEhJvhBRsS0hISEiUG5VKJZfJZJtr1ao1fMKECRWXistBFEWcPXtWjNmxQ+jTvj3sZswoW55rKF27UsCR20s6h9WrV+sTEhJkvXv35kZGRuzYsWOvsuWOqamYOG4c9RFu377sc2g0ZN7l7AzUro24hASsW7cO740dC6fnz4GNG4GLF8lR+/59+nr58qLjiCLJcSdOJMm4oyPVtJfC+vXrkZaWhhkzZqDULGl0NI154ADVRpdEv37Azp2v5UpeFufPn0d6ejquX6eWvn369OEd2rdnqFWLArPizO1yOXqUpOZLlhTcPns28OgRdL/9hiUrVmDWrFlF3egrAx8fMiEz5FxLlkAzcSKWb9vG+/bty1q0aAFRFLFq1Sq9hYUFGzJkiFCWFNhgOKee6rNnAz170ue+axd9nx9RJOO5Vq1eOYkDIHn8vXt0zLffUjBeCI1Gg23btkGtVvN3332XVS9cZ5+fFy+A994D2rZF8qhRiBBF2NjYoFatWli1alVaSkqKx2v3eGbsBoCd4Lz8wfo/hEqlUjDGMmUymfDee++xmvl+L3HO8ejRIwT9/nt64127TDRVqkSGtGnDUy0tHd3c3IR27doZGdK7PJfMzEysWrUqMysrq4dSqQyqjOuRkJAoiGSQJiEhISFRbpRKpU6v13//119/mbyW8VUZJCcnIygoSHCePh12kycDTZvSH/xvyv37lOUrFGgDwIwZM2TDhw/HpUuXxGPHjqFDhw68S5cucL5zB+N27aK6aEMCbYDqhlNSKANtaYlzJ07oAcDG3h7o1o0y35s3Uz302LEUmOdf/E5Opn7SyckkGY+MBLZuLTPQBoDMzEwxIyMDp06dKn01PSSE5lmWbNnJqVjzsYrg7bffRmpqqggAcrkcp0+fZolJScC6dSRvLw0PD5I6v3hRcPuPPwJTpiDhxx9hlppaKfMuwq1bgIuL4dl/X1/4/fKLaGdnx5vnmJAJgoBp06bJLCws2Pr16xESElIx2RDGSAXRoAE9a7GxJMHP/7ylpAD161OP+NxA+8ULYO9eymYrFLQwVEygDQBGRkbw9vaGTqfja9aswZYtW5Ba0r13dAR+/x1o0ADWe/eipb8/nMzNwTlHWlqaKYA3+UFvD2A5GBv5BmP83XhwzmU6na7IaiJjDM6MwTspycKtb1+53Y8/1u//wQcNvvjiCxMvL69yBdp6vR4HDx7MFEVxhxRoS0j8fUjBtoSEhITEa6FUKu8C+ObAgQMZFamSSk5OxvXr1zkAtG7dmoLBkSNJQv2mHD9OvadLwNXVFbNmzZJNnToVffr0YaZ+fkhxc+NyPz+S4JaHYcOApk2h3r4dcampMoVCwV9lWC0tKVi8cYPk7D16kPHX/PkkPV+4kNoo5QaTDRoYfNqRI0cKAFCtWrWSpQCBgRRA+fqWPWDNmiWa1L0OOp0OBw8e5GvWrOEqlQoRERGCp6cnnJyc9D08PESbKVNoQcKoDINpS0vKxv7xR8HtjAH9+8POzAwj9+yBSUVKskti0yZaMDGQ5999hxDGhMGDBxeocTYyMsKoUaPY4MGDcfz4cXbz5s2K+8GqXh2IiCBzvsKt9TIygHHj8pznV6+mPu2WlrTIM7Ls2NXIyAizZ88WGjRowCMjI7F8+fJX3QtEUcTz589x7do1hIaGIj0jgxaiZs+mz3DDBujWrYMM0CiVytfvjce5DkAVAD+DsTJs4f8dCIKwDACcnZ2zHBwcCr558iQtdnToAJOFC+HSvDnq1KkDWTl/F4qiiH379mU+e/bsT41G81GFTV5CQqJMJBm5hISEhMRro1KpGgJ4NGfOnNIlywZy9+5dHDlyBDY2NvquXbvKmjZtmvfmpUvAl19S/fDrBn8BARTA5h+3JPz9oZkwAb+MHAnXXr30PXv2LH+0Hx2NxK5d8ZJzaLdsQdMuXYruwznw+DFlRjmnetmZM19LNn/58mWcPXsWZmZmmD59OoxLqi1v25aCq48/LntQd3dg927KcFcAGzdu1KelpQlNmjSBpaUlc3Z2xqsMXWwsuWD7+Rnmfh4ZSUqFo0eLLMbExsbiwNKlmJaUBDZvHt3fyiIjo3RTsXyIoogHbdtymZcXd1m0qMQH+dGjR9i7dy9cXV3FoUOHVtxqx7FjpKq4cIEM0Y4cocz8woW0cDF/Pn3dogVQt+5rnSIwMBBnz56Fq6srsrKyEB4eDiMjI25qaipyzpGRkSGzs7PTOzk5CVZWVqyZmRnMDhxA+N693PH5c2cztfrxG10j1W9bAOgIzk+90ViVhEqlEgB0AXAeAObNmwej3AWm3N8DCgXQrh052L8mnHOcOHEi+86dO7c1Gk1XpVKZ/eazl5CQMBSp9ZeEhISExJsQr1Ao4s6cOWPVoUMHYzs7uzca7ObNm/rmzZvLBg4cWDSwbdqUMrxZWdTTurwkJwOjR79ygS6VFSuAd96B0bVrqBMUhLt37wo9e/Ys/zkdHJBlY8OtExNhZ2ubFz1nZVFG7/RpYNIkagO2fTv1KB469LXr08+fP8+7dOnC3N3dSzaOu3GD3LwLtX0qERMTqkGvADQaDWJiYmQzZsyAdeEacJ2O6on//NPwAZ2cSNr/66/A1KkF3jIzM0OijQ2CMjN5J29vhsDAilFHFCY1FahXr6icvQROnDiBmlWrslZNm5b6Idva2kIQBNy5c0cYNGhQubOZJeLgQAsDhw7R91evUt/ydesoO795MwXab0Duwtv9+/dfbRswYABr1qyZDCDzwStXrsgCAwOh1+vxwMlJfO/rr4WzmZnZk5cvPwDGTgD4AZwnv9YEONeBsc4A1oOx2uDcsFZifxMqlaqOsbHxwezs7DYAtR98FWjrdFT7b25ODvvFlLwYiiiK8PPz0925c+eFRqPpLQXaEhJ/P5KMXEJCQkLitVEqlSlarbb1vXv3AtatW4eMjIzXGiclJQUqlQqRkZGyzp07F7+TlRXJdb/7jsyoysudO+TkXVZrnNBQqqkWRcDODmZmZkhNTWWRkZHlPyeAU97eYrqHB0NyMgXXAAVnq1ZRyy5/fwqux44l+W5QEEnIXwOdTscSExPFEl2JQ0OpTlytNjyg79u3WNfz1+Ho0aOoVq2aWCTQBqiNV58+5R909GiSwxeqK7e0tIS3tzfOOzkxvy++INfyK1dec+alkJJCJQMGKDtevnyJO3fuoNbSpRDati1xv4CAAKxcuRINGzYUFy5cWHGBNkCqhpMnyfCscWOgSRMgPJyCu3373jjQBoA2bdrA29sb48ePx4cffoiZM2fC1dX11ftmZmbw8PBAp06dAACRkZECGINYr1728lmzPgIQBeAHMPYuGHuNlTUAnB8HUAdAczDW7I0vqoJQqVSNFQrFjc6dO7fsk/O8V6lShR7ev/4iQ0JPT1IXvEGgnZaWhk2bNmUEBwdf12g0nZVK5estXEhISLwRUmZbQkJCQuKNUCqVLwB4Ll269PDly5cH9ejRw6DjRFHEgwcPwDl/JXceMmQIbMqS4rq5key1vKjVZAxVEpxTRmnoUODu3Vc1w56enoiMjBR37dolzJkzx6D2OvmpGRws8IcPyQkaANLSyOwt9zrzm05t3kxBo05XrnPkMnnyZGzYsEFwcnIq2ldaFOmPd1/f8v0RHxdH9+4NEUURYWFh6NevX/E3cMwY6hleXpydqab44kWq9c6Hk5MTvL29sXPnTghvv827rVjB2Lp1Feus/vRpmYsjZ86cQXBwMNfr9axly5ai/enTAqKiKJtciJcvXyIgIAAWFhbw8vIq2rf6TfD3B54/J8dxX19qx/Xzz6R0uHCBnr0KCuzrGiBB79q1K6KiopCdnS3GxMQIDg4ORvcSEzuC8+/BWB0A0wHYgLEIAMdR3tpHynB/AsAGwKByX0QFo1KpmikUigAvLy+rli1bsufPnwMAgoKCZG9bWMBo7Vpg2jRyj3+Dzz0iIgJ79+7N1Ol0P+l0Oh+lUvmvyuxLSPyXkIJtCQkJCYkKQavVply8eBG2trZiixYtSlROpaam4o8//tA/ffpUBkCUyWRcrVbLatasqXd2di77L/2hQyk7268ftX0qwSG5CGvXUs13SUFAXBxJmGfNKmLO5e3tLXz77be4dOkSd3d3L/uv4NxWSYsXo9P58+xey5Zig5MnBbi60h/RJS0oLF9OGVJrawrEBg0CatZEWFgYTp48qdfpdEwQBHh5eQlNmjQpcOiuXbvE8PBwQRRFWBUnEVcqgevXKatZHl6+pB7Wb8jFixehUCjg5uZW9P59+imZd+VKm8tLzZrUB7pQsA0Ajo6OmDx5MrZv345gExNMdHaG2S+/wKSUfthJSUkICQlBhw4dYGRkhFJ7dC9YQC3UGjYs8lZqaipOnjyJp0+fomfPnqx69epwcnIS4OhI9enFsGnTJu7i4sIHDhwoKMpSYZSEVkvBtJUVGe798AOgUgHbtpGyY9AgqnU/cgT47TcqExg1imT8e/eSrHzevNc7dzmQy+UwMTHBkydPhPXr18POzs7UyMjIA8D34PwvAF+AsbYARgDoCcaWg/OIcp5mIgAZGBsC4CQ4/0ek1CqVqrpCofhzwIAB1rkO9LmLjG2vXkXq6dO8+s6dDPXrv/Y5OOe4dOmS/sKFCxk6nW7EwoULf6+QyUtISLw2UrAtISEhIVEhiKI4D0DdQ4cOdbW0tES9evWK3W/fvn08MTFR1rt3bzRv3lzIF8gYnlKTyahe19C2Y3o9ZfOKk+5yDnh7kzv4tWvFHi6Xy+Ho6MiTkpI4gJKD7fHjgQkTaH6XLgGcY9OMGbxt27YCjh0DPvqIJOSHDxd/fEYGSZIDAykwqlIFePddBAcHIzU1Vebt7Y0bN27o9+3bBxMTE16nTh2m0+l4RkYGS0xMFIYPH474+HjUqVOn6NgtWgCDB5d9rwojilRj/gZcvnwZf/75JwYPHly8MuC994CoqNc/gacnsH8/EBNDjtqFqFq1Kj766COWmpqKE8nJvOu5c6x2gwZFFmo0Gg0EQcDOnTuRkJCA8zkO4xYWFrxXr16sQYMGEAQBRkZGePDgAezs7JA8dSoeMIYX69frMzMzUaNGDVmvXr1gbW2NDRs2iBYWFmzixInM3t4+70TGxlTWUEg2HxYWhuzsbNa9e3dmUKDNOZCURM9T8+a0kLJpEz0/P/8MjBhBaoGffwZat6bziWJe3/ZTp4BGjWis9HT6V6ulF+eArS1w5gzVeWdm0rNbwXTq1AmZmZmwsLAQHzx4IIii2LLQNQaDsVsAmgHYBcbOAvg6x3m8bDjnYEwP4FsAVQFsrtgrMAxjY+O1LVu2NM8NtAHA1toaM/76C9f0emzv1IkN4hyG9x0oSFpaGnx9fdVRUVFPtVptP6VS+VfFzFxCQuJNkIJtCQkJCYmKpCsA1Cgm4AGAw4cP6+Pi4mTjxo1D7dq13+xM69YBT56Qe/XevaU7lO/dSxnlwhJZUSRZtyhS+61SaNmyJTt16hTr1q0bLC0t8944dYra8wQGUiZRECjD6u+P1NRUaDQaCpzmzgWePQPmzCn5JDVq0MKAXg9cvkz/nj6NtLQ0vYeHh1C3bl1Wt25dWbdu3XDlyhV+//59ZmZmhvj4eHTv3h0uLi5wKey6zTkwYADVuReWlhtC7dpvJGlVq9Xw9/fnQ4cOZfnrdl+xahXQsuXr1WvnIpdTTfby5VTTXwyCIMDa2hq2Awbo9/7xh9x72DBu+9VXDBMmAACeP3+OrVu3AqCexD169OCtWrVi/v7+PDExkZ85cwa+vr6vboRMJkP9hw/R6v59ZHz+ub5FixYyY2NjPHjwQPz5558FuVwOnU4njBo1CgUCbYAyyPv2AZ9/XmDzlStXuIuLC6ytrYvecM5Jsn70KKk7pkyh7/fvJ4dxBwfKTo8aBdSqRbL6XPL/rCUlUfu7ly+Ld7n+8MO8823YQMaEP/4IbNkCPHpEyo/Ro8kluwKoWbMmxo0bBwDCo0ePcOjQoaJBIgXWt8BYbwDvAvAFY2sBnAHnZTeBp4C7GQAOxoaB84MVMnkDUalUdRUKxYC33347TzKj0QD9+qFa377ouGQJrqxfj99++w1z584tuYtACTx48ACHDh3K5Jyv0Gq1Pkql8s2lKBISEhWCFGxLSEhISFQUIgBMmjQJpsUYaiUlJeH27duymTNnomp5e1aXhK0tBQ2JidRHuCROnwYKt90SRQrU69cnKW0ZNG7cGCdPnkRyfDwF202bAjNmUJDo5UXByc8/FzgmICAAoiiiXbt2FBBOmkStldq3L97kzdyc2mwlJQHVq0O8dAn6ESOQ9PnnQv4ArFq1avDy8hK8vLwAgO3ZswfXr1/nXbp0KRqkJSVRZrpx4zKvsVgcHQ0y/8qPTqfDb7/9JiYlJXGNRiPUrl2bN2nSpPiIPSCAxu/a9fXml4u3NwXtX3wBVKtW4m69evWSx8TE6INr15b1tbYG9u+HbsgQbNq0Ca6urrxPnz7MxMQECoWCAUD//v0ZctQMWq0W0dHRqFatGoyNjSHftQuoUgVNRo58tYrTunVrYffu3Xj48CF69uwJR0fHopPo3Jl6vheiR48ebNu2bfjaxwefuLvD6tIlUkN06wY0awa88w65yffqBSxdSoszNWoYLr/fto0y4Jcvkwt5794l78sYBfUAlV/kysofP6ZA8eRJ4JNPgIcPqQSgVi3D2rWVQlxcnJ5zXrJchfM0AL+AsQsAlABqgrH74PxymYNT/XZNkEP5JXAe/UaTLQfGxsa/dOjQQf6qPWJICBkibtsGtGwJK8Ywfvx4bN++HWvXruWffvqpQatboiji3Llz2mvXrqVotdr+SqWyEhwAJSQk3gTJjVxCQkJCokJQKpUxgiAE7dy5U8wqJDuOjIzEL7/8AmdnZ15hgTZAMmt/f6rX/f774vfhnDJxY8bkbdPrKUjv1IkydWWRkICAU6fQ+ckTsVbbtjTmt9+STLdePQqg82V/c1ruiDdv3sRbb72V14brrbeA+/epxVJJzJpFruQANty7p1v51Vfo1LQpb+LgUOIhXbp0QWpqKktLSwMAZGZmkjP8s2c01pkzgIVF2ddZHDdv0r0ykB07dvDFixdDo9Gge/fuMk9PTzZmzJjijb6ePaMM75Qprze3/BgbU8u2PXvK3DU+Pl6o3aMHlRVs3w5h3TpUrVpVDAsLY4IgoCQJt0KhgJOTE8zNzekzbdIEWLasyH5Dcozebt26VbxsXq3O66uu1wM7dwJr1sDx2TNMWruW975zh5sHB9P9kctJGr51KwXb27eTq37LlsVK5kvF15d6mAP0HEaXI97MvY6jR6n3eps25AMAkH/CrFl0XVOnFnGGN5QHDx6kZ2Vl/VLmjpyHgvNRAKIBjAdjn4CxWgYcFwWgBgAtGDPMyfEN+frrrwcYGxt36dKlixycky/EzJm0qNaq1av7Wq9ePXh6eqJu3boGZaXVajW2bt2qvn79+nWtVusqBdoSEv9OpMy2hISEhESFIYpiv8zMzMSwsDC45dTDhoaGYt++fbC1tRVHjx5dOYu8jFGmrjhu3yZpcd++eduGDiUTtP37Sx4zI4Pqrj09gcaNYdmrF9eMGsWYjw+db+DAEg8NCQlBQECA0LVrV3h4eOS9YWFBdeEbN1I2sDiztgULKHAEIIoic2nSROy2eLGA48fJrbwYHB0dYWNjI/7000+Co6Oj/sWLFzLGGHpfvcobZmTAcu5cZlTI9M1gMjJIam8AAQEBePr0KZs6dSrs7OxKd9IWRQoajxwBund/vbkVxsUFOHiQ5mxuXuwut2/fhkajQcOGDSmjvmEDhLAwDNy1S/itdm1oNBqYl3BsAbRaUkZcv/7q88rFxMQEI0eOxL59+/Dy5UtUL6y6yPUGaNSIeoSfP0/KjPHjcWzkSNTt04fLPT3zbl5xcu/ykJ5OQfKRI3nb4uMpG/262NsD775LX9+7RwF2RAQQHEylFO+9B1StCvz0E/WRL0VtAJDSOzY21gTAdYPnwPmpnCx3R1DG+gI4L2HV7dUxOjDWD4ASjDUot8N5OVCpVFUVCsWmQYMGmcn1ejKpu3WLlDbFqEVyWqGV+YMaFRWFXbt2qTUazXqtVvuFUql8vfYFEhISlY4UbEtISEhIVBhKpTJJpVLh2rVrMDIywvnz58W4uDihUaNGfMyYMZWnphoxggKfzz4jg7Jm+drqajR5cliNhjJ6M2eSlLs41q4lee2FC1SLHR4OREYiaO1a/nbNmgKKMx/Lx71793Ds2DG0b9++YKCdS9WqwO+/Ux/dtDTsO3pUfPTokWBmZoZatWrBJjsb1XfswNHOncEYk8XHx6PvqVOQW1mRSVUJPa+nTp0qXL9+HYmJiTJnZ2c0F0WcrVoVQWo1T/n2W2ZkZIRmzZqJnTt3FsqlLuje3eAM6pMnT+Di4qK3t7cv2+xOEKhGzN/CAAAgAElEQVTmuDT5f3mpW5c++5AQoGPHYneJj4/npqameLX44OCA4JAQmAUFYUqtWoaXOGi1wDfflBhE5ma/CwTuUVH0Gc6cSXP95hsy5sv3nLi+8w67fPkyenh6GjYPQzhxAvj6a1J45C6AvHxZrHv7ayOTAQ0aULANUO24IFC2294eiIykWvHo6LwgPR/JyckAoFYqlTHlOi/nagDnwNh9AH1zgu85AK6UGEhzvg2M7QTQFowlgvMn5TqngRgZGS1r2rSpVX0LC5Li16xJSo5ylmXkotPp8Oeff+qCgoKy9Xr9+IULF/pW8JQlJCQqGElGLiEhISFRYahUKiMAiI2Nxb59+2BlZSUMHz4cY8aMqcBmwSXAGP0hHxhYcPvu3Xk9rt99l2pPe/QA8pucXblC7acAqqO8cYMcm8PDaZupKbKysoQCxmjFwDnHgQMH4OjoiL75M+n5kcvJFOz0aaRmZiI0NFRwd3eHvb29+OLFCx4WHo6mgYHo3q0bGjRowJs3b64XrKwoE2ljU2LLKLlcjg4dOqBv377o2qkTqk6ejOE2NuyTOXOEMWPGwMvLCzExMVi1ahVelkMWjuRkIDW1zN2ePXuGqKgo9OzZs+xA++VLqpUvYeHgjWjdGli/njLnxdCxY0cmCAK+//57fuTIEYiiiID79/WaNWtg17EjGYQZ0ud8166Cz1Ah4uPjYWVlJZqampJc/MQJMqqLjKQ+7rdukaN+YmKB49zc3KBWq1lMTPlizhLx86OFqNDQgkZ3WVmAmVnFnKM4evcmVYiZGT0/NWtSRj+39VybNvRzp9EAoojY2FjI5fJ7r30+zqPB+WYAHwFYAsAbjDmXsr8OwJcAfF77nKWgUqnaARjVo0ULI/TvT90AZs167Wc+Li4Oq1evVl+9evWsTqdzkQJtCYn/H0jBtoSEhIREhaFUKjUA6jHGHtSoUSN76NChKNaBurLYuZPqWj/7jIKt7GyqVzYxoVre774rKKUdOJBqvrOyKMsKUAAwfHiRoV1dXfmhQ4egVquLPbVer8exY8dEhULBJ+Q4XJdIixbA0qUwa9QI1RMS4O7ujjFjxgiffPIJm7ZwIeTJyXDv2BGjR49mQ4cOlQmCQNnBq1fp37KUr8nJdJ05GcRGjRrBzc0NkydPFqpWrSqGhIQYLp3Vag2qwQ0KCuJ169bVVytDLgyAFhzGji1R6v1GdO4MvHhBrbWKwdzcHB999BEbPnw4u3XrFg4dOoTMzEzZ/7F33mFVXFsbf/ecBogFEVEsBI1dFBt2RU3sPSYaNZYYW4qam8ToNTIZjRpNco03mhijxhJL1Ng7iCgCIoqioiICgiC911Nm9vfHBusBDlhi7rd/z3Me4MzMnj0zB+Xda6132bu4sFrknByWil5Wb/Hdu0uteT58+DDy8/MF7NvHsigaN2au+L16PTQSmzmTZU88go2NDerUqUMPHz5MTZaI/tIwGllWR3Dw047yLi5MAL8MikX9l1+y31FKmbFgo0YszbxuXaSmptLOJ04YQcjTDcvLA6VXQWlvsL9xF4KQ0SCkhMb2eAvAJBAyAoRY3nqwDCRJstWo1fvHVq9uXWn8ePbcJ06ssKv/1atX6fr16/NzcnI+nDdv3gBRFOOf11w5HM6LhYttDofD4TxXRFG8azQa3dLT07esWbMm//6z9E+uCFZWLHoWHc1E56+/sj/oN28GnJ1ZT+LitkVNmrA66p49S+59XUTz5s1JYWEhsrKyzG7PycnB5cuXhUmTJhGhtDZkxSxaBHW3bjBWqUKji4V+MUOHAt999/Qxrq7A7NksYlgSfn6sf3SDBmY39+vXTzh37hzZsGGDkmNJLXZGRpkGaYqi4O7du7R169ZlC5b795n7+6JFZZ+7oqxcWbJhHgBCCBo2bIjhw4cjKioKgiCwaL+NDTMiMxpZzXEJCyuQZRbZLsXYrZO1NW0YGAglJoalEDdqBLz+hI48dow95yei8L179yaZmZl0yZIluFbCokGZ3L/ParXT0p6u+aaU3aOKmuY9I8kpKciZNw/Uzo61xLtyBdevX89re/68E4BOIKQZCAkAAPMOcxbAotxzwP7WXQVCJprZp/jG/wfAsAqdxwyVKN3gkZdn77x7N3OKr2B/8vz8fPz5558FR44ciTcajZ0WLly4+XnNkcPhvBx4zTaHw+FwnjuiKOoBTFu0aNGpTZs2bejTp4+1u7t76YZZz4tKlZhh08WLwEcfMbF47Bhz/t24kaWUf/AB29ecoC0BX19f2qRJE1K7BFfwuLg42Nrayk5OTpZFyDp3Bjp3Rt8tW0jNwYMf3/bBByWnKM+cWXJaN6UsWrluXYl1oU2aNMGIESNw4MAB4fr16+hcQm3zA+rWLTMCXRQdFizKYjh7Fti6lbVNe1E0bsyc6kND2cJDCbRu3RrNmzfH4cOHcfDgQbRo0YLVco8ZAwQGMoO8Dh1Y//RH8fFhLblu3Xp60OxsIDcXXVauJGFdu1JhzpySP/RVq7LU4thYYM8eyLKM7du3K1FRUYJarRYAoEqVKjCZTFCpVOXTncULAWZajCE5mT3TiprmlYPs7GxERkYiLy8PycnJhYmJiYaUlJQqANCuXTvlzTffFHJVKqSlpdHv585tI4piAQhpCKC4HiQUhJwFMAvA+wB+t6i3NgBQmgZgBwi5DCbivQHMBqVhj+wjg5BGAAgIGQpKDz7L9a779NPVfS5fHtGqSRMNvLwqHM2OjY3Fn3/+WWA0GjcWmaCVsPLD4XBeZbjY5nA4HM4Lw9PT809Jki75+PgcjoyMrDdy5EgbqwqaA5ULQljbJG9vFjWMjWURy86dmQidPr3cQ3bo0IEcP368xO3VqlWD0WgsV8ZYVpcuaLhwIfJ8fFhbsGJ69QJOnjR/UNOmrKa4RQtWj96q1cNtc+Ywwy5Pz1LP6+rqiv379yMjI4OiqId0iTg5MeFaCioVW18oLCw022P9Afn5wFtvMTH7ItFqgWHDWG10KWIbYO28EhISlJYtWwoPTNPUamDNGvb67Tf29VEjN0dHlhL9JJcvs8/WzJk4uXIlCkwmpSNQ+uLLe+8hPjAQ2TdvwsfHhxYUFKBq1aoPMij27dtHs7KySOXKlfHRRx9B94TzuVlSUlhEtaT0f72embS9ABRFQUxMDG7evGm4fv26yWg0UrVa7Ws0Gm/LsnwXwDUA1wG4XL9+3TMiIqKXi4uLjUqlOrhgwYICACgyLPuiaMjuAKwA1AawDMBmEPI5AB0otSw9gtJbICQcQASAX0DIRgDHQWli0XZTUX33JhDSAJRmVuTajw0c+HlLg2FaixEjNKpZsyoktCml8PPzM/n5+RXIsvyup6fnkYrMhcPhvBpwsc3hcDicF4ooinckSWodExPz05o1a8aNGTPGpk6dOi/+xCNHsrZMx44Be/awKOT160zs1a5d7j+EMzMzUblyZQUllGBFRUUpRf20LR74r4gIpdK8eeRte3vyWLuq3FyWLv7228zR+UnUaubA/mTrJkpLTzF/BEVRYGdnV/ZcY2KYiVUphIaGAkDZQnD8eHZtJS0kPE/c3Vn0vG9fwMGh1F0bNGiA2NjYp5/thx+yGu7r11m0vLjG2d8feDQb4cIFYO5c1tN89WocTU3FteDgkk3yHoG6ueHgunVotncvNO+8o0yfPl1V3Jc9NzcXd+7cIQ4ODtiyZQvdsWMHcXR0VNq2bSvY2dnBbDu3gAB2zRkZJbtex8Wx13Pm7t27+Ouvv/KNRuN9k8n0pyzLmwFELViwwJzqTwEweNGiRWPDw8M/1Ov1/zE7KKVZAIprN9iDJCQVgA6EqAFkA2gNQAZQDZSGlDAOBYuW9wAh8wGsByH/ARAISgtA6W0Q4gjAHoR0B6V+5bn2sz17ftzk/v1vqy5apNK++255Dn1ATk4O9uzZk5+UlHTLZDINE0Xx+T8kDofzUuFim8PhcDgvnEfSyk9s3rx5U69evWw6der04tPKW7d+GNm8dIk5Qp88yaJ+Hh7A6NEWC+9Lly4prq6uJRqLJSYmUhcXl3KZLLm4uAhXwsIgf/EFhM2bmVACgHr1WKpyafMSRRa537YN2LCBpcx//TWLulqIrSU1u6mppbpzJycnAwBmz56NMmvVt25ldfQvg+rVWa1sSAhzxi4FlUqFhIQEQVGUx6+BEFZTvHo1KzlYt461QVu0iBl8RUezZ2BjA4wdy/qGCwJw9CicnJxkd3d3iz4PtWxsFLeoKMFj6lTVo8/c1tYWbm5uAIDu3buTixcvyrIsCxcvXoQgCKhataqSlpYmfPzxx7C3t2dGf126MOdxjabkE+r1ZWYrlIesrCz4+voWhoWF6U0m01hPT8+jlh7r6em5HcD2cp2Q0g0PviekD4BIAF8BGAzAHYRsAfATKA0u4fhlIKQ+gFEA3gIhB0HpCVBqBCFjAEwDYJmzIyG6lBo1vqrs7DxPu3Wryv7RDBULkWUZQUFBiq+vrx7Aj0ajUeS9szmc/w242OZwOBzOS8PT0/MvSZIu+/r6Ho6KinIeOXKkTalpx8+Tdu3Yi1JWh3vvHjBhAquN/te/WBSwfXuzAjcpKQkGg0F4o7iF2BOkpqYiPDxc9UFxLbiF9OrVC46Ojth1/z7GzZ79+Ma1awE3N9aCrCSqVWPzvX6dRVstcQIvQqVSQa/Xl71jp07mo+tFFLuzG8qIfmPsWGDQILM9ll8Yn33GHMavXXvoAP4EBoMBISEhQps2bUpeLPj4Y5ZFcP48S+P392efl6FDmbhdufLBPTp69CgNCQkhAwcOtGgliVKK/LZtyY6GDeWZEREqNDbfrapbt27o1q3bA/EeGxuLqKgo4cyZM1i9ejXauLkpA6ZMEcj33yN/5EiUKqXDwkptW2YpOTk58PX11V+9elUhhPxsNBqXiaKY9swDlwdKA4u+W1T0AgAdgFwQMgGACEobgpC2AG4V9eUGKI0F8B8Q0hVA06K+2xIo/RGErCl6PxqUluzwSEjtu/Xrv59Vo8a/6A8/CHUrILRTUlKwc+fOvLy8vCtGo/F9URRvl3sQDofzysLFNofD4XBeKqIoRkmS1CY2NnbVmjVrJowZM8a67pPp0C8SQli9c4sWLOJ59SqLtv73vyxlePRoJqjc3B4I77CwMDg6OspqtdpspDIwMBCCIKAk87TS8Pb2lnXt2gl4912CBg2AU6fYhurVy24/1b49sGQJS2HevbtEQWkOWZZhkVN8Xl6JPasBPBComtIiqQCLMpcgJF8Y1auzdPutW5m7uBlycnKgKAqGDh1a+ljDhwNbtjDn+sJCVvPs7/+YyVhWVhZCQ0PRo0cPtG3b1qxyz83NRUxMDPLy8pCZmalER0eT9PR0fDhokArt2wN377J5l0H9+vVRv359tG7dGsHBwYiOjKR/jB1L79+7R0wrV2LMmDFo0qSJ+YMLCoCGDcs8R2nExMRgx44dBYqibDCZTItFUUx+pgGfJ5SOBgAQEg+g+EO+BcBeEPIjgJ8BvAtKKSj1ByFBABIB/ABCDoFF2hcBuAHWt/tpCGmXXq3aF7dbthxe+9tvda1dXcs1RZPJhIsXL1IfH58CRVE+lWV5vSiKlrfk43A4/wi42OZwOBzOS6corXzGokWLTmzZsmWLh4eHdefOnVUvxa38UQh5mGbeowcT3evXs9TsMWOAO3eA/v2RkZFR6jB2dnaoXLmyjLLMsMygVquJysaG4oMPyGO9oefPByIiyh5g1iyWfj50KGtlZgGZRancNR41/Sp5gqWK/tzcXABMtNrZ2ZnfaccOYMGCh/2WXybjxj3s7Wzm82VnZwdra2u6ceNGOmnSJKHUVPgJE5jLt8HAas9v3mTZBc7OAIAff/wRKpWK2Nvb49q1azCZTI+9oqOj5djYWJWtra1Jp9ORSpUqCU5OTmTAgAGoWr8+S3m3QGg/Of++J08C58+rlLNncevWLQQEBFB/f380adLE/C9U/fqsFVkFCQsLw/79+/NkWR7u6enpXeGBXjSUZgPwLvq+JQCAkCYA7EApBSEHAdwBpf8CIREARgIQAZwA8DGAayBkOIADRTXfKBpjdJSLS89LXboMd/3qK13Tpk3LNa3o6GgcOHAgr7Cw8ILRaJwliuL1Z75WDofzSsLFNofD4XD+Njw9PfdJktTqzJkzhyIjI11GjRr18tLKn4QQwM4O+OIL9oqPZ1HdbdvQMCgINbp1UyEykkVoHxFkOTk5OHv2LJyLBJelGAwGREdHIzU1VWjUqJGCBQuAIUNYT+alS5mD+qBBzKCsJGJigF9+YSJWpWKR1wkTyjx3sbFWtWrVyp5oUhJrFVUCxe2+sktqR2Y0sjZZrVqxbIKXTaNGrB1XYCBL+X4CQRAwdepUsnLlSnL+/Hl0MbPPA/R6Vhc/dSq7J7t2sT7uly8Dly9Dp9GAqFTUy8tLFgQBgiBApVJBpVIRlUolODg4qEaOHAlbW1vzf3+5uLAFk717y3evPv8cuHMHgiCgefPmqFu3Llm9ejU9evSoPHDgwKcXgI4dq7DYvnDhguzt7Z1rMpl6iaJ4uUKD/J1QGg6guIh/GQATCKkGIAzMgC0EQAGADwDkA5gEZtB2GoRoALwXV6/e1FPDh7v1nT1bV57f+/v37+PEiRN5iYmJuUaj8SNPT8+/ntt1cTicVxLy6EIdh8PhcDh/B5IkabVa7UqNRjNpzJgxNi81rbwMkpOTsXvRIkxzdobG2Zm1gereHfjoIyRRigOHDyM1NRX//ve/LR7z9OnT8PPzgyAI6NSpE/Xw8CBqtRr49lsm5rKzmcHV1q3MwdtctDUnh0Xlt2wBunVjjtgjRjABbkE6+Q8//KCYTCbhnXfeQfXq1VH1yV7SxezZwwT3Rx+VONaSJUtgMpmwcOHCp+uejUY2n5edtfAo+/eze1rKQsTvv/+uxMbGCtbW1sqIESOERubE6PjxTLRHRjIztKFDmVHd/fvA+PG407gxvVmvHoZMn05Qr17F5ipJ7DyWpHnfv8/8B65efapePy4uDps3b0anTp2UPn36PP5QGjYETp9mEe5ykJycjN9++y3HZDK1EkXxbrkOftUhhBRFu+cCqANgDoAEsPRzHYBoBXj9YufOzS8OGdJx3Icf2pT4O/MEaWlp8PLyyo+KijLIsrxQUZR1oiiWYXLA4XD+F+Bim8PhcDivDIsWLRqmVqu39uzZ06ZLly4vP63cDLm5ufjxxx8xePBgODk5wSEnB+T6dRh27kTE1asIadMGPfr2hfO4cSy6XAYZGRlYs2YN3nvvvaej4YoCHD/OhOmAAazHc5MmLMX9SfLzAS8v1k/6Ue7eBXQ65rJexnWtXbsWeXl5cHZ2ViZNmmQ+f9rPj/VtHjmyxLH8/f3h4+ODhQsXPr7h+nVmsJae/lht80tHltnCxMmTD9t3mcFoNGLr1q1K9erVheHDhz+9w4oVQEICM0TLyQFsbdm9qVkToBTr16xRup88KTS5dImlhF+5wtpwlfdzvHkza13WrFnp+5lMwM8/l9gzOykpCevXr0ffvn1phw4d2CQUhUXCv/++VOM7c4SGhuLEiRNH586dO6hcB/4TYf/4fApgM4AACjQ+8+abcdGjR9uNfvfdSjYWlERkZ2fj9OnThdevX5cBLDeZTP8RRTHvBc+cw+G8QpTvX1kOh8PhcF4gnp6eB4xGo+vZs2dvbN26Nb/Y6frvxNbWFpUrV5YPHDiAdevW4Zvt21HYrx+M69fD94038O5HH8H55EmWorx/P4t8ltIq686dO7CysqJm008FgYmhwYPZOL6+THw/yb59zGXbnKnX+PHAk6K3hOv6+OOPodVqaUxMjJCSkmJ+x/R01uKqFFq3bg1FUVBQUPD4hmbN2Pz/TqENsEWQ2bOB/5hv5VyMRqNB1apVqVln9cRE4I8/HkbHK1dmQtvZGYiPx7Hjx+XU3FzScO9eICqK7f/118xV3te3fH2td+xgqeSlMWIEsHFjiUIbABwdHTFq1Ch4e3uTB87zmZns9YTQzs3Nxa5du7Bp0ybs3r0bx48fR3Bw8GMu8/b29lAUpZUkSX//KtiLhkWjzp7u2bOBT9++USf79y/InzOn5qT33y9TaOfn5+PEiRPGn376qSAsLOwXk8lUf8GCBYu50OZw/v/Ba7Y5HA6H80ohimKMJEnt4uLiVqxevXr66NGjrctbD/28+eijj1SFhYWwsrLCqlWrlE2bNhGdTqekVq+uWnvpEp2xaRNR5+YCf/3F0q49PIAbN5ho7t37sbHu3bsHJycnCsC8YBk0CPjxRybmtmwx7wSu1TLxaC5i6uPDUtDT0spsBWZlZYX58+eTVatWKdu2bROmTJmCyk+2hMrIAMLDSx3HxsYGtra2yn//+1/h008/ZTXhZ84Av/8ObNpU6rEvjbFjgTlzWK11zZpPbc7JyYG/vz8NDw9Xvf32208fv24dSx9/NNpcsyYzsXNywp0//hBatmzJygHUahZJDwhgafTTpjHncl9fdi979So92n3sGDsuKwsoKVW5WzegY8cyL7tJkybQ6XTKt99+Kzg5OWFkly6wL7r+Gzdu4PLly6CUIi4ujtavX586OzsLOTk5cmpqKu7cuUNOnDghVKtWTR4wYICqQYMGEAShJiFkBIAyVgNePSRJEgBUKnrZgv0OZgNIAQtAdQNQmShKARWEtp9WqeJpsrHRyrNmoW3bthoHBweUlm0jyzKCg4OV06dP6wHsMplMC0RRjH/hF8bhcF5ZeBo5h8PhcF5ZFi1aNFCtVu/o2rWrTY8ePdSvQlp5fHw8Lly4gEqVKiEoKAiKoqBWrVqYPn36w53S0oCDB5m4qlmTOUwPGQI0bYrd+/YhLS1NmTFjRsnZZZcvAwMHAp6ewOLFrDa3mHnzmJjv37/kSaakAPXqsZTyWrXKvKbCwkIsX74c7dq1w+DBgx/fGBjIaoIfvT4z3LhxA3v37sUXX3wBnU4HnDgBHD4M/PRTmed/aaxezVLsp059apOPjw8NDg4m3bp1Q9euXZ8+9vp14NYtYNSop7dJErJ++QW7Fi/GxIkTHxjQPYbRCAQFsd7fc+cC1tasvVxJae0TJ7Lo+IkTj79/5gy7p3v2WHDBjKSkJHh5eSE1NZXWvHKFvJ2biz8GDZKTk5NVrq6uCiEEDRo0EMy1CsvNzcWFCxeUoKAgwWQygRACRVFmenp6rrXk3JIk1QNwixCymlK6WhTFexZP/BkpisA3JoQM1ul0YwwGQ2tCCNRqtUmtVsuEEBgMBrXRaFQTQlC9evX86oqCYf/+d+WL335rquTurmnh6gorK6syzxUZGYlDhw7lFRYWhur1+qmiKN548VfI4XBedXhkm8PhcDivLJ6enkclSWoREBBwICoqqunbb79tY2tr+7fOqU6dOhgxYgQURUGzZs2wceNGJCYmIiMj42HrK3t71td58mQmeL28gMhI6EeOxGt2dui8erUAg6Hk9Oo2bZh5lUrFaqVlmX1vMgHe3sC775Y+SQcHFoWtVYsdU4ZhmpWVFTp37oyQkBB07twZ9o9GxPV61pfZQoKDg+VudnaqB33MXyXefJNFjfV6JrqLyMjIwPnz50mjRo3M90qPiGBp+8uWmR/3yy9R0Ls3Ery8sG/fPowePfrpfTQaFo0+f549k3feYWJ682a2mNKz5+PR7hUrmKmbuXHq1CnXZRenk2/fvp2o9XoE5eXRlJQU1SeffAIbG5tSSwptbW3Ru3dvoVevXtDr9fDz81MuXLggSpL0q4V9oRsDsKGUzgUQAWB9uSZfDiRJqq5SqQ4SQq5qNJoGarW6o0aj0TZu3Fho2rSplYuLCwgh0Gq1jzWFNxqNwL170KxdWwXLlwM1aqDH6NEaS2ra4+Pj4e3tnXf//v1so9E4g1J6iPfL5nA4xfCabQ6Hw+G80oiiGGcwGDomJCSsXrNmTUF0GfXDLwtvb29s3Ljxwc85OTnmd3ztNWDqVITUq4fVQ4YgpXlzWnfPHtb/ec8eVtNsrsb7zBlmxNWwIauZLjbD8vd/2Bu8NOrUAT7+mIk4C+jbty+sra3pzZs3H99QqRITeGXQvHlzDBs2DD4+Pqrs2bNLrSX+22jSBAgNZffwEXJzc2E0GnHv3j36559/4tdff5Uf20GlAlq2ZC705rCyQkq1avjw55/R/9Fe6eYghN3PffuY+VxkJPDpp+z73buB1FS2n6Mj21ac0q4orF68YUNg1apyXbaiKFizZo2iVquVlj16oNawYWTWrFmwxOSrmISEBCxfvhwBAQGCyWSqBaC9JceJonhKEITTRT8+VrMsSZKtJEnVJEkq+wNmGUZFUVqaTKaZBQUF/UwmUzUAUKvV2itXrhiOHDmCZcuW4dy5cw9rMxQFmowMaOLjHy6EvPtumeZxaWlp2LZtW/7mzZvTYmNj5xoMBhdPT8+DXGhzOJxH4WnkHA6Hw/nHsGjRojfVavWuTp062Xp4eKifajP1EsnIyMB///tf9OnTB9bW1mjXrl2J+969exebN2+Gg4MDnTlzJiGEAPfuMaOqJUuA4GDWUkyrZRHU4qiriwurmZ4+nQns778HLl603EU6Lo71CjeTHmyODRs2ULVajYkTJz4MsR4+zIy4yjLsKkL6+mtmvjZzJnSVKlk2z5eJjw+rJd+69bG38/PzYW1tDYPBgO+//x5t27bFgAED2MaffwaOHmX3ogRu376NsytWIKNOHdq9f3/SqXNny+ekKIDBALz1FstCkCQgN5cJ7kWLWO1+YSGLzO/ZY1FpwKP89ddfNCUlhU6bNk0QfvyROdWXlR1hhk2bNtGYmBgCIBRAF1EULXYwXLRo0VlKaXcArVQqlYdarZ5rNBprCYKgyLKs1mg06YIgZAG4rNfrdxZFiCvUHkuSpKpgAaVMAG5gvbMfMG3atIcZDGPHMkO78+ctGvv+/fvw8vLKi4uLUwCsMplM34iiqEUnOwoAACAASURBVK/IPDkczv8+XGxzOBwO5x+FJEm1dDrd/ho1ariOHj3a5ilDr5fI0qVLodFo8Nlnnz3dXxqAj48P/P39IQgCmjVrpgwdOlRQm0vpzs5mqeZeXkDjxqw2+K23WCT1hx9YG6hWrVidb7Vq5ZukLAOvv84crjt1KnXXyMhIbNu2DXPnzn1Yp3r+PBPaK1ZYdLo8Z2cEursrl93dBY1GQ5s3b6707du37J5oLwtKgZkzgdGjmVGZGWJiYrBlyxaMGDECLevWZXXVM2aU6WZuNBpxYv162vuzz8i1PXvQceDA8s9Pltn9FkUmrM+cYTXzb70FvPFG+ccDsHLlSnnAgAGqpk2bsusYMODplnEWsmrVqpzMzMx+oigGlue4ovppBQBq166dN3DgwEpOTk4QBAGKoiAzMxP5+flITExEcHBwTnp6OlUU5UtFUX4TRVEuY/iyzl0XwL1+/frB3d2d/a6+9RZ79evHsjdKqctWFAW3b9/GpUuX8mJiYgyyLHsWzYuLbA6HUyqqr7/++u+eA4fD4XA4FuPh4ZF76tSp3wsKCnQXL17sUKtWLU316tX/lrm0bt0aQUFBND8/n77++usPosH79++n+/btQ0xMDGnTpg1atWqF3r17E1VJfbh1OqB5c+Ze7urKaqS3b2f13hs3MmMvWQbM9X4uC0Fg4/ftW2oLLkopjh07pmRlZZHMzEzExsYqp0+fpjUrVSI2ioIQgGZnZxM7OzvExsYiPj4eKpUKNjY2kGX5wWKDtlEjvDZ9OpHZuciVK1eE1q1bw9rauvxzfxEUp3EfOMBM6MxQrVo1yLKMS5cuyR26dxfQpAlzlS8joqxSqdCofXtyOTmZemdnk0aNGj3t7l4WggC0aMEWBBITmZna8eNsoWXIkPKNVcSFCxdovXr1hFq1ajGH9IEDgWJ/AQtISUnBnTt3oNPpEBERoc/JyTno4eERaenxkiQRlUoVbWtrq+3SpYtmxIgR2qpVqz5w9iaEwNraGlWqVIGTkxM6dOiga9y4sS4+Pr6X0WgceurUKW8PD4/M8l85w8PDIzsoKKhjs2bNGtUOCmKLT7dusRr6hg1L9DQoKChAUFCQsmvXroJbt27dSE1N/U6W5fGenp4BHh4ez7QAwOFw/n/ADdI4HA6H84+jKNK1UJKkU7t27fqrQ4cOlXv37q0pUcy+IGxtbWE0GsmlS5dIjx49oCgKAgMDERoaSnr37o02bdqg3IZutrasV/b48UxsqVQsrfhZUuZnzgQOHQJ++425pJshODiY3rlzRxAEAREREdTa2pra29urju/dixahoTjHatIVvV4vCIIAGxsbOS8vT1WzZk0kJCRgyJAhtO3q1QTffAPBwQE9e/aEoij45Zdf5F9++UX15Zdf4mU/nxLp0QMIC2Ppww0amN2lQYMGOHfunCq7eXNUsba2OI2eEAK3774j1WfOVPK7dROu7d8PV1dXy+eWk8Pc5OfMAW7fZosvDRuyuu4KUqNGDdX9+/eV1q1bC0hPL1ff84iICOzevTtXpVKdNRqNb8iyXAVAOZqGAwAEWZade/fuDTc3N4sOqFWrFj744INKfn5+bf39/W9+++23h/V6vVhel29JkgghZJJaEHo5V6oETJkCnDvHfqdKoLCwEP7+/qagoCCTIAhH9Hr9MlEUL5XnvBwOhwPwNHIOh8Ph/MORJMlBp9PttrOzaz9mzJhKVUvqS/wCMBqNWL58OQRBYI7GAARBwMCBA0ut4S43hYVMdP/wA2tB9ccf5R/j9m3WK/r775/aZDKZsHLlSnTr1g0GgwE9HzFVy/LygtXy5dB5ewMAsrOzoVarYWNjg3Xr1skJCQmqFi1aIDIsDBM3bkT27t1o1KHDg6hlRkYG1qxZgwULFpTao/ils2YNi2iW0tLsQlCQops6VaiWnY2qV67AxsYGarXabMnAU9y6hfBdu7BXo6GffPIJKXXRRZaZaZtezxzPe/ViZQNbt7K0b3d3tlDy2Wflvszc3Fzs2rULVapUkUe99ZYKCxcCX31Vatr0oxw5csRw8eJFURTFbyVJ6qLRaIYbjcYvy2sEJknSvxwdHRfPmDHDcle2IgoKChAcHCwHBAQYKKVbDAbDvyypF5ck6TWdTre5RXR0h4H79lmr7t9ni1YlPD+j0YgLFy4oZ8+e1RNCDun1+i9FUbxb3vlyOBxOMVxsczgcDucfjyRJglqtnicIwlcjR460Ntcv+EVw+vRpnD17FsOGDcNrr72GVUUu0RMmTICLi8vzP+G5c0B4OOvB/NdfrO64vMyaBUyaBLRt++Cts2fPKiEhIXTWrFmqp4RkZCTw669ma7YVRUFhYSFsbGygDwmBT1ISQkNDqV6vJx06dJCtrKxIREQESUxMJJ9//jkq/Y2GaXl5ecjOzkZubi7y8/PRqmZNkE8/ZeZjT0R6ZVlGTk4Oglavlomfnyq7Rg3caNIElFKo1WpUr15d7tatm+rRiLWiKMjIyHisbRqlFCnNmtEzHTqQpnPnPh3hvnsXSEoCFixgqe0rV7Ke7GlpzEl+xQqgXTvWo7t+fZYCXo7PVVBQEE6ePAkHBwdl2LBhQm2tlqWoJyZaPMaFCxfg4+OzZd68eRMtPsgMkiQNrVmz5rbp06fbVtTYsLCwEPv27cuPjo7WA/jdaDSuFEXxqSi7JEmOWq3264ZhYe+3sbdXNRBFleriRVZKYQZZlnH58mV66tSpQkqpn16v/5T3yeZwOM8DLrY5HA6H8z+DJEmdNRrNQTc3tyr9+vXTvsi05ZCQEJw4cYK2adNG6d+/vwpgUUSVSvXi65N9fID33mNu48Dj/ZnLYvx4JtKHDAGlFGFhYTh8+DA8PDzQyZyB2p07wIYNJfeYBpgI7NcPyMxE5N27OHToEJVlmep0OpqWlqYCADc3NyU3N5fWrVsXPXv2LNeDyc7Oxs6dOxV7e3u0b99ecHR0BKUUiqI8EPCKouD06dNKTk4OdXBwENLT05W4uDii0+lo48aNVb6+vlCr1VStVtOCggKhU6dOypunTwuoWxf48EMAKK7TRlBQEE1PTyczfvsN1Z2coFmyBClubigoKEB+fj6io6OV0NBQQaPRKLVq1RK0Wi1iY2OV3NxcQavVUq1Wq5hMJqFp06YYdusWCWvcmB68dYs0btyYvjVgAMH+/UxUf/MNq8OfMIH1RieEmaF98AGwc+fjKe5paazE4JH+4GVx8OBBpKenK5MmTWLq9uZNFtX+6y+Lx4iNjcX27dvv6fV6FwDjtVptD4PB8JMoilcsHgSAJEk6nU7nVatWrXbvvPOOTXnajj1JQkICQkNDjZcuXVJUKlWyIAjhlNJEQkh1SmlDISfHpaWbG+196ZJOV1AA/Pij2XEopbh+/TpOnjyZZzQar+n1+lmiKAZXeGIcDofzBFxsczgcDud/CkmS7DUazTErKyvX4cOHWzUooSb3Wfnuu++UTp06Cd1L6r38MigsZKZdgYFAs2aWH5ebC+OqVTjRurVy7do10qVLF9K9e3fz6dHx8Uxse3qWOSbMpErHxcXh0qVLKCgoQFRUFIxGI2bMmAEA0Gg0sLOzQ0hICNXr9bR9+/ZCVlYWrly5QsPDw2nVqlVpnTp1VOfPn0ft2rWV5ORkobCwEAAe1H+3aNFCuX//PsnMzCQ6nU7RaDTUZDKRunXrCnXr1kV4eLiSnJxMhg4dSpo3bw4ASE1Nxa+//oqR7duj2cKFSNi7F1CpkJiYiCNHjqBly5bo378/tBkZEA4dYosTT5Qn6PV6BAYGIjs7WzGZTFQQBPTr10+Vm5uL7Oxs5Ofn4+jRowBAnRUFQ3//nRxt1w5v3r2Lqs2bA/PnA3XrPp7OHRwMTJsGnDgB1Kz5+I28eBEYM4YtflhIfn4+Vq1aRYcOHUpatGjBsiIuXmQ93i2EUoq1a9fmJScnqwBYtW/fHleuXMkxmUwdRFEMt3ggAJIkqTUazU86nW5Cr169bNq0afNMpQVGoxFpaWlIS0tDQUEBdDodqlapgnpduoDMns3M5UogIiICx48fz83Ly4spEtk+FZ4Ih8PhlAAX2xwOh8P5n0OSJC2A1ba2thM/++wzi92gKKUW//G/fPlyOnnyZFLzSVH0sjlyhLlLL1vGWkO5u5d5SPCGDXLDuXNVW+bPl0ePG6d60HPYHBERrE3UjRKyaletAvbtA3x9yzzv0qVLYTQaoVaroVarqdFoJFZWVsjLywMAqNVqKIoCa2tr9OjRA6mpqYiLi1M6duwotG7dGoGBgfDz81M6deok1K5dG46Ojti8ebNsa2uL9u3bq5o1awazrdWKMJlM2LVrF7137x70ej0hhKBPfLwSnZcn3Hn9dQDAgAED4O7uDnz9NTNRy8gAiurVy4vp3j1kr1uHu4TQxj/8QM726AH7BQvQsVu3p3f29mZRZ29vs4sWMJlYzfby5RbXWwNAaGgojhw5gmHDhqFFTAxw+TJLWy8H+fn5+O677wAA3bt3p3Z2djh27FiK0Wj0EEXxZnnGKmoB1ker1a5q2rRpwyFDhuhKe2YWk5HB+pDv3cui/46OZndLT0/H4cOH8+Pj49ONRuPHlNKD5a0/53A4HEvhYpvD4XA4/5NIklQfQAwAdO3aVfHw8DDf4xpAWloaduzYoU9LS9NptVpl6NChQosWLUodf9myZXTcuHGkfv36z33uFWL4cBax7NePibEn6pALCwvh5+dHw8PDkZaWRj744APUychgwqRRo5LHvX8f+OgjJqjNER4OhIQA775b5hQVRYHBYIBWq4UgCDCZTLh06RLq16+P9PR0VK5cGQEBARg4cCCqVKlSnqsv8XxhYWFwcXFBXFwcDh48qNjb2+PNN98UHB0dcfbsWWgCAuCeloaMuXNRUFiI14tENzZuZMK3SRPW89pSDAbWqis2FrhwAahTB5g5E9ezshC5aBHqFBSgyn/+g8aNG7P9KQV272bn27sXKC29+sYNZpD3zjvlug9hYWE4cOAAptnYoIYsV8hoLTk5GWvXrqX9+vVDx44dyZUrV+jRo0fzZVn+WFGUzRUwTKus1WoPtWjRouPQoUMtXz14Er2eOewPHw7861/A0qWAmXZrRqMR586dMwUEBBgALDWZTN+Jomio8Hk5HA7HArjY5nA4HM7/LJIk2QEYpFKpviWE1HrnnXdUjZ4Qlv7+/oq3t7dACImllIoAfgeAVq1aycOHD1eZi3RnZGTgl19+wXvvvYd69eq9jEuxiHv37sFm2jRUz8wECQx88L4syzh69CgNCwuDm5sbadiwIRo1asQi4a+/DqxdW/KgGRnMIG3evKe3bd/Oao/7938BV2M5BoMBN2/eRGpqKgRBwLVr1+T8/HyBUgpBEIher4darUbfvn1pu3btyGPPVFGAL74APvkEeO019l5mJnDlCnMInz4dqFGj7ElcvcruR8+ewC+/sIWPRwzsMjIycHrKFNROSaEn33iDtG3bVunVo4dgu3UrE+Vr15YutAHAy4ulmUdHl/se7dy5Ey1OnFBcR40SMGhQuY83R2JiIvbs2ZOXk5OTKcvyr7Is7wJw21LhLUlSUwA33dzcCgYNGmRd7gi3ogB79rCU/PDwEvtl3759G4cOHco3Go2+er1+hiiK98p3Ig6Hw6kYXGxzOBwO538eSZJUALYCeFcsilLGxMQgISEBJ06cAIAhoigeLtpXC2ALgAdKycXFRW9jY6MbMGAA/vjjDyQmJsLZ2ZlOnDiRvCrtrCilWLVqFbIyMtCoenXq0bQpqb1kCU7Mn08vBAcTrVZLR48eTR5zSaeUmXJFRrJezuZIS2OR0E2bnt42bRqrLf7mmxdyTZYQHh6OvXv3Up1OB3t7eyU/P5+0adOGNGzYkFBK4eDg8KA8oMRntX4967m9dCn7eelSYP9+VlO9c2fJfalzcoA//2TGY7a2QJUqTGTXqvXYboqiYN26dUrlypUxbuxYIX/qVOx0dJQbBASoGtSsicJ589C4TZuyL5ZSIDWVnaccRmkAsH37drQ5dEhpNnWqgN69y3Vs6VOiiIuLQ0hISOHt27dlg8EAtVrtW1hYuFgUxaCyjpckqZVWq11hZ2fXfdq0aTYWO5VPm8ba2Z0+zX4282zz8/Nx+PDhgsjIyEyDwTBRFEWvcl0ch8PhPCNcbHM4HA7n/wWSJDUBcKtz586wt7fH4cOHAYCqVKp/f/XVV9+a2d8BQGsAQwGkAfi6eNuoUaNQVpr5y0ZRFCxevBiEEFBKUSMjA23Pn8fJAQPgIcto8/nn5lOz09JYqnNUFODk9PT2jAygTx+WKv4oiYmsLvZvXGxIT0/HunXraK9evdCxY8eKT8RkYi2x/P1ZFFuvZ5HqkBDzfbh9fYEdO4DBg1l6/fjxKE3Aent7K6Ghofj0008FQRBYJoBWi/h69XC8WTMlLi1NmDJlCurWrVv2XEWRRXF37izXJX7//ffKeINBqDV6dMkLK88IpRQhISGIi4tDWFhYgclkGuTp6Xm6rOMkSSI6nc6/bdu2bfv27VvyKgKlzKhv8GDA2pq9SiiBuHPnDv76668CWZY3G43Gzyzpy83hcDjPGy62ORwOh/P/hsWLF7+jKMqfAKDVahfNnz/fomJcSZLqAYgt/nnevHnQlTOy+DL4/fffaWJiIubPn08URUF8fDysjEY4tG4NXLv2ME36SXJyWGS2sJAJmEcpKGD12Pv3P/6+oyOLAE+Z8kKupTQURcHBgwdpWFgYcXV1VQYPHixUtHfzA7y8mBv42LHAkCGAhwfg5vbw+jIygDVr2D5Dh7KU5eHDn3Ipf5Lo6Gjs2LEDU6ZMgaOjIxP2770HdOkCJCUh6ZNPsHbtWnTu3BmvFT2fB/Xc5rh6FTh5Evj8c4svLT09HT///DMWHDoEsnNnuXp1l4eoqChs3boVVlZW+sGDB+v27NkDQshQT0/PQ2UdK0mSi0ajCZs9e7a12X7ssbFAvXrMrG/KFPbVDEajESdPntSHhobmGo3G0aIonnrmC+NwOJwK8oz/M3E4HA6H889h4cKFuwDUAjDUUqFdRGsAmDx5MgRBwKVLl17I/J6VMWPGEFmWyfXr1yEIAurVqweHBg2ArCwmtFu1YinTT1K5MjBzJtCjx9PbBAGwt3/6/atXgcmTn/s1lEVERARWrVql3L17F5MmTcLQoUOfXWgDQMuWzHk9IQHo1g04c4YJ4gMHWMut48fZwsOyZUzsTZxYptDOy8vDrl270KNHDya0jUYWlR0+nNXL79kDhypVUL9+feXy5ct0z5492LFjBw4fPgwfHx9s376dFhQUPD5oq1ZA06bMVdxCQkJCUMvRUSZxceazF54DoaGhypUrVygAFBYW6mrVqgU3NzdQSg9KkmTGfv1xRFGMFgThSFhY2NMbk5NZa7vbt5kZWglCOzExET///HPe1atXTxqNxsZcaHM4nL+b59BrgcPhcDicfw6iKCYBKDPS9gTHVCpVyIEDB1oqiqL18/NDx44dH/R6/ruhlOLChQs0MzOTUEofREgfUCxGJYkJyWPHmGv2o6Jl6VImJovruItRqYBq1R7+bDAArVuzXtDPQ+SWA39/f/j6+qJ79+6kc+fORKPRPL/Ba9cGuncHAgLYIsLt20xkf/stMGkSE8cWOK4XQynF7t27lRo1aqBbt24CcnJYxHzePOYYTwhw6xaEkBBMrltXKF64uHTpEry8vKherycAyIoVK2BnZwcnJyeqUqlogwYNSOtTpwgSEixKJc/NzcXFixfp4B49VJg1q9y13mVx4MCBwvDwcKGgoECtVqtjATgDwOrVq6FWq6MANCCEfAWgTBc9vV4fkJycPAQAm+Tp08C//w2cOwfExJRoVEcpRVBQkHLq1KkCWZZnUkr/4O28OBzOqwAX2xwOh8PhlIEoirIkSR2zsrIOCoLgWlhYWPebb76Bs7Oz3Lp1a1WrVq3+FuF98uRJWRAE1K1bV3X8+HFiZ2cnDxw4UGVrrk8zAIwYwb6ePQskJTGxXVDAUserVwdkmYnOP/9krtoAE9ubNwPffcfEtSyzmmNL6oufIz4+Prhw4QJGjRqFJk2aPHuhuL8/ixBnZACLFgFbtrDrPnkS6NiR3Q9vb8DBoULDBwQEKMnJyZgzZ46AxESWnu7p+XRt9++/s2fx5psAgHbt2qFdu3YEABISErB3716alZVFwsLCCCGE3LhxA7U//hg1HR3Zsyjhc6coCgIDA3Hu3DnatGlTpUXNmirExFToWh7l5s2bOHPmTE7Lli0ry7KMK1euWAGYDCDAZDJFAPAC0AcA1Gp1MKVUlGW5TPdvSZIErVY72NHRUYeICCAujmVj9OvHPnclCG29Xo99+/YVREdH3zOZTANFUYx85ovkcDic5wSv2eZwOBwOp5xIkkQAfEwIaU8pnVC/fn1l8uTJLzXM6+vrK585c+aB0mrZsiXeeuut8g2yfTswaxZzuC5m3z5g0KDHHbjbtAGCglirpbVrgdmzX7ox2pIlS9CzZ09061ZGRnJxZP7gQdbWrHZtlrrt5wfMmQNcusSEdv36wPLlgLs78OGHLFK/fj3www8snf6NN5jIK+89BRAfH4/Nmzdj/PjxqF9QwPo/z5/PsgpKYt8+Fvkupf2Vv78/vL290adPH9pt3jyC998H3n/f7L67du2i9+7dQ9++fUnLli1Brl9njvI//FDu6ynGYDBg/fr1uSkpKft0Ol1zWZYvEkL0RQZkJoD9bhBCblNKXwcAtVq9ecGCBZPMjSdJUmUAg3Q63WBKaVtrKyuXD2fMsNJOmcIWEv78s9T5ZGdnY/PmzXm5ubn7DAbDVFEUCyt8cRwOh/MC4JFtDofD4XDKSVGK6k8AIEnSf2JjY6/8/PPPhhkzZmifS/1wEZRS3Lx5E/Xr14etre2DFlYAoNVqVQCwYMECpKeno4YlvaCfZOxYoH17Vks8cCAT3yNGsN7Fq1YxgQqwCLiiAIGBwM8/M7H9EklNTYUsy0hNSQHS01nUeedO9rV3b8DVlc117VrW4zo+HlixAhg1irmJOzkxY7JPP2VO4wAz3CqGtX9j0dN33wV272Yp5V9/DYwcCeTmsrp2CygsLMTOnTtp+/btSf3kZODHH8sW2pSyRQ+Nhi0MlECxcVhWVpaMGTPUpUXdMzIylC5duqhcXV3ZGxERzASvguj1evzxxx+GzMzMiwA+mDdvngEAJEmaB+DGsmXLIg0Gg68gCNUURTkA4DMAMJlME5csWRK4YMGCXx8dT5IkjUajue7k5FS9WbNmtlWrVEGT4cNBZBnYtq3MxZyEhARs3bo132g0LjGZTN/ytHEOh/MqwsU2h8PhcDjPgCiKoZIk1U1JSYk7efKk3L9/f5XBYIDRaIRZV+VyEBoaigMHDkCj0aB9+/b07t27SEtLI9WrV1dSUlKE2rVrmwRBUNesWbPiJ2ncmBmoWVuz2uwrV1ga9ZAhD/eJiQHy8oCuXVkt84vCYABCQ4EOHYA//mAid8YMCE2bos6wYehZrRrg7Mzc00+cYGnGffsCAwawdOpZs4CPP2ZjnTv3cNzdu9nXsly4L1xgraQaN2btvMaPZ9ddsyaL7LdqVerhlFLs379ftrGxQV9BUGHjRjafTp1KPy8hTPxTymrGu3Qxu5ubmxvq1auHNWvWqFtOmAD98uW0evPmpEa9ek/tazQaH38jJ+eZzNEOHz6sT0hICJVleagoigYAWLx48SQAy+rVq0ednZ0bZWRk9HBwcLBJT08vzMrKym7YsGGVmjVrYu/evT8uWrQo09PT89FQtUpRFIfBffpY1/jqK2D1araQ07VrmUI7IiICu3fvLjCZTJM8PT13V/iiOBwO5wXD08g5HA6Hw3kOSJI0H8BSQRBkRVFUAFC7dm1l4sSJQkXahCUlJWHt2rVwcnJCnz594OXlJVNK0a1bN1VcXBy6deuGEmuzn4U6dVht8fTprEXV+++zCG/TpkxwXrlS8bFNJhaRHjsW2LWLtdH64Qc29pw5TGh17MgE7rffAjk5+LZKFdrC35+0kyTYFBu/PZE9UKlSJTwXs7Rx45hTuJMTm0sx0dFM2I8cyYTzl1+aPTwkJISePHkSn9avT3TXrrH9O3Sw/Pxnz7K2YqmppaaT//bbb6bExET11A0bcKFDB6QOGEBtbGyUYcOGqayLWrctW7YM06ZNg32xk/yxYyyy3b275fN5BH9/f5w9e9Zr/vz5fYvfW7JkSXLPnj0dykrtT0pKwoYNGwqNRmMrURQjit/fOH36mtq9ek0ZsGaNDrt3A7VqlTmPCxcuKN7e3jlGo3GAKIqBFboYDofDeUnwyDaHw+FwOM+H5QDWK4pSA8x5WUlISPgxLS0NThWIKPr6+ipWVlbClClTIAgCpk+f/lh99gvj7l0m9KZMYcJaUViLqwULWOTZHJSyVHSNhonkjz9mrbN++421znJzY2nUK1YAn3zysDa5WCCvWQO0aMHEVn4+e2/+fGRmZkK/ahW52qkTrhenej91agorKyv6wQcfkCpVqlT8ugsKWEr96dNMcD9KcUR8zBi2GHH9OquxXrjwwS7Jyck4fvw4mQxAFx7O0tHLiIQ/RY8erM1VTg5Ldy9BfE6dOpX9/TZ9OupERiLx2jWanp5OfvnlF+rh4UFcXV1BCKGyLD8MEfv6VlhoA4BWqwUhRCn+efHixaMURXEgpUShKaVITk7G6dOn841Gow0AFwBMbBMyfqIgjF1Sq5bO4+RJWD/Z390M3t7ehuDg4CSj0diLG6FxOJx/AlxsczgcDofzHBBFUQGQUvS6KUlSbQA/Vq9evULjOTs7C/fu3VOeTxPpclAsgCdPBt5+m7mO+/iwNN8VK1j98aRJzMV76lTg2jUm4mxtWS/qDRuYWK1d+6HY3LuXuXrb2LDjAFZPPWoU+75Pn8emQCnF9evXcfDgQbz++uvKuHHjSr0H27dv8WAtSAAAIABJREFUp+vXr8f06dNJhVP3Q0NZZNlkKjGNG6NHs69//cVM1oqOMzZvjh07dtBBd++itrMzwXvvMXO2iqDVsog4pcCRI6XvW7ky2g0dinY3bgiKvT0CAwPh6+srHz9+XEUIISkpKXhQYhAU9HhpQDmpXbs2CCFNH3lra//+/dHBTOQ+KSkJQUFBhTdu3KCKouRTSn8CcBpAAAj5BkAdANOyK1c+QAUhu6CgoEyxffHiRSU4ODjZYDC0FUUxtdSdORwO5xWBi20Oh8PhcF4MBQCQl5cHKyurch+cnJxMCwoKXq7QfpTi1OAff2Rp3StXMrEdGMjaMTVrxmqaAcDL62H/5jt3Ho7Rrh372qBBuU7t7+9PT506Rezs7MoU2gAwduxYYcuWLfJ///tfVdeuXZWWLVsKOTk50Gg0xSKx7JOeP8/q1hs1KttI7K232CstDejcGT4//CC7nT5NXN3cBEyc+Oxt0fbsYanySUmAo2PJ+1lbs3T/vDwIDg7o2rUrunbtqrp79y527tyJtLQ0KIoCQRCAXr1Y9kAFMJlM8PX1LaSURhe/p9Fojpw/f36ki4sLqVy5MuLj42E0GhESEpIfExNTSAj53WAwrAbQEsDewQcPUqNGk3ezSZOs2omJN6plZhrtAKN22bKge/fudSxtUery5cv05MmT2UURbS60ORzOPwYutjkcDofDeQGIopgpSRJWr16NN998U+7SpYvFjbhzc3Nx+fJl0rlz5xc5RcuYM4e1YBo0iEVbg4NZOjkhrMYZYKLvGSnq2Yzs7GwEBgaSkSNHwtXV1eLFhgkTJqhu3boFLy8v6ufnB7VaTRVFgSzLpFmzZoqtrS00Go0gCAJu3rypmEwmNG7cGDVr1hRcXV2htrJi0fc6dSyftL09bpw9C81XXwndQ0KI0Lz58+k/bmXFWl81agTs3/90b+5HmTOH7fPBBw/eeu211+Du7q74+/sTWZZpLw8PAXfuPFwQKSfZ2dmIjIzUAfhMkqQZACiAGnq9nmzYsCHRZDJV1+l0NwFkGgyGw7IsrxFFsUCSJFciy4eoIKDhvXuiX48eX50YMIClTkjSXVEUAyill3NzczuWdO5z587JZ8+eTTcajd1EUbxT0n4cDofzKsLFNofD4XA4LwhRFMnixYuTvby8HNzd3aEuxfTqUVKL+l737du3jD1fEoGBD/tXx8QAdnbA0qXA5s0skr1jB2sdVrVqhU+xbds2OTExkWg0GnTq1El40LKqHDRt2hRNmzYtXtQgiqIgKioKwcHBQmpqKvR6PRRFoS1atBCsrKxw69YteuXKFRoQEIBRR4+isHVrcq9SJao/dYo0a9aszFr7jORkJC5ciOZ16xJh/nxW233wIHutX1+Bu/AIKhVw6xYza8vNLTnarlYD//43S8mvVg0Aaz8GQBAEgdrY2BAYDExo29hUaCpRUVEAQDQazeGGDRvaabVaevXqVWsAGfPnz6/96L6SJFkBaLVs2bJp1QoKxn20Zg0MAQEIGzhQGNiunUCPHFEuX74sCILwFYCBKpXK3VzbOkopTp06ZQwODr5fJLTjKjR5DofD+RvhYpvD4XA4nBeIoijdAIRnZ2fD0vrtqkWi1WQyWSzQXyi//Qb8/jtrS1UsjBYuZKZplDJ37rp1maALCGDR1nKQkpKC6Oho1YQJE+BSVnuuciAIAl5//XW8/nj99IOccnd3d5KZmYkAX1+kZmZS+y1bECGKJCM0FOfOncOgQYNgY2ODevXqofITfbblnBzcmjaNOmq1qPXTT+RBdP/mTSZ6KWX3bNKkp9zTLcbJiQn3SZOYQ/kj46Snp8NoNEKWZVQNCEClIjEeERGBXbt2oUaNGsobb7whtG3bFggPZ68K4uvrKwNQ9enTx9Hd3V0ghKBdu3bYtm1briRJKgAqAG01Gs0IlUr1cYOcHKWLv7910qJFqnMqVeGZvXutAODosWMAIACAoih9JUlqb2Vl1axhw4bmzmkMDg6ONxgMHXjqOIfD+afyCvwPzuFwOBzO/zQRGo3GZ82aNR5arVYeN26cpm4ZqcaKwkyfk5KSUKc8ac0viu7dmSv5kxSLv9hY9vX335nr9Zw5rG3YF1+U6cidm5uLEydOwNHRUXFxcXnpNerVqlXDQK0W0OkI3ngDk6dOBQDs3btXDgwMpLIsIy8vT92hQwflzTffFAghQFYWIj7/nKqys9Hs2DHyWHp2v37sFR/PzOQGD2Zp4RV1Sh86lBm3UcqegSAgPDwcu3btAiEEKpUKzuHh6Ovri7t79sDPz0/p3r076dGjx8N7mZEBtG1b4Xs0efJkla2tLXQ6nQAAd+/exdGjR3MMBkM9AHPVavUntra2tvUcHFR9XF1tMkJDqf7gQXrm9OnAAq12E4BQQkhdSukeAL4ARAAhgiB82b59e+2TC0p+fn6m8+fPJxoMhk5caHM4nH8yXGxzOBwOh/MCEUWRSpL0JoB2JpNp6oYNG6Y2aNBAHxUVpXN3d1dee+01oXHjxlCpHpZ0FxuQp6envxpiu2lTIDKS1WprtSXvN3kye1EKZGWx1OetW1mbrL17H9tVr9fj559/RnZ2NmrUqCGPGjXK4pr2587NmywFfvbsB2+NHDnywXzi4uKwbds2olar4dGiBcimTUiKiCAumzdDKKkOuk4dZnAGAPXqsZZnc+dWbH4tWwL9+wP29jg6bhy9cuUK6devH3V3dycAkBEbi5xp0+j5gABqa2uL9u3bP+4IZ2XF2opVkOJe3Tt37jQlJSXpMzMzKwGoTAgJU6vV4htvvKFxd3cXMHgwCrKzsa1fv0LTmDF9xPnzAwFg6dKlK62tradlZ2cDwJ+iKJ4FgBUrVnSoWbPmY889ICBA9vPzSzEajZ1FUUyq8KQ5HA7nFUD19ddf/91z4HA4HA7nfxoPDw/q4eFxv0ePHofOnDmTmJGR4UcIyUlISEi6detWpcDAQKvz58+bMjIyDI0bN9ZYWVlBp9PRw4cPk/j4eKVVq1YW2Gm/YHr3Bjp2BJydy96XENZn2s6OOZkDrJ3W668DLVrg/9i78+goqrQN4E91d1V3FkgAWWJACOiwBAQEEQQdBEQWRUBQQcUFP3dn3BfEFMUiLjPjDC7oMIjIqgLKIgqyCCqC7CEkwRD2QBISsne6a+n6/iiCLAlk6U4HeH7neJJUV937NjPnJG/fe9/XbNYM69avR0pKCvr06YO77rrLFn6hCuCBdOiQ1Rpr2DAr5rPUrl0bjRs3Fn6ZN8+M+flnQXQ4MLdFC/Tr1++MD0nKdP/91vv/17+s/0paiFVEx46YV1Rk7Nq/3/boo4+iVatWp/4/ERIRgYh27YQuLpfQ6e67BbGkfVuJNWuAo0eBKhTc++GHH9SdO3eKHo9nHIDeANCsWbOI0Y884mz28MMCJAl4/XV84vUWuouLH5RleSUAKIrSz+VyvdeoUSNHYWHhT4ZhPN+zZ0/zrbfeelLX9Sd69erlCD15lvznn3/W169ff1zTtK48o01ElwKubBMREVUjWZY/Pf1nRVEa6bo+0Ov19tm5c+ewHj16oHbt2ujWrZuQmJjokyQpeO2/Trd5M1DSs7kibrzR+s80gddeg9q+PX675Ra0S0xE5pQpZvfY2OB+kGCawNdfAz17WgXOytBM19Fj924zo3Fjs+Cpp2zSwoWmJEnli73k361fP6uVl8djbbH/5z/Pv1PgNOuyspDm9drf/PBD2O64w+pjfrr4eGu822479+F9+4BK9nsv0bBhw5K/Gd8KCQnxPPn4465ac+c64XRaleq7dUOOYSC/sBCmaS4CAEVRokVRnNGpU6eQ3377LV/X9cGyLBuKooTZ7fb3R48e7axXrx5M08RPP/2kbdy48djJFe2jVQqWiKiGYLJNREQURLIspwOYrijKNzabLfbDDz9soet6aP/+/ZGenm67rbTkKRi+/96qRK4olXteEIBHH8Xu7dux/qabkN61q2/EiBE2hIcDc+daq+YFBdbqd3UqKLCqfjdsaMVYmsREYNo0NBs40PZxTg6Gaho8Ho+wZ88etGzZsvxzdehg/bd/P/DLL9YZ7Ph4a5v4WUXUMjIy8Mcff8Bms2Hbtm1GYWGhfcTIkbBdfTXQvv25Y997L9C3r9Uy7OzV9vBwoGvX8sdZio4dO9o6duwIVVXhME2XLTsbmDjRqkL/8ssAgCO7dsHhcOzUdV2YOHHic6IovtmlS5fQsLAw0+FwrHnjjTcKTw6ntGjRwog6+YHBhg0bjI0bNx5VVfUGbh0noktJzfi0nIiI6DIny/IJVVWX6boeKgiCuWbNGjMqKkq/UDG1amOz/bklvJJM08SSJUvgCAvDnYpi/Q1y9Ki1Mjpz5p+rsjNnWtW3q8OmTVb7rLLarO3eDUyeDNx2G+o89RR69uyJr776CiEhIb5Kb32PiQG2b7eqt/fvD3z6qbXCflJeXh6mT5+OxMRE365du3zt27e3Pffcc2jWrBlw993A0qVAu3ZnPANRBN5+G/jb386dLzOzcnGWQvr6a9iuugpo1Ag4cuSMYwUnC52FS5L0vyuuuGLiQw89VKdPnz7O1NTUQo/HU7La3cfpdD59yy23hAJAfHy8uW7duhxVVXsw0SaiSw1XtomIiGoIh8PRu3HjxvqgQYMckVbP5Jrze/rBB6ucACckJEAQBAwYMAAul8u6WFKl+5VXTq2QYvx44KqrgLAwYPVqq6p3oOzebSWMAwee+1p8vNXD+s03rZV3AN26dcOJEyfQv39/W5XbspX0LRcE+B55BN7UVHwyZIhRWFhoj42NNU4v0naG3r2BY8fOXYm/4w7r/ZytXr1zt51X1P/+B+TnA6NHA7GxpbYzS09PNz0eT/u6dete8/DDD4c6TxaPO1ld36soiuB0Ot/u37+/q1GjRkhNTcWyZcsKNU3ryTPaRHQpqjm/xImIiC5zNputqHbt2kZkZGTN+/2saUCzZlaSV4k2VpqmYdGiRYiKikL70rZBA38mj6mp1tc5c6yz4oBVcO2FF4Drr6947Ofj9VqrwmcXFYuPB554wmpndtpWcZvNhjvuuMNv0+sAfvn5Z+yMijLruFxCx0aNhO7//S8cL79cduW12rWBp56yCq+1bm31OwesInb5+da/3+m9qydNst5LZRw6ZBWNS0+3tqdHRFhb4UvRpUsXQRRFtG3b9lSifZIJQATwDwDXxsbG4uDBg/jyyy/dmqYNlGW5lE8IiIguftxGTkREVEN4vd5hu3fv9iQlJQU7lHNJEjBv3rlJaTm53W4AQIcyErVS3Xffny3DDMMq8jV3bumr0JVhmtZ4ffqcef3334FHHrFallXkTHYF+Xw+/Oc//zF27dpl9n3gAeGBjz9Gz27dbGKjRhCcTuvDhsLCsgfo0wfo3v3Ma4sWWRXPS+TnWwl5Zba8mybQubP1v/vYsRfcYRAWFoYePXrg5K6MUxwOhw1AJwAvRERE2OPj4zFnzpxiXdfvlGX554oHRkR0cWCyTUREVEPIsnxC1/VbFy5cqHo8nmCHcy5JshLRSjhw4IAJAEWVPff91VfWau1f/vJncty8ObB8uVVorDKOHQP++MNKKEv89JPVE3vNGqtoWgAlJibC5/PZn3nmGaF169YQBMHaPTBvnnXDuHHAqlVWEbfS3uNDD1kr/d26WSvPAPD++9YZ85Lz3MXFwKhRFQssI8P6kCEtzapk/thjlXyHlrCwMNFms50A0DkzM9O2dOlSU9f1O+Pi4lZVaWAiohqOyTYREVENIoriJ4ZhSBs3bqxkBgkoioLJkyebc+bMMdPS0vwX3Jw5fyaCFbR06VKhd+/euOWWW6oWQ+fOwPPPW99PnGit7E6c+GcV84oUA0tIsLZklxQbW7TIGmvdukptlb8QTdPwxx9/4KeffvJNnTq1YPHixWazZs00obQq6IIApKQAgwdbK/xlVaUPCbFWrkvOj9erB9x8s1U9HrCKmCUnly/AEyess9l16wIjR1ofNvih/3lMTIzT6XT+VZblrQBuBlArLi7uxyoPTERUw9W8M2FERESXMV3Xp9tstnXr1q17vkePHihPEa59+/bhyJEjiI+PN/Lz8+0A0KdPHyEtLc03Y8YMoUGDBvqjjz7qsJVS1KpCvvii7PZY53H8+HEYhoHQ0NCqzX+2kSOtr2PGAEOGWN9ffbWVMP71r8Dx41ZbrbKsW2eNIUnWM2vWAMuWASXF26royJEjSEhI0Nq2bSvm5eX5lixZotpstv26rq/RdX0HgGm33377hfflL1hgvZeS7e07dvyZXNtswGefAevXA7NmAdOmWSveYWHW6ydOnNsKrDSqau0S+Ogjaw5ZrvT7Pp3H40Fubi4Mw+gAANw2TkSXEybbRERENUhcXNzHkyZNerZhw4ZFDocj7Hz3qqqK6dOn6ydOnHA0aNDAaNu2rdCxY0eEh4fDbrfj+uuvt/Xq1QvTpk2zf/HFF4iOjkZsbCyuvPLKygWXmgr06wfs3Vuu2w3DwIYNG5Ceng6bzYa250t8q8LhsFanAWs7tdNpJY3/+Y8V8/TpVj/osytyp6RYW9EnT7aemz690ol2SkoK5s6di6ZNm7qbNm0aevToUU9qaqoLwJ5t27Y11DStPoB+siyvAwBFUYbbbDafJEkX/gREkoDoaOt93X+/9X5fecXa7t6kiXVPSMifrdmefRb45BPgppsu/IEDANx1l7VVfeVKa3w/8Hg8WLlypWfXrl2CKIrxpmnO98vAREQXEcE8vUcjERERBd3bb7/9qdfrfeyuu+4qNUH1+XxYvXo1tm7dakZFRZlDhgyx1T7PtufDhw/jl19+MfLy8oSMjAxbmzZtMHz48IoHpqpWFexp0y64wp2QkODbvn27cOjQIcHlchk33HCDvUePHhWfsypM04qzdWsr8a5bF1iyxGotZppWZe0BA6xWVi+9ZCWslXDkyBFMnz4dACCK4lRN054EMA/ATwC+kmU5V1EUUZZlDQAURWngcDhSRo0aVbtJSbJcEbpubZ+fNs2KOSrqz+3ekyYBN94IPPOMVSgtNdXaWn7PPef+2zz+uLWKHREB1K8PXHFFpd7/2VJTU7Fw4cJiwzC+VFX1FVmWj/tlYCKiiwxXtomIiGoYr9f7hCRJ7TZu3HiDy+WyXV1yHvmkOXPm+NLS0mxDhw4VrrnmGqHUM7+nadKkCUaMGGEHgB9++MG3adMm2/Lly9GoUSOoqgqPxwNVVeH1eqGqKjRNg6qq0HUduq4buq6bhmHAMAzUrVtXKBw/XihwuQS73W4+9dRTtpLt4cXFxdiyZQtSU1ONQ4cO2SMjIzFixAg0b968HPuYA6Dk36WkuvuCBUBiovV99+7Wam6tWsCLL1rXmze3zmrbbBXaLh8VFYXmzZtj37598Pl8TQG4ZFn2nn7PaYm24HQ6Z1133XWuSiXagLWyvWmT9f2AAdY28SVLrJ9TU62k+YcfgEaNrPcYE3Pm8/Hx1k6A/HyrpVvr1pWL4yyqqmLFihXeXbt2FWmadrcsy6v9MjAR0UWKyTYREVENI8uyqSjKv9PS0r5MSUnxXn311aeaFmdlZeHw4cO2IUOG4C9/+UuFx77ttttsqqr6UlNThX379pmiKEIURVMURUiSBEmSzNq1awuSJNkkSRJEUbSfvAeiKKLZgw/Ce+ONcL/yClatWoXPPvvMeOaZZ+wAkJSUhHXr1qFjx472AQMGoEGDBn78V/GDYcOsLdOffGIlpVdfDRw9ap1rvv9+qxCZ3Q588IGVrD77rLWtfOpU4OmnrarckmS9pijAokXIFkWsUdUi89dfQ0IaNrTpdvuArps2RQAoq1LbrS6Xq3uvXr0kv7yn5cutXuELFlh9yA8etNqkvfgi8PXXVqXyv//9z/sPHbJWvhMTgfn+29l99OhRzJ8/3+31epdpmvaELMs5fhuciOgixW3kRERENZCiKLEAEqKjo91t27Z1dO3aVQKA6dOn++rXr49BgwYFp6PI4cPWanBkJIqLi/GPf/wDHTp08N1xxx22RYsWGcXFxfb77rsvKKGdl65b264//dRKpqOjrZXs0ng8Vj/xlBQrkW3aFFi4EOja1WqL9e23MGQZCb16oTA8HImxse67588//mPfvk1bpKZmdtyxIwTAVQCSAfwA4DMAsiqKb/3cu/cXHQThynpLllitvXr1stqZbd5sFXXzeKw2X40bV+z9qarVJqxfvz/P1XfrZq3qL1hgFVUbPx7Yvh1wu/8soOYH+/btw/z58926rj8SFxf3pd8GJiK6yLH1FxERUQ0ky/Juu90+LC0tbfOmTZt8uq7D5/MhIyNDaNasWfB+f2dnA2+8AQAICQnBQw89hJ07d9p8Ph+io6PtmZmZRtBiK42uW+3Kpk2zEtA5c6zCYWUl2oC1mm23A61aAe3bA5GRwOjR1tnuXr2AKVNwpLAQ3w4dilV9++JodHTov198senudu2wdMiQWjDN2gDyAHQF8DqAJABTFwwfHuVu2rROnY4drYT6wAGrj/Xu3daZ8j17gL/9DbjhBiupr1cPePVVq8p4t27Arl3Ac89ZK+zZ2dbq9c8/W9fnzrXe19atVuwbN1o9uAsKrBZn110HPPmktT3eT4m2aZrYvn075s+fX6RpWj8m2kREZ+I2ciIiohpq7NixCxVFWel2u5cvXLjwuhYtWoSapilcc801wQvK5wN27jz1Y3R0NOrVq2dOmjRJsNvt0DTNvnv3bsTGxgYvRsAqAPbtt1Zy+c031hbrJ5/02/DR0dF44oknMGPGDEPX9S8B9DMMo65pmn8/Ob8J4MBpjyw49Pbby3r16hVi69LFujJ79p+vlvTRXrDgz/hTU63EubgYaNDAWmG/6SYgL8/638HrtSqQHz1qJdu9e1s9yENCrET+xRet+374ARg0yCqI5ie6rmP+/PnFBw8eLDQM41629CIiOhe3kRMREdVwiqKEASgEgI4dO+qDBg0K7oflqmoVETvZ61nTNCQkJKBx48ZIT0/HkiVLcPfddyNoHwrs329V4j5xAnj9dWtFuhL9wc+nuLgYn3zySbHX613s9XpHAqgPqzDaodLuVxTlCrvdnvbCCy9Ifu83Xhqfz/qQwTCAu+/269CqqmLOnDnu9PT01aqq3lVS/I2IiM7ElW0iIqIaTpblookTJ+649tpr2wc90QaAli2Bd945lcSJooiOHTsCAOrXr4/Dhw9jzZo1vmuuuaZ6t7u73cCDD1qrusuWWe2sbIEJYePGjYbH41mlqupIWZZNlF0QDQAgCMIjrVq1Mqol0QasVf0vvrC++pHH48EXX3zhzs7OXqKq6v2yLNesYwNERDVI8H9hExER0QWJonioUaNGHYIdBwBgyhTrLHMZXC4XnE5n9SXa+/cDH35orV4//DDQp49VNTyAfD6fYJrm0ZOJ9nkpiuKQJOmFLl26VK6Rd0UVFQHr1gETJ/p1Rd/tduPzzz935+XlzVNV9TFZln1+G5yI6BLEZJuIiOgioGnavszMTB9qQnHTVq2s1lFXXVXqyzabDdVyTO3wYeC996zzztHRwBNPANWwcmyaJlJSUop1Xd9UzkeGRUZGhlW6r3ZFvf46cO21Vi9tP/F4PPjss8/cBQUF01RVfb48HzIQEV3ugv8Lm4iIiC7IMIyZ8fHx3rS0tGCHYrWYevnlMl8+fPiwERkZGbj5s7OtCt3vvWcl/DfeaBVAq4ZEOycnB3PmzEFGRkaYaZqzLnS/oig2URQ/6NevX7jg53PjpUpJsSqS33+/34Y0DAPz5s1zFxQUzGOiTURUfky2iYiILgKyLO/QNO3ZGTNmaL/++qsa1GD+7/+svs1lsNls8Hg8/k/ICguBP/6w2mBt2wb8+9/ASy8Bder4faqyrF271pOamgq73f4fWZb1cjxyY2hoqCsmJibgsaGoCHj6aeC776z2ZX6yZMkST3p6+m+qqj7ORJuIqPyYbBMREV0kZFmebhjGsE2bNgU32XY4gI4drUJkpejVq5d93759QmFhoX/m0zRg/Xpg5EhgyRLg99+B998PWPGz82nevLkLAAzDKFdPaVEU7+/UqZN/GltfyPjx1nl1P+4q2Llzp5mcnJypquqdLIZGRFQxTLaJiIguLolut9s2d+7cooKCguBF0bWr1VaqFFFRUYiNjdU/+OADc9u2bVWb56uvgFtuAXTdSiZfesmvyWRFpaamegRBeFOW5d/Kc7/dbu/TtGnTwO8f37DBqhL/2GN+G/LEiRP47rvvPKqqDpJluchvAxMRXSaYbBMREV1EZFneaxhGk/3797szM8/bbSqw3noLcDrLfHnw4MGOwYMHC99//z0++ugjw+erQOFq0wRWrLAqi7vdVpuxXr2ADsEtxp6fn4/k5GTTNM2p5blfUZQ6mqY1iY6ODmxgXq9Vjb1hQ799EGEYBubPn1/k8/lel2V5p18GJSK6zDDZJiIiuogoitJIkqT1giBENm3aNHiBTJhgVf8+j9atW+PFF1+E2+22//rrr+Ub9+efgR9+sIqfDR9u9c3u3t0PAVeNaZqYO3euKgjCAlmWs8v52DUREREeu90e0Niwbh1w663AgAF+G3LVqlVqfn7+RsMwpvhtUCKiywxbfxEREV1cGqqqGnvVVVfp1dJeqyyvvWYV5LoAURRRq1Yt8+jRo+ffSr1rF5CcDMycaSXxq1b5K1K/WL58OTIyMiQA/63AY/VcfixUVqrkZGtVe+pUv/XUTk1NxdatWws1TbuXBdGIiCqPK9tEREQXkZNbejscPnzYd+zYseAFUqcOMHnyBW8rKipCRkaGcFUZPbnh8QAvvgg895x15njRIuD22/0cbNXk5ORgy5YtJT/+rChKebPaXzMzM43ExMTABObzATt3AnfdZfUZ9wOPx4MFCxYUa5o2XJblLL8MSkR0mWKyTUREdJGRZXmnJEm7MjIyKnAQ2s8kCVi5EsgrCT5SAAAgAElEQVTJOe9ttWvXxvDhw7F69WqsWbMGqnqykHphITBmjLVF/IEHgMWLgWuvtcatYXJzc+FyuRIBtLLZbD8A6KQoygV3B8qynK/rep9vv/226PDhw/4PbNEiq4Dcgw/6bchVq1Z5fT7f17Isr/HboERElykm20RERBchr9f7yI8//ugJ2uq2zQYcPlyuglxt2rTBoEGD8PPPP+PjiRPN/DFjoH/2GVC7tpWwd+gAhIdXQ9CV07hxY4ii2Mxms/U1TTMNwGYAvRRFaaYoynmbfMuyvE3X9fu//vprt66Xpy13ORmG1f7sn//025BpaWmIj4/3qKr6vN8GJSK6jDHZJiIiujjtEQThSFZWEHf6Dh8O/O1v5br12hYt8LrTib6mae5avx4fHjqETyIjzaNeb4CDrDpRFDFq1KjQ8PDw90zTHC0IwhEAP9rt9hkATiiKYiqKUmap9Li4uG9VVf1148aN/ulTbZpWpfa33gKaNfPLkD6fD99++22Rrut/k2X5hF8GJSK6zLFAGhER0UVIkqQJjRo1atymTZvgBfH44+dt/wUA0DTrXPHEiZCaN0ebl1+2GePGoU1+PqZMmSJMmzYN3bt3R0REBOrXr4+kpCSYponk5GQjIiLC3rlzZ7Rv37563s95XHHFFXjooYecH330kdcwjP6yLJuKoowAULK1YLuiKHA4HO/puv6eLMvHT3/e6/U+vX79+p3t27cPqVWrVtWCWb3aKk7XrVvVxjnNtm3bzIKCgj2mac7y26BERJc5IaiVTImIiKhS3nnnnX333XdfTOPGjYMXRH4+sH596QXNfD7g6FFg5EggKgr4/HMgJOSMW7Kzs5GUlITNmzebHo9HME0T0dHRhmmaaNq0qd3tdpu7du0STNM0GzRoIHTp0gXt2rWrnvdWhh07dpiLFy8WAMBms/3s8/lu6ty5M66++mq4XC5s3bpVS0lJ+fXVV1+95exn33rrrXdiYmKeHTFiRMi5I5fTiRPA+PHAq69a/65+4PV68f777xd7vd4esixv88ugRETElW0iIqKLkdfrbbxnzx6IooiGDRsGJ4isLCuZzss7s+3Uxo3ABx8A111nnSvu1KnUx+vVq4cePXqgR48ep1f3Pr0ptXDjjTdi06ZNQkpKirl48WIhKioK2dnZqF+/PhwOB0zTREREREDeXmnat28v7N69W7fb7Y5GjRrd1KZNGzRo0ODU65qmiSkpKaV+AqJp2rgDBw7cn5SUFNK6devKBTB5MtC4sd8SbQD45ZdfNADfMdEmIvIvrmwTERFdhBRFuc3hcNwhCMKI5s2bu4YOHRoqBaOSd3Y2UK+e9f2vvwJvvw08/TTg9QKDBvmt9zMAfP3110ZycrJdkiRomgbDMCAIAuLi4vw2R1Xk5eXh008/LfZ4PCPi4uIWl3aPoihdJUla89RTT4VU+EOCzZuBpCRg8GCruJwf5OXl4cMPPyzWdb2lLMsBKJlORHT5YrJNRER0EVMUxSWK4vzY2Njb7rzzTle1B/B//we0aQOIIpCaCnTtCgwbBtjtF362gjRNw5EjR9CsWTN4PB7Mnj3b1HXd9+STT/p/sgoqKCjA//73P3dxcfH4MWPGvHO+eydNmjQxOjr6+VGjRoXabOWsVVtcDDz7rJVo+7EP+aJFi4qTk5M/HDNmzCt+G5SIiACwGjkREdFFTZZlj2EY8Q6HIzi/071eYOJE6/v33wfuuScgiTZgVQWPiYmBIAgICQlBbm6ukJeXZ5s5cyYOHDgQkDnLIysrC9OnT3cXFxe/d6FEGwB0XVfS09P3bNmypfwrHrt2ATExfk20s7KykJSU5NM07S2/DUpERKcw2SYiIrrIiaJYa//+/UZ+fn71T96zJxAdDTzzTLVP/eyzz6J169amx+PBDz/84NN1HUlJSUhISKi2GA4ePIhp06YVFxQUvDRmzJhx5XlGlmXN6/U+sHr1ak9RUdGFH0hOBp54otxt1spr1apVbtM0J8uynOvXgYmICACTbSIiooue1+t9ITc391+zZ892Hzt2DNV6ROzhh63WXoZ/WkhXhMvlwp133mkbPnw4Tpw4YXv33XexePFiLFy4EMePH7/wAFWUkJCAOXPmFGmaNujNN9+cWpFnZVneDWDW2rVrPee90TCAhQuB558Hqtoy7DTHjx/Hvn37dMMw/u23QYmI6Az2cePGBTsGIiIiqoKePXti7dq1P2maFrp9+/b2devWdZ5eITugBAH48kvgzjuB556rnjnPEhISgtatW6NVq1YYOHAgVFU1fvjhBxQVFQm1atVCWFiYX+dzu91Yvny555dffsnRNO2vsiz/Vplx1qxZ89vx48f/3rp1a6nMGKdPB9LTgRdfrErI51i1apUnMzPz/TfffHOFXwcmIqJTmGwTERFdAnr27GnedNNNa9auXeux2+23tGnTpvrae8bEAK1bA3/5S7VNebbQ0FBERkZCEAQ0b97cJgiCuX79eiE+Ph7du3eHUMWq6F6vF3v37sXatWvdy5Yt82VnZ8/RNO12WZYPVXbMnj17Fq9bt87Iz8+/sW3btuI5NxQWWjsHpkwB/NjezO12Y8mSJbqu6yN79uxZjn3sRERUGeyzTUREdGn5es+ePa9+/PHHYbfcckutSvdzroiwMKBlS+C116zWX0EmCAK6d+9uu/766zFt2jTfsmXLbIMGDTrvM6ZpIi8vDzk5OSgsLITb7YZhGDh+/Lhn//79WkFBgcvpdO7weDxzTNOcM3bs2Cx/xOrz+T7du3dv3IkTJ1C3bt3TAwJeeQX46ivgqqv8MdUpW7ZsMex2+5I33ngjw68DExHRGZhsExERXUJkWU5TFKXx8ePH7/jmm2/m1KtXL7RatpTrOrByJTB5sl97a1eFJEno1q2bbc2aNT6cVafGMAwcPHgQ+/btM/bu3VuUnZ3tEgTB7XA4DgJI8/l8x3w+n1fTtBQAPwOIf+WVVzR/xyjLcu6kSZP+sW7dupeHDBkScuqFL7+0Em4/f1iiaRo2bNiger3e8X4dmIiIzsFkm4iI6BIjy7IB4NsJEyY8/b///e8Tm82Gzp07m3369AlcH+7WrYFt24CsLOCKKwI2TUU1bNgQuq6fyv6PHTuGTZs2eRITEwW73b5f07RvDMNYDWCnLMt+Wa2uKF3X/5uUlPRK//794XK5gOPHgdxc6wy8eO7u8qrYtm2bCeCXkwXaiIgogJhsExERXaLefPPNzxVF2WKz2Sb9+uuvgzZu3GgahiHUqVPH53a7vQ6Hw4iKijIzMjJsDRs2tA0dOjQkJCTkwgOXZetW4K9/tRJFR834E+PQoUPwer1Cfn4+li1b5j5w4IDb5/N9YBjGZ2PGjDkS7PgAazfC22+/vWLr1q13dO/e3YYpU4DQUGtrvh8ZhoH169cXe73esX4dmIiISiVUa3sQIiIiCgpFUeoLgnDINE2XzWY75vP5ugNwAugAoADAslGjRiEmJqZqE6WkANdcU/WA/aSoqAhTp06Fx+OB3W7/XlXVIbIse4Md19kURekUGhq6/oWBA0PtixdbPbX9XEU9Pj4ey5cv3/zaa6918evARERUqprxsTMREREFlCzLxwGUtmydrCiKIIriofz8/KpX4nK5gN69gR9/BGy2C98fYCdOnIDX64VhGDAM4/6amGgDgCzLW99XlP1FL78cW/ull/yeaAPAxo0bC7xeb/Ar2BERXSaC/1uQiIiIgkqWZVMQhHVZWVlV3+7WqJG1Bbq42A+RVU1mZiZmzZrl1nV9oCzLgizLJ4Id0/k03759W64kmbjlFr+PnZWVhaysLB+ApX4fnIiISsVkm4iIiKCq6n82bNhgVvl4mSgCS5YACQn+CawKtmzZovp8vn/Jsrw82LFckCC0unXlyps3DB9eGIjht2zZogGYLsuy3yuqExFR6ZhsExEREQAkORyOA8uWLav6knRuLtCzJ5AR3DbOKSkpHsMwvg1qEOU3MalVq/8WFBX5/D2wruvYvn27oWnaVH+PTUREZWOyTURERJBl2a2qavuEhITsxMTEqg1Wpw6QnQ00bOif4CrBNE0UFha6ABwMWhDlJQjPAfj2uzvuyI6IiPB7PZ3k5GTYbLZdsizv9ffYRERUNibbREREBACQZblQVdW7ly5dWuzxeKo2mM0GNG0KZGb6J7gKOnDgAOx2+2EA2UEJoLwEoSmAQQDWucLC7mrZsqXfK6Nt2rSpwOPx/NPf4xIR0fkx2SYiIqJTZFn+zTTNH3777TekpKSg0me4XS5g9GjrDHcQ5OTkQBCE32VZrrk9TgVBAHAvgJeVcePSVVXt0aJFC79OkZeXh/T0dAHAxbKdnojoksFkm4iIiM7g9XqXrV+/HnPnzsX27dvPeE3TNOi6Xr6Bxo4FvvwSKO/9fuR2u6Fp2rFqn7hiRgJoBmAHgF716tXTwsPD/TpBUlKSabfbV9TUlmdERJcyJttERER0tm8EQRhns9ni1q9f787JyQEApKamYvLkyb5PP/3UrWnlKGotCMDEicDWrQEO91xFRUW6YRjBrdB2PoLQCNbfYR/DNA2n0/lghw4davl7mq1btxZ6vd7/+ntcIiK6ML8X4SAiIqKLmyzLOQAUAJg4cWLBJ5988lbXrl3FgwcPagDeyM/Pv3nZsmW3DR48OCQ+Ph61a9dGTEzMuQMJAnD4MODze4HtCzp+/HgxgEPVPnH5PQFAh2nOUhRFdDgcg9q0aSP4c4KsrCzk5eUZAFb7c1wiIiofrmwTERFRmcaOHftvVVU7b9q06auMjIy1pml+rKrqw4mJicYXX3zh/u677w7MnTu34KefftJ8ZSXVUVHA6urL93w+H44cOeIAsLnaJq0Ia1U7BkBJ0bIederUMSIiIvw6zbZt2zQAM2VZNvw6MBERlQtXtomIiOi8ZFlOBHDfaZe8iqJcf+DAgRsBfAMgbOPGjV/v2LGj3fDhw8Oio6MBWP2dDx48CM8rr8DncqGtaZ6sCRZYGzZsMAAkAdgX8MkqShDCYf2bPQTTLLYuCV1iYmJc/pzGMAxs27ZN1zTtY3+OS0RE5cdkm4iIiCpMluVkAMknf8xRFOVGr9c7bObMmTNHjx4dcsUVV2D69Onu3Nzcg4ZhrL/txReH73zttbodBg/2axwejwc5OTmw2+1QVRVJSUn65s2b8zVNG1xDK5EPAbABprmn5ILL5fprdHS05M9J/vjjDwiCkCTL8h/+HJeIiMqPyTYRERFV2cnE9usJEybUmj179gcdO3YMzc7OdmuaFivLspnz3ns3JW3cGIbBg51Vncs0TezevRtr164tyMvLk0RRPGaapkMQBI/P51uvaVqcLMtpfnhb/iUInQD0BfBQySVFUewOh6N7kyZN/DrVrl27ijwezzS/DkpERBXCZJuIiIj85s033/xs/PjxeZs3bx6p6/r0ktXlKc8993+RNtuPTQ8cQHSzZlWaY8WKFZ7t27dnqqr6KIC1Y8eOrf7eYhVl7Z//F4DxMM3Tz1CPqF+/vq1OnTp+m8rn82Hv3r0OAEv8NigREVWYYJo1cYcVERERXWo8Llfmph49QncPHerr1KlTrSuuuAIRERGoV6/eGWe5i4qKkJ6ejry8PNhsNlx55ZWoX78+BEFAfn4+PvjgA7eu61fKspwXxLdTMYLwOICDAFbg5B9fiqJIALyiKKJt27bo1KkTSs67V4bP54Ou60hPT8ecOXOOvv7665UfjIiIqowr20RERFQtXF5vl/pHj6YfP36899q1a++32WzXGIbRNDw8PGTUqFFh+fn5+OWXX9ypqak2SZISTNPcB8BpGEaPsLAw14ABA8IEQYDdbs964403LqZEuw2AngBexmmrHDab7Zk6deoY7dq1sx86dMiYOXOmHQDsdrsJ4NStpmkKp31/6mvJf6dfFwQBpmnCbrcfrZ43R0REZeHKNhEREVUfQZgKYBVMcyEAKIoi2O32jxo0aPB4VlaWW9f110zTnH36qrWiKIIgCLeLojhVVdVoQRDmxsXF3VfmHDWJINgAjACQAdNcVXJZUZT6Dodj/2OPPRZWv359AFbCnJWVBfNk1XabzVbm17O/L/kZAObOnVuQkpLylCzLs4PxlomIyMKVbSIiIqpO2QBOfdIvy7KpKEpcVlZWbU3T3pdleevZD5w8971UUZQVkiS9q6rqouoMuIruBtAPwIOnX5Qk6Z8dOnQQSxJtwFqVPv3nyjBNE4cOHXIAWF+lgYiIqMq4sk1ERETVSxAaAzBgmseCHUpACUI9AI8BWAzTTCy5rCjKdU6n85fnnnsuxOXya3ttZGVlYdq0aVmvv/561bJ2IiKqMluwAyAiIqLLzjwA44IdREBZe7pfBVB8VqItOJ3O6bfeeqvL34k2AOzduxeCIKy68J1ERBRo3EZORERE1e02AMXBDiLAmgDoDKuv9ukGhIaGXt2xY0ehlGeqLDk5Od/r9S4IxNhERFQxTLaJiIioepmmG4LwMQTBA9N8Idjh+J0ghAL4BkBvmOapHuAnV7Xf6d27d7jN5v/NhaZpIiMjQwSwze+DExFRhTHZJiIiomBYAUANdhABMhbAlzDN3LOu93a5XM1at24dkEmzsrLg8/kKARwIyARERFQhPLNNRERE1c80FwM4CkH4a7BD8StB6AkgE8DHp19WFMXudDr/06tXr7BArGoDwL59+yAIwo8nq7cTEVGQMdkmIiKiYHnq5H+XBkFwAXgBwC6YZuHpL9lstqfq1avXtF27dgGb/uDBg4Ver5fF0YiIaghuIyciIqJgeQKmaUIQ7DBNI9jB+MFAADNgmqtPv6goSoTD4Zg0aNCgMKtIeWAcOnQIADYFbAIiIqoQJttEREQUHFai/R6A6wH09OfQiqKEORyO6bquPy3LcrY/xy6VIFwLYDiA585+SRTFsa1atXI0bNgwYNMXFRXB4/HYASQHbBIiIqoQbiMnIiKiYPovgJcDMO5duq7fA+DGAIx9JkGwA7gawNcwzfTTX1IUpSWAp/v06RMSyBCSk5MhiuJ6WZZ9gZyHiIjKj8k2ERERBY9ppgBwQRD+7q8hx48fPxzAzJM//uivcc9jOIARMM2Fp1882errq1tvvdVVu3btgAawefPmAo/H80FAJyEiogphsk1ERETB1hyAX6qST5gwYZRpml+d/LGeLMsef4xbJkEQAQwD8Eopr94kSVKLTp06Be6gNgCv14vjx487AbA4GhFRDcJkm4iIiILLNGcCuAuCUKcqw4wfP/7FkJCQTzp16gSn0/mVLMsn/BRh6axqZzNgFUXbf/bLLpdr/M033xywVl8l9u7dC6fTuU2WZW9AJyIiogphgTQiIiKqCZ4D8DcAMSUXFEUJA1BbluVj53tQURRBEITRoihOvO+++1xbt271er3e6qjK3Q1APQArS4mprdPp7NK+ffuAB7Fjx47C4uLi/wZ8IiIiqhCubBMREVFNMBWnbSVXFKWZw+E47HA4DkyaNOlfiqKU+jeLoiiCJEmfRUZG/nv06NGuqKgoNGvWzClJ0mOKoogBi1YQ6gH4O4B7YJraWTFJkiTN79mzpySKgQsBADweD/bv3y8C+CagExERUYUx2SYiIqLgM00PgKYQhDkn+1Jv6dOnT63Ro0dLuq4/LwhCqS2tBEF4IDw8fPjjjz8e1qBBAwBAbGwsoqOjm4iiOCGAEY8D8AtMM//sFyRJ+k+TJk2a33DDDfYAzg8A2LZtm8/hcHwvy3JuoOciIqKKYbJNRERENUUGAJsoiq+2bt069IYbbnA0bNgQISEhME3zGkVRzmifpShKE7vd/tGwYcPCnE7nqeuCIGDo0KGhNpvtb4qi3Or3KAXhZgAbYJ3XPoOiKDc7HI5Rd911V4h1pDtwfD4ffv31V4/X650U0ImIiKhSmGwTERFRzWCafwAYVf/w4ed69uwZAliJ80svvYS2bdt6JElKUBSlBwAoihIriuKWXr16uaKios4ZKjw8HCNGjAiRJGnxhAkThvgtRkEIA/AogEyYZuHpL51s9TWlb9++oSEhAW2rDQA4cOAAfD7fYVmWtwR8MiIiqjAm20RERFRj7IuJGfbAjBkhdSIjT12z2WwYOnSoa9CgQc1DQkJWvv322wdEUdw8cODA+t26dSuz2GvTpk3x0EMPhTgcjtnjx4+/zU8hXg9r+/jqs18QBGFQSEjI1e3atfPTVOe3a9cuj6qqn1fLZEREVGGsRk5EREQ1xvxHH725a9u2Ri+b7YzzzoIgIDY2Fq1atQo5duxY08jISISHh19wvKioKNx///2hs2bNWjR+/PgRcXFxSyodnCBcCyAOwF1nv6QoSh1RFGcMGjQo4K2+AMAwDCQmJsLn83114buJiCgYuLJNRERENYbdbo+KKi62o3VrwOcr7XU0bty4XIl2iSZNmuDBBx8MdTqd8ydNmvSaoigVP0wtCCKAGwFMg2nmnP2y0+n877XXXhsaExNz7rMBkJKSApvN9ocsy/uqZUIiIqowJttERERUIyiKIvh8vhsib7wRGDSo1GS7sqKjo/HEE0+EREREjJUkaY6iKLEVHOI+AB1hmvPOfkFRlOGSJA3o27evs5TnAmLLli2FHo9nSnXNR0REFcdkm4iIiGqKa+12e+1GjRsDsgzMmePXwSMiIvDYY4+FxcbGDnG5XJsnT578j3I9KAhOAC/B2kJ+BkVRWomi+Pm9994bKkmSX+MtS25uLg4ePGgDwC3kREQ1GJNtIiIiqhHsdnv/mJgYCIIAFBQAzz8PFBX5dQ5JkjBo0CDXs88+G+JyuZ6cOHHi+beVW/27ZgAYCdPMOP0lRVFaSJK08tZbb3VdeeWVfo3zfH777TdVEITpsiwXVNukRERUYUy2iYiIqEYQRbFj8+bNQwEADRsC2dlAgFpohYaG4pFHHgmtXbv2m5Ikfa4oiquMW+8BUAggseSCoijChAkTHhVFcUfv3r2jO3fuXG1/TxUXF2Pbtm0+TdPeq645iYiocphsExERUY2gaVpyWlqaeupCTg4QGWl9DYCIiAg8/vjjoTExMcMkSUop6eF9iiA0AtAcwASYpg4AiqI0dDqdv9SrV+/fo0ePDu/SpYvNWvyuHuvXr9dsNttXsiwfrrZJiYioUtj6i4iIiGoE0zRv2L59uzRo0CDrQt26wGefAWFhAZvT6XTi3nvvDd29e3fo8uXLV7zzzjtJHo/nMwBbX3c47pN0fa8ybtxRKMr1DodjuMPheLpz586OXr16SdXR4ut0OTk52LJli67r+phqnZiIiCqFyTYRERHVFM2bNm1qAvhzqfj224G//x344APAEbg/W2JjY9GyZcvQlJSUTomJia31DRt8P/bvH7K7U6cCh8MxOSwszGjZsqWza9euUp06dQIWR1lM08TSpUvdAN6WZTmt2gMgIqIKY7JNRERENYLP55skSdKHAP5soi1JwK+/AgcOAFdfHdD5HQ4HWrdujdbR0aFYsgTmc8+h+3XXRYqiiLAArq6Xx549e5CWlpat6/o7QQ2EiIjKjck2ERER1RSrDx486DBNE6fOQdtsQHx8wM5tlyohAYiJgdCrFyKrb9YyqaqKpUuXulVVfVCWZfXCTxARUU3AAmlERERUI8iyfMTn8+mFhYVnvmAYQJMmwMaNgQ8iPh548klg7NjAz1VOa9eu1QzD+F6W5bXBjoWIiMqPyTYRERHVGHa7Pc/j8Zx9Edi6FejSJbCTmybw4YfApEkBPR9eEZmZmdiyZYvq9XqfDnYsRERUMUy2iYiIqEYxTfPci3/5CzBgALBvX+Amfu894NprgYEDAzdHBS1fvrzI5/ONlWU5I9ixEBFRxdSMj22JiIjosqcoSojNZmsQERFx7ouCADRqBOTlBWby9HRg3jxg+XJrrhrgwIEDOHbsWKHP5/s42LEQEVHFcWWbiIiIagRRFN++5pprdKfTWfoNn39ube/2ev07sc8HvPsuMHcuEBXl37EryTRNfP/994Wqqr7IomhERBcnJttEREQUdIqitBQE4f/uuOOOkPPe2L8/MHu2fyefMcMqwtaypX/HrYI9e/YgLy8vA8C8YMdCRESVw23kREREFFSKoghOp/PTbt26iRfsZ52cDISHn/+eikhLA0QRePppq81YDeDz+bBixYoir9f7N1mWfcGOh4iIKqdm/FYhIiKiy9ntISEhnXv06HHhRYDwcOCWW4A5c6o+a0n18cxMqwBbDZGYmIji4uJ9AL4PdixERFR5TLaJiIgoaBRFiZAk6ZN+/fqF2e328j00bBgQG1v1yffutc5rP/ts1cfyE5/Ph1WrVhV5vd6XZFkupSw7ERFdLJhsExERUdA4nc6Zbdu2rdeyIueln37aWo1OSan8xLm5wFNPASNHAmUVZAuC3bt3w+Px7APwY7BjISKiqmGyTUREREExfvz4AaIo3tqvX7+KZ7sTJwLz51d+8nXrgA4dgPbtKz+Gn/l8PqxevZqr2kRElwgWSCMiIqJqpyhKG1EU5w4ZMiRUFMWKD7BunfXV56t4YbOtW4GPPgJWrqz4vAHEVW0ioksLV7aJiIioyhRFuUJRFKmc93YVRfHX22+/vXbz5s0rN6EgAP36AX//e8Wfff55YOzYys0bIKZplqxqv8xVbSKiSwNXtomIiKhKFEW5AcBGURSnAnjqPPcJTqfzA1EUHx06dKizVatWVZs4Lg5o1Khiz7z7LvDKK8DNN1dtbj/bs2cPPB5PGoCatdxORESVxpVtIiIiqjRFUZoA2AgAmqY1Pt+9Dofj5dq1az/0/PPPVz3RBoDu3YFNm4BFi8p3f0IC8NtvQKdOVZ/bj0zTxE8//VTo9Xrf5Ko2EdGlg8k2ERERVZokSZNOfvsBgMFl3acoSowgCONGjBgRFhIS4r8ANm8Gfv/9wvcZBrBmDfDqq0BUlP/m94O9e/ciJycnF8DCYMdCRET+w2SbiIiIKkVRlI3oGdAAABWWSURBVKamaQ4fMGAAJEkaAKB2Wfc6nc6pPXr0kOrUqePfIN5/H5g0CSgoOP99X3wBJCUBN9zg3/mryDAMLF++vEjTtGdkWTaCHQ8REfkPk20iIiKqFEmS/nX99dfbO3fujA4dOjSWJClJUZT7FUU5o7y4oigxhmH07Nq1qz0ggdx/PzC4zEV14OhRwOu1CqMJQkBCqKwNGzboxcXFv5umuSTYsRARkX8JpsmjQURERFQ6RVGuBZAmy3L26dcnTJjwSK1atT546qmnQiXJKkKempqKNWvWFGZmZgo2m+1rVVU/BnBAkqQvunbt2ueWW24JTGHWjAzA4QDq1Tv3NdMExowB6tcHXnghINNXVk5ODqZOnerWNK2tLMv7gx0PERH5F6uRExERUakURWkPYIcgCM8D+HfJ9QkTJjzgdDo/HDlyZEhJog0ALVq0QIsWLcLz8vKwdevW+3fs2DGsuLhYioqK0nr06BG4vzkaNgQ++wxISQEmTz7ztcOHgT/+ACZMCNj0lWGaJpYuXeo2TXMyE20ioksTk20iIiIqy46TX0+dxVYUpYUoih+PGjUqpEGDBqU+FBERgV69ejl69eoVfvJSufpvV0lY2LlbxPPygNtvB1atsla+a5CdO3eaaWlp6bquvxvsWIiIKDB4ZpuIiIjOoShKx5Pf7nA4HD4AGD9+/ABRFLf07ds3tFFF+1sH2j33AG+8ASQn/3lt6lTg7ruBMj4UCJbc3FwsX77co6rqUFmW1WDHQ0REgVGzPuYlIiKimmLjya+dxowZ41MU5TqHw/HNyJEjpWbNmgUzrrK9/TawcCGQmAisXg3k5wPjxgU7qjMYhoGvv/666OT28Z3BjoeIiAKHK9tERERUGgkAZFn2KYoiiKL4VKtWrfQam2gDQFwcsG0boGlWkt2/PyAFfgd7RaxcuVLNzs7eouv65AvfTUREFzNWIyciIqIzKIpyNYCUkz92FUXx+Tp16tzx0EMPhYaEhAQztAubMweYOBGYMgW49dZgR3OGhIQELFmyJEPTtDayLJ8IdjxERBRY3EZOREREZ7Db7Q/Url3bl5OTYwOwsVWrVsUDBw4McTqdwQ7two4ft9p9degQ7EjOcPToUSxZssStaVo/JtpERJcHJttERER0BofD8dDgwYNtV111VcmlGr6cfZJpAosWWWe369cPdjSn5OfnY/bs2cW6rj8gy/KOCz9BRESXAibbREREdIqiKB1CQkKuaNKkSbBDqZhDh6xq5CtWADVoq7umaZg1a5Zb07S34+LiFgU7HiIiqj4skEZERESnOJ3OF7t27eoUzu5ZXdPNng24XDUq0TZNEwsWLCjOz8//Ttf1icGOh4iIqhdXtomIiAgAcLLq+B1t27a1BzuWCnn9deC++4DWrYMdyRnWrFmjHTx4cI+qqg/IssyKtERElxmubBMREVGJ9pIkOerUqRPsOMovPh5Yvhxo0gSw15zPCOLj481NmzbleL3evrIse4MdDxERVT+ubBMREREAQJKk56+//nrXRbOF/Pvvgexs4PffgRpUKX3Pnj1YtmxZoaZpvWVZPh7seIiIKDi4sk1ERERQFOUqn893d6dOnWrO8vD5GAbw/PNAeHiNSrQPHDiAhQsXFp1MtBOCHQ8REQUPV7aJiIgITqfzf926dXOEh4cHO5QLy8gApk4Ftmyxku0aIiMjA/PmzSvWdX2wLMubgx0PEREFF5NtIiKiy5yiKK2dTmePG2+88eL4u2DCBMDtrlGJtsfjKWnx9VhcXNyqYMdDRETBd3H8UiUiIqJAur1du3Z2URSDHceFTZkCvPCCVRCthiguLsaMGTPcmqbNjIuLmxPseIiIqGbgmW0iIqLLnN1urxcWFlbzM+2ffgJmzAAiI4Ea8sFAUVERpk+fXpSbm/uZqqpPBzseIiKqOZhsExERXeYMw9h17NixwmDHcV4//2wl2D/+CNStG+xoAACFhYWYPn26Oz8/f6qmaX9jL20iIjodt5ETERHRrvT09GDHULbiYuDhh4F33wW6dw92NACAEydOYObMme7i4uJ/jBkzRg52PEREVPMw2SYiIqLkoqIiR0FBAWrVqhXsWM5UUAAsWQKsWwdERwc7GgBWe6/58+cX67r+ytixYz8KdjxERFQzcRs5ERHRZU6WZdXhcCxPSkoKdijneuIJYOVK4Morgx0JAGDLli3m3LlzC1RVHcREm4iIzocr20RERASv1/ttcnLybV26dKk5/bQWLADGj7cqjwtCUEPRdR3Lli3zJiUlpWua1leW5T+CGhAREdV4XNkmIiIiAFh88OBBl67rwY7DsmQJ8NZbQEQEIElBDSU3NxfTpk1zJyUlrVVVtR0TbSIiKg/BNFk4k4iIiIB33nln73333deicePGwQ1k2zagVi3A4QBiYoIaSlJSEr799ttin8+n6Lr+LiuOExFReXEbOREREQEATNPckJaWFtxkOycHeOABYNIkYPDgoIWh6zq+//57765du3I0TbtTluXfgxYMERFdlLiNnIiIiAAAXq933aFDh4qCFoDbDSQlAbNmBTXRzs3NxaefflqUkJCwUtO0Vky0iYioMriyTURERCU2Hz582Be02R9+GBBFYPbsoIVQ0tbLMIxxuq7/k9vGiYiosphsExERUYndbrfbHpR+27/9Brz0EtCuXfXOe5Jpmvj99999q1evLtR1/a64uLhVQQmEiIguGdxGTkRERAAAWZYNSZJ+2b9/f/VO/NVXwOOPA9dcA7hc1Ts3rPPZ33zzjWfNmjUHNE3ryESbiIj8gck2ERERneLxeNYfOXJEq7YJ//gD6NQJmDsXiIystmlL5OfnY9q0aUV79uxZparqtbIs76v2IIiI6JLEZJuIiIhOMU3z6/j4eN0wjMBPlpEBDB9uJdxt2wZ+vrMcPnwYU6dOLT5x4sQ7qqoOkmU5eMXhiIjoksM+20RERHSGd955J37w4MHtWrZsGbhJiouBI0esRHvgwMDNU4atW7eaK1asKNJ1/Z64uLjl1R4AERFd8lggjYiIiM7g8Xg+2Lp16/stW7YMC8gEpmlVHnc6gZkzAzJFWQzDwPLly70JCQkZmqbdKsvyH9UaABERXTaYbBMREdHZvtm/f/+Huq7D4QjAnwqHDgHDhgH9+/t/7PPweDyYO3euOzMzc6OqqkNkWc6v1gCIiOiywjPbREREdAZZlrPsdnvmiRMn/D/4vHlA377A7bcDYYFZOC9NTk4OPv30U3dGRsYXXq+3LxNtIiIKNK5sExER0TlsNltaXl5e4wYNGvhv0PR0oHlzYNasam3xdfjwYcydO7dY07QxY8eO/U+1TUxERJc1rmwTERHROQRB8Pq1InlmJnDbbYCqAl26+G/c8zAMA2vWrNG/+OKLAq/XO4yJNhERVSeubBMREdE5TNNsEBoa6p/BvF6gqAh49lngppv8M+YFeDwezJkzx338+PGtuq7fI8vysWqZmIiI6CQm20RERHQGRVEkm83WvGHDhlUfzDSB0aOBunWBKVOqPl455OTkYNasWUVFRUWzVVV9WpblamgaTkREdCYm20RERHS26yIjIz1Op1Oq8ki5uUC7dsBjj/khrPMzTRObN2/2rVq1ymua5pu6rv9blmUz4BMTERGVgsk2ERERna1948aNq/43wpdfAuPGAQkJgN1e9ajOQ9d1rFixQo2Pjz+iaVp/9s8mIqJgY7JNREREZ4sMDw93/n979xtbVXnAcfx3Tntv+VMLxGKpVKiFODuEWHWYMDNNCNPohgkFWSJZs2C0UzAY2cIL7MmVLDGOZEOTOgdoCClthTAa/pVBIShNWxmUUqwwUdpaSVPsXYH2XnrPuefsBdZB+DMKp1C4388b4Nze53nuO7495z7PDY0QjZ5/hLy4eMBDu7u7W6WlpZHOzs7qWCw217Ks/wzohAAAXANiGwAA/CgUCo03TfPngUDg+gu5s1N66imprEyaNMm/xV3GqVOntGrVqpjneX+zbfsPlmW5AzohAADXiNgGAACSpFAolC6p2XVdZWZmXt8gjnP+PO0XXhjw0G5sbNTmzZujruu+vnTp0lUDOhkAAP1EbAMAgD5/6vvL+PHj+/9uz5Neekl68EHprbf8XNdFbNvWtm3bepuamr63bftXlmUdGrDJAAC4TsQ2AAAJZtmyZfNd1z1sWdb+C6+npKSMnDhxoh5++GENGTKk/wNHIlIwKBUU+LXUS3R2dqqkpCQSiUT+GYvFfmtZ1tkBmwwAgBtAbAMAkEBCodAISauSkpIOSnr0gutGMBh8ZMqUKZo4cWL/By4vl95/X/rsM8kw/FvwBVpbW7Vu3bqobdt/dF23mGO9AACDGbENAEBiyQ4Gg3ZycnLuu+++22DbtuE4zjbDML4aOnTo2JycnP6P6LrS8ePS4sUDFtpffPGFKioqehzHmV1UVFQ5IJMAAOAjYhsAgMQyLScnx545c+awEydOTFm/fr0kTfY8T6dPn5bjOEpO7sd/Dzo7pRkzpIoK6b77BmTBdXV18aqqqtO2bU/n+9kAgNuFeasXAAAAbqpwJBKJDx06VAcPHoz+cO1npmmGkpOTt1dWVvZd0/79+70VK1Z4X3/99eVH8jypsVF64okBCe14PK7KyspYVVXV97ZtP0poAwBuJ8Q2AACJZW97e3tAkqZOnTpUkkzT/I3rukclnQ2Hw3FJqq6udnft2qWsrCyjvLxcGzdujDc0NKinp+d/I73yitTWJr33nu+L7O7u1sqVK3vq6+s/s237p5ZlNfs+CQAAA4jHyAEASCwPDR8+3JY05IEHHtDLL7+spqamN9rb23tGjx49ZNq0aYGqqiq3rq7OLCgo0NixY3XmzBmtWbNGjY2NmjBhgjtv3jxTXV3SN99Iy5b5vsCOjg6tWbMmEovF/uI4ThEboQEAbkfENgAACcQ0zalZWVkpff/OzMxUZmamKemus2fPqqyszO3q6tL8+fOVkZEhSUpLS9PChQuTli9f7kajUTNWWqpgZaW0a5fv6+vbcTwWixUWFRWt9X0CAABuEmIbAIAEEAqF7pc0QdLBcDgclRS88PVwOKzVq1frnnvu0aJFi8zLbZJWUFBglpWVxb9Zvdp8cNEiX7cd9zxPNTU18T179kTi8fjcoqKi7X6ODwDAzUZsAwCQAAzD+LPnefmBQGDduHHjhl34WldXlz7++GMvJydH+fn5V9zPZbRhaMGWLUl/nTHDaxk1yv2l55mGD0d9nTt3Ths3boy0tLQ0O47zrGVZLTc8KAAAtxgbpAEAkABM03xEkgKBwKwnn3wy0He9tbVVxcXFGjNmjJufn3/1ct65U8a992puYaFRW1trnjlz5obX9cP8kZaWltJYLPYIoQ0AuFNwZxsAgAQQj8dnSzowe/bsISkpP35lW/X19W5WVpbx4osvJl11gMWLpWeflT76SGM8TxkZGfGVK1eac+fONe67jmO/YrGYtmzZcu7o0aM9tm3/3rKs9f0eBACAQYw72wAAJIYvJSk1NVWS5Lqutm7dGm9qajKefvrpq9/R/u47aedO6aGHJMOQaZoqLCxMysvLM9auXava2lrP8659w/D29nYVFxdHjh07tsm27fGENgDgTsSdbQAAEkOBJN19993yPE8bNmyIt7a2GoWFhcaoUaOu/K5t26SDB6X6esm8+Hf006dP14QJE1RWVqa2trb4888/nxQIBK4w0PlN0Orq6rzdu3dHHcdht3EAwB2NO9sAACSGtyTJNE19/vnnXnNzs/Hqq6+aVw1tz5PeeUfKzr4ktPtkZ2drwYIFxsmTJ/Xhhx96XV1dl/253t5ebdiwIVpVVXXKtu3HCW0AwJ2O2AYAIDEcl87fXd63b5+mT59uDhs27OrvMAxp925p3ryr/lhqaqoWLFiQNHLkSO+DDz5QOBy+6PW+x8aPHz++0XGc+y3LOnJjHwUAgMGPx8gBAEgMKyT94vDhw+rt7TXy8vKu7V2XOW/7cg4dOqS2tjYjOzs7npqamiSd/154dXW18+mnn/Z6nvf60qVLP7rexQMAcLshtgEASAz/lqRNmzYpNzfXM03zxg/I1vkzsktKStyOjg5z5syZxqRJk5Kk83ezP/nkk0gkEmlyHCffsqxWP+YDAOB2QWwDAJAYJktSIBBQMBjs3blzZyAlJSUpJSVFwWBQfX8GAgENHz5c6enp/3fAhoYGbd++3cvKytLChQuVmpoqz/NUU1MT37Nnz7l4PP6m53krLctyB/zTAQAwyBDbAAAkhq2S3rBtO6WhoaFH0l2maY5ITk5ON01zpGmaaZ7npUWj0cclacmSJbrwPO4LxWIxlZSUuO3t7eZzzz1nTJ482TAMQx0dHdq0aVNPOBz+ynGcWZZlnbiJnw8AgEHF6M+5mAAA4M4WCoV+IuloQUGBsrOzL3m9qalJmzdv9jIyMrxZs2aZaWlpchxHe/futWtra2Ou6y5xXbeYu9kAgERHbAMAgIu8/fbb0Tlz5gzJzc398ZrjOCotLXW//fZb85lnnvHy8vIMwzB06tQplZeX93R3d1f39vb+zrKsk7dw6QAADBo8Rg4AAC5imuY/jhw5Mjc3N9eUpGPHjqmiosJNT0/Xa6+9phEjRhie5+nAgQPejh07ovF4/E3Xdf9uWRa/wQcA4AfENgAAuEg8Ht/R3Nw8y7btlPXr17vNzc3mjBkzjMcee8wwDEPd3d1au3ZttKur66Rt27+2LOvLW71mAAAGGx4jBwAAFwmFQuMktUjS6NGj3Tlz5phpaWlqa2tTa2urXVNT43met8pxnDctyzp3i5cLAMCgRGwDAIBLhEKhEaZpHnVdd4wkBQIBW1KHaZr/6u3tXW5Z1r5bvEQAAAY1YhsAAFxRKBRKkXSXpPGS6tllHACAa0NsAwAAAADgM/NWLwAAAAAAgDsNsQ0AAAAAgM+IbQAAAAAAfEZsAwAAAADgM2IbAAAAAACfEdsAAAAAAPiM2AYAAAAAwGfENgAAAAAAPiO2AQAAAADwGbENAAAAAIDPiG0AAAAAAHxGbAMAAAAA4DNiGwAAAAAAnxHbAAAAAAD4jNgGAAAAAMBnxDYAAAAAAD4jtgEAAAAA8BmxDQAAAACAz4htAAAAAAB8RmwDAAAAAOAzYhsAAAAAAJ8R2wAAAAAA+IzYBgAAAADAZ8Q2AAAAAAA+I7YBAAAAAPAZsQ0AAAAAgM+IbQAAAAAAfEZsAwAAAADgM2IbAAAAAACfEdsAAAAAAPiM2AYAAAAAwGfENgAAAAAAPiO2AQAAAADwGbENAAAAAIDPiG0AAAAAAHxGbAMAAAAA4DNiGwAAAAAAnxHbAAAAAAD4jNgGAAAAAMBnxDYAAAAAAD4jtgEAAAAA8BmxDQAAAACAz/4L0ZkICrqqch4AAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 1440x1080 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\n",
-    "ax = rs_df.plot(edgecolor='grey', facecolor='w')\n",
-    "f,ax = wf.plot(rs_df, ax=ax, \n",
-    "        edge_kws=dict(color='r', linestyle=':', linewidth=1),\n",
-    "        node_kws=dict(marker=''))\n",
-    "ax.set_title('Rio Grande do Sul: Nonplanar Weights')\n",
-    "ax.set_axis_off()\n",
-    "plt.savefig('rioGrandeDoSul.png')\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docsrc/references.rst b/docsrc/references.rst
deleted file mode 100644
index 09d2529e0..000000000
--- a/docsrc/references.rst
+++ /dev/null
@@ -1,7 +0,0 @@
-.. reference for the docs
-
-References
-==========
-
-.. bibliography:: _static/references.bib
-   :cited:
diff --git a/docsrc/tutorial.rst b/docsrc/tutorial.rst
deleted file mode 100644
index 9b9021775..000000000
--- a/docsrc/tutorial.rst
+++ /dev/null
@@ -1,22 +0,0 @@
-libpysal Tutorial
-=================
-
-
-Spatial Weights
----------------
-
-.. toctree::
-    :glob:
-
-    Spatial Weights <notebooks/weights.ipynb>
-    Voronoi <notebooks/voronoi.ipynb>
-
-
-Example Datasets
-----------------
-
-.. toctree::
-    :glob:
-
-    Example Data <notebooks/examples.ipynb>
-

From c5b219dc5bfe0e9613a0859f482f2b5cc51ac31b Mon Sep 17 00:00:00 2001
From: James Gaboardi <jgaboardi@gmail.com>
Date: Tue, 2 Feb 2021 09:42:53 -0500
Subject: [PATCH 020/162] update conf.py & Makefile

---
 docs/Makefile |  34 +++++
 docs/conf.py  | 334 ++++++++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 368 insertions(+)
 create mode 100644 docs/Makefile
 create mode 100644 docs/conf.py

diff --git a/docs/Makefile b/docs/Makefile
new file mode 100644
index 000000000..45b306f40
--- /dev/null
+++ b/docs/Makefile
@@ -0,0 +1,34 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line.
+SPHINXOPTS    =
+SPHINXBUILD   = sphinx-build
+SPHINXPROJ    = libpysal
+SOURCEDIR     = .
+BUILDDIR      = _build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@rsync -r --exclude '.ipynb_checkpoints/' ../notebooks/ ./notebooks/
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+github:
+	@make html
+
+sync:
+	@rsync -avh _build/html/ ../docs/ --delete
+	@make clean
+	touch .nojekyll
+
+clean:
+	rm -rf $(BUILDDIR)/*
+	rm -rf auto_examples/
+	rm -rf generated/
diff --git a/docs/conf.py b/docs/conf.py
new file mode 100644
index 000000000..1d91a393b
--- /dev/null
+++ b/docs/conf.py
@@ -0,0 +1,334 @@
+# -*- coding: utf-8 -*-
+#
+# libpysal documentation build configuration file, created by
+# sphinx-quickstart on Wed Jun  6 15:54:22 2018.
+#
+# This file is execfile()d with the current directory set to its
+# containing dir.
+#
+# Note that not all possible configuration values are present in this
+# autogenerated file.
+#
+# All configuration values have a default; values that are commented out
+# serve to show the default.
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+#
+import sys, os
+import sphinx_bootstrap_theme
+
+
+sys.path.insert(0, os.path.abspath("../"))
+
+import libpysal
+
+
+# -- General configuration ------------------------------------------------
+
+# If your documentation needs a minimal Sphinx version, state it here.
+#
+# needs_sphinx = '1.0'
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+extensions = [  #'sphinx_gallery.gen_gallery',
+    "sphinx.ext.autodoc",
+    "sphinx.ext.autosummary",
+    "sphinx.ext.viewcode",
+    "sphinxcontrib.bibtex",
+    "sphinx.ext.mathjax",
+    "sphinx.ext.doctest",
+    "sphinx.ext.intersphinx",
+    "numpydoc",
+    #'sphinx.ext.napoleon',
+    "matplotlib.sphinxext.plot_directive",
+    "nbsphinx",
+]
+
+
+# sphinx_gallery_conf = {
+#      # path to your examples scripts
+#      'examples_dirs': '../examples',
+#      # path where to save gallery generated examples
+#      'gallery_dirs': 'auto_examples',
+#      'backreferences_dir': False,
+# }
+
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ["_templates"]
+
+# The suffix(es) of source filenames.
+# You can specify multiple suffix as a list of string:
+#
+# source_suffix = ['.rst', '.md']
+source_suffix = ".rst"
+
+# The master toctree document.
+master_doc = "index"
+
+# General information about the project.
+project = "libpysal"
+copyright = "2018-, pysal developers"
+author = "pysal developers"
+
+# The version info for the project you're documenting, acts as replacement for
+# |version| and |release|, also used in various other places throughout the
+# built documents.
+#
+# The full version.
+version = libpysal.__version__
+release = libpysal.__version__
+
+# The language for content autogenerated by Sphinx. Refer to documentation
+# for a list of supported languages.
+#
+# This is also used if you do content translation via gettext catalogs.
+# Usually you set "language" from the command line for these cases.
+language = None
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+# This patterns also effect to html_static_path and html_extra_path
+exclude_patterns = ["_build", "Thumbs.db", ".DS_Store", "tests/*"]
+
+# The name of the Pygments (syntax highlighting) style to use.
+pygments_style = "sphinx"
+
+# If true, `todo` and `todoList` produce output, else they produce nothing.
+todo_include_todos = False
+
+# -- Options for HTML output ----------------------------------------------
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for
+# a list of builtin themes.
+
+# html_theme = 'alabaster'
+html_theme = "bootstrap"
+html_theme_path = sphinx_bootstrap_theme.get_html_theme_path()
+html_title = "%s v%s Manual" % (project, version)
+
+# (Optional) Logo. Should be small enough to fit the navbar (ideally 24x24).
+# Path should be relative to the ``_static`` files directory.
+# html_logo = "_static/images/CGS_logo.jpg"
+# html_logo = "_static/images/CGS_logo_green.png"
+# html_logo = "_static/images/pysal_logo_small.jpg"
+html_favicon = "_static/images/pysal_favicon.ico"
+
+
+# Theme options are theme-specific and customize the look and feel of a theme
+# further.  For a list of options available for each theme, see the
+# documentation.
+#
+html_theme_options = {
+    # Navigation bar title. (Default: ``project`` value)
+    "navbar_title": project,
+    # Render the next and previous page links in navbar. (Default: true)
+    "navbar_sidebarrel": False,
+    # Render the current pages TOC in the navbar. (Default: true)
+    #'navbar_pagenav': True,
+    #'navbar_pagenav': False,
+    # No sidebar
+    "nosidebar": True,
+    # Tab name for the current pages TOC. (Default: "Page")
+    #'navbar_pagenav_name': "Page",
+    # Global TOC depth for "site" navbar tab. (Default: 1)
+    # Switching to -1 shows all levels.
+    "globaltoc_depth": 2,
+    # Include hidden TOCs in Site navbar?
+    #
+    # Note: If this is "false", you cannot have mixed ``:hidden:`` and
+    # non-hidden ``toctree`` directives in the same page, or else the build
+    # will break.
+    #
+    # Values: "true" (default) or "false"
+    "globaltoc_includehidden": "true",
+    # HTML navbar class (Default: "navbar") to attach to <div> element.
+    # For black navbar, do "navbar navbar-inverse"
+    #'navbar_class': "navbar navbar-inverse",
+    # Fix navigation bar to top of page?
+    # Values: "true" (default) or "false"
+    "navbar_fixed_top": "true",
+    # Location of link to source.
+    # Options are "nav" (default), "footer" or anything else to exclude.
+    "source_link_position": "footer",
+    # Bootswatch (http://bootswatch.com/) theme.
+    #
+    # Options are nothing (default) or the name of a valid theme
+    # such as "amelia" or "cosmo", "yeti", "flatly".
+    "bootswatch_theme": "yeti",
+    # Choose Bootstrap version.
+    # Values: "3" (default) or "2" (in quotes)
+    "bootstrap_version": "3",
+    "navbar_links": [
+        # ("Gallery", "auto_examples/index"),
+        ("Installation", "installation"),
+        ("Tutorial", "tutorial"),
+        ("API", "api"),
+        ("References", "references"),
+    ],
+}
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ["_static"]
+
+# Custom sidebar templates, maps document names to template names.
+# html_sidebars = {}
+# html_sidebars = {'sidebar': ['localtoc.html', 'sourcelink.html', 'searchbox.html']}
+
+# -- Options for HTMLHelp output ------------------------------------------
+
+# Output file base name for HTML help builder.
+htmlhelp_basename = "%sdoc" % project
+
+
+# -- Options for LaTeX output ---------------------------------------------
+
+latex_elements = {
+    # The paper size ('letterpaper' or 'a4paper').
+    #
+    # 'papersize': 'letterpaper',
+    # The font size ('10pt', '11pt' or '12pt').
+    #
+    # 'pointsize': '10pt',
+    # Additional stuff for the LaTeX preamble.
+    #
+    # 'preamble': '',
+    # Latex figure (float) alignment
+    #
+    # 'figure_align': 'htbp',
+}
+
+# Grouping the document tree into LaTeX files. List of tuples
+# (source start file, target name, title,
+#  author, documentclass [howto, manual, or own class]).
+latex_documents = [
+    (
+        master_doc,
+        "%s.tex" % project,
+        u"%s Documentation" % project,
+        u"pysal developers",
+        "manual",
+    ),
+]
+
+
+# -- Options for manual page output ---------------------------------------
+
+# One entry per manual page. List of tuples
+# (source start file, name, description, authors, manual section).
+man_pages = [(master_doc, project, u"%s Documentation" % project, [author], 1)]
+
+
+# -- Options for Texinfo output -------------------------------------------
+
+# Grouping the document tree into Texinfo files. List of tuples
+# (source start file, target name, title, author,
+#  dir menu entry, description, category)
+texinfo_documents = [
+    (
+        master_doc,
+        project,
+        u"%s Documentation" % project,
+        author,
+        project,
+        "One line description of project.",
+        "Miscellaneous",
+    ),
+]
+
+# -----------------------------------------------------------------------------
+# Napoleon configuration
+# -----------------------------------------------------------------------------
+# numpydoc_show_class_members = True
+# numpydoc_class_members_toctree = False
+#
+# napoleon_use_ivar = True
+
+# -----------------------------------------------------------------------------
+# Autosummary
+# -----------------------------------------------------------------------------
+
+# Generate the API documentation when building
+autosummary_generate = True
+
+# avoid showing members twice
+numpydoc_show_class_members = False
+numpydoc_use_plots = True
+class_members_toctree = True
+
+#
+numpydoc_show_inherited_class_members = True
+numpydoc_xref_param_type = True
+
+# automatically document class members
+autodoc_default_options = {"members": True, "undoc-members": True}
+
+# display the source code for Plot directive
+plot_include_source = True
+
+
+def setup(app):
+    app.add_stylesheet("pysal-styles.css")
+
+
+# Example configuration for intersphinx: refer to the Python standard library.
+intersphinx_mapping = {
+    "geopandas": ("https://geopandas.readthedocs.io/en/latest/", None),
+    "libpysal": ("https://pysal.org/libpysal/", None),
+    "matplotlib": ("https://matplotlib.org/", None),
+    "networkx": ("https://networkx.github.io/documentation/stable/", None),
+    "numpy": ("https://docs.scipy.org/doc/numpy", None),
+    "pandas": ("https://pandas.pydata.org/pandas-docs/stable/", None),
+    "python": ("https://docs.python.org/3.8/", None),
+    "scipy": ("https://docs.scipy.org/doc/scipy/reference/", None),
+}
+
+
+# This is processed by Jinja2 and inserted before each notebook
+nbsphinx_prolog = r"""
+{% set docname = env.doc2path(env.docname, base='') %}
+{% set fullpath = env.doc2path(env.docname, base='tree/master/') %}
+
+.. only:: html
+
+    .. role:: raw-html(raw)
+        :format: html
+
+    .. nbinfo::
+
+        This page was generated from `{{ docname }}`__.
+        Interactive online version:
+        :raw-html:`<a href="https://mybinder.org/v2/gh/pysal/libpysal/master?filepath={{ docname }}"><img alt="Binder badge" src="https://mybinder.org/badge_logo.svg" style="vertical-align:text-bottom"></a>`
+
+    __ https://github.com/pysal/libpysal/{{ fullpath }}
+
+.. raw:: latex
+
+    \nbsphinxstartnotebook{\scriptsize\noindent\strut
+    \textcolor{gray}{The following section was generated from
+    \sphinxcode{\sphinxupquote{\strut {{ docname | escape_latex }}}} \dotfill}}
+"""
+
+# This is processed by Jinja2 and inserted after each notebook
+nbsphinx_epilog = r"""
+.. raw:: latex
+
+    \nbsphinxstopnotebook{\scriptsize\noindent\strut
+    \textcolor{gray}{\dotfill\ \sphinxcode{\sphinxupquote{\strut
+    {{ env.doc2path(env.docname, base='doc') | escape_latex }}}} ends here.}}
+"""
+
+# List of arguments to be passed to the kernel that executes the notebooks:
+nbsphinx_execute_arguments = [
+    "--InlineBackend.figure_formats={'svg', 'pdf'}",
+    "--InlineBackend.rc={'figure.dpi': 96}",
+]
+
+mathjax_config = {
+    "TeX": {"equationNumbers": {"autoNumber": "AMS", "useLabelIds": True}},
+}

From a9d6764d6fa8de161ed9bf9bd90840a7ebff5625 Mon Sep 17 00:00:00 2001
From: James Gaboardi <jgaboardi@gmail.com>
Date: Tue, 2 Feb 2021 09:43:26 -0500
Subject: [PATCH 021/162] reorg & clean docs/ dir

---
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-  <h1>Source code for libpysal.cg.alpha_shapes</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">Computation of alpha shape algorithm in 2-D based on original implementation</span>
-<span class="sd">by Tim Kittel (@timkittel) available at:</span>
-
-<span class="sd">    https://github.com/timkittel/alpha-shapes</span>
-
-<span class="sd">Author(s):</span>
-<span class="sd">    Dani Arribas-Bel daniel.arribas.bel@gmail.com</span>
-<span class="sd">&quot;&quot;&quot;</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">scipy.spatial</span> <span class="k">as</span> <span class="nn">spat</span>
-
-<span class="kn">from</span> <span class="nn">..common</span> <span class="kn">import</span> <span class="n">requires</span><span class="p">,</span> <span class="n">jit</span><span class="p">,</span> <span class="n">HAS_JIT</span>
-
-<span class="k">if</span> <span class="ow">not</span> <span class="n">HAS_JIT</span><span class="p">:</span>
-    <span class="kn">from</span> <span class="nn">warnings</span> <span class="kn">import</span> <span class="n">warn</span>
-
-    <span class="n">NUMBA_WARN</span> <span class="o">=</span> <span class="p">(</span>
-        <span class="s2">&quot;Numba not imported, so alpha shape construction may be slower than expected.&quot;</span>
-    <span class="p">)</span>
-
-<span class="k">try</span><span class="p">:</span>
-    <span class="kn">import</span> <span class="nn">pygeos</span>
-
-    <span class="n">HAS_PYGEOS</span> <span class="o">=</span> <span class="kc">True</span>
-<span class="k">except</span> <span class="ne">ModuleNotFoundError</span><span class="p">:</span>
-    <span class="n">HAS_PYGEOS</span> <span class="o">=</span> <span class="kc">False</span>
-
-
-<span class="n">EPS</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">finfo</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span><span class="o">.</span><span class="n">eps</span>
-
-<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;alpha_shape&quot;</span><span class="p">,</span> <span class="s2">&quot;alpha_shape_auto&quot;</span><span class="p">]</span>
-
-
-<span class="nd">@jit</span>
-<span class="k">def</span> <span class="nf">nb_dist</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;numba implementation of distance between points `x` and `y`</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-
-<span class="sd">    x : ndarray</span>
-<span class="sd">        Coordinates of point `x`</span>
-
-<span class="sd">    y : ndarray</span>
-<span class="sd">        Coordinates of point `y`</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-
-<span class="sd">    dist : float</span>
-<span class="sd">        Distance between `x` and `y`</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-
-<span class="sd">    &gt;&gt;&gt; x = np.array([0, 0])</span>
-<span class="sd">    &gt;&gt;&gt; y = np.array([1, 1])</span>
-<span class="sd">    &gt;&gt;&gt; dist = nb_dist(x, y)</span>
-<span class="sd">    &gt;&gt;&gt; dist</span>
-<span class="sd">    1.4142135623730951</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="nb">sum</span> <span class="o">=</span> <span class="mi">0</span>
-    <span class="k">for</span> <span class="n">x_i</span><span class="p">,</span> <span class="n">y_i</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
-        <span class="nb">sum</span> <span class="o">+=</span> <span class="p">(</span><span class="n">x_i</span> <span class="o">-</span> <span class="n">y_i</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span>
-    <span class="n">dist</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="nb">sum</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">dist</span>
-
-
-<span class="nd">@jit</span><span class="p">(</span><span class="n">nopython</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">r_circumcircle_triangle_single</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Computation of the circumcircle of a single triangle</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-
-<span class="sd">    a : ndarray</span>
-<span class="sd">        (2,) Array with coordinates of vertex `a` of the triangle</span>
-<span class="sd">    b : ndarray</span>
-<span class="sd">        (2,) Array with coordinates of vertex `b` of the triangle</span>
-<span class="sd">    c : ndarray</span>
-<span class="sd">        (2,) Array with coordinates of vertex `c` of the triangle</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-
-<span class="sd">    r : float</span>
-<span class="sd">        Circumcircle of the triangle</span>
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-
-<span class="sd">    Source for equations:</span>
-
-<span class="sd">    &gt; https://www.mathopenref.com/trianglecircumcircle.html</span>
-
-<span class="sd">    [Last accessed July 11th. 2018]</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-
-<span class="sd">    &gt;&gt;&gt; a = np.array([0, 0])</span>
-<span class="sd">    &gt;&gt;&gt; b = np.array([0.5, 0])</span>
-<span class="sd">    &gt;&gt;&gt; c = np.array([0.25, 0.25])</span>
-<span class="sd">    &gt;&gt;&gt; r = r_circumcircle_triangle_single(a, b, c)</span>
-<span class="sd">    &gt;&gt;&gt; r</span>
-<span class="sd">    0.2500000000000001</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">ab</span> <span class="o">=</span> <span class="n">nb_dist</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span>
-    <span class="n">bc</span> <span class="o">=</span> <span class="n">nb_dist</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span>
-    <span class="n">ca</span> <span class="o">=</span> <span class="n">nb_dist</span><span class="p">(</span><span class="n">c</span><span class="p">,</span> <span class="n">a</span><span class="p">)</span>
-
-    <span class="n">num</span> <span class="o">=</span> <span class="n">ab</span> <span class="o">*</span> <span class="n">bc</span> <span class="o">*</span> <span class="n">ca</span>
-    <span class="n">den</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">((</span><span class="n">ab</span> <span class="o">+</span> <span class="n">bc</span> <span class="o">+</span> <span class="n">ca</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">bc</span> <span class="o">+</span> <span class="n">ca</span> <span class="o">-</span> <span class="n">ab</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">ca</span> <span class="o">+</span> <span class="n">ab</span> <span class="o">-</span> <span class="n">bc</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">ab</span> <span class="o">+</span> <span class="n">bc</span> <span class="o">-</span> <span class="n">ca</span><span class="p">))</span>
-    <span class="k">if</span> <span class="n">den</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">ab</span><span class="p">,</span> <span class="n">bc</span><span class="p">,</span> <span class="n">ca</span><span class="p">])</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">/</span> <span class="mf">2.0</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">num</span> <span class="o">/</span> <span class="n">den</span>
-
-
-<span class="nd">@jit</span><span class="p">(</span><span class="n">nopython</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">r_circumcircle_triangle</span><span class="p">(</span><span class="n">a_s</span><span class="p">,</span> <span class="n">b_s</span><span class="p">,</span> <span class="n">c_s</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Computation of circumcircles for a series of triangles</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-
-<span class="sd">    a_s : ndarray</span>
-<span class="sd">        (N, 2) array with coordinates of vertices `a` of the triangles</span>
-<span class="sd">    b_s : ndarray</span>
-<span class="sd">        (N, 2) array with coordinates of vertices `b` of the triangles</span>
-<span class="sd">    c_s : ndarray</span>
-<span class="sd">        (N, 2) array with coordinates of vertices `c` of the triangles</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-
-<span class="sd">    radii : ndarray</span>
-<span class="sd">        (N,) array with circumcircles for every triangle</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-
-<span class="sd">    &gt;&gt;&gt; a_s = np.array([[0, 0], [2, 1], [3, 2]])</span>
-<span class="sd">    &gt;&gt;&gt; b_s = np.array([[1, 0], [5, 1], [2, 4]])</span>
-<span class="sd">    &gt;&gt;&gt; c_s = np.array([[0, 7], [1, 3], [4, 2]])</span>
-<span class="sd">    &gt;&gt;&gt; rs = r_circumcircle_triangle(a_s, b_s, c_s)</span>
-<span class="sd">    &gt;&gt;&gt; rs</span>
-<span class="sd">    array([3.53553391, 2.5       , 1.58113883])</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">len_a</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">a_s</span><span class="p">)</span>
-    <span class="n">r2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">len_a</span><span class="p">,))</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">len_a</span><span class="p">):</span>
-        <span class="n">r2</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">r_circumcircle_triangle_single</span><span class="p">(</span><span class="n">a_s</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">b_s</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">c_s</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-    <span class="k">return</span> <span class="n">r2</span>
-
-
-<span class="nd">@jit</span>
-<span class="k">def</span> <span class="nf">get_faces</span><span class="p">(</span><span class="n">triangle</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Extract faces from a single triangle</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-
-<span class="sd">    triangles : ndarray</span>
-<span class="sd">        (3,) array with the vertex indices for a triangle</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-
-<span class="sd">    faces : ndarray</span>
-<span class="sd">        (3, 2) array with a row for each face containing the indices of the two</span>
-<span class="sd">        points that make up the face</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-
-<span class="sd">    &gt;&gt;&gt; triangle = np.array([3, 1, 4], dtype=np.int32)</span>
-<span class="sd">    &gt;&gt;&gt; faces = get_faces(triangle)</span>
-<span class="sd">    &gt;&gt;&gt; faces</span>
-<span class="sd">    array([[3., 1.],</span>
-<span class="sd">           [1., 4.],</span>
-<span class="sd">           [4., 3.]])</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">faces</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
-    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">i0</span><span class="p">,</span> <span class="n">i1</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">([(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]):</span>
-        <span class="n">faces</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">triangle</span><span class="p">[</span><span class="n">i0</span><span class="p">],</span> <span class="n">triangle</span><span class="p">[</span><span class="n">i1</span><span class="p">]</span>
-    <span class="k">return</span> <span class="n">faces</span>
-
-
-<span class="nd">@jit</span>
-<span class="k">def</span> <span class="nf">build_faces</span><span class="p">(</span><span class="n">faces</span><span class="p">,</span> <span class="n">triangles_is</span><span class="p">,</span> <span class="n">num_triangles</span><span class="p">,</span> <span class="n">num_faces_single</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Build facing triangles</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-
-<span class="sd">    faces : ndarray</span>
-<span class="sd">        (num_triangles * num_faces_single, 2) array of zeroes in int form</span>
-
-<span class="sd">    triangles_is : ndarray</span>
-<span class="sd">        (D, 3) array, where D is the number of Delaunay triangles, with the</span>
-<span class="sd">        vertex indices for each triangle</span>
-
-<span class="sd">    num_triangles : int</span>
-<span class="sd">        Number of triangles</span>
-
-<span class="sd">    num_faces_single : int</span>
-<span class="sd">        Number of faces a triangle has (i.e. 3)</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-
-<span class="sd">    faces : ndarray</span>
-<span class="sd">        Two dimensional array with a row for every facing segment containing</span>
-<span class="sd">        the indices of the coordinate points</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-
-<span class="sd">    &gt;&gt;&gt; import scipy.spatial as spat</span>
-<span class="sd">    &gt;&gt;&gt; pts = np.array([[0, 1], [3, 5], [4, 1], [6, 7], [9, 3]])</span>
-<span class="sd">    &gt;&gt;&gt; triangulation = spat.Delaunay(pts)</span>
-<span class="sd">    &gt;&gt;&gt; triangulation.simplices</span>
-<span class="sd">    array([[3, 1, 4],</span>
-<span class="sd">           [1, 2, 4],</span>
-<span class="sd">           [2, 1, 0]], dtype=int32)</span>
-<span class="sd">    &gt;&gt;&gt; num_faces_single = 3</span>
-<span class="sd">    &gt;&gt;&gt; num_triangles = triangulation.simplices.shape[0]</span>
-<span class="sd">    &gt;&gt;&gt; num_faces = num_triangles * num_faces_single</span>
-<span class="sd">    &gt;&gt;&gt; faces = np.zeros((num_faces, 2), dtype=np.int_)</span>
-<span class="sd">    &gt;&gt;&gt; mask = np.ones((num_faces,), dtype=np.bool_)</span>
-<span class="sd">    &gt;&gt;&gt; faces = build_faces(faces, triangulation.simplices, num_triangles, num_faces_single)</span>
-<span class="sd">    &gt;&gt;&gt; faces</span>
-<span class="sd">    array([[3, 1],</span>
-<span class="sd">           [1, 4],</span>
-<span class="sd">           [4, 3],</span>
-<span class="sd">           [1, 2],</span>
-<span class="sd">           [2, 4],</span>
-<span class="sd">           [4, 1],</span>
-<span class="sd">           [2, 1],</span>
-<span class="sd">           [1, 0],</span>
-<span class="sd">           [0, 2]])</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_triangles</span><span class="p">):</span>
-        <span class="n">from_i</span> <span class="o">=</span> <span class="n">num_faces_single</span> <span class="o">*</span> <span class="n">i</span>
-        <span class="n">to_i</span> <span class="o">=</span> <span class="n">num_faces_single</span> <span class="o">*</span> <span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
-        <span class="n">faces</span><span class="p">[</span><span class="n">from_i</span><span class="p">:</span><span class="n">to_i</span><span class="p">]</span> <span class="o">=</span> <span class="n">get_faces</span><span class="p">(</span><span class="n">triangles_is</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-    <span class="k">return</span> <span class="n">faces</span>
-
-
-<span class="nd">@jit</span>
-<span class="k">def</span> <span class="nf">nb_mask_faces</span><span class="p">(</span><span class="n">mask</span><span class="p">,</span> <span class="n">faces</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot; Run over each row in `faces`, if the face in the following row is the</span>
-<span class="sd">    same, then mark both as False on `mask`</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-
-<span class="sd">    mask : ndarray</span>
-<span class="sd">        One-dimensional boolean array set to True with as many observations as</span>
-<span class="sd">        rows in `faces`</span>
-
-<span class="sd">    faces : ndarray</span>
-<span class="sd">        Sorted sequence of faces for all triangles (ie. triangles split by each</span>
-<span class="sd">        segment)</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-
-<span class="sd">    masked : ndarray</span>
-<span class="sd">         Sequence of outward-facing faces</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-
-<span class="sd">    &gt;&gt;&gt; import numpy as np</span>
-<span class="sd">    &gt;&gt;&gt; faces = np.array([[0, 1], [0, 2], [1, 2], [1, 2], [1, 3], [1, 4], [1, 4], [2, 4], [3, 4]])</span>
-<span class="sd">    &gt;&gt;&gt; mask = np.ones((faces.shape[0], ), dtype=np.bool_)</span>
-<span class="sd">    &gt;&gt;&gt; masked = nb_mask_faces(mask, faces)</span>
-<span class="sd">    &gt;&gt;&gt; masked</span>
-<span class="sd">    array([[0, 1],</span>
-<span class="sd">           [0, 2],</span>
-<span class="sd">           [1, 3],</span>
-<span class="sd">           [2, 4],</span>
-<span class="sd">           [3, 4]])</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">faces</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span>
-        <span class="k">if</span> <span class="n">mask</span><span class="p">[</span><span class="n">k</span><span class="p">]:</span>
-            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">faces</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">==</span> <span class="n">faces</span><span class="p">[</span><span class="n">k</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]):</span>
-                <span class="n">mask</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span>
-                <span class="n">mask</span><span class="p">[</span><span class="n">k</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span>
-    <span class="k">return</span> <span class="n">faces</span><span class="p">[</span><span class="n">mask</span><span class="p">]</span>
-
-
-<span class="k">def</span> <span class="nf">get_single_faces</span><span class="p">(</span><span class="n">triangles_is</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Extract outward facing edges from collection of triangles</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-
-<span class="sd">    triangles_is : ndarray</span>
-<span class="sd">        (D, 3) array, where D is the number of Delaunay triangles, with the</span>
-<span class="sd">        vertex indices for each triangle</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-
-<span class="sd">    single_faces : ndarray</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-
-<span class="sd">    &gt;&gt;&gt; import scipy.spatial as spat</span>
-<span class="sd">    &gt;&gt;&gt; pts = np.array([[0, 1], [3, 5], [4, 1], [6, 7], [9, 3]])</span>
-<span class="sd">    &gt;&gt;&gt; alpha = 0.33</span>
-<span class="sd">    &gt;&gt;&gt; triangulation = spat.Delaunay(pts)</span>
-<span class="sd">    &gt;&gt;&gt; triangulation.simplices</span>
-<span class="sd">    array([[3, 1, 4],</span>
-<span class="sd">           [1, 2, 4],</span>
-<span class="sd">           [2, 1, 0]], dtype=int32)</span>
-<span class="sd">    &gt;&gt;&gt; get_single_faces(triangulation.simplices)</span>
-<span class="sd">    array([[0, 1],</span>
-<span class="sd">           [0, 2],</span>
-<span class="sd">           [1, 3],</span>
-<span class="sd">           [2, 4],</span>
-<span class="sd">           [3, 4]])</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">num_faces_single</span> <span class="o">=</span> <span class="mi">3</span>
-    <span class="n">num_triangles</span> <span class="o">=</span> <span class="n">triangles_is</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-    <span class="n">num_faces</span> <span class="o">=</span> <span class="n">num_triangles</span> <span class="o">*</span> <span class="n">num_faces_single</span>
-    <span class="n">faces</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">num_faces</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int_</span><span class="p">)</span>
-    <span class="n">mask</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">num_faces</span><span class="p">,),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">bool_</span><span class="p">)</span>
-
-    <span class="n">faces</span> <span class="o">=</span> <span class="n">build_faces</span><span class="p">(</span><span class="n">faces</span><span class="p">,</span> <span class="n">triangles_is</span><span class="p">,</span> <span class="n">num_triangles</span><span class="p">,</span> <span class="n">num_faces_single</span><span class="p">)</span>
-
-    <span class="n">orderlist</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;x</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">faces</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])]</span>
-    <span class="n">dtype_list</span> <span class="o">=</span> <span class="p">[(</span><span class="n">el</span><span class="p">,</span> <span class="n">faces</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">str</span><span class="p">)</span> <span class="k">for</span> <span class="n">el</span> <span class="ow">in</span> <span class="n">orderlist</span><span class="p">]</span>
-    <span class="c1"># Arranging each face so smallest vertex is first</span>
-    <span class="n">faces</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-    <span class="c1"># Arranging faces in ascending way</span>
-    <span class="n">faces</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">dtype_list</span><span class="p">)</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-    <span class="c1"># Masking</span>
-    <span class="n">single_faces</span> <span class="o">=</span> <span class="n">nb_mask_faces</span><span class="p">(</span><span class="n">mask</span><span class="p">,</span> <span class="n">faces</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">single_faces</span>
-
-
-<span class="nd">@requires</span><span class="p">(</span><span class="s2">&quot;geopandas&quot;</span><span class="p">,</span> <span class="s2">&quot;shapely&quot;</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">alpha_geoms</span><span class="p">(</span><span class="n">alpha</span><span class="p">,</span> <span class="n">triangles</span><span class="p">,</span> <span class="n">radii</span><span class="p">,</span> <span class="n">xys</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Generate alpha-shape polygon(s) from `alpha` value, vertices of</span>
-<span class="sd">    `triangles`, the `radii` for all points, and the points themselves</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-
-<span class="sd">    alpha : float</span>
-<span class="sd">        Alpha value to delineate the alpha-shape</span>
-
-<span class="sd">    triangles : ndarray</span>
-<span class="sd">         (D, 3) array, where D is the number of Delaunay triangles, with the</span>
-<span class="sd">         vertex indices for each triangle</span>
-
-<span class="sd">    radii : ndarray</span>
-<span class="sd">        (N,) array with circumcircles for every triangle</span>
-
-<span class="sd">    xys : ndarray</span>
-<span class="sd">        (N, 2) array with one point per row and coordinates structured as X and Y</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-
-<span class="sd">    geoms : GeoSeries</span>
-<span class="sd">        Polygon(s) resulting from the alpha shape algorithm. The GeoSeries</span>
-<span class="sd">        object remains so even if only a single polygon is returned. There is</span>
-<span class="sd">        no CRS included in the object.</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-
-<span class="sd">    &gt;&gt;&gt; import scipy.spatial as spat</span>
-<span class="sd">    &gt;&gt;&gt; pts = np.array([[0, 1], [3, 5], [4, 1], [6, 7], [9, 3]])</span>
-<span class="sd">    &gt;&gt;&gt; alpha = 0.33</span>
-<span class="sd">    &gt;&gt;&gt; triangulation = spat.Delaunay(pts)</span>
-<span class="sd">    &gt;&gt;&gt; triangles = pts[triangulation.simplices]</span>
-<span class="sd">    &gt;&gt;&gt; triangles</span>
-<span class="sd">    array([[[6, 7],</span>
-<span class="sd">            [3, 5],</span>
-<span class="sd">            [9, 3]],</span>
-<span class="sd">    &lt;BLANKLINE&gt;</span>
-<span class="sd">           [[3, 5],</span>
-<span class="sd">            [4, 1],</span>
-<span class="sd">            [9, 3]],</span>
-<span class="sd">    &lt;BLANKLINE&gt;</span>
-<span class="sd">           [[4, 1],</span>
-<span class="sd">            [3, 5],</span>
-<span class="sd">            [0, 1]]])</span>
-<span class="sd">    &gt;&gt;&gt; a_pts = triangles[:, 0, :]</span>
-<span class="sd">    &gt;&gt;&gt; b_pts = triangles[:, 1, :]</span>
-<span class="sd">    &gt;&gt;&gt; c_pts = triangles[:, 2, :]</span>
-<span class="sd">    &gt;&gt;&gt; radii = r_circumcircle_triangle(a_pts, b_pts, c_pts)</span>
-<span class="sd">    &gt;&gt;&gt; geoms = alpha_geoms(alpha, triangulation.simplices, radii, pts)</span>
-<span class="sd">    &gt;&gt;&gt; geoms</span>
-<span class="sd">    0    POLYGON ((0.00000 1.00000, 3.00000 5.00000, 4....</span>
-<span class="sd">    dtype: geometry</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="kn">from</span> <span class="nn">shapely.geometry</span> <span class="kn">import</span> <span class="n">LineString</span>
-    <span class="kn">from</span> <span class="nn">shapely.ops</span> <span class="kn">import</span> <span class="n">polygonize</span>
-    <span class="kn">from</span> <span class="nn">geopandas</span> <span class="kn">import</span> <span class="n">GeoSeries</span>
-
-    <span class="n">triangles_reduced</span> <span class="o">=</span> <span class="n">triangles</span><span class="p">[</span><span class="n">radii</span> <span class="o">&lt;</span> <span class="mi">1</span> <span class="o">/</span> <span class="n">alpha</span><span class="p">]</span>
-    <span class="n">outer_triangulation</span> <span class="o">=</span> <span class="n">get_single_faces</span><span class="p">(</span><span class="n">triangles_reduced</span><span class="p">)</span>
-    <span class="n">face_pts</span> <span class="o">=</span> <span class="n">xys</span><span class="p">[</span><span class="n">outer_triangulation</span><span class="p">]</span>
-    <span class="n">geoms</span> <span class="o">=</span> <span class="n">GeoSeries</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">polygonize</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">LineString</span><span class="p">,</span> <span class="n">face_pts</span><span class="p">)))))</span>
-    <span class="k">return</span> <span class="n">geoms</span>
-
-
-<div class="viewcode-block" id="alpha_shape"><a class="viewcode-back" href="../../../generated/libpysal.cg.alpha_shape.html#libpysal.cg.alpha_shape">[docs]</a><span class="nd">@requires</span><span class="p">(</span><span class="s2">&quot;geopandas&quot;</span><span class="p">,</span> <span class="s2">&quot;shapely&quot;</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">alpha_shape</span><span class="p">(</span><span class="n">xys</span><span class="p">,</span> <span class="n">alpha</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Alpha-shape delineation (Edelsbrunner, Kirkpatrick &amp; Seidel, 1983) from a collection of points</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-
-<span class="sd">    xys : ndarray</span>
-<span class="sd">        (N, 2) array with one point per row and coordinates structured as X and</span>
-<span class="sd">        Y</span>
-
-<span class="sd">    alpha : float</span>
-<span class="sd">        Alpha value to delineate the alpha-shape</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-
-<span class="sd">    shapes : GeoSeries</span>
-<span class="sd">         Polygon(s) resulting from the alpha shape algorithm. The GeoSeries</span>
-<span class="sd">         object remains so even if only a single polygon is returned. There is</span>
-<span class="sd">         no CRS included in the object.</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-
-<span class="sd">    &gt;&gt;&gt; pts = np.array([[0, 1], [3, 5], [4, 1], [6, 7], [9, 3]])</span>
-<span class="sd">    &gt;&gt;&gt; alpha = 0.1</span>
-<span class="sd">    &gt;&gt;&gt; poly = alpha_shape(pts, alpha)</span>
-<span class="sd">    &gt;&gt;&gt; poly</span>
-<span class="sd">    0    POLYGON ((0.00000 1.00000, 3.00000 5.00000, 6....</span>
-<span class="sd">    dtype: geometry</span>
-<span class="sd">    &gt;&gt;&gt; poly.centroid</span>
-<span class="sd">    0    POINT (4.69048 3.45238)</span>
-<span class="sd">    dtype: geometry</span>
-
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-
-<span class="sd">    Edelsbrunner, H., Kirkpatrick, D., &amp; Seidel, R. (1983). On the shape of</span>
-<span class="sd">        a set of points in the plane. IEEE Transactions on information theory,</span>
-<span class="sd">        29(4), 551-559.</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">HAS_JIT</span><span class="p">:</span>
-        <span class="n">warn</span><span class="p">(</span><span class="n">NUMBA_WARN</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">xys</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mi">4</span><span class="p">:</span>
-        <span class="kn">from</span> <span class="nn">shapely</span> <span class="kn">import</span> <span class="n">ops</span><span class="p">,</span> <span class="n">geometry</span> <span class="k">as</span> <span class="n">geom</span>
-
-        <span class="k">return</span> <span class="n">ops</span><span class="o">.</span><span class="n">cascaded_union</span><span class="p">([</span><span class="n">geom</span><span class="o">.</span><span class="n">Point</span><span class="p">(</span><span class="n">xy</span><span class="p">)</span> <span class="k">for</span> <span class="n">xy</span> <span class="ow">in</span> <span class="n">xys</span><span class="p">])</span><span class="o">.</span><span class="n">convex_hull</span><span class="o">.</span><span class="n">buffer</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-    <span class="n">triangulation</span> <span class="o">=</span> <span class="n">spat</span><span class="o">.</span><span class="n">Delaunay</span><span class="p">(</span><span class="n">xys</span><span class="p">)</span>
-    <span class="n">triangles</span> <span class="o">=</span> <span class="n">xys</span><span class="p">[</span><span class="n">triangulation</span><span class="o">.</span><span class="n">simplices</span><span class="p">]</span>
-    <span class="n">a_pts</span> <span class="o">=</span> <span class="n">triangles</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:]</span>
-    <span class="n">b_pts</span> <span class="o">=</span> <span class="n">triangles</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">,</span> <span class="p">:]</span>
-    <span class="n">c_pts</span> <span class="o">=</span> <span class="n">triangles</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">,</span> <span class="p">:]</span>
-    <span class="n">radii</span> <span class="o">=</span> <span class="n">r_circumcircle_triangle</span><span class="p">(</span><span class="n">a_pts</span><span class="p">,</span> <span class="n">b_pts</span><span class="p">,</span> <span class="n">c_pts</span><span class="p">)</span>
-    <span class="k">del</span> <span class="n">triangles</span><span class="p">,</span> <span class="n">a_pts</span><span class="p">,</span> <span class="n">b_pts</span><span class="p">,</span> <span class="n">c_pts</span>
-    <span class="n">geoms</span> <span class="o">=</span> <span class="n">alpha_geoms</span><span class="p">(</span><span class="n">alpha</span><span class="p">,</span> <span class="n">triangulation</span><span class="o">.</span><span class="n">simplices</span><span class="p">,</span> <span class="n">radii</span><span class="p">,</span> <span class="n">xys</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">geoms</span></div>
-
-
-<span class="k">def</span> <span class="nf">_valid_hull</span><span class="p">(</span><span class="n">geoms</span><span class="p">,</span> <span class="n">points</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Sanity check within ``alpha_shape_auto()`` to verify the generated alpha</span>
-<span class="sd">    shape actually contains the original set of points (xys).</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-
-<span class="sd">    geoms : GeoSeries</span>
-<span class="sd">        See alpha_geoms()</span>
-
-<span class="sd">    points : list</span>
-<span class="sd">        xys parameter cast as shapely.geometry.Point objects</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-
-<span class="sd">    flag : bool</span>
-<span class="sd">        Valid hull for alpha shape [True] or not [False]</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">flag</span> <span class="o">=</span> <span class="kc">True</span>
-    <span class="c1"># if there is not exactly one polygon</span>
-    <span class="k">if</span> <span class="n">geoms</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
-        <span class="k">return</span> <span class="kc">False</span>
-    <span class="c1"># if any (xys) points do not intersect the polygon</span>
-    <span class="k">if</span> <span class="n">HAS_PYGEOS</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">pygeos</span><span class="o">.</span><span class="n">intersects</span><span class="p">(</span><span class="n">pygeos</span><span class="o">.</span><span class="n">from_shapely</span><span class="p">(</span><span class="n">geoms</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">points</span><span class="p">)</span><span class="o">.</span><span class="n">all</span><span class="p">()</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">for</span> <span class="n">point</span> <span class="ow">in</span> <span class="n">points</span><span class="p">:</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="n">point</span><span class="o">.</span><span class="n">intersects</span><span class="p">(</span><span class="n">geoms</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
-                <span class="k">return</span> <span class="kc">False</span>
-        <span class="k">return</span> <span class="kc">True</span>
-
-
-<div class="viewcode-block" id="alpha_shape_auto"><a class="viewcode-back" href="../../../generated/libpysal.cg.alpha_shape_auto.html#libpysal.cg.alpha_shape_auto">[docs]</a><span class="nd">@requires</span><span class="p">(</span><span class="s2">&quot;geopandas&quot;</span><span class="p">,</span> <span class="s2">&quot;shapely&quot;</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">alpha_shape_auto</span><span class="p">(</span>
-    <span class="n">xys</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">return_radius</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">return_circles</span><span class="o">=</span><span class="kc">False</span>
-<span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Computation of alpha-shape delineation with automated selection of alpha.</span>
-
-<span class="sd">    This method uses the algorithm proposed by  Edelsbrunner, Kirkpatrick &amp;</span>
-<span class="sd">    Seidel (1983) to return the tightest polygon that contains all points in</span>
-<span class="sd">    `xys`. The algorithm ranks every point based on its radious and iterates</span>
-<span class="sd">    over each point, checking whether the maximum alpha that would keep the</span>
-<span class="sd">    point and all the other ones in the set with smaller radii results in a</span>
-<span class="sd">    single polygon. If that is the case, it moves to the next point;</span>
-<span class="sd">    otherwise, it retains the previous alpha value and returns the polygon</span>
-<span class="sd">    as `shapely` geometry.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-
-<span class="sd">    xys : ndarray</span>
-<span class="sd">        Nx2 array with one point per row and coordinates structured as X and Y</span>
-
-<span class="sd">    step : int</span>
-<span class="sd">        [Optional. Default=1] Number of points in `xys` to jump ahead after</span>
-<span class="sd">        checking whether the largest possible alpha that includes the point and</span>
-<span class="sd">        all the other ones with smaller radii</span>
-
-<span class="sd">    verbose : Boolean</span>
-<span class="sd">        [Optional. Default=False] If True, it prints alpha values being tried at every step.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    poly : shapely.Polygon</span>
-<span class="sd">         Tightest alpha-shape polygon containing all points in `xys`</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-
-<span class="sd">    &gt;&gt;&gt; pts = np.array([[0, 1], [3, 5], [4, 1], [6, 7], [9, 3]])</span>
-<span class="sd">    &gt;&gt;&gt; poly = alpha_shape_auto(pts)</span>
-<span class="sd">    &gt;&gt;&gt; poly.bounds</span>
-<span class="sd">    (0.0, 1.0, 9.0, 7.0)</span>
-<span class="sd">    &gt;&gt;&gt; poly.centroid.x, poly.centroid.y</span>
-<span class="sd">    (4.690476190476191, 3.4523809523809526)</span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-
-<span class="sd">    Edelsbrunner, H., Kirkpatrick, D., &amp; Seidel, R. (1983). On the shape of</span>
-<span class="sd">        a set of points in the plane. IEEE Transactions on information theory,</span>
-<span class="sd">        29(4), 551-559.</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">HAS_JIT</span><span class="p">:</span>
-        <span class="n">warn</span><span class="p">(</span><span class="n">NUMBA_WARN</span><span class="p">)</span>
-    <span class="kn">from</span> <span class="nn">shapely</span> <span class="kn">import</span> <span class="n">geometry</span> <span class="k">as</span> <span class="n">geom</span>
-
-    <span class="k">if</span> <span class="n">return_circles</span><span class="p">:</span>
-        <span class="n">return_radius</span> <span class="o">=</span> <span class="kc">True</span>
-    <span class="k">if</span> <span class="n">xys</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mi">4</span><span class="p">:</span>
-        <span class="kn">from</span> <span class="nn">shapely</span> <span class="kn">import</span> <span class="n">ops</span>
-
-        <span class="k">if</span> <span class="n">xys</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
-            <span class="n">multipoint</span> <span class="o">=</span> <span class="n">ops</span><span class="o">.</span><span class="n">cascaded_union</span><span class="p">([</span><span class="n">geom</span><span class="o">.</span><span class="n">Point</span><span class="p">(</span><span class="n">xy</span><span class="p">)</span> <span class="k">for</span> <span class="n">xy</span> <span class="ow">in</span> <span class="n">xys</span><span class="p">])</span>
-            <span class="n">alpha_shape</span> <span class="o">=</span> <span class="n">multipoint</span><span class="o">.</span><span class="n">convex_hull</span><span class="o">.</span><span class="n">buffer</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">alpha_shape</span> <span class="o">=</span> <span class="n">geom</span><span class="o">.</span><span class="n">Polygon</span><span class="p">([])</span>
-        <span class="k">if</span> <span class="n">xys</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">return_radius</span><span class="p">:</span>
-                <span class="k">if</span> <span class="n">return_circles</span><span class="p">:</span>
-                    <span class="n">out</span> <span class="o">=</span> <span class="p">[</span><span class="n">alpha_shape</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">alpha_shape</span><span class="p">]</span>
-                <span class="k">return</span> <span class="n">alpha_shape</span><span class="p">,</span> <span class="mi">0</span>
-            <span class="k">return</span> <span class="n">alpha_shape</span>
-        <span class="k">elif</span> <span class="n">xys</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">return_radius</span><span class="p">:</span>
-                <span class="n">r</span> <span class="o">=</span> <span class="n">spat</span><span class="o">.</span><span class="n">distance</span><span class="o">.</span><span class="n">euclidean</span><span class="p">(</span><span class="n">xys</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">xys</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="o">/</span> <span class="mi">2</span>
-                <span class="k">if</span> <span class="n">return_circles</span><span class="p">:</span>
-                    <span class="n">circle</span> <span class="o">=</span> <span class="n">_construct_centers</span><span class="p">(</span><span class="n">xys</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">xys</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">r</span><span class="p">)</span>
-                    <span class="k">return</span> <span class="p">[</span><span class="n">alpha_shape</span><span class="p">,</span> <span class="n">r</span><span class="p">,</span> <span class="n">circle</span><span class="p">]</span>
-                <span class="k">return</span> <span class="p">[</span><span class="n">alpha_shape</span><span class="p">,</span> <span class="n">r</span><span class="p">]</span>
-            <span class="k">return</span> <span class="n">alpha_shape</span>
-        <span class="k">elif</span> <span class="n">return_radius</span><span class="p">:</span>  <span class="c1"># this handles xys.shape[0] == 3</span>
-            <span class="n">radius</span> <span class="o">=</span> <span class="n">r_circumcircle_triangle_single</span><span class="p">(</span><span class="n">xys</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">xys</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">xys</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
-            <span class="k">if</span> <span class="n">return_circles</span><span class="p">:</span>
-                <span class="n">circles</span> <span class="o">=</span> <span class="n">construct_bounding_circles</span><span class="p">(</span><span class="n">alpha_shape</span><span class="p">,</span> <span class="n">radius</span><span class="p">)</span>
-                <span class="k">return</span> <span class="p">[</span><span class="n">alpha_shape</span><span class="p">,</span> <span class="n">radius</span><span class="p">,</span> <span class="n">circles</span><span class="p">]</span>
-            <span class="k">return</span> <span class="p">[</span><span class="n">alpha_shape</span><span class="p">,</span> <span class="n">radius</span><span class="p">]</span>
-        <span class="k">return</span> <span class="n">alpha_shape</span>
-    <span class="n">triangulation</span> <span class="o">=</span> <span class="n">spat</span><span class="o">.</span><span class="n">Delaunay</span><span class="p">(</span><span class="n">xys</span><span class="p">)</span>
-    <span class="n">triangles</span> <span class="o">=</span> <span class="n">xys</span><span class="p">[</span><span class="n">triangulation</span><span class="o">.</span><span class="n">simplices</span><span class="p">]</span>
-    <span class="n">a_pts</span> <span class="o">=</span> <span class="n">triangles</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:]</span>
-    <span class="n">b_pts</span> <span class="o">=</span> <span class="n">triangles</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">,</span> <span class="p">:]</span>
-    <span class="n">c_pts</span> <span class="o">=</span> <span class="n">triangles</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">,</span> <span class="p">:]</span>
-    <span class="n">radii</span> <span class="o">=</span> <span class="n">r_circumcircle_triangle</span><span class="p">(</span><span class="n">a_pts</span><span class="p">,</span> <span class="n">b_pts</span><span class="p">,</span> <span class="n">c_pts</span><span class="p">)</span>
-    <span class="n">radii</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">radii</span><span class="p">)]</span> <span class="o">=</span> <span class="mi">0</span>  <span class="c1"># &quot;Line&quot; triangles to be kept for sure</span>
-    <span class="k">del</span> <span class="n">triangles</span><span class="p">,</span> <span class="n">a_pts</span><span class="p">,</span> <span class="n">b_pts</span><span class="p">,</span> <span class="n">c_pts</span>
-    <span class="n">radii_sorted_i</span> <span class="o">=</span> <span class="n">radii</span><span class="o">.</span><span class="n">argsort</span><span class="p">()</span>
-    <span class="n">triangles</span> <span class="o">=</span> <span class="n">triangulation</span><span class="o">.</span><span class="n">simplices</span><span class="p">[</span><span class="n">radii_sorted_i</span><span class="p">][::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-    <span class="n">radii</span> <span class="o">=</span> <span class="n">radii</span><span class="p">[</span><span class="n">radii_sorted_i</span><span class="p">][::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-    <span class="n">geoms_prev</span> <span class="o">=</span> <span class="n">alpha_geoms</span><span class="p">((</span><span class="mi">1</span> <span class="o">/</span> <span class="n">radii</span><span class="o">.</span><span class="n">max</span><span class="p">())</span> <span class="o">-</span> <span class="n">EPS</span><span class="p">,</span> <span class="n">triangles</span><span class="p">,</span> <span class="n">radii</span><span class="p">,</span> <span class="n">xys</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">HAS_PYGEOS</span><span class="p">:</span>
-        <span class="n">points</span> <span class="o">=</span> <span class="n">pygeos</span><span class="o">.</span><span class="n">points</span><span class="p">(</span><span class="n">xys</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">points</span> <span class="o">=</span> <span class="p">[</span><span class="n">geom</span><span class="o">.</span><span class="n">Point</span><span class="p">(</span><span class="n">pnt</span><span class="p">)</span> <span class="k">for</span> <span class="n">pnt</span> <span class="ow">in</span> <span class="n">xys</span><span class="p">]</span>
-    <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Step set to </span><span class="si">%i</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">step</span><span class="p">)</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">radii</span><span class="p">),</span> <span class="n">step</span><span class="p">):</span>
-        <span class="n">radi</span> <span class="o">=</span> <span class="n">radii</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-        <span class="n">alpha</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="n">radi</span><span class="p">)</span> <span class="o">-</span> <span class="n">EPS</span>
-        <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%.2f%%</span><span class="s2"> | Trying a = </span><span class="si">%f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">((</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="n">radii</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">alpha</span><span class="p">))</span>
-        <span class="n">geoms</span> <span class="o">=</span> <span class="n">alpha_geoms</span><span class="p">(</span><span class="n">alpha</span><span class="p">,</span> <span class="n">triangles</span><span class="p">,</span> <span class="n">radii</span><span class="p">,</span> <span class="n">xys</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">_valid_hull</span><span class="p">(</span><span class="n">geoms</span><span class="p">,</span> <span class="n">points</span><span class="p">):</span>
-            <span class="n">geoms_prev</span> <span class="o">=</span> <span class="n">geoms</span>
-            <span class="n">radi_prev</span> <span class="o">=</span> <span class="n">radi</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">break</span>
-    <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
-        <span class="nb">print</span><span class="p">(</span><span class="n">geoms_prev</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">return_radius</span><span class="p">:</span>
-        <span class="n">out</span> <span class="o">=</span> <span class="p">[</span><span class="n">geoms_prev</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">radi_prev</span><span class="p">]</span>
-        <span class="k">if</span> <span class="n">return_circles</span><span class="p">:</span>
-            <span class="n">out</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">construct_bounding_circles</span><span class="p">(</span><span class="n">out</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">radi_prev</span><span class="p">))</span>
-        <span class="k">return</span> <span class="n">out</span>
-    <span class="c1"># Return a shapely polygon</span>
-    <span class="k">return</span> <span class="n">geoms_prev</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span></div>
-
-
-<span class="k">def</span> <span class="nf">construct_bounding_circles</span><span class="p">(</span><span class="n">alpha_shape</span><span class="p">,</span> <span class="n">radius</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Construct the bounding circles for an alpha shape, given the radius</span>
-<span class="sd">    computed from the `alpha_shape_auto` method.</span>
-
-<span class="sd">    Arguments</span>
-<span class="sd">    ---------</span>
-<span class="sd">    alpha_shape : shapely.Polygon</span>
-<span class="sd">        An alpha-hull with the input radius.</span>
-
-<span class="sd">    radius : float</span>
-<span class="sd">        The radius of the input alpha_shape.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    center : numpy.ndarray of shape (n,2)</span>
-<span class="sd">        The centers of the circles defining the alpha_shape.</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">coordinates</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">alpha_shape</span><span class="o">.</span><span class="n">boundary</span><span class="o">.</span><span class="n">coords</span><span class="p">)</span>
-    <span class="n">n_coordinates</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">coordinates</span><span class="p">)</span>
-    <span class="n">centers</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_coordinates</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span>
-        <span class="n">a</span><span class="p">,</span> <span class="n">b</span> <span class="o">=</span> <span class="n">coordinates</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">coordinates</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span>
-        <span class="n">centers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">_construct_centers</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">radius</span><span class="p">))</span>
-    <span class="k">return</span> <span class="n">centers</span>
-
-
-<span class="nd">@jit</span><span class="p">(</span><span class="n">nopython</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">_construct_centers</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">radius</span><span class="p">):</span>
-    <span class="n">midpoint_x</span> <span class="o">=</span> <span class="p">(</span><span class="n">a</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">b</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">*</span> <span class="mf">0.5</span>
-    <span class="n">midpoint_y</span> <span class="o">=</span> <span class="p">(</span><span class="n">a</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">b</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="o">*</span> <span class="mf">0.5</span>
-    <span class="n">d</span> <span class="o">=</span> <span class="p">((</span><span class="n">a</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">b</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">+</span> <span class="p">(</span><span class="n">a</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">b</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span> <span class="o">**</span> <span class="mf">0.5</span>
-    <span class="k">if</span> <span class="n">b</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">a</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-        <span class="n">m</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span>
-        <span class="n">axis_rotation</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span> <span class="mi">2</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">m</span> <span class="o">=</span> <span class="p">(</span><span class="n">b</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">a</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="o">/</span> <span class="p">(</span><span class="n">b</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">a</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-        <span class="n">axis_rotation</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arctan</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
-    <span class="c1"># altitude is perpendicular bisector of AB</span>
-    <span class="n">interior_angle</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arccos</span><span class="p">(</span><span class="mf">0.5</span> <span class="o">*</span> <span class="n">d</span> <span class="o">/</span> <span class="n">radius</span><span class="p">)</span>
-    <span class="n">chord</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">interior_angle</span><span class="p">)</span> <span class="o">*</span> <span class="n">radius</span>
-
-    <span class="n">dx</span> <span class="o">=</span> <span class="n">chord</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">axis_rotation</span><span class="p">)</span>
-    <span class="n">dy</span> <span class="o">=</span> <span class="n">chord</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">axis_rotation</span><span class="p">)</span>
-
-    <span class="n">up_x</span> <span class="o">=</span> <span class="n">midpoint_x</span> <span class="o">-</span> <span class="n">dx</span>
-    <span class="n">up_y</span> <span class="o">=</span> <span class="n">midpoint_y</span> <span class="o">+</span> <span class="n">dy</span>
-    <span class="n">down_x</span> <span class="o">=</span> <span class="n">midpoint_x</span> <span class="o">+</span> <span class="n">dx</span>
-    <span class="n">down_y</span> <span class="o">=</span> <span class="n">midpoint_y</span> <span class="o">-</span> <span class="n">dy</span>
-
-    <span class="c1"># sign gives us direction of point, since</span>
-    <span class="c1"># shapely shapes are clockwise-defined</span>
-    <span class="n">sign</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sign</span><span class="p">((</span><span class="n">b</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">a</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">*</span> <span class="p">(</span><span class="n">up_y</span> <span class="o">-</span> <span class="n">a</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="o">-</span> <span class="p">(</span><span class="n">b</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">a</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="o">*</span> <span class="p">(</span><span class="n">up_x</span> <span class="o">-</span> <span class="n">a</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
-    <span class="k">if</span> <span class="n">sign</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">up_x</span><span class="p">,</span> <span class="n">up_y</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">down_x</span><span class="p">,</span> <span class="n">down_y</span>
-
-
-<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
-
-    <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-    <span class="kn">import</span> <span class="nn">time</span>
-    <span class="kn">import</span> <span class="nn">geopandas</span> <span class="k">as</span> <span class="nn">gpd</span>
-
-    <span class="n">plt</span><span class="o">.</span><span class="n">close</span><span class="p">(</span><span class="s2">&quot;all&quot;</span><span class="p">)</span>
-    <span class="n">xys</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">((</span><span class="mi">1000</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
-    <span class="n">t0</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
-    <span class="n">geoms</span> <span class="o">=</span> <span class="n">alpha_shape_auto</span><span class="p">(</span><span class="n">xys</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-    <span class="n">t1</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%.2f</span><span class="s2"> Seconds to run algorithm&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">t1</span> <span class="o">-</span> <span class="n">t0</span><span class="p">))</span>
-    <span class="n">f</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
-    <span class="n">gpd</span><span class="o">.</span><span class="n">GeoDataFrame</span><span class="p">({</span><span class="s2">&quot;geometry&quot;</span><span class="p">:</span> <span class="p">[</span><span class="n">geoms</span><span class="p">]})</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;orange&quot;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">xys</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">xys</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">s</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
-    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-
-    </div>
-      
-  </div>
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-  <div class="container">
-    <p class="pull-right">
-      <a href="#">Back to top</a>
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-    <p>
-        &copy; Copyright 2018-, pysal developers.<br/>
-      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 3.1.2.<br/>
-    </p>
-  </div>
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diff --git a/docs/_modules/libpysal/cg/kdtree.html b/docs/_modules/libpysal/cg/kdtree.html
deleted file mode 100644
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-      
-  <h1>Source code for libpysal.cg.kdtree</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">KDTree for PySAL: Python Spatial Analysis Library.</span>
-
-<span class="sd">Adds support for Arc Distance to scipy.spatial.KDTree.</span>
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="kn">import</span> <span class="nn">math</span>
-<span class="kn">import</span> <span class="nn">scipy.spatial</span>
-<span class="kn">import</span> <span class="nn">numpy</span>
-<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">inf</span>
-<span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">sphere</span>
-<span class="kn">from</span> <span class="nn">.sphere</span> <span class="kn">import</span> <span class="n">RADIUS_EARTH_KM</span>
-
-<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Charles R Schmidt &lt;schmidtc@gmail.com&gt;&quot;</span>
-
-<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;DISTANCE_METRICS&quot;</span><span class="p">,</span> <span class="s2">&quot;FLOAT_EPS&quot;</span><span class="p">,</span> <span class="s2">&quot;KDTree&quot;</span><span class="p">]</span>
-
-<span class="n">DISTANCE_METRICS</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;Euclidean&quot;</span><span class="p">,</span> <span class="s2">&quot;Arc&quot;</span><span class="p">]</span>
-<span class="n">FLOAT_EPS</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">finfo</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span><span class="o">.</span><span class="n">eps</span>
-
-
-<div class="viewcode-block" id="KDTree"><a class="viewcode-back" href="../../../generated/libpysal.cg.KDTree.html#libpysal.cg.KDTree">[docs]</a><span class="k">def</span> <span class="nf">KDTree</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">leafsize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">distance_metric</span><span class="o">=</span><span class="s2">&quot;Euclidean&quot;</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="n">RADIUS_EARTH_KM</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;kd-tree built on top of kd-tree functionality in scipy. If using scipy</span>
-<span class="sd">    0.12 or greater uses the scipy.spatial.cKDTree, otherwise uses</span>
-<span class="sd">    scipy.spatial.KDTree. Offers both Arc distance and Euclidean distance. Note</span>
-<span class="sd">    that Arc distance is only appropriate when points in latitude and</span>
-<span class="sd">    longitude, and the radius set to meaningful value (see docs below).</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    data : array</span>
-<span class="sd">        The data points to be indexed. This array is not copied, and so</span>
-<span class="sd">        modifying this data will result in bogus results. Typically nx2.</span>
-
-<span class="sd">    leafsize : int</span>
-<span class="sd">        The number of points at which the algorithm switches over to brute-force. Has to be positive. Optional, default is 10.</span>
-
-<span class="sd">    distance_metric : string</span>
-<span class="sd">        Options: &quot;Euclidean&quot; (default) and &quot;Arc&quot;.</span>
-
-<span class="sd">    radius : float</span>
-<span class="sd">        Radius of the sphere on which to compute distances. Assumes data in</span>
-<span class="sd">        latitude and longitude. Ignored if distance_metric=&quot;Euclidean&quot;. Typical</span>
-<span class="sd">        values: pysal.cg.RADIUS_EARTH_KM (default) pysal.cg.RADIUS_EARTH_MILES</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">if</span> <span class="n">distance_metric</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">==</span> <span class="s2">&quot;euclidean&quot;</span><span class="p">:</span>
-        <span class="k">if</span> <span class="p">(</span>
-            <span class="nb">int</span><span class="p">(</span><span class="n">scipy</span><span class="o">.</span><span class="n">version</span><span class="o">.</span><span class="n">version</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">)[</span><span class="mi">1</span><span class="p">])</span> <span class="o">&lt;</span> <span class="mi">12</span>
-            <span class="ow">and</span> <span class="nb">int</span><span class="p">(</span><span class="n">scipy</span><span class="o">.</span><span class="n">version</span><span class="o">.</span><span class="n">version</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span> <span class="o">==</span> <span class="mi">0</span>
-        <span class="p">):</span>
-            <span class="k">return</span> <span class="n">scipy</span><span class="o">.</span><span class="n">spatial</span><span class="o">.</span><span class="n">KDTree</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">leafsize</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">scipy</span><span class="o">.</span><span class="n">spatial</span><span class="o">.</span><span class="n">cKDTree</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">leafsize</span><span class="p">)</span>
-    <span class="k">elif</span> <span class="n">distance_metric</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">==</span> <span class="s2">&quot;arc&quot;</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">Arc_KDTree</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">leafsize</span><span class="p">,</span> <span class="n">radius</span><span class="p">)</span></div>
-
-
-<span class="c1"># internal hack for the Arc_KDTree class inheritance</span>
-<span class="k">if</span> <span class="p">(</span>
-    <span class="nb">int</span><span class="p">(</span><span class="n">scipy</span><span class="o">.</span><span class="n">version</span><span class="o">.</span><span class="n">version</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">)[</span><span class="mi">1</span><span class="p">])</span> <span class="o">&lt;</span> <span class="mi">12</span>
-    <span class="ow">and</span> <span class="nb">int</span><span class="p">(</span><span class="n">scipy</span><span class="o">.</span><span class="n">version</span><span class="o">.</span><span class="n">version</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span> <span class="o">==</span> <span class="mi">0</span>
-<span class="p">):</span>
-    <span class="n">temp_KDTree</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">spatial</span><span class="o">.</span><span class="n">KDTree</span>
-<span class="k">else</span><span class="p">:</span>
-    <span class="n">temp_KDTree</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">spatial</span><span class="o">.</span><span class="n">cKDTree</span>
-
-
-<span class="k">class</span> <span class="nc">Arc_KDTree</span><span class="p">(</span><span class="n">temp_KDTree</span><span class="p">):</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">leafsize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="mf">1.0</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;KDTree using Arc Distance instead of Euclidean Distance.</span>
-
-<span class="sd">        Returned distances are based on radius. For Example, pass in the radius</span>
-<span class="sd">        of earth in miles to get back miles. Assumes data are Lng/Lat, does not</span>
-<span class="sd">        account for geoids.</span>
-
-<span class="sd">        For more information see docs for scipy.spatial.KDTree</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        &gt;&gt;&gt; pts = [(0,90), (0,0), (180,0), (0,-90)]</span>
-<span class="sd">        &gt;&gt;&gt; kd = Arc_KDTree(pts, radius = sphere.RADIUS_EARTH_KM)</span>
-<span class="sd">        &gt;&gt;&gt; d,i = kd.query((90,0), k=4)</span>
-<span class="sd">        &gt;&gt;&gt; d</span>
-<span class="sd">        array([10007.54339801, 10007.54339801, 10007.54339801, 10007.54339801])</span>
-<span class="sd">        &gt;&gt;&gt; circumference = 2*math.pi*sphere.RADIUS_EARTH_KM</span>
-<span class="sd">        &gt;&gt;&gt; round(d[0],5) == round(circumference/4.0,5)</span>
-<span class="sd">        True</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">radius</span> <span class="o">=</span> <span class="n">radius</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">circumference</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">radius</span>
-        <span class="n">temp_KDTree</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">sphere</span><span class="o">.</span><span class="n">toXYZ</span><span class="p">,</span> <span class="n">data</span><span class="p">)),</span> <span class="n">leafsize</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">_toXYZ</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="nb">issubclass</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
-            <span class="n">x</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span> <span class="ow">and</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>  <span class="c1"># assume point is already in XYZ</span>
-            <span class="k">return</span> <span class="n">x</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>  <span class="c1"># assume point is already in XYZ</span>
-            <span class="k">return</span> <span class="n">x</span>
-        <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">x</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">sphere</span><span class="o">.</span><span class="n">toXYZ</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">x</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">sphere</span><span class="o">.</span><span class="n">toXYZ</span><span class="p">,</span> <span class="n">x</span><span class="p">))</span>
-        <span class="k">return</span> <span class="n">x</span>
-
-    <span class="k">def</span> <span class="nf">count_neighbors</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">r</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;See scipy.spatial.KDTree.count_neighbors</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        p: ignored, kept to maintain compatibility with scipy.spatial.KDTree</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        &gt;&gt;&gt; pts = [(0,90), (0,0), (180,0), (0,-90)]</span>
-<span class="sd">        &gt;&gt;&gt; kd = Arc_KDTree(pts, radius = sphere.RADIUS_EARTH_KM)</span>
-<span class="sd">        &gt;&gt;&gt; kd.count_neighbors(kd,0)</span>
-<span class="sd">        4</span>
-<span class="sd">        &gt;&gt;&gt; circumference = 2.0*math.pi*sphere.RADIUS_EARTH_KM</span>
-<span class="sd">        &gt;&gt;&gt; kd.count_neighbors(kd,circumference/2.0)</span>
-<span class="sd">        16</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="n">r</span> <span class="o">&gt;</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">circumference</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-                <span class="s2">&quot;r, must not exceed 1/2 circumference of the sphere (</span><span class="si">%f</span><span class="s2">).&quot;</span>
-                <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">circumference</span>
-                <span class="o">*</span> <span class="mf">0.5</span>
-            <span class="p">)</span>
-        <span class="n">r</span> <span class="o">=</span> <span class="n">sphere</span><span class="o">.</span><span class="n">arcdist2linear</span><span class="p">(</span><span class="n">r</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">radius</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">temp_KDTree</span><span class="o">.</span><span class="n">count_neighbors</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">r</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">query</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">distance_upper_bound</span><span class="o">=</span><span class="n">inf</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;See scipy.spatial.KDTree.query</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        x : array-like, last dimension self.m</span>
-<span class="sd">            query points are lng/lat.</span>
-<span class="sd">        p: ignored</span>
-<span class="sd">            kept to maintain compatibility with scipy.spatial.KDTree</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        &gt;&gt;&gt; import numpy as np</span>
-<span class="sd">        &gt;&gt;&gt; pts = [(0,90), (0,0), (180,0), (0,-90)]</span>
-<span class="sd">        &gt;&gt;&gt; kd = Arc_KDTree(pts, radius = sphere.RADIUS_EARTH_KM)</span>
-<span class="sd">        &gt;&gt;&gt; d,i = kd.query((90,0), k=4)</span>
-<span class="sd">        &gt;&gt;&gt; d</span>
-<span class="sd">        array([10007.54339801, 10007.54339801, 10007.54339801, 10007.54339801])</span>
-<span class="sd">        &gt;&gt;&gt; circumference = 2*math.pi*sphere.RADIUS_EARTH_KM</span>
-<span class="sd">        &gt;&gt;&gt; round(d[0],5) == round(circumference/4.0,5)</span>
-<span class="sd">        True</span>
-<span class="sd">        &gt;&gt;&gt; d,i = kd.query(kd.data, k=3)</span>
-<span class="sd">        &gt;&gt;&gt; d2,i2 = kd.query(pts, k=3)</span>
-<span class="sd">        &gt;&gt;&gt; (d == d2).all()</span>
-<span class="sd">        True</span>
-<span class="sd">        &gt;&gt;&gt; (i == i2).all()</span>
-<span class="sd">        True</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">eps</span> <span class="o">=</span> <span class="n">sphere</span><span class="o">.</span><span class="n">arcdist2linear</span><span class="p">(</span><span class="n">eps</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">radius</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">distance_upper_bound</span> <span class="o">!=</span> <span class="n">inf</span><span class="p">:</span>
-            <span class="n">distance_upper_bound</span> <span class="o">=</span> <span class="n">sphere</span><span class="o">.</span><span class="n">arcdist2linear</span><span class="p">(</span>
-                <span class="n">distance_upper_bound</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">radius</span>
-            <span class="p">)</span>
-        <span class="n">d</span><span class="p">,</span> <span class="n">i</span> <span class="o">=</span> <span class="n">temp_KDTree</span><span class="o">.</span><span class="n">query</span><span class="p">(</span>
-            <span class="bp">self</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_toXYZ</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="n">k</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="n">eps</span><span class="p">,</span> <span class="n">distance_upper_bound</span><span class="o">=</span><span class="n">distance_upper_bound</span>
-        <span class="p">)</span>
-        <span class="n">dims</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">d</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
-        <span class="n">r</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">radius</span>
-        <span class="k">if</span> <span class="n">dims</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">sphere</span><span class="o">.</span><span class="n">linear2arcdist</span><span class="p">(</span><span class="n">d</span><span class="p">,</span> <span class="n">r</span><span class="p">),</span> <span class="n">i</span>
-        <span class="k">if</span> <span class="n">dims</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="c1"># TODO: implement linear2arcdist on numpy arrays</span>
-            <span class="n">d</span> <span class="o">=</span> <span class="p">[</span><span class="n">sphere</span><span class="o">.</span><span class="n">linear2arcdist</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">r</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">d</span><span class="p">]</span>
-        <span class="k">elif</span> <span class="n">dims</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
-            <span class="n">d</span> <span class="o">=</span> <span class="p">[[</span><span class="n">sphere</span><span class="o">.</span><span class="n">linear2arcdist</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">r</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">row</span><span class="p">]</span> <span class="k">for</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">d</span><span class="p">]</span>
-        <span class="k">return</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">d</span><span class="p">),</span> <span class="n">i</span>
-
-    <span class="k">def</span> <span class="nf">query_ball_point</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">r</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;See scipy.spatial.KDTree.query_ball_point</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        p : ignored</span>
-<span class="sd">            kept to maintain compatibility with scipy.spatial.KDTree</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        &gt;&gt;&gt; import numpy as np</span>
-<span class="sd">        &gt;&gt;&gt; pts = [(0,90), (0,0), (180,0), (0,-90)]</span>
-<span class="sd">        &gt;&gt;&gt; kd = Arc_KDTree(pts, radius = sphere.RADIUS_EARTH_KM)</span>
-<span class="sd">        &gt;&gt;&gt; circumference = 2*math.pi*sphere.RADIUS_EARTH_KM</span>
-<span class="sd">        &gt;&gt;&gt; kd.query_ball_point(pts, circumference/4.)</span>
-<span class="sd">        array([list([0, 1, 2]), list([0, 1, 3]), list([0, 2, 3]), list([1, 2, 3])],</span>
-<span class="sd">              dtype=object)</span>
-<span class="sd">        &gt;&gt;&gt; kd.query_ball_point(pts, circumference/2.)</span>
-<span class="sd">        array([list([0, 1, 2, 3]), list([0, 1, 2, 3]), list([0, 1, 2, 3]),</span>
-<span class="sd">               list([0, 1, 2, 3])], dtype=object)</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">eps</span> <span class="o">=</span> <span class="n">sphere</span><span class="o">.</span><span class="n">arcdist2linear</span><span class="p">(</span><span class="n">eps</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">radius</span><span class="p">)</span>
-        <span class="c1"># scipy.sphere.KDTree.query_ball_point appears to ignore the eps argument.</span>
-        <span class="c1"># we have some floating point errors moving back and forth between cordinate systems,</span>
-        <span class="c1"># so we&#39;ll account for that be adding some to our radius, 3*float&#39;s eps value.</span>
-        <span class="k">if</span> <span class="n">r</span> <span class="o">&gt;</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">circumference</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-                <span class="s2">&quot;r, must not exceed 1/2 circumference of the sphere (</span><span class="si">%f</span><span class="s2">).&quot;</span>
-                <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">circumference</span>
-                <span class="o">*</span> <span class="mf">0.5</span>
-            <span class="p">)</span>
-        <span class="n">r</span> <span class="o">=</span> <span class="n">sphere</span><span class="o">.</span><span class="n">arcdist2linear</span><span class="p">(</span><span class="n">r</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">radius</span><span class="p">)</span> <span class="o">+</span> <span class="n">FLOAT_EPS</span> <span class="o">*</span> <span class="mi">3</span>
-        <span class="k">return</span> <span class="n">temp_KDTree</span><span class="o">.</span><span class="n">query_ball_point</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_toXYZ</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="n">r</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="n">eps</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">query_ball_tree</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">r</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;See scipy.spatial.KDTree.query_ball_tree</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        p : ignored</span>
-<span class="sd">            kept to maintain compatibility with scipy.spatial.KDTree</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        &gt;&gt;&gt; pts = [(0,90), (0,0), (180,0), (0,-90)]</span>
-<span class="sd">        &gt;&gt;&gt; kd = Arc_KDTree(pts, radius = sphere.RADIUS_EARTH_KM)</span>
-<span class="sd">        &gt;&gt;&gt; kd.query_ball_tree(kd, kd.circumference/4.) == [[0, 1, 2], [0, 1, 3], [0, 2, 3], [1, 2, 3]]</span>
-<span class="sd">        True</span>
-<span class="sd">        &gt;&gt;&gt; kd.query_ball_tree(kd, kd.circumference/2.) == [[0, 1, 2, 3], [0, 1, 2, 3], [0, 1, 2, 3], [0, 1, 2, 3]]</span>
-<span class="sd">        True</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">eps</span> <span class="o">=</span> <span class="n">sphere</span><span class="o">.</span><span class="n">arcdist2linear</span><span class="p">(</span><span class="n">eps</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">radius</span><span class="p">)</span>
-        <span class="c1"># scipy.sphere.KDTree.query_ball_point appears to ignore the eps argument.</span>
-        <span class="c1"># we have some floating point errors moving back and forth between cordinate systems,</span>
-        <span class="c1"># so we&#39;ll account for that be adding some to our radius, 3*float&#39;s eps value.</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">radius</span> <span class="o">!=</span> <span class="n">other</span><span class="o">.</span><span class="n">radius</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Both trees must have the same radius.&quot;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">r</span> <span class="o">&gt;</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">circumference</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-                <span class="s2">&quot;r, must not exceed 1/2 circumference of the sphere (</span><span class="si">%f</span><span class="s2">).&quot;</span>
-                <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">circumference</span>
-                <span class="o">*</span> <span class="mf">0.5</span>
-            <span class="p">)</span>
-        <span class="n">r</span> <span class="o">=</span> <span class="n">sphere</span><span class="o">.</span><span class="n">arcdist2linear</span><span class="p">(</span><span class="n">r</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">radius</span><span class="p">)</span> <span class="o">+</span> <span class="n">FLOAT_EPS</span> <span class="o">*</span> <span class="mi">3</span>
-        <span class="k">return</span> <span class="n">temp_KDTree</span><span class="o">.</span><span class="n">query_ball_tree</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">r</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="n">eps</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">query_pairs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">r</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;See scipy.spatial.KDTree.query_pairs</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        p : ignored</span>
-<span class="sd">            kept to maintain compatibility with scipy.spatial.KDTree</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        &gt;&gt;&gt; pts = [(0,90), (0,0), (180,0), (0,-90)]</span>
-<span class="sd">        &gt;&gt;&gt; kd = Arc_KDTree(pts, radius = sphere.RADIUS_EARTH_KM)</span>
-<span class="sd">        &gt;&gt;&gt; kd.query_pairs(kd.circumference/4.) == set([(0, 1), (1, 3), (2, 3), (0, 2)])</span>
-<span class="sd">        True</span>
-<span class="sd">        &gt;&gt;&gt; kd.query_pairs(kd.circumference/2.) == set([(0, 1), (1, 2), (1, 3), (2, 3), (0, 3), (0, 2)])</span>
-<span class="sd">        True</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="n">r</span> <span class="o">&gt;</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">circumference</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-                <span class="s2">&quot;r, must not exceed 1/2 circumference of the sphere (</span><span class="si">%f</span><span class="s2">).&quot;</span>
-                <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">circumference</span>
-                <span class="o">*</span> <span class="mf">0.5</span>
-            <span class="p">)</span>
-        <span class="n">r</span> <span class="o">=</span> <span class="n">sphere</span><span class="o">.</span><span class="n">arcdist2linear</span><span class="p">(</span><span class="n">r</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">radius</span><span class="p">)</span> <span class="o">+</span> <span class="n">FLOAT_EPS</span> <span class="o">*</span> <span class="mi">3</span>
-        <span class="k">return</span> <span class="n">temp_KDTree</span><span class="o">.</span><span class="n">query_pairs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">r</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="n">eps</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">sparse_distance_matrix</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">max_distance</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;See scipy.spatial.KDTree.sparse_distance_matrix</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        p : ignored</span>
-<span class="sd">            kept to maintain compatibility with scipy.spatial.KDTree</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        &gt;&gt;&gt; pts = [(0,90), (0,0), (180,0), (0,-90)]</span>
-<span class="sd">        &gt;&gt;&gt; kd = Arc_KDTree(pts, radius = sphere.RADIUS_EARTH_KM)</span>
-<span class="sd">        &gt;&gt;&gt; kd.sparse_distance_matrix(kd, kd.circumference/4.).todense()</span>
-<span class="sd">        matrix([[    0.        , 10007.54339801, 10007.54339801,     0.        ],</span>
-<span class="sd">                [10007.54339801,     0.        ,     0.        , 10007.54339801],</span>
-<span class="sd">                [10007.54339801,     0.        ,     0.        , 10007.54339801],</span>
-<span class="sd">                [    0.        , 10007.54339801, 10007.54339801,     0.        ]])</span>
-<span class="sd">        &gt;&gt;&gt; kd.sparse_distance_matrix(kd, kd.circumference/2.).todense()</span>
-<span class="sd">        matrix([[    0.        , 10007.54339801, 10007.54339801, 20015.08679602],</span>
-<span class="sd">                [10007.54339801,     0.        , 20015.08679602, 10007.54339801],</span>
-<span class="sd">                [10007.54339801, 20015.08679602,     0.        , 10007.54339801],</span>
-<span class="sd">                [20015.08679602, 10007.54339801, 10007.54339801,     0.        ]])</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">radius</span> <span class="o">!=</span> <span class="n">other</span><span class="o">.</span><span class="n">radius</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Both trees must have the same radius.&quot;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">max_distance</span> <span class="o">&gt;</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">circumference</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-                <span class="s2">&quot;max_distance, must not exceed 1/2 circumference of the sphere (</span><span class="si">%f</span><span class="s2">).&quot;</span>
-                <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">circumference</span>
-                <span class="o">*</span> <span class="mf">0.5</span>
-            <span class="p">)</span>
-        <span class="n">max_distance</span> <span class="o">=</span> <span class="n">sphere</span><span class="o">.</span><span class="n">arcdist2linear</span><span class="p">(</span><span class="n">max_distance</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">radius</span><span class="p">)</span> <span class="o">+</span> <span class="n">FLOAT_EPS</span> <span class="o">*</span> <span class="mi">3</span>
-        <span class="n">D</span> <span class="o">=</span> <span class="n">temp_KDTree</span><span class="o">.</span><span class="n">sparse_distance_matrix</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">max_distance</span><span class="p">)</span>
-        <span class="n">D</span> <span class="o">=</span> <span class="n">D</span><span class="o">.</span><span class="n">tocoo</span><span class="p">()</span>
-        <span class="c1"># print D.data</span>
-        <span class="n">a2l</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">sphere</span><span class="o">.</span><span class="n">linear2arcdist</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">radius</span><span class="p">)</span>
-        <span class="c1"># print map(a2l,D.data)</span>
-        <span class="k">return</span> <span class="n">scipy</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">coo_matrix</span><span class="p">((</span><span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">a2l</span><span class="p">,</span> <span class="n">D</span><span class="o">.</span><span class="n">data</span><span class="p">)),</span> <span class="p">(</span><span class="n">D</span><span class="o">.</span><span class="n">row</span><span class="p">,</span> <span class="n">D</span><span class="o">.</span><span class="n">col</span><span class="p">)))</span><span class="o">.</span><span class="n">todok</span><span class="p">()</span>
-</pre></div>
-
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-  <h1>Source code for libpysal.cg.locators</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">Computational geometry code for PySAL: Python Spatial Analysis Library.</span>
-<span class="sd">&quot;&quot;&quot;</span>
-
-<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Sergio J. Rey, Xinyue Ye, Charles Schmidt, Andrew Winslow&quot;</span>
-<span class="n">__credits__</span> <span class="o">=</span> <span class="s2">&quot;Copyright (c) 2005-2011 Sergio J. Rey&quot;</span>
-
-<span class="kn">import</span> <span class="nn">math</span>
-<span class="kn">import</span> <span class="nn">copy</span>
-<span class="kn">import</span> <span class="nn">warnings</span>
-<span class="kn">from</span> <span class="nn">.rtree</span> <span class="kn">import</span> <span class="o">*</span>
-<span class="kn">from</span> <span class="nn">.standalone</span> <span class="kn">import</span> <span class="o">*</span>
-<span class="kn">from</span> <span class="nn">.shapes</span> <span class="kn">import</span> <span class="o">*</span>
-
-<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;Grid&quot;</span><span class="p">,</span> <span class="s2">&quot;BruteForcePointLocator&quot;</span><span class="p">,</span> <span class="s2">&quot;PointLocator&quot;</span><span class="p">,</span> <span class="s2">&quot;PolygonLocator&quot;</span><span class="p">]</span>
-
-<span class="n">dep_msg</span> <span class="o">=</span> <span class="s2">&quot;is deprecated and will be reoved in libpysal 4.4.0&quot;</span>
-
-
-<div class="viewcode-block" id="Grid"><a class="viewcode-back" href="../../../generated/libpysal.cg.Grid.html#libpysal.cg.Grid">[docs]</a><span class="k">class</span> <span class="nc">Grid</span><span class="p">:</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Representation of a binning data structure.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-<div class="viewcode-block" id="Grid.__init__"><a class="viewcode-back" href="../../../generated/libpysal.cg.Grid.html#libpysal.cg.Grid.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">bounds</span><span class="p">,</span> <span class="n">resolution</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Returns a grid with specified properties.</span>
-
-<span class="sd">        __init__(Rectangle, number) -&gt; Grid</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        bounds      : the area for the grid to encompass</span>
-<span class="sd">        resolution  : the diameter of each bin</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        TODO: complete this doctest</span>
-<span class="sd">        &gt;&gt;&gt; g = Grid(Rectangle(0, 0, 10, 10), 1)</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Grid &quot;</span> <span class="o">+</span> <span class="n">dep_msg</span><span class="p">,</span> <span class="ne">DeprecationWarning</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">resolution</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;Cannot create grid with resolution 0&quot;</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">res</span> <span class="o">=</span> <span class="n">resolution</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">hash</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">x_range</span> <span class="o">=</span> <span class="p">(</span><span class="n">bounds</span><span class="o">.</span><span class="n">left</span><span class="p">,</span> <span class="n">bounds</span><span class="o">.</span><span class="n">right</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">y_range</span> <span class="o">=</span> <span class="p">(</span><span class="n">bounds</span><span class="o">.</span><span class="n">lower</span><span class="p">,</span> <span class="n">bounds</span><span class="o">.</span><span class="n">upper</span><span class="p">)</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">i_range</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span>
-                <span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">x_range</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">x_range</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">res</span><span class="p">)</span>
-            <span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">j_range</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span>
-                <span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">y_range</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">y_range</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">res</span><span class="p">)</span>
-            <span class="p">)</span>
-        <span class="k">except</span> <span class="ne">Exception</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span>
-                <span class="s2">&quot;Invalid arguments for Grid(): (&quot;</span>
-                <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">x_range</span><span class="p">)</span>
-                <span class="o">+</span> <span class="s2">&quot;, &quot;</span>
-                <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">y_range</span><span class="p">)</span>
-                <span class="o">+</span> <span class="s2">&quot;, &quot;</span>
-                <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
-                <span class="o">+</span> <span class="s2">&quot;)&quot;</span>
-            <span class="p">)</span></div>
-
-<div class="viewcode-block" id="Grid.in_grid"><a class="viewcode-back" href="../../../generated/libpysal.cg.Grid.html#libpysal.cg.Grid.in_grid">[docs]</a>    <span class="k">def</span> <span class="nf">in_grid</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">loc</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Returns whether a 2-tuple location _loc_ lies inside the grid bounds.</span>
-
-<span class="sd">        Test tag: &lt;tc&gt;#is#Grid.in_grid&lt;/tc&gt;</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="p">(</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">x_range</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">loc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">x_range</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-            <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">y_range</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">loc</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">y_range</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-        <span class="p">)</span></div>
-
-    <span class="k">def</span> <span class="nf">__grid_loc</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">loc</span><span class="p">):</span>
-        <span class="n">i</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">i_range</span><span class="p">,</span> <span class="nb">max</span><span class="p">(</span><span class="nb">int</span><span class="p">((</span><span class="n">loc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">x_range</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">res</span><span class="p">),</span> <span class="mi">0</span><span class="p">))</span>
-        <span class="n">j</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">j_range</span><span class="p">,</span> <span class="nb">max</span><span class="p">(</span><span class="nb">int</span><span class="p">((</span><span class="n">loc</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">y_range</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">res</span><span class="p">),</span> <span class="mi">0</span><span class="p">))</span>
-        <span class="k">return</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)</span>
-
-<div class="viewcode-block" id="Grid.add"><a class="viewcode-back" href="../../../generated/libpysal.cg.Grid.html#libpysal.cg.Grid.add">[docs]</a>    <span class="k">def</span> <span class="nf">add</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">item</span><span class="p">,</span> <span class="n">pt</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Adds an item to the grid at a specified location.</span>
-
-<span class="sd">        add(x, Point) -&gt; x</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        item  : the item to insert into the grid</span>
-<span class="sd">        pt : the location to insert the item at</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-
-<span class="sd">        &gt;&gt;&gt; g = Grid(Rectangle(0, 0, 10, 10), 1)</span>
-<span class="sd">        &gt;&gt;&gt; g.add(&#39;A&#39;, Point((4.2, 8.7)))</span>
-<span class="sd">        &#39;A&#39;</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">in_grid</span><span class="p">(</span><span class="n">pt</span><span class="p">):</span>
-            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span>
-                <span class="s2">&quot;Attempt to insert item at location outside grid bounds: &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">pt</span><span class="p">)</span>
-            <span class="p">)</span>
-        <span class="n">grid_loc</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__grid_loc</span><span class="p">(</span><span class="n">pt</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">grid_loc</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">hash</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">hash</span><span class="p">[</span><span class="n">grid_loc</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">pt</span><span class="p">,</span> <span class="n">item</span><span class="p">))</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">hash</span><span class="p">[</span><span class="n">grid_loc</span><span class="p">]</span> <span class="o">=</span> <span class="p">[(</span><span class="n">pt</span><span class="p">,</span> <span class="n">item</span><span class="p">)]</span>
-        <span class="k">return</span> <span class="n">item</span></div>
-
-<div class="viewcode-block" id="Grid.remove"><a class="viewcode-back" href="../../../generated/libpysal.cg.Grid.html#libpysal.cg.Grid.remove">[docs]</a>    <span class="k">def</span> <span class="nf">remove</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">item</span><span class="p">,</span> <span class="n">pt</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Removes an item from the grid at a specified location.</span>
-
-<span class="sd">        remove(x, Point) -&gt; x</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        item : the item to remove from the grid</span>
-<span class="sd">        pt : the location the item was added at</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-
-<span class="sd">        &gt;&gt;&gt; g = Grid(Rectangle(0, 0, 10, 10), 1)</span>
-<span class="sd">        &gt;&gt;&gt; g.add(&#39;A&#39;, Point((4.2, 8.7)))</span>
-<span class="sd">        &#39;A&#39;</span>
-<span class="sd">        &gt;&gt;&gt; g.remove(&#39;A&#39;, Point((4.2, 8.7)))</span>
-<span class="sd">        &#39;A&#39;</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">in_grid</span><span class="p">(</span><span class="n">pt</span><span class="p">):</span>
-            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span>
-                <span class="s2">&quot;Attempt to remove item at location outside grid bounds: &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">pt</span><span class="p">)</span>
-            <span class="p">)</span>
-        <span class="n">grid_loc</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__grid_loc</span><span class="p">(</span><span class="n">pt</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">hash</span><span class="p">[</span><span class="n">grid_loc</span><span class="p">]</span><span class="o">.</span><span class="n">remove</span><span class="p">((</span><span class="n">pt</span><span class="p">,</span> <span class="n">item</span><span class="p">))</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">hash</span><span class="p">[</span><span class="n">grid_loc</span><span class="p">]</span> <span class="o">==</span> <span class="p">[]:</span>
-            <span class="k">del</span> <span class="bp">self</span><span class="o">.</span><span class="n">hash</span><span class="p">[</span><span class="n">grid_loc</span><span class="p">]</span>
-        <span class="k">return</span> <span class="n">item</span></div>
-
-<div class="viewcode-block" id="Grid.bounds"><a class="viewcode-back" href="../../../generated/libpysal.cg.Grid.html#libpysal.cg.Grid.bounds">[docs]</a>    <span class="k">def</span> <span class="nf">bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">bounds</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Returns a list of items found in the grid within the bounds specified.</span>
-
-<span class="sd">        bounds(Rectangle) -&gt; x list</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        item     : the item to remove from the grid</span>
-<span class="sd">        pt       : the location the item was added at</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-
-<span class="sd">        &gt;&gt;&gt; g = Grid(Rectangle(0, 0, 10, 10), 1)</span>
-<span class="sd">        &gt;&gt;&gt; g.add(&#39;A&#39;, Point((1.0, 1.0)))</span>
-<span class="sd">        &#39;A&#39;</span>
-<span class="sd">        &gt;&gt;&gt; g.add(&#39;B&#39;, Point((4.0, 4.0)))</span>
-<span class="sd">        &#39;B&#39;</span>
-<span class="sd">        &gt;&gt;&gt; g.bounds(Rectangle(0, 0, 3, 3))</span>
-<span class="sd">        [&#39;A&#39;]</span>
-<span class="sd">        &gt;&gt;&gt; g.bounds(Rectangle(2, 2, 5, 5))</span>
-<span class="sd">        [&#39;B&#39;]</span>
-<span class="sd">        &gt;&gt;&gt; sorted(g.bounds(Rectangle(0, 0, 5, 5)))</span>
-<span class="sd">        [&#39;A&#39;, &#39;B&#39;]</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">x_range</span> <span class="o">=</span> <span class="p">(</span><span class="n">bounds</span><span class="o">.</span><span class="n">left</span><span class="p">,</span> <span class="n">bounds</span><span class="o">.</span><span class="n">right</span><span class="p">)</span>
-        <span class="n">y_range</span> <span class="o">=</span> <span class="p">(</span><span class="n">bounds</span><span class="o">.</span><span class="n">lower</span><span class="p">,</span> <span class="n">bounds</span><span class="o">.</span><span class="n">upper</span><span class="p">)</span>
-        <span class="n">items</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="n">lower_left</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__grid_loc</span><span class="p">((</span><span class="n">x_range</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">y_range</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
-        <span class="n">upper_right</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__grid_loc</span><span class="p">((</span><span class="n">x_range</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">y_range</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">lower_left</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">upper_right</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
-            <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">lower_left</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">upper_right</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
-                <span class="k">if</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">hash</span><span class="p">:</span>
-                    <span class="n">items</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span>
-                        <span class="p">[</span>
-                            <span class="n">item</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-                            <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="p">[</span>
-                                <span class="n">item</span>
-                                <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">hash</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span>
-                                <span class="k">if</span> <span class="n">x_range</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">item</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">x_range</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-                                <span class="ow">and</span> <span class="n">y_range</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">item</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">y_range</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-                            <span class="p">]</span>
-                        <span class="p">]</span>
-                    <span class="p">)</span>
-        <span class="k">return</span> <span class="n">items</span></div>
-
-<div class="viewcode-block" id="Grid.proximity"><a class="viewcode-back" href="../../../generated/libpysal.cg.Grid.html#libpysal.cg.Grid.proximity">[docs]</a>    <span class="k">def</span> <span class="nf">proximity</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pt</span><span class="p">,</span> <span class="n">r</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Returns a list of items found in the grid within a specified distance of a point.</span>
-
-<span class="sd">        proximity(Point, number) -&gt; x list</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        pt : the location to search around</span>
-<span class="sd">        r  : the distance to search around the point</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        &gt;&gt;&gt; g = Grid(Rectangle(0, 0, 10, 10), 1)</span>
-<span class="sd">        &gt;&gt;&gt; g.add(&#39;A&#39;, Point((1.0, 1.0)))</span>
-<span class="sd">        &#39;A&#39;</span>
-<span class="sd">        &gt;&gt;&gt; g.add(&#39;B&#39;, Point((4.0, 4.0)))</span>
-<span class="sd">        &#39;B&#39;</span>
-<span class="sd">        &gt;&gt;&gt; g.proximity(Point((2.0, 1.0)), 2)</span>
-<span class="sd">        [&#39;A&#39;]</span>
-<span class="sd">        &gt;&gt;&gt; g.proximity(Point((6.0, 5.0)), 3.0)</span>
-<span class="sd">        [&#39;B&#39;]</span>
-<span class="sd">        &gt;&gt;&gt; sorted(g.proximity(Point((4.0, 1.0)), 4.0))</span>
-<span class="sd">        [&#39;A&#39;, &#39;B&#39;]</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">items</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="n">lower_left</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__grid_loc</span><span class="p">((</span><span class="n">pt</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">r</span><span class="p">,</span> <span class="n">pt</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">r</span><span class="p">))</span>
-        <span class="n">upper_right</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__grid_loc</span><span class="p">((</span><span class="n">pt</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">r</span><span class="p">,</span> <span class="n">pt</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">r</span><span class="p">))</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">lower_left</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">upper_right</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
-            <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">lower_left</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">upper_right</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
-                <span class="k">if</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">hash</span><span class="p">:</span>
-                    <span class="n">items</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span>
-                        <span class="p">[</span>
-                            <span class="n">item</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-                            <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="p">[</span>
-                                <span class="n">item</span>
-                                <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">hash</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span>
-                                <span class="k">if</span> <span class="n">get_points_dist</span><span class="p">(</span><span class="n">pt</span><span class="p">,</span> <span class="n">item</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">&lt;=</span> <span class="n">r</span>
-                            <span class="p">]</span>
-                        <span class="p">]</span>
-                    <span class="p">)</span>
-        <span class="k">return</span> <span class="n">items</span></div>
-
-<div class="viewcode-block" id="Grid.nearest"><a class="viewcode-back" href="../../../generated/libpysal.cg.Grid.html#libpysal.cg.Grid.nearest">[docs]</a>    <span class="k">def</span> <span class="nf">nearest</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pt</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Returns the nearest item to a point.</span>
-
-<span class="sd">        nearest(Point) -&gt; x</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        pt : the location to search near</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        &gt;&gt;&gt; g = Grid(Rectangle(0, 0, 10, 10), 1)</span>
-<span class="sd">        &gt;&gt;&gt; g.add(&#39;A&#39;, Point((1.0, 1.0)))</span>
-<span class="sd">        &#39;A&#39;</span>
-<span class="sd">        &gt;&gt;&gt; g.add(&#39;B&#39;, Point((4.0, 4.0)))</span>
-<span class="sd">        &#39;B&#39;</span>
-<span class="sd">        &gt;&gt;&gt; g.nearest(Point((2.0, 1.0)))</span>
-<span class="sd">        &#39;A&#39;</span>
-<span class="sd">        &gt;&gt;&gt; g.nearest(Point((7.0, 5.0)))</span>
-<span class="sd">        &#39;B&#39;</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">search_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">res</span>
-        <span class="k">while</span> <span class="bp">self</span><span class="o">.</span><span class="n">proximity</span><span class="p">(</span><span class="n">pt</span><span class="p">,</span> <span class="n">search_size</span><span class="p">)</span> <span class="o">==</span> <span class="p">[]</span> <span class="ow">and</span> <span class="p">(</span>
-            <span class="n">get_points_dist</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">x_range</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">y_range</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">pt</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">search_size</span>
-            <span class="ow">or</span> <span class="n">get_points_dist</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">x_range</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">y_range</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">pt</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">search_size</span>
-            <span class="ow">or</span> <span class="n">get_points_dist</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">x_range</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">y_range</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="n">pt</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">search_size</span>
-            <span class="ow">or</span> <span class="n">get_points_dist</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">x_range</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">y_range</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="n">pt</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">search_size</span>
-        <span class="p">):</span>
-            <span class="n">search_size</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">search_size</span>
-        <span class="n">items</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="n">lower_left</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__grid_loc</span><span class="p">((</span><span class="n">pt</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">search_size</span><span class="p">,</span> <span class="n">pt</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">search_size</span><span class="p">))</span>
-        <span class="n">upper_right</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__grid_loc</span><span class="p">((</span><span class="n">pt</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">search_size</span><span class="p">,</span> <span class="n">pt</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">search_size</span><span class="p">))</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">lower_left</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">upper_right</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
-            <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">lower_left</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">upper_right</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
-                <span class="k">if</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">hash</span><span class="p">:</span>
-                    <span class="n">items</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span>
-                        <span class="p">[</span>
-                            <span class="p">(</span><span class="n">get_points_dist</span><span class="p">(</span><span class="n">pt</span><span class="p">,</span> <span class="n">item</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">item</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-                            <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">hash</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span>
-                        <span class="p">]</span>
-                    <span class="p">)</span>
-        <span class="k">if</span> <span class="n">items</span> <span class="o">==</span> <span class="p">[]:</span>
-            <span class="k">return</span> <span class="kc">None</span>
-        <span class="k">return</span> <span class="nb">min</span><span class="p">(</span><span class="n">items</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span></div></div>
-
-
-<span class="k">class</span> <span class="nc">BruteForcePointLocator</span><span class="p">:</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    A class which does naive linear search on a set of Point objects.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">points</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Creates a naive index of the points specified.</span>
-
-<span class="sd">        __init__(Point list) -&gt; BruteForcePointLocator</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        points : a list of points to index (Point list)</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        &gt;&gt;&gt; pl = BruteForcePointLocator([Point((0, 0)), Point((5, 0)), Point((0, 10))])</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;BruteForcePointLocator &quot;</span> <span class="o">+</span> <span class="n">dep_msg</span><span class="p">,</span> <span class="ne">DeprecationWarning</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_points</span> <span class="o">=</span> <span class="n">points</span>
-
-    <span class="k">def</span> <span class="nf">nearest</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">query_point</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Returns the nearest point indexed to a query point.</span>
-
-<span class="sd">        nearest(Point) -&gt; Point</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        query_point : a point to find the nearest indexed point to</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        &gt;&gt;&gt; points = [Point((0, 0)), Point((1, 6)), Point((5.4, 1.4))]</span>
-<span class="sd">        &gt;&gt;&gt; pl = BruteForcePointLocator(points)</span>
-<span class="sd">        &gt;&gt;&gt; n = pl.nearest(Point((1, 1)))</span>
-<span class="sd">        &gt;&gt;&gt; str(n)</span>
-<span class="sd">        &#39;(0.0, 0.0)&#39;</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="nb">min</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_points</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">p</span><span class="p">:</span> <span class="n">get_points_dist</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">query_point</span><span class="p">))</span>
-
-    <span class="k">def</span> <span class="nf">region</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">region_rect</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Returns the indexed points located inside a rectangular query region.</span>
-
-<span class="sd">        region(Rectangle) -&gt; Point list</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        region_rect : the rectangular range to find indexed points in</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        &gt;&gt;&gt; points = [Point((0, 0)), Point((1, 6)), Point((5.4, 1.4))]</span>
-<span class="sd">        &gt;&gt;&gt; pl = BruteForcePointLocator(points)</span>
-<span class="sd">        &gt;&gt;&gt; pts = pl.region(Rectangle(-1, -1, 10, 10))</span>
-<span class="sd">        &gt;&gt;&gt; len(pts)</span>
-<span class="sd">        3</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="p">[</span>
-            <span class="n">p</span>
-            <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_points</span>
-            <span class="k">if</span> <span class="n">get_rectangle_point_intersect</span><span class="p">(</span><span class="n">region_rect</span><span class="p">,</span> <span class="n">p</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
-        <span class="p">]</span>
-
-    <span class="k">def</span> <span class="nf">proximity</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">origin</span><span class="p">,</span> <span class="n">r</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Returns the indexed points located within some distance of an origin point.</span>
-
-<span class="sd">        proximity(Point, number) -&gt; Point list</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        origin  : the point to find indexed points near</span>
-<span class="sd">        r       : the maximum distance to find indexed point from the origin point</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        &gt;&gt;&gt; points = [Point((0, 0)), Point((1, 6)), Point((5.4, 1.4))]</span>
-<span class="sd">        &gt;&gt;&gt; pl = BruteForcePointLocator(points)</span>
-<span class="sd">        &gt;&gt;&gt; neighs = pl.proximity(Point((1, 0)), 2)</span>
-<span class="sd">        &gt;&gt;&gt; len(neighs)</span>
-<span class="sd">        1</span>
-<span class="sd">        &gt;&gt;&gt; p = neighs[0]</span>
-<span class="sd">        &gt;&gt;&gt; isinstance(p, Point)</span>
-<span class="sd">        True</span>
-<span class="sd">        &gt;&gt;&gt; str(p)</span>
-<span class="sd">        &#39;(0.0, 0.0)&#39;</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="p">[</span><span class="n">p</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_points</span> <span class="k">if</span> <span class="n">get_points_dist</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">origin</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="n">r</span><span class="p">]</span>
-
-
-<div class="viewcode-block" id="PointLocator"><a class="viewcode-back" href="../../../generated/libpysal.cg.PointLocator.html#libpysal.cg.PointLocator">[docs]</a><span class="k">class</span> <span class="nc">PointLocator</span><span class="p">:</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    An abstract representation of a point indexing data structure.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-<div class="viewcode-block" id="PointLocator.__init__"><a class="viewcode-back" href="../../../generated/libpysal.cg.PointLocator.html#libpysal.cg.PointLocator.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">points</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Returns a point locator object.</span>
-
-<span class="sd">        __init__(Point list) -&gt; PointLocator</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        points : a list of points to index</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        &gt;&gt;&gt; points = [Point((0, 0)), Point((1, 6)), Point((5.4, 1.4))]</span>
-<span class="sd">        &gt;&gt;&gt; pl = PointLocator(points)</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;PointLocator &quot;</span> <span class="o">+</span> <span class="n">dep_msg</span><span class="p">,</span> <span class="ne">DeprecationWarning</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_locator</span> <span class="o">=</span> <span class="n">BruteForcePointLocator</span><span class="p">(</span><span class="n">points</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="PointLocator.nearest"><a class="viewcode-back" href="../../../generated/libpysal.cg.PointLocator.html#libpysal.cg.PointLocator.nearest">[docs]</a>    <span class="k">def</span> <span class="nf">nearest</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">query_point</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Returns the nearest point indexed to a query point.</span>
-
-<span class="sd">        nearest(Point) -&gt; Point</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        query_point : a point to find the nearest indexed point to</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        &gt;&gt;&gt; points = [Point((0, 0)), Point((1, 6)), Point((5.4, 1.4))]</span>
-<span class="sd">        &gt;&gt;&gt; pl = PointLocator(points)</span>
-<span class="sd">        &gt;&gt;&gt; n = pl.nearest(Point((1, 1)))</span>
-<span class="sd">        &gt;&gt;&gt; str(n)</span>
-<span class="sd">        &#39;(0.0, 0.0)&#39;</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_locator</span><span class="o">.</span><span class="n">nearest</span><span class="p">(</span><span class="n">query_point</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="PointLocator.region"><a class="viewcode-back" href="../../../generated/libpysal.cg.PointLocator.html#libpysal.cg.PointLocator.region">[docs]</a>    <span class="k">def</span> <span class="nf">region</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">region_rect</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Returns the indexed points located inside a rectangular query region.</span>
-
-<span class="sd">        region(Rectangle) -&gt; Point list</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        region_rect : the rectangular range to find indexed points in</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        &gt;&gt;&gt; points = [Point((0, 0)), Point((1, 6)), Point((5.4, 1.4))]</span>
-<span class="sd">        &gt;&gt;&gt; pl = PointLocator(points)</span>
-<span class="sd">        &gt;&gt;&gt; pts = pl.region(Rectangle(-1, -1, 10, 10))</span>
-<span class="sd">        &gt;&gt;&gt; len(pts)</span>
-<span class="sd">        3</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_locator</span><span class="o">.</span><span class="n">region</span><span class="p">(</span><span class="n">region_rect</span><span class="p">)</span></div>
-
-    <span class="n">overlapping</span> <span class="o">=</span> <span class="n">region</span>
-
-<div class="viewcode-block" id="PointLocator.polygon"><a class="viewcode-back" href="../../../generated/libpysal.cg.PointLocator.html#libpysal.cg.PointLocator.polygon">[docs]</a>    <span class="k">def</span> <span class="nf">polygon</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">polygon</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Returns the indexed points located inside a polygon</span>
-<span class="sd">        &quot;&quot;&quot;</span></div>
-
-        <span class="c1"># get points in polygon bounding box</span>
-
-        <span class="c1"># for points in bounding box, check for inclusion in polygon</span>
-
-<div class="viewcode-block" id="PointLocator.proximity"><a class="viewcode-back" href="../../../generated/libpysal.cg.PointLocator.html#libpysal.cg.PointLocator.proximity">[docs]</a>    <span class="k">def</span> <span class="nf">proximity</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">origin</span><span class="p">,</span> <span class="n">r</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Returns the indexed points located within some distance of an origin point.</span>
-
-<span class="sd">        proximity(Point, number) -&gt; Point list</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        origin  : the point to find indexed points near</span>
-<span class="sd">        r       : the maximum distance to find indexed point from the origin point</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        &gt;&gt;&gt; points = [Point((0, 0)), Point((1, 6)), Point((5.4, 1.4))]</span>
-<span class="sd">        &gt;&gt;&gt; pl = PointLocator(points)</span>
-<span class="sd">        &gt;&gt;&gt; len(pl.proximity(Point((1, 0)), 2))</span>
-<span class="sd">        1</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_locator</span><span class="o">.</span><span class="n">proximity</span><span class="p">(</span><span class="n">origin</span><span class="p">,</span> <span class="n">r</span><span class="p">)</span></div></div>
-
-
-<div class="viewcode-block" id="PolygonLocator"><a class="viewcode-back" href="../../../generated/libpysal.cg.PolygonLocator.html#libpysal.cg.PolygonLocator">[docs]</a><span class="k">class</span> <span class="nc">PolygonLocator</span><span class="p">:</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    An abstract representation of a polygon indexing data structure.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-<div class="viewcode-block" id="PolygonLocator.__init__"><a class="viewcode-back" href="../../../generated/libpysal.cg.PolygonLocator.html#libpysal.cg.PolygonLocator.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">polygons</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Returns a polygon locator object.</span>
-
-<span class="sd">        __init__(Polygon list) -&gt; PolygonLocator</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        polygons : a list of polygons to index</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        &gt;&gt;&gt; p1 = Polygon([Point((0, 1)), Point((4, 5)), Point((5, 1))])</span>
-<span class="sd">        &gt;&gt;&gt; p2 = Polygon([Point((3, 9)), Point((6, 7)), Point((1, 1))])</span>
-<span class="sd">        &gt;&gt;&gt; pl = PolygonLocator([p1, p2])</span>
-<span class="sd">        &gt;&gt;&gt; isinstance(pl, PolygonLocator)</span>
-<span class="sd">        True</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;PolygonLocator &quot;</span> <span class="o">+</span> <span class="n">dep_msg</span><span class="p">,</span> <span class="ne">DeprecationWarning</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_locator</span> <span class="o">=</span> <span class="n">polygons</span>
-        <span class="c1"># create and rtree</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_rtree</span> <span class="o">=</span> <span class="n">RTree</span><span class="p">()</span>
-        <span class="k">for</span> <span class="n">polygon</span> <span class="ow">in</span> <span class="n">polygons</span><span class="p">:</span>
-            <span class="n">x</span> <span class="o">=</span> <span class="n">polygon</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">left</span>
-            <span class="n">y</span> <span class="o">=</span> <span class="n">polygon</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">lower</span>
-            <span class="n">X</span> <span class="o">=</span> <span class="n">polygon</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">right</span>
-            <span class="n">Y</span> <span class="o">=</span> <span class="n">polygon</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">upper</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_rtree</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="n">polygon</span><span class="p">,</span> <span class="n">Rect</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">))</span></div>
-
-<div class="viewcode-block" id="PolygonLocator.inside"><a class="viewcode-back" href="../../../generated/libpysal.cg.PolygonLocator.html#libpysal.cg.PolygonLocator.inside">[docs]</a>    <span class="k">def</span> <span class="nf">inside</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">query_rectangle</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Returns polygons that are inside query_rectangle</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-
-<span class="sd">        &gt;&gt;&gt; p1 = Polygon([Point((0, 1)), Point((4, 5)), Point((5, 1))])</span>
-<span class="sd">        &gt;&gt;&gt; p2 = Polygon([Point((3, 9)), Point((6, 7)), Point((1, 1))])</span>
-<span class="sd">        &gt;&gt;&gt; p3 = Polygon([Point((7, 1)), Point((8, 7)), Point((9, 1))])</span>
-<span class="sd">        &gt;&gt;&gt; pl = PolygonLocator([p1, p2, p3])</span>
-<span class="sd">        &gt;&gt;&gt; qr = Rectangle(0, 0, 5, 5)</span>
-<span class="sd">        &gt;&gt;&gt; res = pl.inside( qr )</span>
-<span class="sd">        &gt;&gt;&gt; len(res)</span>
-<span class="sd">        1</span>
-<span class="sd">        &gt;&gt;&gt; qr = Rectangle(3, 7, 5, 8)</span>
-<span class="sd">        &gt;&gt;&gt; res = pl.inside( qr )</span>
-<span class="sd">        &gt;&gt;&gt; len(res)</span>
-<span class="sd">        0</span>
-<span class="sd">        &gt;&gt;&gt; qr = Rectangle(10, 10, 12, 12)</span>
-<span class="sd">        &gt;&gt;&gt; res = pl.inside( qr )</span>
-<span class="sd">        &gt;&gt;&gt; len(res)</span>
-<span class="sd">        0</span>
-<span class="sd">        &gt;&gt;&gt; qr = Rectangle(0, 0, 12, 12)</span>
-<span class="sd">        &gt;&gt;&gt; res = pl.inside( qr )</span>
-<span class="sd">        &gt;&gt;&gt; len(res)</span>
-<span class="sd">        3</span>
-
-<span class="sd">        Notes</span>
-<span class="sd">        -----</span>
-
-<span class="sd">        inside means the intersection of the query rectangle and a</span>
-<span class="sd">        polygon is not empty and is equal to the area of the polygon</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">left</span> <span class="o">=</span> <span class="n">query_rectangle</span><span class="o">.</span><span class="n">left</span>
-        <span class="n">right</span> <span class="o">=</span> <span class="n">query_rectangle</span><span class="o">.</span><span class="n">right</span>
-        <span class="n">upper</span> <span class="o">=</span> <span class="n">query_rectangle</span><span class="o">.</span><span class="n">upper</span>
-        <span class="n">lower</span> <span class="o">=</span> <span class="n">query_rectangle</span><span class="o">.</span><span class="n">lower</span>
-
-        <span class="c1"># rtree rect</span>
-        <span class="n">qr</span> <span class="o">=</span> <span class="n">Rect</span><span class="p">(</span><span class="n">left</span><span class="p">,</span> <span class="n">lower</span><span class="p">,</span> <span class="n">right</span><span class="p">,</span> <span class="n">upper</span><span class="p">)</span>
-        <span class="c1"># bb overlaps</span>
-        <span class="n">res</span> <span class="o">=</span> <span class="p">[</span><span class="n">r</span><span class="o">.</span><span class="n">leaf_obj</span><span class="p">()</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_rtree</span><span class="o">.</span><span class="n">query_rect</span><span class="p">(</span><span class="n">qr</span><span class="p">)</span> <span class="k">if</span> <span class="n">r</span><span class="o">.</span><span class="n">is_leaf</span><span class="p">()]</span>
-
-        <span class="n">qp</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">(</span>
-            <span class="p">[</span>
-                <span class="n">Point</span><span class="p">((</span><span class="n">left</span><span class="p">,</span> <span class="n">lower</span><span class="p">)),</span>
-                <span class="n">Point</span><span class="p">((</span><span class="n">right</span><span class="p">,</span> <span class="n">lower</span><span class="p">)),</span>
-                <span class="n">Point</span><span class="p">((</span><span class="n">right</span><span class="p">,</span> <span class="n">upper</span><span class="p">)),</span>
-                <span class="n">Point</span><span class="p">((</span><span class="n">left</span><span class="p">,</span> <span class="n">upper</span><span class="p">)),</span>
-            <span class="p">]</span>
-        <span class="p">)</span>
-        <span class="n">ip</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="n">GPPI</span> <span class="o">=</span> <span class="n">get_polygon_point_intersect</span>
-        <span class="k">for</span> <span class="n">poly</span> <span class="ow">in</span> <span class="n">res</span><span class="p">:</span>
-            <span class="n">flag</span> <span class="o">=</span> <span class="kc">True</span>
-            <span class="n">lower</span> <span class="o">=</span> <span class="n">poly</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">lower</span>
-            <span class="n">right</span> <span class="o">=</span> <span class="n">poly</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">right</span>
-            <span class="n">upper</span> <span class="o">=</span> <span class="n">poly</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">upper</span>
-            <span class="n">left</span> <span class="o">=</span> <span class="n">poly</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">left</span>
-            <span class="n">p1</span> <span class="o">=</span> <span class="n">Point</span><span class="p">((</span><span class="n">left</span><span class="p">,</span> <span class="n">lower</span><span class="p">))</span>
-            <span class="n">p2</span> <span class="o">=</span> <span class="n">Point</span><span class="p">((</span><span class="n">right</span><span class="p">,</span> <span class="n">upper</span><span class="p">))</span>
-            <span class="k">if</span> <span class="n">GPPI</span><span class="p">(</span><span class="n">qp</span><span class="p">,</span> <span class="n">p1</span><span class="p">)</span> <span class="ow">and</span> <span class="n">GPPI</span><span class="p">(</span><span class="n">qp</span><span class="p">,</span> <span class="n">p2</span><span class="p">):</span>
-                <span class="n">ip</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">poly</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">ip</span></div>
-
-<div class="viewcode-block" id="PolygonLocator.overlapping"><a class="viewcode-back" href="../../../generated/libpysal.cg.PolygonLocator.html#libpysal.cg.PolygonLocator.overlapping">[docs]</a>    <span class="k">def</span> <span class="nf">overlapping</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">query_rectangle</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Returns list of polygons that overlap query_rectangle</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-
-<span class="sd">        &gt;&gt;&gt; p1 = Polygon([Point((0, 1)), Point((4, 5)), Point((5, 1))])</span>
-<span class="sd">        &gt;&gt;&gt; p2 = Polygon([Point((3, 9)), Point((6, 7)), Point((1, 1))])</span>
-<span class="sd">        &gt;&gt;&gt; p3 = Polygon([Point((7, 1)), Point((8, 7)), Point((9, 1))])</span>
-<span class="sd">        &gt;&gt;&gt; pl = PolygonLocator([p1, p2, p3])</span>
-<span class="sd">        &gt;&gt;&gt; qr = Rectangle(0, 0, 5, 5)</span>
-<span class="sd">        &gt;&gt;&gt; res = pl.overlapping( qr )</span>
-<span class="sd">        &gt;&gt;&gt; len(res)</span>
-<span class="sd">        2</span>
-<span class="sd">        &gt;&gt;&gt; qr = Rectangle(3, 7, 5, 8)</span>
-<span class="sd">        &gt;&gt;&gt; res = pl.overlapping( qr )</span>
-<span class="sd">        &gt;&gt;&gt; len(res)</span>
-<span class="sd">        1</span>
-<span class="sd">        &gt;&gt;&gt; qr = Rectangle(10, 10, 12, 12)</span>
-<span class="sd">        &gt;&gt;&gt; res = pl.overlapping( qr )</span>
-<span class="sd">        &gt;&gt;&gt; len(res)</span>
-<span class="sd">        0</span>
-<span class="sd">        &gt;&gt;&gt; qr = Rectangle(0, 0, 12, 12)</span>
-<span class="sd">        &gt;&gt;&gt; res = pl.overlapping( qr )</span>
-<span class="sd">        &gt;&gt;&gt; len(res)</span>
-<span class="sd">        3</span>
-<span class="sd">        &gt;&gt;&gt; qr = Rectangle(8, 3, 9, 4)</span>
-<span class="sd">        &gt;&gt;&gt; p1 = Polygon([Point((2, 1)), Point((2, 3)), Point((4, 3)), Point((4,1))])</span>
-<span class="sd">        &gt;&gt;&gt; p2 = Polygon([Point((7, 1)), Point((7, 5)), Point((10, 5)), Point((10, 1))])</span>
-<span class="sd">        &gt;&gt;&gt; pl = PolygonLocator([p1, p2])</span>
-<span class="sd">        &gt;&gt;&gt; res = pl.overlapping(qr)</span>
-<span class="sd">        &gt;&gt;&gt; len(res)</span>
-<span class="sd">        1</span>
-
-<span class="sd">        Notes</span>
-<span class="sd">        -----</span>
-<span class="sd">        overlapping means the intersection of the query rectangle and a</span>
-<span class="sd">        polygon is not empty and is no larger than the area of the polygon</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">left</span> <span class="o">=</span> <span class="n">query_rectangle</span><span class="o">.</span><span class="n">left</span>
-        <span class="n">right</span> <span class="o">=</span> <span class="n">query_rectangle</span><span class="o">.</span><span class="n">right</span>
-        <span class="n">upper</span> <span class="o">=</span> <span class="n">query_rectangle</span><span class="o">.</span><span class="n">upper</span>
-        <span class="n">lower</span> <span class="o">=</span> <span class="n">query_rectangle</span><span class="o">.</span><span class="n">lower</span>
-
-        <span class="c1"># rtree rect</span>
-        <span class="n">qr</span> <span class="o">=</span> <span class="n">Rect</span><span class="p">(</span><span class="n">left</span><span class="p">,</span> <span class="n">lower</span><span class="p">,</span> <span class="n">right</span><span class="p">,</span> <span class="n">upper</span><span class="p">)</span>
-
-        <span class="c1"># bb overlaps</span>
-        <span class="n">res</span> <span class="o">=</span> <span class="p">[</span><span class="n">r</span><span class="o">.</span><span class="n">leaf_obj</span><span class="p">()</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_rtree</span><span class="o">.</span><span class="n">query_rect</span><span class="p">(</span><span class="n">qr</span><span class="p">)</span> <span class="k">if</span> <span class="n">r</span><span class="o">.</span><span class="n">is_leaf</span><span class="p">()]</span>
-        <span class="c1"># have to check for polygon overlap using segment intersection</span>
-
-        <span class="c1"># add polys whose bb contains at least one of the corners of the query</span>
-        <span class="c1"># rectangle</span>
-
-        <span class="n">sw</span> <span class="o">=</span> <span class="p">(</span><span class="n">left</span><span class="p">,</span> <span class="n">lower</span><span class="p">)</span>
-        <span class="n">se</span> <span class="o">=</span> <span class="p">(</span><span class="n">right</span><span class="p">,</span> <span class="n">lower</span><span class="p">)</span>
-        <span class="n">ne</span> <span class="o">=</span> <span class="p">(</span><span class="n">right</span><span class="p">,</span> <span class="n">upper</span><span class="p">)</span>
-        <span class="n">nw</span> <span class="o">=</span> <span class="p">(</span><span class="n">left</span><span class="p">,</span> <span class="n">upper</span><span class="p">)</span>
-        <span class="n">pnts</span> <span class="o">=</span> <span class="p">[</span><span class="n">sw</span><span class="p">,</span> <span class="n">se</span><span class="p">,</span> <span class="n">ne</span><span class="p">,</span> <span class="n">nw</span><span class="p">]</span>
-        <span class="n">cs</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">pnt</span> <span class="ow">in</span> <span class="n">pnts</span><span class="p">:</span>
-            <span class="n">c</span> <span class="o">=</span> <span class="p">[</span><span class="n">r</span><span class="o">.</span><span class="n">leaf_obj</span><span class="p">()</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_rtree</span><span class="o">.</span><span class="n">query_point</span><span class="p">(</span><span class="n">pnt</span><span class="p">)</span> <span class="k">if</span> <span class="n">r</span><span class="o">.</span><span class="n">is_leaf</span><span class="p">()]</span>
-            <span class="n">cs</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">c</span><span class="p">)</span>
-
-        <span class="n">cs</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">cs</span><span class="p">))</span>
-
-        <span class="n">overlapping</span> <span class="o">=</span> <span class="p">[]</span>
-
-        <span class="c1"># first find polygons with at least one vertex inside query rectangle</span>
-        <span class="n">remaining</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">polygon</span> <span class="ow">in</span> <span class="n">res</span><span class="p">:</span>
-            <span class="n">vertices</span> <span class="o">=</span> <span class="n">polygon</span><span class="o">.</span><span class="n">vertices</span>
-            <span class="k">for</span> <span class="n">vertex</span> <span class="ow">in</span> <span class="n">vertices</span><span class="p">:</span>
-                <span class="n">xb</span> <span class="o">=</span> <span class="n">vertex</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">left</span>
-                <span class="n">xb</span> <span class="o">*=</span> <span class="n">vertex</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">right</span>
-                <span class="n">yb</span> <span class="o">=</span> <span class="n">vertex</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">lower</span>
-                <span class="n">yb</span> <span class="o">*=</span> <span class="n">vertex</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">upper</span>
-                <span class="k">if</span> <span class="n">xb</span> <span class="o">*</span> <span class="n">yb</span><span class="p">:</span>
-                    <span class="n">overlapping</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">polygon</span><span class="p">)</span>
-                    <span class="n">remaining</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">polygon</span><span class="p">)</span>
-                    <span class="k">break</span>
-
-        <span class="c1"># for remaining polys in bb overlap check if vertex chains intersect</span>
-        <span class="c1"># segments of the query rectangle</span>
-        <span class="n">left_edge</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="n">left</span><span class="p">,</span> <span class="n">lower</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="n">left</span><span class="p">,</span> <span class="n">upper</span><span class="p">)))</span>
-        <span class="n">right_edge</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="n">right</span><span class="p">,</span> <span class="n">lower</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="n">right</span><span class="p">,</span> <span class="n">upper</span><span class="p">)))</span>
-        <span class="n">lower_edge</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="n">left</span><span class="p">,</span> <span class="n">lower</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="n">right</span><span class="p">,</span> <span class="n">lower</span><span class="p">)))</span>
-        <span class="n">upper_edge</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="n">left</span><span class="p">,</span> <span class="n">upper</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="n">right</span><span class="p">,</span> <span class="n">upper</span><span class="p">)))</span>
-        <span class="k">for</span> <span class="n">polygon</span> <span class="ow">in</span> <span class="n">remaining</span><span class="p">:</span>
-            <span class="n">vertices</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">polygon</span><span class="o">.</span><span class="n">vertices</span><span class="p">)</span>
-            <span class="k">if</span> <span class="n">vertices</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">!=</span> <span class="n">vertices</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
-                <span class="n">vertices</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">vertices</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>  <span class="c1"># put on closed cartographic form</span>
-            <span class="n">nv</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">vertices</span><span class="p">)</span>
-            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">nv</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span>
-                <span class="n">head</span> <span class="o">=</span> <span class="n">vertices</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-                <span class="n">tail</span> <span class="o">=</span> <span class="n">vertices</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span>
-                <span class="n">edge</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">head</span><span class="p">,</span> <span class="n">tail</span><span class="p">)</span>
-                <span class="n">li</span> <span class="o">=</span> <span class="n">get_segments_intersect</span><span class="p">(</span><span class="n">edge</span><span class="p">,</span> <span class="n">left_edge</span><span class="p">)</span>
-                <span class="k">if</span> <span class="n">li</span><span class="p">:</span>
-                    <span class="n">overlapping</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">polygon</span><span class="p">)</span>
-                    <span class="k">break</span>
-                <span class="k">elif</span> <span class="n">get_segments_intersect</span><span class="p">(</span><span class="n">edge</span><span class="p">,</span> <span class="n">right_edge</span><span class="p">):</span>
-                    <span class="n">overlapping</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">polygon</span><span class="p">)</span>
-                    <span class="k">break</span>
-                <span class="k">elif</span> <span class="n">get_segments_intersect</span><span class="p">(</span><span class="n">edge</span><span class="p">,</span> <span class="n">lower_edge</span><span class="p">):</span>
-                    <span class="n">overlapping</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">polygon</span><span class="p">)</span>
-                    <span class="k">break</span>
-                <span class="k">elif</span> <span class="n">get_segments_intersect</span><span class="p">(</span><span class="n">edge</span><span class="p">,</span> <span class="n">upper_edge</span><span class="p">):</span>
-                    <span class="n">overlapping</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">polygon</span><span class="p">)</span>
-                    <span class="k">break</span>
-        <span class="c1"># check remaining for explicit containment of the bounding rectangle</span>
-        <span class="c1"># cs has candidates for this check</span>
-        <span class="n">sw</span> <span class="o">=</span> <span class="n">Point</span><span class="p">(</span><span class="n">sw</span><span class="p">)</span>
-        <span class="n">se</span> <span class="o">=</span> <span class="n">Point</span><span class="p">(</span><span class="n">se</span><span class="p">)</span>
-        <span class="n">ne</span> <span class="o">=</span> <span class="n">Point</span><span class="p">(</span><span class="n">ne</span><span class="p">)</span>
-        <span class="n">nw</span> <span class="o">=</span> <span class="n">Point</span><span class="p">(</span><span class="n">nw</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">polygon</span> <span class="ow">in</span> <span class="n">cs</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">get_polygon_point_intersect</span><span class="p">(</span><span class="n">polygon</span><span class="p">,</span> <span class="n">sw</span><span class="p">):</span>
-                <span class="n">overlapping</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">polygon</span><span class="p">)</span>
-                <span class="k">break</span>
-            <span class="k">elif</span> <span class="n">get_polygon_point_intersect</span><span class="p">(</span><span class="n">polygon</span><span class="p">,</span> <span class="n">se</span><span class="p">):</span>
-                <span class="n">overlapping</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">polygon</span><span class="p">)</span>
-                <span class="k">break</span>
-            <span class="k">elif</span> <span class="n">get_polygon_point_intersect</span><span class="p">(</span><span class="n">polygon</span><span class="p">,</span> <span class="n">ne</span><span class="p">):</span>
-                <span class="n">overlapping</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">polygon</span><span class="p">)</span>
-                <span class="k">break</span>
-            <span class="k">elif</span> <span class="n">get_polygon_point_intersect</span><span class="p">(</span><span class="n">polygon</span><span class="p">,</span> <span class="n">nw</span><span class="p">):</span>
-                <span class="n">overlapping</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">polygon</span><span class="p">)</span>
-                <span class="k">break</span>
-        <span class="k">return</span> <span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">overlapping</span><span class="p">))</span></div>
-
-<div class="viewcode-block" id="PolygonLocator.nearest"><a class="viewcode-back" href="../../../generated/libpysal.cg.PolygonLocator.html#libpysal.cg.PolygonLocator.nearest">[docs]</a>    <span class="k">def</span> <span class="nf">nearest</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">query_point</span><span class="p">,</span> <span class="n">rule</span><span class="o">=</span><span class="s2">&quot;vertex&quot;</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Returns the nearest polygon indexed to a query point based on</span>
-<span class="sd">        various rules.</span>
-
-<span class="sd">        nearest(Polygon) -&gt; Polygon</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        query_point  : a point to find the nearest indexed polygon to</span>
-
-<span class="sd">        rule         : representative point for polygon in nearest query.</span>
-<span class="sd">                 vertex -- measures distance between vertices and query_point</span>
-<span class="sd">                 centroid -- measures distance between centroid and</span>
-<span class="sd">                 query_point</span>
-<span class="sd">                 edge   -- measures the distance between edges and query_point</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        &gt;&gt;&gt; p1 = Polygon([Point((0, 1)), Point((4, 5)), Point((5, 1))])</span>
-<span class="sd">        &gt;&gt;&gt; p2 = Polygon([Point((3, 9)), Point((6, 7)), Point((1, 1))])</span>
-<span class="sd">        &gt;&gt;&gt; pl = PolygonLocator([p1, p2])</span>
-<span class="sd">        &gt;&gt;&gt; try: n = pl.nearest(Point((-1, 1)))</span>
-<span class="sd">        ... except NotImplementedError: print(&quot;future test: str(min(n.vertices())) == (0.0, 1.0)&quot;)</span>
-<span class="sd">        future test: str(min(n.vertices())) == (0.0, 1.0)</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">raise</span> <span class="ne">NotImplementedError</span></div>
-
-<div class="viewcode-block" id="PolygonLocator.region"><a class="viewcode-back" href="../../../generated/libpysal.cg.PolygonLocator.html#libpysal.cg.PolygonLocator.region">[docs]</a>    <span class="k">def</span> <span class="nf">region</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">region_rect</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Returns the indexed polygons located inside a rectangular query region.</span>
-
-<span class="sd">        region(Rectangle) -&gt; Polygon list</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        region_rect  : the rectangular range to find indexed polygons in</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        &gt;&gt;&gt; p1 = Polygon([Point((0, 1)), Point((4, 5)), Point((5, 1))])</span>
-<span class="sd">        &gt;&gt;&gt; p2 = Polygon([Point((3, 9)), Point((6, 7)), Point((1, 1))])</span>
-<span class="sd">        &gt;&gt;&gt; pl = PolygonLocator([p1, p2])</span>
-<span class="sd">        &gt;&gt;&gt; n = pl.region(Rectangle(0, 0, 4, 10))</span>
-<span class="sd">        &gt;&gt;&gt; len(n)</span>
-<span class="sd">        2</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">n</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_locator</span>
-        <span class="k">for</span> <span class="n">polygon</span> <span class="ow">in</span> <span class="n">n</span><span class="p">:</span>
-            <span class="n">points</span> <span class="o">=</span> <span class="n">polygon</span><span class="o">.</span><span class="n">vertices</span>
-            <span class="n">pl</span> <span class="o">=</span> <span class="n">BruteForcePointLocator</span><span class="p">(</span><span class="n">points</span><span class="p">)</span>
-            <span class="n">pts</span> <span class="o">=</span> <span class="n">pl</span><span class="o">.</span><span class="n">region</span><span class="p">(</span><span class="n">region_rect</span><span class="p">)</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">pts</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="n">n</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">polygon</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">n</span></div>
-
-<div class="viewcode-block" id="PolygonLocator.contains_point"><a class="viewcode-back" href="../../../generated/libpysal.cg.PolygonLocator.html#libpysal.cg.PolygonLocator.contains_point">[docs]</a>    <span class="k">def</span> <span class="nf">contains_point</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">point</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Returns polygons that contain point</span>
-
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        point: point (x,y)</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        list of polygons containing point</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        &gt;&gt;&gt; p1 = Polygon([Point((0,0)), Point((6,0)), Point((4,4))])</span>
-<span class="sd">        &gt;&gt;&gt; p2 = Polygon([Point((1,2)), Point((4,0)), Point((4,4))])</span>
-<span class="sd">        &gt;&gt;&gt; p1.contains_point((2,2))</span>
-<span class="sd">        1</span>
-<span class="sd">        &gt;&gt;&gt; p2.contains_point((2,2))</span>
-<span class="sd">        1</span>
-<span class="sd">        &gt;&gt;&gt; pl = PolygonLocator([p1, p2])</span>
-<span class="sd">        &gt;&gt;&gt; len(pl.contains_point((2,2)))</span>
-<span class="sd">        2</span>
-<span class="sd">        &gt;&gt;&gt; p2.contains_point((1,1))</span>
-<span class="sd">        0</span>
-<span class="sd">        &gt;&gt;&gt; p1.contains_point((1,1))</span>
-<span class="sd">        1</span>
-<span class="sd">        &gt;&gt;&gt; len(pl.contains_point((1,1)))</span>
-<span class="sd">        1</span>
-<span class="sd">        &gt;&gt;&gt; p1.centroid</span>
-<span class="sd">        (3.3333333333333335, 1.3333333333333333)</span>
-<span class="sd">        &gt;&gt;&gt; pl.contains_point((1,1))[0].centroid</span>
-<span class="sd">        (3.3333333333333335, 1.3333333333333333)</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="c1"># bbounding box containment</span>
-        <span class="n">res</span> <span class="o">=</span> <span class="p">[</span><span class="n">r</span><span class="o">.</span><span class="n">leaf_obj</span><span class="p">()</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_rtree</span><span class="o">.</span><span class="n">query_point</span><span class="p">(</span><span class="n">point</span><span class="p">)</span> <span class="k">if</span> <span class="n">r</span><span class="o">.</span><span class="n">is_leaf</span><span class="p">()]</span>
-        <span class="c1"># explicit containment check for candidate polygons needed</span>
-        <span class="k">return</span> <span class="p">[</span><span class="n">poly</span> <span class="k">for</span> <span class="n">poly</span> <span class="ow">in</span> <span class="n">res</span> <span class="k">if</span> <span class="n">poly</span><span class="o">.</span><span class="n">contains_point</span><span class="p">(</span><span class="n">point</span><span class="p">)]</span></div>
-
-<div class="viewcode-block" id="PolygonLocator.proximity"><a class="viewcode-back" href="../../../generated/libpysal.cg.PolygonLocator.html#libpysal.cg.PolygonLocator.proximity">[docs]</a>    <span class="k">def</span> <span class="nf">proximity</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">origin</span><span class="p">,</span> <span class="n">r</span><span class="p">,</span> <span class="n">rule</span><span class="o">=</span><span class="s2">&quot;vertex&quot;</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Returns the indexed polygons located within some distance of an</span>
-<span class="sd">        origin point based on various rules.</span>
-
-<span class="sd">        proximity(Polygon, number) -&gt; Polygon list</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        origin  : the point to find indexed polygons near</span>
-<span class="sd">        r       : the maximum distance to find indexed polygon from the origin point</span>
-
-<span class="sd">        rule    : representative point for polygon in nearest query.</span>
-<span class="sd">                vertex -- measures distance between vertices and query_point</span>
-<span class="sd">                centroid -- measures distance between centroid and</span>
-<span class="sd">                query_point</span>
-<span class="sd">                edge   -- measures the distance between edges and query_point</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        &gt;&gt;&gt; p1 = Polygon([Point((0, 1)), Point((4, 5)), Point((5, 1))])</span>
-<span class="sd">        &gt;&gt;&gt; p2 = Polygon([Point((3, 9)), Point((6, 7)), Point((1, 1))])</span>
-<span class="sd">        &gt;&gt;&gt; pl = PolygonLocator([p1, p2])</span>
-<span class="sd">        &gt;&gt;&gt; try:</span>
-<span class="sd">        ...     len(pl.proximity(Point((0, 0)), 2))</span>
-<span class="sd">        ... except NotImplementedError:</span>
-<span class="sd">        ...     print(&quot;future test: len(pl.proximity(Point((0, 0)), 2)) == 2&quot;)</span>
-<span class="sd">        future test: len(pl.proximity(Point((0, 0)), 2)) == 2</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">raise</span> <span class="ne">NotImplementedError</span></div></div>
-</pre></div>
-
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-      
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-      
-  <h1>Source code for libpysal.cg.shapes</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">Computational geometry code for PySAL: Python Spatial Analysis Library.</span>
-
-<span class="sd">&quot;&quot;&quot;</span>
-
-<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Sergio J. Rey, Xinyue Ye, Charles Schmidt, Andrew Winslow, Hu Shao&quot;</span>
-
-<span class="kn">import</span> <span class="nn">math</span>
-<span class="kn">from</span> <span class="nn">.sphere</span> <span class="kn">import</span> <span class="n">arcdist</span>
-
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Union</span>
-
-<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="s2">&quot;Point&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;LineSegment&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;Line&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;Ray&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;Chain&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;Polygon&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;Rectangle&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;asShape&quot;</span><span class="p">,</span>
-<span class="p">]</span>
-
-
-<div class="viewcode-block" id="asShape"><a class="viewcode-back" href="../../../generated/libpysal.cg.asShape.html#libpysal.cg.asShape">[docs]</a><span class="k">def</span> <span class="nf">asShape</span><span class="p">(</span><span class="n">obj</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Returns a PySAL shape object from ``obj``, which</span>
-<span class="sd">    must support the ``__geo_interface__``.</span>
-<span class="sd">    </span>
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    obj : {libpysal.cg.{Point, LineSegment, Line, Ray, Chain, Polygon}</span>
-<span class="sd">        A geometric representation of an object.</span>
-<span class="sd">    </span>
-<span class="sd">    Raises</span>
-<span class="sd">    ------</span>
-<span class="sd">    TypeError</span>
-<span class="sd">        Raised when ``obj`` is not a supported shape.</span>
-<span class="sd">    NotImplementedError</span>
-<span class="sd">        Raised when ``geo_type`` is not a supported type.</span>
-<span class="sd">    </span>
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    obj : {libpysal.cg.{Point, LineSegment, Line, Ray, Chain, Polygon}</span>
-<span class="sd">        A new geometric representation of the object.</span>
-<span class="sd">    </span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="p">(</span><span class="n">Point</span><span class="p">,</span> <span class="n">LineSegment</span><span class="p">,</span> <span class="n">Line</span><span class="p">,</span> <span class="n">Ray</span><span class="p">,</span> <span class="n">Chain</span><span class="p">,</span> <span class="n">Polygon</span><span class="p">)):</span>
-        <span class="k">pass</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="s2">&quot;__geo_interface__&quot;</span><span class="p">):</span>
-            <span class="n">geo</span> <span class="o">=</span> <span class="n">obj</span><span class="o">.</span><span class="n">__geo_interface__</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">geo</span> <span class="o">=</span> <span class="n">obj</span>
-
-        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">geo</span><span class="p">,</span> <span class="s2">&quot;type&quot;</span><span class="p">):</span>
-            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%r</span><span class="s2"> does not appear to be a shape object.&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">obj</span><span class="p">))</span>
-
-        <span class="n">geo_type</span> <span class="o">=</span> <span class="n">geo</span><span class="p">[</span><span class="s2">&quot;type&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
-
-        <span class="c1"># if geo_type.startswith(&#39;multi&#39;):</span>
-        <span class="c1">#    raise NotImplementedError, &quot;%s are not supported at this time.&quot;%geo_type</span>
-
-        <span class="k">if</span> <span class="n">geo_type</span> <span class="ow">in</span> <span class="n">_geoJSON_type_to_Pysal_type</span><span class="p">:</span>
-
-            <span class="n">obj</span> <span class="o">=</span> <span class="n">_geoJSON_type_to_Pysal_type</span><span class="p">[</span><span class="n">geo_type</span><span class="p">]</span><span class="o">.</span><span class="n">__from_geo_interface__</span><span class="p">(</span><span class="n">geo</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2"> is not supported at this time.&quot;</span> <span class="o">%</span> <span class="n">geo_type</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">obj</span></div>
-
-
-<span class="k">class</span> <span class="nc">Geometry</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;A base class to help implement ``is_geometry``</span>
-<span class="sd">    and make geometric types extendable.</span>
-<span class="sd">    </span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">pass</span>
-
-
-<div class="viewcode-block" id="Point"><a class="viewcode-back" href="../../../generated/libpysal.cg.Point.html#libpysal.cg.Point">[docs]</a><span class="k">class</span> <span class="nc">Point</span><span class="p">(</span><span class="n">Geometry</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Geometric class for point objects.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    loc : tuple</span>
-<span class="sd">        The point&#39;s location (number :math:`x`-tuple, :math:`x` &gt; 1).</span>
-<span class="sd">    </span>
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; p = Point((1, 3))</span>
-<span class="sd">    </span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-<div class="viewcode-block" id="Point.__init__"><a class="viewcode-back" href="../../../generated/libpysal.cg.Point.html#libpysal.cg.Point.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">loc</span><span class="p">):</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">__loc</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="nb">float</span><span class="p">,</span> <span class="n">loc</span><span class="p">))</span></div>
-
-    <span class="nd">@classmethod</span>
-    <span class="k">def</span> <span class="nf">__from_geo_interface__</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">geo</span><span class="p">):</span>
-        <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span><span class="n">geo</span><span class="p">[</span><span class="s2">&quot;coordinates&quot;</span><span class="p">])</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">__geo_interface__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="p">{</span><span class="s2">&quot;type&quot;</span><span class="p">:</span> <span class="s2">&quot;Point&quot;</span><span class="p">,</span> <span class="s2">&quot;coordinates&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">__loc</span><span class="p">}</span>
-
-    <span class="k">def</span> <span class="fm">__lt__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;Tests if the point is less than another object.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        other : libpysal.cg.Point</span>
-<span class="sd">            An object to test equality against.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; Point((0, 1)) &lt; Point((0, 1))</span>
-<span class="sd">        False</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; Point((0, 1)) &lt; Point((1, 1))</span>
-<span class="sd">        True</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">__loc</span><span class="p">)</span> <span class="o">&lt;</span> <span class="p">(</span><span class="n">other</span><span class="o">.</span><span class="n">__loc</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="fm">__le__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;Tests if the point is less than or equal to another object.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        other : libpysal.cg.Point</span>
-<span class="sd">            An object to test equality against.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; Point((0, 1)) &lt;= Point((0, 1))</span>
-<span class="sd">        True</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; Point((0, 1)) &lt;= Point((1, 1))</span>
-<span class="sd">        True</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">__loc</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="p">(</span><span class="n">other</span><span class="o">.</span><span class="n">__loc</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="fm">__eq__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;Tests if the point is equal to another object.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        other : libpysal.cg.Point</span>
-<span class="sd">            An object to test equality against.</span>
-<span class="sd">        </span>
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; Point((0, 1)) == Point((0, 1))</span>
-<span class="sd">        True</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; Point((0, 1)) == Point((1, 1))</span>
-<span class="sd">        False</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">try</span><span class="p">:</span>
-            <span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">__loc</span><span class="p">)</span> <span class="o">==</span> <span class="p">(</span><span class="n">other</span><span class="o">.</span><span class="n">__loc</span><span class="p">)</span>
-        <span class="k">except</span> <span class="ne">AttributeError</span><span class="p">:</span>
-            <span class="k">return</span> <span class="kc">False</span>
-
-    <span class="k">def</span> <span class="fm">__ne__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;Tests if the point is not equal to another object.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        other : libpysal.cg.Point</span>
-<span class="sd">            An object to test equality against.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; Point((0, 1)) != Point((0, 1))</span>
-<span class="sd">        False</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; Point((0, 1)) != Point((1, 1))</span>
-<span class="sd">        True</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">try</span><span class="p">:</span>
-            <span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">__loc</span><span class="p">)</span> <span class="o">!=</span> <span class="p">(</span><span class="n">other</span><span class="o">.</span><span class="n">__loc</span><span class="p">)</span>
-        <span class="k">except</span> <span class="ne">AttributeError</span><span class="p">:</span>
-            <span class="k">return</span> <span class="kc">True</span>
-
-    <span class="k">def</span> <span class="fm">__gt__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;Tests if the point is greater than another object.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        other : libpysal.cg.Point</span>
-<span class="sd">            An object to test equality against.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; Point((0, 1)) &gt; Point((0, 1))</span>
-<span class="sd">        False</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; Point((0, 1)) &gt; Point((1, 1))</span>
-<span class="sd">        False</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">__loc</span><span class="p">)</span> <span class="o">&gt;</span> <span class="p">(</span><span class="n">other</span><span class="o">.</span><span class="n">__loc</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="fm">__ge__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;Tests if the point is greater than or equal to another object.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        other : libpysal.cg.Point</span>
-<span class="sd">            An object to test equality against.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; Point((0, 1)) &gt;= Point((0, 1))</span>
-<span class="sd">        True</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; Point((0, 1)) &gt;= Point((1, 1))</span>
-<span class="sd">        False</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">__loc</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="p">(</span><span class="n">other</span><span class="o">.</span><span class="n">__loc</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="fm">__hash__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;Returns the hash of the point&#39;s location.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; hash(Point((0, 1))) == hash(Point((0, 1)))</span>
-<span class="sd">        True</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; hash(Point((0, 1))) == hash(Point((1, 1)))</span>
-<span class="sd">        False</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">return</span> <span class="nb">hash</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">__loc</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">]:</span>
-        <span class="sd">&quot;&quot;&quot;Return the coordinate for the given dimension.</span>
-<span class="sd">        </span>
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        *args : tuple</span>
-<span class="sd">            A singleton tuple of :math:`(i)` with :math:`i`</span>
-<span class="sd">            as the index of the desired dimension.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; p = Point((5.5, 4.3))</span>
-<span class="sd">        &gt;&gt;&gt; p[0] == 5.5</span>
-<span class="sd">        True</span>
-<span class="sd">        &gt;&gt;&gt; p[1] == 4.3</span>
-<span class="sd">        True</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">__loc</span><span class="o">.</span><span class="fm">__getitem__</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">__getslice__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">slice</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;Return the coordinates for the given dimensions.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        *args : tuple</span>
-<span class="sd">            A tuple of :math:`(i,j)` with :math:`i` as the index to the start</span>
-<span class="sd">            slice and :math:`j` as the index to end the slice (excluded).</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; p = Point((3, 6, 2))</span>
-<span class="sd">        &gt;&gt;&gt; p[:2] == (3, 6)</span>
-<span class="sd">        True</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; p[1:2] == (6,)</span>
-<span class="sd">        True</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">__loc</span><span class="o">.</span><span class="n">__getslice__</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot; Returns the dimensions of the point.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; len(Point((1, 2)))</span>
-<span class="sd">        2</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">__loc</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;Returns the string representation of the ``Point``.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; Point((0, 1))</span>
-<span class="sd">        (0.0, 1.0)</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">return</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;Returns a string representation of a ``Point`` object.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; p = Point((1, 3))</span>
-<span class="sd">        &gt;&gt;&gt; str(p)</span>
-<span class="sd">        &#39;(1.0, 3.0)&#39;</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">return</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">__loc</span><span class="p">)</span></div>
-        <span class="c1"># return &quot;POINT ({} {})&quot;.format(*self.__loc)</span>
-
-
-<div class="viewcode-block" id="LineSegment"><a class="viewcode-back" href="../../../generated/libpysal.cg.LineSegment.html#libpysal.cg.LineSegment">[docs]</a><span class="k">class</span> <span class="nc">LineSegment</span><span class="p">(</span><span class="n">Geometry</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Geometric representation of line segment objects.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    start_pt : libpysal.cg.Point</span>
-<span class="sd">        The point where the segment begins.</span>
-<span class="sd">    end_pt : libpysal.cg.Point</span>
-<span class="sd">        The point where the segment ends.</span>
-
-<span class="sd">    Attributes</span>
-<span class="sd">    ----------</span>
-<span class="sd">    p1 : libpysal.cg.Point</span>
-<span class="sd">        The starting point of the line segment.</span>
-<span class="sd">    p2 : Point</span>
-<span class="sd">        The ending point of the line segment.</span>
-<span class="sd">    bounding_box : libpysal.cg.Rectangle</span>
-<span class="sd">        The bounding box of the segment.</span>
-<span class="sd">    len : float</span>
-<span class="sd">        The length of the segment.</span>
-<span class="sd">    line : libpysal.cg.Line</span>
-<span class="sd">        The line on which the segment lies.</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; ls = LineSegment(Point((1, 2)), Point((5, 6)))</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-
-<div class="viewcode-block" id="LineSegment.__init__"><a class="viewcode-back" href="../../../generated/libpysal.cg.LineSegment.html#libpysal.cg.LineSegment.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">start_pt</span><span class="p">,</span> <span class="n">end_pt</span><span class="p">):</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">_p1</span> <span class="o">=</span> <span class="n">start_pt</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_p2</span> <span class="o">=</span> <span class="n">end_pt</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_reset_props</span><span class="p">()</span></div>
-
-    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="s2">&quot;LineSegment(&quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_p1</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot;, &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_p2</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot;)&quot;</span>
-        <span class="c1"># return &quot;LINESTRING ({} {}, {} {})&quot;.format(</span>
-        <span class="c1">#    self._p1[0], self._p1[1], self._p2[0], self._p2[1]</span>
-        <span class="c1"># )</span>
-
-    <span class="k">def</span> <span class="fm">__eq__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;Returns ``True`` if ``self`` and ``other`` are the same line segment.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; l1 = LineSegment(Point((1, 2)), Point((5, 6)))</span>
-<span class="sd">        &gt;&gt;&gt; l2 = LineSegment(Point((5, 6)), Point((1, 2)))</span>
-<span class="sd">        &gt;&gt;&gt; l1 == l2</span>
-<span class="sd">        True</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; l2 == l1</span>
-<span class="sd">        True</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">eq</span> <span class="o">=</span> <span class="kc">False</span>
-
-        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="p">):</span>
-            <span class="k">pass</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">other</span><span class="o">.</span><span class="n">p1</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p1</span> <span class="ow">and</span> <span class="n">other</span><span class="o">.</span><span class="n">p2</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p2</span><span class="p">:</span>
-                <span class="n">eq</span> <span class="o">=</span> <span class="kc">True</span>
-            <span class="k">elif</span> <span class="n">other</span><span class="o">.</span><span class="n">p2</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p1</span> <span class="ow">and</span> <span class="n">other</span><span class="o">.</span><span class="n">p1</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p2</span><span class="p">:</span>
-                <span class="n">eq</span> <span class="o">=</span> <span class="kc">True</span>
-
-        <span class="k">return</span> <span class="n">eq</span>
-
-<div class="viewcode-block" id="LineSegment.intersect"><a class="viewcode-back" href="../../../generated/libpysal.cg.LineSegment.html#libpysal.cg.LineSegment.intersect">[docs]</a>    <span class="k">def</span> <span class="nf">intersect</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;Test whether segment intersects with other segment (``True``) or</span>
-<span class="sd">        not (``False``). Handles endpoints of segments being on other segment.</span>
-<span class="sd">        </span>
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        other : libpysal.cg.LineSegment</span>
-<span class="sd">            Another line segment to check against.</span>
-<span class="sd">        </span>
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-
-<span class="sd">        &gt;&gt;&gt; ls = LineSegment(Point((5, 0)), Point((10, 0)))</span>
-<span class="sd">        &gt;&gt;&gt; ls1 = LineSegment(Point((5, 0)), Point((10, 1)))</span>
-<span class="sd">        &gt;&gt;&gt; ls.intersect(ls1)</span>
-<span class="sd">        True</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; ls2 = LineSegment(Point((5, 1)), Point((10, 1)))</span>
-<span class="sd">        &gt;&gt;&gt; ls.intersect(ls2)</span>
-<span class="sd">        False</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; ls2 = LineSegment(Point((7, -1)), Point((7, 2)))</span>
-<span class="sd">        &gt;&gt;&gt; ls.intersect(ls2)</span>
-<span class="sd">        True</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">ccw1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sw_ccw</span><span class="p">(</span><span class="n">other</span><span class="o">.</span><span class="n">p2</span><span class="p">)</span>
-        <span class="n">ccw2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sw_ccw</span><span class="p">(</span><span class="n">other</span><span class="o">.</span><span class="n">p1</span><span class="p">)</span>
-        <span class="n">ccw3</span> <span class="o">=</span> <span class="n">other</span><span class="o">.</span><span class="n">sw_ccw</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">p1</span><span class="p">)</span>
-        <span class="n">ccw4</span> <span class="o">=</span> <span class="n">other</span><span class="o">.</span><span class="n">sw_ccw</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">p2</span><span class="p">)</span>
-
-        <span class="n">intersects</span> <span class="o">=</span> <span class="n">ccw1</span> <span class="o">*</span> <span class="n">ccw2</span> <span class="o">&lt;=</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">ccw3</span> <span class="o">*</span> <span class="n">ccw4</span> <span class="o">&lt;=</span> <span class="mi">0</span>
-
-        <span class="k">return</span> <span class="n">intersects</span></div>
-
-    <span class="k">def</span> <span class="nf">_reset_props</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;**HELPER METHOD. DO NOT CALL.**</span>
-<span class="sd">        Resets attributes which are functions of other attributes.</span>
-<span class="sd">        The getters for these attributes (implemented as properties)</span>
-<span class="sd">        then recompute their values if they have been reset since</span>
-<span class="sd">        the last call to the getter.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; ls = LineSegment(Point((1, 2)), Point((5, 6)))</span>
-<span class="sd">        &gt;&gt;&gt; ls._reset_props()</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">_bounding_box</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_len</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_line</span> <span class="o">=</span> <span class="kc">False</span>
-
-    <span class="k">def</span> <span class="nf">_get_p1</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;**HELPER METHOD. DO NOT CALL.**</span>
-<span class="sd">        Returns the ``p1`` attribute of the line segment.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        self._p1 : libpysal.cg.Point</span>
-<span class="sd">            The ``_p1`` attribute.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; ls = LineSegment(Point((1, 2)), Point((5, 6)))</span>
-<span class="sd">        &gt;&gt;&gt; r = ls._get_p1()</span>
-<span class="sd">        &gt;&gt;&gt; r == Point((1, 2))</span>
-<span class="sd">        True</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p1</span>
-
-    <span class="k">def</span> <span class="nf">_set_p1</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">p1</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;**HELPER METHOD. DO NOT CALL.**</span>
-<span class="sd">        Sets the ``p1`` attribute of the line segment.</span>
-<span class="sd">        </span>
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        p1 : libpysal.cg.Point</span>
-<span class="sd">            A point.</span>
-<span class="sd">        </span>
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        self._p1 : libpysal.cg.Point</span>
-<span class="sd">            The reset ``p1`` attribute.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; ls = LineSegment(Point((1, 2)), Point((5, 6)))</span>
-<span class="sd">        &gt;&gt;&gt; r = ls._set_p1(Point((3, -1)))</span>
-<span class="sd">        &gt;&gt;&gt; r == Point((3.0, -1.0))</span>
-<span class="sd">        True</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">_p1</span> <span class="o">=</span> <span class="n">p1</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_reset_props</span><span class="p">()</span>
-
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p1</span>
-
-    <span class="n">p1</span> <span class="o">=</span> <span class="nb">property</span><span class="p">(</span><span class="n">_get_p1</span><span class="p">,</span> <span class="n">_set_p1</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">_get_p2</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;**HELPER METHOD. DO NOT CALL.**</span>
-<span class="sd">        Returns the ``p2`` attribute of the line segment.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        self._p2 : libpysal.cg.Point</span>
-<span class="sd">            The ``_p2`` attribute.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; ls = LineSegment(Point((1, 2)), Point((5, 6)))</span>
-<span class="sd">        &gt;&gt;&gt; r = ls._get_p2()</span>
-<span class="sd">        &gt;&gt;&gt; r == Point((5, 6))</span>
-<span class="sd">        True</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p2</span>
-
-    <span class="k">def</span> <span class="nf">_set_p2</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">p2</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;**HELPER METHOD. DO NOT CALL.**</span>
-<span class="sd">        Sets the ``p2`` attribute of the line segment.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        p2 : libpysal.cg.Point</span>
-<span class="sd">            A point.</span>
-<span class="sd">        </span>
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        self._p2 : libpysal.cg.Point</span>
-<span class="sd">            The reset ``p2`` attribute.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; ls = LineSegment(Point((1, 2)), Point((5, 6)))</span>
-<span class="sd">        &gt;&gt;&gt; r = ls._set_p2(Point((3, -1)))</span>
-<span class="sd">        &gt;&gt;&gt; r == Point((3.0, -1.0))</span>
-<span class="sd">        True</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">_p2</span> <span class="o">=</span> <span class="n">p2</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_reset_props</span><span class="p">()</span>
-
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p2</span>
-
-    <span class="n">p2</span> <span class="o">=</span> <span class="nb">property</span><span class="p">(</span><span class="n">_get_p2</span><span class="p">,</span> <span class="n">_set_p2</span><span class="p">)</span>
-
-<div class="viewcode-block" id="LineSegment.is_ccw"><a class="viewcode-back" href="../../../generated/libpysal.cg.LineSegment.html#libpysal.cg.LineSegment.is_ccw">[docs]</a>    <span class="k">def</span> <span class="nf">is_ccw</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pt</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;Returns whether a point is counterclockwise of the</span>
-<span class="sd">        segment (``True``) or not (``False``). Exclusive.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        pt : libpysal.cg.Point</span>
-<span class="sd">            A point lying ccw or cw of a segment.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; ls = LineSegment(Point((0, 0)), Point((5, 0)))</span>
-<span class="sd">        &gt;&gt;&gt; ls.is_ccw(Point((2, 2)))</span>
-<span class="sd">        True</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; ls.is_ccw(Point((2, -2)))</span>
-<span class="sd">        False</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">v1</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_p2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p1</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p2</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p1</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-        <span class="n">v2</span> <span class="o">=</span> <span class="p">(</span><span class="n">pt</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p1</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">pt</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p1</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-
-        <span class="k">return</span> <span class="n">v1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">v2</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">v1</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">v2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span></div>
-
-<div class="viewcode-block" id="LineSegment.is_cw"><a class="viewcode-back" href="../../../generated/libpysal.cg.LineSegment.html#libpysal.cg.LineSegment.is_cw">[docs]</a>    <span class="k">def</span> <span class="nf">is_cw</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pt</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;Returns whether a point is clockwise of the</span>
-<span class="sd">        segment (``True``) or not (``False``). Exclusive.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        pt : libpysal.cg.Point</span>
-<span class="sd">            A point lying ccw or cw of a segment.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; ls = LineSegment(Point((0, 0)), Point((5, 0)))</span>
-<span class="sd">        &gt;&gt;&gt; ls.is_cw(Point((2, 2)))</span>
-<span class="sd">        False</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; ls.is_cw(Point((2, -2)))</span>
-<span class="sd">        True</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">v1</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_p2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p1</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p2</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p1</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-        <span class="n">v2</span> <span class="o">=</span> <span class="p">(</span><span class="n">pt</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p1</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">pt</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p1</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-
-        <span class="k">return</span> <span class="n">v1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">v2</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">v1</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">v2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mi">0</span></div>
-
-<div class="viewcode-block" id="LineSegment.sw_ccw"><a class="viewcode-back" href="../../../generated/libpysal.cg.LineSegment.html#libpysal.cg.LineSegment.sw_ccw">[docs]</a>    <span class="k">def</span> <span class="nf">sw_ccw</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pt</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Sedgewick test for ``pt`` being ccw of segment.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        is_ccw : bool</span>
-<span class="sd">            ``1`` if turn from ``self.p1`` to ``self.p2`` to ``pt`` is ccw.</span>
-<span class="sd">            ``-1`` if turn from ``self.p1`` to ``self.p2`` to ``pt`` is cw.</span>
-<span class="sd">            ``-1`` if the points are collinear and ``self.p1`` is in the middle.</span>
-<span class="sd">            ``1`` if the points are collinear and ``self.p2`` is in the middle.</span>
-<span class="sd">            ``0`` if the points are collinear and ``pt`` is in the middle.</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">p0</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">p1</span>
-        <span class="n">p1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">p2</span>
-        <span class="n">p2</span> <span class="o">=</span> <span class="n">pt</span>
-
-        <span class="n">dx1</span> <span class="o">=</span> <span class="n">p1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">p0</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">dy1</span> <span class="o">=</span> <span class="n">p1</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">p0</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-        <span class="n">dx2</span> <span class="o">=</span> <span class="n">p2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">p0</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">dy2</span> <span class="o">=</span> <span class="n">p2</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">p0</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-
-        <span class="k">if</span> <span class="n">dy1</span> <span class="o">*</span> <span class="n">dx2</span> <span class="o">&lt;</span> <span class="n">dy2</span> <span class="o">*</span> <span class="n">dx1</span><span class="p">:</span>
-            <span class="n">is_ccw</span> <span class="o">=</span> <span class="mi">1</span>
-        <span class="k">elif</span> <span class="n">dy1</span> <span class="o">*</span> <span class="n">dx2</span> <span class="o">&gt;</span> <span class="n">dy2</span> <span class="o">*</span> <span class="n">dx1</span><span class="p">:</span>
-            <span class="n">is_ccw</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
-        <span class="k">elif</span> <span class="n">dx1</span> <span class="o">*</span> <span class="n">dx2</span> <span class="o">&lt;</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">dy1</span> <span class="o">*</span> <span class="n">dy2</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">is_ccw</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
-        <span class="k">elif</span> <span class="n">dx1</span> <span class="o">*</span> <span class="n">dx1</span> <span class="o">+</span> <span class="n">dy1</span> <span class="o">*</span> <span class="n">dy1</span> <span class="o">&gt;=</span> <span class="n">dx2</span> <span class="o">*</span> <span class="n">dx2</span> <span class="o">+</span> <span class="n">dy2</span> <span class="o">*</span> <span class="n">dy2</span><span class="p">:</span>
-            <span class="n">is_ccw</span> <span class="o">=</span> <span class="mi">0</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">is_ccw</span> <span class="o">=</span> <span class="mi">1</span>
-
-        <span class="k">return</span> <span class="n">is_ccw</span></div>
-
-<div class="viewcode-block" id="LineSegment.get_swap"><a class="viewcode-back" href="../../../generated/libpysal.cg.LineSegment.html#libpysal.cg.LineSegment.get_swap">[docs]</a>    <span class="k">def</span> <span class="nf">get_swap</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Returns a ``LineSegment`` object which has its endpoints swapped.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        line_seg : libpysal.cg.LineSegment</span>
-<span class="sd">            The ``LineSegment`` object which has its endpoints swapped.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; ls = LineSegment(Point((1, 2)), Point((5, 6)))</span>
-<span class="sd">        &gt;&gt;&gt; swap = ls.get_swap()</span>
-<span class="sd">        &gt;&gt;&gt; swap.p1[0]</span>
-<span class="sd">        5.0</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; swap.p1[1]</span>
-<span class="sd">        6.0</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; swap.p2[0]</span>
-<span class="sd">        1.0</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; swap.p2[1]</span>
-<span class="sd">        2.0</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">line_seg</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_p2</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p1</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">line_seg</span></div>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">bounding_box</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Returns the minimum bounding box of a ``LineSegment`` object.</span>
-<span class="sd">        </span>
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        self._bounding_box : libpysal.cg.Rectangle</span>
-<span class="sd">            The bounding box of the line segment.</span>
-<span class="sd">        </span>
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; ls = LineSegment(Point((1, 2)), Point((5, 6)))</span>
-<span class="sd">        &gt;&gt;&gt; ls.bounding_box.left</span>
-<span class="sd">        1.0</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; ls.bounding_box.lower</span>
-<span class="sd">        2.0</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; ls.bounding_box.right</span>
-<span class="sd">        5.0</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; ls.bounding_box.upper</span>
-<span class="sd">        6.0</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="c1"># If LineSegment attributes p1, p2 changed, recompute</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_bounding_box</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_bounding_box</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span>
-                <span class="nb">min</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">_p1</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p2</span><span class="p">[</span><span class="mi">0</span><span class="p">]]),</span>
-                <span class="nb">min</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">_p1</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p2</span><span class="p">[</span><span class="mi">1</span><span class="p">]]),</span>
-                <span class="nb">max</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">_p1</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p2</span><span class="p">[</span><span class="mi">0</span><span class="p">]]),</span>
-                <span class="nb">max</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">_p1</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p2</span><span class="p">[</span><span class="mi">1</span><span class="p">]]),</span>
-            <span class="p">)</span>
-        <span class="k">return</span> <span class="n">Rectangle</span><span class="p">(</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_bounding_box</span><span class="o">.</span><span class="n">left</span><span class="p">,</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_bounding_box</span><span class="o">.</span><span class="n">lower</span><span class="p">,</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_bounding_box</span><span class="o">.</span><span class="n">right</span><span class="p">,</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_bounding_box</span><span class="o">.</span><span class="n">upper</span><span class="p">,</span>
-        <span class="p">)</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">len</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;Returns the length of a ``LineSegment`` object.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; ls = LineSegment(Point((2, 2)), Point((5, 2)))</span>
-<span class="sd">        &gt;&gt;&gt; ls.len</span>
-<span class="sd">        3.0</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="c1"># If LineSegment attributes p1, p2 changed, recompute</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_len</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_len</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">hypot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_p1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p2</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p1</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p2</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_len</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">line</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Returns a ``Line`` object of the line on which the segment lies.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        self._line : libpysal.cg.Line</span>
-<span class="sd">            The ``Line`` object of the line on which the segment lies.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; ls = LineSegment(Point((2, 2)), Point((3, 3)))</span>
-<span class="sd">        &gt;&gt;&gt; l = ls.line</span>
-<span class="sd">        &gt;&gt;&gt; l.m</span>
-<span class="sd">        1.0</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; l.b</span>
-<span class="sd">        0.0</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_line</span> <span class="o">==</span> <span class="kc">False</span><span class="p">:</span>
-            <span class="n">dx</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="n">dy</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p1</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p2</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-
-            <span class="k">if</span> <span class="n">dx</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">dy</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_line</span> <span class="o">=</span> <span class="kc">None</span>
-            <span class="k">elif</span> <span class="n">dx</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_line</span> <span class="o">=</span> <span class="n">VerticalLine</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_p1</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">m</span> <span class="o">=</span> <span class="n">dy</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="n">dx</span><span class="p">)</span>
-                <span class="c1"># y - mx</span>
-                <span class="n">b</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p1</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">m</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_line</span> <span class="o">=</span> <span class="n">Line</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_line</span></div>
-
-
-<span class="k">class</span> <span class="nc">VerticalLine</span><span class="p">(</span><span class="n">Geometry</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Geometric representation of verticle line objects.</span>
-<span class="sd">    </span>
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    x : {int, float}</span>
-<span class="sd">        The :math:`x`-intercept of the line. ``x`` is also an attribute.</span>
-<span class="sd">    </span>
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; ls = VerticalLine(0)</span>
-<span class="sd">    &gt;&gt;&gt; ls.m</span>
-<span class="sd">    inf</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; ls.b</span>
-<span class="sd">    nan</span>
-<span class="sd">    </span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">_x</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">m</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="s2">&quot;inf&quot;</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">b</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="s2">&quot;nan&quot;</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">x</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;Returns the :math:`x`-value of the line at a particular :math:`y`-value.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        y : {int, float}</span>
-<span class="sd">            The :math:`y`-value at which to compute :math:`x`.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; l = VerticalLine(0)</span>
-<span class="sd">        &gt;&gt;&gt; l.x(0.25)</span>
-<span class="sd">        0.0</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_x</span>
-
-    <span class="k">def</span> <span class="nf">y</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;Returns the :math:`y`-value of the line at a particular :math:`x`-value.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        x : {int, float}</span>
-<span class="sd">            The :math:`x`-value at which to compute :math:`y`.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; l = VerticalLine(1)</span>
-<span class="sd">        &gt;&gt;&gt; l.y(1)</span>
-<span class="sd">        nan</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">return</span> <span class="nb">float</span><span class="p">(</span><span class="s2">&quot;nan&quot;</span><span class="p">)</span>
-
-
-<div class="viewcode-block" id="Line"><a class="viewcode-back" href="../../../generated/libpysal.cg.Line.html#libpysal.cg.Line">[docs]</a><span class="k">class</span> <span class="nc">Line</span><span class="p">(</span><span class="n">Geometry</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Geometric representation of line objects.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    m : {int, float}</span>
-<span class="sd">        The slope of the line. ``m`` is also an attribute.</span>
-<span class="sd">    b : {int, float}</span>
-<span class="sd">        The :math:`y`-intercept of the line. ``b`` is also an attribute.</span>
-<span class="sd">    </span>
-<span class="sd">    Raises</span>
-<span class="sd">    ------</span>
-<span class="sd">    ArithmeticError</span>
-<span class="sd">        Raised when infinity is passed in as the slope.</span>
-<span class="sd">    </span>
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; ls = Line(1, 0)</span>
-<span class="sd">    &gt;&gt;&gt; ls.m</span>
-<span class="sd">    1.0</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; ls.b</span>
-<span class="sd">    0.0</span>
-<span class="sd">    </span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-<div class="viewcode-block" id="Line.__init__"><a class="viewcode-back" href="../../../generated/libpysal.cg.Line.html#libpysal.cg.Line.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">m</span><span class="p">,</span> <span class="n">b</span><span class="p">):</span>
-
-        <span class="k">if</span> <span class="n">m</span> <span class="o">==</span> <span class="nb">float</span><span class="p">(</span><span class="s2">&quot;inf&quot;</span><span class="p">):</span>
-            <span class="k">raise</span> <span class="ne">ArithmeticError</span><span class="p">(</span><span class="s2">&quot;Slope cannot be infinite.&quot;</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">m</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">b</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">b</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="Line.x"><a class="viewcode-back" href="../../../generated/libpysal.cg.Line.html#libpysal.cg.Line.x">[docs]</a>    <span class="k">def</span> <span class="nf">x</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;Returns the :math:`x`-value of the line at a particular :math:`y`-value.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        y : {int, float}</span>
-<span class="sd">            The :math:`y`-value at which to compute :math:`x`.</span>
-<span class="sd">        </span>
-<span class="sd">        Raises</span>
-<span class="sd">        ------</span>
-<span class="sd">        ArithmeticError</span>
-<span class="sd">            Raised when ``0.`` is passed in as the slope.</span>
-<span class="sd">        </span>
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; l = Line(0.5, 0)</span>
-<span class="sd">        &gt;&gt;&gt; l.x(0.25)</span>
-<span class="sd">        0.5</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">m</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ArithmeticError</span><span class="p">(</span><span class="s2">&quot;Cannot solve for &#39;x&#39; when slope is zero.&quot;</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="p">(</span><span class="n">y</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">b</span><span class="p">)</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">m</span></div>
-
-<div class="viewcode-block" id="Line.y"><a class="viewcode-back" href="../../../generated/libpysal.cg.Line.html#libpysal.cg.Line.y">[docs]</a>    <span class="k">def</span> <span class="nf">y</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;Returns the :math:`y`-value of the line at a particular :math:`x`-value.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        x : {int, float}</span>
-<span class="sd">            The :math:`x`-value at which to compute :math:`y`.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; l = Line(1, 0)</span>
-<span class="sd">        &gt;&gt;&gt; l.y(1)</span>
-<span class="sd">        1.0</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">m</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">b</span>
-
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">m</span> <span class="o">*</span> <span class="n">x</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">b</span></div></div>
-
-
-<div class="viewcode-block" id="Ray"><a class="viewcode-back" href="../../../generated/libpysal.cg.Ray.html#libpysal.cg.Ray">[docs]</a><span class="k">class</span> <span class="nc">Ray</span><span class="p">:</span>
-    <span class="sd">&quot;&quot;&quot;Geometric representation of ray objects.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    origin : libpysal.cg.Point</span>
-<span class="sd">        The point where the ray originates.</span>
-<span class="sd">    second_p :</span>
-<span class="sd">        The second point specifying the ray (not ``origin``.)</span>
-
-<span class="sd">    Attributes</span>
-<span class="sd">    ----------</span>
-<span class="sd">    o : libpysal.cg.Point</span>
-<span class="sd">        The origin (point where ray originates). See ``origin``.</span>
-<span class="sd">    p : libpysal.cg.Point</span>
-<span class="sd">        The second point on the ray (not the point where the</span>
-<span class="sd">        ray originates). See ``second_p``.</span>
-<span class="sd">    </span>
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; l = Ray(Point((0, 0)), Point((1, 0)))</span>
-<span class="sd">    &gt;&gt;&gt; str(l.o)</span>
-<span class="sd">    &#39;(0.0, 0.0)&#39;</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; str(l.p)</span>
-<span class="sd">    &#39;(1.0, 0.0)&#39;</span>
-<span class="sd">    </span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-<div class="viewcode-block" id="Ray.__init__"><a class="viewcode-back" href="../../../generated/libpysal.cg.Ray.html#libpysal.cg.Ray.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">origin</span><span class="p">,</span> <span class="n">second_p</span><span class="p">):</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">o</span> <span class="o">=</span> <span class="n">origin</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">p</span> <span class="o">=</span> <span class="n">second_p</span></div></div>
-
-
-<div class="viewcode-block" id="Chain"><a class="viewcode-back" href="../../../generated/libpysal.cg.Chain.html#libpysal.cg.Chain">[docs]</a><span class="k">class</span> <span class="nc">Chain</span><span class="p">(</span><span class="n">Geometry</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Geometric representation of a chain, also known as a polyline.</span>
-<span class="sd">    </span>
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    vertices : list</span>
-<span class="sd">        A point list or list of point lists.</span>
-
-<span class="sd">    Attributes</span>
-<span class="sd">    ----------</span>
-<span class="sd">    vertices : list</span>
-<span class="sd">        The list of points of the vertices of the chain in order.</span>
-<span class="sd">    len : float</span>
-<span class="sd">        The geometric length of the chain.</span>
-<span class="sd">    </span>
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; c = Chain([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((2, 1))])</span>
-<span class="sd">    </span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-<div class="viewcode-block" id="Chain.__init__"><a class="viewcode-back" href="../../../generated/libpysal.cg.Chain.html#libpysal.cg.Chain.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">vertices</span><span class="p">:</span> <span class="nb">list</span><span class="p">):</span>
-
-        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">vertices</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="nb">list</span><span class="p">):</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_vertices</span> <span class="o">=</span> <span class="p">[</span><span class="n">part</span> <span class="k">for</span> <span class="n">part</span> <span class="ow">in</span> <span class="n">vertices</span><span class="p">]</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_vertices</span> <span class="o">=</span> <span class="p">[</span><span class="n">vertices</span><span class="p">]</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_reset_props</span><span class="p">()</span></div>
-
-    <span class="nd">@classmethod</span>
-    <span class="k">def</span> <span class="nf">__from_geo_interface__</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">geo</span><span class="p">:</span> <span class="nb">dict</span><span class="p">):</span>
-        <span class="k">if</span> <span class="n">geo</span><span class="p">[</span><span class="s2">&quot;type&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">==</span> <span class="s2">&quot;linestring&quot;</span><span class="p">:</span>
-            <span class="n">verts</span> <span class="o">=</span> <span class="p">[</span><span class="n">Point</span><span class="p">(</span><span class="n">pt</span><span class="p">)</span> <span class="k">for</span> <span class="n">pt</span> <span class="ow">in</span> <span class="n">geo</span><span class="p">[</span><span class="s2">&quot;coordinates&quot;</span><span class="p">]]</span>
-        <span class="k">elif</span> <span class="n">geo</span><span class="p">[</span><span class="s2">&quot;type&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">==</span> <span class="s2">&quot;multilinestring&quot;</span><span class="p">:</span>
-            <span class="n">verts</span> <span class="o">=</span> <span class="p">[</span><span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">Point</span><span class="p">,</span> <span class="n">part</span><span class="p">))</span> <span class="k">for</span> <span class="n">part</span> <span class="ow">in</span> <span class="n">geo</span><span class="p">[</span><span class="s2">&quot;coordinates&quot;</span><span class="p">]]</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%r</span><span class="s2"> is not a Chain.&quot;</span> <span class="o">%</span> <span class="n">geo</span><span class="p">)</span>
-        <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span><span class="n">verts</span><span class="p">)</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">__geo_interface__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">parts</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="k">return</span> <span class="p">{</span><span class="s2">&quot;type&quot;</span><span class="p">:</span> <span class="s2">&quot;LineString&quot;</span><span class="p">,</span> <span class="s2">&quot;coordinates&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">vertices</span><span class="p">}</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">return</span> <span class="p">{</span><span class="s2">&quot;type&quot;</span><span class="p">:</span> <span class="s2">&quot;MultiLineString&quot;</span><span class="p">,</span> <span class="s2">&quot;coordinates&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">parts</span><span class="p">}</span>
-
-    <span class="k">def</span> <span class="nf">_reset_props</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;**HELPER METHOD. DO NOT CALL.** Resets attributes which are</span>
-<span class="sd">        functions of other attributes. The ``getter``s for these attributes</span>
-<span class="sd">        (implemented as ``properties``) then recompute their values if they</span>
-<span class="sd">        have been reset since the last call to the ``getter``.</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">_len</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_arclen</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_bounding_box</span> <span class="o">=</span> <span class="kc">None</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">vertices</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;Returns the vertices of the chain in clockwise order.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; c = Chain([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((2, 1))])</span>
-<span class="sd">        &gt;&gt;&gt; verts = c.vertices</span>
-<span class="sd">        &gt;&gt;&gt; len(verts)</span>
-<span class="sd">        4</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">return</span> <span class="nb">sum</span><span class="p">([</span><span class="n">part</span> <span class="k">for</span> <span class="n">part</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_vertices</span><span class="p">],</span> <span class="p">[])</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">parts</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;Returns the parts (lists of ``libpysal.cg.Point`` objects) of the chain.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">       </span>
-<span class="sd">        &gt;&gt;&gt; c = Chain(</span>
-<span class="sd">        ...     [</span>
-<span class="sd">        ...         [Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))],</span>
-<span class="sd">        ...         [Point((2, 1)), Point((2, 2)), Point((1, 2)), Point((1, 1))]</span>
-<span class="sd">        ...     ]</span>
-<span class="sd">        ... )</span>
-<span class="sd">        &gt;&gt;&gt; len(c.parts)</span>
-<span class="sd">        2</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">return</span> <span class="p">[[</span><span class="n">v</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">part</span><span class="p">]</span> <span class="k">for</span> <span class="n">part</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_vertices</span><span class="p">]</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">bounding_box</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Returns the bounding box of the chain.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        self._bounding_box : libpysal.cg.Rectangle</span>
-<span class="sd">            The bounding box of the chain.</span>
-<span class="sd">        </span>
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; c = Chain([Point((0, 0)), Point((2, 0)), Point((2, 1)), Point((0, 1))])</span>
-<span class="sd">        &gt;&gt;&gt; c.bounding_box.left</span>
-<span class="sd">        0.0</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; c.bounding_box.lower</span>
-<span class="sd">        0.0</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; c.bounding_box.right</span>
-<span class="sd">        2.0</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; c.bounding_box.upper</span>
-<span class="sd">        1.0</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_bounding_box</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">vertices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">vertices</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_bounding_box</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span>
-                <span class="nb">min</span><span class="p">([</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">vertices</span><span class="p">]),</span>
-                <span class="nb">min</span><span class="p">([</span><span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">vertices</span><span class="p">]),</span>
-                <span class="nb">max</span><span class="p">([</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">vertices</span><span class="p">]),</span>
-                <span class="nb">max</span><span class="p">([</span><span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">vertices</span><span class="p">]),</span>
-            <span class="p">)</span>
-
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_bounding_box</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">len</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;Returns the geometric length of the chain.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; c = Chain([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((2, 1))])</span>
-<span class="sd">        &gt;&gt;&gt; c.len</span>
-<span class="sd">        3.0</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; c = Chain(</span>
-<span class="sd">        ...     [</span>
-<span class="sd">        ...         [Point((0, 0)), Point((1, 0)), Point((1, 1))],</span>
-<span class="sd">        ...         [Point((10, 10)), Point((11, 10)), Point((11, 11))]</span>
-<span class="sd">        ...     ]</span>
-<span class="sd">        ... )</span>
-<span class="sd">        &gt;&gt;&gt; c.len</span>
-<span class="sd">        4.0</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">def</span> <span class="nf">dist</span><span class="p">(</span><span class="n">v1</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">,</span> <span class="n">v2</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">]:</span>
-            <span class="k">return</span> <span class="n">math</span><span class="o">.</span><span class="n">hypot</span><span class="p">(</span><span class="n">v1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">v2</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">v1</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">v2</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-
-        <span class="k">def</span> <span class="nf">part_perimeter</span><span class="p">(</span><span class="n">p</span><span class="p">:</span> <span class="nb">list</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">]:</span>
-            <span class="k">return</span> <span class="nb">sum</span><span class="p">([</span><span class="n">dist</span><span class="p">(</span><span class="n">p</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">p</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">p</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)])</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_len</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_len</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">([</span><span class="n">part_perimeter</span><span class="p">(</span><span class="n">part</span><span class="p">)</span> <span class="k">for</span> <span class="n">part</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_vertices</span><span class="p">])</span>
-
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_len</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">arclen</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">]:</span>
-        <span class="sd">&quot;&quot;&quot;Returns the geometric length of the chain</span>
-<span class="sd">        computed using &#39;arcdistance&#39; (meters).</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">def</span> <span class="nf">part_perimeter</span><span class="p">(</span><span class="n">p</span><span class="p">:</span> <span class="nb">list</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">]:</span>
-            <span class="k">return</span> <span class="nb">sum</span><span class="p">([</span><span class="n">arcdist</span><span class="p">(</span><span class="n">p</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">p</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">])</span> <span class="o">*</span> <span class="mf">1000.0</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">p</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)])</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_arclen</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_arclen</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">([</span><span class="n">part_perimeter</span><span class="p">(</span><span class="n">part</span><span class="p">)</span> <span class="k">for</span> <span class="n">part</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_vertices</span><span class="p">])</span>
-
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_arclen</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">segments</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;Returns the segments that compose the chain.&quot;&quot;&quot;</span>
-
-        <span class="k">return</span> <span class="p">[</span>
-            <span class="p">[</span><span class="n">LineSegment</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span> <span class="k">for</span> <span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">part</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">part</span><span class="p">[</span><span class="mi">1</span><span class="p">:])]</span>
-            <span class="k">for</span> <span class="n">part</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_vertices</span>
-        <span class="p">]</span></div>
-
-
-<span class="k">class</span> <span class="nc">Ring</span><span class="p">(</span><span class="n">Geometry</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Geometric representation of a linear ring. Linear rings must be</span>
-<span class="sd">    closed, the first and last point must be the same. Open rings will</span>
-<span class="sd">    be closed. This class exists primarily as a geometric primitive to</span>
-<span class="sd">    form complex polygons with multiple rings and holes. The ordering</span>
-<span class="sd">    of the vertices is ignored and will not be altered.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    vertices : list</span>
-<span class="sd">        A list of vertices.</span>
-
-<span class="sd">    Attributes</span>
-<span class="sd">    ----------</span>
-<span class="sd">    vertices : list</span>
-<span class="sd">        A list of points with the vertices of the ring.</span>
-<span class="sd">    len : int</span>
-<span class="sd">        The number of vertices.</span>
-<span class="sd">    perimeter : float</span>
-<span class="sd">        The geometric length of the perimeter of the ring.</span>
-<span class="sd">    bounding_box : libpysal.cg.Rectangle</span>
-<span class="sd">        The bounding box of the ring.</span>
-<span class="sd">    area : float</span>
-<span class="sd">        The area enclosed by the ring.</span>
-<span class="sd">    centroid : {tuple, libpysal.cg.Point}</span>
-<span class="sd">        The centroid of the ring defined by the &#39;center of gravity&#39;</span>
-<span class="sd">        or &#39;center or mass&#39;.</span>
-<span class="sd">    _quad_tree_structure : libpysal.cg.QuadTreeStructureSingleRing</span>
-<span class="sd">        The quad tree structure for the ring. This structure helps</span>
-<span class="sd">        test if a point is inside the ring.</span>
-<span class="sd">    </span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">vertices</span><span class="p">):</span>
-        <span class="k">if</span> <span class="n">vertices</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="n">vertices</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]:</span>
-            <span class="n">vertices</span> <span class="o">=</span> <span class="n">vertices</span><span class="p">[:]</span> <span class="o">+</span> <span class="n">vertices</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">1</span><span class="p">]</span>
-            <span class="c1"># msg = &quot;Supplied vertices do not form a closed ring, &quot;</span>
-            <span class="c1"># msg += &quot;the first and last vertices are not the same.&quot;</span>
-            <span class="c1"># raise ValueError(msg)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">vertices</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">vertices</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_perimeter</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_bounding_box</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_area</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_centroid</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_quad_tree_structure</span> <span class="o">=</span> <span class="kc">None</span>
-
-    <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
-        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">vertices</span><span class="p">)</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">len</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
-        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
-
-    <span class="nd">@staticmethod</span>
-    <span class="k">def</span> <span class="nf">dist</span><span class="p">(</span><span class="n">v1</span><span class="p">,</span> <span class="n">v2</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">]:</span>
-
-        <span class="k">return</span> <span class="n">math</span><span class="o">.</span><span class="n">hypot</span><span class="p">(</span><span class="n">v1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">v2</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">v1</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">v2</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">perimeter</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">]:</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_perimeter</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">dist</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dist</span>
-            <span class="n">v</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">vertices</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_perimeter</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span>
-                <span class="p">[</span><span class="n">dist</span><span class="p">(</span><span class="n">v</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">v</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)]</span>
-            <span class="p">)</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_perimeter</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">bounding_box</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Returns the bounding box of the ring.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        self._bounding_box : libpysal.cg.Rectangle</span>
-<span class="sd">            The bounding box of the ring.</span>
-<span class="sd">        </span>
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; r = Ring(</span>
-<span class="sd">        ...     [</span>
-<span class="sd">        ...         Point((0, 0)),</span>
-<span class="sd">        ...         Point((2, 0)),</span>
-<span class="sd">        ...         Point((2, 1)),</span>
-<span class="sd">        ...         Point((0, 1)),</span>
-<span class="sd">        ...         Point((0, 0))</span>
-<span class="sd">        ...     ]</span>
-<span class="sd">        ... )</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; r.bounding_box.left</span>
-<span class="sd">        0.0</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; r.bounding_box.lower</span>
-<span class="sd">        0.0</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; r.bounding_box.right</span>
-<span class="sd">        2.0</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; r.bounding_box.upper</span>
-<span class="sd">        1.0</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_bounding_box</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">vertices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">vertices</span>
-            <span class="n">x</span> <span class="o">=</span> <span class="p">[</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">vertices</span><span class="p">]</span>
-            <span class="n">y</span> <span class="o">=</span> <span class="p">[</span><span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">vertices</span><span class="p">]</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_bounding_box</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="nb">min</span><span class="p">(</span><span class="n">y</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="n">y</span><span class="p">))</span>
-
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_bounding_box</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">area</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">]:</span>
-        <span class="sd">&quot;&quot;&quot;Returns the area of the ring.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; r = Ring(</span>
-<span class="sd">        ...     [</span>
-<span class="sd">        ...         Point((0, 0)),</span>
-<span class="sd">        ...         Point((2, 0)),</span>
-<span class="sd">        ...         Point((2, 1)),</span>
-<span class="sd">        ...         Point((0, 1)),</span>
-<span class="sd">        ...         Point((0, 0))</span>
-<span class="sd">        ...     ]</span>
-<span class="sd">        ... )</span>
-<span class="sd">        &gt;&gt;&gt; r.area</span>
-<span class="sd">        2.0</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">return</span> <span class="nb">abs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">signed_area</span><span class="p">)</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">signed_area</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">]:</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_area</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">vertices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">vertices</span>
-            <span class="n">x</span> <span class="o">=</span> <span class="p">[</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">vertices</span><span class="p">]</span>
-            <span class="n">y</span> <span class="o">=</span> <span class="p">[</span><span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">vertices</span><span class="p">]</span>
-            <span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
-
-            <span class="n">A</span> <span class="o">=</span> <span class="mf">0.0</span>
-            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span>
-                <span class="n">A</span> <span class="o">+=</span> <span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+</span> <span class="n">x</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">])</span> <span class="o">*</span> <span class="p">(</span><span class="n">y</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">-</span> <span class="n">y</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">])</span>
-            <span class="n">A</span> <span class="o">=</span> <span class="n">A</span> <span class="o">*</span> <span class="mf">0.5</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_area</span> <span class="o">=</span> <span class="o">-</span><span class="n">A</span>
-
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_area</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">centroid</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Returns the centroid of the ring.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        self._centroid : libpysal.cg.Point</span>
-<span class="sd">            The ring&#39;s centroid.</span>
-
-<span class="sd">        Notes</span>
-<span class="sd">        -----</span>
-<span class="sd">        </span>
-<span class="sd">        The centroid returned by this method is the geometric centroid.</span>
-<span class="sd">        Also known as the &#39;center of gravity&#39; or &#39;center of mass&#39;.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; r = Ring(</span>
-<span class="sd">        ...     [</span>
-<span class="sd">        ...         Point((0, 0)),</span>
-<span class="sd">        ...         Point((2, 0)),</span>
-<span class="sd">        ...         Point((2, 1)),</span>
-<span class="sd">        ...         Point((0, 1)),</span>
-<span class="sd">        ...         Point((0, 0))</span>
-<span class="sd">        ...     ]</span>
-<span class="sd">        ... )</span>
-<span class="sd">        &gt;&gt;&gt; str(r.centroid)</span>
-<span class="sd">        &#39;(1.0, 0.5)&#39;</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_centroid</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">vertices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">vertices</span>
-            <span class="n">x</span> <span class="o">=</span> <span class="p">[</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">vertices</span><span class="p">]</span>
-            <span class="n">y</span> <span class="o">=</span> <span class="p">[</span><span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">vertices</span><span class="p">]</span>
-            <span class="n">A</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">signed_area</span>
-            <span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
-            <span class="n">cx</span> <span class="o">=</span> <span class="mi">0</span>
-            <span class="n">cy</span> <span class="o">=</span> <span class="mi">0</span>
-            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span>
-                <span class="n">f</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="n">y</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">x</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">y</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-                <span class="n">cx</span> <span class="o">+=</span> <span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+</span> <span class="n">x</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">])</span> <span class="o">*</span> <span class="n">f</span>
-                <span class="n">cy</span> <span class="o">+=</span> <span class="p">(</span><span class="n">y</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+</span> <span class="n">y</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">])</span> <span class="o">*</span> <span class="n">f</span>
-            <span class="n">cx</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="o">/</span> <span class="p">(</span><span class="mi">6</span> <span class="o">*</span> <span class="n">A</span><span class="p">)</span> <span class="o">*</span> <span class="n">cx</span>
-            <span class="n">cy</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="o">/</span> <span class="p">(</span><span class="mi">6</span> <span class="o">*</span> <span class="n">A</span><span class="p">)</span> <span class="o">*</span> <span class="n">cy</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_centroid</span> <span class="o">=</span> <span class="n">Point</span><span class="p">((</span><span class="n">cx</span><span class="p">,</span> <span class="n">cy</span><span class="p">))</span>
-
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_centroid</span>
-
-    <span class="k">def</span> <span class="nf">build_quad_tree_structure</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Build the quad tree structure for this polygon. Once</span>
-<span class="sd">        the structure is built, speed for testing if a point is</span>
-<span class="sd">        inside the ring will be increased significantly.</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">_quad_tree_structure</span> <span class="o">=</span> <span class="n">QuadTreeStructureSingleRing</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">contains_point</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">point</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Point containment using winding number. The implementation is based on</span>
-<span class="sd">        `this &lt;http://www.engr.colostate.edu/~dga/dga/papers/point_in_polygon.pdf&gt;`_.</span>
-<span class="sd">        </span>
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        point : libpysal.cg.Point</span>
-<span class="sd">            The point to test for containment.</span>
-<span class="sd">        </span>
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        point_contained : bool</span>
-<span class="sd">            ``True`` if ``point`` is contained within the polygon, otherwise ``False``.</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">point_contained</span> <span class="o">=</span> <span class="kc">False</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_quad_tree_structure</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">point</span>
-
-            <span class="c1"># bbox checks</span>
-            <span class="n">bbleft</span> <span class="o">=</span> <span class="n">x</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">left</span>
-            <span class="n">bbright</span> <span class="o">=</span> <span class="n">x</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">right</span>
-            <span class="n">bblower</span> <span class="o">=</span> <span class="n">y</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">lower</span>
-            <span class="n">bbupper</span> <span class="o">=</span> <span class="n">y</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">upper</span>
-
-            <span class="k">if</span> <span class="n">bbleft</span> <span class="ow">or</span> <span class="n">bbright</span> <span class="ow">or</span> <span class="n">bblower</span> <span class="ow">or</span> <span class="n">bbupper</span><span class="p">:</span>
-                <span class="k">pass</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">rn</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">vertices</span><span class="p">)</span>
-                <span class="n">xs</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">vertices</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">point</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">rn</span><span class="p">)]</span>
-                <span class="n">ys</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">vertices</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">point</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">rn</span><span class="p">)]</span>
-                <span class="n">w</span> <span class="o">=</span> <span class="mi">0</span>
-
-                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">vertices</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span>
-                    <span class="n">yi</span> <span class="o">=</span> <span class="n">ys</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-                    <span class="n">yj</span> <span class="o">=</span> <span class="n">ys</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span>
-                    <span class="n">xi</span> <span class="o">=</span> <span class="n">xs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-                    <span class="n">xj</span> <span class="o">=</span> <span class="n">xs</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span>
-                    <span class="k">if</span> <span class="n">yi</span> <span class="o">*</span> <span class="n">yj</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
-                        <span class="n">r</span> <span class="o">=</span> <span class="n">xi</span> <span class="o">+</span> <span class="n">yi</span> <span class="o">*</span> <span class="p">(</span><span class="n">xj</span> <span class="o">-</span> <span class="n">xi</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">yi</span> <span class="o">-</span> <span class="n">yj</span><span class="p">)</span>
-                        <span class="k">if</span> <span class="n">r</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                            <span class="k">if</span> <span class="n">yi</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
-                                <span class="n">w</span> <span class="o">+=</span> <span class="mi">1</span>
-                            <span class="k">else</span><span class="p">:</span>
-                                <span class="n">w</span> <span class="o">-=</span> <span class="mi">1</span>
-                    <span class="k">elif</span> <span class="n">yi</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">xi</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                        <span class="k">if</span> <span class="n">yj</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                            <span class="n">w</span> <span class="o">+=</span> <span class="mf">0.5</span>
-                        <span class="k">else</span><span class="p">:</span>
-                            <span class="n">w</span> <span class="o">-=</span> <span class="mf">0.5</span>
-                    <span class="k">elif</span> <span class="n">yj</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">xj</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                        <span class="k">if</span> <span class="n">yi</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
-                            <span class="n">w</span> <span class="o">+=</span> <span class="mf">0.5</span>
-                        <span class="k">else</span><span class="p">:</span>
-                            <span class="n">w</span> <span class="o">-=</span> <span class="mf">0.5</span>
-                <span class="k">if</span> <span class="n">w</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                    <span class="k">pass</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="n">point_contained</span> <span class="o">=</span> <span class="kc">True</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">point_contained</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_quad_tree_structure</span><span class="o">.</span><span class="n">contains_point</span><span class="p">(</span><span class="n">point</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">point_contained</span>
-
-
-<div class="viewcode-block" id="Polygon"><a class="viewcode-back" href="../../../generated/libpysal.cg.Polygon.html#libpysal.cg.Polygon">[docs]</a><span class="k">class</span> <span class="nc">Polygon</span><span class="p">(</span><span class="n">Geometry</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Geometric representation of polygon objects.</span>
-<span class="sd">    Returns a polygon created from the objects specified.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    vertices : list</span>
-<span class="sd">        A list of vertices or a list of lists of vertices.</span>
-<span class="sd">    holes : list</span>
-<span class="sd">        A list of sub-polygons to be considered as holes.</span>
-<span class="sd">        Default is ``None``.</span>
-
-<span class="sd">    Attributes</span>
-<span class="sd">    ----------</span>
-<span class="sd">    vertices : list</span>
-<span class="sd">        A list of points with the vertices of the polygon in clockwise order.</span>
-<span class="sd">    len : int</span>
-<span class="sd">        The number of vertices including holes.</span>
-<span class="sd">    perimeter : float</span>
-<span class="sd">        The geometric length of the perimeter of the polygon.</span>
-<span class="sd">    bounding_box : libpysal.cg.Rectangle</span>
-<span class="sd">        The bounding box of the polygon.</span>
-<span class="sd">    bbox : list</span>
-<span class="sd">        A list representation of the bounding box in the </span>
-<span class="sd">        form ``[left, lower, right, upper]``.</span>
-<span class="sd">    area : float</span>
-<span class="sd">        The area enclosed by the polygon.</span>
-<span class="sd">    centroid : tuple</span>
-<span class="sd">        The &#39;center of gravity&#39;, i.e. the mean point of the polygon.</span>
-<span class="sd">    </span>
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; p1 = Polygon([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))])</span>
-<span class="sd">    </span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-<div class="viewcode-block" id="Polygon.__init__"><a class="viewcode-back" href="../../../generated/libpysal.cg.Polygon.html#libpysal.cg.Polygon.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">vertices</span><span class="p">,</span> <span class="n">holes</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">_part_rings</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_hole_rings</span> <span class="o">=</span> <span class="p">[]</span>
-
-        <span class="k">def</span> <span class="nf">clockwise</span><span class="p">(</span><span class="n">part</span><span class="p">:</span> <span class="nb">list</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">standalone</span><span class="o">.</span><span class="n">is_clockwise</span><span class="p">(</span><span class="n">part</span><span class="p">):</span>
-                <span class="k">return</span> <span class="n">part</span><span class="p">[:]</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="k">return</span> <span class="n">part</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-
-        <span class="n">vl</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">vertices</span><span class="p">)</span>
-        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">vl</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="nb">list</span><span class="p">):</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_part_rings</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">Ring</span><span class="p">,</span> <span class="n">vertices</span><span class="p">))</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_vertices</span> <span class="o">=</span> <span class="p">[</span><span class="n">clockwise</span><span class="p">(</span><span class="n">part</span><span class="p">)</span> <span class="k">for</span> <span class="n">part</span> <span class="ow">in</span> <span class="n">vertices</span><span class="p">]</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_part_rings</span> <span class="o">=</span> <span class="p">[</span><span class="n">Ring</span><span class="p">(</span><span class="n">vertices</span><span class="p">)]</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_vertices</span> <span class="o">=</span> <span class="p">[</span><span class="n">clockwise</span><span class="p">(</span><span class="n">vertices</span><span class="p">)]</span>
-        <span class="k">if</span> <span class="n">holes</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">holes</span> <span class="o">!=</span> <span class="p">[]:</span>
-            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">holes</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="nb">list</span><span class="p">):</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_hole_rings</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">Ring</span><span class="p">,</span> <span class="n">holes</span><span class="p">))</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_holes</span> <span class="o">=</span> <span class="p">[</span><span class="n">clockwise</span><span class="p">(</span><span class="n">hole</span><span class="p">)</span> <span class="k">for</span> <span class="n">hole</span> <span class="ow">in</span> <span class="n">holes</span><span class="p">]</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_hole_rings</span> <span class="o">=</span> <span class="p">[</span><span class="n">Ring</span><span class="p">(</span><span class="n">holes</span><span class="p">)]</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_holes</span> <span class="o">=</span> <span class="p">[</span><span class="n">clockwise</span><span class="p">(</span><span class="n">holes</span><span class="p">)]</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_holes</span> <span class="o">=</span> <span class="p">[[]]</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_reset_props</span><span class="p">()</span></div>
-
-    <span class="nd">@classmethod</span>
-    <span class="k">def</span> <span class="nf">__from_geo_interface__</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">geo</span><span class="p">:</span> <span class="nb">dict</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;While PySAL does not differentiate polygons and multipolygons</span>
-<span class="sd">        GEOS, Shapely, and geoJSON do. In GEOS, etc, polygons may only</span>
-<span class="sd">        have a single exterior ring, all other parts are holes.</span>
-<span class="sd">        MultiPolygons are simply a list of polygons.</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">geo_type</span> <span class="o">=</span> <span class="n">geo</span><span class="p">[</span><span class="s2">&quot;type&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
-        <span class="k">if</span> <span class="n">geo_type</span> <span class="o">==</span> <span class="s2">&quot;multipolygon&quot;</span><span class="p">:</span>
-            <span class="n">parts</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="n">holes</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="k">for</span> <span class="n">polygon</span> <span class="ow">in</span> <span class="n">geo</span><span class="p">[</span><span class="s2">&quot;coordinates&quot;</span><span class="p">]:</span>
-                <span class="n">verts</span> <span class="o">=</span> <span class="p">[[</span><span class="n">Point</span><span class="p">(</span><span class="n">pt</span><span class="p">)</span> <span class="k">for</span> <span class="n">pt</span> <span class="ow">in</span> <span class="n">part</span><span class="p">]</span> <span class="k">for</span> <span class="n">part</span> <span class="ow">in</span> <span class="n">polygon</span><span class="p">]</span>
-                <span class="n">parts</span> <span class="o">+=</span> <span class="n">verts</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">1</span><span class="p">]</span>
-                <span class="n">holes</span> <span class="o">+=</span> <span class="n">verts</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="n">holes</span><span class="p">:</span>
-                <span class="n">holes</span> <span class="o">=</span> <span class="kc">None</span>
-            <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span><span class="n">parts</span><span class="p">,</span> <span class="n">holes</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">verts</span> <span class="o">=</span> <span class="p">[[</span><span class="n">Point</span><span class="p">(</span><span class="n">pt</span><span class="p">)</span> <span class="k">for</span> <span class="n">pt</span> <span class="ow">in</span> <span class="n">part</span><span class="p">]</span> <span class="k">for</span> <span class="n">part</span> <span class="ow">in</span> <span class="n">geo</span><span class="p">[</span><span class="s2">&quot;coordinates&quot;</span><span class="p">]]</span>
-            <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span><span class="n">verts</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">1</span><span class="p">],</span> <span class="n">verts</span><span class="p">[</span><span class="mi">1</span><span class="p">:])</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">__geo_interface__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;Return ``__geo_interface__`` information lookup.&quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">parts</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">geo</span> <span class="o">=</span> <span class="p">{</span>
-                <span class="s2">&quot;type&quot;</span><span class="p">:</span> <span class="s2">&quot;MultiPolygon&quot;</span><span class="p">,</span>
-                <span class="s2">&quot;coordinates&quot;</span><span class="p">:</span> <span class="p">[[</span><span class="n">part</span><span class="p">]</span> <span class="k">for</span> <span class="n">part</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">parts</span><span class="p">],</span>
-            <span class="p">}</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_holes</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
-                <span class="n">geo</span><span class="p">[</span><span class="s2">&quot;coordinates&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_holes</span>
-            <span class="k">return</span> <span class="n">geo</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_holes</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
-            <span class="k">return</span> <span class="p">{</span><span class="s2">&quot;type&quot;</span><span class="p">:</span> <span class="s2">&quot;Polygon&quot;</span><span class="p">,</span> <span class="s2">&quot;coordinates&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">_vertices</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">_holes</span><span class="p">}</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">return</span> <span class="p">{</span><span class="s2">&quot;type&quot;</span><span class="p">:</span> <span class="s2">&quot;Polygon&quot;</span><span class="p">,</span> <span class="s2">&quot;coordinates&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">_vertices</span><span class="p">}</span>
-
-    <span class="k">def</span> <span class="nf">_reset_props</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Resets the geometric properties of the polygon.&quot;&quot;&quot;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_perimeter</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_bounding_box</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_bbox</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_area</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_centroid</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_len</span> <span class="o">=</span> <span class="kc">None</span>
-
-    <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">len</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">len</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;Returns the number of vertices in the polygon.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; p1 = Polygon([Point((0, 0)), Point((0, 1)), Point((1, 1)), Point((1, 0))])</span>
-<span class="sd">        &gt;&gt;&gt; p1.len</span>
-<span class="sd">        4</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; len(p1)</span>
-<span class="sd">        4</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_len</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_len</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">vertices</span><span class="p">)</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_len</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">vertices</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;Returns the vertices of the polygon in clockwise order.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; p1 = Polygon([Point((0, 0)), Point((0, 1)), Point((1, 1)), Point((1, 0))])</span>
-<span class="sd">        &gt;&gt;&gt; len(p1.vertices)</span>
-<span class="sd">        4</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">return</span> <span class="nb">sum</span><span class="p">([</span><span class="n">part</span> <span class="k">for</span> <span class="n">part</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_vertices</span><span class="p">],</span> <span class="p">[])</span> <span class="o">+</span> <span class="nb">sum</span><span class="p">(</span>
-            <span class="p">[</span><span class="n">part</span> <span class="k">for</span> <span class="n">part</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_holes</span><span class="p">],</span> <span class="p">[]</span>
-        <span class="p">)</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">holes</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;Returns the holes of the polygon in clockwise order.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; p = Polygon(</span>
-<span class="sd">        ...     [Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))],</span>
-<span class="sd">        ...     [Point((1, 2)), Point((2, 2)), Point((2, 1)), Point((1, 1))]</span>
-<span class="sd">        ... )</span>
-<span class="sd">        &gt;&gt;&gt; len(p.holes)</span>
-<span class="sd">        1</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">return</span> <span class="p">[[</span><span class="n">v</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">part</span><span class="p">]</span> <span class="k">for</span> <span class="n">part</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_holes</span><span class="p">]</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">parts</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;Returns the parts of the polygon in clockwise order.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; p = Polygon(</span>
-<span class="sd">        ...     [</span>
-<span class="sd">        ...         [Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))],</span>
-<span class="sd">        ...         [Point((2, 1)), Point((2, 2)), Point((1, 2)), Point((1, 1))]</span>
-<span class="sd">        ...     ]</span>
-<span class="sd">        ... )</span>
-<span class="sd">        &gt;&gt;&gt; len(p.parts)</span>
-<span class="sd">        2</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">return</span> <span class="p">[[</span><span class="n">v</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">part</span><span class="p">]</span> <span class="k">for</span> <span class="n">part</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_vertices</span><span class="p">]</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">perimeter</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">]:</span>
-        <span class="sd">&quot;&quot;&quot;Returns the perimeter of the polygon.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; p = Polygon([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))])</span>
-<span class="sd">        &gt;&gt;&gt; p.perimeter</span>
-<span class="sd">        4.0</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">def</span> <span class="nf">dist</span><span class="p">(</span><span class="n">v1</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">],</span> <span class="n">v2</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">math</span><span class="o">.</span><span class="n">hypot</span><span class="p">(</span><span class="n">v1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">v2</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">v1</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">v2</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-
-        <span class="k">def</span> <span class="nf">part_perimeter</span><span class="p">(</span><span class="n">part</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">]:</span>
-            <span class="k">return</span> <span class="nb">sum</span><span class="p">([</span><span class="n">dist</span><span class="p">(</span><span class="n">part</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">part</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">part</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)])</span>
-
-        <span class="n">sum_perim</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">part_type</span><span class="p">:</span> <span class="nb">sum</span><span class="p">([</span><span class="n">part_perimeter</span><span class="p">(</span><span class="n">part</span><span class="p">)</span> <span class="k">for</span> <span class="n">part</span> <span class="ow">in</span> <span class="n">part_type</span><span class="p">])</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_perimeter</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_perimeter</span> <span class="o">=</span> <span class="n">sum_perim</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_vertices</span><span class="p">)</span> <span class="o">+</span> <span class="n">sum_perim</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_holes</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_perimeter</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">bbox</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Returns the bounding box of the polygon as a list.</span>
-<span class="sd">        </span>
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        self._bbox : list</span>
-<span class="sd">            The bounding box of the polygon as a list.</span>
-<span class="sd">        </span>
-<span class="sd">        See Also</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        libpysal.cg.bounding_box</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_bbox</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_bbox</span> <span class="o">=</span> <span class="p">[</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">left</span><span class="p">,</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">lower</span><span class="p">,</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">right</span><span class="p">,</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">upper</span><span class="p">,</span>
-            <span class="p">]</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_bbox</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">bounding_box</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Returns the bounding box of the polygon.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        self._bounding_box : libpysal.cg.Rectangle</span>
-<span class="sd">            The bounding box of the polygon.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; p = Polygon([Point((0, 0)), Point((2, 0)), Point((2, 1)), Point((0, 1))])</span>
-<span class="sd">        &gt;&gt;&gt; p.bounding_box.left</span>
-<span class="sd">        0.0</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; p.bounding_box.lower</span>
-<span class="sd">        0.0</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; p.bounding_box.right</span>
-<span class="sd">        2.0</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; p.bounding_box.upper</span>
-<span class="sd">        1.0</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_bounding_box</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">vertices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">vertices</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_bounding_box</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span>
-                <span class="nb">min</span><span class="p">([</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">vertices</span><span class="p">]),</span>
-                <span class="nb">min</span><span class="p">([</span><span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">vertices</span><span class="p">]),</span>
-                <span class="nb">max</span><span class="p">([</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">vertices</span><span class="p">]),</span>
-                <span class="nb">max</span><span class="p">([</span><span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">vertices</span><span class="p">]),</span>
-            <span class="p">)</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_bounding_box</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">area</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;Returns the area of the polygon.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; p = Polygon([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))])</span>
-<span class="sd">        &gt;&gt;&gt; p.area</span>
-<span class="sd">        1.0</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; p = Polygon(</span>
-<span class="sd">        ...     [Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))],</span>
-<span class="sd">        ...     [Point((2, 1)), Point((2, 2)), Point((1, 2)), Point((1, 1))]</span>
-<span class="sd">        ... )</span>
-<span class="sd">        &gt;&gt;&gt; p.area</span>
-<span class="sd">        99.0</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">def</span> <span class="nf">part_area</span><span class="p">(</span><span class="n">pv</span><span class="p">:</span> <span class="nb">list</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
-            <span class="n">__area</span> <span class="o">=</span> <span class="mi">0</span>
-            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">pv</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span>
-                <span class="n">__area</span> <span class="o">+=</span> <span class="p">(</span><span class="n">pv</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">pv</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span> <span class="o">*</span> <span class="p">(</span><span class="n">pv</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">pv</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">])</span>
-            <span class="n">__area</span> <span class="o">=</span> <span class="n">__area</span> <span class="o">*</span> <span class="mf">0.5</span>
-            <span class="k">if</span> <span class="n">__area</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="n">__area</span> <span class="o">=</span> <span class="o">-</span><span class="n">area</span>
-            <span class="k">return</span> <span class="n">__area</span>
-
-        <span class="n">sum_area</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">part_type</span><span class="p">:</span> <span class="nb">sum</span><span class="p">([</span><span class="n">part_area</span><span class="p">(</span><span class="n">part</span><span class="p">)</span> <span class="k">for</span> <span class="n">part</span> <span class="ow">in</span> <span class="n">part_type</span><span class="p">])</span>
-        <span class="n">_area</span> <span class="o">=</span> <span class="n">sum_area</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_vertices</span><span class="p">)</span> <span class="o">-</span> <span class="n">sum_area</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_holes</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">_area</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">centroid</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;Returns the centroid of the polygon.</span>
-
-<span class="sd">        Notes</span>
-<span class="sd">        -----</span>
-<span class="sd">        </span>
-<span class="sd">        The centroid returned by this method is the geometric</span>
-<span class="sd">        centroid and respects multipart polygons with holes.</span>
-<span class="sd">        Also known as the &#39;center of gravity&#39; or &#39;center of mass&#39;.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; p = Polygon(</span>
-<span class="sd">        ...     [Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))],</span>
-<span class="sd">        ...     [Point((1, 1)), Point((1, 2)), Point((2, 2)), Point((2, 1))]</span>
-<span class="sd">        ... )</span>
-<span class="sd">        &gt;&gt;&gt; p.centroid</span>
-<span class="sd">        (5.0353535353535355, 5.0353535353535355)</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">CP</span> <span class="o">=</span> <span class="p">[</span><span class="n">ring</span><span class="o">.</span><span class="n">centroid</span> <span class="k">for</span> <span class="n">ring</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_part_rings</span><span class="p">]</span>
-        <span class="n">AP</span> <span class="o">=</span> <span class="p">[</span><span class="n">ring</span><span class="o">.</span><span class="n">area</span> <span class="k">for</span> <span class="n">ring</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_part_rings</span><span class="p">]</span>
-        <span class="n">CH</span> <span class="o">=</span> <span class="p">[</span><span class="n">ring</span><span class="o">.</span><span class="n">centroid</span> <span class="k">for</span> <span class="n">ring</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_hole_rings</span><span class="p">]</span>
-        <span class="n">AH</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span class="n">ring</span><span class="o">.</span><span class="n">area</span> <span class="k">for</span> <span class="n">ring</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_hole_rings</span><span class="p">]</span>
-
-        <span class="n">A</span> <span class="o">=</span> <span class="n">AP</span> <span class="o">+</span> <span class="n">AH</span>
-        <span class="n">cx</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">([</span><span class="n">pt</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">area</span> <span class="k">for</span> <span class="n">pt</span><span class="p">,</span> <span class="n">area</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">CP</span> <span class="o">+</span> <span class="n">CH</span><span class="p">,</span> <span class="n">A</span><span class="p">)])</span> <span class="o">/</span> <span class="nb">sum</span><span class="p">(</span><span class="n">A</span><span class="p">)</span>
-        <span class="n">cy</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">([</span><span class="n">pt</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">area</span> <span class="k">for</span> <span class="n">pt</span><span class="p">,</span> <span class="n">area</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">CP</span> <span class="o">+</span> <span class="n">CH</span><span class="p">,</span> <span class="n">A</span><span class="p">)])</span> <span class="o">/</span> <span class="nb">sum</span><span class="p">(</span><span class="n">A</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">cx</span><span class="p">,</span> <span class="n">cy</span>
-
-<div class="viewcode-block" id="Polygon.build_quad_tree_structure"><a class="viewcode-back" href="../../../generated/libpysal.cg.Polygon.html#libpysal.cg.Polygon.build_quad_tree_structure">[docs]</a>    <span class="k">def</span> <span class="nf">build_quad_tree_structure</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Build the quad tree structure for this polygon. Once</span>
-<span class="sd">        the structure is built, speed for testing if a point is</span>
-<span class="sd">        inside the ring will be increased significantly.</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">for</span> <span class="n">ring</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_part_rings</span><span class="p">:</span>
-            <span class="n">ring</span><span class="o">.</span><span class="n">build_quad_tree_structure</span><span class="p">()</span>
-        <span class="k">for</span> <span class="n">ring</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_hole_rings</span><span class="p">:</span>
-            <span class="n">ring</span><span class="o">.</span><span class="n">build_quad_tree_structure</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">is_quad_tree_structure_built</span> <span class="o">=</span> <span class="kc">True</span></div>
-
-<div class="viewcode-block" id="Polygon.contains_point"><a class="viewcode-back" href="../../../generated/libpysal.cg.Polygon.html#libpysal.cg.Polygon.contains_point">[docs]</a>    <span class="k">def</span> <span class="nf">contains_point</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">point</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Test if a polygon contains a point.</span>
-<span class="sd">        </span>
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        point : libpysal.cg.Point</span>
-<span class="sd">            A point to test for containment.</span>
-<span class="sd">        </span>
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        contains : bool</span>
-<span class="sd">            ``True`` if the polygon contains ``point`` otherwise ``False``.</span>
-<span class="sd">        </span>
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; p = Polygon(</span>
-<span class="sd">        ...     [Point((0,0)), Point((4,0)), Point((4,5)), Point((2,3)), Point((0,5))]</span>
-<span class="sd">        ... )</span>
-<span class="sd">        &gt;&gt;&gt; p.contains_point((3,3))</span>
-<span class="sd">        1</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; p.contains_point((0,6))</span>
-<span class="sd">        0</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; p.contains_point((2,2.9))</span>
-<span class="sd">        1</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; p.contains_point((4,5))</span>
-<span class="sd">        0</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; p.contains_point((4,0))</span>
-<span class="sd">        0</span>
-
-<span class="sd">        Handles holes.</span>
-
-<span class="sd">        &gt;&gt;&gt; p = Polygon(</span>
-<span class="sd">        ...     [Point((0, 0)), Point((0, 10)), Point((10, 10)), Point((10, 0))],</span>
-<span class="sd">        ...     [Point((2, 2)), Point((4, 2)), Point((4, 4)), Point((2, 4))]</span>
-<span class="sd">        ... )</span>
-<span class="sd">        &gt;&gt;&gt; p.contains_point((3.0, 3.0))</span>
-<span class="sd">        False</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; p.contains_point((1.0, 1.0))</span>
-<span class="sd">        True</span>
-
-<span class="sd">        Notes</span>
-<span class="sd">        -----</span>
-<span class="sd">        </span>
-<span class="sd">        Points falling exactly on polygon edges may yield unpredictable results.</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">searching</span> <span class="o">=</span> <span class="kc">True</span>
-
-        <span class="k">for</span> <span class="n">ring</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_hole_rings</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">ring</span><span class="o">.</span><span class="n">contains_point</span><span class="p">(</span><span class="n">point</span><span class="p">):</span>
-                <span class="n">contains</span> <span class="o">=</span> <span class="kc">False</span>
-                <span class="n">searching</span> <span class="o">=</span> <span class="kc">False</span>
-                <span class="k">break</span>
-
-        <span class="k">if</span> <span class="n">searching</span><span class="p">:</span>
-            <span class="k">for</span> <span class="n">ring</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_part_rings</span><span class="p">:</span>
-                <span class="k">if</span> <span class="n">ring</span><span class="o">.</span><span class="n">contains_point</span><span class="p">(</span><span class="n">point</span><span class="p">):</span>
-                    <span class="n">contains</span> <span class="o">=</span> <span class="kc">True</span>
-                    <span class="n">searching</span> <span class="o">=</span> <span class="kc">False</span>
-                    <span class="k">break</span>
-            <span class="k">if</span> <span class="n">searching</span><span class="p">:</span>
-                <span class="n">contains</span> <span class="o">=</span> <span class="kc">False</span>
-
-        <span class="k">return</span> <span class="n">contains</span></div></div>
-
-
-<div class="viewcode-block" id="Rectangle"><a class="viewcode-back" href="../../../generated/libpysal.cg.Rectangle.html#libpysal.cg.Rectangle">[docs]</a><span class="k">class</span> <span class="nc">Rectangle</span><span class="p">(</span><span class="n">Geometry</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Geometric representation of rectangle objects.</span>
-
-<span class="sd">    Attributes</span>
-<span class="sd">    ----------</span>
-<span class="sd">    left : float</span>
-<span class="sd">        Minimum x-value of the rectangle.</span>
-<span class="sd">    lower : float</span>
-<span class="sd">        Minimum y-value of the rectangle.</span>
-<span class="sd">    right : float</span>
-<span class="sd">        Maximum x-value of the rectangle.</span>
-<span class="sd">    upper : float</span>
-<span class="sd">        Maximum y-value of the rectangle.</span>
-<span class="sd">    </span>
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; r = Rectangle(-4, 3, 10, 17)</span>
-<span class="sd">    &gt;&gt;&gt; r.left #minx</span>
-<span class="sd">    -4.0</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; r.lower #miny</span>
-<span class="sd">    3.0</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; r.right #maxx</span>
-<span class="sd">    10.0</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; r.upper #maxy</span>
-<span class="sd">    17.0</span>
-<span class="sd">    </span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-<div class="viewcode-block" id="Rectangle.__init__"><a class="viewcode-back" href="../../../generated/libpysal.cg.Rectangle.html#libpysal.cg.Rectangle.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">left</span><span class="p">,</span> <span class="n">lower</span><span class="p">,</span> <span class="n">right</span><span class="p">,</span> <span class="n">upper</span><span class="p">):</span>
-
-        <span class="k">if</span> <span class="n">right</span> <span class="o">&lt;</span> <span class="n">left</span> <span class="ow">or</span> <span class="n">upper</span> <span class="o">&lt;</span> <span class="n">lower</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ArithmeticError</span><span class="p">(</span><span class="s2">&quot;Rectangle must have positive area.&quot;</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">left</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">left</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">lower</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">lower</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">right</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">right</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">upper</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">upper</span><span class="p">)</span></div>
-
-    <span class="k">def</span> <span class="fm">__bool__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Rectangles will evaluate to False if they have zero area.</span>
-<span class="sd">        ``___nonzero__`` is used &quot;to implement truth value</span>
-<span class="sd">        testing and the built-in operation ``bool()``&quot;</span>
-<span class="sd">        ``-- http://docs.python.org/reference/datamodel.html</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; r = Rectangle(0, 0, 0, 0)</span>
-<span class="sd">        &gt;&gt;&gt; bool(r)</span>
-<span class="sd">        False</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; r = Rectangle(0, 0, 1, 1)</span>
-<span class="sd">        &gt;&gt;&gt; bool(r)</span>
-<span class="sd">        True</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">return</span> <span class="nb">bool</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">area</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="fm">__eq__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
-        <span class="k">if</span> <span class="n">other</span><span class="p">:</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="p">[:]</span> <span class="o">==</span> <span class="n">other</span><span class="p">[:]</span>
-        <span class="k">return</span> <span class="kc">False</span>
-
-    <span class="k">def</span> <span class="fm">__add__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
-        <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="bp">self</span><span class="p">[:]</span>
-        <span class="n">x1</span><span class="p">,</span> <span class="n">y2</span><span class="p">,</span> <span class="n">X1</span><span class="p">,</span> <span class="n">Y1</span> <span class="o">=</span> <span class="n">other</span><span class="p">[:]</span>
-
-        <span class="k">return</span> <span class="n">Rectangle</span><span class="p">(</span>
-            <span class="nb">min</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">left</span><span class="p">,</span> <span class="n">other</span><span class="o">.</span><span class="n">left</span><span class="p">),</span>
-            <span class="nb">min</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lower</span><span class="p">,</span> <span class="n">other</span><span class="o">.</span><span class="n">lower</span><span class="p">),</span>
-            <span class="nb">max</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">right</span><span class="p">,</span> <span class="n">other</span><span class="o">.</span><span class="n">right</span><span class="p">),</span>
-            <span class="nb">max</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">upper</span><span class="p">,</span> <span class="n">other</span><span class="o">.</span><span class="n">upper</span><span class="p">),</span>
-        <span class="p">)</span>
-
-    <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        </span>
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; r = Rectangle(-4, 3, 10, 17)</span>
-<span class="sd">        &gt;&gt;&gt; r[:]</span>
-<span class="sd">        [-4.0, 3.0, 10.0, 17.0]</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">l</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">left</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">right</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">upper</span><span class="p">]</span>
-
-        <span class="k">return</span> <span class="n">l</span><span class="o">.</span><span class="fm">__getitem__</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
-
-<div class="viewcode-block" id="Rectangle.set_centroid"><a class="viewcode-back" href="../../../generated/libpysal.cg.Rectangle.html#libpysal.cg.Rectangle.set_centroid">[docs]</a>    <span class="k">def</span> <span class="nf">set_centroid</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">new_center</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Moves the rectangle center to a new specified point.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        new_center : libpysal.cg.Point</span>
-<span class="sd">            The new location of the centroid of the polygon.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; r = Rectangle(0, 0, 4, 4)</span>
-<span class="sd">        &gt;&gt;&gt; r.set_centroid(Point((4, 4)))</span>
-<span class="sd">        &gt;&gt;&gt; r.left</span>
-<span class="sd">        2.0</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; r.right</span>
-<span class="sd">        6.0</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; r.lower</span>
-<span class="sd">        2.0</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; r.upper</span>
-<span class="sd">        6.0</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">shift</span> <span class="o">=</span> <span class="p">(</span>
-            <span class="n">new_center</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">left</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">right</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span><span class="p">,</span>
-            <span class="n">new_center</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lower</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">upper</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span><span class="p">,</span>
-        <span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">left</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">left</span> <span class="o">+</span> <span class="n">shift</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">right</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">right</span> <span class="o">+</span> <span class="n">shift</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">lower</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower</span> <span class="o">+</span> <span class="n">shift</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">upper</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">upper</span> <span class="o">+</span> <span class="n">shift</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span></div>
-
-<div class="viewcode-block" id="Rectangle.set_scale"><a class="viewcode-back" href="../../../generated/libpysal.cg.Rectangle.html#libpysal.cg.Rectangle.set_scale">[docs]</a>    <span class="k">def</span> <span class="nf">set_scale</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">scale</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Rescales the rectangle around its center.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        scale : int, float</span>
-<span class="sd">            The ratio of the new scale to the old</span>
-<span class="sd">            scale (e.g. 1.0 is current size).</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; r = Rectangle(0, 0, 4, 4)</span>
-<span class="sd">        &gt;&gt;&gt; r.set_scale(2)</span>
-<span class="sd">        &gt;&gt;&gt; r.left</span>
-<span class="sd">        -2.0</span>
-<span class="sd">        &gt;&gt;&gt; r.right</span>
-<span class="sd">        6.0</span>
-<span class="sd">        &gt;&gt;&gt; r.lower</span>
-<span class="sd">        -2.0</span>
-<span class="sd">        &gt;&gt;&gt; r.upper</span>
-<span class="sd">        6.0</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">center</span> <span class="o">=</span> <span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">left</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">right</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span><span class="p">,</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lower</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">upper</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">left</span> <span class="o">=</span> <span class="n">center</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">scale</span> <span class="o">*</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">left</span> <span class="o">-</span> <span class="n">center</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">right</span> <span class="o">=</span> <span class="n">center</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">scale</span> <span class="o">*</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">right</span> <span class="o">-</span> <span class="n">center</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">lower</span> <span class="o">=</span> <span class="n">center</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">scale</span> <span class="o">*</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lower</span> <span class="o">-</span> <span class="n">center</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">upper</span> <span class="o">=</span> <span class="n">center</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">scale</span> <span class="o">*</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">upper</span> <span class="o">-</span> <span class="n">center</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span></div>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">area</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">]:</span>
-        <span class="sd">&quot;&quot;&quot;Returns the area of the Rectangle.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; r = Rectangle(0, 0, 4, 4)</span>
-<span class="sd">        &gt;&gt;&gt; r.area</span>
-<span class="sd">        16.0</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">right</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">left</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">upper</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower</span><span class="p">)</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">width</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">]:</span>
-        <span class="sd">&quot;&quot;&quot;Returns the width of the Rectangle.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; r = Rectangle(0, 0, 4, 4)</span>
-<span class="sd">        &gt;&gt;&gt; r.width</span>
-<span class="sd">        4.0</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">right</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">left</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">height</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">]:</span>
-        <span class="sd">&quot;&quot;&quot;Returns the height of the Rectangle.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        </span>
-<span class="sd">        &gt;&gt;&gt; r = Rectangle(0, 0, 4, 4)</span>
-<span class="sd">        &gt;&gt;&gt; r.height</span>
-<span class="sd">        4.0</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">upper</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower</span></div>
-
-
-<span class="n">_geoJSON_type_to_Pysal_type</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="s2">&quot;point&quot;</span><span class="p">:</span> <span class="n">Point</span><span class="p">,</span>
-    <span class="s2">&quot;linestring&quot;</span><span class="p">:</span> <span class="n">Chain</span><span class="p">,</span>
-    <span class="s2">&quot;multilinestring&quot;</span><span class="p">:</span> <span class="n">Chain</span><span class="p">,</span>
-    <span class="s2">&quot;polygon&quot;</span><span class="p">:</span> <span class="n">Polygon</span><span class="p">,</span>
-    <span class="s2">&quot;multipolygon&quot;</span><span class="p">:</span> <span class="n">Polygon</span><span class="p">,</span>
-<span class="p">}</span>
-
-<span class="c1"># moving this to top breaks unit tests !</span>
-<span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">standalone</span>
-<span class="kn">from</span> <span class="nn">.polygonQuadTreeStructure</span> <span class="kn">import</span> <span class="n">QuadTreeStructureSingleRing</span>
-</pre></div>
-
-    </div>
-      
-  </div>
-</div>
-<footer class="footer">
-  <div class="container">
-    <p class="pull-right">
-      <a href="#">Back to top</a>
-      
-        <br/>
-        
-      
-    </p>
-    <p>
-        &copy; Copyright 2018-, pysal developers.<br/>
-      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 3.1.2.<br/>
-    </p>
-  </div>
-</footer>
-  </body>
-</html>
\ No newline at end of file
diff --git a/docs/_modules/libpysal/cg/sphere.html b/docs/_modules/libpysal/cg/sphere.html
deleted file mode 100644
index ee8d82e0a..000000000
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+++ /dev/null
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-<!DOCTYPE html>
-
-<html>
-  <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>libpysal.cg.sphere &#8212; libpysal v4.4.0 Manual</title>
-    <link rel="stylesheet" href="../../../_static/bootstrap-sphinx.css" type="text/css" />
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-          libpysal</a>
-        <span class="navbar-text navbar-version pull-left"><b>4.4.0</b></span>
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-      
-  <h1>Source code for libpysal.cg.sphere</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">sphere: Tools for working with spherical geometry.</span>
-
-<span class="sd">Author(s):</span>
-<span class="sd">    Charles R Schmidt schmidtc@gmail.com</span>
-<span class="sd">    Luc Anselin luc.anselin@asu.edu</span>
-<span class="sd">    Xun Li xun.li@asu.edu</span>
-
-<span class="sd">&quot;&quot;&quot;</span>
-
-<span class="n">__author__</span> <span class="o">=</span> <span class="p">(</span>
-    <span class="s2">&quot;Charles R Schmidt &lt;schmidtc@gmail.com&gt;,&quot;</span>
-    <span class="s2">&quot;Luc Anselin &lt;luc.anselin@asu.edu,&quot;</span>
-    <span class="s2">&quot;Xun Li &lt;xun.li@asu.edu&quot;</span>
-<span class="p">)</span>
-
-<span class="kn">import</span> <span class="nn">math</span>
-<span class="kn">import</span> <span class="nn">numpy</span>
-<span class="kn">import</span> <span class="nn">scipy.spatial</span>
-<span class="kn">import</span> <span class="nn">scipy.constants</span>
-<span class="kn">from</span> <span class="nn">scipy.spatial.distance</span> <span class="kn">import</span> <span class="n">euclidean</span>
-<span class="kn">from</span> <span class="nn">math</span> <span class="kn">import</span> <span class="n">pi</span><span class="p">,</span> <span class="n">cos</span><span class="p">,</span> <span class="n">sin</span>
-
-
-<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="s2">&quot;RADIUS_EARTH_KM&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;RADIUS_EARTH_MILES&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;arcdist&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;arcdist2linear&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;brute_knn&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;fast_knn&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;fast_threshold&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;linear2arcdist&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;toLngLat&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;toXYZ&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;lonlat&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;harcdist&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;geointerpolate&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;geogrid&quot;</span><span class="p">,</span>
-<span class="p">]</span>
-
-
-<span class="n">RADIUS_EARTH_KM</span> <span class="o">=</span> <span class="mf">6371.0</span>
-<span class="n">RADIUS_EARTH_MILES</span> <span class="o">=</span> <span class="p">(</span><span class="n">RADIUS_EARTH_KM</span> <span class="o">*</span> <span class="n">scipy</span><span class="o">.</span><span class="n">constants</span><span class="o">.</span><span class="n">kilo</span><span class="p">)</span> <span class="o">/</span> <span class="n">scipy</span><span class="o">.</span><span class="n">constants</span><span class="o">.</span><span class="n">mile</span>
-
-
-<div class="viewcode-block" id="arcdist"><a class="viewcode-back" href="../../../generated/libpysal.cg.arcdist.html#libpysal.cg.arcdist">[docs]</a><span class="k">def</span> <span class="nf">arcdist</span><span class="p">(</span><span class="n">pt0</span><span class="p">,</span> <span class="n">pt1</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="n">RADIUS_EARTH_KM</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Arc distance between two points on a sphere.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    pt0 : tuple</span>
-<span class="sd">        A point assumed to be in form (longitude,latitude).</span>
-<span class="sd">    pt1 : tuple</span>
-<span class="sd">        A point assumed to be in form (longitude,latitude).</span>
-<span class="sd">    radius : float</span>
-<span class="sd">        The radius of a sphere. Default is Earth&#39;s radius in</span>
-<span class="sd">        kilometers, ``RADIUS_EARTH_KM`` (``6371.0``). Earth&#39;s</span>
-<span class="sd">        radius in miles, ``RADIUS_EARTH_MILES`` (``3958.76``)</span>
-<span class="sd">        is also an option.</span>
-<span class="sd">        Source: http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    dist : float</span>
-<span class="sd">        The arc distance between ``pt0`` and ``pt1`` using supplied ``radius``.</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; pt0 = (0, 0)</span>
-<span class="sd">    &gt;&gt;&gt; pt1 = (180, 0)</span>
-<span class="sd">    &gt;&gt;&gt; d = arcdist(pt0, pt1, RADIUS_EARTH_MILES)</span>
-<span class="sd">    &gt;&gt;&gt; d == math.pi * RADIUS_EARTH_MILES</span>
-<span class="sd">    True</span>
-<span class="sd">    </span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">dist</span> <span class="o">=</span> <span class="n">linear2arcdist</span><span class="p">(</span><span class="n">euclidean</span><span class="p">(</span><span class="n">toXYZ</span><span class="p">(</span><span class="n">pt0</span><span class="p">),</span> <span class="n">toXYZ</span><span class="p">(</span><span class="n">pt1</span><span class="p">)),</span> <span class="n">radius</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">dist</span></div>
-
-
-<div class="viewcode-block" id="arcdist2linear"><a class="viewcode-back" href="../../../generated/libpysal.cg.arcdist2linear.html#libpysal.cg.arcdist2linear">[docs]</a><span class="k">def</span> <span class="nf">arcdist2linear</span><span class="p">(</span><span class="n">arc_dist</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="n">RADIUS_EARTH_KM</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Convert an arc distance (spherical earth)</span>
-<span class="sd">    to a linear distance (R3) in the unit sphere.</span>
-<span class="sd">    </span>
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    arc_dist : float</span>
-<span class="sd">        The arc distance to convert.</span>
-<span class="sd">    radius : float</span>
-<span class="sd">        The radius of a sphere. Default is Earth&#39;s radius in</span>
-<span class="sd">        kilometers, ``RADIUS_EARTH_KM`` (``6371.0``). Earth&#39;s</span>
-<span class="sd">        radius in miles, ``RADIUS_EARTH_MILES`` (``3958.76``)</span>
-<span class="sd">        is also an option.</span>
-<span class="sd">        Source: http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html</span>
-<span class="sd">    </span>
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    linear_dist : float</span>
-<span class="sd">        The linear distance conversion of ``arc_dist``.</span>
-<span class="sd">    </span>
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; pt0 = (0, 0)</span>
-<span class="sd">    &gt;&gt;&gt; pt1 = (180, 0)</span>
-<span class="sd">    &gt;&gt;&gt; d = arcdist(pt0, pt1, RADIUS_EARTH_MILES)</span>
-<span class="sd">    &gt;&gt;&gt; d == math.pi * RADIUS_EARTH_MILES</span>
-<span class="sd">    True</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; arcdist2linear(d, RADIUS_EARTH_MILES)</span>
-<span class="sd">    2.0</span>
-<span class="sd">    </span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">circumference</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">radius</span>
-    <span class="n">linear_dist</span> <span class="o">=</span> <span class="p">(</span>
-        <span class="mi">2</span> <span class="o">-</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">radians</span><span class="p">((</span><span class="n">arc_dist</span> <span class="o">*</span> <span class="mf">360.0</span><span class="p">)</span> <span class="o">/</span> <span class="n">circumference</span><span class="p">)))</span>
-    <span class="p">)</span> <span class="o">**</span> <span class="p">(</span><span class="mf">0.5</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">linear_dist</span></div>
-
-
-<div class="viewcode-block" id="linear2arcdist"><a class="viewcode-back" href="../../../generated/libpysal.cg.linear2arcdist.html#libpysal.cg.linear2arcdist">[docs]</a><span class="k">def</span> <span class="nf">linear2arcdist</span><span class="p">(</span><span class="n">linear_dist</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="n">RADIUS_EARTH_KM</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Convert a linear distance in the unit sphere</span>
-<span class="sd">    (R3) to an arc distance based on supplied radius.</span>
-<span class="sd">    </span>
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    linear_dist : float</span>
-<span class="sd">        The linear distance to convert.</span>
-<span class="sd">    radius : float</span>
-<span class="sd">        The radius of a sphere. Default is Earth&#39;s radius in</span>
-<span class="sd">        kilometers, ``RADIUS_EARTH_KM`` (``6371.0``). Earth&#39;s</span>
-<span class="sd">        radius in miles, ``RADIUS_EARTH_MILES`` (``3958.76``)</span>
-<span class="sd">        is also an option.</span>
-<span class="sd">        Source: http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html</span>
-<span class="sd">    </span>
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    arc_dist : float</span>
-<span class="sd">        The arc distance conversion of ``linear_dist``.</span>
-<span class="sd">    </span>
-<span class="sd">    Raises</span>
-<span class="sd">    ------</span>
-<span class="sd">    ValueError</span>
-<span class="sd">        Raised when ``linear_dist`` exceeds the diameter of the unit sphere.</span>
-<span class="sd">    </span>
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; pt0 = (0, 0)</span>
-<span class="sd">    &gt;&gt;&gt; pt1 = (180, 0)</span>
-<span class="sd">    &gt;&gt;&gt; d = arcdist(pt0, pt1, RADIUS_EARTH_MILES)</span>
-<span class="sd">    &gt;&gt;&gt; d == linear2arcdist(2.0, radius=RADIUS_EARTH_MILES)</span>
-<span class="sd">    True</span>
-<span class="sd">    </span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">if</span> <span class="n">linear_dist</span> <span class="o">==</span> <span class="nb">float</span><span class="p">(</span><span class="s2">&quot;inf&quot;</span><span class="p">):</span>
-        <span class="n">arc_dist</span> <span class="o">=</span> <span class="n">linear_dist</span>
-    <span class="k">elif</span> <span class="n">linear_dist</span> <span class="o">&gt;</span> <span class="mf">2.0</span><span class="p">:</span>
-        <span class="n">msg</span> <span class="o">=</span> <span class="s2">&quot;&#39;linear_dist&#39;, must not exceed the diameter of the unit sphere, 2.0.&quot;</span>
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="n">msg</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">circumference</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">radius</span>
-        <span class="n">a2</span> <span class="o">=</span> <span class="n">linear_dist</span> <span class="o">**</span> <span class="mi">2</span>
-        <span class="n">theta</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">degrees</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">acos</span><span class="p">((</span><span class="mi">2</span> <span class="o">-</span> <span class="n">a2</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="mf">2.0</span><span class="p">)))</span>
-        <span class="n">arc_dist</span> <span class="o">=</span> <span class="p">(</span><span class="n">theta</span> <span class="o">*</span> <span class="n">circumference</span><span class="p">)</span> <span class="o">/</span> <span class="mf">360.0</span>
-
-    <span class="k">return</span> <span class="n">arc_dist</span></div>
-
-
-<div class="viewcode-block" id="toXYZ"><a class="viewcode-back" href="../../../generated/libpysal.cg.toXYZ.html#libpysal.cg.toXYZ">[docs]</a><span class="k">def</span> <span class="nf">toXYZ</span><span class="p">(</span><span class="n">pt</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Convert a point&#39;s latitude and longitude to x,y,z.</span>
-<span class="sd">    </span>
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    pt : tuple</span>
-<span class="sd">        A point assumed to be in form (lng,lat).</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    x, y, z : tuple</span>
-<span class="sd">        A point in form (x, y, z).</span>
-<span class="sd">    </span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">phi</span><span class="p">,</span> <span class="n">theta</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">radians</span><span class="p">,</span> <span class="n">pt</span><span class="p">))</span>
-    <span class="n">phi</span><span class="p">,</span> <span class="n">theta</span> <span class="o">=</span> <span class="n">phi</span> <span class="o">+</span> <span class="n">pi</span><span class="p">,</span> <span class="n">theta</span> <span class="o">+</span> <span class="p">(</span><span class="n">pi</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span>
-    <span class="n">x</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">*</span> <span class="n">sin</span><span class="p">(</span><span class="n">theta</span><span class="p">)</span> <span class="o">*</span> <span class="n">cos</span><span class="p">(</span><span class="n">phi</span><span class="p">)</span>
-    <span class="n">y</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">*</span> <span class="n">sin</span><span class="p">(</span><span class="n">theta</span><span class="p">)</span> <span class="o">*</span> <span class="n">sin</span><span class="p">(</span><span class="n">phi</span><span class="p">)</span>
-    <span class="n">z</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">*</span> <span class="n">cos</span><span class="p">(</span><span class="n">theta</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span></div>
-
-
-<div class="viewcode-block" id="toLngLat"><a class="viewcode-back" href="../../../generated/libpysal.cg.toLngLat.html#libpysal.cg.toLngLat">[docs]</a><span class="k">def</span> <span class="nf">toLngLat</span><span class="p">(</span><span class="n">xyz</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Convert a point&#39;s x,y,z to latitude and longitude.</span>
-<span class="sd">    </span>
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    xyz : tuple</span>
-<span class="sd">        A point assumed to be in form (x,y,z).</span>
-<span class="sd">    </span>
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    phi, theta : tuple</span>
-<span class="sd">        A point in form (phi, theta) [y,x].</span>
-<span class="sd">    </span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span> <span class="o">=</span> <span class="n">xyz</span>
-    <span class="k">if</span> <span class="n">z</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span> <span class="ow">or</span> <span class="n">z</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-        <span class="n">phi</span> <span class="o">=</span> <span class="mi">0</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">phi</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">atan2</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">phi</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">phi</span> <span class="o">=</span> <span class="n">phi</span> <span class="o">-</span> <span class="n">math</span><span class="o">.</span><span class="n">pi</span>
-        <span class="k">elif</span> <span class="n">phi</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">phi</span> <span class="o">=</span> <span class="n">phi</span> <span class="o">+</span> <span class="n">math</span><span class="o">.</span><span class="n">pi</span>
-    <span class="n">theta</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">acos</span><span class="p">(</span><span class="n">z</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">phi</span><span class="p">,</span> <span class="n">theta</span></div>
-
-
-<div class="viewcode-block" id="brute_knn"><a class="viewcode-back" href="../../../generated/libpysal.cg.brute_knn.html#libpysal.cg.brute_knn">[docs]</a><span class="k">def</span> <span class="nf">brute_knn</span><span class="p">(</span><span class="n">pts</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">&quot;arc&quot;</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="n">RADIUS_EARTH_KM</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Computes a brute-force :math:`k` nearest neighbors.</span>
-<span class="sd">    </span>
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    pts : list</span>
-<span class="sd">        A list of :math:`x,y` pairs.</span>
-<span class="sd">    k :  int</span>
-<span class="sd">        The number of points to query.</span>
-<span class="sd">    mode : str</span>
-<span class="sd">        The mode of distance. Valid modes are ``&#39;arc&#39;``</span>
-<span class="sd">        and ``&#39;xyz&#39;``. Default is ``&#39;arc&#39;``.</span>
-<span class="sd">    radius : float</span>
-<span class="sd">        The radius of a sphere. Default is Earth&#39;s radius in</span>
-<span class="sd">        kilometers, ``RADIUS_EARTH_KM`` (``6371.0``). Earth&#39;s</span>
-<span class="sd">        radius in miles, ``RADIUS_EARTH_MILES`` (``3958.76``)</span>
-<span class="sd">        is also an option.</span>
-<span class="sd">        Source: http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html</span>
-<span class="sd">    </span>
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    w : dict</span>
-<span class="sd">        A neighbor ID lookup.</span>
-<span class="sd">    </span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">pts</span><span class="p">)</span>
-    <span class="n">full</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">n</span><span class="p">,</span> <span class="n">n</span><span class="p">))</span>
-
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">n</span><span class="p">):</span>
-            <span class="k">if</span> <span class="n">mode</span> <span class="o">==</span> <span class="s2">&quot;arc&quot;</span><span class="p">:</span>
-                <span class="n">lng0</span><span class="p">,</span> <span class="n">lat0</span> <span class="o">=</span> <span class="n">pts</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-                <span class="n">lng1</span><span class="p">,</span> <span class="n">lat1</span> <span class="o">=</span> <span class="n">pts</span><span class="p">[</span><span class="n">j</span><span class="p">]</span>
-                <span class="n">dist</span> <span class="o">=</span> <span class="n">arcdist</span><span class="p">(</span><span class="n">pts</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">pts</span><span class="p">[</span><span class="n">j</span><span class="p">],</span> <span class="n">radius</span><span class="o">=</span><span class="n">radius</span><span class="p">)</span>
-            <span class="k">elif</span> <span class="n">mode</span> <span class="o">==</span> <span class="s2">&quot;xyz&quot;</span><span class="p">:</span>
-                <span class="n">dist</span> <span class="o">=</span> <span class="n">euclidean</span><span class="p">(</span><span class="n">pts</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">pts</span><span class="p">[</span><span class="n">j</span><span class="p">])</span>
-            <span class="n">full</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">dist</span>
-            <span class="n">full</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">dist</span>
-
-    <span class="n">w</span> <span class="o">=</span> <span class="p">{}</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
-        <span class="n">w</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">full</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">argsort</span><span class="p">()[</span><span class="mi">1</span> <span class="p">:</span> <span class="n">k</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
-
-    <span class="k">return</span> <span class="n">w</span></div>
-
-
-<div class="viewcode-block" id="fast_knn"><a class="viewcode-back" href="../../../generated/libpysal.cg.fast_knn.html#libpysal.cg.fast_knn">[docs]</a><span class="k">def</span> <span class="nf">fast_knn</span><span class="p">(</span><span class="n">pts</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">return_dist</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="n">RADIUS_EARTH_KM</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Computes :math:`k` nearest neighbors on a sphere.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    pts : list</span>
-<span class="sd">        A list of :math:`x,y` pairs.</span>
-<span class="sd">    k :  int</span>
-<span class="sd">        The number of points to query.</span>
-<span class="sd">    return_dist : bool</span>
-<span class="sd">        Return distances in the ``wd`` container object (``True``).</span>
-<span class="sd">        Default is ``False``.</span>
-<span class="sd">    radius : float</span>
-<span class="sd">        The radius of a sphere. Default is Earth&#39;s radius in</span>
-<span class="sd">        kilometers, ``RADIUS_EARTH_KM`` (``6371.0``). Earth&#39;s</span>
-<span class="sd">        radius in miles, ``RADIUS_EARTH_MILES`` (``3958.76``)</span>
-<span class="sd">        is also an option.</span>
-<span class="sd">        Source: http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    wn : dict</span>
-<span class="sd">        A neighbor ID lookup.</span>
-<span class="sd">    wd : dict</span>
-<span class="sd">        A neighbor distance lookup (optional).</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">pts</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">pts</span><span class="p">)</span>
-    <span class="n">kd</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">spatial</span><span class="o">.</span><span class="n">KDTree</span><span class="p">(</span><span class="n">pts</span><span class="p">)</span>
-    <span class="n">d</span><span class="p">,</span> <span class="n">w</span> <span class="o">=</span> <span class="n">kd</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="n">pts</span><span class="p">,</span> <span class="n">k</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
-    <span class="n">w</span> <span class="o">=</span> <span class="n">w</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:]</span>
-    <span class="n">wn</span> <span class="o">=</span> <span class="p">{}</span>
-
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">pts</span><span class="p">)):</span>
-        <span class="n">wn</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">w</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
-
-    <span class="k">if</span> <span class="n">return_dist</span><span class="p">:</span>
-        <span class="n">d</span> <span class="o">=</span> <span class="n">d</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:]</span>
-        <span class="n">wd</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">pts</span><span class="p">)):</span>
-            <span class="n">wd</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">linear2arcdist</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="n">radius</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">d</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()]</span>
-        <span class="k">return</span> <span class="n">wn</span><span class="p">,</span> <span class="n">wd</span>
-    <span class="k">return</span> <span class="n">wn</span></div>
-
-
-<div class="viewcode-block" id="fast_threshold"><a class="viewcode-back" href="../../../generated/libpysal.cg.fast_threshold.html#libpysal.cg.fast_threshold">[docs]</a><span class="k">def</span> <span class="nf">fast_threshold</span><span class="p">(</span><span class="n">pts</span><span class="p">,</span> <span class="n">dist</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="n">RADIUS_EARTH_KM</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Find all neighbors on a sphere within a threshold distance.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    pointslist : list</span>
-<span class="sd">        A list of lat-lon tuples. This **must** be a list, even for one point.</span>
-<span class="sd">    dist: float</span>
-<span class="sd">        The threshold distance.</span>
-<span class="sd">    radius : float</span>
-<span class="sd">        The radius of a sphere. Default is Earth&#39;s radius in</span>
-<span class="sd">        kilometers, ``RADIUS_EARTH_KM`` (``6371.0``). Earth&#39;s</span>
-<span class="sd">        radius in miles, ``RADIUS_EARTH_MILES`` (``3958.76``)</span>
-<span class="sd">        is also an option.</span>
-<span class="sd">        Source: http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    wd : dict</span>
-<span class="sd">        A neighbor distance lookup where the key is the ID</span>
-<span class="sd">        of a point and the value is a list of IDs for other</span>
-<span class="sd">        points within ``dist`` of the key point,</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">d</span> <span class="o">=</span> <span class="n">arcdist2linear</span><span class="p">(</span><span class="n">dist</span><span class="p">,</span> <span class="n">radius</span><span class="p">)</span>
-    <span class="n">kd</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">spatial</span><span class="o">.</span><span class="n">KDTree</span><span class="p">(</span><span class="n">pts</span><span class="p">)</span>
-    <span class="n">r</span> <span class="o">=</span> <span class="n">kd</span><span class="o">.</span><span class="n">query_ball_tree</span><span class="p">(</span><span class="n">kd</span><span class="p">,</span> <span class="n">d</span><span class="p">)</span>
-    <span class="n">wd</span> <span class="o">=</span> <span class="p">{}</span>
-
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">pts</span><span class="p">)):</span>
-        <span class="n">l</span> <span class="o">=</span> <span class="n">r</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-        <span class="n">l</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
-        <span class="n">wd</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">l</span>
-
-    <span class="k">return</span> <span class="n">wd</span></div>
-
-
-<div class="viewcode-block" id="lonlat"><a class="viewcode-back" href="../../../generated/libpysal.cg.lonlat.html#libpysal.cg.lonlat">[docs]</a><span class="k">def</span> <span class="nf">lonlat</span><span class="p">(</span><span class="n">pointslist</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Converts point order from lat-lon tuples to lon-lat (x,y) tuples.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    pointslist : list</span>
-<span class="sd">        A list of lat-lon tuples. This **must** be a list, even for one point.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    newpts : list</span>
-<span class="sd">        A list with tuples of points in lon-lat order.</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; points = [</span>
-<span class="sd">    ...     (41.981417, -87.893517), (41.980396, -87.776787), (41.980906, -87.696450)</span>
-<span class="sd">    ... ]</span>
-<span class="sd">    &gt;&gt;&gt; newpoints = lonlat(points)</span>
-<span class="sd">    &gt;&gt;&gt; newpoints</span>
-<span class="sd">    [(-87.893517, 41.981417), (-87.776787, 41.980396), (-87.69645, 41.980906)]</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">newpts</span> <span class="o">=</span> <span class="p">[(</span><span class="n">i</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">i</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">pointslist</span><span class="p">]</span>
-
-    <span class="k">return</span> <span class="n">newpts</span></div>
-
-
-<span class="k">def</span> <span class="nf">haversine</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Computes the haversine formula.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    x : float</span>
-<span class="sd">        The angle in radians.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    haversine_dist : float</span>
-<span class="sd">        The square of sine of half the radian (the haversine formula).</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; haversine(math.pi) # is 180 in radians, hence sin of 90 = 1</span>
-<span class="sd">    1.0</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">x</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span>
-
-    <span class="n">haversine_dist</span> <span class="o">=</span> <span class="n">x</span> <span class="o">*</span> <span class="n">x</span>
-
-    <span class="k">return</span> <span class="n">haversine_dist</span>
-
-
-<span class="c1"># Lambda functions</span>
-
-<span class="c1"># degree to radian conversion</span>
-<span class="n">d2r</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span> <span class="mf">180.0</span>
-
-<span class="c1"># radian to degree conversion</span>
-<span class="n">r2d</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span> <span class="o">*</span> <span class="mf">180.0</span> <span class="o">/</span> <span class="n">math</span><span class="o">.</span><span class="n">pi</span>
-
-
-<span class="k">def</span> <span class="nf">radangle</span><span class="p">(</span><span class="n">p0</span><span class="p">,</span> <span class="n">p1</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Radian angle between two points on a sphere in lon-lat (x,y).</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    p0 : tuple</span>
-<span class="sd">        The first point in (lon,lat) format.</span>
-<span class="sd">    p1 : tuple</span>
-<span class="sd">        The second point in (lon,lat) format.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    d : float</span>
-<span class="sd">        Radian angle in radians.</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; p0 = (-87.893517, 41.981417)</span>
-<span class="sd">    &gt;&gt;&gt; p1 = (-87.519295, 41.657498)</span>
-<span class="sd">    &gt;&gt;&gt; radangle(p0, p1)</span>
-<span class="sd">    0.007460167953189258</span>
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-<span class="sd">    </span>
-<span class="sd">    Uses haversine formula, function haversine and degree to radian</span>
-<span class="sd">    conversion lambda function ``d2r``.</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">x0</span><span class="p">,</span> <span class="n">y0</span> <span class="o">=</span> <span class="n">d2r</span><span class="p">(</span><span class="n">p0</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">d2r</span><span class="p">(</span><span class="n">p0</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-    <span class="n">x1</span><span class="p">,</span> <span class="n">y1</span> <span class="o">=</span> <span class="n">d2r</span><span class="p">(</span><span class="n">p1</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">d2r</span><span class="p">(</span><span class="n">p1</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-    <span class="n">d</span> <span class="o">=</span> <span class="mf">2.0</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">asin</span><span class="p">(</span>
-        <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">haversine</span><span class="p">(</span><span class="n">y1</span> <span class="o">-</span> <span class="n">y0</span><span class="p">)</span> <span class="o">+</span> <span class="n">math</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">y0</span><span class="p">)</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">y1</span><span class="p">)</span> <span class="o">*</span> <span class="n">haversine</span><span class="p">(</span><span class="n">x1</span> <span class="o">-</span> <span class="n">x0</span><span class="p">))</span>
-    <span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">d</span>
-
-
-<div class="viewcode-block" id="harcdist"><a class="viewcode-back" href="../../../generated/libpysal.cg.harcdist.html#libpysal.cg.harcdist">[docs]</a><span class="k">def</span> <span class="nf">harcdist</span><span class="p">(</span><span class="n">p0</span><span class="p">,</span> <span class="n">p1</span><span class="p">,</span> <span class="n">lonx</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="n">RADIUS_EARTH_KM</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Alternative the arc distance function, uses the haversine formula.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    p0 : tuple</span>
-<span class="sd">        The first point decimal degrees.</span>
-<span class="sd">    p1 : tuple</span>
-<span class="sd">        The second point decimal degrees.</span>
-<span class="sd">    lonx : bool</span>
-<span class="sd">        The method to assess the order of the coordinates.</span>
-<span class="sd">        ``True`` for (lon,lat); ``False`` for (lat,lon).</span>
-<span class="sd">        Default is ``True``.</span>
-<span class="sd">    radius : float</span>
-<span class="sd">        The radius of a sphere. Default is Earth&#39;s radius in</span>
-<span class="sd">        kilometers, ``RADIUS_EARTH_KM`` (``6371.0``). Earth&#39;s</span>
-<span class="sd">        radius in miles, ``RADIUS_EARTH_MILES`` (``3958.76``)</span>
-<span class="sd">        is also an option. Set to ``None`` for radians.</span>
-<span class="sd">        Source: http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    harc_dist : harc_dist</span>
-<span class="sd">        The distance in units specified, km, miles or radians.</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; p0 = (-87.893517, 41.981417)</span>
-<span class="sd">    &gt;&gt;&gt; p1 = (-87.519295, 41.657498)</span>
-<span class="sd">    &gt;&gt;&gt; harcdist(p0, p1)</span>
-<span class="sd">    47.52873002976876</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; harcdist(p0, p1, radius=None)</span>
-<span class="sd">    0.007460167953189258</span>
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-<span class="sd">    </span>
-<span class="sd">    Uses the ``radangle`` function to compute radian angle.</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="n">lonx</span><span class="p">):</span>
-        <span class="n">p</span> <span class="o">=</span> <span class="n">lonlat</span><span class="p">([</span><span class="n">p0</span><span class="p">,</span> <span class="n">p1</span><span class="p">])</span>
-        <span class="n">p0</span> <span class="o">=</span> <span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">p1</span> <span class="o">=</span> <span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-
-    <span class="n">harc_dist</span> <span class="o">=</span> <span class="n">radangle</span><span class="p">(</span><span class="n">p0</span><span class="p">,</span> <span class="n">p1</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">radius</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">harc_dist</span> <span class="o">=</span> <span class="n">harc_dist</span> <span class="o">*</span> <span class="n">radius</span>
-
-    <span class="k">return</span> <span class="n">harc_dist</span></div>
-
-
-<div class="viewcode-block" id="geointerpolate"><a class="viewcode-back" href="../../../generated/libpysal.cg.geointerpolate.html#libpysal.cg.geointerpolate">[docs]</a><span class="k">def</span> <span class="nf">geointerpolate</span><span class="p">(</span><span class="n">p0</span><span class="p">,</span> <span class="n">p1</span><span class="p">,</span> <span class="n">t</span><span class="p">,</span> <span class="n">lonx</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Finds a point on a sphere along the great circle distance between</span>
-<span class="sd">    two points on a sphere also known as a way point in great circle navigation.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    p0 : tuple</span>
-<span class="sd">        The first point decimal degrees.</span>
-<span class="sd">    p1 : tuple</span>
-<span class="sd">        The second point decimal degrees.</span>
-<span class="sd">    t : float</span>
-<span class="sd">        The proportion along great circle distance between ``p0``</span>
-<span class="sd">        and ``p1`` (e.g., :math:`\mathtt{t}=0.5` would find the mid-point).</span>
-<span class="sd">    lonx : bool</span>
-<span class="sd">        The method to assess the order of the coordinates.</span>
-<span class="sd">        ``True`` for (lon,lat); ``False`` for (lat,lon).</span>
-<span class="sd">        Default is ``True``.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    newpx, newpy : tuple</span>
-<span class="sd">        The new point in decimal degrees of (lon-lat) by</span>
-<span class="sd">        default or (lat-lon) if ``lonx`` is set to ``False``.</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; p0 = (-87.893517, 41.981417)</span>
-<span class="sd">    &gt;&gt;&gt; p1 = (-87.519295, 41.657498)</span>
-<span class="sd">    &gt;&gt;&gt; geointerpolate(p0, p1, 0.1)             # using lon-lat</span>
-<span class="sd">    (-87.85592403438788, 41.949079912574796)</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; p3 = (41.981417, -87.893517)</span>
-<span class="sd">    &gt;&gt;&gt; p4 = (41.657498, -87.519295)</span>
-<span class="sd">    &gt;&gt;&gt; geointerpolate(p3, p4, 0.1, lonx=False) # using lat-lon</span>
-<span class="sd">    (41.949079912574796, -87.85592403438788)</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="n">lonx</span><span class="p">):</span>
-        <span class="n">p</span> <span class="o">=</span> <span class="n">lonlat</span><span class="p">([</span><span class="n">p0</span><span class="p">,</span> <span class="n">p1</span><span class="p">])</span>
-        <span class="n">p0</span> <span class="o">=</span> <span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">p1</span> <span class="o">=</span> <span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-
-    <span class="n">d</span> <span class="o">=</span> <span class="n">radangle</span><span class="p">(</span><span class="n">p0</span><span class="p">,</span> <span class="n">p1</span><span class="p">)</span>
-    <span class="n">k</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="o">/</span> <span class="n">math</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">d</span><span class="p">)</span>
-    <span class="n">t</span> <span class="o">=</span> <span class="n">t</span> <span class="o">*</span> <span class="n">d</span>
-    <span class="n">A</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">d</span> <span class="o">-</span> <span class="n">t</span><span class="p">)</span> <span class="o">*</span> <span class="n">k</span>
-    <span class="n">B</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">t</span><span class="p">)</span> <span class="o">*</span> <span class="n">k</span>
-
-    <span class="n">x0</span><span class="p">,</span> <span class="n">y0</span> <span class="o">=</span> <span class="n">d2r</span><span class="p">(</span><span class="n">p0</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">d2r</span><span class="p">(</span><span class="n">p0</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-    <span class="n">x1</span><span class="p">,</span> <span class="n">y1</span> <span class="o">=</span> <span class="n">d2r</span><span class="p">(</span><span class="n">p1</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">d2r</span><span class="p">(</span><span class="n">p1</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-
-    <span class="n">x</span> <span class="o">=</span> <span class="n">A</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">y0</span><span class="p">)</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">x0</span><span class="p">)</span> <span class="o">+</span> <span class="n">B</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">y1</span><span class="p">)</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">x1</span><span class="p">)</span>
-    <span class="n">y</span> <span class="o">=</span> <span class="n">A</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">y0</span><span class="p">)</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x0</span><span class="p">)</span> <span class="o">+</span> <span class="n">B</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">y1</span><span class="p">)</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x1</span><span class="p">)</span>
-    <span class="n">z</span> <span class="o">=</span> <span class="n">A</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">y0</span><span class="p">)</span> <span class="o">+</span> <span class="n">B</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">y1</span><span class="p">)</span>
-
-    <span class="n">newpx</span> <span class="o">=</span> <span class="n">r2d</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">atan2</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">x</span><span class="p">))</span>
-    <span class="n">newpy</span> <span class="o">=</span> <span class="n">r2d</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">atan2</span><span class="p">(</span><span class="n">z</span><span class="p">,</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span> <span class="o">*</span> <span class="n">y</span><span class="p">)))</span>
-
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">lonx</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">newpy</span><span class="p">,</span> <span class="n">newpx</span>
-
-    <span class="k">return</span> <span class="n">newpx</span><span class="p">,</span> <span class="n">newpy</span></div>
-
-
-<div class="viewcode-block" id="geogrid"><a class="viewcode-back" href="../../../generated/libpysal.cg.geogrid.html#libpysal.cg.geogrid">[docs]</a><span class="k">def</span> <span class="nf">geogrid</span><span class="p">(</span><span class="n">pup</span><span class="p">,</span> <span class="n">pdown</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">lonx</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Computes a :math:`k+1` by :math:`k+1` set of grid</span>
-<span class="sd">    points for a bounding box in lat-lon. Uses ``geointerpolate``.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    pup : tuple</span>
-<span class="sd">        The lat-lon or lon-lat for the upper left corner of the bounding box.</span>
-<span class="sd">    pdown : tuple</span>
-<span class="sd">        The lat-lon or lon-lat for The lower right corner of The bounding box.</span>
-<span class="sd">    k : int</span>
-<span class="sd">        The number of grid cells (grid points will be one more).</span>
-<span class="sd">    lonx : bool</span>
-<span class="sd">        The method to assess the order of the coordinates.</span>
-<span class="sd">        ``True`` for (lon,lat); ``False`` for (lat,lon).</span>
-<span class="sd">        Default is ``True``.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    grid : list</span>
-<span class="sd">        A list of tuples with (lat-lon) or (lon-lat) for grid points,</span>
-<span class="sd">        row by row, starting with the top row and moving to the bottom;</span>
-<span class="sd">        coordinate tuples are returned in same order as input.</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; pup = (42.023768, -87.946389)       # Arlington Heights, IL</span>
-<span class="sd">    &gt;&gt;&gt; pdown = (41.644415, -87.524102)     # Hammond, IN</span>
-<span class="sd">    &gt;&gt;&gt; geogrid(pup,pdown, 3, lonx=False)</span>
-<span class="sd">    [(42.023768, -87.946389),</span>
-<span class="sd">     (42.02393997819538, -87.80562679358316),</span>
-<span class="sd">     (42.02393997819538, -87.66486420641684),</span>
-<span class="sd">     (42.023768, -87.524102),</span>
-<span class="sd">     (41.897317, -87.94638900000001),</span>
-<span class="sd">     (41.8974888973743, -87.80562679296166),</span>
-<span class="sd">     (41.8974888973743, -87.66486420703835),</span>
-<span class="sd">     (41.897317, -87.524102),</span>
-<span class="sd">     (41.770866000000005, -87.94638900000001),</span>
-<span class="sd">     (41.77103781320412, -87.80562679234043),</span>
-<span class="sd">     (41.77103781320412, -87.66486420765956),</span>
-<span class="sd">     (41.770866000000005, -87.524102),</span>
-<span class="sd">     (41.644415, -87.946389),</span>
-<span class="sd">     (41.64458672568646, -87.80562679171955),</span>
-<span class="sd">     (41.64458672568646, -87.66486420828045),</span>
-<span class="sd">     (41.644415, -87.524102)]</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">if</span> <span class="n">lonx</span><span class="p">:</span>
-        <span class="n">corners</span> <span class="o">=</span> <span class="p">[</span><span class="n">pup</span><span class="p">,</span> <span class="n">pdown</span><span class="p">]</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">corners</span> <span class="o">=</span> <span class="n">lonlat</span><span class="p">([</span><span class="n">pup</span><span class="p">,</span> <span class="n">pdown</span><span class="p">])</span>
-
-    <span class="n">tpoints</span> <span class="o">=</span> <span class="p">[</span><span class="nb">float</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="o">/</span> <span class="n">k</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">k</span><span class="p">)[</span><span class="mi">1</span><span class="p">:]]</span>
-    <span class="n">leftcorners</span> <span class="o">=</span> <span class="p">[</span><span class="n">corners</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="p">(</span><span class="n">corners</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">corners</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">])]</span>
-    <span class="n">rightcorners</span> <span class="o">=</span> <span class="p">[(</span><span class="n">corners</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">corners</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]),</span> <span class="n">corners</span><span class="p">[</span><span class="mi">1</span><span class="p">]]</span>
-    <span class="n">leftside</span> <span class="o">=</span> <span class="p">[</span><span class="n">leftcorners</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span>
-    <span class="n">rightside</span> <span class="o">=</span> <span class="p">[</span><span class="n">rightcorners</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span>
-
-    <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">tpoints</span><span class="p">:</span>
-        <span class="n">newpl</span> <span class="o">=</span> <span class="n">geointerpolate</span><span class="p">(</span><span class="n">leftcorners</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">leftcorners</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">t</span><span class="p">)</span>
-        <span class="n">leftside</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">newpl</span><span class="p">)</span>
-        <span class="n">newpr</span> <span class="o">=</span> <span class="n">geointerpolate</span><span class="p">(</span><span class="n">rightcorners</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">rightcorners</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">t</span><span class="p">)</span>
-        <span class="n">rightside</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">newpr</span><span class="p">)</span>
-    <span class="n">leftside</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">leftcorners</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-    <span class="n">rightside</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">rightcorners</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-
-    <span class="n">grid</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">leftside</span><span class="p">)):</span>
-        <span class="n">grid</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">leftside</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-        <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">tpoints</span><span class="p">:</span>
-            <span class="n">newp</span> <span class="o">=</span> <span class="n">geointerpolate</span><span class="p">(</span><span class="n">leftside</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">rightside</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">t</span><span class="p">)</span>
-            <span class="n">grid</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">newp</span><span class="p">)</span>
-        <span class="n">grid</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">rightside</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="n">lonx</span><span class="p">):</span>
-        <span class="n">grid</span> <span class="o">=</span> <span class="n">lonlat</span><span class="p">(</span><span class="n">grid</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">grid</span></div>
-</pre></div>
-
-    </div>
-      
-  </div>
-</div>
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-  <div class="container">
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-    <p>
-        &copy; Copyright 2018-, pysal developers.<br/>
-      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 3.1.2.<br/>
-    </p>
-  </div>
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-<html>
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-          libpysal</a>
-        <span class="navbar-text navbar-version pull-left"><b>4.4.0</b></span>
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-  <h1>Source code for libpysal.cg.standalone</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">Helper functions for computational geometry in PySAL.</span>
-
-<span class="sd">&quot;&quot;&quot;</span>
-
-<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Sergio J. Rey, Xinyue Ye, Charles Schmidt, Andrew Winslow&quot;</span>
-<span class="n">__credits__</span> <span class="o">=</span> <span class="s2">&quot;Copyright (c) 2005-2009 Sergio J. Rey&quot;</span>
-
-<span class="kn">import</span> <span class="nn">doctest</span>
-<span class="kn">import</span> <span class="nn">math</span>
-<span class="kn">import</span> <span class="nn">copy</span>
-<span class="kn">import</span> <span class="nn">random</span>
-<span class="kn">from</span> <span class="nn">.shapes</span> <span class="kn">import</span> <span class="o">*</span>
-<span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">islice</span>
-<span class="kn">import</span> <span class="nn">scipy.spatial</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-<span class="n">EPSILON_SCALER</span> <span class="o">=</span> <span class="mi">3</span>
-
-
-<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="s2">&quot;bbcommon&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;get_bounding_box&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;get_angle_between&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;is_collinear&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;get_segments_intersect&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;get_segment_point_intersect&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;get_polygon_point_intersect&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;get_rectangle_point_intersect&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;get_ray_segment_intersect&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;get_rectangle_rectangle_intersection&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;get_polygon_point_dist&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;get_points_dist&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;get_segment_point_dist&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;get_point_at_angle_and_dist&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;convex_hull&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;is_clockwise&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;point_touches_rectangle&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;get_shared_segments&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;distance_matrix&quot;</span><span class="p">,</span>
-<span class="p">]</span>
-
-
-<div class="viewcode-block" id="bbcommon"><a class="viewcode-back" href="../../../generated/libpysal.cg.bbcommon.html#libpysal.cg.bbcommon">[docs]</a><span class="k">def</span> <span class="nf">bbcommon</span><span class="p">(</span><span class="n">bb</span><span class="p">,</span> <span class="n">bbother</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Old Stars method for bounding box overlap testing.</span>
-<span class="sd">    Also defined in ``pysal.weights._cont_binning``.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    bb : list</span>
-<span class="sd">        A bounding box.</span>
-<span class="sd">    bbother : list</span>
-<span class="sd">        The bounding box to test against.</span>
-<span class="sd">    </span>
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    chflag : int</span>
-<span class="sd">        ``1`` if ``bb`` overlaps ``bbother``, otherwise ``0``.</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-
-<span class="sd">    &gt;&gt;&gt; b0 = [0, 0, 10, 10]</span>
-<span class="sd">    &gt;&gt;&gt; b1 = [10, 0, 20, 10]</span>
-<span class="sd">    &gt;&gt;&gt; bbcommon(b0, b1)</span>
-<span class="sd">    1</span>
-<span class="sd">    </span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">chflag</span> <span class="o">=</span> <span class="mi">0</span>
-
-    <span class="k">if</span> <span class="ow">not</span> <span class="p">((</span><span class="n">bbother</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">bb</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="ow">or</span> <span class="p">(</span><span class="n">bbother</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">bb</span><span class="p">[</span><span class="mi">2</span><span class="p">])):</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="p">((</span><span class="n">bbother</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">bb</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="ow">or</span> <span class="p">(</span><span class="n">bbother</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">bb</span><span class="p">[</span><span class="mi">3</span><span class="p">])):</span>
-            <span class="n">chflag</span> <span class="o">=</span> <span class="mi">1</span>
-
-    <span class="k">return</span> <span class="n">chflag</span></div>
-
-
-<div class="viewcode-block" id="get_bounding_box"><a class="viewcode-back" href="../../../generated/libpysal.cg.get_bounding_box.html#libpysal.cg.get_bounding_box">[docs]</a><span class="k">def</span> <span class="nf">get_bounding_box</span><span class="p">(</span><span class="n">items</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Find bounding box for a list of geometries.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    items : list</span>
-<span class="sd">        PySAL shapes.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    rect = libpysal.cg.Rectangle</span>
-<span class="sd">        The bounding box for a list of geometries.</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; bb = get_bounding_box([Point((-1, 5)), Rectangle(0, 6, 11, 12)])</span>
-<span class="sd">    &gt;&gt;&gt; bb.left</span>
-<span class="sd">    -1.0</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; bb.lower</span>
-<span class="sd">    5.0</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; bb.right</span>
-<span class="sd">    11.0</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; bb.upper</span>
-<span class="sd">    12.0</span>
-<span class="sd">    </span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="nf">left</span><span class="p">(</span><span class="n">o</span><span class="p">):</span>
-        <span class="c1"># Polygon, Ellipse</span>
-        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">o</span><span class="p">,</span> <span class="s2">&quot;bounding_box&quot;</span><span class="p">):</span>
-            <span class="k">return</span> <span class="n">o</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">left</span>
-        <span class="c1"># Rectangle</span>
-        <span class="k">elif</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">o</span><span class="p">,</span> <span class="s2">&quot;left&quot;</span><span class="p">):</span>
-            <span class="k">return</span> <span class="n">o</span><span class="o">.</span><span class="n">left</span>
-        <span class="c1"># Point</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">o</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-
-    <span class="k">def</span> <span class="nf">right</span><span class="p">(</span><span class="n">o</span><span class="p">):</span>
-        <span class="c1"># Polygon, Ellipse</span>
-        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">o</span><span class="p">,</span> <span class="s2">&quot;bounding_box&quot;</span><span class="p">):</span>
-            <span class="k">return</span> <span class="n">o</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">right</span>
-        <span class="c1"># Rectangle</span>
-        <span class="k">elif</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">o</span><span class="p">,</span> <span class="s2">&quot;right&quot;</span><span class="p">):</span>
-            <span class="k">return</span> <span class="n">o</span><span class="o">.</span><span class="n">right</span>
-        <span class="c1"># Point</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">o</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-
-    <span class="k">def</span> <span class="nf">lower</span><span class="p">(</span><span class="n">o</span><span class="p">):</span>
-        <span class="c1"># Polygon, Ellipse</span>
-        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">o</span><span class="p">,</span> <span class="s2">&quot;bounding_box&quot;</span><span class="p">):</span>
-            <span class="k">return</span> <span class="n">o</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">lower</span>
-        <span class="c1"># Rectangle</span>
-        <span class="k">elif</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">o</span><span class="p">,</span> <span class="s2">&quot;lower&quot;</span><span class="p">):</span>
-            <span class="k">return</span> <span class="n">o</span><span class="o">.</span><span class="n">lower</span>
-        <span class="c1"># Point</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">o</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-
-    <span class="k">def</span> <span class="nf">upper</span><span class="p">(</span><span class="n">o</span><span class="p">):</span>
-        <span class="c1"># Polygon, Ellipse</span>
-        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">o</span><span class="p">,</span> <span class="s2">&quot;bounding_box&quot;</span><span class="p">):</span>
-            <span class="k">return</span> <span class="n">o</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">upper</span>
-        <span class="c1"># Rectangle</span>
-        <span class="k">elif</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">o</span><span class="p">,</span> <span class="s2">&quot;upper&quot;</span><span class="p">):</span>
-            <span class="k">return</span> <span class="n">o</span><span class="o">.</span><span class="n">upper</span>
-        <span class="c1"># Point</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">o</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-
-    <span class="n">rect</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span>
-        <span class="nb">min</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">left</span><span class="p">,</span> <span class="n">items</span><span class="p">))),</span>
-        <span class="nb">min</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">lower</span><span class="p">,</span> <span class="n">items</span><span class="p">))),</span>
-        <span class="nb">max</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">right</span><span class="p">,</span> <span class="n">items</span><span class="p">))),</span>
-        <span class="nb">max</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">upper</span><span class="p">,</span> <span class="n">items</span><span class="p">))),</span>
-    <span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">rect</span></div>
-
-
-<div class="viewcode-block" id="get_angle_between"><a class="viewcode-back" href="../../../generated/libpysal.cg.get_angle_between.html#libpysal.cg.get_angle_between">[docs]</a><span class="k">def</span> <span class="nf">get_angle_between</span><span class="p">(</span><span class="n">ray1</span><span class="p">,</span> <span class="n">ray2</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Returns the angle formed between a pair of rays which share an origin.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    ray1 : libpysal.cg.Ray</span>
-<span class="sd">        A ray forming the beginning of the angle measured.</span>
-<span class="sd">    ray2 : libpysal.cg.Ray</span>
-<span class="sd">        A ray forming the end of the angle measured.</span>
-<span class="sd">    </span>
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    angle : float</span>
-<span class="sd">        The angle between ``ray1`` and ``ray2``.</span>
-<span class="sd">    </span>
-<span class="sd">    Raises</span>
-<span class="sd">    ------</span>
-<span class="sd">    ValueError</span>
-<span class="sd">        Raised when rays do not have the same origin.</span>
-<span class="sd">    </span>
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; get_angle_between(</span>
-<span class="sd">    ...     Ray(Point((0, 0)), Point((1, 0))),</span>
-<span class="sd">    ...     Ray(Point((0, 0)), Point((1, 0)))</span>
-<span class="sd">    ... )</span>
-<span class="sd">    0.0</span>
-<span class="sd">    </span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">if</span> <span class="n">ray1</span><span class="o">.</span><span class="n">o</span> <span class="o">!=</span> <span class="n">ray2</span><span class="o">.</span><span class="n">o</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Rays must have the same origin.&quot;</span><span class="p">)</span>
-
-    <span class="n">vec1</span> <span class="o">=</span> <span class="p">(</span><span class="n">ray1</span><span class="o">.</span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">ray1</span><span class="o">.</span><span class="n">o</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">ray1</span><span class="o">.</span><span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">ray1</span><span class="o">.</span><span class="n">o</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-    <span class="n">vec2</span> <span class="o">=</span> <span class="p">(</span><span class="n">ray2</span><span class="o">.</span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">ray2</span><span class="o">.</span><span class="n">o</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">ray2</span><span class="o">.</span><span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">ray2</span><span class="o">.</span><span class="n">o</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-
-    <span class="n">rot_theta</span> <span class="o">=</span> <span class="o">-</span><span class="n">math</span><span class="o">.</span><span class="n">atan2</span><span class="p">(</span><span class="n">vec1</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">vec1</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="n">rot_matrix</span> <span class="o">=</span> <span class="p">[</span>
-        <span class="p">[</span><span class="n">math</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">rot_theta</span><span class="p">),</span> <span class="o">-</span><span class="n">math</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">rot_theta</span><span class="p">)],</span>
-        <span class="p">[</span><span class="n">math</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">rot_theta</span><span class="p">),</span> <span class="n">math</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">rot_theta</span><span class="p">)],</span>
-    <span class="p">]</span>
-
-    <span class="n">rot_vec2</span> <span class="o">=</span> <span class="p">(</span>
-        <span class="n">rot_matrix</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">vec2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">rot_matrix</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">vec2</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
-        <span class="n">rot_matrix</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">vec2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">rot_matrix</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">vec2</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
-    <span class="p">)</span>
-
-    <span class="n">angle</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">atan2</span><span class="p">(</span><span class="n">rot_vec2</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">rot_vec2</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-
-    <span class="k">return</span> <span class="n">angle</span></div>
-
-
-<div class="viewcode-block" id="is_collinear"><a class="viewcode-back" href="../../../generated/libpysal.cg.is_collinear.html#libpysal.cg.is_collinear">[docs]</a><span class="k">def</span> <span class="nf">is_collinear</span><span class="p">(</span><span class="n">p1</span><span class="p">,</span> <span class="n">p2</span><span class="p">,</span> <span class="n">p3</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Returns whether a triplet of points is collinear.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    p1 : libpysal.cg.Point</span>
-<span class="sd">        A point.</span>
-<span class="sd">    p2 : libpysal.cg.Point</span>
-<span class="sd">        A point.</span>
-<span class="sd">    p3 : libpysal.cg.Point</span>
-<span class="sd">        A point.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    collinear : bool</span>
-<span class="sd">        ``True`` if ``{p1, p2, p3}`` are collinear, otherwise ``False``.</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; is_collinear(Point((0, 0)), Point((1, 1)), Point((5, 5)))</span>
-<span class="sd">    True</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; is_collinear(Point((0, 0)), Point((1, 1)), Point((5, 0)))</span>
-<span class="sd">    False</span>
-<span class="sd">    </span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">eps</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">finfo</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">p1</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span><span class="o">.</span><span class="n">eps</span>
-
-    <span class="n">slope_diff</span> <span class="o">=</span> <span class="nb">abs</span><span class="p">(</span>
-        <span class="p">(</span><span class="n">p2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">p1</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">*</span> <span class="p">(</span><span class="n">p3</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">p1</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="o">-</span> <span class="p">(</span><span class="n">p2</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">p1</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="o">*</span> <span class="p">(</span><span class="n">p3</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">p1</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="p">)</span>
-
-    <span class="n">very_small_dist</span> <span class="o">=</span> <span class="n">EPSILON_SCALER</span> <span class="o">*</span> <span class="n">eps</span>
-
-    <span class="n">collinear</span> <span class="o">=</span> <span class="n">slope_diff</span> <span class="o">&lt;</span> <span class="n">very_small_dist</span>
-
-    <span class="k">return</span> <span class="n">collinear</span></div>
-
-
-<div class="viewcode-block" id="get_segments_intersect"><a class="viewcode-back" href="../../../generated/libpysal.cg.get_segments_intersect.html#libpysal.cg.get_segments_intersect">[docs]</a><span class="k">def</span> <span class="nf">get_segments_intersect</span><span class="p">(</span><span class="n">seg1</span><span class="p">,</span> <span class="n">seg2</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Returns the intersection of two segments if one exists.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    seg1 : libpysal.cg.LineSegment</span>
-<span class="sd">        A segment to check for an intersection.</span>
-<span class="sd">    seg2 : libpysal.cg.LineSegment</span>
-<span class="sd">        The segment to check against ``seg1`` for an intersection.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    intersection : {libpysal.cg.Point, libpysal.cg.LineSegment, None}</span>
-<span class="sd">        The intersecting point or line between ``seg1`` and</span>
-<span class="sd">        ``seg2`` if an intersection exists or ``None`` if</span>
-<span class="sd">        ``seg1`` and ``seg2`` do not intersect.</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; seg1 = LineSegment(Point((0, 0)), Point((0, 10)))</span>
-<span class="sd">    &gt;&gt;&gt; seg2 = LineSegment(Point((-5, 5)), Point((5, 5)))</span>
-<span class="sd">    &gt;&gt;&gt; i = get_segments_intersect(seg1, seg2)</span>
-<span class="sd">    &gt;&gt;&gt; isinstance(i, Point)</span>
-<span class="sd">    True</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; str(i)</span>
-<span class="sd">    &#39;(0.0, 5.0)&#39;</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; seg3 = LineSegment(Point((100, 100)), Point((100, 101)))</span>
-<span class="sd">    &gt;&gt;&gt; i = get_segments_intersect(seg2, seg3)</span>
-<span class="sd">    </span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">p1</span> <span class="o">=</span> <span class="n">seg1</span><span class="o">.</span><span class="n">p1</span>
-    <span class="n">p2</span> <span class="o">=</span> <span class="n">seg1</span><span class="o">.</span><span class="n">p2</span>
-    <span class="n">p3</span> <span class="o">=</span> <span class="n">seg2</span><span class="o">.</span><span class="n">p1</span>
-    <span class="n">p4</span> <span class="o">=</span> <span class="n">seg2</span><span class="o">.</span><span class="n">p2</span>
-    <span class="n">a</span> <span class="o">=</span> <span class="n">p2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">p1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-    <span class="n">b</span> <span class="o">=</span> <span class="n">p3</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">p4</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-    <span class="n">c</span> <span class="o">=</span> <span class="n">p2</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">p1</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-    <span class="n">d</span> <span class="o">=</span> <span class="n">p3</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">p4</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-    <span class="n">det</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">a</span> <span class="o">*</span> <span class="n">d</span> <span class="o">-</span> <span class="n">b</span> <span class="o">*</span> <span class="n">c</span><span class="p">)</span>
-
-    <span class="n">intersection</span> <span class="o">=</span> <span class="kc">None</span>
-
-    <span class="k">if</span> <span class="n">det</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-        <span class="k">if</span> <span class="n">seg1</span> <span class="o">==</span> <span class="n">seg2</span><span class="p">:</span>
-            <span class="n">intersection</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">seg1</span><span class="o">.</span><span class="n">p1</span><span class="p">,</span> <span class="n">seg1</span><span class="o">.</span><span class="n">p2</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">a</span> <span class="o">=</span> <span class="n">get_segment_point_intersect</span><span class="p">(</span><span class="n">seg2</span><span class="p">,</span> <span class="n">seg1</span><span class="o">.</span><span class="n">p1</span><span class="p">)</span>
-            <span class="n">b</span> <span class="o">=</span> <span class="n">get_segment_point_intersect</span><span class="p">(</span><span class="n">seg2</span><span class="p">,</span> <span class="n">seg1</span><span class="o">.</span><span class="n">p2</span><span class="p">)</span>
-            <span class="n">c</span> <span class="o">=</span> <span class="n">get_segment_point_intersect</span><span class="p">(</span><span class="n">seg1</span><span class="p">,</span> <span class="n">seg2</span><span class="o">.</span><span class="n">p1</span><span class="p">)</span>
-            <span class="n">d</span> <span class="o">=</span> <span class="n">get_segment_point_intersect</span><span class="p">(</span><span class="n">seg1</span><span class="p">,</span> <span class="n">seg2</span><span class="o">.</span><span class="n">p2</span><span class="p">)</span>
-            <span class="k">if</span> <span class="n">a</span> <span class="ow">and</span> <span class="n">b</span><span class="p">:</span>  <span class="c1"># seg1 in seg2</span>
-                <span class="n">intersection</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">seg1</span><span class="o">.</span><span class="n">p1</span><span class="p">,</span> <span class="n">seg1</span><span class="o">.</span><span class="n">p2</span><span class="p">)</span>
-            <span class="k">if</span> <span class="n">c</span> <span class="ow">and</span> <span class="n">d</span><span class="p">:</span>  <span class="c1"># seg2 in seg1</span>
-                <span class="n">intersection</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">seg2</span><span class="o">.</span><span class="n">p1</span><span class="p">,</span> <span class="n">seg2</span><span class="o">.</span><span class="n">p2</span><span class="p">)</span>
-            <span class="k">if</span> <span class="p">(</span><span class="n">a</span> <span class="ow">or</span> <span class="n">b</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">c</span> <span class="ow">or</span> <span class="n">d</span><span class="p">):</span>
-                <span class="n">p1</span> <span class="o">=</span> <span class="n">a</span> <span class="k">if</span> <span class="n">a</span> <span class="k">else</span> <span class="n">b</span>
-                <span class="n">p2</span> <span class="o">=</span> <span class="n">c</span> <span class="k">if</span> <span class="n">c</span> <span class="k">else</span> <span class="n">d</span>
-                <span class="n">intersection</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">p1</span><span class="p">,</span> <span class="n">p2</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">a_inv</span> <span class="o">=</span> <span class="n">d</span> <span class="o">/</span> <span class="n">det</span>
-        <span class="n">b_inv</span> <span class="o">=</span> <span class="o">-</span><span class="n">b</span> <span class="o">/</span> <span class="n">det</span>
-        <span class="n">c_inv</span> <span class="o">=</span> <span class="o">-</span><span class="n">c</span> <span class="o">/</span> <span class="n">det</span>
-        <span class="n">d_inv</span> <span class="o">=</span> <span class="n">a</span> <span class="o">/</span> <span class="n">det</span>
-        <span class="n">m</span> <span class="o">=</span> <span class="n">p3</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">p1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">n</span> <span class="o">=</span> <span class="n">p3</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">p1</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-        <span class="n">x</span> <span class="o">=</span> <span class="n">a_inv</span> <span class="o">*</span> <span class="n">m</span> <span class="o">+</span> <span class="n">b_inv</span> <span class="o">*</span> <span class="n">n</span>
-        <span class="n">y</span> <span class="o">=</span> <span class="n">c_inv</span> <span class="o">*</span> <span class="n">m</span> <span class="o">+</span> <span class="n">d_inv</span> <span class="o">*</span> <span class="n">n</span>
-        <span class="n">intersect_exists</span> <span class="o">=</span> <span class="mi">0</span> <span class="o">&lt;=</span> <span class="n">x</span> <span class="o">&lt;=</span> <span class="mi">1</span> <span class="ow">and</span> <span class="mi">0</span> <span class="o">&lt;=</span> <span class="n">y</span> <span class="o">&lt;=</span> <span class="mi">1</span>
-
-        <span class="k">if</span> <span class="n">intersect_exists</span><span class="p">:</span>
-            <span class="n">intersection</span> <span class="o">=</span> <span class="n">Point</span><span class="p">(</span>
-                <span class="p">(</span><span class="n">p1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">x</span> <span class="o">*</span> <span class="p">(</span><span class="n">p2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">p1</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">p1</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">x</span> <span class="o">*</span> <span class="p">(</span><span class="n">p2</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">p1</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
-            <span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">intersection</span></div>
-
-
-<div class="viewcode-block" id="get_segment_point_intersect"><a class="viewcode-back" href="../../../generated/libpysal.cg.get_segment_point_intersect.html#libpysal.cg.get_segment_point_intersect">[docs]</a><span class="k">def</span> <span class="nf">get_segment_point_intersect</span><span class="p">(</span><span class="n">seg</span><span class="p">,</span> <span class="n">pt</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Returns the intersection of a segment and point.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    seg : libpysal.cg.LineSegment</span>
-<span class="sd">        A segment to check for an intersection.</span>
-<span class="sd">    pt : libpysal.cg.Point</span>
-<span class="sd">        A point to check ``seg`` for an intersection.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    pt : {libpysal.cg.Point, None}</span>
-<span class="sd">        The intersection of a ``seg`` and ``pt`` if one exists, otherwise ``None``.</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; seg = LineSegment(Point((0, 0)), Point((0, 10)))</span>
-<span class="sd">    &gt;&gt;&gt; pt = Point((0, 5))</span>
-<span class="sd">    &gt;&gt;&gt; i = get_segment_point_intersect(seg, pt)</span>
-<span class="sd">    &gt;&gt;&gt; str(i)</span>
-<span class="sd">    &#39;(0.0, 5.0)&#39;</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; pt2 = Point((5, 5))</span>
-<span class="sd">    &gt;&gt;&gt; get_segment_point_intersect(seg, pt2)</span>
-<span class="sd">    </span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">eps</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">finfo</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">pt</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span><span class="o">.</span><span class="n">eps</span>
-
-    <span class="k">if</span> <span class="n">is_collinear</span><span class="p">(</span><span class="n">pt</span><span class="p">,</span> <span class="n">seg</span><span class="o">.</span><span class="n">p1</span><span class="p">,</span> <span class="n">seg</span><span class="o">.</span><span class="n">p2</span><span class="p">):</span>
-        <span class="k">if</span> <span class="n">get_segment_point_dist</span><span class="p">(</span><span class="n">seg</span><span class="p">,</span> <span class="n">pt</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">EPSILON_SCALER</span> <span class="o">*</span> <span class="n">eps</span><span class="p">:</span>
-            <span class="k">pass</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">pt</span> <span class="o">=</span> <span class="kc">None</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">vec1</span> <span class="o">=</span> <span class="p">(</span><span class="n">pt</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">seg</span><span class="o">.</span><span class="n">p1</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">pt</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">seg</span><span class="o">.</span><span class="n">p1</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-        <span class="n">vec2</span> <span class="o">=</span> <span class="p">(</span><span class="n">seg</span><span class="o">.</span><span class="n">p2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">seg</span><span class="o">.</span><span class="n">p1</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">seg</span><span class="o">.</span><span class="n">p2</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">seg</span><span class="o">.</span><span class="n">p1</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-
-        <span class="k">if</span> <span class="nb">abs</span><span class="p">(</span><span class="n">vec1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">vec2</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">vec1</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">vec2</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">&lt;</span> <span class="n">eps</span><span class="p">:</span>
-            <span class="k">pass</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">pt</span> <span class="o">=</span> <span class="kc">None</span>
-
-    <span class="k">return</span> <span class="n">pt</span></div>
-
-
-<div class="viewcode-block" id="get_polygon_point_intersect"><a class="viewcode-back" href="../../../generated/libpysal.cg.get_polygon_point_intersect.html#libpysal.cg.get_polygon_point_intersect">[docs]</a><span class="k">def</span> <span class="nf">get_polygon_point_intersect</span><span class="p">(</span><span class="n">poly</span><span class="p">,</span> <span class="n">pt</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Returns the intersection of a polygon and point.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    poly : libpysal.cg.Polygon</span>
-<span class="sd">        A polygon to check for an intersection.</span>
-<span class="sd">    pt : libpysal.cg.Point</span>
-<span class="sd">        A point to check ``poly`` for an intersection.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    ret : {libpysal.cg.Point, None}</span>
-<span class="sd">        The intersection of a ``poly`` and ``pt`` if one exists, otherwise ``None``.</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; poly = Polygon([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))])</span>
-<span class="sd">    &gt;&gt;&gt; pt = Point((0.5, 0.5))</span>
-<span class="sd">    &gt;&gt;&gt; i = get_polygon_point_intersect(poly, pt)</span>
-<span class="sd">    &gt;&gt;&gt; str(i)</span>
-<span class="sd">    &#39;(0.5, 0.5)&#39;</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; pt2 = Point((2, 2))</span>
-<span class="sd">    &gt;&gt;&gt; get_polygon_point_intersect(poly, pt2)</span>
-<span class="sd">    </span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="nf">pt_lies_on_part_boundary</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">vx</span><span class="p">):</span>
-        <span class="n">vx_range</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">vx</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
-        <span class="n">seg</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">i</span><span class="p">:</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">vx</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">vx</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">])</span>
-        <span class="k">return</span> <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">vx_range</span> <span class="k">if</span> <span class="n">get_segment_point_dist</span><span class="p">(</span><span class="n">seg</span><span class="p">(</span><span class="n">i</span><span class="p">),</span> <span class="n">p</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="p">[]</span>
-
-    <span class="n">ret</span> <span class="o">=</span> <span class="kc">None</span>
-
-    <span class="c1"># Weed out points that aren&#39;t even close</span>
-    <span class="k">if</span> <span class="n">get_rectangle_point_intersect</span><span class="p">(</span><span class="n">poly</span><span class="o">.</span><span class="n">bounding_box</span><span class="p">,</span> <span class="n">pt</span><span class="p">)</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="k">pass</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">if</span> <span class="p">[</span><span class="n">vxs</span> <span class="k">for</span> <span class="n">vxs</span> <span class="ow">in</span> <span class="n">poly</span><span class="o">.</span><span class="n">_vertices</span> <span class="k">if</span> <span class="n">pt_lies_on_part_boundary</span><span class="p">(</span><span class="n">pt</span><span class="p">,</span> <span class="n">vxs</span><span class="p">)]</span> <span class="o">!=</span> <span class="p">[]:</span>
-            <span class="n">ret</span> <span class="o">=</span> <span class="n">pt</span>
-        <span class="k">elif</span> <span class="p">[</span><span class="n">vxs</span> <span class="k">for</span> <span class="n">vxs</span> <span class="ow">in</span> <span class="n">poly</span><span class="o">.</span><span class="n">_vertices</span> <span class="k">if</span> <span class="n">_point_in_vertices</span><span class="p">(</span><span class="n">pt</span><span class="p">,</span> <span class="n">vxs</span><span class="p">)]</span> <span class="o">!=</span> <span class="p">[]:</span>
-            <span class="n">ret</span> <span class="o">=</span> <span class="n">pt</span>
-        <span class="k">if</span> <span class="n">poly</span><span class="o">.</span><span class="n">_holes</span> <span class="o">!=</span> <span class="p">[[]]:</span>
-            <span class="k">if</span> <span class="p">[</span><span class="n">vxs</span> <span class="k">for</span> <span class="n">vxs</span> <span class="ow">in</span> <span class="n">poly</span><span class="o">.</span><span class="n">holes</span> <span class="k">if</span> <span class="n">pt_lies_on_part_boundary</span><span class="p">(</span><span class="n">pt</span><span class="p">,</span> <span class="n">vxs</span><span class="p">)]</span> <span class="o">!=</span> <span class="p">[]:</span>
-                <span class="c1"># pt lies on boundary of hole.</span>
-                <span class="k">pass</span>
-            <span class="k">if</span> <span class="p">[</span><span class="n">vxs</span> <span class="k">for</span> <span class="n">vxs</span> <span class="ow">in</span> <span class="n">poly</span><span class="o">.</span><span class="n">holes</span> <span class="k">if</span> <span class="n">_point_in_vertices</span><span class="p">(</span><span class="n">pt</span><span class="p">,</span> <span class="n">vxs</span><span class="p">)]</span> <span class="o">!=</span> <span class="p">[]:</span>
-                <span class="c1"># pt lines inside a hole.</span>
-                <span class="n">ret</span> <span class="o">=</span> <span class="kc">None</span>
-            <span class="c1"># raise NotImplementedError,</span>
-            <span class="c1"># &#39;Cannot compute containment for polygon with holes&#39;</span>
-
-    <span class="k">return</span> <span class="n">ret</span></div>
-
-
-<div class="viewcode-block" id="get_rectangle_point_intersect"><a class="viewcode-back" href="../../../generated/libpysal.cg.get_rectangle_point_intersect.html#libpysal.cg.get_rectangle_point_intersect">[docs]</a><span class="k">def</span> <span class="nf">get_rectangle_point_intersect</span><span class="p">(</span><span class="n">rect</span><span class="p">,</span> <span class="n">pt</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Returns the intersection of a rectangle and point.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    rect : libpysal.cg.Rectangle</span>
-<span class="sd">        A rectangle to check for an intersection.</span>
-<span class="sd">    pt : libpysal.cg.Point</span>
-<span class="sd">        A point to check ``rect`` for an intersection.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    pt : {libpysal.cg.Point, None}</span>
-<span class="sd">        The intersection of a ``rect`` and ``pt`` if one exists, otherwise ``None``.</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; rect = Rectangle(0, 0, 5, 5)</span>
-<span class="sd">    &gt;&gt;&gt; pt = Point((1, 1))</span>
-<span class="sd">    &gt;&gt;&gt; i = get_rectangle_point_intersect(rect, pt)</span>
-<span class="sd">    &gt;&gt;&gt; str(i)</span>
-<span class="sd">    &#39;(1.0, 1.0)&#39;</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; pt2 = Point((10, 10))</span>
-<span class="sd">    &gt;&gt;&gt; get_rectangle_point_intersect(rect, pt2)</span>
-<span class="sd">    </span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">if</span> <span class="n">rect</span><span class="o">.</span><span class="n">left</span> <span class="o">&lt;=</span> <span class="n">pt</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">rect</span><span class="o">.</span><span class="n">right</span> <span class="ow">and</span> <span class="n">rect</span><span class="o">.</span><span class="n">lower</span> <span class="o">&lt;=</span> <span class="n">pt</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">rect</span><span class="o">.</span><span class="n">upper</span><span class="p">:</span>
-        <span class="k">pass</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">pt</span> <span class="o">=</span> <span class="kc">None</span>
-
-    <span class="k">return</span> <span class="n">pt</span></div>
-
-
-<div class="viewcode-block" id="get_ray_segment_intersect"><a class="viewcode-back" href="../../../generated/libpysal.cg.get_ray_segment_intersect.html#libpysal.cg.get_ray_segment_intersect">[docs]</a><span class="k">def</span> <span class="nf">get_ray_segment_intersect</span><span class="p">(</span><span class="n">ray</span><span class="p">,</span> <span class="n">seg</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Returns the intersection of a ray and line segment.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    ray : libpysal.cg.Ray</span>
-<span class="sd">        A ray to check for an intersection.</span>
-<span class="sd">    seg : libpysal.cg.LineSegment</span>
-<span class="sd">        A segment to check for an intersection against ``ray``.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    intersection : {libpysal.cg.Point, libpysal.cg.LineSegment, None}</span>
-<span class="sd">        The intersecting point or line between ``ray`` and</span>
-<span class="sd">        ``seg`` if an intersection exists or ``None`` if</span>
-<span class="sd">        ``ray`` and ``seg`` do not intersect.</span>
-<span class="sd">    </span>
-<span class="sd">    See Also</span>
-<span class="sd">    --------</span>
-<span class="sd">    </span>
-<span class="sd">    libpysal.cg.get_segments_intersect</span>
-<span class="sd">    </span>
-<span class="sd">    </span>
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; ray = Ray(Point((0, 0)), Point((0, 1)))</span>
-<span class="sd">    &gt;&gt;&gt; seg = LineSegment(Point((-1, 10)), Point((1, 10)))</span>
-<span class="sd">    &gt;&gt;&gt; i = get_ray_segment_intersect(ray, seg)</span>
-<span class="sd">    &gt;&gt;&gt; isinstance(i, Point)</span>
-<span class="sd">    True</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; str(i)</span>
-<span class="sd">    &#39;(0.0, 10.0)&#39;</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; seg2 = LineSegment(Point((10, 10)), Point((10, 11)))</span>
-<span class="sd">    &gt;&gt;&gt; get_ray_segment_intersect(ray, seg2)</span>
-<span class="sd">    </span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="c1"># Upper bound on origin to segment dist (+1)</span>
-    <span class="n">d</span> <span class="o">=</span> <span class="p">(</span>
-        <span class="nb">max</span><span class="p">(</span>
-            <span class="n">math</span><span class="o">.</span><span class="n">hypot</span><span class="p">(</span><span class="n">seg</span><span class="o">.</span><span class="n">p1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">ray</span><span class="o">.</span><span class="n">o</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">seg</span><span class="o">.</span><span class="n">p1</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">ray</span><span class="o">.</span><span class="n">o</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span>
-            <span class="n">math</span><span class="o">.</span><span class="n">hypot</span><span class="p">(</span><span class="n">seg</span><span class="o">.</span><span class="n">p2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">ray</span><span class="o">.</span><span class="n">o</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">seg</span><span class="o">.</span><span class="n">p2</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">ray</span><span class="o">.</span><span class="n">o</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span>
-        <span class="p">)</span>
-        <span class="o">+</span> <span class="mi">1</span>
-    <span class="p">)</span>
-    <span class="n">ratio</span> <span class="o">=</span> <span class="n">d</span> <span class="o">/</span> <span class="n">math</span><span class="o">.</span><span class="n">hypot</span><span class="p">(</span><span class="n">ray</span><span class="o">.</span><span class="n">o</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">ray</span><span class="o">.</span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">ray</span><span class="o">.</span><span class="n">o</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">ray</span><span class="o">.</span><span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-    <span class="n">ray_seg</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span>
-        <span class="n">ray</span><span class="o">.</span><span class="n">o</span><span class="p">,</span>
-        <span class="n">Point</span><span class="p">(</span>
-            <span class="p">(</span>
-                <span class="n">ray</span><span class="o">.</span><span class="n">o</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">ratio</span> <span class="o">*</span> <span class="p">(</span><span class="n">ray</span><span class="o">.</span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">ray</span><span class="o">.</span><span class="n">o</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span>
-                <span class="n">ray</span><span class="o">.</span><span class="n">o</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">ratio</span> <span class="o">*</span> <span class="p">(</span><span class="n">ray</span><span class="o">.</span><span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">ray</span><span class="o">.</span><span class="n">o</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span>
-            <span class="p">)</span>
-        <span class="p">),</span>
-    <span class="p">)</span>
-
-    <span class="n">intersection</span> <span class="o">=</span> <span class="n">get_segments_intersect</span><span class="p">(</span><span class="n">seg</span><span class="p">,</span> <span class="n">ray_seg</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">intersection</span></div>
-
-
-<div class="viewcode-block" id="get_rectangle_rectangle_intersection"><a class="viewcode-back" href="../../../generated/libpysal.cg.get_rectangle_rectangle_intersection.html#libpysal.cg.get_rectangle_rectangle_intersection">[docs]</a><span class="k">def</span> <span class="nf">get_rectangle_rectangle_intersection</span><span class="p">(</span><span class="n">r0</span><span class="p">,</span> <span class="n">r1</span><span class="p">,</span> <span class="n">checkOverlap</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Returns the intersection between two rectangles.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    r0 : libpysal.cg.Rectangle</span>
-<span class="sd">        A rectangle to check for an intersection.</span>
-<span class="sd">    r1 : libpysal.cg.Rectangle</span>
-<span class="sd">        A rectangle to check for an intersection against ``r0``.</span>
-<span class="sd">    checkOverlap : bool</span>
-<span class="sd">        Call ``bbcommon(r0, r1)`` prior to complex geometry</span>
-<span class="sd">        checking. Default is ``True``. Prior to setting as</span>
-<span class="sd">        ``False`` see the Notes section. </span>
-<span class="sd">    </span>
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    intersection : {libpysal.cg.Point, libpysal.cg.LineSegment, libpysal.cg.Rectangle, None}</span>
-<span class="sd">        The intersecting point, line, or rectangle between </span>
-<span class="sd">        `r0`` and ``r1`` if an intersection exists or ``None``</span>
-<span class="sd">        if ``r0`` and ``r1`` do not intersect.</span>
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-<span class="sd">    </span>
-<span class="sd">    The algorithm assumes the rectangles overlap. The keyword</span>
-<span class="sd">    ``checkOverlap=False`` should be used with extreme caution.</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; r0 = Rectangle(0,4,6,9)</span>
-<span class="sd">    &gt;&gt;&gt; r1 = Rectangle(4,0,9,7)</span>
-<span class="sd">    &gt;&gt;&gt; ri = get_rectangle_rectangle_intersection(r0,r1)</span>
-<span class="sd">    &gt;&gt;&gt; ri[:]</span>
-<span class="sd">    [4.0, 4.0, 6.0, 7.0]</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; r0 = Rectangle(0,0,4,4)</span>
-<span class="sd">    &gt;&gt;&gt; r1 = Rectangle(2,1,6,3)</span>
-<span class="sd">    &gt;&gt;&gt; ri = get_rectangle_rectangle_intersection(r0,r1)</span>
-<span class="sd">    &gt;&gt;&gt; ri[:]</span>
-<span class="sd">    [2.0, 1.0, 4.0, 3.0]</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; r0 = Rectangle(0,0,4,4)</span>
-<span class="sd">    &gt;&gt;&gt; r1 = Rectangle(2,1,3,2)</span>
-<span class="sd">    &gt;&gt;&gt; ri = get_rectangle_rectangle_intersection(r0,r1)</span>
-<span class="sd">    &gt;&gt;&gt; ri[:] == r1[:]</span>
-<span class="sd">    True</span>
-<span class="sd">    </span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">intersection</span> <span class="o">=</span> <span class="kc">None</span>
-    <span class="n">common_bb</span> <span class="o">=</span> <span class="kc">True</span>
-
-    <span class="k">if</span> <span class="n">checkOverlap</span><span class="p">:</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">bbcommon</span><span class="p">(</span><span class="n">r0</span><span class="p">,</span> <span class="n">r1</span><span class="p">):</span>
-            <span class="c1"># raise ValueError, &quot;Rectangles do not intersect&quot;</span>
-            <span class="n">common_bb</span> <span class="o">=</span> <span class="kc">False</span>
-
-    <span class="k">if</span> <span class="n">common_bb</span><span class="p">:</span>
-        <span class="n">left</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">r0</span><span class="o">.</span><span class="n">left</span><span class="p">,</span> <span class="n">r1</span><span class="o">.</span><span class="n">left</span><span class="p">)</span>
-        <span class="n">lower</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">r0</span><span class="o">.</span><span class="n">lower</span><span class="p">,</span> <span class="n">r1</span><span class="o">.</span><span class="n">lower</span><span class="p">)</span>
-        <span class="n">right</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">r0</span><span class="o">.</span><span class="n">right</span><span class="p">,</span> <span class="n">r1</span><span class="o">.</span><span class="n">right</span><span class="p">)</span>
-        <span class="n">upper</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">r0</span><span class="o">.</span><span class="n">upper</span><span class="p">,</span> <span class="n">r1</span><span class="o">.</span><span class="n">upper</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">upper</span> <span class="o">==</span> <span class="n">lower</span> <span class="ow">and</span> <span class="n">left</span> <span class="o">==</span> <span class="n">right</span><span class="p">:</span>
-            <span class="n">intersection</span> <span class="o">=</span> <span class="n">Point</span><span class="p">((</span><span class="n">left</span><span class="p">,</span> <span class="n">lower</span><span class="p">))</span>
-        <span class="k">elif</span> <span class="n">upper</span> <span class="o">==</span> <span class="n">lower</span><span class="p">:</span>
-            <span class="n">intersection</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="n">left</span><span class="p">,</span> <span class="n">lower</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="n">right</span><span class="p">,</span> <span class="n">lower</span><span class="p">)))</span>
-        <span class="k">elif</span> <span class="n">left</span> <span class="o">==</span> <span class="n">right</span><span class="p">:</span>
-            <span class="n">intersection</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="n">left</span><span class="p">,</span> <span class="n">lower</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="n">left</span><span class="p">,</span> <span class="n">upper</span><span class="p">)))</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">intersection</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span><span class="n">left</span><span class="p">,</span> <span class="n">lower</span><span class="p">,</span> <span class="n">right</span><span class="p">,</span> <span class="n">upper</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">intersection</span></div>
-
-
-<div class="viewcode-block" id="get_polygon_point_dist"><a class="viewcode-back" href="../../../generated/libpysal.cg.get_polygon_point_dist.html#libpysal.cg.get_polygon_point_dist">[docs]</a><span class="k">def</span> <span class="nf">get_polygon_point_dist</span><span class="p">(</span><span class="n">poly</span><span class="p">,</span> <span class="n">pt</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Returns the distance between a polygon and point.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    poly : libpysal.cg.Polygon</span>
-<span class="sd">        A polygon to compute distance from.</span>
-<span class="sd">    </span>
-<span class="sd">    pt : libpysal.cg.Point</span>
-<span class="sd">        a point to compute distance from</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    dist : float</span>
-<span class="sd">        The distance between ``poly`` and ``point``.</span>
-<span class="sd">    </span>
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; poly = Polygon([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))])</span>
-<span class="sd">    &gt;&gt;&gt; pt = Point((2, 0.5))</span>
-<span class="sd">    &gt;&gt;&gt; get_polygon_point_dist(poly, pt)</span>
-<span class="sd">    1.0</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; pt2 = Point((0.5, 0.5))</span>
-<span class="sd">    &gt;&gt;&gt; get_polygon_point_dist(poly, pt2)</span>
-<span class="sd">    0.0</span>
-<span class="sd">    </span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">if</span> <span class="n">get_polygon_point_intersect</span><span class="p">(</span><span class="n">poly</span><span class="p">,</span> <span class="n">pt</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">dist</span> <span class="o">=</span> <span class="mf">0.0</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">part_prox</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">vertices</span> <span class="ow">in</span> <span class="n">poly</span><span class="o">.</span><span class="n">_vertices</span><span class="p">:</span>
-            <span class="n">vx_range</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">vertices</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
-            <span class="n">seg</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">i</span><span class="p">:</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">vertices</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">vertices</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">])</span>
-            <span class="n">_min_dist</span> <span class="o">=</span> <span class="nb">min</span><span class="p">([</span><span class="n">get_segment_point_dist</span><span class="p">(</span><span class="n">seg</span><span class="p">(</span><span class="n">i</span><span class="p">),</span> <span class="n">pt</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">vx_range</span><span class="p">])</span>
-            <span class="n">part_prox</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">_min_dist</span><span class="p">)</span>
-        <span class="n">dist</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">part_prox</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">dist</span></div>
-
-
-<div class="viewcode-block" id="get_points_dist"><a class="viewcode-back" href="../../../generated/libpysal.cg.get_points_dist.html#libpysal.cg.get_points_dist">[docs]</a><span class="k">def</span> <span class="nf">get_points_dist</span><span class="p">(</span><span class="n">pt1</span><span class="p">,</span> <span class="n">pt2</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Returns the distance between a pair of points.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    pt1 : libpysal.cg.Point</span>
-<span class="sd">        A point.</span>
-<span class="sd">    </span>
-<span class="sd">    pt2 : libpysal.cg.Point</span>
-<span class="sd">        The other point.</span>
-<span class="sd">    </span>
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    dist : float</span>
-<span class="sd">        The distance between ``pt1`` and ``pt2``.</span>
-<span class="sd">    </span>
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; get_points_dist(Point((4, 4)), Point((4, 8)))</span>
-<span class="sd">    4.0</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; get_points_dist(Point((0, 0)), Point((0, 0)))</span>
-<span class="sd">    0.0</span>
-<span class="sd">    </span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">dist</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">hypot</span><span class="p">(</span><span class="n">pt1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">pt2</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">pt1</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">pt2</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-
-    <span class="k">return</span> <span class="n">dist</span></div>
-
-
-<div class="viewcode-block" id="get_segment_point_dist"><a class="viewcode-back" href="../../../generated/libpysal.cg.get_segment_point_dist.html#libpysal.cg.get_segment_point_dist">[docs]</a><span class="k">def</span> <span class="nf">get_segment_point_dist</span><span class="p">(</span><span class="n">seg</span><span class="p">,</span> <span class="n">pt</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Returns (1) the distance between a line segment and point</span>
-<span class="sd">    and (2) the distance along the segment to the closest location on the</span>
-<span class="sd">    segment from the point as a ratio of the length of the segment.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    seg : libpysal.cg.LineSegment</span>
-<span class="sd">        A line segment to compute distance from.</span>
-<span class="sd">    pt : libpysal.cg.Point</span>
-<span class="sd">        A point to compute distance from.</span>
-<span class="sd">    </span>
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    dist : float</span>
-<span class="sd">        The distance between ``seg`` and ``pt``.</span>
-<span class="sd">    ratio : float</span>
-<span class="sd">        The distance along ``seg`` to the closest location on</span>
-<span class="sd">        ``seg`` from ``pt`` as a ratio of the length of ``seg``.</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; seg = LineSegment(Point((0, 0)), Point((10, 0)))</span>
-<span class="sd">    &gt;&gt;&gt; pt = Point((5, 5))</span>
-<span class="sd">    &gt;&gt;&gt; get_segment_point_dist(seg, pt)</span>
-<span class="sd">    (5.0, 0.5)</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; pt2 = Point((0, 0))</span>
-<span class="sd">    &gt;&gt;&gt; get_segment_point_dist(seg, pt2)</span>
-<span class="sd">    (0.0, 0.0)</span>
-<span class="sd">    </span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">src_p</span> <span class="o">=</span> <span class="n">seg</span><span class="o">.</span><span class="n">p1</span>
-    <span class="n">dest_p</span> <span class="o">=</span> <span class="n">seg</span><span class="o">.</span><span class="n">p2</span>
-
-    <span class="c1"># Shift line to go through origin</span>
-    <span class="n">points_0</span> <span class="o">=</span> <span class="n">pt</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">src_p</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-    <span class="n">points_1</span> <span class="o">=</span> <span class="n">pt</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">src_p</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-    <span class="n">points_2</span> <span class="o">=</span> <span class="mi">0</span>
-    <span class="n">points_3</span> <span class="o">=</span> <span class="mi">0</span>
-    <span class="n">points_4</span> <span class="o">=</span> <span class="n">dest_p</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">src_p</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-    <span class="n">points_5</span> <span class="o">=</span> <span class="n">dest_p</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">src_p</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-
-    <span class="n">segment_length</span> <span class="o">=</span> <span class="n">get_points_dist</span><span class="p">(</span><span class="n">src_p</span><span class="p">,</span> <span class="n">dest_p</span><span class="p">)</span>
-
-    <span class="c1"># Meh, robustness...</span>
-    <span class="c1"># maybe should incorporate this into a more general approach later</span>
-    <span class="k">if</span> <span class="n">segment_length</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-        <span class="n">dist</span><span class="p">,</span> <span class="n">ratio</span> <span class="o">=</span> <span class="n">get_points_dist</span><span class="p">(</span><span class="n">pt</span><span class="p">,</span> <span class="n">src_p</span><span class="p">),</span> <span class="mi">0</span>
-
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">u_x</span> <span class="o">=</span> <span class="n">points_4</span> <span class="o">/</span> <span class="n">segment_length</span>
-        <span class="n">u_y</span> <span class="o">=</span> <span class="n">points_5</span> <span class="o">/</span> <span class="n">segment_length</span>
-
-        <span class="n">inter_x</span> <span class="o">=</span> <span class="n">u_x</span> <span class="o">*</span> <span class="n">u_x</span> <span class="o">*</span> <span class="n">points_0</span> <span class="o">+</span> <span class="n">u_x</span> <span class="o">*</span> <span class="n">u_y</span> <span class="o">*</span> <span class="n">points_1</span>
-        <span class="n">inter_y</span> <span class="o">=</span> <span class="n">u_x</span> <span class="o">*</span> <span class="n">u_y</span> <span class="o">*</span> <span class="n">points_0</span> <span class="o">+</span> <span class="n">u_y</span> <span class="o">*</span> <span class="n">u_y</span> <span class="o">*</span> <span class="n">points_1</span>
-
-        <span class="n">src_proj_dist</span> <span class="o">=</span> <span class="n">get_points_dist</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="p">(</span><span class="n">inter_x</span><span class="p">,</span> <span class="n">inter_y</span><span class="p">))</span>
-        <span class="n">dest_proj_dist</span> <span class="o">=</span> <span class="n">get_points_dist</span><span class="p">((</span><span class="n">inter_x</span><span class="p">,</span> <span class="n">inter_y</span><span class="p">),</span> <span class="p">(</span><span class="n">points_4</span><span class="p">,</span> <span class="n">points_5</span><span class="p">))</span>
-
-        <span class="k">if</span> <span class="n">src_proj_dist</span> <span class="o">&gt;</span> <span class="n">segment_length</span> <span class="ow">or</span> <span class="n">dest_proj_dist</span> <span class="o">&gt;</span> <span class="n">segment_length</span><span class="p">:</span>
-            <span class="n">src_pt_dist</span> <span class="o">=</span> <span class="n">get_points_dist</span><span class="p">((</span><span class="n">points_2</span><span class="p">,</span> <span class="n">points_3</span><span class="p">),</span> <span class="p">(</span><span class="n">points_0</span><span class="p">,</span> <span class="n">points_1</span><span class="p">))</span>
-            <span class="n">dest_pt_dist</span> <span class="o">=</span> <span class="n">get_points_dist</span><span class="p">((</span><span class="n">points_4</span><span class="p">,</span> <span class="n">points_5</span><span class="p">),</span> <span class="p">(</span><span class="n">points_0</span><span class="p">,</span> <span class="n">points_1</span><span class="p">))</span>
-
-            <span class="k">if</span> <span class="n">src_pt_dist</span> <span class="o">&lt;</span> <span class="n">dest_pt_dist</span><span class="p">:</span>
-                <span class="n">dist</span><span class="p">,</span> <span class="n">ratio</span> <span class="o">=</span> <span class="n">src_pt_dist</span><span class="p">,</span> <span class="mi">0</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">dist</span><span class="p">,</span> <span class="n">ratio</span> <span class="o">=</span> <span class="n">dest_pt_dist</span><span class="p">,</span> <span class="mi">1</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">dist</span> <span class="o">=</span> <span class="n">get_points_dist</span><span class="p">((</span><span class="n">inter_x</span><span class="p">,</span> <span class="n">inter_y</span><span class="p">),</span> <span class="p">(</span><span class="n">points_0</span><span class="p">,</span> <span class="n">points_1</span><span class="p">))</span>
-            <span class="n">ratio</span> <span class="o">=</span> <span class="n">src_proj_dist</span> <span class="o">/</span> <span class="n">segment_length</span>
-
-    <span class="k">return</span> <span class="n">dist</span><span class="p">,</span> <span class="n">ratio</span></div>
-
-
-<div class="viewcode-block" id="get_point_at_angle_and_dist"><a class="viewcode-back" href="../../../generated/libpysal.cg.get_point_at_angle_and_dist.html#libpysal.cg.get_point_at_angle_and_dist">[docs]</a><span class="k">def</span> <span class="nf">get_point_at_angle_and_dist</span><span class="p">(</span><span class="n">ray</span><span class="p">,</span> <span class="n">angle</span><span class="p">,</span> <span class="n">dist</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Returns the point at a distance and angle relative to the origin of a ray.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    ray : libpysal.cg.Ray</span>
-<span class="sd">        The ray to which ``angle`` and ``dist`` are relative.</span>
-<span class="sd">    angle : float</span>
-<span class="sd">        The angle relative to ``ray`` at which ``point`` is located.</span>
-<span class="sd">    dist : float</span>
-<span class="sd">        The distance from the origin of ``ray`` at which ``point`` is located.</span>
-<span class="sd">    </span>
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    point : libpysal.cg.Point</span>
-<span class="sd">        The point at ``dist`` and ``angle`` relative to the origin of ``ray``.</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; ray = Ray(Point((0, 0)), Point((1, 0)))</span>
-<span class="sd">    &gt;&gt;&gt; pt = get_point_at_angle_and_dist(ray, math.pi, 1.0)</span>
-<span class="sd">    &gt;&gt;&gt; isinstance(pt, Point)</span>
-<span class="sd">    True</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; round(pt[0], 8)</span>
-<span class="sd">    -1.0</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; round(pt[1], 8)</span>
-<span class="sd">    0.0</span>
-<span class="sd">    </span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">v</span> <span class="o">=</span> <span class="p">(</span><span class="n">ray</span><span class="o">.</span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">ray</span><span class="o">.</span><span class="n">o</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">ray</span><span class="o">.</span><span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">ray</span><span class="o">.</span><span class="n">o</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-    <span class="n">cur_angle</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">atan2</span><span class="p">(</span><span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="n">dest_angle</span> <span class="o">=</span> <span class="n">cur_angle</span> <span class="o">+</span> <span class="n">angle</span>
-
-    <span class="n">point</span> <span class="o">=</span> <span class="n">Point</span><span class="p">(</span>
-        <span class="p">(</span><span class="n">ray</span><span class="o">.</span><span class="n">o</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">dist</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">dest_angle</span><span class="p">),</span> <span class="n">ray</span><span class="o">.</span><span class="n">o</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">dist</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">dest_angle</span><span class="p">))</span>
-    <span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">point</span></div>
-
-
-<div class="viewcode-block" id="convex_hull"><a class="viewcode-back" href="../../../generated/libpysal.cg.convex_hull.html#libpysal.cg.convex_hull">[docs]</a><span class="k">def</span> <span class="nf">convex_hull</span><span class="p">(</span><span class="n">points</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Returns the convex hull of a set of points.</span>
-<span class="sd">    </span>
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    points : list</span>
-<span class="sd">        A list of points for computing the convex hull.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    stack : list</span>
-<span class="sd">        A list of points representing the convex hull.</span>
-<span class="sd">    </span>
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; points = [Point((0, 0)), Point((4, 4)), Point((4, 0)), Point((3, 1))]</span>
-<span class="sd">    &gt;&gt;&gt; convex_hull(points)</span>
-<span class="sd">    [(0.0, 0.0), (4.0, 0.0), (4.0, 4.0)]</span>
-<span class="sd">    </span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="nf">right_turn</span><span class="p">(</span><span class="n">p1</span><span class="p">,</span> <span class="n">p2</span><span class="p">,</span> <span class="n">p3</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;Returns if ``p1`` -&gt; ``p2`` -&gt; ``p3`` forms a &#39;right turn&#39;.&quot;&quot;&quot;</span>
-        <span class="n">vec1</span> <span class="o">=</span> <span class="p">(</span><span class="n">p2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">p1</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">p2</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">p1</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-        <span class="n">vec2</span> <span class="o">=</span> <span class="p">(</span><span class="n">p3</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">p2</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">p3</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">p2</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-        <span class="n">_rt</span> <span class="o">=</span> <span class="n">vec2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">vec1</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">vec2</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">vec1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="mi">0</span>
-        <span class="k">return</span> <span class="n">_rt</span>
-
-    <span class="n">points</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">points</span><span class="p">)</span>
-    <span class="n">lowest</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">points</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">p</span><span class="p">:</span> <span class="p">(</span><span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
-
-    <span class="n">points</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">lowest</span><span class="p">)</span>
-    <span class="n">points</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">p</span><span class="p">:</span> <span class="n">math</span><span class="o">.</span><span class="n">atan2</span><span class="p">(</span><span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">lowest</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">lowest</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
-
-    <span class="n">stack</span> <span class="o">=</span> <span class="p">[</span><span class="n">lowest</span><span class="p">]</span>
-
-    <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">points</span><span class="p">:</span>
-        <span class="n">stack</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
-        <span class="k">while</span> <span class="nb">len</span><span class="p">(</span><span class="n">stack</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">3</span> <span class="ow">and</span> <span class="n">right_turn</span><span class="p">(</span><span class="n">stack</span><span class="p">[</span><span class="o">-</span><span class="mi">3</span><span class="p">],</span> <span class="n">stack</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">],</span> <span class="n">stack</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]):</span>
-            <span class="n">stack</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">stack</span></div>
-
-
-<div class="viewcode-block" id="is_clockwise"><a class="viewcode-back" href="../../../generated/libpysal.cg.is_clockwise.html#libpysal.cg.is_clockwise">[docs]</a><span class="k">def</span> <span class="nf">is_clockwise</span><span class="p">(</span><span class="n">vertices</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Returns whether a list of points describing</span>
-<span class="sd">    a polygon are clockwise or counterclockwise.</span>
-<span class="sd">    </span>
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    vertices : list</span>
-<span class="sd">        A list of points that form a single ring.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    clockwise : bool</span>
-<span class="sd">        ``True`` if ``vertices`` are clockwise, otherwise ``False``.</span>
-<span class="sd">    </span>
-<span class="sd">    See Also</span>
-<span class="sd">    --------</span>
-<span class="sd">    </span>
-<span class="sd">    libpysal.cg.ccw</span>
-<span class="sd">    </span>
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; is_clockwise([Point((0, 0)), Point((10, 0)), Point((0, 10))])</span>
-<span class="sd">    False</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; is_clockwise([Point((0, 0)), Point((0, 10)), Point((10, 0))])</span>
-<span class="sd">    True</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; v = [</span>
-<span class="sd">    ...     (-106.57798, 35.174143999999998),</span>
-<span class="sd">    ...     (-106.583412, 35.174141999999996),</span>
-<span class="sd">    ...     (-106.58417999999999, 35.174143000000001),</span>
-<span class="sd">    ...     (-106.58377999999999, 35.175542999999998),</span>
-<span class="sd">    ...     (-106.58287999999999, 35.180543),</span>
-<span class="sd">    ...     (-106.58263099999999, 35.181455),</span>
-<span class="sd">    ...     (-106.58257999999999, 35.181643000000001),</span>
-<span class="sd">    ...     (-106.58198299999999, 35.184615000000001),</span>
-<span class="sd">    ...     (-106.58148, 35.187242999999995),</span>
-<span class="sd">    ...     (-106.58127999999999, 35.188243),</span>
-<span class="sd">    ...     (-106.58138, 35.188243),</span>
-<span class="sd">    ...     (-106.58108, 35.189442999999997),</span>
-<span class="sd">    ...     (-106.58104, 35.189644000000001),</span>
-<span class="sd">    ...     (-106.58028, 35.193442999999995),</span>
-<span class="sd">    ...     (-106.580029, 35.194541000000001),</span>
-<span class="sd">    ...     (-106.57974399999999, 35.195785999999998),</span>
-<span class="sd">    ...     (-106.579475, 35.196961999999999),</span>
-<span class="sd">    ...     (-106.57922699999999, 35.198042999999998),</span>
-<span class="sd">    ...     (-106.578397, 35.201665999999996),</span>
-<span class="sd">    ...     (-106.57827999999999, 35.201642999999997),</span>
-<span class="sd">    ...     (-106.57737999999999, 35.201642999999997),</span>
-<span class="sd">    ...     (-106.57697999999999, 35.201543000000001),</span>
-<span class="sd">    ...     (-106.56436599999999, 35.200311999999997),</span>
-<span class="sd">    ...     (-106.56058, 35.199942999999998),</span>
-<span class="sd">    ...     (-106.56048, 35.197342999999996),</span>
-<span class="sd">    ...     (-106.56048, 35.195842999999996),</span>
-<span class="sd">    ...     (-106.56048, 35.194342999999996),</span>
-<span class="sd">    ...     (-106.56048, 35.193142999999999),</span>
-<span class="sd">    ...     (-106.56048, 35.191873999999999),</span>
-<span class="sd">    ...     (-106.56048, 35.191742999999995),</span>
-<span class="sd">    ...     (-106.56048, 35.190242999999995),</span>
-<span class="sd">    ...     (-106.56037999999999, 35.188642999999999),</span>
-<span class="sd">    ...     (-106.56037999999999, 35.187242999999995),</span>
-<span class="sd">    ...     (-106.56037999999999, 35.186842999999996),</span>
-<span class="sd">    ...     (-106.56037999999999, 35.186552999999996),</span>
-<span class="sd">    ...     (-106.56037999999999, 35.185842999999998),</span>
-<span class="sd">    ...     (-106.56037999999999, 35.184443000000002),</span>
-<span class="sd">    ...     (-106.56037999999999, 35.182943000000002),</span>
-<span class="sd">    ...     (-106.56037999999999, 35.181342999999998),</span>
-<span class="sd">    ...     (-106.56037999999999, 35.180433000000001),</span>
-<span class="sd">    ...     (-106.56037999999999, 35.179943000000002),</span>
-<span class="sd">    ...     (-106.56037999999999, 35.178542999999998),</span>
-<span class="sd">    ...     (-106.56037999999999, 35.177790999999999),</span>
-<span class="sd">    ...     (-106.56037999999999, 35.177143999999998),</span>
-<span class="sd">    ...     (-106.56037999999999, 35.175643999999998),</span>
-<span class="sd">    ...     (-106.56037999999999, 35.174444000000001),</span>
-<span class="sd">    ...     (-106.56037999999999, 35.174043999999995),</span>
-<span class="sd">    ...     (-106.560526, 35.174043999999995),</span>
-<span class="sd">    ...     (-106.56478, 35.174043999999995),</span>
-<span class="sd">    ...     (-106.56627999999999, 35.174143999999998),</span>
-<span class="sd">    ...     (-106.566541, 35.174144999999996),</span>
-<span class="sd">    ...     (-106.569023, 35.174157000000001),</span>
-<span class="sd">    ...     (-106.56917199999999, 35.174157999999998),</span>
-<span class="sd">    ...     (-106.56938, 35.174143999999998),</span>
-<span class="sd">    ...     (-106.57061499999999, 35.174143999999998),</span>
-<span class="sd">    ...     (-106.57097999999999, 35.174143999999998),</span>
-<span class="sd">    ...     (-106.57679999999999, 35.174143999999998),</span>
-<span class="sd">    ...     (-106.57798, 35.174143999999998)</span>
-<span class="sd">    ... ]</span>
-<span class="sd">    &gt;&gt;&gt; is_clockwise(v)</span>
-<span class="sd">    True</span>
-<span class="sd">    </span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">clockwise</span> <span class="o">=</span> <span class="kc">True</span>
-
-    <span class="k">if</span> <span class="ow">not</span> <span class="nb">len</span><span class="p">(</span><span class="n">vertices</span><span class="p">)</span> <span class="o">&lt;</span> <span class="mi">3</span><span class="p">:</span>
-        <span class="n">area</span> <span class="o">=</span> <span class="mf">0.0</span>
-        <span class="n">ax</span><span class="p">,</span> <span class="n">ay</span> <span class="o">=</span> <span class="n">vertices</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="k">for</span> <span class="n">bx</span><span class="p">,</span> <span class="n">by</span> <span class="ow">in</span> <span class="n">vertices</span><span class="p">[</span><span class="mi">1</span><span class="p">:]:</span>
-            <span class="n">area</span> <span class="o">+=</span> <span class="n">ax</span> <span class="o">*</span> <span class="n">by</span> <span class="o">-</span> <span class="n">ay</span> <span class="o">*</span> <span class="n">bx</span>
-            <span class="n">ax</span><span class="p">,</span> <span class="n">ay</span> <span class="o">=</span> <span class="n">bx</span><span class="p">,</span> <span class="n">by</span>
-        <span class="n">bx</span><span class="p">,</span> <span class="n">by</span> <span class="o">=</span> <span class="n">vertices</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">area</span> <span class="o">+=</span> <span class="n">ax</span> <span class="o">*</span> <span class="n">by</span> <span class="o">-</span> <span class="n">ay</span> <span class="o">*</span> <span class="n">bx</span>
-
-        <span class="n">clockwise</span> <span class="o">=</span> <span class="n">area</span> <span class="o">&lt;</span> <span class="mf">0.0</span>
-
-    <span class="k">return</span> <span class="n">clockwise</span></div>
-
-
-<span class="k">def</span> <span class="nf">ccw</span><span class="p">(</span><span class="n">vertices</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Returns whether a list of points is counterclockwise.</span>
-<span class="sd">    </span>
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    vertices : list</span>
-<span class="sd">        A list of points that form a single ring.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    counter_clockwise : bool</span>
-<span class="sd">        ``True`` if ``vertices`` are counter clockwise, otherwise ``False``.</span>
-<span class="sd">    </span>
-<span class="sd">    See Also</span>
-<span class="sd">    --------</span>
-<span class="sd">    </span>
-<span class="sd">    libpysal.cg.is_clockwise</span>
-<span class="sd">    </span>
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; ccw([Point((0, 0)), Point((10, 0)), Point((0, 10))])</span>
-<span class="sd">    True</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; ccw([Point((0, 0)), Point((0, 10)), Point((10, 0))])</span>
-<span class="sd">    False</span>
-<span class="sd">    </span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">counter_clockwise</span> <span class="o">=</span> <span class="kc">True</span>
-
-    <span class="k">if</span> <span class="n">is_clockwise</span><span class="p">(</span><span class="n">vertices</span><span class="p">):</span>
-        <span class="n">counter_clockwise</span> <span class="o">=</span> <span class="kc">False</span>
-
-    <span class="k">return</span> <span class="n">counter_clockwise</span>
-
-
-<span class="k">def</span> <span class="nf">seg_intersect</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">d</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Tests if two segments (a,b) and (c,d) intersect.</span>
-<span class="sd">    </span>
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    a : libpysal.cg.Point</span>
-<span class="sd">        The first vertex for the first segment.</span>
-<span class="sd">    b : libpysal.cg.Point</span>
-<span class="sd">        The second vertex for the first segment.</span>
-<span class="sd">    c : libpysal.cg.Point</span>
-<span class="sd">        The first vertex for the second segment.</span>
-<span class="sd">    d : libpysal.cg.Point</span>
-<span class="sd">        The second vertex for the second segment.</span>
-<span class="sd">    </span>
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    segments_intersect : bool</span>
-<span class="sd">        ``True`` if segments ``(a,b)`` and ``(c,d)``, otherwise ``False``.</span>
-<span class="sd">    </span>
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; a = Point((0,1))</span>
-<span class="sd">    &gt;&gt;&gt; b = Point((0,10))</span>
-<span class="sd">    &gt;&gt;&gt; c = Point((-2,5))</span>
-<span class="sd">    &gt;&gt;&gt; d = Point((2,5))</span>
-<span class="sd">    &gt;&gt;&gt; e = Point((-3,5))</span>
-<span class="sd">    &gt;&gt;&gt; seg_intersect(a, b, c, d)</span>
-<span class="sd">    True</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; seg_intersect(a, b, c, e)</span>
-<span class="sd">    False</span>
-<span class="sd">    </span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">segments_intersect</span> <span class="o">=</span> <span class="kc">True</span>
-
-    <span class="n">acd_bcd</span> <span class="o">=</span> <span class="n">ccw</span><span class="p">([</span><span class="n">a</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">d</span><span class="p">])</span> <span class="o">==</span> <span class="n">ccw</span><span class="p">([</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">d</span><span class="p">])</span>
-
-    <span class="n">abc_abd</span> <span class="o">=</span> <span class="n">ccw</span><span class="p">([</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">])</span> <span class="o">==</span> <span class="n">ccw</span><span class="p">([</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">d</span><span class="p">])</span>
-
-    <span class="k">if</span> <span class="n">acd_bcd</span> <span class="ow">or</span> <span class="n">abc_abd</span><span class="p">:</span>
-        <span class="n">segments_intersect</span> <span class="o">=</span> <span class="kc">False</span>
-
-    <span class="k">return</span> <span class="n">segments_intersect</span>
-
-
-<span class="k">def</span> <span class="nf">_point_in_vertices</span><span class="p">(</span><span class="n">pt</span><span class="p">,</span> <span class="n">vertices</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;**HELPER METHOD. DO NOT CALL.** Returns whether a point</span>
-<span class="sd">    is contained in a polygon specified by a sequence of vertices.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    pt : libpysal.cg.Point</span>
-<span class="sd">        A point.</span>
-<span class="sd">    vertices : list</span>
-<span class="sd">        A list of vertices representing as polygon.</span>
-<span class="sd">    </span>
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    pt_in_poly : bool</span>
-<span class="sd">        ``True`` if ``pt`` is contained in ``vertices``, otherwise ``False``.</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; _point_in_vertices(</span>
-<span class="sd">    ...     Point((1, 1)),</span>
-<span class="sd">    ...     [Point((0, 0)), Point((10, 0)), Point((0, 10))]</span>
-<span class="sd">    ... )</span>
-<span class="sd">    True</span>
-<span class="sd">    </span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="nf">neg_ray_intersect</span><span class="p">(</span><span class="n">p1</span><span class="p">,</span> <span class="n">p2</span><span class="p">,</span> <span class="n">p3</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;Returns whether a ray in the negative-x</span>
-<span class="sd">        direction from ``p3`` intersects the segment between.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="ow">not</span> <span class="nb">min</span><span class="p">(</span><span class="n">p1</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">p2</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="o">&lt;=</span> <span class="n">p3</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="nb">max</span><span class="p">(</span><span class="n">p1</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">p2</span><span class="p">[</span><span class="mi">1</span><span class="p">]):</span>
-            <span class="n">nr_inters</span> <span class="o">=</span> <span class="kc">False</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">p1</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">p2</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
-                <span class="n">vec1</span> <span class="o">=</span> <span class="p">(</span><span class="n">p2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">p1</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">p2</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">p1</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">vec1</span> <span class="o">=</span> <span class="p">(</span><span class="n">p1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">p2</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">p1</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">p2</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-
-            <span class="n">vec2</span> <span class="o">=</span> <span class="p">(</span><span class="n">p3</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">p1</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">p3</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">p1</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-
-            <span class="n">nr_inters</span> <span class="o">=</span> <span class="n">vec1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">vec2</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">vec2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">vec1</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="mi">0</span>
-
-        <span class="k">return</span> <span class="n">nr_inters</span>
-
-    <span class="n">vert_y_set</span> <span class="o">=</span> <span class="nb">set</span><span class="p">([</span><span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">vertices</span><span class="p">])</span>
-    <span class="k">while</span> <span class="n">pt</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="ow">in</span> <span class="n">vert_y_set</span><span class="p">:</span>
-        <span class="c1"># Perturb the location very slightly</span>
-        <span class="n">pt</span> <span class="o">=</span> <span class="n">pt</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">pt</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="o">-</span><span class="mf">1e-14</span> <span class="o">+</span> <span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">()</span> <span class="o">*</span> <span class="mf">2e-14</span>
-
-    <span class="n">inters</span> <span class="o">=</span> <span class="mi">0</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">vertices</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span>
-        <span class="n">v1</span> <span class="o">=</span> <span class="n">vertices</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-        <span class="n">v2</span> <span class="o">=</span> <span class="n">vertices</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span>
-        <span class="k">if</span> <span class="n">neg_ray_intersect</span><span class="p">(</span><span class="n">v1</span><span class="p">,</span> <span class="n">v2</span><span class="p">,</span> <span class="n">pt</span><span class="p">):</span>
-            <span class="n">inters</span> <span class="o">+=</span> <span class="mi">1</span>
-
-    <span class="n">pt_in_poly</span> <span class="o">=</span> <span class="n">inters</span> <span class="o">%</span> <span class="mi">2</span> <span class="o">==</span> <span class="mi">1</span>
-
-    <span class="k">return</span> <span class="n">pt_in_poly</span>
-
-
-<div class="viewcode-block" id="point_touches_rectangle"><a class="viewcode-back" href="../../../generated/libpysal.cg.point_touches_rectangle.html#libpysal.cg.point_touches_rectangle">[docs]</a><span class="k">def</span> <span class="nf">point_touches_rectangle</span><span class="p">(</span><span class="n">point</span><span class="p">,</span> <span class="n">rect</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Returns ``True`` (``1``) if the point is in the rectangle</span>
-<span class="sd">    or touches it&#39;s boundary, otherwise ``False`` (``0``).</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    point : {libpysal.cg.Point, tuple}</span>
-<span class="sd">        A point or point coordinates.</span>
-<span class="sd">    rect : libpysal.cg.Rectangle</span>
-<span class="sd">        A rectangle.</span>
-<span class="sd">    </span>
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    chflag : int</span>
-<span class="sd">        ``1`` if ``point`` is in (or touches</span>
-<span class="sd">        boundary of) ``rect``, otherwise ``0``.</span>
-<span class="sd">    </span>
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; rect = Rectangle(0, 0, 10, 10)</span>
-<span class="sd">    &gt;&gt;&gt; a = Point((5, 5))</span>
-<span class="sd">    &gt;&gt;&gt; b = Point((10, 5))</span>
-<span class="sd">    &gt;&gt;&gt; c = Point((11, 11))</span>
-<span class="sd">    &gt;&gt;&gt; point_touches_rectangle(a, rect)</span>
-<span class="sd">    1</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; point_touches_rectangle(b, rect)</span>
-<span class="sd">    1</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; point_touches_rectangle(c, rect)</span>
-<span class="sd">    0</span>
-<span class="sd">    </span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">chflag</span> <span class="o">=</span> <span class="mi">0</span>
-    <span class="k">if</span> <span class="n">point</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">rect</span><span class="o">.</span><span class="n">left</span> <span class="ow">and</span> <span class="n">point</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">rect</span><span class="o">.</span><span class="n">right</span><span class="p">:</span>
-        <span class="k">if</span> <span class="n">point</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">rect</span><span class="o">.</span><span class="n">lower</span> <span class="ow">and</span> <span class="n">point</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">rect</span><span class="o">.</span><span class="n">upper</span><span class="p">:</span>
-            <span class="n">chflag</span> <span class="o">=</span> <span class="mi">1</span>
-
-    <span class="k">return</span> <span class="n">chflag</span></div>
-
-
-<div class="viewcode-block" id="get_shared_segments"><a class="viewcode-back" href="../../../generated/libpysal.cg.get_shared_segments.html#libpysal.cg.get_shared_segments">[docs]</a><span class="k">def</span> <span class="nf">get_shared_segments</span><span class="p">(</span><span class="n">poly1</span><span class="p">,</span> <span class="n">poly2</span><span class="p">,</span> <span class="n">bool_ret</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Returns the line segments in common to both polygons.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    poly1 : libpysal.cg.Polygon</span>
-<span class="sd">        A Polygon.</span>
-<span class="sd">    poly2 : libpysal.cg.Polygon</span>
-<span class="sd">        A Polygon.</span>
-<span class="sd">    bool_ret : bool</span>
-<span class="sd">        Return only a ``bool``. Default is ``False``.</span>
-<span class="sd">    </span>
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    common : list</span>
-<span class="sd">        The shared line segments between ``poly1`` and ``poly2``.</span>
-<span class="sd">    _ret_bool : bool</span>
-<span class="sd">        Whether ``poly1`` and ``poly2`` share a</span>
-<span class="sd">        segment (``True``) or not (``False``).</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; from libpysal.cg.shapes import Polygon</span>
-<span class="sd">    &gt;&gt;&gt; x = [0, 0, 1, 1]</span>
-<span class="sd">    &gt;&gt;&gt; y = [0, 1, 1, 0]</span>
-<span class="sd">    &gt;&gt;&gt; poly1 = Polygon(list(map(Point, zip(x, y))) )</span>
-<span class="sd">    &gt;&gt;&gt; x = [a+1 for a in x]</span>
-<span class="sd">    &gt;&gt;&gt; poly2 = Polygon(list(map(Point, zip(x, y))) )</span>
-<span class="sd">    &gt;&gt;&gt; get_shared_segments(poly1, poly2, bool_ret=True)</span>
-<span class="sd">    True</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="c1"># get_rectangle_rectangle_intersection inlined for speed.</span>
-    <span class="n">r0</span> <span class="o">=</span> <span class="n">poly1</span><span class="o">.</span><span class="n">bounding_box</span>
-    <span class="n">r1</span> <span class="o">=</span> <span class="n">poly2</span><span class="o">.</span><span class="n">bounding_box</span>
-    <span class="n">wLeft</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">r0</span><span class="o">.</span><span class="n">left</span><span class="p">,</span> <span class="n">r1</span><span class="o">.</span><span class="n">left</span><span class="p">)</span>
-    <span class="n">wLower</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">r0</span><span class="o">.</span><span class="n">lower</span><span class="p">,</span> <span class="n">r1</span><span class="o">.</span><span class="n">lower</span><span class="p">)</span>
-    <span class="n">wRight</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">r0</span><span class="o">.</span><span class="n">right</span><span class="p">,</span> <span class="n">r1</span><span class="o">.</span><span class="n">right</span><span class="p">)</span>
-    <span class="n">wUpper</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">r0</span><span class="o">.</span><span class="n">upper</span><span class="p">,</span> <span class="n">r1</span><span class="o">.</span><span class="n">upper</span><span class="p">)</span>
-
-    <span class="n">segmentsA</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
-    <span class="n">common</span> <span class="o">=</span> <span class="nb">list</span><span class="p">()</span>
-    <span class="n">partsA</span> <span class="o">=</span> <span class="n">poly1</span><span class="o">.</span><span class="n">parts</span>
-
-    <span class="k">for</span> <span class="n">part</span> <span class="ow">in</span> <span class="n">poly1</span><span class="o">.</span><span class="n">parts</span> <span class="o">+</span> <span class="p">[</span><span class="n">p</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">poly1</span><span class="o">.</span><span class="n">holes</span> <span class="k">if</span> <span class="n">p</span><span class="p">]:</span>
-        <span class="k">if</span> <span class="n">part</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="n">part</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]:</span>  <span class="c1"># not closed</span>
-            <span class="n">part</span> <span class="o">=</span> <span class="n">part</span><span class="p">[:]</span> <span class="o">+</span> <span class="n">part</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">1</span><span class="p">]</span>
-        <span class="n">a</span> <span class="o">=</span> <span class="n">part</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-
-        <span class="k">for</span> <span class="n">b</span> <span class="ow">in</span> <span class="n">islice</span><span class="p">(</span><span class="n">part</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="kc">None</span><span class="p">):</span>
-            <span class="c1"># inlining point_touches_rectangle for speed</span>
-            <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">a</span>
-            <span class="c1"># check if point a is in the bounding box intersection</span>
-            <span class="k">if</span> <span class="n">x</span> <span class="o">&gt;=</span> <span class="n">wLeft</span> <span class="ow">and</span> <span class="n">x</span> <span class="o">&lt;=</span> <span class="n">wRight</span> <span class="ow">and</span> <span class="n">y</span> <span class="o">&gt;=</span> <span class="n">wLower</span> <span class="ow">and</span> <span class="n">y</span> <span class="o">&lt;=</span> <span class="n">wUpper</span><span class="p">:</span>
-                <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">b</span>
-                <span class="c1"># check if point b is in the bounding box intersection</span>
-                <span class="k">if</span> <span class="n">x</span> <span class="o">&gt;=</span> <span class="n">wLeft</span> <span class="ow">and</span> <span class="n">x</span> <span class="o">&lt;=</span> <span class="n">wRight</span> <span class="ow">and</span> <span class="n">y</span> <span class="o">&gt;=</span> <span class="n">wLower</span> <span class="ow">and</span> <span class="n">y</span> <span class="o">&lt;=</span> <span class="n">wUpper</span><span class="p">:</span>
-                    <span class="k">if</span> <span class="n">a</span> <span class="o">&gt;</span> <span class="n">b</span><span class="p">:</span>
-                        <span class="n">segmentsA</span><span class="o">.</span><span class="n">add</span><span class="p">((</span><span class="n">b</span><span class="p">,</span> <span class="n">a</span><span class="p">))</span>
-                    <span class="k">else</span><span class="p">:</span>
-                        <span class="n">segmentsA</span><span class="o">.</span><span class="n">add</span><span class="p">((</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">))</span>
-            <span class="n">a</span> <span class="o">=</span> <span class="n">b</span>
-
-    <span class="n">_ret_bool</span> <span class="o">=</span> <span class="kc">False</span>
-
-    <span class="k">for</span> <span class="n">part</span> <span class="ow">in</span> <span class="n">poly2</span><span class="o">.</span><span class="n">parts</span> <span class="o">+</span> <span class="p">[</span><span class="n">p</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">poly2</span><span class="o">.</span><span class="n">holes</span> <span class="k">if</span> <span class="n">p</span><span class="p">]:</span>
-        <span class="k">if</span> <span class="n">part</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="n">part</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]:</span>  <span class="c1"># not closed</span>
-            <span class="n">part</span> <span class="o">=</span> <span class="n">part</span><span class="p">[:]</span> <span class="o">+</span> <span class="n">part</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">1</span><span class="p">]</span>
-        <span class="n">a</span> <span class="o">=</span> <span class="n">part</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-
-        <span class="k">for</span> <span class="n">b</span> <span class="ow">in</span> <span class="n">islice</span><span class="p">(</span><span class="n">part</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="kc">None</span><span class="p">):</span>
-            <span class="c1"># inlining point_touches_rectangle for speed</span>
-            <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">a</span>
-            <span class="k">if</span> <span class="n">x</span> <span class="o">&gt;=</span> <span class="n">wLeft</span> <span class="ow">and</span> <span class="n">x</span> <span class="o">&lt;=</span> <span class="n">wRight</span> <span class="ow">and</span> <span class="n">y</span> <span class="o">&gt;=</span> <span class="n">wLower</span> <span class="ow">and</span> <span class="n">y</span> <span class="o">&lt;=</span> <span class="n">wUpper</span><span class="p">:</span>
-                <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">b</span>
-                <span class="k">if</span> <span class="n">x</span> <span class="o">&gt;=</span> <span class="n">wLeft</span> <span class="ow">and</span> <span class="n">x</span> <span class="o">&lt;=</span> <span class="n">wRight</span> <span class="ow">and</span> <span class="n">y</span> <span class="o">&gt;=</span> <span class="n">wLower</span> <span class="ow">and</span> <span class="n">y</span> <span class="o">&lt;=</span> <span class="n">wUpper</span><span class="p">:</span>
-                    <span class="k">if</span> <span class="n">a</span> <span class="o">&gt;</span> <span class="n">b</span><span class="p">:</span>
-                        <span class="n">seg</span> <span class="o">=</span> <span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">a</span><span class="p">)</span>
-                    <span class="k">else</span><span class="p">:</span>
-                        <span class="n">seg</span> <span class="o">=</span> <span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span>
-                    <span class="k">if</span> <span class="n">seg</span> <span class="ow">in</span> <span class="n">segmentsA</span><span class="p">:</span>
-                        <span class="n">common</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">LineSegment</span><span class="p">(</span><span class="o">*</span><span class="n">seg</span><span class="p">))</span>
-                        <span class="k">if</span> <span class="n">bool_ret</span><span class="p">:</span>
-                            <span class="n">_ret_bool</span> <span class="o">=</span> <span class="kc">True</span>
-                            <span class="k">return</span> <span class="n">_ret_bool</span>
-            <span class="n">a</span> <span class="o">=</span> <span class="n">b</span>
-
-    <span class="k">if</span> <span class="n">bool_ret</span><span class="p">:</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">common</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">_ret_bool</span> <span class="o">=</span> <span class="kc">True</span>
-        <span class="k">return</span> <span class="n">_ret_bool</span>
-
-    <span class="k">return</span> <span class="n">common</span></div>
-
-
-<div class="viewcode-block" id="distance_matrix"><a class="viewcode-back" href="../../../generated/libpysal.cg.distance_matrix.html#libpysal.cg.distance_matrix">[docs]</a><span class="k">def</span> <span class="nf">distance_matrix</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mf">2.0</span><span class="p">,</span> <span class="n">threshold</span><span class="o">=</span><span class="mf">5e7</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Calculate a distance matrix.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    X : numpy.ndarray</span>
-<span class="sd">        An :math:`n \\times k` array where :math:`n` is the number</span>
-<span class="sd">        of observations and :math:`k` is the number of dimensions</span>
-<span class="sd">        (2 for :math:`x,y`).</span>
-<span class="sd">    p : float</span>
-<span class="sd">        Minkowski `p`-norm distance metric parameter where</span>
-<span class="sd">        :math:`1&lt;=\mathtt{p}&lt;=\infty`. ``2`` is Euclidean distance and</span>
-<span class="sd">        ``1`` is Manhattan distance. Default is ``2.0``.</span>
-<span class="sd">    threshold : int</span>
-<span class="sd">        If :math:`(\mathtt{n}**2)*32 &gt; \mathtt{threshold}` use</span>
-<span class="sd">        ``scipy.spatial.distance_matrix`` instead of working in RAM,</span>
-<span class="sd">        this is roughly the amount of RAM (in bytes) that will be used.</span>
-<span class="sd">        Must be positive. Default is ``5e7``.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    D : numpy.ndarray</span>
-<span class="sd">        An n by :math:`m` :math:`p`-norm distance matrix.</span>
-<span class="sd">    </span>
-<span class="sd">    Raises</span>
-<span class="sd">    ------</span>
-<span class="sd">    TypeError</span>
-<span class="sd">        Raised when an invalid dimensional array is passed in.</span>
-<span class="sd">    </span>
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-<span class="sd">    </span>
-<span class="sd">    Needs optimization/integration with other weights in PySAL.</span>
-<span class="sd">    </span>
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; x, y = [r.flatten() for r in np.indices((3, 3))]</span>
-<span class="sd">    &gt;&gt;&gt; data = np.array([x, y]).T</span>
-<span class="sd">    &gt;&gt;&gt; d = distance_matrix(data)</span>
-<span class="sd">    &gt;&gt;&gt; np.array(d)</span>
-<span class="sd">    array([[0.        , 1.        , 2.        , 1.        , 1.41421356,</span>
-<span class="sd">            2.23606798, 2.        , 2.23606798, 2.82842712],</span>
-<span class="sd">           [1.        , 0.        , 1.        , 1.41421356, 1.        ,</span>
-<span class="sd">            1.41421356, 2.23606798, 2.        , 2.23606798],</span>
-<span class="sd">           [2.        , 1.        , 0.        , 2.23606798, 1.41421356,</span>
-<span class="sd">            1.        , 2.82842712, 2.23606798, 2.        ],</span>
-<span class="sd">           [1.        , 1.41421356, 2.23606798, 0.        , 1.        ,</span>
-<span class="sd">            2.        , 1.        , 1.41421356, 2.23606798],</span>
-<span class="sd">           [1.41421356, 1.        , 1.41421356, 1.        , 0.        ,</span>
-<span class="sd">            1.        , 1.41421356, 1.        , 1.41421356],</span>
-<span class="sd">           [2.23606798, 1.41421356, 1.        , 2.        , 1.        ,</span>
-<span class="sd">            0.        , 2.23606798, 1.41421356, 1.        ],</span>
-<span class="sd">           [2.        , 2.23606798, 2.82842712, 1.        , 1.41421356,</span>
-<span class="sd">            2.23606798, 0.        , 1.        , 2.        ],</span>
-<span class="sd">           [2.23606798, 2.        , 2.23606798, 1.41421356, 1.        ,</span>
-<span class="sd">            1.41421356, 1.        , 0.        , 1.        ],</span>
-<span class="sd">           [2.82842712, 2.23606798, 2.        , 2.23606798, 1.41421356,</span>
-<span class="sd">            1.        , 2.        , 1.        , 0.        ]])</span>
-<span class="sd">    </span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">if</span> <span class="n">X</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-        <span class="n">X</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">X</span><span class="o">.</span><span class="n">ndim</span> <span class="o">&gt;</span> <span class="mi">2</span><span class="p">:</span>
-        <span class="n">msg</span> <span class="o">=</span> <span class="s2">&quot;Should be 2D point coordinates: </span><span class="si">%s</span><span class="s2"> dimensions present.&quot;</span> <span class="o">%</span> <span class="n">X</span><span class="o">.</span><span class="n">ndim</span>
-        <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="n">msg</span><span class="p">)</span>
-
-    <span class="n">n</span><span class="p">,</span> <span class="n">k</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span>
-
-    <span class="k">if</span> <span class="p">(</span><span class="n">n</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span> <span class="o">*</span> <span class="mi">32</span> <span class="o">&gt;</span> <span class="n">threshold</span><span class="p">:</span>
-        <span class="n">D</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">spatial</span><span class="o">.</span><span class="n">distance_matrix</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">p</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">M</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">n</span><span class="p">,</span> <span class="n">n</span><span class="p">))</span>
-        <span class="n">D</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">n</span><span class="p">,</span> <span class="n">n</span><span class="p">))</span>
-        <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">k</span><span class="p">):</span>
-            <span class="n">x</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="n">col</span><span class="p">]</span>
-            <span class="n">xM</span> <span class="o">=</span> <span class="n">x</span> <span class="o">*</span> <span class="n">M</span>
-            <span class="n">dx</span> <span class="o">=</span> <span class="n">xM</span> <span class="o">-</span> <span class="n">xM</span><span class="o">.</span><span class="n">T</span>
-            <span class="k">if</span> <span class="n">p</span> <span class="o">%</span> <span class="mi">2</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="n">dx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">dx</span><span class="p">)</span>
-            <span class="n">dx2</span> <span class="o">=</span> <span class="n">dx</span> <span class="o">**</span> <span class="n">p</span>
-            <span class="n">D</span> <span class="o">+=</span> <span class="n">dx2</span>
-        <span class="n">D</span> <span class="o">=</span> <span class="n">D</span> <span class="o">**</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">/</span> <span class="n">p</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">D</span></div>
-</pre></div>
-
-    </div>
-      
-  </div>
-</div>
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-  <div class="container">
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-      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 3.1.2.<br/>
-    </p>
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deleted file mode 100644
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-      
-  <h1>Source code for libpysal.cg.voronoi</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">Voronoi tesslation of 2-d point sets.</span>
-
-<span class="sd">Adapted from https://gist.github.com/pv/8036995</span>
-
-<span class="sd">&quot;&quot;&quot;</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">from</span> <span class="nn">scipy.spatial</span> <span class="kn">import</span> <span class="n">Voronoi</span>
-
-<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Serge Rey &lt;sjsrey@gmail.com&gt;&quot;</span>
-
-<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;voronoi_frames&quot;</span><span class="p">]</span>
-
-
-<span class="k">def</span> <span class="nf">voronoi</span><span class="p">(</span><span class="n">points</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Determine finite Voronoi diagram for a 2-d point set.</span>
-<span class="sd">    See also ``voronoi_regions()``.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    points : array_like</span>
-<span class="sd">        An nx2 array of points.</span>
-<span class="sd">    radius : float (optional)</span>
-<span class="sd">        The distance to &#39;points at infinity&#39;. Default is ``None.``</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    vor : tuple</span>
-<span class="sd">        A two-element tuple consisting of a list and an array. Each element of</span>
-<span class="sd">        the list contains the sequence of the indices of Voronoi vertices</span>
-<span class="sd">        composing a Voronoi polygon (region), whereas the array contains</span>
-<span class="sd">        the Voronoi vertex coordinates.</span>
-<span class="sd">    </span>
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; points = [(10.2, 5.1), (4.7, 2.2), (5.3, 5.7), (2.7, 5.3)]</span>
-<span class="sd">    &gt;&gt;&gt; regions, coordinates = voronoi(points)</span>
-<span class="sd">    &gt;&gt;&gt; regions</span>
-<span class="sd">    [[1, 3, 2], [4, 5, 1, 0], [0, 1, 7, 6], [9, 0, 8]]</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; coordinates</span>
-<span class="sd">    array([[  4.21783296,   4.08408578],</span>
-<span class="sd">           [  7.51956025,   3.51807539],</span>
-<span class="sd">           [  9.4642193 ,  19.3994576 ],</span>
-<span class="sd">           [ 14.98210684, -10.63503022],</span>
-<span class="sd">           [ -9.22691341,  -4.58994414],</span>
-<span class="sd">           [ 14.98210684, -10.63503022],</span>
-<span class="sd">           [  1.78491801,  19.89803294],</span>
-<span class="sd">           [  9.4642193 ,  19.3994576 ],</span>
-<span class="sd">           [  1.78491801,  19.89803294],</span>
-<span class="sd">           [ -9.22691341,  -4.58994414]])</span>
-<span class="sd">    </span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">vor</span> <span class="o">=</span> <span class="n">voronoi_regions</span><span class="p">(</span><span class="n">Voronoi</span><span class="p">(</span><span class="n">points</span><span class="p">),</span> <span class="n">radius</span><span class="o">=</span><span class="n">radius</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">vor</span>
-
-
-<span class="k">def</span> <span class="nf">voronoi_regions</span><span class="p">(</span><span class="n">vor</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Finite voronoi regions for a 2-d point set. See also ``voronoi()``.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    vor : scipy.spatial.Voronoi</span>
-<span class="sd">        A planar Voronoi diagram.</span>
-<span class="sd">    radius : float (optional)</span>
-<span class="sd">        Distance to &#39;points at infinity&#39;. Default is ``None.``</span>
-<span class="sd">    </span>
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    regions_vertices : tuple</span>
-<span class="sd">        A two-element tuple consisting of a list of finite voronoi regions</span>
-<span class="sd">        and an array Voronoi vertex coordinates.</span>
-<span class="sd">    </span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">new_regions</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="n">new_vertices</span> <span class="o">=</span> <span class="n">vor</span><span class="o">.</span><span class="n">vertices</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
-
-    <span class="n">center</span> <span class="o">=</span> <span class="n">vor</span><span class="o">.</span><span class="n">points</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">radius</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">radius</span> <span class="o">=</span> <span class="n">vor</span><span class="o">.</span><span class="n">points</span><span class="o">.</span><span class="n">ptp</span><span class="p">()</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">*</span> <span class="mi">2</span>
-
-    <span class="n">all_ridges</span> <span class="o">=</span> <span class="p">{}</span>
-    <span class="k">for</span> <span class="p">(</span><span class="n">p1</span><span class="p">,</span> <span class="n">p2</span><span class="p">),</span> <span class="p">(</span><span class="n">v1</span><span class="p">,</span> <span class="n">v2</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">vor</span><span class="o">.</span><span class="n">ridge_points</span><span class="p">,</span> <span class="n">vor</span><span class="o">.</span><span class="n">ridge_vertices</span><span class="p">):</span>
-        <span class="n">all_ridges</span><span class="o">.</span><span class="n">setdefault</span><span class="p">(</span><span class="n">p1</span><span class="p">,</span> <span class="p">[])</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">p2</span><span class="p">,</span> <span class="n">v1</span><span class="p">,</span> <span class="n">v2</span><span class="p">))</span>
-        <span class="n">all_ridges</span><span class="o">.</span><span class="n">setdefault</span><span class="p">(</span><span class="n">p2</span><span class="p">,</span> <span class="p">[])</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">p1</span><span class="p">,</span> <span class="n">v1</span><span class="p">,</span> <span class="n">v2</span><span class="p">))</span>
-
-    <span class="k">for</span> <span class="n">p1</span><span class="p">,</span> <span class="n">region</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">vor</span><span class="o">.</span><span class="n">point_region</span><span class="p">):</span>
-        <span class="n">vertices</span> <span class="o">=</span> <span class="n">vor</span><span class="o">.</span><span class="n">regions</span><span class="p">[</span><span class="n">region</span><span class="p">]</span>
-
-        <span class="k">if</span> <span class="nb">all</span><span class="p">(</span><span class="n">v</span> <span class="o">&gt;=</span> <span class="mi">0</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">vertices</span><span class="p">):</span>
-            <span class="n">new_regions</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">vertices</span><span class="p">)</span>
-            <span class="k">continue</span>
-
-        <span class="n">ridges</span> <span class="o">=</span> <span class="n">all_ridges</span><span class="p">[</span><span class="n">p1</span><span class="p">]</span>
-        <span class="n">new_region</span> <span class="o">=</span> <span class="p">[</span><span class="n">v</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">vertices</span> <span class="k">if</span> <span class="n">v</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="p">]</span>
-
-        <span class="k">for</span> <span class="n">p2</span><span class="p">,</span> <span class="n">v1</span><span class="p">,</span> <span class="n">v2</span> <span class="ow">in</span> <span class="n">ridges</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">v2</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="n">v1</span><span class="p">,</span> <span class="n">v2</span> <span class="o">=</span> <span class="n">v2</span><span class="p">,</span> <span class="n">v1</span>
-            <span class="k">if</span> <span class="n">v1</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="k">continue</span>
-
-            <span class="n">t</span> <span class="o">=</span> <span class="n">vor</span><span class="o">.</span><span class="n">points</span><span class="p">[</span><span class="n">p2</span><span class="p">]</span> <span class="o">-</span> <span class="n">vor</span><span class="o">.</span><span class="n">points</span><span class="p">[</span><span class="n">p1</span><span class="p">]</span>
-            <span class="n">t</span> <span class="o">/=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
-            <span class="n">n</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="o">-</span><span class="n">t</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">t</span><span class="p">[</span><span class="mi">0</span><span class="p">]])</span>
-
-            <span class="n">midpoint</span> <span class="o">=</span> <span class="n">vor</span><span class="o">.</span><span class="n">points</span><span class="p">[[</span><span class="n">p1</span><span class="p">,</span> <span class="n">p2</span><span class="p">]]</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-            <span class="n">direction</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sign</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">midpoint</span> <span class="o">-</span> <span class="n">center</span><span class="p">,</span> <span class="n">n</span><span class="p">))</span> <span class="o">*</span> <span class="n">n</span>
-            <span class="n">far_point</span> <span class="o">=</span> <span class="n">vor</span><span class="o">.</span><span class="n">vertices</span><span class="p">[</span><span class="n">v2</span><span class="p">]</span> <span class="o">+</span> <span class="n">direction</span> <span class="o">*</span> <span class="n">radius</span>
-
-            <span class="n">new_region</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">new_vertices</span><span class="p">))</span>
-            <span class="n">new_vertices</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">far_point</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span>
-
-        <span class="n">vs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="n">new_vertices</span><span class="p">[</span><span class="n">v</span><span class="p">]</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">new_region</span><span class="p">])</span>
-        <span class="n">c</span> <span class="o">=</span> <span class="n">vs</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-        <span class="n">angles</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arctan2</span><span class="p">(</span><span class="n">vs</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">c</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">vs</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">c</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-        <span class="n">new_region</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">new_region</span><span class="p">)[</span><span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">angles</span><span class="p">)]</span>
-
-        <span class="n">new_regions</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">new_region</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span>
-
-    <span class="n">regions_vertices</span> <span class="o">=</span> <span class="n">new_regions</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">new_vertices</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">regions_vertices</span>
-
-
-<span class="k">def</span> <span class="nf">as_dataframes</span><span class="p">(</span><span class="n">regions</span><span class="p">,</span> <span class="n">vertices</span><span class="p">,</span> <span class="n">points</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Helper function to store finite Voronoi regions and</span>
-<span class="sd">    originator points as ``geopandas`` (or ``pandas``) dataframes.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    regions : list</span>
-<span class="sd">        Each element of the list contains sequence of the indexes of</span>
-<span class="sd">        voronoi vertices composing a vornoi polygon (region).</span>
-<span class="sd">    vertices : array_like</span>
-<span class="sd">        The coordinates of the vornoi vertices.</span>
-<span class="sd">    points : array_like</span>
-<span class="sd">        The originator points.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    region_df : geopandas.GeoDataFrame</span>
-<span class="sd">        Finite Voronoi polygons as geometries.</span>
-<span class="sd">    points_df : geopandas.GeoDataFrame</span>
-<span class="sd">        Originator points as geometries.</span>
-<span class="sd">    </span>
-<span class="sd">    Raises</span>
-<span class="sd">    ------</span>
-<span class="sd">    ImportError</span>
-<span class="sd">        Raised when ``geopandas`` is not available.</span>
-<span class="sd">    ImportError</span>
-<span class="sd">        Raised when ``shapely`` is not available.</span>
-<span class="sd">    </span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">try</span><span class="p">:</span>
-        <span class="kn">import</span> <span class="nn">geopandas</span> <span class="k">as</span> <span class="nn">gpd</span>
-    <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
-        <span class="n">gpd</span> <span class="o">=</span> <span class="kc">None</span>
-
-    <span class="k">try</span><span class="p">:</span>
-        <span class="kn">from</span> <span class="nn">shapely.geometry</span> <span class="kn">import</span> <span class="n">Polygon</span><span class="p">,</span> <span class="n">Point</span>
-    <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
-        <span class="kn">from</span> <span class="nn">.shapes</span> <span class="kn">import</span> <span class="n">Polygon</span><span class="p">,</span> <span class="n">Point</span>
-
-    <span class="k">if</span> <span class="n">gpd</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">region_df</span> <span class="o">=</span> <span class="n">gpd</span><span class="o">.</span><span class="n">GeoDataFrame</span><span class="p">()</span>
-        <span class="n">region_df</span><span class="p">[</span><span class="s2">&quot;geometry&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">Polygon</span><span class="p">(</span><span class="n">vertices</span><span class="p">[</span><span class="n">region</span><span class="p">])</span> <span class="k">for</span> <span class="n">region</span> <span class="ow">in</span> <span class="n">regions</span><span class="p">]</span>
-
-        <span class="n">point_df</span> <span class="o">=</span> <span class="n">gpd</span><span class="o">.</span><span class="n">GeoDataFrame</span><span class="p">()</span>
-        <span class="n">point_df</span><span class="p">[</span><span class="s2">&quot;geometry&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">gpd</span><span class="o">.</span><span class="n">GeoSeries</span><span class="p">(</span><span class="n">Point</span><span class="p">(</span><span class="n">pnt</span><span class="p">)</span> <span class="k">for</span> <span class="n">pnt</span> <span class="ow">in</span> <span class="n">points</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-
-        <span class="n">region_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">()</span>
-        <span class="n">region_df</span><span class="p">[</span><span class="s2">&quot;geometry&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span>
-            <span class="n">Polygon</span><span class="p">(</span><span class="n">vertices</span><span class="p">[</span><span class="n">region</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span> <span class="k">for</span> <span class="n">region</span> <span class="ow">in</span> <span class="n">regions</span>
-        <span class="p">]</span>
-        <span class="n">point_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">()</span>
-        <span class="n">point_df</span><span class="p">[</span><span class="s2">&quot;geometry&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">Point</span><span class="p">(</span><span class="n">pnt</span><span class="p">)</span> <span class="k">for</span> <span class="n">pnt</span> <span class="ow">in</span> <span class="n">points</span><span class="p">]</span>
-
-    <span class="k">return</span> <span class="n">region_df</span><span class="p">,</span> <span class="n">point_df</span>
-
-
-<div class="viewcode-block" id="voronoi_frames"><a class="viewcode-back" href="../../../generated/libpysal.cg.voronoi_frames.html#libpysal.cg.voronoi_frames">[docs]</a><span class="k">def</span> <span class="nf">voronoi_frames</span><span class="p">(</span><span class="n">points</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">clip</span><span class="o">=</span><span class="s2">&quot;extent&quot;</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Composite helper to return Voronoi regions and</span>
-<span class="sd">    generator points as individual dataframes.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    points : array_like</span>
-<span class="sd">        The originator points.</span>
-<span class="sd">    radius : float</span>
-<span class="sd">        The distance to &#39;points at infinity&#39; used in building voronoi cells.</span>
-<span class="sd">        Default is ``None``.</span>
-<span class="sd">    clip : {str, shapely.geometry.Polygon}</span>
-<span class="sd">        An overloaded option about how to clip the voronoi cells.</span>
-<span class="sd">        Default is ``&#39;extent&#39;``. Options are as follows.</span>
-<span class="sd">        </span>
-<span class="sd">        * ``&#39;none&#39;``/``None`` -- No clip is applied. Voronoi cells may be arbitrarily larger that the source map. Note that this may lead to cells that are many orders of magnitude larger in extent than the original map. Not recommended.</span>
-<span class="sd">        * ``&#39;bbox&#39;``/``&#39;extent&#39;``/``&#39;bounding box&#39;`` -- Clip the voronoi cells to the bounding box of the input points.</span>
-<span class="sd">        * ``&#39;chull``/``&#39;convex hull&#39;`` -- Clip the voronoi cells to the convex hull of the input points.</span>
-<span class="sd">        * ``&#39;ashape&#39;``/``&#39;ahull&#39;`` -- Clip the voronoi cells to the tightest hull that contains all points (e.g. the smallest alphashape, using ``libpysal.cg.alpha_shape_auto``).</span>
-<span class="sd">        * Polygon -- Clip to an arbitrary Polygon.</span>
-<span class="sd">    </span>
-<span class="sd">    tolerance : float</span>
-<span class="sd">        The percent of map width to use to buffer the extent of the map,</span>
-<span class="sd">        if clipping (default: ``.01``, or 1%).</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    reg_vtx : tuple</span>
-<span class="sd">        Two ``geopandas.GeoDataFrame`` (or ``pandas.DataFrame`` if ``geopandas``</span>
-<span class="sd">        is unavailable) objects--``(region_df, points_df)``--of finite </span>
-<span class="sd">        Voronoi polygons and the originator points as geometries.</span>
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-
-<span class="sd">    If ``geopandas`` is not available the return types will be</span>
-<span class="sd">    ``pandas.DataFrame`` objects, each with a geometry column populated</span>
-<span class="sd">    with PySAL shapes. If ``geopandas`` is available, return types are</span>
-<span class="sd">    ``pandas.GeoDataFrame`` objects with a geometry column populated</span>
-<span class="sd">    with shapely geometry types.</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; points = [(10.2, 5.1), (4.7, 2.2), (5.3, 5.7), (2.7, 5.3)]</span>
-<span class="sd">    &gt;&gt;&gt; regions_df, points_df = voronoi_frames(points)</span>
-<span class="sd">    &gt;&gt;&gt; regions_df.shape</span>
-<span class="sd">    (4, 1)</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; regions_df.shape == points_df.shape</span>
-<span class="sd">    True</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">regions</span><span class="p">,</span> <span class="n">vertices</span> <span class="o">=</span> <span class="n">voronoi</span><span class="p">(</span><span class="n">points</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="n">radius</span><span class="p">)</span>
-    <span class="n">regions</span><span class="p">,</span> <span class="n">vertices</span> <span class="o">=</span> <span class="n">as_dataframes</span><span class="p">(</span><span class="n">regions</span><span class="p">,</span> <span class="n">vertices</span><span class="p">,</span> <span class="n">points</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">clip</span><span class="p">:</span>
-        <span class="n">regions</span> <span class="o">=</span> <span class="n">clip_voronoi_frames_to_extent</span><span class="p">(</span><span class="n">regions</span><span class="p">,</span> <span class="n">vertices</span><span class="p">,</span> <span class="n">clip</span><span class="o">=</span><span class="n">clip</span><span class="p">)</span>
-
-    <span class="n">reg_vtx</span> <span class="o">=</span> <span class="n">regions</span><span class="p">,</span> <span class="n">vertices</span>
-    <span class="k">return</span> <span class="n">reg_vtx</span></div>
-
-
-<span class="k">def</span> <span class="nf">clip_voronoi_frames_to_extent</span><span class="p">(</span><span class="n">regions</span><span class="p">,</span> <span class="n">vertices</span><span class="p">,</span> <span class="n">clip</span><span class="o">=</span><span class="s2">&quot;extent&quot;</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Generate a geopandas.GeoDataFrame of Voronoi cells clipped to</span>
-<span class="sd">    a specified extent.</span>
-<span class="sd">    </span>
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    regions : geopandas.GeoDataFrame</span>
-<span class="sd">        A (geo)dataframe containing voronoi cells to clip.</span>
-<span class="sd">    vertices : geopandas.GeoDataFrame</span>
-<span class="sd">        A (geo)dataframe containing vertices used to build voronoi cells.</span>
-<span class="sd">    clip : str, shapely.geometry.Polygon</span>
-<span class="sd">        An overloaded option about how to clip the voronoi cells.</span>
-<span class="sd">        The options are:</span>
-<span class="sd">          - &#39;none&#39;/None: No clip is applied. Voronoi cells may be arbitrarily</span>
-<span class="sd">            larger that the source map. Note that this may lead to cells that</span>
-<span class="sd">            are many orders of magnitude larger in extent than</span>
-<span class="sd">            the original map. Not recommended.</span>
-<span class="sd">          - &#39;bbox&#39;/&#39;extent&#39;/&#39;bounding box&#39;: Clip the voronoi cells to the</span>
-<span class="sd">            bounding box of the input points.</span>
-<span class="sd">          - &#39;chull&#39;/&#39;convex hull&#39;: Clip the voronoi cells to the</span>
-<span class="sd">            convex hull of the input points.</span>
-<span class="sd">          - &#39;ashape&#39;/&#39;ahull&#39;: Clip the voronoi cells to the tightest hull that</span>
-<span class="sd">            contains all points (e.g. the smallest alphashape,</span>
-<span class="sd">            using ``libpysal.cg.alpha_shape_auto``).</span>
-<span class="sd">          - Polygon: Clip to an arbitrary Polygon.</span>
-<span class="sd">    </span>
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    clipped_regions : geopandas.GeoDataFrame</span>
-<span class="sd">        A ``geopandas.GeoDataFrame`` of clipped voronoi regions.</span>
-<span class="sd">    </span>
-<span class="sd">    Raises</span>
-<span class="sd">    ------</span>
-<span class="sd">    ImportError</span>
-<span class="sd">        Raised when ``shapely`` is not available.</span>
-<span class="sd">    ImportError</span>
-<span class="sd">        Raised when ``geopandas`` is not available.</span>
-<span class="sd">    ValueError</span>
-<span class="sd">        Raised when in invalid value for ``clip`` is passed in.</span>
-<span class="sd">    </span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">try</span><span class="p">:</span>
-        <span class="kn">from</span> <span class="nn">shapely.geometry</span> <span class="kn">import</span> <span class="n">Polygon</span>
-    <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">ImportError</span><span class="p">(</span><span class="s2">&quot;Shapely is required to clip voronoi regions.&quot;</span><span class="p">)</span>
-    <span class="k">try</span><span class="p">:</span>
-        <span class="kn">import</span> <span class="nn">geopandas</span>
-    <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">ImportError</span><span class="p">(</span><span class="s2">&quot;Geopandas is required to clip voronoi regions.&quot;</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">clip</span><span class="p">,</span> <span class="n">Polygon</span><span class="p">):</span>
-        <span class="n">clipper</span> <span class="o">=</span> <span class="n">geopandas</span><span class="o">.</span><span class="n">GeoDataFrame</span><span class="p">(</span><span class="n">geometry</span><span class="o">=</span><span class="p">[</span><span class="n">clip</span><span class="p">])</span>
-    <span class="k">elif</span> <span class="n">clip</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">regions</span>
-    <span class="k">elif</span> <span class="n">clip</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">==</span> <span class="s2">&quot;none&quot;</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">regions</span>
-    <span class="k">elif</span> <span class="n">clip</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="ow">in</span> <span class="p">(</span><span class="s2">&quot;bounds&quot;</span><span class="p">,</span> <span class="s2">&quot;bounding box&quot;</span><span class="p">,</span> <span class="s2">&quot;bbox&quot;</span><span class="p">,</span> <span class="s2">&quot;extent&quot;</span><span class="p">):</span>
-        <span class="n">min_x</span><span class="p">,</span> <span class="n">min_y</span><span class="p">,</span> <span class="n">max_x</span><span class="p">,</span> <span class="n">max_y</span> <span class="o">=</span> <span class="n">vertices</span><span class="o">.</span><span class="n">total_bounds</span>
-        <span class="n">bounding_poly</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">(</span>
-            <span class="p">[</span>
-                <span class="p">(</span><span class="n">min_x</span><span class="p">,</span> <span class="n">min_y</span><span class="p">),</span>
-                <span class="p">(</span><span class="n">min_x</span><span class="p">,</span> <span class="n">max_y</span><span class="p">),</span>
-                <span class="p">(</span><span class="n">max_x</span><span class="p">,</span> <span class="n">max_y</span><span class="p">),</span>
-                <span class="p">(</span><span class="n">max_x</span><span class="p">,</span> <span class="n">min_y</span><span class="p">),</span>
-                <span class="p">(</span><span class="n">min_x</span><span class="p">,</span> <span class="n">min_y</span><span class="p">),</span>
-            <span class="p">]</span>
-        <span class="p">)</span>
-        <span class="n">clipper</span> <span class="o">=</span> <span class="n">geopandas</span><span class="o">.</span><span class="n">GeoDataFrame</span><span class="p">(</span><span class="n">geometry</span><span class="o">=</span><span class="p">[</span><span class="n">bounding_poly</span><span class="p">])</span>
-    <span class="k">elif</span> <span class="n">clip</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="ow">in</span> <span class="p">(</span><span class="s2">&quot;chull&quot;</span><span class="p">,</span> <span class="s2">&quot;convex hull&quot;</span><span class="p">,</span> <span class="s2">&quot;convex_hull&quot;</span><span class="p">):</span>
-        <span class="n">clipper</span> <span class="o">=</span> <span class="n">geopandas</span><span class="o">.</span><span class="n">GeoDataFrame</span><span class="p">(</span>
-            <span class="n">geometry</span><span class="o">=</span><span class="p">[</span><span class="n">vertices</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">unary_union</span><span class="o">.</span><span class="n">convex_hull</span><span class="p">]</span>
-        <span class="p">)</span>
-    <span class="k">elif</span> <span class="n">clip</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="ow">in</span> <span class="p">(</span>
-        <span class="s2">&quot;ahull&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;alpha hull&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;alpha_hull&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;ashape&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;alpha shape&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;alpha_shape&quot;</span><span class="p">,</span>
-    <span class="p">):</span>
-        <span class="kn">from</span> <span class="nn">.alpha_shapes</span> <span class="kn">import</span> <span class="n">alpha_shape_auto</span>
-        <span class="kn">from</span> <span class="nn">..weights.distance</span> <span class="kn">import</span> <span class="n">get_points_array</span>
-
-        <span class="n">coordinates</span> <span class="o">=</span> <span class="n">get_points_array</span><span class="p">(</span><span class="n">vertices</span><span class="o">.</span><span class="n">geometry</span><span class="p">)</span>
-        <span class="n">clipper</span> <span class="o">=</span> <span class="n">geopandas</span><span class="o">.</span><span class="n">GeoDataFrame</span><span class="p">(</span><span class="n">geometry</span><span class="o">=</span><span class="p">[</span><span class="n">alpha_shape_auto</span><span class="p">(</span><span class="n">coordinates</span><span class="p">)])</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-            <span class="s2">&quot;Clip type &#39;</span><span class="si">{}</span><span class="s2">&#39; not understood. Try one &quot;</span>
-            <span class="s2">&quot; of the supported options: [None, &#39;extent&#39;, &quot;</span>
-            <span class="s2">&quot;&#39;chull&#39;, &#39;ahull&#39;].&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">clip</span><span class="p">)</span>
-        <span class="p">)</span>
-    <span class="n">clipped_regions</span> <span class="o">=</span> <span class="n">geopandas</span><span class="o">.</span><span class="n">overlay</span><span class="p">(</span><span class="n">regions</span><span class="p">,</span> <span class="n">clipper</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s2">&quot;intersection&quot;</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">clipped_regions</span>
-</pre></div>
-
-    </div>
-      
-  </div>
-</div>
-<footer class="footer">
-  <div class="container">
-    <p class="pull-right">
-      <a href="#">Back to top</a>
-      
-        <br/>
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-    <p>
-        &copy; Copyright 2018-, pysal developers.<br/>
-      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 3.1.2.<br/>
-    </p>
-  </div>
-</footer>
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-
-<html>
-  <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>libpysal.examples &#8212; libpysal v4.4.0 Manual</title>
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-          libpysal</a>
-        <span class="navbar-text navbar-version pull-left"><b>4.4.0</b></span>
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-<ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../installation.html#installing-released-version">Installing released version</a></li>
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-      
-  <h1>Source code for libpysal.examples</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot; The :mod:`libpysal.examples` module includes a number of small built-in</span>
-<span class="sd">    example datasets as well as functions to fetch larger datasets.</span>
-<span class="sd">&quot;&quot;&quot;</span>
-
-<span class="kn">from</span> <span class="nn">.base</span> <span class="kn">import</span> <span class="n">example_manager</span>
-<span class="kn">from</span> <span class="nn">.remotes</span> <span class="kn">import</span> <span class="n">datasets</span> <span class="k">as</span> <span class="n">remote_datasets</span>
-<span class="kn">from</span> <span class="nn">.remotes</span> <span class="kn">import</span> <span class="n">download</span> <span class="k">as</span> <span class="n">fetch_all</span>
-<span class="kn">from</span> <span class="nn">.builtin</span> <span class="kn">import</span> <span class="n">datasets</span> <span class="k">as</span> <span class="n">builtin_datasets</span>
-
-
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Union</span>
-
-<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;get_path&quot;</span><span class="p">,</span> <span class="s2">&quot;available&quot;</span><span class="p">,</span> <span class="s2">&quot;explain&quot;</span><span class="p">,</span> <span class="s2">&quot;fetch_all&quot;</span><span class="p">]</span>
-
-<span class="n">example_manager</span><span class="o">.</span><span class="n">add_examples</span><span class="p">(</span><span class="n">remote_datasets</span><span class="p">)</span>
-<span class="n">example_manager</span><span class="o">.</span><span class="n">add_examples</span><span class="p">(</span><span class="n">builtin_datasets</span><span class="p">)</span>
-
-
-<div class="viewcode-block" id="available"><a class="viewcode-back" href="../../generated/libpysal.examples.available.html#libpysal.examples.available">[docs]</a><span class="k">def</span> <span class="nf">available</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
-    <span class="sd">&quot;&quot;&quot;List available datasets.&quot;&quot;&quot;</span>
-
-    <span class="k">return</span> <span class="n">example_manager</span><span class="o">.</span><span class="n">available</span><span class="p">()</span></div>
-
-
-<div class="viewcode-block" id="explain"><a class="viewcode-back" href="../../generated/libpysal.examples.explain.html#libpysal.examples.explain">[docs]</a><span class="k">def</span> <span class="nf">explain</span><span class="p">(</span><span class="n">name</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
-    <span class="sd">&quot;&quot;&quot;Explain a dataset by name.&quot;&quot;&quot;</span>
-
-    <span class="k">return</span> <span class="n">example_manager</span><span class="o">.</span><span class="n">explain</span><span class="p">(</span><span class="n">name</span><span class="p">)</span></div>
-
-
-<span class="k">def</span> <span class="nf">load_example</span><span class="p">(</span><span class="n">example_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="n">base</span><span class="o">.</span><span class="n">Example</span><span class="p">,</span> <span class="n">builtin</span><span class="o">.</span><span class="n">LocalExample</span><span class="p">]:</span>
-    <span class="sd">&quot;&quot;&quot;Load example dataset instance.&quot;&quot;&quot;</span>
-
-    <span class="k">return</span> <span class="n">example_manager</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">example_name</span><span class="p">)</span>
-
-
-<div class="viewcode-block" id="get_path"><a class="viewcode-back" href="../../generated/libpysal.examples.get_path.html#libpysal.examples.get_path">[docs]</a><span class="k">def</span> <span class="nf">get_path</span><span class="p">(</span><span class="n">file_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
-    <span class="sd">&quot;&quot;&quot;Get the path for a file by searching installed datasets.&quot;&quot;&quot;</span>
-
-    <span class="n">installed</span> <span class="o">=</span> <span class="n">example_manager</span><span class="o">.</span><span class="n">get_installed_names</span><span class="p">()</span>
-    <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">installed</span><span class="p">:</span>
-        <span class="n">example</span> <span class="o">=</span> <span class="n">example_manager</span><span class="o">.</span><span class="n">datasets</span><span class="p">[</span><span class="n">name</span><span class="p">]</span>
-        <span class="n">pth</span> <span class="o">=</span> <span class="n">example</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="n">file_name</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">pth</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">pth</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{}</span><span class="s2"> is not a file in any installed dataset.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">file_name</span><span class="p">))</span></div>
-</pre></div>
-
-    </div>
-      
-  </div>
-</div>
-<footer class="footer">
-  <div class="container">
-    <p class="pull-right">
-      <a href="#">Back to top</a>
-      
-        <br/>
-        
-      
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-    <p>
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-          libpysal</a>
-        <span class="navbar-text navbar-version pull-left"><b>4.4.0</b></span>
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-      
-  <h1>Source code for libpysal.io.fileio</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">FileIO: Module for reading and writing various file types in a Pythonic way.</span>
-<span class="sd">This module should not be used directly, instead...</span>
-
-<span class="sd">```</span>
-<span class="sd">import pysal.core.FileIO as FileIO</span>
-<span class="sd">```</span>
-
-<span class="sd">Readers and Writers will mimic python file objects.</span>
-<span class="sd">    .seek(n) seeks to the n&#39;th object</span>
-<span class="sd">    .read(n) reads n objects, default == all</span>
-<span class="sd">    .next() reads the next object</span>
-
-<span class="sd">&quot;&quot;&quot;</span>
-
-<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Charles R Schmidt &lt;schmidtc@gmail.com&gt;&quot;</span>
-
-<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;FileIO&quot;</span><span class="p">]</span>
-
-<span class="kn">import</span> <span class="nn">os.path</span>
-<span class="kn">from</span> <span class="nn">warnings</span> <span class="kn">import</span> <span class="n">warn</span>
-<span class="kn">from</span> <span class="nn">..common</span> <span class="kn">import</span> <span class="n">MISSINGVALUE</span>
-
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Union</span>
-
-
-<span class="k">class</span> <span class="nc">FileIO_MetaCls</span><span class="p">(</span><span class="nb">type</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;This meta class is instantiated when the class is first defined. All</span>
-<span class="sd">    subclasses of `FileIO` also inherit this meta class, which registers</span>
-<span class="sd">    their abilities with the FileIO registry. Subclasses must contain</span>
-<span class="sd">    ``FORMATS`` and ``MODES`` (both are ``type(list)``).</span>
-<span class="sd">    </span>
-<span class="sd">    Raises</span>
-<span class="sd">    ------</span>
-<span class="sd">    TypeError</span>
-<span class="sd">        FileIO subclasses must have ``FORMATS`` and ``MODES`` defined.</span>
-<span class="sd">    </span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__new__</span><span class="p">(</span><span class="n">mcs</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">bases</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
-
-        <span class="bp">cls</span> <span class="o">=</span> <span class="nb">type</span><span class="o">.</span><span class="fm">__new__</span><span class="p">(</span><span class="n">mcs</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">bases</span><span class="p">,</span> <span class="nb">dict</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">name</span> <span class="o">!=</span> <span class="s2">&quot;FileIO&quot;</span> <span class="ow">and</span> <span class="n">name</span> <span class="o">!=</span> <span class="s2">&quot;DataTable&quot;</span><span class="p">:</span>
-            <span class="k">if</span> <span class="s2">&quot;FORMATS&quot;</span> <span class="ow">in</span> <span class="nb">dict</span> <span class="ow">and</span> <span class="s2">&quot;MODES&quot;</span> <span class="ow">in</span> <span class="nb">dict</span><span class="p">:</span>
-                <span class="c1"># msg = &quot;Registering %s with FileIO.\n\tFormats: %r\n\tModes: %r&quot;</span>
-                <span class="c1"># msg = msg % (name, dict[&quot;FORMATS&quot;], dict[&quot;MODES&quot;])</span>
-                <span class="n">FileIO</span><span class="o">.</span><span class="n">_register</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="nb">dict</span><span class="p">[</span><span class="s2">&quot;FORMATS&quot;</span><span class="p">],</span> <span class="nb">dict</span><span class="p">[</span><span class="s2">&quot;MODES&quot;</span><span class="p">])</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
-                    <span class="s2">&quot;FileIO subclasses must have &#39;FORMATS&#39; and &#39;MODES&#39; defined.&quot;</span>
-                <span class="p">)</span>
-
-        <span class="k">return</span> <span class="bp">cls</span>
-
-
-<div class="viewcode-block" id="FileIO"><a class="viewcode-back" href="../../../generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO">[docs]</a><span class="k">class</span> <span class="nc">FileIO</span><span class="p">(</span><span class="nb">object</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">FileIO_MetaCls</span><span class="p">):</span>  <span class="c1"># should be a type?</span>
-    <span class="sd">&quot;&quot;&quot;Metaclass for supporting spatial data file read and write.</span>
-
-<span class="sd">    How this works:</span>
-<span class="sd">    </span>
-<span class="sd">    ``FileIO.open(\\*args) == FileIO(\\*args)``</span>
-<span class="sd">    </span>
-<span class="sd">    When creating a new instance of `FileIO` the ``.__new__`` method intercepts.</span>
-<span class="sd">    ``.__new__`` parses the filename to determine the ``fileType``. Next,</span>
-<span class="sd">    ``.__registry`` and checked for that type. Each type supports one or more modes</span>
-<span class="sd">    (``[&#39;r&#39;, &#39;w&#39;, &#39;a&#39;, etc.]``). If we support the type and mode, an instance of the</span>
-<span class="sd">    appropriate handler is created and returned. All handlers must inherit from this</span>
-<span class="sd">    class, and by doing so are automatically added to the ``.__registry`` and are</span>
-<span class="sd">    forced to conform to the prescribed API. The metaclass takes care of the</span>
-<span class="sd">    registration by parsing the class definition. It doesn&#39;t make much sense to</span>
-<span class="sd">    treat weights in the same way as shapefiles and dbfs, so...</span>
-<span class="sd">    </span>
-<span class="sd">    * ... for now we&#39;ll just return an instance of `W` on ``mode=&#39;r&#39;``.</span>
-<span class="sd">    * ... on ``mode=&#39;w&#39;``, ``.write`` will expect an instance of `W`.</span>
-<span class="sd">    </span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">__registry</span> <span class="o">=</span> <span class="p">{}</span>  <span class="c1"># {&#39;shp&#39;:{&#39;r&#39;:[OGRshpReader,pysalShpReader]}}</span>
-
-    <span class="k">def</span> <span class="fm">__new__</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">dataPath</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">&quot;r&quot;</span><span class="p">,</span> <span class="n">dataFormat</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Intercepts the instantiation of ``FileIO`` and dispatches</span>
-<span class="sd">        to the correct handler. If no suitable handler is found a</span>
-<span class="sd">        python file object is returned.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="bp">cls</span> <span class="ow">is</span> <span class="n">FileIO</span><span class="p">:</span>
-            <span class="k">try</span><span class="p">:</span>
-                <span class="n">newCls</span> <span class="o">=</span> <span class="nb">object</span><span class="o">.</span><span class="fm">__new__</span><span class="p">(</span>
-                    <span class="bp">cls</span><span class="o">.</span><span class="n">__registry</span><span class="p">[</span><span class="bp">cls</span><span class="o">.</span><span class="n">getType</span><span class="p">(</span><span class="n">dataPath</span><span class="p">,</span> <span class="n">mode</span><span class="p">,</span> <span class="n">dataFormat</span><span class="p">)][</span><span class="n">mode</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
-                <span class="p">)</span>
-            <span class="k">except</span> <span class="ne">KeyError</span><span class="p">:</span>
-                <span class="k">return</span> <span class="nb">open</span><span class="p">(</span><span class="n">dataPath</span><span class="p">,</span> <span class="n">mode</span><span class="p">)</span>
-            <span class="k">return</span> <span class="n">newCls</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">return</span> <span class="nb">object</span><span class="o">.</span><span class="fm">__new__</span><span class="p">(</span><span class="bp">cls</span><span class="p">)</span>
-
-<div class="viewcode-block" id="FileIO.getType"><a class="viewcode-back" href="../../../generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.getType">[docs]</a>    <span class="nd">@staticmethod</span>
-    <span class="k">def</span> <span class="nf">getType</span><span class="p">(</span><span class="n">dataPath</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">mode</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">dataFormat</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;Parse the ``dataPath`` and return the data type.&quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="n">dataFormat</span><span class="p">:</span>
-            <span class="n">ext</span> <span class="o">=</span> <span class="n">dataFormat</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">ext</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">splitext</span><span class="p">(</span><span class="n">dataPath</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span>
-            <span class="n">ext</span> <span class="o">=</span> <span class="n">ext</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">,</span> <span class="s2">&quot;&quot;</span><span class="p">)</span>
-            <span class="n">ext</span> <span class="o">=</span> <span class="n">ext</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
-        <span class="k">if</span> <span class="n">ext</span> <span class="o">==</span> <span class="s2">&quot;txt&quot;</span><span class="p">:</span>
-            <span class="n">f</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="n">dataPath</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span>
-            <span class="n">l1</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">readline</span><span class="p">()</span>
-            <span class="n">l2</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">readline</span><span class="p">()</span>
-            <span class="k">if</span> <span class="n">ext</span> <span class="o">==</span> <span class="s2">&quot;txt&quot;</span><span class="p">:</span>
-                <span class="k">try</span><span class="p">:</span>
-                    <span class="n">n</span><span class="p">,</span> <span class="n">k</span> <span class="o">=</span> <span class="n">l1</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;,&quot;</span><span class="p">)</span>
-                    <span class="n">n</span><span class="p">,</span> <span class="n">k</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">n</span><span class="p">),</span> <span class="nb">int</span><span class="p">(</span><span class="n">k</span><span class="p">)</span>
-                    <span class="n">fields</span> <span class="o">=</span> <span class="n">l2</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;,&quot;</span><span class="p">)</span>
-                    <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">fields</span><span class="p">)</span> <span class="o">==</span> <span class="n">k</span>
-                    <span class="k">return</span> <span class="s2">&quot;geoda_txt&quot;</span>
-                <span class="k">except</span><span class="p">:</span>
-                    <span class="k">return</span> <span class="n">ext</span>
-
-        <span class="k">return</span> <span class="n">ext</span></div>
-
-    <span class="nd">@classmethod</span>
-    <span class="k">def</span> <span class="nf">_register</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">parser</span><span class="p">,</span> <span class="n">formats</span><span class="p">,</span> <span class="n">modes</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;This method is called automatically via the Metaclass of `FileIO` subclasses</span>
-<span class="sd">        This should be private, but that hides it from the Metaclass.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">assert</span> <span class="bp">cls</span> <span class="ow">is</span> <span class="n">FileIO</span>
-
-        <span class="k">for</span> <span class="nb">format</span> <span class="ow">in</span> <span class="n">formats</span><span class="p">:</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="nb">format</span> <span class="ow">in</span> <span class="bp">cls</span><span class="o">.</span><span class="n">__registry</span><span class="p">:</span>
-                <span class="bp">cls</span><span class="o">.</span><span class="n">__registry</span><span class="p">[</span><span class="nb">format</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
-            <span class="k">for</span> <span class="n">mode</span> <span class="ow">in</span> <span class="n">modes</span><span class="p">:</span>
-                <span class="k">if</span> <span class="ow">not</span> <span class="n">mode</span> <span class="ow">in</span> <span class="bp">cls</span><span class="o">.</span><span class="n">__registry</span><span class="p">[</span><span class="nb">format</span><span class="p">]:</span>
-                    <span class="bp">cls</span><span class="o">.</span><span class="n">__registry</span><span class="p">[</span><span class="nb">format</span><span class="p">][</span><span class="n">mode</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
-                <span class="bp">cls</span><span class="o">.</span><span class="n">__registry</span><span class="p">[</span><span class="nb">format</span><span class="p">][</span><span class="n">mode</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">parser</span><span class="p">)</span>
-        <span class="c1"># cls.check()</span>
-
-<div class="viewcode-block" id="FileIO.check"><a class="viewcode-back" href="../../../generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.check">[docs]</a>    <span class="nd">@classmethod</span>
-    <span class="k">def</span> <span class="nf">check</span><span class="p">(</span><span class="bp">cls</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Prints the contents of the registry.&quot;&quot;&quot;</span>
-
-        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;PySAL File I/O understands the following file extensions:&quot;</span><span class="p">)</span>
-
-        <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">val</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="bp">cls</span><span class="o">.</span><span class="n">__registry</span><span class="o">.</span><span class="n">items</span><span class="p">()):</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Ext: &#39;.</span><span class="si">%s</span><span class="s2">&#39;, Modes: </span><span class="si">%r</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="nb">list</span><span class="p">(</span><span class="n">val</span><span class="o">.</span><span class="n">keys</span><span class="p">())))</span></div>
-
-<div class="viewcode-block" id="FileIO.open"><a class="viewcode-back" href="../../../generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.open">[docs]</a>    <span class="nd">@classmethod</span>
-    <span class="k">def</span> <span class="nf">open</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Alias for ``FileIO()``.&quot;&quot;&quot;</span>
-
-        <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
-
-    <span class="k">class</span> <span class="nc">_By_Row</span><span class="p">:</span>
-        <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">parent</span><span class="p">):</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">p</span> <span class="o">=</span> <span class="n">parent</span>
-
-        <span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="o">.</span><span class="n">ids</span><span class="p">:</span>
-                <span class="k">return</span> <span class="s2">&quot;keys: range(0,n)&quot;</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="k">return</span> <span class="s2">&quot;keys: &quot;</span> <span class="o">+</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="o">.</span><span class="n">ids</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span><span class="o">.</span><span class="fm">__repr__</span><span class="p">()</span>
-
-        <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">list</span><span class="p">,</span> <span class="nb">str</span><span class="p">]:</span>
-            <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">key</span><span class="p">)</span> <span class="o">==</span> <span class="nb">list</span><span class="p">:</span>
-                <span class="n">r</span> <span class="o">=</span> <span class="p">[]</span>
-                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="o">.</span><span class="n">ids</span><span class="p">:</span>
-                    <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">key</span><span class="p">:</span>
-                        <span class="n">r</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="o">.</span><span class="n">ids</span><span class="p">[</span><span class="n">k</span><span class="p">]))</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">key</span><span class="p">:</span>
-                        <span class="n">r</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">k</span><span class="p">))</span>
-                <span class="k">return</span> <span class="n">r</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="o">.</span><span class="n">ids</span><span class="p">:</span>
-                <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="o">.</span><span class="n">ids</span><span class="p">[</span><span class="n">key</span><span class="p">])</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
-
-        <span class="fm">__call__</span> <span class="o">=</span> <span class="fm">__getitem__</span>
-
-<div class="viewcode-block" id="FileIO.__init__"><a class="viewcode-back" href="../../../generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataPath</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">&quot;r&quot;</span><span class="p">,</span> <span class="n">dataFormat</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">dataPath</span> <span class="o">=</span> <span class="n">dataPath</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">dataObj</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="o">=</span> <span class="n">mode</span>
-        <span class="c1"># pos Should ALWAYS be in the range 0,...,n</span>
-        <span class="c1"># for custom IDs set the ids property.</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">pos</span> <span class="o">=</span> <span class="mi">0</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">__ids</span> <span class="o">=</span> <span class="kc">None</span>  <span class="c1"># {&#39;id&#39;:n}</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">__rIds</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">closed</span> <span class="o">=</span> <span class="kc">False</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_spec</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">header</span> <span class="o">=</span> <span class="p">[]</span></div>
-
-    <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">):</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">by_row</span><span class="o">.</span><span class="fm">__getitem__</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">by_row</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_By_Row</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">__getIds</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">__ids</span>
-
-    <span class="k">def</span> <span class="nf">__setIds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ids</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">list</span><span class="p">,</span> <span class="nb">dict</span><span class="p">,</span> <span class="kc">None</span><span class="p">]):</span>
-        <span class="sd">&quot;&quot;&quot;Property method for ``.ids``. Takes a list of ids and maps then</span>
-<span class="sd">        to a 0-based index. Need to provide a method to set ID&#39;s based on</span>
-<span class="sd">        a ``fieldName`` preferably without reading the whole file.</span>
-<span class="sd">        </span>
-<span class="sd">        Raises</span>
-<span class="sd">        ------</span>
-<span class="sd">        AssertionError</span>
-<span class="sd">            Raised when IDs are not unique.</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">ids</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
-            <span class="k">try</span><span class="p">:</span>
-                <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">ids</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">ids</span><span class="p">))</span>
-            <span class="k">except</span> <span class="ne">AssertionError</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">KeyError</span><span class="p">(</span><span class="s2">&quot;IDs must be unique.&quot;</span><span class="p">)</span>
-            <span class="c1"># keys: ID values: i</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">__ids</span> <span class="o">=</span> <span class="p">{}</span>
-            <span class="c1"># keys: i values: ID</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">__rIds</span> <span class="o">=</span> <span class="p">{}</span>
-            <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="nb">id</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">ids</span><span class="p">):</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">__ids</span><span class="p">[</span><span class="nb">id</span><span class="p">]</span> <span class="o">=</span> <span class="n">i</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">__rIds</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="nb">id</span>
-        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">ids</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">__ids</span> <span class="o">=</span> <span class="n">ids</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">__rIds</span> <span class="o">=</span> <span class="p">{}</span>
-            <span class="k">for</span> <span class="nb">id</span><span class="p">,</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="n">ids</span><span class="o">.</span><span class="n">items</span><span class="p">()):</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">__rIds</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="nb">id</span>
-        <span class="k">elif</span> <span class="ow">not</span> <span class="n">ids</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">__ids</span> <span class="o">=</span> <span class="kc">None</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">__rIds</span> <span class="o">=</span> <span class="kc">None</span>
-
-    <span class="n">ids</span> <span class="o">=</span> <span class="nb">property</span><span class="p">(</span><span class="n">fget</span><span class="o">=</span><span class="n">__getIds</span><span class="p">,</span> <span class="n">fset</span><span class="o">=</span><span class="n">__setIds</span><span class="p">)</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">rIds</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">dict</span><span class="p">,</span> <span class="kc">None</span><span class="p">]:</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">__rIds</span>
-
-    <span class="k">def</span> <span class="fm">__iter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">seek</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-        <span class="k">return</span> <span class="bp">self</span>
-
-    <span class="nd">@staticmethod</span>
-    <span class="k">def</span> <span class="nf">_complain_ifclosed</span><span class="p">(</span><span class="n">closed</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;From `StringIO`.</span>
-<span class="sd">        </span>
-<span class="sd">        Raises</span>
-<span class="sd">        ------</span>
-<span class="sd">        ValueError</span>
-<span class="sd">            Raised when a file is already closed.</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="n">closed</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;I/O operation on closed file.&quot;</span><span class="p">)</span>
-
-<div class="viewcode-block" id="FileIO.cast"><a class="viewcode-back" href="../../../generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.cast">[docs]</a>    <span class="k">def</span> <span class="nf">cast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="n">typ</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Cast ``key`` as ``typ``.</span>
-<span class="sd">        </span>
-<span class="sd">        Raises</span>
-<span class="sd">        ------</span>
-<span class="sd">        TypeError</span>
-<span class="sd">            Raised when a cast object in not callable.</span>
-<span class="sd">        KeyError</span>
-<span class="sd">            Raised when a key is not present.</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="n">key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">header</span><span class="p">:</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_spec</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_spec</span> <span class="o">=</span> <span class="p">[</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">header</span><span class="p">]</span>
-            <span class="k">if</span> <span class="n">typ</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_spec</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">header</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">key</span><span class="p">)]</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="k">try</span><span class="p">:</span>
-                    <span class="k">assert</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">typ</span><span class="p">,</span> <span class="s2">&quot;__call__&quot;</span><span class="p">)</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">_spec</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">header</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">key</span><span class="p">)]</span> <span class="o">=</span> <span class="n">typ</span>
-                <span class="k">except</span> <span class="ne">AssertionError</span><span class="p">:</span>
-                    <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;Cast objects must be callable.&quot;</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">KeyError</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">key</span><span class="p">)</span></div>
-
-    <span class="k">def</span> <span class="nf">_cast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">row</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        </span>
-<span class="sd">        Raises</span>
-<span class="sd">        ------</span>
-<span class="sd">        ValueError</span>
-<span class="sd">            Raised when a value could not be cast a particular type.</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_spec</span> <span class="ow">and</span> <span class="n">row</span><span class="p">:</span>
-            <span class="k">try</span><span class="p">:</span>
-                <span class="k">return</span> <span class="p">[</span><span class="n">f</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="k">for</span> <span class="n">f</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_spec</span><span class="p">,</span> <span class="n">row</span><span class="p">)]</span>
-            <span class="k">except</span> <span class="ne">ValueError</span><span class="p">:</span>
-                <span class="n">r</span> <span class="o">=</span> <span class="p">[]</span>
-                <span class="k">for</span> <span class="n">f</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_spec</span><span class="p">,</span> <span class="n">row</span><span class="p">):</span>
-                    <span class="k">try</span><span class="p">:</span>
-                        <span class="k">if</span> <span class="ow">not</span> <span class="n">v</span> <span class="ow">and</span> <span class="n">f</span> <span class="o">!=</span> <span class="nb">str</span><span class="p">:</span>
-                            <span class="k">raise</span> <span class="ne">ValueError</span>
-                        <span class="n">r</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">f</span><span class="p">(</span><span class="n">v</span><span class="p">))</span>
-                    <span class="k">except</span> <span class="ne">ValueError</span><span class="p">:</span>
-                        <span class="n">msg</span> <span class="o">=</span> <span class="s2">&quot;Value &#39;</span><span class="si">%r</span><span class="s2">&#39; could not be cast to </span><span class="si">%s</span><span class="s2">, &quot;</span>
-                        <span class="n">msg</span> <span class="o">+=</span> <span class="s2">&quot;value set to MISSINGVALUE.&quot;</span>
-                        <span class="n">msg</span> <span class="o">=</span> <span class="n">msg</span> <span class="o">%</span> <span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">f</span><span class="p">))</span>
-                        <span class="n">warn</span><span class="p">(</span><span class="n">msg</span><span class="p">,</span> <span class="ne">RuntimeWarning</span><span class="p">)</span>
-                        <span class="n">r</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">MISSINGVALUE</span><span class="p">)</span>
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-        <span class="k">else</span><span class="p">:</span>
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-    <span class="k">def</span> <span class="fm">__next__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;A `FileIO` object is its own iterator, see `StringIO`.</span>
-<span class="sd">        </span>
-<span class="sd">        Raises</span>
-<span class="sd">        ------</span>
-<span class="sd">        StopIteration</span>
-<span class="sd">            Raised at the EOF.</span>
-<span class="sd">            </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">_complain_ifclosed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">closed</span><span class="p">)</span>
-        <span class="n">r</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__read</span><span class="p">()</span>
-        <span class="k">if</span> <span class="n">r</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">StopIteration</span>
-
-        <span class="k">return</span> <span class="n">r</span>
-
-<div class="viewcode-block" id="FileIO.close"><a class="viewcode-back" href="../../../generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.close">[docs]</a>    <span class="k">def</span> <span class="nf">close</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Subclasses should clean themselves up and then call this method.&quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">closed</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">closed</span> <span class="o">=</span> <span class="kc">True</span>
-            <span class="k">del</span> <span class="bp">self</span><span class="o">.</span><span class="n">dataObj</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">pos</span></div>
-
-<div class="viewcode-block" id="FileIO.get"><a class="viewcode-back" href="../../../generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.get">[docs]</a>    <span class="k">def</span> <span class="nf">get</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;Seeks the file to ``n`` and returns ``n``. If ``.ids`` is set</span>
-<span class="sd">        ``n`` should be an id, else, ``n`` should be an offset.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">prevPos</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tell</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">seek</span><span class="p">(</span><span class="n">n</span><span class="p">)</span>
-        <span class="n">obj</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__read</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">seek</span><span class="p">(</span><span class="n">prevPos</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">obj</span></div>
-
-<div class="viewcode-block" id="FileIO.seek"><a class="viewcode-back" href="../../../generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.seek">[docs]</a>    <span class="k">def</span> <span class="nf">seek</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Seek the `FileObj` to the beginning of the ``n``&#39;th record.</span>
-<span class="sd">        If IDs are set, seeks to the beginning of the record at ID, ``n``.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">_complain_ifclosed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">closed</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">pos</span> <span class="o">=</span> <span class="n">n</span></div>
-
-<div class="viewcode-block" id="FileIO.tell"><a class="viewcode-back" href="../../../generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.tell">[docs]</a>    <span class="k">def</span> <span class="nf">tell</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;Return ID (or offset) of next object.&quot;&quot;&quot;</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">_complain_ifclosed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">closed</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">pos</span></div>
-
-<div class="viewcode-block" id="FileIO.read"><a class="viewcode-back" href="../../../generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.read">[docs]</a>    <span class="k">def</span> <span class="nf">read</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">list</span><span class="p">,</span> <span class="kc">None</span><span class="p">]:</span>
-        <span class="sd">&quot;&quot;&quot;Read at most ``n`` objects, less if read hits EOF.</span>
-<span class="sd">        If size is negative or omitted read all objects until EOF.</span>
-<span class="sd">        Returns ``None`` if EOF is reached before any objects.</span>
-<span class="sd">        </span>
-<span class="sd">        Raises</span>
-<span class="sd">        ------</span>
-<span class="sd">        StopIteration</span>
-<span class="sd">            Raised at the EOF.</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">_complain_ifclosed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">closed</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">n</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="c1"># return list(self)</span>
-            <span class="n">result</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="k">while</span> <span class="mi">1</span><span class="p">:</span>
-                <span class="k">try</span><span class="p">:</span>
-                    <span class="n">result</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">__read</span><span class="p">())</span>
-                <span class="k">except</span> <span class="ne">StopIteration</span><span class="p">:</span>
-                    <span class="k">break</span>
-            <span class="k">return</span> <span class="n">result</span>
-        <span class="k">elif</span> <span class="n">n</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">return</span> <span class="kc">None</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">result</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">n</span><span class="p">):</span>
-                <span class="k">try</span><span class="p">:</span>
-                    <span class="n">result</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">__read</span><span class="p">())</span>
-                <span class="k">except</span> <span class="ne">StopIteration</span><span class="p">:</span>
-                    <span class="k">break</span>
-            <span class="k">return</span> <span class="n">result</span></div>
-
-    <span class="k">def</span> <span class="nf">__read</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;Gets one row from the file handler, and if necessary casts it&#39;s objects.</span>
-<span class="sd">        </span>
-<span class="sd">        Raises</span>
-<span class="sd">        ------</span>
-<span class="sd">        StopIteration</span>
-<span class="sd">            Raised at the EOF.</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">row</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_read</span><span class="p">()</span>
-        <span class="k">if</span> <span class="n">row</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">StopIteration</span>
-        <span class="n">row</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cast</span><span class="p">(</span><span class="n">row</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">row</span>
-
-    <span class="k">def</span> <span class="nf">_read</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Must be implemented by subclasses that support &#39;r&#39; subclasses.</span>
-<span class="sd">        Should increment ``.pos`` and redefine this doc string.</span>
-<span class="sd">        </span>
-<span class="sd">        Raises</span>
-<span class="sd">        ------</span>
-<span class="sd">        NotImplementedError</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">_complain_ifclosed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">closed</span><span class="p">)</span>
-        <span class="k">raise</span> <span class="ne">NotImplementedError</span>
-
-<div class="viewcode-block" id="FileIO.truncate"><a class="viewcode-back" href="../../../generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.truncate">[docs]</a>    <span class="k">def</span> <span class="nf">truncate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Should be implemented by subclasses and redefine this doc string.</span>
-<span class="sd">        </span>
-<span class="sd">        Raises</span>
-<span class="sd">        ------</span>
-<span class="sd">        NotImplementedError</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">_complain_ifclosed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">closed</span><span class="p">)</span>
-        <span class="k">raise</span> <span class="ne">NotImplementedError</span></div>
-
-<div class="viewcode-block" id="FileIO.write"><a class="viewcode-back" href="../../../generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.write">[docs]</a>    <span class="k">def</span> <span class="nf">write</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">obj</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Must be implemented by subclasses that support &#39;w&#39; subclasses</span>
-<span class="sd">        Should increment ``.pos``. Subclasses should also check if ``obj``</span>
-<span class="sd">        is an instance of type(list) and redefine this doc string.</span>
-<span class="sd">        </span>
-<span class="sd">        Raises</span>
-<span class="sd">        ------</span>
-<span class="sd">        NotImplementedError</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">_complain_ifclosed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">closed</span><span class="p">)</span>
-        <span class="s2">&quot;Write obj to dataObj&quot;</span>
-        <span class="k">raise</span> <span class="ne">NotImplementedError</span></div>
-
-<div class="viewcode-block" id="FileIO.flush"><a class="viewcode-back" href="../../../generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.flush">[docs]</a>    <span class="k">def</span> <span class="nf">flush</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        </span>
-<span class="sd">        Raises</span>
-<span class="sd">        ------</span>
-<span class="sd">        NotImplementedError</span>
-<span class="sd">        </span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">_complain_ifclosed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">closed</span><span class="p">)</span>
-        <span class="k">raise</span> <span class="ne">NotImplementedError</span></div></div>
-</pre></div>
-
-    </div>
-      
-  </div>
-</div>
-<footer class="footer">
-  <div class="container">
-    <p class="pull-right">
-      <a href="#">Back to top</a>
-      
-        <br/>
-        
-      
-    </p>
-    <p>
-        &copy; Copyright 2018-, pysal developers.<br/>
-      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 3.1.2.<br/>
-    </p>
-  </div>
-</footer>
-  </body>
-</html>
\ No newline at end of file
diff --git a/docs/_modules/libpysal/weights/contiguity.html b/docs/_modules/libpysal/weights/contiguity.html
deleted file mode 100644
index 350a7b9c9..000000000
--- a/docs/_modules/libpysal/weights/contiguity.html
+++ /dev/null
@@ -1,780 +0,0 @@
-<!DOCTYPE html>
-
-<html>
-  <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>libpysal.weights.contiguity &#8212; libpysal v4.4.0 Manual</title>
-    <link rel="stylesheet" href="../../../_static/bootstrap-sphinx.css" type="text/css" />
-    <link rel="stylesheet" href="../../../_static/pygments.css" type="text/css" />
-    <link rel="stylesheet" type="text/css" href="../../../_static/pysal-styles.css" />
-    <script id="documentation_options" data-url_root="../../../" src="../../../_static/documentation_options.js"></script>
-    <script src="../../../_static/jquery.js"></script>
-    <script src="../../../_static/underscore.js"></script>
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-    <script src="../../../_static/language_data.js"></script>
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-    <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
-    <script type="text/x-mathjax-config">MathJax.Hub.Config({"tex2jax": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true, "ignoreClass": "document", "processClass": "math|output_area"}})</script>
-    <link rel="shortcut icon" href="../../../_static/pysal_favicon.ico"/>
-    <link rel="index" title="Index" href="../../../genindex.html" />
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-<meta charset='utf-8'>
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-<meta name="apple-mobile-web-app-capable" content="yes">
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-<script type="text/javascript" src="../../../_static/bootstrap-sphinx.js "></script>
-
-  </head><body>
-
-  <div id="navbar" class="navbar navbar-default navbar-fixed-top">
-    <div class="container">
-      <div class="navbar-header">
-        <!-- .btn-navbar is used as the toggle for collapsed navbar content -->
-        <button type="button" class="navbar-toggle" data-toggle="collapse" data-target=".nav-collapse">
-          <span class="icon-bar"></span>
-          <span class="icon-bar"></span>
-          <span class="icon-bar"></span>
-        </button>
-        <a class="navbar-brand" href="../../../index.html">
-          libpysal</a>
-        <span class="navbar-text navbar-version pull-left"><b>4.4.0</b></span>
-      </div>
-
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-          <ul class="nav navbar-nav">
-            
-                <li><a href="../../../installation.html">Installation</a></li>
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-                <li><a href="../../../references.html">References</a></li>
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-            
-              <li class="dropdown globaltoc-container">
-  <a role="button"
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-  <ul class="dropdown-menu globaltoc"
-      role="menu"
-      aria-labelledby="dLabelGlobalToc"><p class="caption"><span class="caption-text">Contents:</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../../../installation.html">Installation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../../installation.html#installing-released-version">Installing released version</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../installation.html#installing-development-version">Installing development version</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../../tutorial.html">Tutorial</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../../tutorial.html#spatial-weights">Spatial Weights</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../tutorial.html#example-datasets">Example Datasets</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../../../api.html">API</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../../api.html#spatial-weights">Spatial Weights</a></li>
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-      
-  <h1>Source code for libpysal.weights.contiguity</h1><div class="highlight"><pre>
-<span></span><span class="kn">import</span> <span class="nn">itertools</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span>
-
-<span class="kn">from</span> <span class="nn">..cg</span> <span class="kn">import</span> <span class="n">voronoi_frames</span>
-<span class="kn">from</span> <span class="nn">..io.fileio</span> <span class="kn">import</span> <span class="n">FileIO</span>
-<span class="kn">from</span> <span class="nn">._contW_lists</span> <span class="kn">import</span> <span class="n">ContiguityWeightsLists</span>
-<span class="kn">from</span> <span class="nn">.util</span> <span class="kn">import</span> <span class="n">get_ids</span><span class="p">,</span> <span class="n">get_points_array</span>
-<span class="kn">from</span> <span class="nn">.weights</span> <span class="kn">import</span> <span class="n">WSP</span><span class="p">,</span> <span class="n">W</span>
-<span class="kn">from</span> <span class="nn">.raster</span> <span class="kn">import</span> <span class="n">da2W</span><span class="p">,</span> <span class="n">da2WSP</span>
-
-<span class="k">try</span><span class="p">:</span>
-    <span class="kn">from</span> <span class="nn">shapely.geometry</span> <span class="kn">import</span> <span class="n">Point</span> <span class="k">as</span> <span class="n">shapely_point</span>
-    <span class="kn">from</span> <span class="nn">..cg.shapes</span> <span class="kn">import</span> <span class="n">Point</span> <span class="k">as</span> <span class="n">pysal_point</span>
-
-    <span class="n">point_type</span> <span class="o">=</span> <span class="p">(</span><span class="n">shapely_point</span><span class="p">,</span> <span class="n">pysal_point</span><span class="p">)</span>
-<span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
-    <span class="kn">from</span> <span class="nn">..cg.shapes</span> <span class="kn">import</span> <span class="n">Point</span> <span class="k">as</span> <span class="n">point_type</span>
-
-<span class="n">WT_TYPE</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;rook&quot;</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span> <span class="s2">&quot;queen&quot;</span><span class="p">:</span> <span class="mi">1</span><span class="p">}</span>  <span class="c1"># for _contW_Binning</span>
-
-<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Sergio J. Rey &lt;srey@asu.edu&gt; , Levi John Wolf &lt;levi.john.wolf@gmail.com&gt;&quot;</span>
-
-<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;Rook&quot;</span><span class="p">,</span> <span class="s2">&quot;Queen&quot;</span><span class="p">,</span> <span class="s2">&quot;Voronoi&quot;</span><span class="p">]</span>
-
-
-<div class="viewcode-block" id="Rook"><a class="viewcode-back" href="../../../generated/libpysal.weights.Rook.html#libpysal.weights.Rook">[docs]</a><span class="k">class</span> <span class="nc">Rook</span><span class="p">(</span><span class="n">W</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Construct a weights object from a collection of pysal polygons that share at least one edge.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    polygons    : list</span>
-<span class="sd">                a collection of PySAL shapes to build weights from</span>
-<span class="sd">    ids         : list</span>
-<span class="sd">                a list of names to use to build the weights</span>
-<span class="sd">    **kw        : keyword arguments</span>
-<span class="sd">                optional arguments for :class:`pysal.weights.W`</span>
-
-<span class="sd">    See Also</span>
-<span class="sd">    ---------</span>
-<span class="sd">    :class:`libpysal.weights.weights.W`</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-<div class="viewcode-block" id="Rook.__init__"><a class="viewcode-back" href="../../../generated/libpysal.weights.Rook.html#libpysal.weights.Rook.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">polygons</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">):</span>
-        <span class="n">criterion</span> <span class="o">=</span> <span class="s2">&quot;rook&quot;</span>
-        <span class="n">ids</span> <span class="o">=</span> <span class="n">kw</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;ids&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
-        <span class="n">polygons</span><span class="p">,</span> <span class="n">backup</span> <span class="o">=</span> <span class="n">itertools</span><span class="o">.</span><span class="n">tee</span><span class="p">(</span><span class="n">polygons</span><span class="p">)</span>
-        <span class="n">first_shape</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="nb">iter</span><span class="p">(</span><span class="n">backup</span><span class="p">))</span>
-        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">first_shape</span><span class="p">,</span> <span class="n">point_type</span><span class="p">):</span>
-            <span class="n">polygons</span><span class="p">,</span> <span class="n">vertices</span> <span class="o">=</span> <span class="n">voronoi_frames</span><span class="p">(</span><span class="n">get_points_array</span><span class="p">(</span><span class="n">polygons</span><span class="p">))</span>
-            <span class="n">polygons</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">polygons</span><span class="o">.</span><span class="n">geometry</span><span class="p">)</span>
-        <span class="n">neighbors</span><span class="p">,</span> <span class="n">ids</span> <span class="o">=</span> <span class="n">_build</span><span class="p">(</span><span class="n">polygons</span><span class="p">,</span> <span class="n">criterion</span><span class="o">=</span><span class="n">criterion</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">)</span>
-        <span class="n">W</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">neighbors</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="Rook.from_shapefile"><a class="viewcode-back" href="../../../generated/libpysal.weights.Rook.html#libpysal.weights.Rook.from_shapefile">[docs]</a>    <span class="nd">@classmethod</span>
-    <span class="k">def</span> <span class="nf">from_shapefile</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">filepath</span><span class="p">,</span> <span class="n">idVariable</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">full</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Rook contiguity weights from a polygon shapefile.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-
-<span class="sd">        shapefile : string</span>
-<span class="sd">                    name of polygon shapefile including suffix.</span>
-<span class="sd">        sparse    : boolean</span>
-<span class="sd">                    If True return WSP instance</span>
-<span class="sd">                    If False return W instance</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-
-<span class="sd">        w          : W</span>
-<span class="sd">                     instance of spatial weights</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        &gt;&gt;&gt; from libpysal.weights import Rook</span>
-<span class="sd">        &gt;&gt;&gt; import libpysal</span>
-<span class="sd">        &gt;&gt;&gt; wr=Rook.from_shapefile(libpysal.examples.get_path(&quot;columbus.shp&quot;), &quot;POLYID&quot;)</span>
-<span class="sd">        &gt;&gt;&gt; &quot;%.3f&quot;%wr.pct_nonzero</span>
-<span class="sd">        &#39;8.330&#39;</span>
-<span class="sd">        &gt;&gt;&gt; wr=Rook.from_shapefile(libpysal.examples.get_path(&quot;columbus.shp&quot;), sparse=True)</span>
-<span class="sd">        &gt;&gt;&gt; pct_sp = wr.sparse.nnz *1. / wr.n**2</span>
-<span class="sd">        &gt;&gt;&gt; &quot;%.3f&quot;%pct_sp</span>
-<span class="sd">        &#39;0.083&#39;</span>
-
-<span class="sd">        Notes</span>
-<span class="sd">        -----</span>
-
-<span class="sd">        Rook contiguity defines as neighbors any pair of polygons that share a</span>
-<span class="sd">        common edge in their polygon definitions.</span>
-
-<span class="sd">        See Also</span>
-<span class="sd">        --------</span>
-<span class="sd">        :class:`libpysal.weights.weights.W`</span>
-<span class="sd">        :class:`libpysal.weights.contiguity.Rook`</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">sparse</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;sparse&quot;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">idVariable</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">ids</span> <span class="o">=</span> <span class="n">get_ids</span><span class="p">(</span><span class="n">filepath</span><span class="p">,</span> <span class="n">idVariable</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">ids</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="n">w</span> <span class="o">=</span> <span class="bp">cls</span><span class="p">(</span><span class="n">FileIO</span><span class="p">(</span><span class="n">filepath</span><span class="p">),</span> <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-        <span class="n">w</span><span class="o">.</span><span class="n">set_shapefile</span><span class="p">(</span><span class="n">filepath</span><span class="p">,</span> <span class="n">idVariable</span><span class="o">=</span><span class="n">idVariable</span><span class="p">,</span> <span class="n">full</span><span class="o">=</span><span class="n">full</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">sparse</span><span class="p">:</span>
-            <span class="n">w</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">to_WSP</span><span class="p">()</span>
-        <span class="k">return</span> <span class="n">w</span></div>
-
-<div class="viewcode-block" id="Rook.from_iterable"><a class="viewcode-back" href="../../../generated/libpysal.weights.Rook.html#libpysal.weights.Rook.from_iterable">[docs]</a>    <span class="nd">@classmethod</span>
-    <span class="k">def</span> <span class="nf">from_iterable</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">iterable</span><span class="p">,</span> <span class="n">sparse</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Construct a weights object from a collection of arbitrary polygons. This</span>
-<span class="sd">        will cast the polygons to PySAL polygons, then build the W.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        iterable    : iterable</span>
-<span class="sd">                      a collection of of shapes to be cast to PySAL shapes. Must</span>
-<span class="sd">                      support iteration. Can be either Shapely or PySAL shapes.</span>
-<span class="sd">        **kw        : keyword arguments</span>
-<span class="sd">                      optional arguments for  :class:`pysal.weights.W`</span>
-<span class="sd">        See Also</span>
-<span class="sd">        --------</span>
-<span class="sd">        :class:`libpysal.weights.weights.W`</span>
-<span class="sd">        :class:`libpysal.weights.contiguity.Rook`</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">new_iterable</span> <span class="o">=</span> <span class="nb">iter</span><span class="p">(</span><span class="n">iterable</span><span class="p">)</span>
-        <span class="n">w</span> <span class="o">=</span> <span class="bp">cls</span><span class="p">(</span><span class="n">new_iterable</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">sparse</span><span class="p">:</span>
-            <span class="n">w</span> <span class="o">=</span> <span class="n">WSP</span><span class="o">.</span><span class="n">from_W</span><span class="p">(</span><span class="n">w</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">w</span></div>
-
-<div class="viewcode-block" id="Rook.from_dataframe"><a class="viewcode-back" href="../../../generated/libpysal.weights.Rook.html#libpysal.weights.Rook.from_dataframe">[docs]</a>    <span class="nd">@classmethod</span>
-    <span class="k">def</span> <span class="nf">from_dataframe</span><span class="p">(</span>
-        <span class="bp">cls</span><span class="p">,</span> <span class="n">df</span><span class="p">,</span> <span class="n">geom_col</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">idVariable</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">id_order</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span>
-    <span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Construct a weights object from a pandas dataframe with a geometry</span>
-<span class="sd">        column. This will cast the polygons to PySAL polygons, then build the W</span>
-<span class="sd">        using ids from the dataframe.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        df          : DataFrame</span>
-<span class="sd">                      a :class: `pandas.DataFrame` containing geometries to use</span>
-<span class="sd">                      for spatial weights</span>
-<span class="sd">        geom_col    : string</span>
-<span class="sd">                      the name of the column in `df` that contains the</span>
-<span class="sd">                      geometries. Defaults to active geometry column.</span>
-<span class="sd">        idVariable  : string</span>
-<span class="sd">                      the name of the column to use as IDs. If nothing is</span>
-<span class="sd">                      provided, the dataframe index is used</span>
-<span class="sd">        ids         : list</span>
-<span class="sd">                      a list of ids to use to index the spatial weights object.</span>
-<span class="sd">                      Order is not respected from this list.</span>
-<span class="sd">        id_order    : list</span>
-<span class="sd">                      an ordered list of ids to use to index the spatial weights</span>
-<span class="sd">                      object. If used, the resulting weights object will iterate</span>
-<span class="sd">                      over results in the order of the names provided in this</span>
-<span class="sd">                      argument. </span>
-
-<span class="sd">        See Also</span>
-<span class="sd">        --------</span>
-<span class="sd">        :class:`libpysal.weights.weights.W`</span>
-<span class="sd">        :class:`libpysal.weights.contiguity.Rook`</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="n">geom_col</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">geom_col</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">name</span>
-        <span class="k">if</span> <span class="n">id_order</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">id_order</span> <span class="ow">is</span> <span class="kc">True</span> <span class="ow">and</span> <span class="p">((</span><span class="n">idVariable</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">or</span> <span class="p">(</span><span class="n">ids</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">)):</span>
-                <span class="c1"># if idVariable is None, we want ids. Otherwise, we want the</span>
-                <span class="c1"># idVariable column</span>
-                <span class="n">id_order</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">idVariable</span><span class="p">,</span> <span class="n">ids</span><span class="p">))</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">id_order</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">id_order</span><span class="p">,</span> <span class="n">ids</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="n">idVariable</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">ids</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">idVariable</span><span class="p">)</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
-        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">ids</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
-            <span class="n">ids</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">ids</span><span class="p">)</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
-        <span class="k">return</span> <span class="bp">cls</span><span class="o">.</span><span class="n">from_iterable</span><span class="p">(</span>
-            <span class="n">df</span><span class="p">[</span><span class="n">geom_col</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">(),</span> <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">,</span> <span class="n">id_order</span><span class="o">=</span><span class="n">id_order</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span>
-        <span class="p">)</span></div>
-
-<div class="viewcode-block" id="Rook.from_xarray"><a class="viewcode-back" href="../../../generated/libpysal.weights.Rook.html#libpysal.weights.Rook.from_xarray">[docs]</a>    <span class="nd">@classmethod</span>
-    <span class="k">def</span> <span class="nf">from_xarray</span><span class="p">(</span>
-        <span class="bp">cls</span><span class="p">,</span>
-        <span class="n">da</span><span class="p">,</span>
-        <span class="n">z_value</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">coords_labels</span><span class="o">=</span><span class="p">{},</span>
-        <span class="n">k</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-        <span class="n">include_nodata</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-        <span class="n">n_jobs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-        <span class="n">sparse</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-        <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
-    <span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Construct a weights object from a xarray.DataArray with an additional</span>
-<span class="sd">        attribute index containing coordinate values of the raster</span>
-<span class="sd">        in the form of Pandas.Index/MultiIndex.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        da : xarray.DataArray</span>
-<span class="sd">            Input 2D or 3D DataArray with shape=(z, y, x)</span>
-<span class="sd">        z_value : int/string/float</span>
-<span class="sd">            Select the z_value of 3D DataArray with multiple layers.</span>
-<span class="sd">        coords_labels : dictionary</span>
-<span class="sd">            Pass dimension labels for coordinates and layers if they do not</span>
-<span class="sd">            belong to default dimensions, which are (band/time, y/lat, x/lon)</span>
-<span class="sd">            e.g. coords_labels = {&quot;y_label&quot;: &quot;latitude&quot;, &quot;x_label&quot;: &quot;longitude&quot;, &quot;z_label&quot;: &quot;year&quot;}</span>
-<span class="sd">            Default is {} empty dictionary.</span>
-<span class="sd">        sparse : boolean</span>
-<span class="sd">            type of weight object. Default is True. For libpysal.weights.W, sparse = False</span>
-<span class="sd">        k : int</span>
-<span class="sd">            Order of contiguity, this will select all neighbors upto kth order.</span>
-<span class="sd">            Default is 1.</span>
-<span class="sd">        include_nodata : boolean</span>
-<span class="sd">            If True, missing values will be assumed as non-missing when</span>
-<span class="sd">            selecting higher_order neighbors, Default is False</span>
-<span class="sd">        n_jobs : int</span>
-<span class="sd">            Number of cores to be used in the sparse weight construction. If -1,</span>
-<span class="sd">            all available cores are used. Default is 1.</span>
-<span class="sd">        **kwargs : keyword arguments</span>
-<span class="sd">            optional arguments passed when sparse = False</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        w : libpysal.weights.W/libpysal.weights.WSP</span>
-<span class="sd">            instance of spatial weights class W or WSP with an index attribute </span>
-
-<span class="sd">        Notes</span>
-<span class="sd">        -----</span>
-<span class="sd">        1. Lower order contiguities are also selected.</span>
-<span class="sd">        2. Returned object contains `index` attribute that includes a </span>
-<span class="sd">        `Pandas.MultiIndex` object from the DataArray.</span>
-
-<span class="sd">        See Also</span>
-<span class="sd">        --------</span>
-<span class="sd">        :class:`libpysal.weights.weights.W`</span>
-<span class="sd">        :class:`libpysal.weights.weights.WSP`</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="n">sparse</span><span class="p">:</span>
-            <span class="n">w</span> <span class="o">=</span> <span class="n">da2WSP</span><span class="p">(</span><span class="n">da</span><span class="p">,</span> <span class="s2">&quot;rook&quot;</span><span class="p">,</span> <span class="n">z_value</span><span class="p">,</span> <span class="n">coords_labels</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">include_nodata</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">w</span> <span class="o">=</span> <span class="n">da2W</span><span class="p">(</span><span class="n">da</span><span class="p">,</span> <span class="s2">&quot;rook&quot;</span><span class="p">,</span> <span class="n">z_value</span><span class="p">,</span> <span class="n">coords_labels</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">include_nodata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">w</span></div></div>
-
-
-<div class="viewcode-block" id="Queen"><a class="viewcode-back" href="../../../generated/libpysal.weights.Queen.html#libpysal.weights.Queen">[docs]</a><span class="k">class</span> <span class="nc">Queen</span><span class="p">(</span><span class="n">W</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Construct a weights object from a collection of pysal polygons that share at least one vertex.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    polygons    : list</span>
-<span class="sd">                  a collection of PySAL shapes to build weights from</span>
-<span class="sd">    ids         : list</span>
-<span class="sd">                  a list of names to use to build the weights</span>
-<span class="sd">    **kw        : keyword arguments</span>
-<span class="sd">                  optional arguments for :class:`pysal.weights.W`</span>
-
-<span class="sd">    See Also</span>
-<span class="sd">    --------</span>
-<span class="sd">    :class:`libpysal.weights.weights.W`</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-<div class="viewcode-block" id="Queen.__init__"><a class="viewcode-back" href="../../../generated/libpysal.weights.Queen.html#libpysal.weights.Queen.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">polygons</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">):</span>
-        <span class="n">criterion</span> <span class="o">=</span> <span class="s2">&quot;queen&quot;</span>
-        <span class="n">ids</span> <span class="o">=</span> <span class="n">kw</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;ids&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
-        <span class="n">polygons</span><span class="p">,</span> <span class="n">backup</span> <span class="o">=</span> <span class="n">itertools</span><span class="o">.</span><span class="n">tee</span><span class="p">(</span><span class="n">polygons</span><span class="p">)</span>
-        <span class="n">first_shape</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="nb">iter</span><span class="p">(</span><span class="n">backup</span><span class="p">))</span>
-        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">first_shape</span><span class="p">,</span> <span class="n">point_type</span><span class="p">):</span>
-            <span class="n">polygons</span><span class="p">,</span> <span class="n">vertices</span> <span class="o">=</span> <span class="n">voronoi_frames</span><span class="p">(</span><span class="n">get_points_array</span><span class="p">(</span><span class="n">polygons</span><span class="p">))</span>
-            <span class="n">polygons</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">polygons</span><span class="o">.</span><span class="n">geometry</span><span class="p">)</span>
-        <span class="n">neighbors</span><span class="p">,</span> <span class="n">ids</span> <span class="o">=</span> <span class="n">_build</span><span class="p">(</span><span class="n">polygons</span><span class="p">,</span> <span class="n">criterion</span><span class="o">=</span><span class="n">criterion</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">)</span>
-        <span class="n">W</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">neighbors</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="Queen.from_shapefile"><a class="viewcode-back" href="../../../generated/libpysal.weights.Queen.html#libpysal.weights.Queen.from_shapefile">[docs]</a>    <span class="nd">@classmethod</span>
-    <span class="k">def</span> <span class="nf">from_shapefile</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">filepath</span><span class="p">,</span> <span class="n">idVariable</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">full</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Queen contiguity weights from a polygon shapefile.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-
-<span class="sd">        shapefile   : string</span>
-<span class="sd">                      name of polygon shapefile including suffix.</span>
-<span class="sd">        idVariable  : string</span>
-<span class="sd">                      name of a column in the shapefile&#39;s DBF to use for ids.</span>
-<span class="sd">        sparse      : boolean</span>
-<span class="sd">                      If True return WSP instance</span>
-<span class="sd">                      If False return W instance</span>
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-
-<span class="sd">        w            : W</span>
-<span class="sd">                       instance of spatial weights</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        &gt;&gt;&gt; from libpysal.weights import Queen</span>
-<span class="sd">        &gt;&gt;&gt; import libpysal</span>
-<span class="sd">        &gt;&gt;&gt; wq=Queen.from_shapefile(libpysal.examples.get_path(&quot;columbus.shp&quot;))</span>
-<span class="sd">        &gt;&gt;&gt; &quot;%.3f&quot;%wq.pct_nonzero</span>
-<span class="sd">        &#39;9.829&#39;</span>
-<span class="sd">        &gt;&gt;&gt; wq=Queen.from_shapefile(libpysal.examples.get_path(&quot;columbus.shp&quot;),&quot;POLYID&quot;)</span>
-<span class="sd">        &gt;&gt;&gt; &quot;%.3f&quot;%wq.pct_nonzero</span>
-<span class="sd">        &#39;9.829&#39;</span>
-<span class="sd">        &gt;&gt;&gt; wq=Queen.from_shapefile(libpysal.examples.get_path(&quot;columbus.shp&quot;), sparse=True)</span>
-<span class="sd">        &gt;&gt;&gt; pct_sp = wq.sparse.nnz *1. / wq.n**2</span>
-<span class="sd">        &gt;&gt;&gt; &quot;%.3f&quot;%pct_sp</span>
-<span class="sd">        &#39;0.098&#39;</span>
-
-<span class="sd">        Notes</span>
-
-<span class="sd">        Queen contiguity defines as neighbors any pair of polygons that share at</span>
-<span class="sd">        least one vertex in their polygon definitions.</span>
-
-<span class="sd">        See Also</span>
-<span class="sd">        --------</span>
-<span class="sd">        :class:`libpysal.weights.weights.W`</span>
-<span class="sd">        :class:`libpysal.weights.contiguity.Queen`</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">sparse</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;sparse&quot;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">idVariable</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">ids</span> <span class="o">=</span> <span class="n">get_ids</span><span class="p">(</span><span class="n">filepath</span><span class="p">,</span> <span class="n">idVariable</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">ids</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="n">w</span> <span class="o">=</span> <span class="bp">cls</span><span class="p">(</span><span class="n">FileIO</span><span class="p">(</span><span class="n">filepath</span><span class="p">),</span> <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-        <span class="n">w</span><span class="o">.</span><span class="n">set_shapefile</span><span class="p">(</span><span class="n">filepath</span><span class="p">,</span> <span class="n">idVariable</span><span class="o">=</span><span class="n">idVariable</span><span class="p">,</span> <span class="n">full</span><span class="o">=</span><span class="n">full</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">sparse</span><span class="p">:</span>
-            <span class="n">w</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">to_WSP</span><span class="p">()</span>
-        <span class="k">return</span> <span class="n">w</span></div>
-
-<div class="viewcode-block" id="Queen.from_iterable"><a class="viewcode-back" href="../../../generated/libpysal.weights.Queen.html#libpysal.weights.Queen.from_iterable">[docs]</a>    <span class="nd">@classmethod</span>
-    <span class="k">def</span> <span class="nf">from_iterable</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">iterable</span><span class="p">,</span> <span class="n">sparse</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Construct a weights object from a collection of arbitrary polygons. This</span>
-<span class="sd">        will cast the polygons to PySAL polygons, then build the W.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        iterable    : iterable</span>
-<span class="sd">                      a collection of of shapes to be cast to PySAL shapes. Must</span>
-<span class="sd">                      support iteration. Contents may either be a shapely or PySAL shape.</span>
-<span class="sd">        **kw        : keyword arguments</span>
-<span class="sd">                      optional arguments for  :class:`pysal.weights.W`</span>
-<span class="sd">        See Also</span>
-<span class="sd">        ---------</span>
-<span class="sd">        :class:`libpysal.weights.weights.W`</span>
-<span class="sd">        :class:`libpysal.weights.contiguiyt.Queen`</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">new_iterable</span> <span class="o">=</span> <span class="nb">iter</span><span class="p">(</span><span class="n">iterable</span><span class="p">)</span>
-        <span class="n">w</span> <span class="o">=</span> <span class="bp">cls</span><span class="p">(</span><span class="n">new_iterable</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">sparse</span><span class="p">:</span>
-            <span class="n">w</span> <span class="o">=</span> <span class="n">WSP</span><span class="o">.</span><span class="n">from_W</span><span class="p">(</span><span class="n">w</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">w</span></div>
-
-<div class="viewcode-block" id="Queen.from_dataframe"><a class="viewcode-back" href="../../../generated/libpysal.weights.Queen.html#libpysal.weights.Queen.from_dataframe">[docs]</a>    <span class="nd">@classmethod</span>
-    <span class="k">def</span> <span class="nf">from_dataframe</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">df</span><span class="p">,</span> <span class="n">geom_col</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Construct a weights object from a pandas dataframe with a geometry</span>
-<span class="sd">        column. This will cast the polygons to PySAL polygons, then build the W</span>
-<span class="sd">        using ids from the dataframe.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        df          : DataFrame</span>
-<span class="sd">                      a :class: `pandas.DataFrame` containing geometries to use</span>
-<span class="sd">                      for spatial weights</span>
-<span class="sd">        geom_col    : string</span>
-<span class="sd">                      the name of the column in `df` that contains the</span>
-<span class="sd">                      geometries. Defaults to active geometry column</span>
-<span class="sd">        idVariable  : string</span>
-<span class="sd">                      the name of the column to use as IDs. If nothing is</span>
-<span class="sd">                      provided, the dataframe index is used</span>
-<span class="sd">        ids         : list</span>
-<span class="sd">                      a list of ids to use to index the spatial weights object.</span>
-<span class="sd">                      Order is not respected from this list.</span>
-<span class="sd">        id_order    : list</span>
-<span class="sd">                      an ordered list of ids to use to index the spatial weights</span>
-<span class="sd">                      object. If used, the resulting weights object will iterate</span>
-<span class="sd">                      over results in the order of the names provided in this</span>
-<span class="sd">                      argument. </span>
-
-<span class="sd">        See Also</span>
-<span class="sd">        --------</span>
-<span class="sd">        :class:`libpysal.weights.weights.W`</span>
-<span class="sd">        :class:`libpysal.weights.contiguity.Queen`</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">idVariable</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;idVariable&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
-        <span class="n">ids</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;ids&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
-        <span class="n">id_order</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;id_order&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">geom_col</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">geom_col</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">name</span>
-        <span class="k">if</span> <span class="n">id_order</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">id_order</span> <span class="ow">is</span> <span class="kc">True</span> <span class="ow">and</span> <span class="p">((</span><span class="n">idVariable</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">or</span> <span class="p">(</span><span class="n">ids</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">)):</span>
-                <span class="c1"># if idVariable is None, we want ids. Otherwise, we want the</span>
-                <span class="c1"># idVariable column</span>
-                <span class="n">ids</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">idVariable</span><span class="p">,</span> <span class="n">ids</span><span class="p">))</span>
-                <span class="n">id_order</span> <span class="o">=</span> <span class="n">ids</span>
-            <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">id_order</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
-                <span class="n">ids</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">id_order</span><span class="p">,</span> <span class="n">ids</span><span class="p">)</span>
-                <span class="n">id_order</span> <span class="o">=</span> <span class="n">ids</span>
-        <span class="k">elif</span> <span class="n">idVariable</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">ids</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">idVariable</span><span class="p">)</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
-        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">ids</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
-            <span class="n">ids</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">ids</span><span class="p">)</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
-        <span class="n">w</span> <span class="o">=</span> <span class="bp">cls</span><span class="o">.</span><span class="n">from_iterable</span><span class="p">(</span>
-            <span class="n">df</span><span class="p">[</span><span class="n">geom_col</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">(),</span> <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">,</span> <span class="n">id_order</span><span class="o">=</span><span class="n">id_order</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span>
-        <span class="p">)</span>
-        <span class="k">return</span> <span class="n">w</span></div>
-
-<div class="viewcode-block" id="Queen.from_xarray"><a class="viewcode-back" href="../../../generated/libpysal.weights.Queen.html#libpysal.weights.Queen.from_xarray">[docs]</a>    <span class="nd">@classmethod</span>
-    <span class="k">def</span> <span class="nf">from_xarray</span><span class="p">(</span>
-        <span class="bp">cls</span><span class="p">,</span>
-        <span class="n">da</span><span class="p">,</span>
-        <span class="n">z_value</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">coords_labels</span><span class="o">=</span><span class="p">{},</span>
-        <span class="n">k</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-        <span class="n">include_nodata</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-        <span class="n">n_jobs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-        <span class="n">sparse</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-        <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
-    <span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Construct a weights object from a xarray.DataArray with an additional</span>
-<span class="sd">        attribute index containing coordinate values of the raster</span>
-<span class="sd">        in the form of Pandas.Index/MultiIndex.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        da : xarray.DataArray</span>
-<span class="sd">            Input 2D or 3D DataArray with shape=(z, y, x)</span>
-<span class="sd">        z_value : int/string/float</span>
-<span class="sd">            Select the z_value of 3D DataArray with multiple layers.</span>
-<span class="sd">        coords_labels : dictionary</span>
-<span class="sd">            Pass dimension labels for coordinates and layers if they do not</span>
-<span class="sd">            belong to default dimensions, which are (band/time, y/lat, x/lon)</span>
-<span class="sd">            e.g. coords_labels = {&quot;y_label&quot;: &quot;latitude&quot;, &quot;x_label&quot;: &quot;longitude&quot;, &quot;z_label&quot;: &quot;year&quot;}</span>
-<span class="sd">            Default is {} empty dictionary.</span>
-<span class="sd">        sparse : boolean</span>
-<span class="sd">            type of weight object. Default is True. For libpysal.weights.W, sparse = False</span>
-<span class="sd">        k : int</span>
-<span class="sd">            Order of contiguity, this will select all neighbors upto kth order.</span>
-<span class="sd">            Default is 1.</span>
-<span class="sd">        include_nodata : boolean</span>
-<span class="sd">            If True, missing values will be assumed as non-missing when</span>
-<span class="sd">            selecting higher_order neighbors, Default is False</span>
-<span class="sd">        n_jobs : int</span>
-<span class="sd">            Number of cores to be used in the sparse weight construction. If -1,</span>
-<span class="sd">            all available cores are used. Default is 1.</span>
-<span class="sd">        **kwargs : keyword arguments</span>
-<span class="sd">            optional arguments passed when sparse = False</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        w : libpysal.weights.W/libpysal.weights.WSP</span>
-<span class="sd">            instance of spatial weights class W or WSP with an index attribute </span>
-
-<span class="sd">        Notes</span>
-<span class="sd">        -----</span>
-<span class="sd">        1. Lower order contiguities are also selected.</span>
-<span class="sd">        2. Returned object contains `index` attribute that includes a </span>
-<span class="sd">        `Pandas.MultiIndex` object from the DataArray.</span>
-
-<span class="sd">        See Also</span>
-<span class="sd">        --------</span>
-<span class="sd">        :class:`libpysal.weights.weights.W`</span>
-<span class="sd">        :class:`libpysal.weights.weights.WSP`</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="n">sparse</span><span class="p">:</span>
-            <span class="n">w</span> <span class="o">=</span> <span class="n">da2WSP</span><span class="p">(</span><span class="n">da</span><span class="p">,</span> <span class="s2">&quot;queen&quot;</span><span class="p">,</span> <span class="n">z_value</span><span class="p">,</span> <span class="n">coords_labels</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">include_nodata</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">w</span> <span class="o">=</span> <span class="n">da2W</span><span class="p">(</span><span class="n">da</span><span class="p">,</span> <span class="s2">&quot;queen&quot;</span><span class="p">,</span> <span class="n">z_value</span><span class="p">,</span> <span class="n">coords_labels</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">include_nodata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">w</span></div></div>
-
-
-<div class="viewcode-block" id="Voronoi"><a class="viewcode-back" href="../../../generated/libpysal.weights.Voronoi.html#libpysal.weights.Voronoi">[docs]</a><span class="k">def</span> <span class="nf">Voronoi</span><span class="p">(</span><span class="n">points</span><span class="p">,</span> <span class="n">criterion</span><span class="o">=</span><span class="s2">&quot;rook&quot;</span><span class="p">,</span> <span class="n">clip</span><span class="o">=</span><span class="s2">&quot;ahull&quot;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Voronoi weights for a 2-d point set</span>
-
-
-<span class="sd">    Points are Voronoi neighbors if their polygons share an edge or vertex.</span>
-
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-
-<span class="sd">    points      : array</span>
-<span class="sd">                  (n,2)</span>
-<span class="sd">                  coordinates for point locations</span>
-<span class="sd">    kwargs      : arguments to pass to Rook, the underlying contiguity class.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-
-<span class="sd">    w           : W</span>
-<span class="sd">                  instance of spatial weights</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    &gt;&gt;&gt; import numpy as np</span>
-<span class="sd">    &gt;&gt;&gt; from libpysal.weights import Voronoi</span>
-<span class="sd">    &gt;&gt;&gt; np.random.seed(12345)</span>
-<span class="sd">    &gt;&gt;&gt; points= np.random.random((5,2))*10 + 10</span>
-<span class="sd">    &gt;&gt;&gt; w = Voronoi(points)</span>
-<span class="sd">    &gt;&gt;&gt; w.neighbors</span>
-<span class="sd">    {0: [2, 3, 4], 1: [2], 2: [0, 1, 4], 3: [0, 4], 4: [0, 2, 3]}</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="kn">from</span> <span class="nn">..cg.voronoi</span> <span class="kn">import</span> <span class="n">voronoi_frames</span>
-
-    <span class="n">region_df</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">voronoi_frames</span><span class="p">(</span><span class="n">points</span><span class="p">,</span> <span class="n">clip</span><span class="o">=</span><span class="n">clip</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">criterion</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">==</span> <span class="s2">&quot;queen&quot;</span><span class="p">:</span>
-        <span class="bp">cls</span> <span class="o">=</span> <span class="n">Queen</span>
-    <span class="k">elif</span> <span class="n">criterion</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">==</span> <span class="s2">&quot;rook&quot;</span><span class="p">:</span>
-        <span class="bp">cls</span> <span class="o">=</span> <span class="n">Rook</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-            <span class="s2">&quot;Contiguity criterion </span><span class="si">{}</span><span class="s2"> not supported. &quot;</span>
-            <span class="s1">&#39;Only &quot;rook&quot; and &quot;queen&quot; are supported.&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">criterion</span><span class="p">)</span>
-        <span class="p">)</span>
-    <span class="k">return</span> <span class="bp">cls</span><span class="o">.</span><span class="n">from_dataframe</span><span class="p">(</span><span class="n">region_df</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
-
-
-<span class="k">def</span> <span class="nf">_from_dataframe</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Construct a voronoi contiguity weight directly from a dataframe. </span>
-<span class="sd">    Note that if criterion=&#39;rook&#39;, this is identical to the delaunay</span>
-<span class="sd">    graph for the points. </span>
-
-<span class="sd">    If the input dataframe is of any other geometry type than &quot;Point&quot;,</span>
-<span class="sd">    a value error is raised. </span>
-
-<span class="sd">    Arguments</span>
-<span class="sd">    ---------</span>
-<span class="sd">    df          :   pandas.DataFrame</span>
-<span class="sd">                    dataframe containing point geometries for a </span>
-<span class="sd">                    voronoi diagram.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    w           :   W</span>
-<span class="sd">                    instance of spatial weights.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">try</span><span class="p">:</span>
-        <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">x</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">df</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">y</span><span class="o">.</span><span class="n">values</span>
-    <span class="k">except</span> <span class="ne">ValueError</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span>
-            <span class="s2">&quot;Voronoi weights are only&quot;</span>
-            <span class="s2">&quot; implemented for point geometries. &quot;</span>
-            <span class="s2">&quot;You may consider using df.centroid.&quot;</span>
-        <span class="p">)</span>
-    <span class="n">coords</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">column_stack</span><span class="p">((</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">))</span>
-    <span class="k">return</span> <span class="n">Voronoi</span><span class="p">(</span><span class="n">coords</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-
-
-<span class="n">Voronoi</span><span class="o">.</span><span class="n">from_dataframe</span> <span class="o">=</span> <span class="n">_from_dataframe</span>
-
-
-<span class="k">def</span> <span class="nf">_build</span><span class="p">(</span><span class="n">polygons</span><span class="p">,</span> <span class="n">criterion</span><span class="o">=</span><span class="s2">&quot;rook&quot;</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    This is a developer-facing function to construct a spatial weights object. </span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ---------</span>
-<span class="sd">    polygons    : list</span>
-<span class="sd">                  list of pysal polygons to use to build contiguity</span>
-<span class="sd">    criterion   : string</span>
-<span class="sd">                  option of which kind of contiguity to build. Is either &quot;rook&quot; or &quot;queen&quot; </span>
-<span class="sd">    ids         : list</span>
-<span class="sd">                  list of ids to use to index the neighbor dictionary</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    tuple containing (neighbors, ids), where neighbors is a dictionary</span>
-<span class="sd">    describing contiguity relations and ids is the list of ids used to index</span>
-<span class="sd">    that dictionary. </span>
-
-<span class="sd">    NOTE: this is different from the prior behavior of buildContiguity, which</span>
-<span class="sd">          returned an actual weights object. Since this just dispatches for the</span>
-<span class="sd">          classes above, this returns the raw ingredients for a spatial weights</span>
-<span class="sd">          object, not the object itself. </span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="n">ids</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">ids</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">ids</span><span class="p">)):</span>
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-            <span class="s2">&quot;The argument to the ids parameter contains duplicate entries.&quot;</span>
-        <span class="p">)</span>
-
-    <span class="n">wttype</span> <span class="o">=</span> <span class="n">WT_TYPE</span><span class="p">[</span><span class="n">criterion</span><span class="o">.</span><span class="n">lower</span><span class="p">()]</span>
-    <span class="n">geo</span> <span class="o">=</span> <span class="n">polygons</span>
-    <span class="k">if</span> <span class="nb">issubclass</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">geo</span><span class="p">),</span> <span class="n">FileIO</span><span class="p">):</span>
-        <span class="n">geo</span><span class="o">.</span><span class="n">seek</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>  <span class="c1"># Make sure we read from the beginning of the file.</span>
-
-    <span class="n">neighbor_data</span> <span class="o">=</span> <span class="n">ContiguityWeightsLists</span><span class="p">(</span><span class="n">polygons</span><span class="p">,</span> <span class="n">wttype</span><span class="o">=</span><span class="n">wttype</span><span class="p">)</span><span class="o">.</span><span class="n">w</span>
-
-    <span class="n">neighbors</span> <span class="o">=</span> <span class="p">{}</span>
-    <span class="c1"># weights={}</span>
-    <span class="k">if</span> <span class="n">ids</span><span class="p">:</span>
-        <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">neighbor_data</span><span class="p">:</span>
-            <span class="n">ida</span> <span class="o">=</span> <span class="n">ids</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
-            <span class="k">if</span> <span class="n">ida</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">neighbors</span><span class="p">:</span>
-                <span class="n">neighbors</span><span class="p">[</span><span class="n">ida</span><span class="p">]</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
-            <span class="n">neighbors</span><span class="p">[</span><span class="n">ida</span><span class="p">]</span><span class="o">.</span><span class="n">update</span><span class="p">([</span><span class="n">ids</span><span class="p">[</span><span class="n">x</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">neighbor_data</span><span class="p">[</span><span class="n">key</span><span class="p">]])</span>
-        <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">neighbors</span><span class="p">:</span>
-            <span class="n">neighbors</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">neighbors</span><span class="p">[</span><span class="n">key</span><span class="p">])</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">neighbor_data</span><span class="p">:</span>
-            <span class="n">neighbors</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">neighbor_data</span><span class="p">[</span><span class="n">key</span><span class="p">])</span>
-    <span class="k">return</span> <span class="p">(</span>
-        <span class="nb">dict</span><span class="p">(</span>
-            <span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">neighbors</span><span class="o">.</span><span class="n">keys</span><span class="p">()),</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">list</span><span class="p">(</span><span class="n">neighbors</span><span class="o">.</span><span class="n">values</span><span class="p">())))))</span>
-        <span class="p">),</span>
-        <span class="n">ids</span><span class="p">,</span>
-    <span class="p">)</span>
-
-
-<span class="k">def</span> <span class="nf">buildContiguity</span><span class="p">(</span><span class="n">polygons</span><span class="p">,</span> <span class="n">criterion</span><span class="o">=</span><span class="s2">&quot;rook&quot;</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    This is a deprecated function.</span>
-
-<span class="sd">    It builds a contiguity W from the polygons provided. As such, it is now</span>
-<span class="sd">    identical to calling the class constructors for Rook or Queen. </span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># Warn(&#39;This function is deprecated. Please use the Rook or Queen classes&#39;,</span>
-    <span class="c1">#        UserWarning)</span>
-    <span class="k">if</span> <span class="n">criterion</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">==</span> <span class="s2">&quot;rook&quot;</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">Rook</span><span class="p">(</span><span class="n">polygons</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">)</span>
-    <span class="k">elif</span> <span class="n">criterion</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">==</span> <span class="s2">&quot;queen&quot;</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">Queen</span><span class="p">(</span><span class="n">polygons</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s1">&#39;Weights criterion &quot;</span><span class="si">{}</span><span class="s1">&quot; was not found.&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">criterion</span><span class="p">))</span>
-</pre></div>
-
-    </div>
-      
-  </div>
-</div>
-<footer class="footer">
-  <div class="container">
-    <p class="pull-right">
-      <a href="#">Back to top</a>
-      
-        <br/>
-        
-      
-    </p>
-    <p>
-        &copy; Copyright 2018-, pysal developers.<br/>
-      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 3.1.2.<br/>
-    </p>
-  </div>
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diff --git a/docs/_modules/libpysal/weights/distance.html b/docs/_modules/libpysal/weights/distance.html
deleted file mode 100644
index 5858a82fe..000000000
--- a/docs/_modules/libpysal/weights/distance.html
+++ /dev/null
@@ -1,1098 +0,0 @@
-<!DOCTYPE html>
-
-<html>
-  <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>libpysal.weights.distance &#8212; libpysal v4.4.0 Manual</title>
-    <link rel="stylesheet" href="../../../_static/bootstrap-sphinx.css" type="text/css" />
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-  <h1>Source code for libpysal.weights.distance</h1><div class="highlight"><pre>
-<span></span><span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;KNN&quot;</span><span class="p">,</span> <span class="s2">&quot;Kernel&quot;</span><span class="p">,</span> <span class="s2">&quot;DistanceBand&quot;</span><span class="p">]</span>
-<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Sergio J. Rey &lt;srey@asu.edu&gt;, Levi John Wolf &lt;levi.john.wolf@gmail.com&gt;&quot;</span>
-
-
-<span class="kn">from</span> <span class="nn">..cg.kdtree</span> <span class="kn">import</span> <span class="n">KDTree</span>
-<span class="kn">from</span> <span class="nn">.weights</span> <span class="kn">import</span> <span class="n">W</span><span class="p">,</span> <span class="n">WSP</span>
-<span class="kn">from</span> <span class="nn">.util</span> <span class="kn">import</span> <span class="p">(</span>
-    <span class="n">isKDTree</span><span class="p">,</span>
-    <span class="n">get_ids</span><span class="p">,</span>
-    <span class="n">get_points_array_from_shapefile</span><span class="p">,</span>
-    <span class="n">get_points_array</span><span class="p">,</span>
-    <span class="n">WSP2W</span><span class="p">,</span>
-<span class="p">)</span>
-
-<span class="kn">import</span> <span class="nn">copy</span>
-<span class="kn">from</span> <span class="nn">warnings</span> <span class="kn">import</span> <span class="n">warn</span> <span class="k">as</span> <span class="n">Warn</span>
-<span class="kn">from</span> <span class="nn">scipy.spatial</span> <span class="kn">import</span> <span class="n">distance_matrix</span>
-<span class="kn">import</span> <span class="nn">scipy.sparse</span> <span class="k">as</span> <span class="nn">sp</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-
-<span class="k">def</span> <span class="nf">knnW</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">distance_metric</span><span class="o">=</span><span class="s2">&quot;euclidean&quot;</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    This is deprecated. Use the pysal.weights.KNN class instead.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># Warn(&#39;This function is deprecated. Please use pysal.weights.KNN&#39;, UserWarning)</span>
-    <span class="k">return</span> <span class="n">KNN</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="n">k</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">p</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="n">radius</span><span class="p">,</span> <span class="n">distance_metric</span><span class="o">=</span><span class="n">distance_metric</span><span class="p">)</span>
-
-
-<div class="viewcode-block" id="KNN"><a class="viewcode-back" href="../../../generated/libpysal.weights.KNN.html#libpysal.weights.KNN">[docs]</a><span class="k">class</span> <span class="nc">KNN</span><span class="p">(</span><span class="n">W</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Creates nearest neighbor weights matrix based on k nearest</span>
-<span class="sd">    neighbors.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    kdtree      : object</span>
-<span class="sd">                  PySAL KDTree or ArcKDTree where KDtree.data is array (n,k)</span>
-<span class="sd">                  n observations on k characteristics used to measure</span>
-<span class="sd">                  distances between the n objects</span>
-<span class="sd">    k           : int</span>
-<span class="sd">                  number of nearest neighbors</span>
-<span class="sd">    p           : float</span>
-<span class="sd">                  Minkowski p-norm distance metric parameter:</span>
-<span class="sd">                  1&lt;=p&lt;=infinity</span>
-<span class="sd">                  2: Euclidean distance</span>
-<span class="sd">                  1: Manhattan distance</span>
-<span class="sd">                  Ignored if the KDTree is an ArcKDTree</span>
-<span class="sd">    ids         : list</span>
-<span class="sd">                  identifiers to attach to each observation</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-
-<span class="sd">    w         : W</span>
-<span class="sd">                instance</span>
-<span class="sd">                Weights object with binary weights</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    &gt;&gt;&gt; import libpysal</span>
-<span class="sd">    &gt;&gt;&gt; import numpy as np</span>
-<span class="sd">    &gt;&gt;&gt; points = [(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]</span>
-<span class="sd">    &gt;&gt;&gt; kd = libpysal.cg.KDTree(np.array(points))</span>
-<span class="sd">    &gt;&gt;&gt; wnn2 = libpysal.weights.KNN(kd, 2)</span>
-<span class="sd">    &gt;&gt;&gt; [1,3] == wnn2.neighbors[0]</span>
-<span class="sd">    True</span>
-<span class="sd">    &gt;&gt;&gt; wnn2 = KNN(kd,2)</span>
-<span class="sd">    &gt;&gt;&gt; wnn2[0]</span>
-<span class="sd">    {1: 1.0, 3: 1.0}</span>
-<span class="sd">    &gt;&gt;&gt; wnn2[1]</span>
-<span class="sd">    {0: 1.0, 3: 1.0}</span>
-
-<span class="sd">    now with 1 rather than 0 offset</span>
-
-<span class="sd">    &gt;&gt;&gt; wnn2 = libpysal.weights.KNN(kd, 2, ids=range(1,7))</span>
-<span class="sd">    &gt;&gt;&gt; wnn2[1]</span>
-<span class="sd">    {2: 1.0, 4: 1.0}</span>
-<span class="sd">    &gt;&gt;&gt; wnn2[2]</span>
-<span class="sd">    {1: 1.0, 4: 1.0}</span>
-<span class="sd">    &gt;&gt;&gt; 0 in wnn2.neighbors</span>
-<span class="sd">    False</span>
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-
-<span class="sd">    Ties between neighbors of equal distance are arbitrarily broken.</span>
-
-<span class="sd">    See Also</span>
-<span class="sd">    --------</span>
-<span class="sd">    :class:`libpysal.weights.weights.W`</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-<div class="viewcode-block" id="KNN.__init__"><a class="viewcode-back" href="../../../generated/libpysal.weights.KNN.html#libpysal.weights.KNN.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">data</span><span class="p">,</span>
-        <span class="n">k</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
-        <span class="n">p</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
-        <span class="n">ids</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">radius</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">distance_metric</span><span class="o">=</span><span class="s2">&quot;euclidean&quot;</span><span class="p">,</span>
-        <span class="o">**</span><span class="n">kwargs</span>
-    <span class="p">):</span>
-        <span class="k">if</span> <span class="n">radius</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">distance_metric</span> <span class="o">=</span> <span class="s2">&quot;arc&quot;</span>
-        <span class="k">if</span> <span class="n">isKDTree</span><span class="p">(</span><span class="n">data</span><span class="p">):</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span> <span class="o">=</span> <span class="n">data</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span><span class="o">.</span><span class="n">data</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span> <span class="o">=</span> <span class="n">KDTree</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="n">radius</span><span class="p">,</span> <span class="n">distance_metric</span><span class="o">=</span><span class="n">distance_metric</span><span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span><span class="o">.</span><span class="n">data</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">k</span> <span class="o">=</span> <span class="n">k</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">p</span> <span class="o">=</span> <span class="n">p</span>
-        <span class="n">this_nnq</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="n">k</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">p</span><span class="p">)</span>
-
-        <span class="n">to_weight</span> <span class="o">=</span> <span class="n">this_nnq</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-        <span class="k">if</span> <span class="n">ids</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">ids</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">to_weight</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
-
-        <span class="n">neighbors</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">row</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">to_weight</span><span class="p">):</span>
-            <span class="n">row</span> <span class="o">=</span> <span class="n">row</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
-            <span class="n">row</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
-            <span class="n">row</span> <span class="o">=</span> <span class="p">[</span><span class="n">ids</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">row</span><span class="p">]</span>
-            <span class="n">focal</span> <span class="o">=</span> <span class="n">ids</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-            <span class="n">neighbors</span><span class="p">[</span><span class="n">focal</span><span class="p">]</span> <span class="o">=</span> <span class="n">row</span>
-        <span class="n">W</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">neighbors</span><span class="p">,</span> <span class="n">id_order</span><span class="o">=</span><span class="n">ids</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="KNN.from_shapefile"><a class="viewcode-back" href="../../../generated/libpysal.weights.KNN.html#libpysal.weights.KNN.from_shapefile">[docs]</a>    <span class="nd">@classmethod</span>
-    <span class="k">def</span> <span class="nf">from_shapefile</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">filepath</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Nearest neighbor weights from a shapefile.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-
-<span class="sd">        data       : string</span>
-<span class="sd">                     shapefile containing attribute data.</span>
-<span class="sd">        k          : int</span>
-<span class="sd">                     number of nearest neighbors</span>
-<span class="sd">        p          : float</span>
-<span class="sd">                     Minkowski p-norm distance metric parameter:</span>
-<span class="sd">                     1&lt;=p&lt;=infinity</span>
-<span class="sd">                     2: Euclidean distance</span>
-<span class="sd">                     1: Manhattan distance</span>
-<span class="sd">        ids        : list</span>
-<span class="sd">                     identifiers to attach to each observation</span>
-<span class="sd">        radius     : float</span>
-<span class="sd">                     If supplied arc_distances will be calculated</span>
-<span class="sd">                     based on the given radius. p will be ignored.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-
-<span class="sd">        w         : KNN</span>
-<span class="sd">                    instance; Weights object with binary weights.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-
-<span class="sd">        Polygon shapefile</span>
-<span class="sd">        &gt;&gt;&gt; import libpysal</span>
-<span class="sd">        &gt;&gt;&gt; from libpysal.weights import KNN</span>
-<span class="sd">        &gt;&gt;&gt; wc=KNN.from_shapefile(libpysal.examples.get_path(&quot;columbus.shp&quot;))</span>
-<span class="sd">        &gt;&gt;&gt; &quot;%.4f&quot;%wc.pct_nonzero</span>
-<span class="sd">        &#39;4.0816&#39;</span>
-<span class="sd">        &gt;&gt;&gt; set([2,1]) == set(wc.neighbors[0])</span>
-<span class="sd">        True</span>
-<span class="sd">        &gt;&gt;&gt; wc3=KNN.from_shapefile(libpysal.examples.get_path(&quot;columbus.shp&quot;),k=3)</span>
-<span class="sd">        &gt;&gt;&gt; set(wc3.neighbors[0]) == set([2,1,3])</span>
-<span class="sd">        True</span>
-<span class="sd">        &gt;&gt;&gt; set(wc3.neighbors[2]) == set([4,3,0])</span>
-<span class="sd">        True</span>
-
-
-<span class="sd">        Point shapefile</span>
-
-<span class="sd">        &gt;&gt;&gt; w=KNN.from_shapefile(libpysal.examples.get_path(&quot;juvenile.shp&quot;))</span>
-<span class="sd">        &gt;&gt;&gt; w.pct_nonzero</span>
-<span class="sd">        1.1904761904761905</span>
-<span class="sd">        &gt;&gt;&gt; w1=KNN.from_shapefile(libpysal.examples.get_path(&quot;juvenile.shp&quot;),k=1)</span>
-<span class="sd">        &gt;&gt;&gt; &quot;%.3f&quot;%w1.pct_nonzero</span>
-<span class="sd">        &#39;0.595&#39;</span>
-
-<span class="sd">        Notes</span>
-<span class="sd">        -----</span>
-
-<span class="sd">        Ties between neighbors of equal distance are arbitrarily broken.</span>
-
-<span class="sd">        See Also</span>
-<span class="sd">        --------</span>
-<span class="sd">        :class:`libpysal.weights.weights.W`</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span><span class="n">get_points_array_from_shapefile</span><span class="p">(</span><span class="n">filepath</span><span class="p">),</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="KNN.from_array"><a class="viewcode-back" href="../../../generated/libpysal.weights.KNN.html#libpysal.weights.KNN.from_array">[docs]</a>    <span class="nd">@classmethod</span>
-    <span class="k">def</span> <span class="nf">from_array</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Creates nearest neighbor weights matrix based on k nearest</span>
-<span class="sd">        neighbors.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        array       : np.ndarray</span>
-<span class="sd">                      (n, k) array representing n observations on</span>
-<span class="sd">                      k characteristics used to measure distances</span>
-<span class="sd">                      between the n objects</span>
-<span class="sd">        **kwargs    : keyword arguments, see Rook</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        w         : W</span>
-<span class="sd">                    instance</span>
-<span class="sd">                    Weights object with binary weights</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        &gt;&gt;&gt; from libpysal.weights import KNN</span>
-<span class="sd">        &gt;&gt;&gt; points = [(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]</span>
-<span class="sd">        &gt;&gt;&gt; wnn2 = KNN.from_array(points, 2)</span>
-<span class="sd">        &gt;&gt;&gt; [1,3] == wnn2.neighbors[0]</span>
-<span class="sd">        True</span>
-<span class="sd">        &gt;&gt;&gt; wnn2 = KNN.from_array(points,2)</span>
-<span class="sd">        &gt;&gt;&gt; wnn2[0]</span>
-<span class="sd">        {1: 1.0, 3: 1.0}</span>
-<span class="sd">        &gt;&gt;&gt; wnn2[1]</span>
-<span class="sd">        {0: 1.0, 3: 1.0}</span>
-
-<span class="sd">        now with 1 rather than 0 offset</span>
-
-<span class="sd">        &gt;&gt;&gt; wnn2 = KNN.from_array(points, 2, ids=range(1,7))</span>
-<span class="sd">        &gt;&gt;&gt; wnn2[1]</span>
-<span class="sd">        {2: 1.0, 4: 1.0}</span>
-<span class="sd">        &gt;&gt;&gt; wnn2[2]</span>
-<span class="sd">        {1: 1.0, 4: 1.0}</span>
-<span class="sd">        &gt;&gt;&gt; 0 in wnn2.neighbors</span>
-<span class="sd">        False</span>
-
-<span class="sd">        Notes</span>
-<span class="sd">        -----</span>
-
-<span class="sd">        Ties between neighbors of equal distance are arbitrarily broken.</span>
-
-<span class="sd">        See Also</span>
-<span class="sd">        --------</span>
-<span class="sd">        :class:`libpysal.weights.weights.W`</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="KNN.from_dataframe"><a class="viewcode-back" href="../../../generated/libpysal.weights.KNN.html#libpysal.weights.KNN.from_dataframe">[docs]</a>    <span class="nd">@classmethod</span>
-    <span class="k">def</span> <span class="nf">from_dataframe</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">df</span><span class="p">,</span> <span class="n">geom_col</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Make KNN weights from a dataframe.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        df      :   pandas.dataframe</span>
-<span class="sd">                    a dataframe with a geometry column that can be used to</span>
-<span class="sd">                    construct a W object</span>
-<span class="sd">        geom_col :  string</span>
-<span class="sd">                    the name of the column in `df` that contains the</span>
-<span class="sd">                    geometries. Defaults to active geometry column.</span>
-<span class="sd">        ids     :   string or iterable</span>
-<span class="sd">                    if string, the column name of the indices from the dataframe</span>
-<span class="sd">                    if iterable, a list of ids to use for the W</span>
-<span class="sd">                    if None, df.index is used.</span>
-
-<span class="sd">        See Also</span>
-<span class="sd">        --------</span>
-<span class="sd">        :class:`libpysal.weights.weights.W`</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="n">geom_col</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">geom_col</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">name</span>
-        <span class="n">pts</span> <span class="o">=</span> <span class="n">get_points_array</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">geom_col</span><span class="p">])</span>
-        <span class="k">if</span> <span class="n">ids</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">ids</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
-        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">ids</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
-            <span class="n">ids</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">ids</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
-        <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span><span class="n">pts</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="KNN.reweight"><a class="viewcode-back" href="../../../generated/libpysal.weights.KNN.html#libpysal.weights.KNN.reweight">[docs]</a>    <span class="k">def</span> <span class="nf">reweight</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">new_data</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">new_ids</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Redo K-Nearest Neighbor weights construction using given parameters</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        new_data    : np.ndarray</span>
-<span class="sd">                      an array containing additional data to use in the KNN</span>
-<span class="sd">                      weight</span>
-<span class="sd">        new_ids     : list</span>
-<span class="sd">                      a list aligned with new_data that provides the ids for</span>
-<span class="sd">                      each new observation</span>
-<span class="sd">        inplace     : bool</span>
-<span class="sd">                      a flag denoting whether to modify the KNN object</span>
-<span class="sd">                      in place or to return a new KNN object</span>
-<span class="sd">        k           : int</span>
-<span class="sd">                      number of nearest neighbors</span>
-<span class="sd">        p           : float</span>
-<span class="sd">                      Minkowski p-norm distance metric parameter:</span>
-<span class="sd">                      1&lt;=p&lt;=infinity</span>
-<span class="sd">                      2: Euclidean distance</span>
-<span class="sd">                      1: Manhattan distance</span>
-<span class="sd">                      Ignored if the KDTree is an ArcKDTree</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        A copy of the object using the new parameterization, or None if the</span>
-<span class="sd">        object is reweighted in place.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="n">new_data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">new_data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">new_data</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
-            <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">new_data</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
-            <span class="k">if</span> <span class="n">new_ids</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">ids</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">id_order</span><span class="p">)</span>
-                <span class="n">ids</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">new_ids</span><span class="p">))</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">ids</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
-        <span class="k">elif</span> <span class="p">(</span><span class="n">new_data</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">new_ids</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">):</span>
-            <span class="c1"># If not, we can use the same kdtree we have</span>
-            <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span>
-            <span class="n">ids</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">id_order</span>
-        <span class="k">elif</span> <span class="p">(</span><span class="n">new_data</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">new_ids</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">):</span>
-            <span class="n">Warn</span><span class="p">(</span><span class="s2">&quot;Remapping ids must be done using w.remap_ids&quot;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">k</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">k</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">k</span>
-        <span class="k">if</span> <span class="n">p</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">p</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">p</span>
-        <span class="k">if</span> <span class="n">inplace</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_reset</span><span class="p">()</span>
-            <span class="bp">self</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="n">k</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">p</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">KNN</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="n">k</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">p</span><span class="p">)</span></div></div>
-
-
-<div class="viewcode-block" id="Kernel"><a class="viewcode-back" href="../../../generated/libpysal.weights.Kernel.html#libpysal.weights.Kernel">[docs]</a><span class="k">class</span> <span class="nc">Kernel</span><span class="p">(</span><span class="n">W</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Spatial weights based on kernel functions.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-
-<span class="sd">    data        : array</span>
-<span class="sd">                  (n,k) or KDTree where KDtree.data is array (n,k)</span>
-<span class="sd">                  n observations on k characteristics used to measure</span>
-<span class="sd">                  distances between the n objects</span>
-<span class="sd">    bandwidth   : float</span>
-<span class="sd">                  or array-like (optional)</span>
-<span class="sd">                  the bandwidth :math:`h_i` for the kernel.</span>
-<span class="sd">    fixed       : binary</span>
-<span class="sd">                  If true then :math:`h_i=h \\forall i`. If false then</span>
-<span class="sd">                  bandwidth is adaptive across observations.</span>
-<span class="sd">    k           : int</span>
-<span class="sd">                  the number of nearest neighbors to use for determining</span>
-<span class="sd">                  bandwidth. For fixed bandwidth, :math:`h_i=max(dknn) \\forall i`</span>
-<span class="sd">                  where :math:`dknn` is a vector of k-nearest neighbor</span>
-<span class="sd">                  distances (the distance to the kth nearest neighbor for each</span>
-<span class="sd">                  observation).  For adaptive bandwidths, :math:`h_i=dknn_i`</span>
-<span class="sd">    diagonal    : boolean</span>
-<span class="sd">                  If true, set diagonal weights = 1.0, if false (default),</span>
-<span class="sd">                  diagonals weights are set to value according to kernel</span>
-<span class="sd">                  function.</span>
-<span class="sd">    function    : {&#39;triangular&#39;,&#39;uniform&#39;,&#39;quadratic&#39;,&#39;quartic&#39;,&#39;gaussian&#39;}</span>
-<span class="sd">                  kernel function defined as follows with</span>
-
-<span class="sd">                  .. math::</span>
-
-<span class="sd">                      z_{i,j} = d_{i,j}/h_i</span>
-
-<span class="sd">                  triangular</span>
-
-<span class="sd">                  .. math::</span>
-
-<span class="sd">                      K(z) = (1 - |z|) \\ if |z| \\le 1</span>
-
-<span class="sd">                  uniform</span>
-
-<span class="sd">                  .. math::</span>
-
-<span class="sd">                      K(z) = 1/2 \\ if |z| \\le 1</span>
-
-<span class="sd">                  quadratic</span>
-
-<span class="sd">                  .. math::</span>
-
-<span class="sd">                      K(z) = (3/4)(1-z^2) \\ if |z| \\le 1</span>
-
-<span class="sd">                  quartic</span>
-
-<span class="sd">                  .. math::</span>
-
-<span class="sd">                      K(z) = (15/16)(1-z^2)^2 \\ if |z| \\le 1</span>
-
-<span class="sd">                  gaussian</span>
-
-<span class="sd">                  .. math::</span>
-
-<span class="sd">                      K(z) = (2\\pi)^{(-1/2)} exp(-z^2 / 2)</span>
-
-<span class="sd">    eps         : float</span>
-<span class="sd">                  adjustment to ensure knn distance range is closed on the</span>
-<span class="sd">                  knnth observations</span>
-
-<span class="sd">    Attributes</span>
-<span class="sd">    ----------</span>
-<span class="sd">    weights : dict</span>
-<span class="sd">              Dictionary keyed by id with a list of weights for each neighbor</span>
-
-<span class="sd">    neighbors : dict</span>
-<span class="sd">                of lists of neighbors keyed by observation id</span>
-
-<span class="sd">    bandwidth : array</span>
-<span class="sd">                array of bandwidths</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    &gt;&gt;&gt; from libpysal.weights import Kernel</span>
-<span class="sd">    &gt;&gt;&gt; points=[(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]</span>
-<span class="sd">    &gt;&gt;&gt; kw=Kernel(points)</span>
-<span class="sd">    &gt;&gt;&gt; kw.weights[0]</span>
-<span class="sd">    [1.0, 0.500000049999995, 0.4409830615267465]</span>
-<span class="sd">    &gt;&gt;&gt; kw.neighbors[0]</span>
-<span class="sd">    [0, 1, 3]</span>
-<span class="sd">    &gt;&gt;&gt; kw.bandwidth</span>
-<span class="sd">    array([[20.000002],</span>
-<span class="sd">           [20.000002],</span>
-<span class="sd">           [20.000002],</span>
-<span class="sd">           [20.000002],</span>
-<span class="sd">           [20.000002],</span>
-<span class="sd">           [20.000002]])</span>
-<span class="sd">    &gt;&gt;&gt; kw15=Kernel(points,bandwidth=15.0)</span>
-<span class="sd">    &gt;&gt;&gt; kw15[0]</span>
-<span class="sd">    {0: 1.0, 1: 0.33333333333333337, 3: 0.2546440075000701}</span>
-<span class="sd">    &gt;&gt;&gt; kw15.neighbors[0]</span>
-<span class="sd">    [0, 1, 3]</span>
-<span class="sd">    &gt;&gt;&gt; kw15.bandwidth</span>
-<span class="sd">    array([[15.],</span>
-<span class="sd">           [15.],</span>
-<span class="sd">           [15.],</span>
-<span class="sd">           [15.],</span>
-<span class="sd">           [15.],</span>
-<span class="sd">           [15.]])</span>
-
-<span class="sd">    Adaptive bandwidths user specified</span>
-
-<span class="sd">    &gt;&gt;&gt; bw=[25.0,15.0,25.0,16.0,14.5,25.0]</span>
-<span class="sd">    &gt;&gt;&gt; kwa=Kernel(points,bandwidth=bw)</span>
-<span class="sd">    &gt;&gt;&gt; kwa.weights[0]</span>
-<span class="sd">    [1.0, 0.6, 0.552786404500042, 0.10557280900008403]</span>
-<span class="sd">    &gt;&gt;&gt; kwa.neighbors[0]</span>
-<span class="sd">    [0, 1, 3, 4]</span>
-<span class="sd">    &gt;&gt;&gt; kwa.bandwidth</span>
-<span class="sd">    array([[25. ],</span>
-<span class="sd">           [15. ],</span>
-<span class="sd">           [25. ],</span>
-<span class="sd">           [16. ],</span>
-<span class="sd">           [14.5],</span>
-<span class="sd">           [25. ]])</span>
-
-<span class="sd">    Endogenous adaptive bandwidths</span>
-
-<span class="sd">    &gt;&gt;&gt; kwea=Kernel(points,fixed=False)</span>
-<span class="sd">    &gt;&gt;&gt; kwea.weights[0]</span>
-<span class="sd">    [1.0, 0.10557289844279438, 9.99999900663795e-08]</span>
-<span class="sd">    &gt;&gt;&gt; kwea.neighbors[0]</span>
-<span class="sd">    [0, 1, 3]</span>
-<span class="sd">    &gt;&gt;&gt; kwea.bandwidth</span>
-<span class="sd">    array([[11.18034101],</span>
-<span class="sd">           [11.18034101],</span>
-<span class="sd">           [20.000002  ],</span>
-<span class="sd">           [11.18034101],</span>
-<span class="sd">           [14.14213704],</span>
-<span class="sd">           [18.02775818]])</span>
-
-<span class="sd">    Endogenous adaptive bandwidths with Gaussian kernel</span>
-
-<span class="sd">    &gt;&gt;&gt; kweag=Kernel(points,fixed=False,function=&#39;gaussian&#39;)</span>
-<span class="sd">    &gt;&gt;&gt; kweag.weights[0]</span>
-<span class="sd">    [0.3989422804014327, 0.2674190291577696, 0.2419707487162134]</span>
-<span class="sd">    &gt;&gt;&gt; kweag.bandwidth</span>
-<span class="sd">    array([[11.18034101],</span>
-<span class="sd">           [11.18034101],</span>
-<span class="sd">           [20.000002  ],</span>
-<span class="sd">           [11.18034101],</span>
-<span class="sd">           [14.14213704],</span>
-<span class="sd">           [18.02775818]])</span>
-
-<span class="sd">    Diagonals to 1.0</span>
-
-<span class="sd">    &gt;&gt;&gt; kq = Kernel(points,function=&#39;gaussian&#39;)</span>
-<span class="sd">    &gt;&gt;&gt; kq.weights</span>
-<span class="sd">    {0: [0.3989422804014327, 0.35206533556593145, 0.3412334260702758], 1: [0.35206533556593145, 0.3989422804014327, 0.2419707487162134, 0.3412334260702758, 0.31069657591175387], 2: [0.2419707487162134, 0.3989422804014327, 0.31069657591175387], 3: [0.3412334260702758, 0.3412334260702758, 0.3989422804014327, 0.3011374490937829, 0.26575287272131043], 4: [0.31069657591175387, 0.31069657591175387, 0.3011374490937829, 0.3989422804014327, 0.35206533556593145], 5: [0.26575287272131043, 0.35206533556593145, 0.3989422804014327]}</span>
-<span class="sd">    &gt;&gt;&gt; kqd = Kernel(points, function=&#39;gaussian&#39;, diagonal=True)</span>
-<span class="sd">    &gt;&gt;&gt; kqd.weights</span>
-<span class="sd">    {0: [1.0, 0.35206533556593145, 0.3412334260702758], 1: [0.35206533556593145, 1.0, 0.2419707487162134, 0.3412334260702758, 0.31069657591175387], 2: [0.2419707487162134, 1.0, 0.31069657591175387], 3: [0.3412334260702758, 0.3412334260702758, 1.0, 0.3011374490937829, 0.26575287272131043], 4: [0.31069657591175387, 0.31069657591175387, 0.3011374490937829, 1.0, 0.35206533556593145], 5: [0.26575287272131043, 0.35206533556593145, 1.0]}</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-
-<div class="viewcode-block" id="Kernel.__init__"><a class="viewcode-back" href="../../../generated/libpysal.weights.Kernel.html#libpysal.weights.Kernel.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">data</span><span class="p">,</span>
-        <span class="n">bandwidth</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">fixed</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-        <span class="n">k</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
-        <span class="n">function</span><span class="o">=</span><span class="s2">&quot;triangular&quot;</span><span class="p">,</span>
-        <span class="n">eps</span><span class="o">=</span><span class="mf">1.0000001</span><span class="p">,</span>
-        <span class="n">ids</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">diagonal</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-        <span class="n">distance_metric</span><span class="o">=</span><span class="s2">&quot;euclidean&quot;</span><span class="p">,</span>
-        <span class="n">radius</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="o">**</span><span class="n">kwargs</span>
-    <span class="p">):</span>
-        <span class="k">if</span> <span class="n">radius</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">distance_metric</span> <span class="o">=</span> <span class="s2">&quot;arc&quot;</span>
-        <span class="k">if</span> <span class="n">isKDTree</span><span class="p">(</span><span class="n">data</span><span class="p">):</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span> <span class="o">=</span> <span class="n">data</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span><span class="o">.</span><span class="n">data</span>
-            <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span> <span class="o">=</span> <span class="n">KDTree</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">distance_metric</span><span class="o">=</span><span class="n">distance_metric</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="n">radius</span><span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span><span class="o">.</span><span class="n">data</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">k</span> <span class="o">=</span> <span class="n">k</span> <span class="o">+</span> <span class="mi">1</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">function</span> <span class="o">=</span> <span class="n">function</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">fixed</span> <span class="o">=</span> <span class="n">fixed</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">eps</span> <span class="o">=</span> <span class="n">eps</span>
-        <span class="k">if</span> <span class="n">bandwidth</span><span class="p">:</span>
-            <span class="k">try</span><span class="p">:</span>
-                <span class="n">bandwidth</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">bandwidth</span><span class="p">)</span>
-                <span class="n">bandwidth</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">bandwidth</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span>
-            <span class="k">except</span><span class="p">:</span>
-                <span class="n">bandwidth</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">),</span> <span class="mi">1</span><span class="p">),</span> <span class="s2">&quot;float&quot;</span><span class="p">)</span> <span class="o">*</span> <span class="n">bandwidth</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span> <span class="o">=</span> <span class="n">bandwidth</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_set_bw</span><span class="p">()</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">_eval_kernel</span><span class="p">()</span>
-        <span class="n">neighbors</span><span class="p">,</span> <span class="n">weights</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_k_to_W</span><span class="p">(</span><span class="n">ids</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">diagonal</span><span class="p">:</span>
-            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">neighbors</span><span class="p">:</span>
-                <span class="n">weights</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">i</span><span class="p">)]</span> <span class="o">=</span> <span class="mf">1.0</span>
-        <span class="n">W</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">neighbors</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">ids</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="Kernel.from_shapefile"><a class="viewcode-back" href="../../../generated/libpysal.weights.Kernel.html#libpysal.weights.Kernel.from_shapefile">[docs]</a>    <span class="nd">@classmethod</span>
-    <span class="k">def</span> <span class="nf">from_shapefile</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">filepath</span><span class="p">,</span> <span class="n">idVariable</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Kernel based weights from shapefile</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        shapefile   : string</span>
-<span class="sd">                      shapefile name with shp suffix</span>
-<span class="sd">        idVariable  : string</span>
-<span class="sd">                      name of column in shapefile&#39;s DBF to use for ids</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        Kernel Weights Object</span>
-
-<span class="sd">        See Also</span>
-<span class="sd">        ---------</span>
-<span class="sd">        :class:`libpysal.weights.weights.W`</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">points</span> <span class="o">=</span> <span class="n">get_points_array_from_shapefile</span><span class="p">(</span><span class="n">filepath</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">idVariable</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">ids</span> <span class="o">=</span> <span class="n">get_ids</span><span class="p">(</span><span class="n">filepath</span><span class="p">,</span> <span class="n">idVariable</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">ids</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="k">return</span> <span class="bp">cls</span><span class="o">.</span><span class="n">from_array</span><span class="p">(</span><span class="n">points</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="Kernel.from_array"><a class="viewcode-back" href="../../../generated/libpysal.weights.Kernel.html#libpysal.weights.Kernel.from_array">[docs]</a>    <span class="nd">@classmethod</span>
-    <span class="k">def</span> <span class="nf">from_array</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Construct a Kernel weights from an array. Supports all the same options</span>
-<span class="sd">        as :class:`libpysal.weights.Kernel`</span>
-
-<span class="sd">        See Also</span>
-<span class="sd">        --------</span>
-<span class="sd">        :class:`libpysal.weights.weights.W`</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="Kernel.from_dataframe"><a class="viewcode-back" href="../../../generated/libpysal.weights.Kernel.html#libpysal.weights.Kernel.from_dataframe">[docs]</a>    <span class="nd">@classmethod</span>
-    <span class="k">def</span> <span class="nf">from_dataframe</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">df</span><span class="p">,</span> <span class="n">geom_col</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Make Kernel weights from a dataframe.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        df      :   pandas.dataframe</span>
-<span class="sd">                    a dataframe with a geometry column that can be used to</span>
-<span class="sd">                    construct a W object</span>
-<span class="sd">        geom_col :  string</span>
-<span class="sd">                    the name of the column in `df` that contains the</span>
-<span class="sd">                    geometries. Defaults to active geometry column.</span>
-<span class="sd">        ids     :   string or iterable</span>
-<span class="sd">                    if string, the column name of the indices from the dataframe</span>
-<span class="sd">                    if iterable, a list of ids to use for the W</span>
-<span class="sd">                    if None, df.index is used.</span>
-
-<span class="sd">        See Also</span>
-<span class="sd">        --------</span>
-<span class="sd">        :class:`libpysal.weights.weights.W`</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="n">geom_col</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">geom_col</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">name</span>
-        <span class="n">pts</span> <span class="o">=</span> <span class="n">get_points_array</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">geom_col</span><span class="p">])</span>
-        <span class="k">if</span> <span class="n">ids</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">ids</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
-        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">ids</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
-            <span class="n">ids</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">ids</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
-        <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span><span class="n">pts</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
-
-    <span class="k">def</span> <span class="nf">_k_to_W</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-        <span class="n">allneighbors</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="n">weights</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="k">if</span> <span class="n">ids</span><span class="p">:</span>
-            <span class="n">ids</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ids</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">ids</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">))</span>
-        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">neighbors</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">kernel</span><span class="p">):</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">neigh</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="n">allneighbors</span><span class="p">[</span><span class="n">ids</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span> <span class="o">=</span> <span class="p">[]</span>
-                <span class="n">weights</span><span class="p">[</span><span class="n">ids</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">allneighbors</span><span class="p">[</span><span class="n">ids</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">ids</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">neigh</span><span class="p">[</span><span class="n">i</span><span class="p">]])</span>
-                <span class="n">weights</span><span class="p">[</span><span class="n">ids</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">kernel</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
-        <span class="k">return</span> <span class="n">allneighbors</span><span class="p">,</span> <span class="n">weights</span>
-
-    <span class="k">def</span> <span class="nf">_set_bw</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="n">dmat</span><span class="p">,</span> <span class="n">neigh</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">k</span><span class="p">)</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">fixed</span><span class="p">:</span>
-            <span class="c1"># use max knn distance as bandwidth</span>
-            <span class="n">bandwidth</span> <span class="o">=</span> <span class="n">dmat</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">eps</span>
-            <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">dmat</span><span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">n</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="s2">&quot;float&quot;</span><span class="p">)</span> <span class="o">*</span> <span class="n">bandwidth</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="c1"># use local max knn distance</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span> <span class="o">=</span> <span class="n">dmat</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">eps</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span><span class="o">.</span><span class="n">size</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-            <span class="c1"># identify knn neighbors for each point</span>
-            <span class="n">nnq</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">k</span><span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">neigh</span> <span class="o">=</span> <span class="n">nnq</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-
-    <span class="k">def</span> <span class="nf">_eval_kernel</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="c1"># get points within bandwidth distance of each point</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;neigh&quot;</span><span class="p">):</span>
-            <span class="n">kdtq</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span><span class="o">.</span><span class="n">query_ball_point</span>
-            <span class="n">neighbors</span> <span class="o">=</span> <span class="p">[</span>
-                <span class="n">kdtq</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">r</span><span class="o">=</span><span class="n">bwi</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">bwi</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span><span class="p">)</span>
-            <span class="p">]</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">neigh</span> <span class="o">=</span> <span class="n">neighbors</span>
-        <span class="c1"># get distances for neighbors</span>
-        <span class="n">bw</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span>
-
-        <span class="n">kdtq</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span><span class="o">.</span><span class="n">query</span>
-        <span class="n">z</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">nids</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">neigh</span><span class="p">):</span>
-            <span class="n">di</span><span class="p">,</span> <span class="n">ni</span> <span class="o">=</span> <span class="n">kdtq</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">k</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">nids</span><span class="p">))</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">di</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
-                <span class="n">di</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="n">di</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">nids</span><span class="p">))</span>
-                <span class="n">ni</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="n">ni</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">nids</span><span class="p">))</span>
-            <span class="n">zi</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="nb">dict</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">ni</span><span class="p">,</span> <span class="n">di</span><span class="p">)))[</span><span class="n">nid</span><span class="p">]</span> <span class="k">for</span> <span class="n">nid</span> <span class="ow">in</span> <span class="n">nids</span><span class="p">])</span> <span class="o">/</span> <span class="n">bw</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-            <span class="n">z</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">zi</span><span class="p">)</span>
-        <span class="n">zs</span> <span class="o">=</span> <span class="n">z</span>
-        <span class="c1"># functions follow Anselin and Rey (2010) table 5.4</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">function</span> <span class="o">==</span> <span class="s2">&quot;triangular&quot;</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">kernel</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span> <span class="o">-</span> <span class="n">zi</span> <span class="k">for</span> <span class="n">zi</span> <span class="ow">in</span> <span class="n">zs</span><span class="p">]</span>
-        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">function</span> <span class="o">==</span> <span class="s2">&quot;uniform&quot;</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">kernel</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">zi</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">*</span> <span class="mf">0.5</span> <span class="k">for</span> <span class="n">zi</span> <span class="ow">in</span> <span class="n">zs</span><span class="p">]</span>
-        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">function</span> <span class="o">==</span> <span class="s2">&quot;quadratic&quot;</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">kernel</span> <span class="o">=</span> <span class="p">[(</span><span class="mf">3.0</span> <span class="o">/</span> <span class="mi">4</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">zi</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span> <span class="k">for</span> <span class="n">zi</span> <span class="ow">in</span> <span class="n">zs</span><span class="p">]</span>
-        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">function</span> <span class="o">==</span> <span class="s2">&quot;quartic&quot;</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">kernel</span> <span class="o">=</span> <span class="p">[(</span><span class="mf">15.0</span> <span class="o">/</span> <span class="mi">16</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">zi</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span> <span class="k">for</span> <span class="n">zi</span> <span class="ow">in</span> <span class="n">zs</span><span class="p">]</span>
-        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">function</span> <span class="o">==</span> <span class="s2">&quot;gaussian&quot;</span><span class="p">:</span>
-            <span class="n">c</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="mi">2</span>
-            <span class="n">c</span> <span class="o">=</span> <span class="n">c</span> <span class="o">**</span> <span class="p">(</span><span class="o">-</span><span class="mf">0.5</span><span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">kernel</span> <span class="o">=</span> <span class="p">[</span><span class="n">c</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="p">(</span><span class="n">zi</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span> <span class="o">/</span> <span class="mf">2.0</span><span class="p">)</span> <span class="k">for</span> <span class="n">zi</span> <span class="ow">in</span> <span class="n">zs</span><span class="p">]</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">((</span><span class="s2">&quot;Unsupported kernel function&quot;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">function</span><span class="p">))</span></div>
-
-
-<div class="viewcode-block" id="DistanceBand"><a class="viewcode-back" href="../../../generated/libpysal.weights.DistanceBand.html#libpysal.weights.DistanceBand">[docs]</a><span class="k">class</span> <span class="nc">DistanceBand</span><span class="p">(</span><span class="n">W</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Spatial weights based on distance band.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-
-<span class="sd">    data        : array</span>
-<span class="sd">                  (n,k) or KDTree where KDtree.data is array (n,k)</span>
-<span class="sd">                  n observations on k characteristics used to measure</span>
-<span class="sd">                  distances between the n objects</span>
-<span class="sd">    threshold  : float</span>
-<span class="sd">                 distance band</span>
-<span class="sd">    p          : float</span>
-<span class="sd">                 Minkowski p-norm distance metric parameter:</span>
-<span class="sd">                 1&lt;=p&lt;=infinity</span>
-<span class="sd">                 2: Euclidean distance</span>
-<span class="sd">                 1: Manhattan distance</span>
-<span class="sd">    binary     : boolean</span>
-<span class="sd">                 If true w_{ij}=1 if d_{i,j}&lt;=threshold, otherwise w_{i,j}=0</span>
-<span class="sd">                 If false wij=dij^{alpha}</span>
-<span class="sd">    alpha      : float</span>
-<span class="sd">                 distance decay parameter for weight (default -1.0)</span>
-<span class="sd">                 if alpha is positive the weights will not decline with</span>
-<span class="sd">                 distance. If binary is True, alpha is ignored</span>
-
-<span class="sd">    ids         : list</span>
-<span class="sd">                  values to use for keys of the neighbors and weights dicts</span>
-
-<span class="sd">    build_sp    : boolean</span>
-<span class="sd">                  True to build sparse distance matrix and false to build dense</span>
-<span class="sd">                  distance matrix; significant speed gains may be obtained</span>
-<span class="sd">                  dending on the sparsity of the of distance_matrix and</span>
-<span class="sd">                  threshold that is applied</span>
-<span class="sd">    silent      : boolean</span>
-<span class="sd">                  By default libpysal will print a warning if the</span>
-<span class="sd">                  dataset contains any disconnected observations or</span>
-<span class="sd">                  islands. To silence this warning set this</span>
-<span class="sd">                  parameter to True.</span>
-
-<span class="sd">    Attributes</span>
-<span class="sd">    ----------</span>
-<span class="sd">    weights : dict</span>
-<span class="sd">              of neighbor weights keyed by observation id</span>
-
-<span class="sd">    neighbors : dict</span>
-<span class="sd">                of neighbors keyed by observation id</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    &gt;&gt;&gt; import libpysal</span>
-<span class="sd">    &gt;&gt;&gt; points=[(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]</span>
-<span class="sd">    &gt;&gt;&gt; wcheck = libpysal.weights.W({0: [1, 3], 1: [0, 3], 2: [], 3: [0, 1], 4: [5], 5: [4]})</span>
-
-<span class="sd">    WARNING: there is one disconnected observation (no neighbors)</span>
-<span class="sd">    Island id:  [2]</span>
-<span class="sd">    &gt;&gt;&gt; w=libpysal.weights.DistanceBand(points,threshold=11.2)</span>
-
-<span class="sd">    WARNING: there is one disconnected observation (no neighbors)</span>
-<span class="sd">    Island id:  [2]</span>
-<span class="sd">    &gt;&gt;&gt; libpysal.weights.util.neighbor_equality(w, wcheck)</span>
-<span class="sd">    True</span>
-<span class="sd">    &gt;&gt;&gt; w=libpysal.weights.DistanceBand(points,threshold=14.2)</span>
-<span class="sd">    &gt;&gt;&gt; wcheck = libpysal.weights.W({0: [1, 3], 1: [0, 3, 4], 2: [4], 3: [1, 0], 4: [5, 2, 1], 5: [4]})</span>
-<span class="sd">    &gt;&gt;&gt; libpysal.weights.util.neighbor_equality(w, wcheck)</span>
-<span class="sd">    True</span>
-
-<span class="sd">    inverse distance weights</span>
-
-<span class="sd">    &gt;&gt;&gt; w=libpysal.weights.DistanceBand(points,threshold=11.2,binary=False)</span>
-
-<span class="sd">    WARNING: there is one disconnected observation (no neighbors)</span>
-<span class="sd">    Island id:  [2]</span>
-<span class="sd">    &gt;&gt;&gt; w.weights[0]</span>
-<span class="sd">    [0.1, 0.08944271909999159]</span>
-<span class="sd">    &gt;&gt;&gt; w.neighbors[0].tolist()</span>
-<span class="sd">    [1, 3]</span>
-
-<span class="sd">    gravity weights</span>
-
-<span class="sd">    &gt;&gt;&gt; w=libpysal.weights.DistanceBand(points,threshold=11.2,binary=False,alpha=-2.)</span>
-
-<span class="sd">    WARNING: there is one disconnected observation (no neighbors)</span>
-<span class="sd">    Island id:  [2]</span>
-<span class="sd">    &gt;&gt;&gt; w.weights[0]</span>
-<span class="sd">    [0.01, 0.007999999999999998]</span>
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-
-<span class="sd">    This was initially implemented running scipy 0.8.0dev (in epd 6.1).</span>
-<span class="sd">    earlier versions of scipy (0.7.0) have a logic bug in scipy/sparse/dok.py</span>
-<span class="sd">    so serge changed line 221 of that file on sal-dev to fix the logic bug.</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-
-<div class="viewcode-block" id="DistanceBand.__init__"><a class="viewcode-back" href="../../../generated/libpysal.weights.DistanceBand.html#libpysal.weights.DistanceBand.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">data</span><span class="p">,</span>
-        <span class="n">threshold</span><span class="p">,</span>
-        <span class="n">p</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
-        <span class="n">alpha</span><span class="o">=-</span><span class="mf">1.0</span><span class="p">,</span>
-        <span class="n">binary</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-        <span class="n">ids</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">build_sp</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-        <span class="n">silence_warnings</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-        <span class="n">distance_metric</span><span class="o">=</span><span class="s2">&quot;euclidean&quot;</span><span class="p">,</span>
-        <span class="n">radius</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Casting to floats is a work around for a bug in scipy.spatial.</span>
-<span class="sd">        See detail in pysal issue #126.</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="n">ids</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">ids</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">ids</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">radius</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">distance_metric</span> <span class="o">=</span> <span class="s2">&quot;arc&quot;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">p</span> <span class="o">=</span> <span class="n">p</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">threshold</span> <span class="o">=</span> <span class="n">threshold</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">binary</span> <span class="o">=</span> <span class="n">binary</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="n">alpha</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">build_sp</span> <span class="o">=</span> <span class="n">build_sp</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">silence_warnings</span> <span class="o">=</span> <span class="n">silence_warnings</span>
-
-        <span class="k">if</span> <span class="n">isKDTree</span><span class="p">(</span><span class="n">data</span><span class="p">):</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span> <span class="o">=</span> <span class="n">data</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span><span class="o">.</span><span class="n">data</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">build_sp</span><span class="p">:</span>
-                <span class="k">try</span><span class="p">:</span>
-                    <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
-                    <span class="k">if</span> <span class="n">data</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">kind</span> <span class="o">!=</span> <span class="s2">&quot;f&quot;</span><span class="p">:</span>
-                        <span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span> <span class="o">=</span> <span class="n">KDTree</span><span class="p">(</span>
-                        <span class="n">data</span><span class="p">,</span> <span class="n">distance_metric</span><span class="o">=</span><span class="n">distance_metric</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="n">radius</span>
-                    <span class="p">)</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span><span class="o">.</span><span class="n">data</span>
-                <span class="k">except</span><span class="p">:</span>
-                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Could not make array from data&quot;</span><span class="p">)</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">data</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_band</span><span class="p">()</span>
-        <span class="n">neighbors</span><span class="p">,</span> <span class="n">weights</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_distance_to_W</span><span class="p">(</span><span class="n">ids</span><span class="p">)</span>
-        <span class="n">W</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
-            <span class="bp">self</span><span class="p">,</span> <span class="n">neighbors</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">ids</span><span class="p">,</span> <span class="n">silence_warnings</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">silence_warnings</span>
-        <span class="p">)</span></div>
-
-<div class="viewcode-block" id="DistanceBand.from_shapefile"><a class="viewcode-back" href="../../../generated/libpysal.weights.DistanceBand.html#libpysal.weights.DistanceBand.from_shapefile">[docs]</a>    <span class="nd">@classmethod</span>
-    <span class="k">def</span> <span class="nf">from_shapefile</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">filepath</span><span class="p">,</span> <span class="n">threshold</span><span class="p">,</span> <span class="n">idVariable</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Distance-band based weights from shapefile</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        shapefile   : string</span>
-<span class="sd">                      shapefile name with shp suffix</span>
-<span class="sd">        idVariable  : string</span>
-<span class="sd">                      name of column in shapefile&#39;s DBF to use for ids</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        --------</span>
-<span class="sd">        Kernel Weights Object</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">points</span> <span class="o">=</span> <span class="n">get_points_array_from_shapefile</span><span class="p">(</span><span class="n">filepath</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">idVariable</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">ids</span> <span class="o">=</span> <span class="n">get_ids</span><span class="p">(</span><span class="n">filepath</span><span class="p">,</span> <span class="n">idVariable</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">ids</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="k">return</span> <span class="bp">cls</span><span class="o">.</span><span class="n">from_array</span><span class="p">(</span><span class="n">points</span><span class="p">,</span> <span class="n">threshold</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="DistanceBand.from_array"><a class="viewcode-back" href="../../../generated/libpysal.weights.DistanceBand.html#libpysal.weights.DistanceBand.from_array">[docs]</a>    <span class="nd">@classmethod</span>
-    <span class="k">def</span> <span class="nf">from_array</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">threshold</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Construct a DistanceBand weights from an array. Supports all the same options</span>
-<span class="sd">        as :class:`libpysal.weights.DistanceBand`</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">threshold</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="DistanceBand.from_dataframe"><a class="viewcode-back" href="../../../generated/libpysal.weights.DistanceBand.html#libpysal.weights.DistanceBand.from_dataframe">[docs]</a>    <span class="nd">@classmethod</span>
-    <span class="k">def</span> <span class="nf">from_dataframe</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">df</span><span class="p">,</span> <span class="n">threshold</span><span class="p">,</span> <span class="n">geom_col</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Make DistanceBand weights from a dataframe.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        df      :   pandas.dataframe</span>
-<span class="sd">                    a dataframe with a geometry column that can be used to</span>
-<span class="sd">                    construct a W object</span>
-<span class="sd">        geom_col :  string</span>
-<span class="sd">                    the name of the column in `df` that contains the</span>
-<span class="sd">                    geometries. Defaults to active geometry column.</span>
-<span class="sd">        ids     :   string or iterable</span>
-<span class="sd">                    if string, the column name of the indices from the dataframe</span>
-<span class="sd">                    if iterable, a list of ids to use for the W</span>
-<span class="sd">                    if None, df.index is used.</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="n">geom_col</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">geom_col</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">name</span>
-        <span class="n">pts</span> <span class="o">=</span> <span class="n">get_points_array</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">geom_col</span><span class="p">])</span>
-        <span class="k">if</span> <span class="n">ids</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">ids</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
-        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">ids</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
-            <span class="n">ids</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">ids</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
-        <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span><span class="n">pts</span><span class="p">,</span> <span class="n">threshold</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
-
-    <span class="k">def</span> <span class="nf">_band</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Find all pairs within threshold.&quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">build_sp</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">dmat</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span><span class="o">.</span><span class="n">sparse_distance_matrix</span><span class="p">(</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span><span class="p">,</span> <span class="n">max_distance</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">threshold</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">p</span>
-            <span class="p">)</span><span class="o">.</span><span class="n">tocsr</span><span class="p">()</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">if</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">:</span><span class="mi">10</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;Arc_KDTree&quot;</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
-                    <span class="s2">&quot;Unable to calculate dense arc distance matrix;&quot;</span>
-                    <span class="s1">&#39; parameter &quot;build_sp&quot; must be set to True for arc&#39;</span>
-                    <span class="s2">&quot; distance type weight&quot;</span>
-                <span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">dmat</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_spdistance_matrix</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">threshold</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">_distance_to_W</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">binary</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">dmat</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">dmat</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">dmat</span><span class="o">.</span><span class="n">eliminate_zeros</span><span class="p">()</span>
-            <span class="n">tempW</span> <span class="o">=</span> <span class="n">WSP2W</span><span class="p">(</span>
-                <span class="n">WSP</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dmat</span><span class="p">,</span> <span class="n">id_order</span><span class="o">=</span><span class="n">ids</span><span class="p">),</span> <span class="n">silence_warnings</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">silence_warnings</span>
-            <span class="p">)</span>
-            <span class="n">neighbors</span> <span class="o">=</span> <span class="n">tempW</span><span class="o">.</span><span class="n">neighbors</span>
-            <span class="n">weight_keys</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">tempW</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
-            <span class="n">weight_vals</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">tempW</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">values</span><span class="p">())</span>
-            <span class="n">weights</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">weight_keys</span><span class="p">,</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="n">weight_vals</span><span class="p">)))))</span>
-            <span class="k">return</span> <span class="n">neighbors</span><span class="p">,</span> <span class="n">weights</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">weighted</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dmat</span><span class="o">.</span><span class="n">power</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span>
-            <span class="n">weighted</span><span class="p">[</span><span class="n">weighted</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
-            <span class="n">weighted</span><span class="o">.</span><span class="n">eliminate_zeros</span><span class="p">()</span>
-            <span class="n">tempW</span> <span class="o">=</span> <span class="n">WSP2W</span><span class="p">(</span>
-                <span class="n">WSP</span><span class="p">(</span><span class="n">weighted</span><span class="p">,</span> <span class="n">id_order</span><span class="o">=</span><span class="n">ids</span><span class="p">),</span> <span class="n">silence_warnings</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">silence_warnings</span>
-            <span class="p">)</span>
-            <span class="n">neighbors</span> <span class="o">=</span> <span class="n">tempW</span><span class="o">.</span><span class="n">neighbors</span>
-            <span class="n">weight_keys</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">tempW</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
-            <span class="n">weight_vals</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">tempW</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">values</span><span class="p">())</span>
-            <span class="n">weights</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">weight_keys</span><span class="p">,</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="n">weight_vals</span><span class="p">)))))</span>
-            <span class="k">return</span> <span class="n">neighbors</span><span class="p">,</span> <span class="n">weights</span>
-
-    <span class="k">def</span> <span class="nf">_spdistance_matrix</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">threshold</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-        <span class="n">dist</span> <span class="o">=</span> <span class="n">distance_matrix</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">threshold</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">zeros</span> <span class="o">=</span> <span class="n">dist</span> <span class="o">&gt;</span> <span class="n">threshold</span>
-            <span class="n">dist</span><span class="p">[</span><span class="n">zeros</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
-        <span class="k">return</span> <span class="n">sp</span><span class="o">.</span><span class="n">csr_matrix</span><span class="p">(</span><span class="n">dist</span><span class="p">)</span></div>
-
-
-<span class="k">def</span> <span class="nf">_test</span><span class="p">():</span>
-    <span class="kn">import</span> <span class="nn">doctest</span>
-
-    <span class="c1"># the following line could be used to define an alternative to the &#39;&lt;BLANKLINE&gt;&#39; flag</span>
-    <span class="c1"># doctest.BLANKLINE_MARKER = &#39;something better than &lt;BLANKLINE&gt;&#39;</span>
-    <span class="n">start_suppress</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">get_printoptions</span><span class="p">()[</span><span class="s2">&quot;suppress&quot;</span><span class="p">]</span>
-    <span class="n">np</span><span class="o">.</span><span class="n">set_printoptions</span><span class="p">(</span><span class="n">suppress</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="n">doctest</span><span class="o">.</span><span class="n">testmod</span><span class="p">()</span>
-    <span class="n">np</span><span class="o">.</span><span class="n">set_printoptions</span><span class="p">(</span><span class="n">suppress</span><span class="o">=</span><span class="n">start_suppress</span><span class="p">)</span>
-
-
-<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
-    <span class="n">_test</span><span class="p">()</span>
-</pre></div>
-
-    </div>
-      
-  </div>
-</div>
-<footer class="footer">
-  <div class="container">
-    <p class="pull-right">
-      <a href="#">Back to top</a>
-      
-        <br/>
-        
-      
-    </p>
-    <p>
-        &copy; Copyright 2018-, pysal developers.<br/>
-      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 3.1.2.<br/>
-    </p>
-  </div>
-</footer>
-  </body>
-</html>
\ No newline at end of file
diff --git a/docs/_modules/libpysal/weights/raster.html b/docs/_modules/libpysal/weights/raster.html
deleted file mode 100644
index 573c49736..000000000
--- a/docs/_modules/libpysal/weights/raster.html
+++ /dev/null
@@ -1,983 +0,0 @@
-<!DOCTYPE html>
-
-<html>
-  <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>libpysal.weights.raster &#8212; libpysal v4.4.0 Manual</title>
-    <link rel="stylesheet" href="../../../_static/bootstrap-sphinx.css" type="text/css" />
-    <link rel="stylesheet" href="../../../_static/pygments.css" type="text/css" />
-    <link rel="stylesheet" type="text/css" href="../../../_static/pysal-styles.css" />
-    <script id="documentation_options" data-url_root="../../../" src="../../../_static/documentation_options.js"></script>
-    <script src="../../../_static/jquery.js"></script>
-    <script src="../../../_static/underscore.js"></script>
-    <script src="../../../_static/doctools.js"></script>
-    <script src="../../../_static/language_data.js"></script>
-    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
-    <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
-    <script type="text/x-mathjax-config">MathJax.Hub.Config({"tex2jax": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true, "ignoreClass": "document", "processClass": "math|output_area"}})</script>
-    <link rel="shortcut icon" href="../../../_static/pysal_favicon.ico"/>
-    <link rel="index" title="Index" href="../../../genindex.html" />
-    <link rel="search" title="Search" href="../../../search.html" />
-<meta charset='utf-8'>
-<meta http-equiv='X-UA-Compatible' content='IE=edge,chrome=1'>
-<meta name='viewport' content='width=device-width, initial-scale=1.0, maximum-scale=1'>
-<meta name="apple-mobile-web-app-capable" content="yes">
-<script type="text/javascript" src="../../../_static/js/jquery-1.11.0.min.js "></script>
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-
-  <div id="navbar" class="navbar navbar-default navbar-fixed-top">
-    <div class="container">
-      <div class="navbar-header">
-        <!-- .btn-navbar is used as the toggle for collapsed navbar content -->
-        <button type="button" class="navbar-toggle" data-toggle="collapse" data-target=".nav-collapse">
-          <span class="icon-bar"></span>
-          <span class="icon-bar"></span>
-          <span class="icon-bar"></span>
-        </button>
-        <a class="navbar-brand" href="../../../index.html">
-          libpysal</a>
-        <span class="navbar-text navbar-version pull-left"><b>4.4.0</b></span>
-      </div>
-
-        <div class="collapse navbar-collapse nav-collapse">
-          <ul class="nav navbar-nav">
-            
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-  <h1>Source code for libpysal.weights.raster</h1><div class="highlight"><pre>
-<span></span><span class="kn">from</span> <span class="nn">.util</span> <span class="kn">import</span> <span class="n">lat2SW</span>
-<span class="kn">from</span> <span class="nn">.weights</span> <span class="kn">import</span> <span class="n">WSP</span><span class="p">,</span> <span class="n">W</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">from</span> <span class="nn">warnings</span> <span class="kn">import</span> <span class="n">warn</span>
-<span class="kn">import</span> <span class="nn">os</span>
-<span class="kn">import</span> <span class="nn">sys</span>
-<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">sparse</span>
-
-<span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">basename</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="ow">in</span> <span class="p">(</span><span class="s2">&quot;pytest&quot;</span><span class="p">,</span> <span class="s2">&quot;py.test&quot;</span><span class="p">):</span>
-
-    <span class="k">def</span> <span class="nf">jit</span><span class="p">(</span><span class="o">*</span><span class="n">dec_args</span><span class="p">,</span> <span class="o">**</span><span class="n">dec_kwargs</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        decorator mimicking numba.jit</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">def</span> <span class="nf">intercepted_function</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="o">*</span><span class="n">f_args</span><span class="p">,</span> <span class="o">**</span><span class="n">f_kwargs</span><span class="p">):</span>
-            <span class="k">return</span> <span class="n">f</span>
-
-        <span class="k">return</span> <span class="n">intercepted_function</span>
-
-
-<span class="k">else</span><span class="p">:</span>
-    <span class="kn">from</span> <span class="nn">..common</span> <span class="kn">import</span> <span class="n">jit</span>
-
-<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Mragank Shekhar &lt;yesthisismrshekhar@gmail.com&gt;&quot;</span>
-
-<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;da2W&quot;</span><span class="p">,</span> <span class="s2">&quot;da2WSP&quot;</span><span class="p">,</span> <span class="s2">&quot;w2da&quot;</span><span class="p">,</span> <span class="s2">&quot;wsp2da&quot;</span><span class="p">,</span> <span class="s2">&quot;testDataArray&quot;</span><span class="p">]</span>
-
-
-<div class="viewcode-block" id="da2W"><a class="viewcode-back" href="../../../generated/libpysal.weights.da2W.html#libpysal.weights.da2W">[docs]</a><span class="k">def</span> <span class="nf">da2W</span><span class="p">(</span>
-    <span class="n">da</span><span class="p">,</span>
-    <span class="n">criterion</span><span class="o">=</span><span class="s2">&quot;queen&quot;</span><span class="p">,</span>
-    <span class="n">z_value</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">coords_labels</span><span class="o">=</span><span class="p">{},</span>
-    <span class="n">k</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-    <span class="n">include_nodata</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-    <span class="n">n_jobs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-    <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
-<span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Create a W object from xarray.DataArray with an additional</span>
-<span class="sd">    attribute index containing coordinate values of the raster</span>
-<span class="sd">    in the form of Pandas.Index/MultiIndex.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    da : xarray.DataArray</span>
-<span class="sd">        Input 2D or 3D DataArray with shape=(z, y, x)</span>
-<span class="sd">    criterion : {&quot;rook&quot;, &quot;queen&quot;}</span>
-<span class="sd">        Type of contiguity. Default is queen.</span>
-<span class="sd">    z_value : int/string/float</span>
-<span class="sd">        Select the z_value of 3D DataArray with multiple layers.</span>
-<span class="sd">    coords_labels : dictionary</span>
-<span class="sd">        Pass dimension labels for coordinates and layers if they do not</span>
-<span class="sd">        belong to default dimensions, which are (band/time, y/lat, x/lon)</span>
-<span class="sd">        e.g. coords_labels = {&quot;y_label&quot;: &quot;latitude&quot;, &quot;x_label&quot;: &quot;longitude&quot;, &quot;z_label&quot;: &quot;year&quot;}</span>
-<span class="sd">        Default is {} empty dictionary.</span>
-<span class="sd">    k : int</span>
-<span class="sd">        Order of contiguity, this will select all neighbors upto kth order.</span>
-<span class="sd">        Default is 1.</span>
-<span class="sd">    include_nodata : boolean</span>
-<span class="sd">        If True, missing values will be assumed as non-missing when</span>
-<span class="sd">        selecting higher_order neighbors, Default is False</span>
-<span class="sd">    n_jobs : int</span>
-<span class="sd">        Number of cores to be used in the sparse weight construction. If -1,</span>
-<span class="sd">        all available cores are used. Default is 1.</span>
-<span class="sd">    **kwargs : keyword arguments</span>
-<span class="sd">        Optional arguments for :class:`libpysal.weights.W`</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    w : libpysal.weights.W</span>
-<span class="sd">       instance of spatial weights class W with an index attribute</span>
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-<span class="sd">    1. Lower order contiguities are also selected.</span>
-<span class="sd">    2. Returned object contains `index` attribute that includes a</span>
-<span class="sd">    `Pandas.MultiIndex` object from the DataArray.</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-
-<span class="sd">    &gt;&gt;&gt; from libpysal.weights.raster import da2W, testDataArray</span>
-<span class="sd">    &gt;&gt;&gt; da = testDataArray().rename(</span>
-<span class="sd">            {&#39;band&#39;: &#39;layer&#39;, &#39;x&#39;: &#39;longitude&#39;, &#39;y&#39;: &#39;latitude&#39;})</span>
-<span class="sd">    &gt;&gt;&gt; da.dims</span>
-<span class="sd">    (&#39;layer&#39;, &#39;latitude&#39;, &#39;longitude&#39;)</span>
-<span class="sd">    &gt;&gt;&gt; da.shape</span>
-<span class="sd">    (3, 4, 4)</span>
-<span class="sd">    &gt;&gt;&gt; da.coords</span>
-<span class="sd">    Coordinates:</span>
-<span class="sd">        * layer      (layer) int64 1 2 3</span>
-<span class="sd">        * latitude   (latitude) float64 90.0 30.0 -30.0 -90.0</span>
-<span class="sd">        * longitude  (longitude) float64 -180.0 -60.0 60.0 180.0</span>
-<span class="sd">    &gt;&gt;&gt; da.attrs</span>
-<span class="sd">    {&#39;nodatavals&#39;: (-32768.0,)}</span>
-<span class="sd">    &gt;&gt;&gt; coords_labels = {</span>
-<span class="sd">        &quot;z_label&quot;: &quot;layer&quot;,</span>
-<span class="sd">        &quot;y_label&quot;: &quot;latitude&quot;,</span>
-<span class="sd">        &quot;x_label&quot;: &quot;longitude&quot;</span>
-<span class="sd">    }</span>
-<span class="sd">    &gt;&gt;&gt; w = da2W(da, z_value=2, coords_labels=coords_labels)</span>
-<span class="sd">    &gt;&gt;&gt; &quot;%.3f&quot;%w.pct_nonzero</span>
-<span class="sd">    &#39;30.000&#39;</span>
-<span class="sd">    &gt;&gt;&gt; w[(2, 90.0, 180.0)] == {(2, 90.0, 60.0): 1, (2, 30.0, 180.0): 1}</span>
-<span class="sd">    True</span>
-<span class="sd">    &gt;&gt;&gt; len(w.index)</span>
-<span class="sd">    10</span>
-<span class="sd">    &gt;&gt;&gt; w.index[:2]</span>
-<span class="sd">    MultiIndex([(2, 90.0,  60.0),</span>
-<span class="sd">                (2, 90.0, 180.0)],</span>
-<span class="sd">               names=[&#39;layer&#39;, &#39;latitude&#39;, &#39;longitude&#39;])</span>
-
-<span class="sd">    See Also</span>
-<span class="sd">    --------</span>
-<span class="sd">    :class:`libpysal.weights.weights.W`</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">warn</span><span class="p">(</span>
-        <span class="s2">&quot;You are trying to build a full W object from &quot;</span>
-        <span class="s2">&quot;xarray.DataArray (raster) object. This computation &quot;</span>
-        <span class="s2">&quot;can be very slow and not scale well. It is recommended, &quot;</span>
-        <span class="s2">&quot;if possible, to instead build WSP object, which is more &quot;</span>
-        <span class="s2">&quot;efficient and faster. You can do this by using da2WSP method.&quot;</span>
-    <span class="p">)</span>
-    <span class="n">wsp</span> <span class="o">=</span> <span class="n">da2WSP</span><span class="p">(</span><span class="n">da</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">z_value</span><span class="p">,</span> <span class="n">coords_labels</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">include_nodata</span><span class="p">,</span> <span class="n">n_jobs</span><span class="p">)</span>
-    <span class="n">w</span> <span class="o">=</span> <span class="n">wsp</span><span class="o">.</span><span class="n">to_W</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-
-    <span class="c1"># temp addition of index attribute</span>
-    <span class="n">w</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="n">wsp</span><span class="o">.</span><span class="n">index</span>
-    <span class="k">return</span> <span class="n">w</span></div>
-
-
-<div class="viewcode-block" id="da2WSP"><a class="viewcode-back" href="../../../generated/libpysal.weights.da2WSP.html#libpysal.weights.da2WSP">[docs]</a><span class="k">def</span> <span class="nf">da2WSP</span><span class="p">(</span>
-    <span class="n">da</span><span class="p">,</span>
-    <span class="n">criterion</span><span class="o">=</span><span class="s2">&quot;queen&quot;</span><span class="p">,</span>
-    <span class="n">z_value</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">coords_labels</span><span class="o">=</span><span class="p">{},</span>
-    <span class="n">k</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-    <span class="n">include_nodata</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-    <span class="n">n_jobs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-<span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Create a WSP object from xarray.DataArray with an additional</span>
-<span class="sd">    attribute index containing coordinate values of the raster</span>
-<span class="sd">    in the form of Pandas.Index/MultiIndex.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    da : xarray.DataArray</span>
-<span class="sd">        Input 2D or 3D DataArray with shape=(z, y, x)</span>
-<span class="sd">    criterion : {&quot;rook&quot;, &quot;queen&quot;}</span>
-<span class="sd">        Type of contiguity. Default is queen.</span>
-<span class="sd">    z_value : int/string/float</span>
-<span class="sd">        Select the z_value of 3D DataArray with multiple layers.</span>
-<span class="sd">    coords_labels : dictionary</span>
-<span class="sd">        Pass dimension labels for coordinates and layers if they do not</span>
-<span class="sd">        belong to default dimensions, which are (band/time, y/lat, x/lon)</span>
-<span class="sd">        e.g. coords_labels = {&quot;y_label&quot;: &quot;latitude&quot;, &quot;x_label&quot;: &quot;longitude&quot;, &quot;z_label&quot;: &quot;year&quot;}</span>
-<span class="sd">        Default is {} empty dictionary.</span>
-<span class="sd">    k : int</span>
-<span class="sd">        Order of contiguity, this will select all neighbors upto kth order.</span>
-<span class="sd">        Default is 1.</span>
-<span class="sd">    include_nodata : boolean</span>
-<span class="sd">        If True, missing values will be assumed as non-missing when</span>
-<span class="sd">        selecting higher_order neighbors, Default is False</span>
-<span class="sd">    n_jobs : int</span>
-<span class="sd">        Number of cores to be used in the sparse weight construction. If -1,</span>
-<span class="sd">        all available cores are used. Default is 1.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    wsp : libpysal.weights.WSP</span>
-<span class="sd">       instance of spatial weights class WSP with an index attribute</span>
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-<span class="sd">    1. Lower order contiguities are also selected.</span>
-<span class="sd">    2. Returned object contains `index` attribute that includes a</span>
-<span class="sd">    `Pandas.MultiIndex` object from the DataArray.</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    &gt;&gt;&gt; from libpysal.weights.raster import da2WSP, testDataArray</span>
-<span class="sd">    &gt;&gt;&gt; da = testDataArray().rename(</span>
-<span class="sd">            {&#39;band&#39;: &#39;layer&#39;, &#39;x&#39;: &#39;longitude&#39;, &#39;y&#39;: &#39;latitude&#39;})</span>
-<span class="sd">    &gt;&gt;&gt; da.dims</span>
-<span class="sd">    (&#39;layer&#39;, &#39;latitude&#39;, &#39;longitude&#39;)</span>
-<span class="sd">    &gt;&gt;&gt; da.shape</span>
-<span class="sd">    (3, 4, 4)</span>
-<span class="sd">    &gt;&gt;&gt; da.coords</span>
-<span class="sd">    Coordinates:</span>
-<span class="sd">        * layer      (layer) int64 1 2 3</span>
-<span class="sd">        * latitude   (latitude) float64 90.0 30.0 -30.0 -90.0</span>
-<span class="sd">        * longitude  (longitude) float64 -180.0 -60.0 60.0 180.0</span>
-<span class="sd">    &gt;&gt;&gt; da.attrs</span>
-<span class="sd">    {&#39;nodatavals&#39;: (-32768.0,)}</span>
-<span class="sd">    &gt;&gt;&gt; coords_labels = {</span>
-<span class="sd">        &quot;z_label&quot;: &quot;layer&quot;,</span>
-<span class="sd">        &quot;y_label&quot;: &quot;latitude&quot;,</span>
-<span class="sd">        &quot;x_label&quot;: &quot;longitude&quot;</span>
-<span class="sd">    }</span>
-<span class="sd">    &gt;&gt;&gt; wsp = da2WSP(da, z_value=2, coords_labels=coords_labels)</span>
-<span class="sd">    &gt;&gt;&gt; wsp.n</span>
-<span class="sd">    10</span>
-<span class="sd">    &gt;&gt;&gt; pct_sp = wsp.sparse.nnz *1. / wsp.n**2</span>
-<span class="sd">    &gt;&gt;&gt; &quot;%.3f&quot;%pct_sp</span>
-<span class="sd">    &#39;0.300&#39;</span>
-<span class="sd">    &gt;&gt;&gt; print(wsp.sparse[4].todense())</span>
-<span class="sd">    [[0 0 1 0 0 1 1 1 0 0]]</span>
-<span class="sd">    &gt;&gt;&gt; wsp.index[:2]</span>
-<span class="sd">    MultiIndex([(2, 90.0,  60.0),</span>
-<span class="sd">                (2, 90.0, 180.0)],</span>
-<span class="sd">               names=[&#39;layer&#39;, &#39;latitude&#39;, &#39;longitude&#39;])</span>
-
-<span class="sd">    See Also</span>
-<span class="sd">    --------</span>
-<span class="sd">    :class:`libpysal.weights.weights.WSP`</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">z_id</span><span class="p">,</span> <span class="n">coords_labels</span> <span class="o">=</span> <span class="n">_da_checker</span><span class="p">(</span><span class="n">da</span><span class="p">,</span> <span class="n">z_value</span><span class="p">,</span> <span class="n">coords_labels</span><span class="p">)</span>
-    <span class="n">shape</span> <span class="o">=</span> <span class="n">da</span><span class="o">.</span><span class="n">shape</span>
-    <span class="k">if</span> <span class="n">z_id</span><span class="p">:</span>
-        <span class="n">slice_dict</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="n">slice_dict</span><span class="p">[</span><span class="n">coords_labels</span><span class="p">[</span><span class="s2">&quot;z_label&quot;</span><span class="p">]]</span> <span class="o">=</span> <span class="mi">0</span>
-        <span class="n">shape</span> <span class="o">=</span> <span class="n">da</span><span class="p">[</span><span class="n">slice_dict</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span>
-        <span class="n">slice_dict</span><span class="p">[</span><span class="n">coords_labels</span><span class="p">[</span><span class="s2">&quot;z_label&quot;</span><span class="p">]]</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="n">z_id</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">z_id</span><span class="p">)</span>
-        <span class="n">da</span> <span class="o">=</span> <span class="n">da</span><span class="p">[</span><span class="n">slice_dict</span><span class="p">]</span>
-
-    <span class="n">ser</span> <span class="o">=</span> <span class="n">da</span><span class="o">.</span><span class="n">to_series</span><span class="p">()</span>
-    <span class="n">dtype</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">int32</span> <span class="k">if</span> <span class="p">(</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="o">&lt;</span> <span class="mi">46340</span> <span class="o">**</span> <span class="mi">2</span> <span class="k">else</span> <span class="n">np</span><span class="o">.</span><span class="n">int64</span>
-    <span class="k">if</span> <span class="s2">&quot;nodatavals&quot;</span> <span class="ow">in</span> <span class="n">da</span><span class="o">.</span><span class="n">attrs</span> <span class="ow">and</span> <span class="n">da</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s2">&quot;nodatavals&quot;</span><span class="p">]:</span>
-        <span class="n">mask</span> <span class="o">=</span> <span class="p">(</span><span class="n">ser</span> <span class="o">!=</span> <span class="n">da</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s2">&quot;nodatavals&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span>
-        <span class="n">ids</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">mask</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">id_map</span> <span class="o">=</span> <span class="n">_idmap</span><span class="p">(</span><span class="n">ids</span><span class="p">,</span> <span class="n">mask</span><span class="p">,</span> <span class="n">dtype</span><span class="p">)</span>
-        <span class="n">ser</span> <span class="o">=</span> <span class="n">ser</span><span class="p">[</span><span class="n">ser</span> <span class="o">!=</span> <span class="n">da</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s2">&quot;nodatavals&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">]]</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">ids</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">ser</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span>
-        <span class="n">id_map</span> <span class="o">=</span> <span class="n">ids</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-
-    <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ids</span><span class="p">)</span>
-
-    <span class="k">try</span><span class="p">:</span>
-        <span class="kn">import</span> <span class="nn">numba</span>
-    <span class="k">except</span> <span class="p">(</span><span class="ne">ModuleNotFoundError</span><span class="p">,</span> <span class="ne">ImportError</span><span class="p">):</span>
-        <span class="n">warn</span><span class="p">(</span>
-            <span class="s2">&quot;numba cannot be imported, parallel processing &quot;</span>
-            <span class="s2">&quot;and include_nodata functionality will be disabled. &quot;</span>
-            <span class="s2">&quot;falling back to slower method&quot;</span>
-        <span class="p">)</span>
-        <span class="n">include_nodata</span> <span class="o">=</span> <span class="kc">False</span>
-        <span class="c1"># Fallback method to build sparse matrix</span>
-        <span class="n">sw</span> <span class="o">=</span> <span class="n">lat2SW</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="n">criterion</span><span class="p">)</span>
-        <span class="k">if</span> <span class="s2">&quot;nodatavals&quot;</span> <span class="ow">in</span> <span class="n">da</span><span class="o">.</span><span class="n">attrs</span> <span class="ow">and</span> <span class="n">da</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s2">&quot;nodatavals&quot;</span><span class="p">]:</span>
-            <span class="n">sw</span> <span class="o">=</span> <span class="n">sw</span><span class="p">[</span><span class="n">mask</span><span class="p">]</span>
-            <span class="n">sw</span> <span class="o">=</span> <span class="n">sw</span><span class="p">[:,</span> <span class="n">mask</span><span class="p">]</span>
-
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">k_nas</span> <span class="o">=</span> <span class="n">k</span> <span class="k">if</span> <span class="n">include_nodata</span> <span class="k">else</span> <span class="mi">1</span>
-
-        <span class="k">if</span> <span class="n">n_jobs</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="k">try</span><span class="p">:</span>
-                <span class="kn">import</span> <span class="nn">joblib</span>
-            <span class="k">except</span> <span class="p">(</span><span class="ne">ModuleNotFoundError</span><span class="p">,</span> <span class="ne">ImportError</span><span class="p">):</span>
-                <span class="n">warn</span><span class="p">(</span>
-                    <span class="sa">f</span><span class="s2">&quot;Parallel processing is requested (n_jobs=</span><span class="si">{</span><span class="n">n_jobs</span><span class="si">}</span><span class="s2">),&quot;</span>
-                    <span class="sa">f</span><span class="s2">&quot; but joblib cannot be imported. n_jobs will be set&quot;</span>
-                    <span class="sa">f</span><span class="s2">&quot; to 1.&quot;</span><span class="p">,</span>
-                    <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
-                <span class="p">)</span>
-                <span class="n">n_jobs</span> <span class="o">=</span> <span class="mi">1</span>
-
-        <span class="k">if</span> <span class="n">n_jobs</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">sw_tup</span> <span class="o">=</span> <span class="n">_SWbuilder</span><span class="p">(</span>
-                <span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="n">ids</span><span class="p">,</span> <span class="n">id_map</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">k_nas</span><span class="p">,</span> <span class="n">dtype</span>
-            <span class="p">)</span>  <span class="c1"># -&gt; (data, (row, col))</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">n_jobs</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span>
-                <span class="n">n_jobs</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">cpu_count</span><span class="p">()</span>
-            <span class="c1"># Parallel implementation</span>
-            <span class="n">sw_tup</span> <span class="o">=</span> <span class="n">_parSWbuilder</span><span class="p">(</span>
-                <span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="n">ids</span><span class="p">,</span> <span class="n">id_map</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">k_nas</span><span class="p">,</span> <span class="n">dtype</span><span class="p">,</span> <span class="n">n_jobs</span>
-            <span class="p">)</span>  <span class="c1"># -&gt; (data, (row, col))</span>
-
-        <span class="n">sw</span> <span class="o">=</span> <span class="n">sparse</span><span class="o">.</span><span class="n">csr_matrix</span><span class="p">(</span><span class="n">sw_tup</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">n</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int8</span><span class="p">,)</span>
-
-    <span class="c1"># Higher_order functionality, this uses idea from</span>
-    <span class="c1"># libpysal#313 for adding higher order neighbors.</span>
-    <span class="c1"># Since diagonal elements are also added in the result,</span>
-    <span class="c1"># this method set the diagonal elements to zero and</span>
-    <span class="c1"># then eliminate zeros from the data. This changes the</span>
-    <span class="c1"># sparcity of the csr_matrix !!</span>
-    <span class="k">if</span> <span class="n">k</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">include_nodata</span><span class="p">:</span>
-        <span class="n">sw</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">sw</span> <span class="o">**</span> <span class="n">x</span><span class="p">,</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">k</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)))</span>
-        <span class="n">sw</span><span class="o">.</span><span class="n">setdiag</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-        <span class="n">sw</span><span class="o">.</span><span class="n">eliminate_zeros</span><span class="p">()</span>
-        <span class="n">sw</span><span class="o">.</span><span class="n">data</span><span class="p">[:]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">sw</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int8</span><span class="p">)</span>
-
-    <span class="n">index</span> <span class="o">=</span> <span class="n">ser</span><span class="o">.</span><span class="n">index</span>
-    <span class="n">wsp</span> <span class="o">=</span> <span class="n">WSP</span><span class="p">(</span><span class="n">sw</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">index</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">wsp</span></div>
-
-
-<div class="viewcode-block" id="w2da"><a class="viewcode-back" href="../../../generated/libpysal.weights.w2da.html#libpysal.weights.w2da">[docs]</a><span class="k">def</span> <span class="nf">w2da</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">attrs</span><span class="o">=</span><span class="p">{},</span> <span class="n">coords</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Creates xarray.DataArray object from passed data aligned with W object.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ---------</span>
-<span class="sd">    data : array/list/pd.Series</span>
-<span class="sd">        1d array-like data with dimensionality conforming to w</span>
-<span class="sd">    w : libpysal.weights.W</span>
-<span class="sd">        Spatial weights object aligned with passed data</span>
-<span class="sd">    attrs : Dictionary</span>
-<span class="sd">        Attributes stored in dict related to DataArray, e.g. da.attrs</span>
-<span class="sd">        Default is {} empty dictionary.</span>
-<span class="sd">    coords : Dictionary/xarray.core.coordinates.DataArrayCoordinates</span>
-<span class="sd">        Coordinates corresponding to DataArray, e.g. da.coords</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    da : xarray.DataArray</span>
-<span class="sd">        instance of xarray.DataArray</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    &gt;&gt;&gt; from libpysal.raster import da2W, testDataArray, w2da</span>
-<span class="sd">    &gt;&gt;&gt; da = testDataArray()</span>
-<span class="sd">    &gt;&gt;&gt; da.shape</span>
-<span class="sd">    (3, 4, 4)</span>
-<span class="sd">    &gt;&gt;&gt; w = da2W(da, z_value=2)</span>
-<span class="sd">    &gt;&gt;&gt; data = np.random.randint(0, 255, len(w.index))</span>
-<span class="sd">    &gt;&gt;&gt; da1 = w2da(data, w)</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">W</span><span class="p">):</span>
-        <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;w must be an instance of weights.W&quot;</span><span class="p">)</span>
-    <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="s2">&quot;index&quot;</span><span class="p">):</span>
-        <span class="n">da</span> <span class="o">=</span> <span class="n">_index2da</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">w</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">attrs</span><span class="p">,</span> <span class="n">coords</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
-            <span class="s2">&quot;This method requires `w` object to include `index` attribute that is built as a `pandas.MultiIndex` object.&quot;</span>
-        <span class="p">)</span>
-    <span class="k">return</span> <span class="n">da</span></div>
-
-
-<div class="viewcode-block" id="wsp2da"><a class="viewcode-back" href="../../../generated/libpysal.weights.wsp2da.html#libpysal.weights.wsp2da">[docs]</a><span class="k">def</span> <span class="nf">wsp2da</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">wsp</span><span class="p">,</span> <span class="n">attrs</span><span class="o">=</span><span class="p">{},</span> <span class="n">coords</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Creates xarray.DataArray object from passed data aligned with WSP object.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ---------</span>
-<span class="sd">    data : array/list/pd.Series</span>
-<span class="sd">        1d array-like data with dimensionality conforming to wsp</span>
-<span class="sd">    wsp : libpysal.weights.WSP</span>
-<span class="sd">        Sparse weights object aligned with passed data</span>
-<span class="sd">    attrs : Dictionary</span>
-<span class="sd">        Attributes stored in dict related to DataArray, e.g. da.attrs</span>
-<span class="sd">        Default is {} empty dictionary.</span>
-<span class="sd">    coords : Dictionary/xarray.core.coordinates.DataArrayCoordinates</span>
-<span class="sd">        coordinates corresponding to DataArray, e.g. da.coords</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    da : xarray.DataArray</span>
-<span class="sd">        instance of xarray.DataArray</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    &gt;&gt;&gt; from libpysal.raster import da2WSP, testDataArray, wsp2da</span>
-<span class="sd">    &gt;&gt;&gt; da = testDataArray()</span>
-<span class="sd">    &gt;&gt;&gt; da.shape</span>
-<span class="sd">    (3, 4, 4)</span>
-<span class="sd">    &gt;&gt;&gt; wsp = da2WSP(da, z_value=2)</span>
-<span class="sd">    &gt;&gt;&gt; data = np.random.randint(0, 255, len(wsp.index))</span>
-<span class="sd">    &gt;&gt;&gt; da1 = w2da(data, wsp)</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">wsp</span><span class="p">,</span> <span class="n">WSP</span><span class="p">):</span>
-        <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;wsp must be an instance of weights.WSP&quot;</span><span class="p">)</span>
-    <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">wsp</span><span class="p">,</span> <span class="s2">&quot;index&quot;</span><span class="p">):</span>
-        <span class="n">da</span> <span class="o">=</span> <span class="n">_index2da</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">wsp</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">attrs</span><span class="p">,</span> <span class="n">coords</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
-            <span class="s2">&quot;This method requires `wsp` object to include `index` attribute that is built as a `pandas.MultiIndex` object.&quot;</span>
-        <span class="p">)</span>
-    <span class="k">return</span> <span class="n">da</span></div>
-
-
-<div class="viewcode-block" id="testDataArray"><a class="viewcode-back" href="../../../generated/libpysal.weights.testDataArray.html#libpysal.weights.testDataArray">[docs]</a><span class="k">def</span> <span class="nf">testDataArray</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span> <span class="n">time</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">rand</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">missing_vals</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Creates 2 or 3 dimensional test xarray.DataArray object</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ---------</span>
-<span class="sd">    shape : tuple</span>
-<span class="sd">        Tuple containing shape of the DataArray aligned with</span>
-<span class="sd">        following dimension = (lat, lon) or (layer, lat, lon)</span>
-<span class="sd">        Default shape = (3, 4, 4)</span>
-<span class="sd">    time : boolean</span>
-<span class="sd">        Type of layer, if True then layer=time else layer=band</span>
-<span class="sd">        Default is False.</span>
-<span class="sd">    rand : boolean</span>
-<span class="sd">        If True, creates a DataArray filled with unique and random data.</span>
-<span class="sd">        Default is false (generates seeded random data)</span>
-<span class="sd">    missing_vals : boolean</span>
-<span class="sd">        Create a DataArray filled with missing values. Default is True.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    da : xarray.DataArray</span>
-<span class="sd">        instance of xarray.DataArray</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">try</span><span class="p">:</span>
-        <span class="kn">from</span> <span class="nn">xarray</span> <span class="kn">import</span> <span class="n">DataArray</span>
-    <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">ModuleNotFoundError</span><span class="p">(</span><span class="s2">&quot;xarray must be installed to use this functionality&quot;</span><span class="p">)</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">rand</span><span class="p">:</span>
-        <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">12345</span><span class="p">)</span>
-    <span class="n">coords</span> <span class="o">=</span> <span class="p">{}</span>
-    <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">n</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
-        <span class="n">layer</span> <span class="o">=</span> <span class="s2">&quot;time&quot;</span> <span class="k">if</span> <span class="n">time</span> <span class="k">else</span> <span class="s2">&quot;band&quot;</span>
-        <span class="n">dims</span> <span class="o">=</span> <span class="p">(</span><span class="n">layer</span><span class="p">,</span> <span class="s2">&quot;y&quot;</span><span class="p">,</span> <span class="s2">&quot;x&quot;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">time</span><span class="p">:</span>
-            <span class="n">layers</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span>
-                <span class="n">np</span><span class="o">.</span><span class="n">datetime64</span><span class="p">(</span><span class="s2">&quot;2020-07-30&quot;</span><span class="p">),</span> <span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s2">&quot;datetime64[D]&quot;</span>
-            <span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">layers</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
-        <span class="n">coords</span><span class="p">[</span><span class="n">dims</span><span class="p">[</span><span class="o">-</span><span class="mi">3</span><span class="p">]]</span> <span class="o">=</span> <span class="n">layers</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">dims</span> <span class="o">=</span> <span class="p">(</span><span class="s2">&quot;y&quot;</span><span class="p">,</span> <span class="s2">&quot;x&quot;</span><span class="p">)</span>
-    <span class="n">coords</span><span class="p">[</span><span class="n">dims</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">]]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">90</span><span class="p">,</span> <span class="o">-</span><span class="mi">90</span><span class="p">,</span> <span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">])</span>
-    <span class="n">coords</span><span class="p">[</span><span class="n">dims</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">180</span><span class="p">,</span> <span class="mi">180</span><span class="p">,</span> <span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
-    <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">255</span><span class="p">,</span> <span class="n">shape</span><span class="p">)</span>
-    <span class="n">attrs</span> <span class="o">=</span> <span class="p">{}</span>
-    <span class="k">if</span> <span class="n">missing_vals</span><span class="p">:</span>
-        <span class="n">attrs</span><span class="p">[</span><span class="s2">&quot;nodatavals&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mf">32768.0</span><span class="p">,)</span>
-        <span class="n">miss_ids</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)</span>
-        <span class="n">data</span><span class="p">[</span><span class="n">miss_ids</span><span class="p">]</span> <span class="o">=</span> <span class="n">attrs</span><span class="p">[</span><span class="s2">&quot;nodatavals&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
-    <span class="n">da</span> <span class="o">=</span> <span class="n">DataArray</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">coords</span><span class="p">,</span> <span class="n">dims</span><span class="p">,</span> <span class="n">attrs</span><span class="o">=</span><span class="n">attrs</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">da</span></div>
-
-
-<span class="k">def</span> <span class="nf">_da_checker</span><span class="p">(</span><span class="n">da</span><span class="p">,</span> <span class="n">z_value</span><span class="p">,</span> <span class="n">coords_labels</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    xarray.dataarray checker for raster interface</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    da : xarray.DataArray</span>
-<span class="sd">        Input 2D or 3D DataArray with shape=(z, y, x)</span>
-<span class="sd">    z_value : int/string/float</span>
-<span class="sd">        Select the z_value of 3D DataArray with multiple layers.</span>
-<span class="sd">    coords_labels : dictionary</span>
-<span class="sd">        Pass dimension labels for coordinates and layers if they do not</span>
-<span class="sd">        belong to default dimensions, which are (band/time, y/lat, x/lon)</span>
-<span class="sd">        e.g. coords_labels = {&quot;y_label&quot;: &quot;latitude&quot;, &quot;x_label&quot;: &quot;longitude&quot;, &quot;z_label&quot;: &quot;year&quot;}</span>
-<span class="sd">        Default is {} empty dictionary.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    z_id : int</span>
-<span class="sd">        Returns the index of layer</span>
-<span class="sd">    dims : dictionary</span>
-<span class="sd">        Mapped dimensions of the DataArray</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">try</span><span class="p">:</span>
-        <span class="kn">from</span> <span class="nn">xarray</span> <span class="kn">import</span> <span class="n">DataArray</span>
-    <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">ModuleNotFoundError</span><span class="p">(</span><span class="s2">&quot;xarray must be installed to use this functionality&quot;</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">da</span><span class="p">,</span> <span class="n">DataArray</span><span class="p">):</span>
-        <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;da must be an instance of xarray.DataArray&quot;</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">da</span><span class="o">.</span><span class="n">ndim</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]:</span>
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;da must be 2D or 3D&quot;</span><span class="p">)</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span>
-        <span class="n">np</span><span class="o">.</span><span class="n">issubdtype</span><span class="p">(</span><span class="n">da</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">integer</span><span class="p">)</span>
-        <span class="ow">or</span> <span class="n">np</span><span class="o">.</span><span class="n">issubdtype</span><span class="p">(</span><span class="n">da</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">floating</span><span class="p">)</span>
-    <span class="p">):</span>
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;da must be an array of integers or float&quot;</span><span class="p">)</span>
-
-    <span class="c1"># default dimensions</span>
-    <span class="n">def_labels</span> <span class="o">=</span> <span class="p">{</span>
-        <span class="s2">&quot;x_label&quot;</span><span class="p">:</span> <span class="n">coords_labels</span><span class="p">[</span><span class="s2">&quot;x_label&quot;</span><span class="p">]</span>
-        <span class="k">if</span> <span class="s2">&quot;x_label&quot;</span> <span class="ow">in</span> <span class="n">coords_labels</span>
-        <span class="k">else</span> <span class="p">(</span><span class="s2">&quot;x&quot;</span> <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">da</span><span class="p">,</span> <span class="s2">&quot;x&quot;</span><span class="p">)</span> <span class="k">else</span> <span class="s2">&quot;lon&quot;</span><span class="p">),</span>
-        <span class="s2">&quot;y_label&quot;</span><span class="p">:</span> <span class="n">coords_labels</span><span class="p">[</span><span class="s2">&quot;y_label&quot;</span><span class="p">]</span>
-        <span class="k">if</span> <span class="s2">&quot;y_label&quot;</span> <span class="ow">in</span> <span class="n">coords_labels</span>
-        <span class="k">else</span> <span class="p">(</span><span class="s2">&quot;y&quot;</span> <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">da</span><span class="p">,</span> <span class="s2">&quot;y&quot;</span><span class="p">)</span> <span class="k">else</span> <span class="s2">&quot;lat&quot;</span><span class="p">),</span>
-    <span class="p">}</span>
-
-    <span class="k">if</span> <span class="n">da</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
-        <span class="n">def_labels</span><span class="p">[</span><span class="s2">&quot;z_label&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
-            <span class="n">coords_labels</span><span class="p">[</span><span class="s2">&quot;z_label&quot;</span><span class="p">]</span>
-            <span class="k">if</span> <span class="s2">&quot;z_label&quot;</span> <span class="ow">in</span> <span class="n">coords_labels</span>
-            <span class="k">else</span> <span class="p">(</span><span class="s2">&quot;band&quot;</span> <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">da</span><span class="p">,</span> <span class="s2">&quot;band&quot;</span><span class="p">)</span> <span class="k">else</span> <span class="s2">&quot;time&quot;</span><span class="p">)</span>
-        <span class="p">)</span>
-
-        <span class="n">z_id</span> <span class="o">=</span> <span class="mi">1</span>
-        <span class="k">if</span> <span class="n">z_value</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">da</span><span class="o">.</span><span class="n">sizes</span><span class="p">[</span><span class="n">def_labels</span><span class="p">[</span><span class="s2">&quot;z_label&quot;</span><span class="p">]]</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
-                <span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Multiple layers detected. Using first layer as default.&quot;</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">z_id</span> <span class="o">+=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">da</span><span class="p">[</span><span class="n">def_labels</span><span class="p">[</span><span class="s2">&quot;z_label&quot;</span><span class="p">]])</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">z_value</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">z_id</span> <span class="o">=</span> <span class="kc">None</span>
-    <span class="k">return</span> <span class="n">z_id</span><span class="p">,</span> <span class="n">def_labels</span>
-
-
-<span class="k">def</span> <span class="nf">_index2da</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">attrs</span><span class="p">,</span> <span class="n">coords</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Creates xarray.DataArray object from passed data</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ---------</span>
-<span class="sd">    data : array/list/pd.Series</span>
-<span class="sd">        1d array-like data with dimensionality conforming to index</span>
-<span class="sd">    index : pd.MultiIndex</span>
-<span class="sd">        indices of the DataArray when converted to pd.Series</span>
-<span class="sd">    attrs : Dictionary</span>
-<span class="sd">        Attributes stored in dict related to DataArray, e.g. da.attrs</span>
-<span class="sd">    coords : Dictionary/xarray.core.coordinates.DataArrayCoordinates</span>
-<span class="sd">        coordinates corresponding to DataArray, e.g. da[n-1:n].coords</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    da : xarray.DataArray</span>
-<span class="sd">        instance of xarray.DataArray</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">try</span><span class="p">:</span>
-        <span class="kn">from</span> <span class="nn">xarray</span> <span class="kn">import</span> <span class="n">DataArray</span>
-    <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">ModuleNotFoundError</span><span class="p">(</span><span class="s2">&quot;xarray must be installed to use this functionality&quot;</span><span class="p">)</span>
-
-    <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">data</span><span class="p">)</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
-    <span class="n">idx</span> <span class="o">=</span> <span class="n">index</span>
-    <span class="n">dims</span> <span class="o">=</span> <span class="n">idx</span><span class="o">.</span><span class="n">names</span>
-    <span class="n">indexer</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">idx</span><span class="o">.</span><span class="n">codes</span><span class="p">)</span>
-    <span class="n">shape</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">lev</span><span class="o">.</span><span class="n">size</span> <span class="k">for</span> <span class="n">lev</span> <span class="ow">in</span> <span class="n">idx</span><span class="o">.</span><span class="n">levels</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">coords</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">missing</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">prod</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">idx</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="k">if</span> <span class="n">missing</span><span class="p">:</span>
-            <span class="k">if</span> <span class="s2">&quot;nodatavals&quot;</span> <span class="ow">in</span> <span class="n">attrs</span><span class="p">:</span>
-                <span class="n">fill_value</span> <span class="o">=</span> <span class="n">attrs</span><span class="p">[</span><span class="s2">&quot;nodatavals&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">min_data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
-                <span class="n">fill_value</span> <span class="o">=</span> <span class="n">min_data</span> <span class="o">-</span> <span class="mi">1</span> <span class="k">if</span> <span class="n">min_data</span> <span class="o">&lt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="o">-</span><span class="mi">1</span>
-                <span class="n">attrs</span><span class="p">[</span><span class="s2">&quot;nodatavals&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">([</span><span class="n">fill_value</span><span class="p">])</span>
-            <span class="n">data_complete</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">fill_value</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">data_complete</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
-        <span class="n">data_complete</span><span class="p">[</span><span class="n">indexer</span><span class="p">]</span> <span class="o">=</span> <span class="n">data</span>
-        <span class="n">coords</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="k">for</span> <span class="n">dim</span><span class="p">,</span> <span class="n">lev</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">dims</span><span class="p">,</span> <span class="n">idx</span><span class="o">.</span><span class="n">levels</span><span class="p">):</span>
-            <span class="n">coords</span><span class="p">[</span><span class="n">dim</span><span class="p">]</span> <span class="o">=</span> <span class="n">lev</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">fill</span> <span class="o">=</span> <span class="n">attrs</span><span class="p">[</span><span class="s2">&quot;nodatavals&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="k">if</span> <span class="s2">&quot;nodatavals&quot;</span> <span class="ow">in</span> <span class="n">attrs</span> <span class="k">else</span> <span class="mi">0</span>
-        <span class="n">data_complete</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">fill</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
-        <span class="n">data_complete</span><span class="p">[</span><span class="n">indexer</span><span class="p">]</span> <span class="o">=</span> <span class="n">data</span>
-        <span class="n">data_complete</span> <span class="o">=</span> <span class="n">data_complete</span><span class="p">[:,</span> <span class="p">::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-
-    <span class="n">da</span> <span class="o">=</span> <span class="n">DataArray</span><span class="p">(</span><span class="n">data_complete</span><span class="p">,</span> <span class="n">coords</span><span class="o">=</span><span class="n">coords</span><span class="p">,</span> <span class="n">dims</span><span class="o">=</span><span class="n">dims</span><span class="p">,</span> <span class="n">attrs</span><span class="o">=</span><span class="n">attrs</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">da</span><span class="o">.</span><span class="n">sortby</span><span class="p">(</span><span class="n">dims</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">],</span> <span class="kc">False</span><span class="p">)</span>
-
-
-<span class="nd">@jit</span><span class="p">(</span><span class="n">nopython</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">fastmath</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">_idmap</span><span class="p">(</span><span class="n">ids</span><span class="p">,</span> <span class="n">mask</span><span class="p">,</span> <span class="n">dtype</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Utility function computes id_map of non-missing raster data</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    ids : ndarray</span>
-<span class="sd">        1D array containing ids of non-missing raster data</span>
-<span class="sd">    mask : ndarray</span>
-<span class="sd">        1D array mask array</span>
-<span class="sd">    dtype : type</span>
-<span class="sd">        Data type of the id_map array</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    id_map : ndarray</span>
-<span class="sd">        1D array containing id_maps of non-missing raster data</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">id_map</span> <span class="o">=</span> <span class="n">mask</span> <span class="o">*</span> <span class="mi">1</span>
-    <span class="n">id_map</span><span class="p">[</span><span class="n">ids</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">ids</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">id_map</span>
-
-
-<span class="nd">@jit</span><span class="p">(</span><span class="n">nopython</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">fastmath</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">_SWbuilder</span><span class="p">(</span>
-    <span class="n">nrows</span><span class="p">,</span> <span class="n">ncols</span><span class="p">,</span> <span class="n">ids</span><span class="p">,</span> <span class="n">id_map</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">dtype</span><span class="p">,</span>
-<span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Computes data and orders rows, cols, data for a single chunk</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    nrows : int</span>
-<span class="sd">        Number of rows in the raster data</span>
-<span class="sd">    ncols : int</span>
-<span class="sd">        Number of columns in the raster data</span>
-<span class="sd">    ids : ndarray</span>
-<span class="sd">        1D array containing ids of non-missing raster data</span>
-<span class="sd">    id_map : ndarray</span>
-<span class="sd">        1D array containing id_maps of non-missing raster data</span>
-<span class="sd">    criterion : str</span>
-<span class="sd">        Type of contiguity.</span>
-<span class="sd">    k : int</span>
-<span class="sd">        Order of contiguity, Default is 1</span>
-<span class="sd">    dtype : type</span>
-<span class="sd">        Data type of the id_map array</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    data : ndarray</span>
-<span class="sd">        1D ones array containing weight of each neighbor</span>
-<span class="sd">    rows : ndarray</span>
-<span class="sd">        1D ones array containing row value of each id</span>
-<span class="sd">        in the sparse weight object</span>
-<span class="sd">    cols : ndarray</span>
-<span class="sd">        1D ones array containing columns value of each id</span>
-<span class="sd">        in the sparse weight object</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">rows</span><span class="p">,</span> <span class="n">cols</span> <span class="o">=</span> <span class="n">_compute_chunk</span><span class="p">(</span><span class="n">nrows</span><span class="p">,</span> <span class="n">ncols</span><span class="p">,</span> <span class="n">ids</span><span class="p">,</span> <span class="n">id_map</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">dtype</span><span class="p">)</span>
-    <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">rows</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int8</span><span class="p">)</span>
-    <span class="k">return</span> <span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="p">(</span><span class="n">rows</span><span class="p">,</span> <span class="n">cols</span><span class="p">))</span>
-
-
-<span class="nd">@jit</span><span class="p">(</span><span class="n">nopython</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">fastmath</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">nogil</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">_compute_chunk</span><span class="p">(</span>
-    <span class="n">nrows</span><span class="p">,</span> <span class="n">ncols</span><span class="p">,</span> <span class="n">ids</span><span class="p">,</span> <span class="n">id_map</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">dtype</span><span class="p">,</span>
-<span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Computes rows cols for a single chunk</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    nrows : int</span>
-<span class="sd">        Number of rows in the raster data</span>
-<span class="sd">    ncols : int</span>
-<span class="sd">        Number of columns in the raster data</span>
-<span class="sd">    ids : ndarray</span>
-<span class="sd">        1D array containing ids of non-missing raster data</span>
-<span class="sd">    id_map : ndarray</span>
-<span class="sd">        1D array containing id_maps of non-missing raster data</span>
-<span class="sd">    criterion : str</span>
-<span class="sd">        Type of contiguity.</span>
-<span class="sd">    k : int</span>
-<span class="sd">        Order of contiguity, Default is 1</span>
-<span class="sd">    dtype : type</span>
-<span class="sd">        Data type of the rows and cols array</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    rows : ndarray</span>
-<span class="sd">        1D ones array containing row value of each id</span>
-<span class="sd">        in the sparse weight object</span>
-<span class="sd">    cols : ndarray</span>
-<span class="sd">        1D ones array containing columns value of each id</span>
-<span class="sd">        in the sparse weight object</span>
-<span class="sd">    ni : int</span>
-<span class="sd">        Number of rows and cols</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ids</span><span class="p">)</span>
-    <span class="c1"># Setting d which is used for row, col preallocation</span>
-    <span class="n">d</span> <span class="o">=</span> <span class="mi">4</span> <span class="k">if</span> <span class="n">criterion</span> <span class="o">==</span> <span class="s2">&quot;rook&quot;</span> <span class="k">else</span> <span class="mi">8</span>
-    <span class="k">if</span> <span class="n">k</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-        <span class="n">d</span> <span class="o">=</span> <span class="nb">int</span><span class="p">((</span><span class="n">k</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="mi">8</span> <span class="o">+</span> <span class="p">(</span><span class="n">k</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="mi">8</span><span class="p">))</span>
-    <span class="n">rows</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">d</span> <span class="o">*</span> <span class="n">n</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span>
-    <span class="n">cols</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty_like</span><span class="p">(</span><span class="n">rows</span><span class="p">)</span>
-    <span class="n">ni</span> <span class="o">=</span> <span class="mi">0</span>  <span class="c1"># -&gt; Pointer to store rows and cols in array</span>
-    <span class="k">for</span> <span class="n">order</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">k</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
-        <span class="n">condition</span> <span class="o">=</span> <span class="p">(</span>
-            <span class="p">(</span><span class="n">order</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
-            <span class="k">if</span> <span class="n">criterion</span> <span class="o">==</span> <span class="s2">&quot;queen&quot;</span>
-            <span class="k">else</span> <span class="p">((</span><span class="n">k</span> <span class="o">-</span> <span class="n">order</span><span class="p">)</span> <span class="k">if</span> <span class="p">((</span><span class="n">k</span> <span class="o">-</span> <span class="n">order</span><span class="p">)</span> <span class="o">&lt;</span> <span class="n">order</span><span class="p">)</span> <span class="k">else</span> <span class="p">(</span><span class="n">order</span> <span class="o">-</span> <span class="mi">1</span><span class="p">))</span>
-        <span class="p">)</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
-            <span class="n">id_i</span> <span class="o">=</span> <span class="n">ids</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-            <span class="n">og_id</span> <span class="o">=</span> <span class="n">id_map</span><span class="p">[</span><span class="n">id_i</span><span class="p">]</span>
-
-            <span class="k">if</span> <span class="p">((</span><span class="n">id_i</span> <span class="o">+</span> <span class="n">order</span><span class="p">)</span> <span class="o">%</span> <span class="n">ncols</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="n">order</span><span class="p">:</span>
-                <span class="c1"># east neighbor</span>
-                <span class="n">id_neighbor</span> <span class="o">=</span> <span class="n">id_map</span><span class="p">[</span><span class="n">id_i</span> <span class="o">+</span> <span class="n">order</span><span class="p">]</span>
-                <span class="k">if</span> <span class="n">id_neighbor</span><span class="p">:</span>
-                    <span class="n">rows</span><span class="p">[</span><span class="n">ni</span><span class="p">],</span> <span class="n">cols</span><span class="p">[</span><span class="n">ni</span><span class="p">]</span> <span class="o">=</span> <span class="n">og_id</span><span class="p">,</span> <span class="n">id_neighbor</span>
-                    <span class="n">ni</span> <span class="o">+=</span> <span class="mi">1</span>
-                    <span class="n">rows</span><span class="p">[</span><span class="n">ni</span><span class="p">],</span> <span class="n">cols</span><span class="p">[</span><span class="n">ni</span><span class="p">]</span> <span class="o">=</span> <span class="n">id_neighbor</span><span class="p">,</span> <span class="n">og_id</span>
-                    <span class="n">ni</span> <span class="o">+=</span> <span class="mi">1</span>
-                <span class="c1"># north-east to south-east neighbors</span>
-                <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">condition</span><span class="p">):</span>
-                    <span class="k">if</span> <span class="p">(</span><span class="n">id_i</span> <span class="o">//</span> <span class="n">ncols</span><span class="p">)</span> <span class="o">&lt;</span> <span class="p">(</span><span class="n">nrows</span> <span class="o">-</span> <span class="n">j</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span>
-                        <span class="n">id_neighbor</span> <span class="o">=</span> <span class="n">id_map</span><span class="p">[(</span><span class="n">id_i</span> <span class="o">+</span> <span class="n">order</span><span class="p">)</span> <span class="o">+</span> <span class="p">(</span><span class="n">ncols</span> <span class="o">*</span> <span class="p">(</span><span class="n">j</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))]</span>
-                        <span class="k">if</span> <span class="n">id_neighbor</span><span class="p">:</span>
-                            <span class="n">rows</span><span class="p">[</span><span class="n">ni</span><span class="p">],</span> <span class="n">cols</span><span class="p">[</span><span class="n">ni</span><span class="p">]</span> <span class="o">=</span> <span class="n">og_id</span><span class="p">,</span> <span class="n">id_neighbor</span>
-                            <span class="n">ni</span> <span class="o">+=</span> <span class="mi">1</span>
-                            <span class="n">rows</span><span class="p">[</span><span class="n">ni</span><span class="p">],</span> <span class="n">cols</span><span class="p">[</span><span class="n">ni</span><span class="p">]</span> <span class="o">=</span> <span class="n">id_neighbor</span><span class="p">,</span> <span class="n">og_id</span>
-                            <span class="n">ni</span> <span class="o">+=</span> <span class="mi">1</span>
-                    <span class="k">if</span> <span class="p">(</span><span class="n">id_i</span> <span class="o">//</span> <span class="n">ncols</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="n">j</span> <span class="o">+</span> <span class="mi">1</span><span class="p">:</span>
-                        <span class="n">id_neighbor</span> <span class="o">=</span> <span class="n">id_map</span><span class="p">[(</span><span class="n">id_i</span> <span class="o">+</span> <span class="n">order</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span><span class="n">ncols</span> <span class="o">*</span> <span class="p">(</span><span class="n">j</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))]</span>
-                        <span class="k">if</span> <span class="n">id_neighbor</span><span class="p">:</span>
-                            <span class="n">rows</span><span class="p">[</span><span class="n">ni</span><span class="p">],</span> <span class="n">cols</span><span class="p">[</span><span class="n">ni</span><span class="p">]</span> <span class="o">=</span> <span class="n">og_id</span><span class="p">,</span> <span class="n">id_neighbor</span>
-                            <span class="n">ni</span> <span class="o">+=</span> <span class="mi">1</span>
-                            <span class="n">rows</span><span class="p">[</span><span class="n">ni</span><span class="p">],</span> <span class="n">cols</span><span class="p">[</span><span class="n">ni</span><span class="p">]</span> <span class="o">=</span> <span class="n">id_neighbor</span><span class="p">,</span> <span class="n">og_id</span>
-                            <span class="n">ni</span> <span class="o">+=</span> <span class="mi">1</span>
-
-            <span class="k">if</span> <span class="p">(</span><span class="n">id_i</span> <span class="o">//</span> <span class="n">ncols</span><span class="p">)</span> <span class="o">&lt;</span> <span class="p">(</span><span class="n">nrows</span> <span class="o">-</span> <span class="n">order</span><span class="p">):</span>
-                <span class="c1"># south neighbor</span>
-                <span class="n">id_neighbor</span> <span class="o">=</span> <span class="n">id_map</span><span class="p">[</span><span class="n">id_i</span> <span class="o">+</span> <span class="p">(</span><span class="n">ncols</span> <span class="o">*</span> <span class="n">order</span><span class="p">)]</span>
-                <span class="k">if</span> <span class="n">id_neighbor</span><span class="p">:</span>
-                    <span class="n">rows</span><span class="p">[</span><span class="n">ni</span><span class="p">],</span> <span class="n">cols</span><span class="p">[</span><span class="n">ni</span><span class="p">]</span> <span class="o">=</span> <span class="n">og_id</span><span class="p">,</span> <span class="n">id_neighbor</span>
-                    <span class="n">ni</span> <span class="o">+=</span> <span class="mi">1</span>
-                    <span class="n">rows</span><span class="p">[</span><span class="n">ni</span><span class="p">],</span> <span class="n">cols</span><span class="p">[</span><span class="n">ni</span><span class="p">]</span> <span class="o">=</span> <span class="n">id_neighbor</span><span class="p">,</span> <span class="n">og_id</span>
-                    <span class="n">ni</span> <span class="o">+=</span> <span class="mi">1</span>
-                <span class="c1"># south-west to south-east neighbors</span>
-                <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">condition</span><span class="p">):</span>
-                    <span class="k">if</span> <span class="p">(</span><span class="n">id_i</span> <span class="o">%</span> <span class="n">ncols</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="n">j</span> <span class="o">+</span> <span class="mi">1</span><span class="p">:</span>
-                        <span class="n">id_neighbor</span> <span class="o">=</span> <span class="n">id_map</span><span class="p">[</span><span class="n">id_i</span> <span class="o">+</span> <span class="p">(</span><span class="n">ncols</span> <span class="o">*</span> <span class="n">order</span><span class="p">)</span> <span class="o">-</span> <span class="n">j</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span>
-                        <span class="k">if</span> <span class="n">id_neighbor</span><span class="p">:</span>
-                            <span class="n">rows</span><span class="p">[</span><span class="n">ni</span><span class="p">],</span> <span class="n">cols</span><span class="p">[</span><span class="n">ni</span><span class="p">]</span> <span class="o">=</span> <span class="n">og_id</span><span class="p">,</span> <span class="n">id_neighbor</span>
-                            <span class="n">ni</span> <span class="o">+=</span> <span class="mi">1</span>
-                            <span class="n">rows</span><span class="p">[</span><span class="n">ni</span><span class="p">],</span> <span class="n">cols</span><span class="p">[</span><span class="n">ni</span><span class="p">]</span> <span class="o">=</span> <span class="n">id_neighbor</span><span class="p">,</span> <span class="n">og_id</span>
-                            <span class="n">ni</span> <span class="o">+=</span> <span class="mi">1</span>
-                    <span class="k">if</span> <span class="p">((</span><span class="n">id_i</span> <span class="o">+</span> <span class="n">j</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">%</span> <span class="n">ncols</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="n">j</span> <span class="o">+</span> <span class="mi">1</span><span class="p">:</span>
-                        <span class="n">id_neighbor</span> <span class="o">=</span> <span class="n">id_map</span><span class="p">[</span><span class="n">id_i</span> <span class="o">+</span> <span class="p">(</span><span class="n">ncols</span> <span class="o">*</span> <span class="n">order</span><span class="p">)</span> <span class="o">+</span> <span class="n">j</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span>
-                        <span class="k">if</span> <span class="n">id_neighbor</span><span class="p">:</span>
-                            <span class="n">rows</span><span class="p">[</span><span class="n">ni</span><span class="p">],</span> <span class="n">cols</span><span class="p">[</span><span class="n">ni</span><span class="p">]</span> <span class="o">=</span> <span class="n">og_id</span><span class="p">,</span> <span class="n">id_neighbor</span>
-                            <span class="n">ni</span> <span class="o">+=</span> <span class="mi">1</span>
-                            <span class="n">rows</span><span class="p">[</span><span class="n">ni</span><span class="p">],</span> <span class="n">cols</span><span class="p">[</span><span class="n">ni</span><span class="p">]</span> <span class="o">=</span> <span class="n">id_neighbor</span><span class="p">,</span> <span class="n">og_id</span>
-                            <span class="n">ni</span> <span class="o">+=</span> <span class="mi">1</span>
-
-                <span class="k">if</span> <span class="n">criterion</span> <span class="o">==</span> <span class="s2">&quot;queen&quot;</span> <span class="ow">or</span> <span class="p">((</span><span class="n">k</span> <span class="o">/</span> <span class="n">order</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="mf">2.0</span><span class="p">):</span>
-                    <span class="k">if</span> <span class="p">(</span><span class="n">id_i</span> <span class="o">%</span> <span class="n">ncols</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="n">order</span><span class="p">:</span>
-                        <span class="c1"># south-west neighbor</span>
-                        <span class="n">id_neighbor</span> <span class="o">=</span> <span class="n">id_map</span><span class="p">[</span><span class="n">id_i</span> <span class="o">+</span> <span class="p">(</span><span class="n">ncols</span> <span class="o">*</span> <span class="n">order</span><span class="p">)</span> <span class="o">-</span> <span class="n">order</span><span class="p">]</span>
-                        <span class="k">if</span> <span class="n">id_neighbor</span><span class="p">:</span>
-                            <span class="n">rows</span><span class="p">[</span><span class="n">ni</span><span class="p">],</span> <span class="n">cols</span><span class="p">[</span><span class="n">ni</span><span class="p">]</span> <span class="o">=</span> <span class="n">og_id</span><span class="p">,</span> <span class="n">id_neighbor</span>
-                            <span class="n">ni</span> <span class="o">+=</span> <span class="mi">1</span>
-                            <span class="n">rows</span><span class="p">[</span><span class="n">ni</span><span class="p">],</span> <span class="n">cols</span><span class="p">[</span><span class="n">ni</span><span class="p">]</span> <span class="o">=</span> <span class="n">id_neighbor</span><span class="p">,</span> <span class="n">og_id</span>
-                            <span class="n">ni</span> <span class="o">+=</span> <span class="mi">1</span>
-                    <span class="k">if</span> <span class="p">((</span><span class="n">id_i</span> <span class="o">+</span> <span class="n">order</span><span class="p">)</span> <span class="o">%</span> <span class="n">ncols</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="n">order</span><span class="p">:</span>
-                        <span class="c1"># south-east neighbor</span>
-                        <span class="n">id_neighbor</span> <span class="o">=</span> <span class="n">id_map</span><span class="p">[</span><span class="n">id_i</span> <span class="o">+</span> <span class="p">(</span><span class="n">ncols</span> <span class="o">*</span> <span class="n">order</span><span class="p">)</span> <span class="o">+</span> <span class="n">order</span><span class="p">]</span>
-                        <span class="k">if</span> <span class="n">id_neighbor</span><span class="p">:</span>
-                            <span class="n">rows</span><span class="p">[</span><span class="n">ni</span><span class="p">],</span> <span class="n">cols</span><span class="p">[</span><span class="n">ni</span><span class="p">]</span> <span class="o">=</span> <span class="n">og_id</span><span class="p">,</span> <span class="n">id_neighbor</span>
-                            <span class="n">ni</span> <span class="o">+=</span> <span class="mi">1</span>
-                            <span class="n">rows</span><span class="p">[</span><span class="n">ni</span><span class="p">],</span> <span class="n">cols</span><span class="p">[</span><span class="n">ni</span><span class="p">]</span> <span class="o">=</span> <span class="n">id_neighbor</span><span class="p">,</span> <span class="n">og_id</span>
-                            <span class="n">ni</span> <span class="o">+=</span> <span class="mi">1</span>
-    <span class="k">return</span> <span class="n">rows</span><span class="p">[:</span><span class="n">ni</span><span class="p">],</span> <span class="n">cols</span><span class="p">[:</span><span class="n">ni</span><span class="p">]</span>
-
-
-<span class="nd">@jit</span><span class="p">(</span><span class="n">nopython</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">fastmath</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">_chunk_generator</span><span class="p">(</span>
-    <span class="n">n_jobs</span><span class="p">,</span> <span class="n">starts</span><span class="p">,</span> <span class="n">ids</span><span class="p">,</span>
-<span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Construct chunks to iterate over within numba in parallel</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    n_jobs : int</span>
-<span class="sd">        Number of cores to be used in the sparse weight construction. If -1,</span>
-<span class="sd">        all available cores are used.</span>
-<span class="sd">    starts : ndarray</span>
-<span class="sd">        (n_chunks+1,) array of positional starts for ids chunk</span>
-<span class="sd">    ids : ndarray</span>
-<span class="sd">        1D array containing ids of non-missing raster data</span>
-
-<span class="sd">    Yields</span>
-<span class="sd">    ------</span>
-<span class="sd">    ids_chunk : numpy.ndarray</span>
-<span class="sd">        (n_chunk,) array containing the chunk of non-missing raster data</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">chunk_size</span> <span class="o">=</span> <span class="n">starts</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">starts</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_jobs</span><span class="p">):</span>
-        <span class="n">start</span> <span class="o">=</span> <span class="n">starts</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-        <span class="n">ids_chunk</span> <span class="o">=</span> <span class="n">ids</span><span class="p">[</span><span class="n">start</span> <span class="p">:</span> <span class="p">(</span><span class="n">start</span> <span class="o">+</span> <span class="n">chunk_size</span><span class="p">)]</span>
-        <span class="k">yield</span> <span class="p">(</span><span class="n">ids_chunk</span><span class="p">,)</span>
-
-
-<span class="k">def</span> <span class="nf">_parSWbuilder</span><span class="p">(</span>
-    <span class="n">nrows</span><span class="p">,</span> <span class="n">ncols</span><span class="p">,</span> <span class="n">ids</span><span class="p">,</span> <span class="n">id_map</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">dtype</span><span class="p">,</span> <span class="n">n_jobs</span><span class="p">,</span>
-<span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Computes data and orders rows, cols, data in parallel using numba</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    nrows : int</span>
-<span class="sd">        Number of rows in the raster data</span>
-<span class="sd">    ncols : int</span>
-<span class="sd">        Number of columns in the raster data</span>
-<span class="sd">    ids : ndarray</span>
-<span class="sd">        1D array containing ids of non-missing raster data</span>
-<span class="sd">    id_map : ndarray</span>
-<span class="sd">        1D array containing id_maps of non-missing raster data</span>
-<span class="sd">    criterion : str</span>
-<span class="sd">        Type of contiguity.</span>
-<span class="sd">    k : int</span>
-<span class="sd">        Order of contiguity, Default is 1</span>
-<span class="sd">    dtype : type</span>
-<span class="sd">        Data type of the rows and cols array</span>
-<span class="sd">    n_jobs : int</span>
-<span class="sd">        Number of cores to be used in the sparse weight construction. If -1,</span>
-<span class="sd">        all available cores are used.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    data : ndarray</span>
-<span class="sd">        1D ones array containing weight of each neighbor</span>
-<span class="sd">    rows : ndarray</span>
-<span class="sd">        1D ones array containing row value of each id</span>
-<span class="sd">        in the sparse weight object</span>
-<span class="sd">    cols : ndarray</span>
-<span class="sd">        1D ones array containing columns value of each id</span>
-<span class="sd">        in the sparse weight object</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="kn">from</span> <span class="nn">joblib</span> <span class="kn">import</span> <span class="n">Parallel</span><span class="p">,</span> <span class="n">delayed</span><span class="p">,</span> <span class="n">parallel_backend</span>
-
-    <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ids</span><span class="p">)</span>
-    <span class="n">chunk_size</span> <span class="o">=</span> <span class="n">n</span> <span class="o">//</span> <span class="n">n_jobs</span> <span class="o">+</span> <span class="mi">1</span>
-    <span class="n">starts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">n_jobs</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">chunk_size</span>
-    <span class="n">chunk</span> <span class="o">=</span> <span class="n">_chunk_generator</span><span class="p">(</span><span class="n">n_jobs</span><span class="p">,</span> <span class="n">starts</span><span class="p">,</span> <span class="n">ids</span><span class="p">)</span>
-    <span class="k">with</span> <span class="n">parallel_backend</span><span class="p">(</span><span class="s2">&quot;threading&quot;</span><span class="p">):</span>
-        <span class="n">worker_out</span> <span class="o">=</span> <span class="n">Parallel</span><span class="p">(</span><span class="n">n_jobs</span><span class="o">=</span><span class="n">n_jobs</span><span class="p">)(</span>
-            <span class="n">delayed</span><span class="p">(</span><span class="n">_compute_chunk</span><span class="p">)(</span><span class="n">nrows</span><span class="p">,</span> <span class="n">ncols</span><span class="p">,</span> <span class="o">*</span><span class="n">ids</span><span class="p">,</span> <span class="n">id_map</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">dtype</span><span class="p">)</span>
-            <span class="k">for</span> <span class="n">ids</span> <span class="ow">in</span> <span class="n">chunk</span>
-        <span class="p">)</span>
-    <span class="n">rows</span><span class="p">,</span> <span class="n">cols</span> <span class="o">=</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">worker_out</span><span class="p">)</span>
-    <span class="n">rows</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">rows</span><span class="p">)</span>
-    <span class="n">cols</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">cols</span><span class="p">)</span>
-    <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">rows</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int8</span><span class="p">)</span>
-    <span class="k">return</span> <span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="p">(</span><span class="n">rows</span><span class="p">,</span> <span class="n">cols</span><span class="p">))</span>
-</pre></div>
-
-    </div>
-      
-  </div>
-</div>
-<footer class="footer">
-  <div class="container">
-    <p class="pull-right">
-      <a href="#">Back to top</a>
-      
-        <br/>
-        
-      
-    </p>
-    <p>
-        &copy; Copyright 2018-, pysal developers.<br/>
-      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 3.1.2.<br/>
-    </p>
-  </div>
-</footer>
-  </body>
-</html>
\ No newline at end of file
diff --git a/docs/_modules/libpysal/weights/set_operations.html b/docs/_modules/libpysal/weights/set_operations.html
deleted file mode 100644
index e466327c2..000000000
--- a/docs/_modules/libpysal/weights/set_operations.html
+++ /dev/null
@@ -1,669 +0,0 @@
-<!DOCTYPE html>
-
-<html>
-  <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>libpysal.weights.set_operations &#8212; libpysal v4.4.0 Manual</title>
-    <link rel="stylesheet" href="../../../_static/bootstrap-sphinx.css" type="text/css" />
-    <link rel="stylesheet" href="../../../_static/pygments.css" type="text/css" />
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-          libpysal</a>
-        <span class="navbar-text navbar-version pull-left"><b>4.4.0</b></span>
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-      aria-labelledby="dLabelGlobalToc"><p class="caption"><span class="caption-text">Contents:</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../../../installation.html">Installation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../../installation.html#installing-released-version">Installing released version</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../../tutorial.html">Tutorial</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../../tutorial.html#spatial-weights">Spatial Weights</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../tutorial.html#example-datasets">Example Datasets</a></li>
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-      
-  <h1>Source code for libpysal.weights.set_operations</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">Set-like manipulation of weights matrices.</span>
-<span class="sd">&quot;&quot;&quot;</span>
-
-<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Sergio J. Rey &lt;srey@asu.edu&gt;, Charles Schmidt &lt;schmidtc@gmail.com&gt;, David Folch &lt;david.folch@asu.edu&gt;, Dani Arribas-Bel &lt;darribas@asu.edu&gt;&quot;</span>
-
-<span class="kn">import</span> <span class="nn">copy</span>
-<span class="kn">from</span> <span class="nn">.weights</span> <span class="kn">import</span> <span class="n">W</span><span class="p">,</span> <span class="n">WSP</span>
-<span class="kn">from</span> <span class="nn">scipy.sparse</span> <span class="kn">import</span> <span class="n">isspmatrix_csr</span>
-<span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <span class="n">ones</span>
-
-<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="s2">&quot;w_union&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;w_intersection&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;w_difference&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;w_symmetric_difference&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;w_subset&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;w_clip&quot;</span><span class="p">,</span>
-<span class="p">]</span>
-
-
-<div class="viewcode-block" id="w_union"><a class="viewcode-back" href="../../../generated/libpysal.weights.w_union.html#libpysal.weights.w_union">[docs]</a><span class="k">def</span> <span class="nf">w_union</span><span class="p">(</span><span class="n">w1</span><span class="p">,</span> <span class="n">w2</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Returns a binary weights object, w, that includes all neighbor pairs that</span>
-<span class="sd">    exist in either w1 or w2.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-
-<span class="sd">    w1                      : W</span>
-<span class="sd">                              object</span>
-<span class="sd">    w2                      : W</span>
-<span class="sd">                              object</span>
-<span class="sd">    **kwargs                : keyword arguments</span>
-<span class="sd">                              optional arguments for :class:`pysal.weights.W`</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-
-<span class="sd">    w       : W</span>
-<span class="sd">              object</span>
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-<span class="sd">    ID comparisons are performed using ==, therefore the integer ID 2 is</span>
-<span class="sd">    equivalent to the float ID 2.0. Returns a matrix with all the unique IDs</span>
-<span class="sd">    from w1 and w2.</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-
-<span class="sd">    Construct rook weights matrices for two regions, one is 4x4 (16 areas)</span>
-<span class="sd">    and the other is 6x4 (24 areas). A union of these two weights matrices</span>
-<span class="sd">    results in the new weights matrix matching the larger one.</span>
-
-<span class="sd">    &gt;&gt;&gt; from libpysal.weights import lat2W, w_union</span>
-<span class="sd">    &gt;&gt;&gt; w1 = lat2W(4,4)</span>
-<span class="sd">    &gt;&gt;&gt; w2 = lat2W(6,4)</span>
-<span class="sd">    &gt;&gt;&gt; w = w_union(w1, w2)</span>
-<span class="sd">    &gt;&gt;&gt; w1[0] == w[0]</span>
-<span class="sd">    True</span>
-<span class="sd">    &gt;&gt;&gt; w1.neighbors[15]</span>
-<span class="sd">    [11, 14]</span>
-<span class="sd">    &gt;&gt;&gt; w2.neighbors[15]</span>
-<span class="sd">    [11, 14, 19]</span>
-<span class="sd">    &gt;&gt;&gt; w.neighbors[15]</span>
-<span class="sd">    [19, 11, 14]</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">neighbors</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">items</span><span class="p">()))</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">w2</span><span class="o">.</span><span class="n">neighbors</span><span class="p">:</span>
-        <span class="k">if</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">neighbors</span><span class="p">:</span>
-            <span class="n">add_neigh</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">])</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">w2</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span>
-            <span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">add_neigh</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">w2</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-    <span class="k">return</span> <span class="n">W</span><span class="p">(</span><span class="n">neighbors</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="w_intersection"><a class="viewcode-back" href="../../../generated/libpysal.weights.w_intersection.html#libpysal.weights.w_intersection">[docs]</a><span class="k">def</span> <span class="nf">w_intersection</span><span class="p">(</span><span class="n">w1</span><span class="p">,</span> <span class="n">w2</span><span class="p">,</span> <span class="n">w_shape</span><span class="o">=</span><span class="s2">&quot;w1&quot;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Returns a binary weights object, w, that includes only</span>
-<span class="sd">    those neighbor pairs that exist in both w1 and w2.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-
-<span class="sd">    w1                      : W</span>
-<span class="sd">                              object</span>
-<span class="sd">    w2                      : W</span>
-<span class="sd">                              object</span>
-<span class="sd">    w_shape                 : string</span>
-<span class="sd">                              Defines the shape of the returned weights matrix. &#39;w1&#39; returns a</span>
-<span class="sd">                              matrix with the same IDs as w1; &#39;all&#39; returns a matrix with all</span>
-<span class="sd">                              the unique IDs from w1 and w2; and &#39;min&#39; returns a matrix with</span>
-<span class="sd">                              only the IDs occurring in both w1 and w2.</span>
-<span class="sd">    **kwargs                : keyword arguments</span>
-<span class="sd">                              optional arguments for :class:`pysal.weights.W`</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-
-<span class="sd">    w       : W</span>
-<span class="sd">              object</span>
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-<span class="sd">    ID comparisons are performed using ==, therefore the integer ID 2 is</span>
-<span class="sd">    equivalent to the float ID 2.0.</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-
-<span class="sd">    Construct rook weights matrices for two regions, one is 4x4 (16 areas)</span>
-<span class="sd">    and the other is 6x4 (24 areas). An intersection of these two weights</span>
-<span class="sd">    matrices results in the new weights matrix matching the smaller one.</span>
-
-<span class="sd">    &gt;&gt;&gt; from libpysal.weights import lat2W, w_intersection</span>
-<span class="sd">    &gt;&gt;&gt; w1 = lat2W(4,4)</span>
-<span class="sd">    &gt;&gt;&gt; w2 = lat2W(6,4)</span>
-<span class="sd">    &gt;&gt;&gt; w = w_intersection(w1, w2)</span>
-<span class="sd">    &gt;&gt;&gt; w1[0] == w[0]</span>
-<span class="sd">    True</span>
-<span class="sd">    &gt;&gt;&gt; w1.neighbors[15]</span>
-<span class="sd">    [11, 14]</span>
-<span class="sd">    &gt;&gt;&gt; w2.neighbors[15]</span>
-<span class="sd">    [11, 14, 19]</span>
-<span class="sd">    &gt;&gt;&gt; w.neighbors[15]</span>
-<span class="sd">    [11, 14]</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">if</span> <span class="n">w_shape</span> <span class="o">==</span> <span class="s2">&quot;w1&quot;</span><span class="p">:</span>
-        <span class="n">neigh_keys</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
-    <span class="k">elif</span> <span class="n">w_shape</span> <span class="o">==</span> <span class="s2">&quot;all&quot;</span><span class="p">:</span>
-        <span class="n">neigh_keys</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">w2</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">keys</span><span class="p">()))</span>
-    <span class="k">elif</span> <span class="n">w_shape</span> <span class="o">==</span> <span class="s2">&quot;min&quot;</span><span class="p">:</span>
-        <span class="n">neigh_keys</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span><span class="o">.</span><span class="n">intersection</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">w2</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">keys</span><span class="p">()))</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;invalid string passed to w_shape&quot;</span><span class="p">)</span>
-
-    <span class="n">neighbors</span> <span class="o">=</span> <span class="p">{}</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">neigh_keys</span><span class="p">:</span>
-        <span class="k">if</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span> <span class="ow">and</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">w2</span><span class="o">.</span><span class="n">neighbors</span><span class="p">:</span>
-            <span class="n">add_neigh</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">])</span><span class="o">.</span><span class="n">intersection</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">w2</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span>
-            <span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">add_neigh</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
-
-    <span class="k">return</span> <span class="n">W</span><span class="p">(</span><span class="n">neighbors</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="w_difference"><a class="viewcode-back" href="../../../generated/libpysal.weights.w_difference.html#libpysal.weights.w_difference">[docs]</a><span class="k">def</span> <span class="nf">w_difference</span><span class="p">(</span><span class="n">w1</span><span class="p">,</span> <span class="n">w2</span><span class="p">,</span> <span class="n">w_shape</span><span class="o">=</span><span class="s2">&quot;w1&quot;</span><span class="p">,</span> <span class="n">constrained</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Returns a binary weights object, w, that includes only neighbor pairs</span>
-<span class="sd">    in w1 that are not in w2. The w_shape and constrained parameters</span>
-<span class="sd">    determine which pairs in w1 that are not in w2 are returned.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-
-<span class="sd">    w1                      : W</span>
-<span class="sd">                              object</span>
-<span class="sd">    w2                      : W</span>
-<span class="sd">                              object</span>
-<span class="sd">    w_shape                 : string</span>
-<span class="sd">                              Defines the shape of the returned weights matrix. &#39;w1&#39; returns a</span>
-<span class="sd">                              matrix with the same IDs as w1; &#39;all&#39; returns a matrix with all</span>
-<span class="sd">                              the unique IDs from w1 and w2; and &#39;min&#39; returns a matrix with</span>
-<span class="sd">                              the IDs occurring in w1 and not in w2.</span>
-<span class="sd">    constrained             : boolean</span>
-<span class="sd">                              If False then the full set of neighbor pairs in w1 that are</span>
-<span class="sd">                              not in w2 are returned. If True then those pairs that would</span>
-<span class="sd">                              not be possible if w_shape=&#39;min&#39; are dropped. Ignored if</span>
-<span class="sd">                              w_shape is set to &#39;min&#39;.</span>
-<span class="sd">    **kwargs                : keyword arguments</span>
-<span class="sd">                              optional arguments for :class:`pysal.weights.W`</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-
-<span class="sd">    w       : W</span>
-<span class="sd">              object</span>
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-<span class="sd">    ID comparisons are performed using ==, therefore the integer ID 2 is</span>
-<span class="sd">    equivalent to the float ID 2.0.</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-
-<span class="sd">    Construct rook (w2) and queen (w1) weights matrices for two 4x4 regions</span>
-<span class="sd">    (16 areas). A queen matrix has all the joins a rook matrix does plus joins</span>
-<span class="sd">    between areas that share a corner. The new matrix formed by the difference</span>
-<span class="sd">    of rook from queen contains only join at corners (typically called a</span>
-<span class="sd">    bishop matrix). Note that the difference of queen from rook would result</span>
-<span class="sd">    in a weights matrix with no joins.</span>
-
-<span class="sd">    &gt;&gt;&gt; from libpysal.weights import lat2W, w_difference</span>
-<span class="sd">    &gt;&gt;&gt; w1 = lat2W(4,4,rook=False)</span>
-<span class="sd">    &gt;&gt;&gt; w2 = lat2W(4,4,rook=True)</span>
-<span class="sd">    &gt;&gt;&gt; w = w_difference(w1, w2, constrained=False)</span>
-<span class="sd">    &gt;&gt;&gt; w1[0] == w[0]</span>
-<span class="sd">    False</span>
-<span class="sd">    &gt;&gt;&gt; w1.neighbors[15]</span>
-<span class="sd">    [10, 11, 14]</span>
-<span class="sd">    &gt;&gt;&gt; w2.neighbors[15]</span>
-<span class="sd">    [11, 14]</span>
-<span class="sd">    &gt;&gt;&gt; w.neighbors[15]</span>
-<span class="sd">    [10]</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">if</span> <span class="n">w_shape</span> <span class="o">==</span> <span class="s2">&quot;w1&quot;</span><span class="p">:</span>
-        <span class="n">neigh_keys</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
-    <span class="k">elif</span> <span class="n">w_shape</span> <span class="o">==</span> <span class="s2">&quot;all&quot;</span><span class="p">:</span>
-        <span class="n">neigh_keys</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">w2</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">keys</span><span class="p">()))</span>
-    <span class="k">elif</span> <span class="n">w_shape</span> <span class="o">==</span> <span class="s2">&quot;min&quot;</span><span class="p">:</span>
-        <span class="n">neigh_keys</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span><span class="o">.</span><span class="n">difference</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">w2</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">keys</span><span class="p">()))</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">neigh_keys</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;returned an empty weights matrix&quot;</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;invalid string passed to w_shape&quot;</span><span class="p">)</span>
-
-    <span class="n">neighbors</span> <span class="o">=</span> <span class="p">{}</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">neigh_keys</span><span class="p">:</span>
-        <span class="k">if</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">w2</span><span class="o">.</span><span class="n">neighbors</span><span class="p">:</span>
-                <span class="n">add_neigh</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">])</span><span class="o">.</span><span class="n">difference</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">w2</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span>
-                <span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">add_neigh</span><span class="p">)</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
-
-    <span class="k">if</span> <span class="n">constrained</span> <span class="ow">or</span> <span class="n">w_shape</span> <span class="o">==</span> <span class="s2">&quot;min&quot;</span><span class="p">:</span>
-        <span class="n">constrained_keys</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span><span class="o">.</span><span class="n">difference</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">w2</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">keys</span><span class="p">()))</span>
-        <span class="n">island_keys</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">neighbors</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span><span class="o">.</span><span class="n">difference</span><span class="p">(</span><span class="n">constrained_keys</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">island_keys</span><span class="p">:</span>
-            <span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">constrained_keys</span><span class="p">:</span>
-            <span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">])</span><span class="o">.</span><span class="n">intersection</span><span class="p">(</span><span class="n">constrained_keys</span><span class="p">))</span>
-
-    <span class="k">return</span> <span class="n">W</span><span class="p">(</span><span class="n">neighbors</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="w_symmetric_difference"><a class="viewcode-back" href="../../../generated/libpysal.weights.w_symmetric_difference.html#libpysal.weights.w_symmetric_difference">[docs]</a><span class="k">def</span> <span class="nf">w_symmetric_difference</span><span class="p">(</span><span class="n">w1</span><span class="p">,</span> <span class="n">w2</span><span class="p">,</span> <span class="n">w_shape</span><span class="o">=</span><span class="s2">&quot;all&quot;</span><span class="p">,</span> <span class="n">constrained</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Returns a binary weights object, w, that includes only neighbor pairs</span>
-<span class="sd">    that are not shared by w1 and w2. The w_shape and constrained parameters</span>
-<span class="sd">    determine which pairs that are not shared by w1 and w2 are returned.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-
-<span class="sd">    w1                      : W</span>
-<span class="sd">                              object</span>
-<span class="sd">    w2                      : W</span>
-<span class="sd">                              object</span>
-<span class="sd">    w_shape                 : string</span>
-<span class="sd">                              Defines the shape of the returned weights matrix. &#39;all&#39; returns a</span>
-<span class="sd">                              matrix with all the unique IDs from w1 and w2; and &#39;min&#39; returns</span>
-<span class="sd">                              a matrix with the IDs not shared by w1 and w2.</span>
-<span class="sd">    constrained             : boolean</span>
-<span class="sd">                              If False then the full set of neighbor pairs that are not</span>
-<span class="sd">                              shared by w1 and w2 are returned. If True then those pairs</span>
-<span class="sd">                              that would not be possible if w_shape=&#39;min&#39; are dropped.</span>
-<span class="sd">                              Ignored if w_shape is set to &#39;min&#39;.</span>
-<span class="sd">    **kwargs                : keyword arguments</span>
-<span class="sd">                              optional arguments for :class:`pysal.weights.W`</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-
-<span class="sd">    w       : W</span>
-<span class="sd">              object</span>
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-<span class="sd">    ID comparisons are performed using ==, therefore the integer ID 2 is</span>
-<span class="sd">    equivalent to the float ID 2.0.</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-
-<span class="sd">    Construct queen weights matrix for a 4x4 (16 areas) region (w1) and a rook</span>
-<span class="sd">    matrix for a 6x4 (24 areas) region (w2). The symmetric difference of these</span>
-<span class="sd">    two matrices (with w_shape set to &#39;all&#39; and constrained set to False)</span>
-<span class="sd">    contains the corner joins in the overlap area, all the joins in the</span>
-<span class="sd">    non-overlap area.</span>
-
-<span class="sd">    &gt;&gt;&gt; from libpysal.weights import lat2W, w_symmetric_difference</span>
-<span class="sd">    &gt;&gt;&gt; w1 = lat2W(4,4,rook=False)</span>
-<span class="sd">    &gt;&gt;&gt; w2 = lat2W(6,4,rook=True)</span>
-<span class="sd">    &gt;&gt;&gt; w = w_symmetric_difference(w1, w2, constrained=False)</span>
-<span class="sd">    &gt;&gt;&gt; w1[0] == w[0]</span>
-<span class="sd">    False</span>
-<span class="sd">    &gt;&gt;&gt; w1.neighbors[15]</span>
-<span class="sd">    [10, 11, 14]</span>
-<span class="sd">    &gt;&gt;&gt; w2.neighbors[15]</span>
-<span class="sd">    [11, 14, 19]</span>
-<span class="sd">    &gt;&gt;&gt; set(w.neighbors[15]) == set([10, 19])</span>
-<span class="sd">    True</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">if</span> <span class="n">w_shape</span> <span class="o">==</span> <span class="s2">&quot;all&quot;</span><span class="p">:</span>
-        <span class="n">neigh_keys</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">w2</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">keys</span><span class="p">()))</span>
-    <span class="k">elif</span> <span class="n">w_shape</span> <span class="o">==</span> <span class="s2">&quot;min&quot;</span><span class="p">:</span>
-        <span class="n">neigh_keys</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span><span class="o">.</span><span class="n">symmetric_difference</span><span class="p">(</span>
-            <span class="nb">set</span><span class="p">(</span><span class="n">w2</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
-        <span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;invalid string passed to w_shape&quot;</span><span class="p">)</span>
-
-    <span class="n">neighbors</span> <span class="o">=</span> <span class="p">{}</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">neigh_keys</span><span class="p">:</span>
-        <span class="k">if</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">w2</span><span class="o">.</span><span class="n">neighbors</span><span class="p">:</span>
-                <span class="n">add_neigh</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">])</span><span class="o">.</span><span class="n">symmetric_difference</span><span class="p">(</span>
-                    <span class="nb">set</span><span class="p">(</span><span class="n">w2</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-                <span class="p">)</span>
-                <span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">add_neigh</span><span class="p">)</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-        <span class="k">elif</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">w2</span><span class="o">.</span><span class="n">neighbors</span><span class="p">:</span>
-            <span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">w2</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
-
-    <span class="k">if</span> <span class="n">constrained</span> <span class="ow">or</span> <span class="n">w_shape</span> <span class="o">==</span> <span class="s2">&quot;min&quot;</span><span class="p">:</span>
-        <span class="n">constrained_keys</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span><span class="o">.</span><span class="n">difference</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">w2</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">keys</span><span class="p">()))</span>
-        <span class="n">island_keys</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">neighbors</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span><span class="o">.</span><span class="n">difference</span><span class="p">(</span><span class="n">constrained_keys</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">island_keys</span><span class="p">:</span>
-            <span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">constrained_keys</span><span class="p">:</span>
-            <span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">])</span><span class="o">.</span><span class="n">intersection</span><span class="p">(</span><span class="n">constrained_keys</span><span class="p">))</span>
-
-    <span class="k">return</span> <span class="n">W</span><span class="p">(</span><span class="n">neighbors</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="w_subset"><a class="viewcode-back" href="../../../generated/libpysal.weights.w_subset.html#libpysal.weights.w_subset">[docs]</a><span class="k">def</span> <span class="nf">w_subset</span><span class="p">(</span><span class="n">w1</span><span class="p">,</span> <span class="n">ids</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Returns a binary weights object, w, that includes only those</span>
-<span class="sd">    observations in ids.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-
-<span class="sd">    w1                      : W</span>
-<span class="sd">                              object</span>
-<span class="sd">    ids                     : list</span>
-<span class="sd">                              A list containing the IDs to be include in the returned weights</span>
-<span class="sd">                              object.</span>
-<span class="sd">    **kwargs                : keyword arguments</span>
-<span class="sd">                              optional arguments for :class:`pysal.weights.W`</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-
-<span class="sd">    w       : W</span>
-<span class="sd">              object</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-
-<span class="sd">    Construct a rook weights matrix for a 6x4 region (24 areas). By default</span>
-<span class="sd">    PySAL assigns integer IDs to the areas in a region. By passing in a list</span>
-<span class="sd">    of integers from 0 to 15, the first 16 areas are extracted from the</span>
-<span class="sd">    previous weights matrix, and only those joins relevant to the new region</span>
-<span class="sd">    are retained.</span>
-
-<span class="sd">    &gt;&gt;&gt; from libpysal.weights import lat2W, w_subset</span>
-<span class="sd">    &gt;&gt;&gt; w1 = lat2W(6,4)</span>
-<span class="sd">    &gt;&gt;&gt; ids = range(16)</span>
-<span class="sd">    &gt;&gt;&gt; w = w_subset(w1, ids)</span>
-<span class="sd">    &gt;&gt;&gt; w1[0] == w[0]</span>
-<span class="sd">    True</span>
-<span class="sd">    &gt;&gt;&gt; w1.neighbors[15]</span>
-<span class="sd">    [11, 14, 19]</span>
-<span class="sd">    &gt;&gt;&gt; w.neighbors[15]</span>
-<span class="sd">    [11, 14]</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">neighbors</span> <span class="o">=</span> <span class="p">{}</span>
-    <span class="n">ids_set</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">ids</span><span class="p">))</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">ids</span><span class="p">:</span>
-        <span class="k">if</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="p">:</span>
-            <span class="n">neigh_add</span> <span class="o">=</span> <span class="n">ids_set</span><span class="o">.</span><span class="n">intersection</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span>
-            <span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">neigh_add</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
-
-    <span class="k">return</span> <span class="n">W</span><span class="p">(</span><span class="n">neighbors</span><span class="p">,</span> <span class="n">id_order</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="n">ids</span><span class="p">),</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="w_clip"><a class="viewcode-back" href="../../../generated/libpysal.weights.w_clip.html#libpysal.weights.w_clip">[docs]</a><span class="k">def</span> <span class="nf">w_clip</span><span class="p">(</span><span class="n">w1</span><span class="p">,</span> <span class="n">w2</span><span class="p">,</span> <span class="n">outSP</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Clip a continuous W object (w1) with a different W object (w2) so only cells where</span>
-<span class="sd">    w2 has a non-zero value remain with non-zero values in w1.</span>
-
-<span class="sd">    Checks on w1 and w2 are performed to make sure they conform to the</span>
-<span class="sd">    appropriate format and, if not, they are converted.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    w1                      : W</span>
-<span class="sd">                              W, scipy.sparse.csr.csr_matrix</span>
-<span class="sd">                              Potentially continuous weights matrix to be clipped. The clipped</span>
-<span class="sd">                              matrix wc will have at most the same elements as w1.</span>
-<span class="sd">    w2                      : W</span>
-<span class="sd">                              W, scipy.sparse.csr.csr_matrix</span>
-<span class="sd">                              Weights matrix to use as shell to clip w1. Automatically</span>
-<span class="sd">                              converted to binary format. Only non-zero elements in w2 will be</span>
-<span class="sd">                              kept non-zero in wc. NOTE: assumed to be of the same shape as w1</span>
-<span class="sd">    outSP                   : boolean</span>
-<span class="sd">                              If True (default) return sparse version of the clipped W, if</span>
-<span class="sd">                              False, return W object of the clipped matrix</span>
-<span class="sd">    **kwargs                : keyword arguments</span>
-<span class="sd">                              optional arguments for :class:`pysal.weights.W`</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    wc      : W</span>
-<span class="sd">              W, scipy.sparse.csr.csr_matrix</span>
-<span class="sd">              Clipped W object (sparse if outSP=Ture). It inherits ``id_order`` from w1.</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    &gt;&gt;&gt; from libpysal.weights import lat2W</span>
-
-<span class="sd">    First create a W object from a lattice using queen contiguity and</span>
-<span class="sd">    row-standardize it (note that these weights will stay when we clip the</span>
-<span class="sd">    object, but they will not neccesarily represent a row-standardization</span>
-<span class="sd">    anymore):</span>
-
-<span class="sd">    &gt;&gt;&gt; w1 = lat2W(3, 2, rook=False)</span>
-<span class="sd">    &gt;&gt;&gt; w1.transform = &#39;R&#39;</span>
-
-<span class="sd">    We will clip that geography assuming observations 0, 2, 3 and 4 belong to</span>
-<span class="sd">    one group and 1, 5 belong to another group and we don&#39;t want both groups</span>
-<span class="sd">    to interact with each other in our weights (i.e. w_ij = 0 if i and j in</span>
-<span class="sd">    different groups). For that, we use the following method:</span>
-
-<span class="sd">    &gt;&gt;&gt; import libpysal</span>
-<span class="sd">    &gt;&gt;&gt; w2 = libpysal.weights.block_weights([&#39;r1&#39;, &#39;r2&#39;, &#39;r1&#39;, &#39;r1&#39;, &#39;r1&#39;, &#39;r2&#39;])</span>
-
-<span class="sd">    To illustrate that w2 will only be considered as binary even when the</span>
-<span class="sd">    object passed is not, we can row-standardize it</span>
-
-<span class="sd">    &gt;&gt;&gt; w2.transform = &#39;R&#39;</span>
-
-<span class="sd">    The clipped object ``wc`` will contain only the spatial queen</span>
-<span class="sd">    relationships that occur within one group (&#39;r1&#39; or &#39;r2&#39;) but will have</span>
-<span class="sd">    gotten rid of those that happen across groups</span>
-
-<span class="sd">    &gt;&gt;&gt; wcs = libpysal.weights.w_clip(w1, w2, outSP=True)</span>
-
-<span class="sd">    This will create a sparse object (recommended when n is large).</span>
-
-<span class="sd">    &gt;&gt;&gt; wcs.sparse.toarray()</span>
-<span class="sd">    array([[0.        , 0.        , 0.33333333, 0.33333333, 0.        ,</span>
-<span class="sd">            0.        ],</span>
-<span class="sd">           [0.        , 0.        , 0.        , 0.        , 0.        ,</span>
-<span class="sd">            0.        ],</span>
-<span class="sd">           [0.2       , 0.        , 0.        , 0.2       , 0.2       ,</span>
-<span class="sd">            0.        ],</span>
-<span class="sd">           [0.2       , 0.        , 0.2       , 0.        , 0.2       ,</span>
-<span class="sd">            0.        ],</span>
-<span class="sd">           [0.        , 0.        , 0.33333333, 0.33333333, 0.        ,</span>
-<span class="sd">            0.        ],</span>
-<span class="sd">           [0.        , 0.        , 0.        , 0.        , 0.        ,</span>
-<span class="sd">            0.        ]])</span>
-
-
-<span class="sd">    If we wanted an original W object, we can control that with the argument</span>
-<span class="sd">    ``outSP``:</span>
-
-<span class="sd">    &gt;&gt;&gt; wc = libpysal.weights.w_clip(w1, w2, outSP=False)</span>
-<span class="sd">    &gt;&gt;&gt; wc.full()[0]</span>
-<span class="sd">    array([[0.        , 0.        , 0.33333333, 0.33333333, 0.        ,</span>
-<span class="sd">            0.        ],</span>
-<span class="sd">           [0.        , 0.        , 0.        , 0.        , 0.        ,</span>
-<span class="sd">            0.        ],</span>
-<span class="sd">           [0.2       , 0.        , 0.        , 0.2       , 0.2       ,</span>
-<span class="sd">            0.        ],</span>
-<span class="sd">           [0.2       , 0.        , 0.2       , 0.        , 0.2       ,</span>
-<span class="sd">            0.        ],</span>
-<span class="sd">           [0.        , 0.        , 0.33333333, 0.33333333, 0.        ,</span>
-<span class="sd">            0.        ],</span>
-<span class="sd">           [0.        , 0.        , 0.        , 0.        , 0.        ,</span>
-<span class="sd">            0.        ]])</span>
-
-<span class="sd">    You can check they are actually the same:</span>
-
-<span class="sd">    &gt;&gt;&gt; wcs.sparse.toarray() == wc.full()[0]</span>
-<span class="sd">    array([[ True,  True,  True,  True,  True,  True],</span>
-<span class="sd">           [ True,  True,  True,  True,  True,  True],</span>
-<span class="sd">           [ True,  True,  True,  True,  True,  True],</span>
-<span class="sd">           [ True,  True,  True,  True,  True,  True],</span>
-<span class="sd">           [ True,  True,  True,  True,  True,  True],</span>
-<span class="sd">           [ True,  True,  True,  True,  True,  True]])</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="kn">from</span> <span class="nn">.util</span> <span class="kn">import</span> <span class="n">WSP2W</span>
-
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">w1</span><span class="o">.</span><span class="n">id_order</span><span class="p">:</span>
-        <span class="n">w1</span><span class="o">.</span><span class="n">id_order</span> <span class="o">=</span> <span class="kc">None</span>
-    <span class="n">id_order</span> <span class="o">=</span> <span class="n">w1</span><span class="o">.</span><span class="n">id_order</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">isspmatrix_csr</span><span class="p">(</span><span class="n">w1</span><span class="p">):</span>
-        <span class="n">w1</span> <span class="o">=</span> <span class="n">w1</span><span class="o">.</span><span class="n">sparse</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">isspmatrix_csr</span><span class="p">(</span><span class="n">w2</span><span class="p">):</span>
-        <span class="n">w2</span> <span class="o">=</span> <span class="n">w2</span><span class="o">.</span><span class="n">sparse</span>
-    <span class="n">w2</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">ones</span><span class="p">(</span><span class="n">w2</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
-    <span class="n">wc</span> <span class="o">=</span> <span class="n">w1</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="n">w2</span><span class="p">)</span>
-    <span class="n">wc</span> <span class="o">=</span> <span class="n">WSP</span><span class="p">(</span><span class="n">wc</span><span class="p">,</span> <span class="n">id_order</span><span class="o">=</span><span class="n">id_order</span><span class="p">)</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">outSP</span><span class="p">:</span>
-        <span class="n">wc</span> <span class="o">=</span> <span class="n">WSP2W</span><span class="p">(</span><span class="n">wc</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">wc</span></div>
-</pre></div>
-
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-      
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-      
-  <h1>Source code for libpysal.weights.spatial_lag</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">Spatial lag operations.</span>
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Sergio J. Rey &lt;srey@asu.edu&gt;, David C. Folch &lt;david.folch@asu.edu&gt;, Levi John Wolf &lt;ljw2@asu.edu&quot;</span>
-<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;lag_spatial&quot;</span><span class="p">,</span> <span class="s2">&quot;lag_categorical&quot;</span><span class="p">]</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-
-<div class="viewcode-block" id="lag_spatial"><a class="viewcode-back" href="../../../generated/libpysal.weights.lag_spatial.html#libpysal.weights.lag_spatial">[docs]</a><span class="k">def</span> <span class="nf">lag_spatial</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Spatial lag operator.</span>
-
-<span class="sd">    If w is row standardized, returns the average of each observation&#39;s neighbors;</span>
-<span class="sd">    if not, returns the weighted sum of each observation&#39;s neighbors.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-
-<span class="sd">    w                   : W</span>
-<span class="sd">                          libpysal spatial weightsobject</span>
-<span class="sd">    y                   : array</span>
-<span class="sd">                          numpy array with dimensionality conforming to w (see examples)</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-
-<span class="sd">    wy                  : array</span>
-<span class="sd">                          array of numeric values for the spatial lag</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-
-<span class="sd">    Setup a 9x9 binary spatial weights matrix and vector of data; compute the</span>
-<span class="sd">    spatial lag of the vector.</span>
-
-<span class="sd">    &gt;&gt;&gt; import libpysal</span>
-<span class="sd">    &gt;&gt;&gt; import numpy as np</span>
-<span class="sd">    &gt;&gt;&gt; w = libpysal.weights.lat2W(3, 3)</span>
-<span class="sd">    &gt;&gt;&gt; y = np.arange(9)</span>
-<span class="sd">    &gt;&gt;&gt; yl = libpysal.weights.lag_spatial(w, y)</span>
-<span class="sd">    &gt;&gt;&gt; yl</span>
-<span class="sd">    array([ 4.,  6.,  6., 10., 16., 14., 10., 18., 12.])</span>
-
-<span class="sd">    Row standardize the weights matrix and recompute the spatial lag</span>
-
-<span class="sd">    &gt;&gt;&gt; w.transform = &#39;r&#39;</span>
-<span class="sd">    &gt;&gt;&gt; yl = libpysal.weights.lag_spatial(w, y)</span>
-<span class="sd">    &gt;&gt;&gt; yl</span>
-<span class="sd">    array([2.        , 2.        , 3.        , 3.33333333, 4.        ,</span>
-<span class="sd">           4.66666667, 5.        , 6.        , 6.        ])</span>
-
-
-<span class="sd">    Explicitly define data vector as 9x1 and recompute the spatial lag</span>
-
-<span class="sd">    &gt;&gt;&gt; y.shape = (9, 1)</span>
-<span class="sd">    &gt;&gt;&gt; yl = libpysal.weights.lag_spatial(w, y)</span>
-<span class="sd">    &gt;&gt;&gt; yl</span>
-<span class="sd">    array([[2.        ],</span>
-<span class="sd">           [2.        ],</span>
-<span class="sd">           [3.        ],</span>
-<span class="sd">           [3.33333333],</span>
-<span class="sd">           [4.        ],</span>
-<span class="sd">           [4.66666667],</span>
-<span class="sd">           [5.        ],</span>
-<span class="sd">           [6.        ],</span>
-<span class="sd">           [6.        ]])</span>
-
-
-<span class="sd">    Take the spatial lag of a 9x2 data matrix</span>
-
-<span class="sd">    &gt;&gt;&gt; yr = np.arange(8, -1, -1)</span>
-<span class="sd">    &gt;&gt;&gt; yr.shape = (9, 1)</span>
-<span class="sd">    &gt;&gt;&gt; x = np.hstack((y, yr))</span>
-<span class="sd">    &gt;&gt;&gt; yl = libpysal.weights.lag_spatial(w, x)</span>
-<span class="sd">    &gt;&gt;&gt; yl</span>
-<span class="sd">    array([[2.        , 6.        ],</span>
-<span class="sd">           [2.        , 6.        ],</span>
-<span class="sd">           [3.        , 5.        ],</span>
-<span class="sd">           [3.33333333, 4.66666667],</span>
-<span class="sd">           [4.        , 4.        ],</span>
-<span class="sd">           [4.66666667, 3.33333333],</span>
-<span class="sd">           [5.        , 3.        ],</span>
-<span class="sd">           [6.        , 2.        ],</span>
-<span class="sd">           [6.        , 2.        ]])</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">return</span> <span class="n">w</span><span class="o">.</span><span class="n">sparse</span> <span class="o">*</span> <span class="n">y</span></div>
-
-
-<div class="viewcode-block" id="lag_categorical"><a class="viewcode-back" href="../../../generated/libpysal.weights.lag_categorical.html#libpysal.weights.lag_categorical">[docs]</a><span class="k">def</span> <span class="nf">lag_categorical</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">ties</span><span class="o">=</span><span class="s2">&quot;tryself&quot;</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Spatial lag operator for categorical variables.</span>
-
-<span class="sd">    Constructs the most common categories of neighboring observations, weighted</span>
-<span class="sd">    by their weight strength.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-
-<span class="sd">    w                   : W</span>
-<span class="sd">                          PySAL spatial weightsobject</span>
-<span class="sd">    y                   : iterable</span>
-<span class="sd">                          iterable collection of categories (either int or</span>
-<span class="sd">                          string) with dimensionality conforming to w (see examples)</span>
-<span class="sd">    ties                : str</span>
-<span class="sd">                          string describing the method to use when resolving</span>
-<span class="sd">                          ties. By default, the option is &quot;tryself&quot;,</span>
-<span class="sd">                          and the category of the focal observation</span>
-<span class="sd">                          is included with its neighbors to try</span>
-<span class="sd">                          and break a tie. If this does not resolve the tie,</span>
-<span class="sd">                          a winner is chosen randomly. To just use random choice to</span>
-<span class="sd">                          break ties, pass &quot;random&quot; instead.</span>
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    an (n x k) column vector containing the most common neighboring observation</span>
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-<span class="sd">    This works on any array where the number of unique elements along the column</span>
-<span class="sd">    axis is less than the number of elements in the array, for any dtype.</span>
-<span class="sd">    That means the routine should work on any dtype that np.unique() can</span>
-<span class="sd">    compare.</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-
-<span class="sd">    Set up a 9x9 weights matrix describing a 3x3 regular lattice. Lag one list of</span>
-<span class="sd">    categorical variables with no ties.</span>
-
-<span class="sd">    &gt;&gt;&gt; import libpysal</span>
-<span class="sd">    &gt;&gt;&gt; import numpy as np</span>
-<span class="sd">    &gt;&gt;&gt; np.random.seed(12345)</span>
-<span class="sd">    &gt;&gt;&gt; w = libpysal.weights.lat2W(3, 3)</span>
-<span class="sd">    &gt;&gt;&gt; y = [&#39;a&#39;,&#39;b&#39;,&#39;a&#39;,&#39;b&#39;,&#39;c&#39;,&#39;b&#39;,&#39;c&#39;,&#39;b&#39;,&#39;c&#39;]</span>
-<span class="sd">    &gt;&gt;&gt; y_l = libpysal.weights.lag_categorical(w, y)</span>
-<span class="sd">    &gt;&gt;&gt; np.array_equal(y_l, np.array([&#39;b&#39;, &#39;a&#39;, &#39;b&#39;, &#39;c&#39;, &#39;b&#39;, &#39;c&#39;, &#39;b&#39;, &#39;c&#39;, &#39;b&#39;]))</span>
-<span class="sd">    True</span>
-
-<span class="sd">    Explicitly reshape y into a (9x1) array and calculate lag again</span>
-
-<span class="sd">    &gt;&gt;&gt; yvect = np.array(y).reshape(9,1)</span>
-<span class="sd">    &gt;&gt;&gt; yvect_l = libpysal.weights.lag_categorical(w,yvect)</span>
-<span class="sd">    &gt;&gt;&gt; check = np.array( [ [i] for i in  [&#39;b&#39;, &#39;a&#39;, &#39;b&#39;, &#39;c&#39;, &#39;b&#39;, &#39;c&#39;, &#39;b&#39;, &#39;c&#39;, &#39;b&#39;]] )</span>
-<span class="sd">    &gt;&gt;&gt; np.array_equal(yvect_l, check)</span>
-<span class="sd">    True</span>
-
-<span class="sd">    compute the lag of a 9x2 matrix of categories</span>
-
-<span class="sd">    &gt;&gt;&gt; y2 = [&#39;a&#39;, &#39;c&#39;, &#39;c&#39;, &#39;d&#39;, &#39;b&#39;, &#39;a&#39;, &#39;d&#39;, &#39;d&#39;, &#39;c&#39;]</span>
-<span class="sd">    &gt;&gt;&gt; ym = np.vstack((y,y2)).T</span>
-<span class="sd">    &gt;&gt;&gt; ym_lag = libpysal.weights.lag_categorical(w,ym)</span>
-<span class="sd">    &gt;&gt;&gt; check = np.array([[&#39;b&#39;, &#39;d&#39;], [&#39;a&#39;, &#39;c&#39;], [&#39;b&#39;, &#39;c&#39;], [&#39;c&#39;, &#39;d&#39;], [&#39;b&#39;, &#39;d&#39;], [&#39;c&#39;, &#39;c&#39;], [&#39;b&#39;, &#39;d&#39;], [&#39;c&#39;, &#39;d&#39;], [&#39;b&#39;, &#39;c&#39;]])</span>
-<span class="sd">    &gt;&gt;&gt; np.array_equal(check, ym_lag)</span>
-<span class="sd">    True</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
-        <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
-    <span class="n">orig_shape</span> <span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">shape</span>
-    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">orig_shape</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-        <span class="k">if</span> <span class="n">orig_shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">([</span><span class="n">lag_categorical</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">col</span><span class="p">)</span> <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">y</span><span class="o">.</span><span class="n">T</span><span class="p">])</span><span class="o">.</span><span class="n">T</span>
-    <span class="n">y</span> <span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
-    <span class="n">output</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
-    <span class="n">labels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
-    <span class="n">normalized_labels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">y</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int</span><span class="p">)</span>
-    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">label</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">labels</span><span class="p">):</span>
-        <span class="n">normalized_labels</span><span class="p">[</span><span class="n">y</span> <span class="o">==</span> <span class="n">label</span><span class="p">]</span> <span class="o">=</span> <span class="n">i</span>
-    <span class="k">for</span> <span class="n">focal_name</span><span class="p">,</span> <span class="n">neighbors</span> <span class="ow">in</span> <span class="n">w</span><span class="p">:</span>
-        <span class="n">focal_idx</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">id2i</span><span class="p">[</span><span class="n">focal_name</span><span class="p">]</span>
-        <span class="n">neighborhood_tally</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">labels</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">neighb_name</span><span class="p">,</span> <span class="n">weight</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="n">neighbors</span><span class="o">.</span><span class="n">items</span><span class="p">()):</span>
-            <span class="n">neighb_idx</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">id2i</span><span class="p">[</span><span class="n">neighb_name</span><span class="p">]</span>
-            <span class="n">neighb_label</span> <span class="o">=</span> <span class="n">normalized_labels</span><span class="p">[</span><span class="n">neighb_idx</span><span class="p">]</span>
-            <span class="n">neighborhood_tally</span><span class="p">[</span><span class="n">neighb_label</span><span class="p">]</span> <span class="o">+=</span> <span class="n">weight</span>
-        <span class="n">out_label_idx</span> <span class="o">=</span> <span class="n">_resolve_ties</span><span class="p">(</span>
-            <span class="n">focal_idx</span><span class="p">,</span> <span class="n">normalized_labels</span><span class="p">,</span> <span class="n">neighborhood_tally</span><span class="p">,</span> <span class="n">neighbors</span><span class="p">,</span> <span class="n">ties</span><span class="p">,</span> <span class="n">w</span>
-        <span class="p">)</span>
-        <span class="n">output</span><span class="p">[</span><span class="n">focal_idx</span><span class="p">]</span> <span class="o">=</span> <span class="n">labels</span><span class="p">[</span><span class="n">out_label_idx</span><span class="p">]</span>
-    <span class="k">return</span> <span class="n">output</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">orig_shape</span><span class="p">)</span></div>
-
-
-<span class="k">def</span> <span class="nf">_resolve_ties</span><span class="p">(</span><span class="n">idx</span><span class="p">,</span> <span class="n">normalized_labels</span><span class="p">,</span> <span class="n">tally</span><span class="p">,</span> <span class="n">neighbors</span><span class="p">,</span> <span class="n">method</span><span class="p">,</span> <span class="n">w</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Helper function to resolve ties if lag is multimodal</span>
-
-<span class="sd">    first, if this function gets called when there&#39;s actually no tie, then the</span>
-<span class="sd">    correct value will be picked.</span>
-
-<span class="sd">    if &#39;random&#39; is selected as the method, a random tiebeaker is picked</span>
-
-<span class="sd">    if &#39;tryself&#39; is selected, then the observation&#39;s own value will be used in</span>
-<span class="sd">    an attempt to break the tie, but if it fails, a random tiebreaker will be</span>
-<span class="sd">    selected.</span>
-
-<span class="sd">    Arguments</span>
-<span class="sd">    ---------</span>
-<span class="sd">    idx                 : int</span>
-<span class="sd">                          index (aligned with `normalized_labels`) of the </span>
-<span class="sd">                          current observation being resolved.</span>
-<span class="sd">    normalized_labels   : (n,) array of ints</span>
-<span class="sd">                          normalized array of labels for each observation</span>
-<span class="sd">    tally               : (p,) array of floats</span>
-<span class="sd">                          current tally of neighbors&#39; labels around `idx` to resolve.</span>
-<span class="sd">    neighbors           : dict of (neighbor_name : weight)</span>
-<span class="sd">                          the elements of the weights object, identical to w[idx]</span>
-<span class="sd">    method              : string</span>
-<span class="sd">                          configuration option to use a specific tiebreaking method. </span>
-<span class="sd">                          supported options are:</span>
-<span class="sd">                          1. tryself: Use the focal observation&#39;s label to tiebreak.</span>
-<span class="sd">                                      If this doesn&#39;t successfully break the tie, </span>
-<span class="sd">                                      (which only occurs if it induces a new tie),</span>
-<span class="sd">                                      decide randomly. </span>
-<span class="sd">                          2. random: Resolve the tie randomly amongst winners.</span>
-<span class="sd">                          3. lowest: Pick the lowest-value label amongst winners.</span>
-<span class="sd">                          4. highest: Pick the highest-value label amongst winners.</span>
-<span class="sd">    w                   : pysal.W object</span>
-<span class="sd">                          a PySAL weights object aligned with normalized_labels. </span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    integer denoting which label to use to label the observation.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="p">(</span><span class="n">ties</span><span class="p">,)</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">tally</span> <span class="o">==</span> <span class="n">tally</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>  <span class="c1"># returns a tuple for flat arrays</span>
-    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">tally</span><span class="p">[</span><span class="n">tally</span> <span class="o">==</span> <span class="n">tally</span><span class="o">.</span><span class="n">max</span><span class="p">()])</span> <span class="o">&lt;=</span> <span class="mi">1</span><span class="p">:</span>  <span class="c1"># no tie, pick the highest</span>
-        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">tally</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
-    <span class="k">elif</span> <span class="n">method</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">==</span> <span class="s2">&quot;random&quot;</span><span class="p">:</span>  <span class="c1"># choose randomly from tally</span>
-        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="n">ties</span><span class="p">))</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
-    <span class="k">elif</span> <span class="n">method</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">==</span> <span class="s2">&quot;lowest&quot;</span><span class="p">:</span>  <span class="c1"># pick lowest tied value</span>
-        <span class="k">return</span> <span class="n">ties</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
-    <span class="k">elif</span> <span class="n">method</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">==</span> <span class="s2">&quot;highest&quot;</span><span class="p">:</span>  <span class="c1"># pick highest tied value</span>
-        <span class="k">return</span> <span class="n">ties</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
-    <span class="k">elif</span> <span class="p">(</span>
-        <span class="n">method</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">==</span> <span class="s2">&quot;tryself&quot;</span>
-    <span class="p">):</span>  <span class="c1"># add self-label as observation, try again, random if fail</span>
-        <span class="n">mean_neighbor_value</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">neighbors</span><span class="o">.</span><span class="n">values</span><span class="p">()))</span>
-        <span class="n">tally</span><span class="p">[</span><span class="n">normalized_labels</span><span class="p">[</span><span class="n">idx</span><span class="p">]]</span> <span class="o">+=</span> <span class="n">mean_neighbor_value</span>
-        <span class="k">return</span> <span class="n">_resolve_ties</span><span class="p">(</span><span class="n">idx</span><span class="p">,</span> <span class="n">normalized_labels</span><span class="p">,</span> <span class="n">tally</span><span class="p">,</span> <span class="n">neighbors</span><span class="p">,</span> <span class="s2">&quot;random&quot;</span><span class="p">,</span> <span class="n">w</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">KeyError</span><span class="p">(</span><span class="s2">&quot;Tie-breaking method for categorical lag not recognized&quot;</span><span class="p">)</span>
-</pre></div>
-
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-  <h1>Source code for libpysal.weights.spintW</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">Spatial weights for spatial interaction including contiguity OD weights (ODW),</span>
-<span class="sd">network based weights (netW), and distance-decay based vector weights (vecW).</span>
-
-<span class="sd">&quot;&quot;&quot;</span>
-
-<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Taylor Oshan  &lt;tayoshan@gmail.com&gt; &quot;</span>
-
-<span class="kn">from</span> <span class="nn">scipy.sparse</span> <span class="kn">import</span> <span class="n">kron</span>
-<span class="kn">from</span> <span class="nn">.weights</span> <span class="kn">import</span> <span class="n">W</span><span class="p">,</span> <span class="n">WSP</span>
-<span class="kn">from</span> <span class="nn">.distance</span> <span class="kn">import</span> <span class="n">DistanceBand</span>
-<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">OrderedDict</span>
-
-
-<div class="viewcode-block" id="ODW"><a class="viewcode-back" href="../../../generated/libpysal.weights.ODW.html#libpysal.weights.ODW">[docs]</a><span class="k">def</span> <span class="nf">ODW</span><span class="p">(</span><span class="n">Wo</span><span class="p">,</span> <span class="n">Wd</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="s2">&quot;r&quot;</span><span class="p">,</span> <span class="n">silence_warnings</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Constructs an o*d by o*d origin-destination style spatial weight for o*d</span>
-<span class="sd">    flows using standard spatial weights on o origins and d destinations. Input</span>
-<span class="sd">    spatial weights must be binary or able to be sutiably transformed to binary.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    Wo          : W object for origin locations</span>
-<span class="sd">                  o x o spatial weight object amongst o origins</span>
-
-<span class="sd">    Wd          : W object for destination locations</span>
-<span class="sd">                  d x d spatial weight object amongst d destinations</span>
-
-<span class="sd">    transform   : Transformation for standardization of final OD spatial weight; default</span>
-<span class="sd">                  is &#39;r&#39; for row standardized</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    W           : spatial contiguity W object for assocations between flows</span>
-<span class="sd">                 o*d x o*d spatial weight object amongst o*d flows between o</span>
-<span class="sd">                 origins and d destinations</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-
-<span class="sd">    &gt;&gt;&gt; import libpysal</span>
-<span class="sd">    &gt;&gt;&gt; O = libpysal.weights.lat2W(2,2)</span>
-<span class="sd">    &gt;&gt;&gt; D = libpysal.weights.lat2W(2,2)</span>
-<span class="sd">    &gt;&gt;&gt; OD = libpysal.weights.ODW(O,D)</span>
-<span class="sd">    &gt;&gt;&gt; OD.weights[0]</span>
-<span class="sd">    [0.25, 0.25, 0.25, 0.25]</span>
-<span class="sd">    &gt;&gt;&gt; OD.neighbors[0]</span>
-<span class="sd">    [5, 6, 9, 10]</span>
-<span class="sd">    &gt;&gt;&gt; OD.full()[0][0]</span>
-<span class="sd">    array([0.  , 0.  , 0.  , 0.  , 0.  , 0.25, 0.25, 0.  , 0.  , 0.25, 0.25,</span>
-<span class="sd">           0.  , 0.  , 0.  , 0.  , 0.  ])</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="n">Wo</span><span class="o">.</span><span class="n">transform</span> <span class="o">!=</span> <span class="s2">&quot;b&quot;</span><span class="p">:</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="n">Wo</span><span class="o">.</span><span class="n">tranform</span> <span class="o">=</span> <span class="s2">&quot;b&quot;</span>
-        <span class="k">except</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
-                <span class="s2">&quot;Wo is not binary and cannot be transformed to &quot;</span>
-                <span class="s2">&quot;binary. Wo must be binary or suitably transformed to binary.&quot;</span>
-            <span class="p">)</span>
-    <span class="k">if</span> <span class="n">Wd</span><span class="o">.</span><span class="n">transform</span> <span class="o">!=</span> <span class="s2">&quot;b&quot;</span><span class="p">:</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="n">Wd</span><span class="o">.</span><span class="n">tranform</span> <span class="o">=</span> <span class="s2">&quot;b&quot;</span>
-        <span class="k">except</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
-                <span class="s2">&quot;Wd is not binary and cannot be transformed to &quot;</span>
-                <span class="s2">&quot;binary. Wd must be binary or suitably transformed to binary.&quot;</span>
-            <span class="p">)</span>
-    <span class="n">Wo</span> <span class="o">=</span> <span class="n">Wo</span><span class="o">.</span><span class="n">sparse</span>
-    <span class="n">Wo</span><span class="o">.</span><span class="n">eliminate_zeros</span><span class="p">()</span>
-    <span class="n">Wd</span> <span class="o">=</span> <span class="n">Wd</span><span class="o">.</span><span class="n">sparse</span>
-    <span class="n">Wd</span><span class="o">.</span><span class="n">eliminate_zeros</span><span class="p">()</span>
-    <span class="n">Ww</span> <span class="o">=</span> <span class="n">kron</span><span class="p">(</span><span class="n">Wo</span><span class="p">,</span> <span class="n">Wd</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="s2">&quot;csr&quot;</span><span class="p">)</span>
-    <span class="n">Ww</span><span class="o">.</span><span class="n">eliminate_zeros</span><span class="p">()</span>
-    <span class="n">Ww</span> <span class="o">=</span> <span class="n">WSP</span><span class="p">(</span><span class="n">Ww</span><span class="p">)</span><span class="o">.</span><span class="n">to_W</span><span class="p">(</span><span class="n">silence_warnings</span><span class="o">=</span><span class="n">silence_warnings</span><span class="p">)</span>
-    <span class="n">Ww</span><span class="o">.</span><span class="n">transform</span> <span class="o">=</span> <span class="n">transform</span>
-    <span class="k">return</span> <span class="n">Ww</span></div>
-
-
-<div class="viewcode-block" id="netW"><a class="viewcode-back" href="../../../generated/libpysal.weights.netW.html#libpysal.weights.netW">[docs]</a><span class="k">def</span> <span class="nf">netW</span><span class="p">(</span><span class="n">link_list</span><span class="p">,</span> <span class="n">share</span><span class="o">=</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="s2">&quot;r&quot;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Create a network-contiguity based weight object based on different nodal</span>
-<span class="sd">    relationships encoded in a network.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    link_list   : list</span>
-<span class="sd">                  of tuples where each tuple is of the form (o,d) where o is an</span>
-<span class="sd">                  origin id and d is a destination id</span>
-
-<span class="sd">    share       : string</span>
-<span class="sd">                  denoting how to define the nodal relationship used to determine neighboring edges; defualt is &#39;A&#39; for any shared nodes between two network edges; options include: O a shared origin node; D a shared destination node; OD; a shared origin or a shared destination node; C a shared node that is the destination of the first edge and the origin of the second edge - i.e., a directed chain is formed moving from edge one to edge two.</span>
-
-<span class="sd">    transform   : Transformation for standardization of final OD spatial weight; default</span>
-<span class="sd">                  is &#39;r&#39; for row standardized</span>
-<span class="sd">    **kwargs    : keyword arguments</span>
-<span class="sd">                  optional arguments for :class:`pysal.weights.W`</span>
-
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">     W          : nodal contiguity W object for networkd edges or flows</span>
-<span class="sd">                  W Object representing the binary adjacency of the network edges</span>
-<span class="sd">                  given a definition of nodal relationshilibpysal.weights.spintW.</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    &gt;&gt;&gt; import libpysal</span>
-<span class="sd">    &gt;&gt;&gt; links = [(&#39;a&#39;,&#39;b&#39;), (&#39;a&#39;,&#39;c&#39;), (&#39;a&#39;,&#39;d&#39;), (&#39;c&#39;,&#39;d&#39;), (&#39;c&#39;, &#39;b&#39;), (&#39;c&#39;,&#39;a&#39;)]</span>
-<span class="sd">    &gt;&gt;&gt; O = libpysal.weights.netW(links, share=&#39;O&#39;)</span>
-<span class="sd">    &gt;&gt;&gt; O.neighbors[(&#39;a&#39;, &#39;b&#39;)]</span>
-<span class="sd">    [(&#39;a&#39;, &#39;c&#39;), (&#39;a&#39;, &#39;d&#39;)]</span>
-<span class="sd">    &gt;&gt;&gt; OD = libpysal.weights.netW(links, share=&#39;OD&#39;)</span>
-<span class="sd">    &gt;&gt;&gt; OD.neighbors[(&#39;a&#39;, &#39;b&#39;)]</span>
-<span class="sd">    [(&#39;a&#39;, &#39;c&#39;), (&#39;a&#39;, &#39;d&#39;), (&#39;c&#39;, &#39;b&#39;)]</span>
-<span class="sd">    &gt;&gt;&gt; any_common = libpysal.weights.netW(links, share=&#39;A&#39;)</span>
-<span class="sd">    &gt;&gt;&gt; any_common.neighbors[(&#39;a&#39;, &#39;b&#39;)]</span>
-<span class="sd">    [(&#39;a&#39;, &#39;c&#39;), (&#39;a&#39;, &#39;d&#39;), (&#39;c&#39;, &#39;b&#39;), (&#39;c&#39;, &#39;a&#39;)]</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">neighbors</span> <span class="o">=</span> <span class="p">{}</span>
-    <span class="n">neighbors</span> <span class="o">=</span> <span class="n">OrderedDict</span><span class="p">()</span>
-    <span class="n">edges</span> <span class="o">=</span> <span class="n">link_list</span>
-    <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">edges</span><span class="p">:</span>
-        <span class="n">neighbors</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">neigh</span> <span class="ow">in</span> <span class="n">edges</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">key</span> <span class="o">==</span> <span class="n">neigh</span><span class="p">:</span>
-                <span class="k">continue</span>
-            <span class="k">if</span> <span class="n">share</span><span class="o">.</span><span class="n">upper</span><span class="p">()</span> <span class="o">==</span> <span class="s2">&quot;OD&quot;</span><span class="p">:</span>
-                <span class="k">if</span> <span class="n">key</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">neigh</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">or</span> <span class="n">key</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">neigh</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
-                    <span class="n">neighbors</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">neigh</span><span class="p">)</span>
-            <span class="k">elif</span> <span class="n">share</span><span class="o">.</span><span class="n">upper</span><span class="p">()</span> <span class="o">==</span> <span class="s2">&quot;O&quot;</span><span class="p">:</span>
-                <span class="k">if</span> <span class="n">key</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">neigh</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
-                    <span class="n">neighbors</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">neigh</span><span class="p">)</span>
-            <span class="k">elif</span> <span class="n">share</span><span class="o">.</span><span class="n">upper</span><span class="p">()</span> <span class="o">==</span> <span class="s2">&quot;D&quot;</span><span class="p">:</span>
-                <span class="k">if</span> <span class="n">key</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">neigh</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
-                    <span class="n">neighbors</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">neigh</span><span class="p">)</span>
-            <span class="k">elif</span> <span class="n">share</span><span class="o">.</span><span class="n">upper</span><span class="p">()</span> <span class="o">==</span> <span class="s2">&quot;C&quot;</span><span class="p">:</span>
-                <span class="k">if</span> <span class="n">key</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">neigh</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
-                    <span class="n">neighbors</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">neigh</span><span class="p">)</span>
-            <span class="k">elif</span> <span class="n">share</span><span class="o">.</span><span class="n">upper</span><span class="p">()</span> <span class="o">==</span> <span class="s2">&quot;A&quot;</span><span class="p">:</span>
-                <span class="k">if</span> <span class="p">(</span>
-                    <span class="n">key</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">neigh</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-                    <span class="ow">or</span> <span class="n">key</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">neigh</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-                    <span class="ow">or</span> <span class="n">key</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">neigh</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-                    <span class="ow">or</span> <span class="n">key</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">neigh</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-                <span class="p">):</span>
-                    <span class="n">neighbors</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">neigh</span><span class="p">)</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
-                    <span class="s2">&quot;Parameter &#39;share&#39; must be &#39;O&#39;, &#39;D&#39;,&quot;</span> <span class="s2">&quot; &#39;OD&#39;, or &#39;C&#39;&quot;</span>
-                <span class="p">)</span>
-    <span class="n">netW</span> <span class="o">=</span> <span class="n">W</span><span class="p">(</span><span class="n">neighbors</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-    <span class="n">netW</span><span class="o">.</span><span class="n">tranform</span> <span class="o">=</span> <span class="n">transform</span>
-    <span class="k">return</span> <span class="n">netW</span></div>
-
-
-<div class="viewcode-block" id="vecW"><a class="viewcode-back" href="../../../generated/libpysal.weights.vecW.html#libpysal.weights.vecW">[docs]</a><span class="k">def</span> <span class="nf">vecW</span><span class="p">(</span>
-    <span class="n">origin_x</span><span class="p">,</span>
-    <span class="n">origin_y</span><span class="p">,</span>
-    <span class="n">dest_x</span><span class="p">,</span>
-    <span class="n">dest_y</span><span class="p">,</span>
-    <span class="n">threshold</span><span class="p">,</span>
-    <span class="n">p</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
-    <span class="n">alpha</span><span class="o">=-</span><span class="mf">1.0</span><span class="p">,</span>
-    <span class="n">binary</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-    <span class="n">ids</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">build_sp</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-    <span class="o">**</span><span class="n">kwargs</span>
-<span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Distance-based spatial weight for vectors that is computed using a</span>
-<span class="sd">    4-dimensional distance between the origin x,y-coordinates and the</span>
-<span class="sd">    destination x,y-coordinates</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    origin_x   : list or array</span>
-<span class="sd">                 of vector origin x-coordinates</span>
-<span class="sd">    origin_y   : list or array</span>
-<span class="sd">                 of vector origin y-coordinates</span>
-<span class="sd">    dest_x     : list or array</span>
-<span class="sd">                 of vector destination x-coordinates</span>
-<span class="sd">    dest_y     : list or array</span>
-<span class="sd">                 of vector destination y-coordinates</span>
-<span class="sd">    threshold  : float</span>
-<span class="sd">                 distance band</span>
-<span class="sd">    p          : float</span>
-<span class="sd">                 Minkowski p-norm distance metric parameter:</span>
-<span class="sd">                 1&lt;=p&lt;=infinity</span>
-<span class="sd">                 2: Euclidean distance</span>
-<span class="sd">                 1: Manhattan distance</span>
-<span class="sd">    binary     : boolean</span>
-<span class="sd">                 If true w_{ij}=1 if d_{i,j}&lt;=threshold, otherwise w_{i,j}=0</span>
-<span class="sd">                 If false wij=dij^{alpha}</span>
-<span class="sd">    alpha      : float</span>
-<span class="sd">                 distance decay parameter for weight (default -1.0)</span>
-<span class="sd">                 if alpha is positive the weights will not decline with</span>
-<span class="sd">                 distance. If binary is True, alpha is ignored</span>
-
-<span class="sd">    ids         : list</span>
-<span class="sd">                  values to use for keys of the neighbors and weights dicts</span>
-<span class="sd">    build_sp    : boolean</span>
-<span class="sd">                  True to build sparse distance matrix and false to build dense</span>
-<span class="sd">                  distance matrix; significant speed gains may be obtained</span>
-<span class="sd">                  dending on the sparsity of the of distance_matrix and</span>
-<span class="sd">                  threshold that is applied</span>
-<span class="sd">    **kwargs    : keyword arguments</span>
-<span class="sd">                  optional arguments for :class:`pysal.weights.W`</span>
-
-
-<span class="sd">    Returns</span>
-<span class="sd">    ------</span>
-<span class="sd">    W           : DistanceBand W object that uses 4-dimenional distances between</span>
-<span class="sd">                  vectors origin and destination coordinates.</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    &gt;&gt;&gt; import libpysal</span>
-<span class="sd">    &gt;&gt;&gt; x1 = [5,6,3]</span>
-<span class="sd">    &gt;&gt;&gt; y1 = [1,8,5]</span>
-<span class="sd">    &gt;&gt;&gt; x2 = [2,4,9]</span>
-<span class="sd">    &gt;&gt;&gt; y2 = [3,6,1]</span>
-<span class="sd">    &gt;&gt;&gt; W1 = libpysal.weights.vecW(x1, y1, x2, y2, threshold=999)</span>
-<span class="sd">    &gt;&gt;&gt; list(W1.neighbors[0])</span>
-<span class="sd">    [1, 2]</span>
-<span class="sd">    &gt;&gt;&gt; W2 = libpysal.weights.vecW(x1, y2, x1, y2, threshold=8.5)</span>
-<span class="sd">    &gt;&gt;&gt; list(W2.neighbors[0])</span>
-<span class="sd">    [1, 2]</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">data</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">origin_x</span><span class="p">,</span> <span class="n">origin_y</span><span class="p">,</span> <span class="n">dest_x</span><span class="p">,</span> <span class="n">dest_y</span><span class="p">))</span>
-    <span class="n">W</span> <span class="o">=</span> <span class="n">DistanceBand</span><span class="p">(</span>
-        <span class="n">data</span><span class="p">,</span>
-        <span class="n">threshold</span><span class="o">=</span><span class="n">threshold</span><span class="p">,</span>
-        <span class="n">p</span><span class="o">=</span><span class="n">p</span><span class="p">,</span>
-        <span class="n">binary</span><span class="o">=</span><span class="n">binary</span><span class="p">,</span>
-        <span class="n">alpha</span><span class="o">=</span><span class="n">alpha</span><span class="p">,</span>
-        <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">,</span>
-        <span class="n">build_sp</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-        <span class="o">**</span><span class="n">kwargs</span>
-    <span class="p">)</span>
-    <span class="k">return</span> <span class="n">W</span></div>
-
-
-<div class="viewcode-block" id="mat2L"><a class="viewcode-back" href="../../../generated/libpysal.weights.mat2L.html#libpysal.weights.mat2L">[docs]</a><span class="k">def</span> <span class="nf">mat2L</span><span class="p">(</span><span class="n">edge_matrix</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Convert a matrix denoting network connectivity (edges or flows) to a list</span>
-<span class="sd">    denoting edges</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    edge_matrix   : array</span>
-<span class="sd">                    where rows denote network edge origins, columns denote</span>
-<span class="sd">                    network edge destinations, and non-zero entries denote the</span>
-<span class="sd">                    existence of an edge between a given origin and destination</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">     edge_list    : list</span>
-<span class="sd">                    of tuples where each tuple is of the form (o,d) where o is an</span>
-<span class="sd">                    origin id and d is a destination id</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">edge_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
-            <span class="s2">&quot;Matrix of network edges should be two dimensions&quot;</span>
-            <span class="s2">&quot;with edge origins on one axis and edge destinations on the&quot;</span>
-            <span class="s2">&quot;second axis with non-zero matrix entires denoting an edge&quot;</span>
-            <span class="s2">&quot;between and origin and destination&quot;</span>
-        <span class="p">)</span>
-    <span class="n">edge_list</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="n">rows</span><span class="p">,</span> <span class="n">cols</span> <span class="o">=</span> <span class="n">edge_matrix</span><span class="o">.</span><span class="n">shape</span>
-    <span class="k">for</span> <span class="n">row</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">rows</span><span class="p">):</span>
-        <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">cols</span><span class="p">):</span>
-            <span class="k">if</span> <span class="n">edge_matrix</span><span class="p">[</span><span class="n">row</span><span class="p">,</span> <span class="n">col</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="n">edge_list</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">row</span><span class="p">,</span> <span class="n">col</span><span class="p">))</span>
-    <span class="k">return</span> <span class="n">edge_list</span></div>
-</pre></div>
-
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-      
-  <h1>Source code for libpysal.weights.user</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">Convenience functions for the construction of spatial weights based on</span>
-<span class="sd">contiguity and distance criteria.</span>
-<span class="sd">&quot;&quot;&quot;</span>
-
-<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Sergio J. Rey &lt;srey@asu.edu&gt; &quot;</span>
-
-<span class="kn">from</span> <span class="nn">.util</span> <span class="kn">import</span> <span class="n">get_points_array_from_shapefile</span><span class="p">,</span> <span class="n">min_threshold_distance</span>
-<span class="kn">from</span> <span class="nn">..io.fileio</span> <span class="kn">import</span> <span class="n">FileIO</span> <span class="k">as</span> <span class="n">ps_open</span>
-<span class="kn">from</span> <span class="nn">..</span> <span class="kn">import</span> <span class="n">cg</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="s2">&quot;min_threshold_dist_from_shapefile&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;build_lattice_shapefile&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;spw_from_gal&quot;</span><span class="p">,</span>
-<span class="p">]</span>
-
-
-<div class="viewcode-block" id="spw_from_gal"><a class="viewcode-back" href="../../../generated/libpysal.weights.spw_from_gal.html#libpysal.weights.spw_from_gal">[docs]</a><span class="k">def</span> <span class="nf">spw_from_gal</span><span class="p">(</span><span class="n">galfile</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Sparse scipy matrix for w from a gal file.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-
-<span class="sd">    galfile  : string</span>
-<span class="sd">               name of gal file including suffix</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-
-<span class="sd">    spw      : sparse_matrix</span>
-<span class="sd">               scipy sparse matrix in CSR format</span>
-
-<span class="sd">    ids      : array</span>
-<span class="sd">               identifiers for rows/cols of spw</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    &gt;&gt;&gt; import libpysal</span>
-<span class="sd">    &gt;&gt;&gt; spw = libpysal.weights.spw_from_gal(libpysal.examples.get_path(&quot;sids2.gal&quot;))</span>
-<span class="sd">    &gt;&gt;&gt; spw.sparse.nnz</span>
-<span class="sd">    462</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">return</span> <span class="n">ps_open</span><span class="p">(</span><span class="n">galfile</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">read</span><span class="p">(</span><span class="n">sparse</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="min_threshold_dist_from_shapefile"><a class="viewcode-back" href="../../../generated/libpysal.weights.min_threshold_dist_from_shapefile.html#libpysal.weights.min_threshold_dist_from_shapefile">[docs]</a><span class="k">def</span> <span class="nf">min_threshold_dist_from_shapefile</span><span class="p">(</span><span class="n">shapefile</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Get the maximum nearest neighbor distance between observations in the</span>
-<span class="sd">    shapefile.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    shapefile  : string</span>
-<span class="sd">                 shapefile name with shp suffix.</span>
-<span class="sd">    radius     : float</span>
-<span class="sd">                 If supplied arc_distances will be calculated</span>
-<span class="sd">                 based on the given radius. p will be ignored.</span>
-<span class="sd">    p          : float</span>
-<span class="sd">                 Minkowski p-norm distance metric parameter:</span>
-<span class="sd">                 1&lt;=p&lt;=infinity</span>
-<span class="sd">                 2: Euclidean distance</span>
-<span class="sd">                 1: Manhattan distance</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    d          : float</span>
-<span class="sd">                 Maximum nearest neighbor distance between the n</span>
-<span class="sd">                 observations.</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    &gt;&gt;&gt; import libpysal</span>
-<span class="sd">    &gt;&gt;&gt; md = libpysal.weights.min_threshold_dist_from_shapefile(libpysal.examples.get_path(&quot;columbus.shp&quot;))</span>
-<span class="sd">    &gt;&gt;&gt; md</span>
-<span class="sd">    0.6188641580768541</span>
-<span class="sd">    &gt;&gt;&gt; libpysal.weights.min_threshold_dist_from_shapefile(libpysal.examples.get_path(&quot;stl_hom.shp&quot;), libpysal.cg.sphere.RADIUS_EARTH_MILES)</span>
-<span class="sd">    31.846942936393717</span>
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-<span class="sd">    Supports polygon or point shapefiles. For polygon shapefiles, distance is</span>
-<span class="sd">    based on polygon centroids. Distances are defined using coordinates in</span>
-<span class="sd">    shapefile which are assumed to be projected and not geographical</span>
-<span class="sd">    coordinates.</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">points</span> <span class="o">=</span> <span class="n">get_points_array_from_shapefile</span><span class="p">(</span><span class="n">shapefile</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">radius</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">kdt</span> <span class="o">=</span> <span class="n">cg</span><span class="o">.</span><span class="n">kdtree</span><span class="o">.</span><span class="n">Arc_KDTree</span><span class="p">(</span><span class="n">points</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="n">radius</span><span class="p">)</span>
-        <span class="n">nn</span> <span class="o">=</span> <span class="n">kdt</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="n">kdt</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
-        <span class="n">nnd</span> <span class="o">=</span> <span class="n">nn</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span>
-        <span class="k">return</span> <span class="n">nnd</span>
-    <span class="k">return</span> <span class="n">min_threshold_distance</span><span class="p">(</span><span class="n">points</span><span class="p">,</span> <span class="n">p</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="build_lattice_shapefile"><a class="viewcode-back" href="../../../generated/libpysal.weights.build_lattice_shapefile.html#libpysal.weights.build_lattice_shapefile">[docs]</a><span class="k">def</span> <span class="nf">build_lattice_shapefile</span><span class="p">(</span><span class="n">nrows</span><span class="p">,</span> <span class="n">ncols</span><span class="p">,</span> <span class="n">outFileName</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Build a lattice shapefile with nrows rows and ncols cols.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-
-<span class="sd">    nrows       : int</span>
-<span class="sd">                  Number of rows</span>
-<span class="sd">    ncols       : int</span>
-<span class="sd">                  Number of cols</span>
-<span class="sd">    outFileName : str</span>
-<span class="sd">                  shapefile name with shp suffix</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    None</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">outFileName</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s2">&quot;.shp&quot;</span><span class="p">):</span>
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;outFileName must end with .shp&quot;</span><span class="p">)</span>
-    <span class="n">o</span> <span class="o">=</span> <span class="n">ps_open</span><span class="p">(</span><span class="n">outFileName</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span>
-    <span class="n">dbf_name</span> <span class="o">=</span> <span class="n">outFileName</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="s2">&quot;.dbf&quot;</span>
-    <span class="n">d</span> <span class="o">=</span> <span class="n">ps_open</span><span class="p">(</span><span class="n">dbf_name</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span>
-    <span class="n">d</span><span class="o">.</span><span class="n">header</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;ID&quot;</span><span class="p">]</span>
-    <span class="n">d</span><span class="o">.</span><span class="n">field_spec</span> <span class="o">=</span> <span class="p">[(</span><span class="s2">&quot;N&quot;</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span>
-    <span class="n">c</span> <span class="o">=</span> <span class="mi">0</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">ncols</span><span class="p">):</span>
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">nrows</span><span class="p">):</span>
-            <span class="n">ll</span> <span class="o">=</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span>
-            <span class="n">ul</span> <span class="o">=</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span> <span class="o">+</span> <span class="mi">1</span>
-            <span class="n">ur</span> <span class="o">=</span> <span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">j</span> <span class="o">+</span> <span class="mi">1</span>
-            <span class="n">lr</span> <span class="o">=</span> <span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">j</span>
-            <span class="n">o</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">cg</span><span class="o">.</span><span class="n">Polygon</span><span class="p">([</span><span class="n">ll</span><span class="p">,</span> <span class="n">ul</span><span class="p">,</span> <span class="n">ur</span><span class="p">,</span> <span class="n">lr</span><span class="p">,</span> <span class="n">ll</span><span class="p">]))</span>
-            <span class="n">d</span><span class="o">.</span><span class="n">write</span><span class="p">([</span><span class="n">c</span><span class="p">])</span>
-            <span class="n">c</span> <span class="o">+=</span> <span class="mi">1</span>
-    <span class="n">d</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
-    <span class="n">o</span><span class="o">.</span><span class="n">close</span><span class="p">()</span></div>
-
-
-<span class="k">def</span> <span class="nf">_test</span><span class="p">():</span>
-    <span class="kn">import</span> <span class="nn">doctest</span>
-
-    <span class="c1"># the following line could be used to define an alternative to the &#39;&lt;BLANKLINE&gt;&#39; flag</span>
-    <span class="c1"># doctest.BLANKLINE_MARKER = &#39;something better than &lt;BLANKLINE&gt;&#39;</span>
-    <span class="n">start_suppress</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">get_printoptions</span><span class="p">()[</span><span class="s2">&quot;suppress&quot;</span><span class="p">]</span>
-    <span class="n">np</span><span class="o">.</span><span class="n">set_printoptions</span><span class="p">(</span><span class="n">suppress</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="n">doctest</span><span class="o">.</span><span class="n">testmod</span><span class="p">()</span>
-    <span class="n">np</span><span class="o">.</span><span class="n">set_printoptions</span><span class="p">(</span><span class="n">suppress</span><span class="o">=</span><span class="n">start_suppress</span><span class="p">)</span>
-
-
-<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
-    <span class="n">_test</span><span class="p">()</span>
-</pre></div>
-
-    </div>
-      
-  </div>
-</div>
-<footer class="footer">
-  <div class="container">
-    <p class="pull-right">
-      <a href="#">Back to top</a>
-      
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-      
-  <h1>Source code for libpysal.weights.util</h1><div class="highlight"><pre>
-<span></span><span class="kn">from</span> <span class="nn">..io.fileio</span> <span class="kn">import</span> <span class="n">FileIO</span> <span class="k">as</span> <span class="n">psopen</span>
-<span class="kn">from</span> <span class="nn">.weights</span> <span class="kn">import</span> <span class="n">W</span><span class="p">,</span> <span class="n">WSP</span>
-<span class="kn">from</span> <span class="nn">.set_operations</span> <span class="kn">import</span> <span class="n">w_subset</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">sparse</span>
-<span class="kn">from</span> <span class="nn">scipy.spatial</span> <span class="kn">import</span> <span class="n">KDTree</span>
-<span class="kn">import</span> <span class="nn">copy</span>
-<span class="kn">import</span> <span class="nn">scipy.spatial</span>
-<span class="kn">import</span> <span class="nn">os</span>
-<span class="kn">import</span> <span class="nn">scipy</span>
-<span class="kn">from</span> <span class="nn">warnings</span> <span class="kn">import</span> <span class="n">warn</span>
-<span class="kn">import</span> <span class="nn">numbers</span>
-<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">defaultdict</span>
-<span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">tee</span>
-<span class="kn">from</span> <span class="nn">..common</span> <span class="kn">import</span> <span class="n">requires</span>
-<span class="kn">from</span> <span class="nn">distutils.version</span> <span class="kn">import</span> <span class="n">LooseVersion</span>
-
-<span class="k">try</span><span class="p">:</span>
-    <span class="kn">import</span> <span class="nn">geopandas</span> <span class="k">as</span> <span class="nn">gpd</span>
-
-    <span class="n">GPD_08</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">gpd</span><span class="o">.</span><span class="n">__version__</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="n">LooseVersion</span><span class="p">(</span><span class="s2">&quot;0.8.0&quot;</span><span class="p">)</span>
-<span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
-    <span class="n">warn</span><span class="p">(</span><span class="s2">&quot;geopandas not available. Some functionality will be disabled.&quot;</span><span class="p">)</span>
-
-<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="s2">&quot;lat2W&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;block_weights&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;comb&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;order&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;higher_order&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;shimbel&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;remap_ids&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;full2W&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;full&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;WSP2W&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;insert_diagonal&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;fill_diagonal&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;get_ids&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;get_points_array_from_shapefile&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;min_threshold_distance&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;lat2SW&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;w_local_cluster&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;higher_order_sp&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;hexLat2W&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;neighbor_equality&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;attach_islands&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;nonplanar_neighbors&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;fuzzy_contiguity&quot;</span><span class="p">,</span>
-<span class="p">]</span>
-
-
-<span class="n">KDTREE_TYPES</span> <span class="o">=</span> <span class="p">[</span><span class="n">scipy</span><span class="o">.</span><span class="n">spatial</span><span class="o">.</span><span class="n">KDTree</span><span class="p">,</span> <span class="n">scipy</span><span class="o">.</span><span class="n">spatial</span><span class="o">.</span><span class="n">cKDTree</span><span class="p">]</span>
-
-
-<div class="viewcode-block" id="hexLat2W"><a class="viewcode-back" href="../../../generated/libpysal.weights.hexLat2W.html#libpysal.weights.hexLat2W">[docs]</a><span class="k">def</span> <span class="nf">hexLat2W</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Create a W object for a hexagonal lattice.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    nrows      : int</span>
-<span class="sd">                 number of rows</span>
-<span class="sd">    ncols      : int</span>
-<span class="sd">                 number of columns</span>
-<span class="sd">    **kwargs   : keyword arguments</span>
-<span class="sd">                 optional arguments for :class:`pysal.weights.W`</span>
-
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    w : W</span>
-<span class="sd">        instance of spatial weights class W</span>
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-<span class="sd">    Observations are row ordered: first k observations are in row 0, next k in row 1, and so on.</span>
-
-<span class="sd">    Construction is based on shifting every other column of a regular lattice</span>
-<span class="sd">    down 1/2 of a cell.</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    &gt;&gt;&gt; from libpysal.weights import lat2W, hexLat2W</span>
-<span class="sd">    &gt;&gt;&gt; w = lat2W()</span>
-<span class="sd">    &gt;&gt;&gt; w.neighbors[1]</span>
-<span class="sd">    [0, 6, 2]</span>
-<span class="sd">    &gt;&gt;&gt; w.neighbors[21]</span>
-<span class="sd">    [16, 20, 22]</span>
-<span class="sd">    &gt;&gt;&gt; wh = hexLat2W()</span>
-<span class="sd">    &gt;&gt;&gt; wh.neighbors[1]</span>
-<span class="sd">    [0, 6, 2, 5, 7]</span>
-<span class="sd">    &gt;&gt;&gt; wh.neighbors[21]</span>
-<span class="sd">    [16, 20, 22]</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">if</span> <span class="n">nrows</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">or</span> <span class="n">ncols</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Hexagon lattice requires at least 2 rows and columns&quot;</span><span class="p">)</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Returning a linear contiguity structure&quot;</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">lat2W</span><span class="p">(</span><span class="n">nrows</span><span class="p">,</span> <span class="n">ncols</span><span class="p">)</span>
-
-    <span class="n">n</span> <span class="o">=</span> <span class="n">nrows</span> <span class="o">*</span> <span class="n">ncols</span>
-    <span class="n">rid</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span> <span class="o">//</span> <span class="n">ncols</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">)]</span>
-    <span class="n">cid</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span> <span class="o">%</span> <span class="n">ncols</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">)]</span>
-    <span class="n">r1</span> <span class="o">=</span> <span class="n">nrows</span> <span class="o">-</span> <span class="mi">1</span>
-    <span class="n">c1</span> <span class="o">=</span> <span class="n">ncols</span> <span class="o">-</span> <span class="mi">1</span>
-
-    <span class="n">w</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">(</span><span class="n">nrows</span><span class="p">,</span> <span class="n">ncols</span><span class="p">)</span><span class="o">.</span><span class="n">neighbors</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
-        <span class="n">odd</span> <span class="o">=</span> <span class="n">cid</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">%</span> <span class="mi">2</span>
-        <span class="k">if</span> <span class="n">odd</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">rid</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">r1</span><span class="p">:</span>  <span class="c1"># odd col index above last row</span>
-                <span class="c1"># new sw neighbor</span>
-                <span class="k">if</span> <span class="n">cid</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                    <span class="n">j</span> <span class="o">=</span> <span class="n">i</span> <span class="o">+</span> <span class="n">ncols</span> <span class="o">-</span> <span class="mi">1</span>
-                    <span class="n">w</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="p">[])</span> <span class="o">+</span> <span class="p">[</span><span class="n">j</span><span class="p">]</span>
-                <span class="c1"># new se neighbor</span>
-                <span class="k">if</span> <span class="n">cid</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">c1</span><span class="p">:</span>
-                    <span class="n">j</span> <span class="o">=</span> <span class="n">i</span> <span class="o">+</span> <span class="n">ncols</span> <span class="o">+</span> <span class="mi">1</span>
-                    <span class="n">w</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="p">[])</span> <span class="o">+</span> <span class="p">[</span><span class="n">j</span><span class="p">]</span>
-
-        <span class="k">else</span><span class="p">:</span>  <span class="c1"># even col</span>
-            <span class="c1"># nw</span>
-            <span class="n">jnw</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span> <span class="o">-</span> <span class="n">ncols</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span>
-            <span class="c1"># ne</span>
-            <span class="n">jne</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span> <span class="o">-</span> <span class="n">ncols</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span>
-            <span class="k">if</span> <span class="n">rid</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="n">w</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-                <span class="k">if</span> <span class="n">cid</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                    <span class="n">w</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="p">[])</span> <span class="o">+</span> <span class="n">jne</span>
-                <span class="k">elif</span> <span class="n">cid</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">==</span> <span class="n">c1</span><span class="p">:</span>
-                    <span class="n">w</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="p">[])</span> <span class="o">+</span> <span class="n">jnw</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="n">w</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="p">[])</span> <span class="o">+</span> <span class="n">jne</span>
-                    <span class="n">w</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="p">[])</span> <span class="o">+</span> <span class="n">jnw</span>
-
-    <span class="k">return</span> <span class="n">W</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="lat2W"><a class="viewcode-back" href="../../../generated/libpysal.weights.lat2W.html#libpysal.weights.lat2W">[docs]</a><span class="k">def</span> <span class="nf">lat2W</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">rook</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">id_type</span><span class="o">=</span><span class="s2">&quot;int&quot;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Create a W object for a regular lattice.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-
-<span class="sd">    nrows      : int</span>
-<span class="sd">                 number of rows</span>
-<span class="sd">    ncols      : int</span>
-<span class="sd">                 number of columns</span>
-<span class="sd">    rook       : boolean</span>
-<span class="sd">                 type of contiguity. Default is rook. For queen, rook =False</span>
-<span class="sd">    id_type    : string</span>
-<span class="sd">                 string defining the type of IDs to use in the final W object;</span>
-<span class="sd">                 options are &#39;int&#39; (0, 1, 2 ...; default), &#39;float&#39; (0.0,</span>
-<span class="sd">                 1.0, 2.0, ...) and &#39;string&#39; (&#39;id0&#39;, &#39;id1&#39;, &#39;id2&#39;, ...)</span>
-<span class="sd">    **kwargs   : keyword arguments</span>
-<span class="sd">                 optional arguments for :class:`pysal.weights.W`</span>
-
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-
-<span class="sd">    w : W</span>
-<span class="sd">        instance of spatial weights class W</span>
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-
-<span class="sd">    Observations are row ordered: first k observations are in row 0, next k in row 1, and so on.</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-
-<span class="sd">    &gt;&gt;&gt; from libpysal.weights import lat2W</span>
-<span class="sd">    &gt;&gt;&gt; w9 = lat2W(3,3)</span>
-<span class="sd">    &gt;&gt;&gt; &quot;%.3f&quot;%w9.pct_nonzero</span>
-<span class="sd">    &#39;29.630&#39;</span>
-<span class="sd">    &gt;&gt;&gt; w9[0] == {1: 1.0, 3: 1.0}</span>
-<span class="sd">    True</span>
-<span class="sd">    &gt;&gt;&gt; w9[3] == {0: 1.0, 4: 1.0, 6: 1.0}</span>
-<span class="sd">    True</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">n</span> <span class="o">=</span> <span class="n">nrows</span> <span class="o">*</span> <span class="n">ncols</span>
-    <span class="n">r1</span> <span class="o">=</span> <span class="n">nrows</span> <span class="o">-</span> <span class="mi">1</span>
-    <span class="n">c1</span> <span class="o">=</span> <span class="n">ncols</span> <span class="o">-</span> <span class="mi">1</span>
-    <span class="n">rid</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span> <span class="o">//</span> <span class="n">ncols</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">)]</span>  <span class="c1"># must be floor!</span>
-    <span class="n">cid</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span> <span class="o">%</span> <span class="n">ncols</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">)]</span>
-    <span class="n">w</span> <span class="o">=</span> <span class="p">{}</span>
-    <span class="n">r</span> <span class="o">=</span> <span class="n">below</span> <span class="o">=</span> <span class="mi">0</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span>
-        <span class="k">if</span> <span class="n">rid</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">r1</span><span class="p">:</span>
-            <span class="n">below</span> <span class="o">=</span> <span class="n">rid</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span>
-            <span class="n">r</span> <span class="o">=</span> <span class="n">below</span> <span class="o">*</span> <span class="n">ncols</span> <span class="o">+</span> <span class="n">cid</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-            <span class="n">w</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="p">[])</span> <span class="o">+</span> <span class="p">[</span><span class="n">r</span><span class="p">]</span>
-            <span class="n">w</span><span class="p">[</span><span class="n">r</span><span class="p">]</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">r</span><span class="p">,</span> <span class="p">[])</span> <span class="o">+</span> <span class="p">[</span><span class="n">i</span><span class="p">]</span>
-        <span class="k">if</span> <span class="n">cid</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">c1</span><span class="p">:</span>
-            <span class="n">right</span> <span class="o">=</span> <span class="n">cid</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span>
-            <span class="n">c</span> <span class="o">=</span> <span class="n">rid</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="n">ncols</span> <span class="o">+</span> <span class="n">right</span>
-            <span class="n">w</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="p">[])</span> <span class="o">+</span> <span class="p">[</span><span class="n">c</span><span class="p">]</span>
-            <span class="n">w</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">c</span><span class="p">,</span> <span class="p">[])</span> <span class="o">+</span> <span class="p">[</span><span class="n">i</span><span class="p">]</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">rook</span><span class="p">:</span>
-            <span class="c1"># southeast bishop</span>
-            <span class="k">if</span> <span class="n">cid</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">c1</span> <span class="ow">and</span> <span class="n">rid</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">r1</span><span class="p">:</span>
-                <span class="n">r</span> <span class="o">=</span> <span class="p">(</span><span class="n">rid</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">ncols</span> <span class="o">+</span> <span class="mi">1</span> <span class="o">+</span> <span class="n">cid</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-                <span class="n">w</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="p">[])</span> <span class="o">+</span> <span class="p">[</span><span class="n">r</span><span class="p">]</span>
-                <span class="n">w</span><span class="p">[</span><span class="n">r</span><span class="p">]</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">r</span><span class="p">,</span> <span class="p">[])</span> <span class="o">+</span> <span class="p">[</span><span class="n">i</span><span class="p">]</span>
-            <span class="c1"># southwest bishop</span>
-            <span class="k">if</span> <span class="n">cid</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">rid</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">r1</span><span class="p">:</span>
-                <span class="n">r</span> <span class="o">=</span> <span class="p">(</span><span class="n">rid</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">ncols</span> <span class="o">-</span> <span class="mi">1</span> <span class="o">+</span> <span class="n">cid</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-                <span class="n">w</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="p">[])</span> <span class="o">+</span> <span class="p">[</span><span class="n">r</span><span class="p">]</span>
-                <span class="n">w</span><span class="p">[</span><span class="n">r</span><span class="p">]</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">r</span><span class="p">,</span> <span class="p">[])</span> <span class="o">+</span> <span class="p">[</span><span class="n">i</span><span class="p">]</span>
-
-    <span class="n">neighbors</span> <span class="o">=</span> <span class="p">{}</span>
-    <span class="n">weights</span> <span class="o">=</span> <span class="p">{}</span>
-    <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">w</span><span class="p">:</span>
-        <span class="n">weights</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">w</span><span class="p">[</span><span class="n">key</span><span class="p">])</span>
-    <span class="n">ids</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">))</span>
-    <span class="k">if</span> <span class="n">id_type</span> <span class="o">==</span> <span class="s2">&quot;string&quot;</span><span class="p">:</span>
-        <span class="n">ids</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;id&quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">ids</span><span class="p">]</span>
-    <span class="k">elif</span> <span class="n">id_type</span> <span class="o">==</span> <span class="s2">&quot;float&quot;</span><span class="p">:</span>
-        <span class="n">ids</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span> <span class="o">*</span> <span class="mf">1.0</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">ids</span><span class="p">]</span>
-    <span class="k">if</span> <span class="n">id_type</span> <span class="o">==</span> <span class="s2">&quot;string&quot;</span> <span class="ow">or</span> <span class="n">id_type</span> <span class="o">==</span> <span class="s2">&quot;float&quot;</span><span class="p">:</span>
-        <span class="n">id_dict</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">)),</span> <span class="n">ids</span><span class="p">)))</span>
-        <span class="n">alt_w</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="n">alt_weights</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">w</span><span class="p">:</span>
-            <span class="n">values</span> <span class="o">=</span> <span class="p">[</span><span class="n">id_dict</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">w</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span>
-            <span class="n">key</span> <span class="o">=</span> <span class="n">id_dict</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-            <span class="n">alt_w</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">values</span>
-            <span class="n">alt_weights</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">weights</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-        <span class="n">w</span> <span class="o">=</span> <span class="n">alt_w</span>
-        <span class="n">weights</span> <span class="o">=</span> <span class="n">alt_weights</span>
-    <span class="k">return</span> <span class="n">W</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">,</span> <span class="n">id_order</span><span class="o">=</span><span class="n">ids</span><span class="p">[:],</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="block_weights"><a class="viewcode-back" href="../../../generated/libpysal.weights.block_weights.html#libpysal.weights.block_weights">[docs]</a><span class="k">def</span> <span class="nf">block_weights</span><span class="p">(</span><span class="n">regimes</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sparse</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Construct spatial weights for regime neighbors.</span>
-
-<span class="sd">    Block contiguity structures are relevant when defining neighbor relations</span>
-<span class="sd">    based on membership in a regime. For example, all counties belonging to</span>
-<span class="sd">    the same state could be defined as neighbors, in an analysis of all</span>
-<span class="sd">    counties in the US.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    regimes     : list, array</span>
-<span class="sd">                  ids of which regime an observation belongs to</span>
-<span class="sd">    ids         : list, array</span>
-<span class="sd">                  Ordered sequence of IDs for the observations</span>
-<span class="sd">    sparse      : boolean</span>
-<span class="sd">                  If True return WSP instance</span>
-<span class="sd">                  If False return W instance</span>
-<span class="sd">    **kwargs    : keyword arguments</span>
-<span class="sd">                  optional arguments for :class:`pysal.weights.W`</span>
-
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-
-<span class="sd">    W : spatial weights instance</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    &gt;&gt;&gt; from libpysal.weights import block_weights</span>
-<span class="sd">    &gt;&gt;&gt; import numpy as np</span>
-<span class="sd">    &gt;&gt;&gt; regimes = np.ones(25)</span>
-<span class="sd">    &gt;&gt;&gt; regimes[range(10,20)] = 2</span>
-<span class="sd">    &gt;&gt;&gt; regimes[range(21,25)] = 3</span>
-<span class="sd">    &gt;&gt;&gt; regimes</span>
-<span class="sd">    array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 2., 2., 2., 2., 2., 2.,</span>
-<span class="sd">           2., 2., 2., 1., 3., 3., 3., 3.])</span>
-<span class="sd">    &gt;&gt;&gt; w = block_weights(regimes)</span>
-<span class="sd">    &gt;&gt;&gt; w.weights[0]</span>
-<span class="sd">    [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]</span>
-<span class="sd">    &gt;&gt;&gt; w.neighbors[0]</span>
-<span class="sd">    [1, 2, 3, 4, 5, 6, 7, 8, 9, 20]</span>
-<span class="sd">    &gt;&gt;&gt; regimes = [&#39;n&#39;,&#39;n&#39;,&#39;s&#39;,&#39;s&#39;,&#39;e&#39;,&#39;e&#39;,&#39;w&#39;,&#39;w&#39;,&#39;e&#39;]</span>
-<span class="sd">    &gt;&gt;&gt; n = len(regimes)</span>
-<span class="sd">    &gt;&gt;&gt; w = block_weights(regimes)</span>
-<span class="sd">    &gt;&gt;&gt; w.neighbors == {0: [1], 1: [0], 2: [3], 3: [2], 4: [5, 8], 5: [4, 8], 6: [7], 7: [6], 8: [4, 5]}</span>
-<span class="sd">    True</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">rids</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">regimes</span><span class="p">)</span>
-    <span class="n">neighbors</span> <span class="o">=</span> <span class="p">{}</span>
-    <span class="n">NPNZ</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nonzero</span>
-    <span class="n">regimes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">regimes</span><span class="p">)</span>
-    <span class="k">for</span> <span class="n">rid</span> <span class="ow">in</span> <span class="n">rids</span><span class="p">:</span>
-        <span class="n">members</span> <span class="o">=</span> <span class="n">NPNZ</span><span class="p">(</span><span class="n">regimes</span> <span class="o">==</span> <span class="n">rid</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="k">for</span> <span class="n">member</span> <span class="ow">in</span> <span class="n">members</span><span class="p">:</span>
-            <span class="n">neighbors</span><span class="p">[</span><span class="n">member</span><span class="p">]</span> <span class="o">=</span> <span class="n">members</span><span class="p">[</span><span class="n">NPNZ</span><span class="p">(</span><span class="n">members</span> <span class="o">!=</span> <span class="n">member</span><span class="p">)[</span><span class="mi">0</span><span class="p">]]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
-    <span class="n">w</span> <span class="o">=</span> <span class="n">W</span><span class="p">(</span><span class="n">neighbors</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">ids</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">w</span><span class="o">.</span><span class="n">remap_ids</span><span class="p">(</span><span class="n">ids</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">sparse</span><span class="p">:</span>
-        <span class="n">w</span> <span class="o">=</span> <span class="n">WSP</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">sparse</span><span class="p">,</span> <span class="n">id_order</span><span class="o">=</span><span class="n">ids</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">w</span></div>
-
-
-<div class="viewcode-block" id="comb"><a class="viewcode-back" href="../../../generated/libpysal.weights.comb.html#libpysal.weights.comb">[docs]</a><span class="k">def</span> <span class="nf">comb</span><span class="p">(</span><span class="n">items</span><span class="p">,</span> <span class="n">n</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Combinations of size n taken from items</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-
-<span class="sd">    items : list</span>
-<span class="sd">            items to be drawn from</span>
-<span class="sd">    n     : integer</span>
-<span class="sd">            size of combinations to take from items</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-
-<span class="sd">    implicit : generator</span>
-<span class="sd">               combinations of size n taken from items</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    &gt;&gt;&gt; x = range(4)</span>
-<span class="sd">    &gt;&gt;&gt; for c in comb(x, 2):</span>
-<span class="sd">    ...     print(c)</span>
-<span class="sd">    ...</span>
-<span class="sd">    [0, 1]</span>
-<span class="sd">    [0, 2]</span>
-<span class="sd">    [0, 3]</span>
-<span class="sd">    [1, 2]</span>
-<span class="sd">    [1, 3]</span>
-<span class="sd">    [2, 3]</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">items</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">items</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">n</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">items</span><span class="p">)</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">items</span><span class="p">))):</span>
-        <span class="n">v</span> <span class="o">=</span> <span class="n">items</span><span class="p">[</span><span class="n">i</span> <span class="p">:</span> <span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span>
-        <span class="k">if</span> <span class="n">n</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="k">yield</span> <span class="n">v</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">rest</span> <span class="o">=</span> <span class="n">items</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span> <span class="p">:]</span>
-            <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">comb</span><span class="p">(</span><span class="n">rest</span><span class="p">,</span> <span class="n">n</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span>
-                <span class="k">yield</span> <span class="n">v</span> <span class="o">+</span> <span class="n">c</span></div>
-
-
-<div class="viewcode-block" id="order"><a class="viewcode-back" href="../../../generated/libpysal.weights.order.html#libpysal.weights.order">[docs]</a><span class="k">def</span> <span class="nf">order</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">kmax</span><span class="o">=</span><span class="mi">3</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Determine the non-redundant order of contiguity up to a specific</span>
-<span class="sd">    order.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-
-<span class="sd">    w       : W</span>
-<span class="sd">              spatial weights object</span>
-
-<span class="sd">    kmax    : int</span>
-<span class="sd">              maximum order of contiguity</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-
-<span class="sd">    info : dictionary</span>
-<span class="sd">           observation id is the key, value is a list of contiguity</span>
-<span class="sd">           orders with a negative 1 in the ith position</span>
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-<span class="sd">    Implements the algorithm in :cite:`Anselin1996b`.</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    &gt;&gt;&gt; from libpysal.weights import order, Rook</span>
-<span class="sd">    &gt;&gt;&gt; import libpysal</span>
-<span class="sd">    &gt;&gt;&gt; w = Rook.from_shapefile(libpysal.examples.get_path(&#39;10740.shp&#39;))</span>
-
-<span class="sd">    WARNING: there is one disconnected observation (no neighbors)</span>
-<span class="sd">    Island id:  [163]</span>
-<span class="sd">    &gt;&gt;&gt; w3 = order(w, kmax = 3)</span>
-<span class="sd">    &gt;&gt;&gt; w3[1][0:5]</span>
-<span class="sd">    [1, -1, 1, 2, 1]</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">ids</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">id_order</span>
-    <span class="n">info</span> <span class="o">=</span> <span class="p">{}</span>
-    <span class="k">for</span> <span class="n">id_</span> <span class="ow">in</span> <span class="n">ids</span><span class="p">:</span>
-        <span class="n">s</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">w</span><span class="o">.</span><span class="n">n</span>
-        <span class="n">s</span><span class="p">[</span><span class="n">ids</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">id_</span><span class="p">)]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">w</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">id_</span><span class="p">]:</span>
-            <span class="n">s</span><span class="p">[</span><span class="n">ids</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">j</span><span class="p">)]</span> <span class="o">=</span> <span class="mi">1</span>
-        <span class="n">k</span> <span class="o">=</span> <span class="mi">1</span>
-        <span class="k">while</span> <span class="n">k</span> <span class="o">&lt;</span> <span class="n">kmax</span><span class="p">:</span>
-            <span class="n">knext</span> <span class="o">=</span> <span class="n">k</span> <span class="o">+</span> <span class="mi">1</span>
-            <span class="k">if</span> <span class="n">s</span><span class="o">.</span><span class="n">count</span><span class="p">(</span><span class="n">k</span><span class="p">):</span>
-                <span class="c1"># get neighbors of order k</span>
-                <span class="n">js</span> <span class="o">=</span> <span class="p">[</span><span class="n">ids</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">val</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">s</span><span class="p">)</span> <span class="k">if</span> <span class="n">val</span> <span class="o">==</span> <span class="n">k</span><span class="p">]</span>
-                <span class="c1"># get first order neighbors for order k neighbors</span>
-                <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">js</span><span class="p">:</span>
-                    <span class="n">next_neighbors</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">j</span><span class="p">]</span>
-                    <span class="k">for</span> <span class="n">neighbor</span> <span class="ow">in</span> <span class="n">next_neighbors</span><span class="p">:</span>
-                        <span class="n">nid</span> <span class="o">=</span> <span class="n">ids</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">neighbor</span><span class="p">)</span>
-                        <span class="k">if</span> <span class="n">s</span><span class="p">[</span><span class="n">nid</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                            <span class="n">s</span><span class="p">[</span><span class="n">nid</span><span class="p">]</span> <span class="o">=</span> <span class="n">knext</span>
-            <span class="n">k</span> <span class="o">=</span> <span class="n">knext</span>
-        <span class="n">info</span><span class="p">[</span><span class="n">id_</span><span class="p">]</span> <span class="o">=</span> <span class="n">s</span>
-    <span class="k">return</span> <span class="n">info</span></div>
-
-
-<div class="viewcode-block" id="higher_order"><a class="viewcode-back" href="../../../generated/libpysal.weights.higher_order.html#libpysal.weights.higher_order">[docs]</a><span class="k">def</span> <span class="nf">higher_order</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Contiguity weights object of order k.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-
-<span class="sd">    w        : W</span>
-<span class="sd">               spatial weights object</span>
-<span class="sd">    k        : int</span>
-<span class="sd">               order of contiguity</span>
-<span class="sd">    **kwargs : keyword arguments</span>
-<span class="sd">               optional arguments for :class:`pysal.weights.W`</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-
-<span class="sd">    implicit : W</span>
-<span class="sd">               spatial weights object</span>
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-<span class="sd">    Proper higher order neighbors are returned such that i and j are k-order</span>
-<span class="sd">    neighbors iff the shortest path from i-j is of length k.</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    &gt;&gt;&gt; from libpysal.weights import lat2W, higher_order</span>
-<span class="sd">    &gt;&gt;&gt; w10 = lat2W(10, 10)</span>
-<span class="sd">    &gt;&gt;&gt; w10_2 = higher_order(w10, 2)</span>
-<span class="sd">    &gt;&gt;&gt; w10_2[0] ==  {2: 1.0, 11: 1.0, 20: 1.0}</span>
-<span class="sd">    True</span>
-<span class="sd">    &gt;&gt;&gt; w5 = lat2W()</span>
-<span class="sd">    &gt;&gt;&gt; w5[0] ==  {1: 1.0, 5: 1.0}</span>
-<span class="sd">    True</span>
-<span class="sd">    &gt;&gt;&gt; w5[1] == {0: 1.0, 2: 1.0, 6: 1.0}</span>
-<span class="sd">    True</span>
-<span class="sd">    &gt;&gt;&gt; w5_2 = higher_order(w5,2)</span>
-<span class="sd">    &gt;&gt;&gt; w5_2[0] == {10: 1.0, 2: 1.0, 6: 1.0}</span>
-<span class="sd">    True</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">return</span> <span class="n">higher_order_sp</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="higher_order_sp"><a class="viewcode-back" href="../../../generated/libpysal.weights.higher_order_sp.html#libpysal.weights.higher_order_sp">[docs]</a><span class="k">def</span> <span class="nf">higher_order_sp</span><span class="p">(</span>
-    <span class="n">w</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">shortest_path</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">diagonal</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">lower_order</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span>
-<span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Contiguity weights for either a sparse W or W for order k.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    w             : W</span>
-<span class="sd">                            sparse_matrix, spatial weights object or</span>
-<span class="sd">                            scipy.sparse.csr.csr_instance</span>
-<span class="sd">    k             : int</span>
-<span class="sd">                    Order of contiguity</span>
-<span class="sd">    shortest_path : boolean</span>
-<span class="sd">                    True: i,j and k-order neighbors if the</span>
-<span class="sd">                    shortest path for i,j is k.</span>
-<span class="sd">                    False: i,j are k-order neighbors if there</span>
-<span class="sd">                    is a path from i,j of length k.</span>
-<span class="sd">    diagonal      : boolean</span>
-<span class="sd">                    True:  keep k-order (i,j) joins when i==j</span>
-<span class="sd">                    False: remove k-order (i,j) joins when i==j</span>
-<span class="sd">    lower_order   : boolean</span>
-<span class="sd">                    True: include lower order contiguities</span>
-<span class="sd">                    False: return only weights of order k</span>
-<span class="sd">    **kwargs      : keyword arguments</span>
-<span class="sd">                    optional arguments for :class:`pysal.weights.W`</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    wk : W</span>
-<span class="sd">             WSP, type matches type of w argument</span>
-
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-
-<span class="sd">    &gt;&gt;&gt; from libpysal.weights import lat2W, higher_order_sp</span>
-<span class="sd">    &gt;&gt;&gt; w25 = lat2W(5,5)</span>
-<span class="sd">    &gt;&gt;&gt; w25.n</span>
-<span class="sd">    25</span>
-<span class="sd">    &gt;&gt;&gt; w25[0] == {1: 1.0, 5: 1.0}</span>
-<span class="sd">    True</span>
-<span class="sd">    &gt;&gt;&gt; w25_2 = higher_order_sp(w25, 2)</span>
-<span class="sd">    &gt;&gt;&gt; w25_2[0] == {10: 1.0, 2: 1.0, 6: 1.0}</span>
-<span class="sd">    True</span>
-<span class="sd">    &gt;&gt;&gt; w25_2 = higher_order_sp(w25, 2, diagonal=True)</span>
-<span class="sd">    &gt;&gt;&gt; w25_2[0] ==  {0: 1.0, 10: 1.0, 2: 1.0, 6: 1.0}</span>
-<span class="sd">    True</span>
-<span class="sd">    &gt;&gt;&gt; w25_3 = higher_order_sp(w25, 3)</span>
-<span class="sd">    &gt;&gt;&gt; w25_3[0] == {15: 1.0, 3: 1.0, 11: 1.0, 7: 1.0}</span>
-<span class="sd">    True</span>
-<span class="sd">    &gt;&gt;&gt; w25_3 = higher_order_sp(w25, 3, shortest_path=False)</span>
-<span class="sd">    &gt;&gt;&gt; w25_3[0] == {1: 1.0, 3: 1.0, 5: 1.0, 7: 1.0, 11: 1.0, 15: 1.0}</span>
-<span class="sd">    True</span>
-<span class="sd">    &gt;&gt;&gt; w25_3 = higher_order_sp(w25, 3, lower_order=True)</span>
-<span class="sd">    &gt;&gt;&gt; w25_3[0] == {5: 1.0, 7: 1.0, 11: 1.0, 2: 1.0, 15: 1.0, 6: 1.0, 10: 1.0, 1: 1.0, 3: 1.0}</span>
-<span class="sd">    True</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">id_order</span> <span class="o">=</span> <span class="kc">None</span>
-    <span class="k">if</span> <span class="nb">issubclass</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">w</span><span class="p">),</span> <span class="n">W</span><span class="p">)</span> <span class="ow">or</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">W</span><span class="p">):</span>
-        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">values</span><span class="p">())))</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">1.0</span><span class="p">]):</span>
-            <span class="n">id_order</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">id_order</span>
-            <span class="n">w</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">sparse</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Weights are not binary (0,1)&quot;</span><span class="p">)</span>
-    <span class="k">elif</span> <span class="n">scipy</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">isspmatrix_csr</span><span class="p">(</span><span class="n">w</span><span class="p">):</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">data</span><span class="p">)</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">1.0</span><span class="p">]):</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-                <span class="s2">&quot;Sparse weights matrix is not binary (0,1) weights matrix.&quot;</span>
-            <span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
-            <span class="s2">&quot;Weights provided are neither a binary W object nor &quot;</span>
-            <span class="s2">&quot;a scipy.sparse.csr_matrix&quot;</span>
-        <span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">lower_order</span><span class="p">:</span>
-        <span class="n">wk</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">w</span> <span class="o">**</span> <span class="n">x</span><span class="p">,</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">k</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)))</span>
-        <span class="n">shortest_path</span> <span class="o">=</span> <span class="kc">False</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">wk</span> <span class="o">=</span> <span class="n">w</span> <span class="o">**</span> <span class="n">k</span>
-
-    <span class="n">rk</span><span class="p">,</span> <span class="n">ck</span> <span class="o">=</span> <span class="n">wk</span><span class="o">.</span><span class="n">nonzero</span><span class="p">()</span>
-    <span class="n">sk</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">rk</span><span class="p">,</span> <span class="n">ck</span><span class="p">))</span>
-
-    <span class="k">if</span> <span class="n">shortest_path</span><span class="p">:</span>
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span>
-            <span class="n">wj</span> <span class="o">=</span> <span class="n">w</span> <span class="o">**</span> <span class="n">j</span>
-            <span class="n">rj</span><span class="p">,</span> <span class="n">cj</span> <span class="o">=</span> <span class="n">wj</span><span class="o">.</span><span class="n">nonzero</span><span class="p">()</span>
-            <span class="n">sj</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">rj</span><span class="p">,</span> <span class="n">cj</span><span class="p">))</span>
-            <span class="n">sk</span><span class="o">.</span><span class="n">difference_update</span><span class="p">(</span><span class="n">sj</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">diagonal</span><span class="p">:</span>
-        <span class="n">sk</span> <span class="o">=</span> <span class="nb">set</span><span class="p">([(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">sk</span> <span class="k">if</span> <span class="n">i</span> <span class="o">!=</span> <span class="n">j</span><span class="p">])</span>
-
-    <span class="k">if</span> <span class="n">id_order</span><span class="p">:</span>
-        <span class="n">d</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">([(</span><span class="n">i</span><span class="p">,</span> <span class="p">[])</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">id_order</span><span class="p">])</span>
-        <span class="k">for</span> <span class="n">pair</span> <span class="ow">in</span> <span class="n">sk</span><span class="p">:</span>
-            <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="o">=</span> <span class="n">pair</span>
-            <span class="n">k</span> <span class="o">=</span> <span class="n">id_order</span><span class="p">[</span><span class="n">k</span><span class="p">]</span>
-            <span class="n">v</span> <span class="o">=</span> <span class="n">id_order</span><span class="p">[</span><span class="n">v</span><span class="p">]</span>
-            <span class="n">d</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">v</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">W</span><span class="p">(</span><span class="n">neighbors</span><span class="o">=</span><span class="n">d</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">d</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="k">for</span> <span class="n">pair</span> <span class="ow">in</span> <span class="n">sk</span><span class="p">:</span>
-            <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="o">=</span> <span class="n">pair</span>
-            <span class="k">if</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">d</span><span class="p">:</span>
-                <span class="n">d</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">v</span><span class="p">)</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">d</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">v</span><span class="p">]</span>
-        <span class="k">return</span> <span class="n">WSP</span><span class="p">(</span><span class="n">W</span><span class="p">(</span><span class="n">neighbors</span><span class="o">=</span><span class="n">d</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span><span class="o">.</span><span class="n">sparse</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="w_local_cluster"><a class="viewcode-back" href="../../../generated/libpysal.weights.w_local_cluster.html#libpysal.weights.w_local_cluster">[docs]</a><span class="k">def</span> <span class="nf">w_local_cluster</span><span class="p">(</span><span class="n">w</span><span class="p">):</span>
-    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Local clustering coefficients for each unit as a node in a graph.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-
-<span class="sd">    w   : W</span>
-<span class="sd">          spatial weights object</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-
-<span class="sd">    c     : array</span>
-<span class="sd">            (w.n,1)</span>
-<span class="sd">            local clustering coefficients</span>
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-
-<span class="sd">    The local clustering coefficient :math:`c_i` quantifies how close the</span>
-<span class="sd">    neighbors of observation :math:`i` are to being a clique:</span>
-
-<span class="sd">    .. math::</span>
-
-<span class="sd">       c_i = | \{w_{j,k}\} |/ (k_i(k_i - 1)): j,k \in N_i</span>
-
-<span class="sd">    where :math:`N_i` is the set of neighbors to :math:`i`, :math:`k_i =</span>
-<span class="sd">    |N_i|` and :math:`\{w_{j,k}\}` is the set of non-zero elements of the</span>
-<span class="sd">    weights between pairs in :math:`N_i` :cite:`Watts1998`.</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    &gt;&gt;&gt; from libpysal.weights import lat2W, w_local_cluster</span>
-<span class="sd">    &gt;&gt;&gt; w = lat2W(3,3, rook=False)</span>
-<span class="sd">    &gt;&gt;&gt; w_local_cluster(w)</span>
-<span class="sd">    array([[1.        ],</span>
-<span class="sd">           [0.6       ],</span>
-<span class="sd">           [1.        ],</span>
-<span class="sd">           [0.6       ],</span>
-<span class="sd">           [0.42857143],</span>
-<span class="sd">           [0.6       ],</span>
-<span class="sd">           [1.        ],</span>
-<span class="sd">           [0.6       ],</span>
-<span class="sd">           [1.        ]])</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">c</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">w</span><span class="o">.</span><span class="n">n</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="nb">float</span><span class="p">)</span>
-    <span class="n">w</span><span class="o">.</span><span class="n">transformation</span> <span class="o">=</span> <span class="s2">&quot;b&quot;</span>
-    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="nb">id</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">id_order</span><span class="p">):</span>
-        <span class="n">ki</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">cardinalities</span><span class="p">[</span><span class="nb">id</span><span class="p">],</span> <span class="mi">1</span><span class="p">)</span>  <span class="c1"># deal with islands</span>
-        <span class="n">Ni</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="nb">id</span><span class="p">]</span>
-        <span class="n">wi</span> <span class="o">=</span> <span class="n">w_subset</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">Ni</span><span class="p">)</span><span class="o">.</span><span class="n">full</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">c</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">wi</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">/</span> <span class="p">(</span><span class="n">ki</span> <span class="o">*</span> <span class="p">(</span><span class="n">ki</span> <span class="o">-</span> <span class="mi">1</span><span class="p">))</span>
-    <span class="k">return</span> <span class="n">c</span></div>
-
-
-<div class="viewcode-block" id="shimbel"><a class="viewcode-back" href="../../../generated/libpysal.weights.shimbel.html#libpysal.weights.shimbel">[docs]</a><span class="k">def</span> <span class="nf">shimbel</span><span class="p">(</span><span class="n">w</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Find the Shimbel matrix for first order contiguity matrix.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    w     : W</span>
-<span class="sd">            spatial weights object</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-
-<span class="sd">    info  : list</span>
-<span class="sd">            list of lists; one list for each observation which stores</span>
-<span class="sd">            the shortest order between it and each of the the other observations.</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    &gt;&gt;&gt; from libpysal.weights import lat2W, shimbel</span>
-<span class="sd">    &gt;&gt;&gt; w5 = lat2W()</span>
-<span class="sd">    &gt;&gt;&gt; w5_shimbel = shimbel(w5)</span>
-<span class="sd">    &gt;&gt;&gt; w5_shimbel[0][24]</span>
-<span class="sd">    8</span>
-<span class="sd">    &gt;&gt;&gt; w5_shimbel[0][0:4]</span>
-<span class="sd">    [-1, 1, 2, 3]</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">info</span> <span class="o">=</span> <span class="p">{}</span>
-    <span class="n">ids</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">id_order</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">ids</span><span class="p">:</span>
-        <span class="n">s</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">w</span><span class="o">.</span><span class="n">n</span>
-        <span class="n">s</span><span class="p">[</span><span class="n">ids</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">i</span><span class="p">)]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
-        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">w</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]:</span>
-            <span class="n">s</span><span class="p">[</span><span class="n">ids</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">j</span><span class="p">)]</span> <span class="o">=</span> <span class="mi">1</span>
-        <span class="n">k</span> <span class="o">=</span> <span class="mi">1</span>
-        <span class="n">flag</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">count</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-        <span class="k">while</span> <span class="n">flag</span><span class="p">:</span>
-            <span class="n">p</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
-            <span class="n">knext</span> <span class="o">=</span> <span class="n">k</span> <span class="o">+</span> <span class="mi">1</span>
-            <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">count</span><span class="p">(</span><span class="n">k</span><span class="p">)):</span>
-                <span class="n">neighbor</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">p</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
-                <span class="n">p</span> <span class="o">=</span> <span class="n">neighbor</span>
-                <span class="n">next_neighbors</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">ids</span><span class="p">[</span><span class="n">p</span><span class="p">]]</span>
-                <span class="k">for</span> <span class="n">neighbor</span> <span class="ow">in</span> <span class="n">next_neighbors</span><span class="p">:</span>
-                    <span class="n">nid</span> <span class="o">=</span> <span class="n">ids</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">neighbor</span><span class="p">)</span>
-                    <span class="k">if</span> <span class="n">s</span><span class="p">[</span><span class="n">nid</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                        <span class="n">s</span><span class="p">[</span><span class="n">nid</span><span class="p">]</span> <span class="o">=</span> <span class="n">knext</span>
-            <span class="n">k</span> <span class="o">=</span> <span class="n">knext</span>
-            <span class="n">flag</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">count</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-        <span class="n">info</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">s</span>
-    <span class="k">return</span> <span class="n">info</span></div>
-
-
-<div class="viewcode-block" id="full"><a class="viewcode-back" href="../../../generated/libpysal.weights.full.html#libpysal.weights.full">[docs]</a><span class="k">def</span> <span class="nf">full</span><span class="p">(</span><span class="n">w</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Generate a full numpy array.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    w        : W</span>
-<span class="sd">               spatial weights object</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    (fullw, keys) : tuple</span>
-<span class="sd">                    first element being the full numpy array and second element</span>
-<span class="sd">                    keys being the ids associated with each row in the array.</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    &gt;&gt;&gt; from libpysal.weights import W, full</span>
-<span class="sd">    &gt;&gt;&gt; neighbors = {&#39;first&#39;:[&#39;second&#39;],&#39;second&#39;:[&#39;first&#39;,&#39;third&#39;],&#39;third&#39;:[&#39;second&#39;]}</span>
-<span class="sd">    &gt;&gt;&gt; weights = {&#39;first&#39;:[1],&#39;second&#39;:[1,1],&#39;third&#39;:[1]}</span>
-<span class="sd">    &gt;&gt;&gt; w = W(neighbors, weights)</span>
-<span class="sd">    &gt;&gt;&gt; wf, ids = full(w)</span>
-<span class="sd">    &gt;&gt;&gt; wf</span>
-<span class="sd">    array([[0., 1., 0.],</span>
-<span class="sd">           [1., 0., 1.],</span>
-<span class="sd">           [0., 1., 0.]])</span>
-<span class="sd">    &gt;&gt;&gt; ids</span>
-<span class="sd">    [&#39;first&#39;, &#39;second&#39;, &#39;third&#39;]</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">return</span> <span class="n">w</span><span class="o">.</span><span class="n">full</span><span class="p">()</span></div>
-
-
-<div class="viewcode-block" id="full2W"><a class="viewcode-back" href="../../../generated/libpysal.weights.full2W.html#libpysal.weights.full2W">[docs]</a><span class="k">def</span> <span class="nf">full2W</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Create a PySAL W object from a full array.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    m          : array</span>
-<span class="sd">                 nxn array with the full weights matrix</span>
-<span class="sd">    ids        : list</span>
-<span class="sd">                 User ids assumed to be aligned with m</span>
-<span class="sd">    **kwargs   : keyword arguments</span>
-<span class="sd">                 optional arguments for :class:`pysal.weights.W`</span>
-
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    w       : W</span>
-<span class="sd">              PySAL weights object</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    &gt;&gt;&gt; from libpysal.weights import full2W</span>
-<span class="sd">    &gt;&gt;&gt; import numpy as np</span>
-
-<span class="sd">    Create an array of zeros</span>
-
-<span class="sd">    &gt;&gt;&gt; a = np.zeros((4, 4))</span>
-
-<span class="sd">    For loop to fill it with random numbers</span>
-
-<span class="sd">    &gt;&gt;&gt; for i in range(len(a)):</span>
-<span class="sd">    ...     for j in range(len(a[i])):</span>
-<span class="sd">    ...         if i!=j:</span>
-<span class="sd">    ...             a[i, j] = np.random.random(1)</span>
-
-<span class="sd">    Create W object</span>
-
-<span class="sd">    &gt;&gt;&gt; w = full2W(a)</span>
-<span class="sd">    &gt;&gt;&gt; w.full()[0] == a</span>
-<span class="sd">    array([[ True,  True,  True,  True],</span>
-<span class="sd">           [ True,  True,  True,  True],</span>
-<span class="sd">           [ True,  True,  True,  True],</span>
-<span class="sd">           [ True,  True,  True,  True]])</span>
-
-<span class="sd">    Create list of user ids</span>
-
-<span class="sd">    &gt;&gt;&gt; ids = [&#39;myID0&#39;, &#39;myID1&#39;, &#39;myID2&#39;, &#39;myID3&#39;]</span>
-<span class="sd">    &gt;&gt;&gt; w = full2W(a, ids=ids)</span>
-<span class="sd">    &gt;&gt;&gt; w.full()[0] == a</span>
-<span class="sd">    array([[ True,  True,  True,  True],</span>
-<span class="sd">           [ True,  True,  True,  True],</span>
-<span class="sd">           [ True,  True,  True,  True],</span>
-<span class="sd">           [ True,  True,  True,  True]])</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="n">m</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="n">m</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Your array is not square&quot;</span><span class="p">)</span>
-    <span class="n">neighbors</span><span class="p">,</span> <span class="n">weights</span> <span class="o">=</span> <span class="p">{},</span> <span class="p">{}</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
-        <span class="c1"># for i, row in enumerate(m):</span>
-        <span class="n">row</span> <span class="o">=</span> <span class="n">m</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-        <span class="k">if</span> <span class="n">ids</span><span class="p">:</span>
-            <span class="n">i</span> <span class="o">=</span> <span class="n">ids</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-        <span class="n">ngh</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">row</span><span class="o">.</span><span class="n">nonzero</span><span class="p">()[</span><span class="mi">0</span><span class="p">])</span>
-        <span class="n">weights</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">row</span><span class="p">[</span><span class="n">ngh</span><span class="p">])</span>
-        <span class="n">ngh</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">ngh</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">ids</span><span class="p">:</span>
-            <span class="n">ngh</span> <span class="o">=</span> <span class="p">[</span><span class="n">ids</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">ngh</span><span class="p">]</span>
-        <span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">ngh</span>
-    <span class="k">return</span> <span class="n">W</span><span class="p">(</span><span class="n">neighbors</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">id_order</span><span class="o">=</span><span class="n">ids</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="WSP2W"><a class="viewcode-back" href="../../../generated/libpysal.weights.WSP2W.html#libpysal.weights.WSP2W">[docs]</a><span class="k">def</span> <span class="nf">WSP2W</span><span class="p">(</span><span class="n">wsp</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Convert a pysal WSP object (thin weights matrix) to a pysal W object.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    wsp                     : WSP</span>
-<span class="sd">                              PySAL sparse weights object</span>
-<span class="sd">    **kwargs                : keyword arguments</span>
-<span class="sd">                              optional arguments for :class:`pysal.weights.W`</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    w       : W</span>
-<span class="sd">              PySAL weights object</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    &gt;&gt;&gt; from libpysal.weights import lat2W, WSP, WSP2W</span>
-
-<span class="sd">    Build a 10x10 scipy.sparse matrix for a rectangular 2x5 region of cells</span>
-<span class="sd">    (rook contiguity), then construct a PySAL sparse weights object (wsp).</span>
-
-<span class="sd">    &gt;&gt;&gt; sp = lat2SW(2, 5)</span>
-<span class="sd">    &gt;&gt;&gt; wsp = WSP(sp)</span>
-<span class="sd">    &gt;&gt;&gt; wsp.n</span>
-<span class="sd">    10</span>
-<span class="sd">    &gt;&gt;&gt; wsp.sparse[0].todense()</span>
-<span class="sd">    matrix([[0, 1, 0, 0, 0, 1, 0, 0, 0, 0]], dtype=int8)</span>
-
-<span class="sd">    Convert this sparse weights object to a standard PySAL weights object.</span>
-
-<span class="sd">    &gt;&gt;&gt; w = WSP2W(wsp)</span>
-<span class="sd">    &gt;&gt;&gt; w.n</span>
-<span class="sd">    10</span>
-<span class="sd">    &gt;&gt;&gt; print(w.full()[0][0])</span>
-<span class="sd">    [0. 1. 0. 0. 0. 1. 0. 0. 0. 0.]</span>
-
-
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">wsp</span><span class="o">.</span><span class="n">sparse</span>
-    <span class="n">indices</span> <span class="o">=</span> <span class="n">wsp</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">indices</span>
-    <span class="n">data</span> <span class="o">=</span> <span class="n">wsp</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">data</span>
-    <span class="n">indptr</span> <span class="o">=</span> <span class="n">wsp</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">indptr</span>
-    <span class="n">id_order</span> <span class="o">=</span> <span class="n">wsp</span><span class="o">.</span><span class="n">id_order</span>
-    <span class="k">if</span> <span class="n">id_order</span><span class="p">:</span>
-        <span class="c1"># replace indices with user IDs</span>
-        <span class="n">indices</span> <span class="o">=</span> <span class="p">[</span><span class="n">id_order</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">indices</span><span class="p">]</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">id_order</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">wsp</span><span class="o">.</span><span class="n">n</span><span class="p">))</span>
-    <span class="n">neighbors</span><span class="p">,</span> <span class="n">weights</span> <span class="o">=</span> <span class="p">{},</span> <span class="p">{}</span>
-    <span class="n">start</span> <span class="o">=</span> <span class="n">indptr</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">wsp</span><span class="o">.</span><span class="n">n</span><span class="p">):</span>
-        <span class="n">oid</span> <span class="o">=</span> <span class="n">id_order</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-        <span class="n">end</span> <span class="o">=</span> <span class="n">indptr</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span>
-        <span class="n">neighbors</span><span class="p">[</span><span class="n">oid</span><span class="p">]</span> <span class="o">=</span> <span class="n">indices</span><span class="p">[</span><span class="n">start</span><span class="p">:</span><span class="n">end</span><span class="p">]</span>
-        <span class="n">weights</span><span class="p">[</span><span class="n">oid</span><span class="p">]</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">start</span><span class="p">:</span><span class="n">end</span><span class="p">]</span>
-        <span class="n">start</span> <span class="o">=</span> <span class="n">end</span>
-    <span class="n">ids</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">wsp</span><span class="o">.</span><span class="n">id_order</span><span class="p">)</span>
-    <span class="n">w</span> <span class="o">=</span> <span class="n">W</span><span class="p">(</span><span class="n">neighbors</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">ids</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-    <span class="n">w</span><span class="o">.</span><span class="n">_sparse</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">wsp</span><span class="o">.</span><span class="n">sparse</span><span class="p">)</span>
-    <span class="n">w</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;sparse&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">_sparse</span>
-    <span class="k">return</span> <span class="n">w</span></div>
-
-
-<span class="k">def</span> <span class="nf">insert_diagonal</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">val</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">wsp</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-    <span class="n">warn</span><span class="p">(</span><span class="s2">&quot;This function is deprecated. Use fill_diagonal instead.&quot;</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">fill_diagonal</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">val</span><span class="o">=</span><span class="n">val</span><span class="p">,</span> <span class="n">wsp</span><span class="o">=</span><span class="n">wsp</span><span class="p">)</span>
-
-
-<div class="viewcode-block" id="fill_diagonal"><a class="viewcode-back" href="../../../generated/libpysal.weights.fill_diagonal.html#libpysal.weights.fill_diagonal">[docs]</a><span class="k">def</span> <span class="nf">fill_diagonal</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">val</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">wsp</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Returns a new weights object with values inserted along the main diagonal.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    w        : W</span>
-<span class="sd">               Spatial weights object</span>
-
-<span class="sd">    diagonal : float, int or array</span>
-<span class="sd">               Defines the value(s) to which the weights matrix diagonal should</span>
-<span class="sd">               be set. If a constant is passed then each element along the</span>
-<span class="sd">               diagonal will get this value (default is 1.0). An array of length</span>
-<span class="sd">               w.n can be passed to set explicit values to each element along</span>
-<span class="sd">               the diagonal (assumed to be in the same order as w.id_order).</span>
-
-<span class="sd">    wsp      : boolean</span>
-<span class="sd">               If True return a thin weights object of the type WSP, if False</span>
-<span class="sd">               return the standard W object.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    w        : W</span>
-<span class="sd">               Spatial weights object</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    &gt;&gt;&gt; from libpysal.weights import lat2W</span>
-<span class="sd">    &gt;&gt;&gt; import numpy as np</span>
-
-<span class="sd">    Build a basic rook weights matrix, which has zeros on the diagonal, then</span>
-<span class="sd">    insert ones along the diagonal.</span>
-
-<span class="sd">    &gt;&gt;&gt; w = lat2W(5, 5, id_type=&#39;string&#39;)</span>
-<span class="sd">    &gt;&gt;&gt; w_const = insert_diagonal(w)</span>
-<span class="sd">    &gt;&gt;&gt; w[&#39;id0&#39;] ==  {&#39;id5&#39;: 1.0, &#39;id1&#39;: 1.0}</span>
-<span class="sd">    True</span>
-<span class="sd">    &gt;&gt;&gt; w_const[&#39;id0&#39;] == {&#39;id5&#39;: 1.0, &#39;id0&#39;: 1.0, &#39;id1&#39;: 1.0}</span>
-<span class="sd">    True</span>
-
-<span class="sd">    Insert different values along the main diagonal.</span>
-
-<span class="sd">    &gt;&gt;&gt; diag = np.arange(100, 125)</span>
-<span class="sd">    &gt;&gt;&gt; w_var = insert_diagonal(w, diag)</span>
-<span class="sd">    &gt;&gt;&gt; w_var[&#39;id0&#39;] == {&#39;id5&#39;: 1.0, &#39;id0&#39;: 100.0, &#39;id1&#39;: 1.0}</span>
-<span class="sd">    True</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">w_new</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">sparse</span><span class="p">)</span>
-    <span class="n">w_new</span> <span class="o">=</span> <span class="n">w_new</span><span class="o">.</span><span class="n">tolil</span><span class="p">()</span>
-    <span class="k">if</span> <span class="nb">issubclass</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">val</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
-        <span class="k">if</span> <span class="n">w</span><span class="o">.</span><span class="n">n</span> <span class="o">!=</span> <span class="n">val</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
-            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;shape of w and diagonal do not match&quot;</span><span class="p">)</span>
-        <span class="n">w_new</span><span class="o">.</span><span class="n">setdiag</span><span class="p">(</span><span class="n">val</span><span class="p">)</span>
-    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">val</span><span class="p">,</span> <span class="n">numbers</span><span class="o">.</span><span class="n">Number</span><span class="p">):</span>
-        <span class="n">w_new</span><span class="o">.</span><span class="n">setdiag</span><span class="p">([</span><span class="n">val</span><span class="p">]</span> <span class="o">*</span> <span class="n">w</span><span class="o">.</span><span class="n">n</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;Invalid value passed to diagonal&quot;</span><span class="p">)</span>
-    <span class="n">w_out</span> <span class="o">=</span> <span class="n">WSP</span><span class="p">(</span><span class="n">w_new</span><span class="p">,</span> <span class="n">copy</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">id_order</span><span class="p">))</span>
-    <span class="k">if</span> <span class="n">wsp</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">w_out</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">WSP2W</span><span class="p">(</span><span class="n">w_out</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="remap_ids"><a class="viewcode-back" href="../../../generated/libpysal.weights.remap_ids.html#libpysal.weights.remap_ids">[docs]</a><span class="k">def</span> <span class="nf">remap_ids</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">old2new</span><span class="p">,</span> <span class="n">id_order</span><span class="o">=</span><span class="p">[],</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Remaps the IDs in a spatial weights object.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    w        : W</span>
-<span class="sd">               Spatial weights object</span>
-
-<span class="sd">    old2new  : dictionary</span>
-<span class="sd">               Dictionary where the keys are the IDs in w (i.e. &quot;old IDs&quot;) and</span>
-<span class="sd">               the values are the IDs to replace them (i.e. &quot;new IDs&quot;)</span>
-
-<span class="sd">    id_order : list</span>
-<span class="sd">               An ordered list of new IDs, which defines the order of observations when</span>
-<span class="sd">               iterating over W. If not set then the id_order in w will be</span>
-<span class="sd">               used.</span>
-<span class="sd">    **kwargs : keyword arguments</span>
-<span class="sd">               optional arguments for :class:`pysal.weights.W`</span>
-
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-
-<span class="sd">    implicit : W</span>
-<span class="sd">               Spatial weights object with new IDs</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    &gt;&gt;&gt; from libpysal.weights import lat2W</span>
-<span class="sd">    &gt;&gt;&gt; w = lat2W(3,2)</span>
-<span class="sd">    &gt;&gt;&gt; w.id_order</span>
-<span class="sd">    [0, 1, 2, 3, 4, 5]</span>
-<span class="sd">    &gt;&gt;&gt; w.neighbors[0]</span>
-<span class="sd">    [2, 1]</span>
-<span class="sd">    &gt;&gt;&gt; old_to_new = {0:&#39;a&#39;, 1:&#39;b&#39;, 2:&#39;c&#39;, 3:&#39;d&#39;, 4:&#39;e&#39;, 5:&#39;f&#39;}</span>
-<span class="sd">    &gt;&gt;&gt; w_new = remap_ids(w, old_to_new)</span>
-<span class="sd">    &gt;&gt;&gt; w_new.id_order</span>
-<span class="sd">    [&#39;a&#39;, &#39;b&#39;, &#39;c&#39;, &#39;d&#39;, &#39;e&#39;, &#39;f&#39;]</span>
-<span class="sd">    &gt;&gt;&gt; w_new.neighbors[&#39;a&#39;]</span>
-<span class="sd">    [&#39;c&#39;, &#39;b&#39;]</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">W</span><span class="p">):</span>
-        <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;w must be a spatial weights object&quot;</span><span class="p">)</span>
-    <span class="n">new_neigh</span> <span class="o">=</span> <span class="p">{}</span>
-    <span class="n">new_weights</span> <span class="o">=</span> <span class="p">{}</span>
-    <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">items</span><span class="p">()):</span>
-        <span class="n">new_values</span> <span class="o">=</span> <span class="p">[</span><span class="n">old2new</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">value</span><span class="p">]</span>
-        <span class="n">new_key</span> <span class="o">=</span> <span class="n">old2new</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
-        <span class="n">new_neigh</span><span class="p">[</span><span class="n">new_key</span><span class="p">]</span> <span class="o">=</span> <span class="n">new_values</span>
-        <span class="n">new_weights</span><span class="p">[</span><span class="n">new_key</span><span class="p">]</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="n">key</span><span class="p">])</span>
-    <span class="k">if</span> <span class="n">id_order</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">W</span><span class="p">(</span><span class="n">new_neigh</span><span class="p">,</span> <span class="n">new_weights</span><span class="p">,</span> <span class="n">id_order</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">if</span> <span class="n">w</span><span class="o">.</span><span class="n">id_order</span><span class="p">:</span>
-            <span class="n">id_order</span> <span class="o">=</span> <span class="p">[</span><span class="n">old2new</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">w</span><span class="o">.</span><span class="n">id_order</span><span class="p">]</span>
-            <span class="k">return</span> <span class="n">W</span><span class="p">(</span><span class="n">new_neigh</span><span class="p">,</span> <span class="n">new_weights</span><span class="p">,</span> <span class="n">id_order</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">W</span><span class="p">(</span><span class="n">new_neigh</span><span class="p">,</span> <span class="n">new_weights</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="get_ids"><a class="viewcode-back" href="../../../generated/libpysal.weights.get_ids.html#libpysal.weights.get_ids">[docs]</a><span class="k">def</span> <span class="nf">get_ids</span><span class="p">(</span><span class="n">in_shps</span><span class="p">,</span> <span class="n">idVariable</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Gets the IDs from the DBF file that moves with a given shape file or</span>
-<span class="sd">    a geopandas.GeoDataFrame.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    in_shps      : str or geopandas.GeoDataFrame</span>
-<span class="sd">                   The input geographic data. Either</span>
-<span class="sd">                   (1) a path to a shapefile including suffix (str); or</span>
-<span class="sd">                   (2) a geopandas.GeoDataFrame.</span>
-<span class="sd">    idVariable   : str</span>
-<span class="sd">                   name of a column in the shapefile&#39;s DBF or the</span>
-<span class="sd">                   geopandas.GeoDataFrame to use for ids.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    ids          : list</span>
-<span class="sd">                   a list of IDs</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    &gt;&gt;&gt; from libpysal.weights.util import get_ids</span>
-<span class="sd">    &gt;&gt;&gt; import libpysal</span>
-<span class="sd">    &gt;&gt;&gt; polyids = get_ids(libpysal.examples.get_path(&quot;columbus.shp&quot;), &quot;POLYID&quot;)</span>
-<span class="sd">    &gt;&gt;&gt; polyids[:5]</span>
-<span class="sd">    [1, 2, 3, 4, 5]</span>
-
-<span class="sd">    &gt;&gt;&gt; from libpysal.weights.util import get_ids</span>
-<span class="sd">    &gt;&gt;&gt; import libpysal</span>
-<span class="sd">    &gt;&gt;&gt; import geopandas as gpd</span>
-<span class="sd">    &gt;&gt;&gt; gdf = gpd.read_file(libpysal.examples.get_path(&quot;columbus.shp&quot;))</span>
-<span class="sd">    &gt;&gt;&gt; polyids = gdf[&quot;POLYID&quot;]</span>
-<span class="sd">    &gt;&gt;&gt; polyids[:5]</span>
-<span class="sd">    0    1</span>
-<span class="sd">    1    2</span>
-<span class="sd">    2    3</span>
-<span class="sd">    3    4</span>
-<span class="sd">    4    5</span>
-<span class="sd">    Name: POLYID, dtype: int64</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">try</span><span class="p">:</span>
-        <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">in_shps</span><span class="p">)</span> <span class="o">==</span> <span class="nb">str</span><span class="p">:</span>
-            <span class="n">dbname</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">splitext</span><span class="p">(</span><span class="n">in_shps</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="s2">&quot;.dbf&quot;</span>
-            <span class="n">db</span> <span class="o">=</span> <span class="n">psopen</span><span class="p">(</span><span class="n">dbname</span><span class="p">)</span>
-            <span class="n">cols</span> <span class="o">=</span> <span class="n">db</span><span class="o">.</span><span class="n">header</span>
-            <span class="n">var</span> <span class="o">=</span> <span class="n">db</span><span class="o">.</span><span class="n">by_col</span><span class="p">[</span><span class="n">idVariable</span><span class="p">]</span>
-            <span class="n">db</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">cols</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">in_shps</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span>
-            <span class="n">var</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">in_shps</span><span class="p">[</span><span class="n">idVariable</span><span class="p">])</span>
-        <span class="k">return</span> <span class="n">var</span>
-
-    <span class="k">except</span> <span class="ne">IOError</span><span class="p">:</span>
-        <span class="n">msg</span> <span class="o">=</span> <span class="p">(</span>
-            <span class="s1">&#39;The shapefile &quot;</span><span class="si">%s</span><span class="s1">&quot; appears to be missing its DBF file. &#39;</span>
-            <span class="o">+</span> <span class="s1">&#39; The DBF file &quot;</span><span class="si">%s</span><span class="s1">&quot; could not be found.&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">in_shps</span><span class="p">,</span> <span class="n">dbname</span><span class="p">)</span>
-        <span class="p">)</span>
-        <span class="k">raise</span> <span class="ne">IOError</span><span class="p">(</span><span class="n">msg</span><span class="p">)</span>
-    <span class="k">except</span> <span class="p">(</span><span class="ne">AttributeError</span><span class="p">,</span> <span class="ne">KeyError</span><span class="p">):</span>
-        <span class="n">msg</span> <span class="o">=</span> <span class="p">(</span>
-            <span class="s1">&#39;The variable &quot;</span><span class="si">%s</span><span class="s1">&quot; not found in the DBF/GDF. The the following &#39;</span>
-            <span class="o">+</span> <span class="s2">&quot;variables are present: </span><span class="si">%s</span><span class="s2">.&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">idVariable</span><span class="p">,</span> <span class="s2">&quot;,&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">cols</span><span class="p">))</span>
-        <span class="p">)</span>
-        <span class="k">raise</span> <span class="ne">KeyError</span><span class="p">(</span><span class="n">msg</span><span class="p">)</span></div>
-
-
-<span class="k">def</span> <span class="nf">get_points_array</span><span class="p">(</span><span class="n">iterable</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Gets a data array of x and y coordinates from a given iterable</span>
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    iterable      : iterable</span>
-<span class="sd">                    arbitrary collection of shapes that supports iteration</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    points        : array</span>
-<span class="sd">                    (n, 2)</span>
-<span class="sd">                    a data array of x and y coordinates</span>
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-<span class="sd">    If the given shape file includes polygons,</span>
-<span class="sd">    this function returns x and y coordinates of the polygons&#39; centroids</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">first_choice</span><span class="p">,</span> <span class="n">backup</span> <span class="o">=</span> <span class="n">tee</span><span class="p">(</span><span class="n">iterable</span><span class="p">)</span>
-    <span class="k">try</span><span class="p">:</span>
-        <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">shape</span><span class="o">.</span><span class="n">centroid</span><span class="p">)</span> <span class="k">for</span> <span class="n">shape</span> <span class="ow">in</span> <span class="n">first_choice</span><span class="p">])</span>
-    <span class="k">except</span> <span class="ne">AttributeError</span><span class="p">:</span>
-        <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">([</span><span class="n">shape</span> <span class="k">for</span> <span class="n">shape</span> <span class="ow">in</span> <span class="n">backup</span><span class="p">])</span>
-    <span class="k">return</span> <span class="n">data</span>
-
-
-<div class="viewcode-block" id="get_points_array_from_shapefile"><a class="viewcode-back" href="../../../generated/libpysal.weights.get_points_array_from_shapefile.html#libpysal.weights.get_points_array_from_shapefile">[docs]</a><span class="k">def</span> <span class="nf">get_points_array_from_shapefile</span><span class="p">(</span><span class="n">shapefile</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Gets a data array of x and y coordinates from a given shapefile.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    shapefile     : string</span>
-<span class="sd">                    name of a shape file including suffix</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    points        : array</span>
-<span class="sd">                    (n, 2)</span>
-<span class="sd">                    a data array of x and y coordinates</span>
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-<span class="sd">    If the given shape file includes polygons,</span>
-<span class="sd">    this function returns x and y coordinates of the polygons&#39; centroids</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    Point shapefile</span>
-
-<span class="sd">    &gt;&gt;&gt; import libpysal</span>
-<span class="sd">    &gt;&gt;&gt; from libpysal.weights.util import get_points_array_from_shapefile</span>
-<span class="sd">    &gt;&gt;&gt; xy = get_points_array_from_shapefile(libpysal.examples.get_path(&#39;juvenile.shp&#39;))</span>
-<span class="sd">    &gt;&gt;&gt; xy[:3]</span>
-<span class="sd">    array([[94., 93.],</span>
-<span class="sd">           [80., 95.],</span>
-<span class="sd">           [79., 90.]])</span>
-
-
-<span class="sd">    Polygon shapefile</span>
-
-<span class="sd">    &gt;&gt;&gt; xy = get_points_array_from_shapefile(libpysal.examples.get_path(&#39;columbus.shp&#39;))</span>
-<span class="sd">    &gt;&gt;&gt; xy[:3]</span>
-<span class="sd">    array([[ 8.82721847, 14.36907602],</span>
-<span class="sd">           [ 8.33265837, 14.03162401],</span>
-<span class="sd">           [ 9.01226541, 13.81971908]])</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">f</span> <span class="o">=</span> <span class="n">psopen</span><span class="p">(</span><span class="n">shapefile</span><span class="p">)</span>
-    <span class="n">data</span> <span class="o">=</span> <span class="n">get_points_array</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">data</span></div>
-
-
-<div class="viewcode-block" id="min_threshold_distance"><a class="viewcode-back" href="../../../generated/libpysal.weights.min_threshold_distance.html#libpysal.weights.min_threshold_distance">[docs]</a><span class="k">def</span> <span class="nf">min_threshold_distance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Get the maximum nearest neighbor distance.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-
-<span class="sd">    data    : array</span>
-<span class="sd">              (n,k) or KDTree where KDtree.data is array (n,k)</span>
-<span class="sd">              n observations on k attributes</span>
-<span class="sd">    p       : float</span>
-<span class="sd">              Minkowski p-norm distance metric parameter:</span>
-<span class="sd">              1&lt;=p&lt;=infinity</span>
-<span class="sd">              2: Euclidean distance</span>
-<span class="sd">              1: Manhattan distance</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    nnd    : float</span>
-<span class="sd">             maximum nearest neighbor distance between the n observations</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    &gt;&gt;&gt; from libpysal.weights.util import min_threshold_distance</span>
-<span class="sd">    &gt;&gt;&gt; import numpy as np</span>
-<span class="sd">    &gt;&gt;&gt; x, y = np.indices((5, 5))</span>
-<span class="sd">    &gt;&gt;&gt; x.shape = (25, 1)</span>
-<span class="sd">    &gt;&gt;&gt; y.shape = (25, 1)</span>
-<span class="sd">    &gt;&gt;&gt; data = np.hstack([x, y])</span>
-<span class="sd">    &gt;&gt;&gt; min_threshold_distance(data)</span>
-<span class="sd">    1.0</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="nb">issubclass</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">data</span><span class="p">),</span> <span class="n">scipy</span><span class="o">.</span><span class="n">spatial</span><span class="o">.</span><span class="n">KDTree</span><span class="p">):</span>
-        <span class="n">kd</span> <span class="o">=</span> <span class="n">data</span>
-        <span class="n">data</span> <span class="o">=</span> <span class="n">kd</span><span class="o">.</span><span class="n">data</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">kd</span> <span class="o">=</span> <span class="n">KDTree</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
-    <span class="n">nn</span> <span class="o">=</span> <span class="n">kd</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">p</span><span class="p">)</span>
-    <span class="n">nnd</span> <span class="o">=</span> <span class="n">nn</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span>
-    <span class="k">return</span> <span class="n">nnd</span></div>
-
-
-<div class="viewcode-block" id="lat2SW"><a class="viewcode-back" href="../../../generated/libpysal.weights.lat2SW.html#libpysal.weights.lat2SW">[docs]</a><span class="k">def</span> <span class="nf">lat2SW</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">criterion</span><span class="o">=</span><span class="s2">&quot;rook&quot;</span><span class="p">,</span> <span class="n">row_st</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Create a sparse W matrix for a regular lattice.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-
-<span class="sd">    nrows   : int</span>
-<span class="sd">              number of rows</span>
-<span class="sd">    ncols   : int</span>
-<span class="sd">              number of columns</span>
-<span class="sd">    rook    : {&quot;rook&quot;, &quot;queen&quot;, &quot;bishop&quot;}</span>
-<span class="sd">              type of contiguity. Default is rook.</span>
-<span class="sd">    row_st  : boolean</span>
-<span class="sd">              If True, the created sparse W object is row-standardized so</span>
-<span class="sd">              every row sums up to one. Defaults to False.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-
-<span class="sd">    w : scipy.sparse.dia_matrix</span>
-<span class="sd">        instance of a scipy sparse matrix</span>
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-
-<span class="sd">    Observations are row ordered: first k observations are in row 0, next k in row 1, and so on.</span>
-<span class="sd">    This method directly creates the W matrix using the strucuture of the contiguity type.</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-
-<span class="sd">    &gt;&gt;&gt; from libpysal.weights import lat2SW</span>
-<span class="sd">    &gt;&gt;&gt; w9 = lat2SW(3,3)</span>
-<span class="sd">    &gt;&gt;&gt; w9[0,1] == 1</span>
-<span class="sd">    True</span>
-<span class="sd">    &gt;&gt;&gt; w9[3,6] == 1</span>
-<span class="sd">    True</span>
-<span class="sd">    &gt;&gt;&gt; w9r = lat2SW(3,3, row_st=True)</span>
-<span class="sd">    &gt;&gt;&gt; w9r[3,6] == 1./3</span>
-<span class="sd">    True</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">n</span> <span class="o">=</span> <span class="n">nrows</span> <span class="o">*</span> <span class="n">ncols</span>
-    <span class="n">diagonals</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="n">offsets</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="k">if</span> <span class="n">criterion</span> <span class="o">==</span> <span class="s2">&quot;rook&quot;</span> <span class="ow">or</span> <span class="n">criterion</span> <span class="o">==</span> <span class="s2">&quot;queen&quot;</span><span class="p">:</span>
-        <span class="n">d</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="n">n</span><span class="p">))</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">ncols</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">ncols</span><span class="p">):</span>
-            <span class="n">d</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
-        <span class="n">diagonals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">d</span><span class="p">)</span>
-        <span class="n">offsets</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
-
-        <span class="n">d</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="n">n</span><span class="p">))</span>
-        <span class="n">diagonals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">d</span><span class="p">)</span>
-        <span class="n">offsets</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="o">-</span><span class="n">ncols</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">criterion</span> <span class="o">==</span> <span class="s2">&quot;queen&quot;</span> <span class="ow">or</span> <span class="n">criterion</span> <span class="o">==</span> <span class="s2">&quot;bishop&quot;</span><span class="p">:</span>
-        <span class="n">d</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="n">n</span><span class="p">))</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">ncols</span><span class="p">):</span>
-            <span class="n">d</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
-        <span class="n">diagonals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">d</span><span class="p">)</span>
-        <span class="n">offsets</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="o">-</span><span class="p">(</span><span class="n">ncols</span> <span class="o">-</span> <span class="mi">1</span><span class="p">))</span>
-
-        <span class="n">d</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="n">n</span><span class="p">))</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">ncols</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">ncols</span><span class="p">):</span>
-            <span class="n">d</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
-        <span class="n">diagonals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">d</span><span class="p">)</span>
-        <span class="n">offsets</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="o">-</span><span class="p">(</span><span class="n">ncols</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
-    <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">diagonals</span><span class="p">)</span>
-    <span class="n">offsets</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">offsets</span><span class="p">)</span>
-    <span class="n">m</span> <span class="o">=</span> <span class="n">sparse</span><span class="o">.</span><span class="n">dia_matrix</span><span class="p">((</span><span class="n">data</span><span class="p">,</span> <span class="n">offsets</span><span class="p">),</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">n</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int8</span><span class="p">)</span>
-    <span class="n">m</span> <span class="o">=</span> <span class="n">m</span> <span class="o">+</span> <span class="n">m</span><span class="o">.</span><span class="n">T</span>
-    <span class="k">if</span> <span class="n">row_st</span><span class="p">:</span>
-        <span class="n">m</span> <span class="o">=</span> <span class="n">sparse</span><span class="o">.</span><span class="n">spdiags</span><span class="p">(</span><span class="mf">1.0</span> <span class="o">/</span> <span class="n">m</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">*</span><span class="n">m</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">*</span> <span class="n">m</span>
-    <span class="k">return</span> <span class="n">m</span></div>
-
-
-<span class="k">def</span> <span class="nf">write_gal</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">10</span><span class="p">):</span>
-    <span class="n">f</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span>
-    <span class="n">n</span> <span class="o">=</span> <span class="n">k</span> <span class="o">*</span> <span class="n">k</span>
-    <span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s2">&quot;0 </span><span class="si">%d</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">n</span><span class="p">)</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
-        <span class="n">row</span> <span class="o">=</span> <span class="n">i</span> <span class="o">/</span> <span class="n">k</span>
-        <span class="n">col</span> <span class="o">=</span> <span class="n">i</span> <span class="o">%</span> <span class="n">k</span>
-        <span class="n">neighs</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span> <span class="o">-</span> <span class="n">i</span><span class="p">,</span> <span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">i</span> <span class="o">-</span> <span class="n">k</span><span class="p">,</span> <span class="n">i</span> <span class="o">+</span> <span class="n">k</span><span class="p">]</span>
-        <span class="n">neighs</span> <span class="o">=</span> <span class="p">[</span><span class="n">j</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">neighs</span> <span class="k">if</span> <span class="n">j</span> <span class="o">&gt;=</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">j</span> <span class="o">&lt;</span> <span class="n">n</span><span class="p">]</span>
-        <span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="si">%d</span><span class="s2"> </span><span class="si">%d</span><span class="se">\n</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">neighs</span><span class="p">)))</span>
-        <span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s2">&quot; &quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="nb">str</span><span class="p">,</span> <span class="n">neighs</span><span class="p">)))</span>
-    <span class="n">f</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
-
-
-<div class="viewcode-block" id="neighbor_equality"><a class="viewcode-back" href="../../../generated/libpysal.weights.neighbor_equality.html#libpysal.weights.neighbor_equality">[docs]</a><span class="k">def</span> <span class="nf">neighbor_equality</span><span class="p">(</span><span class="n">w1</span><span class="p">,</span> <span class="n">w2</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Test if the neighbor sets are equal between two weights objects</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-
-<span class="sd">    w1 : W</span>
-<span class="sd">        instance of spatial weights class W</span>
-
-<span class="sd">    w2 : W</span>
-<span class="sd">        instance of spatial weights class W</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    Boolean</span>
-
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-<span class="sd">    Only set membership is evaluated, no check of the weight values is carried out.</span>
-
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    &gt;&gt;&gt; from libpysal.weights.util import neighbor_equality</span>
-<span class="sd">    &gt;&gt;&gt; from libpysal.weights import lat2W, W</span>
-<span class="sd">    &gt;&gt;&gt; w1 = lat2W(3,3)</span>
-<span class="sd">    &gt;&gt;&gt; w2 = lat2W(3,3)</span>
-<span class="sd">    &gt;&gt;&gt; neighbor_equality(w1, w2)</span>
-<span class="sd">    True</span>
-<span class="sd">    &gt;&gt;&gt; w3 = lat2W(5,5)</span>
-<span class="sd">    &gt;&gt;&gt; neighbor_equality(w1, w3)</span>
-<span class="sd">    False</span>
-<span class="sd">    &gt;&gt;&gt; n4 = w1.neighbors.copy()</span>
-<span class="sd">    &gt;&gt;&gt; n4[0] = [1]</span>
-<span class="sd">    &gt;&gt;&gt; n4[1] = [4, 2]</span>
-<span class="sd">    &gt;&gt;&gt; w4 = W(n4)</span>
-<span class="sd">    &gt;&gt;&gt; neighbor_equality(w1, w4)</span>
-<span class="sd">    False</span>
-<span class="sd">    &gt;&gt;&gt; n5 = w1.neighbors.copy()</span>
-<span class="sd">    &gt;&gt;&gt; n5[0]</span>
-<span class="sd">    [3, 1]</span>
-<span class="sd">    &gt;&gt;&gt; n5[0] = [1, 3]</span>
-<span class="sd">    &gt;&gt;&gt; w5 = W(n5)</span>
-<span class="sd">    &gt;&gt;&gt; neighbor_equality(w1, w5)</span>
-<span class="sd">    True</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">n1</span> <span class="o">=</span> <span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span>
-    <span class="n">n2</span> <span class="o">=</span> <span class="n">w2</span><span class="o">.</span><span class="n">neighbors</span>
-    <span class="n">ids_1</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">n1</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
-    <span class="n">ids_2</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">n2</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
-    <span class="k">if</span> <span class="n">ids_1</span> <span class="o">!=</span> <span class="n">ids_2</span><span class="p">:</span>
-        <span class="k">return</span> <span class="kc">False</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">ids_1</span><span class="p">:</span>
-        <span class="k">if</span> <span class="nb">set</span><span class="p">(</span><span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="o">!=</span> <span class="nb">set</span><span class="p">(</span><span class="n">w2</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]):</span>
-            <span class="k">return</span> <span class="kc">False</span>
-    <span class="k">return</span> <span class="kc">True</span></div>
-
-
-<span class="k">def</span> <span class="nf">isKDTree</span><span class="p">(</span><span class="n">obj</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    This is a utility function to determine whether or not an object is a</span>
-<span class="sd">    KDTree, since KDTree and cKDTree have no common parent type</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">return</span> <span class="nb">any</span><span class="p">([</span><span class="nb">issubclass</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">obj</span><span class="p">),</span> <span class="n">KDTYPE</span><span class="p">)</span> <span class="k">for</span> <span class="n">KDTYPE</span> <span class="ow">in</span> <span class="n">KDTREE_TYPES</span><span class="p">])</span>
-
-
-<div class="viewcode-block" id="attach_islands"><a class="viewcode-back" href="../../../generated/libpysal.weights.attach_islands.html#libpysal.weights.attach_islands">[docs]</a><span class="k">def</span> <span class="nf">attach_islands</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">w_knn1</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Attach nearest neighbor to islands in spatial weight w.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-
-<span class="sd">    w            : libpysal.weights.W</span>
-<span class="sd">                   pysal spatial weight object (unstandardized).</span>
-<span class="sd">    w_knn1       : libpysal.weights.W</span>
-<span class="sd">                   Nearest neighbor pysal spatial weight object (k=1).</span>
-<span class="sd">    **kwargs     : keyword arguments</span>
-<span class="sd">                   optional arguments for :class:`pysal.weights.W`</span>
-
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">                 : libpysal.weights.W</span>
-<span class="sd">                   pysal spatial weight object w without islands.</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    &gt;&gt;&gt; from libpysal.weights import lat2W, Rook, KNN, attach_islands</span>
-<span class="sd">    &gt;&gt;&gt; import libpysal</span>
-<span class="sd">    &gt;&gt;&gt; w = Rook.from_shapefile(libpysal.examples.get_path(&#39;10740.shp&#39;))</span>
-<span class="sd">    &gt;&gt;&gt; w.islands</span>
-<span class="sd">    [163]</span>
-<span class="sd">    &gt;&gt;&gt; w_knn1 = KNN.from_shapefile(libpysal.examples.get_path(&#39;10740.shp&#39;),k=1)</span>
-<span class="sd">    &gt;&gt;&gt; w_attach = attach_islands(w, w_knn1)</span>
-<span class="sd">    &gt;&gt;&gt; w_attach.islands</span>
-<span class="sd">    []</span>
-<span class="sd">    &gt;&gt;&gt; w_attach[w.islands[0]]</span>
-<span class="sd">    {166: 1.0}</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">neighbors</span><span class="p">,</span> <span class="n">weights</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">neighbors</span><span class="p">),</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">weights</span><span class="p">)</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="nb">len</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">islands</span><span class="p">):</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;There are no disconnected observations (no islands)!&quot;</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">w</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">for</span> <span class="n">island</span> <span class="ow">in</span> <span class="n">w</span><span class="o">.</span><span class="n">islands</span><span class="p">:</span>
-            <span class="n">nb</span> <span class="o">=</span> <span class="n">w_knn1</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">island</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
-            <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">island</span><span class="p">)</span> <span class="ow">is</span> <span class="nb">float</span><span class="p">:</span>
-                <span class="n">nb</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">nb</span><span class="p">)</span>
-            <span class="n">neighbors</span><span class="p">[</span><span class="n">island</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">nb</span><span class="p">]</span>
-            <span class="n">weights</span><span class="p">[</span><span class="n">island</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">]</span>
-            <span class="n">neighbors</span><span class="p">[</span><span class="n">nb</span><span class="p">]</span> <span class="o">=</span> <span class="n">neighbors</span><span class="p">[</span><span class="n">nb</span><span class="p">]</span> <span class="o">+</span> <span class="p">[</span><span class="n">island</span><span class="p">]</span>
-            <span class="n">weights</span><span class="p">[</span><span class="n">nb</span><span class="p">]</span> <span class="o">=</span> <span class="n">weights</span><span class="p">[</span><span class="n">nb</span><span class="p">]</span> <span class="o">+</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">]</span>
-        <span class="k">return</span> <span class="n">W</span><span class="p">(</span><span class="n">neighbors</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">id_order</span><span class="o">=</span><span class="n">w</span><span class="o">.</span><span class="n">id_order</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="nonplanar_neighbors"><a class="viewcode-back" href="../../../generated/libpysal.weights.nonplanar_neighbors.html#libpysal.weights.nonplanar_neighbors">[docs]</a><span class="k">def</span> <span class="nf">nonplanar_neighbors</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">geodataframe</span><span class="p">,</span> <span class="n">tolerance</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Detect neighbors for non-planar polygon collections</span>
-
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-
-<span class="sd">    w:   pysal W</span>
-<span class="sd">         A spatial weights object with reported islands</span>
-
-
-<span class="sd">    geodataframe: GeoDataframe</span>
-<span class="sd">                  The polygon dataframe from which w was constructed.</span>
-
-<span class="sd">    tolerance: float</span>
-<span class="sd">               The percentage of the minimum horizontal or vertical extent (minextent) of</span>
-<span class="sd">               the dataframe to use in defining  a buffering distance to allow for fuzzy</span>
-<span class="sd">               contiguity detection. The buffering distance is equal to tolerance*minextent.</span>
-<span class="sd">    **kwargs:  keyword arguments</span>
-<span class="sd">               optional arguments for :class:`pysal.weights.W`</span>
-
-
-<span class="sd">    Attributes</span>
-<span class="sd">    ----------</span>
-
-<span class="sd">    non_planar_joins : dictionary</span>
-<span class="sd">               Stores the new joins detected. Key is the id of the focal unit, value is a list of neighbor ids.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-
-<span class="sd">    w: pysal W</span>
-<span class="sd">       Spatial weights object that encodes fuzzy neighbors.</span>
-<span class="sd">       This will have an attribute `non_planar_joins` to indicate what new joins were detected.</span>
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-
-<span class="sd">    This relaxes the notion of contiguity neighbors for the case of shapefiles</span>
-<span class="sd">    that violate the condition of planar enforcement. It handles three types</span>
-<span class="sd">    of conditions present in such files that would result in islands when using</span>
-<span class="sd">    the regular PySAL contiguity methods. The first are edges for nearby</span>
-<span class="sd">    polygons that should be shared, but are digitized separately for the</span>
-<span class="sd">    individual polygons and the resulting edges do not coincide, but instead</span>
-<span class="sd">    the edges intersect. The second case is similar to the first, only the</span>
-<span class="sd">    resultant edges do not intersect but are &quot;close&quot;. The final case arises</span>
-<span class="sd">    when one polygon is &quot;inside&quot; a second polygon but is not encoded to</span>
-<span class="sd">    represent a hole in the containing polygon.</span>
-
-<span class="sd">    The buffering check assumes the geometry coordinates are projected.</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-
-<span class="sd">    &gt;&gt;&gt; import geopandas as gpd</span>
-<span class="sd">    &gt;&gt;&gt; import libpysal</span>
-<span class="sd">    &gt;&gt;&gt; df = gpd.read_file(libpysal.examples.get_path(&#39;map_RS_BR.shp&#39;))</span>
-<span class="sd">    &gt;&gt;&gt; w = libpysal.weights.Queen.from_dataframe(df)</span>
-<span class="sd">    &gt;&gt;&gt; w.islands</span>
-<span class="sd">    [0, 4, 23, 27, 80, 94, 101, 107, 109, 119, 122, 139, 169, 175, 223, 239, 247, 253, 254, 255, 256, 261, 276, 291, 294, 303, 321, 357, 374]</span>
-<span class="sd">    &gt;&gt;&gt; wnp = libpysal.weights.nonplanar_neighbors(w, df)</span>
-<span class="sd">    &gt;&gt;&gt; wnp.islands</span>
-<span class="sd">    []</span>
-<span class="sd">    &gt;&gt;&gt; w.neighbors[0]</span>
-<span class="sd">    []</span>
-<span class="sd">    &gt;&gt;&gt; wnp.neighbors[0]</span>
-<span class="sd">    [23, 59, 152, 239]</span>
-<span class="sd">    &gt;&gt;&gt; wnp.neighbors[23]</span>
-<span class="sd">    [0, 45, 59, 107, 152, 185, 246]</span>
-
-<span class="sd">    Also see `nonplanarweights.ipynb`</span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-
-<span class="sd">    Planar Enforcement: http://ibis.geog.ubc.ca/courses/klink/gis.notes/ncgia/u12.html#SEC12.6</span>
-
-
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">gdf</span> <span class="o">=</span> <span class="n">geodataframe</span>
-    <span class="k">assert</span> <span class="p">(</span>
-        <span class="n">gdf</span><span class="o">.</span><span class="n">sindex</span>
-    <span class="p">),</span> <span class="s2">&quot;GeoDataFrame must have a spatial index. Please make sure you have `libspatialindex` installed&quot;</span>
-    <span class="n">islands</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">islands</span>
-    <span class="n">joins</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">neighbors</span><span class="p">)</span>
-    <span class="n">candidates</span> <span class="o">=</span> <span class="n">gdf</span><span class="o">.</span><span class="n">geometry</span>
-    <span class="n">fixes</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">list</span><span class="p">)</span>
-
-    <span class="c1"># first check for intersecting polygons</span>
-    <span class="k">for</span> <span class="n">island</span> <span class="ow">in</span> <span class="n">islands</span><span class="p">:</span>
-        <span class="n">focal</span> <span class="o">=</span> <span class="n">gdf</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">island</span><span class="p">]</span><span class="o">.</span><span class="n">geometry</span>
-        <span class="n">neighbors</span> <span class="o">=</span> <span class="p">[</span>
-            <span class="n">j</span>
-            <span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">candidate</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">candidates</span><span class="p">)</span>
-            <span class="k">if</span> <span class="n">focal</span><span class="o">.</span><span class="n">intersects</span><span class="p">(</span><span class="n">candidate</span><span class="p">)</span> <span class="ow">and</span> <span class="n">j</span> <span class="o">!=</span> <span class="n">island</span>
-        <span class="p">]</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">neighbors</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">for</span> <span class="n">neighbor</span> <span class="ow">in</span> <span class="n">neighbors</span><span class="p">:</span>
-                <span class="k">if</span> <span class="n">neighbor</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">joins</span><span class="p">[</span><span class="n">island</span><span class="p">]:</span>
-                    <span class="n">fixes</span><span class="p">[</span><span class="n">island</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">neighbor</span><span class="p">)</span>
-                    <span class="n">joins</span><span class="p">[</span><span class="n">island</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">neighbor</span><span class="p">)</span>
-                <span class="k">if</span> <span class="n">island</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">joins</span><span class="p">[</span><span class="n">neighbor</span><span class="p">]:</span>
-                    <span class="n">fixes</span><span class="p">[</span><span class="n">neighbor</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">island</span><span class="p">)</span>
-                    <span class="n">joins</span><span class="p">[</span><span class="n">neighbor</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">island</span><span class="p">)</span>
-
-    <span class="c1"># if any islands remain, dilate them and check for intersection</span>
-    <span class="k">if</span> <span class="n">islands</span><span class="p">:</span>
-        <span class="n">x0</span><span class="p">,</span> <span class="n">y0</span><span class="p">,</span> <span class="n">x1</span><span class="p">,</span> <span class="n">y1</span> <span class="o">=</span> <span class="n">gdf</span><span class="o">.</span><span class="n">total_bounds</span>
-        <span class="n">distance</span> <span class="o">=</span> <span class="n">tolerance</span> <span class="o">*</span> <span class="nb">min</span><span class="p">(</span><span class="n">x1</span> <span class="o">-</span> <span class="n">x0</span><span class="p">,</span> <span class="n">y1</span> <span class="o">-</span> <span class="n">y0</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">island</span> <span class="ow">in</span> <span class="n">islands</span><span class="p">:</span>
-            <span class="n">dilated</span> <span class="o">=</span> <span class="n">gdf</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">island</span><span class="p">]</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">buffer</span><span class="p">(</span><span class="n">distance</span><span class="p">)</span>
-            <span class="n">neighbors</span> <span class="o">=</span> <span class="p">[</span>
-                <span class="n">j</span>
-                <span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">candidate</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">candidates</span><span class="p">)</span>
-                <span class="k">if</span> <span class="n">dilated</span><span class="o">.</span><span class="n">intersects</span><span class="p">(</span><span class="n">candidate</span><span class="p">)</span> <span class="ow">and</span> <span class="n">j</span> <span class="o">!=</span> <span class="n">island</span>
-            <span class="p">]</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">neighbors</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="k">for</span> <span class="n">neighbor</span> <span class="ow">in</span> <span class="n">neighbors</span><span class="p">:</span>
-                    <span class="k">if</span> <span class="n">neighbor</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">joins</span><span class="p">[</span><span class="n">island</span><span class="p">]:</span>
-                        <span class="n">fixes</span><span class="p">[</span><span class="n">island</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">neighbor</span><span class="p">)</span>
-                        <span class="n">joins</span><span class="p">[</span><span class="n">island</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">neighbor</span><span class="p">)</span>
-                    <span class="k">if</span> <span class="n">island</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">joins</span><span class="p">[</span><span class="n">neighbor</span><span class="p">]:</span>
-                        <span class="n">fixes</span><span class="p">[</span><span class="n">neighbor</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">island</span><span class="p">)</span>
-                        <span class="n">joins</span><span class="p">[</span><span class="n">neighbor</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">island</span><span class="p">)</span>
-
-    <span class="n">w</span> <span class="o">=</span> <span class="n">W</span><span class="p">(</span><span class="n">joins</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-    <span class="n">w</span><span class="o">.</span><span class="n">non_planar_joins</span> <span class="o">=</span> <span class="n">fixes</span>
-    <span class="k">return</span> <span class="n">w</span></div>
-
-<div class="viewcode-block" id="fuzzy_contiguity"><a class="viewcode-back" href="../../../generated/libpysal.weights.fuzzy_contiguity.html#libpysal.weights.fuzzy_contiguity">[docs]</a><span class="nd">@requires</span><span class="p">(</span><span class="s1">&#39;geopandas&#39;</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">fuzzy_contiguity</span><span class="p">(</span><span class="n">gdf</span><span class="p">,</span> <span class="n">tolerance</span><span class="o">=</span><span class="mf">0.005</span><span class="p">,</span> <span class="n">buffering</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">drop</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">buffer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">predicate</span><span class="o">=</span><span class="s1">&#39;intersects&#39;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Fuzzy contiguity spatial weights</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-
-<span class="sd">    gdf:   GeoDataFrame</span>
-
-<span class="sd">    tolerance: float</span>
-<span class="sd">               The percentage of the length of the minimum side of the bounding rectangle for the GeoDataFrame to use in determining the buffering distance.</span>
-
-<span class="sd">    buffering: boolean</span>
-<span class="sd">               If False (default) joins will only be detected for features that intersect (touch, contain, within).</span>
-<span class="sd">               If True then features will be buffered and intersections will be based on buffered features.</span>
-
-<span class="sd">    drop: boolean</span>
-<span class="sd">          If True (default), the buffered features are removed from the GeoDataFrame. If False, buffered features are added to the GeoDataFrame.</span>
-
-<span class="sd">    buffer : float</span>
-<span class="sd">             Specify exact buffering distance. Ignores `tolerance`.</span>
-
-<span class="sd">    predicate : {&#39;intersects&#39;, &#39;within&#39;, &#39;contains&#39;, &#39;overlaps&#39;, &#39;crosses&#39;, &#39;touches&#39;}</span>
-<span class="sd">                The predicate to use for determination of neighbors. Default is &#39;intersects&#39;. If None is passed, neighbours are determined based on</span>
-<span class="sd">                the intersection of bounding boxes.</span>
-
-<span class="sd">    **kwargs: keyword arguments</span>
-<span class="sd">              optional arguments for :class:`pysal.weights.W`</span>
-
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-
-<span class="sd">    w:  PySAL W</span>
-<span class="sd">        Spatial weights based on fuzzy contiguity. Weights are binary.</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-
-<span class="sd">    &gt;&gt;&gt; import libpysal</span>
-<span class="sd">    &gt;&gt;&gt; from libpysal.weights import fuzzy_contiguity</span>
-<span class="sd">    &gt;&gt;&gt; import geopandas as gpd</span>
-<span class="sd">    &gt;&gt;&gt; rs = libpysal.examples.get_path(&#39;map_RS_BR.shp&#39;)</span>
-<span class="sd">    &gt;&gt;&gt; rs_df = gpd.read_file(rs)</span>
-<span class="sd">    &gt;&gt;&gt; wq = libpysal.weights.Queen.from_dataframe(rs_df)</span>
-<span class="sd">    &gt;&gt;&gt; len(wq.islands)</span>
-<span class="sd">    29</span>
-<span class="sd">    &gt;&gt;&gt; wq[0]</span>
-<span class="sd">    {}</span>
-<span class="sd">    &gt;&gt;&gt; wf = fuzzy_contiguity(rs_df)</span>
-<span class="sd">    &gt;&gt;&gt; wf.islands</span>
-<span class="sd">    []</span>
-<span class="sd">    &gt;&gt;&gt; wf[0] == dict({239: 1.0, 59: 1.0, 152: 1.0, 23: 1.0, 107: 1.0})</span>
-<span class="sd">    True</span>
-
-<span class="sd">    Example needing to use buffering</span>
-
-<span class="sd">    &gt;&gt;&gt; from shapely.geometry import Polygon</span>
-<span class="sd">    &gt;&gt;&gt; p0 = Polygon([(0,0), (10,0), (10,10)])</span>
-<span class="sd">    &gt;&gt;&gt; p1 = Polygon([(10,1), (10,2), (15,2)])</span>
-<span class="sd">    &gt;&gt;&gt; p2 = Polygon([(12,2.001), (14, 2.001), (13,10)])</span>
-<span class="sd">    &gt;&gt;&gt; gs = gpd.GeoSeries([p0,p1,p2])</span>
-<span class="sd">    &gt;&gt;&gt; gdf = gpd.GeoDataFrame(geometry=gs)</span>
-<span class="sd">    &gt;&gt;&gt; wf = fuzzy_contiguity(gdf)</span>
-<span class="sd">    &gt;&gt;&gt; wf.islands</span>
-<span class="sd">    [2]</span>
-<span class="sd">    &gt;&gt;&gt; wfb = fuzzy_contiguity(gdf, buffering=True)</span>
-<span class="sd">    &gt;&gt;&gt; wfb.islands</span>
-<span class="sd">    []</span>
-<span class="sd">    &gt;&gt;&gt; wfb[2]</span>
-<span class="sd">    {1: 1.0}</span>
-
-<span class="sd">    Example with a custom index</span>
-
-<span class="sd">    &gt;&gt;&gt; rs_df_ix = rs_df.set_index(&quot;NM_MUNICIP&quot;)</span>
-<span class="sd">    &gt;&gt;&gt; wf_ix = fuzzy_contiguity(rs_df)</span>
-<span class="sd">    &gt;&gt;&gt; wf_ix.neighbors[&quot;TAVARES&quot;]</span>
-<span class="sd">    [&#39;SÃO JOSÉ DO NORTE&#39;, &#39;MOSTARDAS&#39;]</span>
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-
-<span class="sd">    This relaxes the notion of contiguity neighbors for the case of feature</span>
-<span class="sd">    collections that violate the condition of planar enforcement. It handles</span>
-<span class="sd">    three types of conditions present in such collections that would result in</span>
-<span class="sd">    islands when using the regular PySAL contiguity methods. The first are</span>
-<span class="sd">    edges for nearby polygons that should be shared, but are digitized</span>
-<span class="sd">    separately for the individual polygons and the resulting edges do not</span>
-<span class="sd">    coincide, but instead the edges intersect. The second case is similar to</span>
-<span class="sd">    the first, only the resultant edges do not intersect but are &quot;close&quot;. The</span>
-<span class="sd">    final case arises when one polygon is &quot;inside&quot; a second polygon but is not</span>
-<span class="sd">    encoded to represent a hole in the containing polygon.</span>
-
-<span class="sd">    Detection of the second case will require setting buffering=True and exploring different values for tolerance.</span>
-
-<span class="sd">    The buffering check assumes the geometry coordinates are projected.</span>
-
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-
-<span class="sd">    Planar Enforcement: http://ibis.geog.ubc.ca/courses/klink/gis.notes/ncgia/u12.html#SEC12.6</span>
-
-
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="n">buffering</span><span class="p">:</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">buffer</span><span class="p">:</span>
-            <span class="c1"># buffer each shape</span>
-            <span class="n">minx</span><span class="p">,</span> <span class="n">miny</span><span class="p">,</span> <span class="n">maxx</span><span class="p">,</span> <span class="n">maxy</span> <span class="o">=</span> <span class="n">gdf</span><span class="o">.</span><span class="n">total_bounds</span>
-            <span class="n">buffer</span> <span class="o">=</span> <span class="n">tolerance</span> <span class="o">*</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="nb">abs</span><span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="n">maxx</span> <span class="o">-</span> <span class="n">minx</span><span class="p">,</span> <span class="n">maxy</span> <span class="o">-</span> <span class="n">miny</span><span class="p">))</span>
-        <span class="c1"># create new geometry column</span>
-        <span class="n">new_geometry</span> <span class="o">=</span> <span class="n">gdf</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">buffer</span><span class="p">(</span><span class="n">buffer</span><span class="p">)</span>
-        <span class="n">gdf</span><span class="p">[</span><span class="s2">&quot;_buffer&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">new_geometry</span>
-        <span class="n">old_geometry_name</span> <span class="o">=</span> <span class="n">gdf</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">name</span>
-        <span class="n">gdf</span><span class="o">.</span><span class="n">set_geometry</span><span class="p">(</span><span class="s1">&#39;_buffer&#39;</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-    <span class="n">neighbors</span> <span class="o">=</span> <span class="p">{}</span>
-    <span class="k">if</span> <span class="n">GPD_08</span><span class="p">:</span>
-        <span class="c1"># query tree based on set predicate</span>
-        <span class="n">inp</span><span class="p">,</span> <span class="n">res</span> <span class="o">=</span> <span class="n">gdf</span><span class="o">.</span><span class="n">sindex</span><span class="o">.</span><span class="n">query_bulk</span><span class="p">(</span><span class="n">gdf</span><span class="o">.</span><span class="n">geometry</span><span class="p">,</span> <span class="n">predicate</span><span class="o">=</span><span class="n">predicate</span><span class="p">)</span>
-        <span class="c1"># remove self hits</span>
-        <span class="n">itself</span> <span class="o">=</span> <span class="n">inp</span> <span class="o">==</span> <span class="n">res</span>
-        <span class="n">inp</span> <span class="o">=</span> <span class="n">inp</span><span class="p">[</span><span class="o">~</span><span class="n">itself</span><span class="p">]</span>
-        <span class="n">res</span> <span class="o">=</span> <span class="n">res</span><span class="p">[</span><span class="o">~</span><span class="n">itself</span><span class="p">]</span>
-
-        <span class="c1"># extract index values of neighbors</span>
-        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">ix</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">gdf</span><span class="o">.</span><span class="n">index</span><span class="p">):</span>
-            <span class="n">ids</span> <span class="o">=</span> <span class="n">gdf</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">res</span><span class="p">[</span><span class="n">inp</span> <span class="o">==</span> <span class="n">i</span><span class="p">]]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
-            <span class="n">neighbors</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span> <span class="o">=</span> <span class="n">ids</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">if</span> <span class="n">predicate</span> <span class="o">!=</span> <span class="s1">&#39;intersects&#39;</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Predicate `</span><span class="si">{</span><span class="n">predicate</span><span class="si">}</span><span class="s1">` requires geopandas &gt;= 0.8.0.&#39;</span><span class="p">)</span>
-        <span class="n">tree</span> <span class="o">=</span> <span class="n">gdf</span><span class="o">.</span><span class="n">sindex</span>
-        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">ix</span><span class="p">,</span> <span class="n">geom</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">gdf</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">iteritems</span><span class="p">()):</span>
-            <span class="n">hits</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">tree</span><span class="o">.</span><span class="n">intersection</span><span class="p">(</span><span class="n">geom</span><span class="o">.</span><span class="n">bounds</span><span class="p">))</span>
-            <span class="n">hits</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
-            <span class="n">possible</span> <span class="o">=</span> <span class="n">gdf</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">hits</span><span class="p">]</span>
-            <span class="n">ids</span> <span class="o">=</span> <span class="n">possible</span><span class="p">[</span><span class="n">possible</span><span class="o">.</span><span class="n">intersects</span><span class="p">(</span><span class="n">geom</span><span class="p">)]</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
-            <span class="n">neighbors</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span> <span class="o">=</span> <span class="n">ids</span>
-
-    <span class="k">if</span> <span class="n">buffering</span><span class="p">:</span>
-        <span class="n">gdf</span><span class="o">.</span><span class="n">set_geometry</span><span class="p">(</span><span class="n">old_geometry_name</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">drop</span><span class="p">:</span>
-            <span class="n">gdf</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;_buffer&quot;</span><span class="p">],</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">W</span><span class="p">(</span><span class="n">neighbors</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
-
-
-<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
-
-    <span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span>
-
-    <span class="k">assert</span> <span class="p">(</span><span class="n">lat2W</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">todense</span><span class="p">()</span> <span class="o">==</span> <span class="n">lat2SW</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span><span class="o">.</span><span class="n">todense</span><span class="p">())</span><span class="o">.</span><span class="n">all</span><span class="p">()</span>
-    <span class="k">assert</span> <span class="p">(</span><span class="n">lat2W</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">todense</span><span class="p">()</span> <span class="o">==</span> <span class="n">lat2SW</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span><span class="o">.</span><span class="n">todense</span><span class="p">())</span><span class="o">.</span><span class="n">all</span><span class="p">()</span>
-    <span class="k">assert</span> <span class="p">(</span>
-        <span class="n">lat2W</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">rook</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">todense</span><span class="p">()</span> <span class="o">==</span> <span class="n">lat2SW</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="s2">&quot;queen&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">todense</span><span class="p">()</span>
-    <span class="p">)</span><span class="o">.</span><span class="n">all</span><span class="p">()</span>
-    <span class="k">assert</span> <span class="p">(</span>
-        <span class="n">lat2W</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="n">rook</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">todense</span><span class="p">()</span> <span class="o">==</span> <span class="n">lat2SW</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="s2">&quot;queen&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">todense</span><span class="p">()</span>
-    <span class="p">)</span><span class="o">.</span><span class="n">all</span><span class="p">()</span>
-</pre></div>
-
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-      
-  </div>
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-  <div class="container">
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-      <a href="#">Back to top</a>
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-    <p>
-        &copy; Copyright 2018-, pysal developers.<br/>
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diff --git a/docs/_modules/libpysal/weights/weights.html b/docs/_modules/libpysal/weights/weights.html
deleted file mode 100644
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-<html>
-  <head>
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-<ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../../tutorial.html#spatial-weights">Spatial Weights</a></li>
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-      
-  <h1>Source code for libpysal.weights.weights</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">Weights.</span>
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Sergio J. Rey &lt;srey@asu.edu&gt;&quot;</span>
-
-<span class="kn">import</span> <span class="nn">copy</span>
-<span class="kn">from</span> <span class="nn">os.path</span> <span class="kn">import</span> <span class="n">basename</span> <span class="k">as</span> <span class="n">BASENAME</span>
-<span class="kn">import</span> <span class="nn">math</span>
-<span class="kn">import</span> <span class="nn">warnings</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">scipy.sparse</span>
-<span class="kn">from</span> <span class="nn">scipy.sparse.csgraph</span> <span class="kn">import</span> <span class="n">connected_components</span>
-
-<span class="c1"># from .util import full, WSP2W resolve import cycle by</span>
-<span class="c1"># forcing these into methods</span>
-<span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">adjtools</span>
-<span class="kn">from</span> <span class="nn">..io.fileio</span> <span class="kn">import</span> <span class="n">FileIO</span> <span class="k">as</span> <span class="n">popen</span>
-
-<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;W&quot;</span><span class="p">,</span> <span class="s2">&quot;WSP&quot;</span><span class="p">]</span>
-
-
-<div class="viewcode-block" id="W"><a class="viewcode-back" href="../../../generated/libpysal.weights.W.html#libpysal.weights.W">[docs]</a><span class="k">class</span> <span class="nc">W</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Spatial weights class. Class attributes are described by their</span>
-<span class="sd">    docstrings. to view, use the ``help`` function.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    </span>
-<span class="sd">    neighbors : dict</span>
-<span class="sd">        Key is region ID, value is a list of neighbor IDS.</span>
-<span class="sd">        For example, ``{&#39;a&#39;:[&#39;b&#39;],&#39;b&#39;:[&#39;a&#39;,&#39;c&#39;],&#39;c&#39;:[&#39;b&#39;]}``.</span>
-<span class="sd">    weights : dict</span>
-<span class="sd">       Key is region ID, value is a list of edge weights.</span>
-<span class="sd">       If not supplied all edge weights are assumed to have a weight of 1.</span>
-<span class="sd">       For example, ``{&#39;a&#39;:[0.5],&#39;b&#39;:[0.5,1.5],&#39;c&#39;:[1.5]}``.</span>
-<span class="sd">    id_order : list</span>
-<span class="sd">       An ordered list of ids, defines the order of observations when</span>
-<span class="sd">       iterating over ``W`` if not set, lexicographical ordering is used</span>
-<span class="sd">       to iterate and the ``id_order_set`` property will return ``False``.</span>
-<span class="sd">       This can be set after creation by setting the ``id_order`` property.</span>
-<span class="sd">    silence_warnings : bool</span>
-<span class="sd">       By default ``libpysal`` will print a warning if the dataset contains</span>
-<span class="sd">       any disconnected components or islands. To silence this warning set this</span>
-<span class="sd">       parameter to ``True``.</span>
-<span class="sd">    ids : list</span>
-<span class="sd">        Values to use for keys of the neighbors and weights ``dict`` objects.</span>
-
-<span class="sd">    Attributes</span>
-<span class="sd">    ----------</span>
-<span class="sd">    </span>
-<span class="sd">    asymmetries</span>
-<span class="sd">    cardinalities</span>
-<span class="sd">    component_labels</span>
-<span class="sd">    diagW2</span>
-<span class="sd">    diagWtW</span>
-<span class="sd">    diagWtW_WW</span>
-<span class="sd">    histogram</span>
-<span class="sd">    id2i</span>
-<span class="sd">    id_order</span>
-<span class="sd">    id_order_set</span>
-<span class="sd">    islands</span>
-<span class="sd">    max_neighbors</span>
-<span class="sd">    mean_neighbors</span>
-<span class="sd">    min_neighbors</span>
-<span class="sd">    n</span>
-<span class="sd">    n_components</span>
-<span class="sd">    neighbor_offsets</span>
-<span class="sd">    nonzero</span>
-<span class="sd">    pct_nonzero</span>
-<span class="sd">    s0</span>
-<span class="sd">    s1</span>
-<span class="sd">    s2</span>
-<span class="sd">    s2array</span>
-<span class="sd">    sd</span>
-<span class="sd">    sparse</span>
-<span class="sd">    trcW2</span>
-<span class="sd">    trcWtW</span>
-<span class="sd">    trcWtW_WW</span>
-<span class="sd">    transform</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; from libpysal.weights import W</span>
-<span class="sd">    &gt;&gt;&gt; neighbors = {0: [3, 1], 1: [0, 4, 2], 2: [1, 5], 3: [0, 6, 4], 4: [1, 3, 7, 5], 5: [2, 4, 8], 6: [3, 7], 7: [4, 6, 8], 8: [5, 7]}</span>
-<span class="sd">    &gt;&gt;&gt; weights = {0: [1, 1], 1: [1, 1, 1], 2: [1, 1], 3: [1, 1, 1], 4: [1, 1, 1, 1], 5: [1, 1, 1], 6: [1, 1], 7: [1, 1, 1], 8: [1, 1]}</span>
-<span class="sd">    &gt;&gt;&gt; w = W(neighbors, weights)</span>
-<span class="sd">    &gt;&gt;&gt; &quot;%.3f&quot;%w.pct_nonzero</span>
-<span class="sd">    &#39;29.630&#39;</span>
-
-<span class="sd">    Read from external `.gal file &lt;https://geodacenter.github.io/workbook/4a_contig_weights/lab4a.html#gal-weights-file&gt;`_.</span>
-
-<span class="sd">    &gt;&gt;&gt; import libpysal</span>
-<span class="sd">    &gt;&gt;&gt; w = libpysal.io.open(libpysal.examples.get_path(&quot;stl.gal&quot;)).read()</span>
-<span class="sd">    &gt;&gt;&gt; w.n</span>
-<span class="sd">    78</span>
-<span class="sd">    &gt;&gt;&gt; &quot;%.3f&quot;%w.pct_nonzero</span>
-<span class="sd">    &#39;6.542&#39;</span>
-
-<span class="sd">    Set weights implicitly.</span>
-
-<span class="sd">    &gt;&gt;&gt; neighbors = {0: [3, 1], 1: [0, 4, 2], 2: [1, 5], 3: [0, 6, 4], 4: [1, 3, 7, 5], 5: [2, 4, 8], 6: [3, 7], 7: [4, 6, 8], 8: [5, 7]}</span>
-<span class="sd">    &gt;&gt;&gt; w = W(neighbors)</span>
-<span class="sd">    &gt;&gt;&gt; round(w.pct_nonzero,3)</span>
-<span class="sd">    29.63</span>
-<span class="sd">    &gt;&gt;&gt; from libpysal.weights import lat2W</span>
-<span class="sd">    &gt;&gt;&gt; w = lat2W(100, 100)</span>
-<span class="sd">    &gt;&gt;&gt; w.trcW2</span>
-<span class="sd">    39600.0</span>
-<span class="sd">    &gt;&gt;&gt; w.trcWtW</span>
-<span class="sd">    39600.0</span>
-<span class="sd">    &gt;&gt;&gt; w.transform=&#39;r&#39;</span>
-<span class="sd">    &gt;&gt;&gt; round(w.trcW2, 3)</span>
-<span class="sd">    2530.722</span>
-<span class="sd">    &gt;&gt;&gt; round(w.trcWtW, 3)</span>
-<span class="sd">    2533.667</span>
-
-<span class="sd">    Cardinality Histogram:</span>
-<span class="sd">    </span>
-<span class="sd">    &gt;&gt;&gt; w.histogram</span>
-<span class="sd">    [(2, 4), (3, 392), (4, 9604)]</span>
-
-<span class="sd">    Disconnected observations (islands):</span>
-
-<span class="sd">    &gt;&gt;&gt; from libpysal.weights import W</span>
-<span class="sd">    &gt;&gt;&gt; w = W({1:[0],0:[1],2:[], 3:[]})</span>
-
-<span class="sd">    UserWarning: The weights matrix is not fully connected:</span>
-<span class="sd">    There are 3 disconnected components.</span>
-<span class="sd">    There are 2 islands with ids: 2, 3.</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-
-<div class="viewcode-block" id="W.__init__"><a class="viewcode-back" href="../../../generated/libpysal.weights.W.html#libpysal.weights.W.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span> <span class="n">neighbors</span><span class="p">,</span> <span class="n">weights</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">id_order</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">silence_warnings</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="kc">None</span>
-    <span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">silence_warnings</span> <span class="o">=</span> <span class="n">silence_warnings</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">transformations</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">neighbors</span> <span class="o">=</span> <span class="n">neighbors</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">weights</span><span class="p">:</span>
-            <span class="n">weights</span> <span class="o">=</span> <span class="p">{}</span>
-            <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">neighbors</span><span class="p">:</span>
-                <span class="n">weights</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">neighbors</span><span class="p">[</span><span class="n">key</span><span class="p">])</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">weights</span> <span class="o">=</span> <span class="n">weights</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">[</span><span class="s2">&quot;O&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>  <span class="c1"># original weights</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">transform</span> <span class="o">=</span> <span class="s2">&quot;O&quot;</span>
-        <span class="k">if</span> <span class="n">id_order</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_id_order</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_id_order</span><span class="o">.</span><span class="n">sort</span><span class="p">()</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_id_order_set</span> <span class="o">=</span> <span class="kc">False</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_id_order</span> <span class="o">=</span> <span class="n">id_order</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_id_order_set</span> <span class="o">=</span> <span class="kc">True</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_reset</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">)</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">silence_warnings</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_components</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">message</span> <span class="o">=</span> <span class="p">(</span>
-                <span class="s2">&quot;The weights matrix is not fully connected: &quot;</span>
-                <span class="s2">&quot;</span><span class="se">\n</span><span class="s2"> There are </span><span class="si">%d</span><span class="s2"> disconnected components.&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_components</span>
-            <span class="p">)</span>
-            <span class="n">ni</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">islands</span><span class="p">)</span>
-            <span class="k">if</span> <span class="n">ni</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-                <span class="n">message</span> <span class="o">=</span> <span class="n">message</span> <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2"> There is 1 island with id: &quot;</span> <span class="s2">&quot;</span><span class="si">%s</span><span class="s2">.&quot;</span> <span class="o">%</span> <span class="p">(</span>
-                    <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">islands</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-                <span class="p">)</span>
-            <span class="k">elif</span> <span class="n">ni</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                <span class="n">message</span> <span class="o">=</span> <span class="n">message</span> <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2"> There are </span><span class="si">%d</span><span class="s2"> islands with ids: </span><span class="si">%s</span><span class="s2">.&quot;</span> <span class="o">%</span> <span class="p">(</span>
-                    <span class="n">ni</span><span class="p">,</span>
-                    <span class="s2">&quot;, &quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">island</span><span class="p">)</span> <span class="k">for</span> <span class="n">island</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">islands</span><span class="p">),</span>
-                <span class="p">)</span>
-            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="n">message</span><span class="p">)</span></div>
-
-    <span class="k">def</span> <span class="nf">_reset</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Reset properties.&quot;&quot;&quot;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span> <span class="o">=</span> <span class="p">{}</span>
-
-<div class="viewcode-block" id="W.to_file"><a class="viewcode-back" href="../../../generated/libpysal.weights.W.html#libpysal.weights.W.to_file">[docs]</a>    <span class="k">def</span> <span class="nf">to_file</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Write a weights to a file. The format is guessed automatically </span>
-<span class="sd">        from the path, but can be overridden with the format argument. </span>
-
-<span class="sd">        See libpysal.io.FileIO for more information. </span>
-
-<span class="sd">        Arguments</span>
-<span class="sd">        ---------</span>
-<span class="sd">        path    :   string</span>
-<span class="sd">                    location to save the file</span>
-<span class="sd">        format  :   string</span>
-<span class="sd">                    string denoting the format to write the weights to. </span>
-
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        None</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">f</span> <span class="o">=</span> <span class="n">popen</span><span class="p">(</span><span class="n">dataPath</span><span class="o">=</span><span class="n">path</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span> <span class="n">dataFormat</span><span class="o">=</span><span class="nb">format</span><span class="p">)</span>
-        <span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
-        <span class="n">f</span><span class="o">.</span><span class="n">close</span><span class="p">()</span></div>
-
-<div class="viewcode-block" id="W.from_file"><a class="viewcode-back" href="../../../generated/libpysal.weights.W.html#libpysal.weights.W.from_file">[docs]</a>    <span class="nd">@classmethod</span>
-    <span class="k">def</span> <span class="nf">from_file</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">path</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Read a weights file into a W object. </span>
-
-<span class="sd">        Arguments</span>
-<span class="sd">        ---------</span>
-<span class="sd">        path    :   string</span>
-<span class="sd">                    location to save the file</span>
-<span class="sd">        format  :   string</span>
-<span class="sd">                    string denoting the format to write the weights to. </span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        W object</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">f</span> <span class="o">=</span> <span class="n">popen</span><span class="p">(</span><span class="n">dataPath</span><span class="o">=</span><span class="n">path</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">&quot;r&quot;</span><span class="p">,</span> <span class="n">dataFormat</span><span class="o">=</span><span class="nb">format</span><span class="p">)</span>
-        <span class="n">w</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
-        <span class="n">f</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
-        <span class="k">return</span> <span class="n">w</span></div>
-
-<div class="viewcode-block" id="W.from_shapefile"><a class="viewcode-back" href="../../../generated/libpysal.weights.W.html#libpysal.weights.W.from_shapefile">[docs]</a>    <span class="nd">@classmethod</span>
-    <span class="k">def</span> <span class="nf">from_shapefile</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="c1"># we could also just &quot;do the right thing,&quot; but I think it&#39;d make sense to</span>
-        <span class="c1"># try and get people to use `Rook.from_shapefile(shapefile)` rather than</span>
-        <span class="c1"># W.from_shapefile(shapefile, type=`rook`), otherwise we&#39;d need to build</span>
-        <span class="c1"># a type dispatch table. Generic W should be for stuff we don&#39;t know</span>
-        <span class="c1"># anything about.</span>
-        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span>
-            <span class="s2">&quot;Use type-specific constructors, like Rook,&quot;</span>
-            <span class="s2">&quot; Queen, DistanceBand, or Kernel&quot;</span>
-        <span class="p">)</span></div>
-
-<div class="viewcode-block" id="W.from_WSP"><a class="viewcode-back" href="../../../generated/libpysal.weights.W.html#libpysal.weights.W.from_WSP">[docs]</a>    <span class="nd">@classmethod</span>
-    <span class="k">def</span> <span class="nf">from_WSP</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">WSP</span><span class="p">,</span> <span class="n">silence_warnings</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-        <span class="k">return</span> <span class="n">WSP2W</span><span class="p">(</span><span class="n">WSP</span><span class="p">,</span> <span class="n">silence_warnings</span><span class="o">=</span><span class="n">silence_warnings</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="W.from_adjlist"><a class="viewcode-back" href="../../../generated/libpysal.weights.W.html#libpysal.weights.W.from_adjlist">[docs]</a>    <span class="nd">@classmethod</span>
-    <span class="k">def</span> <span class="nf">from_adjlist</span><span class="p">(</span>
-        <span class="bp">cls</span><span class="p">,</span> <span class="n">adjlist</span><span class="p">,</span> <span class="n">focal_col</span><span class="o">=</span><span class="s2">&quot;focal&quot;</span><span class="p">,</span> <span class="n">neighbor_col</span><span class="o">=</span><span class="s2">&quot;neighbor&quot;</span><span class="p">,</span> <span class="n">weight_col</span><span class="o">=</span><span class="kc">None</span>
-    <span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Return an adjacency list representation of a weights object.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        </span>
-<span class="sd">        adjlist : pandas.DataFrame</span>
-<span class="sd">            Adjacency list with a minimum of two columns.</span>
-<span class="sd">        focal_col : str</span>
-<span class="sd">            Name of the column with the &quot;source&quot; node ids.</span>
-<span class="sd">        neighbor_col : str</span>
-<span class="sd">            Name of the column with the &quot;destination&quot; node ids.</span>
-<span class="sd">        weight_col : str</span>
-<span class="sd">            Name of the column with the weight information. If not provided and</span>
-<span class="sd">            the dataframe has no column named &quot;weight&quot; then all weights</span>
-<span class="sd">            are assumed to be 1.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="n">weight_col</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">weight_col</span> <span class="o">=</span> <span class="s2">&quot;weight&quot;</span>
-        <span class="n">try_weightcol</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">adjlist</span><span class="p">,</span> <span class="n">weight_col</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">try_weightcol</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">adjlist</span> <span class="o">=</span> <span class="n">adjlist</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">deep</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-            <span class="n">adjlist</span><span class="p">[</span><span class="s2">&quot;weight&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
-        <span class="n">all_ids</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">adjlist</span><span class="p">[</span><span class="n">focal_col</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span>
-        <span class="n">all_ids</span> <span class="o">|=</span> <span class="nb">set</span><span class="p">(</span><span class="n">adjlist</span><span class="p">[</span><span class="n">neighbor_col</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span>
-        <span class="n">grouper</span> <span class="o">=</span> <span class="n">adjlist</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="n">focal_col</span><span class="p">)</span>
-        <span class="n">neighbors</span> <span class="o">=</span> <span class="n">grouper</span><span class="p">[</span><span class="n">neighbor_col</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="nb">list</span><span class="p">)</span><span class="o">.</span><span class="n">to_dict</span><span class="p">()</span>
-        <span class="n">weights</span> <span class="o">=</span> <span class="n">grouper</span><span class="p">[</span><span class="n">weight_col</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="nb">list</span><span class="p">)</span><span class="o">.</span><span class="n">to_dict</span><span class="p">()</span>
-        <span class="n">neighbors</span><span class="o">.</span><span class="n">update</span><span class="p">({</span><span class="n">k</span><span class="p">:</span> <span class="p">[]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">all_ids</span><span class="o">.</span><span class="n">difference</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">neighbors</span><span class="o">.</span><span class="n">keys</span><span class="p">()))})</span>
-        <span class="n">weights</span><span class="o">.</span><span class="n">update</span><span class="p">({</span><span class="n">k</span><span class="p">:</span> <span class="p">[]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">all_ids</span><span class="o">.</span><span class="n">difference</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">weights</span><span class="o">.</span><span class="n">keys</span><span class="p">()))})</span>
-        <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span><span class="n">neighbors</span><span class="o">=</span><span class="n">neighbors</span><span class="p">,</span> <span class="n">weights</span><span class="o">=</span><span class="n">weights</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="W.to_adjlist"><a class="viewcode-back" href="../../../generated/libpysal.weights.W.html#libpysal.weights.W.to_adjlist">[docs]</a>    <span class="k">def</span> <span class="nf">to_adjlist</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">remove_symmetric</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-        <span class="n">focal_col</span><span class="o">=</span><span class="s2">&quot;focal&quot;</span><span class="p">,</span>
-        <span class="n">neighbor_col</span><span class="o">=</span><span class="s2">&quot;neighbor&quot;</span><span class="p">,</span>
-        <span class="n">weight_col</span><span class="o">=</span><span class="s2">&quot;weight&quot;</span><span class="p">,</span>
-    <span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Compute an adjacency list representation of a weights object.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        remove_symmetric : bool</span>
-<span class="sd">            Whether or not to remove symmetric entries. If the ``W``</span>
-<span class="sd">            is symmetric, a standard directed adjacency list will contain</span>
-<span class="sd">            both the forward and backward links by default because adjacency</span>
-<span class="sd">            lists are a directed graph representation. If this is ``True``,</span>
-<span class="sd">            a ``W`` created from this adjacency list **MAY NOT BE THE SAME**</span>
-<span class="sd">            as the original ``W``. If you would like to consider (1,2) and</span>
-<span class="sd">            (2,1) as distinct links, leave this as ``False``.</span>
-<span class="sd">        focal_col : str</span>
-<span class="sd">            Name of the column in which to store &quot;source&quot; node ids.</span>
-<span class="sd">        neighbor_col : str</span>
-<span class="sd">            Name of the column in which to store &quot;destination&quot; node ids.</span>
-<span class="sd">        weight_col : str</span>
-<span class="sd">            Name of the column in which to store weight information.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-        <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ImportError</span><span class="p">(</span><span class="s2">&quot;pandas must be installed to use this method&quot;</span><span class="p">)</span>
-        <span class="n">n_islands</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">islands</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">n_islands</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="p">(</span><span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">silence_warnings</span><span class="p">):</span>
-            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
-                <span class="s2">&quot;</span><span class="si">{}</span><span class="s2"> islands in this weights matrix. Conversion to an &quot;</span>
-                <span class="s2">&quot;adjacency list will drop these observations!&quot;</span>
-            <span class="p">)</span>
-        <span class="n">adjlist</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span>
-            <span class="p">((</span><span class="n">idx</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">w</span><span class="p">)</span> <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">neighb</span> <span class="ow">in</span> <span class="bp">self</span> <span class="k">for</span> <span class="n">n</span><span class="p">,</span> <span class="n">w</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="n">neighb</span><span class="o">.</span><span class="n">items</span><span class="p">())),</span>
-            <span class="n">columns</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;focal&quot;</span><span class="p">,</span> <span class="s2">&quot;neighbor&quot;</span><span class="p">,</span> <span class="s2">&quot;weight&quot;</span><span class="p">),</span>
-        <span class="p">)</span>
-        <span class="k">return</span> <span class="n">adjtools</span><span class="o">.</span><span class="n">filter_adjlist</span><span class="p">(</span><span class="n">adjlist</span><span class="p">)</span> <span class="k">if</span> <span class="n">remove_symmetric</span> <span class="k">else</span> <span class="n">adjlist</span></div>
-
-<div class="viewcode-block" id="W.to_networkx"><a class="viewcode-back" href="../../../generated/libpysal.weights.W.html#libpysal.weights.W.to_networkx">[docs]</a>    <span class="k">def</span> <span class="nf">to_networkx</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Convert a weights object to a ``networkx`` graph.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        A ``networkx`` graph representation of the ``W`` object.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="kn">import</span> <span class="nn">networkx</span> <span class="k">as</span> <span class="nn">nx</span>
-        <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ImportError</span><span class="p">(</span><span class="s2">&quot;NetworkX is required to use this function.&quot;</span><span class="p">)</span>
-        <span class="n">G</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">DiGraph</span><span class="p">()</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">asymmetries</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="n">nx</span><span class="o">.</span><span class="n">Graph</span><span class="p">()</span>
-        <span class="k">return</span> <span class="n">nx</span><span class="o">.</span><span class="n">from_scipy_sparse_matrix</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sparse</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="n">G</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="W.from_networkx"><a class="viewcode-back" href="../../../generated/libpysal.weights.W.html#libpysal.weights.W.from_networkx">[docs]</a>    <span class="nd">@classmethod</span>
-    <span class="k">def</span> <span class="nf">from_networkx</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">graph</span><span class="p">,</span> <span class="n">weight_col</span><span class="o">=</span><span class="s2">&quot;weight&quot;</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Convert a ``networkx`` graph to a PySAL ``W`` object.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        graph : networkx.Graph</span>
-<span class="sd">            The graph to convert to a ``W``.</span>
-<span class="sd">        weight_col : string</span>
-<span class="sd">            If the graph is labeled, this should be the name of the field</span>
-<span class="sd">            to use as the weight for the ``W``.</span>
-<span class="sd">        </span>
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        w : libpysal.weights.W</span>
-<span class="sd">            A ``W`` object containing the same graph as the ``networkx`` graph.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="kn">import</span> <span class="nn">networkx</span> <span class="k">as</span> <span class="nn">nx</span>
-        <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ImportError</span><span class="p">(</span><span class="s2">&quot;NetworkX is required to use this function.&quot;</span><span class="p">)</span>
-        <span class="n">sparse_matrix</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">to_scipy_sparse_matrix</span><span class="p">(</span><span class="n">graph</span><span class="p">)</span>
-        <span class="n">w</span> <span class="o">=</span> <span class="n">WSP</span><span class="p">(</span><span class="n">sparse_matrix</span><span class="p">)</span><span class="o">.</span><span class="n">to_W</span><span class="p">()</span>
-        <span class="k">return</span> <span class="n">w</span></div>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">sparse</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Sparse matrix object. For any matrix manipulations required for w,</span>
-<span class="sd">        ``w.sparse`` should be used. This is based on ``scipy.sparse``.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="s2">&quot;sparse&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_sparse</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_build_sparse</span><span class="p">()</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;sparse&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sparse</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sparse</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">n_components</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Store whether the adjacency matrix is fully connected.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="s2">&quot;n_components&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_n_components</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_component_labels</span> <span class="o">=</span> <span class="n">connected_components</span><span class="p">(</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span>
-            <span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;n_components&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_n_components</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;component_labels&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_component_labels</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_n_components</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">component_labels</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Store the graph component in which each observation falls.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="s2">&quot;component_labels&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_n_components</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_component_labels</span> <span class="o">=</span> <span class="n">connected_components</span><span class="p">(</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span>
-            <span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;n_components&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_n_components</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;component_labels&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_component_labels</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_component_labels</span>
-
-    <span class="k">def</span> <span class="nf">_build_sparse</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Construct the sparse attribute.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">row</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="n">col</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="n">data</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="n">id2i</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">id2i</span>
-        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">neigh_list</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">neighbor_offsets</span><span class="o">.</span><span class="n">items</span><span class="p">()):</span>
-            <span class="n">card</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cardinalities</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-            <span class="n">row</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span><span class="n">id2i</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span> <span class="o">*</span> <span class="n">card</span><span class="p">)</span>
-            <span class="n">col</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">neigh_list</span><span class="p">)</span>
-            <span class="n">data</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-        <span class="n">row</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">row</span><span class="p">)</span>
-        <span class="n">col</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">col</span><span class="p">)</span>
-        <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
-        <span class="n">s</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">csr_matrix</span><span class="p">((</span><span class="n">data</span><span class="p">,</span> <span class="p">(</span><span class="n">row</span><span class="p">,</span> <span class="n">col</span><span class="p">)),</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">n</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">n</span><span class="p">))</span>
-        <span class="k">return</span> <span class="n">s</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">id2i</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Dictionary where the key is an ID and the value is that ID&#39;s</span>
-<span class="sd">        index in ``W.id_order``.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="s2">&quot;id2i&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_id2i</span> <span class="o">=</span> <span class="p">{}</span>
-            <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">id_i</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_id_order</span><span class="p">):</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_id2i</span><span class="p">[</span><span class="n">id_i</span><span class="p">]</span> <span class="o">=</span> <span class="n">i</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_id2i</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_id2i</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;id2i&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_id2i</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_id2i</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">n</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Number of units.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="s2">&quot;n&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">neighbors</span><span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;n&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_n</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_n</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">s0</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sa">r</span><span class="sd">&quot;&quot;&quot;``s0`` is defined as</span>
-
-<span class="sd">        .. math::</span>
-
-<span class="sd">               s0=\sum_i \sum_j w_{i,j}</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="s2">&quot;s0&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_s0</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;s0&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_s0</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_s0</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">s1</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sa">r</span><span class="sd">&quot;&quot;&quot;``s1`` is defined as</span>
-
-<span class="sd">        .. math::</span>
-
-<span class="sd">               s1=1/2 \sum_i \sum_j \Big(w_{i,j} + w_{j,i}\Big)^2</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="s2">&quot;s1&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
-            <span class="n">t</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">transpose</span><span class="p">()</span>
-            <span class="n">t</span> <span class="o">=</span> <span class="n">t</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span>
-            <span class="n">t2</span> <span class="o">=</span> <span class="n">t</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>  <span class="c1"># element-wise square</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_s1</span> <span class="o">=</span> <span class="n">t2</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">/</span> <span class="mf">2.0</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;s1&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_s1</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_s1</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">s2array</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Individual elements comprising ``s2``.</span>
-
-<span class="sd">        See Also</span>
-<span class="sd">        --------</span>
-<span class="sd">        s2</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="s2">&quot;s2array&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
-            <span class="n">s</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_s2array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="n">s</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">transpose</span><span class="p">())</span> <span class="o">**</span> <span class="mi">2</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;s2array&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_s2array</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_s2array</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">s2</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sa">r</span><span class="sd">&quot;&quot;&quot;``s2`` is defined as</span>
-
-<span class="sd">        .. math::</span>
-
-<span class="sd">                s2=\sum_j \Big(\sum_i w_{i,j} + \sum_i w_{j,i}\Big)^2</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="s2">&quot;s2&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_s2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">s2array</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;s2&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_s2</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_s2</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">trcW2</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Trace of :math:`WW`.</span>
-
-<span class="sd">        See Also</span>
-<span class="sd">        --------</span>
-<span class="sd">        diagW2</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="s2">&quot;trcW2&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_trcW2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">diagW2</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;trcw2&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_trcW2</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_trcW2</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">diagW2</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Diagonal of :math:`WW`.</span>
-
-<span class="sd">        See Also</span>
-<span class="sd">        --------</span>
-<span class="sd">        trcW2</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="s2">&quot;diagw2&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_diagW2</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sparse</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span><span class="p">)</span><span class="o">.</span><span class="n">diagonal</span><span class="p">()</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;diagW2&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_diagW2</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_diagW2</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">diagWtW</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Diagonal of :math:`W^{&#39;}W`.</span>
-
-<span class="sd">        See Also</span>
-<span class="sd">        --------</span>
-<span class="sd">        trcWtW</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="s2">&quot;diagWtW&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_diagWtW</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">transpose</span><span class="p">()</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span><span class="p">)</span><span class="o">.</span><span class="n">diagonal</span><span class="p">()</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;diagWtW&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_diagWtW</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_diagWtW</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">trcWtW</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Trace of :math:`W^{&#39;}W`.</span>
-
-<span class="sd">        See Also</span>
-<span class="sd">        --------</span>
-<span class="sd">        diagWtW</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="s2">&quot;trcWtW&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_trcWtW</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">diagWtW</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;trcWtW&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_trcWtW</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_trcWtW</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">diagWtW_WW</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Diagonal of :math:`W^{&#39;}W + WW`.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="s2">&quot;diagWtW_WW&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
-            <span class="n">wt</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">transpose</span><span class="p">()</span>
-            <span class="n">w</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_diagWtW_WW</span> <span class="o">=</span> <span class="p">(</span><span class="n">wt</span> <span class="o">*</span> <span class="n">w</span> <span class="o">+</span> <span class="n">w</span> <span class="o">*</span> <span class="n">w</span><span class="p">)</span><span class="o">.</span><span class="n">diagonal</span><span class="p">()</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;diagWtW_WW&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_diagWtW_WW</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_diagWtW_WW</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">trcWtW_WW</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Trace of :math:`W^{&#39;}W + WW`.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="s2">&quot;trcWtW_WW&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_trcWtW_WW</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">diagWtW_WW</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;trcWtW_WW&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_trcWtW_WW</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_trcWtW_WW</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">pct_nonzero</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Percentage of nonzero weights.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="s2">&quot;pct_nonzero&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_pct_nonzero</span> <span class="o">=</span> <span class="mf">100.0</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">nnz</span> <span class="o">/</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_n</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;pct_nonzero&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pct_nonzero</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pct_nonzero</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">cardinalities</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Number of neighbors for each observation.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="s2">&quot;cardinalities&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
-            <span class="n">c</span> <span class="o">=</span> <span class="p">{}</span>
-            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_id_order</span><span class="p">:</span>
-                <span class="n">c</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_cardinalities</span> <span class="o">=</span> <span class="n">c</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;cardinalities&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cardinalities</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cardinalities</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">max_neighbors</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Largest number of neighbors.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="s2">&quot;max_neighbors&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_max_neighbors</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cardinalities</span><span class="o">.</span><span class="n">values</span><span class="p">())</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;max_neighbors&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_max_neighbors</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_max_neighbors</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">mean_neighbors</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Average number of neighbors.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="s2">&quot;mean_neighbors&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_mean_neighbors</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cardinalities</span><span class="o">.</span><span class="n">values</span><span class="p">()))</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;mean_neighbors&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_mean_neighbors</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_mean_neighbors</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">min_neighbors</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Minimum number of neighbors.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="s2">&quot;min_neighbors&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_min_neighbors</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cardinalities</span><span class="o">.</span><span class="n">values</span><span class="p">())</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;min_neighbors&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_min_neighbors</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_min_neighbors</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">nonzero</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Number of nonzero weights.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="s2">&quot;nonzero&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_nonzero</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">nnz</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;nonzero&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_nonzero</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_nonzero</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">sd</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Standard deviation of number of neighbors.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="s2">&quot;sd&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_sd</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cardinalities</span><span class="o">.</span><span class="n">values</span><span class="p">()))</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;sd&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sd</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sd</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">asymmetries</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;List of id pairs with asymmetric weights.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="s2">&quot;asymmetries&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_asymmetries</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">asymmetry</span><span class="p">()</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;asymmetries&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_asymmetries</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_asymmetries</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">islands</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;List of ids without any neighbors.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="s2">&quot;islands&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_islands</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">c</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cardinalities</span><span class="o">.</span><span class="n">items</span><span class="p">())</span> <span class="k">if</span> <span class="n">c</span> <span class="o">==</span> <span class="mi">0</span><span class="p">]</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;islands&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_islands</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_islands</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">histogram</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Cardinality histogram as a dictionary where key is the id and</span>
-<span class="sd">        value is the number of neighbors for that unit.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="s2">&quot;histogram&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
-            <span class="n">ct</span><span class="p">,</span> <span class="nb">bin</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">histogram</span><span class="p">(</span>
-                <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cardinalities</span><span class="o">.</span><span class="n">values</span><span class="p">()),</span>
-                <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">min_neighbors</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_neighbors</span> <span class="o">+</span> <span class="mi">2</span><span class="p">)),</span>
-            <span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_histogram</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="nb">bin</span><span class="p">,</span> <span class="n">ct</span><span class="p">))</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;histogram&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_histogram</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_histogram</span>
-
-    <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Allow a dictionary like interaction with the weights class.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        &gt;&gt;&gt; from libpysal.weights import lat2W</span>
-<span class="sd">        &gt;&gt;&gt; w = lat2W()</span>
-
-<span class="sd">        &gt;&gt;&gt; w[0] == dict({1: 1.0, 5: 1.0})</span>
-<span class="sd">        True</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">key</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="n">key</span><span class="p">])))</span>
-
-    <span class="k">def</span> <span class="fm">__iter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Support iteration over weights.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        &gt;&gt;&gt; from libpysal.weights import lat2W</span>
-<span class="sd">        &gt;&gt;&gt; w=lat2W(3,3)</span>
-<span class="sd">        &gt;&gt;&gt; for i,wi in enumerate(w):</span>
-<span class="sd">        ...     print(i,wi[0])</span>
-<span class="sd">        ...</span>
-<span class="sd">        0 0</span>
-<span class="sd">        1 1</span>
-<span class="sd">        2 2</span>
-<span class="sd">        3 3</span>
-<span class="sd">        4 4</span>
-<span class="sd">        5 5</span>
-<span class="sd">        6 6</span>
-<span class="sd">        7 7</span>
-<span class="sd">        8 8</span>
-<span class="sd">        &gt;&gt;&gt;</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_id_order</span><span class="p">:</span>
-            <span class="k">yield</span> <span class="n">i</span><span class="p">,</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="n">i</span><span class="p">])))</span>
-
-<div class="viewcode-block" id="W.remap_ids"><a class="viewcode-back" href="../../../generated/libpysal.weights.W.html#libpysal.weights.W.remap_ids">[docs]</a>    <span class="k">def</span> <span class="nf">remap_ids</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">new_ids</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        In place modification throughout ``W`` of id values from</span>
-<span class="sd">        ``w.id_order`` to ``new_ids`` in all.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-
-<span class="sd">        new_ids : list, numpy.ndarray</span>
-<span class="sd">            Aligned list of new ids to be inserted. Note that first</span>
-<span class="sd">            element of ``new_ids`` will replace first element of</span>
-<span class="sd">            ``w.id_order``, second element of ``new_ids`` replaces second</span>
-<span class="sd">            element of ``w.id_order`` and so on.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-
-<span class="sd">        &gt;&gt;&gt; from libpysal.weights import lat2W</span>
-<span class="sd">        &gt;&gt;&gt; w = lat2W(3, 3)</span>
-<span class="sd">        &gt;&gt;&gt; w.id_order</span>
-<span class="sd">        [0, 1, 2, 3, 4, 5, 6, 7, 8]</span>
-<span class="sd">        &gt;&gt;&gt; w.neighbors[0]</span>
-<span class="sd">        [3, 1]</span>
-<span class="sd">        &gt;&gt;&gt; new_ids = [&#39;id%i&#39;%id for id in w.id_order]</span>
-<span class="sd">        &gt;&gt;&gt; _ = w.remap_ids(new_ids)</span>
-<span class="sd">        &gt;&gt;&gt; w.id_order</span>
-<span class="sd">        [&#39;id0&#39;, &#39;id1&#39;, &#39;id2&#39;, &#39;id3&#39;, &#39;id4&#39;, &#39;id5&#39;, &#39;id6&#39;, &#39;id7&#39;, &#39;id8&#39;]</span>
-<span class="sd">        &gt;&gt;&gt; w.neighbors[&#39;id0&#39;]</span>
-<span class="sd">        [&#39;id3&#39;, &#39;id1&#39;]</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">old_ids</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_id_order</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">old_ids</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">new_ids</span><span class="p">):</span>
-            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span>
-                <span class="s2">&quot;W.remap_ids: length of `old_ids` does not match </span><span class="se">\</span>
-<span class="s2">            that of new_ids&quot;</span>
-            <span class="p">)</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">new_ids</span><span class="p">))</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">new_ids</span><span class="p">):</span>
-            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;W.remap_ids: list `new_ids` contains duplicates&quot;</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">new_neighbors</span> <span class="o">=</span> <span class="p">{}</span>
-            <span class="n">new_weights</span> <span class="o">=</span> <span class="p">{}</span>
-            <span class="n">old_transformations</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">[</span><span class="s2">&quot;O&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-            <span class="n">new_transformations</span> <span class="o">=</span> <span class="p">{}</span>
-            <span class="k">for</span> <span class="n">o</span><span class="p">,</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">old_ids</span><span class="p">,</span> <span class="n">new_ids</span><span class="p">):</span>
-                <span class="n">o_neighbors</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">o</span><span class="p">]</span>
-                <span class="n">o_weights</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="n">o</span><span class="p">]</span>
-                <span class="n">n_neighbors</span> <span class="o">=</span> <span class="p">[</span><span class="n">new_ids</span><span class="p">[</span><span class="n">old_ids</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">j</span><span class="p">)]</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">o_neighbors</span><span class="p">]</span>
-                <span class="n">new_neighbors</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="n">n_neighbors</span>
-                <span class="n">new_weights</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="n">o_weights</span><span class="p">[:]</span>
-                <span class="n">new_transformations</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="n">old_transformations</span><span class="p">[</span><span class="n">o</span><span class="p">]</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">neighbors</span> <span class="o">=</span> <span class="n">new_neighbors</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">weights</span> <span class="o">=</span> <span class="n">new_weights</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">[</span><span class="s2">&quot;O&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">new_transformations</span>
-
-            <span class="n">id_order</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_id_order</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">o</span><span class="p">)</span> <span class="k">for</span> <span class="n">o</span> <span class="ow">in</span> <span class="n">old_ids</span><span class="p">]</span>
-            <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">id_</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">id_order</span><span class="p">):</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">id_order</span><span class="p">[</span><span class="n">id_</span><span class="p">]</span> <span class="o">=</span> <span class="n">new_ids</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-
-            <span class="bp">self</span><span class="o">.</span><span class="n">_reset</span><span class="p">()</span></div>
-
-    <span class="k">def</span> <span class="nf">__set_id_order</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ordered_ids</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Set the iteration order in w. ``W`` can be iterated over. On </span>
-<span class="sd">        construction the iteration order is set to the lexicographic order of </span>
-<span class="sd">        the keys in the ``w.weights`` dictionary. If a specific order</span>
-<span class="sd">        is required it can be set with this method.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-
-<span class="sd">        ordered_ids : sequence</span>
-<span class="sd">            Identifiers for observations in specified order.</span>
-
-<span class="sd">        Notes</span>
-<span class="sd">        -----</span>
-
-<span class="sd">        The ``ordered_ids`` parameter is checked against the ids implied</span>
-<span class="sd">        by the keys in ``w.weights``. If they are not equivalent sets an</span>
-<span class="sd">        exception is raised and the iteration order is not changed.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-
-<span class="sd">        &gt;&gt;&gt; from libpysal.weights import lat2W</span>
-<span class="sd">        &gt;&gt;&gt; w=lat2W(3,3)</span>
-<span class="sd">        &gt;&gt;&gt; for i,wi in enumerate(w):</span>
-<span class="sd">        ...     print(i, wi[0])</span>
-<span class="sd">        ...</span>
-<span class="sd">        0 0</span>
-<span class="sd">        1 1</span>
-<span class="sd">        2 2</span>
-<span class="sd">        3 3</span>
-<span class="sd">        4 4</span>
-<span class="sd">        5 5</span>
-<span class="sd">        6 6</span>
-<span class="sd">        7 7</span>
-<span class="sd">        8 8</span>
-<span class="sd">        &gt;&gt;&gt; w.id_order</span>
-<span class="sd">        [0, 1, 2, 3, 4, 5, 6, 7, 8]</span>
-<span class="sd">        &gt;&gt;&gt; w.id_order=range(8,-1,-1)</span>
-<span class="sd">        &gt;&gt;&gt; list(w.id_order)</span>
-<span class="sd">        [8, 7, 6, 5, 4, 3, 2, 1, 0]</span>
-<span class="sd">        &gt;&gt;&gt; for i,w_i in enumerate(w):</span>
-<span class="sd">        ...     print(i,w_i[0])</span>
-<span class="sd">        ...</span>
-<span class="sd">        0 8</span>
-<span class="sd">        1 7</span>
-<span class="sd">        2 6</span>
-<span class="sd">        3 5</span>
-<span class="sd">        4 4</span>
-<span class="sd">        5 3</span>
-<span class="sd">        6 2</span>
-<span class="sd">        7 1</span>
-<span class="sd">        8 0</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_id_order</span><span class="p">)</span> <span class="o">==</span> <span class="nb">set</span><span class="p">(</span><span class="n">ordered_ids</span><span class="p">):</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_id_order</span> <span class="o">=</span> <span class="n">ordered_ids</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_id_order_set</span> <span class="o">=</span> <span class="kc">True</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_reset</span><span class="p">()</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;ordered_ids do not align with W ids&quot;</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">__get_id_order</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Returns the ids for the observations in the order in which they</span>
-<span class="sd">        would be encountered if iterating over the weights.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_id_order</span>
-
-    <span class="n">id_order</span> <span class="o">=</span> <span class="nb">property</span><span class="p">(</span><span class="n">__get_id_order</span><span class="p">,</span> <span class="n">__set_id_order</span><span class="p">)</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">id_order_set</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot; Returns ``True`` if user has set ``id_order``, ``False`` if not.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        &gt;&gt;&gt; from libpysal.weights import lat2W</span>
-<span class="sd">        &gt;&gt;&gt; w=lat2W()</span>
-<span class="sd">        &gt;&gt;&gt; w.id_order_set</span>
-<span class="sd">        True</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_id_order_set</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">neighbor_offsets</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Given the current ``id_order``, ``neighbor_offsets[id]`` is the</span>
-<span class="sd">        offsets of the id&#39;s neighbors in ``id_order``.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        neighbor_list : list</span>
-<span class="sd">            Offsets of the id&#39;s neighbors in ``id_order``.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        &gt;&gt;&gt; from libpysal.weights import W</span>
-<span class="sd">        &gt;&gt;&gt; neighbors={&#39;c&#39;: [&#39;b&#39;], &#39;b&#39;: [&#39;c&#39;, &#39;a&#39;], &#39;a&#39;: [&#39;b&#39;]}</span>
-<span class="sd">        &gt;&gt;&gt; weights ={&#39;c&#39;: [1.0], &#39;b&#39;: [1.0, 1.0], &#39;a&#39;: [1.0]}</span>
-<span class="sd">        &gt;&gt;&gt; w=W(neighbors,weights)</span>
-<span class="sd">        &gt;&gt;&gt; w.id_order = [&#39;a&#39;,&#39;b&#39;,&#39;c&#39;]</span>
-<span class="sd">        &gt;&gt;&gt; w.neighbor_offsets[&#39;b&#39;]</span>
-<span class="sd">        [2, 0]</span>
-<span class="sd">        &gt;&gt;&gt; w.id_order = [&#39;b&#39;,&#39;a&#39;,&#39;c&#39;]</span>
-<span class="sd">        &gt;&gt;&gt; w.neighbor_offsets[&#39;b&#39;]</span>
-<span class="sd">        [2, 1]</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="s2">&quot;neighbors_0&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">__neighbors_0</span> <span class="o">=</span> <span class="p">{}</span>
-            <span class="n">id2i</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">id2i</span>
-            <span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">neigh_list</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">items</span><span class="p">()):</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">__neighbors_0</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">id2i</span><span class="p">[</span><span class="n">neigh</span><span class="p">]</span> <span class="k">for</span> <span class="n">neigh</span> <span class="ow">in</span> <span class="n">neigh_list</span><span class="p">]</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;neighbors_0&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__neighbors_0</span>
-
-        <span class="n">neighbor_list</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__neighbors_0</span>
-
-        <span class="k">return</span> <span class="n">neighbor_list</span>
-
-<div class="viewcode-block" id="W.get_transform"><a class="viewcode-back" href="../../../generated/libpysal.weights.W.html#libpysal.weights.W.get_transform">[docs]</a>    <span class="k">def</span> <span class="nf">get_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Getter for transform property.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        transformation : str, None</span>
-<span class="sd">            Valid transformation value. See the ``transform`` </span>
-<span class="sd">            parameters in ``set_transform()`` for a detailed description.</span>
-<span class="sd">            </span>
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        &gt;&gt;&gt; from libpysal.weights import lat2W</span>
-<span class="sd">        &gt;&gt;&gt; w=lat2W()</span>
-<span class="sd">        &gt;&gt;&gt; w.weights[0]</span>
-<span class="sd">        [1.0, 1.0]</span>
-<span class="sd">        &gt;&gt;&gt; w.transform</span>
-<span class="sd">        &#39;O&#39;</span>
-<span class="sd">        &gt;&gt;&gt; w.transform=&#39;r&#39;</span>
-<span class="sd">        &gt;&gt;&gt; w.weights[0]</span>
-<span class="sd">        [0.5, 0.5]</span>
-<span class="sd">        &gt;&gt;&gt; w.transform=&#39;b&#39;</span>
-<span class="sd">        &gt;&gt;&gt; w.weights[0]</span>
-<span class="sd">        [1.0, 1.0]</span>
-
-<span class="sd">        See also</span>
-<span class="sd">        --------</span>
-<span class="sd">        set_transform</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_transform</span></div>
-
-<div class="viewcode-block" id="W.set_transform"><a class="viewcode-back" href="../../../generated/libpysal.weights.W.html#libpysal.weights.W.set_transform">[docs]</a>    <span class="k">def</span> <span class="nf">set_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="o">=</span><span class="s2">&quot;B&quot;</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Transformations of weights.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        transform : str</span>
-<span class="sd">            This parameter is not case sensitive. The following are</span>
-<span class="sd">            valid transformations.</span>
-<span class="sd">            </span>
-<span class="sd">            * **B** -- Binary</span>
-<span class="sd">            * **R** -- Row-standardization (global sum :math:`=n`)</span>
-<span class="sd">            * **D** -- Double-standardization (global sum :math:`=1`)</span>
-<span class="sd">            * **V** -- Variance stabilizing</span>
-<span class="sd">            * **O** -- Restore original transformation (from instantiation)</span>
-<span class="sd">        </span>
-<span class="sd">        Notes</span>
-<span class="sd">        -----</span>
-
-<span class="sd">        Transformations are applied only to the value of the weights at</span>
-<span class="sd">        instantiation. Chaining of transformations cannot be done on a ``W``</span>
-<span class="sd">        instance.</span>
-
-<span class="sd">        </span>
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        &gt;&gt;&gt; from libpysal.weights import lat2W</span>
-<span class="sd">        &gt;&gt;&gt; w=lat2W()</span>
-<span class="sd">        &gt;&gt;&gt; w.weights[0]</span>
-<span class="sd">        [1.0, 1.0]</span>
-<span class="sd">        &gt;&gt;&gt; w.transform</span>
-<span class="sd">        &#39;O&#39;</span>
-<span class="sd">        &gt;&gt;&gt; w.transform=&#39;r&#39;</span>
-<span class="sd">        &gt;&gt;&gt; w.weights[0]</span>
-<span class="sd">        [0.5, 0.5]</span>
-<span class="sd">        &gt;&gt;&gt; w.transform=&#39;b&#39;</span>
-<span class="sd">        &gt;&gt;&gt; w.weights[0]</span>
-<span class="sd">        [1.0, 1.0]</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">value</span> <span class="o">=</span> <span class="n">value</span><span class="o">.</span><span class="n">upper</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_transform</span> <span class="o">=</span> <span class="n">value</span>
-        <span class="k">if</span> <span class="n">value</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">weights</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">[</span><span class="n">value</span><span class="p">]</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_reset</span><span class="p">()</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">value</span> <span class="o">==</span> <span class="s2">&quot;R&quot;</span><span class="p">:</span>
-                <span class="c1"># row standardized weights</span>
-                <span class="n">weights</span> <span class="o">=</span> <span class="p">{}</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">weights</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">[</span><span class="s2">&quot;O&quot;</span><span class="p">]</span>
-                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">:</span>
-                    <span class="n">wijs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-                    <span class="n">row_sum</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">wijs</span><span class="p">)</span> <span class="o">*</span> <span class="mf">1.0</span>
-                    <span class="k">if</span> <span class="n">row_sum</span> <span class="o">==</span> <span class="mf">0.0</span><span class="p">:</span>
-                        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">silence_warnings</span><span class="p">:</span>
-                            <span class="nb">print</span><span class="p">((</span><span class="s2">&quot;WARNING: &quot;</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="s2">&quot; is an island (no neighbors)&quot;</span><span class="p">))</span>
-                    <span class="n">weights</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">wij</span> <span class="o">/</span> <span class="n">row_sum</span> <span class="k">for</span> <span class="n">wij</span> <span class="ow">in</span> <span class="n">wijs</span><span class="p">]</span>
-                <span class="n">weights</span> <span class="o">=</span> <span class="n">weights</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">[</span><span class="n">value</span><span class="p">]</span> <span class="o">=</span> <span class="n">weights</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">weights</span> <span class="o">=</span> <span class="n">weights</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_reset</span><span class="p">()</span>
-            <span class="k">elif</span> <span class="n">value</span> <span class="o">==</span> <span class="s2">&quot;D&quot;</span><span class="p">:</span>
-                <span class="c1"># doubly-standardized weights</span>
-                <span class="c1"># update current chars before doing global sum</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_reset</span><span class="p">()</span>
-                <span class="n">s0</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">s0</span>
-                <span class="n">ws</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="o">/</span> <span class="n">s0</span>
-                <span class="n">weights</span> <span class="o">=</span> <span class="p">{}</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">weights</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">[</span><span class="s2">&quot;O&quot;</span><span class="p">]</span>
-                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">:</span>
-                    <span class="n">wijs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-                    <span class="n">weights</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">wij</span> <span class="o">*</span> <span class="n">ws</span> <span class="k">for</span> <span class="n">wij</span> <span class="ow">in</span> <span class="n">wijs</span><span class="p">]</span>
-                <span class="n">weights</span> <span class="o">=</span> <span class="n">weights</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">[</span><span class="n">value</span><span class="p">]</span> <span class="o">=</span> <span class="n">weights</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">weights</span> <span class="o">=</span> <span class="n">weights</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_reset</span><span class="p">()</span>
-            <span class="k">elif</span> <span class="n">value</span> <span class="o">==</span> <span class="s2">&quot;B&quot;</span><span class="p">:</span>
-                <span class="c1"># binary transformation</span>
-                <span class="n">weights</span> <span class="o">=</span> <span class="p">{}</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">weights</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">[</span><span class="s2">&quot;O&quot;</span><span class="p">]</span>
-                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">:</span>
-                    <span class="n">wijs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-                    <span class="n">weights</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mf">1.0</span> <span class="k">for</span> <span class="n">wij</span> <span class="ow">in</span> <span class="n">wijs</span><span class="p">]</span>
-                <span class="n">weights</span> <span class="o">=</span> <span class="n">weights</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">[</span><span class="n">value</span><span class="p">]</span> <span class="o">=</span> <span class="n">weights</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">weights</span> <span class="o">=</span> <span class="n">weights</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_reset</span><span class="p">()</span>
-            <span class="k">elif</span> <span class="n">value</span> <span class="o">==</span> <span class="s2">&quot;V&quot;</span><span class="p">:</span>
-                <span class="c1"># variance stabilizing</span>
-                <span class="n">weights</span> <span class="o">=</span> <span class="p">{}</span>
-                <span class="n">q</span> <span class="o">=</span> <span class="p">{}</span>
-                <span class="n">k</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cardinalities</span>
-                <span class="n">s</span> <span class="o">=</span> <span class="p">{}</span>
-                <span class="n">Q</span> <span class="o">=</span> <span class="mf">0.0</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">weights</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">[</span><span class="s2">&quot;O&quot;</span><span class="p">]</span>
-                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">:</span>
-                    <span class="n">wijs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-                    <span class="n">q</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="nb">sum</span><span class="p">([</span><span class="n">wij</span> <span class="o">*</span> <span class="n">wij</span> <span class="k">for</span> <span class="n">wij</span> <span class="ow">in</span> <span class="n">wijs</span><span class="p">]))</span>
-                    <span class="n">s</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">wij</span> <span class="o">/</span> <span class="n">q</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">wij</span> <span class="ow">in</span> <span class="n">wijs</span><span class="p">]</span>
-                    <span class="n">Q</span> <span class="o">+=</span> <span class="nb">sum</span><span class="p">([</span><span class="n">si</span> <span class="k">for</span> <span class="n">si</span> <span class="ow">in</span> <span class="n">s</span><span class="p">[</span><span class="n">i</span><span class="p">]])</span>
-                <span class="n">nQ</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">n</span> <span class="o">/</span> <span class="n">Q</span>
-                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">:</span>
-                    <span class="n">weights</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">w</span> <span class="o">*</span> <span class="n">nQ</span> <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">s</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span>
-                <span class="n">weights</span> <span class="o">=</span> <span class="n">weights</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">[</span><span class="n">value</span><span class="p">]</span> <span class="o">=</span> <span class="n">weights</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">weights</span> <span class="o">=</span> <span class="n">weights</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_reset</span><span class="p">()</span>
-            <span class="k">elif</span> <span class="n">value</span> <span class="o">==</span> <span class="s2">&quot;O&quot;</span><span class="p">:</span>
-                <span class="c1"># put weights back to original transformation</span>
-                <span class="n">weights</span> <span class="o">=</span> <span class="p">{}</span>
-                <span class="n">original</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">[</span><span class="n">value</span><span class="p">]</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">weights</span> <span class="o">=</span> <span class="n">original</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_reset</span><span class="p">()</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;unsupported weights transformation&quot;</span><span class="p">)</span></div>
-
-    <span class="n">transform</span> <span class="o">=</span> <span class="nb">property</span><span class="p">(</span><span class="n">get_transform</span><span class="p">,</span> <span class="n">set_transform</span><span class="p">)</span>
-
-<div class="viewcode-block" id="W.asymmetry"><a class="viewcode-back" href="../../../generated/libpysal.weights.W.html#libpysal.weights.W.asymmetry">[docs]</a>    <span class="k">def</span> <span class="nf">asymmetry</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">intrinsic</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-        <span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Asymmetry check.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        intrinsic : bool</span>
-<span class="sd">            Default is ``True``. Intrinsic symmetry is defined as</span>
-<span class="sd">            </span>
-<span class="sd">            .. math::</span>
-<span class="sd">                </span>
-<span class="sd">                w_{i,j} == w_{j,i}</span>
-<span class="sd">            </span>
-<span class="sd">            If ``intrinsic`` is ``False`` symmetry is defined as</span>
-<span class="sd">            </span>
-<span class="sd">            .. math:: </span>
-<span class="sd">            </span>
-<span class="sd">                i \in N_j \ \&amp; \ j \in N_i</span>
-<span class="sd">                </span>
-<span class="sd">            where :math:`N_j` is the set of neighbors for :math:`j`.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        asymmetries : list</span>
-<span class="sd">            Empty if no asymmetries are found if asymmetries, then a</span>
-<span class="sd">            ``list`` of ``(i,j)`` tuples is returned.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-
-<span class="sd">        &gt;&gt;&gt; from libpysal.weights import lat2W</span>
-<span class="sd">        &gt;&gt;&gt; w=lat2W(3,3)</span>
-<span class="sd">        &gt;&gt;&gt; w.asymmetry()</span>
-<span class="sd">        []</span>
-<span class="sd">        &gt;&gt;&gt; w.transform=&#39;r&#39;</span>
-<span class="sd">        &gt;&gt;&gt; w.asymmetry()</span>
-<span class="sd">        [(0, 1), (0, 3), (1, 0), (1, 2), (1, 4), (2, 1), (2, 5), (3, 0), (3, 4), (3, 6), (4, 1), (4, 3), (4, 5), (4, 7), (5, 2), (5, 4), (5, 8), (6, 3), (6, 7), (7, 4), (7, 6), (7, 8), (8, 5), (8, 7)]</span>
-<span class="sd">        &gt;&gt;&gt; result = w.asymmetry(intrinsic=False)</span>
-<span class="sd">        &gt;&gt;&gt; result</span>
-<span class="sd">        []</span>
-<span class="sd">        &gt;&gt;&gt; neighbors={0:[1,2,3], 1:[1,2,3], 2:[0,1], 3:[0,1]}</span>
-<span class="sd">        &gt;&gt;&gt; weights={0:[1,1,1], 1:[1,1,1], 2:[1,1], 3:[1,1]}</span>
-<span class="sd">        &gt;&gt;&gt; w=W(neighbors,weights)</span>
-<span class="sd">        &gt;&gt;&gt; w.asymmetry()</span>
-<span class="sd">        [(0, 1), (1, 0)]</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="n">intrinsic</span><span class="p">:</span>
-            <span class="n">wd</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">transpose</span><span class="p">()</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">transform</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transform</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">transform</span> <span class="o">=</span> <span class="s2">&quot;b&quot;</span>
-            <span class="n">wd</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">transpose</span><span class="p">()</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">transform</span> <span class="o">=</span> <span class="n">transform</span>
-
-        <span class="n">ids</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nonzero</span><span class="p">(</span><span class="n">wd</span><span class="p">)</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">ids</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">return</span> <span class="p">[]</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">ijs</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">ids</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">ids</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
-            <span class="n">ijs</span><span class="o">.</span><span class="n">sort</span><span class="p">()</span>
-            <span class="k">return</span> <span class="n">ijs</span></div>
-
-<div class="viewcode-block" id="W.symmetrize"><a class="viewcode-back" href="../../../generated/libpysal.weights.W.html#libpysal.weights.W.symmetrize">[docs]</a>    <span class="k">def</span> <span class="nf">symmetrize</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Construct a symmetric KNN weight. This ensures that the neighbors</span>
-<span class="sd">        of each focal observation consider the focal observation itself as</span>
-<span class="sd">        a neighbor. This returns a generic ``W`` object, since the object is no</span>
-<span class="sd">        longer guaranteed to have ``k`` neighbors for each observation.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">inplace</span><span class="p">:</span>
-            <span class="n">neighbors</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">neighbors</span><span class="p">)</span>
-            <span class="n">weights</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">)</span>
-            <span class="n">out_W</span> <span class="o">=</span> <span class="n">W</span><span class="p">(</span><span class="n">neighbors</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">id_order</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">id_order</span><span class="p">)</span>
-            <span class="n">out_W</span><span class="o">.</span><span class="n">symmetrize</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-            <span class="k">return</span> <span class="n">out_W</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">for</span> <span class="n">focal</span><span class="p">,</span> <span class="n">fneighbs</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">items</span><span class="p">()):</span>
-                <span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">neighbor</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">fneighbs</span><span class="p">):</span>
-                    <span class="n">neighb_neighbors</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">neighbor</span><span class="p">]</span>
-                    <span class="k">if</span> <span class="n">focal</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">neighb_neighbors</span><span class="p">:</span>
-                        <span class="bp">self</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">neighbor</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">focal</span><span class="p">)</span>
-                        <span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="n">neighbor</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="n">focal</span><span class="p">][</span><span class="n">j</span><span class="p">])</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
-            <span class="k">return</span></div>
-
-<div class="viewcode-block" id="W.full"><a class="viewcode-back" href="../../../generated/libpysal.weights.W.html#libpysal.weights.W.full">[docs]</a>    <span class="k">def</span> <span class="nf">full</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Generate a full ``numpy.ndarray``.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        self : libpysal.weights.W</span>
-<span class="sd">            spatial weights object</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        (fullw, keys) : tuple</span>
-<span class="sd">            The first element being the full ``numpy.ndarray`` and second</span>
-<span class="sd">            element keys being the ids associated with each row in the array.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        &gt;&gt;&gt; from libpysal.weights import W, full</span>
-<span class="sd">        &gt;&gt;&gt; neighbors = {&#39;first&#39;:[&#39;second&#39;],&#39;second&#39;:[&#39;first&#39;,&#39;third&#39;],&#39;third&#39;:[&#39;second&#39;]}</span>
-<span class="sd">        &gt;&gt;&gt; weights = {&#39;first&#39;:[1],&#39;second&#39;:[1,1],&#39;third&#39;:[1]}</span>
-<span class="sd">        &gt;&gt;&gt; w = W(neighbors, weights)</span>
-<span class="sd">        &gt;&gt;&gt; wf, ids = full(w)</span>
-<span class="sd">        &gt;&gt;&gt; wf</span>
-<span class="sd">        array([[0., 1., 0.],</span>
-<span class="sd">               [1., 0., 1.],</span>
-<span class="sd">               [0., 1., 0.]])</span>
-<span class="sd">        &gt;&gt;&gt; ids</span>
-<span class="sd">        [&#39;first&#39;, &#39;second&#39;, &#39;third&#39;]</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">wfull</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">n</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">n</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
-        <span class="n">keys</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">id_order</span><span class="p">:</span>
-            <span class="n">keys</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">id_order</span>
-        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">keys</span><span class="p">):</span>
-            <span class="n">n_i</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
-            <span class="n">w_i</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
-            <span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">wij</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">n_i</span><span class="p">,</span> <span class="n">w_i</span><span class="p">):</span>
-                <span class="n">c</span> <span class="o">=</span> <span class="n">keys</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">j</span><span class="p">)</span>
-                <span class="n">wfull</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">wij</span>
-        <span class="k">return</span> <span class="p">(</span><span class="n">wfull</span><span class="p">,</span> <span class="n">keys</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="W.to_WSP"><a class="viewcode-back" href="../../../generated/libpysal.weights.W.html#libpysal.weights.W.to_WSP">[docs]</a>    <span class="k">def</span> <span class="nf">to_WSP</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Generate a ``WSP`` object.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-
-<span class="sd">        implicit : libpysal.weights.WSP</span>
-<span class="sd">            Thin ``W`` class</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        &gt;&gt;&gt; from libpysal.weights import W, WSP</span>
-<span class="sd">        &gt;&gt;&gt; neighbors={&#39;first&#39;:[&#39;second&#39;],&#39;second&#39;:[&#39;first&#39;,&#39;third&#39;],&#39;third&#39;:[&#39;second&#39;]}</span>
-<span class="sd">        &gt;&gt;&gt; weights={&#39;first&#39;:[1],&#39;second&#39;:[1,1],&#39;third&#39;:[1]}</span>
-<span class="sd">        &gt;&gt;&gt; w=W(neighbors,weights)</span>
-<span class="sd">        &gt;&gt;&gt; wsp=w.to_WSP()</span>
-<span class="sd">        &gt;&gt;&gt; isinstance(wsp, WSP)</span>
-<span class="sd">        True</span>
-<span class="sd">        &gt;&gt;&gt; wsp.n</span>
-<span class="sd">        3</span>
-<span class="sd">        &gt;&gt;&gt; wsp.s0</span>
-<span class="sd">        4</span>
-
-<span class="sd">        See also</span>
-<span class="sd">        --------</span>
-<span class="sd">        WSP</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="n">WSP</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sparse</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_id_order</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="W.set_shapefile"><a class="viewcode-back" href="../../../generated/libpysal.weights.W.html#libpysal.weights.W.set_shapefile">[docs]</a>    <span class="k">def</span> <span class="nf">set_shapefile</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">shapefile</span><span class="p">,</span> <span class="n">idVariable</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">full</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Adding metadata for writing headers of ``.gal`` and ``.gwt`` files.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        shapefile : str</span>
-<span class="sd">            The shapefile name used to construct weights.</span>
-<span class="sd">        idVariable : str</span>
-<span class="sd">            The name of the attribute in the shapefile to associate</span>
-<span class="sd">            with ids in the weights.</span>
-<span class="sd">        full : bool</span>
-<span class="sd">            Write out the entire path for a shapefile (``True``) or</span>
-<span class="sd">            only the base of the shapefile without extension (``False``).</span>
-<span class="sd">            Default is ``True``.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="n">full</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_shpName</span> <span class="o">=</span> <span class="n">shapefile</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_shpName</span> <span class="o">=</span> <span class="n">BASENAME</span><span class="p">(</span><span class="n">shapefile</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">_varName</span> <span class="o">=</span> <span class="n">idVariable</span></div>
-
-<div class="viewcode-block" id="W.plot"><a class="viewcode-back" href="../../../generated/libpysal.weights.W.html#libpysal.weights.W.plot">[docs]</a>    <span class="k">def</span> <span class="nf">plot</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span> <span class="n">gdf</span><span class="p">,</span> <span class="n">indexed_on</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;k&quot;</span><span class="p">,</span> <span class="n">node_kws</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">edge_kws</span><span class="o">=</span><span class="kc">None</span>
-    <span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Plot spatial weights objects. **Requires** ``matplotlib``, and</span>
-<span class="sd">        implicitly requires a ``geopandas.GeoDataFrame`` as input.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        gdf : geopandas.GeoDataFrame</span>
-<span class="sd">            The original shapes whose topological relations are modelled in ``W``.</span>
-<span class="sd">        indexed_on : str </span>
-<span class="sd">            Column of ``geopandas.GeoDataFrame`` that the weights object uses</span>
-<span class="sd">            as an index. Default is ``None``, so the index of the </span>
-<span class="sd">            ``geopandas.GeoDataFrame`` is used.</span>
-<span class="sd">        ax : matplotlib.axes.Axes</span>
-<span class="sd">            Axis on which to plot the weights. Default is ``None``, so</span>
-<span class="sd">            plots on the current figure.</span>
-<span class="sd">        color : str</span>
-<span class="sd">            ``matplotlib`` color string, will color both nodes and edges</span>
-<span class="sd">            the same by default.</span>
-<span class="sd">        node_kws : dict</span>
-<span class="sd">            Keyword arguments dictionary to send to ``pyplot.scatter``,</span>
-<span class="sd">            which provides fine-grained control over the aesthetics</span>
-<span class="sd">            of the nodes in the plot.</span>
-<span class="sd">        edge_kws : dict</span>
-<span class="sd">            Keyword arguments dictionary to send to ``pyplot.plot``,</span>
-<span class="sd">            which provides fine-grained control over the aesthetics</span>
-<span class="sd">            of the edges in the plot.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        f : matplotlib.figure.Figure</span>
-<span class="sd">            Figure on which the plot is made.</span>
-<span class="sd">        ax : matplotlib.axes.Axes</span>
-<span class="sd">            Axis on which the plot is made.</span>
-<span class="sd">        </span>
-<span class="sd">        Notes</span>
-<span class="sd">        -----</span>
-<span class="sd">        If you&#39;d like to overlay the actual shapes from the </span>
-<span class="sd">        ``geopandas.GeoDataFrame``, call ``gdf.plot(ax=ax)`` after this.</span>
-<span class="sd">        To plot underneath, adjust the z-order of the plot as follows: </span>
-<span class="sd">        ``gdf.plot(ax=ax,zorder=0)``.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-
-<span class="sd">        &gt;&gt;&gt; from libpysal.weights import Queen</span>
-<span class="sd">        &gt;&gt;&gt; import libpysal as lp</span>
-<span class="sd">        &gt;&gt;&gt; import geopandas</span>
-<span class="sd">        &gt;&gt;&gt; gdf = geopandas.read_file(lp.examples.get_path(&quot;columbus.shp&quot;))</span>
-<span class="sd">        &gt;&gt;&gt; weights = Queen.from_dataframe(gdf)</span>
-<span class="sd">        &gt;&gt;&gt; tmp = weights.plot(gdf, color=&#39;firebrickred&#39;, node_kws=dict(marker=&#39;*&#39;, color=&#39;k&#39;))</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-        <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ImportError</span><span class="p">(</span>
-                <span class="s2">&quot;W.plot depends on matplotlib.pyplot, and this was&quot;</span>
-                <span class="s2">&quot;not able to be imported. </span><span class="se">\n</span><span class="s2">Install matplotlib to&quot;</span>
-                <span class="s2">&quot;plot spatial weights.&quot;</span>
-            <span class="p">)</span>
-        <span class="k">if</span> <span class="n">ax</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">f</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
-            <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">f</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gcf</span><span class="p">()</span>
-        <span class="k">if</span> <span class="n">node_kws</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">if</span> <span class="s2">&quot;color&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">node_kws</span><span class="p">:</span>
-                <span class="n">node_kws</span><span class="p">[</span><span class="s2">&quot;color&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">color</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">node_kws</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">edge_kws</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">if</span> <span class="s2">&quot;color&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">edge_kws</span><span class="p">:</span>
-                <span class="n">edge_kws</span><span class="p">[</span><span class="s2">&quot;color&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">color</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">edge_kws</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">)</span>
-
-        <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">neighbors</span> <span class="ow">in</span> <span class="bp">self</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">idx</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">islands</span><span class="p">:</span>
-                <span class="k">continue</span>
-            <span class="k">if</span> <span class="n">indexed_on</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">neighbors</span> <span class="o">=</span> <span class="n">gdf</span><span class="p">[</span><span class="n">gdf</span><span class="p">[</span><span class="n">indexed_on</span><span class="p">]</span><span class="o">.</span><span class="n">isin</span><span class="p">(</span><span class="n">neighbors</span><span class="p">)]</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
-                <span class="n">idx</span> <span class="o">=</span> <span class="n">gdf</span><span class="p">[</span><span class="n">gdf</span><span class="p">[</span><span class="n">indexed_on</span><span class="p">]</span> <span class="o">==</span> <span class="n">idx</span><span class="p">]</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">tolist</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="n">centroids</span> <span class="o">=</span> <span class="n">gdf</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">neighbors</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">p</span><span class="p">:</span> <span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">x</span><span class="p">,</span> <span class="n">p</span><span class="o">.</span><span class="n">y</span><span class="p">))</span>
-            <span class="n">centroids</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">(</span><span class="n">centroids</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
-            <span class="n">focal</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">(</span><span class="n">gdf</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">centroid</span><span class="o">.</span><span class="n">xy</span><span class="p">)</span>
-            <span class="n">seen</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
-            <span class="k">for</span> <span class="n">nidx</span><span class="p">,</span> <span class="n">neighbor</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">neighbors</span><span class="p">,</span> <span class="n">centroids</span><span class="p">):</span>
-                <span class="k">if</span> <span class="p">(</span><span class="n">idx</span><span class="p">,</span> <span class="n">nidx</span><span class="p">)</span> <span class="ow">in</span> <span class="n">seen</span><span class="p">:</span>
-                    <span class="k">continue</span>
-                <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="o">*</span><span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">focal</span><span class="p">,</span> <span class="n">neighbor</span><span class="p">)),</span> <span class="n">marker</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">edge_kws</span><span class="p">)</span>
-                <span class="n">seen</span><span class="o">.</span><span class="n">update</span><span class="p">((</span><span class="n">idx</span><span class="p">,</span> <span class="n">nidx</span><span class="p">))</span>
-                <span class="n">seen</span><span class="o">.</span><span class="n">update</span><span class="p">((</span><span class="n">nidx</span><span class="p">,</span> <span class="n">idx</span><span class="p">))</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
-            <span class="n">gdf</span><span class="o">.</span><span class="n">centroid</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">p</span><span class="p">:</span> <span class="n">p</span><span class="o">.</span><span class="n">x</span><span class="p">),</span>
-            <span class="n">gdf</span><span class="o">.</span><span class="n">centroid</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">p</span><span class="p">:</span> <span class="n">p</span><span class="o">.</span><span class="n">y</span><span class="p">),</span>
-            <span class="o">**</span><span class="n">node_kws</span>
-        <span class="p">)</span>
-        <span class="k">return</span> <span class="n">f</span><span class="p">,</span> <span class="n">ax</span></div></div>
-
-
-<div class="viewcode-block" id="WSP"><a class="viewcode-back" href="../../../generated/libpysal.weights.WSP.html#libpysal.weights.WSP">[docs]</a><span class="k">class</span> <span class="nc">WSP</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Thin ``W`` class for ``spreg``.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-
-<span class="sd">    sparse : scipy.sparse.{matrix-type}</span>
-<span class="sd">        NxN object from ``scipy.sparse``</span>
-
-<span class="sd">    Attributes</span>
-<span class="sd">    ----------</span>
-
-<span class="sd">    n           : int</span>
-<span class="sd">                  description</span>
-<span class="sd">    s0          : float</span>
-<span class="sd">                  description</span>
-<span class="sd">    trcWtW_WW   : float</span>
-<span class="sd">                  description</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-
-<span class="sd">    From GAL information</span>
-
-<span class="sd">    &gt;&gt;&gt; import scipy.sparse</span>
-<span class="sd">    &gt;&gt;&gt; from libpysal.weights import WSP</span>
-<span class="sd">    &gt;&gt;&gt; rows = [0, 1, 1, 2, 2, 3]</span>
-<span class="sd">    &gt;&gt;&gt; cols = [1, 0, 2, 1, 3, 3]</span>
-<span class="sd">    &gt;&gt;&gt; weights =  [1, 0.75, 0.25, 0.9, 0.1, 1]</span>
-<span class="sd">    &gt;&gt;&gt; sparse = scipy.sparse.csr_matrix((weights, (rows, cols)), shape=(4,4))</span>
-<span class="sd">    &gt;&gt;&gt; w = WSP(sparse)</span>
-<span class="sd">    &gt;&gt;&gt; w.s0</span>
-<span class="sd">    4.0</span>
-<span class="sd">    &gt;&gt;&gt; w.trcWtW_WW</span>
-<span class="sd">    6.395</span>
-<span class="sd">    &gt;&gt;&gt; w.n</span>
-<span class="sd">    4</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-
-<div class="viewcode-block" id="WSP.__init__"><a class="viewcode-back" href="../../../generated/libpysal.weights.WSP.html#libpysal.weights.WSP.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sparse</span><span class="p">,</span> <span class="n">id_order</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">scipy</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">issparse</span><span class="p">(</span><span class="n">sparse</span><span class="p">):</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;must pass a scipy sparse object&quot;</span><span class="p">)</span>
-        <span class="n">rows</span><span class="p">,</span> <span class="n">cols</span> <span class="o">=</span> <span class="n">sparse</span><span class="o">.</span><span class="n">shape</span>
-        <span class="k">if</span> <span class="n">rows</span> <span class="o">!=</span> <span class="n">cols</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Weights object must be square&quot;</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span> <span class="o">=</span> <span class="n">sparse</span><span class="o">.</span><span class="n">tocsr</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">n</span> <span class="o">=</span> <span class="n">sparse</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="k">if</span> <span class="n">id_order</span><span class="p">:</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">id_order</span><span class="p">)</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">n</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-                    <span class="s2">&quot;Number of values in id_order must match shape of sparse&quot;</span>
-                <span class="p">)</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_id_order</span> <span class="o">=</span> <span class="n">id_order</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;id_order&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_id_order</span>
-        <span class="c1"># temp addition of index attribute</span>
-        <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>  <span class="c1"># will be removed after refactoring is done</span>
-        <span class="k">if</span> <span class="n">index</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">Index</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">RangeIndex</span><span class="p">)):</span>
-                <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;index must be an instance of pandas.Index dtype&quot;</span><span class="p">)</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">n</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-                    <span class="s2">&quot;Number of values in index must match shape of sparse&quot;</span>
-                <span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">index</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">RangeIndex</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">n</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="n">index</span></div>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">id_order</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;An ordered list of ids, assumed to match the ordering in ``sparse``.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="c1"># Temporary solution until the refactoring is finished</span>
-        <span class="k">if</span> <span class="s2">&quot;id_order&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
-            <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;index&quot;</span><span class="p">):</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_id_order</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_id_order</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">n</span><span class="p">))</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;id_order&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_id_order</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_id_order</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">s0</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sa">r</span><span class="sd">&quot;&quot;&quot;``s0`` is defined as:</span>
-
-<span class="sd">        .. math::</span>
-
-<span class="sd">               s0=\sum_i \sum_j w_{i,j}</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="s2">&quot;s0&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_s0</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;s0&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_s0</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_s0</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">trcWtW_WW</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Trace of :math:`W^{&#39;}W + WW`.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="s2">&quot;trcWtW_WW&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_trcWtW_WW</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">diagWtW_WW</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;trcWtW_WW&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_trcWtW_WW</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_trcWtW_WW</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">diagWtW_WW</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Diagonal of :math:`W^{&#39;}W + WW`.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="s2">&quot;diagWtW_WW&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
-            <span class="n">wt</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">transpose</span><span class="p">()</span>
-            <span class="n">w</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_diagWtW_WW</span> <span class="o">=</span> <span class="p">(</span><span class="n">wt</span> <span class="o">*</span> <span class="n">w</span> <span class="o">+</span> <span class="n">w</span> <span class="o">*</span> <span class="n">w</span><span class="p">)</span><span class="o">.</span><span class="n">diagonal</span><span class="p">()</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;diagWtW_WW&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_diagWtW_WW</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_diagWtW_WW</span>
-
-<div class="viewcode-block" id="WSP.from_W"><a class="viewcode-back" href="../../../generated/libpysal.weights.WSP.html#libpysal.weights.WSP.from_W">[docs]</a>    <span class="nd">@classmethod</span>
-    <span class="k">def</span> <span class="nf">from_W</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">W</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Constructs a ``WSP`` object from the ``W``&#39;s sparse matrix.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        W : libpysal.weights.W</span>
-<span class="sd">            A PySAL weights object with a sparse form and ids.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        A ``WSP`` instance.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span><span class="n">W</span><span class="o">.</span><span class="n">sparse</span><span class="p">,</span> <span class="n">id_order</span><span class="o">=</span><span class="n">W</span><span class="o">.</span><span class="n">id_order</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="WSP.to_W"><a class="viewcode-back" href="../../../generated/libpysal.weights.WSP.html#libpysal.weights.WSP.to_W">[docs]</a>    <span class="k">def</span> <span class="nf">to_W</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">silence_warnings</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Convert a pysal WSP object (thin weights matrix) to a pysal W object.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        self : WSP</span>
-<span class="sd">            PySAL sparse weights object.</span>
-<span class="sd">        silence_warnings : bool</span>
-<span class="sd">            Switch to ``True`` to turn off print statements for every</span>
-<span class="sd">            observation with islands. Default is ``False``, which does</span>
-<span class="sd">            not silence warnings.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        w : W</span>
-<span class="sd">            PySAL weights object.</span>
-
-<span class="sd">        Examples</span>
-<span class="sd">        --------</span>
-<span class="sd">        &gt;&gt;&gt; from libpysal.weights import lat2SW, WSP, WSP2W</span>
-
-<span class="sd">        Build a 10x10 ``scipy.sparse`` matrix for a rectangular 2x5</span>
-<span class="sd">        region of cells (rook contiguity), then construct a ``libpysal``</span>
-<span class="sd">        sparse weights object (``self``).</span>
-
-<span class="sd">        &gt;&gt;&gt; sp = lat2SW(2, 5)</span>
-<span class="sd">        &gt;&gt;&gt; self = WSP(sp)</span>
-<span class="sd">        &gt;&gt;&gt; self.n</span>
-<span class="sd">        10</span>
-<span class="sd">        &gt;&gt;&gt; print(self.sparse[0].todense())</span>
-<span class="sd">        [[0 1 0 0 0 1 0 0 0 0]]</span>
-
-<span class="sd">        Convert this sparse weights object to a standard PySAL weights object.</span>
-
-<span class="sd">        &gt;&gt;&gt; w = WSP2W(self)</span>
-<span class="sd">        &gt;&gt;&gt; w.n</span>
-<span class="sd">        10</span>
-<span class="sd">        &gt;&gt;&gt; print(w.full()[0][0])</span>
-<span class="sd">        [0. 1. 0. 0. 0. 1. 0. 0. 0. 0.]</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">indices</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">indices</span><span class="p">)</span>
-        <span class="n">data</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>
-        <span class="n">indptr</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">indptr</span><span class="p">)</span>
-        <span class="n">id_order</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">id_order</span>
-        <span class="k">if</span> <span class="n">id_order</span><span class="p">:</span>
-            <span class="c1"># replace indices with user IDs</span>
-            <span class="n">indices</span> <span class="o">=</span> <span class="p">[</span><span class="n">id_order</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">indices</span><span class="p">]</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">id_order</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">n</span><span class="p">))</span>
-        <span class="n">neighbors</span><span class="p">,</span> <span class="n">weights</span> <span class="o">=</span> <span class="p">{},</span> <span class="p">{}</span>
-        <span class="n">start</span> <span class="o">=</span> <span class="n">indptr</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">n</span><span class="p">):</span>
-            <span class="n">oid</span> <span class="o">=</span> <span class="n">id_order</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-            <span class="n">end</span> <span class="o">=</span> <span class="n">indptr</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span>
-            <span class="n">neighbors</span><span class="p">[</span><span class="n">oid</span><span class="p">]</span> <span class="o">=</span> <span class="n">indices</span><span class="p">[</span><span class="n">start</span><span class="p">:</span><span class="n">end</span><span class="p">]</span>
-            <span class="n">weights</span><span class="p">[</span><span class="n">oid</span><span class="p">]</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">start</span><span class="p">:</span><span class="n">end</span><span class="p">]</span>
-            <span class="n">start</span> <span class="o">=</span> <span class="n">end</span>
-        <span class="n">ids</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">id_order</span><span class="p">)</span>
-        <span class="n">w</span> <span class="o">=</span> <span class="n">W</span><span class="p">(</span><span class="n">neighbors</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">ids</span><span class="p">,</span> <span class="n">silence_warnings</span><span class="o">=</span><span class="n">silence_warnings</span><span class="p">)</span>
-        <span class="n">w</span><span class="o">.</span><span class="n">_sparse</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sparse</span><span class="p">)</span>
-        <span class="n">w</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;sparse&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">_sparse</span>
-        <span class="k">return</span> <span class="n">w</span></div></div>
-</pre></div>
-
-    </div>
-      
-  </div>
-</div>
-<footer class="footer">
-  <div class="container">
-    <p class="pull-right">
-      <a href="#">Back to top</a>
-      
-        <br/>
-        
-      
-    </p>
-    <p>
-        &copy; Copyright 2018-, pysal developers.<br/>
-      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 3.1.2.<br/>
-    </p>
-  </div>
-</footer>
-  </body>
-</html>
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.cg.Chain.rst.txt b/docs/_sources/generated/libpysal.cg.Chain.rst.txt
deleted file mode 100644
index f91f22f1f..000000000
--- a/docs/_sources/generated/libpysal.cg.Chain.rst.txt
+++ /dev/null
@@ -1,33 +0,0 @@
-libpysal.cg.Chain
-=================
-
-.. currentmodule:: libpysal.cg
-
-.. autoclass:: Chain
-
-   
-   .. automethod:: __init__
-
-   
-   .. rubric:: Methods
-
-   .. autosummary::
-   
-      ~Chain.__init__
-   
-   
-
-   
-   
-   .. rubric:: Attributes
-
-   .. autosummary::
-   
-      ~Chain.arclen
-      ~Chain.bounding_box
-      ~Chain.len
-      ~Chain.parts
-      ~Chain.segments
-      ~Chain.vertices
-   
-   
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.cg.Grid.rst.txt b/docs/_sources/generated/libpysal.cg.Grid.rst.txt
deleted file mode 100644
index c7b6aa23b..000000000
--- a/docs/_sources/generated/libpysal.cg.Grid.rst.txt
+++ /dev/null
@@ -1,28 +0,0 @@
-libpysal.cg.Grid
-================
-
-.. currentmodule:: libpysal.cg
-
-.. autoclass:: Grid
-
-   
-   .. automethod:: __init__
-
-   
-   .. rubric:: Methods
-
-   .. autosummary::
-   
-      ~Grid.__init__
-      ~Grid.add
-      ~Grid.bounds
-      ~Grid.in_grid
-      ~Grid.nearest
-      ~Grid.proximity
-      ~Grid.remove
-   
-   
-
-   
-   
-   
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.cg.KDTree.rst.txt b/docs/_sources/generated/libpysal.cg.KDTree.rst.txt
deleted file mode 100644
index 020fb9649..000000000
--- a/docs/_sources/generated/libpysal.cg.KDTree.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.KDTree
-==================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: KDTree
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.cg.Line.rst.txt b/docs/_sources/generated/libpysal.cg.Line.rst.txt
deleted file mode 100644
index b04ae005b..000000000
--- a/docs/_sources/generated/libpysal.cg.Line.rst.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-libpysal.cg.Line
-================
-
-.. currentmodule:: libpysal.cg
-
-.. autoclass:: Line
-
-   
-   .. automethod:: __init__
-
-   
-   .. rubric:: Methods
-
-   .. autosummary::
-   
-      ~Line.__init__
-      ~Line.x
-      ~Line.y
-   
-   
-
-   
-   
-   
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.cg.LineSegment.rst.txt b/docs/_sources/generated/libpysal.cg.LineSegment.rst.txt
deleted file mode 100644
index dba1ae0b8..000000000
--- a/docs/_sources/generated/libpysal.cg.LineSegment.rst.txt
+++ /dev/null
@@ -1,37 +0,0 @@
-libpysal.cg.LineSegment
-=======================
-
-.. currentmodule:: libpysal.cg
-
-.. autoclass:: LineSegment
-
-   
-   .. automethod:: __init__
-
-   
-   .. rubric:: Methods
-
-   .. autosummary::
-   
-      ~LineSegment.__init__
-      ~LineSegment.get_swap
-      ~LineSegment.intersect
-      ~LineSegment.is_ccw
-      ~LineSegment.is_cw
-      ~LineSegment.sw_ccw
-   
-   
-
-   
-   
-   .. rubric:: Attributes
-
-   .. autosummary::
-   
-      ~LineSegment.bounding_box
-      ~LineSegment.len
-      ~LineSegment.line
-      ~LineSegment.p1
-      ~LineSegment.p2
-   
-   
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.cg.Point.rst.txt b/docs/_sources/generated/libpysal.cg.Point.rst.txt
deleted file mode 100644
index 133d91fbd..000000000
--- a/docs/_sources/generated/libpysal.cg.Point.rst.txt
+++ /dev/null
@@ -1,22 +0,0 @@
-libpysal.cg.Point
-=================
-
-.. currentmodule:: libpysal.cg
-
-.. autoclass:: Point
-
-   
-   .. automethod:: __init__
-
-   
-   .. rubric:: Methods
-
-   .. autosummary::
-   
-      ~Point.__init__
-   
-   
-
-   
-   
-   
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.cg.PointLocator.rst.txt b/docs/_sources/generated/libpysal.cg.PointLocator.rst.txt
deleted file mode 100644
index 1a65fb787..000000000
--- a/docs/_sources/generated/libpysal.cg.PointLocator.rst.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-libpysal.cg.PointLocator
-========================
-
-.. currentmodule:: libpysal.cg
-
-.. autoclass:: PointLocator
-
-   
-   .. automethod:: __init__
-
-   
-   .. rubric:: Methods
-
-   .. autosummary::
-   
-      ~PointLocator.__init__
-      ~PointLocator.nearest
-      ~PointLocator.overlapping
-      ~PointLocator.polygon
-      ~PointLocator.proximity
-      ~PointLocator.region
-   
-   
-
-   
-   
-   
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.cg.Polygon.rst.txt b/docs/_sources/generated/libpysal.cg.Polygon.rst.txt
deleted file mode 100644
index a0fab3ee9..000000000
--- a/docs/_sources/generated/libpysal.cg.Polygon.rst.txt
+++ /dev/null
@@ -1,38 +0,0 @@
-libpysal.cg.Polygon
-===================
-
-.. currentmodule:: libpysal.cg
-
-.. autoclass:: Polygon
-
-   
-   .. automethod:: __init__
-
-   
-   .. rubric:: Methods
-
-   .. autosummary::
-   
-      ~Polygon.__init__
-      ~Polygon.build_quad_tree_structure
-      ~Polygon.contains_point
-   
-   
-
-   
-   
-   .. rubric:: Attributes
-
-   .. autosummary::
-   
-      ~Polygon.area
-      ~Polygon.bbox
-      ~Polygon.bounding_box
-      ~Polygon.centroid
-      ~Polygon.holes
-      ~Polygon.len
-      ~Polygon.parts
-      ~Polygon.perimeter
-      ~Polygon.vertices
-   
-   
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.cg.PolygonLocator.rst.txt b/docs/_sources/generated/libpysal.cg.PolygonLocator.rst.txt
deleted file mode 100644
index 482bc76b9..000000000
--- a/docs/_sources/generated/libpysal.cg.PolygonLocator.rst.txt
+++ /dev/null
@@ -1,28 +0,0 @@
-libpysal.cg.PolygonLocator
-==========================
-
-.. currentmodule:: libpysal.cg
-
-.. autoclass:: PolygonLocator
-
-   
-   .. automethod:: __init__
-
-   
-   .. rubric:: Methods
-
-   .. autosummary::
-   
-      ~PolygonLocator.__init__
-      ~PolygonLocator.contains_point
-      ~PolygonLocator.inside
-      ~PolygonLocator.nearest
-      ~PolygonLocator.overlapping
-      ~PolygonLocator.proximity
-      ~PolygonLocator.region
-   
-   
-
-   
-   
-   
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.cg.RADIUS_EARTH_KM.rst.txt b/docs/_sources/generated/libpysal.cg.RADIUS_EARTH_KM.rst.txt
deleted file mode 100644
index 001acab4d..000000000
--- a/docs/_sources/generated/libpysal.cg.RADIUS_EARTH_KM.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.RADIUS\_EARTH\_KM
-=============================
-
-.. currentmodule:: libpysal.cg
-
-.. autodata:: RADIUS_EARTH_KM
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.cg.RADIUS_EARTH_MILES.rst.txt b/docs/_sources/generated/libpysal.cg.RADIUS_EARTH_MILES.rst.txt
deleted file mode 100644
index 36c27f184..000000000
--- a/docs/_sources/generated/libpysal.cg.RADIUS_EARTH_MILES.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.RADIUS\_EARTH\_MILES
-================================
-
-.. currentmodule:: libpysal.cg
-
-.. autodata:: RADIUS_EARTH_MILES
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.cg.Ray.rst.txt b/docs/_sources/generated/libpysal.cg.Ray.rst.txt
deleted file mode 100644
index 69efdb894..000000000
--- a/docs/_sources/generated/libpysal.cg.Ray.rst.txt
+++ /dev/null
@@ -1,22 +0,0 @@
-libpysal.cg.Ray
-===============
-
-.. currentmodule:: libpysal.cg
-
-.. autoclass:: Ray
-
-   
-   .. automethod:: __init__
-
-   
-   .. rubric:: Methods
-
-   .. autosummary::
-   
-      ~Ray.__init__
-   
-   
-
-   
-   
-   
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.cg.Rectangle.rst.txt b/docs/_sources/generated/libpysal.cg.Rectangle.rst.txt
deleted file mode 100644
index aefa995d1..000000000
--- a/docs/_sources/generated/libpysal.cg.Rectangle.rst.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-libpysal.cg.Rectangle
-=====================
-
-.. currentmodule:: libpysal.cg
-
-.. autoclass:: Rectangle
-
-   
-   .. automethod:: __init__
-
-   
-   .. rubric:: Methods
-
-   .. autosummary::
-   
-      ~Rectangle.__init__
-      ~Rectangle.set_centroid
-      ~Rectangle.set_scale
-   
-   
-
-   
-   
-   .. rubric:: Attributes
-
-   .. autosummary::
-   
-      ~Rectangle.area
-      ~Rectangle.height
-      ~Rectangle.width
-   
-   
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.cg.alpha_shape.rst.txt b/docs/_sources/generated/libpysal.cg.alpha_shape.rst.txt
deleted file mode 100644
index d7b00aabd..000000000
--- a/docs/_sources/generated/libpysal.cg.alpha_shape.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.alpha\_shape
-========================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: alpha_shape
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.cg.alpha_shape_auto.rst.txt b/docs/_sources/generated/libpysal.cg.alpha_shape_auto.rst.txt
deleted file mode 100644
index eabc9198d..000000000
--- a/docs/_sources/generated/libpysal.cg.alpha_shape_auto.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.alpha\_shape\_auto
-==============================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: alpha_shape_auto
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.cg.arcdist.rst.txt b/docs/_sources/generated/libpysal.cg.arcdist.rst.txt
deleted file mode 100644
index f3ebba8ee..000000000
--- a/docs/_sources/generated/libpysal.cg.arcdist.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.arcdist
-===================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: arcdist
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.cg.arcdist2linear.rst.txt b/docs/_sources/generated/libpysal.cg.arcdist2linear.rst.txt
deleted file mode 100644
index e3afcf98a..000000000
--- a/docs/_sources/generated/libpysal.cg.arcdist2linear.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.arcdist2linear
-==========================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: arcdist2linear
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.cg.asShape.rst.txt b/docs/_sources/generated/libpysal.cg.asShape.rst.txt
deleted file mode 100644
index 2bd215bed..000000000
--- a/docs/_sources/generated/libpysal.cg.asShape.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.asShape
-===================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: asShape
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.cg.bbcommon.rst.txt b/docs/_sources/generated/libpysal.cg.bbcommon.rst.txt
deleted file mode 100644
index 3a774e5c5..000000000
--- a/docs/_sources/generated/libpysal.cg.bbcommon.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.bbcommon
-====================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: bbcommon
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.cg.brute_knn.rst.txt b/docs/_sources/generated/libpysal.cg.brute_knn.rst.txt
deleted file mode 100644
index 549e67cb3..000000000
--- a/docs/_sources/generated/libpysal.cg.brute_knn.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.brute\_knn
-======================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: brute_knn
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.cg.convex_hull.rst.txt b/docs/_sources/generated/libpysal.cg.convex_hull.rst.txt
deleted file mode 100644
index 18c1c230c..000000000
--- a/docs/_sources/generated/libpysal.cg.convex_hull.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.convex\_hull
-========================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: convex_hull
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.cg.distance_matrix.rst.txt b/docs/_sources/generated/libpysal.cg.distance_matrix.rst.txt
deleted file mode 100644
index d4e57bc66..000000000
--- a/docs/_sources/generated/libpysal.cg.distance_matrix.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.distance\_matrix
-============================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: distance_matrix
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.cg.fast_knn.rst.txt b/docs/_sources/generated/libpysal.cg.fast_knn.rst.txt
deleted file mode 100644
index 7155f9186..000000000
--- a/docs/_sources/generated/libpysal.cg.fast_knn.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.fast\_knn
-=====================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: fast_knn
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.cg.fast_threshold.rst.txt b/docs/_sources/generated/libpysal.cg.fast_threshold.rst.txt
deleted file mode 100644
index f86c422fd..000000000
--- a/docs/_sources/generated/libpysal.cg.fast_threshold.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.fast\_threshold
-===========================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: fast_threshold
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.cg.geogrid.rst.txt b/docs/_sources/generated/libpysal.cg.geogrid.rst.txt
deleted file mode 100644
index 2f375ae00..000000000
--- a/docs/_sources/generated/libpysal.cg.geogrid.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.geogrid
-===================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: geogrid
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.cg.geointerpolate.rst.txt b/docs/_sources/generated/libpysal.cg.geointerpolate.rst.txt
deleted file mode 100644
index 73f93e37c..000000000
--- a/docs/_sources/generated/libpysal.cg.geointerpolate.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.geointerpolate
-==========================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: geointerpolate
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.cg.get_angle_between.rst.txt b/docs/_sources/generated/libpysal.cg.get_angle_between.rst.txt
deleted file mode 100644
index 9e5c317bd..000000000
--- a/docs/_sources/generated/libpysal.cg.get_angle_between.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.get\_angle\_between
-===============================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: get_angle_between
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.cg.get_bounding_box.rst.txt b/docs/_sources/generated/libpysal.cg.get_bounding_box.rst.txt
deleted file mode 100644
index 089f0fa7b..000000000
--- a/docs/_sources/generated/libpysal.cg.get_bounding_box.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.get\_bounding\_box
-==============================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: get_bounding_box
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.cg.get_point_at_angle_and_dist.rst.txt b/docs/_sources/generated/libpysal.cg.get_point_at_angle_and_dist.rst.txt
deleted file mode 100644
index 66a05376b..000000000
--- a/docs/_sources/generated/libpysal.cg.get_point_at_angle_and_dist.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.get\_point\_at\_angle\_and\_dist
-============================================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: get_point_at_angle_and_dist
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.cg.get_points_dist.rst.txt b/docs/_sources/generated/libpysal.cg.get_points_dist.rst.txt
deleted file mode 100644
index e19e232cd..000000000
--- a/docs/_sources/generated/libpysal.cg.get_points_dist.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.get\_points\_dist
-=============================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: get_points_dist
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.cg.get_polygon_point_dist.rst.txt b/docs/_sources/generated/libpysal.cg.get_polygon_point_dist.rst.txt
deleted file mode 100644
index 3d53d2328..000000000
--- a/docs/_sources/generated/libpysal.cg.get_polygon_point_dist.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.get\_polygon\_point\_dist
-=====================================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: get_polygon_point_dist
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.cg.get_polygon_point_intersect.rst.txt b/docs/_sources/generated/libpysal.cg.get_polygon_point_intersect.rst.txt
deleted file mode 100644
index 1cd7529f3..000000000
--- a/docs/_sources/generated/libpysal.cg.get_polygon_point_intersect.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.get\_polygon\_point\_intersect
-==========================================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: get_polygon_point_intersect
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.cg.get_ray_segment_intersect.rst.txt b/docs/_sources/generated/libpysal.cg.get_ray_segment_intersect.rst.txt
deleted file mode 100644
index cfe51618c..000000000
--- a/docs/_sources/generated/libpysal.cg.get_ray_segment_intersect.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.get\_ray\_segment\_intersect
-========================================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: get_ray_segment_intersect
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.cg.get_rectangle_point_intersect.rst.txt b/docs/_sources/generated/libpysal.cg.get_rectangle_point_intersect.rst.txt
deleted file mode 100644
index b31e074e5..000000000
--- a/docs/_sources/generated/libpysal.cg.get_rectangle_point_intersect.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.get\_rectangle\_point\_intersect
-============================================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: get_rectangle_point_intersect
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.cg.get_rectangle_rectangle_intersection.rst.txt b/docs/_sources/generated/libpysal.cg.get_rectangle_rectangle_intersection.rst.txt
deleted file mode 100644
index bcdbf5ef2..000000000
--- a/docs/_sources/generated/libpysal.cg.get_rectangle_rectangle_intersection.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.get\_rectangle\_rectangle\_intersection
-===================================================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: get_rectangle_rectangle_intersection
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.cg.get_segment_point_dist.rst.txt b/docs/_sources/generated/libpysal.cg.get_segment_point_dist.rst.txt
deleted file mode 100644
index fd32bb258..000000000
--- a/docs/_sources/generated/libpysal.cg.get_segment_point_dist.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.get\_segment\_point\_dist
-=====================================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: get_segment_point_dist
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.cg.get_segment_point_intersect.rst.txt b/docs/_sources/generated/libpysal.cg.get_segment_point_intersect.rst.txt
deleted file mode 100644
index c54747be1..000000000
--- a/docs/_sources/generated/libpysal.cg.get_segment_point_intersect.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.get\_segment\_point\_intersect
-==========================================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: get_segment_point_intersect
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.cg.get_segments_intersect.rst.txt b/docs/_sources/generated/libpysal.cg.get_segments_intersect.rst.txt
deleted file mode 100644
index a0325a346..000000000
--- a/docs/_sources/generated/libpysal.cg.get_segments_intersect.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.get\_segments\_intersect
-====================================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: get_segments_intersect
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.cg.get_shared_segments.rst.txt b/docs/_sources/generated/libpysal.cg.get_shared_segments.rst.txt
deleted file mode 100644
index 23d6c5174..000000000
--- a/docs/_sources/generated/libpysal.cg.get_shared_segments.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.get\_shared\_segments
-=================================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: get_shared_segments
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.cg.harcdist.rst.txt b/docs/_sources/generated/libpysal.cg.harcdist.rst.txt
deleted file mode 100644
index fd1897c42..000000000
--- a/docs/_sources/generated/libpysal.cg.harcdist.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.harcdist
-====================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: harcdist
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.cg.is_clockwise.rst.txt b/docs/_sources/generated/libpysal.cg.is_clockwise.rst.txt
deleted file mode 100644
index 02e3491bb..000000000
--- a/docs/_sources/generated/libpysal.cg.is_clockwise.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.is\_clockwise
-=========================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: is_clockwise
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.cg.is_collinear.rst.txt b/docs/_sources/generated/libpysal.cg.is_collinear.rst.txt
deleted file mode 100644
index 35252dea5..000000000
--- a/docs/_sources/generated/libpysal.cg.is_collinear.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.is\_collinear
-=========================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: is_collinear
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.cg.linear2arcdist.rst.txt b/docs/_sources/generated/libpysal.cg.linear2arcdist.rst.txt
deleted file mode 100644
index 25004601e..000000000
--- a/docs/_sources/generated/libpysal.cg.linear2arcdist.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.linear2arcdist
-==========================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: linear2arcdist
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.cg.lonlat.rst.txt b/docs/_sources/generated/libpysal.cg.lonlat.rst.txt
deleted file mode 100644
index 21197e585..000000000
--- a/docs/_sources/generated/libpysal.cg.lonlat.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.lonlat
-==================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: lonlat
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.cg.point_touches_rectangle.rst.txt b/docs/_sources/generated/libpysal.cg.point_touches_rectangle.rst.txt
deleted file mode 100644
index 19475cfc2..000000000
--- a/docs/_sources/generated/libpysal.cg.point_touches_rectangle.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.point\_touches\_rectangle
-=====================================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: point_touches_rectangle
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.cg.toLngLat.rst.txt b/docs/_sources/generated/libpysal.cg.toLngLat.rst.txt
deleted file mode 100644
index 32a85fc4a..000000000
--- a/docs/_sources/generated/libpysal.cg.toLngLat.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.toLngLat
-====================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: toLngLat
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.cg.toXYZ.rst.txt b/docs/_sources/generated/libpysal.cg.toXYZ.rst.txt
deleted file mode 100644
index cd18210ec..000000000
--- a/docs/_sources/generated/libpysal.cg.toXYZ.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.toXYZ
-=================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: toXYZ
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.cg.voronoi_frames.rst.txt b/docs/_sources/generated/libpysal.cg.voronoi_frames.rst.txt
deleted file mode 100644
index 5270c27a4..000000000
--- a/docs/_sources/generated/libpysal.cg.voronoi_frames.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.cg.voronoi\_frames
-===========================
-
-.. currentmodule:: libpysal.cg
-
-.. autofunction:: voronoi_frames
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.examples.available.rst.txt b/docs/_sources/generated/libpysal.examples.available.rst.txt
deleted file mode 100644
index 263a1b59c..000000000
--- a/docs/_sources/generated/libpysal.examples.available.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.examples.available
-===========================
-
-.. currentmodule:: libpysal.examples
-
-.. autofunction:: available
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.examples.explain.rst.txt b/docs/_sources/generated/libpysal.examples.explain.rst.txt
deleted file mode 100644
index 3964e1189..000000000
--- a/docs/_sources/generated/libpysal.examples.explain.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.examples.explain
-=========================
-
-.. currentmodule:: libpysal.examples
-
-.. autofunction:: explain
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.examples.get_path.rst.txt b/docs/_sources/generated/libpysal.examples.get_path.rst.txt
deleted file mode 100644
index 8750fd88d..000000000
--- a/docs/_sources/generated/libpysal.examples.get_path.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.examples.get\_path
-===========================
-
-.. currentmodule:: libpysal.examples
-
-.. autofunction:: get_path
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.io.fileio.FileIO.rst.txt b/docs/_sources/generated/libpysal.io.fileio.FileIO.rst.txt
deleted file mode 100644
index 822071cd6..000000000
--- a/docs/_sources/generated/libpysal.io.fileio.FileIO.rst.txt
+++ /dev/null
@@ -1,42 +0,0 @@
-libpysal.io.fileio.FileIO
-=========================
-
-.. currentmodule:: libpysal.io.fileio
-
-.. autoclass:: FileIO
-
-   
-   .. automethod:: __init__
-
-   
-   .. rubric:: Methods
-
-   .. autosummary::
-   
-      ~FileIO.__init__
-      ~FileIO.cast
-      ~FileIO.check
-      ~FileIO.close
-      ~FileIO.flush
-      ~FileIO.get
-      ~FileIO.getType
-      ~FileIO.open
-      ~FileIO.read
-      ~FileIO.seek
-      ~FileIO.tell
-      ~FileIO.truncate
-      ~FileIO.write
-   
-   
-
-   
-   
-   .. rubric:: Attributes
-
-   .. autosummary::
-   
-      ~FileIO.by_row
-      ~FileIO.ids
-      ~FileIO.rIds
-   
-   
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.io.open.rst.txt b/docs/_sources/generated/libpysal.io.open.rst.txt
deleted file mode 100644
index 28508b74f..000000000
--- a/docs/_sources/generated/libpysal.io.open.rst.txt
+++ /dev/null
@@ -1,42 +0,0 @@
-libpysal.io.open
-================
-
-.. currentmodule:: libpysal.io
-
-.. autoclass:: open
-
-   
-   .. automethod:: __init__
-
-   
-   .. rubric:: Methods
-
-   .. autosummary::
-   
-      ~open.__init__
-      ~open.cast
-      ~open.check
-      ~open.close
-      ~open.flush
-      ~open.get
-      ~open.getType
-      ~open.open
-      ~open.read
-      ~open.seek
-      ~open.tell
-      ~open.truncate
-      ~open.write
-   
-   
-
-   
-   
-   .. rubric:: Attributes
-
-   .. autosummary::
-   
-      ~open.by_row
-      ~open.ids
-      ~open.rIds
-   
-   
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.weights.DistanceBand.rst.txt b/docs/_sources/generated/libpysal.weights.DistanceBand.rst.txt
deleted file mode 100644
index 528076369..000000000
--- a/docs/_sources/generated/libpysal.weights.DistanceBand.rst.txt
+++ /dev/null
@@ -1,75 +0,0 @@
-libpysal.weights.DistanceBand
-=============================
-
-.. currentmodule:: libpysal.weights
-
-.. autoclass:: DistanceBand
-
-   
-   .. automethod:: __init__
-
-   
-   .. rubric:: Methods
-
-   .. autosummary::
-   
-      ~DistanceBand.__init__
-      ~DistanceBand.asymmetry
-      ~DistanceBand.from_WSP
-      ~DistanceBand.from_adjlist
-      ~DistanceBand.from_array
-      ~DistanceBand.from_dataframe
-      ~DistanceBand.from_file
-      ~DistanceBand.from_networkx
-      ~DistanceBand.from_shapefile
-      ~DistanceBand.full
-      ~DistanceBand.get_transform
-      ~DistanceBand.plot
-      ~DistanceBand.remap_ids
-      ~DistanceBand.set_shapefile
-      ~DistanceBand.set_transform
-      ~DistanceBand.symmetrize
-      ~DistanceBand.to_WSP
-      ~DistanceBand.to_adjlist
-      ~DistanceBand.to_file
-      ~DistanceBand.to_networkx
-   
-   
-
-   
-   
-   .. rubric:: Attributes
-
-   .. autosummary::
-   
-      ~DistanceBand.asymmetries
-      ~DistanceBand.cardinalities
-      ~DistanceBand.component_labels
-      ~DistanceBand.diagW2
-      ~DistanceBand.diagWtW
-      ~DistanceBand.diagWtW_WW
-      ~DistanceBand.histogram
-      ~DistanceBand.id2i
-      ~DistanceBand.id_order
-      ~DistanceBand.id_order_set
-      ~DistanceBand.islands
-      ~DistanceBand.max_neighbors
-      ~DistanceBand.mean_neighbors
-      ~DistanceBand.min_neighbors
-      ~DistanceBand.n
-      ~DistanceBand.n_components
-      ~DistanceBand.neighbor_offsets
-      ~DistanceBand.nonzero
-      ~DistanceBand.pct_nonzero
-      ~DistanceBand.s0
-      ~DistanceBand.s1
-      ~DistanceBand.s2
-      ~DistanceBand.s2array
-      ~DistanceBand.sd
-      ~DistanceBand.sparse
-      ~DistanceBand.transform
-      ~DistanceBand.trcW2
-      ~DistanceBand.trcWtW
-      ~DistanceBand.trcWtW_WW
-   
-   
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.weights.KNN.rst.txt b/docs/_sources/generated/libpysal.weights.KNN.rst.txt
deleted file mode 100644
index 7eca1d31d..000000000
--- a/docs/_sources/generated/libpysal.weights.KNN.rst.txt
+++ /dev/null
@@ -1,76 +0,0 @@
-libpysal.weights.KNN
-====================
-
-.. currentmodule:: libpysal.weights
-
-.. autoclass:: KNN
-
-   
-   .. automethod:: __init__
-
-   
-   .. rubric:: Methods
-
-   .. autosummary::
-   
-      ~KNN.__init__
-      ~KNN.asymmetry
-      ~KNN.from_WSP
-      ~KNN.from_adjlist
-      ~KNN.from_array
-      ~KNN.from_dataframe
-      ~KNN.from_file
-      ~KNN.from_networkx
-      ~KNN.from_shapefile
-      ~KNN.full
-      ~KNN.get_transform
-      ~KNN.plot
-      ~KNN.remap_ids
-      ~KNN.reweight
-      ~KNN.set_shapefile
-      ~KNN.set_transform
-      ~KNN.symmetrize
-      ~KNN.to_WSP
-      ~KNN.to_adjlist
-      ~KNN.to_file
-      ~KNN.to_networkx
-   
-   
-
-   
-   
-   .. rubric:: Attributes
-
-   .. autosummary::
-   
-      ~KNN.asymmetries
-      ~KNN.cardinalities
-      ~KNN.component_labels
-      ~KNN.diagW2
-      ~KNN.diagWtW
-      ~KNN.diagWtW_WW
-      ~KNN.histogram
-      ~KNN.id2i
-      ~KNN.id_order
-      ~KNN.id_order_set
-      ~KNN.islands
-      ~KNN.max_neighbors
-      ~KNN.mean_neighbors
-      ~KNN.min_neighbors
-      ~KNN.n
-      ~KNN.n_components
-      ~KNN.neighbor_offsets
-      ~KNN.nonzero
-      ~KNN.pct_nonzero
-      ~KNN.s0
-      ~KNN.s1
-      ~KNN.s2
-      ~KNN.s2array
-      ~KNN.sd
-      ~KNN.sparse
-      ~KNN.transform
-      ~KNN.trcW2
-      ~KNN.trcWtW
-      ~KNN.trcWtW_WW
-   
-   
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.weights.Kernel.rst.txt b/docs/_sources/generated/libpysal.weights.Kernel.rst.txt
deleted file mode 100644
index fce92dedc..000000000
--- a/docs/_sources/generated/libpysal.weights.Kernel.rst.txt
+++ /dev/null
@@ -1,75 +0,0 @@
-libpysal.weights.Kernel
-=======================
-
-.. currentmodule:: libpysal.weights
-
-.. autoclass:: Kernel
-
-   
-   .. automethod:: __init__
-
-   
-   .. rubric:: Methods
-
-   .. autosummary::
-   
-      ~Kernel.__init__
-      ~Kernel.asymmetry
-      ~Kernel.from_WSP
-      ~Kernel.from_adjlist
-      ~Kernel.from_array
-      ~Kernel.from_dataframe
-      ~Kernel.from_file
-      ~Kernel.from_networkx
-      ~Kernel.from_shapefile
-      ~Kernel.full
-      ~Kernel.get_transform
-      ~Kernel.plot
-      ~Kernel.remap_ids
-      ~Kernel.set_shapefile
-      ~Kernel.set_transform
-      ~Kernel.symmetrize
-      ~Kernel.to_WSP
-      ~Kernel.to_adjlist
-      ~Kernel.to_file
-      ~Kernel.to_networkx
-   
-   
-
-   
-   
-   .. rubric:: Attributes
-
-   .. autosummary::
-   
-      ~Kernel.asymmetries
-      ~Kernel.cardinalities
-      ~Kernel.component_labels
-      ~Kernel.diagW2
-      ~Kernel.diagWtW
-      ~Kernel.diagWtW_WW
-      ~Kernel.histogram
-      ~Kernel.id2i
-      ~Kernel.id_order
-      ~Kernel.id_order_set
-      ~Kernel.islands
-      ~Kernel.max_neighbors
-      ~Kernel.mean_neighbors
-      ~Kernel.min_neighbors
-      ~Kernel.n
-      ~Kernel.n_components
-      ~Kernel.neighbor_offsets
-      ~Kernel.nonzero
-      ~Kernel.pct_nonzero
-      ~Kernel.s0
-      ~Kernel.s1
-      ~Kernel.s2
-      ~Kernel.s2array
-      ~Kernel.sd
-      ~Kernel.sparse
-      ~Kernel.transform
-      ~Kernel.trcW2
-      ~Kernel.trcWtW
-      ~Kernel.trcWtW_WW
-   
-   
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.weights.ODW.rst.txt b/docs/_sources/generated/libpysal.weights.ODW.rst.txt
deleted file mode 100644
index bd6e87340..000000000
--- a/docs/_sources/generated/libpysal.weights.ODW.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.ODW
-====================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: ODW
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.weights.Queen.rst.txt b/docs/_sources/generated/libpysal.weights.Queen.rst.txt
deleted file mode 100644
index 36858f566..000000000
--- a/docs/_sources/generated/libpysal.weights.Queen.rst.txt
+++ /dev/null
@@ -1,76 +0,0 @@
-libpysal.weights.Queen
-======================
-
-.. currentmodule:: libpysal.weights
-
-.. autoclass:: Queen
-
-   
-   .. automethod:: __init__
-
-   
-   .. rubric:: Methods
-
-   .. autosummary::
-   
-      ~Queen.__init__
-      ~Queen.asymmetry
-      ~Queen.from_WSP
-      ~Queen.from_adjlist
-      ~Queen.from_dataframe
-      ~Queen.from_file
-      ~Queen.from_iterable
-      ~Queen.from_networkx
-      ~Queen.from_shapefile
-      ~Queen.from_xarray
-      ~Queen.full
-      ~Queen.get_transform
-      ~Queen.plot
-      ~Queen.remap_ids
-      ~Queen.set_shapefile
-      ~Queen.set_transform
-      ~Queen.symmetrize
-      ~Queen.to_WSP
-      ~Queen.to_adjlist
-      ~Queen.to_file
-      ~Queen.to_networkx
-   
-   
-
-   
-   
-   .. rubric:: Attributes
-
-   .. autosummary::
-   
-      ~Queen.asymmetries
-      ~Queen.cardinalities
-      ~Queen.component_labels
-      ~Queen.diagW2
-      ~Queen.diagWtW
-      ~Queen.diagWtW_WW
-      ~Queen.histogram
-      ~Queen.id2i
-      ~Queen.id_order
-      ~Queen.id_order_set
-      ~Queen.islands
-      ~Queen.max_neighbors
-      ~Queen.mean_neighbors
-      ~Queen.min_neighbors
-      ~Queen.n
-      ~Queen.n_components
-      ~Queen.neighbor_offsets
-      ~Queen.nonzero
-      ~Queen.pct_nonzero
-      ~Queen.s0
-      ~Queen.s1
-      ~Queen.s2
-      ~Queen.s2array
-      ~Queen.sd
-      ~Queen.sparse
-      ~Queen.transform
-      ~Queen.trcW2
-      ~Queen.trcWtW
-      ~Queen.trcWtW_WW
-   
-   
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.weights.Rook.rst.txt b/docs/_sources/generated/libpysal.weights.Rook.rst.txt
deleted file mode 100644
index 364b3b2cb..000000000
--- a/docs/_sources/generated/libpysal.weights.Rook.rst.txt
+++ /dev/null
@@ -1,76 +0,0 @@
-libpysal.weights.Rook
-=====================
-
-.. currentmodule:: libpysal.weights
-
-.. autoclass:: Rook
-
-   
-   .. automethod:: __init__
-
-   
-   .. rubric:: Methods
-
-   .. autosummary::
-   
-      ~Rook.__init__
-      ~Rook.asymmetry
-      ~Rook.from_WSP
-      ~Rook.from_adjlist
-      ~Rook.from_dataframe
-      ~Rook.from_file
-      ~Rook.from_iterable
-      ~Rook.from_networkx
-      ~Rook.from_shapefile
-      ~Rook.from_xarray
-      ~Rook.full
-      ~Rook.get_transform
-      ~Rook.plot
-      ~Rook.remap_ids
-      ~Rook.set_shapefile
-      ~Rook.set_transform
-      ~Rook.symmetrize
-      ~Rook.to_WSP
-      ~Rook.to_adjlist
-      ~Rook.to_file
-      ~Rook.to_networkx
-   
-   
-
-   
-   
-   .. rubric:: Attributes
-
-   .. autosummary::
-   
-      ~Rook.asymmetries
-      ~Rook.cardinalities
-      ~Rook.component_labels
-      ~Rook.diagW2
-      ~Rook.diagWtW
-      ~Rook.diagWtW_WW
-      ~Rook.histogram
-      ~Rook.id2i
-      ~Rook.id_order
-      ~Rook.id_order_set
-      ~Rook.islands
-      ~Rook.max_neighbors
-      ~Rook.mean_neighbors
-      ~Rook.min_neighbors
-      ~Rook.n
-      ~Rook.n_components
-      ~Rook.neighbor_offsets
-      ~Rook.nonzero
-      ~Rook.pct_nonzero
-      ~Rook.s0
-      ~Rook.s1
-      ~Rook.s2
-      ~Rook.s2array
-      ~Rook.sd
-      ~Rook.sparse
-      ~Rook.transform
-      ~Rook.trcW2
-      ~Rook.trcWtW
-      ~Rook.trcWtW_WW
-   
-   
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.weights.Voronoi.rst.txt b/docs/_sources/generated/libpysal.weights.Voronoi.rst.txt
deleted file mode 100644
index ed097d5c9..000000000
--- a/docs/_sources/generated/libpysal.weights.Voronoi.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.Voronoi
-========================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: Voronoi
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.weights.W.rst.txt b/docs/_sources/generated/libpysal.weights.W.rst.txt
deleted file mode 100644
index 21e1d5c00..000000000
--- a/docs/_sources/generated/libpysal.weights.W.rst.txt
+++ /dev/null
@@ -1,73 +0,0 @@
-libpysal.weights.W
-==================
-
-.. currentmodule:: libpysal.weights
-
-.. autoclass:: W
-
-   
-   .. automethod:: __init__
-
-   
-   .. rubric:: Methods
-
-   .. autosummary::
-   
-      ~W.__init__
-      ~W.asymmetry
-      ~W.from_WSP
-      ~W.from_adjlist
-      ~W.from_file
-      ~W.from_networkx
-      ~W.from_shapefile
-      ~W.full
-      ~W.get_transform
-      ~W.plot
-      ~W.remap_ids
-      ~W.set_shapefile
-      ~W.set_transform
-      ~W.symmetrize
-      ~W.to_WSP
-      ~W.to_adjlist
-      ~W.to_file
-      ~W.to_networkx
-   
-   
-
-   
-   
-   .. rubric:: Attributes
-
-   .. autosummary::
-   
-      ~W.asymmetries
-      ~W.cardinalities
-      ~W.component_labels
-      ~W.diagW2
-      ~W.diagWtW
-      ~W.diagWtW_WW
-      ~W.histogram
-      ~W.id2i
-      ~W.id_order
-      ~W.id_order_set
-      ~W.islands
-      ~W.max_neighbors
-      ~W.mean_neighbors
-      ~W.min_neighbors
-      ~W.n
-      ~W.n_components
-      ~W.neighbor_offsets
-      ~W.nonzero
-      ~W.pct_nonzero
-      ~W.s0
-      ~W.s1
-      ~W.s2
-      ~W.s2array
-      ~W.sd
-      ~W.sparse
-      ~W.transform
-      ~W.trcW2
-      ~W.trcWtW
-      ~W.trcWtW_WW
-   
-   
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.weights.WSP.rst.txt b/docs/_sources/generated/libpysal.weights.WSP.rst.txt
deleted file mode 100644
index 8192bd7d1..000000000
--- a/docs/_sources/generated/libpysal.weights.WSP.rst.txt
+++ /dev/null
@@ -1,33 +0,0 @@
-libpysal.weights.WSP
-====================
-
-.. currentmodule:: libpysal.weights
-
-.. autoclass:: WSP
-
-   
-   .. automethod:: __init__
-
-   
-   .. rubric:: Methods
-
-   .. autosummary::
-   
-      ~WSP.__init__
-      ~WSP.from_W
-      ~WSP.to_W
-   
-   
-
-   
-   
-   .. rubric:: Attributes
-
-   .. autosummary::
-   
-      ~WSP.diagWtW_WW
-      ~WSP.id_order
-      ~WSP.s0
-      ~WSP.trcWtW_WW
-   
-   
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.weights.WSP2W.rst.txt b/docs/_sources/generated/libpysal.weights.WSP2W.rst.txt
deleted file mode 100644
index 0b05884a3..000000000
--- a/docs/_sources/generated/libpysal.weights.WSP2W.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.WSP2W
-======================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: WSP2W
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.weights.attach_islands.rst.txt b/docs/_sources/generated/libpysal.weights.attach_islands.rst.txt
deleted file mode 100644
index 49cb88ab5..000000000
--- a/docs/_sources/generated/libpysal.weights.attach_islands.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.attach\_islands
-================================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: attach_islands
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.weights.block_weights.rst.txt b/docs/_sources/generated/libpysal.weights.block_weights.rst.txt
deleted file mode 100644
index 30a79c630..000000000
--- a/docs/_sources/generated/libpysal.weights.block_weights.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.block\_weights
-===============================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: block_weights
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.weights.build_lattice_shapefile.rst.txt b/docs/_sources/generated/libpysal.weights.build_lattice_shapefile.rst.txt
deleted file mode 100644
index c742797cf..000000000
--- a/docs/_sources/generated/libpysal.weights.build_lattice_shapefile.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.build\_lattice\_shapefile
-==========================================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: build_lattice_shapefile
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.weights.comb.rst.txt b/docs/_sources/generated/libpysal.weights.comb.rst.txt
deleted file mode 100644
index fd4fc858b..000000000
--- a/docs/_sources/generated/libpysal.weights.comb.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.comb
-=====================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: comb
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.weights.da2W.rst.txt b/docs/_sources/generated/libpysal.weights.da2W.rst.txt
deleted file mode 100644
index 5dacd87c0..000000000
--- a/docs/_sources/generated/libpysal.weights.da2W.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.da2W
-=====================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: da2W
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.weights.da2WSP.rst.txt b/docs/_sources/generated/libpysal.weights.da2WSP.rst.txt
deleted file mode 100644
index e5bfd8c6f..000000000
--- a/docs/_sources/generated/libpysal.weights.da2WSP.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.da2WSP
-=======================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: da2WSP
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.weights.fill_diagonal.rst.txt b/docs/_sources/generated/libpysal.weights.fill_diagonal.rst.txt
deleted file mode 100644
index 84d067071..000000000
--- a/docs/_sources/generated/libpysal.weights.fill_diagonal.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.fill\_diagonal
-===============================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: fill_diagonal
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.weights.full.rst.txt b/docs/_sources/generated/libpysal.weights.full.rst.txt
deleted file mode 100644
index cd584af47..000000000
--- a/docs/_sources/generated/libpysal.weights.full.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.full
-=====================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: full
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.weights.full2W.rst.txt b/docs/_sources/generated/libpysal.weights.full2W.rst.txt
deleted file mode 100644
index 8cd2e8c77..000000000
--- a/docs/_sources/generated/libpysal.weights.full2W.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.full2W
-=======================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: full2W
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.weights.fuzzy_contiguity.rst.txt b/docs/_sources/generated/libpysal.weights.fuzzy_contiguity.rst.txt
deleted file mode 100644
index e8d510ec9..000000000
--- a/docs/_sources/generated/libpysal.weights.fuzzy_contiguity.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.fuzzy\_contiguity
-==================================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: fuzzy_contiguity
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.weights.get_ids.rst.txt b/docs/_sources/generated/libpysal.weights.get_ids.rst.txt
deleted file mode 100644
index c49fa48e6..000000000
--- a/docs/_sources/generated/libpysal.weights.get_ids.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.get\_ids
-=========================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: get_ids
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.weights.get_points_array_from_shapefile.rst.txt b/docs/_sources/generated/libpysal.weights.get_points_array_from_shapefile.rst.txt
deleted file mode 100644
index 3098243ae..000000000
--- a/docs/_sources/generated/libpysal.weights.get_points_array_from_shapefile.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.get\_points\_array\_from\_shapefile
-====================================================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: get_points_array_from_shapefile
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.weights.hexLat2W.rst.txt b/docs/_sources/generated/libpysal.weights.hexLat2W.rst.txt
deleted file mode 100644
index aeac82591..000000000
--- a/docs/_sources/generated/libpysal.weights.hexLat2W.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.hexLat2W
-=========================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: hexLat2W
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.weights.higher_order.rst.txt b/docs/_sources/generated/libpysal.weights.higher_order.rst.txt
deleted file mode 100644
index d51b29b23..000000000
--- a/docs/_sources/generated/libpysal.weights.higher_order.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.higher\_order
-==============================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: higher_order
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.weights.higher_order_sp.rst.txt b/docs/_sources/generated/libpysal.weights.higher_order_sp.rst.txt
deleted file mode 100644
index b7e831e72..000000000
--- a/docs/_sources/generated/libpysal.weights.higher_order_sp.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.higher\_order\_sp
-==================================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: higher_order_sp
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.weights.lag_categorical.rst.txt b/docs/_sources/generated/libpysal.weights.lag_categorical.rst.txt
deleted file mode 100644
index 1a2fdd7e2..000000000
--- a/docs/_sources/generated/libpysal.weights.lag_categorical.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.lag\_categorical
-=================================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: lag_categorical
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.weights.lag_spatial.rst.txt b/docs/_sources/generated/libpysal.weights.lag_spatial.rst.txt
deleted file mode 100644
index 204f5d807..000000000
--- a/docs/_sources/generated/libpysal.weights.lag_spatial.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.lag\_spatial
-=============================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: lag_spatial
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.weights.lat2SW.rst.txt b/docs/_sources/generated/libpysal.weights.lat2SW.rst.txt
deleted file mode 100644
index b0b48ae9b..000000000
--- a/docs/_sources/generated/libpysal.weights.lat2SW.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.lat2SW
-=======================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: lat2SW
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.weights.lat2W.rst.txt b/docs/_sources/generated/libpysal.weights.lat2W.rst.txt
deleted file mode 100644
index 93724cf5f..000000000
--- a/docs/_sources/generated/libpysal.weights.lat2W.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.lat2W
-======================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: lat2W
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.weights.mat2L.rst.txt b/docs/_sources/generated/libpysal.weights.mat2L.rst.txt
deleted file mode 100644
index 8d1808c3e..000000000
--- a/docs/_sources/generated/libpysal.weights.mat2L.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.mat2L
-======================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: mat2L
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.weights.min_threshold_dist_from_shapefile.rst.txt b/docs/_sources/generated/libpysal.weights.min_threshold_dist_from_shapefile.rst.txt
deleted file mode 100644
index 8944feb4d..000000000
--- a/docs/_sources/generated/libpysal.weights.min_threshold_dist_from_shapefile.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.min\_threshold\_dist\_from\_shapefile
-======================================================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: min_threshold_dist_from_shapefile
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.weights.min_threshold_distance.rst.txt b/docs/_sources/generated/libpysal.weights.min_threshold_distance.rst.txt
deleted file mode 100644
index 6a2941498..000000000
--- a/docs/_sources/generated/libpysal.weights.min_threshold_distance.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.min\_threshold\_distance
-=========================================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: min_threshold_distance
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.weights.neighbor_equality.rst.txt b/docs/_sources/generated/libpysal.weights.neighbor_equality.rst.txt
deleted file mode 100644
index 714af0a7c..000000000
--- a/docs/_sources/generated/libpysal.weights.neighbor_equality.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.neighbor\_equality
-===================================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: neighbor_equality
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.weights.netW.rst.txt b/docs/_sources/generated/libpysal.weights.netW.rst.txt
deleted file mode 100644
index 492b0d729..000000000
--- a/docs/_sources/generated/libpysal.weights.netW.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.netW
-=====================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: netW
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.weights.nonplanar_neighbors.rst.txt b/docs/_sources/generated/libpysal.weights.nonplanar_neighbors.rst.txt
deleted file mode 100644
index d19855dd8..000000000
--- a/docs/_sources/generated/libpysal.weights.nonplanar_neighbors.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.nonplanar\_neighbors
-=====================================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: nonplanar_neighbors
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.weights.order.rst.txt b/docs/_sources/generated/libpysal.weights.order.rst.txt
deleted file mode 100644
index 77e9ed195..000000000
--- a/docs/_sources/generated/libpysal.weights.order.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.order
-======================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: order
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.weights.remap_ids.rst.txt b/docs/_sources/generated/libpysal.weights.remap_ids.rst.txt
deleted file mode 100644
index 4899fc477..000000000
--- a/docs/_sources/generated/libpysal.weights.remap_ids.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.remap\_ids
-===========================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: remap_ids
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.weights.shimbel.rst.txt b/docs/_sources/generated/libpysal.weights.shimbel.rst.txt
deleted file mode 100644
index ed151c58c..000000000
--- a/docs/_sources/generated/libpysal.weights.shimbel.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.shimbel
-========================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: shimbel
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.weights.spw_from_gal.rst.txt b/docs/_sources/generated/libpysal.weights.spw_from_gal.rst.txt
deleted file mode 100644
index fc4592a3e..000000000
--- a/docs/_sources/generated/libpysal.weights.spw_from_gal.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.spw\_from\_gal
-===============================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: spw_from_gal
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.weights.testDataArray.rst.txt b/docs/_sources/generated/libpysal.weights.testDataArray.rst.txt
deleted file mode 100644
index ded0f3098..000000000
--- a/docs/_sources/generated/libpysal.weights.testDataArray.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.testDataArray
-==============================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: testDataArray
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.weights.vecW.rst.txt b/docs/_sources/generated/libpysal.weights.vecW.rst.txt
deleted file mode 100644
index 8baf549d2..000000000
--- a/docs/_sources/generated/libpysal.weights.vecW.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.vecW
-=====================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: vecW
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.weights.w2da.rst.txt b/docs/_sources/generated/libpysal.weights.w2da.rst.txt
deleted file mode 100644
index 6a11417f1..000000000
--- a/docs/_sources/generated/libpysal.weights.w2da.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.w2da
-=====================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: w2da
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.weights.w_clip.rst.txt b/docs/_sources/generated/libpysal.weights.w_clip.rst.txt
deleted file mode 100644
index d1731b1e1..000000000
--- a/docs/_sources/generated/libpysal.weights.w_clip.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.w\_clip
-========================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: w_clip
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.weights.w_difference.rst.txt b/docs/_sources/generated/libpysal.weights.w_difference.rst.txt
deleted file mode 100644
index 6f0210d95..000000000
--- a/docs/_sources/generated/libpysal.weights.w_difference.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.w\_difference
-==============================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: w_difference
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.weights.w_intersection.rst.txt b/docs/_sources/generated/libpysal.weights.w_intersection.rst.txt
deleted file mode 100644
index bf25a21e0..000000000
--- a/docs/_sources/generated/libpysal.weights.w_intersection.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.w\_intersection
-================================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: w_intersection
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.weights.w_local_cluster.rst.txt b/docs/_sources/generated/libpysal.weights.w_local_cluster.rst.txt
deleted file mode 100644
index 56e84ef20..000000000
--- a/docs/_sources/generated/libpysal.weights.w_local_cluster.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.w\_local\_cluster
-==================================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: w_local_cluster
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.weights.w_subset.rst.txt b/docs/_sources/generated/libpysal.weights.w_subset.rst.txt
deleted file mode 100644
index 2164fd6fd..000000000
--- a/docs/_sources/generated/libpysal.weights.w_subset.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.w\_subset
-==========================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: w_subset
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.weights.w_symmetric_difference.rst.txt b/docs/_sources/generated/libpysal.weights.w_symmetric_difference.rst.txt
deleted file mode 100644
index c06c0b5f3..000000000
--- a/docs/_sources/generated/libpysal.weights.w_symmetric_difference.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.w\_symmetric\_difference
-=========================================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: w_symmetric_difference
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.weights.w_union.rst.txt b/docs/_sources/generated/libpysal.weights.w_union.rst.txt
deleted file mode 100644
index 278a9c33f..000000000
--- a/docs/_sources/generated/libpysal.weights.w_union.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.w\_union
-=========================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: w_union
\ No newline at end of file
diff --git a/docs/_sources/generated/libpysal.weights.wsp2da.rst.txt b/docs/_sources/generated/libpysal.weights.wsp2da.rst.txt
deleted file mode 100644
index 7b71f7f45..000000000
--- a/docs/_sources/generated/libpysal.weights.wsp2da.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-libpysal.weights.wsp2da
-=======================
-
-.. currentmodule:: libpysal.weights
-
-.. autofunction:: wsp2da
\ No newline at end of file
diff --git a/docs/_sources/notebooks/Raster_awareness_API.ipynb.txt b/docs/_sources/notebooks/Raster_awareness_API.ipynb.txt
deleted file mode 100644
index a38dc049d..000000000
--- a/docs/_sources/notebooks/Raster_awareness_API.ipynb.txt
+++ /dev/null
@@ -1,754 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Raster awareness API\n",
-    "\n",
-    "This notebook will give an overview of newly developed raster interface. We'll cover \n",
-    "basic usage of the functionality offered by the interface which mainly involves:\n",
-    "1. converting `xarray.DataArray` object to the PySAL's weights object (`libpysal.weights.W`/`WSP`).\n",
-    "2. going back to the `xarray.DataArray` from weights object.\n",
-    "\n",
-    "using different datasets:\n",
-    "- with missing values.\n",
-    "- with multiple layers.\n",
-    "- with non conventional dimension names."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "\n",
-    "from libpysal.weights import Rook, Queen, raster\n",
-    "import matplotlib.pyplot as plt\n",
-    "from splot import libpysal as splot\n",
-    "import numpy as np\n",
-    "import xarray as xr\n",
-    "import pandas as pd\n",
-    "from esda import Moran_Local"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Loading Data\n",
-    "\n",
-    "*The interface only accepts `xarray.DataArray`*, this can be easily obtained from raster data\n",
-    "format using `xarray`'s I/O functionality which can read from a variety of data formats some of them are listed below: \n",
-    "- [GDAL Raster Formats](https://svn.osgeo.org/gdal/tags/gdal_1_2_5/frmts/formats_list.html) via `open_rasterio` method.\n",
-    "- [NetCDF](https://www.unidata.ucar.edu/software/netcdf/) via `open_dataset` method.\n",
-    "\n",
-    "In this notebook we'll work with `NetCDF` and `GeoTIFF` data. \n",
-    "\n",
-    "### Using xarray example dataset\n",
-    "First lets load up a `netCDF` dataset offered by xarray."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<xarray.DataArray 'air' (time: 2920, lat: 25, lon: 53)>\n",
-      "[3869000 values with dtype=float32]\n",
-      "Coordinates:\n",
-      "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-      "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
-      "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-      "Attributes:\n",
-      "    long_name:     4xDaily Air temperature at sigma level 995\n",
-      "    units:         degK\n",
-      "    precision:     2\n",
-      "    GRIB_id:       11\n",
-      "    GRIB_name:     TMP\n",
-      "    var_desc:      Air temperature\n",
-      "    dataset:       NMC Reanalysis\n",
-      "    level_desc:    Surface\n",
-      "    statistic:     Individual Obs\n",
-      "    parent_stat:   Other\n",
-      "    actual_range:  [185.16 322.1 ]\n"
-     ]
-    }
-   ],
-   "source": [
-    "ds = xr.tutorial.open_dataset(\"air_temperature.nc\")  # -> returns a xarray.Dataset object\n",
-    "da = ds[\"air\"]  # we'll use the \"air\" data variable for further analysis\n",
-    "print(da)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`xarray`'s data structures like `Dataset` and `DataArray` provides `pandas` like functionality for multidimensional-array or ndarray. \n",
-    "\n",
-    "In our case we'll mainly deal with `DataArray`, we can see above that the `da` holds the data for air temperature, it has 2 dims coordinate dimensions `x` and `y`, and it's layered on `time` dimension so in total 3 dims (`time`, `lat`, `lon`).\n",
-    "\n",
-    "We'll now group `da` by month and take average over the `time` dimension\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Coordinates:\n",
-      "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-      "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
-      "  * month    (month) int64 1 2 3 4 5 6 7 8 9 10 11 12\n"
-     ]
-    }
-   ],
-   "source": [
-    "da = da.groupby('time.month').mean()\n",
-    "print(da.coords)  # as a result time dim is replaced by month "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<xarray.plot.facetgrid.FacetGrid at 0x7ff60521aeb0>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x648 with 13 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# let's plot over month, each facet will represent the mean air temperature in a given month.\n",
-    "da.plot(col=\"month\", col_wrap=4,) "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can use `from_xarray` method from the contiguity classes like `Rook` and `Queen`, and also from `KNN`.\n",
-    "\n",
-    "This uses a util function in `raster.py` file called `da2W`, which can also be called directly to build `W` object, similarly `da2WSP` for building `WSP` object.\n",
-    "\n",
-    "**Weight builders (`from_xarray`, `da2W`, `da2WSP`) can recognise dimensions belonging to this list `[band, time, lat, y, lon, x]`, if any of the dimension in the `DataArray` does not belong to the mentioned list then we need to pass a dictionary (specifying that dimension's name) to the weight builder.** \n",
-    "\n",
-    "e.g. `dims` dictionary:\n",
-    "```python\n",
-    ">>> da.dims                  # none of the dimension belong to the default dimension list\n",
-    "('year', 'height', 'width')\n",
-    ">>> coords_labels = {                 # dimension values should be properly aligned with the following keys\n",
-    "        \"z_label\": \"year\",\n",
-    "        \"y_label\": \"height\",\n",
-    "        \"x_label\": \"width\"\n",
-    "    }\n",
-    "```\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "coords_labels = {}\n",
-    "coords_labels[\"z_label\"] = \"month\"  # since month does not belong to the default list we need to pass it using a dictionary\n",
-    "w_queen = Queen.from_xarray(\n",
-    "    da, z_value=12, coords_labels=coords_labels, sparse=False)  # We'll use data from 12th layer (in our case layer=month)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`index` is a newly added attribute to the weights object, this holds the multi-indices of the non-missing values belonging to `pandas.Series` created from the passed `DataArray`, this series can be easily obtained using `DataArray.to_series()` method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "MultiIndex([(12, 75.0, 200.0),\n",
-       "            (12, 75.0, 202.5),\n",
-       "            (12, 75.0, 205.0),\n",
-       "            (12, 75.0, 207.5),\n",
-       "            (12, 75.0, 210.0)],\n",
-       "           names=['month', 'lat', 'lon'])"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w_queen.index[:5]  # indices are aligned to the ids of the weight object"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can then obtain raster data by converting the `DataArray` to `Series` and then using indices from `index` attribute to get non-missing values by subsetting the `Series`. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = da.to_series()[w_queen.index]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We now have the required data for further analysis (we can now use methods such as ESDA/spatial regression), for this example let's compute a local Moran statistic for the extracted data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Quickly computing and loading a LISA\n",
-    "np.random.seed(12345)\n",
-    "lisa = Moran_Local(np.array(data, dtype=np.float64), w_queen)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "After getting our calculated results it's time to store them back to the `DataArray`, we can use `w2da` function directly to convert the `W` object back to `DataArray`. \n",
-    "\n",
-    "*Your use case might differ but the steps for using the interface will be similar to this example.* "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<xarray.DataArray (month: 1, lat: 25, lon: 53)>\n",
-      "array([[[0.018, 0.001, 0.001, ..., 0.001, 0.001, 0.001],\n",
-      "        [0.001, 0.001, 0.001, ..., 0.001, 0.001, 0.001],\n",
-      "        [0.003, 0.001, 0.001, ..., 0.001, 0.001, 0.001],\n",
-      "        ...,\n",
-      "        [0.002, 0.001, 0.001, ..., 0.001, 0.001, 0.003],\n",
-      "        [0.001, 0.001, 0.001, ..., 0.001, 0.001, 0.003],\n",
-      "        [0.002, 0.001, 0.001, ..., 0.001, 0.002, 0.006]]])\n",
-      "Coordinates:\n",
-      "  * month    (month) int64 12\n",
-      "  * lat      (lat) float64 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-      "  * lon      (lon) float64 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Converting obtained data back to DataArray\n",
-    "moran_da = raster.w2da(lisa.p_sim, w_queen)  # w2da accepts list/1d array/pd.Series and a weight object aligned to passed data\n",
-    "print(moran_da)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7ff6013e4b80>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "moran_da.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Using local `NetCDF` dataset\n",
-    "\n",
-    "In the earlier example we used an example dataset from xarray for building weights object. Additonally, we had to pass the custom layer name to the builder. \n",
-    "\n",
-    "In this small example we'll build `KNN` distance weight object using a local `NetCDF` dataset with different dimensions names which doesn't belong to the default list of dimensions.\n",
-    "\n",
-    "We'll also see how to speed up the reverse journey (from weights object to `DataArray`) by passing prebuilt `coords` and `attrs` to `w2da` method. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<xarray.Dataset>\n",
-      "Dimensions:    (latitude: 73, longitude: 144, time: 62)\n",
-      "Coordinates:\n",
-      "  * longitude  (longitude) float32 0.0 2.5 5.0 7.5 ... 350.0 352.5 355.0 357.5\n",
-      "  * latitude   (latitude) float32 90.0 87.5 85.0 82.5 ... -85.0 -87.5 -90.0\n",
-      "  * time       (time) datetime64[ns] 2002-07-01T12:00:00 ... 2002-07-31T18:00:00\n",
-      "Data variables:\n",
-      "    tcw        (time, latitude, longitude) float32 ...\n",
-      "    tcwv       (time, latitude, longitude) float32 ...\n",
-      "    lsp        (time, latitude, longitude) float32 ...\n",
-      "    cp         (time, latitude, longitude) float32 ...\n",
-      "    msl        (time, latitude, longitude) float32 ...\n",
-      "    blh        (time, latitude, longitude) float32 ...\n",
-      "    tcc        (time, latitude, longitude) float32 ...\n",
-      "    p10u       (time, latitude, longitude) float32 ...\n",
-      "    p10v       (time, latitude, longitude) float32 ...\n",
-      "    p2t        (time, latitude, longitude) float32 ...\n",
-      "    p2d        (time, latitude, longitude) float32 ...\n",
-      "    e          (time, latitude, longitude) float32 ...\n",
-      "    lcc        (time, latitude, longitude) float32 ...\n",
-      "    mcc        (time, latitude, longitude) float32 ...\n",
-      "    hcc        (time, latitude, longitude) float32 ...\n",
-      "    tco3       (time, latitude, longitude) float32 ...\n",
-      "    tp         (time, latitude, longitude) float32 ...\n",
-      "Attributes:\n",
-      "    Conventions:  CF-1.0\n",
-      "    history:      2004-09-15 17:04:29 GMT by mars2netcdf-0.92\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Lets load a netCDF Surface dataset\n",
-    "ds = xr.open_dataset('ECMWF_ERA-40_subset.nc')    # After loading netCDF dataset we obtained a xarray.Dataset object\n",
-    "print(ds)                                         # This Dataset object containes several data variables"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Out of 17 data variables we'll use `p2t` for our analysis. This will give us our desired `DataArray` object `da`, we will further group `da` by day, taking average over the `time` dimension."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "('day', 'latitude', 'longitude')\n"
-     ]
-    }
-   ],
-   "source": [
-    "da = ds[\"p2t\"]  # this will give us the required DataArray with p2t (2 metre temperature) data variable\n",
-    "da = da.groupby('time.day').mean()\n",
-    "print(da.dims)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**We can see that the none of dimensions of `da` matches with the default dimensions (`[band, time, lat, y, lon, x]`)**\n",
-    "\n",
-    "This means we have to create a dictionary mentioning the dimensions and ship it to weight builder, similar to our last example. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "coords_labels = {}\n",
-    "coords_labels[\"y_label\"] = \"latitude\"\n",
-    "coords_labels[\"x_label\"] = \"longitude\"\n",
-    "coords_labels[\"z_label\"] = \"day\"\n",
-    "w_rook = Rook.from_xarray(da, z_value=13, coords_labels=coords_labels, sparse=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = da.to_series()[w_rook.index]  # we derived the data from DataArray similar to our last example "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the last example we only passed the `data` values and weight object to `w2da` method, which then created the necessary `coords` to build our required `DataArray`. This process can be speed up by passing `coords` from the existing `DataArray` `da` which we used earlier.\n",
-    "\n",
-    "Along with `coords` we can also pass `attrs` of the same `DataArray` this will help `w2da` to retain all the properties of original `DataArray`.\n",
-    "\n",
-    "Let's compare the `DataArray` returned by `w2da` and original `DataArray`. For this we'll ship the derived data straight to `w2da` without any statistical analysis."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "da1 = raster.wsp2da(data, w_rook, attrs=da.attrs, coords=da[12:13].coords)\n",
-    "xr.DataArray.equals(da[12:13], da1)  # method to compare 2 DataArray, if true then w2da was successfull"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Using local `GeoTIFF` dataset\n",
-    "\n",
-    "Up until now we've only played with `netCDF` datasets but in this example we'll use a `raster.tif` file to see how interface interacts with it. We'll also see how these methods handle missing data. \n",
-    "\n",
-    "Unlike earlier we'll use weight builder methods from `raster.py`, which we can call directly. Just a reminder that `from_xarray` uses methods from `raster.py` and therefore only difference exists in the API. \n",
-    "\n",
-    "To access GDAL Raster Formats `xarray` offers `open_rasterio` method which uses `rasterio` as backend. It loads metadata, coordinate values from the raster file and assign them to the `DataArray`.  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<xarray.DataArray (band: 1, y: 880, x: 940)>\n",
-      "[827200 values with dtype=float32]\n",
-      "Coordinates:\n",
-      "  * band     (band) int64 1\n",
-      "  * y        (y) float64 50.18 50.18 50.18 50.18 ... 49.45 49.45 49.45 49.45\n",
-      "  * x        (x) float64 5.745 5.746 5.747 5.747 ... 6.525 6.526 6.527 6.527\n",
-      "Attributes:\n",
-      "    transform:      (0.0008333333297872345, 0.0, 5.744583325, 0.0, -0.0008333...\n",
-      "    crs:            +init=epsg:4326\n",
-      "    res:            (0.0008333333297872345, 0.0008333333295454553)\n",
-      "    is_tiled:       0\n",
-      "    nodatavals:     (-99999.0,)\n",
-      "    scales:         (1.0,)\n",
-      "    offsets:        (0.0,)\n",
-      "    AREA_OR_POINT:  Area\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Loading raster data with missing values\n",
-    "da = xr.open_rasterio('lux_ppp_2019.tif')\n",
-    "print(da)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7ff600e744f0>"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "da.where(da.values>da.attrs[\"nodatavals\"][0]).plot() # we can see that the DataArray contains missing values."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We'll look at how weight builders handle missing values. Firstly we'll slice the `DataArray` to reduce overall size for easier visualization.\n",
-    "\n",
-    "This time we'll create `WSP` object using `da2WSP` method inside `raster.py`. Since our DataArray is single banded and all of its dimensions belong to the default list, we only have to ship the DataArray and the type of contiguity we need."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Slicing the dataarray\n",
-    "da_s = da[:, 330:340, 129:139]\n",
-    "w_queen = raster.da2WSP(da_s)  # default contiguity is queen\n",
-    "w_rook = raster.da2WSP(da_s, \"rook\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "After plotting both contiguities and sliced `DataArray`, we can see that the missing values are ignored by the `da2WSP` method and only indices of non missing values are stored in `index` attribute of `WSP` object. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1152x288 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "f,ax = plt.subplots(1,3,figsize=(4*4,4), subplot_kw=dict(aspect='equal'))\n",
-    "da_s.where(da_s.values>da_s.attrs[\"nodatavals\"][0]).plot(ax=ax[0])\n",
-    "ax[0].set_title(\"Sliced raster\")\n",
-    "splot.plot_spatial_weights(w_rook, da=da_s, ax=ax[1])\n",
-    "ax[1].set_title(\"Rook contiguity\")\n",
-    "splot.plot_spatial_weights(w_queen, da=da_s, ax=ax[2])\n",
-    "ax[2].set_title(\"Queen contiguity\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### `higher_order` neighbors\n",
-    "\n",
-    "In some cases `Rook` and `Queen` contiguities don't provide sufficient neighbors when performing spatial analysis on a raster data, this is because `Rook` contiguity provides max 4 neighbors and `Queen` provides max 8.\n",
-    "\n",
-    "Therefore we've added `higher_order` functionality inside the builder method. We can now pass `k` value to the weight builder to obtain upto kth order neighbors. Since this can be computionally expensive we can take advantage of parallel processing using `n_jobs` argument. Now lets take a look at this functionality."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Building a test DataArray \n",
-    "da_s = raster.testDataArray((1,5,10), rand=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Below we can see that builder selected all the neighbors of order less than equal to 2, with `rook` contiguity"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/magito/anaconda3/lib/python3.8/site-packages/scipy/sparse/_index.py:124: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.\n",
-      "  self._set_arrayXarray(i, j, x)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(<Figure size 720x720 with 1 Axes>, <AxesSubplot:>)"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "w_rook2 = raster.da2WSP(da_s, \"rook\", k=2, n_jobs=-1)\n",
-    "splot.plot_spatial_weights(w_rook2, da=da_s)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Few times we require the kth order neighbors for our analysis even if lower order neighbors are absent, hence we can use `include_nas` argument to do the same.\n",
-    "\n",
-    "We can also look in both the examples we used `n_jobs` parameter, and assigned -1 which equats to all the cores present in the computer for multithreading"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(<Figure size 720x720 with 1 Axes>, <AxesSubplot:>)"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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A1WDIRuN1ruM4pwGXAV80xlzQ3V80xtxpjFlpjFnZ0NCQuQpFfEQBqyKZkY2A1HQpYNX/Mt54OY5Tn/p7N/AkcAawyxgzGiD19+4ufvcBx3FmOY4zq7i4ONOliviGAlZF3JXNgNR0KWDV3zLaeBljiowxA9q+By4B3gOeAeam/ttc4OlM1iESNApYFXGPFwGp6VLAqn9lesZrJLDCGLMaeBNY6jjOi8APgUpjzGagMvVvEekBBayKuMOrgNR0KWDVnzJ6tHYcp8ZxnFNTX1Mdx/l+avlex3E+6TjOxNTf+zJZh0hXampqwEn97UPtA1bf2lLP8uXLfTuWoPL7NhZkbetm/5GEJwGp6WofsPrS5v0kEglfb2dtAasPbUoE+liml8mSt2pqanjh968DDsuWLfPtTl4cgTNNLa/GS3h3Y62vxxI0NTU1vFD9Cn7fxoKopqaGp9e1vmnr2Kvz2brVn+smbMFsNvFu8xCSx2zfb2dnJt5nx+FjvNPY1/dj6YpxHMfrGrpl1qxZzsqVK70uQzxUXV3t6u0tX76clclRHCubAYkjhEIhwmF3p7m7ur1Ewt1X1/FEEttKnW7I8liCxP31ksA2BWAMHGshZBytl17KzD7TBzC0ph6Z1m9dZLq4QQeXn3c7uzlfj8WBljj9lv+U6dOnM3v2bFfvorKy0tXb64ox5u12wfEf8f+JYZFeKi8vZ8OyZTSWzSB0cDvXlLYQjUZdvY/KyvM7Xe5mE7njKDwSCxP58yKOnHk7xk5yVXgrEyZkZyxB4nZz/8uNNvt313KseALYSU7ru5eLJgx39T7yYb2Au+tm2xGLJdv60PdPD9J09h0M/N1/UVVVlYH9v/MneDfHEk/CfR8UYK17CTveROKUz3B5JMa0CeWu3QdkZyzQOhO5bNkyGs+9i1AoRHm5u+PIBTrVKHkrGo1SVVUFQGTEWLb2n+RxRT3XmIDFdRGuKUty+fmtL6zK+9msCJ3scWXybH0BocII15a2AIbL+tax2ikj1qTDrpfaAlJvGHOMT194FmAy0nRlQ9KGB2sLOXO4zRUzxjKzpD+nFe7jd0zybcBq23E5FAr5dr2ciI4Aktei0SgY+MJEm/WNId7e759ohngSFsYizC5OMK7I/mgscyZFFLDqsdcaQmyPW9xWnvhovZwyYQzXlzbzRH2Yvc1eV5ifOgaktq0bPz652zY8VFtIRT+bc4YdIxqNMnv2bConDvV9wGo0GiUcDvtyvXSHGi8RWi9QnVcRZ8WeAjb7IJrBtmFBrJAZg5KcOvjjR1cFrHpn9QGL1QdDzCtvxuqwKZX1c7h0ZIJfxyIcyZOwyFzhp4DU7ni8PkzfkMNloz8egRGkgNUgyv1nGJEs6R+COWPiLN0ZZmfc62qO76FYmNKIzfnFnT97K2DVGx82WSxvCDO3PE6kiytoTx5oc+bQFhbUFpL0//O/L/gxIPV4XtpVwMEWw43HicBoC1h9OgABq0GjxkuknZERuLIkwWPbcvezw57YXkDIas28OZ72AaubfDCL53cNcXi6PsyNZXEGnmCi4cxhxxjf32ZhrX9PB/mJXwNSO/Pm3j5sPtSHeWM/PqPa0S1jEuxqtlgegIDVINHRWKSDaJHNhanPDsu1C1Rf2R1ib7PFzWXd6wrbAlaf3xmm/qjL7y+XjxxOwm+2Rfj0qASj+3bvd6pGtjAw5LBku04HZdKLOwtoShpfBqR29H6jxev7CrijIk6oG8/ebQGr7zWGeMdH168GnRovkU7MGGxzyqAkv8qhC1Tf3teHDY0h5lac+JVue8URuLokwZK6Qg76/7kn5yRsWLA1wnnDWpg0oGcby3WlCZqShhd26nRQJry+tw81TRa3d2N2KNdtP2p4cVeYW8vj9OvBBFbYgrkVcf6wp4AtmvnOCVoLIl24MPXZYQ/nwAWqmw5ZrNhbwLyKOOFe7LUVRTYXFSdYFIsQ10XdrrFtWLC1kMkDk5w+tOfXDlkW3D62mQ+bLP60VzMSblrfaPHW/gLmjW3u1uxQLtufgCV1hVxbkmBYYc9/f0Dq+tXnfHD9aj7w+eYokllXlLRgAU96eIHqjqPw/M4wt4yJ0z+NSzVOGWwzc3CSBbquyDWP1IUpLrT55Ijed7MhC+aNbWbl/gLWHdQh2Q3bjli81IvZoVwUT8JDsQiVIxKUF/V+xx0ZgStGp65f9f+lbr6mvVzkBG4Zk6Ch2eJVDy5QbQtIvaokQXEk/ds7b3iSMf1sFsW8n8Xzu2frC0jacE1p+s9i/UJwW3mc6t1hBaymqS0g9brSBEN9vpm3BaTOGtLC1EHpv1oa39/mguIWFtbm3vWr+UR7uMgJtF2gmu2A1Y4BqW65fHSLAlbT1D4g1S1DwihgNU0dA1L9rGNAqltmDj7m+4BVv1PjJdIN2Q5YPVFAaroUsNp7xwtITZcCVnsvnwJS06WAVW+p8RLppmwGrJ4oIDVdCljtne4EpKZLAas9l48BqelSwKp31HiJ9EA2Ala7G5CaLgWs9kxPAlLTpYDVnsnXgNR0KWDVGzraivRQJgNWexqQmi4FrHZPbwJS06WA1e7J54DUdClg1RtqvER6IRMBq70NSE2XAlaP76OA1OE9D0hNlwJWj08BqelTwGr26VEW6SU3A1bTDUhNV0WRzcUKWP2YtoDUKQOTnD4k+9cOKWC1awpIdY8CVrPL55uriLfcCFh1KyA1XdMVsPoxbQGpF6cRkJouBax+XKxJAaluU8Bq9mgvFklTOgGrbgekpksBq3/hZkBquhSw+hf7EvBEvQJSM0EBq9mR33uwiAt6G7CaqYDUdClgNTMBqelSwKoCUrNBAauZp8ZLxAVtF6h2N2C1LSD11AwFpKYrnwNWMxmQmq6yfg6X5GnAqgJSs0cBq5nl87Pjkk8qKyszcrv3blzh2m3PaDjMV59cy6fOm8akEQOwbRurk2fvpw8WM2tcX755yUmu3G8bN8dyUdLmtkUrOTi8hOtnlnU5liAZctIsVix7n1/cOpORA9w79+vmeqkERr1TxyNr6ll066y8WC+2bfPE3mFccNJAvnrRBFdv281101FX62ZDwTiOFR7kF3Nm5uy6qwS+/NvV/PlYmG9fOrnLsWTqsZsfe4PKyvMzctte7zO5ucZFfCpa3J9vV53M159+j12H4p3u3N95fj2FIcv1pstt4ZDF/XNm8Ju361jxwZ6cfYJwy4d7m/jei+9z75XTXG26MuGm08o4u2Iof7d4ldelZMU3n13P8P6FrjddmdbZPrPk3Tr+9OE+7rsxd5uuNv957XS2NDTxiz9+mPO19oTXYwnOIymSI2aNHcIXzqngnsWrOZL46/NB96+ooXbfUX509TSPquuZIf3C/Pia6fzHK5t5f9chr8vJmH1NCf7hibV8o3ISk0YO8Lqcbvny7AmMGFDIN59d73UpGfXjVzez70iC739mstelpO21LXt45O067r9pBmEfZGBYlsX8m2ZQvXE3z6zd4XU5gZH7a17Ehy6fOprLpo7krkdXkUxdofrk6np+t2kP9994quevuHqiYlgR37nsZL7xzHvsbAxeyE88keRvH1vF3DPLOXvcMK/L6ZF/u3wy+48k+PGrm70uJSN+s3Ibb8X287MbZvhqn+nMuh0H+dGrm/nv605hcD//XDsVCYf4+Y0zePDPtbz+4V6vywkEf2/JIjnss2dVMHlUf77y+Br+WLOHRW/GuO+mGUTC/ru08rQxQ/jb86Lcs2Q1TYngXNVt2zZ3L17FRZOGc/UpJV6X02OWZfHTG2bwVmw/v35rm9fluOrVTbt5fNV25s/xx+zQ8Ww/cJRvPbuef/n0FMYM6ed1OT02vH8hP7p6Gj+s3sSm3cGd+c4Wf2/NIjnuny45mUTS5l9f2MiPr53O0CL/vNLtqGrySK6YNoq7Hnn3o1k8v/vaU+9RMbSIu8+Lel1Kr4VDFvPnzOCJ1dt5ZeNur8txxertB/nJ8g/47+tOYWDEv/sMwOF4ki/9djX3XDCeGWWDvC6n16LF/flW1Ul8/en3aDgcvJnvbFLjJZJBuw7F2XUozpB+Bby0YZfX5aRt7pljOaV0EH+/ZLXXpaTtBy9tpDlp891P+//aoYGRMD+5/lT+5/cfsHr7Qa/LSUvd/iP889L1fO8zUyjz4exQe4mkzZ2PvsM1p5bwqZNHeF1O2s4YO5TPn13B3z328etXpfvUeIlkSFMiyRcXr+buc6Ms+JvTeGVTA0+tqfe6rLR9/VOT6BcO8e2l/r2o+8E/b2XdzkZ+cv0pXpfimtLBffm3z0zhn5euZ9v+I16X0yuN8QRffnwNX5k9nukl/p0dgtbT2H+/ZBWnjxnCrZ8o97oc13xm2miqJo/k7kdXYQdk5jvb1HiJZEDStrnr0Xe5fOpILps66qMLVBe+EQvEBar/cdVUth84ys9e+8DrUnrshXU7WbpuF/PnzCTk8wu2O5pWMoivzB7Plx9fw4EjuZO63x2ts0OruGFmGRdN8v/s0LeXbmBg3wK+9smJXpfius+fU8Gkkf358uNrvS7Fl4J11BHJEV/67RqmjhrIHWdVfLQsSBeoWpbF/TfN5LUte/ntu3Vel9NtK2P7mf+nD/nZjadS5MM3OXTHRZNGcNNpZdy9eBWJpD9mJGzb5ouLV3FWxVDmnF7mdTlp+8nyLew8FOffr5zqdSkZ83+rTsYy8L0XN3hdiu+o8RJx2fECUjsGrPpZx4DVXOengNR0tQ9Y9cPpoLaA1K/M9ldAamf8FJCarvYBq9J9wd4qRLKsOwGpxwtY9Ru/BKz6MSA1XX4JWFVAqn8pYLV3gr9lSCD44VV7ZwGpndVt23anAau5rqux5HrAamcBqV2NxY+6qtu27ZwPWO0sINWv66azgFQ/1J0O27Z9GbDq9XoxjuN4WkB3zZo1y1m5cqXXZYiHqqurM3K7/74xwj+elF7DsOmQxQs7w3xuXJz+7S4d6uoDZNvG8mx9AftbDLePdedCaDfG0pUTjWXtAYvle8J8oSJOJEcun7Jt+N+thUzof4yLR/xldvFEY3GbF+sFWseTtOGBDwuZMTjJOcOOZaSG3lh30OKVhjCfr4jTrwf7jNvcWDf7E7CwNsK1JQnKi/7ypJ6pD5DOJW3rZVccHtkWYc6YOKNcOJP/P1si/P2E7O8zbjLGvO04zqyOyzXjJZKmHUfh+Z1hbhnz101Xd1xR0oIFPLm9ICO1ZdP0wTYzBydZUFtIrrzQf6QuTHGh/VdNV74JWTBvbDMr9xew7mBuHPJjTRbVu8PcWv7XTZcfxZPwUCxC5Yi/brryzcgIXDE6wWPbIjS2eF1NbsuNvVDEpxoTsLguwlUlCYp7+SrvljEJGpotXt3t82cg4LzhScb0s1kU8z5t/Nn6ApI2XFOqZ4F+IbitPE717jCxJm8P+/sS8ER9mOtLmxnq/WaSlqQND9YWMmtIC1MH5W/T1WZ8f5sLiltYWBshoYejS2q8RHopnoSFsQizixOMS+OVrmXBvIpm1jeGeHt/Hxcr9Mblo1sIW/B4nXezeK81hNget7it3F9ZVpk0JAzXlzbzRH2Yvc3e1HAkCQ/HIlwyMkFZP39c5tIV24aHagup6Gfn1Clcr80cfIzpA5M8uDV3Zr5zjRovkV6wbVgQK+TUQUlOHZz+0SVswdyKOCv2FLD5kP93yzllCfa1WLy8K/vN1+oDFqsPhphX3kzA383fY2X9HC4ZmeDXsQhHsnz2NWnDgtpCzhjSwpSB/n9Gfrw+TN+Qw2WjNaPa0ewRSUYX2vx6m8+nNDNEhyWRXngoFqY0YnNBsXvPXgNCMGdMnKU7w+zMvTcH9ohlwR1jm9l4qA9v7cveLN6HTRbLG8LMLc+dC/xzzZSBNmcMbWFBbSHZyle1bVhUW8j4IpuzAjA79NKuAg62GG4s1YxqV65MneJ/OgDXr7pNjZdIDz2xvYCQ1XphvNv+6gJVnx/TQxbMq4jzp70FbMrCLF5DHJ6uD3NjWZyBeqF9XGcNO8b4IpuFWXojxJLtYfqHHKpG+X926M29fdh8qA/zxmpG9UT+ZkyCnc0WywNw/aqbtNmI9MCru0Psbba4uSxzXdH4/jYXFidYGPP/BapFIbhlTJznd4apP5q5+zmchN9si3D5qASj+2bufoKkalQLA0MOS7Zntkt9cWcBTUnD9QGYHXq/0eL1fQXcUREnD/JR02ZZcEdFM+81hngnANevukWbjkg3vb2vDxsaQ8ytyPwr3RmDbU4ZFIwLVIsjcHVJgiV1EQ5m4Lk3YcPCrRHOG97CxAE+f7Cy7LrSBE1Jwws7M3M66PW9fahpsrg9ALND248aXtwVjAiMbGq7fvUPewrYEoDrV92gR0GkGzYdslixt4C5FXHCWdprLixOUhKxeTgAF6hWFNlcXJxgUSxC3MWLum0bFm4tZPLAJKcP8f+1Q9lmWXD72GY+bLJ4fa+7MxLrDlq8tb+AeWObfT87dCABS+oKubYkwbBCr6vxn7brV5/bGWaXz69fdYPPdweRzEsnIDVdClg9vkfqwgzP84DUdLUFrL7lYsBq0AJSFykgNW1t168+qoBVNV4ix+NGQGq6FLDaOQWkusfNgFUFpEpXFLDaSo2XSBfcCkhNlwJWP04Bqe5zI2BVAalyIgpYVeMl0im3A1LTpYDVv1BAauakE7CqgFTprnwPWNVhS6QTmQhITZcCVhWQmg29CVi1bViogFTpgXwOWFXjJdJBJgNS05XPAasKSM2engasLtkeZoACUqWH8jVgVZuVSDvZCEhNVz4GrCogNfu6G7CqgFTprXwNWNWmJZKSzYDUdOVTwKoCUr1zooBVBaRKuvIxYNWzURpjLjXGbDTGbDHGfMOrOkTAm4DUdOVDwKoCUr11vIBVBaSKW/ItYNWT3cUY0wf4GXAZMAW42RgzxYtaRMC7gNR0BT1gVQGp3ussYLUtIPW2gMwOKSDVe/kUsOrV65QzgC2O49Q4jpMAHgWu8qgWyWPvbYmBA2eYWs8CUtPVFrD65ObD4EBNTY3XJfVaW8Dq/26G/313H01Hm30fkFpTU+P79dI+YPV3mxpYHLM4x3zIEJ9Ptm7Z0rpuos5uBaTmgLaA1V/VhEgkEr7eZ47Hq8arFNjW7t91qWUiWfP+lq0sbWrd7FZXP+HbndyyYLa9gU2NfQCHZcuW+XYsAJOPbmR/01H20peWV+/39VhqampY+sY6grBehoThHPMhbx0dQp+1L/B29VO+Hk9NTQ1L18YA2Pbyr309liAZtG8zztZ3SDrG9/tMV4zjZD9d2BhzA1DlOM7nU/++DTjDcZy/7/D/7gTuBCgvLz+9trY267VK7qiurnbttmwb/nuDQ3L3VuzRkwEHMGBcuwsATBc36ODyfue0/dHu/lweS9c3mImxdPOuey0fxtJVAWlwOn4TgH3GcaAlTigUIhx2dwrP7dvLRYmEu+9mTSQSJJNJSDTR7/UFTJ8+ndmzZ7t6H5WVla7eXleMMW87jjOr43Kvzs7XAWPa/bsMqO/4nxzHeQB4AGDWrFn+/vwJySkPxcKMKjjEgfdfpHHEeMyxFq7uV8ukCVFX76erHdzNJhJaX70vW7aMxou+CslmPhWp4/QJ7k4iZ2MsHzZZPFnXh8IVv+LwJ/6GgX+cT1VVFdGo/9ZLUxIe+KCAPmue5+hJn8z6WCCD29m5d/l63cBfxpJMJgmFQhkay/mu3l4uyvR6KS8vd/X2c4FXjddbwERjzDhgOzAHuMWjWiTPtAWk3jypkJpQFU/GIdr3KMutyUyw/fm2+Gg0SlVVFUsScF5kJyuc8UxM+CtotC0g9ebyOEcvOpcn45l5MsyGpA0LtkY4t7iF4aeP9/VY2mvbzoIwnraxxGIxysvLfT2WIMmH9eJJ4+U4TtIYcw+wDOgD/MpxnHVe1CL5pS0g9XMVrZ8CHI1GCW8Jc82E/jy13eY328LcOtafQZDRaBQ2wrkTRlJ0oDVg9a6oP+IxPhaQmlovfjzo2qnPLJw04BhnDD0GQ/07ls5EfbxuOopGo4EYR9AEfb14dkh2HOd5x3EmOY4z3nGc73tVh+SPEwWkXl3agg08E4BoBj8FrAYtIPWxujCDCxwqR/r73Zgikhk+eC0skr7uBqTeOibBjmaL3wfgs8P8ELAatIDUpTsKiNuGa0v8OWsqIpmnxksCb8fR7gektn122JrGEKsO+P+zw3I9YDVIAakr9oSIHbGYW+7P6wRFJDt0eJBAa0zA4roIV5Ukuh2QGrZg7tg4v28o4IPD/t9F2gJWX82xWbxn6wtI2vg+IBVg7UGLdw6EuCMAn1koIpmlQ4QEVjwJC2MRZhcnGNfDjwIZWAA3jYnz7A7/f3aYZcG8imbWN4Z4Z39uzOK91hBie9zitnL/n5KrbbJ4dXeY28vjRHKrtxWRHKTGSwLJtmFBrJBTByU5dXDvLtgeFYHPjGr97LBDPj8TFrZgbkWcP+wpYPMhb3f71QcsVh8MMS8Ap+T2NsOT9WFuKGtmcO5eSiciOcTnhz2Rzj0UC1MasbmgOL2OacIAm/OHt7Bwa4SEz99wNyAEc8bEWbozzE6PZvE+bLJY3hBmbgBmh5qS8HAswmWjEpT0Vb6ziHSPGi8JnLaA1CtK3Ll26LQhx5g6MMkCH0QznMjICFxZkuCxbREas3yWry0g9cYyfwW7dqYtIPXsYS2cFIAIDBHJHjVeEihtAak3l7nbVVw0IsmIwtaAVb+LFtlcWNwasJqtWbyPBaT62McCUkVEekCNlwTGiQJS06WA1d5RQKqIyF+o8ZJA6G5AaroUsNoztg0LFJAqIvIRNV7iez0JSE2XAlZ75pG6MMUKSBUR+YgOH+JrvQlITZcCVrtHAakiIh+nQ4j4VjoBqelSwOrxKSBVRKRzarzEl9wISE1XUANWt6QZsKqAVBGRrvn8sCj5yq2A1HQFMWD1uTQCVhWQKiJyfGq8xHfcDkhNlwJWWykgVUTkxNR4ia+8kqGA1HTle8CqAlJFRLpHjZf4RqYDUtOVrwGridTskAJSRUROzOdXYUi++GPNHt5o7MeDt5/G0CJ3Z5Xmx96gsvJ8V27rItvmsw+/Q6z/cD53dgW2bWN10iVWVla6cn8d/fvGFa7ddiXw/156n2cPxvnpDTM6HYtt2yxpGMJl04dy93lRV+63jZvrpaOu1su7ZizhoiZ+fuOpnf68t7wYC2RuO/NiPJkaSyYdb90EhdZLzwV7i5BA2LTrEPe+vJkfXzvd9abLbSHL4v45M3hx/S6WvrfD9wfdf7rkZPpYFv/y/IZOx/K1p96jYmiR601XpnU2loferGX19oP81OWmK9P8VGt3BGk8QRpLkHi9XrRVSE7bdSjO1595j29fejLjhhV5XU639AuH+PlNp/KL17fyZu0+r8tJ239eM42avU3MX1HzV8t/8NJGEkmb7356skeVueelDbt4au0O5t80k5CeLEUkg3SEkZzVlEjyxcWrueucccwqH+J1OT1S3D/CvVdN4/vLNlLTcNjrctJiWRbzb5rBK5saeHpNPQAP/nkr63Y28t/Xn+Jxdel7t+4AP19Rw0+vn0F/v2dgiEjOU+MlOSlp29z16Lt8ZuooLps6yutyemXSiAF8o3ISX3vqPfYcbva6nLREwiHuv2kmC96I8dPff8DSdbuYP8f/s0O1+47w3ec38IMrpjJ6UJY+c0pE8pq/j5oSWF/67RqmjhrIvLPGel1KWs4eN4w7zhrL3y1eRTzh73j7oUVh7jiznKfW7uD/XDyBorC/Z4f2H0nw1SfW8LWLJzJ51ECvyxGRPKHGS3LOd55fT2HI4puXnOR1Ka64cvpoPnXSCO56bBW2jxNWP9zbxC//XMs950f54cub2HXIvx9SmUja3P3YKm45vYwLJgz3uhwRySNqvCSn3Leihtp9R/nR1dO8LsVVd547jvHDi/jqE2u9LqVX9jUl+Icn1vKNyklcfWoJXzingnsWr+aID2fxbNvm7sfe5fzoMK6fWeZ1OSKSZ9R4Sc54cnU9v9vUwP0+ezt/d/3zZZM55sC/LXvf61J6JJ5Icvdj7zL3zHLOHjcMgMunjuayqSO569FVJH02i/ePz6yjZFCEey4c73UpIpKHgvfsJr70x5o9LHozxv03zSTi82uHjue/rpvOpl2H+eXrW70upVts2+buxau4eFIxV59S8lc/++xZFUwe1Z+vPL7Go+p67t6XN3EonuRfAxCBISL+pMZLPOengNR0dQxYzXUnCkj9p0tOJpQKWM11fg1IFZFg0dGnHb9e+NxZ3X4ZS8eAVL/UnY5IyPJFwGrHgNSutrMfdxGwmks6BqTmw3YmIrkpuOd0esGyLKqrq70uo8c6+6wsP4wlnoRfbI1wwfAE+zeupHqjPz/3q6csy2LV63/gulHwL0vXcfOYOCNdi5CKuLLeX2sIsf5QH+6saP7o9o63nd04Ah5Ye5QD9Vs5bcixtO8fIJFwZyy1TRZP1YeZOzbO63/4HZAf25mI5CbNeIknbBsWxAqZMSjJqYPzc/ZhVAQ+MyrBo9siHMqhNweuPmCx+mCIeeXNdPeMXNiCeRVx/rCngC2HcuewsrcZnqwPc0NZM4ODfRZbRHwid46QklceioUpjdicX5xDHYcHJgywOX94Cwu3RkjkQP9Z02SxvCHM3PI4Pf30nP4hmDMmznM7w+zMgYivpiQ8HItw2agEJX0dr8sREQHUeIkHntheQMiCK0pavC4lJ5w25BhTByZZsLUQLy89aojDM/VhbiyLM7CXs0MjI3BlSYLHtkVo9HD1Jm1YsDXC2cNaOGlADnS0IiIparwkq17ZHWJvs8XNZQmvS8kpF41IMqLQ5jfbvDkfdjgJv9kW4fJRCUb3Te+2okU2FxYnWFjrzSyebcOC2kImDTjGGUPdud5MRMQtarwka97e14f3G0PMrej+tUP55OrSFmzgme0FWb3fRGp26LzhLUx0aXZoxmCbUwYledCDWbzH6sIMLnCoHKkZVRHJPXr6k6zYdMhixd4C5lbECWur69KtYxLsaLb4/e7svOHYtmHB1kKmDExyukvvRmxzYXGSkojNw1mcxVu6o4Bm23C9ZlRFJEfpKVAybsdReH5nmFvGxOmvAJPjsiy4o6KZNY0hVh3ok/H7e6QuTHGhzcUjMvMmhytKWrAMPJmFWbwVe0LEjljcXt6c8fsSEektNV6SUY0JWFwX4aqSBMWuZVUFW9iCuWPj/L6hgA8OZ24Xfba+gKQN15Rm9pTcLWUJGpotXs3gLN7agxbvHAhxx1idxhaR3KZDlGRMPAkLYxFmFycYV6R3lvXEwAK4aUycZ3eE2ZWBaIbXGkJsj1vcVp75U3KWBfMqmlnfGOKd/e7P4tU2Wby6O8ztvYjAEBHJNjVekhEKSE1fpgJWV/UiIDVdmQpYVUCqiPiNGi/JCAWkusPtgNWaJovf9zIgNV1uB6wqIFVE/EiNl7hOAanucitg1Y2A1HS5FbCqgFQR8Ss1XuIqBaRmRroBq24GpKYr3YBVBaSKiJ+p8RLXKCA1s3obsJqJgNR0pROwqoBUEfEzPT2KKxSQmh09DVjNZEBqunoTsKqAVBHxOz1FStrqFZCaNT0NWM10QGq6ehKwqoBUEQkCNV6SlsYELFFAalZ1N2A1WwGp6epOwKoCUkUkKHQIk15TQKp3ThSwms2A1HSdKGBVAakiEiRqvKRXFJDqva4CVr0ISE1XVwGrCkgVkaDR68csqKmpIRaLUV5eTjQa9bqctLSNZdPoCykdoIBUr00YYHN+sjVg9RLWgzOFV3f14fPj/Dc71Baw+si2COfv+oDmxBgWbS3g0yUKSBWR4PDJ62H/qqmpYdmyZaxdu5Zly5ZRU1PjdUm91jaWt6zxHNy/l6nxjV6XJLQGrJY6e3jyUBkABW8+yp46f25nIyNwhqnl5Xgpx6wCzKYVFDRs8bosERHX+Ow1sf/EYjESNsQv+jLYSZ6MhwhvcfecyfzYG50uTyTcvdo9kZhA8twKCPeFlmaejI/I2lgy6eDRFq7/ZXbv1+11E2+xoG/rOwOPnHqNr7ezeGI09CkAB0zsHWIDJvt+plhEpI0arwwrLy9nw4YNHPvwzyTLZ3JVeCsTJrj7JFJZeX6ny6urq129n7YZr8bz/haM4ZPhGDMmlLl6H12NJZOu/+Ub/PZzZ2b1Pt1cNwkb7tscwtn4O5onXcTAP86nqqrK9WYlW9vZrzYdY9/ubZjGBprP+SxlA7a7evsiIl7SqcYMi0ajVFVVcdqAI1T0TfJan8lpfd6el9rGEupjcVZkN793JnBEl3h5qi0gdfoQuHLaKMBkpOnKlqU7CrDC/bi2pJnT+x2grAj+VHCy12WJiLgmY42XMea7xpjtxphVqa9Pt/vZN40xW4wxG40xVZmqIVdEo1Fmz57NjZP6UhRy+O12/749KxqNEg6HuXBCMWcNbeHBrRGSPm0kg6B9QGo0GgWDb5uu9gGpbfvMLZMKux2wKiLiB5me8fpPx3FmpL6eBzDGTAHmAFOBS4GfG2NOHMEdEDeUJjicNLy40/9PJGcOO8bEAcdYUNvzz9uT9PklILU7jheQ2p2AVRERv/DiVONVwKOO4zQ7jvMhsAU4w4M6PGFZcPvYZmqaLF7f6/9+85KRLQwqcFjs41k8P/JTQOqJnCgg9UQBqyIifpLpxuseY8waY8yvjDFDUstKgW3t/k9dalneCFkwb2wzb+0rYH2j/y+zu64kwdGk4YUd/p/F8wM/BqR2pbsBqV0FrIqI+E1aRzBjzMvGmPc6+boKuA8YD8wAdgA/avu1Tm6q03REY8ydxpiVxpiVDQ0N6ZSac/qF4NaxcV7aFWbbEX8/kVgW3Da2ma1HLP4UgFm8XFbTZPH7hjBzA/DxOU1JeDgW4bJR3QtIbQtYfW5nmJ2dfEySiIgfpPWM7zjOpxzHmdbJ19OO4+xyHOeY4zg28Av+cjqxDhjT7mbKgPoubv8Bx3FmOY4zq7i4OJ1Sc9LQMFxXmuDx7WH2+/yMUciCO8Y2s3J/AesO+ruRzFUNcXimPsyNZXEG+vzMbtKGBVsjnD2shZMGdP8CwZERuLIkwWPbIjT6/9I2EclDmXxX4+h2/7wGeC/1/TPAHGNMoTFmHDAReDNTdeS6Mf1sLhmZ4KFYxPfRDJEQ3FYep3p3mFiTmi83HU7Cb7ZFuHxUgtF9va4mPbYNC2oLmTTgGGcMPdbj348W2VxYnGBhbYSE3tQhIj6TyWfHe40xa40xa4CLgK8COI6zDlgMrAdeBL7oOE7Pj74BMmWgzSeGtLCgttD30QxDwnBDWTNP1IfZ2+x1NcGQSM0OnTe8hYk9mB3KVY/VhRlc4FA5svdTVjMG25wyKMmDW/WOWhHxl4w1Xo7j3OY4znTHcU5xHOdKx3F2tPvZ9x3HGe84zkmO47yQqRr85Oxhx4gW2SwKQDRDaV+HS0cmeDgAs3heawtInTIwyelD/P/6ZOmOApptw/Vl6Z9bv7A4SUnE5uFtPj/vKiJ5ReeDcsilo1p8H7Da5uSBtgJWXdA+INXv2gekuuWKkhYFrIqIr6jxyjEKWJU2+RKQmi4FrIqIn6jxyjEKWBXIr4DUdClgVUT8RI1XDlLAan7Lx4DUdClgVUT8QkeoHKWA1fyUzwGp6VLAqoj4gb+f0QNOAav5RQGp6furgFWf7zMiEkx6BsxxCljNDwpIdc9HAasxBayKSO7Rs58PKGA12BSQ6j4FrIpIrlLj5RNBDFj9dQBm8dKlgNTMUcCqiOQiNV4+ooDV4FFAamYpYFVEco0aL59pC1hdFoCA1TPyPGBVAanZoYBVEcklOhJ1UFlZ6XUJJ3RR0ub2h1eyZ8hobp41Btu2sTp5tsvUWObH3qCy8nxXbqsS+Kdn1/FK3OHeq6Z1OZagqRswkX2mgQWfO52Qi+O9d+OKjK33rtbN8Mmf4LUXNvDALTMZPSiSkftO14WJJLc//DZnjRzDVaeU5M12JiK5R0ceHwqHLB6YM4Mlq7bzu027ff8E8m+XT+bgkQQ/emWz78fSHUvX7WDpul3MnzPT1aYr0zpbN7X7jvDd5zfwgyum5mzTBRAJh7j/ppkseCPG6x/uzYvtTERyk44+PjUwEuYn153Cfy//gLX1B70uJy2WZfE/N8zg7W37efitmNflZNTK2v384k9b+dmNp1IU9veE8/4jCb76xBq+dvFEJo8a6HU5JzS0KMyPrp7GD6s3sWn3Ia/LEZE8pcbLx8qG9ONfLp/Ct59bz/YDR70uJy2ts3in8eTqel5+f7fX5WTEh3ub+N6y97n3ymmMHJC7s0PdkUja3P3YKm45vYwLJgz3upxuixb359tVJ/P1p99j1yHF24tI9qnx8rlTSwfx9xeO50u/XU1j3Pu38KejfyTET64/lZ++9gGr6vw9i9fRvqYE//DEWr7+qYlMGjnA63LSYts2dz/2LudHh3H9zDKvy+mxWWOH8IVzKrhn8WqOJPz/blIR8Rc1XgHwyZNGcN2MUu56dBUJn2czlA7uy/evmMJ3nl/Ptv1HvC7HFfFEkrsfe5e5Z5ZzbtQ/s0Nd+cdn1lE6qC/3XDje61J67fKpo7ls6kjuenQVyXx8S62IeEaNV0DcMmsMnygfwheXrML2+RPJ1NGD+NrFE/ny42s4cMTfs3i2bXP34lVcPKmYq08p8bqctN378iYOxZN87zNTvC4lbZ89q4LJo/rzlcfXeF2KiOQRNV4B8g8XT2RovzDfem6D16Wk7YIJw7n59DLufszfs3hfe+o9KoYWcfd5Ua9LSdtDb9ayevtBfnrjqV6X4pp/uuRkQpbFvzzv/31GRPxBjVfA/OCKKew53Mx/Ld/idSlpu2FmGeeMG8rfLn7Xl7N4P3hpI81Jm+9+erLXpaRt2YZdPLV2B/Nv8lcERnf8+Jpp1OxtYv6KGq9LEZE8EKwjqGBZFj+7cQZ/3rqPR1Zu87qctH1p9gRGDojwj8+s87qUHnnwz1tZt7ORn1x/itelpO3dugPct6KGn14/g/4Rf0dgdMayLObfNINXNjXw1Jp6r8sRkYBT4xVAHQNW/e7fLp9M49EWfvTKZq9L6Ra/BqR2xi8BqelqC1hdmApYFRHJFH8/K0iXFLDqDQWk+pcCVkUkG9R4BZRt274MWO3qWq6QRc4HrHYWkNrZePxwvVpnAal+qDsdtm37MmC1q/Xi1/Xl17q7y6/j82vducjfL8mlS5ZlUV1dDcDsoRZ3P7KSz1fE6efCGk8kIh/dttu6+oBny7J4/Q+vMGc0/OerG6l9fy3lRe4cCNwYz+Ek/PLDCJeNSlC75s/UppZ3Np7268ZtDumPxbbhV7WFjO1nM2jPBqqrW9/x54cPkE9H+/Vy7mCLLzz8FndF44RdeHnq1T6Tqfv0YjxBkcn1kklBXy/ZpBmvPDB1kM0nhrSwoLYQHyczADA4DDeUNfNEfZi9zV5X0yphw4KtEc4b3sKkAT5/gIHH6sIMLnCoHNnidSmemTHY5pRBSR7cWohe6IuIm9R45Ymzhx0jWmSzqNb/TySlfR0uHZng4ViEIx5/4ottw4KthUwZmOT0Ice8LcYFS3cU0Gwbri/zd3CtGy4sTlISsXl4W9jrUkQkQNR45ZFLR7VQFHL47Xb/P5GcPNDm7KEtPLg14uks3iN1YYoLbS4e4f/P/FuxJ0TsiMXt5TkylZgDrihpwTLw5PYCr0sRkYBQ45VnbihNcDhpWLbT/08kZww7xsQBx1jg0Szes/UFJG24ptT/p+TWHrB450CIO8Y24/MEDNfdUpagodni1d26JFZE0qdDbJ6xLLh9bDMfNFn8eW8fr8tJ2yUjWxhU4LA4y7N4rzWE2B63uK3c/6fkapssXm0Ic3t5nADmo6bNsmBeRTPrG0O8vd//+4yIeEuNVx4KWTBvbDNv7itgfaP/N4HrShIcTRpe2JGdWbzVByxWHwwxr9z/s0N7m+HJ+jA3lDUz2P9noDMmbMG8ijgr9hSw+ZDPV7qIeEpHkDzVLwS3jo3z0q4w2474ezOwLLhtbDNbj1j8KcOzeDVNFssbwswNwOxQUxIejrVGYJT0dbwuJ+f1D8GcMXGW7gyz0x8RXyKSg/z9jCtpGRqGa0sSPL49zH6fnzELWXDH2GZW7i9g3cHMbNYNcXimPsyNZXEG+nx2KJmKwDh7WAsnBSACI1tGRuDKkgSPbYvQ6PN9RkS8ocYrz5UX2VSOSPBQDkQzpCsSgtvL41TvDhNrcnfTPpyE32yL8OlRCUb3dfWms862YUFtIZMGHOOMof6PwMi2aJHNhcUJFsYiJNSzikgPqfESBayewEcBqcMUkCqtFLAqIr2lxksABax2pS0gdfLAJKcHYHZo6Y4C4rbh2hKdJ0uXAlZFpDfUeMlHFLD6cW0BqZ8MUEDq3AC8GzNXKGBVRHpKh1/5KwpY/QsFpEp3KGBVRHpCh2D5KwpYbaWAVOkuBayKSE+o8ZKPCWTA6rHuB6wqIFV6SgGrItJdOkJIpwIXsFrevYBVBaRKbylgVUS6w9/PqJJR+RawqoBUSZcCVkXkRNR4yXHlS8Bq0AJSH1RAqmcUsCoix6PGS04o6AGrTgADUocoINVTbQGrv1LAqoh0oMZLuqUtYPV/N0MikaCmpsbrknqtfcDqe5u2ctR2KHH2+j4gtaamBhxoPJpQQGoOuLA4SWnE5pebHd/vM+3V1NQEajxBUVNTw/Lly7VefECNl3TbpCMbObx3J0ksli1b5usd/OSBNpPYxdLEOLBtdr28yNfjqamp4bm3PwAg+ep9bN3q37EEydT4Rg7s30fSMb7fZ6B1O1u2bBnJZDIQ4wmKtvWydu1arRcfMI7jj3c7zZo1y1m5cqXXZfhKdXW1q7e3fPlyVq9dS3z2PVAQAQwYV+8Cc5wbdHB5W3U++uOje8/WeDI2FseBljihUIhw2N13CLh9e7kokXB3pjCRSJBMJsEYcJysr5egjScoMrVe+uzaSOH7LzN9+nRmz57t6n1UVla6env5wBjztuM4szou9/kb5iWbysvL2bBhA9bynxIKhaiqqiIajbp6H8fbud1uJNu/es/2ePw5lvNdvb1cFLT1ErTxBEWm10t5ebmrty/uUuMl3RaNRqmqqiIWi1FeXu76ATfbgjSeII0lSIK2XoI2nqDQevEXNV7SI9FoNFA7dZDGE6SxBEnQ1kvQxhMUWi/+oYvrRURERLJEjZeIiIhIlqjxEhEREckSNV4iIiIiWaLGS0RERCRL0mq8jDE3GGPWGWNsY8ysDj/7pjFmizFmozGmqt3y040xa1M/+4kxxuXIShEREZHclO6M13vAtcBr7RcaY6YAc4CpwKXAz40xfVI/vg+4E5iY+ro0zRpEREREfCGtxstxnA2O42zs5EdXAY86jtPsOM6HwBbgDGPMaGCg4zivO62fVbQIuDqdGkRERET8IlPXeJUC29r9uy61rDT1fcflIiIiIoF3wuR6Y8zLwKhOfvQtx3Ge7urXOlnmHGd5V/d9J62nJfXZUyIiIuJ7J2y8HMf5VC9utw4Y0+7fZUB9anlZJ8u7uu8HgAcAZs2a1WWDJiIiIuIHmTrV+AwwxxhTaIwZR+tF9G86jrMDOGSMOSv1bsbbga5mzUREREQCJd04iWuMMXXA2cBSY8wyAMdx1gGLgfXAi8AXHcc5lvq1vwX+l9YL7j8AXkinBhERERG/OOGpxuNxHOdJ4MkufvZ94PudLF8JTEvnfkVERET8SMn1IiIiIlmixktEREQkS9R4iYiIiGSJGi8RERGRLFHjJSIiIpIlpvUjE3PfrFmznJUrV3pdhmSYbdtYVnBeDwRpPEEaS5AEbb0EbTxBofXSc8aYtx3HmdVxuR5FySlB27GDNJ4gjSVIgrZegjaeoNB6cY8eSREREZEsUeMlIiIikiVqvERERESyRI2XiIiISJao8RIRERHJEjVeIiIiIlmixktEREQkS9R4iYiIiGSJGi8RERGRLFHjJSIiIpIlarxEREREskSNl4iIiEiWGMdxvK6hW4wxDUBthm5+OLAnQ7ftBxq/xq/x5y+NX+PX+DNjrOM4xR0X+qbxyiRjzErHcWZ5XYdXNH6NX+PX+L2uwysav8af7fHrVKOIiIhIlqjxEhEREckSNV6tHvC6AI9p/PlN489vGn9+0/izTNd4iYiIiGSJZrxEREREskSNl4iIiEiW5FXjZYz5D2PM+8aYNcaYJ40xg9v97JvGmC3GmI3GmKp2y083xqxN/ewnxhjjSfEuMMbcYIxZZ4yxjTGz2i2vMMYcNcasSn3d3+5ngR9/6meBX//tGWO+a4zZ3m6df7rdzzp9LILGGHNpaoxbjDHf8LqebDDGbE1tz6uMMStTy4YaY6qNMZtTfw/xuk63GGN+ZYzZbYx5r92yLscbtG2/i/Hnzb5vjBljjPmdMWZD6tj/5dRyb7cBx3Hy5gu4BAilvv934N9T308BVgOFwDjgA6BP6mdvAmcDBngBuMzrcaQx/snAScByYFa75RXAe138Tj6MPy/Wf4fH4rvA/+lkeZePRZC+gD6psUWBcGrMU7yuKwvj3goM77DsXuAbqe+/0XZcDMIXcAFwWvvjW1fjDeK238X482bfB0YDp6W+HwBsSo3T020gr2a8HMd5yXGcZOqffwbKUt9fBTzqOE6z4zgfAluAM4wxo4GBjuO87rSulUXA1dmu2y2O42xwHGdjd/9/Ho0/L9Z/N3X6WHhcUyacAWxxHKfGcZwE8CitY89HVwELU98vJEDbuOM4rwH7OizuaryB2/a7GH9Xgjj+HY7jvJP6/hCwASjF420grxqvDj5L6wwGtK6Ibe1+VpdaVpr6vuPyIBpnjHnXGPN7Y8z5qWX5Mv58Xf/3pE67/6rdVHtXj0XQ5Ms4O3KAl4wxbxtj7kwtG+k4zg5ofaICRnhWXXZ0Nd582ibybt83xlQAM4E38HgbCLl9g14zxrwMjOrkR99yHOfp1P/5FpAEft32a538f+c4y3NWd8bfiR1AueM4e40xpwNPGWOmkj/jD8z6b+94jwVwH/A9WsfzPeBHtL4Y8fWYeyBfxtnRuY7j1BtjRgDVxpj3vS4oh+TLNpF3+74xpj/wOPAVx3Eaj3OpblYeg8A1Xo7jfOp4PzfGzAU+A3wydfoIWrvaMe3+WxlQn1pe1snynHWi8XfxO81Ac+r7t40xHwCTyJPxE6D13153HwtjzC+A51L/7OqxCJp8GedfcRynPvX3bmPMk7SeRtlljBntOM6O1On13Z4WmXldjTcvtgnHcXa1fZ8P+74xpoDWpuvXjuM8kVrs6TaQV6cajTGXAv8IXOk4zpF2P3oGmGOMKTTGjAMmAm+mpiAPGWPOSr2b7Xagq1kT3zLGFBtj+qS+j9I6/pp8GT95uP5TB5s21wBt73rq9LHIdn1Z8BYw0RgzzhgTBubQOvbAMsYUGWMGtH1P65uN3qN13HNT/20uAdnGj6Or8ebFtp9P+37quP1LYIPjOD9u9yNvtwGv33WQzS9aL5TbBqxKfd3f7mffovUdDBtp9841YBatG+YHwE9Jpf378YvWnayO1tmtXcCy1PLrgHW0vpvjHeCKfBp/vqz/Do/FQ8BaYA2tB5vRJ3osgvYFfJrWdzl9QOupaM9ryvB4o6l9fHVqf/9Wavkw4BVgc+rvoV7X6uKYH6H1UoqW1L7/ueONN2jbfhfjz5t9HziP1lOFa9o973/a621AHxkkIiIikiV5dapRRERExEtqvERERESyRI2XiIiISJao8RIRERHJEjVeIiIiIlmixktEREQkS9R4iYiIiGTJ/w+i7TQoMJn7fQAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 720x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "w_rook2 = raster.da2WSP(da_s, \"rook\", k=2, n_jobs=-1, include_nadata=True)\n",
-    "splot.plot_spatial_weights(w_rook2, da=da_s)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Additional resources\n",
-    "\n",
-    "1. [Reading and writing files using Xarray](http://xarray.pydata.org/en/stable/io.html)\n",
-    "2. [Xarray Data Structures](http://xarray.pydata.org/en/stable/data-structures.html)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
\ No newline at end of file
diff --git a/docs/_sources/notebooks/examples.ipynb.txt b/docs/_sources/notebooks/examples.ipynb.txt
deleted file mode 100644
index 2c7203878..000000000
--- a/docs/_sources/notebooks/examples.ipynb.txt
+++ /dev/null
@@ -1,1092 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Datasets for use with libpysal\n",
-    "As of version 4.2, libpysal has refactored the `examples` package to:\n",
-    "\n",
-    "- reduce the size of the source installation\n",
-    "- allow the use of remote datasets from the [Center for Spatial Data Science at the Unversity of Chicago](https://spatial.uchicago.edu/), and other remotes\n",
-    "\n",
-    "This notebook highlights the new functionality"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Backwards compatibility is maintained"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you were familiar with previous versions of libpysal, the newest version maintains backwards compatibility so any code that relied on the previous API should work. \n",
-    "\n",
-    "For example:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from libpysal.examples import get_path \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'/home/jovyan/libpysal/examples/mexico/mexicojoin.dbf'"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "get_path(\"mexicojoin.dbf\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "An important thing to note here is that the path to the file for this particular example is within the source distribution that was installed. Such an example data set is now referred to as a `builtin` dataset."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import libpysal\n",
-    "dbf = libpysal.io.open(get_path(\"mexicojoin.dbf\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['POLY_ID',\n",
-       " 'AREA',\n",
-       " 'CODE',\n",
-       " 'NAME',\n",
-       " 'PERIMETER',\n",
-       " 'ACRES',\n",
-       " 'HECTARES',\n",
-       " 'PCGDP1940',\n",
-       " 'PCGDP1950',\n",
-       " 'PCGDP1960',\n",
-       " 'PCGDP1970',\n",
-       " 'PCGDP1980',\n",
-       " 'PCGDP1990',\n",
-       " 'PCGDP2000',\n",
-       " 'HANSON03',\n",
-       " 'HANSON98',\n",
-       " 'ESQUIVEL99',\n",
-       " 'INEGI',\n",
-       " 'INEGI2',\n",
-       " 'MAXP',\n",
-       " 'GR4000',\n",
-       " 'GR5000',\n",
-       " 'GR6000',\n",
-       " 'GR7000',\n",
-       " 'GR8000',\n",
-       " 'GR9000',\n",
-       " 'LPCGDP40',\n",
-       " 'LPCGDP50',\n",
-       " 'LPCGDP60',\n",
-       " 'LPCGDP70',\n",
-       " 'LPCGDP80',\n",
-       " 'LPCGDP90',\n",
-       " 'LPCGDP00',\n",
-       " 'TEST']"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dbf.header"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## `available` is updated"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The function `available` is also available but has been updated to be more informative:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                      Name                                        Description  Installed\n",
-      "0                    10740    Albuquerque, New Mexico, Census 2000 Tract Data       True\n",
-      "1                   AirBnB  Airbnb rentals, socioeconomics, and crime in C...       True\n",
-      "2                  Atlanta       Atlanta, GA region homicide counts and rates       True\n",
-      "3                Baltimore          Baltimore house sales prices and hedonics       True\n",
-      "4                Bostonhsg               Boston housing and neighborhood data       True\n",
-      "5              Buenosaires      Electoral Data for 1999 Argentinean Elections       True\n",
-      "6              Charleston1   2000 Census Tract Data for Charleston, SC MSA...       True\n",
-      "7              Charleston2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "8           Chicago Health                   Chicago Health + Socio-Economics       True\n",
-      "9              Chile Labor                 Labor Markets in Chile (1982-2002)       True\n",
-      "10         Chile Migration            Internal Migration in Chile (1977-2002)       True\n",
-      "11              Cincinnati         2008 Cincinnati Crime + Socio-Demographics       True\n",
-      "12               Cleveland       2015 sales prices of homes in Cleveland, OH.       True\n",
-      "13                Columbus                        Columbus neighborhood crime       True\n",
-      "14                  Denver   Demographics and housing in Denver neighborho...       True\n",
-      "15               Elections               2012 and 2016 Presidential Elections       True\n",
-      "16                 Grid100                      Grid with simulated variables       True\n",
-      "17               Groceries                          2015 Chicago supermarkets       True\n",
-      "18                  Guerry          Moral statistics of France (Guerry, 1833)       True\n",
-      "19       Health Indicators                Chicago Health Indicators (2005-11)       True\n",
-      "20                 Health+                    2000 Health, Income + Diversity       True\n",
-      "21                Hickory1   2000 Census Tract Data for Hickory, NC MSA an...       True\n",
-      "22                Hickory2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "23              Home Sales              2014-15 Home Sales in King County, WA       True\n",
-      "24                 Houston       Houston, TX region homicide counts and rates       True\n",
-      "25                Juvenile             Cardiff juvenile delinquent residences       True\n",
-      "26                Lansing1   2000 Census Tract Data for Lansing, MI MSA an...       True\n",
-      "27                Lansing2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "28                 Laozone   Ozone measures at monitoring stations in Los ...       True\n",
-      "29                LasRosas   Corn yield, fertilizer and field data for pre...       True\n",
-      "30                    Line                                     Line Shapefile       True\n",
-      "31           Liquor Stores                         2015 Chicago Liquor Stores       True\n",
-      "32                 Malaria   Malaria incidence and population (1973, 95, 9...       True\n",
-      "33              Milwaukee1       2000 Census Tract Data for Milwaukee, WI MSA       True\n",
-      "34              Milwaukee2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "35                   NCOVR                      US county homicides 1960-1990       True\n",
-      "36                    NDVI        Normalized Difference Vegetation Index grid       True\n",
-      "37                     NYC   Demographic and housing data for New York Cit...       True\n",
-      "38            NYC Earnings              Block-level Earnings in NYC (2002-14)       True\n",
-      "39           NYC Education                               NYC Education (2000)       True\n",
-      "40       NYC Neighborhoods       Demographics for New York City neighborhoods       True\n",
-      "41  NYC Socio-Demographics                 NYC Education + Socio-Demographics       True\n",
-      "42              Natregimes                    NCOVR with regimes (book/PySAL)       True\n",
-      "43                   Nepal   Health, poverty and education indicators for ...       True\n",
-      "44                Ohiolung            Ohio lung cancer data, 1968, 1978, 1988       True\n",
-      "45                Orlando1   2000 Census Tract Data for Orlando, FL MSA an...       True\n",
-      "46                Orlando2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "47                  Oz9799                        Monthly ozone data, 1997-99       True\n",
-      "48             Phoenix ACS   Phoenix American Community Survey Data (2010,...       True\n",
-      "49              Pittsburgh                      Pittsburgh homicide locations       True\n",
-      "50                   Point                                    Point Shapefile       True\n",
-      "51                  Police           Police expenditures Mississippi counties       True\n",
-      "52                 Polygon                                  Polygon Shapefile       True\n",
-      "53           Polygon_Holes                 Example to test treatment of holes       True\n",
-      "54       Rio Grande do Sul  Cities of the Brazilian State of Rio Grande do...       True\n",
-      "55                    SIDS            North Carolina county SIDS death counts       True\n",
-      "56                   SIDS2   North Carolina county SIDS death counts and r...       True\n",
-      "57             Sacramento1          2000 Census Tract Data for Sacramento MSA       True\n",
-      "58             Sacramento2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "59           SanFran Crime   July-Dec 2012 crime incidents in San Francisc...       True\n",
-      "60               Savannah1   2000 Census Tract Data for Savannah, GA MSA a...       True\n",
-      "61               Savannah2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "62                 Scotlip               Male lip cancer in Scotland, 1975-80       True\n",
-      "63                Seattle1   2000 Census Tract Data for Seattle, WA MSA an...       True\n",
-      "64                Seattle2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "65                   South             US Southern county homicides 1960-1990       True\n",
-      "66                 StLouis   St Louis region county homicide counts and rates       True\n",
-      "67                  Tampa1   2000 Census Tract Data for Tampa, FL MSA and ...       True\n",
-      "68                  arcgis                               arcgis testing files       True\n",
-      "69                  baltim     Baltimore house sales prices and hedonics 1978       True\n",
-      "70                  berlin  Prenzlauer Berg neighborhood AirBnB data from ...       True\n",
-      "71                    book       Synthetic data to illustrate spatial weights       True\n",
-      "72                 burkitt  Burkitt's lymphoma in the Western Nile distric...       True\n",
-      "73                  calemp         Employment density for California counties       True\n",
-      "74                 chicago                              Chicago neighborhoods       True\n",
-      "75              clearwater                               mgwr testing dataset       True\n",
-      "76                columbus              Columbus neighborhood crime data 1980       True\n",
-      "77                 desmith    Small dataset to illustrate Moran's I statistic       True\n",
-      "78                geodanet        Datasets from geodanet for network analysis       True\n",
-      "79                 georgia  Various socio-economic variables for counties ...       True\n",
-      "80                juvenile    Residences of juvenile offenders in Cardiff, UK       True\n",
-      "81                  mexico  Decennial per capita incomes of Mexican states...       True\n",
-      "82                networks                  Datasets used for network testing       True\n",
-      "83                newHaven                            Network testing dataset       True\n",
-      "84               nyc_bikes                           New York City Bike Trips       True\n",
-      "85                   sids2  North Carolina county SIDS death counts and rates       True\n",
-      "86               snow_maps  Public water pumps and Cholera deaths in Londo...       True\n",
-      "87                     stl  Homicides and selected socio-economic characte...       True\n",
-      "88          street_net_pts                              Street network points       True\n",
-      "89                     taz           Traffic Analysis Zones in So. California       True\n",
-      "90                   tokyo                               Tokyo Mortality data       True\n",
-      "91               us_income  Per-capita income for the lower 48 US states 1...       True\n",
-      "92                virginia                        Virginia counties shapefile       True\n",
-      "93                    wmat          Datasets used for spatial weights testing       True\n"
-     ]
-    }
-   ],
-   "source": [
-    "from libpysal.examples import available\n",
-    "available()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We see that the new column `Installed` is added to the tablular output of `available`. This tells the user whether the dataset has aready been installed on the local machine. All the builtin datasets are by defninition installed. The interesting cases are the remote data sets that are new in this release. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Remote datasets\n",
-    "\n",
-    "The listing from `available` above shows that the dataset `Tampa1` is avalable, but has not yet been installed."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"700\"\n",
-       "            height=\"350\"\n",
-       "            src=\"https://geodacenter.github.io/data-and-lab/tampa1/\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.IFrame at 0x7f2c8867c828>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from libpysal.examples import explain\n",
-    "explain('Tampa1')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from libpysal.examples import load_example\n",
-    "tampa1 = load_example('Tampa1')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                      Name                                        Description  Installed\n",
-      "0                    10740    Albuquerque, New Mexico, Census 2000 Tract Data       True\n",
-      "1                   AirBnB  Airbnb rentals, socioeconomics, and crime in C...       True\n",
-      "2                  Atlanta       Atlanta, GA region homicide counts and rates       True\n",
-      "3                Baltimore          Baltimore house sales prices and hedonics       True\n",
-      "4                Bostonhsg               Boston housing and neighborhood data       True\n",
-      "5              Buenosaires      Electoral Data for 1999 Argentinean Elections       True\n",
-      "6              Charleston1   2000 Census Tract Data for Charleston, SC MSA...       True\n",
-      "7              Charleston2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "8           Chicago Health                   Chicago Health + Socio-Economics       True\n",
-      "9              Chile Labor                 Labor Markets in Chile (1982-2002)       True\n",
-      "10         Chile Migration            Internal Migration in Chile (1977-2002)       True\n",
-      "11              Cincinnati         2008 Cincinnati Crime + Socio-Demographics       True\n",
-      "12               Cleveland       2015 sales prices of homes in Cleveland, OH.       True\n",
-      "13                Columbus                        Columbus neighborhood crime       True\n",
-      "14                  Denver   Demographics and housing in Denver neighborho...       True\n",
-      "15               Elections               2012 and 2016 Presidential Elections       True\n",
-      "16                 Grid100                      Grid with simulated variables       True\n",
-      "17               Groceries                          2015 Chicago supermarkets       True\n",
-      "18                  Guerry          Moral statistics of France (Guerry, 1833)       True\n",
-      "19       Health Indicators                Chicago Health Indicators (2005-11)       True\n",
-      "20                 Health+                    2000 Health, Income + Diversity       True\n",
-      "21                Hickory1   2000 Census Tract Data for Hickory, NC MSA an...       True\n",
-      "22                Hickory2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "23              Home Sales              2014-15 Home Sales in King County, WA       True\n",
-      "24                 Houston       Houston, TX region homicide counts and rates       True\n",
-      "25                Juvenile             Cardiff juvenile delinquent residences       True\n",
-      "26                Lansing1   2000 Census Tract Data for Lansing, MI MSA an...       True\n",
-      "27                Lansing2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "28                 Laozone   Ozone measures at monitoring stations in Los ...       True\n",
-      "29                LasRosas   Corn yield, fertilizer and field data for pre...       True\n",
-      "30                    Line                                     Line Shapefile       True\n",
-      "31           Liquor Stores                         2015 Chicago Liquor Stores       True\n",
-      "32                 Malaria   Malaria incidence and population (1973, 95, 9...       True\n",
-      "33              Milwaukee1       2000 Census Tract Data for Milwaukee, WI MSA       True\n",
-      "34              Milwaukee2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "35                   NCOVR                      US county homicides 1960-1990       True\n",
-      "36                    NDVI        Normalized Difference Vegetation Index grid       True\n",
-      "37                     NYC   Demographic and housing data for New York Cit...       True\n",
-      "38            NYC Earnings              Block-level Earnings in NYC (2002-14)       True\n",
-      "39           NYC Education                               NYC Education (2000)       True\n",
-      "40       NYC Neighborhoods       Demographics for New York City neighborhoods       True\n",
-      "41  NYC Socio-Demographics                 NYC Education + Socio-Demographics       True\n",
-      "42              Natregimes                    NCOVR with regimes (book/PySAL)       True\n",
-      "43                   Nepal   Health, poverty and education indicators for ...       True\n",
-      "44                Ohiolung            Ohio lung cancer data, 1968, 1978, 1988       True\n",
-      "45                Orlando1   2000 Census Tract Data for Orlando, FL MSA an...       True\n",
-      "46                Orlando2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "47                  Oz9799                        Monthly ozone data, 1997-99       True\n",
-      "48             Phoenix ACS   Phoenix American Community Survey Data (2010,...       True\n",
-      "49              Pittsburgh                      Pittsburgh homicide locations       True\n",
-      "50                   Point                                    Point Shapefile       True\n",
-      "51                  Police           Police expenditures Mississippi counties       True\n",
-      "52                 Polygon                                  Polygon Shapefile       True\n",
-      "53           Polygon_Holes                 Example to test treatment of holes       True\n",
-      "54       Rio Grande do Sul  Cities of the Brazilian State of Rio Grande do...       True\n",
-      "55                    SIDS            North Carolina county SIDS death counts       True\n",
-      "56                   SIDS2   North Carolina county SIDS death counts and r...       True\n",
-      "57             Sacramento1          2000 Census Tract Data for Sacramento MSA       True\n",
-      "58             Sacramento2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "59           SanFran Crime   July-Dec 2012 crime incidents in San Francisc...       True\n",
-      "60               Savannah1   2000 Census Tract Data for Savannah, GA MSA a...       True\n",
-      "61               Savannah2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "62                 Scotlip               Male lip cancer in Scotland, 1975-80       True\n",
-      "63                Seattle1   2000 Census Tract Data for Seattle, WA MSA an...       True\n",
-      "64                Seattle2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "65                   South             US Southern county homicides 1960-1990       True\n",
-      "66                 StLouis   St Louis region county homicide counts and rates       True\n",
-      "67                  Tampa1   2000 Census Tract Data for Tampa, FL MSA and ...       True\n",
-      "68                  arcgis                               arcgis testing files       True\n",
-      "69                  baltim     Baltimore house sales prices and hedonics 1978       True\n",
-      "70                  berlin  Prenzlauer Berg neighborhood AirBnB data from ...       True\n",
-      "71                    book       Synthetic data to illustrate spatial weights       True\n",
-      "72                 burkitt  Burkitt's lymphoma in the Western Nile distric...       True\n",
-      "73                  calemp         Employment density for California counties       True\n",
-      "74                 chicago                              Chicago neighborhoods       True\n",
-      "75              clearwater                               mgwr testing dataset       True\n",
-      "76                columbus              Columbus neighborhood crime data 1980       True\n",
-      "77                 desmith    Small dataset to illustrate Moran's I statistic       True\n",
-      "78                geodanet        Datasets from geodanet for network analysis       True\n",
-      "79                 georgia  Various socio-economic variables for counties ...       True\n",
-      "80                juvenile    Residences of juvenile offenders in Cardiff, UK       True\n",
-      "81                  mexico  Decennial per capita incomes of Mexican states...       True\n",
-      "82                networks                  Datasets used for network testing       True\n",
-      "83                newHaven                            Network testing dataset       True\n",
-      "84               nyc_bikes                           New York City Bike Trips       True\n",
-      "85                   sids2  North Carolina county SIDS death counts and rates       True\n",
-      "86               snow_maps  Public water pumps and Cholera deaths in Londo...       True\n",
-      "87                     stl  Homicides and selected socio-economic characte...       True\n",
-      "88          street_net_pts                              Street network points       True\n",
-      "89                     taz           Traffic Analysis Zones in So. California       True\n",
-      "90                   tokyo                               Tokyo Mortality data       True\n",
-      "91               us_income  Per-capita income for the lower 48 US states 1...       True\n",
-      "92                virginia                        Virginia counties shapefile       True\n",
-      "93                    wmat          Datasets used for spatial weights testing       True\n"
-     ]
-    }
-   ],
-   "source": [
-    "available()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tampa1.installed"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['/home/jovyan/pysal_data/Tampa1/__MACOSX/._TampaMSA',\n",
-       " '/home/jovyan/pysal_data/Tampa1/__MACOSX/TampaMSA/._tampa_counties.sbx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/__MACOSX/TampaMSA/._tampa_final_census2.sbx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/__MACOSX/TampaMSA/._2000 Census Data Variables_Documentation.pdf',\n",
-       " '/home/jovyan/pysal_data/Tampa1/__MACOSX/TampaMSA/._tampa_final_census2.sbn',\n",
-       " '/home/jovyan/pysal_data/Tampa1/__MACOSX/TampaMSA/._tampa_counties.sbn',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.geojson',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.gpkg',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.xlsx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.mid',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.spx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.gdbindexes',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByDestItemTypeID.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000002.gdbtable',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.CatItemsByType.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000003.gdbtablx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/gdb',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.gdbtable',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000002.gdbtablx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.gdbtablx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByForwardLabel.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByBackwardLabel.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByOriginItemTypeID.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.CatItemTypesByParentTypeID.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000003.gdbtable',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.gdbindexes',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.gdbindexes',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.gdbindexes',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.gdbtable',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.spx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.gdbindexes',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.FDO_UUID.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.CatRelsByOriginID.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.gdbtable',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000003.gdbindexes',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.gdbtablx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.CatItemsByPhysicalName.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.gdbtablx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.CatRelsByType.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.gdbindexes',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByUUID.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.gdbtablx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.CatItemTypesByUUID.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.gdbtablx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.gdbtable',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.gdbtable',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.gdbtablx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.spx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.FDO_UUID.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.CatRelsByDestinationID.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.gdbtable',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.gdbtable',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/timestamps',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByName.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.gdbindexes',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.CatItemTypesByName.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.TablesByName.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.gdbtablx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.sbn',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.xlsx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/2000 Census Data Variables_Documentation.pdf',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.mid',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.dbf',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.gpkg',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.mif',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.kml',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.sqlite',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.dbf',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.prj',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.mif',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.shx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.geojson',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.shx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.sbn',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.sbx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.prj',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.shp',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.shp',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.sqlite',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.sbx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.kml']"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tampa1.get_file_list()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tampa_counties_shp = tampa1.load('tampa_counties.shp')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<libpysal.io.iohandlers.pyShpIO.PurePyShpWrapper at 0x7f2c886869b0>"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tampa_counties_shp"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import geopandas"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tampa_df = geopandas.read_file(tampa1.get_path('tampa_counties.shp'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2c885f5e48>"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "%matplotlib inline\n",
-    "tampa_df.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Other Remotes\n",
-    "\n",
-    "In addition to the remote datasets from the GeoData Data Science Center, there are several large remotes available at github repositories. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Rio_Grande_do_Sul\n",
-      "======================\n",
-      "\n",
-      "Cities of the Brazilian State of Rio Grande do Sul\n",
-      "-------------------------------------------------------\n",
-      "\n",
-      "* 43MUE250GC_SIR.dbf: attribute data (k=2)\n",
-      "* 43MUE250GC_SIR.shp: Polygon shapefile (n=499)\n",
-      "* 43MUE250GC_SIR.shx: spatial index\n",
-      "* 43MUE250GC_SIR.cpg: encoding file \n",
-      "* 43MUE250GC_SIR.prj: projection information \n",
-      "* map_RS_BR.dbf: attribute data (k=3)\n",
-      "* map_RS_BR.shp: Polygon shapefile (no lakes) (n=497)\n",
-      "* map_RS_BR.prj: projection information\n",
-      "* map_RS_BR.shx: spatial index\n",
-      "\n",
-      "\n",
-      "\n",
-      "Source: Renan Xavier Cortes \n",
-      "Reference: https://github.com/pysal/pysal/issues/889#issuecomment-396693495\n",
-      "\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "explain('Rio Grande do Sul')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note that the `explain` function generates a textual description of this example dataset - no rendering of the map is done as the source repository does not include that functionality."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rio = load_example('Rio Grande do Sul')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                      Name                                        Description  Installed\n",
-      "0                    10740    Albuquerque, New Mexico, Census 2000 Tract Data       True\n",
-      "1                   AirBnB  Airbnb rentals, socioeconomics, and crime in C...       True\n",
-      "2                  Atlanta       Atlanta, GA region homicide counts and rates       True\n",
-      "3                Baltimore          Baltimore house sales prices and hedonics       True\n",
-      "4                Bostonhsg               Boston housing and neighborhood data       True\n",
-      "5              Buenosaires      Electoral Data for 1999 Argentinean Elections       True\n",
-      "6              Charleston1   2000 Census Tract Data for Charleston, SC MSA...       True\n",
-      "7              Charleston2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "8           Chicago Health                   Chicago Health + Socio-Economics       True\n",
-      "9              Chile Labor                 Labor Markets in Chile (1982-2002)       True\n",
-      "10         Chile Migration            Internal Migration in Chile (1977-2002)       True\n",
-      "11              Cincinnati         2008 Cincinnati Crime + Socio-Demographics       True\n",
-      "12               Cleveland       2015 sales prices of homes in Cleveland, OH.       True\n",
-      "13                Columbus                        Columbus neighborhood crime       True\n",
-      "14                  Denver   Demographics and housing in Denver neighborho...       True\n",
-      "15               Elections               2012 and 2016 Presidential Elections       True\n",
-      "16                 Grid100                      Grid with simulated variables       True\n",
-      "17               Groceries                          2015 Chicago supermarkets       True\n",
-      "18                  Guerry          Moral statistics of France (Guerry, 1833)       True\n",
-      "19       Health Indicators                Chicago Health Indicators (2005-11)       True\n",
-      "20                 Health+                    2000 Health, Income + Diversity       True\n",
-      "21                Hickory1   2000 Census Tract Data for Hickory, NC MSA an...       True\n",
-      "22                Hickory2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "23              Home Sales              2014-15 Home Sales in King County, WA       True\n",
-      "24                 Houston       Houston, TX region homicide counts and rates       True\n",
-      "25                Juvenile             Cardiff juvenile delinquent residences       True\n",
-      "26                Lansing1   2000 Census Tract Data for Lansing, MI MSA an...       True\n",
-      "27                Lansing2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "28                 Laozone   Ozone measures at monitoring stations in Los ...       True\n",
-      "29                LasRosas   Corn yield, fertilizer and field data for pre...       True\n",
-      "30                    Line                                     Line Shapefile       True\n",
-      "31           Liquor Stores                         2015 Chicago Liquor Stores       True\n",
-      "32                 Malaria   Malaria incidence and population (1973, 95, 9...       True\n",
-      "33              Milwaukee1       2000 Census Tract Data for Milwaukee, WI MSA       True\n",
-      "34              Milwaukee2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "35                   NCOVR                      US county homicides 1960-1990       True\n",
-      "36                    NDVI        Normalized Difference Vegetation Index grid       True\n",
-      "37                     NYC   Demographic and housing data for New York Cit...       True\n",
-      "38            NYC Earnings              Block-level Earnings in NYC (2002-14)       True\n",
-      "39           NYC Education                               NYC Education (2000)       True\n",
-      "40       NYC Neighborhoods       Demographics for New York City neighborhoods       True\n",
-      "41  NYC Socio-Demographics                 NYC Education + Socio-Demographics       True\n",
-      "42              Natregimes                    NCOVR with regimes (book/PySAL)       True\n",
-      "43                   Nepal   Health, poverty and education indicators for ...       True\n",
-      "44                Ohiolung            Ohio lung cancer data, 1968, 1978, 1988       True\n",
-      "45                Orlando1   2000 Census Tract Data for Orlando, FL MSA an...       True\n",
-      "46                Orlando2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "47                  Oz9799                        Monthly ozone data, 1997-99       True\n",
-      "48             Phoenix ACS   Phoenix American Community Survey Data (2010,...       True\n",
-      "49              Pittsburgh                      Pittsburgh homicide locations       True\n",
-      "50                   Point                                    Point Shapefile       True\n",
-      "51                  Police           Police expenditures Mississippi counties       True\n",
-      "52                 Polygon                                  Polygon Shapefile       True\n",
-      "53           Polygon_Holes                 Example to test treatment of holes       True\n",
-      "54       Rio Grande do Sul  Cities of the Brazilian State of Rio Grande do...       True\n",
-      "55                    SIDS            North Carolina county SIDS death counts       True\n",
-      "56                   SIDS2   North Carolina county SIDS death counts and r...       True\n",
-      "57             Sacramento1          2000 Census Tract Data for Sacramento MSA       True\n",
-      "58             Sacramento2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "59           SanFran Crime   July-Dec 2012 crime incidents in San Francisc...       True\n",
-      "60               Savannah1   2000 Census Tract Data for Savannah, GA MSA a...       True\n",
-      "61               Savannah2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "62                 Scotlip               Male lip cancer in Scotland, 1975-80       True\n",
-      "63                Seattle1   2000 Census Tract Data for Seattle, WA MSA an...       True\n",
-      "64                Seattle2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "65                   South             US Southern county homicides 1960-1990       True\n",
-      "66                 StLouis   St Louis region county homicide counts and rates       True\n",
-      "67                  Tampa1   2000 Census Tract Data for Tampa, FL MSA and ...       True\n",
-      "68                  arcgis                               arcgis testing files       True\n",
-      "69                  baltim     Baltimore house sales prices and hedonics 1978       True\n",
-      "70                  berlin  Prenzlauer Berg neighborhood AirBnB data from ...       True\n",
-      "71                    book       Synthetic data to illustrate spatial weights       True\n",
-      "72                 burkitt  Burkitt's lymphoma in the Western Nile distric...       True\n",
-      "73                  calemp         Employment density for California counties       True\n",
-      "74                 chicago                              Chicago neighborhoods       True\n",
-      "75              clearwater                               mgwr testing dataset       True\n",
-      "76                columbus              Columbus neighborhood crime data 1980       True\n",
-      "77                 desmith    Small dataset to illustrate Moran's I statistic       True\n",
-      "78                geodanet        Datasets from geodanet for network analysis       True\n",
-      "79                 georgia  Various socio-economic variables for counties ...       True\n",
-      "80                juvenile    Residences of juvenile offenders in Cardiff, UK       True\n",
-      "81                  mexico  Decennial per capita incomes of Mexican states...       True\n",
-      "82                networks                  Datasets used for network testing       True\n",
-      "83                newHaven                            Network testing dataset       True\n",
-      "84               nyc_bikes                           New York City Bike Trips       True\n",
-      "85                   sids2  North Carolina county SIDS death counts and rates       True\n",
-      "86               snow_maps  Public water pumps and Cholera deaths in Londo...       True\n",
-      "87                     stl  Homicides and selected socio-economic characte...       True\n",
-      "88          street_net_pts                              Street network points       True\n",
-      "89                     taz           Traffic Analysis Zones in So. California       True\n",
-      "90                   tokyo                               Tokyo Mortality data       True\n",
-      "91               us_income  Per-capita income for the lower 48 US states 1...       True\n",
-      "92                virginia                        Virginia counties shapefile       True\n",
-      "93                    wmat          Datasets used for spatial weights testing       True\n"
-     ]
-    }
-   ],
-   "source": [
-    "available()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Grabbing all the remotes\n",
-    "\n",
-    "All the remote datasets can be downloaded with:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "AirBnB\n",
-      "Already downloaded\n",
-      "Atlanta\n",
-      "Already downloaded\n",
-      "Baltimore\n",
-      "Already downloaded\n",
-      "Bostonhsg\n",
-      "Already downloaded\n",
-      "Buenosaires\n",
-      "Already downloaded\n",
-      "Charleston1\n",
-      "Already downloaded\n",
-      "Charleston2\n",
-      "Already downloaded\n",
-      "Chicago Health\n",
-      "Already downloaded\n",
-      "Chile Labor\n",
-      "Already downloaded\n",
-      "Chile Migration\n",
-      "Already downloaded\n",
-      "Cincinnati\n",
-      "Already downloaded\n",
-      "Cleveland\n",
-      "Already downloaded\n",
-      "Columbus\n",
-      "Already downloaded\n",
-      "Denver\n",
-      "Already downloaded\n",
-      "Elections\n",
-      "Already downloaded\n",
-      "Grid100\n",
-      "Already downloaded\n",
-      "Groceries\n",
-      "Already downloaded\n",
-      "Guerry\n",
-      "Already downloaded\n",
-      "Health Indicators\n",
-      "Already downloaded\n",
-      "Health+\n",
-      "Already downloaded\n",
-      "Hickory1\n",
-      "Already downloaded\n",
-      "Hickory2\n",
-      "Already downloaded\n",
-      "Home Sales\n",
-      "Already downloaded\n",
-      "Houston\n",
-      "Already downloaded\n",
-      "Juvenile\n",
-      "Already downloaded\n",
-      "Lansing1\n",
-      "Already downloaded\n",
-      "Lansing2\n",
-      "Already downloaded\n",
-      "Laozone\n",
-      "Already downloaded\n",
-      "LasRosas\n",
-      "Already downloaded\n",
-      "Liquor Stores\n",
-      "Already downloaded\n",
-      "Malaria\n",
-      "Already downloaded\n",
-      "Milwaukee1\n",
-      "Already downloaded\n",
-      "Milwaukee2\n",
-      "Already downloaded\n",
-      "NCOVR\n",
-      "Already downloaded\n",
-      "NDVI\n",
-      "Already downloaded\n",
-      "NYC\n",
-      "Already downloaded\n",
-      "NYC Earnings\n",
-      "Already downloaded\n",
-      "NYC Education\n",
-      "Already downloaded\n",
-      "NYC Neighborhoods\n",
-      "Already downloaded\n",
-      "NYC Socio-Demographics\n",
-      "Already downloaded\n",
-      "Natregimes\n",
-      "Already downloaded\n",
-      "Nepal\n",
-      "Already downloaded\n",
-      "Ohiolung\n",
-      "Already downloaded\n",
-      "Orlando1\n",
-      "Already downloaded\n",
-      "Orlando2\n",
-      "Already downloaded\n",
-      "Oz9799\n",
-      "Already downloaded\n",
-      "Phoenix ACS\n",
-      "Already downloaded\n",
-      "Pittsburgh\n",
-      "Already downloaded\n",
-      "Police\n",
-      "Already downloaded\n",
-      "Rio Grande do Sul\n",
-      "Already downloaded\n",
-      "SIDS\n",
-      "Already downloaded\n",
-      "SIDS2\n",
-      "Already downloaded\n",
-      "Sacramento1\n",
-      "Already downloaded\n",
-      "Sacramento2\n",
-      "Already downloaded\n",
-      "SanFran Crime\n",
-      "Already downloaded\n",
-      "Savannah1\n",
-      "Already downloaded\n",
-      "Savannah2\n",
-      "Already downloaded\n",
-      "Scotlip\n",
-      "Already downloaded\n",
-      "Seattle1\n",
-      "Already downloaded\n",
-      "Seattle2\n",
-      "Already downloaded\n",
-      "South\n",
-      "Already downloaded\n",
-      "StLouis\n",
-      "Already downloaded\n",
-      "Tampa1\n",
-      "Already downloaded\n",
-      "clearwater\n",
-      "Already downloaded\n",
-      "newHaven\n",
-      "Already downloaded\n",
-      "nyc_bikes\n",
-      "Already downloaded\n",
-      "taz\n",
-      "Already downloaded\n"
-     ]
-    }
-   ],
-   "source": [
-    "libpysal.examples.fetch_all()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                      Name                                        Description  Installed\n",
-      "0                    10740    Albuquerque, New Mexico, Census 2000 Tract Data       True\n",
-      "1                   AirBnB  Airbnb rentals, socioeconomics, and crime in C...       True\n",
-      "2                  Atlanta       Atlanta, GA region homicide counts and rates       True\n",
-      "3                Baltimore          Baltimore house sales prices and hedonics       True\n",
-      "4                Bostonhsg               Boston housing and neighborhood data       True\n",
-      "5              Buenosaires      Electoral Data for 1999 Argentinean Elections       True\n",
-      "6              Charleston1   2000 Census Tract Data for Charleston, SC MSA...       True\n",
-      "7              Charleston2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "8           Chicago Health                   Chicago Health + Socio-Economics       True\n",
-      "9              Chile Labor                 Labor Markets in Chile (1982-2002)       True\n",
-      "10         Chile Migration            Internal Migration in Chile (1977-2002)       True\n",
-      "11              Cincinnati         2008 Cincinnati Crime + Socio-Demographics       True\n",
-      "12               Cleveland       2015 sales prices of homes in Cleveland, OH.       True\n",
-      "13                Columbus                        Columbus neighborhood crime       True\n",
-      "14                  Denver   Demographics and housing in Denver neighborho...       True\n",
-      "15               Elections               2012 and 2016 Presidential Elections       True\n",
-      "16                 Grid100                      Grid with simulated variables       True\n",
-      "17               Groceries                          2015 Chicago supermarkets       True\n",
-      "18                  Guerry          Moral statistics of France (Guerry, 1833)       True\n",
-      "19       Health Indicators                Chicago Health Indicators (2005-11)       True\n",
-      "20                 Health+                    2000 Health, Income + Diversity       True\n",
-      "21                Hickory1   2000 Census Tract Data for Hickory, NC MSA an...       True\n",
-      "22                Hickory2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "23              Home Sales              2014-15 Home Sales in King County, WA       True\n",
-      "24                 Houston       Houston, TX region homicide counts and rates       True\n",
-      "25                Juvenile             Cardiff juvenile delinquent residences       True\n",
-      "26                Lansing1   2000 Census Tract Data for Lansing, MI MSA an...       True\n",
-      "27                Lansing2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "28                 Laozone   Ozone measures at monitoring stations in Los ...       True\n",
-      "29                LasRosas   Corn yield, fertilizer and field data for pre...       True\n",
-      "30                    Line                                     Line Shapefile       True\n",
-      "31           Liquor Stores                         2015 Chicago Liquor Stores       True\n",
-      "32                 Malaria   Malaria incidence and population (1973, 95, 9...       True\n",
-      "33              Milwaukee1       2000 Census Tract Data for Milwaukee, WI MSA       True\n",
-      "34              Milwaukee2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "35                   NCOVR                      US county homicides 1960-1990       True\n",
-      "36                    NDVI        Normalized Difference Vegetation Index grid       True\n",
-      "37                     NYC   Demographic and housing data for New York Cit...       True\n",
-      "38            NYC Earnings              Block-level Earnings in NYC (2002-14)       True\n",
-      "39           NYC Education                               NYC Education (2000)       True\n",
-      "40       NYC Neighborhoods       Demographics for New York City neighborhoods       True\n",
-      "41  NYC Socio-Demographics                 NYC Education + Socio-Demographics       True\n",
-      "42              Natregimes                    NCOVR with regimes (book/PySAL)       True\n",
-      "43                   Nepal   Health, poverty and education indicators for ...       True\n",
-      "44                Ohiolung            Ohio lung cancer data, 1968, 1978, 1988       True\n",
-      "45                Orlando1   2000 Census Tract Data for Orlando, FL MSA an...       True\n",
-      "46                Orlando2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "47                  Oz9799                        Monthly ozone data, 1997-99       True\n",
-      "48             Phoenix ACS   Phoenix American Community Survey Data (2010,...       True\n",
-      "49              Pittsburgh                      Pittsburgh homicide locations       True\n",
-      "50                   Point                                    Point Shapefile       True\n",
-      "51                  Police           Police expenditures Mississippi counties       True\n",
-      "52                 Polygon                                  Polygon Shapefile       True\n",
-      "53           Polygon_Holes                 Example to test treatment of holes       True\n",
-      "54       Rio Grande do Sul  Cities of the Brazilian State of Rio Grande do...       True\n",
-      "55                    SIDS            North Carolina county SIDS death counts       True\n",
-      "56                   SIDS2   North Carolina county SIDS death counts and r...       True\n",
-      "57             Sacramento1          2000 Census Tract Data for Sacramento MSA       True\n",
-      "58             Sacramento2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "59           SanFran Crime   July-Dec 2012 crime incidents in San Francisc...       True\n",
-      "60               Savannah1   2000 Census Tract Data for Savannah, GA MSA a...       True\n",
-      "61               Savannah2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "62                 Scotlip               Male lip cancer in Scotland, 1975-80       True\n",
-      "63                Seattle1   2000 Census Tract Data for Seattle, WA MSA an...       True\n",
-      "64                Seattle2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "65                   South             US Southern county homicides 1960-1990       True\n",
-      "66                 StLouis   St Louis region county homicide counts and rates       True\n",
-      "67                  Tampa1   2000 Census Tract Data for Tampa, FL MSA and ...       True\n",
-      "68                  arcgis                               arcgis testing files       True\n",
-      "69                  baltim     Baltimore house sales prices and hedonics 1978       True\n",
-      "70                  berlin  Prenzlauer Berg neighborhood AirBnB data from ...       True\n",
-      "71                    book       Synthetic data to illustrate spatial weights       True\n",
-      "72                 burkitt  Burkitt's lymphoma in the Western Nile distric...       True\n",
-      "73                  calemp         Employment density for California counties       True\n",
-      "74                 chicago                              Chicago neighborhoods       True\n",
-      "75              clearwater                               mgwr testing dataset       True\n",
-      "76                columbus              Columbus neighborhood crime data 1980       True\n",
-      "77                 desmith    Small dataset to illustrate Moran's I statistic       True\n",
-      "78                geodanet        Datasets from geodanet for network analysis       True\n",
-      "79                 georgia  Various socio-economic variables for counties ...       True\n",
-      "80                juvenile    Residences of juvenile offenders in Cardiff, UK       True\n",
-      "81                  mexico  Decennial per capita incomes of Mexican states...       True\n",
-      "82                networks                  Datasets used for network testing       True\n",
-      "83                newHaven                            Network testing dataset       True\n",
-      "84               nyc_bikes                           New York City Bike Trips       True\n",
-      "85                   sids2  North Carolina county SIDS death counts and rates       True\n",
-      "86               snow_maps  Public water pumps and Cholera deaths in Londo...       True\n",
-      "87                     stl  Homicides and selected socio-economic characte...       True\n",
-      "88          street_net_pts                              Street network points       True\n",
-      "89                     taz           Traffic Analysis Zones in So. California       True\n",
-      "90                   tokyo                               Tokyo Mortality data       True\n",
-      "91               us_income  Per-capita income for the lower 48 US states 1...       True\n",
-      "92                virginia                        Virginia counties shapefile       True\n",
-      "93                    wmat          Datasets used for spatial weights testing       True\n"
-     ]
-    }
-   ],
-   "source": [
-    "available()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/_sources/notebooks/voronoi.ipynb.txt b/docs/_sources/notebooks/voronoi.ipynb.txt
deleted file mode 100644
index 75d7263d3..000000000
--- a/docs/_sources/notebooks/voronoi.ipynb.txt
+++ /dev/null
@@ -1,478 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Voronoi Polygons for 2-D Point Sets\n",
-    "\n",
-    "Author: Serge Rey (http://github.com/sjsrey)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Basic Usage"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sys\n",
-    "import os\n",
-    "sys.path.append(os.path.abspath('..'))\n",
-    "import libpysal"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from libpysal.cg.voronoi  import voronoi, voronoi_frames"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "points = [(10.2, 5.1), (4.7, 2.2), (5.3, 5.7), (2.7, 5.3)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "regions, vertices = voronoi(points)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[[1, 3, 2], [4, 5, 1, 0], [0, 1, 7, 6], [9, 0, 8]]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "regions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[  4.21783296,   4.08408578],\n",
-       "       [  7.51956025,   3.51807539],\n",
-       "       [  9.4642193 ,  19.3994576 ],\n",
-       "       [ 14.98210684, -10.63503022],\n",
-       "       [ -9.22691341,  -4.58994414],\n",
-       "       [ 14.98210684, -10.63503022],\n",
-       "       [  1.78491801,  19.89803294],\n",
-       "       [  9.4642193 ,  19.3994576 ],\n",
-       "       [  1.78491801,  19.89803294],\n",
-       "       [ -9.22691341,  -4.58994414]])"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "vertices"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "region_df, point_df = voronoi_frames(points)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "import matplotlib\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3304d59e48>"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots()\n",
-    "region_df.plot(ax=ax, color='blue',edgecolor='black', alpha=0.3)\n",
-    "point_df.plot(ax=ax, color='red')\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Larger Problem"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "n_points = 200\n",
-    "np.random.seed(12345)\n",
-    "points = np.random.random((n_points,2))*10 + 10\n",
-    "results = voronoi(points)\n",
-    "mins = points.min(axis=0)\n",
-    "maxs = points.max(axis=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "regions, vertices = voronoi(points)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "regions_df, points_df = voronoi_frames(points)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f32fd486b38>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots()\n",
-    "points_df.plot(ax=ax, color='red')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f32fd3c76a0>"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots()\n",
-    "regions_df.plot(ax=ax, color='blue',edgecolor='black', alpha=0.3)\n",
-    "points_df.plot(ax=ax, color='red')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Trimming"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "points = np.array(points)\n",
-    "maxs = points.max(axis=0)\n",
-    "mins = points.min(axis=0)\n",
-    "xr = maxs[0] - mins[0]\n",
-    "yr = maxs[1] - mins[1]\n",
-    "buff = 0.05\n",
-    "r = max(yr, xr) * buff\n",
-    "minx = mins[0] - r\n",
-    "miny = mins[1] - r\n",
-    "maxx = maxs[0] + r\n",
-    "maxy = maxs[1] + r"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots()\n",
-    "regions_df.plot(ax=ax, edgecolor='black', facecolor='blue', alpha=0.2 )\n",
-    "points_df.plot(ax=ax, color='red')\n",
-    "plt.xlim(minx, maxx)\n",
-    "plt.ylim(miny, maxy)\n",
-    "plt.title(\"buffer: %f, n: %d\"%(r,n_points))\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Voronoi Weights"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from libpysal.weights.contiguity import Voronoi as Vornoi_weights"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "w = Vornoi_weights(points)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "200"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w.n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.685"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w.pct_nonzero"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(1, 1),\n",
-       " (2, 6),\n",
-       " (3, 17),\n",
-       " (4, 34),\n",
-       " (5, 41),\n",
-       " (6, 63),\n",
-       " (7, 24),\n",
-       " (8, 7),\n",
-       " (9, 5),\n",
-       " (10, 1),\n",
-       " (11, 0),\n",
-       " (12, 1)]"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w.histogram"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "idx = [i for i in range(w.n) if w.cardinalities[i]==12]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[16.50851787, 13.12932895]])"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "points[idx]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/_sources/notebooks/weights.ipynb.txt b/docs/_sources/notebooks/weights.ipynb.txt
deleted file mode 100644
index 3f867ad7b..000000000
--- a/docs/_sources/notebooks/weights.ipynb.txt
+++ /dev/null
@@ -1,1313 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sys\n",
-    "import os"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sys.path.append(os.path.abspath('..'))\n",
-    "import libpysal"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                      Name                                        Description  Installed\n",
-      "0                    10740    Albuquerque, New Mexico, Census 2000 Tract Data       True\n",
-      "1                   AirBnB  Airbnb rentals, socioeconomics, and crime in C...       True\n",
-      "2                  Atlanta       Atlanta, GA region homicide counts and rates       True\n",
-      "3                Baltimore          Baltimore house sales prices and hedonics       True\n",
-      "4                Bostonhsg               Boston housing and neighborhood data       True\n",
-      "5              Buenosaires      Electoral Data for 1999 Argentinean Elections       True\n",
-      "6              Charleston1   2000 Census Tract Data for Charleston, SC MSA...       True\n",
-      "7              Charleston2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "8           Chicago Health                   Chicago Health + Socio-Economics       True\n",
-      "9              Chile Labor                 Labor Markets in Chile (1982-2002)       True\n",
-      "10         Chile Migration            Internal Migration in Chile (1977-2002)       True\n",
-      "11              Cincinnati         2008 Cincinnati Crime + Socio-Demographics       True\n",
-      "12               Cleveland       2015 sales prices of homes in Cleveland, OH.       True\n",
-      "13                Columbus                        Columbus neighborhood crime       True\n",
-      "14                  Denver   Demographics and housing in Denver neighborho...       True\n",
-      "15               Elections               2012 and 2016 Presidential Elections       True\n",
-      "16                 Grid100                      Grid with simulated variables       True\n",
-      "17               Groceries                          2015 Chicago supermarkets       True\n",
-      "18                  Guerry          Moral statistics of France (Guerry, 1833)       True\n",
-      "19       Health Indicators                Chicago Health Indicators (2005-11)       True\n",
-      "20                 Health+                    2000 Health, Income + Diversity       True\n",
-      "21                Hickory1   2000 Census Tract Data for Hickory, NC MSA an...       True\n",
-      "22                Hickory2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "23              Home Sales              2014-15 Home Sales in King County, WA       True\n",
-      "24                 Houston       Houston, TX region homicide counts and rates       True\n",
-      "25                Juvenile             Cardiff juvenile delinquent residences       True\n",
-      "26                Lansing1   2000 Census Tract Data for Lansing, MI MSA an...       True\n",
-      "27                Lansing2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "28                 Laozone   Ozone measures at monitoring stations in Los ...       True\n",
-      "29                LasRosas   Corn yield, fertilizer and field data for pre...       True\n",
-      "30                    Line                                     Line Shapefile       True\n",
-      "31           Liquor Stores                         2015 Chicago Liquor Stores       True\n",
-      "32                 Malaria   Malaria incidence and population (1973, 95, 9...       True\n",
-      "33              Milwaukee1       2000 Census Tract Data for Milwaukee, WI MSA       True\n",
-      "34              Milwaukee2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "35                   NCOVR                      US county homicides 1960-1990       True\n",
-      "36                    NDVI        Normalized Difference Vegetation Index grid       True\n",
-      "37                     NYC   Demographic and housing data for New York Cit...       True\n",
-      "38            NYC Earnings              Block-level Earnings in NYC (2002-14)       True\n",
-      "39           NYC Education                               NYC Education (2000)       True\n",
-      "40       NYC Neighborhoods       Demographics for New York City neighborhoods       True\n",
-      "41  NYC Socio-Demographics                 NYC Education + Socio-Demographics       True\n",
-      "42              Natregimes                    NCOVR with regimes (book/PySAL)       True\n",
-      "43                   Nepal   Health, poverty and education indicators for ...       True\n",
-      "44                Ohiolung            Ohio lung cancer data, 1968, 1978, 1988       True\n",
-      "45                Orlando1   2000 Census Tract Data for Orlando, FL MSA an...       True\n",
-      "46                Orlando2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "47                  Oz9799                        Monthly ozone data, 1997-99       True\n",
-      "48             Phoenix ACS   Phoenix American Community Survey Data (2010,...       True\n",
-      "49              Pittsburgh                      Pittsburgh homicide locations       True\n",
-      "50                   Point                                    Point Shapefile       True\n",
-      "51                  Police           Police expenditures Mississippi counties       True\n",
-      "52                 Polygon                                  Polygon Shapefile       True\n",
-      "53           Polygon_Holes                 Example to test treatment of holes       True\n",
-      "54       Rio Grande do Sul  Cities of the Brazilian State of Rio Grande do...       True\n",
-      "55                    SIDS            North Carolina county SIDS death counts       True\n",
-      "56                   SIDS2   North Carolina county SIDS death counts and r...       True\n",
-      "57             Sacramento1          2000 Census Tract Data for Sacramento MSA       True\n",
-      "58             Sacramento2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "59           SanFran Crime   July-Dec 2012 crime incidents in San Francisc...       True\n",
-      "60               Savannah1   2000 Census Tract Data for Savannah, GA MSA a...       True\n",
-      "61               Savannah2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "62                 Scotlip               Male lip cancer in Scotland, 1975-80       True\n",
-      "63                Seattle1   2000 Census Tract Data for Seattle, WA MSA an...       True\n",
-      "64                Seattle2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "65                   South             US Southern county homicides 1960-1990       True\n",
-      "66                 StLouis   St Louis region county homicide counts and rates       True\n",
-      "67                  Tampa1   2000 Census Tract Data for Tampa, FL MSA and ...       True\n",
-      "68                  arcgis                               arcgis testing files       True\n",
-      "69                  baltim     Baltimore house sales prices and hedonics 1978       True\n",
-      "70                  berlin  Prenzlauer Berg neighborhood AirBnB data from ...       True\n",
-      "71                    book       Synthetic data to illustrate spatial weights       True\n",
-      "72                 burkitt  Burkitt's lymphoma in the Western Nile distric...       True\n",
-      "73                  calemp         Employment density for California counties       True\n",
-      "74                 chicago                              Chicago neighborhoods       True\n",
-      "75              clearwater                               mgwr testing dataset       True\n",
-      "76                columbus              Columbus neighborhood crime data 1980       True\n",
-      "77                 desmith    Small dataset to illustrate Moran's I statistic       True\n",
-      "78                geodanet        Datasets from geodanet for network analysis       True\n",
-      "79                 georgia  Various socio-economic variables for counties ...       True\n",
-      "80                juvenile    Residences of juvenile offenders in Cardiff, UK       True\n",
-      "81                  mexico  Decennial per capita incomes of Mexican states...       True\n",
-      "82                networks                  Datasets used for network testing       True\n",
-      "83                newHaven                            Network testing dataset       True\n",
-      "84               nyc_bikes                           New York City Bike Trips       True\n",
-      "85                   sids2  North Carolina county SIDS death counts and rates       True\n",
-      "86               snow_maps  Public water pumps and Cholera deaths in Londo...       True\n",
-      "87                     stl  Homicides and selected socio-economic characte...       True\n",
-      "88          street_net_pts                              Street network points       True\n",
-      "89                     taz           Traffic Analysis Zones in So. California       True\n",
-      "90                   tokyo                               Tokyo Mortality data       True\n",
-      "91               us_income  Per-capita income for the lower 48 US states 1...       True\n",
-      "92                virginia                        Virginia counties shapefile       True\n",
-      "93                    wmat          Datasets used for spatial weights testing       True\n"
-     ]
-    }
-   ],
-   "source": [
-    "libpysal.examples.available()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mexico\n",
-      "======\n",
-      "\n",
-      "Decennial per capita incomes of Mexican states 1940-2000\n",
-      "--------------------------------------------------------\n",
-      "\n",
-      "* mexico.csv: attribute data. (n=32, k=13)\n",
-      "* mexico.gal: spatial weights in GAL format.\n",
-      "* mexicojoin.shp: Polygon shapefile. (n=32)\n",
-      "\n",
-      "Data used in Rey, S.J. and M.L. Sastre Gutierrez. (2010) \"Interregional inequality dynamics in Mexico.\" Spatial Economic Analysis, 5: 277-298.\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "libpysal.examples.explain('mexico')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Weights from GeoDataFrames"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import geopandas\n",
-    "pth = libpysal.examples.get_path(\"mexicojoin.shp\")\n",
-    "gdf = geopandas.read_file(pth)\n",
-    "\n",
-    "from libpysal.weights import Queen, Rook, KNN"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "import matplotlib.pyplot as plt\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = gdf.plot()\n",
-    "ax.set_axis_off()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Contiguity Weights\n",
-    "\n",
-    "The first set of spatial weights we illustrate use notions of contiguity to define neighboring observations. **Rook** neighbors are those states that share an edge on their respective borders:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "w_rook = Rook.from_dataframe(gdf)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "32"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w_rook.n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "12.6953125"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w_rook.pct_nonzero"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = gdf.plot(edgecolor='grey', facecolor='w')\n",
-    "f,ax = w_rook.plot(gdf, ax=ax, \n",
-    "        edge_kws=dict(color='r', linestyle=':', linewidth=1),\n",
-    "        node_kws=dict(marker=''))\n",
-    "ax.set_axis_off()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>POLY_ID</th>\n",
-       "      <th>AREA</th>\n",
-       "      <th>CODE</th>\n",
-       "      <th>NAME</th>\n",
-       "      <th>PERIMETER</th>\n",
-       "      <th>ACRES</th>\n",
-       "      <th>HECTARES</th>\n",
-       "      <th>PCGDP1940</th>\n",
-       "      <th>PCGDP1950</th>\n",
-       "      <th>PCGDP1960</th>\n",
-       "      <th>...</th>\n",
-       "      <th>GR9000</th>\n",
-       "      <th>LPCGDP40</th>\n",
-       "      <th>LPCGDP50</th>\n",
-       "      <th>LPCGDP60</th>\n",
-       "      <th>LPCGDP70</th>\n",
-       "      <th>LPCGDP80</th>\n",
-       "      <th>LPCGDP90</th>\n",
-       "      <th>LPCGDP00</th>\n",
-       "      <th>TEST</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>7.252751e+10</td>\n",
-       "      <td>MX02</td>\n",
-       "      <td>Baja California Norte</td>\n",
-       "      <td>2040312.385</td>\n",
-       "      <td>1.792187e+07</td>\n",
-       "      <td>7252751.376</td>\n",
-       "      <td>22361.0</td>\n",
-       "      <td>20977.0</td>\n",
-       "      <td>17865.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.05</td>\n",
-       "      <td>4.35</td>\n",
-       "      <td>4.32</td>\n",
-       "      <td>4.25</td>\n",
-       "      <td>4.40</td>\n",
-       "      <td>4.47</td>\n",
-       "      <td>4.43</td>\n",
-       "      <td>4.48</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>MULTIPOLYGON (((-113.13972 29.01778, -113.2405...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>7.225988e+10</td>\n",
-       "      <td>MX03</td>\n",
-       "      <td>Baja California Sur</td>\n",
-       "      <td>2912880.772</td>\n",
-       "      <td>1.785573e+07</td>\n",
-       "      <td>7225987.769</td>\n",
-       "      <td>9573.0</td>\n",
-       "      <td>16013.0</td>\n",
-       "      <td>16707.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>3.98</td>\n",
-       "      <td>4.20</td>\n",
-       "      <td>4.22</td>\n",
-       "      <td>4.39</td>\n",
-       "      <td>4.46</td>\n",
-       "      <td>4.41</td>\n",
-       "      <td>4.42</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>MULTIPOLYGON (((-111.20612 25.80278, -111.2302...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>2.731957e+10</td>\n",
-       "      <td>MX18</td>\n",
-       "      <td>Nayarit</td>\n",
-       "      <td>1034770.341</td>\n",
-       "      <td>6.750785e+06</td>\n",
-       "      <td>2731956.859</td>\n",
-       "      <td>4836.0</td>\n",
-       "      <td>7515.0</td>\n",
-       "      <td>7621.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.05</td>\n",
-       "      <td>3.68</td>\n",
-       "      <td>3.88</td>\n",
-       "      <td>3.88</td>\n",
-       "      <td>4.04</td>\n",
-       "      <td>4.13</td>\n",
-       "      <td>4.11</td>\n",
-       "      <td>4.06</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>MULTIPOLYGON (((-106.62108 21.56531, -106.6475...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>7.961008e+10</td>\n",
-       "      <td>MX14</td>\n",
-       "      <td>Jalisco</td>\n",
-       "      <td>2324727.436</td>\n",
-       "      <td>1.967200e+07</td>\n",
-       "      <td>7961008.285</td>\n",
-       "      <td>5309.0</td>\n",
-       "      <td>8232.0</td>\n",
-       "      <td>9953.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.03</td>\n",
-       "      <td>3.73</td>\n",
-       "      <td>3.92</td>\n",
-       "      <td>4.00</td>\n",
-       "      <td>4.21</td>\n",
-       "      <td>4.32</td>\n",
-       "      <td>4.30</td>\n",
-       "      <td>4.33</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>POLYGON ((-101.52490 21.85664, -101.58830 21.7...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>5.467030e+09</td>\n",
-       "      <td>MX01</td>\n",
-       "      <td>Aguascalientes</td>\n",
-       "      <td>313895.530</td>\n",
-       "      <td>1.350927e+06</td>\n",
-       "      <td>546702.985</td>\n",
-       "      <td>10384.0</td>\n",
-       "      <td>6234.0</td>\n",
-       "      <td>8714.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.13</td>\n",
-       "      <td>4.02</td>\n",
-       "      <td>3.79</td>\n",
-       "      <td>3.94</td>\n",
-       "      <td>4.21</td>\n",
-       "      <td>4.32</td>\n",
-       "      <td>4.32</td>\n",
-       "      <td>4.44</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>POLYGON ((-101.84620 22.01176, -101.96530 21.8...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 35 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   POLY_ID          AREA  CODE                   NAME    PERIMETER  \\\n",
-       "0        1  7.252751e+10  MX02  Baja California Norte  2040312.385   \n",
-       "1        2  7.225988e+10  MX03    Baja California Sur  2912880.772   \n",
-       "2        3  2.731957e+10  MX18                Nayarit  1034770.341   \n",
-       "3        4  7.961008e+10  MX14                Jalisco  2324727.436   \n",
-       "4        5  5.467030e+09  MX01         Aguascalientes   313895.530   \n",
-       "\n",
-       "          ACRES     HECTARES  PCGDP1940  PCGDP1950  PCGDP1960  ...  GR9000  \\\n",
-       "0  1.792187e+07  7252751.376    22361.0    20977.0    17865.0  ...    0.05   \n",
-       "1  1.785573e+07  7225987.769     9573.0    16013.0    16707.0  ...    0.00   \n",
-       "2  6.750785e+06  2731956.859     4836.0     7515.0     7621.0  ...   -0.05   \n",
-       "3  1.967200e+07  7961008.285     5309.0     8232.0     9953.0  ...    0.03   \n",
-       "4  1.350927e+06   546702.985    10384.0     6234.0     8714.0  ...    0.13   \n",
-       "\n",
-       "   LPCGDP40  LPCGDP50  LPCGDP60  LPCGDP70  LPCGDP80  LPCGDP90  LPCGDP00  TEST  \\\n",
-       "0      4.35      4.32      4.25      4.40      4.47      4.43      4.48   1.0   \n",
-       "1      3.98      4.20      4.22      4.39      4.46      4.41      4.42   2.0   \n",
-       "2      3.68      3.88      3.88      4.04      4.13      4.11      4.06   3.0   \n",
-       "3      3.73      3.92      4.00      4.21      4.32      4.30      4.33   4.0   \n",
-       "4      4.02      3.79      3.94      4.21      4.32      4.32      4.44   5.0   \n",
-       "\n",
-       "                                            geometry  \n",
-       "0  MULTIPOLYGON (((-113.13972 29.01778, -113.2405...  \n",
-       "1  MULTIPOLYGON (((-111.20612 25.80278, -111.2302...  \n",
-       "2  MULTIPOLYGON (((-106.62108 21.56531, -106.6475...  \n",
-       "3  POLYGON ((-101.52490 21.85664, -101.58830 21.7...  \n",
-       "4  POLYGON ((-101.84620 22.01176, -101.96530 21.8...  \n",
-       "\n",
-       "[5 rows x 35 columns]"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gdf.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 22]"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w_rook.neighbors[0] # the first location has two neighbors at locations 1 and 22"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0     Baja California Norte\n",
-       "1       Baja California Sur\n",
-       "22                   Sonora\n",
-       "Name: NAME, dtype: object"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gdf['NAME'][[0, 1,22]]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "So, Baja California Norte has 2 rook neighbors: Baja California Sur and Sonora."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Queen** neighbors are based on a more inclusive condition that requires only a shared vertex between two states:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "w_queen = Queen.from_dataframe(gdf)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w_queen.n == w_rook.n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(w_queen.pct_nonzero > w_rook.pct_nonzero) == (w_queen.n == w_rook.n)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = gdf.plot(edgecolor='grey', facecolor='w')\n",
-    "f,ax = w_queen.plot(gdf, ax=ax, \n",
-    "        edge_kws=dict(color='r', linestyle=':', linewidth=1),\n",
-    "        node_kws=dict(marker=''))\n",
-    "ax.set_axis_off()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(1, 1), (2, 6), (3, 6), (4, 6), (5, 5), (6, 2), (7, 3), (8, 2), (9, 1)]"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w_queen.histogram"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(1, 1), (2, 6), (3, 7), (4, 7), (5, 3), (6, 4), (7, 3), (8, 1)]"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w_rook.histogram"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "c9 = [idx for idx,c in w_queen.cardinalities.items() if c==9]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "28    San Luis Potosi\n",
-       "Name: NAME, dtype: object"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gdf['NAME'][c9]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[5, 6, 7, 27, 29, 30, 31]"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w_rook.neighbors[28]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[3, 5, 6, 7, 24, 27, 29, 30, 31]"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w_queen.neighbors[28]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([-105.,  -95.,   21.,   26.])"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x432 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "f,ax = plt.subplots(1,2,figsize=(10, 6), subplot_kw=dict(aspect='equal'))\n",
-    "gdf.plot(edgecolor='grey', facecolor='w', ax=ax[0])\n",
-    "w_rook.plot(gdf, ax=ax[0], \n",
-    "        edge_kws=dict(color='r', linestyle=':', linewidth=1),\n",
-    "        node_kws=dict(marker=''))\n",
-    "ax[0].set_title('Rook')\n",
-    "ax[0].axis(np.asarray([-105.0, -95.0, 21, 26]))\n",
-    "\n",
-    "ax[0].axis('off')\n",
-    "gdf.plot(edgecolor='grey', facecolor='w', ax=ax[1])\n",
-    "w_queen.plot(gdf, ax=ax[1], \n",
-    "        edge_kws=dict(color='r', linestyle=':', linewidth=1),\n",
-    "        node_kws=dict(marker=''))\n",
-    "ax[1].set_title('Queen')\n",
-    "ax[1].axis('off')\n",
-    "ax[1].axis(np.asarray([-105.0, -95.0, 21, 26]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "w_knn = KNN.from_dataframe(gdf, k=4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(4, 32)]"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w_knn.histogram"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = gdf.plot(edgecolor='grey', facecolor='w')\n",
-    "f,ax = w_knn.plot(gdf, ax=ax, \n",
-    "        edge_kws=dict(color='r', linestyle=':', linewidth=1),\n",
-    "        node_kws=dict(marker=''))\n",
-    "ax.set_axis_off()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Weights from shapefiles (without geopandas)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pth = libpysal.examples.get_path(\"mexicojoin.shp\")\n",
-    "from libpysal.weights import Queen, Rook, KNN"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "w_queen = Queen.from_shapefile(pth)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "w_rook = Rook.from_shapefile(pth)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/jovyan/libpysal/weights/weights.py:167: UserWarning: The weights matrix is not fully connected: \n",
-      " There are 2 disconnected components.\n",
-      "  warnings.warn(message)\n"
-     ]
-    }
-   ],
-   "source": [
-    "w_knn1 = KNN.from_shapefile(pth)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The warning alerts us to the fact that using a first nearest neighbor criterion to define the neighbors results in a connectivity graph that has more than a single component. In this particular case there are 2 components which can be seen in the following plot:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = gdf.plot(edgecolor='grey', facecolor='w')\n",
-    "f,ax = w_knn1.plot(gdf, ax=ax, \n",
-    "        edge_kws=dict(color='r', linestyle=':', linewidth=1),\n",
-    "        node_kws=dict(marker=''))\n",
-    "ax.set_axis_off()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The two components are separated in the southern part of the country, with the smaller component to the east and the larger component running through the rest of the country to the west. For certain types of spatial analytical methods, it is necessary to have a adjacency structure that consists of a single component. To ensure this for the case of Mexican states, we can increase the number of nearest neighbors to three:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "w_knn3 = KNN.from_shapefile(pth,k=3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = gdf.plot(edgecolor='grey', facecolor='w')\n",
-    "f,ax = w_knn3.plot(gdf, ax=ax, \n",
-    "        edge_kws=dict(color='r', linestyle=':', linewidth=1),\n",
-    "        node_kws=dict(marker=''))\n",
-    "ax.set_axis_off()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Lattice Weights"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from libpysal.weights import lat2W"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "w = lat2W(4,3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "12"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w.n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "23.61111111111111"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w.pct_nonzero"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0: [3, 1],\n",
-       " 3: [0, 6, 4],\n",
-       " 1: [0, 4, 2],\n",
-       " 4: [1, 3, 7, 5],\n",
-       " 2: [1, 5],\n",
-       " 5: [2, 4, 8],\n",
-       " 6: [3, 9, 7],\n",
-       " 7: [4, 6, 10, 8],\n",
-       " 8: [5, 7, 11],\n",
-       " 9: [6, 10],\n",
-       " 10: [7, 9, 11],\n",
-       " 11: [8, 10]}"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w.neighbors"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Handling nonplanar geometries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rs = libpysal.examples.get_path('map_RS_BR.shp')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import geopandas as gpd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/jovyan/libpysal/weights/weights.py:167: UserWarning: The weights matrix is not fully connected: \n",
-      " There are 30 disconnected components.\n",
-      " There are 29 islands with ids: 0, 4, 23, 27, 80, 94, 101, 107, 109, 119, 122, 139, 169, 175, 223, 239, 247, 253, 254, 255, 256, 261, 276, 291, 294, 303, 321, 357, 374.\n",
-      "  warnings.warn(message)\n"
-     ]
-    }
-   ],
-   "source": [
-    "rs_df = gpd.read_file(rs)\n",
-    "wq = libpysal.weights.Queen.from_dataframe(rs_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "29"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(wq.islands)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{}"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wq[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wf = libpysal.weights.fuzzy_contiguity(rs_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[]"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wf.islands"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{239: 1.0, 59: 1.0, 152: 1.0, 23: 1.0, 107: 1.0}"
-      ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wf[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x1080 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.rcParams[\"figure.figsize\"] = (20,15)\n",
-    "ax = rs_df.plot(edgecolor='grey', facecolor='w')\n",
-    "f,ax = wq.plot(rs_df, ax=ax, \n",
-    "        edge_kws=dict(color='r', linestyle=':', linewidth=1),\n",
-    "        node_kws=dict(marker=''))\n",
-    "\n",
-    "ax.set_axis_off()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x1080 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\n",
-    "ax = rs_df.plot(edgecolor='grey', facecolor='w')\n",
-    "f,ax = wf.plot(rs_df, ax=ax, \n",
-    "        edge_kws=dict(color='r', linestyle=':', linewidth=1),\n",
-    "        node_kws=dict(marker=''))\n",
-    "ax.set_title('Rio Grande do Sul: Nonplanar Weights')\n",
-    "ax.set_axis_off()\n",
-    "plt.savefig('rioGrandeDoSul.png')\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/_static/basic.css b/docs/_static/basic.css
deleted file mode 100644
index 2e3cf3230..000000000
--- a/docs/_static/basic.css
+++ /dev/null
@@ -1,855 +0,0 @@
-/*
- * basic.css
- * ~~~~~~~~~
- *
- * Sphinx stylesheet -- basic theme.
- *
- * :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS.
- * :license: BSD, see LICENSE for details.
- *
- */
-
-/* -- main layout ----------------------------------------------------------- */
-
-div.clearer {
-    clear: both;
-}
-
-div.section::after {
-    display: block;
-    content: '';
-    clear: left;
-}
-
-/* -- relbar ---------------------------------------------------------------- */
-
-div.related {
-    width: 100%;
-    font-size: 90%;
-}
-
-div.related h3 {
-    display: none;
-}
-
-div.related ul {
-    margin: 0;
-    padding: 0 0 0 10px;
-    list-style: none;
-}
-
-div.related li {
-    display: inline;
-}
-
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-    float: right;
-    margin-right: 5px;
-}
-
-/* -- sidebar --------------------------------------------------------------- */
-
-div.sphinxsidebarwrapper {
-    padding: 10px 5px 0 10px;
-}
-
-div.sphinxsidebar {
-    float: left;
-    width: 230px;
-    margin-left: -100%;
-    font-size: 90%;
-    word-wrap: break-word;
-    overflow-wrap : break-word;
-}
-
-div.sphinxsidebar ul {
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-}
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-div.sphinxsidebar ul ul,
-div.sphinxsidebar ul.want-points {
-    margin-left: 20px;
-    list-style: square;
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-div.sphinxsidebar ul ul {
-    margin-top: 0;
-    margin-bottom: 0;
-}
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-div.sphinxsidebar form {
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-}
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-div.sphinxsidebar input {
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-    font-family: sans-serif;
-    font-size: 1em;
-}
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-div.sphinxsidebar #searchbox form.search {
-    overflow: hidden;
-}
-
-div.sphinxsidebar #searchbox input[type="text"] {
-    float: left;
-    width: 80%;
-    padding: 0.25em;
-    box-sizing: border-box;
-}
-
-div.sphinxsidebar #searchbox input[type="submit"] {
-    float: left;
-    width: 20%;
-    border-left: none;
-    padding: 0.25em;
-    box-sizing: border-box;
-}
-
-
-img {
-    border: 0;
-    max-width: 100%;
-}
-
-/* -- search page ----------------------------------------------------------- */
-
-ul.search {
-    margin: 10px 0 0 20px;
-    padding: 0;
-}
-
-ul.search li {
-    padding: 5px 0 5px 20px;
-    background-image: url(file.png);
-    background-repeat: no-repeat;
-    background-position: 0 7px;
-}
-
-ul.search li a {
-    font-weight: bold;
-}
-
-ul.search li div.context {
-    color: #888;
-    margin: 2px 0 0 30px;
-    text-align: left;
-}
-
-ul.keywordmatches li.goodmatch a {
-    font-weight: bold;
-}
-
-/* -- index page ------------------------------------------------------------ */
-
-table.contentstable {
-    width: 90%;
-    margin-left: auto;
-    margin-right: auto;
-}
-
-table.contentstable p.biglink {
-    line-height: 150%;
-}
-
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-    font-size: 1.3em;
-}
-
-span.linkdescr {
-    font-style: italic;
-    padding-top: 5px;
-    font-size: 90%;
-}
-
-/* -- general index --------------------------------------------------------- */
-
-table.indextable {
-    width: 100%;
-}
-
-table.indextable td {
-    text-align: left;
-    vertical-align: top;
-}
-
-table.indextable ul {
-    margin-top: 0;
-    margin-bottom: 0;
-    list-style-type: none;
-}
-
-table.indextable > tbody > tr > td > ul {
-    padding-left: 0em;
-}
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-table.indextable tr.pcap {
-    height: 10px;
-}
-
-table.indextable tr.cap {
-    margin-top: 10px;
-    background-color: #f2f2f2;
-}
-
-img.toggler {
-    margin-right: 3px;
-    margin-top: 3px;
-    cursor: pointer;
-}
-
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-    border-bottom: 1px solid #ddd;
-    margin: 1em 0 1em 0;
-    padding: 0.4em;
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-    border-bottom: 1px solid #ddd;
-    margin: 1em 0 1em 0;
-    padding: 0.4em;
-}
-
-/* -- domain module index --------------------------------------------------- */
-
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-    border-collapse: collapse;
-}
-
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-    max-width: 800px;
-}
-
-div.body p, div.body dd, div.body li, div.body blockquote {
-    -moz-hyphens: auto;
-    -ms-hyphens: auto;
-    -webkit-hyphens: auto;
-    hyphens: auto;
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-
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-    visibility: hidden;
-}
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-span.brackets > a:before{
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diff --git a/docs/_static/bootstrap-2.3.2/css/bootstrap-responsive.css b/docs/_static/bootstrap-2.3.2/css/bootstrap-responsive.css
deleted file mode 100644
index 09e88ce3f..000000000
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diff --git a/docs/_static/bootstrap-2.3.2/css/bootstrap-responsive.min.css b/docs/_static/bootstrap-2.3.2/css/bootstrap-responsive.min.css
deleted file mode 100644
index f4ede63f3..000000000
--- a/docs/_static/bootstrap-2.3.2/css/bootstrap-responsive.min.css
+++ /dev/null
@@ -1,9 +0,0 @@
-/*!
- * Bootstrap Responsive v2.3.2
- *
- * Copyright 2012 Twitter, Inc
- * Licensed under the Apache License v2.0
- * http://www.apache.org/licenses/LICENSE-2.0
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diff --git a/docs/_static/bootstrap-2.3.2/js/bootstrap.js b/docs/_static/bootstrap-2.3.2/js/bootstrap.js
deleted file mode 100644
index 638bb1877..000000000
--- a/docs/_static/bootstrap-2.3.2/js/bootstrap.js
+++ /dev/null
@@ -1,2287 +0,0 @@
-/* ===================================================
- * bootstrap-transition.js v2.3.2
- * http://twitter.github.com/bootstrap/javascript.html#transitions
- * ===================================================
- * Copyright 2012 Twitter, Inc.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ========================================================== */
-
-
-!function ($) {
-
-  "use strict"; // jshint ;_;
-
-
-  /* CSS TRANSITION SUPPORT (http://www.modernizr.com/)
-   * ======================================================= */
-
-  $(function () {
-
-    $.support.transition = (function () {
-
-      var transitionEnd = (function () {
-
-        var el = document.createElement('bootstrap')
-          , transEndEventNames = {
-               'WebkitTransition' : 'webkitTransitionEnd'
-            ,  'MozTransition'    : 'transitionend'
-            ,  'OTransition'      : 'oTransitionEnd otransitionend'
-            ,  'transition'       : 'transitionend'
-            }
-          , name
-
-        for (name in transEndEventNames){
-          if (el.style[name] !== undefined) {
-            return transEndEventNames[name]
-          }
-        }
-
-      }())
-
-      return transitionEnd && {
-        end: transitionEnd
-      }
-
-    })()
-
-  })
-
-}(window.$jqTheme || window.jQuery);
-/* ==========================================================
- * bootstrap-alert.js v2.3.2
- * http://twitter.github.com/bootstrap/javascript.html#alerts
- * ==========================================================
- * Copyright 2012 Twitter, Inc.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ========================================================== */
-
-
-!function ($) {
-
-  "use strict"; // jshint ;_;
-
-
- /* ALERT CLASS DEFINITION
-  * ====================== */
-
-  var dismiss = '[data-dismiss="alert"]'
-    , Alert = function (el) {
-        $(el).on('click', dismiss, this.close)
-      }
-
-  Alert.prototype.close = function (e) {
-    var $this = $(this)
-      , selector = $this.attr('data-target')
-      , $parent
-
-    if (!selector) {
-      selector = $this.attr('href')
-      selector = selector && selector.replace(/.*(?=#[^\s]*$)/, '') //strip for ie7
-    }
-
-    $parent = $(selector)
-
-    e && e.preventDefault()
-
-    $parent.length || ($parent = $this.hasClass('alert') ? $this : $this.parent())
-
-    $parent.trigger(e = $.Event('close'))
-
-    if (e.isDefaultPrevented()) return
-
-    $parent.removeClass('in')
-
-    function removeElement() {
-      $parent
-        .trigger('closed')
-        .remove()
-    }
-
-    $.support.transition && $parent.hasClass('fade') ?
-      $parent.on($.support.transition.end, removeElement) :
-      removeElement()
-  }
-
-
- /* ALERT PLUGIN DEFINITION
-  * ======================= */
-
-  var old = $.fn.alert
-
-  $.fn.alert = function (option) {
-    return this.each(function () {
-      var $this = $(this)
-        , data = $this.data('alert')
-      if (!data) $this.data('alert', (data = new Alert(this)))
-      if (typeof option == 'string') data[option].call($this)
-    })
-  }
-
-  $.fn.alert.Constructor = Alert
-
-
- /* ALERT NO CONFLICT
-  * ================= */
-
-  $.fn.alert.noConflict = function () {
-    $.fn.alert = old
-    return this
-  }
-
-
- /* ALERT DATA-API
-  * ============== */
-
-  $(document).on('click.alert.data-api', dismiss, Alert.prototype.close)
-
-}(window.$jqTheme || window.jQuery);
-/* ============================================================
- * bootstrap-button.js v2.3.2
- * http://twitter.github.com/bootstrap/javascript.html#buttons
- * ============================================================
- * Copyright 2012 Twitter, Inc.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ============================================================ */
-
-
-!function ($) {
-
-  "use strict"; // jshint ;_;
-
-
- /* BUTTON PUBLIC CLASS DEFINITION
-  * ============================== */
-
-  var Button = function (element, options) {
-    this.$element = $(element)
-    this.options = $.extend({}, $.fn.button.defaults, options)
-  }
-
-  Button.prototype.setState = function (state) {
-    var d = 'disabled'
-      , $el = this.$element
-      , data = $el.data()
-      , val = $el.is('input') ? 'val' : 'html'
-
-    state = state + 'Text'
-    data.resetText || $el.data('resetText', $el[val]())
-
-    $el[val](data[state] || this.options[state])
-
-    // push to event loop to allow forms to submit
-    setTimeout(function () {
-      state == 'loadingText' ?
-        $el.addClass(d).attr(d, d) :
-        $el.removeClass(d).removeAttr(d)
-    }, 0)
-  }
-
-  Button.prototype.toggle = function () {
-    var $parent = this.$element.closest('[data-toggle="buttons-radio"]')
-
-    $parent && $parent
-      .find('.active')
-      .removeClass('active')
-
-    this.$element.toggleClass('active')
-  }
-
-
- /* BUTTON PLUGIN DEFINITION
-  * ======================== */
-
-  var old = $.fn.button
-
-  $.fn.button = function (option) {
-    return this.each(function () {
-      var $this = $(this)
-        , data = $this.data('button')
-        , options = typeof option == 'object' && option
-      if (!data) $this.data('button', (data = new Button(this, options)))
-      if (option == 'toggle') data.toggle()
-      else if (option) data.setState(option)
-    })
-  }
-
-  $.fn.button.defaults = {
-    loadingText: 'loading...'
-  }
-
-  $.fn.button.Constructor = Button
-
-
- /* BUTTON NO CONFLICT
-  * ================== */
-
-  $.fn.button.noConflict = function () {
-    $.fn.button = old
-    return this
-  }
-
-
- /* BUTTON DATA-API
-  * =============== */
-
-  $(document).on('click.button.data-api', '[data-toggle^=button]', function (e) {
-    var $btn = $(e.target)
-    if (!$btn.hasClass('btn')) $btn = $btn.closest('.btn')
-    $btn.button('toggle')
-  })
-
-}(window.$jqTheme || window.jQuery);
-/* ==========================================================
- * bootstrap-carousel.js v2.3.2
- * http://twitter.github.com/bootstrap/javascript.html#carousel
- * ==========================================================
- * Copyright 2012 Twitter, Inc.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ========================================================== */
-
-
-!function ($) {
-
-  "use strict"; // jshint ;_;
-
-
- /* CAROUSEL CLASS DEFINITION
-  * ========================= */
-
-  var Carousel = function (element, options) {
-    this.$element = $(element)
-    this.$indicators = this.$element.find('.carousel-indicators')
-    this.options = options
-    this.options.pause == 'hover' && this.$element
-      .on('mouseenter', $.proxy(this.pause, this))
-      .on('mouseleave', $.proxy(this.cycle, this))
-  }
-
-  Carousel.prototype = {
-
-    cycle: function (e) {
-      if (!e) this.paused = false
-      if (this.interval) clearInterval(this.interval);
-      this.options.interval
-        && !this.paused
-        && (this.interval = setInterval($.proxy(this.next, this), this.options.interval))
-      return this
-    }
-
-  , getActiveIndex: function () {
-      this.$active = this.$element.find('.item.active')
-      this.$items = this.$active.parent().children()
-      return this.$items.index(this.$active)
-    }
-
-  , to: function (pos) {
-      var activeIndex = this.getActiveIndex()
-        , that = this
-
-      if (pos > (this.$items.length - 1) || pos < 0) return
-
-      if (this.sliding) {
-        return this.$element.one('slid', function () {
-          that.to(pos)
-        })
-      }
-
-      if (activeIndex == pos) {
-        return this.pause().cycle()
-      }
-
-      return this.slide(pos > activeIndex ? 'next' : 'prev', $(this.$items[pos]))
-    }
-
-  , pause: function (e) {
-      if (!e) this.paused = true
-      if (this.$element.find('.next, .prev').length && $.support.transition.end) {
-        this.$element.trigger($.support.transition.end)
-        this.cycle(true)
-      }
-      clearInterval(this.interval)
-      this.interval = null
-      return this
-    }
-
-  , next: function () {
-      if (this.sliding) return
-      return this.slide('next')
-    }
-
-  , prev: function () {
-      if (this.sliding) return
-      return this.slide('prev')
-    }
-
-  , slide: function (type, next) {
-      var $active = this.$element.find('.item.active')
-        , $next = next || $active[type]()
-        , isCycling = this.interval
-        , direction = type == 'next' ? 'left' : 'right'
-        , fallback  = type == 'next' ? 'first' : 'last'
-        , that = this
-        , e
-
-      this.sliding = true
-
-      isCycling && this.pause()
-
-      $next = $next.length ? $next : this.$element.find('.item')[fallback]()
-
-      e = $.Event('slide', {
-        relatedTarget: $next[0]
-      , direction: direction
-      })
-
-      if ($next.hasClass('active')) return
-
-      if (this.$indicators.length) {
-        this.$indicators.find('.active').removeClass('active')
-        this.$element.one('slid', function () {
-          var $nextIndicator = $(that.$indicators.children()[that.getActiveIndex()])
-          $nextIndicator && $nextIndicator.addClass('active')
-        })
-      }
-
-      if ($.support.transition && this.$element.hasClass('slide')) {
-        this.$element.trigger(e)
-        if (e.isDefaultPrevented()) return
-        $next.addClass(type)
-        $next[0].offsetWidth // force reflow
-        $active.addClass(direction)
-        $next.addClass(direction)
-        this.$element.one($.support.transition.end, function () {
-          $next.removeClass([type, direction].join(' ')).addClass('active')
-          $active.removeClass(['active', direction].join(' '))
-          that.sliding = false
-          setTimeout(function () { that.$element.trigger('slid') }, 0)
-        })
-      } else {
-        this.$element.trigger(e)
-        if (e.isDefaultPrevented()) return
-        $active.removeClass('active')
-        $next.addClass('active')
-        this.sliding = false
-        this.$element.trigger('slid')
-      }
-
-      isCycling && this.cycle()
-
-      return this
-    }
-
-  }
-
-
- /* CAROUSEL PLUGIN DEFINITION
-  * ========================== */
-
-  var old = $.fn.carousel
-
-  $.fn.carousel = function (option) {
-    return this.each(function () {
-      var $this = $(this)
-        , data = $this.data('carousel')
-        , options = $.extend({}, $.fn.carousel.defaults, typeof option == 'object' && option)
-        , action = typeof option == 'string' ? option : options.slide
-      if (!data) $this.data('carousel', (data = new Carousel(this, options)))
-      if (typeof option == 'number') data.to(option)
-      else if (action) data[action]()
-      else if (options.interval) data.pause().cycle()
-    })
-  }
-
-  $.fn.carousel.defaults = {
-    interval: 5000
-  , pause: 'hover'
-  }
-
-  $.fn.carousel.Constructor = Carousel
-
-
- /* CAROUSEL NO CONFLICT
-  * ==================== */
-
-  $.fn.carousel.noConflict = function () {
-    $.fn.carousel = old
-    return this
-  }
-
- /* CAROUSEL DATA-API
-  * ================= */
-
-  $(document).on('click.carousel.data-api', '[data-slide], [data-slide-to]', function (e) {
-    var $this = $(this), href
-      , $target = $($this.attr('data-target') || (href = $this.attr('href')) && href.replace(/.*(?=#[^\s]+$)/, '')) //strip for ie7
-      , options = $.extend({}, $target.data(), $this.data())
-      , slideIndex
-
-    $target.carousel(options)
-
-    if (slideIndex = $this.attr('data-slide-to')) {
-      $target.data('carousel').pause().to(slideIndex).cycle()
-    }
-
-    e.preventDefault()
-  })
-
-}(window.$jqTheme || window.jQuery);
-/* =============================================================
- * bootstrap-collapse.js v2.3.2
- * http://twitter.github.com/bootstrap/javascript.html#collapse
- * =============================================================
- * Copyright 2012 Twitter, Inc.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ============================================================ */
-
-
-!function ($) {
-
-  "use strict"; // jshint ;_;
-
-
- /* COLLAPSE PUBLIC CLASS DEFINITION
-  * ================================ */
-
-  var Collapse = function (element, options) {
-    this.$element = $(element)
-    this.options = $.extend({}, $.fn.collapse.defaults, options)
-
-    if (this.options.parent) {
-      this.$parent = $(this.options.parent)
-    }
-
-    this.options.toggle && this.toggle()
-  }
-
-  Collapse.prototype = {
-
-    constructor: Collapse
-
-  , dimension: function () {
-      var hasWidth = this.$element.hasClass('width')
-      return hasWidth ? 'width' : 'height'
-    }
-
-  , show: function () {
-      var dimension
-        , scroll
-        , actives
-        , hasData
-
-      if (this.transitioning || this.$element.hasClass('in')) return
-
-      dimension = this.dimension()
-      scroll = $.camelCase(['scroll', dimension].join('-'))
-      actives = this.$parent && this.$parent.find('> .accordion-group > .in')
-
-      if (actives && actives.length) {
-        hasData = actives.data('collapse')
-        if (hasData && hasData.transitioning) return
-        actives.collapse('hide')
-        hasData || actives.data('collapse', null)
-      }
-
-      this.$element[dimension](0)
-      this.transition('addClass', $.Event('show'), 'shown')
-      $.support.transition && this.$element[dimension](this.$element[0][scroll])
-    }
-
-  , hide: function () {
-      var dimension
-      if (this.transitioning || !this.$element.hasClass('in')) return
-      dimension = this.dimension()
-      this.reset(this.$element[dimension]())
-      this.transition('removeClass', $.Event('hide'), 'hidden')
-      this.$element[dimension](0)
-    }
-
-  , reset: function (size) {
-      var dimension = this.dimension()
-
-      this.$element
-        .removeClass('collapse')
-        [dimension](size || 'auto')
-        [0].offsetWidth
-
-      this.$element[size !== null ? 'addClass' : 'removeClass']('collapse')
-
-      return this
-    }
-
-  , transition: function (method, startEvent, completeEvent) {
-      var that = this
-        , complete = function () {
-            if (startEvent.type == 'show') that.reset()
-            that.transitioning = 0
-            that.$element.trigger(completeEvent)
-          }
-
-      this.$element.trigger(startEvent)
-
-      if (startEvent.isDefaultPrevented()) return
-
-      this.transitioning = 1
-
-      this.$element[method]('in')
-
-      $.support.transition && this.$element.hasClass('collapse') ?
-        this.$element.one($.support.transition.end, complete) :
-        complete()
-    }
-
-  , toggle: function () {
-      this[this.$element.hasClass('in') ? 'hide' : 'show']()
-    }
-
-  }
-
-
- /* COLLAPSE PLUGIN DEFINITION
-  * ========================== */
-
-  var old = $.fn.collapse
-
-  $.fn.collapse = function (option) {
-    return this.each(function () {
-      var $this = $(this)
-        , data = $this.data('collapse')
-        , options = $.extend({}, $.fn.collapse.defaults, $this.data(), typeof option == 'object' && option)
-      if (!data) $this.data('collapse', (data = new Collapse(this, options)))
-      if (typeof option == 'string') data[option]()
-    })
-  }
-
-  $.fn.collapse.defaults = {
-    toggle: true
-  }
-
-  $.fn.collapse.Constructor = Collapse
-
-
- /* COLLAPSE NO CONFLICT
-  * ==================== */
-
-  $.fn.collapse.noConflict = function () {
-    $.fn.collapse = old
-    return this
-  }
-
-
- /* COLLAPSE DATA-API
-  * ================= */
-
-  $(document).on('click.collapse.data-api', '[data-toggle=collapse]', function (e) {
-    var $this = $(this), href
-      , target = $this.attr('data-target')
-        || e.preventDefault()
-        || (href = $this.attr('href')) && href.replace(/.*(?=#[^\s]+$)/, '') //strip for ie7
-      , option = $(target).data('collapse') ? 'toggle' : $this.data()
-    $this[$(target).hasClass('in') ? 'addClass' : 'removeClass']('collapsed')
-    $(target).collapse(option)
-  })
-
-}(window.$jqTheme || window.jQuery);
-/* ============================================================
- * bootstrap-dropdown.js v2.3.2
- * http://twitter.github.com/bootstrap/javascript.html#dropdowns
- * ============================================================
- * Copyright 2012 Twitter, Inc.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ============================================================ */
-
-
-!function ($) {
-
-  "use strict"; // jshint ;_;
-
-
- /* DROPDOWN CLASS DEFINITION
-  * ========================= */
-
-  var toggle = '[data-toggle=dropdown]'
-    , Dropdown = function (element) {
-        var $el = $(element).on('click.dropdown.data-api', this.toggle)
-        $('html').on('click.dropdown.data-api', function () {
-          $el.parent().removeClass('open')
-        })
-      }
-
-  Dropdown.prototype = {
-
-    constructor: Dropdown
-
-  , toggle: function (e) {
-      var $this = $(this)
-        , $parent
-        , isActive
-
-      if ($this.is('.disabled, :disabled')) return
-
-      $parent = getParent($this)
-
-      isActive = $parent.hasClass('open')
-
-      clearMenus()
-
-      if (!isActive) {
-        if ('ontouchstart' in document.documentElement) {
-          // if mobile we we use a backdrop because click events don't delegate
-          $('<div class="dropdown-backdrop"/>').insertBefore($(this)).on('click', clearMenus)
-        }
-        $parent.toggleClass('open')
-      }
-
-      $this.focus()
-
-      return false
-    }
-
-  , keydown: function (e) {
-      var $this
-        , $items
-        , $active
-        , $parent
-        , isActive
-        , index
-
-      if (!/(38|40|27)/.test(e.keyCode)) return
-
-      $this = $(this)
-
-      e.preventDefault()
-      e.stopPropagation()
-
-      if ($this.is('.disabled, :disabled')) return
-
-      $parent = getParent($this)
-
-      isActive = $parent.hasClass('open')
-
-      if (!isActive || (isActive && e.keyCode == 27)) {
-        if (e.which == 27) $parent.find(toggle).focus()
-        return $this.click()
-      }
-
-      $items = $('[role=menu] li:not(.divider):visible a', $parent)
-
-      if (!$items.length) return
-
-      index = $items.index($items.filter(':focus'))
-
-      if (e.keyCode == 38 && index > 0) index--                                        // up
-      if (e.keyCode == 40 && index < $items.length - 1) index++                        // down
-      if (!~index) index = 0
-
-      $items
-        .eq(index)
-        .focus()
-    }
-
-  }
-
-  function clearMenus() {
-    $('.dropdown-backdrop').remove()
-    $(toggle).each(function () {
-      getParent($(this)).removeClass('open')
-    })
-  }
-
-  function getParent($this) {
-    var selector = $this.attr('data-target')
-      , $parent
-
-    if (!selector) {
-      selector = $this.attr('href')
-      selector = selector && /#/.test(selector) && selector.replace(/.*(?=#[^\s]*$)/, '') //strip for ie7
-    }
-
-    $parent = selector && $(selector)
-
-    if (!$parent || !$parent.length) $parent = $this.parent()
-
-    return $parent
-  }
-
-
-  /* DROPDOWN PLUGIN DEFINITION
-   * ========================== */
-
-  var old = $.fn.dropdown
-
-  $.fn.dropdown = function (option) {
-    return this.each(function () {
-      var $this = $(this)
-        , data = $this.data('dropdown')
-      if (!data) $this.data('dropdown', (data = new Dropdown(this)))
-      if (typeof option == 'string') data[option].call($this)
-    })
-  }
-
-  $.fn.dropdown.Constructor = Dropdown
-
-
- /* DROPDOWN NO CONFLICT
-  * ==================== */
-
-  $.fn.dropdown.noConflict = function () {
-    $.fn.dropdown = old
-    return this
-  }
-
-
-  /* APPLY TO STANDARD DROPDOWN ELEMENTS
-   * =================================== */
-
-  $(document)
-    .on('click.dropdown.data-api', clearMenus)
-    .on('click.dropdown.data-api', '.dropdown form', function (e) { e.stopPropagation() })
-    .on('click.dropdown.data-api'  , toggle, Dropdown.prototype.toggle)
-    .on('keydown.dropdown.data-api', toggle + ', [role=menu]' , Dropdown.prototype.keydown)
-
-}(window.$jqTheme || window.jQuery);
-/* =========================================================
- * bootstrap-modal.js v2.3.2
- * http://twitter.github.com/bootstrap/javascript.html#modals
- * =========================================================
- * Copyright 2012 Twitter, Inc.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ========================================================= */
-
-
-!function ($) {
-
-  "use strict"; // jshint ;_;
-
-
- /* MODAL CLASS DEFINITION
-  * ====================== */
-
-  var Modal = function (element, options) {
-    this.options = options
-    this.$element = $(element)
-      .delegate('[data-dismiss="modal"]', 'click.dismiss.modal', $.proxy(this.hide, this))
-    this.options.remote && this.$element.find('.modal-body').load(this.options.remote)
-  }
-
-  Modal.prototype = {
-
-      constructor: Modal
-
-    , toggle: function () {
-        return this[!this.isShown ? 'show' : 'hide']()
-      }
-
-    , show: function () {
-        var that = this
-          , e = $.Event('show')
-
-        this.$element.trigger(e)
-
-        if (this.isShown || e.isDefaultPrevented()) return
-
-        this.isShown = true
-
-        this.escape()
-
-        this.backdrop(function () {
-          var transition = $.support.transition && that.$element.hasClass('fade')
-
-          if (!that.$element.parent().length) {
-            that.$element.appendTo(document.body) //don't move modals dom position
-          }
-
-          that.$element.show()
-
-          if (transition) {
-            that.$element[0].offsetWidth // force reflow
-          }
-
-          that.$element
-            .addClass('in')
-            .attr('aria-hidden', false)
-
-          that.enforceFocus()
-
-          transition ?
-            that.$element.one($.support.transition.end, function () { that.$element.focus().trigger('shown') }) :
-            that.$element.focus().trigger('shown')
-
-        })
-      }
-
-    , hide: function (e) {
-        e && e.preventDefault()
-
-        var that = this
-
-        e = $.Event('hide')
-
-        this.$element.trigger(e)
-
-        if (!this.isShown || e.isDefaultPrevented()) return
-
-        this.isShown = false
-
-        this.escape()
-
-        $(document).off('focusin.modal')
-
-        this.$element
-          .removeClass('in')
-          .attr('aria-hidden', true)
-
-        $.support.transition && this.$element.hasClass('fade') ?
-          this.hideWithTransition() :
-          this.hideModal()
-      }
-
-    , enforceFocus: function () {
-        var that = this
-        $(document).on('focusin.modal', function (e) {
-          if (that.$element[0] !== e.target && !that.$element.has(e.target).length) {
-            that.$element.focus()
-          }
-        })
-      }
-
-    , escape: function () {
-        var that = this
-        if (this.isShown && this.options.keyboard) {
-          this.$element.on('keyup.dismiss.modal', function ( e ) {
-            e.which == 27 && that.hide()
-          })
-        } else if (!this.isShown) {
-          this.$element.off('keyup.dismiss.modal')
-        }
-      }
-
-    , hideWithTransition: function () {
-        var that = this
-          , timeout = setTimeout(function () {
-              that.$element.off($.support.transition.end)
-              that.hideModal()
-            }, 500)
-
-        this.$element.one($.support.transition.end, function () {
-          clearTimeout(timeout)
-          that.hideModal()
-        })
-      }
-
-    , hideModal: function () {
-        var that = this
-        this.$element.hide()
-        this.backdrop(function () {
-          that.removeBackdrop()
-          that.$element.trigger('hidden')
-        })
-      }
-
-    , removeBackdrop: function () {
-        this.$backdrop && this.$backdrop.remove()
-        this.$backdrop = null
-      }
-
-    , backdrop: function (callback) {
-        var that = this
-          , animate = this.$element.hasClass('fade') ? 'fade' : ''
-
-        if (this.isShown && this.options.backdrop) {
-          var doAnimate = $.support.transition && animate
-
-          this.$backdrop = $('<div class="modal-backdrop ' + animate + '" />')
-            .appendTo(document.body)
-
-          this.$backdrop.click(
-            this.options.backdrop == 'static' ?
-              $.proxy(this.$element[0].focus, this.$element[0])
-            : $.proxy(this.hide, this)
-          )
-
-          if (doAnimate) this.$backdrop[0].offsetWidth // force reflow
-
-          this.$backdrop.addClass('in')
-
-          if (!callback) return
-
-          doAnimate ?
-            this.$backdrop.one($.support.transition.end, callback) :
-            callback()
-
-        } else if (!this.isShown && this.$backdrop) {
-          this.$backdrop.removeClass('in')
-
-          $.support.transition && this.$element.hasClass('fade')?
-            this.$backdrop.one($.support.transition.end, callback) :
-            callback()
-
-        } else if (callback) {
-          callback()
-        }
-      }
-  }
-
-
- /* MODAL PLUGIN DEFINITION
-  * ======================= */
-
-  var old = $.fn.modal
-
-  $.fn.modal = function (option) {
-    return this.each(function () {
-      var $this = $(this)
-        , data = $this.data('modal')
-        , options = $.extend({}, $.fn.modal.defaults, $this.data(), typeof option == 'object' && option)
-      if (!data) $this.data('modal', (data = new Modal(this, options)))
-      if (typeof option == 'string') data[option]()
-      else if (options.show) data.show()
-    })
-  }
-
-  $.fn.modal.defaults = {
-      backdrop: true
-    , keyboard: true
-    , show: true
-  }
-
-  $.fn.modal.Constructor = Modal
-
-
- /* MODAL NO CONFLICT
-  * ================= */
-
-  $.fn.modal.noConflict = function () {
-    $.fn.modal = old
-    return this
-  }
-
-
- /* MODAL DATA-API
-  * ============== */
-
-  $(document).on('click.modal.data-api', '[data-toggle="modal"]', function (e) {
-    var $this = $(this)
-      , href = $this.attr('href')
-      , $target = $($this.attr('data-target') || (href && href.replace(/.*(?=#[^\s]+$)/, ''))) //strip for ie7
-      , option = $target.data('modal') ? 'toggle' : $.extend({ remote:!/#/.test(href) && href }, $target.data(), $this.data())
-
-    e.preventDefault()
-
-    $target
-      .modal(option)
-      .one('hide', function () {
-        $this.focus()
-      })
-  })
-
-}(window.$jqTheme || window.jQuery);
-/* ===========================================================
- * bootstrap-tooltip.js v2.3.2
- * http://twitter.github.com/bootstrap/javascript.html#tooltips
- * Inspired by the original jQuery.tipsy by Jason Frame
- * ===========================================================
- * Copyright 2012 Twitter, Inc.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ========================================================== */
-
-
-!function ($) {
-
-  "use strict"; // jshint ;_;
-
-
- /* TOOLTIP PUBLIC CLASS DEFINITION
-  * =============================== */
-
-  var Tooltip = function (element, options) {
-    this.init('tooltip', element, options)
-  }
-
-  Tooltip.prototype = {
-
-    constructor: Tooltip
-
-  , init: function (type, element, options) {
-      var eventIn
-        , eventOut
-        , triggers
-        , trigger
-        , i
-
-      this.type = type
-      this.$element = $(element)
-      this.options = this.getOptions(options)
-      this.enabled = true
-
-      triggers = this.options.trigger.split(' ')
-
-      for (i = triggers.length; i--;) {
-        trigger = triggers[i]
-        if (trigger == 'click') {
-          this.$element.on('click.' + this.type, this.options.selector, $.proxy(this.toggle, this))
-        } else if (trigger != 'manual') {
-          eventIn = trigger == 'hover' ? 'mouseenter' : 'focus'
-          eventOut = trigger == 'hover' ? 'mouseleave' : 'blur'
-          this.$element.on(eventIn + '.' + this.type, this.options.selector, $.proxy(this.enter, this))
-          this.$element.on(eventOut + '.' + this.type, this.options.selector, $.proxy(this.leave, this))
-        }
-      }
-
-      this.options.selector ?
-        (this._options = $.extend({}, this.options, { trigger: 'manual', selector: '' })) :
-        this.fixTitle()
-    }
-
-  , getOptions: function (options) {
-      options = $.extend({}, $.fn[this.type].defaults, this.$element.data(), options)
-
-      if (options.delay && typeof options.delay == 'number') {
-        options.delay = {
-          show: options.delay
-        , hide: options.delay
-        }
-      }
-
-      return options
-    }
-
-  , enter: function (e) {
-      var defaults = $.fn[this.type].defaults
-        , options = {}
-        , self
-
-      this._options && $.each(this._options, function (key, value) {
-        if (defaults[key] != value) options[key] = value
-      }, this)
-
-      self = $(e.currentTarget)[this.type](options).data(this.type)
-
-      if (!self.options.delay || !self.options.delay.show) return self.show()
-
-      clearTimeout(this.timeout)
-      self.hoverState = 'in'
-      this.timeout = setTimeout(function() {
-        if (self.hoverState == 'in') self.show()
-      }, self.options.delay.show)
-    }
-
-  , leave: function (e) {
-      var self = $(e.currentTarget)[this.type](this._options).data(this.type)
-
-      if (this.timeout) clearTimeout(this.timeout)
-      if (!self.options.delay || !self.options.delay.hide) return self.hide()
-
-      self.hoverState = 'out'
-      this.timeout = setTimeout(function() {
-        if (self.hoverState == 'out') self.hide()
-      }, self.options.delay.hide)
-    }
-
-  , show: function () {
-      var $tip
-        , pos
-        , actualWidth
-        , actualHeight
-        , placement
-        , tp
-        , e = $.Event('show')
-
-      if (this.hasContent() && this.enabled) {
-        this.$element.trigger(e)
-        if (e.isDefaultPrevented()) return
-        $tip = this.tip()
-        this.setContent()
-
-        if (this.options.animation) {
-          $tip.addClass('fade')
-        }
-
-        placement = typeof this.options.placement == 'function' ?
-          this.options.placement.call(this, $tip[0], this.$element[0]) :
-          this.options.placement
-
-        $tip
-          .detach()
-          .css({ top: 0, left: 0, display: 'block' })
-
-        this.options.container ? $tip.appendTo(this.options.container) : $tip.insertAfter(this.$element)
-
-        pos = this.getPosition()
-
-        actualWidth = $tip[0].offsetWidth
-        actualHeight = $tip[0].offsetHeight
-
-        switch (placement) {
-          case 'bottom':
-            tp = {top: pos.top + pos.height, left: pos.left + pos.width / 2 - actualWidth / 2}
-            break
-          case 'top':
-            tp = {top: pos.top - actualHeight, left: pos.left + pos.width / 2 - actualWidth / 2}
-            break
-          case 'left':
-            tp = {top: pos.top + pos.height / 2 - actualHeight / 2, left: pos.left - actualWidth}
-            break
-          case 'right':
-            tp = {top: pos.top + pos.height / 2 - actualHeight / 2, left: pos.left + pos.width}
-            break
-        }
-
-        this.applyPlacement(tp, placement)
-        this.$element.trigger('shown')
-      }
-    }
-
-  , applyPlacement: function(offset, placement){
-      var $tip = this.tip()
-        , width = $tip[0].offsetWidth
-        , height = $tip[0].offsetHeight
-        , actualWidth
-        , actualHeight
-        , delta
-        , replace
-
-      $tip
-        .offset(offset)
-        .addClass(placement)
-        .addClass('in')
-
-      actualWidth = $tip[0].offsetWidth
-      actualHeight = $tip[0].offsetHeight
-
-      if (placement == 'top' && actualHeight != height) {
-        offset.top = offset.top + height - actualHeight
-        replace = true
-      }
-
-      if (placement == 'bottom' || placement == 'top') {
-        delta = 0
-
-        if (offset.left < 0){
-          delta = offset.left * -2
-          offset.left = 0
-          $tip.offset(offset)
-          actualWidth = $tip[0].offsetWidth
-          actualHeight = $tip[0].offsetHeight
-        }
-
-        this.replaceArrow(delta - width + actualWidth, actualWidth, 'left')
-      } else {
-        this.replaceArrow(actualHeight - height, actualHeight, 'top')
-      }
-
-      if (replace) $tip.offset(offset)
-    }
-
-  , replaceArrow: function(delta, dimension, position){
-      this
-        .arrow()
-        .css(position, delta ? (50 * (1 - delta / dimension) + "%") : '')
-    }
-
-  , setContent: function () {
-      var $tip = this.tip()
-        , title = this.getTitle()
-
-      $tip.find('.tooltip-inner')[this.options.html ? 'html' : 'text'](title)
-      $tip.removeClass('fade in top bottom left right')
-    }
-
-  , hide: function () {
-      var that = this
-        , $tip = this.tip()
-        , e = $.Event('hide')
-
-      this.$element.trigger(e)
-      if (e.isDefaultPrevented()) return
-
-      $tip.removeClass('in')
-
-      function removeWithAnimation() {
-        var timeout = setTimeout(function () {
-          $tip.off($.support.transition.end).detach()
-        }, 500)
-
-        $tip.one($.support.transition.end, function () {
-          clearTimeout(timeout)
-          $tip.detach()
-        })
-      }
-
-      $.support.transition && this.$tip.hasClass('fade') ?
-        removeWithAnimation() :
-        $tip.detach()
-
-      this.$element.trigger('hidden')
-
-      return this
-    }
-
-  , fixTitle: function () {
-      var $e = this.$element
-      if ($e.attr('title') || typeof($e.attr('data-original-title')) != 'string') {
-        $e.attr('data-original-title', $e.attr('title') || '').attr('title', '')
-      }
-    }
-
-  , hasContent: function () {
-      return this.getTitle()
-    }
-
-  , getPosition: function () {
-      var el = this.$element[0]
-      return $.extend({}, (typeof el.getBoundingClientRect == 'function') ? el.getBoundingClientRect() : {
-        width: el.offsetWidth
-      , height: el.offsetHeight
-      }, this.$element.offset())
-    }
-
-  , getTitle: function () {
-      var title
-        , $e = this.$element
-        , o = this.options
-
-      title = $e.attr('data-original-title')
-        || (typeof o.title == 'function' ? o.title.call($e[0]) :  o.title)
-
-      return title
-    }
-
-  , tip: function () {
-      return this.$tip = this.$tip || $(this.options.template)
-    }
-
-  , arrow: function(){
-      return this.$arrow = this.$arrow || this.tip().find(".tooltip-arrow")
-    }
-
-  , validate: function () {
-      if (!this.$element[0].parentNode) {
-        this.hide()
-        this.$element = null
-        this.options = null
-      }
-    }
-
-  , enable: function () {
-      this.enabled = true
-    }
-
-  , disable: function () {
-      this.enabled = false
-    }
-
-  , toggleEnabled: function () {
-      this.enabled = !this.enabled
-    }
-
-  , toggle: function (e) {
-      var self = e ? $(e.currentTarget)[this.type](this._options).data(this.type) : this
-      self.tip().hasClass('in') ? self.hide() : self.show()
-    }
-
-  , destroy: function () {
-      this.hide().$element.off('.' + this.type).removeData(this.type)
-    }
-
-  }
-
-
- /* TOOLTIP PLUGIN DEFINITION
-  * ========================= */
-
-  var old = $.fn.tooltip
-
-  $.fn.tooltip = function ( option ) {
-    return this.each(function () {
-      var $this = $(this)
-        , data = $this.data('tooltip')
-        , options = typeof option == 'object' && option
-      if (!data) $this.data('tooltip', (data = new Tooltip(this, options)))
-      if (typeof option == 'string') data[option]()
-    })
-  }
-
-  $.fn.tooltip.Constructor = Tooltip
-
-  $.fn.tooltip.defaults = {
-    animation: true
-  , placement: 'top'
-  , selector: false
-  , template: '<div class="tooltip"><div class="tooltip-arrow"></div><div class="tooltip-inner"></div></div>'
-  , trigger: 'hover focus'
-  , title: ''
-  , delay: 0
-  , html: false
-  , container: false
-  }
-
-
- /* TOOLTIP NO CONFLICT
-  * =================== */
-
-  $.fn.tooltip.noConflict = function () {
-    $.fn.tooltip = old
-    return this
-  }
-
-}(window.$jqTheme || window.jQuery);
-/* ===========================================================
- * bootstrap-popover.js v2.3.2
- * http://twitter.github.com/bootstrap/javascript.html#popovers
- * ===========================================================
- * Copyright 2012 Twitter, Inc.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =========================================================== */
-
-
-!function ($) {
-
-  "use strict"; // jshint ;_;
-
-
- /* POPOVER PUBLIC CLASS DEFINITION
-  * =============================== */
-
-  var Popover = function (element, options) {
-    this.init('popover', element, options)
-  }
-
-
-  /* NOTE: POPOVER EXTENDS BOOTSTRAP-TOOLTIP.js
-     ========================================== */
-
-  Popover.prototype = $.extend({}, $.fn.tooltip.Constructor.prototype, {
-
-    constructor: Popover
-
-  , setContent: function () {
-      var $tip = this.tip()
-        , title = this.getTitle()
-        , content = this.getContent()
-
-      $tip.find('.popover-title')[this.options.html ? 'html' : 'text'](title)
-      $tip.find('.popover-content')[this.options.html ? 'html' : 'text'](content)
-
-      $tip.removeClass('fade top bottom left right in')
-    }
-
-  , hasContent: function () {
-      return this.getTitle() || this.getContent()
-    }
-
-  , getContent: function () {
-      var content
-        , $e = this.$element
-        , o = this.options
-
-      content = (typeof o.content == 'function' ? o.content.call($e[0]) :  o.content)
-        || $e.attr('data-content')
-
-      return content
-    }
-
-  , tip: function () {
-      if (!this.$tip) {
-        this.$tip = $(this.options.template)
-      }
-      return this.$tip
-    }
-
-  , destroy: function () {
-      this.hide().$element.off('.' + this.type).removeData(this.type)
-    }
-
-  })
-
-
- /* POPOVER PLUGIN DEFINITION
-  * ======================= */
-
-  var old = $.fn.popover
-
-  $.fn.popover = function (option) {
-    return this.each(function () {
-      var $this = $(this)
-        , data = $this.data('popover')
-        , options = typeof option == 'object' && option
-      if (!data) $this.data('popover', (data = new Popover(this, options)))
-      if (typeof option == 'string') data[option]()
-    })
-  }
-
-  $.fn.popover.Constructor = Popover
-
-  $.fn.popover.defaults = $.extend({} , $.fn.tooltip.defaults, {
-    placement: 'right'
-  , trigger: 'click'
-  , content: ''
-  , template: '<div class="popover"><div class="arrow"></div><h3 class="popover-title"></h3><div class="popover-content"></div></div>'
-  })
-
-
- /* POPOVER NO CONFLICT
-  * =================== */
-
-  $.fn.popover.noConflict = function () {
-    $.fn.popover = old
-    return this
-  }
-
-}(window.$jqTheme || window.jQuery);
-/* =============================================================
- * bootstrap-scrollspy.js v2.3.2
- * http://twitter.github.com/bootstrap/javascript.html#scrollspy
- * =============================================================
- * Copyright 2012 Twitter, Inc.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ============================================================== */
-
-
-!function ($) {
-
-  "use strict"; // jshint ;_;
-
-
- /* SCROLLSPY CLASS DEFINITION
-  * ========================== */
-
-  function ScrollSpy(element, options) {
-    var process = $.proxy(this.process, this)
-      , $element = $(element).is('body') ? $(window) : $(element)
-      , href
-    this.options = $.extend({}, $.fn.scrollspy.defaults, options)
-    this.$scrollElement = $element.on('scroll.scroll-spy.data-api', process)
-    this.selector = (this.options.target
-      || ((href = $(element).attr('href')) && href.replace(/.*(?=#[^\s]+$)/, '')) //strip for ie7
-      || '') + ' .nav li > a'
-    this.$body = $('body')
-    this.refresh()
-    this.process()
-  }
-
-  ScrollSpy.prototype = {
-
-      constructor: ScrollSpy
-
-    , refresh: function () {
-        var self = this
-          , $targets
-
-        this.offsets = $([])
-        this.targets = $([])
-
-        $targets = this.$body
-          .find(this.selector)
-          .map(function () {
-            var $el = $(this)
-              , href = $el.data('target') || $el.attr('href')
-              , $href = /^#\w/.test(href) && $(href)
-            return ( $href
-              && $href.length
-              && [[ $href.position().top + (!$.isWindow(self.$scrollElement.get(0)) && self.$scrollElement.scrollTop()), href ]] ) || null
-          })
-          .sort(function (a, b) { return a[0] - b[0] })
-          .each(function () {
-            self.offsets.push(this[0])
-            self.targets.push(this[1])
-          })
-      }
-
-    , process: function () {
-        var scrollTop = this.$scrollElement.scrollTop() + this.options.offset
-          , scrollHeight = this.$scrollElement[0].scrollHeight || this.$body[0].scrollHeight
-          , maxScroll = scrollHeight - this.$scrollElement.height()
-          , offsets = this.offsets
-          , targets = this.targets
-          , activeTarget = this.activeTarget
-          , i
-
-        if (scrollTop >= maxScroll) {
-          return activeTarget != (i = targets.last()[0])
-            && this.activate ( i )
-        }
-
-        for (i = offsets.length; i--;) {
-          activeTarget != targets[i]
-            && scrollTop >= offsets[i]
-            && (!offsets[i + 1] || scrollTop <= offsets[i + 1])
-            && this.activate( targets[i] )
-        }
-      }
-
-    , activate: function (target) {
-        var active
-          , selector
-
-        this.activeTarget = target
-
-        $(this.selector)
-          .parent('.active')
-          .removeClass('active')
-
-        selector = this.selector
-          + '[data-target="' + target + '"],'
-          + this.selector + '[href="' + target + '"]'
-
-        active = $(selector)
-          .parent('li')
-          .addClass('active')
-
-        if (active.parent('.dropdown-menu').length)  {
-          active = active.closest('li.dropdown').addClass('active')
-        }
-
-        active.trigger('activate')
-      }
-
-  }
-
-
- /* SCROLLSPY PLUGIN DEFINITION
-  * =========================== */
-
-  var old = $.fn.scrollspy
-
-  $.fn.scrollspy = function (option) {
-    return this.each(function () {
-      var $this = $(this)
-        , data = $this.data('scrollspy')
-        , options = typeof option == 'object' && option
-      if (!data) $this.data('scrollspy', (data = new ScrollSpy(this, options)))
-      if (typeof option == 'string') data[option]()
-    })
-  }
-
-  $.fn.scrollspy.Constructor = ScrollSpy
-
-  $.fn.scrollspy.defaults = {
-    offset: 10
-  }
-
-
- /* SCROLLSPY NO CONFLICT
-  * ===================== */
-
-  $.fn.scrollspy.noConflict = function () {
-    $.fn.scrollspy = old
-    return this
-  }
-
-
- /* SCROLLSPY DATA-API
-  * ================== */
-
-  $(window).on('load', function () {
-    $('[data-spy="scroll"]').each(function () {
-      var $spy = $(this)
-      $spy.scrollspy($spy.data())
-    })
-  })
-
-}(window.$jqTheme || window.jQuery);
-/* ========================================================
- * bootstrap-tab.js v2.3.2
- * http://twitter.github.com/bootstrap/javascript.html#tabs
- * ========================================================
- * Copyright 2012 Twitter, Inc.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ======================================================== */
-
-
-!function ($) {
-
-  "use strict"; // jshint ;_;
-
-
- /* TAB CLASS DEFINITION
-  * ==================== */
-
-  var Tab = function (element) {
-    this.element = $(element)
-  }
-
-  Tab.prototype = {
-
-    constructor: Tab
-
-  , show: function () {
-      var $this = this.element
-        , $ul = $this.closest('ul:not(.dropdown-menu)')
-        , selector = $this.attr('data-target')
-        , previous
-        , $target
-        , e
-
-      if (!selector) {
-        selector = $this.attr('href')
-        selector = selector && selector.replace(/.*(?=#[^\s]*$)/, '') //strip for ie7
-      }
-
-      if ( $this.parent('li').hasClass('active') ) return
-
-      previous = $ul.find('.active:last a')[0]
-
-      e = $.Event('show', {
-        relatedTarget: previous
-      })
-
-      $this.trigger(e)
-
-      if (e.isDefaultPrevented()) return
-
-      $target = $(selector)
-
-      this.activate($this.parent('li'), $ul)
-      this.activate($target, $target.parent(), function () {
-        $this.trigger({
-          type: 'shown'
-        , relatedTarget: previous
-        })
-      })
-    }
-
-  , activate: function ( element, container, callback) {
-      var $active = container.find('> .active')
-        , transition = callback
-            && $.support.transition
-            && $active.hasClass('fade')
-
-      function next() {
-        $active
-          .removeClass('active')
-          .find('> .dropdown-menu > .active')
-          .removeClass('active')
-
-        element.addClass('active')
-
-        if (transition) {
-          element[0].offsetWidth // reflow for transition
-          element.addClass('in')
-        } else {
-          element.removeClass('fade')
-        }
-
-        if ( element.parent('.dropdown-menu') ) {
-          element.closest('li.dropdown').addClass('active')
-        }
-
-        callback && callback()
-      }
-
-      transition ?
-        $active.one($.support.transition.end, next) :
-        next()
-
-      $active.removeClass('in')
-    }
-  }
-
-
- /* TAB PLUGIN DEFINITION
-  * ===================== */
-
-  var old = $.fn.tab
-
-  $.fn.tab = function ( option ) {
-    return this.each(function () {
-      var $this = $(this)
-        , data = $this.data('tab')
-      if (!data) $this.data('tab', (data = new Tab(this)))
-      if (typeof option == 'string') data[option]()
-    })
-  }
-
-  $.fn.tab.Constructor = Tab
-
-
- /* TAB NO CONFLICT
-  * =============== */
-
-  $.fn.tab.noConflict = function () {
-    $.fn.tab = old
-    return this
-  }
-
-
- /* TAB DATA-API
-  * ============ */
-
-  $(document).on('click.tab.data-api', '[data-toggle="tab"], [data-toggle="pill"]', function (e) {
-    e.preventDefault()
-    $(this).tab('show')
-  })
-
-}(window.$jqTheme || window.jQuery);
-/* =============================================================
- * bootstrap-typeahead.js v2.3.2
- * http://twitter.github.com/bootstrap/javascript.html#typeahead
- * =============================================================
- * Copyright 2012 Twitter, Inc.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * ============================================================ */
-
-
-!function($){
-
-  "use strict"; // jshint ;_;
-
-
- /* TYPEAHEAD PUBLIC CLASS DEFINITION
-  * ================================= */
-
-  var Typeahead = function (element, options) {
-    this.$element = $(element)
-    this.options = $.extend({}, $.fn.typeahead.defaults, options)
-    this.matcher = this.options.matcher || this.matcher
-    this.sorter = this.options.sorter || this.sorter
-    this.highlighter = this.options.highlighter || this.highlighter
-    this.updater = this.options.updater || this.updater
-    this.source = this.options.source
-    this.$menu = $(this.options.menu)
-    this.shown = false
-    this.listen()
-  }
-
-  Typeahead.prototype = {
-
-    constructor: Typeahead
-
-  , select: function () {
-      var val = this.$menu.find('.active').attr('data-value')
-      this.$element
-        .val(this.updater(val))
-        .change()
-      return this.hide()
-    }
-
-  , updater: function (item) {
-      return item
-    }
-
-  , show: function () {
-      var pos = $.extend({}, this.$element.position(), {
-        height: this.$element[0].offsetHeight
-      })
-
-      this.$menu
-        .insertAfter(this.$element)
-        .css({
-          top: pos.top + pos.height
-        , left: pos.left
-        })
-        .show()
-
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-
-
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-  $.fn.affix.Constructor = Affix
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-      $spy.affix(data)
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-
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diff --git a/docs/_static/bootstrap-2.3.2/js/bootstrap.min.js b/docs/_static/bootstrap-2.3.2/js/bootstrap.min.js
deleted file mode 100644
index 3a3394bd4..000000000
--- a/docs/_static/bootstrap-2.3.2/js/bootstrap.min.js
+++ /dev/null
@@ -1,6 +0,0 @@
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deleted file mode 100644
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filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#ffe0e0e0', GradientType=0);\n  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n  background-repeat: repeat-x;\n  border-color: #dbdbdb;\n  text-shadow: 0 1px 0 #fff;\n  border-color: #ccc;\n}\n.btn-default:hover,\n.btn-default:focus {\n  background-color: #e0e0e0;\n  background-position: 0 -15px;\n}\n.btn-default:active,\n.btn-default.active {\n  background-color: #e0e0e0;\n  border-color: #dbdbdb;\n}\n.btn-default.disabled,\n.btn-default[disabled],\nfieldset[disabled] .btn-default,\n.btn-default.disabled:hover,\n.btn-default[disabled]:hover,\nfieldset[disabled] .btn-default:hover,\n.btn-default.disabled:focus,\n.btn-default[disabled]:focus,\nfieldset[disabled] .btn-default:focus,\n.btn-default.disabled.focus,\n.btn-default[disabled].focus,\nfieldset[disabled] .btn-default.focus,\n.btn-default.disabled:active,\n.btn-default[disabled]:active,\nfieldset[disabled] .btn-default:active,\n.btn-default.disabled.active,\n.btn-default[disabled].active,\nfieldset[disabled] .btn-default.active {\n  background-color: #e0e0e0;\n  background-image: none;\n}\n.btn-primary {\n  background-image: -webkit-linear-gradient(top, #337ab7 0%, #265a88 100%);\n  background-image: -o-linear-gradient(top, #337ab7 0%, #265a88 100%);\n  background-image: linear-gradient(to bottom, #337ab7 0%, #265a88 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff265a88', GradientType=0);\n  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n  background-repeat: repeat-x;\n  border-color: #245580;\n}\n.btn-primary:hover,\n.btn-primary:focus {\n  background-color: #265a88;\n  background-position: 0 -15px;\n}\n.btn-primary:active,\n.btn-primary.active {\n  background-color: #265a88;\n  border-color: #245580;\n}\n.btn-primary.disabled,\n.btn-primary[disabled],\nfieldset[disabled] .btn-primary,\n.btn-primary.disabled:hover,\n.btn-primary[disabled]:hover,\nfieldset[disabled] .btn-primary:hover,\n.btn-primary.disabled:focus,\n.btn-primary[disabled]:focus,\nfieldset[disabled] .btn-primary:focus,\n.btn-primary.disabled.focus,\n.btn-primary[disabled].focus,\nfieldset[disabled] .btn-primary.focus,\n.btn-primary.disabled:active,\n.btn-primary[disabled]:active,\nfieldset[disabled] .btn-primary:active,\n.btn-primary.disabled.active,\n.btn-primary[disabled].active,\nfieldset[disabled] .btn-primary.active {\n  background-color: #265a88;\n  background-image: none;\n}\n.btn-success {\n  background-image: -webkit-linear-gradient(top, #5cb85c 0%, #419641 100%);\n  background-image: -o-linear-gradient(top, #5cb85c 0%, #419641 100%);\n  background-image: linear-gradient(to bottom, #5cb85c 0%, #419641 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5cb85c', endColorstr='#ff419641', GradientType=0);\n  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n  background-repeat: repeat-x;\n  border-color: #3e8f3e;\n}\n.btn-success:hover,\n.btn-success:focus {\n  background-color: #419641;\n  background-position: 0 -15px;\n}\n.btn-success:active,\n.btn-success.active {\n  background-color: #419641;\n  border-color: #3e8f3e;\n}\n.btn-success.disabled,\n.btn-success[disabled],\nfieldset[disabled] .btn-success,\n.btn-success.disabled:hover,\n.btn-success[disabled]:hover,\nfieldset[disabled] .btn-success:hover,\n.btn-success.disabled:focus,\n.btn-success[disabled]:focus,\nfieldset[disabled] .btn-success:focus,\n.btn-success.disabled.focus,\n.btn-success[disabled].focus,\nfieldset[disabled] .btn-success.focus,\n.btn-success.disabled:active,\n.btn-success[disabled]:active,\nfieldset[disabled] .btn-success:active,\n.btn-success.disabled.active,\n.btn-success[disabled].active,\nfieldset[disabled] .btn-success.active {\n  background-color: #419641;\n  background-image: none;\n}\n.btn-info {\n  background-image: -webkit-linear-gradient(top, #5bc0de 0%, #2aabd2 100%);\n  background-image: -o-linear-gradient(top, #5bc0de 0%, #2aabd2 100%);\n  background-image: linear-gradient(to bottom, #5bc0de 0%, #2aabd2 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff2aabd2', GradientType=0);\n  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n  background-repeat: repeat-x;\n  border-color: #28a4c9;\n}\n.btn-info:hover,\n.btn-info:focus {\n  background-color: #2aabd2;\n  background-position: 0 -15px;\n}\n.btn-info:active,\n.btn-info.active {\n  background-color: #2aabd2;\n  border-color: #28a4c9;\n}\n.btn-info.disabled,\n.btn-info[disabled],\nfieldset[disabled] .btn-info,\n.btn-info.disabled:hover,\n.btn-info[disabled]:hover,\nfieldset[disabled] .btn-info:hover,\n.btn-info.disabled:focus,\n.btn-info[disabled]:focus,\nfieldset[disabled] .btn-info:focus,\n.btn-info.disabled.focus,\n.btn-info[disabled].focus,\nfieldset[disabled] .btn-info.focus,\n.btn-info.disabled:active,\n.btn-info[disabled]:active,\nfieldset[disabled] .btn-info:active,\n.btn-info.disabled.active,\n.btn-info[disabled].active,\nfieldset[disabled] .btn-info.active {\n  background-color: #2aabd2;\n  background-image: none;\n}\n.btn-warning {\n  background-image: -webkit-linear-gradient(top, #f0ad4e 0%, #eb9316 100%);\n  background-image: -o-linear-gradient(top, #f0ad4e 0%, #eb9316 100%);\n  background-image: linear-gradient(to bottom, #f0ad4e 0%, #eb9316 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff0ad4e', endColorstr='#ffeb9316', GradientType=0);\n  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n  background-repeat: repeat-x;\n  border-color: #e38d13;\n}\n.btn-warning:hover,\n.btn-warning:focus {\n  background-color: #eb9316;\n  background-position: 0 -15px;\n}\n.btn-warning:active,\n.btn-warning.active {\n  background-color: #eb9316;\n  border-color: #e38d13;\n}\n.btn-warning.disabled,\n.btn-warning[disabled],\nfieldset[disabled] .btn-warning,\n.btn-warning.disabled:hover,\n.btn-warning[disabled]:hover,\nfieldset[disabled] .btn-warning:hover,\n.btn-warning.disabled:focus,\n.btn-warning[disabled]:focus,\nfieldset[disabled] .btn-warning:focus,\n.btn-warning.disabled.focus,\n.btn-warning[disabled].focus,\nfieldset[disabled] .btn-warning.focus,\n.btn-warning.disabled:active,\n.btn-warning[disabled]:active,\nfieldset[disabled] .btn-warning:active,\n.btn-warning.disabled.active,\n.btn-warning[disabled].active,\nfieldset[disabled] .btn-warning.active {\n  background-color: #eb9316;\n  background-image: none;\n}\n.btn-danger {\n  background-image: -webkit-linear-gradient(top, #d9534f 0%, #c12e2a 100%);\n  background-image: -o-linear-gradient(top, #d9534f 0%, #c12e2a 100%);\n  background-image: linear-gradient(to bottom, #d9534f 0%, #c12e2a 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9534f', endColorstr='#ffc12e2a', GradientType=0);\n  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n  background-repeat: repeat-x;\n  border-color: #b92c28;\n}\n.btn-danger:hover,\n.btn-danger:focus {\n  background-color: #c12e2a;\n  background-position: 0 -15px;\n}\n.btn-danger:active,\n.btn-danger.active {\n  background-color: #c12e2a;\n  border-color: #b92c28;\n}\n.btn-danger.disabled,\n.btn-danger[disabled],\nfieldset[disabled] .btn-danger,\n.btn-danger.disabled:hover,\n.btn-danger[disabled]:hover,\nfieldset[disabled] .btn-danger:hover,\n.btn-danger.disabled:focus,\n.btn-danger[disabled]:focus,\nfieldset[disabled] .btn-danger:focus,\n.btn-danger.disabled.focus,\n.btn-danger[disabled].focus,\nfieldset[disabled] .btn-danger.focus,\n.btn-danger.disabled:active,\n.btn-danger[disabled]:active,\nfieldset[disabled] .btn-danger:active,\n.btn-danger.disabled.active,\n.btn-danger[disabled].active,\nfieldset[disabled] .btn-danger.active {\n  background-color: #c12e2a;\n  background-image: none;\n}\n.thumbnail,\n.img-thumbnail {\n  -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075);\n  box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075);\n}\n.dropdown-menu > li > a:hover,\n.dropdown-menu > li > a:focus {\n  background-image: -webkit-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);\n  background-image: -o-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);\n  background-image: linear-gradient(to bottom, #f5f5f5 0%, #e8e8e8 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#ffe8e8e8', GradientType=0);\n  background-color: #e8e8e8;\n}\n.dropdown-menu > .active > a,\n.dropdown-menu > .active > a:hover,\n.dropdown-menu > .active > a:focus {\n  background-image: -webkit-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n  background-image: -o-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n  background-image: linear-gradient(to bottom, #337ab7 0%, #2e6da4 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);\n  background-color: #2e6da4;\n}\n.navbar-default {\n  background-image: -webkit-linear-gradient(top, #ffffff 0%, #f8f8f8 100%);\n  background-image: -o-linear-gradient(top, #ffffff 0%, #f8f8f8 100%);\n  background-image: linear-gradient(to bottom, #ffffff 0%, #f8f8f8 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#fff8f8f8', GradientType=0);\n  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n  border-radius: 4px;\n  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 5px rgba(0, 0, 0, 0.075);\n  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 5px rgba(0, 0, 0, 0.075);\n}\n.navbar-default .navbar-nav > .open > a,\n.navbar-default .navbar-nav > .active > a {\n  background-image: -webkit-linear-gradient(top, #dbdbdb 0%, #e2e2e2 100%);\n  background-image: -o-linear-gradient(top, #dbdbdb 0%, #e2e2e2 100%);\n  background-image: linear-gradient(to bottom, #dbdbdb 0%, #e2e2e2 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdbdbdb', endColorstr='#ffe2e2e2', GradientType=0);\n  -webkit-box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.075);\n  box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.075);\n}\n.navbar-brand,\n.navbar-nav > li > a {\n  text-shadow: 0 1px 0 rgba(255, 255, 255, 0.25);\n}\n.navbar-inverse {\n  background-image: -webkit-linear-gradient(top, #3c3c3c 0%, #222 100%);\n  background-image: -o-linear-gradient(top, #3c3c3c 0%, #222 100%);\n  background-image: linear-gradient(to bottom, #3c3c3c 0%, #222 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff3c3c3c', endColorstr='#ff222222', GradientType=0);\n  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n  border-radius: 4px;\n}\n.navbar-inverse .navbar-nav > .open > a,\n.navbar-inverse .navbar-nav > .active > a {\n  background-image: -webkit-linear-gradient(top, #080808 0%, #0f0f0f 100%);\n  background-image: -o-linear-gradient(top, #080808 0%, #0f0f0f 100%);\n  background-image: linear-gradient(to bottom, #080808 0%, #0f0f0f 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff080808', endColorstr='#ff0f0f0f', GradientType=0);\n  -webkit-box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.25);\n  box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.25);\n}\n.navbar-inverse .navbar-brand,\n.navbar-inverse .navbar-nav > li > a {\n  text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25);\n}\n.navbar-static-top,\n.navbar-fixed-top,\n.navbar-fixed-bottom {\n  border-radius: 0;\n}\n@media (max-width: 767px) {\n  .navbar .navbar-nav .open .dropdown-menu > .active > a,\n  .navbar .navbar-nav .open .dropdown-menu > .active > a:hover,\n  .navbar .navbar-nav .open .dropdown-menu > .active > a:focus {\n    color: #fff;\n    background-image: -webkit-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n    background-image: -o-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n    background-image: linear-gradient(to bottom, #337ab7 0%, #2e6da4 100%);\n    background-repeat: repeat-x;\n    filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);\n  }\n}\n.alert {\n  text-shadow: 0 1px 0 rgba(255, 255, 255, 0.2);\n  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.25), 0 1px 2px rgba(0, 0, 0, 0.05);\n  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.25), 0 1px 2px rgba(0, 0, 0, 0.05);\n}\n.alert-success {\n  background-image: -webkit-linear-gradient(top, #dff0d8 0%, #c8e5bc 100%);\n  background-image: -o-linear-gradient(top, #dff0d8 0%, #c8e5bc 100%);\n  background-image: linear-gradient(to bottom, #dff0d8 0%, #c8e5bc 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdff0d8', endColorstr='#ffc8e5bc', GradientType=0);\n  border-color: #b2dba1;\n}\n.alert-info {\n  background-image: -webkit-linear-gradient(top, #d9edf7 0%, #b9def0 100%);\n  background-image: -o-linear-gradient(top, #d9edf7 0%, #b9def0 100%);\n  background-image: linear-gradient(to bottom, #d9edf7 0%, #b9def0 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9edf7', endColorstr='#ffb9def0', GradientType=0);\n  border-color: #9acfea;\n}\n.alert-warning {\n  background-image: -webkit-linear-gradient(top, #fcf8e3 0%, #f8efc0 100%);\n  background-image: -o-linear-gradient(top, #fcf8e3 0%, #f8efc0 100%);\n  background-image: linear-gradient(to bottom, #fcf8e3 0%, #f8efc0 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffcf8e3', endColorstr='#fff8efc0', GradientType=0);\n  border-color: #f5e79e;\n}\n.alert-danger {\n  background-image: -webkit-linear-gradient(top, #f2dede 0%, #e7c3c3 100%);\n  background-image: -o-linear-gradient(top, #f2dede 0%, #e7c3c3 100%);\n  background-image: linear-gradient(to bottom, #f2dede 0%, #e7c3c3 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2dede', endColorstr='#ffe7c3c3', GradientType=0);\n  border-color: #dca7a7;\n}\n.progress {\n  background-image: -webkit-linear-gradient(top, #ebebeb 0%, #f5f5f5 100%);\n  background-image: -o-linear-gradient(top, #ebebeb 0%, #f5f5f5 100%);\n  background-image: linear-gradient(to bottom, #ebebeb 0%, #f5f5f5 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffebebeb', endColorstr='#fff5f5f5', GradientType=0);\n}\n.progress-bar {\n  background-image: -webkit-linear-gradient(top, #337ab7 0%, #286090 100%);\n  background-image: -o-linear-gradient(top, #337ab7 0%, #286090 100%);\n  background-image: linear-gradient(to bottom, #337ab7 0%, #286090 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff286090', GradientType=0);\n}\n.progress-bar-success {\n  background-image: -webkit-linear-gradient(top, #5cb85c 0%, #449d44 100%);\n  background-image: -o-linear-gradient(top, #5cb85c 0%, #449d44 100%);\n  background-image: linear-gradient(to bottom, #5cb85c 0%, #449d44 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5cb85c', endColorstr='#ff449d44', GradientType=0);\n}\n.progress-bar-info {\n  background-image: -webkit-linear-gradient(top, #5bc0de 0%, #31b0d5 100%);\n  background-image: -o-linear-gradient(top, #5bc0de 0%, #31b0d5 100%);\n  background-image: linear-gradient(to bottom, #5bc0de 0%, #31b0d5 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff31b0d5', GradientType=0);\n}\n.progress-bar-warning {\n  background-image: -webkit-linear-gradient(top, #f0ad4e 0%, #ec971f 100%);\n  background-image: -o-linear-gradient(top, #f0ad4e 0%, #ec971f 100%);\n  background-image: linear-gradient(to bottom, #f0ad4e 0%, #ec971f 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff0ad4e', endColorstr='#ffec971f', GradientType=0);\n}\n.progress-bar-danger {\n  background-image: -webkit-linear-gradient(top, #d9534f 0%, #c9302c 100%);\n  background-image: -o-linear-gradient(top, #d9534f 0%, #c9302c 100%);\n  background-image: linear-gradient(to bottom, #d9534f 0%, #c9302c 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9534f', endColorstr='#ffc9302c', GradientType=0);\n}\n.progress-bar-striped {\n  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n}\n.list-group {\n  border-radius: 4px;\n  -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075);\n  box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075);\n}\n.list-group-item.active,\n.list-group-item.active:hover,\n.list-group-item.active:focus {\n  text-shadow: 0 -1px 0 #286090;\n  background-image: -webkit-linear-gradient(top, #337ab7 0%, #2b669a 100%);\n  background-image: -o-linear-gradient(top, #337ab7 0%, #2b669a 100%);\n  background-image: linear-gradient(to bottom, #337ab7 0%, #2b669a 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2b669a', GradientType=0);\n  border-color: #2b669a;\n}\n.list-group-item.active .badge,\n.list-group-item.active:hover .badge,\n.list-group-item.active:focus .badge {\n  text-shadow: none;\n}\n.panel {\n  -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);\n  box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);\n}\n.panel-default > .panel-heading {\n  background-image: -webkit-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);\n  background-image: -o-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);\n  background-image: linear-gradient(to bottom, #f5f5f5 0%, #e8e8e8 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#ffe8e8e8', GradientType=0);\n}\n.panel-primary > .panel-heading {\n  background-image: -webkit-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n  background-image: -o-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n  background-image: linear-gradient(to bottom, #337ab7 0%, #2e6da4 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);\n}\n.panel-success > .panel-heading {\n  background-image: -webkit-linear-gradient(top, #dff0d8 0%, #d0e9c6 100%);\n  background-image: -o-linear-gradient(top, #dff0d8 0%, #d0e9c6 100%);\n  background-image: linear-gradient(to bottom, #dff0d8 0%, #d0e9c6 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdff0d8', endColorstr='#ffd0e9c6', GradientType=0);\n}\n.panel-info > .panel-heading {\n  background-image: -webkit-linear-gradient(top, #d9edf7 0%, #c4e3f3 100%);\n  background-image: -o-linear-gradient(top, #d9edf7 0%, #c4e3f3 100%);\n  background-image: linear-gradient(to bottom, #d9edf7 0%, #c4e3f3 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9edf7', endColorstr='#ffc4e3f3', GradientType=0);\n}\n.panel-warning > .panel-heading {\n  background-image: -webkit-linear-gradient(top, #fcf8e3 0%, #faf2cc 100%);\n  background-image: -o-linear-gradient(top, #fcf8e3 0%, #faf2cc 100%);\n  background-image: linear-gradient(to bottom, #fcf8e3 0%, #faf2cc 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffcf8e3', endColorstr='#fffaf2cc', GradientType=0);\n}\n.panel-danger > .panel-heading {\n  background-image: -webkit-linear-gradient(top, #f2dede 0%, #ebcccc 100%);\n  background-image: -o-linear-gradient(top, #f2dede 0%, #ebcccc 100%);\n  background-image: linear-gradient(to bottom, #f2dede 0%, #ebcccc 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2dede', endColorstr='#ffebcccc', GradientType=0);\n}\n.well {\n  background-image: -webkit-linear-gradient(top, #e8e8e8 0%, #f5f5f5 100%);\n  background-image: -o-linear-gradient(top, #e8e8e8 0%, #f5f5f5 100%);\n  background-image: linear-gradient(to bottom, #e8e8e8 0%, #f5f5f5 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffe8e8e8', endColorstr='#fff5f5f5', GradientType=0);\n  border-color: #dcdcdc;\n  -webkit-box-shadow: inset 0 1px 3px rgba(0, 0, 0, 0.05), 0 1px 0 rgba(255, 255, 255, 0.1);\n  box-shadow: inset 0 1px 3px rgba(0, 0, 0, 0.05), 0 1px 0 rgba(255, 255, 255, 0.1);\n}\n/*# sourceMappingURL=bootstrap-theme.css.map */","/*!\n * Bootstrap v3.3.7 (http://getbootstrap.com)\n * Copyright 2011-2016 Twitter, Inc.\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)\n */\n\n//\n// Load core variables and mixins\n// --------------------------------------------------\n\n@import \"variables.less\";\n@import \"mixins.less\";\n\n\n//\n// Buttons\n// --------------------------------------------------\n\n// Common styles\n.btn-default,\n.btn-primary,\n.btn-success,\n.btn-info,\n.btn-warning,\n.btn-danger {\n  text-shadow: 0 -1px 0 rgba(0,0,0,.2);\n  @shadow: inset 0 1px 0 rgba(255,255,255,.15), 0 1px 1px rgba(0,0,0,.075);\n  .box-shadow(@shadow);\n\n  // Reset the shadow\n  &:active,\n  &.active {\n    .box-shadow(inset 0 3px 5px rgba(0,0,0,.125));\n  }\n\n  &.disabled,\n  &[disabled],\n  fieldset[disabled] & {\n    .box-shadow(none);\n  }\n\n  .badge {\n    text-shadow: none;\n  }\n}\n\n// Mixin for generating new styles\n.btn-styles(@btn-color: #555) {\n  #gradient > .vertical(@start-color: @btn-color; @end-color: darken(@btn-color, 12%));\n  .reset-filter(); // Disable gradients for IE9 because filter bleeds through rounded corners; see https://github.com/twbs/bootstrap/issues/10620\n  background-repeat: repeat-x;\n  border-color: darken(@btn-color, 14%);\n\n  &:hover,\n  &:focus  {\n    background-color: darken(@btn-color, 12%);\n    background-position: 0 -15px;\n  }\n\n  &:active,\n  &.active {\n    background-color: darken(@btn-color, 12%);\n    border-color: darken(@btn-color, 14%);\n  }\n\n  &.disabled,\n  &[disabled],\n  fieldset[disabled] & {\n    &,\n    &:hover,\n    &:focus,\n    &.focus,\n    &:active,\n    &.active {\n      background-color: darken(@btn-color, 12%);\n      background-image: none;\n    }\n  }\n}\n\n// Common styles\n.btn {\n  // Remove the gradient for the pressed/active state\n  &:active,\n  &.active {\n    background-image: none;\n  }\n}\n\n// Apply the mixin to the buttons\n.btn-default { .btn-styles(@btn-default-bg); text-shadow: 0 1px 0 #fff; border-color: #ccc; }\n.btn-primary { .btn-styles(@btn-primary-bg); }\n.btn-success { .btn-styles(@btn-success-bg); }\n.btn-info    { .btn-styles(@btn-info-bg); }\n.btn-warning { .btn-styles(@btn-warning-bg); }\n.btn-danger  { .btn-styles(@btn-danger-bg); }\n\n\n//\n// Images\n// --------------------------------------------------\n\n.thumbnail,\n.img-thumbnail {\n  .box-shadow(0 1px 2px rgba(0,0,0,.075));\n}\n\n\n//\n// Dropdowns\n// --------------------------------------------------\n\n.dropdown-menu > li > a:hover,\n.dropdown-menu > li > a:focus {\n  #gradient > .vertical(@start-color: @dropdown-link-hover-bg; @end-color: darken(@dropdown-link-hover-bg, 5%));\n  background-color: darken(@dropdown-link-hover-bg, 5%);\n}\n.dropdown-menu > .active > a,\n.dropdown-menu > .active > a:hover,\n.dropdown-menu > .active > a:focus {\n  #gradient > .vertical(@start-color: @dropdown-link-active-bg; @end-color: darken(@dropdown-link-active-bg, 5%));\n  background-color: darken(@dropdown-link-active-bg, 5%);\n}\n\n\n//\n// Navbar\n// --------------------------------------------------\n\n// Default navbar\n.navbar-default {\n  #gradient > .vertical(@start-color: lighten(@navbar-default-bg, 10%); @end-color: @navbar-default-bg);\n  .reset-filter(); // Remove gradient in IE<10 to fix bug where dropdowns don't get triggered\n  border-radius: @navbar-border-radius;\n  @shadow: inset 0 1px 0 rgba(255,255,255,.15), 0 1px 5px rgba(0,0,0,.075);\n  .box-shadow(@shadow);\n\n  .navbar-nav > .open > a,\n  .navbar-nav > .active > a {\n    #gradient > .vertical(@start-color: darken(@navbar-default-link-active-bg, 5%); @end-color: darken(@navbar-default-link-active-bg, 2%));\n    .box-shadow(inset 0 3px 9px rgba(0,0,0,.075));\n  }\n}\n.navbar-brand,\n.navbar-nav > li > a {\n  text-shadow: 0 1px 0 rgba(255,255,255,.25);\n}\n\n// Inverted navbar\n.navbar-inverse {\n  #gradient > .vertical(@start-color: lighten(@navbar-inverse-bg, 10%); @end-color: @navbar-inverse-bg);\n  .reset-filter(); // Remove gradient in IE<10 to fix bug where dropdowns don't get triggered; see https://github.com/twbs/bootstrap/issues/10257\n  border-radius: @navbar-border-radius;\n  .navbar-nav > .open > a,\n  .navbar-nav > .active > a {\n    #gradient > .vertical(@start-color: @navbar-inverse-link-active-bg; @end-color: lighten(@navbar-inverse-link-active-bg, 2.5%));\n    .box-shadow(inset 0 3px 9px rgba(0,0,0,.25));\n  }\n\n  .navbar-brand,\n  .navbar-nav > li > a {\n    text-shadow: 0 -1px 0 rgba(0,0,0,.25);\n  }\n}\n\n// Undo rounded corners in static and fixed navbars\n.navbar-static-top,\n.navbar-fixed-top,\n.navbar-fixed-bottom {\n  border-radius: 0;\n}\n\n// Fix active state of dropdown items in collapsed mode\n@media (max-width: @grid-float-breakpoint-max) {\n  .navbar .navbar-nav .open .dropdown-menu > .active > a {\n    &,\n    &:hover,\n    &:focus {\n      color: #fff;\n      #gradient > .vertical(@start-color: @dropdown-link-active-bg; @end-color: darken(@dropdown-link-active-bg, 5%));\n    }\n  }\n}\n\n\n//\n// Alerts\n// --------------------------------------------------\n\n// Common styles\n.alert {\n  text-shadow: 0 1px 0 rgba(255,255,255,.2);\n  @shadow: inset 0 1px 0 rgba(255,255,255,.25), 0 1px 2px rgba(0,0,0,.05);\n  .box-shadow(@shadow);\n}\n\n// Mixin for generating new styles\n.alert-styles(@color) {\n  #gradient > .vertical(@start-color: @color; @end-color: darken(@color, 7.5%));\n  border-color: darken(@color, 15%);\n}\n\n// Apply the mixin to the alerts\n.alert-success    { .alert-styles(@alert-success-bg); }\n.alert-info       { .alert-styles(@alert-info-bg); }\n.alert-warning    { .alert-styles(@alert-warning-bg); }\n.alert-danger     { .alert-styles(@alert-danger-bg); }\n\n\n//\n// Progress bars\n// --------------------------------------------------\n\n// Give the progress background some depth\n.progress {\n  #gradient > .vertical(@start-color: darken(@progress-bg, 4%); @end-color: @progress-bg)\n}\n\n// Mixin for generating new styles\n.progress-bar-styles(@color) {\n  #gradient > .vertical(@start-color: @color; @end-color: darken(@color, 10%));\n}\n\n// Apply the mixin to the progress bars\n.progress-bar            { .progress-bar-styles(@progress-bar-bg); }\n.progress-bar-success    { .progress-bar-styles(@progress-bar-success-bg); }\n.progress-bar-info       { .progress-bar-styles(@progress-bar-info-bg); }\n.progress-bar-warning    { .progress-bar-styles(@progress-bar-warning-bg); }\n.progress-bar-danger     { .progress-bar-styles(@progress-bar-danger-bg); }\n\n// Reset the striped class because our mixins don't do multiple gradients and\n// the above custom styles override the new `.progress-bar-striped` in v3.2.0.\n.progress-bar-striped {\n  #gradient > .striped();\n}\n\n\n//\n// List groups\n// --------------------------------------------------\n\n.list-group {\n  border-radius: @border-radius-base;\n  .box-shadow(0 1px 2px rgba(0,0,0,.075));\n}\n.list-group-item.active,\n.list-group-item.active:hover,\n.list-group-item.active:focus {\n  text-shadow: 0 -1px 0 darken(@list-group-active-bg, 10%);\n  #gradient > .vertical(@start-color: @list-group-active-bg; @end-color: darken(@list-group-active-bg, 7.5%));\n  border-color: darken(@list-group-active-border, 7.5%);\n\n  .badge {\n    text-shadow: none;\n  }\n}\n\n\n//\n// Panels\n// --------------------------------------------------\n\n// Common styles\n.panel {\n  .box-shadow(0 1px 2px rgba(0,0,0,.05));\n}\n\n// Mixin for generating new styles\n.panel-heading-styles(@color) {\n  #gradient > .vertical(@start-color: @color; @end-color: darken(@color, 5%));\n}\n\n// Apply the mixin to the panel headings only\n.panel-default > .panel-heading   { .panel-heading-styles(@panel-default-heading-bg); }\n.panel-primary > .panel-heading   { .panel-heading-styles(@panel-primary-heading-bg); }\n.panel-success > .panel-heading   { .panel-heading-styles(@panel-success-heading-bg); }\n.panel-info > .panel-heading      { .panel-heading-styles(@panel-info-heading-bg); }\n.panel-warning > .panel-heading   { .panel-heading-styles(@panel-warning-heading-bg); }\n.panel-danger > .panel-heading    { .panel-heading-styles(@panel-danger-heading-bg); }\n\n\n//\n// Wells\n// --------------------------------------------------\n\n.well {\n  #gradient > .vertical(@start-color: darken(@well-bg, 5%); @end-color: @well-bg);\n  border-color: darken(@well-bg, 10%);\n  @shadow: inset 0 1px 3px rgba(0,0,0,.05), 0 1px 0 rgba(255,255,255,.1);\n  .box-shadow(@shadow);\n}\n","// Vendor Prefixes\n//\n// All vendor mixins are deprecated as of v3.2.0 due to the introduction of\n// Autoprefixer in our Gruntfile. They have been removed in v4.\n\n// - Animations\n// - Backface visibility\n// - Box shadow\n// - Box sizing\n// - Content columns\n// - Hyphens\n// - Placeholder text\n// - Transformations\n// - Transitions\n// - User Select\n\n\n// Animations\n.animation(@animation) {\n  -webkit-animation: @animation;\n       -o-animation: @animation;\n          animation: @animation;\n}\n.animation-name(@name) {\n  -webkit-animation-name: @name;\n          animation-name: @name;\n}\n.animation-duration(@duration) {\n  -webkit-animation-duration: @duration;\n          animation-duration: @duration;\n}\n.animation-timing-function(@timing-function) {\n  -webkit-animation-timing-function: @timing-function;\n          animation-timing-function: @timing-function;\n}\n.animation-delay(@delay) {\n  -webkit-animation-delay: @delay;\n          animation-delay: @delay;\n}\n.animation-iteration-count(@iteration-count) {\n  -webkit-animation-iteration-count: @iteration-count;\n          animation-iteration-count: @iteration-count;\n}\n.animation-direction(@direction) {\n  -webkit-animation-direction: @direction;\n          animation-direction: @direction;\n}\n.animation-fill-mode(@fill-mode) {\n  -webkit-animation-fill-mode: @fill-mode;\n          animation-fill-mode: @fill-mode;\n}\n\n// Backface visibility\n// Prevent browsers from flickering when using CSS 3D transforms.\n// Default value is `visible`, but can be changed to `hidden`\n\n.backface-visibility(@visibility) {\n  -webkit-backface-visibility: @visibility;\n     -moz-backface-visibility: @visibility;\n          backface-visibility: @visibility;\n}\n\n// Drop shadows\n//\n// Note: Deprecated `.box-shadow()` as of v3.1.0 since all of Bootstrap's\n// supported browsers that have box shadow capabilities now support it.\n\n.box-shadow(@shadow) {\n  -webkit-box-shadow: @shadow; // iOS <4.3 & Android <4.1\n          box-shadow: @shadow;\n}\n\n// Box sizing\n.box-sizing(@boxmodel) {\n  -webkit-box-sizing: @boxmodel;\n     -moz-box-sizing: @boxmodel;\n          box-sizing: @boxmodel;\n}\n\n// CSS3 Content Columns\n.content-columns(@column-count; @column-gap: @grid-gutter-width) {\n  -webkit-column-count: @column-count;\n     -moz-column-count: @column-count;\n          column-count: @column-count;\n  -webkit-column-gap: @column-gap;\n     -moz-column-gap: @column-gap;\n          column-gap: @column-gap;\n}\n\n// Optional hyphenation\n.hyphens(@mode: auto) {\n  word-wrap: break-word;\n  -webkit-hyphens: @mode;\n     -moz-hyphens: @mode;\n      -ms-hyphens: @mode; // IE10+\n       -o-hyphens: @mode;\n          hyphens: @mode;\n}\n\n// Placeholder text\n.placeholder(@color: @input-color-placeholder) {\n  // Firefox\n  &::-moz-placeholder {\n    color: @color;\n    opacity: 1; // Override Firefox's unusual default opacity; see https://github.com/twbs/bootstrap/pull/11526\n  }\n  &:-ms-input-placeholder { color: @color; } // Internet Explorer 10+\n  &::-webkit-input-placeholder  { color: @color; } // Safari and Chrome\n}\n\n// Transformations\n.scale(@ratio) {\n  -webkit-transform: scale(@ratio);\n      -ms-transform: scale(@ratio); // IE9 only\n       -o-transform: scale(@ratio);\n          transform: scale(@ratio);\n}\n.scale(@ratioX; @ratioY) {\n  -webkit-transform: scale(@ratioX, @ratioY);\n      -ms-transform: scale(@ratioX, @ratioY); // IE9 only\n       -o-transform: scale(@ratioX, @ratioY);\n          transform: scale(@ratioX, @ratioY);\n}\n.scaleX(@ratio) {\n  -webkit-transform: scaleX(@ratio);\n      -ms-transform: scaleX(@ratio); // IE9 only\n       -o-transform: scaleX(@ratio);\n          transform: scaleX(@ratio);\n}\n.scaleY(@ratio) {\n  -webkit-transform: scaleY(@ratio);\n      -ms-transform: scaleY(@ratio); // IE9 only\n       -o-transform: scaleY(@ratio);\n          transform: scaleY(@ratio);\n}\n.skew(@x; @y) {\n  -webkit-transform: skewX(@x) skewY(@y);\n      -ms-transform: skewX(@x) skewY(@y); // See https://github.com/twbs/bootstrap/issues/4885; IE9+\n       -o-transform: skewX(@x) skewY(@y);\n          transform: skewX(@x) skewY(@y);\n}\n.translate(@x; @y) {\n  -webkit-transform: translate(@x, @y);\n      -ms-transform: translate(@x, @y); // IE9 only\n       -o-transform: translate(@x, @y);\n          transform: translate(@x, @y);\n}\n.translate3d(@x; @y; @z) {\n  -webkit-transform: translate3d(@x, @y, @z);\n          transform: translate3d(@x, @y, @z);\n}\n.rotate(@degrees) {\n  -webkit-transform: rotate(@degrees);\n      -ms-transform: rotate(@degrees); // IE9 only\n       -o-transform: rotate(@degrees);\n          transform: rotate(@degrees);\n}\n.rotateX(@degrees) {\n  -webkit-transform: rotateX(@degrees);\n      -ms-transform: rotateX(@degrees); // IE9 only\n       -o-transform: rotateX(@degrees);\n          transform: rotateX(@degrees);\n}\n.rotateY(@degrees) {\n  -webkit-transform: rotateY(@degrees);\n      -ms-transform: rotateY(@degrees); // IE9 only\n       -o-transform: rotateY(@degrees);\n          transform: rotateY(@degrees);\n}\n.perspective(@perspective) {\n  -webkit-perspective: @perspective;\n     -moz-perspective: @perspective;\n          perspective: @perspective;\n}\n.perspective-origin(@perspective) {\n  -webkit-perspective-origin: @perspective;\n     -moz-perspective-origin: @perspective;\n          perspective-origin: @perspective;\n}\n.transform-origin(@origin) {\n  -webkit-transform-origin: @origin;\n     -moz-transform-origin: @origin;\n      -ms-transform-origin: @origin; // IE9 only\n          transform-origin: @origin;\n}\n\n\n// Transitions\n\n.transition(@transition) {\n  -webkit-transition: @transition;\n       -o-transition: @transition;\n          transition: @transition;\n}\n.transition-property(@transition-property) {\n  -webkit-transition-property: @transition-property;\n          transition-property: @transition-property;\n}\n.transition-delay(@transition-delay) {\n  -webkit-transition-delay: @transition-delay;\n          transition-delay: @transition-delay;\n}\n.transition-duration(@transition-duration) {\n  -webkit-transition-duration: @transition-duration;\n          transition-duration: @transition-duration;\n}\n.transition-timing-function(@timing-function) {\n  -webkit-transition-timing-function: @timing-function;\n          transition-timing-function: @timing-function;\n}\n.transition-transform(@transition) {\n  -webkit-transition: -webkit-transform @transition;\n     -moz-transition: -moz-transform @transition;\n       -o-transition: -o-transform @transition;\n          transition: transform @transition;\n}\n\n\n// User select\n// For selecting text on the page\n\n.user-select(@select) {\n  -webkit-user-select: @select;\n     -moz-user-select: @select;\n      -ms-user-select: @select; // IE10+\n          user-select: @select;\n}\n","// Gradients\n\n#gradient {\n\n  // Horizontal gradient, from left to right\n  //\n  // Creates two color stops, start and end, by specifying a color and position for each color stop.\n  // Color stops are not available in IE9 and below.\n  .horizontal(@start-color: #555; @end-color: #333; @start-percent: 0%; @end-percent: 100%) {\n    background-image: -webkit-linear-gradient(left, @start-color @start-percent, @end-color @end-percent); // Safari 5.1-6, Chrome 10+\n    background-image: -o-linear-gradient(left, @start-color @start-percent, @end-color @end-percent); // Opera 12\n    background-image: linear-gradient(to right, @start-color @start-percent, @end-color @end-percent); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n    background-repeat: repeat-x;\n    filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=1)\",argb(@start-color),argb(@end-color))); // IE9 and down\n  }\n\n  // Vertical gradient, from top to bottom\n  //\n  // Creates two color stops, start and end, by specifying a color and position for each color stop.\n  // Color stops are not available in IE9 and below.\n  .vertical(@start-color: #555; @end-color: #333; @start-percent: 0%; @end-percent: 100%) {\n    background-image: -webkit-linear-gradient(top, @start-color @start-percent, @end-color @end-percent);  // Safari 5.1-6, Chrome 10+\n    background-image: -o-linear-gradient(top, @start-color @start-percent, @end-color @end-percent);  // Opera 12\n    background-image: linear-gradient(to bottom, @start-color @start-percent, @end-color @end-percent); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n    background-repeat: repeat-x;\n    filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=0)\",argb(@start-color),argb(@end-color))); // IE9 and down\n  }\n\n  .directional(@start-color: #555; @end-color: #333; @deg: 45deg) {\n    background-repeat: repeat-x;\n    background-image: -webkit-linear-gradient(@deg, @start-color, @end-color); // Safari 5.1-6, Chrome 10+\n    background-image: -o-linear-gradient(@deg, @start-color, @end-color); // Opera 12\n    background-image: linear-gradient(@deg, @start-color, @end-color); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n  }\n  .horizontal-three-colors(@start-color: #00b3ee; @mid-color: #7a43b6; @color-stop: 50%; @end-color: #c3325f) {\n    background-image: -webkit-linear-gradient(left, @start-color, @mid-color @color-stop, @end-color);\n    background-image: -o-linear-gradient(left, @start-color, @mid-color @color-stop, @end-color);\n    background-image: linear-gradient(to right, @start-color, @mid-color @color-stop, @end-color);\n    background-repeat: no-repeat;\n    filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=1)\",argb(@start-color),argb(@end-color))); // IE9 and down, gets no color-stop at all for proper fallback\n  }\n  .vertical-three-colors(@start-color: #00b3ee; @mid-color: #7a43b6; @color-stop: 50%; @end-color: #c3325f) {\n    background-image: -webkit-linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n    background-image: -o-linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n    background-image: linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n    background-repeat: no-repeat;\n    filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=0)\",argb(@start-color),argb(@end-color))); // IE9 and down, gets no color-stop at all for proper fallback\n  }\n  .radial(@inner-color: #555; @outer-color: #333) {\n    background-image: -webkit-radial-gradient(circle, @inner-color, @outer-color);\n    background-image: radial-gradient(circle, @inner-color, @outer-color);\n    background-repeat: no-repeat;\n  }\n  .striped(@color: rgba(255,255,255,.15); @angle: 45deg) {\n    background-image: -webkit-linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n    background-image: -o-linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n    background-image: linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n  }\n}\n","// Reset filters for IE\n//\n// When you need to remove a gradient background, do not forget to use this to reset\n// the IE filter for IE9 and below.\n\n.reset-filter() {\n  filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(enabled = false)\"));\n}\n"]}
\ No newline at end of file
diff --git a/docs/_static/bootstrap-3.3.7/css/bootstrap-theme.min.css b/docs/_static/bootstrap-3.3.7/css/bootstrap-theme.min.css
deleted file mode 100644
index 5e3940195..000000000
--- a/docs/_static/bootstrap-3.3.7/css/bootstrap-theme.min.css
+++ /dev/null
@@ -1,6 +0,0 @@
-/*!
- * Bootstrap v3.3.7 (http://getbootstrap.com)
- * Copyright 2011-2016 Twitter, Inc.
- * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
- */.btn-danger,.btn-default,.btn-info,.btn-primary,.btn-success,.btn-warning{text-shadow:0 -1px 0 rgba(0,0,0,.2);-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.15),0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 0 rgba(255,255,255,.15),0 1px 1px rgba(0,0,0,.075)}.btn-danger.active,.btn-danger:active,.btn-default.active,.btn-default:active,.btn-info.active,.btn-info:active,.btn-primary.active,.btn-primary:active,.btn-success.active,.btn-success:active,.btn-warning.active,.btn-warning:active{-webkit-box-shadow:inset 0 3px 5px rgba(0,0,0,.125);box-shadow:inset 0 3px 5px rgba(0,0,0,.125)}.btn-danger.disabled,.btn-danger[disabled],.btn-default.disabled,.btn-default[disabled],.btn-info.disabled,.btn-info[disabled],.btn-primary.disabled,.btn-primary[disabled],.btn-success.disabled,.btn-success[disabled],.btn-warning.disabled,.btn-warning[disabled],fieldset[disabled] .btn-danger,fieldset[disabled] .btn-default,fieldset[disabled] .btn-info,fieldset[disabled] 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-15px}.btn-default.active,.btn-default:active{background-color:#e0e0e0;border-color:#dbdbdb}.btn-default.disabled,.btn-default.disabled.active,.btn-default.disabled.focus,.btn-default.disabled:active,.btn-default.disabled:focus,.btn-default.disabled:hover,.btn-default[disabled],.btn-default[disabled].active,.btn-default[disabled].focus,.btn-default[disabled]:active,.btn-default[disabled]:focus,.btn-default[disabled]:hover,fieldset[disabled] .btn-default,fieldset[disabled] .btn-default.active,fieldset[disabled] .btn-default.focus,fieldset[disabled] .btn-default:active,fieldset[disabled] .btn-default:focus,fieldset[disabled] .btn-default:hover{background-color:#e0e0e0;background-image:none}.btn-primary{background-image:-webkit-linear-gradient(top,#337ab7 0,#265a88 100%);background-image:-o-linear-gradient(top,#337ab7 0,#265a88 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#337ab7),to(#265a88));background-image:linear-gradient(to bottom,#337ab7 0,#265a88 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff265a88', GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false);background-repeat:repeat-x;border-color:#245580}.btn-primary:focus,.btn-primary:hover{background-color:#265a88;background-position:0 -15px}.btn-primary.active,.btn-primary:active{background-color:#265a88;border-color:#245580}.btn-primary.disabled,.btn-primary.disabled.active,.btn-primary.disabled.focus,.btn-primary.disabled:active,.btn-primary.disabled:focus,.btn-primary.disabled:hover,.btn-primary[disabled],.btn-primary[disabled].active,.btn-primary[disabled].focus,.btn-primary[disabled]:active,.btn-primary[disabled]:focus,.btn-primary[disabled]:hover,fieldset[disabled] .btn-primary,fieldset[disabled] .btn-primary.active,fieldset[disabled] .btn-primary.focus,fieldset[disabled] .btn-primary:active,fieldset[disabled] .btn-primary:focus,fieldset[disabled] .btn-primary:hover{background-color:#265a88;background-image:none}.btn-success{background-image:-webkit-linear-gradient(top,#5cb85c 0,#419641 100%);background-image:-o-linear-gradient(top,#5cb85c 0,#419641 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#5cb85c),to(#419641));background-image:linear-gradient(to bottom,#5cb85c 0,#419641 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5cb85c', endColorstr='#ff419641', GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false);background-repeat:repeat-x;border-color:#3e8f3e}.btn-success:focus,.btn-success:hover{background-color:#419641;background-position:0 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100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff2aabd2', GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false);background-repeat:repeat-x;border-color:#28a4c9}.btn-info:focus,.btn-info:hover{background-color:#2aabd2;background-position:0 -15px}.btn-info.active,.btn-info:active{background-color:#2aabd2;border-color:#28a4c9}.btn-info.disabled,.btn-info.disabled.active,.btn-info.disabled.focus,.btn-info.disabled:active,.btn-info.disabled:focus,.btn-info.disabled:hover,.btn-info[disabled],.btn-info[disabled].active,.btn-info[disabled].focus,.btn-info[disabled]:active,.btn-info[disabled]:focus,.btn-info[disabled]:hover,fieldset[disabled] .btn-info,fieldset[disabled] .btn-info.active,fieldset[disabled] .btn-info.focus,fieldset[disabled] .btn-info:active,fieldset[disabled] .btn-info:focus,fieldset[disabled] 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 .reset-filter(); // Remove gradient in IE<10 to fix bug where dropdowns don't get triggered; see https://github.com/twbs/bootstrap/issues/10257\n  border-radius: @navbar-border-radius;\n  .navbar-nav > .open > a,\n  .navbar-nav > .active > a {\n    #gradient > .vertical(@start-color: @navbar-inverse-link-active-bg; @end-color: lighten(@navbar-inverse-link-active-bg, 2.5%));\n    .box-shadow(inset 0 3px 9px rgba(0,0,0,.25));\n  }\n\n  .navbar-brand,\n  .navbar-nav > li > a {\n    text-shadow: 0 -1px 0 rgba(0,0,0,.25);\n  }\n}\n\n// Undo rounded corners in static and fixed navbars\n.navbar-static-top,\n.navbar-fixed-top,\n.navbar-fixed-bottom {\n  border-radius: 0;\n}\n\n// Fix active state of dropdown items in collapsed mode\n@media (max-width: @grid-float-breakpoint-max) {\n  .navbar .navbar-nav .open .dropdown-menu > .active > a {\n    &,\n    &:hover,\n    &:focus {\n      color: #fff;\n      #gradient > .vertical(@start-color: @dropdown-link-active-bg; @end-color: 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They have been removed in v4.\n\n// - Animations\n// - Backface visibility\n// - Box shadow\n// - Box sizing\n// - Content columns\n// - Hyphens\n// - Placeholder text\n// - Transformations\n// - Transitions\n// - User Select\n\n\n// Animations\n.animation(@animation) {\n  -webkit-animation: @animation;\n       -o-animation: @animation;\n          animation: @animation;\n}\n.animation-name(@name) {\n  -webkit-animation-name: @name;\n          animation-name: @name;\n}\n.animation-duration(@duration) {\n  -webkit-animation-duration: @duration;\n          animation-duration: @duration;\n}\n.animation-timing-function(@timing-function) {\n  -webkit-animation-timing-function: @timing-function;\n          animation-timing-function: @timing-function;\n}\n.animation-delay(@delay) {\n  -webkit-animation-delay: @delay;\n          animation-delay: @delay;\n}\n.animation-iteration-count(@iteration-count) {\n  -webkit-animation-iteration-count: @iteration-count;\n          animation-iteration-count: @iteration-count;\n}\n.animation-direction(@direction) {\n  -webkit-animation-direction: @direction;\n          animation-direction: @direction;\n}\n.animation-fill-mode(@fill-mode) {\n  -webkit-animation-fill-mode: @fill-mode;\n          animation-fill-mode: @fill-mode;\n}\n\n// Backface visibility\n// Prevent browsers from flickering when using CSS 3D transforms.\n// Default value is `visible`, but can be changed to `hidden`\n\n.backface-visibility(@visibility) {\n  -webkit-backface-visibility: @visibility;\n     -moz-backface-visibility: @visibility;\n          backface-visibility: @visibility;\n}\n\n// Drop shadows\n//\n// Note: Deprecated `.box-shadow()` as of v3.1.0 since all of Bootstrap's\n// supported browsers that have box shadow capabilities now support it.\n\n.box-shadow(@shadow) {\n  -webkit-box-shadow: @shadow; // iOS <4.3 & Android <4.1\n          box-shadow: @shadow;\n}\n\n// Box sizing\n.box-sizing(@boxmodel) {\n  -webkit-box-sizing: @boxmodel;\n     -moz-box-sizing: @boxmodel;\n          box-sizing: @boxmodel;\n}\n\n// CSS3 Content Columns\n.content-columns(@column-count; @column-gap: @grid-gutter-width) {\n  -webkit-column-count: @column-count;\n     -moz-column-count: @column-count;\n          column-count: @column-count;\n  -webkit-column-gap: @column-gap;\n     -moz-column-gap: @column-gap;\n          column-gap: @column-gap;\n}\n\n// Optional hyphenation\n.hyphens(@mode: auto) {\n  word-wrap: break-word;\n  -webkit-hyphens: @mode;\n     -moz-hyphens: @mode;\n      -ms-hyphens: @mode; // IE10+\n       -o-hyphens: @mode;\n          hyphens: @mode;\n}\n\n// Placeholder text\n.placeholder(@color: @input-color-placeholder) {\n  // Firefox\n  &::-moz-placeholder {\n    color: @color;\n    opacity: 1; // Override Firefox's unusual default opacity; see https://github.com/twbs/bootstrap/pull/11526\n  }\n  &:-ms-input-placeholder { color: @color; 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// See https://github.com/twbs/bootstrap/issues/4885; IE9+\n       -o-transform: skewX(@x) skewY(@y);\n          transform: skewX(@x) skewY(@y);\n}\n.translate(@x; @y) {\n  -webkit-transform: translate(@x, @y);\n      -ms-transform: translate(@x, @y); // IE9 only\n       -o-transform: translate(@x, @y);\n          transform: translate(@x, @y);\n}\n.translate3d(@x; @y; @z) {\n  -webkit-transform: translate3d(@x, @y, @z);\n          transform: translate3d(@x, @y, @z);\n}\n.rotate(@degrees) {\n  -webkit-transform: rotate(@degrees);\n      -ms-transform: rotate(@degrees); // IE9 only\n       -o-transform: rotate(@degrees);\n          transform: rotate(@degrees);\n}\n.rotateX(@degrees) {\n  -webkit-transform: rotateX(@degrees);\n      -ms-transform: rotateX(@degrees); // IE9 only\n       -o-transform: rotateX(@degrees);\n          transform: rotateX(@degrees);\n}\n.rotateY(@degrees) {\n  -webkit-transform: rotateY(@degrees);\n      -ms-transform: rotateY(@degrees); // IE9 only\n       -o-transform: rotateY(@degrees);\n          transform: rotateY(@degrees);\n}\n.perspective(@perspective) {\n  -webkit-perspective: @perspective;\n     -moz-perspective: @perspective;\n          perspective: @perspective;\n}\n.perspective-origin(@perspective) {\n  -webkit-perspective-origin: @perspective;\n     -moz-perspective-origin: @perspective;\n          perspective-origin: @perspective;\n}\n.transform-origin(@origin) {\n  -webkit-transform-origin: @origin;\n     -moz-transform-origin: @origin;\n      -ms-transform-origin: @origin; // IE9 only\n          transform-origin: @origin;\n}\n\n\n// Transitions\n\n.transition(@transition) {\n  -webkit-transition: @transition;\n       -o-transition: @transition;\n          transition: @transition;\n}\n.transition-property(@transition-property) {\n  -webkit-transition-property: @transition-property;\n          transition-property: @transition-property;\n}\n.transition-delay(@transition-delay) {\n  -webkit-transition-delay: @transition-delay;\n          transition-delay: @transition-delay;\n}\n.transition-duration(@transition-duration) {\n  -webkit-transition-duration: @transition-duration;\n          transition-duration: @transition-duration;\n}\n.transition-timing-function(@timing-function) {\n  -webkit-transition-timing-function: @timing-function;\n          transition-timing-function: @timing-function;\n}\n.transition-transform(@transition) {\n  -webkit-transition: -webkit-transform @transition;\n     -moz-transition: -moz-transform @transition;\n       -o-transition: -o-transform @transition;\n          transition: transform @transition;\n}\n\n\n// User select\n// For selecting text on the page\n\n.user-select(@select) {\n  -webkit-user-select: @select;\n     -moz-user-select: @select;\n      -ms-user-select: @select; // IE10+\n          user-select: @select;\n}\n","// Gradients\n\n#gradient {\n\n  // Horizontal gradient, from left to right\n  //\n  // Creates two color stops, start and end, by specifying a color and position for each color stop.\n  // Color stops are not available in IE9 and below.\n  .horizontal(@start-color: #555; @end-color: #333; @start-percent: 0%; @end-percent: 100%) {\n    background-image: -webkit-linear-gradient(left, @start-color @start-percent, @end-color @end-percent); // Safari 5.1-6, Chrome 10+\n    background-image: -o-linear-gradient(left, @start-color @start-percent, @end-color @end-percent); // Opera 12\n    background-image: linear-gradient(to right, @start-color @start-percent, @end-color @end-percent); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n    background-repeat: repeat-x;\n    filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=1)\",argb(@start-color),argb(@end-color))); // IE9 and down\n  }\n\n  // Vertical gradient, from top to bottom\n  //\n  // Creates two color stops, start and end, by specifying a color and position for each color stop.\n  // Color stops are not available in IE9 and below.\n  .vertical(@start-color: #555; @end-color: #333; @start-percent: 0%; @end-percent: 100%) {\n    background-image: -webkit-linear-gradient(top, @start-color @start-percent, @end-color @end-percent);  // Safari 5.1-6, Chrome 10+\n    background-image: -o-linear-gradient(top, @start-color @start-percent, @end-color @end-percent);  // Opera 12\n    background-image: linear-gradient(to bottom, @start-color @start-percent, @end-color @end-percent); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n    background-repeat: repeat-x;\n    filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=0)\",argb(@start-color),argb(@end-color))); // IE9 and down\n  }\n\n  .directional(@start-color: #555; @end-color: #333; @deg: 45deg) {\n    background-repeat: repeat-x;\n    background-image: -webkit-linear-gradient(@deg, @start-color, @end-color); // Safari 5.1-6, Chrome 10+\n    background-image: -o-linear-gradient(@deg, @start-color, @end-color); // Opera 12\n    background-image: linear-gradient(@deg, @start-color, @end-color); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n  }\n  .horizontal-three-colors(@start-color: #00b3ee; @mid-color: #7a43b6; @color-stop: 50%; @end-color: #c3325f) {\n    background-image: -webkit-linear-gradient(left, @start-color, @mid-color @color-stop, @end-color);\n    background-image: -o-linear-gradient(left, @start-color, @mid-color @color-stop, @end-color);\n    background-image: linear-gradient(to right, @start-color, @mid-color @color-stop, @end-color);\n    background-repeat: no-repeat;\n    filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=1)\",argb(@start-color),argb(@end-color))); // IE9 and down, gets no color-stop at all for proper fallback\n  }\n  .vertical-three-colors(@start-color: #00b3ee; @mid-color: #7a43b6; @color-stop: 50%; @end-color: #c3325f) {\n    background-image: -webkit-linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n    background-image: -o-linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n    background-image: linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n    background-repeat: no-repeat;\n    filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=0)\",argb(@start-color),argb(@end-color))); // IE9 and down, gets no color-stop at all for proper fallback\n  }\n  .radial(@inner-color: #555; @outer-color: #333) {\n    background-image: -webkit-radial-gradient(circle, @inner-color, @outer-color);\n    background-image: radial-gradient(circle, @inner-color, @outer-color);\n    background-repeat: no-repeat;\n  }\n  .striped(@color: rgba(255,255,255,.15); @angle: 45deg) {\n    background-image: -webkit-linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n    background-image: -o-linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n    background-image: linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n  }\n}\n","// Reset filters for IE\n//\n// When you need to remove a gradient background, do not forget to use this to reset\n// the IE filter for IE9 and below.\n\n.reset-filter() {\n  filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(enabled = false)\"));\n}\n"]}
\ No newline at end of file
diff --git a/docs/_static/bootstrap-3.3.7/css/bootstrap.css b/docs/_static/bootstrap-3.3.7/css/bootstrap.css
deleted file mode 100644
index 6167622ce..000000000
--- a/docs/_static/bootstrap-3.3.7/css/bootstrap.css
+++ /dev/null
@@ -1,6757 +0,0 @@
-/*!
- * Bootstrap v3.3.7 (http://getbootstrap.com)
- * Copyright 2011-2016 Twitter, Inc.
- * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
- */
-/*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */
-html {
-  font-family: sans-serif;
-  -webkit-text-size-adjust: 100%;
-      -ms-text-size-adjust: 100%;
-}
-body {
-  margin: 0;
-}
-article,
-aside,
-details,
-figcaption,
-figure,
-footer,
-header,
-hgroup,
-main,
-menu,
-nav,
-section,
-summary {
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-}
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-canvas,
-progress,
-video {
-  display: inline-block;
-  vertical-align: baseline;
-}
-audio:not([controls]) {
-  display: none;
-  height: 0;
-}
-[hidden],
-template {
-  display: none;
-}
-a {
-  background-color: transparent;
-}
-a:active,
-a:hover {
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-}
-abbr[title] {
-  border-bottom: 1px dotted;
-}
-b,
-strong {
-  font-weight: bold;
-}
-dfn {
-  font-style: italic;
-}
-h1 {
-  margin: .67em 0;
-  font-size: 2em;
-}
-mark {
-  color: #000;
-  background: #ff0;
-}
-small {
-  font-size: 80%;
-}
-sub,
-sup {
-  position: relative;
-  font-size: 75%;
-  line-height: 0;
-  vertical-align: baseline;
-}
-sup {
-  top: -.5em;
-}
-sub {
-  bottom: -.25em;
-}
-img {
-  border: 0;
-}
-svg:not(:root) {
-  overflow: hidden;
-}
-figure {
-  margin: 1em 40px;
-}
-hr {
-  height: 0;
-  -webkit-box-sizing: content-box;
-     -moz-box-sizing: content-box;
-          box-sizing: content-box;
-}
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-  overflow: auto;
-}
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-kbd,
-pre,
-samp {
-  font-family: monospace, monospace;
-  font-size: 1em;
-}
-button,
-input,
-optgroup,
-select,
-textarea {
-  margin: 0;
-  font: inherit;
-  color: inherit;
-}
-button {
-  overflow: visible;
-}
-button,
-select {
-  text-transform: none;
-}
-button,
-html input[type="button"],
-input[type="reset"],
-input[type="submit"] {
-  -webkit-appearance: button;
-  cursor: pointer;
-}
-button[disabled],
-html input[disabled] {
-  cursor: default;
-}
-button::-moz-focus-inner,
-input::-moz-focus-inner {
-  padding: 0;
-  border: 0;
-}
-input {
-  line-height: normal;
-}
-input[type="checkbox"],
-input[type="radio"] {
-  -webkit-box-sizing: border-box;
-     -moz-box-sizing: border-box;
-          box-sizing: border-box;
-  padding: 0;
-}
-input[type="number"]::-webkit-inner-spin-button,
-input[type="number"]::-webkit-outer-spin-button {
-  height: auto;
-}
-input[type="search"] {
-  -webkit-box-sizing: content-box;
-     -moz-box-sizing: content-box;
-          box-sizing: content-box;
-  -webkit-appearance: textfield;
-}
-input[type="search"]::-webkit-search-cancel-button,
-input[type="search"]::-webkit-search-decoration {
-  -webkit-appearance: none;
-}
-fieldset {
-  padding: .35em .625em .75em;
-  margin: 0 2px;
-  border: 1px solid #c0c0c0;
-}
-legend {
-  padding: 0;
-  border: 0;
-}
-textarea {
-  overflow: auto;
-}
-optgroup {
-  font-weight: bold;
-}
-table {
-  border-spacing: 0;
-  border-collapse: collapse;
-}
-td,
-th {
-  padding: 0;
-}
-/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */
-@media print {
-  *,
-  *:before,
-  *:after {
-    color: #000 !important;
-    text-shadow: none !important;
-    background: transparent !important;
-    -webkit-box-shadow: none !important;
-            box-shadow: none !important;
-  }
-  a,
-  a:visited {
-    text-decoration: underline;
-  }
-  a[href]:after {
-    content: " (" attr(href) ")";
-  }
-  abbr[title]:after {
-    content: " (" attr(title) ")";
-  }
-  a[href^="#"]:after,
-  a[href^="javascript:"]:after {
-    content: "";
-  }
-  pre,
-  blockquote {
-    border: 1px solid #999;
-
-    page-break-inside: avoid;
-  }
-  thead {
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-  }
-  tr,
-  img {
-    page-break-inside: avoid;
-  }
-  img {
-    max-width: 100% !important;
-  }
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-  h2,
-  h3 {
-    orphans: 3;
-    widows: 3;
-  }
-  h2,
-  h3 {
-    page-break-after: avoid;
-  }
-  .navbar {
-    display: none;
-  }
-  .btn > .caret,
-  .dropup > .btn > .caret {
-    border-top-color: #000 !important;
-  }
-  .label {
-    border: 1px solid #000;
-  }
-  .table {
-    border-collapse: collapse !important;
-  }
-  .table td,
-  .table th {
-    background-color: #fff !important;
-  }
-  .table-bordered th,
-  .table-bordered td {
-    border: 1px solid #ddd !important;
-  }
-}
-@font-face {
-  font-family: 'Glyphicons Halflings';
-
-  src: url('../fonts/glyphicons-halflings-regular.eot');
-  src: url('../fonts/glyphicons-halflings-regular.eot?#iefix') format('embedded-opentype'), url('../fonts/glyphicons-halflings-regular.woff2') format('woff2'), url('../fonts/glyphicons-halflings-regular.woff') format('woff'), url('../fonts/glyphicons-halflings-regular.ttf') format('truetype'), url('../fonts/glyphicons-halflings-regular.svg#glyphicons_halflingsregular') format('svg');
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-.glyphicon-heart:before {
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deleted file mode 100644
index f010c82d1..000000000
--- a/docs/_static/bootstrap-3.3.7/css/bootstrap.css.map
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color: #000;\n}\nsmall {\n  font-size: 80%;\n}\nsub,\nsup {\n  font-size: 75%;\n  line-height: 0;\n  position: relative;\n  vertical-align: baseline;\n}\nsup {\n  top: -0.5em;\n}\nsub {\n  bottom: -0.25em;\n}\nimg {\n  border: 0;\n}\nsvg:not(:root) {\n  overflow: hidden;\n}\nfigure {\n  margin: 1em 40px;\n}\nhr {\n  box-sizing: content-box;\n  height: 0;\n}\npre {\n  overflow: auto;\n}\ncode,\nkbd,\npre,\nsamp {\n  font-family: monospace, monospace;\n  font-size: 1em;\n}\nbutton,\ninput,\noptgroup,\nselect,\ntextarea {\n  color: inherit;\n  font: inherit;\n  margin: 0;\n}\nbutton {\n  overflow: visible;\n}\nbutton,\nselect {\n  text-transform: none;\n}\nbutton,\nhtml input[type=\"button\"],\ninput[type=\"reset\"],\ninput[type=\"submit\"] {\n  -webkit-appearance: button;\n  cursor: pointer;\n}\nbutton[disabled],\nhtml input[disabled] {\n  cursor: default;\n}\nbutton::-moz-focus-inner,\ninput::-moz-focus-inner {\n  border: 0;\n  padding: 0;\n}\ninput {\n  line-height: normal;\n}\ninput[type=\"checkbox\"],\ninput[type=\"radio\"] {\n  box-sizing: border-box;\n  padding: 0;\n}\ninput[type=\"number\"]::-webkit-inner-spin-button,\ninput[type=\"number\"]::-webkit-outer-spin-button {\n  height: auto;\n}\ninput[type=\"search\"] {\n  -webkit-appearance: textfield;\n  box-sizing: content-box;\n}\ninput[type=\"search\"]::-webkit-search-cancel-button,\ninput[type=\"search\"]::-webkit-search-decoration {\n  -webkit-appearance: none;\n}\nfieldset {\n  border: 1px solid #c0c0c0;\n  margin: 0 2px;\n  padding: 0.35em 0.625em 0.75em;\n}\nlegend {\n  border: 0;\n  padding: 0;\n}\ntextarea {\n  overflow: auto;\n}\noptgroup {\n  font-weight: bold;\n}\ntable {\n  border-collapse: collapse;\n  border-spacing: 0;\n}\ntd,\nth {\n  padding: 0;\n}\n/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */\n@media print {\n  *,\n  *:before,\n  *:after {\n    background: transparent !important;\n    color: #000 !important;\n    box-shadow: none !important;\n    text-shadow: none !important;\n  }\n  a,\n  a:visited {\n    text-decoration: underline;\n  }\n  a[href]:after {\n    content: \" (\" attr(href) \")\";\n  }\n  abbr[title]:after {\n    content: \" (\" attr(title) \")\";\n  }\n  a[href^=\"#\"]:after,\n  a[href^=\"javascript:\"]:after {\n    content: \"\";\n  }\n  pre,\n  blockquote {\n    border: 1px solid #999;\n    page-break-inside: avoid;\n  }\n  thead {\n    display: table-header-group;\n  }\n  tr,\n  img {\n    page-break-inside: avoid;\n  }\n  img {\n    max-width: 100% !important;\n  }\n  p,\n  h2,\n  h3 {\n    orphans: 3;\n    widows: 3;\n  }\n  h2,\n  h3 {\n    page-break-after: avoid;\n  }\n  .navbar {\n    display: none;\n  }\n  .btn > .caret,\n  .dropup > .btn > .caret {\n    border-top-color: #000 !important;\n  }\n  .label {\n    border: 1px solid #000;\n  }\n  .table {\n    border-collapse: collapse !important;\n  }\n  .table td,\n  .table th {\n    background-color: #fff !important;\n  }\n  .table-bordered th,\n  .table-bordered td {\n    border: 1px solid #ddd !important;\n  }\n}\n@font-face {\n  font-family: 'Glyphicons Halflings';\n  src: url('../fonts/glyphicons-halflings-regular.eot');\n  src: url('../fonts/glyphicons-halflings-regular.eot?#iefix') format('embedded-opentype'), url('../fonts/glyphicons-halflings-regular.woff2') format('woff2'), url('../fonts/glyphicons-halflings-regular.woff') format('woff'), url('../fonts/glyphicons-halflings-regular.ttf') format('truetype'), url('../fonts/glyphicons-halflings-regular.svg#glyphicons_halflingsregular') format('svg');\n}\n.glyphicon {\n  position: relative;\n  top: 1px;\n  display: inline-block;\n  font-family: 'Glyphicons Halflings';\n  font-style: normal;\n  font-weight: normal;\n  line-height: 1;\n  -webkit-font-smoothing: antialiased;\n  -moz-osx-font-smoothing: grayscale;\n}\n.glyphicon-asterisk:before {\n  content: \"\\002a\";\n}\n.glyphicon-plus:before {\n  content: \"\\002b\";\n}\n.glyphicon-euro:before,\n.glyphicon-eur:before {\n  content: \"\\20ac\";\n}\n.glyphicon-minus:before {\n  content: \"\\2212\";\n}\n.glyphicon-cloud:before {\n  content: \"\\2601\";\n}\n.glyphicon-envelope:before {\n  content: \"\\2709\";\n}\n.glyphicon-pencil:before {\n  content: \"\\270f\";\n}\n.glyphicon-glass:before {\n  content: \"\\e001\";\n}\n.glyphicon-music:before {\n  content: \"\\e002\";\n}\n.glyphicon-search:before {\n  content: \"\\e003\";\n}\n.glyphicon-heart:before {\n  content: \"\\e005\";\n}\n.glyphicon-star:before {\n  content: \"\\e006\";\n}\n.glyphicon-star-empty:before {\n  content: \"\\e007\";\n}\n.glyphicon-user:before {\n  content: \"\\e008\";\n}\n.glyphicon-film:before {\n  content: \"\\e009\";\n}\n.glyphicon-th-large:before {\n  content: \"\\e010\";\n}\n.glyphicon-th:before {\n  content: \"\\e011\";\n}\n.glyphicon-th-list:before {\n  content: \"\\e012\";\n}\n.glyphicon-ok:before {\n  content: \"\\e013\";\n}\n.glyphicon-remove:before {\n  content: \"\\e014\";\n}\n.glyphicon-zoom-in:before {\n  content: \"\\e015\";\n}\n.glyphicon-zoom-out:before {\n  content: \"\\e016\";\n}\n.glyphicon-off:before {\n  content: \"\\e017\";\n}\n.glyphicon-signal:before {\n  content: \"\\e018\";\n}\n.glyphicon-cog:before {\n  content: \"\\e019\";\n}\n.glyphicon-trash:before {\n  content: \"\\e020\";\n}\n.glyphicon-home:before {\n  content: \"\\e021\";\n}\n.glyphicon-file:before {\n  content: \"\\e022\";\n}\n.glyphicon-time:before {\n  content: \"\\e023\";\n}\n.glyphicon-road:before {\n  content: \"\\e024\";\n}\n.glyphicon-download-alt:before {\n  content: \"\\e025\";\n}\n.glyphicon-download:before {\n  content: \"\\e026\";\n}\n.glyphicon-upload:before {\n  content: \"\\e027\";\n}\n.glyphicon-inbox:before {\n  content: \"\\e028\";\n}\n.glyphicon-play-circle:before {\n  content: \"\\e029\";\n}\n.glyphicon-repeat:before {\n  content: \"\\e030\";\n}\n.glyphicon-refresh:before {\n  content: \"\\e031\";\n}\n.glyphicon-list-alt:before {\n  content: \"\\e032\";\n}\n.glyphicon-lock:before {\n  content: \"\\e033\";\n}\n.glyphicon-flag:before {\n  content: \"\\e034\";\n}\n.glyphicon-headphones:before {\n  content: \"\\e035\";\n}\n.glyphicon-volume-off:before {\n  content: \"\\e036\";\n}\n.glyphicon-volume-down:before {\n  content: \"\\e037\";\n}\n.glyphicon-volume-up:before {\n  content: \"\\e038\";\n}\n.glyphicon-qrcode:before {\n  content: \"\\e039\";\n}\n.glyphicon-barcode:before {\n  content: \"\\e040\";\n}\n.glyphicon-tag:before {\n  content: \"\\e041\";\n}\n.glyphicon-tags:before {\n  content: \"\\e042\";\n}\n.glyphicon-book:before {\n  content: \"\\e043\";\n}\n.glyphicon-bookmark:before {\n  content: \"\\e044\";\n}\n.glyphicon-print:before {\n  content: \"\\e045\";\n}\n.glyphicon-camera:before {\n  content: \"\\e046\";\n}\n.glyphicon-font:before {\n  content: \"\\e047\";\n}\n.glyphicon-bold:before {\n  content: \"\\e048\";\n}\n.glyphicon-italic:before {\n  content: \"\\e049\";\n}\n.glyphicon-text-height:before {\n  content: \"\\e050\";\n}\n.glyphicon-text-width:before {\n  content: \"\\e051\";\n}\n.glyphicon-align-left:before {\n  content: \"\\e052\";\n}\n.glyphicon-align-center:before {\n  content: \"\\e053\";\n}\n.glyphicon-align-right:before {\n  content: \"\\e054\";\n}\n.glyphicon-align-justify:before {\n  content: \"\\e055\";\n}\n.glyphicon-list:before {\n  content: \"\\e056\";\n}\n.glyphicon-indent-left:before {\n  content: \"\\e057\";\n}\n.glyphicon-indent-right:before {\n  content: \"\\e058\";\n}\n.glyphicon-facetime-video:before {\n  content: \"\\e059\";\n}\n.glyphicon-picture:before {\n  content: \"\\e060\";\n}\n.glyphicon-map-marker:before {\n  content: \"\\e062\";\n}\n.glyphicon-adjust:before {\n  content: \"\\e063\";\n}\n.glyphicon-tint:before {\n  content: \"\\e064\";\n}\n.glyphicon-edit:before {\n  content: \"\\e065\";\n}\n.glyphicon-share:before {\n  content: \"\\e066\";\n}\n.glyphicon-check:before {\n  content: \"\\e067\";\n}\n.glyphicon-move:before {\n  content: \"\\e068\";\n}\n.glyphicon-step-backward:before {\n  content: \"\\e069\";\n}\n.glyphicon-fast-backward:before {\n  content: \"\\e070\";\n}\n.glyphicon-backward:before {\n  content: \"\\e071\";\n}\n.glyphicon-play:before {\n  content: \"\\e072\";\n}\n.glyphicon-pause:before {\n  content: \"\\e073\";\n}\n.glyphicon-stop:before {\n  content: \"\\e074\";\n}\n.glyphicon-forward:before {\n  content: \"\\e075\";\n}\n.glyphicon-fast-forward:before {\n  content: \"\\e076\";\n}\n.glyphicon-step-forward:before {\n  content: \"\\e077\";\n}\n.glyphicon-eject:before {\n  content: \"\\e078\";\n}\n.glyphicon-chevron-left:before {\n  content: \"\\e079\";\n}\n.glyphicon-chevron-right:before {\n  content: \"\\e080\";\n}\n.glyphicon-plus-sign:before {\n  content: \"\\e081\";\n}\n.glyphicon-minus-sign:before {\n  content: \"\\e082\";\n}\n.glyphicon-remove-sign:before {\n  content: \"\\e083\";\n}\n.glyphicon-ok-sign:before {\n  content: \"\\e084\";\n}\n.glyphicon-question-sign:before {\n  content: \"\\e085\";\n}\n.glyphicon-info-sign:before {\n  content: \"\\e086\";\n}\n.glyphicon-screenshot:before {\n  content: \"\\e087\";\n}\n.glyphicon-remove-circle:before {\n  content: \"\\e088\";\n}\n.glyphicon-ok-circle:before {\n  content: \"\\e089\";\n}\n.glyphicon-ban-circle:before {\n  content: \"\\e090\";\n}\n.glyphicon-arrow-left:before {\n  content: \"\\e091\";\n}\n.glyphicon-arrow-right:before {\n  content: \"\\e092\";\n}\n.glyphicon-arrow-up:before {\n  content: \"\\e093\";\n}\n.glyphicon-arrow-down:before {\n  content: \"\\e094\";\n}\n.glyphicon-share-alt:before {\n  content: \"\\e095\";\n}\n.glyphicon-resize-full:before {\n  content: \"\\e096\";\n}\n.glyphicon-resize-small:before {\n  content: \"\\e097\";\n}\n.glyphicon-exclamation-sign:before {\n  content: \"\\e101\";\n}\n.glyphicon-gift:before {\n  content: \"\\e102\";\n}\n.glyphicon-leaf:before {\n  content: \"\\e103\";\n}\n.glyphicon-fire:before {\n  content: \"\\e104\";\n}\n.glyphicon-eye-open:before {\n  content: \"\\e105\";\n}\n.glyphicon-eye-close:before {\n  content: \"\\e106\";\n}\n.glyphicon-warning-sign:before {\n  content: \"\\e107\";\n}\n.glyphicon-plane:before {\n  content: \"\\e108\";\n}\n.glyphicon-calendar:before {\n  content: \"\\e109\";\n}\n.glyphicon-random:before {\n  content: \"\\e110\";\n}\n.glyphicon-comment:before {\n  content: \"\\e111\";\n}\n.glyphicon-magnet:before {\n  content: \"\\e112\";\n}\n.glyphicon-chevron-up:before {\n  content: \"\\e113\";\n}\n.glyphicon-chevron-down:before {\n  content: \"\\e114\";\n}\n.glyphicon-retweet:before {\n  content: \"\\e115\";\n}\n.glyphicon-shopping-cart:before {\n  content: \"\\e116\";\n}\n.glyphicon-folder-close:before {\n  content: \"\\e117\";\n}\n.glyphicon-folder-open:before {\n  content: \"\\e118\";\n}\n.glyphicon-resize-vertical:before {\n  content: \"\\e119\";\n}\n.glyphicon-resize-horizontal:before {\n  content: \"\\e120\";\n}\n.glyphicon-hdd:before {\n  content: \"\\e121\";\n}\n.glyphicon-bullhorn:before {\n  content: \"\\e122\";\n}\n.glyphicon-bell:before {\n  content: \"\\e123\";\n}\n.glyphicon-certificate:before {\n  content: \"\\e124\";\n}\n.glyphicon-thumbs-up:before {\n  content: \"\\e125\";\n}\n.glyphicon-thumbs-down:before {\n  content: \"\\e126\";\n}\n.glyphicon-hand-right:before {\n  content: \"\\e127\";\n}\n.glyphicon-hand-left:before {\n  content: \"\\e128\";\n}\n.glyphicon-hand-up:before {\n  content: \"\\e129\";\n}\n.glyphicon-hand-down:before {\n  content: \"\\e130\";\n}\n.glyphicon-circle-arrow-right:before {\n  content: \"\\e131\";\n}\n.glyphicon-circle-arrow-left:before {\n  content: \"\\e132\";\n}\n.glyphicon-circle-arrow-up:before {\n  content: \"\\e133\";\n}\n.glyphicon-circle-arrow-down:before {\n  content: \"\\e134\";\n}\n.glyphicon-globe:before {\n  content: \"\\e135\";\n}\n.glyphicon-wrench:before {\n  content: \"\\e136\";\n}\n.glyphicon-tasks:before {\n  content: \"\\e137\";\n}\n.glyphicon-filter:before {\n  content: \"\\e138\";\n}\n.glyphicon-briefcase:before {\n  content: \"\\e139\";\n}\n.glyphicon-fullscreen:before {\n  content: \"\\e140\";\n}\n.glyphicon-dashboard:before {\n  content: \"\\e141\";\n}\n.glyphicon-paperclip:before {\n  content: \"\\e142\";\n}\n.glyphicon-heart-empty:before {\n  content: \"\\e143\";\n}\n.glyphicon-link:before {\n  content: \"\\e144\";\n}\n.glyphicon-phone:before {\n  content: \"\\e145\";\n}\n.glyphicon-pushpin:before {\n  content: \"\\e146\";\n}\n.glyphicon-usd:before {\n  content: \"\\e148\";\n}\n.glyphicon-gbp:before {\n  content: \"\\e149\";\n}\n.glyphicon-sort:before {\n  content: \"\\e150\";\n}\n.glyphicon-sort-by-alphabet:before {\n  content: \"\\e151\";\n}\n.glyphicon-sort-by-alphabet-alt:before {\n  content: \"\\e152\";\n}\n.glyphicon-sort-by-order:before {\n  content: \"\\e153\";\n}\n.glyphicon-sort-by-order-alt:before {\n  content: \"\\e154\";\n}\n.glyphicon-sort-by-attributes:before {\n  content: \"\\e155\";\n}\n.glyphicon-sort-by-attributes-alt:before {\n  content: \"\\e156\";\n}\n.glyphicon-unchecked:before {\n  content: \"\\e157\";\n}\n.glyphicon-expand:before {\n  content: \"\\e158\";\n}\n.glyphicon-collapse-down:before {\n  content: \"\\e159\";\n}\n.glyphicon-collapse-up:before {\n  content: \"\\e160\";\n}\n.glyphicon-log-in:before {\n  content: \"\\e161\";\n}\n.glyphicon-flash:before {\n  content: \"\\e162\";\n}\n.glyphicon-log-out:before {\n  content: \"\\e163\";\n}\n.glyphicon-new-window:before {\n  content: \"\\e164\";\n}\n.glyphicon-record:before {\n  content: \"\\e165\";\n}\n.glyphicon-save:before {\n  content: \"\\e166\";\n}\n.glyphicon-open:before {\n  content: \"\\e167\";\n}\n.glyphicon-saved:before {\n  content: \"\\e168\";\n}\n.glyphicon-import:before {\n  content: \"\\e169\";\n}\n.glyphicon-export:before {\n  content: \"\\e170\";\n}\n.glyphicon-send:before {\n  content: \"\\e171\";\n}\n.glyphicon-floppy-disk:before {\n  content: \"\\e172\";\n}\n.glyphicon-floppy-saved:before {\n  content: \"\\e173\";\n}\n.glyphicon-floppy-remove:before {\n  content: \"\\e174\";\n}\n.glyphicon-floppy-save:before {\n  content: \"\\e175\";\n}\n.glyphicon-floppy-open:before {\n  content: \"\\e176\";\n}\n.glyphicon-credit-card:before {\n  content: \"\\e177\";\n}\n.glyphicon-transfer:before {\n  content: \"\\e178\";\n}\n.glyphicon-cutlery:before {\n  content: \"\\e179\";\n}\n.glyphicon-header:before {\n  content: \"\\e180\";\n}\n.glyphicon-compressed:before {\n  content: \"\\e181\";\n}\n.glyphicon-earphone:before {\n  content: \"\\e182\";\n}\n.glyphicon-phone-alt:before {\n  content: \"\\e183\";\n}\n.glyphicon-tower:before {\n  content: \"\\e184\";\n}\n.glyphicon-stats:before {\n  content: \"\\e185\";\n}\n.glyphicon-sd-video:before {\n  content: \"\\e186\";\n}\n.glyphicon-hd-video:before {\n  content: \"\\e187\";\n}\n.glyphicon-subtitles:before {\n  content: \"\\e188\";\n}\n.glyphicon-sound-stereo:before {\n  content: \"\\e189\";\n}\n.glyphicon-sound-dolby:before {\n  content: \"\\e190\";\n}\n.glyphicon-sound-5-1:before {\n  content: \"\\e191\";\n}\n.glyphicon-sound-6-1:before {\n  content: \"\\e192\";\n}\n.glyphicon-sound-7-1:before {\n  content: \"\\e193\";\n}\n.glyphicon-copyright-mark:before {\n  content: \"\\e194\";\n}\n.glyphicon-registration-mark:before {\n  content: \"\\e195\";\n}\n.glyphicon-cloud-download:before {\n  content: \"\\e197\";\n}\n.glyphicon-cloud-upload:before {\n  content: \"\\e198\";\n}\n.glyphicon-tree-conifer:before {\n  content: \"\\e199\";\n}\n.glyphicon-tree-deciduous:before {\n  content: \"\\e200\";\n}\n.glyphicon-cd:before {\n  content: \"\\e201\";\n}\n.glyphicon-save-file:before {\n  content: \"\\e202\";\n}\n.glyphicon-open-file:before {\n  content: \"\\e203\";\n}\n.glyphicon-level-up:before {\n  content: \"\\e204\";\n}\n.glyphicon-copy:before {\n  content: \"\\e205\";\n}\n.glyphicon-paste:before {\n  content: \"\\e206\";\n}\n.glyphicon-alert:before {\n  content: \"\\e209\";\n}\n.glyphicon-equalizer:before {\n  content: \"\\e210\";\n}\n.glyphicon-king:before {\n  content: \"\\e211\";\n}\n.glyphicon-queen:before {\n  content: \"\\e212\";\n}\n.glyphicon-pawn:before {\n  content: \"\\e213\";\n}\n.glyphicon-bishop:before {\n  content: \"\\e214\";\n}\n.glyphicon-knight:before {\n  content: \"\\e215\";\n}\n.glyphicon-baby-formula:before {\n  content: \"\\e216\";\n}\n.glyphicon-tent:before {\n  content: \"\\26fa\";\n}\n.glyphicon-blackboard:before {\n  content: \"\\e218\";\n}\n.glyphicon-bed:before {\n  content: \"\\e219\";\n}\n.glyphicon-apple:before {\n  content: \"\\f8ff\";\n}\n.glyphicon-erase:before {\n  content: \"\\e221\";\n}\n.glyphicon-hourglass:before {\n  content: \"\\231b\";\n}\n.glyphicon-lamp:before {\n  content: \"\\e223\";\n}\n.glyphicon-duplicate:before {\n  content: \"\\e224\";\n}\n.glyphicon-piggy-bank:before {\n  content: \"\\e225\";\n}\n.glyphicon-scissors:before {\n  content: \"\\e226\";\n}\n.glyphicon-bitcoin:before {\n  content: \"\\e227\";\n}\n.glyphicon-btc:before {\n  content: \"\\e227\";\n}\n.glyphicon-xbt:before {\n  content: \"\\e227\";\n}\n.glyphicon-yen:before {\n  content: \"\\00a5\";\n}\n.glyphicon-jpy:before {\n  content: \"\\00a5\";\n}\n.glyphicon-ruble:before {\n  content: \"\\20bd\";\n}\n.glyphicon-rub:before {\n  content: \"\\20bd\";\n}\n.glyphicon-scale:before {\n  content: \"\\e230\";\n}\n.glyphicon-ice-lolly:before {\n  content: \"\\e231\";\n}\n.glyphicon-ice-lolly-tasted:before {\n  content: \"\\e232\";\n}\n.glyphicon-education:before {\n  content: \"\\e233\";\n}\n.glyphicon-option-horizontal:before {\n  content: \"\\e234\";\n}\n.glyphicon-option-vertical:before {\n  content: \"\\e235\";\n}\n.glyphicon-menu-hamburger:before {\n  content: \"\\e236\";\n}\n.glyphicon-modal-window:before {\n  content: \"\\e237\";\n}\n.glyphicon-oil:before {\n  content: \"\\e238\";\n}\n.glyphicon-grain:before {\n  content: \"\\e239\";\n}\n.glyphicon-sunglasses:before {\n  content: \"\\e240\";\n}\n.glyphicon-text-size:before {\n  content: \"\\e241\";\n}\n.glyphicon-text-color:before {\n  content: \"\\e242\";\n}\n.glyphicon-text-background:before {\n  content: \"\\e243\";\n}\n.glyphicon-object-align-top:before {\n  content: \"\\e244\";\n}\n.glyphicon-object-align-bottom:before {\n  content: \"\\e245\";\n}\n.glyphicon-object-align-horizontal:before {\n  content: \"\\e246\";\n}\n.glyphicon-object-align-left:before {\n  content: \"\\e247\";\n}\n.glyphicon-object-align-vertical:before {\n  content: \"\\e248\";\n}\n.glyphicon-object-align-right:before {\n  content: \"\\e249\";\n}\n.glyphicon-triangle-right:before {\n  content: \"\\e250\";\n}\n.glyphicon-triangle-left:before {\n  content: \"\\e251\";\n}\n.glyphicon-triangle-bottom:before {\n  content: \"\\e252\";\n}\n.glyphicon-triangle-top:before {\n  content: \"\\e253\";\n}\n.glyphicon-console:before {\n  content: \"\\e254\";\n}\n.glyphicon-superscript:before {\n  content: \"\\e255\";\n}\n.glyphicon-subscript:before {\n  content: \"\\e256\";\n}\n.glyphicon-menu-left:before {\n  content: \"\\e257\";\n}\n.glyphicon-menu-right:before {\n  content: \"\\e258\";\n}\n.glyphicon-menu-down:before {\n  content: \"\\e259\";\n}\n.glyphicon-menu-up:before {\n  content: \"\\e260\";\n}\n* {\n  -webkit-box-sizing: border-box;\n  -moz-box-sizing: border-box;\n  box-sizing: border-box;\n}\n*:before,\n*:after {\n  -webkit-box-sizing: border-box;\n  -moz-box-sizing: border-box;\n  box-sizing: border-box;\n}\nhtml {\n  font-size: 10px;\n  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);\n}\nbody {\n  font-family: \"Helvetica Neue\", Helvetica, Arial, sans-serif;\n  font-size: 14px;\n  line-height: 1.42857143;\n  color: #333333;\n  background-color: #fff;\n}\ninput,\nbutton,\nselect,\ntextarea {\n  font-family: inherit;\n  font-size: inherit;\n  line-height: inherit;\n}\na {\n  color: #337ab7;\n  text-decoration: none;\n}\na:hover,\na:focus {\n  color: #23527c;\n  text-decoration: underline;\n}\na:focus {\n  outline: 5px auto -webkit-focus-ring-color;\n  outline-offset: -2px;\n}\nfigure {\n  margin: 0;\n}\nimg {\n  vertical-align: middle;\n}\n.img-responsive,\n.thumbnail > img,\n.thumbnail a > img,\n.carousel-inner > .item > img,\n.carousel-inner > .item > a > img {\n  display: block;\n  max-width: 100%;\n  height: auto;\n}\n.img-rounded {\n  border-radius: 6px;\n}\n.img-thumbnail {\n  padding: 4px;\n  line-height: 1.42857143;\n  background-color: #fff;\n  border: 1px solid #ddd;\n  border-radius: 4px;\n  -webkit-transition: all 0.2s ease-in-out;\n  -o-transition: all 0.2s ease-in-out;\n  transition: all 0.2s ease-in-out;\n  display: inline-block;\n  max-width: 100%;\n  height: auto;\n}\n.img-circle {\n  border-radius: 50%;\n}\nhr {\n  margin-top: 20px;\n  margin-bottom: 20px;\n  border: 0;\n  border-top: 1px solid #eeeeee;\n}\n.sr-only {\n  position: absolute;\n  width: 1px;\n  height: 1px;\n  margin: -1px;\n  padding: 0;\n  overflow: hidden;\n  clip: rect(0, 0, 0, 0);\n  border: 0;\n}\n.sr-only-focusable:active,\n.sr-only-focusable:focus {\n  position: static;\n  width: auto;\n  height: auto;\n  margin: 0;\n  overflow: visible;\n  clip: auto;\n}\n[role=\"button\"] {\n  cursor: pointer;\n}\nh1,\nh2,\nh3,\nh4,\nh5,\nh6,\n.h1,\n.h2,\n.h3,\n.h4,\n.h5,\n.h6 {\n  font-family: inherit;\n  font-weight: 500;\n  line-height: 1.1;\n  color: inherit;\n}\nh1 small,\nh2 small,\nh3 small,\nh4 small,\nh5 small,\nh6 small,\n.h1 small,\n.h2 small,\n.h3 small,\n.h4 small,\n.h5 small,\n.h6 small,\nh1 .small,\nh2 .small,\nh3 .small,\nh4 .small,\nh5 .small,\nh6 .small,\n.h1 .small,\n.h2 .small,\n.h3 .small,\n.h4 .small,\n.h5 .small,\n.h6 .small {\n  font-weight: normal;\n  line-height: 1;\n  color: #777777;\n}\nh1,\n.h1,\nh2,\n.h2,\nh3,\n.h3 {\n  margin-top: 20px;\n  margin-bottom: 10px;\n}\nh1 small,\n.h1 small,\nh2 small,\n.h2 small,\nh3 small,\n.h3 small,\nh1 .small,\n.h1 .small,\nh2 .small,\n.h2 .small,\nh3 .small,\n.h3 .small {\n  font-size: 65%;\n}\nh4,\n.h4,\nh5,\n.h5,\nh6,\n.h6 {\n  margin-top: 10px;\n  margin-bottom: 10px;\n}\nh4 small,\n.h4 small,\nh5 small,\n.h5 small,\nh6 small,\n.h6 small,\nh4 .small,\n.h4 .small,\nh5 .small,\n.h5 .small,\nh6 .small,\n.h6 .small {\n  font-size: 75%;\n}\nh1,\n.h1 {\n  font-size: 36px;\n}\nh2,\n.h2 {\n  font-size: 30px;\n}\nh3,\n.h3 {\n  font-size: 24px;\n}\nh4,\n.h4 {\n  font-size: 18px;\n}\nh5,\n.h5 {\n  font-size: 14px;\n}\nh6,\n.h6 {\n  font-size: 12px;\n}\np {\n  margin: 0 0 10px;\n}\n.lead {\n  margin-bottom: 20px;\n  font-size: 16px;\n  font-weight: 300;\n  line-height: 1.4;\n}\n@media (min-width: 768px) {\n  .lead {\n    font-size: 21px;\n  }\n}\nsmall,\n.small {\n  font-size: 85%;\n}\nmark,\n.mark {\n  background-color: #fcf8e3;\n  padding: .2em;\n}\n.text-left {\n  text-align: left;\n}\n.text-right {\n  text-align: right;\n}\n.text-center {\n  text-align: center;\n}\n.text-justify {\n  text-align: justify;\n}\n.text-nowrap {\n  white-space: nowrap;\n}\n.text-lowercase {\n  text-transform: lowercase;\n}\n.text-uppercase {\n  text-transform: uppercase;\n}\n.text-capitalize {\n  text-transform: capitalize;\n}\n.text-muted {\n  color: #777777;\n}\n.text-primary {\n  color: #337ab7;\n}\na.text-primary:hover,\na.text-primary:focus {\n  color: #286090;\n}\n.text-success {\n  color: #3c763d;\n}\na.text-success:hover,\na.text-success:focus {\n  color: #2b542c;\n}\n.text-info {\n  color: #31708f;\n}\na.text-info:hover,\na.text-info:focus {\n  color: #245269;\n}\n.text-warning {\n  color: #8a6d3b;\n}\na.text-warning:hover,\na.text-warning:focus {\n  color: #66512c;\n}\n.text-danger {\n  color: #a94442;\n}\na.text-danger:hover,\na.text-danger:focus {\n  color: #843534;\n}\n.bg-primary {\n  color: #fff;\n  background-color: #337ab7;\n}\na.bg-primary:hover,\na.bg-primary:focus {\n  background-color: #286090;\n}\n.bg-success {\n  background-color: #dff0d8;\n}\na.bg-success:hover,\na.bg-success:focus {\n  background-color: #c1e2b3;\n}\n.bg-info {\n  background-color: #d9edf7;\n}\na.bg-info:hover,\na.bg-info:focus {\n  background-color: #afd9ee;\n}\n.bg-warning {\n  background-color: #fcf8e3;\n}\na.bg-warning:hover,\na.bg-warning:focus {\n  background-color: #f7ecb5;\n}\n.bg-danger {\n  background-color: #f2dede;\n}\na.bg-danger:hover,\na.bg-danger:focus {\n  background-color: #e4b9b9;\n}\n.page-header {\n  padding-bottom: 9px;\n  margin: 40px 0 20px;\n  border-bottom: 1px solid #eeeeee;\n}\nul,\nol {\n  margin-top: 0;\n  margin-bottom: 10px;\n}\nul ul,\nol ul,\nul ol,\nol ol {\n  margin-bottom: 0;\n}\n.list-unstyled {\n  padding-left: 0;\n  list-style: none;\n}\n.list-inline {\n  padding-left: 0;\n  list-style: none;\n  margin-left: -5px;\n}\n.list-inline > li {\n  display: inline-block;\n  padding-left: 5px;\n  padding-right: 5px;\n}\ndl {\n  margin-top: 0;\n  margin-bottom: 20px;\n}\ndt,\ndd {\n  line-height: 1.42857143;\n}\ndt {\n  font-weight: bold;\n}\ndd {\n  margin-left: 0;\n}\n@media (min-width: 768px) {\n  .dl-horizontal dt {\n    float: left;\n    width: 160px;\n    clear: left;\n    text-align: right;\n    overflow: hidden;\n    text-overflow: ellipsis;\n    white-space: nowrap;\n  }\n  .dl-horizontal dd {\n    margin-left: 180px;\n  }\n}\nabbr[title],\nabbr[data-original-title] {\n  cursor: help;\n  border-bottom: 1px dotted #777777;\n}\n.initialism {\n  font-size: 90%;\n  text-transform: uppercase;\n}\nblockquote {\n  padding: 10px 20px;\n  margin: 0 0 20px;\n  font-size: 17.5px;\n  border-left: 5px solid #eeeeee;\n}\nblockquote p:last-child,\nblockquote ul:last-child,\nblockquote ol:last-child {\n  margin-bottom: 0;\n}\nblockquote footer,\nblockquote small,\nblockquote .small {\n  display: block;\n  font-size: 80%;\n  line-height: 1.42857143;\n  color: #777777;\n}\nblockquote footer:before,\nblockquote small:before,\nblockquote .small:before {\n  content: '\\2014 \\00A0';\n}\n.blockquote-reverse,\nblockquote.pull-right {\n  padding-right: 15px;\n  padding-left: 0;\n  border-right: 5px solid #eeeeee;\n  border-left: 0;\n  text-align: right;\n}\n.blockquote-reverse footer:before,\nblockquote.pull-right footer:before,\n.blockquote-reverse small:before,\nblockquote.pull-right small:before,\n.blockquote-reverse .small:before,\nblockquote.pull-right .small:before {\n  content: '';\n}\n.blockquote-reverse footer:after,\nblockquote.pull-right footer:after,\n.blockquote-reverse small:after,\nblockquote.pull-right small:after,\n.blockquote-reverse .small:after,\nblockquote.pull-right .small:after {\n  content: '\\00A0 \\2014';\n}\naddress {\n  margin-bottom: 20px;\n  font-style: normal;\n  line-height: 1.42857143;\n}\ncode,\nkbd,\npre,\nsamp {\n  font-family: Menlo, Monaco, Consolas, \"Courier New\", monospace;\n}\ncode {\n  padding: 2px 4px;\n  font-size: 90%;\n  color: #c7254e;\n  background-color: #f9f2f4;\n  border-radius: 4px;\n}\nkbd {\n  padding: 2px 4px;\n  font-size: 90%;\n  color: #fff;\n  background-color: #333;\n  border-radius: 3px;\n  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);\n}\nkbd kbd {\n  padding: 0;\n  font-size: 100%;\n  font-weight: bold;\n  box-shadow: none;\n}\npre {\n  display: block;\n  padding: 9.5px;\n  margin: 0 0 10px;\n  font-size: 13px;\n  line-height: 1.42857143;\n  word-break: break-all;\n  word-wrap: break-word;\n  color: #333333;\n  background-color: #f5f5f5;\n  border: 1px solid #ccc;\n  border-radius: 4px;\n}\npre code {\n  padding: 0;\n  font-size: inherit;\n  color: inherit;\n  white-space: pre-wrap;\n  background-color: transparent;\n  border-radius: 0;\n}\n.pre-scrollable {\n  max-height: 340px;\n  overflow-y: scroll;\n}\n.container {\n  margin-right: auto;\n  margin-left: auto;\n  padding-left: 15px;\n  padding-right: 15px;\n}\n@media (min-width: 768px) {\n  .container {\n    width: 750px;\n  }\n}\n@media (min-width: 992px) {\n  .container {\n    width: 970px;\n  }\n}\n@media (min-width: 1200px) {\n  .container {\n    width: 1170px;\n  }\n}\n.container-fluid {\n  margin-right: auto;\n  margin-left: auto;\n  padding-left: 15px;\n  padding-right: 15px;\n}\n.row {\n  margin-left: -15px;\n  margin-right: -15px;\n}\n.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 {\n  position: relative;\n  min-height: 1px;\n  padding-left: 15px;\n  padding-right: 15px;\n}\n.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 {\n  float: left;\n}\n.col-xs-12 {\n  width: 100%;\n}\n.col-xs-11 {\n  width: 91.66666667%;\n}\n.col-xs-10 {\n  width: 83.33333333%;\n}\n.col-xs-9 {\n  width: 75%;\n}\n.col-xs-8 {\n  width: 66.66666667%;\n}\n.col-xs-7 {\n  width: 58.33333333%;\n}\n.col-xs-6 {\n  width: 50%;\n}\n.col-xs-5 {\n  width: 41.66666667%;\n}\n.col-xs-4 {\n  width: 33.33333333%;\n}\n.col-xs-3 {\n  width: 25%;\n}\n.col-xs-2 {\n  width: 16.66666667%;\n}\n.col-xs-1 {\n  width: 8.33333333%;\n}\n.col-xs-pull-12 {\n  right: 100%;\n}\n.col-xs-pull-11 {\n  right: 91.66666667%;\n}\n.col-xs-pull-10 {\n  right: 83.33333333%;\n}\n.col-xs-pull-9 {\n  right: 75%;\n}\n.col-xs-pull-8 {\n  right: 66.66666667%;\n}\n.col-xs-pull-7 {\n  right: 58.33333333%;\n}\n.col-xs-pull-6 {\n  right: 50%;\n}\n.col-xs-pull-5 {\n  right: 41.66666667%;\n}\n.col-xs-pull-4 {\n  right: 33.33333333%;\n}\n.col-xs-pull-3 {\n  right: 25%;\n}\n.col-xs-pull-2 {\n  right: 16.66666667%;\n}\n.col-xs-pull-1 {\n  right: 8.33333333%;\n}\n.col-xs-pull-0 {\n  right: auto;\n}\n.col-xs-push-12 {\n  left: 100%;\n}\n.col-xs-push-11 {\n  left: 91.66666667%;\n}\n.col-xs-push-10 {\n  left: 83.33333333%;\n}\n.col-xs-push-9 {\n  left: 75%;\n}\n.col-xs-push-8 {\n  left: 66.66666667%;\n}\n.col-xs-push-7 {\n  left: 58.33333333%;\n}\n.col-xs-push-6 {\n  left: 50%;\n}\n.col-xs-push-5 {\n  left: 41.66666667%;\n}\n.col-xs-push-4 {\n  left: 33.33333333%;\n}\n.col-xs-push-3 {\n  left: 25%;\n}\n.col-xs-push-2 {\n  left: 16.66666667%;\n}\n.col-xs-push-1 {\n  left: 8.33333333%;\n}\n.col-xs-push-0 {\n  left: auto;\n}\n.col-xs-offset-12 {\n  margin-left: 100%;\n}\n.col-xs-offset-11 {\n  margin-left: 91.66666667%;\n}\n.col-xs-offset-10 {\n  margin-left: 83.33333333%;\n}\n.col-xs-offset-9 {\n  margin-left: 75%;\n}\n.col-xs-offset-8 {\n  margin-left: 66.66666667%;\n}\n.col-xs-offset-7 {\n  margin-left: 58.33333333%;\n}\n.col-xs-offset-6 {\n  margin-left: 50%;\n}\n.col-xs-offset-5 {\n  margin-left: 41.66666667%;\n}\n.col-xs-offset-4 {\n  margin-left: 33.33333333%;\n}\n.col-xs-offset-3 {\n  margin-left: 25%;\n}\n.col-xs-offset-2 {\n  margin-left: 16.66666667%;\n}\n.col-xs-offset-1 {\n  margin-left: 8.33333333%;\n}\n.col-xs-offset-0 {\n  margin-left: 0%;\n}\n@media (min-width: 768px) {\n  .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {\n    float: left;\n  }\n  .col-sm-12 {\n    width: 100%;\n  }\n  .col-sm-11 {\n    width: 91.66666667%;\n  }\n  .col-sm-10 {\n    width: 83.33333333%;\n  }\n  .col-sm-9 {\n    width: 75%;\n  }\n  .col-sm-8 {\n    width: 66.66666667%;\n  }\n  .col-sm-7 {\n    width: 58.33333333%;\n  }\n  .col-sm-6 {\n    width: 50%;\n  }\n  .col-sm-5 {\n    width: 41.66666667%;\n  }\n  .col-sm-4 {\n    width: 33.33333333%;\n  }\n  .col-sm-3 {\n    width: 25%;\n  }\n  .col-sm-2 {\n    width: 16.66666667%;\n  }\n  .col-sm-1 {\n    width: 8.33333333%;\n  }\n  .col-sm-pull-12 {\n    right: 100%;\n  }\n  .col-sm-pull-11 {\n    right: 91.66666667%;\n  }\n  .col-sm-pull-10 {\n    right: 83.33333333%;\n  }\n  .col-sm-pull-9 {\n    right: 75%;\n  }\n  .col-sm-pull-8 {\n    right: 66.66666667%;\n  }\n  .col-sm-pull-7 {\n    right: 58.33333333%;\n  }\n  .col-sm-pull-6 {\n    right: 50%;\n  }\n  .col-sm-pull-5 {\n    right: 41.66666667%;\n  }\n  .col-sm-pull-4 {\n    right: 33.33333333%;\n  }\n  .col-sm-pull-3 {\n    right: 25%;\n  }\n  .col-sm-pull-2 {\n    right: 16.66666667%;\n  }\n  .col-sm-pull-1 {\n    right: 8.33333333%;\n  }\n  .col-sm-pull-0 {\n    right: auto;\n  }\n  .col-sm-push-12 {\n    left: 100%;\n  }\n  .col-sm-push-11 {\n    left: 91.66666667%;\n  }\n  .col-sm-push-10 {\n    left: 83.33333333%;\n  }\n  .col-sm-push-9 {\n    left: 75%;\n  }\n  .col-sm-push-8 {\n    left: 66.66666667%;\n  }\n  .col-sm-push-7 {\n    left: 58.33333333%;\n  }\n  .col-sm-push-6 {\n    left: 50%;\n  }\n  .col-sm-push-5 {\n    left: 41.66666667%;\n  }\n  .col-sm-push-4 {\n    left: 33.33333333%;\n  }\n  .col-sm-push-3 {\n    left: 25%;\n  }\n  .col-sm-push-2 {\n    left: 16.66666667%;\n  }\n  .col-sm-push-1 {\n    left: 8.33333333%;\n  }\n  .col-sm-push-0 {\n    left: auto;\n  }\n  .col-sm-offset-12 {\n    margin-left: 100%;\n  }\n  .col-sm-offset-11 {\n    margin-left: 91.66666667%;\n  }\n  .col-sm-offset-10 {\n    margin-left: 83.33333333%;\n  }\n  .col-sm-offset-9 {\n    margin-left: 75%;\n  }\n  .col-sm-offset-8 {\n    margin-left: 66.66666667%;\n  }\n  .col-sm-offset-7 {\n    margin-left: 58.33333333%;\n  }\n  .col-sm-offset-6 {\n    margin-left: 50%;\n  }\n  .col-sm-offset-5 {\n    margin-left: 41.66666667%;\n  }\n  .col-sm-offset-4 {\n    margin-left: 33.33333333%;\n  }\n  .col-sm-offset-3 {\n    margin-left: 25%;\n  }\n  .col-sm-offset-2 {\n    margin-left: 16.66666667%;\n  }\n  .col-sm-offset-1 {\n    margin-left: 8.33333333%;\n  }\n  .col-sm-offset-0 {\n    margin-left: 0%;\n  }\n}\n@media (min-width: 992px) {\n  .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {\n    float: left;\n  }\n  .col-md-12 {\n    width: 100%;\n  }\n  .col-md-11 {\n    width: 91.66666667%;\n  }\n  .col-md-10 {\n    width: 83.33333333%;\n  }\n  .col-md-9 {\n    width: 75%;\n  }\n  .col-md-8 {\n    width: 66.66666667%;\n  }\n  .col-md-7 {\n    width: 58.33333333%;\n  }\n  .col-md-6 {\n    width: 50%;\n  }\n  .col-md-5 {\n    width: 41.66666667%;\n  }\n  .col-md-4 {\n    width: 33.33333333%;\n  }\n  .col-md-3 {\n    width: 25%;\n  }\n  .col-md-2 {\n    width: 16.66666667%;\n  }\n  .col-md-1 {\n    width: 8.33333333%;\n  }\n  .col-md-pull-12 {\n    right: 100%;\n  }\n  .col-md-pull-11 {\n    right: 91.66666667%;\n  }\n  .col-md-pull-10 {\n    right: 83.33333333%;\n  }\n  .col-md-pull-9 {\n    right: 75%;\n  }\n  .col-md-pull-8 {\n    right: 66.66666667%;\n  }\n  .col-md-pull-7 {\n    right: 58.33333333%;\n  }\n  .col-md-pull-6 {\n    right: 50%;\n  }\n  .col-md-pull-5 {\n    right: 41.66666667%;\n  }\n  .col-md-pull-4 {\n    right: 33.33333333%;\n  }\n  .col-md-pull-3 {\n    right: 25%;\n  }\n  .col-md-pull-2 {\n    right: 16.66666667%;\n  }\n  .col-md-pull-1 {\n    right: 8.33333333%;\n  }\n  .col-md-pull-0 {\n    right: auto;\n  }\n  .col-md-push-12 {\n    left: 100%;\n  }\n  .col-md-push-11 {\n    left: 91.66666667%;\n  }\n  .col-md-push-10 {\n    left: 83.33333333%;\n  }\n  .col-md-push-9 {\n    left: 75%;\n  }\n  .col-md-push-8 {\n    left: 66.66666667%;\n  }\n  .col-md-push-7 {\n    left: 58.33333333%;\n  }\n  .col-md-push-6 {\n    left: 50%;\n  }\n  .col-md-push-5 {\n    left: 41.66666667%;\n  }\n  .col-md-push-4 {\n    left: 33.33333333%;\n  }\n  .col-md-push-3 {\n    left: 25%;\n  }\n  .col-md-push-2 {\n    left: 16.66666667%;\n  }\n  .col-md-push-1 {\n    left: 8.33333333%;\n  }\n  .col-md-push-0 {\n    left: auto;\n  }\n  .col-md-offset-12 {\n    margin-left: 100%;\n  }\n  .col-md-offset-11 {\n    margin-left: 91.66666667%;\n  }\n  .col-md-offset-10 {\n    margin-left: 83.33333333%;\n  }\n  .col-md-offset-9 {\n    margin-left: 75%;\n  }\n  .col-md-offset-8 {\n    margin-left: 66.66666667%;\n  }\n  .col-md-offset-7 {\n    margin-left: 58.33333333%;\n  }\n  .col-md-offset-6 {\n    margin-left: 50%;\n  }\n  .col-md-offset-5 {\n    margin-left: 41.66666667%;\n  }\n  .col-md-offset-4 {\n    margin-left: 33.33333333%;\n  }\n  .col-md-offset-3 {\n    margin-left: 25%;\n  }\n  .col-md-offset-2 {\n    margin-left: 16.66666667%;\n  }\n  .col-md-offset-1 {\n    margin-left: 8.33333333%;\n  }\n  .col-md-offset-0 {\n    margin-left: 0%;\n  }\n}\n@media (min-width: 1200px) {\n  .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {\n    float: left;\n  }\n  .col-lg-12 {\n    width: 100%;\n  }\n  .col-lg-11 {\n    width: 91.66666667%;\n  }\n  .col-lg-10 {\n    width: 83.33333333%;\n  }\n  .col-lg-9 {\n    width: 75%;\n  }\n  .col-lg-8 {\n    width: 66.66666667%;\n  }\n  .col-lg-7 {\n    width: 58.33333333%;\n  }\n  .col-lg-6 {\n    width: 50%;\n  }\n  .col-lg-5 {\n    width: 41.66666667%;\n  }\n  .col-lg-4 {\n    width: 33.33333333%;\n  }\n  .col-lg-3 {\n    width: 25%;\n  }\n  .col-lg-2 {\n    width: 16.66666667%;\n  }\n  .col-lg-1 {\n    width: 8.33333333%;\n  }\n  .col-lg-pull-12 {\n    right: 100%;\n  }\n  .col-lg-pull-11 {\n    right: 91.66666667%;\n  }\n  .col-lg-pull-10 {\n    right: 83.33333333%;\n  }\n  .col-lg-pull-9 {\n    right: 75%;\n  }\n  .col-lg-pull-8 {\n    right: 66.66666667%;\n  }\n  .col-lg-pull-7 {\n    right: 58.33333333%;\n  }\n  .col-lg-pull-6 {\n    right: 50%;\n  }\n  .col-lg-pull-5 {\n    right: 41.66666667%;\n  }\n  .col-lg-pull-4 {\n    right: 33.33333333%;\n  }\n  .col-lg-pull-3 {\n    right: 25%;\n  }\n  .col-lg-pull-2 {\n    right: 16.66666667%;\n  }\n  .col-lg-pull-1 {\n    right: 8.33333333%;\n  }\n  .col-lg-pull-0 {\n    right: auto;\n  }\n  .col-lg-push-12 {\n    left: 100%;\n  }\n  .col-lg-push-11 {\n    left: 91.66666667%;\n  }\n  .col-lg-push-10 {\n    left: 83.33333333%;\n  }\n  .col-lg-push-9 {\n    left: 75%;\n  }\n  .col-lg-push-8 {\n    left: 66.66666667%;\n  }\n  .col-lg-push-7 {\n    left: 58.33333333%;\n  }\n  .col-lg-push-6 {\n    left: 50%;\n  }\n  .col-lg-push-5 {\n    left: 41.66666667%;\n  }\n  .col-lg-push-4 {\n    left: 33.33333333%;\n  }\n  .col-lg-push-3 {\n    left: 25%;\n  }\n  .col-lg-push-2 {\n    left: 16.66666667%;\n  }\n  .col-lg-push-1 {\n    left: 8.33333333%;\n  }\n  .col-lg-push-0 {\n    left: auto;\n  }\n  .col-lg-offset-12 {\n    margin-left: 100%;\n  }\n  .col-lg-offset-11 {\n    margin-left: 91.66666667%;\n  }\n  .col-lg-offset-10 {\n    margin-left: 83.33333333%;\n  }\n  .col-lg-offset-9 {\n    margin-left: 75%;\n  }\n  .col-lg-offset-8 {\n    margin-left: 66.66666667%;\n  }\n  .col-lg-offset-7 {\n    margin-left: 58.33333333%;\n  }\n  .col-lg-offset-6 {\n    margin-left: 50%;\n  }\n  .col-lg-offset-5 {\n    margin-left: 41.66666667%;\n  }\n  .col-lg-offset-4 {\n    margin-left: 33.33333333%;\n  }\n  .col-lg-offset-3 {\n    margin-left: 25%;\n  }\n  .col-lg-offset-2 {\n    margin-left: 16.66666667%;\n  }\n  .col-lg-offset-1 {\n    margin-left: 8.33333333%;\n  }\n  .col-lg-offset-0 {\n    margin-left: 0%;\n  }\n}\ntable {\n  background-color: transparent;\n}\ncaption {\n  padding-top: 8px;\n  padding-bottom: 8px;\n  color: #777777;\n  text-align: left;\n}\nth {\n  text-align: left;\n}\n.table {\n  width: 100%;\n  max-width: 100%;\n  margin-bottom: 20px;\n}\n.table > thead > tr > th,\n.table > tbody > tr > th,\n.table > tfoot > tr > th,\n.table > thead > tr > td,\n.table > tbody > tr > td,\n.table > tfoot > tr > td {\n  padding: 8px;\n  line-height: 1.42857143;\n  vertical-align: top;\n  border-top: 1px solid #ddd;\n}\n.table > thead > tr > th {\n  vertical-align: bottom;\n  border-bottom: 2px solid #ddd;\n}\n.table > caption + thead > tr:first-child > th,\n.table > colgroup + thead > tr:first-child > th,\n.table > thead:first-child > tr:first-child > th,\n.table > caption + thead > tr:first-child > td,\n.table > colgroup + thead > tr:first-child > td,\n.table > thead:first-child > tr:first-child > td {\n  border-top: 0;\n}\n.table > tbody + tbody {\n  border-top: 2px solid #ddd;\n}\n.table .table {\n  background-color: #fff;\n}\n.table-condensed > thead > tr > th,\n.table-condensed > tbody > tr > th,\n.table-condensed > tfoot > tr > th,\n.table-condensed > thead > tr > td,\n.table-condensed > tbody > tr > td,\n.table-condensed > tfoot > tr > td {\n  padding: 5px;\n}\n.table-bordered {\n  border: 1px solid #ddd;\n}\n.table-bordered > thead > tr > th,\n.table-bordered > tbody > tr > th,\n.table-bordered > tfoot > tr > th,\n.table-bordered > thead > tr > td,\n.table-bordered > tbody > tr > td,\n.table-bordered > tfoot > tr > td {\n  border: 1px solid #ddd;\n}\n.table-bordered > thead > tr > th,\n.table-bordered > thead > tr > td {\n  border-bottom-width: 2px;\n}\n.table-striped > tbody > tr:nth-of-type(odd) {\n  background-color: #f9f9f9;\n}\n.table-hover > tbody > tr:hover {\n  background-color: #f5f5f5;\n}\ntable col[class*=\"col-\"] {\n  position: static;\n  float: none;\n  display: table-column;\n}\ntable td[class*=\"col-\"],\ntable th[class*=\"col-\"] {\n  position: static;\n  float: none;\n  display: table-cell;\n}\n.table > thead > tr > td.active,\n.table > tbody > tr > td.active,\n.table > tfoot > tr > td.active,\n.table > thead > tr > th.active,\n.table > tbody > tr > th.active,\n.table > tfoot > tr > th.active,\n.table > thead > tr.active > td,\n.table > tbody > tr.active > td,\n.table > tfoot > tr.active > td,\n.table > thead > tr.active > th,\n.table > tbody > tr.active > th,\n.table > tfoot > tr.active > th {\n  background-color: #f5f5f5;\n}\n.table-hover > tbody > tr > td.active:hover,\n.table-hover > tbody > tr > th.active:hover,\n.table-hover > tbody > tr.active:hover > td,\n.table-hover > tbody > tr:hover > .active,\n.table-hover > tbody > tr.active:hover > th {\n  background-color: #e8e8e8;\n}\n.table > thead > tr > td.success,\n.table > tbody > tr > td.success,\n.table > tfoot > tr > td.success,\n.table > thead > tr > th.success,\n.table > tbody > tr > th.success,\n.table > tfoot > tr > th.success,\n.table > thead > tr.success > td,\n.table > tbody > tr.success > td,\n.table > tfoot > tr.success > td,\n.table > thead > tr.success > th,\n.table > tbody > tr.success > th,\n.table > tfoot > tr.success > th {\n  background-color: #dff0d8;\n}\n.table-hover > tbody > tr > td.success:hover,\n.table-hover > tbody > tr > th.success:hover,\n.table-hover > tbody > tr.success:hover > td,\n.table-hover > tbody > tr:hover > .success,\n.table-hover > tbody > tr.success:hover > th {\n  background-color: #d0e9c6;\n}\n.table > thead > tr > td.info,\n.table > tbody > tr > td.info,\n.table > tfoot > tr > td.info,\n.table > thead > tr > th.info,\n.table > tbody > tr > th.info,\n.table > tfoot > tr > th.info,\n.table > thead > tr.info > td,\n.table > tbody > tr.info > td,\n.table > tfoot > tr.info > td,\n.table > thead > tr.info > th,\n.table > tbody > tr.info > th,\n.table > tfoot > tr.info > th {\n  background-color: #d9edf7;\n}\n.table-hover > tbody > tr > td.info:hover,\n.table-hover > tbody > tr > th.info:hover,\n.table-hover > tbody > tr.info:hover > td,\n.table-hover > tbody > tr:hover > .info,\n.table-hover > tbody > tr.info:hover > th {\n  background-color: #c4e3f3;\n}\n.table > thead > tr > td.warning,\n.table > tbody > tr > td.warning,\n.table > tfoot > tr > td.warning,\n.table > thead > tr > th.warning,\n.table > tbody > tr > th.warning,\n.table > tfoot > tr > th.warning,\n.table > thead > tr.warning > td,\n.table > tbody > tr.warning > td,\n.table > tfoot > tr.warning > td,\n.table > thead > tr.warning > th,\n.table > tbody > tr.warning > th,\n.table > tfoot > tr.warning > th {\n  background-color: #fcf8e3;\n}\n.table-hover > tbody > tr > td.warning:hover,\n.table-hover > tbody > tr > th.warning:hover,\n.table-hover > tbody > tr.warning:hover > td,\n.table-hover > tbody > tr:hover > .warning,\n.table-hover > tbody > tr.warning:hover > th {\n  background-color: #faf2cc;\n}\n.table > thead > tr > td.danger,\n.table > tbody > tr > td.danger,\n.table > tfoot > tr > td.danger,\n.table > thead > tr > th.danger,\n.table > tbody > tr > th.danger,\n.table > tfoot > tr > th.danger,\n.table > thead > tr.danger > td,\n.table > tbody > tr.danger > td,\n.table > tfoot > tr.danger > td,\n.table > thead > tr.danger > th,\n.table > tbody > tr.danger > th,\n.table > tfoot > tr.danger > th {\n  background-color: #f2dede;\n}\n.table-hover > tbody > tr > td.danger:hover,\n.table-hover > tbody > tr > th.danger:hover,\n.table-hover > tbody > tr.danger:hover > td,\n.table-hover > tbody > tr:hover > .danger,\n.table-hover > tbody > tr.danger:hover > th {\n  background-color: #ebcccc;\n}\n.table-responsive {\n  overflow-x: auto;\n  min-height: 0.01%;\n}\n@media screen and (max-width: 767px) {\n  .table-responsive {\n    width: 100%;\n    margin-bottom: 15px;\n    overflow-y: hidden;\n    -ms-overflow-style: -ms-autohiding-scrollbar;\n    border: 1px solid #ddd;\n  }\n  .table-responsive > .table {\n    margin-bottom: 0;\n  }\n  .table-responsive > .table > thead > tr > th,\n  .table-responsive > .table > tbody > tr > th,\n  .table-responsive > .table > tfoot > tr > th,\n  .table-responsive > .table > thead > tr > td,\n  .table-responsive > .table > tbody > tr > td,\n  .table-responsive > .table > tfoot > tr > td {\n    white-space: nowrap;\n  }\n  .table-responsive > .table-bordered {\n    border: 0;\n  }\n  .table-responsive > .table-bordered > thead > tr > th:first-child,\n  .table-responsive > .table-bordered > tbody > tr > th:first-child,\n  .table-responsive > .table-bordered > tfoot > tr > th:first-child,\n  .table-responsive > .table-bordered > thead > tr > td:first-child,\n  .table-responsive > .table-bordered > tbody > tr > td:first-child,\n  .table-responsive > .table-bordered > tfoot > tr > td:first-child {\n    border-left: 0;\n  }\n  .table-responsive > .table-bordered > thead > tr > th:last-child,\n  .table-responsive > .table-bordered > tbody > tr > th:last-child,\n  .table-responsive > .table-bordered > tfoot > tr > th:last-child,\n  .table-responsive > .table-bordered > thead > tr > td:last-child,\n  .table-responsive > .table-bordered > tbody > tr > td:last-child,\n  .table-responsive > .table-bordered > tfoot > tr > td:last-child {\n    border-right: 0;\n  }\n  .table-responsive > .table-bordered > tbody > tr:last-child > th,\n  .table-responsive > .table-bordered > tfoot > tr:last-child > th,\n  .table-responsive > .table-bordered > tbody > tr:last-child > td,\n  .table-responsive > .table-bordered > tfoot > tr:last-child > td {\n    border-bottom: 0;\n  }\n}\nfieldset {\n  padding: 0;\n  margin: 0;\n  border: 0;\n  min-width: 0;\n}\nlegend {\n  display: block;\n  width: 100%;\n  padding: 0;\n  margin-bottom: 20px;\n  font-size: 21px;\n  line-height: inherit;\n  color: #333333;\n  border: 0;\n  border-bottom: 1px solid #e5e5e5;\n}\nlabel {\n  display: inline-block;\n  max-width: 100%;\n  margin-bottom: 5px;\n  font-weight: bold;\n}\ninput[type=\"search\"] {\n  -webkit-box-sizing: border-box;\n  -moz-box-sizing: border-box;\n  box-sizing: border-box;\n}\ninput[type=\"radio\"],\ninput[type=\"checkbox\"] {\n  margin: 4px 0 0;\n  margin-top: 1px \\9;\n  line-height: normal;\n}\ninput[type=\"file\"] {\n  display: block;\n}\ninput[type=\"range\"] {\n  display: block;\n  width: 100%;\n}\nselect[multiple],\nselect[size] {\n  height: auto;\n}\ninput[type=\"file\"]:focus,\ninput[type=\"radio\"]:focus,\ninput[type=\"checkbox\"]:focus {\n  outline: 5px auto -webkit-focus-ring-color;\n  outline-offset: -2px;\n}\noutput {\n  display: block;\n  padding-top: 7px;\n  font-size: 14px;\n  line-height: 1.42857143;\n  color: #555555;\n}\n.form-control {\n  display: block;\n  width: 100%;\n  height: 34px;\n  padding: 6px 12px;\n  font-size: 14px;\n  line-height: 1.42857143;\n  color: #555555;\n  background-color: #fff;\n  background-image: none;\n  border: 1px solid #ccc;\n  border-radius: 4px;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;\n  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;\n  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;\n}\n.form-control:focus {\n  border-color: #66afe9;\n  outline: 0;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);\n  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);\n}\n.form-control::-moz-placeholder {\n  color: #999;\n  opacity: 1;\n}\n.form-control:-ms-input-placeholder {\n  color: #999;\n}\n.form-control::-webkit-input-placeholder {\n  color: #999;\n}\n.form-control::-ms-expand {\n  border: 0;\n  background-color: transparent;\n}\n.form-control[disabled],\n.form-control[readonly],\nfieldset[disabled] .form-control {\n  background-color: #eeeeee;\n  opacity: 1;\n}\n.form-control[disabled],\nfieldset[disabled] .form-control {\n  cursor: not-allowed;\n}\ntextarea.form-control {\n  height: auto;\n}\ninput[type=\"search\"] {\n  -webkit-appearance: none;\n}\n@media screen and (-webkit-min-device-pixel-ratio: 0) {\n  input[type=\"date\"].form-control,\n  input[type=\"time\"].form-control,\n  input[type=\"datetime-local\"].form-control,\n  input[type=\"month\"].form-control {\n    line-height: 34px;\n  }\n  input[type=\"date\"].input-sm,\n  input[type=\"time\"].input-sm,\n  input[type=\"datetime-local\"].input-sm,\n  input[type=\"month\"].input-sm,\n  .input-group-sm input[type=\"date\"],\n  .input-group-sm input[type=\"time\"],\n  .input-group-sm input[type=\"datetime-local\"],\n  .input-group-sm input[type=\"month\"] {\n    line-height: 30px;\n  }\n  input[type=\"date\"].input-lg,\n  input[type=\"time\"].input-lg,\n  input[type=\"datetime-local\"].input-lg,\n  input[type=\"month\"].input-lg,\n  .input-group-lg input[type=\"date\"],\n  .input-group-lg input[type=\"time\"],\n  .input-group-lg input[type=\"datetime-local\"],\n  .input-group-lg input[type=\"month\"] {\n    line-height: 46px;\n  }\n}\n.form-group {\n  margin-bottom: 15px;\n}\n.radio,\n.checkbox {\n  position: relative;\n  display: block;\n  margin-top: 10px;\n  margin-bottom: 10px;\n}\n.radio label,\n.checkbox label {\n  min-height: 20px;\n  padding-left: 20px;\n  margin-bottom: 0;\n  font-weight: normal;\n  cursor: pointer;\n}\n.radio input[type=\"radio\"],\n.radio-inline input[type=\"radio\"],\n.checkbox input[type=\"checkbox\"],\n.checkbox-inline input[type=\"checkbox\"] {\n  position: absolute;\n  margin-left: -20px;\n  margin-top: 4px \\9;\n}\n.radio + .radio,\n.checkbox + .checkbox {\n  margin-top: -5px;\n}\n.radio-inline,\n.checkbox-inline {\n  position: relative;\n  display: inline-block;\n  padding-left: 20px;\n  margin-bottom: 0;\n  vertical-align: middle;\n  font-weight: normal;\n  cursor: pointer;\n}\n.radio-inline + .radio-inline,\n.checkbox-inline + .checkbox-inline {\n  margin-top: 0;\n  margin-left: 10px;\n}\ninput[type=\"radio\"][disabled],\ninput[type=\"checkbox\"][disabled],\ninput[type=\"radio\"].disabled,\ninput[type=\"checkbox\"].disabled,\nfieldset[disabled] input[type=\"radio\"],\nfieldset[disabled] input[type=\"checkbox\"] {\n  cursor: not-allowed;\n}\n.radio-inline.disabled,\n.checkbox-inline.disabled,\nfieldset[disabled] .radio-inline,\nfieldset[disabled] .checkbox-inline {\n  cursor: not-allowed;\n}\n.radio.disabled label,\n.checkbox.disabled label,\nfieldset[disabled] .radio label,\nfieldset[disabled] .checkbox label {\n  cursor: not-allowed;\n}\n.form-control-static {\n  padding-top: 7px;\n  padding-bottom: 7px;\n  margin-bottom: 0;\n  min-height: 34px;\n}\n.form-control-static.input-lg,\n.form-control-static.input-sm {\n  padding-left: 0;\n  padding-right: 0;\n}\n.input-sm {\n  height: 30px;\n  padding: 5px 10px;\n  font-size: 12px;\n  line-height: 1.5;\n  border-radius: 3px;\n}\nselect.input-sm {\n  height: 30px;\n  line-height: 30px;\n}\ntextarea.input-sm,\nselect[multiple].input-sm {\n  height: auto;\n}\n.form-group-sm .form-control {\n  height: 30px;\n  padding: 5px 10px;\n  font-size: 12px;\n  line-height: 1.5;\n  border-radius: 3px;\n}\n.form-group-sm select.form-control {\n  height: 30px;\n  line-height: 30px;\n}\n.form-group-sm textarea.form-control,\n.form-group-sm select[multiple].form-control {\n  height: auto;\n}\n.form-group-sm .form-control-static {\n  height: 30px;\n  min-height: 32px;\n  padding: 6px 10px;\n  font-size: 12px;\n  line-height: 1.5;\n}\n.input-lg {\n  height: 46px;\n  padding: 10px 16px;\n  font-size: 18px;\n  line-height: 1.3333333;\n  border-radius: 6px;\n}\nselect.input-lg {\n  height: 46px;\n  line-height: 46px;\n}\ntextarea.input-lg,\nselect[multiple].input-lg {\n  height: auto;\n}\n.form-group-lg .form-control {\n  height: 46px;\n  padding: 10px 16px;\n  font-size: 18px;\n  line-height: 1.3333333;\n  border-radius: 6px;\n}\n.form-group-lg select.form-control {\n  height: 46px;\n  line-height: 46px;\n}\n.form-group-lg textarea.form-control,\n.form-group-lg select[multiple].form-control {\n  height: auto;\n}\n.form-group-lg .form-control-static {\n  height: 46px;\n  min-height: 38px;\n  padding: 11px 16px;\n  font-size: 18px;\n  line-height: 1.3333333;\n}\n.has-feedback {\n  position: relative;\n}\n.has-feedback .form-control {\n  padding-right: 42.5px;\n}\n.form-control-feedback {\n  position: absolute;\n  top: 0;\n  right: 0;\n  z-index: 2;\n  display: block;\n  width: 34px;\n  height: 34px;\n  line-height: 34px;\n  text-align: center;\n  pointer-events: none;\n}\n.input-lg + .form-control-feedback,\n.input-group-lg + .form-control-feedback,\n.form-group-lg .form-control + .form-control-feedback {\n  width: 46px;\n  height: 46px;\n  line-height: 46px;\n}\n.input-sm + .form-control-feedback,\n.input-group-sm + .form-control-feedback,\n.form-group-sm .form-control + .form-control-feedback {\n  width: 30px;\n  height: 30px;\n  line-height: 30px;\n}\n.has-success .help-block,\n.has-success .control-label,\n.has-success .radio,\n.has-success .checkbox,\n.has-success .radio-inline,\n.has-success .checkbox-inline,\n.has-success.radio label,\n.has-success.checkbox label,\n.has-success.radio-inline label,\n.has-success.checkbox-inline label {\n  color: #3c763d;\n}\n.has-success .form-control {\n  border-color: #3c763d;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n}\n.has-success .form-control:focus {\n  border-color: #2b542c;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;\n  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;\n}\n.has-success .input-group-addon {\n  color: #3c763d;\n  border-color: #3c763d;\n  background-color: #dff0d8;\n}\n.has-success .form-control-feedback {\n  color: #3c763d;\n}\n.has-warning .help-block,\n.has-warning .control-label,\n.has-warning .radio,\n.has-warning .checkbox,\n.has-warning .radio-inline,\n.has-warning .checkbox-inline,\n.has-warning.radio label,\n.has-warning.checkbox label,\n.has-warning.radio-inline label,\n.has-warning.checkbox-inline label {\n  color: #8a6d3b;\n}\n.has-warning .form-control {\n  border-color: #8a6d3b;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n}\n.has-warning .form-control:focus {\n  border-color: #66512c;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;\n  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;\n}\n.has-warning .input-group-addon {\n  color: #8a6d3b;\n  border-color: #8a6d3b;\n  background-color: #fcf8e3;\n}\n.has-warning .form-control-feedback {\n  color: #8a6d3b;\n}\n.has-error .help-block,\n.has-error .control-label,\n.has-error .radio,\n.has-error .checkbox,\n.has-error .radio-inline,\n.has-error .checkbox-inline,\n.has-error.radio label,\n.has-error.checkbox label,\n.has-error.radio-inline label,\n.has-error.checkbox-inline label {\n  color: #a94442;\n}\n.has-error .form-control {\n  border-color: #a94442;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n}\n.has-error .form-control:focus {\n  border-color: #843534;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;\n  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;\n}\n.has-error .input-group-addon {\n  color: #a94442;\n  border-color: #a94442;\n  background-color: #f2dede;\n}\n.has-error .form-control-feedback {\n  color: #a94442;\n}\n.has-feedback label ~ .form-control-feedback {\n  top: 25px;\n}\n.has-feedback label.sr-only ~ .form-control-feedback {\n  top: 0;\n}\n.help-block {\n  display: block;\n  margin-top: 5px;\n  margin-bottom: 10px;\n  color: #737373;\n}\n@media (min-width: 768px) {\n  .form-inline .form-group {\n    display: inline-block;\n    margin-bottom: 0;\n    vertical-align: middle;\n  }\n  .form-inline .form-control {\n    display: inline-block;\n    width: auto;\n    vertical-align: middle;\n  }\n  .form-inline .form-control-static {\n    display: inline-block;\n  }\n  .form-inline .input-group {\n    display: inline-table;\n    vertical-align: middle;\n  }\n  .form-inline .input-group .input-group-addon,\n  .form-inline .input-group .input-group-btn,\n  .form-inline .input-group .form-control {\n    width: auto;\n  }\n  .form-inline .input-group > .form-control {\n    width: 100%;\n  }\n  .form-inline .control-label {\n    margin-bottom: 0;\n    vertical-align: middle;\n  }\n  .form-inline .radio,\n  .form-inline .checkbox {\n    display: inline-block;\n    margin-top: 0;\n    margin-bottom: 0;\n    vertical-align: middle;\n  }\n  .form-inline .radio label,\n  .form-inline .checkbox label {\n    padding-left: 0;\n  }\n  .form-inline .radio input[type=\"radio\"],\n  .form-inline .checkbox input[type=\"checkbox\"] {\n    position: relative;\n    margin-left: 0;\n  }\n  .form-inline .has-feedback .form-control-feedback {\n    top: 0;\n  }\n}\n.form-horizontal .radio,\n.form-horizontal .checkbox,\n.form-horizontal .radio-inline,\n.form-horizontal .checkbox-inline {\n  margin-top: 0;\n  margin-bottom: 0;\n  padding-top: 7px;\n}\n.form-horizontal .radio,\n.form-horizontal .checkbox {\n  min-height: 27px;\n}\n.form-horizontal .form-group {\n  margin-left: -15px;\n  margin-right: -15px;\n}\n@media (min-width: 768px) {\n  .form-horizontal .control-label {\n    text-align: right;\n    margin-bottom: 0;\n    padding-top: 7px;\n  }\n}\n.form-horizontal .has-feedback .form-control-feedback {\n  right: 15px;\n}\n@media (min-width: 768px) {\n  .form-horizontal .form-group-lg .control-label {\n    padding-top: 11px;\n    font-size: 18px;\n  }\n}\n@media (min-width: 768px) {\n  .form-horizontal .form-group-sm .control-label {\n    padding-top: 6px;\n    font-size: 12px;\n  }\n}\n.btn {\n  display: inline-block;\n  margin-bottom: 0;\n  font-weight: normal;\n  text-align: center;\n  vertical-align: middle;\n  touch-action: manipulation;\n  cursor: pointer;\n  background-image: none;\n  border: 1px solid transparent;\n  white-space: nowrap;\n  padding: 6px 12px;\n  font-size: 14px;\n  line-height: 1.42857143;\n  border-radius: 4px;\n  -webkit-user-select: none;\n  -moz-user-select: none;\n  -ms-user-select: none;\n  user-select: none;\n}\n.btn:focus,\n.btn:active:focus,\n.btn.active:focus,\n.btn.focus,\n.btn:active.focus,\n.btn.active.focus {\n  outline: 5px auto -webkit-focus-ring-color;\n  outline-offset: -2px;\n}\n.btn:hover,\n.btn:focus,\n.btn.focus {\n  color: #333;\n  text-decoration: none;\n}\n.btn:active,\n.btn.active {\n  outline: 0;\n  background-image: none;\n  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n}\n.btn.disabled,\n.btn[disabled],\nfieldset[disabled] .btn {\n  cursor: not-allowed;\n  opacity: 0.65;\n  filter: alpha(opacity=65);\n  -webkit-box-shadow: none;\n  box-shadow: none;\n}\na.btn.disabled,\nfieldset[disabled] a.btn {\n  pointer-events: none;\n}\n.btn-default {\n  color: #333;\n  background-color: #fff;\n  border-color: #ccc;\n}\n.btn-default:focus,\n.btn-default.focus {\n  color: #333;\n  background-color: #e6e6e6;\n  border-color: #8c8c8c;\n}\n.btn-default:hover {\n  color: #333;\n  background-color: #e6e6e6;\n  border-color: #adadad;\n}\n.btn-default:active,\n.btn-default.active,\n.open > .dropdown-toggle.btn-default {\n  color: #333;\n  background-color: #e6e6e6;\n  border-color: #adadad;\n}\n.btn-default:active:hover,\n.btn-default.active:hover,\n.open > .dropdown-toggle.btn-default:hover,\n.btn-default:active:focus,\n.btn-default.active:focus,\n.open > .dropdown-toggle.btn-default:focus,\n.btn-default:active.focus,\n.btn-default.active.focus,\n.open > .dropdown-toggle.btn-default.focus {\n  color: #333;\n  background-color: #d4d4d4;\n  border-color: #8c8c8c;\n}\n.btn-default:active,\n.btn-default.active,\n.open > .dropdown-toggle.btn-default {\n  background-image: none;\n}\n.btn-default.disabled:hover,\n.btn-default[disabled]:hover,\nfieldset[disabled] .btn-default:hover,\n.btn-default.disabled:focus,\n.btn-default[disabled]:focus,\nfieldset[disabled] .btn-default:focus,\n.btn-default.disabled.focus,\n.btn-default[disabled].focus,\nfieldset[disabled] .btn-default.focus {\n  background-color: #fff;\n  border-color: #ccc;\n}\n.btn-default .badge {\n  color: #fff;\n  background-color: #333;\n}\n.btn-primary {\n  color: #fff;\n  background-color: #337ab7;\n  border-color: #2e6da4;\n}\n.btn-primary:focus,\n.btn-primary.focus {\n  color: #fff;\n  background-color: #286090;\n  border-color: #122b40;\n}\n.btn-primary:hover {\n  color: #fff;\n  background-color: #286090;\n  border-color: #204d74;\n}\n.btn-primary:active,\n.btn-primary.active,\n.open > .dropdown-toggle.btn-primary {\n  color: #fff;\n  background-color: #286090;\n  border-color: #204d74;\n}\n.btn-primary:active:hover,\n.btn-primary.active:hover,\n.open > .dropdown-toggle.btn-primary:hover,\n.btn-primary:active:focus,\n.btn-primary.active:focus,\n.open > .dropdown-toggle.btn-primary:focus,\n.btn-primary:active.focus,\n.btn-primary.active.focus,\n.open > .dropdown-toggle.btn-primary.focus {\n  color: #fff;\n  background-color: #204d74;\n  border-color: #122b40;\n}\n.btn-primary:active,\n.btn-primary.active,\n.open > .dropdown-toggle.btn-primary {\n  background-image: none;\n}\n.btn-primary.disabled:hover,\n.btn-primary[disabled]:hover,\nfieldset[disabled] .btn-primary:hover,\n.btn-primary.disabled:focus,\n.btn-primary[disabled]:focus,\nfieldset[disabled] .btn-primary:focus,\n.btn-primary.disabled.focus,\n.btn-primary[disabled].focus,\nfieldset[disabled] .btn-primary.focus {\n  background-color: #337ab7;\n  border-color: #2e6da4;\n}\n.btn-primary .badge {\n  color: #337ab7;\n  background-color: #fff;\n}\n.btn-success {\n  color: #fff;\n  background-color: #5cb85c;\n  border-color: #4cae4c;\n}\n.btn-success:focus,\n.btn-success.focus {\n  color: #fff;\n  background-color: #449d44;\n  border-color: #255625;\n}\n.btn-success:hover {\n  color: #fff;\n  background-color: #449d44;\n  border-color: #398439;\n}\n.btn-success:active,\n.btn-success.active,\n.open > .dropdown-toggle.btn-success {\n  color: #fff;\n  background-color: #449d44;\n  border-color: #398439;\n}\n.btn-success:active:hover,\n.btn-success.active:hover,\n.open > .dropdown-toggle.btn-success:hover,\n.btn-success:active:focus,\n.btn-success.active:focus,\n.open > .dropdown-toggle.btn-success:focus,\n.btn-success:active.focus,\n.btn-success.active.focus,\n.open > .dropdown-toggle.btn-success.focus {\n  color: #fff;\n  background-color: #398439;\n  border-color: #255625;\n}\n.btn-success:active,\n.btn-success.active,\n.open > .dropdown-toggle.btn-success {\n  background-image: none;\n}\n.btn-success.disabled:hover,\n.btn-success[disabled]:hover,\nfieldset[disabled] .btn-success:hover,\n.btn-success.disabled:focus,\n.btn-success[disabled]:focus,\nfieldset[disabled] .btn-success:focus,\n.btn-success.disabled.focus,\n.btn-success[disabled].focus,\nfieldset[disabled] .btn-success.focus {\n  background-color: #5cb85c;\n  border-color: #4cae4c;\n}\n.btn-success .badge {\n  color: #5cb85c;\n  background-color: #fff;\n}\n.btn-info {\n  color: #fff;\n  background-color: #5bc0de;\n  border-color: #46b8da;\n}\n.btn-info:focus,\n.btn-info.focus {\n  color: #fff;\n  background-color: #31b0d5;\n  border-color: #1b6d85;\n}\n.btn-info:hover {\n  color: #fff;\n  background-color: #31b0d5;\n  border-color: #269abc;\n}\n.btn-info:active,\n.btn-info.active,\n.open > .dropdown-toggle.btn-info {\n  color: #fff;\n  background-color: #31b0d5;\n  border-color: #269abc;\n}\n.btn-info:active:hover,\n.btn-info.active:hover,\n.open > .dropdown-toggle.btn-info:hover,\n.btn-info:active:focus,\n.btn-info.active:focus,\n.open > .dropdown-toggle.btn-info:focus,\n.btn-info:active.focus,\n.btn-info.active.focus,\n.open > .dropdown-toggle.btn-info.focus {\n  color: #fff;\n  background-color: #269abc;\n  border-color: #1b6d85;\n}\n.btn-info:active,\n.btn-info.active,\n.open > .dropdown-toggle.btn-info {\n  background-image: none;\n}\n.btn-info.disabled:hover,\n.btn-info[disabled]:hover,\nfieldset[disabled] .btn-info:hover,\n.btn-info.disabled:focus,\n.btn-info[disabled]:focus,\nfieldset[disabled] .btn-info:focus,\n.btn-info.disabled.focus,\n.btn-info[disabled].focus,\nfieldset[disabled] .btn-info.focus {\n  background-color: #5bc0de;\n  border-color: #46b8da;\n}\n.btn-info .badge {\n  color: #5bc0de;\n  background-color: #fff;\n}\n.btn-warning {\n  color: #fff;\n  background-color: #f0ad4e;\n  border-color: #eea236;\n}\n.btn-warning:focus,\n.btn-warning.focus {\n  color: #fff;\n  background-color: #ec971f;\n  border-color: #985f0d;\n}\n.btn-warning:hover {\n  color: #fff;\n  background-color: #ec971f;\n  border-color: #d58512;\n}\n.btn-warning:active,\n.btn-warning.active,\n.open > .dropdown-toggle.btn-warning {\n  color: #fff;\n  background-color: #ec971f;\n  border-color: #d58512;\n}\n.btn-warning:active:hover,\n.btn-warning.active:hover,\n.open > .dropdown-toggle.btn-warning:hover,\n.btn-warning:active:focus,\n.btn-warning.active:focus,\n.open > .dropdown-toggle.btn-warning:focus,\n.btn-warning:active.focus,\n.btn-warning.active.focus,\n.open > .dropdown-toggle.btn-warning.focus {\n  color: #fff;\n  background-color: #d58512;\n  border-color: #985f0d;\n}\n.btn-warning:active,\n.btn-warning.active,\n.open > .dropdown-toggle.btn-warning {\n  background-image: none;\n}\n.btn-warning.disabled:hover,\n.btn-warning[disabled]:hover,\nfieldset[disabled] .btn-warning:hover,\n.btn-warning.disabled:focus,\n.btn-warning[disabled]:focus,\nfieldset[disabled] .btn-warning:focus,\n.btn-warning.disabled.focus,\n.btn-warning[disabled].focus,\nfieldset[disabled] .btn-warning.focus {\n  background-color: #f0ad4e;\n  border-color: #eea236;\n}\n.btn-warning .badge {\n  color: #f0ad4e;\n  background-color: #fff;\n}\n.btn-danger {\n  color: #fff;\n  background-color: #d9534f;\n  border-color: #d43f3a;\n}\n.btn-danger:focus,\n.btn-danger.focus {\n  color: #fff;\n  background-color: #c9302c;\n  border-color: #761c19;\n}\n.btn-danger:hover {\n  color: #fff;\n  background-color: #c9302c;\n  border-color: #ac2925;\n}\n.btn-danger:active,\n.btn-danger.active,\n.open > .dropdown-toggle.btn-danger {\n  color: #fff;\n  background-color: #c9302c;\n  border-color: #ac2925;\n}\n.btn-danger:active:hover,\n.btn-danger.active:hover,\n.open > .dropdown-toggle.btn-danger:hover,\n.btn-danger:active:focus,\n.btn-danger.active:focus,\n.open > .dropdown-toggle.btn-danger:focus,\n.btn-danger:active.focus,\n.btn-danger.active.focus,\n.open > .dropdown-toggle.btn-danger.focus {\n  color: #fff;\n  background-color: #ac2925;\n  border-color: #761c19;\n}\n.btn-danger:active,\n.btn-danger.active,\n.open > .dropdown-toggle.btn-danger {\n  background-image: none;\n}\n.btn-danger.disabled:hover,\n.btn-danger[disabled]:hover,\nfieldset[disabled] .btn-danger:hover,\n.btn-danger.disabled:focus,\n.btn-danger[disabled]:focus,\nfieldset[disabled] .btn-danger:focus,\n.btn-danger.disabled.focus,\n.btn-danger[disabled].focus,\nfieldset[disabled] .btn-danger.focus {\n  background-color: #d9534f;\n  border-color: #d43f3a;\n}\n.btn-danger .badge {\n  color: #d9534f;\n  background-color: #fff;\n}\n.btn-link {\n  color: #337ab7;\n  font-weight: normal;\n  border-radius: 0;\n}\n.btn-link,\n.btn-link:active,\n.btn-link.active,\n.btn-link[disabled],\nfieldset[disabled] .btn-link {\n  background-color: transparent;\n  -webkit-box-shadow: none;\n  box-shadow: none;\n}\n.btn-link,\n.btn-link:hover,\n.btn-link:focus,\n.btn-link:active {\n  border-color: transparent;\n}\n.btn-link:hover,\n.btn-link:focus {\n  color: #23527c;\n  text-decoration: underline;\n  background-color: transparent;\n}\n.btn-link[disabled]:hover,\nfieldset[disabled] .btn-link:hover,\n.btn-link[disabled]:focus,\nfieldset[disabled] .btn-link:focus {\n  color: #777777;\n  text-decoration: none;\n}\n.btn-lg,\n.btn-group-lg > .btn {\n  padding: 10px 16px;\n  font-size: 18px;\n  line-height: 1.3333333;\n  border-radius: 6px;\n}\n.btn-sm,\n.btn-group-sm > .btn {\n  padding: 5px 10px;\n  font-size: 12px;\n  line-height: 1.5;\n  border-radius: 3px;\n}\n.btn-xs,\n.btn-group-xs > .btn {\n  padding: 1px 5px;\n  font-size: 12px;\n  line-height: 1.5;\n  border-radius: 3px;\n}\n.btn-block {\n  display: block;\n  width: 100%;\n}\n.btn-block + .btn-block {\n  margin-top: 5px;\n}\ninput[type=\"submit\"].btn-block,\ninput[type=\"reset\"].btn-block,\ninput[type=\"button\"].btn-block {\n  width: 100%;\n}\n.fade {\n  opacity: 0;\n  -webkit-transition: opacity 0.15s linear;\n  -o-transition: opacity 0.15s linear;\n  transition: opacity 0.15s linear;\n}\n.fade.in {\n  opacity: 1;\n}\n.collapse {\n  display: none;\n}\n.collapse.in {\n  display: block;\n}\ntr.collapse.in {\n  display: table-row;\n}\ntbody.collapse.in {\n  display: table-row-group;\n}\n.collapsing {\n  position: relative;\n  height: 0;\n  overflow: hidden;\n  -webkit-transition-property: height, visibility;\n  transition-property: height, visibility;\n  -webkit-transition-duration: 0.35s;\n  transition-duration: 0.35s;\n  -webkit-transition-timing-function: ease;\n  transition-timing-function: ease;\n}\n.caret {\n  display: inline-block;\n  width: 0;\n  height: 0;\n  margin-left: 2px;\n  vertical-align: middle;\n  border-top: 4px dashed;\n  border-top: 4px solid \\9;\n  border-right: 4px solid transparent;\n  border-left: 4px solid transparent;\n}\n.dropup,\n.dropdown {\n  position: relative;\n}\n.dropdown-toggle:focus {\n  outline: 0;\n}\n.dropdown-menu {\n  position: absolute;\n  top: 100%;\n  left: 0;\n  z-index: 1000;\n  display: none;\n  float: left;\n  min-width: 160px;\n  padding: 5px 0;\n  margin: 2px 0 0;\n  list-style: none;\n  font-size: 14px;\n  text-align: left;\n  background-color: #fff;\n  border: 1px solid #ccc;\n  border: 1px solid rgba(0, 0, 0, 0.15);\n  border-radius: 4px;\n  -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);\n  box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);\n  background-clip: padding-box;\n}\n.dropdown-menu.pull-right {\n  right: 0;\n  left: auto;\n}\n.dropdown-menu .divider {\n  height: 1px;\n  margin: 9px 0;\n  overflow: hidden;\n  background-color: #e5e5e5;\n}\n.dropdown-menu > li > a {\n  display: block;\n  padding: 3px 20px;\n  clear: both;\n  font-weight: normal;\n  line-height: 1.42857143;\n  color: #333333;\n  white-space: nowrap;\n}\n.dropdown-menu > li > a:hover,\n.dropdown-menu > li > a:focus {\n  text-decoration: none;\n  color: #262626;\n  background-color: #f5f5f5;\n}\n.dropdown-menu > .active > a,\n.dropdown-menu > .active > a:hover,\n.dropdown-menu > .active > a:focus {\n  color: #fff;\n  text-decoration: none;\n  outline: 0;\n  background-color: #337ab7;\n}\n.dropdown-menu > .disabled > a,\n.dropdown-menu > .disabled > a:hover,\n.dropdown-menu > .disabled > a:focus {\n  color: #777777;\n}\n.dropdown-menu > .disabled > a:hover,\n.dropdown-menu > .disabled > a:focus {\n  text-decoration: none;\n  background-color: transparent;\n  background-image: none;\n  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n  cursor: not-allowed;\n}\n.open > .dropdown-menu {\n  display: block;\n}\n.open > a {\n  outline: 0;\n}\n.dropdown-menu-right {\n  left: auto;\n  right: 0;\n}\n.dropdown-menu-left {\n  left: 0;\n  right: auto;\n}\n.dropdown-header {\n  display: block;\n  padding: 3px 20px;\n  font-size: 12px;\n  line-height: 1.42857143;\n  color: #777777;\n  white-space: nowrap;\n}\n.dropdown-backdrop {\n  position: fixed;\n  left: 0;\n  right: 0;\n  bottom: 0;\n  top: 0;\n  z-index: 990;\n}\n.pull-right > .dropdown-menu {\n  right: 0;\n  left: auto;\n}\n.dropup .caret,\n.navbar-fixed-bottom .dropdown .caret {\n  border-top: 0;\n  border-bottom: 4px dashed;\n  border-bottom: 4px solid \\9;\n  content: \"\";\n}\n.dropup .dropdown-menu,\n.navbar-fixed-bottom .dropdown .dropdown-menu {\n  top: auto;\n  bottom: 100%;\n  margin-bottom: 2px;\n}\n@media (min-width: 768px) {\n  .navbar-right .dropdown-menu {\n    left: auto;\n    right: 0;\n  }\n  .navbar-right .dropdown-menu-left {\n    left: 0;\n    right: auto;\n  }\n}\n.btn-group,\n.btn-group-vertical {\n  position: relative;\n  display: inline-block;\n  vertical-align: middle;\n}\n.btn-group > .btn,\n.btn-group-vertical > .btn {\n  position: relative;\n  float: left;\n}\n.btn-group > .btn:hover,\n.btn-group-vertical > .btn:hover,\n.btn-group > .btn:focus,\n.btn-group-vertical > .btn:focus,\n.btn-group > .btn:active,\n.btn-group-vertical > .btn:active,\n.btn-group > .btn.active,\n.btn-group-vertical > .btn.active {\n  z-index: 2;\n}\n.btn-group .btn + .btn,\n.btn-group .btn + .btn-group,\n.btn-group .btn-group + .btn,\n.btn-group .btn-group + .btn-group {\n  margin-left: -1px;\n}\n.btn-toolbar {\n  margin-left: -5px;\n}\n.btn-toolbar .btn,\n.btn-toolbar .btn-group,\n.btn-toolbar .input-group {\n  float: left;\n}\n.btn-toolbar > .btn,\n.btn-toolbar > .btn-group,\n.btn-toolbar > .input-group {\n  margin-left: 5px;\n}\n.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {\n  border-radius: 0;\n}\n.btn-group > .btn:first-child {\n  margin-left: 0;\n}\n.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {\n  border-bottom-right-radius: 0;\n  border-top-right-radius: 0;\n}\n.btn-group > .btn:last-child:not(:first-child),\n.btn-group > .dropdown-toggle:not(:first-child) {\n  border-bottom-left-radius: 0;\n  border-top-left-radius: 0;\n}\n.btn-group > .btn-group {\n  float: left;\n}\n.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {\n  border-radius: 0;\n}\n.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,\n.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {\n  border-bottom-right-radius: 0;\n  border-top-right-radius: 0;\n}\n.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {\n  border-bottom-left-radius: 0;\n  border-top-left-radius: 0;\n}\n.btn-group .dropdown-toggle:active,\n.btn-group.open .dropdown-toggle {\n  outline: 0;\n}\n.btn-group > .btn + .dropdown-toggle {\n  padding-left: 8px;\n  padding-right: 8px;\n}\n.btn-group > .btn-lg + .dropdown-toggle {\n  padding-left: 12px;\n  padding-right: 12px;\n}\n.btn-group.open .dropdown-toggle {\n  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n}\n.btn-group.open .dropdown-toggle.btn-link {\n  -webkit-box-shadow: none;\n  box-shadow: none;\n}\n.btn .caret {\n  margin-left: 0;\n}\n.btn-lg .caret {\n  border-width: 5px 5px 0;\n  border-bottom-width: 0;\n}\n.dropup .btn-lg .caret {\n  border-width: 0 5px 5px;\n}\n.btn-group-vertical > .btn,\n.btn-group-vertical > .btn-group,\n.btn-group-vertical > .btn-group > .btn {\n  display: block;\n  float: none;\n  width: 100%;\n  max-width: 100%;\n}\n.btn-group-vertical > .btn-group > .btn {\n  float: none;\n}\n.btn-group-vertical > .btn + .btn,\n.btn-group-vertical > .btn + .btn-group,\n.btn-group-vertical > .btn-group + .btn,\n.btn-group-vertical > .btn-group + .btn-group {\n  margin-top: -1px;\n  margin-left: 0;\n}\n.btn-group-vertical > .btn:not(:first-child):not(:last-child) {\n  border-radius: 0;\n}\n.btn-group-vertical > .btn:first-child:not(:last-child) {\n  border-top-right-radius: 4px;\n  border-top-left-radius: 4px;\n  border-bottom-right-radius: 0;\n  border-bottom-left-radius: 0;\n}\n.btn-group-vertical > .btn:last-child:not(:first-child) {\n  border-top-right-radius: 0;\n  border-top-left-radius: 0;\n  border-bottom-right-radius: 4px;\n  border-bottom-left-radius: 4px;\n}\n.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {\n  border-radius: 0;\n}\n.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,\n.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {\n  border-bottom-right-radius: 0;\n  border-bottom-left-radius: 0;\n}\n.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {\n  border-top-right-radius: 0;\n  border-top-left-radius: 0;\n}\n.btn-group-justified {\n  display: table;\n  width: 100%;\n  table-layout: fixed;\n  border-collapse: separate;\n}\n.btn-group-justified > .btn,\n.btn-group-justified > .btn-group {\n  float: none;\n  display: table-cell;\n  width: 1%;\n}\n.btn-group-justified > .btn-group .btn {\n  width: 100%;\n}\n.btn-group-justified > .btn-group .dropdown-menu {\n  left: auto;\n}\n[data-toggle=\"buttons\"] > .btn input[type=\"radio\"],\n[data-toggle=\"buttons\"] > .btn-group > .btn input[type=\"radio\"],\n[data-toggle=\"buttons\"] > .btn input[type=\"checkbox\"],\n[data-toggle=\"buttons\"] > .btn-group > .btn input[type=\"checkbox\"] {\n  position: absolute;\n  clip: rect(0, 0, 0, 0);\n  pointer-events: none;\n}\n.input-group {\n  position: relative;\n  display: table;\n  border-collapse: separate;\n}\n.input-group[class*=\"col-\"] {\n  float: none;\n  padding-left: 0;\n  padding-right: 0;\n}\n.input-group .form-control {\n  position: relative;\n  z-index: 2;\n  float: left;\n  width: 100%;\n  margin-bottom: 0;\n}\n.input-group .form-control:focus {\n  z-index: 3;\n}\n.input-group-lg > .form-control,\n.input-group-lg > .input-group-addon,\n.input-group-lg > .input-group-btn > .btn {\n  height: 46px;\n  padding: 10px 16px;\n  font-size: 18px;\n  line-height: 1.3333333;\n  border-radius: 6px;\n}\nselect.input-group-lg > .form-control,\nselect.input-group-lg > .input-group-addon,\nselect.input-group-lg > .input-group-btn > .btn {\n  height: 46px;\n  line-height: 46px;\n}\ntextarea.input-group-lg > .form-control,\ntextarea.input-group-lg > .input-group-addon,\ntextarea.input-group-lg > .input-group-btn > .btn,\nselect[multiple].input-group-lg > .form-control,\nselect[multiple].input-group-lg > .input-group-addon,\nselect[multiple].input-group-lg > .input-group-btn > .btn {\n  height: auto;\n}\n.input-group-sm > .form-control,\n.input-group-sm > .input-group-addon,\n.input-group-sm > .input-group-btn > .btn {\n  height: 30px;\n  padding: 5px 10px;\n  font-size: 12px;\n  line-height: 1.5;\n  border-radius: 3px;\n}\nselect.input-group-sm > .form-control,\nselect.input-group-sm > .input-group-addon,\nselect.input-group-sm > .input-group-btn > .btn {\n  height: 30px;\n  line-height: 30px;\n}\ntextarea.input-group-sm > .form-control,\ntextarea.input-group-sm > .input-group-addon,\ntextarea.input-group-sm > .input-group-btn > .btn,\nselect[multiple].input-group-sm > .form-control,\nselect[multiple].input-group-sm > .input-group-addon,\nselect[multiple].input-group-sm > .input-group-btn > .btn {\n  height: auto;\n}\n.input-group-addon,\n.input-group-btn,\n.input-group .form-control {\n  display: table-cell;\n}\n.input-group-addon:not(:first-child):not(:last-child),\n.input-group-btn:not(:first-child):not(:last-child),\n.input-group .form-control:not(:first-child):not(:last-child) {\n  border-radius: 0;\n}\n.input-group-addon,\n.input-group-btn {\n  width: 1%;\n  white-space: nowrap;\n  vertical-align: middle;\n}\n.input-group-addon {\n  padding: 6px 12px;\n  font-size: 14px;\n  font-weight: normal;\n  line-height: 1;\n  color: #555555;\n  text-align: center;\n  background-color: #eeeeee;\n  border: 1px solid #ccc;\n  border-radius: 4px;\n}\n.input-group-addon.input-sm {\n  padding: 5px 10px;\n  font-size: 12px;\n  border-radius: 3px;\n}\n.input-group-addon.input-lg {\n  padding: 10px 16px;\n  font-size: 18px;\n  border-radius: 6px;\n}\n.input-group-addon input[type=\"radio\"],\n.input-group-addon input[type=\"checkbox\"] {\n  margin-top: 0;\n}\n.input-group .form-control:first-child,\n.input-group-addon:first-child,\n.input-group-btn:first-child > .btn,\n.input-group-btn:first-child > .btn-group > .btn,\n.input-group-btn:first-child > .dropdown-toggle,\n.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),\n.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {\n  border-bottom-right-radius: 0;\n  border-top-right-radius: 0;\n}\n.input-group-addon:first-child {\n  border-right: 0;\n}\n.input-group .form-control:last-child,\n.input-group-addon:last-child,\n.input-group-btn:last-child > .btn,\n.input-group-btn:last-child > .btn-group > .btn,\n.input-group-btn:last-child > .dropdown-toggle,\n.input-group-btn:first-child > .btn:not(:first-child),\n.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {\n  border-bottom-left-radius: 0;\n  border-top-left-radius: 0;\n}\n.input-group-addon:last-child {\n  border-left: 0;\n}\n.input-group-btn {\n  position: relative;\n  font-size: 0;\n  white-space: nowrap;\n}\n.input-group-btn > .btn {\n  position: relative;\n}\n.input-group-btn > .btn + .btn {\n  margin-left: -1px;\n}\n.input-group-btn > .btn:hover,\n.input-group-btn > .btn:focus,\n.input-group-btn > .btn:active {\n  z-index: 2;\n}\n.input-group-btn:first-child > .btn,\n.input-group-btn:first-child > .btn-group {\n  margin-right: -1px;\n}\n.input-group-btn:last-child > .btn,\n.input-group-btn:last-child > .btn-group {\n  z-index: 2;\n  margin-left: -1px;\n}\n.nav {\n  margin-bottom: 0;\n  padding-left: 0;\n  list-style: none;\n}\n.nav > li {\n  position: relative;\n  display: block;\n}\n.nav > li > a {\n  position: relative;\n  display: block;\n  padding: 10px 15px;\n}\n.nav > li > a:hover,\n.nav > li > a:focus {\n  text-decoration: none;\n  background-color: #eeeeee;\n}\n.nav > li.disabled > a {\n  color: #777777;\n}\n.nav > li.disabled > a:hover,\n.nav > li.disabled > a:focus {\n  color: #777777;\n  text-decoration: none;\n  background-color: transparent;\n  cursor: not-allowed;\n}\n.nav .open > a,\n.nav .open > a:hover,\n.nav .open > a:focus {\n  background-color: #eeeeee;\n  border-color: #337ab7;\n}\n.nav .nav-divider {\n  height: 1px;\n  margin: 9px 0;\n  overflow: hidden;\n  background-color: #e5e5e5;\n}\n.nav > li > a > img {\n  max-width: none;\n}\n.nav-tabs {\n  border-bottom: 1px solid #ddd;\n}\n.nav-tabs > li {\n  float: left;\n  margin-bottom: -1px;\n}\n.nav-tabs > li > a {\n  margin-right: 2px;\n  line-height: 1.42857143;\n  border: 1px solid transparent;\n  border-radius: 4px 4px 0 0;\n}\n.nav-tabs > li > a:hover {\n  border-color: #eeeeee #eeeeee #ddd;\n}\n.nav-tabs > li.active > a,\n.nav-tabs > li.active > a:hover,\n.nav-tabs > li.active > a:focus {\n  color: #555555;\n  background-color: #fff;\n  border: 1px solid #ddd;\n  border-bottom-color: transparent;\n  cursor: default;\n}\n.nav-tabs.nav-justified {\n  width: 100%;\n  border-bottom: 0;\n}\n.nav-tabs.nav-justified > li {\n  float: none;\n}\n.nav-tabs.nav-justified > li > a {\n  text-align: center;\n  margin-bottom: 5px;\n}\n.nav-tabs.nav-justified > .dropdown .dropdown-menu {\n  top: auto;\n  left: auto;\n}\n@media (min-width: 768px) {\n  .nav-tabs.nav-justified > li {\n    display: table-cell;\n    width: 1%;\n  }\n  .nav-tabs.nav-justified > li > a {\n    margin-bottom: 0;\n  }\n}\n.nav-tabs.nav-justified > li > a {\n  margin-right: 0;\n  border-radius: 4px;\n}\n.nav-tabs.nav-justified > .active > a,\n.nav-tabs.nav-justified > .active > a:hover,\n.nav-tabs.nav-justified > .active > a:focus {\n  border: 1px solid #ddd;\n}\n@media (min-width: 768px) {\n  .nav-tabs.nav-justified > li > a {\n    border-bottom: 1px solid #ddd;\n    border-radius: 4px 4px 0 0;\n  }\n  .nav-tabs.nav-justified > .active > a,\n  .nav-tabs.nav-justified > .active > a:hover,\n  .nav-tabs.nav-justified > .active > a:focus {\n    border-bottom-color: #fff;\n  }\n}\n.nav-pills > li {\n  float: left;\n}\n.nav-pills > li > a {\n  border-radius: 4px;\n}\n.nav-pills > li + li {\n  margin-left: 2px;\n}\n.nav-pills > li.active > a,\n.nav-pills > li.active > a:hover,\n.nav-pills > li.active > a:focus {\n  color: #fff;\n  background-color: #337ab7;\n}\n.nav-stacked > li {\n  float: none;\n}\n.nav-stacked > li + li {\n  margin-top: 2px;\n  margin-left: 0;\n}\n.nav-justified {\n  width: 100%;\n}\n.nav-justified > li {\n  float: none;\n}\n.nav-justified > li > a {\n  text-align: center;\n  margin-bottom: 5px;\n}\n.nav-justified > .dropdown .dropdown-menu {\n  top: auto;\n  left: auto;\n}\n@media (min-width: 768px) {\n  .nav-justified > li {\n    display: table-cell;\n    width: 1%;\n  }\n  .nav-justified > li > a {\n    margin-bottom: 0;\n  }\n}\n.nav-tabs-justified {\n  border-bottom: 0;\n}\n.nav-tabs-justified > li > a {\n  margin-right: 0;\n  border-radius: 4px;\n}\n.nav-tabs-justified > .active > a,\n.nav-tabs-justified > .active > a:hover,\n.nav-tabs-justified > .active > a:focus {\n  border: 1px solid #ddd;\n}\n@media (min-width: 768px) {\n  .nav-tabs-justified > li > a {\n    border-bottom: 1px solid #ddd;\n    border-radius: 4px 4px 0 0;\n  }\n  .nav-tabs-justified > .active > a,\n  .nav-tabs-justified > .active > a:hover,\n  .nav-tabs-justified > .active > a:focus {\n    border-bottom-color: #fff;\n  }\n}\n.tab-content > .tab-pane {\n  display: none;\n}\n.tab-content > .active {\n  display: block;\n}\n.nav-tabs .dropdown-menu {\n  margin-top: -1px;\n  border-top-right-radius: 0;\n  border-top-left-radius: 0;\n}\n.navbar {\n  position: relative;\n  min-height: 50px;\n  margin-bottom: 20px;\n  border: 1px solid transparent;\n}\n@media (min-width: 768px) {\n  .navbar {\n    border-radius: 4px;\n  }\n}\n@media (min-width: 768px) {\n  .navbar-header {\n    float: left;\n  }\n}\n.navbar-collapse {\n  overflow-x: visible;\n  padding-right: 15px;\n  padding-left: 15px;\n  border-top: 1px solid transparent;\n  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);\n  -webkit-overflow-scrolling: touch;\n}\n.navbar-collapse.in {\n  overflow-y: auto;\n}\n@media (min-width: 768px) {\n  .navbar-collapse {\n    width: auto;\n    border-top: 0;\n    box-shadow: none;\n  }\n  .navbar-collapse.collapse {\n    display: block !important;\n    height: auto !important;\n    padding-bottom: 0;\n    overflow: visible !important;\n  }\n  .navbar-collapse.in {\n    overflow-y: visible;\n  }\n  .navbar-fixed-top .navbar-collapse,\n  .navbar-static-top .navbar-collapse,\n  .navbar-fixed-bottom .navbar-collapse {\n    padding-left: 0;\n    padding-right: 0;\n  }\n}\n.navbar-fixed-top .navbar-collapse,\n.navbar-fixed-bottom .navbar-collapse {\n  max-height: 340px;\n}\n@media (max-device-width: 480px) and (orientation: landscape) {\n  .navbar-fixed-top .navbar-collapse,\n  .navbar-fixed-bottom .navbar-collapse {\n    max-height: 200px;\n  }\n}\n.container > .navbar-header,\n.container-fluid > .navbar-header,\n.container > .navbar-collapse,\n.container-fluid > .navbar-collapse {\n  margin-right: -15px;\n  margin-left: -15px;\n}\n@media (min-width: 768px) {\n  .container > .navbar-header,\n  .container-fluid > .navbar-header,\n  .container > .navbar-collapse,\n  .container-fluid > .navbar-collapse {\n    margin-right: 0;\n    margin-left: 0;\n  }\n}\n.navbar-static-top {\n  z-index: 1000;\n  border-width: 0 0 1px;\n}\n@media (min-width: 768px) {\n  .navbar-static-top {\n    border-radius: 0;\n  }\n}\n.navbar-fixed-top,\n.navbar-fixed-bottom {\n  position: fixed;\n  right: 0;\n  left: 0;\n  z-index: 1030;\n}\n@media (min-width: 768px) {\n  .navbar-fixed-top,\n  .navbar-fixed-bottom {\n    border-radius: 0;\n  }\n}\n.navbar-fixed-top {\n  top: 0;\n  border-width: 0 0 1px;\n}\n.navbar-fixed-bottom {\n  bottom: 0;\n  margin-bottom: 0;\n  border-width: 1px 0 0;\n}\n.navbar-brand {\n  float: left;\n  padding: 15px 15px;\n  font-size: 18px;\n  line-height: 20px;\n  height: 50px;\n}\n.navbar-brand:hover,\n.navbar-brand:focus {\n  text-decoration: none;\n}\n.navbar-brand > img {\n  display: block;\n}\n@media (min-width: 768px) {\n  .navbar > .container .navbar-brand,\n  .navbar > .container-fluid .navbar-brand {\n    margin-left: -15px;\n  }\n}\n.navbar-toggle {\n  position: relative;\n  float: right;\n  margin-right: 15px;\n  padding: 9px 10px;\n  margin-top: 8px;\n  margin-bottom: 8px;\n  background-color: transparent;\n  background-image: none;\n  border: 1px solid transparent;\n  border-radius: 4px;\n}\n.navbar-toggle:focus {\n  outline: 0;\n}\n.navbar-toggle .icon-bar {\n  display: block;\n  width: 22px;\n  height: 2px;\n  border-radius: 1px;\n}\n.navbar-toggle .icon-bar + .icon-bar {\n  margin-top: 4px;\n}\n@media (min-width: 768px) {\n  .navbar-toggle {\n    display: none;\n  }\n}\n.navbar-nav {\n  margin: 7.5px -15px;\n}\n.navbar-nav > li > a {\n  padding-top: 10px;\n  padding-bottom: 10px;\n  line-height: 20px;\n}\n@media (max-width: 767px) {\n  .navbar-nav .open .dropdown-menu {\n    position: static;\n    float: none;\n    width: auto;\n    margin-top: 0;\n    background-color: transparent;\n    border: 0;\n    box-shadow: none;\n  }\n  .navbar-nav .open .dropdown-menu > li > a,\n  .navbar-nav .open .dropdown-menu .dropdown-header {\n    padding: 5px 15px 5px 25px;\n  }\n  .navbar-nav .open .dropdown-menu > li > a {\n    line-height: 20px;\n  }\n  .navbar-nav .open .dropdown-menu > li > a:hover,\n  .navbar-nav .open .dropdown-menu > li > a:focus {\n    background-image: none;\n  }\n}\n@media (min-width: 768px) {\n  .navbar-nav {\n    float: left;\n    margin: 0;\n  }\n  .navbar-nav > li {\n    float: left;\n  }\n  .navbar-nav > li > a {\n    padding-top: 15px;\n    padding-bottom: 15px;\n  }\n}\n.navbar-form {\n  margin-left: -15px;\n  margin-right: -15px;\n  padding: 10px 15px;\n  border-top: 1px solid transparent;\n  border-bottom: 1px solid transparent;\n  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);\n  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);\n  margin-top: 8px;\n  margin-bottom: 8px;\n}\n@media (min-width: 768px) {\n  .navbar-form .form-group {\n    display: inline-block;\n    margin-bottom: 0;\n    vertical-align: middle;\n  }\n  .navbar-form .form-control {\n    display: inline-block;\n    width: auto;\n    vertical-align: middle;\n  }\n  .navbar-form .form-control-static {\n    display: inline-block;\n  }\n  .navbar-form .input-group {\n    display: inline-table;\n    vertical-align: middle;\n  }\n  .navbar-form .input-group .input-group-addon,\n  .navbar-form .input-group .input-group-btn,\n  .navbar-form .input-group .form-control {\n    width: auto;\n  }\n  .navbar-form .input-group > .form-control {\n    width: 100%;\n  }\n  .navbar-form .control-label {\n    margin-bottom: 0;\n    vertical-align: middle;\n  }\n  .navbar-form .radio,\n  .navbar-form .checkbox {\n    display: inline-block;\n    margin-top: 0;\n    margin-bottom: 0;\n    vertical-align: middle;\n  }\n  .navbar-form .radio label,\n  .navbar-form .checkbox label {\n    padding-left: 0;\n  }\n  .navbar-form .radio input[type=\"radio\"],\n  .navbar-form .checkbox input[type=\"checkbox\"] {\n    position: relative;\n    margin-left: 0;\n  }\n  .navbar-form .has-feedback .form-control-feedback {\n    top: 0;\n  }\n}\n@media (max-width: 767px) {\n  .navbar-form .form-group {\n    margin-bottom: 5px;\n  }\n  .navbar-form .form-group:last-child {\n    margin-bottom: 0;\n  }\n}\n@media (min-width: 768px) {\n  .navbar-form {\n    width: auto;\n    border: 0;\n    margin-left: 0;\n    margin-right: 0;\n    padding-top: 0;\n    padding-bottom: 0;\n    -webkit-box-shadow: none;\n    box-shadow: none;\n  }\n}\n.navbar-nav > li > .dropdown-menu {\n  margin-top: 0;\n  border-top-right-radius: 0;\n  border-top-left-radius: 0;\n}\n.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {\n  margin-bottom: 0;\n  border-top-right-radius: 4px;\n  border-top-left-radius: 4px;\n  border-bottom-right-radius: 0;\n  border-bottom-left-radius: 0;\n}\n.navbar-btn {\n  margin-top: 8px;\n  margin-bottom: 8px;\n}\n.navbar-btn.btn-sm {\n  margin-top: 10px;\n  margin-bottom: 10px;\n}\n.navbar-btn.btn-xs {\n  margin-top: 14px;\n  margin-bottom: 14px;\n}\n.navbar-text {\n  margin-top: 15px;\n  margin-bottom: 15px;\n}\n@media (min-width: 768px) {\n  .navbar-text {\n    float: left;\n    margin-left: 15px;\n    margin-right: 15px;\n  }\n}\n@media (min-width: 768px) {\n  .navbar-left {\n    float: left !important;\n  }\n  .navbar-right {\n    float: right !important;\n    margin-right: -15px;\n  }\n  .navbar-right ~ .navbar-right {\n    margin-right: 0;\n  }\n}\n.navbar-default {\n  background-color: #f8f8f8;\n  border-color: #e7e7e7;\n}\n.navbar-default .navbar-brand {\n  color: #777;\n}\n.navbar-default .navbar-brand:hover,\n.navbar-default .navbar-brand:focus {\n  color: #5e5e5e;\n  background-color: transparent;\n}\n.navbar-default .navbar-text {\n  color: #777;\n}\n.navbar-default .navbar-nav > li > a {\n  color: #777;\n}\n.navbar-default .navbar-nav > li > a:hover,\n.navbar-default .navbar-nav > li > a:focus {\n  color: #333;\n  background-color: transparent;\n}\n.navbar-default .navbar-nav > .active > a,\n.navbar-default .navbar-nav > .active > a:hover,\n.navbar-default .navbar-nav > .active > a:focus {\n  color: #555;\n  background-color: #e7e7e7;\n}\n.navbar-default .navbar-nav > .disabled > a,\n.navbar-default .navbar-nav > .disabled > a:hover,\n.navbar-default .navbar-nav > .disabled > a:focus {\n  color: #ccc;\n  background-color: transparent;\n}\n.navbar-default .navbar-toggle {\n  border-color: #ddd;\n}\n.navbar-default .navbar-toggle:hover,\n.navbar-default .navbar-toggle:focus {\n  background-color: #ddd;\n}\n.navbar-default .navbar-toggle .icon-bar {\n  background-color: #888;\n}\n.navbar-default .navbar-collapse,\n.navbar-default .navbar-form {\n  border-color: #e7e7e7;\n}\n.navbar-default .navbar-nav > .open > a,\n.navbar-default .navbar-nav > .open > a:hover,\n.navbar-default .navbar-nav > .open > a:focus {\n  background-color: #e7e7e7;\n  color: #555;\n}\n@media (max-width: 767px) {\n  .navbar-default .navbar-nav .open .dropdown-menu > li > a {\n    color: #777;\n  }\n  .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,\n  .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {\n    color: #333;\n    background-color: transparent;\n  }\n  .navbar-default .navbar-nav .open .dropdown-menu > .active > a,\n  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,\n  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {\n    color: #555;\n    background-color: #e7e7e7;\n  }\n  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,\n  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,\n  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {\n    color: #ccc;\n    background-color: transparent;\n  }\n}\n.navbar-default .navbar-link {\n  color: #777;\n}\n.navbar-default .navbar-link:hover {\n  color: #333;\n}\n.navbar-default .btn-link {\n  color: #777;\n}\n.navbar-default .btn-link:hover,\n.navbar-default .btn-link:focus {\n  color: #333;\n}\n.navbar-default .btn-link[disabled]:hover,\nfieldset[disabled] .navbar-default .btn-link:hover,\n.navbar-default .btn-link[disabled]:focus,\nfieldset[disabled] .navbar-default .btn-link:focus {\n  color: #ccc;\n}\n.navbar-inverse {\n  background-color: #222;\n  border-color: #080808;\n}\n.navbar-inverse .navbar-brand {\n  color: #9d9d9d;\n}\n.navbar-inverse .navbar-brand:hover,\n.navbar-inverse .navbar-brand:focus {\n  color: #fff;\n  background-color: transparent;\n}\n.navbar-inverse .navbar-text {\n  color: #9d9d9d;\n}\n.navbar-inverse .navbar-nav > li > a {\n  color: #9d9d9d;\n}\n.navbar-inverse .navbar-nav > li > a:hover,\n.navbar-inverse .navbar-nav > li > a:focus {\n  color: #fff;\n  background-color: transparent;\n}\n.navbar-inverse .navbar-nav > .active > a,\n.navbar-inverse .navbar-nav > .active > a:hover,\n.navbar-inverse .navbar-nav > .active > a:focus {\n  color: #fff;\n  background-color: #080808;\n}\n.navbar-inverse .navbar-nav > .disabled > a,\n.navbar-inverse .navbar-nav > .disabled > a:hover,\n.navbar-inverse .navbar-nav > .disabled > a:focus {\n  color: #444;\n  background-color: transparent;\n}\n.navbar-inverse .navbar-toggle {\n  border-color: #333;\n}\n.navbar-inverse .navbar-toggle:hover,\n.navbar-inverse .navbar-toggle:focus {\n  background-color: #333;\n}\n.navbar-inverse .navbar-toggle .icon-bar {\n  background-color: #fff;\n}\n.navbar-inverse .navbar-collapse,\n.navbar-inverse .navbar-form {\n  border-color: #101010;\n}\n.navbar-inverse .navbar-nav > .open > a,\n.navbar-inverse .navbar-nav > .open > a:hover,\n.navbar-inverse .navbar-nav > .open > a:focus {\n  background-color: #080808;\n  color: #fff;\n}\n@media (max-width: 767px) {\n  .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {\n    border-color: #080808;\n  }\n  .navbar-inverse .navbar-nav .open .dropdown-menu .divider {\n    background-color: #080808;\n  }\n  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {\n    color: #9d9d9d;\n  }\n  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,\n  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {\n    color: #fff;\n    background-color: transparent;\n  }\n  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,\n  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,\n  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {\n    color: #fff;\n    background-color: #080808;\n  }\n  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,\n  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,\n  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {\n    color: #444;\n    background-color: transparent;\n  }\n}\n.navbar-inverse .navbar-link {\n  color: #9d9d9d;\n}\n.navbar-inverse .navbar-link:hover {\n  color: #fff;\n}\n.navbar-inverse .btn-link {\n  color: #9d9d9d;\n}\n.navbar-inverse .btn-link:hover,\n.navbar-inverse .btn-link:focus {\n  color: #fff;\n}\n.navbar-inverse .btn-link[disabled]:hover,\nfieldset[disabled] .navbar-inverse .btn-link:hover,\n.navbar-inverse .btn-link[disabled]:focus,\nfieldset[disabled] .navbar-inverse .btn-link:focus {\n  color: #444;\n}\n.breadcrumb {\n  padding: 8px 15px;\n  margin-bottom: 20px;\n  list-style: none;\n  background-color: #f5f5f5;\n  border-radius: 4px;\n}\n.breadcrumb > li {\n  display: inline-block;\n}\n.breadcrumb > li + li:before {\n  content: \"/\\00a0\";\n  padding: 0 5px;\n  color: #ccc;\n}\n.breadcrumb > .active {\n  color: #777777;\n}\n.pagination {\n  display: inline-block;\n  padding-left: 0;\n  margin: 20px 0;\n  border-radius: 4px;\n}\n.pagination > li {\n  display: inline;\n}\n.pagination > li > a,\n.pagination > li > span {\n  position: relative;\n  float: left;\n  padding: 6px 12px;\n  line-height: 1.42857143;\n  text-decoration: none;\n  color: #337ab7;\n  background-color: #fff;\n  border: 1px solid #ddd;\n  margin-left: -1px;\n}\n.pagination > li:first-child > a,\n.pagination > li:first-child > span {\n  margin-left: 0;\n  border-bottom-left-radius: 4px;\n  border-top-left-radius: 4px;\n}\n.pagination > li:last-child > a,\n.pagination > li:last-child > span {\n  border-bottom-right-radius: 4px;\n  border-top-right-radius: 4px;\n}\n.pagination > li > a:hover,\n.pagination > li > span:hover,\n.pagination > li > a:focus,\n.pagination > li > span:focus {\n  z-index: 2;\n  color: #23527c;\n  background-color: #eeeeee;\n  border-color: #ddd;\n}\n.pagination > .active > a,\n.pagination > .active > span,\n.pagination > .active > a:hover,\n.pagination > .active > span:hover,\n.pagination > .active > a:focus,\n.pagination > .active > span:focus {\n  z-index: 3;\n  color: #fff;\n  background-color: #337ab7;\n  border-color: #337ab7;\n  cursor: default;\n}\n.pagination > .disabled > span,\n.pagination > .disabled > span:hover,\n.pagination > .disabled > span:focus,\n.pagination > .disabled > a,\n.pagination > .disabled > a:hover,\n.pagination > .disabled > a:focus {\n  color: #777777;\n  background-color: #fff;\n  border-color: #ddd;\n  cursor: not-allowed;\n}\n.pagination-lg > li > a,\n.pagination-lg > li > span {\n  padding: 10px 16px;\n  font-size: 18px;\n  line-height: 1.3333333;\n}\n.pagination-lg > li:first-child > a,\n.pagination-lg > li:first-child > span {\n  border-bottom-left-radius: 6px;\n  border-top-left-radius: 6px;\n}\n.pagination-lg > li:last-child > a,\n.pagination-lg > li:last-child > span {\n  border-bottom-right-radius: 6px;\n  border-top-right-radius: 6px;\n}\n.pagination-sm > li > a,\n.pagination-sm > li > span {\n  padding: 5px 10px;\n  font-size: 12px;\n  line-height: 1.5;\n}\n.pagination-sm > li:first-child > a,\n.pagination-sm > li:first-child > span {\n  border-bottom-left-radius: 3px;\n  border-top-left-radius: 3px;\n}\n.pagination-sm > li:last-child > a,\n.pagination-sm > li:last-child > span {\n  border-bottom-right-radius: 3px;\n  border-top-right-radius: 3px;\n}\n.pager {\n  padding-left: 0;\n  margin: 20px 0;\n  list-style: none;\n  text-align: center;\n}\n.pager li {\n  display: inline;\n}\n.pager li > a,\n.pager li > span {\n  display: inline-block;\n  padding: 5px 14px;\n  background-color: #fff;\n  border: 1px solid #ddd;\n  border-radius: 15px;\n}\n.pager li > a:hover,\n.pager li > a:focus {\n  text-decoration: none;\n  background-color: #eeeeee;\n}\n.pager .next > a,\n.pager .next > span {\n  float: right;\n}\n.pager .previous > a,\n.pager .previous > span {\n  float: left;\n}\n.pager .disabled > a,\n.pager .disabled > a:hover,\n.pager .disabled > a:focus,\n.pager .disabled > span {\n  color: #777777;\n  background-color: #fff;\n  cursor: not-allowed;\n}\n.label {\n  display: inline;\n  padding: .2em .6em .3em;\n  font-size: 75%;\n  font-weight: bold;\n  line-height: 1;\n  color: #fff;\n  text-align: center;\n  white-space: nowrap;\n  vertical-align: baseline;\n  border-radius: .25em;\n}\na.label:hover,\na.label:focus {\n  color: #fff;\n  text-decoration: none;\n  cursor: pointer;\n}\n.label:empty {\n  display: none;\n}\n.btn .label {\n  position: relative;\n  top: -1px;\n}\n.label-default {\n  background-color: #777777;\n}\n.label-default[href]:hover,\n.label-default[href]:focus {\n  background-color: #5e5e5e;\n}\n.label-primary {\n  background-color: #337ab7;\n}\n.label-primary[href]:hover,\n.label-primary[href]:focus {\n  background-color: #286090;\n}\n.label-success {\n  background-color: #5cb85c;\n}\n.label-success[href]:hover,\n.label-success[href]:focus {\n  background-color: #449d44;\n}\n.label-info {\n  background-color: #5bc0de;\n}\n.label-info[href]:hover,\n.label-info[href]:focus {\n  background-color: #31b0d5;\n}\n.label-warning {\n  background-color: #f0ad4e;\n}\n.label-warning[href]:hover,\n.label-warning[href]:focus {\n  background-color: #ec971f;\n}\n.label-danger {\n  background-color: #d9534f;\n}\n.label-danger[href]:hover,\n.label-danger[href]:focus {\n  background-color: #c9302c;\n}\n.badge {\n  display: inline-block;\n  min-width: 10px;\n  padding: 3px 7px;\n  font-size: 12px;\n  font-weight: bold;\n  color: #fff;\n  line-height: 1;\n  vertical-align: middle;\n  white-space: nowrap;\n  text-align: center;\n  background-color: #777777;\n  border-radius: 10px;\n}\n.badge:empty {\n  display: none;\n}\n.btn .badge {\n  position: relative;\n  top: -1px;\n}\n.btn-xs .badge,\n.btn-group-xs > .btn .badge {\n  top: 0;\n  padding: 1px 5px;\n}\na.badge:hover,\na.badge:focus {\n  color: #fff;\n  text-decoration: none;\n  cursor: pointer;\n}\n.list-group-item.active > .badge,\n.nav-pills > .active > a > .badge {\n  color: #337ab7;\n  background-color: #fff;\n}\n.list-group-item > .badge {\n  float: right;\n}\n.list-group-item > .badge + .badge {\n  margin-right: 5px;\n}\n.nav-pills > li > a > .badge {\n  margin-left: 3px;\n}\n.jumbotron {\n  padding-top: 30px;\n  padding-bottom: 30px;\n  margin-bottom: 30px;\n  color: inherit;\n  background-color: #eeeeee;\n}\n.jumbotron h1,\n.jumbotron .h1 {\n  color: inherit;\n}\n.jumbotron p {\n  margin-bottom: 15px;\n  font-size: 21px;\n  font-weight: 200;\n}\n.jumbotron > hr {\n  border-top-color: #d5d5d5;\n}\n.container .jumbotron,\n.container-fluid .jumbotron {\n  border-radius: 6px;\n  padding-left: 15px;\n  padding-right: 15px;\n}\n.jumbotron .container {\n  max-width: 100%;\n}\n@media screen and (min-width: 768px) {\n  .jumbotron {\n    padding-top: 48px;\n    padding-bottom: 48px;\n  }\n  .container .jumbotron,\n  .container-fluid .jumbotron {\n    padding-left: 60px;\n    padding-right: 60px;\n  }\n  .jumbotron h1,\n  .jumbotron .h1 {\n    font-size: 63px;\n  }\n}\n.thumbnail {\n  display: block;\n  padding: 4px;\n  margin-bottom: 20px;\n  line-height: 1.42857143;\n  background-color: #fff;\n  border: 1px solid #ddd;\n  border-radius: 4px;\n  -webkit-transition: border 0.2s ease-in-out;\n  -o-transition: border 0.2s ease-in-out;\n  transition: border 0.2s ease-in-out;\n}\n.thumbnail > img,\n.thumbnail a > img {\n  margin-left: auto;\n  margin-right: auto;\n}\na.thumbnail:hover,\na.thumbnail:focus,\na.thumbnail.active {\n  border-color: #337ab7;\n}\n.thumbnail .caption {\n  padding: 9px;\n  color: #333333;\n}\n.alert {\n  padding: 15px;\n  margin-bottom: 20px;\n  border: 1px solid transparent;\n  border-radius: 4px;\n}\n.alert h4 {\n  margin-top: 0;\n  color: inherit;\n}\n.alert .alert-link {\n  font-weight: bold;\n}\n.alert > p,\n.alert > ul {\n  margin-bottom: 0;\n}\n.alert > p + p {\n  margin-top: 5px;\n}\n.alert-dismissable,\n.alert-dismissible {\n  padding-right: 35px;\n}\n.alert-dismissable .close,\n.alert-dismissible .close {\n  position: relative;\n  top: -2px;\n  right: -21px;\n  color: inherit;\n}\n.alert-success {\n  background-color: #dff0d8;\n  border-color: #d6e9c6;\n  color: #3c763d;\n}\n.alert-success hr {\n  border-top-color: #c9e2b3;\n}\n.alert-success .alert-link {\n  color: #2b542c;\n}\n.alert-info {\n  background-color: #d9edf7;\n  border-color: #bce8f1;\n  color: #31708f;\n}\n.alert-info hr {\n  border-top-color: #a6e1ec;\n}\n.alert-info .alert-link {\n  color: #245269;\n}\n.alert-warning {\n  background-color: #fcf8e3;\n  border-color: #faebcc;\n  color: #8a6d3b;\n}\n.alert-warning hr {\n  border-top-color: #f7e1b5;\n}\n.alert-warning .alert-link {\n  color: #66512c;\n}\n.alert-danger {\n  background-color: #f2dede;\n  border-color: #ebccd1;\n  color: #a94442;\n}\n.alert-danger hr {\n  border-top-color: #e4b9c0;\n}\n.alert-danger .alert-link {\n  color: #843534;\n}\n@-webkit-keyframes progress-bar-stripes {\n  from {\n    background-position: 40px 0;\n  }\n  to {\n    background-position: 0 0;\n  }\n}\n@keyframes progress-bar-stripes {\n  from {\n    background-position: 40px 0;\n  }\n  to {\n    background-position: 0 0;\n  }\n}\n.progress {\n  overflow: hidden;\n  height: 20px;\n  margin-bottom: 20px;\n  background-color: #f5f5f5;\n  border-radius: 4px;\n  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);\n  box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);\n}\n.progress-bar {\n  float: left;\n  width: 0%;\n  height: 100%;\n  font-size: 12px;\n  line-height: 20px;\n  color: #fff;\n  text-align: center;\n  background-color: #337ab7;\n  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);\n  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);\n  -webkit-transition: width 0.6s ease;\n  -o-transition: width 0.6s ease;\n  transition: width 0.6s ease;\n}\n.progress-striped .progress-bar,\n.progress-bar-striped {\n  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-size: 40px 40px;\n}\n.progress.active .progress-bar,\n.progress-bar.active {\n  -webkit-animation: progress-bar-stripes 2s linear infinite;\n  -o-animation: progress-bar-stripes 2s linear infinite;\n  animation: progress-bar-stripes 2s linear infinite;\n}\n.progress-bar-success {\n  background-color: #5cb85c;\n}\n.progress-striped .progress-bar-success {\n  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n}\n.progress-bar-info {\n  background-color: #5bc0de;\n}\n.progress-striped .progress-bar-info {\n  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n}\n.progress-bar-warning {\n  background-color: #f0ad4e;\n}\n.progress-striped .progress-bar-warning {\n  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n}\n.progress-bar-danger {\n  background-color: #d9534f;\n}\n.progress-striped .progress-bar-danger {\n  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n}\n.media {\n  margin-top: 15px;\n}\n.media:first-child {\n  margin-top: 0;\n}\n.media,\n.media-body {\n  zoom: 1;\n  overflow: hidden;\n}\n.media-body {\n  width: 10000px;\n}\n.media-object {\n  display: block;\n}\n.media-object.img-thumbnail {\n  max-width: none;\n}\n.media-right,\n.media > .pull-right {\n  padding-left: 10px;\n}\n.media-left,\n.media > .pull-left {\n  padding-right: 10px;\n}\n.media-left,\n.media-right,\n.media-body {\n  display: table-cell;\n  vertical-align: top;\n}\n.media-middle {\n  vertical-align: middle;\n}\n.media-bottom {\n  vertical-align: bottom;\n}\n.media-heading {\n  margin-top: 0;\n  margin-bottom: 5px;\n}\n.media-list {\n  padding-left: 0;\n  list-style: none;\n}\n.list-group {\n  margin-bottom: 20px;\n  padding-left: 0;\n}\n.list-group-item {\n  position: relative;\n  display: block;\n  padding: 10px 15px;\n  margin-bottom: -1px;\n  background-color: #fff;\n  border: 1px solid #ddd;\n}\n.list-group-item:first-child {\n  border-top-right-radius: 4px;\n  border-top-left-radius: 4px;\n}\n.list-group-item:last-child {\n  margin-bottom: 0;\n  border-bottom-right-radius: 4px;\n  border-bottom-left-radius: 4px;\n}\na.list-group-item,\nbutton.list-group-item {\n  color: #555;\n}\na.list-group-item .list-group-item-heading,\nbutton.list-group-item .list-group-item-heading {\n  color: #333;\n}\na.list-group-item:hover,\nbutton.list-group-item:hover,\na.list-group-item:focus,\nbutton.list-group-item:focus {\n  text-decoration: none;\n  color: #555;\n  background-color: #f5f5f5;\n}\nbutton.list-group-item {\n  width: 100%;\n  text-align: left;\n}\n.list-group-item.disabled,\n.list-group-item.disabled:hover,\n.list-group-item.disabled:focus {\n  background-color: #eeeeee;\n  color: #777777;\n  cursor: not-allowed;\n}\n.list-group-item.disabled .list-group-item-heading,\n.list-group-item.disabled:hover .list-group-item-heading,\n.list-group-item.disabled:focus .list-group-item-heading {\n  color: inherit;\n}\n.list-group-item.disabled .list-group-item-text,\n.list-group-item.disabled:hover .list-group-item-text,\n.list-group-item.disabled:focus .list-group-item-text {\n  color: #777777;\n}\n.list-group-item.active,\n.list-group-item.active:hover,\n.list-group-item.active:focus {\n  z-index: 2;\n  color: #fff;\n  background-color: #337ab7;\n  border-color: #337ab7;\n}\n.list-group-item.active .list-group-item-heading,\n.list-group-item.active:hover .list-group-item-heading,\n.list-group-item.active:focus .list-group-item-heading,\n.list-group-item.active .list-group-item-heading > small,\n.list-group-item.active:hover .list-group-item-heading > small,\n.list-group-item.active:focus .list-group-item-heading > small,\n.list-group-item.active .list-group-item-heading > .small,\n.list-group-item.active:hover .list-group-item-heading > .small,\n.list-group-item.active:focus .list-group-item-heading > .small {\n  color: inherit;\n}\n.list-group-item.active .list-group-item-text,\n.list-group-item.active:hover .list-group-item-text,\n.list-group-item.active:focus .list-group-item-text {\n  color: #c7ddef;\n}\n.list-group-item-success {\n  color: #3c763d;\n  background-color: #dff0d8;\n}\na.list-group-item-success,\nbutton.list-group-item-success {\n  color: #3c763d;\n}\na.list-group-item-success .list-group-item-heading,\nbutton.list-group-item-success .list-group-item-heading {\n  color: inherit;\n}\na.list-group-item-success:hover,\nbutton.list-group-item-success:hover,\na.list-group-item-success:focus,\nbutton.list-group-item-success:focus {\n  color: #3c763d;\n  background-color: #d0e9c6;\n}\na.list-group-item-success.active,\nbutton.list-group-item-success.active,\na.list-group-item-success.active:hover,\nbutton.list-group-item-success.active:hover,\na.list-group-item-success.active:focus,\nbutton.list-group-item-success.active:focus {\n  color: #fff;\n  background-color: #3c763d;\n  border-color: #3c763d;\n}\n.list-group-item-info {\n  color: #31708f;\n  background-color: #d9edf7;\n}\na.list-group-item-info,\nbutton.list-group-item-info {\n  color: #31708f;\n}\na.list-group-item-info .list-group-item-heading,\nbutton.list-group-item-info .list-group-item-heading {\n  color: inherit;\n}\na.list-group-item-info:hover,\nbutton.list-group-item-info:hover,\na.list-group-item-info:focus,\nbutton.list-group-item-info:focus {\n  color: #31708f;\n  background-color: #c4e3f3;\n}\na.list-group-item-info.active,\nbutton.list-group-item-info.active,\na.list-group-item-info.active:hover,\nbutton.list-group-item-info.active:hover,\na.list-group-item-info.active:focus,\nbutton.list-group-item-info.active:focus {\n  color: #fff;\n  background-color: #31708f;\n  border-color: #31708f;\n}\n.list-group-item-warning {\n  color: #8a6d3b;\n  background-color: #fcf8e3;\n}\na.list-group-item-warning,\nbutton.list-group-item-warning {\n  color: #8a6d3b;\n}\na.list-group-item-warning .list-group-item-heading,\nbutton.list-group-item-warning .list-group-item-heading {\n  color: inherit;\n}\na.list-group-item-warning:hover,\nbutton.list-group-item-warning:hover,\na.list-group-item-warning:focus,\nbutton.list-group-item-warning:focus {\n  color: #8a6d3b;\n  background-color: #faf2cc;\n}\na.list-group-item-warning.active,\nbutton.list-group-item-warning.active,\na.list-group-item-warning.active:hover,\nbutton.list-group-item-warning.active:hover,\na.list-group-item-warning.active:focus,\nbutton.list-group-item-warning.active:focus {\n  color: #fff;\n  background-color: #8a6d3b;\n  border-color: #8a6d3b;\n}\n.list-group-item-danger {\n  color: #a94442;\n  background-color: #f2dede;\n}\na.list-group-item-danger,\nbutton.list-group-item-danger {\n  color: #a94442;\n}\na.list-group-item-danger .list-group-item-heading,\nbutton.list-group-item-danger .list-group-item-heading {\n  color: inherit;\n}\na.list-group-item-danger:hover,\nbutton.list-group-item-danger:hover,\na.list-group-item-danger:focus,\nbutton.list-group-item-danger:focus {\n  color: #a94442;\n  background-color: #ebcccc;\n}\na.list-group-item-danger.active,\nbutton.list-group-item-danger.active,\na.list-group-item-danger.active:hover,\nbutton.list-group-item-danger.active:hover,\na.list-group-item-danger.active:focus,\nbutton.list-group-item-danger.active:focus {\n  color: #fff;\n  background-color: #a94442;\n  border-color: #a94442;\n}\n.list-group-item-heading {\n  margin-top: 0;\n  margin-bottom: 5px;\n}\n.list-group-item-text {\n  margin-bottom: 0;\n  line-height: 1.3;\n}\n.panel {\n  margin-bottom: 20px;\n  background-color: #fff;\n  border: 1px solid transparent;\n  border-radius: 4px;\n  -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);\n  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);\n}\n.panel-body {\n  padding: 15px;\n}\n.panel-heading {\n  padding: 10px 15px;\n  border-bottom: 1px solid transparent;\n  border-top-right-radius: 3px;\n  border-top-left-radius: 3px;\n}\n.panel-heading > .dropdown .dropdown-toggle {\n  color: inherit;\n}\n.panel-title {\n  margin-top: 0;\n  margin-bottom: 0;\n  font-size: 16px;\n  color: inherit;\n}\n.panel-title > a,\n.panel-title > small,\n.panel-title > .small,\n.panel-title > small > a,\n.panel-title > .small > a {\n  color: inherit;\n}\n.panel-footer {\n  padding: 10px 15px;\n  background-color: #f5f5f5;\n  border-top: 1px solid #ddd;\n  border-bottom-right-radius: 3px;\n  border-bottom-left-radius: 3px;\n}\n.panel > .list-group,\n.panel > .panel-collapse > .list-group {\n  margin-bottom: 0;\n}\n.panel > .list-group .list-group-item,\n.panel > .panel-collapse > .list-group .list-group-item {\n  border-width: 1px 0;\n  border-radius: 0;\n}\n.panel > .list-group:first-child .list-group-item:first-child,\n.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {\n  border-top: 0;\n  border-top-right-radius: 3px;\n  border-top-left-radius: 3px;\n}\n.panel > .list-group:last-child .list-group-item:last-child,\n.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {\n  border-bottom: 0;\n  border-bottom-right-radius: 3px;\n  border-bottom-left-radius: 3px;\n}\n.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {\n  border-top-right-radius: 0;\n  border-top-left-radius: 0;\n}\n.panel-heading + .list-group .list-group-item:first-child {\n  border-top-width: 0;\n}\n.list-group + .panel-footer {\n  border-top-width: 0;\n}\n.panel > .table,\n.panel > .table-responsive > .table,\n.panel > .panel-collapse > .table {\n  margin-bottom: 0;\n}\n.panel > .table caption,\n.panel > .table-responsive > .table caption,\n.panel > .panel-collapse > .table caption {\n  padding-left: 15px;\n  padding-right: 15px;\n}\n.panel > .table:first-child,\n.panel > .table-responsive:first-child > .table:first-child {\n  border-top-right-radius: 3px;\n  border-top-left-radius: 3px;\n}\n.panel > .table:first-child > thead:first-child > tr:first-child,\n.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,\n.panel > .table:first-child > tbody:first-child > tr:first-child,\n.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {\n  border-top-left-radius: 3px;\n  border-top-right-radius: 3px;\n}\n.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,\n.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,\n.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,\n.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,\n.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,\n.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,\n.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,\n.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {\n  border-top-left-radius: 3px;\n}\n.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,\n.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,\n.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,\n.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,\n.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,\n.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,\n.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,\n.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {\n  border-top-right-radius: 3px;\n}\n.panel > .table:last-child,\n.panel > .table-responsive:last-child > .table:last-child {\n  border-bottom-right-radius: 3px;\n  border-bottom-left-radius: 3px;\n}\n.panel > .table:last-child > tbody:last-child > tr:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,\n.panel > .table:last-child > tfoot:last-child > tr:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {\n  border-bottom-left-radius: 3px;\n  border-bottom-right-radius: 3px;\n}\n.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,\n.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,\n.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,\n.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,\n.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,\n.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,\n.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,\n.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {\n  border-bottom-left-radius: 3px;\n}\n.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,\n.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,\n.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,\n.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {\n  border-bottom-right-radius: 3px;\n}\n.panel > .panel-body + .table,\n.panel > .panel-body + .table-responsive,\n.panel > .table + .panel-body,\n.panel > .table-responsive + .panel-body {\n  border-top: 1px solid #ddd;\n}\n.panel > .table > tbody:first-child > tr:first-child th,\n.panel > .table > tbody:first-child > tr:first-child td {\n  border-top: 0;\n}\n.panel > .table-bordered,\n.panel > .table-responsive > .table-bordered {\n  border: 0;\n}\n.panel > .table-bordered > thead > tr > th:first-child,\n.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,\n.panel > .table-bordered > tbody > tr > th:first-child,\n.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,\n.panel > .table-bordered > tfoot > tr > th:first-child,\n.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,\n.panel > .table-bordered > thead > tr > td:first-child,\n.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,\n.panel > .table-bordered > tbody > tr > td:first-child,\n.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,\n.panel > .table-bordered > tfoot > tr > td:first-child,\n.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {\n  border-left: 0;\n}\n.panel > .table-bordered > thead > tr > th:last-child,\n.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,\n.panel > .table-bordered > tbody > tr > th:last-child,\n.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,\n.panel > .table-bordered > tfoot > tr > th:last-child,\n.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,\n.panel > .table-bordered > thead > tr > td:last-child,\n.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,\n.panel > .table-bordered > tbody > tr > td:last-child,\n.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,\n.panel > .table-bordered > tfoot > tr > td:last-child,\n.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {\n  border-right: 0;\n}\n.panel > .table-bordered > thead > tr:first-child > td,\n.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,\n.panel > .table-bordered > tbody > tr:first-child > td,\n.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,\n.panel > .table-bordered > thead > tr:first-child > th,\n.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,\n.panel > .table-bordered > tbody > tr:first-child > th,\n.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {\n  border-bottom: 0;\n}\n.panel > .table-bordered > tbody > tr:last-child > td,\n.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,\n.panel > .table-bordered > tfoot > tr:last-child > td,\n.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,\n.panel > .table-bordered > tbody > tr:last-child > th,\n.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,\n.panel > .table-bordered > tfoot > tr:last-child > th,\n.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {\n  border-bottom: 0;\n}\n.panel > .table-responsive {\n  border: 0;\n  margin-bottom: 0;\n}\n.panel-group {\n  margin-bottom: 20px;\n}\n.panel-group .panel {\n  margin-bottom: 0;\n  border-radius: 4px;\n}\n.panel-group .panel + .panel {\n  margin-top: 5px;\n}\n.panel-group .panel-heading {\n  border-bottom: 0;\n}\n.panel-group .panel-heading + .panel-collapse > .panel-body,\n.panel-group .panel-heading + .panel-collapse > .list-group {\n  border-top: 1px solid #ddd;\n}\n.panel-group .panel-footer {\n  border-top: 0;\n}\n.panel-group .panel-footer + .panel-collapse .panel-body {\n  border-bottom: 1px solid #ddd;\n}\n.panel-default {\n  border-color: #ddd;\n}\n.panel-default > .panel-heading {\n  color: #333333;\n  background-color: #f5f5f5;\n  border-color: #ddd;\n}\n.panel-default > .panel-heading + .panel-collapse > .panel-body {\n  border-top-color: #ddd;\n}\n.panel-default > .panel-heading .badge {\n  color: #f5f5f5;\n  background-color: #333333;\n}\n.panel-default > .panel-footer + .panel-collapse > .panel-body {\n  border-bottom-color: #ddd;\n}\n.panel-primary {\n  border-color: #337ab7;\n}\n.panel-primary > .panel-heading {\n  color: #fff;\n  background-color: #337ab7;\n  border-color: #337ab7;\n}\n.panel-primary > .panel-heading + .panel-collapse > .panel-body {\n  border-top-color: #337ab7;\n}\n.panel-primary > .panel-heading .badge {\n  color: #337ab7;\n  background-color: #fff;\n}\n.panel-primary > .panel-footer + .panel-collapse > .panel-body {\n  border-bottom-color: #337ab7;\n}\n.panel-success {\n  border-color: #d6e9c6;\n}\n.panel-success > .panel-heading {\n  color: #3c763d;\n  background-color: #dff0d8;\n  border-color: #d6e9c6;\n}\n.panel-success > .panel-heading + .panel-collapse > .panel-body {\n  border-top-color: #d6e9c6;\n}\n.panel-success > .panel-heading .badge {\n  color: #dff0d8;\n  background-color: #3c763d;\n}\n.panel-success > .panel-footer + .panel-collapse > .panel-body {\n  border-bottom-color: #d6e9c6;\n}\n.panel-info {\n  border-color: #bce8f1;\n}\n.panel-info > .panel-heading {\n  color: #31708f;\n  background-color: #d9edf7;\n  border-color: #bce8f1;\n}\n.panel-info > .panel-heading + .panel-collapse > .panel-body {\n  border-top-color: #bce8f1;\n}\n.panel-info > .panel-heading .badge {\n  color: #d9edf7;\n  background-color: #31708f;\n}\n.panel-info > .panel-footer + .panel-collapse > .panel-body {\n  border-bottom-color: #bce8f1;\n}\n.panel-warning {\n  border-color: #faebcc;\n}\n.panel-warning > .panel-heading {\n  color: #8a6d3b;\n  background-color: #fcf8e3;\n  border-color: #faebcc;\n}\n.panel-warning > .panel-heading + .panel-collapse > .panel-body {\n  border-top-color: #faebcc;\n}\n.panel-warning > .panel-heading .badge {\n  color: #fcf8e3;\n  background-color: #8a6d3b;\n}\n.panel-warning > .panel-footer + .panel-collapse > .panel-body {\n  border-bottom-color: #faebcc;\n}\n.panel-danger {\n  border-color: #ebccd1;\n}\n.panel-danger > .panel-heading {\n  color: #a94442;\n  background-color: #f2dede;\n  border-color: #ebccd1;\n}\n.panel-danger > .panel-heading + .panel-collapse > .panel-body {\n  border-top-color: #ebccd1;\n}\n.panel-danger > .panel-heading .badge {\n  color: #f2dede;\n  background-color: #a94442;\n}\n.panel-danger > .panel-footer + .panel-collapse > .panel-body {\n  border-bottom-color: #ebccd1;\n}\n.embed-responsive {\n  position: relative;\n  display: block;\n  height: 0;\n  padding: 0;\n  overflow: hidden;\n}\n.embed-responsive .embed-responsive-item,\n.embed-responsive iframe,\n.embed-responsive embed,\n.embed-responsive object,\n.embed-responsive video {\n  position: absolute;\n  top: 0;\n  left: 0;\n  bottom: 0;\n  height: 100%;\n  width: 100%;\n  border: 0;\n}\n.embed-responsive-16by9 {\n  padding-bottom: 56.25%;\n}\n.embed-responsive-4by3 {\n  padding-bottom: 75%;\n}\n.well {\n  min-height: 20px;\n  padding: 19px;\n  margin-bottom: 20px;\n  background-color: #f5f5f5;\n  border: 1px solid #e3e3e3;\n  border-radius: 4px;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);\n  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);\n}\n.well blockquote {\n  border-color: #ddd;\n  border-color: rgba(0, 0, 0, 0.15);\n}\n.well-lg {\n  padding: 24px;\n  border-radius: 6px;\n}\n.well-sm {\n  padding: 9px;\n  border-radius: 3px;\n}\n.close {\n  float: right;\n  font-size: 21px;\n  font-weight: bold;\n  line-height: 1;\n  color: #000;\n  text-shadow: 0 1px 0 #fff;\n  opacity: 0.2;\n  filter: alpha(opacity=20);\n}\n.close:hover,\n.close:focus {\n  color: #000;\n  text-decoration: none;\n  cursor: pointer;\n  opacity: 0.5;\n  filter: alpha(opacity=50);\n}\nbutton.close {\n  padding: 0;\n  cursor: pointer;\n  background: transparent;\n  border: 0;\n  -webkit-appearance: none;\n}\n.modal-open {\n  overflow: hidden;\n}\n.modal {\n  display: none;\n  overflow: hidden;\n  position: fixed;\n  top: 0;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  z-index: 1050;\n  -webkit-overflow-scrolling: touch;\n  outline: 0;\n}\n.modal.fade .modal-dialog {\n  -webkit-transform: translate(0, -25%);\n  -ms-transform: translate(0, -25%);\n  -o-transform: translate(0, -25%);\n  transform: translate(0, -25%);\n  -webkit-transition: -webkit-transform 0.3s ease-out;\n  -moz-transition: -moz-transform 0.3s ease-out;\n  -o-transition: -o-transform 0.3s ease-out;\n  transition: transform 0.3s ease-out;\n}\n.modal.in .modal-dialog {\n  -webkit-transform: translate(0, 0);\n  -ms-transform: translate(0, 0);\n  -o-transform: translate(0, 0);\n  transform: translate(0, 0);\n}\n.modal-open .modal {\n  overflow-x: hidden;\n  overflow-y: auto;\n}\n.modal-dialog {\n  position: relative;\n  width: auto;\n  margin: 10px;\n}\n.modal-content {\n  position: relative;\n  background-color: #fff;\n  border: 1px solid #999;\n  border: 1px solid rgba(0, 0, 0, 0.2);\n  border-radius: 6px;\n  -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);\n  box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);\n  background-clip: padding-box;\n  outline: 0;\n}\n.modal-backdrop {\n  position: fixed;\n  top: 0;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  z-index: 1040;\n  background-color: #000;\n}\n.modal-backdrop.fade {\n  opacity: 0;\n  filter: alpha(opacity=0);\n}\n.modal-backdrop.in {\n  opacity: 0.5;\n  filter: alpha(opacity=50);\n}\n.modal-header {\n  padding: 15px;\n  border-bottom: 1px solid #e5e5e5;\n}\n.modal-header .close {\n  margin-top: -2px;\n}\n.modal-title {\n  margin: 0;\n  line-height: 1.42857143;\n}\n.modal-body {\n  position: relative;\n  padding: 15px;\n}\n.modal-footer {\n  padding: 15px;\n  text-align: right;\n  border-top: 1px solid #e5e5e5;\n}\n.modal-footer .btn + .btn {\n  margin-left: 5px;\n  margin-bottom: 0;\n}\n.modal-footer .btn-group .btn + .btn {\n  margin-left: -1px;\n}\n.modal-footer .btn-block + .btn-block {\n  margin-left: 0;\n}\n.modal-scrollbar-measure {\n  position: absolute;\n  top: -9999px;\n  width: 50px;\n  height: 50px;\n  overflow: scroll;\n}\n@media (min-width: 768px) {\n  .modal-dialog {\n    width: 600px;\n    margin: 30px auto;\n  }\n  .modal-content {\n    -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);\n    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);\n  }\n  .modal-sm {\n    width: 300px;\n  }\n}\n@media (min-width: 992px) {\n  .modal-lg {\n    width: 900px;\n  }\n}\n.tooltip {\n  position: absolute;\n  z-index: 1070;\n  display: block;\n  font-family: \"Helvetica Neue\", Helvetica, Arial, sans-serif;\n  font-style: normal;\n  font-weight: normal;\n  letter-spacing: normal;\n  line-break: auto;\n  line-height: 1.42857143;\n  text-align: left;\n  text-align: start;\n  text-decoration: none;\n  text-shadow: none;\n  text-transform: none;\n  white-space: normal;\n  word-break: normal;\n  word-spacing: normal;\n  word-wrap: normal;\n  font-size: 12px;\n  opacity: 0;\n  filter: alpha(opacity=0);\n}\n.tooltip.in {\n  opacity: 0.9;\n  filter: alpha(opacity=90);\n}\n.tooltip.top {\n  margin-top: -3px;\n  padding: 5px 0;\n}\n.tooltip.right {\n  margin-left: 3px;\n  padding: 0 5px;\n}\n.tooltip.bottom {\n  margin-top: 3px;\n  padding: 5px 0;\n}\n.tooltip.left {\n  margin-left: -3px;\n  padding: 0 5px;\n}\n.tooltip-inner {\n  max-width: 200px;\n  padding: 3px 8px;\n  color: #fff;\n  text-align: center;\n  background-color: #000;\n  border-radius: 4px;\n}\n.tooltip-arrow {\n  position: absolute;\n  width: 0;\n  height: 0;\n  border-color: transparent;\n  border-style: solid;\n}\n.tooltip.top .tooltip-arrow {\n  bottom: 0;\n  left: 50%;\n  margin-left: -5px;\n  border-width: 5px 5px 0;\n  border-top-color: #000;\n}\n.tooltip.top-left .tooltip-arrow {\n  bottom: 0;\n  right: 5px;\n  margin-bottom: -5px;\n  border-width: 5px 5px 0;\n  border-top-color: #000;\n}\n.tooltip.top-right .tooltip-arrow {\n  bottom: 0;\n  left: 5px;\n  margin-bottom: -5px;\n  border-width: 5px 5px 0;\n  border-top-color: #000;\n}\n.tooltip.right .tooltip-arrow {\n  top: 50%;\n  left: 0;\n  margin-top: -5px;\n  border-width: 5px 5px 5px 0;\n  border-right-color: #000;\n}\n.tooltip.left .tooltip-arrow {\n  top: 50%;\n  right: 0;\n  margin-top: -5px;\n  border-width: 5px 0 5px 5px;\n  border-left-color: #000;\n}\n.tooltip.bottom .tooltip-arrow {\n  top: 0;\n  left: 50%;\n  margin-left: -5px;\n  border-width: 0 5px 5px;\n  border-bottom-color: #000;\n}\n.tooltip.bottom-left .tooltip-arrow {\n  top: 0;\n  right: 5px;\n  margin-top: -5px;\n  border-width: 0 5px 5px;\n  border-bottom-color: #000;\n}\n.tooltip.bottom-right .tooltip-arrow {\n  top: 0;\n  left: 5px;\n  margin-top: -5px;\n  border-width: 0 5px 5px;\n  border-bottom-color: #000;\n}\n.popover {\n  position: absolute;\n  top: 0;\n  left: 0;\n  z-index: 1060;\n  display: none;\n  max-width: 276px;\n  padding: 1px;\n  font-family: \"Helvetica Neue\", Helvetica, Arial, sans-serif;\n  font-style: normal;\n  font-weight: normal;\n  letter-spacing: normal;\n  line-break: auto;\n  line-height: 1.42857143;\n  text-align: left;\n  text-align: start;\n  text-decoration: none;\n  text-shadow: none;\n  text-transform: none;\n  white-space: normal;\n  word-break: normal;\n  word-spacing: normal;\n  word-wrap: normal;\n  font-size: 14px;\n  background-color: #fff;\n  background-clip: padding-box;\n  border: 1px solid #ccc;\n  border: 1px solid rgba(0, 0, 0, 0.2);\n  border-radius: 6px;\n  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);\n  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);\n}\n.popover.top {\n  margin-top: -10px;\n}\n.popover.right {\n  margin-left: 10px;\n}\n.popover.bottom {\n  margin-top: 10px;\n}\n.popover.left {\n  margin-left: -10px;\n}\n.popover-title {\n  margin: 0;\n  padding: 8px 14px;\n  font-size: 14px;\n  background-color: #f7f7f7;\n  border-bottom: 1px solid #ebebeb;\n  border-radius: 5px 5px 0 0;\n}\n.popover-content {\n  padding: 9px 14px;\n}\n.popover > .arrow,\n.popover > .arrow:after {\n  position: absolute;\n  display: block;\n  width: 0;\n  height: 0;\n  border-color: transparent;\n  border-style: solid;\n}\n.popover > .arrow {\n  border-width: 11px;\n}\n.popover > .arrow:after {\n  border-width: 10px;\n  content: \"\";\n}\n.popover.top > .arrow {\n  left: 50%;\n  margin-left: -11px;\n  border-bottom-width: 0;\n  border-top-color: #999999;\n  border-top-color: rgba(0, 0, 0, 0.25);\n  bottom: -11px;\n}\n.popover.top > .arrow:after {\n  content: \" \";\n  bottom: 1px;\n  margin-left: -10px;\n  border-bottom-width: 0;\n  border-top-color: #fff;\n}\n.popover.right > .arrow {\n  top: 50%;\n  left: -11px;\n  margin-top: -11px;\n  border-left-width: 0;\n  border-right-color: #999999;\n  border-right-color: rgba(0, 0, 0, 0.25);\n}\n.popover.right > .arrow:after {\n  content: \" \";\n  left: 1px;\n  bottom: -10px;\n  border-left-width: 0;\n  border-right-color: #fff;\n}\n.popover.bottom > .arrow {\n  left: 50%;\n  margin-left: -11px;\n  border-top-width: 0;\n  border-bottom-color: #999999;\n  border-bottom-color: rgba(0, 0, 0, 0.25);\n  top: -11px;\n}\n.popover.bottom > .arrow:after {\n  content: \" \";\n  top: 1px;\n  margin-left: -10px;\n  border-top-width: 0;\n  border-bottom-color: #fff;\n}\n.popover.left > .arrow {\n  top: 50%;\n  right: -11px;\n  margin-top: -11px;\n  border-right-width: 0;\n  border-left-color: #999999;\n  border-left-color: rgba(0, 0, 0, 0.25);\n}\n.popover.left > .arrow:after {\n  content: \" \";\n  right: 1px;\n  border-right-width: 0;\n  border-left-color: #fff;\n  bottom: -10px;\n}\n.carousel {\n  position: relative;\n}\n.carousel-inner {\n  position: relative;\n  overflow: hidden;\n  width: 100%;\n}\n.carousel-inner > .item {\n  display: none;\n  position: relative;\n  -webkit-transition: 0.6s ease-in-out left;\n  -o-transition: 0.6s ease-in-out left;\n  transition: 0.6s ease-in-out left;\n}\n.carousel-inner > .item > img,\n.carousel-inner > .item > a > img {\n  line-height: 1;\n}\n@media all and (transform-3d), (-webkit-transform-3d) {\n  .carousel-inner > .item {\n    -webkit-transition: -webkit-transform 0.6s ease-in-out;\n    -moz-transition: -moz-transform 0.6s ease-in-out;\n    -o-transition: -o-transform 0.6s ease-in-out;\n    transition: transform 0.6s ease-in-out;\n    -webkit-backface-visibility: hidden;\n    -moz-backface-visibility: hidden;\n    backface-visibility: hidden;\n    -webkit-perspective: 1000px;\n    -moz-perspective: 1000px;\n    perspective: 1000px;\n  }\n  .carousel-inner > .item.next,\n  .carousel-inner > .item.active.right {\n    -webkit-transform: translate3d(100%, 0, 0);\n    transform: translate3d(100%, 0, 0);\n    left: 0;\n  }\n  .carousel-inner > .item.prev,\n  .carousel-inner > .item.active.left {\n    -webkit-transform: translate3d(-100%, 0, 0);\n    transform: translate3d(-100%, 0, 0);\n    left: 0;\n  }\n  .carousel-inner > .item.next.left,\n  .carousel-inner > .item.prev.right,\n  .carousel-inner > .item.active {\n    -webkit-transform: translate3d(0, 0, 0);\n    transform: translate3d(0, 0, 0);\n    left: 0;\n  }\n}\n.carousel-inner > .active,\n.carousel-inner > .next,\n.carousel-inner > .prev {\n  display: block;\n}\n.carousel-inner > .active {\n  left: 0;\n}\n.carousel-inner > .next,\n.carousel-inner > .prev {\n  position: absolute;\n  top: 0;\n  width: 100%;\n}\n.carousel-inner > .next {\n  left: 100%;\n}\n.carousel-inner > .prev {\n  left: -100%;\n}\n.carousel-inner > .next.left,\n.carousel-inner > .prev.right {\n  left: 0;\n}\n.carousel-inner > .active.left {\n  left: -100%;\n}\n.carousel-inner > .active.right {\n  left: 100%;\n}\n.carousel-control {\n  position: absolute;\n  top: 0;\n  left: 0;\n  bottom: 0;\n  width: 15%;\n  opacity: 0.5;\n  filter: alpha(opacity=50);\n  font-size: 20px;\n  color: #fff;\n  text-align: center;\n  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);\n  background-color: rgba(0, 0, 0, 0);\n}\n.carousel-control.left {\n  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);\n  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);\n  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);\n}\n.carousel-control.right {\n  left: auto;\n  right: 0;\n  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);\n  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);\n  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);\n}\n.carousel-control:hover,\n.carousel-control:focus {\n  outline: 0;\n  color: #fff;\n  text-decoration: none;\n  opacity: 0.9;\n  filter: alpha(opacity=90);\n}\n.carousel-control .icon-prev,\n.carousel-control .icon-next,\n.carousel-control .glyphicon-chevron-left,\n.carousel-control .glyphicon-chevron-right {\n  position: absolute;\n  top: 50%;\n  margin-top: -10px;\n  z-index: 5;\n  display: inline-block;\n}\n.carousel-control .icon-prev,\n.carousel-control .glyphicon-chevron-left {\n  left: 50%;\n  margin-left: -10px;\n}\n.carousel-control .icon-next,\n.carousel-control .glyphicon-chevron-right {\n  right: 50%;\n  margin-right: -10px;\n}\n.carousel-control .icon-prev,\n.carousel-control .icon-next {\n  width: 20px;\n  height: 20px;\n  line-height: 1;\n  font-family: serif;\n}\n.carousel-control .icon-prev:before {\n  content: '\\2039';\n}\n.carousel-control .icon-next:before {\n  content: '\\203a';\n}\n.carousel-indicators {\n  position: absolute;\n  bottom: 10px;\n  left: 50%;\n  z-index: 15;\n  width: 60%;\n  margin-left: -30%;\n  padding-left: 0;\n  list-style: none;\n  text-align: center;\n}\n.carousel-indicators li {\n  display: inline-block;\n  width: 10px;\n  height: 10px;\n  margin: 1px;\n  text-indent: -999px;\n  border: 1px solid #fff;\n  border-radius: 10px;\n  cursor: pointer;\n  background-color: #000 \\9;\n  background-color: rgba(0, 0, 0, 0);\n}\n.carousel-indicators .active {\n  margin: 0;\n  width: 12px;\n  height: 12px;\n  background-color: #fff;\n}\n.carousel-caption {\n  position: absolute;\n  left: 15%;\n  right: 15%;\n  bottom: 20px;\n  z-index: 10;\n  padding-top: 20px;\n  padding-bottom: 20px;\n  color: #fff;\n  text-align: center;\n  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);\n}\n.carousel-caption .btn {\n  text-shadow: none;\n}\n@media screen and (min-width: 768px) {\n  .carousel-control .glyphicon-chevron-left,\n  .carousel-control .glyphicon-chevron-right,\n  .carousel-control .icon-prev,\n  .carousel-control .icon-next {\n    width: 30px;\n    height: 30px;\n    margin-top: -10px;\n    font-size: 30px;\n  }\n  .carousel-control .glyphicon-chevron-left,\n  .carousel-control .icon-prev {\n    margin-left: -10px;\n  }\n  .carousel-control .glyphicon-chevron-right,\n  .carousel-control .icon-next {\n    margin-right: -10px;\n  }\n  .carousel-caption {\n    left: 20%;\n    right: 20%;\n    padding-bottom: 30px;\n  }\n  .carousel-indicators {\n    bottom: 20px;\n  }\n}\n.clearfix:before,\n.clearfix:after,\n.dl-horizontal dd:before,\n.dl-horizontal dd:after,\n.container:before,\n.container:after,\n.container-fluid:before,\n.container-fluid:after,\n.row:before,\n.row:after,\n.form-horizontal .form-group:before,\n.form-horizontal .form-group:after,\n.btn-toolbar:before,\n.btn-toolbar:after,\n.btn-group-vertical > .btn-group:before,\n.btn-group-vertical > .btn-group:after,\n.nav:before,\n.nav:after,\n.navbar:before,\n.navbar:after,\n.navbar-header:before,\n.navbar-header:after,\n.navbar-collapse:before,\n.navbar-collapse:after,\n.pager:before,\n.pager:after,\n.panel-body:before,\n.panel-body:after,\n.modal-header:before,\n.modal-header:after,\n.modal-footer:before,\n.modal-footer:after {\n  content: \" \";\n  display: table;\n}\n.clearfix:after,\n.dl-horizontal dd:after,\n.container:after,\n.container-fluid:after,\n.row:after,\n.form-horizontal .form-group:after,\n.btn-toolbar:after,\n.btn-group-vertical > .btn-group:after,\n.nav:after,\n.navbar:after,\n.navbar-header:after,\n.navbar-collapse:after,\n.pager:after,\n.panel-body:after,\n.modal-header:after,\n.modal-footer:after {\n  clear: both;\n}\n.center-block {\n  display: block;\n  margin-left: auto;\n  margin-right: auto;\n}\n.pull-right {\n  float: right !important;\n}\n.pull-left {\n  float: left !important;\n}\n.hide {\n  display: none !important;\n}\n.show {\n  display: block !important;\n}\n.invisible {\n  visibility: hidden;\n}\n.text-hide {\n  font: 0/0 a;\n  color: transparent;\n  text-shadow: none;\n  background-color: transparent;\n  border: 0;\n}\n.hidden {\n  display: none !important;\n}\n.affix {\n  position: fixed;\n}\n@-ms-viewport {\n  width: device-width;\n}\n.visible-xs,\n.visible-sm,\n.visible-md,\n.visible-lg {\n  display: none !important;\n}\n.visible-xs-block,\n.visible-xs-inline,\n.visible-xs-inline-block,\n.visible-sm-block,\n.visible-sm-inline,\n.visible-sm-inline-block,\n.visible-md-block,\n.visible-md-inline,\n.visible-md-inline-block,\n.visible-lg-block,\n.visible-lg-inline,\n.visible-lg-inline-block {\n  display: none !important;\n}\n@media (max-width: 767px) {\n  .visible-xs {\n    display: block !important;\n  }\n  table.visible-xs {\n    display: table !important;\n  }\n  tr.visible-xs {\n    display: table-row !important;\n  }\n  th.visible-xs,\n  td.visible-xs {\n    display: table-cell !important;\n  }\n}\n@media (max-width: 767px) {\n  .visible-xs-block {\n    display: block !important;\n  }\n}\n@media (max-width: 767px) {\n  .visible-xs-inline {\n    display: inline !important;\n  }\n}\n@media (max-width: 767px) {\n  .visible-xs-inline-block {\n    display: inline-block !important;\n  }\n}\n@media (min-width: 768px) and (max-width: 991px) {\n  .visible-sm {\n    display: block !important;\n  }\n  table.visible-sm {\n    display: table !important;\n  }\n  tr.visible-sm {\n    display: table-row !important;\n  }\n  th.visible-sm,\n  td.visible-sm {\n    display: table-cell !important;\n  }\n}\n@media (min-width: 768px) and (max-width: 991px) {\n  .visible-sm-block {\n    display: block !important;\n  }\n}\n@media (min-width: 768px) and (max-width: 991px) {\n  .visible-sm-inline {\n    display: inline !important;\n  }\n}\n@media (min-width: 768px) and (max-width: 991px) {\n  .visible-sm-inline-block {\n    display: inline-block !important;\n  }\n}\n@media (min-width: 992px) and (max-width: 1199px) {\n  .visible-md {\n    display: block !important;\n  }\n  table.visible-md {\n    display: table !important;\n  }\n  tr.visible-md {\n    display: table-row !important;\n  }\n  th.visible-md,\n  td.visible-md {\n    display: table-cell !important;\n  }\n}\n@media (min-width: 992px) and (max-width: 1199px) {\n  .visible-md-block {\n    display: block !important;\n  }\n}\n@media (min-width: 992px) and (max-width: 1199px) {\n  .visible-md-inline {\n    display: inline !important;\n  }\n}\n@media (min-width: 992px) and (max-width: 1199px) {\n  .visible-md-inline-block {\n    display: inline-block !important;\n  }\n}\n@media (min-width: 1200px) {\n  .visible-lg {\n    display: block !important;\n  }\n  table.visible-lg {\n    display: table !important;\n  }\n  tr.visible-lg {\n    display: table-row !important;\n  }\n  th.visible-lg,\n  td.visible-lg {\n    display: table-cell !important;\n  }\n}\n@media (min-width: 1200px) {\n  .visible-lg-block {\n    display: block !important;\n  }\n}\n@media (min-width: 1200px) {\n  .visible-lg-inline {\n    display: inline !important;\n  }\n}\n@media (min-width: 1200px) {\n  .visible-lg-inline-block {\n    display: inline-block !important;\n  }\n}\n@media (max-width: 767px) {\n  .hidden-xs {\n    display: none !important;\n  }\n}\n@media (min-width: 768px) and (max-width: 991px) {\n  .hidden-sm {\n    display: none !important;\n  }\n}\n@media (min-width: 992px) and (max-width: 1199px) {\n  .hidden-md {\n    display: none !important;\n  }\n}\n@media (min-width: 1200px) {\n  .hidden-lg {\n    display: none !important;\n  }\n}\n.visible-print {\n  display: none !important;\n}\n@media print {\n  .visible-print {\n    display: block !important;\n  }\n  table.visible-print {\n    display: table !important;\n  }\n  tr.visible-print {\n    display: table-row !important;\n  }\n  th.visible-print,\n  td.visible-print {\n    display: table-cell !important;\n  }\n}\n.visible-print-block {\n  display: none !important;\n}\n@media print {\n  .visible-print-block {\n    display: block !important;\n  }\n}\n.visible-print-inline {\n  display: none !important;\n}\n@media print {\n  .visible-print-inline {\n    display: inline !important;\n  }\n}\n.visible-print-inline-block {\n  display: none !important;\n}\n@media print {\n  .visible-print-inline-block {\n    display: inline-block !important;\n  }\n}\n@media print {\n  .hidden-print {\n    display: none !important;\n  }\n}\n/*# sourceMappingURL=bootstrap.css.map */","/*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */\n\n//\n// 1. Set default font family to sans-serif.\n// 2. Prevent iOS and IE text size adjust after device orientation change,\n//    without disabling user zoom.\n//\n\nhtml {\n  font-family: sans-serif; // 1\n  -ms-text-size-adjust: 100%; // 2\n  -webkit-text-size-adjust: 100%; // 2\n}\n\n//\n// Remove default margin.\n//\n\nbody {\n  margin: 0;\n}\n\n// HTML5 display definitions\n// ==========================================================================\n\n//\n// Correct `block` display not defined for any HTML5 element in IE 8/9.\n// Correct `block` display not defined for `details` or `summary` in IE 10/11\n// and Firefox.\n// Correct `block` display not defined for `main` in IE 11.\n//\n\narticle,\naside,\ndetails,\nfigcaption,\nfigure,\nfooter,\nheader,\nhgroup,\nmain,\nmenu,\nnav,\nsection,\nsummary {\n  display: block;\n}\n\n//\n// 1. Correct `inline-block` display not defined in IE 8/9.\n// 2. Normalize vertical alignment of `progress` in Chrome, Firefox, and Opera.\n//\n\naudio,\ncanvas,\nprogress,\nvideo {\n  display: inline-block; // 1\n  vertical-align: baseline; // 2\n}\n\n//\n// Prevent modern browsers from displaying `audio` without controls.\n// Remove excess height in iOS 5 devices.\n//\n\naudio:not([controls]) {\n  display: none;\n  height: 0;\n}\n\n//\n// Address `[hidden]` styling not present in IE 8/9/10.\n// Hide the `template` element in IE 8/9/10/11, Safari, and Firefox < 22.\n//\n\n[hidden],\ntemplate {\n  display: none;\n}\n\n// Links\n// ==========================================================================\n\n//\n// Remove the gray background color from active links in IE 10.\n//\n\na {\n  background-color: transparent;\n}\n\n//\n// Improve readability of focused elements when they are also in an\n// active/hover state.\n//\n\na:active,\na:hover {\n  outline: 0;\n}\n\n// Text-level semantics\n// ==========================================================================\n\n//\n// Address styling not present in IE 8/9/10/11, Safari, and Chrome.\n//\n\nabbr[title] {\n  border-bottom: 1px dotted;\n}\n\n//\n// Address style set to `bolder` in Firefox 4+, Safari, and Chrome.\n//\n\nb,\nstrong {\n  font-weight: bold;\n}\n\n//\n// Address styling not present in Safari and Chrome.\n//\n\ndfn {\n  font-style: italic;\n}\n\n//\n// Address variable `h1` font-size and margin within `section` and `article`\n// contexts in Firefox 4+, Safari, and Chrome.\n//\n\nh1 {\n  font-size: 2em;\n  margin: 0.67em 0;\n}\n\n//\n// Address styling not present in IE 8/9.\n//\n\nmark {\n  background: #ff0;\n  color: #000;\n}\n\n//\n// Address inconsistent and variable font size in all browsers.\n//\n\nsmall {\n  font-size: 80%;\n}\n\n//\n// Prevent `sub` and `sup` affecting `line-height` in all browsers.\n//\n\nsub,\nsup {\n  font-size: 75%;\n  line-height: 0;\n  position: relative;\n  vertical-align: baseline;\n}\n\nsup {\n  top: -0.5em;\n}\n\nsub {\n  bottom: -0.25em;\n}\n\n// Embedded content\n// ==========================================================================\n\n//\n// Remove border when inside `a` element in IE 8/9/10.\n//\n\nimg {\n  border: 0;\n}\n\n//\n// Correct overflow not hidden in IE 9/10/11.\n//\n\nsvg:not(:root) {\n  overflow: hidden;\n}\n\n// Grouping content\n// ==========================================================================\n\n//\n// Address margin not present in IE 8/9 and Safari.\n//\n\nfigure {\n  margin: 1em 40px;\n}\n\n//\n// Address differences between Firefox and other browsers.\n//\n\nhr {\n  box-sizing: content-box;\n  height: 0;\n}\n\n//\n// Contain overflow in all browsers.\n//\n\npre {\n  overflow: auto;\n}\n\n//\n// Address odd `em`-unit font size rendering in all browsers.\n//\n\ncode,\nkbd,\npre,\nsamp {\n  font-family: monospace, monospace;\n  font-size: 1em;\n}\n\n// Forms\n// ==========================================================================\n\n//\n// Known limitation: by default, Chrome and Safari on OS X allow very limited\n// styling of `select`, unless a `border` property is set.\n//\n\n//\n// 1. Correct color not being inherited.\n//    Known issue: affects color of disabled elements.\n// 2. Correct font properties not being inherited.\n// 3. Address margins set differently in Firefox 4+, Safari, and Chrome.\n//\n\nbutton,\ninput,\noptgroup,\nselect,\ntextarea {\n  color: inherit; // 1\n  font: inherit; // 2\n  margin: 0; // 3\n}\n\n//\n// Address `overflow` set to `hidden` in IE 8/9/10/11.\n//\n\nbutton {\n  overflow: visible;\n}\n\n//\n// Address inconsistent `text-transform` inheritance for `button` and `select`.\n// All other form control elements do not inherit `text-transform` values.\n// Correct `button` style inheritance in Firefox, IE 8/9/10/11, and Opera.\n// Correct `select` style inheritance in Firefox.\n//\n\nbutton,\nselect {\n  text-transform: none;\n}\n\n//\n// 1. Avoid the WebKit bug in Android 4.0.* where (2) destroys native `audio`\n//    and `video` controls.\n// 2. Correct inability to style clickable `input` types in iOS.\n// 3. Improve usability and consistency of cursor style between image-type\n//    `input` and others.\n//\n\nbutton,\nhtml input[type=\"button\"], // 1\ninput[type=\"reset\"],\ninput[type=\"submit\"] {\n  -webkit-appearance: button; // 2\n  cursor: pointer; // 3\n}\n\n//\n// Re-set default cursor for disabled elements.\n//\n\nbutton[disabled],\nhtml input[disabled] {\n  cursor: default;\n}\n\n//\n// Remove inner padding and border in Firefox 4+.\n//\n\nbutton::-moz-focus-inner,\ninput::-moz-focus-inner {\n  border: 0;\n  padding: 0;\n}\n\n//\n// Address Firefox 4+ setting `line-height` on `input` using `!important` in\n// the UA stylesheet.\n//\n\ninput {\n  line-height: normal;\n}\n\n//\n// It's recommended that you don't attempt to style these elements.\n// Firefox's implementation doesn't respect box-sizing, padding, or width.\n//\n// 1. Address box sizing set to `content-box` in IE 8/9/10.\n// 2. Remove excess padding in IE 8/9/10.\n//\n\ninput[type=\"checkbox\"],\ninput[type=\"radio\"] {\n  box-sizing: border-box; // 1\n  padding: 0; // 2\n}\n\n//\n// Fix the cursor style for Chrome's increment/decrement buttons. For certain\n// `font-size` values of the `input`, it causes the cursor style of the\n// decrement button to change from `default` to `text`.\n//\n\ninput[type=\"number\"]::-webkit-inner-spin-button,\ninput[type=\"number\"]::-webkit-outer-spin-button {\n  height: auto;\n}\n\n//\n// 1. Address `appearance` set to `searchfield` in Safari and Chrome.\n// 2. Address `box-sizing` set to `border-box` in Safari and Chrome.\n//\n\ninput[type=\"search\"] {\n  -webkit-appearance: textfield; // 1\n  box-sizing: content-box; //2\n}\n\n//\n// Remove inner padding and search cancel button in Safari and Chrome on OS X.\n// Safari (but not Chrome) clips the cancel button when the search input has\n// padding (and `textfield` appearance).\n//\n\ninput[type=\"search\"]::-webkit-search-cancel-button,\ninput[type=\"search\"]::-webkit-search-decoration {\n  -webkit-appearance: none;\n}\n\n//\n// Define consistent border, margin, and padding.\n//\n\nfieldset {\n  border: 1px solid #c0c0c0;\n  margin: 0 2px;\n  padding: 0.35em 0.625em 0.75em;\n}\n\n//\n// 1. Correct `color` not being inherited in IE 8/9/10/11.\n// 2. Remove padding so people aren't caught out if they zero out fieldsets.\n//\n\nlegend {\n  border: 0; // 1\n  padding: 0; // 2\n}\n\n//\n// Remove default vertical scrollbar in IE 8/9/10/11.\n//\n\ntextarea {\n  overflow: auto;\n}\n\n//\n// Don't inherit the `font-weight` (applied by a rule above).\n// NOTE: the default cannot safely be changed in Chrome and Safari on OS X.\n//\n\noptgroup {\n  font-weight: bold;\n}\n\n// Tables\n// ==========================================================================\n\n//\n// Remove most spacing between table cells.\n//\n\ntable {\n  border-collapse: collapse;\n  border-spacing: 0;\n}\n\ntd,\nth {\n  padding: 0;\n}\n","/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */\n\n// ==========================================================================\n// Print styles.\n// Inlined to avoid the additional HTTP request: h5bp.com/r\n// ==========================================================================\n\n@media print {\n    *,\n    *:before,\n    *:after {\n        background: transparent !important;\n        color: #000 !important; // Black prints faster: h5bp.com/s\n        box-shadow: none !important;\n        text-shadow: none !important;\n    }\n\n    a,\n    a:visited {\n        text-decoration: underline;\n    }\n\n    a[href]:after {\n        content: \" (\" attr(href) \")\";\n    }\n\n    abbr[title]:after {\n        content: \" (\" attr(title) \")\";\n    }\n\n    // Don't show links that are fragment identifiers,\n    // or use the `javascript:` pseudo protocol\n    a[href^=\"#\"]:after,\n    a[href^=\"javascript:\"]:after {\n        content: \"\";\n    }\n\n    pre,\n    blockquote {\n        border: 1px solid #999;\n        page-break-inside: avoid;\n    }\n\n    thead {\n        display: table-header-group; // h5bp.com/t\n    }\n\n    tr,\n    img {\n        page-break-inside: avoid;\n    }\n\n    img {\n        max-width: 100% !important;\n    }\n\n    p,\n    h2,\n    h3 {\n        orphans: 3;\n        widows: 3;\n    }\n\n    h2,\n    h3 {\n        page-break-after: avoid;\n    }\n\n    // Bootstrap specific changes start\n\n    // Bootstrap components\n    .navbar {\n        display: none;\n    }\n    .btn,\n    .dropup > .btn {\n        > .caret {\n            border-top-color: #000 !important;\n        }\n    }\n    .label {\n        border: 1px solid #000;\n    }\n\n    .table {\n        border-collapse: collapse !important;\n\n        td,\n        th {\n            background-color: #fff !important;\n        }\n    }\n    .table-bordered {\n        th,\n        td {\n            border: 1px solid #ddd !important;\n        }\n    }\n\n    // Bootstrap specific changes end\n}\n","//\n// Glyphicons for Bootstrap\n//\n// Since icons are fonts, they can be placed anywhere text is placed and are\n// thus automatically sized to match the surrounding child. To use, create an\n// inline element with the appropriate classes, like so:\n//\n// <a href=\"#\"><span class=\"glyphicon glyphicon-star\"></span> Star</a>\n\n// Import the fonts\n@font-face {\n  font-family: 'Glyphicons Halflings';\n  src: url('@{icon-font-path}@{icon-font-name}.eot');\n  src: url('@{icon-font-path}@{icon-font-name}.eot?#iefix') format('embedded-opentype'),\n       url('@{icon-font-path}@{icon-font-name}.woff2') format('woff2'),\n       url('@{icon-font-path}@{icon-font-name}.woff') format('woff'),\n       url('@{icon-font-path}@{icon-font-name}.ttf') format('truetype'),\n       url('@{icon-font-path}@{icon-font-name}.svg#@{icon-font-svg-id}') format('svg');\n}\n\n// Catchall baseclass\n.glyphicon {\n  position: relative;\n  top: 1px;\n  display: inline-block;\n  font-family: 'Glyphicons Halflings';\n  font-style: normal;\n  font-weight: normal;\n  line-height: 1;\n  -webkit-font-smoothing: antialiased;\n  -moz-osx-font-smoothing: grayscale;\n}\n\n// Individual icons\n.glyphicon-asterisk               { &:before { content: \"\\002a\"; } }\n.glyphicon-plus                   { &:before { content: \"\\002b\"; } }\n.glyphicon-euro,\n.glyphicon-eur                    { &:before { content: \"\\20ac\"; } }\n.glyphicon-minus                  { &:before { content: \"\\2212\"; } }\n.glyphicon-cloud                  { &:before { content: \"\\2601\"; } }\n.glyphicon-envelope               { &:before { content: \"\\2709\"; } }\n.glyphicon-pencil                 { &:before { content: \"\\270f\"; } }\n.glyphicon-glass                  { &:before { content: \"\\e001\"; } }\n.glyphicon-music                  { &:before { content: \"\\e002\"; } }\n.glyphicon-search                 { &:before { content: \"\\e003\"; } }\n.glyphicon-heart                  { &:before { content: \"\\e005\"; } }\n.glyphicon-star                   { &:before { content: \"\\e006\"; } }\n.glyphicon-star-empty             { &:before { content: \"\\e007\"; } }\n.glyphicon-user                   { &:before { content: \"\\e008\"; } }\n.glyphicon-film                   { &:before { content: \"\\e009\"; } }\n.glyphicon-th-large               { &:before { content: \"\\e010\"; } }\n.glyphicon-th                     { &:before { content: \"\\e011\"; } }\n.glyphicon-th-list                { &:before { content: \"\\e012\"; } }\n.glyphicon-ok                     { &:before { content: \"\\e013\"; } }\n.glyphicon-remove                 { &:before { content: \"\\e014\"; } }\n.glyphicon-zoom-in                { &:before { content: \"\\e015\"; } }\n.glyphicon-zoom-out               { &:before { content: \"\\e016\"; } }\n.glyphicon-off                    { &:before { content: \"\\e017\"; } }\n.glyphicon-signal                 { &:before { content: \"\\e018\"; } }\n.glyphicon-cog                    { &:before { content: \"\\e019\"; } }\n.glyphicon-trash                  { &:before { content: \"\\e020\"; } }\n.glyphicon-home                   { &:before { content: \"\\e021\"; } }\n.glyphicon-file                   { &:before { content: \"\\e022\"; } }\n.glyphicon-time                   { &:before { content: \"\\e023\"; } }\n.glyphicon-road                   { &:before { content: \"\\e024\"; } }\n.glyphicon-download-alt           { &:before { content: \"\\e025\"; } }\n.glyphicon-download               { &:before { content: \"\\e026\"; } }\n.glyphicon-upload                 { &:before { content: \"\\e027\"; } }\n.glyphicon-inbox                  { &:before { content: \"\\e028\"; } }\n.glyphicon-play-circle            { &:before { content: \"\\e029\"; } }\n.glyphicon-repeat                 { &:before { content: \"\\e030\"; } }\n.glyphicon-refresh                { &:before { content: \"\\e031\"; } }\n.glyphicon-list-alt               { &:before { content: \"\\e032\"; } }\n.glyphicon-lock                   { &:before { content: \"\\e033\"; } }\n.glyphicon-flag                   { &:before { content: \"\\e034\"; } }\n.glyphicon-headphones             { &:before { content: \"\\e035\"; } }\n.glyphicon-volume-off             { &:before { content: \"\\e036\"; } }\n.glyphicon-volume-down            { &:before { content: \"\\e037\"; } }\n.glyphicon-volume-up              { &:before { content: \"\\e038\"; } }\n.glyphicon-qrcode                 { &:before { content: \"\\e039\"; } }\n.glyphicon-barcode                { &:before { content: \"\\e040\"; } }\n.glyphicon-tag                    { &:before { content: \"\\e041\"; } }\n.glyphicon-tags                   { &:before { content: \"\\e042\"; } }\n.glyphicon-book                   { &:before { content: \"\\e043\"; } }\n.glyphicon-bookmark               { &:before { content: \"\\e044\"; } }\n.glyphicon-print                  { &:before { content: \"\\e045\"; } }\n.glyphicon-camera                 { &:before { content: \"\\e046\"; } }\n.glyphicon-font                   { &:before { content: \"\\e047\"; } }\n.glyphicon-bold                   { &:before { content: \"\\e048\"; } }\n.glyphicon-italic                 { &:before { content: \"\\e049\"; } }\n.glyphicon-text-height            { &:before { content: \"\\e050\"; } }\n.glyphicon-text-width             { &:before { content: \"\\e051\"; } }\n.glyphicon-align-left             { &:before { content: \"\\e052\"; } }\n.glyphicon-align-center           { &:before { content: \"\\e053\"; } }\n.glyphicon-align-right            { &:before { content: \"\\e054\"; } }\n.glyphicon-align-justify          { &:before { content: \"\\e055\"; } }\n.glyphicon-list                   { &:before { content: \"\\e056\"; } }\n.glyphicon-indent-left            { &:before { content: \"\\e057\"; } }\n.glyphicon-indent-right           { &:before { content: \"\\e058\"; } }\n.glyphicon-facetime-video         { &:before { content: \"\\e059\"; } }\n.glyphicon-picture                { &:before { content: \"\\e060\"; } }\n.glyphicon-map-marker             { &:before { content: \"\\e062\"; } }\n.glyphicon-adjust                 { &:before { content: \"\\e063\"; } }\n.glyphicon-tint                   { &:before { content: \"\\e064\"; } }\n.glyphicon-edit                   { &:before { content: \"\\e065\"; } }\n.glyphicon-share                  { &:before { content: \"\\e066\"; } }\n.glyphicon-check                  { &:before { content: \"\\e067\"; } }\n.glyphicon-move                   { &:before { content: \"\\e068\"; } }\n.glyphicon-step-backward          { &:before { content: \"\\e069\"; } }\n.glyphicon-fast-backward          { &:before { content: \"\\e070\"; } }\n.glyphicon-backward               { &:before { content: \"\\e071\"; } }\n.glyphicon-play                   { &:before { content: \"\\e072\"; } }\n.glyphicon-pause                  { &:before { content: \"\\e073\"; } }\n.glyphicon-stop                   { &:before { content: \"\\e074\"; } }\n.glyphicon-forward                { &:before { content: \"\\e075\"; } }\n.glyphicon-fast-forward           { &:before { content: \"\\e076\"; } }\n.glyphicon-step-forward           { &:before { content: \"\\e077\"; } }\n.glyphicon-eject                  { &:before { content: \"\\e078\"; } }\n.glyphicon-chevron-left           { &:before { content: \"\\e079\"; } }\n.glyphicon-chevron-right          { &:before { content: \"\\e080\"; } }\n.glyphicon-plus-sign              { &:before { content: \"\\e081\"; } }\n.glyphicon-minus-sign             { &:before { content: \"\\e082\"; } }\n.glyphicon-remove-sign            { &:before { content: \"\\e083\"; } }\n.glyphicon-ok-sign                { &:before { content: \"\\e084\"; } }\n.glyphicon-question-sign          { &:before { content: \"\\e085\"; } }\n.glyphicon-info-sign              { &:before { content: \"\\e086\"; } }\n.glyphicon-screenshot             { &:before { content: \"\\e087\"; } }\n.glyphicon-remove-circle          { &:before { content: \"\\e088\"; } }\n.glyphicon-ok-circle              { &:before { content: \"\\e089\"; } }\n.glyphicon-ban-circle             { &:before { content: \"\\e090\"; } }\n.glyphicon-arrow-left             { &:before { content: \"\\e091\"; } }\n.glyphicon-arrow-right            { &:before { content: \"\\e092\"; } }\n.glyphicon-arrow-up               { &:before { content: \"\\e093\"; } }\n.glyphicon-arrow-down             { &:before { content: \"\\e094\"; } }\n.glyphicon-share-alt              { &:before { content: \"\\e095\"; } }\n.glyphicon-resize-full            { &:before { content: \"\\e096\"; } }\n.glyphicon-resize-small           { &:before { content: \"\\e097\"; } }\n.glyphicon-exclamation-sign       { &:before { content: \"\\e101\"; } }\n.glyphicon-gift                   { &:before { content: \"\\e102\"; } }\n.glyphicon-leaf                   { &:before { content: \"\\e103\"; } }\n.glyphicon-fire                   { &:before { content: \"\\e104\"; } }\n.glyphicon-eye-open               { &:before { content: \"\\e105\"; } }\n.glyphicon-eye-close              { &:before { content: \"\\e106\"; } }\n.glyphicon-warning-sign           { &:before { content: \"\\e107\"; } }\n.glyphicon-plane                  { &:before { content: \"\\e108\"; } }\n.glyphicon-calendar               { &:before { content: \"\\e109\"; } }\n.glyphicon-random                 { &:before { content: \"\\e110\"; } }\n.glyphicon-comment                { &:before { content: \"\\e111\"; } }\n.glyphicon-magnet                 { &:before { content: \"\\e112\"; } }\n.glyphicon-chevron-up             { &:before { content: \"\\e113\"; } }\n.glyphicon-chevron-down           { &:before { content: \"\\e114\"; } }\n.glyphicon-retweet                { &:before { content: \"\\e115\"; } }\n.glyphicon-shopping-cart          { &:before { content: \"\\e116\"; } }\n.glyphicon-folder-close           { &:before { content: \"\\e117\"; } }\n.glyphicon-folder-open            { &:before { content: \"\\e118\"; } }\n.glyphicon-resize-vertical        { &:before { content: \"\\e119\"; } }\n.glyphicon-resize-horizontal      { &:before { content: \"\\e120\"; } }\n.glyphicon-hdd                    { &:before { content: \"\\e121\"; } }\n.glyphicon-bullhorn               { &:before { content: \"\\e122\"; } }\n.glyphicon-bell                   { &:before { content: \"\\e123\"; } }\n.glyphicon-certificate            { &:before { content: \"\\e124\"; } }\n.glyphicon-thumbs-up              { &:before { content: \"\\e125\"; } }\n.glyphicon-thumbs-down            { &:before { content: \"\\e126\"; } }\n.glyphicon-hand-right             { &:before { content: \"\\e127\"; } }\n.glyphicon-hand-left              { &:before { content: \"\\e128\"; } }\n.glyphicon-hand-up                { &:before { content: \"\\e129\"; } }\n.glyphicon-hand-down              { &:before { content: \"\\e130\"; } }\n.glyphicon-circle-arrow-right     { &:before { content: \"\\e131\"; } }\n.glyphicon-circle-arrow-left      { &:before { content: \"\\e132\"; } }\n.glyphicon-circle-arrow-up        { &:before { content: \"\\e133\"; } }\n.glyphicon-circle-arrow-down      { &:before { content: \"\\e134\"; } }\n.glyphicon-globe                  { &:before { content: \"\\e135\"; } }\n.glyphicon-wrench                 { &:before { content: \"\\e136\"; } }\n.glyphicon-tasks                  { &:before { content: \"\\e137\"; } }\n.glyphicon-filter                 { &:before { content: \"\\e138\"; } }\n.glyphicon-briefcase              { &:before { content: \"\\e139\"; } }\n.glyphicon-fullscreen             { &:before { content: \"\\e140\"; } }\n.glyphicon-dashboard              { &:before { content: \"\\e141\"; } }\n.glyphicon-paperclip              { &:before { content: \"\\e142\"; } }\n.glyphicon-heart-empty            { &:before { content: \"\\e143\"; } }\n.glyphicon-link                   { &:before { content: \"\\e144\"; } }\n.glyphicon-phone                  { &:before { content: \"\\e145\"; } }\n.glyphicon-pushpin                { &:before { content: \"\\e146\"; } }\n.glyphicon-usd                    { &:before { content: \"\\e148\"; } }\n.glyphicon-gbp                    { &:before { content: \"\\e149\"; } }\n.glyphicon-sort                   { &:before { content: \"\\e150\"; } }\n.glyphicon-sort-by-alphabet       { &:before { content: \"\\e151\"; } }\n.glyphicon-sort-by-alphabet-alt   { &:before { content: \"\\e152\"; } }\n.glyphicon-sort-by-order          { &:before { content: \"\\e153\"; } }\n.glyphicon-sort-by-order-alt      { &:before { content: \"\\e154\"; } }\n.glyphicon-sort-by-attributes     { &:before { content: \"\\e155\"; } }\n.glyphicon-sort-by-attributes-alt { &:before { content: \"\\e156\"; } }\n.glyphicon-unchecked              { &:before { content: \"\\e157\"; } }\n.glyphicon-expand                 { &:before { content: \"\\e158\"; } }\n.glyphicon-collapse-down          { &:before { content: \"\\e159\"; } }\n.glyphicon-collapse-up            { &:before { content: \"\\e160\"; } }\n.glyphicon-log-in                 { &:before { content: \"\\e161\"; } }\n.glyphicon-flash                  { &:before { content: \"\\e162\"; } }\n.glyphicon-log-out                { &:before { content: \"\\e163\"; } }\n.glyphicon-new-window             { &:before { content: \"\\e164\"; } }\n.glyphicon-record                 { &:before { content: \"\\e165\"; } }\n.glyphicon-save                   { &:before { content: \"\\e166\"; } }\n.glyphicon-open                   { &:before { content: \"\\e167\"; } }\n.glyphicon-saved                  { &:before { content: \"\\e168\"; } }\n.glyphicon-import                 { &:before { content: \"\\e169\"; } }\n.glyphicon-export                 { &:before { content: \"\\e170\"; } }\n.glyphicon-send                   { &:before { content: \"\\e171\"; } }\n.glyphicon-floppy-disk            { &:before { content: \"\\e172\"; } }\n.glyphicon-floppy-saved           { &:before { content: \"\\e173\"; } }\n.glyphicon-floppy-remove          { &:before { content: \"\\e174\"; } }\n.glyphicon-floppy-save            { &:before { content: \"\\e175\"; } }\n.glyphicon-floppy-open            { &:before { content: \"\\e176\"; } }\n.glyphicon-credit-card            { &:before { content: \"\\e177\"; } }\n.glyphicon-transfer               { &:before { content: \"\\e178\"; } }\n.glyphicon-cutlery                { &:before { content: \"\\e179\"; } }\n.glyphicon-header                 { &:before { content: \"\\e180\"; } }\n.glyphicon-compressed             { &:before { content: \"\\e181\"; } }\n.glyphicon-earphone               { &:before { content: \"\\e182\"; } }\n.glyphicon-phone-alt              { &:before { content: \"\\e183\"; } }\n.glyphicon-tower                  { &:before { content: \"\\e184\"; } }\n.glyphicon-stats                  { &:before { content: \"\\e185\"; } }\n.glyphicon-sd-video               { &:before { content: \"\\e186\"; } }\n.glyphicon-hd-video               { &:before { content: \"\\e187\"; } }\n.glyphicon-subtitles              { &:before { content: \"\\e188\"; } }\n.glyphicon-sound-stereo           { &:before { content: \"\\e189\"; } }\n.glyphicon-sound-dolby            { &:before { content: \"\\e190\"; } }\n.glyphicon-sound-5-1              { &:before { content: \"\\e191\"; } }\n.glyphicon-sound-6-1              { &:before { content: \"\\e192\"; } }\n.glyphicon-sound-7-1              { &:before { content: \"\\e193\"; } }\n.glyphicon-copyright-mark         { &:before { content: \"\\e194\"; } }\n.glyphicon-registration-mark      { &:before { content: \"\\e195\"; } }\n.glyphicon-cloud-download         { &:before { content: \"\\e197\"; } }\n.glyphicon-cloud-upload           { &:before { content: \"\\e198\"; } }\n.glyphicon-tree-conifer           { &:before { content: \"\\e199\"; } }\n.glyphicon-tree-deciduous         { &:before { content: \"\\e200\"; } }\n.glyphicon-cd                     { &:before { content: \"\\e201\"; } }\n.glyphicon-save-file              { &:before { content: \"\\e202\"; } }\n.glyphicon-open-file              { &:before { content: \"\\e203\"; } }\n.glyphicon-level-up               { &:before { content: \"\\e204\"; } }\n.glyphicon-copy                   { &:before { content: \"\\e205\"; } }\n.glyphicon-paste                  { &:before { content: \"\\e206\"; } }\n// The following 2 Glyphicons are omitted for the time being because\n// they currently use Unicode codepoints that are outside the\n// Basic Multilingual Plane (BMP). Older buggy versions of WebKit can't handle\n// non-BMP codepoints in CSS string escapes, and thus can't display these two icons.\n// Notably, the bug affects some older versions of the Android Browser.\n// More info: https://github.com/twbs/bootstrap/issues/10106\n// .glyphicon-door                   { &:before { content: \"\\1f6aa\"; } }\n// .glyphicon-key                    { &:before { content: \"\\1f511\"; } }\n.glyphicon-alert                  { &:before { content: \"\\e209\"; } }\n.glyphicon-equalizer              { &:before { content: \"\\e210\"; } }\n.glyphicon-king                   { &:before { content: \"\\e211\"; } }\n.glyphicon-queen                  { &:before { content: \"\\e212\"; } }\n.glyphicon-pawn                   { &:before { content: \"\\e213\"; } }\n.glyphicon-bishop                 { &:before { content: \"\\e214\"; } }\n.glyphicon-knight                 { &:before { content: \"\\e215\"; } }\n.glyphicon-baby-formula           { &:before { content: \"\\e216\"; } }\n.glyphicon-tent                   { &:before { content: \"\\26fa\"; } }\n.glyphicon-blackboard             { &:before { content: \"\\e218\"; } }\n.glyphicon-bed                    { &:before { content: \"\\e219\"; } }\n.glyphicon-apple                  { &:before { content: \"\\f8ff\"; } }\n.glyphicon-erase                  { &:before { content: \"\\e221\"; } }\n.glyphicon-hourglass              { &:before { content: \"\\231b\"; } }\n.glyphicon-lamp                   { &:before { content: \"\\e223\"; } }\n.glyphicon-duplicate              { &:before { content: \"\\e224\"; } }\n.glyphicon-piggy-bank             { &:before { content: \"\\e225\"; } }\n.glyphicon-scissors               { &:before { content: \"\\e226\"; } }\n.glyphicon-bitcoin                { &:before { content: \"\\e227\"; } }\n.glyphicon-btc                    { &:before { content: \"\\e227\"; } }\n.glyphicon-xbt                    { &:before { content: \"\\e227\"; } }\n.glyphicon-yen                    { &:before { content: \"\\00a5\"; } }\n.glyphicon-jpy                    { &:before { content: \"\\00a5\"; } }\n.glyphicon-ruble                  { &:before { content: \"\\20bd\"; } }\n.glyphicon-rub                    { &:before { content: \"\\20bd\"; } }\n.glyphicon-scale                  { &:before { content: \"\\e230\"; } }\n.glyphicon-ice-lolly              { &:before { content: \"\\e231\"; } }\n.glyphicon-ice-lolly-tasted       { &:before { content: \"\\e232\"; } }\n.glyphicon-education              { &:before { content: \"\\e233\"; } }\n.glyphicon-option-horizontal      { &:before { content: \"\\e234\"; } }\n.glyphicon-option-vertical        { &:before { content: \"\\e235\"; } }\n.glyphicon-menu-hamburger         { &:before { content: \"\\e236\"; } }\n.glyphicon-modal-window           { &:before { content: \"\\e237\"; } }\n.glyphicon-oil                    { &:before { content: \"\\e238\"; } }\n.glyphicon-grain                  { &:before { content: \"\\e239\"; } }\n.glyphicon-sunglasses             { &:before { content: \"\\e240\"; } }\n.glyphicon-text-size              { &:before { content: \"\\e241\"; } }\n.glyphicon-text-color             { &:before { content: \"\\e242\"; } }\n.glyphicon-text-background        { &:before { content: \"\\e243\"; } }\n.glyphicon-object-align-top       { &:before { content: \"\\e244\"; } }\n.glyphicon-object-align-bottom    { &:before { content: \"\\e245\"; } }\n.glyphicon-object-align-horizontal{ &:before { content: \"\\e246\"; } }\n.glyphicon-object-align-left      { &:before { content: \"\\e247\"; } }\n.glyphicon-object-align-vertical  { &:before { content: \"\\e248\"; } }\n.glyphicon-object-align-right     { &:before { content: \"\\e249\"; } }\n.glyphicon-triangle-right         { &:before { content: \"\\e250\"; } }\n.glyphicon-triangle-left          { &:before { content: \"\\e251\"; } }\n.glyphicon-triangle-bottom        { &:before { content: \"\\e252\"; } }\n.glyphicon-triangle-top           { &:before { content: \"\\e253\"; } }\n.glyphicon-console                { &:before { content: \"\\e254\"; } }\n.glyphicon-superscript            { &:before { content: \"\\e255\"; } }\n.glyphicon-subscript              { &:before { content: \"\\e256\"; } }\n.glyphicon-menu-left              { &:before { content: \"\\e257\"; } }\n.glyphicon-menu-right             { &:before { content: \"\\e258\"; } }\n.glyphicon-menu-down              { &:before { content: \"\\e259\"; } }\n.glyphicon-menu-up                { &:before { content: \"\\e260\"; } }\n","//\n// Scaffolding\n// --------------------------------------------------\n\n\n// Reset the box-sizing\n//\n// Heads up! This reset may cause conflicts with some third-party widgets.\n// For recommendations on resolving such conflicts, see\n// http://getbootstrap.com/getting-started/#third-box-sizing\n* {\n  .box-sizing(border-box);\n}\n*:before,\n*:after {\n  .box-sizing(border-box);\n}\n\n\n// Body reset\n\nhtml {\n  font-size: 10px;\n  -webkit-tap-highlight-color: rgba(0,0,0,0);\n}\n\nbody {\n  font-family: @font-family-base;\n  font-size: @font-size-base;\n  line-height: @line-height-base;\n  color: @text-color;\n  background-color: @body-bg;\n}\n\n// Reset fonts for relevant elements\ninput,\nbutton,\nselect,\ntextarea {\n  font-family: inherit;\n  font-size: inherit;\n  line-height: inherit;\n}\n\n\n// Links\n\na {\n  color: @link-color;\n  text-decoration: none;\n\n  &:hover,\n  &:focus {\n    color: @link-hover-color;\n    text-decoration: @link-hover-decoration;\n  }\n\n  &:focus {\n    .tab-focus();\n  }\n}\n\n\n// Figures\n//\n// We reset this here because previously Normalize had no `figure` margins. This\n// ensures we don't break anyone's use of the element.\n\nfigure {\n  margin: 0;\n}\n\n\n// Images\n\nimg {\n  vertical-align: middle;\n}\n\n// Responsive images (ensure images don't scale beyond their parents)\n.img-responsive {\n  .img-responsive();\n}\n\n// Rounded corners\n.img-rounded {\n  border-radius: @border-radius-large;\n}\n\n// Image thumbnails\n//\n// Heads up! This is mixin-ed into thumbnails.less for `.thumbnail`.\n.img-thumbnail {\n  padding: @thumbnail-padding;\n  line-height: @line-height-base;\n  background-color: @thumbnail-bg;\n  border: 1px solid @thumbnail-border;\n  border-radius: @thumbnail-border-radius;\n  .transition(all .2s ease-in-out);\n\n  // Keep them at most 100% wide\n  .img-responsive(inline-block);\n}\n\n// Perfect circle\n.img-circle {\n  border-radius: 50%; // set radius in percents\n}\n\n\n// Horizontal rules\n\nhr {\n  margin-top:    @line-height-computed;\n  margin-bottom: @line-height-computed;\n  border: 0;\n  border-top: 1px solid @hr-border;\n}\n\n\n// Only display content to screen readers\n//\n// See: http://a11yproject.com/posts/how-to-hide-content\n\n.sr-only {\n  position: absolute;\n  width: 1px;\n  height: 1px;\n  margin: -1px;\n  padding: 0;\n  overflow: hidden;\n  clip: rect(0,0,0,0);\n  border: 0;\n}\n\n// Use in conjunction with .sr-only to only display content when it's focused.\n// Useful for \"Skip to main content\" links; see http://www.w3.org/TR/2013/NOTE-WCAG20-TECHS-20130905/G1\n// Credit: HTML5 Boilerplate\n\n.sr-only-focusable {\n  &:active,\n  &:focus {\n    position: static;\n    width: auto;\n    height: auto;\n    margin: 0;\n    overflow: visible;\n    clip: auto;\n  }\n}\n\n\n// iOS \"clickable elements\" fix for role=\"button\"\n//\n// Fixes \"clickability\" issue (and more generally, the firing of events such as focus as well)\n// for traditionally non-focusable elements with role=\"button\"\n// see https://developer.mozilla.org/en-US/docs/Web/Events/click#Safari_Mobile\n\n[role=\"button\"] {\n  cursor: pointer;\n}\n","// Vendor Prefixes\n//\n// All vendor mixins are deprecated as of v3.2.0 due to the introduction of\n// Autoprefixer in our Gruntfile. They have been removed in v4.\n\n// - Animations\n// - Backface visibility\n// - Box shadow\n// - Box sizing\n// - Content columns\n// - Hyphens\n// - Placeholder text\n// - Transformations\n// - Transitions\n// - User Select\n\n\n// Animations\n.animation(@animation) {\n  -webkit-animation: @animation;\n       -o-animation: @animation;\n          animation: @animation;\n}\n.animation-name(@name) {\n  -webkit-animation-name: @name;\n          animation-name: @name;\n}\n.animation-duration(@duration) {\n  -webkit-animation-duration: @duration;\n          animation-duration: @duration;\n}\n.animation-timing-function(@timing-function) {\n  -webkit-animation-timing-function: @timing-function;\n          animation-timing-function: @timing-function;\n}\n.animation-delay(@delay) {\n  -webkit-animation-delay: @delay;\n          animation-delay: @delay;\n}\n.animation-iteration-count(@iteration-count) {\n  -webkit-animation-iteration-count: @iteration-count;\n          animation-iteration-count: @iteration-count;\n}\n.animation-direction(@direction) {\n  -webkit-animation-direction: @direction;\n          animation-direction: @direction;\n}\n.animation-fill-mode(@fill-mode) {\n  -webkit-animation-fill-mode: @fill-mode;\n          animation-fill-mode: @fill-mode;\n}\n\n// Backface visibility\n// Prevent browsers from flickering when using CSS 3D transforms.\n// Default value is `visible`, but can be changed to `hidden`\n\n.backface-visibility(@visibility) {\n  -webkit-backface-visibility: @visibility;\n     -moz-backface-visibility: @visibility;\n          backface-visibility: @visibility;\n}\n\n// Drop shadows\n//\n// Note: Deprecated `.box-shadow()` as of v3.1.0 since all of Bootstrap's\n// supported browsers that have box shadow capabilities now support it.\n\n.box-shadow(@shadow) {\n  -webkit-box-shadow: @shadow; // iOS <4.3 & Android <4.1\n          box-shadow: @shadow;\n}\n\n// Box sizing\n.box-sizing(@boxmodel) {\n  -webkit-box-sizing: @boxmodel;\n     -moz-box-sizing: @boxmodel;\n          box-sizing: @boxmodel;\n}\n\n// CSS3 Content Columns\n.content-columns(@column-count; @column-gap: @grid-gutter-width) {\n  -webkit-column-count: @column-count;\n     -moz-column-count: @column-count;\n          column-count: @column-count;\n  -webkit-column-gap: @column-gap;\n     -moz-column-gap: @column-gap;\n          column-gap: @column-gap;\n}\n\n// Optional hyphenation\n.hyphens(@mode: auto) {\n  word-wrap: break-word;\n  -webkit-hyphens: @mode;\n     -moz-hyphens: @mode;\n      -ms-hyphens: @mode; // IE10+\n       -o-hyphens: @mode;\n          hyphens: @mode;\n}\n\n// Placeholder text\n.placeholder(@color: @input-color-placeholder) {\n  // Firefox\n  &::-moz-placeholder {\n    color: @color;\n    opacity: 1; // Override Firefox's unusual default opacity; see https://github.com/twbs/bootstrap/pull/11526\n  }\n  &:-ms-input-placeholder { color: @color; } // Internet Explorer 10+\n  &::-webkit-input-placeholder  { color: @color; } // Safari and Chrome\n}\n\n// Transformations\n.scale(@ratio) {\n  -webkit-transform: scale(@ratio);\n      -ms-transform: scale(@ratio); // IE9 only\n       -o-transform: scale(@ratio);\n          transform: scale(@ratio);\n}\n.scale(@ratioX; @ratioY) {\n  -webkit-transform: scale(@ratioX, @ratioY);\n      -ms-transform: scale(@ratioX, @ratioY); // IE9 only\n       -o-transform: scale(@ratioX, @ratioY);\n          transform: scale(@ratioX, @ratioY);\n}\n.scaleX(@ratio) {\n  -webkit-transform: scaleX(@ratio);\n      -ms-transform: scaleX(@ratio); // IE9 only\n       -o-transform: scaleX(@ratio);\n          transform: scaleX(@ratio);\n}\n.scaleY(@ratio) {\n  -webkit-transform: scaleY(@ratio);\n      -ms-transform: scaleY(@ratio); // IE9 only\n       -o-transform: scaleY(@ratio);\n          transform: scaleY(@ratio);\n}\n.skew(@x; @y) {\n  -webkit-transform: skewX(@x) skewY(@y);\n      -ms-transform: skewX(@x) skewY(@y); // See https://github.com/twbs/bootstrap/issues/4885; IE9+\n       -o-transform: skewX(@x) skewY(@y);\n          transform: skewX(@x) skewY(@y);\n}\n.translate(@x; @y) {\n  -webkit-transform: translate(@x, @y);\n      -ms-transform: translate(@x, @y); // IE9 only\n       -o-transform: translate(@x, @y);\n          transform: translate(@x, @y);\n}\n.translate3d(@x; @y; @z) {\n  -webkit-transform: translate3d(@x, @y, @z);\n          transform: translate3d(@x, @y, @z);\n}\n.rotate(@degrees) {\n  -webkit-transform: rotate(@degrees);\n      -ms-transform: rotate(@degrees); // IE9 only\n       -o-transform: rotate(@degrees);\n          transform: rotate(@degrees);\n}\n.rotateX(@degrees) {\n  -webkit-transform: rotateX(@degrees);\n      -ms-transform: rotateX(@degrees); // IE9 only\n       -o-transform: rotateX(@degrees);\n          transform: rotateX(@degrees);\n}\n.rotateY(@degrees) {\n  -webkit-transform: rotateY(@degrees);\n      -ms-transform: rotateY(@degrees); // IE9 only\n       -o-transform: rotateY(@degrees);\n          transform: rotateY(@degrees);\n}\n.perspective(@perspective) {\n  -webkit-perspective: @perspective;\n     -moz-perspective: @perspective;\n          perspective: @perspective;\n}\n.perspective-origin(@perspective) {\n  -webkit-perspective-origin: @perspective;\n     -moz-perspective-origin: @perspective;\n          perspective-origin: @perspective;\n}\n.transform-origin(@origin) {\n  -webkit-transform-origin: @origin;\n     -moz-transform-origin: @origin;\n      -ms-transform-origin: @origin; // IE9 only\n          transform-origin: @origin;\n}\n\n\n// Transitions\n\n.transition(@transition) {\n  -webkit-transition: @transition;\n       -o-transition: @transition;\n          transition: @transition;\n}\n.transition-property(@transition-property) {\n  -webkit-transition-property: @transition-property;\n          transition-property: @transition-property;\n}\n.transition-delay(@transition-delay) {\n  -webkit-transition-delay: @transition-delay;\n          transition-delay: @transition-delay;\n}\n.transition-duration(@transition-duration) {\n  -webkit-transition-duration: @transition-duration;\n          transition-duration: @transition-duration;\n}\n.transition-timing-function(@timing-function) {\n  -webkit-transition-timing-function: @timing-function;\n          transition-timing-function: @timing-function;\n}\n.transition-transform(@transition) {\n  -webkit-transition: -webkit-transform @transition;\n     -moz-transition: -moz-transform @transition;\n       -o-transition: -o-transform @transition;\n          transition: transform @transition;\n}\n\n\n// User select\n// For selecting text on the page\n\n.user-select(@select) {\n  -webkit-user-select: @select;\n     -moz-user-select: @select;\n      -ms-user-select: @select; // IE10+\n          user-select: @select;\n}\n","// WebKit-style focus\n\n.tab-focus() {\n  // WebKit-specific. Other browsers will keep their default outline style.\n  // (Initially tried to also force default via `outline: initial`,\n  // but that seems to erroneously remove the outline in Firefox altogether.)\n  outline: 5px auto -webkit-focus-ring-color;\n  outline-offset: -2px;\n}\n","// Image Mixins\n// - Responsive image\n// - Retina image\n\n\n// Responsive image\n//\n// Keep images from scaling beyond the width of their parents.\n.img-responsive(@display: block) {\n  display: @display;\n  max-width: 100%; // Part 1: Set a maximum relative to the parent\n  height: auto; // Part 2: Scale the height according to the width, otherwise you get stretching\n}\n\n\n// Retina image\n//\n// Short retina mixin for setting background-image and -size. Note that the\n// spelling of `min--moz-device-pixel-ratio` is intentional.\n.img-retina(@file-1x; @file-2x; @width-1x; @height-1x) {\n  background-image: url(\"@{file-1x}\");\n\n  @media\n  only screen and (-webkit-min-device-pixel-ratio: 2),\n  only screen and (   min--moz-device-pixel-ratio: 2),\n  only screen and (     -o-min-device-pixel-ratio: 2/1),\n  only screen and (        min-device-pixel-ratio: 2),\n  only screen and (                min-resolution: 192dpi),\n  only screen and (                min-resolution: 2dppx) {\n    background-image: url(\"@{file-2x}\");\n    background-size: @width-1x @height-1x;\n  }\n}\n","//\n// Typography\n// --------------------------------------------------\n\n\n// Headings\n// -------------------------\n\nh1, h2, h3, h4, h5, h6,\n.h1, .h2, .h3, .h4, .h5, .h6 {\n  font-family: @headings-font-family;\n  font-weight: @headings-font-weight;\n  line-height: @headings-line-height;\n  color: @headings-color;\n\n  small,\n  .small {\n    font-weight: normal;\n    line-height: 1;\n    color: @headings-small-color;\n  }\n}\n\nh1, .h1,\nh2, .h2,\nh3, .h3 {\n  margin-top: @line-height-computed;\n  margin-bottom: (@line-height-computed / 2);\n\n  small,\n  .small {\n    font-size: 65%;\n  }\n}\nh4, .h4,\nh5, .h5,\nh6, .h6 {\n  margin-top: (@line-height-computed / 2);\n  margin-bottom: (@line-height-computed / 2);\n\n  small,\n  .small {\n    font-size: 75%;\n  }\n}\n\nh1, .h1 { font-size: @font-size-h1; }\nh2, .h2 { font-size: @font-size-h2; }\nh3, .h3 { font-size: @font-size-h3; }\nh4, .h4 { font-size: @font-size-h4; }\nh5, .h5 { font-size: @font-size-h5; }\nh6, .h6 { font-size: @font-size-h6; }\n\n\n// Body text\n// -------------------------\n\np {\n  margin: 0 0 (@line-height-computed / 2);\n}\n\n.lead {\n  margin-bottom: @line-height-computed;\n  font-size: floor((@font-size-base * 1.15));\n  font-weight: 300;\n  line-height: 1.4;\n\n  @media (min-width: @screen-sm-min) {\n    font-size: (@font-size-base * 1.5);\n  }\n}\n\n\n// Emphasis & misc\n// -------------------------\n\n// Ex: (12px small font / 14px base font) * 100% = about 85%\nsmall,\n.small {\n  font-size: floor((100% * @font-size-small / @font-size-base));\n}\n\nmark,\n.mark {\n  background-color: @state-warning-bg;\n  padding: .2em;\n}\n\n// Alignment\n.text-left           { text-align: left; }\n.text-right          { text-align: right; }\n.text-center         { text-align: center; }\n.text-justify        { text-align: justify; }\n.text-nowrap         { white-space: nowrap; }\n\n// Transformation\n.text-lowercase      { text-transform: lowercase; }\n.text-uppercase      { text-transform: uppercase; }\n.text-capitalize     { text-transform: capitalize; }\n\n// Contextual colors\n.text-muted {\n  color: @text-muted;\n}\n.text-primary {\n  .text-emphasis-variant(@brand-primary);\n}\n.text-success {\n  .text-emphasis-variant(@state-success-text);\n}\n.text-info {\n  .text-emphasis-variant(@state-info-text);\n}\n.text-warning {\n  .text-emphasis-variant(@state-warning-text);\n}\n.text-danger {\n  .text-emphasis-variant(@state-danger-text);\n}\n\n// Contextual backgrounds\n// For now we'll leave these alongside the text classes until v4 when we can\n// safely shift things around (per SemVer rules).\n.bg-primary {\n  // Given the contrast here, this is the only class to have its color inverted\n  // automatically.\n  color: #fff;\n  .bg-variant(@brand-primary);\n}\n.bg-success {\n  .bg-variant(@state-success-bg);\n}\n.bg-info {\n  .bg-variant(@state-info-bg);\n}\n.bg-warning {\n  .bg-variant(@state-warning-bg);\n}\n.bg-danger {\n  .bg-variant(@state-danger-bg);\n}\n\n\n// Page header\n// -------------------------\n\n.page-header {\n  padding-bottom: ((@line-height-computed / 2) - 1);\n  margin: (@line-height-computed * 2) 0 @line-height-computed;\n  border-bottom: 1px solid @page-header-border-color;\n}\n\n\n// Lists\n// -------------------------\n\n// Unordered and Ordered lists\nul,\nol {\n  margin-top: 0;\n  margin-bottom: (@line-height-computed / 2);\n  ul,\n  ol {\n    margin-bottom: 0;\n  }\n}\n\n// List options\n\n// Unstyled keeps list items block level, just removes default browser padding and list-style\n.list-unstyled {\n  padding-left: 0;\n  list-style: none;\n}\n\n// Inline turns list items into inline-block\n.list-inline {\n  .list-unstyled();\n  margin-left: -5px;\n\n  > li {\n    display: inline-block;\n    padding-left: 5px;\n    padding-right: 5px;\n  }\n}\n\n// Description Lists\ndl {\n  margin-top: 0; // Remove browser default\n  margin-bottom: @line-height-computed;\n}\ndt,\ndd {\n  line-height: @line-height-base;\n}\ndt {\n  font-weight: bold;\n}\ndd {\n  margin-left: 0; // Undo browser default\n}\n\n// Horizontal description lists\n//\n// Defaults to being stacked without any of the below styles applied, until the\n// grid breakpoint is reached (default of ~768px).\n\n.dl-horizontal {\n  dd {\n    &:extend(.clearfix all); // Clear the floated `dt` if an empty `dd` is present\n  }\n\n  @media (min-width: @dl-horizontal-breakpoint) {\n    dt {\n      float: left;\n      width: (@dl-horizontal-offset - 20);\n      clear: left;\n      text-align: right;\n      .text-overflow();\n    }\n    dd {\n      margin-left: @dl-horizontal-offset;\n    }\n  }\n}\n\n\n// Misc\n// -------------------------\n\n// Abbreviations and acronyms\nabbr[title],\n// Add data-* attribute to help out our tooltip plugin, per https://github.com/twbs/bootstrap/issues/5257\nabbr[data-original-title] {\n  cursor: help;\n  border-bottom: 1px dotted @abbr-border-color;\n}\n.initialism {\n  font-size: 90%;\n  .text-uppercase();\n}\n\n// Blockquotes\nblockquote {\n  padding: (@line-height-computed / 2) @line-height-computed;\n  margin: 0 0 @line-height-computed;\n  font-size: @blockquote-font-size;\n  border-left: 5px solid @blockquote-border-color;\n\n  p,\n  ul,\n  ol {\n    &:last-child {\n      margin-bottom: 0;\n    }\n  }\n\n  // Note: Deprecated small and .small as of v3.1.0\n  // Context: https://github.com/twbs/bootstrap/issues/11660\n  footer,\n  small,\n  .small {\n    display: block;\n    font-size: 80%; // back to default font-size\n    line-height: @line-height-base;\n    color: @blockquote-small-color;\n\n    &:before {\n      content: '\\2014 \\00A0'; // em dash, nbsp\n    }\n  }\n}\n\n// Opposite alignment of blockquote\n//\n// Heads up: `blockquote.pull-right` has been deprecated as of v3.1.0.\n.blockquote-reverse,\nblockquote.pull-right {\n  padding-right: 15px;\n  padding-left: 0;\n  border-right: 5px solid @blockquote-border-color;\n  border-left: 0;\n  text-align: right;\n\n  // Account for citation\n  footer,\n  small,\n  .small {\n    &:before { content: ''; }\n    &:after {\n      content: '\\00A0 \\2014'; // nbsp, em dash\n    }\n  }\n}\n\n// Addresses\naddress {\n  margin-bottom: @line-height-computed;\n  font-style: normal;\n  line-height: @line-height-base;\n}\n","// Typography\n\n.text-emphasis-variant(@color) {\n  color: @color;\n  a&:hover,\n  a&:focus {\n    color: darken(@color, 10%);\n  }\n}\n","// Contextual backgrounds\n\n.bg-variant(@color) {\n  background-color: @color;\n  a&:hover,\n  a&:focus {\n    background-color: darken(@color, 10%);\n  }\n}\n","// Text overflow\n// Requires inline-block or block for proper styling\n\n.text-overflow() {\n  overflow: hidden;\n  text-overflow: ellipsis;\n  white-space: nowrap;\n}\n","//\n// Code (inline and block)\n// --------------------------------------------------\n\n\n// Inline and block code styles\ncode,\nkbd,\npre,\nsamp {\n  font-family: @font-family-monospace;\n}\n\n// Inline code\ncode {\n  padding: 2px 4px;\n  font-size: 90%;\n  color: @code-color;\n  background-color: @code-bg;\n  border-radius: @border-radius-base;\n}\n\n// User input typically entered via keyboard\nkbd {\n  padding: 2px 4px;\n  font-size: 90%;\n  color: @kbd-color;\n  background-color: @kbd-bg;\n  border-radius: @border-radius-small;\n  box-shadow: inset 0 -1px 0 rgba(0,0,0,.25);\n\n  kbd {\n    padding: 0;\n    font-size: 100%;\n    font-weight: bold;\n    box-shadow: none;\n  }\n}\n\n// Blocks of code\npre {\n  display: block;\n  padding: ((@line-height-computed - 1) / 2);\n  margin: 0 0 (@line-height-computed / 2);\n  font-size: (@font-size-base - 1); // 14px to 13px\n  line-height: @line-height-base;\n  word-break: break-all;\n  word-wrap: break-word;\n  color: @pre-color;\n  background-color: @pre-bg;\n  border: 1px solid @pre-border-color;\n  border-radius: @border-radius-base;\n\n  // Account for some code outputs that place code tags in pre tags\n  code {\n    padding: 0;\n    font-size: inherit;\n    color: inherit;\n    white-space: pre-wrap;\n    background-color: transparent;\n    border-radius: 0;\n  }\n}\n\n// Enable scrollable blocks of code\n.pre-scrollable {\n  max-height: @pre-scrollable-max-height;\n  overflow-y: scroll;\n}\n","//\n// Grid system\n// --------------------------------------------------\n\n\n// Container widths\n//\n// Set the container width, and override it for fixed navbars in media queries.\n\n.container {\n  .container-fixed();\n\n  @media (min-width: @screen-sm-min) {\n    width: @container-sm;\n  }\n  @media (min-width: @screen-md-min) {\n    width: @container-md;\n  }\n  @media (min-width: @screen-lg-min) {\n    width: @container-lg;\n  }\n}\n\n\n// Fluid container\n//\n// Utilizes the mixin meant for fixed width containers, but without any defined\n// width for fluid, full width layouts.\n\n.container-fluid {\n  .container-fixed();\n}\n\n\n// Row\n//\n// Rows contain and clear the floats of your columns.\n\n.row {\n  .make-row();\n}\n\n\n// Columns\n//\n// Common styles for small and large grid columns\n\n.make-grid-columns();\n\n\n// Extra small grid\n//\n// Columns, offsets, pushes, and pulls for extra small devices like\n// smartphones.\n\n.make-grid(xs);\n\n\n// Small grid\n//\n// Columns, offsets, pushes, and pulls for the small device range, from phones\n// to tablets.\n\n@media (min-width: @screen-sm-min) {\n  .make-grid(sm);\n}\n\n\n// Medium grid\n//\n// Columns, offsets, pushes, and pulls for the desktop device range.\n\n@media (min-width: @screen-md-min) {\n  .make-grid(md);\n}\n\n\n// Large grid\n//\n// Columns, offsets, pushes, and pulls for the large desktop device range.\n\n@media (min-width: @screen-lg-min) {\n  .make-grid(lg);\n}\n","// Grid system\n//\n// Generate semantic grid columns with these mixins.\n\n// Centered container element\n.container-fixed(@gutter: @grid-gutter-width) {\n  margin-right: auto;\n  margin-left: auto;\n  padding-left:  floor((@gutter / 2));\n  padding-right: ceil((@gutter / 2));\n  &:extend(.clearfix all);\n}\n\n// Creates a wrapper for a series of columns\n.make-row(@gutter: @grid-gutter-width) {\n  margin-left:  ceil((@gutter / -2));\n  margin-right: floor((@gutter / -2));\n  &:extend(.clearfix all);\n}\n\n// Generate the extra small columns\n.make-xs-column(@columns; @gutter: @grid-gutter-width) {\n  position: relative;\n  float: left;\n  width: percentage((@columns / @grid-columns));\n  min-height: 1px;\n  padding-left:  (@gutter / 2);\n  padding-right: (@gutter / 2);\n}\n.make-xs-column-offset(@columns) {\n  margin-left: percentage((@columns / @grid-columns));\n}\n.make-xs-column-push(@columns) {\n  left: percentage((@columns / @grid-columns));\n}\n.make-xs-column-pull(@columns) {\n  right: percentage((@columns / @grid-columns));\n}\n\n// Generate the small columns\n.make-sm-column(@columns; @gutter: @grid-gutter-width) {\n  position: relative;\n  min-height: 1px;\n  padding-left:  (@gutter / 2);\n  padding-right: (@gutter / 2);\n\n  @media (min-width: @screen-sm-min) {\n    float: left;\n    width: percentage((@columns / @grid-columns));\n  }\n}\n.make-sm-column-offset(@columns) {\n  @media (min-width: @screen-sm-min) {\n    margin-left: percentage((@columns / @grid-columns));\n  }\n}\n.make-sm-column-push(@columns) {\n  @media (min-width: @screen-sm-min) {\n    left: percentage((@columns / @grid-columns));\n  }\n}\n.make-sm-column-pull(@columns) {\n  @media (min-width: @screen-sm-min) {\n    right: percentage((@columns / @grid-columns));\n  }\n}\n\n// Generate the medium columns\n.make-md-column(@columns; @gutter: @grid-gutter-width) {\n  position: relative;\n  min-height: 1px;\n  padding-left:  (@gutter / 2);\n  padding-right: (@gutter / 2);\n\n  @media (min-width: @screen-md-min) {\n    float: left;\n    width: percentage((@columns / @grid-columns));\n  }\n}\n.make-md-column-offset(@columns) {\n  @media (min-width: @screen-md-min) {\n    margin-left: percentage((@columns / @grid-columns));\n  }\n}\n.make-md-column-push(@columns) {\n  @media (min-width: @screen-md-min) {\n    left: percentage((@columns / @grid-columns));\n  }\n}\n.make-md-column-pull(@columns) {\n  @media (min-width: @screen-md-min) {\n    right: percentage((@columns / @grid-columns));\n  }\n}\n\n// Generate the large columns\n.make-lg-column(@columns; @gutter: @grid-gutter-width) {\n  position: relative;\n  min-height: 1px;\n  padding-left:  (@gutter / 2);\n  padding-right: (@gutter / 2);\n\n  @media (min-width: @screen-lg-min) {\n    float: left;\n    width: percentage((@columns / @grid-columns));\n  }\n}\n.make-lg-column-offset(@columns) {\n  @media (min-width: @screen-lg-min) {\n    margin-left: percentage((@columns / @grid-columns));\n  }\n}\n.make-lg-column-push(@columns) {\n  @media (min-width: @screen-lg-min) {\n    left: percentage((@columns / @grid-columns));\n  }\n}\n.make-lg-column-pull(@columns) {\n  @media (min-width: @screen-lg-min) {\n    right: percentage((@columns / @grid-columns));\n  }\n}\n","// Framework grid generation\n//\n// Used only by Bootstrap to generate the correct number of grid classes given\n// any value of `@grid-columns`.\n\n.make-grid-columns() {\n  // Common styles for all sizes of grid columns, widths 1-12\n  .col(@index) { // initial\n    @item: ~\".col-xs-@{index}, .col-sm-@{index}, .col-md-@{index}, .col-lg-@{index}\";\n    .col((@index + 1), @item);\n  }\n  .col(@index, @list) when (@index =< @grid-columns) { // general; \"=<\" isn't a typo\n    @item: ~\".col-xs-@{index}, .col-sm-@{index}, .col-md-@{index}, .col-lg-@{index}\";\n    .col((@index + 1), ~\"@{list}, @{item}\");\n  }\n  .col(@index, @list) when (@index > @grid-columns) { // terminal\n    @{list} {\n      position: relative;\n      // Prevent columns from collapsing when empty\n      min-height: 1px;\n      // Inner gutter via padding\n      padding-left:  ceil((@grid-gutter-width / 2));\n      padding-right: floor((@grid-gutter-width / 2));\n    }\n  }\n  .col(1); // kickstart it\n}\n\n.float-grid-columns(@class) {\n  .col(@index) { // initial\n    @item: ~\".col-@{class}-@{index}\";\n    .col((@index + 1), @item);\n  }\n  .col(@index, @list) when (@index =< @grid-columns) { // general\n    @item: ~\".col-@{class}-@{index}\";\n    .col((@index + 1), ~\"@{list}, @{item}\");\n  }\n  .col(@index, @list) when (@index > @grid-columns) { // terminal\n    @{list} {\n      float: left;\n    }\n  }\n  .col(1); // kickstart it\n}\n\n.calc-grid-column(@index, @class, @type) when (@type = width) and (@index > 0) {\n  .col-@{class}-@{index} {\n    width: percentage((@index / @grid-columns));\n  }\n}\n.calc-grid-column(@index, @class, @type) when (@type = push) and (@index > 0) {\n  .col-@{class}-push-@{index} {\n    left: percentage((@index / @grid-columns));\n  }\n}\n.calc-grid-column(@index, @class, @type) when (@type = push) and (@index = 0) {\n  .col-@{class}-push-0 {\n    left: auto;\n  }\n}\n.calc-grid-column(@index, @class, @type) when (@type = pull) and (@index > 0) {\n  .col-@{class}-pull-@{index} {\n    right: percentage((@index / @grid-columns));\n  }\n}\n.calc-grid-column(@index, @class, @type) when (@type = pull) and (@index = 0) {\n  .col-@{class}-pull-0 {\n    right: auto;\n  }\n}\n.calc-grid-column(@index, @class, @type) when (@type = offset) {\n  .col-@{class}-offset-@{index} {\n    margin-left: percentage((@index / @grid-columns));\n  }\n}\n\n// Basic looping in LESS\n.loop-grid-columns(@index, @class, @type) when (@index >= 0) {\n  .calc-grid-column(@index, @class, @type);\n  // next iteration\n  .loop-grid-columns((@index - 1), @class, @type);\n}\n\n// Create grid for specific class\n.make-grid(@class) {\n  .float-grid-columns(@class);\n  .loop-grid-columns(@grid-columns, @class, width);\n  .loop-grid-columns(@grid-columns, @class, pull);\n  .loop-grid-columns(@grid-columns, @class, push);\n  .loop-grid-columns(@grid-columns, @class, offset);\n}\n","//\n// Tables\n// --------------------------------------------------\n\n\ntable {\n  background-color: @table-bg;\n}\ncaption {\n  padding-top: @table-cell-padding;\n  padding-bottom: @table-cell-padding;\n  color: @text-muted;\n  text-align: left;\n}\nth {\n  text-align: left;\n}\n\n\n// Baseline styles\n\n.table {\n  width: 100%;\n  max-width: 100%;\n  margin-bottom: @line-height-computed;\n  // Cells\n  > thead,\n  > tbody,\n  > tfoot {\n    > tr {\n      > th,\n      > td {\n        padding: @table-cell-padding;\n        line-height: @line-height-base;\n        vertical-align: top;\n        border-top: 1px solid @table-border-color;\n      }\n    }\n  }\n  // Bottom align for column headings\n  > thead > tr > th {\n    vertical-align: bottom;\n    border-bottom: 2px solid @table-border-color;\n  }\n  // Remove top border from thead by default\n  > caption + thead,\n  > colgroup + thead,\n  > thead:first-child {\n    > tr:first-child {\n      > th,\n      > td {\n        border-top: 0;\n      }\n    }\n  }\n  // Account for multiple tbody instances\n  > tbody + tbody {\n    border-top: 2px solid @table-border-color;\n  }\n\n  // Nesting\n  .table {\n    background-color: @body-bg;\n  }\n}\n\n\n// Condensed table w/ half padding\n\n.table-condensed {\n  > thead,\n  > tbody,\n  > tfoot {\n    > tr {\n      > th,\n      > td {\n        padding: @table-condensed-cell-padding;\n      }\n    }\n  }\n}\n\n\n// Bordered version\n//\n// Add borders all around the table and between all the columns.\n\n.table-bordered {\n  border: 1px solid @table-border-color;\n  > thead,\n  > tbody,\n  > tfoot {\n    > tr {\n      > th,\n      > td {\n        border: 1px solid @table-border-color;\n      }\n    }\n  }\n  > thead > tr {\n    > th,\n    > td {\n      border-bottom-width: 2px;\n    }\n  }\n}\n\n\n// Zebra-striping\n//\n// Default zebra-stripe styles (alternating gray and transparent backgrounds)\n\n.table-striped {\n  > tbody > tr:nth-of-type(odd) {\n    background-color: @table-bg-accent;\n  }\n}\n\n\n// Hover effect\n//\n// Placed here since it has to come after the potential zebra striping\n\n.table-hover {\n  > tbody > tr:hover {\n    background-color: @table-bg-hover;\n  }\n}\n\n\n// Table cell sizing\n//\n// Reset default table behavior\n\ntable col[class*=\"col-\"] {\n  position: static; // Prevent border hiding in Firefox and IE9-11 (see https://github.com/twbs/bootstrap/issues/11623)\n  float: none;\n  display: table-column;\n}\ntable {\n  td,\n  th {\n    &[class*=\"col-\"] {\n      position: static; // Prevent border hiding in Firefox and IE9-11 (see https://github.com/twbs/bootstrap/issues/11623)\n      float: none;\n      display: table-cell;\n    }\n  }\n}\n\n\n// Table backgrounds\n//\n// Exact selectors below required to override `.table-striped` and prevent\n// inheritance to nested tables.\n\n// Generate the contextual variants\n.table-row-variant(active; @table-bg-active);\n.table-row-variant(success; @state-success-bg);\n.table-row-variant(info; @state-info-bg);\n.table-row-variant(warning; @state-warning-bg);\n.table-row-variant(danger; @state-danger-bg);\n\n\n// Responsive tables\n//\n// Wrap your tables in `.table-responsive` and we'll make them mobile friendly\n// by enabling horizontal scrolling. Only applies <768px. Everything above that\n// will display normally.\n\n.table-responsive {\n  overflow-x: auto;\n  min-height: 0.01%; // Workaround for IE9 bug (see https://github.com/twbs/bootstrap/issues/14837)\n\n  @media screen and (max-width: @screen-xs-max) {\n    width: 100%;\n    margin-bottom: (@line-height-computed * 0.75);\n    overflow-y: hidden;\n    -ms-overflow-style: -ms-autohiding-scrollbar;\n    border: 1px solid @table-border-color;\n\n    // Tighten up spacing\n    > .table {\n      margin-bottom: 0;\n\n      // Ensure the content doesn't wrap\n      > thead,\n      > tbody,\n      > tfoot {\n        > tr {\n          > th,\n          > td {\n            white-space: nowrap;\n          }\n        }\n      }\n    }\n\n    // Special overrides for the bordered tables\n    > .table-bordered {\n      border: 0;\n\n      // Nuke the appropriate borders so that the parent can handle them\n      > thead,\n      > tbody,\n      > tfoot {\n        > tr {\n          > th:first-child,\n          > td:first-child {\n            border-left: 0;\n          }\n          > th:last-child,\n          > td:last-child {\n            border-right: 0;\n          }\n        }\n      }\n\n      // Only nuke the last row's bottom-border in `tbody` and `tfoot` since\n      // chances are there will be only one `tr` in a `thead` and that would\n      // remove the border altogether.\n      > tbody,\n      > tfoot {\n        > tr:last-child {\n          > th,\n          > td {\n            border-bottom: 0;\n          }\n        }\n      }\n\n    }\n  }\n}\n","// Tables\n\n.table-row-variant(@state; @background) {\n  // Exact selectors below required to override `.table-striped` and prevent\n  // inheritance to nested tables.\n  .table > thead > tr,\n  .table > tbody > tr,\n  .table > tfoot > tr {\n    > td.@{state},\n    > th.@{state},\n    &.@{state} > td,\n    &.@{state} > th {\n      background-color: @background;\n    }\n  }\n\n  // Hover states for `.table-hover`\n  // Note: this is not available for cells or rows within `thead` or `tfoot`.\n  .table-hover > tbody > tr {\n    > td.@{state}:hover,\n    > th.@{state}:hover,\n    &.@{state}:hover > td,\n    &:hover > .@{state},\n    &.@{state}:hover > th {\n      background-color: darken(@background, 5%);\n    }\n  }\n}\n","//\n// Forms\n// --------------------------------------------------\n\n\n// Normalize non-controls\n//\n// Restyle and baseline non-control form elements.\n\nfieldset {\n  padding: 0;\n  margin: 0;\n  border: 0;\n  // Chrome and Firefox set a `min-width: min-content;` on fieldsets,\n  // so we reset that to ensure it behaves more like a standard block element.\n  // See https://github.com/twbs/bootstrap/issues/12359.\n  min-width: 0;\n}\n\nlegend {\n  display: block;\n  width: 100%;\n  padding: 0;\n  margin-bottom: @line-height-computed;\n  font-size: (@font-size-base * 1.5);\n  line-height: inherit;\n  color: @legend-color;\n  border: 0;\n  border-bottom: 1px solid @legend-border-color;\n}\n\nlabel {\n  display: inline-block;\n  max-width: 100%; // Force IE8 to wrap long content (see https://github.com/twbs/bootstrap/issues/13141)\n  margin-bottom: 5px;\n  font-weight: bold;\n}\n\n\n// Normalize form controls\n//\n// While most of our form styles require extra classes, some basic normalization\n// is required to ensure optimum display with or without those classes to better\n// address browser inconsistencies.\n\n// Override content-box in Normalize (* isn't specific enough)\ninput[type=\"search\"] {\n  .box-sizing(border-box);\n}\n\n// Position radios and checkboxes better\ninput[type=\"radio\"],\ninput[type=\"checkbox\"] {\n  margin: 4px 0 0;\n  margin-top: 1px \\9; // IE8-9\n  line-height: normal;\n}\n\ninput[type=\"file\"] {\n  display: block;\n}\n\n// Make range inputs behave like textual form controls\ninput[type=\"range\"] {\n  display: block;\n  width: 100%;\n}\n\n// Make multiple select elements height not fixed\nselect[multiple],\nselect[size] {\n  height: auto;\n}\n\n// Focus for file, radio, and checkbox\ninput[type=\"file\"]:focus,\ninput[type=\"radio\"]:focus,\ninput[type=\"checkbox\"]:focus {\n  .tab-focus();\n}\n\n// Adjust output element\noutput {\n  display: block;\n  padding-top: (@padding-base-vertical + 1);\n  font-size: @font-size-base;\n  line-height: @line-height-base;\n  color: @input-color;\n}\n\n\n// Common form controls\n//\n// Shared size and type resets for form controls. Apply `.form-control` to any\n// of the following form controls:\n//\n// select\n// textarea\n// input[type=\"text\"]\n// input[type=\"password\"]\n// input[type=\"datetime\"]\n// input[type=\"datetime-local\"]\n// input[type=\"date\"]\n// input[type=\"month\"]\n// input[type=\"time\"]\n// input[type=\"week\"]\n// input[type=\"number\"]\n// input[type=\"email\"]\n// input[type=\"url\"]\n// input[type=\"search\"]\n// input[type=\"tel\"]\n// input[type=\"color\"]\n\n.form-control {\n  display: block;\n  width: 100%;\n  height: @input-height-base; // Make inputs at least the height of their button counterpart (base line-height + padding + border)\n  padding: @padding-base-vertical @padding-base-horizontal;\n  font-size: @font-size-base;\n  line-height: @line-height-base;\n  color: @input-color;\n  background-color: @input-bg;\n  background-image: none; // Reset unusual Firefox-on-Android default style; see https://github.com/necolas/normalize.css/issues/214\n  border: 1px solid @input-border;\n  border-radius: @input-border-radius; // Note: This has no effect on <select>s in some browsers, due to the limited stylability of <select>s in CSS.\n  .box-shadow(inset 0 1px 1px rgba(0,0,0,.075));\n  .transition(~\"border-color ease-in-out .15s, box-shadow ease-in-out .15s\");\n\n  // Customize the `:focus` state to imitate native WebKit styles.\n  .form-control-focus();\n\n  // Placeholder\n  .placeholder();\n\n  // Unstyle the caret on `<select>`s in IE10+.\n  &::-ms-expand {\n    border: 0;\n    background-color: transparent;\n  }\n\n  // Disabled and read-only inputs\n  //\n  // HTML5 says that controls under a fieldset > legend:first-child won't be\n  // disabled if the fieldset is disabled. Due to implementation difficulty, we\n  // don't honor that edge case; we style them as disabled anyway.\n  &[disabled],\n  &[readonly],\n  fieldset[disabled] & {\n    background-color: @input-bg-disabled;\n    opacity: 1; // iOS fix for unreadable disabled content; see https://github.com/twbs/bootstrap/issues/11655\n  }\n\n  &[disabled],\n  fieldset[disabled] & {\n    cursor: @cursor-disabled;\n  }\n\n  // Reset height for `textarea`s\n  textarea& {\n    height: auto;\n  }\n}\n\n\n// Search inputs in iOS\n//\n// This overrides the extra rounded corners on search inputs in iOS so that our\n// `.form-control` class can properly style them. Note that this cannot simply\n// be added to `.form-control` as it's not specific enough. For details, see\n// https://github.com/twbs/bootstrap/issues/11586.\n\ninput[type=\"search\"] {\n  -webkit-appearance: none;\n}\n\n\n// Special styles for iOS temporal inputs\n//\n// In Mobile Safari, setting `display: block` on temporal inputs causes the\n// text within the input to become vertically misaligned. As a workaround, we\n// set a pixel line-height that matches the given height of the input, but only\n// for Safari. See https://bugs.webkit.org/show_bug.cgi?id=139848\n//\n// Note that as of 9.3, iOS doesn't support `week`.\n\n@media screen and (-webkit-min-device-pixel-ratio: 0) {\n  input[type=\"date\"],\n  input[type=\"time\"],\n  input[type=\"datetime-local\"],\n  input[type=\"month\"] {\n    &.form-control {\n      line-height: @input-height-base;\n    }\n\n    &.input-sm,\n    .input-group-sm & {\n      line-height: @input-height-small;\n    }\n\n    &.input-lg,\n    .input-group-lg & {\n      line-height: @input-height-large;\n    }\n  }\n}\n\n\n// Form groups\n//\n// Designed to help with the organization and spacing of vertical forms. For\n// horizontal forms, use the predefined grid classes.\n\n.form-group {\n  margin-bottom: @form-group-margin-bottom;\n}\n\n\n// Checkboxes and radios\n//\n// Indent the labels to position radios/checkboxes as hanging controls.\n\n.radio,\n.checkbox {\n  position: relative;\n  display: block;\n  margin-top: 10px;\n  margin-bottom: 10px;\n\n  label {\n    min-height: @line-height-computed; // Ensure the input doesn't jump when there is no text\n    padding-left: 20px;\n    margin-bottom: 0;\n    font-weight: normal;\n    cursor: pointer;\n  }\n}\n.radio input[type=\"radio\"],\n.radio-inline input[type=\"radio\"],\n.checkbox input[type=\"checkbox\"],\n.checkbox-inline input[type=\"checkbox\"] {\n  position: absolute;\n  margin-left: -20px;\n  margin-top: 4px \\9;\n}\n\n.radio + .radio,\n.checkbox + .checkbox {\n  margin-top: -5px; // Move up sibling radios or checkboxes for tighter spacing\n}\n\n// Radios and checkboxes on same line\n.radio-inline,\n.checkbox-inline {\n  position: relative;\n  display: inline-block;\n  padding-left: 20px;\n  margin-bottom: 0;\n  vertical-align: middle;\n  font-weight: normal;\n  cursor: pointer;\n}\n.radio-inline + .radio-inline,\n.checkbox-inline + .checkbox-inline {\n  margin-top: 0;\n  margin-left: 10px; // space out consecutive inline controls\n}\n\n// Apply same disabled cursor tweak as for inputs\n// Some special care is needed because <label>s don't inherit their parent's `cursor`.\n//\n// Note: Neither radios nor checkboxes can be readonly.\ninput[type=\"radio\"],\ninput[type=\"checkbox\"] {\n  &[disabled],\n  &.disabled,\n  fieldset[disabled] & {\n    cursor: @cursor-disabled;\n  }\n}\n// These classes are used directly on <label>s\n.radio-inline,\n.checkbox-inline {\n  &.disabled,\n  fieldset[disabled] & {\n    cursor: @cursor-disabled;\n  }\n}\n// These classes are used on elements with <label> descendants\n.radio,\n.checkbox {\n  &.disabled,\n  fieldset[disabled] & {\n    label {\n      cursor: @cursor-disabled;\n    }\n  }\n}\n\n\n// Static form control text\n//\n// Apply class to a `p` element to make any string of text align with labels in\n// a horizontal form layout.\n\n.form-control-static {\n  // Size it appropriately next to real form controls\n  padding-top: (@padding-base-vertical + 1);\n  padding-bottom: (@padding-base-vertical + 1);\n  // Remove default margin from `p`\n  margin-bottom: 0;\n  min-height: (@line-height-computed + @font-size-base);\n\n  &.input-lg,\n  &.input-sm {\n    padding-left: 0;\n    padding-right: 0;\n  }\n}\n\n\n// Form control sizing\n//\n// Build on `.form-control` with modifier classes to decrease or increase the\n// height and font-size of form controls.\n//\n// The `.form-group-* form-control` variations are sadly duplicated to avoid the\n// issue documented in https://github.com/twbs/bootstrap/issues/15074.\n\n.input-sm {\n  .input-size(@input-height-small; @padding-small-vertical; @padding-small-horizontal; @font-size-small; @line-height-small; @input-border-radius-small);\n}\n.form-group-sm {\n  .form-control {\n    height: @input-height-small;\n    padding: @padding-small-vertical @padding-small-horizontal;\n    font-size: @font-size-small;\n    line-height: @line-height-small;\n    border-radius: @input-border-radius-small;\n  }\n  select.form-control {\n    height: @input-height-small;\n    line-height: @input-height-small;\n  }\n  textarea.form-control,\n  select[multiple].form-control {\n    height: auto;\n  }\n  .form-control-static {\n    height: @input-height-small;\n    min-height: (@line-height-computed + @font-size-small);\n    padding: (@padding-small-vertical + 1) @padding-small-horizontal;\n    font-size: @font-size-small;\n    line-height: @line-height-small;\n  }\n}\n\n.input-lg {\n  .input-size(@input-height-large; @padding-large-vertical; @padding-large-horizontal; @font-size-large; @line-height-large; @input-border-radius-large);\n}\n.form-group-lg {\n  .form-control {\n    height: @input-height-large;\n    padding: @padding-large-vertical @padding-large-horizontal;\n    font-size: @font-size-large;\n    line-height: @line-height-large;\n    border-radius: @input-border-radius-large;\n  }\n  select.form-control {\n    height: @input-height-large;\n    line-height: @input-height-large;\n  }\n  textarea.form-control,\n  select[multiple].form-control {\n    height: auto;\n  }\n  .form-control-static {\n    height: @input-height-large;\n    min-height: (@line-height-computed + @font-size-large);\n    padding: (@padding-large-vertical + 1) @padding-large-horizontal;\n    font-size: @font-size-large;\n    line-height: @line-height-large;\n  }\n}\n\n\n// Form control feedback states\n//\n// Apply contextual and semantic states to individual form controls.\n\n.has-feedback {\n  // Enable absolute positioning\n  position: relative;\n\n  // Ensure icons don't overlap text\n  .form-control {\n    padding-right: (@input-height-base * 1.25);\n  }\n}\n// Feedback icon (requires .glyphicon classes)\n.form-control-feedback {\n  position: absolute;\n  top: 0;\n  right: 0;\n  z-index: 2; // Ensure icon is above input groups\n  display: block;\n  width: @input-height-base;\n  height: @input-height-base;\n  line-height: @input-height-base;\n  text-align: center;\n  pointer-events: none;\n}\n.input-lg + .form-control-feedback,\n.input-group-lg + .form-control-feedback,\n.form-group-lg .form-control + .form-control-feedback {\n  width: @input-height-large;\n  height: @input-height-large;\n  line-height: @input-height-large;\n}\n.input-sm + .form-control-feedback,\n.input-group-sm + .form-control-feedback,\n.form-group-sm .form-control + .form-control-feedback {\n  width: @input-height-small;\n  height: @input-height-small;\n  line-height: @input-height-small;\n}\n\n// Feedback states\n.has-success {\n  .form-control-validation(@state-success-text; @state-success-text; @state-success-bg);\n}\n.has-warning {\n  .form-control-validation(@state-warning-text; @state-warning-text; @state-warning-bg);\n}\n.has-error {\n  .form-control-validation(@state-danger-text; @state-danger-text; @state-danger-bg);\n}\n\n// Reposition feedback icon if input has visible label above\n.has-feedback label {\n\n  & ~ .form-control-feedback {\n    top: (@line-height-computed + 5); // Height of the `label` and its margin\n  }\n  &.sr-only ~ .form-control-feedback {\n    top: 0;\n  }\n}\n\n\n// Help text\n//\n// Apply to any element you wish to create light text for placement immediately\n// below a form control. Use for general help, formatting, or instructional text.\n\n.help-block {\n  display: block; // account for any element using help-block\n  margin-top: 5px;\n  margin-bottom: 10px;\n  color: lighten(@text-color, 25%); // lighten the text some for contrast\n}\n\n\n// Inline forms\n//\n// Make forms appear inline(-block) by adding the `.form-inline` class. Inline\n// forms begin stacked on extra small (mobile) devices and then go inline when\n// viewports reach <768px.\n//\n// Requires wrapping inputs and labels with `.form-group` for proper display of\n// default HTML form controls and our custom form controls (e.g., input groups).\n//\n// Heads up! This is mixin-ed into `.navbar-form` in navbars.less.\n\n.form-inline {\n\n  // Kick in the inline\n  @media (min-width: @screen-sm-min) {\n    // Inline-block all the things for \"inline\"\n    .form-group {\n      display: inline-block;\n      margin-bottom: 0;\n      vertical-align: middle;\n    }\n\n    // In navbar-form, allow folks to *not* use `.form-group`\n    .form-control {\n      display: inline-block;\n      width: auto; // Prevent labels from stacking above inputs in `.form-group`\n      vertical-align: middle;\n    }\n\n    // Make static controls behave like regular ones\n    .form-control-static {\n      display: inline-block;\n    }\n\n    .input-group {\n      display: inline-table;\n      vertical-align: middle;\n\n      .input-group-addon,\n      .input-group-btn,\n      .form-control {\n        width: auto;\n      }\n    }\n\n    // Input groups need that 100% width though\n    .input-group > .form-control {\n      width: 100%;\n    }\n\n    .control-label {\n      margin-bottom: 0;\n      vertical-align: middle;\n    }\n\n    // Remove default margin on radios/checkboxes that were used for stacking, and\n    // then undo the floating of radios and checkboxes to match.\n    .radio,\n    .checkbox {\n      display: inline-block;\n      margin-top: 0;\n      margin-bottom: 0;\n      vertical-align: middle;\n\n      label {\n        padding-left: 0;\n      }\n    }\n    .radio input[type=\"radio\"],\n    .checkbox input[type=\"checkbox\"] {\n      position: relative;\n      margin-left: 0;\n    }\n\n    // Re-override the feedback icon.\n    .has-feedback .form-control-feedback {\n      top: 0;\n    }\n  }\n}\n\n\n// Horizontal forms\n//\n// Horizontal forms are built on grid classes and allow you to create forms with\n// labels on the left and inputs on the right.\n\n.form-horizontal {\n\n  // Consistent vertical alignment of radios and checkboxes\n  //\n  // Labels also get some reset styles, but that is scoped to a media query below.\n  .radio,\n  .checkbox,\n  .radio-inline,\n  .checkbox-inline {\n    margin-top: 0;\n    margin-bottom: 0;\n    padding-top: (@padding-base-vertical + 1); // Default padding plus a border\n  }\n  // Account for padding we're adding to ensure the alignment and of help text\n  // and other content below items\n  .radio,\n  .checkbox {\n    min-height: (@line-height-computed + (@padding-base-vertical + 1));\n  }\n\n  // Make form groups behave like rows\n  .form-group {\n    .make-row();\n  }\n\n  // Reset spacing and right align labels, but scope to media queries so that\n  // labels on narrow viewports stack the same as a default form example.\n  @media (min-width: @screen-sm-min) {\n    .control-label {\n      text-align: right;\n      margin-bottom: 0;\n      padding-top: (@padding-base-vertical + 1); // Default padding plus a border\n    }\n  }\n\n  // Validation states\n  //\n  // Reposition the icon because it's now within a grid column and columns have\n  // `position: relative;` on them. Also accounts for the grid gutter padding.\n  .has-feedback .form-control-feedback {\n    right: floor((@grid-gutter-width / 2));\n  }\n\n  // Form group sizes\n  //\n  // Quick utility class for applying `.input-lg` and `.input-sm` styles to the\n  // inputs and labels within a `.form-group`.\n  .form-group-lg {\n    @media (min-width: @screen-sm-min) {\n      .control-label {\n        padding-top: (@padding-large-vertical + 1);\n        font-size: @font-size-large;\n      }\n    }\n  }\n  .form-group-sm {\n    @media (min-width: @screen-sm-min) {\n      .control-label {\n        padding-top: (@padding-small-vertical + 1);\n        font-size: @font-size-small;\n      }\n    }\n  }\n}\n","// Form validation states\n//\n// Used in forms.less to generate the form validation CSS for warnings, errors,\n// and successes.\n\n.form-control-validation(@text-color: #555; @border-color: #ccc; @background-color: #f5f5f5) {\n  // Color the label and help text\n  .help-block,\n  .control-label,\n  .radio,\n  .checkbox,\n  .radio-inline,\n  .checkbox-inline,\n  &.radio label,\n  &.checkbox label,\n  &.radio-inline label,\n  &.checkbox-inline label  {\n    color: @text-color;\n  }\n  // Set the border and box shadow on specific inputs to match\n  .form-control {\n    border-color: @border-color;\n    .box-shadow(inset 0 1px 1px rgba(0,0,0,.075)); // Redeclare so transitions work\n    &:focus {\n      border-color: darken(@border-color, 10%);\n      @shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 6px lighten(@border-color, 20%);\n      .box-shadow(@shadow);\n    }\n  }\n  // Set validation states also for addons\n  .input-group-addon {\n    color: @text-color;\n    border-color: @border-color;\n    background-color: @background-color;\n  }\n  // Optional feedback icon\n  .form-control-feedback {\n    color: @text-color;\n  }\n}\n\n\n// Form control focus state\n//\n// Generate a customized focus state and for any input with the specified color,\n// which defaults to the `@input-border-focus` variable.\n//\n// We highly encourage you to not customize the default value, but instead use\n// this to tweak colors on an as-needed basis. This aesthetic change is based on\n// WebKit's default styles, but applicable to a wider range of browsers. Its\n// usability and accessibility should be taken into account with any change.\n//\n// Example usage: change the default blue border and shadow to white for better\n// contrast against a dark gray background.\n.form-control-focus(@color: @input-border-focus) {\n  @color-rgba: rgba(red(@color), green(@color), blue(@color), .6);\n  &:focus {\n    border-color: @color;\n    outline: 0;\n    .box-shadow(~\"inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px @{color-rgba}\");\n  }\n}\n\n// Form control sizing\n//\n// Relative text size, padding, and border-radii changes for form controls. For\n// horizontal sizing, wrap controls in the predefined grid classes. `<select>`\n// element gets special love because it's special, and that's a fact!\n.input-size(@input-height; @padding-vertical; @padding-horizontal; @font-size; @line-height; @border-radius) {\n  height: @input-height;\n  padding: @padding-vertical @padding-horizontal;\n  font-size: @font-size;\n  line-height: @line-height;\n  border-radius: @border-radius;\n\n  select& {\n    height: @input-height;\n    line-height: @input-height;\n  }\n\n  textarea&,\n  select[multiple]& {\n    height: auto;\n  }\n}\n","//\n// Buttons\n// --------------------------------------------------\n\n\n// Base styles\n// --------------------------------------------------\n\n.btn {\n  display: inline-block;\n  margin-bottom: 0; // For input.btn\n  font-weight: @btn-font-weight;\n  text-align: center;\n  vertical-align: middle;\n  touch-action: manipulation;\n  cursor: pointer;\n  background-image: none; // Reset unusual Firefox-on-Android default style; see https://github.com/necolas/normalize.css/issues/214\n  border: 1px solid transparent;\n  white-space: nowrap;\n  .button-size(@padding-base-vertical; @padding-base-horizontal; @font-size-base; @line-height-base; @btn-border-radius-base);\n  .user-select(none);\n\n  &,\n  &:active,\n  &.active {\n    &:focus,\n    &.focus {\n      .tab-focus();\n    }\n  }\n\n  &:hover,\n  &:focus,\n  &.focus {\n    color: @btn-default-color;\n    text-decoration: none;\n  }\n\n  &:active,\n  &.active {\n    outline: 0;\n    background-image: none;\n    .box-shadow(inset 0 3px 5px rgba(0,0,0,.125));\n  }\n\n  &.disabled,\n  &[disabled],\n  fieldset[disabled] & {\n    cursor: @cursor-disabled;\n    .opacity(.65);\n    .box-shadow(none);\n  }\n\n  a& {\n    &.disabled,\n    fieldset[disabled] & {\n      pointer-events: none; // Future-proof disabling of clicks on `<a>` elements\n    }\n  }\n}\n\n\n// Alternate buttons\n// --------------------------------------------------\n\n.btn-default {\n  .button-variant(@btn-default-color; @btn-default-bg; @btn-default-border);\n}\n.btn-primary {\n  .button-variant(@btn-primary-color; @btn-primary-bg; @btn-primary-border);\n}\n// Success appears as green\n.btn-success {\n  .button-variant(@btn-success-color; @btn-success-bg; @btn-success-border);\n}\n// Info appears as blue-green\n.btn-info {\n  .button-variant(@btn-info-color; @btn-info-bg; @btn-info-border);\n}\n// Warning appears as orange\n.btn-warning {\n  .button-variant(@btn-warning-color; @btn-warning-bg; @btn-warning-border);\n}\n// Danger and error appear as red\n.btn-danger {\n  .button-variant(@btn-danger-color; @btn-danger-bg; @btn-danger-border);\n}\n\n\n// Link buttons\n// -------------------------\n\n// Make a button look and behave like a link\n.btn-link {\n  color: @link-color;\n  font-weight: normal;\n  border-radius: 0;\n\n  &,\n  &:active,\n  &.active,\n  &[disabled],\n  fieldset[disabled] & {\n    background-color: transparent;\n    .box-shadow(none);\n  }\n  &,\n  &:hover,\n  &:focus,\n  &:active {\n    border-color: transparent;\n  }\n  &:hover,\n  &:focus {\n    color: @link-hover-color;\n    text-decoration: @link-hover-decoration;\n    background-color: transparent;\n  }\n  &[disabled],\n  fieldset[disabled] & {\n    &:hover,\n    &:focus {\n      color: @btn-link-disabled-color;\n      text-decoration: none;\n    }\n  }\n}\n\n\n// Button Sizes\n// --------------------------------------------------\n\n.btn-lg {\n  // line-height: ensure even-numbered height of button next to large input\n  .button-size(@padding-large-vertical; @padding-large-horizontal; @font-size-large; @line-height-large; @btn-border-radius-large);\n}\n.btn-sm {\n  // line-height: ensure proper height of button next to small input\n  .button-size(@padding-small-vertical; @padding-small-horizontal; @font-size-small; @line-height-small; @btn-border-radius-small);\n}\n.btn-xs {\n  .button-size(@padding-xs-vertical; @padding-xs-horizontal; @font-size-small; @line-height-small; @btn-border-radius-small);\n}\n\n\n// Block button\n// --------------------------------------------------\n\n.btn-block {\n  display: block;\n  width: 100%;\n}\n\n// Vertically space out multiple block buttons\n.btn-block + .btn-block {\n  margin-top: 5px;\n}\n\n// Specificity overrides\ninput[type=\"submit\"],\ninput[type=\"reset\"],\ninput[type=\"button\"] {\n  &.btn-block {\n    width: 100%;\n  }\n}\n","// Button variants\n//\n// Easily pump out default styles, as well as :hover, :focus, :active,\n// and disabled options for all buttons\n\n.button-variant(@color; @background; @border) {\n  color: @color;\n  background-color: @background;\n  border-color: @border;\n\n  &:focus,\n  &.focus {\n    color: @color;\n    background-color: darken(@background, 10%);\n        border-color: darken(@border, 25%);\n  }\n  &:hover {\n    color: @color;\n    background-color: darken(@background, 10%);\n        border-color: darken(@border, 12%);\n  }\n  &:active,\n  &.active,\n  .open > .dropdown-toggle& {\n    color: @color;\n    background-color: darken(@background, 10%);\n        border-color: darken(@border, 12%);\n\n    &:hover,\n    &:focus,\n    &.focus {\n      color: @color;\n      background-color: darken(@background, 17%);\n          border-color: darken(@border, 25%);\n    }\n  }\n  &:active,\n  &.active,\n  .open > .dropdown-toggle& {\n    background-image: none;\n  }\n  &.disabled,\n  &[disabled],\n  fieldset[disabled] & {\n    &:hover,\n    &:focus,\n    &.focus {\n      background-color: @background;\n          border-color: @border;\n    }\n  }\n\n  .badge {\n    color: @background;\n    background-color: @color;\n  }\n}\n\n// Button sizes\n.button-size(@padding-vertical; @padding-horizontal; @font-size; @line-height; @border-radius) {\n  padding: @padding-vertical @padding-horizontal;\n  font-size: @font-size;\n  line-height: @line-height;\n  border-radius: @border-radius;\n}\n","// Opacity\n\n.opacity(@opacity) {\n  opacity: @opacity;\n  // IE8 filter\n  @opacity-ie: (@opacity * 100);\n  filter: ~\"alpha(opacity=@{opacity-ie})\";\n}\n","//\n// Component animations\n// --------------------------------------------------\n\n// Heads up!\n//\n// We don't use the `.opacity()` mixin here since it causes a bug with text\n// fields in IE7-8. Source: https://github.com/twbs/bootstrap/pull/3552.\n\n.fade {\n  opacity: 0;\n  .transition(opacity .15s linear);\n  &.in {\n    opacity: 1;\n  }\n}\n\n.collapse {\n  display: none;\n\n  &.in      { display: block; }\n  tr&.in    { display: table-row; }\n  tbody&.in { display: table-row-group; }\n}\n\n.collapsing {\n  position: relative;\n  height: 0;\n  overflow: hidden;\n  .transition-property(~\"height, visibility\");\n  .transition-duration(.35s);\n  .transition-timing-function(ease);\n}\n","//\n// Dropdown menus\n// --------------------------------------------------\n\n\n// Dropdown arrow/caret\n.caret {\n  display: inline-block;\n  width: 0;\n  height: 0;\n  margin-left: 2px;\n  vertical-align: middle;\n  border-top:   @caret-width-base dashed;\n  border-top:   @caret-width-base solid ~\"\\9\"; // IE8\n  border-right: @caret-width-base solid transparent;\n  border-left:  @caret-width-base solid transparent;\n}\n\n// The dropdown wrapper (div)\n.dropup,\n.dropdown {\n  position: relative;\n}\n\n// Prevent the focus on the dropdown toggle when closing dropdowns\n.dropdown-toggle:focus {\n  outline: 0;\n}\n\n// The dropdown menu (ul)\n.dropdown-menu {\n  position: absolute;\n  top: 100%;\n  left: 0;\n  z-index: @zindex-dropdown;\n  display: none; // none by default, but block on \"open\" of the menu\n  float: left;\n  min-width: 160px;\n  padding: 5px 0;\n  margin: 2px 0 0; // override default ul\n  list-style: none;\n  font-size: @font-size-base;\n  text-align: left; // Ensures proper alignment if parent has it changed (e.g., modal footer)\n  background-color: @dropdown-bg;\n  border: 1px solid @dropdown-fallback-border; // IE8 fallback\n  border: 1px solid @dropdown-border;\n  border-radius: @border-radius-base;\n  .box-shadow(0 6px 12px rgba(0,0,0,.175));\n  background-clip: padding-box;\n\n  // Aligns the dropdown menu to right\n  //\n  // Deprecated as of 3.1.0 in favor of `.dropdown-menu-[dir]`\n  &.pull-right {\n    right: 0;\n    left: auto;\n  }\n\n  // Dividers (basically an hr) within the dropdown\n  .divider {\n    .nav-divider(@dropdown-divider-bg);\n  }\n\n  // Links within the dropdown menu\n  > li > a {\n    display: block;\n    padding: 3px 20px;\n    clear: both;\n    font-weight: normal;\n    line-height: @line-height-base;\n    color: @dropdown-link-color;\n    white-space: nowrap; // prevent links from randomly breaking onto new lines\n  }\n}\n\n// Hover/Focus state\n.dropdown-menu > li > a {\n  &:hover,\n  &:focus {\n    text-decoration: none;\n    color: @dropdown-link-hover-color;\n    background-color: @dropdown-link-hover-bg;\n  }\n}\n\n// Active state\n.dropdown-menu > .active > a {\n  &,\n  &:hover,\n  &:focus {\n    color: @dropdown-link-active-color;\n    text-decoration: none;\n    outline: 0;\n    background-color: @dropdown-link-active-bg;\n  }\n}\n\n// Disabled state\n//\n// Gray out text and ensure the hover/focus state remains gray\n\n.dropdown-menu > .disabled > a {\n  &,\n  &:hover,\n  &:focus {\n    color: @dropdown-link-disabled-color;\n  }\n\n  // Nuke hover/focus effects\n  &:hover,\n  &:focus {\n    text-decoration: none;\n    background-color: transparent;\n    background-image: none; // Remove CSS gradient\n    .reset-filter();\n    cursor: @cursor-disabled;\n  }\n}\n\n// Open state for the dropdown\n.open {\n  // Show the menu\n  > .dropdown-menu {\n    display: block;\n  }\n\n  // Remove the outline when :focus is triggered\n  > a {\n    outline: 0;\n  }\n}\n\n// Menu positioning\n//\n// Add extra class to `.dropdown-menu` to flip the alignment of the dropdown\n// menu with the parent.\n.dropdown-menu-right {\n  left: auto; // Reset the default from `.dropdown-menu`\n  right: 0;\n}\n// With v3, we enabled auto-flipping if you have a dropdown within a right\n// aligned nav component. To enable the undoing of that, we provide an override\n// to restore the default dropdown menu alignment.\n//\n// This is only for left-aligning a dropdown menu within a `.navbar-right` or\n// `.pull-right` nav component.\n.dropdown-menu-left {\n  left: 0;\n  right: auto;\n}\n\n// Dropdown section headers\n.dropdown-header {\n  display: block;\n  padding: 3px 20px;\n  font-size: @font-size-small;\n  line-height: @line-height-base;\n  color: @dropdown-header-color;\n  white-space: nowrap; // as with > li > a\n}\n\n// Backdrop to catch body clicks on mobile, etc.\n.dropdown-backdrop {\n  position: fixed;\n  left: 0;\n  right: 0;\n  bottom: 0;\n  top: 0;\n  z-index: (@zindex-dropdown - 10);\n}\n\n// Right aligned dropdowns\n.pull-right > .dropdown-menu {\n  right: 0;\n  left: auto;\n}\n\n// Allow for dropdowns to go bottom up (aka, dropup-menu)\n//\n// Just add .dropup after the standard .dropdown class and you're set, bro.\n// TODO: abstract this so that the navbar fixed styles are not placed here?\n\n.dropup,\n.navbar-fixed-bottom .dropdown {\n  // Reverse the caret\n  .caret {\n    border-top: 0;\n    border-bottom: @caret-width-base dashed;\n    border-bottom: @caret-width-base solid ~\"\\9\"; // IE8\n    content: \"\";\n  }\n  // Different positioning for bottom up menu\n  .dropdown-menu {\n    top: auto;\n    bottom: 100%;\n    margin-bottom: 2px;\n  }\n}\n\n\n// Component alignment\n//\n// Reiterate per navbar.less and the modified component alignment there.\n\n@media (min-width: @grid-float-breakpoint) {\n  .navbar-right {\n    .dropdown-menu {\n      .dropdown-menu-right();\n    }\n    // Necessary for overrides of the default right aligned menu.\n    // Will remove come v4 in all likelihood.\n    .dropdown-menu-left {\n      .dropdown-menu-left();\n    }\n  }\n}\n","// Horizontal dividers\n//\n// Dividers (basically an hr) within dropdowns and nav lists\n\n.nav-divider(@color: #e5e5e5) {\n  height: 1px;\n  margin: ((@line-height-computed / 2) - 1) 0;\n  overflow: hidden;\n  background-color: @color;\n}\n","// Reset filters for IE\n//\n// When you need to remove a gradient background, do not forget to use this to reset\n// the IE filter for IE9 and below.\n\n.reset-filter() {\n  filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(enabled = false)\"));\n}\n","//\n// Button groups\n// --------------------------------------------------\n\n// Make the div behave like a button\n.btn-group,\n.btn-group-vertical {\n  position: relative;\n  display: inline-block;\n  vertical-align: middle; // match .btn alignment given font-size hack above\n  > .btn {\n    position: relative;\n    float: left;\n    // Bring the \"active\" button to the front\n    &:hover,\n    &:focus,\n    &:active,\n    &.active {\n      z-index: 2;\n    }\n  }\n}\n\n// Prevent double borders when buttons are next to each other\n.btn-group {\n  .btn + .btn,\n  .btn + .btn-group,\n  .btn-group + .btn,\n  .btn-group + .btn-group {\n    margin-left: -1px;\n  }\n}\n\n// Optional: Group multiple button groups together for a toolbar\n.btn-toolbar {\n  margin-left: -5px; // Offset the first child's margin\n  &:extend(.clearfix all);\n\n  .btn,\n  .btn-group,\n  .input-group {\n    float: left;\n  }\n  > .btn,\n  > .btn-group,\n  > .input-group {\n    margin-left: 5px;\n  }\n}\n\n.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {\n  border-radius: 0;\n}\n\n// Set corners individual because sometimes a single button can be in a .btn-group and we need :first-child and :last-child to both match\n.btn-group > .btn:first-child {\n  margin-left: 0;\n  &:not(:last-child):not(.dropdown-toggle) {\n    .border-right-radius(0);\n  }\n}\n// Need .dropdown-toggle since :last-child doesn't apply, given that a .dropdown-menu is used immediately after it\n.btn-group > .btn:last-child:not(:first-child),\n.btn-group > .dropdown-toggle:not(:first-child) {\n  .border-left-radius(0);\n}\n\n// Custom edits for including btn-groups within btn-groups (useful for including dropdown buttons within a btn-group)\n.btn-group > .btn-group {\n  float: left;\n}\n.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {\n  border-radius: 0;\n}\n.btn-group > .btn-group:first-child:not(:last-child) {\n  > .btn:last-child,\n  > .dropdown-toggle {\n    .border-right-radius(0);\n  }\n}\n.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {\n  .border-left-radius(0);\n}\n\n// On active and open, don't show outline\n.btn-group .dropdown-toggle:active,\n.btn-group.open .dropdown-toggle {\n  outline: 0;\n}\n\n\n// Sizing\n//\n// Remix the default button sizing classes into new ones for easier manipulation.\n\n.btn-group-xs > .btn { &:extend(.btn-xs); }\n.btn-group-sm > .btn { &:extend(.btn-sm); }\n.btn-group-lg > .btn { &:extend(.btn-lg); }\n\n\n// Split button dropdowns\n// ----------------------\n\n// Give the line between buttons some depth\n.btn-group > .btn + .dropdown-toggle {\n  padding-left: 8px;\n  padding-right: 8px;\n}\n.btn-group > .btn-lg + .dropdown-toggle {\n  padding-left: 12px;\n  padding-right: 12px;\n}\n\n// The clickable button for toggling the menu\n// Remove the gradient and set the same inset shadow as the :active state\n.btn-group.open .dropdown-toggle {\n  .box-shadow(inset 0 3px 5px rgba(0,0,0,.125));\n\n  // Show no shadow for `.btn-link` since it has no other button styles.\n  &.btn-link {\n    .box-shadow(none);\n  }\n}\n\n\n// Reposition the caret\n.btn .caret {\n  margin-left: 0;\n}\n// Carets in other button sizes\n.btn-lg .caret {\n  border-width: @caret-width-large @caret-width-large 0;\n  border-bottom-width: 0;\n}\n// Upside down carets for .dropup\n.dropup .btn-lg .caret {\n  border-width: 0 @caret-width-large @caret-width-large;\n}\n\n\n// Vertical button groups\n// ----------------------\n\n.btn-group-vertical {\n  > .btn,\n  > .btn-group,\n  > .btn-group > .btn {\n    display: block;\n    float: none;\n    width: 100%;\n    max-width: 100%;\n  }\n\n  // Clear floats so dropdown menus can be properly placed\n  > .btn-group {\n    &:extend(.clearfix all);\n    > .btn {\n      float: none;\n    }\n  }\n\n  > .btn + .btn,\n  > .btn + .btn-group,\n  > .btn-group + .btn,\n  > .btn-group + .btn-group {\n    margin-top: -1px;\n    margin-left: 0;\n  }\n}\n\n.btn-group-vertical > .btn {\n  &:not(:first-child):not(:last-child) {\n    border-radius: 0;\n  }\n  &:first-child:not(:last-child) {\n    .border-top-radius(@btn-border-radius-base);\n    .border-bottom-radius(0);\n  }\n  &:last-child:not(:first-child) {\n    .border-top-radius(0);\n    .border-bottom-radius(@btn-border-radius-base);\n  }\n}\n.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {\n  border-radius: 0;\n}\n.btn-group-vertical > .btn-group:first-child:not(:last-child) {\n  > .btn:last-child,\n  > .dropdown-toggle {\n    .border-bottom-radius(0);\n  }\n}\n.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {\n  .border-top-radius(0);\n}\n\n\n// Justified button groups\n// ----------------------\n\n.btn-group-justified {\n  display: table;\n  width: 100%;\n  table-layout: fixed;\n  border-collapse: separate;\n  > .btn,\n  > .btn-group {\n    float: none;\n    display: table-cell;\n    width: 1%;\n  }\n  > .btn-group .btn {\n    width: 100%;\n  }\n\n  > .btn-group .dropdown-menu {\n    left: auto;\n  }\n}\n\n\n// Checkbox and radio options\n//\n// In order to support the browser's form validation feedback, powered by the\n// `required` attribute, we have to \"hide\" the inputs via `clip`. We cannot use\n// `display: none;` or `visibility: hidden;` as that also hides the popover.\n// Simply visually hiding the inputs via `opacity` would leave them clickable in\n// certain cases which is prevented by using `clip` and `pointer-events`.\n// This way, we ensure a DOM element is visible to position the popover from.\n//\n// See https://github.com/twbs/bootstrap/pull/12794 and\n// https://github.com/twbs/bootstrap/pull/14559 for more information.\n\n[data-toggle=\"buttons\"] {\n  > .btn,\n  > .btn-group > .btn {\n    input[type=\"radio\"],\n    input[type=\"checkbox\"] {\n      position: absolute;\n      clip: rect(0,0,0,0);\n      pointer-events: none;\n    }\n  }\n}\n","// Single side border-radius\n\n.border-top-radius(@radius) {\n  border-top-right-radius: @radius;\n   border-top-left-radius: @radius;\n}\n.border-right-radius(@radius) {\n  border-bottom-right-radius: @radius;\n     border-top-right-radius: @radius;\n}\n.border-bottom-radius(@radius) {\n  border-bottom-right-radius: @radius;\n   border-bottom-left-radius: @radius;\n}\n.border-left-radius(@radius) {\n  border-bottom-left-radius: @radius;\n     border-top-left-radius: @radius;\n}\n","//\n// Input groups\n// --------------------------------------------------\n\n// Base styles\n// -------------------------\n.input-group {\n  position: relative; // For dropdowns\n  display: table;\n  border-collapse: separate; // prevent input groups from inheriting border styles from table cells when placed within a table\n\n  // Undo padding and float of grid classes\n  &[class*=\"col-\"] {\n    float: none;\n    padding-left: 0;\n    padding-right: 0;\n  }\n\n  .form-control {\n    // Ensure that the input is always above the *appended* addon button for\n    // proper border colors.\n    position: relative;\n    z-index: 2;\n\n    // IE9 fubars the placeholder attribute in text inputs and the arrows on\n    // select elements in input groups. To fix it, we float the input. Details:\n    // https://github.com/twbs/bootstrap/issues/11561#issuecomment-28936855\n    float: left;\n\n    width: 100%;\n    margin-bottom: 0;\n\n    &:focus {\n      z-index: 3;\n    }\n  }\n}\n\n// Sizing options\n//\n// Remix the default form control sizing classes into new ones for easier\n// manipulation.\n\n.input-group-lg > .form-control,\n.input-group-lg > .input-group-addon,\n.input-group-lg > .input-group-btn > .btn {\n  .input-lg();\n}\n.input-group-sm > .form-control,\n.input-group-sm > .input-group-addon,\n.input-group-sm > .input-group-btn > .btn {\n  .input-sm();\n}\n\n\n// Display as table-cell\n// -------------------------\n.input-group-addon,\n.input-group-btn,\n.input-group .form-control {\n  display: table-cell;\n\n  &:not(:first-child):not(:last-child) {\n    border-radius: 0;\n  }\n}\n// Addon and addon wrapper for buttons\n.input-group-addon,\n.input-group-btn {\n  width: 1%;\n  white-space: nowrap;\n  vertical-align: middle; // Match the inputs\n}\n\n// Text input groups\n// -------------------------\n.input-group-addon {\n  padding: @padding-base-vertical @padding-base-horizontal;\n  font-size: @font-size-base;\n  font-weight: normal;\n  line-height: 1;\n  color: @input-color;\n  text-align: center;\n  background-color: @input-group-addon-bg;\n  border: 1px solid @input-group-addon-border-color;\n  border-radius: @input-border-radius;\n\n  // Sizing\n  &.input-sm {\n    padding: @padding-small-vertical @padding-small-horizontal;\n    font-size: @font-size-small;\n    border-radius: @input-border-radius-small;\n  }\n  &.input-lg {\n    padding: @padding-large-vertical @padding-large-horizontal;\n    font-size: @font-size-large;\n    border-radius: @input-border-radius-large;\n  }\n\n  // Nuke default margins from checkboxes and radios to vertically center within.\n  input[type=\"radio\"],\n  input[type=\"checkbox\"] {\n    margin-top: 0;\n  }\n}\n\n// Reset rounded corners\n.input-group .form-control:first-child,\n.input-group-addon:first-child,\n.input-group-btn:first-child > .btn,\n.input-group-btn:first-child > .btn-group > .btn,\n.input-group-btn:first-child > .dropdown-toggle,\n.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),\n.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {\n  .border-right-radius(0);\n}\n.input-group-addon:first-child {\n  border-right: 0;\n}\n.input-group .form-control:last-child,\n.input-group-addon:last-child,\n.input-group-btn:last-child > .btn,\n.input-group-btn:last-child > .btn-group > .btn,\n.input-group-btn:last-child > .dropdown-toggle,\n.input-group-btn:first-child > .btn:not(:first-child),\n.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {\n  .border-left-radius(0);\n}\n.input-group-addon:last-child {\n  border-left: 0;\n}\n\n// Button input groups\n// -------------------------\n.input-group-btn {\n  position: relative;\n  // Jankily prevent input button groups from wrapping with `white-space` and\n  // `font-size` in combination with `inline-block` on buttons.\n  font-size: 0;\n  white-space: nowrap;\n\n  // Negative margin for spacing, position for bringing hovered/focused/actived\n  // element above the siblings.\n  > .btn {\n    position: relative;\n    + .btn {\n      margin-left: -1px;\n    }\n    // Bring the \"active\" button to the front\n    &:hover,\n    &:focus,\n    &:active {\n      z-index: 2;\n    }\n  }\n\n  // Negative margin to only have a 1px border between the two\n  &:first-child {\n    > .btn,\n    > .btn-group {\n      margin-right: -1px;\n    }\n  }\n  &:last-child {\n    > .btn,\n    > .btn-group {\n      z-index: 2;\n      margin-left: -1px;\n    }\n  }\n}\n","//\n// Navs\n// --------------------------------------------------\n\n\n// Base class\n// --------------------------------------------------\n\n.nav {\n  margin-bottom: 0;\n  padding-left: 0; // Override default ul/ol\n  list-style: none;\n  &:extend(.clearfix all);\n\n  > li {\n    position: relative;\n    display: block;\n\n    > a {\n      position: relative;\n      display: block;\n      padding: @nav-link-padding;\n      &:hover,\n      &:focus {\n        text-decoration: none;\n        background-color: @nav-link-hover-bg;\n      }\n    }\n\n    // Disabled state sets text to gray and nukes hover/tab effects\n    &.disabled > a {\n      color: @nav-disabled-link-color;\n\n      &:hover,\n      &:focus {\n        color: @nav-disabled-link-hover-color;\n        text-decoration: none;\n        background-color: transparent;\n        cursor: @cursor-disabled;\n      }\n    }\n  }\n\n  // Open dropdowns\n  .open > a {\n    &,\n    &:hover,\n    &:focus {\n      background-color: @nav-link-hover-bg;\n      border-color: @link-color;\n    }\n  }\n\n  // Nav dividers (deprecated with v3.0.1)\n  //\n  // This should have been removed in v3 with the dropping of `.nav-list`, but\n  // we missed it. We don't currently support this anywhere, but in the interest\n  // of maintaining backward compatibility in case you use it, it's deprecated.\n  .nav-divider {\n    .nav-divider();\n  }\n\n  // Prevent IE8 from misplacing imgs\n  //\n  // See https://github.com/h5bp/html5-boilerplate/issues/984#issuecomment-3985989\n  > li > a > img {\n    max-width: none;\n  }\n}\n\n\n// Tabs\n// -------------------------\n\n// Give the tabs something to sit on\n.nav-tabs {\n  border-bottom: 1px solid @nav-tabs-border-color;\n  > li {\n    float: left;\n    // Make the list-items overlay the bottom border\n    margin-bottom: -1px;\n\n    // Actual tabs (as links)\n    > a {\n      margin-right: 2px;\n      line-height: @line-height-base;\n      border: 1px solid transparent;\n      border-radius: @border-radius-base @border-radius-base 0 0;\n      &:hover {\n        border-color: @nav-tabs-link-hover-border-color @nav-tabs-link-hover-border-color @nav-tabs-border-color;\n      }\n    }\n\n    // Active state, and its :hover to override normal :hover\n    &.active > a {\n      &,\n      &:hover,\n      &:focus {\n        color: @nav-tabs-active-link-hover-color;\n        background-color: @nav-tabs-active-link-hover-bg;\n        border: 1px solid @nav-tabs-active-link-hover-border-color;\n        border-bottom-color: transparent;\n        cursor: default;\n      }\n    }\n  }\n  // pulling this in mainly for less shorthand\n  &.nav-justified {\n    .nav-justified();\n    .nav-tabs-justified();\n  }\n}\n\n\n// Pills\n// -------------------------\n.nav-pills {\n  > li {\n    float: left;\n\n    // Links rendered as pills\n    > a {\n      border-radius: @nav-pills-border-radius;\n    }\n    + li {\n      margin-left: 2px;\n    }\n\n    // Active state\n    &.active > a {\n      &,\n      &:hover,\n      &:focus {\n        color: @nav-pills-active-link-hover-color;\n        background-color: @nav-pills-active-link-hover-bg;\n      }\n    }\n  }\n}\n\n\n// Stacked pills\n.nav-stacked {\n  > li {\n    float: none;\n    + li {\n      margin-top: 2px;\n      margin-left: 0; // no need for this gap between nav items\n    }\n  }\n}\n\n\n// Nav variations\n// --------------------------------------------------\n\n// Justified nav links\n// -------------------------\n\n.nav-justified {\n  width: 100%;\n\n  > li {\n    float: none;\n    > a {\n      text-align: center;\n      margin-bottom: 5px;\n    }\n  }\n\n  > .dropdown .dropdown-menu {\n    top: auto;\n    left: auto;\n  }\n\n  @media (min-width: @screen-sm-min) {\n    > li {\n      display: table-cell;\n      width: 1%;\n      > a {\n        margin-bottom: 0;\n      }\n    }\n  }\n}\n\n// Move borders to anchors instead of bottom of list\n//\n// Mixin for adding on top the shared `.nav-justified` styles for our tabs\n.nav-tabs-justified {\n  border-bottom: 0;\n\n  > li > a {\n    // Override margin from .nav-tabs\n    margin-right: 0;\n    border-radius: @border-radius-base;\n  }\n\n  > .active > a,\n  > .active > a:hover,\n  > .active > a:focus {\n    border: 1px solid @nav-tabs-justified-link-border-color;\n  }\n\n  @media (min-width: @screen-sm-min) {\n    > li > a {\n      border-bottom: 1px solid @nav-tabs-justified-link-border-color;\n      border-radius: @border-radius-base @border-radius-base 0 0;\n    }\n    > .active > a,\n    > .active > a:hover,\n    > .active > a:focus {\n      border-bottom-color: @nav-tabs-justified-active-link-border-color;\n    }\n  }\n}\n\n\n// Tabbable tabs\n// -------------------------\n\n// Hide tabbable panes to start, show them when `.active`\n.tab-content {\n  > .tab-pane {\n    display: none;\n  }\n  > .active {\n    display: block;\n  }\n}\n\n\n// Dropdowns\n// -------------------------\n\n// Specific dropdowns\n.nav-tabs .dropdown-menu {\n  // make dropdown border overlap tab border\n  margin-top: -1px;\n  // Remove the top rounded corners here since there is a hard edge above the menu\n  .border-top-radius(0);\n}\n","//\n// Navbars\n// --------------------------------------------------\n\n\n// Wrapper and base class\n//\n// Provide a static navbar from which we expand to create full-width, fixed, and\n// other navbar variations.\n\n.navbar {\n  position: relative;\n  min-height: @navbar-height; // Ensure a navbar always shows (e.g., without a .navbar-brand in collapsed mode)\n  margin-bottom: @navbar-margin-bottom;\n  border: 1px solid transparent;\n\n  // Prevent floats from breaking the navbar\n  &:extend(.clearfix all);\n\n  @media (min-width: @grid-float-breakpoint) {\n    border-radius: @navbar-border-radius;\n  }\n}\n\n\n// Navbar heading\n//\n// Groups `.navbar-brand` and `.navbar-toggle` into a single component for easy\n// styling of responsive aspects.\n\n.navbar-header {\n  &:extend(.clearfix all);\n\n  @media (min-width: @grid-float-breakpoint) {\n    float: left;\n  }\n}\n\n\n// Navbar collapse (body)\n//\n// Group your navbar content into this for easy collapsing and expanding across\n// various device sizes. By default, this content is collapsed when <768px, but\n// will expand past that for a horizontal display.\n//\n// To start (on mobile devices) the navbar links, forms, and buttons are stacked\n// vertically and include a `max-height` to overflow in case you have too much\n// content for the user's viewport.\n\n.navbar-collapse {\n  overflow-x: visible;\n  padding-right: @navbar-padding-horizontal;\n  padding-left:  @navbar-padding-horizontal;\n  border-top: 1px solid transparent;\n  box-shadow: inset 0 1px 0 rgba(255,255,255,.1);\n  &:extend(.clearfix all);\n  -webkit-overflow-scrolling: touch;\n\n  &.in {\n    overflow-y: auto;\n  }\n\n  @media (min-width: @grid-float-breakpoint) {\n    width: auto;\n    border-top: 0;\n    box-shadow: none;\n\n    &.collapse {\n      display: block !important;\n      height: auto !important;\n      padding-bottom: 0; // Override default setting\n      overflow: visible !important;\n    }\n\n    &.in {\n      overflow-y: visible;\n    }\n\n    // Undo the collapse side padding for navbars with containers to ensure\n    // alignment of right-aligned contents.\n    .navbar-fixed-top &,\n    .navbar-static-top &,\n    .navbar-fixed-bottom & {\n      padding-left: 0;\n      padding-right: 0;\n    }\n  }\n}\n\n.navbar-fixed-top,\n.navbar-fixed-bottom {\n  .navbar-collapse {\n    max-height: @navbar-collapse-max-height;\n\n    @media (max-device-width: @screen-xs-min) and (orientation: landscape) {\n      max-height: 200px;\n    }\n  }\n}\n\n\n// Both navbar header and collapse\n//\n// When a container is present, change the behavior of the header and collapse.\n\n.container,\n.container-fluid {\n  > .navbar-header,\n  > .navbar-collapse {\n    margin-right: -@navbar-padding-horizontal;\n    margin-left:  -@navbar-padding-horizontal;\n\n    @media (min-width: @grid-float-breakpoint) {\n      margin-right: 0;\n      margin-left:  0;\n    }\n  }\n}\n\n\n//\n// Navbar alignment options\n//\n// Display the navbar across the entirety of the page or fixed it to the top or\n// bottom of the page.\n\n// Static top (unfixed, but 100% wide) navbar\n.navbar-static-top {\n  z-index: @zindex-navbar;\n  border-width: 0 0 1px;\n\n  @media (min-width: @grid-float-breakpoint) {\n    border-radius: 0;\n  }\n}\n\n// Fix the top/bottom navbars when screen real estate supports it\n.navbar-fixed-top,\n.navbar-fixed-bottom {\n  position: fixed;\n  right: 0;\n  left: 0;\n  z-index: @zindex-navbar-fixed;\n\n  // Undo the rounded corners\n  @media (min-width: @grid-float-breakpoint) {\n    border-radius: 0;\n  }\n}\n.navbar-fixed-top {\n  top: 0;\n  border-width: 0 0 1px;\n}\n.navbar-fixed-bottom {\n  bottom: 0;\n  margin-bottom: 0; // override .navbar defaults\n  border-width: 1px 0 0;\n}\n\n\n// Brand/project name\n\n.navbar-brand {\n  float: left;\n  padding: @navbar-padding-vertical @navbar-padding-horizontal;\n  font-size: @font-size-large;\n  line-height: @line-height-computed;\n  height: @navbar-height;\n\n  &:hover,\n  &:focus {\n    text-decoration: none;\n  }\n\n  > img {\n    display: block;\n  }\n\n  @media (min-width: @grid-float-breakpoint) {\n    .navbar > .container &,\n    .navbar > .container-fluid & {\n      margin-left: -@navbar-padding-horizontal;\n    }\n  }\n}\n\n\n// Navbar toggle\n//\n// Custom button for toggling the `.navbar-collapse`, powered by the collapse\n// JavaScript plugin.\n\n.navbar-toggle {\n  position: relative;\n  float: right;\n  margin-right: @navbar-padding-horizontal;\n  padding: 9px 10px;\n  .navbar-vertical-align(34px);\n  background-color: transparent;\n  background-image: none; // Reset unusual Firefox-on-Android default style; see https://github.com/necolas/normalize.css/issues/214\n  border: 1px solid transparent;\n  border-radius: @border-radius-base;\n\n  // We remove the `outline` here, but later compensate by attaching `:hover`\n  // styles to `:focus`.\n  &:focus {\n    outline: 0;\n  }\n\n  // Bars\n  .icon-bar {\n    display: block;\n    width: 22px;\n    height: 2px;\n    border-radius: 1px;\n  }\n  .icon-bar + .icon-bar {\n    margin-top: 4px;\n  }\n\n  @media (min-width: @grid-float-breakpoint) {\n    display: none;\n  }\n}\n\n\n// Navbar nav links\n//\n// Builds on top of the `.nav` components with its own modifier class to make\n// the nav the full height of the horizontal nav (above 768px).\n\n.navbar-nav {\n  margin: (@navbar-padding-vertical / 2) -@navbar-padding-horizontal;\n\n  > li > a {\n    padding-top:    10px;\n    padding-bottom: 10px;\n    line-height: @line-height-computed;\n  }\n\n  @media (max-width: @grid-float-breakpoint-max) {\n    // Dropdowns get custom display when collapsed\n    .open .dropdown-menu {\n      position: static;\n      float: none;\n      width: auto;\n      margin-top: 0;\n      background-color: transparent;\n      border: 0;\n      box-shadow: none;\n      > li > a,\n      .dropdown-header {\n        padding: 5px 15px 5px 25px;\n      }\n      > li > a {\n        line-height: @line-height-computed;\n        &:hover,\n        &:focus {\n          background-image: none;\n        }\n      }\n    }\n  }\n\n  // Uncollapse the nav\n  @media (min-width: @grid-float-breakpoint) {\n    float: left;\n    margin: 0;\n\n    > li {\n      float: left;\n      > a {\n        padding-top:    @navbar-padding-vertical;\n        padding-bottom: @navbar-padding-vertical;\n      }\n    }\n  }\n}\n\n\n// Navbar form\n//\n// Extension of the `.form-inline` with some extra flavor for optimum display in\n// our navbars.\n\n.navbar-form {\n  margin-left: -@navbar-padding-horizontal;\n  margin-right: -@navbar-padding-horizontal;\n  padding: 10px @navbar-padding-horizontal;\n  border-top: 1px solid transparent;\n  border-bottom: 1px solid transparent;\n  @shadow: inset 0 1px 0 rgba(255,255,255,.1), 0 1px 0 rgba(255,255,255,.1);\n  .box-shadow(@shadow);\n\n  // Mixin behavior for optimum display\n  .form-inline();\n\n  .form-group {\n    @media (max-width: @grid-float-breakpoint-max) {\n      margin-bottom: 5px;\n\n      &:last-child {\n        margin-bottom: 0;\n      }\n    }\n  }\n\n  // Vertically center in expanded, horizontal navbar\n  .navbar-vertical-align(@input-height-base);\n\n  // Undo 100% width for pull classes\n  @media (min-width: @grid-float-breakpoint) {\n    width: auto;\n    border: 0;\n    margin-left: 0;\n    margin-right: 0;\n    padding-top: 0;\n    padding-bottom: 0;\n    .box-shadow(none);\n  }\n}\n\n\n// Dropdown menus\n\n// Menu position and menu carets\n.navbar-nav > li > .dropdown-menu {\n  margin-top: 0;\n  .border-top-radius(0);\n}\n// Menu position and menu caret support for dropups via extra dropup class\n.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {\n  margin-bottom: 0;\n  .border-top-radius(@navbar-border-radius);\n  .border-bottom-radius(0);\n}\n\n\n// Buttons in navbars\n//\n// Vertically center a button within a navbar (when *not* in a form).\n\n.navbar-btn {\n  .navbar-vertical-align(@input-height-base);\n\n  &.btn-sm {\n    .navbar-vertical-align(@input-height-small);\n  }\n  &.btn-xs {\n    .navbar-vertical-align(22);\n  }\n}\n\n\n// Text in navbars\n//\n// Add a class to make any element properly align itself vertically within the navbars.\n\n.navbar-text {\n  .navbar-vertical-align(@line-height-computed);\n\n  @media (min-width: @grid-float-breakpoint) {\n    float: left;\n    margin-left: @navbar-padding-horizontal;\n    margin-right: @navbar-padding-horizontal;\n  }\n}\n\n\n// Component alignment\n//\n// Repurpose the pull utilities as their own navbar utilities to avoid specificity\n// issues with parents and chaining. Only do this when the navbar is uncollapsed\n// though so that navbar contents properly stack and align in mobile.\n//\n// Declared after the navbar components to ensure more specificity on the margins.\n\n@media (min-width: @grid-float-breakpoint) {\n  .navbar-left  { .pull-left(); }\n  .navbar-right {\n    .pull-right();\n    margin-right: -@navbar-padding-horizontal;\n\n    ~ .navbar-right {\n      margin-right: 0;\n    }\n  }\n}\n\n\n// Alternate navbars\n// --------------------------------------------------\n\n// Default navbar\n.navbar-default {\n  background-color: @navbar-default-bg;\n  border-color: @navbar-default-border;\n\n  .navbar-brand {\n    color: @navbar-default-brand-color;\n    &:hover,\n    &:focus {\n      color: @navbar-default-brand-hover-color;\n      background-color: @navbar-default-brand-hover-bg;\n    }\n  }\n\n  .navbar-text {\n    color: @navbar-default-color;\n  }\n\n  .navbar-nav {\n    > li > a {\n      color: @navbar-default-link-color;\n\n      &:hover,\n      &:focus {\n        color: @navbar-default-link-hover-color;\n        background-color: @navbar-default-link-hover-bg;\n      }\n    }\n    > .active > a {\n      &,\n      &:hover,\n      &:focus {\n        color: @navbar-default-link-active-color;\n        background-color: @navbar-default-link-active-bg;\n      }\n    }\n    > .disabled > a {\n      &,\n      &:hover,\n      &:focus {\n        color: @navbar-default-link-disabled-color;\n        background-color: @navbar-default-link-disabled-bg;\n      }\n    }\n  }\n\n  .navbar-toggle {\n    border-color: @navbar-default-toggle-border-color;\n    &:hover,\n    &:focus {\n      background-color: @navbar-default-toggle-hover-bg;\n    }\n    .icon-bar {\n      background-color: @navbar-default-toggle-icon-bar-bg;\n    }\n  }\n\n  .navbar-collapse,\n  .navbar-form {\n    border-color: @navbar-default-border;\n  }\n\n  // Dropdown menu items\n  .navbar-nav {\n    // Remove background color from open dropdown\n    > .open > a {\n      &,\n      &:hover,\n      &:focus {\n        background-color: @navbar-default-link-active-bg;\n        color: @navbar-default-link-active-color;\n      }\n    }\n\n    @media (max-width: @grid-float-breakpoint-max) {\n      // Dropdowns get custom display when collapsed\n      .open .dropdown-menu {\n        > li > a {\n          color: @navbar-default-link-color;\n          &:hover,\n          &:focus {\n            color: @navbar-default-link-hover-color;\n            background-color: @navbar-default-link-hover-bg;\n          }\n        }\n        > .active > a {\n          &,\n          &:hover,\n          &:focus {\n            color: @navbar-default-link-active-color;\n            background-color: @navbar-default-link-active-bg;\n          }\n        }\n        > .disabled > a {\n          &,\n          &:hover,\n          &:focus {\n            color: @navbar-default-link-disabled-color;\n            background-color: @navbar-default-link-disabled-bg;\n          }\n        }\n      }\n    }\n  }\n\n\n  // Links in navbars\n  //\n  // Add a class to ensure links outside the navbar nav are colored correctly.\n\n  .navbar-link {\n    color: @navbar-default-link-color;\n    &:hover {\n      color: @navbar-default-link-hover-color;\n    }\n  }\n\n  .btn-link {\n    color: @navbar-default-link-color;\n    &:hover,\n    &:focus {\n      color: @navbar-default-link-hover-color;\n    }\n    &[disabled],\n    fieldset[disabled] & {\n      &:hover,\n      &:focus {\n        color: @navbar-default-link-disabled-color;\n      }\n    }\n  }\n}\n\n// Inverse navbar\n\n.navbar-inverse {\n  background-color: @navbar-inverse-bg;\n  border-color: @navbar-inverse-border;\n\n  .navbar-brand {\n    color: @navbar-inverse-brand-color;\n    &:hover,\n    &:focus {\n      color: @navbar-inverse-brand-hover-color;\n      background-color: @navbar-inverse-brand-hover-bg;\n    }\n  }\n\n  .navbar-text {\n    color: @navbar-inverse-color;\n  }\n\n  .navbar-nav {\n    > li > a {\n      color: @navbar-inverse-link-color;\n\n      &:hover,\n      &:focus {\n        color: @navbar-inverse-link-hover-color;\n        background-color: @navbar-inverse-link-hover-bg;\n      }\n    }\n    > .active > a {\n      &,\n      &:hover,\n      &:focus {\n        color: @navbar-inverse-link-active-color;\n        background-color: @navbar-inverse-link-active-bg;\n      }\n    }\n    > .disabled > a {\n      &,\n      &:hover,\n      &:focus {\n        color: @navbar-inverse-link-disabled-color;\n        background-color: @navbar-inverse-link-disabled-bg;\n      }\n    }\n  }\n\n  // Darken the responsive nav toggle\n  .navbar-toggle {\n    border-color: @navbar-inverse-toggle-border-color;\n    &:hover,\n    &:focus {\n      background-color: @navbar-inverse-toggle-hover-bg;\n    }\n    .icon-bar {\n      background-color: @navbar-inverse-toggle-icon-bar-bg;\n    }\n  }\n\n  .navbar-collapse,\n  .navbar-form {\n    border-color: darken(@navbar-inverse-bg, 7%);\n  }\n\n  // Dropdowns\n  .navbar-nav {\n    > .open > a {\n      &,\n      &:hover,\n      &:focus {\n        background-color: @navbar-inverse-link-active-bg;\n        color: @navbar-inverse-link-active-color;\n      }\n    }\n\n    @media (max-width: @grid-float-breakpoint-max) {\n      // Dropdowns get custom display\n      .open .dropdown-menu {\n        > .dropdown-header {\n          border-color: @navbar-inverse-border;\n        }\n        .divider {\n          background-color: @navbar-inverse-border;\n        }\n        > li > a {\n          color: @navbar-inverse-link-color;\n          &:hover,\n          &:focus {\n            color: @navbar-inverse-link-hover-color;\n            background-color: @navbar-inverse-link-hover-bg;\n          }\n        }\n        > .active > a {\n          &,\n          &:hover,\n          &:focus {\n            color: @navbar-inverse-link-active-color;\n            background-color: @navbar-inverse-link-active-bg;\n          }\n        }\n        > .disabled > a {\n          &,\n          &:hover,\n          &:focus {\n            color: @navbar-inverse-link-disabled-color;\n            background-color: @navbar-inverse-link-disabled-bg;\n          }\n        }\n      }\n    }\n  }\n\n  .navbar-link {\n    color: @navbar-inverse-link-color;\n    &:hover {\n      color: @navbar-inverse-link-hover-color;\n    }\n  }\n\n  .btn-link {\n    color: @navbar-inverse-link-color;\n    &:hover,\n    &:focus {\n      color: @navbar-inverse-link-hover-color;\n    }\n    &[disabled],\n    fieldset[disabled] & {\n      &:hover,\n      &:focus {\n        color: @navbar-inverse-link-disabled-color;\n      }\n    }\n  }\n}\n","// Navbar vertical align\n//\n// Vertically center elements in the navbar.\n// Example: an element has a height of 30px, so write out `.navbar-vertical-align(30px);` to calculate the appropriate top margin.\n\n.navbar-vertical-align(@element-height) {\n  margin-top: ((@navbar-height - @element-height) / 2);\n  margin-bottom: ((@navbar-height - @element-height) / 2);\n}\n","//\n// Utility classes\n// --------------------------------------------------\n\n\n// Floats\n// -------------------------\n\n.clearfix {\n  .clearfix();\n}\n.center-block {\n  .center-block();\n}\n.pull-right {\n  float: right !important;\n}\n.pull-left {\n  float: left !important;\n}\n\n\n// Toggling content\n// -------------------------\n\n// Note: Deprecated .hide in favor of .hidden or .sr-only (as appropriate) in v3.0.1\n.hide {\n  display: none !important;\n}\n.show {\n  display: block !important;\n}\n.invisible {\n  visibility: hidden;\n}\n.text-hide {\n  .text-hide();\n}\n\n\n// Hide from screenreaders and browsers\n//\n// Credit: HTML5 Boilerplate\n\n.hidden {\n  display: none !important;\n}\n\n\n// For Affix plugin\n// -------------------------\n\n.affix {\n  position: fixed;\n}\n","//\n// Breadcrumbs\n// --------------------------------------------------\n\n\n.breadcrumb {\n  padding: @breadcrumb-padding-vertical @breadcrumb-padding-horizontal;\n  margin-bottom: @line-height-computed;\n  list-style: none;\n  background-color: @breadcrumb-bg;\n  border-radius: @border-radius-base;\n\n  > li {\n    display: inline-block;\n\n    + li:before {\n      content: \"@{breadcrumb-separator}\\00a0\"; // Unicode space added since inline-block means non-collapsing white-space\n      padding: 0 5px;\n      color: @breadcrumb-color;\n    }\n  }\n\n  > .active {\n    color: @breadcrumb-active-color;\n  }\n}\n","//\n// Pagination (multiple pages)\n// --------------------------------------------------\n.pagination {\n  display: inline-block;\n  padding-left: 0;\n  margin: @line-height-computed 0;\n  border-radius: @border-radius-base;\n\n  > li {\n    display: inline; // Remove list-style and block-level defaults\n    > a,\n    > span {\n      position: relative;\n      float: left; // Collapse white-space\n      padding: @padding-base-vertical @padding-base-horizontal;\n      line-height: @line-height-base;\n      text-decoration: none;\n      color: @pagination-color;\n      background-color: @pagination-bg;\n      border: 1px solid @pagination-border;\n      margin-left: -1px;\n    }\n    &:first-child {\n      > a,\n      > span {\n        margin-left: 0;\n        .border-left-radius(@border-radius-base);\n      }\n    }\n    &:last-child {\n      > a,\n      > span {\n        .border-right-radius(@border-radius-base);\n      }\n    }\n  }\n\n  > li > a,\n  > li > span {\n    &:hover,\n    &:focus {\n      z-index: 2;\n      color: @pagination-hover-color;\n      background-color: @pagination-hover-bg;\n      border-color: @pagination-hover-border;\n    }\n  }\n\n  > .active > a,\n  > .active > span {\n    &,\n    &:hover,\n    &:focus {\n      z-index: 3;\n      color: @pagination-active-color;\n      background-color: @pagination-active-bg;\n      border-color: @pagination-active-border;\n      cursor: default;\n    }\n  }\n\n  > .disabled {\n    > span,\n    > span:hover,\n    > span:focus,\n    > a,\n    > a:hover,\n    > a:focus {\n      color: @pagination-disabled-color;\n      background-color: @pagination-disabled-bg;\n      border-color: @pagination-disabled-border;\n      cursor: @cursor-disabled;\n    }\n  }\n}\n\n// Sizing\n// --------------------------------------------------\n\n// Large\n.pagination-lg {\n  .pagination-size(@padding-large-vertical; @padding-large-horizontal; @font-size-large; @line-height-large; @border-radius-large);\n}\n\n// Small\n.pagination-sm {\n  .pagination-size(@padding-small-vertical; @padding-small-horizontal; @font-size-small; @line-height-small; @border-radius-small);\n}\n","// Pagination\n\n.pagination-size(@padding-vertical; @padding-horizontal; @font-size; @line-height; @border-radius) {\n  > li {\n    > a,\n    > span {\n      padding: @padding-vertical @padding-horizontal;\n      font-size: @font-size;\n      line-height: @line-height;\n    }\n    &:first-child {\n      > a,\n      > span {\n        .border-left-radius(@border-radius);\n      }\n    }\n    &:last-child {\n      > a,\n      > span {\n        .border-right-radius(@border-radius);\n      }\n    }\n  }\n}\n","//\n// Pager pagination\n// --------------------------------------------------\n\n\n.pager {\n  padding-left: 0;\n  margin: @line-height-computed 0;\n  list-style: none;\n  text-align: center;\n  &:extend(.clearfix all);\n  li {\n    display: inline;\n    > a,\n    > span {\n      display: inline-block;\n      padding: 5px 14px;\n      background-color: @pager-bg;\n      border: 1px solid @pager-border;\n      border-radius: @pager-border-radius;\n    }\n\n    > a:hover,\n    > a:focus {\n      text-decoration: none;\n      background-color: @pager-hover-bg;\n    }\n  }\n\n  .next {\n    > a,\n    > span {\n      float: right;\n    }\n  }\n\n  .previous {\n    > a,\n    > span {\n      float: left;\n    }\n  }\n\n  .disabled {\n    > a,\n    > a:hover,\n    > a:focus,\n    > span {\n      color: @pager-disabled-color;\n      background-color: @pager-bg;\n      cursor: @cursor-disabled;\n    }\n  }\n}\n","//\n// Labels\n// --------------------------------------------------\n\n.label {\n  display: inline;\n  padding: .2em .6em .3em;\n  font-size: 75%;\n  font-weight: bold;\n  line-height: 1;\n  color: @label-color;\n  text-align: center;\n  white-space: nowrap;\n  vertical-align: baseline;\n  border-radius: .25em;\n\n  // Add hover effects, but only for links\n  a& {\n    &:hover,\n    &:focus {\n      color: @label-link-hover-color;\n      text-decoration: none;\n      cursor: pointer;\n    }\n  }\n\n  // Empty labels collapse automatically (not available in IE8)\n  &:empty {\n    display: none;\n  }\n\n  // Quick fix for labels in buttons\n  .btn & {\n    position: relative;\n    top: -1px;\n  }\n}\n\n// Colors\n// Contextual variations (linked labels get darker on :hover)\n\n.label-default {\n  .label-variant(@label-default-bg);\n}\n\n.label-primary {\n  .label-variant(@label-primary-bg);\n}\n\n.label-success {\n  .label-variant(@label-success-bg);\n}\n\n.label-info {\n  .label-variant(@label-info-bg);\n}\n\n.label-warning {\n  .label-variant(@label-warning-bg);\n}\n\n.label-danger {\n  .label-variant(@label-danger-bg);\n}\n","// Labels\n\n.label-variant(@color) {\n  background-color: @color;\n\n  &[href] {\n    &:hover,\n    &:focus {\n      background-color: darken(@color, 10%);\n    }\n  }\n}\n","//\n// Badges\n// --------------------------------------------------\n\n\n// Base class\n.badge {\n  display: inline-block;\n  min-width: 10px;\n  padding: 3px 7px;\n  font-size: @font-size-small;\n  font-weight: @badge-font-weight;\n  color: @badge-color;\n  line-height: @badge-line-height;\n  vertical-align: middle;\n  white-space: nowrap;\n  text-align: center;\n  background-color: @badge-bg;\n  border-radius: @badge-border-radius;\n\n  // Empty badges collapse automatically (not available in IE8)\n  &:empty {\n    display: none;\n  }\n\n  // Quick fix for badges in buttons\n  .btn & {\n    position: relative;\n    top: -1px;\n  }\n\n  .btn-xs &,\n  .btn-group-xs > .btn & {\n    top: 0;\n    padding: 1px 5px;\n  }\n\n  // Hover state, but only for links\n  a& {\n    &:hover,\n    &:focus {\n      color: @badge-link-hover-color;\n      text-decoration: none;\n      cursor: pointer;\n    }\n  }\n\n  // Account for badges in navs\n  .list-group-item.active > &,\n  .nav-pills > .active > a > & {\n    color: @badge-active-color;\n    background-color: @badge-active-bg;\n  }\n\n  .list-group-item > & {\n    float: right;\n  }\n\n  .list-group-item > & + & {\n    margin-right: 5px;\n  }\n\n  .nav-pills > li > a > & {\n    margin-left: 3px;\n  }\n}\n","//\n// Jumbotron\n// --------------------------------------------------\n\n\n.jumbotron {\n  padding-top:    @jumbotron-padding;\n  padding-bottom: @jumbotron-padding;\n  margin-bottom: @jumbotron-padding;\n  color: @jumbotron-color;\n  background-color: @jumbotron-bg;\n\n  h1,\n  .h1 {\n    color: @jumbotron-heading-color;\n  }\n\n  p {\n    margin-bottom: (@jumbotron-padding / 2);\n    font-size: @jumbotron-font-size;\n    font-weight: 200;\n  }\n\n  > hr {\n    border-top-color: darken(@jumbotron-bg, 10%);\n  }\n\n  .container &,\n  .container-fluid & {\n    border-radius: @border-radius-large; // Only round corners at higher resolutions if contained in a container\n    padding-left:  (@grid-gutter-width / 2);\n    padding-right: (@grid-gutter-width / 2);\n  }\n\n  .container {\n    max-width: 100%;\n  }\n\n  @media screen and (min-width: @screen-sm-min) {\n    padding-top:    (@jumbotron-padding * 1.6);\n    padding-bottom: (@jumbotron-padding * 1.6);\n\n    .container &,\n    .container-fluid & {\n      padding-left:  (@jumbotron-padding * 2);\n      padding-right: (@jumbotron-padding * 2);\n    }\n\n    h1,\n    .h1 {\n      font-size: @jumbotron-heading-font-size;\n    }\n  }\n}\n","//\n// Thumbnails\n// --------------------------------------------------\n\n\n// Mixin and adjust the regular image class\n.thumbnail {\n  display: block;\n  padding: @thumbnail-padding;\n  margin-bottom: @line-height-computed;\n  line-height: @line-height-base;\n  background-color: @thumbnail-bg;\n  border: 1px solid @thumbnail-border;\n  border-radius: @thumbnail-border-radius;\n  .transition(border .2s ease-in-out);\n\n  > img,\n  a > img {\n    &:extend(.img-responsive);\n    margin-left: auto;\n    margin-right: auto;\n  }\n\n  // Add a hover state for linked versions only\n  a&:hover,\n  a&:focus,\n  a&.active {\n    border-color: @link-color;\n  }\n\n  // Image captions\n  .caption {\n    padding: @thumbnail-caption-padding;\n    color: @thumbnail-caption-color;\n  }\n}\n","//\n// Alerts\n// --------------------------------------------------\n\n\n// Base styles\n// -------------------------\n\n.alert {\n  padding: @alert-padding;\n  margin-bottom: @line-height-computed;\n  border: 1px solid transparent;\n  border-radius: @alert-border-radius;\n\n  // Headings for larger alerts\n  h4 {\n    margin-top: 0;\n    // Specified for the h4 to prevent conflicts of changing @headings-color\n    color: inherit;\n  }\n\n  // Provide class for links that match alerts\n  .alert-link {\n    font-weight: @alert-link-font-weight;\n  }\n\n  // Improve alignment and spacing of inner content\n  > p,\n  > ul {\n    margin-bottom: 0;\n  }\n\n  > p + p {\n    margin-top: 5px;\n  }\n}\n\n// Dismissible alerts\n//\n// Expand the right padding and account for the close button's positioning.\n\n.alert-dismissable, // The misspelled .alert-dismissable was deprecated in 3.2.0.\n.alert-dismissible {\n  padding-right: (@alert-padding + 20);\n\n  // Adjust close link position\n  .close {\n    position: relative;\n    top: -2px;\n    right: -21px;\n    color: inherit;\n  }\n}\n\n// Alternate styles\n//\n// Generate contextual modifier classes for colorizing the alert.\n\n.alert-success {\n  .alert-variant(@alert-success-bg; @alert-success-border; @alert-success-text);\n}\n\n.alert-info {\n  .alert-variant(@alert-info-bg; @alert-info-border; @alert-info-text);\n}\n\n.alert-warning {\n  .alert-variant(@alert-warning-bg; @alert-warning-border; @alert-warning-text);\n}\n\n.alert-danger {\n  .alert-variant(@alert-danger-bg; @alert-danger-border; @alert-danger-text);\n}\n","// Alerts\n\n.alert-variant(@background; @border; @text-color) {\n  background-color: @background;\n  border-color: @border;\n  color: @text-color;\n\n  hr {\n    border-top-color: darken(@border, 5%);\n  }\n  .alert-link {\n    color: darken(@text-color, 10%);\n  }\n}\n","//\n// Progress bars\n// --------------------------------------------------\n\n\n// Bar animations\n// -------------------------\n\n// WebKit\n@-webkit-keyframes progress-bar-stripes {\n  from  { background-position: 40px 0; }\n  to    { background-position: 0 0; }\n}\n\n// Spec and IE10+\n@keyframes progress-bar-stripes {\n  from  { background-position: 40px 0; }\n  to    { background-position: 0 0; }\n}\n\n\n// Bar itself\n// -------------------------\n\n// Outer container\n.progress {\n  overflow: hidden;\n  height: @line-height-computed;\n  margin-bottom: @line-height-computed;\n  background-color: @progress-bg;\n  border-radius: @progress-border-radius;\n  .box-shadow(inset 0 1px 2px rgba(0,0,0,.1));\n}\n\n// Bar of progress\n.progress-bar {\n  float: left;\n  width: 0%;\n  height: 100%;\n  font-size: @font-size-small;\n  line-height: @line-height-computed;\n  color: @progress-bar-color;\n  text-align: center;\n  background-color: @progress-bar-bg;\n  .box-shadow(inset 0 -1px 0 rgba(0,0,0,.15));\n  .transition(width .6s ease);\n}\n\n// Striped bars\n//\n// `.progress-striped .progress-bar` is deprecated as of v3.2.0 in favor of the\n// `.progress-bar-striped` class, which you just add to an existing\n// `.progress-bar`.\n.progress-striped .progress-bar,\n.progress-bar-striped {\n  #gradient > .striped();\n  background-size: 40px 40px;\n}\n\n// Call animation for the active one\n//\n// `.progress.active .progress-bar` is deprecated as of v3.2.0 in favor of the\n// `.progress-bar.active` approach.\n.progress.active .progress-bar,\n.progress-bar.active {\n  .animation(progress-bar-stripes 2s linear infinite);\n}\n\n\n// Variations\n// -------------------------\n\n.progress-bar-success {\n  .progress-bar-variant(@progress-bar-success-bg);\n}\n\n.progress-bar-info {\n  .progress-bar-variant(@progress-bar-info-bg);\n}\n\n.progress-bar-warning {\n  .progress-bar-variant(@progress-bar-warning-bg);\n}\n\n.progress-bar-danger {\n  .progress-bar-variant(@progress-bar-danger-bg);\n}\n","// Gradients\n\n#gradient {\n\n  // Horizontal gradient, from left to right\n  //\n  // Creates two color stops, start and end, by specifying a color and position for each color stop.\n  // Color stops are not available in IE9 and below.\n  .horizontal(@start-color: #555; @end-color: #333; @start-percent: 0%; @end-percent: 100%) {\n    background-image: -webkit-linear-gradient(left, @start-color @start-percent, @end-color @end-percent); // Safari 5.1-6, Chrome 10+\n    background-image: -o-linear-gradient(left, @start-color @start-percent, @end-color @end-percent); // Opera 12\n    background-image: linear-gradient(to right, @start-color @start-percent, @end-color @end-percent); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n    background-repeat: repeat-x;\n    filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=1)\",argb(@start-color),argb(@end-color))); // IE9 and down\n  }\n\n  // Vertical gradient, from top to bottom\n  //\n  // Creates two color stops, start and end, by specifying a color and position for each color stop.\n  // Color stops are not available in IE9 and below.\n  .vertical(@start-color: #555; @end-color: #333; @start-percent: 0%; @end-percent: 100%) {\n    background-image: -webkit-linear-gradient(top, @start-color @start-percent, @end-color @end-percent);  // Safari 5.1-6, Chrome 10+\n    background-image: -o-linear-gradient(top, @start-color @start-percent, @end-color @end-percent);  // Opera 12\n    background-image: linear-gradient(to bottom, @start-color @start-percent, @end-color @end-percent); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n    background-repeat: repeat-x;\n    filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=0)\",argb(@start-color),argb(@end-color))); // IE9 and down\n  }\n\n  .directional(@start-color: #555; @end-color: #333; @deg: 45deg) {\n    background-repeat: repeat-x;\n    background-image: -webkit-linear-gradient(@deg, @start-color, @end-color); // Safari 5.1-6, Chrome 10+\n    background-image: -o-linear-gradient(@deg, @start-color, @end-color); // Opera 12\n    background-image: linear-gradient(@deg, @start-color, @end-color); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n  }\n  .horizontal-three-colors(@start-color: #00b3ee; @mid-color: #7a43b6; @color-stop: 50%; @end-color: #c3325f) {\n    background-image: -webkit-linear-gradient(left, @start-color, @mid-color @color-stop, @end-color);\n    background-image: -o-linear-gradient(left, @start-color, @mid-color @color-stop, @end-color);\n    background-image: linear-gradient(to right, @start-color, @mid-color @color-stop, @end-color);\n    background-repeat: no-repeat;\n    filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=1)\",argb(@start-color),argb(@end-color))); // IE9 and down, gets no color-stop at all for proper fallback\n  }\n  .vertical-three-colors(@start-color: #00b3ee; @mid-color: #7a43b6; @color-stop: 50%; @end-color: #c3325f) {\n    background-image: -webkit-linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n    background-image: -o-linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n    background-image: linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n    background-repeat: no-repeat;\n    filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=0)\",argb(@start-color),argb(@end-color))); // IE9 and down, gets no color-stop at all for proper fallback\n  }\n  .radial(@inner-color: #555; @outer-color: #333) {\n    background-image: -webkit-radial-gradient(circle, @inner-color, @outer-color);\n    background-image: radial-gradient(circle, @inner-color, @outer-color);\n    background-repeat: no-repeat;\n  }\n  .striped(@color: rgba(255,255,255,.15); @angle: 45deg) {\n    background-image: -webkit-linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n    background-image: -o-linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n    background-image: linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n  }\n}\n","// Progress bars\n\n.progress-bar-variant(@color) {\n  background-color: @color;\n\n  // Deprecated parent class requirement as of v3.2.0\n  .progress-striped & {\n    #gradient > .striped();\n  }\n}\n",".media {\n  // Proper spacing between instances of .media\n  margin-top: 15px;\n\n  &:first-child {\n    margin-top: 0;\n  }\n}\n\n.media,\n.media-body {\n  zoom: 1;\n  overflow: hidden;\n}\n\n.media-body {\n  width: 10000px;\n}\n\n.media-object {\n  display: block;\n\n  // Fix collapse in webkit from max-width: 100% and display: table-cell.\n  &.img-thumbnail {\n    max-width: none;\n  }\n}\n\n.media-right,\n.media > .pull-right {\n  padding-left: 10px;\n}\n\n.media-left,\n.media > .pull-left {\n  padding-right: 10px;\n}\n\n.media-left,\n.media-right,\n.media-body {\n  display: table-cell;\n  vertical-align: top;\n}\n\n.media-middle {\n  vertical-align: middle;\n}\n\n.media-bottom {\n  vertical-align: bottom;\n}\n\n// Reset margins on headings for tighter default spacing\n.media-heading {\n  margin-top: 0;\n  margin-bottom: 5px;\n}\n\n// Media list variation\n//\n// Undo default ul/ol styles\n.media-list {\n  padding-left: 0;\n  list-style: none;\n}\n","//\n// List groups\n// --------------------------------------------------\n\n\n// Base class\n//\n// Easily usable on <ul>, <ol>, or <div>.\n\n.list-group {\n  // No need to set list-style: none; since .list-group-item is block level\n  margin-bottom: 20px;\n  padding-left: 0; // reset padding because ul and ol\n}\n\n\n// Individual list items\n//\n// Use on `li`s or `div`s within the `.list-group` parent.\n\n.list-group-item {\n  position: relative;\n  display: block;\n  padding: 10px 15px;\n  // Place the border on the list items and negative margin up for better styling\n  margin-bottom: -1px;\n  background-color: @list-group-bg;\n  border: 1px solid @list-group-border;\n\n  // Round the first and last items\n  &:first-child {\n    .border-top-radius(@list-group-border-radius);\n  }\n  &:last-child {\n    margin-bottom: 0;\n    .border-bottom-radius(@list-group-border-radius);\n  }\n}\n\n\n// Interactive list items\n//\n// Use anchor or button elements instead of `li`s or `div`s to create interactive items.\n// Includes an extra `.active` modifier class for showing selected items.\n\na.list-group-item,\nbutton.list-group-item {\n  color: @list-group-link-color;\n\n  .list-group-item-heading {\n    color: @list-group-link-heading-color;\n  }\n\n  // Hover state\n  &:hover,\n  &:focus {\n    text-decoration: none;\n    color: @list-group-link-hover-color;\n    background-color: @list-group-hover-bg;\n  }\n}\n\nbutton.list-group-item {\n  width: 100%;\n  text-align: left;\n}\n\n.list-group-item {\n  // Disabled state\n  &.disabled,\n  &.disabled:hover,\n  &.disabled:focus {\n    background-color: @list-group-disabled-bg;\n    color: @list-group-disabled-color;\n    cursor: @cursor-disabled;\n\n    // Force color to inherit for custom content\n    .list-group-item-heading {\n      color: inherit;\n    }\n    .list-group-item-text {\n      color: @list-group-disabled-text-color;\n    }\n  }\n\n  // Active class on item itself, not parent\n  &.active,\n  &.active:hover,\n  &.active:focus {\n    z-index: 2; // Place active items above their siblings for proper border styling\n    color: @list-group-active-color;\n    background-color: @list-group-active-bg;\n    border-color: @list-group-active-border;\n\n    // Force color to inherit for custom content\n    .list-group-item-heading,\n    .list-group-item-heading > small,\n    .list-group-item-heading > .small {\n      color: inherit;\n    }\n    .list-group-item-text {\n      color: @list-group-active-text-color;\n    }\n  }\n}\n\n\n// Contextual variants\n//\n// Add modifier classes to change text and background color on individual items.\n// Organizationally, this must come after the `:hover` states.\n\n.list-group-item-variant(success; @state-success-bg; @state-success-text);\n.list-group-item-variant(info; @state-info-bg; @state-info-text);\n.list-group-item-variant(warning; @state-warning-bg; @state-warning-text);\n.list-group-item-variant(danger; @state-danger-bg; @state-danger-text);\n\n\n// Custom content options\n//\n// Extra classes for creating well-formatted content within `.list-group-item`s.\n\n.list-group-item-heading {\n  margin-top: 0;\n  margin-bottom: 5px;\n}\n.list-group-item-text {\n  margin-bottom: 0;\n  line-height: 1.3;\n}\n","// List Groups\n\n.list-group-item-variant(@state; @background; @color) {\n  .list-group-item-@{state} {\n    color: @color;\n    background-color: @background;\n\n    a&,\n    button& {\n      color: @color;\n\n      .list-group-item-heading {\n        color: inherit;\n      }\n\n      &:hover,\n      &:focus {\n        color: @color;\n        background-color: darken(@background, 5%);\n      }\n      &.active,\n      &.active:hover,\n      &.active:focus {\n        color: #fff;\n        background-color: @color;\n        border-color: @color;\n      }\n    }\n  }\n}\n","//\n// Panels\n// --------------------------------------------------\n\n\n// Base class\n.panel {\n  margin-bottom: @line-height-computed;\n  background-color: @panel-bg;\n  border: 1px solid transparent;\n  border-radius: @panel-border-radius;\n  .box-shadow(0 1px 1px rgba(0,0,0,.05));\n}\n\n// Panel contents\n.panel-body {\n  padding: @panel-body-padding;\n  &:extend(.clearfix all);\n}\n\n// Optional heading\n.panel-heading {\n  padding: @panel-heading-padding;\n  border-bottom: 1px solid transparent;\n  .border-top-radius((@panel-border-radius - 1));\n\n  > .dropdown .dropdown-toggle {\n    color: inherit;\n  }\n}\n\n// Within heading, strip any `h*` tag of its default margins for spacing.\n.panel-title {\n  margin-top: 0;\n  margin-bottom: 0;\n  font-size: ceil((@font-size-base * 1.125));\n  color: inherit;\n\n  > a,\n  > small,\n  > .small,\n  > small > a,\n  > .small > a {\n    color: inherit;\n  }\n}\n\n// Optional footer (stays gray in every modifier class)\n.panel-footer {\n  padding: @panel-footer-padding;\n  background-color: @panel-footer-bg;\n  border-top: 1px solid @panel-inner-border;\n  .border-bottom-radius((@panel-border-radius - 1));\n}\n\n\n// List groups in panels\n//\n// By default, space out list group content from panel headings to account for\n// any kind of custom content between the two.\n\n.panel {\n  > .list-group,\n  > .panel-collapse > .list-group {\n    margin-bottom: 0;\n\n    .list-group-item {\n      border-width: 1px 0;\n      border-radius: 0;\n    }\n\n    // Add border top radius for first one\n    &:first-child {\n      .list-group-item:first-child {\n        border-top: 0;\n        .border-top-radius((@panel-border-radius - 1));\n      }\n    }\n\n    // Add border bottom radius for last one\n    &:last-child {\n      .list-group-item:last-child {\n        border-bottom: 0;\n        .border-bottom-radius((@panel-border-radius - 1));\n      }\n    }\n  }\n  > .panel-heading + .panel-collapse > .list-group {\n    .list-group-item:first-child {\n      .border-top-radius(0);\n    }\n  }\n}\n// Collapse space between when there's no additional content.\n.panel-heading + .list-group {\n  .list-group-item:first-child {\n    border-top-width: 0;\n  }\n}\n.list-group + .panel-footer {\n  border-top-width: 0;\n}\n\n// Tables in panels\n//\n// Place a non-bordered `.table` within a panel (not within a `.panel-body`) and\n// watch it go full width.\n\n.panel {\n  > .table,\n  > .table-responsive > .table,\n  > .panel-collapse > .table {\n    margin-bottom: 0;\n\n    caption {\n      padding-left: @panel-body-padding;\n      padding-right: @panel-body-padding;\n    }\n  }\n  // Add border top radius for first one\n  > .table:first-child,\n  > .table-responsive:first-child > .table:first-child {\n    .border-top-radius((@panel-border-radius - 1));\n\n    > thead:first-child,\n    > tbody:first-child {\n      > tr:first-child {\n        border-top-left-radius: (@panel-border-radius - 1);\n        border-top-right-radius: (@panel-border-radius - 1);\n\n        td:first-child,\n        th:first-child {\n          border-top-left-radius: (@panel-border-radius - 1);\n        }\n        td:last-child,\n        th:last-child {\n          border-top-right-radius: (@panel-border-radius - 1);\n        }\n      }\n    }\n  }\n  // Add border bottom radius for last one\n  > .table:last-child,\n  > .table-responsive:last-child > .table:last-child {\n    .border-bottom-radius((@panel-border-radius - 1));\n\n    > tbody:last-child,\n    > tfoot:last-child {\n      > tr:last-child {\n        border-bottom-left-radius: (@panel-border-radius - 1);\n        border-bottom-right-radius: (@panel-border-radius - 1);\n\n        td:first-child,\n        th:first-child {\n          border-bottom-left-radius: (@panel-border-radius - 1);\n        }\n        td:last-child,\n        th:last-child {\n          border-bottom-right-radius: (@panel-border-radius - 1);\n        }\n      }\n    }\n  }\n  > .panel-body + .table,\n  > .panel-body + .table-responsive,\n  > .table + .panel-body,\n  > .table-responsive + .panel-body {\n    border-top: 1px solid @table-border-color;\n  }\n  > .table > tbody:first-child > tr:first-child th,\n  > .table > tbody:first-child > tr:first-child td {\n    border-top: 0;\n  }\n  > .table-bordered,\n  > .table-responsive > .table-bordered {\n    border: 0;\n    > thead,\n    > tbody,\n    > tfoot {\n      > tr {\n        > th:first-child,\n        > td:first-child {\n          border-left: 0;\n        }\n        > th:last-child,\n        > td:last-child {\n          border-right: 0;\n        }\n      }\n    }\n    > thead,\n    > tbody {\n      > tr:first-child {\n        > td,\n        > th {\n          border-bottom: 0;\n        }\n      }\n    }\n    > tbody,\n    > tfoot {\n      > tr:last-child {\n        > td,\n        > th {\n          border-bottom: 0;\n        }\n      }\n    }\n  }\n  > .table-responsive {\n    border: 0;\n    margin-bottom: 0;\n  }\n}\n\n\n// Collapsible panels (aka, accordion)\n//\n// Wrap a series of panels in `.panel-group` to turn them into an accordion with\n// the help of our collapse JavaScript plugin.\n\n.panel-group {\n  margin-bottom: @line-height-computed;\n\n  // Tighten up margin so it's only between panels\n  .panel {\n    margin-bottom: 0;\n    border-radius: @panel-border-radius;\n\n    + .panel {\n      margin-top: 5px;\n    }\n  }\n\n  .panel-heading {\n    border-bottom: 0;\n\n    + .panel-collapse > .panel-body,\n    + .panel-collapse > .list-group {\n      border-top: 1px solid @panel-inner-border;\n    }\n  }\n\n  .panel-footer {\n    border-top: 0;\n    + .panel-collapse .panel-body {\n      border-bottom: 1px solid @panel-inner-border;\n    }\n  }\n}\n\n\n// Contextual variations\n.panel-default {\n  .panel-variant(@panel-default-border; @panel-default-text; @panel-default-heading-bg; @panel-default-border);\n}\n.panel-primary {\n  .panel-variant(@panel-primary-border; @panel-primary-text; @panel-primary-heading-bg; @panel-primary-border);\n}\n.panel-success {\n  .panel-variant(@panel-success-border; @panel-success-text; @panel-success-heading-bg; @panel-success-border);\n}\n.panel-info {\n  .panel-variant(@panel-info-border; @panel-info-text; @panel-info-heading-bg; @panel-info-border);\n}\n.panel-warning {\n  .panel-variant(@panel-warning-border; @panel-warning-text; @panel-warning-heading-bg; @panel-warning-border);\n}\n.panel-danger {\n  .panel-variant(@panel-danger-border; @panel-danger-text; @panel-danger-heading-bg; @panel-danger-border);\n}\n","// Panels\n\n.panel-variant(@border; @heading-text-color; @heading-bg-color; @heading-border) {\n  border-color: @border;\n\n  & > .panel-heading {\n    color: @heading-text-color;\n    background-color: @heading-bg-color;\n    border-color: @heading-border;\n\n    + .panel-collapse > .panel-body {\n      border-top-color: @border;\n    }\n    .badge {\n      color: @heading-bg-color;\n      background-color: @heading-text-color;\n    }\n  }\n  & > .panel-footer {\n    + .panel-collapse > .panel-body {\n      border-bottom-color: @border;\n    }\n  }\n}\n","// Embeds responsive\n//\n// Credit: Nicolas Gallagher and SUIT CSS.\n\n.embed-responsive {\n  position: relative;\n  display: block;\n  height: 0;\n  padding: 0;\n  overflow: hidden;\n\n  .embed-responsive-item,\n  iframe,\n  embed,\n  object,\n  video {\n    position: absolute;\n    top: 0;\n    left: 0;\n    bottom: 0;\n    height: 100%;\n    width: 100%;\n    border: 0;\n  }\n}\n\n// Modifier class for 16:9 aspect ratio\n.embed-responsive-16by9 {\n  padding-bottom: 56.25%;\n}\n\n// Modifier class for 4:3 aspect ratio\n.embed-responsive-4by3 {\n  padding-bottom: 75%;\n}\n","//\n// Wells\n// --------------------------------------------------\n\n\n// Base class\n.well {\n  min-height: 20px;\n  padding: 19px;\n  margin-bottom: 20px;\n  background-color: @well-bg;\n  border: 1px solid @well-border;\n  border-radius: @border-radius-base;\n  .box-shadow(inset 0 1px 1px rgba(0,0,0,.05));\n  blockquote {\n    border-color: #ddd;\n    border-color: rgba(0,0,0,.15);\n  }\n}\n\n// Sizes\n.well-lg {\n  padding: 24px;\n  border-radius: @border-radius-large;\n}\n.well-sm {\n  padding: 9px;\n  border-radius: @border-radius-small;\n}\n","//\n// Close icons\n// --------------------------------------------------\n\n\n.close {\n  float: right;\n  font-size: (@font-size-base * 1.5);\n  font-weight: @close-font-weight;\n  line-height: 1;\n  color: @close-color;\n  text-shadow: @close-text-shadow;\n  .opacity(.2);\n\n  &:hover,\n  &:focus {\n    color: @close-color;\n    text-decoration: none;\n    cursor: pointer;\n    .opacity(.5);\n  }\n\n  // Additional properties for button version\n  // iOS requires the button element instead of an anchor tag.\n  // If you want the anchor version, it requires `href=\"#\"`.\n  // See https://developer.mozilla.org/en-US/docs/Web/Events/click#Safari_Mobile\n  button& {\n    padding: 0;\n    cursor: pointer;\n    background: transparent;\n    border: 0;\n    -webkit-appearance: none;\n  }\n}\n","//\n// Modals\n// --------------------------------------------------\n\n// .modal-open      - body class for killing the scroll\n// .modal           - container to scroll within\n// .modal-dialog    - positioning shell for the actual modal\n// .modal-content   - actual modal w/ bg and corners and shit\n\n// Kill the scroll on the body\n.modal-open {\n  overflow: hidden;\n}\n\n// Container that the modal scrolls within\n.modal {\n  display: none;\n  overflow: hidden;\n  position: fixed;\n  top: 0;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  z-index: @zindex-modal;\n  -webkit-overflow-scrolling: touch;\n\n  // Prevent Chrome on Windows from adding a focus outline. For details, see\n  // https://github.com/twbs/bootstrap/pull/10951.\n  outline: 0;\n\n  // When fading in the modal, animate it to slide down\n  &.fade .modal-dialog {\n    .translate(0, -25%);\n    .transition-transform(~\"0.3s ease-out\");\n  }\n  &.in .modal-dialog { .translate(0, 0) }\n}\n.modal-open .modal {\n  overflow-x: hidden;\n  overflow-y: auto;\n}\n\n// Shell div to position the modal with bottom padding\n.modal-dialog {\n  position: relative;\n  width: auto;\n  margin: 10px;\n}\n\n// Actual modal\n.modal-content {\n  position: relative;\n  background-color: @modal-content-bg;\n  border: 1px solid @modal-content-fallback-border-color; //old browsers fallback (ie8 etc)\n  border: 1px solid @modal-content-border-color;\n  border-radius: @border-radius-large;\n  .box-shadow(0 3px 9px rgba(0,0,0,.5));\n  background-clip: padding-box;\n  // Remove focus outline from opened modal\n  outline: 0;\n}\n\n// Modal background\n.modal-backdrop {\n  position: fixed;\n  top: 0;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  z-index: @zindex-modal-background;\n  background-color: @modal-backdrop-bg;\n  // Fade for backdrop\n  &.fade { .opacity(0); }\n  &.in { .opacity(@modal-backdrop-opacity); }\n}\n\n// Modal header\n// Top section of the modal w/ title and dismiss\n.modal-header {\n  padding: @modal-title-padding;\n  border-bottom: 1px solid @modal-header-border-color;\n  &:extend(.clearfix all);\n}\n// Close icon\n.modal-header .close {\n  margin-top: -2px;\n}\n\n// Title text within header\n.modal-title {\n  margin: 0;\n  line-height: @modal-title-line-height;\n}\n\n// Modal body\n// Where all modal content resides (sibling of .modal-header and .modal-footer)\n.modal-body {\n  position: relative;\n  padding: @modal-inner-padding;\n}\n\n// Footer (for actions)\n.modal-footer {\n  padding: @modal-inner-padding;\n  text-align: right; // right align buttons\n  border-top: 1px solid @modal-footer-border-color;\n  &:extend(.clearfix all); // clear it in case folks use .pull-* classes on buttons\n\n  // Properly space out buttons\n  .btn + .btn {\n    margin-left: 5px;\n    margin-bottom: 0; // account for input[type=\"submit\"] which gets the bottom margin like all other inputs\n  }\n  // but override that for button groups\n  .btn-group .btn + .btn {\n    margin-left: -1px;\n  }\n  // and override it for block buttons as well\n  .btn-block + .btn-block {\n    margin-left: 0;\n  }\n}\n\n// Measure scrollbar width for padding body during modal show/hide\n.modal-scrollbar-measure {\n  position: absolute;\n  top: -9999px;\n  width: 50px;\n  height: 50px;\n  overflow: scroll;\n}\n\n// Scale up the modal\n@media (min-width: @screen-sm-min) {\n  // Automatically set modal's width for larger viewports\n  .modal-dialog {\n    width: @modal-md;\n    margin: 30px auto;\n  }\n  .modal-content {\n    .box-shadow(0 5px 15px rgba(0,0,0,.5));\n  }\n\n  // Modal sizes\n  .modal-sm { width: @modal-sm; }\n}\n\n@media (min-width: @screen-md-min) {\n  .modal-lg { width: @modal-lg; }\n}\n","//\n// Tooltips\n// --------------------------------------------------\n\n\n// Base class\n.tooltip {\n  position: absolute;\n  z-index: @zindex-tooltip;\n  display: block;\n  // Our parent element can be arbitrary since tooltips are by default inserted as a sibling of their target element.\n  // So reset our font and text properties to avoid inheriting weird values.\n  .reset-text();\n  font-size: @font-size-small;\n\n  .opacity(0);\n\n  &.in     { .opacity(@tooltip-opacity); }\n  &.top    { margin-top:  -3px; padding: @tooltip-arrow-width 0; }\n  &.right  { margin-left:  3px; padding: 0 @tooltip-arrow-width; }\n  &.bottom { margin-top:   3px; padding: @tooltip-arrow-width 0; }\n  &.left   { margin-left: -3px; padding: 0 @tooltip-arrow-width; }\n}\n\n// Wrapper for the tooltip content\n.tooltip-inner {\n  max-width: @tooltip-max-width;\n  padding: 3px 8px;\n  color: @tooltip-color;\n  text-align: center;\n  background-color: @tooltip-bg;\n  border-radius: @border-radius-base;\n}\n\n// Arrows\n.tooltip-arrow {\n  position: absolute;\n  width: 0;\n  height: 0;\n  border-color: transparent;\n  border-style: solid;\n}\n// Note: Deprecated .top-left, .top-right, .bottom-left, and .bottom-right as of v3.3.1\n.tooltip {\n  &.top .tooltip-arrow {\n    bottom: 0;\n    left: 50%;\n    margin-left: -@tooltip-arrow-width;\n    border-width: @tooltip-arrow-width @tooltip-arrow-width 0;\n    border-top-color: @tooltip-arrow-color;\n  }\n  &.top-left .tooltip-arrow {\n    bottom: 0;\n    right: @tooltip-arrow-width;\n    margin-bottom: -@tooltip-arrow-width;\n    border-width: @tooltip-arrow-width @tooltip-arrow-width 0;\n    border-top-color: @tooltip-arrow-color;\n  }\n  &.top-right .tooltip-arrow {\n    bottom: 0;\n    left: @tooltip-arrow-width;\n    margin-bottom: -@tooltip-arrow-width;\n    border-width: @tooltip-arrow-width @tooltip-arrow-width 0;\n    border-top-color: @tooltip-arrow-color;\n  }\n  &.right .tooltip-arrow {\n    top: 50%;\n    left: 0;\n    margin-top: -@tooltip-arrow-width;\n    border-width: @tooltip-arrow-width @tooltip-arrow-width @tooltip-arrow-width 0;\n    border-right-color: @tooltip-arrow-color;\n  }\n  &.left .tooltip-arrow {\n    top: 50%;\n    right: 0;\n    margin-top: -@tooltip-arrow-width;\n    border-width: @tooltip-arrow-width 0 @tooltip-arrow-width @tooltip-arrow-width;\n    border-left-color: @tooltip-arrow-color;\n  }\n  &.bottom .tooltip-arrow {\n    top: 0;\n    left: 50%;\n    margin-left: -@tooltip-arrow-width;\n    border-width: 0 @tooltip-arrow-width @tooltip-arrow-width;\n    border-bottom-color: @tooltip-arrow-color;\n  }\n  &.bottom-left .tooltip-arrow {\n    top: 0;\n    right: @tooltip-arrow-width;\n    margin-top: -@tooltip-arrow-width;\n    border-width: 0 @tooltip-arrow-width @tooltip-arrow-width;\n    border-bottom-color: @tooltip-arrow-color;\n  }\n  &.bottom-right .tooltip-arrow {\n    top: 0;\n    left: @tooltip-arrow-width;\n    margin-top: -@tooltip-arrow-width;\n    border-width: 0 @tooltip-arrow-width @tooltip-arrow-width;\n    border-bottom-color: @tooltip-arrow-color;\n  }\n}\n",".reset-text() {\n  font-family: @font-family-base;\n  // We deliberately do NOT reset font-size.\n  font-style: normal;\n  font-weight: normal;\n  letter-spacing: normal;\n  line-break: auto;\n  line-height: @line-height-base;\n  text-align: left; // Fallback for where `start` is not supported\n  text-align: start;\n  text-decoration: none;\n  text-shadow: none;\n  text-transform: none;\n  white-space: normal;\n  word-break: normal;\n  word-spacing: normal;\n  word-wrap: normal;\n}\n","//\n// Popovers\n// --------------------------------------------------\n\n\n.popover {\n  position: absolute;\n  top: 0;\n  left: 0;\n  z-index: @zindex-popover;\n  display: none;\n  max-width: @popover-max-width;\n  padding: 1px;\n  // Our parent element can be arbitrary since popovers are by default inserted as a sibling of their target element.\n  // So reset our font and text properties to avoid inheriting weird values.\n  .reset-text();\n  font-size: @font-size-base;\n\n  background-color: @popover-bg;\n  background-clip: padding-box;\n  border: 1px solid @popover-fallback-border-color;\n  border: 1px solid @popover-border-color;\n  border-radius: @border-radius-large;\n  .box-shadow(0 5px 10px rgba(0,0,0,.2));\n\n  // Offset the popover to account for the popover arrow\n  &.top     { margin-top: -@popover-arrow-width; }\n  &.right   { margin-left: @popover-arrow-width; }\n  &.bottom  { margin-top: @popover-arrow-width; }\n  &.left    { margin-left: -@popover-arrow-width; }\n}\n\n.popover-title {\n  margin: 0; // reset heading margin\n  padding: 8px 14px;\n  font-size: @font-size-base;\n  background-color: @popover-title-bg;\n  border-bottom: 1px solid darken(@popover-title-bg, 5%);\n  border-radius: (@border-radius-large - 1) (@border-radius-large - 1) 0 0;\n}\n\n.popover-content {\n  padding: 9px 14px;\n}\n\n// Arrows\n//\n// .arrow is outer, .arrow:after is inner\n\n.popover > .arrow {\n  &,\n  &:after {\n    position: absolute;\n    display: block;\n    width: 0;\n    height: 0;\n    border-color: transparent;\n    border-style: solid;\n  }\n}\n.popover > .arrow {\n  border-width: @popover-arrow-outer-width;\n}\n.popover > .arrow:after {\n  border-width: @popover-arrow-width;\n  content: \"\";\n}\n\n.popover {\n  &.top > .arrow {\n    left: 50%;\n    margin-left: -@popover-arrow-outer-width;\n    border-bottom-width: 0;\n    border-top-color: @popover-arrow-outer-fallback-color; // IE8 fallback\n    border-top-color: @popover-arrow-outer-color;\n    bottom: -@popover-arrow-outer-width;\n    &:after {\n      content: \" \";\n      bottom: 1px;\n      margin-left: -@popover-arrow-width;\n      border-bottom-width: 0;\n      border-top-color: @popover-arrow-color;\n    }\n  }\n  &.right > .arrow {\n    top: 50%;\n    left: -@popover-arrow-outer-width;\n    margin-top: -@popover-arrow-outer-width;\n    border-left-width: 0;\n    border-right-color: @popover-arrow-outer-fallback-color; // IE8 fallback\n    border-right-color: @popover-arrow-outer-color;\n    &:after {\n      content: \" \";\n      left: 1px;\n      bottom: -@popover-arrow-width;\n      border-left-width: 0;\n      border-right-color: @popover-arrow-color;\n    }\n  }\n  &.bottom > .arrow {\n    left: 50%;\n    margin-left: -@popover-arrow-outer-width;\n    border-top-width: 0;\n    border-bottom-color: @popover-arrow-outer-fallback-color; // IE8 fallback\n    border-bottom-color: @popover-arrow-outer-color;\n    top: -@popover-arrow-outer-width;\n    &:after {\n      content: \" \";\n      top: 1px;\n      margin-left: -@popover-arrow-width;\n      border-top-width: 0;\n      border-bottom-color: @popover-arrow-color;\n    }\n  }\n\n  &.left > .arrow {\n    top: 50%;\n    right: -@popover-arrow-outer-width;\n    margin-top: -@popover-arrow-outer-width;\n    border-right-width: 0;\n    border-left-color: @popover-arrow-outer-fallback-color; // IE8 fallback\n    border-left-color: @popover-arrow-outer-color;\n    &:after {\n      content: \" \";\n      right: 1px;\n      border-right-width: 0;\n      border-left-color: @popover-arrow-color;\n      bottom: -@popover-arrow-width;\n    }\n  }\n}\n","//\n// Carousel\n// --------------------------------------------------\n\n\n// Wrapper for the slide container and indicators\n.carousel {\n  position: relative;\n}\n\n.carousel-inner {\n  position: relative;\n  overflow: hidden;\n  width: 100%;\n\n  > .item {\n    display: none;\n    position: relative;\n    .transition(.6s ease-in-out left);\n\n    // Account for jankitude on images\n    > img,\n    > a > img {\n      &:extend(.img-responsive);\n      line-height: 1;\n    }\n\n    // WebKit CSS3 transforms for supported devices\n    @media all and (transform-3d), (-webkit-transform-3d) {\n      .transition-transform(~'0.6s ease-in-out');\n      .backface-visibility(~'hidden');\n      .perspective(1000px);\n\n      &.next,\n      &.active.right {\n        .translate3d(100%, 0, 0);\n        left: 0;\n      }\n      &.prev,\n      &.active.left {\n        .translate3d(-100%, 0, 0);\n        left: 0;\n      }\n      &.next.left,\n      &.prev.right,\n      &.active {\n        .translate3d(0, 0, 0);\n        left: 0;\n      }\n    }\n  }\n\n  > .active,\n  > .next,\n  > .prev {\n    display: block;\n  }\n\n  > .active {\n    left: 0;\n  }\n\n  > .next,\n  > .prev {\n    position: absolute;\n    top: 0;\n    width: 100%;\n  }\n\n  > .next {\n    left: 100%;\n  }\n  > .prev {\n    left: -100%;\n  }\n  > .next.left,\n  > .prev.right {\n    left: 0;\n  }\n\n  > .active.left {\n    left: -100%;\n  }\n  > .active.right {\n    left: 100%;\n  }\n\n}\n\n// Left/right controls for nav\n// ---------------------------\n\n.carousel-control {\n  position: absolute;\n  top: 0;\n  left: 0;\n  bottom: 0;\n  width: @carousel-control-width;\n  .opacity(@carousel-control-opacity);\n  font-size: @carousel-control-font-size;\n  color: @carousel-control-color;\n  text-align: center;\n  text-shadow: @carousel-text-shadow;\n  background-color: rgba(0, 0, 0, 0); // Fix IE9 click-thru bug\n  // We can't have this transition here because WebKit cancels the carousel\n  // animation if you trip this while in the middle of another animation.\n\n  // Set gradients for backgrounds\n  &.left {\n    #gradient > .horizontal(@start-color: rgba(0,0,0,.5); @end-color: rgba(0,0,0,.0001));\n  }\n  &.right {\n    left: auto;\n    right: 0;\n    #gradient > .horizontal(@start-color: rgba(0,0,0,.0001); @end-color: rgba(0,0,0,.5));\n  }\n\n  // Hover/focus state\n  &:hover,\n  &:focus {\n    outline: 0;\n    color: @carousel-control-color;\n    text-decoration: none;\n    .opacity(.9);\n  }\n\n  // Toggles\n  .icon-prev,\n  .icon-next,\n  .glyphicon-chevron-left,\n  .glyphicon-chevron-right {\n    position: absolute;\n    top: 50%;\n    margin-top: -10px;\n    z-index: 5;\n    display: inline-block;\n  }\n  .icon-prev,\n  .glyphicon-chevron-left {\n    left: 50%;\n    margin-left: -10px;\n  }\n  .icon-next,\n  .glyphicon-chevron-right {\n    right: 50%;\n    margin-right: -10px;\n  }\n  .icon-prev,\n  .icon-next {\n    width:  20px;\n    height: 20px;\n    line-height: 1;\n    font-family: serif;\n  }\n\n\n  .icon-prev {\n    &:before {\n      content: '\\2039';// SINGLE LEFT-POINTING ANGLE QUOTATION MARK (U+2039)\n    }\n  }\n  .icon-next {\n    &:before {\n      content: '\\203a';// SINGLE RIGHT-POINTING ANGLE QUOTATION MARK (U+203A)\n    }\n  }\n}\n\n// Optional indicator pips\n//\n// Add an unordered list with the following class and add a list item for each\n// slide your carousel holds.\n\n.carousel-indicators {\n  position: absolute;\n  bottom: 10px;\n  left: 50%;\n  z-index: 15;\n  width: 60%;\n  margin-left: -30%;\n  padding-left: 0;\n  list-style: none;\n  text-align: center;\n\n  li {\n    display: inline-block;\n    width:  10px;\n    height: 10px;\n    margin: 1px;\n    text-indent: -999px;\n    border: 1px solid @carousel-indicator-border-color;\n    border-radius: 10px;\n    cursor: pointer;\n\n    // IE8-9 hack for event handling\n    //\n    // Internet Explorer 8-9 does not support clicks on elements without a set\n    // `background-color`. We cannot use `filter` since that's not viewed as a\n    // background color by the browser. Thus, a hack is needed.\n    // See https://developer.mozilla.org/en-US/docs/Web/Events/click#Internet_Explorer\n    //\n    // For IE8, we set solid black as it doesn't support `rgba()`. For IE9, we\n    // set alpha transparency for the best results possible.\n    background-color: #000 \\9; // IE8\n    background-color: rgba(0,0,0,0); // IE9\n  }\n  .active {\n    margin: 0;\n    width:  12px;\n    height: 12px;\n    background-color: @carousel-indicator-active-bg;\n  }\n}\n\n// Optional captions\n// -----------------------------\n// Hidden by default for smaller viewports\n.carousel-caption {\n  position: absolute;\n  left: 15%;\n  right: 15%;\n  bottom: 20px;\n  z-index: 10;\n  padding-top: 20px;\n  padding-bottom: 20px;\n  color: @carousel-caption-color;\n  text-align: center;\n  text-shadow: @carousel-text-shadow;\n  & .btn {\n    text-shadow: none; // No shadow for button elements in carousel-caption\n  }\n}\n\n\n// Scale up controls for tablets and up\n@media screen and (min-width: @screen-sm-min) {\n\n  // Scale up the controls a smidge\n  .carousel-control {\n    .glyphicon-chevron-left,\n    .glyphicon-chevron-right,\n    .icon-prev,\n    .icon-next {\n      width: (@carousel-control-font-size * 1.5);\n      height: (@carousel-control-font-size * 1.5);\n      margin-top: (@carousel-control-font-size / -2);\n      font-size: (@carousel-control-font-size * 1.5);\n    }\n    .glyphicon-chevron-left,\n    .icon-prev {\n      margin-left: (@carousel-control-font-size / -2);\n    }\n    .glyphicon-chevron-right,\n    .icon-next {\n      margin-right: (@carousel-control-font-size / -2);\n    }\n  }\n\n  // Show and left align the captions\n  .carousel-caption {\n    left: 20%;\n    right: 20%;\n    padding-bottom: 30px;\n  }\n\n  // Move up the indicators\n  .carousel-indicators {\n    bottom: 20px;\n  }\n}\n","// Clearfix\n//\n// For modern browsers\n// 1. The space content is one way to avoid an Opera bug when the\n//    contenteditable attribute is included anywhere else in the document.\n//    Otherwise it causes space to appear at the top and bottom of elements\n//    that are clearfixed.\n// 2. The use of `table` rather than `block` is only necessary if using\n//    `:before` to contain the top-margins of child elements.\n//\n// Source: http://nicolasgallagher.com/micro-clearfix-hack/\n\n.clearfix() {\n  &:before,\n  &:after {\n    content: \" \"; // 1\n    display: table; // 2\n  }\n  &:after {\n    clear: both;\n  }\n}\n","// Center-align a block level element\n\n.center-block() {\n  display: block;\n  margin-left: auto;\n  margin-right: auto;\n}\n","// CSS image replacement\n//\n// Heads up! v3 launched with only `.hide-text()`, but per our pattern for\n// mixins being reused as classes with the same name, this doesn't hold up. As\n// of v3.0.1 we have added `.text-hide()` and deprecated `.hide-text()`.\n//\n// Source: https://github.com/h5bp/html5-boilerplate/commit/aa0396eae757\n\n// Deprecated as of v3.0.1 (has been removed in v4)\n.hide-text() {\n  font: ~\"0/0\" a;\n  color: transparent;\n  text-shadow: none;\n  background-color: transparent;\n  border: 0;\n}\n\n// New mixin to use as of v3.0.1\n.text-hide() {\n  .hide-text();\n}\n","//\n// Responsive: Utility classes\n// --------------------------------------------------\n\n\n// IE10 in Windows (Phone) 8\n//\n// Support for responsive views via media queries is kind of borked in IE10, for\n// Surface/desktop in split view and for Windows Phone 8. This particular fix\n// must be accompanied by a snippet of JavaScript to sniff the user agent and\n// apply some conditional CSS to *only* the Surface/desktop Windows 8. Look at\n// our Getting Started page for more information on this bug.\n//\n// For more information, see the following:\n//\n// Issue: https://github.com/twbs/bootstrap/issues/10497\n// Docs: http://getbootstrap.com/getting-started/#support-ie10-width\n// Source: http://timkadlec.com/2013/01/windows-phone-8-and-device-width/\n// Source: http://timkadlec.com/2012/10/ie10-snap-mode-and-responsive-design/\n\n@-ms-viewport {\n  width: device-width;\n}\n\n\n// Visibility utilities\n// Note: Deprecated .visible-xs, .visible-sm, .visible-md, and .visible-lg as of v3.2.0\n.visible-xs,\n.visible-sm,\n.visible-md,\n.visible-lg {\n  .responsive-invisibility();\n}\n\n.visible-xs-block,\n.visible-xs-inline,\n.visible-xs-inline-block,\n.visible-sm-block,\n.visible-sm-inline,\n.visible-sm-inline-block,\n.visible-md-block,\n.visible-md-inline,\n.visible-md-inline-block,\n.visible-lg-block,\n.visible-lg-inline,\n.visible-lg-inline-block {\n  display: none !important;\n}\n\n.visible-xs {\n  @media (max-width: @screen-xs-max) {\n    .responsive-visibility();\n  }\n}\n.visible-xs-block {\n  @media (max-width: @screen-xs-max) {\n    display: block !important;\n  }\n}\n.visible-xs-inline {\n  @media (max-width: @screen-xs-max) {\n    display: inline !important;\n  }\n}\n.visible-xs-inline-block {\n  @media (max-width: @screen-xs-max) {\n    display: inline-block !important;\n  }\n}\n\n.visible-sm {\n  @media (min-width: @screen-sm-min) and (max-width: @screen-sm-max) {\n    .responsive-visibility();\n  }\n}\n.visible-sm-block {\n  @media (min-width: @screen-sm-min) and (max-width: @screen-sm-max) {\n    display: block !important;\n  }\n}\n.visible-sm-inline {\n  @media (min-width: @screen-sm-min) and (max-width: @screen-sm-max) {\n    display: inline !important;\n  }\n}\n.visible-sm-inline-block {\n  @media (min-width: @screen-sm-min) and (max-width: @screen-sm-max) {\n    display: inline-block !important;\n  }\n}\n\n.visible-md {\n  @media (min-width: @screen-md-min) and (max-width: @screen-md-max) {\n    .responsive-visibility();\n  }\n}\n.visible-md-block {\n  @media (min-width: @screen-md-min) and (max-width: @screen-md-max) {\n    display: block !important;\n  }\n}\n.visible-md-inline {\n  @media (min-width: @screen-md-min) and (max-width: @screen-md-max) {\n    display: inline !important;\n  }\n}\n.visible-md-inline-block {\n  @media (min-width: @screen-md-min) and (max-width: @screen-md-max) {\n    display: inline-block !important;\n  }\n}\n\n.visible-lg {\n  @media (min-width: @screen-lg-min) {\n    .responsive-visibility();\n  }\n}\n.visible-lg-block {\n  @media (min-width: @screen-lg-min) {\n    display: block !important;\n  }\n}\n.visible-lg-inline {\n  @media (min-width: @screen-lg-min) {\n    display: inline !important;\n  }\n}\n.visible-lg-inline-block {\n  @media (min-width: @screen-lg-min) {\n    display: inline-block !important;\n  }\n}\n\n.hidden-xs {\n  @media (max-width: @screen-xs-max) {\n    .responsive-invisibility();\n  }\n}\n.hidden-sm {\n  @media (min-width: @screen-sm-min) and (max-width: @screen-sm-max) {\n    .responsive-invisibility();\n  }\n}\n.hidden-md {\n  @media (min-width: @screen-md-min) and (max-width: @screen-md-max) {\n    .responsive-invisibility();\n  }\n}\n.hidden-lg {\n  @media (min-width: @screen-lg-min) {\n    .responsive-invisibility();\n  }\n}\n\n\n// Print utilities\n//\n// Media queries are placed on the inside to be mixin-friendly.\n\n// Note: Deprecated .visible-print as of v3.2.0\n.visible-print {\n  .responsive-invisibility();\n\n  @media print {\n    .responsive-visibility();\n  }\n}\n.visible-print-block {\n  display: none !important;\n\n  @media print {\n    display: block !important;\n  }\n}\n.visible-print-inline {\n  display: none !important;\n\n  @media print {\n    display: inline !important;\n  }\n}\n.visible-print-inline-block {\n  display: none !important;\n\n  @media print {\n    display: inline-block !important;\n  }\n}\n\n.hidden-print {\n  @media print {\n    .responsive-invisibility();\n  }\n}\n","// Responsive utilities\n\n//\n// More easily include all the states for responsive-utilities.less.\n.responsive-visibility() {\n  display: block !important;\n  table&  { display: table !important; }\n  tr&     { display: table-row !important; }\n  th&,\n  td&     { display: table-cell !important; }\n}\n\n.responsive-invisibility() {\n  display: none !important;\n}\n"]}
\ No newline at end of file
diff --git a/docs/_static/bootstrap-3.3.7/css/bootstrap.min.css b/docs/_static/bootstrap-3.3.7/css/bootstrap.min.css
deleted file mode 100644
index ed3905e0e..000000000
--- a/docs/_static/bootstrap-3.3.7/css/bootstrap.min.css
+++ /dev/null
@@ -1,6 +0,0 @@
-/*!
- * Bootstrap v3.3.7 (http://getbootstrap.com)
- * Copyright 2011-2016 Twitter, Inc.
- * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
- *//*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */html{font-family:sans-serif;-webkit-text-size-adjust:100%;-ms-text-size-adjust:100%}body{margin:0}article,aside,details,figcaption,figure,footer,header,hgroup,main,menu,nav,section,summary{display:block}audio,canvas,progress,video{display:inline-block;vertical-align:baseline}audio:not([controls]){display:none;height:0}[hidden],template{display:none}a{background-color:transparent}a:active,a:hover{outline:0}abbr[title]{border-bottom:1px dotted}b,strong{font-weight:700}dfn{font-style:italic}h1{margin:.67em 0;font-size:2em}mark{color:#000;background:#ff0}small{font-size:80%}sub,sup{position:relative;font-size:75%;line-height:0;vertical-align:baseline}sup{top:-.5em}sub{bottom:-.25em}img{border:0}svg:not(:root){overflow:hidden}figure{margin:1em 40px}hr{height:0;-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box}pre{overflow:auto}code,kbd,pre,samp{font-family:monospace,monospace;font-size:1em}button,input,optgroup,select,textarea{margin:0;font:inherit;color:inherit}button{overflow:visible}button,select{text-transform:none}button,html input[type=button],input[type=reset],input[type=submit]{-webkit-appearance:button;cursor:pointer}button[disabled],html input[disabled]{cursor:default}button::-moz-focus-inner,input::-moz-focus-inner{padding:0;border:0}input{line-height:normal}input[type=checkbox],input[type=radio]{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;padding:0}input[type=number]::-webkit-inner-spin-button,input[type=number]::-webkit-outer-spin-button{height:auto}input[type=search]{-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box;-webkit-appearance:textfield}input[type=search]::-webkit-search-cancel-button,input[type=search]::-webkit-search-decoration{-webkit-appearance:none}fieldset{padding:.35em .625em .75em;margin:0 2px;border:1px solid silver}legend{padding:0;border:0}textarea{overflow:auto}optgroup{font-weight:700}table{border-spacing:0;border-collapse:collapse}td,th{padding:0}/*! 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normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */\n\n//\n// 1. Set default font family to sans-serif.\n// 2. Prevent iOS and IE text size adjust after device orientation change,\n//    without disabling user zoom.\n//\n\nhtml {\n  font-family: sans-serif; // 1\n  -ms-text-size-adjust: 100%; // 2\n  -webkit-text-size-adjust: 100%; // 2\n}\n\n//\n// Remove default margin.\n//\n\nbody {\n  margin: 0;\n}\n\n// HTML5 display definitions\n// ==========================================================================\n\n//\n// Correct `block` display not defined for any HTML5 element in IE 8/9.\n// Correct `block` display not defined for `details` or `summary` in IE 10/11\n// and Firefox.\n// Correct `block` display not defined for `main` in IE 11.\n//\n\narticle,\naside,\ndetails,\nfigcaption,\nfigure,\nfooter,\nheader,\nhgroup,\nmain,\nmenu,\nnav,\nsection,\nsummary {\n  display: block;\n}\n\n//\n// 1. Correct `inline-block` display not defined in IE 8/9.\n// 2. Normalize vertical alignment of `progress` in Chrome, Firefox, and Opera.\n//\n\naudio,\ncanvas,\nprogress,\nvideo {\n  display: inline-block; // 1\n  vertical-align: baseline; // 2\n}\n\n//\n// Prevent modern browsers from displaying `audio` without controls.\n// Remove excess height in iOS 5 devices.\n//\n\naudio:not([controls]) {\n  display: none;\n  height: 0;\n}\n\n//\n// Address `[hidden]` styling not present in IE 8/9/10.\n// Hide the `template` element in IE 8/9/10/11, Safari, and Firefox < 22.\n//\n\n[hidden],\ntemplate {\n  display: none;\n}\n\n// Links\n// ==========================================================================\n\n//\n// Remove the gray background color from active links in IE 10.\n//\n\na {\n  background-color: transparent;\n}\n\n//\n// Improve readability of focused elements when they are also in an\n// active/hover state.\n//\n\na:active,\na:hover {\n  outline: 0;\n}\n\n// Text-level semantics\n// ==========================================================================\n\n//\n// Address styling not present in IE 8/9/10/11, Safari, and Chrome.\n//\n\nabbr[title] {\n  border-bottom: 1px dotted;\n}\n\n//\n// Address style set to `bolder` in Firefox 4+, Safari, and Chrome.\n//\n\nb,\nstrong {\n  font-weight: bold;\n}\n\n//\n// Address styling not present in Safari and Chrome.\n//\n\ndfn {\n  font-style: italic;\n}\n\n//\n// Address variable `h1` font-size and margin within `section` and `article`\n// contexts in Firefox 4+, Safari, and Chrome.\n//\n\nh1 {\n  font-size: 2em;\n  margin: 0.67em 0;\n}\n\n//\n// Address styling not present in IE 8/9.\n//\n\nmark {\n  background: #ff0;\n  color: #000;\n}\n\n//\n// Address inconsistent and variable font size in all browsers.\n//\n\nsmall {\n  font-size: 80%;\n}\n\n//\n// Prevent `sub` and `sup` affecting `line-height` in all browsers.\n//\n\nsub,\nsup {\n  font-size: 75%;\n  line-height: 0;\n  position: relative;\n  vertical-align: baseline;\n}\n\nsup {\n  top: -0.5em;\n}\n\nsub {\n  bottom: -0.25em;\n}\n\n// Embedded content\n// ==========================================================================\n\n//\n// Remove border when inside `a` element in IE 8/9/10.\n//\n\nimg {\n  border: 0;\n}\n\n//\n// Correct overflow not hidden in IE 9/10/11.\n//\n\nsvg:not(:root) {\n  overflow: hidden;\n}\n\n// Grouping content\n// ==========================================================================\n\n//\n// Address margin not present in IE 8/9 and Safari.\n//\n\nfigure {\n  margin: 1em 40px;\n}\n\n//\n// Address differences between Firefox and other browsers.\n//\n\nhr {\n  box-sizing: content-box;\n  height: 0;\n}\n\n//\n// Contain overflow in all browsers.\n//\n\npre {\n  overflow: auto;\n}\n\n//\n// Address odd `em`-unit font size rendering in all browsers.\n//\n\ncode,\nkbd,\npre,\nsamp {\n  font-family: monospace, monospace;\n  font-size: 1em;\n}\n\n// Forms\n// ==========================================================================\n\n//\n// Known limitation: by default, Chrome and Safari on OS X allow very limited\n// styling of `select`, unless a `border` property is set.\n//\n\n//\n// 1. Correct color not being inherited.\n//    Known issue: affects color of disabled elements.\n// 2. Correct font properties not being inherited.\n// 3. Address margins set differently in Firefox 4+, Safari, and Chrome.\n//\n\nbutton,\ninput,\noptgroup,\nselect,\ntextarea {\n  color: inherit; // 1\n  font: inherit; // 2\n  margin: 0; // 3\n}\n\n//\n// Address `overflow` set to `hidden` in IE 8/9/10/11.\n//\n\nbutton {\n  overflow: visible;\n}\n\n//\n// Address inconsistent `text-transform` inheritance for `button` and `select`.\n// All other form control elements do not inherit `text-transform` values.\n// Correct `button` style inheritance in Firefox, IE 8/9/10/11, and Opera.\n// Correct `select` style inheritance in Firefox.\n//\n\nbutton,\nselect {\n  text-transform: none;\n}\n\n//\n// 1. Avoid the WebKit bug in Android 4.0.* where (2) destroys native `audio`\n//    and `video` controls.\n// 2. Correct inability to style clickable `input` types in iOS.\n// 3. Improve usability and consistency of cursor style between image-type\n//    `input` and others.\n//\n\nbutton,\nhtml input[type=\"button\"], // 1\ninput[type=\"reset\"],\ninput[type=\"submit\"] {\n  -webkit-appearance: button; // 2\n  cursor: pointer; // 3\n}\n\n//\n// Re-set default cursor for disabled elements.\n//\n\nbutton[disabled],\nhtml input[disabled] {\n  cursor: default;\n}\n\n//\n// Remove inner padding and border in Firefox 4+.\n//\n\nbutton::-moz-focus-inner,\ninput::-moz-focus-inner {\n  border: 0;\n  padding: 0;\n}\n\n//\n// Address Firefox 4+ setting `line-height` on `input` using `!important` in\n// the UA stylesheet.\n//\n\ninput {\n  line-height: normal;\n}\n\n//\n// It's recommended that you don't attempt to style these elements.\n// Firefox's implementation doesn't respect box-sizing, padding, or width.\n//\n// 1. Address box sizing set to `content-box` in IE 8/9/10.\n// 2. Remove excess padding in IE 8/9/10.\n//\n\ninput[type=\"checkbox\"],\ninput[type=\"radio\"] {\n  box-sizing: border-box; // 1\n  padding: 0; // 2\n}\n\n//\n// Fix the cursor style for Chrome's increment/decrement buttons. For certain\n// `font-size` values of the `input`, it causes the cursor style of the\n// decrement button to change from `default` to `text`.\n//\n\ninput[type=\"number\"]::-webkit-inner-spin-button,\ninput[type=\"number\"]::-webkit-outer-spin-button {\n  height: auto;\n}\n\n//\n// 1. Address `appearance` set to `searchfield` in Safari and Chrome.\n// 2. Address `box-sizing` set to `border-box` in Safari and Chrome.\n//\n\ninput[type=\"search\"] {\n  -webkit-appearance: textfield; // 1\n  box-sizing: content-box; //2\n}\n\n//\n// Remove inner padding and search cancel button in Safari and Chrome on OS X.\n// Safari (but not Chrome) clips the cancel button when the search input has\n// padding (and `textfield` appearance).\n//\n\ninput[type=\"search\"]::-webkit-search-cancel-button,\ninput[type=\"search\"]::-webkit-search-decoration {\n  -webkit-appearance: none;\n}\n\n//\n// Define consistent border, margin, and padding.\n//\n\nfieldset {\n  border: 1px solid #c0c0c0;\n  margin: 0 2px;\n  padding: 0.35em 0.625em 0.75em;\n}\n\n//\n// 1. Correct `color` not being inherited in IE 8/9/10/11.\n// 2. Remove padding so people aren't caught out if they zero out fieldsets.\n//\n\nlegend {\n  border: 0; // 1\n  padding: 0; // 2\n}\n\n//\n// Remove default vertical scrollbar in IE 8/9/10/11.\n//\n\ntextarea {\n  overflow: auto;\n}\n\n//\n// Don't inherit the `font-weight` (applied by a rule above).\n// NOTE: the default cannot safely be changed in Chrome and Safari on OS X.\n//\n\noptgroup {\n  font-weight: bold;\n}\n\n// Tables\n// ==========================================================================\n\n//\n// Remove most spacing between table cells.\n//\n\ntable {\n  border-collapse: collapse;\n  border-spacing: 0;\n}\n\ntd,\nth {\n  padding: 0;\n}\n","/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */\n\n// ==========================================================================\n// Print styles.\n// Inlined to avoid the additional HTTP request: h5bp.com/r\n// ==========================================================================\n\n@media print {\n    *,\n    *:before,\n    *:after {\n        background: transparent !important;\n        color: #000 !important; // Black prints faster: h5bp.com/s\n        box-shadow: none !important;\n        text-shadow: none !important;\n    }\n\n    a,\n    a:visited {\n        text-decoration: underline;\n    }\n\n    a[href]:after {\n        content: \" (\" attr(href) \")\";\n    }\n\n    abbr[title]:after {\n        content: \" (\" attr(title) \")\";\n    }\n\n    // Don't show links that are fragment identifiers,\n    // or use the `javascript:` pseudo protocol\n    a[href^=\"#\"]:after,\n    a[href^=\"javascript:\"]:after {\n        content: \"\";\n    }\n\n    pre,\n    blockquote {\n        border: 1px solid #999;\n        page-break-inside: avoid;\n    }\n\n    thead {\n        display: table-header-group; // h5bp.com/t\n    }\n\n    tr,\n    img {\n        page-break-inside: avoid;\n    }\n\n    img {\n        max-width: 100% !important;\n    }\n\n    p,\n    h2,\n    h3 {\n        orphans: 3;\n        widows: 3;\n    }\n\n    h2,\n    h3 {\n        page-break-after: avoid;\n    }\n\n    // Bootstrap specific changes start\n\n    // Bootstrap components\n    .navbar {\n        display: none;\n    }\n    .btn,\n    .dropup > .btn {\n        > .caret {\n            border-top-color: #000 !important;\n        }\n    }\n    .label {\n        border: 1px solid #000;\n    }\n\n    .table {\n        border-collapse: collapse !important;\n\n        td,\n        th {\n            background-color: #fff !important;\n        }\n    }\n    .table-bordered {\n        th,\n        td {\n            border: 1px solid #ddd !important;\n        }\n    }\n\n    // Bootstrap specific changes end\n}\n","/*!\n * Bootstrap v3.3.7 (http://getbootstrap.com)\n * Copyright 2011-2016 Twitter, Inc.\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)\n */\n/*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */\nhtml {\n  font-family: sans-serif;\n  -ms-text-size-adjust: 100%;\n  -webkit-text-size-adjust: 100%;\n}\nbody {\n  margin: 0;\n}\narticle,\naside,\ndetails,\nfigcaption,\nfigure,\nfooter,\nheader,\nhgroup,\nmain,\nmenu,\nnav,\nsection,\nsummary {\n  display: block;\n}\naudio,\ncanvas,\nprogress,\nvideo {\n  display: inline-block;\n  vertical-align: baseline;\n}\naudio:not([controls]) {\n  display: none;\n  height: 0;\n}\n[hidden],\ntemplate {\n  display: none;\n}\na {\n  background-color: transparent;\n}\na:active,\na:hover {\n  outline: 0;\n}\nabbr[title] {\n  border-bottom: 1px dotted;\n}\nb,\nstrong {\n  font-weight: bold;\n}\ndfn {\n  font-style: italic;\n}\nh1 {\n  font-size: 2em;\n  margin: 0.67em 0;\n}\nmark {\n  background: #ff0;\n  color: #000;\n}\nsmall {\n  font-size: 80%;\n}\nsub,\nsup {\n  font-size: 75%;\n  line-height: 0;\n  position: relative;\n  vertical-align: baseline;\n}\nsup {\n  top: -0.5em;\n}\nsub {\n  bottom: -0.25em;\n}\nimg {\n  border: 0;\n}\nsvg:not(:root) {\n  overflow: hidden;\n}\nfigure {\n  margin: 1em 40px;\n}\nhr {\n  box-sizing: content-box;\n  height: 0;\n}\npre {\n  overflow: auto;\n}\ncode,\nkbd,\npre,\nsamp {\n  font-family: monospace, monospace;\n  font-size: 1em;\n}\nbutton,\ninput,\noptgroup,\nselect,\ntextarea {\n  color: inherit;\n  font: inherit;\n  margin: 0;\n}\nbutton {\n  overflow: visible;\n}\nbutton,\nselect {\n  text-transform: none;\n}\nbutton,\nhtml input[type=\"button\"],\ninput[type=\"reset\"],\ninput[type=\"submit\"] {\n  -webkit-appearance: button;\n  cursor: pointer;\n}\nbutton[disabled],\nhtml input[disabled] {\n  cursor: default;\n}\nbutton::-moz-focus-inner,\ninput::-moz-focus-inner {\n  border: 0;\n  padding: 0;\n}\ninput {\n  line-height: normal;\n}\ninput[type=\"checkbox\"],\ninput[type=\"radio\"] {\n  box-sizing: border-box;\n  padding: 0;\n}\ninput[type=\"number\"]::-webkit-inner-spin-button,\ninput[type=\"number\"]::-webkit-outer-spin-button {\n  height: auto;\n}\ninput[type=\"search\"] {\n  -webkit-appearance: textfield;\n  box-sizing: content-box;\n}\ninput[type=\"search\"]::-webkit-search-cancel-button,\ninput[type=\"search\"]::-webkit-search-decoration {\n  -webkit-appearance: none;\n}\nfieldset {\n  border: 1px solid #c0c0c0;\n  margin: 0 2px;\n  padding: 0.35em 0.625em 0.75em;\n}\nlegend {\n  border: 0;\n  padding: 0;\n}\ntextarea {\n  overflow: auto;\n}\noptgroup {\n  font-weight: bold;\n}\ntable {\n  border-collapse: collapse;\n  border-spacing: 0;\n}\ntd,\nth {\n  padding: 0;\n}\n/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */\n@media print {\n  *,\n  *:before,\n  *:after {\n    background: transparent !important;\n    color: #000 !important;\n    box-shadow: none !important;\n    text-shadow: none !important;\n  }\n  a,\n  a:visited {\n    text-decoration: underline;\n  }\n  a[href]:after {\n    content: \" (\" attr(href) \")\";\n  }\n  abbr[title]:after {\n    content: \" (\" attr(title) \")\";\n  }\n  a[href^=\"#\"]:after,\n  a[href^=\"javascript:\"]:after {\n    content: \"\";\n  }\n  pre,\n  blockquote {\n    border: 1px solid #999;\n    page-break-inside: avoid;\n  }\n  thead {\n    display: table-header-group;\n  }\n  tr,\n  img {\n    page-break-inside: avoid;\n  }\n  img {\n    max-width: 100% !important;\n  }\n  p,\n  h2,\n  h3 {\n    orphans: 3;\n    widows: 3;\n  }\n  h2,\n  h3 {\n    page-break-after: avoid;\n  }\n  .navbar {\n    display: none;\n  }\n  .btn > .caret,\n  .dropup > .btn > .caret {\n    border-top-color: #000 !important;\n  }\n  .label {\n    border: 1px solid #000;\n  }\n  .table {\n    border-collapse: collapse !important;\n  }\n  .table td,\n  .table th {\n    background-color: #fff !important;\n  }\n  .table-bordered th,\n  .table-bordered td {\n    border: 1px solid #ddd !important;\n  }\n}\n@font-face {\n  font-family: 'Glyphicons Halflings';\n  src: url('../fonts/glyphicons-halflings-regular.eot');\n  src: url('../fonts/glyphicons-halflings-regular.eot?#iefix') format('embedded-opentype'), url('../fonts/glyphicons-halflings-regular.woff2') format('woff2'), url('../fonts/glyphicons-halflings-regular.woff') format('woff'), url('../fonts/glyphicons-halflings-regular.ttf') format('truetype'), url('../fonts/glyphicons-halflings-regular.svg#glyphicons_halflingsregular') format('svg');\n}\n.glyphicon {\n  position: relative;\n  top: 1px;\n  display: inline-block;\n  font-family: 'Glyphicons Halflings';\n  font-style: normal;\n  font-weight: normal;\n  line-height: 1;\n  -webkit-font-smoothing: antialiased;\n  -moz-osx-font-smoothing: grayscale;\n}\n.glyphicon-asterisk:before {\n  content: \"\\002a\";\n}\n.glyphicon-plus:before {\n  content: \"\\002b\";\n}\n.glyphicon-euro:before,\n.glyphicon-eur:before {\n  content: \"\\20ac\";\n}\n.glyphicon-minus:before {\n  content: \"\\2212\";\n}\n.glyphicon-cloud:before {\n  content: \"\\2601\";\n}\n.glyphicon-envelope:before {\n  content: \"\\2709\";\n}\n.glyphicon-pencil:before {\n  content: \"\\270f\";\n}\n.glyphicon-glass:before {\n  content: \"\\e001\";\n}\n.glyphicon-music:before {\n  content: \"\\e002\";\n}\n.glyphicon-search:before {\n  content: \"\\e003\";\n}\n.glyphicon-heart:before {\n  content: \"\\e005\";\n}\n.glyphicon-star:before {\n  content: \"\\e006\";\n}\n.glyphicon-star-empty:before {\n  content: \"\\e007\";\n}\n.glyphicon-user:before {\n  content: \"\\e008\";\n}\n.glyphicon-film:before {\n  content: \"\\e009\";\n}\n.glyphicon-th-large:before {\n  content: \"\\e010\";\n}\n.glyphicon-th:before {\n  content: \"\\e011\";\n}\n.glyphicon-th-list:before {\n  content: \"\\e012\";\n}\n.glyphicon-ok:before {\n  content: \"\\e013\";\n}\n.glyphicon-remove:before {\n  content: \"\\e014\";\n}\n.glyphicon-zoom-in:before {\n  content: \"\\e015\";\n}\n.glyphicon-zoom-out:before {\n  content: \"\\e016\";\n}\n.glyphicon-off:before {\n  content: \"\\e017\";\n}\n.glyphicon-signal:before {\n  content: \"\\e018\";\n}\n.glyphicon-cog:before {\n  content: \"\\e019\";\n}\n.glyphicon-trash:before {\n  content: \"\\e020\";\n}\n.glyphicon-home:before {\n  content: \"\\e021\";\n}\n.glyphicon-file:before {\n  content: \"\\e022\";\n}\n.glyphicon-time:before {\n  content: \"\\e023\";\n}\n.glyphicon-road:before {\n  content: \"\\e024\";\n}\n.glyphicon-download-alt:before {\n  content: \"\\e025\";\n}\n.glyphicon-download:before {\n  content: \"\\e026\";\n}\n.glyphicon-upload:before {\n  content: \"\\e027\";\n}\n.glyphicon-inbox:before {\n  content: \"\\e028\";\n}\n.glyphicon-play-circle:before {\n  content: \"\\e029\";\n}\n.glyphicon-repeat:before {\n  content: \"\\e030\";\n}\n.glyphicon-refresh:before {\n  content: \"\\e031\";\n}\n.glyphicon-list-alt:before {\n  content: \"\\e032\";\n}\n.glyphicon-lock:before {\n  content: \"\\e033\";\n}\n.glyphicon-flag:before {\n  content: \"\\e034\";\n}\n.glyphicon-headphones:before {\n  content: \"\\e035\";\n}\n.glyphicon-volume-off:before {\n  content: \"\\e036\";\n}\n.glyphicon-volume-down:before {\n  content: \"\\e037\";\n}\n.glyphicon-volume-up:before {\n  content: \"\\e038\";\n}\n.glyphicon-qrcode:before {\n  content: \"\\e039\";\n}\n.glyphicon-barcode:before {\n  content: \"\\e040\";\n}\n.glyphicon-tag:before {\n  content: \"\\e041\";\n}\n.glyphicon-tags:before {\n  content: \"\\e042\";\n}\n.glyphicon-book:before {\n  content: \"\\e043\";\n}\n.glyphicon-bookmark:before {\n  content: \"\\e044\";\n}\n.glyphicon-print:before {\n  content: \"\\e045\";\n}\n.glyphicon-camera:before {\n  content: \"\\e046\";\n}\n.glyphicon-font:before {\n  content: \"\\e047\";\n}\n.glyphicon-bold:before {\n  content: \"\\e048\";\n}\n.glyphicon-italic:before {\n  content: \"\\e049\";\n}\n.glyphicon-text-height:before {\n  content: \"\\e050\";\n}\n.glyphicon-text-width:before {\n  content: \"\\e051\";\n}\n.glyphicon-align-left:before {\n  content: \"\\e052\";\n}\n.glyphicon-align-center:before {\n  content: \"\\e053\";\n}\n.glyphicon-align-right:before {\n  content: \"\\e054\";\n}\n.glyphicon-align-justify:before {\n  content: \"\\e055\";\n}\n.glyphicon-list:before {\n  content: \"\\e056\";\n}\n.glyphicon-indent-left:before {\n  content: \"\\e057\";\n}\n.glyphicon-indent-right:before {\n  content: \"\\e058\";\n}\n.glyphicon-facetime-video:before {\n  content: \"\\e059\";\n}\n.glyphicon-picture:before {\n  content: \"\\e060\";\n}\n.glyphicon-map-marker:before {\n  content: \"\\e062\";\n}\n.glyphicon-adjust:before {\n  content: \"\\e063\";\n}\n.glyphicon-tint:before {\n  content: \"\\e064\";\n}\n.glyphicon-edit:before {\n  content: \"\\e065\";\n}\n.glyphicon-share:before {\n  content: \"\\e066\";\n}\n.glyphicon-check:before {\n  content: \"\\e067\";\n}\n.glyphicon-move:before {\n  content: \"\\e068\";\n}\n.glyphicon-step-backward:before {\n  content: \"\\e069\";\n}\n.glyphicon-fast-backward:before {\n  content: \"\\e070\";\n}\n.glyphicon-backward:before {\n  content: \"\\e071\";\n}\n.glyphicon-play:before {\n  content: \"\\e072\";\n}\n.glyphicon-pause:before {\n  content: \"\\e073\";\n}\n.glyphicon-stop:before {\n  content: \"\\e074\";\n}\n.glyphicon-forward:before {\n  content: \"\\e075\";\n}\n.glyphicon-fast-forward:before {\n  content: \"\\e076\";\n}\n.glyphicon-step-forward:before {\n  content: \"\\e077\";\n}\n.glyphicon-eject:before {\n  content: \"\\e078\";\n}\n.glyphicon-chevron-left:before {\n  content: \"\\e079\";\n}\n.glyphicon-chevron-right:before {\n  content: \"\\e080\";\n}\n.glyphicon-plus-sign:before {\n  content: \"\\e081\";\n}\n.glyphicon-minus-sign:before {\n  content: \"\\e082\";\n}\n.glyphicon-remove-sign:before {\n  content: \"\\e083\";\n}\n.glyphicon-ok-sign:before {\n  content: \"\\e084\";\n}\n.glyphicon-question-sign:before {\n  content: \"\\e085\";\n}\n.glyphicon-info-sign:before {\n  content: \"\\e086\";\n}\n.glyphicon-screenshot:before {\n  content: \"\\e087\";\n}\n.glyphicon-remove-circle:before {\n  content: \"\\e088\";\n}\n.glyphicon-ok-circle:before {\n  content: \"\\e089\";\n}\n.glyphicon-ban-circle:before {\n  content: \"\\e090\";\n}\n.glyphicon-arrow-left:before {\n  content: \"\\e091\";\n}\n.glyphicon-arrow-right:before {\n  content: \"\\e092\";\n}\n.glyphicon-arrow-up:before {\n  content: \"\\e093\";\n}\n.glyphicon-arrow-down:before {\n  content: \"\\e094\";\n}\n.glyphicon-share-alt:before {\n  content: \"\\e095\";\n}\n.glyphicon-resize-full:before {\n  content: \"\\e096\";\n}\n.glyphicon-resize-small:before {\n  content: \"\\e097\";\n}\n.glyphicon-exclamation-sign:before {\n  content: \"\\e101\";\n}\n.glyphicon-gift:before {\n  content: \"\\e102\";\n}\n.glyphicon-leaf:before {\n  content: \"\\e103\";\n}\n.glyphicon-fire:before {\n  content: \"\\e104\";\n}\n.glyphicon-eye-open:before {\n  content: \"\\e105\";\n}\n.glyphicon-eye-close:before {\n  content: \"\\e106\";\n}\n.glyphicon-warning-sign:before {\n  content: \"\\e107\";\n}\n.glyphicon-plane:before {\n  content: \"\\e108\";\n}\n.glyphicon-calendar:before {\n  content: \"\\e109\";\n}\n.glyphicon-random:before {\n  content: \"\\e110\";\n}\n.glyphicon-comment:before {\n  content: \"\\e111\";\n}\n.glyphicon-magnet:before {\n  content: \"\\e112\";\n}\n.glyphicon-chevron-up:before {\n  content: \"\\e113\";\n}\n.glyphicon-chevron-down:before {\n  content: \"\\e114\";\n}\n.glyphicon-retweet:before {\n  content: \"\\e115\";\n}\n.glyphicon-shopping-cart:before {\n  content: \"\\e116\";\n}\n.glyphicon-folder-close:before {\n  content: \"\\e117\";\n}\n.glyphicon-folder-open:before {\n  content: \"\\e118\";\n}\n.glyphicon-resize-vertical:before {\n  content: \"\\e119\";\n}\n.glyphicon-resize-horizontal:before {\n  content: \"\\e120\";\n}\n.glyphicon-hdd:before {\n  content: \"\\e121\";\n}\n.glyphicon-bullhorn:before {\n  content: \"\\e122\";\n}\n.glyphicon-bell:before {\n  content: \"\\e123\";\n}\n.glyphicon-certificate:before {\n  content: \"\\e124\";\n}\n.glyphicon-thumbs-up:before {\n  content: \"\\e125\";\n}\n.glyphicon-thumbs-down:before {\n  content: \"\\e126\";\n}\n.glyphicon-hand-right:before {\n  content: \"\\e127\";\n}\n.glyphicon-hand-left:before {\n  content: \"\\e128\";\n}\n.glyphicon-hand-up:before {\n  content: \"\\e129\";\n}\n.glyphicon-hand-down:before {\n  content: \"\\e130\";\n}\n.glyphicon-circle-arrow-right:before {\n  content: \"\\e131\";\n}\n.glyphicon-circle-arrow-left:before {\n  content: \"\\e132\";\n}\n.glyphicon-circle-arrow-up:before {\n  content: \"\\e133\";\n}\n.glyphicon-circle-arrow-down:before {\n  content: \"\\e134\";\n}\n.glyphicon-globe:before {\n  content: \"\\e135\";\n}\n.glyphicon-wrench:before {\n  content: \"\\e136\";\n}\n.glyphicon-tasks:before {\n  content: \"\\e137\";\n}\n.glyphicon-filter:before {\n  content: \"\\e138\";\n}\n.glyphicon-briefcase:before {\n  content: \"\\e139\";\n}\n.glyphicon-fullscreen:before {\n  content: \"\\e140\";\n}\n.glyphicon-dashboard:before {\n  content: \"\\e141\";\n}\n.glyphicon-paperclip:before {\n  content: \"\\e142\";\n}\n.glyphicon-heart-empty:before {\n  content: \"\\e143\";\n}\n.glyphicon-link:before {\n  content: \"\\e144\";\n}\n.glyphicon-phone:before {\n  content: \"\\e145\";\n}\n.glyphicon-pushpin:before {\n  content: \"\\e146\";\n}\n.glyphicon-usd:before {\n  content: \"\\e148\";\n}\n.glyphicon-gbp:before {\n  content: \"\\e149\";\n}\n.glyphicon-sort:before {\n  content: \"\\e150\";\n}\n.glyphicon-sort-by-alphabet:before {\n  content: \"\\e151\";\n}\n.glyphicon-sort-by-alphabet-alt:before {\n  content: \"\\e152\";\n}\n.glyphicon-sort-by-order:before {\n  content: \"\\e153\";\n}\n.glyphicon-sort-by-order-alt:before {\n  content: \"\\e154\";\n}\n.glyphicon-sort-by-attributes:before {\n  content: \"\\e155\";\n}\n.glyphicon-sort-by-attributes-alt:before {\n  content: \"\\e156\";\n}\n.glyphicon-unchecked:before {\n  content: \"\\e157\";\n}\n.glyphicon-expand:before {\n  content: \"\\e158\";\n}\n.glyphicon-collapse-down:before {\n  content: \"\\e159\";\n}\n.glyphicon-collapse-up:before {\n  content: \"\\e160\";\n}\n.glyphicon-log-in:before {\n  content: \"\\e161\";\n}\n.glyphicon-flash:before {\n  content: \"\\e162\";\n}\n.glyphicon-log-out:before {\n  content: \"\\e163\";\n}\n.glyphicon-new-window:before {\n  content: \"\\e164\";\n}\n.glyphicon-record:before {\n  content: \"\\e165\";\n}\n.glyphicon-save:before {\n  content: \"\\e166\";\n}\n.glyphicon-open:before {\n  content: \"\\e167\";\n}\n.glyphicon-saved:before {\n  content: \"\\e168\";\n}\n.glyphicon-import:before {\n  content: \"\\e169\";\n}\n.glyphicon-export:before {\n  content: \"\\e170\";\n}\n.glyphicon-send:before {\n  content: \"\\e171\";\n}\n.glyphicon-floppy-disk:before {\n  content: \"\\e172\";\n}\n.glyphicon-floppy-saved:before {\n  content: \"\\e173\";\n}\n.glyphicon-floppy-remove:before {\n  content: \"\\e174\";\n}\n.glyphicon-floppy-save:before {\n  content: \"\\e175\";\n}\n.glyphicon-floppy-open:before {\n  content: \"\\e176\";\n}\n.glyphicon-credit-card:before {\n  content: \"\\e177\";\n}\n.glyphicon-transfer:before {\n  content: \"\\e178\";\n}\n.glyphicon-cutlery:before {\n  content: \"\\e179\";\n}\n.glyphicon-header:before {\n  content: \"\\e180\";\n}\n.glyphicon-compressed:before {\n  content: \"\\e181\";\n}\n.glyphicon-earphone:before {\n  content: \"\\e182\";\n}\n.glyphicon-phone-alt:before {\n  content: \"\\e183\";\n}\n.glyphicon-tower:before {\n  content: \"\\e184\";\n}\n.glyphicon-stats:before {\n  content: \"\\e185\";\n}\n.glyphicon-sd-video:before {\n  content: \"\\e186\";\n}\n.glyphicon-hd-video:before {\n  content: \"\\e187\";\n}\n.glyphicon-subtitles:before {\n  content: \"\\e188\";\n}\n.glyphicon-sound-stereo:before {\n  content: \"\\e189\";\n}\n.glyphicon-sound-dolby:before {\n  content: \"\\e190\";\n}\n.glyphicon-sound-5-1:before {\n  content: \"\\e191\";\n}\n.glyphicon-sound-6-1:before {\n  content: \"\\e192\";\n}\n.glyphicon-sound-7-1:before {\n  content: \"\\e193\";\n}\n.glyphicon-copyright-mark:before {\n  content: \"\\e194\";\n}\n.glyphicon-registration-mark:before {\n  content: \"\\e195\";\n}\n.glyphicon-cloud-download:before {\n  content: \"\\e197\";\n}\n.glyphicon-cloud-upload:before {\n  content: \"\\e198\";\n}\n.glyphicon-tree-conifer:before {\n  content: \"\\e199\";\n}\n.glyphicon-tree-deciduous:before {\n  content: \"\\e200\";\n}\n.glyphicon-cd:before {\n  content: \"\\e201\";\n}\n.glyphicon-save-file:before {\n  content: \"\\e202\";\n}\n.glyphicon-open-file:before {\n  content: \"\\e203\";\n}\n.glyphicon-level-up:before {\n  content: \"\\e204\";\n}\n.glyphicon-copy:before {\n  content: \"\\e205\";\n}\n.glyphicon-paste:before {\n  content: \"\\e206\";\n}\n.glyphicon-alert:before {\n  content: \"\\e209\";\n}\n.glyphicon-equalizer:before {\n  content: \"\\e210\";\n}\n.glyphicon-king:before {\n  content: \"\\e211\";\n}\n.glyphicon-queen:before {\n  content: \"\\e212\";\n}\n.glyphicon-pawn:before {\n  content: \"\\e213\";\n}\n.glyphicon-bishop:before {\n  content: \"\\e214\";\n}\n.glyphicon-knight:before {\n  content: \"\\e215\";\n}\n.glyphicon-baby-formula:before {\n  content: \"\\e216\";\n}\n.glyphicon-tent:before {\n  content: \"\\26fa\";\n}\n.glyphicon-blackboard:before {\n  content: \"\\e218\";\n}\n.glyphicon-bed:before {\n  content: \"\\e219\";\n}\n.glyphicon-apple:before {\n  content: \"\\f8ff\";\n}\n.glyphicon-erase:before {\n  content: \"\\e221\";\n}\n.glyphicon-hourglass:before {\n  content: \"\\231b\";\n}\n.glyphicon-lamp:before {\n  content: \"\\e223\";\n}\n.glyphicon-duplicate:before {\n  content: \"\\e224\";\n}\n.glyphicon-piggy-bank:before {\n  content: \"\\e225\";\n}\n.glyphicon-scissors:before {\n  content: \"\\e226\";\n}\n.glyphicon-bitcoin:before {\n  content: \"\\e227\";\n}\n.glyphicon-btc:before {\n  content: \"\\e227\";\n}\n.glyphicon-xbt:before {\n  content: \"\\e227\";\n}\n.glyphicon-yen:before {\n  content: \"\\00a5\";\n}\n.glyphicon-jpy:before {\n  content: \"\\00a5\";\n}\n.glyphicon-ruble:before {\n  content: \"\\20bd\";\n}\n.glyphicon-rub:before {\n  content: \"\\20bd\";\n}\n.glyphicon-scale:before {\n  content: \"\\e230\";\n}\n.glyphicon-ice-lolly:before {\n  content: \"\\e231\";\n}\n.glyphicon-ice-lolly-tasted:before {\n  content: \"\\e232\";\n}\n.glyphicon-education:before {\n  content: \"\\e233\";\n}\n.glyphicon-option-horizontal:before {\n  content: \"\\e234\";\n}\n.glyphicon-option-vertical:before {\n  content: \"\\e235\";\n}\n.glyphicon-menu-hamburger:before {\n  content: \"\\e236\";\n}\n.glyphicon-modal-window:before {\n  content: \"\\e237\";\n}\n.glyphicon-oil:before {\n  content: \"\\e238\";\n}\n.glyphicon-grain:before {\n  content: \"\\e239\";\n}\n.glyphicon-sunglasses:before {\n  content: \"\\e240\";\n}\n.glyphicon-text-size:before {\n  content: \"\\e241\";\n}\n.glyphicon-text-color:before {\n  content: \"\\e242\";\n}\n.glyphicon-text-background:before {\n  content: \"\\e243\";\n}\n.glyphicon-object-align-top:before {\n  content: \"\\e244\";\n}\n.glyphicon-object-align-bottom:before {\n  content: \"\\e245\";\n}\n.glyphicon-object-align-horizontal:before {\n  content: \"\\e246\";\n}\n.glyphicon-object-align-left:before {\n  content: \"\\e247\";\n}\n.glyphicon-object-align-vertical:before {\n  content: \"\\e248\";\n}\n.glyphicon-object-align-right:before {\n  content: \"\\e249\";\n}\n.glyphicon-triangle-right:before {\n  content: \"\\e250\";\n}\n.glyphicon-triangle-left:before {\n  content: \"\\e251\";\n}\n.glyphicon-triangle-bottom:before {\n  content: \"\\e252\";\n}\n.glyphicon-triangle-top:before {\n  content: \"\\e253\";\n}\n.glyphicon-console:before {\n  content: \"\\e254\";\n}\n.glyphicon-superscript:before {\n  content: \"\\e255\";\n}\n.glyphicon-subscript:before {\n  content: \"\\e256\";\n}\n.glyphicon-menu-left:before {\n  content: \"\\e257\";\n}\n.glyphicon-menu-right:before {\n  content: \"\\e258\";\n}\n.glyphicon-menu-down:before {\n  content: \"\\e259\";\n}\n.glyphicon-menu-up:before {\n  content: \"\\e260\";\n}\n* {\n  -webkit-box-sizing: border-box;\n  -moz-box-sizing: border-box;\n  box-sizing: border-box;\n}\n*:before,\n*:after {\n  -webkit-box-sizing: border-box;\n  -moz-box-sizing: border-box;\n  box-sizing: border-box;\n}\nhtml {\n  font-size: 10px;\n  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);\n}\nbody {\n  font-family: \"Helvetica Neue\", Helvetica, Arial, sans-serif;\n  font-size: 14px;\n  line-height: 1.42857143;\n  color: #333333;\n  background-color: #fff;\n}\ninput,\nbutton,\nselect,\ntextarea {\n  font-family: inherit;\n  font-size: inherit;\n  line-height: inherit;\n}\na {\n  color: #337ab7;\n  text-decoration: none;\n}\na:hover,\na:focus {\n  color: #23527c;\n  text-decoration: underline;\n}\na:focus {\n  outline: 5px auto -webkit-focus-ring-color;\n  outline-offset: -2px;\n}\nfigure {\n  margin: 0;\n}\nimg {\n  vertical-align: middle;\n}\n.img-responsive,\n.thumbnail > img,\n.thumbnail a > img,\n.carousel-inner > .item > img,\n.carousel-inner > .item > a > img {\n  display: block;\n  max-width: 100%;\n  height: auto;\n}\n.img-rounded {\n  border-radius: 6px;\n}\n.img-thumbnail {\n  padding: 4px;\n  line-height: 1.42857143;\n  background-color: #fff;\n  border: 1px solid #ddd;\n  border-radius: 4px;\n  -webkit-transition: all 0.2s ease-in-out;\n  -o-transition: all 0.2s ease-in-out;\n  transition: all 0.2s ease-in-out;\n  display: inline-block;\n  max-width: 100%;\n  height: auto;\n}\n.img-circle {\n  border-radius: 50%;\n}\nhr {\n  margin-top: 20px;\n  margin-bottom: 20px;\n  border: 0;\n  border-top: 1px solid #eeeeee;\n}\n.sr-only {\n  position: absolute;\n  width: 1px;\n  height: 1px;\n  margin: -1px;\n  padding: 0;\n  overflow: hidden;\n  clip: rect(0, 0, 0, 0);\n  border: 0;\n}\n.sr-only-focusable:active,\n.sr-only-focusable:focus {\n  position: static;\n  width: auto;\n  height: auto;\n  margin: 0;\n  overflow: visible;\n  clip: auto;\n}\n[role=\"button\"] {\n  cursor: pointer;\n}\nh1,\nh2,\nh3,\nh4,\nh5,\nh6,\n.h1,\n.h2,\n.h3,\n.h4,\n.h5,\n.h6 {\n  font-family: inherit;\n  font-weight: 500;\n  line-height: 1.1;\n  color: inherit;\n}\nh1 small,\nh2 small,\nh3 small,\nh4 small,\nh5 small,\nh6 small,\n.h1 small,\n.h2 small,\n.h3 small,\n.h4 small,\n.h5 small,\n.h6 small,\nh1 .small,\nh2 .small,\nh3 .small,\nh4 .small,\nh5 .small,\nh6 .small,\n.h1 .small,\n.h2 .small,\n.h3 .small,\n.h4 .small,\n.h5 .small,\n.h6 .small {\n  font-weight: normal;\n  line-height: 1;\n  color: #777777;\n}\nh1,\n.h1,\nh2,\n.h2,\nh3,\n.h3 {\n  margin-top: 20px;\n  margin-bottom: 10px;\n}\nh1 small,\n.h1 small,\nh2 small,\n.h2 small,\nh3 small,\n.h3 small,\nh1 .small,\n.h1 .small,\nh2 .small,\n.h2 .small,\nh3 .small,\n.h3 .small {\n  font-size: 65%;\n}\nh4,\n.h4,\nh5,\n.h5,\nh6,\n.h6 {\n  margin-top: 10px;\n  margin-bottom: 10px;\n}\nh4 small,\n.h4 small,\nh5 small,\n.h5 small,\nh6 small,\n.h6 small,\nh4 .small,\n.h4 .small,\nh5 .small,\n.h5 .small,\nh6 .small,\n.h6 .small {\n  font-size: 75%;\n}\nh1,\n.h1 {\n  font-size: 36px;\n}\nh2,\n.h2 {\n  font-size: 30px;\n}\nh3,\n.h3 {\n  font-size: 24px;\n}\nh4,\n.h4 {\n  font-size: 18px;\n}\nh5,\n.h5 {\n  font-size: 14px;\n}\nh6,\n.h6 {\n  font-size: 12px;\n}\np {\n  margin: 0 0 10px;\n}\n.lead {\n  margin-bottom: 20px;\n  font-size: 16px;\n  font-weight: 300;\n  line-height: 1.4;\n}\n@media (min-width: 768px) {\n  .lead {\n    font-size: 21px;\n  }\n}\nsmall,\n.small {\n  font-size: 85%;\n}\nmark,\n.mark {\n  background-color: #fcf8e3;\n  padding: .2em;\n}\n.text-left {\n  text-align: left;\n}\n.text-right {\n  text-align: right;\n}\n.text-center {\n  text-align: center;\n}\n.text-justify {\n  text-align: justify;\n}\n.text-nowrap {\n  white-space: nowrap;\n}\n.text-lowercase {\n  text-transform: lowercase;\n}\n.text-uppercase {\n  text-transform: uppercase;\n}\n.text-capitalize {\n  text-transform: capitalize;\n}\n.text-muted {\n  color: #777777;\n}\n.text-primary {\n  color: #337ab7;\n}\na.text-primary:hover,\na.text-primary:focus {\n  color: #286090;\n}\n.text-success {\n  color: #3c763d;\n}\na.text-success:hover,\na.text-success:focus {\n  color: #2b542c;\n}\n.text-info {\n  color: #31708f;\n}\na.text-info:hover,\na.text-info:focus {\n  color: #245269;\n}\n.text-warning {\n  color: #8a6d3b;\n}\na.text-warning:hover,\na.text-warning:focus {\n  color: #66512c;\n}\n.text-danger {\n  color: #a94442;\n}\na.text-danger:hover,\na.text-danger:focus {\n  color: #843534;\n}\n.bg-primary {\n  color: #fff;\n  background-color: #337ab7;\n}\na.bg-primary:hover,\na.bg-primary:focus {\n  background-color: #286090;\n}\n.bg-success {\n  background-color: #dff0d8;\n}\na.bg-success:hover,\na.bg-success:focus {\n  background-color: #c1e2b3;\n}\n.bg-info {\n  background-color: #d9edf7;\n}\na.bg-info:hover,\na.bg-info:focus {\n  background-color: #afd9ee;\n}\n.bg-warning {\n  background-color: #fcf8e3;\n}\na.bg-warning:hover,\na.bg-warning:focus {\n  background-color: #f7ecb5;\n}\n.bg-danger {\n  background-color: #f2dede;\n}\na.bg-danger:hover,\na.bg-danger:focus {\n  background-color: #e4b9b9;\n}\n.page-header {\n  padding-bottom: 9px;\n  margin: 40px 0 20px;\n  border-bottom: 1px solid #eeeeee;\n}\nul,\nol {\n  margin-top: 0;\n  margin-bottom: 10px;\n}\nul ul,\nol ul,\nul ol,\nol ol {\n  margin-bottom: 0;\n}\n.list-unstyled {\n  padding-left: 0;\n  list-style: none;\n}\n.list-inline {\n  padding-left: 0;\n  list-style: none;\n  margin-left: -5px;\n}\n.list-inline > li {\n  display: inline-block;\n  padding-left: 5px;\n  padding-right: 5px;\n}\ndl {\n  margin-top: 0;\n  margin-bottom: 20px;\n}\ndt,\ndd {\n  line-height: 1.42857143;\n}\ndt {\n  font-weight: bold;\n}\ndd {\n  margin-left: 0;\n}\n@media (min-width: 768px) {\n  .dl-horizontal dt {\n    float: left;\n    width: 160px;\n    clear: left;\n    text-align: right;\n    overflow: hidden;\n    text-overflow: ellipsis;\n    white-space: nowrap;\n  }\n  .dl-horizontal dd {\n    margin-left: 180px;\n  }\n}\nabbr[title],\nabbr[data-original-title] {\n  cursor: help;\n  border-bottom: 1px dotted #777777;\n}\n.initialism {\n  font-size: 90%;\n  text-transform: uppercase;\n}\nblockquote {\n  padding: 10px 20px;\n  margin: 0 0 20px;\n  font-size: 17.5px;\n  border-left: 5px solid #eeeeee;\n}\nblockquote p:last-child,\nblockquote ul:last-child,\nblockquote ol:last-child {\n  margin-bottom: 0;\n}\nblockquote footer,\nblockquote small,\nblockquote .small {\n  display: block;\n  font-size: 80%;\n  line-height: 1.42857143;\n  color: #777777;\n}\nblockquote footer:before,\nblockquote small:before,\nblockquote .small:before {\n  content: '\\2014 \\00A0';\n}\n.blockquote-reverse,\nblockquote.pull-right {\n  padding-right: 15px;\n  padding-left: 0;\n  border-right: 5px solid #eeeeee;\n  border-left: 0;\n  text-align: right;\n}\n.blockquote-reverse footer:before,\nblockquote.pull-right footer:before,\n.blockquote-reverse small:before,\nblockquote.pull-right small:before,\n.blockquote-reverse .small:before,\nblockquote.pull-right .small:before {\n  content: '';\n}\n.blockquote-reverse footer:after,\nblockquote.pull-right footer:after,\n.blockquote-reverse small:after,\nblockquote.pull-right small:after,\n.blockquote-reverse .small:after,\nblockquote.pull-right .small:after {\n  content: '\\00A0 \\2014';\n}\naddress {\n  margin-bottom: 20px;\n  font-style: normal;\n  line-height: 1.42857143;\n}\ncode,\nkbd,\npre,\nsamp {\n  font-family: Menlo, Monaco, Consolas, \"Courier New\", monospace;\n}\ncode {\n  padding: 2px 4px;\n  font-size: 90%;\n  color: #c7254e;\n  background-color: #f9f2f4;\n  border-radius: 4px;\n}\nkbd {\n  padding: 2px 4px;\n  font-size: 90%;\n  color: #fff;\n  background-color: #333;\n  border-radius: 3px;\n  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);\n}\nkbd kbd {\n  padding: 0;\n  font-size: 100%;\n  font-weight: bold;\n  box-shadow: none;\n}\npre {\n  display: block;\n  padding: 9.5px;\n  margin: 0 0 10px;\n  font-size: 13px;\n  line-height: 1.42857143;\n  word-break: break-all;\n  word-wrap: break-word;\n  color: #333333;\n  background-color: #f5f5f5;\n  border: 1px solid #ccc;\n  border-radius: 4px;\n}\npre code {\n  padding: 0;\n  font-size: inherit;\n  color: inherit;\n  white-space: pre-wrap;\n  background-color: transparent;\n  border-radius: 0;\n}\n.pre-scrollable {\n  max-height: 340px;\n  overflow-y: scroll;\n}\n.container {\n  margin-right: auto;\n  margin-left: auto;\n  padding-left: 15px;\n  padding-right: 15px;\n}\n@media (min-width: 768px) {\n  .container {\n    width: 750px;\n  }\n}\n@media (min-width: 992px) {\n  .container {\n    width: 970px;\n  }\n}\n@media (min-width: 1200px) {\n  .container {\n    width: 1170px;\n  }\n}\n.container-fluid {\n  margin-right: auto;\n  margin-left: auto;\n  padding-left: 15px;\n  padding-right: 15px;\n}\n.row {\n  margin-left: -15px;\n  margin-right: -15px;\n}\n.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 {\n  position: relative;\n  min-height: 1px;\n  padding-left: 15px;\n  padding-right: 15px;\n}\n.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 {\n  float: left;\n}\n.col-xs-12 {\n  width: 100%;\n}\n.col-xs-11 {\n  width: 91.66666667%;\n}\n.col-xs-10 {\n  width: 83.33333333%;\n}\n.col-xs-9 {\n  width: 75%;\n}\n.col-xs-8 {\n  width: 66.66666667%;\n}\n.col-xs-7 {\n  width: 58.33333333%;\n}\n.col-xs-6 {\n  width: 50%;\n}\n.col-xs-5 {\n  width: 41.66666667%;\n}\n.col-xs-4 {\n  width: 33.33333333%;\n}\n.col-xs-3 {\n  width: 25%;\n}\n.col-xs-2 {\n  width: 16.66666667%;\n}\n.col-xs-1 {\n  width: 8.33333333%;\n}\n.col-xs-pull-12 {\n  right: 100%;\n}\n.col-xs-pull-11 {\n  right: 91.66666667%;\n}\n.col-xs-pull-10 {\n  right: 83.33333333%;\n}\n.col-xs-pull-9 {\n  right: 75%;\n}\n.col-xs-pull-8 {\n  right: 66.66666667%;\n}\n.col-xs-pull-7 {\n  right: 58.33333333%;\n}\n.col-xs-pull-6 {\n  right: 50%;\n}\n.col-xs-pull-5 {\n  right: 41.66666667%;\n}\n.col-xs-pull-4 {\n  right: 33.33333333%;\n}\n.col-xs-pull-3 {\n  right: 25%;\n}\n.col-xs-pull-2 {\n  right: 16.66666667%;\n}\n.col-xs-pull-1 {\n  right: 8.33333333%;\n}\n.col-xs-pull-0 {\n  right: auto;\n}\n.col-xs-push-12 {\n  left: 100%;\n}\n.col-xs-push-11 {\n  left: 91.66666667%;\n}\n.col-xs-push-10 {\n  left: 83.33333333%;\n}\n.col-xs-push-9 {\n  left: 75%;\n}\n.col-xs-push-8 {\n  left: 66.66666667%;\n}\n.col-xs-push-7 {\n  left: 58.33333333%;\n}\n.col-xs-push-6 {\n  left: 50%;\n}\n.col-xs-push-5 {\n  left: 41.66666667%;\n}\n.col-xs-push-4 {\n  left: 33.33333333%;\n}\n.col-xs-push-3 {\n  left: 25%;\n}\n.col-xs-push-2 {\n  left: 16.66666667%;\n}\n.col-xs-push-1 {\n  left: 8.33333333%;\n}\n.col-xs-push-0 {\n  left: auto;\n}\n.col-xs-offset-12 {\n  margin-left: 100%;\n}\n.col-xs-offset-11 {\n  margin-left: 91.66666667%;\n}\n.col-xs-offset-10 {\n  margin-left: 83.33333333%;\n}\n.col-xs-offset-9 {\n  margin-left: 75%;\n}\n.col-xs-offset-8 {\n  margin-left: 66.66666667%;\n}\n.col-xs-offset-7 {\n  margin-left: 58.33333333%;\n}\n.col-xs-offset-6 {\n  margin-left: 50%;\n}\n.col-xs-offset-5 {\n  margin-left: 41.66666667%;\n}\n.col-xs-offset-4 {\n  margin-left: 33.33333333%;\n}\n.col-xs-offset-3 {\n  margin-left: 25%;\n}\n.col-xs-offset-2 {\n  margin-left: 16.66666667%;\n}\n.col-xs-offset-1 {\n  margin-left: 8.33333333%;\n}\n.col-xs-offset-0 {\n  margin-left: 0%;\n}\n@media (min-width: 768px) {\n  .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {\n    float: left;\n  }\n  .col-sm-12 {\n    width: 100%;\n  }\n  .col-sm-11 {\n    width: 91.66666667%;\n  }\n  .col-sm-10 {\n    width: 83.33333333%;\n  }\n  .col-sm-9 {\n    width: 75%;\n  }\n  .col-sm-8 {\n    width: 66.66666667%;\n  }\n  .col-sm-7 {\n    width: 58.33333333%;\n  }\n  .col-sm-6 {\n    width: 50%;\n  }\n  .col-sm-5 {\n    width: 41.66666667%;\n  }\n  .col-sm-4 {\n    width: 33.33333333%;\n  }\n  .col-sm-3 {\n    width: 25%;\n  }\n  .col-sm-2 {\n    width: 16.66666667%;\n  }\n  .col-sm-1 {\n    width: 8.33333333%;\n  }\n  .col-sm-pull-12 {\n    right: 100%;\n  }\n  .col-sm-pull-11 {\n    right: 91.66666667%;\n  }\n  .col-sm-pull-10 {\n    right: 83.33333333%;\n  }\n  .col-sm-pull-9 {\n    right: 75%;\n  }\n  .col-sm-pull-8 {\n    right: 66.66666667%;\n  }\n  .col-sm-pull-7 {\n    right: 58.33333333%;\n  }\n  .col-sm-pull-6 {\n    right: 50%;\n  }\n  .col-sm-pull-5 {\n    right: 41.66666667%;\n  }\n  .col-sm-pull-4 {\n    right: 33.33333333%;\n  }\n  .col-sm-pull-3 {\n    right: 25%;\n  }\n  .col-sm-pull-2 {\n    right: 16.66666667%;\n  }\n  .col-sm-pull-1 {\n    right: 8.33333333%;\n  }\n  .col-sm-pull-0 {\n    right: auto;\n  }\n  .col-sm-push-12 {\n    left: 100%;\n  }\n  .col-sm-push-11 {\n    left: 91.66666667%;\n  }\n  .col-sm-push-10 {\n    left: 83.33333333%;\n  }\n  .col-sm-push-9 {\n    left: 75%;\n  }\n  .col-sm-push-8 {\n    left: 66.66666667%;\n  }\n  .col-sm-push-7 {\n    left: 58.33333333%;\n  }\n  .col-sm-push-6 {\n    left: 50%;\n  }\n  .col-sm-push-5 {\n    left: 41.66666667%;\n  }\n  .col-sm-push-4 {\n    left: 33.33333333%;\n  }\n  .col-sm-push-3 {\n    left: 25%;\n  }\n  .col-sm-push-2 {\n    left: 16.66666667%;\n  }\n  .col-sm-push-1 {\n    left: 8.33333333%;\n  }\n  .col-sm-push-0 {\n    left: auto;\n  }\n  .col-sm-offset-12 {\n    margin-left: 100%;\n  }\n  .col-sm-offset-11 {\n    margin-left: 91.66666667%;\n  }\n  .col-sm-offset-10 {\n    margin-left: 83.33333333%;\n  }\n  .col-sm-offset-9 {\n    margin-left: 75%;\n  }\n  .col-sm-offset-8 {\n    margin-left: 66.66666667%;\n  }\n  .col-sm-offset-7 {\n    margin-left: 58.33333333%;\n  }\n  .col-sm-offset-6 {\n    margin-left: 50%;\n  }\n  .col-sm-offset-5 {\n    margin-left: 41.66666667%;\n  }\n  .col-sm-offset-4 {\n    margin-left: 33.33333333%;\n  }\n  .col-sm-offset-3 {\n    margin-left: 25%;\n  }\n  .col-sm-offset-2 {\n    margin-left: 16.66666667%;\n  }\n  .col-sm-offset-1 {\n    margin-left: 8.33333333%;\n  }\n  .col-sm-offset-0 {\n    margin-left: 0%;\n  }\n}\n@media (min-width: 992px) {\n  .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {\n    float: left;\n  }\n  .col-md-12 {\n    width: 100%;\n  }\n  .col-md-11 {\n    width: 91.66666667%;\n  }\n  .col-md-10 {\n    width: 83.33333333%;\n  }\n  .col-md-9 {\n    width: 75%;\n  }\n  .col-md-8 {\n    width: 66.66666667%;\n  }\n  .col-md-7 {\n    width: 58.33333333%;\n  }\n  .col-md-6 {\n    width: 50%;\n  }\n  .col-md-5 {\n    width: 41.66666667%;\n  }\n  .col-md-4 {\n    width: 33.33333333%;\n  }\n  .col-md-3 {\n    width: 25%;\n  }\n  .col-md-2 {\n    width: 16.66666667%;\n  }\n  .col-md-1 {\n    width: 8.33333333%;\n  }\n  .col-md-pull-12 {\n    right: 100%;\n  }\n  .col-md-pull-11 {\n    right: 91.66666667%;\n  }\n  .col-md-pull-10 {\n    right: 83.33333333%;\n  }\n  .col-md-pull-9 {\n    right: 75%;\n  }\n  .col-md-pull-8 {\n    right: 66.66666667%;\n  }\n  .col-md-pull-7 {\n    right: 58.33333333%;\n  }\n  .col-md-pull-6 {\n    right: 50%;\n  }\n  .col-md-pull-5 {\n    right: 41.66666667%;\n  }\n  .col-md-pull-4 {\n    right: 33.33333333%;\n  }\n  .col-md-pull-3 {\n    right: 25%;\n  }\n  .col-md-pull-2 {\n    right: 16.66666667%;\n  }\n  .col-md-pull-1 {\n    right: 8.33333333%;\n  }\n  .col-md-pull-0 {\n    right: auto;\n  }\n  .col-md-push-12 {\n    left: 100%;\n  }\n  .col-md-push-11 {\n    left: 91.66666667%;\n  }\n  .col-md-push-10 {\n    left: 83.33333333%;\n  }\n  .col-md-push-9 {\n    left: 75%;\n  }\n  .col-md-push-8 {\n    left: 66.66666667%;\n  }\n  .col-md-push-7 {\n    left: 58.33333333%;\n  }\n  .col-md-push-6 {\n    left: 50%;\n  }\n  .col-md-push-5 {\n    left: 41.66666667%;\n  }\n  .col-md-push-4 {\n    left: 33.33333333%;\n  }\n  .col-md-push-3 {\n    left: 25%;\n  }\n  .col-md-push-2 {\n    left: 16.66666667%;\n  }\n  .col-md-push-1 {\n    left: 8.33333333%;\n  }\n  .col-md-push-0 {\n    left: auto;\n  }\n  .col-md-offset-12 {\n    margin-left: 100%;\n  }\n  .col-md-offset-11 {\n    margin-left: 91.66666667%;\n  }\n  .col-md-offset-10 {\n    margin-left: 83.33333333%;\n  }\n  .col-md-offset-9 {\n    margin-left: 75%;\n  }\n  .col-md-offset-8 {\n    margin-left: 66.66666667%;\n  }\n  .col-md-offset-7 {\n    margin-left: 58.33333333%;\n  }\n  .col-md-offset-6 {\n    margin-left: 50%;\n  }\n  .col-md-offset-5 {\n    margin-left: 41.66666667%;\n  }\n  .col-md-offset-4 {\n    margin-left: 33.33333333%;\n  }\n  .col-md-offset-3 {\n    margin-left: 25%;\n  }\n  .col-md-offset-2 {\n    margin-left: 16.66666667%;\n  }\n  .col-md-offset-1 {\n    margin-left: 8.33333333%;\n  }\n  .col-md-offset-0 {\n    margin-left: 0%;\n  }\n}\n@media (min-width: 1200px) {\n  .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {\n    float: left;\n  }\n  .col-lg-12 {\n    width: 100%;\n  }\n  .col-lg-11 {\n    width: 91.66666667%;\n  }\n  .col-lg-10 {\n    width: 83.33333333%;\n  }\n  .col-lg-9 {\n    width: 75%;\n  }\n  .col-lg-8 {\n    width: 66.66666667%;\n  }\n  .col-lg-7 {\n    width: 58.33333333%;\n  }\n  .col-lg-6 {\n    width: 50%;\n  }\n  .col-lg-5 {\n    width: 41.66666667%;\n  }\n  .col-lg-4 {\n    width: 33.33333333%;\n  }\n  .col-lg-3 {\n    width: 25%;\n  }\n  .col-lg-2 {\n    width: 16.66666667%;\n  }\n  .col-lg-1 {\n    width: 8.33333333%;\n  }\n  .col-lg-pull-12 {\n    right: 100%;\n  }\n  .col-lg-pull-11 {\n    right: 91.66666667%;\n  }\n  .col-lg-pull-10 {\n    right: 83.33333333%;\n  }\n  .col-lg-pull-9 {\n    right: 75%;\n  }\n  .col-lg-pull-8 {\n    right: 66.66666667%;\n  }\n  .col-lg-pull-7 {\n    right: 58.33333333%;\n  }\n  .col-lg-pull-6 {\n    right: 50%;\n  }\n  .col-lg-pull-5 {\n    right: 41.66666667%;\n  }\n  .col-lg-pull-4 {\n    right: 33.33333333%;\n  }\n  .col-lg-pull-3 {\n    right: 25%;\n  }\n  .col-lg-pull-2 {\n    right: 16.66666667%;\n  }\n  .col-lg-pull-1 {\n    right: 8.33333333%;\n  }\n  .col-lg-pull-0 {\n    right: auto;\n  }\n  .col-lg-push-12 {\n    left: 100%;\n  }\n  .col-lg-push-11 {\n    left: 91.66666667%;\n  }\n  .col-lg-push-10 {\n    left: 83.33333333%;\n  }\n  .col-lg-push-9 {\n    left: 75%;\n  }\n  .col-lg-push-8 {\n    left: 66.66666667%;\n  }\n  .col-lg-push-7 {\n    left: 58.33333333%;\n  }\n  .col-lg-push-6 {\n    left: 50%;\n  }\n  .col-lg-push-5 {\n    left: 41.66666667%;\n  }\n  .col-lg-push-4 {\n    left: 33.33333333%;\n  }\n  .col-lg-push-3 {\n    left: 25%;\n  }\n  .col-lg-push-2 {\n    left: 16.66666667%;\n  }\n  .col-lg-push-1 {\n    left: 8.33333333%;\n  }\n  .col-lg-push-0 {\n    left: auto;\n  }\n  .col-lg-offset-12 {\n    margin-left: 100%;\n  }\n  .col-lg-offset-11 {\n    margin-left: 91.66666667%;\n  }\n  .col-lg-offset-10 {\n    margin-left: 83.33333333%;\n  }\n  .col-lg-offset-9 {\n    margin-left: 75%;\n  }\n  .col-lg-offset-8 {\n    margin-left: 66.66666667%;\n  }\n  .col-lg-offset-7 {\n    margin-left: 58.33333333%;\n  }\n  .col-lg-offset-6 {\n    margin-left: 50%;\n  }\n  .col-lg-offset-5 {\n    margin-left: 41.66666667%;\n  }\n  .col-lg-offset-4 {\n    margin-left: 33.33333333%;\n  }\n  .col-lg-offset-3 {\n    margin-left: 25%;\n  }\n  .col-lg-offset-2 {\n    margin-left: 16.66666667%;\n  }\n  .col-lg-offset-1 {\n    margin-left: 8.33333333%;\n  }\n  .col-lg-offset-0 {\n    margin-left: 0%;\n  }\n}\ntable {\n  background-color: transparent;\n}\ncaption {\n  padding-top: 8px;\n  padding-bottom: 8px;\n  color: #777777;\n  text-align: left;\n}\nth {\n  text-align: left;\n}\n.table {\n  width: 100%;\n  max-width: 100%;\n  margin-bottom: 20px;\n}\n.table > thead > tr > th,\n.table > tbody > tr > th,\n.table > tfoot > tr > th,\n.table > thead > tr > td,\n.table > tbody > tr > td,\n.table > tfoot > tr > td {\n  padding: 8px;\n  line-height: 1.42857143;\n  vertical-align: top;\n  border-top: 1px solid #ddd;\n}\n.table > thead > tr > th {\n  vertical-align: bottom;\n  border-bottom: 2px solid #ddd;\n}\n.table > caption + thead > tr:first-child > th,\n.table > colgroup + thead > tr:first-child > th,\n.table > thead:first-child > tr:first-child > th,\n.table > caption + thead > tr:first-child > td,\n.table > colgroup + thead > tr:first-child > td,\n.table > thead:first-child > tr:first-child > td {\n  border-top: 0;\n}\n.table > tbody + tbody {\n  border-top: 2px solid #ddd;\n}\n.table .table {\n  background-color: #fff;\n}\n.table-condensed > thead > tr > th,\n.table-condensed > tbody > tr > th,\n.table-condensed > tfoot > tr > th,\n.table-condensed > thead > tr > td,\n.table-condensed > tbody > tr > td,\n.table-condensed > tfoot > tr > td {\n  padding: 5px;\n}\n.table-bordered {\n  border: 1px solid #ddd;\n}\n.table-bordered > thead > tr > th,\n.table-bordered > tbody > tr > th,\n.table-bordered > tfoot > tr > th,\n.table-bordered > thead > tr > td,\n.table-bordered > tbody > tr > td,\n.table-bordered > tfoot > tr > td {\n  border: 1px solid #ddd;\n}\n.table-bordered > thead > tr > th,\n.table-bordered > thead > tr > td {\n  border-bottom-width: 2px;\n}\n.table-striped > tbody > tr:nth-of-type(odd) {\n  background-color: #f9f9f9;\n}\n.table-hover > tbody > tr:hover {\n  background-color: #f5f5f5;\n}\ntable col[class*=\"col-\"] {\n  position: static;\n  float: none;\n  display: table-column;\n}\ntable td[class*=\"col-\"],\ntable th[class*=\"col-\"] {\n  position: static;\n  float: none;\n  display: table-cell;\n}\n.table > thead > tr > td.active,\n.table > tbody > tr > td.active,\n.table > tfoot > tr > td.active,\n.table > thead > tr > th.active,\n.table > tbody > tr > th.active,\n.table > tfoot > tr > th.active,\n.table > thead > tr.active > td,\n.table > tbody > tr.active > td,\n.table > tfoot > tr.active > td,\n.table > thead > tr.active > th,\n.table > tbody > tr.active > th,\n.table > tfoot > tr.active > th {\n  background-color: #f5f5f5;\n}\n.table-hover > tbody > tr > td.active:hover,\n.table-hover > tbody > tr > th.active:hover,\n.table-hover > tbody > tr.active:hover > td,\n.table-hover > tbody > tr:hover > .active,\n.table-hover > tbody > tr.active:hover > th {\n  background-color: #e8e8e8;\n}\n.table > thead > tr > td.success,\n.table > tbody > tr > td.success,\n.table > tfoot > tr > td.success,\n.table > thead > tr > th.success,\n.table > tbody > tr > th.success,\n.table > tfoot > tr > th.success,\n.table > thead > tr.success > td,\n.table > tbody > tr.success > td,\n.table > tfoot > tr.success > td,\n.table > thead > tr.success > th,\n.table > tbody > tr.success > th,\n.table > tfoot > tr.success > th {\n  background-color: #dff0d8;\n}\n.table-hover > tbody > tr > td.success:hover,\n.table-hover > tbody > tr > th.success:hover,\n.table-hover > tbody > tr.success:hover > td,\n.table-hover > tbody > tr:hover > .success,\n.table-hover > tbody > tr.success:hover > th {\n  background-color: #d0e9c6;\n}\n.table > thead > tr > td.info,\n.table > tbody > tr > td.info,\n.table > tfoot > tr > td.info,\n.table > thead > tr > th.info,\n.table > tbody > tr > th.info,\n.table > tfoot > tr > th.info,\n.table > thead > tr.info > td,\n.table > tbody > tr.info > td,\n.table > tfoot > tr.info > td,\n.table > thead > tr.info > th,\n.table > tbody > tr.info > th,\n.table > tfoot > tr.info > th {\n  background-color: #d9edf7;\n}\n.table-hover > tbody > tr > td.info:hover,\n.table-hover > tbody > tr > th.info:hover,\n.table-hover > tbody > tr.info:hover > td,\n.table-hover > tbody > tr:hover > .info,\n.table-hover > tbody > tr.info:hover > th {\n  background-color: #c4e3f3;\n}\n.table > thead > tr > td.warning,\n.table > tbody > tr > td.warning,\n.table > tfoot > tr > td.warning,\n.table > thead > tr > th.warning,\n.table > tbody > tr > th.warning,\n.table > tfoot > tr > th.warning,\n.table > thead > tr.warning > td,\n.table > tbody > tr.warning > td,\n.table > tfoot > tr.warning > td,\n.table > thead > tr.warning > th,\n.table > tbody > tr.warning > th,\n.table > tfoot > tr.warning > th {\n  background-color: #fcf8e3;\n}\n.table-hover > tbody > tr > td.warning:hover,\n.table-hover > tbody > tr > th.warning:hover,\n.table-hover > tbody > tr.warning:hover > td,\n.table-hover > tbody > tr:hover > .warning,\n.table-hover > tbody > tr.warning:hover > th {\n  background-color: #faf2cc;\n}\n.table > thead > tr > td.danger,\n.table > tbody > tr > td.danger,\n.table > tfoot > tr > td.danger,\n.table > thead > tr > th.danger,\n.table > tbody > tr > th.danger,\n.table > tfoot > tr > th.danger,\n.table > thead > tr.danger > td,\n.table > tbody > tr.danger > td,\n.table > tfoot > tr.danger > td,\n.table > thead > tr.danger > th,\n.table > tbody > tr.danger > th,\n.table > tfoot > tr.danger > th {\n  background-color: #f2dede;\n}\n.table-hover > tbody > tr > td.danger:hover,\n.table-hover > tbody > tr > th.danger:hover,\n.table-hover > tbody > tr.danger:hover > td,\n.table-hover > tbody > tr:hover > .danger,\n.table-hover > tbody > tr.danger:hover > th {\n  background-color: #ebcccc;\n}\n.table-responsive {\n  overflow-x: auto;\n  min-height: 0.01%;\n}\n@media screen and (max-width: 767px) {\n  .table-responsive {\n    width: 100%;\n    margin-bottom: 15px;\n    overflow-y: hidden;\n    -ms-overflow-style: -ms-autohiding-scrollbar;\n    border: 1px solid #ddd;\n  }\n  .table-responsive > .table {\n    margin-bottom: 0;\n  }\n  .table-responsive > .table > thead > tr > th,\n  .table-responsive > .table > tbody > tr > th,\n  .table-responsive > .table > tfoot > tr > th,\n  .table-responsive > .table > thead > tr > td,\n  .table-responsive > .table > tbody > tr > td,\n  .table-responsive > .table > tfoot > tr > td {\n    white-space: nowrap;\n  }\n  .table-responsive > .table-bordered {\n    border: 0;\n  }\n  .table-responsive > .table-bordered > thead > tr > th:first-child,\n  .table-responsive > .table-bordered > tbody > tr > th:first-child,\n  .table-responsive > .table-bordered > tfoot > tr > th:first-child,\n  .table-responsive > .table-bordered > thead > tr > td:first-child,\n  .table-responsive > .table-bordered > tbody > tr > td:first-child,\n  .table-responsive > .table-bordered > tfoot > tr > td:first-child {\n    border-left: 0;\n  }\n  .table-responsive > .table-bordered > thead > tr > th:last-child,\n  .table-responsive > .table-bordered > tbody > tr > th:last-child,\n  .table-responsive > .table-bordered > tfoot > tr > th:last-child,\n  .table-responsive > .table-bordered > thead > tr > td:last-child,\n  .table-responsive > .table-bordered > tbody > tr > td:last-child,\n  .table-responsive > .table-bordered > tfoot > tr > td:last-child {\n    border-right: 0;\n  }\n  .table-responsive > .table-bordered > tbody > tr:last-child > th,\n  .table-responsive > .table-bordered > tfoot > tr:last-child > th,\n  .table-responsive > .table-bordered > tbody > tr:last-child > td,\n  .table-responsive > .table-bordered > tfoot > tr:last-child > td {\n    border-bottom: 0;\n  }\n}\nfieldset {\n  padding: 0;\n  margin: 0;\n  border: 0;\n  min-width: 0;\n}\nlegend {\n  display: block;\n  width: 100%;\n  padding: 0;\n  margin-bottom: 20px;\n  font-size: 21px;\n  line-height: inherit;\n  color: #333333;\n  border: 0;\n  border-bottom: 1px solid #e5e5e5;\n}\nlabel {\n  display: inline-block;\n  max-width: 100%;\n  margin-bottom: 5px;\n  font-weight: bold;\n}\ninput[type=\"search\"] {\n  -webkit-box-sizing: border-box;\n  -moz-box-sizing: border-box;\n  box-sizing: border-box;\n}\ninput[type=\"radio\"],\ninput[type=\"checkbox\"] {\n  margin: 4px 0 0;\n  margin-top: 1px \\9;\n  line-height: normal;\n}\ninput[type=\"file\"] {\n  display: block;\n}\ninput[type=\"range\"] {\n  display: block;\n  width: 100%;\n}\nselect[multiple],\nselect[size] {\n  height: auto;\n}\ninput[type=\"file\"]:focus,\ninput[type=\"radio\"]:focus,\ninput[type=\"checkbox\"]:focus {\n  outline: 5px auto -webkit-focus-ring-color;\n  outline-offset: -2px;\n}\noutput {\n  display: block;\n  padding-top: 7px;\n  font-size: 14px;\n  line-height: 1.42857143;\n  color: #555555;\n}\n.form-control {\n  display: block;\n  width: 100%;\n  height: 34px;\n  padding: 6px 12px;\n  font-size: 14px;\n  line-height: 1.42857143;\n  color: #555555;\n  background-color: #fff;\n  background-image: none;\n  border: 1px solid #ccc;\n  border-radius: 4px;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;\n  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;\n  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;\n}\n.form-control:focus {\n  border-color: #66afe9;\n  outline: 0;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);\n  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);\n}\n.form-control::-moz-placeholder {\n  color: #999;\n  opacity: 1;\n}\n.form-control:-ms-input-placeholder {\n  color: #999;\n}\n.form-control::-webkit-input-placeholder {\n  color: #999;\n}\n.form-control::-ms-expand {\n  border: 0;\n  background-color: transparent;\n}\n.form-control[disabled],\n.form-control[readonly],\nfieldset[disabled] .form-control {\n  background-color: #eeeeee;\n  opacity: 1;\n}\n.form-control[disabled],\nfieldset[disabled] .form-control {\n  cursor: not-allowed;\n}\ntextarea.form-control {\n  height: auto;\n}\ninput[type=\"search\"] {\n  -webkit-appearance: none;\n}\n@media screen and (-webkit-min-device-pixel-ratio: 0) {\n  input[type=\"date\"].form-control,\n  input[type=\"time\"].form-control,\n  input[type=\"datetime-local\"].form-control,\n  input[type=\"month\"].form-control {\n    line-height: 34px;\n  }\n  input[type=\"date\"].input-sm,\n  input[type=\"time\"].input-sm,\n  input[type=\"datetime-local\"].input-sm,\n  input[type=\"month\"].input-sm,\n  .input-group-sm input[type=\"date\"],\n  .input-group-sm input[type=\"time\"],\n  .input-group-sm input[type=\"datetime-local\"],\n  .input-group-sm input[type=\"month\"] {\n    line-height: 30px;\n  }\n  input[type=\"date\"].input-lg,\n  input[type=\"time\"].input-lg,\n  input[type=\"datetime-local\"].input-lg,\n  input[type=\"month\"].input-lg,\n  .input-group-lg input[type=\"date\"],\n  .input-group-lg input[type=\"time\"],\n  .input-group-lg input[type=\"datetime-local\"],\n  .input-group-lg input[type=\"month\"] {\n    line-height: 46px;\n  }\n}\n.form-group {\n  margin-bottom: 15px;\n}\n.radio,\n.checkbox {\n  position: relative;\n  display: block;\n  margin-top: 10px;\n  margin-bottom: 10px;\n}\n.radio label,\n.checkbox label {\n  min-height: 20px;\n  padding-left: 20px;\n  margin-bottom: 0;\n  font-weight: normal;\n  cursor: pointer;\n}\n.radio input[type=\"radio\"],\n.radio-inline input[type=\"radio\"],\n.checkbox input[type=\"checkbox\"],\n.checkbox-inline input[type=\"checkbox\"] {\n  position: absolute;\n  margin-left: -20px;\n  margin-top: 4px \\9;\n}\n.radio + .radio,\n.checkbox + .checkbox {\n  margin-top: -5px;\n}\n.radio-inline,\n.checkbox-inline {\n  position: relative;\n  display: inline-block;\n  padding-left: 20px;\n  margin-bottom: 0;\n  vertical-align: middle;\n  font-weight: normal;\n  cursor: pointer;\n}\n.radio-inline + .radio-inline,\n.checkbox-inline + .checkbox-inline {\n  margin-top: 0;\n  margin-left: 10px;\n}\ninput[type=\"radio\"][disabled],\ninput[type=\"checkbox\"][disabled],\ninput[type=\"radio\"].disabled,\ninput[type=\"checkbox\"].disabled,\nfieldset[disabled] input[type=\"radio\"],\nfieldset[disabled] input[type=\"checkbox\"] {\n  cursor: not-allowed;\n}\n.radio-inline.disabled,\n.checkbox-inline.disabled,\nfieldset[disabled] .radio-inline,\nfieldset[disabled] .checkbox-inline {\n  cursor: not-allowed;\n}\n.radio.disabled label,\n.checkbox.disabled label,\nfieldset[disabled] .radio label,\nfieldset[disabled] .checkbox label {\n  cursor: not-allowed;\n}\n.form-control-static {\n  padding-top: 7px;\n  padding-bottom: 7px;\n  margin-bottom: 0;\n  min-height: 34px;\n}\n.form-control-static.input-lg,\n.form-control-static.input-sm {\n  padding-left: 0;\n  padding-right: 0;\n}\n.input-sm {\n  height: 30px;\n  padding: 5px 10px;\n  font-size: 12px;\n  line-height: 1.5;\n  border-radius: 3px;\n}\nselect.input-sm {\n  height: 30px;\n  line-height: 30px;\n}\ntextarea.input-sm,\nselect[multiple].input-sm {\n  height: auto;\n}\n.form-group-sm .form-control {\n  height: 30px;\n  padding: 5px 10px;\n  font-size: 12px;\n  line-height: 1.5;\n  border-radius: 3px;\n}\n.form-group-sm select.form-control {\n  height: 30px;\n  line-height: 30px;\n}\n.form-group-sm textarea.form-control,\n.form-group-sm select[multiple].form-control {\n  height: auto;\n}\n.form-group-sm .form-control-static {\n  height: 30px;\n  min-height: 32px;\n  padding: 6px 10px;\n  font-size: 12px;\n  line-height: 1.5;\n}\n.input-lg {\n  height: 46px;\n  padding: 10px 16px;\n  font-size: 18px;\n  line-height: 1.3333333;\n  border-radius: 6px;\n}\nselect.input-lg {\n  height: 46px;\n  line-height: 46px;\n}\ntextarea.input-lg,\nselect[multiple].input-lg {\n  height: auto;\n}\n.form-group-lg .form-control {\n  height: 46px;\n  padding: 10px 16px;\n  font-size: 18px;\n  line-height: 1.3333333;\n  border-radius: 6px;\n}\n.form-group-lg select.form-control {\n  height: 46px;\n  line-height: 46px;\n}\n.form-group-lg textarea.form-control,\n.form-group-lg select[multiple].form-control {\n  height: auto;\n}\n.form-group-lg .form-control-static {\n  height: 46px;\n  min-height: 38px;\n  padding: 11px 16px;\n  font-size: 18px;\n  line-height: 1.3333333;\n}\n.has-feedback {\n  position: relative;\n}\n.has-feedback .form-control {\n  padding-right: 42.5px;\n}\n.form-control-feedback {\n  position: absolute;\n  top: 0;\n  right: 0;\n  z-index: 2;\n  display: block;\n  width: 34px;\n  height: 34px;\n  line-height: 34px;\n  text-align: center;\n  pointer-events: none;\n}\n.input-lg + .form-control-feedback,\n.input-group-lg + .form-control-feedback,\n.form-group-lg .form-control + .form-control-feedback {\n  width: 46px;\n  height: 46px;\n  line-height: 46px;\n}\n.input-sm + .form-control-feedback,\n.input-group-sm + .form-control-feedback,\n.form-group-sm .form-control + .form-control-feedback {\n  width: 30px;\n  height: 30px;\n  line-height: 30px;\n}\n.has-success .help-block,\n.has-success .control-label,\n.has-success .radio,\n.has-success .checkbox,\n.has-success .radio-inline,\n.has-success .checkbox-inline,\n.has-success.radio label,\n.has-success.checkbox label,\n.has-success.radio-inline label,\n.has-success.checkbox-inline label {\n  color: #3c763d;\n}\n.has-success .form-control {\n  border-color: #3c763d;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n}\n.has-success .form-control:focus {\n  border-color: #2b542c;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;\n  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;\n}\n.has-success .input-group-addon {\n  color: #3c763d;\n  border-color: #3c763d;\n  background-color: #dff0d8;\n}\n.has-success .form-control-feedback {\n  color: #3c763d;\n}\n.has-warning .help-block,\n.has-warning .control-label,\n.has-warning .radio,\n.has-warning .checkbox,\n.has-warning .radio-inline,\n.has-warning .checkbox-inline,\n.has-warning.radio label,\n.has-warning.checkbox label,\n.has-warning.radio-inline label,\n.has-warning.checkbox-inline label {\n  color: #8a6d3b;\n}\n.has-warning .form-control {\n  border-color: #8a6d3b;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n}\n.has-warning .form-control:focus {\n  border-color: #66512c;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;\n  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;\n}\n.has-warning .input-group-addon {\n  color: #8a6d3b;\n  border-color: #8a6d3b;\n  background-color: #fcf8e3;\n}\n.has-warning .form-control-feedback {\n  color: #8a6d3b;\n}\n.has-error .help-block,\n.has-error .control-label,\n.has-error .radio,\n.has-error .checkbox,\n.has-error .radio-inline,\n.has-error .checkbox-inline,\n.has-error.radio label,\n.has-error.checkbox label,\n.has-error.radio-inline label,\n.has-error.checkbox-inline label {\n  color: #a94442;\n}\n.has-error .form-control {\n  border-color: #a94442;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n}\n.has-error .form-control:focus {\n  border-color: #843534;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;\n  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;\n}\n.has-error .input-group-addon {\n  color: #a94442;\n  border-color: #a94442;\n  background-color: #f2dede;\n}\n.has-error .form-control-feedback {\n  color: #a94442;\n}\n.has-feedback label ~ .form-control-feedback {\n  top: 25px;\n}\n.has-feedback label.sr-only ~ .form-control-feedback {\n  top: 0;\n}\n.help-block {\n  display: block;\n  margin-top: 5px;\n  margin-bottom: 10px;\n  color: #737373;\n}\n@media (min-width: 768px) {\n  .form-inline .form-group {\n    display: inline-block;\n    margin-bottom: 0;\n    vertical-align: middle;\n  }\n  .form-inline .form-control {\n    display: inline-block;\n    width: auto;\n    vertical-align: middle;\n  }\n  .form-inline .form-control-static {\n    display: inline-block;\n  }\n  .form-inline .input-group {\n    display: inline-table;\n    vertical-align: middle;\n  }\n  .form-inline .input-group .input-group-addon,\n  .form-inline .input-group .input-group-btn,\n  .form-inline .input-group .form-control {\n    width: auto;\n  }\n  .form-inline .input-group > .form-control {\n    width: 100%;\n  }\n  .form-inline .control-label {\n    margin-bottom: 0;\n    vertical-align: middle;\n  }\n  .form-inline .radio,\n  .form-inline .checkbox {\n    display: inline-block;\n    margin-top: 0;\n    margin-bottom: 0;\n    vertical-align: middle;\n  }\n  .form-inline .radio label,\n  .form-inline .checkbox label {\n    padding-left: 0;\n  }\n  .form-inline .radio input[type=\"radio\"],\n  .form-inline .checkbox input[type=\"checkbox\"] {\n    position: relative;\n    margin-left: 0;\n  }\n  .form-inline .has-feedback .form-control-feedback {\n    top: 0;\n  }\n}\n.form-horizontal .radio,\n.form-horizontal .checkbox,\n.form-horizontal .radio-inline,\n.form-horizontal .checkbox-inline {\n  margin-top: 0;\n  margin-bottom: 0;\n  padding-top: 7px;\n}\n.form-horizontal .radio,\n.form-horizontal .checkbox {\n  min-height: 27px;\n}\n.form-horizontal .form-group {\n  margin-left: -15px;\n  margin-right: -15px;\n}\n@media (min-width: 768px) {\n  .form-horizontal .control-label {\n    text-align: right;\n    margin-bottom: 0;\n    padding-top: 7px;\n  }\n}\n.form-horizontal .has-feedback .form-control-feedback {\n  right: 15px;\n}\n@media (min-width: 768px) {\n  .form-horizontal .form-group-lg .control-label {\n    padding-top: 11px;\n    font-size: 18px;\n  }\n}\n@media (min-width: 768px) {\n  .form-horizontal .form-group-sm .control-label {\n    padding-top: 6px;\n    font-size: 12px;\n  }\n}\n.btn {\n  display: inline-block;\n  margin-bottom: 0;\n  font-weight: normal;\n  text-align: center;\n  vertical-align: middle;\n  touch-action: manipulation;\n  cursor: pointer;\n  background-image: none;\n  border: 1px solid transparent;\n  white-space: nowrap;\n  padding: 6px 12px;\n  font-size: 14px;\n  line-height: 1.42857143;\n  border-radius: 4px;\n  -webkit-user-select: none;\n  -moz-user-select: none;\n  -ms-user-select: none;\n  user-select: none;\n}\n.btn:focus,\n.btn:active:focus,\n.btn.active:focus,\n.btn.focus,\n.btn:active.focus,\n.btn.active.focus {\n  outline: 5px auto -webkit-focus-ring-color;\n  outline-offset: -2px;\n}\n.btn:hover,\n.btn:focus,\n.btn.focus {\n  color: #333;\n  text-decoration: none;\n}\n.btn:active,\n.btn.active {\n  outline: 0;\n  background-image: none;\n  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n}\n.btn.disabled,\n.btn[disabled],\nfieldset[disabled] .btn {\n  cursor: not-allowed;\n  opacity: 0.65;\n  filter: alpha(opacity=65);\n  -webkit-box-shadow: none;\n  box-shadow: none;\n}\na.btn.disabled,\nfieldset[disabled] a.btn {\n  pointer-events: none;\n}\n.btn-default {\n  color: #333;\n  background-color: #fff;\n  border-color: #ccc;\n}\n.btn-default:focus,\n.btn-default.focus {\n  color: #333;\n  background-color: #e6e6e6;\n  border-color: #8c8c8c;\n}\n.btn-default:hover {\n  color: #333;\n  background-color: #e6e6e6;\n  border-color: #adadad;\n}\n.btn-default:active,\n.btn-default.active,\n.open > .dropdown-toggle.btn-default {\n  color: #333;\n  background-color: #e6e6e6;\n  border-color: #adadad;\n}\n.btn-default:active:hover,\n.btn-default.active:hover,\n.open > .dropdown-toggle.btn-default:hover,\n.btn-default:active:focus,\n.btn-default.active:focus,\n.open > .dropdown-toggle.btn-default:focus,\n.btn-default:active.focus,\n.btn-default.active.focus,\n.open > .dropdown-toggle.btn-default.focus {\n  color: #333;\n  background-color: #d4d4d4;\n  border-color: #8c8c8c;\n}\n.btn-default:active,\n.btn-default.active,\n.open > .dropdown-toggle.btn-default {\n  background-image: none;\n}\n.btn-default.disabled:hover,\n.btn-default[disabled]:hover,\nfieldset[disabled] .btn-default:hover,\n.btn-default.disabled:focus,\n.btn-default[disabled]:focus,\nfieldset[disabled] .btn-default:focus,\n.btn-default.disabled.focus,\n.btn-default[disabled].focus,\nfieldset[disabled] .btn-default.focus {\n  background-color: #fff;\n  border-color: #ccc;\n}\n.btn-default .badge {\n  color: #fff;\n  background-color: #333;\n}\n.btn-primary {\n  color: #fff;\n  background-color: #337ab7;\n  border-color: #2e6da4;\n}\n.btn-primary:focus,\n.btn-primary.focus {\n  color: #fff;\n  background-color: #286090;\n  border-color: #122b40;\n}\n.btn-primary:hover {\n  color: #fff;\n  background-color: #286090;\n  border-color: #204d74;\n}\n.btn-primary:active,\n.btn-primary.active,\n.open > .dropdown-toggle.btn-primary {\n  color: #fff;\n  background-color: #286090;\n  border-color: #204d74;\n}\n.btn-primary:active:hover,\n.btn-primary.active:hover,\n.open > .dropdown-toggle.btn-primary:hover,\n.btn-primary:active:focus,\n.btn-primary.active:focus,\n.open > .dropdown-toggle.btn-primary:focus,\n.btn-primary:active.focus,\n.btn-primary.active.focus,\n.open > .dropdown-toggle.btn-primary.focus {\n  color: #fff;\n  background-color: #204d74;\n  border-color: #122b40;\n}\n.btn-primary:active,\n.btn-primary.active,\n.open > .dropdown-toggle.btn-primary {\n  background-image: none;\n}\n.btn-primary.disabled:hover,\n.btn-primary[disabled]:hover,\nfieldset[disabled] .btn-primary:hover,\n.btn-primary.disabled:focus,\n.btn-primary[disabled]:focus,\nfieldset[disabled] .btn-primary:focus,\n.btn-primary.disabled.focus,\n.btn-primary[disabled].focus,\nfieldset[disabled] .btn-primary.focus {\n  background-color: #337ab7;\n  border-color: #2e6da4;\n}\n.btn-primary .badge {\n  color: #337ab7;\n  background-color: #fff;\n}\n.btn-success {\n  color: #fff;\n  background-color: #5cb85c;\n  border-color: #4cae4c;\n}\n.btn-success:focus,\n.btn-success.focus {\n  color: #fff;\n  background-color: #449d44;\n  border-color: #255625;\n}\n.btn-success:hover {\n  color: #fff;\n  background-color: #449d44;\n  border-color: #398439;\n}\n.btn-success:active,\n.btn-success.active,\n.open > .dropdown-toggle.btn-success {\n  color: #fff;\n  background-color: #449d44;\n  border-color: #398439;\n}\n.btn-success:active:hover,\n.btn-success.active:hover,\n.open > .dropdown-toggle.btn-success:hover,\n.btn-success:active:focus,\n.btn-success.active:focus,\n.open > .dropdown-toggle.btn-success:focus,\n.btn-success:active.focus,\n.btn-success.active.focus,\n.open > .dropdown-toggle.btn-success.focus {\n  color: #fff;\n  background-color: #398439;\n  border-color: #255625;\n}\n.btn-success:active,\n.btn-success.active,\n.open > .dropdown-toggle.btn-success {\n  background-image: none;\n}\n.btn-success.disabled:hover,\n.btn-success[disabled]:hover,\nfieldset[disabled] .btn-success:hover,\n.btn-success.disabled:focus,\n.btn-success[disabled]:focus,\nfieldset[disabled] .btn-success:focus,\n.btn-success.disabled.focus,\n.btn-success[disabled].focus,\nfieldset[disabled] .btn-success.focus {\n  background-color: #5cb85c;\n  border-color: #4cae4c;\n}\n.btn-success .badge {\n  color: #5cb85c;\n  background-color: #fff;\n}\n.btn-info {\n  color: #fff;\n  background-color: #5bc0de;\n  border-color: #46b8da;\n}\n.btn-info:focus,\n.btn-info.focus {\n  color: #fff;\n  background-color: #31b0d5;\n  border-color: #1b6d85;\n}\n.btn-info:hover {\n  color: #fff;\n  background-color: #31b0d5;\n  border-color: #269abc;\n}\n.btn-info:active,\n.btn-info.active,\n.open > .dropdown-toggle.btn-info {\n  color: #fff;\n  background-color: #31b0d5;\n  border-color: #269abc;\n}\n.btn-info:active:hover,\n.btn-info.active:hover,\n.open > .dropdown-toggle.btn-info:hover,\n.btn-info:active:focus,\n.btn-info.active:focus,\n.open > .dropdown-toggle.btn-info:focus,\n.btn-info:active.focus,\n.btn-info.active.focus,\n.open > .dropdown-toggle.btn-info.focus {\n  color: #fff;\n  background-color: #269abc;\n  border-color: #1b6d85;\n}\n.btn-info:active,\n.btn-info.active,\n.open > .dropdown-toggle.btn-info {\n  background-image: none;\n}\n.btn-info.disabled:hover,\n.btn-info[disabled]:hover,\nfieldset[disabled] .btn-info:hover,\n.btn-info.disabled:focus,\n.btn-info[disabled]:focus,\nfieldset[disabled] .btn-info:focus,\n.btn-info.disabled.focus,\n.btn-info[disabled].focus,\nfieldset[disabled] .btn-info.focus {\n  background-color: #5bc0de;\n  border-color: #46b8da;\n}\n.btn-info .badge {\n  color: #5bc0de;\n  background-color: #fff;\n}\n.btn-warning {\n  color: #fff;\n  background-color: #f0ad4e;\n  border-color: #eea236;\n}\n.btn-warning:focus,\n.btn-warning.focus {\n  color: #fff;\n  background-color: #ec971f;\n  border-color: #985f0d;\n}\n.btn-warning:hover {\n  color: #fff;\n  background-color: #ec971f;\n  border-color: #d58512;\n}\n.btn-warning:active,\n.btn-warning.active,\n.open > .dropdown-toggle.btn-warning {\n  color: #fff;\n  background-color: #ec971f;\n  border-color: #d58512;\n}\n.btn-warning:active:hover,\n.btn-warning.active:hover,\n.open > .dropdown-toggle.btn-warning:hover,\n.btn-warning:active:focus,\n.btn-warning.active:focus,\n.open > .dropdown-toggle.btn-warning:focus,\n.btn-warning:active.focus,\n.btn-warning.active.focus,\n.open > .dropdown-toggle.btn-warning.focus {\n  color: #fff;\n  background-color: #d58512;\n  border-color: #985f0d;\n}\n.btn-warning:active,\n.btn-warning.active,\n.open > .dropdown-toggle.btn-warning {\n  background-image: none;\n}\n.btn-warning.disabled:hover,\n.btn-warning[disabled]:hover,\nfieldset[disabled] .btn-warning:hover,\n.btn-warning.disabled:focus,\n.btn-warning[disabled]:focus,\nfieldset[disabled] .btn-warning:focus,\n.btn-warning.disabled.focus,\n.btn-warning[disabled].focus,\nfieldset[disabled] .btn-warning.focus {\n  background-color: #f0ad4e;\n  border-color: #eea236;\n}\n.btn-warning .badge {\n  color: #f0ad4e;\n  background-color: #fff;\n}\n.btn-danger {\n  color: #fff;\n  background-color: #d9534f;\n  border-color: #d43f3a;\n}\n.btn-danger:focus,\n.btn-danger.focus {\n  color: #fff;\n  background-color: #c9302c;\n  border-color: #761c19;\n}\n.btn-danger:hover {\n  color: #fff;\n  background-color: #c9302c;\n  border-color: #ac2925;\n}\n.btn-danger:active,\n.btn-danger.active,\n.open > .dropdown-toggle.btn-danger {\n  color: #fff;\n  background-color: #c9302c;\n  border-color: #ac2925;\n}\n.btn-danger:active:hover,\n.btn-danger.active:hover,\n.open > .dropdown-toggle.btn-danger:hover,\n.btn-danger:active:focus,\n.btn-danger.active:focus,\n.open > .dropdown-toggle.btn-danger:focus,\n.btn-danger:active.focus,\n.btn-danger.active.focus,\n.open > .dropdown-toggle.btn-danger.focus {\n  color: #fff;\n  background-color: #ac2925;\n  border-color: #761c19;\n}\n.btn-danger:active,\n.btn-danger.active,\n.open > .dropdown-toggle.btn-danger {\n  background-image: none;\n}\n.btn-danger.disabled:hover,\n.btn-danger[disabled]:hover,\nfieldset[disabled] .btn-danger:hover,\n.btn-danger.disabled:focus,\n.btn-danger[disabled]:focus,\nfieldset[disabled] .btn-danger:focus,\n.btn-danger.disabled.focus,\n.btn-danger[disabled].focus,\nfieldset[disabled] .btn-danger.focus {\n  background-color: #d9534f;\n  border-color: #d43f3a;\n}\n.btn-danger .badge {\n  color: #d9534f;\n  background-color: #fff;\n}\n.btn-link {\n  color: #337ab7;\n  font-weight: normal;\n  border-radius: 0;\n}\n.btn-link,\n.btn-link:active,\n.btn-link.active,\n.btn-link[disabled],\nfieldset[disabled] .btn-link {\n  background-color: transparent;\n  -webkit-box-shadow: none;\n  box-shadow: none;\n}\n.btn-link,\n.btn-link:hover,\n.btn-link:focus,\n.btn-link:active {\n  border-color: transparent;\n}\n.btn-link:hover,\n.btn-link:focus {\n  color: #23527c;\n  text-decoration: underline;\n  background-color: transparent;\n}\n.btn-link[disabled]:hover,\nfieldset[disabled] .btn-link:hover,\n.btn-link[disabled]:focus,\nfieldset[disabled] .btn-link:focus {\n  color: #777777;\n  text-decoration: none;\n}\n.btn-lg,\n.btn-group-lg > .btn {\n  padding: 10px 16px;\n  font-size: 18px;\n  line-height: 1.3333333;\n  border-radius: 6px;\n}\n.btn-sm,\n.btn-group-sm > .btn {\n  padding: 5px 10px;\n  font-size: 12px;\n  line-height: 1.5;\n  border-radius: 3px;\n}\n.btn-xs,\n.btn-group-xs > .btn {\n  padding: 1px 5px;\n  font-size: 12px;\n  line-height: 1.5;\n  border-radius: 3px;\n}\n.btn-block {\n  display: block;\n  width: 100%;\n}\n.btn-block + .btn-block {\n  margin-top: 5px;\n}\ninput[type=\"submit\"].btn-block,\ninput[type=\"reset\"].btn-block,\ninput[type=\"button\"].btn-block {\n  width: 100%;\n}\n.fade {\n  opacity: 0;\n  -webkit-transition: opacity 0.15s linear;\n  -o-transition: opacity 0.15s linear;\n  transition: opacity 0.15s linear;\n}\n.fade.in {\n  opacity: 1;\n}\n.collapse {\n  display: none;\n}\n.collapse.in {\n  display: block;\n}\ntr.collapse.in {\n  display: table-row;\n}\ntbody.collapse.in {\n  display: table-row-group;\n}\n.collapsing {\n  position: relative;\n  height: 0;\n  overflow: hidden;\n  -webkit-transition-property: height, visibility;\n  transition-property: height, visibility;\n  -webkit-transition-duration: 0.35s;\n  transition-duration: 0.35s;\n  -webkit-transition-timing-function: ease;\n  transition-timing-function: ease;\n}\n.caret {\n  display: inline-block;\n  width: 0;\n  height: 0;\n  margin-left: 2px;\n  vertical-align: middle;\n  border-top: 4px dashed;\n  border-top: 4px solid \\9;\n  border-right: 4px solid transparent;\n  border-left: 4px solid transparent;\n}\n.dropup,\n.dropdown {\n  position: relative;\n}\n.dropdown-toggle:focus {\n  outline: 0;\n}\n.dropdown-menu {\n  position: absolute;\n  top: 100%;\n  left: 0;\n  z-index: 1000;\n  display: none;\n  float: left;\n  min-width: 160px;\n  padding: 5px 0;\n  margin: 2px 0 0;\n  list-style: none;\n  font-size: 14px;\n  text-align: left;\n  background-color: #fff;\n  border: 1px solid #ccc;\n  border: 1px solid rgba(0, 0, 0, 0.15);\n  border-radius: 4px;\n  -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);\n  box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);\n  background-clip: padding-box;\n}\n.dropdown-menu.pull-right {\n  right: 0;\n  left: auto;\n}\n.dropdown-menu .divider {\n  height: 1px;\n  margin: 9px 0;\n  overflow: hidden;\n  background-color: #e5e5e5;\n}\n.dropdown-menu > li > a {\n  display: block;\n  padding: 3px 20px;\n  clear: both;\n  font-weight: normal;\n  line-height: 1.42857143;\n  color: #333333;\n  white-space: nowrap;\n}\n.dropdown-menu > li > a:hover,\n.dropdown-menu > li > a:focus {\n  text-decoration: none;\n  color: #262626;\n  background-color: #f5f5f5;\n}\n.dropdown-menu > .active > a,\n.dropdown-menu > .active > a:hover,\n.dropdown-menu > .active > a:focus {\n  color: #fff;\n  text-decoration: none;\n  outline: 0;\n  background-color: #337ab7;\n}\n.dropdown-menu > .disabled > a,\n.dropdown-menu > .disabled > a:hover,\n.dropdown-menu > .disabled > a:focus {\n  color: #777777;\n}\n.dropdown-menu > .disabled > a:hover,\n.dropdown-menu > .disabled > a:focus {\n  text-decoration: none;\n  background-color: transparent;\n  background-image: none;\n  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n  cursor: not-allowed;\n}\n.open > .dropdown-menu {\n  display: block;\n}\n.open > a {\n  outline: 0;\n}\n.dropdown-menu-right {\n  left: auto;\n  right: 0;\n}\n.dropdown-menu-left {\n  left: 0;\n  right: auto;\n}\n.dropdown-header {\n  display: block;\n  padding: 3px 20px;\n  font-size: 12px;\n  line-height: 1.42857143;\n  color: #777777;\n  white-space: nowrap;\n}\n.dropdown-backdrop {\n  position: fixed;\n  left: 0;\n  right: 0;\n  bottom: 0;\n  top: 0;\n  z-index: 990;\n}\n.pull-right > .dropdown-menu {\n  right: 0;\n  left: auto;\n}\n.dropup .caret,\n.navbar-fixed-bottom .dropdown .caret {\n  border-top: 0;\n  border-bottom: 4px dashed;\n  border-bottom: 4px solid \\9;\n  content: \"\";\n}\n.dropup .dropdown-menu,\n.navbar-fixed-bottom .dropdown .dropdown-menu {\n  top: auto;\n  bottom: 100%;\n  margin-bottom: 2px;\n}\n@media (min-width: 768px) {\n  .navbar-right .dropdown-menu {\n    left: auto;\n    right: 0;\n  }\n  .navbar-right .dropdown-menu-left {\n    left: 0;\n    right: auto;\n  }\n}\n.btn-group,\n.btn-group-vertical {\n  position: relative;\n  display: inline-block;\n  vertical-align: middle;\n}\n.btn-group > .btn,\n.btn-group-vertical > .btn {\n  position: relative;\n  float: left;\n}\n.btn-group > .btn:hover,\n.btn-group-vertical > .btn:hover,\n.btn-group > .btn:focus,\n.btn-group-vertical > .btn:focus,\n.btn-group > .btn:active,\n.btn-group-vertical > .btn:active,\n.btn-group > .btn.active,\n.btn-group-vertical > .btn.active {\n  z-index: 2;\n}\n.btn-group .btn + .btn,\n.btn-group .btn + .btn-group,\n.btn-group .btn-group + .btn,\n.btn-group .btn-group + .btn-group {\n  margin-left: -1px;\n}\n.btn-toolbar {\n  margin-left: -5px;\n}\n.btn-toolbar .btn,\n.btn-toolbar .btn-group,\n.btn-toolbar .input-group {\n  float: left;\n}\n.btn-toolbar > .btn,\n.btn-toolbar > .btn-group,\n.btn-toolbar > .input-group {\n  margin-left: 5px;\n}\n.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {\n  border-radius: 0;\n}\n.btn-group > .btn:first-child {\n  margin-left: 0;\n}\n.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {\n  border-bottom-right-radius: 0;\n  border-top-right-radius: 0;\n}\n.btn-group > .btn:last-child:not(:first-child),\n.btn-group > .dropdown-toggle:not(:first-child) {\n  border-bottom-left-radius: 0;\n  border-top-left-radius: 0;\n}\n.btn-group > .btn-group {\n  float: left;\n}\n.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {\n  border-radius: 0;\n}\n.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,\n.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {\n  border-bottom-right-radius: 0;\n  border-top-right-radius: 0;\n}\n.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {\n  border-bottom-left-radius: 0;\n  border-top-left-radius: 0;\n}\n.btn-group .dropdown-toggle:active,\n.btn-group.open .dropdown-toggle {\n  outline: 0;\n}\n.btn-group > .btn + .dropdown-toggle {\n  padding-left: 8px;\n  padding-right: 8px;\n}\n.btn-group > .btn-lg + .dropdown-toggle {\n  padding-left: 12px;\n  padding-right: 12px;\n}\n.btn-group.open .dropdown-toggle {\n  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n}\n.btn-group.open .dropdown-toggle.btn-link {\n  -webkit-box-shadow: none;\n  box-shadow: none;\n}\n.btn .caret {\n  margin-left: 0;\n}\n.btn-lg .caret {\n  border-width: 5px 5px 0;\n  border-bottom-width: 0;\n}\n.dropup .btn-lg .caret {\n  border-width: 0 5px 5px;\n}\n.btn-group-vertical > .btn,\n.btn-group-vertical > .btn-group,\n.btn-group-vertical > .btn-group > .btn {\n  display: block;\n  float: none;\n  width: 100%;\n  max-width: 100%;\n}\n.btn-group-vertical > .btn-group > .btn {\n  float: none;\n}\n.btn-group-vertical > .btn + .btn,\n.btn-group-vertical > .btn + .btn-group,\n.btn-group-vertical > .btn-group + .btn,\n.btn-group-vertical > .btn-group + .btn-group {\n  margin-top: -1px;\n  margin-left: 0;\n}\n.btn-group-vertical > .btn:not(:first-child):not(:last-child) {\n  border-radius: 0;\n}\n.btn-group-vertical > .btn:first-child:not(:last-child) {\n  border-top-right-radius: 4px;\n  border-top-left-radius: 4px;\n  border-bottom-right-radius: 0;\n  border-bottom-left-radius: 0;\n}\n.btn-group-vertical > .btn:last-child:not(:first-child) {\n  border-top-right-radius: 0;\n  border-top-left-radius: 0;\n  border-bottom-right-radius: 4px;\n  border-bottom-left-radius: 4px;\n}\n.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {\n  border-radius: 0;\n}\n.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,\n.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {\n  border-bottom-right-radius: 0;\n  border-bottom-left-radius: 0;\n}\n.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {\n  border-top-right-radius: 0;\n  border-top-left-radius: 0;\n}\n.btn-group-justified {\n  display: table;\n  width: 100%;\n  table-layout: fixed;\n  border-collapse: separate;\n}\n.btn-group-justified > .btn,\n.btn-group-justified > .btn-group {\n  float: none;\n  display: table-cell;\n  width: 1%;\n}\n.btn-group-justified > .btn-group .btn {\n  width: 100%;\n}\n.btn-group-justified > .btn-group .dropdown-menu {\n  left: auto;\n}\n[data-toggle=\"buttons\"] > .btn input[type=\"radio\"],\n[data-toggle=\"buttons\"] > .btn-group > .btn input[type=\"radio\"],\n[data-toggle=\"buttons\"] > .btn input[type=\"checkbox\"],\n[data-toggle=\"buttons\"] > .btn-group > .btn input[type=\"checkbox\"] {\n  position: absolute;\n  clip: rect(0, 0, 0, 0);\n  pointer-events: none;\n}\n.input-group {\n  position: relative;\n  display: table;\n  border-collapse: separate;\n}\n.input-group[class*=\"col-\"] {\n  float: none;\n  padding-left: 0;\n  padding-right: 0;\n}\n.input-group .form-control {\n  position: relative;\n  z-index: 2;\n  float: left;\n  width: 100%;\n  margin-bottom: 0;\n}\n.input-group .form-control:focus {\n  z-index: 3;\n}\n.input-group-lg > .form-control,\n.input-group-lg > .input-group-addon,\n.input-group-lg > .input-group-btn > .btn {\n  height: 46px;\n  padding: 10px 16px;\n  font-size: 18px;\n  line-height: 1.3333333;\n  border-radius: 6px;\n}\nselect.input-group-lg > .form-control,\nselect.input-group-lg > .input-group-addon,\nselect.input-group-lg > .input-group-btn > .btn {\n  height: 46px;\n  line-height: 46px;\n}\ntextarea.input-group-lg > .form-control,\ntextarea.input-group-lg > .input-group-addon,\ntextarea.input-group-lg > .input-group-btn > .btn,\nselect[multiple].input-group-lg > .form-control,\nselect[multiple].input-group-lg > .input-group-addon,\nselect[multiple].input-group-lg > .input-group-btn > .btn {\n  height: auto;\n}\n.input-group-sm > .form-control,\n.input-group-sm > .input-group-addon,\n.input-group-sm > .input-group-btn > .btn {\n  height: 30px;\n  padding: 5px 10px;\n  font-size: 12px;\n  line-height: 1.5;\n  border-radius: 3px;\n}\nselect.input-group-sm > .form-control,\nselect.input-group-sm > .input-group-addon,\nselect.input-group-sm > .input-group-btn > .btn {\n  height: 30px;\n  line-height: 30px;\n}\ntextarea.input-group-sm > .form-control,\ntextarea.input-group-sm > .input-group-addon,\ntextarea.input-group-sm > .input-group-btn > .btn,\nselect[multiple].input-group-sm > .form-control,\nselect[multiple].input-group-sm > .input-group-addon,\nselect[multiple].input-group-sm > .input-group-btn > .btn {\n  height: auto;\n}\n.input-group-addon,\n.input-group-btn,\n.input-group .form-control {\n  display: table-cell;\n}\n.input-group-addon:not(:first-child):not(:last-child),\n.input-group-btn:not(:first-child):not(:last-child),\n.input-group .form-control:not(:first-child):not(:last-child) {\n  border-radius: 0;\n}\n.input-group-addon,\n.input-group-btn {\n  width: 1%;\n  white-space: nowrap;\n  vertical-align: middle;\n}\n.input-group-addon {\n  padding: 6px 12px;\n  font-size: 14px;\n  font-weight: normal;\n  line-height: 1;\n  color: #555555;\n  text-align: center;\n  background-color: #eeeeee;\n  border: 1px solid #ccc;\n  border-radius: 4px;\n}\n.input-group-addon.input-sm {\n  padding: 5px 10px;\n  font-size: 12px;\n  border-radius: 3px;\n}\n.input-group-addon.input-lg {\n  padding: 10px 16px;\n  font-size: 18px;\n  border-radius: 6px;\n}\n.input-group-addon input[type=\"radio\"],\n.input-group-addon input[type=\"checkbox\"] {\n  margin-top: 0;\n}\n.input-group .form-control:first-child,\n.input-group-addon:first-child,\n.input-group-btn:first-child > .btn,\n.input-group-btn:first-child > .btn-group > .btn,\n.input-group-btn:first-child > .dropdown-toggle,\n.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),\n.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {\n  border-bottom-right-radius: 0;\n  border-top-right-radius: 0;\n}\n.input-group-addon:first-child {\n  border-right: 0;\n}\n.input-group .form-control:last-child,\n.input-group-addon:last-child,\n.input-group-btn:last-child > .btn,\n.input-group-btn:last-child > .btn-group > .btn,\n.input-group-btn:last-child > .dropdown-toggle,\n.input-group-btn:first-child > .btn:not(:first-child),\n.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {\n  border-bottom-left-radius: 0;\n  border-top-left-radius: 0;\n}\n.input-group-addon:last-child {\n  border-left: 0;\n}\n.input-group-btn {\n  position: relative;\n  font-size: 0;\n  white-space: nowrap;\n}\n.input-group-btn > .btn {\n  position: relative;\n}\n.input-group-btn > .btn + .btn {\n  margin-left: -1px;\n}\n.input-group-btn > .btn:hover,\n.input-group-btn > .btn:focus,\n.input-group-btn > .btn:active {\n  z-index: 2;\n}\n.input-group-btn:first-child > .btn,\n.input-group-btn:first-child > .btn-group {\n  margin-right: -1px;\n}\n.input-group-btn:last-child > .btn,\n.input-group-btn:last-child > .btn-group {\n  z-index: 2;\n  margin-left: -1px;\n}\n.nav {\n  margin-bottom: 0;\n  padding-left: 0;\n  list-style: none;\n}\n.nav > li {\n  position: relative;\n  display: block;\n}\n.nav > li > a {\n  position: relative;\n  display: block;\n  padding: 10px 15px;\n}\n.nav > li > a:hover,\n.nav > li > a:focus {\n  text-decoration: none;\n  background-color: #eeeeee;\n}\n.nav > li.disabled > a {\n  color: #777777;\n}\n.nav > li.disabled > a:hover,\n.nav > li.disabled > a:focus {\n  color: #777777;\n  text-decoration: none;\n  background-color: transparent;\n  cursor: not-allowed;\n}\n.nav .open > a,\n.nav .open > a:hover,\n.nav .open > a:focus {\n  background-color: #eeeeee;\n  border-color: #337ab7;\n}\n.nav .nav-divider {\n  height: 1px;\n  margin: 9px 0;\n  overflow: hidden;\n  background-color: #e5e5e5;\n}\n.nav > li > a > img {\n  max-width: none;\n}\n.nav-tabs {\n  border-bottom: 1px solid #ddd;\n}\n.nav-tabs > li {\n  float: left;\n  margin-bottom: -1px;\n}\n.nav-tabs > li > a {\n  margin-right: 2px;\n  line-height: 1.42857143;\n  border: 1px solid transparent;\n  border-radius: 4px 4px 0 0;\n}\n.nav-tabs > li > a:hover {\n  border-color: #eeeeee #eeeeee #ddd;\n}\n.nav-tabs > li.active > a,\n.nav-tabs > li.active > a:hover,\n.nav-tabs > li.active > a:focus {\n  color: #555555;\n  background-color: #fff;\n  border: 1px solid #ddd;\n  border-bottom-color: transparent;\n  cursor: default;\n}\n.nav-tabs.nav-justified {\n  width: 100%;\n  border-bottom: 0;\n}\n.nav-tabs.nav-justified > li {\n  float: none;\n}\n.nav-tabs.nav-justified > li > a {\n  text-align: center;\n  margin-bottom: 5px;\n}\n.nav-tabs.nav-justified > .dropdown .dropdown-menu {\n  top: auto;\n  left: auto;\n}\n@media (min-width: 768px) {\n  .nav-tabs.nav-justified > li {\n    display: table-cell;\n    width: 1%;\n  }\n  .nav-tabs.nav-justified > li > a {\n    margin-bottom: 0;\n  }\n}\n.nav-tabs.nav-justified > li > a {\n  margin-right: 0;\n  border-radius: 4px;\n}\n.nav-tabs.nav-justified > .active > a,\n.nav-tabs.nav-justified > .active > a:hover,\n.nav-tabs.nav-justified > .active > a:focus {\n  border: 1px solid #ddd;\n}\n@media (min-width: 768px) {\n  .nav-tabs.nav-justified > li > a {\n    border-bottom: 1px solid #ddd;\n    border-radius: 4px 4px 0 0;\n  }\n  .nav-tabs.nav-justified > .active > a,\n  .nav-tabs.nav-justified > .active > a:hover,\n  .nav-tabs.nav-justified > .active > a:focus {\n    border-bottom-color: #fff;\n  }\n}\n.nav-pills > li {\n  float: left;\n}\n.nav-pills > li > a {\n  border-radius: 4px;\n}\n.nav-pills > li + li {\n  margin-left: 2px;\n}\n.nav-pills > li.active > a,\n.nav-pills > li.active > a:hover,\n.nav-pills > li.active > a:focus {\n  color: #fff;\n  background-color: #337ab7;\n}\n.nav-stacked > li {\n  float: none;\n}\n.nav-stacked > li + li {\n  margin-top: 2px;\n  margin-left: 0;\n}\n.nav-justified {\n  width: 100%;\n}\n.nav-justified > li {\n  float: none;\n}\n.nav-justified > li > a {\n  text-align: center;\n  margin-bottom: 5px;\n}\n.nav-justified > .dropdown .dropdown-menu {\n  top: auto;\n  left: auto;\n}\n@media (min-width: 768px) {\n  .nav-justified > li {\n    display: table-cell;\n    width: 1%;\n  }\n  .nav-justified > li > a {\n    margin-bottom: 0;\n  }\n}\n.nav-tabs-justified {\n  border-bottom: 0;\n}\n.nav-tabs-justified > li > a {\n  margin-right: 0;\n  border-radius: 4px;\n}\n.nav-tabs-justified > .active > a,\n.nav-tabs-justified > .active > a:hover,\n.nav-tabs-justified > .active > a:focus {\n  border: 1px solid #ddd;\n}\n@media (min-width: 768px) {\n  .nav-tabs-justified > li > a {\n    border-bottom: 1px solid #ddd;\n    border-radius: 4px 4px 0 0;\n  }\n  .nav-tabs-justified > .active > a,\n  .nav-tabs-justified > .active > a:hover,\n  .nav-tabs-justified > .active > a:focus {\n    border-bottom-color: #fff;\n  }\n}\n.tab-content > .tab-pane {\n  display: none;\n}\n.tab-content > .active {\n  display: block;\n}\n.nav-tabs .dropdown-menu {\n  margin-top: -1px;\n  border-top-right-radius: 0;\n  border-top-left-radius: 0;\n}\n.navbar {\n  position: relative;\n  min-height: 50px;\n  margin-bottom: 20px;\n  border: 1px solid transparent;\n}\n@media (min-width: 768px) {\n  .navbar {\n    border-radius: 4px;\n  }\n}\n@media (min-width: 768px) {\n  .navbar-header {\n    float: left;\n  }\n}\n.navbar-collapse {\n  overflow-x: visible;\n  padding-right: 15px;\n  padding-left: 15px;\n  border-top: 1px solid transparent;\n  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);\n  -webkit-overflow-scrolling: touch;\n}\n.navbar-collapse.in {\n  overflow-y: auto;\n}\n@media (min-width: 768px) {\n  .navbar-collapse {\n    width: auto;\n    border-top: 0;\n    box-shadow: none;\n  }\n  .navbar-collapse.collapse {\n    display: block !important;\n    height: auto !important;\n    padding-bottom: 0;\n    overflow: visible !important;\n  }\n  .navbar-collapse.in {\n    overflow-y: visible;\n  }\n  .navbar-fixed-top .navbar-collapse,\n  .navbar-static-top .navbar-collapse,\n  .navbar-fixed-bottom .navbar-collapse {\n    padding-left: 0;\n    padding-right: 0;\n  }\n}\n.navbar-fixed-top .navbar-collapse,\n.navbar-fixed-bottom .navbar-collapse {\n  max-height: 340px;\n}\n@media (max-device-width: 480px) and (orientation: landscape) {\n  .navbar-fixed-top .navbar-collapse,\n  .navbar-fixed-bottom .navbar-collapse {\n    max-height: 200px;\n  }\n}\n.container > .navbar-header,\n.container-fluid > .navbar-header,\n.container > .navbar-collapse,\n.container-fluid > .navbar-collapse {\n  margin-right: -15px;\n  margin-left: -15px;\n}\n@media (min-width: 768px) {\n  .container > .navbar-header,\n  .container-fluid > .navbar-header,\n  .container > .navbar-collapse,\n  .container-fluid > .navbar-collapse {\n    margin-right: 0;\n    margin-left: 0;\n  }\n}\n.navbar-static-top {\n  z-index: 1000;\n  border-width: 0 0 1px;\n}\n@media (min-width: 768px) {\n  .navbar-static-top {\n    border-radius: 0;\n  }\n}\n.navbar-fixed-top,\n.navbar-fixed-bottom {\n  position: fixed;\n  right: 0;\n  left: 0;\n  z-index: 1030;\n}\n@media (min-width: 768px) {\n  .navbar-fixed-top,\n  .navbar-fixed-bottom {\n    border-radius: 0;\n  }\n}\n.navbar-fixed-top {\n  top: 0;\n  border-width: 0 0 1px;\n}\n.navbar-fixed-bottom {\n  bottom: 0;\n  margin-bottom: 0;\n  border-width: 1px 0 0;\n}\n.navbar-brand {\n  float: left;\n  padding: 15px 15px;\n  font-size: 18px;\n  line-height: 20px;\n  height: 50px;\n}\n.navbar-brand:hover,\n.navbar-brand:focus {\n  text-decoration: none;\n}\n.navbar-brand > img {\n  display: block;\n}\n@media (min-width: 768px) {\n  .navbar > .container .navbar-brand,\n  .navbar > .container-fluid .navbar-brand {\n    margin-left: -15px;\n  }\n}\n.navbar-toggle {\n  position: relative;\n  float: right;\n  margin-right: 15px;\n  padding: 9px 10px;\n  margin-top: 8px;\n  margin-bottom: 8px;\n  background-color: transparent;\n  background-image: none;\n  border: 1px solid transparent;\n  border-radius: 4px;\n}\n.navbar-toggle:focus {\n  outline: 0;\n}\n.navbar-toggle .icon-bar {\n  display: block;\n  width: 22px;\n  height: 2px;\n  border-radius: 1px;\n}\n.navbar-toggle .icon-bar + .icon-bar {\n  margin-top: 4px;\n}\n@media (min-width: 768px) {\n  .navbar-toggle {\n    display: none;\n  }\n}\n.navbar-nav {\n  margin: 7.5px -15px;\n}\n.navbar-nav > li > a {\n  padding-top: 10px;\n  padding-bottom: 10px;\n  line-height: 20px;\n}\n@media (max-width: 767px) {\n  .navbar-nav .open .dropdown-menu {\n    position: static;\n    float: none;\n    width: auto;\n    margin-top: 0;\n    background-color: transparent;\n    border: 0;\n    box-shadow: none;\n  }\n  .navbar-nav .open .dropdown-menu > li > a,\n  .navbar-nav .open .dropdown-menu .dropdown-header {\n    padding: 5px 15px 5px 25px;\n  }\n  .navbar-nav .open .dropdown-menu > li > a {\n    line-height: 20px;\n  }\n  .navbar-nav .open .dropdown-menu > li > a:hover,\n  .navbar-nav .open .dropdown-menu > li > a:focus {\n    background-image: none;\n  }\n}\n@media (min-width: 768px) {\n  .navbar-nav {\n    float: left;\n    margin: 0;\n  }\n  .navbar-nav > li {\n    float: left;\n  }\n  .navbar-nav > li > a {\n    padding-top: 15px;\n    padding-bottom: 15px;\n  }\n}\n.navbar-form {\n  margin-left: -15px;\n  margin-right: -15px;\n  padding: 10px 15px;\n  border-top: 1px solid transparent;\n  border-bottom: 1px solid transparent;\n  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);\n  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);\n  margin-top: 8px;\n  margin-bottom: 8px;\n}\n@media (min-width: 768px) {\n  .navbar-form .form-group {\n    display: inline-block;\n    margin-bottom: 0;\n    vertical-align: middle;\n  }\n  .navbar-form .form-control {\n    display: inline-block;\n    width: auto;\n    vertical-align: middle;\n  }\n  .navbar-form .form-control-static {\n    display: inline-block;\n  }\n  .navbar-form .input-group {\n    display: inline-table;\n    vertical-align: middle;\n  }\n  .navbar-form .input-group .input-group-addon,\n  .navbar-form .input-group .input-group-btn,\n  .navbar-form .input-group .form-control {\n    width: auto;\n  }\n  .navbar-form .input-group > .form-control {\n    width: 100%;\n  }\n  .navbar-form .control-label {\n    margin-bottom: 0;\n    vertical-align: middle;\n  }\n  .navbar-form .radio,\n  .navbar-form .checkbox {\n    display: inline-block;\n    margin-top: 0;\n    margin-bottom: 0;\n    vertical-align: middle;\n  }\n  .navbar-form .radio label,\n  .navbar-form .checkbox label {\n    padding-left: 0;\n  }\n  .navbar-form .radio input[type=\"radio\"],\n  .navbar-form .checkbox input[type=\"checkbox\"] {\n    position: relative;\n    margin-left: 0;\n  }\n  .navbar-form .has-feedback .form-control-feedback {\n    top: 0;\n  }\n}\n@media (max-width: 767px) {\n  .navbar-form .form-group {\n    margin-bottom: 5px;\n  }\n  .navbar-form .form-group:last-child {\n    margin-bottom: 0;\n  }\n}\n@media (min-width: 768px) {\n  .navbar-form {\n    width: auto;\n    border: 0;\n    margin-left: 0;\n    margin-right: 0;\n    padding-top: 0;\n    padding-bottom: 0;\n    -webkit-box-shadow: none;\n    box-shadow: none;\n  }\n}\n.navbar-nav > li > .dropdown-menu {\n  margin-top: 0;\n  border-top-right-radius: 0;\n  border-top-left-radius: 0;\n}\n.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {\n  margin-bottom: 0;\n  border-top-right-radius: 4px;\n  border-top-left-radius: 4px;\n  border-bottom-right-radius: 0;\n  border-bottom-left-radius: 0;\n}\n.navbar-btn {\n  margin-top: 8px;\n  margin-bottom: 8px;\n}\n.navbar-btn.btn-sm {\n  margin-top: 10px;\n  margin-bottom: 10px;\n}\n.navbar-btn.btn-xs {\n  margin-top: 14px;\n  margin-bottom: 14px;\n}\n.navbar-text {\n  margin-top: 15px;\n  margin-bottom: 15px;\n}\n@media (min-width: 768px) {\n  .navbar-text {\n    float: left;\n    margin-left: 15px;\n    margin-right: 15px;\n  }\n}\n@media (min-width: 768px) {\n  .navbar-left {\n    float: left !important;\n  }\n  .navbar-right {\n    float: right !important;\n    margin-right: -15px;\n  }\n  .navbar-right ~ .navbar-right {\n    margin-right: 0;\n  }\n}\n.navbar-default {\n  background-color: #f8f8f8;\n  border-color: #e7e7e7;\n}\n.navbar-default .navbar-brand {\n  color: #777;\n}\n.navbar-default .navbar-brand:hover,\n.navbar-default .navbar-brand:focus {\n  color: #5e5e5e;\n  background-color: transparent;\n}\n.navbar-default .navbar-text {\n  color: #777;\n}\n.navbar-default .navbar-nav > li > a {\n  color: #777;\n}\n.navbar-default .navbar-nav > li > a:hover,\n.navbar-default .navbar-nav > li > a:focus {\n  color: #333;\n  background-color: transparent;\n}\n.navbar-default .navbar-nav > .active > a,\n.navbar-default .navbar-nav > .active > a:hover,\n.navbar-default .navbar-nav > .active > a:focus {\n  color: #555;\n  background-color: #e7e7e7;\n}\n.navbar-default .navbar-nav > .disabled > a,\n.navbar-default .navbar-nav > .disabled > a:hover,\n.navbar-default .navbar-nav > .disabled > a:focus {\n  color: #ccc;\n  background-color: transparent;\n}\n.navbar-default .navbar-toggle {\n  border-color: #ddd;\n}\n.navbar-default .navbar-toggle:hover,\n.navbar-default .navbar-toggle:focus {\n  background-color: #ddd;\n}\n.navbar-default .navbar-toggle .icon-bar {\n  background-color: #888;\n}\n.navbar-default .navbar-collapse,\n.navbar-default .navbar-form {\n  border-color: #e7e7e7;\n}\n.navbar-default .navbar-nav > .open > a,\n.navbar-default .navbar-nav > .open > a:hover,\n.navbar-default .navbar-nav > .open > a:focus {\n  background-color: #e7e7e7;\n  color: #555;\n}\n@media (max-width: 767px) {\n  .navbar-default .navbar-nav .open .dropdown-menu > li > a {\n    color: #777;\n  }\n  .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,\n  .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {\n    color: #333;\n    background-color: transparent;\n  }\n  .navbar-default .navbar-nav .open .dropdown-menu > .active > a,\n  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,\n  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {\n    color: #555;\n    background-color: #e7e7e7;\n  }\n  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,\n  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,\n  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {\n    color: #ccc;\n    background-color: transparent;\n  }\n}\n.navbar-default .navbar-link {\n  color: #777;\n}\n.navbar-default .navbar-link:hover {\n  color: #333;\n}\n.navbar-default .btn-link {\n  color: #777;\n}\n.navbar-default .btn-link:hover,\n.navbar-default .btn-link:focus {\n  color: #333;\n}\n.navbar-default .btn-link[disabled]:hover,\nfieldset[disabled] .navbar-default .btn-link:hover,\n.navbar-default .btn-link[disabled]:focus,\nfieldset[disabled] .navbar-default .btn-link:focus {\n  color: #ccc;\n}\n.navbar-inverse {\n  background-color: #222;\n  border-color: #080808;\n}\n.navbar-inverse .navbar-brand {\n  color: #9d9d9d;\n}\n.navbar-inverse .navbar-brand:hover,\n.navbar-inverse .navbar-brand:focus {\n  color: #fff;\n  background-color: transparent;\n}\n.navbar-inverse .navbar-text {\n  color: #9d9d9d;\n}\n.navbar-inverse .navbar-nav > li > a {\n  color: #9d9d9d;\n}\n.navbar-inverse .navbar-nav > li > a:hover,\n.navbar-inverse .navbar-nav > li > a:focus {\n  color: #fff;\n  background-color: transparent;\n}\n.navbar-inverse .navbar-nav > .active > a,\n.navbar-inverse .navbar-nav > .active > a:hover,\n.navbar-inverse .navbar-nav > .active > a:focus {\n  color: #fff;\n  background-color: #080808;\n}\n.navbar-inverse .navbar-nav > .disabled > a,\n.navbar-inverse .navbar-nav > .disabled > a:hover,\n.navbar-inverse .navbar-nav > .disabled > a:focus {\n  color: #444;\n  background-color: transparent;\n}\n.navbar-inverse .navbar-toggle {\n  border-color: #333;\n}\n.navbar-inverse .navbar-toggle:hover,\n.navbar-inverse .navbar-toggle:focus {\n  background-color: #333;\n}\n.navbar-inverse .navbar-toggle .icon-bar {\n  background-color: #fff;\n}\n.navbar-inverse .navbar-collapse,\n.navbar-inverse .navbar-form {\n  border-color: #101010;\n}\n.navbar-inverse .navbar-nav > .open > a,\n.navbar-inverse .navbar-nav > .open > a:hover,\n.navbar-inverse .navbar-nav > .open > a:focus {\n  background-color: #080808;\n  color: #fff;\n}\n@media (max-width: 767px) {\n  .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {\n    border-color: #080808;\n  }\n  .navbar-inverse .navbar-nav .open .dropdown-menu .divider {\n    background-color: #080808;\n  }\n  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {\n    color: #9d9d9d;\n  }\n  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,\n  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {\n    color: #fff;\n    background-color: transparent;\n  }\n  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,\n  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,\n  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {\n    color: #fff;\n    background-color: #080808;\n  }\n  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,\n  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,\n  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {\n    color: #444;\n    background-color: transparent;\n  }\n}\n.navbar-inverse .navbar-link {\n  color: #9d9d9d;\n}\n.navbar-inverse .navbar-link:hover {\n  color: #fff;\n}\n.navbar-inverse .btn-link {\n  color: #9d9d9d;\n}\n.navbar-inverse .btn-link:hover,\n.navbar-inverse .btn-link:focus {\n  color: #fff;\n}\n.navbar-inverse .btn-link[disabled]:hover,\nfieldset[disabled] .navbar-inverse .btn-link:hover,\n.navbar-inverse .btn-link[disabled]:focus,\nfieldset[disabled] .navbar-inverse .btn-link:focus {\n  color: #444;\n}\n.breadcrumb {\n  padding: 8px 15px;\n  margin-bottom: 20px;\n  list-style: none;\n  background-color: #f5f5f5;\n  border-radius: 4px;\n}\n.breadcrumb > li {\n  display: inline-block;\n}\n.breadcrumb > li + li:before {\n  content: \"/\\00a0\";\n  padding: 0 5px;\n  color: #ccc;\n}\n.breadcrumb > .active {\n  color: #777777;\n}\n.pagination {\n  display: inline-block;\n  padding-left: 0;\n  margin: 20px 0;\n  border-radius: 4px;\n}\n.pagination > li {\n  display: inline;\n}\n.pagination > li > a,\n.pagination > li > span {\n  position: relative;\n  float: left;\n  padding: 6px 12px;\n  line-height: 1.42857143;\n  text-decoration: none;\n  color: #337ab7;\n  background-color: #fff;\n  border: 1px solid #ddd;\n  margin-left: -1px;\n}\n.pagination > li:first-child > a,\n.pagination > li:first-child > span {\n  margin-left: 0;\n  border-bottom-left-radius: 4px;\n  border-top-left-radius: 4px;\n}\n.pagination > li:last-child > a,\n.pagination > li:last-child > span {\n  border-bottom-right-radius: 4px;\n  border-top-right-radius: 4px;\n}\n.pagination > li > a:hover,\n.pagination > li > span:hover,\n.pagination > li > a:focus,\n.pagination > li > span:focus {\n  z-index: 2;\n  color: #23527c;\n  background-color: #eeeeee;\n  border-color: #ddd;\n}\n.pagination > .active > a,\n.pagination > .active > span,\n.pagination > .active > a:hover,\n.pagination > .active > span:hover,\n.pagination > .active > a:focus,\n.pagination > .active > span:focus {\n  z-index: 3;\n  color: #fff;\n  background-color: #337ab7;\n  border-color: #337ab7;\n  cursor: default;\n}\n.pagination > .disabled > span,\n.pagination > .disabled > span:hover,\n.pagination > .disabled > span:focus,\n.pagination > .disabled > a,\n.pagination > .disabled > a:hover,\n.pagination > .disabled > a:focus {\n  color: #777777;\n  background-color: #fff;\n  border-color: #ddd;\n  cursor: not-allowed;\n}\n.pagination-lg > li > a,\n.pagination-lg > li > span {\n  padding: 10px 16px;\n  font-size: 18px;\n  line-height: 1.3333333;\n}\n.pagination-lg > li:first-child > a,\n.pagination-lg > li:first-child > span {\n  border-bottom-left-radius: 6px;\n  border-top-left-radius: 6px;\n}\n.pagination-lg > li:last-child > a,\n.pagination-lg > li:last-child > span {\n  border-bottom-right-radius: 6px;\n  border-top-right-radius: 6px;\n}\n.pagination-sm > li > a,\n.pagination-sm > li > span {\n  padding: 5px 10px;\n  font-size: 12px;\n  line-height: 1.5;\n}\n.pagination-sm > li:first-child > a,\n.pagination-sm > li:first-child > span {\n  border-bottom-left-radius: 3px;\n  border-top-left-radius: 3px;\n}\n.pagination-sm > li:last-child > a,\n.pagination-sm > li:last-child > span {\n  border-bottom-right-radius: 3px;\n  border-top-right-radius: 3px;\n}\n.pager {\n  padding-left: 0;\n  margin: 20px 0;\n  list-style: none;\n  text-align: center;\n}\n.pager li {\n  display: inline;\n}\n.pager li > a,\n.pager li > span {\n  display: inline-block;\n  padding: 5px 14px;\n  background-color: #fff;\n  border: 1px solid #ddd;\n  border-radius: 15px;\n}\n.pager li > a:hover,\n.pager li > a:focus {\n  text-decoration: none;\n  background-color: #eeeeee;\n}\n.pager .next > a,\n.pager .next > span {\n  float: right;\n}\n.pager .previous > a,\n.pager .previous > span {\n  float: left;\n}\n.pager .disabled > a,\n.pager .disabled > a:hover,\n.pager .disabled > a:focus,\n.pager .disabled > span {\n  color: #777777;\n  background-color: #fff;\n  cursor: not-allowed;\n}\n.label {\n  display: inline;\n  padding: .2em .6em .3em;\n  font-size: 75%;\n  font-weight: bold;\n  line-height: 1;\n  color: #fff;\n  text-align: center;\n  white-space: nowrap;\n  vertical-align: baseline;\n  border-radius: .25em;\n}\na.label:hover,\na.label:focus {\n  color: #fff;\n  text-decoration: none;\n  cursor: pointer;\n}\n.label:empty {\n  display: none;\n}\n.btn .label {\n  position: relative;\n  top: -1px;\n}\n.label-default {\n  background-color: #777777;\n}\n.label-default[href]:hover,\n.label-default[href]:focus {\n  background-color: #5e5e5e;\n}\n.label-primary {\n  background-color: #337ab7;\n}\n.label-primary[href]:hover,\n.label-primary[href]:focus {\n  background-color: #286090;\n}\n.label-success {\n  background-color: #5cb85c;\n}\n.label-success[href]:hover,\n.label-success[href]:focus {\n  background-color: #449d44;\n}\n.label-info {\n  background-color: #5bc0de;\n}\n.label-info[href]:hover,\n.label-info[href]:focus {\n  background-color: #31b0d5;\n}\n.label-warning {\n  background-color: #f0ad4e;\n}\n.label-warning[href]:hover,\n.label-warning[href]:focus {\n  background-color: #ec971f;\n}\n.label-danger {\n  background-color: #d9534f;\n}\n.label-danger[href]:hover,\n.label-danger[href]:focus {\n  background-color: #c9302c;\n}\n.badge {\n  display: inline-block;\n  min-width: 10px;\n  padding: 3px 7px;\n  font-size: 12px;\n  font-weight: bold;\n  color: #fff;\n  line-height: 1;\n  vertical-align: middle;\n  white-space: nowrap;\n  text-align: center;\n  background-color: #777777;\n  border-radius: 10px;\n}\n.badge:empty {\n  display: none;\n}\n.btn .badge {\n  position: relative;\n  top: -1px;\n}\n.btn-xs .badge,\n.btn-group-xs > .btn .badge {\n  top: 0;\n  padding: 1px 5px;\n}\na.badge:hover,\na.badge:focus {\n  color: #fff;\n  text-decoration: none;\n  cursor: pointer;\n}\n.list-group-item.active > .badge,\n.nav-pills > .active > a > .badge {\n  color: #337ab7;\n  background-color: #fff;\n}\n.list-group-item > .badge {\n  float: right;\n}\n.list-group-item > .badge + .badge {\n  margin-right: 5px;\n}\n.nav-pills > li > a > .badge {\n  margin-left: 3px;\n}\n.jumbotron {\n  padding-top: 30px;\n  padding-bottom: 30px;\n  margin-bottom: 30px;\n  color: inherit;\n  background-color: #eeeeee;\n}\n.jumbotron h1,\n.jumbotron .h1 {\n  color: inherit;\n}\n.jumbotron p {\n  margin-bottom: 15px;\n  font-size: 21px;\n  font-weight: 200;\n}\n.jumbotron > hr {\n  border-top-color: #d5d5d5;\n}\n.container .jumbotron,\n.container-fluid .jumbotron {\n  border-radius: 6px;\n  padding-left: 15px;\n  padding-right: 15px;\n}\n.jumbotron .container {\n  max-width: 100%;\n}\n@media screen and (min-width: 768px) {\n  .jumbotron {\n    padding-top: 48px;\n    padding-bottom: 48px;\n  }\n  .container .jumbotron,\n  .container-fluid .jumbotron {\n    padding-left: 60px;\n    padding-right: 60px;\n  }\n  .jumbotron h1,\n  .jumbotron .h1 {\n    font-size: 63px;\n  }\n}\n.thumbnail {\n  display: block;\n  padding: 4px;\n  margin-bottom: 20px;\n  line-height: 1.42857143;\n  background-color: #fff;\n  border: 1px solid #ddd;\n  border-radius: 4px;\n  -webkit-transition: border 0.2s ease-in-out;\n  -o-transition: border 0.2s ease-in-out;\n  transition: border 0.2s ease-in-out;\n}\n.thumbnail > img,\n.thumbnail a > img {\n  margin-left: auto;\n  margin-right: auto;\n}\na.thumbnail:hover,\na.thumbnail:focus,\na.thumbnail.active {\n  border-color: #337ab7;\n}\n.thumbnail .caption {\n  padding: 9px;\n  color: #333333;\n}\n.alert {\n  padding: 15px;\n  margin-bottom: 20px;\n  border: 1px solid transparent;\n  border-radius: 4px;\n}\n.alert h4 {\n  margin-top: 0;\n  color: inherit;\n}\n.alert .alert-link {\n  font-weight: bold;\n}\n.alert > p,\n.alert > ul {\n  margin-bottom: 0;\n}\n.alert > p + p {\n  margin-top: 5px;\n}\n.alert-dismissable,\n.alert-dismissible {\n  padding-right: 35px;\n}\n.alert-dismissable .close,\n.alert-dismissible .close {\n  position: relative;\n  top: -2px;\n  right: -21px;\n  color: inherit;\n}\n.alert-success {\n  background-color: #dff0d8;\n  border-color: #d6e9c6;\n  color: #3c763d;\n}\n.alert-success hr {\n  border-top-color: #c9e2b3;\n}\n.alert-success .alert-link {\n  color: #2b542c;\n}\n.alert-info {\n  background-color: #d9edf7;\n  border-color: #bce8f1;\n  color: #31708f;\n}\n.alert-info hr {\n  border-top-color: #a6e1ec;\n}\n.alert-info .alert-link {\n  color: #245269;\n}\n.alert-warning {\n  background-color: #fcf8e3;\n  border-color: #faebcc;\n  color: #8a6d3b;\n}\n.alert-warning hr {\n  border-top-color: #f7e1b5;\n}\n.alert-warning .alert-link {\n  color: #66512c;\n}\n.alert-danger {\n  background-color: #f2dede;\n  border-color: #ebccd1;\n  color: #a94442;\n}\n.alert-danger hr {\n  border-top-color: #e4b9c0;\n}\n.alert-danger .alert-link {\n  color: #843534;\n}\n@-webkit-keyframes progress-bar-stripes {\n  from {\n    background-position: 40px 0;\n  }\n  to {\n    background-position: 0 0;\n  }\n}\n@keyframes progress-bar-stripes {\n  from {\n    background-position: 40px 0;\n  }\n  to {\n    background-position: 0 0;\n  }\n}\n.progress {\n  overflow: hidden;\n  height: 20px;\n  margin-bottom: 20px;\n  background-color: #f5f5f5;\n  border-radius: 4px;\n  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);\n  box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);\n}\n.progress-bar {\n  float: left;\n  width: 0%;\n  height: 100%;\n  font-size: 12px;\n  line-height: 20px;\n  color: #fff;\n  text-align: center;\n  background-color: #337ab7;\n  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);\n  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);\n  -webkit-transition: width 0.6s ease;\n  -o-transition: width 0.6s ease;\n  transition: width 0.6s ease;\n}\n.progress-striped .progress-bar,\n.progress-bar-striped {\n  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-size: 40px 40px;\n}\n.progress.active .progress-bar,\n.progress-bar.active {\n  -webkit-animation: progress-bar-stripes 2s linear infinite;\n  -o-animation: progress-bar-stripes 2s linear infinite;\n  animation: progress-bar-stripes 2s linear infinite;\n}\n.progress-bar-success {\n  background-color: #5cb85c;\n}\n.progress-striped .progress-bar-success {\n  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n}\n.progress-bar-info {\n  background-color: #5bc0de;\n}\n.progress-striped .progress-bar-info {\n  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n}\n.progress-bar-warning {\n  background-color: #f0ad4e;\n}\n.progress-striped .progress-bar-warning {\n  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n}\n.progress-bar-danger {\n  background-color: #d9534f;\n}\n.progress-striped .progress-bar-danger {\n  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n}\n.media {\n  margin-top: 15px;\n}\n.media:first-child {\n  margin-top: 0;\n}\n.media,\n.media-body {\n  zoom: 1;\n  overflow: hidden;\n}\n.media-body {\n  width: 10000px;\n}\n.media-object {\n  display: block;\n}\n.media-object.img-thumbnail {\n  max-width: none;\n}\n.media-right,\n.media > .pull-right {\n  padding-left: 10px;\n}\n.media-left,\n.media > .pull-left {\n  padding-right: 10px;\n}\n.media-left,\n.media-right,\n.media-body {\n  display: table-cell;\n  vertical-align: top;\n}\n.media-middle {\n  vertical-align: middle;\n}\n.media-bottom {\n  vertical-align: bottom;\n}\n.media-heading {\n  margin-top: 0;\n  margin-bottom: 5px;\n}\n.media-list {\n  padding-left: 0;\n  list-style: none;\n}\n.list-group {\n  margin-bottom: 20px;\n  padding-left: 0;\n}\n.list-group-item {\n  position: relative;\n  display: block;\n  padding: 10px 15px;\n  margin-bottom: -1px;\n  background-color: #fff;\n  border: 1px solid #ddd;\n}\n.list-group-item:first-child {\n  border-top-right-radius: 4px;\n  border-top-left-radius: 4px;\n}\n.list-group-item:last-child {\n  margin-bottom: 0;\n  border-bottom-right-radius: 4px;\n  border-bottom-left-radius: 4px;\n}\na.list-group-item,\nbutton.list-group-item {\n  color: #555;\n}\na.list-group-item .list-group-item-heading,\nbutton.list-group-item .list-group-item-heading {\n  color: #333;\n}\na.list-group-item:hover,\nbutton.list-group-item:hover,\na.list-group-item:focus,\nbutton.list-group-item:focus {\n  text-decoration: none;\n  color: #555;\n  background-color: #f5f5f5;\n}\nbutton.list-group-item {\n  width: 100%;\n  text-align: left;\n}\n.list-group-item.disabled,\n.list-group-item.disabled:hover,\n.list-group-item.disabled:focus {\n  background-color: #eeeeee;\n  color: #777777;\n  cursor: not-allowed;\n}\n.list-group-item.disabled .list-group-item-heading,\n.list-group-item.disabled:hover .list-group-item-heading,\n.list-group-item.disabled:focus .list-group-item-heading {\n  color: inherit;\n}\n.list-group-item.disabled .list-group-item-text,\n.list-group-item.disabled:hover .list-group-item-text,\n.list-group-item.disabled:focus .list-group-item-text {\n  color: #777777;\n}\n.list-group-item.active,\n.list-group-item.active:hover,\n.list-group-item.active:focus {\n  z-index: 2;\n  color: #fff;\n  background-color: #337ab7;\n  border-color: #337ab7;\n}\n.list-group-item.active .list-group-item-heading,\n.list-group-item.active:hover .list-group-item-heading,\n.list-group-item.active:focus .list-group-item-heading,\n.list-group-item.active .list-group-item-heading > small,\n.list-group-item.active:hover .list-group-item-heading > small,\n.list-group-item.active:focus .list-group-item-heading > small,\n.list-group-item.active .list-group-item-heading > .small,\n.list-group-item.active:hover .list-group-item-heading > .small,\n.list-group-item.active:focus .list-group-item-heading > .small {\n  color: inherit;\n}\n.list-group-item.active .list-group-item-text,\n.list-group-item.active:hover .list-group-item-text,\n.list-group-item.active:focus .list-group-item-text {\n  color: #c7ddef;\n}\n.list-group-item-success {\n  color: #3c763d;\n  background-color: #dff0d8;\n}\na.list-group-item-success,\nbutton.list-group-item-success {\n  color: #3c763d;\n}\na.list-group-item-success .list-group-item-heading,\nbutton.list-group-item-success .list-group-item-heading {\n  color: inherit;\n}\na.list-group-item-success:hover,\nbutton.list-group-item-success:hover,\na.list-group-item-success:focus,\nbutton.list-group-item-success:focus {\n  color: #3c763d;\n  background-color: #d0e9c6;\n}\na.list-group-item-success.active,\nbutton.list-group-item-success.active,\na.list-group-item-success.active:hover,\nbutton.list-group-item-success.active:hover,\na.list-group-item-success.active:focus,\nbutton.list-group-item-success.active:focus {\n  color: #fff;\n  background-color: #3c763d;\n  border-color: #3c763d;\n}\n.list-group-item-info {\n  color: #31708f;\n  background-color: #d9edf7;\n}\na.list-group-item-info,\nbutton.list-group-item-info {\n  color: #31708f;\n}\na.list-group-item-info .list-group-item-heading,\nbutton.list-group-item-info .list-group-item-heading {\n  color: inherit;\n}\na.list-group-item-info:hover,\nbutton.list-group-item-info:hover,\na.list-group-item-info:focus,\nbutton.list-group-item-info:focus {\n  color: #31708f;\n  background-color: #c4e3f3;\n}\na.list-group-item-info.active,\nbutton.list-group-item-info.active,\na.list-group-item-info.active:hover,\nbutton.list-group-item-info.active:hover,\na.list-group-item-info.active:focus,\nbutton.list-group-item-info.active:focus {\n  color: #fff;\n  background-color: #31708f;\n  border-color: #31708f;\n}\n.list-group-item-warning {\n  color: #8a6d3b;\n  background-color: #fcf8e3;\n}\na.list-group-item-warning,\nbutton.list-group-item-warning {\n  color: #8a6d3b;\n}\na.list-group-item-warning .list-group-item-heading,\nbutton.list-group-item-warning .list-group-item-heading {\n  color: inherit;\n}\na.list-group-item-warning:hover,\nbutton.list-group-item-warning:hover,\na.list-group-item-warning:focus,\nbutton.list-group-item-warning:focus {\n  color: #8a6d3b;\n  background-color: #faf2cc;\n}\na.list-group-item-warning.active,\nbutton.list-group-item-warning.active,\na.list-group-item-warning.active:hover,\nbutton.list-group-item-warning.active:hover,\na.list-group-item-warning.active:focus,\nbutton.list-group-item-warning.active:focus {\n  color: #fff;\n  background-color: #8a6d3b;\n  border-color: #8a6d3b;\n}\n.list-group-item-danger {\n  color: #a94442;\n  background-color: #f2dede;\n}\na.list-group-item-danger,\nbutton.list-group-item-danger {\n  color: #a94442;\n}\na.list-group-item-danger .list-group-item-heading,\nbutton.list-group-item-danger .list-group-item-heading {\n  color: inherit;\n}\na.list-group-item-danger:hover,\nbutton.list-group-item-danger:hover,\na.list-group-item-danger:focus,\nbutton.list-group-item-danger:focus {\n  color: #a94442;\n  background-color: #ebcccc;\n}\na.list-group-item-danger.active,\nbutton.list-group-item-danger.active,\na.list-group-item-danger.active:hover,\nbutton.list-group-item-danger.active:hover,\na.list-group-item-danger.active:focus,\nbutton.list-group-item-danger.active:focus {\n  color: #fff;\n  background-color: #a94442;\n  border-color: #a94442;\n}\n.list-group-item-heading {\n  margin-top: 0;\n  margin-bottom: 5px;\n}\n.list-group-item-text {\n  margin-bottom: 0;\n  line-height: 1.3;\n}\n.panel {\n  margin-bottom: 20px;\n  background-color: #fff;\n  border: 1px solid transparent;\n  border-radius: 4px;\n  -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);\n  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);\n}\n.panel-body {\n  padding: 15px;\n}\n.panel-heading {\n  padding: 10px 15px;\n  border-bottom: 1px solid transparent;\n  border-top-right-radius: 3px;\n  border-top-left-radius: 3px;\n}\n.panel-heading > .dropdown .dropdown-toggle {\n  color: inherit;\n}\n.panel-title {\n  margin-top: 0;\n  margin-bottom: 0;\n  font-size: 16px;\n  color: inherit;\n}\n.panel-title > a,\n.panel-title > small,\n.panel-title > .small,\n.panel-title > small > a,\n.panel-title > .small > a {\n  color: inherit;\n}\n.panel-footer {\n  padding: 10px 15px;\n  background-color: #f5f5f5;\n  border-top: 1px solid #ddd;\n  border-bottom-right-radius: 3px;\n  border-bottom-left-radius: 3px;\n}\n.panel > .list-group,\n.panel > .panel-collapse > .list-group {\n  margin-bottom: 0;\n}\n.panel > .list-group .list-group-item,\n.panel > .panel-collapse > .list-group .list-group-item {\n  border-width: 1px 0;\n  border-radius: 0;\n}\n.panel > .list-group:first-child .list-group-item:first-child,\n.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {\n  border-top: 0;\n  border-top-right-radius: 3px;\n  border-top-left-radius: 3px;\n}\n.panel > .list-group:last-child .list-group-item:last-child,\n.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {\n  border-bottom: 0;\n  border-bottom-right-radius: 3px;\n  border-bottom-left-radius: 3px;\n}\n.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {\n  border-top-right-radius: 0;\n  border-top-left-radius: 0;\n}\n.panel-heading + .list-group .list-group-item:first-child {\n  border-top-width: 0;\n}\n.list-group + .panel-footer {\n  border-top-width: 0;\n}\n.panel > .table,\n.panel > .table-responsive > .table,\n.panel > .panel-collapse > .table {\n  margin-bottom: 0;\n}\n.panel > .table caption,\n.panel > .table-responsive > .table caption,\n.panel > .panel-collapse > .table caption {\n  padding-left: 15px;\n  padding-right: 15px;\n}\n.panel > .table:first-child,\n.panel > .table-responsive:first-child > .table:first-child {\n  border-top-right-radius: 3px;\n  border-top-left-radius: 3px;\n}\n.panel > .table:first-child > thead:first-child > tr:first-child,\n.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,\n.panel > .table:first-child > tbody:first-child > tr:first-child,\n.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {\n  border-top-left-radius: 3px;\n  border-top-right-radius: 3px;\n}\n.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,\n.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,\n.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,\n.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,\n.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,\n.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,\n.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,\n.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {\n  border-top-left-radius: 3px;\n}\n.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,\n.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,\n.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,\n.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,\n.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,\n.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,\n.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,\n.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {\n  border-top-right-radius: 3px;\n}\n.panel > .table:last-child,\n.panel > .table-responsive:last-child > .table:last-child {\n  border-bottom-right-radius: 3px;\n  border-bottom-left-radius: 3px;\n}\n.panel > .table:last-child > tbody:last-child > tr:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,\n.panel > .table:last-child > tfoot:last-child > tr:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {\n  border-bottom-left-radius: 3px;\n  border-bottom-right-radius: 3px;\n}\n.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,\n.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,\n.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,\n.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,\n.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,\n.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,\n.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,\n.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {\n  border-bottom-left-radius: 3px;\n}\n.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,\n.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,\n.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,\n.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {\n  border-bottom-right-radius: 3px;\n}\n.panel > .panel-body + .table,\n.panel > .panel-body + .table-responsive,\n.panel > .table + .panel-body,\n.panel > .table-responsive + .panel-body {\n  border-top: 1px solid #ddd;\n}\n.panel > .table > tbody:first-child > tr:first-child th,\n.panel > .table > tbody:first-child > tr:first-child td {\n  border-top: 0;\n}\n.panel > .table-bordered,\n.panel > .table-responsive > .table-bordered {\n  border: 0;\n}\n.panel > .table-bordered > thead > tr > th:first-child,\n.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,\n.panel > .table-bordered > tbody > tr > th:first-child,\n.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,\n.panel > .table-bordered > tfoot > tr > th:first-child,\n.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,\n.panel > .table-bordered > thead > tr > td:first-child,\n.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,\n.panel > .table-bordered > tbody > tr > td:first-child,\n.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,\n.panel > .table-bordered > tfoot > tr > td:first-child,\n.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {\n  border-left: 0;\n}\n.panel > .table-bordered > thead > tr > th:last-child,\n.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,\n.panel > .table-bordered > tbody > tr > th:last-child,\n.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,\n.panel > .table-bordered > tfoot > tr > th:last-child,\n.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,\n.panel > .table-bordered > thead > tr > td:last-child,\n.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,\n.panel > .table-bordered > tbody > tr > td:last-child,\n.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,\n.panel > .table-bordered > tfoot > tr > td:last-child,\n.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {\n  border-right: 0;\n}\n.panel > .table-bordered > thead > tr:first-child > td,\n.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,\n.panel > .table-bordered > tbody > tr:first-child > td,\n.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,\n.panel > .table-bordered > thead > tr:first-child > th,\n.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,\n.panel > .table-bordered > tbody > tr:first-child > th,\n.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {\n  border-bottom: 0;\n}\n.panel > .table-bordered > tbody > tr:last-child > td,\n.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,\n.panel > .table-bordered > tfoot > tr:last-child > td,\n.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,\n.panel > .table-bordered > tbody > tr:last-child > th,\n.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,\n.panel > .table-bordered > tfoot > tr:last-child > th,\n.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {\n  border-bottom: 0;\n}\n.panel > .table-responsive {\n  border: 0;\n  margin-bottom: 0;\n}\n.panel-group {\n  margin-bottom: 20px;\n}\n.panel-group .panel {\n  margin-bottom: 0;\n  border-radius: 4px;\n}\n.panel-group .panel + .panel {\n  margin-top: 5px;\n}\n.panel-group .panel-heading {\n  border-bottom: 0;\n}\n.panel-group .panel-heading + .panel-collapse > .panel-body,\n.panel-group .panel-heading + .panel-collapse > .list-group {\n  border-top: 1px solid #ddd;\n}\n.panel-group .panel-footer {\n  border-top: 0;\n}\n.panel-group .panel-footer + .panel-collapse .panel-body {\n  border-bottom: 1px solid #ddd;\n}\n.panel-default {\n  border-color: #ddd;\n}\n.panel-default > .panel-heading {\n  color: #333333;\n  background-color: #f5f5f5;\n  border-color: #ddd;\n}\n.panel-default > .panel-heading + .panel-collapse > .panel-body {\n  border-top-color: #ddd;\n}\n.panel-default > .panel-heading .badge {\n  color: #f5f5f5;\n  background-color: #333333;\n}\n.panel-default > .panel-footer + .panel-collapse > .panel-body {\n  border-bottom-color: #ddd;\n}\n.panel-primary {\n  border-color: #337ab7;\n}\n.panel-primary > .panel-heading {\n  color: #fff;\n  background-color: #337ab7;\n  border-color: #337ab7;\n}\n.panel-primary > .panel-heading + .panel-collapse > .panel-body {\n  border-top-color: #337ab7;\n}\n.panel-primary > .panel-heading .badge {\n  color: #337ab7;\n  background-color: #fff;\n}\n.panel-primary > .panel-footer + .panel-collapse > .panel-body {\n  border-bottom-color: #337ab7;\n}\n.panel-success {\n  border-color: #d6e9c6;\n}\n.panel-success > .panel-heading {\n  color: #3c763d;\n  background-color: #dff0d8;\n  border-color: #d6e9c6;\n}\n.panel-success > .panel-heading + .panel-collapse > .panel-body {\n  border-top-color: #d6e9c6;\n}\n.panel-success > .panel-heading .badge {\n  color: #dff0d8;\n  background-color: #3c763d;\n}\n.panel-success > .panel-footer + .panel-collapse > .panel-body {\n  border-bottom-color: #d6e9c6;\n}\n.panel-info {\n  border-color: #bce8f1;\n}\n.panel-info > .panel-heading {\n  color: #31708f;\n  background-color: #d9edf7;\n  border-color: #bce8f1;\n}\n.panel-info > .panel-heading + .panel-collapse > .panel-body {\n  border-top-color: #bce8f1;\n}\n.panel-info > .panel-heading .badge {\n  color: #d9edf7;\n  background-color: #31708f;\n}\n.panel-info > .panel-footer + .panel-collapse > .panel-body {\n  border-bottom-color: #bce8f1;\n}\n.panel-warning {\n  border-color: #faebcc;\n}\n.panel-warning > .panel-heading {\n  color: #8a6d3b;\n  background-color: #fcf8e3;\n  border-color: #faebcc;\n}\n.panel-warning > .panel-heading + .panel-collapse > .panel-body {\n  border-top-color: #faebcc;\n}\n.panel-warning > .panel-heading .badge {\n  color: #fcf8e3;\n  background-color: #8a6d3b;\n}\n.panel-warning > .panel-footer + .panel-collapse > .panel-body {\n  border-bottom-color: #faebcc;\n}\n.panel-danger {\n  border-color: #ebccd1;\n}\n.panel-danger > .panel-heading {\n  color: #a94442;\n  background-color: #f2dede;\n  border-color: #ebccd1;\n}\n.panel-danger > .panel-heading + .panel-collapse > .panel-body {\n  border-top-color: #ebccd1;\n}\n.panel-danger > .panel-heading .badge {\n  color: #f2dede;\n  background-color: #a94442;\n}\n.panel-danger > .panel-footer + .panel-collapse > .panel-body {\n  border-bottom-color: #ebccd1;\n}\n.embed-responsive {\n  position: relative;\n  display: block;\n  height: 0;\n  padding: 0;\n  overflow: hidden;\n}\n.embed-responsive .embed-responsive-item,\n.embed-responsive iframe,\n.embed-responsive embed,\n.embed-responsive object,\n.embed-responsive video {\n  position: absolute;\n  top: 0;\n  left: 0;\n  bottom: 0;\n  height: 100%;\n  width: 100%;\n  border: 0;\n}\n.embed-responsive-16by9 {\n  padding-bottom: 56.25%;\n}\n.embed-responsive-4by3 {\n  padding-bottom: 75%;\n}\n.well {\n  min-height: 20px;\n  padding: 19px;\n  margin-bottom: 20px;\n  background-color: #f5f5f5;\n  border: 1px solid #e3e3e3;\n  border-radius: 4px;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);\n  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);\n}\n.well blockquote {\n  border-color: #ddd;\n  border-color: rgba(0, 0, 0, 0.15);\n}\n.well-lg {\n  padding: 24px;\n  border-radius: 6px;\n}\n.well-sm {\n  padding: 9px;\n  border-radius: 3px;\n}\n.close {\n  float: right;\n  font-size: 21px;\n  font-weight: bold;\n  line-height: 1;\n  color: #000;\n  text-shadow: 0 1px 0 #fff;\n  opacity: 0.2;\n  filter: alpha(opacity=20);\n}\n.close:hover,\n.close:focus {\n  color: #000;\n  text-decoration: none;\n  cursor: pointer;\n  opacity: 0.5;\n  filter: alpha(opacity=50);\n}\nbutton.close {\n  padding: 0;\n  cursor: pointer;\n  background: transparent;\n  border: 0;\n  -webkit-appearance: none;\n}\n.modal-open {\n  overflow: hidden;\n}\n.modal {\n  display: none;\n  overflow: hidden;\n  position: fixed;\n  top: 0;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  z-index: 1050;\n  -webkit-overflow-scrolling: touch;\n  outline: 0;\n}\n.modal.fade .modal-dialog {\n  -webkit-transform: translate(0, -25%);\n  -ms-transform: translate(0, -25%);\n  -o-transform: translate(0, -25%);\n  transform: translate(0, -25%);\n  -webkit-transition: -webkit-transform 0.3s ease-out;\n  -moz-transition: -moz-transform 0.3s ease-out;\n  -o-transition: -o-transform 0.3s ease-out;\n  transition: transform 0.3s ease-out;\n}\n.modal.in .modal-dialog {\n  -webkit-transform: translate(0, 0);\n  -ms-transform: translate(0, 0);\n  -o-transform: translate(0, 0);\n  transform: translate(0, 0);\n}\n.modal-open .modal {\n  overflow-x: hidden;\n  overflow-y: auto;\n}\n.modal-dialog {\n  position: relative;\n  width: auto;\n  margin: 10px;\n}\n.modal-content {\n  position: relative;\n  background-color: #fff;\n  border: 1px solid #999;\n  border: 1px solid rgba(0, 0, 0, 0.2);\n  border-radius: 6px;\n  -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);\n  box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);\n  background-clip: padding-box;\n  outline: 0;\n}\n.modal-backdrop {\n  position: fixed;\n  top: 0;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  z-index: 1040;\n  background-color: #000;\n}\n.modal-backdrop.fade {\n  opacity: 0;\n  filter: alpha(opacity=0);\n}\n.modal-backdrop.in {\n  opacity: 0.5;\n  filter: alpha(opacity=50);\n}\n.modal-header {\n  padding: 15px;\n  border-bottom: 1px solid #e5e5e5;\n}\n.modal-header .close {\n  margin-top: -2px;\n}\n.modal-title {\n  margin: 0;\n  line-height: 1.42857143;\n}\n.modal-body {\n  position: relative;\n  padding: 15px;\n}\n.modal-footer {\n  padding: 15px;\n  text-align: right;\n  border-top: 1px solid #e5e5e5;\n}\n.modal-footer .btn + .btn {\n  margin-left: 5px;\n  margin-bottom: 0;\n}\n.modal-footer .btn-group .btn + .btn {\n  margin-left: -1px;\n}\n.modal-footer .btn-block + .btn-block {\n  margin-left: 0;\n}\n.modal-scrollbar-measure {\n  position: absolute;\n  top: -9999px;\n  width: 50px;\n  height: 50px;\n  overflow: scroll;\n}\n@media (min-width: 768px) {\n  .modal-dialog {\n    width: 600px;\n    margin: 30px auto;\n  }\n  .modal-content {\n    -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);\n    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);\n  }\n  .modal-sm {\n    width: 300px;\n  }\n}\n@media (min-width: 992px) {\n  .modal-lg {\n    width: 900px;\n  }\n}\n.tooltip {\n  position: absolute;\n  z-index: 1070;\n  display: block;\n  font-family: \"Helvetica Neue\", Helvetica, Arial, sans-serif;\n  font-style: normal;\n  font-weight: normal;\n  letter-spacing: normal;\n  line-break: auto;\n  line-height: 1.42857143;\n  text-align: left;\n  text-align: start;\n  text-decoration: none;\n  text-shadow: none;\n  text-transform: none;\n  white-space: normal;\n  word-break: normal;\n  word-spacing: normal;\n  word-wrap: normal;\n  font-size: 12px;\n  opacity: 0;\n  filter: alpha(opacity=0);\n}\n.tooltip.in {\n  opacity: 0.9;\n  filter: alpha(opacity=90);\n}\n.tooltip.top {\n  margin-top: -3px;\n  padding: 5px 0;\n}\n.tooltip.right {\n  margin-left: 3px;\n  padding: 0 5px;\n}\n.tooltip.bottom {\n  margin-top: 3px;\n  padding: 5px 0;\n}\n.tooltip.left {\n  margin-left: -3px;\n  padding: 0 5px;\n}\n.tooltip-inner {\n  max-width: 200px;\n  padding: 3px 8px;\n  color: #fff;\n  text-align: center;\n  background-color: #000;\n  border-radius: 4px;\n}\n.tooltip-arrow {\n  position: absolute;\n  width: 0;\n  height: 0;\n  border-color: transparent;\n  border-style: solid;\n}\n.tooltip.top .tooltip-arrow {\n  bottom: 0;\n  left: 50%;\n  margin-left: -5px;\n  border-width: 5px 5px 0;\n  border-top-color: #000;\n}\n.tooltip.top-left .tooltip-arrow {\n  bottom: 0;\n  right: 5px;\n  margin-bottom: -5px;\n  border-width: 5px 5px 0;\n  border-top-color: #000;\n}\n.tooltip.top-right .tooltip-arrow {\n  bottom: 0;\n  left: 5px;\n  margin-bottom: -5px;\n  border-width: 5px 5px 0;\n  border-top-color: #000;\n}\n.tooltip.right .tooltip-arrow {\n  top: 50%;\n  left: 0;\n  margin-top: -5px;\n  border-width: 5px 5px 5px 0;\n  border-right-color: #000;\n}\n.tooltip.left .tooltip-arrow {\n  top: 50%;\n  right: 0;\n  margin-top: -5px;\n  border-width: 5px 0 5px 5px;\n  border-left-color: #000;\n}\n.tooltip.bottom .tooltip-arrow {\n  top: 0;\n  left: 50%;\n  margin-left: -5px;\n  border-width: 0 5px 5px;\n  border-bottom-color: #000;\n}\n.tooltip.bottom-left .tooltip-arrow {\n  top: 0;\n  right: 5px;\n  margin-top: -5px;\n  border-width: 0 5px 5px;\n  border-bottom-color: #000;\n}\n.tooltip.bottom-right .tooltip-arrow {\n  top: 0;\n  left: 5px;\n  margin-top: -5px;\n  border-width: 0 5px 5px;\n  border-bottom-color: #000;\n}\n.popover {\n  position: absolute;\n  top: 0;\n  left: 0;\n  z-index: 1060;\n  display: none;\n  max-width: 276px;\n  padding: 1px;\n  font-family: \"Helvetica Neue\", Helvetica, Arial, sans-serif;\n  font-style: normal;\n  font-weight: normal;\n  letter-spacing: normal;\n  line-break: auto;\n  line-height: 1.42857143;\n  text-align: left;\n  text-align: start;\n  text-decoration: none;\n  text-shadow: none;\n  text-transform: none;\n  white-space: normal;\n  word-break: normal;\n  word-spacing: normal;\n  word-wrap: normal;\n  font-size: 14px;\n  background-color: #fff;\n  background-clip: padding-box;\n  border: 1px solid #ccc;\n  border: 1px solid rgba(0, 0, 0, 0.2);\n  border-radius: 6px;\n  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);\n  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);\n}\n.popover.top {\n  margin-top: -10px;\n}\n.popover.right {\n  margin-left: 10px;\n}\n.popover.bottom {\n  margin-top: 10px;\n}\n.popover.left {\n  margin-left: -10px;\n}\n.popover-title {\n  margin: 0;\n  padding: 8px 14px;\n  font-size: 14px;\n  background-color: #f7f7f7;\n  border-bottom: 1px solid #ebebeb;\n  border-radius: 5px 5px 0 0;\n}\n.popover-content {\n  padding: 9px 14px;\n}\n.popover > .arrow,\n.popover > .arrow:after {\n  position: absolute;\n  display: block;\n  width: 0;\n  height: 0;\n  border-color: transparent;\n  border-style: solid;\n}\n.popover > .arrow {\n  border-width: 11px;\n}\n.popover > .arrow:after {\n  border-width: 10px;\n  content: \"\";\n}\n.popover.top > .arrow {\n  left: 50%;\n  margin-left: -11px;\n  border-bottom-width: 0;\n  border-top-color: #999999;\n  border-top-color: rgba(0, 0, 0, 0.25);\n  bottom: -11px;\n}\n.popover.top > .arrow:after {\n  content: \" \";\n  bottom: 1px;\n  margin-left: -10px;\n  border-bottom-width: 0;\n  border-top-color: #fff;\n}\n.popover.right > .arrow {\n  top: 50%;\n  left: -11px;\n  margin-top: -11px;\n  border-left-width: 0;\n  border-right-color: #999999;\n  border-right-color: rgba(0, 0, 0, 0.25);\n}\n.popover.right > .arrow:after {\n  content: \" \";\n  left: 1px;\n  bottom: -10px;\n  border-left-width: 0;\n  border-right-color: #fff;\n}\n.popover.bottom > .arrow {\n  left: 50%;\n  margin-left: -11px;\n  border-top-width: 0;\n  border-bottom-color: #999999;\n  border-bottom-color: rgba(0, 0, 0, 0.25);\n  top: -11px;\n}\n.popover.bottom > .arrow:after {\n  content: \" \";\n  top: 1px;\n  margin-left: -10px;\n  border-top-width: 0;\n  border-bottom-color: #fff;\n}\n.popover.left > .arrow {\n  top: 50%;\n  right: -11px;\n  margin-top: -11px;\n  border-right-width: 0;\n  border-left-color: #999999;\n  border-left-color: rgba(0, 0, 0, 0.25);\n}\n.popover.left > .arrow:after {\n  content: \" \";\n  right: 1px;\n  border-right-width: 0;\n  border-left-color: #fff;\n  bottom: -10px;\n}\n.carousel {\n  position: relative;\n}\n.carousel-inner {\n  position: relative;\n  overflow: hidden;\n  width: 100%;\n}\n.carousel-inner > .item {\n  display: none;\n  position: relative;\n  -webkit-transition: 0.6s ease-in-out left;\n  -o-transition: 0.6s ease-in-out left;\n  transition: 0.6s ease-in-out left;\n}\n.carousel-inner > .item > img,\n.carousel-inner > .item > a > img {\n  line-height: 1;\n}\n@media all and (transform-3d), (-webkit-transform-3d) {\n  .carousel-inner > .item {\n    -webkit-transition: -webkit-transform 0.6s ease-in-out;\n    -moz-transition: -moz-transform 0.6s ease-in-out;\n    -o-transition: -o-transform 0.6s ease-in-out;\n    transition: transform 0.6s ease-in-out;\n    -webkit-backface-visibility: hidden;\n    -moz-backface-visibility: hidden;\n    backface-visibility: hidden;\n    -webkit-perspective: 1000px;\n    -moz-perspective: 1000px;\n    perspective: 1000px;\n  }\n  .carousel-inner > .item.next,\n  .carousel-inner > .item.active.right {\n    -webkit-transform: translate3d(100%, 0, 0);\n    transform: translate3d(100%, 0, 0);\n    left: 0;\n  }\n  .carousel-inner > .item.prev,\n  .carousel-inner > .item.active.left {\n    -webkit-transform: translate3d(-100%, 0, 0);\n    transform: translate3d(-100%, 0, 0);\n    left: 0;\n  }\n  .carousel-inner > .item.next.left,\n  .carousel-inner > .item.prev.right,\n  .carousel-inner > .item.active {\n    -webkit-transform: translate3d(0, 0, 0);\n    transform: translate3d(0, 0, 0);\n    left: 0;\n  }\n}\n.carousel-inner > .active,\n.carousel-inner > .next,\n.carousel-inner > .prev {\n  display: block;\n}\n.carousel-inner > .active {\n  left: 0;\n}\n.carousel-inner > .next,\n.carousel-inner > .prev {\n  position: absolute;\n  top: 0;\n  width: 100%;\n}\n.carousel-inner > .next {\n  left: 100%;\n}\n.carousel-inner > .prev {\n  left: -100%;\n}\n.carousel-inner > .next.left,\n.carousel-inner > .prev.right {\n  left: 0;\n}\n.carousel-inner > .active.left {\n  left: -100%;\n}\n.carousel-inner > .active.right {\n  left: 100%;\n}\n.carousel-control {\n  position: absolute;\n  top: 0;\n  left: 0;\n  bottom: 0;\n  width: 15%;\n  opacity: 0.5;\n  filter: alpha(opacity=50);\n  font-size: 20px;\n  color: #fff;\n  text-align: center;\n  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);\n  background-color: rgba(0, 0, 0, 0);\n}\n.carousel-control.left {\n  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);\n  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);\n  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);\n}\n.carousel-control.right {\n  left: auto;\n  right: 0;\n  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);\n  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);\n  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);\n}\n.carousel-control:hover,\n.carousel-control:focus {\n  outline: 0;\n  color: #fff;\n  text-decoration: none;\n  opacity: 0.9;\n  filter: alpha(opacity=90);\n}\n.carousel-control .icon-prev,\n.carousel-control .icon-next,\n.carousel-control .glyphicon-chevron-left,\n.carousel-control .glyphicon-chevron-right {\n  position: absolute;\n  top: 50%;\n  margin-top: -10px;\n  z-index: 5;\n  display: inline-block;\n}\n.carousel-control .icon-prev,\n.carousel-control .glyphicon-chevron-left {\n  left: 50%;\n  margin-left: -10px;\n}\n.carousel-control .icon-next,\n.carousel-control .glyphicon-chevron-right {\n  right: 50%;\n  margin-right: -10px;\n}\n.carousel-control .icon-prev,\n.carousel-control .icon-next {\n  width: 20px;\n  height: 20px;\n  line-height: 1;\n  font-family: serif;\n}\n.carousel-control .icon-prev:before {\n  content: '\\2039';\n}\n.carousel-control .icon-next:before {\n  content: '\\203a';\n}\n.carousel-indicators {\n  position: absolute;\n  bottom: 10px;\n  left: 50%;\n  z-index: 15;\n  width: 60%;\n  margin-left: -30%;\n  padding-left: 0;\n  list-style: none;\n  text-align: center;\n}\n.carousel-indicators li {\n  display: inline-block;\n  width: 10px;\n  height: 10px;\n  margin: 1px;\n  text-indent: -999px;\n  border: 1px solid #fff;\n  border-radius: 10px;\n  cursor: pointer;\n  background-color: #000 \\9;\n  background-color: rgba(0, 0, 0, 0);\n}\n.carousel-indicators .active {\n  margin: 0;\n  width: 12px;\n  height: 12px;\n  background-color: #fff;\n}\n.carousel-caption {\n  position: absolute;\n  left: 15%;\n  right: 15%;\n  bottom: 20px;\n  z-index: 10;\n  padding-top: 20px;\n  padding-bottom: 20px;\n  color: #fff;\n  text-align: center;\n  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);\n}\n.carousel-caption .btn {\n  text-shadow: none;\n}\n@media screen and (min-width: 768px) {\n  .carousel-control .glyphicon-chevron-left,\n  .carousel-control .glyphicon-chevron-right,\n  .carousel-control .icon-prev,\n  .carousel-control .icon-next {\n    width: 30px;\n    height: 30px;\n    margin-top: -10px;\n    font-size: 30px;\n  }\n  .carousel-control .glyphicon-chevron-left,\n  .carousel-control .icon-prev {\n    margin-left: -10px;\n  }\n  .carousel-control .glyphicon-chevron-right,\n  .carousel-control .icon-next {\n    margin-right: -10px;\n  }\n  .carousel-caption {\n    left: 20%;\n    right: 20%;\n    padding-bottom: 30px;\n  }\n  .carousel-indicators {\n    bottom: 20px;\n  }\n}\n.clearfix:before,\n.clearfix:after,\n.dl-horizontal dd:before,\n.dl-horizontal dd:after,\n.container:before,\n.container:after,\n.container-fluid:before,\n.container-fluid:after,\n.row:before,\n.row:after,\n.form-horizontal .form-group:before,\n.form-horizontal .form-group:after,\n.btn-toolbar:before,\n.btn-toolbar:after,\n.btn-group-vertical > .btn-group:before,\n.btn-group-vertical > .btn-group:after,\n.nav:before,\n.nav:after,\n.navbar:before,\n.navbar:after,\n.navbar-header:before,\n.navbar-header:after,\n.navbar-collapse:before,\n.navbar-collapse:after,\n.pager:before,\n.pager:after,\n.panel-body:before,\n.panel-body:after,\n.modal-header:before,\n.modal-header:after,\n.modal-footer:before,\n.modal-footer:after {\n  content: \" \";\n  display: table;\n}\n.clearfix:after,\n.dl-horizontal dd:after,\n.container:after,\n.container-fluid:after,\n.row:after,\n.form-horizontal .form-group:after,\n.btn-toolbar:after,\n.btn-group-vertical > .btn-group:after,\n.nav:after,\n.navbar:after,\n.navbar-header:after,\n.navbar-collapse:after,\n.pager:after,\n.panel-body:after,\n.modal-header:after,\n.modal-footer:after {\n  clear: both;\n}\n.center-block {\n  display: block;\n  margin-left: auto;\n  margin-right: auto;\n}\n.pull-right {\n  float: right !important;\n}\n.pull-left {\n  float: left !important;\n}\n.hide {\n  display: none !important;\n}\n.show {\n  display: block !important;\n}\n.invisible {\n  visibility: hidden;\n}\n.text-hide {\n  font: 0/0 a;\n  color: transparent;\n  text-shadow: none;\n  background-color: transparent;\n  border: 0;\n}\n.hidden {\n  display: none !important;\n}\n.affix {\n  position: fixed;\n}\n@-ms-viewport {\n  width: device-width;\n}\n.visible-xs,\n.visible-sm,\n.visible-md,\n.visible-lg {\n  display: none !important;\n}\n.visible-xs-block,\n.visible-xs-inline,\n.visible-xs-inline-block,\n.visible-sm-block,\n.visible-sm-inline,\n.visible-sm-inline-block,\n.visible-md-block,\n.visible-md-inline,\n.visible-md-inline-block,\n.visible-lg-block,\n.visible-lg-inline,\n.visible-lg-inline-block {\n  display: none !important;\n}\n@media (max-width: 767px) {\n  .visible-xs {\n    display: block !important;\n  }\n  table.visible-xs {\n    display: table !important;\n  }\n  tr.visible-xs {\n    display: table-row !important;\n  }\n  th.visible-xs,\n  td.visible-xs {\n    display: table-cell !important;\n  }\n}\n@media (max-width: 767px) {\n  .visible-xs-block {\n    display: block !important;\n  }\n}\n@media (max-width: 767px) {\n  .visible-xs-inline {\n    display: inline !important;\n  }\n}\n@media (max-width: 767px) {\n  .visible-xs-inline-block {\n    display: inline-block !important;\n  }\n}\n@media (min-width: 768px) and (max-width: 991px) {\n  .visible-sm {\n    display: block !important;\n  }\n  table.visible-sm {\n    display: table !important;\n  }\n  tr.visible-sm {\n    display: table-row !important;\n  }\n  th.visible-sm,\n  td.visible-sm {\n    display: table-cell !important;\n  }\n}\n@media (min-width: 768px) and (max-width: 991px) {\n  .visible-sm-block {\n    display: block !important;\n  }\n}\n@media (min-width: 768px) and (max-width: 991px) {\n  .visible-sm-inline {\n    display: inline !important;\n  }\n}\n@media (min-width: 768px) and (max-width: 991px) {\n  .visible-sm-inline-block {\n    display: inline-block !important;\n  }\n}\n@media (min-width: 992px) and (max-width: 1199px) {\n  .visible-md {\n    display: block !important;\n  }\n  table.visible-md {\n    display: table !important;\n  }\n  tr.visible-md {\n    display: table-row !important;\n  }\n  th.visible-md,\n  td.visible-md {\n    display: table-cell !important;\n  }\n}\n@media (min-width: 992px) and (max-width: 1199px) {\n  .visible-md-block {\n    display: block !important;\n  }\n}\n@media (min-width: 992px) and (max-width: 1199px) {\n  .visible-md-inline {\n    display: inline !important;\n  }\n}\n@media (min-width: 992px) and (max-width: 1199px) {\n  .visible-md-inline-block {\n    display: inline-block !important;\n  }\n}\n@media (min-width: 1200px) {\n  .visible-lg {\n    display: block !important;\n  }\n  table.visible-lg {\n    display: table !important;\n  }\n  tr.visible-lg {\n    display: table-row !important;\n  }\n  th.visible-lg,\n  td.visible-lg {\n    display: table-cell !important;\n  }\n}\n@media (min-width: 1200px) {\n  .visible-lg-block {\n    display: block !important;\n  }\n}\n@media (min-width: 1200px) {\n  .visible-lg-inline {\n    display: inline !important;\n  }\n}\n@media (min-width: 1200px) {\n  .visible-lg-inline-block {\n    display: inline-block !important;\n  }\n}\n@media (max-width: 767px) {\n  .hidden-xs {\n    display: none !important;\n  }\n}\n@media (min-width: 768px) and (max-width: 991px) {\n  .hidden-sm {\n    display: none !important;\n  }\n}\n@media (min-width: 992px) and (max-width: 1199px) {\n  .hidden-md {\n    display: none !important;\n  }\n}\n@media (min-width: 1200px) {\n  .hidden-lg {\n    display: none !important;\n  }\n}\n.visible-print {\n  display: none !important;\n}\n@media print {\n  .visible-print {\n    display: block !important;\n  }\n  table.visible-print {\n    display: table !important;\n  }\n  tr.visible-print {\n    display: table-row !important;\n  }\n  th.visible-print,\n  td.visible-print {\n    display: table-cell !important;\n  }\n}\n.visible-print-block {\n  display: none !important;\n}\n@media print {\n  .visible-print-block {\n    display: block !important;\n  }\n}\n.visible-print-inline {\n  display: none !important;\n}\n@media print {\n  .visible-print-inline {\n    display: inline !important;\n  }\n}\n.visible-print-inline-block {\n  display: none !important;\n}\n@media print {\n  .visible-print-inline-block {\n    display: inline-block !important;\n  }\n}\n@media print {\n  .hidden-print {\n    display: none !important;\n  }\n}\n/*# sourceMappingURL=bootstrap.css.map */","/*!\n * Bootstrap v3.3.7 (http://getbootstrap.com)\n * Copyright 2011-2016 Twitter, Inc.\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)\n */\n/*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */\nhtml {\n  font-family: sans-serif;\n  -webkit-text-size-adjust: 100%;\n      -ms-text-size-adjust: 100%;\n}\nbody {\n  margin: 0;\n}\narticle,\naside,\ndetails,\nfigcaption,\nfigure,\nfooter,\nheader,\nhgroup,\nmain,\nmenu,\nnav,\nsection,\nsummary {\n  display: block;\n}\naudio,\ncanvas,\nprogress,\nvideo {\n  display: inline-block;\n  vertical-align: baseline;\n}\naudio:not([controls]) {\n  display: none;\n  height: 0;\n}\n[hidden],\ntemplate {\n  display: none;\n}\na {\n  background-color: transparent;\n}\na:active,\na:hover {\n  outline: 0;\n}\nabbr[title] {\n  border-bottom: 1px dotted;\n}\nb,\nstrong {\n  font-weight: bold;\n}\ndfn {\n  font-style: italic;\n}\nh1 {\n  margin: .67em 0;\n  font-size: 2em;\n}\nmark {\n  color: #000;\n  background: #ff0;\n}\nsmall {\n  font-size: 80%;\n}\nsub,\nsup {\n  position: relative;\n  font-size: 75%;\n  line-height: 0;\n  vertical-align: baseline;\n}\nsup {\n  top: -.5em;\n}\nsub {\n  bottom: -.25em;\n}\nimg {\n  border: 0;\n}\nsvg:not(:root) {\n  overflow: hidden;\n}\nfigure {\n  margin: 1em 40px;\n}\nhr {\n  height: 0;\n  -webkit-box-sizing: content-box;\n     -moz-box-sizing: content-box;\n          box-sizing: content-box;\n}\npre {\n  overflow: auto;\n}\ncode,\nkbd,\npre,\nsamp {\n  font-family: monospace, monospace;\n  font-size: 1em;\n}\nbutton,\ninput,\noptgroup,\nselect,\ntextarea {\n  margin: 0;\n  font: inherit;\n  color: inherit;\n}\nbutton {\n  overflow: visible;\n}\nbutton,\nselect {\n  text-transform: none;\n}\nbutton,\nhtml input[type=\"button\"],\ninput[type=\"reset\"],\ninput[type=\"submit\"] {\n  -webkit-appearance: button;\n  cursor: pointer;\n}\nbutton[disabled],\nhtml input[disabled] {\n  cursor: default;\n}\nbutton::-moz-focus-inner,\ninput::-moz-focus-inner {\n  padding: 0;\n  border: 0;\n}\ninput {\n  line-height: normal;\n}\ninput[type=\"checkbox\"],\ninput[type=\"radio\"] {\n  -webkit-box-sizing: border-box;\n     -moz-box-sizing: border-box;\n          box-sizing: border-box;\n  padding: 0;\n}\ninput[type=\"number\"]::-webkit-inner-spin-button,\ninput[type=\"number\"]::-webkit-outer-spin-button {\n  height: auto;\n}\ninput[type=\"search\"] {\n  -webkit-box-sizing: content-box;\n     -moz-box-sizing: content-box;\n          box-sizing: content-box;\n  -webkit-appearance: textfield;\n}\ninput[type=\"search\"]::-webkit-search-cancel-button,\ninput[type=\"search\"]::-webkit-search-decoration {\n  -webkit-appearance: none;\n}\nfieldset {\n  padding: .35em .625em .75em;\n  margin: 0 2px;\n  border: 1px solid #c0c0c0;\n}\nlegend {\n  padding: 0;\n  border: 0;\n}\ntextarea {\n  overflow: auto;\n}\noptgroup {\n  font-weight: bold;\n}\ntable {\n  border-spacing: 0;\n  border-collapse: collapse;\n}\ntd,\nth {\n  padding: 0;\n}\n/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */\n@media print {\n  *,\n  *:before,\n  *:after {\n    color: #000 !important;\n    text-shadow: none !important;\n    background: transparent !important;\n    -webkit-box-shadow: none !important;\n            box-shadow: none !important;\n  }\n  a,\n  a:visited {\n    text-decoration: underline;\n  }\n  a[href]:after {\n    content: \" (\" attr(href) \")\";\n  }\n  abbr[title]:after {\n    content: \" (\" attr(title) \")\";\n  }\n  a[href^=\"#\"]:after,\n  a[href^=\"javascript:\"]:after {\n    content: \"\";\n  }\n  pre,\n  blockquote {\n    border: 1px solid #999;\n\n    page-break-inside: avoid;\n  }\n  thead {\n    display: table-header-group;\n  }\n  tr,\n  img {\n    page-break-inside: avoid;\n  }\n  img {\n    max-width: 100% !important;\n  }\n  p,\n  h2,\n  h3 {\n    orphans: 3;\n    widows: 3;\n  }\n  h2,\n  h3 {\n    page-break-after: avoid;\n  }\n  .navbar {\n    display: none;\n  }\n  .btn > .caret,\n  .dropup > .btn > .caret {\n    border-top-color: #000 !important;\n  }\n  .label {\n    border: 1px solid #000;\n  }\n  .table {\n    border-collapse: collapse !important;\n  }\n  .table td,\n  .table th {\n    background-color: #fff !important;\n  }\n  .table-bordered th,\n  .table-bordered td {\n    border: 1px solid #ddd !important;\n  }\n}\n@font-face {\n  font-family: 'Glyphicons Halflings';\n\n  src: url('../fonts/glyphicons-halflings-regular.eot');\n  src: url('../fonts/glyphicons-halflings-regular.eot?#iefix') format('embedded-opentype'), url('../fonts/glyphicons-halflings-regular.woff2') format('woff2'), url('../fonts/glyphicons-halflings-regular.woff') format('woff'), url('../fonts/glyphicons-halflings-regular.ttf') format('truetype'), url('../fonts/glyphicons-halflings-regular.svg#glyphicons_halflingsregular') format('svg');\n}\n.glyphicon {\n  position: relative;\n  top: 1px;\n  display: inline-block;\n  font-family: 'Glyphicons Halflings';\n  font-style: normal;\n  font-weight: normal;\n  line-height: 1;\n\n  -webkit-font-smoothing: antialiased;\n  -moz-osx-font-smoothing: grayscale;\n}\n.glyphicon-asterisk:before {\n  content: \"\\002a\";\n}\n.glyphicon-plus:before {\n  content: \"\\002b\";\n}\n.glyphicon-euro:before,\n.glyphicon-eur:before {\n  content: \"\\20ac\";\n}\n.glyphicon-minus:before {\n  content: \"\\2212\";\n}\n.glyphicon-cloud:before {\n  content: \"\\2601\";\n}\n.glyphicon-envelope:before {\n  content: \"\\2709\";\n}\n.glyphicon-pencil:before {\n  content: \"\\270f\";\n}\n.glyphicon-glass:before {\n  content: \"\\e001\";\n}\n.glyphicon-music:before {\n  content: \"\\e002\";\n}\n.glyphicon-search:before {\n  content: \"\\e003\";\n}\n.glyphicon-heart:before {\n  content: \"\\e005\";\n}\n.glyphicon-star:before {\n  content: \"\\e006\";\n}\n.glyphicon-star-empty:before {\n  content: \"\\e007\";\n}\n.glyphicon-user:before {\n  content: \"\\e008\";\n}\n.glyphicon-film:before {\n  content: \"\\e009\";\n}\n.glyphicon-th-large:before {\n  content: \"\\e010\";\n}\n.glyphicon-th:before {\n  content: \"\\e011\";\n}\n.glyphicon-th-list:before {\n  content: \"\\e012\";\n}\n.glyphicon-ok:before {\n  content: \"\\e013\";\n}\n.glyphicon-remove:before {\n  content: \"\\e014\";\n}\n.glyphicon-zoom-in:before {\n  content: \"\\e015\";\n}\n.glyphicon-zoom-out:before {\n  content: \"\\e016\";\n}\n.glyphicon-off:before {\n  content: \"\\e017\";\n}\n.glyphicon-signal:before {\n  content: \"\\e018\";\n}\n.glyphicon-cog:before {\n  content: \"\\e019\";\n}\n.glyphicon-trash:before {\n  content: \"\\e020\";\n}\n.glyphicon-home:before {\n  content: \"\\e021\";\n}\n.glyphicon-file:before {\n  content: \"\\e022\";\n}\n.glyphicon-time:before {\n  content: \"\\e023\";\n}\n.glyphicon-road:before {\n  content: \"\\e024\";\n}\n.glyphicon-download-alt:before {\n  content: \"\\e025\";\n}\n.glyphicon-download:before {\n  content: \"\\e026\";\n}\n.glyphicon-upload:before {\n  content: \"\\e027\";\n}\n.glyphicon-inbox:before {\n  content: \"\\e028\";\n}\n.glyphicon-play-circle:before {\n  content: \"\\e029\";\n}\n.glyphicon-repeat:before {\n  content: \"\\e030\";\n}\n.glyphicon-refresh:before {\n  content: \"\\e031\";\n}\n.glyphicon-list-alt:before {\n  content: \"\\e032\";\n}\n.glyphicon-lock:before {\n  content: \"\\e033\";\n}\n.glyphicon-flag:before {\n  content: \"\\e034\";\n}\n.glyphicon-headphones:before {\n  content: \"\\e035\";\n}\n.glyphicon-volume-off:before {\n  content: \"\\e036\";\n}\n.glyphicon-volume-down:before {\n  content: \"\\e037\";\n}\n.glyphicon-volume-up:before {\n  content: \"\\e038\";\n}\n.glyphicon-qrcode:before {\n  content: \"\\e039\";\n}\n.glyphicon-barcode:before {\n  content: \"\\e040\";\n}\n.glyphicon-tag:before {\n  content: \"\\e041\";\n}\n.glyphicon-tags:before {\n  content: \"\\e042\";\n}\n.glyphicon-book:before {\n  content: \"\\e043\";\n}\n.glyphicon-bookmark:before {\n  content: \"\\e044\";\n}\n.glyphicon-print:before {\n  content: \"\\e045\";\n}\n.glyphicon-camera:before {\n  content: \"\\e046\";\n}\n.glyphicon-font:before {\n  content: \"\\e047\";\n}\n.glyphicon-bold:before {\n  content: \"\\e048\";\n}\n.glyphicon-italic:before {\n  content: \"\\e049\";\n}\n.glyphicon-text-height:before {\n  content: \"\\e050\";\n}\n.glyphicon-text-width:before {\n  content: \"\\e051\";\n}\n.glyphicon-align-left:before {\n  content: \"\\e052\";\n}\n.glyphicon-align-center:before {\n  content: \"\\e053\";\n}\n.glyphicon-align-right:before {\n  content: \"\\e054\";\n}\n.glyphicon-align-justify:before {\n  content: \"\\e055\";\n}\n.glyphicon-list:before {\n  content: \"\\e056\";\n}\n.glyphicon-indent-left:before {\n  content: \"\\e057\";\n}\n.glyphicon-indent-right:before {\n  content: \"\\e058\";\n}\n.glyphicon-facetime-video:before {\n  content: \"\\e059\";\n}\n.glyphicon-picture:before {\n  content: \"\\e060\";\n}\n.glyphicon-map-marker:before {\n  content: \"\\e062\";\n}\n.glyphicon-adjust:before {\n  content: \"\\e063\";\n}\n.glyphicon-tint:before {\n  content: \"\\e064\";\n}\n.glyphicon-edit:before {\n  content: \"\\e065\";\n}\n.glyphicon-share:before {\n  content: \"\\e066\";\n}\n.glyphicon-check:before {\n  content: \"\\e067\";\n}\n.glyphicon-move:before {\n  content: \"\\e068\";\n}\n.glyphicon-step-backward:before {\n  content: \"\\e069\";\n}\n.glyphicon-fast-backward:before {\n  content: \"\\e070\";\n}\n.glyphicon-backward:before {\n  content: \"\\e071\";\n}\n.glyphicon-play:before {\n  content: \"\\e072\";\n}\n.glyphicon-pause:before {\n  content: \"\\e073\";\n}\n.glyphicon-stop:before {\n  content: \"\\e074\";\n}\n.glyphicon-forward:before {\n  content: \"\\e075\";\n}\n.glyphicon-fast-forward:before {\n  content: \"\\e076\";\n}\n.glyphicon-step-forward:before {\n  content: \"\\e077\";\n}\n.glyphicon-eject:before {\n  content: \"\\e078\";\n}\n.glyphicon-chevron-left:before {\n  content: \"\\e079\";\n}\n.glyphicon-chevron-right:before {\n  content: \"\\e080\";\n}\n.glyphicon-plus-sign:before {\n  content: \"\\e081\";\n}\n.glyphicon-minus-sign:before {\n  content: \"\\e082\";\n}\n.glyphicon-remove-sign:before {\n  content: \"\\e083\";\n}\n.glyphicon-ok-sign:before {\n  content: \"\\e084\";\n}\n.glyphicon-question-sign:before {\n  content: \"\\e085\";\n}\n.glyphicon-info-sign:before {\n  content: \"\\e086\";\n}\n.glyphicon-screenshot:before {\n  content: \"\\e087\";\n}\n.glyphicon-remove-circle:before {\n  content: \"\\e088\";\n}\n.glyphicon-ok-circle:before {\n  content: \"\\e089\";\n}\n.glyphicon-ban-circle:before {\n  content: \"\\e090\";\n}\n.glyphicon-arrow-left:before {\n  content: \"\\e091\";\n}\n.glyphicon-arrow-right:before {\n  content: \"\\e092\";\n}\n.glyphicon-arrow-up:before {\n  content: \"\\e093\";\n}\n.glyphicon-arrow-down:before {\n  content: \"\\e094\";\n}\n.glyphicon-share-alt:before {\n  content: \"\\e095\";\n}\n.glyphicon-resize-full:before {\n  content: \"\\e096\";\n}\n.glyphicon-resize-small:before {\n  content: \"\\e097\";\n}\n.glyphicon-exclamation-sign:before {\n  content: \"\\e101\";\n}\n.glyphicon-gift:before {\n  content: \"\\e102\";\n}\n.glyphicon-leaf:before {\n  content: \"\\e103\";\n}\n.glyphicon-fire:before {\n  content: \"\\e104\";\n}\n.glyphicon-eye-open:before {\n  content: \"\\e105\";\n}\n.glyphicon-eye-close:before {\n  content: \"\\e106\";\n}\n.glyphicon-warning-sign:before {\n  content: \"\\e107\";\n}\n.glyphicon-plane:before {\n  content: \"\\e108\";\n}\n.glyphicon-calendar:before {\n  content: \"\\e109\";\n}\n.glyphicon-random:before {\n  content: \"\\e110\";\n}\n.glyphicon-comment:before {\n  content: \"\\e111\";\n}\n.glyphicon-magnet:before {\n  content: \"\\e112\";\n}\n.glyphicon-chevron-up:before {\n  content: \"\\e113\";\n}\n.glyphicon-chevron-down:before {\n  content: \"\\e114\";\n}\n.glyphicon-retweet:before {\n  content: \"\\e115\";\n}\n.glyphicon-shopping-cart:before {\n  content: \"\\e116\";\n}\n.glyphicon-folder-close:before {\n  content: \"\\e117\";\n}\n.glyphicon-folder-open:before {\n  content: \"\\e118\";\n}\n.glyphicon-resize-vertical:before {\n  content: \"\\e119\";\n}\n.glyphicon-resize-horizontal:before {\n  content: \"\\e120\";\n}\n.glyphicon-hdd:before {\n  content: \"\\e121\";\n}\n.glyphicon-bullhorn:before {\n  content: \"\\e122\";\n}\n.glyphicon-bell:before {\n  content: \"\\e123\";\n}\n.glyphicon-certificate:before {\n  content: \"\\e124\";\n}\n.glyphicon-thumbs-up:before {\n  content: \"\\e125\";\n}\n.glyphicon-thumbs-down:before {\n  content: \"\\e126\";\n}\n.glyphicon-hand-right:before {\n  content: \"\\e127\";\n}\n.glyphicon-hand-left:before {\n  content: \"\\e128\";\n}\n.glyphicon-hand-up:before {\n  content: \"\\e129\";\n}\n.glyphicon-hand-down:before {\n  content: \"\\e130\";\n}\n.glyphicon-circle-arrow-right:before {\n  content: \"\\e131\";\n}\n.glyphicon-circle-arrow-left:before {\n  content: \"\\e132\";\n}\n.glyphicon-circle-arrow-up:before {\n  content: \"\\e133\";\n}\n.glyphicon-circle-arrow-down:before {\n  content: \"\\e134\";\n}\n.glyphicon-globe:before {\n  content: \"\\e135\";\n}\n.glyphicon-wrench:before {\n  content: \"\\e136\";\n}\n.glyphicon-tasks:before {\n  content: \"\\e137\";\n}\n.glyphicon-filter:before {\n  content: \"\\e138\";\n}\n.glyphicon-briefcase:before {\n  content: \"\\e139\";\n}\n.glyphicon-fullscreen:before {\n  content: \"\\e140\";\n}\n.glyphicon-dashboard:before {\n  content: \"\\e141\";\n}\n.glyphicon-paperclip:before {\n  content: \"\\e142\";\n}\n.glyphicon-heart-empty:before {\n  content: \"\\e143\";\n}\n.glyphicon-link:before {\n  content: \"\\e144\";\n}\n.glyphicon-phone:before {\n  content: \"\\e145\";\n}\n.glyphicon-pushpin:before {\n  content: \"\\e146\";\n}\n.glyphicon-usd:before {\n  content: \"\\e148\";\n}\n.glyphicon-gbp:before {\n  content: \"\\e149\";\n}\n.glyphicon-sort:before {\n  content: \"\\e150\";\n}\n.glyphicon-sort-by-alphabet:before {\n  content: \"\\e151\";\n}\n.glyphicon-sort-by-alphabet-alt:before {\n  content: \"\\e152\";\n}\n.glyphicon-sort-by-order:before {\n  content: \"\\e153\";\n}\n.glyphicon-sort-by-order-alt:before {\n  content: \"\\e154\";\n}\n.glyphicon-sort-by-attributes:before {\n  content: \"\\e155\";\n}\n.glyphicon-sort-by-attributes-alt:before {\n  content: \"\\e156\";\n}\n.glyphicon-unchecked:before {\n  content: \"\\e157\";\n}\n.glyphicon-expand:before {\n  content: \"\\e158\";\n}\n.glyphicon-collapse-down:before {\n  content: \"\\e159\";\n}\n.glyphicon-collapse-up:before {\n  content: \"\\e160\";\n}\n.glyphicon-log-in:before {\n  content: \"\\e161\";\n}\n.glyphicon-flash:before {\n  content: \"\\e162\";\n}\n.glyphicon-log-out:before {\n  content: \"\\e163\";\n}\n.glyphicon-new-window:before {\n  content: \"\\e164\";\n}\n.glyphicon-record:before {\n  content: \"\\e165\";\n}\n.glyphicon-save:before {\n  content: \"\\e166\";\n}\n.glyphicon-open:before {\n  content: \"\\e167\";\n}\n.glyphicon-saved:before {\n  content: \"\\e168\";\n}\n.glyphicon-import:before {\n  content: \"\\e169\";\n}\n.glyphicon-export:before {\n  content: \"\\e170\";\n}\n.glyphicon-send:before {\n  content: \"\\e171\";\n}\n.glyphicon-floppy-disk:before {\n  content: \"\\e172\";\n}\n.glyphicon-floppy-saved:before {\n  content: \"\\e173\";\n}\n.glyphicon-floppy-remove:before {\n  content: \"\\e174\";\n}\n.glyphicon-floppy-save:before {\n  content: \"\\e175\";\n}\n.glyphicon-floppy-open:before {\n  content: \"\\e176\";\n}\n.glyphicon-credit-card:before {\n  content: \"\\e177\";\n}\n.glyphicon-transfer:before {\n  content: \"\\e178\";\n}\n.glyphicon-cutlery:before {\n  content: \"\\e179\";\n}\n.glyphicon-header:before {\n  content: \"\\e180\";\n}\n.glyphicon-compressed:before {\n  content: \"\\e181\";\n}\n.glyphicon-earphone:before {\n  content: \"\\e182\";\n}\n.glyphicon-phone-alt:before {\n  content: \"\\e183\";\n}\n.glyphicon-tower:before {\n  content: \"\\e184\";\n}\n.glyphicon-stats:before {\n  content: \"\\e185\";\n}\n.glyphicon-sd-video:before {\n  content: \"\\e186\";\n}\n.glyphicon-hd-video:before {\n  content: \"\\e187\";\n}\n.glyphicon-subtitles:before {\n  content: \"\\e188\";\n}\n.glyphicon-sound-stereo:before {\n  content: \"\\e189\";\n}\n.glyphicon-sound-dolby:before {\n  content: \"\\e190\";\n}\n.glyphicon-sound-5-1:before {\n  content: \"\\e191\";\n}\n.glyphicon-sound-6-1:before {\n  content: \"\\e192\";\n}\n.glyphicon-sound-7-1:before {\n  content: \"\\e193\";\n}\n.glyphicon-copyright-mark:before {\n  content: \"\\e194\";\n}\n.glyphicon-registration-mark:before {\n  content: \"\\e195\";\n}\n.glyphicon-cloud-download:before {\n  content: \"\\e197\";\n}\n.glyphicon-cloud-upload:before {\n  content: \"\\e198\";\n}\n.glyphicon-tree-conifer:before {\n  content: \"\\e199\";\n}\n.glyphicon-tree-deciduous:before {\n  content: \"\\e200\";\n}\n.glyphicon-cd:before {\n  content: \"\\e201\";\n}\n.glyphicon-save-file:before {\n  content: \"\\e202\";\n}\n.glyphicon-open-file:before {\n  content: \"\\e203\";\n}\n.glyphicon-level-up:before {\n  content: \"\\e204\";\n}\n.glyphicon-copy:before {\n  content: \"\\e205\";\n}\n.glyphicon-paste:before {\n  content: \"\\e206\";\n}\n.glyphicon-alert:before {\n  content: \"\\e209\";\n}\n.glyphicon-equalizer:before {\n  content: \"\\e210\";\n}\n.glyphicon-king:before {\n  content: \"\\e211\";\n}\n.glyphicon-queen:before {\n  content: \"\\e212\";\n}\n.glyphicon-pawn:before {\n  content: \"\\e213\";\n}\n.glyphicon-bishop:before {\n  content: \"\\e214\";\n}\n.glyphicon-knight:before {\n  content: \"\\e215\";\n}\n.glyphicon-baby-formula:before {\n  content: \"\\e216\";\n}\n.glyphicon-tent:before {\n  content: \"\\26fa\";\n}\n.glyphicon-blackboard:before {\n  content: \"\\e218\";\n}\n.glyphicon-bed:before {\n  content: \"\\e219\";\n}\n.glyphicon-apple:before {\n  content: \"\\f8ff\";\n}\n.glyphicon-erase:before {\n  content: \"\\e221\";\n}\n.glyphicon-hourglass:before {\n  content: \"\\231b\";\n}\n.glyphicon-lamp:before {\n  content: \"\\e223\";\n}\n.glyphicon-duplicate:before {\n  content: \"\\e224\";\n}\n.glyphicon-piggy-bank:before {\n  content: \"\\e225\";\n}\n.glyphicon-scissors:before {\n  content: \"\\e226\";\n}\n.glyphicon-bitcoin:before {\n  content: \"\\e227\";\n}\n.glyphicon-btc:before {\n  content: \"\\e227\";\n}\n.glyphicon-xbt:before {\n  content: \"\\e227\";\n}\n.glyphicon-yen:before {\n  content: \"\\00a5\";\n}\n.glyphicon-jpy:before {\n  content: \"\\00a5\";\n}\n.glyphicon-ruble:before {\n  content: \"\\20bd\";\n}\n.glyphicon-rub:before {\n  content: \"\\20bd\";\n}\n.glyphicon-scale:before {\n  content: \"\\e230\";\n}\n.glyphicon-ice-lolly:before {\n  content: \"\\e231\";\n}\n.glyphicon-ice-lolly-tasted:before {\n  content: \"\\e232\";\n}\n.glyphicon-education:before {\n  content: \"\\e233\";\n}\n.glyphicon-option-horizontal:before {\n  content: \"\\e234\";\n}\n.glyphicon-option-vertical:before {\n  content: \"\\e235\";\n}\n.glyphicon-menu-hamburger:before {\n  content: \"\\e236\";\n}\n.glyphicon-modal-window:before {\n  content: \"\\e237\";\n}\n.glyphicon-oil:before {\n  content: \"\\e238\";\n}\n.glyphicon-grain:before {\n  content: \"\\e239\";\n}\n.glyphicon-sunglasses:before {\n  content: \"\\e240\";\n}\n.glyphicon-text-size:before {\n  content: \"\\e241\";\n}\n.glyphicon-text-color:before {\n  content: \"\\e242\";\n}\n.glyphicon-text-background:before {\n  content: \"\\e243\";\n}\n.glyphicon-object-align-top:before {\n  content: \"\\e244\";\n}\n.glyphicon-object-align-bottom:before {\n  content: \"\\e245\";\n}\n.glyphicon-object-align-horizontal:before {\n  content: \"\\e246\";\n}\n.glyphicon-object-align-left:before {\n  content: \"\\e247\";\n}\n.glyphicon-object-align-vertical:before {\n  content: \"\\e248\";\n}\n.glyphicon-object-align-right:before {\n  content: \"\\e249\";\n}\n.glyphicon-triangle-right:before {\n  content: \"\\e250\";\n}\n.glyphicon-triangle-left:before {\n  content: \"\\e251\";\n}\n.glyphicon-triangle-bottom:before {\n  content: \"\\e252\";\n}\n.glyphicon-triangle-top:before {\n  content: \"\\e253\";\n}\n.glyphicon-console:before {\n  content: \"\\e254\";\n}\n.glyphicon-superscript:before {\n  content: \"\\e255\";\n}\n.glyphicon-subscript:before {\n  content: \"\\e256\";\n}\n.glyphicon-menu-left:before {\n  content: \"\\e257\";\n}\n.glyphicon-menu-right:before {\n  content: \"\\e258\";\n}\n.glyphicon-menu-down:before {\n  content: \"\\e259\";\n}\n.glyphicon-menu-up:before {\n  content: \"\\e260\";\n}\n* {\n  -webkit-box-sizing: border-box;\n     -moz-box-sizing: border-box;\n          box-sizing: border-box;\n}\n*:before,\n*:after {\n  -webkit-box-sizing: border-box;\n     -moz-box-sizing: border-box;\n          box-sizing: border-box;\n}\nhtml {\n  font-size: 10px;\n\n  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);\n}\nbody {\n  font-family: \"Helvetica Neue\", Helvetica, Arial, sans-serif;\n  font-size: 14px;\n  line-height: 1.42857143;\n  color: #333;\n  background-color: #fff;\n}\ninput,\nbutton,\nselect,\ntextarea {\n  font-family: inherit;\n  font-size: inherit;\n  line-height: inherit;\n}\na {\n  color: #337ab7;\n  text-decoration: none;\n}\na:hover,\na:focus {\n  color: #23527c;\n  text-decoration: underline;\n}\na:focus {\n  outline: 5px auto -webkit-focus-ring-color;\n  outline-offset: -2px;\n}\nfigure {\n  margin: 0;\n}\nimg {\n  vertical-align: middle;\n}\n.img-responsive,\n.thumbnail > img,\n.thumbnail a > img,\n.carousel-inner > .item > img,\n.carousel-inner > .item > a > img {\n  display: block;\n  max-width: 100%;\n  height: auto;\n}\n.img-rounded {\n  border-radius: 6px;\n}\n.img-thumbnail {\n  display: inline-block;\n  max-width: 100%;\n  height: auto;\n  padding: 4px;\n  line-height: 1.42857143;\n  background-color: #fff;\n  border: 1px solid #ddd;\n  border-radius: 4px;\n  -webkit-transition: all .2s ease-in-out;\n       -o-transition: all .2s ease-in-out;\n          transition: all .2s ease-in-out;\n}\n.img-circle {\n  border-radius: 50%;\n}\nhr {\n  margin-top: 20px;\n  margin-bottom: 20px;\n  border: 0;\n  border-top: 1px solid #eee;\n}\n.sr-only {\n  position: absolute;\n  width: 1px;\n  height: 1px;\n  padding: 0;\n  margin: -1px;\n  overflow: hidden;\n  clip: rect(0, 0, 0, 0);\n  border: 0;\n}\n.sr-only-focusable:active,\n.sr-only-focusable:focus {\n  position: static;\n  width: auto;\n  height: auto;\n  margin: 0;\n  overflow: visible;\n  clip: auto;\n}\n[role=\"button\"] {\n  cursor: pointer;\n}\nh1,\nh2,\nh3,\nh4,\nh5,\nh6,\n.h1,\n.h2,\n.h3,\n.h4,\n.h5,\n.h6 {\n  font-family: inherit;\n  font-weight: 500;\n  line-height: 1.1;\n  color: inherit;\n}\nh1 small,\nh2 small,\nh3 small,\nh4 small,\nh5 small,\nh6 small,\n.h1 small,\n.h2 small,\n.h3 small,\n.h4 small,\n.h5 small,\n.h6 small,\nh1 .small,\nh2 .small,\nh3 .small,\nh4 .small,\nh5 .small,\nh6 .small,\n.h1 .small,\n.h2 .small,\n.h3 .small,\n.h4 .small,\n.h5 .small,\n.h6 .small {\n  font-weight: normal;\n  line-height: 1;\n  color: #777;\n}\nh1,\n.h1,\nh2,\n.h2,\nh3,\n.h3 {\n  margin-top: 20px;\n  margin-bottom: 10px;\n}\nh1 small,\n.h1 small,\nh2 small,\n.h2 small,\nh3 small,\n.h3 small,\nh1 .small,\n.h1 .small,\nh2 .small,\n.h2 .small,\nh3 .small,\n.h3 .small {\n  font-size: 65%;\n}\nh4,\n.h4,\nh5,\n.h5,\nh6,\n.h6 {\n  margin-top: 10px;\n  margin-bottom: 10px;\n}\nh4 small,\n.h4 small,\nh5 small,\n.h5 small,\nh6 small,\n.h6 small,\nh4 .small,\n.h4 .small,\nh5 .small,\n.h5 .small,\nh6 .small,\n.h6 .small {\n  font-size: 75%;\n}\nh1,\n.h1 {\n  font-size: 36px;\n}\nh2,\n.h2 {\n  font-size: 30px;\n}\nh3,\n.h3 {\n  font-size: 24px;\n}\nh4,\n.h4 {\n  font-size: 18px;\n}\nh5,\n.h5 {\n  font-size: 14px;\n}\nh6,\n.h6 {\n  font-size: 12px;\n}\np {\n  margin: 0 0 10px;\n}\n.lead {\n  margin-bottom: 20px;\n  font-size: 16px;\n  font-weight: 300;\n  line-height: 1.4;\n}\n@media (min-width: 768px) {\n  .lead {\n    font-size: 21px;\n  }\n}\nsmall,\n.small {\n  font-size: 85%;\n}\nmark,\n.mark {\n  padding: .2em;\n  background-color: #fcf8e3;\n}\n.text-left {\n  text-align: left;\n}\n.text-right {\n  text-align: right;\n}\n.text-center {\n  text-align: center;\n}\n.text-justify {\n  text-align: justify;\n}\n.text-nowrap {\n  white-space: nowrap;\n}\n.text-lowercase {\n  text-transform: lowercase;\n}\n.text-uppercase {\n  text-transform: uppercase;\n}\n.text-capitalize {\n  text-transform: capitalize;\n}\n.text-muted {\n  color: #777;\n}\n.text-primary {\n  color: #337ab7;\n}\na.text-primary:hover,\na.text-primary:focus {\n  color: #286090;\n}\n.text-success {\n  color: #3c763d;\n}\na.text-success:hover,\na.text-success:focus {\n  color: #2b542c;\n}\n.text-info {\n  color: #31708f;\n}\na.text-info:hover,\na.text-info:focus {\n  color: #245269;\n}\n.text-warning {\n  color: #8a6d3b;\n}\na.text-warning:hover,\na.text-warning:focus {\n  color: #66512c;\n}\n.text-danger {\n  color: #a94442;\n}\na.text-danger:hover,\na.text-danger:focus {\n  color: #843534;\n}\n.bg-primary {\n  color: #fff;\n  background-color: #337ab7;\n}\na.bg-primary:hover,\na.bg-primary:focus {\n  background-color: #286090;\n}\n.bg-success {\n  background-color: #dff0d8;\n}\na.bg-success:hover,\na.bg-success:focus {\n  background-color: #c1e2b3;\n}\n.bg-info {\n  background-color: #d9edf7;\n}\na.bg-info:hover,\na.bg-info:focus {\n  background-color: #afd9ee;\n}\n.bg-warning {\n  background-color: #fcf8e3;\n}\na.bg-warning:hover,\na.bg-warning:focus {\n  background-color: #f7ecb5;\n}\n.bg-danger {\n  background-color: #f2dede;\n}\na.bg-danger:hover,\na.bg-danger:focus {\n  background-color: #e4b9b9;\n}\n.page-header {\n  padding-bottom: 9px;\n  margin: 40px 0 20px;\n  border-bottom: 1px solid #eee;\n}\nul,\nol {\n  margin-top: 0;\n  margin-bottom: 10px;\n}\nul ul,\nol ul,\nul ol,\nol ol {\n  margin-bottom: 0;\n}\n.list-unstyled {\n  padding-left: 0;\n  list-style: none;\n}\n.list-inline {\n  padding-left: 0;\n  margin-left: -5px;\n  list-style: none;\n}\n.list-inline > li {\n  display: inline-block;\n  padding-right: 5px;\n  padding-left: 5px;\n}\ndl {\n  margin-top: 0;\n  margin-bottom: 20px;\n}\ndt,\ndd {\n  line-height: 1.42857143;\n}\ndt {\n  font-weight: bold;\n}\ndd {\n  margin-left: 0;\n}\n@media (min-width: 768px) {\n  .dl-horizontal dt {\n    float: left;\n    width: 160px;\n    overflow: hidden;\n    clear: left;\n    text-align: right;\n    text-overflow: ellipsis;\n    white-space: nowrap;\n  }\n  .dl-horizontal dd {\n    margin-left: 180px;\n  }\n}\nabbr[title],\nabbr[data-original-title] {\n  cursor: help;\n  border-bottom: 1px dotted #777;\n}\n.initialism {\n  font-size: 90%;\n  text-transform: uppercase;\n}\nblockquote {\n  padding: 10px 20px;\n  margin: 0 0 20px;\n  font-size: 17.5px;\n  border-left: 5px solid #eee;\n}\nblockquote p:last-child,\nblockquote ul:last-child,\nblockquote ol:last-child {\n  margin-bottom: 0;\n}\nblockquote footer,\nblockquote small,\nblockquote .small {\n  display: block;\n  font-size: 80%;\n  line-height: 1.42857143;\n  color: #777;\n}\nblockquote footer:before,\nblockquote small:before,\nblockquote .small:before {\n  content: '\\2014 \\00A0';\n}\n.blockquote-reverse,\nblockquote.pull-right {\n  padding-right: 15px;\n  padding-left: 0;\n  text-align: right;\n  border-right: 5px solid #eee;\n  border-left: 0;\n}\n.blockquote-reverse footer:before,\nblockquote.pull-right footer:before,\n.blockquote-reverse small:before,\nblockquote.pull-right small:before,\n.blockquote-reverse .small:before,\nblockquote.pull-right .small:before {\n  content: '';\n}\n.blockquote-reverse footer:after,\nblockquote.pull-right footer:after,\n.blockquote-reverse small:after,\nblockquote.pull-right small:after,\n.blockquote-reverse .small:after,\nblockquote.pull-right .small:after {\n  content: '\\00A0 \\2014';\n}\naddress {\n  margin-bottom: 20px;\n  font-style: normal;\n  line-height: 1.42857143;\n}\ncode,\nkbd,\npre,\nsamp {\n  font-family: Menlo, Monaco, Consolas, \"Courier New\", monospace;\n}\ncode {\n  padding: 2px 4px;\n  font-size: 90%;\n  color: #c7254e;\n  background-color: #f9f2f4;\n  border-radius: 4px;\n}\nkbd {\n  padding: 2px 4px;\n  font-size: 90%;\n  color: #fff;\n  background-color: #333;\n  border-radius: 3px;\n  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, .25);\n          box-shadow: inset 0 -1px 0 rgba(0, 0, 0, .25);\n}\nkbd kbd {\n  padding: 0;\n  font-size: 100%;\n  font-weight: bold;\n  -webkit-box-shadow: none;\n          box-shadow: none;\n}\npre {\n  display: block;\n  padding: 9.5px;\n  margin: 0 0 10px;\n  font-size: 13px;\n  line-height: 1.42857143;\n  color: #333;\n  word-break: break-all;\n  word-wrap: break-word;\n  background-color: #f5f5f5;\n  border: 1px solid #ccc;\n  border-radius: 4px;\n}\npre code {\n  padding: 0;\n  font-size: inherit;\n  color: inherit;\n  white-space: pre-wrap;\n  background-color: transparent;\n  border-radius: 0;\n}\n.pre-scrollable {\n  max-height: 340px;\n  overflow-y: scroll;\n}\n.container {\n  padding-right: 15px;\n  padding-left: 15px;\n  margin-right: auto;\n  margin-left: auto;\n}\n@media (min-width: 768px) {\n  .container {\n    width: 750px;\n  }\n}\n@media (min-width: 992px) {\n  .container {\n    width: 970px;\n  }\n}\n@media (min-width: 1200px) {\n  .container {\n    width: 1170px;\n  }\n}\n.container-fluid {\n  padding-right: 15px;\n  padding-left: 15px;\n  margin-right: auto;\n  margin-left: auto;\n}\n.row {\n  margin-right: -15px;\n  margin-left: -15px;\n}\n.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 {\n  position: relative;\n  min-height: 1px;\n  padding-right: 15px;\n  padding-left: 15px;\n}\n.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 {\n  float: left;\n}\n.col-xs-12 {\n  width: 100%;\n}\n.col-xs-11 {\n  width: 91.66666667%;\n}\n.col-xs-10 {\n  width: 83.33333333%;\n}\n.col-xs-9 {\n  width: 75%;\n}\n.col-xs-8 {\n  width: 66.66666667%;\n}\n.col-xs-7 {\n  width: 58.33333333%;\n}\n.col-xs-6 {\n  width: 50%;\n}\n.col-xs-5 {\n  width: 41.66666667%;\n}\n.col-xs-4 {\n  width: 33.33333333%;\n}\n.col-xs-3 {\n  width: 25%;\n}\n.col-xs-2 {\n  width: 16.66666667%;\n}\n.col-xs-1 {\n  width: 8.33333333%;\n}\n.col-xs-pull-12 {\n  right: 100%;\n}\n.col-xs-pull-11 {\n  right: 91.66666667%;\n}\n.col-xs-pull-10 {\n  right: 83.33333333%;\n}\n.col-xs-pull-9 {\n  right: 75%;\n}\n.col-xs-pull-8 {\n  right: 66.66666667%;\n}\n.col-xs-pull-7 {\n  right: 58.33333333%;\n}\n.col-xs-pull-6 {\n  right: 50%;\n}\n.col-xs-pull-5 {\n  right: 41.66666667%;\n}\n.col-xs-pull-4 {\n  right: 33.33333333%;\n}\n.col-xs-pull-3 {\n  right: 25%;\n}\n.col-xs-pull-2 {\n  right: 16.66666667%;\n}\n.col-xs-pull-1 {\n  right: 8.33333333%;\n}\n.col-xs-pull-0 {\n  right: auto;\n}\n.col-xs-push-12 {\n  left: 100%;\n}\n.col-xs-push-11 {\n  left: 91.66666667%;\n}\n.col-xs-push-10 {\n  left: 83.33333333%;\n}\n.col-xs-push-9 {\n  left: 75%;\n}\n.col-xs-push-8 {\n  left: 66.66666667%;\n}\n.col-xs-push-7 {\n  left: 58.33333333%;\n}\n.col-xs-push-6 {\n  left: 50%;\n}\n.col-xs-push-5 {\n  left: 41.66666667%;\n}\n.col-xs-push-4 {\n  left: 33.33333333%;\n}\n.col-xs-push-3 {\n  left: 25%;\n}\n.col-xs-push-2 {\n  left: 16.66666667%;\n}\n.col-xs-push-1 {\n  left: 8.33333333%;\n}\n.col-xs-push-0 {\n  left: auto;\n}\n.col-xs-offset-12 {\n  margin-left: 100%;\n}\n.col-xs-offset-11 {\n  margin-left: 91.66666667%;\n}\n.col-xs-offset-10 {\n  margin-left: 83.33333333%;\n}\n.col-xs-offset-9 {\n  margin-left: 75%;\n}\n.col-xs-offset-8 {\n  margin-left: 66.66666667%;\n}\n.col-xs-offset-7 {\n  margin-left: 58.33333333%;\n}\n.col-xs-offset-6 {\n  margin-left: 50%;\n}\n.col-xs-offset-5 {\n  margin-left: 41.66666667%;\n}\n.col-xs-offset-4 {\n  margin-left: 33.33333333%;\n}\n.col-xs-offset-3 {\n  margin-left: 25%;\n}\n.col-xs-offset-2 {\n  margin-left: 16.66666667%;\n}\n.col-xs-offset-1 {\n  margin-left: 8.33333333%;\n}\n.col-xs-offset-0 {\n  margin-left: 0;\n}\n@media (min-width: 768px) {\n  .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {\n    float: left;\n  }\n  .col-sm-12 {\n    width: 100%;\n  }\n  .col-sm-11 {\n    width: 91.66666667%;\n  }\n  .col-sm-10 {\n    width: 83.33333333%;\n  }\n  .col-sm-9 {\n    width: 75%;\n  }\n  .col-sm-8 {\n    width: 66.66666667%;\n  }\n  .col-sm-7 {\n    width: 58.33333333%;\n  }\n  .col-sm-6 {\n    width: 50%;\n  }\n  .col-sm-5 {\n    width: 41.66666667%;\n  }\n  .col-sm-4 {\n    width: 33.33333333%;\n  }\n  .col-sm-3 {\n    width: 25%;\n  }\n  .col-sm-2 {\n    width: 16.66666667%;\n  }\n  .col-sm-1 {\n    width: 8.33333333%;\n  }\n  .col-sm-pull-12 {\n    right: 100%;\n  }\n  .col-sm-pull-11 {\n    right: 91.66666667%;\n  }\n  .col-sm-pull-10 {\n    right: 83.33333333%;\n  }\n  .col-sm-pull-9 {\n    right: 75%;\n  }\n  .col-sm-pull-8 {\n    right: 66.66666667%;\n  }\n  .col-sm-pull-7 {\n    right: 58.33333333%;\n  }\n  .col-sm-pull-6 {\n    right: 50%;\n  }\n  .col-sm-pull-5 {\n    right: 41.66666667%;\n  }\n  .col-sm-pull-4 {\n    right: 33.33333333%;\n  }\n  .col-sm-pull-3 {\n    right: 25%;\n  }\n  .col-sm-pull-2 {\n    right: 16.66666667%;\n  }\n  .col-sm-pull-1 {\n    right: 8.33333333%;\n  }\n  .col-sm-pull-0 {\n    right: auto;\n  }\n  .col-sm-push-12 {\n    left: 100%;\n  }\n  .col-sm-push-11 {\n    left: 91.66666667%;\n  }\n  .col-sm-push-10 {\n    left: 83.33333333%;\n  }\n  .col-sm-push-9 {\n    left: 75%;\n  }\n  .col-sm-push-8 {\n    left: 66.66666667%;\n  }\n  .col-sm-push-7 {\n    left: 58.33333333%;\n  }\n  .col-sm-push-6 {\n    left: 50%;\n  }\n  .col-sm-push-5 {\n    left: 41.66666667%;\n  }\n  .col-sm-push-4 {\n    left: 33.33333333%;\n  }\n  .col-sm-push-3 {\n    left: 25%;\n  }\n  .col-sm-push-2 {\n    left: 16.66666667%;\n  }\n  .col-sm-push-1 {\n    left: 8.33333333%;\n  }\n  .col-sm-push-0 {\n    left: auto;\n  }\n  .col-sm-offset-12 {\n    margin-left: 100%;\n  }\n  .col-sm-offset-11 {\n    margin-left: 91.66666667%;\n  }\n  .col-sm-offset-10 {\n    margin-left: 83.33333333%;\n  }\n  .col-sm-offset-9 {\n    margin-left: 75%;\n  }\n  .col-sm-offset-8 {\n    margin-left: 66.66666667%;\n  }\n  .col-sm-offset-7 {\n    margin-left: 58.33333333%;\n  }\n  .col-sm-offset-6 {\n    margin-left: 50%;\n  }\n  .col-sm-offset-5 {\n    margin-left: 41.66666667%;\n  }\n  .col-sm-offset-4 {\n    margin-left: 33.33333333%;\n  }\n  .col-sm-offset-3 {\n    margin-left: 25%;\n  }\n  .col-sm-offset-2 {\n    margin-left: 16.66666667%;\n  }\n  .col-sm-offset-1 {\n    margin-left: 8.33333333%;\n  }\n  .col-sm-offset-0 {\n    margin-left: 0;\n  }\n}\n@media (min-width: 992px) {\n  .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {\n    float: left;\n  }\n  .col-md-12 {\n    width: 100%;\n  }\n  .col-md-11 {\n    width: 91.66666667%;\n  }\n  .col-md-10 {\n    width: 83.33333333%;\n  }\n  .col-md-9 {\n    width: 75%;\n  }\n  .col-md-8 {\n    width: 66.66666667%;\n  }\n  .col-md-7 {\n    width: 58.33333333%;\n  }\n  .col-md-6 {\n    width: 50%;\n  }\n  .col-md-5 {\n    width: 41.66666667%;\n  }\n  .col-md-4 {\n    width: 33.33333333%;\n  }\n  .col-md-3 {\n    width: 25%;\n  }\n  .col-md-2 {\n    width: 16.66666667%;\n  }\n  .col-md-1 {\n    width: 8.33333333%;\n  }\n  .col-md-pull-12 {\n    right: 100%;\n  }\n  .col-md-pull-11 {\n    right: 91.66666667%;\n  }\n  .col-md-pull-10 {\n    right: 83.33333333%;\n  }\n  .col-md-pull-9 {\n    right: 75%;\n  }\n  .col-md-pull-8 {\n    right: 66.66666667%;\n  }\n  .col-md-pull-7 {\n    right: 58.33333333%;\n  }\n  .col-md-pull-6 {\n    right: 50%;\n  }\n  .col-md-pull-5 {\n    right: 41.66666667%;\n  }\n  .col-md-pull-4 {\n    right: 33.33333333%;\n  }\n  .col-md-pull-3 {\n    right: 25%;\n  }\n  .col-md-pull-2 {\n    right: 16.66666667%;\n  }\n  .col-md-pull-1 {\n    right: 8.33333333%;\n  }\n  .col-md-pull-0 {\n    right: auto;\n  }\n  .col-md-push-12 {\n    left: 100%;\n  }\n  .col-md-push-11 {\n    left: 91.66666667%;\n  }\n  .col-md-push-10 {\n    left: 83.33333333%;\n  }\n  .col-md-push-9 {\n    left: 75%;\n  }\n  .col-md-push-8 {\n    left: 66.66666667%;\n  }\n  .col-md-push-7 {\n    left: 58.33333333%;\n  }\n  .col-md-push-6 {\n    left: 50%;\n  }\n  .col-md-push-5 {\n    left: 41.66666667%;\n  }\n  .col-md-push-4 {\n    left: 33.33333333%;\n  }\n  .col-md-push-3 {\n    left: 25%;\n  }\n  .col-md-push-2 {\n    left: 16.66666667%;\n  }\n  .col-md-push-1 {\n    left: 8.33333333%;\n  }\n  .col-md-push-0 {\n    left: auto;\n  }\n  .col-md-offset-12 {\n    margin-left: 100%;\n  }\n  .col-md-offset-11 {\n    margin-left: 91.66666667%;\n  }\n  .col-md-offset-10 {\n    margin-left: 83.33333333%;\n  }\n  .col-md-offset-9 {\n    margin-left: 75%;\n  }\n  .col-md-offset-8 {\n    margin-left: 66.66666667%;\n  }\n  .col-md-offset-7 {\n    margin-left: 58.33333333%;\n  }\n  .col-md-offset-6 {\n    margin-left: 50%;\n  }\n  .col-md-offset-5 {\n    margin-left: 41.66666667%;\n  }\n  .col-md-offset-4 {\n    margin-left: 33.33333333%;\n  }\n  .col-md-offset-3 {\n    margin-left: 25%;\n  }\n  .col-md-offset-2 {\n    margin-left: 16.66666667%;\n  }\n  .col-md-offset-1 {\n    margin-left: 8.33333333%;\n  }\n  .col-md-offset-0 {\n    margin-left: 0;\n  }\n}\n@media (min-width: 1200px) {\n  .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {\n    float: left;\n  }\n  .col-lg-12 {\n    width: 100%;\n  }\n  .col-lg-11 {\n    width: 91.66666667%;\n  }\n  .col-lg-10 {\n    width: 83.33333333%;\n  }\n  .col-lg-9 {\n    width: 75%;\n  }\n  .col-lg-8 {\n    width: 66.66666667%;\n  }\n  .col-lg-7 {\n    width: 58.33333333%;\n  }\n  .col-lg-6 {\n    width: 50%;\n  }\n  .col-lg-5 {\n    width: 41.66666667%;\n  }\n  .col-lg-4 {\n    width: 33.33333333%;\n  }\n  .col-lg-3 {\n    width: 25%;\n  }\n  .col-lg-2 {\n    width: 16.66666667%;\n  }\n  .col-lg-1 {\n    width: 8.33333333%;\n  }\n  .col-lg-pull-12 {\n    right: 100%;\n  }\n  .col-lg-pull-11 {\n    right: 91.66666667%;\n  }\n  .col-lg-pull-10 {\n    right: 83.33333333%;\n  }\n  .col-lg-pull-9 {\n    right: 75%;\n  }\n  .col-lg-pull-8 {\n    right: 66.66666667%;\n  }\n  .col-lg-pull-7 {\n    right: 58.33333333%;\n  }\n  .col-lg-pull-6 {\n    right: 50%;\n  }\n  .col-lg-pull-5 {\n    right: 41.66666667%;\n  }\n  .col-lg-pull-4 {\n    right: 33.33333333%;\n  }\n  .col-lg-pull-3 {\n    right: 25%;\n  }\n  .col-lg-pull-2 {\n    right: 16.66666667%;\n  }\n  .col-lg-pull-1 {\n    right: 8.33333333%;\n  }\n  .col-lg-pull-0 {\n    right: auto;\n  }\n  .col-lg-push-12 {\n    left: 100%;\n  }\n  .col-lg-push-11 {\n    left: 91.66666667%;\n  }\n  .col-lg-push-10 {\n    left: 83.33333333%;\n  }\n  .col-lg-push-9 {\n    left: 75%;\n  }\n  .col-lg-push-8 {\n    left: 66.66666667%;\n  }\n  .col-lg-push-7 {\n    left: 58.33333333%;\n  }\n  .col-lg-push-6 {\n    left: 50%;\n  }\n  .col-lg-push-5 {\n    left: 41.66666667%;\n  }\n  .col-lg-push-4 {\n    left: 33.33333333%;\n  }\n  .col-lg-push-3 {\n    left: 25%;\n  }\n  .col-lg-push-2 {\n    left: 16.66666667%;\n  }\n  .col-lg-push-1 {\n    left: 8.33333333%;\n  }\n  .col-lg-push-0 {\n    left: auto;\n  }\n  .col-lg-offset-12 {\n    margin-left: 100%;\n  }\n  .col-lg-offset-11 {\n    margin-left: 91.66666667%;\n  }\n  .col-lg-offset-10 {\n    margin-left: 83.33333333%;\n  }\n  .col-lg-offset-9 {\n    margin-left: 75%;\n  }\n  .col-lg-offset-8 {\n    margin-left: 66.66666667%;\n  }\n  .col-lg-offset-7 {\n    margin-left: 58.33333333%;\n  }\n  .col-lg-offset-6 {\n    margin-left: 50%;\n  }\n  .col-lg-offset-5 {\n    margin-left: 41.66666667%;\n  }\n  .col-lg-offset-4 {\n    margin-left: 33.33333333%;\n  }\n  .col-lg-offset-3 {\n    margin-left: 25%;\n  }\n  .col-lg-offset-2 {\n    margin-left: 16.66666667%;\n  }\n  .col-lg-offset-1 {\n    margin-left: 8.33333333%;\n  }\n  .col-lg-offset-0 {\n    margin-left: 0;\n  }\n}\ntable {\n  background-color: transparent;\n}\ncaption {\n  padding-top: 8px;\n  padding-bottom: 8px;\n  color: #777;\n  text-align: left;\n}\nth {\n  text-align: left;\n}\n.table {\n  width: 100%;\n  max-width: 100%;\n  margin-bottom: 20px;\n}\n.table > thead > tr > th,\n.table > tbody > tr > th,\n.table > tfoot > tr > th,\n.table > thead > tr > td,\n.table > tbody > tr > td,\n.table > tfoot > tr > td {\n  padding: 8px;\n  line-height: 1.42857143;\n  vertical-align: top;\n  border-top: 1px solid #ddd;\n}\n.table > thead > tr > th {\n  vertical-align: bottom;\n  border-bottom: 2px solid #ddd;\n}\n.table > caption + thead > tr:first-child > th,\n.table > colgroup + thead > tr:first-child > th,\n.table > thead:first-child > tr:first-child > th,\n.table > caption + thead > tr:first-child > td,\n.table > colgroup + thead > tr:first-child > td,\n.table > thead:first-child > tr:first-child > td {\n  border-top: 0;\n}\n.table > tbody + tbody {\n  border-top: 2px solid #ddd;\n}\n.table .table {\n  background-color: #fff;\n}\n.table-condensed > thead > tr > th,\n.table-condensed > tbody > tr > th,\n.table-condensed > tfoot > tr > th,\n.table-condensed > thead > tr > td,\n.table-condensed > tbody > tr > td,\n.table-condensed > tfoot > tr > td {\n  padding: 5px;\n}\n.table-bordered {\n  border: 1px solid #ddd;\n}\n.table-bordered > thead > tr > th,\n.table-bordered > tbody > tr > th,\n.table-bordered > tfoot > tr > th,\n.table-bordered > thead > tr > td,\n.table-bordered > tbody > tr > td,\n.table-bordered > tfoot > tr > td {\n  border: 1px solid #ddd;\n}\n.table-bordered > thead > tr > th,\n.table-bordered > thead > tr > td {\n  border-bottom-width: 2px;\n}\n.table-striped > tbody > tr:nth-of-type(odd) {\n  background-color: #f9f9f9;\n}\n.table-hover > tbody > tr:hover {\n  background-color: #f5f5f5;\n}\ntable col[class*=\"col-\"] {\n  position: static;\n  display: table-column;\n  float: none;\n}\ntable td[class*=\"col-\"],\ntable th[class*=\"col-\"] {\n  position: static;\n  display: table-cell;\n  float: none;\n}\n.table > thead > tr > td.active,\n.table > tbody > tr > td.active,\n.table > tfoot > tr > td.active,\n.table > thead > tr > th.active,\n.table > tbody > tr > th.active,\n.table > tfoot > tr > th.active,\n.table > thead > tr.active > td,\n.table > tbody > tr.active > td,\n.table > tfoot > tr.active > td,\n.table > thead > tr.active > th,\n.table > tbody > tr.active > th,\n.table > tfoot > tr.active > th {\n  background-color: #f5f5f5;\n}\n.table-hover > tbody > tr > td.active:hover,\n.table-hover > tbody > tr > th.active:hover,\n.table-hover > tbody > tr.active:hover > td,\n.table-hover > tbody > tr:hover > .active,\n.table-hover > tbody > tr.active:hover > th {\n  background-color: #e8e8e8;\n}\n.table > thead > tr > td.success,\n.table > tbody > tr > td.success,\n.table > tfoot > tr > td.success,\n.table > thead > tr > th.success,\n.table > tbody > tr > th.success,\n.table > tfoot > tr > th.success,\n.table > thead > tr.success > td,\n.table > tbody > tr.success > td,\n.table > tfoot > tr.success > td,\n.table > thead > tr.success > th,\n.table > tbody > tr.success > th,\n.table > tfoot > tr.success > th {\n  background-color: #dff0d8;\n}\n.table-hover > tbody > tr > td.success:hover,\n.table-hover > tbody > tr > th.success:hover,\n.table-hover > tbody > tr.success:hover > td,\n.table-hover > tbody > tr:hover > .success,\n.table-hover > tbody > tr.success:hover > th {\n  background-color: #d0e9c6;\n}\n.table > thead > tr > td.info,\n.table > tbody > tr > td.info,\n.table > tfoot > tr > td.info,\n.table > thead > tr > th.info,\n.table > tbody > tr > th.info,\n.table > tfoot > tr > th.info,\n.table > thead > tr.info > td,\n.table > tbody > tr.info > td,\n.table > tfoot > tr.info > td,\n.table > thead > tr.info > th,\n.table > tbody > tr.info > th,\n.table > tfoot > tr.info > th {\n  background-color: #d9edf7;\n}\n.table-hover > tbody > tr > td.info:hover,\n.table-hover > tbody > tr > th.info:hover,\n.table-hover > tbody > tr.info:hover > td,\n.table-hover > tbody > tr:hover > .info,\n.table-hover > tbody > tr.info:hover > th {\n  background-color: #c4e3f3;\n}\n.table > thead > tr > td.warning,\n.table > tbody > tr > td.warning,\n.table > tfoot > tr > td.warning,\n.table > thead > tr > th.warning,\n.table > tbody > tr > th.warning,\n.table > tfoot > tr > th.warning,\n.table > thead > tr.warning > td,\n.table > tbody > tr.warning > td,\n.table > tfoot > tr.warning > td,\n.table > thead > tr.warning > th,\n.table > tbody > tr.warning > th,\n.table > tfoot > tr.warning > th {\n  background-color: #fcf8e3;\n}\n.table-hover > tbody > tr > td.warning:hover,\n.table-hover > tbody > tr > th.warning:hover,\n.table-hover > tbody > tr.warning:hover > td,\n.table-hover > tbody > tr:hover > .warning,\n.table-hover > tbody > tr.warning:hover > th {\n  background-color: #faf2cc;\n}\n.table > thead > tr > td.danger,\n.table > tbody > tr > td.danger,\n.table > tfoot > tr > td.danger,\n.table > thead > tr > th.danger,\n.table > tbody > tr > th.danger,\n.table > tfoot > tr > th.danger,\n.table > thead > tr.danger > td,\n.table > tbody > tr.danger > td,\n.table > tfoot > tr.danger > td,\n.table > thead > tr.danger > th,\n.table > tbody > tr.danger > th,\n.table > tfoot > tr.danger > th {\n  background-color: #f2dede;\n}\n.table-hover > tbody > tr > td.danger:hover,\n.table-hover > tbody > tr > th.danger:hover,\n.table-hover > tbody > tr.danger:hover > td,\n.table-hover > tbody > tr:hover > .danger,\n.table-hover > tbody > tr.danger:hover > th {\n  background-color: #ebcccc;\n}\n.table-responsive {\n  min-height: .01%;\n  overflow-x: auto;\n}\n@media screen and (max-width: 767px) {\n  .table-responsive {\n    width: 100%;\n    margin-bottom: 15px;\n    overflow-y: hidden;\n    -ms-overflow-style: -ms-autohiding-scrollbar;\n    border: 1px solid #ddd;\n  }\n  .table-responsive > .table {\n    margin-bottom: 0;\n  }\n  .table-responsive > .table > thead > tr > th,\n  .table-responsive > .table > tbody > tr > th,\n  .table-responsive > .table > tfoot > tr > th,\n  .table-responsive > .table > thead > tr > td,\n  .table-responsive > .table > tbody > tr > td,\n  .table-responsive > .table > tfoot > tr > td {\n    white-space: nowrap;\n  }\n  .table-responsive > .table-bordered {\n    border: 0;\n  }\n  .table-responsive > .table-bordered > thead > tr > th:first-child,\n  .table-responsive > .table-bordered > tbody > tr > th:first-child,\n  .table-responsive > .table-bordered > tfoot > tr > th:first-child,\n  .table-responsive > .table-bordered > thead > tr > td:first-child,\n  .table-responsive > .table-bordered > tbody > tr > td:first-child,\n  .table-responsive > .table-bordered > tfoot > tr > td:first-child {\n    border-left: 0;\n  }\n  .table-responsive > .table-bordered > thead > tr > th:last-child,\n  .table-responsive > .table-bordered > tbody > tr > th:last-child,\n  .table-responsive > .table-bordered > tfoot > tr > th:last-child,\n  .table-responsive > .table-bordered > thead > tr > td:last-child,\n  .table-responsive > .table-bordered > tbody > tr > td:last-child,\n  .table-responsive > .table-bordered > tfoot > tr > td:last-child {\n    border-right: 0;\n  }\n  .table-responsive > .table-bordered > tbody > tr:last-child > th,\n  .table-responsive > .table-bordered > tfoot > tr:last-child > th,\n  .table-responsive > .table-bordered > tbody > tr:last-child > td,\n  .table-responsive > .table-bordered > tfoot > tr:last-child > td {\n    border-bottom: 0;\n  }\n}\nfieldset {\n  min-width: 0;\n  padding: 0;\n  margin: 0;\n  border: 0;\n}\nlegend {\n  display: block;\n  width: 100%;\n  padding: 0;\n  margin-bottom: 20px;\n  font-size: 21px;\n  line-height: inherit;\n  color: #333;\n  border: 0;\n  border-bottom: 1px solid #e5e5e5;\n}\nlabel {\n  display: inline-block;\n  max-width: 100%;\n  margin-bottom: 5px;\n  font-weight: bold;\n}\ninput[type=\"search\"] {\n  -webkit-box-sizing: border-box;\n     -moz-box-sizing: border-box;\n          box-sizing: border-box;\n}\ninput[type=\"radio\"],\ninput[type=\"checkbox\"] {\n  margin: 4px 0 0;\n  margin-top: 1px \\9;\n  line-height: normal;\n}\ninput[type=\"file\"] {\n  display: block;\n}\ninput[type=\"range\"] {\n  display: block;\n  width: 100%;\n}\nselect[multiple],\nselect[size] {\n  height: auto;\n}\ninput[type=\"file\"]:focus,\ninput[type=\"radio\"]:focus,\ninput[type=\"checkbox\"]:focus {\n  outline: 5px auto -webkit-focus-ring-color;\n  outline-offset: -2px;\n}\noutput {\n  display: block;\n  padding-top: 7px;\n  font-size: 14px;\n  line-height: 1.42857143;\n  color: #555;\n}\n.form-control {\n  display: block;\n  width: 100%;\n  height: 34px;\n  padding: 6px 12px;\n  font-size: 14px;\n  line-height: 1.42857143;\n  color: #555;\n  background-color: #fff;\n  background-image: none;\n  border: 1px solid #ccc;\n  border-radius: 4px;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075);\n          box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075);\n  -webkit-transition: border-color ease-in-out .15s, -webkit-box-shadow ease-in-out .15s;\n       -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;\n          transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;\n}\n.form-control:focus {\n  border-color: #66afe9;\n  outline: 0;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, .6);\n          box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, .6);\n}\n.form-control::-moz-placeholder {\n  color: #999;\n  opacity: 1;\n}\n.form-control:-ms-input-placeholder {\n  color: #999;\n}\n.form-control::-webkit-input-placeholder {\n  color: #999;\n}\n.form-control::-ms-expand {\n  background-color: transparent;\n  border: 0;\n}\n.form-control[disabled],\n.form-control[readonly],\nfieldset[disabled] .form-control {\n  background-color: #eee;\n  opacity: 1;\n}\n.form-control[disabled],\nfieldset[disabled] .form-control {\n  cursor: not-allowed;\n}\ntextarea.form-control {\n  height: auto;\n}\ninput[type=\"search\"] {\n  -webkit-appearance: none;\n}\n@media screen and (-webkit-min-device-pixel-ratio: 0) {\n  input[type=\"date\"].form-control,\n  input[type=\"time\"].form-control,\n  input[type=\"datetime-local\"].form-control,\n  input[type=\"month\"].form-control {\n    line-height: 34px;\n  }\n  input[type=\"date\"].input-sm,\n  input[type=\"time\"].input-sm,\n  input[type=\"datetime-local\"].input-sm,\n  input[type=\"month\"].input-sm,\n  .input-group-sm input[type=\"date\"],\n  .input-group-sm input[type=\"time\"],\n  .input-group-sm input[type=\"datetime-local\"],\n  .input-group-sm input[type=\"month\"] {\n    line-height: 30px;\n  }\n  input[type=\"date\"].input-lg,\n  input[type=\"time\"].input-lg,\n  input[type=\"datetime-local\"].input-lg,\n  input[type=\"month\"].input-lg,\n  .input-group-lg input[type=\"date\"],\n  .input-group-lg input[type=\"time\"],\n  .input-group-lg input[type=\"datetime-local\"],\n  .input-group-lg input[type=\"month\"] {\n    line-height: 46px;\n  }\n}\n.form-group {\n  margin-bottom: 15px;\n}\n.radio,\n.checkbox {\n  position: relative;\n  display: block;\n  margin-top: 10px;\n  margin-bottom: 10px;\n}\n.radio label,\n.checkbox label {\n  min-height: 20px;\n  padding-left: 20px;\n  margin-bottom: 0;\n  font-weight: normal;\n  cursor: pointer;\n}\n.radio input[type=\"radio\"],\n.radio-inline input[type=\"radio\"],\n.checkbox input[type=\"checkbox\"],\n.checkbox-inline input[type=\"checkbox\"] {\n  position: absolute;\n  margin-top: 4px \\9;\n  margin-left: -20px;\n}\n.radio + .radio,\n.checkbox + .checkbox {\n  margin-top: -5px;\n}\n.radio-inline,\n.checkbox-inline {\n  position: relative;\n  display: inline-block;\n  padding-left: 20px;\n  margin-bottom: 0;\n  font-weight: normal;\n  vertical-align: middle;\n  cursor: pointer;\n}\n.radio-inline + .radio-inline,\n.checkbox-inline + .checkbox-inline {\n  margin-top: 0;\n  margin-left: 10px;\n}\ninput[type=\"radio\"][disabled],\ninput[type=\"checkbox\"][disabled],\ninput[type=\"radio\"].disabled,\ninput[type=\"checkbox\"].disabled,\nfieldset[disabled] input[type=\"radio\"],\nfieldset[disabled] input[type=\"checkbox\"] {\n  cursor: not-allowed;\n}\n.radio-inline.disabled,\n.checkbox-inline.disabled,\nfieldset[disabled] .radio-inline,\nfieldset[disabled] .checkbox-inline {\n  cursor: not-allowed;\n}\n.radio.disabled label,\n.checkbox.disabled label,\nfieldset[disabled] .radio label,\nfieldset[disabled] .checkbox label {\n  cursor: not-allowed;\n}\n.form-control-static {\n  min-height: 34px;\n  padding-top: 7px;\n  padding-bottom: 7px;\n  margin-bottom: 0;\n}\n.form-control-static.input-lg,\n.form-control-static.input-sm {\n  padding-right: 0;\n  padding-left: 0;\n}\n.input-sm {\n  height: 30px;\n  padding: 5px 10px;\n  font-size: 12px;\n  line-height: 1.5;\n  border-radius: 3px;\n}\nselect.input-sm {\n  height: 30px;\n  line-height: 30px;\n}\ntextarea.input-sm,\nselect[multiple].input-sm {\n  height: auto;\n}\n.form-group-sm .form-control {\n  height: 30px;\n  padding: 5px 10px;\n  font-size: 12px;\n  line-height: 1.5;\n  border-radius: 3px;\n}\n.form-group-sm select.form-control {\n  height: 30px;\n  line-height: 30px;\n}\n.form-group-sm textarea.form-control,\n.form-group-sm select[multiple].form-control {\n  height: auto;\n}\n.form-group-sm .form-control-static {\n  height: 30px;\n  min-height: 32px;\n  padding: 6px 10px;\n  font-size: 12px;\n  line-height: 1.5;\n}\n.input-lg {\n  height: 46px;\n  padding: 10px 16px;\n  font-size: 18px;\n  line-height: 1.3333333;\n  border-radius: 6px;\n}\nselect.input-lg {\n  height: 46px;\n  line-height: 46px;\n}\ntextarea.input-lg,\nselect[multiple].input-lg {\n  height: auto;\n}\n.form-group-lg .form-control {\n  height: 46px;\n  padding: 10px 16px;\n  font-size: 18px;\n  line-height: 1.3333333;\n  border-radius: 6px;\n}\n.form-group-lg select.form-control {\n  height: 46px;\n  line-height: 46px;\n}\n.form-group-lg textarea.form-control,\n.form-group-lg select[multiple].form-control {\n  height: auto;\n}\n.form-group-lg .form-control-static {\n  height: 46px;\n  min-height: 38px;\n  padding: 11px 16px;\n  font-size: 18px;\n  line-height: 1.3333333;\n}\n.has-feedback {\n  position: relative;\n}\n.has-feedback .form-control {\n  padding-right: 42.5px;\n}\n.form-control-feedback {\n  position: absolute;\n  top: 0;\n  right: 0;\n  z-index: 2;\n  display: block;\n  width: 34px;\n  height: 34px;\n  line-height: 34px;\n  text-align: center;\n  pointer-events: none;\n}\n.input-lg + .form-control-feedback,\n.input-group-lg + .form-control-feedback,\n.form-group-lg .form-control + .form-control-feedback {\n  width: 46px;\n  height: 46px;\n  line-height: 46px;\n}\n.input-sm + .form-control-feedback,\n.input-group-sm + .form-control-feedback,\n.form-group-sm .form-control + .form-control-feedback {\n  width: 30px;\n  height: 30px;\n  line-height: 30px;\n}\n.has-success .help-block,\n.has-success .control-label,\n.has-success .radio,\n.has-success .checkbox,\n.has-success .radio-inline,\n.has-success .checkbox-inline,\n.has-success.radio label,\n.has-success.checkbox label,\n.has-success.radio-inline label,\n.has-success.checkbox-inline label {\n  color: #3c763d;\n}\n.has-success .form-control {\n  border-color: #3c763d;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075);\n          box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075);\n}\n.has-success .form-control:focus {\n  border-color: #2b542c;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075), 0 0 6px #67b168;\n          box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075), 0 0 6px #67b168;\n}\n.has-success .input-group-addon {\n  color: #3c763d;\n  background-color: #dff0d8;\n  border-color: #3c763d;\n}\n.has-success .form-control-feedback {\n  color: #3c763d;\n}\n.has-warning .help-block,\n.has-warning .control-label,\n.has-warning .radio,\n.has-warning .checkbox,\n.has-warning .radio-inline,\n.has-warning .checkbox-inline,\n.has-warning.radio label,\n.has-warning.checkbox label,\n.has-warning.radio-inline label,\n.has-warning.checkbox-inline label {\n  color: #8a6d3b;\n}\n.has-warning .form-control {\n  border-color: #8a6d3b;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075);\n          box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075);\n}\n.has-warning .form-control:focus {\n  border-color: #66512c;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075), 0 0 6px #c0a16b;\n          box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075), 0 0 6px #c0a16b;\n}\n.has-warning .input-group-addon {\n  color: #8a6d3b;\n  background-color: #fcf8e3;\n  border-color: #8a6d3b;\n}\n.has-warning .form-control-feedback {\n  color: #8a6d3b;\n}\n.has-error .help-block,\n.has-error .control-label,\n.has-error .radio,\n.has-error .checkbox,\n.has-error .radio-inline,\n.has-error .checkbox-inline,\n.has-error.radio label,\n.has-error.checkbox label,\n.has-error.radio-inline label,\n.has-error.checkbox-inline label {\n  color: #a94442;\n}\n.has-error .form-control {\n  border-color: #a94442;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075);\n          box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075);\n}\n.has-error .form-control:focus {\n  border-color: #843534;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075), 0 0 6px #ce8483;\n          box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075), 0 0 6px #ce8483;\n}\n.has-error .input-group-addon {\n  color: #a94442;\n  background-color: #f2dede;\n  border-color: #a94442;\n}\n.has-error .form-control-feedback {\n  color: #a94442;\n}\n.has-feedback label ~ .form-control-feedback {\n  top: 25px;\n}\n.has-feedback label.sr-only ~ .form-control-feedback {\n  top: 0;\n}\n.help-block {\n  display: block;\n  margin-top: 5px;\n  margin-bottom: 10px;\n  color: #737373;\n}\n@media (min-width: 768px) {\n  .form-inline .form-group {\n    display: inline-block;\n    margin-bottom: 0;\n    vertical-align: middle;\n  }\n  .form-inline .form-control {\n    display: inline-block;\n    width: auto;\n    vertical-align: middle;\n  }\n  .form-inline .form-control-static {\n    display: inline-block;\n  }\n  .form-inline .input-group {\n    display: inline-table;\n    vertical-align: middle;\n  }\n  .form-inline .input-group .input-group-addon,\n  .form-inline .input-group .input-group-btn,\n  .form-inline .input-group .form-control {\n    width: auto;\n  }\n  .form-inline .input-group > .form-control {\n    width: 100%;\n  }\n  .form-inline .control-label {\n    margin-bottom: 0;\n    vertical-align: middle;\n  }\n  .form-inline .radio,\n  .form-inline .checkbox {\n    display: inline-block;\n    margin-top: 0;\n    margin-bottom: 0;\n    vertical-align: middle;\n  }\n  .form-inline .radio label,\n  .form-inline .checkbox label {\n    padding-left: 0;\n  }\n  .form-inline .radio input[type=\"radio\"],\n  .form-inline .checkbox input[type=\"checkbox\"] {\n    position: relative;\n    margin-left: 0;\n  }\n  .form-inline .has-feedback .form-control-feedback {\n    top: 0;\n  }\n}\n.form-horizontal .radio,\n.form-horizontal .checkbox,\n.form-horizontal .radio-inline,\n.form-horizontal .checkbox-inline {\n  padding-top: 7px;\n  margin-top: 0;\n  margin-bottom: 0;\n}\n.form-horizontal .radio,\n.form-horizontal .checkbox {\n  min-height: 27px;\n}\n.form-horizontal .form-group {\n  margin-right: -15px;\n  margin-left: -15px;\n}\n@media (min-width: 768px) {\n  .form-horizontal .control-label {\n    padding-top: 7px;\n    margin-bottom: 0;\n    text-align: right;\n  }\n}\n.form-horizontal .has-feedback .form-control-feedback {\n  right: 15px;\n}\n@media (min-width: 768px) {\n  .form-horizontal .form-group-lg .control-label {\n    padding-top: 11px;\n    font-size: 18px;\n  }\n}\n@media (min-width: 768px) {\n  .form-horizontal .form-group-sm .control-label {\n    padding-top: 6px;\n    font-size: 12px;\n  }\n}\n.btn {\n  display: inline-block;\n  padding: 6px 12px;\n  margin-bottom: 0;\n  font-size: 14px;\n  font-weight: normal;\n  line-height: 1.42857143;\n  text-align: center;\n  white-space: nowrap;\n  vertical-align: middle;\n  -ms-touch-action: manipulation;\n      touch-action: manipulation;\n  cursor: pointer;\n  -webkit-user-select: none;\n     -moz-user-select: none;\n      -ms-user-select: none;\n          user-select: none;\n  background-image: none;\n  border: 1px solid transparent;\n  border-radius: 4px;\n}\n.btn:focus,\n.btn:active:focus,\n.btn.active:focus,\n.btn.focus,\n.btn:active.focus,\n.btn.active.focus {\n  outline: 5px auto -webkit-focus-ring-color;\n  outline-offset: -2px;\n}\n.btn:hover,\n.btn:focus,\n.btn.focus {\n  color: #333;\n  text-decoration: none;\n}\n.btn:active,\n.btn.active {\n  background-image: none;\n  outline: 0;\n  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, .125);\n          box-shadow: inset 0 3px 5px rgba(0, 0, 0, .125);\n}\n.btn.disabled,\n.btn[disabled],\nfieldset[disabled] .btn {\n  cursor: not-allowed;\n  filter: alpha(opacity=65);\n  -webkit-box-shadow: none;\n          box-shadow: none;\n  opacity: .65;\n}\na.btn.disabled,\nfieldset[disabled] a.btn {\n  pointer-events: none;\n}\n.btn-default {\n  color: #333;\n  background-color: #fff;\n  border-color: #ccc;\n}\n.btn-default:focus,\n.btn-default.focus {\n  color: #333;\n  background-color: #e6e6e6;\n  border-color: #8c8c8c;\n}\n.btn-default:hover {\n  color: #333;\n  background-color: #e6e6e6;\n  border-color: #adadad;\n}\n.btn-default:active,\n.btn-default.active,\n.open > .dropdown-toggle.btn-default {\n  color: #333;\n  background-color: #e6e6e6;\n  border-color: #adadad;\n}\n.btn-default:active:hover,\n.btn-default.active:hover,\n.open > .dropdown-toggle.btn-default:hover,\n.btn-default:active:focus,\n.btn-default.active:focus,\n.open > .dropdown-toggle.btn-default:focus,\n.btn-default:active.focus,\n.btn-default.active.focus,\n.open > .dropdown-toggle.btn-default.focus {\n  color: #333;\n  background-color: #d4d4d4;\n  border-color: #8c8c8c;\n}\n.btn-default:active,\n.btn-default.active,\n.open > .dropdown-toggle.btn-default {\n  background-image: none;\n}\n.btn-default.disabled:hover,\n.btn-default[disabled]:hover,\nfieldset[disabled] .btn-default:hover,\n.btn-default.disabled:focus,\n.btn-default[disabled]:focus,\nfieldset[disabled] .btn-default:focus,\n.btn-default.disabled.focus,\n.btn-default[disabled].focus,\nfieldset[disabled] .btn-default.focus {\n  background-color: #fff;\n  border-color: #ccc;\n}\n.btn-default .badge {\n  color: #fff;\n  background-color: #333;\n}\n.btn-primary {\n  color: #fff;\n  background-color: #337ab7;\n  border-color: #2e6da4;\n}\n.btn-primary:focus,\n.btn-primary.focus {\n  color: #fff;\n  background-color: #286090;\n  border-color: #122b40;\n}\n.btn-primary:hover {\n  color: #fff;\n  background-color: #286090;\n  border-color: #204d74;\n}\n.btn-primary:active,\n.btn-primary.active,\n.open > .dropdown-toggle.btn-primary {\n  color: #fff;\n  background-color: #286090;\n  border-color: #204d74;\n}\n.btn-primary:active:hover,\n.btn-primary.active:hover,\n.open > .dropdown-toggle.btn-primary:hover,\n.btn-primary:active:focus,\n.btn-primary.active:focus,\n.open > .dropdown-toggle.btn-primary:focus,\n.btn-primary:active.focus,\n.btn-primary.active.focus,\n.open > .dropdown-toggle.btn-primary.focus {\n  color: #fff;\n  background-color: #204d74;\n  border-color: #122b40;\n}\n.btn-primary:active,\n.btn-primary.active,\n.open > .dropdown-toggle.btn-primary {\n  background-image: none;\n}\n.btn-primary.disabled:hover,\n.btn-primary[disabled]:hover,\nfieldset[disabled] .btn-primary:hover,\n.btn-primary.disabled:focus,\n.btn-primary[disabled]:focus,\nfieldset[disabled] .btn-primary:focus,\n.btn-primary.disabled.focus,\n.btn-primary[disabled].focus,\nfieldset[disabled] .btn-primary.focus {\n  background-color: #337ab7;\n  border-color: #2e6da4;\n}\n.btn-primary .badge {\n  color: #337ab7;\n  background-color: #fff;\n}\n.btn-success {\n  color: #fff;\n  background-color: #5cb85c;\n  border-color: #4cae4c;\n}\n.btn-success:focus,\n.btn-success.focus {\n  color: #fff;\n  background-color: #449d44;\n  border-color: #255625;\n}\n.btn-success:hover {\n  color: #fff;\n  background-color: #449d44;\n  border-color: #398439;\n}\n.btn-success:active,\n.btn-success.active,\n.open > .dropdown-toggle.btn-success {\n  color: #fff;\n  background-color: #449d44;\n  border-color: #398439;\n}\n.btn-success:active:hover,\n.btn-success.active:hover,\n.open > .dropdown-toggle.btn-success:hover,\n.btn-success:active:focus,\n.btn-success.active:focus,\n.open > .dropdown-toggle.btn-success:focus,\n.btn-success:active.focus,\n.btn-success.active.focus,\n.open > .dropdown-toggle.btn-success.focus {\n  color: #fff;\n  background-color: #398439;\n  border-color: #255625;\n}\n.btn-success:active,\n.btn-success.active,\n.open > .dropdown-toggle.btn-success {\n  background-image: none;\n}\n.btn-success.disabled:hover,\n.btn-success[disabled]:hover,\nfieldset[disabled] .btn-success:hover,\n.btn-success.disabled:focus,\n.btn-success[disabled]:focus,\nfieldset[disabled] .btn-success:focus,\n.btn-success.disabled.focus,\n.btn-success[disabled].focus,\nfieldset[disabled] .btn-success.focus {\n  background-color: #5cb85c;\n  border-color: #4cae4c;\n}\n.btn-success .badge {\n  color: #5cb85c;\n  background-color: #fff;\n}\n.btn-info {\n  color: #fff;\n  background-color: #5bc0de;\n  border-color: #46b8da;\n}\n.btn-info:focus,\n.btn-info.focus {\n  color: #fff;\n  background-color: #31b0d5;\n  border-color: #1b6d85;\n}\n.btn-info:hover {\n  color: #fff;\n  background-color: #31b0d5;\n  border-color: #269abc;\n}\n.btn-info:active,\n.btn-info.active,\n.open > .dropdown-toggle.btn-info {\n  color: #fff;\n  background-color: #31b0d5;\n  border-color: #269abc;\n}\n.btn-info:active:hover,\n.btn-info.active:hover,\n.open > .dropdown-toggle.btn-info:hover,\n.btn-info:active:focus,\n.btn-info.active:focus,\n.open > .dropdown-toggle.btn-info:focus,\n.btn-info:active.focus,\n.btn-info.active.focus,\n.open > .dropdown-toggle.btn-info.focus {\n  color: #fff;\n  background-color: #269abc;\n  border-color: #1b6d85;\n}\n.btn-info:active,\n.btn-info.active,\n.open > .dropdown-toggle.btn-info {\n  background-image: none;\n}\n.btn-info.disabled:hover,\n.btn-info[disabled]:hover,\nfieldset[disabled] .btn-info:hover,\n.btn-info.disabled:focus,\n.btn-info[disabled]:focus,\nfieldset[disabled] .btn-info:focus,\n.btn-info.disabled.focus,\n.btn-info[disabled].focus,\nfieldset[disabled] .btn-info.focus {\n  background-color: #5bc0de;\n  border-color: #46b8da;\n}\n.btn-info .badge {\n  color: #5bc0de;\n  background-color: #fff;\n}\n.btn-warning {\n  color: #fff;\n  background-color: #f0ad4e;\n  border-color: #eea236;\n}\n.btn-warning:focus,\n.btn-warning.focus {\n  color: #fff;\n  background-color: #ec971f;\n  border-color: #985f0d;\n}\n.btn-warning:hover {\n  color: #fff;\n  background-color: #ec971f;\n  border-color: #d58512;\n}\n.btn-warning:active,\n.btn-warning.active,\n.open > .dropdown-toggle.btn-warning {\n  color: #fff;\n  background-color: #ec971f;\n  border-color: #d58512;\n}\n.btn-warning:active:hover,\n.btn-warning.active:hover,\n.open > .dropdown-toggle.btn-warning:hover,\n.btn-warning:active:focus,\n.btn-warning.active:focus,\n.open > .dropdown-toggle.btn-warning:focus,\n.btn-warning:active.focus,\n.btn-warning.active.focus,\n.open > .dropdown-toggle.btn-warning.focus {\n  color: #fff;\n  background-color: #d58512;\n  border-color: #985f0d;\n}\n.btn-warning:active,\n.btn-warning.active,\n.open > .dropdown-toggle.btn-warning {\n  background-image: none;\n}\n.btn-warning.disabled:hover,\n.btn-warning[disabled]:hover,\nfieldset[disabled] .btn-warning:hover,\n.btn-warning.disabled:focus,\n.btn-warning[disabled]:focus,\nfieldset[disabled] .btn-warning:focus,\n.btn-warning.disabled.focus,\n.btn-warning[disabled].focus,\nfieldset[disabled] .btn-warning.focus {\n  background-color: #f0ad4e;\n  border-color: #eea236;\n}\n.btn-warning .badge {\n  color: #f0ad4e;\n  background-color: #fff;\n}\n.btn-danger {\n  color: #fff;\n  background-color: #d9534f;\n  border-color: #d43f3a;\n}\n.btn-danger:focus,\n.btn-danger.focus {\n  color: #fff;\n  background-color: #c9302c;\n  border-color: #761c19;\n}\n.btn-danger:hover {\n  color: #fff;\n  background-color: #c9302c;\n  border-color: #ac2925;\n}\n.btn-danger:active,\n.btn-danger.active,\n.open > .dropdown-toggle.btn-danger {\n  color: #fff;\n  background-color: #c9302c;\n  border-color: #ac2925;\n}\n.btn-danger:active:hover,\n.btn-danger.active:hover,\n.open > .dropdown-toggle.btn-danger:hover,\n.btn-danger:active:focus,\n.btn-danger.active:focus,\n.open > .dropdown-toggle.btn-danger:focus,\n.btn-danger:active.focus,\n.btn-danger.active.focus,\n.open > .dropdown-toggle.btn-danger.focus {\n  color: #fff;\n  background-color: #ac2925;\n  border-color: #761c19;\n}\n.btn-danger:active,\n.btn-danger.active,\n.open > .dropdown-toggle.btn-danger {\n  background-image: none;\n}\n.btn-danger.disabled:hover,\n.btn-danger[disabled]:hover,\nfieldset[disabled] .btn-danger:hover,\n.btn-danger.disabled:focus,\n.btn-danger[disabled]:focus,\nfieldset[disabled] .btn-danger:focus,\n.btn-danger.disabled.focus,\n.btn-danger[disabled].focus,\nfieldset[disabled] .btn-danger.focus {\n  background-color: #d9534f;\n  border-color: #d43f3a;\n}\n.btn-danger .badge {\n  color: #d9534f;\n  background-color: #fff;\n}\n.btn-link {\n  font-weight: normal;\n  color: #337ab7;\n  border-radius: 0;\n}\n.btn-link,\n.btn-link:active,\n.btn-link.active,\n.btn-link[disabled],\nfieldset[disabled] .btn-link {\n  background-color: transparent;\n  -webkit-box-shadow: none;\n          box-shadow: none;\n}\n.btn-link,\n.btn-link:hover,\n.btn-link:focus,\n.btn-link:active {\n  border-color: transparent;\n}\n.btn-link:hover,\n.btn-link:focus {\n  color: #23527c;\n  text-decoration: underline;\n  background-color: transparent;\n}\n.btn-link[disabled]:hover,\nfieldset[disabled] .btn-link:hover,\n.btn-link[disabled]:focus,\nfieldset[disabled] .btn-link:focus {\n  color: #777;\n  text-decoration: none;\n}\n.btn-lg,\n.btn-group-lg > .btn {\n  padding: 10px 16px;\n  font-size: 18px;\n  line-height: 1.3333333;\n  border-radius: 6px;\n}\n.btn-sm,\n.btn-group-sm > .btn {\n  padding: 5px 10px;\n  font-size: 12px;\n  line-height: 1.5;\n  border-radius: 3px;\n}\n.btn-xs,\n.btn-group-xs > .btn {\n  padding: 1px 5px;\n  font-size: 12px;\n  line-height: 1.5;\n  border-radius: 3px;\n}\n.btn-block {\n  display: block;\n  width: 100%;\n}\n.btn-block + .btn-block {\n  margin-top: 5px;\n}\ninput[type=\"submit\"].btn-block,\ninput[type=\"reset\"].btn-block,\ninput[type=\"button\"].btn-block {\n  width: 100%;\n}\n.fade {\n  opacity: 0;\n  -webkit-transition: opacity .15s linear;\n       -o-transition: opacity .15s linear;\n          transition: opacity .15s linear;\n}\n.fade.in {\n  opacity: 1;\n}\n.collapse {\n  display: none;\n}\n.collapse.in {\n  display: block;\n}\ntr.collapse.in {\n  display: table-row;\n}\ntbody.collapse.in {\n  display: table-row-group;\n}\n.collapsing {\n  position: relative;\n  height: 0;\n  overflow: hidden;\n  -webkit-transition-timing-function: ease;\n       -o-transition-timing-function: ease;\n          transition-timing-function: ease;\n  -webkit-transition-duration: .35s;\n       -o-transition-duration: .35s;\n          transition-duration: .35s;\n  -webkit-transition-property: height, visibility;\n       -o-transition-property: height, visibility;\n          transition-property: height, visibility;\n}\n.caret {\n  display: inline-block;\n  width: 0;\n  height: 0;\n  margin-left: 2px;\n  vertical-align: middle;\n  border-top: 4px dashed;\n  border-top: 4px solid \\9;\n  border-right: 4px solid transparent;\n  border-left: 4px solid transparent;\n}\n.dropup,\n.dropdown {\n  position: relative;\n}\n.dropdown-toggle:focus {\n  outline: 0;\n}\n.dropdown-menu {\n  position: absolute;\n  top: 100%;\n  left: 0;\n  z-index: 1000;\n  display: none;\n  float: left;\n  min-width: 160px;\n  padding: 5px 0;\n  margin: 2px 0 0;\n  font-size: 14px;\n  text-align: left;\n  list-style: none;\n  background-color: #fff;\n  -webkit-background-clip: padding-box;\n          background-clip: padding-box;\n  border: 1px solid #ccc;\n  border: 1px solid rgba(0, 0, 0, .15);\n  border-radius: 4px;\n  -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, .175);\n          box-shadow: 0 6px 12px rgba(0, 0, 0, .175);\n}\n.dropdown-menu.pull-right {\n  right: 0;\n  left: auto;\n}\n.dropdown-menu .divider {\n  height: 1px;\n  margin: 9px 0;\n  overflow: hidden;\n  background-color: #e5e5e5;\n}\n.dropdown-menu > li > a {\n  display: block;\n  padding: 3px 20px;\n  clear: both;\n  font-weight: normal;\n  line-height: 1.42857143;\n  color: #333;\n  white-space: nowrap;\n}\n.dropdown-menu > li > a:hover,\n.dropdown-menu > li > a:focus {\n  color: #262626;\n  text-decoration: none;\n  background-color: #f5f5f5;\n}\n.dropdown-menu > .active > a,\n.dropdown-menu > .active > a:hover,\n.dropdown-menu > .active > a:focus {\n  color: #fff;\n  text-decoration: none;\n  background-color: #337ab7;\n  outline: 0;\n}\n.dropdown-menu > .disabled > a,\n.dropdown-menu > .disabled > a:hover,\n.dropdown-menu > .disabled > a:focus {\n  color: #777;\n}\n.dropdown-menu > .disabled > a:hover,\n.dropdown-menu > .disabled > a:focus {\n  text-decoration: none;\n  cursor: not-allowed;\n  background-color: transparent;\n  background-image: none;\n  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n}\n.open > .dropdown-menu {\n  display: block;\n}\n.open > a {\n  outline: 0;\n}\n.dropdown-menu-right {\n  right: 0;\n  left: auto;\n}\n.dropdown-menu-left {\n  right: auto;\n  left: 0;\n}\n.dropdown-header {\n  display: block;\n  padding: 3px 20px;\n  font-size: 12px;\n  line-height: 1.42857143;\n  color: #777;\n  white-space: nowrap;\n}\n.dropdown-backdrop {\n  position: fixed;\n  top: 0;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  z-index: 990;\n}\n.pull-right > .dropdown-menu {\n  right: 0;\n  left: auto;\n}\n.dropup .caret,\n.navbar-fixed-bottom .dropdown .caret {\n  content: \"\";\n  border-top: 0;\n  border-bottom: 4px dashed;\n  border-bottom: 4px solid \\9;\n}\n.dropup .dropdown-menu,\n.navbar-fixed-bottom .dropdown .dropdown-menu {\n  top: auto;\n  bottom: 100%;\n  margin-bottom: 2px;\n}\n@media (min-width: 768px) {\n  .navbar-right .dropdown-menu {\n    right: 0;\n    left: auto;\n  }\n  .navbar-right .dropdown-menu-left {\n    right: auto;\n    left: 0;\n  }\n}\n.btn-group,\n.btn-group-vertical {\n  position: relative;\n  display: inline-block;\n  vertical-align: middle;\n}\n.btn-group > .btn,\n.btn-group-vertical > .btn {\n  position: relative;\n  float: left;\n}\n.btn-group > .btn:hover,\n.btn-group-vertical > .btn:hover,\n.btn-group > .btn:focus,\n.btn-group-vertical > .btn:focus,\n.btn-group > .btn:active,\n.btn-group-vertical > .btn:active,\n.btn-group > .btn.active,\n.btn-group-vertical > .btn.active {\n  z-index: 2;\n}\n.btn-group .btn + .btn,\n.btn-group .btn + .btn-group,\n.btn-group .btn-group + .btn,\n.btn-group .btn-group + .btn-group {\n  margin-left: -1px;\n}\n.btn-toolbar {\n  margin-left: -5px;\n}\n.btn-toolbar .btn,\n.btn-toolbar .btn-group,\n.btn-toolbar .input-group {\n  float: left;\n}\n.btn-toolbar > .btn,\n.btn-toolbar > .btn-group,\n.btn-toolbar > .input-group {\n  margin-left: 5px;\n}\n.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {\n  border-radius: 0;\n}\n.btn-group > .btn:first-child {\n  margin-left: 0;\n}\n.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {\n  border-top-right-radius: 0;\n  border-bottom-right-radius: 0;\n}\n.btn-group > .btn:last-child:not(:first-child),\n.btn-group > .dropdown-toggle:not(:first-child) {\n  border-top-left-radius: 0;\n  border-bottom-left-radius: 0;\n}\n.btn-group > .btn-group {\n  float: left;\n}\n.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {\n  border-radius: 0;\n}\n.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,\n.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {\n  border-top-right-radius: 0;\n  border-bottom-right-radius: 0;\n}\n.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {\n  border-top-left-radius: 0;\n  border-bottom-left-radius: 0;\n}\n.btn-group .dropdown-toggle:active,\n.btn-group.open .dropdown-toggle {\n  outline: 0;\n}\n.btn-group > .btn + .dropdown-toggle {\n  padding-right: 8px;\n  padding-left: 8px;\n}\n.btn-group > .btn-lg + .dropdown-toggle {\n  padding-right: 12px;\n  padding-left: 12px;\n}\n.btn-group.open .dropdown-toggle {\n  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, .125);\n          box-shadow: inset 0 3px 5px rgba(0, 0, 0, .125);\n}\n.btn-group.open .dropdown-toggle.btn-link {\n  -webkit-box-shadow: none;\n          box-shadow: none;\n}\n.btn .caret {\n  margin-left: 0;\n}\n.btn-lg .caret {\n  border-width: 5px 5px 0;\n  border-bottom-width: 0;\n}\n.dropup .btn-lg .caret {\n  border-width: 0 5px 5px;\n}\n.btn-group-vertical > .btn,\n.btn-group-vertical > .btn-group,\n.btn-group-vertical > .btn-group > .btn {\n  display: block;\n  float: none;\n  width: 100%;\n  max-width: 100%;\n}\n.btn-group-vertical > .btn-group > .btn {\n  float: none;\n}\n.btn-group-vertical > .btn + .btn,\n.btn-group-vertical > .btn + .btn-group,\n.btn-group-vertical > .btn-group + .btn,\n.btn-group-vertical > .btn-group + .btn-group {\n  margin-top: -1px;\n  margin-left: 0;\n}\n.btn-group-vertical > .btn:not(:first-child):not(:last-child) {\n  border-radius: 0;\n}\n.btn-group-vertical > .btn:first-child:not(:last-child) {\n  border-top-left-radius: 4px;\n  border-top-right-radius: 4px;\n  border-bottom-right-radius: 0;\n  border-bottom-left-radius: 0;\n}\n.btn-group-vertical > .btn:last-child:not(:first-child) {\n  border-top-left-radius: 0;\n  border-top-right-radius: 0;\n  border-bottom-right-radius: 4px;\n  border-bottom-left-radius: 4px;\n}\n.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {\n  border-radius: 0;\n}\n.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,\n.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {\n  border-bottom-right-radius: 0;\n  border-bottom-left-radius: 0;\n}\n.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {\n  border-top-left-radius: 0;\n  border-top-right-radius: 0;\n}\n.btn-group-justified {\n  display: table;\n  width: 100%;\n  table-layout: fixed;\n  border-collapse: separate;\n}\n.btn-group-justified > .btn,\n.btn-group-justified > .btn-group {\n  display: table-cell;\n  float: none;\n  width: 1%;\n}\n.btn-group-justified > .btn-group .btn {\n  width: 100%;\n}\n.btn-group-justified > .btn-group .dropdown-menu {\n  left: auto;\n}\n[data-toggle=\"buttons\"] > .btn input[type=\"radio\"],\n[data-toggle=\"buttons\"] > .btn-group > .btn input[type=\"radio\"],\n[data-toggle=\"buttons\"] > .btn input[type=\"checkbox\"],\n[data-toggle=\"buttons\"] > .btn-group > .btn input[type=\"checkbox\"] {\n  position: absolute;\n  clip: rect(0, 0, 0, 0);\n  pointer-events: none;\n}\n.input-group {\n  position: relative;\n  display: table;\n  border-collapse: separate;\n}\n.input-group[class*=\"col-\"] {\n  float: none;\n  padding-right: 0;\n  padding-left: 0;\n}\n.input-group .form-control {\n  position: relative;\n  z-index: 2;\n  float: left;\n  width: 100%;\n  margin-bottom: 0;\n}\n.input-group .form-control:focus {\n  z-index: 3;\n}\n.input-group-lg > .form-control,\n.input-group-lg > .input-group-addon,\n.input-group-lg > .input-group-btn > .btn {\n  height: 46px;\n  padding: 10px 16px;\n  font-size: 18px;\n  line-height: 1.3333333;\n  border-radius: 6px;\n}\nselect.input-group-lg > .form-control,\nselect.input-group-lg > .input-group-addon,\nselect.input-group-lg > .input-group-btn > .btn {\n  height: 46px;\n  line-height: 46px;\n}\ntextarea.input-group-lg > .form-control,\ntextarea.input-group-lg > .input-group-addon,\ntextarea.input-group-lg > .input-group-btn > .btn,\nselect[multiple].input-group-lg > .form-control,\nselect[multiple].input-group-lg > .input-group-addon,\nselect[multiple].input-group-lg > .input-group-btn > .btn {\n  height: auto;\n}\n.input-group-sm > .form-control,\n.input-group-sm > .input-group-addon,\n.input-group-sm > .input-group-btn > .btn {\n  height: 30px;\n  padding: 5px 10px;\n  font-size: 12px;\n  line-height: 1.5;\n  border-radius: 3px;\n}\nselect.input-group-sm > .form-control,\nselect.input-group-sm > .input-group-addon,\nselect.input-group-sm > .input-group-btn > .btn {\n  height: 30px;\n  line-height: 30px;\n}\ntextarea.input-group-sm > .form-control,\ntextarea.input-group-sm > .input-group-addon,\ntextarea.input-group-sm > .input-group-btn > .btn,\nselect[multiple].input-group-sm > .form-control,\nselect[multiple].input-group-sm > .input-group-addon,\nselect[multiple].input-group-sm > .input-group-btn > .btn {\n  height: auto;\n}\n.input-group-addon,\n.input-group-btn,\n.input-group .form-control {\n  display: table-cell;\n}\n.input-group-addon:not(:first-child):not(:last-child),\n.input-group-btn:not(:first-child):not(:last-child),\n.input-group .form-control:not(:first-child):not(:last-child) {\n  border-radius: 0;\n}\n.input-group-addon,\n.input-group-btn {\n  width: 1%;\n  white-space: nowrap;\n  vertical-align: middle;\n}\n.input-group-addon {\n  padding: 6px 12px;\n  font-size: 14px;\n  font-weight: normal;\n  line-height: 1;\n  color: #555;\n  text-align: center;\n  background-color: #eee;\n  border: 1px solid #ccc;\n  border-radius: 4px;\n}\n.input-group-addon.input-sm {\n  padding: 5px 10px;\n  font-size: 12px;\n  border-radius: 3px;\n}\n.input-group-addon.input-lg {\n  padding: 10px 16px;\n  font-size: 18px;\n  border-radius: 6px;\n}\n.input-group-addon input[type=\"radio\"],\n.input-group-addon input[type=\"checkbox\"] {\n  margin-top: 0;\n}\n.input-group .form-control:first-child,\n.input-group-addon:first-child,\n.input-group-btn:first-child > .btn,\n.input-group-btn:first-child > .btn-group > .btn,\n.input-group-btn:first-child > .dropdown-toggle,\n.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),\n.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {\n  border-top-right-radius: 0;\n  border-bottom-right-radius: 0;\n}\n.input-group-addon:first-child {\n  border-right: 0;\n}\n.input-group .form-control:last-child,\n.input-group-addon:last-child,\n.input-group-btn:last-child > .btn,\n.input-group-btn:last-child > .btn-group > .btn,\n.input-group-btn:last-child > .dropdown-toggle,\n.input-group-btn:first-child > .btn:not(:first-child),\n.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {\n  border-top-left-radius: 0;\n  border-bottom-left-radius: 0;\n}\n.input-group-addon:last-child {\n  border-left: 0;\n}\n.input-group-btn {\n  position: relative;\n  font-size: 0;\n  white-space: nowrap;\n}\n.input-group-btn > .btn {\n  position: relative;\n}\n.input-group-btn > .btn + .btn {\n  margin-left: -1px;\n}\n.input-group-btn > .btn:hover,\n.input-group-btn > .btn:focus,\n.input-group-btn > .btn:active {\n  z-index: 2;\n}\n.input-group-btn:first-child > .btn,\n.input-group-btn:first-child > .btn-group {\n  margin-right: -1px;\n}\n.input-group-btn:last-child > .btn,\n.input-group-btn:last-child > .btn-group {\n  z-index: 2;\n  margin-left: -1px;\n}\n.nav {\n  padding-left: 0;\n  margin-bottom: 0;\n  list-style: none;\n}\n.nav > li {\n  position: relative;\n  display: block;\n}\n.nav > li > a {\n  position: relative;\n  display: block;\n  padding: 10px 15px;\n}\n.nav > li > a:hover,\n.nav > li > a:focus {\n  text-decoration: none;\n  background-color: #eee;\n}\n.nav > li.disabled > a {\n  color: #777;\n}\n.nav > li.disabled > a:hover,\n.nav > li.disabled > a:focus {\n  color: #777;\n  text-decoration: none;\n  cursor: not-allowed;\n  background-color: transparent;\n}\n.nav .open > a,\n.nav .open > a:hover,\n.nav .open > a:focus {\n  background-color: #eee;\n  border-color: #337ab7;\n}\n.nav .nav-divider {\n  height: 1px;\n  margin: 9px 0;\n  overflow: hidden;\n  background-color: #e5e5e5;\n}\n.nav > li > a > img {\n  max-width: none;\n}\n.nav-tabs {\n  border-bottom: 1px solid #ddd;\n}\n.nav-tabs > li {\n  float: left;\n  margin-bottom: -1px;\n}\n.nav-tabs > li > a {\n  margin-right: 2px;\n  line-height: 1.42857143;\n  border: 1px solid transparent;\n  border-radius: 4px 4px 0 0;\n}\n.nav-tabs > li > a:hover {\n  border-color: #eee #eee #ddd;\n}\n.nav-tabs > li.active > a,\n.nav-tabs > li.active > a:hover,\n.nav-tabs > li.active > a:focus {\n  color: #555;\n  cursor: default;\n  background-color: #fff;\n  border: 1px solid #ddd;\n  border-bottom-color: transparent;\n}\n.nav-tabs.nav-justified {\n  width: 100%;\n  border-bottom: 0;\n}\n.nav-tabs.nav-justified > li {\n  float: none;\n}\n.nav-tabs.nav-justified > li > a {\n  margin-bottom: 5px;\n  text-align: center;\n}\n.nav-tabs.nav-justified > .dropdown .dropdown-menu {\n  top: auto;\n  left: auto;\n}\n@media (min-width: 768px) {\n  .nav-tabs.nav-justified > li {\n    display: table-cell;\n    width: 1%;\n  }\n  .nav-tabs.nav-justified > li > a {\n    margin-bottom: 0;\n  }\n}\n.nav-tabs.nav-justified > li > a {\n  margin-right: 0;\n  border-radius: 4px;\n}\n.nav-tabs.nav-justified > .active > a,\n.nav-tabs.nav-justified > .active > a:hover,\n.nav-tabs.nav-justified > .active > a:focus {\n  border: 1px solid #ddd;\n}\n@media (min-width: 768px) {\n  .nav-tabs.nav-justified > li > a {\n    border-bottom: 1px solid #ddd;\n    border-radius: 4px 4px 0 0;\n  }\n  .nav-tabs.nav-justified > .active > a,\n  .nav-tabs.nav-justified > .active > a:hover,\n  .nav-tabs.nav-justified > .active > a:focus {\n    border-bottom-color: #fff;\n  }\n}\n.nav-pills > li {\n  float: left;\n}\n.nav-pills > li > a {\n  border-radius: 4px;\n}\n.nav-pills > li + li {\n  margin-left: 2px;\n}\n.nav-pills > li.active > a,\n.nav-pills > li.active > a:hover,\n.nav-pills > li.active > a:focus {\n  color: #fff;\n  background-color: #337ab7;\n}\n.nav-stacked > li {\n  float: none;\n}\n.nav-stacked > li + li {\n  margin-top: 2px;\n  margin-left: 0;\n}\n.nav-justified {\n  width: 100%;\n}\n.nav-justified > li {\n  float: none;\n}\n.nav-justified > li > a {\n  margin-bottom: 5px;\n  text-align: center;\n}\n.nav-justified > .dropdown .dropdown-menu {\n  top: auto;\n  left: auto;\n}\n@media (min-width: 768px) {\n  .nav-justified > li {\n    display: table-cell;\n    width: 1%;\n  }\n  .nav-justified > li > a {\n    margin-bottom: 0;\n  }\n}\n.nav-tabs-justified {\n  border-bottom: 0;\n}\n.nav-tabs-justified > li > a {\n  margin-right: 0;\n  border-radius: 4px;\n}\n.nav-tabs-justified > .active > a,\n.nav-tabs-justified > .active > a:hover,\n.nav-tabs-justified > .active > a:focus {\n  border: 1px solid #ddd;\n}\n@media (min-width: 768px) {\n  .nav-tabs-justified > li > a {\n    border-bottom: 1px solid #ddd;\n    border-radius: 4px 4px 0 0;\n  }\n  .nav-tabs-justified > .active > a,\n  .nav-tabs-justified > .active > a:hover,\n  .nav-tabs-justified > .active > a:focus {\n    border-bottom-color: #fff;\n  }\n}\n.tab-content > .tab-pane {\n  display: none;\n}\n.tab-content > .active {\n  display: block;\n}\n.nav-tabs .dropdown-menu {\n  margin-top: -1px;\n  border-top-left-radius: 0;\n  border-top-right-radius: 0;\n}\n.navbar {\n  position: relative;\n  min-height: 50px;\n  margin-bottom: 20px;\n  border: 1px solid transparent;\n}\n@media (min-width: 768px) {\n  .navbar {\n    border-radius: 4px;\n  }\n}\n@media (min-width: 768px) {\n  .navbar-header {\n    float: left;\n  }\n}\n.navbar-collapse {\n  padding-right: 15px;\n  padding-left: 15px;\n  overflow-x: visible;\n  -webkit-overflow-scrolling: touch;\n  border-top: 1px solid transparent;\n  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, .1);\n          box-shadow: inset 0 1px 0 rgba(255, 255, 255, .1);\n}\n.navbar-collapse.in {\n  overflow-y: auto;\n}\n@media (min-width: 768px) {\n  .navbar-collapse {\n    width: auto;\n    border-top: 0;\n    -webkit-box-shadow: none;\n            box-shadow: none;\n  }\n  .navbar-collapse.collapse {\n    display: block !important;\n    height: auto !important;\n    padding-bottom: 0;\n    overflow: visible !important;\n  }\n  .navbar-collapse.in {\n    overflow-y: visible;\n  }\n  .navbar-fixed-top .navbar-collapse,\n  .navbar-static-top .navbar-collapse,\n  .navbar-fixed-bottom .navbar-collapse {\n    padding-right: 0;\n    padding-left: 0;\n  }\n}\n.navbar-fixed-top .navbar-collapse,\n.navbar-fixed-bottom .navbar-collapse {\n  max-height: 340px;\n}\n@media (max-device-width: 480px) and (orientation: landscape) {\n  .navbar-fixed-top .navbar-collapse,\n  .navbar-fixed-bottom .navbar-collapse {\n    max-height: 200px;\n  }\n}\n.container > .navbar-header,\n.container-fluid > .navbar-header,\n.container > .navbar-collapse,\n.container-fluid > .navbar-collapse {\n  margin-right: -15px;\n  margin-left: -15px;\n}\n@media (min-width: 768px) {\n  .container > .navbar-header,\n  .container-fluid > .navbar-header,\n  .container > .navbar-collapse,\n  .container-fluid > .navbar-collapse {\n    margin-right: 0;\n    margin-left: 0;\n  }\n}\n.navbar-static-top {\n  z-index: 1000;\n  border-width: 0 0 1px;\n}\n@media (min-width: 768px) {\n  .navbar-static-top {\n    border-radius: 0;\n  }\n}\n.navbar-fixed-top,\n.navbar-fixed-bottom {\n  position: fixed;\n  right: 0;\n  left: 0;\n  z-index: 1030;\n}\n@media (min-width: 768px) {\n  .navbar-fixed-top,\n  .navbar-fixed-bottom {\n    border-radius: 0;\n  }\n}\n.navbar-fixed-top {\n  top: 0;\n  border-width: 0 0 1px;\n}\n.navbar-fixed-bottom {\n  bottom: 0;\n  margin-bottom: 0;\n  border-width: 1px 0 0;\n}\n.navbar-brand {\n  float: left;\n  height: 50px;\n  padding: 15px 15px;\n  font-size: 18px;\n  line-height: 20px;\n}\n.navbar-brand:hover,\n.navbar-brand:focus {\n  text-decoration: none;\n}\n.navbar-brand > img {\n  display: block;\n}\n@media (min-width: 768px) {\n  .navbar > .container .navbar-brand,\n  .navbar > .container-fluid .navbar-brand {\n    margin-left: -15px;\n  }\n}\n.navbar-toggle {\n  position: relative;\n  float: right;\n  padding: 9px 10px;\n  margin-top: 8px;\n  margin-right: 15px;\n  margin-bottom: 8px;\n  background-color: transparent;\n  background-image: none;\n  border: 1px solid transparent;\n  border-radius: 4px;\n}\n.navbar-toggle:focus {\n  outline: 0;\n}\n.navbar-toggle .icon-bar {\n  display: block;\n  width: 22px;\n  height: 2px;\n  border-radius: 1px;\n}\n.navbar-toggle .icon-bar + .icon-bar {\n  margin-top: 4px;\n}\n@media (min-width: 768px) {\n  .navbar-toggle {\n    display: none;\n  }\n}\n.navbar-nav {\n  margin: 7.5px -15px;\n}\n.navbar-nav > li > a {\n  padding-top: 10px;\n  padding-bottom: 10px;\n  line-height: 20px;\n}\n@media (max-width: 767px) {\n  .navbar-nav .open .dropdown-menu {\n    position: static;\n    float: none;\n    width: auto;\n    margin-top: 0;\n    background-color: transparent;\n    border: 0;\n    -webkit-box-shadow: none;\n            box-shadow: none;\n  }\n  .navbar-nav .open .dropdown-menu > li > a,\n  .navbar-nav .open .dropdown-menu .dropdown-header {\n    padding: 5px 15px 5px 25px;\n  }\n  .navbar-nav .open .dropdown-menu > li > a {\n    line-height: 20px;\n  }\n  .navbar-nav .open .dropdown-menu > li > a:hover,\n  .navbar-nav .open .dropdown-menu > li > a:focus {\n    background-image: none;\n  }\n}\n@media (min-width: 768px) {\n  .navbar-nav {\n    float: left;\n    margin: 0;\n  }\n  .navbar-nav > li {\n    float: left;\n  }\n  .navbar-nav > li > a {\n    padding-top: 15px;\n    padding-bottom: 15px;\n  }\n}\n.navbar-form {\n  padding: 10px 15px;\n  margin-top: 8px;\n  margin-right: -15px;\n  margin-bottom: 8px;\n  margin-left: -15px;\n  border-top: 1px solid transparent;\n  border-bottom: 1px solid transparent;\n  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, .1), 0 1px 0 rgba(255, 255, 255, .1);\n          box-shadow: inset 0 1px 0 rgba(255, 255, 255, .1), 0 1px 0 rgba(255, 255, 255, .1);\n}\n@media (min-width: 768px) {\n  .navbar-form .form-group {\n    display: inline-block;\n    margin-bottom: 0;\n    vertical-align: middle;\n  }\n  .navbar-form .form-control {\n    display: inline-block;\n    width: auto;\n    vertical-align: middle;\n  }\n  .navbar-form .form-control-static {\n    display: inline-block;\n  }\n  .navbar-form .input-group {\n    display: inline-table;\n    vertical-align: middle;\n  }\n  .navbar-form .input-group .input-group-addon,\n  .navbar-form .input-group .input-group-btn,\n  .navbar-form .input-group .form-control {\n    width: auto;\n  }\n  .navbar-form .input-group > .form-control {\n    width: 100%;\n  }\n  .navbar-form .control-label {\n    margin-bottom: 0;\n    vertical-align: middle;\n  }\n  .navbar-form .radio,\n  .navbar-form .checkbox {\n    display: inline-block;\n    margin-top: 0;\n    margin-bottom: 0;\n    vertical-align: middle;\n  }\n  .navbar-form .radio label,\n  .navbar-form .checkbox label {\n    padding-left: 0;\n  }\n  .navbar-form .radio input[type=\"radio\"],\n  .navbar-form .checkbox input[type=\"checkbox\"] {\n    position: relative;\n    margin-left: 0;\n  }\n  .navbar-form .has-feedback .form-control-feedback {\n    top: 0;\n  }\n}\n@media (max-width: 767px) {\n  .navbar-form .form-group {\n    margin-bottom: 5px;\n  }\n  .navbar-form .form-group:last-child {\n    margin-bottom: 0;\n  }\n}\n@media (min-width: 768px) {\n  .navbar-form {\n    width: auto;\n    padding-top: 0;\n    padding-bottom: 0;\n    margin-right: 0;\n    margin-left: 0;\n    border: 0;\n    -webkit-box-shadow: none;\n            box-shadow: none;\n  }\n}\n.navbar-nav > li > .dropdown-menu {\n  margin-top: 0;\n  border-top-left-radius: 0;\n  border-top-right-radius: 0;\n}\n.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {\n  margin-bottom: 0;\n  border-top-left-radius: 4px;\n  border-top-right-radius: 4px;\n  border-bottom-right-radius: 0;\n  border-bottom-left-radius: 0;\n}\n.navbar-btn {\n  margin-top: 8px;\n  margin-bottom: 8px;\n}\n.navbar-btn.btn-sm {\n  margin-top: 10px;\n  margin-bottom: 10px;\n}\n.navbar-btn.btn-xs {\n  margin-top: 14px;\n  margin-bottom: 14px;\n}\n.navbar-text {\n  margin-top: 15px;\n  margin-bottom: 15px;\n}\n@media (min-width: 768px) {\n  .navbar-text {\n    float: left;\n    margin-right: 15px;\n    margin-left: 15px;\n  }\n}\n@media (min-width: 768px) {\n  .navbar-left {\n    float: left !important;\n  }\n  .navbar-right {\n    float: right !important;\n    margin-right: -15px;\n  }\n  .navbar-right ~ .navbar-right {\n    margin-right: 0;\n  }\n}\n.navbar-default {\n  background-color: #f8f8f8;\n  border-color: #e7e7e7;\n}\n.navbar-default .navbar-brand {\n  color: #777;\n}\n.navbar-default .navbar-brand:hover,\n.navbar-default .navbar-brand:focus {\n  color: #5e5e5e;\n  background-color: transparent;\n}\n.navbar-default .navbar-text {\n  color: #777;\n}\n.navbar-default .navbar-nav > li > a {\n  color: #777;\n}\n.navbar-default .navbar-nav > li > a:hover,\n.navbar-default .navbar-nav > li > a:focus {\n  color: #333;\n  background-color: transparent;\n}\n.navbar-default .navbar-nav > .active > a,\n.navbar-default .navbar-nav > .active > a:hover,\n.navbar-default .navbar-nav > .active > a:focus {\n  color: #555;\n  background-color: #e7e7e7;\n}\n.navbar-default .navbar-nav > .disabled > a,\n.navbar-default .navbar-nav > .disabled > a:hover,\n.navbar-default .navbar-nav > .disabled > a:focus {\n  color: #ccc;\n  background-color: transparent;\n}\n.navbar-default .navbar-toggle {\n  border-color: #ddd;\n}\n.navbar-default .navbar-toggle:hover,\n.navbar-default .navbar-toggle:focus {\n  background-color: #ddd;\n}\n.navbar-default .navbar-toggle .icon-bar {\n  background-color: #888;\n}\n.navbar-default .navbar-collapse,\n.navbar-default .navbar-form {\n  border-color: #e7e7e7;\n}\n.navbar-default .navbar-nav > .open > a,\n.navbar-default .navbar-nav > .open > a:hover,\n.navbar-default .navbar-nav > .open > a:focus {\n  color: #555;\n  background-color: #e7e7e7;\n}\n@media (max-width: 767px) {\n  .navbar-default .navbar-nav .open .dropdown-menu > li > a {\n    color: #777;\n  }\n  .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,\n  .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {\n    color: #333;\n    background-color: transparent;\n  }\n  .navbar-default .navbar-nav .open .dropdown-menu > .active > a,\n  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,\n  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {\n    color: #555;\n    background-color: #e7e7e7;\n  }\n  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,\n  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,\n  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {\n    color: #ccc;\n    background-color: transparent;\n  }\n}\n.navbar-default .navbar-link {\n  color: #777;\n}\n.navbar-default .navbar-link:hover {\n  color: #333;\n}\n.navbar-default .btn-link {\n  color: #777;\n}\n.navbar-default .btn-link:hover,\n.navbar-default .btn-link:focus {\n  color: #333;\n}\n.navbar-default .btn-link[disabled]:hover,\nfieldset[disabled] .navbar-default .btn-link:hover,\n.navbar-default .btn-link[disabled]:focus,\nfieldset[disabled] .navbar-default .btn-link:focus {\n  color: #ccc;\n}\n.navbar-inverse {\n  background-color: #222;\n  border-color: #080808;\n}\n.navbar-inverse .navbar-brand {\n  color: #9d9d9d;\n}\n.navbar-inverse .navbar-brand:hover,\n.navbar-inverse .navbar-brand:focus {\n  color: #fff;\n  background-color: transparent;\n}\n.navbar-inverse .navbar-text {\n  color: #9d9d9d;\n}\n.navbar-inverse .navbar-nav > li > a {\n  color: #9d9d9d;\n}\n.navbar-inverse .navbar-nav > li > a:hover,\n.navbar-inverse .navbar-nav > li > a:focus {\n  color: #fff;\n  background-color: transparent;\n}\n.navbar-inverse .navbar-nav > .active > a,\n.navbar-inverse .navbar-nav > .active > a:hover,\n.navbar-inverse .navbar-nav > .active > a:focus {\n  color: #fff;\n  background-color: #080808;\n}\n.navbar-inverse .navbar-nav > .disabled > a,\n.navbar-inverse .navbar-nav > .disabled > a:hover,\n.navbar-inverse .navbar-nav > .disabled > a:focus {\n  color: #444;\n  background-color: transparent;\n}\n.navbar-inverse .navbar-toggle {\n  border-color: #333;\n}\n.navbar-inverse .navbar-toggle:hover,\n.navbar-inverse .navbar-toggle:focus {\n  background-color: #333;\n}\n.navbar-inverse .navbar-toggle .icon-bar {\n  background-color: #fff;\n}\n.navbar-inverse .navbar-collapse,\n.navbar-inverse .navbar-form {\n  border-color: #101010;\n}\n.navbar-inverse .navbar-nav > .open > a,\n.navbar-inverse .navbar-nav > .open > a:hover,\n.navbar-inverse .navbar-nav > .open > a:focus {\n  color: #fff;\n  background-color: #080808;\n}\n@media (max-width: 767px) {\n  .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {\n    border-color: #080808;\n  }\n  .navbar-inverse .navbar-nav .open .dropdown-menu .divider {\n    background-color: #080808;\n  }\n  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {\n    color: #9d9d9d;\n  }\n  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,\n  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {\n    color: #fff;\n    background-color: transparent;\n  }\n  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,\n  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,\n  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {\n    color: #fff;\n    background-color: #080808;\n  }\n  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,\n  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,\n  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {\n    color: #444;\n    background-color: transparent;\n  }\n}\n.navbar-inverse .navbar-link {\n  color: #9d9d9d;\n}\n.navbar-inverse .navbar-link:hover {\n  color: #fff;\n}\n.navbar-inverse .btn-link {\n  color: #9d9d9d;\n}\n.navbar-inverse .btn-link:hover,\n.navbar-inverse .btn-link:focus {\n  color: #fff;\n}\n.navbar-inverse .btn-link[disabled]:hover,\nfieldset[disabled] .navbar-inverse .btn-link:hover,\n.navbar-inverse .btn-link[disabled]:focus,\nfieldset[disabled] .navbar-inverse .btn-link:focus {\n  color: #444;\n}\n.breadcrumb {\n  padding: 8px 15px;\n  margin-bottom: 20px;\n  list-style: none;\n  background-color: #f5f5f5;\n  border-radius: 4px;\n}\n.breadcrumb > li {\n  display: inline-block;\n}\n.breadcrumb > li + li:before {\n  padding: 0 5px;\n  color: #ccc;\n  content: \"/\\00a0\";\n}\n.breadcrumb > .active {\n  color: #777;\n}\n.pagination {\n  display: inline-block;\n  padding-left: 0;\n  margin: 20px 0;\n  border-radius: 4px;\n}\n.pagination > li {\n  display: inline;\n}\n.pagination > li > a,\n.pagination > li > span {\n  position: relative;\n  float: left;\n  padding: 6px 12px;\n  margin-left: -1px;\n  line-height: 1.42857143;\n  color: #337ab7;\n  text-decoration: none;\n  background-color: #fff;\n  border: 1px solid #ddd;\n}\n.pagination > li:first-child > a,\n.pagination > li:first-child > span {\n  margin-left: 0;\n  border-top-left-radius: 4px;\n  border-bottom-left-radius: 4px;\n}\n.pagination > li:last-child > a,\n.pagination > li:last-child > span {\n  border-top-right-radius: 4px;\n  border-bottom-right-radius: 4px;\n}\n.pagination > li > a:hover,\n.pagination > li > span:hover,\n.pagination > li > a:focus,\n.pagination > li > span:focus {\n  z-index: 2;\n  color: #23527c;\n  background-color: #eee;\n  border-color: #ddd;\n}\n.pagination > .active > a,\n.pagination > .active > span,\n.pagination > .active > a:hover,\n.pagination > .active > span:hover,\n.pagination > .active > a:focus,\n.pagination > .active > span:focus {\n  z-index: 3;\n  color: #fff;\n  cursor: default;\n  background-color: #337ab7;\n  border-color: #337ab7;\n}\n.pagination > .disabled > span,\n.pagination > .disabled > span:hover,\n.pagination > .disabled > span:focus,\n.pagination > .disabled > a,\n.pagination > .disabled > a:hover,\n.pagination > .disabled > a:focus {\n  color: #777;\n  cursor: not-allowed;\n  background-color: #fff;\n  border-color: #ddd;\n}\n.pagination-lg > li > a,\n.pagination-lg > li > span {\n  padding: 10px 16px;\n  font-size: 18px;\n  line-height: 1.3333333;\n}\n.pagination-lg > li:first-child > a,\n.pagination-lg > li:first-child > span {\n  border-top-left-radius: 6px;\n  border-bottom-left-radius: 6px;\n}\n.pagination-lg > li:last-child > a,\n.pagination-lg > li:last-child > span {\n  border-top-right-radius: 6px;\n  border-bottom-right-radius: 6px;\n}\n.pagination-sm > li > a,\n.pagination-sm > li > span {\n  padding: 5px 10px;\n  font-size: 12px;\n  line-height: 1.5;\n}\n.pagination-sm > li:first-child > a,\n.pagination-sm > li:first-child > span {\n  border-top-left-radius: 3px;\n  border-bottom-left-radius: 3px;\n}\n.pagination-sm > li:last-child > a,\n.pagination-sm > li:last-child > span {\n  border-top-right-radius: 3px;\n  border-bottom-right-radius: 3px;\n}\n.pager {\n  padding-left: 0;\n  margin: 20px 0;\n  text-align: center;\n  list-style: none;\n}\n.pager li {\n  display: inline;\n}\n.pager li > a,\n.pager li > span {\n  display: inline-block;\n  padding: 5px 14px;\n  background-color: #fff;\n  border: 1px solid #ddd;\n  border-radius: 15px;\n}\n.pager li > a:hover,\n.pager li > a:focus {\n  text-decoration: none;\n  background-color: #eee;\n}\n.pager .next > a,\n.pager .next > span {\n  float: right;\n}\n.pager .previous > a,\n.pager .previous > span {\n  float: left;\n}\n.pager .disabled > a,\n.pager .disabled > a:hover,\n.pager .disabled > a:focus,\n.pager .disabled > span {\n  color: #777;\n  cursor: not-allowed;\n  background-color: #fff;\n}\n.label {\n  display: inline;\n  padding: .2em .6em .3em;\n  font-size: 75%;\n  font-weight: bold;\n  line-height: 1;\n  color: #fff;\n  text-align: center;\n  white-space: nowrap;\n  vertical-align: baseline;\n  border-radius: .25em;\n}\na.label:hover,\na.label:focus {\n  color: #fff;\n  text-decoration: none;\n  cursor: pointer;\n}\n.label:empty {\n  display: none;\n}\n.btn .label {\n  position: relative;\n  top: -1px;\n}\n.label-default {\n  background-color: #777;\n}\n.label-default[href]:hover,\n.label-default[href]:focus {\n  background-color: #5e5e5e;\n}\n.label-primary {\n  background-color: #337ab7;\n}\n.label-primary[href]:hover,\n.label-primary[href]:focus {\n  background-color: #286090;\n}\n.label-success {\n  background-color: #5cb85c;\n}\n.label-success[href]:hover,\n.label-success[href]:focus {\n  background-color: #449d44;\n}\n.label-info {\n  background-color: #5bc0de;\n}\n.label-info[href]:hover,\n.label-info[href]:focus {\n  background-color: #31b0d5;\n}\n.label-warning {\n  background-color: #f0ad4e;\n}\n.label-warning[href]:hover,\n.label-warning[href]:focus {\n  background-color: #ec971f;\n}\n.label-danger {\n  background-color: #d9534f;\n}\n.label-danger[href]:hover,\n.label-danger[href]:focus {\n  background-color: #c9302c;\n}\n.badge {\n  display: inline-block;\n  min-width: 10px;\n  padding: 3px 7px;\n  font-size: 12px;\n  font-weight: bold;\n  line-height: 1;\n  color: #fff;\n  text-align: center;\n  white-space: nowrap;\n  vertical-align: middle;\n  background-color: #777;\n  border-radius: 10px;\n}\n.badge:empty {\n  display: none;\n}\n.btn .badge {\n  position: relative;\n  top: -1px;\n}\n.btn-xs .badge,\n.btn-group-xs > .btn .badge {\n  top: 0;\n  padding: 1px 5px;\n}\na.badge:hover,\na.badge:focus {\n  color: #fff;\n  text-decoration: none;\n  cursor: pointer;\n}\n.list-group-item.active > .badge,\n.nav-pills > .active > a > .badge {\n  color: #337ab7;\n  background-color: #fff;\n}\n.list-group-item > .badge {\n  float: right;\n}\n.list-group-item > .badge + .badge {\n  margin-right: 5px;\n}\n.nav-pills > li > a > .badge {\n  margin-left: 3px;\n}\n.jumbotron {\n  padding-top: 30px;\n  padding-bottom: 30px;\n  margin-bottom: 30px;\n  color: inherit;\n  background-color: #eee;\n}\n.jumbotron h1,\n.jumbotron .h1 {\n  color: inherit;\n}\n.jumbotron p {\n  margin-bottom: 15px;\n  font-size: 21px;\n  font-weight: 200;\n}\n.jumbotron > hr {\n  border-top-color: #d5d5d5;\n}\n.container .jumbotron,\n.container-fluid .jumbotron {\n  padding-right: 15px;\n  padding-left: 15px;\n  border-radius: 6px;\n}\n.jumbotron .container {\n  max-width: 100%;\n}\n@media screen and (min-width: 768px) {\n  .jumbotron {\n    padding-top: 48px;\n    padding-bottom: 48px;\n  }\n  .container .jumbotron,\n  .container-fluid .jumbotron {\n    padding-right: 60px;\n    padding-left: 60px;\n  }\n  .jumbotron h1,\n  .jumbotron .h1 {\n    font-size: 63px;\n  }\n}\n.thumbnail {\n  display: block;\n  padding: 4px;\n  margin-bottom: 20px;\n  line-height: 1.42857143;\n  background-color: #fff;\n  border: 1px solid #ddd;\n  border-radius: 4px;\n  -webkit-transition: border .2s ease-in-out;\n       -o-transition: border .2s ease-in-out;\n          transition: border .2s ease-in-out;\n}\n.thumbnail > img,\n.thumbnail a > img {\n  margin-right: auto;\n  margin-left: auto;\n}\na.thumbnail:hover,\na.thumbnail:focus,\na.thumbnail.active {\n  border-color: #337ab7;\n}\n.thumbnail .caption {\n  padding: 9px;\n  color: #333;\n}\n.alert {\n  padding: 15px;\n  margin-bottom: 20px;\n  border: 1px solid transparent;\n  border-radius: 4px;\n}\n.alert h4 {\n  margin-top: 0;\n  color: inherit;\n}\n.alert .alert-link {\n  font-weight: bold;\n}\n.alert > p,\n.alert > ul {\n  margin-bottom: 0;\n}\n.alert > p + p {\n  margin-top: 5px;\n}\n.alert-dismissable,\n.alert-dismissible {\n  padding-right: 35px;\n}\n.alert-dismissable .close,\n.alert-dismissible .close {\n  position: relative;\n  top: -2px;\n  right: -21px;\n  color: inherit;\n}\n.alert-success {\n  color: #3c763d;\n  background-color: #dff0d8;\n  border-color: #d6e9c6;\n}\n.alert-success hr {\n  border-top-color: #c9e2b3;\n}\n.alert-success .alert-link {\n  color: #2b542c;\n}\n.alert-info {\n  color: #31708f;\n  background-color: #d9edf7;\n  border-color: #bce8f1;\n}\n.alert-info hr {\n  border-top-color: #a6e1ec;\n}\n.alert-info .alert-link {\n  color: #245269;\n}\n.alert-warning {\n  color: #8a6d3b;\n  background-color: #fcf8e3;\n  border-color: #faebcc;\n}\n.alert-warning hr {\n  border-top-color: #f7e1b5;\n}\n.alert-warning .alert-link {\n  color: #66512c;\n}\n.alert-danger {\n  color: #a94442;\n  background-color: #f2dede;\n  border-color: #ebccd1;\n}\n.alert-danger hr {\n  border-top-color: #e4b9c0;\n}\n.alert-danger .alert-link {\n  color: #843534;\n}\n@-webkit-keyframes progress-bar-stripes {\n  from {\n    background-position: 40px 0;\n  }\n  to {\n    background-position: 0 0;\n  }\n}\n@-o-keyframes progress-bar-stripes {\n  from {\n    background-position: 40px 0;\n  }\n  to {\n    background-position: 0 0;\n  }\n}\n@keyframes progress-bar-stripes {\n  from {\n    background-position: 40px 0;\n  }\n  to {\n    background-position: 0 0;\n  }\n}\n.progress {\n  height: 20px;\n  margin-bottom: 20px;\n  overflow: hidden;\n  background-color: #f5f5f5;\n  border-radius: 4px;\n  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, .1);\n          box-shadow: inset 0 1px 2px rgba(0, 0, 0, .1);\n}\n.progress-bar {\n  float: left;\n  width: 0;\n  height: 100%;\n  font-size: 12px;\n  line-height: 20px;\n  color: #fff;\n  text-align: center;\n  background-color: #337ab7;\n  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, .15);\n          box-shadow: inset 0 -1px 0 rgba(0, 0, 0, .15);\n  -webkit-transition: width .6s ease;\n       -o-transition: width .6s ease;\n          transition: width .6s ease;\n}\n.progress-striped .progress-bar,\n.progress-bar-striped {\n  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent);\n  background-image:      -o-linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent);\n  background-image:         linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent);\n  -webkit-background-size: 40px 40px;\n          background-size: 40px 40px;\n}\n.progress.active .progress-bar,\n.progress-bar.active {\n  -webkit-animation: progress-bar-stripes 2s linear infinite;\n       -o-animation: progress-bar-stripes 2s linear infinite;\n          animation: progress-bar-stripes 2s linear infinite;\n}\n.progress-bar-success {\n  background-color: #5cb85c;\n}\n.progress-striped .progress-bar-success {\n  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent);\n  background-image:      -o-linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent);\n  background-image:         linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent);\n}\n.progress-bar-info {\n  background-color: #5bc0de;\n}\n.progress-striped .progress-bar-info {\n  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent);\n  background-image:      -o-linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent);\n  background-image:         linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent);\n}\n.progress-bar-warning {\n  background-color: #f0ad4e;\n}\n.progress-striped .progress-bar-warning {\n  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent);\n  background-image:      -o-linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent);\n  background-image:         linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent);\n}\n.progress-bar-danger {\n  background-color: #d9534f;\n}\n.progress-striped .progress-bar-danger {\n  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent);\n  background-image:      -o-linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent);\n  background-image:         linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent);\n}\n.media {\n  margin-top: 15px;\n}\n.media:first-child {\n  margin-top: 0;\n}\n.media,\n.media-body {\n  overflow: hidden;\n  zoom: 1;\n}\n.media-body {\n  width: 10000px;\n}\n.media-object {\n  display: block;\n}\n.media-object.img-thumbnail {\n  max-width: none;\n}\n.media-right,\n.media > .pull-right {\n  padding-left: 10px;\n}\n.media-left,\n.media > .pull-left {\n  padding-right: 10px;\n}\n.media-left,\n.media-right,\n.media-body {\n  display: table-cell;\n  vertical-align: top;\n}\n.media-middle {\n  vertical-align: middle;\n}\n.media-bottom {\n  vertical-align: bottom;\n}\n.media-heading {\n  margin-top: 0;\n  margin-bottom: 5px;\n}\n.media-list {\n  padding-left: 0;\n  list-style: none;\n}\n.list-group {\n  padding-left: 0;\n  margin-bottom: 20px;\n}\n.list-group-item {\n  position: relative;\n  display: block;\n  padding: 10px 15px;\n  margin-bottom: -1px;\n  background-color: #fff;\n  border: 1px solid #ddd;\n}\n.list-group-item:first-child {\n  border-top-left-radius: 4px;\n  border-top-right-radius: 4px;\n}\n.list-group-item:last-child {\n  margin-bottom: 0;\n  border-bottom-right-radius: 4px;\n  border-bottom-left-radius: 4px;\n}\na.list-group-item,\nbutton.list-group-item {\n  color: #555;\n}\na.list-group-item .list-group-item-heading,\nbutton.list-group-item .list-group-item-heading {\n  color: #333;\n}\na.list-group-item:hover,\nbutton.list-group-item:hover,\na.list-group-item:focus,\nbutton.list-group-item:focus {\n  color: #555;\n  text-decoration: none;\n  background-color: #f5f5f5;\n}\nbutton.list-group-item {\n  width: 100%;\n  text-align: left;\n}\n.list-group-item.disabled,\n.list-group-item.disabled:hover,\n.list-group-item.disabled:focus {\n  color: #777;\n  cursor: not-allowed;\n  background-color: #eee;\n}\n.list-group-item.disabled .list-group-item-heading,\n.list-group-item.disabled:hover .list-group-item-heading,\n.list-group-item.disabled:focus .list-group-item-heading {\n  color: inherit;\n}\n.list-group-item.disabled .list-group-item-text,\n.list-group-item.disabled:hover .list-group-item-text,\n.list-group-item.disabled:focus .list-group-item-text {\n  color: #777;\n}\n.list-group-item.active,\n.list-group-item.active:hover,\n.list-group-item.active:focus {\n  z-index: 2;\n  color: #fff;\n  background-color: #337ab7;\n  border-color: #337ab7;\n}\n.list-group-item.active .list-group-item-heading,\n.list-group-item.active:hover .list-group-item-heading,\n.list-group-item.active:focus .list-group-item-heading,\n.list-group-item.active .list-group-item-heading > small,\n.list-group-item.active:hover .list-group-item-heading > small,\n.list-group-item.active:focus .list-group-item-heading > small,\n.list-group-item.active .list-group-item-heading > .small,\n.list-group-item.active:hover .list-group-item-heading > .small,\n.list-group-item.active:focus .list-group-item-heading > .small {\n  color: inherit;\n}\n.list-group-item.active .list-group-item-text,\n.list-group-item.active:hover .list-group-item-text,\n.list-group-item.active:focus .list-group-item-text {\n  color: #c7ddef;\n}\n.list-group-item-success {\n  color: #3c763d;\n  background-color: #dff0d8;\n}\na.list-group-item-success,\nbutton.list-group-item-success {\n  color: #3c763d;\n}\na.list-group-item-success .list-group-item-heading,\nbutton.list-group-item-success .list-group-item-heading {\n  color: inherit;\n}\na.list-group-item-success:hover,\nbutton.list-group-item-success:hover,\na.list-group-item-success:focus,\nbutton.list-group-item-success:focus {\n  color: #3c763d;\n  background-color: #d0e9c6;\n}\na.list-group-item-success.active,\nbutton.list-group-item-success.active,\na.list-group-item-success.active:hover,\nbutton.list-group-item-success.active:hover,\na.list-group-item-success.active:focus,\nbutton.list-group-item-success.active:focus {\n  color: #fff;\n  background-color: #3c763d;\n  border-color: #3c763d;\n}\n.list-group-item-info {\n  color: #31708f;\n  background-color: #d9edf7;\n}\na.list-group-item-info,\nbutton.list-group-item-info {\n  color: #31708f;\n}\na.list-group-item-info .list-group-item-heading,\nbutton.list-group-item-info .list-group-item-heading {\n  color: inherit;\n}\na.list-group-item-info:hover,\nbutton.list-group-item-info:hover,\na.list-group-item-info:focus,\nbutton.list-group-item-info:focus {\n  color: #31708f;\n  background-color: #c4e3f3;\n}\na.list-group-item-info.active,\nbutton.list-group-item-info.active,\na.list-group-item-info.active:hover,\nbutton.list-group-item-info.active:hover,\na.list-group-item-info.active:focus,\nbutton.list-group-item-info.active:focus {\n  color: #fff;\n  background-color: #31708f;\n  border-color: #31708f;\n}\n.list-group-item-warning {\n  color: #8a6d3b;\n  background-color: #fcf8e3;\n}\na.list-group-item-warning,\nbutton.list-group-item-warning {\n  color: #8a6d3b;\n}\na.list-group-item-warning .list-group-item-heading,\nbutton.list-group-item-warning .list-group-item-heading {\n  color: inherit;\n}\na.list-group-item-warning:hover,\nbutton.list-group-item-warning:hover,\na.list-group-item-warning:focus,\nbutton.list-group-item-warning:focus {\n  color: #8a6d3b;\n  background-color: #faf2cc;\n}\na.list-group-item-warning.active,\nbutton.list-group-item-warning.active,\na.list-group-item-warning.active:hover,\nbutton.list-group-item-warning.active:hover,\na.list-group-item-warning.active:focus,\nbutton.list-group-item-warning.active:focus {\n  color: #fff;\n  background-color: #8a6d3b;\n  border-color: #8a6d3b;\n}\n.list-group-item-danger {\n  color: #a94442;\n  background-color: #f2dede;\n}\na.list-group-item-danger,\nbutton.list-group-item-danger {\n  color: #a94442;\n}\na.list-group-item-danger .list-group-item-heading,\nbutton.list-group-item-danger .list-group-item-heading {\n  color: inherit;\n}\na.list-group-item-danger:hover,\nbutton.list-group-item-danger:hover,\na.list-group-item-danger:focus,\nbutton.list-group-item-danger:focus {\n  color: #a94442;\n  background-color: #ebcccc;\n}\na.list-group-item-danger.active,\nbutton.list-group-item-danger.active,\na.list-group-item-danger.active:hover,\nbutton.list-group-item-danger.active:hover,\na.list-group-item-danger.active:focus,\nbutton.list-group-item-danger.active:focus {\n  color: #fff;\n  background-color: #a94442;\n  border-color: #a94442;\n}\n.list-group-item-heading {\n  margin-top: 0;\n  margin-bottom: 5px;\n}\n.list-group-item-text {\n  margin-bottom: 0;\n  line-height: 1.3;\n}\n.panel {\n  margin-bottom: 20px;\n  background-color: #fff;\n  border: 1px solid transparent;\n  border-radius: 4px;\n  -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, .05);\n          box-shadow: 0 1px 1px rgba(0, 0, 0, .05);\n}\n.panel-body {\n  padding: 15px;\n}\n.panel-heading {\n  padding: 10px 15px;\n  border-bottom: 1px solid transparent;\n  border-top-left-radius: 3px;\n  border-top-right-radius: 3px;\n}\n.panel-heading > .dropdown .dropdown-toggle {\n  color: inherit;\n}\n.panel-title {\n  margin-top: 0;\n  margin-bottom: 0;\n  font-size: 16px;\n  color: inherit;\n}\n.panel-title > a,\n.panel-title > small,\n.panel-title > .small,\n.panel-title > small > a,\n.panel-title > .small > a {\n  color: inherit;\n}\n.panel-footer {\n  padding: 10px 15px;\n  background-color: #f5f5f5;\n  border-top: 1px solid #ddd;\n  border-bottom-right-radius: 3px;\n  border-bottom-left-radius: 3px;\n}\n.panel > .list-group,\n.panel > .panel-collapse > .list-group {\n  margin-bottom: 0;\n}\n.panel > .list-group .list-group-item,\n.panel > .panel-collapse > .list-group .list-group-item {\n  border-width: 1px 0;\n  border-radius: 0;\n}\n.panel > .list-group:first-child .list-group-item:first-child,\n.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {\n  border-top: 0;\n  border-top-left-radius: 3px;\n  border-top-right-radius: 3px;\n}\n.panel > .list-group:last-child .list-group-item:last-child,\n.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {\n  border-bottom: 0;\n  border-bottom-right-radius: 3px;\n  border-bottom-left-radius: 3px;\n}\n.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {\n  border-top-left-radius: 0;\n  border-top-right-radius: 0;\n}\n.panel-heading + .list-group .list-group-item:first-child {\n  border-top-width: 0;\n}\n.list-group + .panel-footer {\n  border-top-width: 0;\n}\n.panel > .table,\n.panel > .table-responsive > .table,\n.panel > .panel-collapse > .table {\n  margin-bottom: 0;\n}\n.panel > .table caption,\n.panel > .table-responsive > .table caption,\n.panel > .panel-collapse > .table caption {\n  padding-right: 15px;\n  padding-left: 15px;\n}\n.panel > .table:first-child,\n.panel > .table-responsive:first-child > .table:first-child {\n  border-top-left-radius: 3px;\n  border-top-right-radius: 3px;\n}\n.panel > .table:first-child > thead:first-child > tr:first-child,\n.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,\n.panel > .table:first-child > tbody:first-child > tr:first-child,\n.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {\n  border-top-left-radius: 3px;\n  border-top-right-radius: 3px;\n}\n.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,\n.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,\n.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,\n.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,\n.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,\n.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,\n.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,\n.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {\n  border-top-left-radius: 3px;\n}\n.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,\n.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,\n.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,\n.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,\n.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,\n.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,\n.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,\n.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {\n  border-top-right-radius: 3px;\n}\n.panel > .table:last-child,\n.panel > .table-responsive:last-child > .table:last-child {\n  border-bottom-right-radius: 3px;\n  border-bottom-left-radius: 3px;\n}\n.panel > .table:last-child > tbody:last-child > tr:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,\n.panel > .table:last-child > tfoot:last-child > tr:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {\n  border-bottom-right-radius: 3px;\n  border-bottom-left-radius: 3px;\n}\n.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,\n.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,\n.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,\n.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,\n.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,\n.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,\n.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,\n.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {\n  border-bottom-left-radius: 3px;\n}\n.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,\n.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,\n.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,\n.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {\n  border-bottom-right-radius: 3px;\n}\n.panel > .panel-body + .table,\n.panel > .panel-body + .table-responsive,\n.panel > .table + .panel-body,\n.panel > .table-responsive + .panel-body {\n  border-top: 1px solid #ddd;\n}\n.panel > .table > tbody:first-child > tr:first-child th,\n.panel > .table > tbody:first-child > tr:first-child td {\n  border-top: 0;\n}\n.panel > .table-bordered,\n.panel > .table-responsive > .table-bordered {\n  border: 0;\n}\n.panel > .table-bordered > thead > tr > th:first-child,\n.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,\n.panel > .table-bordered > tbody > tr > th:first-child,\n.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,\n.panel > .table-bordered > tfoot > tr > th:first-child,\n.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,\n.panel > .table-bordered > thead > tr > td:first-child,\n.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,\n.panel > .table-bordered > tbody > tr > td:first-child,\n.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,\n.panel > .table-bordered > tfoot > tr > td:first-child,\n.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {\n  border-left: 0;\n}\n.panel > .table-bordered > thead > tr > th:last-child,\n.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,\n.panel > .table-bordered > tbody > tr > th:last-child,\n.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,\n.panel > .table-bordered > tfoot > tr > th:last-child,\n.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,\n.panel > .table-bordered > thead > tr > td:last-child,\n.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,\n.panel > .table-bordered > tbody > tr > td:last-child,\n.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,\n.panel > .table-bordered > tfoot > tr > td:last-child,\n.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {\n  border-right: 0;\n}\n.panel > .table-bordered > thead > tr:first-child > td,\n.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,\n.panel > .table-bordered > tbody > tr:first-child > td,\n.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,\n.panel > .table-bordered > thead > tr:first-child > th,\n.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,\n.panel > .table-bordered > tbody > tr:first-child > th,\n.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {\n  border-bottom: 0;\n}\n.panel > .table-bordered > tbody > tr:last-child > td,\n.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,\n.panel > .table-bordered > tfoot > tr:last-child > td,\n.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,\n.panel > .table-bordered > tbody > tr:last-child > th,\n.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,\n.panel > .table-bordered > tfoot > tr:last-child > th,\n.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {\n  border-bottom: 0;\n}\n.panel > .table-responsive {\n  margin-bottom: 0;\n  border: 0;\n}\n.panel-group {\n  margin-bottom: 20px;\n}\n.panel-group .panel {\n  margin-bottom: 0;\n  border-radius: 4px;\n}\n.panel-group .panel + .panel {\n  margin-top: 5px;\n}\n.panel-group .panel-heading {\n  border-bottom: 0;\n}\n.panel-group .panel-heading + .panel-collapse > .panel-body,\n.panel-group .panel-heading + .panel-collapse > .list-group {\n  border-top: 1px solid #ddd;\n}\n.panel-group .panel-footer {\n  border-top: 0;\n}\n.panel-group .panel-footer + .panel-collapse .panel-body {\n  border-bottom: 1px solid #ddd;\n}\n.panel-default {\n  border-color: #ddd;\n}\n.panel-default > .panel-heading {\n  color: #333;\n  background-color: #f5f5f5;\n  border-color: #ddd;\n}\n.panel-default > .panel-heading + .panel-collapse > .panel-body {\n  border-top-color: #ddd;\n}\n.panel-default > .panel-heading .badge {\n  color: #f5f5f5;\n  background-color: #333;\n}\n.panel-default > .panel-footer + .panel-collapse > .panel-body {\n  border-bottom-color: #ddd;\n}\n.panel-primary {\n  border-color: #337ab7;\n}\n.panel-primary > .panel-heading {\n  color: #fff;\n  background-color: #337ab7;\n  border-color: #337ab7;\n}\n.panel-primary > .panel-heading + .panel-collapse > .panel-body {\n  border-top-color: #337ab7;\n}\n.panel-primary > .panel-heading .badge {\n  color: #337ab7;\n  background-color: #fff;\n}\n.panel-primary > .panel-footer + .panel-collapse > .panel-body {\n  border-bottom-color: #337ab7;\n}\n.panel-success {\n  border-color: #d6e9c6;\n}\n.panel-success > .panel-heading {\n  color: #3c763d;\n  background-color: #dff0d8;\n  border-color: #d6e9c6;\n}\n.panel-success > .panel-heading + .panel-collapse > .panel-body {\n  border-top-color: #d6e9c6;\n}\n.panel-success > .panel-heading .badge {\n  color: #dff0d8;\n  background-color: #3c763d;\n}\n.panel-success > .panel-footer + .panel-collapse > .panel-body {\n  border-bottom-color: #d6e9c6;\n}\n.panel-info {\n  border-color: #bce8f1;\n}\n.panel-info > .panel-heading {\n  color: #31708f;\n  background-color: #d9edf7;\n  border-color: #bce8f1;\n}\n.panel-info > .panel-heading + .panel-collapse > .panel-body {\n  border-top-color: #bce8f1;\n}\n.panel-info > .panel-heading .badge {\n  color: #d9edf7;\n  background-color: #31708f;\n}\n.panel-info > .panel-footer + .panel-collapse > .panel-body {\n  border-bottom-color: #bce8f1;\n}\n.panel-warning {\n  border-color: #faebcc;\n}\n.panel-warning > .panel-heading {\n  color: #8a6d3b;\n  background-color: #fcf8e3;\n  border-color: #faebcc;\n}\n.panel-warning > .panel-heading + .panel-collapse > .panel-body {\n  border-top-color: #faebcc;\n}\n.panel-warning > .panel-heading .badge {\n  color: #fcf8e3;\n  background-color: #8a6d3b;\n}\n.panel-warning > .panel-footer + .panel-collapse > .panel-body {\n  border-bottom-color: #faebcc;\n}\n.panel-danger {\n  border-color: #ebccd1;\n}\n.panel-danger > .panel-heading {\n  color: #a94442;\n  background-color: #f2dede;\n  border-color: #ebccd1;\n}\n.panel-danger > .panel-heading + .panel-collapse > .panel-body {\n  border-top-color: #ebccd1;\n}\n.panel-danger > .panel-heading .badge {\n  color: #f2dede;\n  background-color: #a94442;\n}\n.panel-danger > .panel-footer + .panel-collapse > .panel-body {\n  border-bottom-color: #ebccd1;\n}\n.embed-responsive {\n  position: relative;\n  display: block;\n  height: 0;\n  padding: 0;\n  overflow: hidden;\n}\n.embed-responsive .embed-responsive-item,\n.embed-responsive iframe,\n.embed-responsive embed,\n.embed-responsive object,\n.embed-responsive video {\n  position: absolute;\n  top: 0;\n  bottom: 0;\n  left: 0;\n  width: 100%;\n  height: 100%;\n  border: 0;\n}\n.embed-responsive-16by9 {\n  padding-bottom: 56.25%;\n}\n.embed-responsive-4by3 {\n  padding-bottom: 75%;\n}\n.well {\n  min-height: 20px;\n  padding: 19px;\n  margin-bottom: 20px;\n  background-color: #f5f5f5;\n  border: 1px solid #e3e3e3;\n  border-radius: 4px;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, .05);\n          box-shadow: inset 0 1px 1px rgba(0, 0, 0, .05);\n}\n.well blockquote {\n  border-color: #ddd;\n  border-color: rgba(0, 0, 0, .15);\n}\n.well-lg {\n  padding: 24px;\n  border-radius: 6px;\n}\n.well-sm {\n  padding: 9px;\n  border-radius: 3px;\n}\n.close {\n  float: right;\n  font-size: 21px;\n  font-weight: bold;\n  line-height: 1;\n  color: #000;\n  text-shadow: 0 1px 0 #fff;\n  filter: alpha(opacity=20);\n  opacity: .2;\n}\n.close:hover,\n.close:focus {\n  color: #000;\n  text-decoration: none;\n  cursor: pointer;\n  filter: alpha(opacity=50);\n  opacity: .5;\n}\nbutton.close {\n  -webkit-appearance: none;\n  padding: 0;\n  cursor: pointer;\n  background: transparent;\n  border: 0;\n}\n.modal-open {\n  overflow: hidden;\n}\n.modal {\n  position: fixed;\n  top: 0;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  z-index: 1050;\n  display: none;\n  overflow: hidden;\n  -webkit-overflow-scrolling: touch;\n  outline: 0;\n}\n.modal.fade .modal-dialog {\n  -webkit-transition: -webkit-transform .3s ease-out;\n       -o-transition:      -o-transform .3s ease-out;\n          transition:         transform .3s ease-out;\n  -webkit-transform: translate(0, -25%);\n      -ms-transform: translate(0, -25%);\n       -o-transform: translate(0, -25%);\n          transform: translate(0, -25%);\n}\n.modal.in .modal-dialog {\n  -webkit-transform: translate(0, 0);\n      -ms-transform: translate(0, 0);\n       -o-transform: translate(0, 0);\n          transform: translate(0, 0);\n}\n.modal-open .modal {\n  overflow-x: hidden;\n  overflow-y: auto;\n}\n.modal-dialog {\n  position: relative;\n  width: auto;\n  margin: 10px;\n}\n.modal-content {\n  position: relative;\n  background-color: #fff;\n  -webkit-background-clip: padding-box;\n          background-clip: padding-box;\n  border: 1px solid #999;\n  border: 1px solid rgba(0, 0, 0, .2);\n  border-radius: 6px;\n  outline: 0;\n  -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, .5);\n          box-shadow: 0 3px 9px rgba(0, 0, 0, .5);\n}\n.modal-backdrop {\n  position: fixed;\n  top: 0;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  z-index: 1040;\n  background-color: #000;\n}\n.modal-backdrop.fade {\n  filter: alpha(opacity=0);\n  opacity: 0;\n}\n.modal-backdrop.in {\n  filter: alpha(opacity=50);\n  opacity: .5;\n}\n.modal-header {\n  padding: 15px;\n  border-bottom: 1px solid #e5e5e5;\n}\n.modal-header .close {\n  margin-top: -2px;\n}\n.modal-title {\n  margin: 0;\n  line-height: 1.42857143;\n}\n.modal-body {\n  position: relative;\n  padding: 15px;\n}\n.modal-footer {\n  padding: 15px;\n  text-align: right;\n  border-top: 1px solid #e5e5e5;\n}\n.modal-footer .btn + .btn {\n  margin-bottom: 0;\n  margin-left: 5px;\n}\n.modal-footer .btn-group .btn + .btn {\n  margin-left: -1px;\n}\n.modal-footer .btn-block + .btn-block {\n  margin-left: 0;\n}\n.modal-scrollbar-measure {\n  position: absolute;\n  top: -9999px;\n  width: 50px;\n  height: 50px;\n  overflow: scroll;\n}\n@media (min-width: 768px) {\n  .modal-dialog {\n    width: 600px;\n    margin: 30px auto;\n  }\n  .modal-content {\n    -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, .5);\n            box-shadow: 0 5px 15px rgba(0, 0, 0, .5);\n  }\n  .modal-sm {\n    width: 300px;\n  }\n}\n@media (min-width: 992px) {\n  .modal-lg {\n    width: 900px;\n  }\n}\n.tooltip {\n  position: absolute;\n  z-index: 1070;\n  display: block;\n  font-family: \"Helvetica Neue\", Helvetica, Arial, sans-serif;\n  font-size: 12px;\n  font-style: normal;\n  font-weight: normal;\n  line-height: 1.42857143;\n  text-align: left;\n  text-align: start;\n  text-decoration: none;\n  text-shadow: none;\n  text-transform: none;\n  letter-spacing: normal;\n  word-break: normal;\n  word-spacing: normal;\n  word-wrap: normal;\n  white-space: normal;\n  filter: alpha(opacity=0);\n  opacity: 0;\n\n  line-break: auto;\n}\n.tooltip.in {\n  filter: alpha(opacity=90);\n  opacity: .9;\n}\n.tooltip.top {\n  padding: 5px 0;\n  margin-top: -3px;\n}\n.tooltip.right {\n  padding: 0 5px;\n  margin-left: 3px;\n}\n.tooltip.bottom {\n  padding: 5px 0;\n  margin-top: 3px;\n}\n.tooltip.left {\n  padding: 0 5px;\n  margin-left: -3px;\n}\n.tooltip-inner {\n  max-width: 200px;\n  padding: 3px 8px;\n  color: #fff;\n  text-align: center;\n  background-color: #000;\n  border-radius: 4px;\n}\n.tooltip-arrow {\n  position: absolute;\n  width: 0;\n  height: 0;\n  border-color: transparent;\n  border-style: solid;\n}\n.tooltip.top .tooltip-arrow {\n  bottom: 0;\n  left: 50%;\n  margin-left: -5px;\n  border-width: 5px 5px 0;\n  border-top-color: #000;\n}\n.tooltip.top-left .tooltip-arrow {\n  right: 5px;\n  bottom: 0;\n  margin-bottom: -5px;\n  border-width: 5px 5px 0;\n  border-top-color: #000;\n}\n.tooltip.top-right .tooltip-arrow {\n  bottom: 0;\n  left: 5px;\n  margin-bottom: -5px;\n  border-width: 5px 5px 0;\n  border-top-color: #000;\n}\n.tooltip.right .tooltip-arrow {\n  top: 50%;\n  left: 0;\n  margin-top: -5px;\n  border-width: 5px 5px 5px 0;\n  border-right-color: #000;\n}\n.tooltip.left .tooltip-arrow {\n  top: 50%;\n  right: 0;\n  margin-top: -5px;\n  border-width: 5px 0 5px 5px;\n  border-left-color: #000;\n}\n.tooltip.bottom .tooltip-arrow {\n  top: 0;\n  left: 50%;\n  margin-left: -5px;\n  border-width: 0 5px 5px;\n  border-bottom-color: #000;\n}\n.tooltip.bottom-left .tooltip-arrow {\n  top: 0;\n  right: 5px;\n  margin-top: -5px;\n  border-width: 0 5px 5px;\n  border-bottom-color: #000;\n}\n.tooltip.bottom-right .tooltip-arrow {\n  top: 0;\n  left: 5px;\n  margin-top: -5px;\n  border-width: 0 5px 5px;\n  border-bottom-color: #000;\n}\n.popover {\n  position: absolute;\n  top: 0;\n  left: 0;\n  z-index: 1060;\n  display: none;\n  max-width: 276px;\n  padding: 1px;\n  font-family: \"Helvetica Neue\", Helvetica, Arial, sans-serif;\n  font-size: 14px;\n  font-style: normal;\n  font-weight: normal;\n  line-height: 1.42857143;\n  text-align: left;\n  text-align: start;\n  text-decoration: none;\n  text-shadow: none;\n  text-transform: none;\n  letter-spacing: normal;\n  word-break: normal;\n  word-spacing: normal;\n  word-wrap: normal;\n  white-space: normal;\n  background-color: #fff;\n  -webkit-background-clip: padding-box;\n          background-clip: padding-box;\n  border: 1px solid #ccc;\n  border: 1px solid rgba(0, 0, 0, .2);\n  border-radius: 6px;\n  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, .2);\n          box-shadow: 0 5px 10px rgba(0, 0, 0, .2);\n\n  line-break: auto;\n}\n.popover.top {\n  margin-top: -10px;\n}\n.popover.right {\n  margin-left: 10px;\n}\n.popover.bottom {\n  margin-top: 10px;\n}\n.popover.left {\n  margin-left: -10px;\n}\n.popover-title {\n  padding: 8px 14px;\n  margin: 0;\n  font-size: 14px;\n  background-color: #f7f7f7;\n  border-bottom: 1px solid #ebebeb;\n  border-radius: 5px 5px 0 0;\n}\n.popover-content {\n  padding: 9px 14px;\n}\n.popover > .arrow,\n.popover > .arrow:after {\n  position: absolute;\n  display: block;\n  width: 0;\n  height: 0;\n  border-color: transparent;\n  border-style: solid;\n}\n.popover > .arrow {\n  border-width: 11px;\n}\n.popover > .arrow:after {\n  content: \"\";\n  border-width: 10px;\n}\n.popover.top > .arrow {\n  bottom: -11px;\n  left: 50%;\n  margin-left: -11px;\n  border-top-color: #999;\n  border-top-color: rgba(0, 0, 0, .25);\n  border-bottom-width: 0;\n}\n.popover.top > .arrow:after {\n  bottom: 1px;\n  margin-left: -10px;\n  content: \" \";\n  border-top-color: #fff;\n  border-bottom-width: 0;\n}\n.popover.right > .arrow {\n  top: 50%;\n  left: -11px;\n  margin-top: -11px;\n  border-right-color: #999;\n  border-right-color: rgba(0, 0, 0, .25);\n  border-left-width: 0;\n}\n.popover.right > .arrow:after {\n  bottom: -10px;\n  left: 1px;\n  content: \" \";\n  border-right-color: #fff;\n  border-left-width: 0;\n}\n.popover.bottom > .arrow {\n  top: -11px;\n  left: 50%;\n  margin-left: -11px;\n  border-top-width: 0;\n  border-bottom-color: #999;\n  border-bottom-color: rgba(0, 0, 0, .25);\n}\n.popover.bottom > .arrow:after {\n  top: 1px;\n  margin-left: -10px;\n  content: \" \";\n  border-top-width: 0;\n  border-bottom-color: #fff;\n}\n.popover.left > .arrow {\n  top: 50%;\n  right: -11px;\n  margin-top: -11px;\n  border-right-width: 0;\n  border-left-color: #999;\n  border-left-color: rgba(0, 0, 0, .25);\n}\n.popover.left > .arrow:after {\n  right: 1px;\n  bottom: -10px;\n  content: \" \";\n  border-right-width: 0;\n  border-left-color: #fff;\n}\n.carousel {\n  position: relative;\n}\n.carousel-inner {\n  position: relative;\n  width: 100%;\n  overflow: hidden;\n}\n.carousel-inner > .item {\n  position: relative;\n  display: none;\n  -webkit-transition: .6s ease-in-out left;\n       -o-transition: .6s ease-in-out left;\n          transition: .6s ease-in-out left;\n}\n.carousel-inner > .item > img,\n.carousel-inner > .item > a > img {\n  line-height: 1;\n}\n@media all and (transform-3d), (-webkit-transform-3d) {\n  .carousel-inner > .item {\n    -webkit-transition: -webkit-transform .6s ease-in-out;\n         -o-transition:      -o-transform .6s ease-in-out;\n            transition:         transform .6s ease-in-out;\n\n    -webkit-backface-visibility: hidden;\n            backface-visibility: hidden;\n    -webkit-perspective: 1000px;\n            perspective: 1000px;\n  }\n  .carousel-inner > .item.next,\n  .carousel-inner > .item.active.right {\n    left: 0;\n    -webkit-transform: translate3d(100%, 0, 0);\n            transform: translate3d(100%, 0, 0);\n  }\n  .carousel-inner > .item.prev,\n  .carousel-inner > .item.active.left {\n    left: 0;\n    -webkit-transform: translate3d(-100%, 0, 0);\n            transform: translate3d(-100%, 0, 0);\n  }\n  .carousel-inner > .item.next.left,\n  .carousel-inner > .item.prev.right,\n  .carousel-inner > .item.active {\n    left: 0;\n    -webkit-transform: translate3d(0, 0, 0);\n            transform: translate3d(0, 0, 0);\n  }\n}\n.carousel-inner > .active,\n.carousel-inner > .next,\n.carousel-inner > .prev {\n  display: block;\n}\n.carousel-inner > .active {\n  left: 0;\n}\n.carousel-inner > .next,\n.carousel-inner > .prev {\n  position: absolute;\n  top: 0;\n  width: 100%;\n}\n.carousel-inner > .next {\n  left: 100%;\n}\n.carousel-inner > .prev {\n  left: -100%;\n}\n.carousel-inner > .next.left,\n.carousel-inner > .prev.right {\n  left: 0;\n}\n.carousel-inner > .active.left {\n  left: -100%;\n}\n.carousel-inner > .active.right {\n  left: 100%;\n}\n.carousel-control {\n  position: absolute;\n  top: 0;\n  bottom: 0;\n  left: 0;\n  width: 15%;\n  font-size: 20px;\n  color: #fff;\n  text-align: center;\n  text-shadow: 0 1px 2px rgba(0, 0, 0, .6);\n  background-color: rgba(0, 0, 0, 0);\n  filter: alpha(opacity=50);\n  opacity: .5;\n}\n.carousel-control.left {\n  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, .5) 0%, rgba(0, 0, 0, .0001) 100%);\n  background-image:      -o-linear-gradient(left, rgba(0, 0, 0, .5) 0%, rgba(0, 0, 0, .0001) 100%);\n  background-image: -webkit-gradient(linear, left top, right top, from(rgba(0, 0, 0, .5)), to(rgba(0, 0, 0, .0001)));\n  background-image:         linear-gradient(to right, rgba(0, 0, 0, .5) 0%, rgba(0, 0, 0, .0001) 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);\n  background-repeat: repeat-x;\n}\n.carousel-control.right {\n  right: 0;\n  left: auto;\n  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, .0001) 0%, rgba(0, 0, 0, .5) 100%);\n  background-image:      -o-linear-gradient(left, rgba(0, 0, 0, .0001) 0%, rgba(0, 0, 0, .5) 100%);\n  background-image: -webkit-gradient(linear, left top, right top, from(rgba(0, 0, 0, .0001)), to(rgba(0, 0, 0, .5)));\n  background-image:         linear-gradient(to right, rgba(0, 0, 0, .0001) 0%, rgba(0, 0, 0, .5) 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);\n  background-repeat: repeat-x;\n}\n.carousel-control:hover,\n.carousel-control:focus {\n  color: #fff;\n  text-decoration: none;\n  filter: alpha(opacity=90);\n  outline: 0;\n  opacity: .9;\n}\n.carousel-control .icon-prev,\n.carousel-control .icon-next,\n.carousel-control .glyphicon-chevron-left,\n.carousel-control .glyphicon-chevron-right {\n  position: absolute;\n  top: 50%;\n  z-index: 5;\n  display: inline-block;\n  margin-top: -10px;\n}\n.carousel-control .icon-prev,\n.carousel-control .glyphicon-chevron-left {\n  left: 50%;\n  margin-left: -10px;\n}\n.carousel-control .icon-next,\n.carousel-control .glyphicon-chevron-right {\n  right: 50%;\n  margin-right: -10px;\n}\n.carousel-control .icon-prev,\n.carousel-control .icon-next {\n  width: 20px;\n  height: 20px;\n  font-family: serif;\n  line-height: 1;\n}\n.carousel-control .icon-prev:before {\n  content: '\\2039';\n}\n.carousel-control .icon-next:before {\n  content: '\\203a';\n}\n.carousel-indicators {\n  position: absolute;\n  bottom: 10px;\n  left: 50%;\n  z-index: 15;\n  width: 60%;\n  padding-left: 0;\n  margin-left: -30%;\n  text-align: center;\n  list-style: none;\n}\n.carousel-indicators li {\n  display: inline-block;\n  width: 10px;\n  height: 10px;\n  margin: 1px;\n  text-indent: -999px;\n  cursor: pointer;\n  background-color: #000 \\9;\n  background-color: rgba(0, 0, 0, 0);\n  border: 1px solid #fff;\n  border-radius: 10px;\n}\n.carousel-indicators .active {\n  width: 12px;\n  height: 12px;\n  margin: 0;\n  background-color: #fff;\n}\n.carousel-caption {\n  position: absolute;\n  right: 15%;\n  bottom: 20px;\n  left: 15%;\n  z-index: 10;\n  padding-top: 20px;\n  padding-bottom: 20px;\n  color: #fff;\n  text-align: center;\n  text-shadow: 0 1px 2px rgba(0, 0, 0, .6);\n}\n.carousel-caption .btn {\n  text-shadow: none;\n}\n@media screen and (min-width: 768px) {\n  .carousel-control .glyphicon-chevron-left,\n  .carousel-control .glyphicon-chevron-right,\n  .carousel-control .icon-prev,\n  .carousel-control .icon-next {\n    width: 30px;\n    height: 30px;\n    margin-top: -10px;\n    font-size: 30px;\n  }\n  .carousel-control .glyphicon-chevron-left,\n  .carousel-control .icon-prev {\n    margin-left: -10px;\n  }\n  .carousel-control .glyphicon-chevron-right,\n  .carousel-control .icon-next {\n    margin-right: -10px;\n  }\n  .carousel-caption {\n    right: 20%;\n    left: 20%;\n    padding-bottom: 30px;\n  }\n  .carousel-indicators {\n    bottom: 20px;\n  }\n}\n.clearfix:before,\n.clearfix:after,\n.dl-horizontal dd:before,\n.dl-horizontal dd:after,\n.container:before,\n.container:after,\n.container-fluid:before,\n.container-fluid:after,\n.row:before,\n.row:after,\n.form-horizontal .form-group:before,\n.form-horizontal .form-group:after,\n.btn-toolbar:before,\n.btn-toolbar:after,\n.btn-group-vertical > .btn-group:before,\n.btn-group-vertical > .btn-group:after,\n.nav:before,\n.nav:after,\n.navbar:before,\n.navbar:after,\n.navbar-header:before,\n.navbar-header:after,\n.navbar-collapse:before,\n.navbar-collapse:after,\n.pager:before,\n.pager:after,\n.panel-body:before,\n.panel-body:after,\n.modal-header:before,\n.modal-header:after,\n.modal-footer:before,\n.modal-footer:after {\n  display: table;\n  content: \" \";\n}\n.clearfix:after,\n.dl-horizontal dd:after,\n.container:after,\n.container-fluid:after,\n.row:after,\n.form-horizontal .form-group:after,\n.btn-toolbar:after,\n.btn-group-vertical > .btn-group:after,\n.nav:after,\n.navbar:after,\n.navbar-header:after,\n.navbar-collapse:after,\n.pager:after,\n.panel-body:after,\n.modal-header:after,\n.modal-footer:after {\n  clear: both;\n}\n.center-block {\n  display: block;\n  margin-right: auto;\n  margin-left: auto;\n}\n.pull-right {\n  float: right !important;\n}\n.pull-left {\n  float: left !important;\n}\n.hide {\n  display: none !important;\n}\n.show {\n  display: block !important;\n}\n.invisible {\n  visibility: hidden;\n}\n.text-hide {\n  font: 0/0 a;\n  color: transparent;\n  text-shadow: none;\n  background-color: transparent;\n  border: 0;\n}\n.hidden {\n  display: none !important;\n}\n.affix {\n  position: fixed;\n}\n@-ms-viewport {\n  width: device-width;\n}\n.visible-xs,\n.visible-sm,\n.visible-md,\n.visible-lg {\n  display: none !important;\n}\n.visible-xs-block,\n.visible-xs-inline,\n.visible-xs-inline-block,\n.visible-sm-block,\n.visible-sm-inline,\n.visible-sm-inline-block,\n.visible-md-block,\n.visible-md-inline,\n.visible-md-inline-block,\n.visible-lg-block,\n.visible-lg-inline,\n.visible-lg-inline-block {\n  display: none !important;\n}\n@media (max-width: 767px) {\n  .visible-xs {\n    display: block !important;\n  }\n  table.visible-xs {\n    display: table !important;\n  }\n  tr.visible-xs {\n    display: table-row !important;\n  }\n  th.visible-xs,\n  td.visible-xs {\n    display: table-cell !important;\n  }\n}\n@media (max-width: 767px) {\n  .visible-xs-block {\n    display: block !important;\n  }\n}\n@media (max-width: 767px) {\n  .visible-xs-inline {\n    display: inline !important;\n  }\n}\n@media (max-width: 767px) {\n  .visible-xs-inline-block {\n    display: inline-block !important;\n  }\n}\n@media (min-width: 768px) and (max-width: 991px) {\n  .visible-sm {\n    display: block !important;\n  }\n  table.visible-sm {\n    display: table !important;\n  }\n  tr.visible-sm {\n    display: table-row !important;\n  }\n  th.visible-sm,\n  td.visible-sm {\n    display: table-cell !important;\n  }\n}\n@media (min-width: 768px) and (max-width: 991px) {\n  .visible-sm-block {\n    display: block !important;\n  }\n}\n@media (min-width: 768px) and (max-width: 991px) {\n  .visible-sm-inline {\n    display: inline !important;\n  }\n}\n@media (min-width: 768px) and (max-width: 991px) {\n  .visible-sm-inline-block {\n    display: inline-block !important;\n  }\n}\n@media (min-width: 992px) and (max-width: 1199px) {\n  .visible-md {\n    display: block !important;\n  }\n  table.visible-md {\n    display: table !important;\n  }\n  tr.visible-md {\n    display: table-row !important;\n  }\n  th.visible-md,\n  td.visible-md {\n    display: table-cell !important;\n  }\n}\n@media (min-width: 992px) and (max-width: 1199px) {\n  .visible-md-block {\n    display: block !important;\n  }\n}\n@media (min-width: 992px) and (max-width: 1199px) {\n  .visible-md-inline {\n    display: inline !important;\n  }\n}\n@media (min-width: 992px) and (max-width: 1199px) {\n  .visible-md-inline-block {\n    display: inline-block !important;\n  }\n}\n@media (min-width: 1200px) {\n  .visible-lg {\n    display: block !important;\n  }\n  table.visible-lg {\n    display: table !important;\n  }\n  tr.visible-lg {\n    display: table-row !important;\n  }\n  th.visible-lg,\n  td.visible-lg {\n    display: table-cell !important;\n  }\n}\n@media (min-width: 1200px) {\n  .visible-lg-block {\n    display: block !important;\n  }\n}\n@media (min-width: 1200px) {\n  .visible-lg-inline {\n    display: inline !important;\n  }\n}\n@media (min-width: 1200px) {\n  .visible-lg-inline-block {\n    display: inline-block !important;\n  }\n}\n@media (max-width: 767px) {\n  .hidden-xs {\n    display: none !important;\n  }\n}\n@media (min-width: 768px) and (max-width: 991px) {\n  .hidden-sm {\n    display: none !important;\n  }\n}\n@media (min-width: 992px) and (max-width: 1199px) {\n  .hidden-md {\n    display: none !important;\n  }\n}\n@media (min-width: 1200px) {\n  .hidden-lg {\n    display: none !important;\n  }\n}\n.visible-print {\n  display: none !important;\n}\n@media print {\n  .visible-print {\n    display: block !important;\n  }\n  table.visible-print {\n    display: table !important;\n  }\n  tr.visible-print {\n    display: table-row !important;\n  }\n  th.visible-print,\n  td.visible-print {\n    display: table-cell !important;\n  }\n}\n.visible-print-block {\n  display: none !important;\n}\n@media print {\n  .visible-print-block {\n    display: block !important;\n  }\n}\n.visible-print-inline {\n  display: none !important;\n}\n@media print {\n  .visible-print-inline {\n    display: inline !important;\n  }\n}\n.visible-print-inline-block {\n  display: none !important;\n}\n@media print {\n  .visible-print-inline-block {\n    display: inline-block !important;\n  }\n}\n@media print {\n  .hidden-print {\n    display: none !important;\n  }\n}\n/*# sourceMappingURL=bootstrap.css.map */\n","//\n// Glyphicons for Bootstrap\n//\n// Since icons are fonts, they can be placed anywhere text is placed and are\n// thus automatically sized to match the surrounding child. To use, create an\n// inline element with the appropriate classes, like so:\n//\n// <a href=\"#\"><span class=\"glyphicon glyphicon-star\"></span> Star</a>\n\n// Import the fonts\n@font-face {\n  font-family: 'Glyphicons Halflings';\n  src: url('@{icon-font-path}@{icon-font-name}.eot');\n  src: url('@{icon-font-path}@{icon-font-name}.eot?#iefix') format('embedded-opentype'),\n       url('@{icon-font-path}@{icon-font-name}.woff2') format('woff2'),\n       url('@{icon-font-path}@{icon-font-name}.woff') format('woff'),\n       url('@{icon-font-path}@{icon-font-name}.ttf') format('truetype'),\n       url('@{icon-font-path}@{icon-font-name}.svg#@{icon-font-svg-id}') format('svg');\n}\n\n// Catchall baseclass\n.glyphicon {\n  position: relative;\n  top: 1px;\n  display: inline-block;\n  font-family: 'Glyphicons Halflings';\n  font-style: normal;\n  font-weight: normal;\n  line-height: 1;\n  -webkit-font-smoothing: antialiased;\n  -moz-osx-font-smoothing: grayscale;\n}\n\n// Individual icons\n.glyphicon-asterisk               { &:before { content: \"\\002a\"; } }\n.glyphicon-plus                   { &:before { content: \"\\002b\"; } }\n.glyphicon-euro,\n.glyphicon-eur                    { &:before { content: \"\\20ac\"; } }\n.glyphicon-minus                  { &:before { content: \"\\2212\"; } }\n.glyphicon-cloud                  { &:before { content: \"\\2601\"; } }\n.glyphicon-envelope               { &:before { content: \"\\2709\"; } }\n.glyphicon-pencil                 { &:before { content: \"\\270f\"; } }\n.glyphicon-glass                  { &:before { content: \"\\e001\"; } }\n.glyphicon-music                  { &:before { content: \"\\e002\"; } }\n.glyphicon-search                 { &:before { content: \"\\e003\"; } }\n.glyphicon-heart                  { &:before { content: \"\\e005\"; } }\n.glyphicon-star                   { &:before { content: \"\\e006\"; } }\n.glyphicon-star-empty             { &:before { content: \"\\e007\"; } }\n.glyphicon-user                   { &:before { content: \"\\e008\"; } }\n.glyphicon-film                   { &:before { content: \"\\e009\"; } }\n.glyphicon-th-large               { &:before { content: \"\\e010\"; } }\n.glyphicon-th                     { &:before { content: \"\\e011\"; } }\n.glyphicon-th-list                { &:before { content: \"\\e012\"; } }\n.glyphicon-ok                     { &:before { content: \"\\e013\"; } }\n.glyphicon-remove                 { &:before { content: \"\\e014\"; } }\n.glyphicon-zoom-in                { &:before { content: \"\\e015\"; } }\n.glyphicon-zoom-out               { &:before { content: \"\\e016\"; } }\n.glyphicon-off                    { &:before { content: \"\\e017\"; } }\n.glyphicon-signal                 { &:before { content: \"\\e018\"; } }\n.glyphicon-cog                    { &:before { content: \"\\e019\"; } }\n.glyphicon-trash                  { &:before { content: \"\\e020\"; } }\n.glyphicon-home                   { &:before { content: \"\\e021\"; } }\n.glyphicon-file                   { &:before { content: \"\\e022\"; } }\n.glyphicon-time                   { &:before { content: \"\\e023\"; } }\n.glyphicon-road                   { &:before { content: \"\\e024\"; } }\n.glyphicon-download-alt           { &:before { content: \"\\e025\"; } }\n.glyphicon-download               { &:before { content: \"\\e026\"; } }\n.glyphicon-upload                 { &:before { content: \"\\e027\"; } }\n.glyphicon-inbox                  { &:before { content: \"\\e028\"; } }\n.glyphicon-play-circle            { &:before { content: \"\\e029\"; } }\n.glyphicon-repeat                 { &:before { content: \"\\e030\"; } }\n.glyphicon-refresh                { &:before { content: \"\\e031\"; } }\n.glyphicon-list-alt               { &:before { content: \"\\e032\"; } }\n.glyphicon-lock                   { &:before { content: \"\\e033\"; } }\n.glyphicon-flag                   { &:before { content: \"\\e034\"; } }\n.glyphicon-headphones             { &:before { content: \"\\e035\"; } }\n.glyphicon-volume-off             { &:before { content: \"\\e036\"; } }\n.glyphicon-volume-down            { &:before { content: \"\\e037\"; } }\n.glyphicon-volume-up              { &:before { content: \"\\e038\"; } }\n.glyphicon-qrcode                 { &:before { content: \"\\e039\"; } }\n.glyphicon-barcode                { &:before { content: \"\\e040\"; } }\n.glyphicon-tag                    { &:before { content: \"\\e041\"; } }\n.glyphicon-tags                   { &:before { content: \"\\e042\"; } }\n.glyphicon-book                   { &:before { content: \"\\e043\"; } }\n.glyphicon-bookmark               { &:before { content: \"\\e044\"; } }\n.glyphicon-print                  { &:before { content: \"\\e045\"; } }\n.glyphicon-camera                 { &:before { content: \"\\e046\"; } }\n.glyphicon-font                   { &:before { content: \"\\e047\"; } }\n.glyphicon-bold                   { &:before { content: \"\\e048\"; } }\n.glyphicon-italic                 { &:before { content: \"\\e049\"; } }\n.glyphicon-text-height            { &:before { content: \"\\e050\"; } }\n.glyphicon-text-width             { &:before { content: \"\\e051\"; } }\n.glyphicon-align-left             { &:before { content: \"\\e052\"; } }\n.glyphicon-align-center           { &:before { content: \"\\e053\"; } }\n.glyphicon-align-right            { &:before { content: \"\\e054\"; } }\n.glyphicon-align-justify          { &:before { content: \"\\e055\"; } }\n.glyphicon-list                   { &:before { content: \"\\e056\"; } }\n.glyphicon-indent-left            { &:before { content: \"\\e057\"; } }\n.glyphicon-indent-right           { &:before { content: \"\\e058\"; } }\n.glyphicon-facetime-video         { &:before { content: \"\\e059\"; } }\n.glyphicon-picture                { &:before { content: \"\\e060\"; } }\n.glyphicon-map-marker             { &:before { content: \"\\e062\"; } }\n.glyphicon-adjust                 { &:before { content: \"\\e063\"; } }\n.glyphicon-tint                   { &:before { content: \"\\e064\"; } }\n.glyphicon-edit                   { &:before { content: \"\\e065\"; } }\n.glyphicon-share                  { &:before { content: \"\\e066\"; } }\n.glyphicon-check                  { &:before { content: \"\\e067\"; } }\n.glyphicon-move                   { &:before { content: \"\\e068\"; } }\n.glyphicon-step-backward          { &:before { content: \"\\e069\"; } }\n.glyphicon-fast-backward          { &:before { content: \"\\e070\"; } }\n.glyphicon-backward               { &:before { content: \"\\e071\"; } }\n.glyphicon-play                   { &:before { content: \"\\e072\"; } }\n.glyphicon-pause                  { &:before { content: \"\\e073\"; } }\n.glyphicon-stop                   { &:before { content: \"\\e074\"; } }\n.glyphicon-forward                { &:before { content: \"\\e075\"; } }\n.glyphicon-fast-forward           { &:before { content: \"\\e076\"; } }\n.glyphicon-step-forward           { &:before { content: \"\\e077\"; } }\n.glyphicon-eject                  { &:before { content: \"\\e078\"; } }\n.glyphicon-chevron-left           { &:before { content: \"\\e079\"; } }\n.glyphicon-chevron-right          { &:before { content: \"\\e080\"; } }\n.glyphicon-plus-sign              { &:before { content: \"\\e081\"; } }\n.glyphicon-minus-sign             { &:before { content: \"\\e082\"; } }\n.glyphicon-remove-sign            { &:before { content: \"\\e083\"; } }\n.glyphicon-ok-sign                { &:before { content: \"\\e084\"; } }\n.glyphicon-question-sign          { &:before { content: \"\\e085\"; } }\n.glyphicon-info-sign              { &:before { content: \"\\e086\"; } }\n.glyphicon-screenshot             { &:before { content: \"\\e087\"; } }\n.glyphicon-remove-circle          { &:before { content: \"\\e088\"; } }\n.glyphicon-ok-circle              { &:before { content: \"\\e089\"; } }\n.glyphicon-ban-circle             { &:before { content: \"\\e090\"; } }\n.glyphicon-arrow-left             { &:before { content: \"\\e091\"; } }\n.glyphicon-arrow-right            { &:before { content: \"\\e092\"; } }\n.glyphicon-arrow-up               { &:before { content: \"\\e093\"; } }\n.glyphicon-arrow-down             { &:before { content: \"\\e094\"; } }\n.glyphicon-share-alt              { &:before { content: \"\\e095\"; } }\n.glyphicon-resize-full            { &:before { content: \"\\e096\"; } }\n.glyphicon-resize-small           { &:before { content: \"\\e097\"; } }\n.glyphicon-exclamation-sign       { &:before { content: \"\\e101\"; } }\n.glyphicon-gift                   { &:before { content: \"\\e102\"; } }\n.glyphicon-leaf                   { &:before { content: \"\\e103\"; } }\n.glyphicon-fire                   { &:before { content: \"\\e104\"; } }\n.glyphicon-eye-open               { &:before { content: \"\\e105\"; } }\n.glyphicon-eye-close              { &:before { content: \"\\e106\"; } }\n.glyphicon-warning-sign           { &:before { content: \"\\e107\"; } }\n.glyphicon-plane                  { &:before { content: \"\\e108\"; } }\n.glyphicon-calendar               { &:before { content: \"\\e109\"; } }\n.glyphicon-random                 { &:before { content: \"\\e110\"; } }\n.glyphicon-comment                { &:before { content: \"\\e111\"; } }\n.glyphicon-magnet                 { &:before { content: \"\\e112\"; } }\n.glyphicon-chevron-up             { &:before { content: \"\\e113\"; } }\n.glyphicon-chevron-down           { &:before { content: \"\\e114\"; } }\n.glyphicon-retweet                { &:before { content: \"\\e115\"; } }\n.glyphicon-shopping-cart          { &:before { content: \"\\e116\"; } }\n.glyphicon-folder-close           { &:before { content: \"\\e117\"; } }\n.glyphicon-folder-open            { &:before { content: \"\\e118\"; } }\n.glyphicon-resize-vertical        { &:before { content: \"\\e119\"; } }\n.glyphicon-resize-horizontal      { &:before { content: \"\\e120\"; } }\n.glyphicon-hdd                    { &:before { content: \"\\e121\"; } }\n.glyphicon-bullhorn               { &:before { content: \"\\e122\"; } }\n.glyphicon-bell                   { &:before { content: \"\\e123\"; } }\n.glyphicon-certificate            { &:before { content: \"\\e124\"; } }\n.glyphicon-thumbs-up              { &:before { content: \"\\e125\"; } }\n.glyphicon-thumbs-down            { &:before { content: \"\\e126\"; } }\n.glyphicon-hand-right             { &:before { content: \"\\e127\"; } }\n.glyphicon-hand-left              { &:before { content: \"\\e128\"; } }\n.glyphicon-hand-up                { &:before { content: \"\\e129\"; } }\n.glyphicon-hand-down              { &:before { content: \"\\e130\"; } }\n.glyphicon-circle-arrow-right     { &:before { content: \"\\e131\"; } }\n.glyphicon-circle-arrow-left      { &:before { content: \"\\e132\"; } }\n.glyphicon-circle-arrow-up        { &:before { content: \"\\e133\"; } }\n.glyphicon-circle-arrow-down      { &:before { content: \"\\e134\"; } }\n.glyphicon-globe                  { &:before { content: \"\\e135\"; } }\n.glyphicon-wrench                 { &:before { content: \"\\e136\"; } }\n.glyphicon-tasks                  { &:before { content: \"\\e137\"; } }\n.glyphicon-filter                 { &:before { content: \"\\e138\"; } }\n.glyphicon-briefcase              { &:before { content: \"\\e139\"; } }\n.glyphicon-fullscreen             { &:before { content: \"\\e140\"; } }\n.glyphicon-dashboard              { &:before { content: \"\\e141\"; } }\n.glyphicon-paperclip              { &:before { content: \"\\e142\"; } }\n.glyphicon-heart-empty            { &:before { content: \"\\e143\"; } }\n.glyphicon-link                   { &:before { content: \"\\e144\"; } }\n.glyphicon-phone                  { &:before { content: \"\\e145\"; } }\n.glyphicon-pushpin                { &:before { content: \"\\e146\"; } }\n.glyphicon-usd                    { &:before { content: \"\\e148\"; } }\n.glyphicon-gbp                    { &:before { content: \"\\e149\"; } }\n.glyphicon-sort                   { &:before { content: \"\\e150\"; } }\n.glyphicon-sort-by-alphabet       { &:before { content: \"\\e151\"; } }\n.glyphicon-sort-by-alphabet-alt   { &:before { content: \"\\e152\"; } }\n.glyphicon-sort-by-order          { &:before { content: \"\\e153\"; } }\n.glyphicon-sort-by-order-alt      { &:before { content: \"\\e154\"; } }\n.glyphicon-sort-by-attributes     { &:before { content: \"\\e155\"; } }\n.glyphicon-sort-by-attributes-alt { &:before { content: \"\\e156\"; } }\n.glyphicon-unchecked              { &:before { content: \"\\e157\"; } }\n.glyphicon-expand                 { &:before { content: \"\\e158\"; } }\n.glyphicon-collapse-down          { &:before { content: \"\\e159\"; } }\n.glyphicon-collapse-up            { &:before { content: \"\\e160\"; } }\n.glyphicon-log-in                 { &:before { content: \"\\e161\"; } }\n.glyphicon-flash                  { &:before { content: \"\\e162\"; } }\n.glyphicon-log-out                { &:before { content: \"\\e163\"; } }\n.glyphicon-new-window             { &:before { content: \"\\e164\"; } }\n.glyphicon-record                 { &:before { content: \"\\e165\"; } }\n.glyphicon-save                   { &:before { content: \"\\e166\"; } }\n.glyphicon-open                   { &:before { content: \"\\e167\"; } }\n.glyphicon-saved                  { &:before { content: \"\\e168\"; } }\n.glyphicon-import                 { &:before { content: \"\\e169\"; } }\n.glyphicon-export                 { &:before { content: \"\\e170\"; } }\n.glyphicon-send                   { &:before { content: \"\\e171\"; } }\n.glyphicon-floppy-disk            { &:before { content: \"\\e172\"; } }\n.glyphicon-floppy-saved           { &:before { content: \"\\e173\"; } }\n.glyphicon-floppy-remove          { &:before { content: \"\\e174\"; } }\n.glyphicon-floppy-save            { &:before { content: \"\\e175\"; } }\n.glyphicon-floppy-open            { &:before { content: \"\\e176\"; } }\n.glyphicon-credit-card            { &:before { content: \"\\e177\"; } }\n.glyphicon-transfer               { &:before { content: \"\\e178\"; } }\n.glyphicon-cutlery                { &:before { content: \"\\e179\"; } }\n.glyphicon-header                 { &:before { content: \"\\e180\"; } }\n.glyphicon-compressed             { &:before { content: \"\\e181\"; } }\n.glyphicon-earphone               { &:before { content: \"\\e182\"; } }\n.glyphicon-phone-alt              { &:before { content: \"\\e183\"; } }\n.glyphicon-tower                  { &:before { content: \"\\e184\"; } }\n.glyphicon-stats                  { &:before { content: \"\\e185\"; } }\n.glyphicon-sd-video               { &:before { content: \"\\e186\"; } }\n.glyphicon-hd-video               { &:before { content: \"\\e187\"; } }\n.glyphicon-subtitles              { &:before { content: \"\\e188\"; } }\n.glyphicon-sound-stereo           { &:before { content: \"\\e189\"; } }\n.glyphicon-sound-dolby            { &:before { content: \"\\e190\"; } }\n.glyphicon-sound-5-1              { &:before { content: \"\\e191\"; } }\n.glyphicon-sound-6-1              { &:before { content: \"\\e192\"; } }\n.glyphicon-sound-7-1              { &:before { content: \"\\e193\"; } }\n.glyphicon-copyright-mark         { &:before { content: \"\\e194\"; } }\n.glyphicon-registration-mark      { &:before { content: \"\\e195\"; } }\n.glyphicon-cloud-download         { &:before { content: \"\\e197\"; } }\n.glyphicon-cloud-upload           { &:before { content: \"\\e198\"; } }\n.glyphicon-tree-conifer           { &:before { content: \"\\e199\"; } }\n.glyphicon-tree-deciduous         { &:before { content: \"\\e200\"; } }\n.glyphicon-cd                     { &:before { content: \"\\e201\"; } }\n.glyphicon-save-file              { &:before { content: \"\\e202\"; } }\n.glyphicon-open-file              { &:before { content: \"\\e203\"; } }\n.glyphicon-level-up               { &:before { content: \"\\e204\"; } }\n.glyphicon-copy                   { &:before { content: \"\\e205\"; } }\n.glyphicon-paste                  { &:before { content: \"\\e206\"; } }\n// The following 2 Glyphicons are omitted for the time being because\n// they currently use Unicode codepoints that are outside the\n// Basic Multilingual Plane (BMP). Older buggy versions of WebKit can't handle\n// non-BMP codepoints in CSS string escapes, and thus can't display these two icons.\n// Notably, the bug affects some older versions of the Android Browser.\n// More info: https://github.com/twbs/bootstrap/issues/10106\n// .glyphicon-door                   { &:before { content: \"\\1f6aa\"; } }\n// .glyphicon-key                    { &:before { content: \"\\1f511\"; } }\n.glyphicon-alert                  { &:before { content: \"\\e209\"; } }\n.glyphicon-equalizer              { &:before { content: \"\\e210\"; } }\n.glyphicon-king                   { &:before { content: \"\\e211\"; } }\n.glyphicon-queen                  { &:before { content: \"\\e212\"; } }\n.glyphicon-pawn                   { &:before { content: \"\\e213\"; } }\n.glyphicon-bishop                 { &:before { content: \"\\e214\"; } }\n.glyphicon-knight                 { &:before { content: \"\\e215\"; } }\n.glyphicon-baby-formula           { &:before { content: \"\\e216\"; } }\n.glyphicon-tent                   { &:before { content: \"\\26fa\"; } }\n.glyphicon-blackboard             { &:before { content: \"\\e218\"; } }\n.glyphicon-bed                    { &:before { content: \"\\e219\"; } }\n.glyphicon-apple                  { &:before { content: \"\\f8ff\"; } }\n.glyphicon-erase                  { &:before { content: \"\\e221\"; } }\n.glyphicon-hourglass              { &:before { content: \"\\231b\"; } }\n.glyphicon-lamp                   { &:before { content: \"\\e223\"; } }\n.glyphicon-duplicate              { &:before { content: \"\\e224\"; } }\n.glyphicon-piggy-bank             { &:before { content: \"\\e225\"; } }\n.glyphicon-scissors               { &:before { content: \"\\e226\"; } }\n.glyphicon-bitcoin                { &:before { content: \"\\e227\"; } }\n.glyphicon-btc                    { &:before { content: \"\\e227\"; } }\n.glyphicon-xbt                    { &:before { content: \"\\e227\"; } }\n.glyphicon-yen                    { &:before { content: \"\\00a5\"; } }\n.glyphicon-jpy                    { &:before { content: \"\\00a5\"; } }\n.glyphicon-ruble                  { &:before { content: \"\\20bd\"; } }\n.glyphicon-rub                    { &:before { content: \"\\20bd\"; } }\n.glyphicon-scale                  { &:before { content: \"\\e230\"; } }\n.glyphicon-ice-lolly              { &:before { content: \"\\e231\"; } }\n.glyphicon-ice-lolly-tasted       { &:before { content: \"\\e232\"; } }\n.glyphicon-education              { &:before { content: \"\\e233\"; } }\n.glyphicon-option-horizontal      { &:before { content: \"\\e234\"; } }\n.glyphicon-option-vertical        { &:before { content: \"\\e235\"; } }\n.glyphicon-menu-hamburger         { &:before { content: \"\\e236\"; } }\n.glyphicon-modal-window           { &:before { content: \"\\e237\"; } }\n.glyphicon-oil                    { &:before { content: \"\\e238\"; } }\n.glyphicon-grain                  { &:before { content: \"\\e239\"; } }\n.glyphicon-sunglasses             { &:before { content: \"\\e240\"; } }\n.glyphicon-text-size              { &:before { content: \"\\e241\"; } }\n.glyphicon-text-color             { &:before { content: \"\\e242\"; } }\n.glyphicon-text-background        { &:before { content: \"\\e243\"; } }\n.glyphicon-object-align-top       { &:before { content: \"\\e244\"; } }\n.glyphicon-object-align-bottom    { &:before { content: \"\\e245\"; } }\n.glyphicon-object-align-horizontal{ &:before { content: \"\\e246\"; } }\n.glyphicon-object-align-left      { &:before { content: \"\\e247\"; } }\n.glyphicon-object-align-vertical  { &:before { content: \"\\e248\"; } }\n.glyphicon-object-align-right     { &:before { content: \"\\e249\"; } }\n.glyphicon-triangle-right         { &:before { content: \"\\e250\"; } }\n.glyphicon-triangle-left          { &:before { content: \"\\e251\"; } }\n.glyphicon-triangle-bottom        { &:before { content: \"\\e252\"; } }\n.glyphicon-triangle-top           { &:before { content: \"\\e253\"; } }\n.glyphicon-console                { &:before { content: \"\\e254\"; } }\n.glyphicon-superscript            { &:before { content: \"\\e255\"; } }\n.glyphicon-subscript              { &:before { content: \"\\e256\"; } }\n.glyphicon-menu-left              { &:before { content: \"\\e257\"; } }\n.glyphicon-menu-right             { &:before { content: \"\\e258\"; } }\n.glyphicon-menu-down              { &:before { content: \"\\e259\"; } }\n.glyphicon-menu-up                { &:before { content: \"\\e260\"; } }\n","//\n// Scaffolding\n// --------------------------------------------------\n\n\n// Reset the box-sizing\n//\n// Heads up! This reset may cause conflicts with some third-party widgets.\n// For recommendations on resolving such conflicts, see\n// http://getbootstrap.com/getting-started/#third-box-sizing\n* {\n  .box-sizing(border-box);\n}\n*:before,\n*:after {\n  .box-sizing(border-box);\n}\n\n\n// Body reset\n\nhtml {\n  font-size: 10px;\n  -webkit-tap-highlight-color: rgba(0,0,0,0);\n}\n\nbody {\n  font-family: @font-family-base;\n  font-size: @font-size-base;\n  line-height: @line-height-base;\n  color: @text-color;\n  background-color: @body-bg;\n}\n\n// Reset fonts for relevant elements\ninput,\nbutton,\nselect,\ntextarea {\n  font-family: inherit;\n  font-size: inherit;\n  line-height: inherit;\n}\n\n\n// Links\n\na {\n  color: @link-color;\n  text-decoration: none;\n\n  &:hover,\n  &:focus {\n    color: @link-hover-color;\n    text-decoration: @link-hover-decoration;\n  }\n\n  &:focus {\n    .tab-focus();\n  }\n}\n\n\n// Figures\n//\n// We reset this here because previously Normalize had no `figure` margins. This\n// ensures we don't break anyone's use of the element.\n\nfigure {\n  margin: 0;\n}\n\n\n// Images\n\nimg {\n  vertical-align: middle;\n}\n\n// Responsive images (ensure images don't scale beyond their parents)\n.img-responsive {\n  .img-responsive();\n}\n\n// Rounded corners\n.img-rounded {\n  border-radius: @border-radius-large;\n}\n\n// Image thumbnails\n//\n// Heads up! This is mixin-ed into thumbnails.less for `.thumbnail`.\n.img-thumbnail {\n  padding: @thumbnail-padding;\n  line-height: @line-height-base;\n  background-color: @thumbnail-bg;\n  border: 1px solid @thumbnail-border;\n  border-radius: @thumbnail-border-radius;\n  .transition(all .2s ease-in-out);\n\n  // Keep them at most 100% wide\n  .img-responsive(inline-block);\n}\n\n// Perfect circle\n.img-circle {\n  border-radius: 50%; // set radius in percents\n}\n\n\n// Horizontal rules\n\nhr {\n  margin-top:    @line-height-computed;\n  margin-bottom: @line-height-computed;\n  border: 0;\n  border-top: 1px solid @hr-border;\n}\n\n\n// Only display content to screen readers\n//\n// See: http://a11yproject.com/posts/how-to-hide-content\n\n.sr-only {\n  position: absolute;\n  width: 1px;\n  height: 1px;\n  margin: -1px;\n  padding: 0;\n  overflow: hidden;\n  clip: rect(0,0,0,0);\n  border: 0;\n}\n\n// Use in conjunction with .sr-only to only display content when it's focused.\n// Useful for \"Skip to main content\" links; see http://www.w3.org/TR/2013/NOTE-WCAG20-TECHS-20130905/G1\n// Credit: HTML5 Boilerplate\n\n.sr-only-focusable {\n  &:active,\n  &:focus {\n    position: static;\n    width: auto;\n    height: auto;\n    margin: 0;\n    overflow: visible;\n    clip: auto;\n  }\n}\n\n\n// iOS \"clickable elements\" fix for role=\"button\"\n//\n// Fixes \"clickability\" issue (and more generally, the firing of events such as focus as well)\n// for traditionally non-focusable elements with role=\"button\"\n// see https://developer.mozilla.org/en-US/docs/Web/Events/click#Safari_Mobile\n\n[role=\"button\"] {\n  cursor: pointer;\n}\n","// Vendor Prefixes\n//\n// All vendor mixins are deprecated as of v3.2.0 due to the introduction of\n// Autoprefixer in our Gruntfile. They have been removed in v4.\n\n// - Animations\n// - Backface visibility\n// - Box shadow\n// - Box sizing\n// - Content columns\n// - Hyphens\n// - Placeholder text\n// - Transformations\n// - Transitions\n// - User Select\n\n\n// Animations\n.animation(@animation) {\n  -webkit-animation: @animation;\n       -o-animation: @animation;\n          animation: @animation;\n}\n.animation-name(@name) {\n  -webkit-animation-name: @name;\n          animation-name: @name;\n}\n.animation-duration(@duration) {\n  -webkit-animation-duration: @duration;\n          animation-duration: @duration;\n}\n.animation-timing-function(@timing-function) {\n  -webkit-animation-timing-function: @timing-function;\n          animation-timing-function: @timing-function;\n}\n.animation-delay(@delay) {\n  -webkit-animation-delay: @delay;\n          animation-delay: @delay;\n}\n.animation-iteration-count(@iteration-count) {\n  -webkit-animation-iteration-count: @iteration-count;\n          animation-iteration-count: @iteration-count;\n}\n.animation-direction(@direction) {\n  -webkit-animation-direction: @direction;\n          animation-direction: @direction;\n}\n.animation-fill-mode(@fill-mode) {\n  -webkit-animation-fill-mode: @fill-mode;\n          animation-fill-mode: @fill-mode;\n}\n\n// Backface visibility\n// Prevent browsers from flickering when using CSS 3D transforms.\n// Default value is `visible`, but can be changed to `hidden`\n\n.backface-visibility(@visibility) {\n  -webkit-backface-visibility: @visibility;\n     -moz-backface-visibility: @visibility;\n          backface-visibility: @visibility;\n}\n\n// Drop shadows\n//\n// Note: Deprecated `.box-shadow()` as of v3.1.0 since all of Bootstrap's\n// supported browsers that have box shadow capabilities now support it.\n\n.box-shadow(@shadow) {\n  -webkit-box-shadow: @shadow; // iOS <4.3 & Android <4.1\n          box-shadow: @shadow;\n}\n\n// Box sizing\n.box-sizing(@boxmodel) {\n  -webkit-box-sizing: @boxmodel;\n     -moz-box-sizing: @boxmodel;\n          box-sizing: @boxmodel;\n}\n\n// CSS3 Content Columns\n.content-columns(@column-count; @column-gap: @grid-gutter-width) {\n  -webkit-column-count: @column-count;\n     -moz-column-count: @column-count;\n          column-count: @column-count;\n  -webkit-column-gap: @column-gap;\n     -moz-column-gap: @column-gap;\n          column-gap: @column-gap;\n}\n\n// Optional hyphenation\n.hyphens(@mode: auto) {\n  word-wrap: break-word;\n  -webkit-hyphens: @mode;\n     -moz-hyphens: @mode;\n      -ms-hyphens: @mode; // IE10+\n       -o-hyphens: @mode;\n          hyphens: @mode;\n}\n\n// Placeholder text\n.placeholder(@color: @input-color-placeholder) {\n  // Firefox\n  &::-moz-placeholder {\n    color: @color;\n    opacity: 1; // Override Firefox's unusual default opacity; see https://github.com/twbs/bootstrap/pull/11526\n  }\n  &:-ms-input-placeholder { color: @color; } // Internet Explorer 10+\n  &::-webkit-input-placeholder  { color: @color; } // Safari and Chrome\n}\n\n// Transformations\n.scale(@ratio) {\n  -webkit-transform: scale(@ratio);\n      -ms-transform: scale(@ratio); // IE9 only\n       -o-transform: scale(@ratio);\n          transform: scale(@ratio);\n}\n.scale(@ratioX; @ratioY) {\n  -webkit-transform: scale(@ratioX, @ratioY);\n      -ms-transform: scale(@ratioX, @ratioY); // IE9 only\n       -o-transform: scale(@ratioX, @ratioY);\n          transform: scale(@ratioX, @ratioY);\n}\n.scaleX(@ratio) {\n  -webkit-transform: scaleX(@ratio);\n      -ms-transform: scaleX(@ratio); // IE9 only\n       -o-transform: scaleX(@ratio);\n          transform: scaleX(@ratio);\n}\n.scaleY(@ratio) {\n  -webkit-transform: scaleY(@ratio);\n      -ms-transform: scaleY(@ratio); // IE9 only\n       -o-transform: scaleY(@ratio);\n          transform: scaleY(@ratio);\n}\n.skew(@x; @y) {\n  -webkit-transform: skewX(@x) skewY(@y);\n      -ms-transform: skewX(@x) skewY(@y); // See https://github.com/twbs/bootstrap/issues/4885; IE9+\n       -o-transform: skewX(@x) skewY(@y);\n          transform: skewX(@x) skewY(@y);\n}\n.translate(@x; @y) {\n  -webkit-transform: translate(@x, @y);\n      -ms-transform: translate(@x, @y); // IE9 only\n       -o-transform: translate(@x, @y);\n          transform: translate(@x, @y);\n}\n.translate3d(@x; @y; @z) {\n  -webkit-transform: translate3d(@x, @y, @z);\n          transform: translate3d(@x, @y, @z);\n}\n.rotate(@degrees) {\n  -webkit-transform: rotate(@degrees);\n      -ms-transform: rotate(@degrees); // IE9 only\n       -o-transform: rotate(@degrees);\n          transform: rotate(@degrees);\n}\n.rotateX(@degrees) {\n  -webkit-transform: rotateX(@degrees);\n      -ms-transform: rotateX(@degrees); // IE9 only\n       -o-transform: rotateX(@degrees);\n          transform: rotateX(@degrees);\n}\n.rotateY(@degrees) {\n  -webkit-transform: rotateY(@degrees);\n      -ms-transform: rotateY(@degrees); // IE9 only\n       -o-transform: rotateY(@degrees);\n          transform: rotateY(@degrees);\n}\n.perspective(@perspective) {\n  -webkit-perspective: @perspective;\n     -moz-perspective: @perspective;\n          perspective: @perspective;\n}\n.perspective-origin(@perspective) {\n  -webkit-perspective-origin: @perspective;\n     -moz-perspective-origin: @perspective;\n          perspective-origin: @perspective;\n}\n.transform-origin(@origin) {\n  -webkit-transform-origin: @origin;\n     -moz-transform-origin: @origin;\n      -ms-transform-origin: @origin; // IE9 only\n          transform-origin: @origin;\n}\n\n\n// Transitions\n\n.transition(@transition) {\n  -webkit-transition: @transition;\n       -o-transition: @transition;\n          transition: @transition;\n}\n.transition-property(@transition-property) {\n  -webkit-transition-property: @transition-property;\n          transition-property: @transition-property;\n}\n.transition-delay(@transition-delay) {\n  -webkit-transition-delay: @transition-delay;\n          transition-delay: @transition-delay;\n}\n.transition-duration(@transition-duration) {\n  -webkit-transition-duration: @transition-duration;\n          transition-duration: @transition-duration;\n}\n.transition-timing-function(@timing-function) {\n  -webkit-transition-timing-function: @timing-function;\n          transition-timing-function: @timing-function;\n}\n.transition-transform(@transition) {\n  -webkit-transition: -webkit-transform @transition;\n     -moz-transition: -moz-transform @transition;\n       -o-transition: -o-transform @transition;\n          transition: transform @transition;\n}\n\n\n// User select\n// For selecting text on the page\n\n.user-select(@select) {\n  -webkit-user-select: @select;\n     -moz-user-select: @select;\n      -ms-user-select: @select; // IE10+\n          user-select: @select;\n}\n","// WebKit-style focus\n\n.tab-focus() {\n  // WebKit-specific. Other browsers will keep their default outline style.\n  // (Initially tried to also force default via `outline: initial`,\n  // but that seems to erroneously remove the outline in Firefox altogether.)\n  outline: 5px auto -webkit-focus-ring-color;\n  outline-offset: -2px;\n}\n","// Image Mixins\n// - Responsive image\n// - Retina image\n\n\n// Responsive image\n//\n// Keep images from scaling beyond the width of their parents.\n.img-responsive(@display: block) {\n  display: @display;\n  max-width: 100%; // Part 1: Set a maximum relative to the parent\n  height: auto; // Part 2: Scale the height according to the width, otherwise you get stretching\n}\n\n\n// Retina image\n//\n// Short retina mixin for setting background-image and -size. Note that the\n// spelling of `min--moz-device-pixel-ratio` is intentional.\n.img-retina(@file-1x; @file-2x; @width-1x; @height-1x) {\n  background-image: url(\"@{file-1x}\");\n\n  @media\n  only screen and (-webkit-min-device-pixel-ratio: 2),\n  only screen and (   min--moz-device-pixel-ratio: 2),\n  only screen and (     -o-min-device-pixel-ratio: 2/1),\n  only screen and (        min-device-pixel-ratio: 2),\n  only screen and (                min-resolution: 192dpi),\n  only screen and (                min-resolution: 2dppx) {\n    background-image: url(\"@{file-2x}\");\n    background-size: @width-1x @height-1x;\n  }\n}\n","//\n// Typography\n// --------------------------------------------------\n\n\n// Headings\n// -------------------------\n\nh1, h2, h3, h4, h5, h6,\n.h1, .h2, .h3, .h4, .h5, .h6 {\n  font-family: @headings-font-family;\n  font-weight: @headings-font-weight;\n  line-height: @headings-line-height;\n  color: @headings-color;\n\n  small,\n  .small {\n    font-weight: normal;\n    line-height: 1;\n    color: @headings-small-color;\n  }\n}\n\nh1, .h1,\nh2, .h2,\nh3, .h3 {\n  margin-top: @line-height-computed;\n  margin-bottom: (@line-height-computed / 2);\n\n  small,\n  .small {\n    font-size: 65%;\n  }\n}\nh4, .h4,\nh5, .h5,\nh6, .h6 {\n  margin-top: (@line-height-computed / 2);\n  margin-bottom: (@line-height-computed / 2);\n\n  small,\n  .small {\n    font-size: 75%;\n  }\n}\n\nh1, .h1 { font-size: @font-size-h1; }\nh2, .h2 { font-size: @font-size-h2; }\nh3, .h3 { font-size: @font-size-h3; }\nh4, .h4 { font-size: @font-size-h4; }\nh5, .h5 { font-size: @font-size-h5; }\nh6, .h6 { font-size: @font-size-h6; }\n\n\n// Body text\n// -------------------------\n\np {\n  margin: 0 0 (@line-height-computed / 2);\n}\n\n.lead {\n  margin-bottom: @line-height-computed;\n  font-size: floor((@font-size-base * 1.15));\n  font-weight: 300;\n  line-height: 1.4;\n\n  @media (min-width: @screen-sm-min) {\n    font-size: (@font-size-base * 1.5);\n  }\n}\n\n\n// Emphasis & misc\n// -------------------------\n\n// Ex: (12px small font / 14px base font) * 100% = about 85%\nsmall,\n.small {\n  font-size: floor((100% * @font-size-small / @font-size-base));\n}\n\nmark,\n.mark {\n  background-color: @state-warning-bg;\n  padding: .2em;\n}\n\n// Alignment\n.text-left           { text-align: left; }\n.text-right          { text-align: right; }\n.text-center         { text-align: center; }\n.text-justify        { text-align: justify; }\n.text-nowrap         { white-space: nowrap; }\n\n// Transformation\n.text-lowercase      { text-transform: lowercase; }\n.text-uppercase      { text-transform: uppercase; }\n.text-capitalize     { text-transform: capitalize; }\n\n// Contextual colors\n.text-muted {\n  color: @text-muted;\n}\n.text-primary {\n  .text-emphasis-variant(@brand-primary);\n}\n.text-success {\n  .text-emphasis-variant(@state-success-text);\n}\n.text-info {\n  .text-emphasis-variant(@state-info-text);\n}\n.text-warning {\n  .text-emphasis-variant(@state-warning-text);\n}\n.text-danger {\n  .text-emphasis-variant(@state-danger-text);\n}\n\n// Contextual backgrounds\n// For now we'll leave these alongside the text classes until v4 when we can\n// safely shift things around (per SemVer rules).\n.bg-primary {\n  // Given the contrast here, this is the only class to have its color inverted\n  // automatically.\n  color: #fff;\n  .bg-variant(@brand-primary);\n}\n.bg-success {\n  .bg-variant(@state-success-bg);\n}\n.bg-info {\n  .bg-variant(@state-info-bg);\n}\n.bg-warning {\n  .bg-variant(@state-warning-bg);\n}\n.bg-danger {\n  .bg-variant(@state-danger-bg);\n}\n\n\n// Page header\n// -------------------------\n\n.page-header {\n  padding-bottom: ((@line-height-computed / 2) - 1);\n  margin: (@line-height-computed * 2) 0 @line-height-computed;\n  border-bottom: 1px solid @page-header-border-color;\n}\n\n\n// Lists\n// -------------------------\n\n// Unordered and Ordered lists\nul,\nol {\n  margin-top: 0;\n  margin-bottom: (@line-height-computed / 2);\n  ul,\n  ol {\n    margin-bottom: 0;\n  }\n}\n\n// List options\n\n// Unstyled keeps list items block level, just removes default browser padding and list-style\n.list-unstyled {\n  padding-left: 0;\n  list-style: none;\n}\n\n// Inline turns list items into inline-block\n.list-inline {\n  .list-unstyled();\n  margin-left: -5px;\n\n  > li {\n    display: inline-block;\n    padding-left: 5px;\n    padding-right: 5px;\n  }\n}\n\n// Description Lists\ndl {\n  margin-top: 0; // Remove browser default\n  margin-bottom: @line-height-computed;\n}\ndt,\ndd {\n  line-height: @line-height-base;\n}\ndt {\n  font-weight: bold;\n}\ndd {\n  margin-left: 0; // Undo browser default\n}\n\n// Horizontal description lists\n//\n// Defaults to being stacked without any of the below styles applied, until the\n// grid breakpoint is reached (default of ~768px).\n\n.dl-horizontal {\n  dd {\n    &:extend(.clearfix all); // Clear the floated `dt` if an empty `dd` is present\n  }\n\n  @media (min-width: @dl-horizontal-breakpoint) {\n    dt {\n      float: left;\n      width: (@dl-horizontal-offset - 20);\n      clear: left;\n      text-align: right;\n      .text-overflow();\n    }\n    dd {\n      margin-left: @dl-horizontal-offset;\n    }\n  }\n}\n\n\n// Misc\n// -------------------------\n\n// Abbreviations and acronyms\nabbr[title],\n// Add data-* attribute to help out our tooltip plugin, per https://github.com/twbs/bootstrap/issues/5257\nabbr[data-original-title] {\n  cursor: help;\n  border-bottom: 1px dotted @abbr-border-color;\n}\n.initialism {\n  font-size: 90%;\n  .text-uppercase();\n}\n\n// Blockquotes\nblockquote {\n  padding: (@line-height-computed / 2) @line-height-computed;\n  margin: 0 0 @line-height-computed;\n  font-size: @blockquote-font-size;\n  border-left: 5px solid @blockquote-border-color;\n\n  p,\n  ul,\n  ol {\n    &:last-child {\n      margin-bottom: 0;\n    }\n  }\n\n  // Note: Deprecated small and .small as of v3.1.0\n  // Context: https://github.com/twbs/bootstrap/issues/11660\n  footer,\n  small,\n  .small {\n    display: block;\n    font-size: 80%; // back to default font-size\n    line-height: @line-height-base;\n    color: @blockquote-small-color;\n\n    &:before {\n      content: '\\2014 \\00A0'; // em dash, nbsp\n    }\n  }\n}\n\n// Opposite alignment of blockquote\n//\n// Heads up: `blockquote.pull-right` has been deprecated as of v3.1.0.\n.blockquote-reverse,\nblockquote.pull-right {\n  padding-right: 15px;\n  padding-left: 0;\n  border-right: 5px solid @blockquote-border-color;\n  border-left: 0;\n  text-align: right;\n\n  // Account for citation\n  footer,\n  small,\n  .small {\n    &:before { content: ''; }\n    &:after {\n      content: '\\00A0 \\2014'; // nbsp, em dash\n    }\n  }\n}\n\n// Addresses\naddress {\n  margin-bottom: @line-height-computed;\n  font-style: normal;\n  line-height: @line-height-base;\n}\n","// Typography\n\n.text-emphasis-variant(@color) {\n  color: @color;\n  a&:hover,\n  a&:focus {\n    color: darken(@color, 10%);\n  }\n}\n","// Contextual backgrounds\n\n.bg-variant(@color) {\n  background-color: @color;\n  a&:hover,\n  a&:focus {\n    background-color: darken(@color, 10%);\n  }\n}\n","// Text overflow\n// Requires inline-block or block for proper styling\n\n.text-overflow() {\n  overflow: hidden;\n  text-overflow: ellipsis;\n  white-space: nowrap;\n}\n","//\n// Code (inline and block)\n// --------------------------------------------------\n\n\n// Inline and block code styles\ncode,\nkbd,\npre,\nsamp {\n  font-family: @font-family-monospace;\n}\n\n// Inline code\ncode {\n  padding: 2px 4px;\n  font-size: 90%;\n  color: @code-color;\n  background-color: @code-bg;\n  border-radius: @border-radius-base;\n}\n\n// User input typically entered via keyboard\nkbd {\n  padding: 2px 4px;\n  font-size: 90%;\n  color: @kbd-color;\n  background-color: @kbd-bg;\n  border-radius: @border-radius-small;\n  box-shadow: inset 0 -1px 0 rgba(0,0,0,.25);\n\n  kbd {\n    padding: 0;\n    font-size: 100%;\n    font-weight: bold;\n    box-shadow: none;\n  }\n}\n\n// Blocks of code\npre {\n  display: block;\n  padding: ((@line-height-computed - 1) / 2);\n  margin: 0 0 (@line-height-computed / 2);\n  font-size: (@font-size-base - 1); // 14px to 13px\n  line-height: @line-height-base;\n  word-break: break-all;\n  word-wrap: break-word;\n  color: @pre-color;\n  background-color: @pre-bg;\n  border: 1px solid @pre-border-color;\n  border-radius: @border-radius-base;\n\n  // Account for some code outputs that place code tags in pre tags\n  code {\n    padding: 0;\n    font-size: inherit;\n    color: inherit;\n    white-space: pre-wrap;\n    background-color: transparent;\n    border-radius: 0;\n  }\n}\n\n// Enable scrollable blocks of code\n.pre-scrollable {\n  max-height: @pre-scrollable-max-height;\n  overflow-y: scroll;\n}\n","//\n// Grid system\n// --------------------------------------------------\n\n\n// Container widths\n//\n// Set the container width, and override it for fixed navbars in media queries.\n\n.container {\n  .container-fixed();\n\n  @media (min-width: @screen-sm-min) {\n    width: @container-sm;\n  }\n  @media (min-width: @screen-md-min) {\n    width: @container-md;\n  }\n  @media (min-width: @screen-lg-min) {\n    width: @container-lg;\n  }\n}\n\n\n// Fluid container\n//\n// Utilizes the mixin meant for fixed width containers, but without any defined\n// width for fluid, full width layouts.\n\n.container-fluid {\n  .container-fixed();\n}\n\n\n// Row\n//\n// Rows contain and clear the floats of your columns.\n\n.row {\n  .make-row();\n}\n\n\n// Columns\n//\n// Common styles for small and large grid columns\n\n.make-grid-columns();\n\n\n// Extra small grid\n//\n// Columns, offsets, pushes, and pulls for extra small devices like\n// smartphones.\n\n.make-grid(xs);\n\n\n// Small grid\n//\n// Columns, offsets, pushes, and pulls for the small device range, from phones\n// to tablets.\n\n@media (min-width: @screen-sm-min) {\n  .make-grid(sm);\n}\n\n\n// Medium grid\n//\n// Columns, offsets, pushes, and pulls for the desktop device range.\n\n@media (min-width: @screen-md-min) {\n  .make-grid(md);\n}\n\n\n// Large grid\n//\n// Columns, offsets, pushes, and pulls for the large desktop device range.\n\n@media (min-width: @screen-lg-min) {\n  .make-grid(lg);\n}\n","// Grid system\n//\n// Generate semantic grid columns with these mixins.\n\n// Centered container element\n.container-fixed(@gutter: @grid-gutter-width) {\n  margin-right: auto;\n  margin-left: auto;\n  padding-left:  floor((@gutter / 2));\n  padding-right: ceil((@gutter / 2));\n  &:extend(.clearfix all);\n}\n\n// Creates a wrapper for a series of columns\n.make-row(@gutter: @grid-gutter-width) {\n  margin-left:  ceil((@gutter / -2));\n  margin-right: floor((@gutter / -2));\n  &:extend(.clearfix all);\n}\n\n// Generate the extra small columns\n.make-xs-column(@columns; @gutter: @grid-gutter-width) {\n  position: relative;\n  float: left;\n  width: percentage((@columns / @grid-columns));\n  min-height: 1px;\n  padding-left:  (@gutter / 2);\n  padding-right: (@gutter / 2);\n}\n.make-xs-column-offset(@columns) {\n  margin-left: percentage((@columns / @grid-columns));\n}\n.make-xs-column-push(@columns) {\n  left: percentage((@columns / @grid-columns));\n}\n.make-xs-column-pull(@columns) {\n  right: percentage((@columns / @grid-columns));\n}\n\n// Generate the small columns\n.make-sm-column(@columns; @gutter: @grid-gutter-width) {\n  position: relative;\n  min-height: 1px;\n  padding-left:  (@gutter / 2);\n  padding-right: (@gutter / 2);\n\n  @media (min-width: @screen-sm-min) {\n    float: left;\n    width: percentage((@columns / @grid-columns));\n  }\n}\n.make-sm-column-offset(@columns) {\n  @media (min-width: @screen-sm-min) {\n    margin-left: percentage((@columns / @grid-columns));\n  }\n}\n.make-sm-column-push(@columns) {\n  @media (min-width: @screen-sm-min) {\n    left: percentage((@columns / @grid-columns));\n  }\n}\n.make-sm-column-pull(@columns) {\n  @media (min-width: @screen-sm-min) {\n    right: percentage((@columns / @grid-columns));\n  }\n}\n\n// Generate the medium columns\n.make-md-column(@columns; @gutter: @grid-gutter-width) {\n  position: relative;\n  min-height: 1px;\n  padding-left:  (@gutter / 2);\n  padding-right: (@gutter / 2);\n\n  @media (min-width: @screen-md-min) {\n    float: left;\n    width: percentage((@columns / @grid-columns));\n  }\n}\n.make-md-column-offset(@columns) {\n  @media (min-width: @screen-md-min) {\n    margin-left: percentage((@columns / @grid-columns));\n  }\n}\n.make-md-column-push(@columns) {\n  @media (min-width: @screen-md-min) {\n    left: percentage((@columns / @grid-columns));\n  }\n}\n.make-md-column-pull(@columns) {\n  @media (min-width: @screen-md-min) {\n    right: percentage((@columns / @grid-columns));\n  }\n}\n\n// Generate the large columns\n.make-lg-column(@columns; @gutter: @grid-gutter-width) {\n  position: relative;\n  min-height: 1px;\n  padding-left:  (@gutter / 2);\n  padding-right: (@gutter / 2);\n\n  @media (min-width: @screen-lg-min) {\n    float: left;\n    width: percentage((@columns / @grid-columns));\n  }\n}\n.make-lg-column-offset(@columns) {\n  @media (min-width: @screen-lg-min) {\n    margin-left: percentage((@columns / @grid-columns));\n  }\n}\n.make-lg-column-push(@columns) {\n  @media (min-width: @screen-lg-min) {\n    left: percentage((@columns / @grid-columns));\n  }\n}\n.make-lg-column-pull(@columns) {\n  @media (min-width: @screen-lg-min) {\n    right: percentage((@columns / @grid-columns));\n  }\n}\n","// Framework grid generation\n//\n// Used only by Bootstrap to generate the correct number of grid classes given\n// any value of `@grid-columns`.\n\n.make-grid-columns() {\n  // Common styles for all sizes of grid columns, widths 1-12\n  .col(@index) { // initial\n    @item: ~\".col-xs-@{index}, .col-sm-@{index}, .col-md-@{index}, .col-lg-@{index}\";\n    .col((@index + 1), @item);\n  }\n  .col(@index, @list) when (@index =< @grid-columns) { // general; \"=<\" isn't a typo\n    @item: ~\".col-xs-@{index}, .col-sm-@{index}, .col-md-@{index}, .col-lg-@{index}\";\n    .col((@index + 1), ~\"@{list}, @{item}\");\n  }\n  .col(@index, @list) when (@index > @grid-columns) { // terminal\n    @{list} {\n      position: relative;\n      // Prevent columns from collapsing when empty\n      min-height: 1px;\n      // Inner gutter via padding\n      padding-left:  ceil((@grid-gutter-width / 2));\n      padding-right: floor((@grid-gutter-width / 2));\n    }\n  }\n  .col(1); // kickstart it\n}\n\n.float-grid-columns(@class) {\n  .col(@index) { // initial\n    @item: ~\".col-@{class}-@{index}\";\n    .col((@index + 1), @item);\n  }\n  .col(@index, @list) when (@index =< @grid-columns) { // general\n    @item: ~\".col-@{class}-@{index}\";\n    .col((@index + 1), ~\"@{list}, @{item}\");\n  }\n  .col(@index, @list) when (@index > @grid-columns) { // terminal\n    @{list} {\n      float: left;\n    }\n  }\n  .col(1); // kickstart it\n}\n\n.calc-grid-column(@index, @class, @type) when (@type = width) and (@index > 0) {\n  .col-@{class}-@{index} {\n    width: percentage((@index / @grid-columns));\n  }\n}\n.calc-grid-column(@index, @class, @type) when (@type = push) and (@index > 0) {\n  .col-@{class}-push-@{index} {\n    left: percentage((@index / @grid-columns));\n  }\n}\n.calc-grid-column(@index, @class, @type) when (@type = push) and (@index = 0) {\n  .col-@{class}-push-0 {\n    left: auto;\n  }\n}\n.calc-grid-column(@index, @class, @type) when (@type = pull) and (@index > 0) {\n  .col-@{class}-pull-@{index} {\n    right: percentage((@index / @grid-columns));\n  }\n}\n.calc-grid-column(@index, @class, @type) when (@type = pull) and (@index = 0) {\n  .col-@{class}-pull-0 {\n    right: auto;\n  }\n}\n.calc-grid-column(@index, @class, @type) when (@type = offset) {\n  .col-@{class}-offset-@{index} {\n    margin-left: percentage((@index / @grid-columns));\n  }\n}\n\n// Basic looping in LESS\n.loop-grid-columns(@index, @class, @type) when (@index >= 0) {\n  .calc-grid-column(@index, @class, @type);\n  // next iteration\n  .loop-grid-columns((@index - 1), @class, @type);\n}\n\n// Create grid for specific class\n.make-grid(@class) {\n  .float-grid-columns(@class);\n  .loop-grid-columns(@grid-columns, @class, width);\n  .loop-grid-columns(@grid-columns, @class, pull);\n  .loop-grid-columns(@grid-columns, @class, push);\n  .loop-grid-columns(@grid-columns, @class, offset);\n}\n","//\n// Tables\n// --------------------------------------------------\n\n\ntable {\n  background-color: @table-bg;\n}\ncaption {\n  padding-top: @table-cell-padding;\n  padding-bottom: @table-cell-padding;\n  color: @text-muted;\n  text-align: left;\n}\nth {\n  text-align: left;\n}\n\n\n// Baseline styles\n\n.table {\n  width: 100%;\n  max-width: 100%;\n  margin-bottom: @line-height-computed;\n  // Cells\n  > thead,\n  > tbody,\n  > tfoot {\n    > tr {\n      > th,\n      > td {\n        padding: @table-cell-padding;\n        line-height: @line-height-base;\n        vertical-align: top;\n        border-top: 1px solid @table-border-color;\n      }\n    }\n  }\n  // Bottom align for column headings\n  > thead > tr > th {\n    vertical-align: bottom;\n    border-bottom: 2px solid @table-border-color;\n  }\n  // Remove top border from thead by default\n  > caption + thead,\n  > colgroup + thead,\n  > thead:first-child {\n    > tr:first-child {\n      > th,\n      > td {\n        border-top: 0;\n      }\n    }\n  }\n  // Account for multiple tbody instances\n  > tbody + tbody {\n    border-top: 2px solid @table-border-color;\n  }\n\n  // Nesting\n  .table {\n    background-color: @body-bg;\n  }\n}\n\n\n// Condensed table w/ half padding\n\n.table-condensed {\n  > thead,\n  > tbody,\n  > tfoot {\n    > tr {\n      > th,\n      > td {\n        padding: @table-condensed-cell-padding;\n      }\n    }\n  }\n}\n\n\n// Bordered version\n//\n// Add borders all around the table and between all the columns.\n\n.table-bordered {\n  border: 1px solid @table-border-color;\n  > thead,\n  > tbody,\n  > tfoot {\n    > tr {\n      > th,\n      > td {\n        border: 1px solid @table-border-color;\n      }\n    }\n  }\n  > thead > tr {\n    > th,\n    > td {\n      border-bottom-width: 2px;\n    }\n  }\n}\n\n\n// Zebra-striping\n//\n// Default zebra-stripe styles (alternating gray and transparent backgrounds)\n\n.table-striped {\n  > tbody > tr:nth-of-type(odd) {\n    background-color: @table-bg-accent;\n  }\n}\n\n\n// Hover effect\n//\n// Placed here since it has to come after the potential zebra striping\n\n.table-hover {\n  > tbody > tr:hover {\n    background-color: @table-bg-hover;\n  }\n}\n\n\n// Table cell sizing\n//\n// Reset default table behavior\n\ntable col[class*=\"col-\"] {\n  position: static; // Prevent border hiding in Firefox and IE9-11 (see https://github.com/twbs/bootstrap/issues/11623)\n  float: none;\n  display: table-column;\n}\ntable {\n  td,\n  th {\n    &[class*=\"col-\"] {\n      position: static; // Prevent border hiding in Firefox and IE9-11 (see https://github.com/twbs/bootstrap/issues/11623)\n      float: none;\n      display: table-cell;\n    }\n  }\n}\n\n\n// Table backgrounds\n//\n// Exact selectors below required to override `.table-striped` and prevent\n// inheritance to nested tables.\n\n// Generate the contextual variants\n.table-row-variant(active; @table-bg-active);\n.table-row-variant(success; @state-success-bg);\n.table-row-variant(info; @state-info-bg);\n.table-row-variant(warning; @state-warning-bg);\n.table-row-variant(danger; @state-danger-bg);\n\n\n// Responsive tables\n//\n// Wrap your tables in `.table-responsive` and we'll make them mobile friendly\n// by enabling horizontal scrolling. Only applies <768px. Everything above that\n// will display normally.\n\n.table-responsive {\n  overflow-x: auto;\n  min-height: 0.01%; // Workaround for IE9 bug (see https://github.com/twbs/bootstrap/issues/14837)\n\n  @media screen and (max-width: @screen-xs-max) {\n    width: 100%;\n    margin-bottom: (@line-height-computed * 0.75);\n    overflow-y: hidden;\n    -ms-overflow-style: -ms-autohiding-scrollbar;\n    border: 1px solid @table-border-color;\n\n    // Tighten up spacing\n    > .table {\n      margin-bottom: 0;\n\n      // Ensure the content doesn't wrap\n      > thead,\n      > tbody,\n      > tfoot {\n        > tr {\n          > th,\n          > td {\n            white-space: nowrap;\n          }\n        }\n      }\n    }\n\n    // Special overrides for the bordered tables\n    > .table-bordered {\n      border: 0;\n\n      // Nuke the appropriate borders so that the parent can handle them\n      > thead,\n      > tbody,\n      > tfoot {\n        > tr {\n          > th:first-child,\n          > td:first-child {\n            border-left: 0;\n          }\n          > th:last-child,\n          > td:last-child {\n            border-right: 0;\n          }\n        }\n      }\n\n      // Only nuke the last row's bottom-border in `tbody` and `tfoot` since\n      // chances are there will be only one `tr` in a `thead` and that would\n      // remove the border altogether.\n      > tbody,\n      > tfoot {\n        > tr:last-child {\n          > th,\n          > td {\n            border-bottom: 0;\n          }\n        }\n      }\n\n    }\n  }\n}\n","// Tables\n\n.table-row-variant(@state; @background) {\n  // Exact selectors below required to override `.table-striped` and prevent\n  // inheritance to nested tables.\n  .table > thead > tr,\n  .table > tbody > tr,\n  .table > tfoot > tr {\n    > td.@{state},\n    > th.@{state},\n    &.@{state} > td,\n    &.@{state} > th {\n      background-color: @background;\n    }\n  }\n\n  // Hover states for `.table-hover`\n  // Note: this is not available for cells or rows within `thead` or `tfoot`.\n  .table-hover > tbody > tr {\n    > td.@{state}:hover,\n    > th.@{state}:hover,\n    &.@{state}:hover > td,\n    &:hover > .@{state},\n    &.@{state}:hover > th {\n      background-color: darken(@background, 5%);\n    }\n  }\n}\n","//\n// Forms\n// --------------------------------------------------\n\n\n// Normalize non-controls\n//\n// Restyle and baseline non-control form elements.\n\nfieldset {\n  padding: 0;\n  margin: 0;\n  border: 0;\n  // Chrome and Firefox set a `min-width: min-content;` on fieldsets,\n  // so we reset that to ensure it behaves more like a standard block element.\n  // See https://github.com/twbs/bootstrap/issues/12359.\n  min-width: 0;\n}\n\nlegend {\n  display: block;\n  width: 100%;\n  padding: 0;\n  margin-bottom: @line-height-computed;\n  font-size: (@font-size-base * 1.5);\n  line-height: inherit;\n  color: @legend-color;\n  border: 0;\n  border-bottom: 1px solid @legend-border-color;\n}\n\nlabel {\n  display: inline-block;\n  max-width: 100%; // Force IE8 to wrap long content (see https://github.com/twbs/bootstrap/issues/13141)\n  margin-bottom: 5px;\n  font-weight: bold;\n}\n\n\n// Normalize form controls\n//\n// While most of our form styles require extra classes, some basic normalization\n// is required to ensure optimum display with or without those classes to better\n// address browser inconsistencies.\n\n// Override content-box in Normalize (* isn't specific enough)\ninput[type=\"search\"] {\n  .box-sizing(border-box);\n}\n\n// Position radios and checkboxes better\ninput[type=\"radio\"],\ninput[type=\"checkbox\"] {\n  margin: 4px 0 0;\n  margin-top: 1px \\9; // IE8-9\n  line-height: normal;\n}\n\ninput[type=\"file\"] {\n  display: block;\n}\n\n// Make range inputs behave like textual form controls\ninput[type=\"range\"] {\n  display: block;\n  width: 100%;\n}\n\n// Make multiple select elements height not fixed\nselect[multiple],\nselect[size] {\n  height: auto;\n}\n\n// Focus for file, radio, and checkbox\ninput[type=\"file\"]:focus,\ninput[type=\"radio\"]:focus,\ninput[type=\"checkbox\"]:focus {\n  .tab-focus();\n}\n\n// Adjust output element\noutput {\n  display: block;\n  padding-top: (@padding-base-vertical + 1);\n  font-size: @font-size-base;\n  line-height: @line-height-base;\n  color: @input-color;\n}\n\n\n// Common form controls\n//\n// Shared size and type resets for form controls. Apply `.form-control` to any\n// of the following form controls:\n//\n// select\n// textarea\n// input[type=\"text\"]\n// input[type=\"password\"]\n// input[type=\"datetime\"]\n// input[type=\"datetime-local\"]\n// input[type=\"date\"]\n// input[type=\"month\"]\n// input[type=\"time\"]\n// input[type=\"week\"]\n// input[type=\"number\"]\n// input[type=\"email\"]\n// input[type=\"url\"]\n// input[type=\"search\"]\n// input[type=\"tel\"]\n// input[type=\"color\"]\n\n.form-control {\n  display: block;\n  width: 100%;\n  height: @input-height-base; // Make inputs at least the height of their button counterpart (base line-height + padding + border)\n  padding: @padding-base-vertical @padding-base-horizontal;\n  font-size: @font-size-base;\n  line-height: @line-height-base;\n  color: @input-color;\n  background-color: @input-bg;\n  background-image: none; // Reset unusual Firefox-on-Android default style; see https://github.com/necolas/normalize.css/issues/214\n  border: 1px solid @input-border;\n  border-radius: @input-border-radius; // Note: This has no effect on <select>s in some browsers, due to the limited stylability of <select>s in CSS.\n  .box-shadow(inset 0 1px 1px rgba(0,0,0,.075));\n  .transition(~\"border-color ease-in-out .15s, box-shadow ease-in-out .15s\");\n\n  // Customize the `:focus` state to imitate native WebKit styles.\n  .form-control-focus();\n\n  // Placeholder\n  .placeholder();\n\n  // Unstyle the caret on `<select>`s in IE10+.\n  &::-ms-expand {\n    border: 0;\n    background-color: transparent;\n  }\n\n  // Disabled and read-only inputs\n  //\n  // HTML5 says that controls under a fieldset > legend:first-child won't be\n  // disabled if the fieldset is disabled. Due to implementation difficulty, we\n  // don't honor that edge case; we style them as disabled anyway.\n  &[disabled],\n  &[readonly],\n  fieldset[disabled] & {\n    background-color: @input-bg-disabled;\n    opacity: 1; // iOS fix for unreadable disabled content; see https://github.com/twbs/bootstrap/issues/11655\n  }\n\n  &[disabled],\n  fieldset[disabled] & {\n    cursor: @cursor-disabled;\n  }\n\n  // Reset height for `textarea`s\n  textarea& {\n    height: auto;\n  }\n}\n\n\n// Search inputs in iOS\n//\n// This overrides the extra rounded corners on search inputs in iOS so that our\n// `.form-control` class can properly style them. Note that this cannot simply\n// be added to `.form-control` as it's not specific enough. For details, see\n// https://github.com/twbs/bootstrap/issues/11586.\n\ninput[type=\"search\"] {\n  -webkit-appearance: none;\n}\n\n\n// Special styles for iOS temporal inputs\n//\n// In Mobile Safari, setting `display: block` on temporal inputs causes the\n// text within the input to become vertically misaligned. As a workaround, we\n// set a pixel line-height that matches the given height of the input, but only\n// for Safari. See https://bugs.webkit.org/show_bug.cgi?id=139848\n//\n// Note that as of 9.3, iOS doesn't support `week`.\n\n@media screen and (-webkit-min-device-pixel-ratio: 0) {\n  input[type=\"date\"],\n  input[type=\"time\"],\n  input[type=\"datetime-local\"],\n  input[type=\"month\"] {\n    &.form-control {\n      line-height: @input-height-base;\n    }\n\n    &.input-sm,\n    .input-group-sm & {\n      line-height: @input-height-small;\n    }\n\n    &.input-lg,\n    .input-group-lg & {\n      line-height: @input-height-large;\n    }\n  }\n}\n\n\n// Form groups\n//\n// Designed to help with the organization and spacing of vertical forms. For\n// horizontal forms, use the predefined grid classes.\n\n.form-group {\n  margin-bottom: @form-group-margin-bottom;\n}\n\n\n// Checkboxes and radios\n//\n// Indent the labels to position radios/checkboxes as hanging controls.\n\n.radio,\n.checkbox {\n  position: relative;\n  display: block;\n  margin-top: 10px;\n  margin-bottom: 10px;\n\n  label {\n    min-height: @line-height-computed; // Ensure the input doesn't jump when there is no text\n    padding-left: 20px;\n    margin-bottom: 0;\n    font-weight: normal;\n    cursor: pointer;\n  }\n}\n.radio input[type=\"radio\"],\n.radio-inline input[type=\"radio\"],\n.checkbox input[type=\"checkbox\"],\n.checkbox-inline input[type=\"checkbox\"] {\n  position: absolute;\n  margin-left: -20px;\n  margin-top: 4px \\9;\n}\n\n.radio + .radio,\n.checkbox + .checkbox {\n  margin-top: -5px; // Move up sibling radios or checkboxes for tighter spacing\n}\n\n// Radios and checkboxes on same line\n.radio-inline,\n.checkbox-inline {\n  position: relative;\n  display: inline-block;\n  padding-left: 20px;\n  margin-bottom: 0;\n  vertical-align: middle;\n  font-weight: normal;\n  cursor: pointer;\n}\n.radio-inline + .radio-inline,\n.checkbox-inline + .checkbox-inline {\n  margin-top: 0;\n  margin-left: 10px; // space out consecutive inline controls\n}\n\n// Apply same disabled cursor tweak as for inputs\n// Some special care is needed because <label>s don't inherit their parent's `cursor`.\n//\n// Note: Neither radios nor checkboxes can be readonly.\ninput[type=\"radio\"],\ninput[type=\"checkbox\"] {\n  &[disabled],\n  &.disabled,\n  fieldset[disabled] & {\n    cursor: @cursor-disabled;\n  }\n}\n// These classes are used directly on <label>s\n.radio-inline,\n.checkbox-inline {\n  &.disabled,\n  fieldset[disabled] & {\n    cursor: @cursor-disabled;\n  }\n}\n// These classes are used on elements with <label> descendants\n.radio,\n.checkbox {\n  &.disabled,\n  fieldset[disabled] & {\n    label {\n      cursor: @cursor-disabled;\n    }\n  }\n}\n\n\n// Static form control text\n//\n// Apply class to a `p` element to make any string of text align with labels in\n// a horizontal form layout.\n\n.form-control-static {\n  // Size it appropriately next to real form controls\n  padding-top: (@padding-base-vertical + 1);\n  padding-bottom: (@padding-base-vertical + 1);\n  // Remove default margin from `p`\n  margin-bottom: 0;\n  min-height: (@line-height-computed + @font-size-base);\n\n  &.input-lg,\n  &.input-sm {\n    padding-left: 0;\n    padding-right: 0;\n  }\n}\n\n\n// Form control sizing\n//\n// Build on `.form-control` with modifier classes to decrease or increase the\n// height and font-size of form controls.\n//\n// The `.form-group-* form-control` variations are sadly duplicated to avoid the\n// issue documented in https://github.com/twbs/bootstrap/issues/15074.\n\n.input-sm {\n  .input-size(@input-height-small; @padding-small-vertical; @padding-small-horizontal; @font-size-small; @line-height-small; @input-border-radius-small);\n}\n.form-group-sm {\n  .form-control {\n    height: @input-height-small;\n    padding: @padding-small-vertical @padding-small-horizontal;\n    font-size: @font-size-small;\n    line-height: @line-height-small;\n    border-radius: @input-border-radius-small;\n  }\n  select.form-control {\n    height: @input-height-small;\n    line-height: @input-height-small;\n  }\n  textarea.form-control,\n  select[multiple].form-control {\n    height: auto;\n  }\n  .form-control-static {\n    height: @input-height-small;\n    min-height: (@line-height-computed + @font-size-small);\n    padding: (@padding-small-vertical + 1) @padding-small-horizontal;\n    font-size: @font-size-small;\n    line-height: @line-height-small;\n  }\n}\n\n.input-lg {\n  .input-size(@input-height-large; @padding-large-vertical; @padding-large-horizontal; @font-size-large; @line-height-large; @input-border-radius-large);\n}\n.form-group-lg {\n  .form-control {\n    height: @input-height-large;\n    padding: @padding-large-vertical @padding-large-horizontal;\n    font-size: @font-size-large;\n    line-height: @line-height-large;\n    border-radius: @input-border-radius-large;\n  }\n  select.form-control {\n    height: @input-height-large;\n    line-height: @input-height-large;\n  }\n  textarea.form-control,\n  select[multiple].form-control {\n    height: auto;\n  }\n  .form-control-static {\n    height: @input-height-large;\n    min-height: (@line-height-computed + @font-size-large);\n    padding: (@padding-large-vertical + 1) @padding-large-horizontal;\n    font-size: @font-size-large;\n    line-height: @line-height-large;\n  }\n}\n\n\n// Form control feedback states\n//\n// Apply contextual and semantic states to individual form controls.\n\n.has-feedback {\n  // Enable absolute positioning\n  position: relative;\n\n  // Ensure icons don't overlap text\n  .form-control {\n    padding-right: (@input-height-base * 1.25);\n  }\n}\n// Feedback icon (requires .glyphicon classes)\n.form-control-feedback {\n  position: absolute;\n  top: 0;\n  right: 0;\n  z-index: 2; // Ensure icon is above input groups\n  display: block;\n  width: @input-height-base;\n  height: @input-height-base;\n  line-height: @input-height-base;\n  text-align: center;\n  pointer-events: none;\n}\n.input-lg + .form-control-feedback,\n.input-group-lg + .form-control-feedback,\n.form-group-lg .form-control + .form-control-feedback {\n  width: @input-height-large;\n  height: @input-height-large;\n  line-height: @input-height-large;\n}\n.input-sm + .form-control-feedback,\n.input-group-sm + .form-control-feedback,\n.form-group-sm .form-control + .form-control-feedback {\n  width: @input-height-small;\n  height: @input-height-small;\n  line-height: @input-height-small;\n}\n\n// Feedback states\n.has-success {\n  .form-control-validation(@state-success-text; @state-success-text; @state-success-bg);\n}\n.has-warning {\n  .form-control-validation(@state-warning-text; @state-warning-text; @state-warning-bg);\n}\n.has-error {\n  .form-control-validation(@state-danger-text; @state-danger-text; @state-danger-bg);\n}\n\n// Reposition feedback icon if input has visible label above\n.has-feedback label {\n\n  & ~ .form-control-feedback {\n    top: (@line-height-computed + 5); // Height of the `label` and its margin\n  }\n  &.sr-only ~ .form-control-feedback {\n    top: 0;\n  }\n}\n\n\n// Help text\n//\n// Apply to any element you wish to create light text for placement immediately\n// below a form control. Use for general help, formatting, or instructional text.\n\n.help-block {\n  display: block; // account for any element using help-block\n  margin-top: 5px;\n  margin-bottom: 10px;\n  color: lighten(@text-color, 25%); // lighten the text some for contrast\n}\n\n\n// Inline forms\n//\n// Make forms appear inline(-block) by adding the `.form-inline` class. Inline\n// forms begin stacked on extra small (mobile) devices and then go inline when\n// viewports reach <768px.\n//\n// Requires wrapping inputs and labels with `.form-group` for proper display of\n// default HTML form controls and our custom form controls (e.g., input groups).\n//\n// Heads up! This is mixin-ed into `.navbar-form` in navbars.less.\n\n.form-inline {\n\n  // Kick in the inline\n  @media (min-width: @screen-sm-min) {\n    // Inline-block all the things for \"inline\"\n    .form-group {\n      display: inline-block;\n      margin-bottom: 0;\n      vertical-align: middle;\n    }\n\n    // In navbar-form, allow folks to *not* use `.form-group`\n    .form-control {\n      display: inline-block;\n      width: auto; // Prevent labels from stacking above inputs in `.form-group`\n      vertical-align: middle;\n    }\n\n    // Make static controls behave like regular ones\n    .form-control-static {\n      display: inline-block;\n    }\n\n    .input-group {\n      display: inline-table;\n      vertical-align: middle;\n\n      .input-group-addon,\n      .input-group-btn,\n      .form-control {\n        width: auto;\n      }\n    }\n\n    // Input groups need that 100% width though\n    .input-group > .form-control {\n      width: 100%;\n    }\n\n    .control-label {\n      margin-bottom: 0;\n      vertical-align: middle;\n    }\n\n    // Remove default margin on radios/checkboxes that were used for stacking, and\n    // then undo the floating of radios and checkboxes to match.\n    .radio,\n    .checkbox {\n      display: inline-block;\n      margin-top: 0;\n      margin-bottom: 0;\n      vertical-align: middle;\n\n      label {\n        padding-left: 0;\n      }\n    }\n    .radio input[type=\"radio\"],\n    .checkbox input[type=\"checkbox\"] {\n      position: relative;\n      margin-left: 0;\n    }\n\n    // Re-override the feedback icon.\n    .has-feedback .form-control-feedback {\n      top: 0;\n    }\n  }\n}\n\n\n// Horizontal forms\n//\n// Horizontal forms are built on grid classes and allow you to create forms with\n// labels on the left and inputs on the right.\n\n.form-horizontal {\n\n  // Consistent vertical alignment of radios and checkboxes\n  //\n  // Labels also get some reset styles, but that is scoped to a media query below.\n  .radio,\n  .checkbox,\n  .radio-inline,\n  .checkbox-inline {\n    margin-top: 0;\n    margin-bottom: 0;\n    padding-top: (@padding-base-vertical + 1); // Default padding plus a border\n  }\n  // Account for padding we're adding to ensure the alignment and of help text\n  // and other content below items\n  .radio,\n  .checkbox {\n    min-height: (@line-height-computed + (@padding-base-vertical + 1));\n  }\n\n  // Make form groups behave like rows\n  .form-group {\n    .make-row();\n  }\n\n  // Reset spacing and right align labels, but scope to media queries so that\n  // labels on narrow viewports stack the same as a default form example.\n  @media (min-width: @screen-sm-min) {\n    .control-label {\n      text-align: right;\n      margin-bottom: 0;\n      padding-top: (@padding-base-vertical + 1); // Default padding plus a border\n    }\n  }\n\n  // Validation states\n  //\n  // Reposition the icon because it's now within a grid column and columns have\n  // `position: relative;` on them. Also accounts for the grid gutter padding.\n  .has-feedback .form-control-feedback {\n    right: floor((@grid-gutter-width / 2));\n  }\n\n  // Form group sizes\n  //\n  // Quick utility class for applying `.input-lg` and `.input-sm` styles to the\n  // inputs and labels within a `.form-group`.\n  .form-group-lg {\n    @media (min-width: @screen-sm-min) {\n      .control-label {\n        padding-top: (@padding-large-vertical + 1);\n        font-size: @font-size-large;\n      }\n    }\n  }\n  .form-group-sm {\n    @media (min-width: @screen-sm-min) {\n      .control-label {\n        padding-top: (@padding-small-vertical + 1);\n        font-size: @font-size-small;\n      }\n    }\n  }\n}\n","// Form validation states\n//\n// Used in forms.less to generate the form validation CSS for warnings, errors,\n// and successes.\n\n.form-control-validation(@text-color: #555; @border-color: #ccc; @background-color: #f5f5f5) {\n  // Color the label and help text\n  .help-block,\n  .control-label,\n  .radio,\n  .checkbox,\n  .radio-inline,\n  .checkbox-inline,\n  &.radio label,\n  &.checkbox label,\n  &.radio-inline label,\n  &.checkbox-inline label  {\n    color: @text-color;\n  }\n  // Set the border and box shadow on specific inputs to match\n  .form-control {\n    border-color: @border-color;\n    .box-shadow(inset 0 1px 1px rgba(0,0,0,.075)); // Redeclare so transitions work\n    &:focus {\n      border-color: darken(@border-color, 10%);\n      @shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 6px lighten(@border-color, 20%);\n      .box-shadow(@shadow);\n    }\n  }\n  // Set validation states also for addons\n  .input-group-addon {\n    color: @text-color;\n    border-color: @border-color;\n    background-color: @background-color;\n  }\n  // Optional feedback icon\n  .form-control-feedback {\n    color: @text-color;\n  }\n}\n\n\n// Form control focus state\n//\n// Generate a customized focus state and for any input with the specified color,\n// which defaults to the `@input-border-focus` variable.\n//\n// We highly encourage you to not customize the default value, but instead use\n// this to tweak colors on an as-needed basis. This aesthetic change is based on\n// WebKit's default styles, but applicable to a wider range of browsers. Its\n// usability and accessibility should be taken into account with any change.\n//\n// Example usage: change the default blue border and shadow to white for better\n// contrast against a dark gray background.\n.form-control-focus(@color: @input-border-focus) {\n  @color-rgba: rgba(red(@color), green(@color), blue(@color), .6);\n  &:focus {\n    border-color: @color;\n    outline: 0;\n    .box-shadow(~\"inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px @{color-rgba}\");\n  }\n}\n\n// Form control sizing\n//\n// Relative text size, padding, and border-radii changes for form controls. For\n// horizontal sizing, wrap controls in the predefined grid classes. `<select>`\n// element gets special love because it's special, and that's a fact!\n.input-size(@input-height; @padding-vertical; @padding-horizontal; @font-size; @line-height; @border-radius) {\n  height: @input-height;\n  padding: @padding-vertical @padding-horizontal;\n  font-size: @font-size;\n  line-height: @line-height;\n  border-radius: @border-radius;\n\n  select& {\n    height: @input-height;\n    line-height: @input-height;\n  }\n\n  textarea&,\n  select[multiple]& {\n    height: auto;\n  }\n}\n","//\n// Buttons\n// --------------------------------------------------\n\n\n// Base styles\n// --------------------------------------------------\n\n.btn {\n  display: inline-block;\n  margin-bottom: 0; // For input.btn\n  font-weight: @btn-font-weight;\n  text-align: center;\n  vertical-align: middle;\n  touch-action: manipulation;\n  cursor: pointer;\n  background-image: none; // Reset unusual Firefox-on-Android default style; see https://github.com/necolas/normalize.css/issues/214\n  border: 1px solid transparent;\n  white-space: nowrap;\n  .button-size(@padding-base-vertical; @padding-base-horizontal; @font-size-base; @line-height-base; @btn-border-radius-base);\n  .user-select(none);\n\n  &,\n  &:active,\n  &.active {\n    &:focus,\n    &.focus {\n      .tab-focus();\n    }\n  }\n\n  &:hover,\n  &:focus,\n  &.focus {\n    color: @btn-default-color;\n    text-decoration: none;\n  }\n\n  &:active,\n  &.active {\n    outline: 0;\n    background-image: none;\n    .box-shadow(inset 0 3px 5px rgba(0,0,0,.125));\n  }\n\n  &.disabled,\n  &[disabled],\n  fieldset[disabled] & {\n    cursor: @cursor-disabled;\n    .opacity(.65);\n    .box-shadow(none);\n  }\n\n  a& {\n    &.disabled,\n    fieldset[disabled] & {\n      pointer-events: none; // Future-proof disabling of clicks on `<a>` elements\n    }\n  }\n}\n\n\n// Alternate buttons\n// --------------------------------------------------\n\n.btn-default {\n  .button-variant(@btn-default-color; @btn-default-bg; @btn-default-border);\n}\n.btn-primary {\n  .button-variant(@btn-primary-color; @btn-primary-bg; @btn-primary-border);\n}\n// Success appears as green\n.btn-success {\n  .button-variant(@btn-success-color; @btn-success-bg; @btn-success-border);\n}\n// Info appears as blue-green\n.btn-info {\n  .button-variant(@btn-info-color; @btn-info-bg; @btn-info-border);\n}\n// Warning appears as orange\n.btn-warning {\n  .button-variant(@btn-warning-color; @btn-warning-bg; @btn-warning-border);\n}\n// Danger and error appear as red\n.btn-danger {\n  .button-variant(@btn-danger-color; @btn-danger-bg; @btn-danger-border);\n}\n\n\n// Link buttons\n// -------------------------\n\n// Make a button look and behave like a link\n.btn-link {\n  color: @link-color;\n  font-weight: normal;\n  border-radius: 0;\n\n  &,\n  &:active,\n  &.active,\n  &[disabled],\n  fieldset[disabled] & {\n    background-color: transparent;\n    .box-shadow(none);\n  }\n  &,\n  &:hover,\n  &:focus,\n  &:active {\n    border-color: transparent;\n  }\n  &:hover,\n  &:focus {\n    color: @link-hover-color;\n    text-decoration: @link-hover-decoration;\n    background-color: transparent;\n  }\n  &[disabled],\n  fieldset[disabled] & {\n    &:hover,\n    &:focus {\n      color: @btn-link-disabled-color;\n      text-decoration: none;\n    }\n  }\n}\n\n\n// Button Sizes\n// --------------------------------------------------\n\n.btn-lg {\n  // line-height: ensure even-numbered height of button next to large input\n  .button-size(@padding-large-vertical; @padding-large-horizontal; @font-size-large; @line-height-large; @btn-border-radius-large);\n}\n.btn-sm {\n  // line-height: ensure proper height of button next to small input\n  .button-size(@padding-small-vertical; @padding-small-horizontal; @font-size-small; @line-height-small; @btn-border-radius-small);\n}\n.btn-xs {\n  .button-size(@padding-xs-vertical; @padding-xs-horizontal; @font-size-small; @line-height-small; @btn-border-radius-small);\n}\n\n\n// Block button\n// --------------------------------------------------\n\n.btn-block {\n  display: block;\n  width: 100%;\n}\n\n// Vertically space out multiple block buttons\n.btn-block + .btn-block {\n  margin-top: 5px;\n}\n\n// Specificity overrides\ninput[type=\"submit\"],\ninput[type=\"reset\"],\ninput[type=\"button\"] {\n  &.btn-block {\n    width: 100%;\n  }\n}\n","// Button variants\n//\n// Easily pump out default styles, as well as :hover, :focus, :active,\n// and disabled options for all buttons\n\n.button-variant(@color; @background; @border) {\n  color: @color;\n  background-color: @background;\n  border-color: @border;\n\n  &:focus,\n  &.focus {\n    color: @color;\n    background-color: darken(@background, 10%);\n        border-color: darken(@border, 25%);\n  }\n  &:hover {\n    color: @color;\n    background-color: darken(@background, 10%);\n        border-color: darken(@border, 12%);\n  }\n  &:active,\n  &.active,\n  .open > .dropdown-toggle& {\n    color: @color;\n    background-color: darken(@background, 10%);\n        border-color: darken(@border, 12%);\n\n    &:hover,\n    &:focus,\n    &.focus {\n      color: @color;\n      background-color: darken(@background, 17%);\n          border-color: darken(@border, 25%);\n    }\n  }\n  &:active,\n  &.active,\n  .open > .dropdown-toggle& {\n    background-image: none;\n  }\n  &.disabled,\n  &[disabled],\n  fieldset[disabled] & {\n    &:hover,\n    &:focus,\n    &.focus {\n      background-color: @background;\n          border-color: @border;\n    }\n  }\n\n  .badge {\n    color: @background;\n    background-color: @color;\n  }\n}\n\n// Button sizes\n.button-size(@padding-vertical; @padding-horizontal; @font-size; @line-height; @border-radius) {\n  padding: @padding-vertical @padding-horizontal;\n  font-size: @font-size;\n  line-height: @line-height;\n  border-radius: @border-radius;\n}\n","// Opacity\n\n.opacity(@opacity) {\n  opacity: @opacity;\n  // IE8 filter\n  @opacity-ie: (@opacity * 100);\n  filter: ~\"alpha(opacity=@{opacity-ie})\";\n}\n","//\n// Component animations\n// --------------------------------------------------\n\n// Heads up!\n//\n// We don't use the `.opacity()` mixin here since it causes a bug with text\n// fields in IE7-8. Source: https://github.com/twbs/bootstrap/pull/3552.\n\n.fade {\n  opacity: 0;\n  .transition(opacity .15s linear);\n  &.in {\n    opacity: 1;\n  }\n}\n\n.collapse {\n  display: none;\n\n  &.in      { display: block; }\n  tr&.in    { display: table-row; }\n  tbody&.in { display: table-row-group; }\n}\n\n.collapsing {\n  position: relative;\n  height: 0;\n  overflow: hidden;\n  .transition-property(~\"height, visibility\");\n  .transition-duration(.35s);\n  .transition-timing-function(ease);\n}\n","//\n// Dropdown menus\n// --------------------------------------------------\n\n\n// Dropdown arrow/caret\n.caret {\n  display: inline-block;\n  width: 0;\n  height: 0;\n  margin-left: 2px;\n  vertical-align: middle;\n  border-top:   @caret-width-base dashed;\n  border-top:   @caret-width-base solid ~\"\\9\"; // IE8\n  border-right: @caret-width-base solid transparent;\n  border-left:  @caret-width-base solid transparent;\n}\n\n// The dropdown wrapper (div)\n.dropup,\n.dropdown {\n  position: relative;\n}\n\n// Prevent the focus on the dropdown toggle when closing dropdowns\n.dropdown-toggle:focus {\n  outline: 0;\n}\n\n// The dropdown menu (ul)\n.dropdown-menu {\n  position: absolute;\n  top: 100%;\n  left: 0;\n  z-index: @zindex-dropdown;\n  display: none; // none by default, but block on \"open\" of the menu\n  float: left;\n  min-width: 160px;\n  padding: 5px 0;\n  margin: 2px 0 0; // override default ul\n  list-style: none;\n  font-size: @font-size-base;\n  text-align: left; // Ensures proper alignment if parent has it changed (e.g., modal footer)\n  background-color: @dropdown-bg;\n  border: 1px solid @dropdown-fallback-border; // IE8 fallback\n  border: 1px solid @dropdown-border;\n  border-radius: @border-radius-base;\n  .box-shadow(0 6px 12px rgba(0,0,0,.175));\n  background-clip: padding-box;\n\n  // Aligns the dropdown menu to right\n  //\n  // Deprecated as of 3.1.0 in favor of `.dropdown-menu-[dir]`\n  &.pull-right {\n    right: 0;\n    left: auto;\n  }\n\n  // Dividers (basically an hr) within the dropdown\n  .divider {\n    .nav-divider(@dropdown-divider-bg);\n  }\n\n  // Links within the dropdown menu\n  > li > a {\n    display: block;\n    padding: 3px 20px;\n    clear: both;\n    font-weight: normal;\n    line-height: @line-height-base;\n    color: @dropdown-link-color;\n    white-space: nowrap; // prevent links from randomly breaking onto new lines\n  }\n}\n\n// Hover/Focus state\n.dropdown-menu > li > a {\n  &:hover,\n  &:focus {\n    text-decoration: none;\n    color: @dropdown-link-hover-color;\n    background-color: @dropdown-link-hover-bg;\n  }\n}\n\n// Active state\n.dropdown-menu > .active > a {\n  &,\n  &:hover,\n  &:focus {\n    color: @dropdown-link-active-color;\n    text-decoration: none;\n    outline: 0;\n    background-color: @dropdown-link-active-bg;\n  }\n}\n\n// Disabled state\n//\n// Gray out text and ensure the hover/focus state remains gray\n\n.dropdown-menu > .disabled > a {\n  &,\n  &:hover,\n  &:focus {\n    color: @dropdown-link-disabled-color;\n  }\n\n  // Nuke hover/focus effects\n  &:hover,\n  &:focus {\n    text-decoration: none;\n    background-color: transparent;\n    background-image: none; // Remove CSS gradient\n    .reset-filter();\n    cursor: @cursor-disabled;\n  }\n}\n\n// Open state for the dropdown\n.open {\n  // Show the menu\n  > .dropdown-menu {\n    display: block;\n  }\n\n  // Remove the outline when :focus is triggered\n  > a {\n    outline: 0;\n  }\n}\n\n// Menu positioning\n//\n// Add extra class to `.dropdown-menu` to flip the alignment of the dropdown\n// menu with the parent.\n.dropdown-menu-right {\n  left: auto; // Reset the default from `.dropdown-menu`\n  right: 0;\n}\n// With v3, we enabled auto-flipping if you have a dropdown within a right\n// aligned nav component. To enable the undoing of that, we provide an override\n// to restore the default dropdown menu alignment.\n//\n// This is only for left-aligning a dropdown menu within a `.navbar-right` or\n// `.pull-right` nav component.\n.dropdown-menu-left {\n  left: 0;\n  right: auto;\n}\n\n// Dropdown section headers\n.dropdown-header {\n  display: block;\n  padding: 3px 20px;\n  font-size: @font-size-small;\n  line-height: @line-height-base;\n  color: @dropdown-header-color;\n  white-space: nowrap; // as with > li > a\n}\n\n// Backdrop to catch body clicks on mobile, etc.\n.dropdown-backdrop {\n  position: fixed;\n  left: 0;\n  right: 0;\n  bottom: 0;\n  top: 0;\n  z-index: (@zindex-dropdown - 10);\n}\n\n// Right aligned dropdowns\n.pull-right > .dropdown-menu {\n  right: 0;\n  left: auto;\n}\n\n// Allow for dropdowns to go bottom up (aka, dropup-menu)\n//\n// Just add .dropup after the standard .dropdown class and you're set, bro.\n// TODO: abstract this so that the navbar fixed styles are not placed here?\n\n.dropup,\n.navbar-fixed-bottom .dropdown {\n  // Reverse the caret\n  .caret {\n    border-top: 0;\n    border-bottom: @caret-width-base dashed;\n    border-bottom: @caret-width-base solid ~\"\\9\"; // IE8\n    content: \"\";\n  }\n  // Different positioning for bottom up menu\n  .dropdown-menu {\n    top: auto;\n    bottom: 100%;\n    margin-bottom: 2px;\n  }\n}\n\n\n// Component alignment\n//\n// Reiterate per navbar.less and the modified component alignment there.\n\n@media (min-width: @grid-float-breakpoint) {\n  .navbar-right {\n    .dropdown-menu {\n      .dropdown-menu-right();\n    }\n    // Necessary for overrides of the default right aligned menu.\n    // Will remove come v4 in all likelihood.\n    .dropdown-menu-left {\n      .dropdown-menu-left();\n    }\n  }\n}\n","// Horizontal dividers\n//\n// Dividers (basically an hr) within dropdowns and nav lists\n\n.nav-divider(@color: #e5e5e5) {\n  height: 1px;\n  margin: ((@line-height-computed / 2) - 1) 0;\n  overflow: hidden;\n  background-color: @color;\n}\n","// Reset filters for IE\n//\n// When you need to remove a gradient background, do not forget to use this to reset\n// the IE filter for IE9 and below.\n\n.reset-filter() {\n  filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(enabled = false)\"));\n}\n","//\n// Button groups\n// --------------------------------------------------\n\n// Make the div behave like a button\n.btn-group,\n.btn-group-vertical {\n  position: relative;\n  display: inline-block;\n  vertical-align: middle; // match .btn alignment given font-size hack above\n  > .btn {\n    position: relative;\n    float: left;\n    // Bring the \"active\" button to the front\n    &:hover,\n    &:focus,\n    &:active,\n    &.active {\n      z-index: 2;\n    }\n  }\n}\n\n// Prevent double borders when buttons are next to each other\n.btn-group {\n  .btn + .btn,\n  .btn + .btn-group,\n  .btn-group + .btn,\n  .btn-group + .btn-group {\n    margin-left: -1px;\n  }\n}\n\n// Optional: Group multiple button groups together for a toolbar\n.btn-toolbar {\n  margin-left: -5px; // Offset the first child's margin\n  &:extend(.clearfix all);\n\n  .btn,\n  .btn-group,\n  .input-group {\n    float: left;\n  }\n  > .btn,\n  > .btn-group,\n  > .input-group {\n    margin-left: 5px;\n  }\n}\n\n.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {\n  border-radius: 0;\n}\n\n// Set corners individual because sometimes a single button can be in a .btn-group and we need :first-child and :last-child to both match\n.btn-group > .btn:first-child {\n  margin-left: 0;\n  &:not(:last-child):not(.dropdown-toggle) {\n    .border-right-radius(0);\n  }\n}\n// Need .dropdown-toggle since :last-child doesn't apply, given that a .dropdown-menu is used immediately after it\n.btn-group > .btn:last-child:not(:first-child),\n.btn-group > .dropdown-toggle:not(:first-child) {\n  .border-left-radius(0);\n}\n\n// Custom edits for including btn-groups within btn-groups (useful for including dropdown buttons within a btn-group)\n.btn-group > .btn-group {\n  float: left;\n}\n.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {\n  border-radius: 0;\n}\n.btn-group > .btn-group:first-child:not(:last-child) {\n  > .btn:last-child,\n  > .dropdown-toggle {\n    .border-right-radius(0);\n  }\n}\n.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {\n  .border-left-radius(0);\n}\n\n// On active and open, don't show outline\n.btn-group .dropdown-toggle:active,\n.btn-group.open .dropdown-toggle {\n  outline: 0;\n}\n\n\n// Sizing\n//\n// Remix the default button sizing classes into new ones for easier manipulation.\n\n.btn-group-xs > .btn { &:extend(.btn-xs); }\n.btn-group-sm > .btn { &:extend(.btn-sm); }\n.btn-group-lg > .btn { &:extend(.btn-lg); }\n\n\n// Split button dropdowns\n// ----------------------\n\n// Give the line between buttons some depth\n.btn-group > .btn + .dropdown-toggle {\n  padding-left: 8px;\n  padding-right: 8px;\n}\n.btn-group > .btn-lg + .dropdown-toggle {\n  padding-left: 12px;\n  padding-right: 12px;\n}\n\n// The clickable button for toggling the menu\n// Remove the gradient and set the same inset shadow as the :active state\n.btn-group.open .dropdown-toggle {\n  .box-shadow(inset 0 3px 5px rgba(0,0,0,.125));\n\n  // Show no shadow for `.btn-link` since it has no other button styles.\n  &.btn-link {\n    .box-shadow(none);\n  }\n}\n\n\n// Reposition the caret\n.btn .caret {\n  margin-left: 0;\n}\n// Carets in other button sizes\n.btn-lg .caret {\n  border-width: @caret-width-large @caret-width-large 0;\n  border-bottom-width: 0;\n}\n// Upside down carets for .dropup\n.dropup .btn-lg .caret {\n  border-width: 0 @caret-width-large @caret-width-large;\n}\n\n\n// Vertical button groups\n// ----------------------\n\n.btn-group-vertical {\n  > .btn,\n  > .btn-group,\n  > .btn-group > .btn {\n    display: block;\n    float: none;\n    width: 100%;\n    max-width: 100%;\n  }\n\n  // Clear floats so dropdown menus can be properly placed\n  > .btn-group {\n    &:extend(.clearfix all);\n    > .btn {\n      float: none;\n    }\n  }\n\n  > .btn + .btn,\n  > .btn + .btn-group,\n  > .btn-group + .btn,\n  > .btn-group + .btn-group {\n    margin-top: -1px;\n    margin-left: 0;\n  }\n}\n\n.btn-group-vertical > .btn {\n  &:not(:first-child):not(:last-child) {\n    border-radius: 0;\n  }\n  &:first-child:not(:last-child) {\n    .border-top-radius(@btn-border-radius-base);\n    .border-bottom-radius(0);\n  }\n  &:last-child:not(:first-child) {\n    .border-top-radius(0);\n    .border-bottom-radius(@btn-border-radius-base);\n  }\n}\n.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {\n  border-radius: 0;\n}\n.btn-group-vertical > .btn-group:first-child:not(:last-child) {\n  > .btn:last-child,\n  > .dropdown-toggle {\n    .border-bottom-radius(0);\n  }\n}\n.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {\n  .border-top-radius(0);\n}\n\n\n// Justified button groups\n// ----------------------\n\n.btn-group-justified {\n  display: table;\n  width: 100%;\n  table-layout: fixed;\n  border-collapse: separate;\n  > .btn,\n  > .btn-group {\n    float: none;\n    display: table-cell;\n    width: 1%;\n  }\n  > .btn-group .btn {\n    width: 100%;\n  }\n\n  > .btn-group .dropdown-menu {\n    left: auto;\n  }\n}\n\n\n// Checkbox and radio options\n//\n// In order to support the browser's form validation feedback, powered by the\n// `required` attribute, we have to \"hide\" the inputs via `clip`. We cannot use\n// `display: none;` or `visibility: hidden;` as that also hides the popover.\n// Simply visually hiding the inputs via `opacity` would leave them clickable in\n// certain cases which is prevented by using `clip` and `pointer-events`.\n// This way, we ensure a DOM element is visible to position the popover from.\n//\n// See https://github.com/twbs/bootstrap/pull/12794 and\n// https://github.com/twbs/bootstrap/pull/14559 for more information.\n\n[data-toggle=\"buttons\"] {\n  > .btn,\n  > .btn-group > .btn {\n    input[type=\"radio\"],\n    input[type=\"checkbox\"] {\n      position: absolute;\n      clip: rect(0,0,0,0);\n      pointer-events: none;\n    }\n  }\n}\n","// Single side border-radius\n\n.border-top-radius(@radius) {\n  border-top-right-radius: @radius;\n   border-top-left-radius: @radius;\n}\n.border-right-radius(@radius) {\n  border-bottom-right-radius: @radius;\n     border-top-right-radius: @radius;\n}\n.border-bottom-radius(@radius) {\n  border-bottom-right-radius: @radius;\n   border-bottom-left-radius: @radius;\n}\n.border-left-radius(@radius) {\n  border-bottom-left-radius: @radius;\n     border-top-left-radius: @radius;\n}\n","//\n// Input groups\n// --------------------------------------------------\n\n// Base styles\n// -------------------------\n.input-group {\n  position: relative; // For dropdowns\n  display: table;\n  border-collapse: separate; // prevent input groups from inheriting border styles from table cells when placed within a table\n\n  // Undo padding and float of grid classes\n  &[class*=\"col-\"] {\n    float: none;\n    padding-left: 0;\n    padding-right: 0;\n  }\n\n  .form-control {\n    // Ensure that the input is always above the *appended* addon button for\n    // proper border colors.\n    position: relative;\n    z-index: 2;\n\n    // IE9 fubars the placeholder attribute in text inputs and the arrows on\n    // select elements in input groups. To fix it, we float the input. Details:\n    // https://github.com/twbs/bootstrap/issues/11561#issuecomment-28936855\n    float: left;\n\n    width: 100%;\n    margin-bottom: 0;\n\n    &:focus {\n      z-index: 3;\n    }\n  }\n}\n\n// Sizing options\n//\n// Remix the default form control sizing classes into new ones for easier\n// manipulation.\n\n.input-group-lg > .form-control,\n.input-group-lg > .input-group-addon,\n.input-group-lg > .input-group-btn > .btn {\n  .input-lg();\n}\n.input-group-sm > .form-control,\n.input-group-sm > .input-group-addon,\n.input-group-sm > .input-group-btn > .btn {\n  .input-sm();\n}\n\n\n// Display as table-cell\n// -------------------------\n.input-group-addon,\n.input-group-btn,\n.input-group .form-control {\n  display: table-cell;\n\n  &:not(:first-child):not(:last-child) {\n    border-radius: 0;\n  }\n}\n// Addon and addon wrapper for buttons\n.input-group-addon,\n.input-group-btn {\n  width: 1%;\n  white-space: nowrap;\n  vertical-align: middle; // Match the inputs\n}\n\n// Text input groups\n// -------------------------\n.input-group-addon {\n  padding: @padding-base-vertical @padding-base-horizontal;\n  font-size: @font-size-base;\n  font-weight: normal;\n  line-height: 1;\n  color: @input-color;\n  text-align: center;\n  background-color: @input-group-addon-bg;\n  border: 1px solid @input-group-addon-border-color;\n  border-radius: @input-border-radius;\n\n  // Sizing\n  &.input-sm {\n    padding: @padding-small-vertical @padding-small-horizontal;\n    font-size: @font-size-small;\n    border-radius: @input-border-radius-small;\n  }\n  &.input-lg {\n    padding: @padding-large-vertical @padding-large-horizontal;\n    font-size: @font-size-large;\n    border-radius: @input-border-radius-large;\n  }\n\n  // Nuke default margins from checkboxes and radios to vertically center within.\n  input[type=\"radio\"],\n  input[type=\"checkbox\"] {\n    margin-top: 0;\n  }\n}\n\n// Reset rounded corners\n.input-group .form-control:first-child,\n.input-group-addon:first-child,\n.input-group-btn:first-child > .btn,\n.input-group-btn:first-child > .btn-group > .btn,\n.input-group-btn:first-child > .dropdown-toggle,\n.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),\n.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {\n  .border-right-radius(0);\n}\n.input-group-addon:first-child {\n  border-right: 0;\n}\n.input-group .form-control:last-child,\n.input-group-addon:last-child,\n.input-group-btn:last-child > .btn,\n.input-group-btn:last-child > .btn-group > .btn,\n.input-group-btn:last-child > .dropdown-toggle,\n.input-group-btn:first-child > .btn:not(:first-child),\n.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {\n  .border-left-radius(0);\n}\n.input-group-addon:last-child {\n  border-left: 0;\n}\n\n// Button input groups\n// -------------------------\n.input-group-btn {\n  position: relative;\n  // Jankily prevent input button groups from wrapping with `white-space` and\n  // `font-size` in combination with `inline-block` on buttons.\n  font-size: 0;\n  white-space: nowrap;\n\n  // Negative margin for spacing, position for bringing hovered/focused/actived\n  // element above the siblings.\n  > .btn {\n    position: relative;\n    + .btn {\n      margin-left: -1px;\n    }\n    // Bring the \"active\" button to the front\n    &:hover,\n    &:focus,\n    &:active {\n      z-index: 2;\n    }\n  }\n\n  // Negative margin to only have a 1px border between the two\n  &:first-child {\n    > .btn,\n    > .btn-group {\n      margin-right: -1px;\n    }\n  }\n  &:last-child {\n    > .btn,\n    > .btn-group {\n      z-index: 2;\n      margin-left: -1px;\n    }\n  }\n}\n","//\n// Navs\n// --------------------------------------------------\n\n\n// Base class\n// --------------------------------------------------\n\n.nav {\n  margin-bottom: 0;\n  padding-left: 0; // Override default ul/ol\n  list-style: none;\n  &:extend(.clearfix all);\n\n  > li {\n    position: relative;\n    display: block;\n\n    > a {\n      position: relative;\n      display: block;\n      padding: @nav-link-padding;\n      &:hover,\n      &:focus {\n        text-decoration: none;\n        background-color: @nav-link-hover-bg;\n      }\n    }\n\n    // Disabled state sets text to gray and nukes hover/tab effects\n    &.disabled > a {\n      color: @nav-disabled-link-color;\n\n      &:hover,\n      &:focus {\n        color: @nav-disabled-link-hover-color;\n        text-decoration: none;\n        background-color: transparent;\n        cursor: @cursor-disabled;\n      }\n    }\n  }\n\n  // Open dropdowns\n  .open > a {\n    &,\n    &:hover,\n    &:focus {\n      background-color: @nav-link-hover-bg;\n      border-color: @link-color;\n    }\n  }\n\n  // Nav dividers (deprecated with v3.0.1)\n  //\n  // This should have been removed in v3 with the dropping of `.nav-list`, but\n  // we missed it. We don't currently support this anywhere, but in the interest\n  // of maintaining backward compatibility in case you use it, it's deprecated.\n  .nav-divider {\n    .nav-divider();\n  }\n\n  // Prevent IE8 from misplacing imgs\n  //\n  // See https://github.com/h5bp/html5-boilerplate/issues/984#issuecomment-3985989\n  > li > a > img {\n    max-width: none;\n  }\n}\n\n\n// Tabs\n// -------------------------\n\n// Give the tabs something to sit on\n.nav-tabs {\n  border-bottom: 1px solid @nav-tabs-border-color;\n  > li {\n    float: left;\n    // Make the list-items overlay the bottom border\n    margin-bottom: -1px;\n\n    // Actual tabs (as links)\n    > a {\n      margin-right: 2px;\n      line-height: @line-height-base;\n      border: 1px solid transparent;\n      border-radius: @border-radius-base @border-radius-base 0 0;\n      &:hover {\n        border-color: @nav-tabs-link-hover-border-color @nav-tabs-link-hover-border-color @nav-tabs-border-color;\n      }\n    }\n\n    // Active state, and its :hover to override normal :hover\n    &.active > a {\n      &,\n      &:hover,\n      &:focus {\n        color: @nav-tabs-active-link-hover-color;\n        background-color: @nav-tabs-active-link-hover-bg;\n        border: 1px solid @nav-tabs-active-link-hover-border-color;\n        border-bottom-color: transparent;\n        cursor: default;\n      }\n    }\n  }\n  // pulling this in mainly for less shorthand\n  &.nav-justified {\n    .nav-justified();\n    .nav-tabs-justified();\n  }\n}\n\n\n// Pills\n// -------------------------\n.nav-pills {\n  > li {\n    float: left;\n\n    // Links rendered as pills\n    > a {\n      border-radius: @nav-pills-border-radius;\n    }\n    + li {\n      margin-left: 2px;\n    }\n\n    // Active state\n    &.active > a {\n      &,\n      &:hover,\n      &:focus {\n        color: @nav-pills-active-link-hover-color;\n        background-color: @nav-pills-active-link-hover-bg;\n      }\n    }\n  }\n}\n\n\n// Stacked pills\n.nav-stacked {\n  > li {\n    float: none;\n    + li {\n      margin-top: 2px;\n      margin-left: 0; // no need for this gap between nav items\n    }\n  }\n}\n\n\n// Nav variations\n// --------------------------------------------------\n\n// Justified nav links\n// -------------------------\n\n.nav-justified {\n  width: 100%;\n\n  > li {\n    float: none;\n    > a {\n      text-align: center;\n      margin-bottom: 5px;\n    }\n  }\n\n  > .dropdown .dropdown-menu {\n    top: auto;\n    left: auto;\n  }\n\n  @media (min-width: @screen-sm-min) {\n    > li {\n      display: table-cell;\n      width: 1%;\n      > a {\n        margin-bottom: 0;\n      }\n    }\n  }\n}\n\n// Move borders to anchors instead of bottom of list\n//\n// Mixin for adding on top the shared `.nav-justified` styles for our tabs\n.nav-tabs-justified {\n  border-bottom: 0;\n\n  > li > a {\n    // Override margin from .nav-tabs\n    margin-right: 0;\n    border-radius: @border-radius-base;\n  }\n\n  > .active > a,\n  > .active > a:hover,\n  > .active > a:focus {\n    border: 1px solid @nav-tabs-justified-link-border-color;\n  }\n\n  @media (min-width: @screen-sm-min) {\n    > li > a {\n      border-bottom: 1px solid @nav-tabs-justified-link-border-color;\n      border-radius: @border-radius-base @border-radius-base 0 0;\n    }\n    > .active > a,\n    > .active > a:hover,\n    > .active > a:focus {\n      border-bottom-color: @nav-tabs-justified-active-link-border-color;\n    }\n  }\n}\n\n\n// Tabbable tabs\n// -------------------------\n\n// Hide tabbable panes to start, show them when `.active`\n.tab-content {\n  > .tab-pane {\n    display: none;\n  }\n  > .active {\n    display: block;\n  }\n}\n\n\n// Dropdowns\n// -------------------------\n\n// Specific dropdowns\n.nav-tabs .dropdown-menu {\n  // make dropdown border overlap tab border\n  margin-top: -1px;\n  // Remove the top rounded corners here since there is a hard edge above the menu\n  .border-top-radius(0);\n}\n","//\n// Navbars\n// --------------------------------------------------\n\n\n// Wrapper and base class\n//\n// Provide a static navbar from which we expand to create full-width, fixed, and\n// other navbar variations.\n\n.navbar {\n  position: relative;\n  min-height: @navbar-height; // Ensure a navbar always shows (e.g., without a .navbar-brand in collapsed mode)\n  margin-bottom: @navbar-margin-bottom;\n  border: 1px solid transparent;\n\n  // Prevent floats from breaking the navbar\n  &:extend(.clearfix all);\n\n  @media (min-width: @grid-float-breakpoint) {\n    border-radius: @navbar-border-radius;\n  }\n}\n\n\n// Navbar heading\n//\n// Groups `.navbar-brand` and `.navbar-toggle` into a single component for easy\n// styling of responsive aspects.\n\n.navbar-header {\n  &:extend(.clearfix all);\n\n  @media (min-width: @grid-float-breakpoint) {\n    float: left;\n  }\n}\n\n\n// Navbar collapse (body)\n//\n// Group your navbar content into this for easy collapsing and expanding across\n// various device sizes. By default, this content is collapsed when <768px, but\n// will expand past that for a horizontal display.\n//\n// To start (on mobile devices) the navbar links, forms, and buttons are stacked\n// vertically and include a `max-height` to overflow in case you have too much\n// content for the user's viewport.\n\n.navbar-collapse {\n  overflow-x: visible;\n  padding-right: @navbar-padding-horizontal;\n  padding-left:  @navbar-padding-horizontal;\n  border-top: 1px solid transparent;\n  box-shadow: inset 0 1px 0 rgba(255,255,255,.1);\n  &:extend(.clearfix all);\n  -webkit-overflow-scrolling: touch;\n\n  &.in {\n    overflow-y: auto;\n  }\n\n  @media (min-width: @grid-float-breakpoint) {\n    width: auto;\n    border-top: 0;\n    box-shadow: none;\n\n    &.collapse {\n      display: block !important;\n      height: auto !important;\n      padding-bottom: 0; // Override default setting\n      overflow: visible !important;\n    }\n\n    &.in {\n      overflow-y: visible;\n    }\n\n    // Undo the collapse side padding for navbars with containers to ensure\n    // alignment of right-aligned contents.\n    .navbar-fixed-top &,\n    .navbar-static-top &,\n    .navbar-fixed-bottom & {\n      padding-left: 0;\n      padding-right: 0;\n    }\n  }\n}\n\n.navbar-fixed-top,\n.navbar-fixed-bottom {\n  .navbar-collapse {\n    max-height: @navbar-collapse-max-height;\n\n    @media (max-device-width: @screen-xs-min) and (orientation: landscape) {\n      max-height: 200px;\n    }\n  }\n}\n\n\n// Both navbar header and collapse\n//\n// When a container is present, change the behavior of the header and collapse.\n\n.container,\n.container-fluid {\n  > .navbar-header,\n  > .navbar-collapse {\n    margin-right: -@navbar-padding-horizontal;\n    margin-left:  -@navbar-padding-horizontal;\n\n    @media (min-width: @grid-float-breakpoint) {\n      margin-right: 0;\n      margin-left:  0;\n    }\n  }\n}\n\n\n//\n// Navbar alignment options\n//\n// Display the navbar across the entirety of the page or fixed it to the top or\n// bottom of the page.\n\n// Static top (unfixed, but 100% wide) navbar\n.navbar-static-top {\n  z-index: @zindex-navbar;\n  border-width: 0 0 1px;\n\n  @media (min-width: @grid-float-breakpoint) {\n    border-radius: 0;\n  }\n}\n\n// Fix the top/bottom navbars when screen real estate supports it\n.navbar-fixed-top,\n.navbar-fixed-bottom {\n  position: fixed;\n  right: 0;\n  left: 0;\n  z-index: @zindex-navbar-fixed;\n\n  // Undo the rounded corners\n  @media (min-width: @grid-float-breakpoint) {\n    border-radius: 0;\n  }\n}\n.navbar-fixed-top {\n  top: 0;\n  border-width: 0 0 1px;\n}\n.navbar-fixed-bottom {\n  bottom: 0;\n  margin-bottom: 0; // override .navbar defaults\n  border-width: 1px 0 0;\n}\n\n\n// Brand/project name\n\n.navbar-brand {\n  float: left;\n  padding: @navbar-padding-vertical @navbar-padding-horizontal;\n  font-size: @font-size-large;\n  line-height: @line-height-computed;\n  height: @navbar-height;\n\n  &:hover,\n  &:focus {\n    text-decoration: none;\n  }\n\n  > img {\n    display: block;\n  }\n\n  @media (min-width: @grid-float-breakpoint) {\n    .navbar > .container &,\n    .navbar > .container-fluid & {\n      margin-left: -@navbar-padding-horizontal;\n    }\n  }\n}\n\n\n// Navbar toggle\n//\n// Custom button for toggling the `.navbar-collapse`, powered by the collapse\n// JavaScript plugin.\n\n.navbar-toggle {\n  position: relative;\n  float: right;\n  margin-right: @navbar-padding-horizontal;\n  padding: 9px 10px;\n  .navbar-vertical-align(34px);\n  background-color: transparent;\n  background-image: none; // Reset unusual Firefox-on-Android default style; see https://github.com/necolas/normalize.css/issues/214\n  border: 1px solid transparent;\n  border-radius: @border-radius-base;\n\n  // We remove the `outline` here, but later compensate by attaching `:hover`\n  // styles to `:focus`.\n  &:focus {\n    outline: 0;\n  }\n\n  // Bars\n  .icon-bar {\n    display: block;\n    width: 22px;\n    height: 2px;\n    border-radius: 1px;\n  }\n  .icon-bar + .icon-bar {\n    margin-top: 4px;\n  }\n\n  @media (min-width: @grid-float-breakpoint) {\n    display: none;\n  }\n}\n\n\n// Navbar nav links\n//\n// Builds on top of the `.nav` components with its own modifier class to make\n// the nav the full height of the horizontal nav (above 768px).\n\n.navbar-nav {\n  margin: (@navbar-padding-vertical / 2) -@navbar-padding-horizontal;\n\n  > li > a {\n    padding-top:    10px;\n    padding-bottom: 10px;\n    line-height: @line-height-computed;\n  }\n\n  @media (max-width: @grid-float-breakpoint-max) {\n    // Dropdowns get custom display when collapsed\n    .open .dropdown-menu {\n      position: static;\n      float: none;\n      width: auto;\n      margin-top: 0;\n      background-color: transparent;\n      border: 0;\n      box-shadow: none;\n      > li > a,\n      .dropdown-header {\n        padding: 5px 15px 5px 25px;\n      }\n      > li > a {\n        line-height: @line-height-computed;\n        &:hover,\n        &:focus {\n          background-image: none;\n        }\n      }\n    }\n  }\n\n  // Uncollapse the nav\n  @media (min-width: @grid-float-breakpoint) {\n    float: left;\n    margin: 0;\n\n    > li {\n      float: left;\n      > a {\n        padding-top:    @navbar-padding-vertical;\n        padding-bottom: @navbar-padding-vertical;\n      }\n    }\n  }\n}\n\n\n// Navbar form\n//\n// Extension of the `.form-inline` with some extra flavor for optimum display in\n// our navbars.\n\n.navbar-form {\n  margin-left: -@navbar-padding-horizontal;\n  margin-right: -@navbar-padding-horizontal;\n  padding: 10px @navbar-padding-horizontal;\n  border-top: 1px solid transparent;\n  border-bottom: 1px solid transparent;\n  @shadow: inset 0 1px 0 rgba(255,255,255,.1), 0 1px 0 rgba(255,255,255,.1);\n  .box-shadow(@shadow);\n\n  // Mixin behavior for optimum display\n  .form-inline();\n\n  .form-group {\n    @media (max-width: @grid-float-breakpoint-max) {\n      margin-bottom: 5px;\n\n      &:last-child {\n        margin-bottom: 0;\n      }\n    }\n  }\n\n  // Vertically center in expanded, horizontal navbar\n  .navbar-vertical-align(@input-height-base);\n\n  // Undo 100% width for pull classes\n  @media (min-width: @grid-float-breakpoint) {\n    width: auto;\n    border: 0;\n    margin-left: 0;\n    margin-right: 0;\n    padding-top: 0;\n    padding-bottom: 0;\n    .box-shadow(none);\n  }\n}\n\n\n// Dropdown menus\n\n// Menu position and menu carets\n.navbar-nav > li > .dropdown-menu {\n  margin-top: 0;\n  .border-top-radius(0);\n}\n// Menu position and menu caret support for dropups via extra dropup class\n.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {\n  margin-bottom: 0;\n  .border-top-radius(@navbar-border-radius);\n  .border-bottom-radius(0);\n}\n\n\n// Buttons in navbars\n//\n// Vertically center a button within a navbar (when *not* in a form).\n\n.navbar-btn {\n  .navbar-vertical-align(@input-height-base);\n\n  &.btn-sm {\n    .navbar-vertical-align(@input-height-small);\n  }\n  &.btn-xs {\n    .navbar-vertical-align(22);\n  }\n}\n\n\n// Text in navbars\n//\n// Add a class to make any element properly align itself vertically within the navbars.\n\n.navbar-text {\n  .navbar-vertical-align(@line-height-computed);\n\n  @media (min-width: @grid-float-breakpoint) {\n    float: left;\n    margin-left: @navbar-padding-horizontal;\n    margin-right: @navbar-padding-horizontal;\n  }\n}\n\n\n// Component alignment\n//\n// Repurpose the pull utilities as their own navbar utilities to avoid specificity\n// issues with parents and chaining. Only do this when the navbar is uncollapsed\n// though so that navbar contents properly stack and align in mobile.\n//\n// Declared after the navbar components to ensure more specificity on the margins.\n\n@media (min-width: @grid-float-breakpoint) {\n  .navbar-left  { .pull-left(); }\n  .navbar-right {\n    .pull-right();\n    margin-right: -@navbar-padding-horizontal;\n\n    ~ .navbar-right {\n      margin-right: 0;\n    }\n  }\n}\n\n\n// Alternate navbars\n// --------------------------------------------------\n\n// Default navbar\n.navbar-default {\n  background-color: @navbar-default-bg;\n  border-color: @navbar-default-border;\n\n  .navbar-brand {\n    color: @navbar-default-brand-color;\n    &:hover,\n    &:focus {\n      color: @navbar-default-brand-hover-color;\n      background-color: @navbar-default-brand-hover-bg;\n    }\n  }\n\n  .navbar-text {\n    color: @navbar-default-color;\n  }\n\n  .navbar-nav {\n    > li > a {\n      color: @navbar-default-link-color;\n\n      &:hover,\n      &:focus {\n        color: @navbar-default-link-hover-color;\n        background-color: @navbar-default-link-hover-bg;\n      }\n    }\n    > .active > a {\n      &,\n      &:hover,\n      &:focus {\n        color: @navbar-default-link-active-color;\n        background-color: @navbar-default-link-active-bg;\n      }\n    }\n    > .disabled > a {\n      &,\n      &:hover,\n      &:focus {\n        color: @navbar-default-link-disabled-color;\n        background-color: @navbar-default-link-disabled-bg;\n      }\n    }\n  }\n\n  .navbar-toggle {\n    border-color: @navbar-default-toggle-border-color;\n    &:hover,\n    &:focus {\n      background-color: @navbar-default-toggle-hover-bg;\n    }\n    .icon-bar {\n      background-color: @navbar-default-toggle-icon-bar-bg;\n    }\n  }\n\n  .navbar-collapse,\n  .navbar-form {\n    border-color: @navbar-default-border;\n  }\n\n  // Dropdown menu items\n  .navbar-nav {\n    // Remove background color from open dropdown\n    > .open > a {\n      &,\n      &:hover,\n      &:focus {\n        background-color: @navbar-default-link-active-bg;\n        color: @navbar-default-link-active-color;\n      }\n    }\n\n    @media (max-width: @grid-float-breakpoint-max) {\n      // Dropdowns get custom display when collapsed\n      .open .dropdown-menu {\n        > li > a {\n          color: @navbar-default-link-color;\n          &:hover,\n          &:focus {\n            color: @navbar-default-link-hover-color;\n            background-color: @navbar-default-link-hover-bg;\n          }\n        }\n        > .active > a {\n          &,\n          &:hover,\n          &:focus {\n            color: @navbar-default-link-active-color;\n            background-color: @navbar-default-link-active-bg;\n          }\n        }\n        > .disabled > a {\n          &,\n          &:hover,\n          &:focus {\n            color: @navbar-default-link-disabled-color;\n            background-color: @navbar-default-link-disabled-bg;\n          }\n        }\n      }\n    }\n  }\n\n\n  // Links in navbars\n  //\n  // Add a class to ensure links outside the navbar nav are colored correctly.\n\n  .navbar-link {\n    color: @navbar-default-link-color;\n    &:hover {\n      color: @navbar-default-link-hover-color;\n    }\n  }\n\n  .btn-link {\n    color: @navbar-default-link-color;\n    &:hover,\n    &:focus {\n      color: @navbar-default-link-hover-color;\n    }\n    &[disabled],\n    fieldset[disabled] & {\n      &:hover,\n      &:focus {\n        color: @navbar-default-link-disabled-color;\n      }\n    }\n  }\n}\n\n// Inverse navbar\n\n.navbar-inverse {\n  background-color: @navbar-inverse-bg;\n  border-color: @navbar-inverse-border;\n\n  .navbar-brand {\n    color: @navbar-inverse-brand-color;\n    &:hover,\n    &:focus {\n      color: @navbar-inverse-brand-hover-color;\n      background-color: @navbar-inverse-brand-hover-bg;\n    }\n  }\n\n  .navbar-text {\n    color: @navbar-inverse-color;\n  }\n\n  .navbar-nav {\n    > li > a {\n      color: @navbar-inverse-link-color;\n\n      &:hover,\n      &:focus {\n        color: @navbar-inverse-link-hover-color;\n        background-color: @navbar-inverse-link-hover-bg;\n      }\n    }\n    > .active > a {\n      &,\n      &:hover,\n      &:focus {\n        color: @navbar-inverse-link-active-color;\n        background-color: @navbar-inverse-link-active-bg;\n      }\n    }\n    > .disabled > a {\n      &,\n      &:hover,\n      &:focus {\n        color: @navbar-inverse-link-disabled-color;\n        background-color: @navbar-inverse-link-disabled-bg;\n      }\n    }\n  }\n\n  // Darken the responsive nav toggle\n  .navbar-toggle {\n    border-color: @navbar-inverse-toggle-border-color;\n    &:hover,\n    &:focus {\n      background-color: @navbar-inverse-toggle-hover-bg;\n    }\n    .icon-bar {\n      background-color: @navbar-inverse-toggle-icon-bar-bg;\n    }\n  }\n\n  .navbar-collapse,\n  .navbar-form {\n    border-color: darken(@navbar-inverse-bg, 7%);\n  }\n\n  // Dropdowns\n  .navbar-nav {\n    > .open > a {\n      &,\n      &:hover,\n      &:focus {\n        background-color: @navbar-inverse-link-active-bg;\n        color: @navbar-inverse-link-active-color;\n      }\n    }\n\n    @media (max-width: @grid-float-breakpoint-max) {\n      // Dropdowns get custom display\n      .open .dropdown-menu {\n        > .dropdown-header {\n          border-color: @navbar-inverse-border;\n        }\n        .divider {\n          background-color: @navbar-inverse-border;\n        }\n        > li > a {\n          color: @navbar-inverse-link-color;\n          &:hover,\n          &:focus {\n            color: @navbar-inverse-link-hover-color;\n            background-color: @navbar-inverse-link-hover-bg;\n          }\n        }\n        > .active > a {\n          &,\n          &:hover,\n          &:focus {\n            color: @navbar-inverse-link-active-color;\n            background-color: @navbar-inverse-link-active-bg;\n          }\n        }\n        > .disabled > a {\n          &,\n          &:hover,\n          &:focus {\n            color: @navbar-inverse-link-disabled-color;\n            background-color: @navbar-inverse-link-disabled-bg;\n          }\n        }\n      }\n    }\n  }\n\n  .navbar-link {\n    color: @navbar-inverse-link-color;\n    &:hover {\n      color: @navbar-inverse-link-hover-color;\n    }\n  }\n\n  .btn-link {\n    color: @navbar-inverse-link-color;\n    &:hover,\n    &:focus {\n      color: @navbar-inverse-link-hover-color;\n    }\n    &[disabled],\n    fieldset[disabled] & {\n      &:hover,\n      &:focus {\n        color: @navbar-inverse-link-disabled-color;\n      }\n    }\n  }\n}\n","// Navbar vertical align\n//\n// Vertically center elements in the navbar.\n// Example: an element has a height of 30px, so write out `.navbar-vertical-align(30px);` to calculate the appropriate top margin.\n\n.navbar-vertical-align(@element-height) {\n  margin-top: ((@navbar-height - @element-height) / 2);\n  margin-bottom: ((@navbar-height - @element-height) / 2);\n}\n","//\n// Utility classes\n// --------------------------------------------------\n\n\n// Floats\n// -------------------------\n\n.clearfix {\n  .clearfix();\n}\n.center-block {\n  .center-block();\n}\n.pull-right {\n  float: right !important;\n}\n.pull-left {\n  float: left !important;\n}\n\n\n// Toggling content\n// -------------------------\n\n// Note: Deprecated .hide in favor of .hidden or .sr-only (as appropriate) in v3.0.1\n.hide {\n  display: none !important;\n}\n.show {\n  display: block !important;\n}\n.invisible {\n  visibility: hidden;\n}\n.text-hide {\n  .text-hide();\n}\n\n\n// Hide from screenreaders and browsers\n//\n// Credit: HTML5 Boilerplate\n\n.hidden {\n  display: none !important;\n}\n\n\n// For Affix plugin\n// -------------------------\n\n.affix {\n  position: fixed;\n}\n","//\n// Breadcrumbs\n// --------------------------------------------------\n\n\n.breadcrumb {\n  padding: @breadcrumb-padding-vertical @breadcrumb-padding-horizontal;\n  margin-bottom: @line-height-computed;\n  list-style: none;\n  background-color: @breadcrumb-bg;\n  border-radius: @border-radius-base;\n\n  > li {\n    display: inline-block;\n\n    + li:before {\n      content: \"@{breadcrumb-separator}\\00a0\"; // Unicode space added since inline-block means non-collapsing white-space\n      padding: 0 5px;\n      color: @breadcrumb-color;\n    }\n  }\n\n  > .active {\n    color: @breadcrumb-active-color;\n  }\n}\n","//\n// Pagination (multiple pages)\n// --------------------------------------------------\n.pagination {\n  display: inline-block;\n  padding-left: 0;\n  margin: @line-height-computed 0;\n  border-radius: @border-radius-base;\n\n  > li {\n    display: inline; // Remove list-style and block-level defaults\n    > a,\n    > span {\n      position: relative;\n      float: left; // Collapse white-space\n      padding: @padding-base-vertical @padding-base-horizontal;\n      line-height: @line-height-base;\n      text-decoration: none;\n      color: @pagination-color;\n      background-color: @pagination-bg;\n      border: 1px solid @pagination-border;\n      margin-left: -1px;\n    }\n    &:first-child {\n      > a,\n      > span {\n        margin-left: 0;\n        .border-left-radius(@border-radius-base);\n      }\n    }\n    &:last-child {\n      > a,\n      > span {\n        .border-right-radius(@border-radius-base);\n      }\n    }\n  }\n\n  > li > a,\n  > li > span {\n    &:hover,\n    &:focus {\n      z-index: 2;\n      color: @pagination-hover-color;\n      background-color: @pagination-hover-bg;\n      border-color: @pagination-hover-border;\n    }\n  }\n\n  > .active > a,\n  > .active > span {\n    &,\n    &:hover,\n    &:focus {\n      z-index: 3;\n      color: @pagination-active-color;\n      background-color: @pagination-active-bg;\n      border-color: @pagination-active-border;\n      cursor: default;\n    }\n  }\n\n  > .disabled {\n    > span,\n    > span:hover,\n    > span:focus,\n    > a,\n    > a:hover,\n    > a:focus {\n      color: @pagination-disabled-color;\n      background-color: @pagination-disabled-bg;\n      border-color: @pagination-disabled-border;\n      cursor: @cursor-disabled;\n    }\n  }\n}\n\n// Sizing\n// --------------------------------------------------\n\n// Large\n.pagination-lg {\n  .pagination-size(@padding-large-vertical; @padding-large-horizontal; @font-size-large; @line-height-large; @border-radius-large);\n}\n\n// Small\n.pagination-sm {\n  .pagination-size(@padding-small-vertical; @padding-small-horizontal; @font-size-small; @line-height-small; @border-radius-small);\n}\n","// Pagination\n\n.pagination-size(@padding-vertical; @padding-horizontal; @font-size; @line-height; @border-radius) {\n  > li {\n    > a,\n    > span {\n      padding: @padding-vertical @padding-horizontal;\n      font-size: @font-size;\n      line-height: @line-height;\n    }\n    &:first-child {\n      > a,\n      > span {\n        .border-left-radius(@border-radius);\n      }\n    }\n    &:last-child {\n      > a,\n      > span {\n        .border-right-radius(@border-radius);\n      }\n    }\n  }\n}\n","//\n// Pager pagination\n// --------------------------------------------------\n\n\n.pager {\n  padding-left: 0;\n  margin: @line-height-computed 0;\n  list-style: none;\n  text-align: center;\n  &:extend(.clearfix all);\n  li {\n    display: inline;\n    > a,\n    > span {\n      display: inline-block;\n      padding: 5px 14px;\n      background-color: @pager-bg;\n      border: 1px solid @pager-border;\n      border-radius: @pager-border-radius;\n    }\n\n    > a:hover,\n    > a:focus {\n      text-decoration: none;\n      background-color: @pager-hover-bg;\n    }\n  }\n\n  .next {\n    > a,\n    > span {\n      float: right;\n    }\n  }\n\n  .previous {\n    > a,\n    > span {\n      float: left;\n    }\n  }\n\n  .disabled {\n    > a,\n    > a:hover,\n    > a:focus,\n    > span {\n      color: @pager-disabled-color;\n      background-color: @pager-bg;\n      cursor: @cursor-disabled;\n    }\n  }\n}\n","//\n// Labels\n// --------------------------------------------------\n\n.label {\n  display: inline;\n  padding: .2em .6em .3em;\n  font-size: 75%;\n  font-weight: bold;\n  line-height: 1;\n  color: @label-color;\n  text-align: center;\n  white-space: nowrap;\n  vertical-align: baseline;\n  border-radius: .25em;\n\n  // Add hover effects, but only for links\n  a& {\n    &:hover,\n    &:focus {\n      color: @label-link-hover-color;\n      text-decoration: none;\n      cursor: pointer;\n    }\n  }\n\n  // Empty labels collapse automatically (not available in IE8)\n  &:empty {\n    display: none;\n  }\n\n  // Quick fix for labels in buttons\n  .btn & {\n    position: relative;\n    top: -1px;\n  }\n}\n\n// Colors\n// Contextual variations (linked labels get darker on :hover)\n\n.label-default {\n  .label-variant(@label-default-bg);\n}\n\n.label-primary {\n  .label-variant(@label-primary-bg);\n}\n\n.label-success {\n  .label-variant(@label-success-bg);\n}\n\n.label-info {\n  .label-variant(@label-info-bg);\n}\n\n.label-warning {\n  .label-variant(@label-warning-bg);\n}\n\n.label-danger {\n  .label-variant(@label-danger-bg);\n}\n","// Labels\n\n.label-variant(@color) {\n  background-color: @color;\n\n  &[href] {\n    &:hover,\n    &:focus {\n      background-color: darken(@color, 10%);\n    }\n  }\n}\n","//\n// Badges\n// --------------------------------------------------\n\n\n// Base class\n.badge {\n  display: inline-block;\n  min-width: 10px;\n  padding: 3px 7px;\n  font-size: @font-size-small;\n  font-weight: @badge-font-weight;\n  color: @badge-color;\n  line-height: @badge-line-height;\n  vertical-align: middle;\n  white-space: nowrap;\n  text-align: center;\n  background-color: @badge-bg;\n  border-radius: @badge-border-radius;\n\n  // Empty badges collapse automatically (not available in IE8)\n  &:empty {\n    display: none;\n  }\n\n  // Quick fix for badges in buttons\n  .btn & {\n    position: relative;\n    top: -1px;\n  }\n\n  .btn-xs &,\n  .btn-group-xs > .btn & {\n    top: 0;\n    padding: 1px 5px;\n  }\n\n  // Hover state, but only for links\n  a& {\n    &:hover,\n    &:focus {\n      color: @badge-link-hover-color;\n      text-decoration: none;\n      cursor: pointer;\n    }\n  }\n\n  // Account for badges in navs\n  .list-group-item.active > &,\n  .nav-pills > .active > a > & {\n    color: @badge-active-color;\n    background-color: @badge-active-bg;\n  }\n\n  .list-group-item > & {\n    float: right;\n  }\n\n  .list-group-item > & + & {\n    margin-right: 5px;\n  }\n\n  .nav-pills > li > a > & {\n    margin-left: 3px;\n  }\n}\n","//\n// Jumbotron\n// --------------------------------------------------\n\n\n.jumbotron {\n  padding-top:    @jumbotron-padding;\n  padding-bottom: @jumbotron-padding;\n  margin-bottom: @jumbotron-padding;\n  color: @jumbotron-color;\n  background-color: @jumbotron-bg;\n\n  h1,\n  .h1 {\n    color: @jumbotron-heading-color;\n  }\n\n  p {\n    margin-bottom: (@jumbotron-padding / 2);\n    font-size: @jumbotron-font-size;\n    font-weight: 200;\n  }\n\n  > hr {\n    border-top-color: darken(@jumbotron-bg, 10%);\n  }\n\n  .container &,\n  .container-fluid & {\n    border-radius: @border-radius-large; // Only round corners at higher resolutions if contained in a container\n    padding-left:  (@grid-gutter-width / 2);\n    padding-right: (@grid-gutter-width / 2);\n  }\n\n  .container {\n    max-width: 100%;\n  }\n\n  @media screen and (min-width: @screen-sm-min) {\n    padding-top:    (@jumbotron-padding * 1.6);\n    padding-bottom: (@jumbotron-padding * 1.6);\n\n    .container &,\n    .container-fluid & {\n      padding-left:  (@jumbotron-padding * 2);\n      padding-right: (@jumbotron-padding * 2);\n    }\n\n    h1,\n    .h1 {\n      font-size: @jumbotron-heading-font-size;\n    }\n  }\n}\n","//\n// Thumbnails\n// --------------------------------------------------\n\n\n// Mixin and adjust the regular image class\n.thumbnail {\n  display: block;\n  padding: @thumbnail-padding;\n  margin-bottom: @line-height-computed;\n  line-height: @line-height-base;\n  background-color: @thumbnail-bg;\n  border: 1px solid @thumbnail-border;\n  border-radius: @thumbnail-border-radius;\n  .transition(border .2s ease-in-out);\n\n  > img,\n  a > img {\n    &:extend(.img-responsive);\n    margin-left: auto;\n    margin-right: auto;\n  }\n\n  // Add a hover state for linked versions only\n  a&:hover,\n  a&:focus,\n  a&.active {\n    border-color: @link-color;\n  }\n\n  // Image captions\n  .caption {\n    padding: @thumbnail-caption-padding;\n    color: @thumbnail-caption-color;\n  }\n}\n","//\n// Alerts\n// --------------------------------------------------\n\n\n// Base styles\n// -------------------------\n\n.alert {\n  padding: @alert-padding;\n  margin-bottom: @line-height-computed;\n  border: 1px solid transparent;\n  border-radius: @alert-border-radius;\n\n  // Headings for larger alerts\n  h4 {\n    margin-top: 0;\n    // Specified for the h4 to prevent conflicts of changing @headings-color\n    color: inherit;\n  }\n\n  // Provide class for links that match alerts\n  .alert-link {\n    font-weight: @alert-link-font-weight;\n  }\n\n  // Improve alignment and spacing of inner content\n  > p,\n  > ul {\n    margin-bottom: 0;\n  }\n\n  > p + p {\n    margin-top: 5px;\n  }\n}\n\n// Dismissible alerts\n//\n// Expand the right padding and account for the close button's positioning.\n\n.alert-dismissable, // The misspelled .alert-dismissable was deprecated in 3.2.0.\n.alert-dismissible {\n  padding-right: (@alert-padding + 20);\n\n  // Adjust close link position\n  .close {\n    position: relative;\n    top: -2px;\n    right: -21px;\n    color: inherit;\n  }\n}\n\n// Alternate styles\n//\n// Generate contextual modifier classes for colorizing the alert.\n\n.alert-success {\n  .alert-variant(@alert-success-bg; @alert-success-border; @alert-success-text);\n}\n\n.alert-info {\n  .alert-variant(@alert-info-bg; @alert-info-border; @alert-info-text);\n}\n\n.alert-warning {\n  .alert-variant(@alert-warning-bg; @alert-warning-border; @alert-warning-text);\n}\n\n.alert-danger {\n  .alert-variant(@alert-danger-bg; @alert-danger-border; @alert-danger-text);\n}\n","// Alerts\n\n.alert-variant(@background; @border; @text-color) {\n  background-color: @background;\n  border-color: @border;\n  color: @text-color;\n\n  hr {\n    border-top-color: darken(@border, 5%);\n  }\n  .alert-link {\n    color: darken(@text-color, 10%);\n  }\n}\n","//\n// Progress bars\n// --------------------------------------------------\n\n\n// Bar animations\n// -------------------------\n\n// WebKit\n@-webkit-keyframes progress-bar-stripes {\n  from  { background-position: 40px 0; }\n  to    { background-position: 0 0; }\n}\n\n// Spec and IE10+\n@keyframes progress-bar-stripes {\n  from  { background-position: 40px 0; }\n  to    { background-position: 0 0; }\n}\n\n\n// Bar itself\n// -------------------------\n\n// Outer container\n.progress {\n  overflow: hidden;\n  height: @line-height-computed;\n  margin-bottom: @line-height-computed;\n  background-color: @progress-bg;\n  border-radius: @progress-border-radius;\n  .box-shadow(inset 0 1px 2px rgba(0,0,0,.1));\n}\n\n// Bar of progress\n.progress-bar {\n  float: left;\n  width: 0%;\n  height: 100%;\n  font-size: @font-size-small;\n  line-height: @line-height-computed;\n  color: @progress-bar-color;\n  text-align: center;\n  background-color: @progress-bar-bg;\n  .box-shadow(inset 0 -1px 0 rgba(0,0,0,.15));\n  .transition(width .6s ease);\n}\n\n// Striped bars\n//\n// `.progress-striped .progress-bar` is deprecated as of v3.2.0 in favor of the\n// `.progress-bar-striped` class, which you just add to an existing\n// `.progress-bar`.\n.progress-striped .progress-bar,\n.progress-bar-striped {\n  #gradient > .striped();\n  background-size: 40px 40px;\n}\n\n// Call animation for the active one\n//\n// `.progress.active .progress-bar` is deprecated as of v3.2.0 in favor of the\n// `.progress-bar.active` approach.\n.progress.active .progress-bar,\n.progress-bar.active {\n  .animation(progress-bar-stripes 2s linear infinite);\n}\n\n\n// Variations\n// -------------------------\n\n.progress-bar-success {\n  .progress-bar-variant(@progress-bar-success-bg);\n}\n\n.progress-bar-info {\n  .progress-bar-variant(@progress-bar-info-bg);\n}\n\n.progress-bar-warning {\n  .progress-bar-variant(@progress-bar-warning-bg);\n}\n\n.progress-bar-danger {\n  .progress-bar-variant(@progress-bar-danger-bg);\n}\n","// Gradients\n\n#gradient {\n\n  // Horizontal gradient, from left to right\n  //\n  // Creates two color stops, start and end, by specifying a color and position for each color stop.\n  // Color stops are not available in IE9 and below.\n  .horizontal(@start-color: #555; @end-color: #333; @start-percent: 0%; @end-percent: 100%) {\n    background-image: -webkit-linear-gradient(left, @start-color @start-percent, @end-color @end-percent); // Safari 5.1-6, Chrome 10+\n    background-image: -o-linear-gradient(left, @start-color @start-percent, @end-color @end-percent); // Opera 12\n    background-image: linear-gradient(to right, @start-color @start-percent, @end-color @end-percent); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n    background-repeat: repeat-x;\n    filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=1)\",argb(@start-color),argb(@end-color))); // IE9 and down\n  }\n\n  // Vertical gradient, from top to bottom\n  //\n  // Creates two color stops, start and end, by specifying a color and position for each color stop.\n  // Color stops are not available in IE9 and below.\n  .vertical(@start-color: #555; @end-color: #333; @start-percent: 0%; @end-percent: 100%) {\n    background-image: -webkit-linear-gradient(top, @start-color @start-percent, @end-color @end-percent);  // Safari 5.1-6, Chrome 10+\n    background-image: -o-linear-gradient(top, @start-color @start-percent, @end-color @end-percent);  // Opera 12\n    background-image: linear-gradient(to bottom, @start-color @start-percent, @end-color @end-percent); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n    background-repeat: repeat-x;\n    filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=0)\",argb(@start-color),argb(@end-color))); // IE9 and down\n  }\n\n  .directional(@start-color: #555; @end-color: #333; @deg: 45deg) {\n    background-repeat: repeat-x;\n    background-image: -webkit-linear-gradient(@deg, @start-color, @end-color); // Safari 5.1-6, Chrome 10+\n    background-image: -o-linear-gradient(@deg, @start-color, @end-color); // Opera 12\n    background-image: linear-gradient(@deg, @start-color, @end-color); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n  }\n  .horizontal-three-colors(@start-color: #00b3ee; @mid-color: #7a43b6; @color-stop: 50%; @end-color: #c3325f) {\n    background-image: -webkit-linear-gradient(left, @start-color, @mid-color @color-stop, @end-color);\n    background-image: -o-linear-gradient(left, @start-color, @mid-color @color-stop, @end-color);\n    background-image: linear-gradient(to right, @start-color, @mid-color @color-stop, @end-color);\n    background-repeat: no-repeat;\n    filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=1)\",argb(@start-color),argb(@end-color))); // IE9 and down, gets no color-stop at all for proper fallback\n  }\n  .vertical-three-colors(@start-color: #00b3ee; @mid-color: #7a43b6; @color-stop: 50%; @end-color: #c3325f) {\n    background-image: -webkit-linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n    background-image: -o-linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n    background-image: linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n    background-repeat: no-repeat;\n    filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=0)\",argb(@start-color),argb(@end-color))); // IE9 and down, gets no color-stop at all for proper fallback\n  }\n  .radial(@inner-color: #555; @outer-color: #333) {\n    background-image: -webkit-radial-gradient(circle, @inner-color, @outer-color);\n    background-image: radial-gradient(circle, @inner-color, @outer-color);\n    background-repeat: no-repeat;\n  }\n  .striped(@color: rgba(255,255,255,.15); @angle: 45deg) {\n    background-image: -webkit-linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n    background-image: -o-linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n    background-image: linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n  }\n}\n","// Progress bars\n\n.progress-bar-variant(@color) {\n  background-color: @color;\n\n  // Deprecated parent class requirement as of v3.2.0\n  .progress-striped & {\n    #gradient > .striped();\n  }\n}\n",".media {\n  // Proper spacing between instances of .media\n  margin-top: 15px;\n\n  &:first-child {\n    margin-top: 0;\n  }\n}\n\n.media,\n.media-body {\n  zoom: 1;\n  overflow: hidden;\n}\n\n.media-body {\n  width: 10000px;\n}\n\n.media-object {\n  display: block;\n\n  // Fix collapse in webkit from max-width: 100% and display: table-cell.\n  &.img-thumbnail {\n    max-width: none;\n  }\n}\n\n.media-right,\n.media > .pull-right {\n  padding-left: 10px;\n}\n\n.media-left,\n.media > .pull-left {\n  padding-right: 10px;\n}\n\n.media-left,\n.media-right,\n.media-body {\n  display: table-cell;\n  vertical-align: top;\n}\n\n.media-middle {\n  vertical-align: middle;\n}\n\n.media-bottom {\n  vertical-align: bottom;\n}\n\n// Reset margins on headings for tighter default spacing\n.media-heading {\n  margin-top: 0;\n  margin-bottom: 5px;\n}\n\n// Media list variation\n//\n// Undo default ul/ol styles\n.media-list {\n  padding-left: 0;\n  list-style: none;\n}\n","//\n// List groups\n// --------------------------------------------------\n\n\n// Base class\n//\n// Easily usable on <ul>, <ol>, or <div>.\n\n.list-group {\n  // No need to set list-style: none; since .list-group-item is block level\n  margin-bottom: 20px;\n  padding-left: 0; // reset padding because ul and ol\n}\n\n\n// Individual list items\n//\n// Use on `li`s or `div`s within the `.list-group` parent.\n\n.list-group-item {\n  position: relative;\n  display: block;\n  padding: 10px 15px;\n  // Place the border on the list items and negative margin up for better styling\n  margin-bottom: -1px;\n  background-color: @list-group-bg;\n  border: 1px solid @list-group-border;\n\n  // Round the first and last items\n  &:first-child {\n    .border-top-radius(@list-group-border-radius);\n  }\n  &:last-child {\n    margin-bottom: 0;\n    .border-bottom-radius(@list-group-border-radius);\n  }\n}\n\n\n// Interactive list items\n//\n// Use anchor or button elements instead of `li`s or `div`s to create interactive items.\n// Includes an extra `.active` modifier class for showing selected items.\n\na.list-group-item,\nbutton.list-group-item {\n  color: @list-group-link-color;\n\n  .list-group-item-heading {\n    color: @list-group-link-heading-color;\n  }\n\n  // Hover state\n  &:hover,\n  &:focus {\n    text-decoration: none;\n    color: @list-group-link-hover-color;\n    background-color: @list-group-hover-bg;\n  }\n}\n\nbutton.list-group-item {\n  width: 100%;\n  text-align: left;\n}\n\n.list-group-item {\n  // Disabled state\n  &.disabled,\n  &.disabled:hover,\n  &.disabled:focus {\n    background-color: @list-group-disabled-bg;\n    color: @list-group-disabled-color;\n    cursor: @cursor-disabled;\n\n    // Force color to inherit for custom content\n    .list-group-item-heading {\n      color: inherit;\n    }\n    .list-group-item-text {\n      color: @list-group-disabled-text-color;\n    }\n  }\n\n  // Active class on item itself, not parent\n  &.active,\n  &.active:hover,\n  &.active:focus {\n    z-index: 2; // Place active items above their siblings for proper border styling\n    color: @list-group-active-color;\n    background-color: @list-group-active-bg;\n    border-color: @list-group-active-border;\n\n    // Force color to inherit for custom content\n    .list-group-item-heading,\n    .list-group-item-heading > small,\n    .list-group-item-heading > .small {\n      color: inherit;\n    }\n    .list-group-item-text {\n      color: @list-group-active-text-color;\n    }\n  }\n}\n\n\n// Contextual variants\n//\n// Add modifier classes to change text and background color on individual items.\n// Organizationally, this must come after the `:hover` states.\n\n.list-group-item-variant(success; @state-success-bg; @state-success-text);\n.list-group-item-variant(info; @state-info-bg; @state-info-text);\n.list-group-item-variant(warning; @state-warning-bg; @state-warning-text);\n.list-group-item-variant(danger; @state-danger-bg; @state-danger-text);\n\n\n// Custom content options\n//\n// Extra classes for creating well-formatted content within `.list-group-item`s.\n\n.list-group-item-heading {\n  margin-top: 0;\n  margin-bottom: 5px;\n}\n.list-group-item-text {\n  margin-bottom: 0;\n  line-height: 1.3;\n}\n","// List Groups\n\n.list-group-item-variant(@state; @background; @color) {\n  .list-group-item-@{state} {\n    color: @color;\n    background-color: @background;\n\n    a&,\n    button& {\n      color: @color;\n\n      .list-group-item-heading {\n        color: inherit;\n      }\n\n      &:hover,\n      &:focus {\n        color: @color;\n        background-color: darken(@background, 5%);\n      }\n      &.active,\n      &.active:hover,\n      &.active:focus {\n        color: #fff;\n        background-color: @color;\n        border-color: @color;\n      }\n    }\n  }\n}\n","//\n// Panels\n// --------------------------------------------------\n\n\n// Base class\n.panel {\n  margin-bottom: @line-height-computed;\n  background-color: @panel-bg;\n  border: 1px solid transparent;\n  border-radius: @panel-border-radius;\n  .box-shadow(0 1px 1px rgba(0,0,0,.05));\n}\n\n// Panel contents\n.panel-body {\n  padding: @panel-body-padding;\n  &:extend(.clearfix all);\n}\n\n// Optional heading\n.panel-heading {\n  padding: @panel-heading-padding;\n  border-bottom: 1px solid transparent;\n  .border-top-radius((@panel-border-radius - 1));\n\n  > .dropdown .dropdown-toggle {\n    color: inherit;\n  }\n}\n\n// Within heading, strip any `h*` tag of its default margins for spacing.\n.panel-title {\n  margin-top: 0;\n  margin-bottom: 0;\n  font-size: ceil((@font-size-base * 1.125));\n  color: inherit;\n\n  > a,\n  > small,\n  > .small,\n  > small > a,\n  > .small > a {\n    color: inherit;\n  }\n}\n\n// Optional footer (stays gray in every modifier class)\n.panel-footer {\n  padding: @panel-footer-padding;\n  background-color: @panel-footer-bg;\n  border-top: 1px solid @panel-inner-border;\n  .border-bottom-radius((@panel-border-radius - 1));\n}\n\n\n// List groups in panels\n//\n// By default, space out list group content from panel headings to account for\n// any kind of custom content between the two.\n\n.panel {\n  > .list-group,\n  > .panel-collapse > .list-group {\n    margin-bottom: 0;\n\n    .list-group-item {\n      border-width: 1px 0;\n      border-radius: 0;\n    }\n\n    // Add border top radius for first one\n    &:first-child {\n      .list-group-item:first-child {\n        border-top: 0;\n        .border-top-radius((@panel-border-radius - 1));\n      }\n    }\n\n    // Add border bottom radius for last one\n    &:last-child {\n      .list-group-item:last-child {\n        border-bottom: 0;\n        .border-bottom-radius((@panel-border-radius - 1));\n      }\n    }\n  }\n  > .panel-heading + .panel-collapse > .list-group {\n    .list-group-item:first-child {\n      .border-top-radius(0);\n    }\n  }\n}\n// Collapse space between when there's no additional content.\n.panel-heading + .list-group {\n  .list-group-item:first-child {\n    border-top-width: 0;\n  }\n}\n.list-group + .panel-footer {\n  border-top-width: 0;\n}\n\n// Tables in panels\n//\n// Place a non-bordered `.table` within a panel (not within a `.panel-body`) and\n// watch it go full width.\n\n.panel {\n  > .table,\n  > .table-responsive > .table,\n  > .panel-collapse > .table {\n    margin-bottom: 0;\n\n    caption {\n      padding-left: @panel-body-padding;\n      padding-right: @panel-body-padding;\n    }\n  }\n  // Add border top radius for first one\n  > .table:first-child,\n  > .table-responsive:first-child > .table:first-child {\n    .border-top-radius((@panel-border-radius - 1));\n\n    > thead:first-child,\n    > tbody:first-child {\n      > tr:first-child {\n        border-top-left-radius: (@panel-border-radius - 1);\n        border-top-right-radius: (@panel-border-radius - 1);\n\n        td:first-child,\n        th:first-child {\n          border-top-left-radius: (@panel-border-radius - 1);\n        }\n        td:last-child,\n        th:last-child {\n          border-top-right-radius: (@panel-border-radius - 1);\n        }\n      }\n    }\n  }\n  // Add border bottom radius for last one\n  > .table:last-child,\n  > .table-responsive:last-child > .table:last-child {\n    .border-bottom-radius((@panel-border-radius - 1));\n\n    > tbody:last-child,\n    > tfoot:last-child {\n      > tr:last-child {\n        border-bottom-left-radius: (@panel-border-radius - 1);\n        border-bottom-right-radius: (@panel-border-radius - 1);\n\n        td:first-child,\n        th:first-child {\n          border-bottom-left-radius: (@panel-border-radius - 1);\n        }\n        td:last-child,\n        th:last-child {\n          border-bottom-right-radius: (@panel-border-radius - 1);\n        }\n      }\n    }\n  }\n  > .panel-body + .table,\n  > .panel-body + .table-responsive,\n  > .table + .panel-body,\n  > .table-responsive + .panel-body {\n    border-top: 1px solid @table-border-color;\n  }\n  > .table > tbody:first-child > tr:first-child th,\n  > .table > tbody:first-child > tr:first-child td {\n    border-top: 0;\n  }\n  > .table-bordered,\n  > .table-responsive > .table-bordered {\n    border: 0;\n    > thead,\n    > tbody,\n    > tfoot {\n      > tr {\n        > th:first-child,\n        > td:first-child {\n          border-left: 0;\n        }\n        > th:last-child,\n        > td:last-child {\n          border-right: 0;\n        }\n      }\n    }\n    > thead,\n    > tbody {\n      > tr:first-child {\n        > td,\n        > th {\n          border-bottom: 0;\n        }\n      }\n    }\n    > tbody,\n    > tfoot {\n      > tr:last-child {\n        > td,\n        > th {\n          border-bottom: 0;\n        }\n      }\n    }\n  }\n  > .table-responsive {\n    border: 0;\n    margin-bottom: 0;\n  }\n}\n\n\n// Collapsible panels (aka, accordion)\n//\n// Wrap a series of panels in `.panel-group` to turn them into an accordion with\n// the help of our collapse JavaScript plugin.\n\n.panel-group {\n  margin-bottom: @line-height-computed;\n\n  // Tighten up margin so it's only between panels\n  .panel {\n    margin-bottom: 0;\n    border-radius: @panel-border-radius;\n\n    + .panel {\n      margin-top: 5px;\n    }\n  }\n\n  .panel-heading {\n    border-bottom: 0;\n\n    + .panel-collapse > .panel-body,\n    + .panel-collapse > .list-group {\n      border-top: 1px solid @panel-inner-border;\n    }\n  }\n\n  .panel-footer {\n    border-top: 0;\n    + .panel-collapse .panel-body {\n      border-bottom: 1px solid @panel-inner-border;\n    }\n  }\n}\n\n\n// Contextual variations\n.panel-default {\n  .panel-variant(@panel-default-border; @panel-default-text; @panel-default-heading-bg; @panel-default-border);\n}\n.panel-primary {\n  .panel-variant(@panel-primary-border; @panel-primary-text; @panel-primary-heading-bg; @panel-primary-border);\n}\n.panel-success {\n  .panel-variant(@panel-success-border; @panel-success-text; @panel-success-heading-bg; @panel-success-border);\n}\n.panel-info {\n  .panel-variant(@panel-info-border; @panel-info-text; @panel-info-heading-bg; @panel-info-border);\n}\n.panel-warning {\n  .panel-variant(@panel-warning-border; @panel-warning-text; @panel-warning-heading-bg; @panel-warning-border);\n}\n.panel-danger {\n  .panel-variant(@panel-danger-border; @panel-danger-text; @panel-danger-heading-bg; @panel-danger-border);\n}\n","// Panels\n\n.panel-variant(@border; @heading-text-color; @heading-bg-color; @heading-border) {\n  border-color: @border;\n\n  & > .panel-heading {\n    color: @heading-text-color;\n    background-color: @heading-bg-color;\n    border-color: @heading-border;\n\n    + .panel-collapse > .panel-body {\n      border-top-color: @border;\n    }\n    .badge {\n      color: @heading-bg-color;\n      background-color: @heading-text-color;\n    }\n  }\n  & > .panel-footer {\n    + .panel-collapse > .panel-body {\n      border-bottom-color: @border;\n    }\n  }\n}\n","// Embeds responsive\n//\n// Credit: Nicolas Gallagher and SUIT CSS.\n\n.embed-responsive {\n  position: relative;\n  display: block;\n  height: 0;\n  padding: 0;\n  overflow: hidden;\n\n  .embed-responsive-item,\n  iframe,\n  embed,\n  object,\n  video {\n    position: absolute;\n    top: 0;\n    left: 0;\n    bottom: 0;\n    height: 100%;\n    width: 100%;\n    border: 0;\n  }\n}\n\n// Modifier class for 16:9 aspect ratio\n.embed-responsive-16by9 {\n  padding-bottom: 56.25%;\n}\n\n// Modifier class for 4:3 aspect ratio\n.embed-responsive-4by3 {\n  padding-bottom: 75%;\n}\n","//\n// Wells\n// --------------------------------------------------\n\n\n// Base class\n.well {\n  min-height: 20px;\n  padding: 19px;\n  margin-bottom: 20px;\n  background-color: @well-bg;\n  border: 1px solid @well-border;\n  border-radius: @border-radius-base;\n  .box-shadow(inset 0 1px 1px rgba(0,0,0,.05));\n  blockquote {\n    border-color: #ddd;\n    border-color: rgba(0,0,0,.15);\n  }\n}\n\n// Sizes\n.well-lg {\n  padding: 24px;\n  border-radius: @border-radius-large;\n}\n.well-sm {\n  padding: 9px;\n  border-radius: @border-radius-small;\n}\n","//\n// Close icons\n// --------------------------------------------------\n\n\n.close {\n  float: right;\n  font-size: (@font-size-base * 1.5);\n  font-weight: @close-font-weight;\n  line-height: 1;\n  color: @close-color;\n  text-shadow: @close-text-shadow;\n  .opacity(.2);\n\n  &:hover,\n  &:focus {\n    color: @close-color;\n    text-decoration: none;\n    cursor: pointer;\n    .opacity(.5);\n  }\n\n  // Additional properties for button version\n  // iOS requires the button element instead of an anchor tag.\n  // If you want the anchor version, it requires `href=\"#\"`.\n  // See https://developer.mozilla.org/en-US/docs/Web/Events/click#Safari_Mobile\n  button& {\n    padding: 0;\n    cursor: pointer;\n    background: transparent;\n    border: 0;\n    -webkit-appearance: none;\n  }\n}\n","//\n// Modals\n// --------------------------------------------------\n\n// .modal-open      - body class for killing the scroll\n// .modal           - container to scroll within\n// .modal-dialog    - positioning shell for the actual modal\n// .modal-content   - actual modal w/ bg and corners and shit\n\n// Kill the scroll on the body\n.modal-open {\n  overflow: hidden;\n}\n\n// Container that the modal scrolls within\n.modal {\n  display: none;\n  overflow: hidden;\n  position: fixed;\n  top: 0;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  z-index: @zindex-modal;\n  -webkit-overflow-scrolling: touch;\n\n  // Prevent Chrome on Windows from adding a focus outline. For details, see\n  // https://github.com/twbs/bootstrap/pull/10951.\n  outline: 0;\n\n  // When fading in the modal, animate it to slide down\n  &.fade .modal-dialog {\n    .translate(0, -25%);\n    .transition-transform(~\"0.3s ease-out\");\n  }\n  &.in .modal-dialog { .translate(0, 0) }\n}\n.modal-open .modal {\n  overflow-x: hidden;\n  overflow-y: auto;\n}\n\n// Shell div to position the modal with bottom padding\n.modal-dialog {\n  position: relative;\n  width: auto;\n  margin: 10px;\n}\n\n// Actual modal\n.modal-content {\n  position: relative;\n  background-color: @modal-content-bg;\n  border: 1px solid @modal-content-fallback-border-color; //old browsers fallback (ie8 etc)\n  border: 1px solid @modal-content-border-color;\n  border-radius: @border-radius-large;\n  .box-shadow(0 3px 9px rgba(0,0,0,.5));\n  background-clip: padding-box;\n  // Remove focus outline from opened modal\n  outline: 0;\n}\n\n// Modal background\n.modal-backdrop {\n  position: fixed;\n  top: 0;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  z-index: @zindex-modal-background;\n  background-color: @modal-backdrop-bg;\n  // Fade for backdrop\n  &.fade { .opacity(0); }\n  &.in { .opacity(@modal-backdrop-opacity); }\n}\n\n// Modal header\n// Top section of the modal w/ title and dismiss\n.modal-header {\n  padding: @modal-title-padding;\n  border-bottom: 1px solid @modal-header-border-color;\n  &:extend(.clearfix all);\n}\n// Close icon\n.modal-header .close {\n  margin-top: -2px;\n}\n\n// Title text within header\n.modal-title {\n  margin: 0;\n  line-height: @modal-title-line-height;\n}\n\n// Modal body\n// Where all modal content resides (sibling of .modal-header and .modal-footer)\n.modal-body {\n  position: relative;\n  padding: @modal-inner-padding;\n}\n\n// Footer (for actions)\n.modal-footer {\n  padding: @modal-inner-padding;\n  text-align: right; // right align buttons\n  border-top: 1px solid @modal-footer-border-color;\n  &:extend(.clearfix all); // clear it in case folks use .pull-* classes on buttons\n\n  // Properly space out buttons\n  .btn + .btn {\n    margin-left: 5px;\n    margin-bottom: 0; // account for input[type=\"submit\"] which gets the bottom margin like all other inputs\n  }\n  // but override that for button groups\n  .btn-group .btn + .btn {\n    margin-left: -1px;\n  }\n  // and override it for block buttons as well\n  .btn-block + .btn-block {\n    margin-left: 0;\n  }\n}\n\n// Measure scrollbar width for padding body during modal show/hide\n.modal-scrollbar-measure {\n  position: absolute;\n  top: -9999px;\n  width: 50px;\n  height: 50px;\n  overflow: scroll;\n}\n\n// Scale up the modal\n@media (min-width: @screen-sm-min) {\n  // Automatically set modal's width for larger viewports\n  .modal-dialog {\n    width: @modal-md;\n    margin: 30px auto;\n  }\n  .modal-content {\n    .box-shadow(0 5px 15px rgba(0,0,0,.5));\n  }\n\n  // Modal sizes\n  .modal-sm { width: @modal-sm; }\n}\n\n@media (min-width: @screen-md-min) {\n  .modal-lg { width: @modal-lg; }\n}\n","//\n// Tooltips\n// --------------------------------------------------\n\n\n// Base class\n.tooltip {\n  position: absolute;\n  z-index: @zindex-tooltip;\n  display: block;\n  // Our parent element can be arbitrary since tooltips are by default inserted as a sibling of their target element.\n  // So reset our font and text properties to avoid inheriting weird values.\n  .reset-text();\n  font-size: @font-size-small;\n\n  .opacity(0);\n\n  &.in     { .opacity(@tooltip-opacity); }\n  &.top    { margin-top:  -3px; padding: @tooltip-arrow-width 0; }\n  &.right  { margin-left:  3px; padding: 0 @tooltip-arrow-width; }\n  &.bottom { margin-top:   3px; padding: @tooltip-arrow-width 0; }\n  &.left   { margin-left: -3px; padding: 0 @tooltip-arrow-width; }\n}\n\n// Wrapper for the tooltip content\n.tooltip-inner {\n  max-width: @tooltip-max-width;\n  padding: 3px 8px;\n  color: @tooltip-color;\n  text-align: center;\n  background-color: @tooltip-bg;\n  border-radius: @border-radius-base;\n}\n\n// Arrows\n.tooltip-arrow {\n  position: absolute;\n  width: 0;\n  height: 0;\n  border-color: transparent;\n  border-style: solid;\n}\n// Note: Deprecated .top-left, .top-right, .bottom-left, and .bottom-right as of v3.3.1\n.tooltip {\n  &.top .tooltip-arrow {\n    bottom: 0;\n    left: 50%;\n    margin-left: -@tooltip-arrow-width;\n    border-width: @tooltip-arrow-width @tooltip-arrow-width 0;\n    border-top-color: @tooltip-arrow-color;\n  }\n  &.top-left .tooltip-arrow {\n    bottom: 0;\n    right: @tooltip-arrow-width;\n    margin-bottom: -@tooltip-arrow-width;\n    border-width: @tooltip-arrow-width @tooltip-arrow-width 0;\n    border-top-color: @tooltip-arrow-color;\n  }\n  &.top-right .tooltip-arrow {\n    bottom: 0;\n    left: @tooltip-arrow-width;\n    margin-bottom: -@tooltip-arrow-width;\n    border-width: @tooltip-arrow-width @tooltip-arrow-width 0;\n    border-top-color: @tooltip-arrow-color;\n  }\n  &.right .tooltip-arrow {\n    top: 50%;\n    left: 0;\n    margin-top: -@tooltip-arrow-width;\n    border-width: @tooltip-arrow-width @tooltip-arrow-width @tooltip-arrow-width 0;\n    border-right-color: @tooltip-arrow-color;\n  }\n  &.left .tooltip-arrow {\n    top: 50%;\n    right: 0;\n    margin-top: -@tooltip-arrow-width;\n    border-width: @tooltip-arrow-width 0 @tooltip-arrow-width @tooltip-arrow-width;\n    border-left-color: @tooltip-arrow-color;\n  }\n  &.bottom .tooltip-arrow {\n    top: 0;\n    left: 50%;\n    margin-left: -@tooltip-arrow-width;\n    border-width: 0 @tooltip-arrow-width @tooltip-arrow-width;\n    border-bottom-color: @tooltip-arrow-color;\n  }\n  &.bottom-left .tooltip-arrow {\n    top: 0;\n    right: @tooltip-arrow-width;\n    margin-top: -@tooltip-arrow-width;\n    border-width: 0 @tooltip-arrow-width @tooltip-arrow-width;\n    border-bottom-color: @tooltip-arrow-color;\n  }\n  &.bottom-right .tooltip-arrow {\n    top: 0;\n    left: @tooltip-arrow-width;\n    margin-top: -@tooltip-arrow-width;\n    border-width: 0 @tooltip-arrow-width @tooltip-arrow-width;\n    border-bottom-color: @tooltip-arrow-color;\n  }\n}\n",".reset-text() {\n  font-family: @font-family-base;\n  // We deliberately do NOT reset font-size.\n  font-style: normal;\n  font-weight: normal;\n  letter-spacing: normal;\n  line-break: auto;\n  line-height: @line-height-base;\n  text-align: left; // Fallback for where `start` is not supported\n  text-align: start;\n  text-decoration: none;\n  text-shadow: none;\n  text-transform: none;\n  white-space: normal;\n  word-break: normal;\n  word-spacing: normal;\n  word-wrap: normal;\n}\n","//\n// Popovers\n// --------------------------------------------------\n\n\n.popover {\n  position: absolute;\n  top: 0;\n  left: 0;\n  z-index: @zindex-popover;\n  display: none;\n  max-width: @popover-max-width;\n  padding: 1px;\n  // Our parent element can be arbitrary since popovers are by default inserted as a sibling of their target element.\n  // So reset our font and text properties to avoid inheriting weird values.\n  .reset-text();\n  font-size: @font-size-base;\n\n  background-color: @popover-bg;\n  background-clip: padding-box;\n  border: 1px solid @popover-fallback-border-color;\n  border: 1px solid @popover-border-color;\n  border-radius: @border-radius-large;\n  .box-shadow(0 5px 10px rgba(0,0,0,.2));\n\n  // Offset the popover to account for the popover arrow\n  &.top     { margin-top: -@popover-arrow-width; }\n  &.right   { margin-left: @popover-arrow-width; }\n  &.bottom  { margin-top: @popover-arrow-width; }\n  &.left    { margin-left: -@popover-arrow-width; }\n}\n\n.popover-title {\n  margin: 0; // reset heading margin\n  padding: 8px 14px;\n  font-size: @font-size-base;\n  background-color: @popover-title-bg;\n  border-bottom: 1px solid darken(@popover-title-bg, 5%);\n  border-radius: (@border-radius-large - 1) (@border-radius-large - 1) 0 0;\n}\n\n.popover-content {\n  padding: 9px 14px;\n}\n\n// Arrows\n//\n// .arrow is outer, .arrow:after is inner\n\n.popover > .arrow {\n  &,\n  &:after {\n    position: absolute;\n    display: block;\n    width: 0;\n    height: 0;\n    border-color: transparent;\n    border-style: solid;\n  }\n}\n.popover > .arrow {\n  border-width: @popover-arrow-outer-width;\n}\n.popover > .arrow:after {\n  border-width: @popover-arrow-width;\n  content: \"\";\n}\n\n.popover {\n  &.top > .arrow {\n    left: 50%;\n    margin-left: -@popover-arrow-outer-width;\n    border-bottom-width: 0;\n    border-top-color: @popover-arrow-outer-fallback-color; // IE8 fallback\n    border-top-color: @popover-arrow-outer-color;\n    bottom: -@popover-arrow-outer-width;\n    &:after {\n      content: \" \";\n      bottom: 1px;\n      margin-left: -@popover-arrow-width;\n      border-bottom-width: 0;\n      border-top-color: @popover-arrow-color;\n    }\n  }\n  &.right > .arrow {\n    top: 50%;\n    left: -@popover-arrow-outer-width;\n    margin-top: -@popover-arrow-outer-width;\n    border-left-width: 0;\n    border-right-color: @popover-arrow-outer-fallback-color; // IE8 fallback\n    border-right-color: @popover-arrow-outer-color;\n    &:after {\n      content: \" \";\n      left: 1px;\n      bottom: -@popover-arrow-width;\n      border-left-width: 0;\n      border-right-color: @popover-arrow-color;\n    }\n  }\n  &.bottom > .arrow {\n    left: 50%;\n    margin-left: -@popover-arrow-outer-width;\n    border-top-width: 0;\n    border-bottom-color: @popover-arrow-outer-fallback-color; // IE8 fallback\n    border-bottom-color: @popover-arrow-outer-color;\n    top: -@popover-arrow-outer-width;\n    &:after {\n      content: \" \";\n      top: 1px;\n      margin-left: -@popover-arrow-width;\n      border-top-width: 0;\n      border-bottom-color: @popover-arrow-color;\n    }\n  }\n\n  &.left > .arrow {\n    top: 50%;\n    right: -@popover-arrow-outer-width;\n    margin-top: -@popover-arrow-outer-width;\n    border-right-width: 0;\n    border-left-color: @popover-arrow-outer-fallback-color; // IE8 fallback\n    border-left-color: @popover-arrow-outer-color;\n    &:after {\n      content: \" \";\n      right: 1px;\n      border-right-width: 0;\n      border-left-color: @popover-arrow-color;\n      bottom: -@popover-arrow-width;\n    }\n  }\n}\n","//\n// Carousel\n// --------------------------------------------------\n\n\n// Wrapper for the slide container and indicators\n.carousel {\n  position: relative;\n}\n\n.carousel-inner {\n  position: relative;\n  overflow: hidden;\n  width: 100%;\n\n  > .item {\n    display: none;\n    position: relative;\n    .transition(.6s ease-in-out left);\n\n    // Account for jankitude on images\n    > img,\n    > a > img {\n      &:extend(.img-responsive);\n      line-height: 1;\n    }\n\n    // WebKit CSS3 transforms for supported devices\n    @media all and (transform-3d), (-webkit-transform-3d) {\n      .transition-transform(~'0.6s ease-in-out');\n      .backface-visibility(~'hidden');\n      .perspective(1000px);\n\n      &.next,\n      &.active.right {\n        .translate3d(100%, 0, 0);\n        left: 0;\n      }\n      &.prev,\n      &.active.left {\n        .translate3d(-100%, 0, 0);\n        left: 0;\n      }\n      &.next.left,\n      &.prev.right,\n      &.active {\n        .translate3d(0, 0, 0);\n        left: 0;\n      }\n    }\n  }\n\n  > .active,\n  > .next,\n  > .prev {\n    display: block;\n  }\n\n  > .active {\n    left: 0;\n  }\n\n  > .next,\n  > .prev {\n    position: absolute;\n    top: 0;\n    width: 100%;\n  }\n\n  > .next {\n    left: 100%;\n  }\n  > .prev {\n    left: -100%;\n  }\n  > .next.left,\n  > .prev.right {\n    left: 0;\n  }\n\n  > .active.left {\n    left: -100%;\n  }\n  > .active.right {\n    left: 100%;\n  }\n\n}\n\n// Left/right controls for nav\n// ---------------------------\n\n.carousel-control {\n  position: absolute;\n  top: 0;\n  left: 0;\n  bottom: 0;\n  width: @carousel-control-width;\n  .opacity(@carousel-control-opacity);\n  font-size: @carousel-control-font-size;\n  color: @carousel-control-color;\n  text-align: center;\n  text-shadow: @carousel-text-shadow;\n  background-color: rgba(0, 0, 0, 0); // Fix IE9 click-thru bug\n  // We can't have this transition here because WebKit cancels the carousel\n  // animation if you trip this while in the middle of another animation.\n\n  // Set gradients for backgrounds\n  &.left {\n    #gradient > .horizontal(@start-color: rgba(0,0,0,.5); @end-color: rgba(0,0,0,.0001));\n  }\n  &.right {\n    left: auto;\n    right: 0;\n    #gradient > .horizontal(@start-color: rgba(0,0,0,.0001); @end-color: rgba(0,0,0,.5));\n  }\n\n  // Hover/focus state\n  &:hover,\n  &:focus {\n    outline: 0;\n    color: @carousel-control-color;\n    text-decoration: none;\n    .opacity(.9);\n  }\n\n  // Toggles\n  .icon-prev,\n  .icon-next,\n  .glyphicon-chevron-left,\n  .glyphicon-chevron-right {\n    position: absolute;\n    top: 50%;\n    margin-top: -10px;\n    z-index: 5;\n    display: inline-block;\n  }\n  .icon-prev,\n  .glyphicon-chevron-left {\n    left: 50%;\n    margin-left: -10px;\n  }\n  .icon-next,\n  .glyphicon-chevron-right {\n    right: 50%;\n    margin-right: -10px;\n  }\n  .icon-prev,\n  .icon-next {\n    width:  20px;\n    height: 20px;\n    line-height: 1;\n    font-family: serif;\n  }\n\n\n  .icon-prev {\n    &:before {\n      content: '\\2039';// SINGLE LEFT-POINTING ANGLE QUOTATION MARK (U+2039)\n    }\n  }\n  .icon-next {\n    &:before {\n      content: '\\203a';// SINGLE RIGHT-POINTING ANGLE QUOTATION MARK (U+203A)\n    }\n  }\n}\n\n// Optional indicator pips\n//\n// Add an unordered list with the following class and add a list item for each\n// slide your carousel holds.\n\n.carousel-indicators {\n  position: absolute;\n  bottom: 10px;\n  left: 50%;\n  z-index: 15;\n  width: 60%;\n  margin-left: -30%;\n  padding-left: 0;\n  list-style: none;\n  text-align: center;\n\n  li {\n    display: inline-block;\n    width:  10px;\n    height: 10px;\n    margin: 1px;\n    text-indent: -999px;\n    border: 1px solid @carousel-indicator-border-color;\n    border-radius: 10px;\n    cursor: pointer;\n\n    // IE8-9 hack for event handling\n    //\n    // Internet Explorer 8-9 does not support clicks on elements without a set\n    // `background-color`. We cannot use `filter` since that's not viewed as a\n    // background color by the browser. Thus, a hack is needed.\n    // See https://developer.mozilla.org/en-US/docs/Web/Events/click#Internet_Explorer\n    //\n    // For IE8, we set solid black as it doesn't support `rgba()`. For IE9, we\n    // set alpha transparency for the best results possible.\n    background-color: #000 \\9; // IE8\n    background-color: rgba(0,0,0,0); // IE9\n  }\n  .active {\n    margin: 0;\n    width:  12px;\n    height: 12px;\n    background-color: @carousel-indicator-active-bg;\n  }\n}\n\n// Optional captions\n// -----------------------------\n// Hidden by default for smaller viewports\n.carousel-caption {\n  position: absolute;\n  left: 15%;\n  right: 15%;\n  bottom: 20px;\n  z-index: 10;\n  padding-top: 20px;\n  padding-bottom: 20px;\n  color: @carousel-caption-color;\n  text-align: center;\n  text-shadow: @carousel-text-shadow;\n  & .btn {\n    text-shadow: none; // No shadow for button elements in carousel-caption\n  }\n}\n\n\n// Scale up controls for tablets and up\n@media screen and (min-width: @screen-sm-min) {\n\n  // Scale up the controls a smidge\n  .carousel-control {\n    .glyphicon-chevron-left,\n    .glyphicon-chevron-right,\n    .icon-prev,\n    .icon-next {\n      width: (@carousel-control-font-size * 1.5);\n      height: (@carousel-control-font-size * 1.5);\n      margin-top: (@carousel-control-font-size / -2);\n      font-size: (@carousel-control-font-size * 1.5);\n    }\n    .glyphicon-chevron-left,\n    .icon-prev {\n      margin-left: (@carousel-control-font-size / -2);\n    }\n    .glyphicon-chevron-right,\n    .icon-next {\n      margin-right: (@carousel-control-font-size / -2);\n    }\n  }\n\n  // Show and left align the captions\n  .carousel-caption {\n    left: 20%;\n    right: 20%;\n    padding-bottom: 30px;\n  }\n\n  // Move up the indicators\n  .carousel-indicators {\n    bottom: 20px;\n  }\n}\n","// Clearfix\n//\n// For modern browsers\n// 1. The space content is one way to avoid an Opera bug when the\n//    contenteditable attribute is included anywhere else in the document.\n//    Otherwise it causes space to appear at the top and bottom of elements\n//    that are clearfixed.\n// 2. The use of `table` rather than `block` is only necessary if using\n//    `:before` to contain the top-margins of child elements.\n//\n// Source: http://nicolasgallagher.com/micro-clearfix-hack/\n\n.clearfix() {\n  &:before,\n  &:after {\n    content: \" \"; // 1\n    display: table; // 2\n  }\n  &:after {\n    clear: both;\n  }\n}\n","// Center-align a block level element\n\n.center-block() {\n  display: block;\n  margin-left: auto;\n  margin-right: auto;\n}\n","// CSS image replacement\n//\n// Heads up! v3 launched with only `.hide-text()`, but per our pattern for\n// mixins being reused as classes with the same name, this doesn't hold up. As\n// of v3.0.1 we have added `.text-hide()` and deprecated `.hide-text()`.\n//\n// Source: https://github.com/h5bp/html5-boilerplate/commit/aa0396eae757\n\n// Deprecated as of v3.0.1 (has been removed in v4)\n.hide-text() {\n  font: ~\"0/0\" a;\n  color: transparent;\n  text-shadow: none;\n  background-color: transparent;\n  border: 0;\n}\n\n// New mixin to use as of v3.0.1\n.text-hide() {\n  .hide-text();\n}\n","//\n// Responsive: Utility classes\n// --------------------------------------------------\n\n\n// IE10 in Windows (Phone) 8\n//\n// Support for responsive views via media queries is kind of borked in IE10, for\n// Surface/desktop in split view and for Windows Phone 8. This particular fix\n// must be accompanied by a snippet of JavaScript to sniff the user agent and\n// apply some conditional CSS to *only* the Surface/desktop Windows 8. Look at\n// our Getting Started page for more information on this bug.\n//\n// For more information, see the following:\n//\n// Issue: https://github.com/twbs/bootstrap/issues/10497\n// Docs: http://getbootstrap.com/getting-started/#support-ie10-width\n// Source: http://timkadlec.com/2013/01/windows-phone-8-and-device-width/\n// Source: http://timkadlec.com/2012/10/ie10-snap-mode-and-responsive-design/\n\n@-ms-viewport {\n  width: device-width;\n}\n\n\n// Visibility utilities\n// Note: Deprecated .visible-xs, .visible-sm, .visible-md, and .visible-lg as of v3.2.0\n.visible-xs,\n.visible-sm,\n.visible-md,\n.visible-lg {\n  .responsive-invisibility();\n}\n\n.visible-xs-block,\n.visible-xs-inline,\n.visible-xs-inline-block,\n.visible-sm-block,\n.visible-sm-inline,\n.visible-sm-inline-block,\n.visible-md-block,\n.visible-md-inline,\n.visible-md-inline-block,\n.visible-lg-block,\n.visible-lg-inline,\n.visible-lg-inline-block {\n  display: none !important;\n}\n\n.visible-xs {\n  @media (max-width: @screen-xs-max) {\n    .responsive-visibility();\n  }\n}\n.visible-xs-block {\n  @media (max-width: @screen-xs-max) {\n    display: block !important;\n  }\n}\n.visible-xs-inline {\n  @media (max-width: @screen-xs-max) {\n    display: inline !important;\n  }\n}\n.visible-xs-inline-block {\n  @media (max-width: @screen-xs-max) {\n    display: inline-block !important;\n  }\n}\n\n.visible-sm {\n  @media (min-width: @screen-sm-min) and (max-width: @screen-sm-max) {\n    .responsive-visibility();\n  }\n}\n.visible-sm-block {\n  @media (min-width: @screen-sm-min) and (max-width: @screen-sm-max) {\n    display: block !important;\n  }\n}\n.visible-sm-inline {\n  @media (min-width: @screen-sm-min) and (max-width: @screen-sm-max) {\n    display: inline !important;\n  }\n}\n.visible-sm-inline-block {\n  @media (min-width: @screen-sm-min) and (max-width: @screen-sm-max) {\n    display: inline-block !important;\n  }\n}\n\n.visible-md {\n  @media (min-width: @screen-md-min) and (max-width: @screen-md-max) {\n    .responsive-visibility();\n  }\n}\n.visible-md-block {\n  @media (min-width: @screen-md-min) and (max-width: @screen-md-max) {\n    display: block !important;\n  }\n}\n.visible-md-inline {\n  @media (min-width: @screen-md-min) and (max-width: @screen-md-max) {\n    display: inline !important;\n  }\n}\n.visible-md-inline-block {\n  @media (min-width: @screen-md-min) and (max-width: @screen-md-max) {\n    display: inline-block !important;\n  }\n}\n\n.visible-lg {\n  @media (min-width: @screen-lg-min) {\n    .responsive-visibility();\n  }\n}\n.visible-lg-block {\n  @media (min-width: @screen-lg-min) {\n    display: block !important;\n  }\n}\n.visible-lg-inline {\n  @media (min-width: @screen-lg-min) {\n    display: inline !important;\n  }\n}\n.visible-lg-inline-block {\n  @media (min-width: @screen-lg-min) {\n    display: inline-block !important;\n  }\n}\n\n.hidden-xs {\n  @media (max-width: @screen-xs-max) {\n    .responsive-invisibility();\n  }\n}\n.hidden-sm {\n  @media (min-width: @screen-sm-min) and (max-width: @screen-sm-max) {\n    .responsive-invisibility();\n  }\n}\n.hidden-md {\n  @media (min-width: @screen-md-min) and (max-width: @screen-md-max) {\n    .responsive-invisibility();\n  }\n}\n.hidden-lg {\n  @media (min-width: @screen-lg-min) {\n    .responsive-invisibility();\n  }\n}\n\n\n// Print utilities\n//\n// Media queries are placed on the inside to be mixin-friendly.\n\n// Note: Deprecated .visible-print as of v3.2.0\n.visible-print {\n  .responsive-invisibility();\n\n  @media print {\n    .responsive-visibility();\n  }\n}\n.visible-print-block {\n  display: none !important;\n\n  @media print {\n    display: block !important;\n  }\n}\n.visible-print-inline {\n  display: none !important;\n\n  @media print {\n    display: inline !important;\n  }\n}\n.visible-print-inline-block {\n  display: none !important;\n\n  @media print {\n    display: inline-block !important;\n  }\n}\n\n.hidden-print {\n  @media print {\n    .responsive-invisibility();\n  }\n}\n","// Responsive utilities\n\n//\n// More easily include all the states for responsive-utilities.less.\n.responsive-visibility() {\n  display: block !important;\n  table&  { display: table !important; }\n  tr&     { display: table-row !important; }\n  th&,\n  td&     { display: table-cell !important; }\n}\n\n.responsive-invisibility() {\n  display: none !important;\n}\n"]}
\ No newline at end of file
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diff --git a/docs/_static/bootstrap-3.3.7/js/bootstrap.js b/docs/_static/bootstrap-3.3.7/js/bootstrap.js
deleted file mode 100644
index 6317cf39a..000000000
--- a/docs/_static/bootstrap-3.3.7/js/bootstrap.js
+++ /dev/null
@@ -1,2377 +0,0 @@
-/*!
- * Bootstrap v3.3.7 (http://getbootstrap.com)
- * Copyright 2011-2016 Twitter, Inc.
- * Licensed under the MIT license
- */
-
-if (typeof (window.$jqTheme || window.jQuery) === 'undefined') {
-  throw new Error('Bootstrap\'s JavaScript requires jQuery')
-}
-
-+function ($) {
-  'use strict';
-  var version = $.fn.jquery.split(' ')[0].split('.')
-  if ((version[0] < 2 && version[1] < 9) || (version[0] == 1 && version[1] == 9 && version[2] < 1) || (version[0] > 3)) {
-    throw new Error('Bootstrap\'s JavaScript requires jQuery version 1.9.1 or higher, but lower than version 4')
-  }
-}(window.$jqTheme || window.jQuery);
-
-/* ========================================================================
- * Bootstrap: transition.js v3.3.7
- * http://getbootstrap.com/javascript/#transitions
- * ========================================================================
- * Copyright 2011-2016 Twitter, Inc.
- * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
- * ======================================================================== */
-
-
-+function ($) {
-  'use strict';
-
-  // CSS TRANSITION SUPPORT (Shoutout: http://www.modernizr.com/)
-  // ============================================================
-
-  function transitionEnd() {
-    var el = document.createElement('bootstrap')
-
-    var transEndEventNames = {
-      WebkitTransition : 'webkitTransitionEnd',
-      MozTransition    : 'transitionend',
-      OTransition      : 'oTransitionEnd otransitionend',
-      transition       : 'transitionend'
-    }
-
-    for (var name in transEndEventNames) {
-      if (el.style[name] !== undefined) {
-        return { end: transEndEventNames[name] }
-      }
-    }
-
-    return false // explicit for ie8 (  ._.)
-  }
-
-  // http://blog.alexmaccaw.com/css-transitions
-  $.fn.emulateTransitionEnd = function (duration) {
-    var called = false
-    var $el = this
-    $(this).one('bsTransitionEnd', function () { called = true })
-    var callback = function () { if (!called) $($el).trigger($.support.transition.end) }
-    setTimeout(callback, duration)
-    return this
-  }
-
-  $(function () {
-    $.support.transition = transitionEnd()
-
-    if (!$.support.transition) return
-
-    $.event.special.bsTransitionEnd = {
-      bindType: $.support.transition.end,
-      delegateType: $.support.transition.end,
-      handle: function (e) {
-        if ($(e.target).is(this)) return e.handleObj.handler.apply(this, arguments)
-      }
-    }
-  })
-
-}(window.$jqTheme || window.jQuery);
-
-/* ========================================================================
- * Bootstrap: alert.js v3.3.7
- * http://getbootstrap.com/javascript/#alerts
- * ========================================================================
- * Copyright 2011-2016 Twitter, Inc.
- * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
- * ======================================================================== */
-
-
-+function ($) {
-  'use strict';
-
-  // ALERT CLASS DEFINITION
-  // ======================
-
-  var dismiss = '[data-dismiss="alert"]'
-  var Alert   = function (el) {
-    $(el).on('click', dismiss, this.close)
-  }
-
-  Alert.VERSION = '3.3.7'
-
-  Alert.TRANSITION_DURATION = 150
-
-  Alert.prototype.close = function (e) {
-    var $this    = $(this)
-    var selector = $this.attr('data-target')
-
-    if (!selector) {
-      selector = $this.attr('href')
-      selector = selector && selector.replace(/.*(?=#[^\s]*$)/, '') // strip for ie7
-    }
-
-    var $parent = $(selector === '#' ? [] : selector)
-
-    if (e) e.preventDefault()
-
-    if (!$parent.length) {
-      $parent = $this.closest('.alert')
-    }
-
-    $parent.trigger(e = $.Event('close.bs.alert'))
-
-    if (e.isDefaultPrevented()) return
-
-    $parent.removeClass('in')
-
-    function removeElement() {
-      // detach from parent, fire event then clean up data
-      $parent.detach().trigger('closed.bs.alert').remove()
-    }
-
-    $.support.transition && $parent.hasClass('fade') ?
-      $parent
-        .one('bsTransitionEnd', removeElement)
-        .emulateTransitionEnd(Alert.TRANSITION_DURATION) :
-      removeElement()
-  }
-
-
-  // ALERT PLUGIN DEFINITION
-  // =======================
-
-  function Plugin(option) {
-    return this.each(function () {
-      var $this = $(this)
-      var data  = $this.data('bs.alert')
-
-      if (!data) $this.data('bs.alert', (data = new Alert(this)))
-      if (typeof option == 'string') data[option].call($this)
-    })
-  }
-
-  var old = $.fn.alert
-
-  $.fn.alert             = Plugin
-  $.fn.alert.Constructor = Alert
-
-
-  // ALERT NO CONFLICT
-  // =================
-
-  $.fn.alert.noConflict = function () {
-    $.fn.alert = old
-    return this
-  }
-
-
-  // ALERT DATA-API
-  // ==============
-
-  $(document).on('click.bs.alert.data-api', dismiss, Alert.prototype.close)
-
-}(window.$jqTheme || window.jQuery);
-
-/* ========================================================================
- * Bootstrap: button.js v3.3.7
- * http://getbootstrap.com/javascript/#buttons
- * ========================================================================
- * Copyright 2011-2016 Twitter, Inc.
- * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
- * ======================================================================== */
-
-
-+function ($) {
-  'use strict';
-
-  // BUTTON PUBLIC CLASS DEFINITION
-  // ==============================
-
-  var Button = function (element, options) {
-    this.$element  = $(element)
-    this.options   = $.extend({}, Button.DEFAULTS, options)
-    this.isLoading = false
-  }
-
-  Button.VERSION  = '3.3.7'
-
-  Button.DEFAULTS = {
-    loadingText: 'loading...'
-  }
-
-  Button.prototype.setState = function (state) {
-    var d    = 'disabled'
-    var $el  = this.$element
-    var val  = $el.is('input') ? 'val' : 'html'
-    var data = $el.data()
-
-    state += 'Text'
-
-    if (data.resetText == null) $el.data('resetText', $el[val]())
-
-    // push to event loop to allow forms to submit
-    setTimeout($.proxy(function () {
-      $el[val](data[state] == null ? this.options[state] : data[state])
-
-      if (state == 'loadingText') {
-        this.isLoading = true
-        $el.addClass(d).attr(d, d).prop(d, true)
-      } else if (this.isLoading) {
-        this.isLoading = false
-        $el.removeClass(d).removeAttr(d).prop(d, false)
-      }
-    }, this), 0)
-  }
-
-  Button.prototype.toggle = function () {
-    var changed = true
-    var $parent = this.$element.closest('[data-toggle="buttons"]')
-
-    if ($parent.length) {
-      var $input = this.$element.find('input')
-      if ($input.prop('type') == 'radio') {
-        if ($input.prop('checked')) changed = false
-        $parent.find('.active').removeClass('active')
-        this.$element.addClass('active')
-      } else if ($input.prop('type') == 'checkbox') {
-        if (($input.prop('checked')) !== this.$element.hasClass('active')) changed = false
-        this.$element.toggleClass('active')
-      }
-      $input.prop('checked', this.$element.hasClass('active'))
-      if (changed) $input.trigger('change')
-    } else {
-      this.$element.attr('aria-pressed', !this.$element.hasClass('active'))
-      this.$element.toggleClass('active')
-    }
-  }
-
-
-  // BUTTON PLUGIN DEFINITION
-  // ========================
-
-  function Plugin(option) {
-    return this.each(function () {
-      var $this   = $(this)
-      var data    = $this.data('bs.button')
-      var options = typeof option == 'object' && option
-
-      if (!data) $this.data('bs.button', (data = new Button(this, options)))
-
-      if (option == 'toggle') data.toggle()
-      else if (option) data.setState(option)
-    })
-  }
-
-  var old = $.fn.button
-
-  $.fn.button             = Plugin
-  $.fn.button.Constructor = Button
-
-
-  // BUTTON NO CONFLICT
-  // ==================
-
-  $.fn.button.noConflict = function () {
-    $.fn.button = old
-    return this
-  }
-
-
-  // BUTTON DATA-API
-  // ===============
-
-  $(document)
-    .on('click.bs.button.data-api', '[data-toggle^="button"]', function (e) {
-      var $btn = $(e.target).closest('.btn')
-      Plugin.call($btn, 'toggle')
-      if (!($(e.target).is('input[type="radio"], input[type="checkbox"]'))) {
-        // Prevent double click on radios, and the double selections (so cancellation) on checkboxes
-        e.preventDefault()
-        // The target component still receive the focus
-        if ($btn.is('input,button')) $btn.trigger('focus')
-        else $btn.find('input:visible,button:visible').first().trigger('focus')
-      }
-    })
-    .on('focus.bs.button.data-api blur.bs.button.data-api', '[data-toggle^="button"]', function (e) {
-      $(e.target).closest('.btn').toggleClass('focus', /^focus(in)?$/.test(e.type))
-    })
-
-}(window.$jqTheme || window.jQuery);
-
-/* ========================================================================
- * Bootstrap: carousel.js v3.3.7
- * http://getbootstrap.com/javascript/#carousel
- * ========================================================================
- * Copyright 2011-2016 Twitter, Inc.
- * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
- * ======================================================================== */
-
-
-+function ($) {
-  'use strict';
-
-  // CAROUSEL CLASS DEFINITION
-  // =========================
-
-  var Carousel = function (element, options) {
-    this.$element    = $(element)
-    this.$indicators = this.$element.find('.carousel-indicators')
-    this.options     = options
-    this.paused      = null
-    this.sliding     = null
-    this.interval    = null
-    this.$active     = null
-    this.$items      = null
-
-    this.options.keyboard && this.$element.on('keydown.bs.carousel', $.proxy(this.keydown, this))
-
-    this.options.pause == 'hover' && !('ontouchstart' in document.documentElement) && this.$element
-      .on('mouseenter.bs.carousel', $.proxy(this.pause, this))
-      .on('mouseleave.bs.carousel', $.proxy(this.cycle, this))
-  }
-
-  Carousel.VERSION  = '3.3.7'
-
-  Carousel.TRANSITION_DURATION = 600
-
-  Carousel.DEFAULTS = {
-    interval: 5000,
-    pause: 'hover',
-    wrap: true,
-    keyboard: true
-  }
-
-  Carousel.prototype.keydown = function (e) {
-    if (/input|textarea/i.test(e.target.tagName)) return
-    switch (e.which) {
-      case 37: this.prev(); break
-      case 39: this.next(); break
-      default: return
-    }
-
-    e.preventDefault()
-  }
-
-  Carousel.prototype.cycle = function (e) {
-    e || (this.paused = false)
-
-    this.interval && clearInterval(this.interval)
-
-    this.options.interval
-      && !this.paused
-      && (this.interval = setInterval($.proxy(this.next, this), this.options.interval))
-
-    return this
-  }
-
-  Carousel.prototype.getItemIndex = function (item) {
-    this.$items = item.parent().children('.item')
-    return this.$items.index(item || this.$active)
-  }
-
-  Carousel.prototype.getItemForDirection = function (direction, active) {
-    var activeIndex = this.getItemIndex(active)
-    var willWrap = (direction == 'prev' && activeIndex === 0)
-                || (direction == 'next' && activeIndex == (this.$items.length - 1))
-    if (willWrap && !this.options.wrap) return active
-    var delta = direction == 'prev' ? -1 : 1
-    var itemIndex = (activeIndex + delta) % this.$items.length
-    return this.$items.eq(itemIndex)
-  }
-
-  Carousel.prototype.to = function (pos) {
-    var that        = this
-    var activeIndex = this.getItemIndex(this.$active = this.$element.find('.item.active'))
-
-    if (pos > (this.$items.length - 1) || pos < 0) return
-
-    if (this.sliding)       return this.$element.one('slid.bs.carousel', function () { that.to(pos) }) // yes, "slid"
-    if (activeIndex == pos) return this.pause().cycle()
-
-    return this.slide(pos > activeIndex ? 'next' : 'prev', this.$items.eq(pos))
-  }
-
-  Carousel.prototype.pause = function (e) {
-    e || (this.paused = true)
-
-    if (this.$element.find('.next, .prev').length && $.support.transition) {
-      this.$element.trigger($.support.transition.end)
-      this.cycle(true)
-    }
-
-    this.interval = clearInterval(this.interval)
-
-    return this
-  }
-
-  Carousel.prototype.next = function () {
-    if (this.sliding) return
-    return this.slide('next')
-  }
-
-  Carousel.prototype.prev = function () {
-    if (this.sliding) return
-    return this.slide('prev')
-  }
-
-  Carousel.prototype.slide = function (type, next) {
-    var $active   = this.$element.find('.item.active')
-    var $next     = next || this.getItemForDirection(type, $active)
-    var isCycling = this.interval
-    var direction = type == 'next' ? 'left' : 'right'
-    var that      = this
-
-    if ($next.hasClass('active')) return (this.sliding = false)
-
-    var relatedTarget = $next[0]
-    var slideEvent = $.Event('slide.bs.carousel', {
-      relatedTarget: relatedTarget,
-      direction: direction
-    })
-    this.$element.trigger(slideEvent)
-    if (slideEvent.isDefaultPrevented()) return
-
-    this.sliding = true
-
-    isCycling && this.pause()
-
-    if (this.$indicators.length) {
-      this.$indicators.find('.active').removeClass('active')
-      var $nextIndicator = $(this.$indicators.children()[this.getItemIndex($next)])
-      $nextIndicator && $nextIndicator.addClass('active')
-    }
-
-    var slidEvent = $.Event('slid.bs.carousel', { relatedTarget: relatedTarget, direction: direction }) // yes, "slid"
-    if ($.support.transition && this.$element.hasClass('slide')) {
-      $next.addClass(type)
-      $next[0].offsetWidth // force reflow
-      $active.addClass(direction)
-      $next.addClass(direction)
-      $active
-        .one('bsTransitionEnd', function () {
-          $next.removeClass([type, direction].join(' ')).addClass('active')
-          $active.removeClass(['active', direction].join(' '))
-          that.sliding = false
-          setTimeout(function () {
-            that.$element.trigger(slidEvent)
-          }, 0)
-        })
-        .emulateTransitionEnd(Carousel.TRANSITION_DURATION)
-    } else {
-      $active.removeClass('active')
-      $next.addClass('active')
-      this.sliding = false
-      this.$element.trigger(slidEvent)
-    }
-
-    isCycling && this.cycle()
-
-    return this
-  }
-
-
-  // CAROUSEL PLUGIN DEFINITION
-  // ==========================
-
-  function Plugin(option) {
-    return this.each(function () {
-      var $this   = $(this)
-      var data    = $this.data('bs.carousel')
-      var options = $.extend({}, Carousel.DEFAULTS, $this.data(), typeof option == 'object' && option)
-      var action  = typeof option == 'string' ? option : options.slide
-
-      if (!data) $this.data('bs.carousel', (data = new Carousel(this, options)))
-      if (typeof option == 'number') data.to(option)
-      else if (action) data[action]()
-      else if (options.interval) data.pause().cycle()
-    })
-  }
-
-  var old = $.fn.carousel
-
-  $.fn.carousel             = Plugin
-  $.fn.carousel.Constructor = Carousel
-
-
-  // CAROUSEL NO CONFLICT
-  // ====================
-
-  $.fn.carousel.noConflict = function () {
-    $.fn.carousel = old
-    return this
-  }
-
-
-  // CAROUSEL DATA-API
-  // =================
-
-  var clickHandler = function (e) {
-    var href
-    var $this   = $(this)
-    var $target = $($this.attr('data-target') || (href = $this.attr('href')) && href.replace(/.*(?=#[^\s]+$)/, '')) // strip for ie7
-    if (!$target.hasClass('carousel')) return
-    var options = $.extend({}, $target.data(), $this.data())
-    var slideIndex = $this.attr('data-slide-to')
-    if (slideIndex) options.interval = false
-
-    Plugin.call($target, options)
-
-    if (slideIndex) {
-      $target.data('bs.carousel').to(slideIndex)
-    }
-
-    e.preventDefault()
-  }
-
-  $(document)
-    .on('click.bs.carousel.data-api', '[data-slide]', clickHandler)
-    .on('click.bs.carousel.data-api', '[data-slide-to]', clickHandler)
-
-  $(window).on('load', function () {
-    $('[data-ride="carousel"]').each(function () {
-      var $carousel = $(this)
-      Plugin.call($carousel, $carousel.data())
-    })
-  })
-
-}(window.$jqTheme || window.jQuery);
-
-/* ========================================================================
- * Bootstrap: collapse.js v3.3.7
- * http://getbootstrap.com/javascript/#collapse
- * ========================================================================
- * Copyright 2011-2016 Twitter, Inc.
- * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
- * ======================================================================== */
-
-/* jshint latedef: false */
-
-+function ($) {
-  'use strict';
-
-  // COLLAPSE PUBLIC CLASS DEFINITION
-  // ================================
-
-  var Collapse = function (element, options) {
-    this.$element      = $(element)
-    this.options       = $.extend({}, Collapse.DEFAULTS, options)
-    this.$trigger      = $('[data-toggle="collapse"][href="#' + element.id + '"],' +
-                           '[data-toggle="collapse"][data-target="#' + element.id + '"]')
-    this.transitioning = null
-
-    if (this.options.parent) {
-      this.$parent = this.getParent()
-    } else {
-      this.addAriaAndCollapsedClass(this.$element, this.$trigger)
-    }
-
-    if (this.options.toggle) this.toggle()
-  }
-
-  Collapse.VERSION  = '3.3.7'
-
-  Collapse.TRANSITION_DURATION = 350
-
-  Collapse.DEFAULTS = {
-    toggle: true
-  }
-
-  Collapse.prototype.dimension = function () {
-    var hasWidth = this.$element.hasClass('width')
-    return hasWidth ? 'width' : 'height'
-  }
-
-  Collapse.prototype.show = function () {
-    if (this.transitioning || this.$element.hasClass('in')) return
-
-    var activesData
-    var actives = this.$parent && this.$parent.children('.panel').children('.in, .collapsing')
-
-    if (actives && actives.length) {
-      activesData = actives.data('bs.collapse')
-      if (activesData && activesData.transitioning) return
-    }
-
-    var startEvent = $.Event('show.bs.collapse')
-    this.$element.trigger(startEvent)
-    if (startEvent.isDefaultPrevented()) return
-
-    if (actives && actives.length) {
-      Plugin.call(actives, 'hide')
-      activesData || actives.data('bs.collapse', null)
-    }
-
-    var dimension = this.dimension()
-
-    this.$element
-      .removeClass('collapse')
-      .addClass('collapsing')[dimension](0)
-      .attr('aria-expanded', true)
-
-    this.$trigger
-      .removeClass('collapsed')
-      .attr('aria-expanded', true)
-
-    this.transitioning = 1
-
-    var complete = function () {
-      this.$element
-        .removeClass('collapsing')
-        .addClass('collapse in')[dimension]('')
-      this.transitioning = 0
-      this.$element
-        .trigger('shown.bs.collapse')
-    }
-
-    if (!$.support.transition) return complete.call(this)
-
-    var scrollSize = $.camelCase(['scroll', dimension].join('-'))
-
-    this.$element
-      .one('bsTransitionEnd', $.proxy(complete, this))
-      .emulateTransitionEnd(Collapse.TRANSITION_DURATION)[dimension](this.$element[0][scrollSize])
-  }
-
-  Collapse.prototype.hide = function () {
-    if (this.transitioning || !this.$element.hasClass('in')) return
-
-    var startEvent = $.Event('hide.bs.collapse')
-    this.$element.trigger(startEvent)
-    if (startEvent.isDefaultPrevented()) return
-
-    var dimension = this.dimension()
-
-    this.$element[dimension](this.$element[dimension]())[0].offsetHeight
-
-    this.$element
-      .addClass('collapsing')
-      .removeClass('collapse in')
-      .attr('aria-expanded', false)
-
-    this.$trigger
-      .addClass('collapsed')
-      .attr('aria-expanded', false)
-
-    this.transitioning = 1
-
-    var complete = function () {
-      this.transitioning = 0
-      this.$element
-        .removeClass('collapsing')
-        .addClass('collapse')
-        .trigger('hidden.bs.collapse')
-    }
-
-    if (!$.support.transition) return complete.call(this)
-
-    this.$element
-      [dimension](0)
-      .one('bsTransitionEnd', $.proxy(complete, this))
-      .emulateTransitionEnd(Collapse.TRANSITION_DURATION)
-  }
-
-  Collapse.prototype.toggle = function () {
-    this[this.$element.hasClass('in') ? 'hide' : 'show']()
-  }
-
-  Collapse.prototype.getParent = function () {
-    return $(this.options.parent)
-      .find('[data-toggle="collapse"][data-parent="' + this.options.parent + '"]')
-      .each($.proxy(function (i, element) {
-        var $element = $(element)
-        this.addAriaAndCollapsedClass(getTargetFromTrigger($element), $element)
-      }, this))
-      .end()
-  }
-
-  Collapse.prototype.addAriaAndCollapsedClass = function ($element, $trigger) {
-    var isOpen = $element.hasClass('in')
-
-    $element.attr('aria-expanded', isOpen)
-    $trigger
-      .toggleClass('collapsed', !isOpen)
-      .attr('aria-expanded', isOpen)
-  }
-
-  function getTargetFromTrigger($trigger) {
-    var href
-    var target = $trigger.attr('data-target')
-      || (href = $trigger.attr('href')) && href.replace(/.*(?=#[^\s]+$)/, '') // strip for ie7
-
-    return $(target)
-  }
-
-
-  // COLLAPSE PLUGIN DEFINITION
-  // ==========================
-
-  function Plugin(option) {
-    return this.each(function () {
-      var $this   = $(this)
-      var data    = $this.data('bs.collapse')
-      var options = $.extend({}, Collapse.DEFAULTS, $this.data(), typeof option == 'object' && option)
-
-      if (!data && options.toggle && /show|hide/.test(option)) options.toggle = false
-      if (!data) $this.data('bs.collapse', (data = new Collapse(this, options)))
-      if (typeof option == 'string') data[option]()
-    })
-  }
-
-  var old = $.fn.collapse
-
-  $.fn.collapse             = Plugin
-  $.fn.collapse.Constructor = Collapse
-
-
-  // COLLAPSE NO CONFLICT
-  // ====================
-
-  $.fn.collapse.noConflict = function () {
-    $.fn.collapse = old
-    return this
-  }
-
-
-  // COLLAPSE DATA-API
-  // =================
-
-  $(document).on('click.bs.collapse.data-api', '[data-toggle="collapse"]', function (e) {
-    var $this   = $(this)
-
-    if (!$this.attr('data-target')) e.preventDefault()
-
-    var $target = getTargetFromTrigger($this)
-    var data    = $target.data('bs.collapse')
-    var option  = data ? 'toggle' : $this.data()
-
-    Plugin.call($target, option)
-  })
-
-}(window.$jqTheme || window.jQuery);
-
-/* ========================================================================
- * Bootstrap: dropdown.js v3.3.7
- * http://getbootstrap.com/javascript/#dropdowns
- * ========================================================================
- * Copyright 2011-2016 Twitter, Inc.
- * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
- * ======================================================================== */
-
-
-+function ($) {
-  'use strict';
-
-  // DROPDOWN CLASS DEFINITION
-  // =========================
-
-  var backdrop = '.dropdown-backdrop'
-  var toggle   = '[data-toggle="dropdown"]'
-  var Dropdown = function (element) {
-    $(element).on('click.bs.dropdown', this.toggle)
-  }
-
-  Dropdown.VERSION = '3.3.7'
-
-  function getParent($this) {
-    var selector = $this.attr('data-target')
-
-    if (!selector) {
-      selector = $this.attr('href')
-      selector = selector && /#[A-Za-z]/.test(selector) && selector.replace(/.*(?=#[^\s]*$)/, '') // strip for ie7
-    }
-
-    var $parent = selector && $(selector)
-
-    return $parent && $parent.length ? $parent : $this.parent()
-  }
-
-  function clearMenus(e) {
-    if (e && e.which === 3) return
-    $(backdrop).remove()
-    $(toggle).each(function () {
-      var $this         = $(this)
-      var $parent       = getParent($this)
-      var relatedTarget = { relatedTarget: this }
-
-      if (!$parent.hasClass('open')) return
-
-      if (e && e.type == 'click' && /input|textarea/i.test(e.target.tagName) && $.contains($parent[0], e.target)) return
-
-      $parent.trigger(e = $.Event('hide.bs.dropdown', relatedTarget))
-
-      if (e.isDefaultPrevented()) return
-
-      $this.attr('aria-expanded', 'false')
-      $parent.removeClass('open').trigger($.Event('hidden.bs.dropdown', relatedTarget))
-    })
-  }
-
-  Dropdown.prototype.toggle = function (e) {
-    var $this = $(this)
-
-    if ($this.is('.disabled, :disabled')) return
-
-    var $parent  = getParent($this)
-    var isActive = $parent.hasClass('open')
-
-    clearMenus()
-
-    if (!isActive) {
-      if ('ontouchstart' in document.documentElement && !$parent.closest('.navbar-nav').length) {
-        // if mobile we use a backdrop because click events don't delegate
-        $(document.createElement('div'))
-          .addClass('dropdown-backdrop')
-          .insertAfter($(this))
-          .on('click', clearMenus)
-      }
-
-      var relatedTarget = { relatedTarget: this }
-      $parent.trigger(e = $.Event('show.bs.dropdown', relatedTarget))
-
-      if (e.isDefaultPrevented()) return
-
-      $this
-        .trigger('focus')
-        .attr('aria-expanded', 'true')
-
-      $parent
-        .toggleClass('open')
-        .trigger($.Event('shown.bs.dropdown', relatedTarget))
-    }
-
-    return false
-  }
-
-  Dropdown.prototype.keydown = function (e) {
-    if (!/(38|40|27|32)/.test(e.which) || /input|textarea/i.test(e.target.tagName)) return
-
-    var $this = $(this)
-
-    e.preventDefault()
-    e.stopPropagation()
-
-    if ($this.is('.disabled, :disabled')) return
-
-    var $parent  = getParent($this)
-    var isActive = $parent.hasClass('open')
-
-    if (!isActive && e.which != 27 || isActive && e.which == 27) {
-      if (e.which == 27) $parent.find(toggle).trigger('focus')
-      return $this.trigger('click')
-    }
-
-    var desc = ' li:not(.disabled):visible a'
-    var $items = $parent.find('.dropdown-menu' + desc)
-
-    if (!$items.length) return
-
-    var index = $items.index(e.target)
-
-    if (e.which == 38 && index > 0)                 index--         // up
-    if (e.which == 40 && index < $items.length - 1) index++         // down
-    if (!~index)                                    index = 0
-
-    $items.eq(index).trigger('focus')
-  }
-
-
-  // DROPDOWN PLUGIN DEFINITION
-  // ==========================
-
-  function Plugin(option) {
-    return this.each(function () {
-      var $this = $(this)
-      var data  = $this.data('bs.dropdown')
-
-      if (!data) $this.data('bs.dropdown', (data = new Dropdown(this)))
-      if (typeof option == 'string') data[option].call($this)
-    })
-  }
-
-  var old = $.fn.dropdown
-
-  $.fn.dropdown             = Plugin
-  $.fn.dropdown.Constructor = Dropdown
-
-
-  // DROPDOWN NO CONFLICT
-  // ====================
-
-  $.fn.dropdown.noConflict = function () {
-    $.fn.dropdown = old
-    return this
-  }
-
-
-  // APPLY TO STANDARD DROPDOWN ELEMENTS
-  // ===================================
-
-  $(document)
-    .on('click.bs.dropdown.data-api', clearMenus)
-    .on('click.bs.dropdown.data-api', '.dropdown form', function (e) { e.stopPropagation() })
-    .on('click.bs.dropdown.data-api', toggle, Dropdown.prototype.toggle)
-    .on('keydown.bs.dropdown.data-api', toggle, Dropdown.prototype.keydown)
-    .on('keydown.bs.dropdown.data-api', '.dropdown-menu', Dropdown.prototype.keydown)
-
-}(window.$jqTheme || window.jQuery);
-
-/* ========================================================================
- * Bootstrap: modal.js v3.3.7
- * http://getbootstrap.com/javascript/#modals
- * ========================================================================
- * Copyright 2011-2016 Twitter, Inc.
- * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
- * ======================================================================== */
-
-
-+function ($) {
-  'use strict';
-
-  // MODAL CLASS DEFINITION
-  // ======================
-
-  var Modal = function (element, options) {
-    this.options             = options
-    this.$body               = $(document.body)
-    this.$element            = $(element)
-    this.$dialog             = this.$element.find('.modal-dialog')
-    this.$backdrop           = null
-    this.isShown             = null
-    this.originalBodyPad     = null
-    this.scrollbarWidth      = 0
-    this.ignoreBackdropClick = false
-
-    if (this.options.remote) {
-      this.$element
-        .find('.modal-content')
-        .load(this.options.remote, $.proxy(function () {
-          this.$element.trigger('loaded.bs.modal')
-        }, this))
-    }
-  }
-
-  Modal.VERSION  = '3.3.7'
-
-  Modal.TRANSITION_DURATION = 300
-  Modal.BACKDROP_TRANSITION_DURATION = 150
-
-  Modal.DEFAULTS = {
-    backdrop: true,
-    keyboard: true,
-    show: true
-  }
-
-  Modal.prototype.toggle = function (_relatedTarget) {
-    return this.isShown ? this.hide() : this.show(_relatedTarget)
-  }
-
-  Modal.prototype.show = function (_relatedTarget) {
-    var that = this
-    var e    = $.Event('show.bs.modal', { relatedTarget: _relatedTarget })
-
-    this.$element.trigger(e)
-
-    if (this.isShown || e.isDefaultPrevented()) return
-
-    this.isShown = true
-
-    this.checkScrollbar()
-    this.setScrollbar()
-    this.$body.addClass('modal-open')
-
-    this.escape()
-    this.resize()
-
-    this.$element.on('click.dismiss.bs.modal', '[data-dismiss="modal"]', $.proxy(this.hide, this))
-
-    this.$dialog.on('mousedown.dismiss.bs.modal', function () {
-      that.$element.one('mouseup.dismiss.bs.modal', function (e) {
-        if ($(e.target).is(that.$element)) that.ignoreBackdropClick = true
-      })
-    })
-
-    this.backdrop(function () {
-      var transition = $.support.transition && that.$element.hasClass('fade')
-
-      if (!that.$element.parent().length) {
-        that.$element.appendTo(that.$body) // don't move modals dom position
-      }
-
-      that.$element
-        .show()
-        .scrollTop(0)
-
-      that.adjustDialog()
-
-      if (transition) {
-        that.$element[0].offsetWidth // force reflow
-      }
-
-      that.$element.addClass('in')
-
-      that.enforceFocus()
-
-      var e = $.Event('shown.bs.modal', { relatedTarget: _relatedTarget })
-
-      transition ?
-        that.$dialog // wait for modal to slide in
-          .one('bsTransitionEnd', function () {
-            that.$element.trigger('focus').trigger(e)
-          })
-          .emulateTransitionEnd(Modal.TRANSITION_DURATION) :
-        that.$element.trigger('focus').trigger(e)
-    })
-  }
-
-  Modal.prototype.hide = function (e) {
-    if (e) e.preventDefault()
-
-    e = $.Event('hide.bs.modal')
-
-    this.$element.trigger(e)
-
-    if (!this.isShown || e.isDefaultPrevented()) return
-
-    this.isShown = false
-
-    this.escape()
-    this.resize()
-
-    $(document).off('focusin.bs.modal')
-
-    this.$element
-      .removeClass('in')
-      .off('click.dismiss.bs.modal')
-      .off('mouseup.dismiss.bs.modal')
-
-    this.$dialog.off('mousedown.dismiss.bs.modal')
-
-    $.support.transition && this.$element.hasClass('fade') ?
-      this.$element
-        .one('bsTransitionEnd', $.proxy(this.hideModal, this))
-        .emulateTransitionEnd(Modal.TRANSITION_DURATION) :
-      this.hideModal()
-  }
-
-  Modal.prototype.enforceFocus = function () {
-    $(document)
-      .off('focusin.bs.modal') // guard against infinite focus loop
-      .on('focusin.bs.modal', $.proxy(function (e) {
-        if (document !== e.target &&
-            this.$element[0] !== e.target &&
-            !this.$element.has(e.target).length) {
-          this.$element.trigger('focus')
-        }
-      }, this))
-  }
-
-  Modal.prototype.escape = function () {
-    if (this.isShown && this.options.keyboard) {
-      this.$element.on('keydown.dismiss.bs.modal', $.proxy(function (e) {
-        e.which == 27 && this.hide()
-      }, this))
-    } else if (!this.isShown) {
-      this.$element.off('keydown.dismiss.bs.modal')
-    }
-  }
-
-  Modal.prototype.resize = function () {
-    if (this.isShown) {
-      $(window).on('resize.bs.modal', $.proxy(this.handleUpdate, this))
-    } else {
-      $(window).off('resize.bs.modal')
-    }
-  }
-
-  Modal.prototype.hideModal = function () {
-    var that = this
-    this.$element.hide()
-    this.backdrop(function () {
-      that.$body.removeClass('modal-open')
-      that.resetAdjustments()
-      that.resetScrollbar()
-      that.$element.trigger('hidden.bs.modal')
-    })
-  }
-
-  Modal.prototype.removeBackdrop = function () {
-    this.$backdrop && this.$backdrop.remove()
-    this.$backdrop = null
-  }
-
-  Modal.prototype.backdrop = function (callback) {
-    var that = this
-    var animate = this.$element.hasClass('fade') ? 'fade' : ''
-
-    if (this.isShown && this.options.backdrop) {
-      var doAnimate = $.support.transition && animate
-
-      this.$backdrop = $(document.createElement('div'))
-        .addClass('modal-backdrop ' + animate)
-        .appendTo(this.$body)
-
-      this.$element.on('click.dismiss.bs.modal', $.proxy(function (e) {
-        if (this.ignoreBackdropClick) {
-          this.ignoreBackdropClick = false
-          return
-        }
-        if (e.target !== e.currentTarget) return
-        this.options.backdrop == 'static'
-          ? this.$element[0].focus()
-          : this.hide()
-      }, this))
-
-      if (doAnimate) this.$backdrop[0].offsetWidth // force reflow
-
-      this.$backdrop.addClass('in')
-
-      if (!callback) return
-
-      doAnimate ?
-        this.$backdrop
-          .one('bsTransitionEnd', callback)
-          .emulateTransitionEnd(Modal.BACKDROP_TRANSITION_DURATION) :
-        callback()
-
-    } else if (!this.isShown && this.$backdrop) {
-      this.$backdrop.removeClass('in')
-
-      var callbackRemove = function () {
-        that.removeBackdrop()
-        callback && callback()
-      }
-      $.support.transition && this.$element.hasClass('fade') ?
-        this.$backdrop
-          .one('bsTransitionEnd', callbackRemove)
-          .emulateTransitionEnd(Modal.BACKDROP_TRANSITION_DURATION) :
-        callbackRemove()
-
-    } else if (callback) {
-      callback()
-    }
-  }
-
-  // these following methods are used to handle overflowing modals
-
-  Modal.prototype.handleUpdate = function () {
-    this.adjustDialog()
-  }
-
-  Modal.prototype.adjustDialog = function () {
-    var modalIsOverflowing = this.$element[0].scrollHeight > document.documentElement.clientHeight
-
-    this.$element.css({
-      paddingLeft:  !this.bodyIsOverflowing && modalIsOverflowing ? this.scrollbarWidth : '',
-      paddingRight: this.bodyIsOverflowing && !modalIsOverflowing ? this.scrollbarWidth : ''
-    })
-  }
-
-  Modal.prototype.resetAdjustments = function () {
-    this.$element.css({
-      paddingLeft: '',
-      paddingRight: ''
-    })
-  }
-
-  Modal.prototype.checkScrollbar = function () {
-    var fullWindowWidth = window.innerWidth
-    if (!fullWindowWidth) { // workaround for missing window.innerWidth in IE8
-      var documentElementRect = document.documentElement.getBoundingClientRect()
-      fullWindowWidth = documentElementRect.right - Math.abs(documentElementRect.left)
-    }
-    this.bodyIsOverflowing = document.body.clientWidth < fullWindowWidth
-    this.scrollbarWidth = this.measureScrollbar()
-  }
-
-  Modal.prototype.setScrollbar = function () {
-    var bodyPad = parseInt((this.$body.css('padding-right') || 0), 10)
-    this.originalBodyPad = document.body.style.paddingRight || ''
-    if (this.bodyIsOverflowing) this.$body.css('padding-right', bodyPad + this.scrollbarWidth)
-  }
-
-  Modal.prototype.resetScrollbar = function () {
-    this.$body.css('padding-right', this.originalBodyPad)
-  }
-
-  Modal.prototype.measureScrollbar = function () { // thx walsh
-    var scrollDiv = document.createElement('div')
-    scrollDiv.className = 'modal-scrollbar-measure'
-    this.$body.append(scrollDiv)
-    var scrollbarWidth = scrollDiv.offsetWidth - scrollDiv.clientWidth
-    this.$body[0].removeChild(scrollDiv)
-    return scrollbarWidth
-  }
-
-
-  // MODAL PLUGIN DEFINITION
-  // =======================
-
-  function Plugin(option, _relatedTarget) {
-    return this.each(function () {
-      var $this   = $(this)
-      var data    = $this.data('bs.modal')
-      var options = $.extend({}, Modal.DEFAULTS, $this.data(), typeof option == 'object' && option)
-
-      if (!data) $this.data('bs.modal', (data = new Modal(this, options)))
-      if (typeof option == 'string') data[option](_relatedTarget)
-      else if (options.show) data.show(_relatedTarget)
-    })
-  }
-
-  var old = $.fn.modal
-
-  $.fn.modal             = Plugin
-  $.fn.modal.Constructor = Modal
-
-
-  // MODAL NO CONFLICT
-  // =================
-
-  $.fn.modal.noConflict = function () {
-    $.fn.modal = old
-    return this
-  }
-
-
-  // MODAL DATA-API
-  // ==============
-
-  $(document).on('click.bs.modal.data-api', '[data-toggle="modal"]', function (e) {
-    var $this   = $(this)
-    var href    = $this.attr('href')
-    var $target = $($this.attr('data-target') || (href && href.replace(/.*(?=#[^\s]+$)/, ''))) // strip for ie7
-    var option  = $target.data('bs.modal') ? 'toggle' : $.extend({ remote: !/#/.test(href) && href }, $target.data(), $this.data())
-
-    if ($this.is('a')) e.preventDefault()
-
-    $target.one('show.bs.modal', function (showEvent) {
-      if (showEvent.isDefaultPrevented()) return // only register focus restorer if modal will actually get shown
-      $target.one('hidden.bs.modal', function () {
-        $this.is(':visible') && $this.trigger('focus')
-      })
-    })
-    Plugin.call($target, option, this)
-  })
-
-}(window.$jqTheme || window.jQuery);
-
-/* ========================================================================
- * Bootstrap: tooltip.js v3.3.7
- * http://getbootstrap.com/javascript/#tooltip
- * Inspired by the original jQuery.tipsy by Jason Frame
- * ========================================================================
- * Copyright 2011-2016 Twitter, Inc.
- * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
- * ======================================================================== */
-
-
-+function ($) {
-  'use strict';
-
-  // TOOLTIP PUBLIC CLASS DEFINITION
-  // ===============================
-
-  var Tooltip = function (element, options) {
-    this.type       = null
-    this.options    = null
-    this.enabled    = null
-    this.timeout    = null
-    this.hoverState = null
-    this.$element   = null
-    this.inState    = null
-
-    this.init('tooltip', element, options)
-  }
-
-  Tooltip.VERSION  = '3.3.7'
-
-  Tooltip.TRANSITION_DURATION = 150
-
-  Tooltip.DEFAULTS = {
-    animation: true,
-    placement: 'top',
-    selector: false,
-    template: '<div class="tooltip" role="tooltip"><div class="tooltip-arrow"></div><div class="tooltip-inner"></div></div>',
-    trigger: 'hover focus',
-    title: '',
-    delay: 0,
-    html: false,
-    container: false,
-    viewport: {
-      selector: 'body',
-      padding: 0
-    }
-  }
-
-  Tooltip.prototype.init = function (type, element, options) {
-    this.enabled   = true
-    this.type      = type
-    this.$element  = $(element)
-    this.options   = this.getOptions(options)
-    this.$viewport = this.options.viewport && $($.isFunction(this.options.viewport) ? this.options.viewport.call(this, this.$element) : (this.options.viewport.selector || this.options.viewport))
-    this.inState   = { click: false, hover: false, focus: false }
-
-    if (this.$element[0] instanceof document.constructor && !this.options.selector) {
-      throw new Error('`selector` option must be specified when initializing ' + this.type + ' on the window.document object!')
-    }
-
-    var triggers = this.options.trigger.split(' ')
-
-    for (var i = triggers.length; i--;) {
-      var trigger = triggers[i]
-
-      if (trigger == 'click') {
-        this.$element.on('click.' + this.type, this.options.selector, $.proxy(this.toggle, this))
-      } else if (trigger != 'manual') {
-        var eventIn  = trigger == 'hover' ? 'mouseenter' : 'focusin'
-        var eventOut = trigger == 'hover' ? 'mouseleave' : 'focusout'
-
-        this.$element.on(eventIn  + '.' + this.type, this.options.selector, $.proxy(this.enter, this))
-        this.$element.on(eventOut + '.' + this.type, this.options.selector, $.proxy(this.leave, this))
-      }
-    }
-
-    this.options.selector ?
-      (this._options = $.extend({}, this.options, { trigger: 'manual', selector: '' })) :
-      this.fixTitle()
-  }
-
-  Tooltip.prototype.getDefaults = function () {
-    return Tooltip.DEFAULTS
-  }
-
-  Tooltip.prototype.getOptions = function (options) {
-    options = $.extend({}, this.getDefaults(), this.$element.data(), options)
-
-    if (options.delay && typeof options.delay == 'number') {
-      options.delay = {
-        show: options.delay,
-        hide: options.delay
-      }
-    }
-
-    return options
-  }
-
-  Tooltip.prototype.getDelegateOptions = function () {
-    var options  = {}
-    var defaults = this.getDefaults()
-
-    this._options && $.each(this._options, function (key, value) {
-      if (defaults[key] != value) options[key] = value
-    })
-
-    return options
-  }
-
-  Tooltip.prototype.enter = function (obj) {
-    var self = obj instanceof this.constructor ?
-      obj : $(obj.currentTarget).data('bs.' + this.type)
-
-    if (!self) {
-      self = new this.constructor(obj.currentTarget, this.getDelegateOptions())
-      $(obj.currentTarget).data('bs.' + this.type, self)
-    }
-
-    if (obj instanceof $.Event) {
-      self.inState[obj.type == 'focusin' ? 'focus' : 'hover'] = true
-    }
-
-    if (self.tip().hasClass('in') || self.hoverState == 'in') {
-      self.hoverState = 'in'
-      return
-    }
-
-    clearTimeout(self.timeout)
-
-    self.hoverState = 'in'
-
-    if (!self.options.delay || !self.options.delay.show) return self.show()
-
-    self.timeout = setTimeout(function () {
-      if (self.hoverState == 'in') self.show()
-    }, self.options.delay.show)
-  }
-
-  Tooltip.prototype.isInStateTrue = function () {
-    for (var key in this.inState) {
-      if (this.inState[key]) return true
-    }
-
-    return false
-  }
-
-  Tooltip.prototype.leave = function (obj) {
-    var self = obj instanceof this.constructor ?
-      obj : $(obj.currentTarget).data('bs.' + this.type)
-
-    if (!self) {
-      self = new this.constructor(obj.currentTarget, this.getDelegateOptions())
-      $(obj.currentTarget).data('bs.' + this.type, self)
-    }
-
-    if (obj instanceof $.Event) {
-      self.inState[obj.type == 'focusout' ? 'focus' : 'hover'] = false
-    }
-
-    if (self.isInStateTrue()) return
-
-    clearTimeout(self.timeout)
-
-    self.hoverState = 'out'
-
-    if (!self.options.delay || !self.options.delay.hide) return self.hide()
-
-    self.timeout = setTimeout(function () {
-      if (self.hoverState == 'out') self.hide()
-    }, self.options.delay.hide)
-  }
-
-  Tooltip.prototype.show = function () {
-    var e = $.Event('show.bs.' + this.type)
-
-    if (this.hasContent() && this.enabled) {
-      this.$element.trigger(e)
-
-      var inDom = $.contains(this.$element[0].ownerDocument.documentElement, this.$element[0])
-      if (e.isDefaultPrevented() || !inDom) return
-      var that = this
-
-      var $tip = this.tip()
-
-      var tipId = this.getUID(this.type)
-
-      this.setContent()
-      $tip.attr('id', tipId)
-      this.$element.attr('aria-describedby', tipId)
-
-      if (this.options.animation) $tip.addClass('fade')
-
-      var placement = typeof this.options.placement == 'function' ?
-        this.options.placement.call(this, $tip[0], this.$element[0]) :
-        this.options.placement
-
-      var autoToken = /\s?auto?\s?/i
-      var autoPlace = autoToken.test(placement)
-      if (autoPlace) placement = placement.replace(autoToken, '') || 'top'
-
-      $tip
-        .detach()
-        .css({ top: 0, left: 0, display: 'block' })
-        .addClass(placement)
-        .data('bs.' + this.type, this)
-
-      this.options.container ? $tip.appendTo(this.options.container) : $tip.insertAfter(this.$element)
-      this.$element.trigger('inserted.bs.' + this.type)
-
-      var pos          = this.getPosition()
-      var actualWidth  = $tip[0].offsetWidth
-      var actualHeight = $tip[0].offsetHeight
-
-      if (autoPlace) {
-        var orgPlacement = placement
-        var viewportDim = this.getPosition(this.$viewport)
-
-        placement = placement == 'bottom' && pos.bottom + actualHeight > viewportDim.bottom ? 'top'    :
-                    placement == 'top'    && pos.top    - actualHeight < viewportDim.top    ? 'bottom' :
-                    placement == 'right'  && pos.right  + actualWidth  > viewportDim.width  ? 'left'   :
-                    placement == 'left'   && pos.left   - actualWidth  < viewportDim.left   ? 'right'  :
-                    placement
-
-        $tip
-          .removeClass(orgPlacement)
-          .addClass(placement)
-      }
-
-      var calculatedOffset = this.getCalculatedOffset(placement, pos, actualWidth, actualHeight)
-
-      this.applyPlacement(calculatedOffset, placement)
-
-      var complete = function () {
-        var prevHoverState = that.hoverState
-        that.$element.trigger('shown.bs.' + that.type)
-        that.hoverState = null
-
-        if (prevHoverState == 'out') that.leave(that)
-      }
-
-      $.support.transition && this.$tip.hasClass('fade') ?
-        $tip
-          .one('bsTransitionEnd', complete)
-          .emulateTransitionEnd(Tooltip.TRANSITION_DURATION) :
-        complete()
-    }
-  }
-
-  Tooltip.prototype.applyPlacement = function (offset, placement) {
-    var $tip   = this.tip()
-    var width  = $tip[0].offsetWidth
-    var height = $tip[0].offsetHeight
-
-    // manually read margins because getBoundingClientRect includes difference
-    var marginTop = parseInt($tip.css('margin-top'), 10)
-    var marginLeft = parseInt($tip.css('margin-left'), 10)
-
-    // we must check for NaN for ie 8/9
-    if (isNaN(marginTop))  marginTop  = 0
-    if (isNaN(marginLeft)) marginLeft = 0
-
-    offset.top  += marginTop
-    offset.left += marginLeft
-
-    // $.fn.offset doesn't round pixel values
-    // so we use setOffset directly with our own function B-0
-    $.offset.setOffset($tip[0], $.extend({
-      using: function (props) {
-        $tip.css({
-          top: Math.round(props.top),
-          left: Math.round(props.left)
-        })
-      }
-    }, offset), 0)
-
-    $tip.addClass('in')
-
-    // check to see if placing tip in new offset caused the tip to resize itself
-    var actualWidth  = $tip[0].offsetWidth
-    var actualHeight = $tip[0].offsetHeight
-
-    if (placement == 'top' && actualHeight != height) {
-      offset.top = offset.top + height - actualHeight
-    }
-
-    var delta = this.getViewportAdjustedDelta(placement, offset, actualWidth, actualHeight)
-
-    if (delta.left) offset.left += delta.left
-    else offset.top += delta.top
-
-    var isVertical          = /top|bottom/.test(placement)
-    var arrowDelta          = isVertical ? delta.left * 2 - width + actualWidth : delta.top * 2 - height + actualHeight
-    var arrowOffsetPosition = isVertical ? 'offsetWidth' : 'offsetHeight'
-
-    $tip.offset(offset)
-    this.replaceArrow(arrowDelta, $tip[0][arrowOffsetPosition], isVertical)
-  }
-
-  Tooltip.prototype.replaceArrow = function (delta, dimension, isVertical) {
-    this.arrow()
-      .css(isVertical ? 'left' : 'top', 50 * (1 - delta / dimension) + '%')
-      .css(isVertical ? 'top' : 'left', '')
-  }
-
-  Tooltip.prototype.setContent = function () {
-    var $tip  = this.tip()
-    var title = this.getTitle()
-
-    $tip.find('.tooltip-inner')[this.options.html ? 'html' : 'text'](title)
-    $tip.removeClass('fade in top bottom left right')
-  }
-
-  Tooltip.prototype.hide = function (callback) {
-    var that = this
-    var $tip = $(this.$tip)
-    var e    = $.Event('hide.bs.' + this.type)
-
-    function complete() {
-      if (that.hoverState != 'in') $tip.detach()
-      if (that.$element) { // TODO: Check whether guarding this code with this `if` is really necessary.
-        that.$element
-          .removeAttr('aria-describedby')
-          .trigger('hidden.bs.' + that.type)
-      }
-      callback && callback()
-    }
-
-    this.$element.trigger(e)
-
-    if (e.isDefaultPrevented()) return
-
-    $tip.removeClass('in')
-
-    $.support.transition && $tip.hasClass('fade') ?
-      $tip
-        .one('bsTransitionEnd', complete)
-        .emulateTransitionEnd(Tooltip.TRANSITION_DURATION) :
-      complete()
-
-    this.hoverState = null
-
-    return this
-  }
-
-  Tooltip.prototype.fixTitle = function () {
-    var $e = this.$element
-    if ($e.attr('title') || typeof $e.attr('data-original-title') != 'string') {
-      $e.attr('data-original-title', $e.attr('title') || '').attr('title', '')
-    }
-  }
-
-  Tooltip.prototype.hasContent = function () {
-    return this.getTitle()
-  }
-
-  Tooltip.prototype.getPosition = function ($element) {
-    $element   = $element || this.$element
-
-    var el     = $element[0]
-    var isBody = el.tagName == 'BODY'
-
-    var elRect    = el.getBoundingClientRect()
-    if (elRect.width == null) {
-      // width and height are missing in IE8, so compute them manually; see https://github.com/twbs/bootstrap/issues/14093
-      elRect = $.extend({}, elRect, { width: elRect.right - elRect.left, height: elRect.bottom - elRect.top })
-    }
-    var isSvg = window.SVGElement && el instanceof window.SVGElement
-    // Avoid using $.offset() on SVGs since it gives incorrect results in jQuery 3.
-    // See https://github.com/twbs/bootstrap/issues/20280
-    var elOffset  = isBody ? { top: 0, left: 0 } : (isSvg ? null : $element.offset())
-    var scroll    = { scroll: isBody ? document.documentElement.scrollTop || document.body.scrollTop : $element.scrollTop() }
-    var outerDims = isBody ? { width: $(window).width(), height: $(window).height() } : null
-
-    return $.extend({}, elRect, scroll, outerDims, elOffset)
-  }
-
-  Tooltip.prototype.getCalculatedOffset = function (placement, pos, actualWidth, actualHeight) {
-    return placement == 'bottom' ? { top: pos.top + pos.height,   left: pos.left + pos.width / 2 - actualWidth / 2 } :
-           placement == 'top'    ? { top: pos.top - actualHeight, left: pos.left + pos.width / 2 - actualWidth / 2 } :
-           placement == 'left'   ? { top: pos.top + pos.height / 2 - actualHeight / 2, left: pos.left - actualWidth } :
-        /* placement == 'right' */ { top: pos.top + pos.height / 2 - actualHeight / 2, left: pos.left + pos.width }
-
-  }
-
-  Tooltip.prototype.getViewportAdjustedDelta = function (placement, pos, actualWidth, actualHeight) {
-    var delta = { top: 0, left: 0 }
-    if (!this.$viewport) return delta
-
-    var viewportPadding = this.options.viewport && this.options.viewport.padding || 0
-    var viewportDimensions = this.getPosition(this.$viewport)
-
-    if (/right|left/.test(placement)) {
-      var topEdgeOffset    = pos.top - viewportPadding - viewportDimensions.scroll
-      var bottomEdgeOffset = pos.top + viewportPadding - viewportDimensions.scroll + actualHeight
-      if (topEdgeOffset < viewportDimensions.top) { // top overflow
-        delta.top = viewportDimensions.top - topEdgeOffset
-      } else if (bottomEdgeOffset > viewportDimensions.top + viewportDimensions.height) { // bottom overflow
-        delta.top = viewportDimensions.top + viewportDimensions.height - bottomEdgeOffset
-      }
-    } else {
-      var leftEdgeOffset  = pos.left - viewportPadding
-      var rightEdgeOffset = pos.left + viewportPadding + actualWidth
-      if (leftEdgeOffset < viewportDimensions.left) { // left overflow
-        delta.left = viewportDimensions.left - leftEdgeOffset
-      } else if (rightEdgeOffset > viewportDimensions.right) { // right overflow
-        delta.left = viewportDimensions.left + viewportDimensions.width - rightEdgeOffset
-      }
-    }
-
-    return delta
-  }
-
-  Tooltip.prototype.getTitle = function () {
-    var title
-    var $e = this.$element
-    var o  = this.options
-
-    title = $e.attr('data-original-title')
-      || (typeof o.title == 'function' ? o.title.call($e[0]) :  o.title)
-
-    return title
-  }
-
-  Tooltip.prototype.getUID = function (prefix) {
-    do prefix += ~~(Math.random() * 1000000)
-    while (document.getElementById(prefix))
-    return prefix
-  }
-
-  Tooltip.prototype.tip = function () {
-    if (!this.$tip) {
-      this.$tip = $(this.options.template)
-      if (this.$tip.length != 1) {
-        throw new Error(this.type + ' `template` option must consist of exactly 1 top-level element!')
-      }
-    }
-    return this.$tip
-  }
-
-  Tooltip.prototype.arrow = function () {
-    return (this.$arrow = this.$arrow || this.tip().find('.tooltip-arrow'))
-  }
-
-  Tooltip.prototype.enable = function () {
-    this.enabled = true
-  }
-
-  Tooltip.prototype.disable = function () {
-    this.enabled = false
-  }
-
-  Tooltip.prototype.toggleEnabled = function () {
-    this.enabled = !this.enabled
-  }
-
-  Tooltip.prototype.toggle = function (e) {
-    var self = this
-    if (e) {
-      self = $(e.currentTarget).data('bs.' + this.type)
-      if (!self) {
-        self = new this.constructor(e.currentTarget, this.getDelegateOptions())
-        $(e.currentTarget).data('bs.' + this.type, self)
-      }
-    }
-
-    if (e) {
-      self.inState.click = !self.inState.click
-      if (self.isInStateTrue()) self.enter(self)
-      else self.leave(self)
-    } else {
-      self.tip().hasClass('in') ? self.leave(self) : self.enter(self)
-    }
-  }
-
-  Tooltip.prototype.destroy = function () {
-    var that = this
-    clearTimeout(this.timeout)
-    this.hide(function () {
-      that.$element.off('.' + that.type).removeData('bs.' + that.type)
-      if (that.$tip) {
-        that.$tip.detach()
-      }
-      that.$tip = null
-      that.$arrow = null
-      that.$viewport = null
-      that.$element = null
-    })
-  }
-
-
-  // TOOLTIP PLUGIN DEFINITION
-  // =========================
-
-  function Plugin(option) {
-    return this.each(function () {
-      var $this   = $(this)
-      var data    = $this.data('bs.tooltip')
-      var options = typeof option == 'object' && option
-
-      if (!data && /destroy|hide/.test(option)) return
-      if (!data) $this.data('bs.tooltip', (data = new Tooltip(this, options)))
-      if (typeof option == 'string') data[option]()
-    })
-  }
-
-  var old = $.fn.tooltip
-
-  $.fn.tooltip             = Plugin
-  $.fn.tooltip.Constructor = Tooltip
-
-
-  // TOOLTIP NO CONFLICT
-  // ===================
-
-  $.fn.tooltip.noConflict = function () {
-    $.fn.tooltip = old
-    return this
-  }
-
-}(window.$jqTheme || window.jQuery);
-
-/* ========================================================================
- * Bootstrap: popover.js v3.3.7
- * http://getbootstrap.com/javascript/#popovers
- * ========================================================================
- * Copyright 2011-2016 Twitter, Inc.
- * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
- * ======================================================================== */
-
-
-+function ($) {
-  'use strict';
-
-  // POPOVER PUBLIC CLASS DEFINITION
-  // ===============================
-
-  var Popover = function (element, options) {
-    this.init('popover', element, options)
-  }
-
-  if (!$.fn.tooltip) throw new Error('Popover requires tooltip.js')
-
-  Popover.VERSION  = '3.3.7'
-
-  Popover.DEFAULTS = $.extend({}, $.fn.tooltip.Constructor.DEFAULTS, {
-    placement: 'right',
-    trigger: 'click',
-    content: '',
-    template: '<div class="popover" role="tooltip"><div class="arrow"></div><h3 class="popover-title"></h3><div class="popover-content"></div></div>'
-  })
-
-
-  // NOTE: POPOVER EXTENDS tooltip.js
-  // ================================
-
-  Popover.prototype = $.extend({}, $.fn.tooltip.Constructor.prototype)
-
-  Popover.prototype.constructor = Popover
-
-  Popover.prototype.getDefaults = function () {
-    return Popover.DEFAULTS
-  }
-
-  Popover.prototype.setContent = function () {
-    var $tip    = this.tip()
-    var title   = this.getTitle()
-    var content = this.getContent()
-
-    $tip.find('.popover-title')[this.options.html ? 'html' : 'text'](title)
-    $tip.find('.popover-content').children().detach().end()[ // we use append for html objects to maintain js events
-      this.options.html ? (typeof content == 'string' ? 'html' : 'append') : 'text'
-    ](content)
-
-    $tip.removeClass('fade top bottom left right in')
-
-    // IE8 doesn't accept hiding via the `:empty` pseudo selector, we have to do
-    // this manually by checking the contents.
-    if (!$tip.find('.popover-title').html()) $tip.find('.popover-title').hide()
-  }
-
-  Popover.prototype.hasContent = function () {
-    return this.getTitle() || this.getContent()
-  }
-
-  Popover.prototype.getContent = function () {
-    var $e = this.$element
-    var o  = this.options
-
-    return $e.attr('data-content')
-      || (typeof o.content == 'function' ?
-            o.content.call($e[0]) :
-            o.content)
-  }
-
-  Popover.prototype.arrow = function () {
-    return (this.$arrow = this.$arrow || this.tip().find('.arrow'))
-  }
-
-
-  // POPOVER PLUGIN DEFINITION
-  // =========================
-
-  function Plugin(option) {
-    return this.each(function () {
-      var $this   = $(this)
-      var data    = $this.data('bs.popover')
-      var options = typeof option == 'object' && option
-
-      if (!data && /destroy|hide/.test(option)) return
-      if (!data) $this.data('bs.popover', (data = new Popover(this, options)))
-      if (typeof option == 'string') data[option]()
-    })
-  }
-
-  var old = $.fn.popover
-
-  $.fn.popover             = Plugin
-  $.fn.popover.Constructor = Popover
-
-
-  // POPOVER NO CONFLICT
-  // ===================
-
-  $.fn.popover.noConflict = function () {
-    $.fn.popover = old
-    return this
-  }
-
-}(window.$jqTheme || window.jQuery);
-
-/* ========================================================================
- * Bootstrap: scrollspy.js v3.3.7
- * http://getbootstrap.com/javascript/#scrollspy
- * ========================================================================
- * Copyright 2011-2016 Twitter, Inc.
- * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
- * ======================================================================== */
-
-
-+function ($) {
-  'use strict';
-
-  // SCROLLSPY CLASS DEFINITION
-  // ==========================
-
-  function ScrollSpy(element, options) {
-    this.$body          = $(document.body)
-    this.$scrollElement = $(element).is(document.body) ? $(window) : $(element)
-    this.options        = $.extend({}, ScrollSpy.DEFAULTS, options)
-    this.selector       = (this.options.target || '') + ' .nav li > a'
-    this.offsets        = []
-    this.targets        = []
-    this.activeTarget   = null
-    this.scrollHeight   = 0
-
-    this.$scrollElement.on('scroll.bs.scrollspy', $.proxy(this.process, this))
-    this.refresh()
-    this.process()
-  }
-
-  ScrollSpy.VERSION  = '3.3.7'
-
-  ScrollSpy.DEFAULTS = {
-    offset: 10
-  }
-
-  ScrollSpy.prototype.getScrollHeight = function () {
-    return this.$scrollElement[0].scrollHeight || Math.max(this.$body[0].scrollHeight, document.documentElement.scrollHeight)
-  }
-
-  ScrollSpy.prototype.refresh = function () {
-    var that          = this
-    var offsetMethod  = 'offset'
-    var offsetBase    = 0
-
-    this.offsets      = []
-    this.targets      = []
-    this.scrollHeight = this.getScrollHeight()
-
-    if (!$.isWindow(this.$scrollElement[0])) {
-      offsetMethod = 'position'
-      offsetBase   = this.$scrollElement.scrollTop()
-    }
-
-    this.$body
-      .find(this.selector)
-      .map(function () {
-        var $el   = $(this)
-        var href  = $el.data('target') || $el.attr('href')
-        var $href = /^#./.test(href) && $(href)
-
-        return ($href
-          && $href.length
-          && $href.is(':visible')
-          && [[$href[offsetMethod]().top + offsetBase, href]]) || null
-      })
-      .sort(function (a, b) { return a[0] - b[0] })
-      .each(function () {
-        that.offsets.push(this[0])
-        that.targets.push(this[1])
-      })
-  }
-
-  ScrollSpy.prototype.process = function () {
-    var scrollTop    = this.$scrollElement.scrollTop() + this.options.offset
-    var scrollHeight = this.getScrollHeight()
-    var maxScroll    = this.options.offset + scrollHeight - this.$scrollElement.height()
-    var offsets      = this.offsets
-    var targets      = this.targets
-    var activeTarget = this.activeTarget
-    var i
-
-    if (this.scrollHeight != scrollHeight) {
-      this.refresh()
-    }
-
-    if (scrollTop >= maxScroll) {
-      return activeTarget != (i = targets[targets.length - 1]) && this.activate(i)
-    }
-
-    if (activeTarget && scrollTop < offsets[0]) {
-      this.activeTarget = null
-      return this.clear()
-    }
-
-    for (i = offsets.length; i--;) {
-      activeTarget != targets[i]
-        && scrollTop >= offsets[i]
-        && (offsets[i + 1] === undefined || scrollTop < offsets[i + 1])
-        && this.activate(targets[i])
-    }
-  }
-
-  ScrollSpy.prototype.activate = function (target) {
-    this.activeTarget = target
-
-    this.clear()
-
-    var selector = this.selector +
-      '[data-target="' + target + '"],' +
-      this.selector + '[href="' + target + '"]'
-
-    var active = $(selector)
-      .parents('li')
-      .addClass('active')
-
-    if (active.parent('.dropdown-menu').length) {
-      active = active
-        .closest('li.dropdown')
-        .addClass('active')
-    }
-
-    active.trigger('activate.bs.scrollspy')
-  }
-
-  ScrollSpy.prototype.clear = function () {
-    $(this.selector)
-      .parentsUntil(this.options.target, '.active')
-      .removeClass('active')
-  }
-
-
-  // SCROLLSPY PLUGIN DEFINITION
-  // ===========================
-
-  function Plugin(option) {
-    return this.each(function () {
-      var $this   = $(this)
-      var data    = $this.data('bs.scrollspy')
-      var options = typeof option == 'object' && option
-
-      if (!data) $this.data('bs.scrollspy', (data = new ScrollSpy(this, options)))
-      if (typeof option == 'string') data[option]()
-    })
-  }
-
-  var old = $.fn.scrollspy
-
-  $.fn.scrollspy             = Plugin
-  $.fn.scrollspy.Constructor = ScrollSpy
-
-
-  // SCROLLSPY NO CONFLICT
-  // =====================
-
-  $.fn.scrollspy.noConflict = function () {
-    $.fn.scrollspy = old
-    return this
-  }
-
-
-  // SCROLLSPY DATA-API
-  // ==================
-
-  $(window).on('load.bs.scrollspy.data-api', function () {
-    $('[data-spy="scroll"]').each(function () {
-      var $spy = $(this)
-      Plugin.call($spy, $spy.data())
-    })
-  })
-
-}(window.$jqTheme || window.jQuery);
-
-/* ========================================================================
- * Bootstrap: tab.js v3.3.7
- * http://getbootstrap.com/javascript/#tabs
- * ========================================================================
- * Copyright 2011-2016 Twitter, Inc.
- * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
- * ======================================================================== */
-
-
-+function ($) {
-  'use strict';
-
-  // TAB CLASS DEFINITION
-  // ====================
-
-  var Tab = function (element) {
-    // jscs:disable requireDollarBeforejQueryAssignment
-    this.element = $(element)
-    // jscs:enable requireDollarBeforejQueryAssignment
-  }
-
-  Tab.VERSION = '3.3.7'
-
-  Tab.TRANSITION_DURATION = 150
-
-  Tab.prototype.show = function () {
-    var $this    = this.element
-    var $ul      = $this.closest('ul:not(.dropdown-menu)')
-    var selector = $this.data('target')
-
-    if (!selector) {
-      selector = $this.attr('href')
-      selector = selector && selector.replace(/.*(?=#[^\s]*$)/, '') // strip for ie7
-    }
-
-    if ($this.parent('li').hasClass('active')) return
-
-    var $previous = $ul.find('.active:last a')
-    var hideEvent = $.Event('hide.bs.tab', {
-      relatedTarget: $this[0]
-    })
-    var showEvent = $.Event('show.bs.tab', {
-      relatedTarget: $previous[0]
-    })
-
-    $previous.trigger(hideEvent)
-    $this.trigger(showEvent)
-
-    if (showEvent.isDefaultPrevented() || hideEvent.isDefaultPrevented()) return
-
-    var $target = $(selector)
-
-    this.activate($this.closest('li'), $ul)
-    this.activate($target, $target.parent(), function () {
-      $previous.trigger({
-        type: 'hidden.bs.tab',
-        relatedTarget: $this[0]
-      })
-      $this.trigger({
-        type: 'shown.bs.tab',
-        relatedTarget: $previous[0]
-      })
-    })
-  }
-
-  Tab.prototype.activate = function (element, container, callback) {
-    var $active    = container.find('> .active')
-    var transition = callback
-      && $.support.transition
-      && ($active.length && $active.hasClass('fade') || !!container.find('> .fade').length)
-
-    function next() {
-      $active
-        .removeClass('active')
-        .find('> .dropdown-menu > .active')
-          .removeClass('active')
-        .end()
-        .find('[data-toggle="tab"]')
-          .attr('aria-expanded', false)
-
-      element
-        .addClass('active')
-        .find('[data-toggle="tab"]')
-          .attr('aria-expanded', true)
-
-      if (transition) {
-        element[0].offsetWidth // reflow for transition
-        element.addClass('in')
-      } else {
-        element.removeClass('fade')
-      }
-
-      if (element.parent('.dropdown-menu').length) {
-        element
-          .closest('li.dropdown')
-            .addClass('active')
-          .end()
-          .find('[data-toggle="tab"]')
-            .attr('aria-expanded', true)
-      }
-
-      callback && callback()
-    }
-
-    $active.length && transition ?
-      $active
-        .one('bsTransitionEnd', next)
-        .emulateTransitionEnd(Tab.TRANSITION_DURATION) :
-      next()
-
-    $active.removeClass('in')
-  }
-
-
-  // TAB PLUGIN DEFINITION
-  // =====================
-
-  function Plugin(option) {
-    return this.each(function () {
-      var $this = $(this)
-      var data  = $this.data('bs.tab')
-
-      if (!data) $this.data('bs.tab', (data = new Tab(this)))
-      if (typeof option == 'string') data[option]()
-    })
-  }
-
-  var old = $.fn.tab
-
-  $.fn.tab             = Plugin
-  $.fn.tab.Constructor = Tab
-
-
-  // TAB NO CONFLICT
-  // ===============
-
-  $.fn.tab.noConflict = function () {
-    $.fn.tab = old
-    return this
-  }
-
-
-  // TAB DATA-API
-  // ============
-
-  var clickHandler = function (e) {
-    e.preventDefault()
-    Plugin.call($(this), 'show')
-  }
-
-  $(document)
-    .on('click.bs.tab.data-api', '[data-toggle="tab"]', clickHandler)
-    .on('click.bs.tab.data-api', '[data-toggle="pill"]', clickHandler)
-
-}(window.$jqTheme || window.jQuery);
-
-/* ========================================================================
- * Bootstrap: affix.js v3.3.7
- * http://getbootstrap.com/javascript/#affix
- * ========================================================================
- * Copyright 2011-2016 Twitter, Inc.
- * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
- * ======================================================================== */
-
-
-+function ($) {
-  'use strict';
-
-  // AFFIX CLASS DEFINITION
-  // ======================
-
-  var Affix = function (element, options) {
-    this.options = $.extend({}, Affix.DEFAULTS, options)
-
-    this.$target = $(this.options.target)
-      .on('scroll.bs.affix.data-api', $.proxy(this.checkPosition, this))
-      .on('click.bs.affix.data-api',  $.proxy(this.checkPositionWithEventLoop, this))
-
-    this.$element     = $(element)
-    this.affixed      = null
-    this.unpin        = null
-    this.pinnedOffset = null
-
-    this.checkPosition()
-  }
-
-  Affix.VERSION  = '3.3.7'
-
-  Affix.RESET    = 'affix affix-top affix-bottom'
-
-  Affix.DEFAULTS = {
-    offset: 0,
-    target: window
-  }
-
-  Affix.prototype.getState = function (scrollHeight, height, offsetTop, offsetBottom) {
-    var scrollTop    = this.$target.scrollTop()
-    var position     = this.$element.offset()
-    var targetHeight = this.$target.height()
-
-    if (offsetTop != null && this.affixed == 'top') return scrollTop < offsetTop ? 'top' : false
-
-    if (this.affixed == 'bottom') {
-      if (offsetTop != null) return (scrollTop + this.unpin <= position.top) ? false : 'bottom'
-      return (scrollTop + targetHeight <= scrollHeight - offsetBottom) ? false : 'bottom'
-    }
-
-    var initializing   = this.affixed == null
-    var colliderTop    = initializing ? scrollTop : position.top
-    var colliderHeight = initializing ? targetHeight : height
-
-    if (offsetTop != null && scrollTop <= offsetTop) return 'top'
-    if (offsetBottom != null && (colliderTop + colliderHeight >= scrollHeight - offsetBottom)) return 'bottom'
-
-    return false
-  }
-
-  Affix.prototype.getPinnedOffset = function () {
-    if (this.pinnedOffset) return this.pinnedOffset
-    this.$element.removeClass(Affix.RESET).addClass('affix')
-    var scrollTop = this.$target.scrollTop()
-    var position  = this.$element.offset()
-    return (this.pinnedOffset = position.top - scrollTop)
-  }
-
-  Affix.prototype.checkPositionWithEventLoop = function () {
-    setTimeout($.proxy(this.checkPosition, this), 1)
-  }
-
-  Affix.prototype.checkPosition = function () {
-    if (!this.$element.is(':visible')) return
-
-    var height       = this.$element.height()
-    var offset       = this.options.offset
-    var offsetTop    = offset.top
-    var offsetBottom = offset.bottom
-    var scrollHeight = Math.max($(document).height(), $(document.body).height())
-
-    if (typeof offset != 'object')         offsetBottom = offsetTop = offset
-    if (typeof offsetTop == 'function')    offsetTop    = offset.top(this.$element)
-    if (typeof offsetBottom == 'function') offsetBottom = offset.bottom(this.$element)
-
-    var affix = this.getState(scrollHeight, height, offsetTop, offsetBottom)
-
-    if (this.affixed != affix) {
-      if (this.unpin != null) this.$element.css('top', '')
-
-      var affixType = 'affix' + (affix ? '-' + affix : '')
-      var e         = $.Event(affixType + '.bs.affix')
-
-      this.$element.trigger(e)
-
-      if (e.isDefaultPrevented()) return
-
-      this.affixed = affix
-      this.unpin = affix == 'bottom' ? this.getPinnedOffset() : null
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index bc849bd59..000000000
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diff --git a/docs/_static/bootstrap-3.3.7/js/npm.js b/docs/_static/bootstrap-3.3.7/js/npm.js
deleted file mode 100644
index bf6aa8060..000000000
--- a/docs/_static/bootstrap-3.3.7/js/npm.js
+++ /dev/null
@@ -1,13 +0,0 @@
-// This file is autogenerated via the `commonjs` Grunt task. You can require() this file in a CommonJS environment.
-require('../../js/transition.js')
-require('../../js/alert.js')
-require('../../js/button.js')
-require('../../js/carousel.js')
-require('../../js/collapse.js')
-require('../../js/dropdown.js')
-require('../../js/modal.js')
-require('../../js/tooltip.js')
-require('../../js/popover.js')
-require('../../js/scrollspy.js')
-require('../../js/tab.js')
-require('../../js/affix.js')
\ No newline at end of file
diff --git a/docs/_static/bootstrap-sphinx.css b/docs/_static/bootstrap-sphinx.css
deleted file mode 100644
index 5c08c3dbc..000000000
--- a/docs/_static/bootstrap-sphinx.css
+++ /dev/null
@@ -1,223 +0,0 @@
-/*
- * bootstrap-sphinx.css
- * ~~~~~~~~~~~~~~~~~~~~
- *
- * Sphinx stylesheet -- Bootstrap theme.
- */
-
-/*
- * Imports to aggregate everything together.
- */
-
-@import url("./basic.css");
-
-
-  
-
-
-
-  
-  
-    @import url("./bootswatch-3.3.7/yeti/bootstrap.min.css");
-  
-
-
-/*
- * Styles
- */
-
-.navbar-inverse .brand {
-  color: #FFF;
-}
-
-/*
- * Reset navbar styles from overrides in:
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- */
-.navbar .container {
-  padding-top: 0;
-}
-
-/*
- * Reset the logo image dimensions. Sites like RTD can override with bad
- * results on mobile (mega-huge logo...)
- *
- * https://github.com/ryan-roemer/sphinx-bootstrap-theme/issues/142
- */
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-  width: auto;
-  height: 100%;
-}
-
-.page-top {
-  top: 0px;
-}
-
-
-
-
-
-  body {
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diff --git a/docs/_static/bootstrap-sphinx.js b/docs/_static/bootstrap-sphinx.js
deleted file mode 100644
index 17e81981d..000000000
--- a/docs/_static/bootstrap-sphinx.js
+++ /dev/null
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-(function ($) {
-  /**
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-
-    // Update sourcelink to remove outerdiv (fixes appearance in navbar).
-    var $srcLink = $(".nav #sourcelink");
-    $srcLink.parent().html($srcLink.html());
-  });
-}(window.$jqTheme || window.jQuery));
\ No newline at end of file
diff --git a/docs/_static/bootswatch-2.3.2/amelia/bootstrap.min.css b/docs/_static/bootswatch-2.3.2/amelia/bootstrap.min.css
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index b8cea3283..000000000
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deleted file mode 100644
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diff --git a/docs/_static/bootswatch-2.3.2/readable/bootstrap.min.css b/docs/_static/bootswatch-2.3.2/readable/bootstrap.min.css
deleted file mode 100644
index 0efc48fb6..000000000
--- a/docs/_static/bootswatch-2.3.2/readable/bootstrap.min.css
+++ /dev/null
@@ -1,9 +0,0 @@
-@import url('//fonts.googleapis.com/css?family=Lora:400,700,400italic,700italic');/*!
- * Bootstrap v2.3.2
- *
- * Copyright 2012 Twitter, Inc
- * Licensed under the Apache License v2.0
- * http://www.apache.org/licenses/LICENSE-2.0
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diff --git a/docs/_static/bootswatch-2.3.2/spacelab/bootstrap.min.css b/docs/_static/bootswatch-2.3.2/spacelab/bootstrap.min.css
deleted file mode 100644
index 7b7b4bb4a..000000000
--- a/docs/_static/bootswatch-2.3.2/spacelab/bootstrap.min.css
+++ /dev/null
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diff --git a/docs/_static/bootswatch-3.3.7/cyborg/bootstrap.min.css b/docs/_static/bootswatch-3.3.7/cyborg/bootstrap.min.css
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index 94fb5490a..000000000
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diff --git a/docs/_static/bootswatch-3.3.7/solar/bootstrap.min.css b/docs/_static/bootswatch-3.3.7/solar/bootstrap.min.css
deleted file mode 100644
index 42ab992f1..000000000
--- a/docs/_static/bootswatch-3.3.7/solar/bootstrap.min.css
+++ /dev/null
@@ -1,11 +0,0 @@
-@import url("https://fonts.googleapis.com/css?family=Source+Sans+Pro:300,400,700");/*!
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- * Homepage: http://bootswatch.com
- * Copyright 2012-2017 Thomas Park
- * Licensed under MIT
- * Based on Bootstrap
-*//*!
- * Bootstrap v3.3.7 (http://getbootstrap.com)
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diff --git a/docs/_static/bootswatch-3.3.7/superhero/bootstrap.min.css b/docs/_static/bootswatch-3.3.7/superhero/bootstrap.min.css
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diff --git a/docs/_static/doctools.js b/docs/_static/doctools.js
deleted file mode 100644
index daccd209d..000000000
--- a/docs/_static/doctools.js
+++ /dev/null
@@ -1,315 +0,0 @@
-/*
- * doctools.js
- * ~~~~~~~~~~~
- *
- * Sphinx JavaScript utilities for all documentation.
- *
- * :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS.
- * :license: BSD, see LICENSE for details.
- *
- */
-
-/**
- * select a different prefix for underscore
- */
-$u = _.noConflict();
-
-/**
- * make the code below compatible with browsers without
- * an installed firebug like debugger
-if (!window.console || !console.firebug) {
-  var names = ["log", "debug", "info", "warn", "error", "assert", "dir",
-    "dirxml", "group", "groupEnd", "time", "timeEnd", "count", "trace",
-    "profile", "profileEnd"];
-  window.console = {};
-  for (var i = 0; i < names.length; ++i)
-    window.console[names[i]] = function() {};
-}
- */
-
-/**
- * small helper function to urldecode strings
- */
-jQuery.urldecode = function(x) {
-  return decodeURIComponent(x).replace(/\+/g, ' ');
-};
-
-/**
- * small helper function to urlencode strings
- */
-jQuery.urlencode = encodeURIComponent;
-
-/**
- * This function returns the parsed url parameters of the
- * current request. Multiple values per key are supported,
- * it will always return arrays of strings for the value parts.
- */
-jQuery.getQueryParameters = function(s) {
-  if (typeof s === 'undefined')
-    s = document.location.search;
-  var parts = s.substr(s.indexOf('?') + 1).split('&');
-  var result = {};
-  for (var i = 0; i < parts.length; i++) {
-    var tmp = parts[i].split('=', 2);
-    var key = jQuery.urldecode(tmp[0]);
-    var value = jQuery.urldecode(tmp[1]);
-    if (key in result)
-      result[key].push(value);
-    else
-      result[key] = [value];
-  }
-  return result;
-};
-
-/**
- * highlight a given string on a jquery object by wrapping it in
- * span elements with the given class name.
- */
-jQuery.fn.highlightText = function(text, className) {
-  function highlight(node, addItems) {
-    if (node.nodeType === 3) {
-      var val = node.nodeValue;
-      var pos = val.toLowerCase().indexOf(text);
-      if (pos >= 0 &&
-          !jQuery(node.parentNode).hasClass(className) &&
-          !jQuery(node.parentNode).hasClass("nohighlight")) {
-        var span;
-        var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg");
-        if (isInSVG) {
-          span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
-        } else {
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-          span.className = className;
-        }
-        span.appendChild(document.createTextNode(val.substr(pos, text.length)));
-        node.parentNode.insertBefore(span, node.parentNode.insertBefore(
-          document.createTextNode(val.substr(pos + text.length)),
-          node.nextSibling));
-        node.nodeValue = val.substr(0, pos);
-        if (isInSVG) {
-          var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect");
-          var bbox = node.parentElement.getBBox();
-          rect.x.baseVal.value = bbox.x;
-          rect.y.baseVal.value = bbox.y;
-          rect.width.baseVal.value = bbox.width;
-          rect.height.baseVal.value = bbox.height;
-          rect.setAttribute('class', className);
-          addItems.push({
-              "parent": node.parentNode,
-              "target": rect});
-        }
-      }
-    }
-    else if (!jQuery(node).is("button, select, textarea")) {
-      jQuery.each(node.childNodes, function() {
-        highlight(this, addItems);
-      });
-    }
-  }
-  var addItems = [];
-  var result = this.each(function() {
-    highlight(this, addItems);
-  });
-  for (var i = 0; i < addItems.length; ++i) {
-    jQuery(addItems[i].parent).before(addItems[i].target);
-  }
-  return result;
-};
-
-/*
- * backward compatibility for jQuery.browser
- * This will be supported until firefox bug is fixed.
- */
-if (!jQuery.browser) {
-  jQuery.uaMatch = function(ua) {
-    ua = ua.toLowerCase();
-
-    var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
-      /(webkit)[ \/]([\w.]+)/.exec(ua) ||
-      /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) ||
-      /(msie) ([\w.]+)/.exec(ua) ||
-      ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) ||
-      [];
-
-    return {
-      browser: match[ 1 ] || "",
-      version: match[ 2 ] || "0"
-    };
-  };
-  jQuery.browser = {};
-  jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
-}
-
-/**
- * Small JavaScript module for the documentation.
- */
-var Documentation = {
-
-  init : function() {
-    this.fixFirefoxAnchorBug();
-    this.highlightSearchWords();
-    this.initIndexTable();
-    if (DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) {
-      this.initOnKeyListeners();
-    }
-  },
-
-  /**
-   * i18n support
-   */
-  TRANSLATIONS : {},
-  PLURAL_EXPR : function(n) { return n === 1 ? 0 : 1; },
-  LOCALE : 'unknown',
-
-  // gettext and ngettext don't access this so that the functions
-  // can safely bound to a different name (_ = Documentation.gettext)
-  gettext : function(string) {
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-    if (typeof translated === 'undefined')
-      return string;
-    return (typeof translated === 'string') ? translated : translated[0];
-  },
-
-  ngettext : function(singular, plural, n) {
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-    if (typeof translated === 'undefined')
-      return (n == 1) ? singular : plural;
-    return translated[Documentation.PLURALEXPR(n)];
-  },
-
-  addTranslations : function(catalog) {
-    for (var key in catalog.messages)
-      this.TRANSLATIONS[key] = catalog.messages[key];
-    this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')');
-    this.LOCALE = catalog.locale;
-  },
-
-  /**
-   * add context elements like header anchor links
-   */
-  addContextElements : function() {
-    $('div[id] > :header:first').each(function() {
-      $('<a class="headerlink">\u00B6</a>').
-      attr('href', '#' + this.id).
-      attr('title', _('Permalink to this headline')).
-      appendTo(this);
-    });
-    $('dt[id]').each(function() {
-      $('<a class="headerlink">\u00B6</a>').
-      attr('href', '#' + this.id).
-      attr('title', _('Permalink to this definition')).
-      appendTo(this);
-    });
-  },
-
-  /**
-   * workaround a firefox stupidity
-   * see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075
-   */
-  fixFirefoxAnchorBug : function() {
-    if (document.location.hash && $.browser.mozilla)
-      window.setTimeout(function() {
-        document.location.href += '';
-      }, 10);
-  },
-
-  /**
-   * highlight the search words provided in the url in the text
-   */
-  highlightSearchWords : function() {
-    var params = $.getQueryParameters();
-    var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : [];
-    if (terms.length) {
-      var body = $('div.body');
-      if (!body.length) {
-        body = $('body');
-      }
-      window.setTimeout(function() {
-        $.each(terms, function() {
-          body.highlightText(this.toLowerCase(), 'highlighted');
-        });
-      }, 10);
-      $('<p class="highlight-link"><a href="javascript:Documentation.' +
-        'hideSearchWords()">' + _('Hide Search Matches') + '</a></p>')
-          .appendTo($('#searchbox'));
-    }
-  },
-
-  /**
-   * init the domain index toggle buttons
-   */
-  initIndexTable : function() {
-    var togglers = $('img.toggler').click(function() {
-      var src = $(this).attr('src');
-      var idnum = $(this).attr('id').substr(7);
-      $('tr.cg-' + idnum).toggle();
-      if (src.substr(-9) === 'minus.png')
-        $(this).attr('src', src.substr(0, src.length-9) + 'plus.png');
-      else
-        $(this).attr('src', src.substr(0, src.length-8) + 'minus.png');
-    }).css('display', '');
-    if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) {
-        togglers.click();
-    }
-  },
-
-  /**
-   * helper function to hide the search marks again
-   */
-  hideSearchWords : function() {
-    $('#searchbox .highlight-link').fadeOut(300);
-    $('span.highlighted').removeClass('highlighted');
-  },
-
-  /**
-   * make the url absolute
-   */
-  makeURL : function(relativeURL) {
-    return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL;
-  },
-
-  /**
-   * get the current relative url
-   */
-  getCurrentURL : function() {
-    var path = document.location.pathname;
-    var parts = path.split(/\//);
-    $.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() {
-      if (this === '..')
-        parts.pop();
-    });
-    var url = parts.join('/');
-    return path.substring(url.lastIndexOf('/') + 1, path.length - 1);
-  },
-
-  initOnKeyListeners: function() {
-    $(document).keydown(function(event) {
-      var activeElementType = document.activeElement.tagName;
-      // don't navigate when in search box or textarea
-      if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT'
-          && !event.altKey && !event.ctrlKey && !event.metaKey && !event.shiftKey) {
-        switch (event.keyCode) {
-          case 37: // left
-            var prevHref = $('link[rel="prev"]').prop('href');
-            if (prevHref) {
-              window.location.href = prevHref;
-              return false;
-            }
-          case 39: // right
-            var nextHref = $('link[rel="next"]').prop('href');
-            if (nextHref) {
-              window.location.href = nextHref;
-              return false;
-            }
-        }
-      }
-    });
-  }
-};
-
-// quick alias for translations
-_ = Documentation.gettext;
-
-$(document).ready(function() {
-  Documentation.init();
-});
diff --git a/docs/_static/documentation_options.js b/docs/_static/documentation_options.js
deleted file mode 100644
index 4ea69b7c6..000000000
--- a/docs/_static/documentation_options.js
+++ /dev/null
@@ -1,12 +0,0 @@
-var DOCUMENTATION_OPTIONS = {
-    URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
-    VERSION: '4.4.0',
-    LANGUAGE: 'None',
-    COLLAPSE_INDEX: false,
-    BUILDER: 'html',
-    FILE_SUFFIX: '.html',
-    LINK_SUFFIX: '.html',
-    HAS_SOURCE: true,
-    SOURCELINK_SUFFIX: '.txt',
-    NAVIGATION_WITH_KEYS: false
-};
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diff --git a/docs/_static/jquery-3.5.1.js b/docs/_static/jquery-3.5.1.js
deleted file mode 100644
index 50937333b..000000000
--- a/docs/_static/jquery-3.5.1.js
+++ /dev/null
@@ -1,10872 +0,0 @@
-/*!
- * jQuery JavaScript Library v3.5.1
- * https://jquery.com/
- *
- * Includes Sizzle.js
- * https://sizzlejs.com/
- *
- * Copyright JS Foundation and other contributors
- * Released under the MIT license
- * https://jquery.org/license
- *
- * Date: 2020-05-04T22:49Z
- */
-( function( global, factory ) {
-
-	"use strict";
-
-	if ( typeof module === "object" && typeof module.exports === "object" ) {
-
-		// For CommonJS and CommonJS-like environments where a proper `window`
-		// is present, execute the factory and get jQuery.
-		// For environments that do not have a `window` with a `document`
-		// (such as Node.js), expose a factory as module.exports.
-		// This accentuates the need for the creation of a real `window`.
-		// e.g. var jQuery = require("jquery")(window);
-		// See ticket #14549 for more info.
-		module.exports = global.document ?
-			factory( global, true ) :
-			function( w ) {
-				if ( !w.document ) {
-					throw new Error( "jQuery requires a window with a document" );
-				}
-				return factory( w );
-			};
-	} else {
-		factory( global );
-	}
-
-// Pass this if window is not defined yet
-} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) {
-
-// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1
-// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode
-// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common
-// enough that all such attempts are guarded in a try block.
-"use strict";
-
-var arr = [];
-
-var getProto = Object.getPrototypeOf;
-
-var slice = arr.slice;
-
-var flat = arr.flat ? function( array ) {
-	return arr.flat.call( array );
-} : function( array ) {
-	return arr.concat.apply( [], array );
-};
-
-
-var push = arr.push;
-
-var indexOf = arr.indexOf;
-
-var class2type = {};
-
-var toString = class2type.toString;
-
-var hasOwn = class2type.hasOwnProperty;
-
-var fnToString = hasOwn.toString;
-
-var ObjectFunctionString = fnToString.call( Object );
-
-var support = {};
-
-var isFunction = function isFunction( obj ) {
-
-      // Support: Chrome <=57, Firefox <=52
-      // In some browsers, typeof returns "function" for HTML <object> elements
-      // (i.e., `typeof document.createElement( "object" ) === "function"`).
-      // We don't want to classify *any* DOM node as a function.
-      return typeof obj === "function" && typeof obj.nodeType !== "number";
-  };
-
-
-var isWindow = function isWindow( obj ) {
-		return obj != null && obj === obj.window;
-	};
-
-
-var document = window.document;
-
-
-
-	var preservedScriptAttributes = {
-		type: true,
-		src: true,
-		nonce: true,
-		noModule: true
-	};
-
-	function DOMEval( code, node, doc ) {
-		doc = doc || document;
-
-		var i, val,
-			script = doc.createElement( "script" );
-
-		script.text = code;
-		if ( node ) {
-			for ( i in preservedScriptAttributes ) {
-
-				// Support: Firefox 64+, Edge 18+
-				// Some browsers don't support the "nonce" property on scripts.
-				// On the other hand, just using `getAttribute` is not enough as
-				// the `nonce` attribute is reset to an empty string whenever it
-				// becomes browsing-context connected.
-				// See https://github.com/whatwg/html/issues/2369
-				// See https://html.spec.whatwg.org/#nonce-attributes
-				// The `node.getAttribute` check was added for the sake of
-				// `jQuery.globalEval` so that it can fake a nonce-containing node
-				// via an object.
-				val = node[ i ] || node.getAttribute && node.getAttribute( i );
-				if ( val ) {
-					script.setAttribute( i, val );
-				}
-			}
-		}
-		doc.head.appendChild( script ).parentNode.removeChild( script );
-	}
-
-
-function toType( obj ) {
-	if ( obj == null ) {
-		return obj + "";
-	}
-
-	// Support: Android <=2.3 only (functionish RegExp)
-	return typeof obj === "object" || typeof obj === "function" ?
-		class2type[ toString.call( obj ) ] || "object" :
-		typeof obj;
-}
-/* global Symbol */
-// Defining this global in .eslintrc.json would create a danger of using the global
-// unguarded in another place, it seems safer to define global only for this module
-
-
-
-var
-	version = "3.5.1",
-
-	// Define a local copy of jQuery
-	jQuery = function( selector, context ) {
-
-		// The jQuery object is actually just the init constructor 'enhanced'
-		// Need init if jQuery is called (just allow error to be thrown if not included)
-		return new jQuery.fn.init( selector, context );
-	};
-
-jQuery.fn = jQuery.prototype = {
-
-	// The current version of jQuery being used
-	jquery: version,
-
-	constructor: jQuery,
-
-	// The default length of a jQuery object is 0
-	length: 0,
-
-	toArray: function() {
-		return slice.call( this );
-	},
-
-	// Get the Nth element in the matched element set OR
-	// Get the whole matched element set as a clean array
-	get: function( num ) {
-
-		// Return all the elements in a clean array
-		if ( num == null ) {
-			return slice.call( this );
-		}
-
-		// Return just the one element from the set
-		return num < 0 ? this[ num + this.length ] : this[ num ];
-	},
-
-	// Take an array of elements and push it onto the stack
-	// (returning the new matched element set)
-	pushStack: function( elems ) {
-
-		// Build a new jQuery matched element set
-		var ret = jQuery.merge( this.constructor(), elems );
-
-		// Add the old object onto the stack (as a reference)
-		ret.prevObject = this;
-
-		// Return the newly-formed element set
-		return ret;
-	},
-
-	// Execute a callback for every element in the matched set.
-	each: function( callback ) {
-		return jQuery.each( this, callback );
-	},
-
-	map: function( callback ) {
-		return this.pushStack( jQuery.map( this, function( elem, i ) {
-			return callback.call( elem, i, elem );
-		} ) );
-	},
-
-	slice: function() {
-		return this.pushStack( slice.apply( this, arguments ) );
-	},
-
-	first: function() {
-		return this.eq( 0 );
-	},
-
-	last: function() {
-		return this.eq( -1 );
-	},
-
-	even: function() {
-		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
-			return ( i + 1 ) % 2;
-		} ) );
-	},
-
-	odd: function() {
-		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
-			return i % 2;
-		} ) );
-	},
-
-	eq: function( i ) {
-		var len = this.length,
-			j = +i + ( i < 0 ? len : 0 );
-		return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] );
-	},
-
-	end: function() {
-		return this.prevObject || this.constructor();
-	},
-
-	// For internal use only.
-	// Behaves like an Array's method, not like a jQuery method.
-	push: push,
-	sort: arr.sort,
-	splice: arr.splice
-};
-
-jQuery.extend = jQuery.fn.extend = function() {
-	var options, name, src, copy, copyIsArray, clone,
-		target = arguments[ 0 ] || {},
-		i = 1,
-		length = arguments.length,
-		deep = false;
-
-	// Handle a deep copy situation
-	if ( typeof target === "boolean" ) {
-		deep = target;
-
-		// Skip the boolean and the target
-		target = arguments[ i ] || {};
-		i++;
-	}
-
-	// Handle case when target is a string or something (possible in deep copy)
-	if ( typeof target !== "object" && !isFunction( target ) ) {
-		target = {};
-	}
-
-	// Extend jQuery itself if only one argument is passed
-	if ( i === length ) {
-		target = this;
-		i--;
-	}
-
-	for ( ; i < length; i++ ) {
-
-		// Only deal with non-null/undefined values
-		if ( ( options = arguments[ i ] ) != null ) {
-
-			// Extend the base object
-			for ( name in options ) {
-				copy = options[ name ];
-
-				// Prevent Object.prototype pollution
-				// Prevent never-ending loop
-				if ( name === "__proto__" || target === copy ) {
-					continue;
-				}
-
-				// Recurse if we're merging plain objects or arrays
-				if ( deep && copy && ( jQuery.isPlainObject( copy ) ||
-					( copyIsArray = Array.isArray( copy ) ) ) ) {
-					src = target[ name ];
-
-					// Ensure proper type for the source value
-					if ( copyIsArray && !Array.isArray( src ) ) {
-						clone = [];
-					} else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) {
-						clone = {};
-					} else {
-						clone = src;
-					}
-					copyIsArray = false;
-
-					// Never move original objects, clone them
-					target[ name ] = jQuery.extend( deep, clone, copy );
-
-				// Don't bring in undefined values
-				} else if ( copy !== undefined ) {
-					target[ name ] = copy;
-				}
-			}
-		}
-	}
-
-	// Return the modified object
-	return target;
-};
-
-jQuery.extend( {
-
-	// Unique for each copy of jQuery on the page
-	expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ),
-
-	// Assume jQuery is ready without the ready module
-	isReady: true,
-
-	error: function( msg ) {
-		throw new Error( msg );
-	},
-
-	noop: function() {},
-
-	isPlainObject: function( obj ) {
-		var proto, Ctor;
-
-		// Detect obvious negatives
-		// Use toString instead of jQuery.type to catch host objects
-		if ( !obj || toString.call( obj ) !== "[object Object]" ) {
-			return false;
-		}
-
-		proto = getProto( obj );
-
-		// Objects with no prototype (e.g., `Object.create( null )`) are plain
-		if ( !proto ) {
-			return true;
-		}
-
-		// Objects with prototype are plain iff they were constructed by a global Object function
-		Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor;
-		return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString;
-	},
-
-	isEmptyObject: function( obj ) {
-		var name;
-
-		for ( name in obj ) {
-			return false;
-		}
-		return true;
-	},
-
-	// Evaluates a script in a provided context; falls back to the global one
-	// if not specified.
-	globalEval: function( code, options, doc ) {
-		DOMEval( code, { nonce: options && options.nonce }, doc );
-	},
-
-	each: function( obj, callback ) {
-		var length, i = 0;
-
-		if ( isArrayLike( obj ) ) {
-			length = obj.length;
-			for ( ; i < length; i++ ) {
-				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
-					break;
-				}
-			}
-		} else {
-			for ( i in obj ) {
-				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
-					break;
-				}
-			}
-		}
-
-		return obj;
-	},
-
-	// results is for internal usage only
-	makeArray: function( arr, results ) {
-		var ret = results || [];
-
-		if ( arr != null ) {
-			if ( isArrayLike( Object( arr ) ) ) {
-				jQuery.merge( ret,
-					typeof arr === "string" ?
-					[ arr ] : arr
-				);
-			} else {
-				push.call( ret, arr );
-			}
-		}
-
-		return ret;
-	},
-
-	inArray: function( elem, arr, i ) {
-		return arr == null ? -1 : indexOf.call( arr, elem, i );
-	},
-
-	// Support: Android <=4.0 only, PhantomJS 1 only
-	// push.apply(_, arraylike) throws on ancient WebKit
-	merge: function( first, second ) {
-		var len = +second.length,
-			j = 0,
-			i = first.length;
-
-		for ( ; j < len; j++ ) {
-			first[ i++ ] = second[ j ];
-		}
-
-		first.length = i;
-
-		return first;
-	},
-
-	grep: function( elems, callback, invert ) {
-		var callbackInverse,
-			matches = [],
-			i = 0,
-			length = elems.length,
-			callbackExpect = !invert;
-
-		// Go through the array, only saving the items
-		// that pass the validator function
-		for ( ; i < length; i++ ) {
-			callbackInverse = !callback( elems[ i ], i );
-			if ( callbackInverse !== callbackExpect ) {
-				matches.push( elems[ i ] );
-			}
-		}
-
-		return matches;
-	},
-
-	// arg is for internal usage only
-	map: function( elems, callback, arg ) {
-		var length, value,
-			i = 0,
-			ret = [];
-
-		// Go through the array, translating each of the items to their new values
-		if ( isArrayLike( elems ) ) {
-			length = elems.length;
-			for ( ; i < length; i++ ) {
-				value = callback( elems[ i ], i, arg );
-
-				if ( value != null ) {
-					ret.push( value );
-				}
-			}
-
-		// Go through every key on the object,
-		} else {
-			for ( i in elems ) {
-				value = callback( elems[ i ], i, arg );
-
-				if ( value != null ) {
-					ret.push( value );
-				}
-			}
-		}
-
-		// Flatten any nested arrays
-		return flat( ret );
-	},
-
-	// A global GUID counter for objects
-	guid: 1,
-
-	// jQuery.support is not used in Core but other projects attach their
-	// properties to it so it needs to exist.
-	support: support
-} );
-
-if ( typeof Symbol === "function" ) {
-	jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ];
-}
-
-// Populate the class2type map
-jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ),
-function( _i, name ) {
-	class2type[ "[object " + name + "]" ] = name.toLowerCase();
-} );
-
-function isArrayLike( obj ) {
-
-	// Support: real iOS 8.2 only (not reproducible in simulator)
-	// `in` check used to prevent JIT error (gh-2145)
-	// hasOwn isn't used here due to false negatives
-	// regarding Nodelist length in IE
-	var length = !!obj && "length" in obj && obj.length,
-		type = toType( obj );
-
-	if ( isFunction( obj ) || isWindow( obj ) ) {
-		return false;
-	}
-
-	return type === "array" || length === 0 ||
-		typeof length === "number" && length > 0 && ( length - 1 ) in obj;
-}
-var Sizzle =
-/*!
- * Sizzle CSS Selector Engine v2.3.5
- * https://sizzlejs.com/
- *
- * Copyright JS Foundation and other contributors
- * Released under the MIT license
- * https://js.foundation/
- *
- * Date: 2020-03-14
- */
-( function( window ) {
-var i,
-	support,
-	Expr,
-	getText,
-	isXML,
-	tokenize,
-	compile,
-	select,
-	outermostContext,
-	sortInput,
-	hasDuplicate,
-
-	// Local document vars
-	setDocument,
-	document,
-	docElem,
-	documentIsHTML,
-	rbuggyQSA,
-	rbuggyMatches,
-	matches,
-	contains,
-
-	// Instance-specific data
-	expando = "sizzle" + 1 * new Date(),
-	preferredDoc = window.document,
-	dirruns = 0,
-	done = 0,
-	classCache = createCache(),
-	tokenCache = createCache(),
-	compilerCache = createCache(),
-	nonnativeSelectorCache = createCache(),
-	sortOrder = function( a, b ) {
-		if ( a === b ) {
-			hasDuplicate = true;
-		}
-		return 0;
-	},
-
-	// Instance methods
-	hasOwn = ( {} ).hasOwnProperty,
-	arr = [],
-	pop = arr.pop,
-	pushNative = arr.push,
-	push = arr.push,
-	slice = arr.slice,
-
-	// Use a stripped-down indexOf as it's faster than native
-	// https://jsperf.com/thor-indexof-vs-for/5
-	indexOf = function( list, elem ) {
-		var i = 0,
-			len = list.length;
-		for ( ; i < len; i++ ) {
-			if ( list[ i ] === elem ) {
-				return i;
-			}
-		}
-		return -1;
-	},
-
-	booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" +
-		"ismap|loop|multiple|open|readonly|required|scoped",
-
-	// Regular expressions
-
-	// http://www.w3.org/TR/css3-selectors/#whitespace
-	whitespace = "[\\x20\\t\\r\\n\\f]",
-
-	// https://www.w3.org/TR/css-syntax-3/#ident-token-diagram
-	identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace +
-		"?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+",
-
-	// Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors
-	attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace +
-
-		// Operator (capture 2)
-		"*([*^$|!~]?=)" + whitespace +
-
-		// "Attribute values must be CSS identifiers [capture 5]
-		// or strings [capture 3 or capture 4]"
-		"*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" +
-		whitespace + "*\\]",
-
-	pseudos = ":(" + identifier + ")(?:\\((" +
-
-		// To reduce the number of selectors needing tokenize in the preFilter, prefer arguments:
-		// 1. quoted (capture 3; capture 4 or capture 5)
-		"('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" +
-
-		// 2. simple (capture 6)
-		"((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" +
-
-		// 3. anything else (capture 2)
-		".*" +
-		")\\)|)",
-
-	// Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter
-	rwhitespace = new RegExp( whitespace + "+", "g" ),
-	rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" +
-		whitespace + "+$", "g" ),
-
-	rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ),
-	rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace +
-		"*" ),
-	rdescend = new RegExp( whitespace + "|>" ),
-
-	rpseudo = new RegExp( pseudos ),
-	ridentifier = new RegExp( "^" + identifier + "$" ),
-
-	matchExpr = {
-		"ID": new RegExp( "^#(" + identifier + ")" ),
-		"CLASS": new RegExp( "^\\.(" + identifier + ")" ),
-		"TAG": new RegExp( "^(" + identifier + "|[*])" ),
-		"ATTR": new RegExp( "^" + attributes ),
-		"PSEUDO": new RegExp( "^" + pseudos ),
-		"CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" +
-			whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" +
-			whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ),
-		"bool": new RegExp( "^(?:" + booleans + ")$", "i" ),
-
-		// For use in libraries implementing .is()
-		// We use this for POS matching in `select`
-		"needsContext": new RegExp( "^" + whitespace +
-			"*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace +
-			"*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" )
-	},
-
-	rhtml = /HTML$/i,
-	rinputs = /^(?:input|select|textarea|button)$/i,
-	rheader = /^h\d$/i,
-
-	rnative = /^[^{]+\{\s*\[native \w/,
-
-	// Easily-parseable/retrievable ID or TAG or CLASS selectors
-	rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/,
-
-	rsibling = /[+~]/,
-
-	// CSS escapes
-	// http://www.w3.org/TR/CSS21/syndata.html#escaped-characters
-	runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ),
-	funescape = function( escape, nonHex ) {
-		var high = "0x" + escape.slice( 1 ) - 0x10000;
-
-		return nonHex ?
-
-			// Strip the backslash prefix from a non-hex escape sequence
-			nonHex :
-
-			// Replace a hexadecimal escape sequence with the encoded Unicode code point
-			// Support: IE <=11+
-			// For values outside the Basic Multilingual Plane (BMP), manually construct a
-			// surrogate pair
-			high < 0 ?
-				String.fromCharCode( high + 0x10000 ) :
-				String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 );
-	},
-
-	// CSS string/identifier serialization
-	// https://drafts.csswg.org/cssom/#common-serializing-idioms
-	rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g,
-	fcssescape = function( ch, asCodePoint ) {
-		if ( asCodePoint ) {
-
-			// U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER
-			if ( ch === "\0" ) {
-				return "\uFFFD";
-			}
-
-			// Control characters and (dependent upon position) numbers get escaped as code points
-			return ch.slice( 0, -1 ) + "\\" +
-				ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " ";
-		}
-
-		// Other potentially-special ASCII characters get backslash-escaped
-		return "\\" + ch;
-	},
-
-	// Used for iframes
-	// See setDocument()
-	// Removing the function wrapper causes a "Permission Denied"
-	// error in IE
-	unloadHandler = function() {
-		setDocument();
-	},
-
-	inDisabledFieldset = addCombinator(
-		function( elem ) {
-			return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset";
-		},
-		{ dir: "parentNode", next: "legend" }
-	);
-
-// Optimize for push.apply( _, NodeList )
-try {
-	push.apply(
-		( arr = slice.call( preferredDoc.childNodes ) ),
-		preferredDoc.childNodes
-	);
-
-	// Support: Android<4.0
-	// Detect silently failing push.apply
-	// eslint-disable-next-line no-unused-expressions
-	arr[ preferredDoc.childNodes.length ].nodeType;
-} catch ( e ) {
-	push = { apply: arr.length ?
-
-		// Leverage slice if possible
-		function( target, els ) {
-			pushNative.apply( target, slice.call( els ) );
-		} :
-
-		// Support: IE<9
-		// Otherwise append directly
-		function( target, els ) {
-			var j = target.length,
-				i = 0;
-
-			// Can't trust NodeList.length
-			while ( ( target[ j++ ] = els[ i++ ] ) ) {}
-			target.length = j - 1;
-		}
-	};
-}
-
-function Sizzle( selector, context, results, seed ) {
-	var m, i, elem, nid, match, groups, newSelector,
-		newContext = context && context.ownerDocument,
-
-		// nodeType defaults to 9, since context defaults to document
-		nodeType = context ? context.nodeType : 9;
-
-	results = results || [];
-
-	// Return early from calls with invalid selector or context
-	if ( typeof selector !== "string" || !selector ||
-		nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) {
-
-		return results;
-	}
-
-	// Try to shortcut find operations (as opposed to filters) in HTML documents
-	if ( !seed ) {
-		setDocument( context );
-		context = context || document;
-
-		if ( documentIsHTML ) {
-
-			// If the selector is sufficiently simple, try using a "get*By*" DOM method
-			// (excepting DocumentFragment context, where the methods don't exist)
-			if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) {
-
-				// ID selector
-				if ( ( m = match[ 1 ] ) ) {
-
-					// Document context
-					if ( nodeType === 9 ) {
-						if ( ( elem = context.getElementById( m ) ) ) {
-
-							// Support: IE, Opera, Webkit
-							// TODO: identify versions
-							// getElementById can match elements by name instead of ID
-							if ( elem.id === m ) {
-								results.push( elem );
-								return results;
-							}
-						} else {
-							return results;
-						}
-
-					// Element context
-					} else {
-
-						// Support: IE, Opera, Webkit
-						// TODO: identify versions
-						// getElementById can match elements by name instead of ID
-						if ( newContext && ( elem = newContext.getElementById( m ) ) &&
-							contains( context, elem ) &&
-							elem.id === m ) {
-
-							results.push( elem );
-							return results;
-						}
-					}
-
-				// Type selector
-				} else if ( match[ 2 ] ) {
-					push.apply( results, context.getElementsByTagName( selector ) );
-					return results;
-
-				// Class selector
-				} else if ( ( m = match[ 3 ] ) && support.getElementsByClassName &&
-					context.getElementsByClassName ) {
-
-					push.apply( results, context.getElementsByClassName( m ) );
-					return results;
-				}
-			}
-
-			// Take advantage of querySelectorAll
-			if ( support.qsa &&
-				!nonnativeSelectorCache[ selector + " " ] &&
-				( !rbuggyQSA || !rbuggyQSA.test( selector ) ) &&
-
-				// Support: IE 8 only
-				// Exclude object elements
-				( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) {
-
-				newSelector = selector;
-				newContext = context;
-
-				// qSA considers elements outside a scoping root when evaluating child or
-				// descendant combinators, which is not what we want.
-				// In such cases, we work around the behavior by prefixing every selector in the
-				// list with an ID selector referencing the scope context.
-				// The technique has to be used as well when a leading combinator is used
-				// as such selectors are not recognized by querySelectorAll.
-				// Thanks to Andrew Dupont for this technique.
-				if ( nodeType === 1 &&
-					( rdescend.test( selector ) || rcombinators.test( selector ) ) ) {
-
-					// Expand context for sibling selectors
-					newContext = rsibling.test( selector ) && testContext( context.parentNode ) ||
-						context;
-
-					// We can use :scope instead of the ID hack if the browser
-					// supports it & if we're not changing the context.
-					if ( newContext !== context || !support.scope ) {
-
-						// Capture the context ID, setting it first if necessary
-						if ( ( nid = context.getAttribute( "id" ) ) ) {
-							nid = nid.replace( rcssescape, fcssescape );
-						} else {
-							context.setAttribute( "id", ( nid = expando ) );
-						}
-					}
-
-					// Prefix every selector in the list
-					groups = tokenize( selector );
-					i = groups.length;
-					while ( i-- ) {
-						groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " +
-							toSelector( groups[ i ] );
-					}
-					newSelector = groups.join( "," );
-				}
-
-				try {
-					push.apply( results,
-						newContext.querySelectorAll( newSelector )
-					);
-					return results;
-				} catch ( qsaError ) {
-					nonnativeSelectorCache( selector, true );
-				} finally {
-					if ( nid === expando ) {
-						context.removeAttribute( "id" );
-					}
-				}
-			}
-		}
-	}
-
-	// All others
-	return select( selector.replace( rtrim, "$1" ), context, results, seed );
-}
-
-/**
- * Create key-value caches of limited size
- * @returns {function(string, object)} Returns the Object data after storing it on itself with
- *	property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength)
- *	deleting the oldest entry
- */
-function createCache() {
-	var keys = [];
-
-	function cache( key, value ) {
-
-		// Use (key + " ") to avoid collision with native prototype properties (see Issue #157)
-		if ( keys.push( key + " " ) > Expr.cacheLength ) {
-
-			// Only keep the most recent entries
-			delete cache[ keys.shift() ];
-		}
-		return ( cache[ key + " " ] = value );
-	}
-	return cache;
-}
-
-/**
- * Mark a function for special use by Sizzle
- * @param {Function} fn The function to mark
- */
-function markFunction( fn ) {
-	fn[ expando ] = true;
-	return fn;
-}
-
-/**
- * Support testing using an element
- * @param {Function} fn Passed the created element and returns a boolean result
- */
-function assert( fn ) {
-	var el = document.createElement( "fieldset" );
-
-	try {
-		return !!fn( el );
-	} catch ( e ) {
-		return false;
-	} finally {
-
-		// Remove from its parent by default
-		if ( el.parentNode ) {
-			el.parentNode.removeChild( el );
-		}
-
-		// release memory in IE
-		el = null;
-	}
-}
-
-/**
- * Adds the same handler for all of the specified attrs
- * @param {String} attrs Pipe-separated list of attributes
- * @param {Function} handler The method that will be applied
- */
-function addHandle( attrs, handler ) {
-	var arr = attrs.split( "|" ),
-		i = arr.length;
-
-	while ( i-- ) {
-		Expr.attrHandle[ arr[ i ] ] = handler;
-	}
-}
-
-/**
- * Checks document order of two siblings
- * @param {Element} a
- * @param {Element} b
- * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b
- */
-function siblingCheck( a, b ) {
-	var cur = b && a,
-		diff = cur && a.nodeType === 1 && b.nodeType === 1 &&
-			a.sourceIndex - b.sourceIndex;
-
-	// Use IE sourceIndex if available on both nodes
-	if ( diff ) {
-		return diff;
-	}
-
-	// Check if b follows a
-	if ( cur ) {
-		while ( ( cur = cur.nextSibling ) ) {
-			if ( cur === b ) {
-				return -1;
-			}
-		}
-	}
-
-	return a ? 1 : -1;
-}
-
-/**
- * Returns a function to use in pseudos for input types
- * @param {String} type
- */
-function createInputPseudo( type ) {
-	return function( elem ) {
-		var name = elem.nodeName.toLowerCase();
-		return name === "input" && elem.type === type;
-	};
-}
-
-/**
- * Returns a function to use in pseudos for buttons
- * @param {String} type
- */
-function createButtonPseudo( type ) {
-	return function( elem ) {
-		var name = elem.nodeName.toLowerCase();
-		return ( name === "input" || name === "button" ) && elem.type === type;
-	};
-}
-
-/**
- * Returns a function to use in pseudos for :enabled/:disabled
- * @param {Boolean} disabled true for :disabled; false for :enabled
- */
-function createDisabledPseudo( disabled ) {
-
-	// Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable
-	return function( elem ) {
-
-		// Only certain elements can match :enabled or :disabled
-		// https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled
-		// https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled
-		if ( "form" in elem ) {
-
-			// Check for inherited disabledness on relevant non-disabled elements:
-			// * listed form-associated elements in a disabled fieldset
-			//   https://html.spec.whatwg.org/multipage/forms.html#category-listed
-			//   https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled
-			// * option elements in a disabled optgroup
-			//   https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled
-			// All such elements have a "form" property.
-			if ( elem.parentNode && elem.disabled === false ) {
-
-				// Option elements defer to a parent optgroup if present
-				if ( "label" in elem ) {
-					if ( "label" in elem.parentNode ) {
-						return elem.parentNode.disabled === disabled;
-					} else {
-						return elem.disabled === disabled;
-					}
-				}
-
-				// Support: IE 6 - 11
-				// Use the isDisabled shortcut property to check for disabled fieldset ancestors
-				return elem.isDisabled === disabled ||
-
-					// Where there is no isDisabled, check manually
-					/* jshint -W018 */
-					elem.isDisabled !== !disabled &&
-					inDisabledFieldset( elem ) === disabled;
-			}
-
-			return elem.disabled === disabled;
-
-		// Try to winnow out elements that can't be disabled before trusting the disabled property.
-		// Some victims get caught in our net (label, legend, menu, track), but it shouldn't
-		// even exist on them, let alone have a boolean value.
-		} else if ( "label" in elem ) {
-			return elem.disabled === disabled;
-		}
-
-		// Remaining elements are neither :enabled nor :disabled
-		return false;
-	};
-}
-
-/**
- * Returns a function to use in pseudos for positionals
- * @param {Function} fn
- */
-function createPositionalPseudo( fn ) {
-	return markFunction( function( argument ) {
-		argument = +argument;
-		return markFunction( function( seed, matches ) {
-			var j,
-				matchIndexes = fn( [], seed.length, argument ),
-				i = matchIndexes.length;
-
-			// Match elements found at the specified indexes
-			while ( i-- ) {
-				if ( seed[ ( j = matchIndexes[ i ] ) ] ) {
-					seed[ j ] = !( matches[ j ] = seed[ j ] );
-				}
-			}
-		} );
-	} );
-}
-
-/**
- * Checks a node for validity as a Sizzle context
- * @param {Element|Object=} context
- * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value
- */
-function testContext( context ) {
-	return context && typeof context.getElementsByTagName !== "undefined" && context;
-}
-
-// Expose support vars for convenience
-support = Sizzle.support = {};
-
-/**
- * Detects XML nodes
- * @param {Element|Object} elem An element or a document
- * @returns {Boolean} True iff elem is a non-HTML XML node
- */
-isXML = Sizzle.isXML = function( elem ) {
-	var namespace = elem.namespaceURI,
-		docElem = ( elem.ownerDocument || elem ).documentElement;
-
-	// Support: IE <=8
-	// Assume HTML when documentElement doesn't yet exist, such as inside loading iframes
-	// https://bugs.jquery.com/ticket/4833
-	return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" );
-};
-
-/**
- * Sets document-related variables once based on the current document
- * @param {Element|Object} [doc] An element or document object to use to set the document
- * @returns {Object} Returns the current document
- */
-setDocument = Sizzle.setDocument = function( node ) {
-	var hasCompare, subWindow,
-		doc = node ? node.ownerDocument || node : preferredDoc;
-
-	// Return early if doc is invalid or already selected
-	// Support: IE 11+, Edge 17 - 18+
-	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-	// two documents; shallow comparisons work.
-	// eslint-disable-next-line eqeqeq
-	if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) {
-		return document;
-	}
-
-	// Update global variables
-	document = doc;
-	docElem = document.documentElement;
-	documentIsHTML = !isXML( document );
-
-	// Support: IE 9 - 11+, Edge 12 - 18+
-	// Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936)
-	// Support: IE 11+, Edge 17 - 18+
-	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-	// two documents; shallow comparisons work.
-	// eslint-disable-next-line eqeqeq
-	if ( preferredDoc != document &&
-		( subWindow = document.defaultView ) && subWindow.top !== subWindow ) {
-
-		// Support: IE 11, Edge
-		if ( subWindow.addEventListener ) {
-			subWindow.addEventListener( "unload", unloadHandler, false );
-
-		// Support: IE 9 - 10 only
-		} else if ( subWindow.attachEvent ) {
-			subWindow.attachEvent( "onunload", unloadHandler );
-		}
-	}
-
-	// Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only,
-	// Safari 4 - 5 only, Opera <=11.6 - 12.x only
-	// IE/Edge & older browsers don't support the :scope pseudo-class.
-	// Support: Safari 6.0 only
-	// Safari 6.0 supports :scope but it's an alias of :root there.
-	support.scope = assert( function( el ) {
-		docElem.appendChild( el ).appendChild( document.createElement( "div" ) );
-		return typeof el.querySelectorAll !== "undefined" &&
-			!el.querySelectorAll( ":scope fieldset div" ).length;
-	} );
-
-	/* Attributes
-	---------------------------------------------------------------------- */
-
-	// Support: IE<8
-	// Verify that getAttribute really returns attributes and not properties
-	// (excepting IE8 booleans)
-	support.attributes = assert( function( el ) {
-		el.className = "i";
-		return !el.getAttribute( "className" );
-	} );
-
-	/* getElement(s)By*
-	---------------------------------------------------------------------- */
-
-	// Check if getElementsByTagName("*") returns only elements
-	support.getElementsByTagName = assert( function( el ) {
-		el.appendChild( document.createComment( "" ) );
-		return !el.getElementsByTagName( "*" ).length;
-	} );
-
-	// Support: IE<9
-	support.getElementsByClassName = rnative.test( document.getElementsByClassName );
-
-	// Support: IE<10
-	// Check if getElementById returns elements by name
-	// The broken getElementById methods don't pick up programmatically-set names,
-	// so use a roundabout getElementsByName test
-	support.getById = assert( function( el ) {
-		docElem.appendChild( el ).id = expando;
-		return !document.getElementsByName || !document.getElementsByName( expando ).length;
-	} );
-
-	// ID filter and find
-	if ( support.getById ) {
-		Expr.filter[ "ID" ] = function( id ) {
-			var attrId = id.replace( runescape, funescape );
-			return function( elem ) {
-				return elem.getAttribute( "id" ) === attrId;
-			};
-		};
-		Expr.find[ "ID" ] = function( id, context ) {
-			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
-				var elem = context.getElementById( id );
-				return elem ? [ elem ] : [];
-			}
-		};
-	} else {
-		Expr.filter[ "ID" ] =  function( id ) {
-			var attrId = id.replace( runescape, funescape );
-			return function( elem ) {
-				var node = typeof elem.getAttributeNode !== "undefined" &&
-					elem.getAttributeNode( "id" );
-				return node && node.value === attrId;
-			};
-		};
-
-		// Support: IE 6 - 7 only
-		// getElementById is not reliable as a find shortcut
-		Expr.find[ "ID" ] = function( id, context ) {
-			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
-				var node, i, elems,
-					elem = context.getElementById( id );
-
-				if ( elem ) {
-
-					// Verify the id attribute
-					node = elem.getAttributeNode( "id" );
-					if ( node && node.value === id ) {
-						return [ elem ];
-					}
-
-					// Fall back on getElementsByName
-					elems = context.getElementsByName( id );
-					i = 0;
-					while ( ( elem = elems[ i++ ] ) ) {
-						node = elem.getAttributeNode( "id" );
-						if ( node && node.value === id ) {
-							return [ elem ];
-						}
-					}
-				}
-
-				return [];
-			}
-		};
-	}
-
-	// Tag
-	Expr.find[ "TAG" ] = support.getElementsByTagName ?
-		function( tag, context ) {
-			if ( typeof context.getElementsByTagName !== "undefined" ) {
-				return context.getElementsByTagName( tag );
-
-			// DocumentFragment nodes don't have gEBTN
-			} else if ( support.qsa ) {
-				return context.querySelectorAll( tag );
-			}
-		} :
-
-		function( tag, context ) {
-			var elem,
-				tmp = [],
-				i = 0,
-
-				// By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too
-				results = context.getElementsByTagName( tag );
-
-			// Filter out possible comments
-			if ( tag === "*" ) {
-				while ( ( elem = results[ i++ ] ) ) {
-					if ( elem.nodeType === 1 ) {
-						tmp.push( elem );
-					}
-				}
-
-				return tmp;
-			}
-			return results;
-		};
-
-	// Class
-	Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) {
-		if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) {
-			return context.getElementsByClassName( className );
-		}
-	};
-
-	/* QSA/matchesSelector
-	---------------------------------------------------------------------- */
-
-	// QSA and matchesSelector support
-
-	// matchesSelector(:active) reports false when true (IE9/Opera 11.5)
-	rbuggyMatches = [];
-
-	// qSa(:focus) reports false when true (Chrome 21)
-	// We allow this because of a bug in IE8/9 that throws an error
-	// whenever `document.activeElement` is accessed on an iframe
-	// So, we allow :focus to pass through QSA all the time to avoid the IE error
-	// See https://bugs.jquery.com/ticket/13378
-	rbuggyQSA = [];
-
-	if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) {
-
-		// Build QSA regex
-		// Regex strategy adopted from Diego Perini
-		assert( function( el ) {
-
-			var input;
-
-			// Select is set to empty string on purpose
-			// This is to test IE's treatment of not explicitly
-			// setting a boolean content attribute,
-			// since its presence should be enough
-			// https://bugs.jquery.com/ticket/12359
-			docElem.appendChild( el ).innerHTML = "<a id='" + expando + "'></a>" +
-				"<select id='" + expando + "-\r\\' msallowcapture=''>" +
-				"<option selected=''></option></select>";
-
-			// Support: IE8, Opera 11-12.16
-			// Nothing should be selected when empty strings follow ^= or $= or *=
-			// The test attribute must be unknown in Opera but "safe" for WinRT
-			// https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section
-			if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) {
-				rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" );
-			}
-
-			// Support: IE8
-			// Boolean attributes and "value" are not treated correctly
-			if ( !el.querySelectorAll( "[selected]" ).length ) {
-				rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" );
-			}
-
-			// Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+
-			if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) {
-				rbuggyQSA.push( "~=" );
-			}
-
-			// Support: IE 11+, Edge 15 - 18+
-			// IE 11/Edge don't find elements on a `[name='']` query in some cases.
-			// Adding a temporary attribute to the document before the selection works
-			// around the issue.
-			// Interestingly, IE 10 & older don't seem to have the issue.
-			input = document.createElement( "input" );
-			input.setAttribute( "name", "" );
-			el.appendChild( input );
-			if ( !el.querySelectorAll( "[name='']" ).length ) {
-				rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" +
-					whitespace + "*(?:''|\"\")" );
-			}
-
-			// Webkit/Opera - :checked should return selected option elements
-			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
-			// IE8 throws error here and will not see later tests
-			if ( !el.querySelectorAll( ":checked" ).length ) {
-				rbuggyQSA.push( ":checked" );
-			}
-
-			// Support: Safari 8+, iOS 8+
-			// https://bugs.webkit.org/show_bug.cgi?id=136851
-			// In-page `selector#id sibling-combinator selector` fails
-			if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) {
-				rbuggyQSA.push( ".#.+[+~]" );
-			}
-
-			// Support: Firefox <=3.6 - 5 only
-			// Old Firefox doesn't throw on a badly-escaped identifier.
-			el.querySelectorAll( "\\\f" );
-			rbuggyQSA.push( "[\\r\\n\\f]" );
-		} );
-
-		assert( function( el ) {
-			el.innerHTML = "<a href='' disabled='disabled'></a>" +
-				"<select disabled='disabled'><option/></select>";
-
-			// Support: Windows 8 Native Apps
-			// The type and name attributes are restricted during .innerHTML assignment
-			var input = document.createElement( "input" );
-			input.setAttribute( "type", "hidden" );
-			el.appendChild( input ).setAttribute( "name", "D" );
-
-			// Support: IE8
-			// Enforce case-sensitivity of name attribute
-			if ( el.querySelectorAll( "[name=d]" ).length ) {
-				rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" );
-			}
-
-			// FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled)
-			// IE8 throws error here and will not see later tests
-			if ( el.querySelectorAll( ":enabled" ).length !== 2 ) {
-				rbuggyQSA.push( ":enabled", ":disabled" );
-			}
-
-			// Support: IE9-11+
-			// IE's :disabled selector does not pick up the children of disabled fieldsets
-			docElem.appendChild( el ).disabled = true;
-			if ( el.querySelectorAll( ":disabled" ).length !== 2 ) {
-				rbuggyQSA.push( ":enabled", ":disabled" );
-			}
-
-			// Support: Opera 10 - 11 only
-			// Opera 10-11 does not throw on post-comma invalid pseudos
-			el.querySelectorAll( "*,:x" );
-			rbuggyQSA.push( ",.*:" );
-		} );
-	}
-
-	if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches ||
-		docElem.webkitMatchesSelector ||
-		docElem.mozMatchesSelector ||
-		docElem.oMatchesSelector ||
-		docElem.msMatchesSelector ) ) ) ) {
-
-		assert( function( el ) {
-
-			// Check to see if it's possible to do matchesSelector
-			// on a disconnected node (IE 9)
-			support.disconnectedMatch = matches.call( el, "*" );
-
-			// This should fail with an exception
-			// Gecko does not error, returns false instead
-			matches.call( el, "[s!='']:x" );
-			rbuggyMatches.push( "!=", pseudos );
-		} );
-	}
-
-	rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) );
-	rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) );
-
-	/* Contains
-	---------------------------------------------------------------------- */
-	hasCompare = rnative.test( docElem.compareDocumentPosition );
-
-	// Element contains another
-	// Purposefully self-exclusive
-	// As in, an element does not contain itself
-	contains = hasCompare || rnative.test( docElem.contains ) ?
-		function( a, b ) {
-			var adown = a.nodeType === 9 ? a.documentElement : a,
-				bup = b && b.parentNode;
-			return a === bup || !!( bup && bup.nodeType === 1 && (
-				adown.contains ?
-					adown.contains( bup ) :
-					a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16
-			) );
-		} :
-		function( a, b ) {
-			if ( b ) {
-				while ( ( b = b.parentNode ) ) {
-					if ( b === a ) {
-						return true;
-					}
-				}
-			}
-			return false;
-		};
-
-	/* Sorting
-	---------------------------------------------------------------------- */
-
-	// Document order sorting
-	sortOrder = hasCompare ?
-	function( a, b ) {
-
-		// Flag for duplicate removal
-		if ( a === b ) {
-			hasDuplicate = true;
-			return 0;
-		}
-
-		// Sort on method existence if only one input has compareDocumentPosition
-		var compare = !a.compareDocumentPosition - !b.compareDocumentPosition;
-		if ( compare ) {
-			return compare;
-		}
-
-		// Calculate position if both inputs belong to the same document
-		// Support: IE 11+, Edge 17 - 18+
-		// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-		// two documents; shallow comparisons work.
-		// eslint-disable-next-line eqeqeq
-		compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ?
-			a.compareDocumentPosition( b ) :
-
-			// Otherwise we know they are disconnected
-			1;
-
-		// Disconnected nodes
-		if ( compare & 1 ||
-			( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) {
-
-			// Choose the first element that is related to our preferred document
-			// Support: IE 11+, Edge 17 - 18+
-			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-			// two documents; shallow comparisons work.
-			// eslint-disable-next-line eqeqeq
-			if ( a == document || a.ownerDocument == preferredDoc &&
-				contains( preferredDoc, a ) ) {
-				return -1;
-			}
-
-			// Support: IE 11+, Edge 17 - 18+
-			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-			// two documents; shallow comparisons work.
-			// eslint-disable-next-line eqeqeq
-			if ( b == document || b.ownerDocument == preferredDoc &&
-				contains( preferredDoc, b ) ) {
-				return 1;
-			}
-
-			// Maintain original order
-			return sortInput ?
-				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
-				0;
-		}
-
-		return compare & 4 ? -1 : 1;
-	} :
-	function( a, b ) {
-
-		// Exit early if the nodes are identical
-		if ( a === b ) {
-			hasDuplicate = true;
-			return 0;
-		}
-
-		var cur,
-			i = 0,
-			aup = a.parentNode,
-			bup = b.parentNode,
-			ap = [ a ],
-			bp = [ b ];
-
-		// Parentless nodes are either documents or disconnected
-		if ( !aup || !bup ) {
-
-			// Support: IE 11+, Edge 17 - 18+
-			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-			// two documents; shallow comparisons work.
-			/* eslint-disable eqeqeq */
-			return a == document ? -1 :
-				b == document ? 1 :
-				/* eslint-enable eqeqeq */
-				aup ? -1 :
-				bup ? 1 :
-				sortInput ?
-				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
-				0;
-
-		// If the nodes are siblings, we can do a quick check
-		} else if ( aup === bup ) {
-			return siblingCheck( a, b );
-		}
-
-		// Otherwise we need full lists of their ancestors for comparison
-		cur = a;
-		while ( ( cur = cur.parentNode ) ) {
-			ap.unshift( cur );
-		}
-		cur = b;
-		while ( ( cur = cur.parentNode ) ) {
-			bp.unshift( cur );
-		}
-
-		// Walk down the tree looking for a discrepancy
-		while ( ap[ i ] === bp[ i ] ) {
-			i++;
-		}
-
-		return i ?
-
-			// Do a sibling check if the nodes have a common ancestor
-			siblingCheck( ap[ i ], bp[ i ] ) :
-
-			// Otherwise nodes in our document sort first
-			// Support: IE 11+, Edge 17 - 18+
-			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-			// two documents; shallow comparisons work.
-			/* eslint-disable eqeqeq */
-			ap[ i ] == preferredDoc ? -1 :
-			bp[ i ] == preferredDoc ? 1 :
-			/* eslint-enable eqeqeq */
-			0;
-	};
-
-	return document;
-};
-
-Sizzle.matches = function( expr, elements ) {
-	return Sizzle( expr, null, null, elements );
-};
-
-Sizzle.matchesSelector = function( elem, expr ) {
-	setDocument( elem );
-
-	if ( support.matchesSelector && documentIsHTML &&
-		!nonnativeSelectorCache[ expr + " " ] &&
-		( !rbuggyMatches || !rbuggyMatches.test( expr ) ) &&
-		( !rbuggyQSA     || !rbuggyQSA.test( expr ) ) ) {
-
-		try {
-			var ret = matches.call( elem, expr );
-
-			// IE 9's matchesSelector returns false on disconnected nodes
-			if ( ret || support.disconnectedMatch ||
-
-				// As well, disconnected nodes are said to be in a document
-				// fragment in IE 9
-				elem.document && elem.document.nodeType !== 11 ) {
-				return ret;
-			}
-		} catch ( e ) {
-			nonnativeSelectorCache( expr, true );
-		}
-	}
-
-	return Sizzle( expr, document, null, [ elem ] ).length > 0;
-};
-
-Sizzle.contains = function( context, elem ) {
-
-	// Set document vars if needed
-	// Support: IE 11+, Edge 17 - 18+
-	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-	// two documents; shallow comparisons work.
-	// eslint-disable-next-line eqeqeq
-	if ( ( context.ownerDocument || context ) != document ) {
-		setDocument( context );
-	}
-	return contains( context, elem );
-};
-
-Sizzle.attr = function( elem, name ) {
-
-	// Set document vars if needed
-	// Support: IE 11+, Edge 17 - 18+
-	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-	// two documents; shallow comparisons work.
-	// eslint-disable-next-line eqeqeq
-	if ( ( elem.ownerDocument || elem ) != document ) {
-		setDocument( elem );
-	}
-
-	var fn = Expr.attrHandle[ name.toLowerCase() ],
-
-		// Don't get fooled by Object.prototype properties (jQuery #13807)
-		val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ?
-			fn( elem, name, !documentIsHTML ) :
-			undefined;
-
-	return val !== undefined ?
-		val :
-		support.attributes || !documentIsHTML ?
-			elem.getAttribute( name ) :
-			( val = elem.getAttributeNode( name ) ) && val.specified ?
-				val.value :
-				null;
-};
-
-Sizzle.escape = function( sel ) {
-	return ( sel + "" ).replace( rcssescape, fcssescape );
-};
-
-Sizzle.error = function( msg ) {
-	throw new Error( "Syntax error, unrecognized expression: " + msg );
-};
-
-/**
- * Document sorting and removing duplicates
- * @param {ArrayLike} results
- */
-Sizzle.uniqueSort = function( results ) {
-	var elem,
-		duplicates = [],
-		j = 0,
-		i = 0;
-
-	// Unless we *know* we can detect duplicates, assume their presence
-	hasDuplicate = !support.detectDuplicates;
-	sortInput = !support.sortStable && results.slice( 0 );
-	results.sort( sortOrder );
-
-	if ( hasDuplicate ) {
-		while ( ( elem = results[ i++ ] ) ) {
-			if ( elem === results[ i ] ) {
-				j = duplicates.push( i );
-			}
-		}
-		while ( j-- ) {
-			results.splice( duplicates[ j ], 1 );
-		}
-	}
-
-	// Clear input after sorting to release objects
-	// See https://github.com/jquery/sizzle/pull/225
-	sortInput = null;
-
-	return results;
-};
-
-/**
- * Utility function for retrieving the text value of an array of DOM nodes
- * @param {Array|Element} elem
- */
-getText = Sizzle.getText = function( elem ) {
-	var node,
-		ret = "",
-		i = 0,
-		nodeType = elem.nodeType;
-
-	if ( !nodeType ) {
-
-		// If no nodeType, this is expected to be an array
-		while ( ( node = elem[ i++ ] ) ) {
-
-			// Do not traverse comment nodes
-			ret += getText( node );
-		}
-	} else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) {
-
-		// Use textContent for elements
-		// innerText usage removed for consistency of new lines (jQuery #11153)
-		if ( typeof elem.textContent === "string" ) {
-			return elem.textContent;
-		} else {
-
-			// Traverse its children
-			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
-				ret += getText( elem );
-			}
-		}
-	} else if ( nodeType === 3 || nodeType === 4 ) {
-		return elem.nodeValue;
-	}
-
-	// Do not include comment or processing instruction nodes
-
-	return ret;
-};
-
-Expr = Sizzle.selectors = {
-
-	// Can be adjusted by the user
-	cacheLength: 50,
-
-	createPseudo: markFunction,
-
-	match: matchExpr,
-
-	attrHandle: {},
-
-	find: {},
-
-	relative: {
-		">": { dir: "parentNode", first: true },
-		" ": { dir: "parentNode" },
-		"+": { dir: "previousSibling", first: true },
-		"~": { dir: "previousSibling" }
-	},
-
-	preFilter: {
-		"ATTR": function( match ) {
-			match[ 1 ] = match[ 1 ].replace( runescape, funescape );
-
-			// Move the given value to match[3] whether quoted or unquoted
-			match[ 3 ] = ( match[ 3 ] || match[ 4 ] ||
-				match[ 5 ] || "" ).replace( runescape, funescape );
-
-			if ( match[ 2 ] === "~=" ) {
-				match[ 3 ] = " " + match[ 3 ] + " ";
-			}
-
-			return match.slice( 0, 4 );
-		},
-
-		"CHILD": function( match ) {
-
-			/* matches from matchExpr["CHILD"]
-				1 type (only|nth|...)
-				2 what (child|of-type)
-				3 argument (even|odd|\d*|\d*n([+-]\d+)?|...)
-				4 xn-component of xn+y argument ([+-]?\d*n|)
-				5 sign of xn-component
-				6 x of xn-component
-				7 sign of y-component
-				8 y of y-component
-			*/
-			match[ 1 ] = match[ 1 ].toLowerCase();
-
-			if ( match[ 1 ].slice( 0, 3 ) === "nth" ) {
-
-				// nth-* requires argument
-				if ( !match[ 3 ] ) {
-					Sizzle.error( match[ 0 ] );
-				}
-
-				// numeric x and y parameters for Expr.filter.CHILD
-				// remember that false/true cast respectively to 0/1
-				match[ 4 ] = +( match[ 4 ] ?
-					match[ 5 ] + ( match[ 6 ] || 1 ) :
-					2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) );
-				match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" );
-
-				// other types prohibit arguments
-			} else if ( match[ 3 ] ) {
-				Sizzle.error( match[ 0 ] );
-			}
-
-			return match;
-		},
-
-		"PSEUDO": function( match ) {
-			var excess,
-				unquoted = !match[ 6 ] && match[ 2 ];
-
-			if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) {
-				return null;
-			}
-
-			// Accept quoted arguments as-is
-			if ( match[ 3 ] ) {
-				match[ 2 ] = match[ 4 ] || match[ 5 ] || "";
-
-			// Strip excess characters from unquoted arguments
-			} else if ( unquoted && rpseudo.test( unquoted ) &&
-
-				// Get excess from tokenize (recursively)
-				( excess = tokenize( unquoted, true ) ) &&
-
-				// advance to the next closing parenthesis
-				( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) {
-
-				// excess is a negative index
-				match[ 0 ] = match[ 0 ].slice( 0, excess );
-				match[ 2 ] = unquoted.slice( 0, excess );
-			}
-
-			// Return only captures needed by the pseudo filter method (type and argument)
-			return match.slice( 0, 3 );
-		}
-	},
-
-	filter: {
-
-		"TAG": function( nodeNameSelector ) {
-			var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase();
-			return nodeNameSelector === "*" ?
-				function() {
-					return true;
-				} :
-				function( elem ) {
-					return elem.nodeName && elem.nodeName.toLowerCase() === nodeName;
-				};
-		},
-
-		"CLASS": function( className ) {
-			var pattern = classCache[ className + " " ];
-
-			return pattern ||
-				( pattern = new RegExp( "(^|" + whitespace +
-					")" + className + "(" + whitespace + "|$)" ) ) && classCache(
-						className, function( elem ) {
-							return pattern.test(
-								typeof elem.className === "string" && elem.className ||
-								typeof elem.getAttribute !== "undefined" &&
-									elem.getAttribute( "class" ) ||
-								""
-							);
-				} );
-		},
-
-		"ATTR": function( name, operator, check ) {
-			return function( elem ) {
-				var result = Sizzle.attr( elem, name );
-
-				if ( result == null ) {
-					return operator === "!=";
-				}
-				if ( !operator ) {
-					return true;
-				}
-
-				result += "";
-
-				/* eslint-disable max-len */
-
-				return operator === "=" ? result === check :
-					operator === "!=" ? result !== check :
-					operator === "^=" ? check && result.indexOf( check ) === 0 :
-					operator === "*=" ? check && result.indexOf( check ) > -1 :
-					operator === "$=" ? check && result.slice( -check.length ) === check :
-					operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 :
-					operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" :
-					false;
-				/* eslint-enable max-len */
-
-			};
-		},
-
-		"CHILD": function( type, what, _argument, first, last ) {
-			var simple = type.slice( 0, 3 ) !== "nth",
-				forward = type.slice( -4 ) !== "last",
-				ofType = what === "of-type";
-
-			return first === 1 && last === 0 ?
-
-				// Shortcut for :nth-*(n)
-				function( elem ) {
-					return !!elem.parentNode;
-				} :
-
-				function( elem, _context, xml ) {
-					var cache, uniqueCache, outerCache, node, nodeIndex, start,
-						dir = simple !== forward ? "nextSibling" : "previousSibling",
-						parent = elem.parentNode,
-						name = ofType && elem.nodeName.toLowerCase(),
-						useCache = !xml && !ofType,
-						diff = false;
-
-					if ( parent ) {
-
-						// :(first|last|only)-(child|of-type)
-						if ( simple ) {
-							while ( dir ) {
-								node = elem;
-								while ( ( node = node[ dir ] ) ) {
-									if ( ofType ?
-										node.nodeName.toLowerCase() === name :
-										node.nodeType === 1 ) {
-
-										return false;
-									}
-								}
-
-								// Reverse direction for :only-* (if we haven't yet done so)
-								start = dir = type === "only" && !start && "nextSibling";
-							}
-							return true;
-						}
-
-						start = [ forward ? parent.firstChild : parent.lastChild ];
-
-						// non-xml :nth-child(...) stores cache data on `parent`
-						if ( forward && useCache ) {
-
-							// Seek `elem` from a previously-cached index
-
-							// ...in a gzip-friendly way
-							node = parent;
-							outerCache = node[ expando ] || ( node[ expando ] = {} );
-
-							// Support: IE <9 only
-							// Defend against cloned attroperties (jQuery gh-1709)
-							uniqueCache = outerCache[ node.uniqueID ] ||
-								( outerCache[ node.uniqueID ] = {} );
-
-							cache = uniqueCache[ type ] || [];
-							nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
-							diff = nodeIndex && cache[ 2 ];
-							node = nodeIndex && parent.childNodes[ nodeIndex ];
-
-							while ( ( node = ++nodeIndex && node && node[ dir ] ||
-
-								// Fallback to seeking `elem` from the start
-								( diff = nodeIndex = 0 ) || start.pop() ) ) {
-
-								// When found, cache indexes on `parent` and break
-								if ( node.nodeType === 1 && ++diff && node === elem ) {
-									uniqueCache[ type ] = [ dirruns, nodeIndex, diff ];
-									break;
-								}
-							}
-
-						} else {
-
-							// Use previously-cached element index if available
-							if ( useCache ) {
-
-								// ...in a gzip-friendly way
-								node = elem;
-								outerCache = node[ expando ] || ( node[ expando ] = {} );
-
-								// Support: IE <9 only
-								// Defend against cloned attroperties (jQuery gh-1709)
-								uniqueCache = outerCache[ node.uniqueID ] ||
-									( outerCache[ node.uniqueID ] = {} );
-
-								cache = uniqueCache[ type ] || [];
-								nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
-								diff = nodeIndex;
-							}
-
-							// xml :nth-child(...)
-							// or :nth-last-child(...) or :nth(-last)?-of-type(...)
-							if ( diff === false ) {
-
-								// Use the same loop as above to seek `elem` from the start
-								while ( ( node = ++nodeIndex && node && node[ dir ] ||
-									( diff = nodeIndex = 0 ) || start.pop() ) ) {
-
-									if ( ( ofType ?
-										node.nodeName.toLowerCase() === name :
-										node.nodeType === 1 ) &&
-										++diff ) {
-
-										// Cache the index of each encountered element
-										if ( useCache ) {
-											outerCache = node[ expando ] ||
-												( node[ expando ] = {} );
-
-											// Support: IE <9 only
-											// Defend against cloned attroperties (jQuery gh-1709)
-											uniqueCache = outerCache[ node.uniqueID ] ||
-												( outerCache[ node.uniqueID ] = {} );
-
-											uniqueCache[ type ] = [ dirruns, diff ];
-										}
-
-										if ( node === elem ) {
-											break;
-										}
-									}
-								}
-							}
-						}
-
-						// Incorporate the offset, then check against cycle size
-						diff -= last;
-						return diff === first || ( diff % first === 0 && diff / first >= 0 );
-					}
-				};
-		},
-
-		"PSEUDO": function( pseudo, argument ) {
-
-			// pseudo-class names are case-insensitive
-			// http://www.w3.org/TR/selectors/#pseudo-classes
-			// Prioritize by case sensitivity in case custom pseudos are added with uppercase letters
-			// Remember that setFilters inherits from pseudos
-			var args,
-				fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] ||
-					Sizzle.error( "unsupported pseudo: " + pseudo );
-
-			// The user may use createPseudo to indicate that
-			// arguments are needed to create the filter function
-			// just as Sizzle does
-			if ( fn[ expando ] ) {
-				return fn( argument );
-			}
-
-			// But maintain support for old signatures
-			if ( fn.length > 1 ) {
-				args = [ pseudo, pseudo, "", argument ];
-				return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ?
-					markFunction( function( seed, matches ) {
-						var idx,
-							matched = fn( seed, argument ),
-							i = matched.length;
-						while ( i-- ) {
-							idx = indexOf( seed, matched[ i ] );
-							seed[ idx ] = !( matches[ idx ] = matched[ i ] );
-						}
-					} ) :
-					function( elem ) {
-						return fn( elem, 0, args );
-					};
-			}
-
-			return fn;
-		}
-	},
-
-	pseudos: {
-
-		// Potentially complex pseudos
-		"not": markFunction( function( selector ) {
-
-			// Trim the selector passed to compile
-			// to avoid treating leading and trailing
-			// spaces as combinators
-			var input = [],
-				results = [],
-				matcher = compile( selector.replace( rtrim, "$1" ) );
-
-			return matcher[ expando ] ?
-				markFunction( function( seed, matches, _context, xml ) {
-					var elem,
-						unmatched = matcher( seed, null, xml, [] ),
-						i = seed.length;
-
-					// Match elements unmatched by `matcher`
-					while ( i-- ) {
-						if ( ( elem = unmatched[ i ] ) ) {
-							seed[ i ] = !( matches[ i ] = elem );
-						}
-					}
-				} ) :
-				function( elem, _context, xml ) {
-					input[ 0 ] = elem;
-					matcher( input, null, xml, results );
-
-					// Don't keep the element (issue #299)
-					input[ 0 ] = null;
-					return !results.pop();
-				};
-		} ),
-
-		"has": markFunction( function( selector ) {
-			return function( elem ) {
-				return Sizzle( selector, elem ).length > 0;
-			};
-		} ),
-
-		"contains": markFunction( function( text ) {
-			text = text.replace( runescape, funescape );
-			return function( elem ) {
-				return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1;
-			};
-		} ),
-
-		// "Whether an element is represented by a :lang() selector
-		// is based solely on the element's language value
-		// being equal to the identifier C,
-		// or beginning with the identifier C immediately followed by "-".
-		// The matching of C against the element's language value is performed case-insensitively.
-		// The identifier C does not have to be a valid language name."
-		// http://www.w3.org/TR/selectors/#lang-pseudo
-		"lang": markFunction( function( lang ) {
-
-			// lang value must be a valid identifier
-			if ( !ridentifier.test( lang || "" ) ) {
-				Sizzle.error( "unsupported lang: " + lang );
-			}
-			lang = lang.replace( runescape, funescape ).toLowerCase();
-			return function( elem ) {
-				var elemLang;
-				do {
-					if ( ( elemLang = documentIsHTML ?
-						elem.lang :
-						elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) {
-
-						elemLang = elemLang.toLowerCase();
-						return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0;
-					}
-				} while ( ( elem = elem.parentNode ) && elem.nodeType === 1 );
-				return false;
-			};
-		} ),
-
-		// Miscellaneous
-		"target": function( elem ) {
-			var hash = window.location && window.location.hash;
-			return hash && hash.slice( 1 ) === elem.id;
-		},
-
-		"root": function( elem ) {
-			return elem === docElem;
-		},
-
-		"focus": function( elem ) {
-			return elem === document.activeElement &&
-				( !document.hasFocus || document.hasFocus() ) &&
-				!!( elem.type || elem.href || ~elem.tabIndex );
-		},
-
-		// Boolean properties
-		"enabled": createDisabledPseudo( false ),
-		"disabled": createDisabledPseudo( true ),
-
-		"checked": function( elem ) {
-
-			// In CSS3, :checked should return both checked and selected elements
-			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
-			var nodeName = elem.nodeName.toLowerCase();
-			return ( nodeName === "input" && !!elem.checked ) ||
-				( nodeName === "option" && !!elem.selected );
-		},
-
-		"selected": function( elem ) {
-
-			// Accessing this property makes selected-by-default
-			// options in Safari work properly
-			if ( elem.parentNode ) {
-				// eslint-disable-next-line no-unused-expressions
-				elem.parentNode.selectedIndex;
-			}
-
-			return elem.selected === true;
-		},
-
-		// Contents
-		"empty": function( elem ) {
-
-			// http://www.w3.org/TR/selectors/#empty-pseudo
-			// :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5),
-			//   but not by others (comment: 8; processing instruction: 7; etc.)
-			// nodeType < 6 works because attributes (2) do not appear as children
-			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
-				if ( elem.nodeType < 6 ) {
-					return false;
-				}
-			}
-			return true;
-		},
-
-		"parent": function( elem ) {
-			return !Expr.pseudos[ "empty" ]( elem );
-		},
-
-		// Element/input types
-		"header": function( elem ) {
-			return rheader.test( elem.nodeName );
-		},
-
-		"input": function( elem ) {
-			return rinputs.test( elem.nodeName );
-		},
-
-		"button": function( elem ) {
-			var name = elem.nodeName.toLowerCase();
-			return name === "input" && elem.type === "button" || name === "button";
-		},
-
-		"text": function( elem ) {
-			var attr;
-			return elem.nodeName.toLowerCase() === "input" &&
-				elem.type === "text" &&
-
-				// Support: IE<8
-				// New HTML5 attribute values (e.g., "search") appear with elem.type === "text"
-				( ( attr = elem.getAttribute( "type" ) ) == null ||
-					attr.toLowerCase() === "text" );
-		},
-
-		// Position-in-collection
-		"first": createPositionalPseudo( function() {
-			return [ 0 ];
-		} ),
-
-		"last": createPositionalPseudo( function( _matchIndexes, length ) {
-			return [ length - 1 ];
-		} ),
-
-		"eq": createPositionalPseudo( function( _matchIndexes, length, argument ) {
-			return [ argument < 0 ? argument + length : argument ];
-		} ),
-
-		"even": createPositionalPseudo( function( matchIndexes, length ) {
-			var i = 0;
-			for ( ; i < length; i += 2 ) {
-				matchIndexes.push( i );
-			}
-			return matchIndexes;
-		} ),
-
-		"odd": createPositionalPseudo( function( matchIndexes, length ) {
-			var i = 1;
-			for ( ; i < length; i += 2 ) {
-				matchIndexes.push( i );
-			}
-			return matchIndexes;
-		} ),
-
-		"lt": createPositionalPseudo( function( matchIndexes, length, argument ) {
-			var i = argument < 0 ?
-				argument + length :
-				argument > length ?
-					length :
-					argument;
-			for ( ; --i >= 0; ) {
-				matchIndexes.push( i );
-			}
-			return matchIndexes;
-		} ),
-
-		"gt": createPositionalPseudo( function( matchIndexes, length, argument ) {
-			var i = argument < 0 ? argument + length : argument;
-			for ( ; ++i < length; ) {
-				matchIndexes.push( i );
-			}
-			return matchIndexes;
-		} )
-	}
-};
-
-Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ];
-
-// Add button/input type pseudos
-for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) {
-	Expr.pseudos[ i ] = createInputPseudo( i );
-}
-for ( i in { submit: true, reset: true } ) {
-	Expr.pseudos[ i ] = createButtonPseudo( i );
-}
-
-// Easy API for creating new setFilters
-function setFilters() {}
-setFilters.prototype = Expr.filters = Expr.pseudos;
-Expr.setFilters = new setFilters();
-
-tokenize = Sizzle.tokenize = function( selector, parseOnly ) {
-	var matched, match, tokens, type,
-		soFar, groups, preFilters,
-		cached = tokenCache[ selector + " " ];
-
-	if ( cached ) {
-		return parseOnly ? 0 : cached.slice( 0 );
-	}
-
-	soFar = selector;
-	groups = [];
-	preFilters = Expr.preFilter;
-
-	while ( soFar ) {
-
-		// Comma and first run
-		if ( !matched || ( match = rcomma.exec( soFar ) ) ) {
-			if ( match ) {
-
-				// Don't consume trailing commas as valid
-				soFar = soFar.slice( match[ 0 ].length ) || soFar;
-			}
-			groups.push( ( tokens = [] ) );
-		}
-
-		matched = false;
-
-		// Combinators
-		if ( ( match = rcombinators.exec( soFar ) ) ) {
-			matched = match.shift();
-			tokens.push( {
-				value: matched,
-
-				// Cast descendant combinators to space
-				type: match[ 0 ].replace( rtrim, " " )
-			} );
-			soFar = soFar.slice( matched.length );
-		}
-
-		// Filters
-		for ( type in Expr.filter ) {
-			if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] ||
-				( match = preFilters[ type ]( match ) ) ) ) {
-				matched = match.shift();
-				tokens.push( {
-					value: matched,
-					type: type,
-					matches: match
-				} );
-				soFar = soFar.slice( matched.length );
-			}
-		}
-
-		if ( !matched ) {
-			break;
-		}
-	}
-
-	// Return the length of the invalid excess
-	// if we're just parsing
-	// Otherwise, throw an error or return tokens
-	return parseOnly ?
-		soFar.length :
-		soFar ?
-			Sizzle.error( selector ) :
-
-			// Cache the tokens
-			tokenCache( selector, groups ).slice( 0 );
-};
-
-function toSelector( tokens ) {
-	var i = 0,
-		len = tokens.length,
-		selector = "";
-	for ( ; i < len; i++ ) {
-		selector += tokens[ i ].value;
-	}
-	return selector;
-}
-
-function addCombinator( matcher, combinator, base ) {
-	var dir = combinator.dir,
-		skip = combinator.next,
-		key = skip || dir,
-		checkNonElements = base && key === "parentNode",
-		doneName = done++;
-
-	return combinator.first ?
-
-		// Check against closest ancestor/preceding element
-		function( elem, context, xml ) {
-			while ( ( elem = elem[ dir ] ) ) {
-				if ( elem.nodeType === 1 || checkNonElements ) {
-					return matcher( elem, context, xml );
-				}
-			}
-			return false;
-		} :
-
-		// Check against all ancestor/preceding elements
-		function( elem, context, xml ) {
-			var oldCache, uniqueCache, outerCache,
-				newCache = [ dirruns, doneName ];
-
-			// We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching
-			if ( xml ) {
-				while ( ( elem = elem[ dir ] ) ) {
-					if ( elem.nodeType === 1 || checkNonElements ) {
-						if ( matcher( elem, context, xml ) ) {
-							return true;
-						}
-					}
-				}
-			} else {
-				while ( ( elem = elem[ dir ] ) ) {
-					if ( elem.nodeType === 1 || checkNonElements ) {
-						outerCache = elem[ expando ] || ( elem[ expando ] = {} );
-
-						// Support: IE <9 only
-						// Defend against cloned attroperties (jQuery gh-1709)
-						uniqueCache = outerCache[ elem.uniqueID ] ||
-							( outerCache[ elem.uniqueID ] = {} );
-
-						if ( skip && skip === elem.nodeName.toLowerCase() ) {
-							elem = elem[ dir ] || elem;
-						} else if ( ( oldCache = uniqueCache[ key ] ) &&
-							oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) {
-
-							// Assign to newCache so results back-propagate to previous elements
-							return ( newCache[ 2 ] = oldCache[ 2 ] );
-						} else {
-
-							// Reuse newcache so results back-propagate to previous elements
-							uniqueCache[ key ] = newCache;
-
-							// A match means we're done; a fail means we have to keep checking
-							if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) {
-								return true;
-							}
-						}
-					}
-				}
-			}
-			return false;
-		};
-}
-
-function elementMatcher( matchers ) {
-	return matchers.length > 1 ?
-		function( elem, context, xml ) {
-			var i = matchers.length;
-			while ( i-- ) {
-				if ( !matchers[ i ]( elem, context, xml ) ) {
-					return false;
-				}
-			}
-			return true;
-		} :
-		matchers[ 0 ];
-}
-
-function multipleContexts( selector, contexts, results ) {
-	var i = 0,
-		len = contexts.length;
-	for ( ; i < len; i++ ) {
-		Sizzle( selector, contexts[ i ], results );
-	}
-	return results;
-}
-
-function condense( unmatched, map, filter, context, xml ) {
-	var elem,
-		newUnmatched = [],
-		i = 0,
-		len = unmatched.length,
-		mapped = map != null;
-
-	for ( ; i < len; i++ ) {
-		if ( ( elem = unmatched[ i ] ) ) {
-			if ( !filter || filter( elem, context, xml ) ) {
-				newUnmatched.push( elem );
-				if ( mapped ) {
-					map.push( i );
-				}
-			}
-		}
-	}
-
-	return newUnmatched;
-}
-
-function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) {
-	if ( postFilter && !postFilter[ expando ] ) {
-		postFilter = setMatcher( postFilter );
-	}
-	if ( postFinder && !postFinder[ expando ] ) {
-		postFinder = setMatcher( postFinder, postSelector );
-	}
-	return markFunction( function( seed, results, context, xml ) {
-		var temp, i, elem,
-			preMap = [],
-			postMap = [],
-			preexisting = results.length,
-
-			// Get initial elements from seed or context
-			elems = seed || multipleContexts(
-				selector || "*",
-				context.nodeType ? [ context ] : context,
-				[]
-			),
-
-			// Prefilter to get matcher input, preserving a map for seed-results synchronization
-			matcherIn = preFilter && ( seed || !selector ) ?
-				condense( elems, preMap, preFilter, context, xml ) :
-				elems,
-
-			matcherOut = matcher ?
-
-				// If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results,
-				postFinder || ( seed ? preFilter : preexisting || postFilter ) ?
-
-					// ...intermediate processing is necessary
-					[] :
-
-					// ...otherwise use results directly
-					results :
-				matcherIn;
-
-		// Find primary matches
-		if ( matcher ) {
-			matcher( matcherIn, matcherOut, context, xml );
-		}
-
-		// Apply postFilter
-		if ( postFilter ) {
-			temp = condense( matcherOut, postMap );
-			postFilter( temp, [], context, xml );
-
-			// Un-match failing elements by moving them back to matcherIn
-			i = temp.length;
-			while ( i-- ) {
-				if ( ( elem = temp[ i ] ) ) {
-					matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem );
-				}
-			}
-		}
-
-		if ( seed ) {
-			if ( postFinder || preFilter ) {
-				if ( postFinder ) {
-
-					// Get the final matcherOut by condensing this intermediate into postFinder contexts
-					temp = [];
-					i = matcherOut.length;
-					while ( i-- ) {
-						if ( ( elem = matcherOut[ i ] ) ) {
-
-							// Restore matcherIn since elem is not yet a final match
-							temp.push( ( matcherIn[ i ] = elem ) );
-						}
-					}
-					postFinder( null, ( matcherOut = [] ), temp, xml );
-				}
-
-				// Move matched elements from seed to results to keep them synchronized
-				i = matcherOut.length;
-				while ( i-- ) {
-					if ( ( elem = matcherOut[ i ] ) &&
-						( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) {
-
-						seed[ temp ] = !( results[ temp ] = elem );
-					}
-				}
-			}
-
-		// Add elements to results, through postFinder if defined
-		} else {
-			matcherOut = condense(
-				matcherOut === results ?
-					matcherOut.splice( preexisting, matcherOut.length ) :
-					matcherOut
-			);
-			if ( postFinder ) {
-				postFinder( null, results, matcherOut, xml );
-			} else {
-				push.apply( results, matcherOut );
-			}
-		}
-	} );
-}
-
-function matcherFromTokens( tokens ) {
-	var checkContext, matcher, j,
-		len = tokens.length,
-		leadingRelative = Expr.relative[ tokens[ 0 ].type ],
-		implicitRelative = leadingRelative || Expr.relative[ " " ],
-		i = leadingRelative ? 1 : 0,
-
-		// The foundational matcher ensures that elements are reachable from top-level context(s)
-		matchContext = addCombinator( function( elem ) {
-			return elem === checkContext;
-		}, implicitRelative, true ),
-		matchAnyContext = addCombinator( function( elem ) {
-			return indexOf( checkContext, elem ) > -1;
-		}, implicitRelative, true ),
-		matchers = [ function( elem, context, xml ) {
-			var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || (
-				( checkContext = context ).nodeType ?
-					matchContext( elem, context, xml ) :
-					matchAnyContext( elem, context, xml ) );
-
-			// Avoid hanging onto element (issue #299)
-			checkContext = null;
-			return ret;
-		} ];
-
-	for ( ; i < len; i++ ) {
-		if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) {
-			matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ];
-		} else {
-			matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches );
-
-			// Return special upon seeing a positional matcher
-			if ( matcher[ expando ] ) {
-
-				// Find the next relative operator (if any) for proper handling
-				j = ++i;
-				for ( ; j < len; j++ ) {
-					if ( Expr.relative[ tokens[ j ].type ] ) {
-						break;
-					}
-				}
-				return setMatcher(
-					i > 1 && elementMatcher( matchers ),
-					i > 1 && toSelector(
-
-					// If the preceding token was a descendant combinator, insert an implicit any-element `*`
-					tokens
-						.slice( 0, i - 1 )
-						.concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } )
-					).replace( rtrim, "$1" ),
-					matcher,
-					i < j && matcherFromTokens( tokens.slice( i, j ) ),
-					j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ),
-					j < len && toSelector( tokens )
-				);
-			}
-			matchers.push( matcher );
-		}
-	}
-
-	return elementMatcher( matchers );
-}
-
-function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
-	var bySet = setMatchers.length > 0,
-		byElement = elementMatchers.length > 0,
-		superMatcher = function( seed, context, xml, results, outermost ) {
-			var elem, j, matcher,
-				matchedCount = 0,
-				i = "0",
-				unmatched = seed && [],
-				setMatched = [],
-				contextBackup = outermostContext,
-
-				// We must always have either seed elements or outermost context
-				elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ),
-
-				// Use integer dirruns iff this is the outermost matcher
-				dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ),
-				len = elems.length;
-
-			if ( outermost ) {
-
-				// Support: IE 11+, Edge 17 - 18+
-				// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-				// two documents; shallow comparisons work.
-				// eslint-disable-next-line eqeqeq
-				outermostContext = context == document || context || outermost;
-			}
-
-			// Add elements passing elementMatchers directly to results
-			// Support: IE<9, Safari
-			// Tolerate NodeList properties (IE: "length"; Safari: <number>) matching elements by id
-			for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) {
-				if ( byElement && elem ) {
-					j = 0;
-
-					// Support: IE 11+, Edge 17 - 18+
-					// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-					// two documents; shallow comparisons work.
-					// eslint-disable-next-line eqeqeq
-					if ( !context && elem.ownerDocument != document ) {
-						setDocument( elem );
-						xml = !documentIsHTML;
-					}
-					while ( ( matcher = elementMatchers[ j++ ] ) ) {
-						if ( matcher( elem, context || document, xml ) ) {
-							results.push( elem );
-							break;
-						}
-					}
-					if ( outermost ) {
-						dirruns = dirrunsUnique;
-					}
-				}
-
-				// Track unmatched elements for set filters
-				if ( bySet ) {
-
-					// They will have gone through all possible matchers
-					if ( ( elem = !matcher && elem ) ) {
-						matchedCount--;
-					}
-
-					// Lengthen the array for every element, matched or not
-					if ( seed ) {
-						unmatched.push( elem );
-					}
-				}
-			}
-
-			// `i` is now the count of elements visited above, and adding it to `matchedCount`
-			// makes the latter nonnegative.
-			matchedCount += i;
-
-			// Apply set filters to unmatched elements
-			// NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount`
-			// equals `i`), unless we didn't visit _any_ elements in the above loop because we have
-			// no element matchers and no seed.
-			// Incrementing an initially-string "0" `i` allows `i` to remain a string only in that
-			// case, which will result in a "00" `matchedCount` that differs from `i` but is also
-			// numerically zero.
-			if ( bySet && i !== matchedCount ) {
-				j = 0;
-				while ( ( matcher = setMatchers[ j++ ] ) ) {
-					matcher( unmatched, setMatched, context, xml );
-				}
-
-				if ( seed ) {
-
-					// Reintegrate element matches to eliminate the need for sorting
-					if ( matchedCount > 0 ) {
-						while ( i-- ) {
-							if ( !( unmatched[ i ] || setMatched[ i ] ) ) {
-								setMatched[ i ] = pop.call( results );
-							}
-						}
-					}
-
-					// Discard index placeholder values to get only actual matches
-					setMatched = condense( setMatched );
-				}
-
-				// Add matches to results
-				push.apply( results, setMatched );
-
-				// Seedless set matches succeeding multiple successful matchers stipulate sorting
-				if ( outermost && !seed && setMatched.length > 0 &&
-					( matchedCount + setMatchers.length ) > 1 ) {
-
-					Sizzle.uniqueSort( results );
-				}
-			}
-
-			// Override manipulation of globals by nested matchers
-			if ( outermost ) {
-				dirruns = dirrunsUnique;
-				outermostContext = contextBackup;
-			}
-
-			return unmatched;
-		};
-
-	return bySet ?
-		markFunction( superMatcher ) :
-		superMatcher;
-}
-
-compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
-	var i,
-		setMatchers = [],
-		elementMatchers = [],
-		cached = compilerCache[ selector + " " ];
-
-	if ( !cached ) {
-
-		// Generate a function of recursive functions that can be used to check each element
-		if ( !match ) {
-			match = tokenize( selector );
-		}
-		i = match.length;
-		while ( i-- ) {
-			cached = matcherFromTokens( match[ i ] );
-			if ( cached[ expando ] ) {
-				setMatchers.push( cached );
-			} else {
-				elementMatchers.push( cached );
-			}
-		}
-
-		// Cache the compiled function
-		cached = compilerCache(
-			selector,
-			matcherFromGroupMatchers( elementMatchers, setMatchers )
-		);
-
-		// Save selector and tokenization
-		cached.selector = selector;
-	}
-	return cached;
-};
-
-/**
- * A low-level selection function that works with Sizzle's compiled
- *  selector functions
- * @param {String|Function} selector A selector or a pre-compiled
- *  selector function built with Sizzle.compile
- * @param {Element} context
- * @param {Array} [results]
- * @param {Array} [seed] A set of elements to match against
- */
-select = Sizzle.select = function( selector, context, results, seed ) {
-	var i, tokens, token, type, find,
-		compiled = typeof selector === "function" && selector,
-		match = !seed && tokenize( ( selector = compiled.selector || selector ) );
-
-	results = results || [];
-
-	// Try to minimize operations if there is only one selector in the list and no seed
-	// (the latter of which guarantees us context)
-	if ( match.length === 1 ) {
-
-		// Reduce context if the leading compound selector is an ID
-		tokens = match[ 0 ] = match[ 0 ].slice( 0 );
-		if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" &&
-			context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) {
-
-			context = ( Expr.find[ "ID" ]( token.matches[ 0 ]
-				.replace( runescape, funescape ), context ) || [] )[ 0 ];
-			if ( !context ) {
-				return results;
-
-			// Precompiled matchers will still verify ancestry, so step up a level
-			} else if ( compiled ) {
-				context = context.parentNode;
-			}
-
-			selector = selector.slice( tokens.shift().value.length );
-		}
-
-		// Fetch a seed set for right-to-left matching
-		i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length;
-		while ( i-- ) {
-			token = tokens[ i ];
-
-			// Abort if we hit a combinator
-			if ( Expr.relative[ ( type = token.type ) ] ) {
-				break;
-			}
-			if ( ( find = Expr.find[ type ] ) ) {
-
-				// Search, expanding context for leading sibling combinators
-				if ( ( seed = find(
-					token.matches[ 0 ].replace( runescape, funescape ),
-					rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) ||
-						context
-				) ) ) {
-
-					// If seed is empty or no tokens remain, we can return early
-					tokens.splice( i, 1 );
-					selector = seed.length && toSelector( tokens );
-					if ( !selector ) {
-						push.apply( results, seed );
-						return results;
-					}
-
-					break;
-				}
-			}
-		}
-	}
-
-	// Compile and execute a filtering function if one is not provided
-	// Provide `match` to avoid retokenization if we modified the selector above
-	( compiled || compile( selector, match ) )(
-		seed,
-		context,
-		!documentIsHTML,
-		results,
-		!context || rsibling.test( selector ) && testContext( context.parentNode ) || context
-	);
-	return results;
-};
-
-// One-time assignments
-
-// Sort stability
-support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando;
-
-// Support: Chrome 14-35+
-// Always assume duplicates if they aren't passed to the comparison function
-support.detectDuplicates = !!hasDuplicate;
-
-// Initialize against the default document
-setDocument();
-
-// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27)
-// Detached nodes confoundingly follow *each other*
-support.sortDetached = assert( function( el ) {
-
-	// Should return 1, but returns 4 (following)
-	return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1;
-} );
-
-// Support: IE<8
-// Prevent attribute/property "interpolation"
-// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx
-if ( !assert( function( el ) {
-	el.innerHTML = "<a href='#'></a>";
-	return el.firstChild.getAttribute( "href" ) === "#";
-} ) ) {
-	addHandle( "type|href|height|width", function( elem, name, isXML ) {
-		if ( !isXML ) {
-			return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 );
-		}
-	} );
-}
-
-// Support: IE<9
-// Use defaultValue in place of getAttribute("value")
-if ( !support.attributes || !assert( function( el ) {
-	el.innerHTML = "<input/>";
-	el.firstChild.setAttribute( "value", "" );
-	return el.firstChild.getAttribute( "value" ) === "";
-} ) ) {
-	addHandle( "value", function( elem, _name, isXML ) {
-		if ( !isXML && elem.nodeName.toLowerCase() === "input" ) {
-			return elem.defaultValue;
-		}
-	} );
-}
-
-// Support: IE<9
-// Use getAttributeNode to fetch booleans when getAttribute lies
-if ( !assert( function( el ) {
-	return el.getAttribute( "disabled" ) == null;
-} ) ) {
-	addHandle( booleans, function( elem, name, isXML ) {
-		var val;
-		if ( !isXML ) {
-			return elem[ name ] === true ? name.toLowerCase() :
-				( val = elem.getAttributeNode( name ) ) && val.specified ?
-					val.value :
-					null;
-		}
-	} );
-}
-
-return Sizzle;
-
-} )( window );
-
-
-
-jQuery.find = Sizzle;
-jQuery.expr = Sizzle.selectors;
-
-// Deprecated
-jQuery.expr[ ":" ] = jQuery.expr.pseudos;
-jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort;
-jQuery.text = Sizzle.getText;
-jQuery.isXMLDoc = Sizzle.isXML;
-jQuery.contains = Sizzle.contains;
-jQuery.escapeSelector = Sizzle.escape;
-
-
-
-
-var dir = function( elem, dir, until ) {
-	var matched = [],
-		truncate = until !== undefined;
-
-	while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) {
-		if ( elem.nodeType === 1 ) {
-			if ( truncate && jQuery( elem ).is( until ) ) {
-				break;
-			}
-			matched.push( elem );
-		}
-	}
-	return matched;
-};
-
-
-var siblings = function( n, elem ) {
-	var matched = [];
-
-	for ( ; n; n = n.nextSibling ) {
-		if ( n.nodeType === 1 && n !== elem ) {
-			matched.push( n );
-		}
-	}
-
-	return matched;
-};
-
-
-var rneedsContext = jQuery.expr.match.needsContext;
-
-
-
-function nodeName( elem, name ) {
-
-  return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase();
-
-};
-var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i );
-
-
-
-// Implement the identical functionality for filter and not
-function winnow( elements, qualifier, not ) {
-	if ( isFunction( qualifier ) ) {
-		return jQuery.grep( elements, function( elem, i ) {
-			return !!qualifier.call( elem, i, elem ) !== not;
-		} );
-	}
-
-	// Single element
-	if ( qualifier.nodeType ) {
-		return jQuery.grep( elements, function( elem ) {
-			return ( elem === qualifier ) !== not;
-		} );
-	}
-
-	// Arraylike of elements (jQuery, arguments, Array)
-	if ( typeof qualifier !== "string" ) {
-		return jQuery.grep( elements, function( elem ) {
-			return ( indexOf.call( qualifier, elem ) > -1 ) !== not;
-		} );
-	}
-
-	// Filtered directly for both simple and complex selectors
-	return jQuery.filter( qualifier, elements, not );
-}
-
-jQuery.filter = function( expr, elems, not ) {
-	var elem = elems[ 0 ];
-
-	if ( not ) {
-		expr = ":not(" + expr + ")";
-	}
-
-	if ( elems.length === 1 && elem.nodeType === 1 ) {
-		return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : [];
-	}
-
-	return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) {
-		return elem.nodeType === 1;
-	} ) );
-};
-
-jQuery.fn.extend( {
-	find: function( selector ) {
-		var i, ret,
-			len = this.length,
-			self = this;
-
-		if ( typeof selector !== "string" ) {
-			return this.pushStack( jQuery( selector ).filter( function() {
-				for ( i = 0; i < len; i++ ) {
-					if ( jQuery.contains( self[ i ], this ) ) {
-						return true;
-					}
-				}
-			} ) );
-		}
-
-		ret = this.pushStack( [] );
-
-		for ( i = 0; i < len; i++ ) {
-			jQuery.find( selector, self[ i ], ret );
-		}
-
-		return len > 1 ? jQuery.uniqueSort( ret ) : ret;
-	},
-	filter: function( selector ) {
-		return this.pushStack( winnow( this, selector || [], false ) );
-	},
-	not: function( selector ) {
-		return this.pushStack( winnow( this, selector || [], true ) );
-	},
-	is: function( selector ) {
-		return !!winnow(
-			this,
-
-			// If this is a positional/relative selector, check membership in the returned set
-			// so $("p:first").is("p:last") won't return true for a doc with two "p".
-			typeof selector === "string" && rneedsContext.test( selector ) ?
-				jQuery( selector ) :
-				selector || [],
-			false
-		).length;
-	}
-} );
-
-
-// Initialize a jQuery object
-
-
-// A central reference to the root jQuery(document)
-var rootjQuery,
-
-	// A simple way to check for HTML strings
-	// Prioritize #id over <tag> to avoid XSS via location.hash (#9521)
-	// Strict HTML recognition (#11290: must start with <)
-	// Shortcut simple #id case for speed
-	rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/,
-
-	init = jQuery.fn.init = function( selector, context, root ) {
-		var match, elem;
-
-		// HANDLE: $(""), $(null), $(undefined), $(false)
-		if ( !selector ) {
-			return this;
-		}
-
-		// Method init() accepts an alternate rootjQuery
-		// so migrate can support jQuery.sub (gh-2101)
-		root = root || rootjQuery;
-
-		// Handle HTML strings
-		if ( typeof selector === "string" ) {
-			if ( selector[ 0 ] === "<" &&
-				selector[ selector.length - 1 ] === ">" &&
-				selector.length >= 3 ) {
-
-				// Assume that strings that start and end with <> are HTML and skip the regex check
-				match = [ null, selector, null ];
-
-			} else {
-				match = rquickExpr.exec( selector );
-			}
-
-			// Match html or make sure no context is specified for #id
-			if ( match && ( match[ 1 ] || !context ) ) {
-
-				// HANDLE: $(html) -> $(array)
-				if ( match[ 1 ] ) {
-					context = context instanceof jQuery ? context[ 0 ] : context;
-
-					// Option to run scripts is true for back-compat
-					// Intentionally let the error be thrown if parseHTML is not present
-					jQuery.merge( this, jQuery.parseHTML(
-						match[ 1 ],
-						context && context.nodeType ? context.ownerDocument || context : document,
-						true
-					) );
-
-					// HANDLE: $(html, props)
-					if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) {
-						for ( match in context ) {
-
-							// Properties of context are called as methods if possible
-							if ( isFunction( this[ match ] ) ) {
-								this[ match ]( context[ match ] );
-
-							// ...and otherwise set as attributes
-							} else {
-								this.attr( match, context[ match ] );
-							}
-						}
-					}
-
-					return this;
-
-				// HANDLE: $(#id)
-				} else {
-					elem = document.getElementById( match[ 2 ] );
-
-					if ( elem ) {
-
-						// Inject the element directly into the jQuery object
-						this[ 0 ] = elem;
-						this.length = 1;
-					}
-					return this;
-				}
-
-			// HANDLE: $(expr, $(...))
-			} else if ( !context || context.jquery ) {
-				return ( context || root ).find( selector );
-
-			// HANDLE: $(expr, context)
-			// (which is just equivalent to: $(context).find(expr)
-			} else {
-				return this.constructor( context ).find( selector );
-			}
-
-		// HANDLE: $(DOMElement)
-		} else if ( selector.nodeType ) {
-			this[ 0 ] = selector;
-			this.length = 1;
-			return this;
-
-		// HANDLE: $(function)
-		// Shortcut for document ready
-		} else if ( isFunction( selector ) ) {
-			return root.ready !== undefined ?
-				root.ready( selector ) :
-
-				// Execute immediately if ready is not present
-				selector( jQuery );
-		}
-
-		return jQuery.makeArray( selector, this );
-	};
-
-// Give the init function the jQuery prototype for later instantiation
-init.prototype = jQuery.fn;
-
-// Initialize central reference
-rootjQuery = jQuery( document );
-
-
-var rparentsprev = /^(?:parents|prev(?:Until|All))/,
-
-	// Methods guaranteed to produce a unique set when starting from a unique set
-	guaranteedUnique = {
-		children: true,
-		contents: true,
-		next: true,
-		prev: true
-	};
-
-jQuery.fn.extend( {
-	has: function( target ) {
-		var targets = jQuery( target, this ),
-			l = targets.length;
-
-		return this.filter( function() {
-			var i = 0;
-			for ( ; i < l; i++ ) {
-				if ( jQuery.contains( this, targets[ i ] ) ) {
-					return true;
-				}
-			}
-		} );
-	},
-
-	closest: function( selectors, context ) {
-		var cur,
-			i = 0,
-			l = this.length,
-			matched = [],
-			targets = typeof selectors !== "string" && jQuery( selectors );
-
-		// Positional selectors never match, since there's no _selection_ context
-		if ( !rneedsContext.test( selectors ) ) {
-			for ( ; i < l; i++ ) {
-				for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) {
-
-					// Always skip document fragments
-					if ( cur.nodeType < 11 && ( targets ?
-						targets.index( cur ) > -1 :
-
-						// Don't pass non-elements to Sizzle
-						cur.nodeType === 1 &&
-							jQuery.find.matchesSelector( cur, selectors ) ) ) {
-
-						matched.push( cur );
-						break;
-					}
-				}
-			}
-		}
-
-		return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched );
-	},
-
-	// Determine the position of an element within the set
-	index: function( elem ) {
-
-		// No argument, return index in parent
-		if ( !elem ) {
-			return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1;
-		}
-
-		// Index in selector
-		if ( typeof elem === "string" ) {
-			return indexOf.call( jQuery( elem ), this[ 0 ] );
-		}
-
-		// Locate the position of the desired element
-		return indexOf.call( this,
-
-			// If it receives a jQuery object, the first element is used
-			elem.jquery ? elem[ 0 ] : elem
-		);
-	},
-
-	add: function( selector, context ) {
-		return this.pushStack(
-			jQuery.uniqueSort(
-				jQuery.merge( this.get(), jQuery( selector, context ) )
-			)
-		);
-	},
-
-	addBack: function( selector ) {
-		return this.add( selector == null ?
-			this.prevObject : this.prevObject.filter( selector )
-		);
-	}
-} );
-
-function sibling( cur, dir ) {
-	while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {}
-	return cur;
-}
-
-jQuery.each( {
-	parent: function( elem ) {
-		var parent = elem.parentNode;
-		return parent && parent.nodeType !== 11 ? parent : null;
-	},
-	parents: function( elem ) {
-		return dir( elem, "parentNode" );
-	},
-	parentsUntil: function( elem, _i, until ) {
-		return dir( elem, "parentNode", until );
-	},
-	next: function( elem ) {
-		return sibling( elem, "nextSibling" );
-	},
-	prev: function( elem ) {
-		return sibling( elem, "previousSibling" );
-	},
-	nextAll: function( elem ) {
-		return dir( elem, "nextSibling" );
-	},
-	prevAll: function( elem ) {
-		return dir( elem, "previousSibling" );
-	},
-	nextUntil: function( elem, _i, until ) {
-		return dir( elem, "nextSibling", until );
-	},
-	prevUntil: function( elem, _i, until ) {
-		return dir( elem, "previousSibling", until );
-	},
-	siblings: function( elem ) {
-		return siblings( ( elem.parentNode || {} ).firstChild, elem );
-	},
-	children: function( elem ) {
-		return siblings( elem.firstChild );
-	},
-	contents: function( elem ) {
-		if ( elem.contentDocument != null &&
-
-			// Support: IE 11+
-			// <object> elements with no `data` attribute has an object
-			// `contentDocument` with a `null` prototype.
-			getProto( elem.contentDocument ) ) {
-
-			return elem.contentDocument;
-		}
-
-		// Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only
-		// Treat the template element as a regular one in browsers that
-		// don't support it.
-		if ( nodeName( elem, "template" ) ) {
-			elem = elem.content || elem;
-		}
-
-		return jQuery.merge( [], elem.childNodes );
-	}
-}, function( name, fn ) {
-	jQuery.fn[ name ] = function( until, selector ) {
-		var matched = jQuery.map( this, fn, until );
-
-		if ( name.slice( -5 ) !== "Until" ) {
-			selector = until;
-		}
-
-		if ( selector && typeof selector === "string" ) {
-			matched = jQuery.filter( selector, matched );
-		}
-
-		if ( this.length > 1 ) {
-
-			// Remove duplicates
-			if ( !guaranteedUnique[ name ] ) {
-				jQuery.uniqueSort( matched );
-			}
-
-			// Reverse order for parents* and prev-derivatives
-			if ( rparentsprev.test( name ) ) {
-				matched.reverse();
-			}
-		}
-
-		return this.pushStack( matched );
-	};
-} );
-var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g );
-
-
-
-// Convert String-formatted options into Object-formatted ones
-function createOptions( options ) {
-	var object = {};
-	jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) {
-		object[ flag ] = true;
-	} );
-	return object;
-}
-
-/*
- * Create a callback list using the following parameters:
- *
- *	options: an optional list of space-separated options that will change how
- *			the callback list behaves or a more traditional option object
- *
- * By default a callback list will act like an event callback list and can be
- * "fired" multiple times.
- *
- * Possible options:
- *
- *	once:			will ensure the callback list can only be fired once (like a Deferred)
- *
- *	memory:			will keep track of previous values and will call any callback added
- *					after the list has been fired right away with the latest "memorized"
- *					values (like a Deferred)
- *
- *	unique:			will ensure a callback can only be added once (no duplicate in the list)
- *
- *	stopOnFalse:	interrupt callings when a callback returns false
- *
- */
-jQuery.Callbacks = function( options ) {
-
-	// Convert options from String-formatted to Object-formatted if needed
-	// (we check in cache first)
-	options = typeof options === "string" ?
-		createOptions( options ) :
-		jQuery.extend( {}, options );
-
-	var // Flag to know if list is currently firing
-		firing,
-
-		// Last fire value for non-forgettable lists
-		memory,
-
-		// Flag to know if list was already fired
-		fired,
-
-		// Flag to prevent firing
-		locked,
-
-		// Actual callback list
-		list = [],
-
-		// Queue of execution data for repeatable lists
-		queue = [],
-
-		// Index of currently firing callback (modified by add/remove as needed)
-		firingIndex = -1,
-
-		// Fire callbacks
-		fire = function() {
-
-			// Enforce single-firing
-			locked = locked || options.once;
-
-			// Execute callbacks for all pending executions,
-			// respecting firingIndex overrides and runtime changes
-			fired = firing = true;
-			for ( ; queue.length; firingIndex = -1 ) {
-				memory = queue.shift();
-				while ( ++firingIndex < list.length ) {
-
-					// Run callback and check for early termination
-					if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false &&
-						options.stopOnFalse ) {
-
-						// Jump to end and forget the data so .add doesn't re-fire
-						firingIndex = list.length;
-						memory = false;
-					}
-				}
-			}
-
-			// Forget the data if we're done with it
-			if ( !options.memory ) {
-				memory = false;
-			}
-
-			firing = false;
-
-			// Clean up if we're done firing for good
-			if ( locked ) {
-
-				// Keep an empty list if we have data for future add calls
-				if ( memory ) {
-					list = [];
-
-				// Otherwise, this object is spent
-				} else {
-					list = "";
-				}
-			}
-		},
-
-		// Actual Callbacks object
-		self = {
-
-			// Add a callback or a collection of callbacks to the list
-			add: function() {
-				if ( list ) {
-
-					// If we have memory from a past run, we should fire after adding
-					if ( memory && !firing ) {
-						firingIndex = list.length - 1;
-						queue.push( memory );
-					}
-
-					( function add( args ) {
-						jQuery.each( args, function( _, arg ) {
-							if ( isFunction( arg ) ) {
-								if ( !options.unique || !self.has( arg ) ) {
-									list.push( arg );
-								}
-							} else if ( arg && arg.length && toType( arg ) !== "string" ) {
-
-								// Inspect recursively
-								add( arg );
-							}
-						} );
-					} )( arguments );
-
-					if ( memory && !firing ) {
-						fire();
-					}
-				}
-				return this;
-			},
-
-			// Remove a callback from the list
-			remove: function() {
-				jQuery.each( arguments, function( _, arg ) {
-					var index;
-					while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) {
-						list.splice( index, 1 );
-
-						// Handle firing indexes
-						if ( index <= firingIndex ) {
-							firingIndex--;
-						}
-					}
-				} );
-				return this;
-			},
-
-			// Check if a given callback is in the list.
-			// If no argument is given, return whether or not list has callbacks attached.
-			has: function( fn ) {
-				return fn ?
-					jQuery.inArray( fn, list ) > -1 :
-					list.length > 0;
-			},
-
-			// Remove all callbacks from the list
-			empty: function() {
-				if ( list ) {
-					list = [];
-				}
-				return this;
-			},
-
-			// Disable .fire and .add
-			// Abort any current/pending executions
-			// Clear all callbacks and values
-			disable: function() {
-				locked = queue = [];
-				list = memory = "";
-				return this;
-			},
-			disabled: function() {
-				return !list;
-			},
-
-			// Disable .fire
-			// Also disable .add unless we have memory (since it would have no effect)
-			// Abort any pending executions
-			lock: function() {
-				locked = queue = [];
-				if ( !memory && !firing ) {
-					list = memory = "";
-				}
-				return this;
-			},
-			locked: function() {
-				return !!locked;
-			},
-
-			// Call all callbacks with the given context and arguments
-			fireWith: function( context, args ) {
-				if ( !locked ) {
-					args = args || [];
-					args = [ context, args.slice ? args.slice() : args ];
-					queue.push( args );
-					if ( !firing ) {
-						fire();
-					}
-				}
-				return this;
-			},
-
-			// Call all the callbacks with the given arguments
-			fire: function() {
-				self.fireWith( this, arguments );
-				return this;
-			},
-
-			// To know if the callbacks have already been called at least once
-			fired: function() {
-				return !!fired;
-			}
-		};
-
-	return self;
-};
-
-
-function Identity( v ) {
-	return v;
-}
-function Thrower( ex ) {
-	throw ex;
-}
-
-function adoptValue( value, resolve, reject, noValue ) {
-	var method;
-
-	try {
-
-		// Check for promise aspect first to privilege synchronous behavior
-		if ( value && isFunction( ( method = value.promise ) ) ) {
-			method.call( value ).done( resolve ).fail( reject );
-
-		// Other thenables
-		} else if ( value && isFunction( ( method = value.then ) ) ) {
-			method.call( value, resolve, reject );
-
-		// Other non-thenables
-		} else {
-
-			// Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer:
-			// * false: [ value ].slice( 0 ) => resolve( value )
-			// * true: [ value ].slice( 1 ) => resolve()
-			resolve.apply( undefined, [ value ].slice( noValue ) );
-		}
-
-	// For Promises/A+, convert exceptions into rejections
-	// Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in
-	// Deferred#then to conditionally suppress rejection.
-	} catch ( value ) {
-
-		// Support: Android 4.0 only
-		// Strict mode functions invoked without .call/.apply get global-object context
-		reject.apply( undefined, [ value ] );
-	}
-}
-
-jQuery.extend( {
-
-	Deferred: function( func ) {
-		var tuples = [
-
-				// action, add listener, callbacks,
-				// ... .then handlers, argument index, [final state]
-				[ "notify", "progress", jQuery.Callbacks( "memory" ),
-					jQuery.Callbacks( "memory" ), 2 ],
-				[ "resolve", "done", jQuery.Callbacks( "once memory" ),
-					jQuery.Callbacks( "once memory" ), 0, "resolved" ],
-				[ "reject", "fail", jQuery.Callbacks( "once memory" ),
-					jQuery.Callbacks( "once memory" ), 1, "rejected" ]
-			],
-			state = "pending",
-			promise = {
-				state: function() {
-					return state;
-				},
-				always: function() {
-					deferred.done( arguments ).fail( arguments );
-					return this;
-				},
-				"catch": function( fn ) {
-					return promise.then( null, fn );
-				},
-
-				// Keep pipe for back-compat
-				pipe: function( /* fnDone, fnFail, fnProgress */ ) {
-					var fns = arguments;
-
-					return jQuery.Deferred( function( newDefer ) {
-						jQuery.each( tuples, function( _i, tuple ) {
-
-							// Map tuples (progress, done, fail) to arguments (done, fail, progress)
-							var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ];
-
-							// deferred.progress(function() { bind to newDefer or newDefer.notify })
-							// deferred.done(function() { bind to newDefer or newDefer.resolve })
-							// deferred.fail(function() { bind to newDefer or newDefer.reject })
-							deferred[ tuple[ 1 ] ]( function() {
-								var returned = fn && fn.apply( this, arguments );
-								if ( returned && isFunction( returned.promise ) ) {
-									returned.promise()
-										.progress( newDefer.notify )
-										.done( newDefer.resolve )
-										.fail( newDefer.reject );
-								} else {
-									newDefer[ tuple[ 0 ] + "With" ](
-										this,
-										fn ? [ returned ] : arguments
-									);
-								}
-							} );
-						} );
-						fns = null;
-					} ).promise();
-				},
-				then: function( onFulfilled, onRejected, onProgress ) {
-					var maxDepth = 0;
-					function resolve( depth, deferred, handler, special ) {
-						return function() {
-							var that = this,
-								args = arguments,
-								mightThrow = function() {
-									var returned, then;
-
-									// Support: Promises/A+ section 2.3.3.3.3
-									// https://promisesaplus.com/#point-59
-									// Ignore double-resolution attempts
-									if ( depth < maxDepth ) {
-										return;
-									}
-
-									returned = handler.apply( that, args );
-
-									// Support: Promises/A+ section 2.3.1
-									// https://promisesaplus.com/#point-48
-									if ( returned === deferred.promise() ) {
-										throw new TypeError( "Thenable self-resolution" );
-									}
-
-									// Support: Promises/A+ sections 2.3.3.1, 3.5
-									// https://promisesaplus.com/#point-54
-									// https://promisesaplus.com/#point-75
-									// Retrieve `then` only once
-									then = returned &&
-
-										// Support: Promises/A+ section 2.3.4
-										// https://promisesaplus.com/#point-64
-										// Only check objects and functions for thenability
-										( typeof returned === "object" ||
-											typeof returned === "function" ) &&
-										returned.then;
-
-									// Handle a returned thenable
-									if ( isFunction( then ) ) {
-
-										// Special processors (notify) just wait for resolution
-										if ( special ) {
-											then.call(
-												returned,
-												resolve( maxDepth, deferred, Identity, special ),
-												resolve( maxDepth, deferred, Thrower, special )
-											);
-
-										// Normal processors (resolve) also hook into progress
-										} else {
-
-											// ...and disregard older resolution values
-											maxDepth++;
-
-											then.call(
-												returned,
-												resolve( maxDepth, deferred, Identity, special ),
-												resolve( maxDepth, deferred, Thrower, special ),
-												resolve( maxDepth, deferred, Identity,
-													deferred.notifyWith )
-											);
-										}
-
-									// Handle all other returned values
-									} else {
-
-										// Only substitute handlers pass on context
-										// and multiple values (non-spec behavior)
-										if ( handler !== Identity ) {
-											that = undefined;
-											args = [ returned ];
-										}
-
-										// Process the value(s)
-										// Default process is resolve
-										( special || deferred.resolveWith )( that, args );
-									}
-								},
-
-								// Only normal processors (resolve) catch and reject exceptions
-								process = special ?
-									mightThrow :
-									function() {
-										try {
-											mightThrow();
-										} catch ( e ) {
-
-											if ( jQuery.Deferred.exceptionHook ) {
-												jQuery.Deferred.exceptionHook( e,
-													process.stackTrace );
-											}
-
-											// Support: Promises/A+ section 2.3.3.3.4.1
-											// https://promisesaplus.com/#point-61
-											// Ignore post-resolution exceptions
-											if ( depth + 1 >= maxDepth ) {
-
-												// Only substitute handlers pass on context
-												// and multiple values (non-spec behavior)
-												if ( handler !== Thrower ) {
-													that = undefined;
-													args = [ e ];
-												}
-
-												deferred.rejectWith( that, args );
-											}
-										}
-									};
-
-							// Support: Promises/A+ section 2.3.3.3.1
-							// https://promisesaplus.com/#point-57
-							// Re-resolve promises immediately to dodge false rejection from
-							// subsequent errors
-							if ( depth ) {
-								process();
-							} else {
-
-								// Call an optional hook to record the stack, in case of exception
-								// since it's otherwise lost when execution goes async
-								if ( jQuery.Deferred.getStackHook ) {
-									process.stackTrace = jQuery.Deferred.getStackHook();
-								}
-								window.setTimeout( process );
-							}
-						};
-					}
-
-					return jQuery.Deferred( function( newDefer ) {
-
-						// progress_handlers.add( ... )
-						tuples[ 0 ][ 3 ].add(
-							resolve(
-								0,
-								newDefer,
-								isFunction( onProgress ) ?
-									onProgress :
-									Identity,
-								newDefer.notifyWith
-							)
-						);
-
-						// fulfilled_handlers.add( ... )
-						tuples[ 1 ][ 3 ].add(
-							resolve(
-								0,
-								newDefer,
-								isFunction( onFulfilled ) ?
-									onFulfilled :
-									Identity
-							)
-						);
-
-						// rejected_handlers.add( ... )
-						tuples[ 2 ][ 3 ].add(
-							resolve(
-								0,
-								newDefer,
-								isFunction( onRejected ) ?
-									onRejected :
-									Thrower
-							)
-						);
-					} ).promise();
-				},
-
-				// Get a promise for this deferred
-				// If obj is provided, the promise aspect is added to the object
-				promise: function( obj ) {
-					return obj != null ? jQuery.extend( obj, promise ) : promise;
-				}
-			},
-			deferred = {};
-
-		// Add list-specific methods
-		jQuery.each( tuples, function( i, tuple ) {
-			var list = tuple[ 2 ],
-				stateString = tuple[ 5 ];
-
-			// promise.progress = list.add
-			// promise.done = list.add
-			// promise.fail = list.add
-			promise[ tuple[ 1 ] ] = list.add;
-
-			// Handle state
-			if ( stateString ) {
-				list.add(
-					function() {
-
-						// state = "resolved" (i.e., fulfilled)
-						// state = "rejected"
-						state = stateString;
-					},
-
-					// rejected_callbacks.disable
-					// fulfilled_callbacks.disable
-					tuples[ 3 - i ][ 2 ].disable,
-
-					// rejected_handlers.disable
-					// fulfilled_handlers.disable
-					tuples[ 3 - i ][ 3 ].disable,
-
-					// progress_callbacks.lock
-					tuples[ 0 ][ 2 ].lock,
-
-					// progress_handlers.lock
-					tuples[ 0 ][ 3 ].lock
-				);
-			}
-
-			// progress_handlers.fire
-			// fulfilled_handlers.fire
-			// rejected_handlers.fire
-			list.add( tuple[ 3 ].fire );
-
-			// deferred.notify = function() { deferred.notifyWith(...) }
-			// deferred.resolve = function() { deferred.resolveWith(...) }
-			// deferred.reject = function() { deferred.rejectWith(...) }
-			deferred[ tuple[ 0 ] ] = function() {
-				deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments );
-				return this;
-			};
-
-			// deferred.notifyWith = list.fireWith
-			// deferred.resolveWith = list.fireWith
-			// deferred.rejectWith = list.fireWith
-			deferred[ tuple[ 0 ] + "With" ] = list.fireWith;
-		} );
-
-		// Make the deferred a promise
-		promise.promise( deferred );
-
-		// Call given func if any
-		if ( func ) {
-			func.call( deferred, deferred );
-		}
-
-		// All done!
-		return deferred;
-	},
-
-	// Deferred helper
-	when: function( singleValue ) {
-		var
-
-			// count of uncompleted subordinates
-			remaining = arguments.length,
-
-			// count of unprocessed arguments
-			i = remaining,
-
-			// subordinate fulfillment data
-			resolveContexts = Array( i ),
-			resolveValues = slice.call( arguments ),
-
-			// the master Deferred
-			master = jQuery.Deferred(),
-
-			// subordinate callback factory
-			updateFunc = function( i ) {
-				return function( value ) {
-					resolveContexts[ i ] = this;
-					resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value;
-					if ( !( --remaining ) ) {
-						master.resolveWith( resolveContexts, resolveValues );
-					}
-				};
-			};
-
-		// Single- and empty arguments are adopted like Promise.resolve
-		if ( remaining <= 1 ) {
-			adoptValue( singleValue, master.done( updateFunc( i ) ).resolve, master.reject,
-				!remaining );
-
-			// Use .then() to unwrap secondary thenables (cf. gh-3000)
-			if ( master.state() === "pending" ||
-				isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) {
-
-				return master.then();
-			}
-		}
-
-		// Multiple arguments are aggregated like Promise.all array elements
-		while ( i-- ) {
-			adoptValue( resolveValues[ i ], updateFunc( i ), master.reject );
-		}
-
-		return master.promise();
-	}
-} );
-
-
-// These usually indicate a programmer mistake during development,
-// warn about them ASAP rather than swallowing them by default.
-var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/;
-
-jQuery.Deferred.exceptionHook = function( error, stack ) {
-
-	// Support: IE 8 - 9 only
-	// Console exists when dev tools are open, which can happen at any time
-	if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) {
-		window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack );
-	}
-};
-
-
-
-
-jQuery.readyException = function( error ) {
-	window.setTimeout( function() {
-		throw error;
-	} );
-};
-
-
-
-
-// The deferred used on DOM ready
-var readyList = jQuery.Deferred();
-
-jQuery.fn.ready = function( fn ) {
-
-	readyList
-		.then( fn )
-
-		// Wrap jQuery.readyException in a function so that the lookup
-		// happens at the time of error handling instead of callback
-		// registration.
-		.catch( function( error ) {
-			jQuery.readyException( error );
-		} );
-
-	return this;
-};
-
-jQuery.extend( {
-
-	// Is the DOM ready to be used? Set to true once it occurs.
-	isReady: false,
-
-	// A counter to track how many items to wait for before
-	// the ready event fires. See #6781
-	readyWait: 1,
-
-	// Handle when the DOM is ready
-	ready: function( wait ) {
-
-		// Abort if there are pending holds or we're already ready
-		if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) {
-			return;
-		}
-
-		// Remember that the DOM is ready
-		jQuery.isReady = true;
-
-		// If a normal DOM Ready event fired, decrement, and wait if need be
-		if ( wait !== true && --jQuery.readyWait > 0 ) {
-			return;
-		}
-
-		// If there are functions bound, to execute
-		readyList.resolveWith( document, [ jQuery ] );
-	}
-} );
-
-jQuery.ready.then = readyList.then;
-
-// The ready event handler and self cleanup method
-function completed() {
-	document.removeEventListener( "DOMContentLoaded", completed );
-	window.removeEventListener( "load", completed );
-	jQuery.ready();
-}
-
-// Catch cases where $(document).ready() is called
-// after the browser event has already occurred.
-// Support: IE <=9 - 10 only
-// Older IE sometimes signals "interactive" too soon
-if ( document.readyState === "complete" ||
-	( document.readyState !== "loading" && !document.documentElement.doScroll ) ) {
-
-	// Handle it asynchronously to allow scripts the opportunity to delay ready
-	window.setTimeout( jQuery.ready );
-
-} else {
-
-	// Use the handy event callback
-	document.addEventListener( "DOMContentLoaded", completed );
-
-	// A fallback to window.onload, that will always work
-	window.addEventListener( "load", completed );
-}
-
-
-
-
-// Multifunctional method to get and set values of a collection
-// The value/s can optionally be executed if it's a function
-var access = function( elems, fn, key, value, chainable, emptyGet, raw ) {
-	var i = 0,
-		len = elems.length,
-		bulk = key == null;
-
-	// Sets many values
-	if ( toType( key ) === "object" ) {
-		chainable = true;
-		for ( i in key ) {
-			access( elems, fn, i, key[ i ], true, emptyGet, raw );
-		}
-
-	// Sets one value
-	} else if ( value !== undefined ) {
-		chainable = true;
-
-		if ( !isFunction( value ) ) {
-			raw = true;
-		}
-
-		if ( bulk ) {
-
-			// Bulk operations run against the entire set
-			if ( raw ) {
-				fn.call( elems, value );
-				fn = null;
-
-			// ...except when executing function values
-			} else {
-				bulk = fn;
-				fn = function( elem, _key, value ) {
-					return bulk.call( jQuery( elem ), value );
-				};
-			}
-		}
-
-		if ( fn ) {
-			for ( ; i < len; i++ ) {
-				fn(
-					elems[ i ], key, raw ?
-					value :
-					value.call( elems[ i ], i, fn( elems[ i ], key ) )
-				);
-			}
-		}
-	}
-
-	if ( chainable ) {
-		return elems;
-	}
-
-	// Gets
-	if ( bulk ) {
-		return fn.call( elems );
-	}
-
-	return len ? fn( elems[ 0 ], key ) : emptyGet;
-};
-
-
-// Matches dashed string for camelizing
-var rmsPrefix = /^-ms-/,
-	rdashAlpha = /-([a-z])/g;
-
-// Used by camelCase as callback to replace()
-function fcamelCase( _all, letter ) {
-	return letter.toUpperCase();
-}
-
-// Convert dashed to camelCase; used by the css and data modules
-// Support: IE <=9 - 11, Edge 12 - 15
-// Microsoft forgot to hump their vendor prefix (#9572)
-function camelCase( string ) {
-	return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase );
-}
-var acceptData = function( owner ) {
-
-	// Accepts only:
-	//  - Node
-	//    - Node.ELEMENT_NODE
-	//    - Node.DOCUMENT_NODE
-	//  - Object
-	//    - Any
-	return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType );
-};
-
-
-
-
-function Data() {
-	this.expando = jQuery.expando + Data.uid++;
-}
-
-Data.uid = 1;
-
-Data.prototype = {
-
-	cache: function( owner ) {
-
-		// Check if the owner object already has a cache
-		var value = owner[ this.expando ];
-
-		// If not, create one
-		if ( !value ) {
-			value = {};
-
-			// We can accept data for non-element nodes in modern browsers,
-			// but we should not, see #8335.
-			// Always return an empty object.
-			if ( acceptData( owner ) ) {
-
-				// If it is a node unlikely to be stringify-ed or looped over
-				// use plain assignment
-				if ( owner.nodeType ) {
-					owner[ this.expando ] = value;
-
-				// Otherwise secure it in a non-enumerable property
-				// configurable must be true to allow the property to be
-				// deleted when data is removed
-				} else {
-					Object.defineProperty( owner, this.expando, {
-						value: value,
-						configurable: true
-					} );
-				}
-			}
-		}
-
-		return value;
-	},
-	set: function( owner, data, value ) {
-		var prop,
-			cache = this.cache( owner );
-
-		// Handle: [ owner, key, value ] args
-		// Always use camelCase key (gh-2257)
-		if ( typeof data === "string" ) {
-			cache[ camelCase( data ) ] = value;
-
-		// Handle: [ owner, { properties } ] args
-		} else {
-
-			// Copy the properties one-by-one to the cache object
-			for ( prop in data ) {
-				cache[ camelCase( prop ) ] = data[ prop ];
-			}
-		}
-		return cache;
-	},
-	get: function( owner, key ) {
-		return key === undefined ?
-			this.cache( owner ) :
-
-			// Always use camelCase key (gh-2257)
-			owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ];
-	},
-	access: function( owner, key, value ) {
-
-		// In cases where either:
-		//
-		//   1. No key was specified
-		//   2. A string key was specified, but no value provided
-		//
-		// Take the "read" path and allow the get method to determine
-		// which value to return, respectively either:
-		//
-		//   1. The entire cache object
-		//   2. The data stored at the key
-		//
-		if ( key === undefined ||
-				( ( key && typeof key === "string" ) && value === undefined ) ) {
-
-			return this.get( owner, key );
-		}
-
-		// When the key is not a string, or both a key and value
-		// are specified, set or extend (existing objects) with either:
-		//
-		//   1. An object of properties
-		//   2. A key and value
-		//
-		this.set( owner, key, value );
-
-		// Since the "set" path can have two possible entry points
-		// return the expected data based on which path was taken[*]
-		return value !== undefined ? value : key;
-	},
-	remove: function( owner, key ) {
-		var i,
-			cache = owner[ this.expando ];
-
-		if ( cache === undefined ) {
-			return;
-		}
-
-		if ( key !== undefined ) {
-
-			// Support array or space separated string of keys
-			if ( Array.isArray( key ) ) {
-
-				// If key is an array of keys...
-				// We always set camelCase keys, so remove that.
-				key = key.map( camelCase );
-			} else {
-				key = camelCase( key );
-
-				// If a key with the spaces exists, use it.
-				// Otherwise, create an array by matching non-whitespace
-				key = key in cache ?
-					[ key ] :
-					( key.match( rnothtmlwhite ) || [] );
-			}
-
-			i = key.length;
-
-			while ( i-- ) {
-				delete cache[ key[ i ] ];
-			}
-		}
-
-		// Remove the expando if there's no more data
-		if ( key === undefined || jQuery.isEmptyObject( cache ) ) {
-
-			// Support: Chrome <=35 - 45
-			// Webkit & Blink performance suffers when deleting properties
-			// from DOM nodes, so set to undefined instead
-			// https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted)
-			if ( owner.nodeType ) {
-				owner[ this.expando ] = undefined;
-			} else {
-				delete owner[ this.expando ];
-			}
-		}
-	},
-	hasData: function( owner ) {
-		var cache = owner[ this.expando ];
-		return cache !== undefined && !jQuery.isEmptyObject( cache );
-	}
-};
-var dataPriv = new Data();
-
-var dataUser = new Data();
-
-
-
-//	Implementation Summary
-//
-//	1. Enforce API surface and semantic compatibility with 1.9.x branch
-//	2. Improve the module's maintainability by reducing the storage
-//		paths to a single mechanism.
-//	3. Use the same single mechanism to support "private" and "user" data.
-//	4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData)
-//	5. Avoid exposing implementation details on user objects (eg. expando properties)
-//	6. Provide a clear path for implementation upgrade to WeakMap in 2014
-
-var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/,
-	rmultiDash = /[A-Z]/g;
-
-function getData( data ) {
-	if ( data === "true" ) {
-		return true;
-	}
-
-	if ( data === "false" ) {
-		return false;
-	}
-
-	if ( data === "null" ) {
-		return null;
-	}
-
-	// Only convert to a number if it doesn't change the string
-	if ( data === +data + "" ) {
-		return +data;
-	}
-
-	if ( rbrace.test( data ) ) {
-		return JSON.parse( data );
-	}
-
-	return data;
-}
-
-function dataAttr( elem, key, data ) {
-	var name;
-
-	// If nothing was found internally, try to fetch any
-	// data from the HTML5 data-* attribute
-	if ( data === undefined && elem.nodeType === 1 ) {
-		name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase();
-		data = elem.getAttribute( name );
-
-		if ( typeof data === "string" ) {
-			try {
-				data = getData( data );
-			} catch ( e ) {}
-
-			// Make sure we set the data so it isn't changed later
-			dataUser.set( elem, key, data );
-		} else {
-			data = undefined;
-		}
-	}
-	return data;
-}
-
-jQuery.extend( {
-	hasData: function( elem ) {
-		return dataUser.hasData( elem ) || dataPriv.hasData( elem );
-	},
-
-	data: function( elem, name, data ) {
-		return dataUser.access( elem, name, data );
-	},
-
-	removeData: function( elem, name ) {
-		dataUser.remove( elem, name );
-	},
-
-	// TODO: Now that all calls to _data and _removeData have been replaced
-	// with direct calls to dataPriv methods, these can be deprecated.
-	_data: function( elem, name, data ) {
-		return dataPriv.access( elem, name, data );
-	},
-
-	_removeData: function( elem, name ) {
-		dataPriv.remove( elem, name );
-	}
-} );
-
-jQuery.fn.extend( {
-	data: function( key, value ) {
-		var i, name, data,
-			elem = this[ 0 ],
-			attrs = elem && elem.attributes;
-
-		// Gets all values
-		if ( key === undefined ) {
-			if ( this.length ) {
-				data = dataUser.get( elem );
-
-				if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) {
-					i = attrs.length;
-					while ( i-- ) {
-
-						// Support: IE 11 only
-						// The attrs elements can be null (#14894)
-						if ( attrs[ i ] ) {
-							name = attrs[ i ].name;
-							if ( name.indexOf( "data-" ) === 0 ) {
-								name = camelCase( name.slice( 5 ) );
-								dataAttr( elem, name, data[ name ] );
-							}
-						}
-					}
-					dataPriv.set( elem, "hasDataAttrs", true );
-				}
-			}
-
-			return data;
-		}
-
-		// Sets multiple values
-		if ( typeof key === "object" ) {
-			return this.each( function() {
-				dataUser.set( this, key );
-			} );
-		}
-
-		return access( this, function( value ) {
-			var data;
-
-			// The calling jQuery object (element matches) is not empty
-			// (and therefore has an element appears at this[ 0 ]) and the
-			// `value` parameter was not undefined. An empty jQuery object
-			// will result in `undefined` for elem = this[ 0 ] which will
-			// throw an exception if an attempt to read a data cache is made.
-			if ( elem && value === undefined ) {
-
-				// Attempt to get data from the cache
-				// The key will always be camelCased in Data
-				data = dataUser.get( elem, key );
-				if ( data !== undefined ) {
-					return data;
-				}
-
-				// Attempt to "discover" the data in
-				// HTML5 custom data-* attrs
-				data = dataAttr( elem, key );
-				if ( data !== undefined ) {
-					return data;
-				}
-
-				// We tried really hard, but the data doesn't exist.
-				return;
-			}
-
-			// Set the data...
-			this.each( function() {
-
-				// We always store the camelCased key
-				dataUser.set( this, key, value );
-			} );
-		}, null, value, arguments.length > 1, null, true );
-	},
-
-	removeData: function( key ) {
-		return this.each( function() {
-			dataUser.remove( this, key );
-		} );
-	}
-} );
-
-
-jQuery.extend( {
-	queue: function( elem, type, data ) {
-		var queue;
-
-		if ( elem ) {
-			type = ( type || "fx" ) + "queue";
-			queue = dataPriv.get( elem, type );
-
-			// Speed up dequeue by getting out quickly if this is just a lookup
-			if ( data ) {
-				if ( !queue || Array.isArray( data ) ) {
-					queue = dataPriv.access( elem, type, jQuery.makeArray( data ) );
-				} else {
-					queue.push( data );
-				}
-			}
-			return queue || [];
-		}
-	},
-
-	dequeue: function( elem, type ) {
-		type = type || "fx";
-
-		var queue = jQuery.queue( elem, type ),
-			startLength = queue.length,
-			fn = queue.shift(),
-			hooks = jQuery._queueHooks( elem, type ),
-			next = function() {
-				jQuery.dequeue( elem, type );
-			};
-
-		// If the fx queue is dequeued, always remove the progress sentinel
-		if ( fn === "inprogress" ) {
-			fn = queue.shift();
-			startLength--;
-		}
-
-		if ( fn ) {
-
-			// Add a progress sentinel to prevent the fx queue from being
-			// automatically dequeued
-			if ( type === "fx" ) {
-				queue.unshift( "inprogress" );
-			}
-
-			// Clear up the last queue stop function
-			delete hooks.stop;
-			fn.call( elem, next, hooks );
-		}
-
-		if ( !startLength && hooks ) {
-			hooks.empty.fire();
-		}
-	},
-
-	// Not public - generate a queueHooks object, or return the current one
-	_queueHooks: function( elem, type ) {
-		var key = type + "queueHooks";
-		return dataPriv.get( elem, key ) || dataPriv.access( elem, key, {
-			empty: jQuery.Callbacks( "once memory" ).add( function() {
-				dataPriv.remove( elem, [ type + "queue", key ] );
-			} )
-		} );
-	}
-} );
-
-jQuery.fn.extend( {
-	queue: function( type, data ) {
-		var setter = 2;
-
-		if ( typeof type !== "string" ) {
-			data = type;
-			type = "fx";
-			setter--;
-		}
-
-		if ( arguments.length < setter ) {
-			return jQuery.queue( this[ 0 ], type );
-		}
-
-		return data === undefined ?
-			this :
-			this.each( function() {
-				var queue = jQuery.queue( this, type, data );
-
-				// Ensure a hooks for this queue
-				jQuery._queueHooks( this, type );
-
-				if ( type === "fx" && queue[ 0 ] !== "inprogress" ) {
-					jQuery.dequeue( this, type );
-				}
-			} );
-	},
-	dequeue: function( type ) {
-		return this.each( function() {
-			jQuery.dequeue( this, type );
-		} );
-	},
-	clearQueue: function( type ) {
-		return this.queue( type || "fx", [] );
-	},
-
-	// Get a promise resolved when queues of a certain type
-	// are emptied (fx is the type by default)
-	promise: function( type, obj ) {
-		var tmp,
-			count = 1,
-			defer = jQuery.Deferred(),
-			elements = this,
-			i = this.length,
-			resolve = function() {
-				if ( !( --count ) ) {
-					defer.resolveWith( elements, [ elements ] );
-				}
-			};
-
-		if ( typeof type !== "string" ) {
-			obj = type;
-			type = undefined;
-		}
-		type = type || "fx";
-
-		while ( i-- ) {
-			tmp = dataPriv.get( elements[ i ], type + "queueHooks" );
-			if ( tmp && tmp.empty ) {
-				count++;
-				tmp.empty.add( resolve );
-			}
-		}
-		resolve();
-		return defer.promise( obj );
-	}
-} );
-var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source;
-
-var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" );
-
-
-var cssExpand = [ "Top", "Right", "Bottom", "Left" ];
-
-var documentElement = document.documentElement;
-
-
-
-	var isAttached = function( elem ) {
-			return jQuery.contains( elem.ownerDocument, elem );
-		},
-		composed = { composed: true };
-
-	// Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only
-	// Check attachment across shadow DOM boundaries when possible (gh-3504)
-	// Support: iOS 10.0-10.2 only
-	// Early iOS 10 versions support `attachShadow` but not `getRootNode`,
-	// leading to errors. We need to check for `getRootNode`.
-	if ( documentElement.getRootNode ) {
-		isAttached = function( elem ) {
-			return jQuery.contains( elem.ownerDocument, elem ) ||
-				elem.getRootNode( composed ) === elem.ownerDocument;
-		};
-	}
-var isHiddenWithinTree = function( elem, el ) {
-
-		// isHiddenWithinTree might be called from jQuery#filter function;
-		// in that case, element will be second argument
-		elem = el || elem;
-
-		// Inline style trumps all
-		return elem.style.display === "none" ||
-			elem.style.display === "" &&
-
-			// Otherwise, check computed style
-			// Support: Firefox <=43 - 45
-			// Disconnected elements can have computed display: none, so first confirm that elem is
-			// in the document.
-			isAttached( elem ) &&
-
-			jQuery.css( elem, "display" ) === "none";
-	};
-
-
-
-function adjustCSS( elem, prop, valueParts, tween ) {
-	var adjusted, scale,
-		maxIterations = 20,
-		currentValue = tween ?
-			function() {
-				return tween.cur();
-			} :
-			function() {
-				return jQuery.css( elem, prop, "" );
-			},
-		initial = currentValue(),
-		unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ),
-
-		// Starting value computation is required for potential unit mismatches
-		initialInUnit = elem.nodeType &&
-			( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) &&
-			rcssNum.exec( jQuery.css( elem, prop ) );
-
-	if ( initialInUnit && initialInUnit[ 3 ] !== unit ) {
-
-		// Support: Firefox <=54
-		// Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144)
-		initial = initial / 2;
-
-		// Trust units reported by jQuery.css
-		unit = unit || initialInUnit[ 3 ];
-
-		// Iteratively approximate from a nonzero starting point
-		initialInUnit = +initial || 1;
-
-		while ( maxIterations-- ) {
-
-			// Evaluate and update our best guess (doubling guesses that zero out).
-			// Finish if the scale equals or crosses 1 (making the old*new product non-positive).
-			jQuery.style( elem, prop, initialInUnit + unit );
-			if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) {
-				maxIterations = 0;
-			}
-			initialInUnit = initialInUnit / scale;
-
-		}
-
-		initialInUnit = initialInUnit * 2;
-		jQuery.style( elem, prop, initialInUnit + unit );
-
-		// Make sure we update the tween properties later on
-		valueParts = valueParts || [];
-	}
-
-	if ( valueParts ) {
-		initialInUnit = +initialInUnit || +initial || 0;
-
-		// Apply relative offset (+=/-=) if specified
-		adjusted = valueParts[ 1 ] ?
-			initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] :
-			+valueParts[ 2 ];
-		if ( tween ) {
-			tween.unit = unit;
-			tween.start = initialInUnit;
-			tween.end = adjusted;
-		}
-	}
-	return adjusted;
-}
-
-
-var defaultDisplayMap = {};
-
-function getDefaultDisplay( elem ) {
-	var temp,
-		doc = elem.ownerDocument,
-		nodeName = elem.nodeName,
-		display = defaultDisplayMap[ nodeName ];
-
-	if ( display ) {
-		return display;
-	}
-
-	temp = doc.body.appendChild( doc.createElement( nodeName ) );
-	display = jQuery.css( temp, "display" );
-
-	temp.parentNode.removeChild( temp );
-
-	if ( display === "none" ) {
-		display = "block";
-	}
-	defaultDisplayMap[ nodeName ] = display;
-
-	return display;
-}
-
-function showHide( elements, show ) {
-	var display, elem,
-		values = [],
-		index = 0,
-		length = elements.length;
-
-	// Determine new display value for elements that need to change
-	for ( ; index < length; index++ ) {
-		elem = elements[ index ];
-		if ( !elem.style ) {
-			continue;
-		}
-
-		display = elem.style.display;
-		if ( show ) {
-
-			// Since we force visibility upon cascade-hidden elements, an immediate (and slow)
-			// check is required in this first loop unless we have a nonempty display value (either
-			// inline or about-to-be-restored)
-			if ( display === "none" ) {
-				values[ index ] = dataPriv.get( elem, "display" ) || null;
-				if ( !values[ index ] ) {
-					elem.style.display = "";
-				}
-			}
-			if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) {
-				values[ index ] = getDefaultDisplay( elem );
-			}
-		} else {
-			if ( display !== "none" ) {
-				values[ index ] = "none";
-
-				// Remember what we're overwriting
-				dataPriv.set( elem, "display", display );
-			}
-		}
-	}
-
-	// Set the display of the elements in a second loop to avoid constant reflow
-	for ( index = 0; index < length; index++ ) {
-		if ( values[ index ] != null ) {
-			elements[ index ].style.display = values[ index ];
-		}
-	}
-
-	return elements;
-}
-
-jQuery.fn.extend( {
-	show: function() {
-		return showHide( this, true );
-	},
-	hide: function() {
-		return showHide( this );
-	},
-	toggle: function( state ) {
-		if ( typeof state === "boolean" ) {
-			return state ? this.show() : this.hide();
-		}
-
-		return this.each( function() {
-			if ( isHiddenWithinTree( this ) ) {
-				jQuery( this ).show();
-			} else {
-				jQuery( this ).hide();
-			}
-		} );
-	}
-} );
-var rcheckableType = ( /^(?:checkbox|radio)$/i );
-
-var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i );
-
-var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i );
-
-
-
-( function() {
-	var fragment = document.createDocumentFragment(),
-		div = fragment.appendChild( document.createElement( "div" ) ),
-		input = document.createElement( "input" );
-
-	// Support: Android 4.0 - 4.3 only
-	// Check state lost if the name is set (#11217)
-	// Support: Windows Web Apps (WWA)
-	// `name` and `type` must use .setAttribute for WWA (#14901)
-	input.setAttribute( "type", "radio" );
-	input.setAttribute( "checked", "checked" );
-	input.setAttribute( "name", "t" );
-
-	div.appendChild( input );
-
-	// Support: Android <=4.1 only
-	// Older WebKit doesn't clone checked state correctly in fragments
-	support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked;
-
-	// Support: IE <=11 only
-	// Make sure textarea (and checkbox) defaultValue is properly cloned
-	div.innerHTML = "<textarea>x</textarea>";
-	support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue;
-
-	// Support: IE <=9 only
-	// IE <=9 replaces <option> tags with their contents when inserted outside of
-	// the select element.
-	div.innerHTML = "<option></option>";
-	support.option = !!div.lastChild;
-} )();
-
-
-// We have to close these tags to support XHTML (#13200)
-var wrapMap = {
-
-	// XHTML parsers do not magically insert elements in the
-	// same way that tag soup parsers do. So we cannot shorten
-	// this by omitting <tbody> or other required elements.
-	thead: [ 1, "<table>", "</table>" ],
-	col: [ 2, "<table><colgroup>", "</colgroup></table>" ],
-	tr: [ 2, "<table><tbody>", "</tbody></table>" ],
-	td: [ 3, "<table><tbody><tr>", "</tr></tbody></table>" ],
-
-	_default: [ 0, "", "" ]
-};
-
-wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead;
-wrapMap.th = wrapMap.td;
-
-// Support: IE <=9 only
-if ( !support.option ) {
-	wrapMap.optgroup = wrapMap.option = [ 1, "<select multiple='multiple'>", "</select>" ];
-}
-
-
-function getAll( context, tag ) {
-
-	// Support: IE <=9 - 11 only
-	// Use typeof to avoid zero-argument method invocation on host objects (#15151)
-	var ret;
-
-	if ( typeof context.getElementsByTagName !== "undefined" ) {
-		ret = context.getElementsByTagName( tag || "*" );
-
-	} else if ( typeof context.querySelectorAll !== "undefined" ) {
-		ret = context.querySelectorAll( tag || "*" );
-
-	} else {
-		ret = [];
-	}
-
-	if ( tag === undefined || tag && nodeName( context, tag ) ) {
-		return jQuery.merge( [ context ], ret );
-	}
-
-	return ret;
-}
-
-
-// Mark scripts as having already been evaluated
-function setGlobalEval( elems, refElements ) {
-	var i = 0,
-		l = elems.length;
-
-	for ( ; i < l; i++ ) {
-		dataPriv.set(
-			elems[ i ],
-			"globalEval",
-			!refElements || dataPriv.get( refElements[ i ], "globalEval" )
-		);
-	}
-}
-
-
-var rhtml = /<|&#?\w+;/;
-
-function buildFragment( elems, context, scripts, selection, ignored ) {
-	var elem, tmp, tag, wrap, attached, j,
-		fragment = context.createDocumentFragment(),
-		nodes = [],
-		i = 0,
-		l = elems.length;
-
-	for ( ; i < l; i++ ) {
-		elem = elems[ i ];
-
-		if ( elem || elem === 0 ) {
-
-			// Add nodes directly
-			if ( toType( elem ) === "object" ) {
-
-				// Support: Android <=4.0 only, PhantomJS 1 only
-				// push.apply(_, arraylike) throws on ancient WebKit
-				jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem );
-
-			// Convert non-html into a text node
-			} else if ( !rhtml.test( elem ) ) {
-				nodes.push( context.createTextNode( elem ) );
-
-			// Convert html into DOM nodes
-			} else {
-				tmp = tmp || fragment.appendChild( context.createElement( "div" ) );
-
-				// Deserialize a standard representation
-				tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase();
-				wrap = wrapMap[ tag ] || wrapMap._default;
-				tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ];
-
-				// Descend through wrappers to the right content
-				j = wrap[ 0 ];
-				while ( j-- ) {
-					tmp = tmp.lastChild;
-				}
-
-				// Support: Android <=4.0 only, PhantomJS 1 only
-				// push.apply(_, arraylike) throws on ancient WebKit
-				jQuery.merge( nodes, tmp.childNodes );
-
-				// Remember the top-level container
-				tmp = fragment.firstChild;
-
-				// Ensure the created nodes are orphaned (#12392)
-				tmp.textContent = "";
-			}
-		}
-	}
-
-	// Remove wrapper from fragment
-	fragment.textContent = "";
-
-	i = 0;
-	while ( ( elem = nodes[ i++ ] ) ) {
-
-		// Skip elements already in the context collection (trac-4087)
-		if ( selection && jQuery.inArray( elem, selection ) > -1 ) {
-			if ( ignored ) {
-				ignored.push( elem );
-			}
-			continue;
-		}
-
-		attached = isAttached( elem );
-
-		// Append to fragment
-		tmp = getAll( fragment.appendChild( elem ), "script" );
-
-		// Preserve script evaluation history
-		if ( attached ) {
-			setGlobalEval( tmp );
-		}
-
-		// Capture executables
-		if ( scripts ) {
-			j = 0;
-			while ( ( elem = tmp[ j++ ] ) ) {
-				if ( rscriptType.test( elem.type || "" ) ) {
-					scripts.push( elem );
-				}
-			}
-		}
-	}
-
-	return fragment;
-}
-
-
-var
-	rkeyEvent = /^key/,
-	rmouseEvent = /^(?:mouse|pointer|contextmenu|drag|drop)|click/,
-	rtypenamespace = /^([^.]*)(?:\.(.+)|)/;
-
-function returnTrue() {
-	return true;
-}
-
-function returnFalse() {
-	return false;
-}
-
-// Support: IE <=9 - 11+
-// focus() and blur() are asynchronous, except when they are no-op.
-// So expect focus to be synchronous when the element is already active,
-// and blur to be synchronous when the element is not already active.
-// (focus and blur are always synchronous in other supported browsers,
-// this just defines when we can count on it).
-function expectSync( elem, type ) {
-	return ( elem === safeActiveElement() ) === ( type === "focus" );
-}
-
-// Support: IE <=9 only
-// Accessing document.activeElement can throw unexpectedly
-// https://bugs.jquery.com/ticket/13393
-function safeActiveElement() {
-	try {
-		return document.activeElement;
-	} catch ( err ) { }
-}
-
-function on( elem, types, selector, data, fn, one ) {
-	var origFn, type;
-
-	// Types can be a map of types/handlers
-	if ( typeof types === "object" ) {
-
-		// ( types-Object, selector, data )
-		if ( typeof selector !== "string" ) {
-
-			// ( types-Object, data )
-			data = data || selector;
-			selector = undefined;
-		}
-		for ( type in types ) {
-			on( elem, type, selector, data, types[ type ], one );
-		}
-		return elem;
-	}
-
-	if ( data == null && fn == null ) {
-
-		// ( types, fn )
-		fn = selector;
-		data = selector = undefined;
-	} else if ( fn == null ) {
-		if ( typeof selector === "string" ) {
-
-			// ( types, selector, fn )
-			fn = data;
-			data = undefined;
-		} else {
-
-			// ( types, data, fn )
-			fn = data;
-			data = selector;
-			selector = undefined;
-		}
-	}
-	if ( fn === false ) {
-		fn = returnFalse;
-	} else if ( !fn ) {
-		return elem;
-	}
-
-	if ( one === 1 ) {
-		origFn = fn;
-		fn = function( event ) {
-
-			// Can use an empty set, since event contains the info
-			jQuery().off( event );
-			return origFn.apply( this, arguments );
-		};
-
-		// Use same guid so caller can remove using origFn
-		fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ );
-	}
-	return elem.each( function() {
-		jQuery.event.add( this, types, fn, data, selector );
-	} );
-}
-
-/*
- * Helper functions for managing events -- not part of the public interface.
- * Props to Dean Edwards' addEvent library for many of the ideas.
- */
-jQuery.event = {
-
-	global: {},
-
-	add: function( elem, types, handler, data, selector ) {
-
-		var handleObjIn, eventHandle, tmp,
-			events, t, handleObj,
-			special, handlers, type, namespaces, origType,
-			elemData = dataPriv.get( elem );
-
-		// Only attach events to objects that accept data
-		if ( !acceptData( elem ) ) {
-			return;
-		}
-
-		// Caller can pass in an object of custom data in lieu of the handler
-		if ( handler.handler ) {
-			handleObjIn = handler;
-			handler = handleObjIn.handler;
-			selector = handleObjIn.selector;
-		}
-
-		// Ensure that invalid selectors throw exceptions at attach time
-		// Evaluate against documentElement in case elem is a non-element node (e.g., document)
-		if ( selector ) {
-			jQuery.find.matchesSelector( documentElement, selector );
-		}
-
-		// Make sure that the handler has a unique ID, used to find/remove it later
-		if ( !handler.guid ) {
-			handler.guid = jQuery.guid++;
-		}
-
-		// Init the element's event structure and main handler, if this is the first
-		if ( !( events = elemData.events ) ) {
-			events = elemData.events = Object.create( null );
-		}
-		if ( !( eventHandle = elemData.handle ) ) {
-			eventHandle = elemData.handle = function( e ) {
-
-				// Discard the second event of a jQuery.event.trigger() and
-				// when an event is called after a page has unloaded
-				return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ?
-					jQuery.event.dispatch.apply( elem, arguments ) : undefined;
-			};
-		}
-
-		// Handle multiple events separated by a space
-		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
-		t = types.length;
-		while ( t-- ) {
-			tmp = rtypenamespace.exec( types[ t ] ) || [];
-			type = origType = tmp[ 1 ];
-			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
-
-			// There *must* be a type, no attaching namespace-only handlers
-			if ( !type ) {
-				continue;
-			}
-
-			// If event changes its type, use the special event handlers for the changed type
-			special = jQuery.event.special[ type ] || {};
-
-			// If selector defined, determine special event api type, otherwise given type
-			type = ( selector ? special.delegateType : special.bindType ) || type;
-
-			// Update special based on newly reset type
-			special = jQuery.event.special[ type ] || {};
-
-			// handleObj is passed to all event handlers
-			handleObj = jQuery.extend( {
-				type: type,
-				origType: origType,
-				data: data,
-				handler: handler,
-				guid: handler.guid,
-				selector: selector,
-				needsContext: selector && jQuery.expr.match.needsContext.test( selector ),
-				namespace: namespaces.join( "." )
-			}, handleObjIn );
-
-			// Init the event handler queue if we're the first
-			if ( !( handlers = events[ type ] ) ) {
-				handlers = events[ type ] = [];
-				handlers.delegateCount = 0;
-
-				// Only use addEventListener if the special events handler returns false
-				if ( !special.setup ||
-					special.setup.call( elem, data, namespaces, eventHandle ) === false ) {
-
-					if ( elem.addEventListener ) {
-						elem.addEventListener( type, eventHandle );
-					}
-				}
-			}
-
-			if ( special.add ) {
-				special.add.call( elem, handleObj );
-
-				if ( !handleObj.handler.guid ) {
-					handleObj.handler.guid = handler.guid;
-				}
-			}
-
-			// Add to the element's handler list, delegates in front
-			if ( selector ) {
-				handlers.splice( handlers.delegateCount++, 0, handleObj );
-			} else {
-				handlers.push( handleObj );
-			}
-
-			// Keep track of which events have ever been used, for event optimization
-			jQuery.event.global[ type ] = true;
-		}
-
-	},
-
-	// Detach an event or set of events from an element
-	remove: function( elem, types, handler, selector, mappedTypes ) {
-
-		var j, origCount, tmp,
-			events, t, handleObj,
-			special, handlers, type, namespaces, origType,
-			elemData = dataPriv.hasData( elem ) && dataPriv.get( elem );
-
-		if ( !elemData || !( events = elemData.events ) ) {
-			return;
-		}
-
-		// Once for each type.namespace in types; type may be omitted
-		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
-		t = types.length;
-		while ( t-- ) {
-			tmp = rtypenamespace.exec( types[ t ] ) || [];
-			type = origType = tmp[ 1 ];
-			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
-
-			// Unbind all events (on this namespace, if provided) for the element
-			if ( !type ) {
-				for ( type in events ) {
-					jQuery.event.remove( elem, type + types[ t ], handler, selector, true );
-				}
-				continue;
-			}
-
-			special = jQuery.event.special[ type ] || {};
-			type = ( selector ? special.delegateType : special.bindType ) || type;
-			handlers = events[ type ] || [];
-			tmp = tmp[ 2 ] &&
-				new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" );
-
-			// Remove matching events
-			origCount = j = handlers.length;
-			while ( j-- ) {
-				handleObj = handlers[ j ];
-
-				if ( ( mappedTypes || origType === handleObj.origType ) &&
-					( !handler || handler.guid === handleObj.guid ) &&
-					( !tmp || tmp.test( handleObj.namespace ) ) &&
-					( !selector || selector === handleObj.selector ||
-						selector === "**" && handleObj.selector ) ) {
-					handlers.splice( j, 1 );
-
-					if ( handleObj.selector ) {
-						handlers.delegateCount--;
-					}
-					if ( special.remove ) {
-						special.remove.call( elem, handleObj );
-					}
-				}
-			}
-
-			// Remove generic event handler if we removed something and no more handlers exist
-			// (avoids potential for endless recursion during removal of special event handlers)
-			if ( origCount && !handlers.length ) {
-				if ( !special.teardown ||
-					special.teardown.call( elem, namespaces, elemData.handle ) === false ) {
-
-					jQuery.removeEvent( elem, type, elemData.handle );
-				}
-
-				delete events[ type ];
-			}
-		}
-
-		// Remove data and the expando if it's no longer used
-		if ( jQuery.isEmptyObject( events ) ) {
-			dataPriv.remove( elem, "handle events" );
-		}
-	},
-
-	dispatch: function( nativeEvent ) {
-
-		var i, j, ret, matched, handleObj, handlerQueue,
-			args = new Array( arguments.length ),
-
-			// Make a writable jQuery.Event from the native event object
-			event = jQuery.event.fix( nativeEvent ),
-
-			handlers = (
-					dataPriv.get( this, "events" ) || Object.create( null )
-				)[ event.type ] || [],
-			special = jQuery.event.special[ event.type ] || {};
-
-		// Use the fix-ed jQuery.Event rather than the (read-only) native event
-		args[ 0 ] = event;
-
-		for ( i = 1; i < arguments.length; i++ ) {
-			args[ i ] = arguments[ i ];
-		}
-
-		event.delegateTarget = this;
-
-		// Call the preDispatch hook for the mapped type, and let it bail if desired
-		if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) {
-			return;
-		}
-
-		// Determine handlers
-		handlerQueue = jQuery.event.handlers.call( this, event, handlers );
-
-		// Run delegates first; they may want to stop propagation beneath us
-		i = 0;
-		while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) {
-			event.currentTarget = matched.elem;
-
-			j = 0;
-			while ( ( handleObj = matched.handlers[ j++ ] ) &&
-				!event.isImmediatePropagationStopped() ) {
-
-				// If the event is namespaced, then each handler is only invoked if it is
-				// specially universal or its namespaces are a superset of the event's.
-				if ( !event.rnamespace || handleObj.namespace === false ||
-					event.rnamespace.test( handleObj.namespace ) ) {
-
-					event.handleObj = handleObj;
-					event.data = handleObj.data;
-
-					ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle ||
-						handleObj.handler ).apply( matched.elem, args );
-
-					if ( ret !== undefined ) {
-						if ( ( event.result = ret ) === false ) {
-							event.preventDefault();
-							event.stopPropagation();
-						}
-					}
-				}
-			}
-		}
-
-		// Call the postDispatch hook for the mapped type
-		if ( special.postDispatch ) {
-			special.postDispatch.call( this, event );
-		}
-
-		return event.result;
-	},
-
-	handlers: function( event, handlers ) {
-		var i, handleObj, sel, matchedHandlers, matchedSelectors,
-			handlerQueue = [],
-			delegateCount = handlers.delegateCount,
-			cur = event.target;
-
-		// Find delegate handlers
-		if ( delegateCount &&
-
-			// Support: IE <=9
-			// Black-hole SVG <use> instance trees (trac-13180)
-			cur.nodeType &&
-
-			// Support: Firefox <=42
-			// Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861)
-			// https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click
-			// Support: IE 11 only
-			// ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343)
-			!( event.type === "click" && event.button >= 1 ) ) {
-
-			for ( ; cur !== this; cur = cur.parentNode || this ) {
-
-				// Don't check non-elements (#13208)
-				// Don't process clicks on disabled elements (#6911, #8165, #11382, #11764)
-				if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) {
-					matchedHandlers = [];
-					matchedSelectors = {};
-					for ( i = 0; i < delegateCount; i++ ) {
-						handleObj = handlers[ i ];
-
-						// Don't conflict with Object.prototype properties (#13203)
-						sel = handleObj.selector + " ";
-
-						if ( matchedSelectors[ sel ] === undefined ) {
-							matchedSelectors[ sel ] = handleObj.needsContext ?
-								jQuery( sel, this ).index( cur ) > -1 :
-								jQuery.find( sel, this, null, [ cur ] ).length;
-						}
-						if ( matchedSelectors[ sel ] ) {
-							matchedHandlers.push( handleObj );
-						}
-					}
-					if ( matchedHandlers.length ) {
-						handlerQueue.push( { elem: cur, handlers: matchedHandlers } );
-					}
-				}
-			}
-		}
-
-		// Add the remaining (directly-bound) handlers
-		cur = this;
-		if ( delegateCount < handlers.length ) {
-			handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } );
-		}
-
-		return handlerQueue;
-	},
-
-	addProp: function( name, hook ) {
-		Object.defineProperty( jQuery.Event.prototype, name, {
-			enumerable: true,
-			configurable: true,
-
-			get: isFunction( hook ) ?
-				function() {
-					if ( this.originalEvent ) {
-							return hook( this.originalEvent );
-					}
-				} :
-				function() {
-					if ( this.originalEvent ) {
-							return this.originalEvent[ name ];
-					}
-				},
-
-			set: function( value ) {
-				Object.defineProperty( this, name, {
-					enumerable: true,
-					configurable: true,
-					writable: true,
-					value: value
-				} );
-			}
-		} );
-	},
-
-	fix: function( originalEvent ) {
-		return originalEvent[ jQuery.expando ] ?
-			originalEvent :
-			new jQuery.Event( originalEvent );
-	},
-
-	special: {
-		load: {
-
-			// Prevent triggered image.load events from bubbling to window.load
-			noBubble: true
-		},
-		click: {
-
-			// Utilize native event to ensure correct state for checkable inputs
-			setup: function( data ) {
-
-				// For mutual compressibility with _default, replace `this` access with a local var.
-				// `|| data` is dead code meant only to preserve the variable through minification.
-				var el = this || data;
-
-				// Claim the first handler
-				if ( rcheckableType.test( el.type ) &&
-					el.click && nodeName( el, "input" ) ) {
-
-					// dataPriv.set( el, "click", ... )
-					leverageNative( el, "click", returnTrue );
-				}
-
-				// Return false to allow normal processing in the caller
-				return false;
-			},
-			trigger: function( data ) {
-
-				// For mutual compressibility with _default, replace `this` access with a local var.
-				// `|| data` is dead code meant only to preserve the variable through minification.
-				var el = this || data;
-
-				// Force setup before triggering a click
-				if ( rcheckableType.test( el.type ) &&
-					el.click && nodeName( el, "input" ) ) {
-
-					leverageNative( el, "click" );
-				}
-
-				// Return non-false to allow normal event-path propagation
-				return true;
-			},
-
-			// For cross-browser consistency, suppress native .click() on links
-			// Also prevent it if we're currently inside a leveraged native-event stack
-			_default: function( event ) {
-				var target = event.target;
-				return rcheckableType.test( target.type ) &&
-					target.click && nodeName( target, "input" ) &&
-					dataPriv.get( target, "click" ) ||
-					nodeName( target, "a" );
-			}
-		},
-
-		beforeunload: {
-			postDispatch: function( event ) {
-
-				// Support: Firefox 20+
-				// Firefox doesn't alert if the returnValue field is not set.
-				if ( event.result !== undefined && event.originalEvent ) {
-					event.originalEvent.returnValue = event.result;
-				}
-			}
-		}
-	}
-};
-
-// Ensure the presence of an event listener that handles manually-triggered
-// synthetic events by interrupting progress until reinvoked in response to
-// *native* events that it fires directly, ensuring that state changes have
-// already occurred before other listeners are invoked.
-function leverageNative( el, type, expectSync ) {
-
-	// Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add
-	if ( !expectSync ) {
-		if ( dataPriv.get( el, type ) === undefined ) {
-			jQuery.event.add( el, type, returnTrue );
-		}
-		return;
-	}
-
-	// Register the controller as a special universal handler for all event namespaces
-	dataPriv.set( el, type, false );
-	jQuery.event.add( el, type, {
-		namespace: false,
-		handler: function( event ) {
-			var notAsync, result,
-				saved = dataPriv.get( this, type );
-
-			if ( ( event.isTrigger & 1 ) && this[ type ] ) {
-
-				// Interrupt processing of the outer synthetic .trigger()ed event
-				// Saved data should be false in such cases, but might be a leftover capture object
-				// from an async native handler (gh-4350)
-				if ( !saved.length ) {
-
-					// Store arguments for use when handling the inner native event
-					// There will always be at least one argument (an event object), so this array
-					// will not be confused with a leftover capture object.
-					saved = slice.call( arguments );
-					dataPriv.set( this, type, saved );
-
-					// Trigger the native event and capture its result
-					// Support: IE <=9 - 11+
-					// focus() and blur() are asynchronous
-					notAsync = expectSync( this, type );
-					this[ type ]();
-					result = dataPriv.get( this, type );
-					if ( saved !== result || notAsync ) {
-						dataPriv.set( this, type, false );
-					} else {
-						result = {};
-					}
-					if ( saved !== result ) {
-
-						// Cancel the outer synthetic event
-						event.stopImmediatePropagation();
-						event.preventDefault();
-						return result.value;
-					}
-
-				// If this is an inner synthetic event for an event with a bubbling surrogate
-				// (focus or blur), assume that the surrogate already propagated from triggering the
-				// native event and prevent that from happening again here.
-				// This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the
-				// bubbling surrogate propagates *after* the non-bubbling base), but that seems
-				// less bad than duplication.
-				} else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) {
-					event.stopPropagation();
-				}
-
-			// If this is a native event triggered above, everything is now in order
-			// Fire an inner synthetic event with the original arguments
-			} else if ( saved.length ) {
-
-				// ...and capture the result
-				dataPriv.set( this, type, {
-					value: jQuery.event.trigger(
-
-						// Support: IE <=9 - 11+
-						// Extend with the prototype to reset the above stopImmediatePropagation()
-						jQuery.extend( saved[ 0 ], jQuery.Event.prototype ),
-						saved.slice( 1 ),
-						this
-					)
-				} );
-
-				// Abort handling of the native event
-				event.stopImmediatePropagation();
-			}
-		}
-	} );
-}
-
-jQuery.removeEvent = function( elem, type, handle ) {
-
-	// This "if" is needed for plain objects
-	if ( elem.removeEventListener ) {
-		elem.removeEventListener( type, handle );
-	}
-};
-
-jQuery.Event = function( src, props ) {
-
-	// Allow instantiation without the 'new' keyword
-	if ( !( this instanceof jQuery.Event ) ) {
-		return new jQuery.Event( src, props );
-	}
-
-	// Event object
-	if ( src && src.type ) {
-		this.originalEvent = src;
-		this.type = src.type;
-
-		// Events bubbling up the document may have been marked as prevented
-		// by a handler lower down the tree; reflect the correct value.
-		this.isDefaultPrevented = src.defaultPrevented ||
-				src.defaultPrevented === undefined &&
-
-				// Support: Android <=2.3 only
-				src.returnValue === false ?
-			returnTrue :
-			returnFalse;
-
-		// Create target properties
-		// Support: Safari <=6 - 7 only
-		// Target should not be a text node (#504, #13143)
-		this.target = ( src.target && src.target.nodeType === 3 ) ?
-			src.target.parentNode :
-			src.target;
-
-		this.currentTarget = src.currentTarget;
-		this.relatedTarget = src.relatedTarget;
-
-	// Event type
-	} else {
-		this.type = src;
-	}
-
-	// Put explicitly provided properties onto the event object
-	if ( props ) {
-		jQuery.extend( this, props );
-	}
-
-	// Create a timestamp if incoming event doesn't have one
-	this.timeStamp = src && src.timeStamp || Date.now();
-
-	// Mark it as fixed
-	this[ jQuery.expando ] = true;
-};
-
-// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding
-// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html
-jQuery.Event.prototype = {
-	constructor: jQuery.Event,
-	isDefaultPrevented: returnFalse,
-	isPropagationStopped: returnFalse,
-	isImmediatePropagationStopped: returnFalse,
-	isSimulated: false,
-
-	preventDefault: function() {
-		var e = this.originalEvent;
-
-		this.isDefaultPrevented = returnTrue;
-
-		if ( e && !this.isSimulated ) {
-			e.preventDefault();
-		}
-	},
-	stopPropagation: function() {
-		var e = this.originalEvent;
-
-		this.isPropagationStopped = returnTrue;
-
-		if ( e && !this.isSimulated ) {
-			e.stopPropagation();
-		}
-	},
-	stopImmediatePropagation: function() {
-		var e = this.originalEvent;
-
-		this.isImmediatePropagationStopped = returnTrue;
-
-		if ( e && !this.isSimulated ) {
-			e.stopImmediatePropagation();
-		}
-
-		this.stopPropagation();
-	}
-};
-
-// Includes all common event props including KeyEvent and MouseEvent specific props
-jQuery.each( {
-	altKey: true,
-	bubbles: true,
-	cancelable: true,
-	changedTouches: true,
-	ctrlKey: true,
-	detail: true,
-	eventPhase: true,
-	metaKey: true,
-	pageX: true,
-	pageY: true,
-	shiftKey: true,
-	view: true,
-	"char": true,
-	code: true,
-	charCode: true,
-	key: true,
-	keyCode: true,
-	button: true,
-	buttons: true,
-	clientX: true,
-	clientY: true,
-	offsetX: true,
-	offsetY: true,
-	pointerId: true,
-	pointerType: true,
-	screenX: true,
-	screenY: true,
-	targetTouches: true,
-	toElement: true,
-	touches: true,
-
-	which: function( event ) {
-		var button = event.button;
-
-		// Add which for key events
-		if ( event.which == null && rkeyEvent.test( event.type ) ) {
-			return event.charCode != null ? event.charCode : event.keyCode;
-		}
-
-		// Add which for click: 1 === left; 2 === middle; 3 === right
-		if ( !event.which && button !== undefined && rmouseEvent.test( event.type ) ) {
-			if ( button & 1 ) {
-				return 1;
-			}
-
-			if ( button & 2 ) {
-				return 3;
-			}
-
-			if ( button & 4 ) {
-				return 2;
-			}
-
-			return 0;
-		}
-
-		return event.which;
-	}
-}, jQuery.event.addProp );
-
-jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) {
-	jQuery.event.special[ type ] = {
-
-		// Utilize native event if possible so blur/focus sequence is correct
-		setup: function() {
-
-			// Claim the first handler
-			// dataPriv.set( this, "focus", ... )
-			// dataPriv.set( this, "blur", ... )
-			leverageNative( this, type, expectSync );
-
-			// Return false to allow normal processing in the caller
-			return false;
-		},
-		trigger: function() {
-
-			// Force setup before trigger
-			leverageNative( this, type );
-
-			// Return non-false to allow normal event-path propagation
-			return true;
-		},
-
-		delegateType: delegateType
-	};
-} );
-
-// Create mouseenter/leave events using mouseover/out and event-time checks
-// so that event delegation works in jQuery.
-// Do the same for pointerenter/pointerleave and pointerover/pointerout
-//
-// Support: Safari 7 only
-// Safari sends mouseenter too often; see:
-// https://bugs.chromium.org/p/chromium/issues/detail?id=470258
-// for the description of the bug (it existed in older Chrome versions as well).
-jQuery.each( {
-	mouseenter: "mouseover",
-	mouseleave: "mouseout",
-	pointerenter: "pointerover",
-	pointerleave: "pointerout"
-}, function( orig, fix ) {
-	jQuery.event.special[ orig ] = {
-		delegateType: fix,
-		bindType: fix,
-
-		handle: function( event ) {
-			var ret,
-				target = this,
-				related = event.relatedTarget,
-				handleObj = event.handleObj;
-
-			// For mouseenter/leave call the handler if related is outside the target.
-			// NB: No relatedTarget if the mouse left/entered the browser window
-			if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) {
-				event.type = handleObj.origType;
-				ret = handleObj.handler.apply( this, arguments );
-				event.type = fix;
-			}
-			return ret;
-		}
-	};
-} );
-
-jQuery.fn.extend( {
-
-	on: function( types, selector, data, fn ) {
-		return on( this, types, selector, data, fn );
-	},
-	one: function( types, selector, data, fn ) {
-		return on( this, types, selector, data, fn, 1 );
-	},
-	off: function( types, selector, fn ) {
-		var handleObj, type;
-		if ( types && types.preventDefault && types.handleObj ) {
-
-			// ( event )  dispatched jQuery.Event
-			handleObj = types.handleObj;
-			jQuery( types.delegateTarget ).off(
-				handleObj.namespace ?
-					handleObj.origType + "." + handleObj.namespace :
-					handleObj.origType,
-				handleObj.selector,
-				handleObj.handler
-			);
-			return this;
-		}
-		if ( typeof types === "object" ) {
-
-			// ( types-object [, selector] )
-			for ( type in types ) {
-				this.off( type, selector, types[ type ] );
-			}
-			return this;
-		}
-		if ( selector === false || typeof selector === "function" ) {
-
-			// ( types [, fn] )
-			fn = selector;
-			selector = undefined;
-		}
-		if ( fn === false ) {
-			fn = returnFalse;
-		}
-		return this.each( function() {
-			jQuery.event.remove( this, types, fn, selector );
-		} );
-	}
-} );
-
-
-var
-
-	// Support: IE <=10 - 11, Edge 12 - 13 only
-	// In IE/Edge using regex groups here causes severe slowdowns.
-	// See https://connect.microsoft.com/IE/feedback/details/1736512/
-	rnoInnerhtml = /<script|<style|<link/i,
-
-	// checked="checked" or checked
-	rchecked = /checked\s*(?:[^=]|=\s*.checked.)/i,
-	rcleanScript = /^\s*<!(?:\[CDATA\[|--)|(?:\]\]|--)>\s*$/g;
-
-// Prefer a tbody over its parent table for containing new rows
-function manipulationTarget( elem, content ) {
-	if ( nodeName( elem, "table" ) &&
-		nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) {
-
-		return jQuery( elem ).children( "tbody" )[ 0 ] || elem;
-	}
-
-	return elem;
-}
-
-// Replace/restore the type attribute of script elements for safe DOM manipulation
-function disableScript( elem ) {
-	elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type;
-	return elem;
-}
-function restoreScript( elem ) {
-	if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) {
-		elem.type = elem.type.slice( 5 );
-	} else {
-		elem.removeAttribute( "type" );
-	}
-
-	return elem;
-}
-
-function cloneCopyEvent( src, dest ) {
-	var i, l, type, pdataOld, udataOld, udataCur, events;
-
-	if ( dest.nodeType !== 1 ) {
-		return;
-	}
-
-	// 1. Copy private data: events, handlers, etc.
-	if ( dataPriv.hasData( src ) ) {
-		pdataOld = dataPriv.get( src );
-		events = pdataOld.events;
-
-		if ( events ) {
-			dataPriv.remove( dest, "handle events" );
-
-			for ( type in events ) {
-				for ( i = 0, l = events[ type ].length; i < l; i++ ) {
-					jQuery.event.add( dest, type, events[ type ][ i ] );
-				}
-			}
-		}
-	}
-
-	// 2. Copy user data
-	if ( dataUser.hasData( src ) ) {
-		udataOld = dataUser.access( src );
-		udataCur = jQuery.extend( {}, udataOld );
-
-		dataUser.set( dest, udataCur );
-	}
-}
-
-// Fix IE bugs, see support tests
-function fixInput( src, dest ) {
-	var nodeName = dest.nodeName.toLowerCase();
-
-	// Fails to persist the checked state of a cloned checkbox or radio button.
-	if ( nodeName === "input" && rcheckableType.test( src.type ) ) {
-		dest.checked = src.checked;
-
-	// Fails to return the selected option to the default selected state when cloning options
-	} else if ( nodeName === "input" || nodeName === "textarea" ) {
-		dest.defaultValue = src.defaultValue;
-	}
-}
-
-function domManip( collection, args, callback, ignored ) {
-
-	// Flatten any nested arrays
-	args = flat( args );
-
-	var fragment, first, scripts, hasScripts, node, doc,
-		i = 0,
-		l = collection.length,
-		iNoClone = l - 1,
-		value = args[ 0 ],
-		valueIsFunction = isFunction( value );
-
-	// We can't cloneNode fragments that contain checked, in WebKit
-	if ( valueIsFunction ||
-			( l > 1 && typeof value === "string" &&
-				!support.checkClone && rchecked.test( value ) ) ) {
-		return collection.each( function( index ) {
-			var self = collection.eq( index );
-			if ( valueIsFunction ) {
-				args[ 0 ] = value.call( this, index, self.html() );
-			}
-			domManip( self, args, callback, ignored );
-		} );
-	}
-
-	if ( l ) {
-		fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored );
-		first = fragment.firstChild;
-
-		if ( fragment.childNodes.length === 1 ) {
-			fragment = first;
-		}
-
-		// Require either new content or an interest in ignored elements to invoke the callback
-		if ( first || ignored ) {
-			scripts = jQuery.map( getAll( fragment, "script" ), disableScript );
-			hasScripts = scripts.length;
-
-			// Use the original fragment for the last item
-			// instead of the first because it can end up
-			// being emptied incorrectly in certain situations (#8070).
-			for ( ; i < l; i++ ) {
-				node = fragment;
-
-				if ( i !== iNoClone ) {
-					node = jQuery.clone( node, true, true );
-
-					// Keep references to cloned scripts for later restoration
-					if ( hasScripts ) {
-
-						// Support: Android <=4.0 only, PhantomJS 1 only
-						// push.apply(_, arraylike) throws on ancient WebKit
-						jQuery.merge( scripts, getAll( node, "script" ) );
-					}
-				}
-
-				callback.call( collection[ i ], node, i );
-			}
-
-			if ( hasScripts ) {
-				doc = scripts[ scripts.length - 1 ].ownerDocument;
-
-				// Reenable scripts
-				jQuery.map( scripts, restoreScript );
-
-				// Evaluate executable scripts on first document insertion
-				for ( i = 0; i < hasScripts; i++ ) {
-					node = scripts[ i ];
-					if ( rscriptType.test( node.type || "" ) &&
-						!dataPriv.access( node, "globalEval" ) &&
-						jQuery.contains( doc, node ) ) {
-
-						if ( node.src && ( node.type || "" ).toLowerCase()  !== "module" ) {
-
-							// Optional AJAX dependency, but won't run scripts if not present
-							if ( jQuery._evalUrl && !node.noModule ) {
-								jQuery._evalUrl( node.src, {
-									nonce: node.nonce || node.getAttribute( "nonce" )
-								}, doc );
-							}
-						} else {
-							DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc );
-						}
-					}
-				}
-			}
-		}
-	}
-
-	return collection;
-}
-
-function remove( elem, selector, keepData ) {
-	var node,
-		nodes = selector ? jQuery.filter( selector, elem ) : elem,
-		i = 0;
-
-	for ( ; ( node = nodes[ i ] ) != null; i++ ) {
-		if ( !keepData && node.nodeType === 1 ) {
-			jQuery.cleanData( getAll( node ) );
-		}
-
-		if ( node.parentNode ) {
-			if ( keepData && isAttached( node ) ) {
-				setGlobalEval( getAll( node, "script" ) );
-			}
-			node.parentNode.removeChild( node );
-		}
-	}
-
-	return elem;
-}
-
-jQuery.extend( {
-	htmlPrefilter: function( html ) {
-		return html;
-	},
-
-	clone: function( elem, dataAndEvents, deepDataAndEvents ) {
-		var i, l, srcElements, destElements,
-			clone = elem.cloneNode( true ),
-			inPage = isAttached( elem );
-
-		// Fix IE cloning issues
-		if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) &&
-				!jQuery.isXMLDoc( elem ) ) {
-
-			// We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2
-			destElements = getAll( clone );
-			srcElements = getAll( elem );
-
-			for ( i = 0, l = srcElements.length; i < l; i++ ) {
-				fixInput( srcElements[ i ], destElements[ i ] );
-			}
-		}
-
-		// Copy the events from the original to the clone
-		if ( dataAndEvents ) {
-			if ( deepDataAndEvents ) {
-				srcElements = srcElements || getAll( elem );
-				destElements = destElements || getAll( clone );
-
-				for ( i = 0, l = srcElements.length; i < l; i++ ) {
-					cloneCopyEvent( srcElements[ i ], destElements[ i ] );
-				}
-			} else {
-				cloneCopyEvent( elem, clone );
-			}
-		}
-
-		// Preserve script evaluation history
-		destElements = getAll( clone, "script" );
-		if ( destElements.length > 0 ) {
-			setGlobalEval( destElements, !inPage && getAll( elem, "script" ) );
-		}
-
-		// Return the cloned set
-		return clone;
-	},
-
-	cleanData: function( elems ) {
-		var data, elem, type,
-			special = jQuery.event.special,
-			i = 0;
-
-		for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) {
-			if ( acceptData( elem ) ) {
-				if ( ( data = elem[ dataPriv.expando ] ) ) {
-					if ( data.events ) {
-						for ( type in data.events ) {
-							if ( special[ type ] ) {
-								jQuery.event.remove( elem, type );
-
-							// This is a shortcut to avoid jQuery.event.remove's overhead
-							} else {
-								jQuery.removeEvent( elem, type, data.handle );
-							}
-						}
-					}
-
-					// Support: Chrome <=35 - 45+
-					// Assign undefined instead of using delete, see Data#remove
-					elem[ dataPriv.expando ] = undefined;
-				}
-				if ( elem[ dataUser.expando ] ) {
-
-					// Support: Chrome <=35 - 45+
-					// Assign undefined instead of using delete, see Data#remove
-					elem[ dataUser.expando ] = undefined;
-				}
-			}
-		}
-	}
-} );
-
-jQuery.fn.extend( {
-	detach: function( selector ) {
-		return remove( this, selector, true );
-	},
-
-	remove: function( selector ) {
-		return remove( this, selector );
-	},
-
-	text: function( value ) {
-		return access( this, function( value ) {
-			return value === undefined ?
-				jQuery.text( this ) :
-				this.empty().each( function() {
-					if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
-						this.textContent = value;
-					}
-				} );
-		}, null, value, arguments.length );
-	},
-
-	append: function() {
-		return domManip( this, arguments, function( elem ) {
-			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
-				var target = manipulationTarget( this, elem );
-				target.appendChild( elem );
-			}
-		} );
-	},
-
-	prepend: function() {
-		return domManip( this, arguments, function( elem ) {
-			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
-				var target = manipulationTarget( this, elem );
-				target.insertBefore( elem, target.firstChild );
-			}
-		} );
-	},
-
-	before: function() {
-		return domManip( this, arguments, function( elem ) {
-			if ( this.parentNode ) {
-				this.parentNode.insertBefore( elem, this );
-			}
-		} );
-	},
-
-	after: function() {
-		return domManip( this, arguments, function( elem ) {
-			if ( this.parentNode ) {
-				this.parentNode.insertBefore( elem, this.nextSibling );
-			}
-		} );
-	},
-
-	empty: function() {
-		var elem,
-			i = 0;
-
-		for ( ; ( elem = this[ i ] ) != null; i++ ) {
-			if ( elem.nodeType === 1 ) {
-
-				// Prevent memory leaks
-				jQuery.cleanData( getAll( elem, false ) );
-
-				// Remove any remaining nodes
-				elem.textContent = "";
-			}
-		}
-
-		return this;
-	},
-
-	clone: function( dataAndEvents, deepDataAndEvents ) {
-		dataAndEvents = dataAndEvents == null ? false : dataAndEvents;
-		deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents;
-
-		return this.map( function() {
-			return jQuery.clone( this, dataAndEvents, deepDataAndEvents );
-		} );
-	},
-
-	html: function( value ) {
-		return access( this, function( value ) {
-			var elem = this[ 0 ] || {},
-				i = 0,
-				l = this.length;
-
-			if ( value === undefined && elem.nodeType === 1 ) {
-				return elem.innerHTML;
-			}
-
-			// See if we can take a shortcut and just use innerHTML
-			if ( typeof value === "string" && !rnoInnerhtml.test( value ) &&
-				!wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) {
-
-				value = jQuery.htmlPrefilter( value );
-
-				try {
-					for ( ; i < l; i++ ) {
-						elem = this[ i ] || {};
-
-						// Remove element nodes and prevent memory leaks
-						if ( elem.nodeType === 1 ) {
-							jQuery.cleanData( getAll( elem, false ) );
-							elem.innerHTML = value;
-						}
-					}
-
-					elem = 0;
-
-				// If using innerHTML throws an exception, use the fallback method
-				} catch ( e ) {}
-			}
-
-			if ( elem ) {
-				this.empty().append( value );
-			}
-		}, null, value, arguments.length );
-	},
-
-	replaceWith: function() {
-		var ignored = [];
-
-		// Make the changes, replacing each non-ignored context element with the new content
-		return domManip( this, arguments, function( elem ) {
-			var parent = this.parentNode;
-
-			if ( jQuery.inArray( this, ignored ) < 0 ) {
-				jQuery.cleanData( getAll( this ) );
-				if ( parent ) {
-					parent.replaceChild( elem, this );
-				}
-			}
-
-		// Force callback invocation
-		}, ignored );
-	}
-} );
-
-jQuery.each( {
-	appendTo: "append",
-	prependTo: "prepend",
-	insertBefore: "before",
-	insertAfter: "after",
-	replaceAll: "replaceWith"
-}, function( name, original ) {
-	jQuery.fn[ name ] = function( selector ) {
-		var elems,
-			ret = [],
-			insert = jQuery( selector ),
-			last = insert.length - 1,
-			i = 0;
-
-		for ( ; i <= last; i++ ) {
-			elems = i === last ? this : this.clone( true );
-			jQuery( insert[ i ] )[ original ]( elems );
-
-			// Support: Android <=4.0 only, PhantomJS 1 only
-			// .get() because push.apply(_, arraylike) throws on ancient WebKit
-			push.apply( ret, elems.get() );
-		}
-
-		return this.pushStack( ret );
-	};
-} );
-var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" );
-
-var getStyles = function( elem ) {
-
-		// Support: IE <=11 only, Firefox <=30 (#15098, #14150)
-		// IE throws on elements created in popups
-		// FF meanwhile throws on frame elements through "defaultView.getComputedStyle"
-		var view = elem.ownerDocument.defaultView;
-
-		if ( !view || !view.opener ) {
-			view = window;
-		}
-
-		return view.getComputedStyle( elem );
-	};
-
-var swap = function( elem, options, callback ) {
-	var ret, name,
-		old = {};
-
-	// Remember the old values, and insert the new ones
-	for ( name in options ) {
-		old[ name ] = elem.style[ name ];
-		elem.style[ name ] = options[ name ];
-	}
-
-	ret = callback.call( elem );
-
-	// Revert the old values
-	for ( name in options ) {
-		elem.style[ name ] = old[ name ];
-	}
-
-	return ret;
-};
-
-
-var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" );
-
-
-
-( function() {
-
-	// Executing both pixelPosition & boxSizingReliable tests require only one layout
-	// so they're executed at the same time to save the second computation.
-	function computeStyleTests() {
-
-		// This is a singleton, we need to execute it only once
-		if ( !div ) {
-			return;
-		}
-
-		container.style.cssText = "position:absolute;left:-11111px;width:60px;" +
-			"margin-top:1px;padding:0;border:0";
-		div.style.cssText =
-			"position:relative;display:block;box-sizing:border-box;overflow:scroll;" +
-			"margin:auto;border:1px;padding:1px;" +
-			"width:60%;top:1%";
-		documentElement.appendChild( container ).appendChild( div );
-
-		var divStyle = window.getComputedStyle( div );
-		pixelPositionVal = divStyle.top !== "1%";
-
-		// Support: Android 4.0 - 4.3 only, Firefox <=3 - 44
-		reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12;
-
-		// Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3
-		// Some styles come back with percentage values, even though they shouldn't
-		div.style.right = "60%";
-		pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36;
-
-		// Support: IE 9 - 11 only
-		// Detect misreporting of content dimensions for box-sizing:border-box elements
-		boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36;
-
-		// Support: IE 9 only
-		// Detect overflow:scroll screwiness (gh-3699)
-		// Support: Chrome <=64
-		// Don't get tricked when zoom affects offsetWidth (gh-4029)
-		div.style.position = "absolute";
-		scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12;
-
-		documentElement.removeChild( container );
-
-		// Nullify the div so it wouldn't be stored in the memory and
-		// it will also be a sign that checks already performed
-		div = null;
-	}
-
-	function roundPixelMeasures( measure ) {
-		return Math.round( parseFloat( measure ) );
-	}
-
-	var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal,
-		reliableTrDimensionsVal, reliableMarginLeftVal,
-		container = document.createElement( "div" ),
-		div = document.createElement( "div" );
-
-	// Finish early in limited (non-browser) environments
-	if ( !div.style ) {
-		return;
-	}
-
-	// Support: IE <=9 - 11 only
-	// Style of cloned element affects source element cloned (#8908)
-	div.style.backgroundClip = "content-box";
-	div.cloneNode( true ).style.backgroundClip = "";
-	support.clearCloneStyle = div.style.backgroundClip === "content-box";
-
-	jQuery.extend( support, {
-		boxSizingReliable: function() {
-			computeStyleTests();
-			return boxSizingReliableVal;
-		},
-		pixelBoxStyles: function() {
-			computeStyleTests();
-			return pixelBoxStylesVal;
-		},
-		pixelPosition: function() {
-			computeStyleTests();
-			return pixelPositionVal;
-		},
-		reliableMarginLeft: function() {
-			computeStyleTests();
-			return reliableMarginLeftVal;
-		},
-		scrollboxSize: function() {
-			computeStyleTests();
-			return scrollboxSizeVal;
-		},
-
-		// Support: IE 9 - 11+, Edge 15 - 18+
-		// IE/Edge misreport `getComputedStyle` of table rows with width/height
-		// set in CSS while `offset*` properties report correct values.
-		// Behavior in IE 9 is more subtle than in newer versions & it passes
-		// some versions of this test; make sure not to make it pass there!
-		reliableTrDimensions: function() {
-			var table, tr, trChild, trStyle;
-			if ( reliableTrDimensionsVal == null ) {
-				table = document.createElement( "table" );
-				tr = document.createElement( "tr" );
-				trChild = document.createElement( "div" );
-
-				table.style.cssText = "position:absolute;left:-11111px";
-				tr.style.height = "1px";
-				trChild.style.height = "9px";
-
-				documentElement
-					.appendChild( table )
-					.appendChild( tr )
-					.appendChild( trChild );
-
-				trStyle = window.getComputedStyle( tr );
-				reliableTrDimensionsVal = parseInt( trStyle.height ) > 3;
-
-				documentElement.removeChild( table );
-			}
-			return reliableTrDimensionsVal;
-		}
-	} );
-} )();
-
-
-function curCSS( elem, name, computed ) {
-	var width, minWidth, maxWidth, ret,
-
-		// Support: Firefox 51+
-		// Retrieving style before computed somehow
-		// fixes an issue with getting wrong values
-		// on detached elements
-		style = elem.style;
-
-	computed = computed || getStyles( elem );
-
-	// getPropertyValue is needed for:
-	//   .css('filter') (IE 9 only, #12537)
-	//   .css('--customProperty) (#3144)
-	if ( computed ) {
-		ret = computed.getPropertyValue( name ) || computed[ name ];
-
-		if ( ret === "" && !isAttached( elem ) ) {
-			ret = jQuery.style( elem, name );
-		}
-
-		// A tribute to the "awesome hack by Dean Edwards"
-		// Android Browser returns percentage for some values,
-		// but width seems to be reliably pixels.
-		// This is against the CSSOM draft spec:
-		// https://drafts.csswg.org/cssom/#resolved-values
-		if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) {
-
-			// Remember the original values
-			width = style.width;
-			minWidth = style.minWidth;
-			maxWidth = style.maxWidth;
-
-			// Put in the new values to get a computed value out
-			style.minWidth = style.maxWidth = style.width = ret;
-			ret = computed.width;
-
-			// Revert the changed values
-			style.width = width;
-			style.minWidth = minWidth;
-			style.maxWidth = maxWidth;
-		}
-	}
-
-	return ret !== undefined ?
-
-		// Support: IE <=9 - 11 only
-		// IE returns zIndex value as an integer.
-		ret + "" :
-		ret;
-}
-
-
-function addGetHookIf( conditionFn, hookFn ) {
-
-	// Define the hook, we'll check on the first run if it's really needed.
-	return {
-		get: function() {
-			if ( conditionFn() ) {
-
-				// Hook not needed (or it's not possible to use it due
-				// to missing dependency), remove it.
-				delete this.get;
-				return;
-			}
-
-			// Hook needed; redefine it so that the support test is not executed again.
-			return ( this.get = hookFn ).apply( this, arguments );
-		}
-	};
-}
-
-
-var cssPrefixes = [ "Webkit", "Moz", "ms" ],
-	emptyStyle = document.createElement( "div" ).style,
-	vendorProps = {};
-
-// Return a vendor-prefixed property or undefined
-function vendorPropName( name ) {
-
-	// Check for vendor prefixed names
-	var capName = name[ 0 ].toUpperCase() + name.slice( 1 ),
-		i = cssPrefixes.length;
-
-	while ( i-- ) {
-		name = cssPrefixes[ i ] + capName;
-		if ( name in emptyStyle ) {
-			return name;
-		}
-	}
-}
-
-// Return a potentially-mapped jQuery.cssProps or vendor prefixed property
-function finalPropName( name ) {
-	var final = jQuery.cssProps[ name ] || vendorProps[ name ];
-
-	if ( final ) {
-		return final;
-	}
-	if ( name in emptyStyle ) {
-		return name;
-	}
-	return vendorProps[ name ] = vendorPropName( name ) || name;
-}
-
-
-var
-
-	// Swappable if display is none or starts with table
-	// except "table", "table-cell", or "table-caption"
-	// See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display
-	rdisplayswap = /^(none|table(?!-c[ea]).+)/,
-	rcustomProp = /^--/,
-	cssShow = { position: "absolute", visibility: "hidden", display: "block" },
-	cssNormalTransform = {
-		letterSpacing: "0",
-		fontWeight: "400"
-	};
-
-function setPositiveNumber( _elem, value, subtract ) {
-
-	// Any relative (+/-) values have already been
-	// normalized at this point
-	var matches = rcssNum.exec( value );
-	return matches ?
-
-		// Guard against undefined "subtract", e.g., when used as in cssHooks
-		Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) :
-		value;
-}
-
-function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) {
-	var i = dimension === "width" ? 1 : 0,
-		extra = 0,
-		delta = 0;
-
-	// Adjustment may not be necessary
-	if ( box === ( isBorderBox ? "border" : "content" ) ) {
-		return 0;
-	}
-
-	for ( ; i < 4; i += 2 ) {
-
-		// Both box models exclude margin
-		if ( box === "margin" ) {
-			delta += jQuery.css( elem, box + cssExpand[ i ], true, styles );
-		}
-
-		// If we get here with a content-box, we're seeking "padding" or "border" or "margin"
-		if ( !isBorderBox ) {
-
-			// Add padding
-			delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
-
-			// For "border" or "margin", add border
-			if ( box !== "padding" ) {
-				delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
-
-			// But still keep track of it otherwise
-			} else {
-				extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
-			}
-
-		// If we get here with a border-box (content + padding + border), we're seeking "content" or
-		// "padding" or "margin"
-		} else {
-
-			// For "content", subtract padding
-			if ( box === "content" ) {
-				delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
-			}
-
-			// For "content" or "padding", subtract border
-			if ( box !== "margin" ) {
-				delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
-			}
-		}
-	}
-
-	// Account for positive content-box scroll gutter when requested by providing computedVal
-	if ( !isBorderBox && computedVal >= 0 ) {
-
-		// offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border
-		// Assuming integer scroll gutter, subtract the rest and round down
-		delta += Math.max( 0, Math.ceil(
-			elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
-			computedVal -
-			delta -
-			extra -
-			0.5
-
-		// If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter
-		// Use an explicit zero to avoid NaN (gh-3964)
-		) ) || 0;
-	}
-
-	return delta;
-}
-
-function getWidthOrHeight( elem, dimension, extra ) {
-
-	// Start with computed style
-	var styles = getStyles( elem ),
-
-		// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322).
-		// Fake content-box until we know it's needed to know the true value.
-		boxSizingNeeded = !support.boxSizingReliable() || extra,
-		isBorderBox = boxSizingNeeded &&
-			jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
-		valueIsBorderBox = isBorderBox,
-
-		val = curCSS( elem, dimension, styles ),
-		offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 );
-
-	// Support: Firefox <=54
-	// Return a confounding non-pixel value or feign ignorance, as appropriate.
-	if ( rnumnonpx.test( val ) ) {
-		if ( !extra ) {
-			return val;
-		}
-		val = "auto";
-	}
-
-
-	// Support: IE 9 - 11 only
-	// Use offsetWidth/offsetHeight for when box sizing is unreliable.
-	// In those cases, the computed value can be trusted to be border-box.
-	if ( ( !support.boxSizingReliable() && isBorderBox ||
-
-		// Support: IE 10 - 11+, Edge 15 - 18+
-		// IE/Edge misreport `getComputedStyle` of table rows with width/height
-		// set in CSS while `offset*` properties report correct values.
-		// Interestingly, in some cases IE 9 doesn't suffer from this issue.
-		!support.reliableTrDimensions() && nodeName( elem, "tr" ) ||
-
-		// Fall back to offsetWidth/offsetHeight when value is "auto"
-		// This happens for inline elements with no explicit setting (gh-3571)
-		val === "auto" ||
-
-		// Support: Android <=4.1 - 4.3 only
-		// Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602)
-		!parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) &&
-
-		// Make sure the element is visible & connected
-		elem.getClientRects().length ) {
-
-		isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box";
-
-		// Where available, offsetWidth/offsetHeight approximate border box dimensions.
-		// Where not available (e.g., SVG), assume unreliable box-sizing and interpret the
-		// retrieved value as a content box dimension.
-		valueIsBorderBox = offsetProp in elem;
-		if ( valueIsBorderBox ) {
-			val = elem[ offsetProp ];
-		}
-	}
-
-	// Normalize "" and auto
-	val = parseFloat( val ) || 0;
-
-	// Adjust for the element's box model
-	return ( val +
-		boxModelAdjustment(
-			elem,
-			dimension,
-			extra || ( isBorderBox ? "border" : "content" ),
-			valueIsBorderBox,
-			styles,
-
-			// Provide the current computed size to request scroll gutter calculation (gh-3589)
-			val
-		)
-	) + "px";
-}
-
-jQuery.extend( {
-
-	// Add in style property hooks for overriding the default
-	// behavior of getting and setting a style property
-	cssHooks: {
-		opacity: {
-			get: function( elem, computed ) {
-				if ( computed ) {
-
-					// We should always get a number back from opacity
-					var ret = curCSS( elem, "opacity" );
-					return ret === "" ? "1" : ret;
-				}
-			}
-		}
-	},
-
-	// Don't automatically add "px" to these possibly-unitless properties
-	cssNumber: {
-		"animationIterationCount": true,
-		"columnCount": true,
-		"fillOpacity": true,
-		"flexGrow": true,
-		"flexShrink": true,
-		"fontWeight": true,
-		"gridArea": true,
-		"gridColumn": true,
-		"gridColumnEnd": true,
-		"gridColumnStart": true,
-		"gridRow": true,
-		"gridRowEnd": true,
-		"gridRowStart": true,
-		"lineHeight": true,
-		"opacity": true,
-		"order": true,
-		"orphans": true,
-		"widows": true,
-		"zIndex": true,
-		"zoom": true
-	},
-
-	// Add in properties whose names you wish to fix before
-	// setting or getting the value
-	cssProps: {},
-
-	// Get and set the style property on a DOM Node
-	style: function( elem, name, value, extra ) {
-
-		// Don't set styles on text and comment nodes
-		if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) {
-			return;
-		}
-
-		// Make sure that we're working with the right name
-		var ret, type, hooks,
-			origName = camelCase( name ),
-			isCustomProp = rcustomProp.test( name ),
-			style = elem.style;
-
-		// Make sure that we're working with the right name. We don't
-		// want to query the value if it is a CSS custom property
-		// since they are user-defined.
-		if ( !isCustomProp ) {
-			name = finalPropName( origName );
-		}
-
-		// Gets hook for the prefixed version, then unprefixed version
-		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
-
-		// Check if we're setting a value
-		if ( value !== undefined ) {
-			type = typeof value;
-
-			// Convert "+=" or "-=" to relative numbers (#7345)
-			if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) {
-				value = adjustCSS( elem, name, ret );
-
-				// Fixes bug #9237
-				type = "number";
-			}
-
-			// Make sure that null and NaN values aren't set (#7116)
-			if ( value == null || value !== value ) {
-				return;
-			}
-
-			// If a number was passed in, add the unit (except for certain CSS properties)
-			// The isCustomProp check can be removed in jQuery 4.0 when we only auto-append
-			// "px" to a few hardcoded values.
-			if ( type === "number" && !isCustomProp ) {
-				value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" );
-			}
-
-			// background-* props affect original clone's values
-			if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) {
-				style[ name ] = "inherit";
-			}
-
-			// If a hook was provided, use that value, otherwise just set the specified value
-			if ( !hooks || !( "set" in hooks ) ||
-				( value = hooks.set( elem, value, extra ) ) !== undefined ) {
-
-				if ( isCustomProp ) {
-					style.setProperty( name, value );
-				} else {
-					style[ name ] = value;
-				}
-			}
-
-		} else {
-
-			// If a hook was provided get the non-computed value from there
-			if ( hooks && "get" in hooks &&
-				( ret = hooks.get( elem, false, extra ) ) !== undefined ) {
-
-				return ret;
-			}
-
-			// Otherwise just get the value from the style object
-			return style[ name ];
-		}
-	},
-
-	css: function( elem, name, extra, styles ) {
-		var val, num, hooks,
-			origName = camelCase( name ),
-			isCustomProp = rcustomProp.test( name );
-
-		// Make sure that we're working with the right name. We don't
-		// want to modify the value if it is a CSS custom property
-		// since they are user-defined.
-		if ( !isCustomProp ) {
-			name = finalPropName( origName );
-		}
-
-		// Try prefixed name followed by the unprefixed name
-		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
-
-		// If a hook was provided get the computed value from there
-		if ( hooks && "get" in hooks ) {
-			val = hooks.get( elem, true, extra );
-		}
-
-		// Otherwise, if a way to get the computed value exists, use that
-		if ( val === undefined ) {
-			val = curCSS( elem, name, styles );
-		}
-
-		// Convert "normal" to computed value
-		if ( val === "normal" && name in cssNormalTransform ) {
-			val = cssNormalTransform[ name ];
-		}
-
-		// Make numeric if forced or a qualifier was provided and val looks numeric
-		if ( extra === "" || extra ) {
-			num = parseFloat( val );
-			return extra === true || isFinite( num ) ? num || 0 : val;
-		}
-
-		return val;
-	}
-} );
-
-jQuery.each( [ "height", "width" ], function( _i, dimension ) {
-	jQuery.cssHooks[ dimension ] = {
-		get: function( elem, computed, extra ) {
-			if ( computed ) {
-
-				// Certain elements can have dimension info if we invisibly show them
-				// but it must have a current display style that would benefit
-				return rdisplayswap.test( jQuery.css( elem, "display" ) ) &&
-
-					// Support: Safari 8+
-					// Table columns in Safari have non-zero offsetWidth & zero
-					// getBoundingClientRect().width unless display is changed.
-					// Support: IE <=11 only
-					// Running getBoundingClientRect on a disconnected node
-					// in IE throws an error.
-					( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ?
-						swap( elem, cssShow, function() {
-							return getWidthOrHeight( elem, dimension, extra );
-						} ) :
-						getWidthOrHeight( elem, dimension, extra );
-			}
-		},
-
-		set: function( elem, value, extra ) {
-			var matches,
-				styles = getStyles( elem ),
-
-				// Only read styles.position if the test has a chance to fail
-				// to avoid forcing a reflow.
-				scrollboxSizeBuggy = !support.scrollboxSize() &&
-					styles.position === "absolute",
-
-				// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991)
-				boxSizingNeeded = scrollboxSizeBuggy || extra,
-				isBorderBox = boxSizingNeeded &&
-					jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
-				subtract = extra ?
-					boxModelAdjustment(
-						elem,
-						dimension,
-						extra,
-						isBorderBox,
-						styles
-					) :
-					0;
-
-			// Account for unreliable border-box dimensions by comparing offset* to computed and
-			// faking a content-box to get border and padding (gh-3699)
-			if ( isBorderBox && scrollboxSizeBuggy ) {
-				subtract -= Math.ceil(
-					elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
-					parseFloat( styles[ dimension ] ) -
-					boxModelAdjustment( elem, dimension, "border", false, styles ) -
-					0.5
-				);
-			}
-
-			// Convert to pixels if value adjustment is needed
-			if ( subtract && ( matches = rcssNum.exec( value ) ) &&
-				( matches[ 3 ] || "px" ) !== "px" ) {
-
-				elem.style[ dimension ] = value;
-				value = jQuery.css( elem, dimension );
-			}
-
-			return setPositiveNumber( elem, value, subtract );
-		}
-	};
-} );
-
-jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft,
-	function( elem, computed ) {
-		if ( computed ) {
-			return ( parseFloat( curCSS( elem, "marginLeft" ) ) ||
-				elem.getBoundingClientRect().left -
-					swap( elem, { marginLeft: 0 }, function() {
-						return elem.getBoundingClientRect().left;
-					} )
-				) + "px";
-		}
-	}
-);
-
-// These hooks are used by animate to expand properties
-jQuery.each( {
-	margin: "",
-	padding: "",
-	border: "Width"
-}, function( prefix, suffix ) {
-	jQuery.cssHooks[ prefix + suffix ] = {
-		expand: function( value ) {
-			var i = 0,
-				expanded = {},
-
-				// Assumes a single number if not a string
-				parts = typeof value === "string" ? value.split( " " ) : [ value ];
-
-			for ( ; i < 4; i++ ) {
-				expanded[ prefix + cssExpand[ i ] + suffix ] =
-					parts[ i ] || parts[ i - 2 ] || parts[ 0 ];
-			}
-
-			return expanded;
-		}
-	};
-
-	if ( prefix !== "margin" ) {
-		jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber;
-	}
-} );
-
-jQuery.fn.extend( {
-	css: function( name, value ) {
-		return access( this, function( elem, name, value ) {
-			var styles, len,
-				map = {},
-				i = 0;
-
-			if ( Array.isArray( name ) ) {
-				styles = getStyles( elem );
-				len = name.length;
-
-				for ( ; i < len; i++ ) {
-					map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles );
-				}
-
-				return map;
-			}
-
-			return value !== undefined ?
-				jQuery.style( elem, name, value ) :
-				jQuery.css( elem, name );
-		}, name, value, arguments.length > 1 );
-	}
-} );
-
-
-function Tween( elem, options, prop, end, easing ) {
-	return new Tween.prototype.init( elem, options, prop, end, easing );
-}
-jQuery.Tween = Tween;
-
-Tween.prototype = {
-	constructor: Tween,
-	init: function( elem, options, prop, end, easing, unit ) {
-		this.elem = elem;
-		this.prop = prop;
-		this.easing = easing || jQuery.easing._default;
-		this.options = options;
-		this.start = this.now = this.cur();
-		this.end = end;
-		this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" );
-	},
-	cur: function() {
-		var hooks = Tween.propHooks[ this.prop ];
-
-		return hooks && hooks.get ?
-			hooks.get( this ) :
-			Tween.propHooks._default.get( this );
-	},
-	run: function( percent ) {
-		var eased,
-			hooks = Tween.propHooks[ this.prop ];
-
-		if ( this.options.duration ) {
-			this.pos = eased = jQuery.easing[ this.easing ](
-				percent, this.options.duration * percent, 0, 1, this.options.duration
-			);
-		} else {
-			this.pos = eased = percent;
-		}
-		this.now = ( this.end - this.start ) * eased + this.start;
-
-		if ( this.options.step ) {
-			this.options.step.call( this.elem, this.now, this );
-		}
-
-		if ( hooks && hooks.set ) {
-			hooks.set( this );
-		} else {
-			Tween.propHooks._default.set( this );
-		}
-		return this;
-	}
-};
-
-Tween.prototype.init.prototype = Tween.prototype;
-
-Tween.propHooks = {
-	_default: {
-		get: function( tween ) {
-			var result;
-
-			// Use a property on the element directly when it is not a DOM element,
-			// or when there is no matching style property that exists.
-			if ( tween.elem.nodeType !== 1 ||
-				tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) {
-				return tween.elem[ tween.prop ];
-			}
-
-			// Passing an empty string as a 3rd parameter to .css will automatically
-			// attempt a parseFloat and fallback to a string if the parse fails.
-			// Simple values such as "10px" are parsed to Float;
-			// complex values such as "rotate(1rad)" are returned as-is.
-			result = jQuery.css( tween.elem, tween.prop, "" );
-
-			// Empty strings, null, undefined and "auto" are converted to 0.
-			return !result || result === "auto" ? 0 : result;
-		},
-		set: function( tween ) {
-
-			// Use step hook for back compat.
-			// Use cssHook if its there.
-			// Use .style if available and use plain properties where available.
-			if ( jQuery.fx.step[ tween.prop ] ) {
-				jQuery.fx.step[ tween.prop ]( tween );
-			} else if ( tween.elem.nodeType === 1 && (
-					jQuery.cssHooks[ tween.prop ] ||
-					tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) {
-				jQuery.style( tween.elem, tween.prop, tween.now + tween.unit );
-			} else {
-				tween.elem[ tween.prop ] = tween.now;
-			}
-		}
-	}
-};
-
-// Support: IE <=9 only
-// Panic based approach to setting things on disconnected nodes
-Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = {
-	set: function( tween ) {
-		if ( tween.elem.nodeType && tween.elem.parentNode ) {
-			tween.elem[ tween.prop ] = tween.now;
-		}
-	}
-};
-
-jQuery.easing = {
-	linear: function( p ) {
-		return p;
-	},
-	swing: function( p ) {
-		return 0.5 - Math.cos( p * Math.PI ) / 2;
-	},
-	_default: "swing"
-};
-
-jQuery.fx = Tween.prototype.init;
-
-// Back compat <1.8 extension point
-jQuery.fx.step = {};
-
-
-
-
-var
-	fxNow, inProgress,
-	rfxtypes = /^(?:toggle|show|hide)$/,
-	rrun = /queueHooks$/;
-
-function schedule() {
-	if ( inProgress ) {
-		if ( document.hidden === false && window.requestAnimationFrame ) {
-			window.requestAnimationFrame( schedule );
-		} else {
-			window.setTimeout( schedule, jQuery.fx.interval );
-		}
-
-		jQuery.fx.tick();
-	}
-}
-
-// Animations created synchronously will run synchronously
-function createFxNow() {
-	window.setTimeout( function() {
-		fxNow = undefined;
-	} );
-	return ( fxNow = Date.now() );
-}
-
-// Generate parameters to create a standard animation
-function genFx( type, includeWidth ) {
-	var which,
-		i = 0,
-		attrs = { height: type };
-
-	// If we include width, step value is 1 to do all cssExpand values,
-	// otherwise step value is 2 to skip over Left and Right
-	includeWidth = includeWidth ? 1 : 0;
-	for ( ; i < 4; i += 2 - includeWidth ) {
-		which = cssExpand[ i ];
-		attrs[ "margin" + which ] = attrs[ "padding" + which ] = type;
-	}
-
-	if ( includeWidth ) {
-		attrs.opacity = attrs.width = type;
-	}
-
-	return attrs;
-}
-
-function createTween( value, prop, animation ) {
-	var tween,
-		collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ),
-		index = 0,
-		length = collection.length;
-	for ( ; index < length; index++ ) {
-		if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) {
-
-			// We're done with this property
-			return tween;
-		}
-	}
-}
-
-function defaultPrefilter( elem, props, opts ) {
-	var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display,
-		isBox = "width" in props || "height" in props,
-		anim = this,
-		orig = {},
-		style = elem.style,
-		hidden = elem.nodeType && isHiddenWithinTree( elem ),
-		dataShow = dataPriv.get( elem, "fxshow" );
-
-	// Queue-skipping animations hijack the fx hooks
-	if ( !opts.queue ) {
-		hooks = jQuery._queueHooks( elem, "fx" );
-		if ( hooks.unqueued == null ) {
-			hooks.unqueued = 0;
-			oldfire = hooks.empty.fire;
-			hooks.empty.fire = function() {
-				if ( !hooks.unqueued ) {
-					oldfire();
-				}
-			};
-		}
-		hooks.unqueued++;
-
-		anim.always( function() {
-
-			// Ensure the complete handler is called before this completes
-			anim.always( function() {
-				hooks.unqueued--;
-				if ( !jQuery.queue( elem, "fx" ).length ) {
-					hooks.empty.fire();
-				}
-			} );
-		} );
-	}
-
-	// Detect show/hide animations
-	for ( prop in props ) {
-		value = props[ prop ];
-		if ( rfxtypes.test( value ) ) {
-			delete props[ prop ];
-			toggle = toggle || value === "toggle";
-			if ( value === ( hidden ? "hide" : "show" ) ) {
-
-				// Pretend to be hidden if this is a "show" and
-				// there is still data from a stopped show/hide
-				if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) {
-					hidden = true;
-
-				// Ignore all other no-op show/hide data
-				} else {
-					continue;
-				}
-			}
-			orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop );
-		}
-	}
-
-	// Bail out if this is a no-op like .hide().hide()
-	propTween = !jQuery.isEmptyObject( props );
-	if ( !propTween && jQuery.isEmptyObject( orig ) ) {
-		return;
-	}
-
-	// Restrict "overflow" and "display" styles during box animations
-	if ( isBox && elem.nodeType === 1 ) {
-
-		// Support: IE <=9 - 11, Edge 12 - 15
-		// Record all 3 overflow attributes because IE does not infer the shorthand
-		// from identically-valued overflowX and overflowY and Edge just mirrors
-		// the overflowX value there.
-		opts.overflow = [ style.overflow, style.overflowX, style.overflowY ];
-
-		// Identify a display type, preferring old show/hide data over the CSS cascade
-		restoreDisplay = dataShow && dataShow.display;
-		if ( restoreDisplay == null ) {
-			restoreDisplay = dataPriv.get( elem, "display" );
-		}
-		display = jQuery.css( elem, "display" );
-		if ( display === "none" ) {
-			if ( restoreDisplay ) {
-				display = restoreDisplay;
-			} else {
-
-				// Get nonempty value(s) by temporarily forcing visibility
-				showHide( [ elem ], true );
-				restoreDisplay = elem.style.display || restoreDisplay;
-				display = jQuery.css( elem, "display" );
-				showHide( [ elem ] );
-			}
-		}
-
-		// Animate inline elements as inline-block
-		if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) {
-			if ( jQuery.css( elem, "float" ) === "none" ) {
-
-				// Restore the original display value at the end of pure show/hide animations
-				if ( !propTween ) {
-					anim.done( function() {
-						style.display = restoreDisplay;
-					} );
-					if ( restoreDisplay == null ) {
-						display = style.display;
-						restoreDisplay = display === "none" ? "" : display;
-					}
-				}
-				style.display = "inline-block";
-			}
-		}
-	}
-
-	if ( opts.overflow ) {
-		style.overflow = "hidden";
-		anim.always( function() {
-			style.overflow = opts.overflow[ 0 ];
-			style.overflowX = opts.overflow[ 1 ];
-			style.overflowY = opts.overflow[ 2 ];
-		} );
-	}
-
-	// Implement show/hide animations
-	propTween = false;
-	for ( prop in orig ) {
-
-		// General show/hide setup for this element animation
-		if ( !propTween ) {
-			if ( dataShow ) {
-				if ( "hidden" in dataShow ) {
-					hidden = dataShow.hidden;
-				}
-			} else {
-				dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } );
-			}
-
-			// Store hidden/visible for toggle so `.stop().toggle()` "reverses"
-			if ( toggle ) {
-				dataShow.hidden = !hidden;
-			}
-
-			// Show elements before animating them
-			if ( hidden ) {
-				showHide( [ elem ], true );
-			}
-
-			/* eslint-disable no-loop-func */
-
-			anim.done( function() {
-
-			/* eslint-enable no-loop-func */
-
-				// The final step of a "hide" animation is actually hiding the element
-				if ( !hidden ) {
-					showHide( [ elem ] );
-				}
-				dataPriv.remove( elem, "fxshow" );
-				for ( prop in orig ) {
-					jQuery.style( elem, prop, orig[ prop ] );
-				}
-			} );
-		}
-
-		// Per-property setup
-		propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim );
-		if ( !( prop in dataShow ) ) {
-			dataShow[ prop ] = propTween.start;
-			if ( hidden ) {
-				propTween.end = propTween.start;
-				propTween.start = 0;
-			}
-		}
-	}
-}
-
-function propFilter( props, specialEasing ) {
-	var index, name, easing, value, hooks;
-
-	// camelCase, specialEasing and expand cssHook pass
-	for ( index in props ) {
-		name = camelCase( index );
-		easing = specialEasing[ name ];
-		value = props[ index ];
-		if ( Array.isArray( value ) ) {
-			easing = value[ 1 ];
-			value = props[ index ] = value[ 0 ];
-		}
-
-		if ( index !== name ) {
-			props[ name ] = value;
-			delete props[ index ];
-		}
-
-		hooks = jQuery.cssHooks[ name ];
-		if ( hooks && "expand" in hooks ) {
-			value = hooks.expand( value );
-			delete props[ name ];
-
-			// Not quite $.extend, this won't overwrite existing keys.
-			// Reusing 'index' because we have the correct "name"
-			for ( index in value ) {
-				if ( !( index in props ) ) {
-					props[ index ] = value[ index ];
-					specialEasing[ index ] = easing;
-				}
-			}
-		} else {
-			specialEasing[ name ] = easing;
-		}
-	}
-}
-
-function Animation( elem, properties, options ) {
-	var result,
-		stopped,
-		index = 0,
-		length = Animation.prefilters.length,
-		deferred = jQuery.Deferred().always( function() {
-
-			// Don't match elem in the :animated selector
-			delete tick.elem;
-		} ),
-		tick = function() {
-			if ( stopped ) {
-				return false;
-			}
-			var currentTime = fxNow || createFxNow(),
-				remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ),
-
-				// Support: Android 2.3 only
-				// Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497)
-				temp = remaining / animation.duration || 0,
-				percent = 1 - temp,
-				index = 0,
-				length = animation.tweens.length;
-
-			for ( ; index < length; index++ ) {
-				animation.tweens[ index ].run( percent );
-			}
-
-			deferred.notifyWith( elem, [ animation, percent, remaining ] );
-
-			// If there's more to do, yield
-			if ( percent < 1 && length ) {
-				return remaining;
-			}
-
-			// If this was an empty animation, synthesize a final progress notification
-			if ( !length ) {
-				deferred.notifyWith( elem, [ animation, 1, 0 ] );
-			}
-
-			// Resolve the animation and report its conclusion
-			deferred.resolveWith( elem, [ animation ] );
-			return false;
-		},
-		animation = deferred.promise( {
-			elem: elem,
-			props: jQuery.extend( {}, properties ),
-			opts: jQuery.extend( true, {
-				specialEasing: {},
-				easing: jQuery.easing._default
-			}, options ),
-			originalProperties: properties,
-			originalOptions: options,
-			startTime: fxNow || createFxNow(),
-			duration: options.duration,
-			tweens: [],
-			createTween: function( prop, end ) {
-				var tween = jQuery.Tween( elem, animation.opts, prop, end,
-						animation.opts.specialEasing[ prop ] || animation.opts.easing );
-				animation.tweens.push( tween );
-				return tween;
-			},
-			stop: function( gotoEnd ) {
-				var index = 0,
-
-					// If we are going to the end, we want to run all the tweens
-					// otherwise we skip this part
-					length = gotoEnd ? animation.tweens.length : 0;
-				if ( stopped ) {
-					return this;
-				}
-				stopped = true;
-				for ( ; index < length; index++ ) {
-					animation.tweens[ index ].run( 1 );
-				}
-
-				// Resolve when we played the last frame; otherwise, reject
-				if ( gotoEnd ) {
-					deferred.notifyWith( elem, [ animation, 1, 0 ] );
-					deferred.resolveWith( elem, [ animation, gotoEnd ] );
-				} else {
-					deferred.rejectWith( elem, [ animation, gotoEnd ] );
-				}
-				return this;
-			}
-		} ),
-		props = animation.props;
-
-	propFilter( props, animation.opts.specialEasing );
-
-	for ( ; index < length; index++ ) {
-		result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts );
-		if ( result ) {
-			if ( isFunction( result.stop ) ) {
-				jQuery._queueHooks( animation.elem, animation.opts.queue ).stop =
-					result.stop.bind( result );
-			}
-			return result;
-		}
-	}
-
-	jQuery.map( props, createTween, animation );
-
-	if ( isFunction( animation.opts.start ) ) {
-		animation.opts.start.call( elem, animation );
-	}
-
-	// Attach callbacks from options
-	animation
-		.progress( animation.opts.progress )
-		.done( animation.opts.done, animation.opts.complete )
-		.fail( animation.opts.fail )
-		.always( animation.opts.always );
-
-	jQuery.fx.timer(
-		jQuery.extend( tick, {
-			elem: elem,
-			anim: animation,
-			queue: animation.opts.queue
-		} )
-	);
-
-	return animation;
-}
-
-jQuery.Animation = jQuery.extend( Animation, {
-
-	tweeners: {
-		"*": [ function( prop, value ) {
-			var tween = this.createTween( prop, value );
-			adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween );
-			return tween;
-		} ]
-	},
-
-	tweener: function( props, callback ) {
-		if ( isFunction( props ) ) {
-			callback = props;
-			props = [ "*" ];
-		} else {
-			props = props.match( rnothtmlwhite );
-		}
-
-		var prop,
-			index = 0,
-			length = props.length;
-
-		for ( ; index < length; index++ ) {
-			prop = props[ index ];
-			Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || [];
-			Animation.tweeners[ prop ].unshift( callback );
-		}
-	},
-
-	prefilters: [ defaultPrefilter ],
-
-	prefilter: function( callback, prepend ) {
-		if ( prepend ) {
-			Animation.prefilters.unshift( callback );
-		} else {
-			Animation.prefilters.push( callback );
-		}
-	}
-} );
-
-jQuery.speed = function( speed, easing, fn ) {
-	var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : {
-		complete: fn || !fn && easing ||
-			isFunction( speed ) && speed,
-		duration: speed,
-		easing: fn && easing || easing && !isFunction( easing ) && easing
-	};
-
-	// Go to the end state if fx are off
-	if ( jQuery.fx.off ) {
-		opt.duration = 0;
-
-	} else {
-		if ( typeof opt.duration !== "number" ) {
-			if ( opt.duration in jQuery.fx.speeds ) {
-				opt.duration = jQuery.fx.speeds[ opt.duration ];
-
-			} else {
-				opt.duration = jQuery.fx.speeds._default;
-			}
-		}
-	}
-
-	// Normalize opt.queue - true/undefined/null -> "fx"
-	if ( opt.queue == null || opt.queue === true ) {
-		opt.queue = "fx";
-	}
-
-	// Queueing
-	opt.old = opt.complete;
-
-	opt.complete = function() {
-		if ( isFunction( opt.old ) ) {
-			opt.old.call( this );
-		}
-
-		if ( opt.queue ) {
-			jQuery.dequeue( this, opt.queue );
-		}
-	};
-
-	return opt;
-};
-
-jQuery.fn.extend( {
-	fadeTo: function( speed, to, easing, callback ) {
-
-		// Show any hidden elements after setting opacity to 0
-		return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show()
-
-			// Animate to the value specified
-			.end().animate( { opacity: to }, speed, easing, callback );
-	},
-	animate: function( prop, speed, easing, callback ) {
-		var empty = jQuery.isEmptyObject( prop ),
-			optall = jQuery.speed( speed, easing, callback ),
-			doAnimation = function() {
-
-				// Operate on a copy of prop so per-property easing won't be lost
-				var anim = Animation( this, jQuery.extend( {}, prop ), optall );
-
-				// Empty animations, or finishing resolves immediately
-				if ( empty || dataPriv.get( this, "finish" ) ) {
-					anim.stop( true );
-				}
-			};
-			doAnimation.finish = doAnimation;
-
-		return empty || optall.queue === false ?
-			this.each( doAnimation ) :
-			this.queue( optall.queue, doAnimation );
-	},
-	stop: function( type, clearQueue, gotoEnd ) {
-		var stopQueue = function( hooks ) {
-			var stop = hooks.stop;
-			delete hooks.stop;
-			stop( gotoEnd );
-		};
-
-		if ( typeof type !== "string" ) {
-			gotoEnd = clearQueue;
-			clearQueue = type;
-			type = undefined;
-		}
-		if ( clearQueue ) {
-			this.queue( type || "fx", [] );
-		}
-
-		return this.each( function() {
-			var dequeue = true,
-				index = type != null && type + "queueHooks",
-				timers = jQuery.timers,
-				data = dataPriv.get( this );
-
-			if ( index ) {
-				if ( data[ index ] && data[ index ].stop ) {
-					stopQueue( data[ index ] );
-				}
-			} else {
-				for ( index in data ) {
-					if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) {
-						stopQueue( data[ index ] );
-					}
-				}
-			}
-
-			for ( index = timers.length; index--; ) {
-				if ( timers[ index ].elem === this &&
-					( type == null || timers[ index ].queue === type ) ) {
-
-					timers[ index ].anim.stop( gotoEnd );
-					dequeue = false;
-					timers.splice( index, 1 );
-				}
-			}
-
-			// Start the next in the queue if the last step wasn't forced.
-			// Timers currently will call their complete callbacks, which
-			// will dequeue but only if they were gotoEnd.
-			if ( dequeue || !gotoEnd ) {
-				jQuery.dequeue( this, type );
-			}
-		} );
-	},
-	finish: function( type ) {
-		if ( type !== false ) {
-			type = type || "fx";
-		}
-		return this.each( function() {
-			var index,
-				data = dataPriv.get( this ),
-				queue = data[ type + "queue" ],
-				hooks = data[ type + "queueHooks" ],
-				timers = jQuery.timers,
-				length = queue ? queue.length : 0;
-
-			// Enable finishing flag on private data
-			data.finish = true;
-
-			// Empty the queue first
-			jQuery.queue( this, type, [] );
-
-			if ( hooks && hooks.stop ) {
-				hooks.stop.call( this, true );
-			}
-
-			// Look for any active animations, and finish them
-			for ( index = timers.length; index--; ) {
-				if ( timers[ index ].elem === this && timers[ index ].queue === type ) {
-					timers[ index ].anim.stop( true );
-					timers.splice( index, 1 );
-				}
-			}
-
-			// Look for any animations in the old queue and finish them
-			for ( index = 0; index < length; index++ ) {
-				if ( queue[ index ] && queue[ index ].finish ) {
-					queue[ index ].finish.call( this );
-				}
-			}
-
-			// Turn off finishing flag
-			delete data.finish;
-		} );
-	}
-} );
-
-jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) {
-	var cssFn = jQuery.fn[ name ];
-	jQuery.fn[ name ] = function( speed, easing, callback ) {
-		return speed == null || typeof speed === "boolean" ?
-			cssFn.apply( this, arguments ) :
-			this.animate( genFx( name, true ), speed, easing, callback );
-	};
-} );
-
-// Generate shortcuts for custom animations
-jQuery.each( {
-	slideDown: genFx( "show" ),
-	slideUp: genFx( "hide" ),
-	slideToggle: genFx( "toggle" ),
-	fadeIn: { opacity: "show" },
-	fadeOut: { opacity: "hide" },
-	fadeToggle: { opacity: "toggle" }
-}, function( name, props ) {
-	jQuery.fn[ name ] = function( speed, easing, callback ) {
-		return this.animate( props, speed, easing, callback );
-	};
-} );
-
-jQuery.timers = [];
-jQuery.fx.tick = function() {
-	var timer,
-		i = 0,
-		timers = jQuery.timers;
-
-	fxNow = Date.now();
-
-	for ( ; i < timers.length; i++ ) {
-		timer = timers[ i ];
-
-		// Run the timer and safely remove it when done (allowing for external removal)
-		if ( !timer() && timers[ i ] === timer ) {
-			timers.splice( i--, 1 );
-		}
-	}
-
-	if ( !timers.length ) {
-		jQuery.fx.stop();
-	}
-	fxNow = undefined;
-};
-
-jQuery.fx.timer = function( timer ) {
-	jQuery.timers.push( timer );
-	jQuery.fx.start();
-};
-
-jQuery.fx.interval = 13;
-jQuery.fx.start = function() {
-	if ( inProgress ) {
-		return;
-	}
-
-	inProgress = true;
-	schedule();
-};
-
-jQuery.fx.stop = function() {
-	inProgress = null;
-};
-
-jQuery.fx.speeds = {
-	slow: 600,
-	fast: 200,
-
-	// Default speed
-	_default: 400
-};
-
-
-// Based off of the plugin by Clint Helfers, with permission.
-// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/
-jQuery.fn.delay = function( time, type ) {
-	time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time;
-	type = type || "fx";
-
-	return this.queue( type, function( next, hooks ) {
-		var timeout = window.setTimeout( next, time );
-		hooks.stop = function() {
-			window.clearTimeout( timeout );
-		};
-	} );
-};
-
-
-( function() {
-	var input = document.createElement( "input" ),
-		select = document.createElement( "select" ),
-		opt = select.appendChild( document.createElement( "option" ) );
-
-	input.type = "checkbox";
-
-	// Support: Android <=4.3 only
-	// Default value for a checkbox should be "on"
-	support.checkOn = input.value !== "";
-
-	// Support: IE <=11 only
-	// Must access selectedIndex to make default options select
-	support.optSelected = opt.selected;
-
-	// Support: IE <=11 only
-	// An input loses its value after becoming a radio
-	input = document.createElement( "input" );
-	input.value = "t";
-	input.type = "radio";
-	support.radioValue = input.value === "t";
-} )();
-
-
-var boolHook,
-	attrHandle = jQuery.expr.attrHandle;
-
-jQuery.fn.extend( {
-	attr: function( name, value ) {
-		return access( this, jQuery.attr, name, value, arguments.length > 1 );
-	},
-
-	removeAttr: function( name ) {
-		return this.each( function() {
-			jQuery.removeAttr( this, name );
-		} );
-	}
-} );
-
-jQuery.extend( {
-	attr: function( elem, name, value ) {
-		var ret, hooks,
-			nType = elem.nodeType;
-
-		// Don't get/set attributes on text, comment and attribute nodes
-		if ( nType === 3 || nType === 8 || nType === 2 ) {
-			return;
-		}
-
-		// Fallback to prop when attributes are not supported
-		if ( typeof elem.getAttribute === "undefined" ) {
-			return jQuery.prop( elem, name, value );
-		}
-
-		// Attribute hooks are determined by the lowercase version
-		// Grab necessary hook if one is defined
-		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
-			hooks = jQuery.attrHooks[ name.toLowerCase() ] ||
-				( jQuery.expr.match.bool.test( name ) ? boolHook : undefined );
-		}
-
-		if ( value !== undefined ) {
-			if ( value === null ) {
-				jQuery.removeAttr( elem, name );
-				return;
-			}
-
-			if ( hooks && "set" in hooks &&
-				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
-				return ret;
-			}
-
-			elem.setAttribute( name, value + "" );
-			return value;
-		}
-
-		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
-			return ret;
-		}
-
-		ret = jQuery.find.attr( elem, name );
-
-		// Non-existent attributes return null, we normalize to undefined
-		return ret == null ? undefined : ret;
-	},
-
-	attrHooks: {
-		type: {
-			set: function( elem, value ) {
-				if ( !support.radioValue && value === "radio" &&
-					nodeName( elem, "input" ) ) {
-					var val = elem.value;
-					elem.setAttribute( "type", value );
-					if ( val ) {
-						elem.value = val;
-					}
-					return value;
-				}
-			}
-		}
-	},
-
-	removeAttr: function( elem, value ) {
-		var name,
-			i = 0,
-
-			// Attribute names can contain non-HTML whitespace characters
-			// https://html.spec.whatwg.org/multipage/syntax.html#attributes-2
-			attrNames = value && value.match( rnothtmlwhite );
-
-		if ( attrNames && elem.nodeType === 1 ) {
-			while ( ( name = attrNames[ i++ ] ) ) {
-				elem.removeAttribute( name );
-			}
-		}
-	}
-} );
-
-// Hooks for boolean attributes
-boolHook = {
-	set: function( elem, value, name ) {
-		if ( value === false ) {
-
-			// Remove boolean attributes when set to false
-			jQuery.removeAttr( elem, name );
-		} else {
-			elem.setAttribute( name, name );
-		}
-		return name;
-	}
-};
-
-jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) {
-	var getter = attrHandle[ name ] || jQuery.find.attr;
-
-	attrHandle[ name ] = function( elem, name, isXML ) {
-		var ret, handle,
-			lowercaseName = name.toLowerCase();
-
-		if ( !isXML ) {
-
-			// Avoid an infinite loop by temporarily removing this function from the getter
-			handle = attrHandle[ lowercaseName ];
-			attrHandle[ lowercaseName ] = ret;
-			ret = getter( elem, name, isXML ) != null ?
-				lowercaseName :
-				null;
-			attrHandle[ lowercaseName ] = handle;
-		}
-		return ret;
-	};
-} );
-
-
-
-
-var rfocusable = /^(?:input|select|textarea|button)$/i,
-	rclickable = /^(?:a|area)$/i;
-
-jQuery.fn.extend( {
-	prop: function( name, value ) {
-		return access( this, jQuery.prop, name, value, arguments.length > 1 );
-	},
-
-	removeProp: function( name ) {
-		return this.each( function() {
-			delete this[ jQuery.propFix[ name ] || name ];
-		} );
-	}
-} );
-
-jQuery.extend( {
-	prop: function( elem, name, value ) {
-		var ret, hooks,
-			nType = elem.nodeType;
-
-		// Don't get/set properties on text, comment and attribute nodes
-		if ( nType === 3 || nType === 8 || nType === 2 ) {
-			return;
-		}
-
-		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
-
-			// Fix name and attach hooks
-			name = jQuery.propFix[ name ] || name;
-			hooks = jQuery.propHooks[ name ];
-		}
-
-		if ( value !== undefined ) {
-			if ( hooks && "set" in hooks &&
-				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
-				return ret;
-			}
-
-			return ( elem[ name ] = value );
-		}
-
-		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
-			return ret;
-		}
-
-		return elem[ name ];
-	},
-
-	propHooks: {
-		tabIndex: {
-			get: function( elem ) {
-
-				// Support: IE <=9 - 11 only
-				// elem.tabIndex doesn't always return the
-				// correct value when it hasn't been explicitly set
-				// https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/
-				// Use proper attribute retrieval(#12072)
-				var tabindex = jQuery.find.attr( elem, "tabindex" );
-
-				if ( tabindex ) {
-					return parseInt( tabindex, 10 );
-				}
-
-				if (
-					rfocusable.test( elem.nodeName ) ||
-					rclickable.test( elem.nodeName ) &&
-					elem.href
-				) {
-					return 0;
-				}
-
-				return -1;
-			}
-		}
-	},
-
-	propFix: {
-		"for": "htmlFor",
-		"class": "className"
-	}
-} );
-
-// Support: IE <=11 only
-// Accessing the selectedIndex property
-// forces the browser to respect setting selected
-// on the option
-// The getter ensures a default option is selected
-// when in an optgroup
-// eslint rule "no-unused-expressions" is disabled for this code
-// since it considers such accessions noop
-if ( !support.optSelected ) {
-	jQuery.propHooks.selected = {
-		get: function( elem ) {
-
-			/* eslint no-unused-expressions: "off" */
-
-			var parent = elem.parentNode;
-			if ( parent && parent.parentNode ) {
-				parent.parentNode.selectedIndex;
-			}
-			return null;
-		},
-		set: function( elem ) {
-
-			/* eslint no-unused-expressions: "off" */
-
-			var parent = elem.parentNode;
-			if ( parent ) {
-				parent.selectedIndex;
-
-				if ( parent.parentNode ) {
-					parent.parentNode.selectedIndex;
-				}
-			}
-		}
-	};
-}
-
-jQuery.each( [
-	"tabIndex",
-	"readOnly",
-	"maxLength",
-	"cellSpacing",
-	"cellPadding",
-	"rowSpan",
-	"colSpan",
-	"useMap",
-	"frameBorder",
-	"contentEditable"
-], function() {
-	jQuery.propFix[ this.toLowerCase() ] = this;
-} );
-
-
-
-
-	// Strip and collapse whitespace according to HTML spec
-	// https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace
-	function stripAndCollapse( value ) {
-		var tokens = value.match( rnothtmlwhite ) || [];
-		return tokens.join( " " );
-	}
-
-
-function getClass( elem ) {
-	return elem.getAttribute && elem.getAttribute( "class" ) || "";
-}
-
-function classesToArray( value ) {
-	if ( Array.isArray( value ) ) {
-		return value;
-	}
-	if ( typeof value === "string" ) {
-		return value.match( rnothtmlwhite ) || [];
-	}
-	return [];
-}
-
-jQuery.fn.extend( {
-	addClass: function( value ) {
-		var classes, elem, cur, curValue, clazz, j, finalValue,
-			i = 0;
-
-		if ( isFunction( value ) ) {
-			return this.each( function( j ) {
-				jQuery( this ).addClass( value.call( this, j, getClass( this ) ) );
-			} );
-		}
-
-		classes = classesToArray( value );
-
-		if ( classes.length ) {
-			while ( ( elem = this[ i++ ] ) ) {
-				curValue = getClass( elem );
-				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
-
-				if ( cur ) {
-					j = 0;
-					while ( ( clazz = classes[ j++ ] ) ) {
-						if ( cur.indexOf( " " + clazz + " " ) < 0 ) {
-							cur += clazz + " ";
-						}
-					}
-
-					// Only assign if different to avoid unneeded rendering.
-					finalValue = stripAndCollapse( cur );
-					if ( curValue !== finalValue ) {
-						elem.setAttribute( "class", finalValue );
-					}
-				}
-			}
-		}
-
-		return this;
-	},
-
-	removeClass: function( value ) {
-		var classes, elem, cur, curValue, clazz, j, finalValue,
-			i = 0;
-
-		if ( isFunction( value ) ) {
-			return this.each( function( j ) {
-				jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) );
-			} );
-		}
-
-		if ( !arguments.length ) {
-			return this.attr( "class", "" );
-		}
-
-		classes = classesToArray( value );
-
-		if ( classes.length ) {
-			while ( ( elem = this[ i++ ] ) ) {
-				curValue = getClass( elem );
-
-				// This expression is here for better compressibility (see addClass)
-				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
-
-				if ( cur ) {
-					j = 0;
-					while ( ( clazz = classes[ j++ ] ) ) {
-
-						// Remove *all* instances
-						while ( cur.indexOf( " " + clazz + " " ) > -1 ) {
-							cur = cur.replace( " " + clazz + " ", " " );
-						}
-					}
-
-					// Only assign if different to avoid unneeded rendering.
-					finalValue = stripAndCollapse( cur );
-					if ( curValue !== finalValue ) {
-						elem.setAttribute( "class", finalValue );
-					}
-				}
-			}
-		}
-
-		return this;
-	},
-
-	toggleClass: function( value, stateVal ) {
-		var type = typeof value,
-			isValidValue = type === "string" || Array.isArray( value );
-
-		if ( typeof stateVal === "boolean" && isValidValue ) {
-			return stateVal ? this.addClass( value ) : this.removeClass( value );
-		}
-
-		if ( isFunction( value ) ) {
-			return this.each( function( i ) {
-				jQuery( this ).toggleClass(
-					value.call( this, i, getClass( this ), stateVal ),
-					stateVal
-				);
-			} );
-		}
-
-		return this.each( function() {
-			var className, i, self, classNames;
-
-			if ( isValidValue ) {
-
-				// Toggle individual class names
-				i = 0;
-				self = jQuery( this );
-				classNames = classesToArray( value );
-
-				while ( ( className = classNames[ i++ ] ) ) {
-
-					// Check each className given, space separated list
-					if ( self.hasClass( className ) ) {
-						self.removeClass( className );
-					} else {
-						self.addClass( className );
-					}
-				}
-
-			// Toggle whole class name
-			} else if ( value === undefined || type === "boolean" ) {
-				className = getClass( this );
-				if ( className ) {
-
-					// Store className if set
-					dataPriv.set( this, "__className__", className );
-				}
-
-				// If the element has a class name or if we're passed `false`,
-				// then remove the whole classname (if there was one, the above saved it).
-				// Otherwise bring back whatever was previously saved (if anything),
-				// falling back to the empty string if nothing was stored.
-				if ( this.setAttribute ) {
-					this.setAttribute( "class",
-						className || value === false ?
-						"" :
-						dataPriv.get( this, "__className__" ) || ""
-					);
-				}
-			}
-		} );
-	},
-
-	hasClass: function( selector ) {
-		var className, elem,
-			i = 0;
-
-		className = " " + selector + " ";
-		while ( ( elem = this[ i++ ] ) ) {
-			if ( elem.nodeType === 1 &&
-				( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) {
-					return true;
-			}
-		}
-
-		return false;
-	}
-} );
-
-
-
-
-var rreturn = /\r/g;
-
-jQuery.fn.extend( {
-	val: function( value ) {
-		var hooks, ret, valueIsFunction,
-			elem = this[ 0 ];
-
-		if ( !arguments.length ) {
-			if ( elem ) {
-				hooks = jQuery.valHooks[ elem.type ] ||
-					jQuery.valHooks[ elem.nodeName.toLowerCase() ];
-
-				if ( hooks &&
-					"get" in hooks &&
-					( ret = hooks.get( elem, "value" ) ) !== undefined
-				) {
-					return ret;
-				}
-
-				ret = elem.value;
-
-				// Handle most common string cases
-				if ( typeof ret === "string" ) {
-					return ret.replace( rreturn, "" );
-				}
-
-				// Handle cases where value is null/undef or number
-				return ret == null ? "" : ret;
-			}
-
-			return;
-		}
-
-		valueIsFunction = isFunction( value );
-
-		return this.each( function( i ) {
-			var val;
-
-			if ( this.nodeType !== 1 ) {
-				return;
-			}
-
-			if ( valueIsFunction ) {
-				val = value.call( this, i, jQuery( this ).val() );
-			} else {
-				val = value;
-			}
-
-			// Treat null/undefined as ""; convert numbers to string
-			if ( val == null ) {
-				val = "";
-
-			} else if ( typeof val === "number" ) {
-				val += "";
-
-			} else if ( Array.isArray( val ) ) {
-				val = jQuery.map( val, function( value ) {
-					return value == null ? "" : value + "";
-				} );
-			}
-
-			hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ];
-
-			// If set returns undefined, fall back to normal setting
-			if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) {
-				this.value = val;
-			}
-		} );
-	}
-} );
-
-jQuery.extend( {
-	valHooks: {
-		option: {
-			get: function( elem ) {
-
-				var val = jQuery.find.attr( elem, "value" );
-				return val != null ?
-					val :
-
-					// Support: IE <=10 - 11 only
-					// option.text throws exceptions (#14686, #14858)
-					// Strip and collapse whitespace
-					// https://html.spec.whatwg.org/#strip-and-collapse-whitespace
-					stripAndCollapse( jQuery.text( elem ) );
-			}
-		},
-		select: {
-			get: function( elem ) {
-				var value, option, i,
-					options = elem.options,
-					index = elem.selectedIndex,
-					one = elem.type === "select-one",
-					values = one ? null : [],
-					max = one ? index + 1 : options.length;
-
-				if ( index < 0 ) {
-					i = max;
-
-				} else {
-					i = one ? index : 0;
-				}
-
-				// Loop through all the selected options
-				for ( ; i < max; i++ ) {
-					option = options[ i ];
-
-					// Support: IE <=9 only
-					// IE8-9 doesn't update selected after form reset (#2551)
-					if ( ( option.selected || i === index ) &&
-
-							// Don't return options that are disabled or in a disabled optgroup
-							!option.disabled &&
-							( !option.parentNode.disabled ||
-								!nodeName( option.parentNode, "optgroup" ) ) ) {
-
-						// Get the specific value for the option
-						value = jQuery( option ).val();
-
-						// We don't need an array for one selects
-						if ( one ) {
-							return value;
-						}
-
-						// Multi-Selects return an array
-						values.push( value );
-					}
-				}
-
-				return values;
-			},
-
-			set: function( elem, value ) {
-				var optionSet, option,
-					options = elem.options,
-					values = jQuery.makeArray( value ),
-					i = options.length;
-
-				while ( i-- ) {
-					option = options[ i ];
-
-					/* eslint-disable no-cond-assign */
-
-					if ( option.selected =
-						jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1
-					) {
-						optionSet = true;
-					}
-
-					/* eslint-enable no-cond-assign */
-				}
-
-				// Force browsers to behave consistently when non-matching value is set
-				if ( !optionSet ) {
-					elem.selectedIndex = -1;
-				}
-				return values;
-			}
-		}
-	}
-} );
-
-// Radios and checkboxes getter/setter
-jQuery.each( [ "radio", "checkbox" ], function() {
-	jQuery.valHooks[ this ] = {
-		set: function( elem, value ) {
-			if ( Array.isArray( value ) ) {
-				return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 );
-			}
-		}
-	};
-	if ( !support.checkOn ) {
-		jQuery.valHooks[ this ].get = function( elem ) {
-			return elem.getAttribute( "value" ) === null ? "on" : elem.value;
-		};
-	}
-} );
-
-
-
-
-// Return jQuery for attributes-only inclusion
-
-
-support.focusin = "onfocusin" in window;
-
-
-var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/,
-	stopPropagationCallback = function( e ) {
-		e.stopPropagation();
-	};
-
-jQuery.extend( jQuery.event, {
-
-	trigger: function( event, data, elem, onlyHandlers ) {
-
-		var i, cur, tmp, bubbleType, ontype, handle, special, lastElement,
-			eventPath = [ elem || document ],
-			type = hasOwn.call( event, "type" ) ? event.type : event,
-			namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : [];
-
-		cur = lastElement = tmp = elem = elem || document;
-
-		// Don't do events on text and comment nodes
-		if ( elem.nodeType === 3 || elem.nodeType === 8 ) {
-			return;
-		}
-
-		// focus/blur morphs to focusin/out; ensure we're not firing them right now
-		if ( rfocusMorph.test( type + jQuery.event.triggered ) ) {
-			return;
-		}
-
-		if ( type.indexOf( "." ) > -1 ) {
-
-			// Namespaced trigger; create a regexp to match event type in handle()
-			namespaces = type.split( "." );
-			type = namespaces.shift();
-			namespaces.sort();
-		}
-		ontype = type.indexOf( ":" ) < 0 && "on" + type;
-
-		// Caller can pass in a jQuery.Event object, Object, or just an event type string
-		event = event[ jQuery.expando ] ?
-			event :
-			new jQuery.Event( type, typeof event === "object" && event );
-
-		// Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true)
-		event.isTrigger = onlyHandlers ? 2 : 3;
-		event.namespace = namespaces.join( "." );
-		event.rnamespace = event.namespace ?
-			new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) :
-			null;
-
-		// Clean up the event in case it is being reused
-		event.result = undefined;
-		if ( !event.target ) {
-			event.target = elem;
-		}
-
-		// Clone any incoming data and prepend the event, creating the handler arg list
-		data = data == null ?
-			[ event ] :
-			jQuery.makeArray( data, [ event ] );
-
-		// Allow special events to draw outside the lines
-		special = jQuery.event.special[ type ] || {};
-		if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) {
-			return;
-		}
-
-		// Determine event propagation path in advance, per W3C events spec (#9951)
-		// Bubble up to document, then to window; watch for a global ownerDocument var (#9724)
-		if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) {
-
-			bubbleType = special.delegateType || type;
-			if ( !rfocusMorph.test( bubbleType + type ) ) {
-				cur = cur.parentNode;
-			}
-			for ( ; cur; cur = cur.parentNode ) {
-				eventPath.push( cur );
-				tmp = cur;
-			}
-
-			// Only add window if we got to document (e.g., not plain obj or detached DOM)
-			if ( tmp === ( elem.ownerDocument || document ) ) {
-				eventPath.push( tmp.defaultView || tmp.parentWindow || window );
-			}
-		}
-
-		// Fire handlers on the event path
-		i = 0;
-		while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) {
-			lastElement = cur;
-			event.type = i > 1 ?
-				bubbleType :
-				special.bindType || type;
-
-			// jQuery handler
-			handle = (
-					dataPriv.get( cur, "events" ) || Object.create( null )
-				)[ event.type ] &&
-				dataPriv.get( cur, "handle" );
-			if ( handle ) {
-				handle.apply( cur, data );
-			}
-
-			// Native handler
-			handle = ontype && cur[ ontype ];
-			if ( handle && handle.apply && acceptData( cur ) ) {
-				event.result = handle.apply( cur, data );
-				if ( event.result === false ) {
-					event.preventDefault();
-				}
-			}
-		}
-		event.type = type;
-
-		// If nobody prevented the default action, do it now
-		if ( !onlyHandlers && !event.isDefaultPrevented() ) {
-
-			if ( ( !special._default ||
-				special._default.apply( eventPath.pop(), data ) === false ) &&
-				acceptData( elem ) ) {
-
-				// Call a native DOM method on the target with the same name as the event.
-				// Don't do default actions on window, that's where global variables be (#6170)
-				if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) {
-
-					// Don't re-trigger an onFOO event when we call its FOO() method
-					tmp = elem[ ontype ];
-
-					if ( tmp ) {
-						elem[ ontype ] = null;
-					}
-
-					// Prevent re-triggering of the same event, since we already bubbled it above
-					jQuery.event.triggered = type;
-
-					if ( event.isPropagationStopped() ) {
-						lastElement.addEventListener( type, stopPropagationCallback );
-					}
-
-					elem[ type ]();
-
-					if ( event.isPropagationStopped() ) {
-						lastElement.removeEventListener( type, stopPropagationCallback );
-					}
-
-					jQuery.event.triggered = undefined;
-
-					if ( tmp ) {
-						elem[ ontype ] = tmp;
-					}
-				}
-			}
-		}
-
-		return event.result;
-	},
-
-	// Piggyback on a donor event to simulate a different one
-	// Used only for `focus(in | out)` events
-	simulate: function( type, elem, event ) {
-		var e = jQuery.extend(
-			new jQuery.Event(),
-			event,
-			{
-				type: type,
-				isSimulated: true
-			}
-		);
-
-		jQuery.event.trigger( e, null, elem );
-	}
-
-} );
-
-jQuery.fn.extend( {
-
-	trigger: function( type, data ) {
-		return this.each( function() {
-			jQuery.event.trigger( type, data, this );
-		} );
-	},
-	triggerHandler: function( type, data ) {
-		var elem = this[ 0 ];
-		if ( elem ) {
-			return jQuery.event.trigger( type, data, elem, true );
-		}
-	}
-} );
-
-
-// Support: Firefox <=44
-// Firefox doesn't have focus(in | out) events
-// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787
-//
-// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1
-// focus(in | out) events fire after focus & blur events,
-// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order
-// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857
-if ( !support.focusin ) {
-	jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) {
-
-		// Attach a single capturing handler on the document while someone wants focusin/focusout
-		var handler = function( event ) {
-			jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) );
-		};
-
-		jQuery.event.special[ fix ] = {
-			setup: function() {
-
-				// Handle: regular nodes (via `this.ownerDocument`), window
-				// (via `this.document`) & document (via `this`).
-				var doc = this.ownerDocument || this.document || this,
-					attaches = dataPriv.access( doc, fix );
-
-				if ( !attaches ) {
-					doc.addEventListener( orig, handler, true );
-				}
-				dataPriv.access( doc, fix, ( attaches || 0 ) + 1 );
-			},
-			teardown: function() {
-				var doc = this.ownerDocument || this.document || this,
-					attaches = dataPriv.access( doc, fix ) - 1;
-
-				if ( !attaches ) {
-					doc.removeEventListener( orig, handler, true );
-					dataPriv.remove( doc, fix );
-
-				} else {
-					dataPriv.access( doc, fix, attaches );
-				}
-			}
-		};
-	} );
-}
-var location = window.location;
-
-var nonce = { guid: Date.now() };
-
-var rquery = ( /\?/ );
-
-
-
-// Cross-browser xml parsing
-jQuery.parseXML = function( data ) {
-	var xml;
-	if ( !data || typeof data !== "string" ) {
-		return null;
-	}
-
-	// Support: IE 9 - 11 only
-	// IE throws on parseFromString with invalid input.
-	try {
-		xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" );
-	} catch ( e ) {
-		xml = undefined;
-	}
-
-	if ( !xml || xml.getElementsByTagName( "parsererror" ).length ) {
-		jQuery.error( "Invalid XML: " + data );
-	}
-	return xml;
-};
-
-
-var
-	rbracket = /\[\]$/,
-	rCRLF = /\r?\n/g,
-	rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i,
-	rsubmittable = /^(?:input|select|textarea|keygen)/i;
-
-function buildParams( prefix, obj, traditional, add ) {
-	var name;
-
-	if ( Array.isArray( obj ) ) {
-
-		// Serialize array item.
-		jQuery.each( obj, function( i, v ) {
-			if ( traditional || rbracket.test( prefix ) ) {
-
-				// Treat each array item as a scalar.
-				add( prefix, v );
-
-			} else {
-
-				// Item is non-scalar (array or object), encode its numeric index.
-				buildParams(
-					prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]",
-					v,
-					traditional,
-					add
-				);
-			}
-		} );
-
-	} else if ( !traditional && toType( obj ) === "object" ) {
-
-		// Serialize object item.
-		for ( name in obj ) {
-			buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add );
-		}
-
-	} else {
-
-		// Serialize scalar item.
-		add( prefix, obj );
-	}
-}
-
-// Serialize an array of form elements or a set of
-// key/values into a query string
-jQuery.param = function( a, traditional ) {
-	var prefix,
-		s = [],
-		add = function( key, valueOrFunction ) {
-
-			// If value is a function, invoke it and use its return value
-			var value = isFunction( valueOrFunction ) ?
-				valueOrFunction() :
-				valueOrFunction;
-
-			s[ s.length ] = encodeURIComponent( key ) + "=" +
-				encodeURIComponent( value == null ? "" : value );
-		};
-
-	if ( a == null ) {
-		return "";
-	}
-
-	// If an array was passed in, assume that it is an array of form elements.
-	if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) {
-
-		// Serialize the form elements
-		jQuery.each( a, function() {
-			add( this.name, this.value );
-		} );
-
-	} else {
-
-		// If traditional, encode the "old" way (the way 1.3.2 or older
-		// did it), otherwise encode params recursively.
-		for ( prefix in a ) {
-			buildParams( prefix, a[ prefix ], traditional, add );
-		}
-	}
-
-	// Return the resulting serialization
-	return s.join( "&" );
-};
-
-jQuery.fn.extend( {
-	serialize: function() {
-		return jQuery.param( this.serializeArray() );
-	},
-	serializeArray: function() {
-		return this.map( function() {
-
-			// Can add propHook for "elements" to filter or add form elements
-			var elements = jQuery.prop( this, "elements" );
-			return elements ? jQuery.makeArray( elements ) : this;
-		} )
-		.filter( function() {
-			var type = this.type;
-
-			// Use .is( ":disabled" ) so that fieldset[disabled] works
-			return this.name && !jQuery( this ).is( ":disabled" ) &&
-				rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) &&
-				( this.checked || !rcheckableType.test( type ) );
-		} )
-		.map( function( _i, elem ) {
-			var val = jQuery( this ).val();
-
-			if ( val == null ) {
-				return null;
-			}
-
-			if ( Array.isArray( val ) ) {
-				return jQuery.map( val, function( val ) {
-					return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
-				} );
-			}
-
-			return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
-		} ).get();
-	}
-} );
-
-
-var
-	r20 = /%20/g,
-	rhash = /#.*$/,
-	rantiCache = /([?&])_=[^&]*/,
-	rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg,
-
-	// #7653, #8125, #8152: local protocol detection
-	rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/,
-	rnoContent = /^(?:GET|HEAD)$/,
-	rprotocol = /^\/\//,
-
-	/* Prefilters
-	 * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example)
-	 * 2) These are called:
-	 *    - BEFORE asking for a transport
-	 *    - AFTER param serialization (s.data is a string if s.processData is true)
-	 * 3) key is the dataType
-	 * 4) the catchall symbol "*" can be used
-	 * 5) execution will start with transport dataType and THEN continue down to "*" if needed
-	 */
-	prefilters = {},
-
-	/* Transports bindings
-	 * 1) key is the dataType
-	 * 2) the catchall symbol "*" can be used
-	 * 3) selection will start with transport dataType and THEN go to "*" if needed
-	 */
-	transports = {},
-
-	// Avoid comment-prolog char sequence (#10098); must appease lint and evade compression
-	allTypes = "*/".concat( "*" ),
-
-	// Anchor tag for parsing the document origin
-	originAnchor = document.createElement( "a" );
-	originAnchor.href = location.href;
-
-// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport
-function addToPrefiltersOrTransports( structure ) {
-
-	// dataTypeExpression is optional and defaults to "*"
-	return function( dataTypeExpression, func ) {
-
-		if ( typeof dataTypeExpression !== "string" ) {
-			func = dataTypeExpression;
-			dataTypeExpression = "*";
-		}
-
-		var dataType,
-			i = 0,
-			dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || [];
-
-		if ( isFunction( func ) ) {
-
-			// For each dataType in the dataTypeExpression
-			while ( ( dataType = dataTypes[ i++ ] ) ) {
-
-				// Prepend if requested
-				if ( dataType[ 0 ] === "+" ) {
-					dataType = dataType.slice( 1 ) || "*";
-					( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func );
-
-				// Otherwise append
-				} else {
-					( structure[ dataType ] = structure[ dataType ] || [] ).push( func );
-				}
-			}
-		}
-	};
-}
-
-// Base inspection function for prefilters and transports
-function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) {
-
-	var inspected = {},
-		seekingTransport = ( structure === transports );
-
-	function inspect( dataType ) {
-		var selected;
-		inspected[ dataType ] = true;
-		jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) {
-			var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR );
-			if ( typeof dataTypeOrTransport === "string" &&
-				!seekingTransport && !inspected[ dataTypeOrTransport ] ) {
-
-				options.dataTypes.unshift( dataTypeOrTransport );
-				inspect( dataTypeOrTransport );
-				return false;
-			} else if ( seekingTransport ) {
-				return !( selected = dataTypeOrTransport );
-			}
-		} );
-		return selected;
-	}
-
-	return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" );
-}
-
-// A special extend for ajax options
-// that takes "flat" options (not to be deep extended)
-// Fixes #9887
-function ajaxExtend( target, src ) {
-	var key, deep,
-		flatOptions = jQuery.ajaxSettings.flatOptions || {};
-
-	for ( key in src ) {
-		if ( src[ key ] !== undefined ) {
-			( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ];
-		}
-	}
-	if ( deep ) {
-		jQuery.extend( true, target, deep );
-	}
-
-	return target;
-}
-
-/* Handles responses to an ajax request:
- * - finds the right dataType (mediates between content-type and expected dataType)
- * - returns the corresponding response
- */
-function ajaxHandleResponses( s, jqXHR, responses ) {
-
-	var ct, type, finalDataType, firstDataType,
-		contents = s.contents,
-		dataTypes = s.dataTypes;
-
-	// Remove auto dataType and get content-type in the process
-	while ( dataTypes[ 0 ] === "*" ) {
-		dataTypes.shift();
-		if ( ct === undefined ) {
-			ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" );
-		}
-	}
-
-	// Check if we're dealing with a known content-type
-	if ( ct ) {
-		for ( type in contents ) {
-			if ( contents[ type ] && contents[ type ].test( ct ) ) {
-				dataTypes.unshift( type );
-				break;
-			}
-		}
-	}
-
-	// Check to see if we have a response for the expected dataType
-	if ( dataTypes[ 0 ] in responses ) {
-		finalDataType = dataTypes[ 0 ];
-	} else {
-
-		// Try convertible dataTypes
-		for ( type in responses ) {
-			if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) {
-				finalDataType = type;
-				break;
-			}
-			if ( !firstDataType ) {
-				firstDataType = type;
-			}
-		}
-
-		// Or just use first one
-		finalDataType = finalDataType || firstDataType;
-	}
-
-	// If we found a dataType
-	// We add the dataType to the list if needed
-	// and return the corresponding response
-	if ( finalDataType ) {
-		if ( finalDataType !== dataTypes[ 0 ] ) {
-			dataTypes.unshift( finalDataType );
-		}
-		return responses[ finalDataType ];
-	}
-}
-
-/* Chain conversions given the request and the original response
- * Also sets the responseXXX fields on the jqXHR instance
- */
-function ajaxConvert( s, response, jqXHR, isSuccess ) {
-	var conv2, current, conv, tmp, prev,
-		converters = {},
-
-		// Work with a copy of dataTypes in case we need to modify it for conversion
-		dataTypes = s.dataTypes.slice();
-
-	// Create converters map with lowercased keys
-	if ( dataTypes[ 1 ] ) {
-		for ( conv in s.converters ) {
-			converters[ conv.toLowerCase() ] = s.converters[ conv ];
-		}
-	}
-
-	current = dataTypes.shift();
-
-	// Convert to each sequential dataType
-	while ( current ) {
-
-		if ( s.responseFields[ current ] ) {
-			jqXHR[ s.responseFields[ current ] ] = response;
-		}
-
-		// Apply the dataFilter if provided
-		if ( !prev && isSuccess && s.dataFilter ) {
-			response = s.dataFilter( response, s.dataType );
-		}
-
-		prev = current;
-		current = dataTypes.shift();
-
-		if ( current ) {
-
-			// There's only work to do if current dataType is non-auto
-			if ( current === "*" ) {
-
-				current = prev;
-
-			// Convert response if prev dataType is non-auto and differs from current
-			} else if ( prev !== "*" && prev !== current ) {
-
-				// Seek a direct converter
-				conv = converters[ prev + " " + current ] || converters[ "* " + current ];
-
-				// If none found, seek a pair
-				if ( !conv ) {
-					for ( conv2 in converters ) {
-
-						// If conv2 outputs current
-						tmp = conv2.split( " " );
-						if ( tmp[ 1 ] === current ) {
-
-							// If prev can be converted to accepted input
-							conv = converters[ prev + " " + tmp[ 0 ] ] ||
-								converters[ "* " + tmp[ 0 ] ];
-							if ( conv ) {
-
-								// Condense equivalence converters
-								if ( conv === true ) {
-									conv = converters[ conv2 ];
-
-								// Otherwise, insert the intermediate dataType
-								} else if ( converters[ conv2 ] !== true ) {
-									current = tmp[ 0 ];
-									dataTypes.unshift( tmp[ 1 ] );
-								}
-								break;
-							}
-						}
-					}
-				}
-
-				// Apply converter (if not an equivalence)
-				if ( conv !== true ) {
-
-					// Unless errors are allowed to bubble, catch and return them
-					if ( conv && s.throws ) {
-						response = conv( response );
-					} else {
-						try {
-							response = conv( response );
-						} catch ( e ) {
-							return {
-								state: "parsererror",
-								error: conv ? e : "No conversion from " + prev + " to " + current
-							};
-						}
-					}
-				}
-			}
-		}
-	}
-
-	return { state: "success", data: response };
-}
-
-jQuery.extend( {
-
-	// Counter for holding the number of active queries
-	active: 0,
-
-	// Last-Modified header cache for next request
-	lastModified: {},
-	etag: {},
-
-	ajaxSettings: {
-		url: location.href,
-		type: "GET",
-		isLocal: rlocalProtocol.test( location.protocol ),
-		global: true,
-		processData: true,
-		async: true,
-		contentType: "application/x-www-form-urlencoded; charset=UTF-8",
-
-		/*
-		timeout: 0,
-		data: null,
-		dataType: null,
-		username: null,
-		password: null,
-		cache: null,
-		throws: false,
-		traditional: false,
-		headers: {},
-		*/
-
-		accepts: {
-			"*": allTypes,
-			text: "text/plain",
-			html: "text/html",
-			xml: "application/xml, text/xml",
-			json: "application/json, text/javascript"
-		},
-
-		contents: {
-			xml: /\bxml\b/,
-			html: /\bhtml/,
-			json: /\bjson\b/
-		},
-
-		responseFields: {
-			xml: "responseXML",
-			text: "responseText",
-			json: "responseJSON"
-		},
-
-		// Data converters
-		// Keys separate source (or catchall "*") and destination types with a single space
-		converters: {
-
-			// Convert anything to text
-			"* text": String,
-
-			// Text to html (true = no transformation)
-			"text html": true,
-
-			// Evaluate text as a json expression
-			"text json": JSON.parse,
-
-			// Parse text as xml
-			"text xml": jQuery.parseXML
-		},
-
-		// For options that shouldn't be deep extended:
-		// you can add your own custom options here if
-		// and when you create one that shouldn't be
-		// deep extended (see ajaxExtend)
-		flatOptions: {
-			url: true,
-			context: true
-		}
-	},
-
-	// Creates a full fledged settings object into target
-	// with both ajaxSettings and settings fields.
-	// If target is omitted, writes into ajaxSettings.
-	ajaxSetup: function( target, settings ) {
-		return settings ?
-
-			// Building a settings object
-			ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) :
-
-			// Extending ajaxSettings
-			ajaxExtend( jQuery.ajaxSettings, target );
-	},
-
-	ajaxPrefilter: addToPrefiltersOrTransports( prefilters ),
-	ajaxTransport: addToPrefiltersOrTransports( transports ),
-
-	// Main method
-	ajax: function( url, options ) {
-
-		// If url is an object, simulate pre-1.5 signature
-		if ( typeof url === "object" ) {
-			options = url;
-			url = undefined;
-		}
-
-		// Force options to be an object
-		options = options || {};
-
-		var transport,
-
-			// URL without anti-cache param
-			cacheURL,
-
-			// Response headers
-			responseHeadersString,
-			responseHeaders,
-
-			// timeout handle
-			timeoutTimer,
-
-			// Url cleanup var
-			urlAnchor,
-
-			// Request state (becomes false upon send and true upon completion)
-			completed,
-
-			// To know if global events are to be dispatched
-			fireGlobals,
-
-			// Loop variable
-			i,
-
-			// uncached part of the url
-			uncached,
-
-			// Create the final options object
-			s = jQuery.ajaxSetup( {}, options ),
-
-			// Callbacks context
-			callbackContext = s.context || s,
-
-			// Context for global events is callbackContext if it is a DOM node or jQuery collection
-			globalEventContext = s.context &&
-				( callbackContext.nodeType || callbackContext.jquery ) ?
-					jQuery( callbackContext ) :
-					jQuery.event,
-
-			// Deferreds
-			deferred = jQuery.Deferred(),
-			completeDeferred = jQuery.Callbacks( "once memory" ),
-
-			// Status-dependent callbacks
-			statusCode = s.statusCode || {},
-
-			// Headers (they are sent all at once)
-			requestHeaders = {},
-			requestHeadersNames = {},
-
-			// Default abort message
-			strAbort = "canceled",
-
-			// Fake xhr
-			jqXHR = {
-				readyState: 0,
-
-				// Builds headers hashtable if needed
-				getResponseHeader: function( key ) {
-					var match;
-					if ( completed ) {
-						if ( !responseHeaders ) {
-							responseHeaders = {};
-							while ( ( match = rheaders.exec( responseHeadersString ) ) ) {
-								responseHeaders[ match[ 1 ].toLowerCase() + " " ] =
-									( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] )
-										.concat( match[ 2 ] );
-							}
-						}
-						match = responseHeaders[ key.toLowerCase() + " " ];
-					}
-					return match == null ? null : match.join( ", " );
-				},
-
-				// Raw string
-				getAllResponseHeaders: function() {
-					return completed ? responseHeadersString : null;
-				},
-
-				// Caches the header
-				setRequestHeader: function( name, value ) {
-					if ( completed == null ) {
-						name = requestHeadersNames[ name.toLowerCase() ] =
-							requestHeadersNames[ name.toLowerCase() ] || name;
-						requestHeaders[ name ] = value;
-					}
-					return this;
-				},
-
-				// Overrides response content-type header
-				overrideMimeType: function( type ) {
-					if ( completed == null ) {
-						s.mimeType = type;
-					}
-					return this;
-				},
-
-				// Status-dependent callbacks
-				statusCode: function( map ) {
-					var code;
-					if ( map ) {
-						if ( completed ) {
-
-							// Execute the appropriate callbacks
-							jqXHR.always( map[ jqXHR.status ] );
-						} else {
-
-							// Lazy-add the new callbacks in a way that preserves old ones
-							for ( code in map ) {
-								statusCode[ code ] = [ statusCode[ code ], map[ code ] ];
-							}
-						}
-					}
-					return this;
-				},
-
-				// Cancel the request
-				abort: function( statusText ) {
-					var finalText = statusText || strAbort;
-					if ( transport ) {
-						transport.abort( finalText );
-					}
-					done( 0, finalText );
-					return this;
-				}
-			};
-
-		// Attach deferreds
-		deferred.promise( jqXHR );
-
-		// Add protocol if not provided (prefilters might expect it)
-		// Handle falsy url in the settings object (#10093: consistency with old signature)
-		// We also use the url parameter if available
-		s.url = ( ( url || s.url || location.href ) + "" )
-			.replace( rprotocol, location.protocol + "//" );
-
-		// Alias method option to type as per ticket #12004
-		s.type = options.method || options.type || s.method || s.type;
-
-		// Extract dataTypes list
-		s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ];
-
-		// A cross-domain request is in order when the origin doesn't match the current origin.
-		if ( s.crossDomain == null ) {
-			urlAnchor = document.createElement( "a" );
-
-			// Support: IE <=8 - 11, Edge 12 - 15
-			// IE throws exception on accessing the href property if url is malformed,
-			// e.g. http://example.com:80x/
-			try {
-				urlAnchor.href = s.url;
-
-				// Support: IE <=8 - 11 only
-				// Anchor's host property isn't correctly set when s.url is relative
-				urlAnchor.href = urlAnchor.href;
-				s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !==
-					urlAnchor.protocol + "//" + urlAnchor.host;
-			} catch ( e ) {
-
-				// If there is an error parsing the URL, assume it is crossDomain,
-				// it can be rejected by the transport if it is invalid
-				s.crossDomain = true;
-			}
-		}
-
-		// Convert data if not already a string
-		if ( s.data && s.processData && typeof s.data !== "string" ) {
-			s.data = jQuery.param( s.data, s.traditional );
-		}
-
-		// Apply prefilters
-		inspectPrefiltersOrTransports( prefilters, s, options, jqXHR );
-
-		// If request was aborted inside a prefilter, stop there
-		if ( completed ) {
-			return jqXHR;
-		}
-
-		// We can fire global events as of now if asked to
-		// Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118)
-		fireGlobals = jQuery.event && s.global;
-
-		// Watch for a new set of requests
-		if ( fireGlobals && jQuery.active++ === 0 ) {
-			jQuery.event.trigger( "ajaxStart" );
-		}
-
-		// Uppercase the type
-		s.type = s.type.toUpperCase();
-
-		// Determine if request has content
-		s.hasContent = !rnoContent.test( s.type );
-
-		// Save the URL in case we're toying with the If-Modified-Since
-		// and/or If-None-Match header later on
-		// Remove hash to simplify url manipulation
-		cacheURL = s.url.replace( rhash, "" );
-
-		// More options handling for requests with no content
-		if ( !s.hasContent ) {
-
-			// Remember the hash so we can put it back
-			uncached = s.url.slice( cacheURL.length );
-
-			// If data is available and should be processed, append data to url
-			if ( s.data && ( s.processData || typeof s.data === "string" ) ) {
-				cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data;
-
-				// #9682: remove data so that it's not used in an eventual retry
-				delete s.data;
-			}
-
-			// Add or update anti-cache param if needed
-			if ( s.cache === false ) {
-				cacheURL = cacheURL.replace( rantiCache, "$1" );
-				uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) +
-					uncached;
-			}
-
-			// Put hash and anti-cache on the URL that will be requested (gh-1732)
-			s.url = cacheURL + uncached;
-
-		// Change '%20' to '+' if this is encoded form body content (gh-2658)
-		} else if ( s.data && s.processData &&
-			( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) {
-			s.data = s.data.replace( r20, "+" );
-		}
-
-		// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
-		if ( s.ifModified ) {
-			if ( jQuery.lastModified[ cacheURL ] ) {
-				jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] );
-			}
-			if ( jQuery.etag[ cacheURL ] ) {
-				jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] );
-			}
-		}
-
-		// Set the correct header, if data is being sent
-		if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) {
-			jqXHR.setRequestHeader( "Content-Type", s.contentType );
-		}
-
-		// Set the Accepts header for the server, depending on the dataType
-		jqXHR.setRequestHeader(
-			"Accept",
-			s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ?
-				s.accepts[ s.dataTypes[ 0 ] ] +
-					( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) :
-				s.accepts[ "*" ]
-		);
-
-		// Check for headers option
-		for ( i in s.headers ) {
-			jqXHR.setRequestHeader( i, s.headers[ i ] );
-		}
-
-		// Allow custom headers/mimetypes and early abort
-		if ( s.beforeSend &&
-			( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) {
-
-			// Abort if not done already and return
-			return jqXHR.abort();
-		}
-
-		// Aborting is no longer a cancellation
-		strAbort = "abort";
-
-		// Install callbacks on deferreds
-		completeDeferred.add( s.complete );
-		jqXHR.done( s.success );
-		jqXHR.fail( s.error );
-
-		// Get transport
-		transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR );
-
-		// If no transport, we auto-abort
-		if ( !transport ) {
-			done( -1, "No Transport" );
-		} else {
-			jqXHR.readyState = 1;
-
-			// Send global event
-			if ( fireGlobals ) {
-				globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] );
-			}
-
-			// If request was aborted inside ajaxSend, stop there
-			if ( completed ) {
-				return jqXHR;
-			}
-
-			// Timeout
-			if ( s.async && s.timeout > 0 ) {
-				timeoutTimer = window.setTimeout( function() {
-					jqXHR.abort( "timeout" );
-				}, s.timeout );
-			}
-
-			try {
-				completed = false;
-				transport.send( requestHeaders, done );
-			} catch ( e ) {
-
-				// Rethrow post-completion exceptions
-				if ( completed ) {
-					throw e;
-				}
-
-				// Propagate others as results
-				done( -1, e );
-			}
-		}
-
-		// Callback for when everything is done
-		function done( status, nativeStatusText, responses, headers ) {
-			var isSuccess, success, error, response, modified,
-				statusText = nativeStatusText;
-
-			// Ignore repeat invocations
-			if ( completed ) {
-				return;
-			}
-
-			completed = true;
-
-			// Clear timeout if it exists
-			if ( timeoutTimer ) {
-				window.clearTimeout( timeoutTimer );
-			}
-
-			// Dereference transport for early garbage collection
-			// (no matter how long the jqXHR object will be used)
-			transport = undefined;
-
-			// Cache response headers
-			responseHeadersString = headers || "";
-
-			// Set readyState
-			jqXHR.readyState = status > 0 ? 4 : 0;
-
-			// Determine if successful
-			isSuccess = status >= 200 && status < 300 || status === 304;
-
-			// Get response data
-			if ( responses ) {
-				response = ajaxHandleResponses( s, jqXHR, responses );
-			}
-
-			// Use a noop converter for missing script
-			if ( !isSuccess && jQuery.inArray( "script", s.dataTypes ) > -1 ) {
-				s.converters[ "text script" ] = function() {};
-			}
-
-			// Convert no matter what (that way responseXXX fields are always set)
-			response = ajaxConvert( s, response, jqXHR, isSuccess );
-
-			// If successful, handle type chaining
-			if ( isSuccess ) {
-
-				// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
-				if ( s.ifModified ) {
-					modified = jqXHR.getResponseHeader( "Last-Modified" );
-					if ( modified ) {
-						jQuery.lastModified[ cacheURL ] = modified;
-					}
-					modified = jqXHR.getResponseHeader( "etag" );
-					if ( modified ) {
-						jQuery.etag[ cacheURL ] = modified;
-					}
-				}
-
-				// if no content
-				if ( status === 204 || s.type === "HEAD" ) {
-					statusText = "nocontent";
-
-				// if not modified
-				} else if ( status === 304 ) {
-					statusText = "notmodified";
-
-				// If we have data, let's convert it
-				} else {
-					statusText = response.state;
-					success = response.data;
-					error = response.error;
-					isSuccess = !error;
-				}
-			} else {
-
-				// Extract error from statusText and normalize for non-aborts
-				error = statusText;
-				if ( status || !statusText ) {
-					statusText = "error";
-					if ( status < 0 ) {
-						status = 0;
-					}
-				}
-			}
-
-			// Set data for the fake xhr object
-			jqXHR.status = status;
-			jqXHR.statusText = ( nativeStatusText || statusText ) + "";
-
-			// Success/Error
-			if ( isSuccess ) {
-				deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] );
-			} else {
-				deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] );
-			}
-
-			// Status-dependent callbacks
-			jqXHR.statusCode( statusCode );
-			statusCode = undefined;
-
-			if ( fireGlobals ) {
-				globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError",
-					[ jqXHR, s, isSuccess ? success : error ] );
-			}
-
-			// Complete
-			completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] );
-
-			if ( fireGlobals ) {
-				globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] );
-
-				// Handle the global AJAX counter
-				if ( !( --jQuery.active ) ) {
-					jQuery.event.trigger( "ajaxStop" );
-				}
-			}
-		}
-
-		return jqXHR;
-	},
-
-	getJSON: function( url, data, callback ) {
-		return jQuery.get( url, data, callback, "json" );
-	},
-
-	getScript: function( url, callback ) {
-		return jQuery.get( url, undefined, callback, "script" );
-	}
-} );
-
-jQuery.each( [ "get", "post" ], function( _i, method ) {
-	jQuery[ method ] = function( url, data, callback, type ) {
-
-		// Shift arguments if data argument was omitted
-		if ( isFunction( data ) ) {
-			type = type || callback;
-			callback = data;
-			data = undefined;
-		}
-
-		// The url can be an options object (which then must have .url)
-		return jQuery.ajax( jQuery.extend( {
-			url: url,
-			type: method,
-			dataType: type,
-			data: data,
-			success: callback
-		}, jQuery.isPlainObject( url ) && url ) );
-	};
-} );
-
-jQuery.ajaxPrefilter( function( s ) {
-	var i;
-	for ( i in s.headers ) {
-		if ( i.toLowerCase() === "content-type" ) {
-			s.contentType = s.headers[ i ] || "";
-		}
-	}
-} );
-
-
-jQuery._evalUrl = function( url, options, doc ) {
-	return jQuery.ajax( {
-		url: url,
-
-		// Make this explicit, since user can override this through ajaxSetup (#11264)
-		type: "GET",
-		dataType: "script",
-		cache: true,
-		async: false,
-		global: false,
-
-		// Only evaluate the response if it is successful (gh-4126)
-		// dataFilter is not invoked for failure responses, so using it instead
-		// of the default converter is kludgy but it works.
-		converters: {
-			"text script": function() {}
-		},
-		dataFilter: function( response ) {
-			jQuery.globalEval( response, options, doc );
-		}
-	} );
-};
-
-
-jQuery.fn.extend( {
-	wrapAll: function( html ) {
-		var wrap;
-
-		if ( this[ 0 ] ) {
-			if ( isFunction( html ) ) {
-				html = html.call( this[ 0 ] );
-			}
-
-			// The elements to wrap the target around
-			wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true );
-
-			if ( this[ 0 ].parentNode ) {
-				wrap.insertBefore( this[ 0 ] );
-			}
-
-			wrap.map( function() {
-				var elem = this;
-
-				while ( elem.firstElementChild ) {
-					elem = elem.firstElementChild;
-				}
-
-				return elem;
-			} ).append( this );
-		}
-
-		return this;
-	},
-
-	wrapInner: function( html ) {
-		if ( isFunction( html ) ) {
-			return this.each( function( i ) {
-				jQuery( this ).wrapInner( html.call( this, i ) );
-			} );
-		}
-
-		return this.each( function() {
-			var self = jQuery( this ),
-				contents = self.contents();
-
-			if ( contents.length ) {
-				contents.wrapAll( html );
-
-			} else {
-				self.append( html );
-			}
-		} );
-	},
-
-	wrap: function( html ) {
-		var htmlIsFunction = isFunction( html );
-
-		return this.each( function( i ) {
-			jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html );
-		} );
-	},
-
-	unwrap: function( selector ) {
-		this.parent( selector ).not( "body" ).each( function() {
-			jQuery( this ).replaceWith( this.childNodes );
-		} );
-		return this;
-	}
-} );
-
-
-jQuery.expr.pseudos.hidden = function( elem ) {
-	return !jQuery.expr.pseudos.visible( elem );
-};
-jQuery.expr.pseudos.visible = function( elem ) {
-	return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length );
-};
-
-
-
-
-jQuery.ajaxSettings.xhr = function() {
-	try {
-		return new window.XMLHttpRequest();
-	} catch ( e ) {}
-};
-
-var xhrSuccessStatus = {
-
-		// File protocol always yields status code 0, assume 200
-		0: 200,
-
-		// Support: IE <=9 only
-		// #1450: sometimes IE returns 1223 when it should be 204
-		1223: 204
-	},
-	xhrSupported = jQuery.ajaxSettings.xhr();
-
-support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported );
-support.ajax = xhrSupported = !!xhrSupported;
-
-jQuery.ajaxTransport( function( options ) {
-	var callback, errorCallback;
-
-	// Cross domain only allowed if supported through XMLHttpRequest
-	if ( support.cors || xhrSupported && !options.crossDomain ) {
-		return {
-			send: function( headers, complete ) {
-				var i,
-					xhr = options.xhr();
-
-				xhr.open(
-					options.type,
-					options.url,
-					options.async,
-					options.username,
-					options.password
-				);
-
-				// Apply custom fields if provided
-				if ( options.xhrFields ) {
-					for ( i in options.xhrFields ) {
-						xhr[ i ] = options.xhrFields[ i ];
-					}
-				}
-
-				// Override mime type if needed
-				if ( options.mimeType && xhr.overrideMimeType ) {
-					xhr.overrideMimeType( options.mimeType );
-				}
-
-				// X-Requested-With header
-				// For cross-domain requests, seeing as conditions for a preflight are
-				// akin to a jigsaw puzzle, we simply never set it to be sure.
-				// (it can always be set on a per-request basis or even using ajaxSetup)
-				// For same-domain requests, won't change header if already provided.
-				if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) {
-					headers[ "X-Requested-With" ] = "XMLHttpRequest";
-				}
-
-				// Set headers
-				for ( i in headers ) {
-					xhr.setRequestHeader( i, headers[ i ] );
-				}
-
-				// Callback
-				callback = function( type ) {
-					return function() {
-						if ( callback ) {
-							callback = errorCallback = xhr.onload =
-								xhr.onerror = xhr.onabort = xhr.ontimeout =
-									xhr.onreadystatechange = null;
-
-							if ( type === "abort" ) {
-								xhr.abort();
-							} else if ( type === "error" ) {
-
-								// Support: IE <=9 only
-								// On a manual native abort, IE9 throws
-								// errors on any property access that is not readyState
-								if ( typeof xhr.status !== "number" ) {
-									complete( 0, "error" );
-								} else {
-									complete(
-
-										// File: protocol always yields status 0; see #8605, #14207
-										xhr.status,
-										xhr.statusText
-									);
-								}
-							} else {
-								complete(
-									xhrSuccessStatus[ xhr.status ] || xhr.status,
-									xhr.statusText,
-
-									// Support: IE <=9 only
-									// IE9 has no XHR2 but throws on binary (trac-11426)
-									// For XHR2 non-text, let the caller handle it (gh-2498)
-									( xhr.responseType || "text" ) !== "text"  ||
-									typeof xhr.responseText !== "string" ?
-										{ binary: xhr.response } :
-										{ text: xhr.responseText },
-									xhr.getAllResponseHeaders()
-								);
-							}
-						}
-					};
-				};
-
-				// Listen to events
-				xhr.onload = callback();
-				errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" );
-
-				// Support: IE 9 only
-				// Use onreadystatechange to replace onabort
-				// to handle uncaught aborts
-				if ( xhr.onabort !== undefined ) {
-					xhr.onabort = errorCallback;
-				} else {
-					xhr.onreadystatechange = function() {
-
-						// Check readyState before timeout as it changes
-						if ( xhr.readyState === 4 ) {
-
-							// Allow onerror to be called first,
-							// but that will not handle a native abort
-							// Also, save errorCallback to a variable
-							// as xhr.onerror cannot be accessed
-							window.setTimeout( function() {
-								if ( callback ) {
-									errorCallback();
-								}
-							} );
-						}
-					};
-				}
-
-				// Create the abort callback
-				callback = callback( "abort" );
-
-				try {
-
-					// Do send the request (this may raise an exception)
-					xhr.send( options.hasContent && options.data || null );
-				} catch ( e ) {
-
-					// #14683: Only rethrow if this hasn't been notified as an error yet
-					if ( callback ) {
-						throw e;
-					}
-				}
-			},
-
-			abort: function() {
-				if ( callback ) {
-					callback();
-				}
-			}
-		};
-	}
-} );
-
-
-
-
-// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432)
-jQuery.ajaxPrefilter( function( s ) {
-	if ( s.crossDomain ) {
-		s.contents.script = false;
-	}
-} );
-
-// Install script dataType
-jQuery.ajaxSetup( {
-	accepts: {
-		script: "text/javascript, application/javascript, " +
-			"application/ecmascript, application/x-ecmascript"
-	},
-	contents: {
-		script: /\b(?:java|ecma)script\b/
-	},
-	converters: {
-		"text script": function( text ) {
-			jQuery.globalEval( text );
-			return text;
-		}
-	}
-} );
-
-// Handle cache's special case and crossDomain
-jQuery.ajaxPrefilter( "script", function( s ) {
-	if ( s.cache === undefined ) {
-		s.cache = false;
-	}
-	if ( s.crossDomain ) {
-		s.type = "GET";
-	}
-} );
-
-// Bind script tag hack transport
-jQuery.ajaxTransport( "script", function( s ) {
-
-	// This transport only deals with cross domain or forced-by-attrs requests
-	if ( s.crossDomain || s.scriptAttrs ) {
-		var script, callback;
-		return {
-			send: function( _, complete ) {
-				script = jQuery( "<script>" )
-					.attr( s.scriptAttrs || {} )
-					.prop( { charset: s.scriptCharset, src: s.url } )
-					.on( "load error", callback = function( evt ) {
-						script.remove();
-						callback = null;
-						if ( evt ) {
-							complete( evt.type === "error" ? 404 : 200, evt.type );
-						}
-					} );
-
-				// Use native DOM manipulation to avoid our domManip AJAX trickery
-				document.head.appendChild( script[ 0 ] );
-			},
-			abort: function() {
-				if ( callback ) {
-					callback();
-				}
-			}
-		};
-	}
-} );
-
-
-
-
-var oldCallbacks = [],
-	rjsonp = /(=)\?(?=&|$)|\?\?/;
-
-// Default jsonp settings
-jQuery.ajaxSetup( {
-	jsonp: "callback",
-	jsonpCallback: function() {
-		var callback = oldCallbacks.pop() || ( jQuery.expando + "_" + ( nonce.guid++ ) );
-		this[ callback ] = true;
-		return callback;
-	}
-} );
-
-// Detect, normalize options and install callbacks for jsonp requests
-jQuery.ajaxPrefilter( "json jsonp", function( s, originalSettings, jqXHR ) {
-
-	var callbackName, overwritten, responseContainer,
-		jsonProp = s.jsonp !== false && ( rjsonp.test( s.url ) ?
-			"url" :
-			typeof s.data === "string" &&
-				( s.contentType || "" )
-					.indexOf( "application/x-www-form-urlencoded" ) === 0 &&
-				rjsonp.test( s.data ) && "data"
-		);
-
-	// Handle iff the expected data type is "jsonp" or we have a parameter to set
-	if ( jsonProp || s.dataTypes[ 0 ] === "jsonp" ) {
-
-		// Get callback name, remembering preexisting value associated with it
-		callbackName = s.jsonpCallback = isFunction( s.jsonpCallback ) ?
-			s.jsonpCallback() :
-			s.jsonpCallback;
-
-		// Insert callback into url or form data
-		if ( jsonProp ) {
-			s[ jsonProp ] = s[ jsonProp ].replace( rjsonp, "$1" + callbackName );
-		} else if ( s.jsonp !== false ) {
-			s.url += ( rquery.test( s.url ) ? "&" : "?" ) + s.jsonp + "=" + callbackName;
-		}
-
-		// Use data converter to retrieve json after script execution
-		s.converters[ "script json" ] = function() {
-			if ( !responseContainer ) {
-				jQuery.error( callbackName + " was not called" );
-			}
-			return responseContainer[ 0 ];
-		};
-
-		// Force json dataType
-		s.dataTypes[ 0 ] = "json";
-
-		// Install callback
-		overwritten = window[ callbackName ];
-		window[ callbackName ] = function() {
-			responseContainer = arguments;
-		};
-
-		// Clean-up function (fires after converters)
-		jqXHR.always( function() {
-
-			// If previous value didn't exist - remove it
-			if ( overwritten === undefined ) {
-				jQuery( window ).removeProp( callbackName );
-
-			// Otherwise restore preexisting value
-			} else {
-				window[ callbackName ] = overwritten;
-			}
-
-			// Save back as free
-			if ( s[ callbackName ] ) {
-
-				// Make sure that re-using the options doesn't screw things around
-				s.jsonpCallback = originalSettings.jsonpCallback;
-
-				// Save the callback name for future use
-				oldCallbacks.push( callbackName );
-			}
-
-			// Call if it was a function and we have a response
-			if ( responseContainer && isFunction( overwritten ) ) {
-				overwritten( responseContainer[ 0 ] );
-			}
-
-			responseContainer = overwritten = undefined;
-		} );
-
-		// Delegate to script
-		return "script";
-	}
-} );
-
-
-
-
-// Support: Safari 8 only
-// In Safari 8 documents created via document.implementation.createHTMLDocument
-// collapse sibling forms: the second one becomes a child of the first one.
-// Because of that, this security measure has to be disabled in Safari 8.
-// https://bugs.webkit.org/show_bug.cgi?id=137337
-support.createHTMLDocument = ( function() {
-	var body = document.implementation.createHTMLDocument( "" ).body;
-	body.innerHTML = "<form></form><form></form>";
-	return body.childNodes.length === 2;
-} )();
-
-
-// Argument "data" should be string of html
-// context (optional): If specified, the fragment will be created in this context,
-// defaults to document
-// keepScripts (optional): If true, will include scripts passed in the html string
-jQuery.parseHTML = function( data, context, keepScripts ) {
-	if ( typeof data !== "string" ) {
-		return [];
-	}
-	if ( typeof context === "boolean" ) {
-		keepScripts = context;
-		context = false;
-	}
-
-	var base, parsed, scripts;
-
-	if ( !context ) {
-
-		// Stop scripts or inline event handlers from being executed immediately
-		// by using document.implementation
-		if ( support.createHTMLDocument ) {
-			context = document.implementation.createHTMLDocument( "" );
-
-			// Set the base href for the created document
-			// so any parsed elements with URLs
-			// are based on the document's URL (gh-2965)
-			base = context.createElement( "base" );
-			base.href = document.location.href;
-			context.head.appendChild( base );
-		} else {
-			context = document;
-		}
-	}
-
-	parsed = rsingleTag.exec( data );
-	scripts = !keepScripts && [];
-
-	// Single tag
-	if ( parsed ) {
-		return [ context.createElement( parsed[ 1 ] ) ];
-	}
-
-	parsed = buildFragment( [ data ], context, scripts );
-
-	if ( scripts && scripts.length ) {
-		jQuery( scripts ).remove();
-	}
-
-	return jQuery.merge( [], parsed.childNodes );
-};
-
-
-/**
- * Load a url into a page
- */
-jQuery.fn.load = function( url, params, callback ) {
-	var selector, type, response,
-		self = this,
-		off = url.indexOf( " " );
-
-	if ( off > -1 ) {
-		selector = stripAndCollapse( url.slice( off ) );
-		url = url.slice( 0, off );
-	}
-
-	// If it's a function
-	if ( isFunction( params ) ) {
-
-		// We assume that it's the callback
-		callback = params;
-		params = undefined;
-
-	// Otherwise, build a param string
-	} else if ( params && typeof params === "object" ) {
-		type = "POST";
-	}
-
-	// If we have elements to modify, make the request
-	if ( self.length > 0 ) {
-		jQuery.ajax( {
-			url: url,
-
-			// If "type" variable is undefined, then "GET" method will be used.
-			// Make value of this field explicit since
-			// user can override it through ajaxSetup method
-			type: type || "GET",
-			dataType: "html",
-			data: params
-		} ).done( function( responseText ) {
-
-			// Save response for use in complete callback
-			response = arguments;
-
-			self.html( selector ?
-
-				// If a selector was specified, locate the right elements in a dummy div
-				// Exclude scripts to avoid IE 'Permission Denied' errors
-				jQuery( "<div>" ).append( jQuery.parseHTML( responseText ) ).find( selector ) :
-
-				// Otherwise use the full result
-				responseText );
-
-		// If the request succeeds, this function gets "data", "status", "jqXHR"
-		// but they are ignored because response was set above.
-		// If it fails, this function gets "jqXHR", "status", "error"
-		} ).always( callback && function( jqXHR, status ) {
-			self.each( function() {
-				callback.apply( this, response || [ jqXHR.responseText, status, jqXHR ] );
-			} );
-		} );
-	}
-
-	return this;
-};
-
-
-
-
-jQuery.expr.pseudos.animated = function( elem ) {
-	return jQuery.grep( jQuery.timers, function( fn ) {
-		return elem === fn.elem;
-	} ).length;
-};
-
-
-
-
-jQuery.offset = {
-	setOffset: function( elem, options, i ) {
-		var curPosition, curLeft, curCSSTop, curTop, curOffset, curCSSLeft, calculatePosition,
-			position = jQuery.css( elem, "position" ),
-			curElem = jQuery( elem ),
-			props = {};
-
-		// Set position first, in-case top/left are set even on static elem
-		if ( position === "static" ) {
-			elem.style.position = "relative";
-		}
-
-		curOffset = curElem.offset();
-		curCSSTop = jQuery.css( elem, "top" );
-		curCSSLeft = jQuery.css( elem, "left" );
-		calculatePosition = ( position === "absolute" || position === "fixed" ) &&
-			( curCSSTop + curCSSLeft ).indexOf( "auto" ) > -1;
-
-		// Need to be able to calculate position if either
-		// top or left is auto and position is either absolute or fixed
-		if ( calculatePosition ) {
-			curPosition = curElem.position();
-			curTop = curPosition.top;
-			curLeft = curPosition.left;
-
-		} else {
-			curTop = parseFloat( curCSSTop ) || 0;
-			curLeft = parseFloat( curCSSLeft ) || 0;
-		}
-
-		if ( isFunction( options ) ) {
-
-			// Use jQuery.extend here to allow modification of coordinates argument (gh-1848)
-			options = options.call( elem, i, jQuery.extend( {}, curOffset ) );
-		}
-
-		if ( options.top != null ) {
-			props.top = ( options.top - curOffset.top ) + curTop;
-		}
-		if ( options.left != null ) {
-			props.left = ( options.left - curOffset.left ) + curLeft;
-		}
-
-		if ( "using" in options ) {
-			options.using.call( elem, props );
-
-		} else {
-			if ( typeof props.top === "number" ) {
-				props.top += "px";
-			}
-			if ( typeof props.left === "number" ) {
-				props.left += "px";
-			}
-			curElem.css( props );
-		}
-	}
-};
-
-jQuery.fn.extend( {
-
-	// offset() relates an element's border box to the document origin
-	offset: function( options ) {
-
-		// Preserve chaining for setter
-		if ( arguments.length ) {
-			return options === undefined ?
-				this :
-				this.each( function( i ) {
-					jQuery.offset.setOffset( this, options, i );
-				} );
-		}
-
-		var rect, win,
-			elem = this[ 0 ];
-
-		if ( !elem ) {
-			return;
-		}
-
-		// Return zeros for disconnected and hidden (display: none) elements (gh-2310)
-		// Support: IE <=11 only
-		// Running getBoundingClientRect on a
-		// disconnected node in IE throws an error
-		if ( !elem.getClientRects().length ) {
-			return { top: 0, left: 0 };
-		}
-
-		// Get document-relative position by adding viewport scroll to viewport-relative gBCR
-		rect = elem.getBoundingClientRect();
-		win = elem.ownerDocument.defaultView;
-		return {
-			top: rect.top + win.pageYOffset,
-			left: rect.left + win.pageXOffset
-		};
-	},
-
-	// position() relates an element's margin box to its offset parent's padding box
-	// This corresponds to the behavior of CSS absolute positioning
-	position: function() {
-		if ( !this[ 0 ] ) {
-			return;
-		}
-
-		var offsetParent, offset, doc,
-			elem = this[ 0 ],
-			parentOffset = { top: 0, left: 0 };
-
-		// position:fixed elements are offset from the viewport, which itself always has zero offset
-		if ( jQuery.css( elem, "position" ) === "fixed" ) {
-
-			// Assume position:fixed implies availability of getBoundingClientRect
-			offset = elem.getBoundingClientRect();
-
-		} else {
-			offset = this.offset();
-
-			// Account for the *real* offset parent, which can be the document or its root element
-			// when a statically positioned element is identified
-			doc = elem.ownerDocument;
-			offsetParent = elem.offsetParent || doc.documentElement;
-			while ( offsetParent &&
-				( offsetParent === doc.body || offsetParent === doc.documentElement ) &&
-				jQuery.css( offsetParent, "position" ) === "static" ) {
-
-				offsetParent = offsetParent.parentNode;
-			}
-			if ( offsetParent && offsetParent !== elem && offsetParent.nodeType === 1 ) {
-
-				// Incorporate borders into its offset, since they are outside its content origin
-				parentOffset = jQuery( offsetParent ).offset();
-				parentOffset.top += jQuery.css( offsetParent, "borderTopWidth", true );
-				parentOffset.left += jQuery.css( offsetParent, "borderLeftWidth", true );
-			}
-		}
-
-		// Subtract parent offsets and element margins
-		return {
-			top: offset.top - parentOffset.top - jQuery.css( elem, "marginTop", true ),
-			left: offset.left - parentOffset.left - jQuery.css( elem, "marginLeft", true )
-		};
-	},
-
-	// This method will return documentElement in the following cases:
-	// 1) For the element inside the iframe without offsetParent, this method will return
-	//    documentElement of the parent window
-	// 2) For the hidden or detached element
-	// 3) For body or html element, i.e. in case of the html node - it will return itself
-	//
-	// but those exceptions were never presented as a real life use-cases
-	// and might be considered as more preferable results.
-	//
-	// This logic, however, is not guaranteed and can change at any point in the future
-	offsetParent: function() {
-		return this.map( function() {
-			var offsetParent = this.offsetParent;
-
-			while ( offsetParent && jQuery.css( offsetParent, "position" ) === "static" ) {
-				offsetParent = offsetParent.offsetParent;
-			}
-
-			return offsetParent || documentElement;
-		} );
-	}
-} );
-
-// Create scrollLeft and scrollTop methods
-jQuery.each( { scrollLeft: "pageXOffset", scrollTop: "pageYOffset" }, function( method, prop ) {
-	var top = "pageYOffset" === prop;
-
-	jQuery.fn[ method ] = function( val ) {
-		return access( this, function( elem, method, val ) {
-
-			// Coalesce documents and windows
-			var win;
-			if ( isWindow( elem ) ) {
-				win = elem;
-			} else if ( elem.nodeType === 9 ) {
-				win = elem.defaultView;
-			}
-
-			if ( val === undefined ) {
-				return win ? win[ prop ] : elem[ method ];
-			}
-
-			if ( win ) {
-				win.scrollTo(
-					!top ? val : win.pageXOffset,
-					top ? val : win.pageYOffset
-				);
-
-			} else {
-				elem[ method ] = val;
-			}
-		}, method, val, arguments.length );
-	};
-} );
-
-// Support: Safari <=7 - 9.1, Chrome <=37 - 49
-// Add the top/left cssHooks using jQuery.fn.position
-// Webkit bug: https://bugs.webkit.org/show_bug.cgi?id=29084
-// Blink bug: https://bugs.chromium.org/p/chromium/issues/detail?id=589347
-// getComputedStyle returns percent when specified for top/left/bottom/right;
-// rather than make the css module depend on the offset module, just check for it here
-jQuery.each( [ "top", "left" ], function( _i, prop ) {
-	jQuery.cssHooks[ prop ] = addGetHookIf( support.pixelPosition,
-		function( elem, computed ) {
-			if ( computed ) {
-				computed = curCSS( elem, prop );
-
-				// If curCSS returns percentage, fallback to offset
-				return rnumnonpx.test( computed ) ?
-					jQuery( elem ).position()[ prop ] + "px" :
-					computed;
-			}
-		}
-	);
-} );
-
-
-// Create innerHeight, innerWidth, height, width, outerHeight and outerWidth methods
-jQuery.each( { Height: "height", Width: "width" }, function( name, type ) {
-	jQuery.each( { padding: "inner" + name, content: type, "": "outer" + name },
-		function( defaultExtra, funcName ) {
-
-		// Margin is only for outerHeight, outerWidth
-		jQuery.fn[ funcName ] = function( margin, value ) {
-			var chainable = arguments.length && ( defaultExtra || typeof margin !== "boolean" ),
-				extra = defaultExtra || ( margin === true || value === true ? "margin" : "border" );
-
-			return access( this, function( elem, type, value ) {
-				var doc;
-
-				if ( isWindow( elem ) ) {
-
-					// $( window ).outerWidth/Height return w/h including scrollbars (gh-1729)
-					return funcName.indexOf( "outer" ) === 0 ?
-						elem[ "inner" + name ] :
-						elem.document.documentElement[ "client" + name ];
-				}
-
-				// Get document width or height
-				if ( elem.nodeType === 9 ) {
-					doc = elem.documentElement;
-
-					// Either scroll[Width/Height] or offset[Width/Height] or client[Width/Height],
-					// whichever is greatest
-					return Math.max(
-						elem.body[ "scroll" + name ], doc[ "scroll" + name ],
-						elem.body[ "offset" + name ], doc[ "offset" + name ],
-						doc[ "client" + name ]
-					);
-				}
-
-				return value === undefined ?
-
-					// Get width or height on the element, requesting but not forcing parseFloat
-					jQuery.css( elem, type, extra ) :
-
-					// Set width or height on the element
-					jQuery.style( elem, type, value, extra );
-			}, type, chainable ? margin : undefined, chainable );
-		};
-	} );
-} );
-
-
-jQuery.each( [
-	"ajaxStart",
-	"ajaxStop",
-	"ajaxComplete",
-	"ajaxError",
-	"ajaxSuccess",
-	"ajaxSend"
-], function( _i, type ) {
-	jQuery.fn[ type ] = function( fn ) {
-		return this.on( type, fn );
-	};
-} );
-
-
-
-
-jQuery.fn.extend( {
-
-	bind: function( types, data, fn ) {
-		return this.on( types, null, data, fn );
-	},
-	unbind: function( types, fn ) {
-		return this.off( types, null, fn );
-	},
-
-	delegate: function( selector, types, data, fn ) {
-		return this.on( types, selector, data, fn );
-	},
-	undelegate: function( selector, types, fn ) {
-
-		// ( namespace ) or ( selector, types [, fn] )
-		return arguments.length === 1 ?
-			this.off( selector, "**" ) :
-			this.off( types, selector || "**", fn );
-	},
-
-	hover: function( fnOver, fnOut ) {
-		return this.mouseenter( fnOver ).mouseleave( fnOut || fnOver );
-	}
-} );
-
-jQuery.each( ( "blur focus focusin focusout resize scroll click dblclick " +
-	"mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave " +
-	"change select submit keydown keypress keyup contextmenu" ).split( " " ),
-	function( _i, name ) {
-
-		// Handle event binding
-		jQuery.fn[ name ] = function( data, fn ) {
-			return arguments.length > 0 ?
-				this.on( name, null, data, fn ) :
-				this.trigger( name );
-		};
-	} );
-
-
-
-
-// Support: Android <=4.0 only
-// Make sure we trim BOM and NBSP
-var rtrim = /^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g;
-
-// Bind a function to a context, optionally partially applying any
-// arguments.
-// jQuery.proxy is deprecated to promote standards (specifically Function#bind)
-// However, it is not slated for removal any time soon
-jQuery.proxy = function( fn, context ) {
-	var tmp, args, proxy;
-
-	if ( typeof context === "string" ) {
-		tmp = fn[ context ];
-		context = fn;
-		fn = tmp;
-	}
-
-	// Quick check to determine if target is callable, in the spec
-	// this throws a TypeError, but we will just return undefined.
-	if ( !isFunction( fn ) ) {
-		return undefined;
-	}
-
-	// Simulated bind
-	args = slice.call( arguments, 2 );
-	proxy = function() {
-		return fn.apply( context || this, args.concat( slice.call( arguments ) ) );
-	};
-
-	// Set the guid of unique handler to the same of original handler, so it can be removed
-	proxy.guid = fn.guid = fn.guid || jQuery.guid++;
-
-	return proxy;
-};
-
-jQuery.holdReady = function( hold ) {
-	if ( hold ) {
-		jQuery.readyWait++;
-	} else {
-		jQuery.ready( true );
-	}
-};
-jQuery.isArray = Array.isArray;
-jQuery.parseJSON = JSON.parse;
-jQuery.nodeName = nodeName;
-jQuery.isFunction = isFunction;
-jQuery.isWindow = isWindow;
-jQuery.camelCase = camelCase;
-jQuery.type = toType;
-
-jQuery.now = Date.now;
-
-jQuery.isNumeric = function( obj ) {
-
-	// As of jQuery 3.0, isNumeric is limited to
-	// strings and numbers (primitives or objects)
-	// that can be coerced to finite numbers (gh-2662)
-	var type = jQuery.type( obj );
-	return ( type === "number" || type === "string" ) &&
-
-		// parseFloat NaNs numeric-cast false positives ("")
-		// ...but misinterprets leading-number strings, particularly hex literals ("0x...")
-		// subtraction forces infinities to NaN
-		!isNaN( obj - parseFloat( obj ) );
-};
-
-jQuery.trim = function( text ) {
-	return text == null ?
-		"" :
-		( text + "" ).replace( rtrim, "" );
-};
-
-
-
-// Register as a named AMD module, since jQuery can be concatenated with other
-// files that may use define, but not via a proper concatenation script that
-// understands anonymous AMD modules. A named AMD is safest and most robust
-// way to register. Lowercase jquery is used because AMD module names are
-// derived from file names, and jQuery is normally delivered in a lowercase
-// file name. Do this after creating the global so that if an AMD module wants
-// to call noConflict to hide this version of jQuery, it will work.
-
-// Note that for maximum portability, libraries that are not jQuery should
-// declare themselves as anonymous modules, and avoid setting a global if an
-// AMD loader is present. jQuery is a special case. For more information, see
-// https://github.com/jrburke/requirejs/wiki/Updating-existing-libraries#wiki-anon
-
-if ( typeof define === "function" && define.amd ) {
-	define( "jquery", [], function() {
-		return jQuery;
-	} );
-}
-
-
-
-
-var
-
-	// Map over jQuery in case of overwrite
-	_jQuery = window.jQuery,
-
-	// Map over the $ in case of overwrite
-	_$ = window.$;
-
-jQuery.noConflict = function( deep ) {
-	if ( window.$ === jQuery ) {
-		window.$ = _$;
-	}
-
-	if ( deep && window.jQuery === jQuery ) {
-		window.jQuery = _jQuery;
-	}
-
-	return jQuery;
-};
-
-// Expose jQuery and $ identifiers, even in AMD
-// (#7102#comment:10, https://github.com/jquery/jquery/pull/557)
-// and CommonJS for browser emulators (#13566)
-if ( typeof noGlobal === "undefined" ) {
-	window.jQuery = window.$ = jQuery;
-}
-
-
-
-
-return jQuery;
-} );
diff --git a/docs/_static/jquery.js b/docs/_static/jquery.js
deleted file mode 100644
index b0614034a..000000000
--- a/docs/_static/jquery.js
+++ /dev/null
@@ -1,2 +0,0 @@
-/*! jQuery v3.5.1 | (c) JS Foundation and other contributors | jquery.org/license */
-!function(e,t){"use strict";"object"==typeof module&&"object"==typeof module.exports?module.exports=e.document?t(e,!0):function(e){if(!e.document)throw new Error("jQuery requires a window with a document");return t(e)}:t(e)}("undefined"!=typeof window?window:this,function(C,e){"use strict";var t=[],r=Object.getPrototypeOf,s=t.slice,g=t.flat?function(e){return t.flat.call(e)}:function(e){return t.concat.apply([],e)},u=t.push,i=t.indexOf,n={},o=n.toString,v=n.hasOwnProperty,a=v.toString,l=a.call(Object),y={},m=function(e){return"function"==typeof e&&"number"!=typeof e.nodeType},x=function(e){return null!=e&&e===e.window},E=C.document,c={type:!0,src:!0,nonce:!0,noModule:!0};function b(e,t,n){var r,i,o=(n=n||E).createElement("script");if(o.text=e,t)for(r in c)(i=t[r]||t.getAttribute&&t.getAttribute(r))&&o.setAttribute(r,i);n.head.appendChild(o).parentNode.removeChild(o)}function w(e){return null==e?e+"":"object"==typeof e||"function"==typeof e?n[o.call(e)]||"object":typeof e}var 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index a30ca53dc..000000000
--- a/docs/_static/js/jquery-fix.js
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\ No newline at end of file
diff --git a/docs/_static/language_data.js b/docs/_static/language_data.js
deleted file mode 100644
index d2b4ee91b..000000000
--- a/docs/_static/language_data.js
+++ /dev/null
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-/*
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-
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-    tional: 'tion',
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-    anci: 'ance',
-    izer: 'ize',
-    bli: 'ble',
-    alli: 'al',
-    entli: 'ent',
-    eli: 'e',
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-    ization: 'ize',
-    ation: 'ate',
-    ator: 'ate',
-    alism: 'al',
-    iveness: 'ive',
-    fulness: 'ful',
-    ousness: 'ous',
-    aliti: 'al',
-    iviti: 'ive',
-    biliti: 'ble',
-    logi: 'log'
-  };
-
-  var step3list = {
-    icate: 'ic',
-    ative: '',
-    alize: 'al',
-    iciti: 'ic',
-    ical: 'ic',
-    ful: '',
-    ness: ''
-  };
-
-  var c = "[^aeiou]";          // consonant
-  var v = "[aeiouy]";          // vowel
-  var C = c + "[^aeiouy]*";    // consonant sequence
-  var V = v + "[aeiou]*";      // vowel sequence
-
-  var mgr0 = "^(" + C + ")?" + V + C;                      // [C]VC... is m>0
-  var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$";    // [C]VC[V] is m=1
-  var mgr1 = "^(" + C + ")?" + V + C + V + C;              // [C]VCVC... is m>1
-  var s_v   = "^(" + C + ")?" + v;                         // vowel in stem
-
-  this.stemWord = function (w) {
-    var stem;
-    var suffix;
-    var firstch;
-    var origword = w;
-
-    if (w.length < 3)
-      return w;
-
-    var re;
-    var re2;
-    var re3;
-    var re4;
-
-    firstch = w.substr(0,1);
-    if (firstch == "y")
-      w = firstch.toUpperCase() + w.substr(1);
-
-    // Step 1a
-    re = /^(.+?)(ss|i)es$/;
-    re2 = /^(.+?)([^s])s$/;
-
-    if (re.test(w))
-      w = w.replace(re,"$1$2");
-    else if (re2.test(w))
-      w = w.replace(re2,"$1$2");
-
-    // Step 1b
-    re = /^(.+?)eed$/;
-    re2 = /^(.+?)(ed|ing)$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      re = new RegExp(mgr0);
-      if (re.test(fp[1])) {
-        re = /.$/;
-        w = w.replace(re,"");
-      }
-    }
-    else if (re2.test(w)) {
-      var fp = re2.exec(w);
-      stem = fp[1];
-      re2 = new RegExp(s_v);
-      if (re2.test(stem)) {
-        w = stem;
-        re2 = /(at|bl|iz)$/;
-        re3 = new RegExp("([^aeiouylsz])\\1$");
-        re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
-        if (re2.test(w))
-          w = w + "e";
-        else if (re3.test(w)) {
-          re = /.$/;
-          w = w.replace(re,"");
-        }
-        else if (re4.test(w))
-          w = w + "e";
-      }
-    }
-
-    // Step 1c
-    re = /^(.+?)y$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      stem = fp[1];
-      re = new RegExp(s_v);
-      if (re.test(stem))
-        w = stem + "i";
-    }
-
-    // Step 2
-    re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      stem = fp[1];
-      suffix = fp[2];
-      re = new RegExp(mgr0);
-      if (re.test(stem))
-        w = stem + step2list[suffix];
-    }
-
-    // Step 3
-    re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      stem = fp[1];
-      suffix = fp[2];
-      re = new RegExp(mgr0);
-      if (re.test(stem))
-        w = stem + step3list[suffix];
-    }
-
-    // Step 4
-    re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
-    re2 = /^(.+?)(s|t)(ion)$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      stem = fp[1];
-      re = new RegExp(mgr1);
-      if (re.test(stem))
-        w = stem;
-    }
-    else if (re2.test(w)) {
-      var fp = re2.exec(w);
-      stem = fp[1] + fp[2];
-      re2 = new RegExp(mgr1);
-      if (re2.test(stem))
-        w = stem;
-    }
-
-    // Step 5
-    re = /^(.+?)e$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      stem = fp[1];
-      re = new RegExp(mgr1);
-      re2 = new RegExp(meq1);
-      re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
-      if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
-        w = stem;
-    }
-    re = /ll$/;
-    re2 = new RegExp(mgr1);
-    if (re.test(w) && re2.test(w)) {
-      re = /.$/;
-      w = w.replace(re,"");
-    }
-
-    // and turn initial Y back to y
-    if (firstch == "y")
-      w = firstch.toLowerCase() + w.substr(1);
-    return w;
-  }
-}
-
-
-
-
-
-var splitChars = (function() {
-    var result = {};
-    var singles = [96, 180, 187, 191, 215, 247, 749, 885, 903, 907, 909, 930, 1014, 1648,
-         1748, 1809, 2416, 2473, 2481, 2526, 2601, 2609, 2612, 2615, 2653, 2702,
-         2706, 2729, 2737, 2740, 2857, 2865, 2868, 2910, 2928, 2948, 2961, 2971,
-         2973, 3085, 3089, 3113, 3124, 3213, 3217, 3241, 3252, 3295, 3341, 3345,
-         3369, 3506, 3516, 3633, 3715, 3721, 3736, 3744, 3748, 3750, 3756, 3761,
-         3781, 3912, 4239, 4347, 4681, 4695, 4697, 4745, 4785, 4799, 4801, 4823,
-         4881, 5760, 5901, 5997, 6313, 7405, 8024, 8026, 8028, 8030, 8117, 8125,
-         8133, 8181, 8468, 8485, 8487, 8489, 8494, 8527, 11311, 11359, 11687, 11695,
-         11703, 11711, 11719, 11727, 11735, 12448, 12539, 43010, 43014, 43019, 43587,
-         43696, 43713, 64286, 64297, 64311, 64317, 64319, 64322, 64325, 65141];
-    var i, j, start, end;
-    for (i = 0; i < singles.length; i++) {
-        result[singles[i]] = true;
-    }
-    var ranges = [[0, 47], [58, 64], [91, 94], [123, 169], [171, 177], [182, 184], [706, 709],
-         [722, 735], [741, 747], [751, 879], [888, 889], [894, 901], [1154, 1161],
-         [1318, 1328], [1367, 1368], [1370, 1376], [1416, 1487], [1515, 1519], [1523, 1568],
-         [1611, 1631], [1642, 1645], [1750, 1764], [1767, 1773], [1789, 1790], [1792, 1807],
-         [1840, 1868], [1958, 1968], [1970, 1983], [2027, 2035], [2038, 2041], [2043, 2047],
-         [2070, 2073], [2075, 2083], [2085, 2087], [2089, 2307], [2362, 2364], [2366, 2383],
-         [2385, 2391], [2402, 2405], [2419, 2424], [2432, 2436], [2445, 2446], [2449, 2450],
-         [2483, 2485], [2490, 2492], [2494, 2509], [2511, 2523], [2530, 2533], [2546, 2547],
-         [2554, 2564], [2571, 2574], [2577, 2578], [2618, 2648], [2655, 2661], [2672, 2673],
-         [2677, 2692], [2746, 2748], [2750, 2767], [2769, 2783], [2786, 2789], [2800, 2820],
-         [2829, 2830], [2833, 2834], [2874, 2876], [2878, 2907], [2914, 2917], [2930, 2946],
-         [2955, 2957], [2966, 2968], [2976, 2978], [2981, 2983], [2987, 2989], [3002, 3023],
-         [3025, 3045], [3059, 3076], [3130, 3132], [3134, 3159], [3162, 3167], [3170, 3173],
-         [3184, 3191], [3199, 3204], [3258, 3260], [3262, 3293], [3298, 3301], [3312, 3332],
-         [3386, 3388], [3390, 3423], [3426, 3429], [3446, 3449], [3456, 3460], [3479, 3481],
-         [3518, 3519], [3527, 3584], [3636, 3647], [3655, 3663], [3674, 3712], [3717, 3718],
-         [3723, 3724], [3726, 3731], [3752, 3753], [3764, 3772], [3774, 3775], [3783, 3791],
-         [3802, 3803], [3806, 3839], [3841, 3871], [3892, 3903], [3949, 3975], [3980, 4095],
-         [4139, 4158], [4170, 4175], [4182, 4185], [4190, 4192], [4194, 4196], [4199, 4205],
-         [4209, 4212], [4226, 4237], [4250, 4255], [4294, 4303], [4349, 4351], [4686, 4687],
-         [4702, 4703], [4750, 4751], [4790, 4791], [4806, 4807], [4886, 4887], [4955, 4968],
-         [4989, 4991], [5008, 5023], [5109, 5120], [5741, 5742], [5787, 5791], [5867, 5869],
-         [5873, 5887], [5906, 5919], [5938, 5951], [5970, 5983], [6001, 6015], [6068, 6102],
-         [6104, 6107], [6109, 6111], [6122, 6127], [6138, 6159], [6170, 6175], [6264, 6271],
-         [6315, 6319], [6390, 6399], [6429, 6469], [6510, 6511], [6517, 6527], [6572, 6592],
-         [6600, 6607], [6619, 6655], [6679, 6687], [6741, 6783], [6794, 6799], [6810, 6822],
-         [6824, 6916], [6964, 6980], [6988, 6991], [7002, 7042], [7073, 7085], [7098, 7167],
-         [7204, 7231], [7242, 7244], [7294, 7400], [7410, 7423], [7616, 7679], [7958, 7959],
-         [7966, 7967], [8006, 8007], [8014, 8015], [8062, 8063], [8127, 8129], [8141, 8143],
-         [8148, 8149], [8156, 8159], [8173, 8177], [8189, 8303], [8306, 8307], [8314, 8318],
-         [8330, 8335], [8341, 8449], [8451, 8454], [8456, 8457], [8470, 8472], [8478, 8483],
-         [8506, 8507], [8512, 8516], [8522, 8525], [8586, 9311], [9372, 9449], [9472, 10101],
-         [10132, 11263], [11493, 11498], [11503, 11516], [11518, 11519], [11558, 11567],
-         [11622, 11630], [11632, 11647], [11671, 11679], [11743, 11822], [11824, 12292],
-         [12296, 12320], [12330, 12336], [12342, 12343], [12349, 12352], [12439, 12444],
-         [12544, 12548], [12590, 12592], [12687, 12689], [12694, 12703], [12728, 12783],
-         [12800, 12831], [12842, 12880], [12896, 12927], [12938, 12976], [12992, 13311],
-         [19894, 19967], [40908, 40959], [42125, 42191], [42238, 42239], [42509, 42511],
-         [42540, 42559], [42592, 42593], [42607, 42622], [42648, 42655], [42736, 42774],
-         [42784, 42785], [42889, 42890], [42893, 43002], [43043, 43055], [43062, 43071],
-         [43124, 43137], [43188, 43215], [43226, 43249], [43256, 43258], [43260, 43263],
-         [43302, 43311], [43335, 43359], [43389, 43395], [43443, 43470], [43482, 43519],
-         [43561, 43583], [43596, 43599], [43610, 43615], [43639, 43641], [43643, 43647],
-         [43698, 43700], [43703, 43704], [43710, 43711], [43715, 43738], [43742, 43967],
-         [44003, 44015], [44026, 44031], [55204, 55215], [55239, 55242], [55292, 55295],
-         [57344, 63743], [64046, 64047], [64110, 64111], [64218, 64255], [64263, 64274],
-         [64280, 64284], [64434, 64466], [64830, 64847], [64912, 64913], [64968, 65007],
-         [65020, 65135], [65277, 65295], [65306, 65312], [65339, 65344], [65371, 65381],
-         [65471, 65473], [65480, 65481], [65488, 65489], [65496, 65497]];
-    for (i = 0; i < ranges.length; i++) {
-        start = ranges[i][0];
-        end = ranges[i][1];
-        for (j = start; j <= end; j++) {
-            result[j] = true;
-        }
-    }
-    return result;
-})();
-
-function splitQuery(query) {
-    var result = [];
-    var start = -1;
-    for (var i = 0; i < query.length; i++) {
-        if (splitChars[query.charCodeAt(i)]) {
-            if (start !== -1) {
-                result.push(query.slice(start, i));
-                start = -1;
-            }
-        } else if (start === -1) {
-            start = i;
-        }
-    }
-    if (start !== -1) {
-        result.push(query.slice(start));
-    }
-    return result;
-}
-
-
diff --git a/docs/_static/minus.png b/docs/_static/minus.png
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index d96755fdaf8bb2214971e0db9c1fd3077d7c419d..0000000000000000000000000000000000000000
GIT binary patch
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diff --git a/docs/_static/plus.png b/docs/_static/plus.png
deleted file mode 100644
index 7107cec93a979b9a5f64843235a16651d563ce2d..0000000000000000000000000000000000000000
GIT binary patch
literal 0
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diff --git a/docs/_static/searchtools.js b/docs/_static/searchtools.js
deleted file mode 100644
index ab5649965..000000000
--- a/docs/_static/searchtools.js
+++ /dev/null
@@ -1,515 +0,0 @@
-/*
- * searchtools.js
- * ~~~~~~~~~~~~~~~~
- *
- * Sphinx JavaScript utilities for the full-text search.
- *
- * :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS.
- * :license: BSD, see LICENSE for details.
- *
- */
-
-if (!Scorer) {
-  /**
-   * Simple result scoring code.
-   */
-  var Scorer = {
-    // Implement the following function to further tweak the score for each result
-    // The function takes a result array [filename, title, anchor, descr, score]
-    // and returns the new score.
-    /*
-    score: function(result) {
-      return result[4];
-    },
-    */
-
-    // query matches the full name of an object
-    objNameMatch: 11,
-    // or matches in the last dotted part of the object name
-    objPartialMatch: 6,
-    // Additive scores depending on the priority of the object
-    objPrio: {0:  15,   // used to be importantResults
-              1:  5,   // used to be objectResults
-              2: -5},  // used to be unimportantResults
-    //  Used when the priority is not in the mapping.
-    objPrioDefault: 0,
-
-    // query found in title
-    title: 15,
-    partialTitle: 7,
-    // query found in terms
-    term: 5,
-    partialTerm: 2
-  };
-}
-
-if (!splitQuery) {
-  function splitQuery(query) {
-    return query.split(/\s+/);
-  }
-}
-
-/**
- * Search Module
- */
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-
-  _index : null,
-  _queued_query : null,
-  _pulse_status : -1,
-
-  htmlToText : function(htmlString) {
-      var htmlElement = document.createElement('span');
-      htmlElement.innerHTML = htmlString;
-      $(htmlElement).find('.headerlink').remove();
-      docContent = $(htmlElement).find('[role=main]')[0];
-      if(docContent === undefined) {
-          console.warn("Content block not found. Sphinx search tries to obtain it " +
-                       "via '[role=main]'. Could you check your theme or template.");
-          return "";
-      }
-      return docContent.textContent || docContent.innerText;
-  },
-
-  init : function() {
-      var params = $.getQueryParameters();
-      if (params.q) {
-          var query = params.q[0];
-          $('input[name="q"]')[0].value = query;
-          this.performSearch(query);
-      }
-  },
-
-  loadIndex : function(url) {
-    $.ajax({type: "GET", url: url, data: null,
-            dataType: "script", cache: true,
-            complete: function(jqxhr, textstatus) {
-              if (textstatus != "success") {
-                document.getElementById("searchindexloader").src = url;
-              }
-            }});
-  },
-
-  setIndex : function(index) {
-    var q;
-    this._index = index;
-    if ((q = this._queued_query) !== null) {
-      this._queued_query = null;
-      Search.query(q);
-    }
-  },
-
-  hasIndex : function() {
-      return this._index !== null;
-  },
-
-  deferQuery : function(query) {
-      this._queued_query = query;
-  },
-
-  stopPulse : function() {
-      this._pulse_status = 0;
-  },
-
-  startPulse : function() {
-    if (this._pulse_status >= 0)
-        return;
-    function pulse() {
-      var i;
-      Search._pulse_status = (Search._pulse_status + 1) % 4;
-      var dotString = '';
-      for (i = 0; i < Search._pulse_status; i++)
-        dotString += '.';
-      Search.dots.text(dotString);
-      if (Search._pulse_status > -1)
-        window.setTimeout(pulse, 500);
-    }
-    pulse();
-  },
-
-  /**
-   * perform a search for something (or wait until index is loaded)
-   */
-  performSearch : function(query) {
-    // create the required interface elements
-    this.out = $('#search-results');
-    this.title = $('<h2>' + _('Searching') + '</h2>').appendTo(this.out);
-    this.dots = $('<span></span>').appendTo(this.title);
-    this.status = $('<p class="search-summary">&nbsp;</p>').appendTo(this.out);
-    this.output = $('<ul class="search"/>').appendTo(this.out);
-
-    $('#search-progress').text(_('Preparing search...'));
-    this.startPulse();
-
-    // index already loaded, the browser was quick!
-    if (this.hasIndex())
-      this.query(query);
-    else
-      this.deferQuery(query);
-  },
-
-  /**
-   * execute search (requires search index to be loaded)
-   */
-  query : function(query) {
-    var i;
-
-    // stem the searchterms and add them to the correct list
-    var stemmer = new Stemmer();
-    var searchterms = [];
-    var excluded = [];
-    var hlterms = [];
-    var tmp = splitQuery(query);
-    var objectterms = [];
-    for (i = 0; i < tmp.length; i++) {
-      if (tmp[i] !== "") {
-          objectterms.push(tmp[i].toLowerCase());
-      }
-
-      if ($u.indexOf(stopwords, tmp[i].toLowerCase()) != -1 || tmp[i].match(/^\d+$/) ||
-          tmp[i] === "") {
-        // skip this "word"
-        continue;
-      }
-      // stem the word
-      var word = stemmer.stemWord(tmp[i].toLowerCase());
-      // prevent stemmer from cutting word smaller than two chars
-      if(word.length < 3 && tmp[i].length >= 3) {
-        word = tmp[i];
-      }
-      var toAppend;
-      // select the correct list
-      if (word[0] == '-') {
-        toAppend = excluded;
-        word = word.substr(1);
-      }
-      else {
-        toAppend = searchterms;
-        hlterms.push(tmp[i].toLowerCase());
-      }
-      // only add if not already in the list
-      if (!$u.contains(toAppend, word))
-        toAppend.push(word);
-    }
-    var highlightstring = '?highlight=' + $.urlencode(hlterms.join(" "));
-
-    // console.debug('SEARCH: searching for:');
-    // console.info('required: ', searchterms);
-    // console.info('excluded: ', excluded);
-
-    // prepare search
-    var terms = this._index.terms;
-    var titleterms = this._index.titleterms;
-
-    // array of [filename, title, anchor, descr, score]
-    var results = [];
-    $('#search-progress').empty();
-
-    // lookup as object
-    for (i = 0; i < objectterms.length; i++) {
-      var others = [].concat(objectterms.slice(0, i),
-                             objectterms.slice(i+1, objectterms.length));
-      results = results.concat(this.performObjectSearch(objectterms[i], others));
-    }
-
-    // lookup as search terms in fulltext
-    results = results.concat(this.performTermsSearch(searchterms, excluded, terms, titleterms));
-
-    // let the scorer override scores with a custom scoring function
-    if (Scorer.score) {
-      for (i = 0; i < results.length; i++)
-        results[i][4] = Scorer.score(results[i]);
-    }
-
-    // now sort the results by score (in opposite order of appearance, since the
-    // display function below uses pop() to retrieve items) and then
-    // alphabetically
-    results.sort(function(a, b) {
-      var left = a[4];
-      var right = b[4];
-      if (left > right) {
-        return 1;
-      } else if (left < right) {
-        return -1;
-      } else {
-        // same score: sort alphabetically
-        left = a[1].toLowerCase();
-        right = b[1].toLowerCase();
-        return (left > right) ? -1 : ((left < right) ? 1 : 0);
-      }
-    });
-
-    // for debugging
-    //Search.lastresults = results.slice();  // a copy
-    //console.info('search results:', Search.lastresults);
-
-    // print the results
-    var resultCount = results.length;
-    function displayNextItem() {
-      // results left, load the summary and display it
-      if (results.length) {
-        var item = results.pop();
-        var listItem = $('<li style="display:none"></li>');
-        var requestUrl = "";
-        var linkUrl = "";
-        if (DOCUMENTATION_OPTIONS.BUILDER === 'dirhtml') {
-          // dirhtml builder
-          var dirname = item[0] + '/';
-          if (dirname.match(/\/index\/$/)) {
-            dirname = dirname.substring(0, dirname.length-6);
-          } else if (dirname == 'index/') {
-            dirname = '';
-          }
-          requestUrl = DOCUMENTATION_OPTIONS.URL_ROOT + dirname;
-          linkUrl = requestUrl;
-
-        } else {
-          // normal html builders
-          requestUrl = DOCUMENTATION_OPTIONS.URL_ROOT + item[0] + DOCUMENTATION_OPTIONS.FILE_SUFFIX;
-          linkUrl = item[0] + DOCUMENTATION_OPTIONS.LINK_SUFFIX;
-        }
-        listItem.append($('<a/>').attr('href',
-            linkUrl +
-            highlightstring + item[2]).html(item[1]));
-        if (item[3]) {
-          listItem.append($('<span> (' + item[3] + ')</span>'));
-          Search.output.append(listItem);
-          listItem.slideDown(5, function() {
-            displayNextItem();
-          });
-        } else if (DOCUMENTATION_OPTIONS.HAS_SOURCE) {
-          $.ajax({url: requestUrl,
-                  dataType: "text",
-                  complete: function(jqxhr, textstatus) {
-                    var data = jqxhr.responseText;
-                    if (data !== '' && data !== undefined) {
-                      listItem.append(Search.makeSearchSummary(data, searchterms, hlterms));
-                    }
-                    Search.output.append(listItem);
-                    listItem.slideDown(5, function() {
-                      displayNextItem();
-                    });
-                  }});
-        } else {
-          // no source available, just display title
-          Search.output.append(listItem);
-          listItem.slideDown(5, function() {
-            displayNextItem();
-          });
-        }
-      }
-      // search finished, update title and status message
-      else {
-        Search.stopPulse();
-        Search.title.text(_('Search Results'));
-        if (!resultCount)
-          Search.status.text(_('Your search did not match any documents. Please make sure that all words are spelled correctly and that you\'ve selected enough categories.'));
-        else
-            Search.status.text(_('Search finished, found %s page(s) matching the search query.').replace('%s', resultCount));
-        Search.status.fadeIn(500);
-      }
-    }
-    displayNextItem();
-  },
-
-  /**
-   * search for object names
-   */
-  performObjectSearch : function(object, otherterms) {
-    var filenames = this._index.filenames;
-    var docnames = this._index.docnames;
-    var objects = this._index.objects;
-    var objnames = this._index.objnames;
-    var titles = this._index.titles;
-
-    var i;
-    var results = [];
-
-    for (var prefix in objects) {
-      for (var name in objects[prefix]) {
-        var fullname = (prefix ? prefix + '.' : '') + name;
-        var fullnameLower = fullname.toLowerCase()
-        if (fullnameLower.indexOf(object) > -1) {
-          var score = 0;
-          var parts = fullnameLower.split('.');
-          // check for different match types: exact matches of full name or
-          // "last name" (i.e. last dotted part)
-          if (fullnameLower == object || parts[parts.length - 1] == object) {
-            score += Scorer.objNameMatch;
-          // matches in last name
-          } else if (parts[parts.length - 1].indexOf(object) > -1) {
-            score += Scorer.objPartialMatch;
-          }
-          var match = objects[prefix][name];
-          var objname = objnames[match[1]][2];
-          var title = titles[match[0]];
-          // If more than one term searched for, we require other words to be
-          // found in the name/title/description
-          if (otherterms.length > 0) {
-            var haystack = (prefix + ' ' + name + ' ' +
-                            objname + ' ' + title).toLowerCase();
-            var allfound = true;
-            for (i = 0; i < otherterms.length; i++) {
-              if (haystack.indexOf(otherterms[i]) == -1) {
-                allfound = false;
-                break;
-              }
-            }
-            if (!allfound) {
-              continue;
-            }
-          }
-          var descr = objname + _(', in ') + title;
-
-          var anchor = match[3];
-          if (anchor === '')
-            anchor = fullname;
-          else if (anchor == '-')
-            anchor = objnames[match[1]][1] + '-' + fullname;
-          // add custom score for some objects according to scorer
-          if (Scorer.objPrio.hasOwnProperty(match[2])) {
-            score += Scorer.objPrio[match[2]];
-          } else {
-            score += Scorer.objPrioDefault;
-          }
-          results.push([docnames[match[0]], fullname, '#'+anchor, descr, score, filenames[match[0]]]);
-        }
-      }
-    }
-
-    return results;
-  },
-
-  /**
-   * search for full-text terms in the index
-   */
-  performTermsSearch : function(searchterms, excluded, terms, titleterms) {
-    var docnames = this._index.docnames;
-    var filenames = this._index.filenames;
-    var titles = this._index.titles;
-
-    var i, j, file;
-    var fileMap = {};
-    var scoreMap = {};
-    var results = [];
-
-    // perform the search on the required terms
-    for (i = 0; i < searchterms.length; i++) {
-      var word = searchterms[i];
-      var files = [];
-      var _o = [
-        {files: terms[word], score: Scorer.term},
-        {files: titleterms[word], score: Scorer.title}
-      ];
-      // add support for partial matches
-      if (word.length > 2) {
-        for (var w in terms) {
-          if (w.match(word) && !terms[word]) {
-            _o.push({files: terms[w], score: Scorer.partialTerm})
-          }
-        }
-        for (var w in titleterms) {
-          if (w.match(word) && !titleterms[word]) {
-              _o.push({files: titleterms[w], score: Scorer.partialTitle})
-          }
-        }
-      }
-
-      // no match but word was a required one
-      if ($u.every(_o, function(o){return o.files === undefined;})) {
-        break;
-      }
-      // found search word in contents
-      $u.each(_o, function(o) {
-        var _files = o.files;
-        if (_files === undefined)
-          return
-
-        if (_files.length === undefined)
-          _files = [_files];
-        files = files.concat(_files);
-
-        // set score for the word in each file to Scorer.term
-        for (j = 0; j < _files.length; j++) {
-          file = _files[j];
-          if (!(file in scoreMap))
-            scoreMap[file] = {};
-          scoreMap[file][word] = o.score;
-        }
-      });
-
-      // create the mapping
-      for (j = 0; j < files.length; j++) {
-        file = files[j];
-        if (file in fileMap && fileMap[file].indexOf(word) === -1)
-          fileMap[file].push(word);
-        else
-          fileMap[file] = [word];
-      }
-    }
-
-    // now check if the files don't contain excluded terms
-    for (file in fileMap) {
-      var valid = true;
-
-      // check if all requirements are matched
-      var filteredTermCount = // as search terms with length < 3 are discarded: ignore
-        searchterms.filter(function(term){return term.length > 2}).length
-      if (
-        fileMap[file].length != searchterms.length &&
-        fileMap[file].length != filteredTermCount
-      ) continue;
-
-      // ensure that none of the excluded terms is in the search result
-      for (i = 0; i < excluded.length; i++) {
-        if (terms[excluded[i]] == file ||
-            titleterms[excluded[i]] == file ||
-            $u.contains(terms[excluded[i]] || [], file) ||
-            $u.contains(titleterms[excluded[i]] || [], file)) {
-          valid = false;
-          break;
-        }
-      }
-
-      // if we have still a valid result we can add it to the result list
-      if (valid) {
-        // select one (max) score for the file.
-        // for better ranking, we should calculate ranking by using words statistics like basic tf-idf...
-        var score = $u.max($u.map(fileMap[file], function(w){return scoreMap[file][w]}));
-        results.push([docnames[file], titles[file], '', null, score, filenames[file]]);
-      }
-    }
-    return results;
-  },
-
-  /**
-   * helper function to return a node containing the
-   * search summary for a given text. keywords is a list
-   * of stemmed words, hlwords is the list of normal, unstemmed
-   * words. the first one is used to find the occurrence, the
-   * latter for highlighting it.
-   */
-  makeSearchSummary : function(htmlText, keywords, hlwords) {
-    var text = Search.htmlToText(htmlText);
-    var textLower = text.toLowerCase();
-    var start = 0;
-    $.each(keywords, function() {
-      var i = textLower.indexOf(this.toLowerCase());
-      if (i > -1)
-        start = i;
-    });
-    start = Math.max(start - 120, 0);
-    var excerpt = ((start > 0) ? '...' : '') +
-      $.trim(text.substr(start, 240)) +
-      ((start + 240 - text.length) ? '...' : '');
-    var rv = $('<div class="context"></div>').text(excerpt);
-    $.each(hlwords, function() {
-      rv = rv.highlightText(this, 'highlighted');
-    });
-    return rv;
-  }
-};
-
-$(document).ready(function() {
-  Search.init();
-});
diff --git a/docs/_static/underscore-1.3.1.js b/docs/_static/underscore-1.3.1.js
deleted file mode 100644
index 208d4cd89..000000000
--- a/docs/_static/underscore-1.3.1.js
+++ /dev/null
@@ -1,999 +0,0 @@
-//     Underscore.js 1.3.1
-//     (c) 2009-2012 Jeremy Ashkenas, DocumentCloud Inc.
-//     Underscore is freely distributable under the MIT license.
-//     Portions of Underscore are inspired or borrowed from Prototype,
-//     Oliver Steele's Functional, and John Resig's Micro-Templating.
-//     For all details and documentation:
-//     http://documentcloud.github.com/underscore
-
-(function() {
-
-  // Baseline setup
-  // --------------
-
-  // Establish the root object, `window` in the browser, or `global` on the server.
-  var root = this;
-
-  // Save the previous value of the `_` variable.
-  var previousUnderscore = root._;
-
-  // Establish the object that gets returned to break out of a loop iteration.
-  var breaker = {};
-
-  // Save bytes in the minified (but not gzipped) version:
-  var ArrayProto = Array.prototype, ObjProto = Object.prototype, FuncProto = Function.prototype;
-
-  // Create quick reference variables for speed access to core prototypes.
-  var slice            = ArrayProto.slice,
-      unshift          = ArrayProto.unshift,
-      toString         = ObjProto.toString,
-      hasOwnProperty   = ObjProto.hasOwnProperty;
-
-  // All **ECMAScript 5** native function implementations that we hope to use
-  // are declared here.
-  var
-    nativeForEach      = ArrayProto.forEach,
-    nativeMap          = ArrayProto.map,
-    nativeReduce       = ArrayProto.reduce,
-    nativeReduceRight  = ArrayProto.reduceRight,
-    nativeFilter       = ArrayProto.filter,
-    nativeEvery        = ArrayProto.every,
-    nativeSome         = ArrayProto.some,
-    nativeIndexOf      = ArrayProto.indexOf,
-    nativeLastIndexOf  = ArrayProto.lastIndexOf,
-    nativeIsArray      = Array.isArray,
-    nativeKeys         = Object.keys,
-    nativeBind         = FuncProto.bind;
-
-  // Create a safe reference to the Underscore object for use below.
-  var _ = function(obj) { return new wrapper(obj); };
-
-  // Export the Underscore object for **Node.js**, with
-  // backwards-compatibility for the old `require()` API. If we're in
-  // the browser, add `_` as a global object via a string identifier,
-  // for Closure Compiler "advanced" mode.
-  if (typeof exports !== 'undefined') {
-    if (typeof module !== 'undefined' && module.exports) {
-      exports = module.exports = _;
-    }
-    exports._ = _;
-  } else {
-    root['_'] = _;
-  }
-
-  // Current version.
-  _.VERSION = '1.3.1';
-
-  // Collection Functions
-  // --------------------
-
-  // The cornerstone, an `each` implementation, aka `forEach`.
-  // Handles objects with the built-in `forEach`, arrays, and raw objects.
-  // Delegates to **ECMAScript 5**'s native `forEach` if available.
-  var each = _.each = _.forEach = function(obj, iterator, context) {
-    if (obj == null) return;
-    if (nativeForEach && obj.forEach === nativeForEach) {
-      obj.forEach(iterator, context);
-    } else if (obj.length === +obj.length) {
-      for (var i = 0, l = obj.length; i < l; i++) {
-        if (i in obj && iterator.call(context, obj[i], i, obj) === breaker) return;
-      }
-    } else {
-      for (var key in obj) {
-        if (_.has(obj, key)) {
-          if (iterator.call(context, obj[key], key, obj) === breaker) return;
-        }
-      }
-    }
-  };
-
-  // Return the results of applying the iterator to each element.
-  // Delegates to **ECMAScript 5**'s native `map` if available.
-  _.map = _.collect = function(obj, iterator, context) {
-    var results = [];
-    if (obj == null) return results;
-    if (nativeMap && obj.map === nativeMap) return obj.map(iterator, context);
-    each(obj, function(value, index, list) {
-      results[results.length] = iterator.call(context, value, index, list);
-    });
-    if (obj.length === +obj.length) results.length = obj.length;
-    return results;
-  };
-
-  // **Reduce** builds up a single result from a list of values, aka `inject`,
-  // or `foldl`. Delegates to **ECMAScript 5**'s native `reduce` if available.
-  _.reduce = _.foldl = _.inject = function(obj, iterator, memo, context) {
-    var initial = arguments.length > 2;
-    if (obj == null) obj = [];
-    if (nativeReduce && obj.reduce === nativeReduce) {
-      if (context) iterator = _.bind(iterator, context);
-      return initial ? obj.reduce(iterator, memo) : obj.reduce(iterator);
-    }
-    each(obj, function(value, index, list) {
-      if (!initial) {
-        memo = value;
-        initial = true;
-      } else {
-        memo = iterator.call(context, memo, value, index, list);
-      }
-    });
-    if (!initial) throw new TypeError('Reduce of empty array with no initial value');
-    return memo;
-  };
-
-  // The right-associative version of reduce, also known as `foldr`.
-  // Delegates to **ECMAScript 5**'s native `reduceRight` if available.
-  _.reduceRight = _.foldr = function(obj, iterator, memo, context) {
-    var initial = arguments.length > 2;
-    if (obj == null) obj = [];
-    if (nativeReduceRight && obj.reduceRight === nativeReduceRight) {
-      if (context) iterator = _.bind(iterator, context);
-      return initial ? obj.reduceRight(iterator, memo) : obj.reduceRight(iterator);
-    }
-    var reversed = _.toArray(obj).reverse();
-    if (context && !initial) iterator = _.bind(iterator, context);
-    return initial ? _.reduce(reversed, iterator, memo, context) : _.reduce(reversed, iterator);
-  };
-
-  // Return the first value which passes a truth test. Aliased as `detect`.
-  _.find = _.detect = function(obj, iterator, context) {
-    var result;
-    any(obj, function(value, index, list) {
-      if (iterator.call(context, value, index, list)) {
-        result = value;
-        return true;
-      }
-    });
-    return result;
-  };
-
-  // Return all the elements that pass a truth test.
-  // Delegates to **ECMAScript 5**'s native `filter` if available.
-  // Aliased as `select`.
-  _.filter = _.select = function(obj, iterator, context) {
-    var results = [];
-    if (obj == null) return results;
-    if (nativeFilter && obj.filter === nativeFilter) return obj.filter(iterator, context);
-    each(obj, function(value, index, list) {
-      if (iterator.call(context, value, index, list)) results[results.length] = value;
-    });
-    return results;
-  };
-
-  // Return all the elements for which a truth test fails.
-  _.reject = function(obj, iterator, context) {
-    var results = [];
-    if (obj == null) return results;
-    each(obj, function(value, index, list) {
-      if (!iterator.call(context, value, index, list)) results[results.length] = value;
-    });
-    return results;
-  };
-
-  // Determine whether all of the elements match a truth test.
-  // Delegates to **ECMAScript 5**'s native `every` if available.
-  // Aliased as `all`.
-  _.every = _.all = function(obj, iterator, context) {
-    var result = true;
-    if (obj == null) return result;
-    if (nativeEvery && obj.every === nativeEvery) return obj.every(iterator, context);
-    each(obj, function(value, index, list) {
-      if (!(result = result && iterator.call(context, value, index, list))) return breaker;
-    });
-    return result;
-  };
-
-  // Determine if at least one element in the object matches a truth test.
-  // Delegates to **ECMAScript 5**'s native `some` if available.
-  // Aliased as `any`.
-  var any = _.some = _.any = function(obj, iterator, context) {
-    iterator || (iterator = _.identity);
-    var result = false;
-    if (obj == null) return result;
-    if (nativeSome && obj.some === nativeSome) return obj.some(iterator, context);
-    each(obj, function(value, index, list) {
-      if (result || (result = iterator.call(context, value, index, list))) return breaker;
-    });
-    return !!result;
-  };
-
-  // Determine if a given value is included in the array or object using `===`.
-  // Aliased as `contains`.
-  _.include = _.contains = function(obj, target) {
-    var found = false;
-    if (obj == null) return found;
-    if (nativeIndexOf && obj.indexOf === nativeIndexOf) return obj.indexOf(target) != -1;
-    found = any(obj, function(value) {
-      return value === target;
-    });
-    return found;
-  };
-
-  // Invoke a method (with arguments) on every item in a collection.
-  _.invoke = function(obj, method) {
-    var args = slice.call(arguments, 2);
-    return _.map(obj, function(value) {
-      return (_.isFunction(method) ? method || value : value[method]).apply(value, args);
-    });
-  };
-
-  // Convenience version of a common use case of `map`: fetching a property.
-  _.pluck = function(obj, key) {
-    return _.map(obj, function(value){ return value[key]; });
-  };
-
-  // Return the maximum element or (element-based computation).
-  _.max = function(obj, iterator, context) {
-    if (!iterator && _.isArray(obj)) return Math.max.apply(Math, obj);
-    if (!iterator && _.isEmpty(obj)) return -Infinity;
-    var result = {computed : -Infinity};
-    each(obj, function(value, index, list) {
-      var computed = iterator ? iterator.call(context, value, index, list) : value;
-      computed >= result.computed && (result = {value : value, computed : computed});
-    });
-    return result.value;
-  };
-
-  // Return the minimum element (or element-based computation).
-  _.min = function(obj, iterator, context) {
-    if (!iterator && _.isArray(obj)) return Math.min.apply(Math, obj);
-    if (!iterator && _.isEmpty(obj)) return Infinity;
-    var result = {computed : Infinity};
-    each(obj, function(value, index, list) {
-      var computed = iterator ? iterator.call(context, value, index, list) : value;
-      computed < result.computed && (result = {value : value, computed : computed});
-    });
-    return result.value;
-  };
-
-  // Shuffle an array.
-  _.shuffle = function(obj) {
-    var shuffled = [], rand;
-    each(obj, function(value, index, list) {
-      if (index == 0) {
-        shuffled[0] = value;
-      } else {
-        rand = Math.floor(Math.random() * (index + 1));
-        shuffled[index] = shuffled[rand];
-        shuffled[rand] = value;
-      }
-    });
-    return shuffled;
-  };
-
-  // Sort the object's values by a criterion produced by an iterator.
-  _.sortBy = function(obj, iterator, context) {
-    return _.pluck(_.map(obj, function(value, index, list) {
-      return {
-        value : value,
-        criteria : iterator.call(context, value, index, list)
-      };
-    }).sort(function(left, right) {
-      var a = left.criteria, b = right.criteria;
-      return a < b ? -1 : a > b ? 1 : 0;
-    }), 'value');
-  };
-
-  // Groups the object's values by a criterion. Pass either a string attribute
-  // to group by, or a function that returns the criterion.
-  _.groupBy = function(obj, val) {
-    var result = {};
-    var iterator = _.isFunction(val) ? val : function(obj) { return obj[val]; };
-    each(obj, function(value, index) {
-      var key = iterator(value, index);
-      (result[key] || (result[key] = [])).push(value);
-    });
-    return result;
-  };
-
-  // Use a comparator function to figure out at what index an object should
-  // be inserted so as to maintain order. Uses binary search.
-  _.sortedIndex = function(array, obj, iterator) {
-    iterator || (iterator = _.identity);
-    var low = 0, high = array.length;
-    while (low < high) {
-      var mid = (low + high) >> 1;
-      iterator(array[mid]) < iterator(obj) ? low = mid + 1 : high = mid;
-    }
-    return low;
-  };
-
-  // Safely convert anything iterable into a real, live array.
-  _.toArray = function(iterable) {
-    if (!iterable)                return [];
-    if (iterable.toArray)         return iterable.toArray();
-    if (_.isArray(iterable))      return slice.call(iterable);
-    if (_.isArguments(iterable))  return slice.call(iterable);
-    return _.values(iterable);
-  };
-
-  // Return the number of elements in an object.
-  _.size = function(obj) {
-    return _.toArray(obj).length;
-  };
-
-  // Array Functions
-  // ---------------
-
-  // Get the first element of an array. Passing **n** will return the first N
-  // values in the array. Aliased as `head`. The **guard** check allows it to work
-  // with `_.map`.
-  _.first = _.head = function(array, n, guard) {
-    return (n != null) && !guard ? slice.call(array, 0, n) : array[0];
-  };
-
-  // Returns everything but the last entry of the array. Especcialy useful on
-  // the arguments object. Passing **n** will return all the values in
-  // the array, excluding the last N. The **guard** check allows it to work with
-  // `_.map`.
-  _.initial = function(array, n, guard) {
-    return slice.call(array, 0, array.length - ((n == null) || guard ? 1 : n));
-  };
-
-  // Get the last element of an array. Passing **n** will return the last N
-  // values in the array. The **guard** check allows it to work with `_.map`.
-  _.last = function(array, n, guard) {
-    if ((n != null) && !guard) {
-      return slice.call(array, Math.max(array.length - n, 0));
-    } else {
-      return array[array.length - 1];
-    }
-  };
-
-  // Returns everything but the first entry of the array. Aliased as `tail`.
-  // Especially useful on the arguments object. Passing an **index** will return
-  // the rest of the values in the array from that index onward. The **guard**
-  // check allows it to work with `_.map`.
-  _.rest = _.tail = function(array, index, guard) {
-    return slice.call(array, (index == null) || guard ? 1 : index);
-  };
-
-  // Trim out all falsy values from an array.
-  _.compact = function(array) {
-    return _.filter(array, function(value){ return !!value; });
-  };
-
-  // Return a completely flattened version of an array.
-  _.flatten = function(array, shallow) {
-    return _.reduce(array, function(memo, value) {
-      if (_.isArray(value)) return memo.concat(shallow ? value : _.flatten(value));
-      memo[memo.length] = value;
-      return memo;
-    }, []);
-  };
-
-  // Return a version of the array that does not contain the specified value(s).
-  _.without = function(array) {
-    return _.difference(array, slice.call(arguments, 1));
-  };
-
-  // Produce a duplicate-free version of the array. If the array has already
-  // been sorted, you have the option of using a faster algorithm.
-  // Aliased as `unique`.
-  _.uniq = _.unique = function(array, isSorted, iterator) {
-    var initial = iterator ? _.map(array, iterator) : array;
-    var result = [];
-    _.reduce(initial, function(memo, el, i) {
-      if (0 == i || (isSorted === true ? _.last(memo) != el : !_.include(memo, el))) {
-        memo[memo.length] = el;
-        result[result.length] = array[i];
-      }
-      return memo;
-    }, []);
-    return result;
-  };
-
-  // Produce an array that contains the union: each distinct element from all of
-  // the passed-in arrays.
-  _.union = function() {
-    return _.uniq(_.flatten(arguments, true));
-  };
-
-  // Produce an array that contains every item shared between all the
-  // passed-in arrays. (Aliased as "intersect" for back-compat.)
-  _.intersection = _.intersect = function(array) {
-    var rest = slice.call(arguments, 1);
-    return _.filter(_.uniq(array), function(item) {
-      return _.every(rest, function(other) {
-        return _.indexOf(other, item) >= 0;
-      });
-    });
-  };
-
-  // Take the difference between one array and a number of other arrays.
-  // Only the elements present in just the first array will remain.
-  _.difference = function(array) {
-    var rest = _.flatten(slice.call(arguments, 1));
-    return _.filter(array, function(value){ return !_.include(rest, value); });
-  };
-
-  // Zip together multiple lists into a single array -- elements that share
-  // an index go together.
-  _.zip = function() {
-    var args = slice.call(arguments);
-    var length = _.max(_.pluck(args, 'length'));
-    var results = new Array(length);
-    for (var i = 0; i < length; i++) results[i] = _.pluck(args, "" + i);
-    return results;
-  };
-
-  // If the browser doesn't supply us with indexOf (I'm looking at you, **MSIE**),
-  // we need this function. Return the position of the first occurrence of an
-  // item in an array, or -1 if the item is not included in the array.
-  // Delegates to **ECMAScript 5**'s native `indexOf` if available.
-  // If the array is large and already in sort order, pass `true`
-  // for **isSorted** to use binary search.
-  _.indexOf = function(array, item, isSorted) {
-    if (array == null) return -1;
-    var i, l;
-    if (isSorted) {
-      i = _.sortedIndex(array, item);
-      return array[i] === item ? i : -1;
-    }
-    if (nativeIndexOf && array.indexOf === nativeIndexOf) return array.indexOf(item);
-    for (i = 0, l = array.length; i < l; i++) if (i in array && array[i] === item) return i;
-    return -1;
-  };
-
-  // Delegates to **ECMAScript 5**'s native `lastIndexOf` if available.
-  _.lastIndexOf = function(array, item) {
-    if (array == null) return -1;
-    if (nativeLastIndexOf && array.lastIndexOf === nativeLastIndexOf) return array.lastIndexOf(item);
-    var i = array.length;
-    while (i--) if (i in array && array[i] === item) return i;
-    return -1;
-  };
-
-  // Generate an integer Array containing an arithmetic progression. A port of
-  // the native Python `range()` function. See
-  // [the Python documentation](http://docs.python.org/library/functions.html#range).
-  _.range = function(start, stop, step) {
-    if (arguments.length <= 1) {
-      stop = start || 0;
-      start = 0;
-    }
-    step = arguments[2] || 1;
-
-    var len = Math.max(Math.ceil((stop - start) / step), 0);
-    var idx = 0;
-    var range = new Array(len);
-
-    while(idx < len) {
-      range[idx++] = start;
-      start += step;
-    }
-
-    return range;
-  };
-
-  // Function (ahem) Functions
-  // ------------------
-
-  // Reusable constructor function for prototype setting.
-  var ctor = function(){};
-
-  // Create a function bound to a given object (assigning `this`, and arguments,
-  // optionally). Binding with arguments is also known as `curry`.
-  // Delegates to **ECMAScript 5**'s native `Function.bind` if available.
-  // We check for `func.bind` first, to fail fast when `func` is undefined.
-  _.bind = function bind(func, context) {
-    var bound, args;
-    if (func.bind === nativeBind && nativeBind) return nativeBind.apply(func, slice.call(arguments, 1));
-    if (!_.isFunction(func)) throw new TypeError;
-    args = slice.call(arguments, 2);
-    return bound = function() {
-      if (!(this instanceof bound)) return func.apply(context, args.concat(slice.call(arguments)));
-      ctor.prototype = func.prototype;
-      var self = new ctor;
-      var result = func.apply(self, args.concat(slice.call(arguments)));
-      if (Object(result) === result) return result;
-      return self;
-    };
-  };
-
-  // Bind all of an object's methods to that object. Useful for ensuring that
-  // all callbacks defined on an object belong to it.
-  _.bindAll = function(obj) {
-    var funcs = slice.call(arguments, 1);
-    if (funcs.length == 0) funcs = _.functions(obj);
-    each(funcs, function(f) { obj[f] = _.bind(obj[f], obj); });
-    return obj;
-  };
-
-  // Memoize an expensive function by storing its results.
-  _.memoize = function(func, hasher) {
-    var memo = {};
-    hasher || (hasher = _.identity);
-    return function() {
-      var key = hasher.apply(this, arguments);
-      return _.has(memo, key) ? memo[key] : (memo[key] = func.apply(this, arguments));
-    };
-  };
-
-  // Delays a function for the given number of milliseconds, and then calls
-  // it with the arguments supplied.
-  _.delay = function(func, wait) {
-    var args = slice.call(arguments, 2);
-    return setTimeout(function(){ return func.apply(func, args); }, wait);
-  };
-
-  // Defers a function, scheduling it to run after the current call stack has
-  // cleared.
-  _.defer = function(func) {
-    return _.delay.apply(_, [func, 1].concat(slice.call(arguments, 1)));
-  };
-
-  // Returns a function, that, when invoked, will only be triggered at most once
-  // during a given window of time.
-  _.throttle = function(func, wait) {
-    var context, args, timeout, throttling, more;
-    var whenDone = _.debounce(function(){ more = throttling = false; }, wait);
-    return function() {
-      context = this; args = arguments;
-      var later = function() {
-        timeout = null;
-        if (more) func.apply(context, args);
-        whenDone();
-      };
-      if (!timeout) timeout = setTimeout(later, wait);
-      if (throttling) {
-        more = true;
-      } else {
-        func.apply(context, args);
-      }
-      whenDone();
-      throttling = true;
-    };
-  };
-
-  // Returns a function, that, as long as it continues to be invoked, will not
-  // be triggered. The function will be called after it stops being called for
-  // N milliseconds.
-  _.debounce = function(func, wait) {
-    var timeout;
-    return function() {
-      var context = this, args = arguments;
-      var later = function() {
-        timeout = null;
-        func.apply(context, args);
-      };
-      clearTimeout(timeout);
-      timeout = setTimeout(later, wait);
-    };
-  };
-
-  // Returns a function that will be executed at most one time, no matter how
-  // often you call it. Useful for lazy initialization.
-  _.once = function(func) {
-    var ran = false, memo;
-    return function() {
-      if (ran) return memo;
-      ran = true;
-      return memo = func.apply(this, arguments);
-    };
-  };
-
-  // Returns the first function passed as an argument to the second,
-  // allowing you to adjust arguments, run code before and after, and
-  // conditionally execute the original function.
-  _.wrap = function(func, wrapper) {
-    return function() {
-      var args = [func].concat(slice.call(arguments, 0));
-      return wrapper.apply(this, args);
-    };
-  };
-
-  // Returns a function that is the composition of a list of functions, each
-  // consuming the return value of the function that follows.
-  _.compose = function() {
-    var funcs = arguments;
-    return function() {
-      var args = arguments;
-      for (var i = funcs.length - 1; i >= 0; i--) {
-        args = [funcs[i].apply(this, args)];
-      }
-      return args[0];
-    };
-  };
-
-  // Returns a function that will only be executed after being called N times.
-  _.after = function(times, func) {
-    if (times <= 0) return func();
-    return function() {
-      if (--times < 1) { return func.apply(this, arguments); }
-    };
-  };
-
-  // Object Functions
-  // ----------------
-
-  // Retrieve the names of an object's properties.
-  // Delegates to **ECMAScript 5**'s native `Object.keys`
-  _.keys = nativeKeys || function(obj) {
-    if (obj !== Object(obj)) throw new TypeError('Invalid object');
-    var keys = [];
-    for (var key in obj) if (_.has(obj, key)) keys[keys.length] = key;
-    return keys;
-  };
-
-  // Retrieve the values of an object's properties.
-  _.values = function(obj) {
-    return _.map(obj, _.identity);
-  };
-
-  // Return a sorted list of the function names available on the object.
-  // Aliased as `methods`
-  _.functions = _.methods = function(obj) {
-    var names = [];
-    for (var key in obj) {
-      if (_.isFunction(obj[key])) names.push(key);
-    }
-    return names.sort();
-  };
-
-  // Extend a given object with all the properties in passed-in object(s).
-  _.extend = function(obj) {
-    each(slice.call(arguments, 1), function(source) {
-      for (var prop in source) {
-        obj[prop] = source[prop];
-      }
-    });
-    return obj;
-  };
-
-  // Fill in a given object with default properties.
-  _.defaults = function(obj) {
-    each(slice.call(arguments, 1), function(source) {
-      for (var prop in source) {
-        if (obj[prop] == null) obj[prop] = source[prop];
-      }
-    });
-    return obj;
-  };
-
-  // Create a (shallow-cloned) duplicate of an object.
-  _.clone = function(obj) {
-    if (!_.isObject(obj)) return obj;
-    return _.isArray(obj) ? obj.slice() : _.extend({}, obj);
-  };
-
-  // Invokes interceptor with the obj, and then returns obj.
-  // The primary purpose of this method is to "tap into" a method chain, in
-  // order to perform operations on intermediate results within the chain.
-  _.tap = function(obj, interceptor) {
-    interceptor(obj);
-    return obj;
-  };
-
-  // Internal recursive comparison function.
-  function eq(a, b, stack) {
-    // Identical objects are equal. `0 === -0`, but they aren't identical.
-    // See the Harmony `egal` proposal: http://wiki.ecmascript.org/doku.php?id=harmony:egal.
-    if (a === b) return a !== 0 || 1 / a == 1 / b;
-    // A strict comparison is necessary because `null == undefined`.
-    if (a == null || b == null) return a === b;
-    // Unwrap any wrapped objects.
-    if (a._chain) a = a._wrapped;
-    if (b._chain) b = b._wrapped;
-    // Invoke a custom `isEqual` method if one is provided.
-    if (a.isEqual && _.isFunction(a.isEqual)) return a.isEqual(b);
-    if (b.isEqual && _.isFunction(b.isEqual)) return b.isEqual(a);
-    // Compare `[[Class]]` names.
-    var className = toString.call(a);
-    if (className != toString.call(b)) return false;
-    switch (className) {
-      // Strings, numbers, dates, and booleans are compared by value.
-      case '[object String]':
-        // Primitives and their corresponding object wrappers are equivalent; thus, `"5"` is
-        // equivalent to `new String("5")`.
-        return a == String(b);
-      case '[object Number]':
-        // `NaN`s are equivalent, but non-reflexive. An `egal` comparison is performed for
-        // other numeric values.
-        return a != +a ? b != +b : (a == 0 ? 1 / a == 1 / b : a == +b);
-      case '[object Date]':
-      case '[object Boolean]':
-        // Coerce dates and booleans to numeric primitive values. Dates are compared by their
-        // millisecond representations. Note that invalid dates with millisecond representations
-        // of `NaN` are not equivalent.
-        return +a == +b;
-      // RegExps are compared by their source patterns and flags.
-      case '[object RegExp]':
-        return a.source == b.source &&
-               a.global == b.global &&
-               a.multiline == b.multiline &&
-               a.ignoreCase == b.ignoreCase;
-    }
-    if (typeof a != 'object' || typeof b != 'object') return false;
-    // Assume equality for cyclic structures. The algorithm for detecting cyclic
-    // structures is adapted from ES 5.1 section 15.12.3, abstract operation `JO`.
-    var length = stack.length;
-    while (length--) {
-      // Linear search. Performance is inversely proportional to the number of
-      // unique nested structures.
-      if (stack[length] == a) return true;
-    }
-    // Add the first object to the stack of traversed objects.
-    stack.push(a);
-    var size = 0, result = true;
-    // Recursively compare objects and arrays.
-    if (className == '[object Array]') {
-      // Compare array lengths to determine if a deep comparison is necessary.
-      size = a.length;
-      result = size == b.length;
-      if (result) {
-        // Deep compare the contents, ignoring non-numeric properties.
-        while (size--) {
-          // Ensure commutative equality for sparse arrays.
-          if (!(result = size in a == size in b && eq(a[size], b[size], stack))) break;
-        }
-      }
-    } else {
-      // Objects with different constructors are not equivalent.
-      if ('constructor' in a != 'constructor' in b || a.constructor != b.constructor) return false;
-      // Deep compare objects.
-      for (var key in a) {
-        if (_.has(a, key)) {
-          // Count the expected number of properties.
-          size++;
-          // Deep compare each member.
-          if (!(result = _.has(b, key) && eq(a[key], b[key], stack))) break;
-        }
-      }
-      // Ensure that both objects contain the same number of properties.
-      if (result) {
-        for (key in b) {
-          if (_.has(b, key) && !(size--)) break;
-        }
-        result = !size;
-      }
-    }
-    // Remove the first object from the stack of traversed objects.
-    stack.pop();
-    return result;
-  }
-
-  // Perform a deep comparison to check if two objects are equal.
-  _.isEqual = function(a, b) {
-    return eq(a, b, []);
-  };
-
-  // Is a given array, string, or object empty?
-  // An "empty" object has no enumerable own-properties.
-  _.isEmpty = function(obj) {
-    if (_.isArray(obj) || _.isString(obj)) return obj.length === 0;
-    for (var key in obj) if (_.has(obj, key)) return false;
-    return true;
-  };
-
-  // Is a given value a DOM element?
-  _.isElement = function(obj) {
-    return !!(obj && obj.nodeType == 1);
-  };
-
-  // Is a given value an array?
-  // Delegates to ECMA5's native Array.isArray
-  _.isArray = nativeIsArray || function(obj) {
-    return toString.call(obj) == '[object Array]';
-  };
-
-  // Is a given variable an object?
-  _.isObject = function(obj) {
-    return obj === Object(obj);
-  };
-
-  // Is a given variable an arguments object?
-  _.isArguments = function(obj) {
-    return toString.call(obj) == '[object Arguments]';
-  };
-  if (!_.isArguments(arguments)) {
-    _.isArguments = function(obj) {
-      return !!(obj && _.has(obj, 'callee'));
-    };
-  }
-
-  // Is a given value a function?
-  _.isFunction = function(obj) {
-    return toString.call(obj) == '[object Function]';
-  };
-
-  // Is a given value a string?
-  _.isString = function(obj) {
-    return toString.call(obj) == '[object String]';
-  };
-
-  // Is a given value a number?
-  _.isNumber = function(obj) {
-    return toString.call(obj) == '[object Number]';
-  };
-
-  // Is the given value `NaN`?
-  _.isNaN = function(obj) {
-    // `NaN` is the only value for which `===` is not reflexive.
-    return obj !== obj;
-  };
-
-  // Is a given value a boolean?
-  _.isBoolean = function(obj) {
-    return obj === true || obj === false || toString.call(obj) == '[object Boolean]';
-  };
-
-  // Is a given value a date?
-  _.isDate = function(obj) {
-    return toString.call(obj) == '[object Date]';
-  };
-
-  // Is the given value a regular expression?
-  _.isRegExp = function(obj) {
-    return toString.call(obj) == '[object RegExp]';
-  };
-
-  // Is a given value equal to null?
-  _.isNull = function(obj) {
-    return obj === null;
-  };
-
-  // Is a given variable undefined?
-  _.isUndefined = function(obj) {
-    return obj === void 0;
-  };
-
-  // Has own property?
-  _.has = function(obj, key) {
-    return hasOwnProperty.call(obj, key);
-  };
-
-  // Utility Functions
-  // -----------------
-
-  // Run Underscore.js in *noConflict* mode, returning the `_` variable to its
-  // previous owner. Returns a reference to the Underscore object.
-  _.noConflict = function() {
-    root._ = previousUnderscore;
-    return this;
-  };
-
-  // Keep the identity function around for default iterators.
-  _.identity = function(value) {
-    return value;
-  };
-
-  // Run a function **n** times.
-  _.times = function (n, iterator, context) {
-    for (var i = 0; i < n; i++) iterator.call(context, i);
-  };
-
-  // Escape a string for HTML interpolation.
-  _.escape = function(string) {
-    return (''+string).replace(/&/g, '&amp;').replace(/</g, '&lt;').replace(/>/g, '&gt;').replace(/"/g, '&quot;').replace(/'/g, '&#x27;').replace(/\//g,'&#x2F;');
-  };
-
-  // Add your own custom functions to the Underscore object, ensuring that
-  // they're correctly added to the OOP wrapper as well.
-  _.mixin = function(obj) {
-    each(_.functions(obj), function(name){
-      addToWrapper(name, _[name] = obj[name]);
-    });
-  };
-
-  // Generate a unique integer id (unique within the entire client session).
-  // Useful for temporary DOM ids.
-  var idCounter = 0;
-  _.uniqueId = function(prefix) {
-    var id = idCounter++;
-    return prefix ? prefix + id : id;
-  };
-
-  // By default, Underscore uses ERB-style template delimiters, change the
-  // following template settings to use alternative delimiters.
-  _.templateSettings = {
-    evaluate    : /<%([\s\S]+?)%>/g,
-    interpolate : /<%=([\s\S]+?)%>/g,
-    escape      : /<%-([\s\S]+?)%>/g
-  };
-
-  // When customizing `templateSettings`, if you don't want to define an
-  // interpolation, evaluation or escaping regex, we need one that is
-  // guaranteed not to match.
-  var noMatch = /.^/;
-
-  // Within an interpolation, evaluation, or escaping, remove HTML escaping
-  // that had been previously added.
-  var unescape = function(code) {
-    return code.replace(/\\\\/g, '\\').replace(/\\'/g, "'");
-  };
-
-  // JavaScript micro-templating, similar to John Resig's implementation.
-  // Underscore templating handles arbitrary delimiters, preserves whitespace,
-  // and correctly escapes quotes within interpolated code.
-  _.template = function(str, data) {
-    var c  = _.templateSettings;
-    var tmpl = 'var __p=[],print=function(){__p.push.apply(__p,arguments);};' +
-      'with(obj||{}){__p.push(\'' +
-      str.replace(/\\/g, '\\\\')
-         .replace(/'/g, "\\'")
-         .replace(c.escape || noMatch, function(match, code) {
-           return "',_.escape(" + unescape(code) + "),'";
-         })
-         .replace(c.interpolate || noMatch, function(match, code) {
-           return "'," + unescape(code) + ",'";
-         })
-         .replace(c.evaluate || noMatch, function(match, code) {
-           return "');" + unescape(code).replace(/[\r\n\t]/g, ' ') + ";__p.push('";
-         })
-         .replace(/\r/g, '\\r')
-         .replace(/\n/g, '\\n')
-         .replace(/\t/g, '\\t')
-         + "');}return __p.join('');";
-    var func = new Function('obj', '_', tmpl);
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-    <link rel="search" title="Search" href="search.html" />
-    <link rel="next" title="libpysal.weights.W" href="generated/libpysal.weights.W.html" />
-    <link rel="prev" title="Datasets for use with libpysal" href="notebooks/examples.html" />
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-      </div>
-
-        <div class="collapse navbar-collapse nav-collapse">
-          <ul class="nav navbar-nav">
-            
-                <li><a href="installation.html">Installation</a></li>
-                <li><a href="tutorial.html">Tutorial</a></li>
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-      aria-labelledby="dLabelGlobalToc"><p class="caption"><span class="caption-text">Contents:</span></p>
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-<li class="toctree-l2"><a class="reference internal" href="installation.html#installing-released-version">Installing released version</a></li>
-<li class="toctree-l2"><a class="reference internal" href="installation.html#installing-development-version">Installing development version</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="tutorial.html">Tutorial</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="tutorial.html#spatial-weights">Spatial Weights</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorial.html#example-datasets">Example Datasets</a></li>
-</ul>
-</li>
-<li class="toctree-l1 current"><a class="current reference internal" href="#">API</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="#spatial-weights">Spatial Weights</a></li>
-<li class="toctree-l2"><a class="reference internal" href="#cg-computational-geometry">cg: Computational Geometry</a></li>
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-<li><a class="reference internal" href="#">libpysal API reference</a><ul>
-<li><a class="reference internal" href="#spatial-weights">Spatial Weights</a><ul>
-<li><a class="reference internal" href="#distance-weights">Distance Weights</a></li>
-<li><a class="reference internal" href="#contiguity-weights">Contiguity Weights</a></li>
-<li><a class="reference internal" href="#spint-weights">spint Weights</a></li>
-<li><a class="reference internal" href="#weights-tools-to-interface-with-rasters">Weights tools to interface with rasters</a></li>
-<li><a class="reference internal" href="#weights-util-classes-and-functions">Weights Util Classes and Functions</a></li>
-<li><a class="reference internal" href="#weights-user-classes-and-functions">Weights user Classes and Functions</a></li>
-<li><a class="reference internal" href="#set-theoretic-weights">Set Theoretic Weights</a></li>
-<li><a class="reference internal" href="#spatial-lag">Spatial Lag</a></li>
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-<li><a class="reference internal" href="#alpha-shapes">alpha_shapes</a></li>
-<li><a class="reference internal" href="#voronoi">voronoi</a></li>
-<li><a class="reference internal" href="#sphere">sphere</a></li>
-<li><a class="reference internal" href="#shapes">shapes</a></li>
-<li><a class="reference internal" href="#standalone">standalone</a></li>
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-  <div class="section" id="libpysal-api-reference">
-<span id="api-ref"></span><h1>libpysal API reference<a class="headerlink" href="#libpysal-api-reference" title="Permalink to this headline">¶</a></h1>
-<div class="section" id="spatial-weights">
-<h2>Spatial Weights<a class="headerlink" href="#spatial-weights" title="Permalink to this headline">¶</a></h2>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.W</span></code></a>(neighbors[, weights, …])</p></td>
-<td><p>Spatial weights class.</p></td>
-</tr>
-</tbody>
-</table>
-<div class="section" id="distance-weights">
-<h3>Distance Weights<a class="headerlink" href="#distance-weights" title="Permalink to this headline">¶</a></h3>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.DistanceBand.html#libpysal.weights.DistanceBand" title="libpysal.weights.DistanceBand"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.DistanceBand</span></code></a>(data, threshold)</p></td>
-<td><p>Spatial weights based on distance band.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.weights.Kernel.html#libpysal.weights.Kernel" title="libpysal.weights.Kernel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.Kernel</span></code></a>(data[, bandwidth, …])</p></td>
-<td><p>Spatial weights based on kernel functions.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.KNN.html#libpysal.weights.KNN" title="libpysal.weights.KNN"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.KNN</span></code></a>(data[, k, p, ids, …])</p></td>
-<td><p>Creates nearest neighbor weights matrix based on k nearest neighbors.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="section" id="contiguity-weights">
-<h3>Contiguity Weights<a class="headerlink" href="#contiguity-weights" title="Permalink to this headline">¶</a></h3>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.Queen.html#libpysal.weights.Queen" title="libpysal.weights.Queen"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.Queen</span></code></a>(polygons, **kw)</p></td>
-<td><p>Construct a weights object from a collection of pysal polygons that share at least one vertex.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.weights.Rook.html#libpysal.weights.Rook" title="libpysal.weights.Rook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.Rook</span></code></a>(polygons, **kw)</p></td>
-<td><p>Construct a weights object from a collection of pysal polygons that share at least one edge.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.Voronoi.html#libpysal.weights.Voronoi" title="libpysal.weights.Voronoi"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.Voronoi</span></code></a>(points[, …])</p></td>
-<td><p>Voronoi weights for a 2-d point set</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.W</span></code></a>(neighbors[, weights, …])</p></td>
-<td><p>Spatial weights class.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="section" id="spint-weights">
-<h3>spint Weights<a class="headerlink" href="#spint-weights" title="Permalink to this headline">¶</a></h3>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.WSP.html#libpysal.weights.WSP" title="libpysal.weights.WSP"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.WSP</span></code></a>(sparse[, id_order, index])</p></td>
-<td><p>Thin <code class="docutils literal notranslate"><span class="pre">W</span></code> class for <code class="docutils literal notranslate"><span class="pre">spreg</span></code>.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.weights.netW.html#libpysal.weights.netW" title="libpysal.weights.netW"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.netW</span></code></a>(link_list[, share, …])</p></td>
-<td><p>Create a network-contiguity based weight object based on different nodal relationships encoded in a network.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.mat2L.html#libpysal.weights.mat2L" title="libpysal.weights.mat2L"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.mat2L</span></code></a>(edge_matrix)</p></td>
-<td><p>Convert a matrix denoting network connectivity (edges or flows) to a list denoting edges</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.weights.ODW.html#libpysal.weights.ODW" title="libpysal.weights.ODW"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.ODW</span></code></a>(Wo, Wd[, transform, …])</p></td>
-<td><p>Constructs an o*d by o*d origin-destination style spatial weight for o*d flows using standard spatial weights on o origins and d destinations.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.vecW.html#libpysal.weights.vecW" title="libpysal.weights.vecW"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.vecW</span></code></a>(origin_x, origin_y, …)</p></td>
-<td><p>Distance-based spatial weight for vectors that is computed using a 4-dimensional distance between the origin x,y-coordinates and the destination x,y-coordinates</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="section" id="weights-tools-to-interface-with-rasters">
-<h3>Weights tools to interface with rasters<a class="headerlink" href="#weights-tools-to-interface-with-rasters" title="Permalink to this headline">¶</a></h3>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.da2W.html#libpysal.weights.da2W" title="libpysal.weights.da2W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.da2W</span></code></a>(da[, criterion, …])</p></td>
-<td><p>Create a W object from xarray.DataArray with an additional attribute index containing coordinate values of the raster in the form of Pandas.Index/MultiIndex.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.weights.da2WSP.html#libpysal.weights.da2WSP" title="libpysal.weights.da2WSP"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.da2WSP</span></code></a>(da[, criterion, …])</p></td>
-<td><p>Create a WSP object from xarray.DataArray with an additional attribute index containing coordinate values of the raster in the form of Pandas.Index/MultiIndex.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.w2da.html#libpysal.weights.w2da" title="libpysal.weights.w2da"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.w2da</span></code></a>(data, w[, attrs, coords])</p></td>
-<td><p>Creates xarray.DataArray object from passed data aligned with W object.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.weights.wsp2da.html#libpysal.weights.wsp2da" title="libpysal.weights.wsp2da"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.wsp2da</span></code></a>(data, wsp[, attrs, …])</p></td>
-<td><p>Creates xarray.DataArray object from passed data aligned with WSP object.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.testDataArray.html#libpysal.weights.testDataArray" title="libpysal.weights.testDataArray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.testDataArray</span></code></a>([shape, …])</p></td>
-<td><p>Creates 2 or 3 dimensional test xarray.DataArray object</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="section" id="weights-util-classes-and-functions">
-<h3>Weights Util Classes and Functions<a class="headerlink" href="#weights-util-classes-and-functions" title="Permalink to this headline">¶</a></h3>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.block_weights.html#libpysal.weights.block_weights" title="libpysal.weights.block_weights"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.block_weights</span></code></a>(regimes[, …])</p></td>
-<td><p>Construct spatial weights for regime neighbors.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.weights.lat2W.html#libpysal.weights.lat2W" title="libpysal.weights.lat2W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.lat2W</span></code></a>([nrows, ncols, rook, …])</p></td>
-<td><p>Create a W object for a regular lattice.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.comb.html#libpysal.weights.comb" title="libpysal.weights.comb"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.comb</span></code></a>(items[, n])</p></td>
-<td><p>Combinations of size n taken from items</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.weights.order.html#libpysal.weights.order" title="libpysal.weights.order"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.order</span></code></a>(w[, kmax])</p></td>
-<td><p>Determine the non-redundant order of contiguity up to a specific order.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.higher_order.html#libpysal.weights.higher_order" title="libpysal.weights.higher_order"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.higher_order</span></code></a>(w[, k])</p></td>
-<td><p>Contiguity weights object of order k.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.weights.shimbel.html#libpysal.weights.shimbel" title="libpysal.weights.shimbel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.shimbel</span></code></a>(w)</p></td>
-<td><p>Find the Shimbel matrix for first order contiguity matrix.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.remap_ids.html#libpysal.weights.remap_ids" title="libpysal.weights.remap_ids"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.remap_ids</span></code></a>(w, old2new[, …])</p></td>
-<td><p>Remaps the IDs in a spatial weights object.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.weights.full2W.html#libpysal.weights.full2W" title="libpysal.weights.full2W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.full2W</span></code></a>(m[, ids])</p></td>
-<td><p>Create a PySAL W object from a full array.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.full.html#libpysal.weights.full" title="libpysal.weights.full"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.full</span></code></a>(w)</p></td>
-<td><p>Generate a full numpy array.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.weights.WSP2W.html#libpysal.weights.WSP2W" title="libpysal.weights.WSP2W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.WSP2W</span></code></a>(wsp, **kwargs)</p></td>
-<td><p>Convert a pysal WSP object (thin weights matrix) to a pysal W object.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.get_ids.html#libpysal.weights.get_ids" title="libpysal.weights.get_ids"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.get_ids</span></code></a>(in_shps, idVariable)</p></td>
-<td><p>Gets the IDs from the DBF file that moves with a given shape file or a geopandas.GeoDataFrame.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.weights.get_points_array_from_shapefile.html#libpysal.weights.get_points_array_from_shapefile" title="libpysal.weights.get_points_array_from_shapefile"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.get_points_array_from_shapefile</span></code></a>(…)</p></td>
-<td><p>Gets a data array of x and y coordinates from a given shapefile.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.fill_diagonal.html#libpysal.weights.fill_diagonal" title="libpysal.weights.fill_diagonal"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.fill_diagonal</span></code></a>(w[, val, wsp])</p></td>
-<td><p>Returns a new weights object with values inserted along the main diagonal.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="section" id="weights-user-classes-and-functions">
-<h3>Weights user Classes and Functions<a class="headerlink" href="#weights-user-classes-and-functions" title="Permalink to this headline">¶</a></h3>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.min_threshold_distance.html#libpysal.weights.min_threshold_distance" title="libpysal.weights.min_threshold_distance"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.min_threshold_distance</span></code></a>(data)</p></td>
-<td><p>Get the maximum nearest neighbor distance.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.weights.lat2SW.html#libpysal.weights.lat2SW" title="libpysal.weights.lat2SW"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.lat2SW</span></code></a>([nrows, ncols, …])</p></td>
-<td><p>Create a sparse W matrix for a regular lattice.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.w_local_cluster.html#libpysal.weights.w_local_cluster" title="libpysal.weights.w_local_cluster"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.w_local_cluster</span></code></a>(w)</p></td>
-<td><p>Local clustering coefficients for each unit as a node in a graph.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.weights.higher_order_sp.html#libpysal.weights.higher_order_sp" title="libpysal.weights.higher_order_sp"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.higher_order_sp</span></code></a>(w[, k, …])</p></td>
-<td><p>Contiguity weights for either a sparse W or W for order k.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.hexLat2W.html#libpysal.weights.hexLat2W" title="libpysal.weights.hexLat2W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.hexLat2W</span></code></a>([nrows, ncols])</p></td>
-<td><p>Create a W object for a hexagonal lattice.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.weights.attach_islands.html#libpysal.weights.attach_islands" title="libpysal.weights.attach_islands"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.attach_islands</span></code></a>(w, w_knn1, …)</p></td>
-<td><p>Attach nearest neighbor to islands in spatial weight w.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.nonplanar_neighbors.html#libpysal.weights.nonplanar_neighbors" title="libpysal.weights.nonplanar_neighbors"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.nonplanar_neighbors</span></code></a>(w, …)</p></td>
-<td><p>Detect neighbors for non-planar polygon collections</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.weights.fuzzy_contiguity.html#libpysal.weights.fuzzy_contiguity" title="libpysal.weights.fuzzy_contiguity"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.fuzzy_contiguity</span></code></a>(gdf[, …])</p></td>
-<td><p>Fuzzy contiguity spatial weights</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.min_threshold_dist_from_shapefile.html#libpysal.weights.min_threshold_dist_from_shapefile" title="libpysal.weights.min_threshold_dist_from_shapefile"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.min_threshold_dist_from_shapefile</span></code></a>(…)</p></td>
-<td><p>Get the maximum nearest neighbor distance between observations in the shapefile.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.weights.build_lattice_shapefile.html#libpysal.weights.build_lattice_shapefile" title="libpysal.weights.build_lattice_shapefile"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.build_lattice_shapefile</span></code></a>(…)</p></td>
-<td><p>Build a lattice shapefile with nrows rows and ncols cols.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.spw_from_gal.html#libpysal.weights.spw_from_gal" title="libpysal.weights.spw_from_gal"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.spw_from_gal</span></code></a>(galfile)</p></td>
-<td><p>Sparse scipy matrix for w from a gal file.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.weights.neighbor_equality.html#libpysal.weights.neighbor_equality" title="libpysal.weights.neighbor_equality"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.neighbor_equality</span></code></a>(w1, w2)</p></td>
-<td><p>Test if the neighbor sets are equal between two weights objects</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="section" id="set-theoretic-weights">
-<h3>Set Theoretic Weights<a class="headerlink" href="#set-theoretic-weights" title="Permalink to this headline">¶</a></h3>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.w_union.html#libpysal.weights.w_union" title="libpysal.weights.w_union"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.w_union</span></code></a>(w1, w2, **kwargs)</p></td>
-<td><p>Returns a binary weights object, w, that includes all neighbor pairs that exist in either w1 or w2.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.weights.w_intersection.html#libpysal.weights.w_intersection" title="libpysal.weights.w_intersection"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.w_intersection</span></code></a>(w1, w2[, …])</p></td>
-<td><p>Returns a binary weights object, w, that includes only those neighbor pairs that exist in both w1 and w2.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.w_difference.html#libpysal.weights.w_difference" title="libpysal.weights.w_difference"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.w_difference</span></code></a>(w1, w2[, …])</p></td>
-<td><p>Returns a binary weights object, w, that includes only neighbor pairs in w1 that are not in w2.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.weights.w_symmetric_difference.html#libpysal.weights.w_symmetric_difference" title="libpysal.weights.w_symmetric_difference"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.w_symmetric_difference</span></code></a>(w1, w2)</p></td>
-<td><p>Returns a binary weights object, w, that includes only neighbor pairs that are not shared by w1 and w2.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.w_subset.html#libpysal.weights.w_subset" title="libpysal.weights.w_subset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.w_subset</span></code></a>(w1, ids, **kwargs)</p></td>
-<td><p>Returns a binary weights object, w, that includes only those observations in ids.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.weights.w_clip.html#libpysal.weights.w_clip" title="libpysal.weights.w_clip"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.w_clip</span></code></a>(w1, w2[, outSP])</p></td>
-<td><p>Clip a continuous W object (w1) with a different W object (w2) so only cells where w2 has a non-zero value remain with non-zero values in w1.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="section" id="spatial-lag">
-<h3>Spatial Lag<a class="headerlink" href="#spatial-lag" title="Permalink to this headline">¶</a></h3>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.lag_spatial.html#libpysal.weights.lag_spatial" title="libpysal.weights.lag_spatial"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.lag_spatial</span></code></a>(w, y)</p></td>
-<td><p>Spatial lag operator.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.weights.lag_categorical.html#libpysal.weights.lag_categorical" title="libpysal.weights.lag_categorical"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.lag_categorical</span></code></a>(w, y[, ties])</p></td>
-<td><p>Spatial lag operator for categorical variables.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-</div>
-<div class="section" id="cg-computational-geometry">
-<h2>cg: Computational Geometry<a class="headerlink" href="#cg-computational-geometry" title="Permalink to this headline">¶</a></h2>
-<div class="section" id="alpha-shapes">
-<h3>alpha_shapes<a class="headerlink" href="#alpha-shapes" title="Permalink to this headline">¶</a></h3>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.alpha_shape.html#libpysal.cg.alpha_shape" title="libpysal.cg.alpha_shape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.alpha_shape</span></code></a>(xys, alpha)</p></td>
-<td><p>Alpha-shape delineation (Edelsbrunner, Kirkpatrick &amp; Seidel, 1983) from a collection of points</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.cg.alpha_shape_auto.html#libpysal.cg.alpha_shape_auto" title="libpysal.cg.alpha_shape_auto"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.alpha_shape_auto</span></code></a>(xys[, step, …])</p></td>
-<td><p>Computation of alpha-shape delineation with automated selection of alpha.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="section" id="voronoi">
-<h3>voronoi<a class="headerlink" href="#voronoi" title="Permalink to this headline">¶</a></h3>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.voronoi_frames.html#libpysal.cg.voronoi_frames" title="libpysal.cg.voronoi_frames"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.voronoi_frames</span></code></a>(points[, radius, …])</p></td>
-<td><p>Composite helper to return Voronoi regions and generator points as individual dataframes.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="section" id="sphere">
-<h3>sphere<a class="headerlink" href="#sphere" title="Permalink to this headline">¶</a></h3>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.RADIUS_EARTH_KM.html#libpysal.cg.RADIUS_EARTH_KM" title="libpysal.cg.RADIUS_EARTH_KM"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.RADIUS_EARTH_KM</span></code></a></p></td>
-<td><p>Convert a string or number to a floating point number, if possible.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.cg.RADIUS_EARTH_MILES.html#libpysal.cg.RADIUS_EARTH_MILES" title="libpysal.cg.RADIUS_EARTH_MILES"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.RADIUS_EARTH_MILES</span></code></a></p></td>
-<td><p>Convert a string or number to a floating point number, if possible.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.arcdist.html#libpysal.cg.arcdist" title="libpysal.cg.arcdist"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.arcdist</span></code></a>(pt0, pt1[, radius])</p></td>
-<td><p>Arc distance between two points on a sphere.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.cg.arcdist2linear.html#libpysal.cg.arcdist2linear" title="libpysal.cg.arcdist2linear"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.arcdist2linear</span></code></a>(arc_dist[, radius])</p></td>
-<td><p>Convert an arc distance (spherical earth) to a linear distance (R3) in the unit sphere.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.brute_knn.html#libpysal.cg.brute_knn" title="libpysal.cg.brute_knn"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.brute_knn</span></code></a>(pts, k[, mode, radius])</p></td>
-<td><p>Computes a brute-force <span class="math notranslate nohighlight">\(k\)</span> nearest neighbors.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.cg.fast_knn.html#libpysal.cg.fast_knn" title="libpysal.cg.fast_knn"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.fast_knn</span></code></a>(pts, k[, return_dist, …])</p></td>
-<td><p>Computes <span class="math notranslate nohighlight">\(k\)</span> nearest neighbors on a sphere.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.fast_threshold.html#libpysal.cg.fast_threshold" title="libpysal.cg.fast_threshold"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.fast_threshold</span></code></a>(pts, dist[, radius])</p></td>
-<td><p>Find all neighbors on a sphere within a threshold distance.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.cg.linear2arcdist.html#libpysal.cg.linear2arcdist" title="libpysal.cg.linear2arcdist"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.linear2arcdist</span></code></a>(linear_dist[, radius])</p></td>
-<td><p>Convert a linear distance in the unit sphere (R3) to an arc distance based on supplied radius.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.toLngLat.html#libpysal.cg.toLngLat" title="libpysal.cg.toLngLat"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.toLngLat</span></code></a>(xyz)</p></td>
-<td><p>Convert a point’s x,y,z to latitude and longitude.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.cg.toXYZ.html#libpysal.cg.toXYZ" title="libpysal.cg.toXYZ"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.toXYZ</span></code></a>(pt)</p></td>
-<td><p>Convert a point’s latitude and longitude to x,y,z.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.lonlat.html#libpysal.cg.lonlat" title="libpysal.cg.lonlat"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.lonlat</span></code></a>(pointslist)</p></td>
-<td><p>Converts point order from lat-lon tuples to lon-lat (x,y) tuples.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.cg.harcdist.html#libpysal.cg.harcdist" title="libpysal.cg.harcdist"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.harcdist</span></code></a>(p0, p1[, lonx, radius])</p></td>
-<td><p>Alternative the arc distance function, uses the haversine formula.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.geointerpolate.html#libpysal.cg.geointerpolate" title="libpysal.cg.geointerpolate"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.geointerpolate</span></code></a>(p0, p1, t[, lonx])</p></td>
-<td><p>Finds a point on a sphere along the great circle distance between two points on a sphere also known as a way point in great circle navigation.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.cg.geogrid.html#libpysal.cg.geogrid" title="libpysal.cg.geogrid"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.geogrid</span></code></a>(pup, pdown, k[, lonx])</p></td>
-<td><p>Computes a <span class="math notranslate nohighlight">\(k+1\)</span> by <span class="math notranslate nohighlight">\(k+1\)</span> set of grid points for a bounding box in lat-lon.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="section" id="shapes">
-<h3>shapes<a class="headerlink" href="#shapes" title="Permalink to this headline">¶</a></h3>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a>(loc)</p></td>
-<td><p>Geometric class for point objects.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.cg.LineSegment.html#libpysal.cg.LineSegment" title="libpysal.cg.LineSegment"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.LineSegment</span></code></a>(start_pt, end_pt)</p></td>
-<td><p>Geometric representation of line segment objects.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.Line.html#libpysal.cg.Line" title="libpysal.cg.Line"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Line</span></code></a>(m, b)</p></td>
-<td><p>Geometric representation of line objects.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.cg.Ray.html#libpysal.cg.Ray" title="libpysal.cg.Ray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Ray</span></code></a>(origin, second_p)</p></td>
-<td><p>Geometric representation of ray objects.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.Chain.html#libpysal.cg.Chain" title="libpysal.cg.Chain"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Chain</span></code></a>(vertices)</p></td>
-<td><p>Geometric representation of a chain, also known as a polyline.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.cg.Polygon.html#libpysal.cg.Polygon" title="libpysal.cg.Polygon"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Polygon</span></code></a>(vertices[, holes])</p></td>
-<td><p>Geometric representation of polygon objects.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.Rectangle.html#libpysal.cg.Rectangle" title="libpysal.cg.Rectangle"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Rectangle</span></code></a>(left, lower, right, upper)</p></td>
-<td><p>Geometric representation of rectangle objects.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.cg.asShape.html#libpysal.cg.asShape" title="libpysal.cg.asShape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.asShape</span></code></a>(obj)</p></td>
-<td><p>Returns a PySAL shape object from <code class="docutils literal notranslate"><span class="pre">obj</span></code>, which must support the <code class="docutils literal notranslate"><span class="pre">__geo_interface__</span></code>.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="section" id="standalone">
-<h3>standalone<a class="headerlink" href="#standalone" title="Permalink to this headline">¶</a></h3>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.bbcommon.html#libpysal.cg.bbcommon" title="libpysal.cg.bbcommon"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.bbcommon</span></code></a>(bb, bbother)</p></td>
-<td><p>Old Stars method for bounding box overlap testing.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.cg.get_bounding_box.html#libpysal.cg.get_bounding_box" title="libpysal.cg.get_bounding_box"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.get_bounding_box</span></code></a>(items)</p></td>
-<td><p>Find bounding box for a list of geometries.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.get_angle_between.html#libpysal.cg.get_angle_between" title="libpysal.cg.get_angle_between"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.get_angle_between</span></code></a>(ray1, ray2)</p></td>
-<td><p>Returns the angle formed between a pair of rays which share an origin.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.cg.is_collinear.html#libpysal.cg.is_collinear" title="libpysal.cg.is_collinear"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.is_collinear</span></code></a>(p1, p2, p3)</p></td>
-<td><p>Returns whether a triplet of points is collinear.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.get_segments_intersect.html#libpysal.cg.get_segments_intersect" title="libpysal.cg.get_segments_intersect"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.get_segments_intersect</span></code></a>(seg1, seg2)</p></td>
-<td><p>Returns the intersection of two segments if one exists.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.cg.get_segment_point_intersect.html#libpysal.cg.get_segment_point_intersect" title="libpysal.cg.get_segment_point_intersect"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.get_segment_point_intersect</span></code></a>(seg, pt)</p></td>
-<td><p>Returns the intersection of a segment and point.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.get_polygon_point_intersect.html#libpysal.cg.get_polygon_point_intersect" title="libpysal.cg.get_polygon_point_intersect"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.get_polygon_point_intersect</span></code></a>(poly, pt)</p></td>
-<td><p>Returns the intersection of a polygon and point.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.cg.get_rectangle_point_intersect.html#libpysal.cg.get_rectangle_point_intersect" title="libpysal.cg.get_rectangle_point_intersect"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.get_rectangle_point_intersect</span></code></a>(…)</p></td>
-<td><p>Returns the intersection of a rectangle and point.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.get_ray_segment_intersect.html#libpysal.cg.get_ray_segment_intersect" title="libpysal.cg.get_ray_segment_intersect"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.get_ray_segment_intersect</span></code></a>(ray, seg)</p></td>
-<td><p>Returns the intersection of a ray and line segment.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.cg.get_rectangle_rectangle_intersection.html#libpysal.cg.get_rectangle_rectangle_intersection" title="libpysal.cg.get_rectangle_rectangle_intersection"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.get_rectangle_rectangle_intersection</span></code></a>(r0, r1)</p></td>
-<td><p>Returns the intersection between two rectangles.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.get_polygon_point_dist.html#libpysal.cg.get_polygon_point_dist" title="libpysal.cg.get_polygon_point_dist"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.get_polygon_point_dist</span></code></a>(poly, pt)</p></td>
-<td><p>Returns the distance between a polygon and point.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.cg.get_points_dist.html#libpysal.cg.get_points_dist" title="libpysal.cg.get_points_dist"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.get_points_dist</span></code></a>(pt1, pt2)</p></td>
-<td><p>Returns the distance between a pair of points.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.get_segment_point_dist.html#libpysal.cg.get_segment_point_dist" title="libpysal.cg.get_segment_point_dist"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.get_segment_point_dist</span></code></a>(seg, pt)</p></td>
-<td><p>Returns (1) the distance between a line segment and point and (2) the distance along the segment to the closest location on the segment from the point as a ratio of the length of the segment.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.cg.get_point_at_angle_and_dist.html#libpysal.cg.get_point_at_angle_and_dist" title="libpysal.cg.get_point_at_angle_and_dist"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.get_point_at_angle_and_dist</span></code></a>(ray, …)</p></td>
-<td><p>Returns the point at a distance and angle relative to the origin of a ray.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.convex_hull.html#libpysal.cg.convex_hull" title="libpysal.cg.convex_hull"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.convex_hull</span></code></a>(points)</p></td>
-<td><p>Returns the convex hull of a set of points.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.cg.is_clockwise.html#libpysal.cg.is_clockwise" title="libpysal.cg.is_clockwise"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.is_clockwise</span></code></a>(vertices)</p></td>
-<td><p>Returns whether a list of points describing a polygon are clockwise or counterclockwise.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.point_touches_rectangle.html#libpysal.cg.point_touches_rectangle" title="libpysal.cg.point_touches_rectangle"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.point_touches_rectangle</span></code></a>(point, rect)</p></td>
-<td><p>Returns <code class="docutils literal notranslate"><span class="pre">True</span></code> (<code class="docutils literal notranslate"><span class="pre">1</span></code>) if the point is in the rectangle or touches it’s boundary, otherwise <code class="docutils literal notranslate"><span class="pre">False</span></code> (<code class="docutils literal notranslate"><span class="pre">0</span></code>).</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.cg.get_shared_segments.html#libpysal.cg.get_shared_segments" title="libpysal.cg.get_shared_segments"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.get_shared_segments</span></code></a>(poly1, poly2)</p></td>
-<td><p>Returns the line segments in common to both polygons.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.distance_matrix.html#libpysal.cg.distance_matrix" title="libpysal.cg.distance_matrix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.distance_matrix</span></code></a>(X[, p, threshold])</p></td>
-<td><p>Calculate a distance matrix.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="section" id="locators">
-<h3>locators<a class="headerlink" href="#locators" title="Permalink to this headline">¶</a></h3>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.Grid.html#libpysal.cg.Grid" title="libpysal.cg.Grid"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Grid</span></code></a>(bounds, resolution)</p></td>
-<td><p>Representation of a binning data structure.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.cg.PointLocator.html#libpysal.cg.PointLocator" title="libpysal.cg.PointLocator"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.PointLocator</span></code></a>(points)</p></td>
-<td><p>An abstract representation of a point indexing data structure.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.PolygonLocator.html#libpysal.cg.PolygonLocator" title="libpysal.cg.PolygonLocator"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.PolygonLocator</span></code></a>(polygons)</p></td>
-<td><p>An abstract representation of a polygon indexing data structure.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="section" id="kdtree">
-<h3>kdtree<a class="headerlink" href="#kdtree" title="Permalink to this headline">¶</a></h3>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.KDTree.html#libpysal.cg.KDTree" title="libpysal.cg.KDTree"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.KDTree</span></code></a>(data[, leafsize, …])</p></td>
-<td><p>kd-tree built on top of kd-tree functionality in scipy.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-</div>
-<div class="section" id="io">
-<h2>io<a class="headerlink" href="#io" title="Permalink to this headline">¶</a></h2>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.io.open.html#libpysal.io.open" title="libpysal.io.open"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.io.open</span></code></a></p></td>
-<td><p>alias of <a class="reference internal" href="generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO" title="libpysal.io.fileio.FileIO"><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.io.fileio.FileIO</span></code></a></p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO" title="libpysal.io.fileio.FileIO"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.io.fileio.FileIO</span></code></a>([dataPath, mode, …])</p></td>
-<td><p>Metaclass for supporting spatial data file read and write.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="section" id="examples">
-<h2>examples<a class="headerlink" href="#examples" title="Permalink to this headline">¶</a></h2>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.examples.available.html#libpysal.examples.available" title="libpysal.examples.available"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.examples.available</span></code></a>()</p></td>
-<td><p>List available datasets.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.examples.explain.html#libpysal.examples.explain" title="libpysal.examples.explain"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.examples.explain</span></code></a>(name)</p></td>
-<td><p>Explain a dataset by name.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.examples.get_path.html#libpysal.examples.get_path" title="libpysal.examples.get_path"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.examples.get_path</span></code></a>(file_name)</p></td>
-<td><p>Get the path for a file by searching installed datasets.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-</div>
-
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-  <div class="section" id="libpysal-cg-chain">
-<h1>libpysal.cg.Chain<a class="headerlink" href="#libpysal-cg-chain" title="Permalink to this headline">¶</a></h1>
-<dl class="py class">
-<dt id="libpysal.cg.Chain">
-<em class="property">class </em><code class="sig-prename descclassname">libpysal.cg.</code><code class="sig-name descname">Chain</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">vertices</span><span class="p">:</span> <span class="n"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)">list</a></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#Chain"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.Chain" title="Permalink to this definition">¶</a></dt>
-<dd><p>Geometric representation of a chain, also known as a polyline.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>vertices</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>A point list or list of point lists.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">c</span> <span class="o">=</span> <span class="n">Chain</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
-</pre></div>
-</div>
-<dl class="field-list">
-<dt class="field-odd">Attributes</dt>
-<dd class="field-odd"><dl>
-<dt><a class="reference internal" href="#libpysal.cg.Chain.vertices" title="libpysal.cg.Chain.vertices"><code class="xref py py-obj docutils literal notranslate"><span class="pre">vertices</span></code></a><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>Returns the vertices of the chain in clockwise order.</p>
-</dd>
-<dt><a class="reference internal" href="#libpysal.cg.Chain.len" title="libpysal.cg.Chain.len"><code class="xref py py-obj docutils literal notranslate"><span class="pre">len</span></code></a><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>Returns the geometric length of the chain.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<dl class="py method">
-<dt id="libpysal.cg.Chain.__init__">
-<code class="sig-name descname">__init__</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">vertices</span><span class="p">:</span> <span class="n"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)">list</a></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#Chain.__init__"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.Chain.__init__" title="Permalink to this definition">¶</a></dt>
-<dd><p>Initialize self.  See help(type(self)) for accurate signature.</p>
-</dd></dl>
-
-<p class="rubric">Methods</p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.Chain.__init__" title="libpysal.cg.Chain.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(vertices)</p></td>
-<td><p>Initialize self.</p></td>
-</tr>
-</tbody>
-</table>
-<p class="rubric">Attributes</p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.Chain.arclen" title="libpysal.cg.Chain.arclen"><code class="xref py py-obj docutils literal notranslate"><span class="pre">arclen</span></code></a></p></td>
-<td><p>Returns the geometric length of the chain computed using ‘arcdistance’ (meters).</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.cg.Chain.bounding_box" title="libpysal.cg.Chain.bounding_box"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bounding_box</span></code></a></p></td>
-<td><p>Returns the bounding box of the chain.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.Chain.len" title="libpysal.cg.Chain.len"><code class="xref py py-obj docutils literal notranslate"><span class="pre">len</span></code></a></p></td>
-<td><p>Returns the geometric length of the chain.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.cg.Chain.parts" title="libpysal.cg.Chain.parts"><code class="xref py py-obj docutils literal notranslate"><span class="pre">parts</span></code></a></p></td>
-<td><p>Returns the parts (lists of <code class="docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code> objects) of the chain.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.Chain.segments" title="libpysal.cg.Chain.segments"><code class="xref py py-obj docutils literal notranslate"><span class="pre">segments</span></code></a></p></td>
-<td><p>Returns the segments that compose the chain.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.cg.Chain.vertices" title="libpysal.cg.Chain.vertices"><code class="xref py py-obj docutils literal notranslate"><span class="pre">vertices</span></code></a></p></td>
-<td><p>Returns the vertices of the chain in clockwise order.</p></td>
-</tr>
-</tbody>
-</table>
-<dl class="py method">
-<dt id="libpysal.cg.Chain.arclen">
-<em class="property">property </em><code class="sig-name descname">arclen</code><a class="headerlink" href="#libpysal.cg.Chain.arclen" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the geometric length of the chain
-computed using ‘arcdistance’ (meters).</p>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.cg.Chain.bounding_box">
-<em class="property">property </em><code class="sig-name descname">bounding_box</code><a class="headerlink" href="#libpysal.cg.Chain.bounding_box" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the bounding box of the chain.</p>
-<dl class="field-list">
-<dt class="field-odd">Returns</dt>
-<dd class="field-odd"><dl>
-<dt><strong>self._bounding_box</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Rectangle.html#libpysal.cg.Rectangle" title="libpysal.cg.Rectangle"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Rectangle</span></code></a></span></dt><dd><p>The bounding box of the chain.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">c</span> <span class="o">=</span> <span class="n">Chain</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">c</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">left</span>
-<span class="go">0.0</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">c</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">lower</span>
-<span class="go">0.0</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">c</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">right</span>
-<span class="go">2.0</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">c</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">upper</span>
-<span class="go">1.0</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.cg.Chain.len">
-<em class="property">property </em><code class="sig-name descname">len</code><a class="headerlink" href="#libpysal.cg.Chain.len" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the geometric length of the chain.</p>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">c</span> <span class="o">=</span> <span class="n">Chain</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">c</span><span class="o">.</span><span class="n">len</span>
-<span class="go">3.0</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">c</span> <span class="o">=</span> <span class="n">Chain</span><span class="p">(</span>
-<span class="gp">... </span>    <span class="p">[</span>
-<span class="gp">... </span>        <span class="p">[</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))],</span>
-<span class="gp">... </span>        <span class="p">[</span><span class="n">Point</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">11</span><span class="p">,</span> <span class="mi">10</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">11</span><span class="p">,</span> <span class="mi">11</span><span class="p">))]</span>
-<span class="gp">... </span>    <span class="p">]</span>
-<span class="gp">... </span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">c</span><span class="o">.</span><span class="n">len</span>
-<span class="go">4.0</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.cg.Chain.parts">
-<em class="property">property </em><code class="sig-name descname">parts</code><a class="headerlink" href="#libpysal.cg.Chain.parts" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the parts (lists of <code class="docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code> objects) of the chain.</p>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">c</span> <span class="o">=</span> <span class="n">Chain</span><span class="p">(</span>
-<span class="gp">... </span>    <span class="p">[</span>
-<span class="gp">... </span>        <span class="p">[</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">))],</span>
-<span class="gp">... </span>        <span class="p">[</span><span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))]</span>
-<span class="gp">... </span>    <span class="p">]</span>
-<span class="gp">... </span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="n">c</span><span class="o">.</span><span class="n">parts</span><span class="p">)</span>
-<span class="go">2</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.cg.Chain.segments">
-<em class="property">property </em><code class="sig-name descname">segments</code><a class="headerlink" href="#libpysal.cg.Chain.segments" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the segments that compose the chain.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.cg.Chain.vertices">
-<em class="property">property </em><code class="sig-name descname">vertices</code><a class="headerlink" href="#libpysal.cg.Chain.vertices" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the vertices of the chain in clockwise order.</p>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">c</span> <span class="o">=</span> <span class="n">Chain</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">verts</span> <span class="o">=</span> <span class="n">c</span><span class="o">.</span><span class="n">vertices</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="n">verts</span><span class="p">)</span>
-<span class="go">4</span>
-</pre></div>
-</div>
-</dd></dl>
-
-</dd></dl>
-
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-<div id="sourcelink">
-  <a href="../_sources/generated/libpysal.cg.Chain.rst.txt"
-     rel="nofollow">Source</a>
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-  <div class="section" id="libpysal-cg-grid">
-<h1>libpysal.cg.Grid<a class="headerlink" href="#libpysal-cg-grid" title="Permalink to this headline">¶</a></h1>
-<dl class="py class">
-<dt id="libpysal.cg.Grid">
-<em class="property">class </em><code class="sig-prename descclassname">libpysal.cg.</code><code class="sig-name descname">Grid</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">bounds</span></em>, <em class="sig-param"><span class="n">resolution</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/locators.html#Grid"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.Grid" title="Permalink to this definition">¶</a></dt>
-<dd><p>Representation of a binning data structure.</p>
-<dl class="py method">
-<dt id="libpysal.cg.Grid.__init__">
-<code class="sig-name descname">__init__</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">bounds</span></em>, <em class="sig-param"><span class="n">resolution</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/locators.html#Grid.__init__"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.Grid.__init__" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns a grid with specified properties.</p>
-<p>__init__(Rectangle, number) -&gt; Grid</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>bounds</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">area</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">for</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">grid</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">encompass</span></code></span></dt><dd></dd>
-<dt><strong>resolution</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">diameter</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">of</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">each</span></code> <a class="reference external" href="https://docs.python.org/3.8/library/functions.html#bin" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bin</span></code></a></span></dt><dd></dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<p>TODO: complete this doctest
-&gt;&gt;&gt; g = Grid(Rectangle(0, 0, 10, 10), 1)</p>
-</dd></dl>
-
-<p class="rubric">Methods</p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.Grid.__init__" title="libpysal.cg.Grid.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(bounds, resolution)</p></td>
-<td><p>Returns a grid with specified properties.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.cg.Grid.add" title="libpysal.cg.Grid.add"><code class="xref py py-obj docutils literal notranslate"><span class="pre">add</span></code></a>(item, pt)</p></td>
-<td><p>Adds an item to the grid at a specified location.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.Grid.bounds" title="libpysal.cg.Grid.bounds"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bounds</span></code></a>(bounds)</p></td>
-<td><p>Returns a list of items found in the grid within the bounds specified.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.cg.Grid.in_grid" title="libpysal.cg.Grid.in_grid"><code class="xref py py-obj docutils literal notranslate"><span class="pre">in_grid</span></code></a>(loc)</p></td>
-<td><p>Returns whether a 2-tuple location _loc_ lies inside the grid bounds.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.Grid.nearest" title="libpysal.cg.Grid.nearest"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nearest</span></code></a>(pt)</p></td>
-<td><p>Returns the nearest item to a point.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.cg.Grid.proximity" title="libpysal.cg.Grid.proximity"><code class="xref py py-obj docutils literal notranslate"><span class="pre">proximity</span></code></a>(pt, r)</p></td>
-<td><p>Returns a list of items found in the grid within a specified distance of a point.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.Grid.remove" title="libpysal.cg.Grid.remove"><code class="xref py py-obj docutils literal notranslate"><span class="pre">remove</span></code></a>(item, pt)</p></td>
-<td><p>Removes an item from the grid at a specified location.</p></td>
-</tr>
-</tbody>
-</table>
-<dl class="py method">
-<dt id="libpysal.cg.Grid.add">
-<code class="sig-name descname">add</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">item</span></em>, <em class="sig-param"><span class="n">pt</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/locators.html#Grid.add"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.Grid.add" title="Permalink to this definition">¶</a></dt>
-<dd><p>Adds an item to the grid at a specified location.</p>
-<p>add(x, Point) -&gt; x</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>item</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">item</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">insert</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">into</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">grid</span></code></span></dt><dd></dd>
-<dt><strong>pt</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">location</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">insert</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">item</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">at</span></code></span></dt><dd></dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">g</span> <span class="o">=</span> <span class="n">Grid</span><span class="p">(</span><span class="n">Rectangle</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">g</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="n">Point</span><span class="p">((</span><span class="mf">4.2</span><span class="p">,</span> <span class="mf">8.7</span><span class="p">)))</span>
-<span class="go">&#39;A&#39;</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.cg.Grid.bounds">
-<code class="sig-name descname">bounds</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">bounds</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/locators.html#Grid.bounds"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.Grid.bounds" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns a list of items found in the grid within the bounds specified.</p>
-<p>bounds(Rectangle) -&gt; x list</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>item</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">item</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code> <a class="reference internal" href="#libpysal.cg.Grid.remove" title="libpysal.cg.Grid.remove"><code class="xref py py-obj docutils literal notranslate"><span class="pre">remove</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">from</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">grid</span></code></span></dt><dd></dd>
-<dt><strong>pt</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">location</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">item</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">was</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">added</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">at</span></code></span></dt><dd></dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">g</span> <span class="o">=</span> <span class="n">Grid</span><span class="p">(</span><span class="n">Rectangle</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">g</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="n">Point</span><span class="p">((</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)))</span>
-<span class="go">&#39;A&#39;</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">g</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="s1">&#39;B&#39;</span><span class="p">,</span> <span class="n">Point</span><span class="p">((</span><span class="mf">4.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">)))</span>
-<span class="go">&#39;B&#39;</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">g</span><span class="o">.</span><span class="n">bounds</span><span class="p">(</span><span class="n">Rectangle</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
-<span class="go">[&#39;A&#39;]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">g</span><span class="o">.</span><span class="n">bounds</span><span class="p">(</span><span class="n">Rectangle</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
-<span class="go">[&#39;B&#39;]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">sorted</span><span class="p">(</span><span class="n">g</span><span class="o">.</span><span class="n">bounds</span><span class="p">(</span><span class="n">Rectangle</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">)))</span>
-<span class="go">[&#39;A&#39;, &#39;B&#39;]</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.cg.Grid.in_grid">
-<code class="sig-name descname">in_grid</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">loc</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/locators.html#Grid.in_grid"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.Grid.in_grid" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns whether a 2-tuple location _loc_ lies inside the grid bounds.</p>
-<p>Test tag: &lt;tc&gt;#is#Grid.in_grid&lt;/tc&gt;</p>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.cg.Grid.nearest">
-<code class="sig-name descname">nearest</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">pt</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/locators.html#Grid.nearest"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.Grid.nearest" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the nearest item to a point.</p>
-<p>nearest(Point) -&gt; x</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>pt</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">location</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">search</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">near</span></code></span></dt><dd></dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">g</span> <span class="o">=</span> <span class="n">Grid</span><span class="p">(</span><span class="n">Rectangle</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">g</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="n">Point</span><span class="p">((</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)))</span>
-<span class="go">&#39;A&#39;</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">g</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="s1">&#39;B&#39;</span><span class="p">,</span> <span class="n">Point</span><span class="p">((</span><span class="mf">4.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">)))</span>
-<span class="go">&#39;B&#39;</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">g</span><span class="o">.</span><span class="n">nearest</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mf">2.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)))</span>
-<span class="go">&#39;A&#39;</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">g</span><span class="o">.</span><span class="n">nearest</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mf">7.0</span><span class="p">,</span> <span class="mf">5.0</span><span class="p">)))</span>
-<span class="go">&#39;B&#39;</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.cg.Grid.proximity">
-<code class="sig-name descname">proximity</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">pt</span></em>, <em class="sig-param"><span class="n">r</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/locators.html#Grid.proximity"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.Grid.proximity" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns a list of items found in the grid within a specified distance of a point.</p>
-<p>proximity(Point, number) -&gt; x list</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>pt</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">location</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">search</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">around</span></code></span></dt><dd></dd>
-<dt><strong>r</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">distance</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">search</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">around</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">point</span></code></span></dt><dd></dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">g</span> <span class="o">=</span> <span class="n">Grid</span><span class="p">(</span><span class="n">Rectangle</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">g</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="n">Point</span><span class="p">((</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)))</span>
-<span class="go">&#39;A&#39;</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">g</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="s1">&#39;B&#39;</span><span class="p">,</span> <span class="n">Point</span><span class="p">((</span><span class="mf">4.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">)))</span>
-<span class="go">&#39;B&#39;</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">g</span><span class="o">.</span><span class="n">proximity</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mf">2.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)),</span> <span class="mi">2</span><span class="p">)</span>
-<span class="go">[&#39;A&#39;]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">g</span><span class="o">.</span><span class="n">proximity</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mf">6.0</span><span class="p">,</span> <span class="mf">5.0</span><span class="p">)),</span> <span class="mf">3.0</span><span class="p">)</span>
-<span class="go">[&#39;B&#39;]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">sorted</span><span class="p">(</span><span class="n">g</span><span class="o">.</span><span class="n">proximity</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mf">4.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)),</span> <span class="mf">4.0</span><span class="p">))</span>
-<span class="go">[&#39;A&#39;, &#39;B&#39;]</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.cg.Grid.remove">
-<code class="sig-name descname">remove</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">item</span></em>, <em class="sig-param"><span class="n">pt</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/locators.html#Grid.remove"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.Grid.remove" title="Permalink to this definition">¶</a></dt>
-<dd><p>Removes an item from the grid at a specified location.</p>
-<p>remove(x, Point) -&gt; x</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>item</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">item</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code> <a class="reference internal" href="#libpysal.cg.Grid.remove" title="libpysal.cg.Grid.remove"><code class="xref py py-obj docutils literal notranslate"><span class="pre">remove</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">from</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">grid</span></code></span></dt><dd></dd>
-<dt><strong>pt</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">location</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">item</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">was</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">added</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">at</span></code></span></dt><dd></dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">g</span> <span class="o">=</span> <span class="n">Grid</span><span class="p">(</span><span class="n">Rectangle</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">g</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="n">Point</span><span class="p">((</span><span class="mf">4.2</span><span class="p">,</span> <span class="mf">8.7</span><span class="p">)))</span>
-<span class="go">&#39;A&#39;</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">g</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="n">Point</span><span class="p">((</span><span class="mf">4.2</span><span class="p">,</span> <span class="mf">8.7</span><span class="p">)))</span>
-<span class="go">&#39;A&#39;</span>
-</pre></div>
-</div>
-</dd></dl>
-
-</dd></dl>
-
-</div>
-
-
-    </div>
-      
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-</div>
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-  <div class="container">
-    <p class="pull-right">
-      <a href="#">Back to top</a>
-      
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-<h1>libpysal.cg.KDTree<a class="headerlink" href="#libpysal-cg-kdtree" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.cg.KDTree">
-<code class="sig-prename descclassname">libpysal.cg.</code><code class="sig-name descname">KDTree</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">leafsize</span><span class="o">=</span><span class="default_value">10</span></em>, <em class="sig-param"><span class="n">distance_metric</span><span class="o">=</span><span class="default_value">'Euclidean'</span></em>, <em class="sig-param"><span class="n">radius</span><span class="o">=</span><span class="default_value">6371.0</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/kdtree.html#KDTree"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.KDTree" title="Permalink to this definition">¶</a></dt>
-<dd><p>kd-tree built on top of kd-tree functionality in scipy. If using scipy
-0.12 or greater uses the scipy.spatial.cKDTree, otherwise uses
-scipy.spatial.KDTree. Offers both Arc distance and Euclidean distance. Note
-that Arc distance is only appropriate when points in latitude and
-longitude, and the radius set to meaningful value (see docs below).</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>data</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.20)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a></span></dt><dd><p>The data points to be indexed. This array is not copied, and so
-modifying this data will result in bogus results. Typically nx2.</p>
-</dd>
-<dt><strong>leafsize</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#int" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>The number of points at which the algorithm switches over to brute-force. Has to be positive. Optional, default is 10.</p>
-</dd>
-<dt><strong>distance_metric</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>Options: “Euclidean” (default) and “Arc”.</p>
-</dd>
-<dt><strong>radius</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>Radius of the sphere on which to compute distances. Assumes data in
-latitude and longitude. Ignored if distance_metric=”Euclidean”. Typical
-values: pysal.cg.RADIUS_EARTH_KM (default) pysal.cg.RADIUS_EARTH_MILES</p>
-</dd>
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-<h1>libpysal.cg.Line<a class="headerlink" href="#libpysal-cg-line" title="Permalink to this headline">¶</a></h1>
-<dl class="py class">
-<dt id="libpysal.cg.Line">
-<em class="property">class </em><code class="sig-prename descclassname">libpysal.cg.</code><code class="sig-name descname">Line</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">m</span></em>, <em class="sig-param"><span class="n">b</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#Line"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.Line" title="Permalink to this definition">¶</a></dt>
-<dd><p>Geometric representation of line objects.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>m</strong><span class="classifier">{<a class="reference external" href="https://docs.python.org/3.8/library/functions.html#int" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a>, <a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a>}</span></dt><dd><p>The slope of the line. <code class="docutils literal notranslate"><span class="pre">m</span></code> is also an attribute.</p>
-</dd>
-<dt><strong>b</strong><span class="classifier">{<a class="reference external" href="https://docs.python.org/3.8/library/functions.html#int" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a>, <a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a>}</span></dt><dd><p>The <span class="math notranslate nohighlight">\(y\)</span>-intercept of the line. <code class="docutils literal notranslate"><span class="pre">b</span></code> is also an attribute.</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Raises</dt>
-<dd class="field-even"><dl class="simple">
-<dt><a class="reference external" href="https://docs.python.org/3.8/library/exceptions.html#ArithmeticError" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ArithmeticError</span></code></a></dt><dd><p>Raised when infinity is passed in as the slope.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ls</span> <span class="o">=</span> <span class="n">Line</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">ls</span><span class="o">.</span><span class="n">m</span>
-<span class="go">1.0</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ls</span><span class="o">.</span><span class="n">b</span>
-<span class="go">0.0</span>
-</pre></div>
-</div>
-<dl class="py method">
-<dt id="libpysal.cg.Line.__init__">
-<code class="sig-name descname">__init__</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">m</span></em>, <em class="sig-param"><span class="n">b</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#Line.__init__"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.Line.__init__" title="Permalink to this definition">¶</a></dt>
-<dd><p>Initialize self.  See help(type(self)) for accurate signature.</p>
-</dd></dl>
-
-<p class="rubric">Methods</p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.Line.__init__" title="libpysal.cg.Line.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(m, b)</p></td>
-<td><p>Initialize self.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.cg.Line.x" title="libpysal.cg.Line.x"><code class="xref py py-obj docutils literal notranslate"><span class="pre">x</span></code></a>(y)</p></td>
-<td><p>Returns the <span class="math notranslate nohighlight">\(x\)</span>-value of the line at a particular <span class="math notranslate nohighlight">\(y\)</span>-value.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.Line.y" title="libpysal.cg.Line.y"><code class="xref py py-obj docutils literal notranslate"><span class="pre">y</span></code></a>(x)</p></td>
-<td><p>Returns the <span class="math notranslate nohighlight">\(y\)</span>-value of the line at a particular <span class="math notranslate nohighlight">\(x\)</span>-value.</p></td>
-</tr>
-</tbody>
-</table>
-<dl class="py method">
-<dt id="libpysal.cg.Line.x">
-<code class="sig-name descname">x</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">y</span><span class="p">:</span> <span class="n">Union<span class="p">[</span><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#int" title="(in Python v3.8)">int</a><span class="p">, </span><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)">float</a><span class="p">]</span></span></em><span class="sig-paren">)</span> &#x2192; <a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)">float</a><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#Line.x"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.Line.x" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the <span class="math notranslate nohighlight">\(x\)</span>-value of the line at a particular <span class="math notranslate nohighlight">\(y\)</span>-value.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>y</strong><span class="classifier">{<a class="reference external" href="https://docs.python.org/3.8/library/functions.html#int" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a>, <a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a>}</span></dt><dd><p>The <span class="math notranslate nohighlight">\(y\)</span>-value at which to compute <span class="math notranslate nohighlight">\(x\)</span>.</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Raises</dt>
-<dd class="field-even"><dl class="simple">
-<dt><a class="reference external" href="https://docs.python.org/3.8/library/exceptions.html#ArithmeticError" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ArithmeticError</span></code></a></dt><dd><p>Raised when <code class="docutils literal notranslate"><span class="pre">0.</span></code> is passed in as the slope.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">l</span> <span class="o">=</span> <span class="n">Line</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">l</span><span class="o">.</span><span class="n">x</span><span class="p">(</span><span class="mf">0.25</span><span class="p">)</span>
-<span class="go">0.5</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.cg.Line.y">
-<code class="sig-name descname">y</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">x</span><span class="p">:</span> <span class="n">Union<span class="p">[</span><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#int" title="(in Python v3.8)">int</a><span class="p">, </span><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)">float</a><span class="p">]</span></span></em><span class="sig-paren">)</span> &#x2192; <a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)">float</a><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#Line.y"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.Line.y" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the <span class="math notranslate nohighlight">\(y\)</span>-value of the line at a particular <span class="math notranslate nohighlight">\(x\)</span>-value.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>x</strong><span class="classifier">{<a class="reference external" href="https://docs.python.org/3.8/library/functions.html#int" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a>, <a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a>}</span></dt><dd><p>The <span class="math notranslate nohighlight">\(x\)</span>-value at which to compute <span class="math notranslate nohighlight">\(y\)</span>.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">l</span> <span class="o">=</span> <span class="n">Line</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">l</span><span class="o">.</span><span class="n">y</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
-<span class="go">1.0</span>
-</pre></div>
-</div>
-</dd></dl>
-
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-  <div class="section" id="libpysal-cg-linesegment">
-<h1>libpysal.cg.LineSegment<a class="headerlink" href="#libpysal-cg-linesegment" title="Permalink to this headline">¶</a></h1>
-<dl class="py class">
-<dt id="libpysal.cg.LineSegment">
-<em class="property">class </em><code class="sig-prename descclassname">libpysal.cg.</code><code class="sig-name descname">LineSegment</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">start_pt</span></em>, <em class="sig-param"><span class="n">end_pt</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#LineSegment"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.LineSegment" title="Permalink to this definition">¶</a></dt>
-<dd><p>Geometric representation of line segment objects.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>start_pt</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a></span></dt><dd><p>The point where the segment begins.</p>
-</dd>
-<dt><strong>end_pt</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a></span></dt><dd><p>The point where the segment ends.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ls</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">)))</span>
-</pre></div>
-</div>
-<dl class="field-list">
-<dt class="field-odd">Attributes</dt>
-<dd class="field-odd"><dl>
-<dt><a class="reference internal" href="#libpysal.cg.LineSegment.p1" title="libpysal.cg.LineSegment.p1"><code class="xref py py-obj docutils literal notranslate"><span class="pre">p1</span></code></a><span class="classifier"><a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a></span></dt><dd><p><strong>HELPER METHOD. DO NOT CALL.</strong></p>
-</dd>
-<dt><a class="reference internal" href="#libpysal.cg.LineSegment.p2" title="libpysal.cg.LineSegment.p2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">p2</span></code></a><span class="classifier"><a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Point</span></code></a></span></dt><dd><p><strong>HELPER METHOD. DO NOT CALL.</strong></p>
-</dd>
-<dt><a class="reference internal" href="#libpysal.cg.LineSegment.bounding_box" title="libpysal.cg.LineSegment.bounding_box"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bounding_box</span></code></a><span class="classifier"><a class="reference internal" href="libpysal.cg.Rectangle.html#libpysal.cg.Rectangle" title="libpysal.cg.Rectangle"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Rectangle</span></code></a></span></dt><dd><p>Returns the minimum bounding box of a <code class="docutils literal notranslate"><span class="pre">LineSegment</span></code> object.</p>
-</dd>
-<dt><a class="reference internal" href="#libpysal.cg.LineSegment.len" title="libpysal.cg.LineSegment.len"><code class="xref py py-obj docutils literal notranslate"><span class="pre">len</span></code></a><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>Returns the length of a <code class="docutils literal notranslate"><span class="pre">LineSegment</span></code> object.</p>
-</dd>
-<dt><a class="reference internal" href="#libpysal.cg.LineSegment.line" title="libpysal.cg.LineSegment.line"><code class="xref py py-obj docutils literal notranslate"><span class="pre">line</span></code></a><span class="classifier"><a class="reference internal" href="libpysal.cg.Line.html#libpysal.cg.Line" title="libpysal.cg.Line"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Line</span></code></a></span></dt><dd><p>Returns a <code class="docutils literal notranslate"><span class="pre">Line</span></code> object of the line on which the segment lies.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<dl class="py method">
-<dt id="libpysal.cg.LineSegment.__init__">
-<code class="sig-name descname">__init__</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">start_pt</span></em>, <em class="sig-param"><span class="n">end_pt</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#LineSegment.__init__"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.LineSegment.__init__" title="Permalink to this definition">¶</a></dt>
-<dd><p>Initialize self.  See help(type(self)) for accurate signature.</p>
-</dd></dl>
-
-<p class="rubric">Methods</p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.LineSegment.__init__" title="libpysal.cg.LineSegment.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(start_pt, end_pt)</p></td>
-<td><p>Initialize self.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.cg.LineSegment.get_swap" title="libpysal.cg.LineSegment.get_swap"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_swap</span></code></a>()</p></td>
-<td><p>Returns a <code class="docutils literal notranslate"><span class="pre">LineSegment</span></code> object which has its endpoints swapped.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.LineSegment.intersect" title="libpysal.cg.LineSegment.intersect"><code class="xref py py-obj docutils literal notranslate"><span class="pre">intersect</span></code></a>(other)</p></td>
-<td><p>Test whether segment intersects with other segment (<code class="docutils literal notranslate"><span class="pre">True</span></code>) or not (<code class="docutils literal notranslate"><span class="pre">False</span></code>).</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.cg.LineSegment.is_ccw" title="libpysal.cg.LineSegment.is_ccw"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_ccw</span></code></a>(pt)</p></td>
-<td><p>Returns whether a point is counterclockwise of the segment (<code class="docutils literal notranslate"><span class="pre">True</span></code>) or not (<code class="docutils literal notranslate"><span class="pre">False</span></code>).</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.LineSegment.is_cw" title="libpysal.cg.LineSegment.is_cw"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_cw</span></code></a>(pt)</p></td>
-<td><p>Returns whether a point is clockwise of the segment (<code class="docutils literal notranslate"><span class="pre">True</span></code>) or not (<code class="docutils literal notranslate"><span class="pre">False</span></code>).</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.cg.LineSegment.sw_ccw" title="libpysal.cg.LineSegment.sw_ccw"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sw_ccw</span></code></a>(pt)</p></td>
-<td><p>Sedgewick test for <code class="docutils literal notranslate"><span class="pre">pt</span></code> being ccw of segment.</p></td>
-</tr>
-</tbody>
-</table>
-<p class="rubric">Attributes</p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.LineSegment.bounding_box" title="libpysal.cg.LineSegment.bounding_box"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bounding_box</span></code></a></p></td>
-<td><p>Returns the minimum bounding box of a <code class="docutils literal notranslate"><span class="pre">LineSegment</span></code> object.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.cg.LineSegment.len" title="libpysal.cg.LineSegment.len"><code class="xref py py-obj docutils literal notranslate"><span class="pre">len</span></code></a></p></td>
-<td><p>Returns the length of a <code class="docutils literal notranslate"><span class="pre">LineSegment</span></code> object.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.LineSegment.line" title="libpysal.cg.LineSegment.line"><code class="xref py py-obj docutils literal notranslate"><span class="pre">line</span></code></a></p></td>
-<td><p>Returns a <code class="docutils literal notranslate"><span class="pre">Line</span></code> object of the line on which the segment lies.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.cg.LineSegment.p1" title="libpysal.cg.LineSegment.p1"><code class="xref py py-obj docutils literal notranslate"><span class="pre">p1</span></code></a></p></td>
-<td><p><strong>HELPER METHOD.DO NOT CALL.</strong> Returns the <code class="docutils literal notranslate"><span class="pre">p1</span></code> attribute of the line segment..</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.LineSegment.p2" title="libpysal.cg.LineSegment.p2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">p2</span></code></a></p></td>
-<td><p><strong>HELPER METHOD.DO NOT CALL.</strong> Returns the <code class="docutils literal notranslate"><span class="pre">p2</span></code> attribute of the line segment..</p></td>
-</tr>
-</tbody>
-</table>
-<dl class="py method">
-<dt id="libpysal.cg.LineSegment.bounding_box">
-<em class="property">property </em><code class="sig-name descname">bounding_box</code><a class="headerlink" href="#libpysal.cg.LineSegment.bounding_box" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the minimum bounding box of a <code class="docutils literal notranslate"><span class="pre">LineSegment</span></code> object.</p>
-<dl class="field-list">
-<dt class="field-odd">Returns</dt>
-<dd class="field-odd"><dl>
-<dt><strong>self._bounding_box</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Rectangle.html#libpysal.cg.Rectangle" title="libpysal.cg.Rectangle"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Rectangle</span></code></a></span></dt><dd><p>The bounding box of the line segment.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ls</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">)))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">ls</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">left</span>
-<span class="go">1.0</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ls</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">lower</span>
-<span class="go">2.0</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ls</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">right</span>
-<span class="go">5.0</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ls</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">upper</span>
-<span class="go">6.0</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.cg.LineSegment.get_swap">
-<code class="sig-name descname">get_swap</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#LineSegment.get_swap"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.LineSegment.get_swap" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns a <code class="docutils literal notranslate"><span class="pre">LineSegment</span></code> object which has its endpoints swapped.</p>
-<dl class="field-list">
-<dt class="field-odd">Returns</dt>
-<dd class="field-odd"><dl>
-<dt><strong>line_seg</strong><span class="classifier"><a class="reference internal" href="#libpysal.cg.LineSegment" title="libpysal.cg.LineSegment"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.LineSegment</span></code></a></span></dt><dd><p>The <code class="docutils literal notranslate"><span class="pre">LineSegment</span></code> object which has its endpoints swapped.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ls</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">)))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">swap</span> <span class="o">=</span> <span class="n">ls</span><span class="o">.</span><span class="n">get_swap</span><span class="p">()</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">swap</span><span class="o">.</span><span class="n">p1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<span class="go">5.0</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">swap</span><span class="o">.</span><span class="n">p1</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-<span class="go">6.0</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">swap</span><span class="o">.</span><span class="n">p2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<span class="go">1.0</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">swap</span><span class="o">.</span><span class="n">p2</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-<span class="go">2.0</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.cg.LineSegment.intersect">
-<code class="sig-name descname">intersect</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">other</span></em><span class="sig-paren">)</span> &#x2192; <a class="reference external" href="https://docs.python.org/3.8/library/functions.html#bool" title="(in Python v3.8)">bool</a><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#LineSegment.intersect"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.LineSegment.intersect" title="Permalink to this definition">¶</a></dt>
-<dd><p>Test whether segment intersects with other segment (<code class="docutils literal notranslate"><span class="pre">True</span></code>) or
-not (<code class="docutils literal notranslate"><span class="pre">False</span></code>). Handles endpoints of segments being on other segment.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>other</strong><span class="classifier"><a class="reference internal" href="#libpysal.cg.LineSegment" title="libpysal.cg.LineSegment"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.LineSegment</span></code></a></span></dt><dd><p>Another line segment to check against.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ls</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="mi">0</span><span class="p">)))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">ls1</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="mi">1</span><span class="p">)))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">ls</span><span class="o">.</span><span class="n">intersect</span><span class="p">(</span><span class="n">ls1</span><span class="p">)</span>
-<span class="go">True</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ls2</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="mi">1</span><span class="p">)))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">ls</span><span class="o">.</span><span class="n">intersect</span><span class="p">(</span><span class="n">ls2</span><span class="p">)</span>
-<span class="go">False</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ls2</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">7</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">7</span><span class="p">,</span> <span class="mi">2</span><span class="p">)))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">ls</span><span class="o">.</span><span class="n">intersect</span><span class="p">(</span><span class="n">ls2</span><span class="p">)</span>
-<span class="go">True</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.cg.LineSegment.is_ccw">
-<code class="sig-name descname">is_ccw</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">pt</span></em><span class="sig-paren">)</span> &#x2192; <a class="reference external" href="https://docs.python.org/3.8/library/functions.html#bool" title="(in Python v3.8)">bool</a><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#LineSegment.is_ccw"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.LineSegment.is_ccw" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns whether a point is counterclockwise of the
-segment (<code class="docutils literal notranslate"><span class="pre">True</span></code>) or not (<code class="docutils literal notranslate"><span class="pre">False</span></code>). Exclusive.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>pt</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a></span></dt><dd><p>A point lying ccw or cw of a segment.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ls</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">0</span><span class="p">)))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">ls</span><span class="o">.</span><span class="n">is_ccw</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)))</span>
-<span class="go">True</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ls</span><span class="o">.</span><span class="n">is_ccw</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">)))</span>
-<span class="go">False</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.cg.LineSegment.is_cw">
-<code class="sig-name descname">is_cw</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">pt</span></em><span class="sig-paren">)</span> &#x2192; <a class="reference external" href="https://docs.python.org/3.8/library/functions.html#bool" title="(in Python v3.8)">bool</a><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#LineSegment.is_cw"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.LineSegment.is_cw" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns whether a point is clockwise of the
-segment (<code class="docutils literal notranslate"><span class="pre">True</span></code>) or not (<code class="docutils literal notranslate"><span class="pre">False</span></code>). Exclusive.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>pt</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a></span></dt><dd><p>A point lying ccw or cw of a segment.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ls</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">0</span><span class="p">)))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">ls</span><span class="o">.</span><span class="n">is_cw</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)))</span>
-<span class="go">False</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ls</span><span class="o">.</span><span class="n">is_cw</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">)))</span>
-<span class="go">True</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.cg.LineSegment.len">
-<em class="property">property </em><code class="sig-name descname">len</code><a class="headerlink" href="#libpysal.cg.LineSegment.len" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the length of a <code class="docutils literal notranslate"><span class="pre">LineSegment</span></code> object.</p>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ls</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">2</span><span class="p">)))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">ls</span><span class="o">.</span><span class="n">len</span>
-<span class="go">3.0</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.cg.LineSegment.line">
-<em class="property">property </em><code class="sig-name descname">line</code><a class="headerlink" href="#libpysal.cg.LineSegment.line" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns a <code class="docutils literal notranslate"><span class="pre">Line</span></code> object of the line on which the segment lies.</p>
-<dl class="field-list">
-<dt class="field-odd">Returns</dt>
-<dd class="field-odd"><dl>
-<dt><strong>self._line</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Line.html#libpysal.cg.Line" title="libpysal.cg.Line"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Line</span></code></a></span></dt><dd><p>The <code class="docutils literal notranslate"><span class="pre">Line</span></code> object of the line on which the segment lies.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ls</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">l</span> <span class="o">=</span> <span class="n">ls</span><span class="o">.</span><span class="n">line</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">l</span><span class="o">.</span><span class="n">m</span>
-<span class="go">1.0</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">l</span><span class="o">.</span><span class="n">b</span>
-<span class="go">0.0</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.cg.LineSegment.p1">
-<em class="property">property </em><code class="sig-name descname">p1</code><a class="headerlink" href="#libpysal.cg.LineSegment.p1" title="Permalink to this definition">¶</a></dt>
-<dd><p><strong>HELPER METHOD. DO NOT CALL.</strong>
-Returns the <code class="docutils literal notranslate"><span class="pre">p1</span></code> attribute of the line segment.</p>
-<dl class="field-list">
-<dt class="field-odd">Returns</dt>
-<dd class="field-odd"><dl>
-<dt><strong>self._p1</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a></span></dt><dd><p>The <code class="docutils literal notranslate"><span class="pre">_p1</span></code> attribute.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ls</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">)))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">r</span> <span class="o">=</span> <span class="n">ls</span><span class="o">.</span><span class="n">_get_p1</span><span class="p">()</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">r</span> <span class="o">==</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
-<span class="go">True</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.cg.LineSegment.p2">
-<em class="property">property </em><code class="sig-name descname">p2</code><a class="headerlink" href="#libpysal.cg.LineSegment.p2" title="Permalink to this definition">¶</a></dt>
-<dd><p><strong>HELPER METHOD. DO NOT CALL.</strong>
-Returns the <code class="docutils literal notranslate"><span class="pre">p2</span></code> attribute of the line segment.</p>
-<dl class="field-list">
-<dt class="field-odd">Returns</dt>
-<dd class="field-odd"><dl>
-<dt><strong>self._p2</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a></span></dt><dd><p>The <code class="docutils literal notranslate"><span class="pre">_p2</span></code> attribute.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ls</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">)))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">r</span> <span class="o">=</span> <span class="n">ls</span><span class="o">.</span><span class="n">_get_p2</span><span class="p">()</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">r</span> <span class="o">==</span> <span class="n">Point</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
-<span class="go">True</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.cg.LineSegment.sw_ccw">
-<code class="sig-name descname">sw_ccw</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">pt</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#LineSegment.sw_ccw"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.LineSegment.sw_ccw" title="Permalink to this definition">¶</a></dt>
-<dd><p>Sedgewick test for <code class="docutils literal notranslate"><span class="pre">pt</span></code> being ccw of segment.</p>
-<dl class="field-list">
-<dt class="field-odd">Returns</dt>
-<dd class="field-odd"><dl>
-<dt><strong>is_ccw</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.8)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p><code class="docutils literal notranslate"><span class="pre">1</span></code> if turn from <code class="docutils literal notranslate"><span class="pre">self.p1</span></code> to <code class="docutils literal notranslate"><span class="pre">self.p2</span></code> to <code class="docutils literal notranslate"><span class="pre">pt</span></code> is ccw.
-<code class="docutils literal notranslate"><span class="pre">-1</span></code> if turn from <code class="docutils literal notranslate"><span class="pre">self.p1</span></code> to <code class="docutils literal notranslate"><span class="pre">self.p2</span></code> to <code class="docutils literal notranslate"><span class="pre">pt</span></code> is cw.
-<code class="docutils literal notranslate"><span class="pre">-1</span></code> if the points are collinear and <code class="docutils literal notranslate"><span class="pre">self.p1</span></code> is in the middle.
-<code class="docutils literal notranslate"><span class="pre">1</span></code> if the points are collinear and <code class="docutils literal notranslate"><span class="pre">self.p2</span></code> is in the middle.
-<code class="docutils literal notranslate"><span class="pre">0</span></code> if the points are collinear and <code class="docutils literal notranslate"><span class="pre">pt</span></code> is in the middle.</p>
-</dd>
-</dl>
-</dd>
-</dl>
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-</div>
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-<h1>libpysal.cg.Point<a class="headerlink" href="#libpysal-cg-point" title="Permalink to this headline">¶</a></h1>
-<dl class="py class">
-<dt id="libpysal.cg.Point">
-<em class="property">class </em><code class="sig-prename descclassname">libpysal.cg.</code><code class="sig-name descname">Point</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">loc</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#Point"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.Point" title="Permalink to this definition">¶</a></dt>
-<dd><p>Geometric class for point objects.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>loc</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#tuple" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">tuple</span></code></a></span></dt><dd><p>The point’s location (number <span class="math notranslate nohighlight">\(x\)</span>-tuple, <span class="math notranslate nohighlight">\(x\)</span> &gt; 1).</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p</span> <span class="o">=</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
-</pre></div>
-</div>
-<dl class="py method">
-<dt id="libpysal.cg.Point.__init__">
-<code class="sig-name descname">__init__</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">loc</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#Point.__init__"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.Point.__init__" title="Permalink to this definition">¶</a></dt>
-<dd><p>Initialize self.  See help(type(self)) for accurate signature.</p>
-</dd></dl>
-
-<p class="rubric">Methods</p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.Point.__init__" title="libpysal.cg.Point.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(loc)</p></td>
-<td><p>Initialize self.</p></td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
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diff --git a/docs/generated/libpysal.cg.PointLocator.html b/docs/generated/libpysal.cg.PointLocator.html
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-<h1>libpysal.cg.PointLocator<a class="headerlink" href="#libpysal-cg-pointlocator" title="Permalink to this headline">¶</a></h1>
-<dl class="py class">
-<dt id="libpysal.cg.PointLocator">
-<em class="property">class </em><code class="sig-prename descclassname">libpysal.cg.</code><code class="sig-name descname">PointLocator</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">points</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/locators.html#PointLocator"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.PointLocator" title="Permalink to this definition">¶</a></dt>
-<dd><p>An abstract representation of a point indexing data structure.</p>
-<dl class="py method">
-<dt id="libpysal.cg.PointLocator.__init__">
-<code class="sig-name descname">__init__</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">points</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/locators.html#PointLocator.__init__"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.PointLocator.__init__" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns a point locator object.</p>
-<p>__init__(Point list) -&gt; PointLocator</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>points</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">a</span></code> <a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">of</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">points</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">index</span></code></span></dt><dd></dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">points</span> <span class="o">=</span> <span class="p">[</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mf">5.4</span><span class="p">,</span> <span class="mf">1.4</span><span class="p">))]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">pl</span> <span class="o">=</span> <span class="n">PointLocator</span><span class="p">(</span><span class="n">points</span><span class="p">)</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<p class="rubric">Methods</p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.PointLocator.__init__" title="libpysal.cg.PointLocator.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(points)</p></td>
-<td><p>Returns a point locator object.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.cg.PointLocator.nearest" title="libpysal.cg.PointLocator.nearest"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nearest</span></code></a>(query_point)</p></td>
-<td><p>Returns the nearest point indexed to a query point.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.PointLocator.overlapping" title="libpysal.cg.PointLocator.overlapping"><code class="xref py py-obj docutils literal notranslate"><span class="pre">overlapping</span></code></a>(region_rect)</p></td>
-<td><p>Returns the indexed points located inside a rectangular query region.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.cg.PointLocator.polygon" title="libpysal.cg.PointLocator.polygon"><code class="xref py py-obj docutils literal notranslate"><span class="pre">polygon</span></code></a>(polygon)</p></td>
-<td><p>Returns the indexed points located inside a polygon</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.PointLocator.proximity" title="libpysal.cg.PointLocator.proximity"><code class="xref py py-obj docutils literal notranslate"><span class="pre">proximity</span></code></a>(origin, r)</p></td>
-<td><p>Returns the indexed points located within some distance of an origin point.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.cg.PointLocator.region" title="libpysal.cg.PointLocator.region"><code class="xref py py-obj docutils literal notranslate"><span class="pre">region</span></code></a>(region_rect)</p></td>
-<td><p>Returns the indexed points located inside a rectangular query region.</p></td>
-</tr>
-</tbody>
-</table>
-<dl class="py method">
-<dt id="libpysal.cg.PointLocator.nearest">
-<code class="sig-name descname">nearest</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">query_point</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/locators.html#PointLocator.nearest"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.PointLocator.nearest" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the nearest point indexed to a query point.</p>
-<p>nearest(Point) -&gt; Point</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>query_point</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">a</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">point</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">find</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <a class="reference internal" href="#libpysal.cg.PointLocator.nearest" title="libpysal.cg.PointLocator.nearest"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nearest</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">indexed</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">point</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code></span></dt><dd></dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">points</span> <span class="o">=</span> <span class="p">[</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mf">5.4</span><span class="p">,</span> <span class="mf">1.4</span><span class="p">))]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">pl</span> <span class="o">=</span> <span class="n">PointLocator</span><span class="p">(</span><span class="n">points</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">n</span> <span class="o">=</span> <span class="n">pl</span><span class="o">.</span><span class="n">nearest</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">str</span><span class="p">(</span><span class="n">n</span><span class="p">)</span>
-<span class="go">&#39;(0.0, 0.0)&#39;</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.cg.PointLocator.overlapping">
-<code class="sig-name descname">overlapping</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">region_rect</span></em><span class="sig-paren">)</span><a class="headerlink" href="#libpysal.cg.PointLocator.overlapping" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the indexed points located inside a rectangular query region.</p>
-<p>region(Rectangle) -&gt; Point list</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>region_rect</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">rectangular</span></code> <a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#range" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">range</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">find</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">indexed</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">points</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">in</span></code></span></dt><dd></dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">points</span> <span class="o">=</span> <span class="p">[</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mf">5.4</span><span class="p">,</span> <span class="mf">1.4</span><span class="p">))]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">pl</span> <span class="o">=</span> <span class="n">PointLocator</span><span class="p">(</span><span class="n">points</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">pts</span> <span class="o">=</span> <span class="n">pl</span><span class="o">.</span><span class="n">region</span><span class="p">(</span><span class="n">Rectangle</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="n">pts</span><span class="p">)</span>
-<span class="go">3</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.cg.PointLocator.polygon">
-<code class="sig-name descname">polygon</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">polygon</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/locators.html#PointLocator.polygon"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.PointLocator.polygon" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the indexed points located inside a polygon</p>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.cg.PointLocator.proximity">
-<code class="sig-name descname">proximity</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">origin</span></em>, <em class="sig-param"><span class="n">r</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/locators.html#PointLocator.proximity"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.PointLocator.proximity" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the indexed points located within some distance of an origin point.</p>
-<p>proximity(Point, number) -&gt; Point list</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>origin</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">point</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">find</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">indexed</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">points</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">near</span></code></span></dt><dd></dd>
-<dt><strong>r</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">maximum</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">distance</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">find</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">indexed</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">point</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">from</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">origin</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">point</span></code></span></dt><dd></dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">points</span> <span class="o">=</span> <span class="p">[</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mf">5.4</span><span class="p">,</span> <span class="mf">1.4</span><span class="p">))]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">pl</span> <span class="o">=</span> <span class="n">PointLocator</span><span class="p">(</span><span class="n">points</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="n">pl</span><span class="o">.</span><span class="n">proximity</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="mi">2</span><span class="p">))</span>
-<span class="go">1</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.cg.PointLocator.region">
-<code class="sig-name descname">region</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">region_rect</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/locators.html#PointLocator.region"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.PointLocator.region" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the indexed points located inside a rectangular query region.</p>
-<p>region(Rectangle) -&gt; Point list</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>region_rect</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">rectangular</span></code> <a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#range" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">range</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">find</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">indexed</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">points</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">in</span></code></span></dt><dd></dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">points</span> <span class="o">=</span> <span class="p">[</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mf">5.4</span><span class="p">,</span> <span class="mf">1.4</span><span class="p">))]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">pl</span> <span class="o">=</span> <span class="n">PointLocator</span><span class="p">(</span><span class="n">points</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">pts</span> <span class="o">=</span> <span class="n">pl</span><span class="o">.</span><span class="n">region</span><span class="p">(</span><span class="n">Rectangle</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="n">pts</span><span class="p">)</span>
-<span class="go">3</span>
-</pre></div>
-</div>
-</dd></dl>
-
-</dd></dl>
-
-</div>
-
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-  <div class="section" id="libpysal-cg-polygon">
-<h1>libpysal.cg.Polygon<a class="headerlink" href="#libpysal-cg-polygon" title="Permalink to this headline">¶</a></h1>
-<dl class="py class">
-<dt id="libpysal.cg.Polygon">
-<em class="property">class </em><code class="sig-prename descclassname">libpysal.cg.</code><code class="sig-name descname">Polygon</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">vertices</span></em>, <em class="sig-param"><span class="n">holes</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#Polygon"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.Polygon" title="Permalink to this definition">¶</a></dt>
-<dd><p>Geometric representation of polygon objects.
-Returns a polygon created from the objects specified.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>vertices</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>A list of vertices or a list of lists of vertices.</p>
-</dd>
-<dt><strong>holes</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>A list of sub-polygons to be considered as holes.
-Default is <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p1</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
-</pre></div>
-</div>
-<dl class="field-list">
-<dt class="field-odd">Attributes</dt>
-<dd class="field-odd"><dl>
-<dt><a class="reference internal" href="#libpysal.cg.Polygon.vertices" title="libpysal.cg.Polygon.vertices"><code class="xref py py-obj docutils literal notranslate"><span class="pre">vertices</span></code></a><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>Returns the vertices of the polygon in clockwise order.</p>
-</dd>
-<dt><a class="reference internal" href="#libpysal.cg.Polygon.len" title="libpysal.cg.Polygon.len"><code class="xref py py-obj docutils literal notranslate"><span class="pre">len</span></code></a><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#int" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>Returns the number of vertices in the polygon.</p>
-</dd>
-<dt><a class="reference internal" href="#libpysal.cg.Polygon.perimeter" title="libpysal.cg.Polygon.perimeter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">perimeter</span></code></a><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>Returns the perimeter of the polygon.</p>
-</dd>
-<dt><a class="reference internal" href="#libpysal.cg.Polygon.bounding_box" title="libpysal.cg.Polygon.bounding_box"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bounding_box</span></code></a><span class="classifier"><a class="reference internal" href="libpysal.cg.Rectangle.html#libpysal.cg.Rectangle" title="libpysal.cg.Rectangle"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Rectangle</span></code></a></span></dt><dd><p>Returns the bounding box of the polygon.</p>
-</dd>
-<dt><a class="reference internal" href="#libpysal.cg.Polygon.bbox" title="libpysal.cg.Polygon.bbox"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bbox</span></code></a><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>Returns the bounding box of the polygon as a list.</p>
-</dd>
-<dt><a class="reference internal" href="#libpysal.cg.Polygon.area" title="libpysal.cg.Polygon.area"><code class="xref py py-obj docutils literal notranslate"><span class="pre">area</span></code></a><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>Returns the area of the polygon.</p>
-</dd>
-<dt><a class="reference internal" href="#libpysal.cg.Polygon.centroid" title="libpysal.cg.Polygon.centroid"><code class="xref py py-obj docutils literal notranslate"><span class="pre">centroid</span></code></a><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#tuple" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">tuple</span></code></a></span></dt><dd><p>Returns the centroid of the polygon.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<dl class="py method">
-<dt id="libpysal.cg.Polygon.__init__">
-<code class="sig-name descname">__init__</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">vertices</span></em>, <em class="sig-param"><span class="n">holes</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#Polygon.__init__"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.Polygon.__init__" title="Permalink to this definition">¶</a></dt>
-<dd><p>Initialize self.  See help(type(self)) for accurate signature.</p>
-</dd></dl>
-
-<p class="rubric">Methods</p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.Polygon.__init__" title="libpysal.cg.Polygon.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(vertices[, holes])</p></td>
-<td><p>Initialize self.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.cg.Polygon.build_quad_tree_structure" title="libpysal.cg.Polygon.build_quad_tree_structure"><code class="xref py py-obj docutils literal notranslate"><span class="pre">build_quad_tree_structure</span></code></a>()</p></td>
-<td><p>Build the quad tree structure for this polygon.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.Polygon.contains_point" title="libpysal.cg.Polygon.contains_point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">contains_point</span></code></a>(point)</p></td>
-<td><p>Test if a polygon contains a point.</p></td>
-</tr>
-</tbody>
-</table>
-<p class="rubric">Attributes</p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.Polygon.area" title="libpysal.cg.Polygon.area"><code class="xref py py-obj docutils literal notranslate"><span class="pre">area</span></code></a></p></td>
-<td><p>Returns the area of the polygon.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.cg.Polygon.bbox" title="libpysal.cg.Polygon.bbox"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bbox</span></code></a></p></td>
-<td><p>Returns the bounding box of the polygon as a list.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.Polygon.bounding_box" title="libpysal.cg.Polygon.bounding_box"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bounding_box</span></code></a></p></td>
-<td><p>Returns the bounding box of the polygon.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.cg.Polygon.centroid" title="libpysal.cg.Polygon.centroid"><code class="xref py py-obj docutils literal notranslate"><span class="pre">centroid</span></code></a></p></td>
-<td><p>Returns the centroid of the polygon.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.Polygon.holes" title="libpysal.cg.Polygon.holes"><code class="xref py py-obj docutils literal notranslate"><span class="pre">holes</span></code></a></p></td>
-<td><p>Returns the holes of the polygon in clockwise order.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.cg.Polygon.len" title="libpysal.cg.Polygon.len"><code class="xref py py-obj docutils literal notranslate"><span class="pre">len</span></code></a></p></td>
-<td><p>Returns the number of vertices in the polygon.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.Polygon.parts" title="libpysal.cg.Polygon.parts"><code class="xref py py-obj docutils literal notranslate"><span class="pre">parts</span></code></a></p></td>
-<td><p>Returns the parts of the polygon in clockwise order.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.cg.Polygon.perimeter" title="libpysal.cg.Polygon.perimeter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">perimeter</span></code></a></p></td>
-<td><p>Returns the perimeter of the polygon.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.Polygon.vertices" title="libpysal.cg.Polygon.vertices"><code class="xref py py-obj docutils literal notranslate"><span class="pre">vertices</span></code></a></p></td>
-<td><p>Returns the vertices of the polygon in clockwise order.</p></td>
-</tr>
-</tbody>
-</table>
-<dl class="py method">
-<dt id="libpysal.cg.Polygon.area">
-<em class="property">property </em><code class="sig-name descname">area</code><a class="headerlink" href="#libpysal.cg.Polygon.area" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the area of the polygon.</p>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">p</span><span class="o">.</span><span class="n">area</span>
-<span class="go">1.0</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">(</span>
-<span class="gp">... </span>    <span class="p">[</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">))],</span>
-<span class="gp">... </span>    <span class="p">[</span><span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))]</span>
-<span class="gp">... </span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">p</span><span class="o">.</span><span class="n">area</span>
-<span class="go">99.0</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.cg.Polygon.bbox">
-<em class="property">property </em><code class="sig-name descname">bbox</code><a class="headerlink" href="#libpysal.cg.Polygon.bbox" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the bounding box of the polygon as a list.</p>
-<dl class="field-list">
-<dt class="field-odd">Returns</dt>
-<dd class="field-odd"><dl>
-<dt><strong>self._bbox</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>The bounding box of the polygon as a list.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<div class="admonition seealso">
-<p class="admonition-title">See also</p>
-<dl class="simple">
-<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.bounding_box</span></code></dt><dd></dd>
-</dl>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.cg.Polygon.bounding_box">
-<em class="property">property </em><code class="sig-name descname">bounding_box</code><a class="headerlink" href="#libpysal.cg.Polygon.bounding_box" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the bounding box of the polygon.</p>
-<dl class="field-list">
-<dt class="field-odd">Returns</dt>
-<dd class="field-odd"><dl>
-<dt><strong>self._bounding_box</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Rectangle.html#libpysal.cg.Rectangle" title="libpysal.cg.Rectangle"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Rectangle</span></code></a></span></dt><dd><p>The bounding box of the polygon.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">p</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">left</span>
-<span class="go">0.0</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">lower</span>
-<span class="go">0.0</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">right</span>
-<span class="go">2.0</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">upper</span>
-<span class="go">1.0</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.cg.Polygon.build_quad_tree_structure">
-<code class="sig-name descname">build_quad_tree_structure</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#Polygon.build_quad_tree_structure"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.Polygon.build_quad_tree_structure" title="Permalink to this definition">¶</a></dt>
-<dd><p>Build the quad tree structure for this polygon. Once
-the structure is built, speed for testing if a point is
-inside the ring will be increased significantly.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.cg.Polygon.centroid">
-<em class="property">property </em><code class="sig-name descname">centroid</code><a class="headerlink" href="#libpysal.cg.Polygon.centroid" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the centroid of the polygon.</p>
-<p class="rubric">Notes</p>
-<p>The centroid returned by this method is the geometric
-centroid and respects multipart polygons with holes.
-Also known as the ‘center of gravity’ or ‘center of mass’.</p>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">(</span>
-<span class="gp">... </span>    <span class="p">[</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">))],</span>
-<span class="gp">... </span>    <span class="p">[</span><span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">))]</span>
-<span class="gp">... </span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">p</span><span class="o">.</span><span class="n">centroid</span>
-<span class="go">(5.0353535353535355, 5.0353535353535355)</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.cg.Polygon.contains_point">
-<code class="sig-name descname">contains_point</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">point</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#Polygon.contains_point"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.Polygon.contains_point" title="Permalink to this definition">¶</a></dt>
-<dd><p>Test if a polygon contains a point.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>point</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a></span></dt><dd><p>A point to test for containment.</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>contains</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.8)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p><code class="docutils literal notranslate"><span class="pre">True</span></code> if the polygon contains <code class="docutils literal notranslate"><span class="pre">point</span></code> otherwise <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Notes</p>
-<p>Points falling exactly on polygon edges may yield unpredictable results.</p>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">(</span>
-<span class="gp">... </span>    <span class="p">[</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span><span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span><span class="mi">5</span><span class="p">))]</span>
-<span class="gp">... </span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">p</span><span class="o">.</span><span class="n">contains_point</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
-<span class="go">1</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p</span><span class="o">.</span><span class="n">contains_point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
-<span class="go">0</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p</span><span class="o">.</span><span class="n">contains_point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mf">2.9</span><span class="p">))</span>
-<span class="go">1</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p</span><span class="o">.</span><span class="n">contains_point</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">))</span>
-<span class="go">0</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p</span><span class="o">.</span><span class="n">contains_point</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span><span class="mi">0</span><span class="p">))</span>
-<span class="go">0</span>
-</pre></div>
-</div>
-<p>Handles holes.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">(</span>
-<span class="gp">... </span>    <span class="p">[</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="mi">0</span><span class="p">))],</span>
-<span class="gp">... </span>    <span class="p">[</span><span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">))]</span>
-<span class="gp">... </span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">p</span><span class="o">.</span><span class="n">contains_point</span><span class="p">((</span><span class="mf">3.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">))</span>
-<span class="go">False</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p</span><span class="o">.</span><span class="n">contains_point</span><span class="p">((</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">))</span>
-<span class="go">True</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.cg.Polygon.holes">
-<em class="property">property </em><code class="sig-name descname">holes</code><a class="headerlink" href="#libpysal.cg.Polygon.holes" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the holes of the polygon in clockwise order.</p>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">(</span>
-<span class="gp">... </span>    <span class="p">[</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">))],</span>
-<span class="gp">... </span>    <span class="p">[</span><span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))]</span>
-<span class="gp">... </span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">holes</span><span class="p">)</span>
-<span class="go">1</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.cg.Polygon.len">
-<em class="property">property </em><code class="sig-name descname">len</code><a class="headerlink" href="#libpysal.cg.Polygon.len" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the number of vertices in the polygon.</p>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p1</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">))])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">p1</span><span class="o">.</span><span class="n">len</span>
-<span class="go">4</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="n">p1</span><span class="p">)</span>
-<span class="go">4</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.cg.Polygon.parts">
-<em class="property">property </em><code class="sig-name descname">parts</code><a class="headerlink" href="#libpysal.cg.Polygon.parts" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the parts of the polygon in clockwise order.</p>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">(</span>
-<span class="gp">... </span>    <span class="p">[</span>
-<span class="gp">... </span>        <span class="p">[</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">))],</span>
-<span class="gp">... </span>        <span class="p">[</span><span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))]</span>
-<span class="gp">... </span>    <span class="p">]</span>
-<span class="gp">... </span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">parts</span><span class="p">)</span>
-<span class="go">2</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.cg.Polygon.perimeter">
-<em class="property">property </em><code class="sig-name descname">perimeter</code><a class="headerlink" href="#libpysal.cg.Polygon.perimeter" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the perimeter of the polygon.</p>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">p</span><span class="o">.</span><span class="n">perimeter</span>
-<span class="go">4.0</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.cg.Polygon.vertices">
-<em class="property">property </em><code class="sig-name descname">vertices</code><a class="headerlink" href="#libpysal.cg.Polygon.vertices" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the vertices of the polygon in clockwise order.</p>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p1</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">))])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="n">p1</span><span class="o">.</span><span class="n">vertices</span><span class="p">)</span>
-<span class="go">4</span>
-</pre></div>
-</div>
-</dd></dl>
-
-</dd></dl>
-
-</div>
-
-
-    </div>
-      
-  </div>
-</div>
-<footer class="footer">
-  <div class="container">
-    <p class="pull-right">
-      <a href="#">Back to top</a>
-      
-        <br/>
-        
-<div id="sourcelink">
-  <a href="../_sources/generated/libpysal.cg.Polygon.rst.txt"
-     rel="nofollow">Source</a>
-</div>
-      
-    </p>
-    <p>
-        &copy; Copyright 2018-, pysal developers.<br/>
-      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 3.1.2.<br/>
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-<!DOCTYPE html>
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-<html>
-  <head>
-    <meta charset="utf-8" />
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-  <div class="section" id="libpysal-cg-polygonlocator">
-<h1>libpysal.cg.PolygonLocator<a class="headerlink" href="#libpysal-cg-polygonlocator" title="Permalink to this headline">¶</a></h1>
-<dl class="py class">
-<dt id="libpysal.cg.PolygonLocator">
-<em class="property">class </em><code class="sig-prename descclassname">libpysal.cg.</code><code class="sig-name descname">PolygonLocator</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">polygons</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/locators.html#PolygonLocator"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.PolygonLocator" title="Permalink to this definition">¶</a></dt>
-<dd><p>An abstract representation of a polygon indexing data structure.</p>
-<dl class="py method">
-<dt id="libpysal.cg.PolygonLocator.__init__">
-<code class="sig-name descname">__init__</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">polygons</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/locators.html#PolygonLocator.__init__"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.PolygonLocator.__init__" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns a polygon locator object.</p>
-<p>__init__(Polygon list) -&gt; PolygonLocator</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>polygons</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">a</span></code> <a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">of</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">polygons</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">index</span></code></span></dt><dd></dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p1</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">p2</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">9</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">pl</span> <span class="o">=</span> <span class="n">PolygonLocator</span><span class="p">([</span><span class="n">p1</span><span class="p">,</span> <span class="n">p2</span><span class="p">])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">isinstance</span><span class="p">(</span><span class="n">pl</span><span class="p">,</span> <span class="n">PolygonLocator</span><span class="p">)</span>
-<span class="go">True</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<p class="rubric">Methods</p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.PolygonLocator.__init__" title="libpysal.cg.PolygonLocator.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(polygons)</p></td>
-<td><p>Returns a polygon locator object.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.cg.PolygonLocator.contains_point" title="libpysal.cg.PolygonLocator.contains_point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">contains_point</span></code></a>(point)</p></td>
-<td><p>Returns polygons that contain point</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.PolygonLocator.inside" title="libpysal.cg.PolygonLocator.inside"><code class="xref py py-obj docutils literal notranslate"><span class="pre">inside</span></code></a>(query_rectangle)</p></td>
-<td><p>Returns polygons that are inside query_rectangle</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.cg.PolygonLocator.nearest" title="libpysal.cg.PolygonLocator.nearest"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nearest</span></code></a>(query_point[, rule])</p></td>
-<td><p>Returns the nearest polygon indexed to a query point based on various rules.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.PolygonLocator.overlapping" title="libpysal.cg.PolygonLocator.overlapping"><code class="xref py py-obj docutils literal notranslate"><span class="pre">overlapping</span></code></a>(query_rectangle)</p></td>
-<td><p>Returns list of polygons that overlap query_rectangle</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.cg.PolygonLocator.proximity" title="libpysal.cg.PolygonLocator.proximity"><code class="xref py py-obj docutils literal notranslate"><span class="pre">proximity</span></code></a>(origin, r[, rule])</p></td>
-<td><p>Returns the indexed polygons located within some distance of an origin point based on various rules.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.PolygonLocator.region" title="libpysal.cg.PolygonLocator.region"><code class="xref py py-obj docutils literal notranslate"><span class="pre">region</span></code></a>(region_rect)</p></td>
-<td><p>Returns the indexed polygons located inside a rectangular query region.</p></td>
-</tr>
-</tbody>
-</table>
-<dl class="py method">
-<dt id="libpysal.cg.PolygonLocator.contains_point">
-<code class="sig-name descname">contains_point</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">point</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/locators.html#PolygonLocator.contains_point"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.PolygonLocator.contains_point" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns polygons that contain point</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl class="simple">
-<dt><strong>point: point (x,y)</strong></dt><dd></dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl class="simple">
-<dt><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">of</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">polygons</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">containing</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">point</span></code></dt><dd></dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p1</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">6</span><span class="p">,</span><span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">))])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">p2</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span><span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">))])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">p1</span><span class="o">.</span><span class="n">contains_point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span>
-<span class="go">1</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">p2</span><span class="o">.</span><span class="n">contains_point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span>
-<span class="go">1</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">pl</span> <span class="o">=</span> <span class="n">PolygonLocator</span><span class="p">([</span><span class="n">p1</span><span class="p">,</span> <span class="n">p2</span><span class="p">])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="n">pl</span><span class="o">.</span><span class="n">contains_point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">)))</span>
-<span class="go">2</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">p2</span><span class="o">.</span><span class="n">contains_point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
-<span class="go">0</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">p1</span><span class="o">.</span><span class="n">contains_point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
-<span class="go">1</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="n">pl</span><span class="o">.</span><span class="n">contains_point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">)))</span>
-<span class="go">1</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">p1</span><span class="o">.</span><span class="n">centroid</span>
-<span class="go">(3.3333333333333335, 1.3333333333333333)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">pl</span><span class="o">.</span><span class="n">contains_point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">))[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span>
-<span class="go">(3.3333333333333335, 1.3333333333333333)</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.cg.PolygonLocator.inside">
-<code class="sig-name descname">inside</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">query_rectangle</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/locators.html#PolygonLocator.inside"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.PolygonLocator.inside" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns polygons that are inside query_rectangle</p>
-<p class="rubric">Notes</p>
-<p>inside means the intersection of the query rectangle and a
-polygon is not empty and is equal to the area of the polygon</p>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p1</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">p2</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">9</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">p3</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">7</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">8</span><span class="p">,</span> <span class="mi">7</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">9</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">pl</span> <span class="o">=</span> <span class="n">PolygonLocator</span><span class="p">([</span><span class="n">p1</span><span class="p">,</span> <span class="n">p2</span><span class="p">,</span> <span class="n">p3</span><span class="p">])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">qr</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">res</span> <span class="o">=</span> <span class="n">pl</span><span class="o">.</span><span class="n">inside</span><span class="p">(</span> <span class="n">qr</span> <span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
-<span class="go">1</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">qr</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">8</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">res</span> <span class="o">=</span> <span class="n">pl</span><span class="o">.</span><span class="n">inside</span><span class="p">(</span> <span class="n">qr</span> <span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
-<span class="go">0</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">qr</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">12</span><span class="p">,</span> <span class="mi">12</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">res</span> <span class="o">=</span> <span class="n">pl</span><span class="o">.</span><span class="n">inside</span><span class="p">(</span> <span class="n">qr</span> <span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
-<span class="go">0</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">qr</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">12</span><span class="p">,</span> <span class="mi">12</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">res</span> <span class="o">=</span> <span class="n">pl</span><span class="o">.</span><span class="n">inside</span><span class="p">(</span> <span class="n">qr</span> <span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
-<span class="go">3</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.cg.PolygonLocator.nearest">
-<code class="sig-name descname">nearest</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">query_point</span></em>, <em class="sig-param"><span class="n">rule</span><span class="o">=</span><span class="default_value">'vertex'</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/locators.html#PolygonLocator.nearest"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.PolygonLocator.nearest" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the nearest polygon indexed to a query point based on
-various rules.</p>
-<p>nearest(Polygon) -&gt; Polygon</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>query_point</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">a</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">point</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">find</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <a class="reference internal" href="#libpysal.cg.PolygonLocator.nearest" title="libpysal.cg.PolygonLocator.nearest"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nearest</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">indexed</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">polygon</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code></span></dt><dd></dd>
-<dt><strong>rule</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">representative</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">point</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">for</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">polygon</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">in</span></code> <a class="reference internal" href="#libpysal.cg.PolygonLocator.nearest" title="libpysal.cg.PolygonLocator.nearest"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nearest</span></code></a> query.</span></dt><dd><p>vertex – measures distance between vertices and query_point
-centroid – measures distance between centroid and
-query_point
-edge   – measures the distance between edges and query_point</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p1</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">p2</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">9</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">pl</span> <span class="o">=</span> <span class="n">PolygonLocator</span><span class="p">([</span><span class="n">p1</span><span class="p">,</span> <span class="n">p2</span><span class="p">])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="k">try</span><span class="p">:</span> <span class="n">n</span> <span class="o">=</span> <span class="n">pl</span><span class="o">.</span><span class="n">nearest</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)))</span>
-<span class="gp">... </span><span class="k">except</span> <span class="ne">NotImplementedError</span><span class="p">:</span> <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;future test: str(min(n.vertices())) == (0.0, 1.0)&quot;</span><span class="p">)</span>
-<span class="go">future test: str(min(n.vertices())) == (0.0, 1.0)</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.cg.PolygonLocator.overlapping">
-<code class="sig-name descname">overlapping</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">query_rectangle</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/locators.html#PolygonLocator.overlapping"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.PolygonLocator.overlapping" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns list of polygons that overlap query_rectangle</p>
-<p class="rubric">Notes</p>
-<p>overlapping means the intersection of the query rectangle and a
-polygon is not empty and is no larger than the area of the polygon</p>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p1</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">p2</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">9</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">p3</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">7</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">8</span><span class="p">,</span> <span class="mi">7</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">9</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">pl</span> <span class="o">=</span> <span class="n">PolygonLocator</span><span class="p">([</span><span class="n">p1</span><span class="p">,</span> <span class="n">p2</span><span class="p">,</span> <span class="n">p3</span><span class="p">])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">qr</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">res</span> <span class="o">=</span> <span class="n">pl</span><span class="o">.</span><span class="n">overlapping</span><span class="p">(</span> <span class="n">qr</span> <span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
-<span class="go">2</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">qr</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">8</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">res</span> <span class="o">=</span> <span class="n">pl</span><span class="o">.</span><span class="n">overlapping</span><span class="p">(</span> <span class="n">qr</span> <span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
-<span class="go">1</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">qr</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">12</span><span class="p">,</span> <span class="mi">12</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">res</span> <span class="o">=</span> <span class="n">pl</span><span class="o">.</span><span class="n">overlapping</span><span class="p">(</span> <span class="n">qr</span> <span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
-<span class="go">0</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">qr</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">12</span><span class="p">,</span> <span class="mi">12</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">res</span> <span class="o">=</span> <span class="n">pl</span><span class="o">.</span><span class="n">overlapping</span><span class="p">(</span> <span class="n">qr</span> <span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
-<span class="go">3</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">qr</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">p1</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span><span class="mi">1</span><span class="p">))])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">p2</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">7</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">7</span><span class="p">,</span> <span class="mi">5</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">pl</span> <span class="o">=</span> <span class="n">PolygonLocator</span><span class="p">([</span><span class="n">p1</span><span class="p">,</span> <span class="n">p2</span><span class="p">])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">res</span> <span class="o">=</span> <span class="n">pl</span><span class="o">.</span><span class="n">overlapping</span><span class="p">(</span><span class="n">qr</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
-<span class="go">1</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.cg.PolygonLocator.proximity">
-<code class="sig-name descname">proximity</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">origin</span></em>, <em class="sig-param"><span class="n">r</span></em>, <em class="sig-param"><span class="n">rule</span><span class="o">=</span><span class="default_value">'vertex'</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/locators.html#PolygonLocator.proximity"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.PolygonLocator.proximity" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the indexed polygons located within some distance of an
-origin point based on various rules.</p>
-<p>proximity(Polygon, number) -&gt; Polygon list</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>origin</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">point</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">find</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">indexed</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">polygons</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">near</span></code></span></dt><dd></dd>
-<dt><strong>r</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">maximum</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">distance</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">find</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">indexed</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">polygon</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">from</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">origin</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">point</span></code></span></dt><dd></dd>
-<dt><strong>rule</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">representative</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">point</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">for</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">polygon</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">in</span></code> <a class="reference internal" href="#libpysal.cg.PolygonLocator.nearest" title="libpysal.cg.PolygonLocator.nearest"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nearest</span></code></a> query.</span></dt><dd><p>vertex – measures distance between vertices and query_point
-centroid – measures distance between centroid and
-query_point
-edge   – measures the distance between edges and query_point</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p1</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">p2</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">9</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">pl</span> <span class="o">=</span> <span class="n">PolygonLocator</span><span class="p">([</span><span class="n">p1</span><span class="p">,</span> <span class="n">p2</span><span class="p">])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="k">try</span><span class="p">:</span>
-<span class="gp">... </span>    <span class="nb">len</span><span class="p">(</span><span class="n">pl</span><span class="o">.</span><span class="n">proximity</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="mi">2</span><span class="p">))</span>
-<span class="gp">... </span><span class="k">except</span> <span class="ne">NotImplementedError</span><span class="p">:</span>
-<span class="gp">... </span>    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;future test: len(pl.proximity(Point((0, 0)), 2)) == 2&quot;</span><span class="p">)</span>
-<span class="go">future test: len(pl.proximity(Point((0, 0)), 2)) == 2</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.cg.PolygonLocator.region">
-<code class="sig-name descname">region</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">region_rect</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/locators.html#PolygonLocator.region"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.PolygonLocator.region" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the indexed polygons located inside a rectangular query region.</p>
-<p>region(Rectangle) -&gt; Polygon list</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>region_rect</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">rectangular</span></code> <a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#range" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">range</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">find</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">indexed</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">polygons</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">in</span></code></span></dt><dd></dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p1</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">p2</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">9</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">pl</span> <span class="o">=</span> <span class="n">PolygonLocator</span><span class="p">([</span><span class="n">p1</span><span class="p">,</span> <span class="n">p2</span><span class="p">])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">n</span> <span class="o">=</span> <span class="n">pl</span><span class="o">.</span><span class="n">region</span><span class="p">(</span><span class="n">Rectangle</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="n">n</span><span class="p">)</span>
-<span class="go">2</span>
-</pre></div>
-</div>
-</dd></dl>
-
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-  <a href="../_sources/generated/libpysal.cg.PolygonLocator.rst.txt"
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diff --git a/docs/generated/libpysal.cg.RADIUS_EARTH_KM.html b/docs/generated/libpysal.cg.RADIUS_EARTH_KM.html
deleted file mode 100644
index e033de3f3..000000000
--- a/docs/generated/libpysal.cg.RADIUS_EARTH_KM.html
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-  <div class="section" id="libpysal-cg-radius-earth-km">
-<h1>libpysal.cg.RADIUS_EARTH_KM<a class="headerlink" href="#libpysal-cg-radius-earth-km" title="Permalink to this headline">¶</a></h1>
-<dl class="py data">
-<dt id="libpysal.cg.RADIUS_EARTH_KM">
-<code class="sig-prename descclassname">libpysal.cg.</code><code class="sig-name descname">RADIUS_EARTH_KM</code><em class="property"> = 6371.0</em><a class="headerlink" href="#libpysal.cg.RADIUS_EARTH_KM" title="Permalink to this definition">¶</a></dt>
-<dd><p>Convert a string or number to a floating point number, if possible.</p>
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-        &copy; Copyright 2018-, pysal developers.<br/>
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diff --git a/docs/generated/libpysal.cg.RADIUS_EARTH_MILES.html b/docs/generated/libpysal.cg.RADIUS_EARTH_MILES.html
deleted file mode 100644
index 7b266ba03..000000000
--- a/docs/generated/libpysal.cg.RADIUS_EARTH_MILES.html
+++ /dev/null
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-<h1>libpysal.cg.RADIUS_EARTH_MILES<a class="headerlink" href="#libpysal-cg-radius-earth-miles" title="Permalink to this headline">¶</a></h1>
-<dl class="py data">
-<dt id="libpysal.cg.RADIUS_EARTH_MILES">
-<code class="sig-prename descclassname">libpysal.cg.</code><code class="sig-name descname">RADIUS_EARTH_MILES</code><em class="property"> = 3958.755865744055</em><a class="headerlink" href="#libpysal.cg.RADIUS_EARTH_MILES" title="Permalink to this definition">¶</a></dt>
-<dd><p>Convert a string or number to a floating point number, if possible.</p>
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diff --git a/docs/generated/libpysal.cg.Ray.html b/docs/generated/libpysal.cg.Ray.html
deleted file mode 100644
index 07f66d207..000000000
--- a/docs/generated/libpysal.cg.Ray.html
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-<h1>libpysal.cg.Ray<a class="headerlink" href="#libpysal-cg-ray" title="Permalink to this headline">¶</a></h1>
-<dl class="py class">
-<dt id="libpysal.cg.Ray">
-<em class="property">class </em><code class="sig-prename descclassname">libpysal.cg.</code><code class="sig-name descname">Ray</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">origin</span></em>, <em class="sig-param"><span class="n">second_p</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#Ray"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.Ray" title="Permalink to this definition">¶</a></dt>
-<dd><p>Geometric representation of ray objects.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>origin</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a></span></dt><dd><p>The point where the ray originates.</p>
-</dd>
-<dt><strong>second_p :</strong></dt><dd><p>The second point specifying the ray (not <code class="docutils literal notranslate"><span class="pre">origin</span></code>.)</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">l</span> <span class="o">=</span> <span class="n">Ray</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">str</span><span class="p">(</span><span class="n">l</span><span class="o">.</span><span class="n">o</span><span class="p">)</span>
-<span class="go">&#39;(0.0, 0.0)&#39;</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="nb">str</span><span class="p">(</span><span class="n">l</span><span class="o">.</span><span class="n">p</span><span class="p">)</span>
-<span class="go">&#39;(1.0, 0.0)&#39;</span>
-</pre></div>
-</div>
-<dl class="field-list">
-<dt class="field-odd">Attributes</dt>
-<dd class="field-odd"><dl>
-<dt><strong>o</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a></span></dt><dd><p>The origin (point where ray originates). See <code class="docutils literal notranslate"><span class="pre">origin</span></code>.</p>
-</dd>
-<dt><strong>p</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a></span></dt><dd><p>The second point on the ray (not the point where the
-ray originates). See <code class="docutils literal notranslate"><span class="pre">second_p</span></code>.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<dl class="py method">
-<dt id="libpysal.cg.Ray.__init__">
-<code class="sig-name descname">__init__</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">origin</span></em>, <em class="sig-param"><span class="n">second_p</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#Ray.__init__"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.Ray.__init__" title="Permalink to this definition">¶</a></dt>
-<dd><p>Initialize self.  See help(type(self)) for accurate signature.</p>
-</dd></dl>
-
-<p class="rubric">Methods</p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.Ray.__init__" title="libpysal.cg.Ray.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(origin, second_p)</p></td>
-<td><p>Initialize self.</p></td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
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-<h1>libpysal.cg.Rectangle<a class="headerlink" href="#libpysal-cg-rectangle" title="Permalink to this headline">¶</a></h1>
-<dl class="py class">
-<dt id="libpysal.cg.Rectangle">
-<em class="property">class </em><code class="sig-prename descclassname">libpysal.cg.</code><code class="sig-name descname">Rectangle</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">left</span></em>, <em class="sig-param"><span class="n">lower</span></em>, <em class="sig-param"><span class="n">right</span></em>, <em class="sig-param"><span class="n">upper</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#Rectangle"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.Rectangle" title="Permalink to this definition">¶</a></dt>
-<dd><p>Geometric representation of rectangle objects.</p>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">r</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span><span class="o">-</span><span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">17</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">r</span><span class="o">.</span><span class="n">left</span> <span class="c1">#minx</span>
-<span class="go">-4.0</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">r</span><span class="o">.</span><span class="n">lower</span> <span class="c1">#miny</span>
-<span class="go">3.0</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">r</span><span class="o">.</span><span class="n">right</span> <span class="c1">#maxx</span>
-<span class="go">10.0</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">r</span><span class="o">.</span><span class="n">upper</span> <span class="c1">#maxy</span>
-<span class="go">17.0</span>
-</pre></div>
-</div>
-<dl class="field-list">
-<dt class="field-odd">Attributes</dt>
-<dd class="field-odd"><dl>
-<dt><strong>left</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>Minimum x-value of the rectangle.</p>
-</dd>
-<dt><strong>lower</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>Minimum y-value of the rectangle.</p>
-</dd>
-<dt><strong>right</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>Maximum x-value of the rectangle.</p>
-</dd>
-<dt><strong>upper</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>Maximum y-value of the rectangle.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<dl class="py method">
-<dt id="libpysal.cg.Rectangle.__init__">
-<code class="sig-name descname">__init__</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">left</span></em>, <em class="sig-param"><span class="n">lower</span></em>, <em class="sig-param"><span class="n">right</span></em>, <em class="sig-param"><span class="n">upper</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#Rectangle.__init__"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.Rectangle.__init__" title="Permalink to this definition">¶</a></dt>
-<dd><p>Initialize self.  See help(type(self)) for accurate signature.</p>
-</dd></dl>
-
-<p class="rubric">Methods</p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.Rectangle.__init__" title="libpysal.cg.Rectangle.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(left, lower, right, upper)</p></td>
-<td><p>Initialize self.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.cg.Rectangle.set_centroid" title="libpysal.cg.Rectangle.set_centroid"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_centroid</span></code></a>(new_center)</p></td>
-<td><p>Moves the rectangle center to a new specified point.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.Rectangle.set_scale" title="libpysal.cg.Rectangle.set_scale"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_scale</span></code></a>(scale)</p></td>
-<td><p>Rescales the rectangle around its center.</p></td>
-</tr>
-</tbody>
-</table>
-<p class="rubric">Attributes</p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.Rectangle.area" title="libpysal.cg.Rectangle.area"><code class="xref py py-obj docutils literal notranslate"><span class="pre">area</span></code></a></p></td>
-<td><p>Returns the area of the Rectangle.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.cg.Rectangle.height" title="libpysal.cg.Rectangle.height"><code class="xref py py-obj docutils literal notranslate"><span class="pre">height</span></code></a></p></td>
-<td><p>Returns the height of the Rectangle.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.Rectangle.width" title="libpysal.cg.Rectangle.width"><code class="xref py py-obj docutils literal notranslate"><span class="pre">width</span></code></a></p></td>
-<td><p>Returns the width of the Rectangle.</p></td>
-</tr>
-</tbody>
-</table>
-<dl class="py method">
-<dt id="libpysal.cg.Rectangle.area">
-<em class="property">property </em><code class="sig-name descname">area</code><a class="headerlink" href="#libpysal.cg.Rectangle.area" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the area of the Rectangle.</p>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">r</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">r</span><span class="o">.</span><span class="n">area</span>
-<span class="go">16.0</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.cg.Rectangle.height">
-<em class="property">property </em><code class="sig-name descname">height</code><a class="headerlink" href="#libpysal.cg.Rectangle.height" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the height of the Rectangle.</p>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">r</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">r</span><span class="o">.</span><span class="n">height</span>
-<span class="go">4.0</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.cg.Rectangle.set_centroid">
-<code class="sig-name descname">set_centroid</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">new_center</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#Rectangle.set_centroid"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.Rectangle.set_centroid" title="Permalink to this definition">¶</a></dt>
-<dd><p>Moves the rectangle center to a new specified point.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>new_center</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a></span></dt><dd><p>The new location of the centroid of the polygon.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">r</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">r</span><span class="o">.</span><span class="n">set_centroid</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">)))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">r</span><span class="o">.</span><span class="n">left</span>
-<span class="go">2.0</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">r</span><span class="o">.</span><span class="n">right</span>
-<span class="go">6.0</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">r</span><span class="o">.</span><span class="n">lower</span>
-<span class="go">2.0</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">r</span><span class="o">.</span><span class="n">upper</span>
-<span class="go">6.0</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.cg.Rectangle.set_scale">
-<code class="sig-name descname">set_scale</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">scale</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#Rectangle.set_scale"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.Rectangle.set_scale" title="Permalink to this definition">¶</a></dt>
-<dd><p>Rescales the rectangle around its center.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>scale</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#int" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a>, <a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>The ratio of the new scale to the old
-scale (e.g. 1.0 is current size).</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">r</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">r</span><span class="o">.</span><span class="n">set_scale</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">r</span><span class="o">.</span><span class="n">left</span>
-<span class="go">-2.0</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">r</span><span class="o">.</span><span class="n">right</span>
-<span class="go">6.0</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">r</span><span class="o">.</span><span class="n">lower</span>
-<span class="go">-2.0</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">r</span><span class="o">.</span><span class="n">upper</span>
-<span class="go">6.0</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.cg.Rectangle.width">
-<em class="property">property </em><code class="sig-name descname">width</code><a class="headerlink" href="#libpysal.cg.Rectangle.width" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the width of the Rectangle.</p>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">r</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">r</span><span class="o">.</span><span class="n">width</span>
-<span class="go">4.0</span>
-</pre></div>
-</div>
-</dd></dl>
-
-</dd></dl>
-
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-
-
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-      
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-      
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-        
-<div id="sourcelink">
-  <a href="../_sources/generated/libpysal.cg.Rectangle.rst.txt"
-     rel="nofollow">Source</a>
-</div>
-      
-    </p>
-    <p>
-        &copy; Copyright 2018-, pysal developers.<br/>
-      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 3.1.2.<br/>
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diff --git a/docs/generated/libpysal.cg.alpha_shape.html b/docs/generated/libpysal.cg.alpha_shape.html
deleted file mode 100644
index d2ac00480..000000000
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-  <div class="section" id="libpysal-cg-alpha-shape">
-<h1>libpysal.cg.alpha_shape<a class="headerlink" href="#libpysal-cg-alpha-shape" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.cg.alpha_shape">
-<code class="sig-prename descclassname">libpysal.cg.</code><code class="sig-name descname">alpha_shape</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">xys</span></em>, <em class="sig-param"><span class="n">alpha</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/alpha_shapes.html#alpha_shape"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.alpha_shape" title="Permalink to this definition">¶</a></dt>
-<dd><p>Alpha-shape delineation (Edelsbrunner, Kirkpatrick &amp; Seidel, 1983) from a collection of points</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>xys</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.20)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray</span></code></a></span></dt><dd><p>(N, 2) array with one point per row and coordinates structured as X and
-Y</p>
-</dd>
-<dt><strong>alpha</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>Alpha value to delineate the alpha-shape</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>shapes</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GeoSeries</span></code></span></dt><dd><p>Polygon(s) resulting from the alpha shape algorithm. The GeoSeries
-object remains so even if only a single polygon is returned. There is
-no CRS included in the object.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">References</p>
-<dl class="simple">
-<dt>Edelsbrunner, H., Kirkpatrick, D., &amp; Seidel, R. (1983). On the shape of</dt><dd><p>a set of points in the plane. IEEE Transactions on information theory,
-29(4), 551-559.</p>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">pts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">],</span> <span class="p">[</span><span class="mi">9</span><span class="p">,</span> <span class="mi">3</span><span class="p">]])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">alpha</span> <span class="o">=</span> <span class="mf">0.1</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">poly</span> <span class="o">=</span> <span class="n">alpha_shape</span><span class="p">(</span><span class="n">pts</span><span class="p">,</span> <span class="n">alpha</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">poly</span>
-<span class="go">0    POLYGON ((0.00000 1.00000, 3.00000 5.00000, 6....</span>
-<span class="go">dtype: geometry</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">poly</span><span class="o">.</span><span class="n">centroid</span>
-<span class="go">0    POINT (4.69048 3.45238)</span>
-<span class="go">dtype: geometry</span>
-</pre></div>
-</div>
-</dd></dl>
-
-</div>
-
-
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-      
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-</div>
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-      
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-<div id="sourcelink">
-  <a href="../_sources/generated/libpysal.cg.alpha_shape.rst.txt"
-     rel="nofollow">Source</a>
-</div>
-      
-    </p>
-    <p>
-        &copy; Copyright 2018-, pysal developers.<br/>
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--- a/docs/generated/libpysal.cg.alpha_shape_auto.html
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-<h1>libpysal.cg.alpha_shape_auto<a class="headerlink" href="#libpysal-cg-alpha-shape-auto" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.cg.alpha_shape_auto">
-<code class="sig-prename descclassname">libpysal.cg.</code><code class="sig-name descname">alpha_shape_auto</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">xys</span></em>, <em class="sig-param"><span class="n">step</span><span class="o">=</span><span class="default_value">1</span></em>, <em class="sig-param"><span class="n">verbose</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">return_radius</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">return_circles</span><span class="o">=</span><span class="default_value">False</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/alpha_shapes.html#alpha_shape_auto"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.alpha_shape_auto" title="Permalink to this definition">¶</a></dt>
-<dd><p>Computation of alpha-shape delineation with automated selection of alpha.</p>
-<p>This method uses the algorithm proposed by  Edelsbrunner, Kirkpatrick &amp;
-Seidel (1983) to return the tightest polygon that contains all points in
-<cite>xys</cite>. The algorithm ranks every point based on its radious and iterates
-over each point, checking whether the maximum alpha that would keep the
-point and all the other ones in the set with smaller radii results in a
-single polygon. If that is the case, it moves to the next point;
-otherwise, it retains the previous alpha value and returns the polygon
-as <cite>shapely</cite> geometry.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>xys</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.20)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray</span></code></a></span></dt><dd><p>Nx2 array with one point per row and coordinates structured as X and Y</p>
-</dd>
-<dt><strong>step</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#int" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>[Optional. Default=1] Number of points in <cite>xys</cite> to jump ahead after
-checking whether the largest possible alpha that includes the point and
-all the other ones with smaller radii</p>
-</dd>
-<dt><strong>verbose</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Boolean</span></code></span></dt><dd><p>[Optional. Default=False] If True, it prints alpha values being tried at every step.</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>poly</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">shapely.Polygon</span></code></span></dt><dd><p>Tightest alpha-shape polygon containing all points in <cite>xys</cite></p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">References</p>
-<dl class="simple">
-<dt>Edelsbrunner, H., Kirkpatrick, D., &amp; Seidel, R. (1983). On the shape of</dt><dd><p>a set of points in the plane. IEEE Transactions on information theory,
-29(4), 551-559.</p>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">pts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">],</span> <span class="p">[</span><span class="mi">9</span><span class="p">,</span> <span class="mi">3</span><span class="p">]])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">poly</span> <span class="o">=</span> <span class="n">alpha_shape_auto</span><span class="p">(</span><span class="n">pts</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">poly</span><span class="o">.</span><span class="n">bounds</span>
-<span class="go">(0.0, 1.0, 9.0, 7.0)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">poly</span><span class="o">.</span><span class="n">centroid</span><span class="o">.</span><span class="n">x</span><span class="p">,</span> <span class="n">poly</span><span class="o">.</span><span class="n">centroid</span><span class="o">.</span><span class="n">y</span>
-<span class="go">(4.690476190476191, 3.4523809523809526)</span>
-</pre></div>
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-</dd></dl>
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-<h1>libpysal.cg.arcdist<a class="headerlink" href="#libpysal-cg-arcdist" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.cg.arcdist">
-<code class="sig-prename descclassname">libpysal.cg.</code><code class="sig-name descname">arcdist</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">pt0</span></em>, <em class="sig-param"><span class="n">pt1</span></em>, <em class="sig-param"><span class="n">radius</span><span class="o">=</span><span class="default_value">6371.0</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/sphere.html#arcdist"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.arcdist" title="Permalink to this definition">¶</a></dt>
-<dd><p>Arc distance between two points on a sphere.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>pt0</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#tuple" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">tuple</span></code></a></span></dt><dd><p>A point assumed to be in form (longitude,latitude).</p>
-</dd>
-<dt><strong>pt1</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#tuple" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">tuple</span></code></a></span></dt><dd><p>A point assumed to be in form (longitude,latitude).</p>
-</dd>
-<dt><strong>radius</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>The radius of a sphere. Default is Earth’s radius in
-kilometers, <code class="docutils literal notranslate"><span class="pre">RADIUS_EARTH_KM</span></code> (<code class="docutils literal notranslate"><span class="pre">6371.0</span></code>). Earth’s
-radius in miles, <code class="docutils literal notranslate"><span class="pre">RADIUS_EARTH_MILES</span></code> (<code class="docutils literal notranslate"><span class="pre">3958.76</span></code>)
-is also an option.
-Source: <a class="reference external" href="http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html">http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html</a></p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>dist</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>The arc distance between <code class="docutils literal notranslate"><span class="pre">pt0</span></code> and <code class="docutils literal notranslate"><span class="pre">pt1</span></code> using supplied <code class="docutils literal notranslate"><span class="pre">radius</span></code>.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">pt0</span> <span class="o">=</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">pt1</span> <span class="o">=</span> <span class="p">(</span><span class="mi">180</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">d</span> <span class="o">=</span> <span class="n">arcdist</span><span class="p">(</span><span class="n">pt0</span><span class="p">,</span> <span class="n">pt1</span><span class="p">,</span> <span class="n">RADIUS_EARTH_MILES</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">d</span> <span class="o">==</span> <span class="n">math</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">RADIUS_EARTH_MILES</span>
-<span class="go">True</span>
-</pre></div>
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-<h1>libpysal.cg.arcdist2linear<a class="headerlink" href="#libpysal-cg-arcdist2linear" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.cg.arcdist2linear">
-<code class="sig-prename descclassname">libpysal.cg.</code><code class="sig-name descname">arcdist2linear</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">arc_dist</span></em>, <em class="sig-param"><span class="n">radius</span><span class="o">=</span><span class="default_value">6371.0</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/sphere.html#arcdist2linear"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.arcdist2linear" title="Permalink to this definition">¶</a></dt>
-<dd><p>Convert an arc distance (spherical earth)
-to a linear distance (R3) in the unit sphere.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>arc_dist</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>The arc distance to convert.</p>
-</dd>
-<dt><strong>radius</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>The radius of a sphere. Default is Earth’s radius in
-kilometers, <code class="docutils literal notranslate"><span class="pre">RADIUS_EARTH_KM</span></code> (<code class="docutils literal notranslate"><span class="pre">6371.0</span></code>). Earth’s
-radius in miles, <code class="docutils literal notranslate"><span class="pre">RADIUS_EARTH_MILES</span></code> (<code class="docutils literal notranslate"><span class="pre">3958.76</span></code>)
-is also an option.
-Source: <a class="reference external" href="http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html">http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html</a></p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>linear_dist</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>The linear distance conversion of <code class="docutils literal notranslate"><span class="pre">arc_dist</span></code>.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">pt0</span> <span class="o">=</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">pt1</span> <span class="o">=</span> <span class="p">(</span><span class="mi">180</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">d</span> <span class="o">=</span> <span class="n">arcdist</span><span class="p">(</span><span class="n">pt0</span><span class="p">,</span> <span class="n">pt1</span><span class="p">,</span> <span class="n">RADIUS_EARTH_MILES</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">d</span> <span class="o">==</span> <span class="n">math</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">RADIUS_EARTH_MILES</span>
-<span class="go">True</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">arcdist2linear</span><span class="p">(</span><span class="n">d</span><span class="p">,</span> <span class="n">RADIUS_EARTH_MILES</span><span class="p">)</span>
-<span class="go">2.0</span>
-</pre></div>
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-<h1>libpysal.cg.asShape<a class="headerlink" href="#libpysal-cg-asshape" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.cg.asShape">
-<code class="sig-prename descclassname">libpysal.cg.</code><code class="sig-name descname">asShape</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">obj</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#asShape"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.asShape" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns a PySAL shape object from <code class="docutils literal notranslate"><span class="pre">obj</span></code>, which
-must support the <code class="docutils literal notranslate"><span class="pre">__geo_interface__</span></code>.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>obj</strong><span class="classifier">{libpysal.cg.{<a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Point</span></code></a>, <a class="reference internal" href="libpysal.cg.LineSegment.html#libpysal.cg.LineSegment" title="libpysal.cg.LineSegment"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LineSegment</span></code></a>, <a class="reference internal" href="libpysal.cg.Line.html#libpysal.cg.Line" title="libpysal.cg.Line"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Line</span></code></a>, <a class="reference internal" href="libpysal.cg.Ray.html#libpysal.cg.Ray" title="libpysal.cg.Ray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Ray</span></code></a>, <a class="reference internal" href="libpysal.cg.Chain.html#libpysal.cg.Chain" title="libpysal.cg.Chain"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Chain</span></code></a>, <a class="reference internal" href="libpysal.cg.Polygon.html#libpysal.cg.Polygon" title="libpysal.cg.Polygon"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Polygon</span></code></a>}</span></dt><dd><p>A geometric representation of an object.</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>obj</strong><span class="classifier">{libpysal.cg.{<a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Point</span></code></a>, <a class="reference internal" href="libpysal.cg.LineSegment.html#libpysal.cg.LineSegment" title="libpysal.cg.LineSegment"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LineSegment</span></code></a>, <a class="reference internal" href="libpysal.cg.Line.html#libpysal.cg.Line" title="libpysal.cg.Line"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Line</span></code></a>, <a class="reference internal" href="libpysal.cg.Ray.html#libpysal.cg.Ray" title="libpysal.cg.Ray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Ray</span></code></a>, <a class="reference internal" href="libpysal.cg.Chain.html#libpysal.cg.Chain" title="libpysal.cg.Chain"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Chain</span></code></a>, <a class="reference internal" href="libpysal.cg.Polygon.html#libpysal.cg.Polygon" title="libpysal.cg.Polygon"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Polygon</span></code></a>}</span></dt><dd><p>A new geometric representation of the object.</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-odd">Raises</dt>
-<dd class="field-odd"><dl class="simple">
-<dt><a class="reference external" href="https://docs.python.org/3.8/library/exceptions.html#TypeError" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">TypeError</span></code></a></dt><dd><p>Raised when <code class="docutils literal notranslate"><span class="pre">obj</span></code> is not a supported shape.</p>
-</dd>
-<dt><a class="reference external" href="https://docs.python.org/3.8/library/exceptions.html#NotImplementedError" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">NotImplementedError</span></code></a></dt><dd><p>Raised when <code class="docutils literal notranslate"><span class="pre">geo_type</span></code> is not a supported type.</p>
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-</dl>
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-  <div class="section" id="libpysal-cg-bbcommon">
-<h1>libpysal.cg.bbcommon<a class="headerlink" href="#libpysal-cg-bbcommon" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.cg.bbcommon">
-<code class="sig-prename descclassname">libpysal.cg.</code><code class="sig-name descname">bbcommon</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">bb</span></em>, <em class="sig-param"><span class="n">bbother</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/standalone.html#bbcommon"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.bbcommon" title="Permalink to this definition">¶</a></dt>
-<dd><p>Old Stars method for bounding box overlap testing.
-Also defined in <code class="docutils literal notranslate"><span class="pre">pysal.weights._cont_binning</span></code>.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>bb</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>A bounding box.</p>
-</dd>
-<dt><strong>bbother</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>The bounding box to test against.</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>chflag</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#int" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p><code class="docutils literal notranslate"><span class="pre">1</span></code> if <code class="docutils literal notranslate"><span class="pre">bb</span></code> overlaps <code class="docutils literal notranslate"><span class="pre">bbother</span></code>, otherwise <code class="docutils literal notranslate"><span class="pre">0</span></code>.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">b0</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">b1</span> <span class="o">=</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">10</span><span class="p">]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">bbcommon</span><span class="p">(</span><span class="n">b0</span><span class="p">,</span> <span class="n">b1</span><span class="p">)</span>
-<span class="go">1</span>
-</pre></div>
-</div>
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-<h1>libpysal.cg.brute_knn<a class="headerlink" href="#libpysal-cg-brute-knn" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.cg.brute_knn">
-<code class="sig-prename descclassname">libpysal.cg.</code><code class="sig-name descname">brute_knn</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">pts</span></em>, <em class="sig-param"><span class="n">k</span></em>, <em class="sig-param"><span class="n">mode</span><span class="o">=</span><span class="default_value">'arc'</span></em>, <em class="sig-param"><span class="n">radius</span><span class="o">=</span><span class="default_value">6371.0</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/sphere.html#brute_knn"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.brute_knn" title="Permalink to this definition">¶</a></dt>
-<dd><p>Computes a brute-force <span class="math notranslate nohighlight">\(k\)</span> nearest neighbors.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>pts</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>A list of <span class="math notranslate nohighlight">\(x,y\)</span> pairs.</p>
-</dd>
-<dt><strong>k</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#int" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>The number of points to query.</p>
-</dd>
-<dt><strong>mode</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>The mode of distance. Valid modes are <code class="docutils literal notranslate"><span class="pre">'arc'</span></code>
-and <code class="docutils literal notranslate"><span class="pre">'xyz'</span></code>. Default is <code class="docutils literal notranslate"><span class="pre">'arc'</span></code>.</p>
-</dd>
-<dt><strong>radius</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>The radius of a sphere. Default is Earth’s radius in
-kilometers, <code class="docutils literal notranslate"><span class="pre">RADIUS_EARTH_KM</span></code> (<code class="docutils literal notranslate"><span class="pre">6371.0</span></code>). Earth’s
-radius in miles, <code class="docutils literal notranslate"><span class="pre">RADIUS_EARTH_MILES</span></code> (<code class="docutils literal notranslate"><span class="pre">3958.76</span></code>)
-is also an option.
-Source: <a class="reference external" href="http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html">http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html</a></p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>w</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#dict" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">dict</span></code></a></span></dt><dd><p>A neighbor ID lookup.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-</dd></dl>
-
-</div>
-
-
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-<h1>libpysal.cg.convex_hull<a class="headerlink" href="#libpysal-cg-convex-hull" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.cg.convex_hull">
-<code class="sig-prename descclassname">libpysal.cg.</code><code class="sig-name descname">convex_hull</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">points</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/standalone.html#convex_hull"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.convex_hull" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the convex hull of a set of points.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>points</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>A list of points for computing the convex hull.</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>stack</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>A list of points representing the convex hull.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">points</span> <span class="o">=</span> <span class="p">[</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">))]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">convex_hull</span><span class="p">(</span><span class="n">points</span><span class="p">)</span>
-<span class="go">[(0.0, 0.0), (4.0, 0.0), (4.0, 4.0)]</span>
-</pre></div>
-</div>
-</dd></dl>
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-<h1>libpysal.cg.distance_matrix<a class="headerlink" href="#libpysal-cg-distance-matrix" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.cg.distance_matrix">
-<code class="sig-prename descclassname">libpysal.cg.</code><code class="sig-name descname">distance_matrix</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">X</span></em>, <em class="sig-param"><span class="n">p</span><span class="o">=</span><span class="default_value">2.0</span></em>, <em class="sig-param"><span class="n">threshold</span><span class="o">=</span><span class="default_value">50000000.0</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/standalone.html#distance_matrix"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.distance_matrix" title="Permalink to this definition">¶</a></dt>
-<dd><p>Calculate a distance matrix.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>X</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.20)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></span></dt><dd><p>An <span class="math notranslate nohighlight">\(n \times k\)</span> array where <span class="math notranslate nohighlight">\(n\)</span> is the number
-of observations and <span class="math notranslate nohighlight">\(k\)</span> is the number of dimensions
-(2 for <span class="math notranslate nohighlight">\(x,y\)</span>).</p>
-</dd>
-<dt><strong>p</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>Minkowski <cite>p</cite>-norm distance metric parameter where
-<span class="math notranslate nohighlight">\(1&lt;=\mathtt{p}&lt;=\infty\)</span>. <code class="docutils literal notranslate"><span class="pre">2</span></code> is Euclidean distance and
-<code class="docutils literal notranslate"><span class="pre">1</span></code> is Manhattan distance. Default is <code class="docutils literal notranslate"><span class="pre">2.0</span></code>.</p>
-</dd>
-<dt><strong>threshold</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#int" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>If <span class="math notranslate nohighlight">\((\mathtt{n}**2)*32 &gt; \mathtt{threshold}\)</span> use
-<code class="docutils literal notranslate"><span class="pre">scipy.spatial.distance_matrix</span></code> instead of working in RAM,
-this is roughly the amount of RAM (in bytes) that will be used.
-Must be positive. Default is <code class="docutils literal notranslate"><span class="pre">5e7</span></code>.</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>D</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.20)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></span></dt><dd><p>An n by <span class="math notranslate nohighlight">\(m\)</span> <span class="math notranslate nohighlight">\(p\)</span>-norm distance matrix.</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-odd">Raises</dt>
-<dd class="field-odd"><dl class="simple">
-<dt><a class="reference external" href="https://docs.python.org/3.8/library/exceptions.html#TypeError" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">TypeError</span></code></a></dt><dd><p>Raised when an invalid dimensional array is passed in.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Notes</p>
-<p>Needs optimization/integration with other weights in PySAL.</p>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="p">[</span><span class="n">r</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">indices</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">))]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">])</span><span class="o">.</span><span class="n">T</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">d</span> <span class="o">=</span> <span class="n">distance_matrix</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">d</span><span class="p">)</span>
-<span class="go">array([[0.        , 1.        , 2.        , 1.        , 1.41421356,</span>
-<span class="go">        2.23606798, 2.        , 2.23606798, 2.82842712],</span>
-<span class="go">       [1.        , 0.        , 1.        , 1.41421356, 1.        ,</span>
-<span class="go">        1.41421356, 2.23606798, 2.        , 2.23606798],</span>
-<span class="go">       [2.        , 1.        , 0.        , 2.23606798, 1.41421356,</span>
-<span class="go">        1.        , 2.82842712, 2.23606798, 2.        ],</span>
-<span class="go">       [1.        , 1.41421356, 2.23606798, 0.        , 1.        ,</span>
-<span class="go">        2.        , 1.        , 1.41421356, 2.23606798],</span>
-<span class="go">       [1.41421356, 1.        , 1.41421356, 1.        , 0.        ,</span>
-<span class="go">        1.        , 1.41421356, 1.        , 1.41421356],</span>
-<span class="go">       [2.23606798, 1.41421356, 1.        , 2.        , 1.        ,</span>
-<span class="go">        0.        , 2.23606798, 1.41421356, 1.        ],</span>
-<span class="go">       [2.        , 2.23606798, 2.82842712, 1.        , 1.41421356,</span>
-<span class="go">        2.23606798, 0.        , 1.        , 2.        ],</span>
-<span class="go">       [2.23606798, 2.        , 2.23606798, 1.41421356, 1.        ,</span>
-<span class="go">        1.41421356, 1.        , 0.        , 1.        ],</span>
-<span class="go">       [2.82842712, 2.23606798, 2.        , 2.23606798, 1.41421356,</span>
-<span class="go">        1.        , 2.        , 1.        , 0.        ]])</span>
-</pre></div>
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-<h1>libpysal.cg.fast_knn<a class="headerlink" href="#libpysal-cg-fast-knn" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.cg.fast_knn">
-<code class="sig-prename descclassname">libpysal.cg.</code><code class="sig-name descname">fast_knn</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">pts</span></em>, <em class="sig-param"><span class="n">k</span></em>, <em class="sig-param"><span class="n">return_dist</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">radius</span><span class="o">=</span><span class="default_value">6371.0</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/sphere.html#fast_knn"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.fast_knn" title="Permalink to this definition">¶</a></dt>
-<dd><p>Computes <span class="math notranslate nohighlight">\(k\)</span> nearest neighbors on a sphere.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>pts</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>A list of <span class="math notranslate nohighlight">\(x,y\)</span> pairs.</p>
-</dd>
-<dt><strong>k</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#int" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>The number of points to query.</p>
-</dd>
-<dt><strong>return_dist</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.8)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>Return distances in the <code class="docutils literal notranslate"><span class="pre">wd</span></code> container object (<code class="docutils literal notranslate"><span class="pre">True</span></code>).
-Default is <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p>
-</dd>
-<dt><strong>radius</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>The radius of a sphere. Default is Earth’s radius in
-kilometers, <code class="docutils literal notranslate"><span class="pre">RADIUS_EARTH_KM</span></code> (<code class="docutils literal notranslate"><span class="pre">6371.0</span></code>). Earth’s
-radius in miles, <code class="docutils literal notranslate"><span class="pre">RADIUS_EARTH_MILES</span></code> (<code class="docutils literal notranslate"><span class="pre">3958.76</span></code>)
-is also an option.
-Source: <a class="reference external" href="http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html">http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html</a></p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>wn</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#dict" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">dict</span></code></a></span></dt><dd><p>A neighbor ID lookup.</p>
-</dd>
-<dt><strong>wd</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#dict" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">dict</span></code></a></span></dt><dd><p>A neighbor distance lookup (optional).</p>
-</dd>
-</dl>
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-<h1>libpysal.cg.fast_threshold<a class="headerlink" href="#libpysal-cg-fast-threshold" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.cg.fast_threshold">
-<code class="sig-prename descclassname">libpysal.cg.</code><code class="sig-name descname">fast_threshold</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">pts</span></em>, <em class="sig-param"><span class="n">dist</span></em>, <em class="sig-param"><span class="n">radius</span><span class="o">=</span><span class="default_value">6371.0</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/sphere.html#fast_threshold"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.fast_threshold" title="Permalink to this definition">¶</a></dt>
-<dd><p>Find all neighbors on a sphere within a threshold distance.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>pointslist</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>A list of lat-lon tuples. This <strong>must</strong> be a list, even for one point.</p>
-</dd>
-<dt><strong>dist: float</strong></dt><dd><p>The threshold distance.</p>
-</dd>
-<dt><strong>radius</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>The radius of a sphere. Default is Earth’s radius in
-kilometers, <code class="docutils literal notranslate"><span class="pre">RADIUS_EARTH_KM</span></code> (<code class="docutils literal notranslate"><span class="pre">6371.0</span></code>). Earth’s
-radius in miles, <code class="docutils literal notranslate"><span class="pre">RADIUS_EARTH_MILES</span></code> (<code class="docutils literal notranslate"><span class="pre">3958.76</span></code>)
-is also an option.
-Source: <a class="reference external" href="http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html">http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html</a></p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>wd</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#dict" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">dict</span></code></a></span></dt><dd><p>A neighbor distance lookup where the key is the ID
-of a point and the value is a list of IDs for other
-points within <code class="docutils literal notranslate"><span class="pre">dist</span></code> of the key point,</p>
-</dd>
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-<h1>libpysal.cg.geogrid<a class="headerlink" href="#libpysal-cg-geogrid" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.cg.geogrid">
-<code class="sig-prename descclassname">libpysal.cg.</code><code class="sig-name descname">geogrid</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">pup</span></em>, <em class="sig-param"><span class="n">pdown</span></em>, <em class="sig-param"><span class="n">k</span></em>, <em class="sig-param"><span class="n">lonx</span><span class="o">=</span><span class="default_value">True</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/sphere.html#geogrid"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.geogrid" title="Permalink to this definition">¶</a></dt>
-<dd><p>Computes a <span class="math notranslate nohighlight">\(k+1\)</span> by <span class="math notranslate nohighlight">\(k+1\)</span> set of grid
-points for a bounding box in lat-lon. Uses <code class="docutils literal notranslate"><span class="pre">geointerpolate</span></code>.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>pup</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#tuple" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">tuple</span></code></a></span></dt><dd><p>The lat-lon or lon-lat for the upper left corner of the bounding box.</p>
-</dd>
-<dt><strong>pdown</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#tuple" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">tuple</span></code></a></span></dt><dd><p>The lat-lon or lon-lat for The lower right corner of The bounding box.</p>
-</dd>
-<dt><strong>k</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#int" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>The number of grid cells (grid points will be one more).</p>
-</dd>
-<dt><strong>lonx</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.8)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>The method to assess the order of the coordinates.
-<code class="docutils literal notranslate"><span class="pre">True</span></code> for (lon,lat); <code class="docutils literal notranslate"><span class="pre">False</span></code> for (lat,lon).
-Default is <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>grid</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>A list of tuples with (lat-lon) or (lon-lat) for grid points,
-row by row, starting with the top row and moving to the bottom;
-coordinate tuples are returned in same order as input.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">pup</span> <span class="o">=</span> <span class="p">(</span><span class="mf">42.023768</span><span class="p">,</span> <span class="o">-</span><span class="mf">87.946389</span><span class="p">)</span>       <span class="c1"># Arlington Heights, IL</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">pdown</span> <span class="o">=</span> <span class="p">(</span><span class="mf">41.644415</span><span class="p">,</span> <span class="o">-</span><span class="mf">87.524102</span><span class="p">)</span>     <span class="c1"># Hammond, IN</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">geogrid</span><span class="p">(</span><span class="n">pup</span><span class="p">,</span><span class="n">pdown</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">lonx</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-<span class="go">[(42.023768, -87.946389),</span>
-<span class="go"> (42.02393997819538, -87.80562679358316),</span>
-<span class="go"> (42.02393997819538, -87.66486420641684),</span>
-<span class="go"> (42.023768, -87.524102),</span>
-<span class="go"> (41.897317, -87.94638900000001),</span>
-<span class="go"> (41.8974888973743, -87.80562679296166),</span>
-<span class="go"> (41.8974888973743, -87.66486420703835),</span>
-<span class="go"> (41.897317, -87.524102),</span>
-<span class="go"> (41.770866000000005, -87.94638900000001),</span>
-<span class="go"> (41.77103781320412, -87.80562679234043),</span>
-<span class="go"> (41.77103781320412, -87.66486420765956),</span>
-<span class="go"> (41.770866000000005, -87.524102),</span>
-<span class="go"> (41.644415, -87.946389),</span>
-<span class="go"> (41.64458672568646, -87.80562679171955),</span>
-<span class="go"> (41.64458672568646, -87.66486420828045),</span>
-<span class="go"> (41.644415, -87.524102)]</span>
-</pre></div>
-</div>
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-<h1>libpysal.cg.geointerpolate<a class="headerlink" href="#libpysal-cg-geointerpolate" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.cg.geointerpolate">
-<code class="sig-prename descclassname">libpysal.cg.</code><code class="sig-name descname">geointerpolate</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">p0</span></em>, <em class="sig-param"><span class="n">p1</span></em>, <em class="sig-param"><span class="n">t</span></em>, <em class="sig-param"><span class="n">lonx</span><span class="o">=</span><span class="default_value">True</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/sphere.html#geointerpolate"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.geointerpolate" title="Permalink to this definition">¶</a></dt>
-<dd><p>Finds a point on a sphere along the great circle distance between
-two points on a sphere also known as a way point in great circle navigation.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>p0</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#tuple" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">tuple</span></code></a></span></dt><dd><p>The first point decimal degrees.</p>
-</dd>
-<dt><strong>p1</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#tuple" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">tuple</span></code></a></span></dt><dd><p>The second point decimal degrees.</p>
-</dd>
-<dt><strong>t</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>The proportion along great circle distance between <code class="docutils literal notranslate"><span class="pre">p0</span></code>
-and <code class="docutils literal notranslate"><span class="pre">p1</span></code> (e.g., <span class="math notranslate nohighlight">\(\mathtt{t}=0.5\)</span> would find the mid-point).</p>
-</dd>
-<dt><strong>lonx</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.8)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>The method to assess the order of the coordinates.
-<code class="docutils literal notranslate"><span class="pre">True</span></code> for (lon,lat); <code class="docutils literal notranslate"><span class="pre">False</span></code> for (lat,lon).
-Default is <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>newpx, newpy</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#tuple" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">tuple</span></code></a></span></dt><dd><p>The new point in decimal degrees of (lon-lat) by
-default or (lat-lon) if <code class="docutils literal notranslate"><span class="pre">lonx</span></code> is set to <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p0</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mf">87.893517</span><span class="p">,</span> <span class="mf">41.981417</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">p1</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mf">87.519295</span><span class="p">,</span> <span class="mf">41.657498</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">geointerpolate</span><span class="p">(</span><span class="n">p0</span><span class="p">,</span> <span class="n">p1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">)</span>             <span class="c1"># using lon-lat</span>
-<span class="go">(-87.85592403438788, 41.949079912574796)</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p3</span> <span class="o">=</span> <span class="p">(</span><span class="mf">41.981417</span><span class="p">,</span> <span class="o">-</span><span class="mf">87.893517</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">p4</span> <span class="o">=</span> <span class="p">(</span><span class="mf">41.657498</span><span class="p">,</span> <span class="o">-</span><span class="mf">87.519295</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">geointerpolate</span><span class="p">(</span><span class="n">p3</span><span class="p">,</span> <span class="n">p4</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="n">lonx</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> <span class="c1"># using lat-lon</span>
-<span class="go">(41.949079912574796, -87.85592403438788)</span>
-</pre></div>
-</div>
-</dd></dl>
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-<h1>libpysal.cg.get_angle_between<a class="headerlink" href="#libpysal-cg-get-angle-between" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.cg.get_angle_between">
-<code class="sig-prename descclassname">libpysal.cg.</code><code class="sig-name descname">get_angle_between</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ray1</span></em>, <em class="sig-param"><span class="n">ray2</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/standalone.html#get_angle_between"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.get_angle_between" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the angle formed between a pair of rays which share an origin.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>ray1</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Ray.html#libpysal.cg.Ray" title="libpysal.cg.Ray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Ray</span></code></a></span></dt><dd><p>A ray forming the beginning of the angle measured.</p>
-</dd>
-<dt><strong>ray2</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Ray.html#libpysal.cg.Ray" title="libpysal.cg.Ray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Ray</span></code></a></span></dt><dd><p>A ray forming the end of the angle measured.</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>angle</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>The angle between <code class="docutils literal notranslate"><span class="pre">ray1</span></code> and <code class="docutils literal notranslate"><span class="pre">ray2</span></code>.</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-odd">Raises</dt>
-<dd class="field-odd"><dl class="simple">
-<dt><a class="reference external" href="https://docs.python.org/3.8/library/exceptions.html#ValueError" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ValueError</span></code></a></dt><dd><p>Raised when rays do not have the same origin.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">get_angle_between</span><span class="p">(</span>
-<span class="gp">... </span>    <span class="n">Ray</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">))),</span>
-<span class="gp">... </span>    <span class="n">Ray</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)))</span>
-<span class="gp">... </span><span class="p">)</span>
-<span class="go">0.0</span>
-</pre></div>
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-</dd></dl>
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-<h1>libpysal.cg.get_bounding_box<a class="headerlink" href="#libpysal-cg-get-bounding-box" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.cg.get_bounding_box">
-<code class="sig-prename descclassname">libpysal.cg.</code><code class="sig-name descname">get_bounding_box</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">items</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/standalone.html#get_bounding_box"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.get_bounding_box" title="Permalink to this definition">¶</a></dt>
-<dd><p>Find bounding box for a list of geometries.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>items</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>PySAL shapes.</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl class="simple">
-<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">rect</span></code> = <a class="reference internal" href="libpysal.cg.Rectangle.html#libpysal.cg.Rectangle" title="libpysal.cg.Rectangle"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Rectangle</span></code></a></dt><dd><p>The bounding box for a list of geometries.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">bb</span> <span class="o">=</span> <span class="n">get_bounding_box</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">)),</span> <span class="n">Rectangle</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">11</span><span class="p">,</span> <span class="mi">12</span><span class="p">)])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">bb</span><span class="o">.</span><span class="n">left</span>
-<span class="go">-1.0</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">bb</span><span class="o">.</span><span class="n">lower</span>
-<span class="go">5.0</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">bb</span><span class="o">.</span><span class="n">right</span>
-<span class="go">11.0</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">bb</span><span class="o">.</span><span class="n">upper</span>
-<span class="go">12.0</span>
-</pre></div>
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-<h1>libpysal.cg.get_point_at_angle_and_dist<a class="headerlink" href="#libpysal-cg-get-point-at-angle-and-dist" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.cg.get_point_at_angle_and_dist">
-<code class="sig-prename descclassname">libpysal.cg.</code><code class="sig-name descname">get_point_at_angle_and_dist</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ray</span></em>, <em class="sig-param"><span class="n">angle</span></em>, <em class="sig-param"><span class="n">dist</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/standalone.html#get_point_at_angle_and_dist"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.get_point_at_angle_and_dist" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the point at a distance and angle relative to the origin of a ray.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>ray</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Ray.html#libpysal.cg.Ray" title="libpysal.cg.Ray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Ray</span></code></a></span></dt><dd><p>The ray to which <code class="docutils literal notranslate"><span class="pre">angle</span></code> and <code class="docutils literal notranslate"><span class="pre">dist</span></code> are relative.</p>
-</dd>
-<dt><strong>angle</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>The angle relative to <code class="docutils literal notranslate"><span class="pre">ray</span></code> at which <code class="docutils literal notranslate"><span class="pre">point</span></code> is located.</p>
-</dd>
-<dt><strong>dist</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>The distance from the origin of <code class="docutils literal notranslate"><span class="pre">ray</span></code> at which <code class="docutils literal notranslate"><span class="pre">point</span></code> is located.</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>point</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a></span></dt><dd><p>The point at <code class="docutils literal notranslate"><span class="pre">dist</span></code> and <code class="docutils literal notranslate"><span class="pre">angle</span></code> relative to the origin of <code class="docutils literal notranslate"><span class="pre">ray</span></code>.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ray</span> <span class="o">=</span> <span class="n">Ray</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">pt</span> <span class="o">=</span> <span class="n">get_point_at_angle_and_dist</span><span class="p">(</span><span class="n">ray</span><span class="p">,</span> <span class="n">math</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">isinstance</span><span class="p">(</span><span class="n">pt</span><span class="p">,</span> <span class="n">Point</span><span class="p">)</span>
-<span class="go">True</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="nb">round</span><span class="p">(</span><span class="n">pt</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">8</span><span class="p">)</span>
-<span class="go">-1.0</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="nb">round</span><span class="p">(</span><span class="n">pt</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">8</span><span class="p">)</span>
-<span class="go">0.0</span>
-</pre></div>
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-<h1>libpysal.cg.get_points_dist<a class="headerlink" href="#libpysal-cg-get-points-dist" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.cg.get_points_dist">
-<code class="sig-prename descclassname">libpysal.cg.</code><code class="sig-name descname">get_points_dist</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">pt1</span></em>, <em class="sig-param"><span class="n">pt2</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/standalone.html#get_points_dist"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.get_points_dist" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the distance between a pair of points.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>pt1</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a></span></dt><dd><p>A point.</p>
-</dd>
-<dt><strong>pt2</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a></span></dt><dd><p>The other point.</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>dist</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>The distance between <code class="docutils literal notranslate"><span class="pre">pt1</span></code> and <code class="docutils literal notranslate"><span class="pre">pt2</span></code>.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">get_points_dist</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">)))</span>
-<span class="go">4.0</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">get_points_dist</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)))</span>
-<span class="go">0.0</span>
-</pre></div>
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diff --git a/docs/generated/libpysal.cg.get_polygon_point_dist.html b/docs/generated/libpysal.cg.get_polygon_point_dist.html
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-<h1>libpysal.cg.get_polygon_point_dist<a class="headerlink" href="#libpysal-cg-get-polygon-point-dist" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.cg.get_polygon_point_dist">
-<code class="sig-prename descclassname">libpysal.cg.</code><code class="sig-name descname">get_polygon_point_dist</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">poly</span></em>, <em class="sig-param"><span class="n">pt</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/standalone.html#get_polygon_point_dist"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.get_polygon_point_dist" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the distance between a polygon and point.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>poly</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Polygon.html#libpysal.cg.Polygon" title="libpysal.cg.Polygon"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Polygon</span></code></a></span></dt><dd><p>A polygon to compute distance from.</p>
-</dd>
-<dt><strong>pt</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a></span></dt><dd><p>a point to compute distance from</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>dist</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>The distance between <code class="docutils literal notranslate"><span class="pre">poly</span></code> and <code class="docutils literal notranslate"><span class="pre">point</span></code>.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">poly</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">pt</span> <span class="o">=</span> <span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">get_polygon_point_dist</span><span class="p">(</span><span class="n">poly</span><span class="p">,</span> <span class="n">pt</span><span class="p">)</span>
-<span class="go">1.0</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">pt2</span> <span class="o">=</span> <span class="n">Point</span><span class="p">((</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">get_polygon_point_dist</span><span class="p">(</span><span class="n">poly</span><span class="p">,</span> <span class="n">pt2</span><span class="p">)</span>
-<span class="go">0.0</span>
-</pre></div>
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-<h1>libpysal.cg.get_polygon_point_intersect<a class="headerlink" href="#libpysal-cg-get-polygon-point-intersect" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.cg.get_polygon_point_intersect">
-<code class="sig-prename descclassname">libpysal.cg.</code><code class="sig-name descname">get_polygon_point_intersect</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">poly</span></em>, <em class="sig-param"><span class="n">pt</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/standalone.html#get_polygon_point_intersect"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.get_polygon_point_intersect" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the intersection of a polygon and point.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>poly</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Polygon.html#libpysal.cg.Polygon" title="libpysal.cg.Polygon"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Polygon</span></code></a></span></dt><dd><p>A polygon to check for an intersection.</p>
-</dd>
-<dt><strong>pt</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a></span></dt><dd><p>A point to check <code class="docutils literal notranslate"><span class="pre">poly</span></code> for an intersection.</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>ret</strong><span class="classifier">{<a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a>, <a class="reference external" href="https://docs.python.org/3.8/library/constants.html#None" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a>}</span></dt><dd><p>The intersection of a <code class="docutils literal notranslate"><span class="pre">poly</span></code> and <code class="docutils literal notranslate"><span class="pre">pt</span></code> if one exists, otherwise <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">poly</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">pt</span> <span class="o">=</span> <span class="n">Point</span><span class="p">((</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">i</span> <span class="o">=</span> <span class="n">get_polygon_point_intersect</span><span class="p">(</span><span class="n">poly</span><span class="p">,</span> <span class="n">pt</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
-<span class="go">&#39;(0.5, 0.5)&#39;</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">pt2</span> <span class="o">=</span> <span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">get_polygon_point_intersect</span><span class="p">(</span><span class="n">poly</span><span class="p">,</span> <span class="n">pt2</span><span class="p">)</span>
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-<h1>libpysal.cg.get_ray_segment_intersect<a class="headerlink" href="#libpysal-cg-get-ray-segment-intersect" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.cg.get_ray_segment_intersect">
-<code class="sig-prename descclassname">libpysal.cg.</code><code class="sig-name descname">get_ray_segment_intersect</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ray</span></em>, <em class="sig-param"><span class="n">seg</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/standalone.html#get_ray_segment_intersect"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.get_ray_segment_intersect" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the intersection of a ray and line segment.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>ray</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Ray.html#libpysal.cg.Ray" title="libpysal.cg.Ray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Ray</span></code></a></span></dt><dd><p>A ray to check for an intersection.</p>
-</dd>
-<dt><strong>seg</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.LineSegment.html#libpysal.cg.LineSegment" title="libpysal.cg.LineSegment"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.LineSegment</span></code></a></span></dt><dd><p>A segment to check for an intersection against <code class="docutils literal notranslate"><span class="pre">ray</span></code>.</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>intersection</strong><span class="classifier">{<a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a>, <a class="reference internal" href="libpysal.cg.LineSegment.html#libpysal.cg.LineSegment" title="libpysal.cg.LineSegment"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.LineSegment</span></code></a>, <a class="reference external" href="https://docs.python.org/3.8/library/constants.html#None" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a>}</span></dt><dd><p>The intersecting point or line between <code class="docutils literal notranslate"><span class="pre">ray</span></code> and
-<code class="docutils literal notranslate"><span class="pre">seg</span></code> if an intersection exists or <code class="docutils literal notranslate"><span class="pre">None</span></code> if
-<code class="docutils literal notranslate"><span class="pre">ray</span></code> and <code class="docutils literal notranslate"><span class="pre">seg</span></code> do not intersect.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<div class="admonition seealso">
-<p class="admonition-title">See also</p>
-<dl class="simple">
-<dt><a class="reference internal" href="libpysal.cg.get_segments_intersect.html#libpysal.cg.get_segments_intersect" title="libpysal.cg.get_segments_intersect"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.get_segments_intersect</span></code></a></dt><dd></dd>
-</dl>
-</div>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ray</span> <span class="o">=</span> <span class="n">Ray</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">seg</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">)))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">i</span> <span class="o">=</span> <span class="n">get_ray_segment_intersect</span><span class="p">(</span><span class="n">ray</span><span class="p">,</span> <span class="n">seg</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">isinstance</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">Point</span><span class="p">)</span>
-<span class="go">True</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
-<span class="go">&#39;(0.0, 10.0)&#39;</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">seg2</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="mi">11</span><span class="p">)))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">get_ray_segment_intersect</span><span class="p">(</span><span class="n">ray</span><span class="p">,</span> <span class="n">seg2</span><span class="p">)</span>
-</pre></div>
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-<h1>libpysal.cg.get_rectangle_point_intersect<a class="headerlink" href="#libpysal-cg-get-rectangle-point-intersect" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.cg.get_rectangle_point_intersect">
-<code class="sig-prename descclassname">libpysal.cg.</code><code class="sig-name descname">get_rectangle_point_intersect</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">rect</span></em>, <em class="sig-param"><span class="n">pt</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/standalone.html#get_rectangle_point_intersect"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.get_rectangle_point_intersect" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the intersection of a rectangle and point.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>rect</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Rectangle.html#libpysal.cg.Rectangle" title="libpysal.cg.Rectangle"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Rectangle</span></code></a></span></dt><dd><p>A rectangle to check for an intersection.</p>
-</dd>
-<dt><strong>pt</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a></span></dt><dd><p>A point to check <code class="docutils literal notranslate"><span class="pre">rect</span></code> for an intersection.</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>pt</strong><span class="classifier">{<a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a>, <a class="reference external" href="https://docs.python.org/3.8/library/constants.html#None" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a>}</span></dt><dd><p>The intersection of a <code class="docutils literal notranslate"><span class="pre">rect</span></code> and <code class="docutils literal notranslate"><span class="pre">pt</span></code> if one exists, otherwise <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">rect</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">pt</span> <span class="o">=</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">i</span> <span class="o">=</span> <span class="n">get_rectangle_point_intersect</span><span class="p">(</span><span class="n">rect</span><span class="p">,</span> <span class="n">pt</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
-<span class="go">&#39;(1.0, 1.0)&#39;</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">pt2</span> <span class="o">=</span> <span class="n">Point</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">get_rectangle_point_intersect</span><span class="p">(</span><span class="n">rect</span><span class="p">,</span> <span class="n">pt2</span><span class="p">)</span>
-</pre></div>
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-<h1>libpysal.cg.get_rectangle_rectangle_intersection<a class="headerlink" href="#libpysal-cg-get-rectangle-rectangle-intersection" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.cg.get_rectangle_rectangle_intersection">
-<code class="sig-prename descclassname">libpysal.cg.</code><code class="sig-name descname">get_rectangle_rectangle_intersection</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">r0</span></em>, <em class="sig-param"><span class="n">r1</span></em>, <em class="sig-param"><span class="n">checkOverlap</span><span class="o">=</span><span class="default_value">True</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/standalone.html#get_rectangle_rectangle_intersection"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.get_rectangle_rectangle_intersection" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the intersection between two rectangles.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>r0</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Rectangle.html#libpysal.cg.Rectangle" title="libpysal.cg.Rectangle"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Rectangle</span></code></a></span></dt><dd><p>A rectangle to check for an intersection.</p>
-</dd>
-<dt><strong>r1</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Rectangle.html#libpysal.cg.Rectangle" title="libpysal.cg.Rectangle"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Rectangle</span></code></a></span></dt><dd><p>A rectangle to check for an intersection against <code class="docutils literal notranslate"><span class="pre">r0</span></code>.</p>
-</dd>
-<dt><strong>checkOverlap</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.8)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>Call <code class="docutils literal notranslate"><span class="pre">bbcommon(r0,</span> <span class="pre">r1)</span></code> prior to complex geometry
-checking. Default is <code class="docutils literal notranslate"><span class="pre">True</span></code>. Prior to setting as
-<code class="docutils literal notranslate"><span class="pre">False</span></code> see the Notes section.</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>intersection</strong><span class="classifier">{<a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a>, <a class="reference internal" href="libpysal.cg.LineSegment.html#libpysal.cg.LineSegment" title="libpysal.cg.LineSegment"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.LineSegment</span></code></a>, <a class="reference internal" href="libpysal.cg.Rectangle.html#libpysal.cg.Rectangle" title="libpysal.cg.Rectangle"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Rectangle</span></code></a>, <a class="reference external" href="https://docs.python.org/3.8/library/constants.html#None" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a>}</span></dt><dd><p>The intersecting point, line, or rectangle between 
-<cite>r0`</cite> and <code class="docutils literal notranslate"><span class="pre">r1</span></code> if an intersection exists or <code class="docutils literal notranslate"><span class="pre">None</span></code>
-if <code class="docutils literal notranslate"><span class="pre">r0</span></code> and <code class="docutils literal notranslate"><span class="pre">r1</span></code> do not intersect.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Notes</p>
-<p>The algorithm assumes the rectangles overlap. The keyword
-<code class="docutils literal notranslate"><span class="pre">checkOverlap=False</span></code> should be used with extreme caution.</p>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">r0</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">6</span><span class="p">,</span><span class="mi">9</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">r1</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">9</span><span class="p">,</span><span class="mi">7</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">ri</span> <span class="o">=</span> <span class="n">get_rectangle_rectangle_intersection</span><span class="p">(</span><span class="n">r0</span><span class="p">,</span><span class="n">r1</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">ri</span><span class="p">[:]</span>
-<span class="go">[4.0, 4.0, 6.0, 7.0]</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">r0</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">r1</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">6</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">ri</span> <span class="o">=</span> <span class="n">get_rectangle_rectangle_intersection</span><span class="p">(</span><span class="n">r0</span><span class="p">,</span><span class="n">r1</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">ri</span><span class="p">[:]</span>
-<span class="go">[2.0, 1.0, 4.0, 3.0]</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">r0</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">r1</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">ri</span> <span class="o">=</span> <span class="n">get_rectangle_rectangle_intersection</span><span class="p">(</span><span class="n">r0</span><span class="p">,</span><span class="n">r1</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">ri</span><span class="p">[:]</span> <span class="o">==</span> <span class="n">r1</span><span class="p">[:]</span>
-<span class="go">True</span>
-</pre></div>
-</div>
-</dd></dl>
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-<h1>libpysal.cg.get_segment_point_dist<a class="headerlink" href="#libpysal-cg-get-segment-point-dist" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.cg.get_segment_point_dist">
-<code class="sig-prename descclassname">libpysal.cg.</code><code class="sig-name descname">get_segment_point_dist</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">seg</span></em>, <em class="sig-param"><span class="n">pt</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/standalone.html#get_segment_point_dist"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.get_segment_point_dist" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns (1) the distance between a line segment and point
-and (2) the distance along the segment to the closest location on the
-segment from the point as a ratio of the length of the segment.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>seg</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.LineSegment.html#libpysal.cg.LineSegment" title="libpysal.cg.LineSegment"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.LineSegment</span></code></a></span></dt><dd><p>A line segment to compute distance from.</p>
-</dd>
-<dt><strong>pt</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a></span></dt><dd><p>A point to compute distance from.</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>dist</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>The distance between <code class="docutils literal notranslate"><span class="pre">seg</span></code> and <code class="docutils literal notranslate"><span class="pre">pt</span></code>.</p>
-</dd>
-<dt><strong>ratio</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>The distance along <code class="docutils literal notranslate"><span class="pre">seg</span></code> to the closest location on
-<code class="docutils literal notranslate"><span class="pre">seg</span></code> from <code class="docutils literal notranslate"><span class="pre">pt</span></code> as a ratio of the length of <code class="docutils literal notranslate"><span class="pre">seg</span></code>.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">seg</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="mi">0</span><span class="p">)))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">pt</span> <span class="o">=</span> <span class="n">Point</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">get_segment_point_dist</span><span class="p">(</span><span class="n">seg</span><span class="p">,</span> <span class="n">pt</span><span class="p">)</span>
-<span class="go">(5.0, 0.5)</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">pt2</span> <span class="o">=</span> <span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">get_segment_point_dist</span><span class="p">(</span><span class="n">seg</span><span class="p">,</span> <span class="n">pt2</span><span class="p">)</span>
-<span class="go">(0.0, 0.0)</span>
-</pre></div>
-</div>
-</dd></dl>
-
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-<h1>libpysal.cg.get_segment_point_intersect<a class="headerlink" href="#libpysal-cg-get-segment-point-intersect" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.cg.get_segment_point_intersect">
-<code class="sig-prename descclassname">libpysal.cg.</code><code class="sig-name descname">get_segment_point_intersect</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">seg</span></em>, <em class="sig-param"><span class="n">pt</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/standalone.html#get_segment_point_intersect"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.get_segment_point_intersect" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the intersection of a segment and point.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>seg</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.LineSegment.html#libpysal.cg.LineSegment" title="libpysal.cg.LineSegment"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.LineSegment</span></code></a></span></dt><dd><p>A segment to check for an intersection.</p>
-</dd>
-<dt><strong>pt</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a></span></dt><dd><p>A point to check <code class="docutils literal notranslate"><span class="pre">seg</span></code> for an intersection.</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>pt</strong><span class="classifier">{<a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a>, <a class="reference external" href="https://docs.python.org/3.8/library/constants.html#None" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a>}</span></dt><dd><p>The intersection of a <code class="docutils literal notranslate"><span class="pre">seg</span></code> and <code class="docutils literal notranslate"><span class="pre">pt</span></code> if one exists, otherwise <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">seg</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">)))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">pt</span> <span class="o">=</span> <span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">i</span> <span class="o">=</span> <span class="n">get_segment_point_intersect</span><span class="p">(</span><span class="n">seg</span><span class="p">,</span> <span class="n">pt</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
-<span class="go">&#39;(0.0, 5.0)&#39;</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">pt2</span> <span class="o">=</span> <span class="n">Point</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">get_segment_point_intersect</span><span class="p">(</span><span class="n">seg</span><span class="p">,</span> <span class="n">pt2</span><span class="p">)</span>
-</pre></div>
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diff --git a/docs/generated/libpysal.cg.get_segments_intersect.html b/docs/generated/libpysal.cg.get_segments_intersect.html
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-<h1>libpysal.cg.get_segments_intersect<a class="headerlink" href="#libpysal-cg-get-segments-intersect" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.cg.get_segments_intersect">
-<code class="sig-prename descclassname">libpysal.cg.</code><code class="sig-name descname">get_segments_intersect</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">seg1</span></em>, <em class="sig-param"><span class="n">seg2</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/standalone.html#get_segments_intersect"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.get_segments_intersect" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the intersection of two segments if one exists.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>seg1</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.LineSegment.html#libpysal.cg.LineSegment" title="libpysal.cg.LineSegment"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.LineSegment</span></code></a></span></dt><dd><p>A segment to check for an intersection.</p>
-</dd>
-<dt><strong>seg2</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.LineSegment.html#libpysal.cg.LineSegment" title="libpysal.cg.LineSegment"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.LineSegment</span></code></a></span></dt><dd><p>The segment to check against <code class="docutils literal notranslate"><span class="pre">seg1</span></code> for an intersection.</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>intersection</strong><span class="classifier">{<a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a>, <a class="reference internal" href="libpysal.cg.LineSegment.html#libpysal.cg.LineSegment" title="libpysal.cg.LineSegment"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.LineSegment</span></code></a>, <a class="reference external" href="https://docs.python.org/3.8/library/constants.html#None" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a>}</span></dt><dd><p>The intersecting point or line between <code class="docutils literal notranslate"><span class="pre">seg1</span></code> and
-<code class="docutils literal notranslate"><span class="pre">seg2</span></code> if an intersection exists or <code class="docutils literal notranslate"><span class="pre">None</span></code> if
-<code class="docutils literal notranslate"><span class="pre">seg1</span></code> and <code class="docutils literal notranslate"><span class="pre">seg2</span></code> do not intersect.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">seg1</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">)))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">seg2</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="o">-</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">)))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">i</span> <span class="o">=</span> <span class="n">get_segments_intersect</span><span class="p">(</span><span class="n">seg1</span><span class="p">,</span> <span class="n">seg2</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">isinstance</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">Point</span><span class="p">)</span>
-<span class="go">True</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
-<span class="go">&#39;(0.0, 5.0)&#39;</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">seg3</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">100</span><span class="p">,</span> <span class="mi">100</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">100</span><span class="p">,</span> <span class="mi">101</span><span class="p">)))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">i</span> <span class="o">=</span> <span class="n">get_segments_intersect</span><span class="p">(</span><span class="n">seg2</span><span class="p">,</span> <span class="n">seg3</span><span class="p">)</span>
-</pre></div>
-</div>
-</dd></dl>
-
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-  <a href="../_sources/generated/libpysal.cg.get_segments_intersect.rst.txt"
-     rel="nofollow">Source</a>
-</div>
-      
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-    <p>
-        &copy; Copyright 2018-, pysal developers.<br/>
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@@ -1,196 +0,0 @@
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-<h1>libpysal.cg.get_shared_segments<a class="headerlink" href="#libpysal-cg-get-shared-segments" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.cg.get_shared_segments">
-<code class="sig-prename descclassname">libpysal.cg.</code><code class="sig-name descname">get_shared_segments</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">poly1</span></em>, <em class="sig-param"><span class="n">poly2</span></em>, <em class="sig-param"><span class="n">bool_ret</span><span class="o">=</span><span class="default_value">False</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/standalone.html#get_shared_segments"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.get_shared_segments" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the line segments in common to both polygons.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>poly1</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Polygon.html#libpysal.cg.Polygon" title="libpysal.cg.Polygon"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Polygon</span></code></a></span></dt><dd><p>A Polygon.</p>
-</dd>
-<dt><strong>poly2</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Polygon.html#libpysal.cg.Polygon" title="libpysal.cg.Polygon"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Polygon</span></code></a></span></dt><dd><p>A Polygon.</p>
-</dd>
-<dt><strong>bool_ret</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.8)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>Return only a <code class="docutils literal notranslate"><span class="pre">bool</span></code>. Default is <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>common</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>The shared line segments between <code class="docutils literal notranslate"><span class="pre">poly1</span></code> and <code class="docutils literal notranslate"><span class="pre">poly2</span></code>.</p>
-</dd>
-<dt><strong>_ret_bool</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.8)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>Whether <code class="docutils literal notranslate"><span class="pre">poly1</span></code> and <code class="docutils literal notranslate"><span class="pre">poly2</span></code> share a
-segment (<code class="docutils literal notranslate"><span class="pre">True</span></code>) or not (<code class="docutils literal notranslate"><span class="pre">False</span></code>).</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.cg.shapes</span> <span class="kn">import</span> <span class="n">Polygon</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">x</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">y</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">poly1</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">Point</span><span class="p">,</span> <span class="nb">zip</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)))</span> <span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">x</span> <span class="o">=</span> <span class="p">[</span><span class="n">a</span><span class="o">+</span><span class="mi">1</span> <span class="k">for</span> <span class="n">a</span> <span class="ow">in</span> <span class="n">x</span><span class="p">]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">poly2</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">Point</span><span class="p">,</span> <span class="nb">zip</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)))</span> <span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">get_shared_segments</span><span class="p">(</span><span class="n">poly1</span><span class="p">,</span> <span class="n">poly2</span><span class="p">,</span> <span class="n">bool_ret</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="go">True</span>
-</pre></div>
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-<h1>libpysal.cg.harcdist<a class="headerlink" href="#libpysal-cg-harcdist" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.cg.harcdist">
-<code class="sig-prename descclassname">libpysal.cg.</code><code class="sig-name descname">harcdist</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">p0</span></em>, <em class="sig-param"><span class="n">p1</span></em>, <em class="sig-param"><span class="n">lonx</span><span class="o">=</span><span class="default_value">True</span></em>, <em class="sig-param"><span class="n">radius</span><span class="o">=</span><span class="default_value">6371.0</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/sphere.html#harcdist"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.harcdist" title="Permalink to this definition">¶</a></dt>
-<dd><p>Alternative the arc distance function, uses the haversine formula.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>p0</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#tuple" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">tuple</span></code></a></span></dt><dd><p>The first point decimal degrees.</p>
-</dd>
-<dt><strong>p1</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#tuple" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">tuple</span></code></a></span></dt><dd><p>The second point decimal degrees.</p>
-</dd>
-<dt><strong>lonx</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.8)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>The method to assess the order of the coordinates.
-<code class="docutils literal notranslate"><span class="pre">True</span></code> for (lon,lat); <code class="docutils literal notranslate"><span class="pre">False</span></code> for (lat,lon).
-Default is <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p>
-</dd>
-<dt><strong>radius</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>The radius of a sphere. Default is Earth’s radius in
-kilometers, <code class="docutils literal notranslate"><span class="pre">RADIUS_EARTH_KM</span></code> (<code class="docutils literal notranslate"><span class="pre">6371.0</span></code>). Earth’s
-radius in miles, <code class="docutils literal notranslate"><span class="pre">RADIUS_EARTH_MILES</span></code> (<code class="docutils literal notranslate"><span class="pre">3958.76</span></code>)
-is also an option. Set to <code class="docutils literal notranslate"><span class="pre">None</span></code> for radians.
-Source: <a class="reference external" href="http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html">http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html</a></p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>harc_dist</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">harc_dist</span></code></span></dt><dd><p>The distance in units specified, km, miles or radians.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Notes</p>
-<p>Uses the <code class="docutils literal notranslate"><span class="pre">radangle</span></code> function to compute radian angle.</p>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p0</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mf">87.893517</span><span class="p">,</span> <span class="mf">41.981417</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">p1</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mf">87.519295</span><span class="p">,</span> <span class="mf">41.657498</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">harcdist</span><span class="p">(</span><span class="n">p0</span><span class="p">,</span> <span class="n">p1</span><span class="p">)</span>
-<span class="go">47.52873002976876</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">harcdist</span><span class="p">(</span><span class="n">p0</span><span class="p">,</span> <span class="n">p1</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
-<span class="go">0.007460167953189258</span>
-</pre></div>
-</div>
-</dd></dl>
-
-</div>
-
-
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-      
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-  <a href="../_sources/generated/libpysal.cg.harcdist.rst.txt"
-     rel="nofollow">Source</a>
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deleted file mode 100644
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-  <div class="section" id="libpysal-cg-is-clockwise">
-<h1>libpysal.cg.is_clockwise<a class="headerlink" href="#libpysal-cg-is-clockwise" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.cg.is_clockwise">
-<code class="sig-prename descclassname">libpysal.cg.</code><code class="sig-name descname">is_clockwise</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">vertices</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/standalone.html#is_clockwise"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.is_clockwise" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns whether a list of points describing
-a polygon are clockwise or counterclockwise.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>vertices</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>A list of points that form a single ring.</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>clockwise</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.8)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p><code class="docutils literal notranslate"><span class="pre">True</span></code> if <code class="docutils literal notranslate"><span class="pre">vertices</span></code> are clockwise, otherwise <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<div class="admonition seealso">
-<p class="admonition-title">See also</p>
-<dl class="simple">
-<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.ccw</span></code></dt><dd></dd>
-</dl>
-</div>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">is_clockwise</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">))])</span>
-<span class="go">False</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">is_clockwise</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="mi">0</span><span class="p">))])</span>
-<span class="go">True</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">v</span> <span class="o">=</span> <span class="p">[</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.57798</span><span class="p">,</span> <span class="mf">35.174143999999998</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.583412</span><span class="p">,</span> <span class="mf">35.174141999999996</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.58417999999999</span><span class="p">,</span> <span class="mf">35.174143000000001</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.58377999999999</span><span class="p">,</span> <span class="mf">35.175542999999998</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.58287999999999</span><span class="p">,</span> <span class="mf">35.180543</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.58263099999999</span><span class="p">,</span> <span class="mf">35.181455</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.58257999999999</span><span class="p">,</span> <span class="mf">35.181643000000001</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.58198299999999</span><span class="p">,</span> <span class="mf">35.184615000000001</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.58148</span><span class="p">,</span> <span class="mf">35.187242999999995</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.58127999999999</span><span class="p">,</span> <span class="mf">35.188243</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.58138</span><span class="p">,</span> <span class="mf">35.188243</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.58108</span><span class="p">,</span> <span class="mf">35.189442999999997</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.58104</span><span class="p">,</span> <span class="mf">35.189644000000001</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.58028</span><span class="p">,</span> <span class="mf">35.193442999999995</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.580029</span><span class="p">,</span> <span class="mf">35.194541000000001</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.57974399999999</span><span class="p">,</span> <span class="mf">35.195785999999998</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.579475</span><span class="p">,</span> <span class="mf">35.196961999999999</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.57922699999999</span><span class="p">,</span> <span class="mf">35.198042999999998</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.578397</span><span class="p">,</span> <span class="mf">35.201665999999996</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.57827999999999</span><span class="p">,</span> <span class="mf">35.201642999999997</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.57737999999999</span><span class="p">,</span> <span class="mf">35.201642999999997</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.57697999999999</span><span class="p">,</span> <span class="mf">35.201543000000001</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56436599999999</span><span class="p">,</span> <span class="mf">35.200311999999997</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56058</span><span class="p">,</span> <span class="mf">35.199942999999998</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56048</span><span class="p">,</span> <span class="mf">35.197342999999996</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56048</span><span class="p">,</span> <span class="mf">35.195842999999996</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56048</span><span class="p">,</span> <span class="mf">35.194342999999996</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56048</span><span class="p">,</span> <span class="mf">35.193142999999999</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56048</span><span class="p">,</span> <span class="mf">35.191873999999999</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56048</span><span class="p">,</span> <span class="mf">35.191742999999995</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56048</span><span class="p">,</span> <span class="mf">35.190242999999995</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56037999999999</span><span class="p">,</span> <span class="mf">35.188642999999999</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56037999999999</span><span class="p">,</span> <span class="mf">35.187242999999995</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56037999999999</span><span class="p">,</span> <span class="mf">35.186842999999996</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56037999999999</span><span class="p">,</span> <span class="mf">35.186552999999996</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56037999999999</span><span class="p">,</span> <span class="mf">35.185842999999998</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56037999999999</span><span class="p">,</span> <span class="mf">35.184443000000002</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56037999999999</span><span class="p">,</span> <span class="mf">35.182943000000002</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56037999999999</span><span class="p">,</span> <span class="mf">35.181342999999998</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56037999999999</span><span class="p">,</span> <span class="mf">35.180433000000001</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56037999999999</span><span class="p">,</span> <span class="mf">35.179943000000002</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56037999999999</span><span class="p">,</span> <span class="mf">35.178542999999998</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56037999999999</span><span class="p">,</span> <span class="mf">35.177790999999999</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56037999999999</span><span class="p">,</span> <span class="mf">35.177143999999998</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56037999999999</span><span class="p">,</span> <span class="mf">35.175643999999998</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56037999999999</span><span class="p">,</span> <span class="mf">35.174444000000001</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56037999999999</span><span class="p">,</span> <span class="mf">35.174043999999995</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.560526</span><span class="p">,</span> <span class="mf">35.174043999999995</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56478</span><span class="p">,</span> <span class="mf">35.174043999999995</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56627999999999</span><span class="p">,</span> <span class="mf">35.174143999999998</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.566541</span><span class="p">,</span> <span class="mf">35.174144999999996</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.569023</span><span class="p">,</span> <span class="mf">35.174157000000001</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56917199999999</span><span class="p">,</span> <span class="mf">35.174157999999998</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56938</span><span class="p">,</span> <span class="mf">35.174143999999998</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.57061499999999</span><span class="p">,</span> <span class="mf">35.174143999999998</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.57097999999999</span><span class="p">,</span> <span class="mf">35.174143999999998</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.57679999999999</span><span class="p">,</span> <span class="mf">35.174143999999998</span><span class="p">),</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.57798</span><span class="p">,</span> <span class="mf">35.174143999999998</span><span class="p">)</span>
-<span class="gp">... </span><span class="p">]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">is_clockwise</span><span class="p">(</span><span class="n">v</span><span class="p">)</span>
-<span class="go">True</span>
-</pre></div>
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-<h1>libpysal.cg.is_collinear<a class="headerlink" href="#libpysal-cg-is-collinear" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.cg.is_collinear">
-<code class="sig-prename descclassname">libpysal.cg.</code><code class="sig-name descname">is_collinear</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">p1</span></em>, <em class="sig-param"><span class="n">p2</span></em>, <em class="sig-param"><span class="n">p3</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/standalone.html#is_collinear"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.is_collinear" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns whether a triplet of points is collinear.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>p1</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a></span></dt><dd><p>A point.</p>
-</dd>
-<dt><strong>p2</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a></span></dt><dd><p>A point.</p>
-</dd>
-<dt><strong>p3</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a></span></dt><dd><p>A point.</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>collinear</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.8)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p><code class="docutils literal notranslate"><span class="pre">True</span></code> if <code class="docutils literal notranslate"><span class="pre">{p1,</span> <span class="pre">p2,</span> <span class="pre">p3}</span></code> are collinear, otherwise <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">is_collinear</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">)))</span>
-<span class="go">True</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">is_collinear</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">0</span><span class="p">)))</span>
-<span class="go">False</span>
-</pre></div>
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diff --git a/docs/generated/libpysal.cg.linear2arcdist.html b/docs/generated/libpysal.cg.linear2arcdist.html
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-<h1>libpysal.cg.linear2arcdist<a class="headerlink" href="#libpysal-cg-linear2arcdist" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.cg.linear2arcdist">
-<code class="sig-prename descclassname">libpysal.cg.</code><code class="sig-name descname">linear2arcdist</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">linear_dist</span></em>, <em class="sig-param"><span class="n">radius</span><span class="o">=</span><span class="default_value">6371.0</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/sphere.html#linear2arcdist"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.linear2arcdist" title="Permalink to this definition">¶</a></dt>
-<dd><p>Convert a linear distance in the unit sphere
-(R3) to an arc distance based on supplied radius.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>linear_dist</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>The linear distance to convert.</p>
-</dd>
-<dt><strong>radius</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>The radius of a sphere. Default is Earth’s radius in
-kilometers, <code class="docutils literal notranslate"><span class="pre">RADIUS_EARTH_KM</span></code> (<code class="docutils literal notranslate"><span class="pre">6371.0</span></code>). Earth’s
-radius in miles, <code class="docutils literal notranslate"><span class="pre">RADIUS_EARTH_MILES</span></code> (<code class="docutils literal notranslate"><span class="pre">3958.76</span></code>)
-is also an option.
-Source: <a class="reference external" href="http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html">http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html</a></p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>arc_dist</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>The arc distance conversion of <code class="docutils literal notranslate"><span class="pre">linear_dist</span></code>.</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-odd">Raises</dt>
-<dd class="field-odd"><dl class="simple">
-<dt><a class="reference external" href="https://docs.python.org/3.8/library/exceptions.html#ValueError" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ValueError</span></code></a></dt><dd><p>Raised when <code class="docutils literal notranslate"><span class="pre">linear_dist</span></code> exceeds the diameter of the unit sphere.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">pt0</span> <span class="o">=</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">pt1</span> <span class="o">=</span> <span class="p">(</span><span class="mi">180</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">d</span> <span class="o">=</span> <span class="n">arcdist</span><span class="p">(</span><span class="n">pt0</span><span class="p">,</span> <span class="n">pt1</span><span class="p">,</span> <span class="n">RADIUS_EARTH_MILES</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">d</span> <span class="o">==</span> <span class="n">linear2arcdist</span><span class="p">(</span><span class="mf">2.0</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="n">RADIUS_EARTH_MILES</span><span class="p">)</span>
-<span class="go">True</span>
-</pre></div>
-</div>
-</dd></dl>
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-  <div class="section" id="libpysal-cg-lonlat">
-<h1>libpysal.cg.lonlat<a class="headerlink" href="#libpysal-cg-lonlat" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.cg.lonlat">
-<code class="sig-prename descclassname">libpysal.cg.</code><code class="sig-name descname">lonlat</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">pointslist</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/sphere.html#lonlat"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.lonlat" title="Permalink to this definition">¶</a></dt>
-<dd><p>Converts point order from lat-lon tuples to lon-lat (x,y) tuples.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>pointslist</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>A list of lat-lon tuples. This <strong>must</strong> be a list, even for one point.</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>newpts</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>A list with tuples of points in lon-lat order.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">points</span> <span class="o">=</span> <span class="p">[</span>
-<span class="gp">... </span>    <span class="p">(</span><span class="mf">41.981417</span><span class="p">,</span> <span class="o">-</span><span class="mf">87.893517</span><span class="p">),</span> <span class="p">(</span><span class="mf">41.980396</span><span class="p">,</span> <span class="o">-</span><span class="mf">87.776787</span><span class="p">),</span> <span class="p">(</span><span class="mf">41.980906</span><span class="p">,</span> <span class="o">-</span><span class="mf">87.696450</span><span class="p">)</span>
-<span class="gp">... </span><span class="p">]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">newpoints</span> <span class="o">=</span> <span class="n">lonlat</span><span class="p">(</span><span class="n">points</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">newpoints</span>
-<span class="go">[(-87.893517, 41.981417), (-87.776787, 41.980396), (-87.69645, 41.980906)]</span>
-</pre></div>
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-<h1>libpysal.cg.point_touches_rectangle<a class="headerlink" href="#libpysal-cg-point-touches-rectangle" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.cg.point_touches_rectangle">
-<code class="sig-prename descclassname">libpysal.cg.</code><code class="sig-name descname">point_touches_rectangle</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">point</span></em>, <em class="sig-param"><span class="n">rect</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/standalone.html#point_touches_rectangle"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.point_touches_rectangle" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns <code class="docutils literal notranslate"><span class="pre">True</span></code> (<code class="docutils literal notranslate"><span class="pre">1</span></code>) if the point is in the rectangle
-or touches it’s boundary, otherwise <code class="docutils literal notranslate"><span class="pre">False</span></code> (<code class="docutils literal notranslate"><span class="pre">0</span></code>).</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>point</strong><span class="classifier">{<a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a>, <a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#tuple" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">tuple</span></code></a>}</span></dt><dd><p>A point or point coordinates.</p>
-</dd>
-<dt><strong>rect</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Rectangle.html#libpysal.cg.Rectangle" title="libpysal.cg.Rectangle"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Rectangle</span></code></a></span></dt><dd><p>A rectangle.</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>chflag</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#int" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p><code class="docutils literal notranslate"><span class="pre">1</span></code> if <code class="docutils literal notranslate"><span class="pre">point</span></code> is in (or touches
-boundary of) <code class="docutils literal notranslate"><span class="pre">rect</span></code>, otherwise <code class="docutils literal notranslate"><span class="pre">0</span></code>.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">rect</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">a</span> <span class="o">=</span> <span class="n">Point</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">b</span> <span class="o">=</span> <span class="n">Point</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">c</span> <span class="o">=</span> <span class="n">Point</span><span class="p">((</span><span class="mi">11</span><span class="p">,</span> <span class="mi">11</span><span class="p">))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">point_touches_rectangle</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">rect</span><span class="p">)</span>
-<span class="go">1</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">point_touches_rectangle</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">rect</span><span class="p">)</span>
-<span class="go">1</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">point_touches_rectangle</span><span class="p">(</span><span class="n">c</span><span class="p">,</span> <span class="n">rect</span><span class="p">)</span>
-<span class="go">0</span>
-</pre></div>
-</div>
-</dd></dl>
-
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-<h1>libpysal.cg.toLngLat<a class="headerlink" href="#libpysal-cg-tolnglat" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.cg.toLngLat">
-<code class="sig-prename descclassname">libpysal.cg.</code><code class="sig-name descname">toLngLat</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">xyz</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/sphere.html#toLngLat"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.toLngLat" title="Permalink to this definition">¶</a></dt>
-<dd><p>Convert a point’s x,y,z to latitude and longitude.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>xyz</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#tuple" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">tuple</span></code></a></span></dt><dd><p>A point assumed to be in form (x,y,z).</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>phi, theta</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#tuple" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">tuple</span></code></a></span></dt><dd><p>A point in form (phi, theta) [y,x].</p>
-</dd>
-</dl>
-</dd>
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-<h1>libpysal.cg.toXYZ<a class="headerlink" href="#libpysal-cg-toxyz" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.cg.toXYZ">
-<code class="sig-prename descclassname">libpysal.cg.</code><code class="sig-name descname">toXYZ</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">pt</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/sphere.html#toXYZ"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.toXYZ" title="Permalink to this definition">¶</a></dt>
-<dd><p>Convert a point’s latitude and longitude to x,y,z.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
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-<h1>libpysal.cg.voronoi_frames<a class="headerlink" href="#libpysal-cg-voronoi-frames" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.cg.voronoi_frames">
-<code class="sig-prename descclassname">libpysal.cg.</code><code class="sig-name descname">voronoi_frames</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">points</span></em>, <em class="sig-param"><span class="n">radius</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">clip</span><span class="o">=</span><span class="default_value">'extent'</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/voronoi.html#voronoi_frames"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.cg.voronoi_frames" title="Permalink to this definition">¶</a></dt>
-<dd><p>Composite helper to return Voronoi regions and
-generator points as individual dataframes.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>points</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/glossary.html#term-array-like" title="(in NumPy v1.20)"><span>array_like</span></a></span></dt><dd><p>The originator points.</p>
-</dd>
-<dt><strong>radius</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>The distance to ‘points at infinity’ used in building voronoi cells.
-Default is <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p>
-</dd>
-<dt><strong>clip</strong><span class="classifier">{<a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">shapely.geometry.Polygon</span></code>}</span></dt><dd><p>An overloaded option about how to clip the voronoi cells.
-Default is <code class="docutils literal notranslate"><span class="pre">'extent'</span></code>. Options are as follows.</p>
-<ul class="simple">
-<li><p><code class="docutils literal notranslate"><span class="pre">'none'</span></code>/<code class="docutils literal notranslate"><span class="pre">None</span></code> – No clip is applied. Voronoi cells may be arbitrarily larger that the source map. Note that this may lead to cells that are many orders of magnitude larger in extent than the original map. Not recommended.</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">'bbox'</span></code>/<code class="docutils literal notranslate"><span class="pre">'extent'</span></code>/<code class="docutils literal notranslate"><span class="pre">'bounding</span> <span class="pre">box'</span></code> – Clip the voronoi cells to the bounding box of the input points.</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">'chull</span></code>/<code class="docutils literal notranslate"><span class="pre">'convex</span> <span class="pre">hull'</span></code> – Clip the voronoi cells to the convex hull of the input points.</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">'ashape'</span></code>/<code class="docutils literal notranslate"><span class="pre">'ahull'</span></code> – Clip the voronoi cells to the tightest hull that contains all points (e.g. the smallest alphashape, using <code class="docutils literal notranslate"><span class="pre">libpysal.cg.alpha_shape_auto</span></code>).</p></li>
-<li><p>Polygon – Clip to an arbitrary Polygon.</p></li>
-</ul>
-</dd>
-<dt><strong>tolerance</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>The percent of map width to use to buffer the extent of the map,
-if clipping (default: <code class="docutils literal notranslate"><span class="pre">.01</span></code>, or 1%).</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>reg_vtx</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#tuple" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">tuple</span></code></a></span></dt><dd><p>Two <code class="docutils literal notranslate"><span class="pre">geopandas.GeoDataFrame</span></code> (or <code class="docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code> if <code class="docutils literal notranslate"><span class="pre">geopandas</span></code>
-is unavailable) objects–<code class="docutils literal notranslate"><span class="pre">(region_df,</span> <span class="pre">points_df)</span></code>–of finite 
-Voronoi polygons and the originator points as geometries.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Notes</p>
-<p>If <code class="docutils literal notranslate"><span class="pre">geopandas</span></code> is not available the return types will be
-<code class="docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code> objects, each with a geometry column populated
-with PySAL shapes. If <code class="docutils literal notranslate"><span class="pre">geopandas</span></code> is available, return types are
-<code class="docutils literal notranslate"><span class="pre">pandas.GeoDataFrame</span></code> objects with a geometry column populated
-with shapely geometry types.</p>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">points</span> <span class="o">=</span> <span class="p">[(</span><span class="mf">10.2</span><span class="p">,</span> <span class="mf">5.1</span><span class="p">),</span> <span class="p">(</span><span class="mf">4.7</span><span class="p">,</span> <span class="mf">2.2</span><span class="p">),</span> <span class="p">(</span><span class="mf">5.3</span><span class="p">,</span> <span class="mf">5.7</span><span class="p">),</span> <span class="p">(</span><span class="mf">2.7</span><span class="p">,</span> <span class="mf">5.3</span><span class="p">)]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">regions_df</span><span class="p">,</span> <span class="n">points_df</span> <span class="o">=</span> <span class="n">voronoi_frames</span><span class="p">(</span><span class="n">points</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">regions_df</span><span class="o">.</span><span class="n">shape</span>
-<span class="go">(4, 1)</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">regions_df</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="n">points_df</span><span class="o">.</span><span class="n">shape</span>
-<span class="go">True</span>
-</pre></div>
-</div>
-</dd></dl>
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-<h1>libpysal.examples.available<a class="headerlink" href="#libpysal-examples-available" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.examples.available">
-<code class="sig-prename descclassname">libpysal.examples.</code><code class="sig-name descname">available</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; <a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)">str</a><a class="reference internal" href="../_modules/libpysal/examples.html#available"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.examples.available" title="Permalink to this definition">¶</a></dt>
-<dd><p>List available datasets.</p>
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diff --git a/docs/generated/libpysal.examples.explain.html b/docs/generated/libpysal.examples.explain.html
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-<h1>libpysal.examples.explain<a class="headerlink" href="#libpysal-examples-explain" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.examples.explain">
-<code class="sig-prename descclassname">libpysal.examples.</code><code class="sig-name descname">explain</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">name</span><span class="p">:</span> <span class="n"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)">str</a></span></em><span class="sig-paren">)</span> &#x2192; <a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)">str</a><a class="reference internal" href="../_modules/libpysal/examples.html#explain"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.examples.explain" title="Permalink to this definition">¶</a></dt>
-<dd><p>Explain a dataset by name.</p>
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diff --git a/docs/generated/libpysal.examples.get_path.html b/docs/generated/libpysal.examples.get_path.html
deleted file mode 100644
index 31eb6b834..000000000
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+++ /dev/null
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-<h1>libpysal.examples.get_path<a class="headerlink" href="#libpysal-examples-get-path" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.examples.get_path">
-<code class="sig-prename descclassname">libpysal.examples.</code><code class="sig-name descname">get_path</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">file_name</span><span class="p">:</span> <span class="n"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)">str</a></span></em><span class="sig-paren">)</span> &#x2192; <a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)">str</a><a class="reference internal" href="../_modules/libpysal/examples.html#get_path"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.examples.get_path" title="Permalink to this definition">¶</a></dt>
-<dd><p>Get the path for a file by searching installed datasets.</p>
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-  <div class="section" id="libpysal-io-fileio-fileio">
-<h1>libpysal.io.fileio.FileIO<a class="headerlink" href="#libpysal-io-fileio-fileio" title="Permalink to this headline">¶</a></h1>
-<dl class="py class">
-<dt id="libpysal.io.fileio.FileIO">
-<em class="property">class </em><code class="sig-prename descclassname">libpysal.io.fileio.</code><code class="sig-name descname">FileIO</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">dataPath</span><span class="o">=</span><span class="default_value">''</span></em>, <em class="sig-param"><span class="n">mode</span><span class="o">=</span><span class="default_value">'r'</span></em>, <em class="sig-param"><span class="n">dataFormat</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/io/fileio.html#FileIO"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.io.fileio.FileIO" title="Permalink to this definition">¶</a></dt>
-<dd><p>Metaclass for supporting spatial data file read and write.</p>
-<p>How this works:</p>
-<p><code class="docutils literal notranslate"><span class="pre">FileIO.open(\*args)</span> <span class="pre">==</span> <span class="pre">FileIO(\*args)</span></code></p>
-<p>When creating a new instance of <cite>FileIO</cite> the <code class="docutils literal notranslate"><span class="pre">.__new__</span></code> method intercepts.
-<code class="docutils literal notranslate"><span class="pre">.__new__</span></code> parses the filename to determine the <code class="docutils literal notranslate"><span class="pre">fileType</span></code>. Next,
-<code class="docutils literal notranslate"><span class="pre">.__registry</span></code> and checked for that type. Each type supports one or more modes
-(<code class="docutils literal notranslate"><span class="pre">['r',</span> <span class="pre">'w',</span> <span class="pre">'a',</span> <span class="pre">etc.]</span></code>). If we support the type and mode, an instance of the
-appropriate handler is created and returned. All handlers must inherit from this
-class, and by doing so are automatically added to the <code class="docutils literal notranslate"><span class="pre">.__registry</span></code> and are
-forced to conform to the prescribed API. The metaclass takes care of the
-registration by parsing the class definition. It doesn’t make much sense to
-treat weights in the same way as shapefiles and dbfs, so…</p>
-<ul class="simple">
-<li><p>… for now we’ll just return an instance of <cite>W</cite> on <code class="docutils literal notranslate"><span class="pre">mode='r'</span></code>.</p></li>
-<li><p>… on <code class="docutils literal notranslate"><span class="pre">mode='w'</span></code>, <code class="docutils literal notranslate"><span class="pre">.write</span></code> will expect an instance of <cite>W</cite>.</p></li>
-</ul>
-<dl class="py method">
-<dt id="libpysal.io.fileio.FileIO.__init__">
-<code class="sig-name descname">__init__</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">dataPath</span><span class="o">=</span><span class="default_value">''</span></em>, <em class="sig-param"><span class="n">mode</span><span class="o">=</span><span class="default_value">'r'</span></em>, <em class="sig-param"><span class="n">dataFormat</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/io/fileio.html#FileIO.__init__"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.io.fileio.FileIO.__init__" title="Permalink to this definition">¶</a></dt>
-<dd><p>Initialize self.  See help(type(self)) for accurate signature.</p>
-</dd></dl>
-
-<p class="rubric">Methods</p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.io.fileio.FileIO.__init__" title="libpysal.io.fileio.FileIO.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>([dataPath, mode, dataFormat])</p></td>
-<td><p>Initialize self.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.io.fileio.FileIO.cast" title="libpysal.io.fileio.FileIO.cast"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cast</span></code></a>(key, typ)</p></td>
-<td><p>Cast <code class="docutils literal notranslate"><span class="pre">key</span></code> as <code class="docutils literal notranslate"><span class="pre">typ</span></code>.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.io.fileio.FileIO.check" title="libpysal.io.fileio.FileIO.check"><code class="xref py py-obj docutils literal notranslate"><span class="pre">check</span></code></a>()</p></td>
-<td><p>Prints the contents of the registry.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.io.fileio.FileIO.close" title="libpysal.io.fileio.FileIO.close"><code class="xref py py-obj docutils literal notranslate"><span class="pre">close</span></code></a>()</p></td>
-<td><p>Subclasses should clean themselves up and then call this method.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.io.fileio.FileIO.flush" title="libpysal.io.fileio.FileIO.flush"><code class="xref py py-obj docutils literal notranslate"><span class="pre">flush</span></code></a>()</p></td>
-<td><p><dl class="field-list simple">
-<dt class="field-odd">Raises</dt>
-<dd class="field-odd"><p></p></dd>
-</dl>
-</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.io.fileio.FileIO.get" title="libpysal.io.fileio.FileIO.get"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get</span></code></a>(n)</p></td>
-<td><p>Seeks the file to <code class="docutils literal notranslate"><span class="pre">n</span></code> and returns <code class="docutils literal notranslate"><span class="pre">n</span></code>.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.io.fileio.FileIO.getType" title="libpysal.io.fileio.FileIO.getType"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getType</span></code></a>(dataPath, mode[, dataFormat])</p></td>
-<td><p>Parse the <code class="docutils literal notranslate"><span class="pre">dataPath</span></code> and return the data type.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.io.fileio.FileIO.open" title="libpysal.io.fileio.FileIO.open"><code class="xref py py-obj docutils literal notranslate"><span class="pre">open</span></code></a>(*args, **kwargs)</p></td>
-<td><p>Alias for <code class="docutils literal notranslate"><span class="pre">FileIO()</span></code>.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.io.fileio.FileIO.read" title="libpysal.io.fileio.FileIO.read"><code class="xref py py-obj docutils literal notranslate"><span class="pre">read</span></code></a>([n])</p></td>
-<td><p>Read at most <code class="docutils literal notranslate"><span class="pre">n</span></code> objects, less if read hits EOF.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.io.fileio.FileIO.seek" title="libpysal.io.fileio.FileIO.seek"><code class="xref py py-obj docutils literal notranslate"><span class="pre">seek</span></code></a>(n)</p></td>
-<td><p>Seek the <cite>FileObj</cite> to the beginning of the <code class="docutils literal notranslate"><span class="pre">n</span></code>’th record.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.io.fileio.FileIO.tell" title="libpysal.io.fileio.FileIO.tell"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tell</span></code></a>()</p></td>
-<td><p>Return ID (or offset) of next object.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.io.fileio.FileIO.truncate" title="libpysal.io.fileio.FileIO.truncate"><code class="xref py py-obj docutils literal notranslate"><span class="pre">truncate</span></code></a>([size])</p></td>
-<td><p>Should be implemented by subclasses and redefine this doc string.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.io.fileio.FileIO.write" title="libpysal.io.fileio.FileIO.write"><code class="xref py py-obj docutils literal notranslate"><span class="pre">write</span></code></a>(obj)</p></td>
-<td><p>Must be implemented by subclasses that support ‘w’ subclasses Should increment <code class="docutils literal notranslate"><span class="pre">.pos</span></code>.</p></td>
-</tr>
-</tbody>
-</table>
-<p class="rubric">Attributes</p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.io.fileio.FileIO.by_row" title="libpysal.io.fileio.FileIO.by_row"><code class="xref py py-obj docutils literal notranslate"><span class="pre">by_row</span></code></a></p></td>
-<td><p></p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.io.fileio.FileIO.ids" title="libpysal.io.fileio.FileIO.ids"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ids</span></code></a></p></td>
-<td><p></p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.io.fileio.FileIO.rIds" title="libpysal.io.fileio.FileIO.rIds"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rIds</span></code></a></p></td>
-<td><p></p></td>
-</tr>
-</tbody>
-</table>
-<dl class="py method">
-<dt id="libpysal.io.fileio.FileIO.by_row">
-<em class="property">property </em><code class="sig-name descname">by_row</code><a class="headerlink" href="#libpysal.io.fileio.FileIO.by_row" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.io.fileio.FileIO.cast">
-<code class="sig-name descname">cast</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">key</span></em>, <em class="sig-param"><span class="n">typ</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/io/fileio.html#FileIO.cast"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.io.fileio.FileIO.cast" title="Permalink to this definition">¶</a></dt>
-<dd><p>Cast <code class="docutils literal notranslate"><span class="pre">key</span></code> as <code class="docutils literal notranslate"><span class="pre">typ</span></code>.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Raises</dt>
-<dd class="field-odd"><dl class="simple">
-<dt><a class="reference external" href="https://docs.python.org/3.8/library/exceptions.html#TypeError" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">TypeError</span></code></a></dt><dd><p>Raised when a cast object in not callable.</p>
-</dd>
-<dt><a class="reference external" href="https://docs.python.org/3.8/library/exceptions.html#KeyError" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">KeyError</span></code></a></dt><dd><p>Raised when a key is not present.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.io.fileio.FileIO.check">
-<em class="property">classmethod </em><code class="sig-name descname">check</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/io/fileio.html#FileIO.check"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.io.fileio.FileIO.check" title="Permalink to this definition">¶</a></dt>
-<dd><p>Prints the contents of the registry.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.io.fileio.FileIO.close">
-<code class="sig-name descname">close</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/io/fileio.html#FileIO.close"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.io.fileio.FileIO.close" title="Permalink to this definition">¶</a></dt>
-<dd><p>Subclasses should clean themselves up and then call this method.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.io.fileio.FileIO.flush">
-<code class="sig-name descname">flush</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/io/fileio.html#FileIO.flush"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.io.fileio.FileIO.flush" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="field-list simple">
-<dt class="field-odd">Raises</dt>
-<dd class="field-odd"><dl class="simple">
-<dt><a class="reference external" href="https://docs.python.org/3.8/library/exceptions.html#NotImplementedError" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">NotImplementedError</span></code></a></dt><dd></dd>
-</dl>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.io.fileio.FileIO.get">
-<code class="sig-name descname">get</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">n</span><span class="p">:</span> <span class="n"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#int" title="(in Python v3.8)">int</a></span></em><span class="sig-paren">)</span> &#x2192; <a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)">list</a><a class="reference internal" href="../_modules/libpysal/io/fileio.html#FileIO.get"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.io.fileio.FileIO.get" title="Permalink to this definition">¶</a></dt>
-<dd><p>Seeks the file to <code class="docutils literal notranslate"><span class="pre">n</span></code> and returns <code class="docutils literal notranslate"><span class="pre">n</span></code>. If <code class="docutils literal notranslate"><span class="pre">.ids</span></code> is set
-<code class="docutils literal notranslate"><span class="pre">n</span></code> should be an id, else, <code class="docutils literal notranslate"><span class="pre">n</span></code> should be an offset.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.io.fileio.FileIO.getType">
-<em class="property">static </em><code class="sig-name descname">getType</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">dataPath</span><span class="p">:</span> <span class="n"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)">str</a></span></em>, <em class="sig-param"><span class="n">mode</span><span class="p">:</span> <span class="n"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)">str</a></span></em>, <em class="sig-param"><span class="n">dataFormat</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span> &#x2192; <a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)">str</a><a class="reference internal" href="../_modules/libpysal/io/fileio.html#FileIO.getType"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.io.fileio.FileIO.getType" title="Permalink to this definition">¶</a></dt>
-<dd><p>Parse the <code class="docutils literal notranslate"><span class="pre">dataPath</span></code> and return the data type.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.io.fileio.FileIO.ids">
-<em class="property">property </em><code class="sig-name descname">ids</code><a class="headerlink" href="#libpysal.io.fileio.FileIO.ids" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.io.fileio.FileIO.open">
-<em class="property">classmethod </em><code class="sig-name descname">open</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">*</span><span class="n">args</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/io/fileio.html#FileIO.open"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.io.fileio.FileIO.open" title="Permalink to this definition">¶</a></dt>
-<dd><p>Alias for <code class="docutils literal notranslate"><span class="pre">FileIO()</span></code>.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.io.fileio.FileIO.rIds">
-<em class="property">property </em><code class="sig-name descname">rIds</code><a class="headerlink" href="#libpysal.io.fileio.FileIO.rIds" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.io.fileio.FileIO.read">
-<code class="sig-name descname">read</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">n</span><span class="o">=</span><span class="default_value">- 1</span></em><span class="sig-paren">)</span> &#x2192; Optional<span class="p">[</span><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)">list</a><span class="p">]</span><a class="reference internal" href="../_modules/libpysal/io/fileio.html#FileIO.read"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.io.fileio.FileIO.read" title="Permalink to this definition">¶</a></dt>
-<dd><p>Read at most <code class="docutils literal notranslate"><span class="pre">n</span></code> objects, less if read hits EOF.
-If size is negative or omitted read all objects until EOF.
-Returns <code class="docutils literal notranslate"><span class="pre">None</span></code> if EOF is reached before any objects.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Raises</dt>
-<dd class="field-odd"><dl class="simple">
-<dt><a class="reference external" href="https://docs.python.org/3.8/library/exceptions.html#StopIteration" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">StopIteration</span></code></a></dt><dd><p>Raised at the EOF.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.io.fileio.FileIO.seek">
-<code class="sig-name descname">seek</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">n</span><span class="p">:</span> <span class="n"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#int" title="(in Python v3.8)">int</a></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/io/fileio.html#FileIO.seek"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.io.fileio.FileIO.seek" title="Permalink to this definition">¶</a></dt>
-<dd><p>Seek the <cite>FileObj</cite> to the beginning of the <code class="docutils literal notranslate"><span class="pre">n</span></code>’th record.
-If IDs are set, seeks to the beginning of the record at ID, <code class="docutils literal notranslate"><span class="pre">n</span></code>.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.io.fileio.FileIO.tell">
-<code class="sig-name descname">tell</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; <a class="reference external" href="https://docs.python.org/3.8/library/functions.html#int" title="(in Python v3.8)">int</a><a class="reference internal" href="../_modules/libpysal/io/fileio.html#FileIO.tell"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.io.fileio.FileIO.tell" title="Permalink to this definition">¶</a></dt>
-<dd><p>Return ID (or offset) of next object.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.io.fileio.FileIO.truncate">
-<code class="sig-name descname">truncate</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">size</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/io/fileio.html#FileIO.truncate"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.io.fileio.FileIO.truncate" title="Permalink to this definition">¶</a></dt>
-<dd><p>Should be implemented by subclasses and redefine this doc string.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Raises</dt>
-<dd class="field-odd"><dl class="simple">
-<dt><a class="reference external" href="https://docs.python.org/3.8/library/exceptions.html#NotImplementedError" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">NotImplementedError</span></code></a></dt><dd></dd>
-</dl>
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-<dd><p>Must be implemented by subclasses that support ‘w’ subclasses
-Should increment <code class="docutils literal notranslate"><span class="pre">.pos</span></code>. Subclasses should also check if <code class="docutils literal notranslate"><span class="pre">obj</span></code>
-is an instance of type(list) and redefine this doc string.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Raises</dt>
-<dd class="field-odd"><dl class="simple">
-<dt><a class="reference external" href="https://docs.python.org/3.8/library/exceptions.html#NotImplementedError" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">NotImplementedError</span></code></a></dt><dd></dd>
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-<h1>libpysal.io.open<a class="headerlink" href="#libpysal-io-open" title="Permalink to this headline">¶</a></h1>
-<dl class="py attribute">
-<dt id="libpysal.io.open">
-<code class="sig-prename descclassname">libpysal.io.</code><code class="sig-name descname">open</code><a class="headerlink" href="#libpysal.io.open" title="Permalink to this definition">¶</a></dt>
-<dd><p>alias of <a class="reference internal" href="libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO" title="libpysal.io.fileio.FileIO"><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.io.fileio.FileIO</span></code></a></p>
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-<h1>libpysal.weights.DistanceBand<a class="headerlink" href="#libpysal-weights-distanceband" title="Permalink to this headline">¶</a></h1>
-<dl class="py class">
-<dt id="libpysal.weights.DistanceBand">
-<em class="property">class </em><code class="sig-prename descclassname">libpysal.weights.</code><code class="sig-name descname">DistanceBand</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">threshold</span></em>, <em class="sig-param"><span class="n">p</span><span class="o">=</span><span class="default_value">2</span></em>, <em class="sig-param"><span class="n">alpha</span><span class="o">=</span><span class="default_value">- 1.0</span></em>, <em class="sig-param"><span class="n">binary</span><span class="o">=</span><span class="default_value">True</span></em>, <em class="sig-param"><span class="n">ids</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">build_sp</span><span class="o">=</span><span class="default_value">True</span></em>, <em class="sig-param"><span class="n">silence_warnings</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">distance_metric</span><span class="o">=</span><span class="default_value">'euclidean'</span></em>, <em class="sig-param"><span class="n">radius</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/distance.html#DistanceBand"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.DistanceBand" title="Permalink to this definition">¶</a></dt>
-<dd><p>Spatial weights based on distance band.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>data</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.20)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a></span></dt><dd><p>(n,k) or KDTree where KDtree.data is array (n,k)
-n observations on k characteristics used to measure
-distances between the n objects</p>
-</dd>
-<dt><strong>threshold</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>distance band</p>
-</dd>
-<dt><strong>p</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>Minkowski p-norm distance metric parameter:
-1&lt;=p&lt;=infinity
-2: Euclidean distance
-1: Manhattan distance</p>
-</dd>
-<dt><strong>binary</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.8)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>If true w_{ij}=1 if d_{i,j}&lt;=threshold, otherwise w_{i,j}=0
-If false wij=dij^{alpha}</p>
-</dd>
-<dt><strong>alpha</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>distance decay parameter for weight (default -1.0)
-if alpha is positive the weights will not decline with
-distance. If binary is True, alpha is ignored</p>
-</dd>
-<dt><strong>ids</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>values to use for keys of the neighbors and weights dicts</p>
-</dd>
-<dt><strong>build_sp</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.8)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>True to build sparse distance matrix and false to build dense
-distance matrix; significant speed gains may be obtained
-dending on the sparsity of the of distance_matrix and
-threshold that is applied</p>
-</dd>
-<dt><strong>silent</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.8)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>By default libpysal will print a warning if the
-dataset contains any disconnected observations or
-islands. To silence this warning set this
-parameter to True.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Notes</p>
-<p>This was initially implemented running scipy 0.8.0dev (in epd 6.1).
-earlier versions of scipy (0.7.0) have a logic bug in scipy/sparse/dok.py
-so serge changed line 221 of that file on sal-dev to fix the logic bug.</p>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">libpysal</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">points</span><span class="o">=</span><span class="p">[(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="p">(</span><span class="mi">40</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">20</span><span class="p">),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">20</span><span class="p">),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">30</span><span class="p">)]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wcheck</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">W</span><span class="p">({</span><span class="mi">0</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="mi">1</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="mi">2</span><span class="p">:</span> <span class="p">[],</span> <span class="mi">3</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="mi">4</span><span class="p">:</span> <span class="p">[</span><span class="mi">5</span><span class="p">],</span> <span class="mi">5</span><span class="p">:</span> <span class="p">[</span><span class="mi">4</span><span class="p">]})</span>
-</pre></div>
-</div>
-<p>WARNING: there is one disconnected observation (no neighbors)
-Island id:  [2]
-&gt;&gt;&gt; w=libpysal.weights.DistanceBand(points,threshold=11.2)</p>
-<p>WARNING: there is one disconnected observation (no neighbors)
-Island id:  [2]
-&gt;&gt;&gt; libpysal.weights.util.neighbor_equality(w, wcheck)
-True
-&gt;&gt;&gt; w=libpysal.weights.DistanceBand(points,threshold=14.2)
-&gt;&gt;&gt; wcheck = libpysal.weights.W({0: [1, 3], 1: [0, 3, 4], 2: [4], 3: [1, 0], 4: [5, 2, 1], 5: [4]})
-&gt;&gt;&gt; libpysal.weights.util.neighbor_equality(w, wcheck)
-True</p>
-<p>inverse distance weights</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">=</span><span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">DistanceBand</span><span class="p">(</span><span class="n">points</span><span class="p">,</span><span class="n">threshold</span><span class="o">=</span><span class="mf">11.2</span><span class="p">,</span><span class="n">binary</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>WARNING: there is one disconnected observation (no neighbors)
-Island id:  [2]
-&gt;&gt;&gt; w.weights[0]
-[0.1, 0.08944271909999159]
-&gt;&gt;&gt; w.neighbors[0].tolist()
-[1, 3]</p>
-<p>gravity weights</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">=</span><span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">DistanceBand</span><span class="p">(</span><span class="n">points</span><span class="p">,</span><span class="n">threshold</span><span class="o">=</span><span class="mf">11.2</span><span class="p">,</span><span class="n">binary</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span><span class="n">alpha</span><span class="o">=-</span><span class="mf">2.</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>WARNING: there is one disconnected observation (no neighbors)
-Island id:  [2]
-&gt;&gt;&gt; w.weights[0]
-[0.01, 0.007999999999999998]</p>
-<dl class="field-list">
-<dt class="field-odd">Attributes</dt>
-<dd class="field-odd"><dl>
-<dt><strong>weights</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#dict" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">dict</span></code></a></span></dt><dd><p>of neighbor weights keyed by observation id</p>
-</dd>
-<dt><strong>neighbors</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#dict" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">dict</span></code></a></span></dt><dd><p>of neighbors keyed by observation id</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<dl class="py method">
-<dt id="libpysal.weights.DistanceBand.__init__">
-<code class="sig-name descname">__init__</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">threshold</span></em>, <em class="sig-param"><span class="n">p</span><span class="o">=</span><span class="default_value">2</span></em>, <em class="sig-param"><span class="n">alpha</span><span class="o">=</span><span class="default_value">- 1.0</span></em>, <em class="sig-param"><span class="n">binary</span><span class="o">=</span><span class="default_value">True</span></em>, <em class="sig-param"><span class="n">ids</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">build_sp</span><span class="o">=</span><span class="default_value">True</span></em>, <em class="sig-param"><span class="n">silence_warnings</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">distance_metric</span><span class="o">=</span><span class="default_value">'euclidean'</span></em>, <em class="sig-param"><span class="n">radius</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/distance.html#DistanceBand.__init__"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.DistanceBand.__init__" title="Permalink to this definition">¶</a></dt>
-<dd><p>Casting to floats is a work around for a bug in scipy.spatial.
-See detail in pysal issue #126.</p>
-</dd></dl>
-
-<p class="rubric">Methods</p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.DistanceBand.__init__" title="libpysal.weights.DistanceBand.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(data, threshold[, p, alpha, …])</p></td>
-<td><p>Casting to floats is a work around for a bug in scipy.spatial.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">asymmetry</span></code>([intrinsic])</p></td>
-<td><p>Asymmetry check.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_WSP</span></code>(WSP[, silence_warnings])</p></td>
-<td><p></p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_adjlist</span></code>(adjlist[, focal_col, …])</p></td>
-<td><p>Return an adjacency list representation of a weights object.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.DistanceBand.from_array" title="libpysal.weights.DistanceBand.from_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_array</span></code></a>(array, threshold, **kwargs)</p></td>
-<td><p>Construct a DistanceBand weights from an array.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.DistanceBand.from_dataframe" title="libpysal.weights.DistanceBand.from_dataframe"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_dataframe</span></code></a>(df, threshold[, geom_col, ids])</p></td>
-<td><p>Make DistanceBand weights from a dataframe.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_file</span></code>([path, format])</p></td>
-<td><p>Read a weights file into a W object.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_networkx</span></code>(graph[, weight_col])</p></td>
-<td><p>Convert a <code class="docutils literal notranslate"><span class="pre">networkx</span></code> graph to a PySAL <code class="docutils literal notranslate"><span class="pre">W</span></code> object.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.DistanceBand.from_shapefile" title="libpysal.weights.DistanceBand.from_shapefile"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_shapefile</span></code></a>(filepath, threshold[, idVariable])</p></td>
-<td><p>Distance-band based weights from shapefile</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">full</span></code>()</p></td>
-<td><p>Generate a full <code class="docutils literal notranslate"><span class="pre">numpy.ndarray</span></code>.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_transform</span></code>()</p></td>
-<td><p>Getter for transform property.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot</span></code>(gdf[, indexed_on, ax, color, node_kws, …])</p></td>
-<td><p>Plot spatial weights objects.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">remap_ids</span></code>(new_ids)</p></td>
-<td><p>In place modification throughout <code class="docutils literal notranslate"><span class="pre">W</span></code> of id values from <code class="docutils literal notranslate"><span class="pre">w.id_order</span></code> to <code class="docutils literal notranslate"><span class="pre">new_ids</span></code> in all.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_shapefile</span></code>(shapefile[, idVariable, full])</p></td>
-<td><p>Adding metadata for writing headers of <code class="docutils literal notranslate"><span class="pre">.gal</span></code> and <code class="docutils literal notranslate"><span class="pre">.gwt</span></code> files.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_transform</span></code>([value])</p></td>
-<td><p>Transformations of weights.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">symmetrize</span></code>([inplace])</p></td>
-<td><p>Construct a symmetric KNN weight.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_WSP</span></code>()</p></td>
-<td><p>Generate a <code class="docutils literal notranslate"><span class="pre">WSP</span></code> object.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_adjlist</span></code>([remove_symmetric, focal_col, …])</p></td>
-<td><p>Compute an adjacency list representation of a weights object.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_file</span></code>([path, format])</p></td>
-<td><p>Write a weights to a file.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_networkx</span></code>()</p></td>
-<td><p>Convert a weights object to a <code class="docutils literal notranslate"><span class="pre">networkx</span></code> graph.</p></td>
-</tr>
-</tbody>
-</table>
-<p class="rubric">Attributes</p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">asymmetries</span></code></p></td>
-<td><p>List of id pairs with asymmetric weights.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cardinalities</span></code></p></td>
-<td><p>Number of neighbors for each observation.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">component_labels</span></code></p></td>
-<td><p>Store the graph component in which each observation falls.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagW2</span></code></p></td>
-<td><p>Diagonal of <span class="math notranslate nohighlight">\(WW\)</span>.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagWtW</span></code></p></td>
-<td><p>Diagonal of <span class="math notranslate nohighlight">\(W^{'}W\)</span>.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagWtW_WW</span></code></p></td>
-<td><p>Diagonal of <span class="math notranslate nohighlight">\(W^{'}W + WW\)</span>.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">histogram</span></code></p></td>
-<td><p>Cardinality histogram as a dictionary where key is the id and value is the number of neighbors for that unit.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">id2i</span></code></p></td>
-<td><p>Dictionary where the key is an ID and the value is that ID’s index in <code class="docutils literal notranslate"><span class="pre">W.id_order</span></code>.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">id_order</span></code></p></td>
-<td><p>Returns the ids for the observations in the order in which they would be encountered if iterating over the weights.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">id_order_set</span></code></p></td>
-<td><p>Returns <code class="docutils literal notranslate"><span class="pre">True</span></code> if user has set <code class="docutils literal notranslate"><span class="pre">id_order</span></code>, <code class="docutils literal notranslate"><span class="pre">False</span></code> if not.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">islands</span></code></p></td>
-<td><p>List of ids without any neighbors.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">max_neighbors</span></code></p></td>
-<td><p>Largest number of neighbors.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mean_neighbors</span></code></p></td>
-<td><p>Average number of neighbors.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">min_neighbors</span></code></p></td>
-<td><p>Minimum number of neighbors.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">n</span></code></p></td>
-<td><p>Number of units.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">n_components</span></code></p></td>
-<td><p>Store whether the adjacency matrix is fully connected.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">neighbor_offsets</span></code></p></td>
-<td><p>Given the current <code class="docutils literal notranslate"><span class="pre">id_order</span></code>, <code class="docutils literal notranslate"><span class="pre">neighbor_offsets[id]</span></code> is the offsets of the id’s neighbors in <code class="docutils literal notranslate"><span class="pre">id_order</span></code>.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">nonzero</span></code></p></td>
-<td><p>Number of nonzero weights.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">pct_nonzero</span></code></p></td>
-<td><p>Percentage of nonzero weights.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s0</span></code></p></td>
-<td><p><code class="docutils literal notranslate"><span class="pre">s0</span></code> is defined as</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s1</span></code></p></td>
-<td><p><code class="docutils literal notranslate"><span class="pre">s1</span></code> is defined as</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s2</span></code></p></td>
-<td><p><code class="docutils literal notranslate"><span class="pre">s2</span></code> is defined as</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s2array</span></code></p></td>
-<td><p>Individual elements comprising <code class="docutils literal notranslate"><span class="pre">s2</span></code>.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">sd</span></code></p></td>
-<td><p>Standard deviation of number of neighbors.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">sparse</span></code></p></td>
-<td><p>Sparse matrix object.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code></p></td>
-<td><p>Getter for transform property.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcW2</span></code></p></td>
-<td><p>Trace of <span class="math notranslate nohighlight">\(WW\)</span>.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcWtW</span></code></p></td>
-<td><p>Trace of <span class="math notranslate nohighlight">\(W^{'}W\)</span>.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcWtW_WW</span></code></p></td>
-<td><p>Trace of <span class="math notranslate nohighlight">\(W^{'}W + WW\)</span>.</p></td>
-</tr>
-</tbody>
-</table>
-<dl class="py method">
-<dt id="libpysal.weights.DistanceBand.from_array">
-<em class="property">classmethod </em><code class="sig-name descname">from_array</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">array</span></em>, <em class="sig-param"><span class="n">threshold</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/distance.html#DistanceBand.from_array"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.DistanceBand.from_array" title="Permalink to this definition">¶</a></dt>
-<dd><p>Construct a DistanceBand weights from an array. Supports all the same options
-as <a class="reference internal" href="#libpysal.weights.DistanceBand" title="libpysal.weights.DistanceBand"><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.DistanceBand</span></code></a></p>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.DistanceBand.from_dataframe">
-<em class="property">classmethod </em><code class="sig-name descname">from_dataframe</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">df</span></em>, <em class="sig-param"><span class="n">threshold</span></em>, <em class="sig-param"><span class="n">geom_col</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">ids</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/distance.html#DistanceBand.from_dataframe"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.DistanceBand.from_dataframe" title="Permalink to this definition">¶</a></dt>
-<dd><p>Make DistanceBand weights from a dataframe.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>df</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pandas.dataframe</span></code></span></dt><dd><p>a dataframe with a geometry column that can be used to
-construct a W object</p>
-</dd>
-<dt><strong>geom_col</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>the name of the column in <cite>df</cite> that contains the
-geometries. Defaults to active geometry column.</p>
-</dd>
-<dt><strong>ids</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a> or <a class="reference external" href="https://docs.python.org/3.8/glossary.html#term-iterable" title="(in Python v3.8)"><span>iterable</span></a></span></dt><dd><p>if string, the column name of the indices from the dataframe
-if iterable, a list of ids to use for the W
-if None, df.index is used.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.DistanceBand.from_shapefile">
-<em class="property">classmethod </em><code class="sig-name descname">from_shapefile</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">filepath</span></em>, <em class="sig-param"><span class="n">threshold</span></em>, <em class="sig-param"><span class="n">idVariable</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/distance.html#DistanceBand.from_shapefile"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.DistanceBand.from_shapefile" title="Permalink to this definition">¶</a></dt>
-<dd><p>Distance-band based weights from shapefile</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>shapefile</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>shapefile name with shp suffix</p>
-</dd>
-<dt><strong>idVariable</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>name of column in shapefile’s DBF to use for ids</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl class="simple">
-<dt><a class="reference internal" href="libpysal.weights.Kernel.html#libpysal.weights.Kernel" title="libpysal.weights.Kernel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Kernel</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">Weights</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">Object</span></code></dt><dd></dd>
-</dl>
-</dd>
-</dl>
-</dd></dl>
-
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-  <div class="section" id="libpysal-weights-knn">
-<h1>libpysal.weights.KNN<a class="headerlink" href="#libpysal-weights-knn" title="Permalink to this headline">¶</a></h1>
-<dl class="py class">
-<dt id="libpysal.weights.KNN">
-<em class="property">class </em><code class="sig-prename descclassname">libpysal.weights.</code><code class="sig-name descname">KNN</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">k</span><span class="o">=</span><span class="default_value">2</span></em>, <em class="sig-param"><span class="n">p</span><span class="o">=</span><span class="default_value">2</span></em>, <em class="sig-param"><span class="n">ids</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">radius</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">distance_metric</span><span class="o">=</span><span class="default_value">'euclidean'</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/distance.html#KNN"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.KNN" title="Permalink to this definition">¶</a></dt>
-<dd><p>Creates nearest neighbor weights matrix based on k nearest
-neighbors.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>kdtree</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#object" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">object</span></code></a></span></dt><dd><p>PySAL KDTree or ArcKDTree where KDtree.data is array (n,k)
-n observations on k characteristics used to measure
-distances between the n objects</p>
-</dd>
-<dt><strong>k</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#int" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>number of nearest neighbors</p>
-</dd>
-<dt><strong>p</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>Minkowski p-norm distance metric parameter:
-1&lt;=p&lt;=infinity
-2: Euclidean distance
-1: Manhattan distance
-Ignored if the KDTree is an ArcKDTree</p>
-</dd>
-<dt><strong>ids</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>identifiers to attach to each observation</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>instance
-Weights object with binary weights</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<div class="admonition seealso">
-<p class="admonition-title">See also</p>
-<dl class="simple">
-<dt><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.weights.W</span></code></dt><dd></dd>
-</dl>
-</div>
-<p class="rubric">Notes</p>
-<p>Ties between neighbors of equal distance are arbitrarily broken.</p>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">libpysal</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">points</span> <span class="o">=</span> <span class="p">[(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="p">(</span><span class="mi">40</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">20</span><span class="p">),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">20</span><span class="p">),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">30</span><span class="p">)]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">kd</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">cg</span><span class="o">.</span><span class="n">KDTree</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">points</span><span class="p">))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wnn2</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">KNN</span><span class="p">(</span><span class="n">kd</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">]</span> <span class="o">==</span> <span class="n">wnn2</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<span class="go">True</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wnn2</span> <span class="o">=</span> <span class="n">KNN</span><span class="p">(</span><span class="n">kd</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wnn2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<span class="go">{1: 1.0, 3: 1.0}</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wnn2</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-<span class="go">{0: 1.0, 3: 1.0}</span>
-</pre></div>
-</div>
-<p>now with 1 rather than 0 offset</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">wnn2</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">KNN</span><span class="p">(</span><span class="n">kd</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">7</span><span class="p">))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wnn2</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-<span class="go">{2: 1.0, 4: 1.0}</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wnn2</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
-<span class="go">{1: 1.0, 4: 1.0}</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="mi">0</span> <span class="ow">in</span> <span class="n">wnn2</span><span class="o">.</span><span class="n">neighbors</span>
-<span class="go">False</span>
-</pre></div>
-</div>
-<dl class="py method">
-<dt id="libpysal.weights.KNN.__init__">
-<code class="sig-name descname">__init__</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">k</span><span class="o">=</span><span class="default_value">2</span></em>, <em class="sig-param"><span class="n">p</span><span class="o">=</span><span class="default_value">2</span></em>, <em class="sig-param"><span class="n">ids</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">radius</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">distance_metric</span><span class="o">=</span><span class="default_value">'euclidean'</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/distance.html#KNN.__init__"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.KNN.__init__" title="Permalink to this definition">¶</a></dt>
-<dd><p>Initialize self.  See help(type(self)) for accurate signature.</p>
-</dd></dl>
-
-<p class="rubric">Methods</p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.KNN.__init__" title="libpysal.weights.KNN.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(data[, k, p, ids, radius, …])</p></td>
-<td><p>Initialize self.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">asymmetry</span></code>([intrinsic])</p></td>
-<td><p>Asymmetry check.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_WSP</span></code>(WSP[, silence_warnings])</p></td>
-<td><p></p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_adjlist</span></code>(adjlist[, focal_col, …])</p></td>
-<td><p>Return an adjacency list representation of a weights object.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.KNN.from_array" title="libpysal.weights.KNN.from_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_array</span></code></a>(array, *args, **kwargs)</p></td>
-<td><p>Creates nearest neighbor weights matrix based on k nearest neighbors.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.KNN.from_dataframe" title="libpysal.weights.KNN.from_dataframe"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_dataframe</span></code></a>(df[, geom_col, ids])</p></td>
-<td><p>Make KNN weights from a dataframe.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_file</span></code>([path, format])</p></td>
-<td><p>Read a weights file into a W object.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_networkx</span></code>(graph[, weight_col])</p></td>
-<td><p>Convert a <code class="docutils literal notranslate"><span class="pre">networkx</span></code> graph to a PySAL <code class="docutils literal notranslate"><span class="pre">W</span></code> object.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.KNN.from_shapefile" title="libpysal.weights.KNN.from_shapefile"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_shapefile</span></code></a>(filepath, *args, **kwargs)</p></td>
-<td><p>Nearest neighbor weights from a shapefile.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">full</span></code>()</p></td>
-<td><p>Generate a full <code class="docutils literal notranslate"><span class="pre">numpy.ndarray</span></code>.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_transform</span></code>()</p></td>
-<td><p>Getter for transform property.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot</span></code>(gdf[, indexed_on, ax, color, node_kws, …])</p></td>
-<td><p>Plot spatial weights objects.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">remap_ids</span></code>(new_ids)</p></td>
-<td><p>In place modification throughout <code class="docutils literal notranslate"><span class="pre">W</span></code> of id values from <code class="docutils literal notranslate"><span class="pre">w.id_order</span></code> to <code class="docutils literal notranslate"><span class="pre">new_ids</span></code> in all.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.KNN.reweight" title="libpysal.weights.KNN.reweight"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reweight</span></code></a>([k, p, new_data, new_ids, inplace])</p></td>
-<td><p>Redo K-Nearest Neighbor weights construction using given parameters</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_shapefile</span></code>(shapefile[, idVariable, full])</p></td>
-<td><p>Adding metadata for writing headers of <code class="docutils literal notranslate"><span class="pre">.gal</span></code> and <code class="docutils literal notranslate"><span class="pre">.gwt</span></code> files.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_transform</span></code>([value])</p></td>
-<td><p>Transformations of weights.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">symmetrize</span></code>([inplace])</p></td>
-<td><p>Construct a symmetric KNN weight.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_WSP</span></code>()</p></td>
-<td><p>Generate a <code class="docutils literal notranslate"><span class="pre">WSP</span></code> object.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_adjlist</span></code>([remove_symmetric, focal_col, …])</p></td>
-<td><p>Compute an adjacency list representation of a weights object.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_file</span></code>([path, format])</p></td>
-<td><p>Write a weights to a file.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_networkx</span></code>()</p></td>
-<td><p>Convert a weights object to a <code class="docutils literal notranslate"><span class="pre">networkx</span></code> graph.</p></td>
-</tr>
-</tbody>
-</table>
-<p class="rubric">Attributes</p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">asymmetries</span></code></p></td>
-<td><p>List of id pairs with asymmetric weights.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cardinalities</span></code></p></td>
-<td><p>Number of neighbors for each observation.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">component_labels</span></code></p></td>
-<td><p>Store the graph component in which each observation falls.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagW2</span></code></p></td>
-<td><p>Diagonal of <span class="math notranslate nohighlight">\(WW\)</span>.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagWtW</span></code></p></td>
-<td><p>Diagonal of <span class="math notranslate nohighlight">\(W^{'}W\)</span>.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagWtW_WW</span></code></p></td>
-<td><p>Diagonal of <span class="math notranslate nohighlight">\(W^{'}W + WW\)</span>.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">histogram</span></code></p></td>
-<td><p>Cardinality histogram as a dictionary where key is the id and value is the number of neighbors for that unit.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">id2i</span></code></p></td>
-<td><p>Dictionary where the key is an ID and the value is that ID’s index in <code class="docutils literal notranslate"><span class="pre">W.id_order</span></code>.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">id_order</span></code></p></td>
-<td><p>Returns the ids for the observations in the order in which they would be encountered if iterating over the weights.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">id_order_set</span></code></p></td>
-<td><p>Returns <code class="docutils literal notranslate"><span class="pre">True</span></code> if user has set <code class="docutils literal notranslate"><span class="pre">id_order</span></code>, <code class="docutils literal notranslate"><span class="pre">False</span></code> if not.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">islands</span></code></p></td>
-<td><p>List of ids without any neighbors.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">max_neighbors</span></code></p></td>
-<td><p>Largest number of neighbors.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mean_neighbors</span></code></p></td>
-<td><p>Average number of neighbors.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">min_neighbors</span></code></p></td>
-<td><p>Minimum number of neighbors.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">n</span></code></p></td>
-<td><p>Number of units.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">n_components</span></code></p></td>
-<td><p>Store whether the adjacency matrix is fully connected.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">neighbor_offsets</span></code></p></td>
-<td><p>Given the current <code class="docutils literal notranslate"><span class="pre">id_order</span></code>, <code class="docutils literal notranslate"><span class="pre">neighbor_offsets[id]</span></code> is the offsets of the id’s neighbors in <code class="docutils literal notranslate"><span class="pre">id_order</span></code>.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">nonzero</span></code></p></td>
-<td><p>Number of nonzero weights.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">pct_nonzero</span></code></p></td>
-<td><p>Percentage of nonzero weights.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s0</span></code></p></td>
-<td><p><code class="docutils literal notranslate"><span class="pre">s0</span></code> is defined as</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s1</span></code></p></td>
-<td><p><code class="docutils literal notranslate"><span class="pre">s1</span></code> is defined as</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s2</span></code></p></td>
-<td><p><code class="docutils literal notranslate"><span class="pre">s2</span></code> is defined as</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s2array</span></code></p></td>
-<td><p>Individual elements comprising <code class="docutils literal notranslate"><span class="pre">s2</span></code>.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">sd</span></code></p></td>
-<td><p>Standard deviation of number of neighbors.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">sparse</span></code></p></td>
-<td><p>Sparse matrix object.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code></p></td>
-<td><p>Getter for transform property.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcW2</span></code></p></td>
-<td><p>Trace of <span class="math notranslate nohighlight">\(WW\)</span>.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcWtW</span></code></p></td>
-<td><p>Trace of <span class="math notranslate nohighlight">\(W^{'}W\)</span>.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcWtW_WW</span></code></p></td>
-<td><p>Trace of <span class="math notranslate nohighlight">\(W^{'}W + WW\)</span>.</p></td>
-</tr>
-</tbody>
-</table>
-<dl class="py method">
-<dt id="libpysal.weights.KNN.from_array">
-<em class="property">classmethod </em><code class="sig-name descname">from_array</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">array</span></em>, <em class="sig-param"><span class="o">*</span><span class="n">args</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/distance.html#KNN.from_array"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.KNN.from_array" title="Permalink to this definition">¶</a></dt>
-<dd><p>Creates nearest neighbor weights matrix based on k nearest
-neighbors.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>array</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.20)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">np.ndarray</span></code></a></span></dt><dd><p>(n, k) array representing n observations on
-k characteristics used to measure distances
-between the n objects</p>
-</dd>
-<dt><strong>**kwargs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/keyword.html#module-keyword" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">see</span></code> <a class="reference internal" href="libpysal.weights.Rook.html#libpysal.weights.Rook" title="libpysal.weights.Rook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Rook</span></code></a></span></dt><dd></dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>instance
-Weights object with binary weights</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<div class="admonition seealso">
-<p class="admonition-title">See also</p>
-<dl class="simple">
-<dt><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.weights.W</span></code></dt><dd></dd>
-</dl>
-</div>
-<p class="rubric">Notes</p>
-<p>Ties between neighbors of equal distance are arbitrarily broken.</p>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">KNN</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">points</span> <span class="o">=</span> <span class="p">[(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="p">(</span><span class="mi">40</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">20</span><span class="p">),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">20</span><span class="p">),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">30</span><span class="p">)]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wnn2</span> <span class="o">=</span> <span class="n">KNN</span><span class="o">.</span><span class="n">from_array</span><span class="p">(</span><span class="n">points</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">]</span> <span class="o">==</span> <span class="n">wnn2</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<span class="go">True</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wnn2</span> <span class="o">=</span> <span class="n">KNN</span><span class="o">.</span><span class="n">from_array</span><span class="p">(</span><span class="n">points</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wnn2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<span class="go">{1: 1.0, 3: 1.0}</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wnn2</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-<span class="go">{0: 1.0, 3: 1.0}</span>
-</pre></div>
-</div>
-<p>now with 1 rather than 0 offset</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">wnn2</span> <span class="o">=</span> <span class="n">KNN</span><span class="o">.</span><span class="n">from_array</span><span class="p">(</span><span class="n">points</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">7</span><span class="p">))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wnn2</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-<span class="go">{2: 1.0, 4: 1.0}</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wnn2</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
-<span class="go">{1: 1.0, 4: 1.0}</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="mi">0</span> <span class="ow">in</span> <span class="n">wnn2</span><span class="o">.</span><span class="n">neighbors</span>
-<span class="go">False</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.KNN.from_dataframe">
-<em class="property">classmethod </em><code class="sig-name descname">from_dataframe</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">df</span></em>, <em class="sig-param"><span class="n">geom_col</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">ids</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="o">*</span><span class="n">args</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/distance.html#KNN.from_dataframe"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.KNN.from_dataframe" title="Permalink to this definition">¶</a></dt>
-<dd><p>Make KNN weights from a dataframe.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>df</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pandas.dataframe</span></code></span></dt><dd><p>a dataframe with a geometry column that can be used to
-construct a W object</p>
-</dd>
-<dt><strong>geom_col</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>the name of the column in <cite>df</cite> that contains the
-geometries. Defaults to active geometry column.</p>
-</dd>
-<dt><strong>ids</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a> or <a class="reference external" href="https://docs.python.org/3.8/glossary.html#term-iterable" title="(in Python v3.8)"><span>iterable</span></a></span></dt><dd><p>if string, the column name of the indices from the dataframe
-if iterable, a list of ids to use for the W
-if None, df.index is used.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<div class="admonition seealso">
-<p class="admonition-title">See also</p>
-<dl class="simple">
-<dt><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.weights.W</span></code></dt><dd></dd>
-</dl>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.KNN.from_shapefile">
-<em class="property">classmethod </em><code class="sig-name descname">from_shapefile</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">filepath</span></em>, <em class="sig-param"><span class="o">*</span><span class="n">args</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/distance.html#KNN.from_shapefile"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.KNN.from_shapefile" title="Permalink to this definition">¶</a></dt>
-<dd><p>Nearest neighbor weights from a shapefile.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>data</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>shapefile containing attribute data.</p>
-</dd>
-<dt><strong>k</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#int" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>number of nearest neighbors</p>
-</dd>
-<dt><strong>p</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>Minkowski p-norm distance metric parameter:
-1&lt;=p&lt;=infinity
-2: Euclidean distance
-1: Manhattan distance</p>
-</dd>
-<dt><strong>ids</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>identifiers to attach to each observation</p>
-</dd>
-<dt><strong>radius</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>If supplied arc_distances will be calculated
-based on the given radius. p will be ignored.</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="#libpysal.weights.KNN" title="libpysal.weights.KNN"><code class="xref py py-obj docutils literal notranslate"><span class="pre">KNN</span></code></a></span></dt><dd><p>instance; Weights object with binary weights.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<div class="admonition seealso">
-<p class="admonition-title">See also</p>
-<dl class="simple">
-<dt><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.weights.W</span></code></dt><dd></dd>
-</dl>
-</div>
-<p class="rubric">Notes</p>
-<p>Ties between neighbors of equal distance are arbitrarily broken.</p>
-<p class="rubric">Examples</p>
-<p>Polygon shapefile
-&gt;&gt;&gt; import libpysal
-&gt;&gt;&gt; from libpysal.weights import KNN
-&gt;&gt;&gt; wc=KNN.from_shapefile(libpysal.examples.get_path(“columbus.shp”))
-&gt;&gt;&gt; “%.4f”%wc.pct_nonzero
-‘4.0816’
-&gt;&gt;&gt; set([2,1]) == set(wc.neighbors[0])
-True
-&gt;&gt;&gt; wc3=KNN.from_shapefile(libpysal.examples.get_path(“columbus.shp”),k=3)
-&gt;&gt;&gt; set(wc3.neighbors[0]) == set([2,1,3])
-True
-&gt;&gt;&gt; set(wc3.neighbors[2]) == set([4,3,0])
-True</p>
-<p>Point shapefile</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">=</span><span class="n">KNN</span><span class="o">.</span><span class="n">from_shapefile</span><span class="p">(</span><span class="n">libpysal</span><span class="o">.</span><span class="n">examples</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="s2">&quot;juvenile.shp&quot;</span><span class="p">))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">pct_nonzero</span>
-<span class="go">1.1904761904761905</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w1</span><span class="o">=</span><span class="n">KNN</span><span class="o">.</span><span class="n">from_shapefile</span><span class="p">(</span><span class="n">libpysal</span><span class="o">.</span><span class="n">examples</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="s2">&quot;juvenile.shp&quot;</span><span class="p">),</span><span class="n">k</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="s2">&quot;</span><span class="si">%.3f</span><span class="s2">&quot;</span><span class="o">%</span><span class="n">w1</span><span class="o">.</span><span class="n">pct_nonzero</span>
-<span class="go">&#39;0.595&#39;</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.KNN.reweight">
-<code class="sig-name descname">reweight</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">k</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">p</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">new_data</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">new_ids</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">inplace</span><span class="o">=</span><span class="default_value">True</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/distance.html#KNN.reweight"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.KNN.reweight" title="Permalink to this definition">¶</a></dt>
-<dd><p>Redo K-Nearest Neighbor weights construction using given parameters</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>new_data</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.20)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">np.ndarray</span></code></a></span></dt><dd><p>an array containing additional data to use in the KNN
-weight</p>
-</dd>
-<dt><strong>new_ids</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>a list aligned with new_data that provides the ids for
-each new observation</p>
-</dd>
-<dt><strong>inplace</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.8)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>a flag denoting whether to modify the KNN object
-in place or to return a new KNN object</p>
-</dd>
-<dt><strong>k</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#int" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>number of nearest neighbors</p>
-</dd>
-<dt><strong>p</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>Minkowski p-norm distance metric parameter:
-1&lt;=p&lt;=infinity
-2: Euclidean distance
-1: Manhattan distance
-Ignored if the KDTree is an ArcKDTree</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl class="simple">
-<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">A</span></code> <a class="reference external" href="https://docs.python.org/3.8/library/copy.html#module-copy" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">copy</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">of</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <a class="reference external" href="https://docs.python.org/3.8/library/functions.html#object" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">object</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">using</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">new</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">parameterization</span></code>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">or</span></code> <a class="reference external" href="https://docs.python.org/3.8/library/constants.html#None" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">if</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code></dt><dd></dd>
-<dt><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#object" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">object</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">is</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">reweighted</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">in</span></code> place.</dt><dd></dd>
-</dl>
-</dd>
-</dl>
-</dd></dl>
-
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-  <div class="section" id="libpysal-weights-kernel">
-<h1>libpysal.weights.Kernel<a class="headerlink" href="#libpysal-weights-kernel" title="Permalink to this headline">¶</a></h1>
-<dl class="py class">
-<dt id="libpysal.weights.Kernel">
-<em class="property">class </em><code class="sig-prename descclassname">libpysal.weights.</code><code class="sig-name descname">Kernel</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">bandwidth</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">fixed</span><span class="o">=</span><span class="default_value">True</span></em>, <em class="sig-param"><span class="n">k</span><span class="o">=</span><span class="default_value">2</span></em>, <em class="sig-param"><span class="n">function</span><span class="o">=</span><span class="default_value">'triangular'</span></em>, <em class="sig-param"><span class="n">eps</span><span class="o">=</span><span class="default_value">1.0000001</span></em>, <em class="sig-param"><span class="n">ids</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">diagonal</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">distance_metric</span><span class="o">=</span><span class="default_value">'euclidean'</span></em>, <em class="sig-param"><span class="n">radius</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/distance.html#Kernel"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.Kernel" title="Permalink to this definition">¶</a></dt>
-<dd><p>Spatial weights based on kernel functions.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>data</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.20)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a></span></dt><dd><p>(n,k) or KDTree where KDtree.data is array (n,k)
-n observations on k characteristics used to measure
-distances between the n objects</p>
-</dd>
-<dt><strong>bandwidth</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>or array-like (optional)
-the bandwidth <span class="math notranslate nohighlight">\(h_i\)</span> for the kernel.</p>
-</dd>
-<dt><strong>fixed</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">binary</span></code></span></dt><dd><p>If true then <span class="math notranslate nohighlight">\(h_i=h \forall i\)</span>. If false then
-bandwidth is adaptive across observations.</p>
-</dd>
-<dt><strong>k</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#int" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>the number of nearest neighbors to use for determining
-bandwidth. For fixed bandwidth, <span class="math notranslate nohighlight">\(h_i=max(dknn) \forall i\)</span>
-where <span class="math notranslate nohighlight">\(dknn\)</span> is a vector of k-nearest neighbor
-distances (the distance to the kth nearest neighbor for each
-observation).  For adaptive bandwidths, <span class="math notranslate nohighlight">\(h_i=dknn_i\)</span></p>
-</dd>
-<dt><strong>diagonal</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.8)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>If true, set diagonal weights = 1.0, if false (default),
-diagonals weights are set to value according to kernel
-function.</p>
-</dd>
-<dt><strong>function</strong><span class="classifier">{‘triangular’,’uniform’,’quadratic’,’quartic’,’gaussian’}</span></dt><dd><p>kernel function defined as follows with</p>
-<div class="math notranslate nohighlight">
-\[z_{i,j} = d_{i,j}/h_i\]</div>
-<p>triangular</p>
-<div class="math notranslate nohighlight">
-\[K(z) = (1 - |z|) \ if |z| \le 1\]</div>
-<p>uniform</p>
-<div class="math notranslate nohighlight">
-\[K(z) = 1/2 \ if |z| \le 1\]</div>
-<p>quadratic</p>
-<div class="math notranslate nohighlight">
-\[K(z) = (3/4)(1-z^2) \ if |z| \le 1\]</div>
-<p>quartic</p>
-<div class="math notranslate nohighlight">
-\[K(z) = (15/16)(1-z^2)^2 \ if |z| \le 1\]</div>
-<p>gaussian</p>
-<div class="math notranslate nohighlight">
-\[K(z) = (2\pi)^{(-1/2)} exp(-z^2 / 2)\]</div>
-</dd>
-<dt><strong>eps</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>adjustment to ensure knn distance range is closed on the
-knnth observations</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">Kernel</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">points</span><span class="o">=</span><span class="p">[(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="p">(</span><span class="mi">40</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">20</span><span class="p">),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">20</span><span class="p">),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">30</span><span class="p">)]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">kw</span><span class="o">=</span><span class="n">Kernel</span><span class="p">(</span><span class="n">points</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">kw</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<span class="go">[1.0, 0.500000049999995, 0.4409830615267465]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">kw</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<span class="go">[0, 1, 3]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">kw</span><span class="o">.</span><span class="n">bandwidth</span>
-<span class="go">array([[20.000002],</span>
-<span class="go">       [20.000002],</span>
-<span class="go">       [20.000002],</span>
-<span class="go">       [20.000002],</span>
-<span class="go">       [20.000002],</span>
-<span class="go">       [20.000002]])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">kw15</span><span class="o">=</span><span class="n">Kernel</span><span class="p">(</span><span class="n">points</span><span class="p">,</span><span class="n">bandwidth</span><span class="o">=</span><span class="mf">15.0</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">kw15</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<span class="go">{0: 1.0, 1: 0.33333333333333337, 3: 0.2546440075000701}</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">kw15</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<span class="go">[0, 1, 3]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">kw15</span><span class="o">.</span><span class="n">bandwidth</span>
-<span class="go">array([[15.],</span>
-<span class="go">       [15.],</span>
-<span class="go">       [15.],</span>
-<span class="go">       [15.],</span>
-<span class="go">       [15.],</span>
-<span class="go">       [15.]])</span>
-</pre></div>
-</div>
-<p>Adaptive bandwidths user specified</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">bw</span><span class="o">=</span><span class="p">[</span><span class="mf">25.0</span><span class="p">,</span><span class="mf">15.0</span><span class="p">,</span><span class="mf">25.0</span><span class="p">,</span><span class="mf">16.0</span><span class="p">,</span><span class="mf">14.5</span><span class="p">,</span><span class="mf">25.0</span><span class="p">]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">kwa</span><span class="o">=</span><span class="n">Kernel</span><span class="p">(</span><span class="n">points</span><span class="p">,</span><span class="n">bandwidth</span><span class="o">=</span><span class="n">bw</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">kwa</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<span class="go">[1.0, 0.6, 0.552786404500042, 0.10557280900008403]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">kwa</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<span class="go">[0, 1, 3, 4]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">kwa</span><span class="o">.</span><span class="n">bandwidth</span>
-<span class="go">array([[25. ],</span>
-<span class="go">       [15. ],</span>
-<span class="go">       [25. ],</span>
-<span class="go">       [16. ],</span>
-<span class="go">       [14.5],</span>
-<span class="go">       [25. ]])</span>
-</pre></div>
-</div>
-<p>Endogenous adaptive bandwidths</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">kwea</span><span class="o">=</span><span class="n">Kernel</span><span class="p">(</span><span class="n">points</span><span class="p">,</span><span class="n">fixed</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">kwea</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<span class="go">[1.0, 0.10557289844279438, 9.99999900663795e-08]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">kwea</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<span class="go">[0, 1, 3]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">kwea</span><span class="o">.</span><span class="n">bandwidth</span>
-<span class="go">array([[11.18034101],</span>
-<span class="go">       [11.18034101],</span>
-<span class="go">       [20.000002  ],</span>
-<span class="go">       [11.18034101],</span>
-<span class="go">       [14.14213704],</span>
-<span class="go">       [18.02775818]])</span>
-</pre></div>
-</div>
-<p>Endogenous adaptive bandwidths with Gaussian kernel</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">kweag</span><span class="o">=</span><span class="n">Kernel</span><span class="p">(</span><span class="n">points</span><span class="p">,</span><span class="n">fixed</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span><span class="n">function</span><span class="o">=</span><span class="s1">&#39;gaussian&#39;</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">kweag</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<span class="go">[0.3989422804014327, 0.2674190291577696, 0.2419707487162134]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">kweag</span><span class="o">.</span><span class="n">bandwidth</span>
-<span class="go">array([[11.18034101],</span>
-<span class="go">       [11.18034101],</span>
-<span class="go">       [20.000002  ],</span>
-<span class="go">       [11.18034101],</span>
-<span class="go">       [14.14213704],</span>
-<span class="go">       [18.02775818]])</span>
-</pre></div>
-</div>
-<p>Diagonals to 1.0</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">kq</span> <span class="o">=</span> <span class="n">Kernel</span><span class="p">(</span><span class="n">points</span><span class="p">,</span><span class="n">function</span><span class="o">=</span><span class="s1">&#39;gaussian&#39;</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">kq</span><span class="o">.</span><span class="n">weights</span>
-<span class="go">{0: [0.3989422804014327, 0.35206533556593145, 0.3412334260702758], 1: [0.35206533556593145, 0.3989422804014327, 0.2419707487162134, 0.3412334260702758, 0.31069657591175387], 2: [0.2419707487162134, 0.3989422804014327, 0.31069657591175387], 3: [0.3412334260702758, 0.3412334260702758, 0.3989422804014327, 0.3011374490937829, 0.26575287272131043], 4: [0.31069657591175387, 0.31069657591175387, 0.3011374490937829, 0.3989422804014327, 0.35206533556593145], 5: [0.26575287272131043, 0.35206533556593145, 0.3989422804014327]}</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">kqd</span> <span class="o">=</span> <span class="n">Kernel</span><span class="p">(</span><span class="n">points</span><span class="p">,</span> <span class="n">function</span><span class="o">=</span><span class="s1">&#39;gaussian&#39;</span><span class="p">,</span> <span class="n">diagonal</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">kqd</span><span class="o">.</span><span class="n">weights</span>
-<span class="go">{0: [1.0, 0.35206533556593145, 0.3412334260702758], 1: [0.35206533556593145, 1.0, 0.2419707487162134, 0.3412334260702758, 0.31069657591175387], 2: [0.2419707487162134, 1.0, 0.31069657591175387], 3: [0.3412334260702758, 0.3412334260702758, 1.0, 0.3011374490937829, 0.26575287272131043], 4: [0.31069657591175387, 0.31069657591175387, 0.3011374490937829, 1.0, 0.35206533556593145], 5: [0.26575287272131043, 0.35206533556593145, 1.0]}</span>
-</pre></div>
-</div>
-<dl class="field-list">
-<dt class="field-odd">Attributes</dt>
-<dd class="field-odd"><dl>
-<dt><strong>weights</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#dict" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">dict</span></code></a></span></dt><dd><p>Dictionary keyed by id with a list of weights for each neighbor</p>
-</dd>
-<dt><strong>neighbors</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#dict" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">dict</span></code></a></span></dt><dd><p>of lists of neighbors keyed by observation id</p>
-</dd>
-<dt><strong>bandwidth</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.20)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a></span></dt><dd><p>array of bandwidths</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<dl class="py method">
-<dt id="libpysal.weights.Kernel.__init__">
-<code class="sig-name descname">__init__</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">bandwidth</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">fixed</span><span class="o">=</span><span class="default_value">True</span></em>, <em class="sig-param"><span class="n">k</span><span class="o">=</span><span class="default_value">2</span></em>, <em class="sig-param"><span class="n">function</span><span class="o">=</span><span class="default_value">'triangular'</span></em>, <em class="sig-param"><span class="n">eps</span><span class="o">=</span><span class="default_value">1.0000001</span></em>, <em class="sig-param"><span class="n">ids</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">diagonal</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">distance_metric</span><span class="o">=</span><span class="default_value">'euclidean'</span></em>, <em class="sig-param"><span class="n">radius</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/distance.html#Kernel.__init__"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.Kernel.__init__" title="Permalink to this definition">¶</a></dt>
-<dd><p>Initialize self.  See help(type(self)) for accurate signature.</p>
-</dd></dl>
-
-<p class="rubric">Methods</p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.Kernel.__init__" title="libpysal.weights.Kernel.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(data[, bandwidth, fixed, k, …])</p></td>
-<td><p>Initialize self.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">asymmetry</span></code>([intrinsic])</p></td>
-<td><p>Asymmetry check.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_WSP</span></code>(WSP[, silence_warnings])</p></td>
-<td><p></p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_adjlist</span></code>(adjlist[, focal_col, …])</p></td>
-<td><p>Return an adjacency list representation of a weights object.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.Kernel.from_array" title="libpysal.weights.Kernel.from_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_array</span></code></a>(array, **kwargs)</p></td>
-<td><p>Construct a Kernel weights from an array.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.Kernel.from_dataframe" title="libpysal.weights.Kernel.from_dataframe"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_dataframe</span></code></a>(df[, geom_col, ids])</p></td>
-<td><p>Make Kernel weights from a dataframe.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_file</span></code>([path, format])</p></td>
-<td><p>Read a weights file into a W object.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_networkx</span></code>(graph[, weight_col])</p></td>
-<td><p>Convert a <code class="docutils literal notranslate"><span class="pre">networkx</span></code> graph to a PySAL <code class="docutils literal notranslate"><span class="pre">W</span></code> object.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.Kernel.from_shapefile" title="libpysal.weights.Kernel.from_shapefile"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_shapefile</span></code></a>(filepath[, idVariable])</p></td>
-<td><p>Kernel based weights from shapefile</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">full</span></code>()</p></td>
-<td><p>Generate a full <code class="docutils literal notranslate"><span class="pre">numpy.ndarray</span></code>.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_transform</span></code>()</p></td>
-<td><p>Getter for transform property.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot</span></code>(gdf[, indexed_on, ax, color, node_kws, …])</p></td>
-<td><p>Plot spatial weights objects.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">remap_ids</span></code>(new_ids)</p></td>
-<td><p>In place modification throughout <code class="docutils literal notranslate"><span class="pre">W</span></code> of id values from <code class="docutils literal notranslate"><span class="pre">w.id_order</span></code> to <code class="docutils literal notranslate"><span class="pre">new_ids</span></code> in all.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_shapefile</span></code>(shapefile[, idVariable, full])</p></td>
-<td><p>Adding metadata for writing headers of <code class="docutils literal notranslate"><span class="pre">.gal</span></code> and <code class="docutils literal notranslate"><span class="pre">.gwt</span></code> files.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_transform</span></code>([value])</p></td>
-<td><p>Transformations of weights.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">symmetrize</span></code>([inplace])</p></td>
-<td><p>Construct a symmetric KNN weight.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_WSP</span></code>()</p></td>
-<td><p>Generate a <code class="docutils literal notranslate"><span class="pre">WSP</span></code> object.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_adjlist</span></code>([remove_symmetric, focal_col, …])</p></td>
-<td><p>Compute an adjacency list representation of a weights object.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_file</span></code>([path, format])</p></td>
-<td><p>Write a weights to a file.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_networkx</span></code>()</p></td>
-<td><p>Convert a weights object to a <code class="docutils literal notranslate"><span class="pre">networkx</span></code> graph.</p></td>
-</tr>
-</tbody>
-</table>
-<p class="rubric">Attributes</p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">asymmetries</span></code></p></td>
-<td><p>List of id pairs with asymmetric weights.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cardinalities</span></code></p></td>
-<td><p>Number of neighbors for each observation.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">component_labels</span></code></p></td>
-<td><p>Store the graph component in which each observation falls.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagW2</span></code></p></td>
-<td><p>Diagonal of <span class="math notranslate nohighlight">\(WW\)</span>.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagWtW</span></code></p></td>
-<td><p>Diagonal of <span class="math notranslate nohighlight">\(W^{'}W\)</span>.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagWtW_WW</span></code></p></td>
-<td><p>Diagonal of <span class="math notranslate nohighlight">\(W^{'}W + WW\)</span>.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">histogram</span></code></p></td>
-<td><p>Cardinality histogram as a dictionary where key is the id and value is the number of neighbors for that unit.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">id2i</span></code></p></td>
-<td><p>Dictionary where the key is an ID and the value is that ID’s index in <code class="docutils literal notranslate"><span class="pre">W.id_order</span></code>.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">id_order</span></code></p></td>
-<td><p>Returns the ids for the observations in the order in which they would be encountered if iterating over the weights.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">id_order_set</span></code></p></td>
-<td><p>Returns <code class="docutils literal notranslate"><span class="pre">True</span></code> if user has set <code class="docutils literal notranslate"><span class="pre">id_order</span></code>, <code class="docutils literal notranslate"><span class="pre">False</span></code> if not.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">islands</span></code></p></td>
-<td><p>List of ids without any neighbors.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">max_neighbors</span></code></p></td>
-<td><p>Largest number of neighbors.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mean_neighbors</span></code></p></td>
-<td><p>Average number of neighbors.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">min_neighbors</span></code></p></td>
-<td><p>Minimum number of neighbors.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">n</span></code></p></td>
-<td><p>Number of units.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">n_components</span></code></p></td>
-<td><p>Store whether the adjacency matrix is fully connected.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">neighbor_offsets</span></code></p></td>
-<td><p>Given the current <code class="docutils literal notranslate"><span class="pre">id_order</span></code>, <code class="docutils literal notranslate"><span class="pre">neighbor_offsets[id]</span></code> is the offsets of the id’s neighbors in <code class="docutils literal notranslate"><span class="pre">id_order</span></code>.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">nonzero</span></code></p></td>
-<td><p>Number of nonzero weights.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">pct_nonzero</span></code></p></td>
-<td><p>Percentage of nonzero weights.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s0</span></code></p></td>
-<td><p><code class="docutils literal notranslate"><span class="pre">s0</span></code> is defined as</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s1</span></code></p></td>
-<td><p><code class="docutils literal notranslate"><span class="pre">s1</span></code> is defined as</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s2</span></code></p></td>
-<td><p><code class="docutils literal notranslate"><span class="pre">s2</span></code> is defined as</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s2array</span></code></p></td>
-<td><p>Individual elements comprising <code class="docutils literal notranslate"><span class="pre">s2</span></code>.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">sd</span></code></p></td>
-<td><p>Standard deviation of number of neighbors.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">sparse</span></code></p></td>
-<td><p>Sparse matrix object.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code></p></td>
-<td><p>Getter for transform property.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcW2</span></code></p></td>
-<td><p>Trace of <span class="math notranslate nohighlight">\(WW\)</span>.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcWtW</span></code></p></td>
-<td><p>Trace of <span class="math notranslate nohighlight">\(W^{'}W\)</span>.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcWtW_WW</span></code></p></td>
-<td><p>Trace of <span class="math notranslate nohighlight">\(W^{'}W + WW\)</span>.</p></td>
-</tr>
-</tbody>
-</table>
-<dl class="py method">
-<dt id="libpysal.weights.Kernel.from_array">
-<em class="property">classmethod </em><code class="sig-name descname">from_array</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">array</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/distance.html#Kernel.from_array"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.Kernel.from_array" title="Permalink to this definition">¶</a></dt>
-<dd><p>Construct a Kernel weights from an array. Supports all the same options
-as <a class="reference internal" href="#libpysal.weights.Kernel" title="libpysal.weights.Kernel"><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.Kernel</span></code></a></p>
-<div class="admonition seealso">
-<p class="admonition-title">See also</p>
-<dl class="simple">
-<dt><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.weights.W</span></code></dt><dd></dd>
-</dl>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.Kernel.from_dataframe">
-<em class="property">classmethod </em><code class="sig-name descname">from_dataframe</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">df</span></em>, <em class="sig-param"><span class="n">geom_col</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">ids</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/distance.html#Kernel.from_dataframe"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.Kernel.from_dataframe" title="Permalink to this definition">¶</a></dt>
-<dd><p>Make Kernel weights from a dataframe.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>df</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pandas.dataframe</span></code></span></dt><dd><p>a dataframe with a geometry column that can be used to
-construct a W object</p>
-</dd>
-<dt><strong>geom_col</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>the name of the column in <cite>df</cite> that contains the
-geometries. Defaults to active geometry column.</p>
-</dd>
-<dt><strong>ids</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a> or <a class="reference external" href="https://docs.python.org/3.8/glossary.html#term-iterable" title="(in Python v3.8)"><span>iterable</span></a></span></dt><dd><p>if string, the column name of the indices from the dataframe
-if iterable, a list of ids to use for the W
-if None, df.index is used.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<div class="admonition seealso">
-<p class="admonition-title">See also</p>
-<dl class="simple">
-<dt><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.weights.W</span></code></dt><dd></dd>
-</dl>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.Kernel.from_shapefile">
-<em class="property">classmethod </em><code class="sig-name descname">from_shapefile</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">filepath</span></em>, <em class="sig-param"><span class="n">idVariable</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/distance.html#Kernel.from_shapefile"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.Kernel.from_shapefile" title="Permalink to this definition">¶</a></dt>
-<dd><p>Kernel based weights from shapefile</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>shapefile</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>shapefile name with shp suffix</p>
-</dd>
-<dt><strong>idVariable</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>name of column in shapefile’s DBF to use for ids</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl class="simple">
-<dt><a class="reference internal" href="#libpysal.weights.Kernel" title="libpysal.weights.Kernel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Kernel</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">Weights</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">Object</span></code></dt><dd></dd>
-</dl>
-</dd>
-</dl>
-<div class="admonition seealso">
-<p class="admonition-title">See also</p>
-<dl class="simple">
-<dt><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.weights.W</span></code></dt><dd></dd>
-</dl>
-</div>
-</dd></dl>
-
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-  <div class="section" id="libpysal-weights-odw">
-<h1>libpysal.weights.ODW<a class="headerlink" href="#libpysal-weights-odw" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.weights.ODW">
-<code class="sig-prename descclassname">libpysal.weights.</code><code class="sig-name descname">ODW</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">Wo</span></em>, <em class="sig-param"><span class="n">Wd</span></em>, <em class="sig-param"><span class="n">transform</span><span class="o">=</span><span class="default_value">'r'</span></em>, <em class="sig-param"><span class="n">silence_warnings</span><span class="o">=</span><span class="default_value">True</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/spintW.html#ODW"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.ODW" title="Permalink to this definition">¶</a></dt>
-<dd><p>Constructs an o*d by o*d origin-destination style spatial weight for o*d
-flows using standard spatial weights on o origins and d destinations. Input
-spatial weights must be binary or able to be sutiably transformed to binary.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>Wo</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a> <a class="reference external" href="https://docs.python.org/3.8/library/functions.html#object" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">object</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">for</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">origin</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">locations</span></code></span></dt><dd><p>o x o spatial weight object amongst o origins</p>
-</dd>
-<dt><strong>Wd</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a> <a class="reference external" href="https://docs.python.org/3.8/library/functions.html#object" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">object</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">for</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">destination</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">locations</span></code></span></dt><dd><p>d x d spatial weight object amongst d destinations</p>
-</dd>
-<dt><strong>transform</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Transformation</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">for</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">standardization</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">of</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">final</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">OD</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">spatial</span></code> weight; <code class="xref py py-obj docutils literal notranslate"><span class="pre">default</span></code></span></dt><dd><p>is ‘r’ for row standardized</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>W</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">spatial</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">contiguity</span></code> <a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a> <a class="reference external" href="https://docs.python.org/3.8/library/functions.html#object" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">object</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">for</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">assocations</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">between</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">flows</span></code></span></dt><dd><p>o*d x o*d spatial weight object amongst o*d flows between o
-origins and d destinations</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">libpysal</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">O</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">lat2W</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">D</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">lat2W</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">OD</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">ODW</span><span class="p">(</span><span class="n">O</span><span class="p">,</span><span class="n">D</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">OD</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<span class="go">[0.25, 0.25, 0.25, 0.25]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">OD</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<span class="go">[5, 6, 9, 10]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">OD</span><span class="o">.</span><span class="n">full</span><span class="p">()[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
-<span class="go">array([0.  , 0.  , 0.  , 0.  , 0.  , 0.25, 0.25, 0.  , 0.  , 0.25, 0.25,</span>
-<span class="go">       0.  , 0.  , 0.  , 0.  , 0.  ])</span>
-</pre></div>
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-</dd></dl>
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-  <div class="section" id="libpysal-weights-queen">
-<h1>libpysal.weights.Queen<a class="headerlink" href="#libpysal-weights-queen" title="Permalink to this headline">¶</a></h1>
-<dl class="py class">
-<dt id="libpysal.weights.Queen">
-<em class="property">class </em><code class="sig-prename descclassname">libpysal.weights.</code><code class="sig-name descname">Queen</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">polygons</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kw</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/contiguity.html#Queen"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.Queen" title="Permalink to this definition">¶</a></dt>
-<dd><p>Construct a weights object from a collection of pysal polygons that share at least one vertex.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>polygons</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>a collection of PySAL shapes to build weights from</p>
-</dd>
-<dt><strong>ids</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>a list of names to use to build the weights</p>
-</dd>
-<dt><strong>**kw</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/keyword.html#module-keyword" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code></span></dt><dd><p>optional arguments for <code class="xref py py-class docutils literal notranslate"><span class="pre">pysal.weights.W</span></code></p>
-</dd>
-</dl>
-</dd>
-</dl>
-<div class="admonition seealso">
-<p class="admonition-title">See also</p>
-<dl class="simple">
-<dt><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.weights.W</span></code></dt><dd></dd>
-</dl>
-</div>
-<dl class="py method">
-<dt id="libpysal.weights.Queen.__init__">
-<code class="sig-name descname">__init__</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">polygons</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kw</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/contiguity.html#Queen.__init__"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.Queen.__init__" title="Permalink to this definition">¶</a></dt>
-<dd><p>Initialize self.  See help(type(self)) for accurate signature.</p>
-</dd></dl>
-
-<p class="rubric">Methods</p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.Queen.__init__" title="libpysal.weights.Queen.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(polygons, **kw)</p></td>
-<td><p>Initialize self.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">asymmetry</span></code>([intrinsic])</p></td>
-<td><p>Asymmetry check.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_WSP</span></code>(WSP[, silence_warnings])</p></td>
-<td><p></p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_adjlist</span></code>(adjlist[, focal_col, …])</p></td>
-<td><p>Return an adjacency list representation of a weights object.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.Queen.from_dataframe" title="libpysal.weights.Queen.from_dataframe"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_dataframe</span></code></a>(df[, geom_col])</p></td>
-<td><p>Construct a weights object from a pandas dataframe with a geometry column.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_file</span></code>([path, format])</p></td>
-<td><p>Read a weights file into a W object.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.Queen.from_iterable" title="libpysal.weights.Queen.from_iterable"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_iterable</span></code></a>(iterable[, sparse])</p></td>
-<td><p>Construct a weights object from a collection of arbitrary polygons.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_networkx</span></code>(graph[, weight_col])</p></td>
-<td><p>Convert a <code class="docutils literal notranslate"><span class="pre">networkx</span></code> graph to a PySAL <code class="docutils literal notranslate"><span class="pre">W</span></code> object.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.Queen.from_shapefile" title="libpysal.weights.Queen.from_shapefile"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_shapefile</span></code></a>(filepath[, idVariable, full])</p></td>
-<td><p>Queen contiguity weights from a polygon shapefile.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.Queen.from_xarray" title="libpysal.weights.Queen.from_xarray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_xarray</span></code></a>(da[, z_value, coords_labels, k, …])</p></td>
-<td><p>Construct a weights object from a xarray.DataArray with an additional attribute index containing coordinate values of the raster in the form of Pandas.Index/MultiIndex.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">full</span></code>()</p></td>
-<td><p>Generate a full <code class="docutils literal notranslate"><span class="pre">numpy.ndarray</span></code>.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_transform</span></code>()</p></td>
-<td><p>Getter for transform property.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot</span></code>(gdf[, indexed_on, ax, color, node_kws, …])</p></td>
-<td><p>Plot spatial weights objects.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">remap_ids</span></code>(new_ids)</p></td>
-<td><p>In place modification throughout <code class="docutils literal notranslate"><span class="pre">W</span></code> of id values from <code class="docutils literal notranslate"><span class="pre">w.id_order</span></code> to <code class="docutils literal notranslate"><span class="pre">new_ids</span></code> in all.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_shapefile</span></code>(shapefile[, idVariable, full])</p></td>
-<td><p>Adding metadata for writing headers of <code class="docutils literal notranslate"><span class="pre">.gal</span></code> and <code class="docutils literal notranslate"><span class="pre">.gwt</span></code> files.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_transform</span></code>([value])</p></td>
-<td><p>Transformations of weights.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">symmetrize</span></code>([inplace])</p></td>
-<td><p>Construct a symmetric KNN weight.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_WSP</span></code>()</p></td>
-<td><p>Generate a <code class="docutils literal notranslate"><span class="pre">WSP</span></code> object.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_adjlist</span></code>([remove_symmetric, focal_col, …])</p></td>
-<td><p>Compute an adjacency list representation of a weights object.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_file</span></code>([path, format])</p></td>
-<td><p>Write a weights to a file.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_networkx</span></code>()</p></td>
-<td><p>Convert a weights object to a <code class="docutils literal notranslate"><span class="pre">networkx</span></code> graph.</p></td>
-</tr>
-</tbody>
-</table>
-<p class="rubric">Attributes</p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">asymmetries</span></code></p></td>
-<td><p>List of id pairs with asymmetric weights.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cardinalities</span></code></p></td>
-<td><p>Number of neighbors for each observation.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">component_labels</span></code></p></td>
-<td><p>Store the graph component in which each observation falls.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagW2</span></code></p></td>
-<td><p>Diagonal of <span class="math notranslate nohighlight">\(WW\)</span>.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagWtW</span></code></p></td>
-<td><p>Diagonal of <span class="math notranslate nohighlight">\(W^{'}W\)</span>.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagWtW_WW</span></code></p></td>
-<td><p>Diagonal of <span class="math notranslate nohighlight">\(W^{'}W + WW\)</span>.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">histogram</span></code></p></td>
-<td><p>Cardinality histogram as a dictionary where key is the id and value is the number of neighbors for that unit.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">id2i</span></code></p></td>
-<td><p>Dictionary where the key is an ID and the value is that ID’s index in <code class="docutils literal notranslate"><span class="pre">W.id_order</span></code>.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">id_order</span></code></p></td>
-<td><p>Returns the ids for the observations in the order in which they would be encountered if iterating over the weights.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">id_order_set</span></code></p></td>
-<td><p>Returns <code class="docutils literal notranslate"><span class="pre">True</span></code> if user has set <code class="docutils literal notranslate"><span class="pre">id_order</span></code>, <code class="docutils literal notranslate"><span class="pre">False</span></code> if not.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">islands</span></code></p></td>
-<td><p>List of ids without any neighbors.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">max_neighbors</span></code></p></td>
-<td><p>Largest number of neighbors.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mean_neighbors</span></code></p></td>
-<td><p>Average number of neighbors.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">min_neighbors</span></code></p></td>
-<td><p>Minimum number of neighbors.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">n</span></code></p></td>
-<td><p>Number of units.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">n_components</span></code></p></td>
-<td><p>Store whether the adjacency matrix is fully connected.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">neighbor_offsets</span></code></p></td>
-<td><p>Given the current <code class="docutils literal notranslate"><span class="pre">id_order</span></code>, <code class="docutils literal notranslate"><span class="pre">neighbor_offsets[id]</span></code> is the offsets of the id’s neighbors in <code class="docutils literal notranslate"><span class="pre">id_order</span></code>.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">nonzero</span></code></p></td>
-<td><p>Number of nonzero weights.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">pct_nonzero</span></code></p></td>
-<td><p>Percentage of nonzero weights.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s0</span></code></p></td>
-<td><p><code class="docutils literal notranslate"><span class="pre">s0</span></code> is defined as</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s1</span></code></p></td>
-<td><p><code class="docutils literal notranslate"><span class="pre">s1</span></code> is defined as</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s2</span></code></p></td>
-<td><p><code class="docutils literal notranslate"><span class="pre">s2</span></code> is defined as</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s2array</span></code></p></td>
-<td><p>Individual elements comprising <code class="docutils literal notranslate"><span class="pre">s2</span></code>.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">sd</span></code></p></td>
-<td><p>Standard deviation of number of neighbors.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">sparse</span></code></p></td>
-<td><p>Sparse matrix object.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code></p></td>
-<td><p>Getter for transform property.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcW2</span></code></p></td>
-<td><p>Trace of <span class="math notranslate nohighlight">\(WW\)</span>.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcWtW</span></code></p></td>
-<td><p>Trace of <span class="math notranslate nohighlight">\(W^{'}W\)</span>.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcWtW_WW</span></code></p></td>
-<td><p>Trace of <span class="math notranslate nohighlight">\(W^{'}W + WW\)</span>.</p></td>
-</tr>
-</tbody>
-</table>
-<dl class="py method">
-<dt id="libpysal.weights.Queen.from_dataframe">
-<em class="property">classmethod </em><code class="sig-name descname">from_dataframe</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">df</span></em>, <em class="sig-param"><span class="n">geom_col</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/contiguity.html#Queen.from_dataframe"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.Queen.from_dataframe" title="Permalink to this definition">¶</a></dt>
-<dd><p>Construct a weights object from a pandas dataframe with a geometry
-column. This will cast the polygons to PySAL polygons, then build the W
-using ids from the dataframe.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>df</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DataFrame</span></code></span></dt><dd><p>a :class: <cite>pandas.DataFrame</cite> containing geometries to use
-for spatial weights</p>
-</dd>
-<dt><strong>geom_col</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>the name of the column in <cite>df</cite> that contains the
-geometries. Defaults to active geometry column</p>
-</dd>
-<dt><strong>idVariable</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>the name of the column to use as IDs. If nothing is
-provided, the dataframe index is used</p>
-</dd>
-<dt><strong>ids</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>a list of ids to use to index the spatial weights object.
-Order is not respected from this list.</p>
-</dd>
-<dt><strong>id_order</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>an ordered list of ids to use to index the spatial weights
-object. If used, the resulting weights object will iterate
-over results in the order of the names provided in this
-argument.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<div class="admonition seealso">
-<p class="admonition-title">See also</p>
-<dl class="simple">
-<dt><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.weights.W</span></code></dt><dd></dd>
-<dt><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.contiguity.Queen</span></code></dt><dd></dd>
-</dl>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.Queen.from_iterable">
-<em class="property">classmethod </em><code class="sig-name descname">from_iterable</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">iterable</span></em>, <em class="sig-param"><span class="n">sparse</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/contiguity.html#Queen.from_iterable"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.Queen.from_iterable" title="Permalink to this definition">¶</a></dt>
-<dd><p>Construct a weights object from a collection of arbitrary polygons. This
-will cast the polygons to PySAL polygons, then build the W.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>iterable</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/glossary.html#term-iterable" title="(in Python v3.8)"><span>iterable</span></a></span></dt><dd><p>a collection of of shapes to be cast to PySAL shapes. Must
-support iteration. Contents may either be a shapely or PySAL shape.</p>
-</dd>
-<dt><strong>**kw</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/keyword.html#module-keyword" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code></span></dt><dd><p>optional arguments for  <code class="xref py py-class docutils literal notranslate"><span class="pre">pysal.weights.W</span></code></p>
-</dd>
-<dt><strong>See Also</strong></dt><dd></dd>
-<dt><strong>———</strong></dt><dd></dd>
-<dt><strong>:class:`libpysal.weights.weights.W`</strong></dt><dd></dd>
-<dt><strong>:class:`libpysal.weights.contiguiyt.Queen`</strong></dt><dd></dd>
-</dl>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.Queen.from_shapefile">
-<em class="property">classmethod </em><code class="sig-name descname">from_shapefile</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">filepath</span></em>, <em class="sig-param"><span class="n">idVariable</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">full</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/contiguity.html#Queen.from_shapefile"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.Queen.from_shapefile" title="Permalink to this definition">¶</a></dt>
-<dd><p>Queen contiguity weights from a polygon shapefile.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>shapefile</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>name of polygon shapefile including suffix.</p>
-</dd>
-<dt><strong>idVariable</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>name of a column in the shapefile’s DBF to use for ids.</p>
-</dd>
-<dt><strong>sparse</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.8)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>If True return WSP instance
-If False return W instance</p>
-</dd>
-<dt><strong>Returns</strong></dt><dd></dd>
-<dt><strong>——-</strong></dt><dd></dd>
-<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>instance of spatial weights</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<div class="admonition seealso">
-<p class="admonition-title">See also</p>
-<dl class="simple">
-<dt><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.weights.W</span></code></dt><dd></dd>
-<dt><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.contiguity.Queen</span></code></dt><dd></dd>
-</dl>
-</div>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">Queen</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">libpysal</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wq</span><span class="o">=</span><span class="n">Queen</span><span class="o">.</span><span class="n">from_shapefile</span><span class="p">(</span><span class="n">libpysal</span><span class="o">.</span><span class="n">examples</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="s2">&quot;columbus.shp&quot;</span><span class="p">))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="s2">&quot;</span><span class="si">%.3f</span><span class="s2">&quot;</span><span class="o">%</span><span class="n">wq</span><span class="o">.</span><span class="n">pct_nonzero</span>
-<span class="go">&#39;9.829&#39;</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wq</span><span class="o">=</span><span class="n">Queen</span><span class="o">.</span><span class="n">from_shapefile</span><span class="p">(</span><span class="n">libpysal</span><span class="o">.</span><span class="n">examples</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="s2">&quot;columbus.shp&quot;</span><span class="p">),</span><span class="s2">&quot;POLYID&quot;</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="s2">&quot;</span><span class="si">%.3f</span><span class="s2">&quot;</span><span class="o">%</span><span class="n">wq</span><span class="o">.</span><span class="n">pct_nonzero</span>
-<span class="go">&#39;9.829&#39;</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wq</span><span class="o">=</span><span class="n">Queen</span><span class="o">.</span><span class="n">from_shapefile</span><span class="p">(</span><span class="n">libpysal</span><span class="o">.</span><span class="n">examples</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="s2">&quot;columbus.shp&quot;</span><span class="p">),</span> <span class="n">sparse</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">pct_sp</span> <span class="o">=</span> <span class="n">wq</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">nnz</span> <span class="o">*</span><span class="mf">1.</span> <span class="o">/</span> <span class="n">wq</span><span class="o">.</span><span class="n">n</span><span class="o">**</span><span class="mi">2</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="s2">&quot;</span><span class="si">%.3f</span><span class="s2">&quot;</span><span class="o">%</span><span class="n">pct_sp</span>
-<span class="go">&#39;0.098&#39;</span>
-</pre></div>
-</div>
-<p>Notes</p>
-<p>Queen contiguity defines as neighbors any pair of polygons that share at
-least one vertex in their polygon definitions.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.Queen.from_xarray">
-<em class="property">classmethod </em><code class="sig-name descname">from_xarray</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">da</span></em>, <em class="sig-param"><span class="n">z_value</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">coords_labels</span><span class="o">=</span><span class="default_value">{}</span></em>, <em class="sig-param"><span class="n">k</span><span class="o">=</span><span class="default_value">1</span></em>, <em class="sig-param"><span class="n">include_nodata</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">n_jobs</span><span class="o">=</span><span class="default_value">1</span></em>, <em class="sig-param"><span class="n">sparse</span><span class="o">=</span><span class="default_value">True</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/contiguity.html#Queen.from_xarray"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.Queen.from_xarray" title="Permalink to this definition">¶</a></dt>
-<dd><p>Construct a weights object from a xarray.DataArray with an additional
-attribute index containing coordinate values of the raster
-in the form of Pandas.Index/MultiIndex.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>da</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.DataArray</span></code></span></dt><dd><p>Input 2D or 3D DataArray with shape=(z, y, x)</p>
-</dd>
-<dt><strong>z_value</strong><span class="classifier">int/string/float</span></dt><dd><p>Select the z_value of 3D DataArray with multiple layers.</p>
-</dd>
-<dt><strong>coords_labels</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dictionary</span></code></span></dt><dd><p>Pass dimension labels for coordinates and layers if they do not
-belong to default dimensions, which are (band/time, y/lat, x/lon)
-e.g. coords_labels = {“y_label”: “latitude”, “x_label”: “longitude”, “z_label”: “year”}
-Default is {} empty dictionary.</p>
-</dd>
-<dt><strong>sparse</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.8)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>type of weight object. Default is True. For libpysal.weights.W, sparse = False</p>
-</dd>
-<dt><strong>k</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#int" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>Order of contiguity, this will select all neighbors upto kth order.
-Default is 1.</p>
-</dd>
-<dt><strong>include_nodata</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.8)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>If True, missing values will be assumed as non-missing when
-selecting higher_order neighbors, Default is False</p>
-</dd>
-<dt><strong>n_jobs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#int" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>Number of cores to be used in the sparse weight construction. If -1,
-all available cores are used. Default is 1.</p>
-</dd>
-<dt><strong>**kwargs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/keyword.html#module-keyword" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code></span></dt><dd><p>optional arguments passed when sparse = False</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl class="simple">
-<dt><strong>w</strong><span class="classifier">libpysal.weights.W/libpysal.weights.WSP</span></dt><dd><p>instance of spatial weights class W or WSP with an index attribute</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<div class="admonition seealso">
-<p class="admonition-title">See also</p>
-<dl class="simple">
-<dt><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.weights.W</span></code></dt><dd></dd>
-<dt><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.weights.WSP</span></code></dt><dd></dd>
-</dl>
-</div>
-<p class="rubric">Notes</p>
-<ol class="arabic simple">
-<li><p>Lower order contiguities are also selected.</p></li>
-</ol>
-<p>2. Returned object contains <cite>index</cite> attribute that includes a 
-<cite>Pandas.MultiIndex</cite> object from the DataArray.</p>
-</dd></dl>
-
-</dd></dl>
-
-</div>
-
-
-    </div>
-      
-  </div>
-</div>
-<footer class="footer">
-  <div class="container">
-    <p class="pull-right">
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-      
-        <br/>
-        
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-  <a href="../_sources/generated/libpysal.weights.Queen.rst.txt"
-     rel="nofollow">Source</a>
-</div>
-      
-    </p>
-    <p>
-        &copy; Copyright 2018-, pysal developers.<br/>
-      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 3.1.2.<br/>
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-  <div class="section" id="libpysal-weights-rook">
-<h1>libpysal.weights.Rook<a class="headerlink" href="#libpysal-weights-rook" title="Permalink to this headline">¶</a></h1>
-<dl class="py class">
-<dt id="libpysal.weights.Rook">
-<em class="property">class </em><code class="sig-prename descclassname">libpysal.weights.</code><code class="sig-name descname">Rook</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">polygons</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kw</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/contiguity.html#Rook"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.Rook" title="Permalink to this definition">¶</a></dt>
-<dd><p>Construct a weights object from a collection of pysal polygons that share at least one edge.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>polygons</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>a collection of PySAL shapes to build weights from</p>
-</dd>
-<dt><strong>ids</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>a list of names to use to build the weights</p>
-</dd>
-<dt><strong>**kw</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/keyword.html#module-keyword" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code></span></dt><dd><p>optional arguments for <code class="xref py py-class docutils literal notranslate"><span class="pre">pysal.weights.W</span></code></p>
-</dd>
-</dl>
-</dd>
-</dl>
-<div class="admonition seealso">
-<p class="admonition-title">See also</p>
-<dl class="simple">
-<dt><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.weights.W</span></code></dt><dd></dd>
-</dl>
-</div>
-<dl class="py method">
-<dt id="libpysal.weights.Rook.__init__">
-<code class="sig-name descname">__init__</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">polygons</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kw</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/contiguity.html#Rook.__init__"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.Rook.__init__" title="Permalink to this definition">¶</a></dt>
-<dd><p>Initialize self.  See help(type(self)) for accurate signature.</p>
-</dd></dl>
-
-<p class="rubric">Methods</p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.Rook.__init__" title="libpysal.weights.Rook.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(polygons, **kw)</p></td>
-<td><p>Initialize self.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">asymmetry</span></code>([intrinsic])</p></td>
-<td><p>Asymmetry check.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_WSP</span></code>(WSP[, silence_warnings])</p></td>
-<td><p></p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_adjlist</span></code>(adjlist[, focal_col, …])</p></td>
-<td><p>Return an adjacency list representation of a weights object.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.Rook.from_dataframe" title="libpysal.weights.Rook.from_dataframe"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_dataframe</span></code></a>(df[, geom_col, idVariable, …])</p></td>
-<td><p>Construct a weights object from a pandas dataframe with a geometry column.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_file</span></code>([path, format])</p></td>
-<td><p>Read a weights file into a W object.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.Rook.from_iterable" title="libpysal.weights.Rook.from_iterable"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_iterable</span></code></a>(iterable[, sparse])</p></td>
-<td><p>Construct a weights object from a collection of arbitrary polygons.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_networkx</span></code>(graph[, weight_col])</p></td>
-<td><p>Convert a <code class="docutils literal notranslate"><span class="pre">networkx</span></code> graph to a PySAL <code class="docutils literal notranslate"><span class="pre">W</span></code> object.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.Rook.from_shapefile" title="libpysal.weights.Rook.from_shapefile"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_shapefile</span></code></a>(filepath[, idVariable, full])</p></td>
-<td><p>Rook contiguity weights from a polygon shapefile.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.Rook.from_xarray" title="libpysal.weights.Rook.from_xarray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_xarray</span></code></a>(da[, z_value, coords_labels, k, …])</p></td>
-<td><p>Construct a weights object from a xarray.DataArray with an additional attribute index containing coordinate values of the raster in the form of Pandas.Index/MultiIndex.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">full</span></code>()</p></td>
-<td><p>Generate a full <code class="docutils literal notranslate"><span class="pre">numpy.ndarray</span></code>.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_transform</span></code>()</p></td>
-<td><p>Getter for transform property.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot</span></code>(gdf[, indexed_on, ax, color, node_kws, …])</p></td>
-<td><p>Plot spatial weights objects.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">remap_ids</span></code>(new_ids)</p></td>
-<td><p>In place modification throughout <code class="docutils literal notranslate"><span class="pre">W</span></code> of id values from <code class="docutils literal notranslate"><span class="pre">w.id_order</span></code> to <code class="docutils literal notranslate"><span class="pre">new_ids</span></code> in all.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_shapefile</span></code>(shapefile[, idVariable, full])</p></td>
-<td><p>Adding metadata for writing headers of <code class="docutils literal notranslate"><span class="pre">.gal</span></code> and <code class="docutils literal notranslate"><span class="pre">.gwt</span></code> files.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_transform</span></code>([value])</p></td>
-<td><p>Transformations of weights.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">symmetrize</span></code>([inplace])</p></td>
-<td><p>Construct a symmetric KNN weight.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_WSP</span></code>()</p></td>
-<td><p>Generate a <code class="docutils literal notranslate"><span class="pre">WSP</span></code> object.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_adjlist</span></code>([remove_symmetric, focal_col, …])</p></td>
-<td><p>Compute an adjacency list representation of a weights object.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_file</span></code>([path, format])</p></td>
-<td><p>Write a weights to a file.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_networkx</span></code>()</p></td>
-<td><p>Convert a weights object to a <code class="docutils literal notranslate"><span class="pre">networkx</span></code> graph.</p></td>
-</tr>
-</tbody>
-</table>
-<p class="rubric">Attributes</p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">asymmetries</span></code></p></td>
-<td><p>List of id pairs with asymmetric weights.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cardinalities</span></code></p></td>
-<td><p>Number of neighbors for each observation.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">component_labels</span></code></p></td>
-<td><p>Store the graph component in which each observation falls.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagW2</span></code></p></td>
-<td><p>Diagonal of <span class="math notranslate nohighlight">\(WW\)</span>.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagWtW</span></code></p></td>
-<td><p>Diagonal of <span class="math notranslate nohighlight">\(W^{'}W\)</span>.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagWtW_WW</span></code></p></td>
-<td><p>Diagonal of <span class="math notranslate nohighlight">\(W^{'}W + WW\)</span>.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">histogram</span></code></p></td>
-<td><p>Cardinality histogram as a dictionary where key is the id and value is the number of neighbors for that unit.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">id2i</span></code></p></td>
-<td><p>Dictionary where the key is an ID and the value is that ID’s index in <code class="docutils literal notranslate"><span class="pre">W.id_order</span></code>.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">id_order</span></code></p></td>
-<td><p>Returns the ids for the observations in the order in which they would be encountered if iterating over the weights.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">id_order_set</span></code></p></td>
-<td><p>Returns <code class="docutils literal notranslate"><span class="pre">True</span></code> if user has set <code class="docutils literal notranslate"><span class="pre">id_order</span></code>, <code class="docutils literal notranslate"><span class="pre">False</span></code> if not.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">islands</span></code></p></td>
-<td><p>List of ids without any neighbors.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">max_neighbors</span></code></p></td>
-<td><p>Largest number of neighbors.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mean_neighbors</span></code></p></td>
-<td><p>Average number of neighbors.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">min_neighbors</span></code></p></td>
-<td><p>Minimum number of neighbors.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">n</span></code></p></td>
-<td><p>Number of units.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">n_components</span></code></p></td>
-<td><p>Store whether the adjacency matrix is fully connected.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">neighbor_offsets</span></code></p></td>
-<td><p>Given the current <code class="docutils literal notranslate"><span class="pre">id_order</span></code>, <code class="docutils literal notranslate"><span class="pre">neighbor_offsets[id]</span></code> is the offsets of the id’s neighbors in <code class="docutils literal notranslate"><span class="pre">id_order</span></code>.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">nonzero</span></code></p></td>
-<td><p>Number of nonzero weights.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">pct_nonzero</span></code></p></td>
-<td><p>Percentage of nonzero weights.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s0</span></code></p></td>
-<td><p><code class="docutils literal notranslate"><span class="pre">s0</span></code> is defined as</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s1</span></code></p></td>
-<td><p><code class="docutils literal notranslate"><span class="pre">s1</span></code> is defined as</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s2</span></code></p></td>
-<td><p><code class="docutils literal notranslate"><span class="pre">s2</span></code> is defined as</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s2array</span></code></p></td>
-<td><p>Individual elements comprising <code class="docutils literal notranslate"><span class="pre">s2</span></code>.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">sd</span></code></p></td>
-<td><p>Standard deviation of number of neighbors.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">sparse</span></code></p></td>
-<td><p>Sparse matrix object.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code></p></td>
-<td><p>Getter for transform property.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcW2</span></code></p></td>
-<td><p>Trace of <span class="math notranslate nohighlight">\(WW\)</span>.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcWtW</span></code></p></td>
-<td><p>Trace of <span class="math notranslate nohighlight">\(W^{'}W\)</span>.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcWtW_WW</span></code></p></td>
-<td><p>Trace of <span class="math notranslate nohighlight">\(W^{'}W + WW\)</span>.</p></td>
-</tr>
-</tbody>
-</table>
-<dl class="py method">
-<dt id="libpysal.weights.Rook.from_dataframe">
-<em class="property">classmethod </em><code class="sig-name descname">from_dataframe</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">df</span></em>, <em class="sig-param"><span class="n">geom_col</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">idVariable</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">ids</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">id_order</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/contiguity.html#Rook.from_dataframe"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.Rook.from_dataframe" title="Permalink to this definition">¶</a></dt>
-<dd><p>Construct a weights object from a pandas dataframe with a geometry
-column. This will cast the polygons to PySAL polygons, then build the W
-using ids from the dataframe.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>df</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DataFrame</span></code></span></dt><dd><p>a :class: <cite>pandas.DataFrame</cite> containing geometries to use
-for spatial weights</p>
-</dd>
-<dt><strong>geom_col</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>the name of the column in <cite>df</cite> that contains the
-geometries. Defaults to active geometry column.</p>
-</dd>
-<dt><strong>idVariable</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>the name of the column to use as IDs. If nothing is
-provided, the dataframe index is used</p>
-</dd>
-<dt><strong>ids</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>a list of ids to use to index the spatial weights object.
-Order is not respected from this list.</p>
-</dd>
-<dt><strong>id_order</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>an ordered list of ids to use to index the spatial weights
-object. If used, the resulting weights object will iterate
-over results in the order of the names provided in this
-argument.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<div class="admonition seealso">
-<p class="admonition-title">See also</p>
-<dl class="simple">
-<dt><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.weights.W</span></code></dt><dd></dd>
-<dt><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.contiguity.Rook</span></code></dt><dd></dd>
-</dl>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.Rook.from_iterable">
-<em class="property">classmethod </em><code class="sig-name descname">from_iterable</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">iterable</span></em>, <em class="sig-param"><span class="n">sparse</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/contiguity.html#Rook.from_iterable"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.Rook.from_iterable" title="Permalink to this definition">¶</a></dt>
-<dd><p>Construct a weights object from a collection of arbitrary polygons. This
-will cast the polygons to PySAL polygons, then build the W.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>iterable</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/glossary.html#term-iterable" title="(in Python v3.8)"><span>iterable</span></a></span></dt><dd><p>a collection of of shapes to be cast to PySAL shapes. Must
-support iteration. Can be either Shapely or PySAL shapes.</p>
-</dd>
-<dt><strong>**kw</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/keyword.html#module-keyword" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code></span></dt><dd><p>optional arguments for  <code class="xref py py-class docutils literal notranslate"><span class="pre">pysal.weights.W</span></code></p>
-</dd>
-<dt><strong>See Also</strong></dt><dd></dd>
-<dt><strong>——–</strong></dt><dd></dd>
-<dt><strong>:class:`libpysal.weights.weights.W`</strong></dt><dd></dd>
-<dt><strong>:class:`libpysal.weights.contiguity.Rook`</strong></dt><dd></dd>
-</dl>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.Rook.from_shapefile">
-<em class="property">classmethod </em><code class="sig-name descname">from_shapefile</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">filepath</span></em>, <em class="sig-param"><span class="n">idVariable</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">full</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/contiguity.html#Rook.from_shapefile"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.Rook.from_shapefile" title="Permalink to this definition">¶</a></dt>
-<dd><p>Rook contiguity weights from a polygon shapefile.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>shapefile</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>name of polygon shapefile including suffix.</p>
-</dd>
-<dt><strong>sparse</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.8)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>If True return WSP instance
-If False return W instance</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>instance of spatial weights</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<div class="admonition seealso">
-<p class="admonition-title">See also</p>
-<dl class="simple">
-<dt><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.weights.W</span></code></dt><dd></dd>
-<dt><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.contiguity.Rook</span></code></dt><dd></dd>
-</dl>
-</div>
-<p class="rubric">Notes</p>
-<p>Rook contiguity defines as neighbors any pair of polygons that share a
-common edge in their polygon definitions.</p>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">Rook</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">libpysal</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wr</span><span class="o">=</span><span class="n">Rook</span><span class="o">.</span><span class="n">from_shapefile</span><span class="p">(</span><span class="n">libpysal</span><span class="o">.</span><span class="n">examples</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="s2">&quot;columbus.shp&quot;</span><span class="p">),</span> <span class="s2">&quot;POLYID&quot;</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="s2">&quot;</span><span class="si">%.3f</span><span class="s2">&quot;</span><span class="o">%</span><span class="n">wr</span><span class="o">.</span><span class="n">pct_nonzero</span>
-<span class="go">&#39;8.330&#39;</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wr</span><span class="o">=</span><span class="n">Rook</span><span class="o">.</span><span class="n">from_shapefile</span><span class="p">(</span><span class="n">libpysal</span><span class="o">.</span><span class="n">examples</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="s2">&quot;columbus.shp&quot;</span><span class="p">),</span> <span class="n">sparse</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">pct_sp</span> <span class="o">=</span> <span class="n">wr</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">nnz</span> <span class="o">*</span><span class="mf">1.</span> <span class="o">/</span> <span class="n">wr</span><span class="o">.</span><span class="n">n</span><span class="o">**</span><span class="mi">2</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="s2">&quot;</span><span class="si">%.3f</span><span class="s2">&quot;</span><span class="o">%</span><span class="n">pct_sp</span>
-<span class="go">&#39;0.083&#39;</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.Rook.from_xarray">
-<em class="property">classmethod </em><code class="sig-name descname">from_xarray</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">da</span></em>, <em class="sig-param"><span class="n">z_value</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">coords_labels</span><span class="o">=</span><span class="default_value">{}</span></em>, <em class="sig-param"><span class="n">k</span><span class="o">=</span><span class="default_value">1</span></em>, <em class="sig-param"><span class="n">include_nodata</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">n_jobs</span><span class="o">=</span><span class="default_value">1</span></em>, <em class="sig-param"><span class="n">sparse</span><span class="o">=</span><span class="default_value">True</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/contiguity.html#Rook.from_xarray"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.Rook.from_xarray" title="Permalink to this definition">¶</a></dt>
-<dd><p>Construct a weights object from a xarray.DataArray with an additional
-attribute index containing coordinate values of the raster
-in the form of Pandas.Index/MultiIndex.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>da</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.DataArray</span></code></span></dt><dd><p>Input 2D or 3D DataArray with shape=(z, y, x)</p>
-</dd>
-<dt><strong>z_value</strong><span class="classifier">int/string/float</span></dt><dd><p>Select the z_value of 3D DataArray with multiple layers.</p>
-</dd>
-<dt><strong>coords_labels</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dictionary</span></code></span></dt><dd><p>Pass dimension labels for coordinates and layers if they do not
-belong to default dimensions, which are (band/time, y/lat, x/lon)
-e.g. coords_labels = {“y_label”: “latitude”, “x_label”: “longitude”, “z_label”: “year”}
-Default is {} empty dictionary.</p>
-</dd>
-<dt><strong>sparse</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.8)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>type of weight object. Default is True. For libpysal.weights.W, sparse = False</p>
-</dd>
-<dt><strong>k</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#int" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>Order of contiguity, this will select all neighbors upto kth order.
-Default is 1.</p>
-</dd>
-<dt><strong>include_nodata</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.8)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>If True, missing values will be assumed as non-missing when
-selecting higher_order neighbors, Default is False</p>
-</dd>
-<dt><strong>n_jobs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#int" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>Number of cores to be used in the sparse weight construction. If -1,
-all available cores are used. Default is 1.</p>
-</dd>
-<dt><strong>**kwargs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/keyword.html#module-keyword" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code></span></dt><dd><p>optional arguments passed when sparse = False</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl class="simple">
-<dt><strong>w</strong><span class="classifier">libpysal.weights.W/libpysal.weights.WSP</span></dt><dd><p>instance of spatial weights class W or WSP with an index attribute</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<div class="admonition seealso">
-<p class="admonition-title">See also</p>
-<dl class="simple">
-<dt><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.weights.W</span></code></dt><dd></dd>
-<dt><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.weights.WSP</span></code></dt><dd></dd>
-</dl>
-</div>
-<p class="rubric">Notes</p>
-<ol class="arabic simple">
-<li><p>Lower order contiguities are also selected.</p></li>
-</ol>
-<p>2. Returned object contains <cite>index</cite> attribute that includes a 
-<cite>Pandas.MultiIndex</cite> object from the DataArray.</p>
-</dd></dl>
-
-</dd></dl>
-
-</div>
-
-
-    </div>
-      
-  </div>
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-  <a href="../_sources/generated/libpysal.weights.Rook.rst.txt"
-     rel="nofollow">Source</a>
-</div>
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-    <p>
-        &copy; Copyright 2018-, pysal developers.<br/>
-      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 3.1.2.<br/>
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-  <div class="section" id="libpysal-weights-voronoi">
-<h1>libpysal.weights.Voronoi<a class="headerlink" href="#libpysal-weights-voronoi" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.weights.Voronoi">
-<code class="sig-prename descclassname">libpysal.weights.</code><code class="sig-name descname">Voronoi</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">points</span></em>, <em class="sig-param"><span class="n">criterion</span><span class="o">=</span><span class="default_value">'rook'</span></em>, <em class="sig-param"><span class="n">clip</span><span class="o">=</span><span class="default_value">'ahull'</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/contiguity.html#Voronoi"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.Voronoi" title="Permalink to this definition">¶</a></dt>
-<dd><p>Voronoi weights for a 2-d point set</p>
-<p>Points are Voronoi neighbors if their polygons share an edge or vertex.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>points</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.20)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a></span></dt><dd><p>(n,2)
-coordinates for point locations</p>
-</dd>
-<dt><strong>kwargs</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">pass</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code> <a class="reference internal" href="libpysal.weights.Rook.html#libpysal.weights.Rook" title="libpysal.weights.Rook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Rook</span></code></a>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">underlying</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">contiguity</span></code> class.</span></dt><dd></dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>instance of spatial weights</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">Voronoi</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">12345</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">points</span><span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span><span class="o">*</span><span class="mi">10</span> <span class="o">+</span> <span class="mi">10</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">Voronoi</span><span class="p">(</span><span class="n">points</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">neighbors</span>
-<span class="go">{0: [2, 3, 4], 1: [2], 2: [0, 1, 4], 3: [0, 4], 4: [0, 2, 3]}</span>
-</pre></div>
-</div>
-</dd></dl>
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-  <div class="section" id="libpysal-weights-w">
-<h1>libpysal.weights.W<a class="headerlink" href="#libpysal-weights-w" title="Permalink to this headline">¶</a></h1>
-<dl class="py class">
-<dt id="libpysal.weights.W">
-<em class="property">class </em><code class="sig-prename descclassname">libpysal.weights.</code><code class="sig-name descname">W</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">neighbors</span></em>, <em class="sig-param"><span class="n">weights</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">id_order</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">silence_warnings</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">ids</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/weights.html#W"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.W" title="Permalink to this definition">¶</a></dt>
-<dd><p>Spatial weights class. Class attributes are described by their
-docstrings. to view, use the <code class="docutils literal notranslate"><span class="pre">help</span></code> function.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>neighbors</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#dict" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">dict</span></code></a></span></dt><dd><p>Key is region ID, value is a list of neighbor IDS.
-For example, <code class="docutils literal notranslate"><span class="pre">{'a':['b'],'b':['a','c'],'c':['b']}</span></code>.</p>
-</dd>
-<dt><strong>weights</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#dict" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">dict</span></code></a></span></dt><dd><p>Key is region ID, value is a list of edge weights.
-If not supplied all edge weights are assumed to have a weight of 1.
-For example, <code class="docutils literal notranslate"><span class="pre">{'a':[0.5],'b':[0.5,1.5],'c':[1.5]}</span></code>.</p>
-</dd>
-<dt><strong>id_order</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>An ordered list of ids, defines the order of observations when
-iterating over <code class="docutils literal notranslate"><span class="pre">W</span></code> if not set, lexicographical ordering is used
-to iterate and the <code class="docutils literal notranslate"><span class="pre">id_order_set</span></code> property will return <code class="docutils literal notranslate"><span class="pre">False</span></code>.
-This can be set after creation by setting the <code class="docutils literal notranslate"><span class="pre">id_order</span></code> property.</p>
-</dd>
-<dt><strong>silence_warnings</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.8)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>By default <code class="docutils literal notranslate"><span class="pre">libpysal</span></code> will print a warning if the dataset contains
-any disconnected components or islands. To silence this warning set this
-parameter to <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p>
-</dd>
-<dt><strong>ids</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>Values to use for keys of the neighbors and weights <code class="docutils literal notranslate"><span class="pre">dict</span></code> objects.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">W</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">neighbors</span> <span class="o">=</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="mi">1</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="mi">2</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="mi">3</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="mi">4</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="mi">5</span><span class="p">:</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">],</span> <span class="mi">6</span><span class="p">:</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">7</span><span class="p">],</span> <span class="mi">7</span><span class="p">:</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">8</span><span class="p">],</span> <span class="mi">8</span><span class="p">:</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">7</span><span class="p">]}</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">weights</span> <span class="o">=</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="mi">1</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="mi">2</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="mi">3</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="mi">4</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="mi">5</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="mi">6</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="mi">7</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="mi">8</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]}</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">W</span><span class="p">(</span><span class="n">neighbors</span><span class="p">,</span> <span class="n">weights</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="s2">&quot;</span><span class="si">%.3f</span><span class="s2">&quot;</span><span class="o">%</span><span class="n">w</span><span class="o">.</span><span class="n">pct_nonzero</span>
-<span class="go">&#39;29.630&#39;</span>
-</pre></div>
-</div>
-<p>Read from external <a class="reference external" href="https://geodacenter.github.io/workbook/4a_contig_weights/lab4a.html#gal-weights-file">.gal file</a>.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">libpysal</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">libpysal</span><span class="o">.</span><span class="n">examples</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="s2">&quot;stl.gal&quot;</span><span class="p">))</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">n</span>
-<span class="go">78</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="s2">&quot;</span><span class="si">%.3f</span><span class="s2">&quot;</span><span class="o">%</span><span class="n">w</span><span class="o">.</span><span class="n">pct_nonzero</span>
-<span class="go">&#39;6.542&#39;</span>
-</pre></div>
-</div>
-<p>Set weights implicitly.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">neighbors</span> <span class="o">=</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="mi">1</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="mi">2</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="mi">3</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="mi">4</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="mi">5</span><span class="p">:</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">],</span> <span class="mi">6</span><span class="p">:</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">7</span><span class="p">],</span> <span class="mi">7</span><span class="p">:</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">8</span><span class="p">],</span> <span class="mi">8</span><span class="p">:</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">7</span><span class="p">]}</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">W</span><span class="p">(</span><span class="n">neighbors</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">round</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">pct_nonzero</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span>
-<span class="go">29.63</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">trcW2</span>
-<span class="go">39600.0</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">trcWtW</span>
-<span class="go">39600.0</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">transform</span><span class="o">=</span><span class="s1">&#39;r&#39;</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">round</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">trcW2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
-<span class="go">2530.722</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">round</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">trcWtW</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
-<span class="go">2533.667</span>
-</pre></div>
-</div>
-<p>Cardinality Histogram:</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">histogram</span>
-<span class="go">[(2, 4), (3, 392), (4, 9604)]</span>
-</pre></div>
-</div>
-<p>Disconnected observations (islands):</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">W</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">W</span><span class="p">({</span><span class="mi">1</span><span class="p">:[</span><span class="mi">0</span><span class="p">],</span><span class="mi">0</span><span class="p">:[</span><span class="mi">1</span><span class="p">],</span><span class="mi">2</span><span class="p">:[],</span> <span class="mi">3</span><span class="p">:[]})</span>
-</pre></div>
-</div>
-<p>UserWarning: The weights matrix is not fully connected:
-There are 3 disconnected components.
-There are 2 islands with ids: 2, 3.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Attributes</dt>
-<dd class="field-odd"><dl class="simple">
-<dt><a class="reference internal" href="#libpysal.weights.W.asymmetries" title="libpysal.weights.W.asymmetries"><code class="xref py py-obj docutils literal notranslate"><span class="pre">asymmetries</span></code></a></dt><dd><p>List of id pairs with asymmetric weights.</p>
-</dd>
-<dt><a class="reference internal" href="#libpysal.weights.W.cardinalities" title="libpysal.weights.W.cardinalities"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cardinalities</span></code></a></dt><dd><p>Number of neighbors for each observation.</p>
-</dd>
-<dt><a class="reference internal" href="#libpysal.weights.W.component_labels" title="libpysal.weights.W.component_labels"><code class="xref py py-obj docutils literal notranslate"><span class="pre">component_labels</span></code></a></dt><dd><p>Store the graph component in which each observation falls.</p>
-</dd>
-<dt><a class="reference internal" href="#libpysal.weights.W.diagW2" title="libpysal.weights.W.diagW2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagW2</span></code></a></dt><dd><p>Diagonal of <span class="math notranslate nohighlight">\(WW\)</span>.</p>
-</dd>
-<dt><a class="reference internal" href="#libpysal.weights.W.diagWtW" title="libpysal.weights.W.diagWtW"><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagWtW</span></code></a></dt><dd><p>Diagonal of <span class="math notranslate nohighlight">\(W^{'}W\)</span>.</p>
-</dd>
-<dt><a class="reference internal" href="#libpysal.weights.W.diagWtW_WW" title="libpysal.weights.W.diagWtW_WW"><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagWtW_WW</span></code></a></dt><dd><p>Diagonal of <span class="math notranslate nohighlight">\(W^{'}W + WW\)</span>.</p>
-</dd>
-<dt><a class="reference internal" href="#libpysal.weights.W.histogram" title="libpysal.weights.W.histogram"><code class="xref py py-obj docutils literal notranslate"><span class="pre">histogram</span></code></a></dt><dd><p>Cardinality histogram as a dictionary where key is the id and value is the number of neighbors for that unit.</p>
-</dd>
-<dt><a class="reference internal" href="#libpysal.weights.W.id2i" title="libpysal.weights.W.id2i"><code class="xref py py-obj docutils literal notranslate"><span class="pre">id2i</span></code></a></dt><dd><p>Dictionary where the key is an ID and the value is that ID’s index in <code class="docutils literal notranslate"><span class="pre">W.id_order</span></code>.</p>
-</dd>
-<dt><a class="reference internal" href="#libpysal.weights.W.id_order" title="libpysal.weights.W.id_order"><code class="xref py py-obj docutils literal notranslate"><span class="pre">id_order</span></code></a></dt><dd><p>Returns the ids for the observations in the order in which they would be encountered if iterating over the weights.</p>
-</dd>
-<dt><a class="reference internal" href="#libpysal.weights.W.id_order_set" title="libpysal.weights.W.id_order_set"><code class="xref py py-obj docutils literal notranslate"><span class="pre">id_order_set</span></code></a></dt><dd><p>Returns <code class="docutils literal notranslate"><span class="pre">True</span></code> if user has set <code class="docutils literal notranslate"><span class="pre">id_order</span></code>, <code class="docutils literal notranslate"><span class="pre">False</span></code> if not.</p>
-</dd>
-<dt><a class="reference internal" href="#libpysal.weights.W.islands" title="libpysal.weights.W.islands"><code class="xref py py-obj docutils literal notranslate"><span class="pre">islands</span></code></a></dt><dd><p>List of ids without any neighbors.</p>
-</dd>
-<dt><a class="reference internal" href="#libpysal.weights.W.max_neighbors" title="libpysal.weights.W.max_neighbors"><code class="xref py py-obj docutils literal notranslate"><span class="pre">max_neighbors</span></code></a></dt><dd><p>Largest number of neighbors.</p>
-</dd>
-<dt><a class="reference internal" href="#libpysal.weights.W.mean_neighbors" title="libpysal.weights.W.mean_neighbors"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mean_neighbors</span></code></a></dt><dd><p>Average number of neighbors.</p>
-</dd>
-<dt><a class="reference internal" href="#libpysal.weights.W.min_neighbors" title="libpysal.weights.W.min_neighbors"><code class="xref py py-obj docutils literal notranslate"><span class="pre">min_neighbors</span></code></a></dt><dd><p>Minimum number of neighbors.</p>
-</dd>
-<dt><a class="reference internal" href="#libpysal.weights.W.n" title="libpysal.weights.W.n"><code class="xref py py-obj docutils literal notranslate"><span class="pre">n</span></code></a></dt><dd><p>Number of units.</p>
-</dd>
-<dt><a class="reference internal" href="#libpysal.weights.W.n_components" title="libpysal.weights.W.n_components"><code class="xref py py-obj docutils literal notranslate"><span class="pre">n_components</span></code></a></dt><dd><p>Store whether the adjacency matrix is fully connected.</p>
-</dd>
-<dt><a class="reference internal" href="#libpysal.weights.W.neighbor_offsets" title="libpysal.weights.W.neighbor_offsets"><code class="xref py py-obj docutils literal notranslate"><span class="pre">neighbor_offsets</span></code></a></dt><dd><p>Given the current <code class="docutils literal notranslate"><span class="pre">id_order</span></code>, <code class="docutils literal notranslate"><span class="pre">neighbor_offsets[id]</span></code> is the offsets of the id’s neighbors in <code class="docutils literal notranslate"><span class="pre">id_order</span></code>.</p>
-</dd>
-<dt><a class="reference internal" href="#libpysal.weights.W.nonzero" title="libpysal.weights.W.nonzero"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nonzero</span></code></a></dt><dd><p>Number of nonzero weights.</p>
-</dd>
-<dt><a class="reference internal" href="#libpysal.weights.W.pct_nonzero" title="libpysal.weights.W.pct_nonzero"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pct_nonzero</span></code></a></dt><dd><p>Percentage of nonzero weights.</p>
-</dd>
-<dt><a class="reference internal" href="#libpysal.weights.W.s0" title="libpysal.weights.W.s0"><code class="xref py py-obj docutils literal notranslate"><span class="pre">s0</span></code></a></dt><dd><p><code class="docutils literal notranslate"><span class="pre">s0</span></code> is defined as</p>
-</dd>
-<dt><a class="reference internal" href="#libpysal.weights.W.s1" title="libpysal.weights.W.s1"><code class="xref py py-obj docutils literal notranslate"><span class="pre">s1</span></code></a></dt><dd><p><code class="docutils literal notranslate"><span class="pre">s1</span></code> is defined as</p>
-</dd>
-<dt><a class="reference internal" href="#libpysal.weights.W.s2" title="libpysal.weights.W.s2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">s2</span></code></a></dt><dd><p><code class="docutils literal notranslate"><span class="pre">s2</span></code> is defined as</p>
-</dd>
-<dt><a class="reference internal" href="#libpysal.weights.W.s2array" title="libpysal.weights.W.s2array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">s2array</span></code></a></dt><dd><p>Individual elements comprising <code class="docutils literal notranslate"><span class="pre">s2</span></code>.</p>
-</dd>
-<dt><a class="reference internal" href="#libpysal.weights.W.sd" title="libpysal.weights.W.sd"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sd</span></code></a></dt><dd><p>Standard deviation of number of neighbors.</p>
-</dd>
-<dt><a class="reference internal" href="#libpysal.weights.W.sparse" title="libpysal.weights.W.sparse"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sparse</span></code></a></dt><dd><p>Sparse matrix object.</p>
-</dd>
-<dt><a class="reference internal" href="#libpysal.weights.W.trcW2" title="libpysal.weights.W.trcW2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcW2</span></code></a></dt><dd><p>Trace of <span class="math notranslate nohighlight">\(WW\)</span>.</p>
-</dd>
-<dt><a class="reference internal" href="#libpysal.weights.W.trcWtW" title="libpysal.weights.W.trcWtW"><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcWtW</span></code></a></dt><dd><p>Trace of <span class="math notranslate nohighlight">\(W^{'}W\)</span>.</p>
-</dd>
-<dt><a class="reference internal" href="#libpysal.weights.W.trcWtW_WW" title="libpysal.weights.W.trcWtW_WW"><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcWtW_WW</span></code></a></dt><dd><p>Trace of <span class="math notranslate nohighlight">\(W^{'}W + WW\)</span>.</p>
-</dd>
-<dt><a class="reference internal" href="#libpysal.weights.W.transform" title="libpysal.weights.W.transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code></a></dt><dd><p>Getter for transform property.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<dl class="py method">
-<dt id="libpysal.weights.W.__init__">
-<code class="sig-name descname">__init__</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">neighbors</span></em>, <em class="sig-param"><span class="n">weights</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">id_order</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">silence_warnings</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">ids</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/weights.html#W.__init__"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.W.__init__" title="Permalink to this definition">¶</a></dt>
-<dd><p>Initialize self.  See help(type(self)) for accurate signature.</p>
-</dd></dl>
-
-<p class="rubric">Methods</p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.W.__init__" title="libpysal.weights.W.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(neighbors[, weights, id_order, …])</p></td>
-<td><p>Initialize self.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.W.asymmetry" title="libpysal.weights.W.asymmetry"><code class="xref py py-obj docutils literal notranslate"><span class="pre">asymmetry</span></code></a>([intrinsic])</p></td>
-<td><p>Asymmetry check.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.W.from_WSP" title="libpysal.weights.W.from_WSP"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_WSP</span></code></a>(WSP[, silence_warnings])</p></td>
-<td><p></p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.W.from_adjlist" title="libpysal.weights.W.from_adjlist"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_adjlist</span></code></a>(adjlist[, focal_col, …])</p></td>
-<td><p>Return an adjacency list representation of a weights object.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.W.from_file" title="libpysal.weights.W.from_file"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_file</span></code></a>([path, format])</p></td>
-<td><p>Read a weights file into a W object.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.W.from_networkx" title="libpysal.weights.W.from_networkx"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_networkx</span></code></a>(graph[, weight_col])</p></td>
-<td><p>Convert a <code class="docutils literal notranslate"><span class="pre">networkx</span></code> graph to a PySAL <code class="docutils literal notranslate"><span class="pre">W</span></code> object.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.W.from_shapefile" title="libpysal.weights.W.from_shapefile"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_shapefile</span></code></a>(*args, **kwargs)</p></td>
-<td><p></p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.W.full" title="libpysal.weights.W.full"><code class="xref py py-obj docutils literal notranslate"><span class="pre">full</span></code></a>()</p></td>
-<td><p>Generate a full <code class="docutils literal notranslate"><span class="pre">numpy.ndarray</span></code>.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.W.get_transform" title="libpysal.weights.W.get_transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_transform</span></code></a>()</p></td>
-<td><p>Getter for transform property.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.W.plot" title="libpysal.weights.W.plot"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot</span></code></a>(gdf[, indexed_on, ax, color, node_kws, …])</p></td>
-<td><p>Plot spatial weights objects.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.W.remap_ids" title="libpysal.weights.W.remap_ids"><code class="xref py py-obj docutils literal notranslate"><span class="pre">remap_ids</span></code></a>(new_ids)</p></td>
-<td><p>In place modification throughout <code class="docutils literal notranslate"><span class="pre">W</span></code> of id values from <code class="docutils literal notranslate"><span class="pre">w.id_order</span></code> to <code class="docutils literal notranslate"><span class="pre">new_ids</span></code> in all.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.W.set_shapefile" title="libpysal.weights.W.set_shapefile"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_shapefile</span></code></a>(shapefile[, idVariable, full])</p></td>
-<td><p>Adding metadata for writing headers of <code class="docutils literal notranslate"><span class="pre">.gal</span></code> and <code class="docutils literal notranslate"><span class="pre">.gwt</span></code> files.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.W.set_transform" title="libpysal.weights.W.set_transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_transform</span></code></a>([value])</p></td>
-<td><p>Transformations of weights.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.W.symmetrize" title="libpysal.weights.W.symmetrize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">symmetrize</span></code></a>([inplace])</p></td>
-<td><p>Construct a symmetric KNN weight.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.W.to_WSP" title="libpysal.weights.W.to_WSP"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_WSP</span></code></a>()</p></td>
-<td><p>Generate a <code class="docutils literal notranslate"><span class="pre">WSP</span></code> object.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.W.to_adjlist" title="libpysal.weights.W.to_adjlist"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_adjlist</span></code></a>([remove_symmetric, focal_col, …])</p></td>
-<td><p>Compute an adjacency list representation of a weights object.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.W.to_file" title="libpysal.weights.W.to_file"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_file</span></code></a>([path, format])</p></td>
-<td><p>Write a weights to a file.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.W.to_networkx" title="libpysal.weights.W.to_networkx"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_networkx</span></code></a>()</p></td>
-<td><p>Convert a weights object to a <code class="docutils literal notranslate"><span class="pre">networkx</span></code> graph.</p></td>
-</tr>
-</tbody>
-</table>
-<p class="rubric">Attributes</p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.W.asymmetries" title="libpysal.weights.W.asymmetries"><code class="xref py py-obj docutils literal notranslate"><span class="pre">asymmetries</span></code></a></p></td>
-<td><p>List of id pairs with asymmetric weights.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.W.cardinalities" title="libpysal.weights.W.cardinalities"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cardinalities</span></code></a></p></td>
-<td><p>Number of neighbors for each observation.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.W.component_labels" title="libpysal.weights.W.component_labels"><code class="xref py py-obj docutils literal notranslate"><span class="pre">component_labels</span></code></a></p></td>
-<td><p>Store the graph component in which each observation falls.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.W.diagW2" title="libpysal.weights.W.diagW2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagW2</span></code></a></p></td>
-<td><p>Diagonal of <span class="math notranslate nohighlight">\(WW\)</span>.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.W.diagWtW" title="libpysal.weights.W.diagWtW"><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagWtW</span></code></a></p></td>
-<td><p>Diagonal of <span class="math notranslate nohighlight">\(W^{'}W\)</span>.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.W.diagWtW_WW" title="libpysal.weights.W.diagWtW_WW"><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagWtW_WW</span></code></a></p></td>
-<td><p>Diagonal of <span class="math notranslate nohighlight">\(W^{'}W + WW\)</span>.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.W.histogram" title="libpysal.weights.W.histogram"><code class="xref py py-obj docutils literal notranslate"><span class="pre">histogram</span></code></a></p></td>
-<td><p>Cardinality histogram as a dictionary where key is the id and value is the number of neighbors for that unit.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.W.id2i" title="libpysal.weights.W.id2i"><code class="xref py py-obj docutils literal notranslate"><span class="pre">id2i</span></code></a></p></td>
-<td><p>Dictionary where the key is an ID and the value is that ID’s index in <code class="docutils literal notranslate"><span class="pre">W.id_order</span></code>.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.W.id_order" title="libpysal.weights.W.id_order"><code class="xref py py-obj docutils literal notranslate"><span class="pre">id_order</span></code></a></p></td>
-<td><p>Returns the ids for the observations in the order in which they would be encountered if iterating over the weights.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.W.id_order_set" title="libpysal.weights.W.id_order_set"><code class="xref py py-obj docutils literal notranslate"><span class="pre">id_order_set</span></code></a></p></td>
-<td><p>Returns <code class="docutils literal notranslate"><span class="pre">True</span></code> if user has set <code class="docutils literal notranslate"><span class="pre">id_order</span></code>, <code class="docutils literal notranslate"><span class="pre">False</span></code> if not.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.W.islands" title="libpysal.weights.W.islands"><code class="xref py py-obj docutils literal notranslate"><span class="pre">islands</span></code></a></p></td>
-<td><p>List of ids without any neighbors.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.W.max_neighbors" title="libpysal.weights.W.max_neighbors"><code class="xref py py-obj docutils literal notranslate"><span class="pre">max_neighbors</span></code></a></p></td>
-<td><p>Largest number of neighbors.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.W.mean_neighbors" title="libpysal.weights.W.mean_neighbors"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mean_neighbors</span></code></a></p></td>
-<td><p>Average number of neighbors.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.W.min_neighbors" title="libpysal.weights.W.min_neighbors"><code class="xref py py-obj docutils literal notranslate"><span class="pre">min_neighbors</span></code></a></p></td>
-<td><p>Minimum number of neighbors.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.W.n" title="libpysal.weights.W.n"><code class="xref py py-obj docutils literal notranslate"><span class="pre">n</span></code></a></p></td>
-<td><p>Number of units.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.W.n_components" title="libpysal.weights.W.n_components"><code class="xref py py-obj docutils literal notranslate"><span class="pre">n_components</span></code></a></p></td>
-<td><p>Store whether the adjacency matrix is fully connected.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.W.neighbor_offsets" title="libpysal.weights.W.neighbor_offsets"><code class="xref py py-obj docutils literal notranslate"><span class="pre">neighbor_offsets</span></code></a></p></td>
-<td><p>Given the current <code class="docutils literal notranslate"><span class="pre">id_order</span></code>, <code class="docutils literal notranslate"><span class="pre">neighbor_offsets[id]</span></code> is the offsets of the id’s neighbors in <code class="docutils literal notranslate"><span class="pre">id_order</span></code>.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.W.nonzero" title="libpysal.weights.W.nonzero"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nonzero</span></code></a></p></td>
-<td><p>Number of nonzero weights.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.W.pct_nonzero" title="libpysal.weights.W.pct_nonzero"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pct_nonzero</span></code></a></p></td>
-<td><p>Percentage of nonzero weights.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.W.s0" title="libpysal.weights.W.s0"><code class="xref py py-obj docutils literal notranslate"><span class="pre">s0</span></code></a></p></td>
-<td><p><code class="docutils literal notranslate"><span class="pre">s0</span></code> is defined as</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.W.s1" title="libpysal.weights.W.s1"><code class="xref py py-obj docutils literal notranslate"><span class="pre">s1</span></code></a></p></td>
-<td><p><code class="docutils literal notranslate"><span class="pre">s1</span></code> is defined as</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.W.s2" title="libpysal.weights.W.s2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">s2</span></code></a></p></td>
-<td><p><code class="docutils literal notranslate"><span class="pre">s2</span></code> is defined as</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.W.s2array" title="libpysal.weights.W.s2array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">s2array</span></code></a></p></td>
-<td><p>Individual elements comprising <code class="docutils literal notranslate"><span class="pre">s2</span></code>.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.W.sd" title="libpysal.weights.W.sd"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sd</span></code></a></p></td>
-<td><p>Standard deviation of number of neighbors.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.W.sparse" title="libpysal.weights.W.sparse"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sparse</span></code></a></p></td>
-<td><p>Sparse matrix object.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.W.transform" title="libpysal.weights.W.transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code></a></p></td>
-<td><p>Getter for transform property.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.W.trcW2" title="libpysal.weights.W.trcW2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcW2</span></code></a></p></td>
-<td><p>Trace of <span class="math notranslate nohighlight">\(WW\)</span>.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.W.trcWtW" title="libpysal.weights.W.trcWtW"><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcWtW</span></code></a></p></td>
-<td><p>Trace of <span class="math notranslate nohighlight">\(W^{'}W\)</span>.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.W.trcWtW_WW" title="libpysal.weights.W.trcWtW_WW"><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcWtW_WW</span></code></a></p></td>
-<td><p>Trace of <span class="math notranslate nohighlight">\(W^{'}W + WW\)</span>.</p></td>
-</tr>
-</tbody>
-</table>
-<dl class="py method">
-<dt id="libpysal.weights.W.asymmetries">
-<em class="property">property </em><code class="sig-name descname">asymmetries</code><a class="headerlink" href="#libpysal.weights.W.asymmetries" title="Permalink to this definition">¶</a></dt>
-<dd><p>List of id pairs with asymmetric weights.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.W.asymmetry">
-<code class="sig-name descname">asymmetry</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">intrinsic</span><span class="o">=</span><span class="default_value">True</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/weights.html#W.asymmetry"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.W.asymmetry" title="Permalink to this definition">¶</a></dt>
-<dd><p>Asymmetry check.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>intrinsic</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.8)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>Default is <code class="docutils literal notranslate"><span class="pre">True</span></code>. Intrinsic symmetry is defined as</p>
-<div class="math notranslate nohighlight">
-\[w_{i,j} == w_{j,i}\]</div>
-<p>If <code class="docutils literal notranslate"><span class="pre">intrinsic</span></code> is <code class="docutils literal notranslate"><span class="pre">False</span></code> symmetry is defined as</p>
-<div class="math notranslate nohighlight">
-\[i \in N_j \ \&amp; \ j \in N_i\]</div>
-<p>where <span class="math notranslate nohighlight">\(N_j\)</span> is the set of neighbors for <span class="math notranslate nohighlight">\(j\)</span>.</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>asymmetries</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>Empty if no asymmetries are found if asymmetries, then a
-<code class="docutils literal notranslate"><span class="pre">list</span></code> of <code class="docutils literal notranslate"><span class="pre">(i,j)</span></code> tuples is returned.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">=</span><span class="n">lat2W</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">asymmetry</span><span class="p">()</span>
-<span class="go">[]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">transform</span><span class="o">=</span><span class="s1">&#39;r&#39;</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">asymmetry</span><span class="p">()</span>
-<span class="go">[(0, 1), (0, 3), (1, 0), (1, 2), (1, 4), (2, 1), (2, 5), (3, 0), (3, 4), (3, 6), (4, 1), (4, 3), (4, 5), (4, 7), (5, 2), (5, 4), (5, 8), (6, 3), (6, 7), (7, 4), (7, 6), (7, 8), (8, 5), (8, 7)]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">result</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">asymmetry</span><span class="p">(</span><span class="n">intrinsic</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">result</span>
-<span class="go">[]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">neighbors</span><span class="o">=</span><span class="p">{</span><span class="mi">0</span><span class="p">:[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">],</span> <span class="mi">1</span><span class="p">:[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">],</span> <span class="mi">2</span><span class="p">:[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span> <span class="mi">3</span><span class="p">:[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]}</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">weights</span><span class="o">=</span><span class="p">{</span><span class="mi">0</span><span class="p">:[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span> <span class="mi">1</span><span class="p">:[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span> <span class="mi">2</span><span class="p">:[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span> <span class="mi">3</span><span class="p">:[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">]}</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">=</span><span class="n">W</span><span class="p">(</span><span class="n">neighbors</span><span class="p">,</span><span class="n">weights</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">asymmetry</span><span class="p">()</span>
-<span class="go">[(0, 1), (1, 0)]</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.W.cardinalities">
-<em class="property">property </em><code class="sig-name descname">cardinalities</code><a class="headerlink" href="#libpysal.weights.W.cardinalities" title="Permalink to this definition">¶</a></dt>
-<dd><p>Number of neighbors for each observation.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.W.component_labels">
-<em class="property">property </em><code class="sig-name descname">component_labels</code><a class="headerlink" href="#libpysal.weights.W.component_labels" title="Permalink to this definition">¶</a></dt>
-<dd><p>Store the graph component in which each observation falls.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.W.diagW2">
-<em class="property">property </em><code class="sig-name descname">diagW2</code><a class="headerlink" href="#libpysal.weights.W.diagW2" title="Permalink to this definition">¶</a></dt>
-<dd><p>Diagonal of <span class="math notranslate nohighlight">\(WW\)</span>.</p>
-<div class="admonition seealso">
-<p class="admonition-title">See also</p>
-<dl class="simple">
-<dt><a class="reference internal" href="#libpysal.weights.W.trcW2" title="libpysal.weights.W.trcW2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcW2</span></code></a></dt><dd></dd>
-</dl>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.W.diagWtW">
-<em class="property">property </em><code class="sig-name descname">diagWtW</code><a class="headerlink" href="#libpysal.weights.W.diagWtW" title="Permalink to this definition">¶</a></dt>
-<dd><p>Diagonal of <span class="math notranslate nohighlight">\(W^{'}W\)</span>.</p>
-<div class="admonition seealso">
-<p class="admonition-title">See also</p>
-<dl class="simple">
-<dt><a class="reference internal" href="#libpysal.weights.W.trcWtW" title="libpysal.weights.W.trcWtW"><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcWtW</span></code></a></dt><dd></dd>
-</dl>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.W.diagWtW_WW">
-<em class="property">property </em><code class="sig-name descname">diagWtW_WW</code><a class="headerlink" href="#libpysal.weights.W.diagWtW_WW" title="Permalink to this definition">¶</a></dt>
-<dd><p>Diagonal of <span class="math notranslate nohighlight">\(W^{'}W + WW\)</span>.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.W.from_WSP">
-<em class="property">classmethod </em><code class="sig-name descname">from_WSP</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">WSP</span></em>, <em class="sig-param"><span class="n">silence_warnings</span><span class="o">=</span><span class="default_value">True</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/weights.html#W.from_WSP"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.W.from_WSP" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.W.from_adjlist">
-<em class="property">classmethod </em><code class="sig-name descname">from_adjlist</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">adjlist</span></em>, <em class="sig-param"><span class="n">focal_col</span><span class="o">=</span><span class="default_value">'focal'</span></em>, <em class="sig-param"><span class="n">neighbor_col</span><span class="o">=</span><span class="default_value">'neighbor'</span></em>, <em class="sig-param"><span class="n">weight_col</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/weights.html#W.from_adjlist"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.W.from_adjlist" title="Permalink to this definition">¶</a></dt>
-<dd><p>Return an adjacency list representation of a weights object.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>adjlist</strong><span class="classifier"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v1.2.1)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a></span></dt><dd><p>Adjacency list with a minimum of two columns.</p>
-</dd>
-<dt><strong>focal_col</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>Name of the column with the “source” node ids.</p>
-</dd>
-<dt><strong>neighbor_col</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>Name of the column with the “destination” node ids.</p>
-</dd>
-<dt><strong>weight_col</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>Name of the column with the weight information. If not provided and
-the dataframe has no column named “weight” then all weights
-are assumed to be 1.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.W.from_file">
-<em class="property">classmethod </em><code class="sig-name descname">from_file</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">path</span><span class="o">=</span><span class="default_value">''</span></em>, <em class="sig-param"><span class="n">format</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/weights.html#W.from_file"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.W.from_file" title="Permalink to this definition">¶</a></dt>
-<dd><p>Read a weights file into a W object.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Returns</dt>
-<dd class="field-odd"><dl class="simple">
-<dt><a class="reference internal" href="#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a> <a class="reference external" href="https://docs.python.org/3.8/library/functions.html#object" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">object</span></code></a></dt><dd></dd>
-</dl>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.W.from_networkx">
-<em class="property">classmethod </em><code class="sig-name descname">from_networkx</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">graph</span></em>, <em class="sig-param"><span class="n">weight_col</span><span class="o">=</span><span class="default_value">'weight'</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/weights.html#W.from_networkx"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.W.from_networkx" title="Permalink to this definition">¶</a></dt>
-<dd><p>Convert a <code class="docutils literal notranslate"><span class="pre">networkx</span></code> graph to a PySAL <code class="docutils literal notranslate"><span class="pre">W</span></code> object.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>graph</strong><span class="classifier"><a class="reference external" href="https://networkx.org/documentation/stable/reference/classes/graph.html#networkx.Graph" title="(in NetworkX v2.5)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">networkx.Graph</span></code></a></span></dt><dd><p>The graph to convert to a <code class="docutils literal notranslate"><span class="pre">W</span></code>.</p>
-</dd>
-<dt><strong>weight_col</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>If the graph is labeled, this should be the name of the field
-to use as the weight for the <code class="docutils literal notranslate"><span class="pre">W</span></code>.</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.W</span></code></a></span></dt><dd><p>A <code class="docutils literal notranslate"><span class="pre">W</span></code> object containing the same graph as the <code class="docutils literal notranslate"><span class="pre">networkx</span></code> graph.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.W.from_shapefile">
-<em class="property">classmethod </em><code class="sig-name descname">from_shapefile</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">*</span><span class="n">args</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/weights.html#W.from_shapefile"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.W.from_shapefile" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.W.full">
-<code class="sig-name descname">full</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/weights.html#W.full"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.W.full" title="Permalink to this definition">¶</a></dt>
-<dd><p>Generate a full <code class="docutils literal notranslate"><span class="pre">numpy.ndarray</span></code>.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>self</strong><span class="classifier"><a class="reference internal" href="#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.W</span></code></a></span></dt><dd><p>spatial weights object</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>(fullw, keys)</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#tuple" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">tuple</span></code></a></span></dt><dd><p>The first element being the full <code class="docutils literal notranslate"><span class="pre">numpy.ndarray</span></code> and second
-element keys being the ids associated with each row in the array.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">W</span><span class="p">,</span> <span class="n">full</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">neighbors</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;first&#39;</span><span class="p">:[</span><span class="s1">&#39;second&#39;</span><span class="p">],</span><span class="s1">&#39;second&#39;</span><span class="p">:[</span><span class="s1">&#39;first&#39;</span><span class="p">,</span><span class="s1">&#39;third&#39;</span><span class="p">],</span><span class="s1">&#39;third&#39;</span><span class="p">:[</span><span class="s1">&#39;second&#39;</span><span class="p">]}</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">weights</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;first&#39;</span><span class="p">:[</span><span class="mi">1</span><span class="p">],</span><span class="s1">&#39;second&#39;</span><span class="p">:[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span><span class="s1">&#39;third&#39;</span><span class="p">:[</span><span class="mi">1</span><span class="p">]}</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">W</span><span class="p">(</span><span class="n">neighbors</span><span class="p">,</span> <span class="n">weights</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wf</span><span class="p">,</span> <span class="n">ids</span> <span class="o">=</span> <span class="n">full</span><span class="p">(</span><span class="n">w</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wf</span>
-<span class="go">array([[0., 1., 0.],</span>
-<span class="go">       [1., 0., 1.],</span>
-<span class="go">       [0., 1., 0.]])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">ids</span>
-<span class="go">[&#39;first&#39;, &#39;second&#39;, &#39;third&#39;]</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.W.get_transform">
-<code class="sig-name descname">get_transform</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/weights.html#W.get_transform"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.W.get_transform" title="Permalink to this definition">¶</a></dt>
-<dd><p>Getter for transform property.</p>
-<dl class="field-list">
-<dt class="field-odd">Returns</dt>
-<dd class="field-odd"><dl>
-<dt><strong>transformation</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a>, <a class="reference external" href="https://docs.python.org/3.8/library/constants.html#None" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a></span></dt><dd><p>Valid transformation value. See the <code class="docutils literal notranslate"><span class="pre">transform</span></code> 
-parameters in <code class="docutils literal notranslate"><span class="pre">set_transform()</span></code> for a detailed description.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<div class="admonition seealso">
-<p class="admonition-title">See also</p>
-<dl class="simple">
-<dt><a class="reference internal" href="#libpysal.weights.W.set_transform" title="libpysal.weights.W.set_transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_transform</span></code></a></dt><dd></dd>
-</dl>
-</div>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">=</span><span class="n">lat2W</span><span class="p">()</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<span class="go">[1.0, 1.0]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">transform</span>
-<span class="go">&#39;O&#39;</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">transform</span><span class="o">=</span><span class="s1">&#39;r&#39;</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<span class="go">[0.5, 0.5]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">transform</span><span class="o">=</span><span class="s1">&#39;b&#39;</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<span class="go">[1.0, 1.0]</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.W.histogram">
-<em class="property">property </em><code class="sig-name descname">histogram</code><a class="headerlink" href="#libpysal.weights.W.histogram" title="Permalink to this definition">¶</a></dt>
-<dd><p>Cardinality histogram as a dictionary where key is the id and
-value is the number of neighbors for that unit.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.W.id2i">
-<em class="property">property </em><code class="sig-name descname">id2i</code><a class="headerlink" href="#libpysal.weights.W.id2i" title="Permalink to this definition">¶</a></dt>
-<dd><p>Dictionary where the key is an ID and the value is that ID’s
-index in <code class="docutils literal notranslate"><span class="pre">W.id_order</span></code>.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.W.id_order">
-<em class="property">property </em><code class="sig-name descname">id_order</code><a class="headerlink" href="#libpysal.weights.W.id_order" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the ids for the observations in the order in which they
-would be encountered if iterating over the weights.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.W.id_order_set">
-<em class="property">property </em><code class="sig-name descname">id_order_set</code><a class="headerlink" href="#libpysal.weights.W.id_order_set" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns <code class="docutils literal notranslate"><span class="pre">True</span></code> if user has set <code class="docutils literal notranslate"><span class="pre">id_order</span></code>, <code class="docutils literal notranslate"><span class="pre">False</span></code> if not.</p>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">=</span><span class="n">lat2W</span><span class="p">()</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">id_order_set</span>
-<span class="go">True</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.W.islands">
-<em class="property">property </em><code class="sig-name descname">islands</code><a class="headerlink" href="#libpysal.weights.W.islands" title="Permalink to this definition">¶</a></dt>
-<dd><p>List of ids without any neighbors.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.W.max_neighbors">
-<em class="property">property </em><code class="sig-name descname">max_neighbors</code><a class="headerlink" href="#libpysal.weights.W.max_neighbors" title="Permalink to this definition">¶</a></dt>
-<dd><p>Largest number of neighbors.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.W.mean_neighbors">
-<em class="property">property </em><code class="sig-name descname">mean_neighbors</code><a class="headerlink" href="#libpysal.weights.W.mean_neighbors" title="Permalink to this definition">¶</a></dt>
-<dd><p>Average number of neighbors.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.W.min_neighbors">
-<em class="property">property </em><code class="sig-name descname">min_neighbors</code><a class="headerlink" href="#libpysal.weights.W.min_neighbors" title="Permalink to this definition">¶</a></dt>
-<dd><p>Minimum number of neighbors.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.W.n">
-<em class="property">property </em><code class="sig-name descname">n</code><a class="headerlink" href="#libpysal.weights.W.n" title="Permalink to this definition">¶</a></dt>
-<dd><p>Number of units.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.W.n_components">
-<em class="property">property </em><code class="sig-name descname">n_components</code><a class="headerlink" href="#libpysal.weights.W.n_components" title="Permalink to this definition">¶</a></dt>
-<dd><p>Store whether the adjacency matrix is fully connected.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.W.neighbor_offsets">
-<em class="property">property </em><code class="sig-name descname">neighbor_offsets</code><a class="headerlink" href="#libpysal.weights.W.neighbor_offsets" title="Permalink to this definition">¶</a></dt>
-<dd><p>Given the current <code class="docutils literal notranslate"><span class="pre">id_order</span></code>, <code class="docutils literal notranslate"><span class="pre">neighbor_offsets[id]</span></code> is the
-offsets of the id’s neighbors in <code class="docutils literal notranslate"><span class="pre">id_order</span></code>.</p>
-<dl class="field-list">
-<dt class="field-odd">Returns</dt>
-<dd class="field-odd"><dl>
-<dt><strong>neighbor_list</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>Offsets of the id’s neighbors in <code class="docutils literal notranslate"><span class="pre">id_order</span></code>.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">W</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">neighbors</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;c&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;b&#39;</span><span class="p">],</span> <span class="s1">&#39;b&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;c&#39;</span><span class="p">,</span> <span class="s1">&#39;a&#39;</span><span class="p">],</span> <span class="s1">&#39;a&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;b&#39;</span><span class="p">]}</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">weights</span> <span class="o">=</span><span class="p">{</span><span class="s1">&#39;c&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">],</span> <span class="s1">&#39;b&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span> <span class="s1">&#39;a&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">]}</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">=</span><span class="n">W</span><span class="p">(</span><span class="n">neighbors</span><span class="p">,</span><span class="n">weights</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">id_order</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;a&#39;</span><span class="p">,</span><span class="s1">&#39;b&#39;</span><span class="p">,</span><span class="s1">&#39;c&#39;</span><span class="p">]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">neighbor_offsets</span><span class="p">[</span><span class="s1">&#39;b&#39;</span><span class="p">]</span>
-<span class="go">[2, 0]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">id_order</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;b&#39;</span><span class="p">,</span><span class="s1">&#39;a&#39;</span><span class="p">,</span><span class="s1">&#39;c&#39;</span><span class="p">]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">neighbor_offsets</span><span class="p">[</span><span class="s1">&#39;b&#39;</span><span class="p">]</span>
-<span class="go">[2, 1]</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.W.nonzero">
-<em class="property">property </em><code class="sig-name descname">nonzero</code><a class="headerlink" href="#libpysal.weights.W.nonzero" title="Permalink to this definition">¶</a></dt>
-<dd><p>Number of nonzero weights.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.W.pct_nonzero">
-<em class="property">property </em><code class="sig-name descname">pct_nonzero</code><a class="headerlink" href="#libpysal.weights.W.pct_nonzero" title="Permalink to this definition">¶</a></dt>
-<dd><p>Percentage of nonzero weights.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.W.plot">
-<code class="sig-name descname">plot</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">gdf</span></em>, <em class="sig-param"><span class="n">indexed_on</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">ax</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">color</span><span class="o">=</span><span class="default_value">'k'</span></em>, <em class="sig-param"><span class="n">node_kws</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">edge_kws</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/weights.html#W.plot"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.W.plot" title="Permalink to this definition">¶</a></dt>
-<dd><p>Plot spatial weights objects. <strong>Requires</strong> <code class="docutils literal notranslate"><span class="pre">matplotlib</span></code>, and
-implicitly requires a <code class="docutils literal notranslate"><span class="pre">geopandas.GeoDataFrame</span></code> as input.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>gdf</strong><span class="classifier"><a class="reference external" href="https://geopandas.readthedocs.io/en/latest/reference/geopandas.GeoDataFrame.html#geopandas.GeoDataFrame" title="(in GeoPandas v0+untagged.50.g5119410)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">geopandas.GeoDataFrame</span></code></a></span></dt><dd><p>The original shapes whose topological relations are modelled in <code class="docutils literal notranslate"><span class="pre">W</span></code>.</p>
-</dd>
-<dt><strong>indexed_on</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>Column of <code class="docutils literal notranslate"><span class="pre">geopandas.GeoDataFrame</span></code> that the weights object uses
-as an index. Default is <code class="docutils literal notranslate"><span class="pre">None</span></code>, so the index of the 
-<code class="docutils literal notranslate"><span class="pre">geopandas.GeoDataFrame</span></code> is used.</p>
-</dd>
-<dt><strong>ax</strong><span class="classifier"><a class="reference external" href="https://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes" title="(in Matplotlib v3.3.3)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">matplotlib.axes.Axes</span></code></a></span></dt><dd><p>Axis on which to plot the weights. Default is <code class="docutils literal notranslate"><span class="pre">None</span></code>, so
-plots on the current figure.</p>
-</dd>
-<dt><strong>color</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p><code class="docutils literal notranslate"><span class="pre">matplotlib</span></code> color string, will color both nodes and edges
-the same by default.</p>
-</dd>
-<dt><strong>node_kws</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#dict" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">dict</span></code></a></span></dt><dd><p>Keyword arguments dictionary to send to <code class="docutils literal notranslate"><span class="pre">pyplot.scatter</span></code>,
-which provides fine-grained control over the aesthetics
-of the nodes in the plot.</p>
-</dd>
-<dt><strong>edge_kws</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#dict" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">dict</span></code></a></span></dt><dd><p>Keyword arguments dictionary to send to <code class="docutils literal notranslate"><span class="pre">pyplot.plot</span></code>,
-which provides fine-grained control over the aesthetics
-of the edges in the plot.</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>f</strong><span class="classifier"><a class="reference external" href="https://matplotlib.org/api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure" title="(in Matplotlib v3.3.3)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">matplotlib.figure.Figure</span></code></a></span></dt><dd><p>Figure on which the plot is made.</p>
-</dd>
-<dt><strong>ax</strong><span class="classifier"><a class="reference external" href="https://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes" title="(in Matplotlib v3.3.3)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">matplotlib.axes.Axes</span></code></a></span></dt><dd><p>Axis on which the plot is made.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Notes</p>
-<p>If you’d like to overlay the actual shapes from the 
-<code class="docutils literal notranslate"><span class="pre">geopandas.GeoDataFrame</span></code>, call <code class="docutils literal notranslate"><span class="pre">gdf.plot(ax=ax)</span></code> after this.
-To plot underneath, adjust the z-order of the plot as follows: 
-<code class="docutils literal notranslate"><span class="pre">gdf.plot(ax=ax,zorder=0)</span></code>.</p>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">Queen</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">libpysal</span> <span class="k">as</span> <span class="nn">lp</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">geopandas</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">gdf</span> <span class="o">=</span> <span class="n">geopandas</span><span class="o">.</span><span class="n">read_file</span><span class="p">(</span><span class="n">lp</span><span class="o">.</span><span class="n">examples</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="s2">&quot;columbus.shp&quot;</span><span class="p">))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">weights</span> <span class="o">=</span> <span class="n">Queen</span><span class="o">.</span><span class="n">from_dataframe</span><span class="p">(</span><span class="n">gdf</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">tmp</span> <span class="o">=</span> <span class="n">weights</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">gdf</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;firebrickred&#39;</span><span class="p">,</span> <span class="n">node_kws</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">marker</span><span class="o">=</span><span class="s1">&#39;*&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">))</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.W.remap_ids">
-<code class="sig-name descname">remap_ids</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">new_ids</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/weights.html#W.remap_ids"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.W.remap_ids" title="Permalink to this definition">¶</a></dt>
-<dd><p>In place modification throughout <code class="docutils literal notranslate"><span class="pre">W</span></code> of id values from
-<code class="docutils literal notranslate"><span class="pre">w.id_order</span></code> to <code class="docutils literal notranslate"><span class="pre">new_ids</span></code> in all.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>new_ids</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.20)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></span></dt><dd><p>Aligned list of new ids to be inserted. Note that first
-element of <code class="docutils literal notranslate"><span class="pre">new_ids</span></code> will replace first element of
-<code class="docutils literal notranslate"><span class="pre">w.id_order</span></code>, second element of <code class="docutils literal notranslate"><span class="pre">new_ids</span></code> replaces second
-element of <code class="docutils literal notranslate"><span class="pre">w.id_order</span></code> and so on.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">id_order</span>
-<span class="go">[0, 1, 2, 3, 4, 5, 6, 7, 8]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<span class="go">[3, 1]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">new_ids</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;id</span><span class="si">%i</span><span class="s1">&#39;</span><span class="o">%</span><span class="nb">id</span> <span class="k">for</span> <span class="nb">id</span> <span class="ow">in</span> <span class="n">w</span><span class="o">.</span><span class="n">id_order</span><span class="p">]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">_</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">remap_ids</span><span class="p">(</span><span class="n">new_ids</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">id_order</span>
-<span class="go">[&#39;id0&#39;, &#39;id1&#39;, &#39;id2&#39;, &#39;id3&#39;, &#39;id4&#39;, &#39;id5&#39;, &#39;id6&#39;, &#39;id7&#39;, &#39;id8&#39;]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="s1">&#39;id0&#39;</span><span class="p">]</span>
-<span class="go">[&#39;id3&#39;, &#39;id1&#39;]</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.W.s0">
-<em class="property">property </em><code class="sig-name descname">s0</code><a class="headerlink" href="#libpysal.weights.W.s0" title="Permalink to this definition">¶</a></dt>
-<dd><p><code class="docutils literal notranslate"><span class="pre">s0</span></code> is defined as</p>
-<div class="math notranslate nohighlight">
-\[s0=\sum_i \sum_j w_{i,j}\]</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.W.s1">
-<em class="property">property </em><code class="sig-name descname">s1</code><a class="headerlink" href="#libpysal.weights.W.s1" title="Permalink to this definition">¶</a></dt>
-<dd><p><code class="docutils literal notranslate"><span class="pre">s1</span></code> is defined as</p>
-<div class="math notranslate nohighlight">
-\[s1=1/2 \sum_i \sum_j \Big(w_{i,j} + w_{j,i}\Big)^2\]</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.W.s2">
-<em class="property">property </em><code class="sig-name descname">s2</code><a class="headerlink" href="#libpysal.weights.W.s2" title="Permalink to this definition">¶</a></dt>
-<dd><p><code class="docutils literal notranslate"><span class="pre">s2</span></code> is defined as</p>
-<div class="math notranslate nohighlight">
-\[s2=\sum_j \Big(\sum_i w_{i,j} + \sum_i w_{j,i}\Big)^2\]</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.W.s2array">
-<em class="property">property </em><code class="sig-name descname">s2array</code><a class="headerlink" href="#libpysal.weights.W.s2array" title="Permalink to this definition">¶</a></dt>
-<dd><p>Individual elements comprising <code class="docutils literal notranslate"><span class="pre">s2</span></code>.</p>
-<div class="admonition seealso">
-<p class="admonition-title">See also</p>
-<dl class="simple">
-<dt><a class="reference internal" href="#libpysal.weights.W.s2" title="libpysal.weights.W.s2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">s2</span></code></a></dt><dd></dd>
-</dl>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.W.sd">
-<em class="property">property </em><code class="sig-name descname">sd</code><a class="headerlink" href="#libpysal.weights.W.sd" title="Permalink to this definition">¶</a></dt>
-<dd><p>Standard deviation of number of neighbors.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.W.set_shapefile">
-<code class="sig-name descname">set_shapefile</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">shapefile</span></em>, <em class="sig-param"><span class="n">idVariable</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">full</span><span class="o">=</span><span class="default_value">False</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/weights.html#W.set_shapefile"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.W.set_shapefile" title="Permalink to this definition">¶</a></dt>
-<dd><p>Adding metadata for writing headers of <code class="docutils literal notranslate"><span class="pre">.gal</span></code> and <code class="docutils literal notranslate"><span class="pre">.gwt</span></code> files.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>shapefile</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>The shapefile name used to construct weights.</p>
-</dd>
-<dt><strong>idVariable</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>The name of the attribute in the shapefile to associate
-with ids in the weights.</p>
-</dd>
-<dt><strong>full</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.8)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>Write out the entire path for a shapefile (<code class="docutils literal notranslate"><span class="pre">True</span></code>) or
-only the base of the shapefile without extension (<code class="docutils literal notranslate"><span class="pre">False</span></code>).
-Default is <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.W.set_transform">
-<code class="sig-name descname">set_transform</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">value</span><span class="o">=</span><span class="default_value">'B'</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/weights.html#W.set_transform"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.W.set_transform" title="Permalink to this definition">¶</a></dt>
-<dd><p>Transformations of weights.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>transform</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>This parameter is not case sensitive. The following are
-valid transformations.</p>
-<ul class="simple">
-<li><p><strong>B</strong> – Binary</p></li>
-<li><p><strong>R</strong> – Row-standardization (global sum <span class="math notranslate nohighlight">\(=n\)</span>)</p></li>
-<li><p><strong>D</strong> – Double-standardization (global sum <span class="math notranslate nohighlight">\(=1\)</span>)</p></li>
-<li><p><strong>V</strong> – Variance stabilizing</p></li>
-<li><p><strong>O</strong> – Restore original transformation (from instantiation)</p></li>
-</ul>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Notes</p>
-<p>Transformations are applied only to the value of the weights at
-instantiation. Chaining of transformations cannot be done on a <code class="docutils literal notranslate"><span class="pre">W</span></code>
-instance.</p>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">=</span><span class="n">lat2W</span><span class="p">()</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<span class="go">[1.0, 1.0]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">transform</span>
-<span class="go">&#39;O&#39;</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">transform</span><span class="o">=</span><span class="s1">&#39;r&#39;</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<span class="go">[0.5, 0.5]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">transform</span><span class="o">=</span><span class="s1">&#39;b&#39;</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<span class="go">[1.0, 1.0]</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.W.sparse">
-<em class="property">property </em><code class="sig-name descname">sparse</code><a class="headerlink" href="#libpysal.weights.W.sparse" title="Permalink to this definition">¶</a></dt>
-<dd><p>Sparse matrix object. For any matrix manipulations required for w,
-<code class="docutils literal notranslate"><span class="pre">w.sparse</span></code> should be used. This is based on <code class="docutils literal notranslate"><span class="pre">scipy.sparse</span></code>.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.W.symmetrize">
-<code class="sig-name descname">symmetrize</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">inplace</span><span class="o">=</span><span class="default_value">False</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/weights.html#W.symmetrize"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.W.symmetrize" title="Permalink to this definition">¶</a></dt>
-<dd><p>Construct a symmetric KNN weight. This ensures that the neighbors
-of each focal observation consider the focal observation itself as
-a neighbor. This returns a generic <code class="docutils literal notranslate"><span class="pre">W</span></code> object, since the object is no
-longer guaranteed to have <code class="docutils literal notranslate"><span class="pre">k</span></code> neighbors for each observation.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.W.to_WSP">
-<code class="sig-name descname">to_WSP</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/weights.html#W.to_WSP"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.W.to_WSP" title="Permalink to this definition">¶</a></dt>
-<dd><p>Generate a <code class="docutils literal notranslate"><span class="pre">WSP</span></code> object.</p>
-<dl class="field-list">
-<dt class="field-odd">Returns</dt>
-<dd class="field-odd"><dl>
-<dt><strong>implicit</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.WSP.html#libpysal.weights.WSP" title="libpysal.weights.WSP"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.WSP</span></code></a></span></dt><dd><p>Thin <code class="docutils literal notranslate"><span class="pre">W</span></code> class</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<div class="admonition seealso">
-<p class="admonition-title">See also</p>
-<dl class="simple">
-<dt><a class="reference internal" href="libpysal.weights.WSP.html#libpysal.weights.WSP" title="libpysal.weights.WSP"><code class="xref py py-obj docutils literal notranslate"><span class="pre">WSP</span></code></a></dt><dd></dd>
-</dl>
-</div>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">W</span><span class="p">,</span> <span class="n">WSP</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">neighbors</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;first&#39;</span><span class="p">:[</span><span class="s1">&#39;second&#39;</span><span class="p">],</span><span class="s1">&#39;second&#39;</span><span class="p">:[</span><span class="s1">&#39;first&#39;</span><span class="p">,</span><span class="s1">&#39;third&#39;</span><span class="p">],</span><span class="s1">&#39;third&#39;</span><span class="p">:[</span><span class="s1">&#39;second&#39;</span><span class="p">]}</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">weights</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;first&#39;</span><span class="p">:[</span><span class="mi">1</span><span class="p">],</span><span class="s1">&#39;second&#39;</span><span class="p">:[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span><span class="s1">&#39;third&#39;</span><span class="p">:[</span><span class="mi">1</span><span class="p">]}</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">=</span><span class="n">W</span><span class="p">(</span><span class="n">neighbors</span><span class="p">,</span><span class="n">weights</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wsp</span><span class="o">=</span><span class="n">w</span><span class="o">.</span><span class="n">to_WSP</span><span class="p">()</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">isinstance</span><span class="p">(</span><span class="n">wsp</span><span class="p">,</span> <span class="n">WSP</span><span class="p">)</span>
-<span class="go">True</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wsp</span><span class="o">.</span><span class="n">n</span>
-<span class="go">3</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wsp</span><span class="o">.</span><span class="n">s0</span>
-<span class="go">4</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.W.to_adjlist">
-<code class="sig-name descname">to_adjlist</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">remove_symmetric</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">focal_col</span><span class="o">=</span><span class="default_value">'focal'</span></em>, <em class="sig-param"><span class="n">neighbor_col</span><span class="o">=</span><span class="default_value">'neighbor'</span></em>, <em class="sig-param"><span class="n">weight_col</span><span class="o">=</span><span class="default_value">'weight'</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/weights.html#W.to_adjlist"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.W.to_adjlist" title="Permalink to this definition">¶</a></dt>
-<dd><p>Compute an adjacency list representation of a weights object.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>remove_symmetric</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.8)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>Whether or not to remove symmetric entries. If the <code class="docutils literal notranslate"><span class="pre">W</span></code>
-is symmetric, a standard directed adjacency list will contain
-both the forward and backward links by default because adjacency
-lists are a directed graph representation. If this is <code class="docutils literal notranslate"><span class="pre">True</span></code>,
-a <code class="docutils literal notranslate"><span class="pre">W</span></code> created from this adjacency list <strong>MAY NOT BE THE SAME</strong>
-as the original <code class="docutils literal notranslate"><span class="pre">W</span></code>. If you would like to consider (1,2) and
-(2,1) as distinct links, leave this as <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p>
-</dd>
-<dt><strong>focal_col</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>Name of the column in which to store “source” node ids.</p>
-</dd>
-<dt><strong>neighbor_col</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>Name of the column in which to store “destination” node ids.</p>
-</dd>
-<dt><strong>weight_col</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>Name of the column in which to store weight information.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.W.to_file">
-<code class="sig-name descname">to_file</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">path</span><span class="o">=</span><span class="default_value">''</span></em>, <em class="sig-param"><span class="n">format</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/weights.html#W.to_file"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.W.to_file" title="Permalink to this definition">¶</a></dt>
-<dd><p>Write a weights to a file. The format is guessed automatically 
-from the path, but can be overridden with the format argument.</p>
-<p>See libpysal.io.FileIO for more information.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Returns</dt>
-<dd class="field-odd"><dl class="simple">
-<dt><a class="reference external" href="https://docs.python.org/3.8/library/constants.html#None" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a></dt><dd></dd>
-</dl>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.W.to_networkx">
-<code class="sig-name descname">to_networkx</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/weights.html#W.to_networkx"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.W.to_networkx" title="Permalink to this definition">¶</a></dt>
-<dd><p>Convert a weights object to a <code class="docutils literal notranslate"><span class="pre">networkx</span></code> graph.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Returns</dt>
-<dd class="field-odd"><dl class="simple">
-<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">A</span></code> <code class="docutils literal notranslate"><span class="pre">networkx</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">graph</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">representation</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">of</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="docutils literal notranslate"><span class="pre">W</span></code> object.</dt><dd></dd>
-</dl>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.W.transform">
-<em class="property">property </em><code class="sig-name descname">transform</code><a class="headerlink" href="#libpysal.weights.W.transform" title="Permalink to this definition">¶</a></dt>
-<dd><p>Getter for transform property.</p>
-<dl class="field-list">
-<dt class="field-odd">Returns</dt>
-<dd class="field-odd"><dl>
-<dt><strong>transformation</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a>, <a class="reference external" href="https://docs.python.org/3.8/library/constants.html#None" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a></span></dt><dd><p>Valid transformation value. See the <code class="docutils literal notranslate"><span class="pre">transform</span></code> 
-parameters in <code class="docutils literal notranslate"><span class="pre">set_transform()</span></code> for a detailed description.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<div class="admonition seealso">
-<p class="admonition-title">See also</p>
-<dl class="simple">
-<dt><a class="reference internal" href="#libpysal.weights.W.set_transform" title="libpysal.weights.W.set_transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_transform</span></code></a></dt><dd></dd>
-</dl>
-</div>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">=</span><span class="n">lat2W</span><span class="p">()</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<span class="go">[1.0, 1.0]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">transform</span>
-<span class="go">&#39;O&#39;</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">transform</span><span class="o">=</span><span class="s1">&#39;r&#39;</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<span class="go">[0.5, 0.5]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">transform</span><span class="o">=</span><span class="s1">&#39;b&#39;</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<span class="go">[1.0, 1.0]</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.W.trcW2">
-<em class="property">property </em><code class="sig-name descname">trcW2</code><a class="headerlink" href="#libpysal.weights.W.trcW2" title="Permalink to this definition">¶</a></dt>
-<dd><p>Trace of <span class="math notranslate nohighlight">\(WW\)</span>.</p>
-<div class="admonition seealso">
-<p class="admonition-title">See also</p>
-<dl class="simple">
-<dt><a class="reference internal" href="#libpysal.weights.W.diagW2" title="libpysal.weights.W.diagW2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagW2</span></code></a></dt><dd></dd>
-</dl>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.W.trcWtW">
-<em class="property">property </em><code class="sig-name descname">trcWtW</code><a class="headerlink" href="#libpysal.weights.W.trcWtW" title="Permalink to this definition">¶</a></dt>
-<dd><p>Trace of <span class="math notranslate nohighlight">\(W^{'}W\)</span>.</p>
-<div class="admonition seealso">
-<p class="admonition-title">See also</p>
-<dl class="simple">
-<dt><a class="reference internal" href="#libpysal.weights.W.diagWtW" title="libpysal.weights.W.diagWtW"><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagWtW</span></code></a></dt><dd></dd>
-</dl>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.W.trcWtW_WW">
-<em class="property">property </em><code class="sig-name descname">trcWtW_WW</code><a class="headerlink" href="#libpysal.weights.W.trcWtW_WW" title="Permalink to this definition">¶</a></dt>
-<dd><p>Trace of <span class="math notranslate nohighlight">\(W^{'}W + WW\)</span>.</p>
-</dd></dl>
-
-</dd></dl>
-
-</div>
-
-
-    </div>
-      
-  </div>
-</div>
-<footer class="footer">
-  <div class="container">
-    <p class="pull-right">
-      <a href="#">Back to top</a>
-      
-        <br/>
-        
-<div id="sourcelink">
-  <a href="../_sources/generated/libpysal.weights.W.rst.txt"
-     rel="nofollow">Source</a>
-</div>
-      
-    </p>
-    <p>
-        &copy; Copyright 2018-, pysal developers.<br/>
-      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 3.1.2.<br/>
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-  <div class="section" id="libpysal-weights-wsp">
-<h1>libpysal.weights.WSP<a class="headerlink" href="#libpysal-weights-wsp" title="Permalink to this headline">¶</a></h1>
-<dl class="py class">
-<dt id="libpysal.weights.WSP">
-<em class="property">class </em><code class="sig-prename descclassname">libpysal.weights.</code><code class="sig-name descname">WSP</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sparse</span></em>, <em class="sig-param"><span class="n">id_order</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">index</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/weights.html#WSP"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.WSP" title="Permalink to this definition">¶</a></dt>
-<dd><p>Thin <code class="docutils literal notranslate"><span class="pre">W</span></code> class for <code class="docutils literal notranslate"><span class="pre">spreg</span></code>.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl class="simple">
-<dt><strong>sparse</strong><span class="classifier">scipy.sparse.{matrix-type}</span></dt><dd><p>NxN object from <code class="docutils literal notranslate"><span class="pre">scipy.sparse</span></code></p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<p>From GAL information</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">scipy.sparse</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">WSP</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">rows</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">cols</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">weights</span> <span class="o">=</span>  <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mf">0.75</span><span class="p">,</span> <span class="mf">0.25</span><span class="p">,</span> <span class="mf">0.9</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">sparse</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">csr_matrix</span><span class="p">((</span><span class="n">weights</span><span class="p">,</span> <span class="p">(</span><span class="n">rows</span><span class="p">,</span> <span class="n">cols</span><span class="p">)),</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">WSP</span><span class="p">(</span><span class="n">sparse</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">s0</span>
-<span class="go">4.0</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">trcWtW_WW</span>
-<span class="go">6.395</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">n</span>
-<span class="go">4</span>
-</pre></div>
-</div>
-<dl class="field-list">
-<dt class="field-odd">Attributes</dt>
-<dd class="field-odd"><dl>
-<dt><strong>n</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#int" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>description</p>
-</dd>
-<dt><a class="reference internal" href="#libpysal.weights.WSP.s0" title="libpysal.weights.WSP.s0"><code class="xref py py-obj docutils literal notranslate"><span class="pre">s0</span></code></a><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p><code class="docutils literal notranslate"><span class="pre">s0</span></code> is defined as:</p>
-</dd>
-<dt><a class="reference internal" href="#libpysal.weights.WSP.trcWtW_WW" title="libpysal.weights.WSP.trcWtW_WW"><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcWtW_WW</span></code></a><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>Trace of <span class="math notranslate nohighlight">\(W^{'}W + WW\)</span>.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<dl class="py method">
-<dt id="libpysal.weights.WSP.__init__">
-<code class="sig-name descname">__init__</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sparse</span></em>, <em class="sig-param"><span class="n">id_order</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">index</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/weights.html#WSP.__init__"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.WSP.__init__" title="Permalink to this definition">¶</a></dt>
-<dd><p>Initialize self.  See help(type(self)) for accurate signature.</p>
-</dd></dl>
-
-<p class="rubric">Methods</p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.WSP.__init__" title="libpysal.weights.WSP.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(sparse[, id_order, index])</p></td>
-<td><p>Initialize self.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.WSP.from_W" title="libpysal.weights.WSP.from_W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_W</span></code></a>(W)</p></td>
-<td><p>Constructs a <code class="docutils literal notranslate"><span class="pre">WSP</span></code> object from the <code class="docutils literal notranslate"><span class="pre">W</span></code>’s sparse matrix.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.WSP.to_W" title="libpysal.weights.WSP.to_W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_W</span></code></a>([silence_warnings])</p></td>
-<td><p>Convert a pysal WSP object (thin weights matrix) to a pysal W object.</p></td>
-</tr>
-</tbody>
-</table>
-<p class="rubric">Attributes</p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.WSP.diagWtW_WW" title="libpysal.weights.WSP.diagWtW_WW"><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagWtW_WW</span></code></a></p></td>
-<td><p>Diagonal of <span class="math notranslate nohighlight">\(W^{'}W + WW\)</span>.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.WSP.id_order" title="libpysal.weights.WSP.id_order"><code class="xref py py-obj docutils literal notranslate"><span class="pre">id_order</span></code></a></p></td>
-<td><p>An ordered list of ids, assumed to match the ordering in <code class="docutils literal notranslate"><span class="pre">sparse</span></code>.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.WSP.s0" title="libpysal.weights.WSP.s0"><code class="xref py py-obj docutils literal notranslate"><span class="pre">s0</span></code></a></p></td>
-<td><p><code class="docutils literal notranslate"><span class="pre">s0</span></code> is defined as:</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.WSP.trcWtW_WW" title="libpysal.weights.WSP.trcWtW_WW"><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcWtW_WW</span></code></a></p></td>
-<td><p>Trace of <span class="math notranslate nohighlight">\(W^{'}W + WW\)</span>.</p></td>
-</tr>
-</tbody>
-</table>
-<dl class="py method">
-<dt id="libpysal.weights.WSP.diagWtW_WW">
-<em class="property">property </em><code class="sig-name descname">diagWtW_WW</code><a class="headerlink" href="#libpysal.weights.WSP.diagWtW_WW" title="Permalink to this definition">¶</a></dt>
-<dd><p>Diagonal of <span class="math notranslate nohighlight">\(W^{'}W + WW\)</span>.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.WSP.from_W">
-<em class="property">classmethod </em><code class="sig-name descname">from_W</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">W</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/weights.html#WSP.from_W"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.WSP.from_W" title="Permalink to this definition">¶</a></dt>
-<dd><p>Constructs a <code class="docutils literal notranslate"><span class="pre">WSP</span></code> object from the <code class="docutils literal notranslate"><span class="pre">W</span></code>’s sparse matrix.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>W</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.W</span></code></a></span></dt><dd><p>A PySAL weights object with a sparse form and ids.</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl class="simple">
-<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">A</span></code> <code class="docutils literal notranslate"><span class="pre">WSP</span></code> instance.</dt><dd></dd>
-</dl>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.WSP.id_order">
-<em class="property">property </em><code class="sig-name descname">id_order</code><a class="headerlink" href="#libpysal.weights.WSP.id_order" title="Permalink to this definition">¶</a></dt>
-<dd><p>An ordered list of ids, assumed to match the ordering in <code class="docutils literal notranslate"><span class="pre">sparse</span></code>.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.WSP.s0">
-<em class="property">property </em><code class="sig-name descname">s0</code><a class="headerlink" href="#libpysal.weights.WSP.s0" title="Permalink to this definition">¶</a></dt>
-<dd><p><code class="docutils literal notranslate"><span class="pre">s0</span></code> is defined as:</p>
-<div class="math notranslate nohighlight">
-\[s0=\sum_i \sum_j w_{i,j}\]</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.WSP.to_W">
-<code class="sig-name descname">to_W</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">silence_warnings</span><span class="o">=</span><span class="default_value">False</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/weights.html#WSP.to_W"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.WSP.to_W" title="Permalink to this definition">¶</a></dt>
-<dd><p>Convert a pysal WSP object (thin weights matrix) to a pysal W object.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>self</strong><span class="classifier"><a class="reference internal" href="#libpysal.weights.WSP" title="libpysal.weights.WSP"><code class="xref py py-obj docutils literal notranslate"><span class="pre">WSP</span></code></a></span></dt><dd><p>PySAL sparse weights object.</p>
-</dd>
-<dt><strong>silence_warnings</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.8)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>Switch to <code class="docutils literal notranslate"><span class="pre">True</span></code> to turn off print statements for every
-observation with islands. Default is <code class="docutils literal notranslate"><span class="pre">False</span></code>, which does
-not silence warnings.</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>PySAL weights object.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2SW</span><span class="p">,</span> <span class="n">WSP</span><span class="p">,</span> <span class="n">WSP2W</span>
-</pre></div>
-</div>
-<p>Build a 10x10 <code class="docutils literal notranslate"><span class="pre">scipy.sparse</span></code> matrix for a rectangular 2x5
-region of cells (rook contiguity), then construct a <code class="docutils literal notranslate"><span class="pre">libpysal</span></code>
-sparse weights object (<code class="docutils literal notranslate"><span class="pre">self</span></code>).</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">sp</span> <span class="o">=</span> <span class="n">lat2SW</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="bp">self</span> <span class="o">=</span> <span class="n">WSP</span><span class="p">(</span><span class="n">sp</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="bp">self</span><span class="o">.</span><span class="n">n</span>
-<span class="go">10</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sparse</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">todense</span><span class="p">())</span>
-<span class="go">[[0 1 0 0 0 1 0 0 0 0]]</span>
-</pre></div>
-</div>
-<p>Convert this sparse weights object to a standard PySAL weights object.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">WSP2W</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">n</span>
-<span class="go">10</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">full</span><span class="p">()[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span>
-<span class="go">[0. 1. 0. 0. 0. 1. 0. 0. 0. 0.]</span>
-</pre></div>
-</div>
-</dd></dl>
-
-<dl class="py method">
-<dt id="libpysal.weights.WSP.trcWtW_WW">
-<em class="property">property </em><code class="sig-name descname">trcWtW_WW</code><a class="headerlink" href="#libpysal.weights.WSP.trcWtW_WW" title="Permalink to this definition">¶</a></dt>
-<dd><p>Trace of <span class="math notranslate nohighlight">\(W^{'}W + WW\)</span>.</p>
-</dd></dl>
-
-</dd></dl>
-
-</div>
-
-
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-      
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-</div>
-<footer class="footer">
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-<div id="sourcelink">
-  <a href="../_sources/generated/libpysal.weights.WSP.rst.txt"
-     rel="nofollow">Source</a>
-</div>
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-    <p>
-        &copy; Copyright 2018-, pysal developers.<br/>
-      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 3.1.2.<br/>
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diff --git a/docs/generated/libpysal.weights.WSP2W.html b/docs/generated/libpysal.weights.WSP2W.html
deleted file mode 100644
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-<h1>libpysal.weights.WSP2W<a class="headerlink" href="#libpysal-weights-wsp2w" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.weights.WSP2W">
-<code class="sig-prename descclassname">libpysal.weights.</code><code class="sig-name descname">WSP2W</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">wsp</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/util.html#WSP2W"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.WSP2W" title="Permalink to this definition">¶</a></dt>
-<dd><p>Convert a pysal WSP object (thin weights matrix) to a pysal W object.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>wsp</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.WSP.html#libpysal.weights.WSP" title="libpysal.weights.WSP"><code class="xref py py-obj docutils literal notranslate"><span class="pre">WSP</span></code></a></span></dt><dd><p>PySAL sparse weights object</p>
-</dd>
-<dt><strong>**kwargs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/keyword.html#module-keyword" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code></span></dt><dd><p>optional arguments for <code class="xref py py-class docutils literal notranslate"><span class="pre">pysal.weights.W</span></code></p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>PySAL weights object</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span><span class="p">,</span> <span class="n">WSP</span><span class="p">,</span> <span class="n">WSP2W</span>
-</pre></div>
-</div>
-<p>Build a 10x10 scipy.sparse matrix for a rectangular 2x5 region of cells
-(rook contiguity), then construct a PySAL sparse weights object (wsp).</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">sp</span> <span class="o">=</span> <span class="n">lat2SW</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wsp</span> <span class="o">=</span> <span class="n">WSP</span><span class="p">(</span><span class="n">sp</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wsp</span><span class="o">.</span><span class="n">n</span>
-<span class="go">10</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wsp</span><span class="o">.</span><span class="n">sparse</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">todense</span><span class="p">()</span>
-<span class="go">matrix([[0, 1, 0, 0, 0, 1, 0, 0, 0, 0]], dtype=int8)</span>
-</pre></div>
-</div>
-<p>Convert this sparse weights object to a standard PySAL weights object.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">WSP2W</span><span class="p">(</span><span class="n">wsp</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">n</span>
-<span class="go">10</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">full</span><span class="p">()[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span>
-<span class="go">[0. 1. 0. 0. 0. 1. 0. 0. 0. 0.]</span>
-</pre></div>
-</div>
-</dd></dl>
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diff --git a/docs/generated/libpysal.weights.attach_islands.html b/docs/generated/libpysal.weights.attach_islands.html
deleted file mode 100644
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-<h1>libpysal.weights.attach_islands<a class="headerlink" href="#libpysal-weights-attach-islands" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.weights.attach_islands">
-<code class="sig-prename descclassname">libpysal.weights.</code><code class="sig-name descname">attach_islands</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">w</span></em>, <em class="sig-param"><span class="n">w_knn1</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/util.html#attach_islands"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.attach_islands" title="Permalink to this definition">¶</a></dt>
-<dd><p>Attach nearest neighbor to islands in spatial weight w.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.W</span></code></a></span></dt><dd><p>pysal spatial weight object (unstandardized).</p>
-</dd>
-<dt><strong>w_knn1</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.W</span></code></a></span></dt><dd><p>Nearest neighbor pysal spatial weight object (k=1).</p>
-</dd>
-<dt><strong>**kwargs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/keyword.html#module-keyword" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code></span></dt><dd><p>optional arguments for <code class="xref py py-class docutils literal notranslate"><span class="pre">pysal.weights.W</span></code></p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl class="simple">
-<dt>: <a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.W</span></code></a></dt><dd><p>pysal spatial weight object w without islands.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span><span class="p">,</span> <span class="n">Rook</span><span class="p">,</span> <span class="n">KNN</span><span class="p">,</span> <span class="n">attach_islands</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">libpysal</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">Rook</span><span class="o">.</span><span class="n">from_shapefile</span><span class="p">(</span><span class="n">libpysal</span><span class="o">.</span><span class="n">examples</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="s1">&#39;10740.shp&#39;</span><span class="p">))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">islands</span>
-<span class="go">[163]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w_knn1</span> <span class="o">=</span> <span class="n">KNN</span><span class="o">.</span><span class="n">from_shapefile</span><span class="p">(</span><span class="n">libpysal</span><span class="o">.</span><span class="n">examples</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="s1">&#39;10740.shp&#39;</span><span class="p">),</span><span class="n">k</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w_attach</span> <span class="o">=</span> <span class="n">attach_islands</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">w_knn1</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w_attach</span><span class="o">.</span><span class="n">islands</span>
-<span class="go">[]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w_attach</span><span class="p">[</span><span class="n">w</span><span class="o">.</span><span class="n">islands</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span>
-<span class="go">{166: 1.0}</span>
-</pre></div>
-</div>
-</dd></dl>
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diff --git a/docs/generated/libpysal.weights.block_weights.html b/docs/generated/libpysal.weights.block_weights.html
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-<h1>libpysal.weights.block_weights<a class="headerlink" href="#libpysal-weights-block-weights" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.weights.block_weights">
-<code class="sig-prename descclassname">libpysal.weights.</code><code class="sig-name descname">block_weights</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">regimes</span></em>, <em class="sig-param"><span class="n">ids</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">sparse</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/util.html#block_weights"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.block_weights" title="Permalink to this definition">¶</a></dt>
-<dd><p>Construct spatial weights for regime neighbors.</p>
-<p>Block contiguity structures are relevant when defining neighbor relations
-based on membership in a regime. For example, all counties belonging to
-the same state could be defined as neighbors, in an analysis of all
-counties in the US.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>regimes</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.20)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a></span></dt><dd><p>ids of which regime an observation belongs to</p>
-</dd>
-<dt><strong>ids</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.20)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a></span></dt><dd><p>Ordered sequence of IDs for the observations</p>
-</dd>
-<dt><strong>sparse</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.8)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>If True return WSP instance
-If False return W instance</p>
-</dd>
-<dt><strong>**kwargs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/keyword.html#module-keyword" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code></span></dt><dd><p>optional arguments for <code class="xref py py-class docutils literal notranslate"><span class="pre">pysal.weights.W</span></code></p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>W</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">spatial</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">weights</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">instance</span></code></span></dt><dd></dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">block_weights</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">regimes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">25</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">regimes</span><span class="p">[</span><span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">20</span><span class="p">)]</span> <span class="o">=</span> <span class="mi">2</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">regimes</span><span class="p">[</span><span class="nb">range</span><span class="p">(</span><span class="mi">21</span><span class="p">,</span><span class="mi">25</span><span class="p">)]</span> <span class="o">=</span> <span class="mi">3</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">regimes</span>
-<span class="go">array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 2., 2., 2., 2., 2., 2.,</span>
-<span class="go">       2., 2., 2., 1., 3., 3., 3., 3.])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">block_weights</span><span class="p">(</span><span class="n">regimes</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<span class="go">[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<span class="go">[1, 2, 3, 4, 5, 6, 7, 8, 9, 20]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">regimes</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;n&#39;</span><span class="p">,</span><span class="s1">&#39;n&#39;</span><span class="p">,</span><span class="s1">&#39;s&#39;</span><span class="p">,</span><span class="s1">&#39;s&#39;</span><span class="p">,</span><span class="s1">&#39;e&#39;</span><span class="p">,</span><span class="s1">&#39;e&#39;</span><span class="p">,</span><span class="s1">&#39;w&#39;</span><span class="p">,</span><span class="s1">&#39;w&#39;</span><span class="p">,</span><span class="s1">&#39;e&#39;</span><span class="p">]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">regimes</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">block_weights</span><span class="p">(</span><span class="n">regimes</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">neighbors</span> <span class="o">==</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">1</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">2</span><span class="p">:</span> <span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="mi">3</span><span class="p">:</span> <span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="mi">4</span><span class="p">:</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">8</span><span class="p">],</span> <span class="mi">5</span><span class="p">:</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">],</span> <span class="mi">6</span><span class="p">:</span> <span class="p">[</span><span class="mi">7</span><span class="p">],</span> <span class="mi">7</span><span class="p">:</span> <span class="p">[</span><span class="mi">6</span><span class="p">],</span> <span class="mi">8</span><span class="p">:</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">]}</span>
-<span class="go">True</span>
-</pre></div>
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-<h1>libpysal.weights.build_lattice_shapefile<a class="headerlink" href="#libpysal-weights-build-lattice-shapefile" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.weights.build_lattice_shapefile">
-<code class="sig-prename descclassname">libpysal.weights.</code><code class="sig-name descname">build_lattice_shapefile</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">nrows</span></em>, <em class="sig-param"><span class="n">ncols</span></em>, <em class="sig-param"><span class="n">outFileName</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/user.html#build_lattice_shapefile"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.build_lattice_shapefile" title="Permalink to this definition">¶</a></dt>
-<dd><p>Build a lattice shapefile with nrows rows and ncols cols.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>nrows</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#int" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>Number of rows</p>
-</dd>
-<dt><strong>ncols</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#int" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>Number of cols</p>
-</dd>
-<dt><strong>outFileName</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>shapefile name with shp suffix</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl class="simple">
-<dt><a class="reference external" href="https://docs.python.org/3.8/library/constants.html#None" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a></dt><dd></dd>
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-<h1>libpysal.weights.comb<a class="headerlink" href="#libpysal-weights-comb" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.weights.comb">
-<code class="sig-prename descclassname">libpysal.weights.</code><code class="sig-name descname">comb</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">items</span></em>, <em class="sig-param"><span class="n">n</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/util.html#comb"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.comb" title="Permalink to this definition">¶</a></dt>
-<dd><p>Combinations of size n taken from items</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>items</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>items to be drawn from</p>
-</dd>
-<dt><strong>n</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">integer</span></code></span></dt><dd><p>size of combinations to take from items</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>implicit</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/glossary.html#term-generator" title="(in Python v3.8)"><span>generator</span></a></span></dt><dd><p>combinations of size n taken from items</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">x</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">comb</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">2</span><span class="p">):</span>
-<span class="gp">... </span>    <span class="nb">print</span><span class="p">(</span><span class="n">c</span><span class="p">)</span>
-<span class="gp">...</span>
-<span class="go">[0, 1]</span>
-<span class="go">[0, 2]</span>
-<span class="go">[0, 3]</span>
-<span class="go">[1, 2]</span>
-<span class="go">[1, 3]</span>
-<span class="go">[2, 3]</span>
-</pre></div>
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-<h1>libpysal.weights.da2W<a class="headerlink" href="#libpysal-weights-da2w" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.weights.da2W">
-<code class="sig-prename descclassname">libpysal.weights.</code><code class="sig-name descname">da2W</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">da</span></em>, <em class="sig-param"><span class="n">criterion</span><span class="o">=</span><span class="default_value">'queen'</span></em>, <em class="sig-param"><span class="n">z_value</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">coords_labels</span><span class="o">=</span><span class="default_value">{}</span></em>, <em class="sig-param"><span class="n">k</span><span class="o">=</span><span class="default_value">1</span></em>, <em class="sig-param"><span class="n">include_nodata</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">n_jobs</span><span class="o">=</span><span class="default_value">1</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/raster.html#da2W"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.da2W" title="Permalink to this definition">¶</a></dt>
-<dd><p>Create a W object from xarray.DataArray with an additional
-attribute index containing coordinate values of the raster
-in the form of Pandas.Index/MultiIndex.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>da</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.DataArray</span></code></span></dt><dd><p>Input 2D or 3D DataArray with shape=(z, y, x)</p>
-</dd>
-<dt><strong>criterion</strong><span class="classifier">{“rook”, “queen”}</span></dt><dd><p>Type of contiguity. Default is queen.</p>
-</dd>
-<dt><strong>z_value</strong><span class="classifier">int/string/float</span></dt><dd><p>Select the z_value of 3D DataArray with multiple layers.</p>
-</dd>
-<dt><strong>coords_labels</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dictionary</span></code></span></dt><dd><p>Pass dimension labels for coordinates and layers if they do not
-belong to default dimensions, which are (band/time, y/lat, x/lon)
-e.g. coords_labels = {“y_label”: “latitude”, “x_label”: “longitude”, “z_label”: “year”}
-Default is {} empty dictionary.</p>
-</dd>
-<dt><strong>k</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#int" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>Order of contiguity, this will select all neighbors upto kth order.
-Default is 1.</p>
-</dd>
-<dt><strong>include_nodata</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.8)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>If True, missing values will be assumed as non-missing when
-selecting higher_order neighbors, Default is False</p>
-</dd>
-<dt><strong>n_jobs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#int" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>Number of cores to be used in the sparse weight construction. If -1,
-all available cores are used. Default is 1.</p>
-</dd>
-<dt><strong>**kwargs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/keyword.html#module-keyword" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code></span></dt><dd><p>Optional arguments for <a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.W</span></code></a></p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.W</span></code></a></span></dt><dd><p>instance of spatial weights class W with an index attribute</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<div class="admonition seealso">
-<p class="admonition-title">See also</p>
-<dl class="simple">
-<dt><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.weights.W</span></code></dt><dd></dd>
-</dl>
-</div>
-<p class="rubric">Notes</p>
-<ol class="arabic simple">
-<li><p>Lower order contiguities are also selected.</p></li>
-</ol>
-<p>2. Returned object contains <cite>index</cite> attribute that includes a
-<cite>Pandas.MultiIndex</cite> object from the DataArray.</p>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights.raster</span> <span class="kn">import</span> <span class="n">da2W</span><span class="p">,</span> <span class="n">testDataArray</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">da</span> <span class="o">=</span> <span class="n">testDataArray</span><span class="p">()</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span>
-<span class="go">        {&#39;band&#39;: &#39;layer&#39;, &#39;x&#39;: &#39;longitude&#39;, &#39;y&#39;: &#39;latitude&#39;})</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">da</span><span class="o">.</span><span class="n">dims</span>
-<span class="go">(&#39;layer&#39;, &#39;latitude&#39;, &#39;longitude&#39;)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">da</span><span class="o">.</span><span class="n">shape</span>
-<span class="go">(3, 4, 4)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">da</span><span class="o">.</span><span class="n">coords</span>
-<span class="go">Coordinates:</span>
-<span class="go">    * layer      (layer) int64 1 2 3</span>
-<span class="go">    * latitude   (latitude) float64 90.0 30.0 -30.0 -90.0</span>
-<span class="go">    * longitude  (longitude) float64 -180.0 -60.0 60.0 180.0</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">da</span><span class="o">.</span><span class="n">attrs</span>
-<span class="go">{&#39;nodatavals&#39;: (-32768.0,)}</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">coords_labels</span> <span class="o">=</span> <span class="p">{</span>
-<span class="go">    &quot;z_label&quot;: &quot;layer&quot;,</span>
-<span class="go">    &quot;y_label&quot;: &quot;latitude&quot;,</span>
-<span class="go">    &quot;x_label&quot;: &quot;longitude&quot;</span>
-<span class="go">}</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">da2W</span><span class="p">(</span><span class="n">da</span><span class="p">,</span> <span class="n">z_value</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">coords_labels</span><span class="o">=</span><span class="n">coords_labels</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="s2">&quot;</span><span class="si">%.3f</span><span class="s2">&quot;</span><span class="o">%</span><span class="n">w</span><span class="o">.</span><span class="n">pct_nonzero</span>
-<span class="go">&#39;30.000&#39;</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="p">[(</span><span class="mi">2</span><span class="p">,</span> <span class="mf">90.0</span><span class="p">,</span> <span class="mf">180.0</span><span class="p">)]</span> <span class="o">==</span> <span class="p">{(</span><span class="mi">2</span><span class="p">,</span> <span class="mf">90.0</span><span class="p">,</span> <span class="mf">60.0</span><span class="p">):</span> <span class="mi">1</span><span class="p">,</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mf">30.0</span><span class="p">,</span> <span class="mf">180.0</span><span class="p">):</span> <span class="mi">1</span><span class="p">}</span>
-<span class="go">True</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">index</span><span class="p">)</span>
-<span class="go">10</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">index</span><span class="p">[:</span><span class="mi">2</span><span class="p">]</span>
-<span class="go">MultiIndex([(2, 90.0,  60.0),</span>
-<span class="go">            (2, 90.0, 180.0)],</span>
-<span class="go">           names=[&#39;layer&#39;, &#39;latitude&#39;, &#39;longitude&#39;])</span>
-</pre></div>
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-<h1>libpysal.weights.da2WSP<a class="headerlink" href="#libpysal-weights-da2wsp" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.weights.da2WSP">
-<code class="sig-prename descclassname">libpysal.weights.</code><code class="sig-name descname">da2WSP</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">da</span></em>, <em class="sig-param"><span class="n">criterion</span><span class="o">=</span><span class="default_value">'queen'</span></em>, <em class="sig-param"><span class="n">z_value</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">coords_labels</span><span class="o">=</span><span class="default_value">{}</span></em>, <em class="sig-param"><span class="n">k</span><span class="o">=</span><span class="default_value">1</span></em>, <em class="sig-param"><span class="n">include_nodata</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">n_jobs</span><span class="o">=</span><span class="default_value">1</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/raster.html#da2WSP"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.da2WSP" title="Permalink to this definition">¶</a></dt>
-<dd><p>Create a WSP object from xarray.DataArray with an additional
-attribute index containing coordinate values of the raster
-in the form of Pandas.Index/MultiIndex.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>da</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.DataArray</span></code></span></dt><dd><p>Input 2D or 3D DataArray with shape=(z, y, x)</p>
-</dd>
-<dt><strong>criterion</strong><span class="classifier">{“rook”, “queen”}</span></dt><dd><p>Type of contiguity. Default is queen.</p>
-</dd>
-<dt><strong>z_value</strong><span class="classifier">int/string/float</span></dt><dd><p>Select the z_value of 3D DataArray with multiple layers.</p>
-</dd>
-<dt><strong>coords_labels</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dictionary</span></code></span></dt><dd><p>Pass dimension labels for coordinates and layers if they do not
-belong to default dimensions, which are (band/time, y/lat, x/lon)
-e.g. coords_labels = {“y_label”: “latitude”, “x_label”: “longitude”, “z_label”: “year”}
-Default is {} empty dictionary.</p>
-</dd>
-<dt><strong>k</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#int" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>Order of contiguity, this will select all neighbors upto kth order.
-Default is 1.</p>
-</dd>
-<dt><strong>include_nodata</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.8)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>If True, missing values will be assumed as non-missing when
-selecting higher_order neighbors, Default is False</p>
-</dd>
-<dt><strong>n_jobs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#int" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>Number of cores to be used in the sparse weight construction. If -1,
-all available cores are used. Default is 1.</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>wsp</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.WSP.html#libpysal.weights.WSP" title="libpysal.weights.WSP"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.WSP</span></code></a></span></dt><dd><p>instance of spatial weights class WSP with an index attribute</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<div class="admonition seealso">
-<p class="admonition-title">See also</p>
-<dl class="simple">
-<dt><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.weights.WSP</span></code></dt><dd></dd>
-</dl>
-</div>
-<p class="rubric">Notes</p>
-<ol class="arabic simple">
-<li><p>Lower order contiguities are also selected.</p></li>
-</ol>
-<p>2. Returned object contains <cite>index</cite> attribute that includes a
-<cite>Pandas.MultiIndex</cite> object from the DataArray.</p>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights.raster</span> <span class="kn">import</span> <span class="n">da2WSP</span><span class="p">,</span> <span class="n">testDataArray</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">da</span> <span class="o">=</span> <span class="n">testDataArray</span><span class="p">()</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span>
-<span class="go">        {&#39;band&#39;: &#39;layer&#39;, &#39;x&#39;: &#39;longitude&#39;, &#39;y&#39;: &#39;latitude&#39;})</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">da</span><span class="o">.</span><span class="n">dims</span>
-<span class="go">(&#39;layer&#39;, &#39;latitude&#39;, &#39;longitude&#39;)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">da</span><span class="o">.</span><span class="n">shape</span>
-<span class="go">(3, 4, 4)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">da</span><span class="o">.</span><span class="n">coords</span>
-<span class="go">Coordinates:</span>
-<span class="go">    * layer      (layer) int64 1 2 3</span>
-<span class="go">    * latitude   (latitude) float64 90.0 30.0 -30.0 -90.0</span>
-<span class="go">    * longitude  (longitude) float64 -180.0 -60.0 60.0 180.0</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">da</span><span class="o">.</span><span class="n">attrs</span>
-<span class="go">{&#39;nodatavals&#39;: (-32768.0,)}</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">coords_labels</span> <span class="o">=</span> <span class="p">{</span>
-<span class="go">    &quot;z_label&quot;: &quot;layer&quot;,</span>
-<span class="go">    &quot;y_label&quot;: &quot;latitude&quot;,</span>
-<span class="go">    &quot;x_label&quot;: &quot;longitude&quot;</span>
-<span class="go">}</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wsp</span> <span class="o">=</span> <span class="n">da2WSP</span><span class="p">(</span><span class="n">da</span><span class="p">,</span> <span class="n">z_value</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">coords_labels</span><span class="o">=</span><span class="n">coords_labels</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wsp</span><span class="o">.</span><span class="n">n</span>
-<span class="go">10</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">pct_sp</span> <span class="o">=</span> <span class="n">wsp</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">nnz</span> <span class="o">*</span><span class="mf">1.</span> <span class="o">/</span> <span class="n">wsp</span><span class="o">.</span><span class="n">n</span><span class="o">**</span><span class="mi">2</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="s2">&quot;</span><span class="si">%.3f</span><span class="s2">&quot;</span><span class="o">%</span><span class="n">pct_sp</span>
-<span class="go">&#39;0.300&#39;</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">wsp</span><span class="o">.</span><span class="n">sparse</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span><span class="o">.</span><span class="n">todense</span><span class="p">())</span>
-<span class="go">[[0 0 1 0 0 1 1 1 0 0]]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wsp</span><span class="o">.</span><span class="n">index</span><span class="p">[:</span><span class="mi">2</span><span class="p">]</span>
-<span class="go">MultiIndex([(2, 90.0,  60.0),</span>
-<span class="go">            (2, 90.0, 180.0)],</span>
-<span class="go">           names=[&#39;layer&#39;, &#39;latitude&#39;, &#39;longitude&#39;])</span>
-</pre></div>
-</div>
-</dd></dl>
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-     rel="nofollow">Source</a>
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diff --git a/docs/generated/libpysal.weights.fill_diagonal.html b/docs/generated/libpysal.weights.fill_diagonal.html
deleted file mode 100644
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--- a/docs/generated/libpysal.weights.fill_diagonal.html
+++ /dev/null
@@ -1,209 +0,0 @@
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-<h1>libpysal.weights.fill_diagonal<a class="headerlink" href="#libpysal-weights-fill-diagonal" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.weights.fill_diagonal">
-<code class="sig-prename descclassname">libpysal.weights.</code><code class="sig-name descname">fill_diagonal</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">w</span></em>, <em class="sig-param"><span class="n">val</span><span class="o">=</span><span class="default_value">1.0</span></em>, <em class="sig-param"><span class="n">wsp</span><span class="o">=</span><span class="default_value">False</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/util.html#fill_diagonal"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.fill_diagonal" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns a new weights object with values inserted along the main diagonal.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>Spatial weights object</p>
-</dd>
-<dt><strong>diagonal</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a>, <a class="reference external" href="https://docs.python.org/3.8/library/functions.html#int" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a> or <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.20)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a></span></dt><dd><p>Defines the value(s) to which the weights matrix diagonal should
-be set. If a constant is passed then each element along the
-diagonal will get this value (default is 1.0). An array of length
-w.n can be passed to set explicit values to each element along
-the diagonal (assumed to be in the same order as w.id_order).</p>
-</dd>
-<dt><strong>wsp</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.8)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>If True return a thin weights object of the type WSP, if False
-return the standard W object.</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>Spatial weights object</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-</pre></div>
-</div>
-<p>Build a basic rook weights matrix, which has zeros on the diagonal, then
-insert ones along the diagonal.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="n">id_type</span><span class="o">=</span><span class="s1">&#39;string&#39;</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w_const</span> <span class="o">=</span> <span class="n">insert_diagonal</span><span class="p">(</span><span class="n">w</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="p">[</span><span class="s1">&#39;id0&#39;</span><span class="p">]</span> <span class="o">==</span>  <span class="p">{</span><span class="s1">&#39;id5&#39;</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="s1">&#39;id1&#39;</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">}</span>
-<span class="go">True</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w_const</span><span class="p">[</span><span class="s1">&#39;id0&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="p">{</span><span class="s1">&#39;id5&#39;</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="s1">&#39;id0&#39;</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="s1">&#39;id1&#39;</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">}</span>
-<span class="go">True</span>
-</pre></div>
-</div>
-<p>Insert different values along the main diagonal.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">diag</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">125</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w_var</span> <span class="o">=</span> <span class="n">insert_diagonal</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">diag</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w_var</span><span class="p">[</span><span class="s1">&#39;id0&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="p">{</span><span class="s1">&#39;id5&#39;</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="s1">&#39;id0&#39;</span><span class="p">:</span> <span class="mf">100.0</span><span class="p">,</span> <span class="s1">&#39;id1&#39;</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">}</span>
-<span class="go">True</span>
-</pre></div>
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-<h1>libpysal.weights.full<a class="headerlink" href="#libpysal-weights-full" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.weights.full">
-<code class="sig-prename descclassname">libpysal.weights.</code><code class="sig-name descname">full</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">w</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/util.html#full"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.full" title="Permalink to this definition">¶</a></dt>
-<dd><p>Generate a full numpy array.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>spatial weights object</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>(fullw, keys)</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#tuple" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">tuple</span></code></a></span></dt><dd><p>first element being the full numpy array and second element
-keys being the ids associated with each row in the array.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">W</span><span class="p">,</span> <span class="n">full</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">neighbors</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;first&#39;</span><span class="p">:[</span><span class="s1">&#39;second&#39;</span><span class="p">],</span><span class="s1">&#39;second&#39;</span><span class="p">:[</span><span class="s1">&#39;first&#39;</span><span class="p">,</span><span class="s1">&#39;third&#39;</span><span class="p">],</span><span class="s1">&#39;third&#39;</span><span class="p">:[</span><span class="s1">&#39;second&#39;</span><span class="p">]}</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">weights</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;first&#39;</span><span class="p">:[</span><span class="mi">1</span><span class="p">],</span><span class="s1">&#39;second&#39;</span><span class="p">:[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span><span class="s1">&#39;third&#39;</span><span class="p">:[</span><span class="mi">1</span><span class="p">]}</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">W</span><span class="p">(</span><span class="n">neighbors</span><span class="p">,</span> <span class="n">weights</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wf</span><span class="p">,</span> <span class="n">ids</span> <span class="o">=</span> <span class="n">full</span><span class="p">(</span><span class="n">w</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wf</span>
-<span class="go">array([[0., 1., 0.],</span>
-<span class="go">       [1., 0., 1.],</span>
-<span class="go">       [0., 1., 0.]])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">ids</span>
-<span class="go">[&#39;first&#39;, &#39;second&#39;, &#39;third&#39;]</span>
-</pre></div>
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-<h1>libpysal.weights.full2W<a class="headerlink" href="#libpysal-weights-full2w" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.weights.full2W">
-<code class="sig-prename descclassname">libpysal.weights.</code><code class="sig-name descname">full2W</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">m</span></em>, <em class="sig-param"><span class="n">ids</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/util.html#full2W"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.full2W" title="Permalink to this definition">¶</a></dt>
-<dd><p>Create a PySAL W object from a full array.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>m</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.20)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a></span></dt><dd><p>nxn array with the full weights matrix</p>
-</dd>
-<dt><strong>ids</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>User ids assumed to be aligned with m</p>
-</dd>
-<dt><strong>**kwargs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/keyword.html#module-keyword" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code></span></dt><dd><p>optional arguments for <code class="xref py py-class docutils literal notranslate"><span class="pre">pysal.weights.W</span></code></p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>PySAL weights object</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">full2W</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-</pre></div>
-</div>
-<p>Create an array of zeros</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
-</pre></div>
-</div>
-<p>For loop to fill it with random numbers</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">a</span><span class="p">)):</span>
-<span class="gp">... </span>    <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">a</span><span class="p">[</span><span class="n">i</span><span class="p">])):</span>
-<span class="gp">... </span>        <span class="k">if</span> <span class="n">i</span><span class="o">!=</span><span class="n">j</span><span class="p">:</span>
-<span class="gp">... </span>            <span class="n">a</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>Create W object</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">full2W</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">full</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">a</span>
-<span class="go">array([[ True,  True,  True,  True],</span>
-<span class="go">       [ True,  True,  True,  True],</span>
-<span class="go">       [ True,  True,  True,  True],</span>
-<span class="go">       [ True,  True,  True,  True]])</span>
-</pre></div>
-</div>
-<p>Create list of user ids</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ids</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;myID0&#39;</span><span class="p">,</span> <span class="s1">&#39;myID1&#39;</span><span class="p">,</span> <span class="s1">&#39;myID2&#39;</span><span class="p">,</span> <span class="s1">&#39;myID3&#39;</span><span class="p">]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">full2W</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">full</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">a</span>
-<span class="go">array([[ True,  True,  True,  True],</span>
-<span class="go">       [ True,  True,  True,  True],</span>
-<span class="go">       [ True,  True,  True,  True],</span>
-<span class="go">       [ True,  True,  True,  True]])</span>
-</pre></div>
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-<h1>libpysal.weights.fuzzy_contiguity<a class="headerlink" href="#libpysal-weights-fuzzy-contiguity" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.weights.fuzzy_contiguity">
-<code class="sig-prename descclassname">libpysal.weights.</code><code class="sig-name descname">fuzzy_contiguity</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">gdf</span></em>, <em class="sig-param"><span class="n">tolerance</span><span class="o">=</span><span class="default_value">0.005</span></em>, <em class="sig-param"><span class="n">buffering</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">drop</span><span class="o">=</span><span class="default_value">True</span></em>, <em class="sig-param"><span class="n">buffer</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">predicate</span><span class="o">=</span><span class="default_value">'intersects'</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/util.html#fuzzy_contiguity"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.fuzzy_contiguity" title="Permalink to this definition">¶</a></dt>
-<dd><p>Fuzzy contiguity spatial weights</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>gdf:   GeoDataFrame</strong></dt><dd></dd>
-<dt><strong>tolerance: float</strong></dt><dd><p>The percentage of the length of the minimum side of the bounding rectangle for the GeoDataFrame to use in determining the buffering distance.</p>
-</dd>
-<dt><strong>buffering: boolean</strong></dt><dd><p>If False (default) joins will only be detected for features that intersect (touch, contain, within).
-If True then features will be buffered and intersections will be based on buffered features.</p>
-</dd>
-<dt><strong>drop: boolean</strong></dt><dd><p>If True (default), the buffered features are removed from the GeoDataFrame. If False, buffered features are added to the GeoDataFrame.</p>
-</dd>
-<dt><strong>buffer</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>Specify exact buffering distance. Ignores <cite>tolerance</cite>.</p>
-</dd>
-<dt><strong>predicate</strong><span class="classifier">{‘intersects’, ‘within’, ‘contains’, ‘overlaps’, ‘crosses’, ‘touches’}</span></dt><dd><p>The predicate to use for determination of neighbors. Default is ‘intersects’. If None is passed, neighbours are determined based on
-the intersection of bounding boxes.</p>
-</dd>
-<dt><strong>**kwargs: keyword arguments</strong></dt><dd><p>optional arguments for <code class="xref py py-class docutils literal notranslate"><span class="pre">pysal.weights.W</span></code></p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl class="simple">
-<dt>w:  <code class="xref py py-obj docutils literal notranslate"><span class="pre">PySAL</span></code> <a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></dt><dd><p>Spatial weights based on fuzzy contiguity. Weights are binary.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Notes</p>
-<p>This relaxes the notion of contiguity neighbors for the case of feature
-collections that violate the condition of planar enforcement. It handles
-three types of conditions present in such collections that would result in
-islands when using the regular PySAL contiguity methods. The first are
-edges for nearby polygons that should be shared, but are digitized
-separately for the individual polygons and the resulting edges do not
-coincide, but instead the edges intersect. The second case is similar to
-the first, only the resultant edges do not intersect but are “close”. The
-final case arises when one polygon is “inside” a second polygon but is not
-encoded to represent a hole in the containing polygon.</p>
-<p>Detection of the second case will require setting buffering=True and exploring different values for tolerance.</p>
-<p>The buffering check assumes the geometry coordinates are projected.</p>
-<p class="rubric">References</p>
-<p>Planar Enforcement: <a class="reference external" href="http://ibis.geog.ubc.ca/courses/klink/gis.notes/ncgia/u12.html#SEC12.6">http://ibis.geog.ubc.ca/courses/klink/gis.notes/ncgia/u12.html#SEC12.6</a></p>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">libpysal</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">fuzzy_contiguity</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">geopandas</span> <span class="k">as</span> <span class="nn">gpd</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">rs</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">examples</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="s1">&#39;map_RS_BR.shp&#39;</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">rs_df</span> <span class="o">=</span> <span class="n">gpd</span><span class="o">.</span><span class="n">read_file</span><span class="p">(</span><span class="n">rs</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wq</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">Queen</span><span class="o">.</span><span class="n">from_dataframe</span><span class="p">(</span><span class="n">rs_df</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="n">wq</span><span class="o">.</span><span class="n">islands</span><span class="p">)</span>
-<span class="go">29</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wq</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<span class="go">{}</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wf</span> <span class="o">=</span> <span class="n">fuzzy_contiguity</span><span class="p">(</span><span class="n">rs_df</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wf</span><span class="o">.</span><span class="n">islands</span>
-<span class="go">[]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wf</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="nb">dict</span><span class="p">({</span><span class="mi">239</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">59</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">152</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">23</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">107</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">})</span>
-<span class="go">True</span>
-</pre></div>
-</div>
-<p>Example needing to use buffering</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">shapely.geometry</span> <span class="kn">import</span> <span class="n">Polygon</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">p0</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([(</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">),</span> <span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">0</span><span class="p">),</span> <span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">10</span><span class="p">)])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">p1</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([(</span><span class="mi">10</span><span class="p">,</span><span class="mi">1</span><span class="p">),</span> <span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">2</span><span class="p">),</span> <span class="p">(</span><span class="mi">15</span><span class="p">,</span><span class="mi">2</span><span class="p">)])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">p2</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([(</span><span class="mi">12</span><span class="p">,</span><span class="mf">2.001</span><span class="p">),</span> <span class="p">(</span><span class="mi">14</span><span class="p">,</span> <span class="mf">2.001</span><span class="p">),</span> <span class="p">(</span><span class="mi">13</span><span class="p">,</span><span class="mi">10</span><span class="p">)])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">gs</span> <span class="o">=</span> <span class="n">gpd</span><span class="o">.</span><span class="n">GeoSeries</span><span class="p">([</span><span class="n">p0</span><span class="p">,</span><span class="n">p1</span><span class="p">,</span><span class="n">p2</span><span class="p">])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">gdf</span> <span class="o">=</span> <span class="n">gpd</span><span class="o">.</span><span class="n">GeoDataFrame</span><span class="p">(</span><span class="n">geometry</span><span class="o">=</span><span class="n">gs</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wf</span> <span class="o">=</span> <span class="n">fuzzy_contiguity</span><span class="p">(</span><span class="n">gdf</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wf</span><span class="o">.</span><span class="n">islands</span>
-<span class="go">[2]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wfb</span> <span class="o">=</span> <span class="n">fuzzy_contiguity</span><span class="p">(</span><span class="n">gdf</span><span class="p">,</span> <span class="n">buffering</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wfb</span><span class="o">.</span><span class="n">islands</span>
-<span class="go">[]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wfb</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
-<span class="go">{1: 1.0}</span>
-</pre></div>
-</div>
-<p>Example with a custom index</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">rs_df_ix</span> <span class="o">=</span> <span class="n">rs_df</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">&quot;NM_MUNICIP&quot;</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wf_ix</span> <span class="o">=</span> <span class="n">fuzzy_contiguity</span><span class="p">(</span><span class="n">rs_df</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wf_ix</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="s2">&quot;TAVARES&quot;</span><span class="p">]</span>
-<span class="go">[&#39;SÃO JOSÉ DO NORTE&#39;, &#39;MOSTARDAS&#39;]</span>
-</pre></div>
-</div>
-</dd></dl>
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-<h1>libpysal.weights.get_ids<a class="headerlink" href="#libpysal-weights-get-ids" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.weights.get_ids">
-<code class="sig-prename descclassname">libpysal.weights.</code><code class="sig-name descname">get_ids</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">in_shps</span></em>, <em class="sig-param"><span class="n">idVariable</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/util.html#get_ids"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.get_ids" title="Permalink to this definition">¶</a></dt>
-<dd><p>Gets the IDs from the DBF file that moves with a given shape file or
-a geopandas.GeoDataFrame.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>in_shps</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a> or <a class="reference external" href="https://geopandas.readthedocs.io/en/latest/reference/geopandas.GeoDataFrame.html#geopandas.GeoDataFrame" title="(in GeoPandas v0+untagged.50.g5119410)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">geopandas.GeoDataFrame</span></code></a></span></dt><dd><p>The input geographic data. Either
-(1) a path to a shapefile including suffix (str); or
-(2) a geopandas.GeoDataFrame.</p>
-</dd>
-<dt><strong>idVariable</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>name of a column in the shapefile’s DBF or the
-geopandas.GeoDataFrame to use for ids.</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>ids</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>a list of IDs</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights.util</span> <span class="kn">import</span> <span class="n">get_ids</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">libpysal</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">polyids</span> <span class="o">=</span> <span class="n">get_ids</span><span class="p">(</span><span class="n">libpysal</span><span class="o">.</span><span class="n">examples</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="s2">&quot;columbus.shp&quot;</span><span class="p">),</span> <span class="s2">&quot;POLYID&quot;</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">polyids</span><span class="p">[:</span><span class="mi">5</span><span class="p">]</span>
-<span class="go">[1, 2, 3, 4, 5]</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights.util</span> <span class="kn">import</span> <span class="n">get_ids</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">libpysal</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">geopandas</span> <span class="k">as</span> <span class="nn">gpd</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">gdf</span> <span class="o">=</span> <span class="n">gpd</span><span class="o">.</span><span class="n">read_file</span><span class="p">(</span><span class="n">libpysal</span><span class="o">.</span><span class="n">examples</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="s2">&quot;columbus.shp&quot;</span><span class="p">))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">polyids</span> <span class="o">=</span> <span class="n">gdf</span><span class="p">[</span><span class="s2">&quot;POLYID&quot;</span><span class="p">]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">polyids</span><span class="p">[:</span><span class="mi">5</span><span class="p">]</span>
-<span class="go">0    1</span>
-<span class="go">1    2</span>
-<span class="go">2    3</span>
-<span class="go">3    4</span>
-<span class="go">4    5</span>
-<span class="go">Name: POLYID, dtype: int64</span>
-</pre></div>
-</div>
-</dd></dl>
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-<h1>libpysal.weights.get_points_array_from_shapefile<a class="headerlink" href="#libpysal-weights-get-points-array-from-shapefile" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.weights.get_points_array_from_shapefile">
-<code class="sig-prename descclassname">libpysal.weights.</code><code class="sig-name descname">get_points_array_from_shapefile</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">shapefile</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/util.html#get_points_array_from_shapefile"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.get_points_array_from_shapefile" title="Permalink to this definition">¶</a></dt>
-<dd><p>Gets a data array of x and y coordinates from a given shapefile.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>shapefile</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>name of a shape file including suffix</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>points</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.20)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a></span></dt><dd><p>(n, 2)
-a data array of x and y coordinates</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Notes</p>
-<p>If the given shape file includes polygons,
-this function returns x and y coordinates of the polygons’ centroids</p>
-<p class="rubric">Examples</p>
-<p>Point shapefile</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">libpysal</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights.util</span> <span class="kn">import</span> <span class="n">get_points_array_from_shapefile</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">xy</span> <span class="o">=</span> <span class="n">get_points_array_from_shapefile</span><span class="p">(</span><span class="n">libpysal</span><span class="o">.</span><span class="n">examples</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="s1">&#39;juvenile.shp&#39;</span><span class="p">))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">xy</span><span class="p">[:</span><span class="mi">3</span><span class="p">]</span>
-<span class="go">array([[94., 93.],</span>
-<span class="go">       [80., 95.],</span>
-<span class="go">       [79., 90.]])</span>
-</pre></div>
-</div>
-<p>Polygon shapefile</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">xy</span> <span class="o">=</span> <span class="n">get_points_array_from_shapefile</span><span class="p">(</span><span class="n">libpysal</span><span class="o">.</span><span class="n">examples</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="s1">&#39;columbus.shp&#39;</span><span class="p">))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">xy</span><span class="p">[:</span><span class="mi">3</span><span class="p">]</span>
-<span class="go">array([[ 8.82721847, 14.36907602],</span>
-<span class="go">       [ 8.33265837, 14.03162401],</span>
-<span class="go">       [ 9.01226541, 13.81971908]])</span>
-</pre></div>
-</div>
-</dd></dl>
-
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-     rel="nofollow">Source</a>
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-<h1>libpysal.weights.hexLat2W<a class="headerlink" href="#libpysal-weights-hexlat2w" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.weights.hexLat2W">
-<code class="sig-prename descclassname">libpysal.weights.</code><code class="sig-name descname">hexLat2W</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">nrows</span><span class="o">=</span><span class="default_value">5</span></em>, <em class="sig-param"><span class="n">ncols</span><span class="o">=</span><span class="default_value">5</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/util.html#hexLat2W"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.hexLat2W" title="Permalink to this definition">¶</a></dt>
-<dd><p>Create a W object for a hexagonal lattice.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>nrows</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#int" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>number of rows</p>
-</dd>
-<dt><strong>ncols</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#int" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>number of columns</p>
-</dd>
-<dt><strong>**kwargs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/keyword.html#module-keyword" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code></span></dt><dd><p>optional arguments for <code class="xref py py-class docutils literal notranslate"><span class="pre">pysal.weights.W</span></code></p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>instance of spatial weights class W</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Notes</p>
-<p>Observations are row ordered: first k observations are in row 0, next k in row 1, and so on.</p>
-<p>Construction is based on shifting every other column of a regular lattice
-down 1/2 of a cell.</p>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span><span class="p">,</span> <span class="n">hexLat2W</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">()</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-<span class="go">[0, 6, 2]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">21</span><span class="p">]</span>
-<span class="go">[16, 20, 22]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wh</span> <span class="o">=</span> <span class="n">hexLat2W</span><span class="p">()</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wh</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-<span class="go">[0, 6, 2, 5, 7]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wh</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">21</span><span class="p">]</span>
-<span class="go">[16, 20, 22]</span>
-</pre></div>
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-  <div class="section" id="libpysal-weights-higher-order">
-<h1>libpysal.weights.higher_order<a class="headerlink" href="#libpysal-weights-higher-order" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.weights.higher_order">
-<code class="sig-prename descclassname">libpysal.weights.</code><code class="sig-name descname">higher_order</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">w</span></em>, <em class="sig-param"><span class="n">k</span><span class="o">=</span><span class="default_value">2</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/util.html#higher_order"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.higher_order" title="Permalink to this definition">¶</a></dt>
-<dd><p>Contiguity weights object of order k.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>spatial weights object</p>
-</dd>
-<dt><strong>k</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#int" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>order of contiguity</p>
-</dd>
-<dt><strong>**kwargs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/keyword.html#module-keyword" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code></span></dt><dd><p>optional arguments for <code class="xref py py-class docutils literal notranslate"><span class="pre">pysal.weights.W</span></code></p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>implicit</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>spatial weights object</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Notes</p>
-<p>Proper higher order neighbors are returned such that i and j are k-order
-neighbors iff the shortest path from i-j is of length k.</p>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span><span class="p">,</span> <span class="n">higher_order</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w10</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w10_2</span> <span class="o">=</span> <span class="n">higher_order</span><span class="p">(</span><span class="n">w10</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w10_2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span>  <span class="p">{</span><span class="mi">2</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">11</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">20</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">}</span>
-<span class="go">True</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w5</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">()</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w5</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span>  <span class="p">{</span><span class="mi">1</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">5</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">}</span>
-<span class="go">True</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w5</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">2</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">6</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">}</span>
-<span class="go">True</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w5_2</span> <span class="o">=</span> <span class="n">higher_order</span><span class="p">(</span><span class="n">w5</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w5_2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="p">{</span><span class="mi">10</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">2</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">6</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">}</span>
-<span class="go">True</span>
-</pre></div>
-</div>
-</dd></dl>
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-<h1>libpysal.weights.higher_order_sp<a class="headerlink" href="#libpysal-weights-higher-order-sp" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.weights.higher_order_sp">
-<code class="sig-prename descclassname">libpysal.weights.</code><code class="sig-name descname">higher_order_sp</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">w</span></em>, <em class="sig-param"><span class="n">k</span><span class="o">=</span><span class="default_value">2</span></em>, <em class="sig-param"><span class="n">shortest_path</span><span class="o">=</span><span class="default_value">True</span></em>, <em class="sig-param"><span class="n">diagonal</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">lower_order</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/util.html#higher_order_sp"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.higher_order_sp" title="Permalink to this definition">¶</a></dt>
-<dd><p>Contiguity weights for either a sparse W or W for order k.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>sparse_matrix, spatial weights object or
-scipy.sparse.csr.csr_instance</p>
-</dd>
-<dt><strong>k</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#int" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>Order of contiguity</p>
-</dd>
-<dt><strong>shortest_path</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.8)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>True: i,j and k-order neighbors if the
-shortest path for i,j is k.
-False: i,j are k-order neighbors if there
-is a path from i,j of length k.</p>
-</dd>
-<dt><strong>diagonal</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.8)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>True:  keep k-order (i,j) joins when i==j
-False: remove k-order (i,j) joins when i==j</p>
-</dd>
-<dt><strong>lower_order</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.8)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>True: include lower order contiguities
-False: return only weights of order k</p>
-</dd>
-<dt><strong>**kwargs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/keyword.html#module-keyword" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code></span></dt><dd><p>optional arguments for <code class="xref py py-class docutils literal notranslate"><span class="pre">pysal.weights.W</span></code></p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>wk</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>WSP, type matches type of w argument</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span><span class="p">,</span> <span class="n">higher_order_sp</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w25</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span><span class="mi">5</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w25</span><span class="o">.</span><span class="n">n</span>
-<span class="go">25</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w25</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="p">{</span><span class="mi">1</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">5</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">}</span>
-<span class="go">True</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w25_2</span> <span class="o">=</span> <span class="n">higher_order_sp</span><span class="p">(</span><span class="n">w25</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w25_2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="p">{</span><span class="mi">10</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">2</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">6</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">}</span>
-<span class="go">True</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w25_2</span> <span class="o">=</span> <span class="n">higher_order_sp</span><span class="p">(</span><span class="n">w25</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">diagonal</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w25_2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span>  <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">10</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">2</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">6</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">}</span>
-<span class="go">True</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w25_3</span> <span class="o">=</span> <span class="n">higher_order_sp</span><span class="p">(</span><span class="n">w25</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w25_3</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="p">{</span><span class="mi">15</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">3</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">11</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">7</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">}</span>
-<span class="go">True</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w25_3</span> <span class="o">=</span> <span class="n">higher_order_sp</span><span class="p">(</span><span class="n">w25</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">shortest_path</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w25_3</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="p">{</span><span class="mi">1</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">3</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">5</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">7</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">11</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">15</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">}</span>
-<span class="go">True</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w25_3</span> <span class="o">=</span> <span class="n">higher_order_sp</span><span class="p">(</span><span class="n">w25</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">lower_order</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w25_3</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="p">{</span><span class="mi">5</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">7</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">11</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">2</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">15</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">6</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">10</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">1</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">3</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">}</span>
-<span class="go">True</span>
-</pre></div>
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-<h1>libpysal.weights.lag_categorical<a class="headerlink" href="#libpysal-weights-lag-categorical" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.weights.lag_categorical">
-<code class="sig-prename descclassname">libpysal.weights.</code><code class="sig-name descname">lag_categorical</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">w</span></em>, <em class="sig-param"><span class="n">y</span></em>, <em class="sig-param"><span class="n">ties</span><span class="o">=</span><span class="default_value">'tryself'</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/spatial_lag.html#lag_categorical"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.lag_categorical" title="Permalink to this definition">¶</a></dt>
-<dd><p>Spatial lag operator for categorical variables.</p>
-<p>Constructs the most common categories of neighboring observations, weighted
-by their weight strength.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>PySAL spatial weightsobject</p>
-</dd>
-<dt><strong>y</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/glossary.html#term-iterable" title="(in Python v3.8)"><span>iterable</span></a></span></dt><dd><p>iterable collection of categories (either int or
-string) with dimensionality conforming to w (see examples)</p>
-</dd>
-<dt><strong>ties</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>string describing the method to use when resolving
-ties. By default, the option is “tryself”,
-and the category of the focal observation
-is included with its neighbors to try
-and break a tie. If this does not resolve the tie,
-a winner is chosen randomly. To just use random choice to
-break ties, pass “random” instead.</p>
-</dd>
-<dt><strong>Returns</strong></dt><dd></dd>
-<dt><strong>——-</strong></dt><dd></dd>
-<dt><strong>an (n x k) column vector containing the most common neighboring observation</strong></dt><dd></dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Notes</p>
-<p>This works on any array where the number of unique elements along the column
-axis is less than the number of elements in the array, for any dtype.
-That means the routine should work on any dtype that np.unique() can
-compare.</p>
-<p class="rubric">Examples</p>
-<p>Set up a 9x9 weights matrix describing a 3x3 regular lattice. Lag one list of
-categorical variables with no ties.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">libpysal</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">12345</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">lat2W</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">y</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;a&#39;</span><span class="p">,</span><span class="s1">&#39;b&#39;</span><span class="p">,</span><span class="s1">&#39;a&#39;</span><span class="p">,</span><span class="s1">&#39;b&#39;</span><span class="p">,</span><span class="s1">&#39;c&#39;</span><span class="p">,</span><span class="s1">&#39;b&#39;</span><span class="p">,</span><span class="s1">&#39;c&#39;</span><span class="p">,</span><span class="s1">&#39;b&#39;</span><span class="p">,</span><span class="s1">&#39;c&#39;</span><span class="p">]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">y_l</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">lag_categorical</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">array_equal</span><span class="p">(</span><span class="n">y_l</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="s1">&#39;a&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="s1">&#39;c&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="s1">&#39;c&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="s1">&#39;c&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">]))</span>
-<span class="go">True</span>
-</pre></div>
-</div>
-<p>Explicitly reshape y into a (9x1) array and calculate lag again</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">yvect</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">y</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">9</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">yvect_l</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">lag_categorical</span><span class="p">(</span><span class="n">w</span><span class="p">,</span><span class="n">yvect</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">check</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span> <span class="p">[</span> <span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span>  <span class="p">[</span><span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="s1">&#39;a&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="s1">&#39;c&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="s1">&#39;c&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="s1">&#39;c&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">]]</span> <span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">array_equal</span><span class="p">(</span><span class="n">yvect_l</span><span class="p">,</span> <span class="n">check</span><span class="p">)</span>
-<span class="go">True</span>
-</pre></div>
-</div>
-<p>compute the lag of a 9x2 matrix of categories</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">y2</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;a&#39;</span><span class="p">,</span> <span class="s1">&#39;c&#39;</span><span class="p">,</span> <span class="s1">&#39;c&#39;</span><span class="p">,</span> <span class="s1">&#39;d&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="s1">&#39;a&#39;</span><span class="p">,</span> <span class="s1">&#39;d&#39;</span><span class="p">,</span> <span class="s1">&#39;d&#39;</span><span class="p">,</span> <span class="s1">&#39;c&#39;</span><span class="p">]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">ym</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">((</span><span class="n">y</span><span class="p">,</span><span class="n">y2</span><span class="p">))</span><span class="o">.</span><span class="n">T</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">ym_lag</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">lag_categorical</span><span class="p">(</span><span class="n">w</span><span class="p">,</span><span class="n">ym</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">check</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="s1">&#39;d&#39;</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;a&#39;</span><span class="p">,</span> <span class="s1">&#39;c&#39;</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="s1">&#39;c&#39;</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;c&#39;</span><span class="p">,</span> <span class="s1">&#39;d&#39;</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="s1">&#39;d&#39;</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;c&#39;</span><span class="p">,</span> <span class="s1">&#39;c&#39;</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="s1">&#39;d&#39;</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;c&#39;</span><span class="p">,</span> <span class="s1">&#39;d&#39;</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="s1">&#39;c&#39;</span><span class="p">]])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">array_equal</span><span class="p">(</span><span class="n">check</span><span class="p">,</span> <span class="n">ym_lag</span><span class="p">)</span>
-<span class="go">True</span>
-</pre></div>
-</div>
-</dd></dl>
-
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-
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-<h1>libpysal.weights.lag_spatial<a class="headerlink" href="#libpysal-weights-lag-spatial" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.weights.lag_spatial">
-<code class="sig-prename descclassname">libpysal.weights.</code><code class="sig-name descname">lag_spatial</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">w</span></em>, <em class="sig-param"><span class="n">y</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/spatial_lag.html#lag_spatial"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.lag_spatial" title="Permalink to this definition">¶</a></dt>
-<dd><p>Spatial lag operator.</p>
-<p>If w is row standardized, returns the average of each observation’s neighbors;
-if not, returns the weighted sum of each observation’s neighbors.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>libpysal spatial weightsobject</p>
-</dd>
-<dt><strong>y</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.20)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a></span></dt><dd><p>numpy array with dimensionality conforming to w (see examples)</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>wy</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.20)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a></span></dt><dd><p>array of numeric values for the spatial lag</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<p>Setup a 9x9 binary spatial weights matrix and vector of data; compute the
-spatial lag of the vector.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">libpysal</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">lat2W</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">9</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">yl</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">lag_spatial</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">yl</span>
-<span class="go">array([ 4.,  6.,  6., 10., 16., 14., 10., 18., 12.])</span>
-</pre></div>
-</div>
-<p>Row standardize the weights matrix and recompute the spatial lag</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">transform</span> <span class="o">=</span> <span class="s1">&#39;r&#39;</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">yl</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">lag_spatial</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">yl</span>
-<span class="go">array([2.        , 2.        , 3.        , 3.33333333, 4.        ,</span>
-<span class="go">       4.66666667, 5.        , 6.        , 6.        ])</span>
-</pre></div>
-</div>
-<p>Explicitly define data vector as 9x1 and recompute the spatial lag</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">y</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">9</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">yl</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">lag_spatial</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">yl</span>
-<span class="go">array([[2.        ],</span>
-<span class="go">       [2.        ],</span>
-<span class="go">       [3.        ],</span>
-<span class="go">       [3.33333333],</span>
-<span class="go">       [4.        ],</span>
-<span class="go">       [4.66666667],</span>
-<span class="go">       [5.        ],</span>
-<span class="go">       [6.        ],</span>
-<span class="go">       [6.        ]])</span>
-</pre></div>
-</div>
-<p>Take the spatial lag of a 9x2 data matrix</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">yr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">yr</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">9</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">((</span><span class="n">y</span><span class="p">,</span> <span class="n">yr</span><span class="p">))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">yl</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">lag_spatial</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">yl</span>
-<span class="go">array([[2.        , 6.        ],</span>
-<span class="go">       [2.        , 6.        ],</span>
-<span class="go">       [3.        , 5.        ],</span>
-<span class="go">       [3.33333333, 4.66666667],</span>
-<span class="go">       [4.        , 4.        ],</span>
-<span class="go">       [4.66666667, 3.33333333],</span>
-<span class="go">       [5.        , 3.        ],</span>
-<span class="go">       [6.        , 2.        ],</span>
-<span class="go">       [6.        , 2.        ]])</span>
-</pre></div>
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-  <div class="section" id="libpysal-weights-lat2sw">
-<h1>libpysal.weights.lat2SW<a class="headerlink" href="#libpysal-weights-lat2sw" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.weights.lat2SW">
-<code class="sig-prename descclassname">libpysal.weights.</code><code class="sig-name descname">lat2SW</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">nrows</span><span class="o">=</span><span class="default_value">3</span></em>, <em class="sig-param"><span class="n">ncols</span><span class="o">=</span><span class="default_value">5</span></em>, <em class="sig-param"><span class="n">criterion</span><span class="o">=</span><span class="default_value">'rook'</span></em>, <em class="sig-param"><span class="n">row_st</span><span class="o">=</span><span class="default_value">False</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/util.html#lat2SW"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.lat2SW" title="Permalink to this definition">¶</a></dt>
-<dd><p>Create a sparse W matrix for a regular lattice.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>nrows</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#int" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>number of rows</p>
-</dd>
-<dt><strong>ncols</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#int" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>number of columns</p>
-</dd>
-<dt><strong>rook</strong><span class="classifier">{“rook”, “queen”, “bishop”}</span></dt><dd><p>type of contiguity. Default is rook.</p>
-</dd>
-<dt><strong>row_st</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.8)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>If True, the created sparse W object is row-standardized so
-every row sums up to one. Defaults to False.</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>w</strong><span class="classifier"><a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.dia_matrix.html#scipy.sparse.dia_matrix" title="(in SciPy v1.6.0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scipy.sparse.dia_matrix</span></code></a></span></dt><dd><p>instance of a scipy sparse matrix</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Notes</p>
-<p>Observations are row ordered: first k observations are in row 0, next k in row 1, and so on.
-This method directly creates the W matrix using the strucuture of the contiguity type.</p>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2SW</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w9</span> <span class="o">=</span> <span class="n">lat2SW</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w9</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span>
-<span class="go">True</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w9</span><span class="p">[</span><span class="mi">3</span><span class="p">,</span><span class="mi">6</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span>
-<span class="go">True</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w9r</span> <span class="o">=</span> <span class="n">lat2SW</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span> <span class="n">row_st</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w9r</span><span class="p">[</span><span class="mi">3</span><span class="p">,</span><span class="mi">6</span><span class="p">]</span> <span class="o">==</span> <span class="mf">1.</span><span class="o">/</span><span class="mi">3</span>
-<span class="go">True</span>
-</pre></div>
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-</dd></dl>
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-<h1>libpysal.weights.lat2W<a class="headerlink" href="#libpysal-weights-lat2w" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.weights.lat2W">
-<code class="sig-prename descclassname">libpysal.weights.</code><code class="sig-name descname">lat2W</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">nrows</span><span class="o">=</span><span class="default_value">5</span></em>, <em class="sig-param"><span class="n">ncols</span><span class="o">=</span><span class="default_value">5</span></em>, <em class="sig-param"><span class="n">rook</span><span class="o">=</span><span class="default_value">True</span></em>, <em class="sig-param"><span class="n">id_type</span><span class="o">=</span><span class="default_value">'int'</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/util.html#lat2W"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.lat2W" title="Permalink to this definition">¶</a></dt>
-<dd><p>Create a W object for a regular lattice.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>nrows</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#int" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>number of rows</p>
-</dd>
-<dt><strong>ncols</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#int" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>number of columns</p>
-</dd>
-<dt><strong>rook</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.8)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>type of contiguity. Default is rook. For queen, rook =False</p>
-</dd>
-<dt><strong>id_type</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>string defining the type of IDs to use in the final W object;
-options are ‘int’ (0, 1, 2 …; default), ‘float’ (0.0,
-1.0, 2.0, …) and ‘string’ (‘id0’, ‘id1’, ‘id2’, …)</p>
-</dd>
-<dt><strong>**kwargs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/keyword.html#module-keyword" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code></span></dt><dd><p>optional arguments for <code class="xref py py-class docutils literal notranslate"><span class="pre">pysal.weights.W</span></code></p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>instance of spatial weights class W</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Notes</p>
-<p>Observations are row ordered: first k observations are in row 0, next k in row 1, and so on.</p>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w9</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="s2">&quot;</span><span class="si">%.3f</span><span class="s2">&quot;</span><span class="o">%</span><span class="n">w9</span><span class="o">.</span><span class="n">pct_nonzero</span>
-<span class="go">&#39;29.630&#39;</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w9</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="p">{</span><span class="mi">1</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">3</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">}</span>
-<span class="go">True</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w9</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span> <span class="o">==</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">4</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">6</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">}</span>
-<span class="go">True</span>
-</pre></div>
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-<h1>libpysal.weights.mat2L<a class="headerlink" href="#libpysal-weights-mat2l" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.weights.mat2L">
-<code class="sig-prename descclassname">libpysal.weights.</code><code class="sig-name descname">mat2L</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">edge_matrix</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/spintW.html#mat2L"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.mat2L" title="Permalink to this definition">¶</a></dt>
-<dd><p>Convert a matrix denoting network connectivity (edges or flows) to a list
-denoting edges</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>edge_matrix</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.20)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a></span></dt><dd><p>where rows denote network edge origins, columns denote
-network edge destinations, and non-zero entries denote the
-existence of an edge between a given origin and destination</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>edge_list</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>of tuples where each tuple is of the form (o,d) where o is an
-origin id and d is a destination id</p>
-</dd>
-</dl>
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-<h1>libpysal.weights.min_threshold_dist_from_shapefile<a class="headerlink" href="#libpysal-weights-min-threshold-dist-from-shapefile" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.weights.min_threshold_dist_from_shapefile">
-<code class="sig-prename descclassname">libpysal.weights.</code><code class="sig-name descname">min_threshold_dist_from_shapefile</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">shapefile</span></em>, <em class="sig-param"><span class="n">radius</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">p</span><span class="o">=</span><span class="default_value">2</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/user.html#min_threshold_dist_from_shapefile"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.min_threshold_dist_from_shapefile" title="Permalink to this definition">¶</a></dt>
-<dd><p>Get the maximum nearest neighbor distance between observations in the
-shapefile.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>shapefile</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>shapefile name with shp suffix.</p>
-</dd>
-<dt><strong>radius</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>If supplied arc_distances will be calculated
-based on the given radius. p will be ignored.</p>
-</dd>
-<dt><strong>p</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>Minkowski p-norm distance metric parameter:
-1&lt;=p&lt;=infinity
-2: Euclidean distance
-1: Manhattan distance</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>d</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>Maximum nearest neighbor distance between the n
-observations.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Notes</p>
-<p>Supports polygon or point shapefiles. For polygon shapefiles, distance is
-based on polygon centroids. Distances are defined using coordinates in
-shapefile which are assumed to be projected and not geographical
-coordinates.</p>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">libpysal</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">md</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">min_threshold_dist_from_shapefile</span><span class="p">(</span><span class="n">libpysal</span><span class="o">.</span><span class="n">examples</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="s2">&quot;columbus.shp&quot;</span><span class="p">))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">md</span>
-<span class="go">0.6188641580768541</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">min_threshold_dist_from_shapefile</span><span class="p">(</span><span class="n">libpysal</span><span class="o">.</span><span class="n">examples</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="s2">&quot;stl_hom.shp&quot;</span><span class="p">),</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">cg</span><span class="o">.</span><span class="n">sphere</span><span class="o">.</span><span class="n">RADIUS_EARTH_MILES</span><span class="p">)</span>
-<span class="go">31.846942936393717</span>
-</pre></div>
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diff --git a/docs/generated/libpysal.weights.min_threshold_distance.html b/docs/generated/libpysal.weights.min_threshold_distance.html
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-<h1>libpysal.weights.min_threshold_distance<a class="headerlink" href="#libpysal-weights-min-threshold-distance" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.weights.min_threshold_distance">
-<code class="sig-prename descclassname">libpysal.weights.</code><code class="sig-name descname">min_threshold_distance</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">p</span><span class="o">=</span><span class="default_value">2</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/util.html#min_threshold_distance"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.min_threshold_distance" title="Permalink to this definition">¶</a></dt>
-<dd><p>Get the maximum nearest neighbor distance.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>data</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.20)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a></span></dt><dd><p>(n,k) or KDTree where KDtree.data is array (n,k)
-n observations on k attributes</p>
-</dd>
-<dt><strong>p</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>Minkowski p-norm distance metric parameter:
-1&lt;=p&lt;=infinity
-2: Euclidean distance
-1: Manhattan distance</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>nnd</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>maximum nearest neighbor distance between the n observations</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights.util</span> <span class="kn">import</span> <span class="n">min_threshold_distance</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">indices</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">x</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">25</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">y</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">25</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">([</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">min_threshold_distance</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
-<span class="go">1.0</span>
-</pre></div>
-</div>
-</dd></dl>
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-<h1>libpysal.weights.neighbor_equality<a class="headerlink" href="#libpysal-weights-neighbor-equality" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.weights.neighbor_equality">
-<code class="sig-prename descclassname">libpysal.weights.</code><code class="sig-name descname">neighbor_equality</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">w1</span></em>, <em class="sig-param"><span class="n">w2</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/util.html#neighbor_equality"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.neighbor_equality" title="Permalink to this definition">¶</a></dt>
-<dd><p>Test if the neighbor sets are equal between two weights objects</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>w1</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>instance of spatial weights class W</p>
-</dd>
-<dt><strong>w2</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>instance of spatial weights class W</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl class="simple">
-<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">Boolean</span></code></dt><dd></dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Notes</p>
-<p>Only set membership is evaluated, no check of the weight values is carried out.</p>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights.util</span> <span class="kn">import</span> <span class="n">neighbor_equality</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span><span class="p">,</span> <span class="n">W</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w1</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w2</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">neighbor_equality</span><span class="p">(</span><span class="n">w1</span><span class="p">,</span> <span class="n">w2</span><span class="p">)</span>
-<span class="go">True</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w3</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span><span class="mi">5</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">neighbor_equality</span><span class="p">(</span><span class="n">w1</span><span class="p">,</span> <span class="n">w3</span><span class="p">)</span>
-<span class="go">False</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">n4</span> <span class="o">=</span> <span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">n4</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">n4</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w4</span> <span class="o">=</span> <span class="n">W</span><span class="p">(</span><span class="n">n4</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">neighbor_equality</span><span class="p">(</span><span class="n">w1</span><span class="p">,</span> <span class="n">w4</span><span class="p">)</span>
-<span class="go">False</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">n5</span> <span class="o">=</span> <span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">n5</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<span class="go">[3, 1]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">n5</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w5</span> <span class="o">=</span> <span class="n">W</span><span class="p">(</span><span class="n">n5</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">neighbor_equality</span><span class="p">(</span><span class="n">w1</span><span class="p">,</span> <span class="n">w5</span><span class="p">)</span>
-<span class="go">True</span>
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-<h1>libpysal.weights.netW<a class="headerlink" href="#libpysal-weights-netw" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.weights.netW">
-<code class="sig-prename descclassname">libpysal.weights.</code><code class="sig-name descname">netW</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">link_list</span></em>, <em class="sig-param"><span class="n">share</span><span class="o">=</span><span class="default_value">'A'</span></em>, <em class="sig-param"><span class="n">transform</span><span class="o">=</span><span class="default_value">'r'</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/spintW.html#netW"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.netW" title="Permalink to this definition">¶</a></dt>
-<dd><p>Create a network-contiguity based weight object based on different nodal
-relationships encoded in a network.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>link_list</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>of tuples where each tuple is of the form (o,d) where o is an
-origin id and d is a destination id</p>
-</dd>
-<dt><strong>share</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>denoting how to define the nodal relationship used to determine neighboring edges; defualt is ‘A’ for any shared nodes between two network edges; options include: O a shared origin node; D a shared destination node; OD; a shared origin or a shared destination node; C a shared node that is the destination of the first edge and the origin of the second edge - i.e., a directed chain is formed moving from edge one to edge two.</p>
-</dd>
-<dt><strong>transform</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Transformation</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">for</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">standardization</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">of</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">final</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">OD</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">spatial</span></code> weight; <code class="xref py py-obj docutils literal notranslate"><span class="pre">default</span></code></span></dt><dd><p>is ‘r’ for row standardized</p>
-</dd>
-<dt><strong>**kwargs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/keyword.html#module-keyword" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code></span></dt><dd><p>optional arguments for <code class="xref py py-class docutils literal notranslate"><span class="pre">pysal.weights.W</span></code></p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>W</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nodal</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">contiguity</span></code> <a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a> <a class="reference external" href="https://docs.python.org/3.8/library/functions.html#object" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">object</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">for</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">networkd</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">edges</span></code> or <code class="xref py py-obj docutils literal notranslate"><span class="pre">flows</span></code></span></dt><dd><p>W Object representing the binary adjacency of the network edges
-given a definition of nodal relationshilibpysal.weights.spintW.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">libpysal</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">links</span> <span class="o">=</span> <span class="p">[(</span><span class="s1">&#39;a&#39;</span><span class="p">,</span><span class="s1">&#39;b&#39;</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;a&#39;</span><span class="p">,</span><span class="s1">&#39;c&#39;</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;a&#39;</span><span class="p">,</span><span class="s1">&#39;d&#39;</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;c&#39;</span><span class="p">,</span><span class="s1">&#39;d&#39;</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;c&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;c&#39;</span><span class="p">,</span><span class="s1">&#39;a&#39;</span><span class="p">)]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">O</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">netW</span><span class="p">(</span><span class="n">links</span><span class="p">,</span> <span class="n">share</span><span class="o">=</span><span class="s1">&#39;O&#39;</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">O</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[(</span><span class="s1">&#39;a&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">)]</span>
-<span class="go">[(&#39;a&#39;, &#39;c&#39;), (&#39;a&#39;, &#39;d&#39;)]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">OD</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">netW</span><span class="p">(</span><span class="n">links</span><span class="p">,</span> <span class="n">share</span><span class="o">=</span><span class="s1">&#39;OD&#39;</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">OD</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[(</span><span class="s1">&#39;a&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">)]</span>
-<span class="go">[(&#39;a&#39;, &#39;c&#39;), (&#39;a&#39;, &#39;d&#39;), (&#39;c&#39;, &#39;b&#39;)]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">any_common</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">netW</span><span class="p">(</span><span class="n">links</span><span class="p">,</span> <span class="n">share</span><span class="o">=</span><span class="s1">&#39;A&#39;</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">any_common</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[(</span><span class="s1">&#39;a&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">)]</span>
-<span class="go">[(&#39;a&#39;, &#39;c&#39;), (&#39;a&#39;, &#39;d&#39;), (&#39;c&#39;, &#39;b&#39;), (&#39;c&#39;, &#39;a&#39;)]</span>
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-<h1>libpysal.weights.nonplanar_neighbors<a class="headerlink" href="#libpysal-weights-nonplanar-neighbors" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.weights.nonplanar_neighbors">
-<code class="sig-prename descclassname">libpysal.weights.</code><code class="sig-name descname">nonplanar_neighbors</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">w</span></em>, <em class="sig-param"><span class="n">geodataframe</span></em>, <em class="sig-param"><span class="n">tolerance</span><span class="o">=</span><span class="default_value">0.001</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/util.html#nonplanar_neighbors"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.nonplanar_neighbors" title="Permalink to this definition">¶</a></dt>
-<dd><p>Detect neighbors for non-planar polygon collections</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl class="simple">
-<dt><strong>w:   pysal W</strong></dt><dd><p>A spatial weights object with reported islands</p>
-</dd>
-<dt><strong>geodataframe: GeoDataframe</strong></dt><dd><p>The polygon dataframe from which w was constructed.</p>
-</dd>
-<dt><strong>tolerance: float</strong></dt><dd><p>The percentage of the minimum horizontal or vertical extent (minextent) of
-the dataframe to use in defining  a buffering distance to allow for fuzzy
-contiguity detection. The buffering distance is equal to tolerance*minextent.</p>
-</dd>
-<dt><strong>**kwargs:  keyword arguments</strong></dt><dd><p>optional arguments for <code class="xref py py-class docutils literal notranslate"><span class="pre">pysal.weights.W</span></code></p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl class="simple">
-<dt>w: <code class="xref py py-obj docutils literal notranslate"><span class="pre">pysal</span></code> <a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></dt><dd><p>Spatial weights object that encodes fuzzy neighbors.
-This will have an attribute <cite>non_planar_joins</cite> to indicate what new joins were detected.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Notes</p>
-<p>This relaxes the notion of contiguity neighbors for the case of shapefiles
-that violate the condition of planar enforcement. It handles three types
-of conditions present in such files that would result in islands when using
-the regular PySAL contiguity methods. The first are edges for nearby
-polygons that should be shared, but are digitized separately for the
-individual polygons and the resulting edges do not coincide, but instead
-the edges intersect. The second case is similar to the first, only the
-resultant edges do not intersect but are “close”. The final case arises
-when one polygon is “inside” a second polygon but is not encoded to
-represent a hole in the containing polygon.</p>
-<p>The buffering check assumes the geometry coordinates are projected.</p>
-<p class="rubric">References</p>
-<p>Planar Enforcement: <a class="reference external" href="http://ibis.geog.ubc.ca/courses/klink/gis.notes/ncgia/u12.html#SEC12.6">http://ibis.geog.ubc.ca/courses/klink/gis.notes/ncgia/u12.html#SEC12.6</a></p>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">geopandas</span> <span class="k">as</span> <span class="nn">gpd</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">libpysal</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">df</span> <span class="o">=</span> <span class="n">gpd</span><span class="o">.</span><span class="n">read_file</span><span class="p">(</span><span class="n">libpysal</span><span class="o">.</span><span class="n">examples</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="s1">&#39;map_RS_BR.shp&#39;</span><span class="p">))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">Queen</span><span class="o">.</span><span class="n">from_dataframe</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">islands</span>
-<span class="go">[0, 4, 23, 27, 80, 94, 101, 107, 109, 119, 122, 139, 169, 175, 223, 239, 247, 253, 254, 255, 256, 261, 276, 291, 294, 303, 321, 357, 374]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wnp</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">nonplanar_neighbors</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">df</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wnp</span><span class="o">.</span><span class="n">islands</span>
-<span class="go">[]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<span class="go">[]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wnp</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<span class="go">[23, 59, 152, 239]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wnp</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">23</span><span class="p">]</span>
-<span class="go">[0, 45, 59, 107, 152, 185, 246]</span>
-</pre></div>
-</div>
-<p>Also see <cite>nonplanarweights.ipynb</cite></p>
-<dl class="field-list">
-<dt class="field-odd">Attributes</dt>
-<dd class="field-odd"><dl>
-<dt><strong>non_planar_joins</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dictionary</span></code></span></dt><dd><p>Stores the new joins detected. Key is the id of the focal unit, value is a list of neighbor ids.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-</dd></dl>
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-<h1>libpysal.weights.order<a class="headerlink" href="#libpysal-weights-order" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.weights.order">
-<code class="sig-prename descclassname">libpysal.weights.</code><code class="sig-name descname">order</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">w</span></em>, <em class="sig-param"><span class="n">kmax</span><span class="o">=</span><span class="default_value">3</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/util.html#order"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.order" title="Permalink to this definition">¶</a></dt>
-<dd><p>Determine the non-redundant order of contiguity up to a specific
-order.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>spatial weights object</p>
-</dd>
-<dt><strong>kmax</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#int" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>maximum order of contiguity</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>info</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dictionary</span></code></span></dt><dd><p>observation id is the key, value is a list of contiguity
-orders with a negative 1 in the ith position</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Notes</p>
-<p>Implements the algorithm in <a class="bibtex reference internal" href="../references.html#anselin1996b" id="id1">[AS96]</a>.</p>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">order</span><span class="p">,</span> <span class="n">Rook</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">libpysal</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">Rook</span><span class="o">.</span><span class="n">from_shapefile</span><span class="p">(</span><span class="n">libpysal</span><span class="o">.</span><span class="n">examples</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="s1">&#39;10740.shp&#39;</span><span class="p">))</span>
-</pre></div>
-</div>
-<p>WARNING: there is one disconnected observation (no neighbors)
-Island id:  [163]
-&gt;&gt;&gt; w3 = order(w, kmax = 3)
-&gt;&gt;&gt; w3[1][0:5]
-[1, -1, 1, 2, 1]</p>
-</dd></dl>
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-<h1>libpysal.weights.remap_ids<a class="headerlink" href="#libpysal-weights-remap-ids" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.weights.remap_ids">
-<code class="sig-prename descclassname">libpysal.weights.</code><code class="sig-name descname">remap_ids</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">w</span></em>, <em class="sig-param"><span class="n">old2new</span></em>, <em class="sig-param"><span class="n">id_order</span><span class="o">=</span><span class="default_value">[]</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/util.html#remap_ids"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.remap_ids" title="Permalink to this definition">¶</a></dt>
-<dd><p>Remaps the IDs in a spatial weights object.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>Spatial weights object</p>
-</dd>
-<dt><strong>old2new</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dictionary</span></code></span></dt><dd><p>Dictionary where the keys are the IDs in w (i.e. “old IDs”) and
-the values are the IDs to replace them (i.e. “new IDs”)</p>
-</dd>
-<dt><strong>id_order</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>An ordered list of new IDs, which defines the order of observations when
-iterating over W. If not set then the id_order in w will be
-used.</p>
-</dd>
-<dt><strong>**kwargs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/keyword.html#module-keyword" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code></span></dt><dd><p>optional arguments for <code class="xref py py-class docutils literal notranslate"><span class="pre">pysal.weights.W</span></code></p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>implicit</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>Spatial weights object with new IDs</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">id_order</span>
-<span class="go">[0, 1, 2, 3, 4, 5]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<span class="go">[2, 1]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">old_to_new</span> <span class="o">=</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span><span class="s1">&#39;a&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">:</span><span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="mi">2</span><span class="p">:</span><span class="s1">&#39;c&#39;</span><span class="p">,</span> <span class="mi">3</span><span class="p">:</span><span class="s1">&#39;d&#39;</span><span class="p">,</span> <span class="mi">4</span><span class="p">:</span><span class="s1">&#39;e&#39;</span><span class="p">,</span> <span class="mi">5</span><span class="p">:</span><span class="s1">&#39;f&#39;</span><span class="p">}</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w_new</span> <span class="o">=</span> <span class="n">remap_ids</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">old_to_new</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w_new</span><span class="o">.</span><span class="n">id_order</span>
-<span class="go">[&#39;a&#39;, &#39;b&#39;, &#39;c&#39;, &#39;d&#39;, &#39;e&#39;, &#39;f&#39;]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w_new</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="s1">&#39;a&#39;</span><span class="p">]</span>
-<span class="go">[&#39;c&#39;, &#39;b&#39;]</span>
-</pre></div>
-</div>
-</dd></dl>
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-<h1>libpysal.weights.shimbel<a class="headerlink" href="#libpysal-weights-shimbel" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.weights.shimbel">
-<code class="sig-prename descclassname">libpysal.weights.</code><code class="sig-name descname">shimbel</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">w</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/util.html#shimbel"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.shimbel" title="Permalink to this definition">¶</a></dt>
-<dd><p>Find the Shimbel matrix for first order contiguity matrix.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>spatial weights object</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>info</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>list of lists; one list for each observation which stores
-the shortest order between it and each of the the other observations.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span><span class="p">,</span> <span class="n">shimbel</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w5</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">()</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w5_shimbel</span> <span class="o">=</span> <span class="n">shimbel</span><span class="p">(</span><span class="n">w5</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w5_shimbel</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">24</span><span class="p">]</span>
-<span class="go">8</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w5_shimbel</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">:</span><span class="mi">4</span><span class="p">]</span>
-<span class="go">[-1, 1, 2, 3]</span>
-</pre></div>
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diff --git a/docs/generated/libpysal.weights.spw_from_gal.html b/docs/generated/libpysal.weights.spw_from_gal.html
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-<h1>libpysal.weights.spw_from_gal<a class="headerlink" href="#libpysal-weights-spw-from-gal" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.weights.spw_from_gal">
-<code class="sig-prename descclassname">libpysal.weights.</code><code class="sig-name descname">spw_from_gal</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">galfile</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/user.html#spw_from_gal"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.spw_from_gal" title="Permalink to this definition">¶</a></dt>
-<dd><p>Sparse scipy matrix for w from a gal file.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>galfile</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>name of gal file including suffix</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>spw</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sparse_matrix</span></code></span></dt><dd><p>scipy sparse matrix in CSR format</p>
-</dd>
-<dt><strong>ids</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.20)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a></span></dt><dd><p>identifiers for rows/cols of spw</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">libpysal</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">spw</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">spw_from_gal</span><span class="p">(</span><span class="n">libpysal</span><span class="o">.</span><span class="n">examples</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="s2">&quot;sids2.gal&quot;</span><span class="p">))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">spw</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">nnz</span>
-<span class="go">462</span>
-</pre></div>
-</div>
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-     rel="nofollow">Source</a>
-</div>
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diff --git a/docs/generated/libpysal.weights.testDataArray.html b/docs/generated/libpysal.weights.testDataArray.html
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-<h1>libpysal.weights.testDataArray<a class="headerlink" href="#libpysal-weights-testdataarray" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.weights.testDataArray">
-<code class="sig-prename descclassname">libpysal.weights.</code><code class="sig-name descname">testDataArray</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">shape</span><span class="o">=</span><span class="default_value">3, 4, 4</span></em>, <em class="sig-param"><span class="n">time</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">rand</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">missing_vals</span><span class="o">=</span><span class="default_value">True</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/raster.html#testDataArray"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.testDataArray" title="Permalink to this definition">¶</a></dt>
-<dd><p>Creates 2 or 3 dimensional test xarray.DataArray object</p>
-<dl class="simple">
-<dt>shape<span class="classifier">tuple</span></dt><dd><p>Tuple containing shape of the DataArray aligned with
-following dimension = (lat, lon) or (layer, lat, lon)
-Default shape = (3, 4, 4)</p>
-</dd>
-<dt>time<span class="classifier">boolean</span></dt><dd><p>Type of layer, if True then layer=time else layer=band
-Default is False.</p>
-</dd>
-<dt>rand<span class="classifier">boolean</span></dt><dd><p>If True, creates a DataArray filled with unique and random data.
-Default is false (generates seeded random data)</p>
-</dd>
-<dt>missing_vals<span class="classifier">boolean</span></dt><dd><p>Create a DataArray filled with missing values. Default is True.</p>
-</dd>
-</dl>
-<dl class="field-list">
-<dt class="field-odd">Returns</dt>
-<dd class="field-odd"><dl>
-<dt><strong>da</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.DataArray</span></code></span></dt><dd><p>instance of xarray.DataArray</p>
-</dd>
-</dl>
-</dd>
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-<h1>libpysal.weights.vecW<a class="headerlink" href="#libpysal-weights-vecw" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.weights.vecW">
-<code class="sig-prename descclassname">libpysal.weights.</code><code class="sig-name descname">vecW</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">origin_x</span></em>, <em class="sig-param"><span class="n">origin_y</span></em>, <em class="sig-param"><span class="n">dest_x</span></em>, <em class="sig-param"><span class="n">dest_y</span></em>, <em class="sig-param"><span class="n">threshold</span></em>, <em class="sig-param"><span class="n">p</span><span class="o">=</span><span class="default_value">2</span></em>, <em class="sig-param"><span class="n">alpha</span><span class="o">=</span><span class="default_value">- 1.0</span></em>, <em class="sig-param"><span class="n">binary</span><span class="o">=</span><span class="default_value">True</span></em>, <em class="sig-param"><span class="n">ids</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">build_sp</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/spintW.html#vecW"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.vecW" title="Permalink to this definition">¶</a></dt>
-<dd><p>Distance-based spatial weight for vectors that is computed using a
-4-dimensional distance between the origin x,y-coordinates and the
-destination x,y-coordinates</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>origin_x</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a> or <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.20)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a></span></dt><dd><p>of vector origin x-coordinates</p>
-</dd>
-<dt><strong>origin_y</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a> or <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.20)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a></span></dt><dd><p>of vector origin y-coordinates</p>
-</dd>
-<dt><strong>dest_x</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a> or <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.20)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a></span></dt><dd><p>of vector destination x-coordinates</p>
-</dd>
-<dt><strong>dest_y</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a> or <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.20)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a></span></dt><dd><p>of vector destination y-coordinates</p>
-</dd>
-<dt><strong>threshold</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>distance band</p>
-</dd>
-<dt><strong>p</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>Minkowski p-norm distance metric parameter:
-1&lt;=p&lt;=infinity
-2: Euclidean distance
-1: Manhattan distance</p>
-</dd>
-<dt><strong>binary</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.8)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>If true w_{ij}=1 if d_{i,j}&lt;=threshold, otherwise w_{i,j}=0
-If false wij=dij^{alpha}</p>
-</dd>
-<dt><strong>alpha</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/functions.html#float" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>distance decay parameter for weight (default -1.0)
-if alpha is positive the weights will not decline with
-distance. If binary is True, alpha is ignored</p>
-</dd>
-<dt><strong>ids</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>values to use for keys of the neighbors and weights dicts</p>
-</dd>
-<dt><strong>build_sp</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.8)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>True to build sparse distance matrix and false to build dense
-distance matrix; significant speed gains may be obtained
-dending on the sparsity of the of distance_matrix and
-threshold that is applied</p>
-</dd>
-<dt><strong>**kwargs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/keyword.html#module-keyword" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code></span></dt><dd><p>optional arguments for <code class="xref py py-class docutils literal notranslate"><span class="pre">pysal.weights.W</span></code></p>
-</dd>
-<dt><strong>Returns</strong></dt><dd></dd>
-<dt><strong>——</strong></dt><dd></dd>
-<dt><strong>W</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.DistanceBand.html#libpysal.weights.DistanceBand" title="libpysal.weights.DistanceBand"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DistanceBand</span></code></a> <a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a> <a class="reference external" href="https://docs.python.org/3.8/library/functions.html#object" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">object</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">that</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">uses</span></code> 4-dimenional <code class="xref py py-obj docutils literal notranslate"><span class="pre">distances</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">between</span></code></span></dt><dd><p>vectors origin and destination coordinates.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">libpysal</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">x1</span> <span class="o">=</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span><span class="mi">6</span><span class="p">,</span><span class="mi">3</span><span class="p">]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">y1</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">8</span><span class="p">,</span><span class="mi">5</span><span class="p">]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">x2</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">9</span><span class="p">]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">y2</span> <span class="o">=</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span><span class="mi">6</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">W1</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">vecW</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">y1</span><span class="p">,</span> <span class="n">x2</span><span class="p">,</span> <span class="n">y2</span><span class="p">,</span> <span class="n">threshold</span><span class="o">=</span><span class="mi">999</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">list</span><span class="p">(</span><span class="n">W1</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-<span class="go">[1, 2]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">W2</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">vecW</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">y2</span><span class="p">,</span> <span class="n">x1</span><span class="p">,</span> <span class="n">y2</span><span class="p">,</span> <span class="n">threshold</span><span class="o">=</span><span class="mf">8.5</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">list</span><span class="p">(</span><span class="n">W2</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-<span class="go">[1, 2]</span>
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-<h1>libpysal.weights.w2da<a class="headerlink" href="#libpysal-weights-w2da" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.weights.w2da">
-<code class="sig-prename descclassname">libpysal.weights.</code><code class="sig-name descname">w2da</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">w</span></em>, <em class="sig-param"><span class="n">attrs</span><span class="o">=</span><span class="default_value">{}</span></em>, <em class="sig-param"><span class="n">coords</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/raster.html#w2da"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.w2da" title="Permalink to this definition">¶</a></dt>
-<dd><p>Creates xarray.DataArray object from passed data aligned with W object.</p>
-<dl class="simple">
-<dt>data<span class="classifier">array/list/pd.Series</span></dt><dd><p>1d array-like data with dimensionality conforming to w</p>
-</dd>
-<dt>w<span class="classifier">libpysal.weights.W</span></dt><dd><p>Spatial weights object aligned with passed data</p>
-</dd>
-<dt>attrs<span class="classifier">Dictionary</span></dt><dd><p>Attributes stored in dict related to DataArray, e.g. da.attrs
-Default is {} empty dictionary.</p>
-</dd>
-<dt>coords<span class="classifier">Dictionary/xarray.core.coordinates.DataArrayCoordinates</span></dt><dd><p>Coordinates corresponding to DataArray, e.g. da.coords</p>
-</dd>
-</dl>
-<dl class="field-list">
-<dt class="field-odd">Returns</dt>
-<dd class="field-odd"><dl>
-<dt><strong>da</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.DataArray</span></code></span></dt><dd><p>instance of xarray.DataArray</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.raster</span> <span class="kn">import</span> <span class="n">da2W</span><span class="p">,</span> <span class="n">testDataArray</span><span class="p">,</span> <span class="n">w2da</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">da</span> <span class="o">=</span> <span class="n">testDataArray</span><span class="p">()</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">da</span><span class="o">.</span><span class="n">shape</span>
-<span class="go">(3, 4, 4)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">da2W</span><span class="p">(</span><span class="n">da</span><span class="p">,</span> <span class="n">z_value</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">255</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">index</span><span class="p">))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">da1</span> <span class="o">=</span> <span class="n">w2da</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">w</span><span class="p">)</span>
-</pre></div>
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-<h1>libpysal.weights.w_clip<a class="headerlink" href="#libpysal-weights-w-clip" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.weights.w_clip">
-<code class="sig-prename descclassname">libpysal.weights.</code><code class="sig-name descname">w_clip</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">w1</span></em>, <em class="sig-param"><span class="n">w2</span></em>, <em class="sig-param"><span class="n">outSP</span><span class="o">=</span><span class="default_value">True</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/set_operations.html#w_clip"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.w_clip" title="Permalink to this definition">¶</a></dt>
-<dd><p>Clip a continuous W object (w1) with a different W object (w2) so only cells where
-w2 has a non-zero value remain with non-zero values in w1.</p>
-<p>Checks on w1 and w2 are performed to make sure they conform to the
-appropriate format and, if not, they are converted.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>w1</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>W, scipy.sparse.csr.csr_matrix
-Potentially continuous weights matrix to be clipped. The clipped
-matrix wc will have at most the same elements as w1.</p>
-</dd>
-<dt><strong>w2</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>W, scipy.sparse.csr.csr_matrix
-Weights matrix to use as shell to clip w1. Automatically
-converted to binary format. Only non-zero elements in w2 will be
-kept non-zero in wc. NOTE: assumed to be of the same shape as w1</p>
-</dd>
-<dt><strong>outSP</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.8)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>If True (default) return sparse version of the clipped W, if
-False, return W object of the clipped matrix</p>
-</dd>
-<dt><strong>**kwargs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/keyword.html#module-keyword" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code></span></dt><dd><p>optional arguments for <code class="xref py py-class docutils literal notranslate"><span class="pre">pysal.weights.W</span></code></p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>wc</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>W, scipy.sparse.csr.csr_matrix
-Clipped W object (sparse if outSP=Ture). It inherits <code class="docutils literal notranslate"><span class="pre">id_order</span></code> from w1.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span>
-</pre></div>
-</div>
-<p>First create a W object from a lattice using queen contiguity and
-row-standardize it (note that these weights will stay when we clip the
-object, but they will not neccesarily represent a row-standardization
-anymore):</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">w1</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">rook</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w1</span><span class="o">.</span><span class="n">transform</span> <span class="o">=</span> <span class="s1">&#39;R&#39;</span>
-</pre></div>
-</div>
-<p>We will clip that geography assuming observations 0, 2, 3 and 4 belong to
-one group and 1, 5 belong to another group and we don’t want both groups
-to interact with each other in our weights (i.e. w_ij = 0 if i and j in
-different groups). For that, we use the following method:</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">libpysal</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w2</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">block_weights</span><span class="p">([</span><span class="s1">&#39;r1&#39;</span><span class="p">,</span> <span class="s1">&#39;r2&#39;</span><span class="p">,</span> <span class="s1">&#39;r1&#39;</span><span class="p">,</span> <span class="s1">&#39;r1&#39;</span><span class="p">,</span> <span class="s1">&#39;r1&#39;</span><span class="p">,</span> <span class="s1">&#39;r2&#39;</span><span class="p">])</span>
-</pre></div>
-</div>
-<p>To illustrate that w2 will only be considered as binary even when the
-object passed is not, we can row-standardize it</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">w2</span><span class="o">.</span><span class="n">transform</span> <span class="o">=</span> <span class="s1">&#39;R&#39;</span>
-</pre></div>
-</div>
-<p>The clipped object <code class="docutils literal notranslate"><span class="pre">wc</span></code> will contain only the spatial queen
-relationships that occur within one group (‘r1’ or ‘r2’) but will have
-gotten rid of those that happen across groups</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">wcs</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">w_clip</span><span class="p">(</span><span class="n">w1</span><span class="p">,</span> <span class="n">w2</span><span class="p">,</span> <span class="n">outSP</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>This will create a sparse object (recommended when n is large).</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">wcs</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">toarray</span><span class="p">()</span>
-<span class="go">array([[0.        , 0.        , 0.33333333, 0.33333333, 0.        ,</span>
-<span class="go">        0.        ],</span>
-<span class="go">       [0.        , 0.        , 0.        , 0.        , 0.        ,</span>
-<span class="go">        0.        ],</span>
-<span class="go">       [0.2       , 0.        , 0.        , 0.2       , 0.2       ,</span>
-<span class="go">        0.        ],</span>
-<span class="go">       [0.2       , 0.        , 0.2       , 0.        , 0.2       ,</span>
-<span class="go">        0.        ],</span>
-<span class="go">       [0.        , 0.        , 0.33333333, 0.33333333, 0.        ,</span>
-<span class="go">        0.        ],</span>
-<span class="go">       [0.        , 0.        , 0.        , 0.        , 0.        ,</span>
-<span class="go">        0.        ]])</span>
-</pre></div>
-</div>
-<p>If we wanted an original W object, we can control that with the argument
-<code class="docutils literal notranslate"><span class="pre">outSP</span></code>:</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">wc</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">w_clip</span><span class="p">(</span><span class="n">w1</span><span class="p">,</span> <span class="n">w2</span><span class="p">,</span> <span class="n">outSP</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wc</span><span class="o">.</span><span class="n">full</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span>
-<span class="go">array([[0.        , 0.        , 0.33333333, 0.33333333, 0.        ,</span>
-<span class="go">        0.        ],</span>
-<span class="go">       [0.        , 0.        , 0.        , 0.        , 0.        ,</span>
-<span class="go">        0.        ],</span>
-<span class="go">       [0.2       , 0.        , 0.        , 0.2       , 0.2       ,</span>
-<span class="go">        0.        ],</span>
-<span class="go">       [0.2       , 0.        , 0.2       , 0.        , 0.2       ,</span>
-<span class="go">        0.        ],</span>
-<span class="go">       [0.        , 0.        , 0.33333333, 0.33333333, 0.        ,</span>
-<span class="go">        0.        ],</span>
-<span class="go">       [0.        , 0.        , 0.        , 0.        , 0.        ,</span>
-<span class="go">        0.        ]])</span>
-</pre></div>
-</div>
-<p>You can check they are actually the same:</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">wcs</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">toarray</span><span class="p">()</span> <span class="o">==</span> <span class="n">wc</span><span class="o">.</span><span class="n">full</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span>
-<span class="go">array([[ True,  True,  True,  True,  True,  True],</span>
-<span class="go">       [ True,  True,  True,  True,  True,  True],</span>
-<span class="go">       [ True,  True,  True,  True,  True,  True],</span>
-<span class="go">       [ True,  True,  True,  True,  True,  True],</span>
-<span class="go">       [ True,  True,  True,  True,  True,  True],</span>
-<span class="go">       [ True,  True,  True,  True,  True,  True]])</span>
-</pre></div>
-</div>
-</dd></dl>
-
-</div>
-
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-  <div class="section" id="libpysal-weights-w-difference">
-<h1>libpysal.weights.w_difference<a class="headerlink" href="#libpysal-weights-w-difference" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.weights.w_difference">
-<code class="sig-prename descclassname">libpysal.weights.</code><code class="sig-name descname">w_difference</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">w1</span></em>, <em class="sig-param"><span class="n">w2</span></em>, <em class="sig-param"><span class="n">w_shape</span><span class="o">=</span><span class="default_value">'w1'</span></em>, <em class="sig-param"><span class="n">constrained</span><span class="o">=</span><span class="default_value">True</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/set_operations.html#w_difference"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.w_difference" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns a binary weights object, w, that includes only neighbor pairs
-in w1 that are not in w2. The w_shape and constrained parameters
-determine which pairs in w1 that are not in w2 are returned.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>w1</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>object</p>
-</dd>
-<dt><strong>w2</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>object</p>
-</dd>
-<dt><strong>w_shape</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>Defines the shape of the returned weights matrix. ‘w1’ returns a
-matrix with the same IDs as w1; ‘all’ returns a matrix with all
-the unique IDs from w1 and w2; and ‘min’ returns a matrix with
-the IDs occurring in w1 and not in w2.</p>
-</dd>
-<dt><strong>constrained</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.8)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>If False then the full set of neighbor pairs in w1 that are
-not in w2 are returned. If True then those pairs that would
-not be possible if w_shape=’min’ are dropped. Ignored if
-w_shape is set to ‘min’.</p>
-</dd>
-<dt><strong>**kwargs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/keyword.html#module-keyword" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code></span></dt><dd><p>optional arguments for <code class="xref py py-class docutils literal notranslate"><span class="pre">pysal.weights.W</span></code></p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>object</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Notes</p>
-<p>ID comparisons are performed using ==, therefore the integer ID 2 is
-equivalent to the float ID 2.0.</p>
-<p class="rubric">Examples</p>
-<p>Construct rook (w2) and queen (w1) weights matrices for two 4x4 regions
-(16 areas). A queen matrix has all the joins a rook matrix does plus joins
-between areas that share a corner. The new matrix formed by the difference
-of rook from queen contains only join at corners (typically called a
-bishop matrix). Note that the difference of queen from rook would result
-in a weights matrix with no joins.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span><span class="p">,</span> <span class="n">w_difference</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w1</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="n">rook</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w2</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="n">rook</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">w_difference</span><span class="p">(</span><span class="n">w1</span><span class="p">,</span> <span class="n">w2</span><span class="p">,</span> <span class="n">constrained</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">w</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<span class="go">False</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">15</span><span class="p">]</span>
-<span class="go">[10, 11, 14]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w2</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">15</span><span class="p">]</span>
-<span class="go">[11, 14]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">15</span><span class="p">]</span>
-<span class="go">[10]</span>
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-<h1>libpysal.weights.w_intersection<a class="headerlink" href="#libpysal-weights-w-intersection" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.weights.w_intersection">
-<code class="sig-prename descclassname">libpysal.weights.</code><code class="sig-name descname">w_intersection</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">w1</span></em>, <em class="sig-param"><span class="n">w2</span></em>, <em class="sig-param"><span class="n">w_shape</span><span class="o">=</span><span class="default_value">'w1'</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/set_operations.html#w_intersection"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.w_intersection" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns a binary weights object, w, that includes only
-those neighbor pairs that exist in both w1 and w2.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>w1</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>object</p>
-</dd>
-<dt><strong>w2</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>object</p>
-</dd>
-<dt><strong>w_shape</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>Defines the shape of the returned weights matrix. ‘w1’ returns a
-matrix with the same IDs as w1; ‘all’ returns a matrix with all
-the unique IDs from w1 and w2; and ‘min’ returns a matrix with
-only the IDs occurring in both w1 and w2.</p>
-</dd>
-<dt><strong>**kwargs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/keyword.html#module-keyword" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code></span></dt><dd><p>optional arguments for <code class="xref py py-class docutils literal notranslate"><span class="pre">pysal.weights.W</span></code></p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>object</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Notes</p>
-<p>ID comparisons are performed using ==, therefore the integer ID 2 is
-equivalent to the float ID 2.0.</p>
-<p class="rubric">Examples</p>
-<p>Construct rook weights matrices for two regions, one is 4x4 (16 areas)
-and the other is 6x4 (24 areas). An intersection of these two weights
-matrices results in the new weights matrix matching the smaller one.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span><span class="p">,</span> <span class="n">w_intersection</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w1</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w2</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span><span class="mi">4</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">w_intersection</span><span class="p">(</span><span class="n">w1</span><span class="p">,</span> <span class="n">w2</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">w</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<span class="go">True</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">15</span><span class="p">]</span>
-<span class="go">[11, 14]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w2</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">15</span><span class="p">]</span>
-<span class="go">[11, 14, 19]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">15</span><span class="p">]</span>
-<span class="go">[11, 14]</span>
-</pre></div>
-</div>
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-<h1>libpysal.weights.w_local_cluster<a class="headerlink" href="#libpysal-weights-w-local-cluster" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.weights.w_local_cluster">
-<code class="sig-prename descclassname">libpysal.weights.</code><code class="sig-name descname">w_local_cluster</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">w</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/util.html#w_local_cluster"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.w_local_cluster" title="Permalink to this definition">¶</a></dt>
-<dd><p>Local clustering coefficients for each unit as a node in a graph.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>spatial weights object</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>c</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.20)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a></span></dt><dd><p>(w.n,1)
-local clustering coefficients</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Notes</p>
-<p>The local clustering coefficient <span class="math notranslate nohighlight">\(c_i\)</span> quantifies how close the
-neighbors of observation <span class="math notranslate nohighlight">\(i\)</span> are to being a clique:</p>
-<div class="math notranslate nohighlight">
-\[c_i = | \{w_{j,k}\} |/ (k_i(k_i - 1)): j,k \in N_i\]</div>
-<p>where <span class="math notranslate nohighlight">\(N_i\)</span> is the set of neighbors to <span class="math notranslate nohighlight">\(i\)</span>, <span class="math notranslate nohighlight">\(k_i =
-|N_i|\)</span> and <span class="math notranslate nohighlight">\(\{w_{j,k}\}\)</span> is the set of non-zero elements of the
-weights between pairs in <span class="math notranslate nohighlight">\(N_i\)</span> <a class="bibtex reference internal" href="../references.html#watts1998" id="id1">[WS98]</a>.</p>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span><span class="p">,</span> <span class="n">w_local_cluster</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span> <span class="n">rook</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w_local_cluster</span><span class="p">(</span><span class="n">w</span><span class="p">)</span>
-<span class="go">array([[1.        ],</span>
-<span class="go">       [0.6       ],</span>
-<span class="go">       [1.        ],</span>
-<span class="go">       [0.6       ],</span>
-<span class="go">       [0.42857143],</span>
-<span class="go">       [0.6       ],</span>
-<span class="go">       [1.        ],</span>
-<span class="go">       [0.6       ],</span>
-<span class="go">       [1.        ]])</span>
-</pre></div>
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-</dd></dl>
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-  <a href="../_sources/generated/libpysal.weights.w_local_cluster.rst.txt"
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-</div>
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-<h1>libpysal.weights.w_subset<a class="headerlink" href="#libpysal-weights-w-subset" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.weights.w_subset">
-<code class="sig-prename descclassname">libpysal.weights.</code><code class="sig-name descname">w_subset</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">w1</span></em>, <em class="sig-param"><span class="n">ids</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/set_operations.html#w_subset"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.w_subset" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns a binary weights object, w, that includes only those
-observations in ids.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>w1</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>object</p>
-</dd>
-<dt><strong>ids</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#list" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>A list containing the IDs to be include in the returned weights
-object.</p>
-</dd>
-<dt><strong>**kwargs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/keyword.html#module-keyword" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code></span></dt><dd><p>optional arguments for <code class="xref py py-class docutils literal notranslate"><span class="pre">pysal.weights.W</span></code></p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>object</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<p>Construct a rook weights matrix for a 6x4 region (24 areas). By default
-PySAL assigns integer IDs to the areas in a region. By passing in a list
-of integers from 0 to 15, the first 16 areas are extracted from the
-previous weights matrix, and only those joins relevant to the new region
-are retained.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span><span class="p">,</span> <span class="n">w_subset</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w1</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span><span class="mi">4</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">ids</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="mi">16</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">w_subset</span><span class="p">(</span><span class="n">w1</span><span class="p">,</span> <span class="n">ids</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">w</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<span class="go">True</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">15</span><span class="p">]</span>
-<span class="go">[11, 14, 19]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">15</span><span class="p">]</span>
-<span class="go">[11, 14]</span>
-</pre></div>
-</div>
-</dd></dl>
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-<h1>libpysal.weights.w_symmetric_difference<a class="headerlink" href="#libpysal-weights-w-symmetric-difference" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.weights.w_symmetric_difference">
-<code class="sig-prename descclassname">libpysal.weights.</code><code class="sig-name descname">w_symmetric_difference</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">w1</span></em>, <em class="sig-param"><span class="n">w2</span></em>, <em class="sig-param"><span class="n">w_shape</span><span class="o">=</span><span class="default_value">'all'</span></em>, <em class="sig-param"><span class="n">constrained</span><span class="o">=</span><span class="default_value">True</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/set_operations.html#w_symmetric_difference"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.w_symmetric_difference" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns a binary weights object, w, that includes only neighbor pairs
-that are not shared by w1 and w2. The w_shape and constrained parameters
-determine which pairs that are not shared by w1 and w2 are returned.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>w1</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>object</p>
-</dd>
-<dt><strong>w2</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>object</p>
-</dd>
-<dt><strong>w_shape</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>Defines the shape of the returned weights matrix. ‘all’ returns a
-matrix with all the unique IDs from w1 and w2; and ‘min’ returns
-a matrix with the IDs not shared by w1 and w2.</p>
-</dd>
-<dt><strong>constrained</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.8)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>If False then the full set of neighbor pairs that are not
-shared by w1 and w2 are returned. If True then those pairs
-that would not be possible if w_shape=’min’ are dropped.
-Ignored if w_shape is set to ‘min’.</p>
-</dd>
-<dt><strong>**kwargs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/keyword.html#module-keyword" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code></span></dt><dd><p>optional arguments for <code class="xref py py-class docutils literal notranslate"><span class="pre">pysal.weights.W</span></code></p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>object</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Notes</p>
-<p>ID comparisons are performed using ==, therefore the integer ID 2 is
-equivalent to the float ID 2.0.</p>
-<p class="rubric">Examples</p>
-<p>Construct queen weights matrix for a 4x4 (16 areas) region (w1) and a rook
-matrix for a 6x4 (24 areas) region (w2). The symmetric difference of these
-two matrices (with w_shape set to ‘all’ and constrained set to False)
-contains the corner joins in the overlap area, all the joins in the
-non-overlap area.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span><span class="p">,</span> <span class="n">w_symmetric_difference</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w1</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="n">rook</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w2</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="n">rook</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">w_symmetric_difference</span><span class="p">(</span><span class="n">w1</span><span class="p">,</span> <span class="n">w2</span><span class="p">,</span> <span class="n">constrained</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">w</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<span class="go">False</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">15</span><span class="p">]</span>
-<span class="go">[10, 11, 14]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w2</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">15</span><span class="p">]</span>
-<span class="go">[11, 14, 19]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">set</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">15</span><span class="p">])</span> <span class="o">==</span> <span class="nb">set</span><span class="p">([</span><span class="mi">10</span><span class="p">,</span> <span class="mi">19</span><span class="p">])</span>
-<span class="go">True</span>
-</pre></div>
-</div>
-</dd></dl>
-
-</div>
-
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-</div>
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-  <div class="section" id="libpysal-weights-w-union">
-<h1>libpysal.weights.w_union<a class="headerlink" href="#libpysal-weights-w-union" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.weights.w_union">
-<code class="sig-prename descclassname">libpysal.weights.</code><code class="sig-name descname">w_union</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">w1</span></em>, <em class="sig-param"><span class="n">w2</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/set_operations.html#w_union"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.w_union" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns a binary weights object, w, that includes all neighbor pairs that
-exist in either w1 or w2.</p>
-<dl class="field-list">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><dl>
-<dt><strong>w1</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>object</p>
-</dd>
-<dt><strong>w2</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>object</p>
-</dd>
-<dt><strong>**kwargs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.8/library/keyword.html#module-keyword" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code></span></dt><dd><p>optional arguments for <code class="xref py py-class docutils literal notranslate"><span class="pre">pysal.weights.W</span></code></p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><dl>
-<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>object</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Notes</p>
-<p>ID comparisons are performed using ==, therefore the integer ID 2 is
-equivalent to the float ID 2.0. Returns a matrix with all the unique IDs
-from w1 and w2.</p>
-<p class="rubric">Examples</p>
-<p>Construct rook weights matrices for two regions, one is 4x4 (16 areas)
-and the other is 6x4 (24 areas). A union of these two weights matrices
-results in the new weights matrix matching the larger one.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span><span class="p">,</span> <span class="n">w_union</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w1</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w2</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span><span class="mi">4</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">w_union</span><span class="p">(</span><span class="n">w1</span><span class="p">,</span> <span class="n">w2</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">w</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<span class="go">True</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">15</span><span class="p">]</span>
-<span class="go">[11, 14]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w2</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">15</span><span class="p">]</span>
-<span class="go">[11, 14, 19]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">15</span><span class="p">]</span>
-<span class="go">[19, 11, 14]</span>
-</pre></div>
-</div>
-</dd></dl>
-
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-
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-<h1>libpysal.weights.wsp2da<a class="headerlink" href="#libpysal-weights-wsp2da" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt id="libpysal.weights.wsp2da">
-<code class="sig-prename descclassname">libpysal.weights.</code><code class="sig-name descname">wsp2da</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">wsp</span></em>, <em class="sig-param"><span class="n">attrs</span><span class="o">=</span><span class="default_value">{}</span></em>, <em class="sig-param"><span class="n">coords</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/raster.html#wsp2da"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#libpysal.weights.wsp2da" title="Permalink to this definition">¶</a></dt>
-<dd><p>Creates xarray.DataArray object from passed data aligned with WSP object.</p>
-<dl class="simple">
-<dt>data<span class="classifier">array/list/pd.Series</span></dt><dd><p>1d array-like data with dimensionality conforming to wsp</p>
-</dd>
-<dt>wsp<span class="classifier">libpysal.weights.WSP</span></dt><dd><p>Sparse weights object aligned with passed data</p>
-</dd>
-<dt>attrs<span class="classifier">Dictionary</span></dt><dd><p>Attributes stored in dict related to DataArray, e.g. da.attrs
-Default is {} empty dictionary.</p>
-</dd>
-<dt>coords<span class="classifier">Dictionary/xarray.core.coordinates.DataArrayCoordinates</span></dt><dd><p>coordinates corresponding to DataArray, e.g. da.coords</p>
-</dd>
-</dl>
-<dl class="field-list">
-<dt class="field-odd">Returns</dt>
-<dd class="field-odd"><dl>
-<dt><strong>da</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.DataArray</span></code></span></dt><dd><p>instance of xarray.DataArray</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.raster</span> <span class="kn">import</span> <span class="n">da2WSP</span><span class="p">,</span> <span class="n">testDataArray</span><span class="p">,</span> <span class="n">wsp2da</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">da</span> <span class="o">=</span> <span class="n">testDataArray</span><span class="p">()</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">da</span><span class="o">.</span><span class="n">shape</span>
-<span class="go">(3, 4, 4)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">wsp</span> <span class="o">=</span> <span class="n">da2WSP</span><span class="p">(</span><span class="n">da</span><span class="p">,</span> <span class="n">z_value</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">255</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">wsp</span><span class="o">.</span><span class="n">index</span><span class="p">))</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">da1</span> <span class="o">=</span> <span class="n">w2da</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">wsp</span><span class="p">)</span>
-</pre></div>
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-
-      <ul>
-        <li><a href="generated/libpysal.cg.LineSegment.html#libpysal.cg.LineSegment.bounding_box">(libpysal.cg.LineSegment property)</a>
-</li>
-        <li><a href="generated/libpysal.cg.Polygon.html#libpysal.cg.Polygon.bounding_box">(libpysal.cg.Polygon property)</a>
-</li>
-      </ul></li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="generated/libpysal.cg.Grid.html#libpysal.cg.Grid.bounds">bounds() (libpysal.cg.Grid method)</a>
-</li>
-      <li><a href="generated/libpysal.cg.brute_knn.html#libpysal.cg.brute_knn">brute_knn() (in module libpysal.cg)</a>
-</li>
-      <li><a href="generated/libpysal.weights.build_lattice_shapefile.html#libpysal.weights.build_lattice_shapefile">build_lattice_shapefile() (in module libpysal.weights)</a>
-</li>
-      <li><a href="generated/libpysal.cg.Polygon.html#libpysal.cg.Polygon.build_quad_tree_structure">build_quad_tree_structure() (libpysal.cg.Polygon method)</a>
-</li>
-      <li><a href="generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.by_row">by_row() (libpysal.io.fileio.FileIO property)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="C">C</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.cardinalities">cardinalities() (libpysal.weights.W property)</a>
-</li>
-      <li><a href="generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.cast">cast() (libpysal.io.fileio.FileIO method)</a>
-</li>
-      <li><a href="generated/libpysal.cg.Polygon.html#libpysal.cg.Polygon.centroid">centroid() (libpysal.cg.Polygon property)</a>
-</li>
-      <li><a href="generated/libpysal.cg.Chain.html#libpysal.cg.Chain">Chain (class in libpysal.cg)</a>
-</li>
-      <li><a href="generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.check">check() (libpysal.io.fileio.FileIO class method)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.close">close() (libpysal.io.fileio.FileIO method)</a>
-</li>
-      <li><a href="generated/libpysal.weights.comb.html#libpysal.weights.comb">comb() (in module libpysal.weights)</a>
-</li>
-      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.component_labels">component_labels() (libpysal.weights.W property)</a>
-</li>
-      <li><a href="generated/libpysal.cg.Polygon.html#libpysal.cg.Polygon.contains_point">contains_point() (libpysal.cg.Polygon method)</a>
-
-      <ul>
-        <li><a href="generated/libpysal.cg.PolygonLocator.html#libpysal.cg.PolygonLocator.contains_point">(libpysal.cg.PolygonLocator method)</a>
-</li>
-      </ul></li>
-      <li><a href="generated/libpysal.cg.convex_hull.html#libpysal.cg.convex_hull">convex_hull() (in module libpysal.cg)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="D">D</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="generated/libpysal.weights.da2W.html#libpysal.weights.da2W">da2W() (in module libpysal.weights)</a>
-</li>
-      <li><a href="generated/libpysal.weights.da2WSP.html#libpysal.weights.da2WSP">da2WSP() (in module libpysal.weights)</a>
-</li>
-      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.diagW2">diagW2() (libpysal.weights.W property)</a>
-</li>
-      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.diagWtW">diagWtW() (libpysal.weights.W property)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.diagWtW_WW">diagWtW_WW() (libpysal.weights.W property)</a>
-
-      <ul>
-        <li><a href="generated/libpysal.weights.WSP.html#libpysal.weights.WSP.diagWtW_WW">(libpysal.weights.WSP property)</a>
-</li>
-      </ul></li>
-      <li><a href="generated/libpysal.cg.distance_matrix.html#libpysal.cg.distance_matrix">distance_matrix() (in module libpysal.cg)</a>
-</li>
-      <li><a href="generated/libpysal.weights.DistanceBand.html#libpysal.weights.DistanceBand">DistanceBand (class in libpysal.weights)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="E">E</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="generated/libpysal.examples.explain.html#libpysal.examples.explain">explain() (in module libpysal.examples)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="F">F</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="generated/libpysal.cg.fast_knn.html#libpysal.cg.fast_knn">fast_knn() (in module libpysal.cg)</a>
-</li>
-      <li><a href="generated/libpysal.cg.fast_threshold.html#libpysal.cg.fast_threshold">fast_threshold() (in module libpysal.cg)</a>
-</li>
-      <li><a href="generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO">FileIO (class in libpysal.io.fileio)</a>
-</li>
-      <li><a href="generated/libpysal.weights.fill_diagonal.html#libpysal.weights.fill_diagonal">fill_diagonal() (in module libpysal.weights)</a>
-</li>
-      <li><a href="generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.flush">flush() (libpysal.io.fileio.FileIO method)</a>
-</li>
-      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.from_adjlist">from_adjlist() (libpysal.weights.W class method)</a>
-</li>
-      <li><a href="generated/libpysal.weights.DistanceBand.html#libpysal.weights.DistanceBand.from_array">from_array() (libpysal.weights.DistanceBand class method)</a>
-
-      <ul>
-        <li><a href="generated/libpysal.weights.Kernel.html#libpysal.weights.Kernel.from_array">(libpysal.weights.Kernel class method)</a>
-</li>
-        <li><a href="generated/libpysal.weights.KNN.html#libpysal.weights.KNN.from_array">(libpysal.weights.KNN class method)</a>
-</li>
-      </ul></li>
-      <li><a href="generated/libpysal.weights.DistanceBand.html#libpysal.weights.DistanceBand.from_dataframe">from_dataframe() (libpysal.weights.DistanceBand class method)</a>
-
-      <ul>
-        <li><a href="generated/libpysal.weights.Kernel.html#libpysal.weights.Kernel.from_dataframe">(libpysal.weights.Kernel class method)</a>
-</li>
-        <li><a href="generated/libpysal.weights.KNN.html#libpysal.weights.KNN.from_dataframe">(libpysal.weights.KNN class method)</a>
-</li>
-        <li><a href="generated/libpysal.weights.Queen.html#libpysal.weights.Queen.from_dataframe">(libpysal.weights.Queen class method)</a>
-</li>
-        <li><a href="generated/libpysal.weights.Rook.html#libpysal.weights.Rook.from_dataframe">(libpysal.weights.Rook class method)</a>
-</li>
-      </ul></li>
-      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.from_file">from_file() (libpysal.weights.W class method)</a>
-</li>
-      <li><a href="generated/libpysal.weights.Queen.html#libpysal.weights.Queen.from_iterable">from_iterable() (libpysal.weights.Queen class method)</a>
-
-      <ul>
-        <li><a href="generated/libpysal.weights.Rook.html#libpysal.weights.Rook.from_iterable">(libpysal.weights.Rook class method)</a>
-</li>
-      </ul></li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.from_networkx">from_networkx() (libpysal.weights.W class method)</a>
-</li>
-      <li><a href="generated/libpysal.weights.DistanceBand.html#libpysal.weights.DistanceBand.from_shapefile">from_shapefile() (libpysal.weights.DistanceBand class method)</a>
-
-      <ul>
-        <li><a href="generated/libpysal.weights.Kernel.html#libpysal.weights.Kernel.from_shapefile">(libpysal.weights.Kernel class method)</a>
-</li>
-        <li><a href="generated/libpysal.weights.KNN.html#libpysal.weights.KNN.from_shapefile">(libpysal.weights.KNN class method)</a>
-</li>
-        <li><a href="generated/libpysal.weights.Queen.html#libpysal.weights.Queen.from_shapefile">(libpysal.weights.Queen class method)</a>
-</li>
-        <li><a href="generated/libpysal.weights.Rook.html#libpysal.weights.Rook.from_shapefile">(libpysal.weights.Rook class method)</a>
-</li>
-        <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.from_shapefile">(libpysal.weights.W class method)</a>
-</li>
-      </ul></li>
-      <li><a href="generated/libpysal.weights.WSP.html#libpysal.weights.WSP.from_W">from_W() (libpysal.weights.WSP class method)</a>
-</li>
-      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.from_WSP">from_WSP() (libpysal.weights.W class method)</a>
-</li>
-      <li><a href="generated/libpysal.weights.Queen.html#libpysal.weights.Queen.from_xarray">from_xarray() (libpysal.weights.Queen class method)</a>
-
-      <ul>
-        <li><a href="generated/libpysal.weights.Rook.html#libpysal.weights.Rook.from_xarray">(libpysal.weights.Rook class method)</a>
-</li>
-      </ul></li>
-      <li><a href="generated/libpysal.weights.full.html#libpysal.weights.full">full() (in module libpysal.weights)</a>
-
-      <ul>
-        <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.full">(libpysal.weights.W method)</a>
-</li>
-      </ul></li>
-      <li><a href="generated/libpysal.weights.full2W.html#libpysal.weights.full2W">full2W() (in module libpysal.weights)</a>
-</li>
-      <li><a href="generated/libpysal.weights.fuzzy_contiguity.html#libpysal.weights.fuzzy_contiguity">fuzzy_contiguity() (in module libpysal.weights)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="G">G</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="generated/libpysal.cg.geogrid.html#libpysal.cg.geogrid">geogrid() (in module libpysal.cg)</a>
-</li>
-      <li><a href="generated/libpysal.cg.geointerpolate.html#libpysal.cg.geointerpolate">geointerpolate() (in module libpysal.cg)</a>
-</li>
-      <li><a href="generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.get">get() (libpysal.io.fileio.FileIO method)</a>
-</li>
-      <li><a href="generated/libpysal.cg.get_angle_between.html#libpysal.cg.get_angle_between">get_angle_between() (in module libpysal.cg)</a>
-</li>
-      <li><a href="generated/libpysal.cg.get_bounding_box.html#libpysal.cg.get_bounding_box">get_bounding_box() (in module libpysal.cg)</a>
-</li>
-      <li><a href="generated/libpysal.weights.get_ids.html#libpysal.weights.get_ids">get_ids() (in module libpysal.weights)</a>
-</li>
-      <li><a href="generated/libpysal.examples.get_path.html#libpysal.examples.get_path">get_path() (in module libpysal.examples)</a>
-</li>
-      <li><a href="generated/libpysal.cg.get_point_at_angle_and_dist.html#libpysal.cg.get_point_at_angle_and_dist">get_point_at_angle_and_dist() (in module libpysal.cg)</a>
-</li>
-      <li><a href="generated/libpysal.weights.get_points_array_from_shapefile.html#libpysal.weights.get_points_array_from_shapefile">get_points_array_from_shapefile() (in module libpysal.weights)</a>
-</li>
-      <li><a href="generated/libpysal.cg.get_points_dist.html#libpysal.cg.get_points_dist">get_points_dist() (in module libpysal.cg)</a>
-</li>
-      <li><a href="generated/libpysal.cg.get_polygon_point_dist.html#libpysal.cg.get_polygon_point_dist">get_polygon_point_dist() (in module libpysal.cg)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="generated/libpysal.cg.get_polygon_point_intersect.html#libpysal.cg.get_polygon_point_intersect">get_polygon_point_intersect() (in module libpysal.cg)</a>
-</li>
-      <li><a href="generated/libpysal.cg.get_ray_segment_intersect.html#libpysal.cg.get_ray_segment_intersect">get_ray_segment_intersect() (in module libpysal.cg)</a>
-</li>
-      <li><a href="generated/libpysal.cg.get_rectangle_point_intersect.html#libpysal.cg.get_rectangle_point_intersect">get_rectangle_point_intersect() (in module libpysal.cg)</a>
-</li>
-      <li><a href="generated/libpysal.cg.get_rectangle_rectangle_intersection.html#libpysal.cg.get_rectangle_rectangle_intersection">get_rectangle_rectangle_intersection() (in module libpysal.cg)</a>
-</li>
-      <li><a href="generated/libpysal.cg.get_segment_point_dist.html#libpysal.cg.get_segment_point_dist">get_segment_point_dist() (in module libpysal.cg)</a>
-</li>
-      <li><a href="generated/libpysal.cg.get_segment_point_intersect.html#libpysal.cg.get_segment_point_intersect">get_segment_point_intersect() (in module libpysal.cg)</a>
-</li>
-      <li><a href="generated/libpysal.cg.get_segments_intersect.html#libpysal.cg.get_segments_intersect">get_segments_intersect() (in module libpysal.cg)</a>
-</li>
-      <li><a href="generated/libpysal.cg.get_shared_segments.html#libpysal.cg.get_shared_segments">get_shared_segments() (in module libpysal.cg)</a>
-</li>
-      <li><a href="generated/libpysal.cg.LineSegment.html#libpysal.cg.LineSegment.get_swap">get_swap() (libpysal.cg.LineSegment method)</a>
-</li>
-      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.get_transform">get_transform() (libpysal.weights.W method)</a>
-</li>
-      <li><a href="generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.getType">getType() (libpysal.io.fileio.FileIO static method)</a>
-</li>
-      <li><a href="generated/libpysal.cg.Grid.html#libpysal.cg.Grid">Grid (class in libpysal.cg)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="H">H</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="generated/libpysal.cg.harcdist.html#libpysal.cg.harcdist">harcdist() (in module libpysal.cg)</a>
-</li>
-      <li><a href="generated/libpysal.cg.Rectangle.html#libpysal.cg.Rectangle.height">height() (libpysal.cg.Rectangle property)</a>
-</li>
-      <li><a href="generated/libpysal.weights.hexLat2W.html#libpysal.weights.hexLat2W">hexLat2W() (in module libpysal.weights)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="generated/libpysal.weights.higher_order.html#libpysal.weights.higher_order">higher_order() (in module libpysal.weights)</a>
-</li>
-      <li><a href="generated/libpysal.weights.higher_order_sp.html#libpysal.weights.higher_order_sp">higher_order_sp() (in module libpysal.weights)</a>
-</li>
-      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.histogram">histogram() (libpysal.weights.W property)</a>
-</li>
-      <li><a href="generated/libpysal.cg.Polygon.html#libpysal.cg.Polygon.holes">holes() (libpysal.cg.Polygon property)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="I">I</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.id2i">id2i() (libpysal.weights.W property)</a>
-</li>
-      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.id_order">id_order() (libpysal.weights.W property)</a>
-
-      <ul>
-        <li><a href="generated/libpysal.weights.WSP.html#libpysal.weights.WSP.id_order">(libpysal.weights.WSP property)</a>
-</li>
-      </ul></li>
-      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.id_order_set">id_order_set() (libpysal.weights.W property)</a>
-</li>
-      <li><a href="generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.ids">ids() (libpysal.io.fileio.FileIO property)</a>
-</li>
-      <li><a href="generated/libpysal.cg.Grid.html#libpysal.cg.Grid.in_grid">in_grid() (libpysal.cg.Grid method)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="generated/libpysal.cg.PolygonLocator.html#libpysal.cg.PolygonLocator.inside">inside() (libpysal.cg.PolygonLocator method)</a>
-</li>
-      <li><a href="generated/libpysal.cg.LineSegment.html#libpysal.cg.LineSegment.intersect">intersect() (libpysal.cg.LineSegment method)</a>
-</li>
-      <li><a href="generated/libpysal.cg.LineSegment.html#libpysal.cg.LineSegment.is_ccw">is_ccw() (libpysal.cg.LineSegment method)</a>
-</li>
-      <li><a href="generated/libpysal.cg.is_clockwise.html#libpysal.cg.is_clockwise">is_clockwise() (in module libpysal.cg)</a>
-</li>
-      <li><a href="generated/libpysal.cg.is_collinear.html#libpysal.cg.is_collinear">is_collinear() (in module libpysal.cg)</a>
-</li>
-      <li><a href="generated/libpysal.cg.LineSegment.html#libpysal.cg.LineSegment.is_cw">is_cw() (libpysal.cg.LineSegment method)</a>
-</li>
-      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.islands">islands() (libpysal.weights.W property)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="K">K</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="generated/libpysal.cg.KDTree.html#libpysal.cg.KDTree">KDTree() (in module libpysal.cg)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="generated/libpysal.weights.Kernel.html#libpysal.weights.Kernel">Kernel (class in libpysal.weights)</a>
-</li>
-      <li><a href="generated/libpysal.weights.KNN.html#libpysal.weights.KNN">KNN (class in libpysal.weights)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="L">L</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="generated/libpysal.weights.lag_categorical.html#libpysal.weights.lag_categorical">lag_categorical() (in module libpysal.weights)</a>
-</li>
-      <li><a href="generated/libpysal.weights.lag_spatial.html#libpysal.weights.lag_spatial">lag_spatial() (in module libpysal.weights)</a>
-</li>
-      <li><a href="generated/libpysal.weights.lat2SW.html#libpysal.weights.lat2SW">lat2SW() (in module libpysal.weights)</a>
-</li>
-      <li><a href="generated/libpysal.weights.lat2W.html#libpysal.weights.lat2W">lat2W() (in module libpysal.weights)</a>
-</li>
-      <li><a href="generated/libpysal.cg.Chain.html#libpysal.cg.Chain.len">len() (libpysal.cg.Chain property)</a>
-
-      <ul>
-        <li><a href="generated/libpysal.cg.LineSegment.html#libpysal.cg.LineSegment.len">(libpysal.cg.LineSegment property)</a>
-</li>
-        <li><a href="generated/libpysal.cg.Polygon.html#libpysal.cg.Polygon.len">(libpysal.cg.Polygon property)</a>
-</li>
-      </ul></li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="generated/libpysal.cg.Line.html#libpysal.cg.Line">Line (class in libpysal.cg)</a>
-</li>
-      <li><a href="generated/libpysal.cg.LineSegment.html#libpysal.cg.LineSegment.line">line() (libpysal.cg.LineSegment property)</a>
-</li>
-      <li><a href="generated/libpysal.cg.linear2arcdist.html#libpysal.cg.linear2arcdist">linear2arcdist() (in module libpysal.cg)</a>
-</li>
-      <li><a href="generated/libpysal.cg.LineSegment.html#libpysal.cg.LineSegment">LineSegment (class in libpysal.cg)</a>
-</li>
-      <li><a href="generated/libpysal.cg.lonlat.html#libpysal.cg.lonlat">lonlat() (in module libpysal.cg)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="M">M</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="generated/libpysal.weights.mat2L.html#libpysal.weights.mat2L">mat2L() (in module libpysal.weights)</a>
-</li>
-      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.max_neighbors">max_neighbors() (libpysal.weights.W property)</a>
-</li>
-      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.mean_neighbors">mean_neighbors() (libpysal.weights.W property)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.min_neighbors">min_neighbors() (libpysal.weights.W property)</a>
-</li>
-      <li><a href="generated/libpysal.weights.min_threshold_dist_from_shapefile.html#libpysal.weights.min_threshold_dist_from_shapefile">min_threshold_dist_from_shapefile() (in module libpysal.weights)</a>
-</li>
-      <li><a href="generated/libpysal.weights.min_threshold_distance.html#libpysal.weights.min_threshold_distance">min_threshold_distance() (in module libpysal.weights)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="N">N</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.n">n() (libpysal.weights.W property)</a>
-</li>
-      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.n_components">n_components() (libpysal.weights.W property)</a>
-</li>
-      <li><a href="generated/libpysal.cg.Grid.html#libpysal.cg.Grid.nearest">nearest() (libpysal.cg.Grid method)</a>
-
-      <ul>
-        <li><a href="generated/libpysal.cg.PointLocator.html#libpysal.cg.PointLocator.nearest">(libpysal.cg.PointLocator method)</a>
-</li>
-        <li><a href="generated/libpysal.cg.PolygonLocator.html#libpysal.cg.PolygonLocator.nearest">(libpysal.cg.PolygonLocator method)</a>
-</li>
-      </ul></li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="generated/libpysal.weights.neighbor_equality.html#libpysal.weights.neighbor_equality">neighbor_equality() (in module libpysal.weights)</a>
-</li>
-      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.neighbor_offsets">neighbor_offsets() (libpysal.weights.W property)</a>
-</li>
-      <li><a href="generated/libpysal.weights.netW.html#libpysal.weights.netW">netW() (in module libpysal.weights)</a>
-</li>
-      <li><a href="generated/libpysal.weights.nonplanar_neighbors.html#libpysal.weights.nonplanar_neighbors">nonplanar_neighbors() (in module libpysal.weights)</a>
-</li>
-      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.nonzero">nonzero() (libpysal.weights.W property)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="O">O</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="generated/libpysal.weights.ODW.html#libpysal.weights.ODW">ODW() (in module libpysal.weights)</a>
-</li>
-      <li><a href="generated/libpysal.io.open.html#libpysal.io.open">open (in module libpysal.io)</a>
-</li>
-      <li><a href="generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.open">open() (libpysal.io.fileio.FileIO class method)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="generated/libpysal.weights.order.html#libpysal.weights.order">order() (in module libpysal.weights)</a>
-</li>
-      <li><a href="generated/libpysal.cg.PointLocator.html#libpysal.cg.PointLocator.overlapping">overlapping() (libpysal.cg.PointLocator method)</a>
-
-      <ul>
-        <li><a href="generated/libpysal.cg.PolygonLocator.html#libpysal.cg.PolygonLocator.overlapping">(libpysal.cg.PolygonLocator method)</a>
-</li>
-      </ul></li>
-  </ul></td>
-</tr></table>
-
-<h2 id="P">P</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="generated/libpysal.cg.LineSegment.html#libpysal.cg.LineSegment.p1">p1() (libpysal.cg.LineSegment property)</a>
-</li>
-      <li><a href="generated/libpysal.cg.LineSegment.html#libpysal.cg.LineSegment.p2">p2() (libpysal.cg.LineSegment property)</a>
-</li>
-      <li><a href="generated/libpysal.cg.Chain.html#libpysal.cg.Chain.parts">parts() (libpysal.cg.Chain property)</a>
-
-      <ul>
-        <li><a href="generated/libpysal.cg.Polygon.html#libpysal.cg.Polygon.parts">(libpysal.cg.Polygon property)</a>
-</li>
-      </ul></li>
-      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.pct_nonzero">pct_nonzero() (libpysal.weights.W property)</a>
-</li>
-      <li><a href="generated/libpysal.cg.Polygon.html#libpysal.cg.Polygon.perimeter">perimeter() (libpysal.cg.Polygon property)</a>
-</li>
-      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.plot">plot() (libpysal.weights.W method)</a>
-</li>
-      <li><a href="generated/libpysal.cg.Point.html#libpysal.cg.Point">Point (class in libpysal.cg)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="generated/libpysal.cg.point_touches_rectangle.html#libpysal.cg.point_touches_rectangle">point_touches_rectangle() (in module libpysal.cg)</a>
-</li>
-      <li><a href="generated/libpysal.cg.PointLocator.html#libpysal.cg.PointLocator">PointLocator (class in libpysal.cg)</a>
-</li>
-      <li><a href="generated/libpysal.cg.Polygon.html#libpysal.cg.Polygon">Polygon (class in libpysal.cg)</a>
-</li>
-      <li><a href="generated/libpysal.cg.PointLocator.html#libpysal.cg.PointLocator.polygon">polygon() (libpysal.cg.PointLocator method)</a>
-</li>
-      <li><a href="generated/libpysal.cg.PolygonLocator.html#libpysal.cg.PolygonLocator">PolygonLocator (class in libpysal.cg)</a>
-</li>
-      <li><a href="generated/libpysal.cg.Grid.html#libpysal.cg.Grid.proximity">proximity() (libpysal.cg.Grid method)</a>
-
-      <ul>
-        <li><a href="generated/libpysal.cg.PointLocator.html#libpysal.cg.PointLocator.proximity">(libpysal.cg.PointLocator method)</a>
-</li>
-        <li><a href="generated/libpysal.cg.PolygonLocator.html#libpysal.cg.PolygonLocator.proximity">(libpysal.cg.PolygonLocator method)</a>
-</li>
-      </ul></li>
-  </ul></td>
-</tr></table>
-
-<h2 id="Q">Q</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="generated/libpysal.weights.Queen.html#libpysal.weights.Queen">Queen (class in libpysal.weights)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="R">R</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="generated/libpysal.cg.RADIUS_EARTH_KM.html#libpysal.cg.RADIUS_EARTH_KM">RADIUS_EARTH_KM (in module libpysal.cg)</a>
-</li>
-      <li><a href="generated/libpysal.cg.RADIUS_EARTH_MILES.html#libpysal.cg.RADIUS_EARTH_MILES">RADIUS_EARTH_MILES (in module libpysal.cg)</a>
-</li>
-      <li><a href="generated/libpysal.cg.Ray.html#libpysal.cg.Ray">Ray (class in libpysal.cg)</a>
-</li>
-      <li><a href="generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.read">read() (libpysal.io.fileio.FileIO method)</a>
-</li>
-      <li><a href="generated/libpysal.cg.Rectangle.html#libpysal.cg.Rectangle">Rectangle (class in libpysal.cg)</a>
-</li>
-      <li><a href="generated/libpysal.cg.PointLocator.html#libpysal.cg.PointLocator.region">region() (libpysal.cg.PointLocator method)</a>
-
-      <ul>
-        <li><a href="generated/libpysal.cg.PolygonLocator.html#libpysal.cg.PolygonLocator.region">(libpysal.cg.PolygonLocator method)</a>
-</li>
-      </ul></li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="generated/libpysal.weights.remap_ids.html#libpysal.weights.remap_ids">remap_ids() (in module libpysal.weights)</a>
-
-      <ul>
-        <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.remap_ids">(libpysal.weights.W method)</a>
-</li>
-      </ul></li>
-      <li><a href="generated/libpysal.cg.Grid.html#libpysal.cg.Grid.remove">remove() (libpysal.cg.Grid method)</a>
-</li>
-      <li><a href="generated/libpysal.weights.KNN.html#libpysal.weights.KNN.reweight">reweight() (libpysal.weights.KNN method)</a>
-</li>
-      <li><a href="generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.rIds">rIds() (libpysal.io.fileio.FileIO property)</a>
-</li>
-      <li><a href="generated/libpysal.weights.Rook.html#libpysal.weights.Rook">Rook (class in libpysal.weights)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="S">S</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.s0">s0() (libpysal.weights.W property)</a>
-
-      <ul>
-        <li><a href="generated/libpysal.weights.WSP.html#libpysal.weights.WSP.s0">(libpysal.weights.WSP property)</a>
-</li>
-      </ul></li>
-      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.s1">s1() (libpysal.weights.W property)</a>
-</li>
-      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.s2">s2() (libpysal.weights.W property)</a>
-</li>
-      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.s2array">s2array() (libpysal.weights.W property)</a>
-</li>
-      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.sd">sd() (libpysal.weights.W property)</a>
-</li>
-      <li><a href="generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.seek">seek() (libpysal.io.fileio.FileIO method)</a>
-</li>
-      <li><a href="generated/libpysal.cg.Chain.html#libpysal.cg.Chain.segments">segments() (libpysal.cg.Chain property)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="generated/libpysal.cg.Rectangle.html#libpysal.cg.Rectangle.set_centroid">set_centroid() (libpysal.cg.Rectangle method)</a>
-</li>
-      <li><a href="generated/libpysal.cg.Rectangle.html#libpysal.cg.Rectangle.set_scale">set_scale() (libpysal.cg.Rectangle method)</a>
-</li>
-      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.set_shapefile">set_shapefile() (libpysal.weights.W method)</a>
-</li>
-      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.set_transform">set_transform() (libpysal.weights.W method)</a>
-</li>
-      <li><a href="generated/libpysal.weights.shimbel.html#libpysal.weights.shimbel">shimbel() (in module libpysal.weights)</a>
-</li>
-      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.sparse">sparse() (libpysal.weights.W property)</a>
-</li>
-      <li><a href="generated/libpysal.weights.spw_from_gal.html#libpysal.weights.spw_from_gal">spw_from_gal() (in module libpysal.weights)</a>
-</li>
-      <li><a href="generated/libpysal.cg.LineSegment.html#libpysal.cg.LineSegment.sw_ccw">sw_ccw() (libpysal.cg.LineSegment method)</a>
-</li>
-      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.symmetrize">symmetrize() (libpysal.weights.W method)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="T">T</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.tell">tell() (libpysal.io.fileio.FileIO method)</a>
-</li>
-      <li><a href="generated/libpysal.weights.testDataArray.html#libpysal.weights.testDataArray">testDataArray() (in module libpysal.weights)</a>
-</li>
-      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.to_adjlist">to_adjlist() (libpysal.weights.W method)</a>
-</li>
-      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.to_file">to_file() (libpysal.weights.W method)</a>
-</li>
-      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.to_networkx">to_networkx() (libpysal.weights.W method)</a>
-</li>
-      <li><a href="generated/libpysal.weights.WSP.html#libpysal.weights.WSP.to_W">to_W() (libpysal.weights.WSP method)</a>
-</li>
-      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.to_WSP">to_WSP() (libpysal.weights.W method)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="generated/libpysal.cg.toLngLat.html#libpysal.cg.toLngLat">toLngLat() (in module libpysal.cg)</a>
-</li>
-      <li><a href="generated/libpysal.cg.toXYZ.html#libpysal.cg.toXYZ">toXYZ() (in module libpysal.cg)</a>
-</li>
-      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.transform">transform() (libpysal.weights.W property)</a>
-</li>
-      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.trcW2">trcW2() (libpysal.weights.W property)</a>
-</li>
-      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.trcWtW">trcWtW() (libpysal.weights.W property)</a>
-</li>
-      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.trcWtW_WW">trcWtW_WW() (libpysal.weights.W property)</a>
-
-      <ul>
-        <li><a href="generated/libpysal.weights.WSP.html#libpysal.weights.WSP.trcWtW_WW">(libpysal.weights.WSP property)</a>
-</li>
-      </ul></li>
-      <li><a href="generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.truncate">truncate() (libpysal.io.fileio.FileIO method)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="V">V</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="generated/libpysal.weights.vecW.html#libpysal.weights.vecW">vecW() (in module libpysal.weights)</a>
-</li>
-      <li><a href="generated/libpysal.cg.Chain.html#libpysal.cg.Chain.vertices">vertices() (libpysal.cg.Chain property)</a>
-
-      <ul>
-        <li><a href="generated/libpysal.cg.Polygon.html#libpysal.cg.Polygon.vertices">(libpysal.cg.Polygon property)</a>
-</li>
-      </ul></li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="generated/libpysal.weights.Voronoi.html#libpysal.weights.Voronoi">Voronoi() (in module libpysal.weights)</a>
-</li>
-      <li><a href="generated/libpysal.cg.voronoi_frames.html#libpysal.cg.voronoi_frames">voronoi_frames() (in module libpysal.cg)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="W">W</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W">W (class in libpysal.weights)</a>
-</li>
-      <li><a href="generated/libpysal.weights.w2da.html#libpysal.weights.w2da">w2da() (in module libpysal.weights)</a>
-</li>
-      <li><a href="generated/libpysal.weights.w_clip.html#libpysal.weights.w_clip">w_clip() (in module libpysal.weights)</a>
-</li>
-      <li><a href="generated/libpysal.weights.w_difference.html#libpysal.weights.w_difference">w_difference() (in module libpysal.weights)</a>
-</li>
-      <li><a href="generated/libpysal.weights.w_intersection.html#libpysal.weights.w_intersection">w_intersection() (in module libpysal.weights)</a>
-</li>
-      <li><a href="generated/libpysal.weights.w_local_cluster.html#libpysal.weights.w_local_cluster">w_local_cluster() (in module libpysal.weights)</a>
-</li>
-      <li><a href="generated/libpysal.weights.w_subset.html#libpysal.weights.w_subset">w_subset() (in module libpysal.weights)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="generated/libpysal.weights.w_symmetric_difference.html#libpysal.weights.w_symmetric_difference">w_symmetric_difference() (in module libpysal.weights)</a>
-</li>
-      <li><a href="generated/libpysal.weights.w_union.html#libpysal.weights.w_union">w_union() (in module libpysal.weights)</a>
-</li>
-      <li><a href="generated/libpysal.cg.Rectangle.html#libpysal.cg.Rectangle.width">width() (libpysal.cg.Rectangle property)</a>
-</li>
-      <li><a href="generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.write">write() (libpysal.io.fileio.FileIO method)</a>
-</li>
-      <li><a href="generated/libpysal.weights.WSP.html#libpysal.weights.WSP">WSP (class in libpysal.weights)</a>
-</li>
-      <li><a href="generated/libpysal.weights.wsp2da.html#libpysal.weights.wsp2da">wsp2da() (in module libpysal.weights)</a>
-</li>
-      <li><a href="generated/libpysal.weights.WSP2W.html#libpysal.weights.WSP2W">WSP2W() (in module libpysal.weights)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="X">X</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="generated/libpysal.cg.Line.html#libpysal.cg.Line.x">x() (libpysal.cg.Line method)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="Y">Y</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="generated/libpysal.cg.Line.html#libpysal.cg.Line.y">y() (libpysal.cg.Line method)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-
-
-    </div>
-      
-  </div>
-</div>
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-  <div class="container">
-    <p class="pull-right">
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-    </p>
-    <p>
-        &copy; Copyright 2018-, pysal developers.<br/>
-      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 3.1.2.<br/>
-    </p>
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diff --git a/docs/index.html b/docs/index.html
deleted file mode 100644
index 4ebba8cf8..000000000
--- a/docs/index.html
+++ /dev/null
@@ -1,245 +0,0 @@
-<!DOCTYPE html>
-
-<html>
-  <head>
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-  <div class="section" id="libpysal-python-spatial-analysis-library-core">
-<h1>libpysal: Python Spatial Analysis Library Core<a class="headerlink" href="#libpysal-python-spatial-analysis-library-core" title="Permalink to this headline">¶</a></h1>
-<a class="reference external image-reference" href="https://github.com/pysal/libpysal/actions?query=workflow%3A.github%2Fworkflows%2Funittests.yml"><img alt="https://github.com/pysal/libpysal/workflows/.github/workflows/unittests.yml/badge.svg" src="https://github.com/pysal/libpysal/workflows/.github/workflows/unittests.yml/badge.svg" /></a>
-<a class="reference external image-reference" href="https://gitter.im/pysal/pysal"><img alt="https://badges.gitter.im/pysal/pysal.svg" src="https://badges.gitter.im/pysal/pysal.svg" /></a>
-<a class="reference external image-reference" href="https://badge.fury.io/py/libpysal"><img alt="https://badge.fury.io/py/libpysal.svg" src="https://badge.fury.io/py/libpysal.svg" /></a>
-<div class="container-fluid">
-  <div class="row equal-height">
-    <div class="col-sm-1 col-xs-hidden">
-    </div>
-    <div class="col-sm-10 col-xs-12">
-        <a href="http://nbviewer.jupyter.org/github/pysal/libpysal/blob/master/notebooks/weights.ipynb" class="thumbnail">
-            <img src="_static/images/npweights.png" class="img-responsive center-block">
-            <div class="caption text-center">
-            <h6>Weights for nonplanar enforced geometries</h6>
-            </div>
-        </a>
-    </div>
-    <div class="col-sm-1 col-xs-hidden">
-    </div>
-  </div>
-</div><div class="section" id="introduction">
-<h2>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">¶</a></h2>
-<p><strong>libpysal</strong> offers four modules that form the building blocks in many upstream packages in the <a class="reference external" href="https://pysal.org">PySAL family</a>:</p>
-<ul class="simple">
-<li><p>Spatial Weights: libpysal.weights</p></li>
-<li><p>Input-and output: libpysal.io</p></li>
-<li><p>Computational geometry: libpysal.cg</p></li>
-<li><p>Built-in example datasets libpysal.examples</p></li>
-</ul>
-<p>Examples demonstrating some of <strong>libpysal</strong> functionality are available in the <a class="reference external" href="tutorial.html">tutorial</a>.</p>
-<p>Details are available in the <a class="reference external" href="api.html">libpysal api</a>.</p>
-<p>For background information see <a class="bibtex reference internal" href="references.html#pysal2007" id="id1">[RA07]</a>.</p>
-</div>
-<div class="section" id="development">
-<h2>Development<a class="headerlink" href="#development" title="Permalink to this headline">¶</a></h2>
-<p>libpysal development is hosted on <a class="reference external" href="https://github.com/pysal/libpysal">github</a>.</p>
-<p>Discussions of development occurs on the
-<a class="reference external" href="http://groups.google.com/group/pysal-dev">developer list</a>
-as well as <a class="reference external" href="https://gitter.im/pysal/pysal?">gitter</a>.</p>
-</div>
-<div class="section" id="getting-involved">
-<h2>Getting Involved<a class="headerlink" href="#getting-involved" title="Permalink to this headline">¶</a></h2>
-<p>If you are interested in contributing to PySAL please see our
-<a class="reference external" href="https://github.com/pysal/pysal/wiki">development guidelines</a>.</p>
-</div>
-<div class="section" id="bug-reports">
-<h2>Bug reports<a class="headerlink" href="#bug-reports" title="Permalink to this headline">¶</a></h2>
-<p>To search for or report bugs, please see libpysal’s <a class="reference external" href="http://github.com/pysal/libpysal/issues">issues</a>.</p>
-</div>
-<div class="section" id="citing-libpysal">
-<h2>Citing libpysal<a class="headerlink" href="#citing-libpysal" title="Permalink to this headline">¶</a></h2>
-<p>If you use PySAL in a scientific publication, we would appreciate citations to the following paper:</p>
-<blockquote>
-<div><p><a class="reference external" href="http://journal.srsa.org/ojs/index.php/RRS/article/view/134/85">PySAL: A Python Library of Spatial Analytical Methods</a>, <em>Rey, S.J. and L. Anselin</em>, Review of Regional Studies 37, 5-27 2007.</p>
-<p>Bibtex entry:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@Article</span><span class="p">{</span><span class="n">pysal2007</span><span class="p">,</span>
-  <span class="n">author</span><span class="o">=</span><span class="p">{</span><span class="n">Rey</span><span class="p">,</span> <span class="n">Sergio</span> <span class="n">J</span><span class="o">.</span> <span class="ow">and</span> <span class="n">Anselin</span><span class="p">,</span> <span class="n">Luc</span><span class="p">},</span>
-  <span class="n">title</span><span class="o">=</span><span class="p">{{</span><span class="n">PySAL</span><span class="p">:</span> <span class="n">A</span> <span class="n">Python</span> <span class="n">Library</span> <span class="n">of</span> <span class="n">Spatial</span> <span class="n">Analytical</span> <span class="n">Methods</span><span class="p">}},</span>
-  <span class="n">journal</span><span class="o">=</span><span class="p">{</span><span class="n">The</span> <span class="n">Review</span> <span class="n">of</span> <span class="n">Regional</span> <span class="n">Studies</span><span class="p">},</span>
-  <span class="n">year</span><span class="o">=</span><span class="mi">2007</span><span class="p">,</span>
-  <span class="n">volume</span><span class="o">=</span><span class="p">{</span><span class="mi">37</span><span class="p">},</span>
-  <span class="n">number</span><span class="o">=</span><span class="p">{</span><span class="mi">1</span><span class="p">},</span>
-  <span class="n">pages</span><span class="o">=</span><span class="p">{</span><span class="mi">5</span><span class="o">-</span><span class="mi">27</span><span class="p">},</span>
-  <span class="n">keywords</span><span class="o">=</span><span class="p">{</span><span class="n">Open</span> <span class="n">Source</span><span class="p">;</span> <span class="n">Software</span><span class="p">;</span> <span class="n">Spatial</span><span class="p">}</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-</div></blockquote>
-</div>
-<div class="section" id="license-information">
-<h2>License information<a class="headerlink" href="#license-information" title="Permalink to this headline">¶</a></h2>
-<p>See the file “LICENSE.txt” for information on the history of this
-software, terms &amp; conditions for usage, and a DISCLAIMER OF ALL
-WARRANTIES.</p>
-</div>
-</div>
-<div class="section" id="libpysal">
-<h1>libpysal<a class="headerlink" href="#libpysal" title="Permalink to this headline">¶</a></h1>
-<p>Core components of the Python Spatial Analysis Library (<a class="reference external" href="https://github.com/pysal/pysal">PySAL</a>)</p>
-<div class="toctree-wrapper compound">
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-  <div class="section" id="installation">
-<h1>Installation<a class="headerlink" href="#installation" title="Permalink to this headline">¶</a></h1>
-<p>libpysal supports python &gt;= <a class="reference external" href="https://docs.python.org/3.6/">3.6</a> only. Please make sure that you are
-operating in a python 3 environment.</p>
-<div class="section" id="installing-released-version">
-<h2>Installing released version<a class="headerlink" href="#installing-released-version" title="Permalink to this headline">¶</a></h2>
-<div class="section" id="conda">
-<h3>conda<a class="headerlink" href="#conda" title="Permalink to this headline">¶</a></h3>
-<p>libpysal is available through conda:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">conda</span> <span class="n">install</span> <span class="o">-</span><span class="n">c</span> <span class="n">conda</span><span class="o">-</span><span class="n">forge</span> <span class="n">libpysal</span>
-</pre></div>
-</div>
-</div>
-<div class="section" id="pypi">
-<h3>pypi<a class="headerlink" href="#pypi" title="Permalink to this headline">¶</a></h3>
-<p>libpysal is available on the <a class="reference external" href="https://pypi.org/project/libpysal/">Python Package Index</a>. Therefore, you can either
-install directly with <cite>pip</cite> from the command line:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="o">-</span><span class="n">U</span> <span class="n">libpysal</span>
-</pre></div>
-</div>
-<p>or download the source distribution (.tar.gz) and decompress it to your selected
-destination. Open a command shell and navigate to the decompressed folder.
-Type:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="o">.</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="section" id="installing-development-version">
-<h2>Installing development version<a class="headerlink" href="#installing-development-version" title="Permalink to this headline">¶</a></h2>
-<p>Potentially, you might want to use the newest features in the development
-version of libpysal on github - <a class="reference external" href="https://github.com/pysal/libpysal">pysal/libpysal</a> while have not been incorporated
-in the Pypi released version. You can achieve that by installing <a class="reference external" href="https://github.com/pysal/libpysal">pysal/libpysal</a>
-by running the following from a command shell:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">git</span><span class="o">+</span><span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">github</span><span class="o">.</span><span class="n">com</span><span class="o">/</span><span class="n">pysal</span><span class="o">/</span><span class="n">libpysal</span><span class="o">.</span><span class="n">git</span>
-</pre></div>
-</div>
-<p>You can  also <a class="reference external" href="https://help.github.com/articles/fork-a-repo/">fork</a> the <a class="reference external" href="https://github.com/pysal/libpysal">pysal/libpysal</a> repo and create a local clone of
-your fork. By making changes
-to your local clone and submitting a pull request to <a class="reference external" href="https://github.com/pysal/libpysal">pysal/libpysal</a>, you can
-contribute to libpysal development.</p>
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-<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../installation.html#installing-released-version">Installing released version</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../api.html#cg-computational-geometry">cg: Computational Geometry</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../api.html#io">io</a></li>
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-  <ul class="dropdown-menu localtoc"
-      role="menu"
-      aria-labelledby="dLabelLocalToc"><ul>
-<li><a class="reference internal" href="#">Raster awareness API</a><ul>
-<li><a class="reference internal" href="#Loading-Data">Loading Data</a><ul>
-<li><a class="reference internal" href="#Using-xarray-example-dataset">Using xarray example dataset</a></li>
-<li><a class="reference internal" href="#Using-local-NetCDF-dataset">Using local <code class="docutils literal notranslate"><span class="pre">NetCDF</span></code> dataset</a></li>
-<li><a class="reference internal" href="#Using-local-GeoTIFF-dataset">Using local <code class="docutils literal notranslate"><span class="pre">GeoTIFF</span></code> dataset</a></li>
-<li><a class="reference internal" href="#higher_order-neighbors"><code class="docutils literal notranslate"><span class="pre">higher_order</span></code> neighbors</a></li>
-</ul>
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-<div class="section" id="Raster-awareness-API">
-<h1>Raster awareness API<a class="headerlink" href="#Raster-awareness-API" title="Permalink to this headline">¶</a></h1>
-<p>This notebook will give an overview of newly developed raster interface. We’ll cover basic usage of the functionality offered by the interface which mainly involves: 1. converting <code class="docutils literal notranslate"><span class="pre">xarray.DataArray</span></code> object to the PySAL’s weights object (<code class="docutils literal notranslate"><span class="pre">libpysal.weights.W</span></code>/<code class="docutils literal notranslate"><span class="pre">WSP</span></code>). 2. going back to the <code class="docutils literal notranslate"><span class="pre">xarray.DataArray</span></code> from weights object.</p>
-<p>using different datasets: - with missing values. - with multiple layers. - with non conventional dimension names.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="o">%</span><span class="k">matplotlib</span> inline
-
-<span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">Rook</span><span class="p">,</span> <span class="n">Queen</span><span class="p">,</span> <span class="n">raster</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">from</span> <span class="nn">splot</span> <span class="kn">import</span> <span class="n">libpysal</span> <span class="k">as</span> <span class="n">splot</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="kn">from</span> <span class="nn">esda</span> <span class="kn">import</span> <span class="n">Moran_Local</span>
-</pre></div>
-</div>
-</div>
-<div class="section" id="Loading-Data">
-<h2>Loading Data<a class="headerlink" href="#Loading-Data" title="Permalink to this headline">¶</a></h2>
-<p><em>The interface only accepts ``xarray.DataArray``</em>, this can be easily obtained from raster data format using <code class="docutils literal notranslate"><span class="pre">xarray</span></code>’s I/O functionality which can read from a variety of data formats some of them are listed below: - <a class="reference external" href="https://svn.osgeo.org/gdal/tags/gdal_1_2_5/frmts/formats_list.html">GDAL Raster Formats</a> via <code class="docutils literal notranslate"><span class="pre">open_rasterio</span></code> method. - <a class="reference external" href="https://www.unidata.ucar.edu/software/netcdf/">NetCDF</a> via <code class="docutils literal notranslate"><span class="pre">open_dataset</span></code> method.</p>
-<p>In this notebook we’ll work with <code class="docutils literal notranslate"><span class="pre">NetCDF</span></code> and <code class="docutils literal notranslate"><span class="pre">GeoTIFF</span></code> data.</p>
-<div class="section" id="Using-xarray-example-dataset">
-<h3>Using xarray example dataset<a class="headerlink" href="#Using-xarray-example-dataset" title="Permalink to this headline">¶</a></h3>
-<p>First lets load up a <code class="docutils literal notranslate"><span class="pre">netCDF</span></code> dataset offered by xarray.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">tutorial</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="s2">&quot;air_temperature.nc&quot;</span><span class="p">)</span>  <span class="c1"># -&gt; returns a xarray.Dataset object</span>
-<span class="n">da</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s2">&quot;air&quot;</span><span class="p">]</span>  <span class="c1"># we&#39;ll use the &quot;air&quot; data variable for further analysis</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">da</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
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-<div class="highlight"><pre>
-&lt;xarray.DataArray &#39;air&#39; (time: 2920, lat: 25, lon: 53)&gt;
-[3869000 values with dtype=float32]
-Coordinates:
-  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0
-  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0
-  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00
-Attributes:
-    long_name:     4xDaily Air temperature at sigma level 995
-    units:         degK
-    precision:     2
-    GRIB_id:       11
-    GRIB_name:     TMP
-    var_desc:      Air temperature
-    dataset:       NMC Reanalysis
-    level_desc:    Surface
-    statistic:     Individual Obs
-    parent_stat:   Other
-    actual_range:  [185.16 322.1 ]
-</pre></div></div>
-</div>
-<p><code class="docutils literal notranslate"><span class="pre">xarray</span></code>’s data structures like <code class="docutils literal notranslate"><span class="pre">Dataset</span></code> and <code class="docutils literal notranslate"><span class="pre">DataArray</span></code> provides <code class="docutils literal notranslate"><span class="pre">pandas</span></code> like functionality for multidimensional-array or ndarray.</p>
-<p>In our case we’ll mainly deal with <code class="docutils literal notranslate"><span class="pre">DataArray</span></code>, we can see above that the <code class="docutils literal notranslate"><span class="pre">da</span></code> holds the data for air temperature, it has 2 dims coordinate dimensions <code class="docutils literal notranslate"><span class="pre">x</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code>, and it’s layered on <code class="docutils literal notranslate"><span class="pre">time</span></code> dimension so in total 3 dims (<code class="docutils literal notranslate"><span class="pre">time</span></code>, <code class="docutils literal notranslate"><span class="pre">lat</span></code>, <code class="docutils literal notranslate"><span class="pre">lon</span></code>).</p>
-<p>We’ll now group <code class="docutils literal notranslate"><span class="pre">da</span></code> by month and take average over the <code class="docutils literal notranslate"><span class="pre">time</span></code> dimension</p>
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-<span></span><span class="n">da</span> <span class="o">=</span> <span class="n">da</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">&#39;time.month&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">da</span><span class="o">.</span><span class="n">coords</span><span class="p">)</span>  <span class="c1"># as a result time dim is replaced by month</span>
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-Coordinates:
-  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0
-  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0
-  * month    (month) int64 1 2 3 4 5 6 7 8 9 10 11 12
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-<span></span><span class="c1"># let&#39;s plot over month, each facet will represent the mean air temperature in a given month.</span>
-<span class="n">da</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">col</span><span class="o">=</span><span class="s2">&quot;month&quot;</span><span class="p">,</span> <span class="n">col_wrap</span><span class="o">=</span><span class="mi">4</span><span class="p">,)</span>
-</pre></div>
-</div>
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-&lt;xarray.plot.facetgrid.FacetGrid at 0x7ff60521aeb0&gt;
-</pre></div></div>
-</div>
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-</div>
-</div>
-<p>We can use <code class="docutils literal notranslate"><span class="pre">from_xarray</span></code> method from the contiguity classes like <code class="docutils literal notranslate"><span class="pre">Rook</span></code> and <code class="docutils literal notranslate"><span class="pre">Queen</span></code>, and also from <code class="docutils literal notranslate"><span class="pre">KNN</span></code>.</p>
-<p>This uses a util function in <code class="docutils literal notranslate"><span class="pre">raster.py</span></code> file called <code class="docutils literal notranslate"><span class="pre">da2W</span></code>, which can also be called directly to build <code class="docutils literal notranslate"><span class="pre">W</span></code> object, similarly <code class="docutils literal notranslate"><span class="pre">da2WSP</span></code> for building <code class="docutils literal notranslate"><span class="pre">WSP</span></code> object.</p>
-<p><strong>Weight builders (``from_xarray``, ``da2W``, ``da2WSP``) can recognise dimensions belonging to this list ``[band, time, lat, y, lon, x]``, if any of the dimension in the ``DataArray`` does not belong to the mentioned list then we need to pass a dictionary (specifying that dimension’s name) to the weight builder.</strong></p>
-<p>e.g. <code class="docutils literal notranslate"><span class="pre">dims</span></code> dictionary:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">da</span><span class="o">.</span><span class="n">dims</span>                  <span class="c1"># none of the dimension belong to the default dimension list</span>
-<span class="go">(&#39;year&#39;, &#39;height&#39;, &#39;width&#39;)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">coords_labels</span> <span class="o">=</span> <span class="p">{</span>                 <span class="c1"># dimension values should be properly aligned with the following keys</span>
-<span class="go">        &quot;z_label&quot;: &quot;year&quot;,</span>
-<span class="go">        &quot;y_label&quot;: &quot;height&quot;,</span>
-<span class="go">        &quot;x_label&quot;: &quot;width&quot;</span>
-<span class="go">    }</span>
-</pre></div>
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-<span></span><span class="n">coords_labels</span> <span class="o">=</span> <span class="p">{}</span>
-<span class="n">coords_labels</span><span class="p">[</span><span class="s2">&quot;z_label&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;month&quot;</span>  <span class="c1"># since month does not belong to the default list we need to pass it using a dictionary</span>
-<span class="n">w_queen</span> <span class="o">=</span> <span class="n">Queen</span><span class="o">.</span><span class="n">from_xarray</span><span class="p">(</span>
-    <span class="n">da</span><span class="p">,</span> <span class="n">z_value</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span> <span class="n">coords_labels</span><span class="o">=</span><span class="n">coords_labels</span><span class="p">,</span> <span class="n">sparse</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>  <span class="c1"># We&#39;ll use data from 12th layer (in our case layer=month)</span>
-</pre></div>
-</div>
-</div>
-<p><code class="docutils literal notranslate"><span class="pre">index</span></code> is a newly added attribute to the weights object, this holds the multi-indices of the non-missing values belonging to <code class="docutils literal notranslate"><span class="pre">pandas.Series</span></code> created from the passed <code class="docutils literal notranslate"><span class="pre">DataArray</span></code>, this series can be easily obtained using <code class="docutils literal notranslate"><span class="pre">DataArray.to_series()</span></code> method.</p>
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-<span></span><span class="n">w_queen</span><span class="o">.</span><span class="n">index</span><span class="p">[:</span><span class="mi">5</span><span class="p">]</span>  <span class="c1"># indices are aligned to the ids of the weight object</span>
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-MultiIndex([(12, 75.0, 200.0),
-            (12, 75.0, 202.5),
-            (12, 75.0, 205.0),
-            (12, 75.0, 207.5),
-            (12, 75.0, 210.0)],
-           names=[&#39;month&#39;, &#39;lat&#39;, &#39;lon&#39;])
-</pre></div></div>
-</div>
-<p>We can then obtain raster data by converting the <code class="docutils literal notranslate"><span class="pre">DataArray</span></code> to <code class="docutils literal notranslate"><span class="pre">Series</span></code> and then using indices from <code class="docutils literal notranslate"><span class="pre">index</span></code> attribute to get non-missing values by subsetting the <code class="docutils literal notranslate"><span class="pre">Series</span></code>.</p>
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-<span></span><span class="n">data</span> <span class="o">=</span> <span class="n">da</span><span class="o">.</span><span class="n">to_series</span><span class="p">()[</span><span class="n">w_queen</span><span class="o">.</span><span class="n">index</span><span class="p">]</span>
-</pre></div>
-</div>
-</div>
-<p>We now have the required data for further analysis (we can now use methods such as ESDA/spatial regression), for this example let’s compute a local Moran statistic for the extracted data.</p>
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-<span></span><span class="c1"># Quickly computing and loading a LISA</span>
-<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">12345</span><span class="p">)</span>
-<span class="n">lisa</span> <span class="o">=</span> <span class="n">Moran_Local</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">),</span> <span class="n">w_queen</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>After getting our calculated results it’s time to store them back to the <code class="docutils literal notranslate"><span class="pre">DataArray</span></code>, we can use <code class="docutils literal notranslate"><span class="pre">w2da</span></code> function directly to convert the <code class="docutils literal notranslate"><span class="pre">W</span></code> object back to <code class="docutils literal notranslate"><span class="pre">DataArray</span></code>.</p>
-<p><em>Your use case might differ but the steps for using the interface will be similar to this example.</em></p>
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-<span></span><span class="c1"># Converting obtained data back to DataArray</span>
-<span class="n">moran_da</span> <span class="o">=</span> <span class="n">raster</span><span class="o">.</span><span class="n">w2da</span><span class="p">(</span><span class="n">lisa</span><span class="o">.</span><span class="n">p_sim</span><span class="p">,</span> <span class="n">w_queen</span><span class="p">)</span>  <span class="c1"># w2da accepts list/1d array/pd.Series and a weight object aligned to passed data</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">moran_da</span><span class="p">)</span>
-</pre></div>
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-&lt;xarray.DataArray (month: 1, lat: 25, lon: 53)&gt;
-array([[[0.018, 0.001, 0.001, ..., 0.001, 0.001, 0.001],
-        [0.001, 0.001, 0.001, ..., 0.001, 0.001, 0.001],
-        [0.003, 0.001, 0.001, ..., 0.001, 0.001, 0.001],
-        ...,
-        [0.002, 0.001, 0.001, ..., 0.001, 0.001, 0.003],
-        [0.001, 0.001, 0.001, ..., 0.001, 0.001, 0.003],
-        [0.002, 0.001, 0.001, ..., 0.001, 0.002, 0.006]]])
-Coordinates:
-  * month    (month) int64 12
-  * lat      (lat) float64 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0
-  * lon      (lon) float64 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0
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-<span></span><span class="n">moran_da</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
-</pre></div>
-</div>
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-&lt;matplotlib.collections.QuadMesh at 0x7ff6013e4b80&gt;
-</pre></div></div>
-</div>
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-</div>
-</div>
-</div>
-<div class="section" id="Using-local-NetCDF-dataset">
-<h3>Using local <code class="docutils literal notranslate"><span class="pre">NetCDF</span></code> dataset<a class="headerlink" href="#Using-local-NetCDF-dataset" title="Permalink to this headline">¶</a></h3>
-<p>In the earlier example we used an example dataset from xarray for building weights object. Additonally, we had to pass the custom layer name to the builder.</p>
-<p>In this small example we’ll build <code class="docutils literal notranslate"><span class="pre">KNN</span></code> distance weight object using a local <code class="docutils literal notranslate"><span class="pre">NetCDF</span></code> dataset with different dimensions names which doesn’t belong to the default list of dimensions.</p>
-<p>We’ll also see how to speed up the reverse journey (from weights object to <code class="docutils literal notranslate"><span class="pre">DataArray</span></code>) by passing prebuilt <code class="docutils literal notranslate"><span class="pre">coords</span></code> and <code class="docutils literal notranslate"><span class="pre">attrs</span></code> to <code class="docutils literal notranslate"><span class="pre">w2da</span></code> method.</p>
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-<span></span><span class="c1"># Lets load a netCDF Surface dataset</span>
-<span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="s1">&#39;ECMWF_ERA-40_subset.nc&#39;</span><span class="p">)</span>    <span class="c1"># After loading netCDF dataset we obtained a xarray.Dataset object</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">ds</span><span class="p">)</span>                                         <span class="c1"># This Dataset object containes several data variables</span>
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-&lt;xarray.Dataset&gt;
-Dimensions:    (latitude: 73, longitude: 144, time: 62)
-Coordinates:
-  * longitude  (longitude) float32 0.0 2.5 5.0 7.5 ... 350.0 352.5 355.0 357.5
-  * latitude   (latitude) float32 90.0 87.5 85.0 82.5 ... -85.0 -87.5 -90.0
-  * time       (time) datetime64[ns] 2002-07-01T12:00:00 ... 2002-07-31T18:00:00
-Data variables:
-    tcw        (time, latitude, longitude) float32 ...
-    tcwv       (time, latitude, longitude) float32 ...
-    lsp        (time, latitude, longitude) float32 ...
-    cp         (time, latitude, longitude) float32 ...
-    msl        (time, latitude, longitude) float32 ...
-    blh        (time, latitude, longitude) float32 ...
-    tcc        (time, latitude, longitude) float32 ...
-    p10u       (time, latitude, longitude) float32 ...
-    p10v       (time, latitude, longitude) float32 ...
-    p2t        (time, latitude, longitude) float32 ...
-    p2d        (time, latitude, longitude) float32 ...
-    e          (time, latitude, longitude) float32 ...
-    lcc        (time, latitude, longitude) float32 ...
-    mcc        (time, latitude, longitude) float32 ...
-    hcc        (time, latitude, longitude) float32 ...
-    tco3       (time, latitude, longitude) float32 ...
-    tp         (time, latitude, longitude) float32 ...
-Attributes:
-    Conventions:  CF-1.0
-    history:      2004-09-15 17:04:29 GMT by mars2netcdf-0.92
-</pre></div></div>
-</div>
-<p>Out of 17 data variables we’ll use <code class="docutils literal notranslate"><span class="pre">p2t</span></code> for our analysis. This will give us our desired <code class="docutils literal notranslate"><span class="pre">DataArray</span></code> object <code class="docutils literal notranslate"><span class="pre">da</span></code>, we will further group <code class="docutils literal notranslate"><span class="pre">da</span></code> by day, taking average over the <code class="docutils literal notranslate"><span class="pre">time</span></code> dimension.</p>
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-<span></span><span class="n">da</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s2">&quot;p2t&quot;</span><span class="p">]</span>  <span class="c1"># this will give us the required DataArray with p2t (2 metre temperature) data variable</span>
-<span class="n">da</span> <span class="o">=</span> <span class="n">da</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">&#39;time.day&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">da</span><span class="o">.</span><span class="n">dims</span><span class="p">)</span>
-</pre></div>
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-(&#39;day&#39;, &#39;latitude&#39;, &#39;longitude&#39;)
-</pre></div></div>
-</div>
-<p><strong>We can see that the none of dimensions of ``da`` matches with the default dimensions (``[band, time, lat, y, lon, x]``)</strong></p>
-<p>This means we have to create a dictionary mentioning the dimensions and ship it to weight builder, similar to our last example.</p>
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-<span></span><span class="n">coords_labels</span> <span class="o">=</span> <span class="p">{}</span>
-<span class="n">coords_labels</span><span class="p">[</span><span class="s2">&quot;y_label&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;latitude&quot;</span>
-<span class="n">coords_labels</span><span class="p">[</span><span class="s2">&quot;x_label&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;longitude&quot;</span>
-<span class="n">coords_labels</span><span class="p">[</span><span class="s2">&quot;z_label&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;day&quot;</span>
-<span class="n">w_rook</span> <span class="o">=</span> <span class="n">Rook</span><span class="o">.</span><span class="n">from_xarray</span><span class="p">(</span><span class="n">da</span><span class="p">,</span> <span class="n">z_value</span><span class="o">=</span><span class="mi">13</span><span class="p">,</span> <span class="n">coords_labels</span><span class="o">=</span><span class="n">coords_labels</span><span class="p">,</span> <span class="n">sparse</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-</pre></div>
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-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">data</span> <span class="o">=</span> <span class="n">da</span><span class="o">.</span><span class="n">to_series</span><span class="p">()[</span><span class="n">w_rook</span><span class="o">.</span><span class="n">index</span><span class="p">]</span>  <span class="c1"># we derived the data from DataArray similar to our last example</span>
-</pre></div>
-</div>
-</div>
-<p>In the last example we only passed the <code class="docutils literal notranslate"><span class="pre">data</span></code> values and weight object to <code class="docutils literal notranslate"><span class="pre">w2da</span></code> method, which then created the necessary <code class="docutils literal notranslate"><span class="pre">coords</span></code> to build our required <code class="docutils literal notranslate"><span class="pre">DataArray</span></code>. This process can be speed up by passing <code class="docutils literal notranslate"><span class="pre">coords</span></code> from the existing <code class="docutils literal notranslate"><span class="pre">DataArray</span></code> <code class="docutils literal notranslate"><span class="pre">da</span></code> which we used earlier.</p>
-<p>Along with <code class="docutils literal notranslate"><span class="pre">coords</span></code> we can also pass <code class="docutils literal notranslate"><span class="pre">attrs</span></code> of the same <code class="docutils literal notranslate"><span class="pre">DataArray</span></code> this will help <code class="docutils literal notranslate"><span class="pre">w2da</span></code> to retain all the properties of original <code class="docutils literal notranslate"><span class="pre">DataArray</span></code>.</p>
-<p>Let’s compare the <code class="docutils literal notranslate"><span class="pre">DataArray</span></code> returned by <code class="docutils literal notranslate"><span class="pre">w2da</span></code> and original <code class="docutils literal notranslate"><span class="pre">DataArray</span></code>. For this we’ll ship the derived data straight to <code class="docutils literal notranslate"><span class="pre">w2da</span></code> without any statistical analysis.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">da1</span> <span class="o">=</span> <span class="n">raster</span><span class="o">.</span><span class="n">wsp2da</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">w_rook</span><span class="p">,</span> <span class="n">attrs</span><span class="o">=</span><span class="n">da</span><span class="o">.</span><span class="n">attrs</span><span class="p">,</span> <span class="n">coords</span><span class="o">=</span><span class="n">da</span><span class="p">[</span><span class="mi">12</span><span class="p">:</span><span class="mi">13</span><span class="p">]</span><span class="o">.</span><span class="n">coords</span><span class="p">)</span>
-<span class="n">xr</span><span class="o">.</span><span class="n">DataArray</span><span class="o">.</span><span class="n">equals</span><span class="p">(</span><span class="n">da</span><span class="p">[</span><span class="mi">12</span><span class="p">:</span><span class="mi">13</span><span class="p">],</span> <span class="n">da1</span><span class="p">)</span>  <span class="c1"># method to compare 2 DataArray, if true then w2da was successfull</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-True
-</pre></div></div>
-</div>
-</div>
-<div class="section" id="Using-local-GeoTIFF-dataset">
-<h3>Using local <code class="docutils literal notranslate"><span class="pre">GeoTIFF</span></code> dataset<a class="headerlink" href="#Using-local-GeoTIFF-dataset" title="Permalink to this headline">¶</a></h3>
-<p>Up until now we’ve only played with <code class="docutils literal notranslate"><span class="pre">netCDF</span></code> datasets but in this example we’ll use a <code class="docutils literal notranslate"><span class="pre">raster.tif</span></code> file to see how interface interacts with it. We’ll also see how these methods handle missing data.</p>
-<p>Unlike earlier we’ll use weight builder methods from <code class="docutils literal notranslate"><span class="pre">raster.py</span></code>, which we can call directly. Just a reminder that <code class="docutils literal notranslate"><span class="pre">from_xarray</span></code> uses methods from <code class="docutils literal notranslate"><span class="pre">raster.py</span></code> and therefore only difference exists in the API.</p>
-<p>To access GDAL Raster Formats <code class="docutils literal notranslate"><span class="pre">xarray</span></code> offers <code class="docutils literal notranslate"><span class="pre">open_rasterio</span></code> method which uses <code class="docutils literal notranslate"><span class="pre">rasterio</span></code> as backend. It loads metadata, coordinate values from the raster file and assign them to the <code class="docutils literal notranslate"><span class="pre">DataArray</span></code>.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="c1"># Loading raster data with missing values</span>
-<span class="n">da</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_rasterio</span><span class="p">(</span><span class="s1">&#39;lux_ppp_2019.tif&#39;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">da</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-&lt;xarray.DataArray (band: 1, y: 880, x: 940)&gt;
-[827200 values with dtype=float32]
-Coordinates:
-  * band     (band) int64 1
-  * y        (y) float64 50.18 50.18 50.18 50.18 ... 49.45 49.45 49.45 49.45
-  * x        (x) float64 5.745 5.746 5.747 5.747 ... 6.525 6.526 6.527 6.527
-Attributes:
-    transform:      (0.0008333333297872345, 0.0, 5.744583325, 0.0, -0.0008333...
-    crs:            +init=epsg:4326
-    res:            (0.0008333333297872345, 0.0008333333295454553)
-    is_tiled:       0
-    nodatavals:     (-99999.0,)
-    scales:         (1.0,)
-    offsets:        (0.0,)
-    AREA_OR_POINT:  Area
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[17]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">da</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">da</span><span class="o">.</span><span class="n">values</span><span class="o">&gt;</span><span class="n">da</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s2">&quot;nodatavals&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span> <span class="c1"># we can see that the DataArray contains missing values.</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[17]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-&lt;matplotlib.collections.QuadMesh at 0x7ff600e744f0&gt;
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/notebooks_Raster_awareness_API_29_1.png" src="../_images/notebooks_Raster_awareness_API_29_1.png" />
-</div>
-</div>
-<p>We’ll look at how weight builders handle missing values. Firstly we’ll slice the <code class="docutils literal notranslate"><span class="pre">DataArray</span></code> to reduce overall size for easier visualization.</p>
-<p>This time we’ll create <code class="docutils literal notranslate"><span class="pre">WSP</span></code> object using <code class="docutils literal notranslate"><span class="pre">da2WSP</span></code> method inside <code class="docutils literal notranslate"><span class="pre">raster.py</span></code>. Since our DataArray is single banded and all of its dimensions belong to the default list, we only have to ship the DataArray and the type of contiguity we need.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[18]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="c1"># Slicing the dataarray</span>
-<span class="n">da_s</span> <span class="o">=</span> <span class="n">da</span><span class="p">[:,</span> <span class="mi">330</span><span class="p">:</span><span class="mi">340</span><span class="p">,</span> <span class="mi">129</span><span class="p">:</span><span class="mi">139</span><span class="p">]</span>
-<span class="n">w_queen</span> <span class="o">=</span> <span class="n">raster</span><span class="o">.</span><span class="n">da2WSP</span><span class="p">(</span><span class="n">da_s</span><span class="p">)</span>  <span class="c1"># default contiguity is queen</span>
-<span class="n">w_rook</span> <span class="o">=</span> <span class="n">raster</span><span class="o">.</span><span class="n">da2WSP</span><span class="p">(</span><span class="n">da_s</span><span class="p">,</span> <span class="s2">&quot;rook&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>After plotting both contiguities and sliced <code class="docutils literal notranslate"><span class="pre">DataArray</span></code>, we can see that the missing values are ignored by the <code class="docutils literal notranslate"><span class="pre">da2WSP</span></code> method and only indices of non missing values are stored in <code class="docutils literal notranslate"><span class="pre">index</span></code> attribute of <code class="docutils literal notranslate"><span class="pre">WSP</span></code> object.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[19]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">f</span><span class="p">,</span><span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="o">*</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">subplot_kw</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">aspect</span><span class="o">=</span><span class="s1">&#39;equal&#39;</span><span class="p">))</span>
-<span class="n">da_s</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">da_s</span><span class="o">.</span><span class="n">values</span><span class="o">&gt;</span><span class="n">da_s</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s2">&quot;nodatavals&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Sliced raster&quot;</span><span class="p">)</span>
-<span class="n">splot</span><span class="o">.</span><span class="n">plot_spatial_weights</span><span class="p">(</span><span class="n">w_rook</span><span class="p">,</span> <span class="n">da</span><span class="o">=</span><span class="n">da_s</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Rook contiguity&quot;</span><span class="p">)</span>
-<span class="n">splot</span><span class="o">.</span><span class="n">plot_spatial_weights</span><span class="p">(</span><span class="n">w_queen</span><span class="p">,</span> <span class="n">da</span><span class="o">=</span><span class="n">da_s</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
-<span class="n">ax</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Queen contiguity&quot;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/notebooks_Raster_awareness_API_33_0.png" src="../_images/notebooks_Raster_awareness_API_33_0.png" />
-</div>
-</div>
-</div>
-<div class="section" id="higher_order-neighbors">
-<h3><code class="docutils literal notranslate"><span class="pre">higher_order</span></code> neighbors<a class="headerlink" href="#higher_order-neighbors" title="Permalink to this headline">¶</a></h3>
-<p>In some cases <code class="docutils literal notranslate"><span class="pre">Rook</span></code> and <code class="docutils literal notranslate"><span class="pre">Queen</span></code> contiguities don’t provide sufficient neighbors when performing spatial analysis on a raster data, this is because <code class="docutils literal notranslate"><span class="pre">Rook</span></code> contiguity provides max 4 neighbors and <code class="docutils literal notranslate"><span class="pre">Queen</span></code> provides max 8.</p>
-<p>Therefore we’ve added <code class="docutils literal notranslate"><span class="pre">higher_order</span></code> functionality inside the builder method. We can now pass <code class="docutils literal notranslate"><span class="pre">k</span></code> value to the weight builder to obtain upto kth order neighbors. Since this can be computionally expensive we can take advantage of parallel processing using <code class="docutils literal notranslate"><span class="pre">n_jobs</span></code> argument. Now lets take a look at this functionality.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[20]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="c1"># Building a test DataArray</span>
-<span class="n">da_s</span> <span class="o">=</span> <span class="n">raster</span><span class="o">.</span><span class="n">testDataArray</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">10</span><span class="p">),</span> <span class="n">rand</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>Below we can see that builder selected all the neighbors of order less than equal to 2, with <code class="docutils literal notranslate"><span class="pre">rook</span></code> contiguity</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[21]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">w_rook2</span> <span class="o">=</span> <span class="n">raster</span><span class="o">.</span><span class="n">da2WSP</span><span class="p">(</span><span class="n">da_s</span><span class="p">,</span> <span class="s2">&quot;rook&quot;</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
-<span class="n">splot</span><span class="o">.</span><span class="n">plot_spatial_weights</span><span class="p">(</span><span class="n">w_rook2</span><span class="p">,</span> <span class="n">da</span><span class="o">=</span><span class="n">da_s</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area stderr docutils container">
-<div class="highlight"><pre>
-/home/magito/anaconda3/lib/python3.8/site-packages/scipy/sparse/_index.py:124: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.
-  self._set_arrayXarray(i, j, x)
-</pre></div></div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[21]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-(&lt;Figure size 720x720 with 1 Axes&gt;, &lt;AxesSubplot:&gt;)
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/notebooks_Raster_awareness_API_37_2.png" src="../_images/notebooks_Raster_awareness_API_37_2.png" />
-</div>
-</div>
-<p>Few times we require the kth order neighbors for our analysis even if lower order neighbors are absent, hence we can use <code class="docutils literal notranslate"><span class="pre">include_nas</span></code> argument to do the same.</p>
-<p>We can also look in both the examples we used <code class="docutils literal notranslate"><span class="pre">n_jobs</span></code> parameter, and assigned -1 which equats to all the cores present in the computer for multithreading</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[22]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">w_rook2</span> <span class="o">=</span> <span class="n">raster</span><span class="o">.</span><span class="n">da2WSP</span><span class="p">(</span><span class="n">da_s</span><span class="p">,</span> <span class="s2">&quot;rook&quot;</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">include_nadata</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">splot</span><span class="o">.</span><span class="n">plot_spatial_weights</span><span class="p">(</span><span class="n">w_rook2</span><span class="p">,</span> <span class="n">da</span><span class="o">=</span><span class="n">da_s</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[22]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-(&lt;Figure size 720x720 with 1 Axes&gt;, &lt;AxesSubplot:&gt;)
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
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-<div class="output_area docutils container">
-<img alt="../_images/notebooks_Raster_awareness_API_39_1.png" src="../_images/notebooks_Raster_awareness_API_39_1.png" />
-</div>
-</div>
-</div>
-</div>
-<div class="section" id="Additional-resources">
-<h2>Additional resources<a class="headerlink" href="#Additional-resources" title="Permalink to this headline">¶</a></h2>
-<ol class="arabic simple">
-<li><p><a class="reference external" href="http://xarray.pydata.org/en/stable/io.html">Reading and writing files using Xarray</a></p></li>
-<li><p><a class="reference external" href="http://xarray.pydata.org/en/stable/data-structures.html">Xarray Data Structures</a></p></li>
-</ol>
-</div>
-</div>
-
-
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-      
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-</div>
-<footer class="footer">
-  <div class="container">
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-<div id="sourcelink">
-  <a href="../_sources/notebooks/Raster_awareness_API.ipynb.txt"
-     rel="nofollow">Source</a>
-</div>
-      
-    </p>
-    <p>
-        &copy; Copyright 2018-, pysal developers.<br/>
-      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 3.1.2.<br/>
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diff --git a/docs/notebooks/Raster_awareness_API.ipynb b/docs/notebooks/Raster_awareness_API.ipynb
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@@ -1,754 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Raster awareness API\n",
-    "\n",
-    "This notebook will give an overview of newly developed raster interface. We'll cover \n",
-    "basic usage of the functionality offered by the interface which mainly involves:\n",
-    "1. converting `xarray.DataArray` object to the PySAL's weights object (`libpysal.weights.W`/`WSP`).\n",
-    "2. going back to the `xarray.DataArray` from weights object.\n",
-    "\n",
-    "using different datasets:\n",
-    "- with missing values.\n",
-    "- with multiple layers.\n",
-    "- with non conventional dimension names."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "\n",
-    "from libpysal.weights import Rook, Queen, raster\n",
-    "import matplotlib.pyplot as plt\n",
-    "from splot import libpysal as splot\n",
-    "import numpy as np\n",
-    "import xarray as xr\n",
-    "import pandas as pd\n",
-    "from esda import Moran_Local"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Loading Data\n",
-    "\n",
-    "*The interface only accepts `xarray.DataArray`*, this can be easily obtained from raster data\n",
-    "format using `xarray`'s I/O functionality which can read from a variety of data formats some of them are listed below: \n",
-    "- [GDAL Raster Formats](https://svn.osgeo.org/gdal/tags/gdal_1_2_5/frmts/formats_list.html) via `open_rasterio` method.\n",
-    "- [NetCDF](https://www.unidata.ucar.edu/software/netcdf/) via `open_dataset` method.\n",
-    "\n",
-    "In this notebook we'll work with `NetCDF` and `GeoTIFF` data. \n",
-    "\n",
-    "### Using xarray example dataset\n",
-    "First lets load up a `netCDF` dataset offered by xarray."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<xarray.DataArray 'air' (time: 2920, lat: 25, lon: 53)>\n",
-      "[3869000 values with dtype=float32]\n",
-      "Coordinates:\n",
-      "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-      "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
-      "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-      "Attributes:\n",
-      "    long_name:     4xDaily Air temperature at sigma level 995\n",
-      "    units:         degK\n",
-      "    precision:     2\n",
-      "    GRIB_id:       11\n",
-      "    GRIB_name:     TMP\n",
-      "    var_desc:      Air temperature\n",
-      "    dataset:       NMC Reanalysis\n",
-      "    level_desc:    Surface\n",
-      "    statistic:     Individual Obs\n",
-      "    parent_stat:   Other\n",
-      "    actual_range:  [185.16 322.1 ]\n"
-     ]
-    }
-   ],
-   "source": [
-    "ds = xr.tutorial.open_dataset(\"air_temperature.nc\")  # -> returns a xarray.Dataset object\n",
-    "da = ds[\"air\"]  # we'll use the \"air\" data variable for further analysis\n",
-    "print(da)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`xarray`'s data structures like `Dataset` and `DataArray` provides `pandas` like functionality for multidimensional-array or ndarray. \n",
-    "\n",
-    "In our case we'll mainly deal with `DataArray`, we can see above that the `da` holds the data for air temperature, it has 2 dims coordinate dimensions `x` and `y`, and it's layered on `time` dimension so in total 3 dims (`time`, `lat`, `lon`).\n",
-    "\n",
-    "We'll now group `da` by month and take average over the `time` dimension\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Coordinates:\n",
-      "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-      "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
-      "  * month    (month) int64 1 2 3 4 5 6 7 8 9 10 11 12\n"
-     ]
-    }
-   ],
-   "source": [
-    "da = da.groupby('time.month').mean()\n",
-    "print(da.coords)  # as a result time dim is replaced by month "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<xarray.plot.facetgrid.FacetGrid at 0x7ff60521aeb0>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x648 with 13 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# let's plot over month, each facet will represent the mean air temperature in a given month.\n",
-    "da.plot(col=\"month\", col_wrap=4,) "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can use `from_xarray` method from the contiguity classes like `Rook` and `Queen`, and also from `KNN`.\n",
-    "\n",
-    "This uses a util function in `raster.py` file called `da2W`, which can also be called directly to build `W` object, similarly `da2WSP` for building `WSP` object.\n",
-    "\n",
-    "**Weight builders (`from_xarray`, `da2W`, `da2WSP`) can recognise dimensions belonging to this list `[band, time, lat, y, lon, x]`, if any of the dimension in the `DataArray` does not belong to the mentioned list then we need to pass a dictionary (specifying that dimension's name) to the weight builder.** \n",
-    "\n",
-    "e.g. `dims` dictionary:\n",
-    "```python\n",
-    ">>> da.dims                  # none of the dimension belong to the default dimension list\n",
-    "('year', 'height', 'width')\n",
-    ">>> coords_labels = {                 # dimension values should be properly aligned with the following keys\n",
-    "        \"z_label\": \"year\",\n",
-    "        \"y_label\": \"height\",\n",
-    "        \"x_label\": \"width\"\n",
-    "    }\n",
-    "```\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "coords_labels = {}\n",
-    "coords_labels[\"z_label\"] = \"month\"  # since month does not belong to the default list we need to pass it using a dictionary\n",
-    "w_queen = Queen.from_xarray(\n",
-    "    da, z_value=12, coords_labels=coords_labels, sparse=False)  # We'll use data from 12th layer (in our case layer=month)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`index` is a newly added attribute to the weights object, this holds the multi-indices of the non-missing values belonging to `pandas.Series` created from the passed `DataArray`, this series can be easily obtained using `DataArray.to_series()` method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "MultiIndex([(12, 75.0, 200.0),\n",
-       "            (12, 75.0, 202.5),\n",
-       "            (12, 75.0, 205.0),\n",
-       "            (12, 75.0, 207.5),\n",
-       "            (12, 75.0, 210.0)],\n",
-       "           names=['month', 'lat', 'lon'])"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w_queen.index[:5]  # indices are aligned to the ids of the weight object"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can then obtain raster data by converting the `DataArray` to `Series` and then using indices from `index` attribute to get non-missing values by subsetting the `Series`. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = da.to_series()[w_queen.index]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We now have the required data for further analysis (we can now use methods such as ESDA/spatial regression), for this example let's compute a local Moran statistic for the extracted data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Quickly computing and loading a LISA\n",
-    "np.random.seed(12345)\n",
-    "lisa = Moran_Local(np.array(data, dtype=np.float64), w_queen)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "After getting our calculated results it's time to store them back to the `DataArray`, we can use `w2da` function directly to convert the `W` object back to `DataArray`. \n",
-    "\n",
-    "*Your use case might differ but the steps for using the interface will be similar to this example.* "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<xarray.DataArray (month: 1, lat: 25, lon: 53)>\n",
-      "array([[[0.018, 0.001, 0.001, ..., 0.001, 0.001, 0.001],\n",
-      "        [0.001, 0.001, 0.001, ..., 0.001, 0.001, 0.001],\n",
-      "        [0.003, 0.001, 0.001, ..., 0.001, 0.001, 0.001],\n",
-      "        ...,\n",
-      "        [0.002, 0.001, 0.001, ..., 0.001, 0.001, 0.003],\n",
-      "        [0.001, 0.001, 0.001, ..., 0.001, 0.001, 0.003],\n",
-      "        [0.002, 0.001, 0.001, ..., 0.001, 0.002, 0.006]]])\n",
-      "Coordinates:\n",
-      "  * month    (month) int64 12\n",
-      "  * lat      (lat) float64 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-      "  * lon      (lon) float64 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Converting obtained data back to DataArray\n",
-    "moran_da = raster.w2da(lisa.p_sim, w_queen)  # w2da accepts list/1d array/pd.Series and a weight object aligned to passed data\n",
-    "print(moran_da)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7ff6013e4b80>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "moran_da.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Using local `NetCDF` dataset\n",
-    "\n",
-    "In the earlier example we used an example dataset from xarray for building weights object. Additonally, we had to pass the custom layer name to the builder. \n",
-    "\n",
-    "In this small example we'll build `KNN` distance weight object using a local `NetCDF` dataset with different dimensions names which doesn't belong to the default list of dimensions.\n",
-    "\n",
-    "We'll also see how to speed up the reverse journey (from weights object to `DataArray`) by passing prebuilt `coords` and `attrs` to `w2da` method. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<xarray.Dataset>\n",
-      "Dimensions:    (latitude: 73, longitude: 144, time: 62)\n",
-      "Coordinates:\n",
-      "  * longitude  (longitude) float32 0.0 2.5 5.0 7.5 ... 350.0 352.5 355.0 357.5\n",
-      "  * latitude   (latitude) float32 90.0 87.5 85.0 82.5 ... -85.0 -87.5 -90.0\n",
-      "  * time       (time) datetime64[ns] 2002-07-01T12:00:00 ... 2002-07-31T18:00:00\n",
-      "Data variables:\n",
-      "    tcw        (time, latitude, longitude) float32 ...\n",
-      "    tcwv       (time, latitude, longitude) float32 ...\n",
-      "    lsp        (time, latitude, longitude) float32 ...\n",
-      "    cp         (time, latitude, longitude) float32 ...\n",
-      "    msl        (time, latitude, longitude) float32 ...\n",
-      "    blh        (time, latitude, longitude) float32 ...\n",
-      "    tcc        (time, latitude, longitude) float32 ...\n",
-      "    p10u       (time, latitude, longitude) float32 ...\n",
-      "    p10v       (time, latitude, longitude) float32 ...\n",
-      "    p2t        (time, latitude, longitude) float32 ...\n",
-      "    p2d        (time, latitude, longitude) float32 ...\n",
-      "    e          (time, latitude, longitude) float32 ...\n",
-      "    lcc        (time, latitude, longitude) float32 ...\n",
-      "    mcc        (time, latitude, longitude) float32 ...\n",
-      "    hcc        (time, latitude, longitude) float32 ...\n",
-      "    tco3       (time, latitude, longitude) float32 ...\n",
-      "    tp         (time, latitude, longitude) float32 ...\n",
-      "Attributes:\n",
-      "    Conventions:  CF-1.0\n",
-      "    history:      2004-09-15 17:04:29 GMT by mars2netcdf-0.92\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Lets load a netCDF Surface dataset\n",
-    "ds = xr.open_dataset('ECMWF_ERA-40_subset.nc')    # After loading netCDF dataset we obtained a xarray.Dataset object\n",
-    "print(ds)                                         # This Dataset object containes several data variables"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Out of 17 data variables we'll use `p2t` for our analysis. This will give us our desired `DataArray` object `da`, we will further group `da` by day, taking average over the `time` dimension."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "('day', 'latitude', 'longitude')\n"
-     ]
-    }
-   ],
-   "source": [
-    "da = ds[\"p2t\"]  # this will give us the required DataArray with p2t (2 metre temperature) data variable\n",
-    "da = da.groupby('time.day').mean()\n",
-    "print(da.dims)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**We can see that the none of dimensions of `da` matches with the default dimensions (`[band, time, lat, y, lon, x]`)**\n",
-    "\n",
-    "This means we have to create a dictionary mentioning the dimensions and ship it to weight builder, similar to our last example. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "coords_labels = {}\n",
-    "coords_labels[\"y_label\"] = \"latitude\"\n",
-    "coords_labels[\"x_label\"] = \"longitude\"\n",
-    "coords_labels[\"z_label\"] = \"day\"\n",
-    "w_rook = Rook.from_xarray(da, z_value=13, coords_labels=coords_labels, sparse=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = da.to_series()[w_rook.index]  # we derived the data from DataArray similar to our last example "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the last example we only passed the `data` values and weight object to `w2da` method, which then created the necessary `coords` to build our required `DataArray`. This process can be speed up by passing `coords` from the existing `DataArray` `da` which we used earlier.\n",
-    "\n",
-    "Along with `coords` we can also pass `attrs` of the same `DataArray` this will help `w2da` to retain all the properties of original `DataArray`.\n",
-    "\n",
-    "Let's compare the `DataArray` returned by `w2da` and original `DataArray`. For this we'll ship the derived data straight to `w2da` without any statistical analysis."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "da1 = raster.wsp2da(data, w_rook, attrs=da.attrs, coords=da[12:13].coords)\n",
-    "xr.DataArray.equals(da[12:13], da1)  # method to compare 2 DataArray, if true then w2da was successfull"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Using local `GeoTIFF` dataset\n",
-    "\n",
-    "Up until now we've only played with `netCDF` datasets but in this example we'll use a `raster.tif` file to see how interface interacts with it. We'll also see how these methods handle missing data. \n",
-    "\n",
-    "Unlike earlier we'll use weight builder methods from `raster.py`, which we can call directly. Just a reminder that `from_xarray` uses methods from `raster.py` and therefore only difference exists in the API. \n",
-    "\n",
-    "To access GDAL Raster Formats `xarray` offers `open_rasterio` method which uses `rasterio` as backend. It loads metadata, coordinate values from the raster file and assign them to the `DataArray`.  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<xarray.DataArray (band: 1, y: 880, x: 940)>\n",
-      "[827200 values with dtype=float32]\n",
-      "Coordinates:\n",
-      "  * band     (band) int64 1\n",
-      "  * y        (y) float64 50.18 50.18 50.18 50.18 ... 49.45 49.45 49.45 49.45\n",
-      "  * x        (x) float64 5.745 5.746 5.747 5.747 ... 6.525 6.526 6.527 6.527\n",
-      "Attributes:\n",
-      "    transform:      (0.0008333333297872345, 0.0, 5.744583325, 0.0, -0.0008333...\n",
-      "    crs:            +init=epsg:4326\n",
-      "    res:            (0.0008333333297872345, 0.0008333333295454553)\n",
-      "    is_tiled:       0\n",
-      "    nodatavals:     (-99999.0,)\n",
-      "    scales:         (1.0,)\n",
-      "    offsets:        (0.0,)\n",
-      "    AREA_OR_POINT:  Area\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Loading raster data with missing values\n",
-    "da = xr.open_rasterio('lux_ppp_2019.tif')\n",
-    "print(da)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7ff600e744f0>"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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ItFnyp5U9nWvfhjy9xgnXmk4cpl6DOAbfrFp9Vem0KD3zEouGC7kcLYN+tEZzG2HAmAdytqmK4FQ87hCL4KIXHHPPXu4E0bCp4/TjjhMUGdlmE4xAkmBb7cnqkzmUsfQSB4GjVim6UA9dRL4AbOFcq56qPigip4F3Aw8AXwD+mqreKtj3EeBtQAS8Q1Xfssix7gcZTW8vTSrY4z57Pe4Iz33u50mRGvEZ4v5SrSKNOlKvQ6/nTzFihTDmuGUsvcQicafSFr9RVb9KVR/0z98IfEhVnwd8yD8fgIhEwP8NvAz4CuDbReQrDmCse0KWqCy51MOYQrEx/J+tILo97I1bJDduzqfgqUSJuWO+pf8i8gUR+biIfExEHvevnRaRx0Tks/7/qXHHOIyQyyuAd/nH7wK+pWCbrwX+UFU/p6od4Of9fiWmgEQRplbHNBqupVwYApoUGvHhI1OrHwyVMVSQ9H+22STZ3nF89hnYLaNQeuclFgXr+4pO+psBMzvAIRb9i1XggyLyhIi8zr92QVUvA/j/5wv2uwd4Mnj+lH9tCCLyOhF5XEQev3bt2hyHPh5H2Uik/G6pVpFqtf/GNMlXH8fOOh8dlsZMQY4h1U6fZUxH+XMqcXvDsVyiqf72gWkc4AyLZrm8SFUvich54DER+fSU+xVNaYXZPFV9O/B2gAcffPBAeW0TG1ccIjRJXEs5mN3L9XF2xzhJXI/SsF3dIZTqm2oVqcS+h2kydUejMo5eYlGYsQXd2TSM4vF2b7sGD+kcYAX+jX9/wAH2tnQkFmrQVfWS/39VRN6HC6VcEZGLfnAXgasFuz4F3Bc8vxe4tMix7hUTjfoiKImTsJ9WdDmYivuK2G4wVy6Shz7i2NrrZrRHiUBtbzFjKFFiBswQTrkehFFGYa8OcIaFradFZFlEVtPHwMPAJ4BfBl7jN3sN8EsFu/828DwRebaIVIFX+f2OJB6z7x3t/U3J7jhySGPaYrLwy4C87ryQarB72mdGgcwVZKlVbLeH9rrjw0Fj7uVD5pVHdkVV4vZDynKZV+l/6AADAw4wwBgHOMMiLckF4NdF5HeB/wH8F1X9APAW4CER+SzwkH+OiNwtIr8KoKo94G8BjwKfAt6jqp9c4Fjngj0v6WfheB+g8dckwbZbWel91px6zmNIK1cH2EIhayigPobdjdJ9w79pkBr20riX2C/mxXLZpwOcYWEhF1X9HPDCgtdvAC8peP0S8PLg+a8Cv7qo8d0WSHnZqeeaGrRx8eNAjxz68e59U/8WGDYaqZk+Tis+XT2UcgAlDgmqQm9+laIXgPeJCDi7/HOq+gER+W3gPSLyPcCXgLFeSFn6Pyc8ZF6ZGdN5UO3S/dWagXhx2FFoKPyRGvNKjHZ7EBlotw+Mxz2k95KPiaeCYeF4ppDYHTmB5Y49zfFKlJgn5lVYNKsDPAqlQd8DhpbqaRw4MLDOcM3hZKlh9MdMmSeAY58YARHEGNRapF6DxEK3h7bbcxjADENNkkEjHoRMilYNUx1z2m1LQ17igHEUK0VLgz4DCmOuQTx50BOOhj3UvSJjd/i+m7WaM5CnT0KlgtZiEMF0E+gl2EvPZGOZW0hiWg94FEslSZxq5Azx90L530NASX0sMQqlQT9uGGPAjC/qyZJ4k5KfI9+SLF5slpeQagXOnCJZqbu0tiX7H126HuxTgf1ozAxc0JwkfGc4zlCoaNwEuaCQS34Sf8i8sjTqJYCZeegHgtKgB8j/eMMfbhojn8kgWcU06pilBvbWOrbbS98o2tj9D/ttpuyNRt29dfokRJHTOVmqYlodpNUFa9GlmitdA5DUsw2YI6MQhIqOXG/P/P2eqqXd3nMYj6x9ty9cgkdbP8tD0bcdTh1BidsGM5b1Lxx3vEEfR10bem/GH7YmiYtjq04fOkjVEFOGS73mPHJV9NoNtNN1yc7LV1C1qBikEiNLDRd+2dmFXm/6zkbMMSwzL+yFFhncN3cIl0wN8xpFyeH0M37MvtfJ9Pp9H668aigHkPLhS5QA5z/1ygYXRwMHxUFWq6jX885CJ9N2IBID1mI3tjJj4op7Yq+DYKDXw7bavqCgiXZnM+b9Ux2NePVEhK31xqx0UmbMLInpD3Z+DoCHq98x9l6UYZcSKcqQy1FD3hsMWCVziTur5EIAU1oYtdhOB3wIwA3VMWe02Qw2c8dOdnYHzzvLGJlRJiBk9PhK0qkrYqcI/2SFRgX9UKeuVJ3xs8tCavSLlcLPLu/dFzKdgMeSd8903hK3L8oY+lFEyiDJ4sjzjZnuiwOeG8e+aZC5tnemEvcLj2aJO3t+fCoFMPW4xrWgS6mNVS8B0G47/n2OBpmFVabpYDTr55if3Iq6Sk3YNwzhlDj+0NKgH01khveASusHZGC9QTWV2CVOF9nByBtyAOIYY3xjiSRx7d6mPb83rjNPWCOOnXrmEkVgJrS0m+X+5Hu35vfPJ4UJWEW93HY+jo5Usu2yRPes4ypxLHDUkqJHK6J/kAhFoPZIq9vXuYF8IdJCjXkKH8qx3R622cI2m+6v1T6Y848cljrhrXYb/CQxrTZL7kD9v/R5/r288FcyyAZK9eTzxx2in/rJUaLIyfvGlew7VerEHH+ozlecax64cz30Q/amUlqhq/b0pfozdLWfBzKPsz+oBZ9wTKjEh3EQdQZ9XLhjkZz6HLOlMPmaeuuRARUkrdw10o+7H0UFzRJzhpCULJfDR+Y9HZb+R8DESHnPBzqGNOySesBJ0o9XL3Is4+LeqZpizxYmWbMwSF5eYB5Ij5cLv2R5heB8EkWY5SX3eKmBttuOxdTrDYRsbgvGUIl9o4yhHzJCES0Xr/UfyCzx43lh3kaJfhhnUmxbkwSsIpW4MI4803lnvY5piplyx3WGfEFGMp90LUKQRNZu1xl1ESfDUHeFX/R6rl6g2epP1CWOLUotl0NGPq6pSQI9i9RqEEWIVcQsODG5KMxCP0zDG2klae4Yeznv1PATz9iq1DCJuciQ0CyTkVpst+e873abpDMo+ZtOUFKtQrUC3bKj0rGHkhVnHxXcUQY9g1/aA24Z32z135pnGPuolo3PMx497fmKkpSTMI7mOO5c055n1MpgzP79FczgF0UTb9S7PbC2v/IrcaxRslwOCSNZByErIs+QmBXTdqYvarNWtNmMHe4njS39M/Uaplp1zIxxTJIpxjgVUsM867GCQp+Z9t3PJDXu8x/1XcmzYxKLdns8XP2OvY+jxJGH+qToNH8HhTvTQ18gBmLQk0IK0x5rHsnKIIyhQUhpbMy8iGmy1zHM6GWHicWZRMPmUbG6F+RCXhJFaK/Lw5VXoVbLCtJjijLkcsA4UD5wvoBlXsebM/YcNw853PMY2xjDOrJydR7GeI9JXKlWs8lw1CTjqIsWqdSyuHuJ44uS5XInYJLB2K9kbVF1Y9r+bZKuyl6M4V5j4GOOl9IQM972PD3w3HnmKQvs7nMEvW5hviUVUEuVG0scX6iWBn3hmKtHvsCkpsSufDyjt02bqEy3y4x6UL2YFsPMo6FF6InP8R5kOYY4+Oq12wxo6Exxvql6t2ZsnnSnPSaD/XHEV5ROYug4vZlgbGWR0bHFUaMtLvybJiKRiPyOiPyKf/5CEflNEfm4iPxnEVkbsd/3i8gnROSTIvK3pznXZ5743BQDMkhccX+TSssX9WNMy++LjPkU+zqWThfb7WXGxbV466K97vA+YRJ22uTkAsM91vc7ddIDrT0V4aRho8zTH3VN4YQ0bdK76Fj+M5taWXLOE2GJownV6f4OCgfhOnw/8Kng+TuAN6rqnwDeB/xAfgcR+UrgbwBfi+uE/U0i8rx9jUKGGzlnr4/DvOPi+eOmw0gnl2mTekF15aRjFxq1g8AYI5sZxlFGdprJR3MNJ45w7uKh6NvmfswShwtFsNZM9XdQWOiZRORe4C/jjHiKFwAf8Y8fA/6Xgl3/GPBbqrqrqj3g14C/sq/B5IzaVH0+YWAiyFrCzZNOGI5vlm1n5WbPEj6adfuiffcTrvL321SrmHqtL3pVhBGTwth9ph3DtPTTSfBjKwW7jh90yr+DwqKnjrcCfx/XxjjFJ4Bv9o9fCdxXsN8ngG8QkTMisgS8fMR2iMjrRORxEXm8y+RE1ICy3ixL76DCcS8dgYoH0zdEczvmqPPAdMYn7/nPyh/fL5ffH0OTxIWkkoRUd33qcaSaL1NslxntvRj/MqxyZ8MnRaf5mwYF4enTIvKYiHzW/z816RgLM+gi8k3AVVV9IvfWa4HXi8gTwCowJHqhqp8C/hnOg/8A8LtAIf9LVd+uqg+q6oN//E//sYU0Fkj1w4+s4NIkYzSKaiimX1wUGrV88vCwjJan/YW5gmn3A0ZPAmmBVbWKVKuukXe9NmTY9/15585dNr04hpivi54PT78R+JCqPg/4kH8+FotkubwI+GYReTlQB9ZE5GdU9dXAwwAi8nxcSGYIqvqTwE/67f4J8NS0Jw5/OPtZ5ooRL28rSMR0Xt9BIxWN8mMrZH6MiteDa3JRrbrNkgTtdA5m4pqWcbLHxHR6jRJBETXSdVpKIME14R53/rEnmhBaCt4Pv4ulcT8emBdtMQhP/xjwd/3LrwBe7B+/C/gw8IPjjrMwg66qPwT8EICIvBh4g6q+WkTOq+pVETHAjwA/UbR/sN39wF8Fvn4v48j/cKY28HkjIsLCZVH3ULSTGibTaKDWIr2ezw8UGJpQGCvdt9MF36QBq54OuOBimFwl6MjNpqEmjtgPcEqSgHZ7fkLub+P44r66My+0FZ53EvbI+S8bTd/+UMDaqQ36WRF5PHj+dlV9e/D8rbjw9Grw2gVVvQygqpdF5PykkxwGD/3bReT1/vEvAu8EEJG7gXeo6sv9e/9RRM4AXeD1qnprHidPf0QTDXvKYfZe3IGEHfZwjrR1nSaJM0SxM87aag97j7njp4qLqB1YFTpju0BtdB0jU9sfhEtAxyZjs8ySZ8gKrwKZg3wxVl5oa6z08DhPPNWcCRhU00xEpVG/zaHA9B76dVV9sOiNMDztnd8940AMuqp+GLdcQFXfBrytYJtLuORn+vzPL3JMUxl2tYN9JfeCKdgeWZXnXioa0/Lybm826mOwXWZcA3ExCY99GDH0lJKo3Zm99DQnoKncbTIYDx+grqbGuBJDUKAV8vkzjz3/WQbsJwBpNFz4xip2a8vduwmff9lU+vbGnDjmheFp4IqIXPTe+UXg6qQDHbtK0VlwIDSyKYxQWi6+3/NMPfmMCe30GyGbw0sCB+PbS97Cdjr9sE6we5oklTTEBKi1mBNrrntTkjgp5bZjS6ln2BTeh3Dyq1aQeg09exo2t8FaZGkJ2drqTwT+ekYhbbwClEJetxPm8BMZE57+58BrgLf4/7806Vh3rEFfqDGflYOdD0HsYf+pEcSvnUzAoFCXbbcGt52XlEDBOEbF+PvDKfCKZzi22r5mjEQu+SnGe+TLS9h7zmEu34BqBV1qINdvuq5DPl8ixoWyTLUKkXGxdg1UIH0ISJMEmi1Mkrjfd2Kx29sDE3V6r4dWYaGXH0VItdqX3Q0Kp0ojfxQxPSVxj3gL8B4R+R7gSzia91gca4NeZLQfs+9dvGc+L+M3T2XDFKGhm2bb/Yacxhw7wx5olyFTZ6QXrxaI3LXGMebsaeyZNXon6qgIveWIykqVaLeHJAmcP42sbyOqaKuVJYpts+kMff7YuXHZze2hcQ6svsTnLAYuzbWxM42Ga4ohgrTaYGSgjV0pw3tEMedFbC48fQN4ySz7H1uDPspoL8yYz1PZLyzTXwQ0J1p1mCgw5vnJZijkkca9Pd2SPNUyKIpSq5iG286eWqNzZglbM6iAGugtxYhV1MTEGyCry7DbhHsuQLMDzRZs72Sfa/7zHXyeZOfNxpGGriRsIJ1bdfR6btIAzPlzsLWNhtz7gkKpTEog9x0pY/EHCAWdnuVyIDi2Bn2kJ16Q2Nqv4UyTcKZRd13gA8Gs0TuZwZDHqPDDNGPbiye/n2ue9p5Ns13g6WbyCo2Ge8tXiiLp/Rzc1SwteU+2SrK51eeWDwzB/+CiCHP1JtXYIK0OGIOtVRBrkS9c8glRxXa7iDFIu4Nd3xz4HIcm66JVRm4V0f8uDE9UEKwuvCefPHVpxErE/R/QhCmrVI8ASoN+uMj/CPYQf04fhzFO1IKqM0bdLtqeLEOQxmkzNkQ4nimLVaa6hhytbt+riDH0vbQRhDNm1idZ3T2yzWZm5AopiClPvtPBrK4g589CpwOtNrrbzCbLzMD2ehAZ7PZ2/7oCj9806pjlJfdekmBv3ESvXsN645mxYSIDnr8PfhXtPebsuOOue8T9GZiAxlA11U9a+fcHVnzz1g4qMR8cseLxY2fQZw2pSFzJPLtpDZ37gfc96/SHmGxvz/TDcz9kkzFLpuI/71VzBEYalLkjSaBWwyw1oBI7g97tIdb6xGKgE6M2S1y6oSpCgt3YQnab2D/+HJJ6TLzdQT7/NDRbWXs3bbUhMtlniIibSMVgVpa99+7oh3Znd2A1lKcyjppcsgl8mpXGJIxbSRV8xgPfx9xKZhTzpuS2HzBKg36EkBnLyFVadnt9jfIRP9BpmipMhSyOPYYalx4v/SGnhv8oasrkVi/abqOdjgtfnDyB3diExE8sAcMm9ZJDBgpR5Kh/9RrmD74Ez72P5sUlarVnET91Hd3YwtRW3fGMYDe3nQG3CstLIOIaNasdMuQHcR+GzjVlZWyGvEdexP4Zs0p6uPIqPtj9+alO9ciJ17oJN4og8udJLNrtop1udu/KZGwBZissOhAcO4P+mH2vizNOEYZIl93OK2TyD3/eBmEazzkwlAdqmPaC1LNMEkQFaTScNsyNmy7Jlzal8BNTqpWjSeJCHt6wkyTIyRP07jsLIsRXN5ELDXbvqmHO3YNYpXa9jVglurKBdLrY7R23MkhjztOESdIK0igaTlTudVVT5OkX0VKnGZuY7P7kq1zH1hLEFV7a+C4ebf77kYd/KPo2l69IKaxGMi5+eh6pxJkD8VD0baVRL0DZJHrBCAs0xiL9kXmDaVvt/UnY7iUxOeUP+8BCJdMiP+4gbJRCE7CbW4W7h6El58WnUgOJ0940gt64Sdxu03vevbTvO0VnxYBA42mXm4h+/wtgnWSBrK6QpOcqaso84TNZqOhakcHNjHNx0jirPK3V3KolijIKo/GPs+bT4WehNgtHjevGFSZW0+NowtC9y9colOGcApQslwPADEZVe110jLwsOPncwkRe+n695nbvdEb+SPc7zpAxcSgIJskBg+QeOL76NBNUwWQ7IJSVgKiPqVerRLd24dQSSIXGlS4qUP3s5f4EDOhW8cSxL+THOcLz3gvS3MnIsF6qy9Ppem66QZYbSL0OXZcTMPRDL2EyOPW2H2397NBxH668auB6pqkxGJA9gNJTz0GOmId+rFLnU/UUTZFqX09hhNKYb5G+dpEuyMjns8Ivuffk/c8D/v6kJe6mXnP/l5bcX8oDz3mJY483AmFuIu2Paq/fRG7cwux0iFpK7RNfJPqNT5Bcveb6e47qoToPpNcy6pomXeek78KkVYN3ILK/Zgt712nsfReQkyfAi7AVeeJF8fPUmI9dhRaMO7+9GBmcGO5kTKuFfoBG//h56NN6TtMwR9I4as8WGw6f2NRW2x8mldidzzxZqN63vwMOvzbmXqUUREQwJ0/A6jJqDFy5Bj5hNjbZN+OElte0SQ2ZfPESq1frWVz8UFcqkzDhu7TnY1hFugmd88tE9XOYMycwT13B7u66wqggPBKGVAaaaO+HqZMmav1xSk8dQMqk6KGjyNudByc79GbTL/+4opSpDh3QI/eLwNPGiCtp9yJWheMKxaeWGujZk0i7C1euods7k69rgjGfhvWReqdSiT0LIwmYPvsvCEvHWaRtUzi+Cfz78F7ueQIuqpyNIhdyuXSNWscpa7Kz66pLve5Maljy5x0IZ+3XKQjyORJFpVGHA/W+p8GxMujP/9PPgd9hmEOcw0Jof+GXvRJ7TvSM0q+zFBcF+4zV8c4284JTqXG0Ful1+/HR3LmyexfHcGLNGfNegqyuoDtNQh7+yOvII5zoQoMcbD9wv8QV/Ig0HJ1AFbOyjG02nSrinOAUE30islrp67akFapTTMap4ZSay6fQbs8+6YzIL9Drohtd91lv76RvDGwz8TstBjGDRICx++Q/m9x3Mv2u3/GJ0iO2UDxWBh0GudoDBu4g4tCpUfdsg6z7zxQUSlOtgtqpNLSLzpmdbxJjI+Qbpz/yUT/sKMJu72BUHaMkkJWdyhiMey+/QgqNWd5YqUW6LsRjN7fn/hmmevIhBgzeNKEK8ZotkyqEx322+ftAv5+tY6MMx7+ndU6y70Xq7MSVrGhdfIOUVD9+4Ds4QO3Uws/vjjXqJQ998RiZJDuomKs3sJOYDIW77oNnPsmQp+Eb9WXy6QRgO0P15v3EZKsNakk2Noa3ITV6c5ooR+U01KI7TffbSVcU8/wsRzJN9nPIMWOcMo7d11r3OYw4diJe4cQzTR6IYaMvRnyf3KjP2+90xmoLFX2/shqOIFE6bUHTLHi4+h1Z8ncct/4wcNRYLsfKoH/mic/xZ+TZgy8eFkMkf85xCUnN6ZDnMSNDYtRYBnpqjpWcnXweU4nduHyce97Iio9qNaTimjjrToLr6jyn+Pk8EGrHBM26Zx5jQTWoWi/rG7y254kiOI9adYlUErfyCipCZznW0EQRRTxy4rVor+cqd4H3X/nX040rh0zCIw0P+Wbej5x4LQAf2PipPR137rjdDLqI/C3gZ+fV03PhCPnSaSVckhyp4pw+r52pwjFhwu2olP2P9BTnDG23XRx9aYno1ImsInTfrfH2OymEUgfp82rVhW6yxhczHG/E5D+XblajzmcNmvT2vjLMEsDpeBMXckq7Qd11loe/9h8SbezCzi66tQ3GOP0dMWivh3Y6PLrz09khi7SYMnJA2/ZrRkoUYhoP/S7gt0XkfwI/BTyqetQKXh2e/6efw2OPv5uHzCv7gk1h0+QUI348c9MzDyF9BUJg2KMdwzIZGBMHnA8YhwM4b0qJlGrVeXvVqiuqCSe0RVXnTjVA3ygkijJvVKoVaHfQnd3px1E0lqFwx/6GWnTssE1f4Rj2iijCnDoJSw3sUhUSJTmxBKeXkc4psGDaXbi5ju46eeSHq9/Rd1byv4eBfIqvJoYhLrzEFVBbWFAVYkB+eA44aiGXidOdqv4I8DzgJ4H/FfisiPwTEfmyaU4gIpGI/I6I/Ip//kIR+U0R+biI/GcRWRux398RkU+KyCdE5D+ISH3ai3rMvjeLA/uDZdWcA+fwHryp1zCN+thy6b0i866TBET63ks6cYyiDGac9mC7UeGQoEgqFbva56D7Mdy4spD7MhbBtUmj7pg2vR7a67kEntWsyGkmhOynsIhmH/crZQ5JrQpLS30aYVi4NircNurzPyikv5E9jCH7nklfE0niCubMaVheQisx0fUtoi89g9lpE12+idluI50e3LjlPstGHXPxgvvtpQ4YjGdJ+b+BoiurWX7lkROv5ZFT31u4+8OVV833u6y40v9p/g4IU8XQVVVF5BngGaAHnAJ+QUQeU9W/P2H37wc+BaSG+x24Jqi/JiKvBX4A+NFwBxG5B/g/gK9Q1aaIvAd4FfDvprusfoxYmy1MJe6HCPL0vIyNspgflhtH4paKI8YwvNOY5Xp+3/QH5T2Uflx7ivMUIP3CR+fPudPtNp3eSvDjWbgRSg1vFCFx7EMYXms+jrMEoXZ7mHrNSS5Ms+oJWCST9FSmRVr8RBSh65v9PqPjdMwPe4U1DSaMMd9aT6oVtwptttD1DXdfUtXLm+tus0qMWVlGW20XZ7/7LrRRdfr53R72ytXh7lOzDLkSuzGsrvCyB/4O9BLe/9SPAy6xmq1055pUn9+h5oFpYuj/B67j9HWcMf4BVe2KiAE+C4w06CJyL/CXgR8D/q5/+QXAR/zjx4BHyRn0YGwNEekCS8ClaS5oCGr7krhDbymadFwThUVjwT9eTRLXzMFadNczfcYt60cdx/+Ykms3soIWc+ok2mpBq42kTIt5X08RF35lue+dt9qIMaCaKQKK77uZLtXDhszptQx5oOFkmfPQpw25DXQisiZrH5exRCYlllNDPymOP6KeYiGTap6bPsP3RYzFNluI16LvH1L6n4E/ZnKrl90n+9Rl93q14j5X8cwbktkmWv850O25HEvK1Y8ML13+69g03LqA3+BRC7lM46GfBf6qqn4xfFFVrYh804R934oz+KvBa58Avhn4JVwX6/vyO6nq0yLy/8Z1um4CH1TVDxadQEReB7wO4P7775/icsITzRZ7TZeWAwbtCDAu0h+VNpue5ub45QPbTBuH9cYmayKxmSA7zcVL9xZ4z3ZzG+NbwwEuD2Ktj68P9tlMwxwDBVGdHIV1wmeVeZ1SGR2KSPnmAUMpTQqqLVB6TM+bXmPR8zHIqnsjN5lljaPn/b3LrwrHVRCnhjqQYujL8Nr+89x3rl+16idcTzHOs6T25KGrHVyVikHCsGuAx+x7EZlTGOSIGfRpYuhvyhvz4L1PjdrPG/urqvpE7q3XAq8XkSdwhn7IPRaRU8ArgGcDdwPLIvLqEWN4u6o+qKoPnjt3Lnt9roUOqTGv1ZyhGBjAcAjkoCFGMCsrmLNnkBNrLiZZrWbGfey4wtcDQ2Pbrcy7CoWw5uEdpvHWoVh9yo2m74Xq7i5Sq7pYurX9pJv4MBODnqDtuhWEbbaGf8xFIZAC3rf4Yp6xse9xrxXFz/Ne+yTPPFcBLMYgcewldfdojEbF84swYeLLb5OFmoL/RRWs6iUnwvez71YqtrbX3EJwXsDlq8JJXsz8C6DuIHGuFwHfLCIvB+rAmoj8jKq+GngYQESejwvJ5PGXgM+r6jW/3S8Cfxb4mVkGMM2HN1XLupTRAO6HFXZuz5fe54xGZqD2o7U+DXzS1d5az9q8jVw+5yl3BGMMMSpcMCtyRiQrmAFkeQl6CXZrK7unUqu5ylkfJ0fVTaSV2C2rr10f1JIZOt2E0EmBUU+bOGu4+poiHJKeD3CTUiUe1CufdJzcMbNJLY4R0w/n2GZz8FrDxHHaG3WSkuKsKPLSJz3PvxfQbkfWK4ST3hzyC07bxrpGKUZ4tDX/Yidw4ZbbMeSyJ6jqDwE/BCAiL8YlQl8tIudV9aqPwf8I8BMFu38J+DoRWcKFXF4CPL6IcaZGf6JhVx8nHGMsstiqh6nXyPpcwmzL5BmKidQqttVG7PpAs+Nx+2WVrItGmOBMDV+1iqyuQK3mqIhxjGnUXUItvVe9nvNGV1ey51jFNlsDk1XR5zHUbDn0+rONhg2T0znJfbYTEqwSRc6jTxLMvRfdW09dHo6nTzJUYb0BznFIVyEpm2NohSFpqGcKEbdwHEVGdtRKc7+hnSwklQuPpY9rNRdK6vb2nMgfPqX2V9SLZlgfsQYXh8HS/3YR+QzwaVyi850AInK3iPwqgKp+FPgF4H8CH/fjfPsiBzXVUqyI5pWjUIXLRe10nGFKqVKzeGpRhKnEjlKZhk5GhQBwBto2W4NsnlHnK1r+zzLRTDsRpNdRrzmOtvc8iWPs+oZjNbTaTr1xe8eVoy81snZ1ycYmybUbbtuNTefFh0ybos+j8HK1/yNPP4s8bXEUjbFodVNADZVaDVpt7IklzLkzZGJo4bYTvNmQhmebTRc26nUHrzH8rMLPborPUOIKpl4jWl7CNBqklN2MJRVQX+fGTy+4l2rV1Rc0Gm7iC9oTDn23wrDcNJTcYJKzzRba7fHSxnctTMM99dIn/U08jkhdRP6HiPyup2v/A//6aRF5TEQ+6/+fGnecAyn9V9UPAx/2j98GvK1gm0vAy4PnbwbefBDjS/GYfe+gpx5m/mcxeGr7ib1ZGTSp95SGASDrYJN5keMwIpFV+N4Eb7FQ3IzUw8p5vSO82EzXxsNUYmy7ne1nW+3MoGiTfvwat8LRfGn6KE9yiqX/kCc7KrSSn7SGJvDgaTqsbo9obRX5wiVnSML48SxGUcNOQlNmsiexmdKJdWXZMYci4+mEtwYE1/ISwPtG/ljBOLXbyxK803yHpg7FhCu1Xj8pupBqW2CO8fE28BdVdVtEKsCvi8j7gb8KfEhV3yIibwTeCPzgqIMchod+pJF56uFydtKXqMgLmUbjZIS3kSWLfJIv7eAzdLyQ+pb+5eL3aWHQUDHNuHGMYAeIEUyj7roVNRoFsfjCixn4SxNiAz+8tDuPZz1ItdL33Nrt0YZ3Fszizc7g9YbjsrfWHRMnZFAEoZmFhLim+AwyB6Hddlr43Z5bFXkWUMhWGZz8+ucYWsVMg1EOhNrs+5xf1Y491l4+f7/fIkTDmNI7n8ZDV4dt/7Ti/xRHDnmXf/1dwLeMO05p0AuQGfUZf9AzIV1Khi3msrdksgFIE2j5tnhBrDRrNuyLPoZ++MHyWuLKwLI73GbIaFRiZHWF6PTJyZPFOBQYTu10nEduC5gSgVGRKOq3wEvfO2jkz5lYUHXx/jBpmUoY1GuD93i/Y/aT+SSjmDkIqUywMdlEmc8rpKyTodDOfscZnGNhGDPxTEV+2AumZ7mcFZHHg7/XDQ3fVdV/DLgKPOZDzxdU9TKA/39+3HCOldriPPFY8u79h18mwR/HJdD6y0LTaGRLYGfURi+DR45Hnba6qcT9RGNwLQMGp1bDrK6Atdid3azBQxiPzwSYOt0sQSm1qlvCN1tZcdZedUfyrJV0UssfL1N59F2XTKOejXfmStZpE8+TwhBico3CNRtr9nhtFZadNIDeuOUKYKYJoY2D2umah2erRkiuXutPBEVJ4txx5tKcfJFG/DDOE0CmP+V1VX1w3AaqmgBfJSIngfeJyFfOOp7SoI/BUEx9HAqoV4U9Qf37plp1Rgmc/kXYgafqmg/YGzdHn29KY5AVQYVGKWfItNN1lZjViout5qr7BrZNElBBt7ZgKzhWevxZvbEiyhr+fkm/YGXg/CS+M5BvG5dew5QFKenKSJMEqVb6Mfr8Pcrft1FQ20/sBYZSbT8hrq02khr93d1hg7oX5EMjk6AjmDzjdikK8wXHmyvmGb+/jaGq6yLyYeAR4IqIXFTVyyJyEee9j0QZcpmAMPwyVso08LZNteqYBEtLWbx5IDSRxhC7PSSO0Y1NV/683AAj6PYO9sbNfrhh1i95PrYejG/osf+B2u1tetdvuiTuhKX2gChSYPgzNkLI8JiEEZNMur9pNDDVqmP8BAlTej03AUHGu58K6XmMuCRhpYJUK/1jj0uIjrwEze7bUCFNeq86HfTmOvaZa3vTxBnFcNpLbDn/+c8S/tnrOWcd1yjMGqYKQnTzVloEZgm5jB+myDnvmSMiDVwtzqeBX8ZJr+D//9K445Qe+h5Q2OotSKKKWtRaFwoQ48u2B3tTqlUkIuNVpx56ymke+MFPm+FPkd9uzP5Fyc8hYzOG8YGEBSMBt31GDzd/HrXqCkNSymNikXoNu7nl2BFiMm1tQ9152VMyitIKU8d/7vYnhLx3PglFq4oR1+VyAwGrY9Lx85/ZIgzo7egRzzBxh6J1U3H1Zx7LdAnPKXEReJeIRDhH+z2q+isi8pvAe0Tke3D1OWNDBqVBnwJh6CXkM+c1PVKkPSqdN59Ar+BHPBQyGQ5xqFXnOUZRJkg1SjVyJIKEZr8RQX8M0+w/FgPjNfR/NHvQfskZdjGSNUPg5Aqouli0tS6EsbriJstuDza3+k2vJ5wjpZJKMAGMLTqaZswTtgmbM089WUyLKWL82TEHvg9HrMxxHgjIAEBA+e3yWPLu+Z9vTrdQVX8P+FMFr9/AFVZOhdKgT4nH7Hvdkm2a2HWQhJpmu4GXcgJJttvDiMk0ZEwU+cTbFF576qX4vpSEkgBTGowBxo3a8eGCwGDM3JYuMDTZDzL9v7qCNmrOb1lbRnbbyPIy1Kuw20Q3t4ZL48ch/HyCyWNu/VHHnDev9JgvOttTO78pvgMOUT+Ml98v3E7GVJAeBKRfyTrT9zX9vqd5Cz/Bf7Dzc4sb6xGbE0uDPgOGmC+LhjeQmUe5B3lTF0MUxBioVoYZL1MiTejR7SJeXqDwhxYyYiaNy28farsASKPuNMbBdShqO9leAFaW0JUGKoLZ2Mk6GPVlV2f8haVhEzFIFIx/0YYsW9nJ7GOegKHcxTSGMfjcUqXOAc2ifQ8qp3k0Dn4spuLO7xQcB4kGY3Vh/HgXwj0PT8VMLJcDQWnQZ8RMzJd5IAxphBVzo5Jk+ZeSBG0WFAnlfvTjfmSaJNBselZIddDLmzDmkeGAXGhJ/LGJYxdiadRdAUyz5ZQWk8Rpv/QSpNVBdnZg1YVhbGtvk1Q4loGVUZDgzucC5t0Qe6gadz9x7SI6IjAU/hqVU8nyO2Z4bPuBmCzpPMQmGgXN9TGYFPpLVzz+PGGf0oXhCIpzlSyXPWDuEpwzwvgiFVOJMwbIUIER9BN9BayErNhkgnHOtEW6PbetZ3NMzWKZAbbVxm7vOM0W36TAnDnlVBZrNbAJdDrYS5ezZtEsNYjOnx1hyPaHgcrJZA/e/0Ejl6QdQsBmGVtduiAWy746XvkxjavAljTXNOdJd/y4pvw7IJQGfY+YaNRlQrn3XqoE02VrGmuu1QYohNN4Pf1DSf9HPc1+4Q/K6uBKIaCGDV3zlAnATOIglDvodNHdJlhF1zewz1zFPuNpuEniKjOTxHWT3y+KJj7NcfJnwZiqxYljGHG8aauHTb2WTfiDb/c/84VNTiPGZwOe/qIQ6uEvhKJYeNIp/w4IZchlEQgTfEVxyHFhkzwKPGu6vo1Xuz178iqfENzHD8wZiP5XaF8VhUPX6ah+0usiu7uukEiMK0tPufPXbrhwTNbIYAovfdZr3sv1pCEG6WuUzyXhKiGLKP96enj17fqG8y0D38O9OBR+v2ziTicHX7w2Nlm+SBR9pnu9vllPfcQWbaVBnwMkMyiSxQgnqgNCFibJysa7vX5XoKGTBGyM/LKz6AczC/87uIbsrTHe1ICnl280Ma8f70A8N3Gdi1IdlCChaLd3nFaNbyxR1PosGPjg/3mNteBea5IgtdiX/1sIFCb3jBGslCJDWjiBjHo8A8SIi1OLuF6z2zvOoB808oVoIVXR3+dUMnehydHSoB8vZIqGjbrzWJqtvmEZ8wPOBLFSnjk4PjUU7zuOCpnjGYfbz3QtlTj7caYSuYXGIh9PXqQHFni36X3NEEUILjQj1Qqm0XByvNGICWaBdMSiMWvYEGUWSLEnHWqGh3RSgeH2gNPy0meEWoVOx60Qd3bBWiefsBf21F7Hkb/+9LeXqnPKgqmKKbRkuRwrPGbf67yAMK4dRdgJ+s6I1+BIeyimEJNVtiE63FUo5HnvdXlb9CNKvf84diyWbteFc45gEjCTX0jpmEtL0Gy5H3clRpLESSpME36ZBvn7Ne7+h9uGn3N/8NOdK4+C/dLCNqmaTH9nauxnYlML+ErptG/rmLh/4flmzbGMOnYUYc6dARNBuw0ry7CxBc0mL61/p9sujnl0+13jj7UfHLGfSGnQ94mUcqettmv/lteR9l/YgVZ3Rd62j49mlW0MqvU5A1EJduhvN0speWaMisIEo7ysKSmSc8eIH37GTe50+7HcSoycO4NYdYZmewfd3R1Oas467jxzRHM8+7AIBnd/M0G0cYZu2sl4xGtpxXARJXXRyMKKKa1w1HWOMOQDfXb3mMfJVmpWYWfbfQeWGsjZ03zgs/8vAB6ufge02+4/LMRrL2PoxwyzlhOH7JgBPnueb+7/Dyg2Bl1l0g5GIxNleYwL2aQhAtsb3iesNk2SrGI1a0QdriL2mEAc98PvL+eD8I8fr0Rk4RZttRER7GoNExmk20VOn0Sv3UB7vUwfp4hvPhFTGFqpxM64eEpnYUGOv65sos7yBIHoW8B7H+v9FsTTR03UC8EcYvC0Z1hZ5JwKTZK+IqmIE2sLuiDBYgz4EI6YQS9pi4eIkdTHPE0w6OySdfdJDVQYssljFtpcSNvLeZcpv1dqNczKslOPrLjwjMSVwbj2qPPPEE5IYeq10VS99P74zjdiDPaLT2HWd+DaTeytdexTvh2c75I00Ahiv0YvxyyxrTba62Z8/SGD75khphJjTqxlnZ8kdkqPEkWY1VXM2kq/MUlR45DcPc0ULuMKplEf/VksAntgkqS1DGmbvpkm1fB3MRDKUsyZ08hd5zB338XLHvg7M49rT5iWsljSFu8czFx5OkvcfI/JpoEwj/qKPTFIksDurms/5yeTST/KVE53wDuehn0jhmRnd+z1ZAwbn3xEDMmXns6qBfNe8L7iteH5Z2APFR4rjpFTdfe0lzhVzijCrm+4lUSe9z4qsRtOKr0uJAGL56h46QVGfxZef2GDDfH9BCLjjPnpU+jairuX1roitAOAUIZcShRgIL4Oe/Jm5wXn7Rnv+Q6eO+Wu22bTvzRdMZPaIHxQ5K3vJbmbDdgMTBqoazYhldhps2TVngpEfe9ulkTduG3ph8Ukdo1JMJKtCvqH0EwrBmtdmKBeg+1d7MYWUq9l3Y7SyWkqzZO0YQWwJ4XLUQjj3ZMmxRETSNZiMQsniUvgFjkCBcn//Dmy+1ytunHFMbKyjJ5YRrabWQvA9z851IN+YbjjDLrX930ceFpVv0lEXgj8BLACfAH4TlXdzO3zAiAMTj8HeJOqvnXR4z0sPBR92+Ql7Lz50wWwkzjF+aTgBAzoUI9jPez1mlKjpv1QgxhBGnWk4pPI3oDa9Y0htcN0+0F64IgxFpx7IPThVwa2VcwQEiMuBxHHoAo7TdKG0uonSXd+15Vp6pDGgr4PGTUwExAr7gEwrngui5cDGIPt7BbnfQpyAv3jJK5QK46zXgSyvOQmxChC1redtw4HaszduA/2dJNwEB769wOfAtb883cAb1DVXxOR1wI/APxouIOq/gHwVZBNCE8D7zuAsR44pi1R7vfYTAYN6iw/5uAHGGKv1aZjDU4B3W9uoaHCQ3jVxdRT32miURuzuoK99zzSTTAn1rBPXXJ8/3zCt1qBbg+bccf74834ztUKrilGrz+RhNcQRW7yIDBy+etLrOu/Wq1CpzNclDODB7xopNTagfOHnnIUjXYA/MRvm013f1O20agVUsG+WY/dSow0Gq5/ba0Ky3XYbcPWNtr2SVARPnDz387r0qfHnWTQReRe4C8DPwb8Xf/yC4CP+MePAY+SM+g5vAT4I1X94qLGeViYRW8i9ZCccQljw7N57VkvU697sW+M8rrzRn8Uk2UvksAjh9KvvNVuz8WmV5aRVg/pdJ0xHeKFe4+PimPDFF0buGpUYwqrdNMVgm6P15RxjCCFSsUZ82Yrqw7uh6L2QbGcJ4Lvl5hU4jhy9y8yLgntVS7DgqdwX/fQSREMvJ6uakZVuuYretPJVFzhniSJW+HUa9Du8IFb75j/9U+DI6i2uGgP/a3A3wdWg9c+AXwzrjfeK4H7JhzjVcB/GPWmiLwOeB3A/fffv4+hHiz2Kh6kVrOKzr2IRqXGY6w0Qd6rnsSbDuPik+LPfvuMyeHj37bV3rcnmiXQ4hiz1MBubaPXbyJnTkG7g712vR9nt0Hc2SrQGtQkyaPXG+2M5b31gnFJrZYVPw3smiZ2p/0sZ7lHgdE0q6uOl58/36QwXhZiczRBoigLZQ3RKqddgaW0zOA+h9+H8Bql0XCdqXwlKOAmZiO8/wv/YuItWDjuFIMuIt8EXFXVJ0TkxcFbrwV+XETehGuAOrIRpIhUccb/h0Zto6pvB94O8OCDDx6x2zsaA3HbnEczatssTisCVrOElZ2yl2bf8MwwEWRGoaCPanDcMIk2LnmWxWWjCERc4wyrWTu4iV2eRp0fF/8XI85wtT3V8sQq9uQy5qYz9K45R7irIsY6jn0WhvENwYNuQgMTwV4nHWOQRsN5lo06stQg2m2S3Npg5GcyTQJ5DNQq8d13DcTo0+MO1jiMPQhpnYIkiZNXmECPLOy7O3B6GQhjZTUOiXXfcd8AXMS4frv1mushay3v/9JbJ132geFOKv1/EfDNIvJyoA6sicjPqOqrgYcBROT5uJDMKLwM+J+qemWB4zxwPFx5Vd9L9dSr1DssQubhLTXAeI2QRuS8xlRXY14a0HmPK+dJjcNYOd6golKtIrFnPGzv9D38fdIK0wlOrSIk/dZ7iY+vV4bDKtmk6BUshzz07PnepV/Fe7Xa6UDTwIbjAMhSo9+ZaRRyseupOgj5e5neb3vtuj9U4BGHBhWg0x1f05Af1oSOQePez/rkWoXEYtZWvL69xZxcc4ZdxBnvp3586jEdBu6YkIuq/hDes/Ye+htU9dUicl5Vr4qIAX4Ex3gZhW9nTLjldsUHuz/Pyy6+HpaXXCz76nVvlCH01EJvVpMEvfc8KkJ0c8tVQHqa20IrBPfgEY46TlrdKUaySlOHfTQ+8AhZEVKteH0Ti+w2Ma22mzT9/RraJx1iSK2EoVj5XuGahm/56w5WUyH9c0o4Q1wZnniGN8xWHwOdrvx7qTMhxvgm5P1iq0JjnL1HPwaenwDVjrxPmRCdVVfZ2+k6JtJd56DTRc6fdkb8xga02lCrHnljftBFQ9PgMHjo3y4ir/ePfxF4J4CI3A28Q1Vf7p8vAQ8B/49DGOPCoedOkazW0dhQAewz1xgl/ZoyIcwfPYVZW3E6JZ0Cb2qRtMZJS/8JceSB7byXOWSQZg0vBJ65VGLM0lLGebbbO86It9qZ0Fi+ucMQFS89337v34hCpKwvZiDbMHXTh4H7m2tblztmKiU8cHwJjG6aVO+BpvtXK0hUc4ZdzERvXYxglpfcITsdsDoY+gsZQun4fUhMqlV41t1oFJEsVegtV1AD9Wd24MQqbG4ffWOe4k406Kr6YeDD/vHbgCGyqKpeAl4ePN8FzhzE+A4D0k2In76B3tpAg0RZyPpw3nc/RGC3tmB7x5fde42TnDEYJ3u7t4HmGhpk0gP+/VHnCEIs2RI8H8PfQ+l4flwAZmXZJT4rMbZWwbS7mOVl2NmBkyfg2g3Y3R0cb1i8Mu8JcFxycCAhPX2IY+y5guR1amQligZ7reZCaSEtUIz0heXSiXaaexMZxIdHBoTdwlVOUCgV5o20GtO8exmNBNNTaleaSKeHbVR49PdvD2NeVoqWyPD+T/1TXvbA30F7rhHyxCU0/QSeev5yahjyCda+Nzos+p9uMxXCxJlapOaMBbu7SFR1LwcNOUb1NS0MCe3XmKciT1aRM6ewaw1svUK03oRbG44aeOYUGhvkxCpSrWA3tpjLZLKPcc9y78dum6spkLjiG4I3M8PcLwoqLopKq0yHVlbTjFGMWylCdr6+6FhOAz51CqoVFz5Ui3QTKls91AimZ9GKQSPh0Sf+weRzHyHIEZOYLg36ISKlXaXynpMQNma2QRI19XozjewwnpxKlfrlcJpYm4X/7SovG+5xJUZWV92k4sM+Yen9QPelcMIBspXDiHOkx8r48ZOMWurp7jad6FWzi1Yilzhe33CND6ox9vQq0mogvR60jEsAHmQj4RSzrASm+Ez8A3fv/cSqSf/9lPVTGOveC4LPI1ylmXqtWIMmuBZNgLZfRZw7Azstqt0Eu1xDI+GDv/WmvY3pMDHHGLqI3Af8NHAXYIG3q+rbROQ0rmr+AVxl/V9T1VujjlMa9COAUOYz46eP8GZTudoixoNazRgEZmUZGk4Aim4PvPCT1GoutmwtkulqjOaZZy3gvFDWkM5IOrY4duqIPo4tvmjGxbL7q4Z8B580/i1+rLrTHNxmlFEXl8yTWg3WVtxriXWJtZVl7P3nnffX6mJubLpuNt0e6GAoamRF5zwxz4R1ELowayuOPdNquy5CXiulb7wT8nmCcOU21sCLGfqsTCUe6FaVTioDoZ0CpDo2UvWrut1dpGng3Gke/e037+0+HBHMMeTSA/6eqv5PEVkFnhCRx4D/FfiQqr5FRN4IvBH4wVEHKQ36EUNeX/0h88rMy5JaLWtX5xoo94aqPTVJHHNmRzAirsQc5zGnFLXMex7gXY8x6oEAlJjYGexAO8W9bhAx6OoydrlGdH0Trt/A1Gv+B58MaKdn19RoILVqv3oyMgh9kauRFa3qqj6104FLV9x9uec80knQmvPWWxdXqHYturaMXL6e6aKn5++PZW9Vt2MRsEDc8xHFOLMeM2DmSK0Gy0tO12R9w92PdhvtjGANheyUyCAZ82c4JNOPe/fj/kPftRnorOnKjSjiA9ffPtt1H2XMyaCr6mXgsn+8JSKfAu4BXgG82G/2LlwusjTotytCzfSX3ff9EEeuKKXZct7Y1WsDXlNmNNttbNhBSa37sZM+TQ20dZQyn7gc9yPNqg5PnYTVZfdis4W9ct0Vm3R7SLuNiWNsGpKZZLySBKpV7PmTSLPrjlGruiKStMR7cxu036UnDD2JMb7JBX6ycwa7e7KOxoJYi9zcHEzapR5zmBhdAByjo+GkBapVF9/2tL9xDZ5HHCwLbeGT1Npuu36izZZbCbkDjj1mKl2QxbRxnnf2nl9JhZ59OuHtC16g69DK9BeEGTz0syLyePD87b4ocviYIg8Afwr4KHDBG3tU9bKInB93ktKg3yZ42T3fB0sNqMQu0RfHQM/FtptNsuV1yioZ1YEoxIBWSdV1gGkNM2fC5bdaxV6/gWxuuUMGMrHas8V0t3AVAANeJlGEbu9gGnXsUo3kKx/AtBJMpwdfvIR2+5PCgJRrkriORd2e8+RVkWu3XJjJuIIiNdA9USeqncfsnkI+9+RQCGIotjyqyCk0/GOYPdnDNJ8Rxy4Ukt7jNGEZ+4KeNOQ17rj+nokRZGU5C7PoTtOFW4Iw0tgJIihS0iRBejKYsxjIyfSvaej+7AGHkrM4CExv0K+r6oOTNhKRFeA/An9bVTfT7860KA36bQLd2s4EimS3ib12o6/1DYHRnD3xlYUB4niQBgdZ0+oBbe+EIcGlUZ69ySiZzntOJ4XMC2w23fkuX8GcOYVtnMZ0esi1W47XHK4a8nkFcR3fpVbF3n2WpF6ht1IhavaItzvYiqFys4l56gqqtr9imHRvRhnzKe7pgDY7uHxF2+nUDEx2OXnYdN9RBtlN1upoqz4pjVoXAksleLs9mEHWYSCEMmkSyMY6O9QqtNtIFPHSlde4WLq//ts6/KLMtfRfRCo4Y/6zqvqL/uUrInLRe+cXgavjjlEa9NsE2unA1namFmhOn8LeWkeiCjZPeyz44Q3odgTGN0ueJUBa/g4DIkmDhTgppqiilMEmDUXnRaW/bl3fJK5UsMt1xA7z68NYb0pblEqMveccWolIlmJ6DYPpOgpc9dou5tLVrCHH0MSUl9wdR62cssgpXR2Z5SVnaH0iOg35mCVH/bS7u74FXezCS+JWGdpqYzud/pgC3nyq6pik0gFeXz0r3hGBDsV67rkx7gmB8wBBQjuOXTivKGQXFlWJIqrY7R2yAqPbGPPkoYtzxX8S+JSq/n+Ct34ZeA3wFv//l8YdpzTotwnUKrTamOUlJ7vabGJOnoAkIapWPZ+92TeigYct9ZoTwYoiZzS2d9B2e8BDy+Lj9ZpLtp064QzExmbWGCLPepjG0y2sHg1ZO6k4lrqyeNncwvQSJ2QVxyg4hkStBu32gMeuSYJubiOfadH9My+gst4m3jaoEaRn2X7uGo21GvHvfQ6sRVMRKD+GNOHnKnSnFDibgOx6IDPiEsf9LjsAIphGwyVprbokc7XqtNTVDhrzcJIJWEepBEAaO0/L6UcmXefJtAnH5QvNiOOxekTpd6Hf6LxyPMIwOjeay4uA7wI+LiIf86/9MM6Qv0dEvgf4Ek6hdiRKg36bQIw4A16vIWur6PWb2Os3svf73lzQ6UaMYzOsLEO9hi7VkK2mUxz0S+D0xynVCub0KddAoBJjl5z3JHHkmkQ8+bR7HoXnimYz8OmYcrHYLJxge9j1DaTdcTHwlJUSrBpSgxAKcGm3R/Wjf4CcPcPOV17AVoT69Q5Rx1L50nU0jUOK9ENI3iMXI6PVKsNxTmkQTSV21E118XO7u4t2OpiVFYhcMQ4wNJlqbgwDK5LwngUeb7iPNnNyyAGbaF/MmlFIJ5deF9Fo8vbp2HyoTJOEDzZ/bvI+Rxzz8tBV9ddxTn8RXjLtcUqDfrsgirC31l1RRmKR1RW02x1kkgwp3SXQtk5tz7c9y+uBh2wRqhXsSgOztYvZ2nEiSXEEqyuYixecjKlnV9DpOo8sLCiZNsZasF2/HF7Qnd1BLzVJCjv7aEK2KgHQK1eRLz+P1gy9RkRSNS4k0ai70Ae4iaLbRRJLsrkFRJmRj06fIrl5ayhs0Kc4RpmBDO9fPpwlvV42IZlGw3W5980viibA1HBHa65tgHa7br8kcffa3+OQ9pkfX2G4bdQKaV4oSr6PC1MFIZ8Pdn9+gQM7IJTiXCVmwSMnXuuWdJ6Drru76Oa2+x/EVweaNYxCQGFMGRPm7Bl0a9tzlztIs4VJEqdUuLGZ6V7LbhMqriEvcezCH80m0kuyUM9w44hBDIVrCl4PZQamKkfPGw9rqP9/P445e5qNP3s/OxcM3aW7qG5bqus9TM8S39qFRJH1TeJqBXv3edoXlqj9+u87rn5K4UzHlGsmEo5LjGRjlWrVSRwnLlxEZDAry2i7janVsNs7wwyg0Iv2nHqp+fBYow7rG24F4pPDo2Lg8zDaY3Xsp9k/5a17YbBxwmvHwph73El66CWmxEvr3+mMto+jpuqAGdKwSOQ00ENmRBYLzjYdrasSSqbK6oorSqnErjfj9g72xs3B7VNj2247DXavXW5WlmG5AatVpNVGW22kavzYBo1CKEVQaLwDDMgWTEJO7Am1mXiYvbVOZfdeGjeF5hlB1GDjCmu/fxN55gbJ+jrmxJrzrp+8TD05T++rn4/pWidPvN3CbLewV6/149KjhlGtuvth1YW1dnZdJWSjge56eVwK6JwD9yhI9p5wXrp95prTyk+NY57FNOM9GrqGoUlh77OCGHGTvG/xJ7Wa0ycqCK/lC+dud5QGvcQQbLeHWNfkImxWMWD8Uk8wUGYc13ghTZ65J6mRTCAxYAS9cdMZ6FAnPP8jz5KPZMeRyCe20iYE9RrmrnOutH5nN2N36OYWttnCVFwhTCYBXAkMdsqHNnbQMI9iY4RGPCwuOnsGvbXuPFx/f+KdHtsXY9RAbSNh+ZNXYXsHVleQZtMZn1oNzp52HruF3koF6SmmHaEnltELJ4ieuk7v0jO5YQyW3lOvOXmFtHlGFGXx7TBROES/FN/sIc11pIlrYzCnT7quPbtNF25ptd3KycviTuVJ55KoUvPVmmGFcXCcTPdnVk9dDCkXfogKeYyNuQu5HK2YS2nQjwJSY23EFefkvPO+jjYusdeow+7uxLZhWWx5IPaa9M/l6Y5u8+m+mI473oIbt5Azp9DlumvxttOG02uocT9uqVSIul3srXX3POd5mxNrIILd2s4MjbbbWRNioL8aCYS7ZKmBnjuFtLrYlQZac63sOLuGabaRdo/uXWtc/ro6GsOZTyY0nt6h9Zyz7F64yOknrmMuXkCrFXonG5h2D+lZoq0W0S0LsWHzy0+y8qVdoisboEp87gxJunpJKzaXfRf6bg977YYz7OnqKjchj4wlQ9YpKa34lKUl5+nvNtGb6/39fQJ7VOhqHLLK2l7P5VIoDoEN5EJmmTRC9ko63nRFmV9JHTOU8rklijEugZXR1gSb6npPeUyg8Mc5iy55qCUjSw1Hr/MeunQTos0d6CXOEBvfVq/dzjxWs7oCS0uws+Mol0nitFuSBLO26ihvG5sudLG6gn3mapY7MCc9fTJJoJdg7z3vFBUbNf7o70UsNTrsfO4EGivRziqVbVADZz+eoJGw/mURG88+wdmPd1i60qV9zxrSU6pXtog2m6T879a9J6hdc/d29Q83MRu70O3CUgO9ftNNQN2eC4vVa+69ZstVbHa6QzIHefnY9D5mieuh5GigOd/zHrn2Rc0GmoLP4j3nEpfiJ/d8EnUgCT1rHL2owjR8L67NdrzbCaVBL5HiITOWUjqEmeKcOc9oZEOKoEgkfX+kB2iMM2aAvXodbtx0Rj7lrCfWhR6iyCkePn3ZhRFWV0jOrqJyEulZTKeHjQ3S7iK3tmB1GanX0GoFbVSQpfuQ3bZbCSwvoY0q0nUGXTo9tp6zStyyLC9tsbNb41u/8Te5WN0A4P/62IupfrrBxnMi7n33F1h+6izSTeicbmA66QQmaGxoX1zFRoJWhOqtDlqJaJ+tY2NB7AqNp3cwX3yGzYe+HBSqWwn13/i0k0hoNp2GzIBevIsjZ2Gk3D12D1P54sH3ZbmB1L065s4O5sJZdHPbr8T20aIvN5nnJ3IxnsoZmb7Y25RFVJn+OQxPNuHKMrH9VdcxQtngosTBIBB/Mo062usFdLuctx7wmx19TxiQW42DAhARdGPLhRV8rFSbLaTddomwTtcZJl/ABECnC70e0UYTFUGrfsnfsyRrDfTUErYSoZHTXhF18e/IGPTkMrZeIWlE9JYibCTUbnWprfewNcOFf1qltxrz4Ytfzz968zt4unuab/2K3+E/fe7PknShd985xFo0NpiepXrVUQd3nnuKXmON3nJE3LJ0ViOe+ZolVr8EzfMgvoD1XHeJeO1Z1NZ71H7rD5wn7sWxTKNB0nEa5K5i0sW/Zdk3mthpBvz2fjgim1jDiVStmwh2my4PUK0iDQbCN3v9HmT/Rxhpta7YylRi7CTJ4v5O/XBKBNrrYuq1QuXGVKv9g83jw2zJoFo2uCjhMKt3PoBxJdzemJvVVadauLyEtNpYL686RG0MfvSaJJjlJUwcOzpiwHPXhKwoJu/Fa+KW8hJFvgy84/TTE4ucOuk8d3Dl/L5a0zZqxNe3YWcX1lbonl0mvrGLbO3AypKbCBpVok4PW12mstXj0ovq2GpE+64eX/bcy3z29++BquXsR+FNb/5e6rcSSJT7mi1MJ0GsJdpoYpdrVG420WqMrceIwuYDFXbugd6KsPbcW/Q+c4rmOUEFNIbuKohVol//OLERWFrCrNUyMTBV61rfpSsbI26VUq0i3S4sg4lMMUsmjS+rdVW8K8voxmZf+rjTdQnc9P6OMrShamPBOSDUMC/4zvjJXJOEZGd3eP8xkg7p/rKyDM1+jgNRl5gWyUJuj7Z+tvg4xwFHy56XBv0wsC9jDhO9J4icXka77Tyn3WZxU+ngWGmi1PqiHvdWnls+2ltMGTeJT+RlWh1e7tcuVdHYgAXT8bFca10Yp9NFLGijAmYZub7uJoQogrsvULnZpHeiTnUTbAXkjzcxopz/shucauzywq99mvf8+tdx/6OwfV+FqBsTtZXapqXz7GUa17tEOz20Yog221z9UzHVTXjW+3dJahG1z25xcbnnQjw9b1S3tr0sry+p397JYtwhG8k19WiAqpNIsOoMfSVGGg1swIIwK8tIo+7yCJ0OUq07o9dqDzCNCkNe4zxsd8Pdv3SCgaxxiFh1dQhpgVZYfDZldW8+iZo2rdCdpmtc3um6vq7VCnrZ60dFER/Y+KnJx7+NUYZcSjiEP5L9aG8UeOuZtGynQ/L0M9n7k6RVwx6TU7eoK9JlSY3LUsMZZd9JSBLF3PICY+sbroGzD2HET113zTjiCCoV17TBWri5DmuryEqN2oaSfNsN/tLFP6JtY1iDX3v3n2btFW3+xJ/8Ajd+7QGWr/SobCV012I6q4blp1pIojz1l5Z51i/fQiPhWf/qUy5EtL1DlOYFdsDeuIlUq9id3eyabbPlL9MxkAYaZvesE/5Kxb887NbW0L1xN0ihWnGqmeubzsimoatAu2VWvvlAsw6vcy5rq644KTLopSue6STFIZzwu1ZgvIfO5bdTq0hKQ40MbGzBidVjp3k+EkomlndUsHCDLiIR8DjwtKp+k4i8EPgJYAXXI+87VXWzYL+TwDuAr8Tduteq6m8uerwHAZOqzMWx01VJBY5yvN1C9kn4w0u1TaJoJENmZmbEOMbCqO1zY848/GYL2h1kZRkDzqC1O66ytNly70fGacisLJGs1NFqRNzpoidX4Mln4L67SFbrYBXTVewvnOHX7RnUQP2WZa1m+f1fej5xC+o15ZE3/f/46M0H+Nyn7ufLfr7D1R9os3FlldXPQOfMErVPfgmWllwlbLOVUSFJEleUlBrzoBgn1GE3J0/461bsZvC5ZbdjWJEya9W2s+tCTAFSBku+peBEemLATsm6WamrZSCK0M0t2NmdrvFFwWtFBV4SRZjlJRdGiyPodLDPXMVubrukaqPB+//wn48e83HE0bLnB+Khfz/wKWDNP38H8AZV/TUReS3wA8CPFuz3NuADqvqtIlIFlg5grAtF2gzaNOqukXFQMg8M09tSFHHFU+562OYsQCZ/65sHHxhSdoNaR10U43RURNzzOHbL9HNnnBAYOOOw28REAm2DLtUgUXjW3Whs0NjQOVnB9KC2YTEJNE8btu+JSCpQvwVJFUxPefe//0b++d/8Sb7mubd45uVCVw2v+dh307p2kvXn17hw6QT2S087lcpn3QvtjjNM2zuuWMnEmXbKgFFLhby2dxznPElcOKaZ9JPKvklIpr0yQkp2IIa+tORlGWxfmSkZ0Sik8Ha7UJf6lQRq0a3u+DzLKKSsl3SCMAa7szt8L+pVt+pK3DWnnv/7r/3E9Oc6JrijQi4ici/wl4EfA/6uf/kFwEf848eAR8kZdBFZA74B1yAVVe0A89E3PQQ8ZF6Z/YilEiOnTzoe99XrLoYacI1D2leKkQY5x13PjES1OiDHmumdz4LcMjw858A24evpMjyUIkgcP106HR+f9zTK9Q1nxOo1p0q4vQNXrmPWVrAbm07DxFrEGCqNOhWRrIpST67Q/NozxE0lagtJDTSCrXsN/8/X/QzPq9ykQsRdkeUNTz3Ea573W/zaqefz8Y8/i927znPys+c4+Xs34epN7MZW/5rS1m5FZfJiMl68vXAKrcZET10n8vFzKhXPv++AtSS3Nkg99OGOP/3VmPR6mTRw2C1qYrgrLBwrSnaPqCAeiTDUIooYk3WLCqUbSBL0qcsQKFhqr8ejOz89/bmOEe40lstbgb8PrAavfQL4ZpxQ+yuB+wr2ew5wDXinD9E8AXy/qu7kNxSR1wGvA7j//vvnOfZ9I5/87HOW1XGrz5/1Hp1Fr9/wnl2BAQ/DGvnY+hDHGAg7/UyKf48ory9s+DAKYfl4ECpQ60MJnW6WLE2vLDOavR7WOmaE9rpOxMq6Rg/OuFpsy3HMswrNTodzH9p1RUzAzp+44JQVVfkXP/wd/NN/9m/4jn/wN/kLf/ujdGzEf7/5XC5tr3Hm2be4eWKFnc06p3bbTmvFCFgnHyxLDZcMLbp2ta64Z3ML0+thL55zRs0Y103qhG8pZwza7mST61jDrBbbamOqlX6ycka9eX/TpzvfhGOFTJhs9ZgePxUQC9QtAT7Yuf0lcPeMO0ltUUS+Cbiqqk+IyIuDt14L/LiIvAnXjaPI846Brwa+T1U/KiJvA95IQWjGN1p9O8CDDz54xG6vR1iCb5Xk0hXMzZrTEomMM2KehVLY2MC9URxTDxF4yaHHlhmocEgZGyJgRYTiWREBd3362OtQOXkwlkzHfIBF4/tapi3tPA/bxYLNIDtHrUtCrS1Dt4u9fjOLaUcdS1IRUPjhN7yOmloev34flSjh6vYKGzdXiK9UOPVHwvlf+FT2O5Q4dgnEVjvz0KniWSHD12tbbbftzXXXBMSrJNor1wZXSH6CGrzpwyE1MYLd3B4MaeyhgGjPfPVwTOlzzTXk9oqTcLyUEvcLV1h0tEzOIj30FwHfLCIvB+rAmoj8jKq+GngYQESejwvJ5PEU8JSqftQ//wWcQb89kS2j+4JZttlEOh0XU853miky3PmY6CjveeBcvuFCNRoM56Q/Yt8+jEqMpJNOWgyjmvGiJ2LKkExYdDLQdSivsmjViXwlyYADZFZXnWBVJYZuNxMCEwvVWx2ny9LuOiqkKp//7/fQfrabKJ7zLqWyvoF55ubgJFGJnYE+dYLuxRNEOx3MjU2nzyL97k/ZGAMvuHf95sC4NfGFXEmCGB9ii9NeokFrv8jpz0i95nMdXtMGt6qZemU04p7nE6xDyFWuhuMyqytor+cSnTiqJapOP6jEMPZQvLtILKweV1V/SFXvVdUHgFcB/01VXy0i5wHEWZAfwTFe8vs+AzwpIi/wL70E+P1FjXVReMy+l8fse/sv6KBORtrAYGzbsLxxD1+bQvAoNT7GGw+pVjGNuvsBB80ptNNxHYKiCO10sSE3elrkr6EgH1A4bs+LVusacGTeZrXi+ewVolR90BhY33AhgSTBnj1JvNUlvraFuXQduXbL/W01uf8DO1z81Qr3vi+mcyL28gFeDTCNyXe6roF0JSapRSTLTqvFLC/5JGkQP07vZy6/kf6h1vd3VWRt1RUM5YS61KtqaqeL7jQz2lumR75fpBzzSbUK+Umg0XDGO4qyyT1DZNCdJtpq89KV1+x/jMcIojrV30HhMHjo3y4ir/ePfxF4J4CI3A28Q1Vf7t/7PuBnPcPlc8B3H/hI54QBox5gIMY+wjhnZeUwKNA0xX6Ak3g9fxatREjPIjduoe1O39PsdMnobkXHmUbdb2Kh0+Dx0vFlKpBFoRvf7IEocnowqtg0Du/vQXTmFGogavsiJWvR0ycRVSclUDGc+OQtUKV7doVkpU58swLNVtY4Qq3FRBFYS/13Po+qhZVlJ2mQNncOCn5cGIohg5giOuVpjcYglYq7zl6Y6AzCTz1Lsr7eP0xKP91DyGVm5L432nQGO9WUT8dgt7b6jKnE7j20cxxxJ8XQQ6jqh4EP+8dvw1ES89tcAl4ePP8Y8OBBjO+oIi1icRQyQeJecfMLGIrPSuqFN+qOXpYKJDXq6MbWwL6pxnZGgfQl/ES+4tRM2cqswPMeei/gzw8h5H+r9dRAR5HLGCBx7Pt2NrB3nUUSxRqnmKgXztA91cBW3Ll6S4Zot4K5tU31yZtoo4qeOQFnT8Lla6CKObHmujZ96enMW9Zmyz0u6DU6aXJzErgN1xxjTFK6UH8+neDyCoj5e7kX5MNi+TCgimO3RBWyDzvj4idZgjvTVC8BlFouJTweir6t0LsOk1MSRa7RwYlV7FIVc30DNrYwtarzsiOTVWGS6pundLK01Ht7J0vUpTFxzXmcrrhmCalVHVUtVUxMl4riVbMmxGQz5b6MgpmTZM3FpMP903EM9OpcbvS95Gavv6KoxI4e+JnPoyIYVVdNurNLpXsSu1zD1iNEI2w1wmxsOerklosLs9SARt11bfKt9WRjM6Mwamdwsilsp1aQ71CrWeXoSLZKWHhVFOfWoBo1jhHPSR8bE899DgPnHJWPyTGTst3S2oF0LDku/kgJiTsVd1BStEQBHq68ajAJKK4PY7isB2fQzMk1l5Ts9pCmwJIzcMkpnwxs9TCtjjPqqmBvOW9btc8aST2xoDx90Ovz3nKzhbbbTmuk14N2G6lWiU6sOQ32sGAoj5RTHTvq3qjwTbod+IbINl0hOCMhyw2MGGcUez1ILMm1G4E36ZQNs+NbBXUTlF3fdEleazFPtvrJoWoFvecCWonQSkRvqYKoEjV7ROu7JCeXMM0ushP7Rhq+2US+SUX+OgqQNZIAHPe/eBUyabWTJYqTxMkTRzVn2NOcxzhu+uCARp8kNxGB6yaVOefVKo/u/DQPV15Fyp0fKjC606GULejuVLy08V0ubFKruSSJT/6liUqp1xzTIbGZcp/d2sacPU1yaoVkuUL1mU3YbRJ5bWmNnK63rtRdwm/pLqdo+KXLfU+cAqOUS8w6z8uFF8QnQsWIi6l63rEzVAkhzXEgbKA2kIsdPo87eGBgrHWeNo7+lXo6mRGMY9c8IunHnFOWSV/VsT1wLu10XFLX00MBR4P8/FNZV6DaxfMkJxqY3Q567QayXKN9fpmaKrK5FYQ9NPNQJ4VZspZ0Xis+YwqNC0FNgp9AtdkapBTOipDuOoqBBP3vIWSrvEfOvq6kKU5C6aHfWUgNOZGj0rmYcOIZFh1XRn7fXdhqjHnSOrqYSEYj09UltGIwncR5k7UKctk1L3ZxbgPnT7sk3IZr9hxKto40RuGPvKA4KexFme3i9ULAJy3z9LhpCmE8T942Wxiz5Mrnod9gOIpcC7ZWe3T5exCP73usvh9mWviSn7R8VapWIjf5gTP6iVJ/ch195trgymJEsdT48bhCo2xCCCpC94SBugIYW/0Zfp7p9efPnb6eN+ZZuMu6RG6t6kXVEh459b13jtjWXjAney4iPwWktTtf6V87DbwbeACne/XXVPXWuOOUBn2BeOnyXwcjmLXVrA+m1mLssvPkpJ04A6nq+NNrq64lmwhaiSESpNUl/qNLLumXxi/PnUHSkEqzhTQ7zphYHWAiTBUuGGVsAmMikTPmsuYLfns97Ppm1plnVlmBlIoITrBK0rJ5qyQbm4MhlnBCSZsYJwmm6il2vZ6jWAZ89rSRdbafGDC487S6yKVrbvtOB9PuYutVzMXz8NTlAapmFv7KF/0Ued5i0HbbtaoDt7LRCXzwaVBklNPXizCJ0jrqdSNIqi+Uhuf8SuiRs68D4APX3z7j4I8/xM4t5vLvgH8JhBoKbwQ+pKpvEZE3+uc/OO4gpUFfAB459b3I6gr6J5+HGnHl69UIW1vBRkLnRIxGIKk3bKC6mbD0+XWX9DMGabYdU8KHZ8AZKanE8MxVZ1zrNScsdekZshZwMJx0TJGvPIUgnEJ2jvw2muDi6M0WZnkp855n4k3nmRvtdhYyCUMned2T0KiqlwgQUezubiYMNXiJSqqYlB2r10XiCnZr28nWpjH/SoxevuoLeiw64sdZ1B8UyJXbO6aI03Ch//qc2CmmWh2gFKrVgRXMQE4mfZx+rj4WP9BsI/iOpFILg6f1fVyTxH0nofTW81DmVlikqh8RkQdyL78CeLF//C4cU7A06AeFP/8t/5zazQ7meffTvtAg3nG9M21FMD2ledZ1y7GREHUUjSBqq2trBq7BwvZu31iRcpOD5gedDirGJf8aDcebpp9YHGnMYXDJHXh+abJrrAiYD8GkBT1Ta6UH5xnUeUkTca4v5ZDMb54nnYp++XCVK87pDLyfniN8nj7OcgSZ4ctJ3w5VtRYb44yTjSvWUp+07u+zGBaI+4zi4ok0nZSjCJMWMyXWhU4qFeh0smYdtEd/P4YSnp5CaU6fzKpvS/QhzFQ0dFZEHg+ev93LlozDBVW9DKCql9OizHEoDfoe8dCf+zHaZ6okFcky3bZmsJUIW4mo3uoQbbXonlmieq2FWd+i/qUadqUOPYu5ehOWl2Bnl96zLhB/8YrjQ6fMD1/koV4eQILiHzGuvZfd3cWcPtXnjG9s+krIMSGXEUvyIQM9YmmeJg1NNeWSR8PGOHe+TNgp530DmWZI/vWi1zJPOMcI6p9K+hrxIySIw2sbZKEM6s2MDE2lFMulJeTkCaTZxG5ueYmEGVQtCzRUhhgs4b2bwHBJE8HaanuKatetSMCNLY5d2G5AcMv2pQhIJyvP6U/bBYJLYPsQWYkcpjfo11V14XU1pUGfES/9mn+AdBOkGhPvJtjVmO6yAXEUpqidYGOhtxzTXVmhdqPlGhWvLsGTz2QsAlUXdiCxxJ+75DyqM6eQ7V1XDBTH6LpbvqtXT0zpjVJ3ol5Sr6NrK0i749q4RZHrR+mRhVKCmHQekxJ+mWEMQzTVikueJYmjGBbvmO075AnntERSOmLGW88dJ0ReeGwg1ODbxWXPo2hA4Cvtbj+gN54zoBPvBfSN4/UbWSeglNqXT8iOg6n0f36jKjCnDdukKxhtt/vX26i7783SkhOAC5uoaNoU3DqBspVl9PRakNgVR5tc33bGfHtI6LQELHrVckVELnrv/CJwddIOpUGfEo+88EfpnF9GT9ep3Gpj6xHxVodop0fDCLZqqFzfQbZb6LIrWU+WfNik03MsirTaMV+YEhn0vovYpQqSnCSpx3ROVqhuniHe6mCevOIaL7faLoa8upL9yKTZzhg02ulmzJOsn+TqCvQS14S40ylsvBA2ych3bR82oNadJxOZMv33AyOeGdN2u8+G6R+0/zAhM/qzcJwzzncUYU6ddLK16xve2/S6876Nne1tQlxzSVFAeyMmoVGrk9x1mXNnXMXt5av9z5TpjW8YIpFqFTl7Gr1xy/Uy3d3tHyccz6QkKOFn5btY7e5iTp7IGn2H26RKmuKbSHNyDa1Vsr6vu/cuUdlJqNSrRDe3Jl/TnYg5xtBH4JeB1wBv8f9/adIOpUGfAg9/3T+EpSrVS1vY5RrRxi7RFzcGquaiyJA8/34iEccDt5bIU7+sj2n2OdXBt8AbMXP1Jr3nXYSKIdrtUQWaF2rUIyGWu5yMmgW6CfqZz7uwgxHXxzEbhIFeGv9xjBe6PVeQ5MW2ChEkCY2nrw0bAbLHYmzWvs2sLLuq1FR7RdV5sJBpmw+cJ4+CApdpILHrzSmnT7o4sbXI8pJbAXkPk0oMnS7m7Bk3rijCXrtBX054RHxkVOioEmNWV9yk0eog6X3t7S0Bqt2eC5e12q4NXq+H8aG1MFYuRjCNFdTarMdpdq5cmKofUvFFWM2WKxQbusTBnIRcvQGdDqZew959nsYzLaRnIZI7r63cDJgXy0VE/gMuAXpWRJ4C3owz5O8Rke8BvoTrHzEWpUGfArYaIYkizTbR1RvuxdjHG5PEeYetdhY6yQps8lrehE/7lDyJIpL7zmE6FluLsI2YVBa2fbJCdznCVgyV7YTGp5+BtVVXhFSvOcU+6PfmjGP0xk2013Peo7XorfXiBgoBpBI741ytui9pp5st08OmxZmnmhb5VGJYXnIt5nw4w968NTAhFF3/fpB6l3RwBvXGLXd9Rtw1+ASevXbDhbIadWcwjWBOrGLXNwZiyYXjy3vF6njyyc11p4We57lPHvRw+Eptxh4Kw2MDlEsvp6DW9pUi8yi4t6nI10BfVILkt090S4SLvbfb7hytNubWJuapJvaBizz6m2+afG13LHRuIRdV/fYRb71kluOUBn0K/NeP/J889KJ/7LjFcQS9hPdf/r8Htnnpymv6SajUWPhQCEVhjtQwxjGcPIGtRmw+u0FSFeK2svJkm7VPXIftXddy7f4TdNYi6mvLSGKd8TaCrCxjr99057EW4sh1QlrfxO7uDnt0IyBR5BpugPufJO74UcX3vOwNJBrNqqdN+iQanY4rCNradoYnP3nkGTa51/IqjOG+A5NIsL9UK/2EpL+X2mpjmxuD17vrEnxy8bxbrTRbLn8RxuwLim2gry0TMmUmTk4FyVjHva9mCex0VeOSwgUyAUEIbGhlNc3kmEoMpBTOAjpq+rrEBjmxhrTaLuRz7QZmaYlHf/vNk89zJ0M5csyf0qBPicf++4+Mff/R7Xfx0vp3Dnh8eZGn1HgNFMyows11Ks0WZz6vdJ53F9F2F9PqoLHB3nuW9tk6SVVY+4MNF1Jp1NFajLS60EsyI2jXNzDtjtP3brUxYTIsM4gaGIv+l9G22kTNFnr+DNLtuRVCHGcTwlCBT+yMkt30IZ9UGRFc4+N2GxnVLNkdcPB/eosKvN3Ue5UlX1marj58SCvtWpQ15WgOHxdwlNAbN7MJdigclKdzTkPNLLiGAepkOIF5TzjrjBSwWEaeY78rmyLaaNHqo9t1xrzTQeLKHdnweU9YbAx9ZpQGfd6YJUGmrk8l1QrS6dD7srvZfFYN06tl3cQlUarblsp2Qvd0g8q1bdfgYWfXMWU8xMetOXUC2dh0xUy7uy4kYqTvGaZFPElC2LJcjLhxbO24Sabnmm+YRt2FT0zcVwBcXnLD39l1HnrPKSGmfUDTsUi16mL4KeUuR/3Lx+bDsQwlSKMIOXPKMYQuPUNaYWuWl6BaBVWSK9f6+/vVRtpARHd2YWe3f9+LYvuh51q0upiAbCXhWUB2py9qplYhZdYkCeqPOZU0w7QYtU8u4Z2uPsLuSUC2sny0+e+nP+cdjjupBd0dg0dOvDZbek1M7KUeUxRhzp91iUsj9O4+Q/NCndqmpb1mSGruR7b8TEJls0u02cbc2HDnWWo4Slmj4TxlgN0mLLt2YayuIM1WsKTHJ1F7jqGRVmq22lCJXTeaJMHeuIk0Gk7Pe9cZP+vFoaSx7DxvI45W2Ww5Wpxv8pwZjQTY3XWStCvL6JXrQEBPDD39AspkSEM09VomuIUIbG55DroPYzWbTu53cztjyUij0b/VYcel0HhOKoYiMM456uXQRB0yYDyt1KwsQ6WCAV9G71cqSXCOcQVIBRNe0dj6FzoivJXb11RiNyFXXELZ7uz2q3SXl9znuWt5uPod2TEfS949epwlypDLsUQUDSr/wVhGR0a5a7awF8/RPd1wLBZvyyo7SmdNMF3YPR/TPhHRuFnBPmsF01WWPnkZWVtzxrgWI7tt8OwLrbmE6gA3OoVV91evOVbIUsNVp3oD7RKNHR/O8FWC9Ri11nm8ccz2n7yArRiijqV+pUn0R13XkzMwImrVhW12mpgTTpvG3lrPmBeZjrtal5RL9wkNlVo36Z1Yg6UGemIFVUWaHYwRV8bedfRJ9fdUuz2X0FXNKJrpscd+LsFnEz43nvapO7suf0LkQ1YyZEDFl+Ubv4qw5086xkqn46UFgolgQvHWwJjz4ZzIUTLN0pJrML617a7bTDbqAyqU1notfTd2u73j9HpqNaerz2CYrUQBVPtyG0cEpUGfB3KNI0iLZ4qWvrkfqNlpUbu+TnLvWXbuqrJ0pUt31Vm56rYStZS1T91Eepbu+VUnE9BqO156p4O5/x60UXUUs+1dp5veqMOFc+jTlxEiFwJYXeknMUXoPes8veUK1Ru7mKeuoru2z11vNNx27TbJc+6GROHmFlQrrPzW592XuObi5drpZJ6p7XgOtJG+9kuj7lhAafEQYE6fQleXkW7P6dCoIiRZTDy7Xd2uWy2cPUFvtYpGgvSW4N6TmHaCdC1mqwmXrvhenrbP4x6F/Ocyxqii6qiP993lOrzvtrHPXHXj9eO0aVtA3/HHXr/h+nO2wtBWmvSNsolL4srIRGXROLP9Uj2XUL0z3WVcRWmQJNVmwKIJ5IbDFY3xx31p47vKEMw4lB768cMHNn7KJUQLMBBmCPnCSeLib1vbznB1E9Y+32TnnjqtU4Z4F5audJ0BF4Gb68SXrjhKXtVzsM+ccoe9etN14en1XIFIJSJZqSNnVoiubGBPr9A6t4QaiHcTomYXFCrrbYwvGjF3nYdaDa1XUFXsUpXmxSVnJIwQn6r57VYx7YRop+NWBimzx4gzAn6pLlHkpAt6vcGKVTHYGzddl6FuN/PU1SrYPi1PvPofSYLGxhnwxCI9S7JU6cd9PctmUqf7QW8zyj6bUduKEWR5CV1bJlmuYbqJu89hX80hg+s1b5pNFyNPVxyRgR7esNsshBOydtKxZ9+XnBdvqlGWC9FO13nRxiBnT7sJdXvX00U7U7NghhLT4mmMs1Iy72SUBv144tHWzwK+tVyuYGXgR5r+eJKE5NaGez2OMU9ewT73HhpX2jSuCdF2l+jzl9z+atHE9hkSVW/Qdnbh1Ak4ecJ9seo1136tFhM/fcN5tnedRHqWymaH+NoWnXtOsnPvEkuXW3RP1th+4B5EXfLVxkJ3WZyYWFdZutJj6XefxN59FvPUVVfAE8fY0yvYRgWqMaYWY1q+0fSNW06SoNP1cgW4661GmLUV6HRdEljE67Z3svuTYqCU/+wZUEu00SRZq2N22kgnwXzxGVcpu7LsNj19Em6uDykGZseMon58PS3VB8eWCSmBaUjMT0B2cws2t4iqVZcPaLackU7CXQZbtDlj6FUYu37F1kuvMeWE02+xd+Y0qGKfvpwlqtOxyqmTaKMGkcCTz7iagrSsv1qFWhW9ej1TUUwnolFVuUXPU4Odxtfp9SAKvH6i0ksfBaUfwjoiWLhBF5EIeBx4WlW/SUReCPwEsIITbf9OVd0s2O8LwBbuV9A7CGGbeSBNIj0UfdvIGORAoi7pL3ujT30Ref59SDtBnrzsKht3djOvUKoVxBgXMz13huT0smtDt77lCooqEeaZm3DhFL17z7L5nAaNq13i3R67F+vUlivEO13WPnaT3vkTxDtd6k+u0zuzwvrzG9hYOPnZFre+vI5FaJ+KqD5wAdPqohfOOAmDSOicbtBdieg1hPqNGlHbYjoJ0VIN/ewX/YTjjVtknKd79qSjGDY7sLHlNGAClUUJvFkxLmlMzZfqx6afF1CF0ycde2Zzy5X2+3vUD2v0BieGtDPSuTNOZ/7yVTc5JokLfXj2SRZzT41jWpq/1IBuF7nrPFy74cSvut2seGqgAYX/P16+2OURJIrcpLy8hDlzGvXiamZt1U1WImijgq1XkOfd7ya0Jy+h3R7J+vpwWCbBxcEhGFtBiKnIg4+i/vcsigYNVRlLH4HZq4MXjYPw0L8f+BSw5p+/A3iDqv6aiLwW+AHgR0fs+42qev0Axjh3pIb94cqr+nHOIM4+8IP34luIQT7xR87bBdRrnPeFpHxxi7WItURfeCZjtWAMdrmGMSdp3bXMzl0xaoTNZ1cxHWcY2yeqrH4JkJNoJPSWYjqna6jgBMUacOMr66BQbapLyl6sI1on6iimbbEVQWNBrLL6+RbRTtvx4bd3nAcOWaNhZ/h66M4u0uu5MnS8sfFt9KRa6TeFaNShEpOcaKDGEK/vul6gsUE6PZKVOqYau/ZxcQ2Wa0jPOr77TjMLZZhKPGi4/L21T1/OlBohNeDDxjf97F5a/85+orVRR1NGkZdyyCOdRPbStu1ld/3vTnStY9CzJ0lOLCGJJanH2KrBVA3Rxi7Uaq6hh1XHlAmZL/jVhfE8+k6XIXmDIJRjUh0XyJQ8M/qpj/OXnvkYKHdWUlRE7gX+MvBjwN/1L78A+Ih//BjwKKMN+m2PD3Z/3nV86XRdp3mffEqb/vaNuzMupGHkMFmXGifrk2DViit3b7edwfGiVNHNJajXMBeWidvQqyvSATVOjx1Rtu6rAlWWr/TYvhijMag/VdRxNElJlF7dUNvoUf/cDVjf7OuB3HcXWnE0QvP0Nde5KJigxEgmx2q9YUgNoDTqaNvHeHu27xW2287Qnl7DNmpOZqHXw9Yqjqq5tQ3WEq8sw4lVWvedwMbumkzXUm21vSa5Ojpes8Vwu7bhMFiIInpeGkY7CLz/mX819NpL//SbqX7+MnrmFETiJB1SNlKKXKLdNluut+kUhkb9CkWqFRf2yTUSH6n9U6KPOyyG/lbg7wOrwWufAL4Zpxz2SuC+Efsq8EERUeDfjBKDF5HXAa8DuP/+++cz6jkjbN31yOm/MfhmmijLo6ByMfWWXnbub6I9ZwQHwjrVCjTqxNtdqnVDdRPinYTOyZhew6A9SGouVr59T4z0yCrdkhpoBNsXI6pbytrnm671Xc8pAKZiYHLpKnj9GJvnZafeok/cZRWT3Z6jVDa7fUMjpt+JyXPapd3DWJBegsYRsrnteNypAFKni0ZC7fIWrXvWnOfaTkjOnSTq9tDNrYztMq3Q11HmWT/6xD/gkVPfi/7RFwBvZEdoz6d5Gk0SkrSAahzScEyv2w855XIZs4il3bG4Uwy6iKQNT58QkRcHb70W+HEReRNOHnKUG/AiVb3ku3Q8JiKfVtWP5Dfyhv7tAA8++ODRursF+MDNfzv2/Ycrr8oef7Dzc4XbzFKW/Zf+wj+hdqNDXaF1toqNDb01yJrbCsRN96cCpz7bJt7oOAbOyrILpYgAXgWwuz14giK6XSV2qn3bO35VURlMWEpf3jcV1aLTRW/eQk6dxJ5Y8j+UFSf6tbIEWzsukfr5pzCNBnWge3YZ07Gu+cfmFllzkGNkiD5w6x1ZPmZcRylgsHBpSrjm0J6qWJGS1TIT9M4x6MCLgG8WkZcDdWBNRH5GVV8NPAwgIs/HhWSGoKqX/P+rIvI+4Gvph2qOLfYSfx2H//prPwzAw1//j4h3LVHTsnQFqptdOmsV2qciWqcMtQ1l6UqX2tOb0O2hN26RPPc+R/Db2u4b7pz3O9TLMjKO8x7HmOVlp1HuQyEDvUO7PaAFz7kPuglcuebCUiKoMZhWh97ZZTizRLTVwegSEpksoWqu3CD28XQSiw067Exj/GYxekcBM01S+XqHIkmA7POKMLXYqy1qlocoMQWU/urxiGBhBl1Vfwj4IQDvob9BVV8tIue9kTbAj+AYLwMQkWXAqOqWf/ww8A8XNdY7AR/8zcE0xUN/7seobLsippN/2KHxyUvuy9npuj6lqkSXrrsqyWSEEQfX9QYcBU8MnD6JXa2jxhBtNeGe80i75xKSYaLSa8dgLb0zS1S2luBkhd65VbprVSSpIarOA+/00Gs3kErFlfqrYnd2nTEHUl2XiRSywKAd5VBLHkXMqZGce2+oU/GvgR6kgZFPk56pxo/Uauju7sD7JabAHeShj8K3i8jr/eNfBN4JICJ3A+9Q1ZcDF4D3ieMLx8DPqeoHDmGsxxaP/bqTBF77gw2SlRr27EnM5Wv9xJiqM55hYw7fDWiglRnemFcqWUNiaUcuVNJL0JSJ4ql+JD4ZJ45NYXqW+MYu3WefR43QXYuxcb9Pa9RMsHevEq/WiDaamDhGU4VHVbQSIUnIWgHE8FhSHK66nfFY8u6BkNxIpJXKkUGimpukw88wDdH4+HlK3cwaYI/rEVsiwNEr/Rc9YjPMfvDggw/q448/PnnDEhkeOfFa1yjDa6MM6beH1Yt55DXKY9/RZ2U5042n2XLSrNs7fdVFyDjemiSOO336JPR6JOdP0brYcMtZgcbTO5CoY7vEsWvW0em4uL7/MZmTay55u7Xlh6wj8w/HCQ+ZEQ1sRoWT8qGYnLRuum907gyI8P6nfnyOoz1aEJEn9lvbciI+p19/8q9Mte2jN/7tvs83DW6vQGKJ+UPVaaanZfgT4qd5oauU5SJZc4wqdq3hhZ/EqyK2ho/jG0xrt4f6pgrsNF0D7p4iidJ4ahtzfQPT7qKn11zVpK/yFNOPC9ut7b48r5g7wpgDPGbfW/yG2kHvOv88fS2HjMIo0lfxLDEeqeDdpL8DQln6f4cjE2ZKcu3NCppgQL8sfvBF42iKjXpW0dm9a414vYnsmCHRrfS4AwnVes0rRgoYoXatibm24aonb65nx0hb6WWywOCSqUds6XtQGGXUHzKvLA6ZjBCMy/TaE+MapZw7s4DRHkMcsQhH6aHf4dBULRD6nlzABBEjmEqMaTQwjYZv3hC7pFq10t8PvA5IhHQT4hu7yM1NdGt7rGaLGGfAtZPK8ArStSSNCiw3nFJjp4Pd2XUefS4clDdQZWWjw2P2vaM9+ACpzo2pVt1nXK+5yl1VXvbA3zmAkd7GSNs+TvN3QCg99BIORUtwIyAV17ChUceeO4HZbEK74+iI4JUfq85zP7lG87lnqV/edgJdm1tDTSb6p+sXwjjtER8Xt5bGF9fRazewrfawBxRMNvOmeB5HpEZ9ZLwdvEyDb+3X7SGdjstrBNK8JUbgiHnopUEvARR4z2myrBK7UEi7jXQTl+A8c8KVl+82EVXsXachUVSE3pKhd6JO5coNVx2aM+YjE6w+1m4ueemeTtd5/CljI9BcuZ0oh0cFj9n3Dhj1gedqnbRDpjNkEGYrYLszoUeOs18a9DscaQLRyf4OQ7s92Nx0HPOnrjhdmJ1mpu0ip06ilYj2+TrdlYilyy2kk6C7zcIydTFS6FmnevLa9uGZQPmwpM/NB/kQzDQhmRJjcCfK55a4PZCpQ1a/AwhobGrR7R1sGiJJRbgqcb8DUatH1PJ0xF5CtL7jGlingl4BVW4UAyUVwnrk9N/IpG0LW8iVKHGUcMScjdKglwD6HnpYiZgPjQwIcSW+ZdzODhIZzEqNeLPl2tl5YzygwMh06oWp1k3msZfGvMQRhXL0vp+lQS8xgKIY9zhD/LIL/xuAq9a09HW1fTI0FX+alRueP+eokFCJEocGvTMbXJS4DbDXROP7r/zr7PFL/9Sb3QPVvixwQWPsgxxfiRKLxFFLih6r0n8RuQZ8cU6HOwschW5J5TgGUY5jEEdlHHB0xjLNOJ6lquf2cxIR+YA/1zS4rqqP7Od80+BYGfR5QkQePwp9TMtxlOO4HcYBR2csR2Uch4GyUrREiRIljglKg16iRIkSxwSlQR+Nwh6mh4ByHIMoxzGIozIOODpjOSrjOHCUMfQSJUqUOCYoPfQSJUqUOCYoDXqJEiVKHBPc0QZdRL4gIh8XkY+JyFDvOhE5ISL/WUR+V0Q+KSLffYhjOSUi7xOR3xOR/yEiX7mgcZwUkV8QkU+LyKdE5Otz74uI/LiI/KEfy1cf0ji+XER+U0TaIvKGRYxhynF8p78PvycivyEiLzykcbzCj+FjIvK4iPy5wxhHsN3XiEgiIt+6iHFMMxYRebGIbPh78jERedOixnJkoKp37B/wBeDsmPd/GPhn/vE54CZQPaSx/HPgzf7xlwMfWtA43gV8r39cBU7m3n858H5AgK8DPnpI4zgPfA3wY8AbFvgdmTSOPwuc8o9fdoj3Y4V+TuxPAp8+jHH41yPgvwG/CnzrIX42LwZ+ZVHnP4p/d7SHPgUUWBURwf1gbgK98bssDF8BfAhAVT8NPCAiF+Z5AhFZA74B+El/no6qruc2ewXw0+rwW8BJEbl40ONQ1auq+ttAd57n3sM4fkNVb/mnvwXce0jj2FZvxYBl3Hf3wMfh8X3AfwSuznsMexjLHYU73aAr8EEReUJEXlfw/r8E/hhwCfg48P2qC1PjmTSW3wX+KoCIfC3wLOZvPJ4DXAPeKSK/IyLvEJHl3Db3AE8Gz5/yrx30OA4Cs47je3Crl0MZh4j8FRH5NPBfgNcexjhE5B7grwCL7o4x7Wfz9T5k+n4R+eMLHtOh40436C9S1a/GLZVfLyLfkHv/pcDHgLuBrwL+pfcMDmMsbwFOicjHcB7Q7zD/1UIMfDXwr1X1TwE7wBtz2xS0G5q7NzjNOA4CU49DRL4RZ9B/8LDGoarvU9UvB74F+EeHNI63Aj+oqotWrZpmLP8Tp9nyQuD/Av7Tgsd06LijDbqqXvL/rwLvA742t8l3A7/owwt/CHweF78+8LGo6qaqfreqfhXw13Ex/c/PeRhPAU+p6kf981/A/Wjy29wXPL8Xt4I56HEcBKYah4j8SeAdwCtU9cZhjSOFqn4E+DIRmVY4ap7jeBD4eRH5AvCtwL8SkW+Z8zimGov/zWz7x78KVBZwT44U7liDLiLLIrKaPgYeBj6R2+xLwEv8NheAFwCfO4yx+Ix+2rX3e4GPqOrmPMehqs8AT4rIC/xLLwF+P7fZLwN/3bNdvg7YUNXLhzCOhWOacYjI/cAvAt+lqp85xHE81+d68MyjKjDXyWWacajqs1X1AVV9AGdk/3dV/U/zHMe0YxGRu4J78rU4e7eICffI4E7WQ78AvM9/3jHwc6r6ARH5mwCq+hO4Zeu/E5GP40INP6iqi5AHnWYsfwz4aRFJcF/c71nAOMCFc37WTx6fA747N45fxTFd/hDYxa1iDnwcInIX8DiwBlgR+dvAV8x7kps0DuBNwBmcJwrQ08Uo/U0ax/+Cm2i7QBP4tiBJepDjOEhMGsu3Av+biPRw9+RVC7onRwZl6X+JEiVKHBPcsSGXEiVKlDhuKA16iRIlShwTlAa9RIkSJY4JSoNeokSJEscEpUEvUaJEiWOC0qCXKFGixDFBadBLlChR4pigNOgljiW8HvfviUjdV+J+UhakIV+ixFFBWVhU4thCRP4xUAcaON2Pf3rIQypRYqEoDXqJYwtfEv7bQAv4swegAFiixKGiDLmUOM44jWtMsorz1EuUONYoPfQSxxYi8svAzwPPBi6q6t865CGVKLFQ3MlqiyWOMUTkr+OUD39ORCLgN0TkL6rqfzvssZUosSiUHnqJEiVKHBOUMfQSJUqUOCYoDXqJEiVKHBOUBr1EiRIljglKg16iRIkSxwSlQS9RokSJY4LSoJcoUaLEMUFp0EuUKFHimOD/D8x5G2eVRAGfAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "da.where(da.values>da.attrs[\"nodatavals\"][0]).plot() # we can see that the DataArray contains missing values."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We'll look at how weight builders handle missing values. Firstly we'll slice the `DataArray` to reduce overall size for easier visualization.\n",
-    "\n",
-    "This time we'll create `WSP` object using `da2WSP` method inside `raster.py`. Since our DataArray is single banded and all of its dimensions belong to the default list, we only have to ship the DataArray and the type of contiguity we need."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Slicing the dataarray\n",
-    "da_s = da[:, 330:340, 129:139]\n",
-    "w_queen = raster.da2WSP(da_s)  # default contiguity is queen\n",
-    "w_rook = raster.da2WSP(da_s, \"rook\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "After plotting both contiguities and sliced `DataArray`, we can see that the missing values are ignored by the `da2WSP` method and only indices of non missing values are stored in `index` attribute of `WSP` object. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1152x288 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "f,ax = plt.subplots(1,3,figsize=(4*4,4), subplot_kw=dict(aspect='equal'))\n",
-    "da_s.where(da_s.values>da_s.attrs[\"nodatavals\"][0]).plot(ax=ax[0])\n",
-    "ax[0].set_title(\"Sliced raster\")\n",
-    "splot.plot_spatial_weights(w_rook, da=da_s, ax=ax[1])\n",
-    "ax[1].set_title(\"Rook contiguity\")\n",
-    "splot.plot_spatial_weights(w_queen, da=da_s, ax=ax[2])\n",
-    "ax[2].set_title(\"Queen contiguity\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### `higher_order` neighbors\n",
-    "\n",
-    "In some cases `Rook` and `Queen` contiguities don't provide sufficient neighbors when performing spatial analysis on a raster data, this is because `Rook` contiguity provides max 4 neighbors and `Queen` provides max 8.\n",
-    "\n",
-    "Therefore we've added `higher_order` functionality inside the builder method. We can now pass `k` value to the weight builder to obtain upto kth order neighbors. Since this can be computionally expensive we can take advantage of parallel processing using `n_jobs` argument. Now lets take a look at this functionality."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Building a test DataArray \n",
-    "da_s = raster.testDataArray((1,5,10), rand=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Below we can see that builder selected all the neighbors of order less than equal to 2, with `rook` contiguity"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/magito/anaconda3/lib/python3.8/site-packages/scipy/sparse/_index.py:124: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.\n",
-      "  self._set_arrayXarray(i, j, x)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(<Figure size 720x720 with 1 Axes>, <AxesSubplot:>)"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "w_rook2 = raster.da2WSP(da_s, \"rook\", k=2, n_jobs=-1)\n",
-    "splot.plot_spatial_weights(w_rook2, da=da_s)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Few times we require the kth order neighbors for our analysis even if lower order neighbors are absent, hence we can use `include_nas` argument to do the same.\n",
-    "\n",
-    "We can also look in both the examples we used `n_jobs` parameter, and assigned -1 which equats to all the cores present in the computer for multithreading"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(<Figure size 720x720 with 1 Axes>, <AxesSubplot:>)"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "w_rook2 = raster.da2WSP(da_s, \"rook\", k=2, n_jobs=-1, include_nadata=True)\n",
-    "splot.plot_spatial_weights(w_rook2, da=da_s)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Additional resources\n",
-    "\n",
-    "1. [Reading and writing files using Xarray](http://xarray.pydata.org/en/stable/io.html)\n",
-    "2. [Xarray Data Structures](http://xarray.pydata.org/en/stable/data-structures.html)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/docs/notebooks/examples.html b/docs/notebooks/examples.html
deleted file mode 100644
index 161bba4e4..000000000
--- a/docs/notebooks/examples.html
+++ /dev/null
@@ -1,1392 +0,0 @@
-<!DOCTYPE html>
-
-<html>
-  <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>Datasets for use with libpysal &#8212; libpysal v4.4.0 Manual</title>
-    <link rel="stylesheet" href="../_static/bootstrap-sphinx.css" type="text/css" />
-    <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
-    <link rel="stylesheet" type="text/css" href="../_static/pysal-styles.css" />
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-<div class="section" id="Datasets-for-use-with-libpysal">
-<h1>Datasets for use with libpysal<a class="headerlink" href="#Datasets-for-use-with-libpysal" title="Permalink to this headline">¶</a></h1>
-<p>As of version 4.2, libpysal has refactored the <code class="docutils literal notranslate"><span class="pre">examples</span></code> package to:</p>
-<ul class="simple">
-<li><p>reduce the size of the source installation</p></li>
-<li><p>allow the use of remote datasets from the <a class="reference external" href="https://spatial.uchicago.edu/">Center for Spatial Data Science at the Unversity of Chicago</a>, and other remotes</p></li>
-</ul>
-<p>This notebook highlights the new functionality</p>
-</div>
-<div class="section" id="Backwards-compatibility-is-maintained">
-<h1>Backwards compatibility is maintained<a class="headerlink" href="#Backwards-compatibility-is-maintained" title="Permalink to this headline">¶</a></h1>
-<p>If you were familiar with previous versions of libpysal, the newest version maintains backwards compatibility so any code that relied on the previous API should work.</p>
-<p>For example:</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="kn">from</span> <span class="nn">libpysal.examples</span> <span class="kn">import</span> <span class="n">get_path</span>
-
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">get_path</span><span class="p">(</span><span class="s2">&quot;mexicojoin.dbf&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-&#39;/home/jovyan/libpysal/examples/mexico/mexicojoin.dbf&#39;
-</pre></div></div>
-</div>
-<p>An important thing to note here is that the path to the file for this particular example is within the source distribution that was installed. Such an example data set is now referred to as a <code class="docutils literal notranslate"><span class="pre">builtin</span></code> dataset.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="kn">import</span> <span class="nn">libpysal</span>
-<span class="n">dbf</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">get_path</span><span class="p">(</span><span class="s2">&quot;mexicojoin.dbf&quot;</span><span class="p">))</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">dbf</span><span class="o">.</span><span class="n">header</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-[&#39;POLY_ID&#39;,
- &#39;AREA&#39;,
- &#39;CODE&#39;,
- &#39;NAME&#39;,
- &#39;PERIMETER&#39;,
- &#39;ACRES&#39;,
- &#39;HECTARES&#39;,
- &#39;PCGDP1940&#39;,
- &#39;PCGDP1950&#39;,
- &#39;PCGDP1960&#39;,
- &#39;PCGDP1970&#39;,
- &#39;PCGDP1980&#39;,
- &#39;PCGDP1990&#39;,
- &#39;PCGDP2000&#39;,
- &#39;HANSON03&#39;,
- &#39;HANSON98&#39;,
- &#39;ESQUIVEL99&#39;,
- &#39;INEGI&#39;,
- &#39;INEGI2&#39;,
- &#39;MAXP&#39;,
- &#39;GR4000&#39;,
- &#39;GR5000&#39;,
- &#39;GR6000&#39;,
- &#39;GR7000&#39;,
- &#39;GR8000&#39;,
- &#39;GR9000&#39;,
- &#39;LPCGDP40&#39;,
- &#39;LPCGDP50&#39;,
- &#39;LPCGDP60&#39;,
- &#39;LPCGDP70&#39;,
- &#39;LPCGDP80&#39;,
- &#39;LPCGDP90&#39;,
- &#39;LPCGDP00&#39;,
- &#39;TEST&#39;]
-</pre></div></div>
-</div>
-</div>
-<div class="section" id="available-is-updated">
-<h1><code class="docutils literal notranslate"><span class="pre">available</span></code> is updated<a class="headerlink" href="#available-is-updated" title="Permalink to this headline">¶</a></h1>
-<p>The function <code class="docutils literal notranslate"><span class="pre">available</span></code> is also available but has been updated to be more informative:</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="kn">from</span> <span class="nn">libpysal.examples</span> <span class="kn">import</span> <span class="n">available</span>
-<span class="n">available</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-                      Name                                        Description  Installed
-0                    10740    Albuquerque, New Mexico, Census 2000 Tract Data       True
-1                   AirBnB  Airbnb rentals, socioeconomics, and crime in C...       True
-2                  Atlanta       Atlanta, GA region homicide counts and rates       True
-3                Baltimore          Baltimore house sales prices and hedonics       True
-4                Bostonhsg               Boston housing and neighborhood data       True
-5              Buenosaires      Electoral Data for 1999 Argentinean Elections       True
-6              Charleston1   2000 Census Tract Data for Charleston, SC MSA...       True
-7              Charleston2   1998 and 2001 Zip Code Business Patterns (Cen...       True
-8           Chicago Health                   Chicago Health + Socio-Economics       True
-9              Chile Labor                 Labor Markets in Chile (1982-2002)       True
-10         Chile Migration            Internal Migration in Chile (1977-2002)       True
-11              Cincinnati         2008 Cincinnati Crime + Socio-Demographics       True
-12               Cleveland       2015 sales prices of homes in Cleveland, OH.       True
-13                Columbus                        Columbus neighborhood crime       True
-14                  Denver   Demographics and housing in Denver neighborho...       True
-15               Elections               2012 and 2016 Presidential Elections       True
-16                 Grid100                      Grid with simulated variables       True
-17               Groceries                          2015 Chicago supermarkets       True
-18                  Guerry          Moral statistics of France (Guerry, 1833)       True
-19       Health Indicators                Chicago Health Indicators (2005-11)       True
-20                 Health+                    2000 Health, Income + Diversity       True
-21                Hickory1   2000 Census Tract Data for Hickory, NC MSA an...       True
-22                Hickory2   1998 and 2001 Zip Code Business Patterns (Cen...       True
-23              Home Sales              2014-15 Home Sales in King County, WA       True
-24                 Houston       Houston, TX region homicide counts and rates       True
-25                Juvenile             Cardiff juvenile delinquent residences       True
-26                Lansing1   2000 Census Tract Data for Lansing, MI MSA an...       True
-27                Lansing2   1998 and 2001 Zip Code Business Patterns (Cen...       True
-28                 Laozone   Ozone measures at monitoring stations in Los ...       True
-29                LasRosas   Corn yield, fertilizer and field data for pre...       True
-30                    Line                                     Line Shapefile       True
-31           Liquor Stores                         2015 Chicago Liquor Stores       True
-32                 Malaria   Malaria incidence and population (1973, 95, 9...       True
-33              Milwaukee1       2000 Census Tract Data for Milwaukee, WI MSA       True
-34              Milwaukee2   1998 and 2001 Zip Code Business Patterns (Cen...       True
-35                   NCOVR                      US county homicides 1960-1990       True
-36                    NDVI        Normalized Difference Vegetation Index grid       True
-37                     NYC   Demographic and housing data for New York Cit...       True
-38            NYC Earnings              Block-level Earnings in NYC (2002-14)       True
-39           NYC Education                               NYC Education (2000)       True
-40       NYC Neighborhoods       Demographics for New York City neighborhoods       True
-41  NYC Socio-Demographics                 NYC Education + Socio-Demographics       True
-42              Natregimes                    NCOVR with regimes (book/PySAL)       True
-43                   Nepal   Health, poverty and education indicators for ...       True
-44                Ohiolung            Ohio lung cancer data, 1968, 1978, 1988       True
-45                Orlando1   2000 Census Tract Data for Orlando, FL MSA an...       True
-46                Orlando2   1998 and 2001 Zip Code Business Patterns (Cen...       True
-47                  Oz9799                        Monthly ozone data, 1997-99       True
-48             Phoenix ACS   Phoenix American Community Survey Data (2010,...       True
-49              Pittsburgh                      Pittsburgh homicide locations       True
-50                   Point                                    Point Shapefile       True
-51                  Police           Police expenditures Mississippi counties       True
-52                 Polygon                                  Polygon Shapefile       True
-53           Polygon_Holes                 Example to test treatment of holes       True
-54       Rio Grande do Sul  Cities of the Brazilian State of Rio Grande do...       True
-55                    SIDS            North Carolina county SIDS death counts       True
-56                   SIDS2   North Carolina county SIDS death counts and r...       True
-57             Sacramento1          2000 Census Tract Data for Sacramento MSA       True
-58             Sacramento2   1998 and 2001 Zip Code Business Patterns (Cen...       True
-59           SanFran Crime   July-Dec 2012 crime incidents in San Francisc...       True
-60               Savannah1   2000 Census Tract Data for Savannah, GA MSA a...       True
-61               Savannah2   1998 and 2001 Zip Code Business Patterns (Cen...       True
-62                 Scotlip               Male lip cancer in Scotland, 1975-80       True
-63                Seattle1   2000 Census Tract Data for Seattle, WA MSA an...       True
-64                Seattle2   1998 and 2001 Zip Code Business Patterns (Cen...       True
-65                   South             US Southern county homicides 1960-1990       True
-66                 StLouis   St Louis region county homicide counts and rates       True
-67                  Tampa1   2000 Census Tract Data for Tampa, FL MSA and ...       True
-68                  arcgis                               arcgis testing files       True
-69                  baltim     Baltimore house sales prices and hedonics 1978       True
-70                  berlin  Prenzlauer Berg neighborhood AirBnB data from ...       True
-71                    book       Synthetic data to illustrate spatial weights       True
-72                 burkitt  Burkitt&#39;s lymphoma in the Western Nile distric...       True
-73                  calemp         Employment density for California counties       True
-74                 chicago                              Chicago neighborhoods       True
-75              clearwater                               mgwr testing dataset       True
-76                columbus              Columbus neighborhood crime data 1980       True
-77                 desmith    Small dataset to illustrate Moran&#39;s I statistic       True
-78                geodanet        Datasets from geodanet for network analysis       True
-79                 georgia  Various socio-economic variables for counties ...       True
-80                juvenile    Residences of juvenile offenders in Cardiff, UK       True
-81                  mexico  Decennial per capita incomes of Mexican states...       True
-82                networks                  Datasets used for network testing       True
-83                newHaven                            Network testing dataset       True
-84               nyc_bikes                           New York City Bike Trips       True
-85                   sids2  North Carolina county SIDS death counts and rates       True
-86               snow_maps  Public water pumps and Cholera deaths in Londo...       True
-87                     stl  Homicides and selected socio-economic characte...       True
-88          street_net_pts                              Street network points       True
-89                     taz           Traffic Analysis Zones in So. California       True
-90                   tokyo                               Tokyo Mortality data       True
-91               us_income  Per-capita income for the lower 48 US states 1...       True
-92                virginia                        Virginia counties shapefile       True
-93                    wmat          Datasets used for spatial weights testing       True
-</pre></div></div>
-</div>
-<p>We see that the new column <code class="docutils literal notranslate"><span class="pre">Installed</span></code> is added to the tablular output of <code class="docutils literal notranslate"><span class="pre">available</span></code>. This tells the user whether the dataset has aready been installed on the local machine. All the builtin datasets are by defninition installed. The interesting cases are the remote data sets that are new in this release.</p>
-</div>
-<div class="section" id="Remote-datasets">
-<h1>Remote datasets<a class="headerlink" href="#Remote-datasets" title="Permalink to this headline">¶</a></h1>
-<p>The listing from <code class="docutils literal notranslate"><span class="pre">available</span></code> above shows that the dataset <code class="docutils literal notranslate"><span class="pre">Tampa1</span></code> is avalable, but has not yet been installed.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="kn">from</span> <span class="nn">libpysal.examples</span> <span class="kn">import</span> <span class="n">explain</span>
-<span class="n">explain</span><span class="p">(</span><span class="s1">&#39;Tampa1&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
-</pre></div>
-</div>
-<div class="output_area rendered_html docutils container">
-<iframe
-    width="700"
-    height="350"
-    src="https://geodacenter.github.io/data-and-lab/tampa1/"
-    frameborder="0"
-    allowfullscreen
-></iframe></div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="kn">from</span> <span class="nn">libpysal.examples</span> <span class="kn">import</span> <span class="n">load_example</span>
-<span class="n">tampa1</span> <span class="o">=</span> <span class="n">load_example</span><span class="p">(</span><span class="s1">&#39;Tampa1&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">available</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-                      Name                                        Description  Installed
-0                    10740    Albuquerque, New Mexico, Census 2000 Tract Data       True
-1                   AirBnB  Airbnb rentals, socioeconomics, and crime in C...       True
-2                  Atlanta       Atlanta, GA region homicide counts and rates       True
-3                Baltimore          Baltimore house sales prices and hedonics       True
-4                Bostonhsg               Boston housing and neighborhood data       True
-5              Buenosaires      Electoral Data for 1999 Argentinean Elections       True
-6              Charleston1   2000 Census Tract Data for Charleston, SC MSA...       True
-7              Charleston2   1998 and 2001 Zip Code Business Patterns (Cen...       True
-8           Chicago Health                   Chicago Health + Socio-Economics       True
-9              Chile Labor                 Labor Markets in Chile (1982-2002)       True
-10         Chile Migration            Internal Migration in Chile (1977-2002)       True
-11              Cincinnati         2008 Cincinnati Crime + Socio-Demographics       True
-12               Cleveland       2015 sales prices of homes in Cleveland, OH.       True
-13                Columbus                        Columbus neighborhood crime       True
-14                  Denver   Demographics and housing in Denver neighborho...       True
-15               Elections               2012 and 2016 Presidential Elections       True
-16                 Grid100                      Grid with simulated variables       True
-17               Groceries                          2015 Chicago supermarkets       True
-18                  Guerry          Moral statistics of France (Guerry, 1833)       True
-19       Health Indicators                Chicago Health Indicators (2005-11)       True
-20                 Health+                    2000 Health, Income + Diversity       True
-21                Hickory1   2000 Census Tract Data for Hickory, NC MSA an...       True
-22                Hickory2   1998 and 2001 Zip Code Business Patterns (Cen...       True
-23              Home Sales              2014-15 Home Sales in King County, WA       True
-24                 Houston       Houston, TX region homicide counts and rates       True
-25                Juvenile             Cardiff juvenile delinquent residences       True
-26                Lansing1   2000 Census Tract Data for Lansing, MI MSA an...       True
-27                Lansing2   1998 and 2001 Zip Code Business Patterns (Cen...       True
-28                 Laozone   Ozone measures at monitoring stations in Los ...       True
-29                LasRosas   Corn yield, fertilizer and field data for pre...       True
-30                    Line                                     Line Shapefile       True
-31           Liquor Stores                         2015 Chicago Liquor Stores       True
-32                 Malaria   Malaria incidence and population (1973, 95, 9...       True
-33              Milwaukee1       2000 Census Tract Data for Milwaukee, WI MSA       True
-34              Milwaukee2   1998 and 2001 Zip Code Business Patterns (Cen...       True
-35                   NCOVR                      US county homicides 1960-1990       True
-36                    NDVI        Normalized Difference Vegetation Index grid       True
-37                     NYC   Demographic and housing data for New York Cit...       True
-38            NYC Earnings              Block-level Earnings in NYC (2002-14)       True
-39           NYC Education                               NYC Education (2000)       True
-40       NYC Neighborhoods       Demographics for New York City neighborhoods       True
-41  NYC Socio-Demographics                 NYC Education + Socio-Demographics       True
-42              Natregimes                    NCOVR with regimes (book/PySAL)       True
-43                   Nepal   Health, poverty and education indicators for ...       True
-44                Ohiolung            Ohio lung cancer data, 1968, 1978, 1988       True
-45                Orlando1   2000 Census Tract Data for Orlando, FL MSA an...       True
-46                Orlando2   1998 and 2001 Zip Code Business Patterns (Cen...       True
-47                  Oz9799                        Monthly ozone data, 1997-99       True
-48             Phoenix ACS   Phoenix American Community Survey Data (2010,...       True
-49              Pittsburgh                      Pittsburgh homicide locations       True
-50                   Point                                    Point Shapefile       True
-51                  Police           Police expenditures Mississippi counties       True
-52                 Polygon                                  Polygon Shapefile       True
-53           Polygon_Holes                 Example to test treatment of holes       True
-54       Rio Grande do Sul  Cities of the Brazilian State of Rio Grande do...       True
-55                    SIDS            North Carolina county SIDS death counts       True
-56                   SIDS2   North Carolina county SIDS death counts and r...       True
-57             Sacramento1          2000 Census Tract Data for Sacramento MSA       True
-58             Sacramento2   1998 and 2001 Zip Code Business Patterns (Cen...       True
-59           SanFran Crime   July-Dec 2012 crime incidents in San Francisc...       True
-60               Savannah1   2000 Census Tract Data for Savannah, GA MSA a...       True
-61               Savannah2   1998 and 2001 Zip Code Business Patterns (Cen...       True
-62                 Scotlip               Male lip cancer in Scotland, 1975-80       True
-63                Seattle1   2000 Census Tract Data for Seattle, WA MSA an...       True
-64                Seattle2   1998 and 2001 Zip Code Business Patterns (Cen...       True
-65                   South             US Southern county homicides 1960-1990       True
-66                 StLouis   St Louis region county homicide counts and rates       True
-67                  Tampa1   2000 Census Tract Data for Tampa, FL MSA and ...       True
-68                  arcgis                               arcgis testing files       True
-69                  baltim     Baltimore house sales prices and hedonics 1978       True
-70                  berlin  Prenzlauer Berg neighborhood AirBnB data from ...       True
-71                    book       Synthetic data to illustrate spatial weights       True
-72                 burkitt  Burkitt&#39;s lymphoma in the Western Nile distric...       True
-73                  calemp         Employment density for California counties       True
-74                 chicago                              Chicago neighborhoods       True
-75              clearwater                               mgwr testing dataset       True
-76                columbus              Columbus neighborhood crime data 1980       True
-77                 desmith    Small dataset to illustrate Moran&#39;s I statistic       True
-78                geodanet        Datasets from geodanet for network analysis       True
-79                 georgia  Various socio-economic variables for counties ...       True
-80                juvenile    Residences of juvenile offenders in Cardiff, UK       True
-81                  mexico  Decennial per capita incomes of Mexican states...       True
-82                networks                  Datasets used for network testing       True
-83                newHaven                            Network testing dataset       True
-84               nyc_bikes                           New York City Bike Trips       True
-85                   sids2  North Carolina county SIDS death counts and rates       True
-86               snow_maps  Public water pumps and Cholera deaths in Londo...       True
-87                     stl  Homicides and selected socio-economic characte...       True
-88          street_net_pts                              Street network points       True
-89                     taz           Traffic Analysis Zones in So. California       True
-90                   tokyo                               Tokyo Mortality data       True
-91               us_income  Per-capita income for the lower 48 US states 1...       True
-92                virginia                        Virginia counties shapefile       True
-93                    wmat          Datasets used for spatial weights testing       True
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">tampa1</span><span class="o">.</span><span class="n">installed</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-True
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">tampa1</span><span class="o">.</span><span class="n">get_file_list</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-[&#39;/home/jovyan/pysal_data/Tampa1/__MACOSX/._TampaMSA&#39;,
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- &#39;/home/jovyan/pysal_data/Tampa1/__MACOSX/TampaMSA/._tampa_final_census2.sbn&#39;,
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- &#39;/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.gpkg&#39;,
- &#39;/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.xlsx&#39;,
- &#39;/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.mid&#39;,
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- &#39;/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.xlsx&#39;,
- &#39;/home/jovyan/pysal_data/Tampa1/TampaMSA/2000 Census Data Variables_Documentation.pdf&#39;,
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- &#39;/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.dbf&#39;,
- &#39;/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.gpkg&#39;,
- &#39;/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.mif&#39;,
- &#39;/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.kml&#39;,
- &#39;/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.sqlite&#39;,
- &#39;/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.dbf&#39;,
- &#39;/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.prj&#39;,
- &#39;/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.mif&#39;,
- &#39;/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.shx&#39;,
- &#39;/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.geojson&#39;,
- &#39;/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.shx&#39;,
- &#39;/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.sbn&#39;,
- &#39;/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.sbx&#39;,
- &#39;/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.prj&#39;,
- &#39;/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.shp&#39;,
- &#39;/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.shp&#39;,
- &#39;/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.sqlite&#39;,
- &#39;/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.sbx&#39;,
- &#39;/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.kml&#39;]
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-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">tampa_counties_shp</span> <span class="o">=</span> <span class="n">tampa1</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">&#39;tampa_counties.shp&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">tampa_counties_shp</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-&lt;libpysal.io.iohandlers.pyShpIO.PurePyShpWrapper at 0x7f2c886869b0&gt;
-</pre></div></div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="kn">import</span> <span class="nn">geopandas</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">tampa_df</span> <span class="o">=</span> <span class="n">geopandas</span><span class="o">.</span><span class="n">read_file</span><span class="p">(</span><span class="n">tampa1</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="s1">&#39;tampa_counties.shp&#39;</span><span class="p">))</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="o">%</span><span class="k">matplotlib</span> inline
-<span class="n">tampa_df</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-&lt;matplotlib.axes._subplots.AxesSubplot at 0x7f2c885f5e48&gt;
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/notebooks_examples_22_1.png" src="../_images/notebooks_examples_22_1.png" />
-</div>
-</div>
-</div>
-<div class="section" id="Other-Remotes">
-<h1>Other Remotes<a class="headerlink" href="#Other-Remotes" title="Permalink to this headline">¶</a></h1>
-<p>In addition to the remote datasets from the GeoData Data Science Center, there are several large remotes available at github repositories.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">explain</span><span class="p">(</span><span class="s1">&#39;Rio Grande do Sul&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-Rio_Grande_do_Sul
-======================
-
-Cities of the Brazilian State of Rio Grande do Sul
--------------------------------------------------------
-
-* 43MUE250GC_SIR.dbf: attribute data (k=2)
-* 43MUE250GC_SIR.shp: Polygon shapefile (n=499)
-* 43MUE250GC_SIR.shx: spatial index
-* 43MUE250GC_SIR.cpg: encoding file
-* 43MUE250GC_SIR.prj: projection information
-* map_RS_BR.dbf: attribute data (k=3)
-* map_RS_BR.shp: Polygon shapefile (no lakes) (n=497)
-* map_RS_BR.prj: projection information
-* map_RS_BR.shx: spatial index
-
-
-
-Source: Renan Xavier Cortes
-Reference: https://github.com/pysal/pysal/issues/889#issuecomment-396693495
-
-
-</pre></div></div>
-</div>
-<p>Note that the <code class="docutils literal notranslate"><span class="pre">explain</span></code> function generates a textual description of this example dataset - no rendering of the map is done as the source repository does not include that functionality.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[17]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">rio</span> <span class="o">=</span> <span class="n">load_example</span><span class="p">(</span><span class="s1">&#39;Rio Grande do Sul&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[18]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">available</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-                      Name                                        Description  Installed
-0                    10740    Albuquerque, New Mexico, Census 2000 Tract Data       True
-1                   AirBnB  Airbnb rentals, socioeconomics, and crime in C...       True
-2                  Atlanta       Atlanta, GA region homicide counts and rates       True
-3                Baltimore          Baltimore house sales prices and hedonics       True
-4                Bostonhsg               Boston housing and neighborhood data       True
-5              Buenosaires      Electoral Data for 1999 Argentinean Elections       True
-6              Charleston1   2000 Census Tract Data for Charleston, SC MSA...       True
-7              Charleston2   1998 and 2001 Zip Code Business Patterns (Cen...       True
-8           Chicago Health                   Chicago Health + Socio-Economics       True
-9              Chile Labor                 Labor Markets in Chile (1982-2002)       True
-10         Chile Migration            Internal Migration in Chile (1977-2002)       True
-11              Cincinnati         2008 Cincinnati Crime + Socio-Demographics       True
-12               Cleveland       2015 sales prices of homes in Cleveland, OH.       True
-13                Columbus                        Columbus neighborhood crime       True
-14                  Denver   Demographics and housing in Denver neighborho...       True
-15               Elections               2012 and 2016 Presidential Elections       True
-16                 Grid100                      Grid with simulated variables       True
-17               Groceries                          2015 Chicago supermarkets       True
-18                  Guerry          Moral statistics of France (Guerry, 1833)       True
-19       Health Indicators                Chicago Health Indicators (2005-11)       True
-20                 Health+                    2000 Health, Income + Diversity       True
-21                Hickory1   2000 Census Tract Data for Hickory, NC MSA an...       True
-22                Hickory2   1998 and 2001 Zip Code Business Patterns (Cen...       True
-23              Home Sales              2014-15 Home Sales in King County, WA       True
-24                 Houston       Houston, TX region homicide counts and rates       True
-25                Juvenile             Cardiff juvenile delinquent residences       True
-26                Lansing1   2000 Census Tract Data for Lansing, MI MSA an...       True
-27                Lansing2   1998 and 2001 Zip Code Business Patterns (Cen...       True
-28                 Laozone   Ozone measures at monitoring stations in Los ...       True
-29                LasRosas   Corn yield, fertilizer and field data for pre...       True
-30                    Line                                     Line Shapefile       True
-31           Liquor Stores                         2015 Chicago Liquor Stores       True
-32                 Malaria   Malaria incidence and population (1973, 95, 9...       True
-33              Milwaukee1       2000 Census Tract Data for Milwaukee, WI MSA       True
-34              Milwaukee2   1998 and 2001 Zip Code Business Patterns (Cen...       True
-35                   NCOVR                      US county homicides 1960-1990       True
-36                    NDVI        Normalized Difference Vegetation Index grid       True
-37                     NYC   Demographic and housing data for New York Cit...       True
-38            NYC Earnings              Block-level Earnings in NYC (2002-14)       True
-39           NYC Education                               NYC Education (2000)       True
-40       NYC Neighborhoods       Demographics for New York City neighborhoods       True
-41  NYC Socio-Demographics                 NYC Education + Socio-Demographics       True
-42              Natregimes                    NCOVR with regimes (book/PySAL)       True
-43                   Nepal   Health, poverty and education indicators for ...       True
-44                Ohiolung            Ohio lung cancer data, 1968, 1978, 1988       True
-45                Orlando1   2000 Census Tract Data for Orlando, FL MSA an...       True
-46                Orlando2   1998 and 2001 Zip Code Business Patterns (Cen...       True
-47                  Oz9799                        Monthly ozone data, 1997-99       True
-48             Phoenix ACS   Phoenix American Community Survey Data (2010,...       True
-49              Pittsburgh                      Pittsburgh homicide locations       True
-50                   Point                                    Point Shapefile       True
-51                  Police           Police expenditures Mississippi counties       True
-52                 Polygon                                  Polygon Shapefile       True
-53           Polygon_Holes                 Example to test treatment of holes       True
-54       Rio Grande do Sul  Cities of the Brazilian State of Rio Grande do...       True
-55                    SIDS            North Carolina county SIDS death counts       True
-56                   SIDS2   North Carolina county SIDS death counts and r...       True
-57             Sacramento1          2000 Census Tract Data for Sacramento MSA       True
-58             Sacramento2   1998 and 2001 Zip Code Business Patterns (Cen...       True
-59           SanFran Crime   July-Dec 2012 crime incidents in San Francisc...       True
-60               Savannah1   2000 Census Tract Data for Savannah, GA MSA a...       True
-61               Savannah2   1998 and 2001 Zip Code Business Patterns (Cen...       True
-62                 Scotlip               Male lip cancer in Scotland, 1975-80       True
-63                Seattle1   2000 Census Tract Data for Seattle, WA MSA an...       True
-64                Seattle2   1998 and 2001 Zip Code Business Patterns (Cen...       True
-65                   South             US Southern county homicides 1960-1990       True
-66                 StLouis   St Louis region county homicide counts and rates       True
-67                  Tampa1   2000 Census Tract Data for Tampa, FL MSA and ...       True
-68                  arcgis                               arcgis testing files       True
-69                  baltim     Baltimore house sales prices and hedonics 1978       True
-70                  berlin  Prenzlauer Berg neighborhood AirBnB data from ...       True
-71                    book       Synthetic data to illustrate spatial weights       True
-72                 burkitt  Burkitt&#39;s lymphoma in the Western Nile distric...       True
-73                  calemp         Employment density for California counties       True
-74                 chicago                              Chicago neighborhoods       True
-75              clearwater                               mgwr testing dataset       True
-76                columbus              Columbus neighborhood crime data 1980       True
-77                 desmith    Small dataset to illustrate Moran&#39;s I statistic       True
-78                geodanet        Datasets from geodanet for network analysis       True
-79                 georgia  Various socio-economic variables for counties ...       True
-80                juvenile    Residences of juvenile offenders in Cardiff, UK       True
-81                  mexico  Decennial per capita incomes of Mexican states...       True
-82                networks                  Datasets used for network testing       True
-83                newHaven                            Network testing dataset       True
-84               nyc_bikes                           New York City Bike Trips       True
-85                   sids2  North Carolina county SIDS death counts and rates       True
-86               snow_maps  Public water pumps and Cholera deaths in Londo...       True
-87                     stl  Homicides and selected socio-economic characte...       True
-88          street_net_pts                              Street network points       True
-89                     taz           Traffic Analysis Zones in So. California       True
-90                   tokyo                               Tokyo Mortality data       True
-91               us_income  Per-capita income for the lower 48 US states 1...       True
-92                virginia                        Virginia counties shapefile       True
-93                    wmat          Datasets used for spatial weights testing       True
-</pre></div></div>
-</div>
-</div>
-<div class="section" id="Grabbing-all-the-remotes">
-<h1>Grabbing all the remotes<a class="headerlink" href="#Grabbing-all-the-remotes" title="Permalink to this headline">¶</a></h1>
-<p>All the remote datasets can be downloaded with:</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[19]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">libpysal</span><span class="o">.</span><span class="n">examples</span><span class="o">.</span><span class="n">fetch_all</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-AirBnB
-Already downloaded
-Atlanta
-Already downloaded
-Baltimore
-Already downloaded
-Bostonhsg
-Already downloaded
-Buenosaires
-Already downloaded
-Charleston1
-Already downloaded
-Charleston2
-Already downloaded
-Chicago Health
-Already downloaded
-Chile Labor
-Already downloaded
-Chile Migration
-Already downloaded
-Cincinnati
-Already downloaded
-Cleveland
-Already downloaded
-Columbus
-Already downloaded
-Denver
-Already downloaded
-Elections
-Already downloaded
-Grid100
-Already downloaded
-Groceries
-Already downloaded
-Guerry
-Already downloaded
-Health Indicators
-Already downloaded
-Health+
-Already downloaded
-Hickory1
-Already downloaded
-Hickory2
-Already downloaded
-Home Sales
-Already downloaded
-Houston
-Already downloaded
-Juvenile
-Already downloaded
-Lansing1
-Already downloaded
-Lansing2
-Already downloaded
-Laozone
-Already downloaded
-LasRosas
-Already downloaded
-Liquor Stores
-Already downloaded
-Malaria
-Already downloaded
-Milwaukee1
-Already downloaded
-Milwaukee2
-Already downloaded
-NCOVR
-Already downloaded
-NDVI
-Already downloaded
-NYC
-Already downloaded
-NYC Earnings
-Already downloaded
-NYC Education
-Already downloaded
-NYC Neighborhoods
-Already downloaded
-NYC Socio-Demographics
-Already downloaded
-Natregimes
-Already downloaded
-Nepal
-Already downloaded
-Ohiolung
-Already downloaded
-Orlando1
-Already downloaded
-Orlando2
-Already downloaded
-Oz9799
-Already downloaded
-Phoenix ACS
-Already downloaded
-Pittsburgh
-Already downloaded
-Police
-Already downloaded
-Rio Grande do Sul
-Already downloaded
-SIDS
-Already downloaded
-SIDS2
-Already downloaded
-Sacramento1
-Already downloaded
-Sacramento2
-Already downloaded
-SanFran Crime
-Already downloaded
-Savannah1
-Already downloaded
-Savannah2
-Already downloaded
-Scotlip
-Already downloaded
-Seattle1
-Already downloaded
-Seattle2
-Already downloaded
-South
-Already downloaded
-StLouis
-Already downloaded
-Tampa1
-Already downloaded
-clearwater
-Already downloaded
-newHaven
-Already downloaded
-nyc_bikes
-Already downloaded
-taz
-Already downloaded
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[20]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">available</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-                      Name                                        Description  Installed
-0                    10740    Albuquerque, New Mexico, Census 2000 Tract Data       True
-1                   AirBnB  Airbnb rentals, socioeconomics, and crime in C...       True
-2                  Atlanta       Atlanta, GA region homicide counts and rates       True
-3                Baltimore          Baltimore house sales prices and hedonics       True
-4                Bostonhsg               Boston housing and neighborhood data       True
-5              Buenosaires      Electoral Data for 1999 Argentinean Elections       True
-6              Charleston1   2000 Census Tract Data for Charleston, SC MSA...       True
-7              Charleston2   1998 and 2001 Zip Code Business Patterns (Cen...       True
-8           Chicago Health                   Chicago Health + Socio-Economics       True
-9              Chile Labor                 Labor Markets in Chile (1982-2002)       True
-10         Chile Migration            Internal Migration in Chile (1977-2002)       True
-11              Cincinnati         2008 Cincinnati Crime + Socio-Demographics       True
-12               Cleveland       2015 sales prices of homes in Cleveland, OH.       True
-13                Columbus                        Columbus neighborhood crime       True
-14                  Denver   Demographics and housing in Denver neighborho...       True
-15               Elections               2012 and 2016 Presidential Elections       True
-16                 Grid100                      Grid with simulated variables       True
-17               Groceries                          2015 Chicago supermarkets       True
-18                  Guerry          Moral statistics of France (Guerry, 1833)       True
-19       Health Indicators                Chicago Health Indicators (2005-11)       True
-20                 Health+                    2000 Health, Income + Diversity       True
-21                Hickory1   2000 Census Tract Data for Hickory, NC MSA an...       True
-22                Hickory2   1998 and 2001 Zip Code Business Patterns (Cen...       True
-23              Home Sales              2014-15 Home Sales in King County, WA       True
-24                 Houston       Houston, TX region homicide counts and rates       True
-25                Juvenile             Cardiff juvenile delinquent residences       True
-26                Lansing1   2000 Census Tract Data for Lansing, MI MSA an...       True
-27                Lansing2   1998 and 2001 Zip Code Business Patterns (Cen...       True
-28                 Laozone   Ozone measures at monitoring stations in Los ...       True
-29                LasRosas   Corn yield, fertilizer and field data for pre...       True
-30                    Line                                     Line Shapefile       True
-31           Liquor Stores                         2015 Chicago Liquor Stores       True
-32                 Malaria   Malaria incidence and population (1973, 95, 9...       True
-33              Milwaukee1       2000 Census Tract Data for Milwaukee, WI MSA       True
-34              Milwaukee2   1998 and 2001 Zip Code Business Patterns (Cen...       True
-35                   NCOVR                      US county homicides 1960-1990       True
-36                    NDVI        Normalized Difference Vegetation Index grid       True
-37                     NYC   Demographic and housing data for New York Cit...       True
-38            NYC Earnings              Block-level Earnings in NYC (2002-14)       True
-39           NYC Education                               NYC Education (2000)       True
-40       NYC Neighborhoods       Demographics for New York City neighborhoods       True
-41  NYC Socio-Demographics                 NYC Education + Socio-Demographics       True
-42              Natregimes                    NCOVR with regimes (book/PySAL)       True
-43                   Nepal   Health, poverty and education indicators for ...       True
-44                Ohiolung            Ohio lung cancer data, 1968, 1978, 1988       True
-45                Orlando1   2000 Census Tract Data for Orlando, FL MSA an...       True
-46                Orlando2   1998 and 2001 Zip Code Business Patterns (Cen...       True
-47                  Oz9799                        Monthly ozone data, 1997-99       True
-48             Phoenix ACS   Phoenix American Community Survey Data (2010,...       True
-49              Pittsburgh                      Pittsburgh homicide locations       True
-50                   Point                                    Point Shapefile       True
-51                  Police           Police expenditures Mississippi counties       True
-52                 Polygon                                  Polygon Shapefile       True
-53           Polygon_Holes                 Example to test treatment of holes       True
-54       Rio Grande do Sul  Cities of the Brazilian State of Rio Grande do...       True
-55                    SIDS            North Carolina county SIDS death counts       True
-56                   SIDS2   North Carolina county SIDS death counts and r...       True
-57             Sacramento1          2000 Census Tract Data for Sacramento MSA       True
-58             Sacramento2   1998 and 2001 Zip Code Business Patterns (Cen...       True
-59           SanFran Crime   July-Dec 2012 crime incidents in San Francisc...       True
-60               Savannah1   2000 Census Tract Data for Savannah, GA MSA a...       True
-61               Savannah2   1998 and 2001 Zip Code Business Patterns (Cen...       True
-62                 Scotlip               Male lip cancer in Scotland, 1975-80       True
-63                Seattle1   2000 Census Tract Data for Seattle, WA MSA an...       True
-64                Seattle2   1998 and 2001 Zip Code Business Patterns (Cen...       True
-65                   South             US Southern county homicides 1960-1990       True
-66                 StLouis   St Louis region county homicide counts and rates       True
-67                  Tampa1   2000 Census Tract Data for Tampa, FL MSA and ...       True
-68                  arcgis                               arcgis testing files       True
-69                  baltim     Baltimore house sales prices and hedonics 1978       True
-70                  berlin  Prenzlauer Berg neighborhood AirBnB data from ...       True
-71                    book       Synthetic data to illustrate spatial weights       True
-72                 burkitt  Burkitt&#39;s lymphoma in the Western Nile distric...       True
-73                  calemp         Employment density for California counties       True
-74                 chicago                              Chicago neighborhoods       True
-75              clearwater                               mgwr testing dataset       True
-76                columbus              Columbus neighborhood crime data 1980       True
-77                 desmith    Small dataset to illustrate Moran&#39;s I statistic       True
-78                geodanet        Datasets from geodanet for network analysis       True
-79                 georgia  Various socio-economic variables for counties ...       True
-80                juvenile    Residences of juvenile offenders in Cardiff, UK       True
-81                  mexico  Decennial per capita incomes of Mexican states...       True
-82                networks                  Datasets used for network testing       True
-83                newHaven                            Network testing dataset       True
-84               nyc_bikes                           New York City Bike Trips       True
-85                   sids2  North Carolina county SIDS death counts and rates       True
-86               snow_maps  Public water pumps and Cholera deaths in Londo...       True
-87                     stl  Homicides and selected socio-economic characte...       True
-88          street_net_pts                              Street network points       True
-89                     taz           Traffic Analysis Zones in So. California       True
-90                   tokyo                               Tokyo Mortality data       True
-91               us_income  Per-capita income for the lower 48 US states 1...       True
-92                virginia                        Virginia counties shapefile       True
-93                    wmat          Datasets used for spatial weights testing       True
-</pre></div></div>
-</div>
-</div>
-
-
-    </div>
-      
-  </div>
-</div>
-<footer class="footer">
-  <div class="container">
-    <p class="pull-right">
-      <a href="#">Back to top</a>
-      
-        <br/>
-        
-<div id="sourcelink">
-  <a href="../_sources/notebooks/examples.ipynb.txt"
-     rel="nofollow">Source</a>
-</div>
-      
-    </p>
-    <p>
-        &copy; Copyright 2018-, pysal developers.<br/>
-      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 3.1.2.<br/>
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diff --git a/docs/notebooks/examples.ipynb b/docs/notebooks/examples.ipynb
deleted file mode 100644
index 2c7203878..000000000
--- a/docs/notebooks/examples.ipynb
+++ /dev/null
@@ -1,1092 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Datasets for use with libpysal\n",
-    "As of version 4.2, libpysal has refactored the `examples` package to:\n",
-    "\n",
-    "- reduce the size of the source installation\n",
-    "- allow the use of remote datasets from the [Center for Spatial Data Science at the Unversity of Chicago](https://spatial.uchicago.edu/), and other remotes\n",
-    "\n",
-    "This notebook highlights the new functionality"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Backwards compatibility is maintained"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you were familiar with previous versions of libpysal, the newest version maintains backwards compatibility so any code that relied on the previous API should work. \n",
-    "\n",
-    "For example:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from libpysal.examples import get_path \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'/home/jovyan/libpysal/examples/mexico/mexicojoin.dbf'"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "get_path(\"mexicojoin.dbf\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "An important thing to note here is that the path to the file for this particular example is within the source distribution that was installed. Such an example data set is now referred to as a `builtin` dataset."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import libpysal\n",
-    "dbf = libpysal.io.open(get_path(\"mexicojoin.dbf\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['POLY_ID',\n",
-       " 'AREA',\n",
-       " 'CODE',\n",
-       " 'NAME',\n",
-       " 'PERIMETER',\n",
-       " 'ACRES',\n",
-       " 'HECTARES',\n",
-       " 'PCGDP1940',\n",
-       " 'PCGDP1950',\n",
-       " 'PCGDP1960',\n",
-       " 'PCGDP1970',\n",
-       " 'PCGDP1980',\n",
-       " 'PCGDP1990',\n",
-       " 'PCGDP2000',\n",
-       " 'HANSON03',\n",
-       " 'HANSON98',\n",
-       " 'ESQUIVEL99',\n",
-       " 'INEGI',\n",
-       " 'INEGI2',\n",
-       " 'MAXP',\n",
-       " 'GR4000',\n",
-       " 'GR5000',\n",
-       " 'GR6000',\n",
-       " 'GR7000',\n",
-       " 'GR8000',\n",
-       " 'GR9000',\n",
-       " 'LPCGDP40',\n",
-       " 'LPCGDP50',\n",
-       " 'LPCGDP60',\n",
-       " 'LPCGDP70',\n",
-       " 'LPCGDP80',\n",
-       " 'LPCGDP90',\n",
-       " 'LPCGDP00',\n",
-       " 'TEST']"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dbf.header"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## `available` is updated"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The function `available` is also available but has been updated to be more informative:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                      Name                                        Description  Installed\n",
-      "0                    10740    Albuquerque, New Mexico, Census 2000 Tract Data       True\n",
-      "1                   AirBnB  Airbnb rentals, socioeconomics, and crime in C...       True\n",
-      "2                  Atlanta       Atlanta, GA region homicide counts and rates       True\n",
-      "3                Baltimore          Baltimore house sales prices and hedonics       True\n",
-      "4                Bostonhsg               Boston housing and neighborhood data       True\n",
-      "5              Buenosaires      Electoral Data for 1999 Argentinean Elections       True\n",
-      "6              Charleston1   2000 Census Tract Data for Charleston, SC MSA...       True\n",
-      "7              Charleston2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "8           Chicago Health                   Chicago Health + Socio-Economics       True\n",
-      "9              Chile Labor                 Labor Markets in Chile (1982-2002)       True\n",
-      "10         Chile Migration            Internal Migration in Chile (1977-2002)       True\n",
-      "11              Cincinnati         2008 Cincinnati Crime + Socio-Demographics       True\n",
-      "12               Cleveland       2015 sales prices of homes in Cleveland, OH.       True\n",
-      "13                Columbus                        Columbus neighborhood crime       True\n",
-      "14                  Denver   Demographics and housing in Denver neighborho...       True\n",
-      "15               Elections               2012 and 2016 Presidential Elections       True\n",
-      "16                 Grid100                      Grid with simulated variables       True\n",
-      "17               Groceries                          2015 Chicago supermarkets       True\n",
-      "18                  Guerry          Moral statistics of France (Guerry, 1833)       True\n",
-      "19       Health Indicators                Chicago Health Indicators (2005-11)       True\n",
-      "20                 Health+                    2000 Health, Income + Diversity       True\n",
-      "21                Hickory1   2000 Census Tract Data for Hickory, NC MSA an...       True\n",
-      "22                Hickory2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "23              Home Sales              2014-15 Home Sales in King County, WA       True\n",
-      "24                 Houston       Houston, TX region homicide counts and rates       True\n",
-      "25                Juvenile             Cardiff juvenile delinquent residences       True\n",
-      "26                Lansing1   2000 Census Tract Data for Lansing, MI MSA an...       True\n",
-      "27                Lansing2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "28                 Laozone   Ozone measures at monitoring stations in Los ...       True\n",
-      "29                LasRosas   Corn yield, fertilizer and field data for pre...       True\n",
-      "30                    Line                                     Line Shapefile       True\n",
-      "31           Liquor Stores                         2015 Chicago Liquor Stores       True\n",
-      "32                 Malaria   Malaria incidence and population (1973, 95, 9...       True\n",
-      "33              Milwaukee1       2000 Census Tract Data for Milwaukee, WI MSA       True\n",
-      "34              Milwaukee2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "35                   NCOVR                      US county homicides 1960-1990       True\n",
-      "36                    NDVI        Normalized Difference Vegetation Index grid       True\n",
-      "37                     NYC   Demographic and housing data for New York Cit...       True\n",
-      "38            NYC Earnings              Block-level Earnings in NYC (2002-14)       True\n",
-      "39           NYC Education                               NYC Education (2000)       True\n",
-      "40       NYC Neighborhoods       Demographics for New York City neighborhoods       True\n",
-      "41  NYC Socio-Demographics                 NYC Education + Socio-Demographics       True\n",
-      "42              Natregimes                    NCOVR with regimes (book/PySAL)       True\n",
-      "43                   Nepal   Health, poverty and education indicators for ...       True\n",
-      "44                Ohiolung            Ohio lung cancer data, 1968, 1978, 1988       True\n",
-      "45                Orlando1   2000 Census Tract Data for Orlando, FL MSA an...       True\n",
-      "46                Orlando2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "47                  Oz9799                        Monthly ozone data, 1997-99       True\n",
-      "48             Phoenix ACS   Phoenix American Community Survey Data (2010,...       True\n",
-      "49              Pittsburgh                      Pittsburgh homicide locations       True\n",
-      "50                   Point                                    Point Shapefile       True\n",
-      "51                  Police           Police expenditures Mississippi counties       True\n",
-      "52                 Polygon                                  Polygon Shapefile       True\n",
-      "53           Polygon_Holes                 Example to test treatment of holes       True\n",
-      "54       Rio Grande do Sul  Cities of the Brazilian State of Rio Grande do...       True\n",
-      "55                    SIDS            North Carolina county SIDS death counts       True\n",
-      "56                   SIDS2   North Carolina county SIDS death counts and r...       True\n",
-      "57             Sacramento1          2000 Census Tract Data for Sacramento MSA       True\n",
-      "58             Sacramento2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "59           SanFran Crime   July-Dec 2012 crime incidents in San Francisc...       True\n",
-      "60               Savannah1   2000 Census Tract Data for Savannah, GA MSA a...       True\n",
-      "61               Savannah2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "62                 Scotlip               Male lip cancer in Scotland, 1975-80       True\n",
-      "63                Seattle1   2000 Census Tract Data for Seattle, WA MSA an...       True\n",
-      "64                Seattle2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "65                   South             US Southern county homicides 1960-1990       True\n",
-      "66                 StLouis   St Louis region county homicide counts and rates       True\n",
-      "67                  Tampa1   2000 Census Tract Data for Tampa, FL MSA and ...       True\n",
-      "68                  arcgis                               arcgis testing files       True\n",
-      "69                  baltim     Baltimore house sales prices and hedonics 1978       True\n",
-      "70                  berlin  Prenzlauer Berg neighborhood AirBnB data from ...       True\n",
-      "71                    book       Synthetic data to illustrate spatial weights       True\n",
-      "72                 burkitt  Burkitt's lymphoma in the Western Nile distric...       True\n",
-      "73                  calemp         Employment density for California counties       True\n",
-      "74                 chicago                              Chicago neighborhoods       True\n",
-      "75              clearwater                               mgwr testing dataset       True\n",
-      "76                columbus              Columbus neighborhood crime data 1980       True\n",
-      "77                 desmith    Small dataset to illustrate Moran's I statistic       True\n",
-      "78                geodanet        Datasets from geodanet for network analysis       True\n",
-      "79                 georgia  Various socio-economic variables for counties ...       True\n",
-      "80                juvenile    Residences of juvenile offenders in Cardiff, UK       True\n",
-      "81                  mexico  Decennial per capita incomes of Mexican states...       True\n",
-      "82                networks                  Datasets used for network testing       True\n",
-      "83                newHaven                            Network testing dataset       True\n",
-      "84               nyc_bikes                           New York City Bike Trips       True\n",
-      "85                   sids2  North Carolina county SIDS death counts and rates       True\n",
-      "86               snow_maps  Public water pumps and Cholera deaths in Londo...       True\n",
-      "87                     stl  Homicides and selected socio-economic characte...       True\n",
-      "88          street_net_pts                              Street network points       True\n",
-      "89                     taz           Traffic Analysis Zones in So. California       True\n",
-      "90                   tokyo                               Tokyo Mortality data       True\n",
-      "91               us_income  Per-capita income for the lower 48 US states 1...       True\n",
-      "92                virginia                        Virginia counties shapefile       True\n",
-      "93                    wmat          Datasets used for spatial weights testing       True\n"
-     ]
-    }
-   ],
-   "source": [
-    "from libpysal.examples import available\n",
-    "available()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We see that the new column `Installed` is added to the tablular output of `available`. This tells the user whether the dataset has aready been installed on the local machine. All the builtin datasets are by defninition installed. The interesting cases are the remote data sets that are new in this release. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Remote datasets\n",
-    "\n",
-    "The listing from `available` above shows that the dataset `Tampa1` is avalable, but has not yet been installed."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"700\"\n",
-       "            height=\"350\"\n",
-       "            src=\"https://geodacenter.github.io/data-and-lab/tampa1/\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.IFrame at 0x7f2c8867c828>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from libpysal.examples import explain\n",
-    "explain('Tampa1')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from libpysal.examples import load_example\n",
-    "tampa1 = load_example('Tampa1')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                      Name                                        Description  Installed\n",
-      "0                    10740    Albuquerque, New Mexico, Census 2000 Tract Data       True\n",
-      "1                   AirBnB  Airbnb rentals, socioeconomics, and crime in C...       True\n",
-      "2                  Atlanta       Atlanta, GA region homicide counts and rates       True\n",
-      "3                Baltimore          Baltimore house sales prices and hedonics       True\n",
-      "4                Bostonhsg               Boston housing and neighborhood data       True\n",
-      "5              Buenosaires      Electoral Data for 1999 Argentinean Elections       True\n",
-      "6              Charleston1   2000 Census Tract Data for Charleston, SC MSA...       True\n",
-      "7              Charleston2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "8           Chicago Health                   Chicago Health + Socio-Economics       True\n",
-      "9              Chile Labor                 Labor Markets in Chile (1982-2002)       True\n",
-      "10         Chile Migration            Internal Migration in Chile (1977-2002)       True\n",
-      "11              Cincinnati         2008 Cincinnati Crime + Socio-Demographics       True\n",
-      "12               Cleveland       2015 sales prices of homes in Cleveland, OH.       True\n",
-      "13                Columbus                        Columbus neighborhood crime       True\n",
-      "14                  Denver   Demographics and housing in Denver neighborho...       True\n",
-      "15               Elections               2012 and 2016 Presidential Elections       True\n",
-      "16                 Grid100                      Grid with simulated variables       True\n",
-      "17               Groceries                          2015 Chicago supermarkets       True\n",
-      "18                  Guerry          Moral statistics of France (Guerry, 1833)       True\n",
-      "19       Health Indicators                Chicago Health Indicators (2005-11)       True\n",
-      "20                 Health+                    2000 Health, Income + Diversity       True\n",
-      "21                Hickory1   2000 Census Tract Data for Hickory, NC MSA an...       True\n",
-      "22                Hickory2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "23              Home Sales              2014-15 Home Sales in King County, WA       True\n",
-      "24                 Houston       Houston, TX region homicide counts and rates       True\n",
-      "25                Juvenile             Cardiff juvenile delinquent residences       True\n",
-      "26                Lansing1   2000 Census Tract Data for Lansing, MI MSA an...       True\n",
-      "27                Lansing2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "28                 Laozone   Ozone measures at monitoring stations in Los ...       True\n",
-      "29                LasRosas   Corn yield, fertilizer and field data for pre...       True\n",
-      "30                    Line                                     Line Shapefile       True\n",
-      "31           Liquor Stores                         2015 Chicago Liquor Stores       True\n",
-      "32                 Malaria   Malaria incidence and population (1973, 95, 9...       True\n",
-      "33              Milwaukee1       2000 Census Tract Data for Milwaukee, WI MSA       True\n",
-      "34              Milwaukee2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "35                   NCOVR                      US county homicides 1960-1990       True\n",
-      "36                    NDVI        Normalized Difference Vegetation Index grid       True\n",
-      "37                     NYC   Demographic and housing data for New York Cit...       True\n",
-      "38            NYC Earnings              Block-level Earnings in NYC (2002-14)       True\n",
-      "39           NYC Education                               NYC Education (2000)       True\n",
-      "40       NYC Neighborhoods       Demographics for New York City neighborhoods       True\n",
-      "41  NYC Socio-Demographics                 NYC Education + Socio-Demographics       True\n",
-      "42              Natregimes                    NCOVR with regimes (book/PySAL)       True\n",
-      "43                   Nepal   Health, poverty and education indicators for ...       True\n",
-      "44                Ohiolung            Ohio lung cancer data, 1968, 1978, 1988       True\n",
-      "45                Orlando1   2000 Census Tract Data for Orlando, FL MSA an...       True\n",
-      "46                Orlando2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "47                  Oz9799                        Monthly ozone data, 1997-99       True\n",
-      "48             Phoenix ACS   Phoenix American Community Survey Data (2010,...       True\n",
-      "49              Pittsburgh                      Pittsburgh homicide locations       True\n",
-      "50                   Point                                    Point Shapefile       True\n",
-      "51                  Police           Police expenditures Mississippi counties       True\n",
-      "52                 Polygon                                  Polygon Shapefile       True\n",
-      "53           Polygon_Holes                 Example to test treatment of holes       True\n",
-      "54       Rio Grande do Sul  Cities of the Brazilian State of Rio Grande do...       True\n",
-      "55                    SIDS            North Carolina county SIDS death counts       True\n",
-      "56                   SIDS2   North Carolina county SIDS death counts and r...       True\n",
-      "57             Sacramento1          2000 Census Tract Data for Sacramento MSA       True\n",
-      "58             Sacramento2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "59           SanFran Crime   July-Dec 2012 crime incidents in San Francisc...       True\n",
-      "60               Savannah1   2000 Census Tract Data for Savannah, GA MSA a...       True\n",
-      "61               Savannah2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "62                 Scotlip               Male lip cancer in Scotland, 1975-80       True\n",
-      "63                Seattle1   2000 Census Tract Data for Seattle, WA MSA an...       True\n",
-      "64                Seattle2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "65                   South             US Southern county homicides 1960-1990       True\n",
-      "66                 StLouis   St Louis region county homicide counts and rates       True\n",
-      "67                  Tampa1   2000 Census Tract Data for Tampa, FL MSA and ...       True\n",
-      "68                  arcgis                               arcgis testing files       True\n",
-      "69                  baltim     Baltimore house sales prices and hedonics 1978       True\n",
-      "70                  berlin  Prenzlauer Berg neighborhood AirBnB data from ...       True\n",
-      "71                    book       Synthetic data to illustrate spatial weights       True\n",
-      "72                 burkitt  Burkitt's lymphoma in the Western Nile distric...       True\n",
-      "73                  calemp         Employment density for California counties       True\n",
-      "74                 chicago                              Chicago neighborhoods       True\n",
-      "75              clearwater                               mgwr testing dataset       True\n",
-      "76                columbus              Columbus neighborhood crime data 1980       True\n",
-      "77                 desmith    Small dataset to illustrate Moran's I statistic       True\n",
-      "78                geodanet        Datasets from geodanet for network analysis       True\n",
-      "79                 georgia  Various socio-economic variables for counties ...       True\n",
-      "80                juvenile    Residences of juvenile offenders in Cardiff, UK       True\n",
-      "81                  mexico  Decennial per capita incomes of Mexican states...       True\n",
-      "82                networks                  Datasets used for network testing       True\n",
-      "83                newHaven                            Network testing dataset       True\n",
-      "84               nyc_bikes                           New York City Bike Trips       True\n",
-      "85                   sids2  North Carolina county SIDS death counts and rates       True\n",
-      "86               snow_maps  Public water pumps and Cholera deaths in Londo...       True\n",
-      "87                     stl  Homicides and selected socio-economic characte...       True\n",
-      "88          street_net_pts                              Street network points       True\n",
-      "89                     taz           Traffic Analysis Zones in So. California       True\n",
-      "90                   tokyo                               Tokyo Mortality data       True\n",
-      "91               us_income  Per-capita income for the lower 48 US states 1...       True\n",
-      "92                virginia                        Virginia counties shapefile       True\n",
-      "93                    wmat          Datasets used for spatial weights testing       True\n"
-     ]
-    }
-   ],
-   "source": [
-    "available()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tampa1.installed"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['/home/jovyan/pysal_data/Tampa1/__MACOSX/._TampaMSA',\n",
-       " '/home/jovyan/pysal_data/Tampa1/__MACOSX/TampaMSA/._tampa_counties.sbx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/__MACOSX/TampaMSA/._tampa_final_census2.sbx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/__MACOSX/TampaMSA/._2000 Census Data Variables_Documentation.pdf',\n",
-       " '/home/jovyan/pysal_data/Tampa1/__MACOSX/TampaMSA/._tampa_final_census2.sbn',\n",
-       " '/home/jovyan/pysal_data/Tampa1/__MACOSX/TampaMSA/._tampa_counties.sbn',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.geojson',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.gpkg',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.xlsx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.mid',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.spx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.gdbindexes',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByDestItemTypeID.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000002.gdbtable',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.CatItemsByType.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000003.gdbtablx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/gdb',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.gdbtable',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000002.gdbtablx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.gdbtablx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByForwardLabel.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByBackwardLabel.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByOriginItemTypeID.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.CatItemTypesByParentTypeID.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000003.gdbtable',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.gdbindexes',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.gdbindexes',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.gdbindexes',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.gdbtable',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.spx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.gdbindexes',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.FDO_UUID.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.CatRelsByOriginID.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.gdbtable',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000003.gdbindexes',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.gdbtablx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.CatItemsByPhysicalName.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.gdbtablx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.CatRelsByType.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.gdbindexes',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByUUID.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.gdbtablx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.CatItemTypesByUUID.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.gdbtablx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.gdbtable',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.gdbtable',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.gdbtablx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.spx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.FDO_UUID.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.CatRelsByDestinationID.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.gdbtable',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.gdbtable',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/timestamps',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByName.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.gdbindexes',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.CatItemTypesByName.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.TablesByName.atx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.gdbtablx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.sbn',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.xlsx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/2000 Census Data Variables_Documentation.pdf',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.mid',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.dbf',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.gpkg',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.mif',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.kml',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.sqlite',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.dbf',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.prj',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.mif',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.shx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.geojson',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.shx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.sbn',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.sbx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.prj',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.shp',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.shp',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.sqlite',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_final_census2.sbx',\n",
-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.kml']"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tampa1.get_file_list()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tampa_counties_shp = tampa1.load('tampa_counties.shp')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<libpysal.io.iohandlers.pyShpIO.PurePyShpWrapper at 0x7f2c886869b0>"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tampa_counties_shp"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import geopandas"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tampa_df = geopandas.read_file(tampa1.get_path('tampa_counties.shp'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2c885f5e48>"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "%matplotlib inline\n",
-    "tampa_df.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Other Remotes\n",
-    "\n",
-    "In addition to the remote datasets from the GeoData Data Science Center, there are several large remotes available at github repositories. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Rio_Grande_do_Sul\n",
-      "======================\n",
-      "\n",
-      "Cities of the Brazilian State of Rio Grande do Sul\n",
-      "-------------------------------------------------------\n",
-      "\n",
-      "* 43MUE250GC_SIR.dbf: attribute data (k=2)\n",
-      "* 43MUE250GC_SIR.shp: Polygon shapefile (n=499)\n",
-      "* 43MUE250GC_SIR.shx: spatial index\n",
-      "* 43MUE250GC_SIR.cpg: encoding file \n",
-      "* 43MUE250GC_SIR.prj: projection information \n",
-      "* map_RS_BR.dbf: attribute data (k=3)\n",
-      "* map_RS_BR.shp: Polygon shapefile (no lakes) (n=497)\n",
-      "* map_RS_BR.prj: projection information\n",
-      "* map_RS_BR.shx: spatial index\n",
-      "\n",
-      "\n",
-      "\n",
-      "Source: Renan Xavier Cortes \n",
-      "Reference: https://github.com/pysal/pysal/issues/889#issuecomment-396693495\n",
-      "\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "explain('Rio Grande do Sul')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note that the `explain` function generates a textual description of this example dataset - no rendering of the map is done as the source repository does not include that functionality."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rio = load_example('Rio Grande do Sul')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                      Name                                        Description  Installed\n",
-      "0                    10740    Albuquerque, New Mexico, Census 2000 Tract Data       True\n",
-      "1                   AirBnB  Airbnb rentals, socioeconomics, and crime in C...       True\n",
-      "2                  Atlanta       Atlanta, GA region homicide counts and rates       True\n",
-      "3                Baltimore          Baltimore house sales prices and hedonics       True\n",
-      "4                Bostonhsg               Boston housing and neighborhood data       True\n",
-      "5              Buenosaires      Electoral Data for 1999 Argentinean Elections       True\n",
-      "6              Charleston1   2000 Census Tract Data for Charleston, SC MSA...       True\n",
-      "7              Charleston2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "8           Chicago Health                   Chicago Health + Socio-Economics       True\n",
-      "9              Chile Labor                 Labor Markets in Chile (1982-2002)       True\n",
-      "10         Chile Migration            Internal Migration in Chile (1977-2002)       True\n",
-      "11              Cincinnati         2008 Cincinnati Crime + Socio-Demographics       True\n",
-      "12               Cleveland       2015 sales prices of homes in Cleveland, OH.       True\n",
-      "13                Columbus                        Columbus neighborhood crime       True\n",
-      "14                  Denver   Demographics and housing in Denver neighborho...       True\n",
-      "15               Elections               2012 and 2016 Presidential Elections       True\n",
-      "16                 Grid100                      Grid with simulated variables       True\n",
-      "17               Groceries                          2015 Chicago supermarkets       True\n",
-      "18                  Guerry          Moral statistics of France (Guerry, 1833)       True\n",
-      "19       Health Indicators                Chicago Health Indicators (2005-11)       True\n",
-      "20                 Health+                    2000 Health, Income + Diversity       True\n",
-      "21                Hickory1   2000 Census Tract Data for Hickory, NC MSA an...       True\n",
-      "22                Hickory2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "23              Home Sales              2014-15 Home Sales in King County, WA       True\n",
-      "24                 Houston       Houston, TX region homicide counts and rates       True\n",
-      "25                Juvenile             Cardiff juvenile delinquent residences       True\n",
-      "26                Lansing1   2000 Census Tract Data for Lansing, MI MSA an...       True\n",
-      "27                Lansing2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "28                 Laozone   Ozone measures at monitoring stations in Los ...       True\n",
-      "29                LasRosas   Corn yield, fertilizer and field data for pre...       True\n",
-      "30                    Line                                     Line Shapefile       True\n",
-      "31           Liquor Stores                         2015 Chicago Liquor Stores       True\n",
-      "32                 Malaria   Malaria incidence and population (1973, 95, 9...       True\n",
-      "33              Milwaukee1       2000 Census Tract Data for Milwaukee, WI MSA       True\n",
-      "34              Milwaukee2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "35                   NCOVR                      US county homicides 1960-1990       True\n",
-      "36                    NDVI        Normalized Difference Vegetation Index grid       True\n",
-      "37                     NYC   Demographic and housing data for New York Cit...       True\n",
-      "38            NYC Earnings              Block-level Earnings in NYC (2002-14)       True\n",
-      "39           NYC Education                               NYC Education (2000)       True\n",
-      "40       NYC Neighborhoods       Demographics for New York City neighborhoods       True\n",
-      "41  NYC Socio-Demographics                 NYC Education + Socio-Demographics       True\n",
-      "42              Natregimes                    NCOVR with regimes (book/PySAL)       True\n",
-      "43                   Nepal   Health, poverty and education indicators for ...       True\n",
-      "44                Ohiolung            Ohio lung cancer data, 1968, 1978, 1988       True\n",
-      "45                Orlando1   2000 Census Tract Data for Orlando, FL MSA an...       True\n",
-      "46                Orlando2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "47                  Oz9799                        Monthly ozone data, 1997-99       True\n",
-      "48             Phoenix ACS   Phoenix American Community Survey Data (2010,...       True\n",
-      "49              Pittsburgh                      Pittsburgh homicide locations       True\n",
-      "50                   Point                                    Point Shapefile       True\n",
-      "51                  Police           Police expenditures Mississippi counties       True\n",
-      "52                 Polygon                                  Polygon Shapefile       True\n",
-      "53           Polygon_Holes                 Example to test treatment of holes       True\n",
-      "54       Rio Grande do Sul  Cities of the Brazilian State of Rio Grande do...       True\n",
-      "55                    SIDS            North Carolina county SIDS death counts       True\n",
-      "56                   SIDS2   North Carolina county SIDS death counts and r...       True\n",
-      "57             Sacramento1          2000 Census Tract Data for Sacramento MSA       True\n",
-      "58             Sacramento2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "59           SanFran Crime   July-Dec 2012 crime incidents in San Francisc...       True\n",
-      "60               Savannah1   2000 Census Tract Data for Savannah, GA MSA a...       True\n",
-      "61               Savannah2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "62                 Scotlip               Male lip cancer in Scotland, 1975-80       True\n",
-      "63                Seattle1   2000 Census Tract Data for Seattle, WA MSA an...       True\n",
-      "64                Seattle2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "65                   South             US Southern county homicides 1960-1990       True\n",
-      "66                 StLouis   St Louis region county homicide counts and rates       True\n",
-      "67                  Tampa1   2000 Census Tract Data for Tampa, FL MSA and ...       True\n",
-      "68                  arcgis                               arcgis testing files       True\n",
-      "69                  baltim     Baltimore house sales prices and hedonics 1978       True\n",
-      "70                  berlin  Prenzlauer Berg neighborhood AirBnB data from ...       True\n",
-      "71                    book       Synthetic data to illustrate spatial weights       True\n",
-      "72                 burkitt  Burkitt's lymphoma in the Western Nile distric...       True\n",
-      "73                  calemp         Employment density for California counties       True\n",
-      "74                 chicago                              Chicago neighborhoods       True\n",
-      "75              clearwater                               mgwr testing dataset       True\n",
-      "76                columbus              Columbus neighborhood crime data 1980       True\n",
-      "77                 desmith    Small dataset to illustrate Moran's I statistic       True\n",
-      "78                geodanet        Datasets from geodanet for network analysis       True\n",
-      "79                 georgia  Various socio-economic variables for counties ...       True\n",
-      "80                juvenile    Residences of juvenile offenders in Cardiff, UK       True\n",
-      "81                  mexico  Decennial per capita incomes of Mexican states...       True\n",
-      "82                networks                  Datasets used for network testing       True\n",
-      "83                newHaven                            Network testing dataset       True\n",
-      "84               nyc_bikes                           New York City Bike Trips       True\n",
-      "85                   sids2  North Carolina county SIDS death counts and rates       True\n",
-      "86               snow_maps  Public water pumps and Cholera deaths in Londo...       True\n",
-      "87                     stl  Homicides and selected socio-economic characte...       True\n",
-      "88          street_net_pts                              Street network points       True\n",
-      "89                     taz           Traffic Analysis Zones in So. California       True\n",
-      "90                   tokyo                               Tokyo Mortality data       True\n",
-      "91               us_income  Per-capita income for the lower 48 US states 1...       True\n",
-      "92                virginia                        Virginia counties shapefile       True\n",
-      "93                    wmat          Datasets used for spatial weights testing       True\n"
-     ]
-    }
-   ],
-   "source": [
-    "available()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Grabbing all the remotes\n",
-    "\n",
-    "All the remote datasets can be downloaded with:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "AirBnB\n",
-      "Already downloaded\n",
-      "Atlanta\n",
-      "Already downloaded\n",
-      "Baltimore\n",
-      "Already downloaded\n",
-      "Bostonhsg\n",
-      "Already downloaded\n",
-      "Buenosaires\n",
-      "Already downloaded\n",
-      "Charleston1\n",
-      "Already downloaded\n",
-      "Charleston2\n",
-      "Already downloaded\n",
-      "Chicago Health\n",
-      "Already downloaded\n",
-      "Chile Labor\n",
-      "Already downloaded\n",
-      "Chile Migration\n",
-      "Already downloaded\n",
-      "Cincinnati\n",
-      "Already downloaded\n",
-      "Cleveland\n",
-      "Already downloaded\n",
-      "Columbus\n",
-      "Already downloaded\n",
-      "Denver\n",
-      "Already downloaded\n",
-      "Elections\n",
-      "Already downloaded\n",
-      "Grid100\n",
-      "Already downloaded\n",
-      "Groceries\n",
-      "Already downloaded\n",
-      "Guerry\n",
-      "Already downloaded\n",
-      "Health Indicators\n",
-      "Already downloaded\n",
-      "Health+\n",
-      "Already downloaded\n",
-      "Hickory1\n",
-      "Already downloaded\n",
-      "Hickory2\n",
-      "Already downloaded\n",
-      "Home Sales\n",
-      "Already downloaded\n",
-      "Houston\n",
-      "Already downloaded\n",
-      "Juvenile\n",
-      "Already downloaded\n",
-      "Lansing1\n",
-      "Already downloaded\n",
-      "Lansing2\n",
-      "Already downloaded\n",
-      "Laozone\n",
-      "Already downloaded\n",
-      "LasRosas\n",
-      "Already downloaded\n",
-      "Liquor Stores\n",
-      "Already downloaded\n",
-      "Malaria\n",
-      "Already downloaded\n",
-      "Milwaukee1\n",
-      "Already downloaded\n",
-      "Milwaukee2\n",
-      "Already downloaded\n",
-      "NCOVR\n",
-      "Already downloaded\n",
-      "NDVI\n",
-      "Already downloaded\n",
-      "NYC\n",
-      "Already downloaded\n",
-      "NYC Earnings\n",
-      "Already downloaded\n",
-      "NYC Education\n",
-      "Already downloaded\n",
-      "NYC Neighborhoods\n",
-      "Already downloaded\n",
-      "NYC Socio-Demographics\n",
-      "Already downloaded\n",
-      "Natregimes\n",
-      "Already downloaded\n",
-      "Nepal\n",
-      "Already downloaded\n",
-      "Ohiolung\n",
-      "Already downloaded\n",
-      "Orlando1\n",
-      "Already downloaded\n",
-      "Orlando2\n",
-      "Already downloaded\n",
-      "Oz9799\n",
-      "Already downloaded\n",
-      "Phoenix ACS\n",
-      "Already downloaded\n",
-      "Pittsburgh\n",
-      "Already downloaded\n",
-      "Police\n",
-      "Already downloaded\n",
-      "Rio Grande do Sul\n",
-      "Already downloaded\n",
-      "SIDS\n",
-      "Already downloaded\n",
-      "SIDS2\n",
-      "Already downloaded\n",
-      "Sacramento1\n",
-      "Already downloaded\n",
-      "Sacramento2\n",
-      "Already downloaded\n",
-      "SanFran Crime\n",
-      "Already downloaded\n",
-      "Savannah1\n",
-      "Already downloaded\n",
-      "Savannah2\n",
-      "Already downloaded\n",
-      "Scotlip\n",
-      "Already downloaded\n",
-      "Seattle1\n",
-      "Already downloaded\n",
-      "Seattle2\n",
-      "Already downloaded\n",
-      "South\n",
-      "Already downloaded\n",
-      "StLouis\n",
-      "Already downloaded\n",
-      "Tampa1\n",
-      "Already downloaded\n",
-      "clearwater\n",
-      "Already downloaded\n",
-      "newHaven\n",
-      "Already downloaded\n",
-      "nyc_bikes\n",
-      "Already downloaded\n",
-      "taz\n",
-      "Already downloaded\n"
-     ]
-    }
-   ],
-   "source": [
-    "libpysal.examples.fetch_all()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                      Name                                        Description  Installed\n",
-      "0                    10740    Albuquerque, New Mexico, Census 2000 Tract Data       True\n",
-      "1                   AirBnB  Airbnb rentals, socioeconomics, and crime in C...       True\n",
-      "2                  Atlanta       Atlanta, GA region homicide counts and rates       True\n",
-      "3                Baltimore          Baltimore house sales prices and hedonics       True\n",
-      "4                Bostonhsg               Boston housing and neighborhood data       True\n",
-      "5              Buenosaires      Electoral Data for 1999 Argentinean Elections       True\n",
-      "6              Charleston1   2000 Census Tract Data for Charleston, SC MSA...       True\n",
-      "7              Charleston2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "8           Chicago Health                   Chicago Health + Socio-Economics       True\n",
-      "9              Chile Labor                 Labor Markets in Chile (1982-2002)       True\n",
-      "10         Chile Migration            Internal Migration in Chile (1977-2002)       True\n",
-      "11              Cincinnati         2008 Cincinnati Crime + Socio-Demographics       True\n",
-      "12               Cleveland       2015 sales prices of homes in Cleveland, OH.       True\n",
-      "13                Columbus                        Columbus neighborhood crime       True\n",
-      "14                  Denver   Demographics and housing in Denver neighborho...       True\n",
-      "15               Elections               2012 and 2016 Presidential Elections       True\n",
-      "16                 Grid100                      Grid with simulated variables       True\n",
-      "17               Groceries                          2015 Chicago supermarkets       True\n",
-      "18                  Guerry          Moral statistics of France (Guerry, 1833)       True\n",
-      "19       Health Indicators                Chicago Health Indicators (2005-11)       True\n",
-      "20                 Health+                    2000 Health, Income + Diversity       True\n",
-      "21                Hickory1   2000 Census Tract Data for Hickory, NC MSA an...       True\n",
-      "22                Hickory2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "23              Home Sales              2014-15 Home Sales in King County, WA       True\n",
-      "24                 Houston       Houston, TX region homicide counts and rates       True\n",
-      "25                Juvenile             Cardiff juvenile delinquent residences       True\n",
-      "26                Lansing1   2000 Census Tract Data for Lansing, MI MSA an...       True\n",
-      "27                Lansing2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "28                 Laozone   Ozone measures at monitoring stations in Los ...       True\n",
-      "29                LasRosas   Corn yield, fertilizer and field data for pre...       True\n",
-      "30                    Line                                     Line Shapefile       True\n",
-      "31           Liquor Stores                         2015 Chicago Liquor Stores       True\n",
-      "32                 Malaria   Malaria incidence and population (1973, 95, 9...       True\n",
-      "33              Milwaukee1       2000 Census Tract Data for Milwaukee, WI MSA       True\n",
-      "34              Milwaukee2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "35                   NCOVR                      US county homicides 1960-1990       True\n",
-      "36                    NDVI        Normalized Difference Vegetation Index grid       True\n",
-      "37                     NYC   Demographic and housing data for New York Cit...       True\n",
-      "38            NYC Earnings              Block-level Earnings in NYC (2002-14)       True\n",
-      "39           NYC Education                               NYC Education (2000)       True\n",
-      "40       NYC Neighborhoods       Demographics for New York City neighborhoods       True\n",
-      "41  NYC Socio-Demographics                 NYC Education + Socio-Demographics       True\n",
-      "42              Natregimes                    NCOVR with regimes (book/PySAL)       True\n",
-      "43                   Nepal   Health, poverty and education indicators for ...       True\n",
-      "44                Ohiolung            Ohio lung cancer data, 1968, 1978, 1988       True\n",
-      "45                Orlando1   2000 Census Tract Data for Orlando, FL MSA an...       True\n",
-      "46                Orlando2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "47                  Oz9799                        Monthly ozone data, 1997-99       True\n",
-      "48             Phoenix ACS   Phoenix American Community Survey Data (2010,...       True\n",
-      "49              Pittsburgh                      Pittsburgh homicide locations       True\n",
-      "50                   Point                                    Point Shapefile       True\n",
-      "51                  Police           Police expenditures Mississippi counties       True\n",
-      "52                 Polygon                                  Polygon Shapefile       True\n",
-      "53           Polygon_Holes                 Example to test treatment of holes       True\n",
-      "54       Rio Grande do Sul  Cities of the Brazilian State of Rio Grande do...       True\n",
-      "55                    SIDS            North Carolina county SIDS death counts       True\n",
-      "56                   SIDS2   North Carolina county SIDS death counts and r...       True\n",
-      "57             Sacramento1          2000 Census Tract Data for Sacramento MSA       True\n",
-      "58             Sacramento2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "59           SanFran Crime   July-Dec 2012 crime incidents in San Francisc...       True\n",
-      "60               Savannah1   2000 Census Tract Data for Savannah, GA MSA a...       True\n",
-      "61               Savannah2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "62                 Scotlip               Male lip cancer in Scotland, 1975-80       True\n",
-      "63                Seattle1   2000 Census Tract Data for Seattle, WA MSA an...       True\n",
-      "64                Seattle2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "65                   South             US Southern county homicides 1960-1990       True\n",
-      "66                 StLouis   St Louis region county homicide counts and rates       True\n",
-      "67                  Tampa1   2000 Census Tract Data for Tampa, FL MSA and ...       True\n",
-      "68                  arcgis                               arcgis testing files       True\n",
-      "69                  baltim     Baltimore house sales prices and hedonics 1978       True\n",
-      "70                  berlin  Prenzlauer Berg neighborhood AirBnB data from ...       True\n",
-      "71                    book       Synthetic data to illustrate spatial weights       True\n",
-      "72                 burkitt  Burkitt's lymphoma in the Western Nile distric...       True\n",
-      "73                  calemp         Employment density for California counties       True\n",
-      "74                 chicago                              Chicago neighborhoods       True\n",
-      "75              clearwater                               mgwr testing dataset       True\n",
-      "76                columbus              Columbus neighborhood crime data 1980       True\n",
-      "77                 desmith    Small dataset to illustrate Moran's I statistic       True\n",
-      "78                geodanet        Datasets from geodanet for network analysis       True\n",
-      "79                 georgia  Various socio-economic variables for counties ...       True\n",
-      "80                juvenile    Residences of juvenile offenders in Cardiff, UK       True\n",
-      "81                  mexico  Decennial per capita incomes of Mexican states...       True\n",
-      "82                networks                  Datasets used for network testing       True\n",
-      "83                newHaven                            Network testing dataset       True\n",
-      "84               nyc_bikes                           New York City Bike Trips       True\n",
-      "85                   sids2  North Carolina county SIDS death counts and rates       True\n",
-      "86               snow_maps  Public water pumps and Cholera deaths in Londo...       True\n",
-      "87                     stl  Homicides and selected socio-economic characte...       True\n",
-      "88          street_net_pts                              Street network points       True\n",
-      "89                     taz           Traffic Analysis Zones in So. California       True\n",
-      "90                   tokyo                               Tokyo Mortality data       True\n",
-      "91               us_income  Per-capita income for the lower 48 US states 1...       True\n",
-      "92                virginia                        Virginia counties shapefile       True\n",
-      "93                    wmat          Datasets used for spatial weights testing       True\n"
-     ]
-    }
-   ],
-   "source": [
-    "available()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/notebooks/voronoi.html b/docs/notebooks/voronoi.html
deleted file mode 100644
index 428597eb4..000000000
--- a/docs/notebooks/voronoi.html
+++ /dev/null
@@ -1,804 +0,0 @@
-<!DOCTYPE html>
-
-<html>
-  <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>Voronoi Polygons for 2-D Point Sets &#8212; libpysal v4.4.0 Manual</title>
-    <link rel="stylesheet" href="../_static/bootstrap-sphinx.css" type="text/css" />
-    <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
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-    <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
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-    <link rel="shortcut icon" href="../_static/pysal_favicon.ico"/>
-    <link rel="index" title="Index" href="../genindex.html" />
-    <link rel="search" title="Search" href="../search.html" />
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-          libpysal</a>
-        <span class="navbar-text navbar-version pull-left"><b>4.4.0</b></span>
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-            
-                <li><a href="../installation.html">Installation</a></li>
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-<div class="section" id="Voronoi-Polygons-for-2-D-Point-Sets">
-<h1>Voronoi Polygons for 2-D Point Sets<a class="headerlink" href="#Voronoi-Polygons-for-2-D-Point-Sets" title="Permalink to this headline">¶</a></h1>
-<p>Author: Serge Rey (<a class="reference external" href="http://github.com/sjsrey">http://github.com/sjsrey</a>)</p>
-<div class="section" id="Basic-Usage">
-<h2>Basic Usage<a class="headerlink" href="#Basic-Usage" title="Permalink to this headline">¶</a></h2>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="kn">import</span> <span class="nn">sys</span>
-<span class="kn">import</span> <span class="nn">os</span>
-<span class="n">sys</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">abspath</span><span class="p">(</span><span class="s1">&#39;..&#39;</span><span class="p">))</span>
-<span class="kn">import</span> <span class="nn">libpysal</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="kn">from</span> <span class="nn">libpysal.cg.voronoi</span>  <span class="kn">import</span> <span class="n">voronoi</span><span class="p">,</span> <span class="n">voronoi_frames</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">points</span> <span class="o">=</span> <span class="p">[(</span><span class="mf">10.2</span><span class="p">,</span> <span class="mf">5.1</span><span class="p">),</span> <span class="p">(</span><span class="mf">4.7</span><span class="p">,</span> <span class="mf">2.2</span><span class="p">),</span> <span class="p">(</span><span class="mf">5.3</span><span class="p">,</span> <span class="mf">5.7</span><span class="p">),</span> <span class="p">(</span><span class="mf">2.7</span><span class="p">,</span> <span class="mf">5.3</span><span class="p">)]</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">regions</span><span class="p">,</span> <span class="n">vertices</span> <span class="o">=</span> <span class="n">voronoi</span><span class="p">(</span><span class="n">points</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">regions</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
-</pre></div>
-</div>
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-<div class="highlight"><pre>
-[[1, 3, 2], [4, 5, 1, 0], [0, 1, 7, 6], [9, 0, 8]]
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
-</pre></div>
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-<span></span><span class="n">vertices</span>
-</pre></div>
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-</div>
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-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
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-array([[  4.21783296,   4.08408578],
-       [  7.51956025,   3.51807539],
-       [  9.4642193 ,  19.3994576 ],
-       [ 14.98210684, -10.63503022],
-       [ -9.22691341,  -4.58994414],
-       [ 14.98210684, -10.63503022],
-       [  1.78491801,  19.89803294],
-       [  9.4642193 ,  19.3994576 ],
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-<span></span><span class="n">region_df</span><span class="p">,</span> <span class="n">point_df</span> <span class="o">=</span> <span class="n">voronoi_frames</span><span class="p">(</span><span class="n">points</span><span class="p">)</span>
-</pre></div>
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-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
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-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="o">%</span><span class="k">matplotlib</span> inline
-<span class="kn">import</span> <span class="nn">matplotlib</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
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-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
-<span class="n">region_df</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;blue&#39;</span><span class="p">,</span><span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
-<span class="n">point_df</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;red&#39;</span><span class="p">)</span>
-
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-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
-</pre></div>
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-<div class="highlight"><pre>
-&lt;matplotlib.axes._subplots.AxesSubplot at 0x7f3304d59e48&gt;
-</pre></div></div>
-</div>
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-<div class="output_area docutils container">
-<img alt="../_images/notebooks_voronoi_10_1.png" src="../_images/notebooks_voronoi_10_1.png" />
-</div>
-</div>
-</div>
-<div class="section" id="Larger-Problem">
-<h2>Larger Problem<a class="headerlink" href="#Larger-Problem" title="Permalink to this headline">¶</a></h2>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">n_points</span> <span class="o">=</span> <span class="mi">200</span>
-<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">12345</span><span class="p">)</span>
-<span class="n">points</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">((</span><span class="n">n_points</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span><span class="o">*</span><span class="mi">10</span> <span class="o">+</span> <span class="mi">10</span>
-<span class="n">results</span> <span class="o">=</span> <span class="n">voronoi</span><span class="p">(</span><span class="n">points</span><span class="p">)</span>
-<span class="n">mins</span> <span class="o">=</span> <span class="n">points</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-<span class="n">maxs</span> <span class="o">=</span> <span class="n">points</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-</pre></div>
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-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
-</pre></div>
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-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">regions</span><span class="p">,</span> <span class="n">vertices</span> <span class="o">=</span> <span class="n">voronoi</span><span class="p">(</span><span class="n">points</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">regions_df</span><span class="p">,</span> <span class="n">points_df</span> <span class="o">=</span> <span class="n">voronoi_frames</span><span class="p">(</span><span class="n">points</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
-<span class="n">points_df</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;red&#39;</span><span class="p">)</span>
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-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-&lt;matplotlib.axes._subplots.AxesSubplot at 0x7f32fd486b38&gt;
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
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-<img alt="../_images/notebooks_voronoi_15_1.png" src="../_images/notebooks_voronoi_15_1.png" />
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
-<span class="n">regions_df</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;blue&#39;</span><span class="p">,</span><span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
-<span class="n">points_df</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;red&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-&lt;matplotlib.axes._subplots.AxesSubplot at 0x7f32fd3c76a0&gt;
-</pre></div></div>
-</div>
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-<div class="prompt empty docutils container">
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-<div class="output_area docutils container">
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-</div>
-</div>
-</div>
-<div class="section" id="Trimming">
-<h2>Trimming<a class="headerlink" href="#Trimming" title="Permalink to this headline">¶</a></h2>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">points</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">points</span><span class="p">)</span>
-<span class="n">maxs</span> <span class="o">=</span> <span class="n">points</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-<span class="n">mins</span> <span class="o">=</span> <span class="n">points</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-<span class="n">xr</span> <span class="o">=</span> <span class="n">maxs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">mins</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<span class="n">yr</span> <span class="o">=</span> <span class="n">maxs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">mins</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-<span class="n">buff</span> <span class="o">=</span> <span class="mf">0.05</span>
-<span class="n">r</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">yr</span><span class="p">,</span> <span class="n">xr</span><span class="p">)</span> <span class="o">*</span> <span class="n">buff</span>
-<span class="n">minx</span> <span class="o">=</span> <span class="n">mins</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">r</span>
-<span class="n">miny</span> <span class="o">=</span> <span class="n">mins</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">r</span>
-<span class="n">maxx</span> <span class="o">=</span> <span class="n">maxs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">r</span>
-<span class="n">maxy</span> <span class="o">=</span> <span class="n">maxs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">r</span>
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-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
-<span class="n">regions_df</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s1">&#39;blue&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.2</span> <span class="p">)</span>
-<span class="n">points_df</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;red&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="n">minx</span><span class="p">,</span> <span class="n">maxx</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="n">miny</span><span class="p">,</span> <span class="n">maxy</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;buffer: </span><span class="si">%f</span><span class="s2">, n: </span><span class="si">%d</span><span class="s2">&quot;</span><span class="o">%</span><span class="p">(</span><span class="n">r</span><span class="p">,</span><span class="n">n_points</span><span class="p">))</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-
-</pre></div>
-</div>
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-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
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-<div class="output_area docutils container">
-<img alt="../_images/notebooks_voronoi_19_0.png" src="../_images/notebooks_voronoi_19_0.png" />
-</div>
-</div>
-</div>
-</div>
-<div class="section" id="Voronoi-Weights">
-<h1>Voronoi Weights<a class="headerlink" href="#Voronoi-Weights" title="Permalink to this headline">¶</a></h1>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[17]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="kn">from</span> <span class="nn">libpysal.weights.contiguity</span> <span class="kn">import</span> <span class="n">Voronoi</span> <span class="k">as</span> <span class="n">Vornoi_weights</span>
-</pre></div>
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-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[18]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">w</span> <span class="o">=</span> <span class="n">Vornoi_weights</span><span class="p">(</span><span class="n">points</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[19]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">w</span><span class="o">.</span><span class="n">n</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[19]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-200
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[20]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">w</span><span class="o">.</span><span class="n">pct_nonzero</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[20]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-2.685
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[21]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">w</span><span class="o">.</span><span class="n">histogram</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[21]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-[(1, 1),
- (2, 6),
- (3, 17),
- (4, 34),
- (5, 41),
- (6, 63),
- (7, 24),
- (8, 7),
- (9, 5),
- (10, 1),
- (11, 0),
- (12, 1)]
-</pre></div></div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[22]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">idx</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">n</span><span class="p">)</span> <span class="k">if</span> <span class="n">w</span><span class="o">.</span><span class="n">cardinalities</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">==</span><span class="mi">12</span><span class="p">]</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[23]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">points</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[23]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-array([[16.50851787, 13.12932895]])
-</pre></div></div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span>
-</pre></div>
-</div>
-</div>
-</div>
-
-
-    </div>
-      
-  </div>
-</div>
-<footer class="footer">
-  <div class="container">
-    <p class="pull-right">
-      <a href="#">Back to top</a>
-      
-        <br/>
-        
-<div id="sourcelink">
-  <a href="../_sources/notebooks/voronoi.ipynb.txt"
-     rel="nofollow">Source</a>
-</div>
-      
-    </p>
-    <p>
-        &copy; Copyright 2018-, pysal developers.<br/>
-      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 3.1.2.<br/>
-    </p>
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\ No newline at end of file
diff --git a/docs/notebooks/voronoi.ipynb b/docs/notebooks/voronoi.ipynb
deleted file mode 100644
index 75d7263d3..000000000
--- a/docs/notebooks/voronoi.ipynb
+++ /dev/null
@@ -1,478 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Voronoi Polygons for 2-D Point Sets\n",
-    "\n",
-    "Author: Serge Rey (http://github.com/sjsrey)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Basic Usage"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sys\n",
-    "import os\n",
-    "sys.path.append(os.path.abspath('..'))\n",
-    "import libpysal"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from libpysal.cg.voronoi  import voronoi, voronoi_frames"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "points = [(10.2, 5.1), (4.7, 2.2), (5.3, 5.7), (2.7, 5.3)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "regions, vertices = voronoi(points)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[[1, 3, 2], [4, 5, 1, 0], [0, 1, 7, 6], [9, 0, 8]]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "regions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[  4.21783296,   4.08408578],\n",
-       "       [  7.51956025,   3.51807539],\n",
-       "       [  9.4642193 ,  19.3994576 ],\n",
-       "       [ 14.98210684, -10.63503022],\n",
-       "       [ -9.22691341,  -4.58994414],\n",
-       "       [ 14.98210684, -10.63503022],\n",
-       "       [  1.78491801,  19.89803294],\n",
-       "       [  9.4642193 ,  19.3994576 ],\n",
-       "       [  1.78491801,  19.89803294],\n",
-       "       [ -9.22691341,  -4.58994414]])"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "vertices"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "region_df, point_df = voronoi_frames(points)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "import matplotlib\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3304d59e48>"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots()\n",
-    "region_df.plot(ax=ax, color='blue',edgecolor='black', alpha=0.3)\n",
-    "point_df.plot(ax=ax, color='red')\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Larger Problem"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "n_points = 200\n",
-    "np.random.seed(12345)\n",
-    "points = np.random.random((n_points,2))*10 + 10\n",
-    "results = voronoi(points)\n",
-    "mins = points.min(axis=0)\n",
-    "maxs = points.max(axis=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "regions, vertices = voronoi(points)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "regions_df, points_df = voronoi_frames(points)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f32fd486b38>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots()\n",
-    "points_df.plot(ax=ax, color='red')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f32fd3c76a0>"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots()\n",
-    "regions_df.plot(ax=ax, color='blue',edgecolor='black', alpha=0.3)\n",
-    "points_df.plot(ax=ax, color='red')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Trimming"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "points = np.array(points)\n",
-    "maxs = points.max(axis=0)\n",
-    "mins = points.min(axis=0)\n",
-    "xr = maxs[0] - mins[0]\n",
-    "yr = maxs[1] - mins[1]\n",
-    "buff = 0.05\n",
-    "r = max(yr, xr) * buff\n",
-    "minx = mins[0] - r\n",
-    "miny = mins[1] - r\n",
-    "maxx = maxs[0] + r\n",
-    "maxy = maxs[1] + r"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots()\n",
-    "regions_df.plot(ax=ax, edgecolor='black', facecolor='blue', alpha=0.2 )\n",
-    "points_df.plot(ax=ax, color='red')\n",
-    "plt.xlim(minx, maxx)\n",
-    "plt.ylim(miny, maxy)\n",
-    "plt.title(\"buffer: %f, n: %d\"%(r,n_points))\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Voronoi Weights"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from libpysal.weights.contiguity import Voronoi as Vornoi_weights"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "w = Vornoi_weights(points)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "200"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w.n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.685"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
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diff --git a/docs/notebooks/weights.html b/docs/notebooks/weights.html
deleted file mode 100644
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-65                   South             US Southern county homicides 1960-1990       True
-66                 StLouis   St Louis region county homicide counts and rates       True
-67                  Tampa1   2000 Census Tract Data for Tampa, FL MSA and ...       True
-68                  arcgis                               arcgis testing files       True
-69                  baltim     Baltimore house sales prices and hedonics 1978       True
-70                  berlin  Prenzlauer Berg neighborhood AirBnB data from ...       True
-71                    book       Synthetic data to illustrate spatial weights       True
-72                 burkitt  Burkitt&#39;s lymphoma in the Western Nile distric...       True
-73                  calemp         Employment density for California counties       True
-74                 chicago                              Chicago neighborhoods       True
-75              clearwater                               mgwr testing dataset       True
-76                columbus              Columbus neighborhood crime data 1980       True
-77                 desmith    Small dataset to illustrate Moran&#39;s I statistic       True
-78                geodanet        Datasets from geodanet for network analysis       True
-79                 georgia  Various socio-economic variables for counties ...       True
-80                juvenile    Residences of juvenile offenders in Cardiff, UK       True
-81                  mexico  Decennial per capita incomes of Mexican states...       True
-82                networks                  Datasets used for network testing       True
-83                newHaven                            Network testing dataset       True
-84               nyc_bikes                           New York City Bike Trips       True
-85                   sids2  North Carolina county SIDS death counts and rates       True
-86               snow_maps  Public water pumps and Cholera deaths in Londo...       True
-87                     stl  Homicides and selected socio-economic characte...       True
-88          street_net_pts                              Street network points       True
-89                     taz           Traffic Analysis Zones in So. California       True
-90                   tokyo                               Tokyo Mortality data       True
-91               us_income  Per-capita income for the lower 48 US states 1...       True
-92                virginia                        Virginia counties shapefile       True
-93                    wmat          Datasets used for spatial weights testing       True
-</pre></div></div>
-</div>
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-</div>
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-<span></span><span class="n">libpysal</span><span class="o">.</span><span class="n">examples</span><span class="o">.</span><span class="n">explain</span><span class="p">(</span><span class="s1">&#39;mexico&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
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-mexico
-======
-
-Decennial per capita incomes of Mexican states 1940-2000
---------------------------------------------------------
-
-* mexico.csv: attribute data. (n=32, k=13)
-* mexico.gal: spatial weights in GAL format.
-* mexicojoin.shp: Polygon shapefile. (n=32)
-
-Data used in Rey, S.J. and M.L. Sastre Gutierrez. (2010) &#34;Interregional inequality dynamics in Mexico.&#34; Spatial Economic Analysis, 5: 277-298.
-
-</pre></div></div>
-</div>
-<div class="section" id="Weights-from-GeoDataFrames">
-<h1>Weights from GeoDataFrames<a class="headerlink" href="#Weights-from-GeoDataFrames" title="Permalink to this headline">¶</a></h1>
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-</pre></div>
-</div>
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-<span></span><span class="kn">import</span> <span class="nn">geopandas</span>
-<span class="n">pth</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">examples</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="s2">&quot;mexicojoin.shp&quot;</span><span class="p">)</span>
-<span class="n">gdf</span> <span class="o">=</span> <span class="n">geopandas</span><span class="o">.</span><span class="n">read_file</span><span class="p">(</span><span class="n">pth</span><span class="p">)</span>
-
-<span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">Queen</span><span class="p">,</span> <span class="n">Rook</span><span class="p">,</span> <span class="n">KNN</span>
-</pre></div>
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-</div>
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-<span></span><span class="o">%</span><span class="k">matplotlib</span> inline
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-
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-<span></span><span class="n">ax</span> <span class="o">=</span> <span class="n">gdf</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_axis_off</span><span class="p">()</span>
-</pre></div>
-</div>
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-</div>
-<div class="section" id="Contiguity-Weights">
-<h2>Contiguity Weights<a class="headerlink" href="#Contiguity-Weights" title="Permalink to this headline">¶</a></h2>
-<p>The first set of spatial weights we illustrate use notions of contiguity to define neighboring observations. <strong>Rook</strong> neighbors are those states that share an edge on their respective borders:</p>
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-<span></span><span class="n">w_rook</span> <span class="o">=</span> <span class="n">Rook</span><span class="o">.</span><span class="n">from_dataframe</span><span class="p">(</span><span class="n">gdf</span><span class="p">)</span>
-</pre></div>
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-<span></span><span class="n">w_rook</span><span class="o">.</span><span class="n">n</span>
-</pre></div>
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-32
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-<span></span><span class="n">w_rook</span><span class="o">.</span><span class="n">pct_nonzero</span>
-</pre></div>
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-12.6953125
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-</pre></div>
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-<span></span><span class="n">ax</span> <span class="o">=</span> <span class="n">gdf</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;grey&#39;</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s1">&#39;w&#39;</span><span class="p">)</span>
-<span class="n">f</span><span class="p">,</span><span class="n">ax</span> <span class="o">=</span> <span class="n">w_rook</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">gdf</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
-        <span class="n">edge_kws</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;:&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
-        <span class="n">node_kws</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">marker</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">))</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_axis_off</span><span class="p">()</span>
-</pre></div>
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-<span></span><span class="n">gdf</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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-<style scoped>
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-        vertical-align: top;
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-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>POLY_ID</th>
-      <th>AREA</th>
-      <th>CODE</th>
-      <th>NAME</th>
-      <th>PERIMETER</th>
-      <th>ACRES</th>
-      <th>HECTARES</th>
-      <th>PCGDP1940</th>
-      <th>PCGDP1950</th>
-      <th>PCGDP1960</th>
-      <th>...</th>
-      <th>GR9000</th>
-      <th>LPCGDP40</th>
-      <th>LPCGDP50</th>
-      <th>LPCGDP60</th>
-      <th>LPCGDP70</th>
-      <th>LPCGDP80</th>
-      <th>LPCGDP90</th>
-      <th>LPCGDP00</th>
-      <th>TEST</th>
-      <th>geometry</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>1</td>
-      <td>7.252751e+10</td>
-      <td>MX02</td>
-      <td>Baja California Norte</td>
-      <td>2040312.385</td>
-      <td>1.792187e+07</td>
-      <td>7252751.376</td>
-      <td>22361.0</td>
-      <td>20977.0</td>
-      <td>17865.0</td>
-      <td>...</td>
-      <td>0.05</td>
-      <td>4.35</td>
-      <td>4.32</td>
-      <td>4.25</td>
-      <td>4.40</td>
-      <td>4.47</td>
-      <td>4.43</td>
-      <td>4.48</td>
-      <td>1.0</td>
-      <td>MULTIPOLYGON (((-113.13972 29.01778, -113.2405...</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>2</td>
-      <td>7.225988e+10</td>
-      <td>MX03</td>
-      <td>Baja California Sur</td>
-      <td>2912880.772</td>
-      <td>1.785573e+07</td>
-      <td>7225987.769</td>
-      <td>9573.0</td>
-      <td>16013.0</td>
-      <td>16707.0</td>
-      <td>...</td>
-      <td>0.00</td>
-      <td>3.98</td>
-      <td>4.20</td>
-      <td>4.22</td>
-      <td>4.39</td>
-      <td>4.46</td>
-      <td>4.41</td>
-      <td>4.42</td>
-      <td>2.0</td>
-      <td>MULTIPOLYGON (((-111.20612 25.80278, -111.2302...</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>3</td>
-      <td>2.731957e+10</td>
-      <td>MX18</td>
-      <td>Nayarit</td>
-      <td>1034770.341</td>
-      <td>6.750785e+06</td>
-      <td>2731956.859</td>
-      <td>4836.0</td>
-      <td>7515.0</td>
-      <td>7621.0</td>
-      <td>...</td>
-      <td>-0.05</td>
-      <td>3.68</td>
-      <td>3.88</td>
-      <td>3.88</td>
-      <td>4.04</td>
-      <td>4.13</td>
-      <td>4.11</td>
-      <td>4.06</td>
-      <td>3.0</td>
-      <td>MULTIPOLYGON (((-106.62108 21.56531, -106.6475...</td>
-    </tr>
-    <tr>
-      <th>3</th>
-      <td>4</td>
-      <td>7.961008e+10</td>
-      <td>MX14</td>
-      <td>Jalisco</td>
-      <td>2324727.436</td>
-      <td>1.967200e+07</td>
-      <td>7961008.285</td>
-      <td>5309.0</td>
-      <td>8232.0</td>
-      <td>9953.0</td>
-      <td>...</td>
-      <td>0.03</td>
-      <td>3.73</td>
-      <td>3.92</td>
-      <td>4.00</td>
-      <td>4.21</td>
-      <td>4.32</td>
-      <td>4.30</td>
-      <td>4.33</td>
-      <td>4.0</td>
-      <td>POLYGON ((-101.52490 21.85664, -101.58830 21.7...</td>
-    </tr>
-    <tr>
-      <th>4</th>
-      <td>5</td>
-      <td>5.467030e+09</td>
-      <td>MX01</td>
-      <td>Aguascalientes</td>
-      <td>313895.530</td>
-      <td>1.350927e+06</td>
-      <td>546702.985</td>
-      <td>10384.0</td>
-      <td>6234.0</td>
-      <td>8714.0</td>
-      <td>...</td>
-      <td>0.13</td>
-      <td>4.02</td>
-      <td>3.79</td>
-      <td>3.94</td>
-      <td>4.21</td>
-      <td>4.32</td>
-      <td>4.32</td>
-      <td>4.44</td>
-      <td>5.0</td>
-      <td>POLYGON ((-101.84620 22.01176, -101.96530 21.8...</td>
-    </tr>
-  </tbody>
-</table>
-<p>5 rows × 35 columns</p>
-</div></div>
-</div>
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-<span></span><span class="n">w_rook</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="c1"># the first location has two neighbors at locations 1 and 22</span>
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-[1, 22]
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-<span></span><span class="n">gdf</span><span class="p">[</span><span class="s1">&#39;NAME&#39;</span><span class="p">][[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span><span class="mi">22</span><span class="p">]]</span>
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-0     Baja California Norte
-1       Baja California Sur
-22                   Sonora
-Name: NAME, dtype: object
-</pre></div></div>
-</div>
-<p>So, Baja California Norte has 2 rook neighbors: Baja California Sur and Sonora.</p>
-<p><strong>Queen</strong> neighbors are based on a more inclusive condition that requires only a shared vertex between two states:</p>
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-<span></span><span class="n">w_queen</span> <span class="o">=</span> <span class="n">Queen</span><span class="o">.</span><span class="n">from_dataframe</span><span class="p">(</span><span class="n">gdf</span><span class="p">)</span>
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-<span></span><span class="n">w_queen</span><span class="o">.</span><span class="n">n</span> <span class="o">==</span> <span class="n">w_rook</span><span class="o">.</span><span class="n">n</span>
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-True
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-</div>
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-<span></span><span class="p">(</span><span class="n">w_queen</span><span class="o">.</span><span class="n">pct_nonzero</span> <span class="o">&gt;</span> <span class="n">w_rook</span><span class="o">.</span><span class="n">pct_nonzero</span><span class="p">)</span> <span class="o">==</span> <span class="p">(</span><span class="n">w_queen</span><span class="o">.</span><span class="n">n</span> <span class="o">==</span> <span class="n">w_rook</span><span class="o">.</span><span class="n">n</span><span class="p">)</span>
-</pre></div>
-</div>
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-True
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-</pre></div>
-</div>
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-<span></span><span class="n">ax</span> <span class="o">=</span> <span class="n">gdf</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;grey&#39;</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s1">&#39;w&#39;</span><span class="p">)</span>
-<span class="n">f</span><span class="p">,</span><span class="n">ax</span> <span class="o">=</span> <span class="n">w_queen</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">gdf</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
-        <span class="n">edge_kws</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;:&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
-        <span class="n">node_kws</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">marker</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">))</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_axis_off</span><span class="p">()</span>
-</pre></div>
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-<img alt="../_images/notebooks_weights_21_0.png" src="../_images/notebooks_weights_21_0.png" />
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-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[19]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">w_queen</span><span class="o">.</span><span class="n">histogram</span>
-</pre></div>
-</div>
-</div>
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-[(1, 1), (2, 6), (3, 6), (4, 6), (5, 5), (6, 2), (7, 3), (8, 2), (9, 1)]
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-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">w_rook</span><span class="o">.</span><span class="n">histogram</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[20]:
-</pre></div>
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-<div class="highlight"><pre>
-[(1, 1), (2, 6), (3, 7), (4, 7), (5, 3), (6, 4), (7, 3), (8, 1)]
-</pre></div></div>
-</div>
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-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[21]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">c9</span> <span class="o">=</span> <span class="p">[</span><span class="n">idx</span> <span class="k">for</span> <span class="n">idx</span><span class="p">,</span><span class="n">c</span> <span class="ow">in</span> <span class="n">w_queen</span><span class="o">.</span><span class="n">cardinalities</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="n">c</span><span class="o">==</span><span class="mi">9</span><span class="p">]</span>
-</pre></div>
-</div>
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-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[22]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">gdf</span><span class="p">[</span><span class="s1">&#39;NAME&#39;</span><span class="p">][</span><span class="n">c9</span><span class="p">]</span>
-</pre></div>
-</div>
-</div>
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-28    San Luis Potosi
-Name: NAME, dtype: object
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-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[23]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">w_rook</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">28</span><span class="p">]</span>
-</pre></div>
-</div>
-</div>
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-[5, 6, 7, 27, 29, 30, 31]
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-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">w_queen</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">28</span><span class="p">]</span>
-</pre></div>
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-[3, 5, 6, 7, 24, 27, 29, 30, 31]
-</pre></div></div>
-</div>
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-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[25]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="n">f</span><span class="p">,</span><span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">6</span><span class="p">),</span> <span class="n">subplot_kw</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">aspect</span><span class="o">=</span><span class="s1">&#39;equal&#39;</span><span class="p">))</span>
-<span class="n">gdf</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;grey&#39;</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s1">&#39;w&#39;</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-<span class="n">w_rook</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">gdf</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
-        <span class="n">edge_kws</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;:&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
-        <span class="n">node_kws</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">marker</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">))</span>
-<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Rook&#39;</span><span class="p">)</span>
-<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="o">-</span><span class="mf">105.0</span><span class="p">,</span> <span class="o">-</span><span class="mf">95.0</span><span class="p">,</span> <span class="mi">21</span><span class="p">,</span> <span class="mi">26</span><span class="p">]))</span>
-
-<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;off&#39;</span><span class="p">)</span>
-<span class="n">gdf</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;grey&#39;</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s1">&#39;w&#39;</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-<span class="n">w_queen</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">gdf</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
-        <span class="n">edge_kws</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;:&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
-        <span class="n">node_kws</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">marker</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">))</span>
-<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Queen&#39;</span><span class="p">)</span>
-<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;off&#39;</span><span class="p">)</span>
-<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="o">-</span><span class="mf">105.0</span><span class="p">,</span> <span class="o">-</span><span class="mf">95.0</span><span class="p">,</span> <span class="mi">21</span><span class="p">,</span> <span class="mi">26</span><span class="p">]))</span>
-</pre></div>
-</div>
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-array([-105.,  -95.,   21.,   26.])
-</pre></div></div>
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-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">w_knn</span> <span class="o">=</span> <span class="n">KNN</span><span class="o">.</span><span class="n">from_dataframe</span><span class="p">(</span><span class="n">gdf</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
-</pre></div>
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-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[27]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">w_knn</span><span class="o">.</span><span class="n">histogram</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[27]:
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-[(4, 32)]
-</pre></div></div>
-</div>
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-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[28]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">ax</span> <span class="o">=</span> <span class="n">gdf</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;grey&#39;</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s1">&#39;w&#39;</span><span class="p">)</span>
-<span class="n">f</span><span class="p">,</span><span class="n">ax</span> <span class="o">=</span> <span class="n">w_knn</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">gdf</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
-        <span class="n">edge_kws</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;:&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
-        <span class="n">node_kws</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">marker</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">))</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_axis_off</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
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-<img alt="../_images/notebooks_weights_31_0.png" src="../_images/notebooks_weights_31_0.png" />
-</div>
-</div>
-</div>
-</div>
-<div class="section" id="Weights-from-shapefiles-(without-geopandas)">
-<h1>Weights from shapefiles (without geopandas)<a class="headerlink" href="#Weights-from-shapefiles-(without-geopandas)" title="Permalink to this headline">¶</a></h1>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[29]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">pth</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">examples</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="s2">&quot;mexicojoin.shp&quot;</span><span class="p">)</span>
-<span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">Queen</span><span class="p">,</span> <span class="n">Rook</span><span class="p">,</span> <span class="n">KNN</span>
-</pre></div>
-</div>
-</div>
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-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">w_queen</span> <span class="o">=</span> <span class="n">Queen</span><span class="o">.</span><span class="n">from_shapefile</span><span class="p">(</span><span class="n">pth</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
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-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">w_rook</span> <span class="o">=</span> <span class="n">Rook</span><span class="o">.</span><span class="n">from_shapefile</span><span class="p">(</span><span class="n">pth</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[32]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">w_knn1</span> <span class="o">=</span> <span class="n">KNN</span><span class="o">.</span><span class="n">from_shapefile</span><span class="p">(</span><span class="n">pth</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
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-/home/jovyan/libpysal/weights/weights.py:167: UserWarning: The weights matrix is not fully connected:
- There are 2 disconnected components.
-  warnings.warn(message)
-</pre></div></div>
-</div>
-<p>The warning alerts us to the fact that using a first nearest neighbor criterion to define the neighbors results in a connectivity graph that has more than a single component. In this particular case there are 2 components which can be seen in the following plot:</p>
-<div class="nbinput docutils container">
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-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">ax</span> <span class="o">=</span> <span class="n">gdf</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;grey&#39;</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s1">&#39;w&#39;</span><span class="p">)</span>
-<span class="n">f</span><span class="p">,</span><span class="n">ax</span> <span class="o">=</span> <span class="n">w_knn1</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">gdf</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
-        <span class="n">edge_kws</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;:&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
-        <span class="n">node_kws</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">marker</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">))</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_axis_off</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/notebooks_weights_38_0.png" src="../_images/notebooks_weights_38_0.png" />
-</div>
-</div>
-<p>The two components are separated in the southern part of the country, with the smaller component to the east and the larger component running through the rest of the country to the west. For certain types of spatial analytical methods, it is necessary to have a adjacency structure that consists of a single component. To ensure this for the case of Mexican states, we can increase the number of nearest neighbors to three:</p>
-<div class="nbinput nblast docutils container">
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-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">w_knn3</span> <span class="o">=</span> <span class="n">KNN</span><span class="o">.</span><span class="n">from_shapefile</span><span class="p">(</span><span class="n">pth</span><span class="p">,</span><span class="n">k</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[35]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">ax</span> <span class="o">=</span> <span class="n">gdf</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;grey&#39;</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s1">&#39;w&#39;</span><span class="p">)</span>
-<span class="n">f</span><span class="p">,</span><span class="n">ax</span> <span class="o">=</span> <span class="n">w_knn3</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">gdf</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
-        <span class="n">edge_kws</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;:&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
-        <span class="n">node_kws</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">marker</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">))</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_axis_off</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/notebooks_weights_41_0.png" src="../_images/notebooks_weights_41_0.png" />
-</div>
-</div>
-</div>
-<div class="section" id="Lattice-Weights">
-<h1>Lattice Weights<a class="headerlink" href="#Lattice-Weights" title="Permalink to this headline">¶</a></h1>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[36]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[37]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">w</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[38]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">w</span><span class="o">.</span><span class="n">n</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[38]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-12
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[39]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">w</span><span class="o">.</span><span class="n">pct_nonzero</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[39]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-23.61111111111111
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[40]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">w</span><span class="o">.</span><span class="n">neighbors</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[40]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-{0: [3, 1],
- 3: [0, 6, 4],
- 1: [0, 4, 2],
- 4: [1, 3, 7, 5],
- 2: [1, 5],
- 5: [2, 4, 8],
- 6: [3, 9, 7],
- 7: [4, 6, 10, 8],
- 8: [5, 7, 11],
- 9: [6, 10],
- 10: [7, 9, 11],
- 11: [8, 10]}
-</pre></div></div>
-</div>
-</div>
-<div class="section" id="Handling-nonplanar-geometries">
-<h1>Handling nonplanar geometries<a class="headerlink" href="#Handling-nonplanar-geometries" title="Permalink to this headline">¶</a></h1>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[41]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">rs</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">examples</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="s1">&#39;map_RS_BR.shp&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[42]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="kn">import</span> <span class="nn">geopandas</span> <span class="k">as</span> <span class="nn">gpd</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[43]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">rs_df</span> <span class="o">=</span> <span class="n">gpd</span><span class="o">.</span><span class="n">read_file</span><span class="p">(</span><span class="n">rs</span><span class="p">)</span>
-<span class="n">wq</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">Queen</span><span class="o">.</span><span class="n">from_dataframe</span><span class="p">(</span><span class="n">rs_df</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area stderr docutils container">
-<div class="highlight"><pre>
-/home/jovyan/libpysal/weights/weights.py:167: UserWarning: The weights matrix is not fully connected:
- There are 30 disconnected components.
- There are 29 islands with ids: 0, 4, 23, 27, 80, 94, 101, 107, 109, 119, 122, 139, 169, 175, 223, 239, 247, 253, 254, 255, 256, 261, 276, 291, 294, 303, 321, 357, 374.
-  warnings.warn(message)
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[44]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="nb">len</span><span class="p">(</span><span class="n">wq</span><span class="o">.</span><span class="n">islands</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[44]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-29
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[45]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">wq</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[45]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
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-{}
-</pre></div></div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[46]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">wf</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">fuzzy_contiguity</span><span class="p">(</span><span class="n">rs_df</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[47]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">wf</span><span class="o">.</span><span class="n">islands</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[47]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
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-[]
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[48]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">wf</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[48]:
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-{239: 1.0, 59: 1.0, 152: 1.0, 23: 1.0, 107: 1.0}
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-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[49]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s2">&quot;figure.figsize&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mi">20</span><span class="p">,</span><span class="mi">15</span><span class="p">)</span>
-<span class="n">ax</span> <span class="o">=</span> <span class="n">rs_df</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;grey&#39;</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s1">&#39;w&#39;</span><span class="p">)</span>
-<span class="n">f</span><span class="p">,</span><span class="n">ax</span> <span class="o">=</span> <span class="n">wq</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">rs_df</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
-        <span class="n">edge_kws</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;:&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
-        <span class="n">node_kws</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">marker</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">))</span>
-
-<span class="n">ax</span><span class="o">.</span><span class="n">set_axis_off</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/notebooks_weights_57_0.png" src="../_images/notebooks_weights_57_0.png" />
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[50]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-
-<span></span><span class="n">ax</span> <span class="o">=</span> <span class="n">rs_df</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;grey&#39;</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s1">&#39;w&#39;</span><span class="p">)</span>
-<span class="n">f</span><span class="p">,</span><span class="n">ax</span> <span class="o">=</span> <span class="n">wf</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">rs_df</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
-        <span class="n">edge_kws</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;:&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
-        <span class="n">node_kws</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">marker</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">))</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Rio Grande do Sul: Nonplanar Weights&#39;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_axis_off</span><span class="p">()</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">&#39;rioGrandeDoSul.png&#39;</span><span class="p">)</span>
-
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-</div>
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-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span>
-</pre></div>
-</div>
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-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span>
-</pre></div>
-</div>
-</div>
-</div>
-
-
-    </div>
-      
-  </div>
-</div>
-<footer class="footer">
-  <div class="container">
-    <p class="pull-right">
-      <a href="#">Back to top</a>
-      
-        <br/>
-        
-<div id="sourcelink">
-  <a href="../_sources/notebooks/weights.ipynb.txt"
-     rel="nofollow">Source</a>
-</div>
-      
-    </p>
-    <p>
-        &copy; Copyright 2018-, pysal developers.<br/>
-      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 3.1.2.<br/>
-    </p>
-  </div>
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-  </body>
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\ No newline at end of file
diff --git a/docs/notebooks/weights.ipynb b/docs/notebooks/weights.ipynb
deleted file mode 100644
index 3f867ad7b..000000000
--- a/docs/notebooks/weights.ipynb
+++ /dev/null
@@ -1,1313 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sys\n",
-    "import os"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sys.path.append(os.path.abspath('..'))\n",
-    "import libpysal"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                      Name                                        Description  Installed\n",
-      "0                    10740    Albuquerque, New Mexico, Census 2000 Tract Data       True\n",
-      "1                   AirBnB  Airbnb rentals, socioeconomics, and crime in C...       True\n",
-      "2                  Atlanta       Atlanta, GA region homicide counts and rates       True\n",
-      "3                Baltimore          Baltimore house sales prices and hedonics       True\n",
-      "4                Bostonhsg               Boston housing and neighborhood data       True\n",
-      "5              Buenosaires      Electoral Data for 1999 Argentinean Elections       True\n",
-      "6              Charleston1   2000 Census Tract Data for Charleston, SC MSA...       True\n",
-      "7              Charleston2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "8           Chicago Health                   Chicago Health + Socio-Economics       True\n",
-      "9              Chile Labor                 Labor Markets in Chile (1982-2002)       True\n",
-      "10         Chile Migration            Internal Migration in Chile (1977-2002)       True\n",
-      "11              Cincinnati         2008 Cincinnati Crime + Socio-Demographics       True\n",
-      "12               Cleveland       2015 sales prices of homes in Cleveland, OH.       True\n",
-      "13                Columbus                        Columbus neighborhood crime       True\n",
-      "14                  Denver   Demographics and housing in Denver neighborho...       True\n",
-      "15               Elections               2012 and 2016 Presidential Elections       True\n",
-      "16                 Grid100                      Grid with simulated variables       True\n",
-      "17               Groceries                          2015 Chicago supermarkets       True\n",
-      "18                  Guerry          Moral statistics of France (Guerry, 1833)       True\n",
-      "19       Health Indicators                Chicago Health Indicators (2005-11)       True\n",
-      "20                 Health+                    2000 Health, Income + Diversity       True\n",
-      "21                Hickory1   2000 Census Tract Data for Hickory, NC MSA an...       True\n",
-      "22                Hickory2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "23              Home Sales              2014-15 Home Sales in King County, WA       True\n",
-      "24                 Houston       Houston, TX region homicide counts and rates       True\n",
-      "25                Juvenile             Cardiff juvenile delinquent residences       True\n",
-      "26                Lansing1   2000 Census Tract Data for Lansing, MI MSA an...       True\n",
-      "27                Lansing2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "28                 Laozone   Ozone measures at monitoring stations in Los ...       True\n",
-      "29                LasRosas   Corn yield, fertilizer and field data for pre...       True\n",
-      "30                    Line                                     Line Shapefile       True\n",
-      "31           Liquor Stores                         2015 Chicago Liquor Stores       True\n",
-      "32                 Malaria   Malaria incidence and population (1973, 95, 9...       True\n",
-      "33              Milwaukee1       2000 Census Tract Data for Milwaukee, WI MSA       True\n",
-      "34              Milwaukee2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "35                   NCOVR                      US county homicides 1960-1990       True\n",
-      "36                    NDVI        Normalized Difference Vegetation Index grid       True\n",
-      "37                     NYC   Demographic and housing data for New York Cit...       True\n",
-      "38            NYC Earnings              Block-level Earnings in NYC (2002-14)       True\n",
-      "39           NYC Education                               NYC Education (2000)       True\n",
-      "40       NYC Neighborhoods       Demographics for New York City neighborhoods       True\n",
-      "41  NYC Socio-Demographics                 NYC Education + Socio-Demographics       True\n",
-      "42              Natregimes                    NCOVR with regimes (book/PySAL)       True\n",
-      "43                   Nepal   Health, poverty and education indicators for ...       True\n",
-      "44                Ohiolung            Ohio lung cancer data, 1968, 1978, 1988       True\n",
-      "45                Orlando1   2000 Census Tract Data for Orlando, FL MSA an...       True\n",
-      "46                Orlando2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "47                  Oz9799                        Monthly ozone data, 1997-99       True\n",
-      "48             Phoenix ACS   Phoenix American Community Survey Data (2010,...       True\n",
-      "49              Pittsburgh                      Pittsburgh homicide locations       True\n",
-      "50                   Point                                    Point Shapefile       True\n",
-      "51                  Police           Police expenditures Mississippi counties       True\n",
-      "52                 Polygon                                  Polygon Shapefile       True\n",
-      "53           Polygon_Holes                 Example to test treatment of holes       True\n",
-      "54       Rio Grande do Sul  Cities of the Brazilian State of Rio Grande do...       True\n",
-      "55                    SIDS            North Carolina county SIDS death counts       True\n",
-      "56                   SIDS2   North Carolina county SIDS death counts and r...       True\n",
-      "57             Sacramento1          2000 Census Tract Data for Sacramento MSA       True\n",
-      "58             Sacramento2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "59           SanFran Crime   July-Dec 2012 crime incidents in San Francisc...       True\n",
-      "60               Savannah1   2000 Census Tract Data for Savannah, GA MSA a...       True\n",
-      "61               Savannah2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "62                 Scotlip               Male lip cancer in Scotland, 1975-80       True\n",
-      "63                Seattle1   2000 Census Tract Data for Seattle, WA MSA an...       True\n",
-      "64                Seattle2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "65                   South             US Southern county homicides 1960-1990       True\n",
-      "66                 StLouis   St Louis region county homicide counts and rates       True\n",
-      "67                  Tampa1   2000 Census Tract Data for Tampa, FL MSA and ...       True\n",
-      "68                  arcgis                               arcgis testing files       True\n",
-      "69                  baltim     Baltimore house sales prices and hedonics 1978       True\n",
-      "70                  berlin  Prenzlauer Berg neighborhood AirBnB data from ...       True\n",
-      "71                    book       Synthetic data to illustrate spatial weights       True\n",
-      "72                 burkitt  Burkitt's lymphoma in the Western Nile distric...       True\n",
-      "73                  calemp         Employment density for California counties       True\n",
-      "74                 chicago                              Chicago neighborhoods       True\n",
-      "75              clearwater                               mgwr testing dataset       True\n",
-      "76                columbus              Columbus neighborhood crime data 1980       True\n",
-      "77                 desmith    Small dataset to illustrate Moran's I statistic       True\n",
-      "78                geodanet        Datasets from geodanet for network analysis       True\n",
-      "79                 georgia  Various socio-economic variables for counties ...       True\n",
-      "80                juvenile    Residences of juvenile offenders in Cardiff, UK       True\n",
-      "81                  mexico  Decennial per capita incomes of Mexican states...       True\n",
-      "82                networks                  Datasets used for network testing       True\n",
-      "83                newHaven                            Network testing dataset       True\n",
-      "84               nyc_bikes                           New York City Bike Trips       True\n",
-      "85                   sids2  North Carolina county SIDS death counts and rates       True\n",
-      "86               snow_maps  Public water pumps and Cholera deaths in Londo...       True\n",
-      "87                     stl  Homicides and selected socio-economic characte...       True\n",
-      "88          street_net_pts                              Street network points       True\n",
-      "89                     taz           Traffic Analysis Zones in So. California       True\n",
-      "90                   tokyo                               Tokyo Mortality data       True\n",
-      "91               us_income  Per-capita income for the lower 48 US states 1...       True\n",
-      "92                virginia                        Virginia counties shapefile       True\n",
-      "93                    wmat          Datasets used for spatial weights testing       True\n"
-     ]
-    }
-   ],
-   "source": [
-    "libpysal.examples.available()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mexico\n",
-      "======\n",
-      "\n",
-      "Decennial per capita incomes of Mexican states 1940-2000\n",
-      "--------------------------------------------------------\n",
-      "\n",
-      "* mexico.csv: attribute data. (n=32, k=13)\n",
-      "* mexico.gal: spatial weights in GAL format.\n",
-      "* mexicojoin.shp: Polygon shapefile. (n=32)\n",
-      "\n",
-      "Data used in Rey, S.J. and M.L. Sastre Gutierrez. (2010) \"Interregional inequality dynamics in Mexico.\" Spatial Economic Analysis, 5: 277-298.\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "libpysal.examples.explain('mexico')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Weights from GeoDataFrames"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import geopandas\n",
-    "pth = libpysal.examples.get_path(\"mexicojoin.shp\")\n",
-    "gdf = geopandas.read_file(pth)\n",
-    "\n",
-    "from libpysal.weights import Queen, Rook, KNN"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "import matplotlib.pyplot as plt\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = gdf.plot()\n",
-    "ax.set_axis_off()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Contiguity Weights\n",
-    "\n",
-    "The first set of spatial weights we illustrate use notions of contiguity to define neighboring observations. **Rook** neighbors are those states that share an edge on their respective borders:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "w_rook = Rook.from_dataframe(gdf)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "32"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w_rook.n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "12.6953125"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w_rook.pct_nonzero"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = gdf.plot(edgecolor='grey', facecolor='w')\n",
-    "f,ax = w_rook.plot(gdf, ax=ax, \n",
-    "        edge_kws=dict(color='r', linestyle=':', linewidth=1),\n",
-    "        node_kws=dict(marker=''))\n",
-    "ax.set_axis_off()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>POLY_ID</th>\n",
-       "      <th>AREA</th>\n",
-       "      <th>CODE</th>\n",
-       "      <th>NAME</th>\n",
-       "      <th>PERIMETER</th>\n",
-       "      <th>ACRES</th>\n",
-       "      <th>HECTARES</th>\n",
-       "      <th>PCGDP1940</th>\n",
-       "      <th>PCGDP1950</th>\n",
-       "      <th>PCGDP1960</th>\n",
-       "      <th>...</th>\n",
-       "      <th>GR9000</th>\n",
-       "      <th>LPCGDP40</th>\n",
-       "      <th>LPCGDP50</th>\n",
-       "      <th>LPCGDP60</th>\n",
-       "      <th>LPCGDP70</th>\n",
-       "      <th>LPCGDP80</th>\n",
-       "      <th>LPCGDP90</th>\n",
-       "      <th>LPCGDP00</th>\n",
-       "      <th>TEST</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>7.252751e+10</td>\n",
-       "      <td>MX02</td>\n",
-       "      <td>Baja California Norte</td>\n",
-       "      <td>2040312.385</td>\n",
-       "      <td>1.792187e+07</td>\n",
-       "      <td>7252751.376</td>\n",
-       "      <td>22361.0</td>\n",
-       "      <td>20977.0</td>\n",
-       "      <td>17865.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.05</td>\n",
-       "      <td>4.35</td>\n",
-       "      <td>4.32</td>\n",
-       "      <td>4.25</td>\n",
-       "      <td>4.40</td>\n",
-       "      <td>4.47</td>\n",
-       "      <td>4.43</td>\n",
-       "      <td>4.48</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>MULTIPOLYGON (((-113.13972 29.01778, -113.2405...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>7.225988e+10</td>\n",
-       "      <td>MX03</td>\n",
-       "      <td>Baja California Sur</td>\n",
-       "      <td>2912880.772</td>\n",
-       "      <td>1.785573e+07</td>\n",
-       "      <td>7225987.769</td>\n",
-       "      <td>9573.0</td>\n",
-       "      <td>16013.0</td>\n",
-       "      <td>16707.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>3.98</td>\n",
-       "      <td>4.20</td>\n",
-       "      <td>4.22</td>\n",
-       "      <td>4.39</td>\n",
-       "      <td>4.46</td>\n",
-       "      <td>4.41</td>\n",
-       "      <td>4.42</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>MULTIPOLYGON (((-111.20612 25.80278, -111.2302...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>2.731957e+10</td>\n",
-       "      <td>MX18</td>\n",
-       "      <td>Nayarit</td>\n",
-       "      <td>1034770.341</td>\n",
-       "      <td>6.750785e+06</td>\n",
-       "      <td>2731956.859</td>\n",
-       "      <td>4836.0</td>\n",
-       "      <td>7515.0</td>\n",
-       "      <td>7621.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.05</td>\n",
-       "      <td>3.68</td>\n",
-       "      <td>3.88</td>\n",
-       "      <td>3.88</td>\n",
-       "      <td>4.04</td>\n",
-       "      <td>4.13</td>\n",
-       "      <td>4.11</td>\n",
-       "      <td>4.06</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>MULTIPOLYGON (((-106.62108 21.56531, -106.6475...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>7.961008e+10</td>\n",
-       "      <td>MX14</td>\n",
-       "      <td>Jalisco</td>\n",
-       "      <td>2324727.436</td>\n",
-       "      <td>1.967200e+07</td>\n",
-       "      <td>7961008.285</td>\n",
-       "      <td>5309.0</td>\n",
-       "      <td>8232.0</td>\n",
-       "      <td>9953.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.03</td>\n",
-       "      <td>3.73</td>\n",
-       "      <td>3.92</td>\n",
-       "      <td>4.00</td>\n",
-       "      <td>4.21</td>\n",
-       "      <td>4.32</td>\n",
-       "      <td>4.30</td>\n",
-       "      <td>4.33</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>POLYGON ((-101.52490 21.85664, -101.58830 21.7...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>5.467030e+09</td>\n",
-       "      <td>MX01</td>\n",
-       "      <td>Aguascalientes</td>\n",
-       "      <td>313895.530</td>\n",
-       "      <td>1.350927e+06</td>\n",
-       "      <td>546702.985</td>\n",
-       "      <td>10384.0</td>\n",
-       "      <td>6234.0</td>\n",
-       "      <td>8714.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.13</td>\n",
-       "      <td>4.02</td>\n",
-       "      <td>3.79</td>\n",
-       "      <td>3.94</td>\n",
-       "      <td>4.21</td>\n",
-       "      <td>4.32</td>\n",
-       "      <td>4.32</td>\n",
-       "      <td>4.44</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>POLYGON ((-101.84620 22.01176, -101.96530 21.8...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 35 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   POLY_ID          AREA  CODE                   NAME    PERIMETER  \\\n",
-       "0        1  7.252751e+10  MX02  Baja California Norte  2040312.385   \n",
-       "1        2  7.225988e+10  MX03    Baja California Sur  2912880.772   \n",
-       "2        3  2.731957e+10  MX18                Nayarit  1034770.341   \n",
-       "3        4  7.961008e+10  MX14                Jalisco  2324727.436   \n",
-       "4        5  5.467030e+09  MX01         Aguascalientes   313895.530   \n",
-       "\n",
-       "          ACRES     HECTARES  PCGDP1940  PCGDP1950  PCGDP1960  ...  GR9000  \\\n",
-       "0  1.792187e+07  7252751.376    22361.0    20977.0    17865.0  ...    0.05   \n",
-       "1  1.785573e+07  7225987.769     9573.0    16013.0    16707.0  ...    0.00   \n",
-       "2  6.750785e+06  2731956.859     4836.0     7515.0     7621.0  ...   -0.05   \n",
-       "3  1.967200e+07  7961008.285     5309.0     8232.0     9953.0  ...    0.03   \n",
-       "4  1.350927e+06   546702.985    10384.0     6234.0     8714.0  ...    0.13   \n",
-       "\n",
-       "   LPCGDP40  LPCGDP50  LPCGDP60  LPCGDP70  LPCGDP80  LPCGDP90  LPCGDP00  TEST  \\\n",
-       "0      4.35      4.32      4.25      4.40      4.47      4.43      4.48   1.0   \n",
-       "1      3.98      4.20      4.22      4.39      4.46      4.41      4.42   2.0   \n",
-       "2      3.68      3.88      3.88      4.04      4.13      4.11      4.06   3.0   \n",
-       "3      3.73      3.92      4.00      4.21      4.32      4.30      4.33   4.0   \n",
-       "4      4.02      3.79      3.94      4.21      4.32      4.32      4.44   5.0   \n",
-       "\n",
-       "                                            geometry  \n",
-       "0  MULTIPOLYGON (((-113.13972 29.01778, -113.2405...  \n",
-       "1  MULTIPOLYGON (((-111.20612 25.80278, -111.2302...  \n",
-       "2  MULTIPOLYGON (((-106.62108 21.56531, -106.6475...  \n",
-       "3  POLYGON ((-101.52490 21.85664, -101.58830 21.7...  \n",
-       "4  POLYGON ((-101.84620 22.01176, -101.96530 21.8...  \n",
-       "\n",
-       "[5 rows x 35 columns]"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gdf.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 22]"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w_rook.neighbors[0] # the first location has two neighbors at locations 1 and 22"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0     Baja California Norte\n",
-       "1       Baja California Sur\n",
-       "22                   Sonora\n",
-       "Name: NAME, dtype: object"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gdf['NAME'][[0, 1,22]]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "So, Baja California Norte has 2 rook neighbors: Baja California Sur and Sonora."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Queen** neighbors are based on a more inclusive condition that requires only a shared vertex between two states:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "w_queen = Queen.from_dataframe(gdf)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w_queen.n == w_rook.n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(w_queen.pct_nonzero > w_rook.pct_nonzero) == (w_queen.n == w_rook.n)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = gdf.plot(edgecolor='grey', facecolor='w')\n",
-    "f,ax = w_queen.plot(gdf, ax=ax, \n",
-    "        edge_kws=dict(color='r', linestyle=':', linewidth=1),\n",
-    "        node_kws=dict(marker=''))\n",
-    "ax.set_axis_off()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(1, 1), (2, 6), (3, 6), (4, 6), (5, 5), (6, 2), (7, 3), (8, 2), (9, 1)]"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w_queen.histogram"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(1, 1), (2, 6), (3, 7), (4, 7), (5, 3), (6, 4), (7, 3), (8, 1)]"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w_rook.histogram"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "c9 = [idx for idx,c in w_queen.cardinalities.items() if c==9]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "28    San Luis Potosi\n",
-       "Name: NAME, dtype: object"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gdf['NAME'][c9]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[5, 6, 7, 27, 29, 30, 31]"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w_rook.neighbors[28]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[3, 5, 6, 7, 24, 27, 29, 30, 31]"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w_queen.neighbors[28]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([-105.,  -95.,   21.,   26.])"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x432 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "f,ax = plt.subplots(1,2,figsize=(10, 6), subplot_kw=dict(aspect='equal'))\n",
-    "gdf.plot(edgecolor='grey', facecolor='w', ax=ax[0])\n",
-    "w_rook.plot(gdf, ax=ax[0], \n",
-    "        edge_kws=dict(color='r', linestyle=':', linewidth=1),\n",
-    "        node_kws=dict(marker=''))\n",
-    "ax[0].set_title('Rook')\n",
-    "ax[0].axis(np.asarray([-105.0, -95.0, 21, 26]))\n",
-    "\n",
-    "ax[0].axis('off')\n",
-    "gdf.plot(edgecolor='grey', facecolor='w', ax=ax[1])\n",
-    "w_queen.plot(gdf, ax=ax[1], \n",
-    "        edge_kws=dict(color='r', linestyle=':', linewidth=1),\n",
-    "        node_kws=dict(marker=''))\n",
-    "ax[1].set_title('Queen')\n",
-    "ax[1].axis('off')\n",
-    "ax[1].axis(np.asarray([-105.0, -95.0, 21, 26]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "w_knn = KNN.from_dataframe(gdf, k=4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(4, 32)]"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w_knn.histogram"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = gdf.plot(edgecolor='grey', facecolor='w')\n",
-    "f,ax = w_knn.plot(gdf, ax=ax, \n",
-    "        edge_kws=dict(color='r', linestyle=':', linewidth=1),\n",
-    "        node_kws=dict(marker=''))\n",
-    "ax.set_axis_off()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Weights from shapefiles (without geopandas)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pth = libpysal.examples.get_path(\"mexicojoin.shp\")\n",
-    "from libpysal.weights import Queen, Rook, KNN"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "w_queen = Queen.from_shapefile(pth)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "w_rook = Rook.from_shapefile(pth)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/jovyan/libpysal/weights/weights.py:167: UserWarning: The weights matrix is not fully connected: \n",
-      " There are 2 disconnected components.\n",
-      "  warnings.warn(message)\n"
-     ]
-    }
-   ],
-   "source": [
-    "w_knn1 = KNN.from_shapefile(pth)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The warning alerts us to the fact that using a first nearest neighbor criterion to define the neighbors results in a connectivity graph that has more than a single component. In this particular case there are 2 components which can be seen in the following plot:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = gdf.plot(edgecolor='grey', facecolor='w')\n",
-    "f,ax = w_knn1.plot(gdf, ax=ax, \n",
-    "        edge_kws=dict(color='r', linestyle=':', linewidth=1),\n",
-    "        node_kws=dict(marker=''))\n",
-    "ax.set_axis_off()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The two components are separated in the southern part of the country, with the smaller component to the east and the larger component running through the rest of the country to the west. For certain types of spatial analytical methods, it is necessary to have a adjacency structure that consists of a single component. To ensure this for the case of Mexican states, we can increase the number of nearest neighbors to three:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "w_knn3 = KNN.from_shapefile(pth,k=3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = gdf.plot(edgecolor='grey', facecolor='w')\n",
-    "f,ax = w_knn3.plot(gdf, ax=ax, \n",
-    "        edge_kws=dict(color='r', linestyle=':', linewidth=1),\n",
-    "        node_kws=dict(marker=''))\n",
-    "ax.set_axis_off()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Lattice Weights"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from libpysal.weights import lat2W"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "w = lat2W(4,3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "12"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w.n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "23.61111111111111"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w.pct_nonzero"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0: [3, 1],\n",
-       " 3: [0, 6, 4],\n",
-       " 1: [0, 4, 2],\n",
-       " 4: [1, 3, 7, 5],\n",
-       " 2: [1, 5],\n",
-       " 5: [2, 4, 8],\n",
-       " 6: [3, 9, 7],\n",
-       " 7: [4, 6, 10, 8],\n",
-       " 8: [5, 7, 11],\n",
-       " 9: [6, 10],\n",
-       " 10: [7, 9, 11],\n",
-       " 11: [8, 10]}"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w.neighbors"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Handling nonplanar geometries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rs = libpysal.examples.get_path('map_RS_BR.shp')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import geopandas as gpd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/jovyan/libpysal/weights/weights.py:167: UserWarning: The weights matrix is not fully connected: \n",
-      " There are 30 disconnected components.\n",
-      " There are 29 islands with ids: 0, 4, 23, 27, 80, 94, 101, 107, 109, 119, 122, 139, 169, 175, 223, 239, 247, 253, 254, 255, 256, 261, 276, 291, 294, 303, 321, 357, 374.\n",
-      "  warnings.warn(message)\n"
-     ]
-    }
-   ],
-   "source": [
-    "rs_df = gpd.read_file(rs)\n",
-    "wq = libpysal.weights.Queen.from_dataframe(rs_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "29"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(wq.islands)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{}"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wq[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wf = libpysal.weights.fuzzy_contiguity(rs_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[]"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wf.islands"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{239: 1.0, 59: 1.0, 152: 1.0, 23: 1.0, 107: 1.0}"
-      ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wf[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x1080 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.rcParams[\"figure.figsize\"] = (20,15)\n",
-    "ax = rs_df.plot(edgecolor='grey', facecolor='w')\n",
-    "f,ax = wq.plot(rs_df, ax=ax, \n",
-    "        edge_kws=dict(color='r', linestyle=':', linewidth=1),\n",
-    "        node_kws=dict(marker=''))\n",
-    "\n",
-    "ax.set_axis_off()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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wwvnz5z978cUXn/y96p+amrq6Y8eOtycmJirl5eWYOnWqzXWvD7vq7AEgwGq1Zv9edWCMMcauh7uRM8YYY9XEYrGM79Kli/fjjz/u27p1aw+j0fj8ra5TVVRVfdHf3//LhISE1wIDA780m81rNU0zuh+jaZqqaVo9k8k0PzQ0NCIuLu6KcmrWrIkGDRogIiICBoMBwcHBGDFihJcQYqSmaXVutl6apnWZMmXKzEmTJg291nE2m+2lbdu2XQCAvLw8qKp63C1o1zIajUdUVc2YPHny0zdbB8YYY6y6cDdyxhhj7Aa5xiR3AGACsMFqtdrd9wshWtWpIzNmmzZtjDt27HhI07RXrFZrURVleZjN5ulEFFtWVjbaarX+9L+4BgAwGo09u3fv7hUXFwdd141ffPFFwokTJ6wAXtE0LcpsNr+lKEpPk8lkb9CggdKjRw8L0Y0NxzabzRU/3lTXOU3Tunt4eCzq0KGDV1pa2iBN0zKtVuuPVzl8b3FxsacQAj4+PnA4HKGaphkAkNls/jIhISGoZcuW6vvvvz9Z07QF3MLNGGPsVuCwzRhjjN0gk8n0rtlsfshisegFBQW2iRMn9p8wYcJGANA0ra6qqsF+fn4AgICAANSvX185ePDgywDGuZejaVqYyWRaHhUVVT8mJsZjxYoV6zRNC7dareerOq8r5McDiACw2Wq1Fvw316HrenZRkcz/iqKgb9++nh988MGzr7/++gCj0VivXbt2xrZt2xosFovxOkVdYc+ePSCifTfbfV5V1e6JiYle7dq1AxF5rl+//nEAVwvbEQaDwQnA4Ofnh6CgIOTl5X1mMBhiw8LCmt5+++2qoiho0aKF4aeffnoawPOuexgA4NzvOJ6cMcYYu4jDNmOMMXYDNE0jInrsiSeeUH19fXHo0CGfhQsXLtc0rZHVaj1uNpun3XbbbaqHh8fFz3Tv3t3j0KFDT02cODFTCPGh1WrVNU1LMRqNXycmJnp37NhRJSLs2bNHyczM7A1gXqVzGolouNlsHq+qak1/f39nXl6e0DStl9VqTbuBOisAyGq1Ot2322y2WZs3b+4RHx/v5eHhAT8/Pzz11FOWnJycBrVq1XJvnb5ECMDpBNSr/9dBCIE1a9ZcsNlsz16lPvUAmKxW68HK+wwGg7/JZAIAREdHY926dZ3cPucDoLvJZOphMBgSVFWN6d69u6Gitf2+++7z3rVr12Bvb2+lefPmUBQ5Si48PNy0d+/eJgBgNpsXOxyOngaDIVfTtG5Wq3XfNe5bHID6ADb+ty82GGOM/X3xBGmMMcbYDUpNTV3buXPnlNatWxMA/Pjjj45NmzblKIqS5eHh0WLUqFGeFYGxQlZWFpYtW1ZSWFhYpuu6yel0erdq1Qq9evW6eMzmzZuxYcOGL1566aWLY5U1TQs3mUyrQ0NDw5OTk70iIyNBRDhy5AgWLFhwwW63t7vazNuaptUwmUyvOp3OYUII1Wg0zrLZbKOtVmuZaz+ZTKZ/eXl5Da+qzlcoLQUCAwGHA9i6FYiOBlwt+JWvdd68ecdsNls999ZjTdNqmM3mLwDc7tq0zmazPWq1Wk9UHPPGG2/s6NevX6vY2Fjouo7U1FSb3W7vr6pqIoBnwsLCHA0aNPCpVasWQkJC4P5SoyqnTp3C7NmzS0pLS4cJIQ55enpuevrppz137dol1q1bt3PcuHEJVdy3rmaz+W0iqhcUFOTIyckps9vt0VfrccAYY4xdC7dsM8YYYzfIZrOtzMvLaw85ZhsdOnRQw8LCwsvKysLr16+PqkJr3bp18fjjj3seP37cc8uWLWL//v3Yv3+/3qtXr4uTlMbHx2PdunUDNU17DIAgohEA3gsNDRXDhg0j9/HS0dHRSE5O9ty4ceM4AENc3aMHAwgHsImImhiNxsnx8fEeHTt2NBuNRixZsmRwVlZWmKtFXLdarULTtCeJqNG+fftub968+dUvurQUKCoCevcG7rwTSEsDpk4FBg8GunYFXNcshEBaWlqp3W6fVhG0NU3zURRluNFo1Jo0aeLRrVs3sxACGzdu7Lply5b9kyZNelrX9RkAahoMhviIiAgAsmv7gAEDzKtWrZpfq1Ytwx133OEREBBww9/TmTNnMHPmzFK73T58woQJX06aNMnatGlTk6qqiIyMpLVr19au/JkpU6a87eXl9cidd97p2aBBAyiKgrffflux2+2xAP5n4+kZY4z9dXDYZowxxipxBdgkABVTcO8HkGE2mx+OjY29mKiJCDExMdctj4hQp04dRERE0KRJk9CoUSOlqKgIvr6+AABfX1/ExMSIjIyMw06nM6Bu3bp2h8OBY8eOUVpamrNDhw4G9/JCQkJIUZT6rl/v8Pb2/qBRo0bm7du3q0IIxMfHo3fv3hePv/vuuy2fffZZcn5+/seapj1itVqdVqtVTJw48dTSpUuxb98+vV69ekpsbCyCgoIunaikBGjUCDh7Vv6ZMAF4+WWgc2fg2DHg9tuBRx8Fhg7Fpk2bHMeOHcvUdf1fmqYlWyyWCQaDoX1kZKTj9ttv9woLC7tYbKdOndRGjRqpixcvfruoqOhFXdejPD09dYvFcvGYuLg4xMXF+dzYN3a53377DbqufzZhwoSFAGAymZqHhISoAHD+/HkoinK24lhN0wyqqr7s6en5yKhRozwrWsyLiopQUlKiQC6NxhhjjN00DtuMMcZYJWaz+SOz2Xxf3bp1CYA4deqUs6ioyBQdHa1HR0f/V2ULIZCeno6cnBwxdOhQUjMzgR07MCgoyDMvOdnT398f2dnZ5i+++AIAUFhYeEUZBQUFEEJkuOr6ULt27TwTExMpIiJCHDlyhPbt2ycAUK9evaAoClRVxQMPPOA5b968e3NycrpNnjz5K4fDkW80GtsGBgbCbDYrv/zyi7569WrFYDBg4MCBiAsKkuO0x48HoqLkWO3Zs4GBA4GWLWUIDw0F1q8HHnwQx1VVd4aF3UVGYx+z2fxp165dPRs0aAAPD48qBoADoaGhGDVqlNf69eujNm7c+F/d08qICERkcPvdy2iUc73puo7y8vI6kyZNet1kMjUyGo1JwcHBxkGDBnm6d00vLS2FqqpnXn75ZbumaS0sFss/iKim3W4XDodjLoB9APZZrdYz1Vp5xhhjfxk8Zpsxxhhzo2laDVVVs5999lmze0trdcnIyED+e++heO9eHI+IwODVq2HKzgZiY4F9+wBPT3z9wAN6vSZNyO+dd2j+yJEict8+qtO6Ndo/9RQAYMXKlfb09PR/CCGWGI3Gn8aMGePh7e0NACgvL8ebb74Ju92OymPDhRA4duwYjh8/jrKyMt3f319p2bLlxQnFHA4Hli9fLn7euZPGf/EF1IICYMECoEuXigKAykuACYGzixahcNw4BObn37/0nns+6qRp3rVrX9FTu0olJSWYOnUqAMBqtd74jRRCjiE3Xjlhel5eHmbMmFFst9vjrFbrqTfeeGNDQkJCx8TERJhMJvz22284ffo0fH19ERERgcDAwCvKKC0txTvvvGMzGAw7nU5ni06dOnkQEVasWIGQkBC7rusl586d8zAYDL/abLYh15pw7X/FtVZ6HQAnrFar7VbXhzHG/u4M//znP291HRhjjLE/jA0bNjT29/cf3L59+ypbZG9aaSmQnQ388gswdCgC4uMReuAAsk+exO7mzeE1dizCO3QAmjYFgoNxqFcvrMvPp7u6dKEz+/eL4MGDqd6HHyLQZoP/HXcAAQE4360bvNasue3uuXPHxoeEGAO7dyccOQIEBMBgNKJ169bw9fXFzp079aZNm1LFWHIigr+/P+rUqYOoqCgKCwuD+3hwRVEQZzTS6V9/de6pX598WrdGXocOFFjRtfzFF4EvvgD69bv4mbPnzmH+9u3YEhuLs8HBfZIzMkzhFosCkwkICbnu7TEajbDZbDhx4gQSExNhMBiuPOj8eRmqrVYgL0/ez/h4YNIkYO9e4MIFOXGbEEDNmvDy84MQQsnOzn4wLS2ttc1m65mVlQV/f3+EhYUhKCgIdevWRWhoKDw9Pa9ar6ZNm6q1a9eO6NatmzEyMhK1a9fGhg0bEBUVZRg6dKilffv2BiFErZycnPpJSUlf3MRTUe00TbvdaDRutVgsTwJ4bO3atdNSUlIct7JOjDH2d8fdyBljjLHLnSsrK6si8d2EjAzZ5frxx2Uo7NgReP114OmngY4doXbtip/efRdUVATH6dMQ06eD0tNRmpCAAiJEPf20sLRuTQ2WLiWHw4HZ48bp2dnZym2//IJOR46geXCwoW5mpq/fkiXw/+YboEkTYNYsYNQoICMDHrt2ocm8eSiyWpUfCgvRb8IE2Q28cqt0ZUIAffqgr9lsOOHlhS/79NEvLFxIw4YNQ82aNeHZuTNETg5yT51CUFAQVFXFwYMHkZ+fD0VRcDAmxng0OhqjGzaE77hxQGQk8PbbQFVLibmJjY3F1q1bcSIzE1E+PjJY/+MfwL33Ahs2yAnZcnKA48dlD4DWrYEPPwR27gS2bAEsFiA/X74MaNwYUFV0rFnT2MhuD8mJirpna34+ThYXo379+tesR2V+fn7wqzTrekJCgmPHjh0qAL1///6Kqqq6ECKvYr+maQbI5c1Kb+pk/wHX3AI9VFXtbjabRw4aNMgjKCgI7733ngGAEUDZ710HxhhjV8dhmzHGGLtcjI+Pj/P6h7kRAnjnHeBf/5KzdHt5Afv3A2VlwJEj8ndAhk8XRVGEruu0OT0d8QYD/IjwcZ8+ekdvb7o3IIDgcACqClVV8eCDDyqzZ8/Grl279E4HDyr06quocfQo8OSTMsxHRsqg/cgjwOHDwNat8LBY0NrhwK6tW7FywAB0278f9NFHwA8/ADYbMHEisH27HH/t6wts2gScOgVs3AjDjz+irq8vnrv9dmXdunWYOXMmAMDbZEL4oUM4mJkJIkJcXJzYt28fAUBQUBByc3Nh9vGB0rIlsGgR8NNPQFISMGCAnEjNfUbx7Gz5d24uoiZPRkh0NIx33AHUrAmsXSu7iPv5yRcUL74oZz3/7DNg6VJ5zSNHyiXI3n0XGDZMXtOiRfJajh8Hzp5FzfnzUXPzZniWlaFo1y7Y0tPhM2wYUKcOUK8eEBR0/RcQlfTo0UP19vbGunXrlP79+2PXrl0XbDbbxwCgaVqCqqqrdF33Tk1Nnfjiiy9Oqvjcq6++OtxoNL6k6/qy8vLy56xWq369c02aNKmf0Wh8wGazvWC1Wg9X3m8wGJ728fGZ2KRJE0urVq0Mfn5+mDt37gUiettqtRbf1IUxxhirdtyNnDHGGHPRNI3MZvOn7du3r3+jY46xbx/w1VfAmDFAcTHw1FNy1u5WrQB/f2D4cKBDB2DVKmDzZhluT55Eq5QUAoDsY8fQpn59UPv2+GHjRuoyYgR5Pf+87BbtGm9NRIg5dAhnNmygtd7eosmaNWSoVQt0221ykrJjx+TxJ0/K5bmaN0dhWRk+KSrC4dBQnAwLw7nu3Z0N77xTQX4+YLcD4eHAE0/IZb2mTQNeeUUGz9RU2QofGwsAiIyMROvWrREYGIjI8HB0Gj8eHRcvhl9gINLS0shisWDcuHFIS0tzlpeXKx07dnTGxMTIbuR16sjW6R9/BHbskPflk0+AoUOBunUBXQeaNwdycpDfoIFYXqcOJS9YAHh4AH36yGszm4GKruXl5bLebdrIFu8mTYBmzeSxDocM9zk5QNu2QK1acqx5r17Y4++PrUQ4puuI0XWY9u8Hdu8GXnhBluPpeSn8+/tf8+vOyMjAN998gzZt2iA2NharVq0yCCGe3rBhQ7nJZNrYp0+fWt26dVO2bt3abt26df9KSUmxaZrW1mKxLBg0aFDoyZMnm5aUlOQlJyfvqvTsGTZv3vzPLVu2vJOWltZ13bp1rVRVfb1Zs2bxZ8+ebZqUlPR55bps3rx5TMeOHVsmJSUpFosFpaWlWL58uXA6nf1SUlJsbmU33LBhgyElJeXCjT3UjDHGqgO3bDPGGPtL0DQt1Gw2L7bb7a1UVZ340ksvTb6Jz7ZTFKWHxWLp7u3t3bhly5bX/1BOjgxqTzwhw93nnwMFBXL88IYNMqyeOydbZmvVAjIz5Z9+/YAWLaA+/1p7F7sAACAASURBVDwS/PxQ/7PPUODvL0JCQqj+4cM4cewYAufNAwoLZegdORKoUQNeGzYgqaAA83Nz6eOJE0VhdjZFzZkj7mvalJStW2Vg/vlnYMAAHHrhBSxatw5CzoJKoaGh2JeXZ6j722/wSkhA3JAh8hqWLpV1/eUXGWJbtJDhttLEcN7e3mjVqpX8Zft2wGzG1q1bAQD9+/eHqqoYO3asYf369Vi9erWhZcuWuDizt68voGnAM8/I0B0RIbt/nzx5qcX/1VfRKCuLtmdmIjc3FyFVjfU+cwaIi5MvLLp0kUG9ok4GA3D//XJfxf1u0uRi9/XYuDhs3rpVFHh40G/duiEhIUH2Rnj0UWDjRvkiIDNT3o8tW4BrLOdW0a28YcOGAIC6devajh07NlXX9eOenp7BjRs3BhEhPDzcnpmZeSeA+QAaR0VFiaioKHTr1s1r4cKFz2uaNsNtPXKzyWT6Mjg4+I7bb7/ds6CgoFlhYSEaN24MHx8f7Ny5s52maWFWqzXbvS42m21uenp6zzZt2vgAwOnTp2EymTJfeOGFoopjJk6cONBkMs3SdV1omtbVarVuutq1aZrW3Gw2j9N1PdNuty8E0MhisdwrhDhjs9nmAFhzIy3yjDHGJA7bjDHG/vRcM4jvbdOmjV+LFi3U6dOnv6Jp2myr1Zp1nc+R0Wj8TlXVHm3btnX6+PgYmjRpAoPBgFmzZjmPHj1qSExMFF26dLnU11jXgU8/BebNk63I48bJLtzffitbt1evloFv6lS5PNa998rPjR9/6cT5+YCuY+Xbbzu9mzQxwGAg59dfI1bXRUxpKaFRI9k9euZMua71o48C06Yh8N57MXzHDqR16ybS09Pp8OHDdKFPH/jUqCHDYseOyFu6FMdefhmdR4+GKSqKlixZggEDBmDGjBn49ttv4eXlpcfFxSlwOIDHHpMtwi1byknIiork2to7dshx0VXQFy7E7vPnkevlheTkZMS6WsA/+eQTR15entq2bVunxWK5NObdagUOHJD3KzUVOHpUdvPu0UOOaXfdn7p16wIA5syZg7Fjx16cIR0AyoqKkF9cjNDp01FYowb2rFuH9qmpMH1eqbE3MVG+PJgxA3jzTeC114CoKGzatAlERCNGjMDF9b7Pn5eTrc2eLYP8Rx/JdcOv0a1cCIFZs2bBbDaL8PBwAoD+/ft7/fDDDw84nU6RnJzsWTHhXFBQkDkzM7NicXEPVVWViuv08/OrXVhYePz1119PJyKD0WhsHxUVZRkwYICnql75XzNd180ATk6cONGpqmqRqqqHy8vLNwCwnz9//uJ07DVq1IDD4airaVprq9W6AwCIqEWbNm3MqqoqGzduHAigyrCtaZqnqqo/duzY0efs2bNlhw8ffiIwMBDx8fE+paWlYseOHQNKS0s3ArjzKp/3AFBW8QKBMcYYh23GGGN/DXdHRUV5dOrUSQWAiIiI8oyMjNYArhm2iWiI3W7vQUQ4cOAATp8+jbS0NKiq6iwoKDAAwObNmyk3N1cMuf9+wjffyBmwH3tMjsVu1gzo3Fm2qI4bJ1tXO3eW4S0rS05KdpVJucrKy7Hv/HmDT+PGGLh4MT4bPhx33XUXvJo3l2OndV2G0smTZYvuypVQNQ2+MTFILC5W9u7di7KyMuzfvx8tGzeGPmcOVp44IX6pWZOi27Z1Jr/+ukGZMAErLRYxY8YMeu6553Dw4EEsWrBA+bVFCxEVFUUec+bIwBsdLQPx8OHy3OvXy/A+cOAV9T768884a7cjctAg0bx584vJNCcnR9V1HSdPnqSMjAxE1awJstmAdu1kV3Ei2QLdubNs4f6//5M9Ae64AxgxArj3XvTv3x+LFy9GVlYWQkJCsHfvXpzOz0frESPwW9OmmJWcDH3/ftSsUUMPzctTVi1apCclJysXW90B2Sr/xBOuyh4F0tPh4e2NsrIyLFmyBKM7d5a9Dx5/HEhOli81atUCFAX47jsZul9//WrPCwwGAwwGAypCsbe3N/r16+dR+djCwsJyAOdcQxMGR0ZGegCAwWDAqFGjPE+dOuWZl5dXW1EUhISEVN2aX8kTTzxhMBgMAXl5eQmnTp1q7XA40KBBg4vfgZeXFwYMGGD56quv1k+cOHHAhAkTvtd1fe2BAwdGh4WFGZ1O58FrFO8FwNyuXTsQkQWABQDy8/Nx+vRpioqKsuzZs+eKJn9N08xms3kuEfU1mUw/aprW2Wq13tycB4wx9hfFYZsxxtifnsViubNhw4aeAOB0OpGbm2sAkH2tz2ia5mk0Gt8dPHgwjhw5IlRVVU6cOKGfOHFCadOmjdK8eXNkZWXhq6++QumWLaRnZUE5cADw8ZHjjX/++dL43ttuk63BFe65R7bkvvCCDJVRUVecf8+ePQCAzsnJUOfNAwA0atRIBieDATh4ELj7blnG0qXAoEEQNWrgy6lT9QP79ysCQM+ePbFu3Tp9VVmZMnLuXOR4eop7hw6lmJgYAzZtAo4dwxO+vvRWbi6OHDmCmEOH0MhmE0fDwmhdVJRotWIF3bZxIyg0VNYXkBOH+foCzz8PBAYCKSkXW3vLy8sxr0ULdGvbFkO7dr2sCbh27drOmjVrKgCUOXPm4Kkvv4RfkybA3LmXDjIYgLFjZfmuyeKOHDsG9dNPYc/IwJGsLN0cFaUsWLBA2O12qhEQoHuZTGL/8OFo8+SThji7XbaAZ2YqF/bsQXjduli2bBm2bNniHD169KXWdCJg9Ghgzx7gX/9C40aNcLSwED137wa+/FLOkP7ll3JMubvmzYHly6/12KCgoAAArjurWuvWrb2PHj36HoCxHh4eMY0bN3arHiEsLOxSK/t1BAcHi7y8PNq8eTN69+4Nf39/1K9fv8o6xMXFYciQIV6fffbZyokTJ04FoJ09e9ZksVh0AGcrjtM0rR6AMxUTqVmt1vzU1NTc48ePR9SpUwd2ux1ffvnlhczMzHJVVXfb7fZ0h8PxbuXzmUymj8LDw3vcfffdyrvvvtsGQDyAX27owhhj7C+OwzZjjLE/PafT2T48PBwAcPbsWdhsNgeA3652vKZpZDAYJtWpU8dUt25d1K1btyK4XPZ3Y4sFterWRekXXyCtYUNRf8wYClq8GIdDQ1F8+DBatmwpuzuPGyfH+T766KWT3HefbGX9+WfZalqv3mV1KCqSw2pDmzSB1wcfwOPAAfHvf/+bHnjgAQQFBQEPPSS7RffrB9x1F1BQAOFwwPbhh3hh8mS8/9RTaNGiBVq1aqWkp6fjuNGIkSdPKlQx3rh9eyAyEsbRo9EpPx/+/frBNHYsBn7wAZ1/5RXs2L6dvMaOxY+1a8N7zx5RsnEj+fr6olmzZnL5rNmzZQvvtm3Ac88Bqor3339fb1pYSK0feohw4sRl1/PQQw/JsJuaipKsLPFp3750/+OP44r22qefBqZNg+PhhzH944+d586dM/j07Im6+/eLDjt2KG0SE2H29SW/u+6C+uSTCg4ckLOTA7g4ddmhQ/AqL8eAAQOU3Nxc5OfnG0pKSq5cM7tmTWDCBNS57z4MP3ECiwYOBF57DZFxcVU/GImJ8l7ruvzOKsnJyQGAqtcCryQ2NhYjRozw/uCDD5rqui6Ki4sR4D4b+w06d+4c8vLyCACSkpJu6DMhISEwGAzw9/d/sri4+EGn02nx8vIqB1ALAFJTU98zmUwP67pu0zQtyWq17gUAu90+9fvvv58ycuRIr6+//ro0Kytrjd1uv3v8+PG2qs6jaVp7Ly+vgYMGDfIoKyuDzWZTAFyr9Zwxxv5WOGwzxhj701MUpcRut9cAgMDAQDRo0MD422+/5aWmpuYrinJQCOGw2WwthRCzjEajh9ls7uHn5xfYp08fryoLLCkB5swBpaUhsFcvlLz6KvzGjaN5336L/PBw0Lffwm63o06dOggODpbdxSutxwxABuXp0+WSXHPnQq9RA+Xl5UhLS8O2bdsQGxsrgp1Owjvv4PFvv6W33noL77//Pob07YuwxYvh4ekJUbs26KefgNhYKAYD6rVoIZYMGYJib29ZbpcuSLjnHtld/bnnZDfwWrXk+cPCcGbIEDQaMQLGkSNld/dateANoGNmJs6ePIllvXqheVISft26FXa7HevXr3c+8cQTBrVGDTlD+WOPAcuX45vz552FhYWG+kOGgOLjr7xWp1MG1J07EVW7Ng6oKkptV2a04vPn4fH669CbNcPZs2cNAPDUU09BURSCrsvW6JdfliH/wQcvTaLmLjpaThwHYPjw4ViwYIE+Y8YM5aGHHoK3l5dc53zZMuDrr+VY+cWLUb56NeqtXi02Tp5MRc8+K5o1a3Zly7DJJJdG69Hjit4Iu3fvFl9//TUpioIhFRPMXcPSpUudu3fvNgCAzWajadOmYfDgwYh0W/7tRgQEBCAlJQXr16+/Ys3vyhwOB44ePYoVK1YIDw8P8cQTT1gOHz5smTt3LurUqWPKyMi4Z9KkSYrZbB45ZswYy6FDh8zffvvtRk3TngcwF8D7Z86ceerQoUPRBw4cMOm6/j2Ap6ZMmVLfbrfvFkJ8aLVaL36pZrP5uaSkJA+z2Ywff/zRrijKzFdeeaVU07QYyPW/Yx0Ox24Ac/4X644zxtgfDS/9xRhj7E9N0zSzoigvJSQkeHh5eYGI0KhRI2P79u0NcXFxPnXq1KlXq1atqMOHD3sQ0W3+/v6tBw0a5NepUyeTpdKs2xAC+Phj2QX57ruBnj2BOXNgvPdemAYPhqlpUxQUFIjCwkICZDduALJbeceOVU+ulZAAhIcjf84czFyzRqzevp3Onz8vEhISqEOHDmQRAti0CabBg5GYmAi9sFA06tWLVmRmYmtBgf5t06a0+eBBBAYGwtPTE4sWLVIS7r0X3bp1g+fPPwNNm8pJztLT5VJZeXlAw4ZysrPXX4dP795YsW8fbH5+CN++XdZT14FVq1C8YwfS27TBHV26UM+ePdGqVSscPnwYmzdvFl5eXhQcEQH06wfbzJkoXrBAiRk0CM1TUuRyZz4+l7rRCwE0aCCX7EpNRXpZmZ6bm6vk5ubqYWFh5OPjc/F2fP/99/rCWrXo17NnhS4ExcTEID4+HkQk719oqCz3lVeAdevk2PE775QvNCr83/8BJ04AHTvCaDQiLCyMftu2DUe2bRPNxo0jWrwY+ssvY1NsLObt3i3W7t5N6eXlyPP3p87ff4+itWsp4L77UFRUhN27d0NVVXh7e8uys7LkTPI1a148XWFhIWbOnEnBwcH6s88+S/7XWR6suLgYy5YtU3r16oU+ffogOTkZhw4dEmlpaRQUFCRf0NyEWbNmAQCCg4Ov+dkVK1Y4V61apcTHx9PgwYPJYDBAURRs27YNp06dElFRUYE+Pj4pgwYN8vL19UVISAiFh4dbiouL7yguLh6r6/p3TqfzvJeXV/Jtt91m0HU9OTo6umvDhg1bOxyOlNLS0jZJSUlzATkpIRF90K9fP9VoNGLZsmUlJSUl32/btm2iwWB4NT4+vmvjxo3bCyHuKCkpGbx27dqPU1JSeCw3Y+xvhVu2GWOM/ampqvpK3bp1zUFBQZdtNxgMl0081bhxY2zbtg1paWn4/PPPERQU5Hj00Uflv4NCyCWfZsyQQWvePCAsTAZvmw0IDQV5eCDg+HFkZ2dfTNQbN25EoNOJhv36gc6cwamcHPj5+eHo0aMoKCiAzWbDgQMH4OHhgfA9e8QDWVnku2MHyGS6lMrLy2VgBmAymdClVy/CrFno+fzzOGowKPd+9BE++ugj59KlSw3l5eUAgK1btzrj4uIMmDJFlvHee8CCBXIN6zfflOt6f/aZDOBjxyKva1dBISE6EhIMSEwEfHxA9epB/PwzxEcfobCwEADg6emJwYMHK6tWrXIuXrwYixcvhq+vr7PEx0dJCQujtgUFciK1d94Bzp6VLyVWrpQTjb31llySC0Dfvn0N3bt3x9SpU5V9+/aJsLAwys7ORkZGBnJycigyMhIDH3+c5t9zDw6Ul2P//v0Xl9KCzSZfCCxYICdXW74cePVVeZ/GjAFq15YvFDp2lMefOIHgzZvx6MqVWBsejllt24qyBg3ozNy58PDwEH379qXY2Fg4HA6kp6djicWCesXFwr91a9rYqROKo6P1NWvWKLGxsc6kpCRDWFiYbBVv0ODiV7R8+XIdgPLAAw9c2bfcJSMjA999953ueiYpICBANG/e/OLxI0eOJE3TsGPHDriP374RPj4+zlq1ahnirtb93SUmJsawZ88e0blzZ6qYwM1ut8NkMqGkpIQSEhI861UazhAZGYnIyEivPXv2iG+//TYNgIiIiFAbN26Mxo0be1cc17p1a4+33377dk3Tmlmt1l+I6P6YmBhnxRJvSUlJHrt3736uadOmvk2bNoXRKCdJT0xM9HrvvfdqlZeXNwGwA4wx9jdCcglOxhhj7M9H07TaqqoeGj16tMf1uthW2L9/PxYsWAAAMJlM4pnOncl8/LgMkBMmyHG7w4bJ9ZcPHQK8L+YNOJ1ObNu2DbVr18aaNWv0kydPKoayMjQ8c0Ycioqi0lLZU9bDwwNeXl56aWmpUrt2beHr6yuCgoKU1iYTlA8/BCZOBCpeDhw4IMd8L1kig+ubb8rwHBcnJxQDkJ6ejqysLJSWliImJgZZWVn6gQMHlMaNG+sDBw5UIIRsrZ4+HXjySVlueTnQtStQpw7mtmql3/3cc4q6Zo0ci/3RR0BgII7/9BO8mzbFiiFD9HsffFBRpk2TIX3jRmSXl+OX8nIRERFBNpsNTZo0gWn+fDlj97hxcrkwm02uz71okQzcbjZv3owffvgBDRs21FNSUpTp06fDYDAgKCjImZiYaNg9ZQqO1asHu8UCIQSGDRuGuv7+cuz7mjUXX0AAkOuVv/MOkJQkl1arVw/o1Emu3X3kiKyT2YzSwEBs2bJF5OXl6cnJyYbQ0FCQW2+DH374Qd+zZ4945plnDAUffwzfuXOhfPIJcj098cPq1SIrK4vanD2rdzl9WoFrWbFz585h+vTpuP/++1E5qLpbt26dvmXLFiUlJQW5ubno1KnTFd2+NU0DAPzjH/+4bGmz61m9ejU2bdqEhx56CBEREVc9TgiBTz/9VA8KClL69Olz2b6PP/5Y+Pr60j333HPVz+fn50MIcdXW8+XLl5fv3Llzk67rbxmNxs/uv//+oGvdEwAoKSnB22+/XeZwOOpZrdbcqx2naZo3gL4A9lqt1p+vWShjjP1JcNhmjDH2p/Xaa6/9q0WLFo/06NHDdDOfczgc+GnFCmTPmIGuJhM8unSR4bZi3eYFC+QY6I8/vup606mpqSIoKIj6EmHtjz9iX2Qk6tevj9OnTzu7d+9uqFh/+gpvvSXHBa9cKX8/d0623g4eDHz/vQz8Tufls5u7sdlsSE1NRVBuLqI8PfXuAwcqeOQROcu33S7rvG+fXMs7IwPw9sZRXcfSTz5B7+HDER0TIwOyqgKZmbC9/z6m5+aiqdmMlD17oMyfL7vPx8TI7tpmsyxn7Vp5X8aPlxPBHTok69iggTymkvPnz+P7779HQUEBTp8+DbvdjrFjx17srq07nVCefx546SVo778PT1XF4AcfRNjOnXKSsspOngS++gqYMwfYvl2Okf/hB/ly4LHH5PdXXn7Zy5HKPv30U2dERIShi6sFHroOdO8uw/t77+HoqVOY/9FH6GU04nyPHjh9+rTz119/NURHR4tBgwZdcwby8vJyLFiwwHnq1Cl64YUXqkzSJ0+exJIlS0RBQQF5eXnpY8aMUW4kdOu6jtdeew1OpxMvv/wyqlqLu8Ls2bP17Oxs5YUXXrisbm+88QYeffRRVO4BcjPWrFmDtLQ0hIWFFbVq1cqnZcuWl+6Jw3F5V3+XXbt2YdWqVWtffPHFOzRN81MUZbSiKMEOh2M9gKVWq1XXNK2jyWSaFxYW5nvs2DGTruuhVqv13H9cUcYY+4O48deqjDHG2B+IpmkE4IE2bdrcVNAGAPXtt9Fy6lQoRiOO/fIL9GnTZFDo108ueXXiBPDPfwK7dgHFxVd8vqioCDabjVq1aoXADRvQ08MDRITjx48jKirKsHjxYuFwOKo++TPPyMDdt68M2vn5wPvvyyCbnS0nBtu69crP2WzAjh0wDR+O24OD0WvZMoSvWaOIhg3lS4Lhw2UI3rEDOH8eyMmRE315eiIyOhqRbdtizrx52LFpk4CfnzzPQw/B3LUrej/4ILYbDJjbq5c813ffyfWnFUWuVR0eDsTHA926yTWqMzJkuNq7V25v2lRue/hhOc76yBF4Z2VhQL9+CAgIcJSVlQEA3MfIKwaDDM179yIpKQlD/v1v5I0Zg5MVLzeKioBNm4ApU+T9SUgA5s8HQkJk0E5NlS3/P/0kXzIsWyb3ATJ8p6bKn2fPBkpLcerUKZw6dcrQtm3bS/dUUYBvvgHatgVmzEBkXh6imjXTQ2fNwsGNG51nzpyhfv364XpBG5BDAOLj4w0mk+mqrRi1a9fGww8/TI0aNdILCwuVKVOm4JNPPrk0jvn8eWD3buDXX+XvrgYRRVHQv39/AHKitmvVIzIyUiktLUVJScnFbWVlZSAiBAYGXu8yrikrK0sHgAceeMC3ZcuWhNxcubTb55/LcfYTJlzxmYCAANhstk6TJk2aajQa9zdq1OgfycnJY4KDg2cBcKampu7y8PBY2a9fv7CkpCRvInIAKP+vKsoYY38QHLYZY4z9WYUpimK64QCh6zJENmkCnDgBg8GAdiEh+DElBXNGjxZo316GOqsVCAiQrauHD8uu2W7279+PadOmwdfXF02aNAFmzoTn++8jMTERpaWl2Lt3L8rKyqhiaa8rEAGNGsnw+PLLsjW2tFSG+8cfv9RCmJcnu3tfuCBbme+/H9B1HPfx0bdlZGDmyJHYNno05i9frovmzS+fnK1PH/nSYNw4IDgY2LcPd911F/r164eNy5aR0+GQAVlRgPnzER0djcTERBw9ehQLFy4Ul9U1IkKGvtq1ZSvy+PHAgAEyyBcWyjXGv/oK8PSUa2cLIV8c9OsHZGWh+zPPqEN++knUyM1FwaRJwLFj8pqEANLSgNatYbDbsTYlBb+azVgxYQIOx8SgKDwcq+bORemFC0CLFnLm9ZgYeU/uuUcubTZvnmxVb9FCns819hytW8t7rOtyXHlpKSg1FSM+/BC+vr6Apskgr+uyHiNGyBncH3oI9yQkKEFJSXiwc2fDsGHDlOuNk3Z35NAhZ21PTwN0Xb50OHhQvqx44w3g+HHg/fdhGj0a/e64Q3l+6VK02rwZgcuWGURoKDB0qLymZs3kCwxAPiN9+wIAGrVti5q5uch47TU6FRYmx9kPHSrvRbZcUr60tBTp6ekgostebPj4+EBVVZyotFzbzfL19YWlpAS5n3wCvPQSMGSIfDHVtCkwahRw5swVL4pmz54NAGjfvv2zAwcODB0wYIA5KSkJo0aN8m7cuLFQVbX52LFjPfLy8uwLFizI13X9LqvVeuG/qihjjP1B8ARpjDHG/qwCLBaLHYDHdY+cO1dOgDZrlpw8bNQoICQENWvUQMudO7Fy5Ur6YsIEfdCHHyqW0NBLn5s0Sc6Eff/9QHg4ysvLsXjxYsTHx+vt2rVTDIAM5llZiImJwaZNmwBAdO3alWrUqHH1+hDJ0FqxhvUdd8jfbTa5vnZSkmz1XrdOhv5vvgHq14eTCJ+tWKEAgFFVkZOTA4fDoZw5cwbe3t4wGAxyYqp335Xht1Yt2TKfkACkp6NJkybY4unpzEpOVqI8PQnPPguUl8M5ejR2RUejdu3aOHDgAC1atEiOBQdk6/6UKTJQDRkiX0CMHy+7dbdsKVtiL1yQM5GPHy+vr1Mn+bOuwzF9OvalpQnvwkL4HjxIOHJEBuft24H9+4GQECSrKs6npMB06hRK7rwT5++4A0ePHsVekwm3v/EGyo4dg+WBB+RY9t69ZRf2adPkxGnz58t7+M038pxJSTKUZ2fLa585E+f37MGJ+vVR0qyZIzQ3V8XGjbJV3GSSLwuOHpWTybVqBXzwAfDLL8CHH8owW7++PEfXrnJ8+FdfyZcy/fsDzZvL7Y8/DkyejK6zZxssK1dijtks7tM0UoYOlZPGpafL58jPTz4v+/bBs29f3D5/PlZFReFk164I9/WVvRJatbo0Xn3rVjmkAAC++w53R0YifelSbNZ1RGVkoIXBACxcCJSVAUuWIC8vD4WFhahXr57u3j+diBAcHKx/9dVX9PTTT1+3lf4KDgcwZw4Gbt2q7HE4cDwtDX6PPAL/116Tz7Kuy+77OTlyGENk5MVeBkFBQaJRo0bUsWPHy85LRGjQoAGdPHnSYTKZ1Ly8PLuu6wuEEGtvun6MMfYHxWGbMcbYn1VBWVmZ8YaOHD9edtdet04u6VSvnmzVBdCqVSvEZ2XBOWyY8nmNGvrDY8ZcSilms5z8KykJ577+Gp9++y1CQkL03r17K0Qk1+N+5BHAzw9BqgpFUVBWVka7du3S27Vrd+3eY0QyWO3fL8dq//vfQG7upWXEGjSQLdPAxeCSc/LkZUW8/PLLSE1NFbm5uTTd1QL/7LPPwjM6Gpg6FWjcWIa83FzAywu2Bx9ErVq1DAWZmQLvvQds2QLRsycKly+HV9euuPvNN7Fv3z6xYsUKpXlZGWIWLACefhp44AF5wk8/lcFz/Xr54mLuXBlOFy0CnnhCtjq7z7StKPjs4EEUmEwKBQfjnTp1xJh27ciycqWczXz1akBVQfXrw2faNODrr2Fu1w4B27Yh4osv0OT113Hg11+RX1Ag2p87R4Z+/YCAAByrWVMEZGeTT2kpsG0bRL16+KluXeEzYwZo4UKEN2xIlgMHgNtug/jwQxwQQmT9P3vvHVXVubV9X/dau8KmN+lNqjTpIsi2YS8xajRqLDGaGI2mnyTmkB1NNMmJ0WhiNLGbxB5bSgJETAAAIABJREFU1FhAECmCgAqCSkcpSu+w91rr++MGATUn5/nG837fOe9ZvzEYDPdmr3ov5JrzmnPq6xP/4mIWWVk0CHHkCDBjBg1EbNpEM+4ZGTQrnpNDv44epaL+nXeAlSupKD91itayZ2UBUikV0wwD6Oujy88PLYWFKCgpIcXGxoKriwuBsTFgakpLBz76CPjiCyAvD9i9G9LgYJTk5SGf5/l3f/6ZQUAA7SgfEgKo1TR7PGYMvZbDhkFXWYn02loowsIwzc+Pity6OkChAFavhn1GBqynTeOFZzTkGTt2LLN9+3ZUV1c/7tD/T6mtpc37zp2j93v8eGD2bFg5OODYnj1ot7ERRnd3nzv6/ff8c2+8wWzdvFnnynHE8sUXScrkyTzPMGhpaWH/rD/Qw4cPIZVKSffx6dXU1CxsaGgI1Wg0HwFol8vlyxmG8ed5PrOzs/Oz2NjYvL8+cBEREZF/H0SxLSIiIiLyn8oolmX/uhxKEGhTLRsbaonuqTHesoWKZaUS8gkTUHfhAqrOnWNyc3OpPRy01rWF4yDMno3iRYugmzQJ4eHhzOMO15WVwOzZqKurww8//AArKyvupZdeYmUy2T8/rtRUmh3du5cKq5oaWjccF0fHW/1JEytbW1ssX74cW7Zsgb6+vgCAyOVyPjk5mTUwMOBbW1uZo0eP8gCEuQUFLJF1l7OrVKipqkJbYiJITIzgbGND2srLIV+zBjfc3PDHwoXCcicnojp5EqGBgcSwvByVaWnCwBdfJIiOpq6ADz+kDcnkcjpj28am98CmT6c1uy0t4HfuhDB/PvLy8lBXV4eGhgYAgIuLi6DT6YTTp08TQ0NDKB89QlRQEO36HhZGgw+OjrTGetMmICgIzODBUO3Ygc5Jkwh++QU4fhy3bt1Ca1kZuWFhgVv79sE+Jkaora0ljVFRxLypCfN+/BGbfXxgP3euYJiXh/KpU4X6+nqyatUqACBQKGhTNADIz0f9qFEos7GB2+nT0Js9m57X3btAaCi9R97e1BY/eTLNID98CBga0hpxY2OaqY6NBfz9kZmczGkdHVljY2PoBwcTWFnR87p0iQr1Tz+lWfOe9TN2LOrS00EApu7992H63XdAQgJ1Xjz3HODq2u/+19TUPF6Xjw4dgqVEQo/x4kVg7150HDuGpnv3GOeSEujmzu3XSM3a2hq+vr7c+fPnyTNHmPE8nTH+6BHwzTe0fnzuXHod3n8f0NcHAMgaGsAwDOTdTfGampqQU1vL3F+/HlFDhkhIWBgs9+zB1LIy5tG8eSCEwNnZ+and5eXlITU1FS+88AJLl6gKS5Ys0c/MzAy5du3aUZ1ORwIDA/UdHByY0tJSjytXrkz79NNPp//9738/86fPlYiIiMi/GaLYFhERERH5t0ej0TAAhgIIAqCUSqVOAJa0tbVBq9Xi/v37qK+vh6GhISoqKnhfX18GoM2ZUFBAO32XlNCN3b4NNDRQUbNsGX3t1Vdh+s47GJCdzZeUlDBSqRRnz54VmpqaCAAQjsPcoCDhPT09Qvpmbr/4AmhuBrN1KwRBQHV1Nbtp0ybh3XfffbZV9+23qcXaxASPx3UdPkwzp2fPUpF38iTNas6b178OuxsDAwMAgKmpKQ+Atba2Zu7cuQNzc3NGp9OhpKSE4XkeGz09BWOW5Vu/+YbREYLW1lboFiwgE6RSweDTT8mx9nbURUbi4fHjcPf2JgYMQ+u85XLUDxok5E2ZIkTNnUuQmUmz24mJvV3HDQx664p7GDUK1/ftg0VsLK6kpKDA1hYgBHp6ekJUVBTx9PQkJ0+e5PPz82Gs02HGjz8i/oUX+OGffsrAwIAKeq2WNmNTKul1AnBq925YjhyJgI8/RnV1NU6dOoVF4eGoLSiAVqtFUVER6b7XwuwVK8gBd3de1tDATFyyhJybP19wnDiRWbhwIWQyGb3eBQXQbt8OiYcHqvbtEx50dJDrEyYI56ZOJe9t2QLi7k7Pbc0a6g7Qamm2/scfqQB9+216voGBvec+fToAwHHWLPYAALa5GemTJgmTxoyhN/DePSrwy8powKcbhmHwTkUFas6dQ+bUqRj1+++0fMDVlW7z7l36/cgRAICvry/09PSwf/9+1FZXw7LbVQE3N8DeHmTJEozeuxdO27fj5K5dwrSFC0nfDuFBQUHswYMH+cf16pGRNPB04gQNKKxeTWvz9+2jQSA9vX63+MKFC0hOTgYhBEZGRqSiogL79u0Thunp8dHV1SzTc02cnYHvvoNteTm1/T+BTqfDsWPHMHnyZLj2CSgwDAMzMzOiUChIVVWVLD4+XrCysmr29fU1CAgIUN68eXMeAFFsi4iI/Mcgim0RERERkf+jdHcNN/6zUT4ajUYBYBqAjNjY2LvP+rxcLj+uUCiGDxw4UCqXyyVKpZK5ffs2qqqq8P333wsNDQ0EAAYNGiTk5uYy8fHxAIC5c+fCtbmZ2rV7YBhq650yhc7UzsujAqq4GOGTJjHZJ0/ixMCBGDx4sBAaGkqMjY3B8zyYBw8Inn+efs7BgW7ryy8BrRbGxsYYP368cP78edLR0UGOHTsmTJ8+nQqtoiLg44+pgGlspOItIoJ+9fDoEa0d1mppFjE7m86tXruWWsr7IJVKuzdbxBYXF2PmzJnk+vXrCAoKwq1bt3D37l1MmjQJFy5cIJELF7KZgwcje+xYqNVqmJqa4u733wv6/v6C/SuvkOe3bYOgVEJiaQl89hkV23V1uN3eDq1Ox9w+dAjeX39Nbe7KPqXxBQW0lnvBgscvnT59mrteVMQO0mjAx8VhxuHDKF69mpswezbb8zMymQymKpVgodUKSeHhzB2lkolWKkGkUuRt2gThzTchHzIEmYcOCe3t7aTlzh1hzvffk9qjR9Eul2P/jh1CQEAAGWBlBYfLl+E9bRpu3LghdHR0CLNmzWIAYMnSpTRr6+aG6aNHE+zZA5iZof2bb8CdO4e45cuhyshAgVaLhvHjhYULF5Lg1FTyXVYWV/Hee6ythQVtBDdgAC0RCAujNeBxcVQsNzZSW/UzxpMVFBTwABiO49DY2MgDYJGURJ0Vn3xCnRB94XnoVq1CYksLntuxA/z774Oxsemt01apaB0/zz8ue3B2doZhczNM9u2j3d/19YGiIgh37mD7uXNCR0cHzL/+WlC7uzNwdKTr6t13AYaBw+jR6Hr3Xabj22+hMDCg/QsmTKA2dA8P6qooKqIBKT+/p8R2dzdyZubMmdDX18fu3bsxdOhQYVhbG0tovwKKmRkV2YcP08CBt/fjtxobG5GYmAiJRCL4+vr2iyZxHIdffvlFq9PplgK4BKC1srJyVG1t7RSWZX27urp+euqii4iIiPwbI87ZFhERERH5P4ZGo7GSy+Xnurq6/GUy2cXOzs55sbGx1U/8zOtGRkZft7W1cTzPv7l69ertT7zvpVQqM95++209ln2s2xAfH4+UlBTB19eXZGZmwtDQEGFhYbhw4UK/Y3h92DCYq1Q0U9eXxYupbbbn57VacCdOoHXxYlz87DN+ilzOsBMnUtHVw717NNu5aRMVn2PH0oxnd7Zyy5YtQm1tLRkwYIDg8fvvQqu+PqLeeosx1GhoI6snxMtjEhJoRjcri1rb582j9cNff02bcM2eTS3W3Wzbtk2oqqoiEydORFDfQEIfNm3axLP37jETli6FnafnY5GO5GTa3GzGDJqhbm+n1u0hQ+g+Vq1CfXExzvr4IPrIEcH2wgXyOLjQ9zoUFUE3ciSuXLmC3Nxcvra2ljE3N8fcuXOxb+dOfnpyMjNg2TIqVHuyuYIAzJ+P2tpa4YC7O1m0bRsuffKJUP7gASGdnahzcAAhBBzHgdNqMX3IEHhXVkKYPx979+7lu7q6hCVLlrDYv5+Ox1q37ukTFwTg1i3auI1hwC9bhlJnZyHZ25s02tuj1cxMCA0NJWZmZvD09IREqwUcHVHk6io0CwIZNHMmJEolHR/WQ2QkFa3/+AdtajdlCq3pZlnqUuimsrISu3fvxogRI/B4xNjZs8CxY8CKFbT++4cfqGPhyhXaZK2yEmfPnMGd+Hi8um4dFAxDbepNTbSBG0ADNSYmuB8QgPj4eDB//IFIqRSOu3bR90eNQp27OzZbWWHKlCkICAigr69aRffZ45a4dg27Cgs5Wzs7NiYmpvf8cnNph/ZJk3ob333zDa1N9/ICAFRUVOCnn36CIAiQy+UCIQQ6nY7odDrExsY+e10fPEjt88uW0XMCtY8fOnQINjY2nCAIcHNzY4cPH9596wRs3bq1rbm5OYPn+XwABIDQ1dV1E8CRJ393iIiIiPy7w37yySf/fx+DiIiIiMj/JWg0GueEhASPhISE2oSEBDepVJoUFhbmMmfOHLazs9Px4cOHL8TFxe1Vq9XtPZ9JSEgYOmjQoFFTp05V5uXljYyPjzePi4s7r1are96PsbGxmRAYGCjvuy9nZ2dERUURDw8PuLi4oKioiKuqqhL8/f3h5+dH7t27BwCwO32ab7x+XbimVHJubm40PdjZSYXlzJm9YollwXh7g1uxAifOnSM++/ZBr6aG2nQvX6ZWYjMzWnN76RIwfDgVJMuWPRZFLiYmxHrNGtywsiIRlZXokEiYi1IpH7pxI4H0n/RyO3ECOH+eZrFXrqSjqJyc6IitnByaGXdyoseqVMLMzIzcuHEDTU1NfHBw8DMt6z4+PuRyTg7s163DpaoqIefBA8HJyYnI09Npp+4hQ+gotLo62m09OJiKwDFjoLS3h+MnnwhJs2cLzuPGkVu3buH06dP8xYsXcfXqVdQfOoS2U6ew99Ej1NXVwdfXl4wbN46o1WpwHIe4y5eJw6uvwkqno93Vvb2pG6CqCrh2DXqVlWTwzp04JwhCgZ4ephYWEtf4eEw4fBiRkZEYOnQorL/6Sui6fBnWmzaRS5cu8YWFhXj11VdZlmWpoJZKe5uxCQIV199+S89h+XKA46BdsgQ7TE2Fm35+ePHOHRJZU4OhW7YQJycnWFpYgFm0iI4NW7AARZmZ5FxQEIK1WsjGjOmd2Q1Qm/aECTTTbGdHZ6XLZPSczM0fW8oVCgUKCwtRVVUlBAUFEfA8rQGfPp2OYDt9mq4biaR3drmFBawjI3F9xAhEjRpFBfzy5TQw01NGsHUr0NqKU01NKL5zB+PPnIH1kSOQ9ARvZs1ChokJ6uvrhcmTJ5PHPQUMDOh27tyhAQInJ+Tm5jIPHjwQQkND6Q9xHA3AeHjQ8/jkExpQAqjjoqEBcHDA9evXUVJSAl9fX37KlCmMv78/SU9Ph7OzM/y3bqWztmfOfHIR0tpzjYaWSnAcOjkOWVlZaGtrYwwMDEhOTg5JTk4WPD09ib6+Pvz9/aUmJiaOdnZ2Qc7OzkHOzs5BSqVyeF1d3arExMSO+Pj4NLVaLWaKRERE/iMQbeQiIiIiIv8rrF+/fpNMJluiUqm6Ghoa9FiW5ceOHSsNDAwkABATEyPhOM76xo0bBwGM7vPR6ubm5i5zc3P50qVL9Xbu3Lm0sbGxCsCX3e+rVCoV++T++uLg4IBly5Y9/pmOjg4UFhYCAG7eu8e0K5Voun2bMTQ0RJhSCcm4cdQK3V3/3Jfqhw8h0dODMjmZWnRPnqS12RMm0O/PPUdrac+epd3NVSqata6pgdn06RA6OwV9QSAme/aQE7t3C8P/qis5ALi40Jro4GBq2T5wgIpUQmiGdfZsYM8ecIcOIc3EhL+sUjEAMGfOnD/dtkqlwpQpU2B24gRulZaSQpYlZWVlGHjjBqT19WAnTqTNtQwNaY1tUBAwbhwapk5Fy9q1gqy1lehKSshXX30FlUolWFtbC1FRUYyxsTHY3bvBdnZi6tSp8PT0RI+4KysrQ1ZWFieXy9lTp04Jvh9+SLB+PRV848ZRQbxnD2BnB6lCgVy5HNrmZrLfyQme48ZxTgydpsYwDNwHDSI/A8KtfftQXl7OLF68mNZdAxC0WpBjx2hd94oV1KHw4Yf0frq4ANevg+d5HPr1V76LZYVVb7zBMjNnUht4QQG9xi+/TIMO9+4BH38Mux9+gPLiRS7v9Gl20LJl/efJRUVRi7+lJV0PQ4ZQW3d+PhX9r74KXUMDct5/Hw8ePMDixYvpBfnjD2DGDHTV1eHUqVOcf1cX27V8Oe969CiT8ve/c/bTprFcdjbKw8OxYOXK3v0tX06dF90BJ/zwAwBg4Esv8bK2NsZm+HAo+s6XZ1mETpuG8r/9DX0mftHzc3OjQY64OGDECCiVSo7n+d7n6ehRanM/cYIKb19f6naYO5e6Kz79FKishLS7IZ6NjQ0sLS0BAM7OznxNTQ2wZAmD1j8Zjb1yJbXey+WArS3sVq7E31atAk8IlIaGpKWlBZs3byaXLl3C+PHjYWhoCL+e0We9KOvq6nDo0CFNQ0PDJI1GExMbG9v1Z2tfRERE5N8FMbMtIiIiIvK/QlJS0raZM2eajR07Vj506FAmKiqKtbGx6Zd1dXFxYdPT060uXLiQplariwEgISGB6+joWDp06FCZVCqFu7u79Pr165FxcXG/qdXqRwkJCUY6nW5mWFiY/Nl7fhqJRAIfHx94enrCfNcu5Jqbo1EqRXFBAaxsbWERGQkEBYHjuP7iBMCxY8d4Nzc3wdvbmx67hwcVvDwP7N5NxdXDh3Qs03ffAfPn0wxxbS3I1KnQf/11Ej5yJPT09JCTkyNUVVWRzMxMoaKiglhYWKCmpgY1NTXQ6XQghFB7t709FdY9dbIffwywLBrs7SGXy0GUSrT4+OCXK1fAVlQIs/PySKNSKZR2zyr+s+tgZWUFdu5cxGVmQmJggFu3bqHh3j2UPHoEw/p6QZWVRbB0KXQ6HQpNTJD04AGPjRtJjbW1QLZtI17u7hipVCLylVeIj48PY25uDpVKBX0bGyhDQ2Hh6/tYaANAVlYWn5WVxUZFRSEmJobo6+tTIb9qFRVzXV1UOA4ZAkgkyMnJEYKDg/HiggVk0PLlDKKiaJZ6zBhwmzdD5uZGrl27htGjR8PdzQ15hw/ziZs2keYdOzDg6lWkKZWCnqsrkb7/PtjwcCqKu50K586d4woLC8lrr73GSqVSGlSwt6dlA6+9Rn/2zTep2A4MhGrcOIRaWDANR48KP2u1ZODAgWhvb0d5eTnMzc1pxtnTkwpgQuiXvj6gUECwtcXxxETcuX8f0WfP4kh9Pe4WFQkKX19i/v77SLx+HZmZmYxULodjaiopmDsXJU5OJOvqVcHsp5/ItbFjMXJ0n/jT2bPUDeDi0vtaSwssly8n983Nobd4MYwHDux9j2FQkZOD6wxDwvtuJzWVrtehQ4E9eyDExODwb78x9vb28PLyogGETz+lQSQzMxpAyM6m98zMjGblBw8GWlpQuG4dSu3t4eXtzdvY2DAAIAgCKSsr48JdXRk4OT3uWA6APhcXLtBtjx5N7/l77wGzZ0Ny+DCkkycDb78NWXs7qhsbcefOHVy7dg0dHR39mqb1oFQqMXjwYFlubq55a2vrDbVa/VR/BxEREZF/N0SxLSIiIiLyv0J8fHz1vXv3xnt7e0v19fX7ibAeGIaBkZGRrKCgIDIuLm6bWq3mEhISagGscnV11TcwMIBCoQAhhCkvLx8XHx8/XCKRfODi4mKQmpqK0tJSYmJiApVK9S8dE8MwMNBoUDtxolDZ2kqW/fwzsisqkOnuzqWlpeH3338nbm5u0Gq14DgOWq0Wf/zxB5k2bRpR9m0IJgg0e+rkRGtpMzOpkGltpYJl4EBa72pu3q+DuEKhQFpaGmlubiaPHj1CSkoKsrKycOvWLaSnpyM5ORk3btwQWlpbYbF2LWkYMwYqc3M0u7oiv6JCOHTiBMnKz0dTUxOflpYm6MzMhFmffcZInZ3hdP06aT19mqS0tQlewcHkWde7rq4O4HkMnzULQz78EO1yOUZ9/z3yXFwgDwwkeg0NuKinx/3222/M/UeP+NDycsarthaOKhUxf/NN6NfUgF2yhDZC63s9Dh8GkpKAUaP67U8qlZL09HRMmDAB5mZmdLwax9HrplZTy/3AgdSibWKCi1VVRD1iBDE2MaGZ/ZAQWgteUIDPi4uRl5cH69JSmO3di7u1tbz3mjVMI8fhWkgIymxtcc3MjFzr6MCVmzcRFRX1OHBy7do1ISUlhbzyyiuMQV/3QnU13YehIRWTQUHUCv7WWwAA8u23sAwLIw/s7PhLly6RjIwM5OfnCzk5OYJEIiHmQ4eCzcqiAnTVKoBl0dHRgfM3b/LZOh3xs7eH3aVLKAoKEhxv3SKW69bh+44OVFRXY9iwYRj9ww8wtLSE7RdfwG/IEBLe2EiYvXshe+ON/uOxRo+ms8DlvfGlxvZ27KqqQnhGBhynTQNxcup37XW+vig+dw7+kyf3PntnzlC7+pgxwIMHIMXFyNLp0NLSwoWFhjJ4+WVaQjBkSO+Gbt6kDQTt7Oi/zc3BGxmhcft2DHN0hMe8eUzPGr99+7Zw9+5d1nvlSii1WpDuDvIAaPmFvz8t1yCEPid+fjR4FRxMLefGxoCtLThDQ/CurtzwqCgmMSWFHzJkyDPXc1VVFVJTU3U8z3+iVqsbnvoBERERkX8zRLEtIiIiIvIUa9asmZmWlvZrUlLSjEuXLmWq1eqHf/WZ6OjonMuXLzdlZGSoWZZl7OzsmGf9wWxhYYHy8nJ5S0uLc1xc3MnY2FghOTl5ooODg2OPPdXOzo5hGMa4uLjYy8TERFZaWkqamppIdXU1MjIy4Ofnh35i+M/Q6UAiIuA2eTKJjoqCXlsbioYMgZZhGLlcjpqaGpKfn4+rV68iJSUFKSkpsLCwEIaFhxMkJ1NBmZ5Ouznv20fFgrk5FZlhYVS8yWRAczMVlT0WcycnoK0Nlu7u5MqVK5DJZBg3bhxMTU0xb948REREYNiwYaivr4ezszO5lZvLy0tLydlHj0hWfr6QkJdHnE+dQmBVFUmzsEBzczMACPPmzWOlMhng6AjpuHEwbm6G/WefkWyeh4Of32NxxvM8kpOTceDAAaSmpcHrgw9gGBkJdzs7SE6eRLG/PwpraxHn4YEHFRWMHsfh+aYmwcXOjjD79gEvvECbYx07RrP2f/xB7fLGxvS6lpfTLOgTTecMDAzQ2trKJe3fT8zMzYmpRgPExFALeVYWvU7BwcDs2aixsYFsyxb4fPklmLfeok3IDh6kwmzRIhh9+ik87twBEQQYW1ggRSolCeHhqPL25letWkV8//EPeG7cCLlcjvLycmRmZgoFBQXkxo0byM7OJrNnzyY2fWeBd3TQ+9K9f7z5JrWVh4b2Bg1+/RWYNw+DoqNJSEgIgoODoVarSWtrK0lOTuYLCwuFgHHjCDw80GRvjwO//CKYmpmRc+fOETs7O6js7fkzFhZEplCQeUePwlQmg8e6dRg7aRKcHB1pjXlFBV0zbm6AUomNhKC2oQFeXl5QKBSoqKiA8p13wBw4QO9DN83Nzbh/8iQGtbYKqtOnSb8GbgAazp9H8Oef4/7s2TAxNaUvpqTQbuAuLrTR35EjcBs3Dmn37pEImYyQ48epi6LPeDAkJtI1HB39+CVBTw/7qqvhamsLs88/B0aOBPT1YWdnR1QqFX41NkaWsTHP8zxxuHyZZrW/+KJfUz+EhFA7+fz5SLCzw7H4eC712jXheng4f0+hIIEpKfD57DOSFhWFmydPQjFgALHqrpvnOA7Z2dk4cuRIO8dxcwVBSEtISAhPSEjwSkhIqFSr1bq//mUgIiIi8v89Ys22iIiIiEg/NBqNAcuye6dOnSpvbGwULly4kKLRaEbGxsam/dVnP/roo80ajeb3hISEREtLS9uBfa2u3RBCMG3aNL2dO3fObGhokGs0mjNSqTT0yXm7oaGhTE5ODi+Xy+Hj40MSEhIwoLszuETyL/73dfs2HY80fz7t+H38OMZ3v9XR0UEKCwsFF2dnqB0cSOb+/WioqIBjbS3Bpk20oZSvL23y9PPP/buSnzhBRUlqKm3+tH8/bbqWkUGFaU4Ore01MsIKQ0OcqKjAVYBf/re/9fOsT++ezzx8+HAWzc0I8PPDZbmc+Pv7w/rNNwm++gq+zs5CsyDw8+fP71+3LpVC9eabqFSrgbffBl9cDGbmTFyVyXDl6lWhs7PzcaSDODrSTOnu3WBcXTGQZTH0+HFse+stgenqIp4ZGejQ1yf4+uuem0Rt1lIptbVfuQJs307rfgGa5e4OjDx5byeoVGznrl3YpdXCLD4exj0CvaqKitvFiwEHBxzbvh2V0dFwfPdduHR0UMuyTgfY2IA5exb2zz+POJVKKOvqIp0DB2KStzdyc3OF8ePHMxKZDJDJYG1lBWtra9y9e5eTSqVsQ0MD6uvrYW5uDse+Qu/AAVpzn5kJrF9PgwgHDgA1NShtbARfXAxnExOace/OMCsUCigUCgDAiBEj4OXlxezatQtgGOgmTkTptGnCyMxMsuOVVyCVSjFjxgx0dnYyDMPwqampzNdz5sArJESY+PHHNHAzYABdH3V1VHDv2gVs2IDlSUn47rvv8N1338HCwgIPHz6EmbEx/AIDhaG0GzcA4PjPP3NT/viDbbtyhcDNjc6Nd3B4PBJMGRODLcuXwzU7Gy49z9KAAb2N3uztAR8fmN68CSuZTLj7+usYeO4ckcifqM4YMYJ22O/Do0eP0EEImoKDBTQ1ERw4AEyeDKmjI0JDQxG0fj2OBAWRixcvImTxYshApwXY2dnBzc2td0P29oBUivK8PDg5ObE+Pj7geR48z8Ns6VLCyGRYynFEYmeHCxUVvO9XXzE6AN8dPNje3t6eo9VqlwDQk8lkhUql0lyhUPD19fX3NRqNT2xsrNg0TURE5N8OUWyLiIiIiDyJm6GhYae7u7scADE0NNQ/evToWY1G4xsbG/vgX/i80P31pygUCrz88st6cXFx06qrqydERkYqeoRND3K5HK+99hoDAE027JMAAAAgAElEQVRNTUhMTERnZycvCAI2b97MvPvuu48bZv0pPE/ty9bW1C4LUDtrSgoUdXX4UC4nOHsWyMrC6BdfxJ7GRuQ7OcFg40ZU1dXxw4cPf3YDsvR0msUGqKB5/nlq1f3b32hzq4AAapNtaoLRrl0IvngRRY2NDObPpxbkKVMAW9ve0U4AQAgUd+5g7Hvv9b62YAGmjB1LNjz3HKvVanvHd/XB1d8fB0eMQCPPQ/Ldd7x1WRnzwsqV5JpMJuTn5xO5XC5Y2NkRSCQ0K29rC68JE4DRo/HByJEk/eWXwdXX4+ewMDL45Elu8uTJVNSHhtKv7duBI0doZnr7dtq47c4dmqXuy8GDtBv4xYsoO3wY1YmJ2LRpE959913o6enR+eEPH6Krqwtbt27lGhoa2GFqNRyiomjTstBQICMDOmdnbPn9dzTqdGCbm4lUKhUiIiKIlZUVPDw8eq0SU6cCLS3Ye/y4rqamRjJv3jy4uLggNzcXR44cwf379+Hg4EAz2m++ibaGBjR6ecHC3h4Pw8ORl5qKggsXeL+EBOb82LHwbGrS2WVlsYkbNhAzMzPd4sWLJT229K6uLvz222/8wIEDhYaGBvbGjRvCNQ8PYmllhWlTp8LX3//xYRkaGoJwHN76/HP8sHIlwd69tIN9Zia992fP0mz/r78CO3fCxMQEq1evRk5ODn777TfwPI/w0aNRtWsX+dXMjJ89ezYDAL43b7KFLi5wUKmowA4NpdnjhQsBACamphhRXCzo37lD58ED4M+fB/H2fqzYhUWLUOThgUCFgim1tkZLSQmCzc3730dn56cCKY8ePaJLlGUJ/vY3mjF/+WVg9WpArQbb1YUZkZGkbcIEfCcIvMrWljRev46rV68Snufh6OiI+fPnU2v5yZNwnT0bBl5ecJ827an1rAKw5fPPaV34hg1gDx9Gy+LFrG9aWvztiIhYAGMmT56s9OoeSbZ27VoPAEoAbRqNxgi0+SIP4GJsbGzTUzv4H6DRaCSxsbFi1lxEROT/NaKNXERERESkHwkJCQGmpqbTg4KC5ABgbm4OnuellZWVoy5durRDrVbzGo2GJCUlvZaSkrI3KSnpb8nJyfMSExM/v3Llytssy74dGRlpEBAQwD7LRt6DpKsLbjY2koCQELmpTEbrogE6Z5phaJazowOQSCDneURHR8PV1ZVkpqSQTq0WYcHBkLEszcJyXO+Ge7ZDCM0kfvsttb02NNAM7YIFQGUlzVg7O9OxTO++C0REoIDj+MKWFnKvqEgoKipiWJblHR0d+59EURHNWn/wAf13cDBtQPXll9QK/eGHwMSJtDO2QgESHo7zenpck709PEaNIjqtFtJt22g2XC6nVnU9PSrQBwygIrwHY2MwhYV40NTE6w0aREx77MF9IITAwcEB1ysrec85cxjvkBCY3b4N2bFjKLCwIOo//iAmubmQXbxIM9UPHgB799Ju0wcOwLqhAS0aDXLz81FVVcUEBwf3D2IEBVGB3dYGzJkDKBQ0++niQoMYaWm0KzdAG4iFhsLM2Rn29va4efMmysrKBFdXV6IoKYF24kRs7OzkLSwsMGfOHMbDwwOSoiJg82badO7kSXSEhUGxbRu0hEDu6cmtWLGif911D998AwwZgjs1NaivryeNjY1ca2srCQwMJHV1dfyFP/4g5q+/jvNVVcgYPpz3/PhjktjYiCPm5rhRUYHm5mZhqIcHGXz+PGGXLxf88/LYAQMHEt/Fi5Gbm0uys7P54OBgBgB2797N8TzPTJ8+namoqMDvv/9OtDIZFO7u/PjYWIIHD+hILwCNjY24nZ9P0oODYdTVBd85c8Bs2EBLEBobqdBet466Ilavfnw6lpaWiI6ORmtrq1B24gSZcu4cLgcF4datW7yXpydze/duZAUGorqtDX5+fuBfeQVdgwdD0tUFSKXo6upC2tGjRNBqIR8+HFu3boXLgQP4meOQUVIiODg4EJWBASpra2F1+TI6PvsMod3H3A+lkjo5Rox4XJZgaWmJlJQU2Nra0gCGvT2t6a6rozO6164FcXCA1NISiIhARUUFFi1aREaMGAFTU1Okpqbieno6l5yUhLzjxwWr3FwyoK4OBgCtDz97lu4zJARYsABN5eXwUiqJyaVLIFu2IOyzz1jv5OShnh0dntHffiu1trUFIQRarRZJSUk8AE1CQsJgiURyw8HBYaqRkdHU1tbWlfHx8b+q1erGp0+SotFoJAkJCUZqtbrjidfdU1JS0jiO25CcnLwiISGhftiwYZl/th0RERGRP0MU2yIiIiIi/UhJSfl88ODBAX1FpqOjI3P37l2DlpaW+ujo6PTExMTnVSrV1ueff94mNDTU0MzMzDo/P185ffp0/alTp0qcnJyeLbSbm3tnXMfGAj/9RMWumxsVwixLxbBcTu3Zq1bRulVPTyA1FbqaGvhs3IgOpRL+P/5Iu0rPm0c7N2dm0s7SixdTMdAz2qq+nr7v7k7/mP/oo95tOjnRZlndeHt7k2HDhiEyMpIwDIPLly+TgIAA9Mu6//QTFcUREb2vSSQ0yzh3Lj2v+fOpiFarAZkM2dnZqHr0iLlSXIz01lZEbdpERZeeHg0IbNsGnDpFBXtFBTB2LK2ZtrUFJk2CZPNm0tTczNuo1c+MXhgbGyM4OJgMGDAAjJsbEBqKzPh4TDt9mhiEhMBk3DjaKdrVlYrl69dp9rKgAGTdOpjZ2iIpKQmCICAjIwM8z/NOTk69+9LTo8eVkkI7Sn/6KRVaYWHAtGlUoC1dSjO33ZSWluLOnTswMTER4uPjYezoSKovXxa0o0cTO3t75vDhw8g5cQJGhw/zhllZhH3xReDNN9ESGwudVIqAXbuQYWXFxF+/Dm9vb5od70tnJ+Dnh0GhocTOzg5tbW1MUlISCQkJgZ+PD3Gxt4f1+fNwTEsT2tzcBMc9exj/hQsRGRkJtVqNsLAwMsDbmzDLlsHRwYEYbtgAvTVrYGBrCx8fH5KUlMQkJCSgoaFBaGpqgpOTE/H29iYpKSlcdXU14+Pjg4iICGI6dCi1qCsUACGwtLQkoVu3Ih9AhYUF+EGDOJcJE2hTMQcHOgorI4Nez2nTAAuLfqfl7u5OzubmCo6bNpFh0dEkOzsbt3/6ifgXFMD4jTeQlZ2N5ORkJKal4f7PP8N77lykDBuGtGvXuCYHB6a6qwt37t9Hc0cHQm1toZwyBe08j0uXLpG2ujrO5sAB8sjICIESCSEjRz69mAgBfvmFBn8sLdHR0YEvvvgCWq0Wpqamgru7O10Xzs503X/+OfDSS0BKCsjatbB/7z0SIpcTuVIJZs4cDHB3R8jFi4javp2x//JLMnTlSlJtbAzdmDGw3LSJzpYvLqYlBuPHA+XlyFEqheSmJmI7diyMXF0hiYgAuXkT+g4OkDQ20ueYEFRXVyM3N7f0o48+2piWlnYgJibGfdy4cfKAgAB5cXGxtqGhIV2tVuf1nNq6devWp6SkfJiYmBidnJz8Oc/zGxiGeTc5OTny0qVLR9RqtU6j0RCZTHZt5MiRLi+++CJxc3PTy83NHXnx4sULarW6QqPR+KalpV1MSkp6Oy4uriw6Ojr/Wc+kiIiICCDayEVERET+69BoNMrY2Nj2P3lvhoGBwYTQ0NB+9mlCCKKiovSPHz++UKPR7JLJZN9OmjRJr6eDcl1dHQA6nufJUVrIz6fi9NNPqR25rIwKap6nrykU/S3J8+bR70uWAAB0Oh02ffQRr9Vqia2tLa/94gu2vr6ew9tv99YwV1fT74JAGzt99BG1aKvV9L133qEjp8zN6f7+BSIjIxEfH4/KykoYGRnRF3me1hZHRvb/YUKAq1fB5+ejY+tW5Lz5pqDYsUNoW70aWkdHcr+tjbGysuIZhmHa29uF1tZWom9uTo9HJqM13np6NNuZm0st2wcOAF5eQE0N3K5cQUVxMcOnpICxtaVC/JVXaEBCKqXibdYsOlpLJgOUSjR2dhKdsTEsVCqaibe1pXbm4cOp1b2khDbHUqnAAJg1axYOHDgArVaLy5cvM+Xl5cKkCROIkUJBrff379Na5x9/pBZolu2tQ37yngPIzc0FACxYsIBJT08Xjp85g0BTU1KZlYWy8nLBS6HAKI4j95qbmbMjRghTliwhnTodvv32WwCAasUKTDh2TGiQydD03HPE/Em788WLNKgyYgRcXFzAMAzS09MhtLeD+PrC/pVXgF9/hf6mTSQmNpZFtwX/qXr/iAjqTBg0iN5bAHp6enj77beRnZ2NM2fOEI7jCABh/fr1QmdnJ2ttbQ2GYWgHcVdXKvzNzYFLl4DBg6HX0oKXz57F/mnTcM/Ojhi99x6CamrovZs+ndZrX7pEP/vHH7QEoQ/GJia8YXg4q7h2DYsWLWLudXTAZfRosN3HZ25ujkmTJsHCwgJJ4eG4k5fHqfT0mJaWFm7uwYPszaFD+Rd27GBM3noLFkOHIlRPjzx69AhnVq9mBT095AwfLhgePAiXmTOpqH6SoCDg2jXwXl7YsWMHr9PpGAsLC+Tm5pLhw4f3Bj7c3WkJxR9/ALdu0evg6kqfQT8/Gjzy9YX+hAmARAI7iQRrV61CYGAgwgMC6FrS1wdef7133++9h8kAs2HDBuH+3r0wX72aKL77DkxGBn0/OZmWcOzdC0IIBEGQaTQaolQqWXl3Jl6n0+HBgwdSANd6NqvRaAZIpdJVEydOlDc3N8PS0hK2trZgWRaHDh2KKikp2QJgMYBwhUJhGRISQgghMOudZ64FAIVCsT0qKsrXysoKBw8e/KW7Xrzo6YsoIiIiAjy7Fk1ERERE5P9K1q9f/w2AtnXr1hVqNBqPvu9pNBqFVCr9YcaMGXpP1k8DgKOjIxiGGcQwTL2Xl5dJ34Zm7u7uAICff/6ZvnDvHhXWDQ00+5maSu3V+flAbS0VaB99REdB9TTlegY8zcihpaWFUalUKC8vZ8vLy9HS0sKeOXOmf114YiIVnHv3UlEcF0fn/ObmUkv3zZs0M9vTqOsvYBgGgwYNwrFjx3Dq1CmO53l6HocP9++y3IekPXuENo0GV9LTheLp0xkwDOP19ddkirc3GhsaUFZWhqamJvLNN99gx9q1QsWqVeDnzqXi95NPgMBAYMUKYM8emkEeO5ZmDTs7cf755/kGa2vg1VdpUMHQkIr8K1eAtWvpATg50ax6Xh5GxMejNTiYWug5jl6PV1+l53/iBBU4R45Q+25REdw+/hgfRURg1v37WLFpE0rz8oi+rS21jmdlUctzTs7jOdbgOJrJXr8eCA+nr504QS36AFpaWh7fn5CQEDJp0iT4FhcLLzo64r05c8jMe/eIaXAwBlRVCVY2NuT3uDih6OpVITo6GiNHjoSRuTl/YswYYmZmBpfyctoBvS8sS63n3djY2MABEH7YuRPCiBF0JjrDAF99BTyj1v0xn3xCz8PDo1/QgGEYBAYGYsGCBTDpPmcvLy8hODgYJiYm3IMHD4QNGzbgwoULeNjYSLvV+/vTjP/Jk5Bu3oz57e14bs8eJre6GklhYdDFxtJac6mU3tvu7txI6997cObMmWyRrS2uZ2fz0oYGeH/7LVLMzbnjx48L9DY7wdraGhKJBOolS7D0+HF2zqVLZNWqVax5aipG7NvHmBga0metu2u/RW0tZmVnQzZ/PkKHDyd/qNXIX7RI+GnrVn7Hjh3Crl27sHfvXuzfvx9xtbW4fuUKfvzxR9TU1DAsy8LLywtarRbJycm9B9rZSa/dw4d0jZSWUpeGry8Ncg0ZAhgb4+fffuO27tjBf/nllxAEAQqFAuZ2dvT3hKnpU+ePw4exqKqK6H3wAdk4bx46Z89G4enTNJAUFUWz6mfPYoBCAYlEYgwgpL29fXNqamozAKSnp/MMw6TGxsaWazQaZ41GYw+gked5juM4+Pv7w9HRETKZDCzL4rnnnlNKpdIX16xZs5IQMtHDw0PW48ypr69HV1eXAKA7oocBzs7OcHBwgCAIDIDWP19cIiIi/+2ImW0RERGR/yI4jntl1apVuHHjhnNKSsoGABP6vD3GwsJCam9v/8zPyuVyvPXWW4rOzs6nxm6xLAsnKyvO89Ah9l5gIKxOnIBhYSHN4tXW9h8tdPYszXoZGdHMs/B0L7XExETcvXsXjY2NfHt7O+Pq6srNmTOHJYTg119/Fe7evUssLCzoX8OlpTRz3dlJs7U//kgFPkCt3G+/Tf+Y37ePvvbll7SOu7ycinKWfWr/PUybNg0PHjzAzp07WVtbWwTm5aEzJgYVxcVoamrC/fv3+ejoaEalUqGkpATxCgVp3r1beJsQBsHBtBHaBx/AfOdO6E6eJGcjI2EnlwszbGxIw44d5La5Of/r88+TZUFBRAlQG+3UqVTAbNhAs8kvvgjGygr6AwcSsmmTgBUrCIyNqZ0doHb47iZZuH8fuHsX7cOHIyU0FLesrLCqvh4KfX0q3j/5hJ7z/fs0w5+RQWtvR44EfHzA2NnB7f33sZvjwEkkeHj7Nmx65jmnp9O6bbmcNkjbupUGT2bP7s2OfvghtUkHB2Pm+vVky/z5aLl1CypXVwQEBACHDxOwLPD3v1Obv7s7rg0aRG53dsK4sBAjz58nGVOnCiFDhpDQ0FDmj/p6nI+J4d0KClh88QWwc+fjbuF44w06jgw0KFN4+zbmfPYZaTMzo8clkdCs6V8REkJr9p+YGd6DnZ0d3njjDQAg0GoJ7t8HHB3Z6gsXcCMtjUtJSmIVX38tWH7/PcGPP9KAhoEBMGcO+Dlz8JObGzipFKVVVbizaxf/yiuv9Cp6CwtqoRYEGij69lvA0hIsy+LqiBEI4HkBhw4Bzz+PqtpawdbWlkRHR8O2b10/AKxZ01vrb2JCs8179lCrek9PgytXIF+6FCNnzQIAlBQX42FVFdTFxUzlpEngOK7nS5A4OwuyujpYWVkJXV1d7IwZMzBgwACUlpYiLy9PGDVqFH32NmwALl+mme3ISLoOup/35qFDhfvW1rg8darQUlXFDpsxA+fOnQMAPOwJkjAMcPQobbyXn0/XZlsbkJICY3NzBMTEwG/kSCSNGyekpqUJ7wEMWJbW658+DTJrFoYsXKiILyh4n+O4k7W1tRIAiI+P12m12tfXrFnzukwm+0oQBCIIwns6nW7SqVOntmu1WlczMzPd8uXLJQBt2Lho0SLlkSNH1ra0tJDBgwc/blpgZWWFqKgo+dWrV29qNJovpVKpEcMwuHXrFiQSSfpHH31UDQAajUYKwAZAeWxsLP/XC09EROS/AVFsi4iIiPyH0d1x1wdAVmxsbNv/5LMcxyn09fUREBBArly5Eq3RaJiePwylUulYT09P1T/7PMMwT8+33rULSE3FAwsLdnhBAU6fPIkaGxv8/Ycf6Pt9hbZWSwXFiRP03y+9RLPMq1YBGzdCp9MhLi4OKSkpAIAxY8aQkJAQsGyvIvby8iJlZWVCkKEhwQ8/0GZTgkCz2NbW/WqwYWfXr44YABWdK1ZQkdOTde+Tpe+hq6sLmzdv5ltaWhgAOH/4MCyOHsX+l14Cc+iQwLKs0NXVxWRkZMDHxwf5+fkIDQ3FuHHjCKKjabb3iy+ovXjlSngVFZGOS5cg7+oiktdeg2TPHkS7uDA3N2/m4uPjSczQoYzk4kXa0dzRkWb8d++mwYHx4+EzbBg5UlSEBeXlkHp7P9sOTwiEAQOQ6uWFjLAwOr7q5ZeBLVtowOHOHSpUyspoo7S+I566m3UxAPyWLUP577+DffJe/+MfNBt+/jwVqampQEsLHs987raO3zh9mqt3cmJZmUxomzEDKi8vgl9+oTXKNTXU0h8RAW7CBFQOG0ZvlYMDab56Fa1pafzx48dZQRDg6OgINzc39nBJCUKXLYPjqVNUYC9aBJw5A6hUyOM4lH3wgVBtYkL4RYsE94AAQnps/38Gx9FgS0UFHQ+3YwfNAPv60kzz6dPULXDzJnVMqFS05MHdnXa1LymB1f79iGlpYR9Nm8Z57NzJPg5gMAy1t48dC8myZXjr6FEUnDyJe/n5sB837mlHoVJJn4uWFqCiAjojI3z//ffC/N9/xwCWZcGywOrV4H//ndTV1SE+Ph4xMTGw7NsxPDgYaGqi+01MpI6SigrqlABop/jUVHqe3dTV10MVEcEP02rZgXZ2vUEMgKC2luDECfhOmULFejdubm5ITU0lDQ0NMDY0pNn5KVMAAF07d6KwtBTlly7xMWPGMAfee48XurrYUR0dxPWHH8B8/jkKrl7lCpqbWT09PQE9Y83eeovW+2dn0+/+/lR8A0BTExhHR9SsXy/odLr+127iRKCwEIEGBmyrkdH4CgODUaGhoQqdTgdBECRSqfQMx3GOYWFhCAwMxLZt274A8LVWq/0EwL7a2lrJl19+yevr6/MymYwJDQ1llixZ8szff9HR0RIHBwfzvLy8tRYWFhJLS0vs37+/taOjYzUAaDSacKlUeooQomIY5jyAKf98AYqIiPy3IDZIExEREfkPQqPR2Eul0luGhoYLdTrdG/Hx8Qf/WbfdJ0lNTV3o7OxsbGlpiczMzK7Ozs4rarW6TKPRWLEsuz0mJkah/1fZQEGg2auoKFrn2tEB8DyMnntO+FWpJG3d2cb09HTe3t6eGPUVPqdPUzEyoU9CXauF9v338X1TE5+YloaSkhJiY2PDDx48mI+KimKerAEvPXtWsL5yBY4XL5K2O3cgW7qUZnaDgx93T36MsTEVu9bW/euKpVKaAfT2pqLxpZfo+56eAGjN5zfffCMYGxuThQsXEpZlMbSsDBJ9ffi+9RYmTJhAIiIiSEhICHieR35+PrRaLebMmUNrgufMAUaPpgJnwABgzx5oT55Eib4+BlRX4zzH4WxlJSQSiaBWq5mLFy8Kl5OSiPTBA+G+qyspKytDaXk57/jcc6Snltvy7l2kmJnxqo0biXllJZgnm1tptVQsBgdjLwCj+noMO38e1i+/DNLURGuTHR1p/fbo0VTMDxkC9NakPsbc3BzZ2dm4du0a7t+/z9nY2DB6lZXA5Mn0WmVmAm++SS3oq1YBM2bQmvNuruXnk1SFggwaNIgkuLggYuNGgvZ2GhhpaQE2bgR++QWCoyPKAgJQU1ODhw8fInzFCkRt2sSEjx6NgoICvqSkhNy7dw9tbW3Ia2riIyZOJFizhtaya7WAVIo96enC7O3biY9MBuvjxwkbHEzXaE+n7IYGGty5dYuKuK1bqdh+/316HgoFteKXllLhd/YsrQn29aW1x3Z2tMfA8OF0vx98QAXytGnArFmwsbVlftZqYafRCIZr1xL8/e9AYSG9TjNmQOrhAUX3eLmrbW3wmT0bl4qK+KzkZHBxcYT18cGxkydxztSUv373Lu87eTJT4+KCkH/8gzA6HXD3LvD882hvbxcqKirw4MEDkp+fz0dERPRvlieXU2EcE0ODEZWV9DwDAuh63LaNPg/dXLhwAZGTJjFW9fXUAj5kSK+w1tOjwYapU6kD5fEu5EhNTUVdXR3n+8svDPLz0TZ5Mnbs2MFfSE8nXefPI2jzZmL01lu4lZMDU0tLMvTll0FWrAAUCvhNmsRwMpmQpdUSPX19zsbOjj6U+/fTxobjx9PgUo/tXyLBI319/F5TQ+bPnw+jJ4Mo4eGQ3rkD1717JQELFsgtfHzIpk2buPb2dpbneWNCCEpLSxETEwM/Pz9pY2NjcGdn5/jQ0FAJz/NgWZapqalhtFotycnJQXh4+NM1/d2YmJjAzc2NtbW1JbW1tUhPT29cvXr1co1GYySRSK5Pnz7dNCwsTHLjxg1lZGTkxmduRERE5L8OUWyLiIiI/AeRkpLyXXBwcPCcOXOUtbW1zMOHDyuio6NT/9XPJyQk2Ovp6YU5OzszSqVSWlhYODE+Pj5MKpV+GRkZqfL29v5zT/XGjfSP+M2bqW27oYGK5qAgICoKVlZWZNiwYXB3d4e1tTWMjIxw6tQp0tzcDA+P7vLwK1fAq9VILimBsbEx5HI5oKeH/JEj0XD8OBn7/PMkdMQIqNVq4uzs3F9l19UBH3wAi9JSktPaSsyvXMENlQq/u7tzwTNmMH86Ziw0lGbfniEq4e7eOyLM2ZkGDm7exD9++01ob28nb7zxBtHX14erqytMHz2C0cyZMOqTBZdIJHB1dcWQIUOQlZUlEEKIg4MDtab/+uvjRk4wMoLkxx9R7O2NfEtLXtfcTNRxcbCbM4cMcHZGREQE8fP3h9WaNeSCvT13Ky+PaWlpIaampijmONwlBMrycqhzc8lJDw/YuLjA0M6O2pUBKi4JAVpaQEJDcef8eWhlMkRkZMBw1SoaGOkRKgkJVMy88QYVhD1jyvrAsiwiIiKgUqmQkpLClN64IQTPm0cwYQLtqO3qSkW2nh61kY8aRYVZd6BFEASSm5uL0aNHIysri0RHR1MxuGgRDYxUVAAvvYRUlUrI02qJIJOBB+BZWAiTRYvAGBoiICCAJCYmAgC0Wi3s7e3hN3w4wbx5tG7+yBFg1y4YPHiAzIAAUqivD+byZd7o4EHCNDRQK3VmJl2fJSU069tznEOH0izqiy8CW7dC+Pxz1C9bBoWBAciWLRBmzEDJiRPC3WvXBNOoKCI1Nf3TcoOK4mIUpaXBrLKSWK1aBaKnR/cTFkYbnzk5gYuKwq76enRIpXjg5ASL4cOJyZ07sPvlF/IDz2P2unVws7OD/ahRDN/RAZ933iHKlBRq3X/hBcDJCba2tkxoaCiJiIhAQkICyc7O1qWnp/OlpaVk0KBBdPEPGULt40uX0nVnbEy73H/+OQ0U9OHq1auIiYmBPDiYfsbBgQaleuB5umbs7AAA586dE86cOUMAwMjICH537xJMm4YDKSlce3s7Wbx4Mbmv1QpCWRlJNzDg7967x3h6esLJyak3ELBiBQyioojl3r0YuHYt87OpqRDo5kawaBEV9ytX0jp2lQro6kLXsnP/ADwAACAASURBVGXYyLIY6O4uREVFPfsB9/CgdvyuLqC0FFpbW6aiokLgOI4IggBfX1/Oy8uLUSgU8Pb2lg4ZMkTu7OzM2NjYPF5fOp0OFhYWXFBQEMP+k7KSHjiOQ2pqKhsfHy+XyWTf+Pn5mUdEREgKCgpQWFiYEBkZeeAvNyIiIvJfgdggTUREROQ/BI1G4ywIwvSoqCgpANjb2yvkcnnI/2QbOp3uzN27d1sBwN/fn7zwwguWgiBMt7CwMLexsXm6i9TDh1RMCQK1ed67B1hZAefOUbvzE5kmhmFga2uL4OBgEEIIy7LIzMzEjRs3gIIC4MIFpPw/7H13VFTn2v1+zxR6G3pHepUmotiwoCKxYo29RI2JLdFYcr2IscQ0NSZqjLkmUWNvsUZsoCKIoBQBpStNpEsZZphzfn88gKAm97vfWt9aub81ey2WijNnzpzznrPOfvZ+9tPUhKtXr2Lbtm3YsWOHqq6uDvHx8ar+9+8L3Y4ff7MftaqKCH5ICNCrFzSKijDAwACFJ08i6NIl8ILAnTt3DmVlZcjJyQFAoUZHjx4VeJ5H0/79qNfXB8/zSElJwQ8//KDaunWrsHnzZnz11Vf884oKUlwjIsBfvozq+fMhb25mc0aNeqVy5eSQXd7Z+a3HlUtMhHV+Piv//XdSEn//nUgtADQ3k5U+JgbeMTEobGzkiqytUW1oiKrNmwW07bOhoSH09fTw/syZIgMDA9WLFy9w6tQp/tq1a3zKs2f8HpEIv0gkCI+JgUVJCamtSiV9xogR1C+9ejWwZg363LmDekNDNF279mZS+PjxRGbc3akHuj2o6y0ICAiAJmPo3qMHQ2oqWXzb4etL68PAgILu2ueOAzh58iQA6qWmZVRB88n37wdcXCDfswdpM2bw13182LzcXKw9fBhR69ah26+/dhB/rqAAy6ZPh2ZjI0afOQN/f3+GlStJpV22jALf6uvhpa/PvCsqeE25HDFiMbdXXx/CxIkUBBcbS/3oK1YAI0dSVkC3bq+U082bIR8xAvurqvgTu3ejzssL386eLWw5ehTlly+Di43lcjMyyCZfX//G8SmLi4N5r15oValwftw4nDl3jgIINDSoLz0uDuB56OjoQFtbm+/m5CRM27kToZMnI2T3bmZaWooVq1ZB9tNPcJg7l3lyHGzv3YO+XE5p87t2Ud/5vn1km3/+HNLycsybMQMDBw4UV1VViTMzM7uSUE9PWg9z5tA1K5fTTPm3QBAECh177z1SlBsaXv2noWFH2B0AFBYWQqlUwsbGRnhXqeSwYAHQrx9evHjBDRo0iDM0NMTY2bOZ8Nln8N2xg9NsaoL365+rrQ1TBwd0P3kSVxYtwoSoKAYLC2pzmDiREtp9fWmf8/KAu3chMIawsLA/qaS1ITyc1vk//gF3uRwKhYL5+fnx8+fPx7hx494+hvA1jBkzRtRlvvxfQE9PDzNmzNDs16/fP0aOHOkeERGhCQDp6ekNcrn8TPvroqOjJdHR0azt73rR0dEjo6OjZ0RHR/eOjo7+96xeDTXU+K+HumdbDTXUUOO/BCKRaIavry/X3jPdNgqpS0NydHS0o6am5n4AFq2trS9aW1sPATgcFRVV2/aStKqqqg6vtaOjI/r27cunpaWxQ4cOsZUrV9JYn+3biSDOmUP22uZm6mWtqCDC+c9/Ajdu0Ezp11RRnuexb98+VX19PTd79mx279494cqVK8xCSwvmvXohp7AQrq6ugpGRERITE0W7d+8WtLS0OLOEBMZ0dUmFdHAgsnTpEll8bW1JqTQ1BaZMgeHUqeghEoHneejr67OHDx8iIyMDHMeBMSY4OTmx7Oxs9tlnn6FHYiIqzMxQ7uYGDQ0NwcXFBWPGjGFyuRwXL17kfv75Z2HVqlWM53n8ZGQktHz0EZYEBjLDAQPIhmxrS0RxyBA6JqWlVHzIyCA17vBh4OOP4aenh/NBQdTXnJFBZKG+/lVv9fXrMDl1CsHDh8Np0ybcDwiA+5IlDO++Swrz4sWU7K2hgfnz54uePXsGNze3Dqa8adMmhKxZAxuxGOyLL2jbT58Cjo50bubOxcvgYOwMDISye3cAeNN2CxApKSigv3t5kRV80CAKunrtXMbHx2PMoUPQOHeOVNbOaA+hA0itNjAA1q2Dav166Ovrw8DAQHB1dWVSqVSoqKhgZnl5wLlzyJfJkNTcjKaQELw3ejT0TU3pe1y4QKqzhQVQUgIhLAzlgwfzWra2cG1p4RRWVvT/cXFE2nv2BJ48AefsDHtPT846Px/+d+7glpMTftu2TXh3zRrG/iSFPCkpia/IzBRC9+8XHZwyBWJ7e8z5/ntUGhpiSGgoE+nrw23tWrZ161ZhloYGQ3IyKb07dtAafPddID0dJt27o2jtWviHhAiMMaSkpPAAiERNmULf6d13gcOHwRhjxcXFrKioCI6OjsjMzISHhwe0dXS6tlVkZwP37pHlv76eUr2fPCH3RX4+cO0aLPz8YPL775hWW4vMUaMELFvG4OBALRFVVcDcuXRODQzoGn0LiVQqlbh69SofGRnJITiYxp6dOkVrEaA57MXFHa/X1tZmISEhCO3dm3EeHsC5cxAEARoaGkJjY2MHm+3dvz8gkWBVeDi1cLwF4hMn8FRHh2/54AMOU6ZQuNuUKdTPf/Ys0NoKWFlBmpGBbr/+ysfGxrIJEyb8JWM+XFamau7Th1n98gt8mpu5l05OgmVnpf416OrqwtzcnH/58iV8fX05CwuLv9r8G7C1tYWtrW3HPtXW1uIZJeYfBYCNGzdO4DjuoFgsfvHFF1/ki8XiHhYWFko9PT1ReXk539jY+DI6OjoiKirq4X/0wWqoocZ/FdRkWw011FDjvwQSieRdLy+vjqdma2trKJVKp+joaIOoqKi66OhopqGh8XtwcLCHsbExd/r0aVdtbe3eLS0t27Zu3Rovl8svMMYMtLW1Ve3bYIyhsbFRqK+v5zQUCmDwYPwydy5CnzyBvYsL2YTbR/3o6dHs6zFjKODqxg3q2501q8vc6fr6ejx//ly0YMECmJmZITQ0lGUmJ0O0dSu+W7VKaCgvR3BwMBs4cCAMDQ0FqVTKvLy8yFJ+9So9dC9dSv2mxsakZNbVUWqxuzsplW3YvXu3UFlZyQBg3rx5kMlk2L59O8vMzARAI5JCExMhODigae5cmJqaMsaYCADkcjnq6+sRHBzMAODhw4coLS1lS5YsgaGREYVjWVoCLi5UZBgyhMiPpycpyCNG0Hc3MgLi4/Hizh1BKyWFR36+COXlZHNWKEhVfPIEPxUUtBb7+Ii5oiJUBwRAQ1cXxkePdszFxqJFpER6ekLb0fGV9V4QgOJiyACh6tEjxlpbKcxs5UoqfIhEpHD37g2lnh4kublQtine9+/fR3h4eNeFZGtLx7Yd3t7kHBg5khT5TgFzMj09xISFoVlLC3OrqyGTyV69b9u2V6o5xxG527cPrXFxqK6uRlBQEAMozTszM1PlPXGiCBERsAoLg8rSEgOHDuXMzc3p/enppKqamZHSnpyM8pgYHDl4kIMg4PvZs9H07beQSCTCmq+/ZszVlQoM8+eT4rtkCcSffgrj+fMRdu4cbt+5w16OHw+JrS20Jk8G/PxQrVDg5s2bSE9PBwBuaGGhcHnSJN7Uz48bNWoUJxaLYfHll7CYMYO+i5sbTExM+BO3b3NzYmOZlpYWrYPGRuDOHWDQIEiSkuC8Zg2Ma2rY7t27MXbs2K5qZVgY8O23iNmxA01NTUwQBNy6dUs4c+YMa2xshI+PDz927NiuLRC7dpE74sQJUubLykh1bseHHwIAxIsWoebcOVSVlvJwdhZBpSIynpJChYH8ttHPnXrpOyMoKAgVFRWvRgF88QWF+g0YQETfyoqKavPmtW1GGwkJCcg7fx6N776LhrNngbNnwRjjVJ3XE0Cug4wMKhy05SB0ICkJyo8/hub06Zzx5s2v1tD+/RTgZ2TUUfRpPXoURbm5nNefKPPtSElJQUFBgWj83LlQpafD7tNPUTpr1l8qx7q6upg/fz6XnJyMuLg4JCQkYMGCBehYk/8h2s6hCIAtgMdSqXR+eHi41MDAwLqxsdHa1tYWurq6HYmDqampehcvXjwXHR1tFxUV9eZIBjXUUOP/C6jJthpqqKHGfwGio6ONRCJRt85juSQSCRwdHRUFBQW7oqOjN4vF4kWGhoYOXl5e3K5du+Dp6SlERkZyCoVC4/HjxwNLSkr6tLS08H379u2IsW5uboZ01y7RoIYGXB8yBOUNDcLzJ0/Yz+bmWLd0addeo+xs4OVL+vvChaRGffkljf+5fh1Ytw6tKhUePnwIkUiE6upqmJmZwcjICGPq6/m07t256uZm1qNHD1VISIgIAHr16vWKZTQ2UkK2lRX9hIdTGvfq1UQ8xo1747iMHz+e/fDDD1i9ejXaLaCRkZFISEjgx44dSy6AmTMBQYBuJ0Jz4sQJPjs7m2OM4eHt20LlyZPCY1NTburDh7zRjz9yGDiQ+q2//poU9oYGKjpoaZFy2N7X2aYgA0BRQYEq/NIlMZycKIW8neQMGAA0N0NPT48DAF4sxqO293UbORLeAwZQuNuyZXQMRo+mEK89e0jZLy5Gq5cXugcEMC97ewq68vSkAK9jx4hYKZWAnR1kjGHFihXYsmULlEolKisrX6U+t8PMjOzja9e+UrJDQsgGPnMm/Z+REfDgAdyHDoUsKQm7f/kFO3fuxNy5c2HT1sOLkBAK4/ryS/q3TEbna/16GEmlyMnJQa9evTB8+HC299tvRSo9PRTt2IG7VlYo8/MT9PX1GV6+pNC8kyfJ6j14MBVXPvsMFnV18P7wQ2RkZEDZ0oKpv/2GzJAQHm5uInh5UZq4SETHeuJEUulra6FasgRJHIfUujqYVlUhbM0atNTUIMPREUwkgszGBpPDw2Galsawcydr7zPvQI8eHfb82bNni/bs2cPHxMTwo0aNEmHjRnJ9GBlBGRiIMm1tNI0axZeYmHBid3few8ODa7+uGhoa0NLSghdbtsDi/ffh4OaGAicnPH36lAGAh4cH0tPTufT0dAQGBsJAXx8+NTUwPHSIer2dnChJf8wYUqw7W/gBUti7d0dDUxONDWvHmDFUFDp4kK4nS0sKtvP1pRFhbdDQ0ABj7BXJE4loUsCCBdQm4ulJ66AtiG7cuHGofvwYRuHhUFy5AomjIziOw86dO1v19fXffJ7cswfIyiJrOEBp+PPnA7dv43B0tGBvYcFzCoUImzZR0er0adq/ykoi4DwPxQ8/wE0mw7AdO/BcpULOpUsq6bNnjN+yhetlbQ1YW+P73btV9fX1ouHDh8PV1ZVyGIYPh8vWrUT416zpkqjeGTdu3Gi9ffu2WCQSQUdHh2+/Rv83MDAwgJGRkeaLFy+yo6OjJ0okkgBzc/OuqfGd4Ovri/Pnz5sCMAZQ+b/9XDXUUOPvDXVAmhpqqKHGfwFiY2PDrK2tRwYGBnaZ9+Tk5CStra11bG5unuvi4hI4duxYbZ7nkZSUBIVCAXd3dyYSiWBgYABvb2+Rh4eHWEdHh5TEsDBIpk2DuUqFZyUlKLO3xwN3d9YqlcLZ2RlxcXG8oaEhM2m3gj58SLbQdqXKzY1GC1VUoLGsDFWffIKDT5/ihVLJh4aGMi8vL3AcBwgC9BIT2SUDA8i1tDBq1Ciui725oYGswcuXE3Hcv5/IwbZtROQ0NYk0+PvTg7OHB4WlHTuGSnt71F26JIgbGpi5tzeQlAQjFxf4uLsziVxO5Pibb4hYTpsGnD+PupMncS0vjy08dAj9BgyAYXY2el64wHlv2gS7+nqWo1DgslyOVI5DhYkJnnfvLjwJD2cOI0eC5ecD33yDhuBgPCkqgpmZGWpqahAXEwPbrVu5xoAAFI4ejcLycjDGcO/ePVS6uODiiROqgspKka2tLT9r1ixWU1Ojqqqq4noEB8Ood2+ypicnk4I/fTp9/2HDSMU0MMDd/v0Rr6WFAfPng5s7lwhz//5ErLS16Xy2keXHT54gLS0NPXv2xLBhw9gbfaiMEQny8KD++3Y4OxOx+vlncirIZIC7O3T69IFKpcLTp0/x4MED2Nvbw8jIiIoCQ4d2SYDPevYM8RcvYlB8vOC1eTPTpl5l5OXlqZ4pFJz5iRPI6d4dNkFBzKe+HtxHHyHL0FA4YmnJ0hsb+daaGqHV2JgZbtwINmwYHM6ehfTGDVT6+AgDnz1j3ioVx/71Lyq8hIfTd+nXj5LCbW2BqVOhUVGBnqtXwz0wECJHR5wQBGS5uECQSmFUU4O+9+/DODUVzM0NnKMjEcrOoVjBweSo2LQJbNgwmJubs5iYGC4gIABNqamQfvIJUoKC8JOuLjKysmCSlcVeisVolUiY/uLF2FtZiTuJiXj48CEyMzP5svJyFc/zXPCNG0j184OTszOGDBmC0NBQ9OzZE/Hx8agpLYXPtm14UF+P8vnzYeThAYlEQtePWEzEdcKEN+4LcrkcDx48YH379n3FJgcPJsv5woV0/cyYQeeye3daX5GRwOzZKKBZ8YKfn98rgmljQwWflBQi+KWlpHIbGoIxBm1BAOfoCEloaHvLBpKSkngbG5s3bdhhYbTPdXWvZmhnZgITJ4L78ENme+wYF+vgoLL+4QdOY/hwev3y5TRnu7oamDULEqUSTs+f48F776nOlpRwhlVVzE1fn7tcXQ2/qVPR2tCAwowMLvTGDdwWi/kgY2MGiYTcGR4eVCwLCqLz+XbCzaWlpSFixAhEhoUxSVkZFQXkcmrp+PVX2pfgYCoKvffe27bRgVu3bgl6enrM2Nh4mK+vr7aXl9efquuCICA2NpYJghAdGhra+pcbVkMNNf5roVa21VBDDTX+CyCVSke7u7vrv/57bW1tREZGdpnVpauri0mTJuHIkSPs22+/BUDp0v/4xz/o4Tkqih56MzOBkhLoLVwI/0mToJuTg6KiImRmZqKoqAhKpZK7desWWltb4eXlBdy+Tf2nneHrixaRCC9GjUK2tzfeu3kT0sGDOfj6viIw9+5BXFICo379UFtYiMTERIwaNYoeaM+coQd7HR16kM3LI2JsZkZp515elDzd3EwP6zExHXOOFd98g1/KyvDutWusNjmZUrXDwkhJu3aNFM/qamDdOqClhchsQgK0ysogt7VF2YwZcJ0wAT5LljAA0AEAb2+c/eorvrmggFt54QLucRz/KCQEXEICe3nxIgyOHQMuXMCBH37gTTMyuOSICJSUlGDGkSNChr09SzU1FfQfPxYUCgW7desWs7KyUvXdtYsboVJx2levQiaTcS0tLXjy5IkIABR1daSUHjxI37mykooCUik6K66WlpbgeV749ttvMXfuXKbfbvV+/Jjs5CNHEiGQy2H34gUACrR63UIuCALKysqgFRwMI4XizYU2bRod9+7d6TwsXAgAGDx4MFpaWpCUlITm5ma0fQCRqDlzAAAFBQU4duwYhi5fDju5nOGnn8iVAGCWkZEIS5eCT0iAW0gI+C+/FK5oaTHBwUG4L5czY2NjWFlZcUJMDBJPnYLV+PE4GRsLeXExpsfHo8LWlmlUVJCq/8EH1It86BDth6srFShsbanvvLYWmtHR0Fy8GOa9eyMwMBBSqRQvXrzAnj17UGtggAE3byJVXx/DYmMhXr6cXBrLl3cQS+jqkqV+zRrY2dnBzsSELwsI4K5ERKBqxgzo5uWpJk+eLHJ1dcXjyEjEnTgBvYoKNGpqIqB3b4T98gu4d94Bpk7lAHClpaWIsbXlF6emcrJPP+0IaNPW1kbU4sVAVhb4/HyUjBqF2w8fIjY5GQCwbNky6I8cCaZQAHfvUtp4JxgZGVHIGUAOjNxcmqltYUHXy/37dJ0NG0avyc4m8skYXHfuREP7HO52MEbtEl9/TQWu1lbq27a3p/dOm0ZktBNEIlFH20IXSCSvktFlMprNnp0NvHgB8y1bcO/hQ9Q0Noq+GT8eUw0M4GxgQGtq5EhS2PPygMOHodmzJ/o8fSoKNjWF4ptvmLaZGT5UKnHRz0+V/uCByMjQEM1GRpAzRvcQXV26ppYupRT2DRuobeLzz+n7PHpE5PvcOTiWl2N2QAC4ceOgsLCAdMIE4MULKt64uHS0E4AxcpIolcD69bTNt6SWW1lZcUqlkp8+ffob9+rXwfN8+7mziI6OLlRbydVQ4/9PsI6btBpqqKGGGn9bbN26NTUyMrK785+kYb8N9fX14DgOP+7dy/splRjI8xwSE8mmO2YMPdg6O5Mtuk29ViqV+OKLL2BpaSm4uroiPj6eNTc3Y926deBnz0b5u+/i5osXMDAwwOPHj/mWlhZOEAS4WFqqJqSliTiZjB6odXTo4dbEhB7+AeQHBeHAgQPw9fERxrx8yXD4MBHkR4+IFHz4IanYQ4aQvfnhQwrBeg2lpaU4d+6cUFFRwXx8fFSpqakiAPj444+hra2NqqoqmJqa0oufPyfVfNEiIg0aGhAEAXv27BEqKioYAHzwwQcw6RTk9Gz6dNzlOCHP2pq9u2AB7O3t8d3OnULQ6dPsQXh4a70gcNLycm7W2bPI6t1b8MvMZFq7duGL2Fg4OTsjMjKy6w5XV9ODeSc1Pz09Hbd37YJlWRkcnz6Fgakp7A8douO1YAEVGZYs6bKZ58+fY8+ePZg0aRLc290FDg5kOS8sJCLg4QFMnoxzO3ciJScH/fv3V927d09kbGzMm5ubC8+fP2cVFRWcT3w8WjU1oTVvnmr48OFdWYNKRUWLfv1ILW8LeIuOjgYALF26FIaGhkSIrl9Hy9GjSExMFG7dusVUKhXWrVsH1thIZGfAAFJWvbzoOGRnU3iXgwNShg7Fubt30X7udHV1gfPncTImRpUhk4l8UlPhtmEDPNLS0Lh4MVQqFcouXoSHtTXtU2dVPj6eAtyWLgVaW6GaNg31pqYw+vbbLormlT/+EMy3bWMvAgNxp03xD+/VC4HNzRDduUPFDl9fsjGPHElr08sLvESC+pkzIT5yBNrGxmif/V5bW4udO3dCLBajtbUVPM9jyZIlMNq5k9wBEgn111+/TjswZQr1pLcVKJCbSz3Ry5bRNQkiYQ0NDdi2bVvHfg9tbIRFaqpgdvIk40QipKWlged5FBYWCgUFBWzAgAHo/d134BoaaNRXOzIySK3uNF+7HVU9eiBuzBjV2CFDRCgpIcW7HUePEnmXy4m8fvABFcZiYoDvv++ynb179/Le3t5cSEgIFcU0NF4Va5ycKP1+40ZqUXgLtm/f3jp06FCxp6cnuVoyMijAz8yM1qFSSfkImzfT9nx8qLccVDz6+uuvoVAoMH/+fLqOBYEKRgcP0v3lu+/I3WJoSP3wmZnkkgFQLxYjSyJBfHIynLp3pyJgZ0yeTMWJjz+miQWFhdRHn5ZG6/k1Nf/HH39EaWkpevbs2RoeHv5vBa24uDjFnTt3eEEQ6pRKZXhUVNSDf/ceNdRQ478Lahu5GmqoocbfHNHR0QFisXjliBEjJNzrY5z+AhoaGri1bBnvcekSV//8OTMICsJpT09Vnra24OHlxcHYGNi7l+b4fvwxwBgYY7h16xZmz57NmpubkZWVxSQSiaBSqVjWtWu4LpdDwXFoaGjg+/XrxyIiIlifPn3g17Mnx/r1I9vltGlEhHbsICXpH/8Avv8eRoaGcKuuhuyTTxhfUABtBwdKofb1BaKjyRbt7f2q1zksjAhIp8Cumpoa7N+/X7C0tMTkyZOZv78/Z2Njg4aGBv7GjRvCnTt3kJCQwB49eqRy+flnTrR2LUR79gCffEIbaLO+BgUFMX19ff7Jkyfs/v37AAAHW1ugpQUGe/bA4sMP2d3SUgQHB0NXVxeWVlbsZU0N3zcpSWQ2bx7rGxEB2dy5sP3lFybJz4dKWxs3OQ7vvvsuBb11hlhMx2T8eCJxggDza9fQPT0ddlpaMCwsxJ7hw6Ftakpjz/z8iDB069YRHnX79m2cOXMGvXv3VgUFBb0K1Fq2jBS4oCBS7//4Azh9GvX5+eixbRtitLU5XZlMMDc351paWjhjY2M2bdo0OPI8TMrL8XtDA2dlZQXjthnkz/ftg9aGDeAuXqRU6OJivLC2xlfffAPGGCQSCRISElBVVcXXuLqy5wMH4ty5c0JxcbHg5OTEKioqIAgCdGUy6AQEULFDEIiASSREWH7+GZg4EfcePVJVV1eztWvXso5jdukSPJVKrteiRfBevRpmc+dCvn49UqZORZyrqxB05QrTOnSIChKdlcW2c4fTp1FobY1/1dQI9wAWtHw5xN7epMwCqImKErSNjVnPHTuQnJwsKBQKlltcjHSFAnbz5kGvPfE+L48cA8ePAxs3gnl5QbN/f0h//x1swAAihWIxWlQqPL9xAw1iMXw9PeHQrRs8vLzAQkPp/CmVRODDwkglnTSJii4FBVTkUqnoWpkypeOrMMagoaEBT09PJCcno1+/frhRUgKdp0/ZH2lpuJGaiqqqKlVlZaXQ3NwsmDY1QXzkCFOsXQuztuu4A4sX0/rz8Xnj/pDg7IznPC/4p6dzuHyZiOWlS7SenJyA336jpPvMTOrfzs3tMt6tHaWnTzNNmQx2J07QNj75hAh6WBipwv7+1JP/lswFAEhISOBdXFw4k/R0OlarV5P9PDycLP5Tp9Losx9+oPvJr79SISAgAIwxmJubIzMzUxg8eDDjOI6+v5YWOT14nu4hCgXw6adUQBk0iIpSHh44mpjI33/0iKlEIsycORNvzNhOT6eWjvBwSoxPTSX3BMfRXHJnZzo2bfD390diYiKePn3K9enT583tvQZ7e3tRv379xMbGxro5OTlTr1+/fjs0NPTpX75JDTXU+K+C2kauhhpqqPE3QnR0dG+xWDxeJBIZKhSKXEEQ8iQSybfDhw/X7Jj5/O+gUAAJCcCmTbDR1WWxAQF4YWeHhJISABCZmJi8sjS9tBcSMQAAIABJREFU9x4pbQ0NaG1txb2sLGhoaEBPTw+nT59mEokEUqkUsdevY356OgYfPQpNHR0AeJP16+qSmrR2LQU0LV9OI6GCgoADB4DsbMh++QUCx0HjvfeIjAQHvzkHGiAi9f77RNQA1NXV4ejRo6rKykqRi4sLP27cOFH7g6yzszO6devGnTlzBhkZGfD29ORr/vhDtF9fHxg3DuOLi2EbE0P9q1lZaGpqQk5ODjw9PbmAgADcvXsX9+PiVANWrRKlBAQI18LDhZaEBM7V1VVlYWEhAtrG/CxaxKFHD5i4uJB9e/58+r4iEUqTkuD/22+o37IF+ps3dyU8EgmNT6usJHK1ahUwYgQ05s2Dxv37wLp1CC0t5RMSEoSePXuKYGND9u3mZpQFB+PUqVOqpqYm0cSJE+Hs7Nz16T0ggFRTQ0MiSadPA598AkMnJ4j09CBSKhHi5sYCwsK6Ht9+/WB59y569eqF48ePIzIyEiqVCg9u3oS7IAjmJSXMdvt2VI8Zg9vXroF5eUHgOCiVSowYMQK5ublcwfXr/MhvvuGe7tuHMWPGcHfv3sXjx4+F27dvs/r6en706NEcUlNpfbW2EtFsax9Q1tVB2L9f5DBiBLjUVCJ0U6eSw+HSJWiuXEm2/7FjsX/QIOEFzzPv/v2Z0Y0bdOzz88kF0FYkAACFWAzl1q043NiIXn36MMYYbj16JIRmZbF6MzM8Li4WXP74g3vx88+4dOkSeJ5nH330EXiex549e4S9e/eydevWgXN1pf14/30qOJSW0vmMj6dkcIWCzntVFQycnDDlxg0c69UL7gcOwKasDFxBAa3rOXPonCQmkkIOUPCYhQUVouzt6c/Zs996GYvFYohEIgwaNAiDBg2C6tIl9PvxR8ijomBobPxqHWzZgoKCAqFeLGa8IIDrvPaGDycL91vQUbBZu5b+fPGCguYKC0mhHjOG2jF4nlpPdHWpaARQGntyMrBwIYZu24b0tWt5LFrEtSeld4wPA+j6//FHoKaGyPNrEASB9uSTT4CPPqJj/d57r8Lyhgyha6i+nn7n4UHnYtw44Kuv4OjoCFNTU+HLL7+EUqlkgwcPRp8+faj33NqaVOjNm6mg5+REhZk2TJw4kfv+++/R2NiIw4cPq2bOnEnHNTaWCkOXLtH+eHuTS6NvX7KRr19PBRkjI7oPSKXAd9+B4zgEBwcjLi4OdXV1XRwzfwVPT09oaGjoHjly5GJ0dHT3qKioAgCIjo62lEqlGwC8A+CgQqH4RG03V0ON/y6olW011FBDjb8JNm3a9IFUKj0QEhLSz8XFJUAmk/UXiUThgwYNknXv3v0vZ8wCoIfL33+nUUgXLgDr1sFk6VLmMWQIBg8ZgpKSElRXV2PChAnMwMCAHrYZAwwNwUdGouiLL3BaRwdjx46FqakpbG1toVAo+JKSEk6voQEBSUnYIQi4efMmmpqaBBcXl7fv06BBRIjq66kfu7CQrK1yOV5GR2OvlRXKZTL4RkT8aUowAFIq794FevTAnj17VCYmJmzkyJGsd+/e3OsKP8dx0NPTw4MHD9Dt99/Z4GvXEN+/P+RSKR4+fIjclhZeLyiINVhZ4ccff0Rubq6QmJjIAAgm9fUs9fFjTsSYcMXamg0ZPpyNGDECgYGBXUcy6ejQw/6FC/TzySek2slk0PPyQmNuLkRHjiDL1hZ2HNd1xvCCBUS6vv+eFHyZjEj3118DdnawtLRk165d47p37w4tLS1AJsPj1FT8cv8+XF1dhalTp3JvTTWOiSESKxaT9X7hQvDff499yclI8faGtKUFoz7+GKIhQ8hO3A7GgHPn4Pzxx6ipqVHdjotj7hs3gk2Zwl6EhLBr167hSU6OcFVbm41/8gSKpiaUm5hg5qxZ8PLygo+PD7oHBTGNlhZ4fPABY4zBzs4O/fr1Y/n5+TA0NISzszODvj4lTDc1UQFo8WKgoQENNjaw2bABgevXg7t+nWaVz5pFVuO6OlovJSWAnx/sli5lzc3NqIqPF2p79OC77d7NsZcvoerRA1kaGiiVSpGamiqcvHuX5fTtq5rr58e5h4XBwcEBMbW1/P2GBtZ9+nSGe/eQMW8e0w0Kwo0bNzB16lSYmZlBU1MT1tbWLD0lBeYXLvCmTk4MK1bQOb5xgxTas2eJMI8eTa0Ns2YBffoAnp7IGzIEcU+fojwsjI/x92cZ+fl8bb9+vMX48ZzU0ZEIn68vnR9LS+oH/uknUl9zcui4FBQQwaupoXWjqYnaujqkpaWhb9tYPc7ZGeLDh6EZHEwW69xcss1//jlO6+gI95KSWFxcHGpra1+1GXAcqbM9eryxdAoLC1FTUyP4+/tzHet7zRpylvTvT5MHWlpoxvdXXwGhoaQ49+gB3LpFhHfyZJxydobcxoZ38/XlOmbKd4ZUSrZuuZwKbK8hISFB8Kmo4AyWLEG9vz+OHj0qWBYWgvP0ZGIPD7Dhw4kw19TQvS0igkIapVJg/34wjkNKQ4NQW1vLAYBIJBJ8MjIY/P2pkNH+mWvXkmvhtVT3+/fvw9zcXCgqKuL69u1LLQLZ2VRUGTKEXuTnR+dOKqWWlCFDSNlmjIoQDg50TX/+OVr79kV6Rgays7MRGBj4b9XtdshkMqhUKnFZWZmsX79+Z6Kjo23FYvGjwMDAoGHDhhkUFRX5tba2uvft2/f0/2iDaqihxt8CarKthhpqqPE3QHR0tBXHcRcWLlyo5e7uzmxsbODi4iLy9/fXMDMz+2ui3dJCPYknTtDDX1gY9T9bWQFt1l8A8Pb2xsOHD1X37t3jYmNjO1KleZ7HnpISVXK3btyciAg4+voCjEEmk8HT05PJZDIEWFjAzMMDRRYWQm1tLSsvL2eZmZkqAJxSqaQe3nYwRg/5Y8eSEvjBB6TU2thAKzMTrFcvpOflCYGBgax9396KzExgxw6UhIcjKSmJmz59OvsrpSgrPR2Gx45BtnIlThkaQimRQFtbm1+xYgV7qVKxx7GxwtNLl5jHqFGYMWMGMzY2RtyFCwhbtYrVmprivo8PWgE2ePBgGBsbv1L+ampIMfbxoaCpX38lsrNiBYV0OTmBaWjAMjQUub164dmRI4LXhg0MYWH0cA/QA/6vvxKpNDOjec2RkR0W1JMnT6KyshIPHjyg4+LpiZfLlsGsZ08hbNYs7q2uBp4nW6yW1qvfiUSo2LABBo8fI8/FBUqpFPmenkLgzJkM0dH0HbS1ydadlgYEBcHdx4fr6+3NTI8fZ90++wzufn7w8vJCcXEx8/P3h9uqVVBt3AgHBwfBa/x41lEgkUiIcGhrE9lvQ2xsLG9qaso56+vTOqispP7zuDhqWxg0CJfv3lVdcXNj/ceOZejVi8hre3iVnR0RGA8PNJ46hZTMTJ7LykLExYusIj+fk4eEwMjREUfLypD88iXM9u5Fga4uHzFxIjc0IIDTiIwky76+PgICAriikhKUSaV8iFzOuQwdilorKxQWFakePHjAtba2wogxWObmQpqUBOMbN5jBoEFUMPL0JNJpaEg9zN99R7bh9uRzV1cAgKGhIdLT0wV9fX1hxsyZnK6eHntUXi7cTEriWjQ0BKvBg5lYV5f6mHv0oIKLgQH1ne/YQQTezIzI6NGjwL17wOXLaI2Ph+7du7B1cqLX6ujQtT1zJinM9++TujxpEvwDAlhoaCiam5v5ly9fMp922/iTJ3TMp09/Y/kUFRV1Jdvt1y5AfeRhYVQs++UXUqY3b6ZzOHQo/bSlo+fQXHfe09Pzz3tcqqqop75fv9eWMI8HZ89yHjt24IK2Np9bXY28vDz2mONYXGUlLG1tYWxqSkUFMzNas5Mm0X56egIhIVBs2QLd2Fj2xNUVs+fMQW9fX8aGDycXhaPjqw+bOZNs7Z3uORzHwdvbGxUVFUJZWRnz8vSE7ty59N06p787OtLnmptT5sCTJ0S+xWIi866uVBj88UeoZs1C9rVrUGlrC3fv3mUeHh7Q/pNZ568jPz+ff/bs2ZX+/ftfvXPnzprAwMDQ4cOHS/T09ODi4iJJSEhw6d+//6b/0cbUUEONvwXUZFsNNdRQ42+A2NhYO21t7TmDBg2S/vtXd8KiRfRQaGtLttXISCLZb0F7P3ZraysDgNTUVNja2uLAgQMqTX199sFHHzGD3r3pwb4tmKy9J9IgPx/5MTH8LcY4gIKJGhsbuZycHKSmpuLp06cwNTWFXlYWKWD37pH1cvFiImNXr1KokVQKuzt30CQIwtm0NEgkEmbTWXHtDHt7ICwMxy5e5D28vARPT8+/LDpY3b8P15MncdvdHRVtidlSqZT169cP3bp1g/vly8z62TN4//OfAADTQ4fQrKPDrrm7I49UYyYIAgZ4eUFqaEiEQl+fRpMtWEC2eC0tsjBv3EjkZ+ZMIDAQuHgRwqpViLW357WamgT37ds5GBgQuT18mF63ZMkre/yoUWRJbYOZmRmsra1RVFTElEolHB0dkfz774J7aSnTnTTp7V84I4Me8j/9tMuvb2lo8A8Fgc3t3x/WPXvCPiiImRkZUYKytTURDo4jm2xAAJ2r/HxSL9uUSS0tLXh7e8POzg5MJMJNLS3e6tAhzszQsGv/r6srba9T32pdXR0rffSI9x85ksHKis7/hAnAkSNEoh0dcevWLdba2iqEhITQOeV5yg347DOA46AqKMDld97BSQMDiLy8mMuVK0xSW4vEXr2ge/kyfyw5mT3X04PAGAJTUjAsMJCThYWRutijBymNUikYY/Dy9GRuUimHkBDg889h0tSE3p9+yukoFEi9fp3vtnw508vPR9O6dYgrLob3pk0QrVr1StXU0iJVuqqK7OG2tkS2SkoAMzNwurowMjJit27d4oYOHQorKyv06NGDMzExQWJionD79m3Ws2dPUjgzM8lK/s471Ju8dCmtDSsrOq4TJlAYWHAwyjQ1UZOWJjiUlDBkZlKv+8WLpIZHR9P5mj69izvk4sWLTCKRwM/Pj35hYkLnyNb2DRfJG8p2Z/zxB5F/Q0Pq3XZ3p6JTbi4VIvT0OnrmCwoK0NjYyBsZGXFSqRRvLQzZ2JATZOrUDrIrl8tx6tQpvMzLg9LDA4ZDh7JHjx5BpVKxlceOoVxTE76TJkFTS4ve5+tLkwg2boTQqxeq6+tx/vp1/qxKxZoZQ8SZM+AOHBD05sxhLCqqq7MEIHXdzo5yDjrt45kzZ/i0tDSOMYaStDSV6+3bnMYHH3QZadfuYoBIROr24sXkGBg69NVrHB2BefNw+OefVe9v2MD1++orllVVhZKSEt7b2/vfO5MAZGVltZaUlGTfvHlTUyQSRYeFhem0Tx5ISEhQlZaWnu7bt++J/8m21FBDjb8H1D3baqihhhp/D2Q1NjZqtba2vv1h9W0QBODZM1LJCgspzOnbb+n3EyZQb2O3bvRw2AZdXV2uY3QTgIMHD8Ld3Z0bO3YsE4vFFABkbEwP9C4urz5LSwu6vr6csUQiVFVVMQcHB8HS0hKmpqbs999/R2N8PAr37RO06uuZgb4+uLNnSQmMiCDik5BAaeBnz4JNn45hly9zviUlwr9aW5GYmKjy9PQUhYSEdFWApFLwkyZB38uLeY0c+ecPqy0twJIl4D7/HKWXLiH311+hq6vLq1QqFhkZ2fE+6fffw6z9uNXVAbt3w2vHDpTp6iLsX//C+QED0C8pSdD+8Uf2MisLNc7OeFpcDE1ra/gVFoLxPETvvEPkq12he/KE/pTJ0CSXIzc3l1u+Zw89zDs5UZDTkiXUA3vwIJGGxYuBkJAuX8HMzAwJCQmCXC5nd+/eRVJSElT+/qyPpycdt7cVJNzcSC18DbqWloJxdTX42bPhm539qk/21i367p6etE/e3qQU/vEH7eufBFgBgI2bG3clIgLeN24QWRo/nv4jP7/LiDIA0I+PR4Wjo4CcHCKEkycTyV+6lOzh3bqhtLSUAWC//fab4OzszPzlcjSePInMe/cg4nn4pKejfNgwfuLkyZyrqyvqfv0Vd3v2BK+pCa3kZM5aIsFIiQStLi64/fnn8HznHfo+48dTf7GLC5H7nj2pp/2bb6j/duBAulamTUNAQgICVq7kNk+bBmtXV2GmQsFy/fwQo6UlDLe2Zl0YqIcHEdzQUCLKgkCOjc8+AxITER8fzxsaGjKO49irt3jAw8OD++KLL/h79+6xkN69GXfsGLUiREURyR42DPjmG7R+8gk2bdoEXV1djBgxAh4eHmi0tUXqkCHCgLbRdMjKIqfIkSP0AY2N1Dfs79+Re8AYE6qrq9n+/fshEonQ3NyMKWvWYP+sWeC6dROkUinfNnJKaGpq4uRyuWjv3r0qPT09bsqUKQzJyVQ8+uorKgwcPkykW0ODWgGOH6eCjL09Ef21a6EpFiMnJ0eck5MDe3t73tDQUMjOzhYNHToUAW2jxQSpFJgwAWzvXmDZMhQWFuLIkSNwePgQE2JjcWHDBn7eoEFc3759WWtrK7g+fVB09SoM2hP89fQg9OmD55GR0DxwQLj27BkeOzszPT09wc3HB1liMZr09DBj/35WuGcPHJcupWJYZ8hkVJiSy9HZ7i6VSjkA8H74EAIg+mPNGmG8vn7X+01MDB2X9nvpDz8Azc14lp6Of506BV1dXZWenh7jeZ49r6oSvczJgaGDAyYsWIAkbW3uprm5KjQ09N/6yXv37i2tq6ubqlAopvTs2VO/cyEyKSlJ3tLS8sW/24Yaaqjx94J69Jcaaqihxt8EGzdubPr444+1tDrbgv8MEyaQ2vTZZ6SUchz1BO/fTwrOtWukyEoklAjt5QXF8OH46rffoGx7YBwwYADMzMzg6OgIzc69ljU1RI7S04mEAZTM7OgIYeFC8Dzf0YcoLylB/IIF8Lp3DyLGsG/OHLTq6MBTKhVGHT/OxJ3JoCDQQ+uJE0QeTUygOnQIcTNmqB4LAqupqWHdunVj3bt3h76+PqysrJC1YAGeiETC6F272J8msaemkk39+nXs/vlnoaKigi1cuBDmnUdDtSMwkBLQDx3qCLnC+fPAgAF4vnkz9l6+DF4k6kxeIBaLBaVSyUxMTDB/4EBItm4la+1rEAQBGzZsgFFLC5Y0NRERsrEBvviCCg7V1USMUlK6vI/neRw4cEBVUVEhGjZsGG7fvo0XbbOyh8bFCTr6+gLbsIHz9vZGlx7y3FxKRp4374192b59O4zS0jDzn/+kz+6MK1fIAnvzJu3Lvn1vnRncGQ8ePMDly5eFNdOnM2zeTKR1wgRgzx4iNe02ZbkccpkMv86cifkzZ1Io3cmTrzZ06BDkp0/ja3d3dA8ORkNDg6q6uJjTzc1lvFyOgYWFONC/P7zMzflxixZx7eeB37gRpxQK/pFIxLUdbAwvLuaDDQw49OtHa2rMGCosNDWR3fmdd8g9sHs3eAcHpFpYwOPKFWju2QP06oVWKytcHDgQ2Y8fw7Omho84fpy7/MEHSGlqQmBgIOzs7MBxHI0cKytD8IkT0FuwoGNsFADgxQsIUVF4lJoqFC9dyoZPnPjGsSsoKMCJEyd4iydPuJEiEQy3biVXQp8+RJ4PHwYiIhB95AgsLCxQXl6OZcuWIScnB/fu3eMXvf8+h6IisrBHRlIWQlQUrWVXV7I1L1+O5y9f4uSpU4Kenh6ztbVFa2srVCqVEBQdzcrffx+1Tk7gOK7jp6WlBXK5HBKJBPFXr2L10qW0XnNyyLK9Zg1Z1Xv3pnvL0KGv0sifPqVC0rVrUB46hCPh4byKMe6ptjaEt+QwaGlpQaeoCEPT0/Hoo4+QmpoKCAL8U1KgtLDA6F27uhQZH06bxl+yteVWfvYZUlNT8eTJE1XL9esiuVgM/d69hSBLS+ZkZASuRw/KgjA3R+n+/agwMUHIsWPg5HI0LV4MVbduKCkpwfPnz1FZWQnh8WNBNyeHf+TtzZRKJadSqaBSqQAA448dQ52PDx9y5MibNxojI7qeZTIoFAqIxWI8WrYMuufOIX37dt7F1ZWrqqpCQ0OD4OTkxFzaC5UnTyKlrg6JSUmI1NeHyZYt+E8mSnTG0aNHm/Ly8k4rlcrZUVFRyujoaC+xWDyPMcYplcoTUVFRt/5XG1ZDDTX+T6Em22qooYYafxNs2bKlatGiRTKDTvOY34AgUHDPb79Ryq+3N5HjkBAije0P4YsWkRrV0EAPz2fOAE1NeFFaiuynNFmm15dfQuLt/YYyCYAIu74+BQV5epKFOjz8lW1SqSRb69KlUI0ejZtDh/LuQ4dy+/btAwD0i42FQiLBoHPnIJW+5owXBGDnTuoDXbYM2LEDwtatyLOzQ9rjx6qcnByRXC4HAOi+fIlJenqCTVTUm0/wzc1EqHbv7uifzcjIwMmTJ+Ht7Y2xY8fSg21DAymw3t6kOAsC9c36+5NN1sMDANlav/vuO0EmkwkSiYRNmjSJSaVSFBcXIzMzE3fv3oXVs2eYd/8+2MOHb+xOdVUVLn/4IYJEIrgMGEAkaO9eSmJfsoSKIuvXk01YIukYR3X69Gnh6dOnmDt3LtPtdC5aW1tRFBeH6itXEKunJzi4uQmjR4/mOvrcjx0jMvuauq1UKnHq1CkUFRYKKw4dYtySJW8ScpWKVDqepzTl9etpLvrq1WQR1tbukhL/22+/qZRKJZs5cyaHnBw69z17Up+qvj6FT61di/xx43Dg3DnYd+smzMrKYggMpITrNihaWnBx+nRYS6V80IEDHBgDli+H8vx5yB0doXXhAgoKChC/dStmJCaCpadTUJmnJ26XlPCxsbGckZGRqra2VgQAa9eupfV/+DCRwtmzqfdbU5PGaVVVAbW1KE5ORt3Ll4gdOBBaVlZCva4u41++5Gfu2sXVhIbCPCoKdy5eFHIkEtja2rLy8nJVc3OzAIA1NzdzCoWCGdbUYOrp07iwfj3EUqlKV1cX3t7eIgcjI9wZMwY9oqOhXVPzdoeAQoH4SZMEnb59mXe3bmCPH4OtXo3y8nKo9u+H8tEj/OriAj9/f6Snp3cQwL7PnvGDjx3jEBODysREcIMGQZaYSGnfEglUSiWKc3Mh27QJTTEx+Hn5cuGdkSOZl5fXq8/OyKA1/5bxXwCgXLwYtUePwrSigtRyNzcqwAwfTqnjLi70p48PEf7XkZUFmJpC6NMHhTIZ7gwapAoZMEBUZ2qKtLQ0iEQimJqa4nFmJu91/jyX7u2NOpkMA69dg8eIETBdvfqNTbZoaSH7t99g0rMnfvnlF3h5ecHb2xvd9u8H5+JC1/S2bUBCAorlcjx4/32kBARAqqkp8DzPnB49gkVJCeoNDZHXt6+gL5PxMpkMTnl5IrdvvkH53bswMDCARCJBRX4+TA4cQMXcuTh45AhsbGwwYsQIWFpaduxPXFwcUlJSVAqFgsnlcg4A9CUSYV51NdPdtKmLe+h1PH36FGkrVgguqaksf/t2PtzTk2u/9v8TtLS04OjRo00lJSVVjLEHgiCEBQcHS8ViMXf79m25UqnsERUVlfkfb1gNNdT4P4XaRq6GGmqo8feBwPM8AODZs2eora1FWVkZTE1NYWxsDC0tLZjOm0eEsbNaaGREltZz54hErlhBf/fzo4djf/8ONe7XjRsFsbExm2lpCcn331PgD8cRwerViwi8oSH9nDtHVtfKSnq/mxuRtBs3iFDMng0cPgzRyJEY3CbXdO/evTUnJ0fM8TxyvLww9C2WeF4QUDd9Osp794b9jRvQ9vFB69atsLazg3T+fFF1dTVfVlbGaWhoCK7m5rD57juGdeu6jgjjebKCm5qi84Orubk5qU7p6dDZtw+Ga9ei18GDQHo6qk6dgtGkSeBCQogAfvstEf7SUiAvD4klJWhsbGTjx49nDp3UYBsbG9jY2MDV1RXHf/lF2KmtzWqio6GpqYmIiAi0trZC2tSEvL17EZiWBuvt24EHD8guvHQp9UpraNBxfPKEEshTUkgtBJCbmytERkZyuq8VPcRiMZwGDYLTsWPwlMnYnqIi4dChQ8KECROYjo4OfYdORLa2thZxcXHCkydPmFwuh0qlYvlr1sDZxIQKNO2EIC+PCPqwYTSGasUK+v+iIiJbq1eTM+LZMzSGhqJk4kQUP30qMqipweOePeHm4UFhUUeOUIDYiBG0Lv74A7k6OrCysRFmREYy7N1Lx6AT/rhyBY9cXBBRWclh3z6y1Q8dCsmzZ5D89BMgFsPJyQlHzc2h6tMHYqWSjuH69chsamKtra2YMmWKSC6XY+/evXj69Cns7OyouCQI5Dj44w8iYytW0IdGRkI0ciS6bdqESyNGwPnmTTbu/n1kXbrEGW3bBtmRI2jR00OyvT2bPXlyO8nqIvXzPI+8vDzUpqTANDFRKAsKEqlUKhw8eBADBw5E3NChaIyNRfjVq1S8CgnpWsSKi4NFdTU71NSEit27IauuRoqJCZ4/fw49La3WvpWVIk87OzZw4EAEBwfj0dSpECuVUAQHc6U6Ojh+/Dhq9fQQtG0b+mhpoTkiAubm5ih69gy/nTwJDS8v3sLUFKveeYfDhAlUXGsrQOHSJQrk+/LLrheiszOwfj2Uq1bhZwMDrCwupvvBrl1UVGubs44+fSjs8MgRWsedRmcB6ChWsexsVF2+DLuDB5njiRPAsWPwb22lgpixMYYNG8ZBLMYAExPUDRgA3QMHoBEZ+frtAcXFxfhp9WpM9fFBeXk59PX1VaNHj6bzYWBA5P7zzylYztoalvHxODdiBFBRAZlMhmnTpkFTUxNcaSnw2WdghoYMAweK4OhIa2ThQth3KiZ1e/wYuHkTep9/joiICOHChQts7969AICpU6fiwYMHGD5nDmr27eOcvb2Zs7MzGhoaYGBgwMRyOV0DZ8/SsXsL7OzsoNq6lV26dAl6Dx+SA+P5c7rH/gfQ0NDA9OnTtYuKirSrqqps3d3dodNmlc/JyVEUFxfbAFCTbTXU+JtBHZCmhhpqqPE3QVxc3Cf6NZnTAAAgAElEQVRVVVXaV65cUSUlJXGZmZmoqalBUVGRkJKQwB7HxcFs1CgYzZlDamJnWFqSPXjQIOpbTU6mvkJT0w4bcVNTE27evs2UenpIAuC7fj00w8PJKq6pSWrluXOkhN++TdbirVuJ2H72GamdW7YQSXR0JEXZ27tL8JKHhwdX/fixYHznDsv29YVYKhXEYjGrqKhAQkKCcOvWLVy5coXdv38f+cXF/G2FgmUKAgpFIlhcvYrmy5dhZGzMBrz/PoaHhzO3Hj0YXF2JULeT7cZGUlUDAih0qROhbzpyBKKYGDyztMSE48fxB2N44OfHJ/v5sYwjRyCurxcsKysZDA0p9GzyZFKZPTxg4OGBrLIy2OnowMzN7Y3zY2hoiGCxmPl89x0qR4+Gvb094uLi0Hz1KmQ//ghVayv8+/eHhpYW9QVv2kQFjOPHyUo+eTIRoMuXaY7wqVNQ/uMfuC6TsWHDhuFPk9kNDCA9eRLeGzaw5ORk/ubNm9zLly9VtseOcZL9+8GPGoWbN2/yx44dY42NjczT0xPFxcXo378/fIYNgygjg4jn1KlEiq2tqVBx6RIpv7//Tirw5MnU1zp2LLBwIU6ePi28TExkeTo6vG5JCRt//DgaJk6E8Ucf0Wi3UaMo1GzZMiJg27fDTFsbyQkJ0Hv4EKZ5eQwREXS+amtxJyUFKTdvokevXiqX0aM5ZGVRMef8ebK06+iA53mcPHkSFdXVGPThhxQItns34OcHhVLJXpSV8YqsLGbV2AhldrYg379feHTnjmC5YQPT+OILWhfnz9M5bVf8Fy6E3sqVeFhYiHo3N+GdNWuY7pw5sOneHWzlSqCwENdVKuEFx7H84mL+QWoqn52dLSgUClhZWTGA2gmMjY0h69sXLoWFLGDZMnh4euLu3bsoLCwEz/MoE4kwYPduKly88w5ddy4u9O9Zs2B06BAGjBoFp9GjoYiIQE5uLm9nZ8fPmTdPbOXiwryuXoVGUxN0vb3hcOYMzBMScLlnT8SHhEAkk0EikcA0IwNpAGIKC9Hc3KwyMDDgioqKVB+vXCnyHT6cdYSChYSQk6K5mULddHTIoXL/Pp239jn3ERFQaGoiPikJvWfNwpPGRpi8/z5d07/+SoWoESNom9On033mtdFZHWAMRZWVSGFMCP7pJwYdHbp/XPt/7H1nVFVX1/Xc514uHS5FlCIgKKBSFBBUVLCggCXGHokmYEuMJkZjTYxBE3uLLbEn9hI1UbEiCKggYpciXUAEpHduOef7sbgUS568z/e83/uO8d05hkNzyzn37L3PyZ5rzTXXDXpeJSQALi7gDhyATl4exL/8QoZlb+CXn38W5m7cyIxXrcKjx495DQ0NkbOzM73p40NS9337KJCzdy8ULi64V1+v5HmeaWtrC3369GEcx4EZGoINH05Gdr/+SoqH7t0peHPtGl3X6dPUP/vLLwGOg4WFBfPz80NtbS0KCgqQnJyM4uJi9NHTg+uKFcysKZino6NDqhmJhFq2mZu3dT5vhadPn+LEiROor69H9z59eLuffybzxJ490Wyi+A/BGINUKoWFhUUbxVB2djZfVlYmiYqKivTz82v8xwdUQw01/tuhzmyroYYaavwvAcdxVWlpaSa2trZccHAw5HI5muq3GaZMQdWjR9imrw8vbW0MfdMwq2NHylJeuQKMG4fMvn2Rnp8Pr7g4GEdE4MmIEbh444Zga2uLiooKVFRUsK1bt8LZ2Vk5duxYEbp0IZk4z5PpWloaZQj/+INqt/PySFpeW0tZ27/pj83n5vK1pqYimSDg1q1biIiIgEgkgrm5OWtsbBQcHBwwhqS2nEwmQ21tLQwNDcHdu0cZuXPniNhPnUrZt+vX6ZwLF1LGUiSijK7KpEwupw3zpUsQXryASUkJRBoa2Dp/Pr1fVMS5ubkpDZ4/F0m8vBgvlwMZGeCAZuOxB5cv43J4OAakpMDpjz+ILB0/TsSzVQ29uH176A0ciMmTJwMyGQbFxSG3ro4Xams509GjIdbWJvntJ5/QFxQK6qe9aBGN2fPnRDwYAxwcUGNsDCYIkOfktHH0boP+/YGiIhjcvYvZs2eLXr16hYsXL7Jz6elw0NLik48cQXFxMYYMGQIPDw/k5OQgISEBRUVFSolEIsKwYTSu8+ZRPfOxY3RdABl+bdpE895KOZDw7Bmepaay3idOoJ+lJXf+/Hlhk6MjW9GnD9XH8zzNTUkJkclly4ABA6A/eTLGmpuzsro61L5+jdcWFrB59Aj1MTF4EByMuceOQSspSYRBg+g7dXXA/PlEgK5fx7rJk/HRoUNwDgjgER7OYc4cUnGYmaHnkyfItbbmusfFodzMTBj100+sxMiIJenpCYdEIvQbOxZujo4UbJoyhQJOPXtSFjErC69GjQJevxa0LCxa2pdxHDBlCh5ev85GXriADmIxl7xzJ+7fv4/CwkLB883+1HZ2VGedkACJtze+++47VFRUIDExEbdv30alXA5DV1dSTfA88N13VGM9eXILsQwMhO3Mmfjss89aBtzGhtbZvHnAwYPgzp3DnYgIGMlkyi+nTBFxHIf4a9dgu3kz9sycCQDo0aOHqKysDBzHtdQDchy1/RMEmp/GRqqtnjeP5io4mDKxgkCkGwC7dAmSykr8/OmnkOvpYQlAQZmFC1vM9QC6B/8FjIyMUF5ezlXU11M7wL/+onOFh1NJxY4dZFj39CnN/xv4448/eHljI5ffqRNe373Lv3z5Ei5vktGQEDJcZAxYvx51+/ahuk8fUT9NTfTp06ftg4njKANubw88ekQlE0OH0rjU19NYXbhANeqtUFtbq9TR0RHV1dVBUyLB08GDhT4c9+6n3uHD5AFx7x4pg96AWdOxBw4ciB49eoiqBAEGAJ170CAKdpWW0nX9mwgICNBJSUkJBpAAYPu/fSA11FDjPw412VZDDTXU+F8CkUj0cty4cZ26du3KOI4jw6CKCiIN27cjLipKUD55wt7bszUgAMW//IJTWVl8Dc9zVh078vufP+cGxMYKFffusUHBwULvKVOaN/hbtmwRnj17JnJycuK7d+9Or3McbfxtbGjjKBYTOdTXJ4nuu2o2W0GpVMLmzz9F2U01o3369GGRkZEYO3YsnJycAKDNflUikbRkaLy96Zw9exI5vX+fpM7W1pRhk8moHn3BAnIV/uEHckw+dQro0gWy8nIcNzERKgIDGZRK9OrVi09PT+cqKiowxNtbJEpNxRY/P5wD0Cc/XxjQ2MhSUlPh5uaGm7dvKwf4+4v6r1xJRKOmhtynJRLKBL98SSROT49+X2EhMHs2tDQ04JCfzz0cMEC4pqWFL775pq2Rm6cnbe7XN5kIp6fTv3fsAHr0QNHChbBdvRqa7u64df4832/o0Pe7J+3YAfj7w9zcHDNmzOAq/P1x+fp1KBQKzJ49m1Oti5MnTwIAcnJyRNXV1dBXKRKsrVvaw6lgZ0dk4dIlCjA0v2wHbW1t/unTp4KlpaVIKpW2zFtrafjDhzT+n3xC9dvR0WinUEB30CCcXrkSxa9f8zJPT453d8enn34K7e2teMCLFxQc6tgRGDUKkZaWkMtkMJgzB7a9e3PYto0+l5iI+O+/55Pkcq7Q1hap3buD4zi2PCAAZgEBMANY6a5dSr2ffhJh61Zar8HBpHwAiDQWFGBoeDg2bdrEXbp0CcOHD2/5HTNnYkFYGO4GBOD5o0do/O03wbyykvX46ae3uZVIRBnRwsLml6RSKSQSCQ+AO3v2rCIkJEQMX18idKqxjYtrOcbHHxPJAoiI/vorrefz54n4jR4NtGuHEsYgkUiaDbV619UBH32EDpaWePXqFfbs2aPKsL69ZhgjMltURCUf+fkkv9bTI2d2FQQB2nPmwHvAAMR06YKlixbR68eP0/2VkdH2uLa2RJpbt7xqBScnJ3Ac19angTFaWyNG0O8pLSWi+4aSQ6FQIDk5mRs7Zgykfn44d+sWp1AoME7lfK8ar2nTKOgHAJ07ozIzE588f456XV2UZmSgw6+/kkJDZSB45QqR4YQEarF24gSVxJibk9Fb6xZfTRg4cKBo79696N+/PxLv3IHnvHls5evXGD58ON4KwAA0vgcOUBb/DbRv3x5SqVR58+ZNUWRkJEQiEb799luwadPoA8+eUTDz00/JW0JlSvlfQGZmJkQiUZpSqTz4X/6yGmqo8d8KNdlWQw011PhfgLCwME4sFrtaWFi0davdsAG4fh310dGIf/KEMcbQu3fvdx5jb3y8wjshQTygUyfWdcECaGhocJWVlcj44APWt7gY+nv3ctDQILkwABMTE1RVVUGhULx9sKtXgZEjiUg9eUJZt5iYf0m2RQoFuvI87DduxJM9e5CTkwMAkMlkQmNjI9N8x8a2DTiOstbjxlGt86FDRPQDAymjuHEjSZ4HDiTS19RP+MFXXwmXjh5l+vr6/PLly0W1tbXQ19fnqqur8fr1a+iePw+sW4dPBg/G9WvXeNfvvuOONzQIOaamLDk5GdXV1aLm3sQiEZ3z+XP6761bgbNniQTNmEEEc80a2tDPnQsEB6NzdTW7e/Qov3btWiYWi3mpVAotLS1Of8oUOHl4oKvq+iQSoMlpHCByEtu/v3JHu3YiRVQU1+/334H9+9u0JgJA59bVpTFoMm6ShobiIwsLDkeOtPloYGAg0tPTkZGRgca6OuiravbnzaO2bm9CZTYVFNSc3TY1NYWpqSmrqKgQAEDV6/fx48dway0j/u03OraBAa3VHj0gNjGBwTff4NOQEPA8z+3ZswdFRUUtbZxkMsqs//orsGkTyj77DM/PnEHs4MHwGzxYaXzwoAi//kqBCYkE2LwZnUpKOM85c3Bw/ny+QiplSlDbukmTJjFxbS3scnKY3Y0byPzhB9gXF7cpLcCBA4BCAS2ZDK6urnxiYiJXXl7OjxgxgpNKpcDDhzh65oyQEx3NvLy8eK/CQmaemUlGXO9CQAAFUdzdm+uXVbWzVlZWLSeWSGhcjh8ng7r27Wm85HJSVIwbR++vW0cBmPBwCmjt3g2ACFRwcHBbm/gpUzChUyekp6fj0qVLqKurA2OM43m+5bkhCJRVHzCAFCKnT9Of0tK2ba+2b6cykOfP8ejXX4W+zs4tgaL+/WkdvokNG0iq/x5UVFSA53nI5fK33xQEyiBfu0Z13qtWUSArNBQAcPToUV4qlbLONTVMMyQE1qtX87m5udzr168hlUoh/uorIqOXLxPhrqoCJk1C2ZYteHHoEB67uaGnpydG6ejQPWphQcdXmcX99hupZXx9Kdixdi11CEhOprlqZSrXrl07BAYGCpcuXWIG+vrK+xs3ilhFBcLDw2FrawvTN3t4L1xIAaf3ZLe/+uorEUCu/ufPn8fTp0/h6upKb6oy/KtXU9Cld28KEvyrZ2Ur6FIJhqNEIrmyfv16Bc/zneRy+dzly5ef/8cHUUMNNf5b8O/1H1BDDTXUUOM/CrFY/J1UKhUZqWSbeXm0GV21CoiLg0KhgJ6eHiQSyTtbSMTExKCiokLscPQoXI8dYxpNZMPQ0BAenp7QDwqiYymV5Cze0AAvLy8GAPX19S0ZvCNHKOvj5katnQICWly8d+6kjNTfITkZml98AVmr9mIAcO7cObZ27VrU1NT8swHhOMqsnT1LxGTxYsqqHTlCGbInT6hWMjERFceOIWvNGjbZygpzO3QQcTEx0H/9Gnj6FPqFhbCTSsHi4sC6dYNlQwP86+u5B2PH8jZPnrBOWVkovXMHM+PiBI3sbCI+Q4cSWbG1pTGzsKCs4PXrlCUrKCDieusWZVAB6OvrY9asWdwXX3yB4OBgztrKCkEbNsBQT48/FR2NoqIiuq7+/YlQVVc3X2pBQYGo1sAAc2fPpgx6VdXb4ywWE/lftarltTNnKNv9Bry8vJCTk4P2BQW49M034OVyRNjZCVutrHCytJS/N3MmHj58iLq6OlRWVkLm7U0GY+Xlzce4fPkyXr9+zYYNGyYCAFdXV5iZmeHPP/9se7KICMpWqv69di1lZxMTm6aRg0wmUwLAkSNHyP2vqopkxCNG4MzZs9hrbg7BxkZYWlMD3y5dRBg4kAihQkGBF11dtO/WDYVPnqBAW5v7ZOdO5nfuHMvMzGQNwcGApSUchg/nMkePFo52746GN4NHGhokV7e0xEgXF27u3LkoLy/Hzz//jLS0NMh/+QUTXr5kYrEY2traguXatYyLiiJ5/dChRBJbQyIhmfrRo80vyWQyJpVKlf7+/gAoS/twwwbwhoY053/9RbLtZcto7aSmUg/6lBSqMX/0iAI4Q4YAgwahYP16MMZgozL/k8ko8NShA6RSKXr16oVhw4bB0tISgiBg1apV2LRpE3/h55/R4O4O5ZEjVNfcvz9l1efNa+kCoMKVK0BeHtKyslBXV4f+qrIMhYLugz593lpbGDu2pf75HahvWrc3b958+82PPyZjvUGDqEVh+/ZAXBzqamtx4sQJ5Ofnc8HBwUzTwwM4dQpjx47l7O3tlZcuXRLWf/891aCHhdGxGKNAhbk50i9f5vMGDhQWHD+OyrNnka0qyRCJ0FyuAFCwcPp0uvd8fChosHUr8PPPZArI86SuSU0FamvRs3t3Nn/+fNh37CgSHz6M/v37Q0tLS7h9+zZf3upeAUDPq6Iieh4UF79zbHiex/nzxH2lKnM0nqfnbFQUBQYYo4z89u0twb5/AIVCAYVCAXt7e5+goCBff39/a01Nze/+8QHUUEON/zaoybYaaqihxv8wVq1a9ZmOjs7iKVOm6Da/eOsWbcAYA0Qi6OvrQ1NTUymXy9lPP/2ES5cuNX+0rKwMt27dwrhx46Dl6EgZlqbsWBs4O1OG1MwMCA6GblP2iTHG5HfvUq30zZtEyM3MiEyosmA2NkQGmlpyvRcPHwJaWsjPz4euri5vbW0NExMTpertioqKfz4wsbHAyZO08WeMiIOfH3DnDhEHJyfA3BwGmpqw0tHhk27cEKqOH6fruH0bj2fMEPJGjRIU8+eTnDchATh9GpY7dyJg3jzOJyUFxiUlEBhDbmUl233gALLNzXHN1FS4deeOEocOUSb7ww8pY9W3L7m+h4VRxvDw4TY/lzEGQ0NDWFpaIsDfnzN1dsagKVM4X19f5YEDBwRV72yMHk3EoQkqWapgZNRsFAYLCyIHrRESQtnJprZQOHSInMXfAWc7O+W0/fvROT0da2fNwh1dXSbW0IBpp06s29mziDh9Gps2bcK2bduwcdMmXL12jVd88gkUCgWuXr2Khw8fIjg4GKrgD8dx8PDwEACgqqqq5UQnTxKJUuH+fcDYGAp/f5zavx9lTaqBWbNmoaSkhHs4dy6e/fUX/tq0CQ/XrcOzZ8/QwBgynJwEjb17m4M85bGxuBwRAf7bb4GXLyGXy3H43Dm0a9eOD58+XWFhYoJvS0qgFx0N8Dz0yspgf/YsEzgOT548eXtATEyoVtjGBsZGRvjiiy84CwsL5R9//IHo6GgoZTJIJBK+ViVPBojojxhBkuW8vLbHmzePAgZNxMrR0ZFVVFSI9u7di927d2PTmjUovXYNMh+fFiKmoYFmafzcuSSjHzyYlBqqrH8TQaw+dkwY4u4uNPdVz8+nddGqHVXv3r0xffp0uLm5wTYzEx/u2MGlFxXhaseOWJebiypLS6rTVgV25syh80ydSvdyeDgwdSoeP36s7NKlCxOJRCgqKoLs6VMKLL2rnRXHkYw8Kan5pYqKCuTk5KCoqAjHjh0TdHR0kJSUhKioqLbf/f57ygCLRHSMwEBg71689PYW7PfvFyZOnAgTExMyF4yLg46ODiZNmiSaX17OZu7ZA4WTU9usOmPAgQMoMTVl07ZvZ3pTpsDDy0s4fvw4Mt9zX+DZM5L1V1eTnL57dzrO8eMUQPDyorWybh0EW1v8/vvvvPGmTXBOToa3tzemTJnCXr16JezatQu//PJLc/cIALR2d+9+Sx6vgmpdBgcHw/rFC1IOMUYmlvr6LcaWr1/Tuho5kqTl/6JFb1FREU6cOAEAqKmp4Z2dnSEIAniez/7bL6qhhhr/T6CWkauhhhpq/A8iLCysp4aGxuapU6dqGxgYkHR2+nTKOn30UZvPVlRUiCZMmICKigo+MjKSNRmoCc+fPxfc3NzQqVMnkpyOGEFZ4U8/fVuOrKsLLF0K5OTAPCAA7i4uwnWZjLmvW4ciExMIjx+jQ4cO7/6xVVVE5M+/R5koCLSZ/fRTpKelKW1sbEQAMHXqVFFkZCQeP36M/fv3Y9myZe933gZos6lQkJS9qIiM327fJpJw5gxtZkeMAH78EcjKAnfsGNyCgri9e/fiQXk5VqxYAQC4mJvLFAoFOnfuDKmWlpKzshL59u0LnSVLAJkMYokEjWPGoCw/HxanTqHiwAH8VVaGSkdH1jEtDXru7uhqZATN8nIi+aosmFxO2aisLKBfP5LWt7ZOamggEnrsGMAY/Pz8RJWVlfyZM2eEzz77jMOePc0b8j/++APFTYQtJSUFvXr1ojmKiCCX4s2bifRZWRFR+vRTyh5/+y3JaU1MqI699RzMnw93QPTA3R1xffuiV69ewqBBg5hIJAIAho8/xsIrV1A+aBDE2tpoaGjAtXPn8DIiAn+uW8fXi0RccHAwrN4w4YuOjmaMMTS3KHvxgs7dShaPnBwox4/HT2lpGHztGpRLlkAxZ05zDa84KgoaN2+iq4UFHx4QwEykUmHmqlXc5ZkzcXLsWKF3dDTClyxhJU1y9+pu3VB2/jyMra2hoaHBzx48mEOnThxGjyalBUAZzqgo8IGBAIDs7GxBpdpog169KHiiVII7cAADBgwQnThxAlXTpil3PHwoYjzPhraumbe2Jpfq334jZUV+fguR0tCg4x07BsybB21tbUilUqGgoIABgH1aGjo0NkKrZ09Sh6h6Xask/6tW0XqysqJsqIlJi0lXt25I6taN+ael0XcBygRPnPj2vXLyJALq6hDJGNIdHPDVDz9AplTi0fr1iP3xRwRu2QJO1KRE79ABVTIZqqOj8URfX6g4fpxlZWVBoVCIGGN49uwZBEGApLER3r/+Cr/W0vTWyMoCADx69AiXLl1qIxm3tbXFhx9+iKqqKhw9ehQFBQX8R0OGcJyvL5maqVz+m4IXWVlZuOPtzT7y9oZEKqUxevKEZO9NCgC2dCmOVlejfuNGQSwWCyKRSJDJZJy5uTnT0tJCnYkJNMaOBSZPRrecHGa8fbuwWy5nBgYGSl1dXebr68s5OjqSuuHGDbqXs7MpWOfpSZnstWvJqE+V+V+5EidtbJR1lZXMy8wM3KlTwN270JkwAZ8VFor4Gzew96+/2JMnT+Dm5obmoIifH0nk166loForuLq6QnPGDD43Px/2n3/OsU6dKLO9axdlsbt0oWdHaSmNz/LllKGfNo3KFZYubfM8l8vluHz5srK0tJRDkxdGQUEBx/M8lEollEqlU1hY2DcALqn7b6uhxv8c1K2/1FBDDTX+hxAWFiaWSCQxw4cPN+vUqRO9+NlnJPv8/XfKuly+DGhooIgx3Lt3Dy4uLnB1dWX5+fl8UlISl5+fzwRB4IKDgzmRalNtbEwktayspdfum5BKwXXpAofvvmMdCgoQPX8+ZN98AwdHx3dusHmex75Ll4QOly+zW506KQsKCzktLS3o6uq2bDSzsoAtW1A2ezauXr3KjRo1SpWRR2ZmJgoKCiCRSDBgwICW77SGqtbUwYFqxL//nqS3Dx+SJDYwkLJAhw4RCU1LI6fhLl2g0bkztKdORebr10JxcTGztrbG/fv3BYVCwcrLy1Ggp8fp3LwJhy+/hGT+fEAiAZs6FV0MDXErNRVBQUEoLy+HiYkJKisrhdLSUu758+dwffAAYjs7iEeObDEGE4koQ7lnDwUezpwB/P1b3LwjIiCbNw+P/fxgbGKChoYG6OjosLi4OCYWi3E1Pp4Xli7FxexsPqu0lGtsbIQgCHB3d+dNTU1pYCws6HhLlxKZ7NGDCD3HUXZw+nTKdKvqPgHKwL5+DUydCi4gAMccHdFIBnSsuR4doFrQJUugnZsLzcBA6OrqwsXdnWXo6fFdHj/Gh+vWMaPWLtRNePLkCe/u7s7sVC2O6uspAODj0/Lfo0fjtJ8fymtq4Pz11zijrQ1pSQm8zp2DiZYWL6xYwbo6OaGdnh7ro6/PvObOZSJNTTisW8euDR7MOE1N5p6TI0hHjoTv0KHMKTwcSSYmeFFSAs36euY5ZQoaT52ChkxGNdN37xI56dwZ3BdfoFQmQx5jeFFUJDg7O7OHDx/i4sWLiIyMFLy9vRlnYoJCQ0M8qKrC1atXMWDAAGVgdbXIe/16JPTpw54+fars1atX2xugRw8K9BQVUc3y4ME0Fx06UBa2Wzdo6Oigd+/ezNnZGT1FInQ6eFAwKyhgmhYWpCipqiIp+PnzFCSYM4ek+0OG0PG2bCFpemUl7mdkILGgAD6pqRB36kTn2bCBAi2qebl6FTAwQMX06bhRVISknj3Rc/ZsmFtaQiwW4/G9e5iwZg1umZigU1M2WPHRR8i+fBmvjY3x3NycFTOGnj178k5OTiwrKwuhoaHw8/VF74kTEa2pCYm1Ndq/q3d0bS14ExOcMjAQHHv2ZCEhIXBxcYGLiwt8fX2ZpqYmDAwM4OrqioSEBNxLSOA9raw41uR+DgA1334L2fr1OFBbK3iNHw/rUaMYBg+m+Vy2jNb3wYMU3FuyBN18fNClSxfm6OjILCwsuKSkJAYA5eXlwhB/f1hOmcKQmQlMngw9xpjt8uWwtLbmxGIxu3btGu7evctX7dolVMfH47ilJc+lpeGmlhbv3KMHxwYMIJK7ciXg7g7B2BgPHjxAXGIi9/XXXzONgQNJ8r9xIwWXunQBW7QIHWtrcb6yUnCZOpVpTppEx2CMgjTa2kSeAVJATJwI9vHHkGZmslvV1ZwZf2MAACAASURBVKzYzo7vPHcua35m5ORQkO2DDyiIM24cEe0ffqC2fRERpGq4e5fIPGOQyWQ4c+YMx/M8EwQBGhoagq2tLe/q6sq1b98eOjo67aVSqV9paemM27dv6/br1+9t9zY11FDjvx1qsq2GGmqo8X+BsLAwi9u3b38THx+/PTY2dsiNGzdi/fz8at74jFF0dPTn0dHR/aKjo9P9/PzqACAmJmZh+/bthwcGBkoYY1R/6+pK2a/gYNq4JSUB6enQW7cOBk+fot7QEJY1NXAZPJjrP2gQJBKJ4OPjw4yNjdv+MMYoO96/f1uzKICyO4MHA99+C1ZejiKJRLC9fJnl9O4NR2fndxLhp0+f4klaGtr168fY06fc44YGPiEhASkpKXBycmISiYTk2y4uOJyayltYWKB3797NB9LV1UVxcTHKy8tRV1cHCwsL1NfXo6amBjo6OiSH7tWLyMfSpfS3Cg8ekKR+40b67d26EckrLqbNeF0doKODdlOmwP7HHyG5fJkdq62Fg4MD7+LiwlVWVgr19fWszNgYxr6+vEXfvgwKBRRnzqBmxQrc7dwZXl5e6NGjB5ycnFBQUMBev34N49pa9Pz5ZxxvaECPkBAUFRVBJBJBEAQc19ERSoqKWIapKXQuXUJ9x47Q7dABvFKJQ/HxysuOjlzq8+eIi4vD3bt3kZKSAqVSiezsbNjb2wt9b9/mzPz8OK9Jk5CYmAgtLS1UVlYiPz+fd3Bw4JrnMCSEDMgGDCDVw/jxNKcvX9IYeXlRrWdZGRkrSSTAkiXQnDwZA3x9kZ2dzefk5DBjY+MW4sQYzb+LC9VpGxmBMQYLMzPWfuNGxiZPflsRASAzM1N48OABS05OVpaXl3PW5eUQ2dq2tLT6/Xck19Qg3sAAGhoaQm1tLZx69ODNKyuFzvfvc+YxMazD7NngbG3p3CEhJNEPCECRkRHuKRQI3b4dpkVFzM7EhBn37g3dBQvgOWsWvDZuhK2XF4TiYuglJaFu2TJItmyh3xkURNlBDw9oFhcLmllZjOXkwHH4cLbv999RVVUFuVzO0tPThejsbOFRVhbrvWYNLObP5/v27y+ClRVEHh5wGzMGERER3MuXL5GUlIQOHTo0G59BT49q9bdupTZeYjER34NN5s/W1kBJCXSWLoX+6tWoDQzEbx4ezOXSJWgePEgkaulSyqy3b09zoKVF//7gAyJa9+9D+PxzPE5ORp85c2i+srPpPHp6ZAqYlwckJkL46ivszcxEhLc3PGfMwLhx42DeJDFnjKFrt27YKBajSBCEqvJyxlVVITYzk89ydGSBv/wCr5Ur0W/1ath7eLC6ujqkpqYiMDAQ2hoakJib446BgVJPX59rDgK2wrE//uCTeZ61GzKEHzV6NCcWi6Grq9tigNcETU1NuF+4wAoePOAeDBoE56Zny61btxATG4sXjMGkf382dOhQek6MGkXZ5pUriWTu2kWturS0oKmpCUNDQxgZGSE5ORm5ubloaGjAhAkTWLdu3ej7lpaAVAqsXg3prl1o37cv7D084O7uDmdtbeZ09ixTHjjAHDt25Exv3mQRenpcfHw87ty5IxTq66OboyPD/ft4UVCAc3fvYtiwYS018zU1pDJQGUROnAi9CRPw+tUrKPLyYB4ayrB4MQUKdu4kFcKGDaTQcXOj59vQocjr2hXRL17Ax8eHNZusvXxJRpTTplG5jupZLpORcV5wMJVq5OUBP/1Ea0ZHBxlFRUhKSkJQUBDGjx+Pfv36MRcXFw4ARCIRLC0t4eDgILKystJISkoyu3Hjxp7bt2//EBsb6xkVFRXv5+f39/p0NdRQ4z8CJvyLWhA11FBDDTXejZUrVw4XiUSnXF1dRd27d9fMyspSJCQkvJbL5e4rVqwoBICwsDAmkUiSO3fu3EksFiM5OVkQi8UxPM9raWtre06ZMkXHxMSEpJNr11ItZdMGTxAElJWVYceOHfjAzw9JBw6gu48Pety+TcRL1RInIIA2mm9sdjFzJm3uVe646elkKlZRQcQ+KYk2+QAit22D9bZtMFmyBEbTpr3VRzs3NxcHDx7Ep+3awebPP4Fr16BQKLBz505lRUWFyNvbWxiSns5qe/TA1shIfPvtt9S67A1s375dWVZW1uyw3LGwEKESCV330aOU0XqT7NfUEImUSIBffiE5708/0Xu7dtG/MzMBLS0I9+8j69EjaLq7w2LQIKybNQsybW0wxiAIAqytrYUPrl5l0txcXJ0/n9c4fJjpLF7Mevfu3ZzR56uqwI8Zg5ezZuFWbS0yc3Px5v8rzc3NhcmLF7OYPn2QNnSo4HL+PLN8+RJiABocB+7aNZw8eVLo3bs369evX/P32rhGNzaiEcDhw4d5nueZrq4uMjIy2MCBAzFgwIC2YxAfT9fPcTRvx46RhD00lDL/VlZUU/777y09tJtw69YtREZGYtKkSdDU1GwhEKdOUQAjOrqll3hiImXS36GIePXqFdLS0gSRSIT79+8j4OBBOH7wAVOZVlUvX47DNTUImDsX+vr62LVrF0aNGoWeHEfkOj6esuCMUTa6vJyydGlp2B8RwYvFYvbJJ58wCAI55nt50XV1707BgcpKYNo0XB0+XHk3MVEkCAL09fX5+fPnc8jIANasQf7Spfhr2zZ8IJPBysgIuZqaOAhAR1cXNjY2vI6OjtDZ1lbksHQpuIMHARXh2b4d/KxZWLVmTfP1mpmZ4fNWtfVt5iI4mDwAIiOp9jk+nrLfgwYBX3wBITwc+cOHCzn6+uhTU8PEU6ZQL/G/Q2QkaufPx19OThiVlAQ9ExMijx07EsHKz6da6pQUgDH8sm8fSkpKsHz58rbHOXEC+PprVD1/jvDwcGX3NWtEhpWVuLBwoRASEsJ0dXWB/fuhzM7GOTc3Pj0jgw0aNIh5e3sDe/cCvXrh98ePwfO84Ofnx4yMjCCVSsHzPA4cOKCsqanhJgYGMvPTp6mP+N9h0SKUWVpie5NXw2effYbTp08rzc3NRT0lEpj17UvXCZAXgVJJzySFgq7z++9pTD/4gHwTfH1x7sYNPG2SvNvb2/Mff/xxWyXCuXM0PwsWkFy/vJyekadO0bM1OxuYPh1CRASKiopQVFSEP//8E5988gls8/KQt3y5kOHpyQZu3EjHa2wkAztv7zanqaysxM6dO4XRo0ezbt26UdBvyxYKhjUZQ+LChTYt9U6ePInU1FTMnTsXzQHSH39suYfv3KHrbI2sLJqXNWuIgL98CYwYAXloKFbX1GD8+PHo1q3be6egvr4eW7ZsaQBwz9zcvJdMJuPLy8sLAEgEQRDL5fLg77///ubfT6Qaaqjx70JNttVQQw01/k2sXbv2xfjx463tW/VFjYqKUsTHx6fKZDKfFStWVIWFhQ3V1dX9MygoSKu6ulqwtrbmEhMTYWFhARcXF6plraujLIi/P9C3L2pqarB582YIgtBMEjmOA8/z+OKLL6jtDM+TmU5cHBGW6dNJerxpE2U5vbzIHGnGDCJTjY200RSLqX3UG4Q2JSUFV/bswcjz51EwcaLQ8/PPmX7HjhAEAenp6YiKihLKy8vx5ezZTCc+nohdU+1ueno6rvzxBz/41Cnuz6AgaBob8wsWLHivAWdmZib+3LULEpkMs5ydIYmPpzZD76oPBSirI5VSgKCsjKS4/v4UYABo81xTQxmlHTvoGhUK4MQJZPbpA+bvj1ITE2R98w1SU1KgKZNBWlaGOjMzYaa+PtOztaWMMUBj1thINdG//gpoaeHZs2c4c+ZM889ZunQpzdvr10TWGENWRgZyvvsO7Xr1EhSAEF5fz7Vv314xY8aMd3uj5OSQvLiykmTpAGJiYpRRUVEiMzMzeHp6Uv32m5gzh2SpgwdTZtvSktQQu3dTjeg7MtIAcPToUWVGRobI2NhYmDt3Lk2+qoY4MLCFdCYkkGw1Kqr5d70LMTEx/N0LF7ivFiyApEMHICMDaSEhwvMFC4SRo0dzALBnzx6lmZkZG/355xzWrqXgkK0tje+uXbQuIyJQcu8eGnr0wIOAAGHUjz8ynDhB8zFyJJEcPT0qr1AqaX2D6lXT09Nx9uxZhIaGwszMDKirA9erF65OmaJMUCpFhmVl6PnwIexycpD69ddK/6++ErVZY3/+SUGGYcPIoOrpU5QZGEAsFiMtLQ3h4eHN9f9t0NhIv6OmhkzyBgygWt0ZM0gKHhJCZC43FzHDh6MiNBQffPDBe8dShZe5uTh08CDGA7A7cgTc6NE0RvfuUZDi6FGSlDe1hNq4cSPv5+fHPD09297MCgU9F9zcgJQUNJiZobCyEh1dXCASiVBdXQ2xSISi0FAhGUC/PXuYqrUbXFyAsDDUDB2KgwcPKisqKkQ8z0MsFkOhUEBbWxtz5syBjkxGx09NbQnUtEZDA7Wy+uEHgONQW1uLjRs3wtnZGfn5+byNjQ1GT53K4fx5IqcJCWQa9+WXFFB4+ZJKaLZupWxyu3YonzMHj0eORKeLF2GUn4+IrVuVfXbv5swXLWJwdaVgYr9+9Htu3qSSk7t3qV5epaQAKKB09CiwZQvi4+OFq1evMqlUKsydO5dxHIf4n38W+KQk1rdPH/rOs2c0vyUlbS7x3r17uHTpEkbV1QlITYVtWBgzmjiRDBR37SJFTlVVm+daQ0MDNmzYgAEDBlCnhpoa+oyhIQUZBOHtYGNsLD2PoqOb5eO3T57kHQwNuepFiyCZMAF2YWFvf68V8vLyUFBQgJ49e0IkEiEvLw8SiQR1dXU4ffp0nVwuD/3+++9P/stFqoYaavyXoTZIU0MNNdT4NyEIgriurq6ZFAOAn5+fuKampsuTJ09erFu3LprjuGENDQ1a169fR0VFBdPU1ATP8yguLuZ79uxJu7CtW8HzPJ7q6uLunj0oLi6GtrY2/9FHH3EPHjxAbW2tctSoUaKGhgZy6wVoA9erV0vWOiODNr4FBZRtS0mhDE9uLmVEAwJoM/qOWlwA6Nq1K7ps2IDSJUvAffQRe3HlCrKDgxUpSqUYgGBlZYWQkBCSi0dFkZx73ToAQJcuXWA7aBCXdeGCEDB2LHN3d38v0eblchw5fBizDx2CaXAw2KxZwKxZfz/QH39MG2eAJJavXhFJXLyYXuvUibJ+z58DpaUor6vD2chIFBYWgsvLg15QELRFImG6szPjp03Dy8eP8SQlBYFLljCuXTsiMOPH0/gNHUok7Lffmk+vVDabqUNXVxfNtfHt2lEdrYMD7JYtg115OfDyJSs5c4YZTpokTFu27P3/j1W1MWsV8JZKpRwAFBcX4/Lly+8m2zt2tLRv8/GhYzg4UFbsPUQbAIKDg0Xr168XysrKWE5ODmxtbWlzHhxMpH/dOiK3Xl5EokpKKJv6DtTU1CA6KopbcOIEymbOxOnTp5Wd8/I4wdiY9fbxad7xu7m4iOKvXycjtchIMvwrKqLabiMjYPNmxAQHI2rnTng7O6P3p58y5OZStjQqijJ6gkDXu2BBm9+joaGBLl26QEdHB3v37m1+PWTSJAzR0xNlAEIFx7GnEyagXkODH3zypAi5uaT26NrU9fz6dQpaDRtGhIcxqIoxmlsztUZeHo1xnz50P02aREqL4cPJEM/cnIJBw4cDe/bgYU2NcLO4mHUoLMS2bdsU1lIpGz1mjAilpRRc+vZbui65HFi5Eh0cHeE+ebJg4ePDoKdHqoXffqMgmrMztQibPRuqNdLQ0MA5OTm1/Y3ffUfEbeFCyqQnJEArLg62rT6yefNmusb27dmMCxegoyJpjY3A48cAY9BjDHPnzhXxPI+ioiKkpaXh0aNHgqWlJa+joyOCjg7N6/vw9Cl5GTQFK3R1deHh4YHk5GSB4zju8ePHGF1RASxaRPLp27cpgNHQQAGkw4cpiLZmDQWmpFL8HhqqrKysFN0eMQIh/v7oY2Ulkjx4QM+3e/dIcn3gACkM9PSoZvrgQZrvcePoWVhZSX+aSh8qKythYGAg1NbWsps3b8LX1xdyd3f2qrZWQHw8g0xG8xAf3/b6iorgunIlkmxsBMWrV2goK2PKuDgyutuxg9QOn39OzyTVegOgpaUFNzc34ebNm8zHxwfiVavoufPXXzS3Y8a0LaEBKBgREwOcPg25Uok9JSUoLS3lYgC4+foqR+TliRATQ/Pe2puhFTp27IiOTT3hATKyUyEkJETn8OHDB3/66SdnhULx/YoVK9RZODXU+A9CXbOthhpqqPFvIioq6klmZubgnJwcsZOTk4ZYLAZjDI6OjmJHR0ctMzMzp759+4rr6uqE/Px8ZmxsDDs7O/j6+uLOnTusqKgI5eHhvG5NDYvt3p2/ff8+NDU1MWzYMDZ69GhmYGCAmzdvKrt06SKyt7en2ub3obiYyICDA204nZwowxsdTe+fP0/txA4dIkL5DnAcB21tbfyemwupmRk0EhI4y6oqPnjDBs7VzY01k0yplDZ/rbJ1ovXrYRoayszflD+3RmEhWM+e6OjvjxMuLkK6oyPr0KFDi7t1K8hkMty5cwc8z8MoLY3qllX1s927E8kZMwYQiZCeno4dhw6hy48/4kV8PEyHDsVjU1PIjI3R2NgIuaEh6vT0WP8PPwTr3RuGHh5wGDcOzMeH3MUdHIjceXoSkXqjv7CZmRlu3boFIyMjjB07FlKptKWuvbycHJY7d6ZSgC+/hOaYMai9eBE1jx+zDoMGvX88ZDIyumraILdv3549f/5cqKmpYQCQnZ2tbA7ItAbPE9GTyYikpKYSIfubTDQAeHh4sNu3b0OhULTIThkjkiiXUy08Y4C9PZGkN9yUVVizZg006uvhmJaG37W0BPtOnZjvxo2setEihWMrc7F2O3bA6ZdfwObNg8YPP1DWd98+oG9fKPX1kZ2WJpwvLWVdu3XDyJ9+gt6pU/RbKispK7hmDa1fc3Pq71xe3lLPCiKcDx8+FDp16sRsbW2FV69esUeM4WV6OgLCw+G3axfr7+uLzp6eTKSq7b98meZaKiU1SFAQneO77yjL2DQX25tcqRPi44WKZ89Y4549MA4Lg2jECMp2fvwxEbbPP6e537aNPBKys8mp3NgY3JgxzObYMd47MhIvfHxE42bM4DhTU2ob9/vvtH45jsa9Rw88DwzE1aIidqeqCjFOTvAYNgya48fT+nzwgAh6Xh4wbBgiIiPx6tUrDH3zXk5KovWRkEBkftq0NpnV+vp6PHr0CAYGBqiVSOA9bx4ku3ZRwOHTT2l8Wt3XjDHcvHlTiIuLYw0NDWzChAlc83Po7l3KGC9a1DarGhVFr8+f33zuxMRE4c6dO6yxsZEZVVUJn1y7Bt24OIa8PAoo2NrSPE+fThlxqZQCMs7OpNzIzkbnXr24Bw8eQMfQkC8RBOHOvXvstkgEZadOsBo6FKLgYLoP7t0js8AffmgJmhkaUrBx+XL6bd9+C8TE4HZZGev9119MoasrlD18yOQ3bvCpgsDktbV8z6+/5vDiBa3Z27cpGLVqFamQJkyA+O5d9Fi+nFkGBTH5zp0wmjkTEi8vmoOQEBr7Dh1aDASb4OjoyGJiYmBkZIQOHTvSOjA1peCoszOgMiB8EwcOoCwpCTFNzv7jxo3DgNBQjhs9mrLfX31Fz3YNjWb1wz+Bnp4eXF1dNTIyMjzlcvmXsbGxy2JjY7+KjIwM9/PzK/nXR1BDDTX+DmqyrYYaaqjxb8LX1zcrMjJye21trV1iYqKDnp6ehomJCTiOg56eHszNzWFoaIhu3boxjuPg5+cHT09P7N69GzzPQygsFDr99RcXq6ODTLEYoaGhzNfXl5mpWgABuHnzJry9vdu6Q1dVAYWFVKc8bx5tUhcupGzKzJm08R09mjIkKSlk6DR2LJCcTBnLwYMp6+PhQd9lrHmz3FRPrLxVXMxV6+mh+9277FZkJKLT03mPfv0YY4w2tHV1lAmzsKDN/cGDtLl8B3FGUhLJn4ODAUtLXGRMUVxRIaqsrMTLly+Ft2SwADIyMnDhwgU8fvwY/VavRqaTE56VlqK2thaGHTpALBJBuW8fWGAgduzYAQB48OABsioqBN1hw9jIxYvRPyoKHUeNwqPUVEgkEqFf//6seSMbFETy39hYqqm8cIHGbuVKkhTzPNXQ9+oFducObKqrEV9aivJz51ApCIKtnR1DTQ2ZktXV0SZ+yxYiWe3bIyczk7ffsYOL0teHSadO4Hke5eXlUCgU0FJloC9coHELDW2+7u7du7OsrCzU1NTAzMyMubi4tIwNz9N8x8SQvNbIiLJoz59TjeioUW3I6JsQi8UoKiriU1JSWGNjI9+5c2c6dteuUNU848MPqWZ7xQrauL/jeO3bt4e5lhaq+vVD78BA1ruigmlVV6Pjd9+1sDqeh3jIEPyRkyO4rFjBxGfOEJFYsQLF3bpha14enmhrs1m//grbPn2g7+RE6yM3l4IIPXqQodiyZSQvLioCRo6E0twcNVZWuHDhgnDt2jUmkUiYv78/PD09WWxsLACg0sAAIxhjmi4uRHZU6NqVxqm6mgIjBw7Q+g8MpGvt3r3ZAMtWVxc9u3fH4EWLmF5KinDe0ZFVT5mCLv37kwt+eTllgdeupXtp1Sq6tzQ0aBz374eejg7M3N2ZjpUVsxs+HDsMDNDx449h6OpKxFZbm36TgwPAGNpZWKBdu3ZITU2Fu7s77+zszJipKY2FoSFltocOhWL6dNwXBMHax0dwcnJqWR+HD1MQ4OlT+uwnn7QJwJSWlmL79u3Q09NThoaGcoMGDaISgMuX6Zni7U0mbK16eVdVVeH8+fNs8uTJ+PDDD1sM4wC69z08WkzDVBgxgtZq374AgNjYWP7mzZssVEeHDdy3D/GdO7PGujqmPXgwKtLTYTBnDgRBQPaiRdC2s4N43DiUlZVBLpdDaW+PVzEx0PX2xjaOg4Lj0L59e+Hly5esoaGBfWxlhceXLqHgzh04fPopWFAQ1bgPHUqqH09PCjqmpNC8X7tG90/37sCyZXD18UFZZiafzhjXta5OsImP51IcHJRTNm4UiR8+JJXH5s0UqLS3JxWNqysF/0aOBAoL0XD+PJILCtClTx8whYJIPkDKl169SHXyBmprayFfsQJ1hYVCkbs7a9euHdioUXSO92HYMDwyNUXnQ4cEpZOT4DFgANNWSfjd3Cijn5xMRN/a+u+P9QY0NTXh4eGh4eTkpNu7d2/t2tparaKiojw/P787//ggaqihxjuhrtlWQw011PgPICwsbLCWltYaAM5BQUFabUjSG1i1ahU4xrDAzg7FCQk4aWnJDx06lHNzc2vzOZ7nsXrVKnwREgLd1FRIDh+mjZ+TE20mf/iBNsoTJ7bZIAMgSWZ0NJFHI6Nm07Vm/Pwz1ZiqjMmysqjOsXdvQEsL+fn5kMlkSH3yRFl/+LDIMT0d3X75BZwqS7NhAxG03buJ9J8/T9Lm1sjPpz81NWTatGsXIJHgxx9/bJZmL1++HM/JyAk6OjoYM2YM2rVrh8OHDwsSiYQVFhbywsuXnNjSUqmtp8dqamqEmpoakbZcjil79+L09Oko09DAxIkT0blz5xZTttpaNPj7I9zSElnu7vyoMWOo125ryOVEktetI5JUW0tZurFjKYseFkbtvdasAQoKsKFrV8zYuhUNixejg7k5kcCMDNqA19dT9rFnTwBAYWEh4qOjYXPwIKrr6hAzZAhEGhqQyWRwcnISJk6c2LI+3qjTLCsrw/bt2zFkyBD4qMa7tJQ+5+1N8t0jRyi7rUJ4OAVXRo2i39XKlKk1UlJScOrUKQBoW49cXEzXffEiEbucHFoT78vML1tGc6tqwzZ4MG34AZICt2sHxMXh5tKlvJOdHdfhyRPULFuG2zzPx8fHc127dsWYMWMQP2yY4BUdzeR9+kA7JQV1fn7Y4uaGoKAg2NjYwOTwYeDhQ7BDh3B840bh5atXbOyZM0jw9kZqK1MoiUQCGxsbPj09nQMALy8vBEZGEtF5lzFZXR0Rr3XriKAOGEBzLhbTfbB6Na2N+/eBSZOQ+OWX6JKSgporV2AaGopcHx/UuLvDraoK3MCBFPS4eJH+XrWK5qJvX1r7iYnAyJGonzQJGhIJxEePUhazRw8KBsjlQP/+4Kursf74cdh27YqJEye+syuAMiQEspMnhSp7e5gtW8bYhAlEqHmeZPbdulFm+R3+B69evcKePXtgamrKf/7551yzUV9yMsngzcyAL75o/jzP81i1atXba6U1MjKoJ/bSpfQbXrygZw3HQRAE3PjrL95k9WrW2dub6QcGorqiApvT0lrWuyAAggBduRwfnjiB0+PHQ9K+Perr66FQKMBxHDiOg6SuDvViMTwSE2H50UdwOnoUJVu2wGrWLBTY2uKwvb2grK9nQ8eO5T09PVsuvqCAFAGLFlFgbc8eUhaoygAA7Nu3j3/9+jWbNGkSa+O+zvN0H/z0EwVmfH1pbaiwaxfwyy9I37ULF+7dE+afOMEwdSoFFgEqJ6isJKL+DlSPGIGb5ub8Aysrzs3SUvhg9mzGGhvfW3t94MABPi83l1tw9iz0Nm9+W26uwp07lNn+8UdgyZK3jN3+CRITE3Hjxo0/Fy9e/GFYWJgVAAsAz1esWFH5Xz6YGmr8fw412VZDDTXU+A8iLCzMS0ND41xAQIC5u7v7O3dNMpkM4itXwO3eTRv0NzaeCA8H4uKQExgIo+HD8cTDA0W2thjRoQNEX38NDSMjIod/h7Vrqf722DH6e+bMd3+O58mMqGNHIiYbNhAJOHECKpfpY8eO8ZKICIzq14+TVFWRiVFZGZGxHj2oNrOoiLKuANXY8jxtaM3MgOPH25wyJycHx44dg1wuR9euXfmUlBROtanmeR4GBgZCQ0ODMG3aNM64ogL4+GNwrWomeZ5HVlYWbEtKUHb1Kp717w8/P7+3+oPHx8UJVy9fZkt+/x2aCxe2bIIB2nR/+SVtpquqiGgeO0aEjIEmWwAAIABJREFU6w0ZZ1FRETIyMhAVFQWlUglDQ0N89dVXYNeu0XeLi2kMLl8mouXqCuXYsXjw4AFKkpOVngcPimIHD1am8bxIxhgCAwNb6rFHjaLMpsr5GEB2djYOHToEDw8PjBgxgpQBQ4bQ5y5fJtm4u/vbcykINO89ehBpVF1X6zGJj8fVq1cxbtw4dO/e/e21MHs2ESctLVJHXLjQYp7WGg8f0t+WlvT5rVtJEaDCtWvApk04ZGcn6A0ZwjyPHRPO2dszLXt73s7OjvPX1SUHcG1tyI4eBQCc+/BDpDXVt+rr6ytra2tFnXNyoFdcDHlIiJIxhidPnohcHj2CrVQK+5EjcSQ+Xqg1NWX19fX47LPP0L59eyQkJODatWtwZQx9jx+H8f374N4nq+V5mrNVq2jdurpS8MjHh4JZR44AISGQi8XYvW8fSk1NoaWtDalUypeWlnILN2+GBtBinpWSQkSX40hR0rUrSZk5Drt//ZWXNDRwXm5u6C6XE+lLTkbjo0doDAxE/ezZ0Kqqgt66dRDFxlIAoLaW7vURI5AZG4vYe/f4vvfvM5sVK5jmxIkU6Ll3jwIcjx5R8GPPnpZrUyiIjO/fjwgLC77g1CmuS1oadHbvhtuiRZTV/+gjIv0cR/JqJycia0ZGOJ6ZiczCQixZuvSd3QVw+zYFMxITKbM+fz5QXAw+MRFFs2cLF/38ENzYyHTmzGl2uBcEAefOneOfPn3Kzd+wAfmhoZCIxShRKgXtadNw7tw5ZmFhIYwcOZKlpaby/Tp04P5KTlb6fPaZyLCiAvFhYUL//HzGrV7dpgPDzp07UVJSAgMDA8HHx0fw8vLioFSSNP3nn0mxcfMmZYDXr4dy9GiUN7UevH79OgoKCpSff/55ixRAJqN1oalJQRcvL7rX7e1JWRQYCFhZISorCw3h4Xzg999zUBnNAfT8jIggkv8mYmNJGWBvj/LychzYvVvoLQiCz9Kl7/W72LZtm6Cnp8dCQ0NJVRQWRkGh963rX38lFcbIkbSuVb4X/wAxMTFCbGxsrkgkUgqCYKGnpyerqqqScBy3QyaTLV6xYgX/jw+mhhr/n0NNttVQQw01/osICwuzBjAEgBRAAoCnrSP+YWFho6ysrA5NmzbN8J0HqKwkieeWLbRR5jgy1pk7lzIR+flAQQGKlyzBb1u3wtzFRVAqlfzLly9FCoUCLi4uyjFjxogUCgWuXLmiZIwhKChIxBijzfnQoURwra2JQKrqHv8pLlwAdu2CcOkS6oOCcMPSEg86dsTEvn3hdOkSEblVq6jWcNIkCg7MmUNGPrW1VBO5dClJZd8TFNi/fz+fn5/fZmO5aNEilJSUID09HQMGDKDNfVIS1dOeO/f2QUpL6bxr1lDN5xuIi4vDtWvXYJWbi0FBQeikq0sb6FevKLN5/TpdK8cRSVBl3C5eJIf1K1d4Y2NjLiMjAyKRCBMmTEBhYSGiIyKwfPFiynRNmUJS1Z07aawXLwYcHBCuVCrr795lyqFDufLyct4/MpKzrK+H+OBBiFuT12vXiMy2Is+qDKRGYyNmFxdDunEjqQ/mz6f2Rf/A2RrbttFnb9ygoEgrk6Y1a9YI/v7+zNPT8+3vzZhBKomVK0m1EBDwtioCoOucO5dIh4pwAURkJBKa+wMHUBkejsg+fZDj66sMCgoSOVpZUXBn3z76jWvX0vdsbKBwc0NJWRn0nJygp6eHyspKPHr4EBXPn+NJcTF4vmV/P3fuXGROnMjbPXjARf38s9DXz49ZNMl1BUHA06dPce7cOXAKBRZGREBr3z6SDr8PHTrQelqyhNpNvWPd1tfXQyaTUT/pK1eQ8McffKO5Odd//Hi6v6qqaOyePSMSnJtLruC1tcCVK7gTECBcz8tjYrEYCxYsQHp6Om7evKksLy8XqfZiy5YuhUZdHRF+fX3KUmZkIN/NDTVhYdATiWDh7w/uyBFSEqjk7DIZKStevKBrSE2l9x0dUbpzJ4y6dcO2Tz5BiIcH9CMiwO3fT88IBwcioK9f0zEmT6bryM4GLl7Ei+xslEilcPf3BysvpyBEt25E3FqXGNTXEyldtQp8bS2iOY5nT5/Cfd8+zuBN1U3THBUVFcG4tBQSqZSI4cyZQMeOEORysAMHiNxGRgJr10LIyUF9eDgKra1x7vRpjLxwAVpXrsD6jcBYVVUVoqKikJSUhKVLl5I6IDSUAoGqZ+APPwCPH+O1UolDDg7Qd3BASUkJ5HI5Fi5cSN4YgkA1+OfPUwZ7504KZEycSL916FB6fkskiFi9Gn22boVuSgo9x1XIy6Pf/8knbS+e52ktrl7d3KavJCoKV377DRP37YPGO9aeXC5HbGwsEhIShCVLljC8eEH35r177yzdSU5Oho2NDcn+Fyyg60hIoIDDv/B2UJ0vJSUFBgYGsLGxAWMM1dXVOHr0aN3r16+/Wb58+S//8iBqqKEGADXZVkMNNdRoRlhYmCGAmhUrVijf9f7KlSuDNTQ0tgDQt7OzU+rq6mq8ePGioby8XEtDQyOroaHhFIBVHMctcHJy+n78+PFvO5odOPB/2HvPqKqu7nt47nMbvfcuYAGkKCJdUawotlgTNUVjiy0meUxiEoMaE02MSTR2o8beu2JBpClFEERAEWlSBUSudLjn/D8sqqJJfu+HdzxP7hzDoXLvPfecffbZ7LnmXGuRoiqT0UZsxQqyHn/yCVBcDKFnT9TV10Mmk0EkEqG6uhoqKioQi8W4dOmSkJSUxEQiETQ1NYWKigrGcRwkEgn+85//AAC4pCSykB86RMTxq6+IAHXRN/l1qKmpQXJyshAfH4/+58+javBgBDg4MLVWJfjaNdqwjhxJ35GWRqRp3jyylN65Q/bj17XyArBu3TrY2tpCX18ft27dgkKhwJw5c2BgYNB5s1leTupcx9zbjjh6lOyzAQGvvCSXy5GZmYmCggI8fPhQ+DQxkYmSktqLyWlptbsK9u8HNDUh9/FB9PffK5L19UXOrq7geV5hZ2cn6t27NwDgwr596Ld8OTBrFkzWrKHPR0fT57dvB9BiIV+8GMEJCRBlZxMh0tKi152ciIh1JLD79gGTJ3dqobRnyxao37yJifn54B4/JiXMzu6vHQ0v484dyk9v+XzkrVuIiYnBkiVLui6419hIQYeCApqXI0e29/huhSBQAau0NFINQ0LarapxcUQiPDwo4HLxIvjAQHCtub7u7uSKMDMjVXzPnvbCd3l5lMZw4QK9D4BQVwdoaGDt8uVolsnAcRzMzMwU48aNE23evBmShga4pqTAv6gIWufPd8qP3bx5MyoqKrBSTY3mUUv1/DY0NNA53LxJSn5+PhG82bMpkPBSYatOMDeHvKYG6aGh8PLyav95WdmrCmJdHbBzJxr8/ICPP8ZZExPke3igrq4OgYGBsLGxgWkLIe3KOp6QkIBr167B19dX8PDwYGoSCY1RfDyR+6++onzk336jQFdeHhG8xkY8ff4cW7cSL+I47tWe3J99RvPz11/pvjY1depYsCokBNYaGnh31ChyM6SkkJpcWkr2c2Njur6TJ4n8mZvjhViMo01N6D1pErwMDckp0mp119bubJPOyqLAjosLzYmDB6kbgJcX1Q0YPpzmXstneJ7HL59+ihGhoYhcuFCYt2DBKwPG8zw2bNjAm5mZwdXVlXOKjgaTy8lKDgDvvIOGzz/Hs3PnkHjrFmo1NVHs7S1oampi+vTp1HHhwAFy95w/T989ejTNj2fPUJ+cjOKAAGhaW8Pg+HEc2LED4qoqYco337BO9y8igtal+vrOBcvq6igA1lqQEEDzN98g/8ABqN++DeMuugC0Bo80NDSEZcuWtX/Jgwf0PR06OsjlcmzcuBFGRkbC/Pnz6b1Pn1Jw5cqV9lSk/wOuX7+uiI2NXf/VV199+X86gBJK/AuhbP2lhBJK/OsREhIyWCqV7hOJRCaCIOCHH34Ib2ho+GjlypWPWt+zZs2a6WpqatunTp2qZm5u3nFTLG1ubsaRI0d6ZWdnfwPgC6lUqhg0aFB7H6amJtrof/45kdGaGlKFOI5UzcBAIi86Orh+7Rp/69YtDgA8PT2FQYMGsVb75siRI9mTJ094uVzOKioqWL9+/SAWixEbG4sff/wRo5884a3s7TmNQ4fAWvqxSsPCXltVuiN4nkdWVhbi4+MVubm5Ih0dHd7X11fUb8kSsmdXV5MaKZFQsMDDgxSwkyeJlDQ1EYlUUXl9LmEL6uvr0djYiPHjx6O5uRlJSUlCTU0N27FjBwIDA9vylBljROILCmjj2xVGjiSF+SWiBQBaWlro168f+vbti1xnZ/ZEJhNs1q9n2LyZlNg9e9qv38oKD/bvx9n797H4wAHR4O++g0pwMACQDNTcDP7rr9FoYoKIgQPxQCzG9Oxs2NnZEWHrUBX68uXLvPrEiUx09CiDXE7ndfs25cP+/jv1sQ4Nbbdnr1hB7wkMpP9v3Yop33yDDfPn44i/P6b9+CPY8+f/nGgDpLpnZpJKam0N3tubH7B0KffayvZSKfD8OZ3r3btEvBITO1doVyho856VRSSvf39Sq8eMofkNkJWZMWDFCnAKBRXeMjAgV4G5OanhL8PamhS47t1JsfPwwN4jR4Snn37KBFVVgOfh6uqKMWPGiPbv3y+IRCLWJJPhnrc3AtTV21uirVkDqKujW7duqKioQOmMGTAuKqLc7D//pO86fJgKsm3ZQqTnzh0iQRoaRA6TkynY8PI4LVhApOvePdxLTeWjb95kPXr0YHqtKq+BAQVU9u2jsQcoiLJ4MWQKBYRZs2B8+rSgFhHBjDU14TBvHtT09bsk2Y2Njdi5cyeeP3+Ot956C52KoY0fTznhjo4UHAgOpoDa5MmApSXKR41C6cOHOB0eDsYYNDU14ezs3H5wQWgns15eFPBwc6OgSUvQCADc+vQR7t69y+RmZtDq1Yss562fl8uJ6E2cSPc6MRHYsQOaM2ZgnI4OLh06BMeUFGgtXEj3OzSUlHpXV5oPM2dS0K61gKKzMz3LIhEdqwtwHAcVe3v++JQp3EwdHQZLS1LhO1jcOY7Dhx9+yF2+fBmXLl3iE3JyOP9nz2Akl0NdXR01d+9ix/Hj4DU0eJMePZj/5cssQVUVk3/7jUEqpeJ558/TPWSMyPLCheTgkcmQmJ6O+KFDYVlRAW9fX2GSlRXb4OmJlJQUuHVsveXvD3h7ozQjA8eiovjAwEDO0cGB5kXr89GCZwsX4oiKCr58Q7s9QRDw4sULFhISgsmTJ8PBwYGKvZ040Ua2eZ7H4cOHBZlMhvLyclZQUAALCwtK51m0iNYfNzd6bv8P60lOTk61QqFICwkJYcoWYUoo8fegrEauhBJK/KuxatWqYRKJ5NykSZP0x40bx3l5eXESicT2yZMnM27cuLEzICCgDgBu3769esiQIb179uz5ysaY4ziIxWI+LS2NWVpacvPnz5doaGiQwhQTQ/nQc+bQ5njwYKoevWULbdxNTIgI3b0L/PwzcqysBJmODvP09ERcXJzw4MEDwdLSkt24cYN/8uQJy8zMZOPGjWMTJ05Ejx49YGZmhvv37/N2trbM9uef2RU1NZy5dw+RkZGIiopCmpYWIp8+RYNC0dytW7dXpOZnz54hOjqaP3nyJMvMzOSNjY1F06ZNg7+/P9cpqCCVEgkCaEPv6kqE5e5d2uyuXk35wV3ldb6EuLg4oby8nPfx8eEqKytx69YtBhC5lkgkiI2NFSIjI6Gtrc0Me/cmomdm1vXBZDJSE8vL24qTdcLu3YCJCeJv3ECPe/eY7LffiFBUVxOpKisDdHWx5+LFZvfdu7leGzfC8KuvIPbwIGJgZQVUVOBFbCwq1q9Hkq0t32PGDFZWXi7cuXOHFRYWCtKMDN4gOppDcDCys7MRFxeHDz74gInEYgo+zJxJYzd8OAVZWhRflJURQVq6lFTr9HSylfftC8HLC4OWLkWegYFwYexYlMlkrEePHl2Ssr+EigrAcXjSowdinj5lgYWFUDl5st2Z8DKsrEjJy8khkhEe3qY0A6AgxdKlNG9nzKD3KxQUeDl7lgjt+++Ton76NF3roUP0vpMnSU19HczNaRxGj0Zit26KxxUVXK+YGFirqQlvffEFc3Z2BmMM+vr6LDs7W9G7d2+urqFBuF5ZyXz69YPo+HEigqqq0LS2RmJiIu6npaFeXR0aR4+iTE0NcbduwW75crAFC2jOMEZukPBwsgkHBlIwyc2N7lGra0KhIBWW44D582Fpbc2qq6uF0NBQwdXVlRRRxihNxMPj1Z72HAfm6gqbqVPZvbt3hd4pKezwo0fgN2zAlaIiRaNEwll0CBidPn0aeXl5mDZtGnq87ExpaKCggokJFeLy86NUitpa4NAhXFi1SvBbupQ9nz8fjr16CU8KClhJSQlfVVXF9PT0oLZ1K1nVZ89uz7WfMYNIfMucKCgoQFhYGNzc3HgnJyeu09xrbKQ54ONDz/zVqxRMvHED/O7d+Hn0aFQaGCB74EDBY+5chnHjKMiko0NzasIEmvP+/pTnvmgR/VxXl+bNa+Y5z/O4cuUKEwQB9v7+MHJwaHdLdHCGqKiowNnZGb6+vkyQyfA8IoI/WljIkuLiBKPMTDitWsWCx4xhriNGsNOM8WaFhazbyZMMdXVUEG3XLiKoCgUQGIjaK1fw9OFDobiykqWWlkJwchJGL13KarZvZ9LiYmh+9hm7cuUK+vbtC2mrC4QxKPbswflHj4RaExPu3r17gnvPnkxSXEydGzpc4/PAQEFFVVWwHT26ywvft28fpFJpW2HJ9PR0qKqqQubjA9W5c1G0bh3Cb9zgryQn48WLF3jvvfdYRkYGr6enx8zNzekgcjkFMUaPpnvfVRrJX4DjOEleXt6o5ubmNbdv355+48aNfQEBAQ3/+EBKKPEvgpJsK6GEEv9afPfdd59KpdLtb7/9tqqtrS0YYxCLxbCysmLPnj1jz549c71x48aJgIAAPjw8vKeBgYG/ra1tl97ooqIilpWVhREjRjCjjAyykv70E5GNWbNISR0/nmyYb79NSpSGBm0s9fVJDcvIQJ6NDQIWLWK23bvDYMgQFhsfz5Lu3oVEImGZmZkwMDAQRowY0bYhk0gk8HZ0ZA7Hj0N68CCkzs5obGzkg4ODmYGBAcZ+/DESfH3xKC+P43keBgYGYIzh/v37OHv2LB8ZGckaGxv5YcOGcWPHjmUODg6QdVVMKjeX7NKtuYt2dkQ6mpuJWB06RAr3m3qBg/Je9+/fz4YPH86ZmppCXV0dXl5e8PDwQHx8PMrLy/HixQvW0NDASkpKBM+nTxns7bsu0tUKf3+yMnt702a+Iz76CFVpaYiysBCkU6eyU8nJzdFZWcLNxkauZu9e3vTTT9k5S0sht7RU5G9mBgNVVSJZDx60Ow6mTsUld3fFfX9/NueTTzhLS0t4eHiwqqoqZGRkMK3ycmZXVsaECRNw+PBh3sHBgevV0abZap01Nib1TqEgwnr4MOW85+bSNaSlkfI7ciRE3t5gWVkwWb2axd+7xwoKCuDh4dG+kf+HqK+vx67z5wWv0aPR3cKCIT+f8sS3biVl82Vyo61N59bQQHPYyak94FFTQ/blpCTKH28t0lRU1K52jxlD+eKt7YxGjSL76pvuYytMTaGYOBHHTpzgDDMyYJ2fDydXV6YzYkRbsEFLSwuenp7c1atXFRUVFRxjDH7Dh0M0dSpZZAcOhGZBASo8PfmioiL2pKgIzzgOfX75BQ95HsU//oi84mJYWlqC4zjk2ttDZfp0PM7NhVQqRXVNDSLEYnQbMABcdja5Q9auJWL43nsAKDhkb2/PCgsLhbt37/J9+/altcHfnwrZqau/SrhBz8CZhASW6uQEoakJdg8fQqW4mCu9exdqUin2X70qhIaGsrKyMtja2vKDBg3qfHN4no5//z4Fk3ie3CSbNoF/9108ksuRmpvL+h87Bsf+/WHt48O8J0yAfkAAS7t/nxe+/po9kMkElcWLmVZHN0hLITaMHIn7jx/j6NGjcHNzQ1BQENep+GBREVnP168nO/vFi0R0RSLA0RHn6+sVjdra7IMNG1hjr17MrmNFe8YoQNb6x8yM1sO1a9GwejVq1dUhi4ykSvBubrSOdpibjDFkZ2fzNTU1rKikRPCcN4/h+nV69v/zny5TV0zt7GB18iTrNW8ezMzMWE/GmMHEiW2vN/A8blRUMO8ePSAODaUA2MSJFBg6cABpY8fifEMDLNTUmMLbG6ZVVbzN6NGc9ejRaDpwAH9yHNwzM8FnZEDk6gojIyNUV1cjIiICetu2QSSVYtyWLSw8LIw5HzwIjbVrX8mzvn/8uGA+Zgyn10VtjebmZsTGxgoNDQ0MAAwMDMBxHF9cXMxHRkZy0dHR6LF3L5hCAbNJk9iECRNYfX09YmNjmaOjI6UoXL5MY3riBOWcR0e3r2//AKamppyfn5/E398fMTExagqFYm9AQEDlPzqIEkr8y6C0kSuhhBL/SqxevXquhoZGyAcffKCq3aGibSuCgoJU5HL54IKCgrCQkJDRAEqqqqoaAHTJJs1MTKBXUABrKyvabO/e3TlHVBBIObt0ifIdAdpkxsUREV+/HvjpJ3A3b7Lod99FUN++6H72LL44eBA1Z89CV1sbUdnZSE1NFQB03nzv2wckJ0NFRwe9dXXRu3dvDgBsLCwABwfM++QTxMbHIyYmBlFRUZBIJFBRUVE4OzuL3n//fUil0q4r5iQlUX62vT0FDvz8qEiXkREV0hoxgnKfV6yg65gyhVShjjmsL4HjODDGUFXV3kFGRUUFKioqWLJkCW7duiXExcUxJycnQSKR0AbawODNOYYcR0GLX3+lokXNzaSQXrgAzJoFzV9+AaZOxeWnTwFA3KNHD2HIkCGoqqrisubPR/O5c5gSHg7J6tXAo5bMgfJyIlUeHsCePchJS+MCAwPbxl0sFmP8+PFQKBR8Vng4x794Ae3YWNTU1GD48OFdn2er1fzmTSInyclEVvPyiKgcPkwWbmNjaiG1fz/UmppQX18PANDoqof5XyA+Ph7Jycm8mpoazM3N4eXlxYGxdofFnj0U+FEogJ4928mKSESvNTdTekDHZ0QuJ6X+xQtybSQnAzt3Up7r3r0UeNHSonkyaBC5Hv4OyW5BVlYWDh48CMvKSgRfuIC4PXug24EcdUTv3r1F0dHRYIxh3bp14HkeVlZW/PTYWE5SWoq3fvmFG6elhfz+/WG5cCEKXryAf0wMdrm6opHjEBUVBUEQMOj6deQDCH8pBeLO5ctYcPYsZJWVSPP1hW15OQw75GQzxqCrqwu5XN75xLZtI6t9a4X+DlBVVcWMGTOQkZHB+/j4cJJvv0ViYqLC6JtvRJqzZkE2ezazevoUdR4emDhx4qvscf9+sjmfOEH/d3QE3n8fvJMT7n/6qaD66BHelcsZt2YNvX7mDDhnZzj89BMcTp7kStTVhX3q6qzh8WNAWxtmZmaUKsIYIJEg6coVhObkwMbGBikpKaxHjx6wb+2lfekSqeGRkZRO4ur6SqDmkYYGFzBgANOTSBA4YQIF6BoaaO3ogKNHj/L5+fkcAEwKD8ed6mo8dHGBq5MTb+jsjNyrV4VRgweL0t3dhZghQ8AzBp7nWUNDA6dQKDBw4ED64iFDKPCgUNAz31UBQTU1GD1+DCNXVwpqdYC+vr5gIZUKD//8kxN8feFcVwdu4ULKif/xR1xLSVHIXF1F1rq6kE2ZAnh7c2hoAMaMgVH//pDExwv3rl9n/R48QGVjIzZs2CDU1dUxPT09wWjlStbD2poxxuBnaChINm9mP+/dy9t2784NGzYMampqOHnypFA6cCB796XzjoyMRHR0NGQyGZqampitrS2fnZ3NlZeXY8WKFZxYLIZcLsezZ89g9eWX6MEYw/r1gKcndHV1IRaLce/ePcHNzY2x8PD29J6gIEqbePy4y8KSf4UtW7YIFRUVDABbuXJl9j8+gBJK/MugLJCmhBJK/OsQEhKiJRaLi+bMmaNu+IZ2KDzP48KFCw1paWlPAUiCg4NNWgtltUEuJyJ96hRqFi2CUFxMpKjjBrSykgjrvXuU48jzlD/aitRUygtMTESMszPS09P5Dz/8kAPPkwLh4QHY2aHAxwdXHRyED6ZPZ2jtGR0aSnZoS8tONkoAZJUODSWVBsDhw4eRmZkJFRUVfPrppxC9XJW2sZHyUY8dozxfAwM67pgxbb1zAVDV5okTyQoaFkYFjQCyzJ87R9bSN1TM3rdvX7OFhYU4sDVH+SWEtLQcAwDG8zA0NMS8jz56s4W6spLOo6yMCPq+fRQs+PxzUtD09LBz504UFRVh5MiRQv/+/dsPVlEBzJ9PVai3bKGCU+vX03hKJGg4cAAb58wRli5dylRUVF756sbr13F//XrhvK8vCwgIwMCBA19/nh0RFgZMmkSk9PPPaX6EhNAmv2VsBEFATEwMHxYWxmlra/NLly59fdW5l1BWVoYdO3aA4zg0NjZi7ty5MOmq0JxCQfNz+nTKoe04L/bsIQKdk0MBFVNTUhwtLekeFxfT+LYqglIpKZ2VlUQ2/0ZaQUfU1tbizz//pErVenqCkYYGm5iWBtH27cCTJ6iTySCVStHQ0IDKykpoampi48aNAIDZs2ejvLwcZ86cgUgkgouLi6JPXZ3I4scfwaqqyJHh7U1W68WLkfP228gBEBUVhSkvXkAqk8FkxQowxsAYQ01NDdR69QKrrsbGpUvhHxWFZ2ZmQlVwMD9t2jSRWCxGVVUVNm/ejBkzZsDKyqr9QgShvfXW69qOvYQdO3YIxYWFzL64GO+kplLRRCsrciC0jmNqKs3vU6deed6frl4NtmkTdFauhCQggHLQra1JHRYEIrtyOepWrUKlhwcOv/MOqjU0IBaLIZFIeJFIBFdXVy725k28M2sWunXr1lacbVw3zJDQAAAgAElEQVS3boJjWBjDjz9ScGXOHAoevlQnAQCOHTumyMzMFDk4OAjjx49n3JYtNBc+/ZRqL3z4IQqLirBr1y4AgL29PUYOHAjNnBy8sLXFodOnFZqamszIyIhTy82FtKwM1nl50M7MRNHXX+NwTAwEjoOPjw8/ZMiQdnt7aChVHi8sfNWpceMGjV3v3hTI6tjdoK4OisOHEXHoEBL69cNbFy8KTFubWT9/jox338XZhgYsWbIEmn36UPBx/XpyKN26BQA4cOCAUFFRAV9fX6ZYuBCa9fXI++47PnDoUE46diyR2sxM8HfvolhLC2mPH/NPnjxBQUEBp6+vr6gvKhJ9sm4dWGNjp2dv27ZtQmlpKfPw8ODt7e05Ozs7fPfdd3B3d8eoUaNenUANDeRAOXUKcHHB5cuXER8Xh0WpqdD74Yf2NKDKyvbaAz///I/7cIeEhEAqlUIQhEdffvnl36+8qYQS/1IolW0llFDiXwepVLqxZ8+e3JuINkAqbHBwsMzBwcFSEAR0b92sAERkNTQo53PyZOC777AxPx+LBaFto1dfX487d+5A0dgIYcIEQaesjLlFRxN57QhnZwiPH0P4809oT5sGXioVWk6Aeu0CQH4+Hp0+DdMTJ5hi4ECI7tyhTdUPPxA57qraeE4ObSxbyPaUKVMQGhqKu3fv4sCBA0JAQACz1tWlAmTbt5Pa8dlnlMP5xRdoI/Qd8dNPdF7h4aQCz5rV/pqvLynKq1YRcXy58nELRCIRa25ufu24z5w5EzKZDGoA1B0dsf6TT3D69Gk4OzvD1NS0a4VXV5eIYn4+Kew7d9KG+IcfiLB8+y0GGRnBZsECrOc45rB6NTRramgT7uhI6tv27WSrvnKFNsgg8redMYzu1o29SrMJUnNzdA8KYqiqQlFRkQKtRdVeB0EgwjdjBn3vtGk0poJAhdQ2baJxtrAA8/WFn44OV2Zjw+dWVPxtos3zPI4dO6awtbVFr169WEFBgWBiYtL1eYlEFFypraW82fR0Ut8ZoyJi+flEYFJSiGz/+iuRmuBgCg7Mnk3uhtac/bNnKeDyD/LLX8jluJucDPGmTWg2MoKxQoG5u3czlpdH96e6GoKvL/b6+OCDP//ExmXLoNXcLDQAjGlqQhAENDQ0wNXVFc7OzsjNzcWLTz/l8quqhIglS4R3tLU5Nn063VtLS8DUFN3270e3PXswePBgukaZrD0NIioKKnfuAMePQ+jWDZ9bWQFbt+J5bCw7UVnJHT58mJ8xYwZ3/fp1hbm5ObOysup8bxij51xbm5T+v4GpU6eyjRs3IsvcHD9268bP9/PjNMaNI6vvvn2k3v7nP1RZvgPRFgQBmZmZONXcjAmBgYLhkCEMoaFk6b9zh+ZUUhKR5GPHoKKqimYnJ6FaVZV9qlCgXl8fVUFB3OHDh1G4fz8+P3oU4hZV3KNfPxgqFHi0Zg1EDQ2CUXU101VVpRzrLog2AIwePVpUWFiIq1ev4vfff1e8/fbbotTUVKgdPgzHFStw18IC2TExAqRSBpBLRM/CApg/H3oeHlj4zTevztPGRuDYMRSXlWHxr7/itrc3bvM8N2jQoPb+3yNG0DzNyQF+/JGe5VYYGRHhV1WledsKngc2b0ZtRgbyLC0x28MDlU1N7LaTE3/j9m2ux4ULmOjhIWhKJAw7dlDgoraWApFyOaClhalTp7JLly7h0qVLcJs6VfDkeebo6sqhro4KDS5aBEREgJs1C+bp6TC3s+MA4Pnz53jw4IEoPzcXu7/5RpgtErU9MLW1tSgrK2M2NjZ8UFAQBwANDQ3gOA5dObEA0PzNyqLfTZs3Y+TChcg/cwa1cXHQsbBA2wTV0aFznzqV5oaHxxs7R7TOsaysLMTFxfEAODc3NyQnJ3MhISEWK1euLHjjh5VQ4l8OJdlWQgkl/lUICQnxlMlk04KCglT/+t1kE+1EspubKZ+3Xz8qypOc3F5gqIMqIZfLcfjwYehdv46eJSVCwRdfIOLsWZiWl8O4pWDNrl27+NLSUk6hUEAQBKgEBMAqPh4BBQUcZs3qrAqKxfAfPx6bCgqEVBcXNqaoCL0OHCBLYHMzFXiaP79zzrSREeVTt4DjOAQFBcG4uRkVBw6w5Bs3BL1Hj5imtzcRpRUr6DNvQkICETCAPnPwIOUht8LGhja6ly6R+vX774BEgqdPn6K+vh4KhQIVFRUi3S5yWVvRrVs3+kdlJTB2LIYOH46IiAghNTWVAUDv3r2F4OBg9kr+8rvvkvI0ahTlW+rp0eb6zh3AwAD2EgmQlASP4mLsr6tD8PDhsATIXqmrS5v106eBRYsgxMYitbAQiXK5oG5uzvdet06E+Hga55eRlwfNuDgs+v13bNmyRVRSUgJjY+Oulfjycgp+fP01zaPffyeSvXAhBUZ69KAgTk0NEaTaWihCQ6FRWcm9fe8eWYdXrSKF0NaWLPulpTQfm5pQxvM4HhGBKrlc0NPTY+PHj+dUVFTQp6vicR0hkRAxXL2aFOzMTAq87N5NSn/fvrRB19entIcJEyiIERRE77l9m1IL/vOfV1tfdURWFj0neno0f06ehOKbb9C0fz/ClyzBuwkJKPbxwfD168Hc3GhsWgJLzMQEY+fMQa2qKpplMsz65RcGxvDLxx/jkz/+gKx/f8DICFxqKmz79gWCg1lZeTmuZ2ezX7S0hAnnzjHrhgayPm/b1u4oWLuWVORu3Ui1BCjFQxCAgoL2nI0FC6C7YAHGDRzInpaXs3VFRUJzc7NobBcujubmZmTNmYOYpCRheEEBs3gNMe0ILS0tvPvuu9i3bx9qGxu5BokEGrdvE0n8+WeqYL1/PzkRAOTn5yMiIoLPzs7mxGIxLGxtm3uGhIgxYgSprhUV1Ad91iy6lqtXAYkEV69caX48fjwnk8uh/uwZUxeLoW9ggKVyOarWrIF45cr2k5o0CTalpdA4fZrt2blTGDd4MFL8/GCyfLnQ6+V0lhaoqamhe/fu6NatG9uzZw/7/fffYWRkxCsUCtz49FNOuHsXi379laWOH48kDw+Fv78/LZwbNhBRbG5+1REhlQLTp8MmPx+Hp02DrKEB81JTIQ4OpkCEnh59hjEar9xcuubWZ9DOjpT+0lL6eStiYoDERGh+9RXGjBiBeC0tfuSvv3L2AFc5fjwa5XIYr1/PMGoU5bPv308BvG3baK156y2IxWJ4enoiPT1d6BUUxNCjBz0zp04B169TYKpbNyq61mHN0tHRgZeXF/rV1SH00CHW1NSE2tpaHDx4kOc4juN5HgMGDOjEghUKBUpKSt48ke7epYBY794YbmGBP2bNwtcdHTmMkcvBwoLWnKNH26vMd4FTp04pUlNTW3+5cUFBQejXrx+kUqllbGxs1vfff3+wsbFxpZJ0K6FE11CSbSWUUOJ/CiEhIZ4qKirfMsb4urq6EwAOrly5srHltX4SieTquHHjVLuyA78WrZu2ceNow9aqGr1UDIwxhr179zZLpVKutLSUE4vFGODiwjs4OHA9AwORlJSEu3I51LOz4e/sjMrKSm78+PGwtbWFVCptb7E1fTrDnTtECjooWGKxGG+99Ra7cOGC8GLhQlbU1CSY7drFEBlJBXBmzCDCOX06WXyzs9sUWuTl0Wa7rg7uR4+C9/PDcU1NtsncHIOCgwU3Nzem+rINvSNiYqiP75EjNBaNjURqX7bVA0TIpkwB5HJUDxuGc9OnKx4VFIjU1dV5xhgEQcArSmBXaGoCli1Df1dX9O/fn/E8j/j4eFy5coWJRCJh6NChTF1dnci/oSEp2xoaFADp0aN9o92xp7WzM4Y6O6Po4UOkr10LvX79oF5fT/d19Gjg+HFUW1sje/ZswSQxkck//5yf9eGHIkybRsrRvXvUE7gjxGJATw96enowNzdv3r59uxgAFi9ejE5BhchIGq++fdvPads2Coj89htteHv1IlfBjz+2BXEU06fj1vffg1u6FMYODridmgpbT08YGxhQwCclBdDRQd3y5ZA8eQKDoUPxdl4e0+rfn3Fbt9J7xo8n0mpmRrn3tbVENl8mNQYGdF8rK9uLe+3YQfMpNJQIREUFKe+//kqE286OLNqurvT+VjLz7bfUez0ujgI5aWl0jT4+VM9AXx/gecQOGYJINTUMHDgQNitXwqb1XObNaz8vLS0gMBAmS5eiTCpFv6QkIfncOURfu8ZkDQ3ga2tpDowaReM8cSLA8zDU18fXmzfjwpo1LPbePYX1tGkibNhA1759O/UQv3QJ+PJLGu+PP6YgR1paexDtJWjt3Yvay5cxw8aGXUlIEAoLC1lreklBQQHi4+MVqampIrFYDKeHD9mLjz8WcPz4X8r8lZWVSEhIaKvJoK+vTy9UV5MFevt2ZIrFMLCyQry3N+54esLM0pKbM2cOjI2NwXEc3cydOymlIj2d1o8zZ6jyvESC5uZmJCYmij08PCiI2Jqze/Mm1K9ehfq6dRQIeviQFOBvvwVsbWGgqoo5Y8eyB2fPIsrcHAFlZazXX/RqFovFmD17NsfzPEQiEScIAsLDwxEVFYWtX3yBqVOmwHvdOhGOHqX1pZWk7t9PtRe6gJWVFeZv3YqffvpJCNXUxEw1NYaKCnIAffklBUm8vGg9PH+e2pslJtI4JCZS9fbWQn8XL1JF95YUnz/eeQfM0JBzyM2FjY0NPbu6usAvv9CaMnAgpft8+y2tFydOACNHQnHkCC6WlAj2RkborlDQ/F+1is7lwgVKT7l0iQJSXY1Taipss7Nx/vx5pKenQ1dXl5mYmAg2NjZCxw4SUqkUGhoaSEtLg7OzM+zs7NpV/Y7w96dgmYMDJH36gHNyQss9aH9PUREFJOfNo98XU6a8Vt1uJdpBQUHo06dP23cGBgZKfXx8cOPGjXdSUlLe/uGHH840NDS8v3Llyvo3TgwllPiXQUm2lVBCif8ZhISE2IvF4huDBw9Wk0qlSEpKGlhSUrJuzZo1lwBoSySSYRMmTFD7q01iJxw4QGToxg0iQMbGROK6UKoEQYCXl5f42bNnvFgsxvDiYt5y7lwORkaQAVixfDme7NyJdJEIV69eRWNjI54/f45OxF9Dg9TVQ4doI7hvH5GSFlhZWWHe4MEsWV8f++/eZe8/fQqjAQNIQWlqIlu7XE4b7rVraeMXGkr5kiUllAu8aBE4xjCJ5xEbG4vY2Fj+2rVrorfeegsODg7gutp03b1LBLGVwEZGUu7u66zCUikKxoxBaHo6Jq5dK6rZvBnmI0f+bRt029iHhlKQAKTMe3l5QV9fH5cuXeJ///130bJlyyCePp2CCa6ulBN/9iwpey39ujuhoAAoL0fjw4fwSk6GjOdp09mC5g8/xEVfXzwLDhbeP3SILTl6VISJE+me1NbSpj48vHObMSsrausF4P333xfL5XJs3boVO3fuxJIlS6i6+7VrpPTfuEEq3JAhpL7l5bUfp6KCFKfsbBrvlhQCqVQKIyMj4fbduyz6zp22txs1N8PNzQ3eEyfi+fPn2DJ1Knx9fYXJPj4MRUWkgBYX08ZbIqFjXrlC1/Hnn0Q6nj2jObJoEY2DhQUR1vx8IioKBfXOHjSICGxoKF1vXV076XZ3p2Jy165RDnpBARHsc+eI5Lm7t7f7Skhov96jRyEIAhIeP4aqsTECWltsvYSa9HTIpk3DxpAQGBsb8+UpKVxweDgrfvgQdYMHA4whKzqa+ki7u9Pc7NuXyNXly+DkcgSvXo04f39RfUICVCIi6H6WlAAODkQ8wsKIcMTHEzHT0nrttJR16wbrBQuAzz/HtOPH2S9SqWBnZ8dMTEywe/duWFtbc5MnT6Y+31u3ouDwYfbbb78JY8aMYTavKUgll8uRlJSE9PR0BgBerUUGy8uBEyfQ3FIj4fB330Hr3Xcx0tsbg/buhez+fbIDd3xmLS0pqNHqMGhoAJYvB5yd8ejAAcz5+WdIv/sOWoaG5GaoqqJaBa3BlTVrqF3c22+3B9NWrYJ2aio8Y2NReu6ccO/ePcHf3/8vn2fGWBvJS0pKQlRUFACgtrkZ9x89guWGDRQUyMujYOb69e2F316D1NRU1NTUsJyaGtweNoz3dnDgcOAArUWTJ9P8PnGC+r/PnNneK3vUKCLhc+ZQCszmzTQvr18HJkyABWPIzMzE6ePH8bGfHwUcmpooTWfMGBqf0lIK1HAccPs2miMi8OzrryEdNIgNAcC8vOi56N6d1qPWuhSWlq+/oMWLcbamBrLcXAwfPpz38PBoHddOiytjDHPmzMHevXuFU6dOMZ7noampqZg9e7ZIrWPgV6EAduwA7+uL53Fx8Js7VxB1sKgDoPPasoUCh5s20b8XLgQAVFVV4cGDBzAyMsKzZ88AAO+//37nmgQtUFVVxahRo2SBgYHYtWtXcENDwyAAl994A5VQ4l8GJdlWQgkl/mcgFovneXh4SDw8PAAArq6u6oWFheqXLl16t7KyUpg9ezbT09P7+wcUBFK6ANo8nTlDxOXnnwEARUVFCA8PhyAIEAQBPM/DxcUFKioqHAQB6NGDQ79+RDgACIWF0K6vR0paGrS0tPjm5mYuMjISFhYWnTcyjNFG99EjqvZdU0MEBwByc8GNHg2XkycRlp/Pb9u2jfv444+hqaZG5MLdnYhDfj59VhCIfLq4vEKMOSoyBB8fH9Gff/7ZfOLECbGKiorg7OzMPD09SVmrr6cN7LZtnXtd79vXdZ54C0pKSrD/wAE4Dhum0Jk5U6SzYQMRmK4I8OswdSowdOgrP+7evTusrKw41W3bIBw+TJvF4cMpGGBjQ/csJaVz3nBhIZ3/rFmAgQF6zp2LzaamMDpyRKGiogJra2tOX19feD5pEqvV0sL8+fNpwzthAhHWpiYa38xM2nQ/fkyKLkBEISKiLRdfS0sLy5cvx7p164QLISEYFxnJRBcvUr7znj2kHH7yyavXa2RE5Dcigo45ciSRQhUVTJw4kUVHR/OmpqachYUFbty4gZycHFy9ehU9e/bEtm3bBG1tbfj5+TGIRO3Kua1t+5h3zGGdPJnmRmkpkWNjYyKo1dXk2ggLozHbu5dsuc7ONHYtm29s20bEKCuLjm9kRGO0bRu1vAKI3LeipV1WV6iqqoKurm6boltZWYnz588jJycHYrEYjDE0LV8OVVLMOS0HB8H6m29Y6FdfYe7+/bji5wcnJycilT//TCq6ri7de8YAQUBTQgLu7NuHZxkZCBIEIpinT1P+6t275Fj49VfK4f27+OEHyJYvx1vr1uHJRx/h8ujRUFdX59977702Emr+8ccwnj0b9Xv3soMHD8LX11cwMDBgTk5OyM/Px+3bt4Xnz58LpaWlnFQqhbm5uTBp0iSmoaGBpOho4OuvBX13d7a3vp4q/QPQcnYGc3OD7M8/KaBx8yaRpdbgSEYGuVxcXChwU1ND8/jyZdjfvo3bH3wgJOfmItjPj1mfOkW5vL/91n7t48ZR0GXcOPp/QwPNi5YaDJaWliw3N/eV6rqxsbFwcXHBvXv30K9fv06qa0snBAEA09fXV4jFYnh5eYmgpUVKdE0NEcCAAAo0vv8+BXI6BBpboaurC319fUVFRYWoqKiIHvDWntF79lBAKSWFyOPKlTTv16yh+x0VRfO+df4bGdGcKS6G2unTGPfwIcTTpgnCsmWMtXZfGDeO1v5Hj8gJIpWSldzXF/Xbt2PrnDlYvnw5BU2//JLOIz293Q7/5ZcU7HodAgLgoq2NQYcPQ01N7Y0BDE1NTSxatIgJgoC4uDhcuXJFlJKSAm9v7/Y37doFHD6MskOHcGL3bnyWmcng5ta5zZeZGQUi0tJI2f/5Z2DUKJy9d0+RnJwsAtDWClNXV1dhZWX15joUAF68eMEByAKAVatWTZdIJEMbGxuXrFy58vlffVYJJf6XoSTbSiihxP8MJBLJIFtb2067GnNzc7i4uODKlSssLCwMTU1NcHNzg6OjI+rr61FZWQme52HekkfdhqYmyke9cIFU4fJy2vgZGdGGtkcPZP3nP3haUwOn3r0Fxhisra0hk8lo8/fkCRGzDgSXe/EC2itWYPnixQDAhYSEoKGhAadPnxZUVVWZubm5MGrUKPoAY2RXPHaMSMC+fUS4ZTJg0yaIXVzwmUzGnVu5UsCJE6ytL+1nn1Hucb9+wPffk0pnZNQ5f7ELzJw5UxwSEgJfX1+EhYUhISEB7u7uwmhzc4bmZiJjHfHjj69WP2/BgQMH+JycHK5///7C8OHDaZO2eTMV0vLzo3P8O8Wzjh3rnA/eAb169mTX+/UTVKOimMe0aVCLiWkLamDiRCpMdPIkEeDGRtpQRkWRMssYBoBcAnl5eaLs7Gw+MjKSGRgYCIMSE5HcuzcqKioo2NBqT09Kos3pzZuknC5YQIqoWExj29DQ+QR5HvMGDGBXN25EZXo6DKTS9txniaTrzXf//mRZLSoiYi6TERm2soKhoSHGjx/fthGfOXMmNmzYgOrqamzatAkcx7E5c+a8WmH+TWCMbLWtFco75Pe3Ea333qO5Z2pK4wiQvXrXLlLHW4n84cP0918UHXwZjS3H1NHRUQAQNzQ0YPfu3WhqaoKJiQlcXFygLpfD+YcfwMLD2868ubkZKi4uQkRFBXNMT8exjz9WjD17VqQ6YwbqV6yAvF8/5Jqb82E+PkxVVRVVVVUMBgaYtX49BBUVyqevr6diWomJpGqLxXQf162j1mh+fn89hLq66D5kCLNMSkJceTlMra1fIUvis2cxcO1a6F29itjYWCEiIoKdO3euZSg10bNnT660tBSNjY0oLCxkv/zyCxjPo3d2Nszq6tjeloKAPXv2xMOHD1FQUIAjR45AW1tbWLp0KUNjIym2dXVUt0FFhe5lSgqdQEUFzd2LFyHR1saAP/5g3MCB/KEzZ1hzczPep+AVvVcQ0HzkCMQtSnlzdDTEH3xAx2p53luqwHci23/88Qf/5MkT7sqVKwCAmJgYvoWocUVFRUJjYyOTSCQMAKZOnSoyaG0Fx/M0DxMSaH5JJDSXGhooCHTtGv0sI6MtqGCRlISFCQmimw0NijoPD4Zx4xiOHCHimJlJqn5KCq1/1tYUFLK3p3HJzaW1y9ubvlehoH/37g2XwYPxwt0dV+vrcW7MGHz++ec0T2JjKdDxzjv0fiMjOt+hQ6GRlASdykocO3aMnzFjRntl9I5r4/Tpbd0neJ5HXFwcGGNQVVVFXV0dxG5uyG9uxhvTeF6ed4zh4cOHANC5HkNSErUYHD8e2lpa0NDQ4BtWr+ZUVVTAZs7sfJDKSnLpLFwILFyItCNHkNzYKDI1NcWcOXPA83yry+kvF5Xc3FyIRKKUlStXPlq1alWwmpradgMDA3F+fv4DAN//7QtTQon/QYi+/fbb/7/PQQkllFDi/zNCQkJEgiBsHDp0qPjlwlkaGhpQVVVFTU2NQi6Xs7i4OBYfH89HRUWxlJQU3LlzB1KpFJYdrX7nzlF+cn4+qRh795Ji6udH5Kq+HiV9+sBhyxbBOyKC2X33HbPW0mJMVZWUlcGDybrZkVQmJ5N11ccHAODq6gp3d3dERUWx6upqNDU1CU5OTkzSkYg5OZHK1NREm72wMFLlPvoI0NdHQUYGTAcNYio//ECFeGxsaEM5fDiRRE1N+r7kZLJRvgERERGYPn06q6qqUqioqHCWGzeyQkGAxY4dnW2q+flko16xokvSfO7cOWZvb49x48a1v6iuTtbpjAxSifz9/7ot1Jo1lEPcqlq14vZtGA4bBs/+/VnDBx/giFgM9V69YGho2F6UTFeXyP1vv1ERrEWLSOXtcL46OjqwsbFBnz59mFgsRlZWFht99izLdXJCMWOsZ8dq7Kam7TnEH35IRcFaq6JXVxNx69hCZ+ZMqGzciMylS6EZG4v7Zma81dixjL0mD7gNy5ZRUGDcOFL2U1KIPMyY8cp41dfXK1r7FAuCgOjo6L/fduyvkJNDwQ6ep3mcmEj3zdOTzqukhMbVyYkI9kv1C/4OBEHAiRMnhNraWixYsECUnJws7N69m2lrayuWLl3KeXh4wNLSEsYaGmDXrnUq4sRxHNzd3VnU06eKZDMzbsjly1xjXR1O+vryYbdvQ6StLegGBHByLS2UlZUxAwMDYcaMGezQoUN8VVUVVFRUmNaxY0RM1q2judFa5Tk8nFwLjNG8eU2P71Y809HBn01NvHtYGEZGRjLRe+91bp/m7AzMmQNjKyu4u7uzvn37wsbGBsHBwfDy8mJ2dnYwMjJCU1MTPD09MSggAMLOnbB78AAXxo6FtY0Nqqqq8Pbbb2Po0KHw8fFBTU0N8vLyWM61a0La06eC85YtjA0YQHnFOjpEsM+cISuztjYR2aAgmk/r18Nq2jTmP2kSkpKSBJlMxrp164asrCxELlsGg/nzsQkQ4qKjhaenTrEiS0ue8/JiOjo64HkeZ86cUfTu3Zvp6+uzEydOIDQ0lJfL5dzkyZPh5OQEPz8/5OTkCE1NTZyamprC3d2dGzRoUNv6O6yqiuH2bQoOqqvT+qRQEKmdMIFSFl68oGf3ww8pqNOzJ60FvXoBvXpBkZ2NJltbLiovj/kmJpLr4uRJso9Pn07jYGZGa8eyZRSwnD6dnBg1NfTdpqa0Ts+bB0ybBt1+/WDs5wfvgAAWHh4OHx8fiIqKaC355BOa4xIJpYH4+lIw7MEDGD57hsi6Oubk5AT1VldHRzBGqRijR2PPnj18VlYWKioqhJycHKGsrEwQi8V8//fe4/T/QS96AEhLS0NTUxP8/f3pB3I5rfGOjkCLs8DFxYVtVihgERQE3dOnKdgHoLq6GrEZGXxWURF/LjWVRT1+zHrv2wcVJydh8rJlrLX93d9FSy0ArRs3boSqqKhsGzNmjJmTk5MoLS3NNyYmZnxYWFhNREREWkBAgLLfsBL/OiiVbSWUUOK/HiEhITLG2BcKhUIlLy8P3bt3R0fCqoQiSe0AACAASURBVKOjgwGU/yoCgLy8PFRUVHAGBgawsrJCTk4Ojh49imfPnmH06NFkN8zMpJxhY2PalFVWtluHRSLg88+h9+gRQocNg9PIkWQZNDWljXqPHqQ0vZz7LBJ1yvVuLZ4VFBSkuHTpkqiyspLbsGEDvl68mMixpSWpMDo6ZJWuqaEcymnTiPhbWOBOfT2c+/VrJwoAEaLVq9uJ5dat7ZbnvwGO46AlFsO5thaHXrxA+h9/NH/wwQftvy+oiBvAcUhNTUVYWBg/ZcoUztTUFBkZGWCMdU36DA1pY/vbb6Q4X7rUmZS8jDNnXiXkzc10fQsWANeuoefSpYjNzxdOnz7NeJ6Hq6srmIcHjdGvv5I6BnQeny6gqamJ2tpayFNTIYSG8oIgvHpi6uq04Z4yhe71+PFkjV+2jPp7A2TnLSggUpCcjPGTJiHf0xPR+/dzN1evxqJFi/DGVIaVK2l8W+HhQdfb1ETzqcO8Hjx4sMjPzw+7du0SysrKmH4Xltt/jLAwmm+//EJKqZ8fVVIeM4aKpCUlEfHYv5/mpI8P3ZPffqMAzz9Q1k+cOMFnZmZyH330EUpKSnDx4kXWp08fDBkyRNSpboCxcee+yB3g5uYmunr5MvSfP8d1b28EJidzOt7e0N60ibFr1+BcUcFuDhiAhIQEtnv3bohEIq7wwAF0i4qCIj4eoqoqur6sLCpMN28ePS/OzlTQqvU8WgMeQUGvnENOTg7q6+vhefgwk166RLb7Z8/aC+kxRgGwlp7LWlpa0HopJ9zR0RGOjo4AAD4uDnaPH+PUhAkAAHV1dXz11VdtrgUxx2FUYCCMExPh/ssvbO1nn7HL27YhqPXcJk2igmonT9L3HjhA6iVjVAF+zx6grg5s7Vr4DhnC7ty5o6irqxMlJCTAREMDL+bOxbSxY5nx9OlM+OknXK6sFA4ePAgtLS2FhYWFqKamRvTixQv8/PPP0NPTEzw8PDBgwIBOtR7mzp1L/9mzh066rg4T331XtO6zz1CblAS11vmckdFepG3IEPrbyoqeMXNzUpEfPqQ5YGPT9t61tbXgW56TxshISCUSqpK/ZAkdo0X1RVMTkfXSUnJwFBS0F5DsIkUFILeFIAg4evQoP/ybbzjd4GBIfvsNALVzjLO3h2zkSP6Ru7vAq6rCJC1N5DV/Pm9oaNi1BXzjRiA1FRlLlqCkpISbP38+dHV1aXGWy4nMt9Y0+Jto6TMvmJub03F4nu713r20ZrRAQ0MD+vr6zVcPHBDN3bKFsTFjAENDpKWlIaqkhFu2dSu0z51DWXU19MzNESSVsk7V2v8mTExMMGbMGK2TJ0/era+vh7a2NkxNTfHJJ5+oPHr0yP3mzZvbq6qqPl27dm0cY4w1NjZ+u3LlyuJ//EVKKPFfCKWyrYQSSvzXISQkhEVFRS2Ki4v7PSIiYr0gCCG6urr97e3tpXFxcfzNmzeZgYEBjIyM0Nzc/ErBLx0dHZiamrb1K9XV1YW9vT2uXbuGB8nJcPv+ezBnZ1KHWqr4ojXPs0MbMIlEgsj4eDagtUDR4sX0Z98+qoQ7bx7lwtnakgq6fj1t+Nzc6AA8D2RkwLy8nOutrQ2j7duhIZfDbs8ecDdvkkKxbBkpb2PHkuK5fz8dz9kZmD8fsfHxgnOfPkxdXR15eXmQLFiA2qoqiGbObLcUm5jQRszDA5g797X5gxERERgwYADqd+4UDOPjOcsbN9Dd0xPh4eFcdXU1ampqUFJSAklSEuoGDkRofDxiYmKgpqbGoqOj8fjxYz4+Pp4FBwd3bpfWEWIxETQHB7qmwEBSirqCnh5tjFuJSXU1qVuXLlGF32XLAA0N9O7dm2VdvCj0Xb6c5QQEwNjFhSopq6gQ+R09mv79Bpw7dw4vXrxA4Dvv4JaeHuczZgy6bE/GGKnyOjpE5j09SXVTVSUlbfx4Uul27aKAAABtbW306tUL2dnZioiICE4kEsHIyKhTQKgNOjq08bazI4IhkVBawI4dwNKlZA/uMJ9FIhEMDQ1ZSkpKW3Xif0y6a2vJuqutTap1v35Ezo4do9zTI0eIRKurkxK4eDGRTgsLusbCQlIlv/+eggwdq7+/AXl5eYJIJGKenp74448/hG7durEJEya8Oi6PH9PYdtG33dLSEo/T0hTmZWXM79QppmtvD5WNG8H696dq07GxsFq2DE+ePBFGDB3KBjg4wOHcOWjfuwdpSAhYq1U8PJzIV1AQEb3Zs+k63nqLXo+OpnmblkYq6nvvoaCgAOHh4XxsbCxzdnaGg4sLg4sLBdqmTydLd6vLxtCQ5q+Pz5sDEteugX3xBS7PmQNTW1s8f/4cpaWlGDRoUPt7vL3B3b4Ni02bIPr0U0TExKCoqAgRERFtY2rs7EyKvIoKcPUqam/eBMdxiO/VC+dqa+G2bx9w4QLO6OigqqGBKygowIQJExAEQG/uXOhUVUFcXAzJggVwcHTkvLy8UFxcjNTUVCYIAiorK+Hs7Izp06ezbt26UVhv505am06dIsK/aBE9pxoaQGAgIjkOOQBUR4yA1ezZdC0d84hbceIEpWqMG0dKrY0N+H79wDiuzT2Sm5uL58+fw8/PT9G9e/dXSW5TE1m9ly8nF8b16xQ4WreOnqvoaFLPHRxe+ShjDFVVVRCePmWxRkbov349wsLDcerUKSEyMpLl5OQg6PFjZuruzskGD+ZMT51CVlkZu5aTw0dGRsLR0bFzZ4fhw8G/8w4OXrrEOzk58S4uLu3nK5NRTvdfuXw64NSpU4pTp05xdXV1bNiwYfS8f/UVVdf/8stX3EaOjo7c1Vu3mPDJJyjKyxPK//wT4cXFzNPXFz2ys2E2aBC6Dx4M1b596RkWieh3zz+EkZERXFxcEB8fDz8/P6ioqIDjOBgaGqJv375SPT09EzMzs35isbjP8+fP1f39/S/+4y9RQon/QijJthJKKPFfh6ioqJna2tq/BAcHWw8aNEhl6NChnJeXl9TBwQG+vr5MJpMhPDxc0NPTY1u3bkVCQgLS0tJ4CwsLpq6u3qU9TkNDA052dtD87jskzZmD7qWltBlrVVvU1EgtHjOmbQMtkUgQGRkJb29vIrYyGW3Qp0yhTaYgUM7rqFFkw92+nUhyUhIdy9CQWnWVlOCJi4tQkp7O1GfMgHlUFJiVFdgXXxCZsbIixXPBAlJuBg2ijayNDeymToWQkMD+ePpUSE1IgGpKCuIaG3ExLY1lZ2crampqBCsrKw56emQlHTjwtfnSkZGRGDBgABQbNrBKU1NmPmYMZDIZzM3NkZCQwOfl5Qn5+fmC3dq17GZFBV4YGysmT57MBQYG4vbt25DL5czT0xM+LTb514IxIk99+5JN1Nf31VxfQWhvWcVxpLICpHQ1NtK4icXA5s3goqLg/sknLDcigo9RU4P9uHFM1cCA3qupSZ99QzXg9PR0JCUlYdiwYSiMj4f+jBlw61hwqCtwHAUufHzonp48ScGYQ4fI4v/SGKurq8PT05MrLS0VEhISWGJiIp49e8YXFhby1tbWXGtLNJ7n8WLbNly/f1+4nJeH58+fC+Xl5Uyjf3+oaGgQOWCsE2HT0tJCZGQknj59itTUVKSkpKB///5/bQN9+JDs4ps2EUGcOZOIfk4OcP8+3Z/PPqP3xce3q4Zvv01BD4mEzmXoUHJzhIVRYbdz5yiw8xeW+aamJpaSksJLpVJkZWXhvffeY13mnGto0HPYRTVklJejZ2Iit83MjGloaQmm7u6MffABBQrCwoDvvwdTKODs48N03d2hsm0bGm/exM8iEZoUCt7W1pYxxuieffQRBabs7Oj5bFGWAVDAw8wM9ZWVeJCayu/LyWGus2ahUiIRPN99l3l7e7cPdu/eRMhTUijYNmIEzRdPT0pJGTCg62fwwQMKqqxfD9cRI+Do6Ag7OzskJSXBUlsbej170noyYQKp7BwHiMVwc3ODlpYW5HI5ioqKWEZGBiIiIhAREYEyuRw3ZDI+TCxmtefOwfXIEdy1sIBRTAzODxgAq4wMDNXRwaDPP4elSEQBsJISuo/r17edp0gkgqOjI/Pz88MgX1/4VVai59ixtEbt2EEFxyZOJELbty8FFLt3p7ni4YHs3FxcuHcPmlpaePz4sVBYWAh7e3vWZeuqq1dp7rRU44+9exfHeR61Ghq8bVgYg4EBpObmSE9PR35+Pufj49MeWKyvJ1fS228T2f7pp/Ye8Vu3tre0GzeO8sELCtpb1rWAMYZez5+jx6xZyJo0SZFbXMwnJiZyTU1NLDg4GMOGDYPeggX/j73vDovq3L5e75lC700BkSoKSFXEhlhiN7FrNPZertGoid4k10ua0Zho1BiNGo2JGo0xFhQ7RZqACtgAKdKl9zbMOef7YzMUBWPu9/ue3833zHoen3szzJw55T1n9tpr7b1h6O0Na6kUpn5+cLl2DUYrV7KSkhJER0eLycnJLC4ujk9PT+f4khJYDBiApGHDMH3OHK5d8vfSJUqgqrqWvwYiIyNRXV3NACAlJUUc7OjIYGZGa66DLvoymQwVFRXi06dPBfMrV0SH06e5pEGDxNmzZzN4e1OPCFXJjLMzlYcsWvR6fTVegIaGBu7fvy/IZDLY2Ni0bIDjOJibm6Nbt2549uxZ0/Pnz0P8/f1v/uUvUEONvyHUNnI11FDjbweO43r36tVLozP1tE+fPrh69SpLTEyEiYmJMGLECC4pKYkdPnwYPM9j8uTJoqur60uRhNF336FOT0+8+uwZKzI0VM7/4IPWZ6SNDRHCe/dI2aT9gEwmE6urq5mJkRGpYocPk/0RoGAlNZX+/4QJVOv84Ydkie3dm4LA69eBsjI8e/BAGH7zpmS7tzfCR4+GRffumFxaioqqKjjk5JDNddUq4OuvyU7ePCbpt9WrRUVFBRuiVDLvTz8Fq6xEP6USBbW1CA4OlsTHxwsDBw6kwPyDD0gB6dKlZcxLu+MvLQV7+208WrNGLKupYb7Nr9vb22PVqlWtEaK7O2b6+QEaGi3MaOPGjXjw4AEuX74Md3d3WLzYUK0j9O1Lxz9qFBG9ZctaA7yyMkoMqIJoW1si4Ldu0eiejz6i42lspM/o68P1yhXu9v79wuHDh9n69euJbPr6UvLj+PEWwv38+XNERUWJkyZNYllZWTh//rw4atQolJWVieVOTlx9UREGi+Kfk1WJhNRgS0sK2n19Sb1MTGw/GqwNZsyYwQDgypUrKCkp4VJTU4W4uDiYmZnxNTU1kvLycsiHDBFHOzqybj17Ii4uTnjy5IkQwxhbvXo1k65ZQ52zIyJatslxHDZs2ABtbW2kpKTgjz/+wKeffgo7OzvxnXfeYe2Ce1Eklc/Hh5I2a9YQqZJISJFPS6PzundvyxrDkSNEwBctov8+dIhsyidOtG7X1ZW2e+kSJYYGDqR13rNnh64Cnueho6ODuro6Ljg4GC4uLnix10KbN5PduKOGZSEh0PnlF0w/eRJnz55l5ubm1Oxr9WoiewcOEMn65hsiXvr60NXTg72DgxgfH8+6d++OHg4OVBagqrcdOpTO7/797WZ919TU4FhsrNjQty8zMzaGOH8+Rv7jHxwuXCCbeUJC634ZGZHyn5hIa1lXl5JDR44QUba3b38cT54Qyfnkk1biA8Dy118x78wZ/ASgx7hxGKupCYOuXdt91NDQUDVVALm5uThz5gzq6+vBcRyePXuGuro6bvXq1dDU1ISmXI7FcXFQhoVhes+e0L5xA+zxY1J89fTIybB+PfD++7RxhYL2zcMDmDkT0v79SbWeP5+eZ2PH0vMIoDXSsuOtkwsaGhqgr68PmUwm8jzPxo8fz+Lj44Vdu3aB4zhRQ0NDHDx4sMS7uZ4Y/ftTMgfA06dPcf36dQyaMAHRkZGcQ3w8Gs6cEc+OH8+0DQxQV1cHnudpPy9fpiRLnz70nHRzo8SErS0llP7xD9pnY2NKDi1ZQmtCoaB+F6r7RKkEevZE+f79yMvIkDSVlMDDwwOJiYm4cOECOI7DkiVL0GXpUiL3V65A6uMD14oK9FiwgEVFRYkAIJFIJFlZWUJMeDjnrKeHOfPnv5xcqKxs7fD/mpg5cyYXExOD2NhY+NnZ0Xo9fbr1d6cDNPfPkABAUWEh1qSkMKSm0rX77beWSQro3RsIDKT1sGnTX9ovgBrA8TzP3bx5E+7u7i+VSwBAcnJyk1KpvPuXN66GGn9TMPE/qM1QQw011PjfRGBgYA+ZTJawbNkyrc4ss6dOneJLS0sxZMgQiaura8vrsbGxuHnzJmxtbZUKhYLV1dUxPz8/0Ss/XwItLfAeHihfvx41UVGwVRFlFfLyyB5661YLKdyxY4fQv39/rigtjff5+muu6eRJ5qD6PlGkoFVHhwLowkIiI0eOUE3sjh2kJOnoACdOIP3pU5iYmSE3N1c4f/48p1QqAQBb5s6lQEpbmwLB77+nYH7mzNZ9i4+nfXv8GKK9PRonTULGvHm4deWKuHrTplbWqBqn07bzdDPOTpmCiTo6uD57tlBcXMy909xBtx1++40s8seOdXjeL1y4wOfl5WHFihWvX7xbWkrBnaoJmZYWcOoU1auHhpLiV15OCqulJdnCfX1JEXqhW7lSqcTWrVvh7u7Ojxw5UqKlpUXH7OHRUqMZGxuL4OBgWFlZCYWFhdzQoUNFDw8Ptnv3bqz79lt8P2cOYGmJNWvW/Hl379RUIiknThCxsrCgGd+RkWT7VzUv6gSCIODZs2eIjIxEXl4eFi1aBBO5HFyPHnTcRkYQBAHffvutYGFhwYb6+rKuhYV0HiwtyU3xAhQKBRISEhAcHIxu3boJCxcu5FBRQaUEt29TXefJk1SrrLJsFxYS8fb3J+KhssGWl5OTQy5vfe/du7QGOnPGpafTe957j7pA//gjYGcHQRRx48YNPjk5mVVVVXEymUzU09MTdHV1JW+++SYMO7IUA5TIcHMDKl6YIFRRQUpeM0nauXMnb2Jiwk2YMIEZGRmRo+GXXyg54+ND90gbXL9+HY8fPxZXzp/PZD/9RMff9ju/+YaIsZcXeJ7HZ599BgDYvHlz+8RAURHVd7/1Fl3/gweJhHIc/QsOJgIbGkpui4qK9vbp/HxaPzY2pBADlJz7978BQUBdQgK+KSwEz/NYvnz56yWy2kDsKHEkCEQUx4yhfgMWFrR+TU3pPhsxggjr3buUIHz+nBIXvXq1KM6viyNHjiA7OxtyuVw0MDDAkiVLGMdxSE9PhyiKqKqqwvXr19G1a1doa2vD9ehRdJs1CwZz5uDixYu4d+8eFixYgNu3b/NpaWkSJgiY/csvqDIzw4VRozA2JUV0i4tjdV98AS0bG2j3709JuJgYOjZPT7o2SUl0LTIziXgDRHS/+IISI6tW0Rp/6y1KBH70EUpKSvD06VP0798f27dvF+vr65lUKsXw4cPh5+FB9x/HkXU+Pp621RGuXSPHxF/ondERGhsbceTIERQWFoIJAv61cCH9HnXwPH8lRo6ka7ljByXHPv20NbGZn0/r/vDh1nr610Bubi6OHz8uNjQ0MG9vb37cuHGSF0u4AKieTbmbN29+xfBxNdT4/wdqG7kaaqjxt0NAQEBpWFhYeUpKylBvb29ZR4TIzc2N69u3L2dubt7udSsrK3Ach4KCAi4vL4+rqalhdoBgvHUrd7apSUisqRGsBg/mgkQRYQ8eiCYmJqxlTI2eHgWfUimCU1Nx+vRpCILASmJj4VBUxPKWLWPB16/D1NQUZmVlYFpapAZWVFCTHFUTtcOHKSAPCCD1oLku1NjEBJqamjA3N2cymQzp6ekwrKsTvRcuZPWLFkHevz8phcnJpC6qZhffvUtKzfr1AGO4aGGBCyUl4C9cwKz9+9lRCwvx4a1bYszDh0KcTCbEV1eLko8+wm+VlfydO3eEmKgoQf8f/2AJrq6sz759yMrKwuPHj5mZmRnMXrR3375Nx9FJcyEzMzMuPDyc8/8rAbm2NqnYV66QmjhpEgWlY8eSBX/7dgomCwuJAKxeTaS8A9LBcRwcHR1x+/ZtMSQkhJNKpaLeyJGMmz8fiVpaiExN5WNjY7lBgwbh0aNHTEdHB35+fuzixYuCpqam2G/pUlZqZoZn2dlwdnaG3qus0JmZpGD370/7Pnw4kbTTp0nh9vQkxb6khNTdDmq0GWMwMjJCdnY2tLW1+X79+nFMU5OO39ER0NAAYwy2trYsLCyMZeTmir5TpjC88w7Vtrbp0K2CRCKBlZUVtLS0UBUXJ7h7eXEtdtF//5tqiU1NW4Pr8HBSuCoqaA21JZKzZhFJnDSp9TVLS5o57Ozc/r0qGBtTIO/kRDPDU1KAoCAkAwhNSmIjRozgRo8ejeHDh7O+fftyHh4eNKO4M+jrd6yyvfEGqenN3cK1tLS4xMREGBsbs64pKZRgCQggQtFB53w7OzvcuXNHrE5PFxyDg7l251Jfn5To/fuBkSPxKDUVGRkZ4qZNm9hLNeU6OnRfArQWBg2iTvrLlxOBNzWl5JiHB9nxBw6k9zs7k735s8/onA4YQKRn9Ggiiu7uQN++kPn4wNvbG/Hx8WCMwdLSsuN6/47Q1ARWVUUJkKoqun8jIohsHzxIpL9fv9YyiLo6+t/CQrp+AQFExhgjxfg16/EBICUlBT/++KNYU1MDnueZIAhs7dq1TCaTgeM4mJiYwNTUFFZWVnByckJ9fb1YX18vSKOiuNtNTUiurERZWZlQU1PDevfujSFDhnCGhoZCcmoqK+3aFX3q6tA7MlKsBNilgADcUSrFiKdPWUREBMxWr0Z9VBTKV6+GIWO0tkeNovv0+nVa17Ro6HqdOUMzyd3c6NjffhswNIS2tja6deuGuro6REdHM57noa+vj3HjxkGmpUXn7Z13yJGQmkqOhY46kru7U7L2xc72Hh5UrqQaofcKPHjwACdOnEB5eTm6deuGfqdPQyc6Glpb/4PJWu+8Q4mW8+fp/qyupmMH6HeuRw9KTqia/L0GfvzxR16hUHALFy5Enz59uM6cQc+ePUNaWlpNaGgowsLCygICAkr/+gGoocbfB2qyrYYaavwtERISEi+K4gh9fX3bri/YKv8M165dE/Ly8hhjDK4ODkLD1atcvI0NU3p7M+mVK1y/rCzctrZGXV0ds7e3R8v2GcOjoiI8/uMPMUkqZT179hTeeOMN5h4TA7d795jTF1+gurpaTDp1inkvXw4uK4vGgKmUol27qBY2IoLUTx0dasQ0duxLVtukpCQhPz+f8aLIsqytcS0rC06ff44fBYGPUypFYdgwrltBAQV3kyaR/Xf0aADAw9RU6JiYCGM3bmSJvr4wsbFho1euZOZWVpzxkCFcd01NrtepU8xw+XLOzsWF6yWXczY3bjCbbdtgZG4OqVTKMjIy8OzZM97X17e9NFFd3eEYKoAUtMLCQqSkpAgDBw78awV/EgnZIc3NiaCkppIiuncvqenm5jTvfMWKP92Uvr4++vfvz5mbmyMoKIjdvXcP9QqFYH7zJvL79mUzZsxgrq6usLOza2ksJZVKhaVLl0q4jRvRY9MmZObkiA8ePEDfvn1Zh0GjQkFEZd48IpdpadTA7MQJsgy7ulKzIgMDCtzPnycnQn7+S3WVSqUSV65cEZ2cnJitrS19WVMTJWWaG2Pp6ekhOzsbGhoacHNzY2zaNEpQ3LtH5Q1tSa9SCaSkoPrGDXju3s1p+fsTiVPNkm57PJcvk210+PDWOuC2sLEhgqAac6bC+vVUjtB2PFpbMEZ/mzuXCMndu2DdukHz8mXms3QptDpTsTtCSQkRomXL2r8+cyat+WYVvkuXLkhOSoLL6dPMyN2drtG771Li4pNP6Bo03yMAkJOTg8TERFgoFKJjUhL3kjpob0/EMzwcwZWVkMvl6Nu376vXtY0NJVUCAojMmJgQ2V66lBTwGTPouo4eTfb4oCBSRMeNI5Kzdy85C8aObWcLlsvlEEURd8PDkX77tujt5sYQGUnPEqUSLXOmT54kxwfP03X7978pKfXbb+BLS3H3+HGgsRGZcjkMdXUh9fIiEjp0KNnFLS2JAOrq0vkLDaUE17FjRFiLiiih1NhIx/aKUovCwkIkJSUxnueZjY2N6OfnJ9jY2HTYsVtXVxcODg7Mzc2NswoJgWTKFNQyJlhaWrKAgABm32y7r0tLYwZHjoDjeQjV1aKemxtzTUqC79mzGBQQwNx79kTflBTUjxqFiN69xcjQUCaLiBCzJk1iWlpa0Bk0iM512wZmMhkpvceO0T28a1e7/gCCIGDPnj1CfX09A8gW7+joSE4Mc3N6dg8aRPdSWdlLNeAA6NouXUrnrC28vAA/v44bxbVBRUUFjh49CmdnZyxbtgxuvXoh58QJ3PXz490DAjrugv4qqGaMDx9OSZSUFHIMqWBqSkkga+uOeyW0wd27d3H8+HGhpqZGMn/+fFi3mbjREUxMTKCrq6unr68fUFZWtiwiIuLNmzdvhgQEBJT/5eNQQ42/AdQ122qoocbfFTqCIHTrsMHOn6B///7ctWvX+KqqKonl3r1cTysrSHbvxs5duzDNxAQSqRRDhw4VQ0JCREdHRy48PByMMVRXV+NhdjbGJCUxmakpdDw8mK1cTsEZANy+jQkffcT+WLhQfNqtG+spimDa2qS+5eS0vo/nKUju04eCrLQ0Cqy7dGnZx3HjxnElRUVK/927pdzBgzh66RKKjIxQVVUlESQSFBQV8Th9WoIbN8jC2gaiKEJXV1fU19fHAFUtXn4+bBmD7cGDRDyKi+EaGkrdukeMAO7ehU12NlBSgu6CgEnFxbiiry9i2zZSaRcvJotrQgJZJX/7jQLHefOAK1dQXFYmplVUMGltLXx9fCiI09QkRTo0lNTr5vnkGDaMaqhra2m7UVFEWEtLSXFzcSFbOUAB37NnHdql/wwuLi4YOHAgHxkZKUnt21fs7enJTbOwgLTZqWBjY4P5yiMmQQAAIABJREFU8+cjJCQEQ4cOJctjWBggCJgzZw7btm0bTp06JY4fP57pvkg2Z86kfTp5kpSwxkYivEeOkA21S5fWuu2rV4k8JyZSYF1dTRbnZtJ99OhRXk9Pj7VLbPA8kaZ//7uF0Li7u+PcuXPs008/xeLFiyHT0IDJ5s2oc3VFxNSpgolUyrzd3Zl01izU5OfjwowZeOP332HcUa1zWRklLtatowRBRw3kFiwg10VHDe927243xq5TyGREYsLDYbxzJyzz85E3bBikX3wBK1XjsD+DXE5KmwqiSOUDBw+Sktzmdd+ffhK7x8WxQxoaGL11KzgAhnV1gJ0dWH09NAQBHMfh3r17uHLlCvz8/MQAc3NJp6RxyhSUvPsuNOrr0XX+fCaKIvLz86Gvr4/MzEwkJyfzEydOlLxUb85xrfusqhP+5z8pwebuThZ+1Yzy6mo6liVLaL0fPEj3wOHDdO/NmAH88gv8m5rgKggoys1lNXZ20Dl7FszCgpIaOjp0DXv3psSVvT3d14aGtH5WrEBDbS0u19W17hMAVlgIu/JywbC0FCOnT+c01q2jpERUFDURGzKE1m5CAt2fDQ2U4FF1zO/ZkxJKlZVE2O3sAFNTRD98iJiYGAEA16VLF2RnZzMLC4vXIoXS58/hNWAAvAwMWt+flwf8/DPs8/NhMWIEnvn64k5ODgvOycEHX34JzUOHgHv3YCSTASUlMDp1Cj1kMlZ28CBk//wnO2htLYSGhXErfHygu2YNfg8M5CsrK9myZctIgZVISO2Ni6PExdSpLSq+IAhoaGjgJBIJ1YcDrR3/TU3pcxs30rNs40ZKML2I5nGR+P339q+npdHvQCcQRRFxcXEIDg6GqakpP2XKFAnS08GNHYuIyZOxbtOm1y/X6WifCgqoVOTzz+m+Ut0HWlp0PEVF9CzqpJxGZfM3MDDgJk2aBKtX1I2roKWlhX7UVV5jzJgxuHPnTt/w8PCQwMBAjy1btqgJtxr/30GtbKuhhhp/OwQGBjrK5fKIpqYmh+TkZOTn54uurq4dK5AdwNzcHP379+cKLlzgqziO8zxwABoGBnielsanlZRwjdOmie7u7iwsLIzFxMSgoaFBKCkpEQRBEDR1dcV+XbtyQwYPhv3o0Qw9elAwWlpKatDOnehpbs4StLQEvqIC2b17sy6ffALWdowUYxRAl5WRvVwQiIT16kXW4WYkh4cLHrdvc+afforS8nLeJDycs1u1Snz67BnTtLXlZObmMJs1CzkzZyI0MlJ8+PAha2pqQnFxMeRyuejs7NwarMpkpGqqZlRLpfRd8fFEMOfPp7pWmYysp8ePI83GRvDQ1uagpUW1x42NpDi7u5P6PGwY4OKCOltbnMrJYc5vv40uI0bA8c03mbRPHwrUe/Wiuj9vbyICzs5EQnr0oH1xcSFrvasrBexNTaTUHzlC+1hfT8qchcVfGo+jgr29PdevXz9UVlfjWVgYkx0+jMeurjAxNYVcLgdjDPb29q01rWvXApqa4CQSSCQSxMXFsefPn4seHh7tF5ezM6mPRkak+P3wQ2uHdC8vUokXLWoNUiUSIuBr11Ig27MnUF6OG01NSEtJ4eYtWMDaWda7diUlt7a2xfVgYWGBIUOGIDc3lw8NDeXuJyQgwtYWaRKJ6BIUxOx37WI3k5Nx08cHN9zcYG1nh17u7i/XQmdmkqIqCMDs2R2PXhNFugZTppB69yJSUqiRWgc29pfAGGBrCzZsGLRTUpBkZiYYf/stKz5yRPy5spKlZ2ejR48e6DRxJpeT8tjW/VFcTI26VJ/ZvRsYMQJ/TJ3KQt94A2UGBrh//z7u3buHqKgoRFVU4G5REXDypFhua8suXLiAKVOmwNfXl7EnT+h4VA3h2kJDA9HJyaLOo0fMys8P4YmJ4rVr11h0dDTS0tJQVlbGJSQkiJGRkczc3LzjGera2nQORowg1frcOUrSZGTQ2lmzhghsTg5di4oKSmSYmdE91K8fWebnz4d09mwcra3F7bIyhFlZQW/2bFj6+hJhc3Skz1la0hrT0mqnPFdVVSEhIQGbN2+GlZUV0tPTYWxsDB0dHaZz8SKrrq0VrSZPZnBxoW00NgLffUc12nZ2tP2ePcnyvGwZkXFHR1rX5eVEVNPSgD/+gGTnTnTJyWETBgyAn4MDChIT8TQ/n0XExYmeXl6s02Z4SiX98/NrbTCZmEjX2sMDCAyEPCAA5UoloqKiAAADhg2DzNGRnqVr19J92dygTcvLCxrr1qH/oEEsPT1duB0fz3RLSlDevz97/vw569atGzNUjfEzMaHRV0lJVAdtYQGYm4PjOPj7+8Pf3x/Z2dl8VVUV9+jRI7Ffv370m9PURK6bd9+le6uoqNWSrUJgIHD/PiW3VKitpXO7eXOnSaerV6+KoaGhbNSoUZg8eTK9KToaCg0NhGtr4+nTp4Krq+vLpQ2vC8aoxGLnTnqW+fm1/s3Jie5xTc1OR4HJ5XI0l25gzJgxL43Z/DNwHIdu3bqx6upqrdLS0kGDBg06+p8diBpq/PdCrWyroYYafwsEBgYyAD5yuXyNVCqdNnToUDljjH/8+LGksLAQ3377rTB+/HjOwcEBSqWy887GKiQnY3psrKRq2zZImwPkt4uLJc/Pnxfjhg9nhw8fFjmOY3369BHGjBnTPoKoraXAb+hQUoCmTiV78NWrQN++YOvXw83IiIuMjERqcLCYlZUljhs3jmsJiBgjhUNPj7ZlbY3iw4dx4sYNuO3ciXhfXzRpaMCkqEgasn073pLJMGXKFAl27UKDkxO7HBKCgoICnJdI0F0qxaVvvoHYu7cok8lw7tw5ZmBgAMc2pB0ghUQURQqGdHVJrejRg4jUoUMU2D9/3vL+NFtbVEdHU9MgFTw96Xj/+c/Wuj4AT4uLUWtnx/eaNKlV/mjbCKgjG2LbxjvJyUTmd+wgxfzjj2n/6uroPI0aRaRw9+7WhkR/AVpaWhg/fjyHceOQ/PnnYvoff7DbkZHo06cPRo0a1fpGQSBi1KxeDRw4ELGxsXzXrl1bj6uigpIS0dGtJNTMrHUsFkCB9qFDlFD5/ff2+6uri9LSUoR+8gmynz0Tev3wA7cxNBSSjz6i72/73nXrSPX85Zd2x/POO+9IwPNAVRXEf/0L7Nw5BgsL1K9dizIbGxRnZcHExASiKOLEiRNYuXJlK+E+fJiIRVDQq+2hQUE0L74ze6u1NSn6fwXm5tA8cgTDrl3jGhUKlDDGhly5ggrG8HNWFpb8858df66oiPa1sZHWxJYt5HzgOLI6nzsHZGSAHzwYhc32YI7j8M4777Q0n+N5HvZlZaJrUBDb07z2amtrBQBcnVQKuY1NhwFRSUkJYpRK5imToWDHDlRMmyasWLFCYmBgAKlUCqVSicTERJaamorjx49jxYoVeLFPBGpriYjevk1W7aIiIjK1tXQ9cnPJ6VJWRv0JBg8mxb4DR4FMFOHv7y9ev36dAUBQUBB8fHxe6/QbGxuD4zikpqbCxcUF76s6jgMoPX8eoZWVrGtuLtmAhw6l5FdICJHBjRtfXi9yOZFwOzu6J5pxLz4eN3V0MEhPT9A2NeXw/Dmm8zxq9u/H4549mbShgdaVXE7E3dKSFGKOIwV71y5qUJaQQI34xo+n8ps2z/TS0tYy3++++07YKJFwLQmCGTOoweKTJ7RWUlIAALNnz+bS0tLQ09oaPpMns8PHjiErKwu23buTi+TTT2mDy5fTffvzz5Rs8vVt+a65c+dKRFHEF198wY4dOyYMHTqUs7GxoWfY8+f0rNq3r33zSoDU8mY3gQoKmQyS6upOmzDm5eXh3r17bPr06eilmge+bh0waBA0v/oK0x4/xs2bN7ldu3Zh9OjRaOnm/rrYsoUaOc6ZQ84I1XjFtggMJCeUat78C7Czs4O1tTWfl5cn4Xm+84TZK8AYw4gRI+R37979k5mRaqjx94Ra2VZDDTX+6xEYGKijoaERrKmp+ZGfn5/XpEmT5Pb29sza2prz8vKCl5cXUyqVuHr1Krtz547w8OFDvm/fvp0zsro6IDUVnJsbtMaMaX29Z0+k2NmxqEePYCMIgpOnJ1fz8KHgIopcNoD8zz8H09CAloYG2P79FISWlJDKc+4cBZw7dwJmZtDT04OLiwvc3d3ZzZs3xZs3b7KkpCQxLi5OjI2NFWOTk0Vx61Y0ffkljiqVYlJRkcBJpWxaWhrr7+yM/itXwnPePFQOHw4bT0/aP3NzcC4ueJKaign79qFreTlSvbwQMH06hs2ezfLz84Wqqiro6uqy1NRUThRFIT8/HwqFAhcuXBCvXr3KXF1dkblhg8iSk5n28eNkldy7l1SqNsjPz0dOTo7YYm0uKCAle+NGUoB27ULtwIGIjItDWFgY3NzcmJOT01+r087NJeLv60tW8SNHSNW2sqL9ycqirsETJ1IAHhlJRP8f//iPZsCCMZhyHPO6cgWaM2aIoRERjOd5qOpBIQhEMNrU9TLGuNDQUDDGRBsbG1aQno66wkKI48dDEASIokjBsodH+yZcXbuSynXrFhAQgIaGBoSEhODMmTNCVFQUq2lsxMTp01mfxYshmTSptbbXza3VfuzkRNtpWxddWUn/+vQBamvBVq2ic7J0KQotLKDz8cfosWwZJk6bBk9PT2RlZQl37tyBRk0NM9q/H1I/P7Ir29l1fp6qqkjlnTevc7JtZESESCLpuCHUq+DgAOmIEdA3NESXuDjoSKXIr6+H2/PnlNB5kXxoaRHh6tqVFODvvqN7judJcWu2qXNz5mBIQADs7e1x//59PH78GO7u7pg6dSqys7N503794HbgAIu8fh2CRIKnT58ypVKJ9O3bURIdjSO5ubCxsYGRkRHy8/Nx4MABREZG0tzl2bPhX1AAn/79OR13d0gkEjDGWprRubu7o6CgQAgLC2NyuRyG2tqQFxS0jvPy9KQE0+efE5H98UciO1VVpGj/+iutnR07SFFljO63pUspodCcqFMoFDh27BgDqM5ZoVDAysoKnU1laIva2lpERkZi/PjxL5EiTV1dPDY0REhsLLp37w4DAwNSNEeMIGV91Ch6BjT3EOgMhw4d4uPv3uWa5HJM2biRydzdAXd3cGPH4my3buJDTU2mMDWFg7c3cOcOqdaJiXQ+9uyhJM/9+3SvL15M7hBf35fWRGhoqFBeXs4kjGHOgwdMf8ECWteurrSvHEfPjOXLW2qoJRIJzMzMwBYtAiZMwJOCAkiVSsEpKorhu+/alyq4uBC5/+EHSqo1JxoqKytV9cnQ0dHhoqKi4OjoSI0UbWyodtvcnO6Jts0ls7IAd3dE+PsLkdHRLCYmRszevp01bduG4zU14pMnT8Ta2lrGcRz09fXR0NCAM2fOiBoaGq3J3poaSgi8+y5gaAgzMzP4+vpCFEVcu3YNFRUVePz4Maqrq6GnpweNzkpvLlyg83viBO3jkiV0Db7/nsh12znwqn4lZ8506PxQKBS4ceMGx/M8hg4d+ufjEjtBdXU14uPja/39/f+Dbm9qqPHfDbWyrYYaavzXQy6X77O3tx84ZcoUjY5UAA0NDQQEBDATExMxMzMTSUlJUqVS2XGWvbSULM3Pn5Oa9K9/Eck7fhwYMQI+c+fCZ9Ei4M03Jcl79qAuOJhrOnECx2fMwOITJ1B68SJ+HzwYS/Pz0TwwmYLRjRtf7jQLwMDAAO+++y5XXl6OvLw8BqAlGuFcXJC2d69obWXFOfXoAUNDQ0g3bQIaGiDz9cW9N94Q6q2tW5MGS5eCi4vD22+/jYsJCeLc1asZ3N2pWVVjI/RNTBjP86yoqAhWVlYIDw/nAEBTUxMNDQ0MAPbs2YPJ4eGs3NBQMGWMQ9++FJAWFbWzC4uiCEEQWmdDdulCHZJ1dSngPnsWD3fsEO6YmHA9evTAmDFj2kVZSqUS2dnZ4Hke7eahC0KrRfmf/6TrcOQIqdaMtY7ymjmzVUlzdSVlcP58stPeu0d1kefPt7PdvxYGDQL++APWSiWTy+VobGxs/Zso0tpoAz8/P8THxyM0NJTVbtsGjjE8GjZMqPnmG04ikUAmk2HChAlw0dIiUqSyOnMc2fUPHkTpgQPYX1ICMzMzfuDAgZLMzEzk5ORAR0cHnFTaSqavXaPjX7eO7P23b9OooidPKCly9CjVy8fEkHKnSsI043p4uDChooIzbXOPzJw5k7tx+TI0N2wQE+RydkNDA2s3bcIr6bFMRmrgi53oX8Tnn5MC29Ya+woolUqIokidtI2M0DhyJMLi40UuMpJ5FxaSkvnhh3Sd29pWlUq6z6ysqCwhPp7s2I8fkx27Z88WRwBjDDrN5L+pqQmXL1+GoaEh5s2bRyelthb//OorFIaHIzIvT8zJyWHmHIeK5qTCzz///NJ+Ozs7Kwf5+0vh5kbr0srqZZswgLfffptL2rcPed98I2bl56OXjw9jqm7o+/aRUu3pSWRJVdtaWUl/V815/uMPev35c3JzyGSU1OjdGzhyBOy778AplRCkUtTU1AAATpw4geXLl8Pc3Bz19fUoKSmBlpYWtLW1W84FQAk0mUwmampqtmdEogju448x7epVhCYl4eTJk/jggw9QVVWF5ORkmJmZ4fLEiRgqioLLhg1c6bhx+CEmBqIowsbGhm92ErHa2lrk5+dLAGDTpk0vkb0333qLfZ2WhntNTeIbAQGsHXmLjKSkyttvky3bx4fuJScnInoKBV3vZqfN9OnTue3bt8M+ORkGGRl0f6iO1cmJur7n5lICq6CglTQClKwxN0e/fv2Q8v77XFNqKmQdjc4aMoTugU8+AWprUTNgAA4fPiyamJjggw8+YFKpFDt27MDFixfFpUuXMqSlURnAoUPkcmq7ho2MAAcHhNy6xcm0tWFtbc0cfX1hoKuLIUOGsJycHJaYmMjfvn1b0tTUBF1dXWhoaIhz586ldfvgAY1eu3OnnfuFMYYhQ4ZAJpPh+vXrMDAwwIMHDxAcHAx/f38MbZscqa8n58DEidSNPTqa1tfVq0Ska2vJRbNwYXsXQ0AAjWFcvrxdXxEAOHbsmKBQKDhvb2+eMfYf14/fvn1bwXHcqf/082qo8d8MNdlWQw01/tcRGBg4nDE2VBTF81u2bIl74W8DNDQ0pk2YMKFDot0WvXv3Zr1792aJiYlIT0+Hc1tFsKSELMrz51PgZmVFtcqrVlEAnZZGSsrevRTMFBejPDoa96dNE+sjI8UeGRlc6nvvib22bmXTLlyAIioKGjo6pAZcudKqwq5dS8pAm1pWqVSKDsdoATh+7x7Gbd0Kw9jY1tFQWlqAjg6UMpmom55O5FYiIet2ZSX0pkzB85EjWctYFhsb4MoV+B84wDHGeJ7n2ZAhQzilUom8vDxcvHiR53leMjQlBbJHj3B26lQwxjjZ1q1wc3PjJwQFSRASQvbLZkiaa5Zb0L8/qU8gIr7HwQGTd+zglv/+Owyp2Q0UCgWCgoKQm5urrKmpkUqlUjQ2NmLYsGEYqBpt4+ND52fnTiJN/v6U9Gg7WgqgBIa2dut/a2oSsdq3j8iJvz8Fwh9/TPs2duwr10Y7bNkCEy8vsKlTMbqNio3KSlIi58xp9/bVq1cjIyMDLDkZVn36YPT69VxFRQV4nkdGRgbOnTsH6aZNYg9RbCExKSkpuHPnjqhRVSV4Hj0qGbZpk9B/zhwJAAwaNAj79+8Xnjx5wrp06dJKfFSNkj78kAh2XR3Vgm7ZQg3nduygQNnS8qXmZLm5ucgvLeV0ExIooJ4wAfj9d0h/+gmj9+wBbt9mu3/6SdBSKrnOFK+CggI8CwsTfZYuZclXr8Kwvh42r7Kar13bYeM6nudRUFCA6upqyGQy6OnpoaCgABcvXoQgCHBxcRElEgkePHjAwHHMe8kS2GVkUALhyBEK9nv1Ios1x1FTrq+/pm7b6emkNkZEEAnooLmUkZERunXrhpycnBbCvXLlSvqjjg7Ykyfo4uCAKc2Jryo9PYTExQmOjo4sNzcXMpmM1dTUYMOGDdCmNUixkrExrY+9e8nura9PCZrUVFKn33sP7i4ucFmxgh24fFk0WruWJhkIAo0Qe/SIknptx2cFBJAF+fhxUnBVz6wuXSgZBRBxrK4GCgsh/+YbfPzoEYo++QTJcXGQfvklrl+/jv3793d4iYyMjERjY2Nx8uTJ3LNnzwQTE5OXpceKCkqiGRtDqVSioaEBhw8fFgoKCjg9PT2+vr6eM7S0FH97/pybyHGi5cyZzG3zZrH3hAnsXmKiRBAE1NXVobC5rMDIyKhDVfXSpUsCAG7w4MGt+9DYSCRw3Djg0iVa5x9+SMm3vDzq8O3oSCrslSvU1M/CAhr//jfWPniAlLQ0ZO/ZA9cXR/TFx1Py0MeHniWffUbJC4B+B5qa4NitG1KnTRN3p6aymQUFHTf3cnEBduxA3Zw5uNajh2js5yfMmzdPolQqceDAAdTV1WHBggWsZX0sXEjrorCQkgaqZ7qZGcT169H14UOMXLyY7quGBkBDA3aMoW/fvgAgUSqVOHr0KKRSKWbNmsW1lEMdOEC/C52U0AwYMADm5uawtLSEVCpFWFiYEB4ezhUVFYnTpk1j3IcfktK/eDGVK7R1rOTk0DNFT49+B4uLaRyganxjly707D99uv0serrWXEVFBcaPH/8fEe3Y2FhldHR0fV1dXbFCoehgvp8aavz9oSbbaqihxv8qAgMDLSQSSZCvr69mQkLCuq1bt55TKBQrtmzZUhUYGGgHIHLChAlQKBRoaGiAUdtGY51g4MCBwsWLF+Hk5MRxokgk6tw5UgaXLAEmTya16IMPqFP4xYsUYOzb17qR58/h8/33uOngwOknJaHW3h73NTSY3NsbXosXQzpmDCkpCgUpr0eOUDBkYkIB+K1bFCxfvEivdRIkNRgYQJDLqZ5PVefc1AQwhvSBA8U3du4EHxoKycmT1LxHSwuwtUVT24Y4a9YAjx+DXb8O/5EjW4IeuVwOOzs7rFmzRrJ92zbBxcGBewSIAJimpibq6+tJZdTXJzVm2LCO1cyGBrK2Ntut6+vrUV5ZCcXOnTBcvx7KkBBcDg5GcnKyYGJiAn9/f6mVlRXMzMzwzTffiBrl5QwWFhTI/fwzHev9+0Qk2yrLbbFgAal5e/a0vsYYJUcGDiSF9/x5SqKkpREJy8trDRBfBX19yCdNQp+UFKSnp7cq7wYGtE46gP3Zs0T+mhMAqhpoExMTmJmZgX/jDVY+ZgyMPD3x+PFjnDt3Dl5eXqKhk5NEx90dPSIjOUya1DJCq3v37lxWVhYP4OUg1dSU6nZ5ntZOQwM1LmpooHXa3PxJBUEQEBQUJPbo0QOampoMVlakBv/+O6l7P/2EzLIylJeXc++8885Ljg+VDTUmJgZmSqVYOWYM7ly7xgAiTu7u7kJAR+OFHB2plOLw4XYvnzhxQsjIyOB0dXV5AKympoaTSqUYO3Ys9PX1ERQUJDLG4ODgwMaOHdvaVGzxYnKHjBhBTeZmzqTzsHgxOVKys+leW7iQCG4HEEUR9+7dQ05OTstrlpaWAoDW/XdwoGTE/PnAlCnQT0vDW/36cZg8GQDw66+/AgAvlUpfvjbDhhH53bqVyJVSSY3OvLzInmtrCykA/exs4dC+fZLNp09DevgwXfdFizqeU81x5GoID6dO5C9CLm8dGZWbCwCIzc0Fp6uLsV5e6DtsGM6//76YU1XFZE1N8Jg5E1lZWRg4cCDS0tJYbGwsCwkJEbOzs5m5ufnLZPvZsxYi6u/vD9Vc7ClTpsDQ0FACACkpKezXX39FXI8eYr/gYDbh4EGGu3dhu20byjQ1cerUKfA8jyFDhojNz+h231NbW4vk5GQOQOvs+l9+IZW/sJCcNRIJKdyzZ1MS5b33KMGmp0dlA8uW0bP1xg2gogLijh2wqq+H0tiYngn29vQM37OHOoSrEizBwfS5wEB6rhQX03lcvBhjP/6YNXl64tSpU+KKFSuYVtuRYCA3xu+RkXy2v79kztWrzHzwYAl4HtXV1Xje3OOinYV/1CgipyEhlIhtS05Xr0ZfNzfUqcYXenrSb9EXX7S8RSqVYvHixe2vz86d5B55VekH0K5Px7Bhw7j6+nrB/R//4Eo2bYL51q10XlQOirYwMGjfQ2P7dlK9IyJaX+vRg/okeHiQ4t+McePGYdu2baioqHit3+a2qKiowLVr13ie51cACN6yZUvZX9qAGmr8TcBEUfzzd6mhhhpq/D/CJ598stLJyWn722+/raNQKHD58uWGR48eNQmC8D5jbByA8V27dhVyc3M5AJg8eTLc3NxeWRsmCAIOHjwolJWVsaU//8wEExOUHTrUqnRv305BzvPnpJzcuEG1k8nJZJczMgKGDYO4eDGuLFuGe2lp8I6Jgde9e6jy8BB0t27lulZUgB09SmrwoEEUTDs7k/Kydi115z1wgAhl795kf962jchlG1Xhxx9/hKOjI/zT0kjV6d6dLIP29rgaEcHfu3NH4mlqijFlZZQs+PBDNM6ahe3bt+Pjjz9uPegbNyi4fKGREADg5EkUvP8+xJgYWDarN59++ikEQYCJiYno7e0N8ZdfxCoXF6G22SZYXl4uKS8vx/DhwwUfhUKCHj0AExM0NDTg0KFDfHV1tcTX1xd90tKQGhQkxI8dy/z8/Jinp2fLtRF37cL9Eyeg2L0bfomJlOjgOJo/7ONDalNnKC8nVejFkVsq1NQAX35JyYdx46jm8OOPqU66pISSA69CQwMSZ80SSidM4IapLKSlpaRqX77c/r2ZmUT6kpM73Z87w4aJVl99xax9fPD999/zLi4u3JAhQ1oX6ddfU4C/cycA4Pjx42CM8bNmzepYEWpqIuKZlkaKr5sbKVPffkuvXb5Mx81xePr0Kc6ePSuuW7eOujzzPCUdDAxIEb11Cw08jwMHDggVFRXcxo0bVYotRFFEcnIyTp8+jS7GtFuIAAAgAElEQVTV1VhWW0uEhTFcu3YNmZmZeP78OT766KOXGzkpFEQssrKIEDUjIiICMTExwoYNGziA7sfXalqoOu7t22kNGxlR0D9qFN1ns2aRy6MjwtCMqqoq7Gw+xwsWLOhcmd+6lUh93750v44c2eKOuHTpEuLj4wEAOjo66N+/v+js7MxMRZEISFYWJdKWLCESZGPz0v4IJSX48ocfMO76dbG3ri7jDhx4KUnS/gMCJVNOniRC+CdNAAMDAwEAH374IaSxsXQ/ffEFhN9+A/f4MVl+V6wAPDyQkJCAS5cuQS6XC+vWreNeKq85dIgSVVu2vPI7FQpF6zXkeUow6uri4q+/4p6nJ+wdHIQ5c+Z0uOM8z2P//v0oKSnBvNu3YevjQ8nOkpLWWum7d+m6nDlD/11cTNf7xAm0uHgAcheYmOB7HR1BKpOxRYsWMS4jgxRyQ0NSso2M6Dlz8yYl9nbtorIMMzM6zooKIvc9ekAQBBw4cIA3MjLCjBkzJC3PL1HE0aNHhaqqKnHJkiUSbbmcng+jRwMzZmDn998LVVVVHAD4+fnxo0aNohvk8WNK2p0/T+tXdS1v3cKlZ8/4MpmMmzNnDkNBAT2/XjVj++FDeg5ERr48o7szVFRQImngQKTfvInCqioMuHWrfTf/thgzhp5PbW3vogg8fdq+jj0khH4nx49vqaEPDQ1FWFgYNm/e/Hr3dxtUVVXh0KFDdTzPi42NjTKJRLJt8+bN//pLG1FDjb8B/lpLVzXUUEON/2FoaGj8o2/fvjoAKbETJ07UXLRokZ6FhcXXjo6Ow+bOnYvc3NyWAPHs2bNoamp65Ta5+nos/eknbn63buzWwoX86alTxdOnT0OhUNAbkpNJ7R40iGy5lZUUbNbUEOFSKAB7eyR+8w1inzyB75MnguuQIaj5/HPo37vHnfzpJ+TZ2NA85dGjibhfuULB59y5NFe6oYGCQqmUlJVVqyjwsrUlhS48nDqYg4I6XL1KAY8oUjATHo5Ro0ZJ3H18+CZBoAAuPR3YtQuS8nJwqmNRYcQIUtFf7BDd1ATY2CDBz08Q2iRXra2tlfb29rxEIuHv3LkjVHh7c4O+/FKqqVRKZTKZ1NjYmOnq6rLr165J6saPR01YGADg1q1b4Hme8/X1FSMiIvBDejp0y8rYAjc35uXlRUR73jwgJwfMyQld3ngD4eHh2Flby8PLizpcBwe/mmgDZKu9erXzv+vq0jZ69WqdXZuRQQqZszN1TX9VMllTE0b29pzxjz+2vqm+nrbRFllZFFjm5HRKtAVBAGtsZPX5+fj+++/58vJyiaenZ/ts0MqVtK8nTiA0NBRpaWkoLy/niouLO96/c+foe1esaA2C166l/SspIcXs/Hng8WPUpqdDU1OTl8vlREzef5+s1idPkuLa1ARNqRSrVq3iTExM+O+++67la1RE29bWVlzm5UX1rc1kY+TIkXhTNae9I8jltD/N3dtV8PT0RGNjI6dSlzmOe/1AXCYjG3FAACWQhg2jIB+getXbt+l++/JLIiChoaTMNuPp06ci7Zoc+s1zzDvE5s1kgQ8KImdHG8V5zJgxGNPcOJFvaEDOnj3s+bBhqNi1i+5DDw9K1IWE0Jp4kRjX1IDr1g2b3nwTppmZLFPVcftV4Dgi3J9/3l5R7AQDmjuVJyUlUS24hgYQGEhEWxRpvfI8cOIEPOfOxYcffoiNs2dz0o5IvJtbu6aAnaHdNZRIyHHg4YGB5eVwfvIEFoLQ6WclPI83b90Sdaqr8VBXl55xxsbtyZy2No34UsHMDNi+HQ2HDkEoKqLXioqIPM+cie62tmJ5eTkEQSCXRe/eNGv8/n3g7FlKdH79NZ2H336j7xw6lK5F794tddwcx2HevHmSZ8+ecVFRUS0HkZmZiaKiIrZq1SqJtrY2PctPniRnw7ffYt7EiZzKlREbG9uaiYqMJJK/ejU951W4cgXDDx6U5Obm0j7v20dkuzNkZtJvRHT06xHtoiJaz0VFdF8kJ+O0tzdujhnTOdFuaCDLfduSKxV8fWl7KgwdSgnfNu4f3eZnolKp/PP9ewH6+vp47733tDdu3Kizbt06OWNsQ2BgoMdf3pAaavyXQ20jV0MNNf7HERgY2B2ALoAnW7ZseSkCCwwMNATgCaCrtra2jd0L9rguXbpg6dKlLQW7q1atgq6uLi5fvoyysjKhvLyck0ql0NTUhEwmaw0CKypIOdyyBWzYMHQdMwbTTEwkALBnzx7x1q1bbPTo0RQ0WFtTYJudTcGpanzNV18BzQRDoVBg/O3boo9MxmH3biAiAnl376L+yBEY6ukB69ahdsQIZBsbC/UxMXDZtInT1NRsJYEqC7TKoufsTMTAwIBUMQMDaPfvj27HjlHTHg0NCrDOniWL4bFj8Dp0iMtzcGhtyLRmDSQ//oiVe/dCmD8f3LNnpLrK5a2qzu3bFHSGhJAamJGBR/36wbVNMLxgwYKXn/9hYRjftWuLTbCurg4RERE4tnChaAEw47AwJCQkoG/fvuLw4cM5iUQCR0dHWE+axHDqFDUxe+89Sl7k5ADjxsFy3DjM3LcPv5SWSrBhAwW8r4PkZAoE/wyTJ9O5On2alLIPPiBCZG5O6u6AAcC2bcjLy0NhYSHKy8vF+vp6oampCVW2tpx2Xh7zTEujYL1rVwps22LZMiIFJ050+PVVVVW4dOkSeldWIubcOXSfMgVz5sxpCUJboKUFjBiBxs8/RwbHwd7fH0VFRSwoKEhYsGBBewZ0/DgR6nnziHi1JUiMkWqfkUHrdvFi2IaEiPfXreNw+XJrTburK71/924iHGfPQhoejoULF0q++uorCIIAjuNQVVUFAJg3YQJDaWlrXWszzp49y/v5+XESiaRjK8nHH9M+7d7d8pKuri4GDBgg/PTTT5yWlpYwa9YsrmvbBlWvg759qcv45ct0TRYsAKZPB775hsYQGRiQyhYaSuSneS61hoaGOPLRI/bU0BAHt27Fxs8+a62bfRFHjlCSrWvXdokZjufhKwjw1dICMjLQCOCktzfOM4Z333239dru2UPnS5UUEgQ61+vXA3Fx4NavR/H27Tj/5Alcz5yBRCIRRowYwem9WF/ceuKorruysn3N7AtISUlpmTHd4Vxvxqj+GaD7QHVs/fqR4vnuu1R3/vHHRJo/+ogs1v8BFA4O+G7IEFhnZ8Nv714GI6OW5ye9QQGEhUHh5QWdhw+Z3NISNarygBexeTOduzbI7NULoWfOYNCAAai2thbkVVXc72+9BZAjSFJfX4/MzExoaGigS5cukB8/TgT1ww9pA6qGXpGRdB4+/piud3Ex9UX49ltg0yZo9+6NKVOmsDNnzjBvb29oaWlBEATIZDLhpXKCvXtRsX49ijdvRlO3bjC0tsa7bUf/LVlC/377jcoDVI3gMjKgUV4OhULB0h8/htPBg692E8yZQ46okSNffRFqauj5//XX1Btg7FigoABNUikUX3wBmxf6O7RDXh41CX3RtcIYnUdtbWqcpmo+99FHRLgnTQI4DjKZDBzH/Ucjv9pCR0cHgwYN0oyIiFgPYO7/1cbUUOO/DOrRX2qoocb/KLZu3bpbIpEc1dTUXAhgza1bty4HBAS0yHeBgYF+Uqn0vpmZ2QwdHZ0J48eP1/2zsTXa2tqQSqUQRRHx8fEsNjYWsbGxiIqKQmpqKoSmJsFaFBkePSLr9qxZpNS0abBVXFwsJiUlYZCzM8OIEWTBraykmktbWwrCIiKoFnTAAIDjYLBlCx4ALHn8eL5n//4c3ngDZWVlSEpKQncHB8DYGD9ER6PQxUWsz8hAcnAwcxs/ntTtMWMo4Lt5k+zhKmhqUiAzZw4wdSqehoTANSgImrNn02sbNrTaSRsb8dDbW8x0c2Nuq1ZRzWdAANjgwdhfVATv4cMhXbCAulNbWlIHaV9fUulEkUiiuzvQuzeio6PFXr16McNXWRZHjSIyM3p0SyDlsHkztMzNWUhpKbKyssDzPEaPHs3u378vhoaGsuLYWNG2Z0+mFRVFQdiGDVRr260bbbOhAbpjxyLdzg65bm6is4fH682GGTu2wznDHcLIiIhETQ3ZRa2tITo4QOzVCzVWVriycydkX36JaGNjZYNCwfE8zzHGOImWFnvTyAjSoCBaD48e0XeqAv6yMlpLU6a8HIwCOHjwIH/r1i1OoVBAy8FB6PfOO8xnyBCuUxXXyAgpmZmQPnkiTFy7lj3MzBScnZ257m3reKuricC99x65Eiwtqdv2i2CM/r31Fm5pa8Pvq684/SNHwM6coeuur99K0t3ciHD26AGJTIaY2FjR1taWGRgYICgoCEqlEoNOnCClvE1jPwB4/PixmJOTw6ytrZmBgcHL+2FsTOfGy6vdy7a2tszJyQlKpRJRUVFCywi5vwKJhMjK+PGUDDt+nIictjbduzNmEBGZP5+cHba2MHd1ZbKsLOhHRUG7thb8rl3QCw9HfUgINFSqfWkp2d7Hjycl9dEjarJXWUmk88MPW2fQL1wI6dy5sB82DFGxsYiOjoanpyc0NTXpPlMqqYP40KG03UWLKCmwcyfg7Y0uS5YgOTlZyMzMZEVFRezhw4fo3r07OiXcMhlw4waEefOgXLkSkg4SBbq6uoiMjAQAPHr0CLa2tujw2gC0DlQW7LVryc3z9Ck5A1aupCTGpUtESnNz6RmioUFqZ2EhPYtycmgtFRaS+0Mmo3IYiQQSmQy8KOJRTQ2cJ01ihpqadA+qZlP/8APwr38hbvBgnNDRQYO2NsrLy9HQ0CBER0cLoaGh7NatW6y7pSUMPv+cyGez2isIAvbt24dKAwMYlJWhZ2UlkwUGos+bb8LCwgJJSUmiKIosKSkJCQkJuHv3rtinpoZJeb5dXTEAeg7PmEGjsz76iOz/ZmbkXNHSAvbtg0l9PZI1Nfm79+6xHj16MKlUirt377JBgwa1f2Yxhhi5HAXh4fB58ADOs2eLpvb27d/z4AE9R93cWps9vvkmmK8vCuVy4VluLjx+/pl1Wi5w6xat64kT/3zMoZ0dEfuhQ8kJs2EDIJejqakJkZGRMDAwEL28vDreSFwcJZwHDXr5bzIZ3XOjR5NbBqDnUWYmObkCAsAYQ1xcHIYMGQLuT0of/gwZGRlCTk7OvcGDB18IDAzUjomJCQoLC9sbEhJyLSAgoOD/auNqqPG/CLWyrYYaavyP4ZNPPhmro6OzaOXKlRpaWloaFy9e1E5MTFwIYAMABAYGSuRy+Yk333xT11WlvP0FuLi4ICYmRszJyWGDBw9GcXExnj59CoPlyzno6JACdudOh58dHRfHWZ87h4y33oKVRILiqipYL19OlriJEymYPHCAFMw33gC0taFra4sxUVHY6+7O4ZNPAAC2trYYMWIEzpw5I47+/XfWZ+NGGDk7c+Vnz4q9s7JEqBoDMUZ207o6ClBFsX137eb3VFtb4+YXX4iTrawYhg8nlSwri+bumpmh6upV8CUlFMBu20bHaGcHhYUFtu/fj1m//gpHGxuy+33+OdX39u9PKn9CQtsu36LwCpsnAFIvqqtJWZ8+nV4zN4fr1Klw9fLCzz//LGRkZHAHDx6Edk0Nmzx3LozGjWNPr19Hvz/+oMZTT56Qqp+dTUTozh1IysowoaQE+/btYwqFQpwyZcqfE+6pU6lj/Lff/ulbAVBgOG0axJ49UfTZZ0j57DOEDh0KxnFwNTYWnBwdOc/Vq6X4+muy9KuUmqYmsntWVND3qYj2s2dEUtLS2o1EawttbW2uW7duwty5czn29tscevUid8Qr8HtVFeZ06cLh22+hMDcXo6OjkZmZyc+ePVtSnp4O0507SYUzNSXCQs26OgTP86j6+WcMs7RkwZ6e0PzmG5jb2pIqbGNDzeiamsjxsGQJsGgRuMxMdJkzR4yPjxcyMzMlhYWFGDF8ONVBd2C7nzt3ruTXX39FcHCwsHTpUo7neRQVFSEqKgoPHz7ElMmTRbfsbAaeb5eQYIzB0tIS2traLCEhQZKSktJ+OsDroLaW6rdv3KBxTozRMc2dS2Tw/n1S8qXSdqTSaNs21ObmQkhNxW+RkdBqaIBVbi5mNjURUfjjD+D6dSImT58SofjjD0pqJCZSqUN+Pt2LU6cC//oX9P38sHTqVGQvWIDKt96CYUkJEap58+h5ceAAEdW0NCKw8+ZRAoAxLF++nAPIavvFF1/gyZMngqWlZafMpKBfP/y0YAGM165FpZkZ6uRyODk58dOnT5cIgoCcnBz4+fkhJiYGSqUSR44cwZY/qbcGQM84DQ1ao0lJ9JqJCSnyhw/TPX/hAj1nQkNpPajI1eDBRNDlckoE/fIL2bPNzeFXVIQqPT2Yfv893Sv6+kTeBw0i0rhmDfwYQ0V1tXjnzh1mamqK4uJizsLCAp6ennj69Kl46fvvmduyZbxuVpakISUF9fX1uH37NgDgA6kUmrW1rf0U/g973x0W1dl9u94zhTZUKdK7AoKAAmIEscSOPZZYY0uwxJIYTdWg0WCiiSXGllhQoyYqdlGDAhZERCliA0QsSBkpAtJmzrl/bIaOJb989/fd5856Hh9kmHLKe87stdfae7/7LszNzeHr68uKi4tRUFAAuVyOzNhYHNXV5UfPnl1/bKur6Z555gwlVFXNEA8epMc3baLf8/KAK1cw7f33RcmbNwvhz58jcPBgNO1rlJeXh9zcXJS/fIkbPj6QFBYK/TduZLC1bTyG0MOD3Cd795LarJow0L07+PBwwf3qVa6uw3pTHDxI48YuXWqdaD95Qt8vH31E3w0ZGUTOG9jSVR3hra2tW7/fVlY2HvPVFO+/T8kCQajflvfeI9fF3Ll4+vQpAODp06ewban53xuC53lcuXJFUVNTsyo0NFRXKpWednR07GxmZqZ55cqVuQA++MdvroYa/8tQk2011FDjX4OGhsbMXr16aas6umZmZr5UKpUNir7wnpGRkYlbw0Ysb4nAwEB269Yt9AgMBBcUhAdBQTg0aBBcli9v/uTiYgr49u9HlYsLMpydcT8iAt0nT+ZjLl3iXCMilEPOnBGJVYTUzY26Pv/0E6kFQ4fixdKlKDtxgp0+fRr9+vUDx3Hw9/eHn58fY1u2gDk6IiwyUuixZAm7mJWlzP/yS86ia1c+ODhYVPjuu7h79y66fvIJuLg4Ir9NYGJigmwVSV+4kIiRmxuRLQCMMWpkKZUSGawlifPmzcOmTZuEoqIiBicnslOPGEGBcW4uBdanTlGDnf37IenUiYj06/DVV1S3Kwhkx543ry5AnThxInfvzh3kZmYicPhwcPPn43liIiJ//RVdLCxI6du9m5IX3t5Qdd9+UVqKX2s7vYvF4jdTtj/6qFHTrZaQl5eH9PR02NvbIzExEbm5ufzz5885bVdXfoyLC/dOTAwUe/dC09ycgu+iIkqmdO5MAaa1NTkNQkJoW//8k8gCz9PfoqJaJdoAMGrUKPbDDz+wgoICmJqYvFaBqqyshCAIuOLvD/uMDAxgTHTfwwPXrl0THTx4EE4//ICy9u35tjIZp1lbz9+wS3BFRQVu3boFZ2dnGMhkuLRundLpp59EF8aN41O9vLhBlpa0DdeukcofFUWk7/59Wg8//wzcuoWAsjJu36VLEEQiaGlpodumTUQOPm8+eYfjOAwYMAAbNmzgVq5cWdcvQdUsLSU1VXA/epShf3+6dprAwMAAwcHBwsGDB5mPjw/69ev3ymNUB0GgpEN6ej2BGDOG1taECfT3L7+kdbZqVaPRbxoaGnB0dISFhQU0NTURGxuLe66udJ4Buk54nhTbzExSBa2tyYY7fTqp9RkZZFMHyGpbVIQCjoOZXI62RkbkJklIoNr5O3co0ZCdTeUgOjpUa95E6SsqKoIgCMjJyUFlZSWp401QWlqK8PBwuHXtyg/89VeuMjcXpyZMwJMnT9iWLVuEFy9eMMaYYGBgIPTq1Ys7f/48/kniEgD1eJDL6TjExlJy5+ZNOsZJSXS9qOzYAK2lpv+vrsaT5GSkHTmC3hIJkXKZDLhxg5JaHAcwhhcvXiAxMZEFBwejc5OElIeHB3sYHY2ilBTukqGhQiqVMk1NTRFjDHqFhXhUXc23272bg7MzJcOuXCHlWiKBgYEBDAwM4OzsDJuQEFakVLKNAD9z5kyaRNG/P91LDxxofH2amTVuTDZlCjBlCrjbt+GtqcmevnihLJk3T9S+Z0+2evVqvry8nJNIJBAEAXp6enxhYSGnp6cHrR49eGhqijBhAiUgGhLutm0pcVNaSmpzu3ZA9+5wdXHh4mNi4BYcjGYr4MkTOg/79lHSoin27KFGclOmUHKorIzWWsNa91owxqCnpyfEx8czf3//5qUtAN2v58xp/rgKHEf75O1N180339CxO3wYWLcOFw0NlV26dBFZvcqq/oaQSCS8QqF4XyKRTHJzc7MYPHiwZlZWFgRBCAwNDR0tkUi6cRxnXF1dvWvJkiVn/8cfqIYa/5egJttqqKHGvwbGGFNZyQRBQFVVlZgx1tAj7uzo6Kj9qk7ir4OzuTmc160D+vdH8aBBOFlZKXgGBvJoWFf33XdENM6fp8y8tTV03N3RtXt39JHJoD9yJOe7ezeOxcSwrSYmvNXx45yxsTFK7t1Dz/v3oTlmDKlphw7BaMcOTNy1CxFHjghlZWVsVG1Qw3Ec2U81NMCfPctuJCUJbVJT2aRDh9hPYrFo4MCBiIiI4J8+fcpdtrHhZ82bx8nS08myOX8+AFIDUlNT4erqWr+D7dsT0awlEBzHkboSFETjimpJuKamJjQ0NASlUskePnwIOzs7IhBaWhRgentToNSuHWBtDaVEAptevUiVWLSICEZISPNOuKrP5ziydAYEkGIOAEuXov3Bg2iflkZKkJERtF6+RMeUFCJ4XbuS+rR6NSn6338PAI0a2qnGbD1+/BjWKqt5S7Cza7XWNiEhAVeuXBGKi4uZTCYToqKimLW1NW9gYICCggLM+uorTiIWA/HxEPfuTapjQADZzVUJj1696FgdO0aKqLY2HZMNG+jci0TNRlo1hVQqhZWVlfD3338L48aO5erU8hZQXV2NzMxMAEDnLl2A4GA4zpgBR09PZJuZ8Tq7d3O3OnfGcwcHlK5ahamdO4OFhMCK4yCXy3HixAlkZ2fToZFIMDwqCnIzM5HHlSvIP32aQ15efd0kx1GgPmQIqa1t2pBCGRICLFoEy3ffxcjiYhwcMwYLFy6k0UOvIMGGhoaYOXMmUlJS4ODgAFtbW3Ach9jYWNy/f1/AgQP1ZQMtwNPTk+nr62PXrl3w9vZGYWEhzp49q+zWrZuoKfGqw5491D8hMbHx4xoaRJzOnSOldcQICvyVSlKSGyh7Wlpa6NmzJ2JjY9GuYSMu1TGytaV/aWmUfJFK6zthDx9e7wr5/XfwPI+/167l/ffs4exsbOhYhoSQc2DZMiIsCxYQERwzhu49nTrVXa8AJdYmTZqEAwcOsOzs7EZKf0VFBa5evYrLly/DwsJCMWTIEDH69YPs5UuMfvAAlUOGcBcuXICNjQ06dOjAADClUonMzEz+6dOnqK6ubr18oSWUl1N/g4gIIoXDhhF5W7mSnALduhHZrqmh9ZOcTLZrbe1GSYSynBxUzZ4tKAYMYOlFRehkZ0dJwW3biAB26AB8+SVOSyRQKBTwblJuoIKdqyvsxo1j3v7+dXFp8apVUO7fj83Tp3OzTE1hqEoYffABqb5LlzYi0GU7dghXo6MhLyjgoseN4y0KCzn5iBG4p6mJ9ysq6jrwA6D17utLiUlVXTdAic7wcPQvKxM9mTwZ+hER8JwzhzO3sYHc3x+GRkaQyWScIAiqqQv0vfPnn3QMt22rd7eIRNREU2XznjABOQYGfMGpU6zS0FCQTp7c+IuwspLcBZMm1SeGVPj9dypNEoupIeT9++Q+aK0fQS1CQkLY5s2bsXv3bn7mzJnN3RQ1NY2b1LWGn35q7NpxcADS02FnaspJ3N2bTyp4S9Q2qdOKj4//wsrKSurl5cUYY7C3t4eXl5fl8+fPf7OxsZGJRCIWFRU1KjQ0VLOlfjBqqPHfCDXZVkMNNf41VFVV3SooKBgAgGOMYdy4cZoHDhwIDwsLG1dVVTVRKpW6ymSyf1bYpVRS0yyJhNSX8nJc7NgRkqdP0SMoSISKCgqUtm6lQKpzZwrEGigzlrVjryAWQ6OiAu8tXswl7N0rpGdnK1+eOCEadPAgahQKPLt5E/aLFhEx3bkTtsuWoYuTE3+vvJyh4RSHvn2B2bMxbNgwpKens8CxY9lJhYLvM2AAE4lEzNvbm8vPzwcnkXC7oqLgeecO/G/ehLiWbB8+fFhpYWHBBQUF1b/n6NGkWtQHUUypUBCRKCkhxa12PxhjwsWLF4WXL18yMzMzZciZMyI8eEBWekGoV6V/+gn4+WdkREfDxcqKFLhz5+o/S1ubmkWpCPOsWaRuHzpEyq+3N9U5fvQRvbaggGzry5aBT01Fh9RUqtf+5ReyHE6eTO91+zYwfTpEIhGkUilsbW1x6NAh3LhxA5mZmdDW1hYcHByYXC5XmpubcwYGBuzZs2do164dLD78EDo2NlBu3w59fX3wPI9Hjx7h5MmTSrlcLurYsSMGDhwoODs7s6qqKmhoaHA1NTVYuXIlfvzxR1hbW8PKygo94+Ko7rp3b9pX1SzYv/8mxemPP0itSUggpfTgQbLAv+GYnSFDhrANGzawF8nJ0LO0pGZtLeDw4cO4d+8eLCws4KKqwf7sM+DMGYR4enJ8aSm4pUvx0tCQW7t2LR5u3MgXSqWc3nvv4ezZs0J2djYzNTXFAB0dXLp0SbguCMxx4UIY2dpCR0dHcHBw4DmOax7xqpT5jAwqZ1i/HlpZWSj58Uf0f/AA3ObNtA5eE3CbmJigd8PeAwDMzMxw8eJFUdHff8Pw7NlWZ5QDQHJyslJDQ4O7desWi34m5XEAACAASURBVIuLg7u7uygqKgrnz5/nu3TpwnVv2Azs5UsiFl26tEwmOI6u8Q4dqKnWgAHA9u1ExE6ebNbh2djYGKWlpY1nbTdEmzakwrY047oWZ86cgUQiYT4+PvUPHjhAn3vmDCm5GRmkxuvokC1/5kwi3TxfNyPZ3t4ejLG6Oe21Y6eQn58PsVgMZ2dn5ZgxYyg209AgAhYcDM29e+s6pKsgEokwatQobvXq1QgLC8OSJW84NamwkBIxMTFEoAFKNOzeTZ/5/DkdU9pASnzY2VEi7e5dsvD/8ANgY4OaTp2g/+wZ83Z2hvG0aZRc6NmT9psxYOlS3BSJ+Ac3bnDahoYCx3HNM61lZXTdNSSY16/DoFMnZO/aBcXVq43t3Js2kVIcH1/vppg2De2dnFj7GTNQmZmJ4iNHuMNdu6Lg+XMAwJo1a2BmZoapU6fWJ6WmTSObuaqZXAPklZTgSGCgUObtzfpmZfF2Z85w1qpJChMngjUtCbKxoXtq//50v1U1gFu5kn4/cwbC++/D+PhxTu/xY4zLykJNZSU01q6tf4/iYuDjjylZo0JJCb3nzZt0PXTvTsTdz+/1tdyghJOdnZ1w69YtLjo6Gj1UzdoASrgEBjZONrSGnj3p3HfqROUTWlqUYPrlF0FTIvnn2fMGMDU1xeDBgzUaPlbrrNEAoAEA4eHh5VKp9Mjnn3+uJtpq/D8DNdlWQw01/hXU1mOPd3R0rAtora2tMXfuXO1jx44NvH///iOxWKz9jyyPgkDE5PRpInC1nWXLS0vhcOgQe/HVVzAqKAC3fDkpMk0DoabvZWQE8DzY6NHwCwhgfgUFIpiYAOvXI62mRjh64ADr1asX/OPjge7dUTVoECx37hS9/PHHxu81YgTg6Ag3Nze4ubkhJycH6RYW3IgpU6A4dQqPHz8WGGOsvLwcFhYWfKavL0txc+NnnT4t4j/+GMUTJ4rGjx9fN6IoPT0d13/6STl8+HCR5pw54HV1cfnyZTClEonTp/OdBg7kWIP50YwxJpVKmZ2dHX/nzh1RtaUlpA8e1I95WbSIiFTXrjA2NhZOnTsnaL/3HrPx8iLlHyCiWVBAVtIPPiBXQE0NEevqamqEJBaTynj1KgVcT59Scyk3N/A8jyQfH7SLiKD62atX6X2vXqVawv79IaqpgSAI6E4duIXMzEwGAKampkJeXh60tbW5x48fC8nJyYznedy9exfGY8cKRcXFTLl2LRhjDYNtUe385LoAT1WbKBaLMWfOHOTl5SEjI4OPi4vjXrx4IQw9fpzh++9pDc2fT02LOI46Wo8bRwRBJqOAf9cu2j8VCXkNjIyMIJFIUO3u3upsb4VCgXv37sHJyQnjG3b77tqVCM/vv4PbsAGwtIQ2gC+//BJYsIDbrFCoZkazGTNmwOLCBWDrVtisW8fyzMyg6vBdUVEBc3PzVwe8Mhn9W7QIrFs3vNOxY33iITb2zdStJnB2doZEIsGB5GRMEovR8KrjeR4xMTHIzMxEYWEhX1lZKaptcIjx48fDzs4OSqUSGzdu5KKjo9GIbIeE0LbWlh60CisrIohHj9LzO3em1/bpQ03mGENeXh6Ki4thZ2fXepJPR4cU6YqKVkcx5eTkCJ6enqyResxxdB3s2UOW7MjI+rr18HAiM5GRpMCHhgLOzlA6OEChULDz589DJpMhOztbeP78ORs4cCA6d+6MZgkTfX26lkxNKdHVpC5WR0cHvr6+SEhIQFRUFHr27Pn6RlULFpB62nSNd+tGCra/P6mmlpa0j6ru4rt2UUKirIySbyNGQMfEBNE+PvDt0QNWVlaUeDA0rO+JMGYMji9Zwn26di3OjBjR8vbExhLhU5HHw4fJLXDyJCzNzKCVnMyfO3cOo0aN4jiOo3O0bh0lO8eMoXuVtzfd90aOhObYsWiblIRZQF3X/bS0NBw8eBBhYWF499134e/vTwnCVkZiVVdXo6SkhE2cOBHHjx8XLo0bJ4wsK2M2p0+DpaWRam9lVZdEAUBJm4sXqdni9Om0bRxHiUktLbCMDJz49lulwthY9ERfH8OHDat/7ZYtVIKQnFx/HM6do7X99Cmt53v3KCH4qnFhTVBRUYF79+4xHR0dxMTEwMDAAF6qhOCDB3Q/f9PGZu3a0XdE7TG7a2AAx+RkzrnWcfN/AyUlJcqqqqrWs2JqqPFfCHU3cjXUUON/jNDQUF2xWLy0TZs2Qb1795Y2tImLRCK4urqK27Vrpx0YGChpsW7sVQgKooAgNJTqI3m+rqa2fffuuGZkhBQLC2RVVyudRozgXtbU4MaNG4iLi1MePHiQ8/T0hFbD4KSmhtRvIyNSEXbtIlIqlwMbNsDU25tZWlrixIkTeF5aKrSfNYsVffUVzowfzw/bs4exx49pmxijIEVHp45kHT9+HNp6eoJtTQ3bc+MGXspkgrm5OS+Xy7nBgwczQ0ND9vDhQ9536FDuzrNnSJNKEaStjSpjYyQnJ+PEiRPotnUry3z5kreVSjmWnIwkU1Ohr0TCniUlQef2bXZZLOYrqqpYUVERHjx4wIqLi+FUWCgM2riR6YaFgX37LW7fvYu8vDyYuriQArJ4MTxDQ7myly9ZZGQkysvLlXZ2dhS4mptTTZ6RER3fDh1I6TlzhhITUikpK6ampKyEhFBgrKMDrFkDbtMmHPfxQfeIiMZKi5UVKXtZWZAMGoSHBgYInDAB/v7+LCgoCN27d4e3tzfz9fVlXl5ezM/PjwUFBaFHjx7o0aMH/BITWRdtbbhOnAhHR0f4+Pigd+/e6N69O1rrXs8Yg7a2NkxMTNC+fXvm7OyMM2fOMJmuLszHjCHLfFgYESJnZ/qZkkJKn1hMNbs8T/v5hn0Fnj17hps3b6K3oSFEjx61WLeclZWFlJQUTJgwofFaVCjIol9aSs2OVM2bnj4F9u1Duw0b8PTpU8HFyAidNmxgGD4c+OADMGfnRt2snzx5wjIzM9G1a9fGhLu8nBwKBgak7HfpQm6FGTPIgpuWRgF8cHDLHc9fA8YY0tLSlLmMcfnl5cgqKVEaWVhwOjo6+PXXX/k7d+4wqVQKNzc3JpVKhZCQEBYYGAjDWpLPcRxsbW1x69YtVFVVwcHBQXVQaRtfNSe7IVxcqIt8RQUlkS5epMd0dFBcXY3ExEQUFRXh0qVLQkJCAuRyOd++fXsOAIqLi3E1IQFtly+HWCJplXgkJydDR0dHcHR0pGM8aRI5X3x8qGHYb781L8mQSmkdjR0LmJmhsmdP3PvtNzy0t0deURGe5eaioqKCLV68GLa2tmi1vMbAgBJB779PCnyTMUvOzs4QiUTKmJgYLjY2FikpKcqkpCQhPj5eiI+PF4yMjLg2bdrQ9fzJJ3S+x4xpWRlt25YIvoMDXR9SKSXdrl0DTE1R3q8f9hYUCKeCg9kVCwvU7NsHx0ePYL9oEdjIkXQf+eqrOrJdVlaGwuJiXNXXB/P3h6dYzJopqSUlNB7LzIzOn60tWfidnFTj6VhycjLr0qULJCqnA8eR2+fCBSKoKSl0rwsOpiRoLVTH1NTUFD169FB1xxfeeecdxlTqbGkp9SxoAENDQ8jlcmV8fDzmzZsnellRwc5euSIku7jA49NPmWTPHvr+GD2avp8auKYwZgy5LJ49oyTA1Kl0Xzl9GhqRkczh2DHW8cULSIcOpeNbVUUN+SZMoPumIJDTaM4csrn37EnHVCqlREfDuvBWUFvGhaSkJGRmZmLBggWIj4/H7du3kZycDJFIBNP0dIhyc+tcDDzPIzIyEoaGho0t9/UHk5T15cuR8NNP/JHCQibz8BBcSksZ8/Vtti7/E5BKpdKsrKze58+f39GjR4+q//gHqqHGvwA12VZDDTX+MUJDQ3tevXo1ShCEFVZWVp1Hjx4ta6npD2MMMpmsPlB6HV6+BD78kIiLuTkFtTIZqSrBwVRr6OcHUXAwbLy9kcMYnjx5guzsbNy9exfXr19ncrmcMzExEQICAlijIJbnySa8ciWpWRkZpEwtXlwXfBoaGsLV1RXR0dG4mJbGUtu3Fz46dIgTzZ9PdYwaGkQ+V6+m19faay0tLXH+/HmWaGwsONy+zfoOHMj8Bg/mAgICYGRkhPz8fDx8+JC/n5WFKxUVzCw3F36LF+Oko6MyLjGR6927N4yvXEECz3OaH3wAE3t75D1/zreNieFMcnLQ9swZdsHXl2VmZSlTU1NZaWkps7GxQZCrK6s8dQr6qan4S0tLiIuLY7du3UJAQAA4Cwva38hIOM6YARcXF1y4cAHXrl2DtbU102tIaBQKqhX/4AOyLUZHU+Opx4+pK/aYMURqLl+mgM/VFWWdOuHa06fo3nTUDp14oG1bVPv5IfbRI3TdsQNo3x7MzKx1YqHCmTMQ8zx0BwyAiYkJDAwMoKGh8VbzXGUyGdq0aYOTJ0+iTZs2MPHxoQB9yxY6/87OZElNSqJANy+vfma5p+cbBY/p6el48uSJECASMaSnU2KiCQoKCnDr1i307du3fr8FgRS8Pn0okTR9Oll17e1p3FVFBTSCg+FtZsaco6IY2rShwL6FEU+Ghoa4c+QI8/XwAHv5kvYhJITI2b59dD7j4uh6evfd+rVuZ0e13B99RLbcgIDX1oA2RefOnbnu3bvD+ocfkFtUxF0uLeWjoqJYaWkp+/TTTxEQEAAnJyd4enoykUjU7LzLZDIYGBjg3LlzcG3XDjpDhlD5Q0PF8E0gkxGxNTenxnr79wMnTkDRvj1eaGlBLpeD53nG8zzLycnhkpKSEB8fz8fExLDs7GyYHDiAtgsXUsKpBaSlpTFNTU3B2dmZ5pGvX08OialT6diqXAItQUMDNVIpVhUVIc/cHCNycjA0KQmZFhbKd959l7N7A/IEDw9aKxxHymKTpICtrS0XFBQEd3d36OrqcqamppylpSWno6PDRUVFCRoaGqxg+3YoYmPBz50LzdZGhQFUCrFuHblchg0D1q0Dv3QpfpFI+CgvL9Z97Fjm6OiIO3fu4LGVFbqvXw+Zjg74rVtR/vffyLh3T2CrVrFIQ0NlZGQkV15eLvgPHcoG9u7N4OVFpLjh+Z0zh1wJjx9TA7ShQ+vIb05ODo4fP44+ffoIDqrxWtXVRMrd3elaXruWmtT99NNrHRpWVlaIiYlhGhoa1DMiIYHWjJsbampqcPfuXZiYmIAxhvbt23OxsbHM1NQUfn5+sLGxYXFxcay4pIR3mz2bYdYsUoc//piSuJqapG5LpXTfPHSI7qHdulEyZutW6PXqxaoKClCVno7M/v3R1tCQ7qnjx9NxOXyYVPOzZ8mhFRND7hvV+jp4kKzcr0ha19TU4OjRo8qIiAguMzMTJiYmCn9/fy4wMBCOjo6Ii4tDeno6+MhIocrPj1VbWuLAgQPK6OhoPHz4kN28eRPV1dX1ya+muHcPyZmZcBo9mvWZNImx8+eptKCl8WH/MoyMjFhcXJymUqncGxMTU9GjR4+a179KDTX+d6G2kauhhhr/CMuXLx+poaGxe/jw4VqOjo4Qi8VvF6W3BJ4nItehA1kmnz+nGcdmZlSbOXkyWfRUGXZQt+NRo0bh0qVLXJTKGg3A19dX2b9/f1EzS2VJCQXKM2eSgjtxYosBmrGxMWbOnMl27dolCHp6gmj/foY1a4gIrF4N5OeTEtrAVmpkZIQvvvgCANjdLl0E5aZNDL161RFEa2trlJSUiIuKimBpaSnkikTs2qlTvL1IJPL47ju0mzsXoUFBDACORUZCMy6ON5PLRfr798OkbVuWX1CAWRYWACB6+PAhwsPD0XP9esFw/Xp2ZsYMvqS0lJVnZ7M+ffrgwoULvFgspp2fNo1G0Bw/DpPBgzF37lxu9+7d2LFjB/z9/fmePXtyIpGImoV9+y0FoHv2ECHs0IHIaHAwJUE++YT2PywMiI2FcPIknO7epS66tR1p8/PzkZ6eDqVSiZqaGlRUVPClGhocLC1JMReLiay/isy+af3pa9ChQwecPHoUqSkpcPvlF1KGVqygdRURQRbZc+eIkA4ZQg6H0aOJuJ04QWPMXgETExOUl5czxbBhELfS/CkiIgKMscb23m3byCJ64AARpz/+IHJ84gSpg6NHE4nYsoWSQg3J3PHjRLxOnAA2b0bkxIkY8+efYMbGZO/19qZr6dCh+tds3tx8w0pKSD2uqqJjMGMGNch7S3AcB6Off4YsPR3yrCwOACZOnNhy9+MW4O7ujujoaOF+VBQzlUpphNw/Rd++lFRQKlG1cyeurFkDk8pKlAwbhpKXL3lfX19OLBYjKioKJiYmzMjICObm5kJUZSUz/eknmLfytk+ePBHatm3LJW7eDG+xGFxkJCUpfv/9lQkKlZ0+NjYWnESCyd99Bz1dXeDSJVTv3y/qMGkSqZbTp7c4y70RPDwoWWJuTvb5JmCMwdjYGMYNmrIJggBdXV2kb9kCt5wc4eL77wsPw8OZjo4Ob2dnJ+rVqxcAcl/k5ubi9u3b/Mj+/Tkr1XW+YQOqP/sMYQoFhMJCbubMmdDU1FSVN2Ds2LEwre0LcGzwYCV34YLomVjMXNu3B8/zogUrVgB//smkbm50387IoGTI5s2UUFUoyIFRVET7FR7eyFXy9PFj6JaUwMbammHECLoP6etTwujCBerDkJND6zYnh0j7uXOvVH81NDRQUVFBv4SGEmGuqcHt27dx5MgR2Nra8r179+YqKytRXV1dN8N8x44dAAA9PT2qadHUJGfDxYtUWz98OH0vXLhA97awMLKGz51L5zgoCKLUVBiuWIGVaWkQR0bCXKGAaY8epIzv3k2kHKDmbTt2NHZ3dOlCvQFUjT9bSFgeP35ccePGDbGBgQE++eQTSKVSSKXSuhuttbU1PvvsM4jFYjwYNUq4du8ey/rtN9jY2LAhQ4ZwNjY2KCoqwq5du1BWVobS0lJBLpfzPj4+okBVHfqHH0IwNWVGn32G/EOHYDZ7NiX45s59dQnXv4D4+HheEIQTAKwB3Pnuu+8+/Prrr9W2cjX+q6Em22qoocZbY9myZWMkEsmOyZMna6nqRv/HUCqpE/TatZQlnzKFalqLikg5UgXgRkYtvjw1NVVpbm4uKi4uRkBAALy9veuJdlUV2SC7daP3LywkYrl4Mam+xcUU2Hh4ELGPiwP69YNWWhomCgLbnJvLsk+cgO3IkdQ8LCiIgp2FC6kLc4PGShzH4cCBA0LWkCFs2sSJpNbUdmo2NDTEuHHjUFRUhNOnT7O+ffvi7NmznGZVFSbq6gIiEeatX4+L77yDGz4+ONexI8Z5eUHXxwc4dQoWI0ZQEgI073vJZ58B168zrF+PsR06cE9mzYKJiQl+++03wd3dnQPI5mxmZoZjz54p3P74g3Nyd+dOpKYKT548YQBw/fp1Lv3gQYTcvg22YQNZmF1dyZJrb08ElDEKJtu0IcX7xg3a2YwMKHR14X/pEiVD5s0DwsOR5u8vXHv8mJmYmEAkEkEsFnOdu3ShRAlA6m9VFQWlrWHcOCL7+/b9s/WUn09k8vlzzA0Lw7H583n07s2hTRs6H9eukfX0r7/I7vn119Q0zcSEXAuffkrJnvJyvKrLeGFhIQRBALt5kxqE7dnT7DkWFhaQy+X1M9hTU0lV//nneoXKyoqSQMOHE+ny9KSAe9o0ItoLFpDd9bff6No4fZqs0gMGQENDA5tmz8bEiRPhIBbTWn4TSCREcDQ0yIqrasZ08OArlbMWYWQEn2PHcN7DAyNGjGhdFWsFBmVlvPGNGyKcOfN2n9sCqjQ0EOXjI6SXlbEPT5yAlkyGXoIAjBnDqWYKB5AKxwBAqVSyszo6wtU//0SwSMQkqnVaC7lcDoVCwa5cuYIhhw/jpa0tZL/8Qsk6lX24CV68eIHLly/j2rVrAMj1Mn369PonBAbCVanEHokEdnl5vLObG2fr6wvxli2vXG84cIASQnJ5o07nrYExhnfc3dk7168DW7Ywb39/lpWVhaKiIlFqaqpy7dq1olrnES/T1hassrNFZh4eOLl/Pz8oJ4dT9u6N+NhYwXDIEEyePJnp6emB53lIpVIolcr68WWCAL9ff+WerViB4Pfeq08stW1Liu3331Ni4tEjuqaXLyeClpNDNulFi2hte3nR/fiLL4AZM2D322/osHcvDnTowH/QsSPHVOMZR46sf/8vvyR30erVdN+wtye1WalsVvcfGRmprKmpEaWnp/NmZmacs7MzpAEBwMqVOJKdDdU9648//hBEIhHv5eXFmZubs7S0NACURHJwcGicFZFI6N/ff9P+REZSj4t162h7zp6lxMKHHwK//gpJSQmWeHvjert2fPHFi5zpvn10z+3cma79QYPIVdBSd/kPP6R9NzamxFIDZGdnIyUlRTxt2jRYWVm1mrnR1tYGBAEu3bpxLlOnQmjTBoyxukygubk5pkyZgi1btsDY2BhOTk6i5ORkRWBgYB1nGDRwIJ4vWoQ/1q3DyIULcX/WLL7dpElc5Q8/vPW1/zbgOA6MsS4SiaRfu3bt8ODBgyEA1GRbjf9q/LOuwGqoocb/t1ixYsUCLS2t7VOmTPn3iHZwMNnuZs8mO97GjRRQPHlCwUdrSldNDZGh0lK8m5LClTx9ig5Xr0Jr9mzI5XLAzg6Zs2YJ577/Xolhw2hu7pYt9FoDA7IN37tHxC85mf7/6BEpiXl5QFQUdE6fRveOHVEVHi7g/n3qgnzkCJHerCxSFysr6zYpMTER2dnZ7MPZs2Hy559kiWzQSdfR0RGdO3cGz/Pw8vJCQEAAJsyejbaJiYBIBP2XL2Ho54fAwEDMCA3ldG/coPrJ9u1J1VLh8GEia8eOAfb2EI0cCVtbW4SHhwvl5eWspKQEa9asUW7duhV//fWXkKqpKU51cuIehIVBLAisTZs2/KJFi/DuO+9Ai+dR9egRKufNw+4jR/DwzBk65hxHbgJ/fyLhCQmNa3udnCBwHP748ENSZWQy4MULiBQKTN61C1MvX8bk0aMxvm9fDGpIYE6dosD6xx8pKK6ubn5uP/2Ukhlvg8JCClKrqiiAX7EC8PAAHx2NexIJl+zkRLWhKsTFkaI8bBgR3cePSakCKKlSU0MOiqKiVj9SX18fEokEIlNTIq1NUFBQgKysLNTU1BDRzs+nRm3l5c0dFd7elOz5+29agz4+pI4BVFf53nv0f7mciLi7O56NG4f79+8DwFtZ7AGQiyQrq/53xijA/ydjfAwNIZw9C8bzcPwHynSftDSu+vp1hIaGIkc1Y/wfIC8vD2vWrMHt27eFHuPHQ+vGDXIB3LtH51k12qsBRCIRunXrxlzT03Ft5UocbPKco0ePKl1cXPgl7u64P26c8MzQUMA337RKtBMTE/Hzzz/j2rVrcHFxEb755pvGRLsWPXr0wITvvsMza2t2dOhQPLGyonM/fDglg1qCaoybmxuNv3odSkvJpn3wYF0/AXt7e3Tq1AmTJ08WzZs3D7Nnz8a8s2e5aadPi0b88AMenDqF5Lt3OejoYF/v3kqRm5swZ9KkupITjuOwePFimJiYCDt37oRCoQAyM6FTWcnOZWQIsbGx9Z8fEEBraulSsnoDRKx//ZXW8ciRlEA4cIDWeqdOpArL5YBUCpNffkHMwYN49OQJ92jKlGY9EbKvXkUOgON6eoLSy4uuGaWS7rv9+tG16+RExwFASUkJ3NzcwBgTTp06xa9ZswYX168X4pydYWRkhIKCAgQHB3OLFy9mCxcuFA0dOpQBQHl5OQAgIiJCeOX6tLAgS/jx43SOfH2pAdyiRVS64uIC9O4NtmIFbAoKOL2kJFQPGUJJriFDKAnXGtEG6NpcuZKcSg3uS/Hx8fzevXsRFBSEN5p5XVxM34VEtJv92czMDIsXL0ZISAgzo7Kfxk+SStHm/n3YOTgIV+bOxaX8fK7ozh3ELlvWuHP8v4xu3bpxY8eOtfroo49kfn5+4HneMzQ0VM1l1PivhlrZVkMNNd4IoaGhtmKx+AtNTc2J06ZN0zZo2gzobVFZSepwSAgFZCtXUpB05069dS45mX46O5Pte9kyUkN//pn+5uhIQcqSJXCOjWXdQ0Px6NEjPG/TBg+uXVMmjhsnuqtUMpGOjqhPWhqRq2fPyA6toUH1umlppGTu3Fm/bSpiSF/myPzzT6F0/nyh3YwZFHAMH061zT/8QNswahTViOro4NatW4KjoyOMjIwYPvmE6vGagDEGkUiEmpoa9OzZs/4PWlpgYWEIGDqUrIKVlUTA4uKoAy1Z1AkJCaSmhIVR3Wj79qiurkZ+fj7z8fFRVlRUoH///iKFQoHo6GheV1cX5b16ieTbtws6BQWCNDgY0j174PP11zBZtEj4ycaGaWpo4IPFixE5bhxvbG/PyQoLybL5+DFtw4QJzfaF5xtMYHFxAXbuRNW5c0gYNw6D/f1JCR8zpj7JAVBixdGRyE9YGAXWDx40rvmTyWg9vAnmzSO7u7MzqUrTp9P6qCWf2p06oUd5uXDmzBnB09OTArOiIlKS9uwhB4W7O1nJtbVJVWaMHAyjRlFgqqFRZ5FUdTgGgMjISEFbWxtwcmJ1434aQBWkW1pakmU2M5PWfXBw4ydWVxN5srWlRJCDQ33zJ6CZigVQl/Nt27ZBEAQMHDiQt7GxeW3QWVlZif3790NDQwPv3LuHtgMGoC5FoKdHyZsbN+rr9t+QeEdcvozMGTMwcfDglpsrvQp37sBs9WpmpKWFmxERQnh4OKZNm8ZM3rAjvApKpRKnTp1S2tjYsAkTJtQfixUrqPb11i0isefOkQraoMmcnp4eNM+fZwe+/x6S+/frXAg8z6OgoIB719qaYcIE9HN0ZOe9vASnESPQlKIUFxcjKSlJiImJYRzHYezYsXB2dn5lcwKZTIYpU6awffv24ZJSqbQbMECE588p0TN4MBHG2bMbW4Z1dalEwMODyiJe5UKYM4fWtqr5XkMUFUHX1ZWSLitXAtbWJduD8AAAIABJREFUYGIxHHv0QM3ly6iurka2trYoePp0MA8P+kxfXwBEuEeOHMk2btyIlJQUdMrLg+6lSxhSXs6OHDkCPz+/5utApdhnZ9M1P2AAkeDMTNqHX34h55JMRo6TWiQlJwt9+/ZlNrWuhIY4GBEBCxcX3L9xg9V4ePAjTpzgkJZGtm3G6P3HjkW1hgZyAgIEC5mMs/v11zp1Oj09HaeOHeNHLVokip86lXd1dWX6+vrNzpmfnx/8/Pxw5MgRdvDgQeHjjz9uxj8bQTV3PjWVElp791Ly8ulTSgrOmAFTe3uUAsKOjh1Zl+xsvuP9+xzX8D7ZGtzcKGn47bcoW74cO8PDhbKyMjZy5MhGs9tfiRs3Xjs+TDXlwd7eHn///bfo0KFD6Nq1KyyolAkAMLiqipU/eoTsrl35mKAgbrStLRjP/7OE3RtANXsbqJsEYVRVVeUG4NZ/5APVUONfgJpsq6GGGs0QGhqqD6C3RCLpIxaLfRUKhbNEItHw9vZmgYGB0rfuKN4QCgXV/f7wAxGjoqL6ESfXr1MQNm4cBZGff042102b6HGFgqzgKuvigwd1hEqRnIzoNWv4AQsWcFevXlUUZWSIKjU0oKOjA2trayW2bhVh5cp6NRUg8mhqSpbpL76gvzVRFO7cuYPs7Gx89NFH9cG7hwftQ1JS/fifqVOBAwfQu3dvtmvXLly+fBmdO3eGprk5WQRTUxvVdopEIsTExDRWfAEi/RxHSquZGR2TmhqyBn/yCQVq33xDwaQgkOJdS4JfvHgBqVQqDBw4sC7Syc/Px8uXL7mAgACWnJzM3x08GLYnTnCTCgvpdY8ewfbXX9nk8+dxITJSWT54MFfo5IT7/frBuawMunfu1B/7hw+pqVYD8DwPxhgUCgWys7ORmJiofPTokcjc3r5eiU9Pp5/ffUek3dub6l2XLCELfnw8WaP37SNli+OodvzlS1oXjT+Qztfly/QeN2/SemjbltTsVtS+bt26satXr7KLFy8iUEODlK+UlPrxR76+1DVaqSSCHR5O5Przz4msXboEHDuGjOxs7N27F7q6uvz06dO5vLw8Nm7cOFKsT54kktoAdrXHq6CggN4nJaVxLbUKw4fT+dy0iY5FmzZU375iBW3jF180G1MUFhYGQRAwc+ZMmJqavpJoJyQk4NSpU40e0yopwbOYGNTk56O0tBSjR4+Gs7MzKe5Dh75RwFxZWYmYmBikpKRg6o0bsNbXr3cHvAkUCjruc+dCMm0aJkyYwMLDw/mDBw8KM2fOfGPF6tGjR6qaWlFIw3nNKgQF0fn56y/qtdC3LzksGtTCl5aWYu7ataj+8cc6BnL9+nXoVVXB0sIC+PFHyHJycI8xrF27FmVlZejZsyfv7+/PnT59Gjdu3IBIJGKDBg1Cp06dXj+CqxZlZWV48OABFAqFCFIp3UsEgWroExLIfZGRQcRZ1Yk/MJDU4KVLKSHTgqsCYWG0lvv1a/z4unVkbT5xguzdtraN+ieo6pnXrVvHM8Y4fTs7ujdqahJ5rU1SGBsbw8PDQzh99CizPXQIbY4ehZubG27evKncsGEDN2/ePNaoYaYg0GeuW0f78tlnlOC5fJmcMV9+2WIfB6VSyVxdXZspsMXFxbDMyUG3ykqYdOuGy5cvc4/s7THi7l3oR0ZCr39/CDo6uBYcjNi1a9HZ3p7vNmaMSFOppHtQYiKcnZ0x79NPRbh6FfPHj+de15gvODgYq1atYunp6WjXWjM2pZLuB9XV9LO4mKzt77xD52nNGuDlS7BNm6DXtSsb99dfuJ2ezsUGB6PHKz+dwPM8yocPh+zYMTw/dgzl5eVYuHAhe2tny8SJb/Q0ExMT9OzZU5GSksLt3LmTmzVrFm7evAkDAwNck8mU8qFDRYMkEub6ww/Q+PhjSjw3HHH4HwJjDObm5nx6eroX1GRbjf9iqMm2GmqoAQAIDQ0VAxikqan5kUgk6mVpaVnVrl07PTMzM5iYmEBPT+/1HaTfBPPnk018xgyqQ5XLiYT27k3E19ubFCjGqC5VhZMn6/+vUmkaBBcRERGCgYEBPDw80LFjR7FK9XuZkYEBhYVEtPv0aTzixdiYiH1ODjW5KS4mYtvAHlpdXY2amhpWVdVkykinTvRzwwayQk6aBMycCauNGxEcHIzo6GhlTEyMaMyoUXDs2ZPeu4FSN3nyZOzcuROurq6Na9xu3qSfqoRAWBht8+bNZC1OSSH1MTS0XjWpDbTv3LkjyGQyHkAdS1J18o6JiYGLi4swaNAgEXfzJsRhYWS3XroUWLoUloxhQkmJCE+fYk7nziwzPBwbDx2CePVqnud5jLtyhbN6+JASDA0gCAIEQcDmzZuF0tJSODg4cEFBQfD09Kx/kirg/u47+llcTKqyUllPKK5cISI+fjyNZtu7t151qawklbd/fzo3EyaQ2rd6NQXrq1bhdeA4Du7u7kg9e5YPnDuXw44djecMKxREKORyUkH9/GgbPD1pnWpqQvH0KY4dOcK3a9dOUCqVbN26dQAAJycnel1LHdkBjBgxAue2b0cBAJOGypUgUOB97x7Zam1sqJY1N5d+Hz+e1uyFC7Qdy5aRK6CW7GhpaaGsrKxOgQKApKQkREdH8z179uRqamrg5uaGiIgIZGRk1D2nU6dOcHJygmluLv4UBBTV2lH/+OMPANSxWd/TE6Yffgjv48dxeccO2NnZwdraGqdPn8a9e/fQtm1bQaFQsNzc3Lr3Nf/mG1pTb4PKSlI0a10NIpEIQ4cO5TZs2IAjR45gWMNZxE0gCALkcjlkMhnOnz8vmJmZoV+/fsyslXnn0Namsgg3N1L27OyoXOGbbwDGwBjD+f79MVI1VxrAkydPMHLvXogPHAAmT4boiy8wp6yMbdy4UeB5nkVFRdU1ZtTT0+MXLFjwVpbWy5cv4++//wYABDZ0RjBGtuIhQ2h9xMSQArxkCZU4dOhARFqhoHUkCI1VyqNH6V4xYwbdXwWBlORvviErtqEhPX/KlBa3y8PDQ+B5nktPTxeqqqqY5pgx9cmk2Ng6lXrEiBEs3doaV69fx/U9e2Bubi64uLhwcrkc27ZtE/T09DB5/HiGzz+nDuK//kqJwh496PMjI+m6PnWK+mnMn9/ovr5lyxZBqVSyygalOkqlEmVlZdi5cyccKipg5ukJy169oFQqkZ2djZNKJd9vzhwubvhwXu7lhezsbK5Pnz7wmj9fJJVKqVSoXz9K6o0YQc6bP/9sXFLRFJWVQHExxNXV8JHLlclLlogsRo+G7Plz4P59WvdiMd2Ptm+n+9SYMXSv43lKkvToQTPTASLeRkbAqFHQDQpCycKF/NWLF7mOnp4waqEviVwuR2FhIZ49eybExcUxpVIJbV9fYeznnzPnr7/mxWLx20nJt24BLTgFWkPXrl3FXbt2RWhoKNatWwfGGCQSCby9vdmQIUNg3rkzg5kZNUm7ceP1jot/CVZWVroPHjzwAtC8WYYaavyXgP0nayvUUEON/zcQGhoqkUqlZ/X19X38/Pxk7u7uaGmE1/8YCgUFgTExpGI0nbda9zQFOI5DXFwcfH19IW2tfq0Wly5dQmxsLCZPnozHjx/j9u3bgoeHB+vQoQNOzJwpDExIYDINDSJzr9ovnqdtWr6cVNNa/Pzzz0Lfvn1Zhw4dmr8mKYkCKGNjIuxOTnVEOTY2FpcvX4Znx458/8uXOW7BgkYE79SpU8qMjAw2a9Ysrk6VmDGDFOExY+j39u3JNl9SQuRvzRpSHq9fp2A7KYkUYAsLRHzyiWBkZcWCZs0i1ejrr4Fr16DcsQNFW7eijWr01P799B4cRwG8nR3VUC9aREpu376Alxeqq6vx9OlTXLlyBcV5ecrZH38satp5OT8/H5s2bYKWlpYwd+5c9tbrpqyMyHbHjtRt19MT6NWLFF59fdrfuDgK0lNSgIICaib2hqqhCo8ePcKBDRsQsn07dA8daly7TU+gxItKGc/LoxrpSZPI9i2T4UWPHsh4+RIu0dHQ0tJCeno6MjMzlf379xex58/p+dHRjd5WEAT89sknGLxvH3Sjo6GjqnnPzaVAPCyMSilUtaiXLtE6rO22X4fSUlK3BYHOz7vvYtmaNRAEAZ999hm0tbVbVK8bYtasWTAyMoJIpVgPHw78+CMER0fU1NTg+++/b/R8cXU1HDIzcd/FBWKeh6KB0t2+fXvh3r17DKDEkZ2dHa3Dr7+mEUxvgowM2s+7d5vN1I6PjxciIyMZAMycOZNs3k3WVlJSEo7WOh80NDSEWbNmNR5l9ypUVRHJ37aNSOjChXihq4uNP/6I90UiROjr8zzPc8KzZxhtYACb589pLTZYd9XV1bhy5QpiY2PRt29f+LcwY/1VqKysxOrVqxEUFIR27dqh1SSBCqWllBAqK6NrRU+PiOrgwaScbthAz0tIILt8v3703J9+IqfGggWkmjeZKf0qrFq1Cp06dUKfPn1oXf74IyUsTEzqnQ+jRiHJ3x+3zMz4qqoq5Ofns+rqatZWLofP5cvwnDwZYo6j7WmqBg8eTOUny5cTCbexoWRC7X0mNDQUAPDll1+ivLwc27dvJ2W3tjzD69YteIwcCYexYxu9beaePUjfswdPvLx4p/79uZSUFGVxcbFo/vz5aLRGli2jsqG7d0np3b6dnAK6ukSYr18n51X37pSgMDGBcPUqoioq4Dd9OvTE4vrpCjIZEWhd3ZbvT8uW0XO6dyfH1jffUALj/n0IFRX4fvt2DB8+HK5NJiDwPI/ly5dDJpMpZTIZunXrJnJzc8Pdu3ehf+UKLI8fb+4Aeh2+/JLuV6qk8RsiNTUVx44dw+TJk2FpaVmfgOd52ufHjylpYmlJTqz/MCIjIxXXrl2LFQRh7tKlS9P+4x+ohhr/AGqyrYYaaiA0NHR827Ztt0yfPl1H9B+qtQJAKpKREX0pu7hQsN8EeXl52LFjh1BdXc0EQYCTk5Mwfvz4ViV1nuexdu1avlu3blyXLl2wbNkyeHl54fGVKxh09qxwdOJE5iCTKQcnJ4vwyy+v38YXLyjQ++wzCqLWrsWqVasgFot5b29vrnv37s0bUd24QXbMqVOB27cpCJ4xAwApEtu2bRM+PnyYyWbNaqQkVVVVYe/evXxhYSEWLlxI0dk335BVvn9/IlYBAaRku7mRJbFTJwqm9fRIKdbTIzWhrAzpLi4oMjDA/WnT+Pd//53LWLIE5Q8ewPDSJfzdvTumpqVB9PAh1SkvW0aKvqkpfcbcuWRvblA3XVNTg1OnTvFpaWncuHHjYFdTQ9v28CGgrY0XL15gx44dSsYYN3LkSGbZSsOoN4IgEFFTKoHERGoUBVDgNnfuK2sL3wT3r17F4YgIOD1/zg/bvJlrdg6zssjO2rAuXamkjvVVVcC8ebiRmQn5H38IfUNDWVM7PYqLiTDfvdvo4buJibi5ejW0Kiow7MgRevDZM0oozJlDCn1DrF9PqlcrVmw+LQ3c0aNAeTlO3r8vJLq6Ms/OnVFeXq5MT08XAYCDg4MglUoFjuO427dvw8bGRujXrx8zNzdv7E7p04fGn9UqUPn5+TA2NoZcLq8bI1VRUYHK2bPBnz2Lw0uXYtiwYdDQ0IC+vj4qKiqgpaVV/36CQKQvOrpZuUGLuHOHnjtzZot/fvz4MbZv3173u6enJ/z8/GBmZgZBELB+/XrBw8ODOTs7Q09Pr0VF8LUoLSUXw7Nn4FNT8depU/zQefO4HxcuhENpKcZu2QLRiBFk5f4XERkZiYSEBMhkMsyZMweSt5xvjn37iPiGh1OzRC8vIo3FxZQccnKiUgBLS1LDDx586wSVIAhYvnw5fH19MWDAgPo/fPQRXRM7d1KCxdWVCKqqj0d4OCp+/RWn33kHullZQp/du9kbqZzV1ZSo9PWlJJdUitOnT+P+/ftCQEAAzpw5wwwNDXkXFxfOysoKenp6KJs5E9fbtBFsp01jVlZWuHHjBh4/fqyUy+Wi9nfuwO3OHcQOH46OvXohNjYW77//fuMGfoJA17mzM93Xhg+n4+joSM3OJBLaryZz1MPCwgRzc3M2YcIEvNF3ZmUlkXCplBwwBQXUWXz6dLoPe3ri0KhRgu7w4axvk/4MSqUSK1euxIIFC5qP0+N5YNcuWsetzIhvBp6npOa1a9QL5C1x9uxZJCQkYM6cOXVj0QDQ99SkSVQ2NGQIOSDedl2/JZ48eYLff/8dYrG4ijG2u6amJmzp0qWZ/9EPVUONt4TaRq6GGmoAgJauri77jxJtQSAVddIksnOGhFAwWBsA8jyPI0eOCHfu3GH+/v58YGCgqKSkBL/99htLTU2FRwtqzKNHjxAeHg5jY2N4eXkBALS1tflnz56xwG7dmFZ0NHzkcqWbkdGbEW2gXmEbPJiswc+e4WMDA9zy9uYSEhL469evs5CQkMYKWqdORHzDw+uahMHHBwoPD9y8eROCILBH27fDzciIgqxadVtDQwOjR4/m1qxZg7KyMgqkli+vf98XL6gus00bUrfnzKHjNns2qb4hIaTUT50KADg6fbrg4eHBMq9e5cIGD4Y4NVVo06YN/EpL2cDvvkPqiRPwCg4GTp8GP2gQ2I4dYLt2EekRBKqFboA7d+7g3r17CAkJqScyYWHIzMnB2XPnlIWFhSJHR0eMHDmSvTVZOHeOFOqYGFJA+vQhtdfAgIjv/Pl0LiIiaB9HjGhs+34bKJVo99FHGN2pE3bb2XHBCkXzhElGBtl1G0IkIqv68ePA4sXIs7TE8+7deQwYIMLMmY2DWz09Uj4b2nkFAUYrVqBNWRniunXDo5Ur+fHXr3NntLTQ8bvv4K4aX9QQf/1F6l4Dss3zPORyOWpqarD98GFYWlpCnJHBtykv5ybv2oXYrCxk2duLho4YAUdHR0ilUqahocEAUk81NTWbZypqaohMNGhipZqXrPoJ0Jgg7Y0bEb1nD/QFQWkqlYpQG2BrNQ3UGaNyAFVd8avw++90P1i5stWnWFtbw9bWVsjOzmaMMeTm5vLbtm2rY4y6urpCnz59/mdZGF1dKsfYswdcQABG+/tzx7Zvh+zJE4zv2pUcDg1G+/1TVFZWYu3atUJNTQ1TNRV8//33W6/7fR1GjSL3S3Iy9TyoqCBl1saG+h0cOULXdb9+RMbfEvn5+Th37hwvlUpZs2P8zTfkjikvJ1fPqVNEWFeupASlQgGtr75CoJ8ftv72G3MpKoJ1a2R77VpqUrltGxHRTZuIzPM88MEH6NevH1JTU4UTJ05wXbp0Qf/+/RtlDAzefRdPZDIhMTFROH/+PGdra6u0tbVFx44dcR7AcBcXdPTyQnRlJc8Y4xoR7cePSd39+GNKBOfmkrPn/PnmzpcmsLe3Z5mZmYiNjeV79uz56ixGWhqVB127Rtfd+PHU1LE2IatS0B/s3MkmffEF9SJp0HcgJycHEolEkMlkzdc6x1EJ1pIlZA13d3/lpgCgKR/e3v+IaANA3759kZiYKFRUVLBGZHvIEEpY6OjQvfDDD998DOE/wM2bN3Hs2DEAwMyZMzXi4+MnJSUlTQgLC4upqqo6AOD20qVL4/9jG6CGGm8I0bfffvu/vQ1qqKHG/zJiYmKySktLQ3ie19bX128eRP8b+P13Uly8vOrnNRcX11ka//jjD2Vubi774IMPmLu7OycSiaCjowMDAwMcP34cgiDA1ta20Vvu2rVL6e7ujjFjxnAqsvcsKYn1W7qUVU+dCpulS5l2eDgnvnsXmqNGvd32OjmRtfniRUi++QaWK1bA18ODFZWV8SdOnOCabY+xMZHBb78FVq2CcvVq7IuP5x8rFBgyZAhzcXMjknzwIDWEqoVUKoVcLuejo6N5e3t7Tuedd3A7IkI4VFbGe8XEcKKffybbpqMjKb+dO5NlkOOA0aPJehkQAEgkSE9P53me58rKyngfHx82acgQ1klLi5kFBEA4cQKRL1/iSUiIUPjoEfI3bGAF5eV4NGGCsnzuXE4rPR3szh1wVlZEggEkJycLFRUVXFVVFa5duyYcO3aMpYhESuOvvuKkrq5My8aG9e3bl3ttw7yKCiKu+/eTFTonh8h0WhoFn506UTLB0JBcAkeOUDdf1T5HRpKSZ2JCj7+Nyl1dDeHhQxS6uyPawkIoLCpinTp1ar7GU1JQKpfjSG6uajRQ/d/atwe6dcOLVavgY2rKac+fT9tRVlafAFDV2E6bVq/mPHkCnagoOOzbh/zoaLCCAqadnIzrPj4o09ODu7t7XROtwsJCXLt2DXazZ1M9bm0ygOd5JCYmYt++fXjw4AEqKirw8uVLFHIcy7GyQqaDAwIuX0ZfQYDdO+9Aw9YW4gaJj1abJpWXU3KhYTf81iCRoEomg9X8+czowgX2yuZH69dTgN2Ca6URtm4lt0bnzq98WlJSEkpKStiUKVPQs2dPZmVlBYVCgaqqKuHDDz/kXldi8kbgOLov9egB9tNPcMnNRdfNm4lIHj1KCQQnJ3KUZGZSQ7H164kkFRWRTbtnTyrFSEsjwjNhAq3r27dR+tFH2Pz8Od/j8GHOTC6HyNsb07ZtQ9tx48BOnSJiNX06uR0yMihh5+BAx3DtWiItc+fSdZmTQ0kSGxuqn16zhu6lf/5JJDg/n0jdqlXkzsnJIZfI/PlkXw4OpuSRasKDKkGiclR88QX4Bw+w4/BheCQkCL2HDuV0c3MpKebsTHXX2trUoM3Tk/pqJCTQffzyZSL8I0YA7dtDRyaDRCLhjx49ylRJhWbnSy6ne0PXrvQ7Y5To3LoVKCgA8/DAxbg4KJVKNnXq1GZ9Q8TvvQe7tWuZX/fuLDAwEB07duTatWvH2dra4urVq4LQrZvwf9h776iqru0LeO5zuZfeUaqgVFFBUUABC4ho7L0mxtiiMZaYaExe8gtBExNbjMaoJM/YW2wxKlERpAjYKAKK0ou0S+/t3nO+PxaXjjF5ed83xvuYYzBMbjn3nH323mfNteZaq9+lSyw7MJAZTJ9OBQDj44nQz51LhNPVlRwuvr5ULXz6dNpbX9FxIy8vD9nZ2cjKymLW1tZ4ZfqCjw/tfVevUvR3375OBcRkGhoIDg/H6JEjody7N6UNJSZCbmmJQ4cOwdLSUhg0aFDXG5+2Np3rzZt0D//Maf7yZWvXhb8BnucRGhrKRo8e3a5eBAByjDo7k6M4J4fmsaIy+z8MPT093GtO+yktLRVmzZql5O7uriSXyy1UVFQmVFRULL579+4OT0/PHglvD/4/RU9kuwc96AF8fX3L/Pz8vKKioj67d+/eZIlEwhkaGgru7u4a1tbW//kPVFWRzLptPumKFfTaggUAY8jPz2fTpk3r1O7HwcEBqqqquHjxItTU1ODcHPG7ceMG5HI55+3tzdpW/U2rqOCNra2558+eyV8+fix6YGsLmVyOLwTh7xV4mzSJ/oqLwfr1w9TISJGDgwPOnTuHvLw8ft68eVzL7zs6ElFctQqPdXWFyefPc2rx8VBRRPr27yfjUpHf1oxp06ZxZ8+e5Y8cOQLHd98VcioqIJfLuWsiET/rm284JCWRQVheThWqp04lYpeTQ0ZwRQVw9Sqs6+q4dI7Dxx9/TAefO5ci6UFB0PbywpQFC3Dr8WNBJymJs+d5FFpZIdrMjA1dv1445eYmaBYWcq5//MFbBQVx8g8/RGpqqlBRUaEogsUWL16MCxcuiCSNjShJTGQlSko4c+aMfN26dZ2tu8uX6b5PmUJV1T/5hMhdYiKde0gIGe8qKiRF9fMj8nrlCkWJ/f0pQuLuToQ7Pp5IXFgY5Va/jkwZABYuRHlBAQ6MHw8lJSXGGINUKu0kOeYzMhCYmio8FwR26NAhuZubm2jIkCG4e/cuL5VK2dChQ9mNqVPxiY4OEQ5ra5Li//xza2E0MzNyHgD0mU8+AR4+BDt3DuNPnRLOTp/Ors6aBQDIzMxEQEAAHBwcEBQUxOfm5nIAMGrKFHA5OSisqEBsbCzi4+OFuro6BlD1ZX19fZSUlMDIyEjw8PBgvXr1QumKFdDMz6d804AAcr54eb3aKSGVvl6f5mZYWVlhx9y5bNOHH0L54UO6/q5k215eXfdNb4tjx2hs/uQeCoKArKwsBgAmJibYvn075M3t4AwMDFBbW9tZVvt30NREpPn33yniuGULKRRkMpqzb71FpPXnn2mtjR5NBQsHDSKyFhBAcyElhUgMY0BFBUqlUvxx6ZJ8QHGxaPDgwRjQ1IRSiQTRpaV4amEhDNfTY3B2bo1wfvIJEWkDA0r16NOHSLaianRcHK0JFRWSJTNGsv3gYOpSYGhIeei3b9Pr6em019TXE7E2MaHzVlIiR8GSJXQNRUXkfFFVBTQ0EBMfz6soKXEu+vq0S0VF0W/4+BAhdXamOS8IFMn08SHHyY8/dhpaNzc37sGDB8Lhw4cZAMydOxcDBgxo/YC3d2fSxxit/7VrgepqSHhe4MVi1qm6u6IyetvoahssWbKEHT16lNls2QLtOXMwuKiInANVVZQ7rq4OfvlycO+8Q/MZoHF78YLGMy2NHH5dwNLSElFRUbC2tsbx48excuXKdooQADT2R4/SnpaeTqqu0FBKxfm//yMnCihd4vbt27y6ujrUV6/mwHE0177/HkJ0NDQaGqCkpPTqh9eYMaQW+ve/u03LaEFcHCk2/iaysrLA83z3tVROn6Zxy8mh1CRX1/9KK7Dy8nIA1FbRy8uLAVRg0cvLS9zY2CjesWOHDIAKgNp//Md70IO/gJ6c7R70oAft4OfnJwLQD4CHRCL52sbGRm/69Omqf1km3BbPnpGB2LbqNs9T+69x4wBnZ3z11Vd4//33odshN06B8PBwxMTE8OvWreM4jsM333yDxYsXw6w5CouGBsDcHLKAAKSqqyM9JQXj5s5F6v79uFJUhA0bNnQyzBsaGhAXFwdXV9fXI+JxcUSolyxB9YQJ+KW0VNDW1uaXLFnSYkkHowfUAAAgAElEQVTk5+ejIDwcpuvX4+WXXwpDz51juHq11SC8do2cDJGR7Q4tl8uxc+dOmD97hjcWL0Zpv354uXat4PXTTwx6emTwjh9PEaUFCzrn+s6ejQKJBDccHeXLy8tFMDKivO9hw4gEmJqSkezkRIZcYiKRrbffpkhabCxCExMRc/MmhoeEoF5FBUnDhwslamotzwkzMzMUFhbCwcEBBc+e8f0AoaRvX5EkPV2WX1YmkotEWHHgAOOCgqC8axcZq5WVlKfu7k4GWMcKuMXFFKGbNKm1cjJAxqihIRncCshk1CIoN5cK2S1b1n0eKs9TlFxdHf8ODORL6+u5KVOmtDf0Wz7KI3bqVKSbmmLy998jKChIHhMTIxozZgyioqJga2srT05OFjU2NmLdunXQe/6cCI1USuSM5ylvddcuOiddXXImDR9OBZC++w4yiQQ7Tp6ETCYDAIjFYjQ1NbX8CwBqSkp4+/x5FBw/jqvNao62UFFRwaJFi6CnpwfGWOc+xoJAlcujoylS9957rRHDjggLI3J0/nzX73eBXbt28fPnz+fMZ8+mdduhoFoLfvmFVCxdKWRKSigKGhn5p2S7uroae/bsaffa+vXrIZVKcfPmTaipqQkmJiZs4sSJr91mq+UcAgIoN9jXlyK/27ZRKsi335JaRFeXXjt3jhxoil7rfwHbt2+HlZUVP2vWLK7t/llbW4tdu3Zh2LBh8PT0hIaGBnJycmBqavrn16FIUzAxofGfPJlSHY4fJ+KrrExR4VOniNj9RQdjRUUFfvjhh5Zieq9EdDRJ1e3tacwOHUJXrbOqqqpQVlaGixcvCp6enmxo26Jcv/5Kzo02lcCrq6vx22+/8YX5+cLI69dFNRIJBh89Cn3FXq9AURE57V6hWjpw4IDQV0eHpSYkYP3+/SgbPhxhGzagpKREaGho4BtTU0XKDQ3QHzaMn798eevgf/EF3ff4+E7H5HkeO3bsENzd3TFq1Ch29uxZoaamBitWrGjvELhwgaT+Dx7QfTp9mhwT8fHkSGl+1u3evRtisRhLly5tFyHPzsqCYVYWlGbPxqEFC7Bm375Xz4+8PCL306e/Wk6+di3tTa/Z+qsjDh06JJdIJGx52/HqiKwsUnZMn04OuL/5W6/CiRMn+IyMDG7YsGHo378/2gYGKisr8cMPPzTK5fIVgiCc8vX17SE7Pfj/DD0y8h70oAft4OnpKXh6epZ6eno+CQ4OPlxRUTEkNTXVfNCgQeK/ldNdXExEavHi9rI8xsi7npwMuLggKipKcHBwYN1FqgwMDBAXF4ewsDAhOzsbUqmUTZ48mYyPhgYyMo2NwY0fD4NevZCfl4cLxsZIaiYyhoaGLdEwhUc+MzMTV65cgZ6e3p9XAgaI4DEGPHwIib09LCws2POwMAwdP54BQHR0NC5cuIBiQeD5xYt5t927OTZuHEVp5s+n7xoZ0TX7+LQjihzHYdiwYXA8fhwajY3QnD4dRmvXsjwvL+goyEt2NkVDRo7sbETPnw9+/HhknDzJDfzlFzCplCJwn3xCkbmRI4n0NisJkJpKPW8dHUn6rqGBvn37wtjaGkVOTkjPz8eE69cZzxga7ez4+vp6VllZCU11dQzo3x/9v/ySOQQEcIkTJ2LM1q3ccG1t5lRbyxqzspAqlQpGEyeS3HjnToq69OvXOQKVmEhS2a+/ptzJtvNLJKLoU9uK9RxHzgITEyLS/v5EmLqKbO3aRdf12Wd4kZnJ19fXY+rUqZ2Yx5EjR2Q3b97kdGpqBJeVK5mepSV0dXW5hIQEITMzk02bNg2enp6ch4cHbGxsoK+vD87CgmTHUinJ33/8EZgxg6JKQ4aQ8+Ctt2iMLS2BqVPB6eigqKhILpVKOQDo06cPL5PJ2KZNmzBs2DA4OTlBUlcnS6qq4h5XVkJLS6ul37EC6urqqK+vF3R0dJiBgUHna1ZEOkeMIGN+zx4iI4MGdZbESqUUmf4LUtInT57wWlpanKmvL81ff38izB1J//r15FTpqIoRBIoafvYZVWP+E0gkEowcORLh4eEAqOq5sbExDAwMMGDAAAQGBrK8vDw8ePBAGDRoUNeV8GUykjlra9MavHCBigKeOUPR2MpKchwoK5MKo64O2LSJ1sXQofReXR0RBh0dul5Hx9car4iICGHOnDmcZnOrNgXEYjGsrKwQEBCAqKgoPH/+HPfu3UNcXByvpKTUfaHBS5dIar5pE63nCRMo0n3pEkWrm2s3YNgwIlNvv017719ICzp//rxcS0sLbm5ur2bpVVV07G3baI7n5dFYFhbSHGyzPykK6qWlpQm5ubmCk5MTq6mpQV5eHjIFAQ1eXtCysUFtbS1Onjwp3Llzh+nr6/Pe48aJes2bh74ZGdCJjqaIcFun76NHJM2eOrXLU6yvq8OjCxfYgr17kaepicdublAvKxOq+vXjTe3smLGxMfP87Tc4mZiwGw0NbOTIka1k1tOT9qaUFHL6NV9PfX09Tp8+zXMcx2bNmsVEIhEsLS1ZSEgI0tPTBbFYzGQyGWovXULj8OFQ7dePUmfEYko34DhydBkZAVpaSEtLQ3R0dEvFfQXu3LmD369dw/2XL5HYpw8q9PTguG4dmoyMoNyFwxAARfkLCih6P2pUlz3LAZDqZvHiv9Wa6+DBg/KamhrR8uXLX12nQ0eHlD4zZpBqxMzs1Z1A/gZsbW2Znp4eXrx4IURGRrL09HSZk5MTB9Ccs7a2FqWnp48XBMEnKCjokqen559IbnrQg/8Oesh2D3rQg27h6enZFBwc/GtDQ4NdbGysjampqVi7G8letyguphxtb+/O75mbA3v2IFRVFbklJczb27tbz71EIoGLiwvr1asXy87O5nv37i04OjrSh42NKY9y0SKAMZQePAijlSvxwNMTEokEDQ0NsLKywsWLF4WwsDD24MEDIT4+XoiJiWEikQjPnz+HkpKSvE+fPlxNTQ1EItGrIwjjxgE2NuB27oT5xYtMed06lJeW4tz585g5cyYmTZrE+tnacszHh/Lz3nqLok9TpxIx8fCgNlvjx7cj3GKxGKI33wTGjoWIMVyzsRGeZ2TA6ZdfGNauJUN74MDWPuMdoLx0KSRJSYj74ANYKXo3GxqSTNbJib6nMIJzciii9+23dD3NpFZHRwf9LC0xaMIEZPfqBVOZDKMfPWL1OTkoMDXFxq+/RnxeHjLMzWH08iUkWVlCpZ4e1CwtWb6hofBg3jz21NCQH75qFQdz83ZGt6Jlj4TjKMK/eTNFfjsYj7W1tajy90d1WRnUhg/vrDrQ06OIrZkZOR8aG4nkKsYyNJTaOS1ejJjUVCEqKoqTy+VsdId2Wk+fPsX9+/e5mTNnwu3ECaa5bBmgrY3S0lI8fvyYCYKAyZMnQyKRgDEGLS2t1nmhyE00M6PoXlkZKQQqK4nQPX1K0cY2jpH09HSB53luzpw58PT0ZO7u7uA4DsrKylBXV0ffjAxu4K1b4N96i3/27FnLRS9ZsgR6enooKyvj09LSuNjYWDg4OHSObCvAcaRkmD2biKKvL42RqmprjnlMDK1NF5euj9EFsrOzucrKSvmAgQNpEN59lxwfHaNojo50Tzue308/UcuhDz547Ygrx3EIDQ0FANja2qJXm+KCI0aMwMCBA5GYmMgiIiJQUlIi2JmYMFZXR8qI1FSaZ4cOkeOpVy+Kgg4ZQvN/xAi6R3PnkrOnqIiKHC5fTk6K8HAaw8mTyfkTGEhredUqksK/Ilonk8lw79495uLi0mUNDG1tbZiamsLQ0FCQyWT8pEmTOC0tLdy+fZuZmpq2jyq7ulLUetkyug5zc5p3hYVE/r/9lop8tSUzvXoRIT54kHKoX3O8MzIyhJycHM7a2rpbxycAaolVUEDjwXE0z/v2pbZd9++T46LDb+rq6rLQ0FAWFhaGyMhIJCQkoLSwkNc4fpwdz83F/fv30dDQwJSUlITVq1dzenp60NDSgrK3N62nX3+l54iCRD5/Tuutq2JmAQFQmj4d0cOHy/VWruTcPvkEw6ZPh5GFBbM7cYKzWLGCmdvaMo3cXCZbswZR0dF49uwZ/+zZM5aYmIjEp0+RWVQEy1WrwGpqwEaNQmNjI3bu3Imqqiq2fPlyplh/EokEw4cPZy9evEB8fDwfFxMD7w0bWO716+DLyyF+/32IDh9udRTMnk11IAYMQF1dHWJiYmBhYQGFA43neZw6dQqjRo3C2LFjYTJkCBydnJCdkMBH5eSwxidPYKqs3FJbox0GDiQnqrp6lyoDNDWRWmPu3O7JeDf47bffhPT0dG7VqlWvzlFXwNGRUqd++onmyF9sjfdnEIvFMDY2hpWVFXvw4AFqamq4tnu8pqYmnJ2dJSUlJUZlZWWTg4ODT3t6ejb9oyfRgx68Bnpk5D3oQQ9eC1u3bl2gpKR0wNTUVMXHx0fdxMTkz7+UnU1GbERE9y1A/Pxw59Ejod++fcyqm/y4bvHyJRHt+HiK0CmIUHU1Xp4/jxRLS7x48UJeXFwsUlZW5k1MTISFCxeKUlJSUFFRASMjIygrK8Pf3x/jxo2Ta2hoiK5cuQI1NTWoqKhAVVVVWL58OetOYi4IAs6dPi3H3bvc5IsXWfDBg/IZb77ZPvxfWUkGso0NGRsffkgRvkGDSOrZsc/phg1EYHR0ULxvn3Bk3jy2euNGaKupUb72lCmdT6S4GKitRemXX0Ly66/IffwYdhxHx87Lo+jyiRN0PyoqKMqXnEw5qv37tz9meTn9mZqSsRYZidSJEwWTtDRW8vnn6FNXh6LcXFRFRCDbxATuUVHY8fHH0NDXR2VlJQBAT09Pvm7dOlFERATkcjlGjx6N2tpa7N+/XxCVlbF5DQ2wKC4mwqKkhLq6OkRGRvK1tbXCmDFjRPv374f7o0dCvoYGG7F1a0urHplMhszMTPTt27c1Z7C0lCJG2dkUZcvJIafL/fuAiQlOnjyJ9PR0rF27FvodqmTv2bNHqK6uZm8uWABrFxegtBT5RUXIyMiAq6srRCLR66UXZGdTNDskhCLKv/5KhK6D7Pj27duCVCplb7VtL9YWL18CT58i3thYuHHjBvP09BRcXV3bdQlQtLoRi8XYtGnTn/agB0DKjwsXSEmgpUXFnx4+pFz5FSv+/PvNiImJQUREBL9u3bpWD5EgkPx/+3aKGAPk7Dh0iIz6tudQWEjz7y/0eQZae7kvWrSICly1RXExcOECCoyNke/nB9vkZMSeOIGR4eG097i7t+4L2dmUCpKeTlHR7dvJSQdQznJUFJEUNzfas774gqKAHbFiBa2f06fJqfPwYSfHwp07dxAfHy9s3Lix2/2jI/Ly8nD06FGsWbMGOjk5kI8ejYRbt+BUV0fzqaM6obyc9r0ffqA6CB0hCORU+fhjmpOvUSVeEAQEBwfzDx48YLNnz2Z2dnZdf3DdOqql0FER1NREa3DNGmD2bDQsXozH0dGorq4WYmNjmbOzMzw8PHDs2DFUVFQIH7//PmN9+6IuOxsyuRzq6urYvXs3+vbtK8ycOZO1zG+5nEhbZCTtGyIROUp0ddtf+7Fj5ATw9gZycnCkrEyurq7O5s+fz7Xch23bSHVx8SI5TsaPR25uLjIzM9HY2IimpibhxYsXrKamBuqVlahlDL05Tt5gaipSVlbm3377ba5blVdWFiAI4HfsQOn9+wh2deUn//Ybp56R0dn5BKCkpAQHDhzA5s2bW5xnL168wLlz5/Dee+91ygMvKChA9rx5MMrPF2K2bmXjfXyg1lHl0tBA8/7UKZL4t0VxMeX1/41A2+XLl5GQkAAAsLCw4BctWvTnBQoDAuh+XL9Oc/AvEvzXxeHDh3krKyvOx8en03s8z+PixYv1aWlpJz/99NN3/ysn0IMevAJ/reliD3rQg//f4osvvjjX1NRkmp2d/fGxY8dq0tPT//xLUVFEKl8hN/vd0pJ3jItjFh2Ly7wOvLyosJaTU6tBvWYNcOECzJYvh5eXF1avXi36/PPPsXnzZu7NN98UcRwHOzs7uLq6wtzcHOrq6gAAxpjoypUrsLCwQG1tLUQikZCbm8tOnTrFd+WUrKiogCAImDF7tmjwxo3sjwkTYNG/vwjLl5OxqYCWFlXSVlcnY3/fPjrXxESK/DQ0tD/wgAFEzB0coL9pE1u/bx+CN27k5fb2FBHvCp6ekL/7Lk706YPKGTNgt3gxSbArK1v7cScmUuTOzIzuS10dyZ4V/WUVbahmz6aIs1xOEbGQEPR1d2cNKirQ3rULqKmBwddfQ3bjBqxOnYJw+zZWvP8+KisrwRiDrq4ujIyMRAAQFRXF3717F5WVlfjxxx95i9paNiYiAsqCQLnXzYbXnTt35LGxsSwmJkZUV1cHuVwOoaEB6jo6giLCFxkZKd+7dy9Onz6Nb775Bnv37qX8Zz09Mp7ffBNyHx9UPHuGkl9/Ra4gQCqVQjFPu6oFMGrUKAYALxMTga1bUVlbi2PHjiEwMBA5OTmvR7QFga5DRYX+OzeXHEDDhxMxyMtrKUYkkUiYIj+7S9y7B+TlwdHRkX366adwc3Pr1I5PUaOgqakJFy5cEBRtpF4JZWWKQH74Ic2LTZvoHj94QIT7NWFra4vy8nJOUaQMAOWrp6RQHrQCvXsToW2LpUspn/QvEm25XI5zzaRdSyIhNYZUSkR66FAiN6GhMNLWhr6/Pw7961+o1dSkKt4jR9Jay8oiR8Ybb7QWQTt9upVoA6Q8uXChNcd9xAiSwHZcnwCtm7AwWs8ZGXR/V62iSHgznj59Kvfw8Hhtog0ACQkJwoIrV1A1bx6/PyhI+G3CBPx+8yZeWlt3Jtru7iTj//HHrok2QPvL0KF03fv2vda9ZozB29ubmzRpEi5evIh9+/bJz5w5g4CAAFRXV9OH4uLo2rtKZRCLSVb+ySeQP3qE85s24dGNG0JGRgYaGhrAGIOqqirc3d3R0NDAZMrKYCUlUFNXh5aWFkQiEZYtW4aamhph165d2LFjh3D48GE+LiGBlBRvvEGpMDxPygXFegoLo3sRFkbyaBcXYNYszJ49W5SWlsYlx8TQ2gwKojH55htyXhYVAaAiWx4eHvDy8sL48ePZlClTIBKJ+HXbt2PDjBlY9OOPosaMDEyaNKl7og1QxNjeHlyfPjA4dw4jVq7kLk+YgNBHj1oXzRtv0B4MdK7mDbREjZOSkuQdnz1GRkYYEBCAikuXmFZgoCC3tERjx2JnysqU3uLr2zo+CkRGkiLgb2DGjBl4++23sXTpUhQXF+P333/vdH6doEghy84mJ+h/CYwxpKWlwd/fn1e0A1OA4zh4eXmpNDU1LfPz8+spDN2D/9fRIyPvQQ968Nrw9PSUjxkz5lFwcPDDtLS0OW5ubuJujcmiItQ9eoTcFStQUloKHR2dduSlvr4e586dE56lp3MuHh7QLCkhCdzr4OlTMoLXrqW8xbbnoCD4r1lFXSKR4MWLF/yTJ0+YioqKsHbtWubp6QkXFxemr6+PyMhIFhoaivDwcISEhCAyMhJPnjzB3bt3ERYWhoiICDxLSkKJoSGc7O1hcPEiyRrLyymCzHEk6XRyoshMRESr/HjOHDIQx41rPSFnZ4pGPH8ONmoUioyM8LCsjGnV1Ai93n+//WD7+wNFRUiIjRWExERWaWkpH/7llxz69KExaB6XwsJCBIaFQb9PH6ivWUMRazs7ijiUlVEU6OxZOg87OzKM9u6lczYyAjd+PG57eeGWoSHc5XKw8nLo29tDu08fKPXtC/UZM6A2YgTmrl2Le/fuob6+HrW1tcjOzmZyuRyPHz/G4NJSYWhYGPJdXVnamDFCYGAgb2VlxampqSEiIkKwtbXlSkpK+KioKCaRSATLFy+Yy9SpTLdZenjp0iXWv39/pqmpicrKSjQ0NMDc3Bw8zyO/oAB1ysoQ7duHnORkZEdGIiohAbFSKWQyGSwsLGBra4szZ87IEhISBHt7e+7mzZuIiooCz/OYPmAAVCMiEGtsjOTkZADAmDFjcOfOHXlERIRgZmbGKZwy7RAVRXLl4mKau9u3U/7506dEap2cKMK7bBmwaRNU16xBqUjE240bx7qU9e7fT4So7XzogOPHj6O8vBxjxoxBYmIiq6yshK21Nd3rkBA6Fz09yjf18aHo36pV9GdvT5HAqVOJMMbHE/G4epUcFosXUyRKT6/LKJxEIkFUVJRgZ2fXKjHmOCLSEgk5v6ZNIzmxmxs5IVRVyQkRHU0R4Ve0U2oLQRDwPDgYwXfvYvCRIzDLy8NAsZjW0NChNLZLlxJ5nzMH6NcPYl1dhEdFYcmSJbTX5OUBT55AvnAh7kul+NXdnY80NBR09fTadz0oLydHx+eft143x1HE2Nq6+/xyc/PWYmpLllA6wdChqLt0CXfLyzlvb+/Xq5ZeXg7BwgKXtLVZibo6UiwtYe3mxrzXr0dNTQ3CwsIEqVTKNzU1cb169aJri46myOz333dfJBCgeeHuTpX+fX1pv3xF7mx+fj6KiooglUohFosxdOhQrqGhQZ6Tk8PCw8Ohp6fHetXWAh4eyBWJ8OTJE0gkEqipqaG+vh5PnjxBfHw8shjDqfJyDI2LE+Y9fMhcdu9myTk58qSkJM7Ozg6nT5+GkpISvLy8aK2MGdMSJVdXV4eTkxNzcnKCvb09CwsLYykpKRjj6Un7miCQ6mDSJFKSNDXRnFY4FlRVSdny229QKS2F89atUPnlF0hsbcHdv09EXVeX8vklEnIO+fmRg6x5nVdWVuLRo0fM09MTSqamUPL2xvCZM6GhrEzf6QoJCeQIkkhoXwCgvXQp8t5/HxFRUZyrqyvEYjEpIUaOBHr1gkQiQV5envz27dusf//+DADu37+PyspKJCcncwMHDkTHvUcikaB3797I0dVlwYwhLjWVd96yhcHDozVNxMKCzuWPP9q397t6lfaBP2m51/VUImeqtrY2Bg0axG7dusWUlZVhamr6ao8SY3QeWVm0L7xOfZS/CEtLS1ZaWsqnpqZyBQUFcHd3R1unyK1bt+qKi4tvCYJwxvNPeqj3oAf/NHrIdg960IO/jNDQ0EK5XP6Zu7t7917+L75A0h9/4Fx1NeLj4/Ho0SPBw8Oj5aG8Y8cO1NfXs6VLl8LIwICIwtixr5db+NZblDOoKPalwJ49FLHrTvrYDfLz81lhYSF69eoFQ0NDpogsGBoawtnZGSoqKnj58iU0NDSgpaWFoqIieHl5wdvbGzY2NkhPTxdmzpzJ+js4UFGi3r3JqH34kKqxAkQyhgwhAnLwIJ3j8uVkCInFrQbzmDFU8OjYMRTExwtBUikbVFQkyL7+un3hJEEgkpGQgCsLFgiWOTnMpW9fjps7t10ObU5ODo4dOyZUVVWxuoMHYXzkCJRnzaIIYXk5GeAff0zGqaoqFY4aPJiIvKcnRdqNjWFpaYnw6Gj0WbkSuklJZOx6eQF6emBxcTCbNg0iExMYUe43n5GRwQ8ePBgTvLxY38hIwTU9nUtfsgR36+pYSUkJq6mp4erq6tC/f3+EhISwIUOGsJkzZ7KRI0dizJgx7GlMjPxxZSUzdXBg6urqSEhIEHJyclhpaSksLS354uJi9uzZM0RHR6MyLEyeGh2N+DfekI87eZIz79ULLCyM137xguXr6aG4uhqZmZnIycnhJBIJCwwMZLm5uTA0NOSrq6tZQ0YGbwgw/enT8fDhQ6ipqfH37t1j+fn5XENDA5eamioMGjSotSBQUBA5ehwcqMK6opK6pydF7L/6ilIHvLzIkfHRR4BcjqqLFyFVU4NNVhbDnDlE0G7dIuO/Vy9SLowb114JUltLsmxLS+DYMZTfugXxiBH8tC1bmJa3N1984QKzWbIE3P/9H0l78/OJcJw7R+kB5uZ0P21sKFfawoLIV1gYcOQInaudHb02eDBdi4YGyarnzaNo1JMnREx0dBATEyM3MDDgjNoWrgNoXkdEtOaCrlpF6okhQ2ie+vm9ut+uIFBKwMGDQFkZZN9/D7GvLyKMjKBXVgbzlSuhu3w5jZmZGTmMOkRW5XI5wsPDMdDKCupbtwKbNyN5zhycVFJCo6Oj3H3UKJFUKmVpaWmCq6urouIVjdOaNUT62kIqpZSOP0mZiYyMxDkLCz5z5EiY+Pszze++Q9jIkZjEGJjCEdIVPvsM/IEDuKypKaRLpazYykqYtXkz85g+ndnY2EAsFqO5PzUrLy/nHj9+zOscOSIY/PILY4sWkWOjm/oN7cAYXVtiIo2xhUWXhDs9PR1Hjx7Fs2fPkJeXJxQXF3N1dXX8/PnzRc7Ozowxxu5euyYM/PprFu3kJL969y5XVVUl3Lt3j0VGRiI8PBwpKSkoKioSysrKBAcHBzb2yy8ZN3Ei5FevwvbuXS5eWxv34+IAAG+++SYpToqKqKBXhzxgZWVlFBUVISEhAWvXrqXcd8ZoT0pMpI4MJ07Q3qWmRvP47FlSFg0YQKRuyBBc1NdHgJMT3N57D0oTJ9L+GBREsubx42mNBQTQ8+eDD4C8POSZmCApKQljFG39TExIGbJvH6WMdMTvv9P6nziR2sBZWJBDRCrFS2dnlJeXCyNHjiSlw4QJtN6b54W5uTkXERHBHj16hEePHgk5OTnM3NxccHJygp2dXZfqiKamJpw6dQo1mpqokcmYu6kpREOG0L6upkYOM3t7SiEaMqS10GRaGtUA+DtKsg73pqmpiT158qR1LXUHxuhZbWVFz+e33361g+hvQFVVFZaWluzevXvw8vKCZdvOJwAaGhq47OzsPoyxRcHBwdc9PT0r/tET6EEPXoEeOUUPevA/DD8/v94AZonF4sFyubyI5/lwAOG+vr71/+Ghp/fp06dOLBZrdvluZSUS3dwQoqyMdevWIT4+HqGhoe0eyPb29nj58qWgp6fHYGREJC8pqVOxrHZQyBdv3uz8sC4ooNzdlSv/coueSZMmIT09XV5QUCA6cnLKheMAACAASURBVOQIHBwc5LNmzRIBgIaGBkaPHo22hVeSkpJgZ2fXUizL3t6+s7Hx4AEZ8nv2kCEXFERkRkmJSPj8+STn272bIqEXLtD3rl2jfMScHCjNmIGROjro27s3aylkJQhEyMeMAfbsgfydd9DbxITL27MHugMHoq25eisggI+JjuY2HjzIVA4exEWpVJBWVzOtYcMoiqmItr/7LrUDs7EhsjRgABmwixaRcZaYCImxMQRBQEZGBt9v3ToOU6bQtRkZkXGZkQHcugWrCRNgZWVFA5OXB5w7B+OaGoajR2HV1MT0z54VioqKGAAkJiYiMTERKioq6N+/PwBAqVla/kZJiShCTU04c+aM0NyajTU1NQlyuZyJm3vuvvfee9DX0wP69xdh9Wpg40b63alTUS6TCU0nTggzHj1iMUZGyGwmsObm5kJhYSEDgHfeeYerqalBypYtLDY7G2mXLsHQ0JBfvXo1FxkZicDAQJiZmQkFBQVCWloac6isBGJj6X4pJK0iEUWTv/2WomJRUdTqTlWVyKeHB31GJELWZ58hOy6Ox4oVIjg6UnTN358Mc0WbroEDKZ/YxISipq6uRFZLS1Hx7Bm4vDyMGjWKw8qVGDxlCnctJwdJ/fuj7+nTQv8vv2TDFBGra9daJ4KiFkJJCRGNoUOJSLu5ESk/cIDmVVMTSXMBIu2zZtE1ffEFEfADB7Do+++Vnikr89DW5lBX15oTqqREUfTERCKvP/1EBn9SEpHWjuS8srJ1TXz5JbVSi4gA4uPx0sBAOKmtzRrXrcNQZ2e479r157npgoDs2FjhzZMnmUpCAq4PH46ERYuA0FCMnzlTGDZsmAgA7t27x/fp06d184iOJkdGV8Wb+vUj6fsrooCVlZUICwsT7OzsOLFYzJ8cORJNlpZs/rRpYC4utEZmzybip6JC6Rl2dsD165BPmIAz2dnIfPaMzd65E5P69+/US5rjOHg0p4/k5ORwN7duhcHTp4JRXh77K/n2YIzUCwcOUBrBnj2dyG1eXh4AYPXq1dDX1+fq6+uxe/duLisrCxYWFvDw8IBRQQHLCAzE3ZQU0fjx4+Hq6spkMhkCAgJgbGyMpqYmuLu7MwCte6KVFe4YGkI3IwNaOjror6qKyd9911ps8K23uq2YrqGhAZFIRNJquZxI+a1blPsPkOPl0CFy5OjotPSubq5sjrKkJKGhoYHNmjNHUFFRoXM6e5YcjQoSa2lJrwE0H8ViWDx/jgWnTuF2nz4Yv3QpvbdrF80XRd0LBTIyyPk7YACpN0xNydEklQK//IIn338vHz16tKiFNGtr07qbPLn5f7Uxa9YsqKmp4dSpU4zjOEybNo11rGLfFhKJBGPHjkVwcDAEkQjVn35KjovPP6dCft9/T6T76FFySpw5Q9XK798nx9o/gPz8/BZFQ5edADqiqIie3ydOtFbO/wchkUgwdepU3Llzhx89enS7hTRkyBDR4MGD1cPDw/tHRESE+/n5ufj6+kr/8ZPoQQ+6QE+BtB704H8Ufn5+A5SUlB7Y2tqKTE1NVWtra+Wpqak1xcXFEolEElFfXx8kCMJzAHkA5ACaAJQBKPD19X1li4wdO3Y8nDx5ssug5ghqY2Mjbt68iWHDhsHU1BQVy5bheWIi8j79FDNnzkR6ejpOnjwJd3d3KAqY8DwPf39/eWNjIzdu3Dg2MDqajBZf3+5/+F//IuP9ypX2r1dVEbFtKw39G8jPz8dPP/2EmTNn8i2Vzv9TpKcTqVi0iAzczz+nSOHdu2QkHj9OJGPzZvr8zZtk0D1/jhve3vyLFy84a0dHPkMqFfRlMsxYtEikZGeHWpEIRz/9VK6VmSkqNDKCpra2UF1dzYZaW6NOLofhmTP8oLAwriwiAn2vXIFMTw8JP/8My/x8aM+cSUZpeTmRa6m0c+Ga6mo0XLmCY7W18nF79ojqlJXx+9SpWBMcDJ3ff6exrqkhIjZ8OPVrVlOjojwAkc7AQDKA161rMWxDQkIEheNFXV2dnzhxIqenpwfjjlLdqVPBr16NX/LzZbW1tczY2Fg0btw41DbnVctkMmiVlcFTVVVw2rKFdZQnnzx5Uq6joyPSyMqCyunTUJbJ8MekSZC1ITNbtmwhI3HPHjwtKsJFVVW4uLhg0qRJACjVQSKR4PLGjXKngQNFVkePUlurhQtbfygxke5tbGz71mXZ2URsX7xokSHHxsYiIiJCtnbt2s6O7qIikl/b25MEfMoUkjC3kZAeOXJE0NPTw8yZM1uITFNTE7Zv397yGd/u1s/NmxRpT08nZ8DRo1S0CKDI3s6dRBguX+5eJltVhfQPP0SEnZ18cXm5COfOETkfMwbYuJEie1lZrfN9yxaK4i1YQONgbk7zTiKhqOxPP9FndHWpeJeZGSorK7F3715YWFhg0aJFr1cALiwM8n/9CycGDICZXI5Ru3dj5w8/QBAEfPTRR+2k3Ldu3eIfP37MffbZZ7TOtm2jfspd4flzitA3V0NXID09HRoaGtDR0cFPP/0kNzU1xcyZMztLfGJi6B46OpIz0MWF7sPBg8DSpYjPy8OV5r2s2/umQHY2MHUqHu/YAZ0NG2AVGgrW0YHxOhAEakU1ejQR1zaELjQ0FCEhIViyZAn6NvdAP3/+PNLT07F69Wr8/vvvwuC9e9nDKVOQLxZj2rRpcHJyeq2fvX//Pm7duoXV7u4w/PJLYPVqItmMkTNj3jyaC23B8wDH4eG4cYI1zzO94GD6ztat5AhpaKDvmZmRA/PxYwBUSPH06dPIzMyEqqoq6urq4OrqKvj4+DAlJSVaq5qaXaYa8TyPp0+fIujcOQwLD0f9+vWCz+7dDNOnUxtFQaDf3rqVVBaKKv+jRtHaUVSR37WLHEiBgfj6668xd+5chUqB1oiRUZdO4YaGhpbuB6++jQK2bt0KZWVlLFq0CObm5m3fJEfdxYv0vDx6lCTu331HypnAwH+kUFlcXByuXr0KANiwYQN0XidFJCmJ1v3GjbQf/MPgeR779u0TnJycWHdy8ZCQkKbIyMjKpqYmd19f3+R//CR60IMO6Ils96AH/6NQVlb+0t3dXXX06NEKI1A0btw4rdraWqSmpnrn5+ePLiwsrKusrBQEQQDP86y+vl6poaFBZceOHYkNDQ1bBUG47Ovr284j5+fnN0BZWXmQfZsqp1lZWYiNjcWTJ0/AGMOYlBTkzZkjTJ84kQGApaUlfHx8hMDAQFZdXY2ZM2eC4zisWrVKdPnyZdy8eVMYuHEjw8CBZNx2NCIfPybD4dtvu77YTz8lwhob+7fHKyEhAZcvX8bo0aPljo6Of6OheDewtKS/tDQqyrRmDZGqsWOJ7GzYQFGVjz5qjYInJwNPn8ItIIAztrDANRUVboK3N5y9vRF/+jRC3n8fq+/exTQvL5G0sRFvDRgA1eRklqisDDtXV0TPn89ruboyJZkMfZcuBZYtA5PLYVxYSAWV5s5tPb/IyK4NLw0NBOnrQ15VxamHheGUvz/c7O2hk55OETpFH+kHD0hSLQgkmS8rI0KRkEAG/RtvtDvsmDFjmKqqKm7evImxY8dyA7vL03/nHXAODlgxeXK7k9PV1cW6detw48YNYdjOndAzM+tEtAHA3t6eCwgIgL6+Pu/5ww/M6Nkz9u7mzQhdtEjItbDAipUrW/sy29hgoI8P7AcNatfyTSUnB7hwAUPCwji5gwPNsY6KirQ0kqIqiLatLRniCxZQdJjngdhYFBgbIzg4GJqaml1b0RxHhbbq6+l3li8nyWqbonj19fW8ubl5u7kpFouxceNG7N27t+txBIDGRtTMmIEmbW0U+PvDgDG0E2CrqZGTKzKSfu/Eic5VjAFAUxPqX32FnCNHRMK2bWBffUX3/Y03aE5HRpIjIieHiH1MDEXS/viD3jtyhMbH2Znk6jNndvqJxMREaGpq8m+//Tb3yvZ7gkA57o8eAe+9hwgHB2T37o2aXr3kD378UcQYgyAIOHLkCL9hw4aWAw0cOJC7f/8+kpOTobp8OXK9vGCSnQ0zM7PO7f5sbUlxUFXVQkoPHz4sk0qlSkpKSuB5HnK5XLSou8JPQ4fS/dy1i8aloADQ0UFVUBC+P3AAfDOhEolE4Hn+1e0GtbQAFxc4+/vj24ULMbuqCjZ/g2zLeR7CV19B6YsvaK/ZsaPFoePi4oKQkBAcP34cb7/9Nvr164eioiKhsbGR7d+/HyZSKdOqrUW9vj6G2di8NtEGAGdnZ9y6dQs38/Kw5MYNUoM4OxMBvHq1NVLc2EjOv969acyTk1EyfLggU1Ji7gA583x8iNR+/jntlUZGwA8/gC8uRtVnn+HfNjZCdU0NA4CPP/4YGRkZuHTpkpCcnIxxvXqx/tu2QfT0KXieR0FBAdLT04XExEShtLSUA8iBJVJVhUdICDiOY9DQoNSO48dpzn3/PTnSMjMp5cfOjvZABdHmeYrczp8PAJBIJPyTJ0+Yra0t3fCrV2l/6ELK3VXBtK7w4MEDHgDX1NTUnmgD5MD46iva558/pzF74w1y8vTt+58T7cZGoKEBQ0xMoD1yJAJ//RVqycn0u1IpzXN9ffpcQQE52vr1ozFMSaFn0JEjpHxSV3/ttnSvA47j4ODgwDIyMtAd2R4zZow4ISFBtbS0dCCAHrLdg/86esh2D3rwP4ht27bNUVFRmeLi4tKJNKqpqcHR0RGOjo5iAJ3KhMvlcqSkpDjeunXreF1d3RwAC9u+r6ys/IWbm5ukqakJ9+7dQ2hoKBQKmc8++wxVH30EYe1ajJo/v90T1N3dnZmamuLYsWMwMDDAqFGjwHEczMzMUFRUJIDjGHbuBA4f7tyWJC+PSE1XEAQiJc1tp/4uGhsbFQV7/jmi3RZWVmR0yGRkZB04QEbb0aNkeJWUUJR4/34iK5mZeHn1Kv9YT4/7GICqgQFgZ4fBGRmw2LQJatu3w+bMGdiMG0eFuX79FYMmTwYWLMAIMzMOenoUhR0/Hnj5EqKKCiQ8e8Y3qatzk9qeV20tRVEVUc5m8DyP5ORk3t3dnTMyMoKWlpYQlZTEvE6epCI/+/eTc6OggCKD06dTxGLePCpWtHdvq3y5DRhjcHFxwc2bN1FXV9f9eN29S1LqLqIfWgAWZmUxhIa2i8y1hbOzM3N2dgYUXTecnRGurAxXPz+Yr1rFUFHRGjW+dg1YvryV7NTVkcw+MxO4fBl3NDV55yFDRJ2I9osXlAO7e3frazt2UKQfoOMfPgx8/TXCdu9GdXU11q1b1/X8+ugjipAdOkRjaWVFZOLxY+DNNwEDAygrK4uKiooEtJXogqoXL1q0CGfOnMGBAwfkADBixAiRs7MzcPgwUq2tcfajjzD+zh3cjo6GVVoazG1tMbLtQRgjon38OEWlDx2i6+hgCBsaGkIQBFRWVkJbW5ve37KFCKmDAykZGhtbi4qpqlIEUSwmmfq1a+R0Sk2lHuuurjTfZTLgvffAffQRXPT0OM7NjT7j4ECEVSKhNIeqKiIPgwdTdemZMwF3d2SmpQnIyGD6+vqYP38+4uLi+IcPH3LW1tbtbpqZmRlEIhFSPvwQFcOHg3N1lQefOiVqamqCWCyGRCLhJRIJr6GhIdLT02Mjjh5FakoKMvv1Q3p6OjiOU9q4cSOkUimKi4vx+PFjISoqSpg8eXJnpvzLLxRhzM6m6zh+HMjLg8rmzfg4NhZJXl4YcPEiJK+QCwMgx8TmzXSM+HiI1NWFkpISpmiDVlpaiuTkZCEzM5OfMWOGqCtJb11dHW7duiU8efKEAYCFtTW/MC2NUw4KominmhrU1NTw4Ycf4rvvvkNQUBDefvttlJSUMB8fHwQGBmK6tzd6L1uG9X+zuBbQTCZ1dOjv44+pSN+lS7R/HzvW2hv+zh1Sx1hY4KFEwtna2qJF/Hz2LBV809Ag0uvpCaiqIvinn2B//Tpqli9n82fNgllz5Lpfv3748MMPubt37yL1/Hk+39KSyzt1Sv7y5UsRx3GCjo4Ob2dnJzIxMYFMJoNcLseVK1dw+fJl+Zw5c0QtDsPGZsGXosBYVRVdR0ZG+8J/R47QHG1WREydOpU7f/48/v3vf2PZsmVg+/eDeXj8R3nTenp6HABMmDCh6w9wHKUJ1dZSasqQIbTHuLkRAZfJaJwzM2ltiUS0zp48IWeqgQFd34MHRM779qX1+8cftJbHjUNeQQEaY2Lgra4uSKytGdTU6HkgEpFzSFmZHCHGxiTzV1OjdA1/f3pu375N+4yvLxWM+wcQHR0tPHjwgHU7LgDKy8tRUVHBAFwDgG3btk2WSCTvNjQ0+H/xxRcB/8iJ9KAHbdBDtnvQg/8x+Pn56YnF4iOLFi1SVe0mD+5VEIlE6N+/P6ysrNT3798/zc/Pz8vX1/du87F7KykpTXd1dRXt3r0bcrkcOjo6UFNTg6WlJbjGRmhfvkw5Yl3AwsICEydOxB9//IHQ0FD069ePl0gkXO/evYmtu7pSVFSRE/foERnyd+5030fW1pbIZtto7V9EcHAw4uLiYGxsLAfw3yHbCigpkfRYV5euraGBjBofH5K1BgVRFWQDAzgeOMA5urvTZ2/eBJ48gSg0FPo//0zjsWEDRSx69aJI4siRRK7bFo4qKiLytGwZbD/5hDt16hRUVFTg6OgIAwODVjJbU9PaTkcuR/Dvv/ODkpLYgCFDgOXLsSwtjd0wMYF0+HDB1M6OgePovFeupAhVYyNFchRGslxO90+Ra94GHMeBMYZue/gCdM3dyRJPnKDx2LTpL0VFpEpKQvS77+IDnqfvLltGCoOYGFJHpKaSE6G6miJVbm6AsjKE5ihmJ/j6di76NWIEOYYURbVWr0bCkCEoPHUKjoMGdS+Lnju3vXNi0CBKl9ixA/JZs1D40UcozM/HpEmTurxgS0tLzJo1C01NTaLc3FzcuHEDqSkpmHf4MCp79+ZnmZhwaufOYVJpKa/k78+9ePkScXFxiIyMFEQiEZYsWUKR/gEDWgv7eXjQHGuWYfM8j1u3bkEmk6E2NxfaO3dS9fVJk8h4v3OHIvI3bpCs9sgRIhRt75GiD64gUKEpQWhtj9anDwqMjcHxPFIfP4a1VEoG+uXLNI83bKBx0tEh59DOneB5HpEREfzLly85kUiE4uJidvjwYaiqqmLlypWd+hQDgC2AgYmJMPruO6jY2ooU11ZdXY2ysjKuvLycKysrE4qKiuQZWlpc6fPnTGJnJ3dychL5+PhARUUFmpqasLKygoaGBrt27Rrz9PRsrRp94wYRsKlTaf6LxTRXLC0BR0eIfHwQu3o1X52SwpU5OQmGcjlDXFz7PGAFBIFy+l++JLK9Zg1qt29nRc0tq9LS0nCKUjeYqqoqd+HCBX7evHlc2wjpuXPn+BcvXnAAmImJCYYNG4br169z30okmLNvH/qdP4+iL75Ak0iE2NhYHgA3ceJEVDTnRYeEhEDc2Aj9ffta00T+AgoLC3H48GEAwDhFpf2KCro2b2+SFAsCOa0uX24tEDh0aEt3gCFDhtAcGzqUyLZCEXPuHIp270bW3r2ImDwZEe++i80rVkDN1paIYzPh5jgO3h4ewLFj3PMdO9CYnS0aN24cTExMGDrs+YmJiRCLxcjLzBTh0SNKuzAzI4XC48f077RpVBRt7NjO+5SuLjk6m9G/f3+4u7sjMjIS27Ztg2T5ciwyMIAF/iMIYrGYZWRkwNXVtftPqalRJDkpia7j2TNyUFpYELlOSaExNTCgZ5OxMRFrfX36/5Ej6T0NDSLh27YBqqqora3Fz7t2QTJwILZs2cL+tOCZXE4Oih076L58+CE52VatIqfkmDHk9FWkU/0N8DyPmzdvshkzZqBbxRQAHR0d9OrViy8sLNzm5+eXIBaL/UeNGqVx7949n+3bt+9qamr6sqOirwc9+E/QQ7Z70IP/MYjF4s8HDRokaVe5+u8dB15eXmqBgYGfALjb/PJQQ0PDhvz8fBVFr93169e35pf98Qd5wl8hb3RxcYGGhgaamppw9+5dVlVVBRcXF3pSGxqSsR8VRSTS2JiiCN09yBmjKHhzoZm/i+joaL62tpbz8PDgYmNj+cDAQK6pqQnLly9Hp6rL/wQURGz4cDIgFy4k4+zsWXJU2NiQBPmtt0j69+23FD329ydj7/Ztigb98AONj51d5wJDMhkZVikpFG3W1oZFbS2WFhYKuXv3IkosZn0yM9F72jSYJCeT0+LQISJIFhaoLyhgIy0tmYaaGjB2LLRnzwafkoLQgQPZoo0b27eGmjqV/hUEqsabl0ekKyGBcrmHD6dIR/N9zM/PhyAIKCkpIcLfFZqLNbUDzxNpWbeOcn//ohzSyMgIubm5PNavFyE5ma41LIycFVu3Ul9le3si4W3ICsdxncl2Xh5JYDsSpBMnKHIbH9/yUt+BAzEjJAQVoaHwZ4z39PTk2jkaMjKI8HeYx7wgINbHBw9LSoShX3/NZvfpw5uUl3NdVccWiURwaO5h7eTkBLc7dxAXHs7vXrQIQ+/f5zRfvICpri769evHNTk7435MDK5evYphw4YhOjqapaamQlGDAWIxSV2/+YYI0DvvIL+hASd++02YefYsPKZOZVqmpjRf/fwoxUMxHz77jIzqb76h9fzttzQPT5xof8KMtY6djg4wYAAEQcBTFxfIZDIYurrCWqEQUPSAFwSqZPzyJX23shJXAgORmJjIzZ07FwYGBsjKyuLs7e2hoaHR9aYhl2O2hQV+njABWo8fY1FzHi3HcdDS0oKWlhYsqP82kTBHRzr3efO6dMLZ29vjzp078u+//1707tKl6KWhQWRbW7t9H+9Dh4h4a2iAE4ng7O/Pbdu2DeA4pl1WBpURI0jd8ttvrQWsfviBnBD79hHR/v77FiJaWVmJf//733xubi5nbm4uvPnmm4znebZ79272bXO6zZQpUxAUFIS6ujpu9OjRKCwsxIIFCwAAQ4cOxZMnT3CJ5+EUEwN+82YkODpCLhZzU6ZMgbGxMTiOa8kl55OSwDU2vrqifDf45ZdfAADLFi6EwfHjdE0mJq3F6YKCaC11sRcUFxdDJBLB3t6e9jRv7/YV2GfPhjwlBWWmppjWv79gOXEiU9PWprQFa2si8u+8Q0qIX38FmprQf9Ag9G/TsQGCQE7PO3eAFy/w/MkTYVp0NNOsribHm7o6PZe++oruw8OH1D3j3j3Kx2+L+/fJkbB8ebuXR40ahfT0dKirq8N70yYE1dfDws/vL48lAKSmpuLSpUusqamp1XnxKkREEKFevJhIbYdz+6vgeR7XmgswLl269NXpDwDdN5GIyPTKlXS/R40iR520uUbZ55/T8+LoUXpmbdz4l2uwMMYgk8kQHx//SrLNGMOcOXPUb9++va6xsVHu7u6uYWNjAwcHB9Vjx45tqqysrAfwjeLzfn5+ygDsAeT7+voWdnvgHvSgG/SQ7R704H8MHMfNd3FxeY3SoH+OAQMGICAgYIyfn5+Wr69vJQBJcXGxxtmzZ2FkZIQJEya0Eu2CAooOdpAjdwRjDAOaDevi4mLcu3evfTVvZ2cile++S3K3H3/s+kC5uVT59fr19oWp/gbWrVvHXblyRbh79y5rbGxkOjo6UFJS4v39/bkPPviA5LL/Dbz5Jv177hwZHYJAhp22NkmynZ0pAuDjQwZIcjJFGydOJOMxKoqIx927FE2dOZPIaFERGTVffUXEx9aWvt+3L0zMzJiJqyvqHBxw+ocfMHzkSJg4O1Oxn6lTgWnTkJOTg7jjx9no9esRkZAAeZ8+ePDgAV9bW8uJxWIU1dSgVxd9mHHsGJHq2bOp4JZUSkTy22/JGXDjBgCgV69eMDU1xePHj7uPbtfXUzSkLeLiKNq7eXP3RbxegaFDh7KQkBBRxp076BceToRQR4ci9J9+CunQocgqLISzRNJOp81xHJN3PJd336WozyeftH/94487FXrS1NRE+bVreHL3rlz1/n1RQHU1b2dn12qh3r5Nxntzb16AKt5fvXpV4HmeGQ8YINhs2cJ0//iDQ2AgObXWrOm2gjNjDAbV1Rg7diw3KCMDYXPm8BEpKRyflAQ3NzeI9+zBO7//DnmfPlBTU2Pp6elCVlYWG9SWgIjFgLo6pIWFUPb0hEFeHobfvs3bGBmJmLU1zdH0dPqsogBZZiZFvqKiWs/N2bn7PtUdUFVVBZlMBsYYhiuIdluEhhIpU1OjOT99OlRWrQJAexWALiPZ7XDwIERRUSi0sUFhSsqfn5SODpHC5mJdHcFxHDZs2CC6cumSIHJ0ZA0rVkD54MGuj9PFmgnneYRra0Nv4UJ+6e3bnMYPP1A6iY0NESR9fbrWP/4gYgpARUVFSE1NZRKJhNu8eTPU1NQYAJSUlEAxT8VisXD9+nWmq6vLz5o1i7PuohjY4MGDYWVlBSXGwG3ejKkZGeBOnepSLcL961/d18zoDnI5kJ+PxYIA+YkT6DN8OO1hEyeSY0sBQaB9oqamXUFAAAgMDKSodmoqOXc61CYIf/ddRERHY7mTE3r5+7OWVouDB9NxU1KoEGR5OY3p119TvnhEBDm4DA3J4WZiQvUmHB1h4OHBgrW1scTPrzUPuy02bqS53lVRu/37u0x9kclk0NTUFFJSUphVv34Y+R84iIODg/nGxkYOAH7++Wfhk08+6V7ec/06kevkZHIkOjiQQ6ft+P8J7t+/j5CQEMHb25sFBLRXWYeFhQnz5s3r/vcVyoydO8lJKhLRfVi4kBzJ4eFEvH186C8tjZwZ+flUEPWDD1pVDH8Cxhh69+7NS6VSrrq6+pU97vX19bFw4cJ2k01TUxNvvfWW2oEDB/z8/Py+8/X1bQAAZWXlUIlEMqiurk7p22+/DWpoaPiw+SvNGyC8AGT2FFvrQXfoIds96MH/GBhj1U1NTf/IsVRUVGBqatqYnZ09BcAZxtgEmUwmmj17Nuw7FlBijIwxM7PXOvaprv2GcwAAIABJREFUU6fkeXl5bMmSJcyibQTIxYVIx/z5XRqnLSgrIwP2PyTaAF3nwoULWWJiIlJTU2Fvbw9VVVXu1KlTr12w5j/CtGkU1Tx3rqV1DSZPJuOovp7yggcOpBy8b76hCNDWrZQzPGAAkWlFJetDhyjiGx5OBG7IkPa/1SzrVgVQ3r+/UK6szDBuHEnbm43s4uJiaGlp8dHR0VxUVBT09PR4Ly8v5vT/sHfdUVGd23d/984MVboC0lGw0xVRQQR7L7FFfcaWaKIxiTE9jzdRY2JMYkxiEktiSezGFjtgQWwoCEiVoiAKCEiHaff+/jgMvfney/vlvcVey2WCw8yde7/v3rPP2WcfT0/s3r0bBw4cEBctWsSatCnExpKEcto0kmBnZxP5HDGCEgvnzgFLl0ISHY2ysjKx1jCopXNSX1Xw6af0PrGxz3fN8/Io4B4yBHpeXpg8Y4YQeeqU6HT1Ko9p06g6tXEj0KcPLu7dKyQrFNzp06cxYsQIzaBBg3iAyHaDyra2gt/c+BrGKHj89dcGlU07JyfMsbfnq995B6eGD+dOnjwJKysr9O/fnxQML72Ep0+fQhAExMfH49q1axg9ejSrkYgSy5s3j4LQ334jmfvixXSe6+Pjj6lPc8cOcFVVsHJywoyICC7ZywudO3em9WRoCJ3u3WvPo7+/Pws7dkwcN3Ysw86dVAE8cwY4cQI3nJ2FrLFjuVmxsWLg48c85s1rWWmyaRNd//rrYvhwSv7s2NFmRU1737K1tW3Slw6AvA1++IGSSsOGAe+/D5kgwOHOnbYNxgBSI1hZAWvWYFhODiIjIyGKYuvOz5aWRAzKypqXeSsUwMsvY8q777LdL70kSPr2xWxR5Jq8p4FBE7WEg4MDHj58CHd3dxgYGGATx+Gt5cuhv2IF9bbfv0+kTk+PEhg16N69u5iTkyO+/PLLXP3+bK3se9y4cfDx8dEeQKsnpZaQbN5M1zwkhKqM9ZNZCQl0DZvxYGiC0lIyXvP0pD3s5QWzpUuxLz0d493dYdnIMBEA7Znr16kneMmS2h/n5uYCAM26vnCB9mojRMTGitNCQ9H5b39jsLYmAq01FWSMyGZxMd0/nzyhyu6cOUS2e/emBKN2NBtAPiRhYdC1tYVxc0T7++/JNG/hwqZJiYICCJs2IS4jA4VhYWCMQV9fH9evX9eUl5fzPM+zCRMmoO/y5ZCZmrZ9LlvAqFGjuLNnzyI3NxcKhaL5xRsZSdd0715aQ8nJ9P+lpUS8n4NsV1ZWQqFQ1BJta2trTJ48Gfr6+uB5vuXNk5lJ8cDevZQ4WraMRkwClDhavpySZ/7+db/TrRvt8bIy2qv799N1MjOjFqk22oa6devGXb9+HWlpaZSkeU6YmprC1dVVnZmZeVMul28EsFdXV7dqxIgRBr169cL58+eDExIS7jDGxKqqKl0AgomJiaqsrKxSLpc7hoSEVLb03nK53BHAEADhISEhzUi3OvC/ig6y3YEO/I9BrVYfun379lv29vbP37DdDAICAjrt379/6/r168fzPD951qxZ6NY46MrMJJOapKR2v29RURE3YsQIph0xA4ACARsbqpD+9lvLv3zpEj18tbNR/03o27cv+vbtC1EU8fXXX4u+vr6irq7uv2cEmCiSUVJkJEnkV60i0jJjBknIV6ygwN3cnOSkQ4YQOXFyosqgri4F6/Pm1b1nc26rXbsSKXV3bxC4NofAwECcO3cOpqam6P3kCbgXXwQuXkRcXJzg6OgoGhgYwMTERFi6dGntOZgzZw62bdsm7t69Wxw2bBjn4uJSR1Y2bWr4AXZ2VKGPjaVq0A8/UIXD2Bgzv/mG5c+fT47lzaBq40YIvXrBQGvUs2cPzX1ui2hXVRFRGDAAuHgR4nffQThzBk+OHYPm5ZfxzM6OS09IwDMfH5jOnk1EYvFi4PPPMeLuXa74rbdQpFSKFy5c4JOSkoRhw4ZxjLGGZHvMmLpqfXNwcWlalQcAnocsMxPSsDDkHzuGaHt7XA0LE1Z+9hmnTEzEFm0gCiA4OFgcMGBA08jS2pr6zVNTqYJua0vnnefrxhIZGNQpJWoIS0/t7z9+TOuC50nOWVCAuxUVwpvr1nEYN46SM1rjusuXUbx7N1eZmwuLNWsYli6l3xs5kki1FqJICoxXXqGWhsa4fZsSGm2QbYVCAQBQKpUCmvNOePKkYbA9Zw58Ll+G14kTiNqyBb7Ll7f85oJAyptBgyA4OeHaoUNQKpWorq5Gm94WoaF0rWfMaPjztDS6D+XnA8bGmPDhh9wPP/yAo0ePwtTUFB4eHjT7GKBxT/XW0NGjR/Hw4UMAwGQiPty9e/fEL775hk2fPBm9y8qo/eKDD2jv79tHRGXlSkxLS+OQm0sS599/R4WrK3ZGRKCgpARgDG6Npc3tgURC96WICFJNbN9e92+PH9cl8ZrDs2cktZdKaa+XlpKiISwMsLWFPoDKuDgx6s4dNn78+ObfIziYznO9e9adO3cEQ0NDZpKdzTBxYhNfjsePH0OlUjELlYrWxpYtJME/fZoSkqmp1Maybx/5Chw8SMqLIUPQ0ozy8PBw6Onp1crtG+CPPyh5uGBB86Rv9WrcTUwUz02ezCwtLTWiKKKiooLz8/Pj3NzcoK9NHstkpNKpUWM8D0RRxM6dOyGTyWBjYyN06dKl4UxzgO6XKhWtTY6jtevpSWowbUKqBaVGcwgICKhttXrzzTdp5nl7MGQI7bnlyynRqk0caWFmRgnZ4uKmfe+dOtG1AshY8J13KKF8+zYl3Vp4DlRXVwuGhoZc73/i3Goxffp0vXv37rlfuXLlp2fPng1UKBT5hYWFkEgkGDt2rM7YsWN1AEoOajQa6Orqyg4ePMgyMjLOyeXyOSEhIVmN31Mul5vzPB/n4ODAsrKymFwuHxwSEhLb1rHI5XJLAH4hISEtzCds9nckAMYBCA0JCal4jq/egT8JHWS7Ax34H4Nard6YlJT0Vmlpafsfiq2gW7dueOmllwyysrJmu7q6wtzcvOmLMjOJBLZWiW4EGxsbITMzk/fy8qIfFBXRw/f4cXpIh4dTD1dNH2oDfP89Bftbt/6T36plPH36FGFhYaJCoWBZWVnIz89vW5qqRWUlHbOzMwV5MTFEiKZOJVLZsyd9Lx8f+o7W1mSwk5BAgawoUpIhMpKqOHv3EhHv2bPtzwZIHql1dHd2brEKUF5eDrVaDR8fH6ajoyMeP36cnS8txSilUswNDWUPHjzg/P39oaOjg7CwsAYRmUwmw4IFC7jNmzeL+2qSHa+99hosRJGqEs+eNQyEOnWi6qu3N/Dtt6heuhQ5mZlI6d0bljY2dJ6iosh0qOZ4U1JSYJGRgT0//wy33FzBztGRs4uOhm5zpOjJEyK+33xDVVSOI1m2hwf2//KLkCKRcJIpUyDbt0+QyWSCdVISN+nhQ0765Zd1gZ+/P6rfew8XBg2CXna2EDhhAnf+6lU8evSIO3/+vFheXl6XFMrLo2C2tWuybRuthWbAdeqE0d26gT9wAPEnT+Lc2bPcFW9vpJ85AxsbGyEoKIizt7eHRCJpvYTj6krV81On6tbW/fskiQWo8paUVFcRFQQi0XfvkvR98WKSJ+fm4rGTE5cZFYXu2lFO9UY6+fr6Yv/+/SiTydBJS1Zu3iRSpFW3hIWR1NrBofkAfswY+qNQNDtbWIuuXbuixvmaT09Pb5jUCwigylj9GecA9AcOxLcLF8KhtBS+L79M9watwVZ9PHlCJGfRIqjVaigUCrzwwgttE22A1CAxMQ3J9rlzpEhITKSKMABTADNmzMDZs2fFhIQExhirGz/00ksNiEZGjQTfx8entoq/YsUKljVqlJhgasp679pFKoU33qB7RkICfTcXFzr/t26RSuHwYSSXl4sD9fXRJzmZyb76CtyyZXS9P/uM1oefHyVl9PRa9zmQSqn1REu4N28m48OvvqLWHi00GupL3rWL7lFbtpBHwaxZdI0a3XdKS0tRVFTE/BqrMOpj2jR6jlRXQy2R4MqVK7h9+zZnbGwsYsYMItqffNLgV7TTDAwuXqRry3H0unnzyKgwL496quPiKDGxbRudFzc3ukdqe+NrcOXKFYiiCHt7e8He3r7hQs7JoWTp3bvNzucuKSlBhCAIqYMHc8uWLYOJiUnLWcHs7Gb709uDmjUlXrp0ifXv359zd3dv+IJ33qGkQGJiw2Ts5MmU0FixgvZ+bm6bLV9aSCQSrFq1ilu/fj3y8/PbjitOnqSkXUICkWi1uq71oz5mz6b71dq1DSc6NMb8+eTVkJhI6+3JE3qeeHo2IeklJSWsvLwcmZmZrRtwtgKO4+Dm5gYHBwf98PDwhZWVlZp+zcQhUqmUJnMAmDZtmn5ERMTAyMjI1M8++6xYo9EYSiSSRxqNxlwUxfdkMpl3v379dMePHy+9dOmSGBkZ+cGnn35ayPP8GI1G84dKpYoDcDEkJCRN+/5yudxOJpNdVKvVTnK5fExISMj5dn6FvgCOSaXSR3K5PCAkJCTzec9BTZ+6O4D4kJCQFseGyOVyIwDlISEhzTiIdkAL/h+NR+x0oAMd+K9GYGBgdWRkpBPHcW5OTk7/lqqskZER7Ki/s+k/ZmcTQXzOfr6YmBjR2NiYegkFgWR8vr4UEPA8/Sw5mR6q9ZGXR5WF8eP/rfM5ATKe2blzJ/T19TF69Gh29+5dlpKSIlpYWDBdXV16sBYWEmm4fp3IvocHSVqvXqWeww0biPgUF1OFcdQoIhpz5lDQO2sWVa+9vChw5vk6ghIVRRVpf3/6dysrItvOznR+2upTXrWKguO5c1s9N99++60QERHBjIyM4OHhwQICAuA2aBDu6uoKymvXRP3evZmPjw8zNTXFlStX0L9//9qgAqAgY8iQISwgIAARERGwsLBA165dwbp1aypbB+j7BQTgMWOoHjcON/LzhfLx44XRCxZwyMwkCfuIEWR2N3Ag9h85IlRVVbFsOzsMunCB3TMwwO8pKbh74QKyf/tNOBofz1zmzoUiJwf3jYzA1qzBlZ494fL221Q9ZQyYNAm/nz/PJk6ciBkzZmDw4MFs4MCBXJ+4OGYVGwvZokV1xIMxlEskYGfPYhjPM6cbN+C+fj369OmDq1evMpVKhZKSEvj27w+2YweRkNZI2uLF9JrmZOYAeEtLcCtXwtLCAtU//YTk7t3xFICnpyfz9PRsWw6thUxG8thBg8hYyMeHiICBASV4goKIVPj7U6X/6lVaS1lZFOiOGgXVmDG4ceMG7Lp3h3Wj3urs7GyEhoZCoVAgODiYFAxDh1JCJSqKEmTR0bRX//GP1pNtubm0hlesoEptC9i3bx/UajWGDh3akAiXlhJ5aCTtjYuLw70HD9B/0CDYnDpFlXUrq4YJnwcPaB/+/jugq4sDBw4IRUVFLDg4uH1k28WF3sPXl95D25e/aFGT8U1mZmYYMGAAy8jIQHp6OgRB0Njb2XHw8AD+8Q88yc3Fpk2boFAoMHz4cDE4OLhuo2o0KF+zhpm5uMDs9deJ3N+6RWRk9266p0yaRNd46FDAyAjJ7u44VV3NJn75JdNdsACse3daF6amdGx79lCV89AhcnMfMoTuQXFxpII4dIh8FrRVdz09UhdFRFCPfJ8+lMQbOpSSCmVlRGhzcugeNWkSnZfJkynZ1sJ9p6SkRHPz5k2uS5cuzZsichw0P/6ItGfPsPXCBVRUVAgajYapFApWPX++0G3xYtb4vZ8+fYp79+4hkDFwnp50frS97QMGUFKpf39SFbi60nXjeVIiTJ0KiCJKKyuxfft2TWRkJJeWlgZ3d3ehR48ezNLSsu7DRJGI3dmz1E9f7z6sVqtx48YNPFm2TOxkb8/83n6btZmcXbOG9uw/4TsBAHl5eSwtLQ3FxcWoGW9Iz6PoaOCFFyjZ0JgQ5+XRubCzowrx8OG10vn2gDGGrKws8fLly2zw4MHgW1MYzZlDiRst2f/uO/rTWE2go0Nk+8ABes619p6M0XqeM4e+x3vvUUvV1KmU2DQ0RHp6Oi5fvsyMjIzQooLiOaCrq4tevXpJ3dzcZM3GPvXAcRwcHR05X19fSd++fQ0HDRok6969u4WNjY1+RkbGQEEQTL28vLpaW1sjKiqqKi8vr4+Dg4P3xIkTzWQymYexsfHIZ8+eLbl48eLuwMDAMrlcbgHgiSAIZv369VMXFRUV+/v7nwWAdevWvXDt2rVPw8LCrgYGBjaYebpmzZr5Uql0D2NM18vLy6igoCBgyJAhz1WV+OSTT4ZKpdJbUql0KQDu4sWLcZcvX1YEBgY2cGiXy+VjANyTSqWjIiMj3758+fL6yMjIV8LDw68FBgbmPM9n/q+jo7LdgQ78D0KpVH4fHR09a9iwYZJW+xH/Hfj6awrmngMqlQo5OTm8m5sbVQr69aOsdX2nW19fMrPRVrwBqsw5OZGU7F+QibWEiooKGBkZaRbMncvj4UO82akT9lVWCuWTJ/PF5uai3rvvMrzxBgWtokjVEp4nR2Y3N5JwL1hAb1bfJba+e25rOHyYvp8WgwZRlTskhExtlixp6n4LULCxdClVwloJeLVQKpVcUFAQzp49i9LSUgwcOBAGBgYYZ2rK47ffGlQZZDKZUFBQwNk140LMcRx69+6tOX36NB/7009Y+M47LTaIiqKI48nJouuiRWyGgwNXa6o1YQL9efKEAuXZs7HYxYUriItD14cP8dvcuXjr998xdPly5Bw+LPa4fJmr+PJLZOvqChdzc7nyW7fQ/ZtvkB4XB3AcpFIp/Pz8wHEcXFxcNNevX+c9PT0poNuzh6o5K1Y0OT7jqVOh/8UXKPngA+gKAkzj42Hq74/evXuLnTp1Yjdv3kT299+L9r/9xuqP9WkWGzZQNac1GBhAs3kzhp09C/dt27D72TPh0qVLXEFBAaZp3bfbg+3baf1lZdF/r1hBVdcvvqCE1M6dJOUMDSWVwbp1tE5qgsdbt25BKpVqPD09G0S66enp2LdvH2xsbDSzZs3iG9xHZs2ifbtxI/XSHjnStqrFyoqqwy2Nc6uBr68vLl26hC1btsDa2lrw9PTkvIqLycOh0WSA/Px8nDx5Enp6emL/4GCGoCCq5ru7k5GetrK1dy8lPmrMt7Kzs1n37t3rJN5tQV+fxjwNGlTXky+TtTonecqUKTh27BjCwsJ4F0dHdNHVxYkTJ3D37l0AwKJFi2Bra1t7UtU7diA+IwM3p04VX3Z0ZNi8mcyixo6le52tLZ33ESMokdOrF6qrq3HmzBlNQEAAp6ury2qTGNrpAEBDF/gvvqC/V6+m71FWRtfEyoqu4fnzRLAXLKBEDc8TMZs/n47D1JTW2P799PN2QkdHB1OnTuVjYmLEI0eOsLfeegv1e81TUlIQEREhGogiK4mOxrhly+Dh4cEpUlMhcXfH52+8wcUmJgoLFizg6iur0qKixJ4JCUxdVQVJ//4ot7KC/qVLKN2zB5Lff4ehVgVhZUVEWZtcCwkBBAGiqSlOzpwpFtjY8ACpdiZMmMA1SXZFRlKrxuDBTQzcDh8+jJSUFMx/8IA5rF4N1h7PkkOHKDHaioFXS3j8+DESEhIAoG56gChSUk1PD7UGcfWhUFASVKsscHOj/ZGR0fzrm4EgCJg1axb79ttvhRs3bjB/f/+mD5l16+hZHhVV9wwSBFo7QUHNv/GQIZT4+eWXOr+StiCV0jksLaX7+RdfAF9/jZi4OA0AfmELSc7/BHR0dGp9XoyMjGBvbw+O47pkZWUZa31uMjMzAeBDtVr9kZ2dnZGdnZ0MgOz8+fOKO3fu3F67du2vANIBem5aWlpKU1JSZgB4Qy6XW0il0u9dXV0tkpOTh37++edJarV6l1qt3g6gL8/z2+bNmye1sbFBVFSUyBirbuuY5XJ5J5lMdpsxxisUimUcx73p7Oxs4OrqKjt58uRHAD7ieT5ELpeflslkPzPGbisUisUAHnMcp9LR0elfXl6ufX50AqD8957V/350kO0OdOB/E3dVKlXJ48ePDf7VEWCtIi+Pgthm3FfrQxRFpKamIjIyUsPzPB4/fswbGxtr+vXrx8PenrLUr77a8Je6dKEKWHR0HXHV06M+vHaasLWKkhKSvvbuTcF4VBRMFy/GvDVreKSkAO7u4MLDMWftWj61rAxXHj3CJH9/5J87hye5ubC3t4e19pibM/15Xmg0lGAYOLDhz6VSMgc7epR61cPD6VzVr4r88gv97ezcZh+eWq2GSqXCgAEDoKOjI0ZGRiIiIoKZmZlpZs+axZulplLwxhiKi4uhUqm41kjJ1KlT+YSEBAy9dAkKQYBezZgfLUpKShAWFqbJzs7mKioqmP+HH1KlbNky6u/UOvpaW9P1AKCcMwddc3Jg+eQJnikUotLVFeZdujCzr79m+PpryACYvv02p3f/PsrKykQvLy+WkZGBM2fOCGq1mt25c0drHMQzxpAUG4tednYNjXhAI8iOHTsmDBkyhHNwcEBSjx6YkJZWN2N65kxMX72aAUBCfLx4ODOTebz2mmAeHw93d/eWT7SxMVXRvvyy1evBrV6Nb/LysMTFBW/27Mmt2bIF9+7dQ79+/dCtW7fWq0cAVRdnz6ZAmjFKxuzdS/82bBiRzZSUumMCqPpXcz1FUURYWBheeOGFBh8kCAKOHz8u+Pv7Y+jQoc0fhKEh7cPlyykR5OraKvkEQLLz4GBg61Y81tNDTk4OioqKEBcXh8mTJ6N79+4ICAiAs7Mznj17hqSkJJw6dQpef/xB6pBGSrzjx48DAN555x2K7Bmj/fzqq0SsYmJINWFiQsmoGujq6opFRUWMFRdTldPUlCrIjBHBvHOHiMlPP1FF/p13iHBpZePtSGCamJjU9mT/8vPP6D52LB7UuJ+//fbbdTO5a5D066+i+OwZm1NZybilSylp9t57tD/09amCV1REFdvLl3ExOxtXqEWAb9a5vREEQQCnvWfUr/ppK6NTphAxUijoGt29S5MORJESNq6ulBDLzSUVU0AAkaiCApoJ3w6FgIeHBztx4gSuXbuGgQMH4sGDBwgLC0NRURH69+8vDFm5kjd89VVwNeoYHRcX4MwZTLG0xMGDB7ns7GyYm5nRcW3YgDEZGeykRILdRUXQsbGB5PZtpG3YAAgCPJVKPFm6FMXOzuLs+Hhmv3YtiouLkZycDI1GA0dHR5ycMwd5lpZsRpcuMJ8yBUePHxe++OILNnz4cOZdo6hS7dsHfu5c3Fq2DJylZS3BTU5OhrOzM1JTU+EnlcI+PBysjedgLbQu/s8JlUqF7du3QxRFjB07lswVp02jxMeuXS3/4oULlITLrKcmvniR1norZFutVuPKlStISUkR8vPzOcYYpFIpFx4eDnNzczTpi87KontC/f3x9de0B7XPqMZ44QXag7t3U6vF81T7jYyAF1+k+EAUMflvf+Ptra3BXn65eTPD/wcwxuDh4QEPD4/a/hl9fX1RqVS+wPN8g4fDiBEjdHr16mWdkpLyVnZ2dpWfnx969OgBURQRHR1t/NlnnyXLZDKJl5eX6ciRI7msrCyToqIiv7Nnz3pwHDdPEITBgiCItra2CA8PV9y4caNKpVK1btxCmOPo6Gjbr18//QsXLvyuo6MjODo6MhcXF3h7e6s7deokuX79ugOA4xKJxNrExKRbYWHhSKVSuQNAjIuLi3dycrJSpVIVq9XqcSEhIXfrv7lcLufqy8zlcrkXx3FXBEF4MSQk5MS/eIr/K8BEsWNuewc68L+Izz77bGdQUND8GjfjPwfLlxOZ2Ly5xZdoNBocPXpUk56ezrm5uTGO49CjRw84pqWRHLJr15YfsJmZFEzcuUNuxj/+SFXt9kAUSeKup0eE/exZMhsaPpxIyKRJwM8/01zgrCygtBQnrK01VTk5bObKlVzjgHrjxo2CiYkJl5OTAwsLC01JSQlvaWmp6datG9e3b1/W4rzo9iIsjEh1WFjLr8nNpd5kAwM6L927U9Xp9dcpaG7H3OlHjx5hx44d+Pvf/15rbKZUKnHw4EHNw4cP+fGVleh57hz4GzewadMmwcXFRZw0aVKrrO/p06fYtm2bOGjQIFbbowrqqdy7d6+gUCiYt7c38/T0hEx7rYuLiWzv2kUBmTY4evAAePddKK9dQ4KTk+ahmxs3LiKCSTmOekNTUqga0kzfJECkIj09Hbq6utDV1UXM3LnwT0iA3qNH2LVrl1BcXMzc3NzQtWtXdvToUUilUigUCqhUKvSsrBRm8jyHtWuJWCiVRGiHDEHUSy9pLMPD+V9qTL5mzpyJni31bQsCJT7u3KGWgZYglyP51Cmxq1LJjPr3B7Ztw88//yxkZ2dzJiYmWLFiRcuS8tBQWgNPn9L+eestqlx/8AH1TL7wQvO/t2kTyUzNzaFSqbB+/Xq8/fbbDVpELl++jMjISKxevbpB+0ADjB9PxO/DD0kiqlSSbLTG7b4lqJcswaW+fTU3y8t5mUwmVFVVcfXjkPrrMiYmBqdPn8aHH35YmwDSIicnB9u3b4eTkxP+9re/Nf2gjAxaJ8XF1O4RHk7HOHgwMjZvFrOsrNhQhQKM56kXeOdO6jkfPJgqyoMGEbk1MCApdXAwJYi2b6cK3tChrX5PAMjKysIvv/wCWVUVZh48COfmSFZ+Pqq++AJ7i4pgYmKCaRMn0nunpJCC5swZ+t5qNR3TTz/h8M2bYvBHH7Hin35C1/HjIZVKqWc4IgJFRUUICgqCvb097t+/j8TExNpqeufOnTWvvvpq83v53j3aiz17UmLh0SMi1Hp6lOhTqUg1sH49KXpsbOi4fv2VkhA7dtD/b98O9bFjqPDwgI6bG3TNzGqvmyAI+PTTT2vHk2nn17/00ks021yjoXvz1atkapaWVjv6MeKPP8TsvXuZbVGR6FFczGSLFyPXzw+7Dh8GAEy9eBGuTk54+P77kEgksEhOhvjIQZZNAAAgAElEQVTtt8j48EPYTZ6MgwsWiAWdOjFzc3NBFEWUlJRwarUa0Gjw8TffgDt1CuLgwTh58iSSkpLE1atXs8InT3D5/fdFfVFExcSJQn5+PisqKuIEQYBMJgNjTFRUV7PV+/dD/5NPmhrotQRbWzqXnTq17/WgvvetW7eiooL8rj6cNQsSCwt6rnl6tq70Uirp2dE4GSCKtK5q9rhSqcS9e/dQUlKCqKgoUalUMgsLC8HZ2RkDBgzgRFFESkoKkpKSMG7cuDovk4ULaSb25583/ez16+le3cjcrgFWraLj++CDdo/5aowTJ05o4m/c4K2qq8WXSksZ//AhrWcrq3Ybwf2nUFJSgqSkJPTr169J0q0laAsWKpUKffr0aTBBISYmBidOnADP86rx48dLPTw8sHHjxvKKiooQACKAgwBsAAwGsB3kEdEFQA4ANWPsO39//yXDhg1rNouYnJyMY8eOJSoUiqkcx8W+8sorOtXV1bhz506Vubm5jo+PD6ejo4MjR46okpKS3g0JCamdzyeXyycDOCqRSN788MMPN8nlcl+e589rNBojAGNCQkLO/lMn8b8MHZXtDnTgfwByuZwD0B2AL8/z/aRSqZ0gCMPb1Y/4z0IQqBI7cmSLLykvL8e2bdtEtVrNli1bxhoYqyxYQMHjunUtf4aTEwW5R44QSW4sVdWaPmVlEfmYMYOqWmo1/f3225TRl0gooJBKiRj07k0kSDvnugbVBw9Cam/fhGjToTixhIQEuLu7i5MnT+ZLS0tx+/ZtPiUlRXPt2jXe0NBQcHd3Z87Ozqy0tBSxsbEaURQRFBTEN+6FbRaRkVS9aw1WVkTIr10jKbCtLZkkeXq2i2gDJHOTSCQNHtYymQxz587lExISxPizZ4VnJib8tQ0bRDMzM0yYMKHNOVudGcObe/awr0URdnZ2MDIyQmRkpCYhIYE3MTERlyxZwskaJ1RMTIjYXLxITuVbttB13LgRWLoUsv374eHuzicWF+PTqVOxevp06Gs0REISEqgaYmFBgZ5EQpU3xrTycVoD8fF4EBgoWsyYwYzT0/HgwQPOy8sLaWlpwp07d0QnJydx5syZvCAIiI2Nha2NDYc33ySy5eJChmNLlgC7dqH/rFl8amAg8PAh7OzsxMzMTHbgwAE0687LcZQ0aARBEPD48WPY2NiAMQbB0RFJTk7McP16GNnZAREReOmll7jk5GQcOnQIa9asgaenpyCRSDhvb29YanssL1+mfZGYSJ+lUNDx2thQH6RSSeencaW5qopcpWtk8NpRUYmJibW9nyUlJbhx44Y4bNgw1izRViqp5/T77yl4Z4xIYVgYVa/u36dqUzPIzc3FVjs7uDx+jFemTYPFgAG1UfC2bdvw+PFjfPLJJ3jjjTegUqkQGhqKaXv3ojgvD6qPP6bRZTXIzs4GAEydOrXpBwkC7W9bW0qa/Por3RtcXCD06oVj6enMb+JEsPpO+MOGNXvMtVi1ihIM9vaUXCwvp/tXK6oPe3t7WFlZiUUPH7KSlipt0dHgjh3DiOpqmOzZU0fi9fUpgaLdpxIJ8OabqNy7FwkGBkzX3x/qiAjNmbt3WYFUyjHG4OjoqDEzM+P27NnDXFxckFQzHcLBwUEMDAxke/fu5eVyOQYMGIAxY8bQHvnkE5LibthA62fMmDoDx5AQkupu2EBVUMYgVFYCly8jw8cHXQYMgNHp03SMvr44vXkz0r7+GgNu3kTx+fOwKCiAl0oFbssW4LvvwHl4YJCzszr18mVJlYMDSlUqfPzxx3UJJZ6vG1XVrRt950ePgM8/x+Bnz9jTnj2RLJXia4UCyMmB0fnzteZyetu3Q+LkBFetGsTZGQgNhadEgoqHDxGUnc2cnZ0hk8k4jUaDtWvXgjEGTiZDVkwMHJ2dwVauxNgPP0RMTAxbt3YtXt+4EaNNTJjBw4dgjPEAVXsrKiqgp6eH8PBwIToqis9ZvRourZHJxpg7t4lvgSiKUKvVLSa3OI6rJdoSngcXFESKi5Ur2/68VasoudyYbI8aRX3be/YAAHbv3i3k5ORw+vr66N+/P3x8fNCpU6cGTHXgwIEYqFVfiSIlSIYPb9gCpsXZs6TUamtvLVlC6pi1a+n6/xOtb/b29lxMTAy6+PkxfswYavt4/JjGz3300XONOvuzYWxsXHcO2wnGWIuGb+7u7jh37lyVQqF4/fTp05udnZ31unXrxrKzs/9hamqqm5WVNRdAT2dnZy4tLe1jQRBMDQwMyhUKBcdxXIGxsbFZ3759Wzzprq6u4DjOkTEW7erqqjEzM4NEIkH9iTc1pnRqALdq3ND1JBLJiwB+rHnJx3K5fDOAERKJRF8ikVQrFIq85zoJ/8XoINsd6MB/IeRyOQONSjZjjE2VSqUhUqlUx8bGRrS1tTU0MDCAubk5Gsyv/nfj5ZdJlltDAPLy8nDu3DlNQUEBJ5FIRH19fTE/P5+3sbER5s+fX0fYTpwgctDYmbQlvPgiEfMpU8iQ6PPPiZh++SU95CdOpODw9m0KFOfPpyz7wIHkDN4YjaTE9VFUVMQ167YOYMqUKSwgIACdO3dmAPVjBQUFISgoiFer1bh9+zYXGxuruXbtGieTyQQnJydepVKJv/zyC2QymWhqairUjAphBgYGop2dHWdtbc0cHBxgoKNDgeUbb7R9Phgjies335Ahm0RCvZftnAdubm4Oxhiys7PRuA+7T58+rE+fPrxi3Dh0PXWKdZ45k7XLrEuhgJ6/P/r07YvDhw9DrVaja9eubPHixbC0tGyZrEskJA3OyiIpNM8TkakJzlhxMSYlJ+MrV1dsOnYMPj4+wohPP6UZxpmZZOJUUkLVVY2GAs/qavr9X34Bdu0C/+67YmhhIVT797NRo0Zpg5wGX4rjOHhqHbhfeIESQDt3EoE9eJAMABlD93XrgDVrkJOTw7Rk79SpU+LYsWOZcWMytWABEaaayhxA1ZfY2Fi+W7duGh8PD748OlqM792bTXF2JhI7bRq4PXvQe9QomJmZiUVFRSw2NpZjjCEqKgr29vbiPD8/Jpk4kRICNjZE+Hr2JJWGFh9+SJ/dOMBMSqK9UXNNLSwsMHbsWOH8+fNcWFiYyBgTFQoF5+npKQwcOLD567ZzJ+21jz9uGBQHB9PxXL5MPeJyeRPFyqVLlzTm5ub8rJMneaYdz1YDMzMzPH78uOYjdqK6uhrGxsZIGjhQc0ul4h788ANjjMHGxkaYPn069/DhQxgbGwuGhoYNF6gg0DlxdyfTKEGgoP/aNaCoCJWurijr1AlGrSkOGkOhIOn09OnUe6olOuvWUe9oM606giDgxx9/FJ4+fcrpaTRgzakI169HhYMD9gYGAoxhUX3y/9ZbTZMWs2ej4MgR+HXpIvT57jsu79Ytftzrr4Pt2gUJ9cTyAGBpaSmcPXuW69Wrlzh58mQmk8lqL5RUqYRzTg71f/ftSwmZbdvI7CwwkOTqy5ZRVdvXl/aVkRHtgx9/RMH27TDOyMBVS0v0OH8eJnK5aL92LdsaFyeUmptzCxYsAMdxELOzcSY0FH0XL0ZpVRW6BAYCSiWGGhhI+icm4npmJrrm5YG7c4fuZadO0XpITaXE2xtvkLJg4kTg/ffBeXvDUqWCZY8ezPW337Dvzh1UV1cz37g4JAQECNflcq7r8eMo+PFH2EdFEaFdvBjo2xcGw4ej58aN1Ibj5oabt25pmFrNixIJXn/9dUqWqdXA9euQFBbitddeA5eWBllKCvTWrm2wziUSCbR7ffTo0XzfZcvEynffbWLe1iq0CeB6OH/+vObGjRv80KFDhcDAwCY3XUNDQ9jZ2Ag+mzdz3X/4AdzKle2vjD950nx19+uvG7iiOzg4cEVFRRgxYgTMzc1Zm8n66dPpvnWiBSXwe+8R0W8LPXuSz4QgkAHpP6ESc3d3Z3/88Qeqq6vp3NYY4NWOaJw8mVQT69a1OzH93wKO42BgYKBSKBR3eZ6/dP/+/TFTpkwxAIDi4mJ8++23nmPGjBF8fHz4Q4cO6ebn54uvvfaaYVFREYqLi+2dnJzQmrcPx3EYPny4jiiK8Pb2bva5oFaroVQqJXp6el9WVVX5AoAoitU2Njaa/Px8JgiCqY6Ozi21Wu0SEBAgCQsLYwD05HK5KYCKkJCQ/+k+7/+tFdeBDvyPQi6XD5RIJJOlUukQURSteJ63FkVRJpFI1E5OThp/f/8/tze7HtRqNU78/rvGLyyMP2drK+ru3y+UlZUhPz+fd3V15caNG8cqKytZWVkZ/P390aNHj4Y358hIqrC1pxKgVpPUOD6egrChQ0nWOWkSST7j4+tcV+fPp79bkBe3B0+fPmUtye45jmtQWasPiUSizfhrv6v2b6ZWq5GamsoKCgp4mUwGmUyGwsJCZGdni3FxcZqKigq+d36+0OfaNVY2cqTg4+PTeiVZFKl6qFLVGVSNHk0yTje3NqsCgiBAFEVIWgk4dGJi4Prjj+0LlAC6Jlu2YALJQoX79+9zhYWF7XN6Bihgk0jqrm8N1ImJSP/sM0zp0gWnS0rE69evcwEBAWSu5ORUZyYXHk4O2Tdu0Izdc+doPNKAAVgQGMidPHFCLLG0bF81YezYuiq7vT0Z/pw9CxQUgHv/fUyfPh2VlZUIDQ0VFQoFS01NZSYmJujcuTO8vLzqqnSzZjWoXimVSiQmJvLBwcHIzs7mkjZt0gScO8fPuXSJXiCTUQJBVxe4dg0rVqxgAEn4Dhw4ACcnJ5gdPMg+S0/H327fhnViIqR2dnTdG1eV0tObD65VKjQ2d/Px8eF69eqF8vJyduPGDZaYmIiRI0fyzQZfe/aQimLmzOZbP3r2pLYQLVn65JPawFmj0SA9PZ1fuHAh2Kuv0jqtJw0PCgqCmZkZiouLodFoYG5ujqE8D87Pj4e7O9RqNWJiYnD58mV89dVX4HkeJiYmdV9SFEn1MWwYJeT8/KifeM4cUr0MGAD84x+oOHYM3e7fr60StgtPn1KVrP452bCBlAVbttD6ffvtBu7Oa2hGMAcAMoUCxo1NJKuqgO+/h7RTJ7AhQzD8gw/q1o4o0r2tZr2eOHFCk5mZKRoYGPDSzp3xwrVrnIG1NWymTCGFiJMTJRNqRln5+vpyNX3cdMAKBVBaioVhYdC/fRv8mTOUIBgxoo54iCLtoadPaT+dOkU/l0jou929i7tLl8LyyhU8MzPDqNWrIfvqK+zW12fjV67ERMY4mx07oGthAejrQyaT4fz58/i8ZjyjkZERqqqqoLp7Fxg+HLa2tqJHnz4Mjx9Tki0hgUihtrL98CGRtfh4+l5ffEHHFRcH608/xeTRo/FHZSWG3b2L0QcOcA9XrcIzc3PcLCjQ2Gdm8njyhK6/KNI9JSaGErzV1ei3bBnvXVCAI3PninrTpjEMHEjncOxYQKmEhbMzJe1SUhoaVjZGdTUqjYzYTUEQXRvPum4NdnZ19zwA8fHxiI6O5vv27YvIyEiuuroajo6O2ooi/U5lJXp07y7KlEooGYN+e4m2Ukn3iOaUFX36UHLTxQUYMwbBwcEQRRGhoaGCSqViTk5OwqxZs5o+jyoriRSvW9fyZIFHj+i+2V7H80GDaE988ME/NdJTFEVoNJqGsmzG6vxedHXp2XDmDB3X6tXPZfL3V4dMJuOlUunv1dXVdqmpqdXe3t66AHlHfPTRR0yrzCgtLYWenp4IgJmZmcGs0XSHluDl5dVqXGJiYoJ58+ZJVSqVb1JSkhATE8MFBgZKhgwZwgMUd2zbtq1nbm6uQWpqKjiOK+U4brooissAVMvlcueQkJCif+kk/IXRQbY70IG/MORyeScdHZ2ThoaGPh4eHro2NjZ8p06dYGJiAn19fTDG/uN7OCYmRjQ8cIAr3LwZPTt3ZgUFBXzXrl0xadIkdOnSpeWA4/ffKdj59tv2fVBKCvW8hobSw/fmTSI+I0dS4Hv//nONLmkLCoUCoijCo7nRVf8CJBJJUyMZAgPAK5VKpBw8yMVVViLvxg3O29u75SyzKBJZksmojxKgSuyYMZS59/QkyVwrc1CfPn0KAE3GPDXArFn0vhpN6+NYtBg2DBg4ENwPP2DKlCmcUqnEr7/+im+//RYvvPBC6/NOCwvp2H/7jcjt1KkQAgLwe79+4v379/G3X39lxgUF6LNvH4YGBjZwMa4FY+RYP3Ysmfbt20d9y7Gx4G7ehO/+/Siwt6eETe/eVC0zNGw+MaGjQy0I4eFk2HPqFK3d0lLg3j26liYmcHNzY8+ePUNcXByuXbsGAIiIiMCKFSsokREYSCZTIJf7L7/8EgYGBsKgQYM4juMYJk/mkZ0N8/qzpA0MaM2PGUO94oaG0M73DvLxgcmKFch0dMQve/di8datkE2eDP0NG5r2/X36aZ38tz5SU4l4NYKBgQEMDAwwadIkpKamCrGxsax///4NT86DByTzPH68dfMhIyMai3XpEhHdv/8dGDwYmZmZkMlkgrW1NbGHlSvpu169CgAwNTXFsMZyU20v9u7dkEgk6N+/P/r378/FxMSgsLBQjIyMZEePHsWkQYPA8TxV2CZMqHPs19WlNQzQveKHH9D55k14ff89cj78EEfmzdMMnzSJb6JKaAwTE/o+jdG7N+23zz+nRE9pKTB3Lvb8+qsGAO/u7o7Y2FhU6enhRr9+qKVtx49TMmfzZlTY2iLnzBno6emhtLSUqqzJyUQua/ZocnIy8/Hx4SUSCboGBsIgJYXeY8oU2nvJyaSk2LGD5N8AVQnj4+le+eWXwOjR6Pz++9gQGgqT2FgMGDAA3lqirVLRev3gA2pVaZy4ZQzi4sW4tWgRcqdPx5CrV+E3YQL0CgrwUkIC0ufNg2tZGXSvXCHlQ2AguowbBycnJzE3N5ctWLAAp0+f1pibm/N37tzBG2+8AWNj47r1FRtLjtTnztG53rKF1s3Tp7RPO3em63fkCL0+IQEmRUUo+f57XP/tNwQaGsLhp59w6dQpKI8c4Xd7eoqTQkIYZs2CcX4+EfhZs2oTsoVubtj9888Y5OPDpPb21F6Un0/XTyYjxZAg0LNKLm/xXqHesQM3Zs1CxfPKnktLa4l2Tk4OTp48iUGDBiEwMBDx8fE4fvw4bt26hfHjx8PLy4sq/N27w+/cOf6rxYuhuHgRboWFwoQJE9qWHR08SOqhtLTm//3ePdojY8aA4ziMHDkSw4cP50JDQ3H9+nX+wYMHtfegWrz8Mt0PavZuE4gi3dM/+IAqzO3BmjVE/Pfto/d+TiJ87949kTHW8tg1b2/6k5dHe/XRI+onnzGDVDn/5Zg7d65Bbm6uQZcuXaCvr9/gIVk/nsjLy4OXl9ef0sSuXSfdu3fnAgICYGxsXBufchyHBQsWGBQWFsLCwgKRkZH6SqXytaKiIj4lJUUHgB2ADrLdgQ504D8LuVzOdHR0jvbs2XPghAkTdNp0Jv4PICsrC7eOHmULoqOh/8MPbbsP14dM1j6nUVEkcqN1IN6zhyrcz57Rg1IrmX7lFSIOJ08SUZs9u33EsAVIJBL8fxhGyjgO/T75BA5//IFN+/czURSbJ9uiSARMoSCZcH0YGFDWfutWClKnTGnRwCkmJgbGxsa1vY7NgjGSlu7eTYFJW7hyhY5P+51kMixcuJC7fv26eOTIEfbKK6+gWXl+XBxdt4sXa9eScOAAzv/jH4L91q2c7zffwGroUPCpqRjv4sJalU0ePUpB+vnzdePXXF0BAHeMjISKoiK+r0JB/84YrauhQ+mPtXXD4K5vXzqXz54RKdy/n1oVXnmFqs/nz0Mmk8HS0hIjRoyAlZUV4uLikJaWhrt371L/85MnEPz9sf3bb4UnubkcAKxYsaJurFBQUPMOvd7eRJBqvAh0e/fG2LAw8ZGLC2yLi5n/4MFIsbPDrmXLoNJogI0bERwcjP79+0MikYDjODBnZzr2xlAo2nTy79mzJxcRESH2r290lpxM43wiItq35xkjEqinRwTp/n3kduvW0OzttdcarJlmUX9sVT3USP6ZiYkJorduhfqVVyALDaWEXH0YG1MVq14FnfP1BfvlFxT+/rsQtGoVH33uHIb99hsAqo6VlZWhU6dOtXtQEATg4kVwP/xAewxkMqivr09VIT09IjMJCcDnn0OZloYchYKHnh4qKyuFJUuWcLkXL6LboUN1x/XVV0BCAkr37kVOjQ/FTz/9BFEUYWxsLA69dYtZm5rCasIEJCUlQa1WM0tLS/TRmkdVVBDBDw6m5EbPnnS/lErp5xMmkIRaEGjtbt8OODqCE0U45OeLeXl57PTp09DT00Pv7t3pdUOGUDJm06Ymvb2VkyZh/7VryLOywvxFi+AQEkJJv9RUmJaWwmfkSHJzDwqiJGl6OjBsGOaOHMmEjz6CxNAQ8+fP5zfWjBQsLy8nKfbf/073rsmTaR9KpVRxXL2a7vOGhuTnoNFAc/EiKqRSCBYW0PnjDxwLCBA4juMuX74MDw8PmJiYYJCuLvj9+3Fk5UpRmZXFsvbvR5yFhfBiZCQnMzUFq0neVNS031h361ZnyNWnD63ZxETaf7Gx0PzxBxSvvw4WE4Pi3buRW10NJ1dXREVFCQlXrnCLNm1C7tKlGDthQu29VKlUQqVSQU9PD4yxpvdyrbqkpu0jJydHBIDAwEAGAP369UO/fv3wxRdfCNG3b3PKzz4TTUNCWI/Tp3Hy8WOxoqKCAUBqamqze6MJgoJIBdYStm6l6rdSCchkEAQBmzZtEiQSCRs8eHDDBHpWFt2zt29v/TOVSkqUPU+fNMfRc2DWLLrfPCfZjoqKgqmpad3c8ZZgaUkVeZWKVHa//06V+ooKIt5/MTO19sLAwADd6iduW4CXlxdu374NLy8vtDkP/p8EYwwmzYx4lMlktUn+wMBAHQAoLCyERCLRJCcnX167du2Cjz766OifclD/z+gg2x3owF8X/jo6On8Zog0A+/fvh7ejI/RPn24/0T55kipt1683HDnTHB48oABs5EjKimvnU0ulRIiOHKFeUYD+WxRJerhyJT3YQ0PJGKmVvuyWEBUVJejq6jKO457fneVfQUIC0KULUkpK0CLRBuh737lDMrjmIJUSgblxg15bXk4BdL2qXXV1NaKjozFjxoy2v+OECW3PTgao6vLBB+QU3wgDBw5kWVlZmi1btvCrVq1q4HiNtDS6br//Xke0BQG7Tp0Sypyd2Su6utB5912q4EVGAuPGURDWnIGQQkHBWU3va2NwPM80UikFgEuW0Lrx9qYK08GDJMF95x2S5Pv701oSRapMHz9eF5Bv2ULHoE0E1ezLfv36oUePHti2bZt48eJFFh0djUmTJmF3SAi48nJu/PjxcHFxqXNiVygoYdRMQCkIArKePkXZ66+LfTMymBgTA6sHD5hhTbLCzdsbXQcNwszZs/HgwQOcOnUK4eHhCKvnYu/euzeyMjJQtm4dhg0bJg4aNIhOypEjTavd9RAeHo74+HhMnjy57iSKIvW/zpv3fMk1gGTQdnbAhg3ofeEConx86ti1qyv1fi9bRqZtjbF8OSXX6pPU+sjMhPd33+GClRX2vfgi5jc3fx4gxcOdO0RIa6BjaooUmYx7OnMmBpeUiOIHHzA2dy52R0UJNSZ64vDhw5lMJsPPP/8s6MbFcXOCglBdWYmrV6/iOo3cwoABA4RRo0ZRAqVPH2DHDjwODcXC+fNx09cXw1at4gwNDdHVzY0qtsXFRMyDg/Ho+++x89ixWmduDw8PsUePHiwjI0OsyssTT5aVcWZHjiA5ORk6Ojqsa9eudd/J3Z2IYUICyeU1GvIu+OGHWrMrHDxY5xheA8YYXnzxRQYAYWFhCA8NFXvPn88wfTqRnDlzqPrbaA8l5+QgKCwMBlVVEObPr/NW2L2b9mXXrmRodugQtfj06EFKl8OHwc2aRfty4kTo6ekJ/aytOZt336XKvURC66NHD+DTT6FSqZBy7x45Lo8YgQfLlyMvOBgqlQrXAgKgqaiAbU6O6JmdjYKyMrZy0yZc8/CAet48gDHIGAPc3DBjxgyueu5cqBMTkZWYyO0yNUWvp0/Brl6FKIoIDw+HRCKpS16AEi3lu3aBrVqFmAMHYGFkBJ1r18TbvXsz1r+/ULpjB5uwdy87HxSEHF9fTJk0CZUvvADVH3/g9OnT6Nq1K86dO1dLgmU1xNXBwUEzdepUvvbep9FAvHoVUbdu4erVq2JZWRkDakaz1SN6FhYWYll0NHpcuMAeL12K8gED8OTuXUgkEgQHB8PGxqZ9rPD48bZHU/r40DN340ZkZWWhrKyMa2Bcp8XWrXSvbO05Lgi0Bn766fmJ69tvEwn+8kuSlbei0KoPURTx6NEjNrQdEwJqIZWSAgeghNz339NowshIUjS159n3X4jRo0cjNzdXvHjxojBz5sz/98DS3Nwczs7OfEJCgrFUKv1rzGv7E8D/o9Hcyg50oAN/DVy5cmWup6fncBcXl79EqjU7Oxvp589j5i+/gK1a1XKvVmMYGRG50Mobm4O2mn3oEL3uxRebmqT07k1EYeTIur5exsgk5t13qWq+YQNVHoODKViv35PYBo4fPy56e3tzf6qpXHO4cgWHLSw00Q8ecMOGDRPt7e2bssX0dKoYffxx2wGIrS311J4+TQG3uXltT294eDiUSqUwatSotsm2sTGd3/Bwku+3hLQ0qnQsXtzknxhj6Nu3LxcfHy/IZDJW6ysQHk4mQYcONSCcFy5cQEZGBluweDEznDSJiMSoUeQsnJxMVfDGkukffqDvq5392wzS09PFyspK5qYlZIzR+rKxofW0cCGdp8JCqq7s20eJm++/p95jbV8bxxFpevllkiHWG3PG8zx8fHzYlStXxLKyMhYVFYWhZ89iSmAg7EaPhk59A7vUVDrmetX+W7du4eDBg8KFCxdYfHw8Eq2tmVVamnjv9GkxZt480W/tWgaFAmzNGhjUXA8TE8OYzhcAACAASURBVBMMGDAAQ4cOhbm5OUpKSlBeXg7J3buY98sviPTzQ0ZGBvPz84OE5yH+9hvShw5F5pMnaEDeanDkyBFhwoQJrLbtQaEg9/6QELoO/wyMjIChQ6FSKGD/1Vec6Ysv1gWyhYW075tzL7e1JYLcuFojilR5DA8Hy8qC8fz5uJGdjfj4eMHT05M1SUz270/rt16SRk9PD5GRkVAaGUFn6FCWEhoKx6+/RpSuLgsYOxb3UlKEsLAwLjY2VlQoFDB7+lSMy8pip2muuxAUFMT8/PwQFhYGpVLJag2GeB6no6IQZWSEQVZWotXVqwyOjrR+HB3JPO7zz6GSy/HThQvo06cPnJ2d4eDggDFjxjALCwu4WFgwu1WrWKevv0b2o0fqgoICztnZWUhLSxPOnTvH9ejRg5JW1tZkGPngASldPD2pv1hfn6rBjYh2Y5jo6OBmeDjzGD0assmTSQmxfHmzrTnW1taoKC5GZVISHnt5wbZnT9oL7u503338mKqnCxdS0sHVlSr+3t5EzO7fBw4fhvOBAyy1pASGqaniWYkELm+8wSQ1xoQPHjxAbGwsQkNDkZ+fr852cRHSunQRC8vKWHl5ObOwsMDs2bMxZOpUZr1kCSurrGRXAbjPng37+Hjai+PHkyR93TpIPvkEtp6eCAwMRKFMJri99x6LqKhAXBEpVa2srKCdpQ0AZ/fvF48nJrIkV1c8EQQxKSNDLLKxEWf06cP1W7KEec2cyXT8/CCxscG4/fuZ6Zo1MHz3XRhYWyMxMRG3bt1CYWEh/Pz8MG3atNqe66SkJJaTkyP06dOHA4CcvDwc6dpVSEhIQI8ePaCnp4cRI0Y0lD/fuQOXJUu4CF9fmK1bh/jMTOH06dOsoqKCSSQScfbs2U0NGVvC9Ol0blrrPR85kgiyVAqO43Djxg34+vrWOaNHRpKkfv36ZpMxDaAdufXWW89Ptrt0oWMZN472fTv7iRljiI6OhrGxMU2ieF50705rV6OhpHFUFD1nZLLnGs/23wKFQsEyMjLQpFXo/wEJCQniqVOnSgRB8P74448v/H8fz5+Fjsp2Bzrw18V/vsraDKqqqnDixAlNWloaP6x3b7A+fYhwtIWbN6k39tGjugxyc9Bo6EHHcVQBb6mXWCKh6uNrr7Vc7dI6MmdlUYVXIiESZmNDlbl6KCwsxNOnTyGRSJCUlITS0tI6MvafgkYDcedOFPTqxQ2dPh0DBw5ser0//xw4cIBkmu11UdXRIXOzs2epbzMsDHj7bTx48ACGhobtX1NRUSQnb2GUEwAiMjWVvpbg5OQk3rx5U+Pt7c1zV65QhXX7duoPrcG+ffs0mZmZ/KxZs2CqHafk5EQ9k7t3U9/l4MF0Xc3MaN1ER9OxDRjQagDIcRwTRZHkjTExVJl79oxUF3PmEMm4f58k5qdP11XRu3QhQnP4MPWNAhSAHTtGMtczZ4AxY1BUVISIiAhUVVVBrVaz5cuXY+vWrSIvkUDS3B5etYoqk++8g/j4ePzxxx8ix3HM39+fubu7QyKRICEhQYPISOYXHc0FODgQ8a8xwGr+MpD8FABVhxYtQoivL7766ivhxIkTzNvVlT3S08PFkycBxhoQDS14nheLi4vrWgxCQui8rFjR6vVtE/r66DRvHo5fuACn0aPBDhygBEq/fqQSiI0l4qZFbCwF7RMnNn2vhQvpGsbEAC+9hN5qNeKSk8X09HTu0qVLGNl4FGFlJfX+1kui6erqwsvLC9HR0UhITIRRcDC+6NcPw65fx4Aff8SADz7gy7t1w5UrV9iQIUOYwfr1UJiYoOill2Bra1vLIAIDA1loaChu3boFtVoNoEZ23rkzKiZNoj07fjz1un/0EbUnlJbiwYMHUCgUsLGxaSp7vXEDcHWFa8+ecO3ZUxIVFYXQ0FCOMSba2tqKx48dExYOGsTj11+pmp2XVzcfGyBy1Y52GL2xYxEklUJv3bq6xE8z6hQtOi1ejEfnz+NWaCh8tfJgjiN/AwcHUo306UPfd/hw2pfz5pEaZOpUIDcXTxmDZV4eBI5jVTExuHDmDJxcXCCKIo4ePQojIyNNz549MW3aNLrRLVpEya5mkn3V1dWaJzY2fPrDh0KPHj04vPoqtV5cu0brv979YOSoURzeeAN2V6/iqbMzzMzMhHnz5tUxQY0GgXI543r0EEcdP85A65/e4Ntvyaht1y5I/P3RC6gzAYyOhtevv+KBm5smURR5iURSu/60MlqpVMoOHDjA79y5U5RKpUJuYiL/2nffccKTJw2VPgBVhRMSAAcH8EuXolffvjh/4QJUKhVnYGAgBgQEMA8Pj+eLCTIz235Nt25EjidPxuG0NNHKygq6urp1n/PkCa2ztnrTRZGIeWTkP9fSpadHrvi9etE6jopq9zPP0tJSU15e/q9Vam1sKLbQaCjWOHSInm1VVf+S8epfDd26dcO5c+e4ioqKds/5/rMQGhparlKpVgJI+X89kD8ZHZXtDnTgL4pLly5Jy8rKXhgwYICstbEMfyZu374t7ty5k1VXV+P1oCDm/NNP5IDd1vFUVVHfn5NT61XRS5eoT+q990hO2haJt7Ul4uno2HrvqbExsHQpBYNnzlAV3sWFKt5z50LgOGwkyZwmOTlZ1Gg0bNSoUcy2jX7WfzvKy/EoMRFXjYzYqFGjmj74tAR71apW5/m2iO7d6Vzdvg2cOAG+WzckPX3aPKlvDp6eVLFWKFoOevz8yFyoFem+o6Mjd+vWLVaxYwdsr1xh/N//TtLRGpw+fVq8d+8et2zZMjRx1XdxoQA+IICqeAMHErH45hvq7XznHQp+AQpWU1Io2ZKbC+WmTQg7f16w27GD8927FwgOBrdyJZjq/9j77qioru7t59wZht6liQIiXXq1IQRF7BobxJ4YY0kxiSaaLKMxplli3hTjaywxlmhijb3TxEIRBATpRXrvdebe74/NUAQR86b9vuWzFksZZubee+655+xn72fv3UpjI5ORcffSS5S+YGZG/7e3pwJx779PY3DwIEnKPT1pTqmoENFZsABwdMTJmBgkJSVBT09P5ufnx5mYmGDkyJFswMsvM2Zj093wtLICxozBheBgXL9+HZaWlmzhwoUYNGgQk0gkEM+bB6NHj7h+P//MxNu3g5s3j8h+H3LyANDx9u4FzM1hYGXFwsPDURwWBpfsbJbp6CgMHz6cPV706OHDh0hPT+eSk5OZlZUV1M+eJfnpggV9UrFIpVIwxiAIApqbm7tVuy8sLMTNwkLBe/t2hmnTaD45OQFFRTTeb77ZcZyffqLnXF4gDaA2RVFRlI//zjvt9R84joOamhqLj49HXl4eBEHAoM5RvBkzKFrr6trlfPT09BAZGQme5zFjxgxUVFdj4tat4HR0gJwcSPbuhWVgIBT79QM3YAAU3N2h8diYDRgwAM7Ozrh9+zZV+ed56JaVoVlREQYZGYLZo0cMhw9TC63SUpJq29pCe+BA9OvXD2fOnIG9vX1X0pWfT3Ow7XnX0tJCZGQkBhobs/H5+cxwxw5Ox84OSbm5sj12dpxHdDS4adPA2pRAgqEhSisq8PWdOygvLxfMzc1Zl3tRVYVru3YJ11VUWM7IkbKhAwZwcHAg6ewTejwDQLMgQPLFF/ALCcF3zc2IefhQMDQ0ZKpqauCsrclxlpxMTpLCwo7Un02bgDFjIAsKws7KStguX44hPj7o/9tvqMzKkpVHRgr3a2p4VS0tYcWKFSJ5BBgA5ZubmXWkE3WCjY0NV7N/vzDi2DFO2deXnnlBIMfGBx/Q/Pr0U8pZZgyPTExQf+QIVIcOFbKKi7nMzExBUVGRqaurQ4ExRCQn8zE2Npyrh0fXXtceHuSkKyykNaOpiRwne/cCgwaB5ecjuqwMY06eZOP9/KDwmBNLV1cXzs7OuHHjBlRVVYXAoCBOXVsbCp1UMe3473+pLsSHH0LB1xfm5uYICwuDqakpVqxYwYyNjfFMKWW7dpGjW96pozf89hvQvz9CSkp4DQ0Nzt7eHsKJE5AuWgRu+3awvhQ6O3+e8qHle+8fwdix5CDOzYW0f39El5W1dyhQVFTs8frbnDWcpqYm/hSHOceRkmfJEjqXpUsp0q2t3XtxyP8jUFFRQUxMjExDQ4PrtVDq34CEhARpbW3tbAUFhfk3btw47uvrW/uPntBfhOdk+zme41+K0NDQbEEQAmpra40sLCx6bsXzFyMiIkLQ0tJiS5cuZQpHjpABM3587x8qLCTys3w5GSo9obycoq5ffUWGtatr3zzhIhGRtLNn23sxPxWjR9N5lJVRJHTWLLCFC8Hl5AgW8+dzM2fO5FxdXVm/P9Db83/GBx8gq6kJKaqqqKqqkpmZmXHtcuN9+4CPPwa/bh1qVVVRV1fXPRLSF6ioAN7ekBoYQPb229BoaMCAvuRsA+RU+eQTinAuXtzzewYPJjLRw/iVlpbi7t27QlpaGgbdvg2uqIjdsrPjHWfO7HL8yMhIoampiY0ePbrnnHWxmCIdWlok8b56lSJq8gj1kSNkVDo7kxwyLw9xCgpIvnoVmWpqLM3MDHfd3HAjNxehlpZ4aGQEUx8fqL7wAn2npmYH0airo2sKCqJ5bGxM5HjXLop0i8X0fi0tIqNqarBVUMDt3FxMmzaN60L0zp0jJ8T773e8dukSRfanT0dhYSGys7NRVlYGKysrKhollZLTICKCyOjGjZRC4eVFRaT66hB65x3A3R3abm7w9vZmbk1NTLuqCsKkSSwmJkbw8vJiTU1NKCwsREpKCn/mzBkmEol4RUVFVnTqFEx27RJagoKY0uNtxTpBKpVi//79OHPmDMLDwxEaGoqwsDBERESgvLxcuH79OjMxMYGSkhIOHTokDBw4EEM8PBhmziQynZhIaoX167vmSHp7dxDt8nIiO//9L7135MhupFBbWxs2NjaIiYlBTk4O8vPzBUdHR5pIr7/ejWgDZKDLq8iPHz8eHh4elKM6eDCRqowMKgaVnU33fsAAIn1SKTlp8vOBrVuhEBAArXffhV1uLjQCA4U5a9awZEtLDDE2hl5UFENlJRXdqqujyO/t22AXL0JfXx/hubkY4uiILpLgN98kBYOhISAIKIqKgv6RI3A5cwaP9PURYWyMga+8gkslJahpbmZV9fWQ7tqFi0pKKCwsxBl1dUQpK8PcygpJSUksJiYGNTU1vIGBAROJRJBNnQqEhbGsSZPwxrRpHMaOJTLxlHz88xcu8AkSCbPatw/q1tZ49OgRu3XrFiorKwWRSMRSFRSEAY6OjH34IakOEhIoMqitDQQFgVNSQnhkJCZPmQKJtTVUX30VgywtOcvSUs6jqorz4DiOubp2LaI5cyY5YJSVu+4PjY3Axx/jQV2dUGZoyAwPH0apvz80Vq2i51VLiyKx69cDs2Yht6wMGRkZULp0CV79+jG7JUuQl5eH+Ph4oXTHDma0ahV+GzWKmZib82FhYfywYcM6mCJj5Ohat47ScQoLyQnw+ut0riNG4EZiomDVvz8zktcQOXOGnHRt16KoqAhvb2/m7OzMKSsr01h3Xi+vXKF0jbVriai27QHp6el48OABfH19YdhJBdRnyGQ0j9qk+r1i6lSgf39cvXGDq6ypQUVJCX8vORmlZWWsesiQHtNOukAQSB0xYcKz13Z4/KvefBMJCxciee9eRIrFfGZmJn/37l3u5s2bSEtL469fv87c3NzaHXqMMURFRUFPT+9JnT/+GCQSChjMmUNKnDFjSPk0dSrtA/9QEOTPQGlpKZecnMwbGRmxxsZGqKmp/SPn4ezsLHFzc0NhYaFyZWVlga+v792nf+r/Hp6T7ed4jn8pfH19cf369d/LysqCJBKJRmf54t+B+vp6XLlyhVlZWWGQ3Ev9+uu9e6wzMymiPW3ak6uJPnhAhVB4noh2T1Wqe4OeHhkz2tp9Jx4A5Y7KvfMVFchVVORrqqo4i1mzyMD9J6qQvvsu+m3eDF1ra0RGRnI6OjooLy+HVmQkRObmKA0IwPenTuHmzZuIi4uD9x8o/AYAYAxf//IL/8DAgE1wcGCSb78l4tKXDdbSksheT1HV2lqKZvWQjy8IAnbv3i2UlpbC5PJlaGdk4LaVFSvS1OxWyMbOzo6Fh4fDwcGh597cPN+RG7p9OxnyS5ZQpNPGho4/ZQpFSFetAmbMQHZrK4KlUtSpq6NRRQW2Hh6CtY0Ny8nJQX19PaKiohAbG8s7ODgwSWcDXyqlOdY5R1lDg6S20dEULTM2RnsubkQEREuX4r6TE38nLo6Zmpp2VGKV54R3jtZfvUrR3IkTMXDgQBgaGuLBgweIi4vDgA0bBG7XLtb6ySdQPHKEVAOmpkQu33yT5ntkZPe+2j1h2TIyEOWIjAQ8PGDs44Pw8HAhLi4OoaGhLC4uDmlpaSwwMBBTpkxhw1VUYGVigismJsKVlBRmZ2cHZWVlVFdX49atWyguLsa1a9egrq6OHTt2oKampv0Q8grtWlpaQnl5OSorK1lMTAzCw8MhCAJeffVVxhgj8uTvT9XNv/qK7p2tLZHiU6fo3Jcto+J7zs503Z9+Su95AtTU1GBlZYV79+6hoqKC+cojh6tXA/Hx4IcO7VKAMDw8HLm5uQAAT0/Prjn1CgpEeC0sSEFx+jTds/nzicjp6dHP1q24MGAAcoqL4b5kCRynTGHso4/gPnky9E6cYEhOpnzqjRtpDr/6Kq2Npqao/fJLDA4OhqmVFVhLC5Gi1lYq5jZ/PnDiBKSbNiEsJAQtra1o/OADKPj4ILm6Grdv30Z9fT1TU1MTgj76iNWWlKCxvBxJNTWCY2GhMH30aOY+fTpGjRoFnufx8OFD/t65c9yjvXtxyc4OcY6OmDljBrQEgZ7vJ9Q66AwlJSVWevYstL75BkPOnYPn7t1ISUsTMjMzWWJiIvKSkpjhzp3QamgAx/OkBpkyhQjJsWPAN9/AMCEBajExUJRISC1jakoOKxcXUo4sWUJzVlm5I4IoJ4rDhtG/UikR3zt3YFBezkp1dYWLkyfzd+rqOF1dXejr66OxsRHlMhnU1qxB46NHELm746KKCrTGjhXsvL2ZhrU1nJyd2fDhw5mBWIzcfv1gPWMG/Pz8WEhICKejo9O1UrOaGpHjykq6ns8+67JfNDU3s6tlZRi+Zg04qZTI+NCh9D4bm66KrcxMcrqsX9/24SaSKhcWUvpKp7UoLCxMKCkpYVlZWYK3t/ezM7vMTIrsd57bPUAQBJSVlUHF0RFNMhkGKypi1CefsJhp0/ghS5Zw8gJwvfZkPn6c7t+6dc98mp1RVF6OyJwcPqqyko3Nzobf0qVs2PTp3MiRI+Ho6IiKigpWWloqqKmpdSkaqKuri4iICGHEiBHsTw9MSCQ0J5csofXo4EG6f+7uZL/8H6xgXlxcjAcPHrDk5GQhOjqaPXHv/YvBcRwUFRXB87w4KytL58aNG7WhoaHZvr6+rX/7yfyFeE62n+M5/sXw9fVtunHjxtXc3Nwlw4cPF3erDvonQiqVIiMjA3l5eYiOjhZOnz7NtLW1pTNmzODY6tUUYfL3f/IXyI2nCRO65l/KUVNDRCk6mqSgj+VQ9xkcR170vLyej9MXuLqiRF2dS0pNlbmPGsVh2DAyjh496ih69Vd7rZOSAE9PcCNGoLq6GvHx8SgsLETt8eOw/uUXKCxciLMPH/IVFRVMQUEBc+bM6bGdRm/Iz8/HxYsXcfbsWQBgK9etg/KwYTSGCxeSka2v3/u1amhQdO/qVTIuOuPmTYp4r17d5WWe57Fnzx6htrYW70gkzMzMjOmvXs1cZsyAr69vt+g1x3GIjIyUNTQ0cAUFBQIApqGhAVZdTZEtDw/KZZXJSKZ6/DjdL39/cgL060fX0Sn6FRkZiZKSEqxbtw4vvPACbG1tmUQiwb1799rf09zczHJzc+Hs7EzndPw4FQH6/POex2LIECIntbWUQ+3tTQR/4kRYtLay+IwMVLa2dkRVJRKKWmlodBT7UVZuLzLEGIOenh5GjRyJ/IQElPfvzwafOYMzWVlwPHGiqzNJQYHGe/Jkilo/LZdx5UqKoMvzns+dA0xNwQYPhrW1NauoqGDjxo3DxIkTMXLkSCIXjY3A+PFQcHSE3YoVLD09XSgtLRV4nmd79+5Fbm4uMjIy2ucrALz11lsYN24cRo0ahVGjRsHNzQ0uLi7My8uLubq6oqamBvPmzcMLL7zAuqxfHEfjZ2FBRuzEieQMs7WldeTjj4lgzZ/f5z64BQUFSExMhJGRUUdOekoKMuvrsTcykr9+/Tq7e/euUFJSQlW/GxuZuro6fHx8elZUKCvTPdi7l5QUw4Z1RJ4NDIClS3H//n2k8jzcZ8yAqooKzaEPPuh4Ll58kRwdUVFUzM/QEAgIQLqzM843NMDD2Bii0FDKuz56FEhPh+zrrxGtqChck0pZio0Npm3eDGsXFxgZGcHe3h4JCQm8sbGxMHXqVE7XwAD69fWwiYvDyM2bmcWhQ0yprW82Ywympqbw9PTkBvzwA/Ti4xHv6QkTU1PBbN8+KF2/zsqXL+81qiUIApqamtDU1ISw9HQElJdD7OwM+PvD2cODPXjwgB/Vvz8bc+oUJDk5YCEhkAwfTg6KHTtIFWNiAqio4IyzM2+kpsa0ZDKqBbB/P5EWxkixNHcuzfFffqFce6mU2mSNHk3z5coVWrPq6oCPPoLym29i0Pz5bOiwYVxcXJwsNjaWq6iokJ06dYqLjY2FpaUlvtu/H0wQ4LF6NbxMTRlWraL1wsoKCAiAyuTJMFy0CEZGRhCJRLh3755QUFAgODs703y9d48k6m5ulEZy6FBH+klbmomxhgbir1yBg48PJKam5JjjOFoz09I62lbKHXRyoj1/Ps2J//ynR8VYdXU1MjIyGGOMjXqCQyQ/Px8pKSlQVVVFbW0tkpKSoKKigrt37sBg4kTsr6xEdmMjb2dnxxobG5GVlQUdHZ0u8/3hw4fYv38/oi0tUaWpCVVfX8Fkzhw2PCiIMzQ0hLKycnvKwxMJWXY2jY+9/RPnUl+we/duyOrr2SvBwVD98ENa/wcMAGMMysrKsLa2hoKCAgsJCYGHh0e7rFxHRwfBwcHMwcHhjynA+gKRiJ77kSPJgaqvT4ojBYUnq/j+pTA1NcXQoUNhaGjIHjx4AGdn538sug2Qs+TevXt6LS0tgRzHiX18fK79YyfzF+B5gbTneI5/OTZs2JD8+eefN9bW1io9K9l6FiQnJ+PkyZNQU1OTqampcfPmzYOZmZkYNTUkM+ytQMilS2QQFRX1XC07OJgKTjU0kCH6v1b4DAigH2fn3nPCe0F5eTnPaWtTZWmACuFoaFDP6MmTqU1PU9Nf1wLk2DHKA5swoT1X2yA6GnWamrj3xhtQrqlBSkpKOzu5deuWzNTU9Klae0EQIAgCTpw4wSclJbV/fsWKFR2tp6ZPp4hSYCAZR++807v0LyUFuHyZ5kFnjB5NEZnH0NzcjKKiIuYaE4N7ZWWC7J13mJeZGXrrsu7t7S2KjIyUqSoocFHnz8Pb1hZDV6ygfPATJ0hW7+REBu7atRQNkreBewx1dXVISEiAg4NDlxw/Y2NjLF68GHv37gUATJ48GWfPnsWmTZvAcRzGVVbCyda21/OEpyc5jq5dIyN57lzUGRoi64cf+NmZmZxs+fKurO2HH8gYGzyYDNKAAPq3E/HkPvkE837+GcjORqYgIP9JTjUfH5Ivl5cT4WjrI94jJkwg8iyHllZ7ReJ+/fpheqccTI7j6L1nzhBZbCu2NXLkSHb48GEWGxsLIyMjWWBgoEgueT5+/Di8vb3bi9n15AjU0NDArFmznnyOADlN9uyhPOXiYup3+9FHdB5GRs+0Vsg7CRQXF+P777+XCYLAWqqrGZqbmf+kSZy1tTVSUlJYQkKCrKWlRQSQkdcj0f78c4pg/fgjFQmU925/zCAdOXIkkpKSEPXbb7yDpiY38NgxSmkYMYLe8Pvv5BwsK6Mq9G3jZWxsjFpNTeR5eWFQYCDdV1NTQCxGnY4OcpqamJuNDWa89BI0OkmONTU1sXr16q6DPXkyOaESEyn3tjOys4GdOzHg6FFU1tfD6NQpoaysTCjJy2MXbGzAnTolLFu2rH0AysrKsGfPHqipqQk6OjpCTk4O19LSQn9UUoLie+8RYYyNhXjkSLzZ3Mzho49Qa22NH5Yuxfu7d5PUdtUqUibo69M9njULNvPnw/DCBRoLxijan5tL511RQZHY33+n74+LIwcEx1HE39qaip/NmdNR9bwTVq5cKUpPT0dkZKRIQUEBra2tOHPmjACOY7qffw4rExNylL39Nu1TNTW03nf+np9/xmsvvMCu/PabUN+vH6SpqdD99FN6dsaOJeeLigoR46YmWhfr69G6YwfmHzqElEWL4BYURHvK9Ok0nz/5hLpkHD1Ke8ulS1SB/5NPKArcC8GxsrJily9fxugenE2JiYlITk5GUlISAOqGIAgCFegDAEFA7GuvoUZTE6UpKdyePXv4goICjjEGBQUFaGlpCQ0NDWz06NG4desWVFRUoJ+RgbmHD0O0aBFDp9xzd3d3JCYm8sHBwcL06dO770HXrtG9+x8LKdbW1qKmpgaTXn4Z4qtXaW/y8yMHTKfcYk9PT0RFRcn27dvHhg4dyjk6OrY/w/Hx8fDz8/ufzuOpYIzmA0D3tbKSHIYSCTlr+9iy7J+GkpISwsPD4ezszBsYGPyj4XmJRILXX39dcuLECVl2dvY/m0j+F+A52X6O5/iXY+PGjRocx6n9Zd7aNvTv3x9isRhisViwt7fvKKD05ptkfHt6PvnDr75KxGPmzK6vy2RU2Cg1lYzV5cv/nJMVieiYP/1E0fJnhCAIuH//PjdjxoyOF+WeaUtLqt7NcUTkZ8+mIj+lpU+ulP7sJ0CGdZv8mud5GJaWYsKtW6jYsgXXq6r4utBQwcPDg8vPqwCwEwAAIABJREFUzxc4juMyMzNFMTExMDc376jW3QOuXr3K3759m2OMcQCwfv36nsmEigoZtt99R9EVT88n5+OvWEFEu6qqqyTypZcocvrSS13erqykhA+VldHs4YGcYcPY75GRggfPs96UGZ6amvB84w1Rk7s7shobMfjTT4lEaGiQVD00lOaiigpw+DBFlp5Ats+cOQMAqK2t7ais3YYBAwZg/fr1aGhogFgsBsdxSE9PF1T27WOR5ua4AGBCVBQ8eotUaGhQocDMTMhmzMAdU1OhbOlSOLu5QSyXecsdG1FRHZ9TV6donzznmOeJVLzzDs3jmzehtnw5Wn/8sVvf3XaoqBCZi4ykiv9PwogRpJ4AaL59/HEHYewJy5bR8zp7dvtL5ubmEIlEmDlzJmxsbLoY2TMff9b/KHieHAfffUeRY0GgCODRo8+sLlFSUsKcOXPwyy+/wNraWqSkpASnQ4eglpgIbtMmAICzszOcnZ1FAPDrr78iLS0NW7duFRQUFASpVArnnBxOWUMD2Xp6sF+7FvbXr0PUS9skIz09LB06FK2LF3P3nZ0x8Ny5jj+++SY5pKZNozoTCQn03GdnI9nGBhzHUUFAxiiCfuoU4OCAi8ePC0opKRiSl8dEv/5Kc9/fnyK/OjrdZascR45HudQ+P58cBACtZQ8fAhIJtBUVsfillxgCAhh/+DCuHDiAmb6+7YPM8zyOHDkiqKqqMkNDQ9bU1MQCAwNhYmKC6OhoWkfu3iVCf+IEEd/SUmD6dFwaOhRaFRU8P28ex61cCWZqSuv0pk107bm5KJg6la8fPpzzr60laf22bXSvO6ccvf02tRMsKKBo8qlTdKzQUPp95Ej6KSqidamlhboVuLvDYscOREZF8TPPn+eOzpsHp5AQ2Hp5QcvJicjtzZv07A0dSn3CIyLo35MnKYL93XdQefttTFu1irtVXMw/unaNCzxyhJ7P3Fwax08/pfM0NAQuX0ZeXh72amrCaPNm/lUXFw4HDlCtB8bImaqgQMTcxoaKKi5bRp83Nu7onvEEqKqqQl1dHQ0NDd3+FhYWxpeWlrZPBHm/9nnz5kFVVRX8gQPIuHYNN/z90draiqKiIm7cuHFwd3dHSEgISkpKmJqaGkJCQmQtTU3sFZGI0z1wgJ6/5uZuxxs7diy3f/9+FBUVdc8f3737j6vMOiEpKQkSiUSwtLNjOHaMcv+nTqV0k07rEgAsX75cdPbsWVy7do0PCQlhrlSXgaWlpcHX17fntfOvgFztZW1NTsMffqB6FLa2pNCIiSEHW+eUnn8J8vLyUFJSghkzZvwrdPCKiorIzs5mUqm05Z8+lz8bz8n2czzHvxgbN25UlEgkJ+3s7GQSieTJpWL/BOjq6uK1115Dbm6u+PLly1BUVIS7mxvJVZ9UHGvfPoq25eZ2NwBjYqidyNq19Pk/Uk27NwQGUpQgPLzXStg9ITk5GQAEc3Pz7tY8x3UQ7/v3Scp4/TpFKurqyCizt39qHlyvuH2bDNZFiwAAJsnJWBoQALz7LrTNzTEY6DyYDAB27NghO3funMjPzw8ODg7Q1NSU52PC0tIS5eXl0NDQQEZGBtoieJg9e3bPRLvzta5cSffqyy/JGPT37zmPfuVKIm83bnS8Zm3dPX9YEIDDhyF++BDiL7+Eja4ujkdEsOLiYnSrfFpVRUa1hgaRhcxM/BQYCI3+/WW2iooi6OnR8a5cIceAPI+zvJwiUxkZPeaSBwUF4YsvvkB2djZrbm7umo8LKqgjVxM4OzvD2d6eCe+9hz1tudUXLlyAi4tLt4rajyNPIsEFf3/errkZsyIjOZG3NzmA4uKoZRtABtdPP5Gk9JtvqNCbHAcPEmHIz28fB30AYrFYyM7OZuZPMtC+/ZYi0XfvkkOoJ0dcQgI5QkpL6fvt7bv3KJfj1Cmq7G5h0YXgchyHYcOG4cSJE1BSUhIYY1BXV+c1NDQgk8mEpqYmVl9fz1paWpi1tTXv5eUl0pO3SOsNra0Uub52jQhUaCgVHwoMpHvNGBVEi4kh8vUM0tSMjAwAgJ+fH6karKxoXveAxsZGyGQy2NnZQUUigZauLmewbh2qJBK+fsUKFpqUhNzERGY5bBhsevqCuDjg44+RZmzMxwUFcT4LFvDo/OxKpR1jrq0NobERDx894o1v3OCueHpi6tSppDbJyyOH1cWLJPXftg33GxrYfQCaHCe8vXo1w8WLNGaXLhHZdHYGHB07FCmjRnXUwLCwoIj2smVEVDuv3/fuAQYGSKiogEwmw/Xr1wVlZWVmamoKmUyGiooKNmPGDNg/NuZDhw6l/5iaEnFMTSVVy+zZwMsvI+3zz6GmpsZdWrQINebmwqi33mLGr7xC87SpCSgpgbqaGlfc1ETnFhxM80Asbt87eJ5HQ0MDalVV0aSgABOehygpieZ4fj4R9+BgcgoVFtI8MTGh85o4ETAywkArK64uNBR6enqw1dNjWlIpHefiRXr2rl2jKHpFBalmAgMpegpQihOAqqoqBA8axFnU1ws4dIihoYGcA4GBtOaFhrYrF/r16weO49CvXz+BMdaRWw5QZB6g516O33+nOb1qFd3zTZueqBoTBAG1tbVdq+sDiIqKgqmpKVdaWorZs2dDKpXCzMwMampqHeu9lhb6+/rC/f33UV1djbNnz/IXL17kgoODBScnJwQFBckfdBFiYmiteP11WpOyssgJ1mlP79+/P2xsbPhdu3Zx8+bNw2D5upuRQQ71/6EoWk1NDQoKChAcHAyZTEbnVVtL9RyuXaN5PHkyKQvawHEcpk6dCp7nuYiICDx8+JBXV1cXKioquG3btgkrVqxgf7ksmudpbsfHk61z+jQFAeLiaG8DqGaDkRE5Ew0MaO9PTaV94eRJSqUYMoScxw0Nf2tf78zMTOjr68t0dHT+t5Zpz4DKykqoqqqiS52UTrC2thYSExMXf/rpp6J169b14h3+v4XnOdvP8Rz/QmzcuFEUGho6WSKRnDQzM7OfNm2a8t/hqVVVVUX//v1hZGSEM2fOYPC+fUh0c8PAJ1X+XrWKSGfnqHdLC20kR4+SUeHv32WT/NPAGJG0Xbu6R9Sfgl9//ZV3c3PjHjdiukEioZwxCwvaTBUUiBSUlZFH+9Kljv62z4L//pdI4qhRZPRNn065nT30PpbD09OTq62txa1bt3D37l00NTXJrl69ykVHR+PmzZuIiYmRV1cWFixYwOLj42UaGhowMzNjMpkMjY2NT9zg0L8/GVv37pEDRVW1uyfe1ZUMTHlUo7WVzrezlJnniQimp1MESFcXhw8fltXW1jI/P7+OFkRXrlA0a9YsItNLl6Jm2TLsPXGCr2poYHPmzKGqvY2NFNlYtarrcRijc/7pJyKJnZCUlITTp0+jtbUVtra2wpAhQ3ovmMPzwK1bYNu2wWniRNjb2yMrK0u4evUqy87Oljo7Oz/xwdu9e7dssLs757d0KcclJNA1HzhAz8OFC0QSBIEIxdChlKs9bx7ljB44QPf81CkyJDsV+6mvr2ehoaEYNmxYz84Sxmgujh9PEsae2gj170+RUA0NIvIGBj1Hn06eJMfAa6/12NbG3Nwc7u7uMDQ0ZJaWlkxZWZmTyWSciooKp6+vz1lZWTEbGxuWkpKCsLAwJhKJ+IEDB3Y96ZYWIv+hocDWrWRsRkeTo27OHPqxtqbxCAig5+2ll6jjgJUVjU9REb2/l3tZXV2NEydOwNzcHM7OzvRiaSmlQDymzMnKykJYWBhEIhFeffVVNmjMGGY0cCDUv/kGekFBbPDgwcxLTY0N3LkTe3V0EB0dLUtLS2MNDQ1MW0EBkq1bAQ0NNBsb46BUyibOnQsHB4eOk0tMpJxcedsqKyvkNzTgUH09S1ZVhR3HyXzmzqW5VV5OjqyZM4HYWFjOn88Gu7tDWVkZGZmZrEhJibdbuZIxDw9yLJqYkBplyxYy5E+f7qjD8MYbFMV99IgisbNmdYzZuXP0bG7ZAn19fQwcOBDp6eksPj4eXl5eEIvFKCsrE6KiogQHBwf2uJMKMhlFo5cvJ/lzURGRBZCzNjU1VRZw9SonMTBgZwsKEBISAtNZs6ApkwGbN8N471521cZGiC0okF01N2d3L1xgbtOmYWddHa7euYOQkBDcCwtDyeXLsNy0CQpXrkDh+HFSsJib03zfto3UArNnk7NJQ4Oub8sWYPx4PCotRbiqKqqkUtS6u8uGLFnCta/be/bQdxkZ0Rp+8yY5cjo5a5ubm/HDDz9Aq6QEo/btY7UjRgBvvQWlTZvIYWxkRPO5rVifWCxGVVUV4uPjORcXFyj11ibv229pv5TJaG4XFNA8Aajg3mPrc3NzM27duoXU1FR4eXmB4zg0NDTg559/RkFBAcRiMV544QWYmppCUVGx61rRtqYrKChATU0Nbm5uzNPTExoaGiw0NJR5e3uD1dWRJH/JElpH5Yobc3MigJ3aMwKAra0tu3PnjjBgwABmaGiIiooKiObOhay6GuKe1qA+oLCwEDt27EBycjJUVFRkEyZM4AwMDGicVVXp+Q8L6yiW+BjkdQnc3NzYsGHD2PDhw1FSUsKfO3eOs7Gx+fP7SOfnk33j5kbqhMJCmj9iMdVycHenZ7mhgVK1Vq5Ee6vDSZNoTOvq6O8+PkTGJRLaE0xN6X5s20Y/c+fS57W1SVF28ya950+qJ1NeXo7MzEx4eXn95WXVW1tbsWPHDmloaCgnkUigpqaG69ev80ZGRl3WmcGDB3OMMWRnZzuHhoZe8/X1ffRXn9vfASYIwj99Ds/xHM/Rho0bN9opKSl9JpPJXtDS0uJ8fHzU7ezseo9O/kWIDw4WBr74Itu9ZAm0raz4lpYWwd3dXeTl5UWScX19MpI7o66ODMbBg2mz+KurW0qlZEC5uFChkj6A53ls3rwZ8+fPxx/uqy0IRKaWLaOo/v79HcWe+vLZffs6qlvfukUS7T5UZa+oqMCePXv4xsZGTldXF+Xl5XBxcZFVVFSgoKBA5Ofn1x6BysrKwpEjR/Daa6/h+PHjfHFxMRcQECB4eXn1Tj5DQ6moT3o6GaadjZVffiHysnIlOVNWrCAjCCDSevYsSbz37AE0NCCVSrFt2zZ4eHhgtIUF5fW5uJDBEBUFWFigoKgIFy5c4AsKCjhzc3PZlClTRBoaGmTIv/kmtcQxMOh+niEhdG6d5N5SqRRffvklBg4cyI8dO7ZvPUQPHKCoVWpqe4E1QRAQFRWFS5cuwdHRkZ82bVo3wi0n9e+9915HX966OiICI0cSwfvmG/o9LY2MJTU1eiZu3KCcv3v3KDr5GHiex5dffomXX365uxqgMxobyXi/ebPnitILF9I9fPCAiNeaNV3/Hh1NYygW99jL+FmRn5+P/fv3Y9GiRTBubqa5NGgQRVYHDiSpfEsLGaSPqwZef51UKuPHk3x03z6SVn/2GTl68vNprkVHk4Fqb9+tXeB//vMfobq6mi1durRD6nrpEjnK5JJ6UCTt66+/BmQyvPXoEbR37qRjenh0Pa/t24GqKtSvWYNHjx4hOzubL4mOhutvv3GNmppC/JQpvLOfn+jcuXOYO3cuLDo//4aGEDZvRtPs2VBWVkZLYyNgbY3/zJsHs/R0jIuMRFVICESxsehvaAgmj4gmJBD5biMvaWlpOHHiBIYOHQrfxwmNVErrz8WLRB4jIymqVlBA0fLOVfAFgRwO777bJe2D53ls376d9/HxYR4eHozneZw+fVqWmZnJ3n77ba7dQZaZSeT2wQNa/9sKjD1emVv+nceOHcPDhw/RHiXfuRPlly7htI2N4Dl3LssvKEB0dDQGP3iAgO+/h9qpUxDp6EB08iTg5IQ76eky/bIykfmxY/T9mzeTY+nLL+la3n2X1s67dzsi197e4N98E03h4fjv4sVYmZUF0Zo1RB4TE2ndCgsjshIfT/Uf5DnjHAdBEHDkyBFe8d49YYqnpyj73j2hKjSUDczOxv6VK/nZs2dz5iYmdG9OngT09SGVSrFlyxZh8uTJzOFp9UPGjSMyde4cpXTIndhz59I53LlDzt229bm8vBzff/89AGDhwoUoLy/HubY0hQEDBvDz58/nenSg8jylGyQkdFMe8TyPL774AlKpFKo1NVh08SIebN8OUxsbmJiYkPy6svKJSrSTJ08iISEBEokEMqkUFklJSLewwIDBg/kZM2Zw6s8YmT127BiflJTEffDBB92dwT/9RGvC++9TBH3ChD4TzX379vF5eXmcgYEBP3/+fO4Pp+G1tlL0+pVXyHmWl0dF8q5fp2fO2Ljr/L98mVRMMhndU7lSpy+Qyei7YmNpXk6bRs4yuXNr7Fg65vTp9N7z54mwf/MNOUrPnaM9s66O1E5PCdC0tLRg+/btgqWlJT9t2jTRM/VwfwbIZDIEBwcjIiICAODm5oZ79+5Bzj+NjY2biouLFe3t7fmSkhJ4enqKTp8+DQAtGzZs+B8khP8ePJeRP8dz/EuwadOmhQoKCjtHjhypaGtry/XaZuNvgKNUyqqjo+FXVSXU1tayiIgILjExUebl5SVCcTFt6HJIpSSL3rePCgt5ePw9PSjFYoq0nj1LhmQfjhkcHMyLRCLWv3//P36CjJFs8VGb03XvXnpNLKb8tc8+e/Jno6PpfBcupI1xw4Y+tz/T0dHB+++/z3333Xey8vJykbq6Oj9lypQed0g9PT25NxlGRkbC6NGjERYWJsTFxQmBgYHcE/O+fXwogrBnD0U8Nm7sIGKVlZQ/DVBkSW4symQUaYuOJkLeZqDHx8XBMTJS8Fi0iGH9eiJM58+TxI4xPHz4ECdOnIC9vT3z8fGBpaVlx7W89VZH1eee4OtLY3jvHhmVoMJoPM9j4cKFfZOB1NVRP21//y7EjTEGFxcXXLx4EQkJCdygQYNga2sLsViMpKQkhIaGyqqrq0Xjxo3jFRQUOo6lpkZOmG+/JQdKYyMZZTNnUmTbyIiiGF99RTnUT+gJy3EcTE1N+WvXrgnz589/sgWkrExjKc9rfFxuXVhIxK24mCJnnVFURPf3s8/IiP1fUVMD46YmjLtzR+AuXmTt0lwXF5oXT0sj+e47ml9KSmQkKinRGvPrryTzlRelOn6cHAzm5h31FBQVAcYwaNAgFhcXB93Oz9O4cfTTCRKJBEwmw5KXX4b2tGlk2HaW/8rR1AS8+SZUVVVhY2EBm82bOSQlQXrzJgqqqphxUhK7cuUKAEBFRaVLazFpaiq++fFH1G3ZAgcHB1laWprIzskJPM9j+Fdf4dK338ryDh1iw86f59SVlCA7eRLl5eUYHBcHdvp0O9m2tLSErq4uX1JS0n1Oi8U0Dq+/Tr+Xl5MD7OpVKjT34EFHAbBdu2huPjYPOI5DQEAAd/78eTg6OkJRUREDBw7kEhISWHl5OQwMDOh7x4yheZSVRdW0xWIiIhcuUMSubbx4Cwsc37YNhUVFvI6ODrOysmIAUBkUhNBr1zB91y6mEReH4vfeg0wmQ56rK6QXLkDyxhvAhg0Qpk4Ftm1D4+TJiB8/HuZywuDlRXJlgNbajRspzSA/n/7fVguBO3QIR/fsEZSKihgnCBTlv3qVIr337gEREWh2cECrnx8ejR3LD0xP52Tu7sgbOpS/bGHBBsXFYUpRkUgUFATL2bMZWlogZGWh/9277ODBg3BxcRGmODszpKcD+vqIjY1Fa2srs3yas+rXX2nuqqkRQevsRDt8mJ7VL7+k1KWdOwEjo/b+6z4+PpBKpUhLSwMALF++HPr6+k9e43ieCGEPLQI5jsOKWbOgPnEifn/7bf7HefM47awsPuTuXW7AgAH84sWLOWhp0Xp1/Hi39ppjx46FlZUVVFRUMGjpUuS+9BL8Jk3CoUOHWGhoKD9p0qRnkuDZ2dlxSUlJiIiIwAuPK+i8vWk/tbKi/UBNjfanPmD+/PlcaWkpzp49y/bv3y9bsWJF35ikVEpz2sODHDxHj3Y4rqysSAG2YgW9t6cWjI8e0ftqakj9sGtXR57+0yDfg1xd6QegQqpyyKXpO3dSZJznac3X1iZiv28f2RTDhlEE/aefAEdHVJ04gdLISKhHRSFu1iyYlZaCHzgQ2Q0N8PT0ZOHh4SIlJSVh4sSJf7rRxvM89u7di0IqpOoMwC42NnYXx3H/FYlEfEtLy5r8/PwtAJrj4+PVeJ7/4PTp01BUVLzQ3Nz8/42M/DnZfo7n+Bfg008/Xa6kpLRt0aJFyn3KefyrkZoKvPceNENC4G5hwRITEyEWi4X5paUiLFnSUXxH/t6YGKpkfOhQh8z478KkSZTXlZXVRfosCAIqKyuhra0NxhiSkpL427dvC0VFRaLp06f/uQVUbt6kf69coZwsgCT0M2Z0zydnjAjI/PkUifoD0f8lS5aItm3bhs59Rh+HRCKBqqoqL5PJ2Jw5c0Rqamrw9PTktm/fjtjYWMHPz+/JG6umJkm3s7JI9jx5MkUXXn+dCGppKW34AQEUQblwga57/34ywhMSUBsRgUsFBVh98yaTFBdTVedOY3D//n2cPn0aw4cP58eMGcO1R9t5no7zzjtk8D0GnufR0tJCck1lZYrM8jwepqbi6tWrgr6+voCuOe9PxuTJ5KSR51d3gkgkgr29PRITE3H58mXcvHmTb2hoQENDA9evXz/urbfegpqaWvfj6OqS8b97N0XSmpuJbCYkUFQ3KIiI+FP60b744ovcV199haampt6lqVpaRDg4jqJ8nRUeV66QI+TQoa6t9mpqKNq+c2ePY9wn8DxFB0tLySB8+BBYuxYmTk7sZFUVqlRVYWxsLHvJweHpEZN794hIHTxIv8+cScbumjV0DIAcQO+9R/MRICdGWho5CyIjUbx6NZLv3gUUFTuUBgAReBcXyhNug9KDB1izeTO28jzW3b/f8znl5VGUWFOTcm+zsoi0f/89xKqqMNHQgImJCTd06FB888032L17N+bMmQNLS0vA3h7cjz9ioIkJn5GRwcrKymBsbCxzcHMT2RkaYsDAgZi9cKEI06bh2xUrZFdqa0X49lsAwMLJk8HX10O7be1qaWlBeXk5116wsjdoaxNZu3+fiPCkSaRs0NQk59mCBT1+zMHBASEhIbLw8HA2ZswYrra2lonFYsFAXZ3h9ddpnG/cIPIuz80NDqa5LAiU+wwAUimS3dyQnpkJX19f5uTk1N7DPjMzE0kuLrAzMYF2WRlkiYnQKi+HY1gYsuvrkf3xxxjk6gq2aBEy+/dHvLq6qH9rp1a7Pj50L3NzSUavoUHOvvR0mnstLVSR3cICnFjMG8XEiNK8vWH58stgOTn0jMhk4BcswN0BA1A4axZvl5rKjrz8Mj/hxx85LjGRm1VTg4Evv8xgZEREydER2L4dbMwY6GVmsqysLMTGxjI1U1Pe7/JlDsOHtxcm27lzJ955552e78vDh7SeBQTQ7/ICnJ3XACMjUmz88ANFL7dtQ8WKFWCMITQ0FABJ1seMGSPo6en1ToquX6fnvidIpdAeNAiYMQMzli/n2qKf3MWLFxEZGcm1tLRQhNnZuWs3gzaoqamRUqGxERCJYDp9OqClhdGjR7PTp08zRUVFqb+/f5+4RVlZGU6cOAFFRUVhxIgR3a/JwoIcEHfukGM1Pb3PZFtBQQH9+/fHhAkT2IEDB0RSqbTnGhxyde9XX9Fa9sortM7s2UOt+9atoz3tP//p03Fx9SrVqRk7lgrwffMNEfCeiPkfRWfFyo4d9K+pKQUAANprWlsBnsc9Pz/h+unTbEheHj8oIYGV+PjwHmvWiFKdnAR+9GjZiPXrxcVbtvBOoaEMly51FEwNCKDnTBA60gv+AEJCQmSFhYUiAC9t2LDhPoD7AI50esvaxz7y4caNGxXWrl37/1Wf7edk+zme4x/Gpk2b5kskkq8WL16s/E9Hs9uhpEQRai0t5Ofn4+zZsxgzZgwrTUiAkaoqMRmeJ5K7fz9FBo8e/dNPo6KiAnv37oVEIhFaWlqYiooKDwATJkzgBg0ahIqKClRWViK2ogJOr7wiWAYHM6lMhvr6euTl5eH48ePQ0NAQDA0NkZmZyTk6OuLFF1/EXzbOY8d2tAQpKCCD59YtkqMeP05jtnw5RZdmzPjDMnslJSWsI0PtiaRSIpF0axF0//59CIIAW1vbvnmwBw2igj6HD1NO77p1FIkvKSEvvL09Gd1JSSTH/vRT8uKfOwfZhQvA+PHgc3ORXVSE5pQUWLflABYWFuL8+fMYO3Yshg0b1vUafvqJCL2razelAs/zOHjwILKzsyGRSATbcePgMXcu09mxA+eam/lBgwYxf3//vntRVq9+ItnkOA4vvvgixowZg8OHD6O0tJTjOA5Dhw6Fv79/r5XVAVDUePJkyiWdOpVkkPHxROJ6cZJUVlYiKysLjo6OEIvFQk1NDeuVbAMkGTx/ngzSoqIOKfScOWQsqah0RDQFgQirsTEZVc8CmYwidMHBFG3+5ReSr69eTd+nqgq9CRMQWFWF0tJSnD59mjt27JgsKCiod7ZdW9tVvj11avf88ZUriew+fEhpKhYW9BMQANy4gbLGRizbsQOZDg7krNHSou9UVycpvyDQ+J87B3z4IULWr4dMKkVxcTFFbx9HXR3N78hIus4tW7rlvNfW1uLbNpLs5+dHhax4HnjxRXCurlAuL0dLSwsbM2aMyNzcnMZJTu7t7YG8PKwQiUQHTUzQ0NDAGxgY4OezZ7mgI0dwLCYGhQMGQEVFBTzPw8XF5en359Qpaj0kN+zv3CFDf8ECUgH0IqX19fUVnTx5sl3qqV9UxA6++64wJTKSaYpEFOHsTPhHjAC+/BIlEydCpa6OevRKpQgbORJOdnbC8OHD2x/euro6BAcHw9XVFTYTJgC//IKxixbBV10dzWvW4LiuLoTbt2G0Ywcq7ez4lkWLmOj2bVbemTAyRuqNzExyBMphYUHS8IYGctD8/DMWLFjVGJj1AAAgAElEQVQgSti9W8i/eJHdUFER5syZwy6mpMhqDxwQ6X71FdJSU/nVbm4cN3s2HE6eZPjuOxhHRJBzRSLpqBI+b177+qCoqAgTExPZlClTRAc3b+ZsLl2CyltvYejQoQgPDweALsqGdrS20lxOSOjo5GBhAemdO4iNikJ4eDhqa2sRFBRE6+PbbwPFxZCVlyNm7VrBesAAVj9kCB7l50NfX58fMWLE09e333+nCO3UqV1fr64mAhUZSSS2E0aNGoXIyEh88cUXcHJywrSdO8mJ/iTs3UtOvLZrcnJyQkhIiHD//n2xv7//U08RAHJycqCgoCCsXbv2yftRaiqloxw8SHvmiBHPVCelsrKSl0qlXFZWFtrVB8nJtJZVV1NKxcmTdN9NTSkinJLS5+/vguLijlQpHx9aC7y96X688cYf+84/ijaCfM7UlE2ZMgXy2iO2gAgLFmAYwNDaKsbw4Xhp6FAOv//e4ZD88Udaz5OTyWH366/dqsH3BYIgIDw8XL7299I6oys2bNjw/xXRBp4XSHuO5/jHsHHjRvHt27e/V1RUXLdo0SKVfp16qf6jiI2lXKGvvwYYQ2pqqqBy+DCz/c9/sNvCAvn9+ws2yspMtH59RwuuHvqAPitSUlLQ2toKqVSK6urq9sImoaGhaGpqYoIgQF1dnTU0NLD09HRBU1OT7du3D/Hx8SjV0YFbcDBTHzMGF6KihNOnT7OkpCQ4OzvDwsKCPXr0SJgyZQobNmwYlP/qPHI5Zs0iozchgaJijJGhkJFBBrGRERn/ycm06RcWUtQmJ4cM/epqkm+Wl5Ph1NREUdKWlo5crGeMzl+8eBFVVVXw8vLqe+EYFRWKlvI83esPPqAo6SuvkNF25w4ZKKNGUR7lsGHAnDlonTULERERiIiIwP3795GYmIiYmBi+vLycXblyBSoqKgLP8+zEiRNobW2l6rZ796KptRVVixcjv6oK9fX1iI6OhqGhoTwfWNbQ0MACAwOZi4sLu3PnDkw//pjpHTqEEA8PtujVV1mfrqu6msj8+vW9SvgZY1BSUsLDhw9lFhYWXH5+PvLy8mBtbY0+5Samp9PYbdxIRve5cxQN/PpruneamvQMdYr8njlzBrdu3cLt27fR2trK7Ozsem311g4rKzLoioo6ci7NzIiA1NeTQ0wsJqI8ahQ5T542f1payFmUkkJy7atXidTI28S98grNaV3dLsWdlJSUoKurCzMzM3b16lUuJycHdnZ2eGKEW1OTxkUOJyeSRRobk+MPIANWRaWj1/iYMfQ6Y4C5OcR6etjV3IwKDQ24x8dTRNfLiyJwQ4eSYysyEjh7FnFDhuBGcjIUWlow3M4OShUVJNEMCyPjvriYjMuEBHIqfPJJj4qdb7/9FlKpFAAwZcoUqKiokGz0jTcAZWUcPXqUAUBhYSHv4eHBMHw4nbe8INCsWeCOHYNuUBBiExPZkiVL2KhRo6B2/ToEXV3kampCKpVCKpWisbERNjY2kMlkPatyeJ6Kz/n6dpBtJSV6Vk1NqVhfcTFF73qIsh0/fhwNDQ3Q1tbGMmtrDP/2WxjHx7Nf3n4b99zdhYjbt1l0dLTg6upKjiaRCHBwQJGjI7itW/Edzwuq69dj6Nmz7KK5OfPw8GiPJBYVFSEyMhKtNTUyj6IiDqdPgzk5QSyRQHHNGjgWF8Pa2xsYOxbmn37KUktK2IgtW9AvO1swfO21DiJmaEhzra2fepfLF4lQO28eRNOmofLrr5FubMyKFy1CQUEBu3XrFhoaGhjP82iprhYCVVU51UmTwLZuJTXIhg1Utd3bm+oL3LpFz+i2bch49AhHjhxBZmamIJFImLe3NzOwsEBScrJw6d49FhEfzzc3NzOpVIrS0lLe2tq6qyNu1SqKPsqlxwBgYIDYGzf4axUV0NHRYTU1NUhLS6Ne1/r64NTVcVlREQWamsK8CxeYa3U1XDduhH1PRet6woQJJHfujIwMGj9Pzy51LuSQSCRwc3ODRCLB3bt3MWr4cDAbG/qex51RaWnkNF65sstzf//+fb6yspLjOI43NTXt1aHb0tKC33//XdDW1uZdXFyevBANHkzrg5kZrW3BwR0KgT4gISFBqKysFCaZmDC8/TaRx9GjaQ8IDKR10c2N1oje2j32BTExpPQaPpzWe7ns/cABev3PjG73ETk5ObIHDx5w7u7u3ddfkYicLxxHSg55Ks2yZVQfY/NmGvsFC3p1EPcEnudRU1ODux3tKbf6+vrW/s8X9H8UzwukPcdz/APYuHGjoaKi4u+Ghob2gYGBKn8bAewLjh6lzbSth7EglaIgPh6N585BfelSJKxdK4iKiqBsbMzyx45FTX290NDQwLu6uoqG9ZT32AdkZ2fj559/BmMMIpEIjDEwxuDm5sbfvXuXe+ONN9oJh1Qqxb59+2SFhYUiFRUV3snJiRs0aBAuf/MNRoWF4fSMGbCxtUVqaiqUlZX5uro6TldXVzZ8+HCRs7Nzu6GalpaGI0eOwMrK6umRt8chCLRZ19cTkVZSIslYVRVt3L/+Sh5uJyd6n6kpyQNrauizixaREZ+TQwaEjQ0Zy3l5ZBTJ2zmlpZHE1seHjpGTQxLB0aPJcEpPp99nzyZ5Z00NqQ38/WkTraqiz3t4oFFDA2EhIRhQVobBEyZAydycDILcXNpI9fRo8y0oIKJmYEDnWltL56KtTaR/wwYiJLm5FDkdN46i9qdOdRsmmUyGtLQ0REdHCxkZGe3Gl7u7O6Kjo2Fqasrn5ORwqqqqeM3fH61Tp+Lo1Kko09FpN9SlUmn7vHB1deXHjBnDdZEJNzSgtqQEtRyH/vI2QE9Dbi4ZEnIJ3lMgL1SkrKwsTJ48mdm2VSLuEUlJJKkfMAD48ENyTvz0E0U19fRoHHftovvz8CEV8PrxR5Lvubjgto0NUm7dEiwnTICmjg6ztbV9MkntCQsXUjTzxg1y1Jw5Q+qT0FAqNnf4MM2RJzklcnLIoDUwIEJuZ0dRXXnk8Cnt0B7H3r17kZeXh379+mH27NnoliYTF0eGbn1914JnXl40v1au7Pr+5maK2nz9NTmsOhVp3LRpE3iex0cffQQuOZnmbUAAPSd6evQ8GhjgWmkpBqWlway2FqIFC4iw6+kRGXd2pp+FC8nZUFVFjq4exuvo0aMyBQUFUU5OjlBbW8uUBQErv/4aN3fuFCLS09vtK21tbf6tt97ikJhI5CUsjJ7/hARgzBi0vPMONj94gDVr1pCEt6aG5oanJwRBwLFjx/jk5OR2QqKvr8/7+/tzZmZmqK6uphz1tDRSN6xc2aEIkVe79vOj+/bf/1Lhyuho+r1Ta6Svv/5acDYxwQt79zJMmgTo6SGtqQm/ZGZ2uWaO48DzPFRVVQVzMzPm+fbbqPT0FESffsrEYjH0dHRw5LffZIqKimzkyJFcSkoKz3Ec1/LTT3CJiYHSRx9Bd+RISKytad2YNYvm2uXLJNtmDNHR0Th/9iwUGMPkvDxw5eWInjYNzU1N/MwNG7hTS5dKqzQ0mEwmYzKZjPFSKfO7cAHBAQHoX14OzYIC1KmooEZdHUvGj0ehlRVqVqzgC4yNmXJjIyxv32ZHAwOh1doqqJmZMXt/f9jq69N5fP45sH07ZCIRwn/9FTdv3oRIJIKdnR0ePXrEv/HGG3Qf1q1Di44OEn19cfbs2fbxWbVqFdpbTvE8hKQkJOfkIDg7mzc2NkZubi7Ta2jg3fbvF2mHh0NPTw88z+PAgQOyR48eiQYNGiQYGBiw27dvIygoCFaDB9PzvHYtrR3bt/euiGpqor2hoKBDydDURNWur19/aiFRQRCwZcsWTJs2DdYmJj0fS5620kPxxu3btwu1tbVs7dq13VoudkZBQQF++uknrFy5Ek9t0bVsGZHBBQtongQE9F4DormZ1hU3NzRYWSFy4ED4btpEa9/WraR2+Stqypw4QWvTlCm05isokDri2jVy4Kxa9eR19y8Cz/PYtGkTZs+ejV73LTkEgfYwDw9aB11dad94Rnz33XfNFRUVimKxOEUqlY7csGFD2R84/f9v8FxG/hzP8Tfjk08+maugoPCDp6eniq+vr/jvaOnVZzx8SJt0G9HG+fNgCxfCuKSECODrr8NATY3FL10qZAJ8a1OTYGJiwikrK4tu3LgBqVSKxMREfuLEiZxJG/G5du0aYmNj+cDAwPbXHkdCQoKgoKDA/Pz8ICfEoaGhuHXrFmdubi7T1tZut8LFYjFee+010Z07d+Do6NheZdRg0yaobtwIKw8PKI4fj9u3b/Otra0QBAGhoaGis2fP4urVq8KIESMYz/MIDQ2FgYEBSkpKunocpVIybh49og09Lo5ef/CAvL0jRxL5KC2liFdqKhUQEwSSNFpakhTQwICMkrfeIhnZ5s0UZVq+nAjClSvkWXd3/2P3ShBICtfcTES9LUcL775LhrQgEFmYNAlQUsL18+dRYGyMKnV1QE2NH2JiwqGpib5DHj1saaFrrKmh/zc1kREvk5FXnjEysNqKQqGxkZwJixZR1OyxCIhIJIKNjQ2sra1ZRUUFVFVVIZPJoKqqigkTJiAvL487ePAgZlhbI3npUtwMCsLM5cthZGQEsVgMjuNw9OhRPjU1lVuzZg3EYnH3h0VFBerHjkH9ww/pvJ/muLpwge5tH4k2AGhoaMDIyIivrq6Gjo5OdyutrIzyeuVFdXR0SGYZFUXjN3s2RRzlBt4nn3R8dtMmulcvvAA0N8O5qQmGv/7K9CQSqJWVkcrhiy8oAufh8fToyP79dF8iIugerlxJ3y132kyY0NXgq6mhe7hjB5HsV18lZ4SlJa0H/6NxuHjxYqSnp+O3337DDz/8AA8PD37cuHFc+7rn7EzyxccdCufP92xQyw340lKKqnXqB8y3FW3kOI6cBFeuUAT+hx/ICfbZZ4CODmqCg2W/ZGSILIcM4YOCgnpegDdtojWvshIwN0ft6dO4Vl0tU1dX53x8fJhIJEJGRoZIJpNh4cKFLCIiAnVVVfjy3XeBtDQmkUiEgIAAZm5u3pHbb2VFEXo1NYrkKyoCN25AEhUFnaws2cmTJ9m4ceM4rYICksJHRoIxhokTJ3Le3t4oKyuDuro6EhMTcfToUTDGIJPJYGZmxs+PieGYl1fHHIuNparF9+51OEiWLaP7e+4cpYQcPQrY24PneSimpTEPgGTutrbAihUYJJXihVu3oK6ujvPnz2Px4sW4dOkSRCKRoK+vj6KiIln41q0Y3a+fSD8ighw148fj1VdfFZ06dUo4f/68TCstTTQsMlJodnLC5YAAVpyXh3mLFwuDJ09m7fdcWZnm9xtvoGXVKtw/dw4qWloQ9PXRoKQk09DUZCZGRpwyx3FwcsJYHR3x/2PvvcOiOtfu4fXsPQND71UEpSkoIBhBsYG9a4wlGnuPJXqMHhNjwsEkR0+SY4yJURNLjLHEaGyxYwMVaSLSm1TpUpU6s/f3x80wdNGTvN95fy/ruuYyGWbP7PKUe6271U2cCJlMBplUCpmaGjR++w0D3n4b6NkTP23YgBJBwOhLl8Dv24ejH3yAt2trGSwthVJvb07x9ddYZmCA6g8+YLJ//Qu/JCbC4tdf8cPmzcLEggKuaMQIRbiZGT/oo48wddUqhfPs2XxiYiIaC4bw9YXawYPwWL8eV65cEevq6pijo6OopaVFnxFF1LzxBh5OnCjelsng5ubGlZSUUL6zQsFb5udDuz6ajeM4zJs3j9+xYweysrJYbm4ubGxsBEdHRxo33boRSTx8mISSmBiq+9Ca7VBXR/uPkmgfO0Yt9ZT72Uvw/PlzVFdXQ18Z8t6zJ4lQurr0/+HhNK5SUloce+7cOVRUVDCe59sl2gBgYWEBU1NT8bvvvsOMGTOYbfNWk40xYgSJhQYGdC6lpS2Ljj1/TmlMU6cS6T15EkhOxqPZs/Ggrg4+Q4a03rHhz8TDh6q8aXNzWoOV53/6NK1prxGO/boQBAHBwcHgeb5Fr/ZWUVJC4nBEBJ37xx+TjfKKqK2tRXFxsTqADXK5fKefn18bBQT+76DTs92JTvwP4vPPP39fQ0Nj68yZMzW7NC5y8d+COXOoyNjWrWTMGhmRIltZSQVeDhwgpbOVdiMXL15URERE8K6urmJsbCyTyWQKiUTCqqqquJqaGmhrawvvv/8+BwD37t1TSCQS5uXlxQFAYmKieOrUKTZp0iQ0bp8iCMKrFTK7d4+MxXffbTA6nz59inPnzolvTpjAnj95gvMXL6JrVhbUq6qg5eYmmsTEwM3GhqG6mgjjoEF0nba29D0JCeSlNDUlA8bWVlWYqy389huRLh0dqgb8+eeqHNQbN+g7IyPpb6tWtXo//2xkZWUhLCwMMTExEEURn3zyyeu3lLt3j8LeDQyo+uk771Ce4/DhZNCYmdG1tRN6lpKSghMnTmDwwIEYumEDIkeNEv/Q1GSCIMDW1laYNWsWJ5FIUFNTA47jmha9ao7kZPL8HD7cxFvXKt59lwzHVoqitYeQkBDhypUrXO/evfHWW2/Rm/v2kZdy1SryGq5d27JX+pIlRPiUx3QA+/btE9SkUiz08OAQHEyenPffp/tqaUkk/sMPSQAyNycCzXEqoiWKFNq/fDk9h4wMOtd9++j8oqNpDMbEkGE1bx6N/VGjyLj+izoJHD9+HElJSdDR0RHWr19PE3vyZCpG1NzjrVDQXLt9m2oHtIFno0ZBXlqK4M2bERUVhV69eonT3NwYjhwhIj59OlV+P36cyPekSUhOTsavv/6K9957D7pKEtEcaWl0j8aOBaKjcb+kBIYrVyLTygoaH30EnufF69evM6lUig8++ABcXh7qunXDlxs2oK6eaHAch3fffRdNUoS2bSOSvX696r3Ro1Hj6YmvtbRQU1ODTz7+GOzzz2lMtzHuy8vLUVtbCwA48OWXmP/LL7jo5wc9Q0NMGT8ekuRkIDkZD+vvnYeyurESd+4A9vYQly9HWFWV0OfuXU7yySfgPvywCYkrLi7Gt99+C5lMhk3NW8cp0aULCTvKDgETJtC9S06mFIoNGwBbWwgjRuDTv/0NEy5cQE8AWg8ekFCloUG5v48eAWZmyJgyRTRISGC/zJmDJVpaUJs/nwTL/fuJ0Ny+TQQyJoZCXwsLSUAIDQXefReKgQNxdsAA5FhaQsZx6Nuzp+Dx8ccckpJICFOiqgoQBNQVFuLFiRPInTwZBmPGINvFRXB94w1ObedOmhO//orcvDwcPnwYH3xQX89JFClyZeNGRGVlISYmRkxJSWHm5ubo3bu36OLgwOInTsTD6dOF8TNmtBSax4+nrgV2dgCA/Px87N27F9ra2hgxYoTg6urKtbo+R0XRXuzvT9eirDavRGwsiSs9etAcsrGhfOfmlb7bwJEjRxQVFRVoqN79t7+RMKNMt4mPp/s+fXqT4zIzM3Ho0CEAJLB1pK1mZWUlvvzyS+jp6Ynr1q1rf9H55RfaW0pKKHJo1Cjakz//nKJ4bt6kc/L3pzWufgz/8ccfSExMFN9///2/tj2KXE52Qloajef8fLKnrl+nv794QVEF9++rhIu/EGFhYbhz544AgI0cORJubm7tX39YGM2/zz6jPPapU0ng+fXXV05XE0URW+sFZZ7n73Icl7V58+bZLzns/2l0erY70Yn/IWzdunWMurr6p4sXL9bQa178578BT5+SktmlCxlK7u6kXicl0YZx+nS7hUnGjRvHjx49GhKJhI0cORKxsbFcenq6MGrUKHzzzTcAwPLz88EYQ0BAAA8AZmZmOHv2rGhtbQ3GWIsNukNEWxDIG1tZSV7eCxcox1RPD3jjDXQ5cQIrk5MZ4uOB0lJM1tcXZLm57LmlJQvKzWUOgweTd9nenjZJPb2mhvCrtEUKCCBy1bs3GYGffqoqiqOEszMZht98Q4R9wADyWNYXWvqr0LVrV1hYWCC6vk3Wa0GhoFDcSZNorEildH/mzqV7aGVFHpTERArH1NOjTdvBgYxKU1PU1NTgzJkzYkpKChs7ZAj6HjoEHD8Od0dHZltWhpCQEAQHB3O1tbWQSCQv9ZAAoO//8ksibtevt20cBATQ517DW5uWlgYmCBhpYUFq/9ixFKbt60tGlLd3y4OKilT9Ul8BkyZN4g4cOIC8ceNg/u679KYyTL+sjIwiOzsyQIuKaA58+ilFTBgYkOfn5k3yFvftS+9PmUKfz8ykkPVBg0h4MDBot3DWn4WYmBikpaVhxowZOHnyJOfv748R3t4YWFrawuMml8uRmpoKhZsbHh46JHA9ezI1NTVWUlIi2NjYcPn5+WJOTg7kcjnT69cPttra8qrUVGaVkcHrBAez2p49oTZ4MF1zTQ0JhIJARDA4GKEREaKxsTF0dXXbNkCjo4kYjx0LuLigV1kZAp2cUC6T4cWJE7BNT4fVm2+irLxcwXEcDzMzlPz0E7qBKm9z9X2bm6QAVFfTvNfVbbrGfPst4h89gpiUhLfffhuM44hIXL2qaqvVDI1FgpXl5Tg3cSKe5uUhOzcX9l99BT1nZ5xzdRXLYmKYlpaWGBAQINrZ2YlDhw7la2trobC1RZeKCtQ8fIhqW1tOMmsWuLffbpg79eHrisTERF5fX1+sqKhgWVlZ6NpaZEVamkowLCgg4W33borqKS6m8TptGuqOH4duQABS7ewgMTCAm5oaiUdr1pBgVV8EziY0lGVnZKDu669RVFgIy9hYItn29rQfLVxIURiLF9N+deWKqriTjQ0qExIQu3MnZs2apSyKxcHZmdaj2bNV7Qw1NIDPPoNUQwP67u7Qt7AAMjJgriw8uXo1kaTu3WGyfTtqa2vRUNWaMfIi370Lt0mT4ObmxqKjoxESEiKGnznDtK9dQ8C0aZg3cybX6j3T0aFUo3qybWZmhlWrVuH06dMIDQ0V3ZoV42uAmxsJthkZtA4vWECEWEnM339fRbRXr6b1uA3xrKqqCsnJyXB2dkZVVRVKS0uRlZXFb9iwQfWhL7+k3xs9mupz7N6t6hrQCNbW1jA3Nxfy8/O5u3fvdig1SykmdO/e/eVE+OJFErGnTqU9ftMmSpGZPJnmqJYWRS01giiKiIiIQO/evf/6PqSPH1MNC2VklbExje3ycprvWlp07xYupPSJv7A16h9//IHIyEh4e3vD19e3/WKeokiFB99/nwSVuDg61+3bqc5AB4m2svuLjo5OE2FcoVAMAtBOL9T/G+gk253oxP8A/P39e0il0hMzZ8787yTaAHnKbG1p4/LyIu/B3LlUAOnYsZduDoyxhjxbLS0teHp6Mk9PTx4Ali1bhsjISHH//v0MAExMTBTV1dU4fPgwb2ZmxoqKioS5c+eyJoWgRJGMmYwMMhgMDckbDNCGUFJCBW22baO8Yn9/MsRsbOjzAwYQsd20iQzX+vwy+3pDqqioCAX79iG2Rw+Y9O0Lzf+EcJw7R2LEpUtExD7/vCXJVsLCgjwPSUlkXB48SATqwQO6RkfH1z+Pl6DxpiuXy9v3FreGujrK/9y9m8i2REI5oMrIh8WLiUSMGUOGXmQkebeioiB++y0qY2Jwp3dvsVJXl0n19cG2bEG8vj6eZmRghKMj9PT0UFFRAUdHR4Wmpuar5dGXlpK3LiGh9f7V5eX0bAIDW3qD2kNtLVBbi0E//8yNDg6GbOZMIthTprT9jJXYvJk8Ba+Scw3AyMgIPM+LT58+ZebNC3Pp6ana33z/vep9T0/69+5dCsktL6dnlZ9PbW369aP5UllJ/6+h8T9GtPPz83H+/HlMnjwZTk5OWLx4MQ4cOIDAGzcQMGwY8Pnnysr0kMvl2L59OxQKBd6YM0fom5TExamrKxhjcHR05B89eiRaW1sLs2fP5vX19aGpqQme5yVYuxaKkydxbfBgHNLWxnJlJEFuLpGFjz8m4icIUDt6VMiXSPjTp08rJk2axDeeB7m5uXjy5AmcPTxg0ChXUSKRoGTiRKSlpWG6VAqb+/dZRGGh6K2ry+H5c2DZMpgeOYLZPI9bt27h4cOHTb1pNTXUDm71avLUx8WpxqmjI6oXLsSsLl3QTdkOqriYPHhtkO0G5OVBJysLc+qLMIXcvy9oXLvGXTczg7a2NkaNGgUHBweWnp7OTp48ibi4OACAXk0Nxp88ia6FhTD87DORO3+eYfFiEqvq70d8fDw/f/58dOvWjR08eBDR0dFC165dW1rfamok3ty7R+NsxgwSoubNIy9ZPREvWr9eHMcYOz1vHjxnz25o44fERBQ/eIDCmhrUt9dDZWUlKoyNET5sGCZNmkTnVFhI5K+srKHGAUaPplzc48dp/U1Ph46tLVxdXcVjx44xAwMDhb29PT9o0CDoKtNuKirIQ2tnR2JVXByd++TJtK80JkwhIYBMBklCAvQUCrG0tJQ1RCpMmNBkbru4uMDFxYXdWLlSUK+p4eSiiDb3+/ffb7F+GBsbw8fHB2fPnm1/wWCMQssDA+l8hw6lc9m4kSpry+W01pWUtLlv5+Tk4NSpU2JpaSk7c+YMpFIpBEGAvb29Qk1NTfX7VVVEcNPTySvbTij622+/zZ07dw6JiYl8TU1N20KpIACMQaOgAMOqqoSbjx5xvjdvQnf0aLqWnj1JFFy3jkTD6Ggi2JWVtHavWEHX9ZI2YMpaH22lr/2pyMoikUMJpdD57JnKkz1oEF3H8eNNak38WRAEAYGBgXj06BGWLl0Kc3Pz9plycTGJ4oaGZDcNH05EOz+f5kWfPm0eWllZiWfPnjWIbw8fPsQff/zR5DOMsWmiKF7YsmVL7X9+df+70Um2O9GJvxj+/v6aUqn0xpgxY3Q71Cv1/w8IAi2sM2cS2Vu3jop17dpFXtr/EBYWFrCwsOCMjY3FxMRE9jQ1lV81fToCbt/GaJkMmrW1HFJTyRgtKSGCk59PYWOHD5MIsHw5EYguXUjR19Qko1WZ+9gYw4erWgO1AWNjY7i6uirCwrxdOLAAACAASURBVML4sLAwuLm5iVOmTHk1ufnECSI3x4+T0Zef3zFi5etLJPXoUcqPBEhFfvxY1bP7LwDHcVi1ahV2796NkJAQDBo0qOMHp6SQMLBrV0svvKYmhTfK5US0fXxIvHF3b/BWZYwcievffgtfmYyN0dOD4uxZlBcVidk2NuKTCxe4w4mJmL96NTIzM4WBAwe+GjsFyHtZW0sF5ppDFMmrmJTUbvXxBsjlRNqLikhEWLMGWosW4bCjI2xTUjCpeb5ga0hJoXN5FWKPhnZ3Ym1tLaurqxMBdGxMKosVTZ9Or6wsulYHB5rL8+eTl6KsjESQ774jw2rKFDpXQaBCZXp6NK9EkeadgcF/7IW5cOGCqK+vz3rV3wsrKyv4+flB0NbG/bVrcUNNDdeuXYOXlxckEgkYY/D29hZHentzWLECTgkJvLK/89ChQxkA1fh48YLEwKAg8HfvouLbbzH0t99QtnEjER1jY1WrKMaAnTsx/Zdf+IMff4yYmBh+4MCBUAoaNTU1OHToEPT09ISAgABuzfnzqNm5E2YDB+Ly5ctCRkYGp6GhAeuVK6G1eTM25eYyODlRCHxuLsDziI6Oxt27d9GjRw+h4TxFkby32tpU7yE1lYTMvXsBDw88f/4csT16oF/jsblmjap/dXt4/JjWDkNDIDcXXps3c7hyBT2oWn7Dg3NwcMDGjRvBA+BXrIB4/DieBQejpmtX9DYyYli0iASrzZuBmBiwkyfB8zyMjY2RnJyMvLw8tGirV1VFnvpNm2iuAEQg7ezIE80YEe2tWwEHByT94x9CYHw8LwVw9OjRhh7VUl9fqOfni5I7d0R1dXXIZDJRJpPBLjmZ7xoSQuv9e++p0pv69SMxdfp0WuP/+IPW3a1bae339sawFStY4aRJsAwN5XVOnsSllSvxtpWVKtXE25vGtzI6RBSJiDev+TB+PL1u38aq4cNZWVmZav1TitMxMao5Eh4O36VLuU/rx6sy1L8F7tyh9Wrz5iZvBwcHKwRB4FttI9Yc+vr02r6dRJKdO4lwb9sGXL5MKSJyeavdK3755RfI5XK2ZcsWCILQuAd107VXR4fWjKdPaX1oq5d4ZSX05HJ069YNpseOoS43F+p5eXSvjh0jwm5nR2KJRAI8eYLMPXtgd+QId3P5ctQ+fUr7u6YmhV8rFCSQbd5MzyYlhfKw16yhter5c1rHXtISr1evXmJwcDD69ev313q3CwoaohQakJdHQo4yDYbnSWzbupXshY7sRR3E+fPnxcjISAYAs2fPRguRtjkePKA9LiWF6vSEhKjG8KNH5JVvRVipq6trqMEBAH5+fsjPz0dSUhIA1AJ4D8BeABBFsczPz+//PNEGOsl2Jzrxl0MqlX5kZ2dn6OHh8deHMr0u3nmHFtf4eNocExLIGO9Ie6PGUCjIGKquJoNSQ4NClMrKAC8veB47xjxiY/FQVxeKqCh49ugBzbw8IgTu7kSYzMxo09LUJKOocbjlmDEdO4+tW2mjvny53Y+NGzeO79+/P6qrq3HkyBHGGMPEiRNfHr4eHk4GbkEB9VNOT3+1ezVqFBlIGRmqNjbff0/G3ldfkTd4796/JNQsJCQEjDEEBQXBwcGh9R7DrSE8nDbhtnKepVJ6rV1LRlNEBD1DJyeIoogLly6JL8zNmfnq1eCCgsAFBcFo1y5mdPMms8nPR8KJE4g+dQp9OY5zGTyYDPlXrdKfk0Ni0cWLTcP/v/qKhIL4+LaPFUXybikrXCsUqvQJW1voCQLKYmMRGRkJLy+vl9+32FjyGrxiyHp0dDQqKyvZhg0b0FBs6VVQXk6VuuPjyfNlZEQi2rZt5MlITaVrXLKErvn5cyKKyldhIZGAo0cpPHjWLHpfXZ2Ia/fudI8VCvoeK6tWQw3T09ORlJSEN954Ax4eHuzmzZsthAMuKQmDzM3Rp7ISu3fvxvbt26GnpwdRFOHs7MygpUXGaj1xaYFHj2je6OkBwcFIzsxEpba2WGhiwmQlJdDjecrjPXWKep4DRLzXrEGva9eQlZGBmpqahq/LysoCAKxatYrbt2+fWFRezu4dPoyC+/ehUCi4ZcuWNX3uFhYkapSXUwSDnx+S3dxgb2+vmDFjBpEWhYLWoxEjSNhQ5tNu2UJjPCcH10NCkG1jA37cOBqnixZR0cJBg2getVL1GQAdf/CgquBeUBB5PNtYi9QSE+m59uoFtnIljN3cmq4x+vrUDu/IESA7G743bqBi7lxcunRJMWDAANbg1T56lEjcpEnUq33xYhJKLSxI/EhMJBHUwYG6F8TGAh4e8J0wgS8+fVqMjY1la9asgY6ODpG8jAzAyYkhK4vBwIDWxIAA3LxzR2GQk8M3tB3ct4+8qzxPY5cxitYYPJjOPzyc/q2rQ87OnWJpfDwbbGQE7awsDBgxgu7nkiU0J168oFz+CRNovejRg/a9tuDpibAxYxRGdna80eHDtGdaW5OYkJysikhauhTcrFmwtrFBZmYm9uzZg82bN7fsKNCjh6r4ZiOUlpbC09NTZEqmLZeTiJuXR2MpNZWe0+nT9LchQ4iEPn1KooFCQQKMQkHkOC+P1uqjR+kzTk7AzJnQVFcXl//zn4zbuBHcd98R+fr1V1r75s6l6LaVK0lQPnGCwtV9fCgVZ9w4ElhkMppXBQUUFcBxuOXtjVXh4RDT0mheKoXwFStU5LKiAtDSQqyPj/hQV5e9NXkyjP38VDdh2zb6VxlB8PbbJI6sXElzY88eWrd+/rkjZJs9fvy43c/8KTh0iO5xY1hb0/1SFk0DaH4OHkxjuZnQ8rooLi5GTEwMpk2bhq5du7Zdh0KJn3+m+hVTp1L0U+M6HQoF2YCt1DQpKyvDgQMHql68eCEHoAMA/v7+jT+i5ufnt8/f3/8MgF5+fn63/pQL/H8AnWS7E534C+Hv768tlUpXDx8+/L+ot1czVFSQQnz1KhnoGzaQ8dQYStU/LY2MSxMT8lyIIh1TXEyb/rZtRLSVId329mQY2NuTh/zjj1GtpYXLP/+MW/UVsDkjI0EQBMyzsuIs/tM+l0oMGEAeB2WbrDbA83xDK6I333wTJ0+exNChQxsqsZaVlUFDQ4Pa8QBkRM6dS17cQ4deX52WSMhDEBTUtGesujptxAUFZEwoFC8PVX5F5OXlwdnZWUhOTub++OMPwdHRkXN1dW073BEgz82YMWT0/PZb+yRYSXI/+4y8SImJyMnJQXFxMQOA9B9+QG+plO6fmRng5QV9ALaZmQg6dkzhXVDAaezezWBuTka1vr6qcNfLCslZWZHRrzS+ARqjs2e33Zs1LIyM9C5daPwqRRplIaV6dZ/jOEybNg2nTp1CXl5e+2S7sJCK7P34Y/vn2wqUoZeSV2yxBYC8E+npNHYOHoSooYE7t2+LLhs3MqOHD8lA19Ym4rdtG3kIdXTo5eiIJt40ZZhjZSWNx7IyCucsL6ffuH6d5tf48WT0m5jQ59zdkWdqisDLl8UqTU324N49iBwHxhh7/PgxevXqRcRj40Y6D0tLaGtrY/ny5bh7965QW1uLoUOHcg0FJNPS6DciIlTXmZ9P3syrVynXtz6c9NixY4ChIevi54cuERG0jn3zDd2PegiCAIHj4LRgAV64uuIPU1PIZDKhvoIu5+joKADgli5dyu6YmOBNBwdcePRI9PDwYK0+8wsXyOsaEADIZND46iuhZ2wsnz1oEKy6diVyr6ZGxLTx+J00iQS1bdswcNcuxMbG4unTp+ji50fh1xIJEZxG594Cqal03x0dyXuorU3zqjlEkYjYiBEq8tbW+NLRAVauhJieDvOcHHChodB/+hQ6zs6cOGYM2LlztB7wPM2NjAw6x02bgLNnaa13c6P9oFcvGiNz59JcVyigoaHBRFFEQUEBDDQ1KaXh73+nNIjRo2kufvghnmtq4r6lJe++YQOl6airU44uz1Ous5cXzbPWIJVC1r8/EzMzhZ7+/ioliLxvFD1lZkaV/ZXCRF0d/TtoEIlknp7kQVWmWWhq4uG4cfDR16fnc/s23evvv1eR7cxMGgeGhvCMi0NmZiYEQcCBAwfEZY37hQMUSRYURGQ4NpY8jLa2eDszk4+/fh3Cjh3gRJH2sfPn6VwWLaJr1tWl9y0tSfzatYvaPS5cSOvzvXskgCmv6YsvKLUJoGdkaAjTa9fEK8OHs4kSCdXbUO4zlpb0/TU1RM4BCs9XKGgtAKhKv54ePeuDB+m9+kJgGysr8bVEgknGxlTsVDkeR41SXbuWFmpraxEaGsoAUIX29mBvT3NcIqE9ODWVBMRTp2hdamfvUkYTVlZW/mepYu0hLU0VadcYI0e2PkbfeovWpYiIlgU1XxGVlZU4ePCg4ObmBmdnZ9ZuNERxMa1H48dTYc2PPlLNZSUCAmgdaeWZxMbGoqKiQgOAEYDpAMIYY1+JoqisvncVAPz8/AoAdCAs5/8OOsl2JzrxF0IqlW5zdHSUNqlG+9+GkSNpo1AqmUp1PyCAjBKlcebrSx4POzvy5lZWErmZNo2Ot7Eho+olJEEbwIcffgipVIrKykqcO3eOS05ORkpKCiza8uC8KpStf86eJeW2Ax7iHj16QEdHR4yKimJDhw5Famoqjh49Cp7n0Ts7W+EdGMjLSkuR6+oK29xcSBpXtX0d2NvT/VR6aJTw8qLX7t3k/U5N/VOrlZeXlwsODg5cWVmZIjs7m8/OzkZ5eTmioqJgbGwszJo1i9Np7hl7+20yEPz96bzbyeVqwJYtJNwcPw7TXbvgvGQJ8nJyYPPttxS62Yy4mFpb460PPmgaHpyaSt6fjRtprLm4qAyBxm2OGmPBAvKwnT5NBv/w4eTNapwjl5tLlY2HDKG+tRYWNKYnTmz1O2tqahAUFCTeu3ePqampiZaWlu0PqOxsMg47GjVQj+LiYly/fh1vvfVWxwrDKaH0wu/ZQ9fx5Zf0PrW9Y3cAjPvHP4R+69dzGDSInqGxMRn5Dg6AmhoCAwPFW7duMQBYsWIFRFGkUERNTfLGAESilJg2jf6tqyOSXVsL3L2LisJC3L5xA27l5cy1uhpyXV2IhoaIra0VpXPnskgnJ9Fp8WKmFR2N6i5dIPbtC3UDA+jr62PChAktXeQuLvS8FQqa16dPk9BnaUliTiPhh+d5KBQKjBw5UnW+jAGxsRBFETExMTh//jwUCgWMVq8WzHv35tRyc0VjY2POzMwMtra2MDU15QASV3yTk4ETJzD35Mm2n/esWURi673vfUaO5LKio3Hqxx+x/sYNSFatAsaMgUzZXq8xli0DampgWlaGAZ6e+CksDFOOHxd7JSYy9OpFYdIPH9Izag5RpON//pnW6E8+oX7tzcdvWRkR+/x8ihLQ0Wl3jRZFEWlpabh8+bJYsXAhNgYHs1k//MDfDQ1FqVwOg8rKBs9jbW0tbt26JZpVVjIzZ2eYm5iAiSLg54eyefOQvm0bsoyNBe3AQOh+9x2s8vM5WwcHaEdGQpqbS2HPu3YRidy6VZUCsnIl5KWlYLW1MHjnHfJq/vvfqpPs1o3SihSKNtN26urqIJfLuatXr4qjR49uelMMDIj8KXsvf/ghjTNRpHnbtSutUevXk7j0xRdAv36oq6sTmbs7CRcFBbQOrltHAkHv3kTQIyMBxtCrVy84OTlh165ditzsbP7u2bNifw8PJvn4YxJnPv6YxKLcXCLSxsZA794wHzQIAXV14nEjI7yzZg1r6PjQHPn5tK59/TXtE0OGECmuq1OFKjf2FivrEtTnFY8YO5b7NiUFI2tqIGscHfDVV6pjNm6kNfHcOar+rqwL0djr2aymgKamJvT09BT379/nk5OTFd27d+fd3NyaRIsdPXpULCgoaHgmmZmZbedVf/EFebSVnS3u36drl8lI5Nizh6r2t4H8/HwAVIvBrnmY95+F9HTaI5vDwICirZrv8bq61FP+wgX691VrpzTCgQMHFFZWVmzs2LGtV69XIimJ9jpXV1oDJk2icdJ8/pw6RSS8FTg4OOA6iSrv+vn5fe7v72/SiGgDwPhWD+xEJ9nuRCf+KmzdunWWTCZbPG7cuP9er/aePeQN09QkBXbbNlI+ZTIy0hYuJA+fhQWFwzbO12qsVL8ilJ5iLS0tZGZmwsLCAv3+LK+2EsOGkdcyOpo2mJeA4zgMHjwYly5dgrq6Ou7evStM1NCA648/cs9sbLhSPT0xdOlSoVxdnTt/8KBoYGDAWVhYYNyrVCtvjN69SbBIT2+9tdGqVUQUMzMpjE9ZOOk/wIMHD/DixQvO3d0d/fv35+Pi4pCXlyeEh4dzCoUCubm53Pnz58V33nlHtWtXV1OIoZYWbdgDBxIR7ghkMuDNNyHNzMRoDw8U/fOf+Hn6dHHVwIEvVz+0tOi5ubqSJ6m8nIzxhAQyItXVSQDq1o2M4759yaCRSIiQp6aSl6pfPxoLABnsDx6QgazMaT537qWnUllZiXv37jEAcHZ2ZibN21Q1RnU1GV7KHquvgJKSEkil0pd7ehojK4vux+DBJNA0qugviiI4joOXlxeuBQdz7ubmkJSXk+eue3e6Z15eyNi0Cbdu3WKGhoYoLi7G3r17AQATJ05EYWGhoK6uzrm5ucGgteJIUinQpQuePn2KCy9e4FlVFazHjxdd58xhjDFIBQF49gzu1dWsKjgYd44eZQEHDqBP164oPnwYht98g1IdHeR37QqLggJIzczEritXsqcZGbD39gZnaQn5Rx9B8vvvJAA6OpJXq5VqzTzPi2ZmZkx57SWGhih++22h6/37XMnq1fj9998BADKZTCiqrubm2NlBb+5choKC1kOv33qrZR5mY/z+O/U1P3++4S2jN9/EgeRk+F65Akl4OH66fRvlkZGo1dODQqEAz/Po168fDA0NERQUJLq4uLBh/v5wMTBASv/+CDx7ljmfPg0WEUFez337Wu91GxBAAme3bnQOYWEtcyyvX1d5/qZN61AUTkREBG6cPInV+/cz9fh48DdugO/dGwUlJag4cQIGO3YAW7eiqroahw4dEgsLC9nEc+dwztMTfR88QEl8PPrs24dfnz9HnZWVaFNZySS9e7OaFy/Qe+NGhEql8Hn0CCmVlciOjoaV0nt64gSFcy9cCAAQzp3DyCtXkP3pp7BqHpXCcUQCMzIob7oRRFFEYmIifv31V+jq6uLx48ctyTZAx/72GxWw0tNT7RFKD/CSJSoC9fgxYGQE7YwM3sHHh9aWzEx63bhBz8DQkGp3mJhQtFhqKrgvvoClj484a9s2vNDSYg937oSnlhaRLUNDWofWr6d1y9wc4DhER0cjPS6OOTo6KmBk1FJJyM8nUj1qFP2WiYmqNklOjqqA4rVr9GpjnzasF4ujo6Nb7r21tSQm6OqS15vjSBSprGwaYdIGpk+fzp88eVIRHx/PR0dHo3v37g3RYtXV1UhJSWFqamrQ0NAQZ8yYwdok2sqUmPnzm77v7k4Ee9kyGi+bNrUqkhYUFODIkSOQSqUd6zP9usjMVIW8N4ahoSq/vPn6MmkSiS3377+00FtbCA8Px4sXL/glS5a0n/q2ezfNr8OHSZA7eZJqBjQX8ePiaI1Q1pGph1wuR2RkZGNnyGcAPvfz8yv09/f/O4AvAFQD0AVQ8loX8/84Osl2JzrxF8Df37+3VCrdP3/+fI2/LHTpP4VCQR4ZOzsKafrqKzJgt2yh8Nf8fFKyLSyI+M2bR0QrK4tyu8rLyYuWmEiG+2t6X/X19QUnJyeuVc/PfwKOo9DjX34hJb4D3m2lZy9+zx4siIpihs+eMW7KFJh+/DEzNTWFA8DL5XIEBQUJxcXFYlhYGEtPT1e88847vJ6eHvLz8yEIQsc89IyRl+HwYcodbA09e5JY8Ouv9Ax0dV+5srUScrkcN27cwIQJE6D0XPchDzU3YsQIREVF4Y8//sCECRNUN0qZ4/fkCRFYR0ciAK+SS66pCXz4IbSHDIH02TNoPHvGcrOzYdGBPqxNoKurKrg2axaFxMXFkWdo1y4VibayohDjgADKkf3oI8oXnDKFDJ+pUynNYODADv+00pAZPnz4y4vKnTtH3/0a0SzPnz9HXV1dx/vLX7hAXnQPD5qfjcZGbW0tLl++LEilUjZkyBCWlJSkuLl8OUYVFvKYOhVYvx5iSAgO798vdF20iJsHiN1v3GCXL19GaGho/ddfgFQq5erq6pCamorFzdNLGiEiIgL5+fnw9fXF4MGDVeGMHNfQQ1uja1dY9OgB4ylTxEojI5Tu2MHcfX3RtaQE1nl5KAkLQ+Jvv7HoI0egX1SE/N9+g2N8PNSqqmBQXk4pCitXtkq066+ZMcbElJQUdurUKQiCgDcsLJCxYgUSfvwRHxw8iJ3vvINl773H7d27F1UWFtALCyNDWBBa5p53706G6tSprbfAqU+DaIyS4mIMDAiAx5w5kJ84gQkAugwejLLx4xE9cyZycnKQkpIi1NXVsYqKChYcHIxHzs5wi4iATn6+kKyjw2WsWYNuHEdikrFx6+cWH09r9d695I1asED1N1GkMOepU8krfPVqm88NAHlDpVJgwQK4PX2KiwMHonTBAnTR1W2ICOmbnIzbiYmiRUgIy1+9GlqCIL6wtcXyt96Cxr59iGNMdBo1ih187z1RvmkTW5iaCu316xl69yZi/PnngLMzJowYgYP//rfc7sYNSa9Tp2i++PiQ91ZZ/CozE4a7dqFozBgxOCEBrmpq7Pnz5zA2NkZ1dTUqKysVbv7+3HMdHfHOjBliTU0Nq6mpYXV1dUwul0MURQAkksnlci4iIkLs27cvDcgnT+i+3LtHwt0XX7Td0lJbm/795RcAQPE//sESFy9Gbx0diozx9qb1xcmJ1qDCQhI83n+fxqqxMWZMny5J6d8fR8+cwXR7ewo7j4ykvXfuXJqz9vaUA+7vj/LychgZGQkNOf+Nn1FZGe3VDx7QfFeGcDc+3/79ieQtX07rYJ8+bdY86NWrlxgSEoK+ffs2bQ81ezZ572/fpmcDkHDZweKdZmZmWLNmDf/999+LhYWFTEm0CwsLG4Q8fX194d13321/kcvLo/Wt+Z4XH68KHV+1ilIo3nmnxeFHjhxBXV0deJ7v2Hr6unj8uNXfB0D704MHFEHYGIyRSDp5MolMr5GOdvPmTdHd3Z1ptJXWpezS4uBA4+74cdoLt2xp3V6LiqLx0szTfu3aNSEsLIwDIIC6uTQswH5+fl8C+PKVT/7/GDrJdic68SfD39+/m1QqvTZ+/HiNDhef+p9GeTnl0125QrliAJGYqCgyHufNU+VaKcPcyspUxcqePCGPAECfP3yYPI2rVpHq/9VXtMHPm0f9UL29W1YMr4ednZ148+ZN5OTkiDNnzvxzK4JNmULe+7a8x80wT18f5cHBgv2NGxzn68sQHt4i5FIikcDX15cHAFdXVxw7doxPSkrC3bt3FeXl5byOjo6wfv36ju3sixaR52DTprbzoF1ciHBHRpJ3KjCQ8otfEV9//bWgra3NtdW/Vdm2o0k7MDMzUsGVxHHLFvIAzZjR8R8WBGDDBnD//jdkbm7I//RTGHt6UuseZXum14GhIRmAAIXbFhWR0ZOZSUbFiRP0t6+/pt95663XbnN17949EQB78OCB4ODgwLU5rxUKmkNfvrrtcfnyZfHhw4dsyJAhAvcyy1AUSaBJTaVn0gpZuH37thATE8PNmjULMpkMs2bN4vfs2QO9W7dEBwsLVnT3rngtNZWVSKXcsPnzYV1WxvDiBcYaGWGsnx8EQUB8fDx69eqFzz77DIIgtFsZvbq6WgTA+vbt224VZTc3NyA6mqGqCg71JJwzMICBgQEMnJxgPXs2ysrKoK6ujn9/9RVKdHTgxXEiIiIYiotVvYPXrSOxr74AXUxMDADg6dOn7Gh9oSIjIyPFCE9PnvP2Rs6jR/IwFxfOedAgZlBSwtTV1RUFBQW8uasrkbzHj8nb0xg8T2Hry5e3vMfJyTRnG4dciiK433+HUXExYmxs4GVlBW0AiI6Gfm0tBu/dS+vh9esNz7esrAy5ublw+PvfwQ8Zwin8/cGNH0/rwtmzFOL83nvkCVMiLo4Kv40cSfmXb76pWqdyckhMWriQvGYuLq0/CEEgcqimRoJqcjKwbh1CoqJEpKczg88+a7ImOTg4oMvOnSw9PR0v/v1vcfTly0w9PBzc48fAkiWYs2EDw+3beM/dncHeXtUXPjpaJc4NHw6IIqokEhbn5YX+69cTIbx/n4pF5efTvO7TBwgLg0NqKnty9aqQlpYmZGVl8erq6qK5uTmTyWT847Vroa6vz1wMDKCjo9Pw0tPTg0QiQXh4OGJjY5Geno6EhATWt29fijoxNSVPooYGpUk5O7fbXk0QhIb/NrSyEkt69GCoqSHBr2tX1RqmpkbPIDdX5cWsF2LsDQygceWK+Ntvv7FNGzZAtmcPCRBKEhkSQjnTx4/DvU8f3Hr2jGuSYyyKtO76+BBpamt+JSaqIjGUOeFffEHrUSvH9O/fnx04cAA//fQTFi1aRMTM1pa8n809sWvWqNLKOlhPQl9fXywsLGz44XPnzkEQBGzatAkSiaT9Ne78eSKG9UULm0BPD1i6FIKpKZ66usLq55/BZs1qIkgpFIqGdUhdXV1JEv98vHhBe80XX7T+9/HjKYqiNZiZkQ3w1VcUbfQKInZqaipqa2uZVzOxrwEVFTTfZs6ktWD7dopK2LChdVvs+XMS7Xbvbvb2c4SFhXE8z4cqFIqhAGr8/PzEDp9oJwB0ku1OdOKV4O/vbwpgDMdxPSUSiSnHcZqMMX0A3evq6qxFUeQlEok4bNgwqZub239n9XFRpMV2wwZS5I2NqSBUv36UF+XuTuG2hw/Txjp3LqmuBgaqMPIPP1R9X2kp/VtQoKoiWlJCIcSiSJtNQgIt5IcOkfG1Zg2F9k6ejJEFBbz1jBk4feYMnj17BqM/sR0GGKONSDfqWwAAIABJREFUbMWK9r07168D330H86gomE+cyCE3t0NVpLt27QpDQ0Px+vXrzNjYmJswYQJOnjzJJSQkoGfPnqiursaePXsUJiYmmD17Nt+CQ2lpUV7cjRsv76fr5kbPzNCw3fDA1nD+/HmxsrKSW7JkSZtEyNjYGEVFRfjyyy+xbNkyWFy8SMba4cOqD5WX05h4FezYQce5uYGpqcHe1VXxYNEifrCjI3mMPDxa74v9qjA0JKMmPZ08Msr8bju79kOBOwBLS0tmbGwsFhUVcXv37oWvry+GDBnS8oOXL5MI9Yr5/NXV1Xj48CGbO3curK2t2zcKY2NpPH/2GXnQ2qg8GxMTw7m4uMC2PszWyMgI7733HiJdXdnVM2dEJpdj/rFjwKlT0FHm4P/+OxHLvDxwPA9lqy5ra2ukpaWxc+fOwdHREYIg4NSpUwAAd3d30dfXl8XHxzOAUkPaxa+/kljXeA1pBIlEAiMjIwg1NfC5eRNmrq6i2UcfMcTE0HM0MKDic2fO0Hrj6QksWwb1+rxmFxcXmJub4+bNm3j27BmviIwE17075i5cKMHChRQKa2QEsw0bWEhIiOjq6sqwYQON0dYQGdl6b+EffiCPVVCQ6r09e2CSn49LixcL6aGhnJmTExVoUnpIZ82icOGyMlpLd+2Cnp6eqjjh3r3gc3PJozdwIBVeGjCgZZG03Fwi2kuWUJukd9+l98+epeswNqZ1tzWiff8+feeUKfTZ27dJIOraFXXm5rhx7hwbMGBAq8WkNDU14WxjA+dp0xj27CGv3JUr9MctW+i8EhOpCnNYGL0/cSJFody/TwT3p5+wrGtXHhs34umCBeh2+DAR1b//nUSzigqKSuE4ODg4wMHBgaPTvo+YmBhxwYIFtIBlZJA3/1brBY/79OmDixcvAiBi8rufnzDhX//ijn71laBwcQEOHoTzjRvoEhHByfr1a1LwMCoqCmfPnm36haIIh6Qk5hAURILGiRPkAZbJyPu6fDmdUxui7oIFC9iePXsQevq0YgjP803W+/o9r3b3bkQYGQmaQ4cynucZsrPJC//11xTFYmvbPiFTphUosWIFedyvXKGCas2gLEJorq1NnvPcXBKVWlu/ysvpmhcvJluhA6iqqmJWVla4e/eumJycLD59+pSztrYWZDLZy8XEkSNpX24rksvHB0Hx8eLt+HjmamEhjIuJ4XhnZ1RWVkJXVxeMMVRUVAAAFi1a1CGiXVlZidOnT8PY2Bi9evXqWH/ux49J9GvrPD08aE60hUWLVONYme7UAQQEBMDR0VHQ19dvem2iSJ7+L76g565Q0Bg6cIDWhbbO8/p1eu7NWoZdu3YNAKBQKKb6+flVd/gEO9EEnWS7E53oAPz9/dUkEsmHEolkU/fu3RVdunTRkslkTCKRQF1dHTo6OjA2Nm5o7/Gnh0T/mZgxg0i2sl3MTz81FE0BQIvtl1+S4XDoEG2wpaXkUWqvYrWpqcrjqcx7A1QVTGfPVhVYUVOjV3Ex8O67sC8uxozr11Hz449kYE6eTJ4rd3cyzpcvf/3r7d2bjJBLl5q2gwKImM2aRQRmxw4KyXyFIm0ymQxLly5lqampsLW1ZTKZDCNHjsT58+eFtLQ0ztTUFOXl5TwAcffu3cK8efO4FlW/7ezIW/0yss1xZFTn5Kiqznp7v/QcY2JiEB0dzRYvXtx6zi2I4CxevBg7duxAXV0dfvjhB3jl5gpeNjZcwxGCQMLFq6QL7N9PoeeLFwNqaggJCRHj4uL4VG1tZERFiR47d0KD58XugYFcW5EPL8WLF2RISCTkHejfn8iDMnQyPr5FDtqrIj4+XlFUVMSbmJiICoVC7NOnT0vjrT50FfPmvZKHQi6X4/vvvxcsLCzaJ9qCQGGcu3YRUW0nzy87OxsVFRUY1sx409XVxdBJk4C6OoaffybCV1BABEdHh4yyiRPJw+jjQ0TSzAzDhw/HTz/9hKioKDxq1q4oMjKSRUZGAgBmdCTiobiYXu2hvBwF//gHpIzBZONGBgsLIqyurlQwb8MGej18CPzwA2q//RZd7t3DPB8f2MycCa5nT/Tv3x8AwIWFNS0wpqmJpKAgpFy8yI3Zv5+q7NvY0POztyfPTuMc4a1bae59/bXqPVGkNbKR1xNhYeQFP3YMI+Rybv/+/bh165awcOFC1TPt3p1eeXkUUVRVRd4mLy8ibd7e5F2fM4fW3UuXaN1UVtFW3r/ly0k81NOjtVJ5nlu3kqHdPK82NpZ+8403KHw5Lo6iP5Se6/r1PzQ0VATARowYQe+npVH0hJcXrU+LF9N6/vXXFIY9YwZ974wZtC5cv073UFOTRApAFWEC0PVyHG5fv46obdvwvo0NfdePP1Kht7w8qoL93nu0bisruYPqfMjlcpWH0sSEhL/WQuxBKRBKjOI4yH19uTgrKzg4OTV8uFxDAxEmJtC7ehVz5sxBYmIiHj9+jIyMDFEikTAnJydxipMT4/bvp3UlLY3u1VtvkTCwbh3dky5daJ+aM4eKXirFlUYwNTWFjDHRftMm/ufly8V3BIE1bgd26dIlRI4di759+4prd+/m+KtXaTxNnEjktiPr7vbtTQt1SaUUEfH55ySA1ItnSrx48QJMEDB85UoSgOpDvFuFnR2FSnft+vLzqIeRkRGioqKQnZ3NUB8VY2dn93Li+9lnRFDrw/ebQxAEJHp4IDkykr2pq4sCgGX87W/4bdgwyOVy9OnTB0qP76uEkD979gxPnjxBcXExQkNDMWTIEPj6+rZ/UGEh4OKCyspK3Lp1CzY2Nk1rbmho0FyYM6f14yUSKpa3dCkJbB3YB2/duoW8vDz4+Pg0vbDiYhLir16ll7Exzatu3Wg/bGtfEkUqVnjsWIs/mZmZidHR0QxAzktPrBNtopNsd6ITL4G/v7+WmppakKWlZY9JkyZptEVY/tdg2LCmrZEMDIhMN/eQ2dhQqGphIRXpGTOGNm0Pj9drR2VurlJNG1eWLS8HD8DkwAF2dNcuDI6Ohku3bqT2JyVRvvXy5WT86+iQwv/GG2TU8DxtLqtXk5HW2kbFceRJP32a1H3GyCP10UdklK5dS9/5mhXjZTJZgwcQALp3747Lly9zyrxXOzs7xdSpU/mTJ0/i8OHD4sqVK1mTtk79+5Oh9uxZx/K2LC3pvogiebe/+07V47UV3L59WzF48GDOysqqXQYok8nw9ttvIzExEbrHjiHM0JCL1NDARrmc2lAFBJAh9zKipERkJD2jgwcbPIMODg7s1q1bcHR0VERHR/NpkydDpqYmbly3joyreo9Wh5CZSWLJ7t0UUjt7NuV/Nm6lUllJgk1s7H/k3R49ejSflJSEoqIiBoBdu3ZNnDZtWtP7GRFB1Y0fPGh4q7a2FiEhIXB0dGzwnMnlcty8eRN9+/bFqVOnhBcvXnA8z7PZs2e3/Xxqa8moLy+nsP6XGGTl9V7aNtuHTZ1KESbKSsOHD9NLIiED3dSURKj6AnJdxo/HRx99hNraWmzbtg16enriwoULmZ6eHqKioiCRSHDq1KmGHtlthpHX1ZEnp73zz8qC+OmnCCsogM7HH8NI2RpPR4dIR3R0Q/qAws0NSWvX4uyZM/D08JAPz82VwMsLsLcHt2ABhVfPnk0EtpG37gXPA4IAi9xcEhqU1YI//FCVmqDE4MGUotAY8+bRMUoycPIkvS5eBGQydAGwevVqfPfdd1xtba2qdaAS5uYksIki5aBPm0brkVRKIsqsWRQ+e/s2eYTDw8lTLZHQWuHuTnM/Pp5CkKdOpXN5+FBV6KumhnJCt24lQqsMK33xolWjO/XgQTEhNJR5OziA8/am3OD9+ykqKTCQSP3QobQvrFhBBykjBZSezsYeT6XA1Zh41s9tLy8vpF+8KIZPnsxcu3WDuq9vUzFjxw56FkFBdK969YKamhoUCoXqM5qaFN1QXt5kP0pPT8fZs2eFiooKbtq0adB/8gSW8+aBvf9+032vuhriihWInTEDaWlp2LZtG9TV1UU7W1th/IgRvNa2bVD77jt2aOxYeFZVwcXTkwg2QCR//nx6TkqPsZYWzZeDB0ksaAXj6+qQZ2mJNMZYeHi46OXlxehUqhEWFgbtmhpkBwTwefn5YhdtbSYyBvbxx61+VwtUVdEYb743OziQl/jBA1oDZTKUlpai5NkzlCxZAlm/fpDevPnyIqJSKa2z//wnCX4dwJQpU5i9vT3OnDkDnudhbm6OHo2F/bbg4NCqQFpeXo5Tp04Jubm5nEwmE8elp4t2ZWVc/Ny5gsW//sWLVVWQamqioKAA+/fvB8/z2LJlC548eYKQkBAIggAfH582W4BZWFhAJpOJpaWlDEBDW1BBEFBaWtpQVK4JkpIQr62NP3bvFvT09PDo0SNOXV0dDkqBz9YWuHmzTVEIAAk4q1fTnPv73196e3r27InAwMAmKQ54+pQKxnl4EMGOiaH5evs2rbnt4dYtWhuUFd8bwcTERLlYaAFopw9hJ9oD/4+2CvN0ohOdAAAEBwcfsLe3Hzpr1iyNNgtR/G/A5ctkIOzZ07Ql0cSJZMS15SXV0qK2InPnUr7YN9/QRqijQ96YPwkyAwNUamjIr127xlksWgQjZ2fyGKxdSx+YMYM801paRJ7GjydD88cfiYj06kUhXRMmkIq/dCkQHEw5gePHk2obG6vyiBkZEQGfMuW1c3lbQ3V1NTIzM4X+/fsLrq6unJOTE2dsbAxbW1uWkJAg3rlzR9TS0mLGxsakuEsktCHWh1p3CMr8wPv3aaPW0GjzGm7evMm8vb1ZR0QiAwMDONjbw2DdOqSbm+OZnh50dXVhaWlJY2b06I7liz9+TCLIli1NqgVraGhg0KBBcHJy4vr27QsrKys8iopigzZuBGdpSQbJqVPthynGxlJonKcnhd87O5MINGdOS2NBKqXnbWrasv3KK0AmkyE8PBxaWlqoqqpCYWEhMzExQXV1dcPfhZ9+wvM+fbD9+nUEBweL3bp1Y99++y3S0tIQHh6OtLQ0xMfH4/Lly0hPT0dkZCTKy8uZoaGhOG7cuLYrnAcGEol/6622C9s0g7GxMe7cuYOqqqrWjVvGyFs5YADlyj5+TIKNUnBSFucSBCITNjaAszN4noePjw/69+/PlNE75ubmkEgkCA0NxcOHD3Hnzh2YmZmh1ev55hsiKMraD80RFQVcuADB2hqXLC2FrJwcpqWlhcuXLwvZ2dlCj7VrOfzxB503gJycHPzyyy8Y4O2N4cuWcXjzTSKvgkAE9f33yVtqYkLXW+9JtLCwQPCDByK/dClz8PYmb6yREYVzhoYSeV2yhO6TjQ0Jjo1ztu3saK5aW5Pnd8MGEpYaER2ZTIbAwED4+Pi07VljjM538GCKMtiyhYhsv35U06CsjMJBlZX55XIiwO+8Q8/lxQvyAtbUkDDatSu1sgoIoPmxbh2tb/PmqYhiXR2RVImE5s+bb6Ju7lzUzJzJtBmD9dKlKOc4yEaMADdzJoomTYKmpiaYt3fD9dXV1aG2tpbqO2RmNnj3GiM9PR3nzp0TtbS0mCAIEAQBz58/R0VWFl5s3AiD8nKWxnEw//RTaDXPO9XWJnIfFkZj38EBJVpaSE5OFvr376+6mVOnkvDYqPjU77//LhYUFHCzR42C7Y4dMNi0Cezvf285b7KzwQID0WP7dnTr1g0jnJ3hq6nJnHbu5Ey1tKDfqxd01q1DaY8euFpZif6jR5N4JYqUTxwfT+O4cVjuwIGUQqWv3zLnuawMpmfOMHHHDsRSq0umTEU58tNP8qqqKrbswAGmU1GBo2PHsqxhwxQmkyZxiRcuQHPKlJdHy5WX0xh2d2/5N1dXEsyLi1Hn4oId27cjISwMg27cgO7cubBuXryrLezbR/d71aqOfR5AYGCgWFlZCalUKpSWlnJ1dXWKqqoq7sqVK4o7d+4whULBCgsLYWhoSPd33z4aS8roika4cuUKkpKS2OLFizF69GhmMmMG4+fNg7GhIZdVWio4BwWxxO7dYWRkJPTq1YsVFBQgMDAQsbGxkEgkitjYWK6goACu9cLCgwcPEBQUJHbv3p0BwK5duwQdHR1MmjSJ1dXVKYKCgrjY2FhFSkoKu3LlCjMwMIBEIsGVK1eImKurA8uX47CxMSoZY2vXrmWlpaVCeno6y87ORlhYGGzs7aEeFUXrRzNBv7S0FHK5nMQ4ExOKDDE3f2nLyPT0dMTHx8PX1xeaGhoktB0/ToR93jwi3nl5FEXTkdD0CxdozWklBSIoKKg6Pz9fwvO8w5AhQ357+Zd1ojV0ku1OdKId+Pv76wH4eeHCherS/6AX4n8Fnj0jo615FeZ33+1QODIkEvJuDxlCxvm//kVqeq9er10huzm6devGVVZWCrdv38bjx4+FoqIi5uDgQAxJKlXlUY8dSwTT2ZmINkAGwJgxqmsdO5ZCqYKCKKdu2zZSmD08SHBYuLD9sPjXhKamJt544w1mbW3NmZubN+Rjqquro6ioiGVnZ7O4uDiEhYWJJSUlzM7ODlzPnsDf/ob8yZMRHBwMGxubl4e+MUbCgrk5EW41tRaVkQEgODhYtLGxYaZtVKRtgcRE3B8+HFH1+W79+vWjPPojR8jj8LIIgOpqMpQnTmx3XKmrq6OiogLx8fEYOmYMGSPp6dTSZdYsMmqVBrIoUgjn5ctESiorKe93+3YiK9bWbRNpiYS8gMHBLw/VbwOMMQwYMAD9+/eHl5cXQkNDERMTg0ePHiEiIkKMOnmSSa5fx2EbG4AxKBQKlpGRgaqqKtja2opdunRBcnIyKy4uBsdxWLlyJYyNjZGUlITBgwezVlt9VVeTKLRmDZGwESM67PVnjOHp06dibGws69OnT+uGulRK8zcvj4janDkU4aJM9QDo99aupTk+bBillrQSvq6hoQEPDw/U1dUJubm5TEdHB/bKdkSN4elJXtzWImOuX6cxNnAguNmzMcDbm2VnZytiY2NFmUzGJyUlcf28vCD95BMq/mRhAW1tbQQHB0NHR0d0cnKiASCTUbTI1Kk0L65eBQoKkBMYiJrVq8VLz56xq9HRQnV1NZednQ0PDw+oOzuTIBcWRgbns2cqY7+qikJx33uP7sfhw7Te+fhQmPeqVTS2mkUG1dTU4P79+y8PQ2WMvnfYMJq/tbV0Lv/+N43xU6eIPDNGc+DoUbr+3bupt3ZkJAkDY8cSsb53j1IDxowhUn7+PAkGmzfTc541i953cSHBoE8f3MzNFc+ZmbEkGxvEPHmCZC0txd3wcC4qKkq4e/cuKy4uFnr06MFqa2tx6tQpxYULF7j79+8jKytLcCkpYSw2Fhg9Gk+ePMHvv/8u3Lp1i4WFhaG0tJRFR0cjLCwMEXfvovzoUVh99hnCLS3xqGdPMdvcnA0bORJt7q29e1OkQXIy1HfuRIK+vvhG49BZd3dV9e166OnpsbS0NCErNBTmV6+yOHd30ap7d9Yi2iI5GRg2DJpqajCZNw/qSo+0pyfw5puQu7ggNDsbtbW1yMzMhLu7O3lEf/iBoiu++aZlf2RNTSI9N260zJH+5htAXx86U6YgPj5esLa2FpydnTnk5MBu+nSuQFubPZg4UUxxdRVramqYmZkZ6zZnDgvR0xMTL15EHc8zi+7d244aiYsjEaVxIb16VFRUQH30aAh+fpBYWsJ7/Xo86NsXId7eyCgvR0JCgtijRw/WIgKjOXx9aYx2797uWlRUVIQHDx4gISEBsbGxbPHixWz48OGcra0t4uLikJiYqLCwsJB0796dxcXFKRISEpCTkyO49O7NYf58CP3740BgoCI+Ph49e/ZkPM83eHHj4+NRXV2tcHZ2phP49VdoT56MbkePMvM9e+C6bRsG+PoyW1tbeHp6wtXVFb6+vigqKhKzsrI4uVwuhIaGKkpKSrjHjx+LT58+ZSEhIXjw4AHMzMzExYsXc8bGxrCxseGCg4Ohra3NCgsLmbu7O27duoWIiAgUFxcjKSkJBnl5UMvKQkLfvoJCoWADBw5ERESEkJqayikUCkEmk4kBAQFw5HmmZWTUEF0VEhKCa9eu4dq1a0hISKCQdy0tWvPDw+keMwZBEFBVVdVkfsTFxeH06dPw8vJSuFpZcbh1izzZn39O8yUwkNaBVas6VtclJ4fWlOXLW+33HRQUVF1RUfGuKIrbfXx8al7+hZ1oDZ1h5J3oRPswVFNTq5PJZP97mXZdHRmGP/9MHpjm2L6djMeNGzv2fba29PL0JAXVxYW8MWvW/Cmke8SIEZyxsTFyc3P5yMhIqKurt8g9bRUcpwpP3baNvFuJiWQIX72q6heanEzqvL5+q7l1fzYSExNx8+ZNRVlZGaempoaJEycyc3NznD17VkxISGCRkZGws7UVe9rbs/itW8VUMzOWkpKCKVOmdKyFmNLDra5OOXc+Pk28cDKZTFR6YF+K27eBKVMwND8fIaGhoiAI7M6dO6KDgwPDDz+QwNFWmxyACjn94x801jpQ9ExPTw+s3qjgOI7GVHw8Pbfhw8l47NmTCFpeHo3RYcNoTIeHt13BvTk++aRp3u5rQGnkamhoYP78+UhMTES/fv3w3XffMY+ICOSYmwOMYcOGDSgqKkJoaKjC2tqaHzRoEDMwMECfPn3QrVu3hroORkZGKCgoEOLi4pinp+f/x957h0Vxtm3j5z2zsJSFRXqRolhQQUQUCzbsNbEbe0vUJzHGxDwxxhiDMfHRdGONJUZN7LFhJSqKAgIKKGJBQEBFQJC+y+7OzO+Pi6W5CJjn/d7fd3ycx7EHCrs7M/fcc99XOa/zqmlBp6bSHFYqKdP6Gs/VxIkT2ffff4/Nmzdj6dKlhoM3n3xClN2DB4ka+sMPVfXbeug/t3QpBVoSEijjWksgztLSEiNHjuRu3LiBqKgoDKndG/nFCwrs7dv38nns2kVzZ9q0GsHASZMm8QBlsLdv345ilQpma9ZUOlccx8HNzU1UqVQveyCRkfSdeXkAY7i/dKnUQi5nXZ48wRsbN3J5I0ag6MMPYWlpWRWEmT+fMsDffEM0ZmdnWte2bKH1hOfJkfLyojrITZtIVM4AXV+lUoFvzH0zMaHsUlEROZG2tlR+8ddf5MRdvkwZXFEkh3/WLHKW58+n8VMq6f6tWkUOqL4H8e+/k8M6dSqVgVhakmOmR+vWeLRtGwOAuXPnwtbWFiYmJvydO3eQnZ3NdezYEdu3b2dXrlyRBEFAVlYWe//998HzPL7//ntuo0qFls2aCVk7drDHjx9zfn5+XJs2bSBJErXJEwRoK+jHchcX4P59jDYzw549e1hRaipycnLgri8VAJCfn4+nT5+C53moVCqoVCqUmZlJtjyP5vfvczXEIe3siAHw1VeVn29ZWIiPfv6Zw/37uDNrFkIPH2at2reHvb09MQBkMqojnzOHxnXHDho/T0+6vy1aQJIkREZG4sqVK7CzsxPd3NyYUqlkyMqiuRASYtAxAUBsijNnKOChD3A+e0ZzKSgIOp0OGo1G0kRG8rpt2yA7ehTqVauQnJWFRQsWsGbNmunnMgOAGQMGsPx27ZBy5QourFkjDhgwgDPocBcVvRTwSU9PR1RUlHjv3j3OpLwc3dRq+PzrX5Bv2oSPp0yBTqejvuoXLrCNGzdKH3744asd7tOnyak7c4b2UwPIzs7Gjh074OjoKPA8jzfeeIOztbVlAAmyzZw5k0M1ZfA+ffrwGRkZ2L9/P5Us3LqF69HRKLx/n5PJZNJPP/0k2traIj8/H5IkMXd3d7EysAYQ46ddO8DODtyuXbBOSal0ao2MjCp1Sjw9PfnS0lLBycmJl8vl3PXr10WO4zB58mS2f/9+aLVajBgxonJszc3NoVAoJKVSyQRBEIcNG8b169cPxsbGSEtLw/nz58U7p04hGeDy8/O5Dz/8EDzPo7CwkAHApEmTOCsrK2zevFm6lpyM4SoVYq5eRVpaGjIyMtC9e3eYm5sjJSVFKigooPZoI0eieNYsxK9cicDgYGzZskXIzc3l5XI5dDodeJ6XBEFgCoUC/ZRK/vns2VC3bQvno0cRn5AgPli9mms9bJhkFxzMLly+LNglJcHd3Z339vauO0hz+TKtZQb2UkmS8PTpUwWAv1auXFmHgmQTGoImZ7sJTXg10jUaDa9SqfB/LYW8uJgW07qETRSKhtfJVkfr1vQ6c4ZoTJ98QpHVCRP+kRMrk8nQpWIjd3Z2RkhICDQaDYbqs9b14flzCiroVTk7dybnWi/go1CQsXTkCGWEDNHuXhOiKCIqKgrNmzdHdnY2IiMjhZKSEj4wMJBr27Ytc3BwqNz03nnnHa6wsBAhISGiWq1mZa6ukk1SEpMHBSE1NRUnT54Ug4KCuKioKLi7u1fWmxmEXuDp4kUKJnz3XWWm18vLi4+NjRW7dOny6pssCJS1vH8fnFyOTz/9lMXExODixYtMkiSw69frH4APPiADs4GCZAqFAlqttopKB9B5t2hBbWgWLCAnon9/Ml6zsshha0jdX3Xoe8QuXmzY2WskXFxc4OLiAlEU0VqplJ63aCFl+/iw6RMnMnNzc5ibm8Pd3b2Gp+VpoGbczc2Nu3nzJjIyMkj5VpIoUKHTEbV4+vTXpr4bGRlhwYIF+Pnnn3Hu3DkMM6BGDMbIaF64kITH1qyh/x88+HJJg955XrmS5ll4+Cup+fn5+TVrHDMzqd6/+vsliWijd+5QVrGOgEhUVBQkSaK6d56nc7x9G+XGxsjIyOCGDx+Ol2qjP/6YSk8qjtd1xQr2i5WVZGJkxN5Sq0UnUeScfvyRrmXHDspEJiTQWrh0KQmY6bUYYmLonvTtS87ruXPUUufixTrZMY12tvWwtKSstSTROjV5MgWg4uLouDxP1PXBg4lOv3gxZVvHj6dzU6noPrZuTWv+tWv1HvLNN9/E7t27sWPHDvj7+2PkyJHo0KFDpRbFuHHj2LFjx8Ty8nLurbfOxob5AAAgAElEQVTeYnq2zty5c1F47hwc167lw9asgYODA0ZWZ49ERAAhIZCXl1MQoJqeQo8ePfD06VNp7969bNasWWCMwd7eHr/88gsAQKlU6ioESDlTU1OueMoUeMlklCGOjKQAGkABj+Bgum85OUC7drjctasYs2EDysvLufaAZBcZyfDGGxS8OHyY9gR9UMfVFZonT1Bmbw9JkiCKIkpLSxEWFgY3Nzdp8ODBnCiKyD1/HorPP8edn36CLi0NwsOHEASh8iWKYuVPHDkCrzVrpJDp00VBFFmXsDAmKy9nlx89gt2TJ7BSq3mtKCK6uBidNBocEgShddu2/EvimRWwTkxE5q1bED7/nIu+fFl0eucdTq1W48SJE2jbtq0watQoHnl5eNGyJfZv3izl5uYyjuMgCAI8PDzYgP790fb2bTQrKMDVdu0kcedO5tatG1q1bo0ePXqga9eu+Prrr9mZM2fwpl5wzxC6dqVxq9Zx4Pnz5wgLC0Pr1q3h7u6O3377TerevbvYv3//Bk9+uVwOk/x8IDAQRVevIjMzE1qtljk6OooeHh58bm6u4OLigkGDBnEcx9X8XpmM6PNDhxLbbvNmCjjXChq0bNkSLVu2rPysXuQyLy8PkiShe/fuNUpfOI5D165dxUuXLvH29vZgjFXWeleq5O/ZA9HODoMGDKjMPvft25ddu3ZNNDc35xhj6N+/P9Lu3BET1q7lLgweDDc3N0yaNKmS+RMSEiLu3LkTixcv5s+cOYNnzZpJHc+cYY/Hj0d+fj4/ePBg3L9/X+zatStnaWnJLl64ANtDhxCRkCDmK5Xsjrk58zt1Sio/fZobcPw49puasnwbG3h4eHDFxcUsJCREOnr0KPPx8ZE6duzIauxDWi09R7GxBu9LxTVr1Gp1GoD/y8WK/nfBJL2CahOa0ASDWLt2bcq0adNaurxGb+P/dXz6KTkms2f/zx8rKYmMxMBAMqpnz25Q+6z6cPr0aTE5OZkbM2bMq1txxMcT9TIjgzaPadMoy1o9O1xcTIZWRATVe6ekkHG6aNE/rtvW6XTYtWuX+OzZM04QBBgZGWHgwIHw9vauU5ClBrKzofrPfyD/6iv8uHWrVFZWxiRJgpWVFV68eAEAePvtt1HvPJQkEoZq2RKPFi3CiRMnRJ1Ox31UV52sHlOnksG6Z0/lr3bs2IHHjx+jR3q61Cc+nuUcOwZXV1fDUfL//Icyaj17NlixvCKjIX7yySdc5bn/+CMpo0ZEkFG9aBEZeR9//FI2tVFITiajvA6F29dBRkYGEhcsQGtzc7HFvn1cnYJkdUAQBGzcuBEODg6YNGgQ1c6dP0/qtK9iEDQCV69eFcPDw9mHH37I6qz7TEoiAaU5c8hhKy8nR7+uAKMkEevg3/8mFkK16z59+rQUExPDAGDRokVVCvgqVc3vKy8nQ695c3Io6yhPKCsrw3fffQeZTIbPPvuMfjlrFrB4MY49eiQkJibyeuEsuVwu8TwvDVCrWef58xns7Go493l5eTh58iTS09Px8ccfwzw7m5w3X1/KBI8ZQzXS+/YRe2fkSNJ+MDGhdXTnTqLSmpvTWBmi/1fg4cOHOH78uLRkyZKGRUtKSoidkpxMGb6xYymrPnIkOaoREfS+d9+lwJGJCQUJjhwhpzsggGq4ra0bzvioBkmSsGHDBkmj0bAlS5a89Hd93fVLc/zJE6IuV1dAv3+fsuzt2pEWgF6Y0gAOHz6MsrIyIS0trdIRUiqV0uLFiw1/oKiIAiGLFtF6360bzceICAoI5eXh23XrpAk7drB9b72FnhER6MpxMAsLI5X96vWwx48D+/ZBdfw41n36KYyNjaVqaxvjOE4CIDFRxOStW7nQvn2R0aIFHB0dRSMjI4njOHAcJ/E8zyp+guM4FD1/Lht34gQKVq+GkVIJ688/h/bzzyF3cYHRmDFgHTqg/PvvsXv3btHR0ZFr2bIlTp06JWo0Gi4oKEgIDAw06KiWzZmDmwUFYkT37uA4TnJ2duZTU1MxadIkPFu6VMoF2JOgIIwdOxbZ2dkwMTFB+5MnaY5ERwOSBFGrxa2hQ5FiZYUeGzaQHgeq1vopU6ZUiXtVQKPR4OzZs9A9f443LlyAbMgQYMYMiKKIb7/9VnJ1dZUePXrE8TwvtWzZUpowYUKjovfZ2dnYsmkT2gmCmGxqypmbmwtGRkZcSUmJJAgCJ0kS5s+fD9u6Spg0GnpeDhygvWPWrIZpi4Dm9cWLF8WYmBjO1NRU6Nu3L+9XLQBfWFgIY2Njw8mWyZOpxKeWyvtLePwY5UFB2LJwoTRw4EBWXVBVEASsX79ekslkyM/PZ15eXmh/6JD0pKSEJQ0ejEWLFlU9c4WFuDB9uqR8+pSlTpyIR4CktLQUOpw+zd/w8GB2zs5i20GDuPj4eHHu3LkcQOtnamoqjh07hmbNmkm2trbikydPeB8fHyEwOZmXFRbCuI42jAAQGxurb6HHNfXXfn00Zbab0IR6wBgr0mg0/7XvEwQBBw8eFJVKpRQYGMibmZnVXbP2T1FQQFS7VyE4mIzLe/f+2bHatydnOyWFWoYtXEh1u/r66teEg4MDYmJisHfv3ipjWw9JIgXgkhIyQvXtS3btMtzv1MKCMkIxMWTE5uWRmve6dSS+Vr1etZE4duwYnjx5wi1ZsgSMMXAc1zg2hIMDTPPygNhYDBkyRHr27Jnk4+PDWVpa4urVq4iIiMCBAweE0aNH8y2riY7VRlFxMbh//Qs3zpxB0i+/oH2XLuhhoI7vJSxa9BIddtKkSXjw4AEidu5k6ubNEffbb+jcubM4atSomsbUvn0U4Jg/v1Gtwezt7aFWqzn1s2cw+egjmidz5lAN2fHjJAZ19Cg52wEBJPTz228N/v4aaN2aHO2dO8kYex02Ry0c2LdPnGBuzjy++YYzRCWuDxU0S4R/9RXUO3fCZOFCOr/XbYNmAD179uQuXLiA8PBwDKpLCMnBgVggPj4kohgSQgyVw4cNiyAyRnXF5eX073XriD7L84iLi6v0Viqd+4ICYjzk5FD5Rn4+ZWNlMmKXGHhOysvLceTIESE1NZWXyWQYPXp05d+EdeuQ9dlnSGrZkn///fdhYWGB0tJSPHz4kMnj45n9J59gg04ntu3VixswYAA0Gg12794t5uTkcMuWLcNPP/0k3bx5k/Xu3ZucbYC0DyIjqdYxLIzWhdGjqW4+OpqCce7udH82baq3LEGtVsOqsFBCaiqDrS2tSxMnkt7F9u1US60XqZs0idafkyeJph4aSs5DcTEFD6ujXTvK3CUn098TE+l833uPAiV2diRYptGQs755M4353Ll0HR07UkYLoOcqJwewsQFzckJ+Xh4DY1i9ejU+++yzGqUHFY7lyxfq4kLH1mqp5nTz5qrAzaBB9c7lwYMHY8uWLUx/DKVSKc2cObPuAIWlJQVM//1vug8ffUTPcng4re3jx2Pk2rWMP3NGmjtlCgtp00YSPDxYf+Bl4amKLO7dx48x0NoagatXM0RGVhd1ZHjxgmHECODCBfRjDLt374YgCGz+/PmvXkDGjYNy/HiiNAcFwWTAAJprFy4AHAc5AG9vb3bp0iXpyZMn4vjx4/lLly4hJiaGC6ytq1IBs5070Qvgeo0bR/vbxx/j3Llz+PPPP9HaxIQNmTcPNhXsJ5fSUtoPhw6tEpxkDJyxMTrt3QtHPz/s37gRPv37o2vXrnByctI9fvxYdvbs2RrO9sOHD3Ho0CHJwcFBUhUUMPWFC0zx+DEwYwbu3r0LmUyGyZMnc/n5+UhKSmL+/v6NpuLYxcfjg99+Q9yuXdzoXr1gbGysDzawkpIS/PLLL1JZWVnd32tsTGtWeDhd77RpxDppACuI4zgMHDiQ69OnDw4fPsyfOXMGBQUFcHJygpeXF+piGyAvj+yIBpRLwdkZ8gkT8MHs2aw21Z/neUyYMIHdunVLMjMzE729veE1ZgznFRCAoWPHVu3JUVHAV1+h26xZ7MdbtyCqVADA3J884Xo9ecKUCxeiw8CBHMdx8Pf3r5ybZmZm8Pb2RmhoqPj8+XNOo9EwFxcX3IiN5eRnziDR2xvz9B1HDODhw4fgOO7WihUrmhztf4AmZ7sJTagffEP7NNZGeXk5CgsLUVxcjOjoaMnS0lKKjY2trFmKiYkBQL0MFyxY8Hp8UUOIiyPHd9u2+jecd9+lCO1/C56epJCrUpER9uWXFHGuoy6oPigUCk4mk8Hb21tCRR0b1GrKwFpaUkaK56nFz0cf1VC/NohvvyUjdMgQop+tXEkG9bp1tHHq6YmNhJGREdq1a6dTKBSvv66+/Tbw7bfwPnmSqy6a1atXL+h0OtHIyIjbt29fDRqaHs+fP8fevXuFwsJCHgBaBwSIM0JDOfP//Ier9/6++SZlpmvRvxUKBTp37ox2ZWVIk8sRd/o0bt68yRUWFkqTJ0+mPrGnT1f1IW5kW7znJ09iyp49kPr1I4Nw9GhiHJw9S5ndGzeqnOLQUMqi7dlD2a1GKOJWQhAoa9yhg0Exucbg9OnTaHv1KufQps3r14NrtfD4808UKJXiOXNzvDlqFJednQ2O42BhYVG/AnEDoF+7oqKi6na2bWyo7lWtJrr08OHkDN6+TZRtQ2uIXE7rRlYWOVhTpgBOTliyZAm+/fZbiKKIZ8+eoUWLFuTsJSbST72wXc+eVHpScX6pqamIiooSOnXqxDs5OaG4uBiPHj3i3nvvPWzevBmO+raBABIzM2F97hwm/vJLpTFsYWEBv9atARsb5P31F/ooldy5c+cQGRmJCgYfBwA5OTkwN8S4GT6cXoJAAR4LC3KM/Pxobm/dSmUNS5YQi0av/F1YSNnvoUOpfVduLrBoETQnT2Lk779zuHuX6uFDQshJtrGh50ySiPbq6kqOk55i36sXrUkA1VjrBdGaNydmR2goBS8uXaL3H6omEDx+PP1UqcgZMDOjNbKkhMZeL0wXF0f3TaGgkpPSUqB/f3xy4QKuyeVokZ4OFhtLFPUTJ6jeuFMnElPy96dSHYWCstaCQEEsMzNaW0eNous1M6N7LUm07j9/TsKGMhn9VCqBuDhY8jw+6dKFu7hjh8RKSlif3r0Zv307jcvZszQn+/enYHBhIY2TUlnVfULf933VKhJKbNsW7XQ6ICSEwdoa9hkZLCYmBl27doVFbYVwACne3rjr7o7uJ07Q9bZsSU7xwIF0n77/ntbHli3RApUt3ZhWqzUcJBdFCpAYGVEg4+pVejZWraI1raiIxsbYGAEBAczY2Bg3btzg9uzZAwcHB3HixIn1Gxv/+hfNzxcvMCQoCObm5vA6eRI21csW3n+fAiE7d778eWdnWIeG4q358/GbiYkUGRnJUOEPtKgVpD5z5ozQqVMnftiwYSwxMRFnb96UPAYNYp0rKPM8z4MxBhsbG/Su3lqtEeBsbGD18ccIMqDN8ttvvwnOzs6ca339vQWBAoChoTT/791rcDkTQH3cTUxMBK1Wy1+5cgUuLi5iq1at6mYr3b1LAeqGlPno97D09JdU+wGgefPmqGjNWfll/MGDxC66fJkYLGVlwPvvQzF0KGZ1746U+/fRddMmHLG35xARAZ96glrTp0/nJEmCnZ0dncytW+zprVu4ZG+Pw4cPS6NGjWKG1kUPDw/x/v37HYODg5UrV64srP9im2AITc52E5pQDyRJsqhXpdMA7t69i4MHD1b/FQPAnJ2d0b17dzRv3hw3b97E1atXkZ2dzdRq9X/FuAZAWQ+druH1nhERr+zV/FowNaWNPjOTMs137xK1vE+fRmXucnJyRJ1Ox8XHx7Os2FhhmKcn73b6NCn09u9Pxt/27fU72XqYmdHYnD5NhiFAmZJffiElz/79KQvVyPEwNTWtFEd5bfToQUGJ1NSXWmYNGzaMA4Di4mLh8OHDvJ2dndivXz/O09MTRUVF2LRpE3x9fVn//v1hbGwMuVzOYcoUMk4PHiSj9JtvXj5meTllwV4hxmb6xhtov28fBg8ejPDwcKSkpLCIiAi4A1AsWYK7c+aIPSZMaFhIShTJmL13D7lt2iC/c2epVUAAQ0YGjbu9PYnw1KYBWlvT6949yqTPmkX3sTGK8jxPDrsgGBQUagwS4uOlBXfvMtNVq17vC8rKiAng5IS2ixdz5/btQ+qPP4qlpaWVZQiffvpp/ar0DUSNnqwv/5Hm3Pz5RJH+4QfKrH77LQXPvvmmxlpSUFCAP/74QwTA5eXlQfH++yjZtg2f/PwzLk6aBNHZGXZ2dpUtdbBgAZUBXL9Oa03PnnT/qiElJUVKTk7ms7OzBZVKxWu1WpiYmODQoUOCs7MzU6lU3Pfffy8FBgbC0tKShY4ZI83x9a35vL33HiBJsNm9GzYA2rdvj4SEBIiiCE9PT2zYsAEAUUPrLMfgecrIPnhA8/HsWQomSBJll0aOJGc4MJBoySoVZf9Hj6ZgXbNmQO/eUD19ihOffy6888475AFduVJ1DH3msi4NBpWKGDt791LGXaWic/L0pHHUasmZVioNC3WZmpJzDtQsvejcmX4OH171uwEDoNVq8eLFC7wYOxbXDxzAPS8vvD16NExatKAgGGP0vKSk0Dqxfj05/B9+SEHK69fJKbC3pzFZtYoc9CNHiKLfvz8FWwoKyOHUt1S7e5eec1FE15492dXwcOnorVvMRKGQBvTowUzHjqX56OpKzBYTE9o7jIzI2Z8+HTh2jI53/Dg5xebmxNYqKAAmTcKQ8HBYpaUh190dJ6KjJYtOncQ3ZszgAaLO7927F2jTBsPfeIOCwk+fEsPAxoaCLEFBNYLRNjY24Hke+fn5sNNowO7cAWvRgpgQWVm0H0VEELXe0pLuBcfRfEpIoMDGs2fAmDHgd+6Ev4sLWs2YwWJCQsTnFhackbMzBQ1atKC9ytD91SvlBwUBNjbodfgwreNubrSnDRpE+9srNAOMfXzgOG4clr14weLGj8eJEyfQsWNHcfjw4TUWnA4dOvDR0dEYNmwYvL294fH0KZMWLcLOqChkOTujQ4cOIoDXV0Y9ebKqlMsAPDw8+NTUVOnatWuwsLBAx44dDZcx8Tzdc46j/eHYsUY52wAwZswY3s3NDadOncKTJ0+4r7/+GkuWLIHCkAZNZqbBvtR1wtaWgkUNRevWNJ8DA+m5Xb688niuDg5wLSqCpNEg29ERDzMzDXd/qHH4WhT82Fg4DxsGPysrKSEhgX333XdYsGAB6WJUoLy8HImJiWoAZgDsg4ODBQDiypUryxp+IU0Ammq2m9CEerFmzZrc+fPn29YQ+2kAgoODAQAffPABMjMz4eLigpKSkhp1x1lZWdixYwcAwM/PDyNGjPhnJyuKtGmtWFFvr8ZKXL9O9Mb09H927Pqgz6b85z9kJL3zToM/qo6JAWdtDbFLF6gkCRoLCzxXKHBj/Hix3zvvcK+s5TaEhAQyVmtH0iWJqGiffEKZvkYEBg4dOoSioqLKWqnXxm+/kTFayxnR48WLF9i1axeKiooqBZgEQYC1tbW0cOHCl1vcAGQIr19PRoggVDEMCgrISJ0x49WBmfJyMvo4DhqNBr/++qukTU9nHW/exD0vLzyvUN319/cHYwyurq6VvUwrkZ9PTnbfvpQt690bj/z9Efbdd9Kshw8Zhg8nBfL66t/0OHCAHI+4uPrbkdXGkCFEPTx5snGfA3Djxg2EhITAIy0NHTw8pC5fftm4AIskkROyYgWNfevWAGMoKSnB/fv34erqClNTU6xfvx7NmjUT3n333X8s8f/o0SP8/vvvWLZsGYmIlZRUiQbOnElzzdqanMgWLSgTV14OZGVBXLcOjxwdcb99e8iNjKCWJDy1s0OhUolSc3NIFXWqoijCLS0Nuo4dpYkAU/bsSTX8kkTO9fz5tNaMHVujL7IehYWFWL9+PSwtLQWe5yWNRsNNnTqV+/XXXzFu3DicO3dO4nkearVa0mg0XFDr1gjUt74yM6MM6KlT9EwbYFiEhoYioqLuWS6XY8mSJTASRXKu7t2j4Me+fXTd/ftTcOrOHQp6XblCzt1XXzWYBXTx4kVkZGQIs2bNatz9S02lNUAUyZkdPZocTYWCWELr19M5LVtG9zE1tV61eq1WC309sVqtRkxMjJSbmysqlUq+tLQUSUlJKC+nrj7GxsbQarUYMmQItSSqC3p9hR49yOGOiKAg2bVrxAKoFaDOyMhAdHQ09PX1zs7OkCSpUpSs4iU9fvwYmZmZbPny5a8WmLtyhXQmHj6kDLexMWXp8/IoSNGsGVBUBF1CAkJ//VWUXrzgHAsKpOcKBQu6dw9cnz642qqVkH3nDq9ycRFnrl3LITKSrmHfPppXs2dT0OLyZWISJCWhrEMHPPz4Y2iNjcEkCZwowmv9epjcu0c1/N26ARwHSZLw5MkTFFe0ULSxsUFl+0VRpHn28GGl05a8dy/upaUhyMYGirIyKr8KDqaA4vLlpKvg7U3OerNm9GxlZdHa9+QJBba3bKHyjBEjGqb58OJFpT7Bs06dsHXrVgBA586dpdatW7MTJ05IeqX/gQMHwtLSEt5btoDduwfVsmVgvXr98wTBtGm05lRTlK8OURRx8OBBqaSkBE+ePGGurq7SnDlz6l5zO3WiEoa//6b9xlA5WT1QqVRYt24dZDIZli1bZjjguXYt7WXduzfsS3ftIgbd+vUNe39CAq3NHTpQXbg+cFBYSE744sXA6NH44YcfEBAQQMr/DUVJCTF2EhMBuRx5eXnYsGEDWrRoIc2YMaNybG/fvo2//vqrxkc5jvt7xYoVDWzM3gQ9mjLbTWhCPWCMCa/MChlAdHQ0AGD06NGwsrKCVQV9r7bDnpycDAcHB8HX15c/c+YM3NzcJB8fn9fPjubkkBHSmA2wW7f/eUcboMXdz4+yFFFRtGFMnUo/DTm0kkRG8PXrMPn+e6BvX0glJXjh7S39/fbbUrdBg7i0ffs46dKlV9f4GYKXF9E1//ijZgabMXKwIyKoxnH3btpUGxDB7tWrF3bu3Mnl5ubWUDVtNLp1IydQFA3WFTdr1gzTp0/Hrl27UFpaioEDB6JTp04wNzevewz69qXXrl1k1Ny+TQ7K8eNEk5w5s+7zOXCAall/+gkAGeML332XSYMHo7hfP/ReuhSbNm1CYWEhblSIJMXGxsLFxQXW1tZgiYn0HcOGEb2yeXP6rqwsOH/7LXqfO8dw7BhlBRujvD1pEolWaTREt9261WCW++DBg2JqaiqzsbFBQEAA8/X1pfvaSMq7HpcuXRLNTE25sSoV5H5+jZt3hYVUB52YSFTlanNPoVDAv5pa87Rp07B3717+9OnTGDx4cJ01dQ2BXC6HWUkJSleuhPGMGVR6odGQQfrDD6R8npZGjBjGKBjh5oayn3/GL0ZG0rzNm1nzjh2Rz/OSXWYmG6BWw9jICC9KS9EsJwesWTNg7FhIffqAOTszBAfTtXbuTM7CrFlk4L39dg1F6upQKpUYO3asVFZWxvM8j9OnT+Pp06fw8fERjxw5wllZWYnvvfcez3Fc1ZiHhwOPH1OdcpcuVKdp6L5KErTZ2XB79AitjY3RrmtXGI0YQc/BF1+Q0+HuThRxmYwM3agoMnTlcspa5uXRPOvVi5xefXaxDpSXl8PY2Lhx2Yxz58jBd3OjANiIEfTslJdXZUjfe48ccFNTuvbiYsqUGnCuSktL8dtvv0l5eXkMoHZGGo0Gcrlcsra25pOTk6FWq9GpUycMGDAA58+fR3Z2tqhWq7mzZ88iOzsbbxjSe1CpaE04cYLW79Wrgb17kXPlCkofPIDZ48ewOnQIt/r0QUR0tFhWVsY0Gg2Ty+Xw9vYW4uPj+ZSUFDg4OEiMoK8JZ/o2hdevX4ejoyMM6lOIIj3ra9fS/WnblgI6hw7RWKxbR/Ng0SLI+vTBsD599Asp+/GHH5Do7Q37nBzoHj/m3/Lygkl5OYcBA2ht0rOLmjcnuvaZM3R9W7YA06bB7M038WTkSN0tQZDJnZyk0tJS9qy0FMaenoBKBe7KFXTr1g06nQ4XVqyAW06OGN6tG2dsbIwBAwZUXkJlUNTeHkwQcMrSEiOXL4dCzz4AaL7l59N4t2hB1PHwcJoj1tZUo//0KQWxNBp65tasofNvCJo1A/z8IG3ciBtPnwrm5uZs7Nix3J49e1hSUhJMTEyYIAiwsbGRrl+/juLiYhauUEg9e/ZknWSyxtkZhvDgAZW6veJ7OI7DyJEj2d69ewUAvFKpfPWau2IF7eEtWlAw+TWgDwgZGRlJKSkpLCsrCz179qxagwWBAk0LFzb8S/386D7VB0mi0oN16yhYkJ1NwaPVq6lERaWibPeoUZAkCcXFxcjOzsbevXvFadOmNSzQ/+OP5KxX2F02NjaYN28efv31VxYWFoZevXpBJpPBw8MD48ePR3Jysi4hIUEGAKIovt3wi26CHk3OdhOa8AoEBwfLOI6zNlTrZQgajQZarRapqakAAN/a7XNqoSLjzQICAlBaWoq//vqLOTs7w8bGpvEnu2cPGWm3bjXuc5JEjk5CQqPErV4bPj706tKFjCW9WM+ECXT88nIyzMeMIUMjKIg2mWfPwHJy4NSsGZsOsNLSUgDA0KFDGx+ckMspm5+cbJguznGUKTh1iiiKAQGUja9F6UtOTsbp06cFnU7H7OzsmEwmY5s2bYKVlRVmzpxZGWRpFNq3J/GfN9+sMztha2uLIUOG4K+//kJJSYnhGlRDmDGDjEiViqj3//oXXVd9qK6mLgjA6tVgq1fDsls3gDEsXrwYT58+hUKhAMdx2LJlixjy4YecnYMDhubmgrOyIidLT7394w9g/36Iw4bhj6lT0SklBW80NKNdHZ06ARV0umt79yIlJ0fgWrXibWxsUFpaitzcXLG0tJQbM2YMsrKycOrUKZSUlCAwMJAMmrffpoxRraDG5cuXkZ2dXVk/qdPpUFhYCBsbG+h0OjrewDYAACAASURBVDbMzw8WklRVhtAQFBdTwKF3bwpw1EMPd3d3x5gxY3DhwgXpxx9/ZCNHjkS7xtAiy8sBnkf5vHnQpKbCxtMT8pwcet4uXqyay337kvPSsSM53z16UI1gQgL2r14NY1tbZvLtt/CJjQXWrWPVheCs1WrKrl2/DpSUgD18SDWTZ8+SMzhwIDkHzs7kNJ47R4ybZ8/IUXzzTcr2eHgASiU6tGnDYGSE5ORk8Dwv+fr6Mj8/P65CHO3lNOeyZRQsmD+f5ra7O2X+CguJRpudTTTl06cxJC0NCR4eeC5J0tGoKDbwvffgMWJElQBRdDRdR3w8UZ8vXyaDd/JkyjBv3UqBiMGD6Tj1Dn85Glx+pNOR4xQfT6yCHj0oQ+npSWPXogWJoAF034yNaQ4xRgEBtRrIzobE87h//z4uXryIgoICaGltZW+//TYEQcDTp0/h7u4OJyenyslXvWXa2LFjAYCLjIwUz58/z8XFxb3sbMfFUbBm40a610ZGQFoaNDt3Is3TEwqVCic3b8bEPXtw5+5ddBk6lLn36sVSUlLQrl072Nvb81ZWVlJ4eDibO3euwbU7PDwcoaGhFbd4Wc1xFEV6hlasoLECiK69cCEFTYKCaEyWL6d5rlDQHAeQmJgoFRUXM6+AAOj3XBO9NoYoUpb5+HGaQ0+e0O9+/JGo6Xl5wA8/QHf2LKLlcllAQAAGDhzIjh49iuzs7MrTe/ToEe7evQutViu5FxayIKWS81u0CAcPHhRiY2MlAKjOKNVqtUylUvH6LH8N8HyVwKmeveXrS471zZs0x2/epMzn8ePknL/9Nu2XoaGkeTFq1Ku1Unr0QMndu+CPHuUX/PknFBYWeP/992FlZVU9o8sqzhVPN21i4k8/oSw8nBTeXxeCQEymH3+s0hqoA1u2bBFLS0v5Tp06oS7xuEqMG0fBVFGk9SEiotHlQvpAiEqlYocOHYJCoZCuX78uBQUFcV26dCFbSV/i0FC0aEGJhle0S0R5OTETs7IoKCuX03qdlES0+C+/pLaEX39N5wmga9euupiYGBkALjw8vP66+fJySq7Mm1fj105OTrCxsRGuXr3KR0VFoU+fPvDx8YEkSbh3755+0TdbuXKlquEX3QQ9mpztJjThFWCMzba3ty+Xy+UNkgtfs2ZN5b+trKxEVIjy1IX8/Hyhffv2PFAlllan+mV92LGDjI8Kw6LBYIycSZ3u/4yzrYefH7B/PxkLv/9OlDq9uviECbQpZGTQJvX33y+12cnOzoapqano4ODwerTtxYspc9S3r+G+4IxRfaafH21yHEcOo48P1q9fL/E8LxUWFnKBgYGcvb09S0tLEwGIPM9zDx8+ZHFxcVJQUNDrsRTGj6dMSkU22RDatGmDzp07S9HR0SwhIUEcM2YMV7tly0vgOHKAEhKIgp2URMd5FUaMoCyyHsHBVbWJ1YwGZ2dnMqAOH8aioUO5sp9+QmiPHoj44AOpV69eDGo10WNTU6kecvdumDRrhtmZmfjjjz+g1WqlcePGNX68LC1xePJk6VlaGuZt2cKnjhsn3ejZUzQ1NYWvry/fsWNHWFhYoG3btmjRogX++OMPxMXFCR6OjnzHQYPw9Pp15L94AX9/fyiVSiQlJeHy5cuQJIn7+eefBVEUWVFREUfDx0EURab8/nsJ06ezBimaCwIxCk6dome0EQ5z+/bt0bx5cxYWFobDhw9DqVQKixYtqptbm5hIgZS0NGr798cfSLO2lq7rdCzTzQ1mFSUrL4Hj6J7qjdLmzQEXFwyZNw+aTp1geuoUBciCgsjA1wcfTUzo+axN1fzqK+DRI7rm8HAyGM3NiWI8ciQZjhcv0tx64w2ifbZtCyxZgtI//oDp0qX4V24u4959lxyrRYvIad68mRSo//iDxnXBAsqMRUSQAxwcTHXFjo7094AAyq4PHw7e3h6dAYiiyI4cOYI9t25hsIOD2M3CgsPXX5Mh3L8/OSvz51Ng4NQpclzs7at6u3/xBQUD3dzoGuqoldRoNDA1Na1/PufkkPHs6UkOpL09ZfxKS8nZV6nI4a5eKnP0KHDlCkotLVEsipArldLtrVtZ+bFjiOjVC3K5HDKZDK1bt0avXr3gVKHHYKjcxlBAoHPnztz58+cBGi9yukSRAibu7rR2VuvckFBUhPBp0zDgrbfQzs0NrbdtQ/m9e5ilVAJt2jAMGoTmGzdWrhe+vr7swoULyMvLMxhYzsrKAkBsj40bN2L+/PkoKyuDpaUltPn5MO/c+eU2gPPnUy36unU0V7dto/n2/ffAvXs4bGkp3HnwgPfy8sKYMWNevu4DByhT/+efFBCKiKDrHDmS7kVYGFBUBNnEiXB9913B09OTNzIywsSJE2t8TXR0NKKiosR27dpxATNnAkolrADMmzfP4HOrp+rOnDmz7taWOh0xLXr1ovVj7FjKdP71F9GSAwPJgerTh+bQoEEkSPfnn5Td1bc8mzvXYMlBXu/ecNy2TVIcP84wbdpLDDw9jIyM4D5mDJITEsRTKhU3ID+/zvfWC46joFwDgqxmZmZSaWkpbG1tJVtb2/qfqehoClJ0705dMgwIr70K5ubmWL58OR4/fgxra2uYmZmxo0ePslOnTqF9+/Ywe/as8W0oLS1p333+3HCHmFu3aG3r25f2f/38NDKiYOTJkxRAqjXfhg8fLhs+fDhCQ0Px4MGD+p3tK1eodMlAmeHChQv5srIy7N+/H2FhYQgNDQXP8xAEATKZ7M7y5cubHO3XRJOz3YQm1IHg4OA+xsbGP48ePbpBEto//PADAMDT0xPdu3eHh4fHKy3xoqIiFBUV8R4eHigrK4NKpcKgQYNq1KlVzzrUiWfPaNO9ePH12xlt3/5faYX0WvDxIUrg1q1Eob5/n2pHP/mEftepk8GPKRQKaDSa1z9pIyPKav/550tR3hpwcaEs06lTROvq3RtFBQVMkCQGAB06dGC2trZo165d5bkIggCe51+/HGDyZNpcXyHiJZfLMWrUKNa/f39s3LiRu3TpktS6deuGHbNjR8rqFxbS+J4+bZgqL0lk/MfFkbOxaxcZfAEBNbP8evGkAweAGzdgvGMHjDMz4ZWYiOPHjjGftDQojxyhzO7MmTUcFFdXV7zzzjvYsmULS0tLe0kNF6BAVHh4uODk5MSNGjWKVResOXPmjPTw4UM2d+5cyOfNQzudjrVbsYJHt26U5agGd3d3LF68GPHx8Xx6erpw0suL9VqyhBWMHy/9euMGJ4oizMzM0KdPH8nDw4O9ePGCLy4uhre3N27cuCHZ2Niw1ra2sDh0iNU2egwiI4OMqLNnyZCqpqjdUFhaWuKNN95Aq1atcOjQIb6srKxm3/a4OHJCv/mGnNcpU4gOfe0a4OQEzy5d2IFvvoGFhUWVmn91FBSQQxoXV/P3jCFu7VoxMzaWm3roECxHj6as2qVL5IC8ar0QRRLlu3+fnF6OI7rr339TwGX16qr3PnlS+U/NhAn47ptvYBYUBAczM2mGqSnDV19RoE2no7mnUFDGR8/OUKnIGZo8GQgMhGhpiVPnzgEAMjMzxS4vXnCZt27B29sbbdu2BcdxmDBuHB789RfylyzhCouLody1i9YhQSBq+ejRVcZ0WhoFBKrT+BUKcsSaN6+z3EOj0aBeyuv16+Q4e3tTwIHn6Zn74gtyEJRKGsPg4JeOIe7cifL0dGx7+220ychgIz/5BDpbW3Q7cACWr+sAVUAul2Pq1Kk4ePCgVFZWRs/b2rV0//buJcp2BXQ6HU6dOiXNjIpiLs2bA/Pnw/j4cRh7e9M43r5NDKUvvyQnKCSkcv5eunQJ4w1kNfv37w9TU1OYmZnh6tWrOHbsGJKTk9H1+nU4ZWWhZVgYlNWfAYAc+W7dqAZZr/gcGEhMoQcPMGzWLF7t74+HMhmOHDkiWVlZMV9fXzhbW9MzYGtLjh9jFFzMz6c17uJFICUFSatWwebPP3Ht3Xelx3I57z56NAV7agl7BVDWnG7W5s20v4SH1znWGo0GRkZGsLW1fbk2+OBBcq6zsmh+5+ZSttrKioLRCQl0fePHkw7E5s0U+NqwgZhZw4bR3vbrr3QOrVuTEOjChRTEqljDbRwdcbpHD9YxIwPcgwevFgdt3hytsrM5bWYmLm3ahHGff173e+tCXh6tJbdvN+jtCxYs4NeuXSsVFBQYXsNqY8MGeo4ePaLgQ9++9eoa1IaeRq3HiBEjkJSUhEuXLmHEo0evJygrihRIq+5sSxIJBU6ZQoGhIUNqfiYvj57/jh0NMhQkSUJ6ejri4+PhXSshYRD37r2yxamZmRnmzJkDAEhJScHjx4+liIiI2xqNpmuDrrEJBsF/+eWX/9vn0IQm/P8OwcHBPXmeD500aZJpQ8S38vLycPXqVchkMixcuBDW1tavVBEWRRF79+4V7e3txYCAAI7neTx+/FhMTExEeHg4CwsLQ1hYGK5evQqO4wR3d/e6v2z7dqIczZ7duJrX6rC1pYyFgbYU/yOQJMroTJ9OdLeAADIoJIkiup07EyWwTx86NwNjaWRkhCtXrqBZs2Y12gI1Ct7eZFS1a/fqsWOMNtdOnYDsbPT5+2+0nzwZDwsKxOjoaNauXbsaDtA/VpCWyWg8ioqqMmp1wNjYGI8ePRKeP3/O+fv711/bW1REWbR336WIeWYmGSNpaYYj7kOH0jhduUJG2/TpVcrlT5+S0depExms69bRPFQqAcbgUF6O5l9/jdzYWDyZNw8O06eDGchkVfSal06fPo1evXpV3ghRFHH06FEpJiaG9e/fn8vLyxPPnj3LJSYmChqNhrt06ZKUnJzM5syZQyqqJiZEeU9PpwyCmxvNnWqaAEZGRnB1dYWPjw8XEBDAHA8cYD5TpjCPPn3QpUsXDB06FB4eHszKygpOTk5wd3eHqakpPD09mZOTE+Tr1xPNv751ITqanCY7O3JUDLEnGgE7Ozs8fPhQTLt2jXm3b08OYGIiXe+zZ3QPly0j5oKZWWX2+f79+0hKSsLYsWPZS4q0AGWdS0rIMK+FNh07svtZWaLrsmXscUyMmPqvfzGb6GjIQkIMipwBIIrruXPkSG7dSkGGiAiiX8+bRxmVrl1p3tQS4NIJAmJv3ICK51HAcSwqJkYy69yZOXXoQOtAYCDdY39/MkJNTMjRuH6d6ORmZki6dw8XLlxAVlYWysvL2YMHD9CsWTMhIiKCe5GXJ7ZNSWHYvRs2jx7hllIpZX/0EVoGBjLwPNHao6LIMdQ/zyEhlJWvvTb26EHOgp8fPUu1gqI3btyAra0tq26w18DWrVQPPGkSHVe/ZkRE0PM5aVJVOUtCwktG+H13d+wuLoaVkRHmrFkD4z/+gMmQIZDzPNV1T536jwKoCoUCWVlZYsK2bZzXl1/C6KuvKHtcrSZerVZjw4YNoo2NDXr17cs4IyMqD3rrLXJuYmNpbbWwoACbIAAtWoBbtgy3mjWDwtoaHQxkNs3MzNC2bVu0bNkSxcXF0p07dxgAtHr4EE9cXGAXFIS8vDzEx8eL7u7uVaKQPXoAajW0koTNBw5I165dg4O7O2vm4wNjb2/4qFToDCCupES6l57OCgoK4Lt8OQU29bW+eugV1T098duJE7oYjYZzGDgQXIsWmLJ2LTMKCgJbuJDu19WrhoPCCgU5v6/I3sbHxyM/Px/u7u7IysrCk0uXoPn8c1w2M0Oz5ctxOyUFpywthag+fcSIqCjx2o0buHjhArPasEFyMDNj+Pe/aazlcgqyzZwJKScH2itXwEpLwXr3pjk6blyVkKS9PQU9f/8deOMNGBcUIPLZM6kkMVFyjotjRv37G1ZBBwDGwNavhznPI1mrRUhamnjr1i2mUCgarlVSWEh76tChDXo7YwzR0dGir68v3+C9/oMPiDGi3wMaIhj3ChgZGUGhUODatWvodvYsyseNg3FDRWj10OloX9LPlfx8qrE/eZJetc/x0CFiJXzyCTB7NqS33sIRjUYUzM2Zg4MDysrKsGnTJikuLg6+vr5smIE1vAYePybW0cqVDVobjIyMsG/fPiYIwkYAEf369RMad8FN0KPJ2W5CE2ph1apVoxljp0VRNL59+zYqhEJgb2+PtLQ0KJVKMMagVqvBcRx+//13KTQ0lAHAlClT6qVViaKInTt3CmVlZdxbb73FGRsbgzEGX19f1rNnT+bm5oYePXogICAArVu3xrlz55idnZ1hQ3nPHqpVnDv3n2WmZ8wgI6mR0d/XwqlT5JAFBJDBfesWUcjVatp4goMpiu/oSEIwa9eSIVtrI+J5HmZmZggJCcHt27fBGKu7nU9dsLSkGih39/qdJ4CcyPbtATMzmB85gu5eXiwsNxfOLi41Wmb8V6DT0bh0rT+grFQquTt37iAsLAypqak6Pz+/uieDTkdzZciQKlp5aSkdp1MnMvT1OHOGjAOtliLys2dTduT5c8rWvvMOZSiCg4nepnf009OJbbFiBcyXLcPJFi2km+nprEePHgaDAdeuXUNERISkVqu5uLg4ISIiAuHh4dKVK1eYKIrSlClTWJs2beDj48P5+flBEAQWGxsLS0tLafz48eylsQ8IoMzNmjVkdL3/ft3BlOnTAYUCVs+fw7Ke9il49oycx1mz6q7XKy2l8VCpaEwMCUw1BhoN0VtVKrQ9eZK1Wr8e13x94eLmhlhvbzi/8QbYsGHkHNa6xnPnzknnz59nADBo0CDIawsRlpURtf3TT+scH68OHVhMq1Z47OjIuM2bpUtOTvC/fJnU42sHENRqojomJBCNl+Mow67TkaOsf7+jI/0/KoqyUBVGokwmQ0BAADw8PDBq1ChotVp2+/ZtoUuXLjXn89SpVH7y5ZeU8fHwoGM1awZ7e3sUFRVBrVZL//73v1lAQAD8fHw4t5gYOHz1Fbv+5IkUp1CId3v04JIkiQ0ZMoQpcnIoi7hgATmU1dfSNWvI8dUrSVeHgwPRzzt3prWs2tyOjY0VHR0d2UvBWq2WsqF2duTwVFftf/6c1qNRo6padxkZ0XNZqzWfjaMjirZvx/CDB2G6dCllxtPTafwHD6ZgSElJo9osVgfPcbDNzubEv/7C0zZt4DFrVo3re/jwITZv3gxra2vMnTuX41u2pMBp7950zPDwKpX7jh3JSe/WjYJ927Yh3cVFaMdxnIOv7yv3nbZt27IXjx5hwIYN+HvQIGS7uSEhIQGJiYl49OgRMzY2llxdXVl6ejrSMjKQvWULcn/7DXHNmzNTU1PJ1taWubi4AB4eYD16wDg0FC22bWNWWVno1b07TJYtgzh9OlTl5SguLsaLFy+Qk5ODImNjZMhk2Hn0KPLz8zlnZ2ep0MJCyFGp8LB5czwoL0fB5s3M9uRJGJWWErX7xYua2Ue5nLLQrxBj9PT0RGRkpOTy2WcsPS1NeqJWS63CwvCwVy8xd+RIiXXvzrk0b855enpyXl5eXMeOHZnlsWPgtFrm8tNPdIziYuCjj4C5cyEWFEBydsZGX19E2dlJnfbuZUZffknzWi6n9drVFamdO+OuRiM+z8gQnf79b66LpyfLtbaWSs+cYaZubpC/yjnt0gVG06ejTb9+aNayJYuNjcWdO3dw584d0dzcnHEcV5N9Ux1JSeTkN9L3uHz5MnNxcWHW1tYN00Lo04eCe15etCbXV2LVADg5OUFMSxNkR45wm01M4ObmhmaNEdrMzqYyqp49KRgbEUFB6h9+ePk5LS6GOG4cQhUKobBlSwaZjGUaG0u227Zxx2Qy5OTmSqdOnWIKhUL64IMP6i8hA2hfGjy4TpHK2tDpdIiPj9dqtdqBAD7r16/fa/a5bEJT668mNAFAcHCwA4CexsbG0+Ry+dAJEyaY7du3DyqVCnK5XCovL6+0RO3s7JCbm1vj887OzujTpw/a1pOFFEUR27ZtE3Q6HTd79mxW54ZUDWFhYYiMjMSMGTNgYWGBsrIylJWV4UVSEvxmzwZu3gTn6vp6F67Hn3+Sw1mf+MjrQqulLN/9+2RoJidTlPX77ykbsXVr3X2yr10jJ+fHH6nOeNSoGsZZbm4uYmNjERMTg7Zt2yIvL0/w9/fnX9m2pjoOHKDMS0XvWY1Gg6Kiopf7UtbGixd4PG0aSlJSkLZmDYaNGdOw4zUUWi1F/vftM2zo10JoaKjw+PFjPiMjAwEBARgyZAhyc3NrBgH+/ptokYb6bT97Rkbhe++R4I6HB0XUc3LI6dfXL06fTs5URAQ5aNUz1YWF5LSkpVFWaN48wNgY9+/fx/79+zFp0iR41TLiRFHEt99+K3Xv3p1xHAeFQgGdTgdnZ2eYmJiQovnrMjYAyr7HxxN9+fffa4q96aHvR3727Ku/KzycHNTaVD89bt2i92Rk0Bg2UFjxJUgSifyEhFCv6/HjyYnq1w9Hzp8XE+/d48zMzFBWRu1Ovby8YGpqitTUVKG8vJyXy+WSi4sLS0pKAgD07dsX/Qz1dA4Npefx7t36z0mlQnnv3jjTvDl8vvsOnosX01zRZ1NKSyn4V1hIhn/1zFhdokDHj9OcXL+eShRmzqzh6MbHx+Ps2bPS1KlTmaura1XrnKVLyXjWO3+rVtHxli0DAGzfvl1QqVTc+7NnM4SHA3v2QDQ3R2z//pKVvz9LT08X79y5wwYNGoQOHTowvPkmObcbNtQ8z0ePaHxOnHj12GzdSs/Uo0eVn9+8ebPg7+/PB1SnbD5/Ttf45pv0HNWmhV6/DsTE1FQ5HjGCxMhqZcjLyspw/MgRyfjkSTZu/34at7w8csy/+aZqzU1NfT0tjtmzob19G2tGjIDEcVi5cmWNP69evRrm5uZYtGgRlT6JItC2LcQZM8CZm1Mg5eJFosOXldH6PmoUrTH+/vjphx8wd+NGWIwZQwGNV51jZCRUX32FhBUrENCtGwoLCxEVFYX4+HhMmDABrVq1wvr16yWdTieZarVS+7w8PtPVFeVmZmJWVhbHVbTiAgCZSgVWXo63t29HrqMjTo0Zg2K5HDzPg+d5ied5SSaTSY5ZWeh5+jR3aNYsKBQKZmdnBzMzMygUCigUClhYWODUr79idmkplIsW0Vp34wY5vvo5/O23NKfq6vZx8SKwaBGCJ0xAv4sXUejrK/JDhkgjRoyoO/qwfz9yjx7F0Q4dhHlffEHvKyigEovERNy4cQPXTpwQ5y1bxu3evRvNra3F4Z6eXKGVFdI3bpSEqVMZYwwhISFwc3NDWloafLy8xILMTEwrKOBUmzdDplTCvHdvWv8NZeXnzyeWQkYGcP48RFFEcnIy4uPjxXv37nEA0KVLFwwaNOhlx/iXX+i6jx6t+34bwPnz55GQkCCq1Wpu+PDhor+/f/3ZhQsXqvRW1q+nQMM/xd9/Q5OQgEve3kJUVBQ/ePBg9OjRo2GfTUmhNb1DB5oXc+caFtlctQoqAN9ptZCMjGBmZiYKgsAgSZibmcmKra1xrW1b2Nvbo1+/fg0LPjx/TsHxP/98tWBeLaSnp2PXrl1gjD394osvGpnNaIIeTc52E/6fRXBwsLVcLl8nSdJIURStXFxcylu1amUREBDAai9e165dQ2RkpOTj48OioqLA8zwGDBiANm3awMjICJYNVLvcuXOnqFar2Zw5c1hD+1OKoojt27dXKp7KZDLJ5dkzqcTEhOWbmDCR5+Hq6iq6uroyrVYreXh4cC4uLg0+JwBkzHftSkbsfxMpKeRQDx9ORmTHjuT8fP45bXynTjXMIZEkMkQ3baKorKcnGfnVnO4jR44IGo0G1tbWfFxcnPTpp582zEMTRbr2EyfwlDHs3btXUqlUTKlUCoIgMLVazTHGJLlcLnXt2pVzcnKCq6srzp07h6TERPhptfDZuRMOP/wA2dix/93a9+XLyWl9770GXoqII0eOSElJSZXX3rx5c0yePJkyDdu2kUFYlyiaTkesgilT6J5ptWQctWxJ9YBDh1Jk3MvrZad1+3aimvftS5+tlYm7du0awsLC8OGHH9bIeoSFheHWrVvS+++/b7hH+H8DOTkUqNmwgRzL2oEYQaC5pP9pCCUlJNz3yy+GRbG2baPa96VLG957VY/SUnKKBIEEuubNI0eysJCcvWrnpNVqERcXh+fPn4tWVlacXrUZAHr06IHIyMjK/+v7X7/11lsvBwIFgZ6rRrQUe/HsGXZ89x0mxMVJ7h9+yLB5M9Ef9e293NzI0av+DISGEq04L6/uL372jNaGxEQ6nwp2UEWPXajVasyaOJHGf+RIUtGvjjt3aF5Pn47Mx4+xb9Mm+Dx8CEdRhFRaKt319hbdR4/mX+pFe/Ei0bR//dVwEOb4cQocLV786oHR6Wh98ven9cTMDBs2bNAFBgbK/Pz86D3nz9PzvGMH0LEj1Gp1zR7FV68SA+PGjarxKy+n7HlWVg0DWRAEfPPNNxBFEZ729uK0HTs4nD9P41ZaSlluvfBk27YUbKrWduqViIykMoDBg4FOnbD599/FFy9ecB9//HGlUX/8+HEkJCTgw4ULYfH4MRAbi9RNmySWl8du+fig/5dfwsLXt6plmVZL679WS2v4rVs40LEjAocMQfNhw8ghSkykwGrtNeDrr+l5qnX+Bw4cwL179yCTyWBiYiKVlpaySZMm0Tz/6Sfg1i3ofv0VBQUF4DgOjDEwQYBlmzZQ//wzHnXogJLgYGQrFOKI4cM5bsqUmscuKqKATmZmnYGAVatWYeDAgVJPHx+Gn34ixsbz5xQ4Cgl5OZtaWlpVkjBxIs3j33/HRrlcLCkt5TiOg1arxWeffWb43ly5AsTGoqhnT/x47hzGjRtHdboqFfD117g0YIAQGRnJjxo1Cj6rViEpLU1S//orc3Fxwa1ly0S/M2e4fZ99pisvL+c8PT2lMWPG8GvXroVGowHHcdDpdDA1MREXtGrFWX7yCc3H7Gz6/i++oCw9x9EczsurYvFUg06nQ3R0tBQVFcWKi4thb28vzJ07JO/Q7wAAIABJREFUlzc2Nibn3MSEWB2NWOvVajXu37+P0NBQsbS0lBs4cKAQGBhYPw0vJ4dKm1q3pnvz5psNPmadOHCAGDojRuDKlSvipUuXuKVLlzas33heHq2RU6ZQ2UJt1oskUbBm8mSUr1yJK4WFuHnzJoKCglAZtMvMpGDHrl0NCsJXYu9eeh7nzm34ZwAUFBRgw4YNKkEQnFauXFnYqA83oRJNznYT/p9EcHBwOyMjo3BfX19F165d5XZ2dv8se9YAHDhwQHr69CnmzZvHGtyqqS54e5NTtHw5ioqKEBISIul0OiaKIrKzsyWNRsP8/f3h5+dXqUT7fxQnThCNeM4cii5PnkxG04ULZGRMnvyyw9NQXLxIjs2IEbRx13JwHzx4gH379kEul0scx0k9e/bkXjKya2PZMuQJArZaWSEwMFDs0KEDl52dDTMzM8hkMgiCgNzcXOnmzZtSUVERKysrY7a2tuLIkSM5Nzc37Fm37v9j773Dojq3qPH1njND7x0EKSIIYgcrCmJQI4q9m9hbNPHGaEyuscVyNdFEjS2xRWMLKlZExYqCKCKI9KKioDTpdZg55/fHZigKiuXe7/d9cT0PDzrMnDnlLXvttYvQef9+znT2bOgPGNC4Sv+2UCphZ868FYnPz89HVlYWEhIS8OjRI2hoaChmOjjwaN++af2lBaE2n1tTk3JTb94EJkyobySJIkUrzJpF4egTJry2x+uGDRuEAQMGcHVbWP38889C//79uTb/i3oBKSlUUfvgwVerycbEkFGfl9ewcR0ZSeTl5QrxOTlExkaNIsLYQIG3BpGdTccbOpSML6mUyHpExFsV9MnLy4OWlpZSmQNAxiljDJs3b4ZMJkO3bt3Qtm3b+tEa//43kcnY2CZ9T2xsLE6ePIk2Dg6Kfps386pr1lAdg8GDiTwMGFA/91iJkhLIIyIQVFaGwsJCCIKAnJwcRbVnRXylurqdHZ56eAgXevTA0KFDuT9+/x2j09Nhp2zNVX2N2dnZiImJgaenJ9VI8PFB8dy5eLBzJ4xiYiAMHoxHzZsrtNq35+RyObtx4wakUil4nhd8fX25Vvb2lHtvaUm5rg3h0CH6e1MrD0+cSFE7oaHYtGmTvE+fPhIXR0ci8/fuAYsXI1dHB6dOnRLS09O5Fi1aiNbW1ujRvTvDokVgvXqB1U07yM2lceLsXO9rBEHA6tWr4eDggJEjR4KbNIlUfeW8Skqicz53jubwjBlEdF4XUq409Pv0IQWsumjkixcvsGXLFnTv3h19+vTBjQsXEHP2LMZlZ0O/vBy39fWFnPJy9tTcnA02MEDOuXMwOnkSzcrKaD415GTJzcXROXMwzNISfPPmtL45OVG4/vr1ROYMDYmcu7qSI++ldJqdO3cqnj17xgOAr68vdHV1YWNjQ2OhoIBIxZEjNK/kcpqj69bRfai+TwEBAcgICECfsDDRfO5cpuHtTWkBSkRH0/pXN7WmDo4fP46YmBgsXbq01n7YsIHWCH9/cu4p24ddv04OqaIiciq0bw9oaSErKws7d+5E165dxdTUVNHX15drcM+OjyeH55AhgJcX7t69iwsXLsDMzExhrKIC161buX2DB2Pq1KnMxMQERYGB+PPiRfSaOBHnzp2DlZWVMHbsWE4SGUlpGHFxgESinI84ePCgWFZWxhYtWgSpVEr5whcukLPvyBFao4YPp/oEPXrQnpCVRa83kIIlCAIKCwtx9OhRobCwEAMHDuScZsyglItNmxofhw1g//79ePToEbS0tPDFF19A/S2UWchkFCW0fz9FXb1rfRclvvmG7BdXV4iiiFWrVqF///5wa0K6F7ZsoZxpDQ26/ykptbUgBIHsJQ0NckhVIzQ0VIyIiBC+/PLL2nXy7FnaJ16KNmkUcjk5HQYPblL197oQRRFr1qyRy+XyvsuWLbv6Vh/+iBp8rEb+Ef84rFixwk4qld708fHRb9eu3X+XYVcjICBATEtLYzNmzGh6T+SGUFFBKsWdOzVKjI6ODsaNG1f3OlhISAjCw8OF8PBwrkOHDoKhoSEXGxsrenl5MfuGVLnly8kI2bPn3c+tspLIg7ExVSx2dSWDYvVqWuhXriQj7m1bZrwMLy8iTDExZHycPUse+A4dgGplgE6nkuno6LDLly8jJCREdHFxYR4eHggICICPjw/qVrTGd98h7ZtvYOXoqPDw8OABvBJGbmNjw9zc3BgApSpVwyrGL1jAHbOxUegcOsT3OHcOOt9+S9f5viq3kRHdx1u33irEX19fH/r6+mjVqhUyMjKwZ/duHqtXU7jm+PFvPgDHkTL99CkZRzY2pOZ27Ejhodu3kwE2axZFKhw+TM9DmWfaCCQSifj06dOaftFFRUUoKyvj7D6Uc+JNsLcnA1pVlfK6//OfWsXMxYXUx4aItkJB0QUnT9Z/PTWVejD7+NDP61RimYzu6+zZ9Hv0aJrHn35KoedKIvSWbWoaqhGhpqaGoqIiGBgYID09HTdu3MDNmzfx3Xff4cSJE+jXrx/0lixp2lioRkREBJydnUXfoUN5jBhBxvaECaS6KlWjhqpyq6ggeO9eMb55c6aoToPp2LEjX15ejpSUlFe/KCoKN/38xOZ+fnzpjz/CrE8fGJuaAgcPoqS8HGfPnkViYmLN22NiYgQPOzvORUMDGD0a5a6ueP7LL4LHwIFcqzq9uXv06IHi4mI8fvyYJS5dCqt796CZmNi4U0MU6Xn/9FOT7xG2b6e83adPAZmMqQgChYs3bw5s2oTcigrs2rULtra2bPr06Thy5IiYlpbGiatWoVBVFckWFvBNSYG2tjYyMzOhf/iwaJWVxdjevfW+huM46Orqijo6OozjOCISf/1F469lS3KCPH1KY1pTk8LbY2LISbl9+6uqYnk5zWVRpPld557o6OjAxsBALPz9d3Zu40ZYZGejO4BrrVqJaa1aoVhFhfPx8YFHy5bQunMH+adPo6SkBKWiCDZ3LuRFRa9GWRkZIb51ayi++44ekKtrrYM2JoauJSKCziMqiki3XF5vfsnl8pp/m5ub1y+SqadH0VMLF1Iod3Expb0UFtYQ7dTUVNy9excwNcWhgQPZJA0NaEyaRA6Tzz+n4/j7075Wp6VnXTg5OSEmJqb+i1OnkpPh6FG6DlEkJdTGhqI+5HJyNFenWTx48AAGBgZiz5492SeffNKwPZKVRSkuPXvWrA/29vYICAgAz/N8qVwu5pqZsQkTJsCkWu2MNzAQJKLI6QwfDp9jx9BOWcfDyak2cubBA3Bt2sDU1BTz589nmzdvVpw8eRIjRozg2ZAhVOE8Pb027ejYMUoJmDSJyKKLC839SZNeOWWO46Cvr49JkyZxN2/ehP+xY5i/cyfUm9Cj/mVUVFQIADiJRCKGh4eLPXv25JosjuTlkSNq6lRy2rxL9fTaE6FnunYtAChr7YjXr18X3dzcXr/ZR0dTeohy/X/6lCLFtm8ne+bFC5q7yrFXDVEURZlMVv9ivbzI+X3tGtBQelBD3/3w4VsTbYAiqeRyuYTn+QsrVqyQMsZSly5d+obiJh/xMj6S7Y/4R2HFihVqUqk02NvbW+9/RbQDAwMRExPDJk+eDD09vfc72E8/0SYeHf3at/Xo0QM9evTg7t69i5s3byItLU3BcRx/8OBBDBw4EO3bt6/XYgzdu0N0dARE8e0V/uxs2jAGDiSFYtEiytlcsoRCbm1tSdGu0y7mfZGXn48/g4JQ0awZXK2tFb0XLuSlqqrA77+jtZMTrL/5BvHx8QgKCkKzZs3EXr16sRMnTiAiIgKiKCIhIQEtWrSAo6MjOnXqhEJBgHpcHNpYWDRJTnw5ZIzneYwePZqPsLcXDx04gKm3bjHpyZOkprxG6W0SunShjfId8+kfP34s6nKciJQUrkm5m5s2UYh/UBAZFyYm1J6pqoqM9OJicmxs2lTbpsjOjgzBESPI8RESQmO0b18y4r28gOXL0XfmTP78nj3oZm8PbTs7aGlpQSqVIjw8XPT09PyfzMcaZV9JNENCSDnU16cx2q8fGWd1jZgTJygcVxm2p1BQ+OS5cxRp0VjKRmwskey4ODLyjh0jB5SzMxlYb0ms3wZRUVFieno609XVFQsLC5koiti4caNYXl7OHM6cQYd27YiMVKOiogJyubzGCZWcnIyAgADR0dGRJSUlKQoKCnhl9MG169eRGBKimP7gAc9t2UKkaM4cUmTqrB+JiYn4+++/Mf/0adb5wAHIOndGZGSk2KtXL1ZcXIzU1NRXW/no6KBn//58aH6+QiU6mveIjYX2ixeIf/ECfkeO1LzNzc0NvS0s8HTXLlbs7y9eU1Njpb16Ia5HDyzo3/8Vw1dFRQWGhoYwrKhgVm5uOKuhAf3Ll9G3b9+Gb+Djx6QQv8381dCgn06d4K1QsOZ//03ORm9viIxh3++/w8HBQRw2bBgDgPnz53Py7Gwodu0Cf+0aikNChGPHjrGqqiomCAIc09KYpoYGBjawLhcVFbHY2FjUVCAOCKBQ7c2b6f9SKSnEn31G1yKVknNIJqN/Kx0jUVE0p11cKIVBuS88fQpERkKalISJv/3G8kaNwvGCAsS2aQNVMzOMGzeOeUgkkEgk0NbWpvPr0weP3dzE+/v2MfPcXAgSCdJ//RX9+/eHhoYGFApFTYFRgNRPqKnR2tajB6l7n35K13LgAJ3Hli20n1y9SlEDlpbAggVwMTDgXNeuxU/ffQfJihV0DQcOEIH57jtSxnfsoDGZmkqOtDqRnHU7RvBqakg0MhKttmxhePiQHGGrV1PU1Gv22lYODtDNy0PCvn1wun2bIhG6dqV7p6FBdVCeP6c5UVJC/751ixTMhQsBdXV0f/AAsXp64qYlS5hzr17ygQMH1rfLy8rIYWBhAYwcibS0NBw9elTU1dUVOI7j+/fvDzMtLYY1a+qp8rm5uaJK8+Ywc3aGXd2WUFpalFKTnk4Ox4yMmrSNadOm8b/88gt+/PFHDB48GO137iQV196e1r6xY8nB5u9P69msWRTZ88knVHuhAaioqKC3pyfajRuHCIkE7tV1Fd4G+vr6QnFxMdq1a8ddv36dGRsbo2501GthZkbnmJNDDp2KChpz74L794kw16lHYW1tzZKSkl4fIlxSQuNC2UITIAdccjLNQ1NTegaPHtX7WFVVFW7evMl98skn9Y+noUF7a0QEHfdN1/Pnn43XGXkDVFRUsGzZMigUCmlwcDCCg4NbrFq1aoFCodiwbNmyj6HRTcRHsv0R/yjwPP+FlZWV/hu9kB8IgYGBuH//PqZMmVLjcX5nREcTCfjXv5qc7+Tq6oq6lXz9/f1x8eJF8c6dO5gxYwbjeR4xMTFIzs9XPI2P54VNm0Q3NzdWUVEBmUwmPH36FNra2uLo0aP5V9pZRUaSqnnuHHnrra2pkM+8eWToxMZS4aCmeF7fEjzPo7i4GLq6umJaeTn7ycsL7oCi7dq1vE5lJbSmToVbt27Q1NTEyZMnmb29PRYuXIi8vDxoamoiODhY8eTJEwQGBvKBgYHgeR727duLXSws3ovwderUiSUmJgqHKyrEz42NOfj70yY6ceK7t2Xr3JkiAoYPbziv9A2QREaymatWsbrE6rWIiCBjwtOTSOSDB0T4s7MpHDYtjQzjIUNIQVKiqIh+r19PagJARpi9PRmh167BaeNGcBcviuzaNYbwcFQYGKDV+PFiZxUVhqFDidReuEDEt3Pn1+dQvy/mzaPfPj70HadO0TNyda1/Xco2dUuW0P+Tk6nYmoUFGZgvE+1798jIX7uWDKKpU8lA8/Sknu1NrAT7rqioqEBgYCCio6PZuHHj0LJlSyaXy5GSkgKe51lMTIyQGx7OvRxSvGvXLsWLFy94NTU10d7eniUmJsLCwoJFRkaiY8eOrHXr1jA1NWXHjx8Xk+Pi2Ij9+/kTPXpAceaM2Pv6dWZsZUVjpW1byOVy3Lx5E2FhYRBFEeo5OTXOPW9vbwaQEaempoZ169aJenp6wsSJE3mlE8vy2TOMKi3l43/6CeeTk4UJAQHcmUuXxKESCbuvpobnWVkw/+EHsObN4TBzJsPKlcgTBIgDBqAwKwvFxcUNVwnesAHYsQMmsbHwyMvDvn37UFBQII4aNerVyXnr1rvVsBAEYO1aGH/2GVeyeDHUvL0BjkNZaSnKy8sxZMgQVve9kl9+geTqVcDaGuPqtHgsKChA4pgxCDE3x+nTpzH4pXxTNzc3hIWF1b6wbx99d0YGjTOAimYlJlKEjJsbOT1PniSidPcuzctRo6jA3IIFREh++426ELi7U751+/bAw4cw4Hl8mp6OgwcPonPnzo3uZz5PnzKfQYPIwG/XDn/Z2gohISFMIpGI+fn5XHVFfFFHR0eQSCQ0KJREKCaGvlPZx9zNjQpoLltWG1p74wZgaIiEvXsVz3x9JQYGBtDu0YPINkCpHEZGpAZXVlLEyKNHdN0nTtC/27SB7bx5WDRuHFT+8x+c9PISM4KDBUydyqN/fzqfOXOoH/yxY+QAMDQk4pudTcT98mVwjo7wvH1bNBBFBm9vckJaW5OCfvw4VZm+epVCwFu1ougugPZOQQCCg1GRmAitXr3EaVu3ovT+fR4qKuS0njeP1qPjxynK6OuvUVxcjMOHD6Njx45MKpXynTp1IkW/pOSVdYjjOKZhZgaNM2coYq137/opLpaWFIWhokJRKZs3Q8PaGvPnz0dISAgCAgIQAGCapSVMXVzIUbBuHa1pUinVTzh3jtbA0aPpmMOHk5PipSKYrKoKMh8f4WpZGSe9fRtNLl5ajZEjR9ZwlaioKIWfnx+/YMGCpkcJKhSUCuDlRcT0LSJ66uHp01fqdRgYGEChUDS+uefk0H6Sm/vq/l1WRgT+jz/o2Rw+THvL0aNAHacU39Ae6OVF4+TsWXJyN4akJBp/bxm6/+phkhAcHAwAUCgUPwPYAqDivQ76D8JHsv0R/yhIJJKvPT09356xvAOqq2di8uTJ70+009MpLDkysuk5oQ1g2LBhEASBrV69GqtWrYK1tbWYkZHB+iYnc4POnsW5PXsQFxcnSCQSjjHG2djY4N69e+LTp09hbW1NxCMwkMIUlSrmli1ErpYsIaJkZUXKwrvmZDcBampqaNWqlZCeno7p06dzeXl5CAgIYHdUVIRmMTGcwZ9/Cs7Tp3Mp7u5ilYUFy8vLg5GRUU1oeL9+/XiAPMeMMWpHJYoMHTqQulm3Hc9bwsvLi9u5cydihw1Da1VVIl4zZ1Ie3zuQZRgZUShocHCT+5LWhWn//jgUFydOVlV9Pdtv145I/f799V9v3ZoMzl69yEhR5rk2Bk3N2rZY335b+3pUFAAg8vvvcauiQjA5d44rHz5c7P/vfzON3Nzaqua7d9Px3dzIqEtIIOKzdy+Fza1eTYS4b19SlXv3fr9w/YAAMla//54cCQcPkhJ98CBdb0gIGfgzZ1KEweDBRFbGjiVjODOTznP8eFIZHB2JMDBG8/a/XAuiLuRyOfbu3Yvs7Gzo6uoKLVu25ABqqdWqVStkZmYi9+JFLrljR3h/9VW9z1ZUVLBx48ZBFEV25coVwc3NTfT29lZaeRxAxeyy791jCy5cAHfsGB5lZyvCgoN5E2treB49SqTNwwObnjxBSUkJPDw80KlTJ/D//jeN/To5howxzJkzh+Xl5eH8+fPs77//VkycOJFHeTkwYwbKZ89GBMcJRRIJt23wYAz49FO0nT4dbc3MIM/MRPbkyVCbMaOmcJgBAMydi8+UUQp1IQgUhtu7N6mVKiowMzPDzJkzsXfvXuzatUsxZcqU+g7FR4+IcL4NCgrIcA4Jwb4vvxS/XrOGQVcX+PxzPHv2DKqqqgJX90v27ydS2EA3CT0dHXRJS8PDAQMQFRUFW1tbsU2bNjVFBB0dHREWFoaYmBgqkqWqSuRu3z6aa8pxp6JCY3nrVhrL/fqR0jt7NqnZv/xCRGDhQiKJJibklKhb7b0alpaWEARBDAoKYt0bi1SaP58I5+bNgLo6PjMwUF4vS0tLw7Fjx8SSkhI2bMAAnouOpnsQGEihytOmUdTH5s30DBMTac7PnUvOvAEDAFtbGoclJZLc9u0hy8tDhp0dalJRfvyR6hGsX0/tzz7/nBT/adPoWgEIy5eDa98eagBK4+OR0Lw5GxkYyOedPAmD+/dp3dq/n9aEa9dIzXVyInLaqhXtd7NnA66uuGZgIPTu3Zs3rVvpetYs2qc7dKCx17UrEe1Bg+h1ZVHQQYPgl5GhyMrK4s/u3i0O69eP4dmzWuLs7EyRCHPmQHRzQ76Kitjd3l7s1aEDBzMzmlMKBT3jL7+s9xh4nqfIAYAcHwoFre91oaZGDgm5nNbsykpoaGjAu08f9PrpJ6TI5UgYNUowXbaMw5Yt9XOEKyooNN/Kiu6TIFA6gpEROQgqKymfu6ICmDcP5lu3ct5RUcL169fh5ubGveK8byL+9a9/8WvWrEFZWVnTyTbPk22iUJAzqrHuCG9CdnZtjnU1tLS0UFFRwR4/fgybhqJgBg2isfNSKghEkdpCduhAcxMgh0BsLKCiguKhQ3GmY0dBznGcsuvEK1i4kHLoPTwofa8hHD9OY/U99yE/Pz8AAGOssyiKj5ctW/aRaL8FPpLtj/hHQaFQ6L9VcY13RFhYGCIiIjB58uT377+cnU2GR1wcqWnvCY7jMGvWLFy/fh2CIDBfX1+0adWKQS6Hr7o6w0thnXl5eeKhXbuYY0WF0DIrC62ePOGkGzeSxzQri6pzTpxIhltY2CuLfnl5OTIyMpCTkwNVVVWUlZUJT548EaytrfmcnBwhKyuLdevWjbVt27ZJu0FmZib++OMP6OrqitOmTeMB8i5/9tlnHACUlJQgJjqaK3FxgVt0NLMNDUVQZqY4cvNmJqnbjgigQjBKMEbGwcmT70W2zczM4OPjg/Pnzytaf/MNj//8h7zXbm6klAwZ8vYb3+LFZOi8Ldn+9ltYFhXhefPmrMYofxlhYUReq4lSPSQkkNEZHU3v2b278WJSTYSvry/75ZdfWEZGBiavXMk0LSxIidu1i95QvakDIONfU5MMTmWOZmoqheAVFlLqQmEhGR23b9O1DBlCxLh7d/LmL11KhrqGRuM51VpaFNp54kRtm7mcHCLQxcWkfv/wAxlqwcFEWmJiiFgdPUpq99Ch9KMMt/w/gEuXLikKCgq4yZMns+bNm9ezZs+dOyeEh4dzs/39oftSjmVWVhYqKys5W1tbSCQSODg41PtsYGCgGBcXJ7DsbH7qo0eQLFoEtGsHb4APDQ3FdS8vdN67FxoXL0IMDoYwZow4bNgwVlP0ztGxwVBHlWrS26dPH+7woUMCVq6sqUmxd9cuBYqKuFmzZsE4Kgrc0aMMvr6AgwMkiYmw2LixNjpBiT59yAl482Z9B8zq1aQAXbtWr6q3np4eZs6cyfbv38+2bNmimDVrFlVNlsspbeJtchwTEqiOAWPAyZOQr1uHwrt3YWBhARw9imRNTcHExKR24ivX9e+/bzh6Qy4H9u7F6M6dERoaihMnTrBTp05h+vTpMDMzqwnHbqZUsQEqZtVQoaZBg6jegr09OamGDiVl+6+/aA7NnUvO0VatEHTpEmylUtg3MleUJCk3N7fh9ohmZhTVcfcu1SSoA+vmzfFNnz7s1tq1Yv758yzOygouI0YQuVfeg8JCmsvLl1MUyPLlpPitW0ckdvVqiKIIuVyOTp06wcrKCrZ1HdC5uZQCk5pKzpUJE8jR8tNPwFdfId/WFlvi4iBJShI9PDxYxvz5qIqLw51//QspKSlYIgjgtm8nNTIjgwja+fNE3u3sXmmbVFlZydVLD/P3JwLUvTvdA44joq2lRWvRzp315sKUKVP4O3fu4Pr16yw0Olrw8PDgsGQJOQB79yayOmcOsnNyUJCaiu5Pn3KYM4eeYUAAOfTmzaN7mJtLa5a5OTiOYzVkOziYyF18fG0RPSVUVek4okjj0dgYOHsW/MiRuJacDGsrK1rPliyh9/n40OdGj6bUoYgIGssmJqR0A7TfFRTQ55YuJWeHqiq6dOnCBQUFYd26dWLHjh1Zv3cIbc7Ly6txkr8VunWjfSEigiIF3qV2zO+/E2lvAMpaMfVQWkrvfzl9KzycokyOHq1v0xkako1QWoqs7GxRJyOD62Vqiq4v2S01sLAg5+GaNeRIevmeFBXR+HnPFL7w8PCafy9dujT8NW/9iEbwPwml/YiP+P8LOI5bvnPnzvKkpKT/2nckJCTgypUrGDNmTP2iLe+K3r1pMf0ARFsJY2NjjBgxAqNGjUKbNm2IUDQUZpydjTEeHty3f/8Nj9u3ubBOndjWwYPFLDU1KJYuRXn79uKLX39F3L59uLRunbh2925s2bJF8Pf3VwQEBAjBwcHi6dOnxYMHDyIqKkoRHBysuH37NrS1tSWRkZFCcXEx43meu3PnjtDUcz98+LDo5OQkzps3j9fV1X3l71paWujavTucv/oK5jt3os2sWehx8SJL37aNQt5e14Fh/nzasPLzm3o6DcLU1BSlpaV8QEAAtv7xh+KxkRGFfJ0/T5viS33a3wh7ezIQkpPf7nNubpB4e6Nbt27i+fPnxZKSkvp/F0VSak+coN9KozElhSIW1q2jMNKzZymM8/p1KuQiNPlxvYKgoCCRMYa+ffsKFm8a09raZKy6uNS2LNmzh/JQ9fRISVFRISVLSdadnGrzzA8epNfGjat1JNjZERm7f7+2sE9ICBnVixYRaTp3jo537RoZaAApDgoFqVqrV5PyuWwZGbva2vS+941geQ/cvXtXvH37Nj9q1CjWvIHqwJGRkRwA7Jg1CwplSHw1QkJCYGJiIkgaIFjJycm4c+cO87Gx4b8KD4fuwoUUQlqNiRMnAgB+LivD6S5dcGDwYAzbtYs51yUlkyc33E+2Ghb6+jBIT+cKExKIFKmooKSoiA1NUIT0AAAgAElEQVS3tmamEyaACwsjVXPpUiJPK1fSM8vNpXXx+XM6kLk5PSNl0arCQhobkyfT3G/A0aqhoYGpU6dyBgYGbPPmzUJRURGN/2vXSKVrCpQh523bQr54MU6dPg2ZTMZU9fTI4J09G7khIczZ2bnWGl6/nlIOHBwaPuahQ8DateA4Du7u7vj+++8hCAL27dsHuVyOgoICiKKIeqqXREJrRcuWFKJaFyoqZJifPk1K1/jxFIpqYQGEh6PwxAnc2LoVcWfP4uLGjQgPDW1wrfz666+Zmpoatm7diga72RgZkcNSQ6OWVObkUHTI4MHAwoXoNn06w8KFwvHWrbEyMREVdYnK+vVEjBwc6PkpsWgRzbcdO+ARGQkDNTWRMYa2bdsS8Xr8mJRgZerLixe0PgBA//7I7NIFt//4A+eWLxfMzc0FFxcXMSgoCHFxcfD09BRtbW3RUqEQuCNH6N6vWkXjKi6OxtO4cUBcHHJzc3Hq1CmcPn0aV69eRUVFBavZ50+fpnWyVStAKkWxtjayHR1JYZ4/n1JVqotrAYBMJsPGjRvFy5cvQ0tLS7SzsyObvF8/Csnu3p3C0B0dETdrlnBm6FB2cOJE4dnvv5P63q0bOUnU1enZp6bSfdPUhObTp8wgKal2rd65s/EaEUuXkup98CA5Cq5fR0GHDnihrY3u3btz0NMjZ2RsLDkjleNp+nQqHnf+fP3j/fgjsHkz5HfvomrnTpz89luknz4NoaICCxcuhJWVFUtISFA0fDKNIz8/H9u2bQNjrGFHz5vAGO0DynoAb4OEBKrf8JJNp6+vDwsLCyH55b3Zz4+cwjo6r647S5eSM61ZswYd7+Uch4N9+zLO1xc9i4uRO368+NPSpcgKb4Dnjh9PEVc3b776t1u3aFy8hx1aVFSEc+REGQ/gAxi0/0zwy5cv/z99Dh/xEf8z9OzZM/TKlSuRjx49GtK9e/cmVIt6OyQnJ+P48eMYOHCg2KpVq/eL2xFFMlKGDycv9ofs3/wyiotJQVD2c372rLaQi4UFuP/8Bxpz5qCDqytrNm8eM1ixAiEAYrp0EcMGDBCTCguF0tJSeHp6MnNzc/bkyRP27NkzpKSkcDk5OczNzQ3jxo3junbtynXv3p05Ojqic+fOXLt27ZipqSlCQ0M5d3f3N3qr5XI5Ll26xGbNmtW0nsyMAU5O2AcobNXUOMPAQNo0AQp/e/kYHFerFjSllUcjUFdXR0VFhSInJ0c0NzfngoODxR4DBzL4+NB9/eEHyuE1MGiays3zZHgWFLySD9coFi8mlbdrVzRr1ozFxMQgPj6etW/fHkyhoBA2Ly9SLJQKZGkpKSTh4XR/Fiwgsg3QvdHUJIUsI4Pysd8CMpkMfn5+YkpKCpsxYwZatmz54eKrtbRqie4nnxCBMDauDa0cN44IF2Nk/Li7U1TGzZukhM6eTZEaXl5E1m/dIvK8dCmRlhYtyKg1NCRlZ8YMUms1NP6787KJKCsrw9GjR9GnTx/WWAu1Fi1aoN+cOZA7OCjuFRWhTZs2TC6XY+3ateLz58+Zs7OzYG9v/8rF6OvrI/7YMXTLz4fG0KGv9DvW09ODjo4OkpKTkWlggA5XrsAhLQ2qhYW1BNvPj55LQznQpaXghg6FSXm5+HevXkKXPn044coVyPbtY8537oBbu5ZUNFvb+vdaW5tUOTU1em6LFpEaPW0arZs2NpTCcfs2PfvXpHDwPA8XFxeWm5srnD9/nrW9c4eptmvXoCKUnZ2NrVu3QkdHB9pSKUInThR0Q0LYg1GjcCYvT7xy5QoyMzNZz5494eDgQOe3YAHuxsWJzjdvMv1+/YjExcbS/GqsaOHduzSGqwsj8jwPc3NzJCYmimFhYUyhUCAzMxPGxsawrJvWoa1NpMvNjf4tk5FzaNcuIrs3b5La9fQp0LUrBB0dlKxfj+SsLKQXFcGzqEh0v3WLBZSWosOIEZBfvQqJsTGtWZqauLNhg2h89y5zGTMGFnFxtGZratJ+xfP0nV5etPaEh5MjLyCA1q8JE0jJt7ODpZMTKywsxPPnzxEWFoaioiLRQU+P4bvv6LlJpUQm1dQoxQUgZ0qPHmBnz8KS51l4RARup6UhNiJCvBUXp5A9fsztzcxE8I0bKD5yBDlZWbhcUSEGBQXhTnExk+bmom9mJnP19WVOn3zCevbsiV5du8KG55nx11/D+OpVFmxtLdh36sS48+dJ9V+5ktK3Zs6E4uhRHAoLA2dqqigrK0N8fDzT1NSEu7s7rR+CQOuDvT3y8vJwevVqUe/0afb7ixdQiKJoO306U2zYAFhagjk4oLS0FGFhYUwQBEilUqFv27Yce/aM9vuICFLy3dwAjoPNmDHM2toaJaWlwvm7dzktExOYz5pFYfLJybSGjx9PToaHDyEpKED7P/8Ed/EipcfY2JADledpHczKojXO3JxU9A4daHx06wYsXAgNPT1ESaWCRCKBtbU1g7k5RZ2cP09rgHIttbCA4OUFGBoiNDRU/Ouvv1hlZSUqKiqQ9eWXYpVMxoKsrdF1yRJEXryIRxYWkOfnI7uoiHXt2pU1NZxcEARcvnwZz549w8yZM+t3E2kqBg6ksZSURERXmbrUFNy7R3uicj+sg2vXrsHZ2ZnVRJkoFOQg9vWtH+l37Bjty/7+JKI0AqlUihcvXsijoqK4+ObNhTAnJ848JQWdFi+GZMwYmm/KSBCOo+cxbRpFEihVcFGkdAwfH0rreEds375dUVlZyQFw5jgu1cPDI/6dD/YPxscw8o/4J8LAyMjog1dRTE5OxtGjRzFgwACxqSHRr8WaNZRr9bZq5rvAxIS898XFFIqoDJU9d47+lpsLzJwJ7vhxNB80CBWLF0PL0hIenTo1mHtV3SKLZWdnIyUlBY3m94HatlS3l6gf1t0A4uPjoaWlpeA47q2qZlXJ5ajs3p02pLQ0ukaA7q+hYX3CO3EivUcQ3plISaVSDBgwgAfIQRAVFcVkMhlUVFTIwB4xgoicoyOdU1NSDVxcaqu+v6loWFUVGbmTJwOgcN1Ro0axLVu2IODUKQwaMoSM/LIy8nzL5fT+DRuoMFHv3o2f07VrZEDk5dVUsX0dIiMjoaqqiuvXryt4nmczZsxgDbWr+q+D48gA8fIiYyUnh+6pQkHKwIABpDyZmJASUVBAn2vZkgzVGTP+p/nXb4IoivD391cUFxcjLS2N5ziOva7wUDMLC2DJElh37szfOnkSBw4cEMzNzTmJRMI+++wzWFpaNjionh49Cs+bN6H9yy+vEG0lOnbsiI4dO+LRo0dQnToVGi9eENEaM4ZUwiFDGg7bDAkhFXnBAhj36MFaTp3KR1+8KOro67MnzZtjla0tBjGGjo3NQ56vdagUFxOxfPSIakbo6VH48ZdfNqnIHsdx8PX15XV1dYV7a9bAZv581lwQkJubiwcPHqCyshIKhQL37t0DAFzZvRtpKSkwys3ltnt5QZ6cjGbNmmHChAmsWbNm9RyHpeXlUCQnc9YnTpCx/fnnpP69LufUwIDITx04OjpiwYIFbOXKlYiPJ5u3wQ4XmzaR86F5cwp9nTmTyLaBARGyv/6i/6eloVIux5+ff44eLi4YfOIEpOfOMUgk6B4ZKf7t7Czkpqfz0pAQ0dHAQDQqKeHadezIkmNixKvnzzPXBw/I+WdnRxEnP/xARPnaNZorw4aRAuvkVJunXAfe3t5ISkqCQqHA/fv32YB798Bt21arBE6YUFu9WQldXWDDBhifOAG306ehWlUlOh04wKIvXpRo9O+Pmbq6KCkpgfT2bVxo1gzWFhail5cXZ2BggE2bNmGIqyukkZFATg54bW1SfC0soL5uHXg9PVgPH87Ji4rABwTQtfn60vnY2SFGXR39Fi+Gdb9+vLxvX6xevRpz5syh6IWvvybH+JQpAAB/f3+Fc7NmrJWhIdPS0hILCwvxR0qKwlFbm3cbORInNm0StExMOI7j8N133+G3337jCiZOhIFEQpE7+vqk1J87B9jagjEGa2trWFtb8zzPiw8ePGAdO3ake3L9On13u3ak2goCHgcGIprnRW3GhDabN/NahYVgy5eTU71DB8qTV1ente3l/fnKFQDAcDc3LvfkSfH5oUMwNzcHZsyAuH49io8dg3TQICRbWsLst9+QcfAgTg8ZAlSnoN26dQsqPC9q9O8vzp06lS0xMwOWLEFqaCjkP/8s9L90iduwYAHLz82FcRNU13v37uHMmTMAAB8fH9HMzOzdF+KxY+l5yeW1BeuagqdPG1WIzczMhCdPnvBubm50XBMT2kt69qx9U3k5Of6GDatXzbwxDB8+XNK3b19cvHhR5HleIff05PfY2Sm+sLDg0bw5RXnMnk1vNjEhx8zKlbVt6sLDSVioew7vABMTExnHcar5+flOEomkD4Dj73XAfyg+ku2P+EdhxYoVllKpdIunp+c7uEUbR2JiIo4fP44BAwaI7du3f3+LvKiIwrnGjPkAZ/cGyGSk5Do5kSE2fz6Fwqmrk5d88mQiJCEhZDj37Qs1AE3RfU1MTN5YHI7jOEgkEmRlZdVXaF6CKIq4ceOG0KZNm7cuT92tWzc+ICAAFhYWMLSzo40wOpoIlIUFkW9lOGfr1qTwq6i8tXrbECQSCbS0tMTExMRa1dHGhoy89esp9NDE5BXD+hW4uJBHPinp1by7upDLSRWJi6v3skKhgLSiAp9Wh0PuHzJEXnHpEjf6+XNOd8cOet5nzjReaEUJMzNSwJs1I2//a86lsLAQgYGBAADGGP/VV1+9X5/5pqCykn4nJNC9XbKEwjLd3ChfdeRIus4nT+iH50n569aNVJ7Bgyk3vV8/MjoLC0mF7NuXSIyz83/3/JuIGzduCKmpqczW1pYDgAkTJrz+AwsXAkuWoKW2NkapqMDPz49LTU2FkZERrBoo0AUAlZcvI+b4ceh8+qnA9+nzRs9TTe6spSXls58/T6rmoEFU5XrhwtriZfn5FMa/bBlQVQVVd3f0HDEC1zQ0hIdSKbO1tRXTIiL4wMBAtG/fHg0qYJmZNNZ9fGgdq656j6goUnNMTd/KQcIYg2fnzlyaRIKDwcGQREejXBmKXOc9Ts+f49OqKtw1NBSbbd/OZmlqQltbG1KptMEvi42NFUvt7AQ+PZ3H4cO0vnfr9vqT2bCBake8NB85jsPEiRNx6tQpsaqqijm8HIaelUVpH/7+FA1w9Wp9BW/3bpoTGzYA06YhzssLw0+cgIGTE6QaGrR+KBTo0KED69ChAy8IAjIyMlhycjJuxsUJp1684HgfH6ahoSGyX35hePKEnveePbSPzJpFIeOamvQMHj2i/NQ5c16pmK2hoQFPT0+Eh4fLkZYmibx2DbHa2qL4+DFTUVGBrlQKneBglBcWokuXLvX6dUsGD0b6gQPwbN+e8bm56ODoWEuGRBGwtMSMVasAY+OagaOroyNEy+Vcp4sXqbjXTz/R+HN2BtLTYbFhAx5qayNXoYCoqgrL4mJyjnp6AmVlCImKUnRdsICzTkhgD5OSRBUVFURGRLDuHAfp6NH1eiTn5+czi169uJCsLJHjOKFfv378+vXr+YnHjkH+9df4ZNs2dmbKFGH8+PGc6sOHGFFVhV2enuJXM2cyNQ8POj+A1uX9++m+groNREVFsZqWb4JAdkL1eCouLkZ+fj5EfX08HDiQZWdn80FubjCrqlLMVFGhvTM1la55zZraKu4NwGzdOlw7eRKX16+HBs+j2MgIeoWF6LpqFW5eu4YcU1PMevAALUtKYG9vj+HDh9fs59yAAQxeXqwuQe3avTtw4gSHvDws09Oj8XDpEhWQew2URHv+/PnQ1tZ+P/vq4kVaJ4KDSdFvqkIeEUE2WQMwMzPj79y5I0IUGSQSKkpWt7jib7+R0ys6+q0c+Nra2hg+fDgPUC2aX3/9lZcxBpUrV8g+WbyY9rD9+2mNTU+nOgmdO9P6u3hxk7+rMYwdO1Z9w4YNco7jCmUy2dv3bfsIAB/J9kf8w8AYG+Xs7KzaYNXId8QHJ9rp6RQSFhVF4av/LYgiVcj8+Wcq1DFlCpEOAwMyBufNI1W9pIRU2JerVH9AGBgYKBITEzlLS8tG719iYiKKioqY1zv0Ju7evTuysrKEP/74gxs+fDhatmwJ1qEDXffff5NxuHYt3QN3dyIBoaEfhGwDgIuLixgWFia2adOmdqdVVaXNMDycDODHj0nReJ3qu2gREb4dOxp/z8GDZEynpdUSDUGAyYUL8Bw0SDyYn4/KoCCmkpgocUlPF3N27hR1f/6ZwdOz6cREU5PIrLLNTQO58w8fPoSfn5/Ytm1boU+fPjxA4fUfBAoFkQo/P/Luf/MNvfb99+SU2LWLxveNG/T65MmkkLVvT58DyCBRYtgw+i2TkQPG3JycCJs3U86vhQUZpT/9RK2S+vZtvNja/wBFRUUICQnhBg8eDOemkP+CAnIwLF8OjuPg5OSkTHWAr6/vK8feu3evwiQujmt95w6T9usntPrmm7cP8di6lZ7HokXklDl9msL5dXWJ4BQU0Hpz+jQRiitXoGlgAB+gxpnWs0sXnPjxR6xavhzuERGik4sLM582jcj1ypWkih84QI4RU1NyAI0eTU5BY2N63xdfUDTIa29PAYKDg5GQkCCqZmQw18JCVKmqQoXj0LlzZ1hZWcHOzg4x9+8LL378kZOKIrSWLIFnjx5NmjDR0dFC69atecTHk/Kkq/v6isiCQPeqkfXfxsYG48aNY9u2bYOy0wLKy+l+ZmaSmnXlCq0xxcX1yfawYUTMqqu2W0yejMw+fUQLxhg4jgjYrl1UCVxbGxzHwcrKClZWVszLy4ulpabiwbx5SG3RghUuWADdffuoQNayZRS9wHEUJbVuHZH+li3p+efm0vo2Zw7Nn2qoq6ujqqqKzerQAem6urB1dWUymQyVlZWC2Y0botWlS9y28eNZXFycMG/evJpxyD18iP4BAYieNQtuWlq05n3+OV17YSHljivzeouLgaQkDD95klPk55MzWRCIFDk4UITPwIGAszNMR4wQw0xMxJgDB7hFTk5Q27gR+PVXCF5eKFi8mLefOxd49gzaX30FJ01Nxm7eRFZaGo5OmaKoXLuWU1dXF6RSKSorK/nLBw6g4+PH4uyNG3mO48DzPC6HhwttV67kLLt2ZdOyshisrIAVK2AdHs4Mhw4VUhcsYK0XL65dUxMSiKhVk+1CymMX27VrR4MnKgrC99/j8MKFQk5ODissLGTq6upCeXk5p6WlJWhpaXElJSUo09amAoN6ejS2duwgErh9O63jLi60jtbZfyReXrAAmNOiRbAqKECWvz9kMhlau7hALTIS2hMmwHTqVCAzE+NfLoL46acUFdAQlN9x/TqlJU2aRNFYyhobdVBU3VJyzJgx0G4gOuKtIZWSLbNxI61Bypocr0N+PkUCKKuGv4T27dvj5s2bLLVzZ6GFoyNXLydc2Xpvz573SjlSU1ODIAjw9/dHp06d0NLeniLkgoLIZlyzhiKPjh+n+9usGRU1fQ+IoogLFy6gtLRUAsB+2bJlhW/80Ec0iI9k+yP+UVBRUWlrYWHxalncd4SSaPv4+NRufu+Dykoy9I8ff688m9eitJSU3NxcMoQPHaKN9ttvySgJDKTCJ46O9LfXqagfCA4ODnxkZKRobW0N+5f6WIqiiOjoaAQEBMDb25s1VMSpKSgsLBRkMhl3+PBhAMDixYup5de4caTmGBiQkbhtGxkdsbEUXqxsKfOOyM/PR2xsLFdeXt5w6oKbG93/q1cpbHLmzMZVbjc3au3TCMFFZSUZnMOG1Tfk09KA+fPRLSmJCX36CNp+fmJLGxuu2N2d/dm2rbjoNfljjcLamtSzadMoAqLO91XnEIvu7u7o0aMH/9aVYwFy+Jw9S+HdBw5QCN6ePfScFi2ie+HnR9EYvXoRYbO0pPBwZQ9pZf/XpkSIFBRQVMeQIZQzuXNnbZEpxihMc+BASjPYto2uvbF82/8iRFHEkSNHBCsrKzg7OzfNelMoyOirA0EQIIriK6p2WFgYrB484N3t7KA1YgTaenu/u4VoaUmkZv16GoMqKlQx/vRpcnbk5tJa1K4dVe1NSqLx37MnMGsWdBnDxDNnUHH5MnKfPkVsTIxobmXFsHs3GZLq6rXqzY8/0tjfuJGIbF4epUR8/TUpvM2bo0gUwXFcTb5nYGCg+ODBA7GqqoqTU7V71js5GYZt2wrLli2rf925ueh88SKXpKUlxnh5MWUu9etQXl6Oq1evKrKysvihQ4cS8V+wgM5z3z46x6+/fvWD16+T+viaeZNTXWTx1unTikFaWjx++IHuxb/+VUsyf/iBlLy6VcH19Un9z8gA+vfHHS8vceCRIwzLltF8uXaN1rzduym8uHNnup979gDXr8N61y7o37+PXCMj/OnkJMzLyuLAGBn+p07R/EhOprzwtWup/seePeSc8vQkJW7dOuDxY8SNG4eIhw8hZmVxKpcvo8Xu3WhRq+Rz6NED+PJLjGYM/v7+TBAEcCUl5AjevRs/f/89/tWtG6mTvXvTvQ0IoPUvPZ3m9O+/1xQ6TO3eXchq04azkkjo76NGkUPVzo5C60eNQssVK1hQUBAMDAwUKj168DAzQ6KODm5OmoT+/fqJKioqLM/EBKabN7OBp0+jIjQUf/Tvj/Lycn7mzJnIyMjgy8vLYW9vj6BffhFLnz/H77//rujVqxf/+eefIygoCPfv3xdH9eoltrh0icPdu8D582AAPG7d4vL37hXlK1awjLQ0vHjxAhkKhWC5Zw/XVqEAz/MwNjYGx3HMz88PrVq1gktSEjKsrZGSksJ5eHjAxcUFRkZGnFwuh0Qi4QBAXlUFbvRoDqqqND4AWkMXLaIK5ceOkXJrZkZ7z6hRRJa1tKCvr4+QiRPlnWbNkphcvkxE9eBBOISGkiqtDLXfsoWcKQDN6YULG2xpVw+dOtHvhQtp3c7OpgJmkZGAmhri4uIQHR0NU1NT4eUOCe+FgQNpj7h6tWlk+8GD16YRGRoaYu7cuTianc0ZDhmCmsSOZcvIlouMbFLo+OsgkUgwduxYHD16FImJiWjZsiXc3d3R/NtvaS4/eUKRMH//TUVYO3Z8fZrKG5CXlwd/f395RkaGRCKRzFcoFDtWrFixcdmyZaHvdSH/UHwk2x/xjwJjzEqtgRY074IPTrQB2mj69QNWrPggh6uHu3dJVZk6lZTz4cNrN8PCQiJ8lZWkgmzaRAt1RASFghoZkXKUkUHGU4cOpHhLpWREm5rST1YW/Tg6krGlqUmbuYUF/T03F8jPh8LUFHKZDKoGBkBSEnrr60OqULCIlSth0LUrDGxsIAIIT0wUMkJDOVFVVRzVvz+zLyggAiaR0Hfr6tL5VIcNoqSEjHuZjAxxXV36zrIyyO/d48e6ukJdQwOyFy/A37lDeYQlJWSUOTjUhkhPmkSe4S++IEPxLSvBx8fHIyUlRfHw4UOupKSENWvWTPDx8WncWFBXJ1JpakoGwDffUDXcl5VgXV0aIxERDVeW9fYmA+bXX+n/Dx4QGQ4JATIzwX77De6XLnGYMgX45BOUyWSo2LqVlZSUvFvBmWHDSC2uqiJCp66OhIQEHD16FC1bthReS7SrqiiUr21bGpt79xJ5dnCgolBz5pBR3q4d5Z4qFPTMk5LoPnEczRWAjEMllET7bVBVRfd/xoxaR0VDxpWqKjmgbt2i3L+hQ8lZ8z8skiaTyZCZmcn9+9//btoHQkJISSwurneeHTp0EMPCwtiOHTvks2bNqrEF1CMi4BAZKZpMn87wmhzwJuPwYXLGWFrSvDQ0pPlkZEQOG19fImlxceTc0tIiEtC7N2BmBjZzJtQBqP30EwvZtg1VV6+ie/fu0H15buTlkcH555/0fE6cIKfI9Omk+ty/jz9HjBDzGWOdOnWCKIq4f/8+69+/P4uMjBQGDhzIGRkZQTpuHPDVV/Uf6O3bRCYGDcINMzPBvlWrN6aylJeXY/v27aKqqiqmTZsGw8BAcgwpVfbSUsoDbQihobR+vgbO+vro9fCh0CIigkfbtqQevzyHly0jYl9VVd/YP3WKnoe3N/RnzmR/amhggpcXVJUkqVMnem7dutGa4+dHz8vFBVBRQaK/P9LOnYOhpqZYb+wPHkzrvZERzdv+/UmZ7dOHIiuqHQv3/PxQceYM7hw7htF+fnhiackqPDyg/nIKi44OMHw4HPz9YWBgIG7fvl2YM2wYj4QEXDhxQhTqLi7K1Jxjx+geiyJFh1VV0V6npYXOGzZwpxYsEBEWxpCaSvdBJqPxefIkdShQU0Pl2bPo1q0bzwUGAoaGiM7Kgnp+PtLWrmUBbduC53lYlpeLn50+zTQdHNAlJ0e8pK3NNDU10a5Ov+1h3t6sNCmJie3bIzAwUPz222/ZlClTuDNnzuCWlZXQIjKS1NKiIkBHBy0ePsQNGxusO3AAcrkc2traCjMzM95o2DBc79NHMJ43jxNFEVVVVUhLSxMePXrEMu7fZ/EtWsDb27tebZS6TmmJTEZrqJIM14WTE0UFiCLNwV9/JSfJ+vWAnh6M+vWDgjHac9XVSQwAKPd7zBiKgmjThvZLgGyJCxcazYeuqKhAXRvs9u3buHHjhiAIAtO+eVPhambGXfvtN6738eNItrFBkpMTpk+fzr2Tw7YxtG5NtsnVq+T4a0yBVyI39/XpQ/fuwXDUKOgsXao4l5PDjZPJGI4fp3H3xRfvTbSVcHBwgIeHh3D58mWuoqJC2Lt3L9e6dWuMGDGC7KLc3NqOIYcOvdN3ZGRkYPfu3cpOAxIACrlc/gsAcBxnBuAdeqZ9xEey/RH/CKxYsUICYJKamlq3V3Lc3gH/FaItl1O49nv2RKwHUSSD8+pVUgP69iWSNmBAbf/Q8HBS8MzNyVj95hsiM6WltWRDFOn9VVVkBMpk9KNQ0OaqoUHEtSp5Sv8AACAASURBVLCQwhhNTIi4y2R0TMYAqRTis2fIf/AAIc+eQVtLS/T09WV48ABcs2bo1aIFIhMSEH/kCDp4eyMhNVXIKC3lvNTVoWthwWrCE1+8oDxbPT0y6u7epfPq0oU848XF9GNiQkZ9VBSQnQ2r8nIYq6tDX0ODQrafPydjPyaGDMT+/Yl4iyK1z8nNJVK7axcVWWrZkhwJly8TOa9L8F5CUFCQoKGhwXfp0gWdO3dGg1XkGkKnTmT87NlD4eynT9caMUrY2lJF4YbI9s8/kxENUOibjQ0pgBER9Fw//ZQUteqw0uRbt8BxHBVue1fY2ZEaXFEBnD+P2NhY6OjoYMTw4TyLi6Nw3qwsyhHdto1ypkWRFKdp0+i3qipdpyDQNVtaEnG4f5++o26/Y6Wx96Hw9CkR7L17iUwAdC06OjSeXi5AxXHkDDA3JyVcX5+e2XtGQDQVYrU62+QIjx49iPC8NARdXV2ZtrY2goKCJOvWrRN1dHTQ9tEjppWYiIhhw0SbLl0+3LoWH1/bhun0acpd7NiRHDXPn9P6UJfYN9Cn3MjICJ6enmJ8fLx4584dzsbGRhg9ejSnVl3tG4MG0by4erX2mWlpkdG5aRNQUoKOT58yjchIpJqZKeKzsnhRFOHq6gpXV1e6Ofn5dE51w7d376YxMnkyZO7uyPnlF967bl/nBpCTk4M///wThoaGwpQpU3hkZdE68ttvtc9Bmf85YwYpvuPG1R5g2LDGCxQWFwPr1qEoIwNFxcXc86lTxeaDBzf8rKRSWhtbtiQnlXKe//lnzVt69eqF7OxsXHj+HIOWLQMbN47WmH//m8L0790jx0edInPKvrv5+fl8Tk4OjI2NaW/p0qWWZIWE0D7ToQOtD8nJuJ+aiodZWYhJTET/X3/FnLZtUeniAodvv0WSRALHvn2hdugQEXyptKYNI5eVhc969OBkPXtic0GBIP38c7EgKYn/bPhwaN27R6poVBTtZWPH0r179IgU/VOnyKFnb49z+/cjJztbwJw5fM19aN+eVO7yckBNDSUlJXBwcGC3b99WdA0N5dGuHUrMzYUW6emcrkwmev7xB9NXUcHRb78VUgcM4Oy/+op1Cwxk7VJSoP6SI19VIoGquTnc3d0RExMj/vXXX+Jnn33GKc6eVQwNDuZx6BCd34QJwI4dYNevo9uqVQxPnuDq1asYO3Ysb25ujqqCAiRUVXGXL19WlJeX8+7u7vDw8OBKiotR7O4OlT590LWxvOdDhyhUPDj49WlCjNE6u2sXrcPXrwPBwbBYuhQTVVUlyMqi57l+PZ3zpEk0j4cMIXX48GEaxw8e0HrTwHetW7cOFRUV0NHRESQSiSgIAioqKngfHx9OT08PRUVFkgIvL9g9fy46OjuzjqNHI1MigcX27W9XzKwpyMmhc92z581kOz6+8bZpAM2v2bPRz8eH37FjB8q++w4akZEUJfiBxB0l3N3dOXd3d+Tn53NbNmzA89hYEampDJs3k4I+dSqd79WrFG3ShOKQSuTm5mKXsoUmAFNTU0VWVpbyAAWCIAz4oBfzD8JHsv0R/09gxYoVHKgHoBUAfQA2EomktVQqdRJFsQXHcc1MTU0rBw0apP6+yvZ/hWhv2UIFLaKjP8jh8OIFecydnMiQGDuWyJu7OynRx47RBvzttxQm2KNHrQoyahR9Lj6eyNr7ojqU99GjR7iQmSkU2Noy/Y4dWVVVleg5diyra1h3GDkSGzduVFwqLOQt3d0xfPhw6DZUbbcuGvLW18XEiaioqEDYunWsz+LFTc+1LS6m+zR5MjkeVq+mjSs0lO7hqFFkFB44QMbpTz8B+/cj5tIlsSQ/n5s0aVK9gj5NhoYGMHcu3bdBg0g9/eKL2mq+Hh5kCH/5ZW2xKZmMQm+PHyfS6udHOX4XL9Iz/PlnMqJeahsmkUhExhh7Z7Kdn08Ol+XLSVV6+BCDbtxAzsmT2FJZKc5dtYpJ1q+vbd1TVUWKva4uRVU8elR7LKUx09TWZh8COTmkzmzYUEu0ASL/oaGvEu26sLMjNWzPHrq38+bRs/kvVyxvsLdxYzh7lnIzz5595U+Ghobo1q0bdHV1kZCQwHKCgtAsMBBhkyYhUS7nfKuq3tgd4I0YOZIU5+PHab3JzKQ51K8fKW1vAcYYPDw8WK9evdj9+/dx8eJF7siRI+KkiRMZEhNrDWYDg/rElTEKay0qgp2BAXLu3cMAJyfeS00NOS8r97Gx5GhRUSHC8fXXpIBNnw7Y2uLvv/5SSKVSZmlp2ajzTBRFHDt2TLC1tWUjRowgQ/XGDSILDbVls7R8tS3Z9OnkIKhbv0Gpvq5bB6xZg2u2tohKT0dXNTWWkpKCFi1aNNw60cmJDHDldSnRrBk5Pjp1wogRI7AiNhZ5JiaY2KsX2MyZtDfs2aO8qHqHdHV1FQMDA5kgCLV5tIsWQTQ2hkIux5MnT6ClooLHO3bgooUFpFKp2GndOpg9esTiRozAyDFj0Kp6nkvT0lC5Zg3uyWRi+vXr6PfiBZO4udH3T5hA9+7FC2ja2ICNHw+fNm04dT8/lIeFwbxZMwob79SJnvkXX5ATbPx4WmcUinpO0aKyMkEul3NZWVkwffiQnq+zM6m5AQGQy+XYtm2bsigeX/bbb9AwMkLO+vXotXcvEhMThXPbtnFT7t1jdhUVfOpXXwn25uaM8/WF1sKF5Mj8/PNah0qzZoCXFxhjGD9+PPfXX38Ja1evhqqWFm/dsSNatWoF9YQEcq5MnAh06QKpjQ208/MhkUhQWVkJQRAgnToV3o8ewbtVK37z5s2isu+llooKtCZOhPnUqQCAqqoqyGQyCIIAhUIBQaGASnIyqiZPRkVmJhhjUFFRQU5OTs1YkUqlUOaTK33Cf/75JxQKBUTGgPnz0fbRI2FISgqHb76hNd/VlfYWU1Ny3O7bR84OZXu/1NQGSV6LFi0QGxuLoqKimvkze/bshoqoMgwfDgCwOHGCSCRAe8zYsU0vavY6uLlRusORI69PF1PWtpk7t+G/eXlRusY330CIj0ffM2cg270bGi1afHCiDYDskps3kVpQgC+3bYPO998zeHlRfQoTE4pelMvpGc2eTXZlE2FoaIgvvvgCurq6kMlkYIzxR44cKU9PT1cH0H/ZsmUlH/6C/hn4SLY/4v9qrFixwlhFReVHiUQyjud5qZaWlkxdXR0GBgZSExMTDX19fejp6cHIyAgqKirvHcuTnJwMPz8/+Pr6fjiiLYq0Sb2Uq/xOiI4mpfDzz2tz0kaOpFBr5Yaork4b5N27tWRNiR07aNG+dIlI2uTJ5M0eOfKNX52fn4979+6JZWVlgoGBAa+jo4OKigpwHIesrCwxKiqKtW/fnps2bRrKysqwdetWdvbsWaF3795c3QrVc+fO5dPT02FjY/PBYnOfPn0KiUTSdDUQIGVl507y4Gtq1uZ2LV9e+56ICFLPY2KAqioIggD9iRPZzDZtoLN0KRHH0FAidc+fk+e/qTA2JnVh9Woq1NW7N0U9SCS1xrjyXMrKyLteWUlOlk8++f/Ye++wKM7uffx+ZpZdOgiigDRFQRBEQVBRiRV7r4nGrrFGTXtjNFFM7LGjiRq7scRYgx0L9kJRQVAEAZHekbbLzszvj8NSFBSNeX95vx/v6+ISWZidnXnmPOc+5T507w4fJqXXr76qlgQ6OzuzU6dOISwsDC1btnz1HESRAg2SRMcdNowyJOvWkQPk6EhBgWnTqMXA0xPyL76Axc6daCWTsSVffQWtrCwMq1sX9ppsWnXvUw1yc3OxefNmDB48+JU+/veC69fpvE+ffrVN4K+/qu+Jrw7jx1O57d27lKlfuPDNvYp/A9HR0eB5XkLZmJ3XwtS0qhDcS2CMoVmzZmgWGQkhLw+bxo9HNqgEdd26dRg6dChsbGyqJ3E1IT+fHNnt2+la1K9PIj6hoUSepk+nCph9+yi7/ZZgjKFFixYIDQ0VLYKCOKSk0P3SoFcvco4riXCp1Grc7dIFFmfOQLe0VEp7/Jg12rULpllZ9NxoiEFUFAUBkpOJsM2eTccpK1k3MzNjSUlJ1DtcQ7FKYmIi8vLy2GeffUYX7exZsr1//FH9B/rhB3rOPD0p6NOhA52ThpgnJlKW6uJFWlcPHgDa2uiYn4+U/fuFhw8f4tatW/yXX35ZfSsIxxEZGDeO3ktDKpYvr0Iw+vXrhxMnTiClY0dYbtpEz7qdHdmQGTPIfpWhVatWLCYmBjExMUhLSoLN7Nlgu3bhzO3b6jtHj8q0tLTQwMQEerm5aDd0qOTi4sKE4cOhNW8evhg8GDqacV6pqYBcDsXQoRhjYsI2ANKjgADxy5MnOajVZHMPHqTrs2QJdFNTYR8Xh9xu3XDK2BgTP/64igK4BiLHoWjWLGh3745if38onJwgl8vRp08f7urVq8LujRt5HblcnNi4Madtbk5kV6lE8b17UKvVsLGxEZ89e8bJXFyQdfQoSktLub179+KrDRt4ea9eeB4Xhwt9+6KvjQ0tgrp1ySZu2UItYD/8QGvq2TMKdA0cCENDQ0xr1oyTBg3Cbz/9hBOGhjBJT4etjQ2R09hYWnMArK2toaurK+3atYsNHz4cTe/epSqajAy0b99eunDhguTj48Ph99+p+gY0bWLJkiUA6PlVKJX4ZPt2HBw5UnqRmQlp926UlJQwmUwGSZKgp6cnKJVKThAExhiTtLS0JEmSIIoi4zgOs2fPZpo9s7wyq7iY1uKPPxLJ9PSk7HC/fmTXe/emwH0N2dRBgwahY8eO0NXVhVwux86dO8VDhw6x6dOn12xgBg6kL1EkXYbWrSloZGxc4yiuWiMtjdbPzJmk9VAd7t+n/bSm/cDdvbzEPHPVKslUkpixo+P7FdAsLKSgvqsr1KtWQZaairjJk3F7yhRp+tdfv3rtZDIKesydS7aslm1wjDGqUgGNC83Pz8fz5891GGPfS5L0njJB/zfxgWx/wP8sFi1a1EdLS+t3Nzc37TZt2sjLZve+J7nj6hEXFwdRFGFoaPh+iHZ2NpWxXb9OZczvAkmiDV2ppCxAnTpUDvbzzxS5HT+eyumWLSOS5uxcc/bNxIQyfFFRtGnWrUuGOiaGnOgy51iSpCoOeHh4OP766y+YmZnB0NCQT0lJEYuLi0W5XA5RFJm2tjY3duxYWJYZfUNDQ0yYMIEdOnRI3LVrlzht2rRyz1Umk+F9qsWrVCocO3ZMbE+jOGpP4LW1KZtUUvJq77QGGhLo5QXs34/szEzsmj4d3337LUWXu3Qhp2DTJiJ116/TvfnmG3JQfvyRxExqmumtUJCDERhIfaeTJpGT0707OV+CQPf46lXajHfsICf63j06frdur42u6+npQUcux9VLl9AyOJgc/dRU2qTPnqVKCFNTKhE8e5acDk9PIu8AOSuadXDsGK0bR0dwSiWeHDggGhsbs/r167NLly4J9vb2bzWyLTY2VjPmRmjcuPFbj3t7La5fp/LgvXtfcUSKi4uh3rsXBlZWlKmuDZyciCDl5xM5sbSkANV7znKr1WqcOnUKrVq1evOBy/pP8cMPr/+9iAjg55/BHzmCmQ0a4OnTp3jx4gWOHTuGnTt3omnTpmL//v25N1YEabLof/1F5Kxly4qMbV4eOcxNm1JJ77BhZGtu3HjnthltbW1OKyEBorV11Yf62rWKVgqQbTpx4gT1wNapI5m7ubEOubnUp9qiBVVdzJxJxG737oqM6vffU4Cr0j3s3r07FxoaCn9/f2n27NnV3oP4+HjJ1NRU4jiOoaiICMqXX9ZsQwB69vv0obaWqCi6dnl5VPFUWkqkeMUKylyVwdDQEJ999hkviiJ+/PFHHDhwABMnTqz++IzR8UJCKgh2v37UEjNwIACgZcuWuHnzprhDJuPGu7vDwseHnvMpU4C6dfHs2TM8evRISkpKEjMzM3lBECRJktiBzZvxZVERZPr6ePLkCd+9e3dcvXpV9HRz45yzs4FOneg6mZmR2vSgQRRI8POjcuSydiAOQJtmzdijPXtYuiCgnka/oU4dssMODrTGDA0hc3VFRkwM1m7cKDVp0kTs1asXTx+T3urIkSNCZGQk37+oCI+WL4eyRw9h9OjRvKmpKQYMGMBnrloFRERwiWZmuNq5s9g5KYmzUyqhe+QItOvWlUxNTZlVgwZC5Llz/JkLF2BlayvY2NjwUXfviu7nz3P+kyfDwNhYsrS0rFgDhoaUSZw6lfQC+vWje5edTa8nJ4O1bw928CDEmBjR1dgYths3cpAkuiYrV1KWtU0b1G3aFHPmzGEbN24UCwsLOYwcWU7E3dzcuL/++gt79+4VR12/zmnaL3ieR9euXYWQkBBOpVJJ07y9Od2kJExdtozVYIcq21SG2gTvNNoivXpRy9X69XT/jh+ne7RzJwUNBKFaws1xHCnnl8He3p67ffv2G9+27I8rNA5Gj6bg7oULtF+9K+m2tKRqliVLat6DExKqH022bRuVy69aRW1pbdsieeZMJPv4oFENRDs+Ph4GBgYwrTwd4HXYtQs4fRoZ06ZB+uUX6WbHjtI9FxdOt00bFMXHo8/gwTXfs48/rgiE3Lr1TuQ/rWxyhyRJpxYsWFD8hl//gNeAX1g5S/MBH/AvhZ+fn35QUFCToKAg3aCgoIKgoCBfuVx++NNPP9Vv1aoV/95GCr0B9vb2CA4OFrOzs2Fpacn+9szg0lJyhHq9QyuMUknZIx0dKofz9ianDiCn5M8/qe/4o4+I0Dk6krP2JgLQrRs5ZYaGZLBtbCCsWIGSVauwRy4XTp88yV26fBnXrl3DtWvXcP36dURHR0t9+/ZlPXv2ZC4uLvDw8GBt2rThPD09OS8vL87d3Z29PLZDT08P7u7u3NmzZ5m3tzf4t+gtehvExMQgLCyMeXt7s1pvchpkZJBDXov7LAgCtm3bJjVo0EBybd6cgecp0s8YqWWXlfqhYUPavDMyKJr+2WdEoA8fplLIPn0qZvBmZlJEvVGjip7UXbvIKdfSogj/+fN0nN27KRM+fz6RBze3qhvsyZPk+OfkUMmitzcwdSpc9+xB0uDBcF27lpzghg3puK1bUz/pxIlUtqcRjatXjwgTY6+uJTMzIC4Ogo0NAp2d2eSZM5mFhQWuXbvG+fi8na6KtrY27ty5g9zcXM7b27vGTOJbY9s2em5Wraq2muTu3bv4/cUL1Pv00/Iof63AcRR0sbCgDNfZs9Qj/57OOyYmBhs3boQoihg9evSbr8eiReT0lznpr0CSKNiwbBmpNZdlburUqQNzc3N07NgRrVq1wtWrV8Vbt25xbm5ur/T2FxYWIvfzz6EnlxMpevGCCGrr1hWiQM+fU0nvokW0jrp1o6oIHR1ah+3bv72znJAA+Rdf4MagQWhbeZbs3btU2VNp1NfOnTsltVrNJEnC3LlzmVP//qijVlOgy8eHzictjUTQrl8nsquZ0PDS+maMwdzcHMHBwYznedi+NDFCqVTi2LFjaN68OWdra0vPZWgoZfPfhI4d6Z64ulJ/9f79VDUyfDgFwWqwQbGxsQgPD4eJiQmcnJxqrt7RKPLn51OgIziYnmnNngHA09OT5efnIyA2Fi309KAdHAy0bw+xQQOk9OyJkIYNmZ2dHefh4YFBgwaxiEOHYAcguEcP8fzFi0ylUqFfv34sJCREysrIYM1LS8G9PD6xXTuyYw0bUpCwTRvSDsnJgeXYsbBOTMQ1lQoNS0sh++EHuoatWlH1jp0d4OMD+dq1aHf4MPLatWM4dIj7KzQUCdnZgma0YmhoqNSgQQOu09q1KHn4EEVPn7LGnTszAEiMj8e9y5fhmJuLup06IbVtWxYYGAj7Pn1gdPgw7GfNYmcvXGDmMhlr6uDAHjAmTps2jW9oZQXLkBCm4+UFh+nTcffuXRYdHS1ZW1tX7G1yOZGbffvIFnt50T6qpUX3dfp05B4/jsZr1rBnVlaSs4kJw8SJFPCYN48qF7Zto7UglyMsLEwyMzNjVtbWROL19cE1aoSioiIxIiKCS0tKgti3LzLy85GUlATGGGdubs6s1q1jT2JipMZ79tREtP8+1Gran+7do3MLCCB/5t49Cu5HRVGQWKmk9aatXeV5ys7OxtGjR9GvX7/qyshfj4EDqX3syRMKcn79NR37XWytiQk9A3FxZK9fxs2btN++rNPw6BEF4j08gGvXICoUOKxSsTZt2sCikq5IQUEBAgMDERAQINy5c4eLjIxE69atERoaitDQUNHW1pbxPE+BosJCClaMHk3HLi5Gab162PzsGRI6dmS2H33EevTsCZ7noaenJ3br1u31N7djR2o509PDu4hdJiYm4smTJ0mSJH3fsWNH9Vsf4APK8SGz/QH/avj5+dVRKBS7eJ7vrqurq1Sr1bxSqZQrFIrSoUOH6rw8tua/gcGDB3MXLlwQt27dyvT09CR7e3vRzs6Ob9y4Md6qH3zAAHL0/vOftzuBpCSKxgYFkVPWtStlDOLiKOtob09Zz3Hjqs5XrSUkbW0ISiWkb75BztatePDggRhSpw7Hjxoltnnxgh+7YgXE9HSUqNVQKpVQKpUwMTFh79ILL5PJIJfLpby8PFY54v0+4ejoiE6dOuHQoUOYMGEC6tckPFQd0tPp6w3OgCRJOHPmjCiKovTJJ5+8PmqgCaxYW1NQA6Csd9k8UXAc9Wfv2UNZ6qQkUvnt2ZOyTLNm0UZ/+TKVKbZpQwQnNJQEmFxcKLP49dfAqVOUMcnOpgzn0KFE6N3difhs2oSTx45JhUol04iRqdVqxPTsCSk+Ho6Ojm9RClCGRo1wafFiGOnri/pyOadbvz44jpNCQkLg4eFRa8/v2bNnMDIyEvLy8viUlBRYVcpWvhMkifqGS0ooI/Oy8FwZciIihDlr1vD+MhmysrIka2tr1qBBg9r3L9vaUvvBw4d0nRcvfuOc5zchLS0Nv//+Oxo1aiR16tTpzePvRJGqKWrq75YkciIPH6YRZjUEuvT19TFr1ix+3bp14vr16zl7e3uhTZs2fF5aGswPHsRvdeqg940bKLa3h81XX4FVlwEqy9aVO9oFBbSOExIoC65SEekfNar2FyQxESgshNnLYnmpqVQ2CWpr2blzp1hSUsJ9WYlQymQyel4aNiRSVK8ekQVLSwqAnTxZ7fMuiiKCgoLE4uJiDgAuXryIe/fuSUOHDmXmZcGCR48egTEm+vj48OVq+9u21e4zCQKRz4ICIir5+URcXm71eQmNGzfGqFGjsHfvXmzfvl2aOnVqzc/Yli2U7bp4kUhv2bWqjD59+iA8PBy/t2uHwTk5MG7ZEsGjRok2eXncdE1LTRnaP3smGYeHs+MNG3IDBw6EjY0Nk8vlmDlzJrd65UpJ+uUXBj+/qr3iVlYUeNHsTUZG9Ew2aACue3cYpKcj3t4ey93d8b2nJwWVnJyomqdTJyrt37EDXG4uMtavFzxzcni39HREbdvGB23diuDWrVGgUPBDy3q1HbS0YH7oEFtuagq+pASzV6+G5ciRorarK4cvv0RvZ2cUFhYK2/bv57uam0ueS5eyeq1aSYWnT8PgyROqwigupsDpxInAzJmwWLwYffv2xdmzZ5GYmFiFXIHn6fPNnk3r++ZNCmx26gTMnIlLcrmY1b07p+vuzj3r0AE2Gu0Cxshm7N9P9n/5cshkMkmlUtHroliuUN+rVy/O1cQE4vbtOOzlJfEymaTpuVbl53MDkpNxy9dXwPv08SWJ9hhDQwrC6uvTPsMYBXy/+ILWrJ0dBY+NjSmgs20b7W+CQH9bpw7QqRNuRUdDrVbjypUr0uHDh5mdnZ1YUFAAb29vrrS0FCYmJq9vH+I4qnbIyCAi37gx+TyVg2+1AcfR+Z48SdWAL+uXHDtGgVkNiopoHWzfTlVBzZsDly8joWVLFO/eDXd3d4iiiKNHj4rPnz+XXrx4wVtZWQmdO3fmAUhnzpxhK1asQNl95WLPn4dKLkf/8+dhnZwM5bVrMB4xgsTN9PRw9eJFEaGhGD9+fHk1f2fSN3nz1szzdM/at6fPWFmXpBpER0dDEAQEBgaWZmdnaza8BgC8AVys1fX8gGrB3kps5QM+4L+MZcuWHWzatOnAHj16aGnInEqlgpaW1tv1Ef4DUKvVCAsLQ3R0NNLT04XCwkLe0tJScHNz4xs1aoQ6b3CS8OOPtJFr+tfehIgI2oR9fCh6vngxZTlTUymj5OpKZcuvUUUWRREZGRlITk7Gs2fPhKysLJSUlDCVSsVKS0tZaWkp1Go1eFGEbWIiSnV1JZWLi9ixY0fe0dGRNvwrVyhi+vHHtLG9wYC/CatXrxY8PT359u3b/6P3dOPGjaKzszPX6W1mSm/eTE7eG8rag4KCxNu3b7Px48f/M0GDrVtpU7ezo+v9+DERmCdP6PU9e0iky8aGMkHz5pHzY2xM0XJb22orGjQlqPXr10e9evUQHx8vFBcX83K5XCoqKmI+Pj54q+sF4OnTpzh48KA0ITWV1TtzBnj8GMePH0dkZCS+/fbbN95jURRx6tQpISQkhDcwMJBatGjBbt26hU8++eTvtRd89x05St99V2PwRKVSYeOiRRhaWIjMMWNw6dIlIT8/n2/QoIHk5OSEdu3avd0CjYggwZoBA+iZeZtMeRlEUcTu3bsllUolTp48uXblH23bkp1YvvzV1ySJnMcLFygbVYuKkvj4eERGRiLz8WNBjI3leAMDoedvv8m2jxsHqxYt8OzZM8nCwkKytrbmVCoVkpOTpczMTObesiWaR0ai3pAhVYMbgkC2REsLePAAwoABuLdsGeq2bg1zc3NwHIe4uDio1Wo4OTlVrBlRJFK+ciV2BQaKSUlJnJ6enqCtrc23atUKHq6u5c7ymTNnoClRnTt3bvWK+3FxlDm2sKAKoPv3yXk/deqVXw0ICBDCwsJ4S0tL2NjYQEdHBxkZGYiKisLEiRNRr149mHnDQQAAIABJREFUnDhxQigqKuJHDB1KAZbly+m5fRNiY8mWP31KAbHoaCr9vH2bgmQ9e1aUk9eArKws+Pv7Y/jw4eXiY69AqSQyr1AQWfr+e8pO/vJLlV/7888/8fDhQwDAyAsXwPM8csaPh7uHR8UIpMxM3IqJwdmzZ2FoaCjOmTOnivO/atUqceyFC5zp+vVVK0hKS+nL3p7OJyaGAsbW1hR00dfH+cBA3Lp1C19//TUFsIuLqXc+IKBifGWTJkhMTMT27dvx9ddfQ7p5E7IjR1D68cfQnTAB3KefUuWEkVHFVAwjI8o279lTIVpXFrjKyMjA3o0bpZGbNrE/pk8XR4wcyZUmJOD30FDxK3d3Dvv3kx2uZL/27NkjPX36lLm4uAiDPTx4mJhQT/2qVZSNX7CA1vr06RRY79UL+w4dEtLT0yFJEoqLi3lnZ2ehT58+fHkALS8P2LgR19LTxQt16nAdOnTQkKuqpc6PHlGgrPIIwKtXkbp4MU4OGSJNmDjx72+mgkD7fVERvVdsLAV0c3IogJCSQuthxAha72FhFCgC6Lw0wpiSVBG4/uMPIDsb+XI5CqKjIUgSklq0QEmDBsjQ08PTtDRJLpeL+fn5vI+Pj9CpU6fa2byEBAoEHT1K/fMREW/3WdesIRtVecpIUhI9e5WFa+PjKfOsmSwQEQF8/DFSUlKwdetWfPTRR4iLi0Nubq7Url075uTkVK6lUFxcjJ07d0L24AGGNG8OWf364GbPRtY33+B8QoKYravLqeRyuLu7iz169OAYYzh79qwUGhqKuXPnvvv9nDCBggmayQ/VID09Hb+8ZAcALAaQD2DdggULlO/8/h/wIbP9Af9e+Pn5MYVC0dbT01Orctb0b40peo+QyWTw9PSEp6cnAPD5+fm4efMmHxQUJAYEBHBjx459pcwQAHDoEDlW1SgEvwJRpNmXERFEor7/nghgSgptZOPH01fDhtWWUEmShNTUVISHh4sxMTHIzs7mtLS0JF1dXaFevXqyRo0aQU9PD3p6etDX14eenh4MDAzoGl+5AixezLB0KV+u/slxFeWOL14QmXvwgBylNwUXakCXLl34U6dOSYaGhqzyjNL3DWNjYy4/P1/E2/Rt37pFZWavIXmlpaW4ffs217t3b/xT2XlMmlTxfVQUOaXduhHZnjSJ+jktLcnByc6u6jy/huCVlcVKqampTF9fX+jZsydvaWkJIyMjdvr0aTE2NpZ10vRcvgFqtRrbtm0TsrKy+A4dOkhmrVoxTJgAlJaib+/eiIiIQGJiYkUm5yUUFBSA4zgEBASIUVFRPAC8ePGC+fj4QCaT4ffff0fXrl0lLy8v9lZBGUGgaoD27amq4DUK8RcvXpQUubnMatkyWOnro0WLFnxiYiKuXLnCAgMD4enp+Xb2x8UFwu7dYAsWgFu7lrJWtVThLi0tRXR0NCIjI8Xk5GQ2Y8aM2vdZbN5coV5fGaJImeasLPq3lq0bdgYGsOvSBdixg0dBAXD6tCz+s89gfuUK+vTpA7lczgICAlh8fDxycnLEOnXqcCqVCk+OHEHDy5eR17MnUq9eFWJjY2Fubs7srKw4+zZtEPDVV6Lo6orHI0ZwjY4eRfHWrdLejh2ZXC6HIAgSAERFRYmDBg3iGWNQZ2VBGR0NbYUCo0eP5hISEpCSksKnpaWJp06d4lw++QSKmTORM3IkBEGQGGOsd+/eNd8zW1sKUoaHk7O+ZQupLE+aRN8zhsLCQty4cQMhISH8p59+ikYvEV65XC799ttv7OOPP0ZkZCTfv39/aiHQ0XlzILKwkLKC27cT6V+0iOypjw8RYltbIowrVlCGMD6e+tzbtHnF3puamqJx48biwYMHuf/85z/VV1kpFFQqP3Ei8OQJVO7uyE5KwstF/HFxcSIArlu3bmi8YAGwYgUazphBmdWgICpJbdIEHr/+imRXV4SHh3O5ubkwrqTe7+TkxF0PCUH7lBSYaMj2ixfUj75uHfX3Ll1KwosPH1K2vWzNdujQAc+fP5dWr17NjIyMxGHDhnFmgYH03pGREO/cQUH9+rC2toa+vr4QGRnJt+rSBejSBQoAOHoUoRcuoGTSJLHR/ftceL9+Qrfdu3lYWFBVRGAgBQDKCO7u3buF7Oxs5DPGn+jXD6OMjDjjffuQrVCgxa1bDJmZFMwEyJbcvg1cvIiR9+6xjJs3cTs/n4LP/fqRvXn0iKpa5s0je+3tTZ9t9mx8snIlr9EyyM3Nxbp163gbGxu4a8QCyyZcOKanc4oVK+Cq0TQ4cIBU9csE0XD4MFWyVcbhwyi2sfn7WhGBgfR+48eTyOHIkURGDQzo2JJEBO7pUwr2r1tHa/iXXyiTmp9P1wAgP4UxEkqsX79c+M9QFGGYnAxERsL61i0KhmhpAeHhDA4O/FUzM4SfOMF3at++dnOqNb7W4MGkNSNJtNZ27KidKKe/P137IUMqnq3o6IoRfQBlvvv0Ib2BoUPJhyubplK3bl20bNlSCA8P53iel0aNGsVV8QcSE5H93XfgdXXR9fJlGLx4AdmOHUDfvtDT0sKEMr8kLi4O+/fvZ5GRkdDW1kZmZiarX7++Gn+Hr23cSAGuhQvp3KtBdTZSS0vL7Lvvvpv/zu/7AeX4QLY/4F8LjuMmGBoamli877m6/xAMDQ3RvXt3WFpackeOHKmRVEBPr1yVWpIkCIIApVIJlUpVPrZDLklQKJXQnTABLD8fbP162liDgsiJa9WKspvVGEhJkpCWlobw8HDxwYMHTKVSoV69enB3d+ecnZ014m5vfvZ9fKjMOTX11d5WxiqCBePHV4jfvAPc3NyQlJTEQkNDBTc3t3+kcVsURaSlpUlv3RtmY1MxH7gMarUap0+fFlJSUqCvry89f/5cpqurKzRr1uyfaTrXQJJovvfnn1MgJDKSxNY8PckRCg0lR3LtWvp5mzbl82VrAmMMkydPZmWzm6ucf9u2bblff/0Vly5demN2u6CgADt27BB1dXXZ7NmzoaurS95K8+aApye4du3A6tWrsc0iJCREOnnyJAOAunXrSjNnzqTRM5IEmUwGHx8fWFlZ4Y8//kBhYaHYuXPn2gVMBIH6162sKLv8mlYHQRBw+/Zt9rWGKK1cCYCUgUeOHAk/Pz8EBQWhW7dub3xblUqFo0ePCnK5nD179ozlKxTM0ddX/OjbbzkDKys87tkT+lZWaNiw4Ss9toIgIDw8HEFBQZJKpZL09PSkadOmcbUeIzdrFvUzvtxjKEkk/qXJZNZG50Ktpi87OwoUVCLodnZ2VSoNhgwZovmWA+iZe+jvj8jnzxF24AAA8G3atEFMTIzw8OFDofmkSZzK2prLyciQ2nTtis52dsDQoczG1xcpggBPLy9WVFQEf39/bunSpehz7ZoUZWHBHvXti06RkfDx8al8Dpy2trb6aEyMrImtLYofPkRwcDADgICAANjZ2b0qShQaSrbr3DmyyUZGtF4HDKCe4rVrIc6ahR07dkg5OTnMx8fnFaINAL1792YmJib4/fffoaWlJTnq6zNcuEDZ09f1kF68SL3KxsZEVDSOed++1Fo0ZQppbaxYQToMc+fS89+/PwXbGjcm8lPJpo0cOZLz9/cXDhw4wJmamrKioiL07du3XGwwISEB+bm5klmzZix7+3Ypv6QEQmwss9m9W8xVq0V9fX0MGzZM5uHhwV29erViQsE331AGWpKIoDIGREZCy8ICpkFBEgCWnJxchWz7+voifN8+6drGjczJ3ByW2dnQc3MjsrJsGX02U1OyVampFAT76CNg5Upoe3nBx8eH7d27F9nZ2dzWrVvRv1MnNB46FMlHj+LE9euiz0cfcQG9e0PbyIidPXsWurq6cC7LugcmJ+NWXh5azpzJ7bx2DTaMSSgpIUJ37x4RfUEAJkyAWFyMhIQEvmPHjsjOzkbnYcNg4OdHmX9ra7jcucOwdCkFuA8coGC5QgEsWQIuIQG8tjbsz5/ngz/9VGw1fz4Hc/OKZ2vYMAoo2NtTECUlhdbE7NnAtGlIKNtbXtYzQUgILo8di36ZmVCUlpLd6tWravDm4sUKxX2NcvvChXgeH08k8W3w4gXdg5Ur6XObmxMpdnYmn6MyVCoKQsfEULBIT48y96NHU8B30SLqo27YkD7799/TPX4ZHEd22cqq4nOoVLSHnT8Pr8JCICQE0oQJYElJlD1v0YJ8jRoqtQBQcFyjXu7rS6KMa9dSRcf81/DGmBjSlsjKqghQP39eVU8iIIBaL2bPpgy9g0P5S1paWujbt69mD2WQJFpjX3xB13bOHNSTy9HUywu7LS3xn//8B7JqgggNGzbEt99+y06cOIH4+HhxxIgRnKOj49/jatraKNcG6NKFfLuXoFmDHMddFUXxWwBrSktLd/2t9/2Acnwg2x/wr4Sfn5+tTCZbO3jwYL33Joz0X4K2tja0tbVJ1VitRk5ODnJycpCbkgKnr76STo8aJaXzPFe6eDEEQQAAzYxLSUephFFurtT9zz+5R/b2yNfTg7YgoPGIEbjTti3iGjdGqbY25NHRkiIhQVIoFJK2tjZ0dXWho6PDGGMsKioKSqUSZmZm6Nq1K3N1dQXHce8W6p4+nTb4I0dqztKcO0cb2+bNRPo0Ee23gI+PD9auXcsXFRVB9+WZs+8B9+/fh1qtZm9dBuzr+4rDfP/+fURFRTFvb28uPz8fzZs3h4uLyz9PtLt3J0d3/vwKdeHMTCIM48bRpg5Qb1ZuLpWwfv899Sdr1MmbNHnl0Iyxaku7jY2NMXLkSOzatQslJSXo0qVLleh3QUEBzpw5g4SEBKGoqIi3trbG8OHDuVfECrdvB2xtwa1fL+3atYv17NlTcnFxKX/DzMxMBAQEsA4dOqBjx46vkH4NGjVqhHHjxrHt27fj7t27aNKkidClSxfeqKaRLNnZlKEcN46U/mupxpp+/jzsqqlI8fDwwI0bN2Bvb18t6dJAkiSsXr0apaWlvKmpqVi/fn2pf//+LCwsjNspCGh/8SKyUlNFpVyOg3Z2nImJieji4gIvLy8uPz8ff/zxh1RSUiK1aNGC69y5M3tr+/f48auOqFpNirsuLkQAakO0f/+diHtaGpVbv2XVBieKcNmzB+LPP6Nzq1ZYvXo12rRpA19f34r7O20aMHkyQ4sW9P/gYNisXAmbmBigdWvo6+tj2rRp7NSpU9A7dIi1HTQIxTIZLl26BBcXFygUCty5cwf6+vpo5egou5aXh4DQUGiEK3v16oVz587h+vXrMDQ0hFKpFHx9fXmWnk5Ed84cIquSRARl82b62fz5wKFDiF28WCrW1pbmzZv32vvQtm1bxMfHC1paWjz3ww+UzatJVDEjg8impSURqJfHASYkVHw/cyY59vv2UXbR2JjuB0B9syEh9HXvXvmkiLFjx/IHDx6UQkNDAVAfOQAYGBhIpqamUmJSEpfXoQNG+/uzcyNHisP27GEBz55xiW5uHMdxWL58uVTWXshycnJQ/jxv2UL2pHVrIpv5+YAgoEWLFiwqKkoKDw9nGrILUOVXy65dmfaJE9KZAwfEyT/9xN91d4fM2lp0/fxzjvfwAGvYkD5PnTp0D1xcKLBz9y6MywiPKIoYMGAA/jxyBEbjxiHv8mVY1qvHmjGG9AYNUMfDg7t48SKyNarfABo0aABRFKEXFyf2vniRud6+LYOpKRF9Q0PqJR41CpAkcPXqoWPv3iJna4v+UVEcevemsna1Grq//YZDQ4dK49q1Y6UmJijo1An13NzAjI2pB/vTT2Fsa4vo0FBcuHCBM1KrUU+lgpGODpVaR0RUKOM3b077aHExEbbgYNgbGsItLg4nTpzAzJkzK+yrjg6iGzeG1ty5SLh5U3o8ZQrqmJjA8+FDBpmMgsDffFORsb1+naqczM2RdP26JIrim/e558+pPHz58oqWEk9PCja9rt1l+HCqcLh0qbyHvFzgE6D9aP36CgX+mzeJdNamnUIup8CEvT2uXbiACJlM6jBtGkNwMF3Le/eoCuLGDSKMnp4UdGrQ4FWRRY6jQFXZ9YQoUnZ36lTK9CoUVX+fMbIJv/5K5eMAVZL06kX2784dygpPn05fGptVGYJAAZXISLo/S5ZQwKJPH6BNG8hat8alRYugr68vaWtr13iPOI7DgAEDgLepwnsNCgoKAHd3pPv6SrmzZrGAPn0wfNQoOJa1MKpUKixduhQAIIri5QULFtwA8PaKah9QIz6Q7Q/4V0KhUOzw9vbWfisxq38J7O3toaenJ6xevZrnOA5yuVxUKBSimVotczA0ZE07dGAdraxgaGgIfX192mBVKmDlSoadO4GvvmJo2hSW3boBenoQ27ZFkaMjugoCRFFESUkJCgoKWEFBASssLERRURGKioqQl5cHtVqNTp06wc3N7d0JdmXI5SSCcunS60siOY6cQSsr2nBGj6YNrVKm43XQ19eHkZGRcO7cOfTv359/373bRUVFKC4ufnsiHx1NpE2jDg6KAHMch7JRYv8sVCoqRfTyoqj0+vX0vbc3cOIEfZ05U9U54nnKGK1YQU7906fkjO3bR45A06aU+ayFeJ61tTWGDh2KK1euiD///DPn6+uLVq1aIS0tDTt27JAsLCwkb29vztPTEzKZrHrHwNUVUKnwnxUr2NYRI3Dd0JC5VFpLhoaGsLOzE69fv86JooiuL6sXV0L9+vXx5ZdfstTUVJw9exb+/v7geR5yuVyyt7cXO3TowJuYmFCp5fLl5BBrFOHfAFEUUT8lBTqffkr9nC+hT58+ePz4sRQYGIjJkydXe8AXL14gJiYGSqUSZb2W5dfEzs4Oqt698WLWLJhGR3PYsAEvFAo8aNqUC71/X7hy5QoAwNnZGQMGDODeKcj4+DGJEb38eadOJbI8YsRrs/sAyLls3pwE9oKCaD3Vgmi/Mns6MBCsYUO4lY1PY4whIiICVlZWFVU/ubnk0GocV8ZIwO+774iEmZnBMDAQIx4+pH5QAGMB+Pv7Y8uWLXBwcEB4eDgAoGFcHAZcuoT4jh0FmUzGunfvzrm6uiIhIUF49OgREwSBqVQq3uXJEzTw96d7rHluLl6ka7dsGRHusDC8aN4c7Msv2bC1a2sV8Hjx4gXTDQvDJVNTFFlbwzIsDK6urhWVC6JII5K2bSOi169fxWi0yhg0iMTtzM2JsK9eTSW7xsa0ljXn8ttvRB4SEihbde0awPPQt7LChAkTmJ+fH1xcXNCiRQsIgiA5ODgwlOn0MMaAsDB83L49h6++gmdqKhplZSE5OVmIj4/nMzIyoK+vj1eqypo1o89RXEwZwClTYJSYCOWnn6LzrFkIfvJEbPXxxxwWLKDsu44OnEJDmdOkSbzk6opGkyZha3o6M27VCrt//BEwMIBZdDQy/fxgbmEhshYtULhvHzd83TokmpmB79cPAwYMgIuLCywsLJCZlITG3btDfeUKk8+Yge6RkcCGDZBNmoQzZ87AxMQEDg4OCAsLkwxEkbVo1YrLWr8ejx89guOjR0Qk9+6lXmrNdIjkZNR78oS79dtvaBsWBo7nKWgcGQk5z2NAbi574uwsPXV1lZ5oa3NDfvkFNpUqOvKysqBUKiFJEvbt2wcnJydh2LBhPPLyKND4MqnT0Smfua6/fj0GREXhRysrBH32GZwXLcKLwkLk5+dDLZdj/4oV8Dx5khWdO4dbRkbwPHeOjhETQ+uoe3eqoDAxoT2a51FSUiKam5tXH/yNjKT9olkz0gL45BMKqK1eTZUdr9vjU1OpuqVXLwpISRKtT6WSbIpmhKKXF1VnnDhBa/zwYSKemzdXlHrXAvHx8VJhYSGDlhbtv5X2YLx4QYQ7OZnsgp8fXZMvvqBrYWtLWjia9rfPPqN/nz4lXQYtLapSaN26agXQmjX0bGoCQP7+VGWyYQNlx0+fJmHSyvZQraZr2rYtHc/enu775s10LSrZ4rI51tJbT0X5G1Cr1VhVJvDGN27MJgUGov+xYzggk2HBggUAgGvXrqkAyBljJyVJWv1fO7n/Q/hAtj/gX4dFixaN1NfXb+3t7f3PZgv/IXAch5f6Kzl88w2HDh2AmzdRPtVXkojIjR9PJU5ubhSpvXePyq/c3AC5HBwA/f/+x6jA55+T4MvBgxTVrgn6+uQMZmVRJFhHh6LQTk61GskxZswYfuPGjZKDgwMqZ0jeB8LCwoSyfvC3W1PGxlR6CMrAymQy3L9/X9LW1v7nlSWDgigT/egRZWjr1aP1onHee/emrJihIX0/cCCVilUGx1HkXzNr+fp1EpC5coWc9QEDqPfsNUERR0dHODo6co8fP8aRI0cQEREhamtrc7a2ttKIESO4WgVG5HKwgABoxcQgMSEBp0+flrp06cKePn0Ke3t79O/fn1u3bh3yNYrsrz2UHDY2Npg0aRKvVquRkZGBnJwcdvv2bfbLL7/A09BQ6BgYyMunTKl+lEsNCA8PByeK0H2N6rmzszO7c+cOoqKiRCcnp1cW9f79+6WUlBTWqFGjaoMxcrmcypnbtgWaNYNBWBjazZ+Pdj/9xMfb2oLjONjY2LxbpEmpJJtx/36F6GJpKWVAhw6lbF1NvcspKdSreOIE9Qi7u1MwphZO4dOnT3H48GEUFRWhXr16UsuWLZmXlxc4lYpKSstgZWUlnj9/njMwMBC++OILeg737SMnPiOjgvhaWdEYu1mzKPvm7U0ObSVMmzYNx44dKyfa3bt3h1wuh/7WrZitpVXlGR8yZEj5/0NWrMC127fh/dNPqG9khPKrER5OGeJp04ApUyBt3ozCBQuk6CFDpF5793JwcXljr33XNm04fssW6V67dihSqcTgEyf4U6dOYd68eVSFMm0akett26h3tSbk5latwjAwIMe9Rw8iEZUJkZYWkYX0dPqbHj0AxiAeOQKTzEw4OjrC3t4eqDRDufx53b+fqpIuX4blTz/B8uJFuLq68iUlJdizZw8kSRLT09M5MzMzcBphwaNHqeLh2TMiert2AY0aYWBODst4+hTXcnI46+Bg1C+7Lxgzhtblhg1gwcEwBaC3YYO4e/duXiaToXOnTnAfOBCh06fjvihy6enp4HkeOyZOhKhWo9edOzD+80+U3LqFunXrkibG9OmQabQGysrZrc3NwRjDiRMnIEkS+gQGYlhKCmQLF+Lhjh1S8JkzouOtW3xYSgoMrl9HoxUrKlKGBgZwdHfHlZYthfUODtzkoiKmO2kSMGcOuK++Qv25c1E/N5c1s7Vlfn5+EF+6XadOnUJ6ejrMzc2F7OxsvnxCym+/kV2tTqFfg88/Bz7/HJ6bNkktN29m5xiDWKeOUGxpCQcHB3748OHgeB5NvvoKzz/7jAIqABHszp3p2i5cSH3JZddEEASmrvy8xMXR7zdpQgTT2ppGvl29WrM9qA4TJ5I92LGD/ADNGk1LowBG5WPp6FBguG9f6qG2tqaM8ahR1ZeUVwMLCwv2/PlzlJaWvjoJwsCA1p8GEybQmnzyhMrQg4Iom9y1KwUHdHTo+W3RgsYDAtRKk5pKlU9PnlBrnpcXBRLGjKGAzKhRpCj/0Uc0StXCgq6BKNJzoFGI37OHkgs3blS0ddQw7aK0tBQWFhb/NXXfyiNVBZkMumvXwnHTJkzr1w8AkJqaiqtXr8oBQJKkUQsWLMj9b53b/yV8INsf8K/CokWL+svl8q0jR47UeeN4m/8VSBJFVCtvDmfP0ibp4EBkavp0Ms4dO77TuK5/HJ6edI5DhrxZVMnUlDYdgMjOF19QKeQbYGhoqKkCeK9Blvz8fGRlZfHdK1//2qJRIyAyEjk5Odi4cSM4joOBgYH4xvFefweCQBmwefNo7Mjy5eQEJCZWHWvi50eR/d9+I8eiNsJb7drRlyiS45CbS46Tlhb1q8lk5AxVQ6AdHR0xdepUrFu3juM4DuPHj68d0dbAxwdjSkqgnjMHS2fPZk+fPkVmZiYYY9BMxYiKikJBQUG5euubIJPJYGFhAQsLCzg7O3P5QUF45O/PHZfL4evtjRoKzF/BixcvEBAQgPotW6oNpk6t0fD07NkTERERuHPnDpycnF553dXVlaWkpKB9+/ZvFlLT9Kj6+QFXr8Lu3r2KDMy7QKEg0qrp/xRFIngODtQOUZ09PX6ciObs2XTf5XJg+HCkp6fj5I4d4DgOzs7O8PDwwOXLl3H9+nXo6OhAqVSC53lJEATGGENpaSkaNmwIxph09uxZdvXkSUy5dAkGx46Vv1VqairH8zyaNm1addF88gnZvj17qp7bZ5/Ra4sWUbaoEjiOQ5MmTcrJNsdxcN+0iZzpGTOqvz7+/vD46y9kTpwo7YuNhXLZMubg4CB5e3szm5fsU1SHDrgdEYHRABHtMWNIyOw1e1IjUQQmTGC2U6cCAH/z5k1cOHkSD3/6CU2OH8cRZ2dJ2bat9KmZ2etDjxermbBTpw4R4w4dyOmv1CsKoEJA6uxZQKVC4MqV0uQtWxg/dy4JdDVpUv25//EH2QB9fQpoyGTQ1tZGnz59sGXLFu7XX3+Fq709Bq1fT6Tq2jUi/JJEIlrTpgGLF8OmY0eUpqXhjo4O8vLzUV+jcxEdTSRk8mQiLcePY5K/Pw+gQr/hxAnkx8UhMyYG3bp1g6urK3iex40bN1Bia4ubAQFo9/w5LIOCiFTNn0/B6BkzKJAbFITYadPQPiEBTRwcoO3gAKNjxxhTkniyp6cnCzt3jn+kq4sbx47BKz9f2h4UJDWIjeVSUlIkpVIpOTo6cp06deKfz5ghqTdvpv7q0aMpS7p8OVUaJCRAoVBAoVBArVZDpVKhtLQUpaWlaN68udStWzfNnkD/lpYS2asFekybxjB1Kj4BgB49eDx4QCXnWVnA7Nm49/gxWj55Qvd++XIKrvbrV6G6XaktRM5x4IuK6JkODKRzLy2lCqMyMa+3wv37VA79669UFTV9Ou1HGixeTOvP37/iZ336VASqbW0D7J+KAAAgAElEQVTp99PSqKLq8uXXilSWX5MePRAaGoq7d+/CWyMSVxMYo/epnDmfPZuC/ZmZlPX+6y8KqPXtS6Xds2dT4OrAAfp8CQm0n8rl9HcxMRS0HDGCbNP160TUp06lvXL/fvJvZsx4K7ttbm4uPaMA/n+FcL+8R6+OiUE3SYJDly74cfp0iFXtQgaAWs64/IC3wf8jbOYD/l/AokWLhmtpae0YPXq0zv9i+Xi1uHWLsgF//kkk6vPPyWFxcSHS6u5OpYv/dhE4Hx/aXPbtow2ztoiJoY14xgyKIv/5JwAq6y4oKICJiQlkMhmePHmCBg0aQKlU8u9T0Ts+Ph6HDh2SzM3NUadOHQaQANXly5elkJAQ5uHhAZ7npbi4OLFDhw5848aNUVxcjNu3b0utW7dmXHExtJ4/R1ZWFgBg2LBhcHR0/OeI9qZNVMoWEkIEqbCQIuUHD76qLu3oWOFcTJ1KzvjDh7XLHHBcRcbF15fUdUtKiNiYmVFfr63tK8Tb2NgY48ePh4GBQRUxpNqC69oV8iNH4FFUhLDQUPA8jwkTJuDOnTuIiYkRCwoKuJSUFDSpprf8jbh0CYZ79sCka1d2PjMTlmWjX9LS0kSVSiUVFBRALpdLcrmcMcYkQRBgZmbGeXt7c48fP4YkSZg8f74MurpErmqAs7OzFBwczK1bt06aNm0a09LSQnx8POLi4nDt2jU4ODig4cviZK9D585UfrhwIX1//nzN/b414flzUlt//Jj+n59P2avPPiOHv3KATJJohFGPHuQMZ2QQ2dqypfxXdu3aJZaUlHCaz3bu3Dno6OhIABjP8+jZs6ekVqvZ6dOnAQBeXl7oSVUE3P379xG0bRuCtbTAR0YiLy8Pjx49kkpLS1mjRo3g4eFRlWsePVp9OXVxMQWQVq6kz3fuHMBxEAQBpaWlOHLkCADAxsZGcnd3Z2jS5BVSDoBI5LJl5BgPHozuFhasO8g27Nmzhz1+/BhzVq1C+ubNaPzxx4iLi8OpU6fgPnAg4+VyEir74QfK7O3YUX1Lwr17tGZu3iz/UVvGYJSaKhbfu8et6dIFcnNzJissxLJly6Tp06ezarUGoqKo6iG3muSSkRH11fbtS2ukJrFHuRx3AcYOH0Y3S0t6vtu1o+uYl0dZRg02bqT1kJdHLStljreFhQXMzc0lmyNHWIeVK1HQpw90r11DbkEBUiMj0UhXF9pmZnQO/foBa9YgVamEqKMDWZcuFdMTNGv5m2/o+hkbQ1uhAM6dQ0G7dnj06BGCz58XvP/4gx+wbx8qt5d06dIFkiRhUUICml24AMslS+gacxyVJjdqREQIgJOdHRIfPBDjQkI4eX6+4D5lCq+xl3K5HCO7dcOL6Gh84uoKnbp1WYRMJmZmZgqNGjXiiouLudvnziFeECSFj4/UqmlThosXqV8+OprKiGfNAp49w9SlS/FLTg6UurrgOK68baLZyzY3LY36oavr660JmnV19izZ4sBAeobv30dqx47o8OefEubPZxg1ip6Fp0+pfFtHh8qqHzwAMjLQY+VKFu7sTO0I48ZRS0g1gli1giAQoW/ShO51UhKtw8qYNOnVyiiOo71o9+4KFfe+fYnwjxpFqt4vB4xeQlpaGgRBqH6iS23AceXK5+jUic6zqIhsZHY2CSQuXEjP2+TJVKWhCQqtWUMiemU2A7/9Rs/ehAmUATczq3Y8YG1gYmLCIiMjoVKp/pHJOrm5uUhISECzZs3KW1jmzJmDNWvWlP9OYNeucHnwAM0fPEi55+4+EEAigMOMMeG9n9AHAPgwZ/sD/iVYtGhRD4VCcXjMmDG65i8LXfwvY+NGEh/R0iLHNjOTNsBOncjB/l8Sf0tLo8i4v3/FrNXaIioKOffvI93eHrkzZuBMjx4Ax5VvBqIoQiaTSYwxZmBgIKnVanHcuHG8vr4+Xu6XfP78OSwtLav8/OV+UUEQcP78edy5cweOjo5i//79OY1o3a+//ioJgiAZGhpyKSkpUCgUklKpZAAwdepU3L17VwgODuYBQLegAE6xsQhxc4NCoQDHcZKvry9r8TZOVG1w5gyRHlNTItUdOtD1trOjgEV1JWm3bpHjrHlt+HByhCqRpreGKJIjsm0blRTPn0/R/AEDaidw8wYkJCQgKSkJFt9/DzEuDvUfPKiSxX6brHYVHDtGpbQODkh3dkZAQAAEQRDL1gVXr1491KlTB0qlsry/kud53Lt3D6WlpWCMoXPnzmhvb09ZlzeQ3czMTGzcuBEAMG/ePKxZswZFRUXw9PSEr6/vKwrjtUZCAmVzhw0jovym/moNcnMpUPPdd+R4L11Ka+HrrytsjChSX+dHH5ETumYNEe6XsHfvXik2Npb5+vqiTZs2CA8PR0REhNilSxeuchBUEARERUWhpKQELVq0KP/MSqUSyp49EdKrl3ilsJADAIVCAaVSCZlMBj09PWH27NlVA1aLFxNhPXSIznPkSPoMdnZElm/coFab1q2x9/RpKTY2lgFECidPnkzHePiQ7NLLZHjlSnpWtm+vQhSePXuG3bt3w9TUFFanTsFo+nSI2toaETxp6NCh1KtdWEg2W60mgrBsWdXjiyKVpBoaUiAyK4uCkjExkHx98WdRkVhcXMw+/fRTdv36denChQvsk08+qT6gVFRELR7V3JdyPHlC1+f48RqDtEuXLoWHhwd8fX2JTKtUNGd37Fh6ThISiEBxHJUAz5xJBEKjYv3iBTK3bQO/ZAnue3khqFUrmCcnwywzE0+9vcXP583jEpcvh6CvD+XJk2JjW1tOXqcONhkZSW7u7sxHQ+569KCg3oUL9LkYA2JiUNq8OVbOmgWFqank7eUFz3HjmCwkpNp2lk2bNokAOBMTE7g1agQnX186VoMGlPHnOCrLX7IE6ps3cVgQJO9Fi5h15aBCRAQJXG3bRvaxEjkWRREPPTykRnI50yuby45nz+g9Ro0CAOTl5eHIH38IDY4e5W96eaHDjRtI+ugj4UWdOkwo01IRRZGJoghJkuAQFsasY2JwduBASJIEURShVqup1UFfX8zNzS3frBQKhShJEqv89wAJLeoWFKBYRwezf/4ZyZ07iw4vXnCYMoX2iy+/JNvcvDkRaycnYMcOPA4PFw/cvMlp+nHfGZcu0bq4c4eCNd7e5M9URkkJrfnqxggWFVFyYfPmqq+dPQt8+y0FeF9DNn/77TchJyeH//rrr//e53gTsrJofnh2NmWwnz4lW6QRf3v4kPq1Bw8mbQRBeLUP/y1QUFCAQ4cOiRkZGeyLL75g76uCMzMzE1u2bEFpaWmVn3/33XeIj4/Hvn37ND/yYoxt/mHBgjEAjgGYBkk6W90x/fz8zOVyub+WllZhcXFxqiiKmxcsWPD0vZzw/zF8yGx/wL8CCoViad++ff/fIdqiSJkmpZIyhDExtMH07Vt7B/rfhvr1qY8pN5ccuFqUEIeEhKBly5bgnJyw+fhxyezSJeabnAw9fX18OXw48o2MwPM89PT0kJSUxAoLC5GWlsauXbvGb9iwAXK5XGrVqhWLiooSzc3NudjYWE1vqHpqWbnvhg0bhOzsbJ7neYwZMwbW1tZYsmQJRJG667p06cKdP38eT548kYqLi5m+vr44Z86cyp4B27Nnj/D06VN+27ZtkMvlfPv27REWFibqAuh+9SrXbts2REdH49GjR+z48eMoKCiAg4MDDh8+LDg6OvKdO3d+t2ual0drRdMfpqkauHCBAjKxsRXCMy9j4UIiZqNH0/9376bMlFL57s4Ax5H4y3/+Q9kojQDN9u20bk1N6T0tLd9plmtQUJAYFxfH1XFzQwMfH2Gwnl4VD+2diPaff1KmZ8oUwN0d9QCMHz8eqIWSq66uLoKCgtClSxe0k8spy1p5rmoNqFu3Lnr16oVTp07h6tWrcHZ2RnBwMARBeHeiDZCt+OsvUn0+fJgcaM383ZqQkUHO7nffEZH6/HMi6wMH0j1SqSjTHRhIDnpcHGVQq0FAQIAUGxvL9PX14eHhAcYYmjdvjubNm79yLXmer5KJ1ECRmQlFVhY6zZjBJRw4IDo5OaF169acJEk4evSokJyc/Oobd+5ckXEtLKQeck3AQyajzNzUqSheuBDJQ4YwALCyspKaN29Oi/D5c8pQ5eVVHDMvj8jlwoVEGsrsrkqlQkREBP766y+4u7uLvV1cuD+fPRNDb9/mAMDAwEAcPnx4xefViJSNHUvkwN+/aqn6li30nEyfTtU/J09Sn+jYsWAGBhhaaR2WlJQwAEhMTKyWbKvT08Fpab1+4TZpQoGImTOJyFTTdiRJEjIyMug/jJE9GDSIMnUKBdmWzz+nTJ6vL5UIk/oxXX8vL9TNzASOHUO7WbOQWrcuTG/dglt+PgZ88QW3oqhIEouK0PnAAWaSnc35u7piZGYm2h07xk4XFCA+Ph5ZWVlCBz8/vpWXF7VQ3bkD0dMTYXl5OPmf/2Dw4MFo5uvLsG4dBRA05/sSOnTowJKSksSkpCTu6r17otP+/RwaNyYbVK8eEaM7dwBbW7AHD6BITX01Y3jzJvUo29tXDRT//DM4XV08mD0bR+Pi4Pznn4KLiwtvEB0NXiZDaWIiOI5DQkICUjMzucZz58K7uBit/vgDFgYGfLGjIzhjY8gUCshkMvA8D57noXBxgUwmw5iy7CLP8yguLsbz58+hra3NyeVynD9/XrCwsOC9vLw4bW1tKBQKyOXy8ukQmvJfjuOAjz9Gk6QkDkeOlGfz0aIFZbOdnChbW7a+HS0sONy8+apg4dsgNZUyv/PnEwkNCqq++uTJEzqH6lrLdHVpzc2dW6EKDtBacHOjIMzBg9VOyIiLi0NycjI/uxbtZ38blbUpjIwoUHbrVsXrU6ZQEKpxY2or6NiRKs/27qUWnOPHga++okSEgwPZVxeXGhMp+vr6GDt2LLdhwwbpjz/+kEaMGPH2EyeqQWRkpFRaWlp5Uy4GoLNkyRK0LROX43n+mSAIoZIkjfFbuFB/np9fqEySVgGolmwzxmaLojjIw8ODJSYmivHx8XOWLFmSK4qirkwmu6dUKr8EELJgwQJ1dX//ARX4kNn+gP/f4efn10ShUNz/+uuvdfg39QP/r2DePOoDMjOj0jdzc8o8mpvXegTRvxKSRI7alCkVm34l5OfnY8OGDbC1tUVBQYGUlpbG7O3twXEcnjx5gu+++47ETgSBMrhBQdQP/hK2bt0qJScns8aNG4tZWVkQRZEzMzMT9PT0+Pv378PJyUkaNmwYO3fuHEJCQjB58mT4+/vD2dkZkZGRAKifV61WQ0tLCzzPS87OzszGxgb29vbVkjpRFHH27Fnk5+ejf//+1FOoVhMBSkwEOK5KRpMxBhcXFykiIoKNHDlSI0RUe8THA926EdEeObKCvBYXU8bq8uXXlyFevEgZmsotFyNHEiHYsePtzqU2CAsjMtqmDa3tfv0qSghrSbyTk5OxdetWdOrUCT4+PuTAfPvtq8JutYEkUUZRFMmBe8vSc1EU8eOPP0KhUGDixImoe/w4Zcj/+qvWf79582akp6eX/2zy5MmvKji/Kw4dIjL1/fdEJGvKAh0+TJnhkyfp/AsLiVQLAtmali0pk71zJ92n19yr1atXi82bN+depwj/Rly6RGXp1TzXP/30E8aPHw/L6gJIcXFEWCdOJCf9JSQ9f46Dq1fD/f595HTsKPSfN48vd1JVKtK+0FRfFBdT8OnhQ8rgV9pXfv/9d8TExMDW1lYcM2YMx/z8gOPHsWH8eKmoqAhDhw5lNY5269OHnttVq2jNKZX0jP7+O2XQW7So6Al9Cenp6di2bRvUajW+/PLLVyYj3L9/H4nffw+vW7dwZdMmcciQIVxSUhIiIiLQrl07aGtr4+LFi2jYsCER9cOHqZT1558pw1sJS5YskXieZzNmzCgfg1YFgkB9vL/+Sv2nT5+S0FrbtkQSQkKIOFy6BEyYgFOtW4t3U1K44cOHo2nTphAEAXl5efhl0yYYGBgI6vR0vpDn4RwVBdcHD3BoxAgY5Odj8i+/YM0PP0jDMjOZfWoqLk+ejKCgIBgZGWH06NEwuXeP7MnUqRXr19iYRnPl5NAaOnYMyMtDcGamVDclBXaDBzOsXk2EECAS3bYtwPMIDg7GzQsXpBkXLzK2Y0dFoHL+fArg9OtHc8rLArFYsgQwMsL+uv8fe98dFtXZvH0/5yxLRzqCIKAiIKJSBLFgQY29ayyxG2uiiRqNiZFgNDFRoyaxxZLYiBolQY3R2BFsWBCliEiTDgLSy+55vj+GZUEBNW/yS973c65rLwV2z57zlHlm7rlnxpTHxcUxBwcHnpWVJfU6fJgJCgU7/eabnHOOqqoqwcDAQHrnnXfqekX9+tHa+u23uuPr5UUAZSPVvbdv365s166dqHKEUFlJDLht28iBFkUCPFWMlcxMirD26EF2RVZWvXuZc46VK1fik08++XPO9k8/UdpEeDjtncBAcqrrk/x8OscbsmlU6S3R0c8766dPU6T8wIHnUqS+/vprZZs2bYR+/fr93xQSy8+n8XRzo4i2tzc51h070jymp9OYcE4sFxsbGp+UFKpjM3gwgVeMUTHKlBQCzkWRAIWRI2vWGq5fB0aORE5ODrZs3Qo9PT0sWLDgPwNpq6Wqqgrr1q3jlZWVDIArgGJBED6QJKleBFmoqsLy1asvM+AyOK8pCBMQEMAAzAPwbb9+/aq8vb01ACAnJwcFBQUwMjLCw4cP+dmzZyFJEmOMreCcB/n7+0f9xw/xPyqvne3X8o9LQEDAQjc3t8+HDBny57k5/yY5d44O+dBQKihy8yYdmn370mHq6UkHj7s7GRr/bZHuq1cpmvD2288dskeOHEFUFOlbfX19vPHGGzh16hSKi4vVDpZKCguJejlsGBknH31U86fS0lLs3btXys/PFwYOHCipImuZmZnYvn07AKBr165IT0+HmZmZ1K9fP+Ho0aOK+Ph4mZ6eHsaMGQNTU9N6+0e/smzfTlHnWkarUqlEZWUltLW1cfLkSWV4eLhYHU3hvr6+rEuXLg1fLy6OjMsdO2gMajvUa9eSs9GYAaOSKVPIGKptaN+5Q8yDnj3/zJO+nHBO85+cTIZwXh5FLzQ0AA+PBp25goIC/Pjjj9zKyoqPGTOGrEBV0SctrVeLlHNORuCZM+RoNlD5tSFRKBRYvXo1AKgBoD8hnHNs3boVeXl5mDBhwqvlar+MZGVR5CQ0lCjhqhxElVRV0bilpxNo06sXGXlpaWToX7lChrmx8UulrAQEBKB169YY92cKKQE0LxMmUNS1QwdyRnNzyehu0gQ/f/MNfH18YKGqnt28OT3blSt0z7m59Lk7d4DoaJTZ2yM0NJRbWlqy4OBgKJVKjImI4E4AQ3XONgAy2KsjgHj0iIDAHTsoglZrXQUFBUn37t0ThgwZwt3c3Gr+oKisxOdr1mDBggVosG87QIb4okU03h98QAXG2rUjh3ftWnLAG9i3v//+O79x4wYzNTXlMpmMWVlZQVNTU4qMjBQsLS0RHx8PHx8fREREoKysDIIg1LBzahcRlMvlfOzYsczezo6c/vJyKkJZ7cyo1raOjg4vLS1lxsbGfPbs2azBNV5SQo6esTH93LUrRe91dOoAWF9//TWcnZ3RvXt37Ny5U8rPzxcA4JMJE4C2bbH3q6+QXlQEu/v30e3aNRgdPQpFUBAO2dhAfPoUg62tsT8tTVJyLixYsACatdk3Rkakt7p3p/WTkUGATe/e5OiZmmL35cuSt6en4OLrSwBBXBxVrl+3jiLWEycife5c7NyxA2N++YVfGTAAfrNmMVtbW3KYrl2jOXNwIGeqUyfKRwate1EUsXz5crqf4mICWqtp7WvWrOG9evViXrULgwE0bk+fEk3d35/A46dPCUS9dq3+iO/t20BVFQ5dvMh7nT7NzD79lFJ2Nm+mFJB+/cg5GzKEgIQBA9Q6dv9+2lP9+1OKxe7dz32Hytn+UzTyixfpmUxMiDVgbU3z8gyYUyNvvEF2zaJFDV8zKoqAAj+/5/+WmEjPun9/DUB1584dHDt2DKNGjXo+F/6vFlVHGBsbWv8WFjQ/jo6km0SR5tTamnLjHz4kvbpgAa2fxgD22FgC/ZycSB8uWkQsgI8+Ih3l4ID7+vo43asX5l+6BI0DB4jdERFBoGNmJunuVwhA/fDDD6guvva2v7//zoCAAF8Al555Wz8AFwAoAHT56LPP8kWlMvzwuHE/Jrq69pIkSQOAppGRUZNhw4bp1guMVsvevXt5YmIis7e3r0xMTJTL5fL4ysrKkf7+/pEvfdP/n8h/cYjttfwPiYL/r6A+ly+T4RMURC29WrcmmmFhISnrTp0I5e3enaLDnTqRY960KTlYfftSVPPfnMvt40O0vY8+qqGHSZKE1NTUGkd7xYoVNY6ui4tL/cVAVBVJJ06kw+zBA4qkzJ4NHR0dzJ49W9i8eTMiIiJ4u+qIVe2WJtevX4coitzS0pIDwMiRI/8efXbxIhkVtZxtURShXV0BdsCAAWK/fv2Qn5+P3377jd24cUNKT08XBg8erK64C9ChfuECGTJdu9JhXtswlySKPnp7v7igDeeEmH/7bd3fu7lRZHbXLnUf2b9aGKPvcXOjKFFEBK1XVW7dhx/S3Hp4oKS0FDdv3gTnnIeFhbGWLVuqHW2A1nt6OkXoHzxouOhTbVEoiEYLUKTnFQq13bp1C/fu3YOKyjxhwgS1o21jQ2P3Cvn4OTk5NXTdZq/o8L+UWFgQqKWrS4ba2bNkuKnWzbvvEvi1eDGBeNnZtD+vXKGCPvVUTG9IKisrAQDdq/ti10hVFUV+FAq6/tOn5BSUlFCk5vx5dRTy7l3SDe+/T86AhwftHwCKUaOgn5EBjadPScdZWJDeW76cwKFly8hh6dGDnHMvLyiCgpB24ABTZGfDYuRI5YwZM0QADGVl5Ix88AGBNeHhpF89PChKO2VKvfNYUFCANm3aSG5ubuo1OGgQsGIFOOf47rvvsGzZsoYjglpa5BSp8jcNDAgQuX37haCpo6Mju3PnDnJzcxlAwCGqKebx8fGwsrKC37176Jmfj6g334RCoYCLiwu0tbVr8n4551i9ejULCQkhYGfhQmTPnInsefMQ2bMnNHV1ERcXxy0sLPD222+zCxcuICwsjDUKOiYmknPdrBnpqEmTKGoeF0dRVkNDKBQKFBcXQxRFrF27FmZmZqxLly4ICwtDpo4OrGJj0T4/Hzlnz0rN580TThsaYur48RC//hot5XIpLCxMyFi3Dg7W1kLfw4fJ0a6oIKdiyRIqThUVRetGUxOo7sl+7NgxyGQyaKen8zylUujYrBkxFp4+JR3g4EBgTX4+MHUqrM6dw5LAQCT+8gur2r6dG48dS1Wkr1yheUpIIKdq8+Y6BbpMTEwkb29v9aTPmEHjMGAAAEBfX5/n5eUBz1aQlsvJIbKxoTXMmDptobycgECVjlu+nPbCwoWk+z09WbGVFcyaNaOo6erVtK/PnlVff+xYOhfS0+mZOScQZPRoeoazZ+t2OQHwp02pbduIEXXsGDnYrVsT+NWQow3QPnhRl4+qKqpz0K3b8+wce3va8/v2EQDdvDkqqqvIvzJT7FXl0iXSE4MHE7CTm0ssRA8PskcMDUmf+ftT1PqddwiIMDKitRcXp67OX19BXycn9f9VReKcndXtU2/fRtX161wrPFzSGDRIhKEh3dNvv9G+6NCBgKfx4+l14QKB01lZlOZ14QIFbGqBg56enjwlJYUJgvAhgJ0Akmrfkkwmm//xxx+fDggIEAFYAZB9FRCwy/PqVVmvhw9npsydKzytqEDTpk3h7Oz8QmbEW2+9xb744gsMGjRIrqGhgbi4uBYnTpy4GxAQYOnv75/5qlPyvyyvne3X8m+QC/Hx8f/9OR9379LBe+CAGvH096cDVNWaQ5ULFBFBB+e2bWRklpUR2nn4sLpKtJERFSZxdq4/X+qflDFjCIH/9FNARwebN29W5uXlie3atZOGDh36XEuoRqtujhxJ/+7aRfm3s2fTYW9oCC8vL1y+fFngnIMxhmbNmmHChAmQy+UQBAHZ2dmsQ4cOf2/ugYEBGQyNiCAI1D8ZUBYWForR0dGIjo5Wo/Pl5XSA5ufTYdqjR90LTJ9O4EzUS7KwJIkKu9S3Lh49IgPz73K2a4vK8QaIFpibS1TzX34Bpk+H4tw5PFQokGZtzTw9PTFw4MDnT28rKzJGzMxeXAugrIyi6UlJ1Mf1Fat2nzhxAgBgbGwMBwcHRatWrdRn4Icf1jWQXiD37t3DrVu3OKoN8PT0dNjZ2b3S/byUMEZGV2oqRfHmzaP2P4aGZKBmZ1NO8apVNJZOTvSZZ9kVSiVF7LKzaZxzc0k3Xb8OyGQQLCzQ7eJFGN25Q4Z2SQl9x4YNdK1hw6j2hCpao6tLjouTEzlqrVrRtRYsoFxguZzuoxqEESQJWYIgbUtLE1xv3ZIGHz8u4O5dMhgvX6b9X1VFoMu6dUB+PvRFEd3On0flnTvwHj5chLs7RZC7dSPA8vffCVzYsIEM37feotSCBgqMKZVK4ble7oaGkJmbY8yYMTh8+PCL50NZXbC3vJzud8YM0ofLlpGBXKsNU20pKSmBJEmYPn06rK2tUVZWBoVCAf3aFNoff4QoCHi2ACNjrKZfrre3N65fv45Vq1ZBV1dXWdSsmTgkOBg2eno80tGRDx8+XHB0dARjDK1atUJ4eDj/8ccfuapVXx3dXFpKkXknJ3KwPT0JPJHLCTwoLAS6dIHM3x8ubdoor169KrZv3x6DBw9mWVlZCAsLw6+//irNnTtXcHvrLbi99Zbww8OHklaXLhDu3BGwYAF679kj5Do6KoVOncQh5uZ03WvXgDZtwGNiAA8PsLQ0Ak9SU4G2bVEZHY2jR4/C0NAQfn5+uHnzJisoKAALDEQO5zAzM6P3x+igGykAACAASURBVMQQcJmTQ2fGvXvQWrYMt2/elIb9+qug5+BA83TlCjk5779P+2fevOfmp07kX0urjhNjaGjIbt++zUxNTSGKIlq0aIEmgqAGWp48If21aBFFyzU1iV784AGBMj16ED3byqoGfFIGBiJ2wgTJvmXLhj2a4GDa46mpdatf+/rSXi4rI3ZPnz7PfXTr1q1KQRDg6ekpenh4NPgVkCQCLfv0IcdToaDIbmpq4/o1O5v2wouqhXfoQKDs06c0Rs/K0KEEus2cCaxbh7i4OBgaGtYFqv9KOXSI9Ji+PrFpYmJIdzg7E4CxeTPp2169aC5zcmh8QkLoTDUxIfDgp58InPj+e0ob/OCD5zuGNCb6+kgsLJQEQ0MGahdIwKqqkn9mJunpoiK6vrEx6eTcXHURya++QoUggM2Zg13+/grXoCBZN01NxS0fnw/B2Nw2o0ffjnN2PilJ0nauoVGhUCjCAgICeoAi25DJZAo7OztFj3nzZJo9esB87Vpacy8pgiDAzMxMunLlCgYNGiS4ubkJJ06cAGPsckBAQGt/f///jSDaXyCvne3X8m+Q6LKyMo3y8vK/T8H+3fLkCSm/Tz+lQ1YlkyaRQr50idBrlXTooG7To6LwjR5NDteMGXQIxMWRAa2jQw5NZSUh6AMG0Pv/Cor0nxVLS3IaFy1C3mefIS8vT3z33XdhbGz850Py06fTKzm5BlX38PDAuXPnkJCQgJYtW9YYkCqxtrb+K56mcTE3pwPuJXKCx44dK8bGxuL06dMoLS1F2KVLsJk5EwatWhHlrwFDHBUVrzafN2+S8ZiU9Pzf3nuPDLT09IaLq/0dwhgZUzNnksHw5Al4RAQ8T5yAXWIizKKiyKhv0eL5Zx05koyWTZsaBhyePiUnUxSJIvwK7I/S0lJs3LgRAODj4yP16dNHYIypz7/YWIpAvaT+qaysVLWeYgMHDqxTjftvE2trcqoTEwmQUyhoTObOpbHIzaXaAqrWNomJ5EyFhdG8eHpSNCQtjXRMaSkZjpmZgIMDSnR0kNasGeI8POA6fDgEDQ1ytleterm1KUn0XcuX11ugTxAETP7lFyGxpIQfc3ER7s+YgbbXr1NUs7iY3rRsGTnQFy4AP/2Ehz4+CNTWppZdDx6g09ix5NRv3Uo5kGlpFCXMziZ9tHYtRaaeuzUqFuXh4YFTp04hKiqKQLCKCnLyNTVhU1wMfX19HDp0iI8bN+75B05Pp/x5ExMavy+/VEe22rWjvT1pEuU7R0URcDp0aA3z4vr161ypVDKVztKuTxdMmdLoED9+/Bjh4eFo0qQJSkpK4O3tLWpra6PFwoUwmDiRdevZk9UGjOzs7LBo0SL25Zdfss8++wxGRkaYNm0a1axYtoyimH5+xKhRMTMyMgjsiKxmgq5bBxgaYuTOneKI5GSwxYsBzmFlZYU+ffrwM2fO0EacPRvw9kbZmTOwtLQUigMDoV9cDKxejZZWVuyhnx/alZTQefjxx5BiYvDtmDFKM1tb8dGmTRg1ahScnZ2R8eWX2LN3L7S1tPj06dOZnp4e7OzscGbNGkk7OFj4ddAgvL1wYU2lcAC03pKTgVOnkDJrFlICAwXDQYPAPv6YcmxjYmiuo6IoReHJE1Tp6uJAYCD39vGBJEmoUy9myRJyODkH4uMxxNychVy9KpWsWwfbmzeFFAMDOPXoAQ0VDXnIENK1hYUEHAYGko4+elR9zWd0sYaGBioqKnhSUhJiYmIkhULBBw4cSHUIFApirPTuTXPzrK5zdSVWmYEBOYL379dEjQVBwMiRI1FaWirevXsXt27dUnp4eDQMSH//PQGlU6YQwG9sTGfV8OGNrkX8+isVZlQ5io1Jt250vcuX69cl3t7AtGmovHwZRVFRKDAzQ35+fg1Ir9Ktmv9BFXDcvk179+JFGlsbG9JtK1eS8z19Ojmze/eSLVdaSmBkQAC9PviA1tidO3ROBAaSnfLzz3T9li1JByQkvDT1u2fPnuI333yDixcvosezADxAY2VgoC6CWhskqmZnJUVE4PLo0cguKpIVGBpCIYoyXaWyF4BuvpcuLWyRmGjuGhHxy9pPP80cvWePVbKdHb/v7S3NLSwU5MuXy5gkyVBaSvps2zba9w10HqmsrMTu3bsVlZWVYIxBLpczfX19MTIyEoMGDYIgCNDW1kZZWVkrAJoAyl9qIP4/kNfO9mv5NwjnnP95+tM/LXl5FNXet08d5astzZtTTlJDUTtVO42UFHLorKzIWenalejoCgWhzLGxRHM9fZoqRtvYkIHbty8ZBv/XQIWlJZCRgeK9ewFQSy5jVd7ffyK2tuQo6OtD6NkT3Tp35ocPH2azZs36a67/qiKK5Mi8hMjlcqre3KYNUmbMkB4C/Iy1tdj/88+hU59xPWgQGVT797/aPZmYEDDTkKxZQ2vl5s1Xu+5fJYwBpqbInjQJwRoa6GNjA+f0dDJEhg6l5162jCIBKkNy7FiKcNS3T7KyiELHGBk+r5hmsW3bNqmqqkpo3749r3a0675hwwbKxzt//qWu9+jRI2hqavIPP/zw70G8ysuJoeDiQg5kq1ZksHboQNFjpZL+DlDk5fJlGrfOnckIfvNN0hvFxWQU6uiQIV2rLsKz0gSAVlERfnv4EDfOnOETJ05krwR+RkZSWsOzeesxMeQYHTgAjB6NkOhoViCXo/DKFZ6xaxcTwsNR01LszBkCY6ytIc2fD1liIgBAR0dH6tS5s4DOnel9CxYQOKkqYqhU1rRCys7ORmVlJa5fv84TExN5RUWFoCqUKAgCr6qqYqdPnyZne906cv7i41FZWQmZTFZTMbxGkpLIKJ89m+Zh/nx6Hh8fcsp+/52ebe5cinbFx1OU9ccfgYwMZDKGx0FByO3Vi3l16VLDhKhX2rUjfb5uXb1/Pn78OBdFkdVbpfnIEQJiLSzqsBrkcjlmzZqFyMhIxMTESBs3bhQcsrL46O++Y+nu7tw0KIhp1Y7ITZpEe3HLFgLBqH864OQE9ttvdBYNHAh89hna9e7Nzpw5g3379kkTJ04UEBKCnmfOsBOurmg3bRoyzp9H67VrYfT++7Bdvpz06BdfIOHcORjb2sJ+8GBxwN692LZtGz927BiLi4tTRmRmij3v3kW3hATGli4FAOg9eoTh6elC6vbtKLhyhWPaNIZ9++o+v5cXEBmJrI0bMenQIa4ZGckgCOQcAcDRo+CrV6O4Rw/cdndHVtOmeDM4mO2dPBk9w8KYw44d5JC1b09nbbduxFwQReiPHImBnToJ0NJCzrhxCLp1ixtMm8ZsazNZtmyhtT5/PjmuQUG0TjdurPfcLykpQXJyshgTE8Otra2FxMRE9OnThwIOW7fS5+LjaT08K4zR+isspL2/ezedB9XOqapLQGVlJWJjY+tXlhUVBIwuXkzrubKSfhcXV38E+lmZNq3eQob1iqsr7dP4+AZB66rhw3F+0SLuFxqKq35+/ODBg0Lt4pP6+vp87NixrLEc4nqluJh0Y//+FAwJC6P9OW8ePfdXXxEzpVs30p1lZfS5NWvIFps3j5xumYzW/ejR9L6UFLXt1qIF7b/Hj2kPp6WRnebt3eit6ejowMzMjIeGhjI7O7s/xYxy7NABjjt34tGjRygdMQJBQUEQBMEGnLvuXL16s0Kh8EyYMweDPT2tmhobw6FtW9bH1pZh8mQCFDZupGh2QgI924kTNK+HDtX5nvLycmzdulXS1tYWe/bsyZRKJUpKShARESGhVseF7t27V506dUpDLpefB9D5lR/of1ReO9uv5d8gfQwNDSu1tbX/+wqkcU6R7PbtyWCvT/z8yAnfsqVe+lqNNG9OETuADkELC0JgBw8mJW5urjZ8srMpAnH+PBnQTk6E7hsaUqERLy86MP/O6LcoAt99B9Pz56GZk/Oni0zVK6oDtUsXdJkzR8j86ivpzpYt8Fu+/P8+mb1rV3XxoJeRyEhAWxvNHz8WrD/8EFvPnePrd+9mPj4+6NGjB6H0nNNr8OAX52fXJ1VVRKNtSD74gK79DwrnHBcuXJAsLS2FTlOmqPO/fv6Z0ii++ILqF8ybR+u2Y0eKEA0YQAZZdfEiJCWRw2lvT3mNryhJSUkoKioSmjZtyocNG1b/hqguuveycv/+faWxsfGfT1+oqCCAzsuLvptz2vPW1mTI3bxJEZfMTDKCDAxojIYMIRBl9mwqjlVYSKyZykqifI4fT86jKoodEkL5oyYmREUcOpT0UVISgYNFReQkhoQAbm4YPXo0srKysG3bNpaZmflqxt9PP9Wl4X/6KQECX31F99WiBZQODpCmT0f/+Hj++L33cCE2Fo6XL2OUig20di0Z8V264HqfPvzJwYOsla2tJHTr9vy8WVkR2HjvHn3m9m38unq10vOHH8QzfftC2aULHzBggCCXy2FlZQVJklBeXs7KysrUlcAXLKiJkF66dAkFBQUYPXo0/S05mcbw6VOaFwcHGleA9L0qTaN/fzK0KypIX3frRr+/cAEAEO7vLzWVJKFVq1bK/pMni5g9m+Y6K4vWfG0dffx4w+wXAAYGBqygoKD+P8rltHYGDiRwQHUfAMzNzdG7d2/07tlTKG/eHKykhEW/9x6CjIwY//prtG3bVho0aJBQEznU0CBwKzlZ3bbJyEgdTT5yBNDWhtbbb2NGeDj2Tp8uoKgIKCuDM2PMeepUpAcH435UlNIyMlJ82qEDipKSUGJoyM/GxPD0tDSh1YAB6ObvD5lMBl9fX3b8+HGkpKRg9uzZsBBFApUAWqPTpgHffANFs2bQzM9nyocPITYwTlKzZigwMUGzAweIefD55+Tkz54NBmDzRx9h/Pjx8DY3h6BUYiLnqEpKgpYk0VyMGkVRPmdn+tfKihyP6lximaEhiiIiWFl5raBdSAgB4KtWUf4vQDTk774DUlIQU1qK4uJiMMagoaEBDQ0NlJaWom3btsqRI0eKABVpO7VoEXeIiYHD778z+ezZjZ/fs2YRuDZjBjl2KSn0rLVEoVBAFMXnL6JU0lqprKTorKYmgb4GBqhTeLAx6diRQAFT0xe/VxDo+w4caLCY2sGDB5W59vasl7e3oIyLY9fi4ni/sWNZQkKC0t7eXkxLS8MPP/yA2bNnw8jICKmpqTAyMoKenl7DhVDPniXdc/UqAVArVhBYO2ECgfoKBenRuDhaZ6NHE9sDoLlftYoi8n36UGrU7dsEXKrqjKiKkV66RA52v34UGd+xgyLoM2fSPvzuu3pvT1NTE3PnzmV79uyR9uzZI/j6+qLnM8VNMzIyYGJi0ngqHtQ57kFBQeCcawKAQqF4rKmpqRg1daqMMVY3Yl2tn7BhA72qqgjsfviQnO+iIloTmzYBnTrh9OnT0NLSwttvv81qs0CioqK4k5OTEoAIAN7e3hrXrl2TCgoKfAICAjwB3PH391c2evP/H8hrZ/u1/OOiqan5YefOnV8h2eVfIgoFHUybNpHibUy0tEih+fi8uG8uoO55qlCoI1MuLnRgfPcd0dbbt6fX+++TIZCQQAr/hx8oqtK+vbqi6pAhRBP8D6PfpaWlqKqqQn5+Pvbs2QMLCwve8cABvKGrC6fPP//rPftVqwAAnZKThZKwMI7lyynq938Zxb95k1DtF80b50SJXbWKDrJz5yAAmOfkxFJSUnDw4EFcvXoV3bt3h++qVTQff7ZF16FDZAg35HDr6JAxP2kSHf7/gKSmpiIzM1NYtGhR3UIrzs70GjeOIkGMERUxL4+MtzfeUOfc3r9P0U5vbzWV7iUlJiYGwcHBvKKigmlqamLy5Mn1r09JIqf34kW1odyIpKSkID4+XhxQXTypXlEoyNDv1o2opFFRZOQ5OpLTa29PoEJBgbq/sCBQRNPBge5H1e9761aKJiYlUaeDHTvoun5+RFVWpRLs2kXfU13oDGVlVPAHoLWgVBJN8t136XcZGeq+1KNGEbuiqAgWc+ZA54MPuHL+fKBrV4YlS8hxXrBATQ9/tlYA57RHunShqMiiRUSrdHEhBozKwFYqYcY5N5PLWWRBAbe1s+MDBgxQz0tBQc09mXh7s8TQUD6hokKojxpeE8lfuJBAGMbQYfhwMcfVlY+ZMYPpfP+9wKKiiHpfbYw/1/Jv8+aa6JyFhQWPi4uDflUVQ2KimnXybGTy++/pOWv3t/bwoAj3jBk0B7WK9hn37Suc1tDA8unTRfTuTbo6PJzo9j/9RPrdxYWioRcu1B/JrBYjIyOeSyyb+teynh7poEWLiDVSO/e7tBT45BNolZQAa9ei7dtvo40koVo3sTVr1qBt27a8oKCAN23aVBp45IgMxcXqqGltqb5u5JIliNi5E5O9vAhQ/uwzGtMzZ1C8bBnu370ruu/dC9nIkdzs0CE0efddNuPrr1mVnR0qIiKgv2oV0LMnmhkbY1RgIHfcv1/EoUMELg8cSOOor09Ok6MjmksSDKyslF+NHSuOjItD6+oiZ2lpaQgKClJqamqiqrJSNB4/XnI5dUrE6dM0Jm++SSyEsjLoOTpKubm5QvPmzQEAckDdouvMGQJXV64k0FShICfLyorO2bAwxH37LQYePw7rQYPoMw8eEKNj/vy6+cvVlOX7Q4bwvKdPWdS4cRLnHEqlEkqlklVUVLDaFfFHjxgB2e+/s6KbN1FeUQG5qoBoQyKTEUVeVRAzN5ecvlpngkKh4M8520+e0H7//HN1Ff/MzFfK1QXntL8dHV/+M4aGqAwOxn0LC7QZNaomZbCwsBAKhQJpaWmiu7s75H37wuXwYbgcPcrQpAm8x41TeXbsxx9/VGzZskUmCEINS8XOzk45adKkusDnhg2Up//991Q358MPaS3t3Uushfx8AslWrKCzaMIE0snVgAoAAti0tAh4MjUFTp2iMys3lyLZiYmkj0+eJHbBvHk0nubmFFhJS6P9bGVFnysuJoCznoKeEydOFI4ePYqQkBB4eHjAwMAACoUCGzZs4KWlpUwul/OWLVsyHx8f2NjYPPf5pKQkXL58uaaArCAI4atXr/6aMRZcVVVVef78edHPz69x+0yVEjNnDj3TypV0NmlrI2X8eHiEhaHDpUuCqFTW0OQfPnyIJ0+eiBMmTKhzKUdHR+E6gWXhoijGBwQEzAdwBoAGAKW/v39lo/fyPyivne3X8o9KQECAq6ampne7BnJE/tXyySeUn3fixItprTo6hNTn5LzQWZQkCTWFbGQydbXPW7fIWL53j4zt3FxS8q1a0cGncmJ696bDMDtbXSxrxQqKfLVsSUa4tzc5/k2avFL0e/369TXtaLS0tNC6dWtW/N57UqeSEoFlZREy+jdI8pIlCA8PR+ugIDJoqTLs/40YGlIEtjHZtIkcn2PHCAl/pn1Q8+bNsWTJEsTcuYPff/mF+65Zw/6jsZo2ra5hUJ/Y2Kgpcf+AWFtbw8LCgq9fv57NmzcPpvVFQFTVsi9coPV8/jwd+lpaRD20siIn+803CUwyMaHoywvWrCRJOHz4MHR1ddmkSZNgZGTUcD2IsjJycBtxtFWt3mQyGQ4cOADvjh3RPjubHJirVynSt3Ur7as+fSiiMXQoRQlKStQ5yRs2EGjTtClFDgByulQyfvzzX/7bb/SeoUMJnCgooAiyrS1FI1q1oj1hYUEOtarwWVkZvZKSCGy7epUYGirHd8kS9Xeo6JolJYjbtAlViYlMr2NH0hecE3g3ezbl9G3aRMZ59+7kkL73nrqv9LJlxKhp0eL5Am3btqFw3Tp+f8YMuHz6KWbY2z8/iXv21MytiYkJku3sGOzsyHA2Na3LMJEkMpj79SNj2dQUOhs3gr33HpM7O4NNnUqRqMuX1Tnez1LcVQUpHRzQsW1bFn/oECp79SIH/s6d+nubb9hAxfmelf79gXv3UBUaiuSrV3HDxQWPHz8GYwxKpRLl5eXQUjljrVurC0MeP07AR3AwpQ/170/j7uNDjqaxMTl9dnYoyspiFeXljedb2dkROLJ6Nc1H+/YE1tjZEXvq/n1yJED5vXZ2dli8eDHbsWMHoqKimJaWFktNTRUiIiLgYGQkjf7qK4F5eNB1npE27u4I7dABO27dwuBNmyR3Dw8BEycCZ8+iNYBlXbui1NQUzTdsEEuDgqCTlgYGQJ6VBbmPDzl+UVHQatkSto8esZoUkcxMWsNPn9IarnbqBEHAlA0bxMSZMxH4889wdXXlLVu2ZMePH+cdOnQQtLW1Yfvuu9zSwkKs6du9fz/tjeoe4k0KC1WARfUyktRg4KefomrIEKS1bg3rOXMg69AB2LwZkiSh6NNPIUkSKu7dg31BAcoUChQtWwajAweQtmABioqLUXH9OiRJUjnUkCQJj9q0wfBr1+A7frzw7LkAFf128WK0CQkBbtxAQFkZPEJC6hbOq0eaNWuGVr6+tH6MjMj5mz+fwIEWLVBZWYmysrK6aQs5OQQOdOxI654xyjH/+GMC5F4gQUFBKC4uhlFhIdzfegvNqiuVFxcXo6SkRJ0OUo8kZWbiQsuWkO7cwamUFD558mQWGBgolZaWCjKZDJxzddu9MWNIH4eE0NqvBkOmTJkikyQJmZmZMDc3Z+Xl5Vi/fr1YUVFB+dx795JuNDYm3XT1KjGoXFzoObW0SF8VFlLk+p13SBc7ONBaq52n7u6uBhe//15d3NbUlPaTqSldZ8AA0vMuLmRjlZaSfbZjBznoBQWkc6OiSJdfukTnt6oQLmhd29jYIDo6GmfPnsXjx4+VBQUFop6eHps9ezYKCgrYyZMnsXv3bhgYGEhvvPGGUFlZifz8fOjp6eHcuXPc0tISRUVFDACUSuVH1dedIknSwNDQ0PMWFhY16QXPyc6dNB5jx9aASvz335EydCiOBwVJzRQKoevHH8PM2Jie+4cfgG7dkJuQAENDQ0lXV7eOAdyvXz/4+vpCJpMhKCjI5sGDBycZY1Wccw2ZTJa3evXqDz7++OPdL1xw/0Pyus/2a/nHJCAggGlqal7w9fXt1rlz539xr6t6JCiIDgAzs8ZbYzwrAweSIVWfsQbgyZMn2L59O5RKJdzc3JQDBw4U66VIqdDFAQPI2F65knKMVq6s30AEyGhJSyMD9MgRUppaWvSvoSE5Cba2DX5eoVDgiy++wJIlS6BQKKBQKNSH44EDdMj//PPfQl0vLS3F+vXrsXjxYmgnJ9Ph6OtLSHGLFn/599WRmBhCw+srGBMaSuP6+DFF8FQ0/wakatgwPI6MRNqePehWi+b5yrJsGTkzquhKQ1JQQId8Y32//2KRJAkhISHc2dmZxcbG4uLFiy/fM1WhIHq1ri454ozRXI8eTTRJSSLn0cyM2B/BweQMvv02AUvNmwOSBElHBxtPnYK7s7PUY+hQAebmDa/LsrL6+3yfOkW04fR0PJo0iR8YMYJN+OknKHV0eOtr1xhMTcmRy8oipsHOnfSzrS3dx38qp07Rft63j5ySamcOpaXkZDs7k+G2cCFF4+7cobHYvJlojip2QG4uUSU//5wc5HbtKBJXWFgHFJIkCcHBwTwmJoYNHjyYu7q6NswEEAQyHO3siKFhbU3G8bZtBAo8O5Z5eUBeHq58+63yhr09mzFjhvBclBmgKHPr1sCaNUhNTUVgYKC0ZMkSAQEBBFh8/jlFms6epWf49Vf6rvLympZRG9esUc7esUPU2raNdFpurpqe/sUXBEbWLq4F0Bh/+SX2DRiAMgAzVdTpZyU5mWjejbSoKzp+HOXTpmH7rFlQamjAyspKOWHCBFHnRR0lFApa6127UnRbqaR55pzWOWN4qK0NRXIyd9bVZTWRRQsLck6bNqU1oaFBcxEVRfrJxIQiVaJI51Yj51V4eDhOnjyJtm3b8srKShYXFwfrJk14X1dXZqOtDXTujLy8vBrgKTo6moeGhjIjQ0OIkZEYZ2UF/bAwihBzThXHjY1R/PgxyktLoRwxAvoxMdA9fVq9Tn7/HVnZ2fjxxx/50qVLGRQKYNgw8F278PDRI1TExMBi8GCYV485P3MGJe3b496jRzhPNRY455y907YtDE1NCYyzsaHn3LuXXtHRdPbl5CB+7lzpaMeOTENDgysUCl5RUSFqampKI8zMhD/y8pQ5xcUiAGiVlWH4W2/h4OHDgEwGmUwGxhhXKpVMFEUwpZL3//lnpiNJ/PSsWXzqRx8JEX36SPGdOnHXixfZnREjuCAIEEURfn5+YrMZM4harmqRCBDIkpVF+7CoCGjXDr/++ivy8vKeo0bXttWfPn0KAwMD5fRp00TMmkWsExcXcgi1tMBbtcJnX39d05996NChaK+nR6DUypVq9kR2NunS7Oz621fVkqSkJBw4cABeXl68+ebNqEpKYlHLlvE33niDbd++nVdUVDBLS0upR48eQrNmzaCjo4PHjx8jKChIaWdnh44dO4p7v/kGS48exaXPPpNi8vN5Xl6e+NZbb0FLSwuRkZHSjRs3BE9PT/Tp04cAkEOHKGq/e3cNQFRbQkJCcOPGDSweNIh0rpcX7XFbW/V5HRREQNHMmfS74mJysvv1I515/TpFpeVy0l8qmTqV9ISfnzpta8cONYtx1Sq6r4QE+vn2bTqHvviCbKqFC0nnjBqlZgIFBJAuXrqU9rpCUQOw5ufn48iRIzwnJ4d5enrC1NQUtra2qi4n4JyjsrISJ0+elKKiogSlUgkDAwMuiiIvKSkRJk6cCGtra+zatUuRmppaE0gVRTFVqVR+A+CrQYMGwd3dve7aio2lcfjkExQfPYp7paXITU9H74kTcXzCBN7B15c5LF0KduMGzUFKCoGA69eD79iB1e+8gw8cHKA5dGiDKTCcc5SWlqKgoABlZWU4dOhQpSAI+wBIgiB0USgUxQqFop+/v39+o4vwv1heO9uv5R+TgICAN42MjHbNmzdPV3zJ6o3/Cjl7lg7M48frPQAAMlxTUlKgVCrRtGlT6KpaaFQf+HB2rrdi78WLF/mDIwWPOAAAIABJREFUBw/4mDFjhB07dkg+Pj7o1q3bi4GIBw8IDQ4Pp8PU0ZH6VzcmnNP9ZGaqaclOThQx1denQ8bJCTAywtOnT2uqOfv7+z9/LYWComtr1vxt0e1Vq1Zh7ty5VCStooIOth071P0mX6ZH85+RS5eIvbB2rfp3qqIrbm4UIZw5s/FrFBTQHDk748y5c4hKTla+9957dRZ9ZWUlqqqq1GulMRk+nOZ73LjG33fxIjmqWVkNsi+Sk5ORl5eH5s2b1xzs9UlmZibS0tJgY2NTY/jWJ0+ePMF3tXLUtLW1yWFqTCoqyLnYtImiCLdvk7FjakqA0smTFD01NSWAQakkQz0hgZ7NwoJAEX194Pp1lObl4fjTp+ieksKblpQw+PnROndxIaaHnZ26R/iGDbR+s7LI6IqKovdYW1PKRps2iB0zhj9cuZK1Ki2FY8eOEP7OHrCPHhHgkJxMhvC5c+RAqww2B4eaKsnw9CSnWZIIgDExIRpgTg453M/OU2wsPatMRgaoqvewpSViCgpw+PBhTJ06Fc1fBSzYs4dYN8uWEYPGwoJYPyr99sUXZMQmJ6OiogLBwcFSQkICe++9954vwHb+PDmKXl7IyMjA999/j2XLlkEuihSpdXEhhzw4mPp5q3JUExOJMh8Sgv3790M/OloaWlgoYMkSNfAhSbS+FAq6zrZtZGT6+ABz5kCpr48NFy9CQ0NDuWDBgvoPpAEDyEifP7/RIXmcnIzYWbOgWVaGC716wcDAQNmuXTvhhVTOY8doPJcuJcfhGdm4cSN3adWK9fH0pLkrK6O9EB1NIFTz5gQuxMfTnJSXU9RNLqd1smAB3X+XLrQWPD3rGMgpKSn44YcfsGDBAhgaGiIjIwNHjhxBswsX0C80FGmXLuHQ0aNgjFHbMh0dqU9pqeB44QLuVVXxMpmMdQkJAbtzh6KS6en0TKtXI27fPsnu+HEheOhQDD94ELLKShr/lBQURUZii4EBX7pwIYNcjrIBA3CoY0eeraeHVrGxgJERH7F+vYCjRxH8+++IsLGBhoYG9PX1le+++664Y8cO/ub69cxg8mRahypZtIgAFzc3cpj++AOKdesQ+cMPuP/gAS8sLGRTp07FoY8+wujdu7F1zhzYuLlxV1dXdvToUXQNCUGrx4+he+XKc8ycmE2bkH/5Mu988CCDTEbrS6kk523hQlqf48cT22XBApqPw4eJ0qwCPN58k5gv1S0JX1YuXLiAxMRE5bRp00Rs3Ur6QJV2MmUKIEnY27u3MjExURQEAaYZGejRqxecS0rUqSW5uaTjHj1SV6J/RlJTUxEcHKxs166dWFxcjEePHvF33nmHITER+U+eYN+VK7ywsJC5ubkpvby8xDt37vCrV68yADA0NERRURFcXFykmJgYoaqqCqamppj3+DGyPT2xNSoKGhoamDx5MppVf398fDyCg4N5cXExGzFiBFxdXWkcjxwhx/WZ1oz79uzh1nFx6Ll7N0NYGDGhVq0iUNLVlWwSW1t1qyx3d3K0VT2zbWwoqi+X0/lYu6NEcDCtG5X+CAykn1VsLIWC7CdLS3WAIiGB5nP7dooUL19O7/nySzWrRpLoXJ46lebt4kXS94MH1/3+RqSkpATVzna9f9++fTvPyclhfn5++OOPPyAIwiVJks7IZLJZNjY2Zh07dtRydHSEkJ4OTJ0KZadOuMw5v6Kjw7S0tHiTJk24p5kZazd+PGN379L6SEsj4ODoUXWAo7gY3377rXLC+vVi+fvvQ+HgAKu8PMhmzWo06JKbm4uHDx/W/P/27dsAMNbf3/9Qgx/6L5fXzvZr+UckICDASENDI2HixImG9eWg/GslNJSiwDo6jUZUT5w4obx//76goaEhlZWViaamptLw4cMFCwsLMj6++IKokc8opH379inNzMzEfv364fHjx9i7dy/GjBkDh5doO1Ujn3xCir1XL1KOL5mLCoCcyMREMt5+/x1ZWVkosrBA/uPHvMjUlLVZsgRN27Wj6MmzUlRE/URV1dL/Q8nKysLFixeRn58vmZmZCbGxsRg/fjzsn6WCdulC0aoVK2g8X7FS9QvlwQOKfFXnjyM6msZ1zRpiKrwMULRoEa2d69fx8OFDBAYGQiaToVOnTmjZsiUkScLRo0dRWloKAwMDPn78eGZubo7S0lJUVFSgvLwcUVFRMDIygoeHB1hqKh3yL3M4Z2UB5ubgoOhESUkJHB0dwTnH+fPnlTdv3hT19fWVBQUF4ooVK56LqJSXl2PHjh28qKiIGRgYKAsKCkRDQ0Our6/PfX19BdV8pKam4u7du7h79y4sLCwUI0eOlOXl5UFfX5/64tYnqiiprS0ZiZ98QgWZVJTImzfJKAHU+dtnzxIQNHly3bzZWvLHH3/g6tWrMDY2xrvvvkvfU1hIxs3Dh7RW5XJqXbNlC9EOHz8mh1ySiC1SLbGxsfzIkSNs5syZjYIM/7FUVdF9eXuTEb5qFTlTfn50nyqjb/9+0kGDBpERGh5OxlpqKhmIH32kzgffuZM+Wx+r4MkTGr+hQwFRROW+fbgzfDiPGTaMT5o9WxBeZh/l5BAt89tvaQzLyggAO3mSqNvR0XT/KsMe5ND9+OOPWLx4MZ6L9kZEoCw1FRVdu2LTpk0QBAFLly6l4kClpTTn5eWU2147X7SkBBg5Eo+++w77DxyAk5OT9GavXgL69CEnfeDAut9z/TpF9zQ0gCNHwLt3x+effw6FQoHWrVtze3t7lpeXh6KiIgwePBg6OjqIjY1FxscfS8rJk4XeQ4Y0Oixbt26Fxq1bGJeeDnbgAK7evo3Q0FDo6upK7u7ugouLS/2U2wcPyGmyssKz1bazs7OxdetWLFq06Pnc8/rk5ElaR5zTWDFG8z1lCunnRYtojr76ioCdsDBUvvUWDunpof/8+TDNzAR690axQoFjx48j/f59mKemosW4cejq5QXpxAkIn3xC45iaCkkQcKO4GDrdu6Pdrl00J92709oMDkbJ3bsQZ85E+A8/SN2+/VbA9u3EsoiIQMU772Cfl5c0/bvvhMA1axBfVAR3d3cMHDgQyuHDca2wELZ79sBi+XLcjI9Hi6NHYWlpSXt1xAhc19XlmaNG8aHDh6sXrUJB4MqUKfTcW7cSaHX/PhAZics5OXiQmMhnTJzIygsLkXLzJiqsrREREaFMSEgQAaCPoyM6lZdDeLYl2/79eLJlC359803l9IaAGYD2goMDgXsODnS+OjiQvhsxguoniOIrs8HOnz+PlJQU5ZQpU0RUVhLL7tYt5CsUuHHxomQZFSUk6Ogo7xYViWbZ2Rh36hRO+frCZNw49O3bl/atqSnpO0vLer9DoVBgzZo1aN++PY+OjmYVFRUYOnQob+/qytCnDxAUhFINDRQXF6tZB5wjLi4OBw8eBAC0aNFCOXHiRLG4uBiHDx/mOjo60tgRI0Q+aRIeLV8OGweH59p5cc6xcuVKdOnSBb1796ZfBgdTakRoqLr3t78/Kg4dwtrRo+Hn48M9u3dnGkuXEttl0iSqHyCKlELTqROB846OZCuo9v6kSRSU4JzYebVl6lQCtFVstYIC+qwqzUklbdpQSshnn9HPRUX02ZEjaY5v3CAm4xtv0H5UpQeUltIadXMjZsH27VSDwtiY2Bl/gXDOkZKSgpCQkJKEhARdABBF8YgkSf2cbG3lo+zs5PzuXTz64QceMXgw9507V2haO1gyciSdxbdv0xj5+9M4JiYSA0cQcPr0aVy7dg3gHO0iIuB+5w7+WLZMGnX8uKAXEAANVVHJBuTx48fYvbuGUd7M398//S95+H+ZvM7Zfi3/iMjl8k1t27bV/q9ytHNyCF399lt1HnUDEh0dLY4ZMwYtWrQQFQoFTpw4wXbt2kUobt++FMEpLa05OJ48eYLw8HApJSVF7F5d4MTGxgb9+vXDzz//jOHDh8NZhai+SFRKPzubcod0delwcXMjB5HzBh3S23FxiI6Ohp+fHyxGj8a2lSuhW1SEdk2bSn5lZaK4YwcdUubmhAwPGkSHjb4+vbp2pSh5Y1XXX0JOnjyJ8PBwuLi4SI6OjkJSUpLEORfqzfsNC6N/Fy2icVW1evmrRF+frhkfT21Stm6liGevXi/+bEoKgSvr1pFhCMDBwQHLli1DcnIyAgMDERoaCsYYOnXqhB49euDgwYN827Zt9VpfWlpavKSkROo+daqIU6eIcvsiycgAunZF5b172Lt3bw21UBRF6OnpYfLkycjMzBRDQ0M5q+Vpp6WlITc3F2FhYZIgCPzDDz8UBUEQ8/LyEBERwWJjY9nevXvRvHlznpmZyTjnqKqqAgBMnTpVJggCDOspBlMjqtoDSUkEPKmiK3I5OYvTppFj3KsXGadt29Jr1iyi4r3xBrEKHBzqtNxTKBR0+ANkWAK03lX34uVF8/nll5TSsWsXGeSqVi/PGNY5OTlcLpfD3Nz87yvtr+rzOnw4OScaGhR5PnCAxkYlW7dSUa2QEPq5Vy9yJhcvJmBi1iyin3/zDX02K4uMaVX0v7aogIpqKr48IwNeycksLDsbWR98AEtra8rfa0y2bCGHSpVLra1NIEnHjqQLtLTo2WoxgDQ1NcEYw/379+FVHb29e/cuDw8Pl7xOnRK0IiPZTxMmoEmTJnXZHzo6pNu6dSMWQ21nW1cXOHQIofv3cwBszJgxAhgjlsStW2TwqiLFlZW0pwcOJFDS1BSMMfTq1YunpKTw/Px8nD59mlX3i0VcXBz09fUl9+BgQezYUQi5cweR8fF82LBhrEU9gGtKSgqys7Nh5u7OdXfuZJg2Db0Yg9WiRUhLT8eVK1dw+fJlmJiYoFevXmjVqpW60rCmJlVGXr2a5rOWofrHH39wURRZXFwc3Bsr1sg5ATW7dtF6GTeO9tiGDVTYLD+fnLu7d9U5uxkZAOdI09ODJIpI/PlnyfTsWQGurtDr3x/jzc2h2L0bYufOYIcPA+7uEPr0oT21cSOQnAwhIACJrVpJJi4uQjtvb3JKNm4kYCM2FoWPHuFW797IKixkbVu3hiSXQz8gAPL4eFR07w4hO1uoaNIE5dHRcOnXD4OruykIwcGI27VLurx3r6Dn7g7J3V3qYmkpID0dkr4+YuVyfqtpUza6a9e6+zMmhhxr1TqfM4fqDlSDELbvvYe7FhZKzJol0yoqQuvqnthBQUEiADDGcObBA7QeNAimKhYVQHqrqAgPZ82CUK3TG5RBgwgE09OjCPtvv9EeCQ2lNTluHI3RgAG0rl/S6VbVdQGACs6RPXw4kubO5edbtWJNmzYVcvX1+ZA1a8Q35s7FcS0tfuWtt9Bj9my2b98+ZGdnSxOWLhXY0qXA/PkqMFXJGMPw4cNFS0tLCIKApKQkCIKAwYMHs+7duyM5ORlt27ZlNawZfX3oCEIdwIwxBkdHRwwbNgwpKSn83r174q1bt+Dh4YFp06YxVFesZhoaaHX9urow3TPCGKOotkqGDiV9OGYMnRO9ewN9+kBz0CCM0NVF5oIF4KNHo8jODul//AEHJyfExcbiSGAgBpmbS+1sbAShqIh0Z0AAsdS2b6fxnjWrfjZeRQUBtCpRFeu7eZN0m0oOHaJzWAUc6+sTgyE+ngon7t1Le2zNGvrX3Z10pmrcoqLIed2+nRgRZmbEuCosBIyMoGqLKwgCioqKcPv2bXh5eUG7ka4FANUZ2b17N09PT2e9e/fWNjExUYaHh4tKpXIUOEeLzZsLM62sNCK9vLjg4CCMWLGCyZ4F73fupPPw00/ptXKlujvAokXAnDlwcnLCtWvXsPiDD1BYWAhlcTHs7t4VcpKT+a0VK5ibjw9MfvqJ9mI9dmdsbGwVqHAaZDLZ559//rlJVVXVFH9//yeNPuB/mYiffvrpP30Pr+X/MwkICOgkl8vXjB8/Xvu5zf1vlbQ0Mtrmz6+X3ves3L9/X6qqqmIODg4QBAFOTk4sKysLCQkJSlc3NwF+fkTv7dwZtx494gcPHmSSJPFhw4Yx21oVTa2srGBoaIhgqhTKDQ0NmcpYfaHo6hJlkDEyKtu1Iwfc0ZFoVE+f0sFffa0bN27gt99+Q35+PiIiIpCSkoL8ggLMXbIELoMGCUL//mQ8dO1KUbacHIqs3b5NlXjDwuggycig737Vfpi1JCQkBPb29nzEiBGCvb093NzcmK+v73MoeB3p3p2i3Do6ZEiPHfvStKxGpahInTNWVUVjUCvy2aioCvRMm1bnoBFFESYmJnByckK7du0QFRUFKysrNG3aFF5eXszX1xcdOnRA27ZtYW1tDUNDQ2hrayM3N5fp6+tzZxMTAUOHvtzzmZoC6emQ9eyJR8nJyg4dOghDhgxBly5d4OvrKzRp0gRpaWmIiopimpqa3MbGhgHAt99+i+joaDg6OrIJEybURDq1tbVhb28PDw8PpKamIikpiSmVSsyZMweurq7Q0tJCq8bGZ9EiGs9Zsyji4OxMSL4k0cH+3nu0x95+myKyz/aQ19CguZ4wgSiQJ04Qe6O4uCaCd+nSJQwfPhxt2rSpu1cKC8lhy88nA+vBA/qZMTKikpLIkKv+jFKpxMWLF3lZWRk6d+781zvbGRnkBHh5EWgwdaq6p2tSEhmZtWsorF1Lxv7ChfSzTEbj2aIF7UdLS6Iyzp1L1ZS//pqeb+ZMet6GnDTGAH19sHffhaijwx4fOQJ9PT3oenkRHXPkSHIYns31S0+nfVbb8AsPp6jszz8TC0GpJFBDUxNVVVXYs2cPLy8vZx4eHjXU3BMnTvDMzEzxoYEBe+DkBCMrKz506FChSe2CUsnJ5CDOm0eOpJNTXZp8r15oWlHBbhkZoVOnTtRir1kzyod9/33619KS5rdtW2IAPHlC4+XuDhsbG9a2bVvm6enJevTogS5duqBHjx6oqKhQaGtpsQ4//siyevZEh4EDce/ePRYZGQm5XF6nOnBCQgL27NkDAFi8eDFhV87OYE+fwszLCy3atmXdfH1hZGSEhIQEHhMTw65fvy61b9+eyeVyMuiHDqUx/O47WuPVY/7LL78wzjkePXqETp06od7UK6WSgJaVK0kft21LzqaKMdK/P83j11+Tc3f7Np0LFhaAXI4rmpo8uqyM6Xp5McevvwYzMqI9OXgwBH19sCdPCPwZP5725/vv0xieOQO4uyOhSROmGRiICltbbrx0KROqqsiRPHYM+gEB0Pf1xQVRZDdNTXErJgbpyclo06wZNH/6CdZ37+LkqFHKQgsLNm7fPiZzd6czpKoK7QcOZGWamtLA/ftZ559+YmJlJeDkhD/i4qSIbt0w6u232XOsk3PnaH/Uro3BGDk3SiWyFyxAzP373Kd/fwETJtTUL4iOjoZcLucymYxVVlaia+fO0Fy3jiKgGRnAsmX4ubRUeZNzwcTEhLdr104A57TWnz6ldTphAgEb779PZ8CMGaSr/PyIgfXgAdkSixZRmpKXFwGLfn7kgB87RuvyGdq0ShITExEVFSXcuHGDX758mT2pqlK2KSwU+q5ahc6dO6Ndp05M09AQGt98A92RI9lFhYL17dsXLi1b4uxvv7Eod3fecelSBsawZcsWpb6+vqBUKlloaCi7ffu2BICdOnWK9+jRg9vY2DBNTU1YWFiQLlW1C2ykyGvTpk3h6OjIdHR0cOLECVRUVNQ9Ezp2JNCrRYvnAAbGGBITE6U7d+7wjh070h7inBhzHTtS6siUKcSie/oUZj/9BKPcXBbZpg0/P3SoFBEbi+vXr8Nl4ULmkJbG/+jeHa4rVzIxJwdChw70nStW0Pxs2ULR5fq6etjZkRNdGzAePJj0Xu15sbCg88TSkvS3vr6aRWJgQOdVbi7ZAOXllH6oorlratIaNTMj4NTamq4xaBDd45gx2HHwIP/t1ClWWlrKf/31V5aYmIiwsDCUlZXB1ta2Rg9UVFTg0aNHKC8vx82bN6UbN25ISUlJAgAkJCSw9PR0wdrampeWljK9vDxYZWRonuvUqbx7YKD8aocO8H6W/QOQXi8vp4h2URHp061byX5MTIRy2DCERUVh9LFj0L97F/oKBQwWLEBLHx+YlJczs8hI3MvIQPPbt8G6d3++QCUAURRFpVJZbm5uXlVeXu5RWlrqCOCzHj16/E9VLH/tbL+W/3O5du1aYO/evVu9Ul7gPymcUxRNLif68EuIlpYWu3HjhtLHx6fGwzI3N8fZs2cFLy8vyDQ1Sek2aYJLOTmStbW1MG7cOFZfJNDCwgKWlpa4fPmyFBoaKjx8+FBydXWt0+tQkiSUlpZCFEXUS/9s3pwUpaUlPYu9PdC9O0pOnsS3yclSSmCgdC0jQ9DU1MTMmTNx48YNSJLEBw4cCFtb27qnoaYmOXBeXmSE9+5NDpOpKTk+oaFUvTgpiQq2mJnRwdyYo/yMnDp1ijs5OT3/3Y2JTEbPl51NharGjqVIt63tn6eWK5X0jE+eEAWxX7+Xo43fv08Uwc8+e75tTi3R09NDkyZN0LJlS5w5cwaXL19GWloa2rdvD21t7ZqcrGPHjqGkpAQjR45EO3t7QYqJQZaDA6KiopCXl4fHjx/DxMQE9YJXgkBGyq5duEv5WEKbNm3q9O1s1qwZ7t27h/v377PmzZtDQ0MDYWFhaNKkiTR58mRWH7jDGMONGzcURUVFgoGBAbp16wZjY+Oafp91pLycxs7bm9aDpycBFrUP34QEGuP582mMly4lxD8zs06Er0bkcmJV9OlDa/vAAWDlSuRYWuJeUhLcfHwov18lISEU9S0rI2MxLo4MrT596O8+PvT/4cPJoOrRA8nJyQgJCWFz5sxhL4okvJJUVCD/yBFc/uYb5GVk8N+1tJBnY4MW9vZUpb59e4rC1Xa0FQraY998ozZQRZHWeYsWNK729gR8ubmRQbp+PdHje/QgI/677+g6jaTAWFpaIlJHB6cLC/EkJ0dpY2AgyIcMId0XGEjXyc6m3GIzM6I+1pbISNr3b71FNM1Dh8jxa9YMMDXFxYsXmSiKfMCAATU6zNLSksXGxnLzhATW+/x5ZPj6sq5du9bVZf7+VJ9h8WIy1JOS6JoqWqmvL0o8PXEzNhbh4eE8MzOTPX36FMfCwmDWsyeMKisp2mliQiwCQaCczS++oHlvoIBZy5YtBWelkuWNHImTiYl8yJAhzM/PD7q6uvz06dPMwMCAKM0gRk5eXh40NDTg6+tLFzAxobU1Zgxw/DjY6NFo2rQpOnXqxDp37ozo6Gikp6ezNqqK7uPHE8jUrRt9tvr5wsLCwBiDrq4uQkJCcO3aNSkiIgLFxcXM3t6e1vWbb6Lq3Dn8Ons2fo6MxM3ISEm7Vy9mqaLOammRU7l8OTl/y5aR892kCdCkCVq3bs0yMzMVUVFRQkZGBhXJk8vJgdDWptfNm5TvPmMGrYFHj2gdTJgA0/Hj0SwuDiWFhYhPSOBN/x977x0W1Z2Fj7+fO0Nn6L1LF5QOAoIIdrFj75pY10SjSTa7KcZsuppidBONpqgxUaNGwS5SBaSDdATpINI7M8y9vz+OgAWM2d3f9/vdfXKeh2eUGebe+2nnvKe858gRlunnB+Px48GOH4dkwQKYhYYiZPJkaN24wXv+8gtTDg9H18qVyLl5E47vvstNjY1l4ro6chQ8zMjgxGLYbtjAlAMDIepnnN64EadqatiyZcuYgYEBmpqaUFRUhJycHBQVFfFqu3cjwdeXz7h3D9nZ2fKsrCw+PT1dSO3oEFIsLOCydi0mnj3LsUmT6Hzctw/w9kbcL7/A19KSGTo7w8HODjajR9NejIwkp7uODsrv3GGTZ89mvtXVHLdoETkOw8IIWAUG0nj5+9PZ/8IL9PuQENq//evOwYHA9vTpBNAPHCB94eU1OK5OTjQOXl6kg6KigNGj0d3djfLycnlYWBg3btw4+IaGcjrR0VC0tibw98UXBPRsbKARGYkkCwu+prYWii++yLxTU1E4dSq8vL3ZyZMnhfr6em7jxo3M19eXBQYGUkT/+nVmbGwszJw5k3vq7F+3jvRbf4r3M8TExATFxcW4d++eEPBo5oGGBjkZDQyGdFzb29uzyMhIpqSkBHN1dVpz27bReG3bRmeblxeRZCYmQvntt2H+zjvM08+P83NzYxqdnUxj4kTYrFjBXDmOHbSwELxPnmRidXWyUdato7N13z7aY0MFUD75hJzrj7Lwy+U016Ghg+ngAO1RH5+nGftdXMjuWb2a/nbBAjoLGhooUqynR45IgM5yFxeyYdTUSA+dOQPHTz9lyl1dSFZXZyqqqpBKpVBSUhIqKipYfHw8xo8fj8LCQhw/fly4c+cO7ty5g9raWrS1tYmUlZV5QRCYWCwGx3FoaWlhC7S1MWb/fpyZPx+GdXWcWmMjl+XpiZqaGujq6j7Ngn/qFNlRSUk0bl9+SfrDzQ33MjNRr6gIt3feAVu9mkhrt2yhcZg1C0qtrdC4fBmdurpQ/fLLITM3tLS04OTkJNbW1lZITEzsEwTBc+fOnZXPXFj/hfIn2P5T/o/Krl27vBQVFf8+d+5cheeqCfy/LT09lAb0yiukTJ8zzau9vR35+fnCoyzrqqqqyMjIkCsoKDAzMzOGsDCgoQF91dWsqLOT9/T0HHZAdHR0MGbMGC4gIADx8fH8/fv3hZEjR3LNzc24ePGi/LfffuOSkpKQmpoKDw+PoUFXv/RHnF98EZfkcui3tQlz9u8X2X/xBSbJ5dAAELRkCfz8/JihoeHvPzBj5P21tCSP7Jo1g62UOI6M/6tXKTPg/HkyCrW1ybgdYjxzc3ORk5PDli5dyv6lNaKpSYYtQIrN0ZE81H+kLq6rixRjRgYpl9Onaf5/j1G4X2pqCGDMn/9c15VIJBg7dixUVVWF5ORk5uLiMpAmJpFIoKamhoqKCj43N5eVR0RgxA8/4AdVVeHu3bssPz8fVVVVQmVlJe/i4jL0gMlkwLx5yPf3F9QNDAbqrB+V9vZ2VFaz3VUeAAAgAElEQVRWIjs7Gx0dHaivr8fSpUuZ5tPtagYkMTFR6O7u5tavXz90HWl6OqXQT59ODpDgYEqtexLsZWYS+P3880FnxoULg8Ry/fM5lHAcYGCAag8PZOvooPWbb+BdXy+YSSRMrKVFa/ODD8jIWr6cwGJiIhk5Q5EISiSAgQHkxsZIzc5GT0+PMG7cuH8rql1cXIy2tjYkJiZihKkp5PPm4cG5c7gUGIjq0aOZkkTCKhMS2OjAQCj6+DwWXR+Qzz+nmuV+boJ+efttAqCffUZ7b+NGMtZnzSIDv7qa/n3wIGUUeHhQVOfh3urp6UFNTQ2OHz/OX7t2jd26dQv19fWQy+VQ09JiV3t7WXFpKZ+jpye0Ozqy7MJCue24cVwHx4FbvBiilhYymn/+mcZzz57Bdc8YAdn2duDYMXAiEXxXrEBiUhK7deuWUFJSAi0tLZaUlMS3tLQwe21t3qi8nIs1MEBWVpagp6fHdHV1ydhesYJqHkUiigpdvUrgIzAQja2tuHXtGvD99yizsMC4ceNQWVkpZGVlsa6uLtytqoLXvn0QpaTQGPanoCspkZOnsPCxVjxPyaJFkJiaolhNTSguLmYuLi4wNTVlMTExKCoqwqhRo6CqqorY2Fh0d3djwYIFT7e662dMrq0lcErs1mhpaUFtba2gpqbG7t+/DwNNTVqz5uYUIV2zBhCJEBUVBRMTE2Hz5s3MysoKampqTEVFBUlJSayzqgr2n38OPjERe+bNg8zICKGhoVBRVWVNFy7AeNo03KmsRPZXX/HGe/YwxTVraA+uXEn3FBgI3LmDRl9f3EpI4Hp7e8HzPCsqKkJ2dvYAF0N9bCw6GEO5WIz6WbMgVlKCSmEh5EeOoPb8eZwMCxNifXxY8GefsWwNDZw1MmJFxcXwfO01KE2ZArS0QCc2FkpWVkgTi4Xyzk5Wo68vFJeWIl1LC7PDwhhUVQlI+fmRc+7WLYrKf/UVgasPPqCz2NoaZWVl8tjYWC4uLg7p6emoqanpk8lkHKurY/q5uSzG0pJrbm2FmZmZSENDg9PW1uZMOI4ztLFh5ufOMcXWVrAJEwjwrVoFbN0KhQ8+gEdCAqw//BBmo0eTg6K7m5zUD1OGJdeuMTUnJ6gGB1M02saGQEZQEOkfDw9AWRltbW2oqKjAvXv3EB4ezickJAiJiYmoioqC2gcfsB8AIenePdyWyYT7jAmxUqkwYtEilqinJ4S7uQnJ+flCpaEh4ltb0X7qFJSPHWNHFRR4vzVrWC9jnNv06VC7fZvuv66OnGB6ekTCtm0b+EmTkPr990KVoSF7UFXFVFeuxB1ra0GqoIDKykpWWlrKli9fDu2HhG0PW1Axb29veHt7D61/lZXJcfOoI/MZoqSkhIKCAhb4ZIZBf2BhiFIoBQUFZGdnC26//MJ0du4kp+GWLWQ3SKXkmNi7lwB7RMRjnBQsLAyGSUnUw/3ttyGKiUFDUxMzOXwYKg4OpIvMzckJOXfu45kPj0pREX3uUb4cJSVyOFlaPl1XbW1NZ/TmzXQG94tEQuvW0JAchkFBtL4dHYkp/eZNAqn9NhtjdD6PGgU0NaG7sxOZ2toIiInBrKws+H/7LcYGBbGcnBz09vZCT08Pp06dgqenJ9PQ0GAtLS1Cd3c3N2LECLmnp6dowoQJmDx5Mtrb24Xme/eYJD4exm+9BdMxYzD6yy+507NnQ85xMDU15a9fv84qKirkLi4uHFJS6Iy1siKn9549pE8uXqQzXUkJyidP4rfp0xF4/jyVl6xZM6ibPv4Y8vfeQ5qXF8yPHoXi7xDnHj58WNrb2/vOzp07zzzzg/+l8l+Sw/un/C/Irl27vBQUFK5MmTLlvyd9/I03yOu8evUfAmvd3d0Qi8VPsQ8GBASIrl69iurqavmoUaNEdpmZsNm/nyVs2/Zc38txHNasWSM6cOCAcPjwYf7+/fucqakpW79+PTo6OvDTTz+htbV1+J7CANra2nDmzBleKpUKPT09ohHOzhC1t8OUMfKsGxmBOTgQ4/rnnz8/wBy8SUpZ/ewz8or294mtrSXwFR5OqYjd3aSI9PUJXNjYIOnOHURGRmLGjBmCWCxmPT09SElJ4XV0dODs7PzHkDdjdE2Oo6iqsTH1h3yWyOWkVMLCSAGuXEmeb5mMUsV+j/gtK4uAYXY2EVP9QfH29maXL19GTk7OYGSMfg9PT08uPz8fI4yNofrKK/ibvT2TSqXgeR59fX1s7969ou+//55fuXIl91SKqURCxtjhwwN90p+UiRMnYuLEiYiPj0d0dDQA4MaNG8R4O4y4uLiICgoK5Do6Oo9/5swZMgAbGig9EqD6r+HknXfIYJ0zZ/B3X31F8/Ec/V8BYlWPrKgAgoPx1qpVTHTnDkUhsrLIYbJp02D95ubNFCVxc3v6i0JD0dvbi2ZLS6jb2MB3375/CWi3tbVBLpdDSUkJJ06cgIJUitCICJQqKgqF69ez9OpqgDHMmTMHNjY26FZTQ75IBM89e4b+wgkTKCL55Dk0Zw5FeeVyAmc1NfRcp08TOH3jDUojb2qilNa2NiAgALyJCT7y8EBfXx8AgOM4bsWKFVBRUYGenl5/eiIrKytDUVER16GtjfzGRqGvuVlUamuLWHNzjH37bTj09FDbv7i4x42tR2XuXIra7NkD8b17UJVKoWlpyXR0dOQnTpwQyWQybuXKlRhhbi4S5s9HSF0d0tPTceLECah0duLl/fuR/+23yJRKeSMjIzZt2jSGv/4VLevWIf6VV+R3zMw4654eeEVGsh5nZ1y/fp1t376dSSQS3IuNRVxkpHDOy4t5LlgAW0GgsVmwgO7t1VcJaK9cOXRbLEHAbU9PPra2ltPQ1X2srnHt2rVISUmRf/PNNyJdXV20tbUJU6dOZY5Dpf9qatKZEhpKBvuvvwIAHBwcWHx8PPvtt9/Q09ODNFVVBFdUwHj2bIh9fICqKrQ9XLcODg6MMQYLC4t+xnjmqqUFlblzUWJmJlx44QWo6uiwVatWQV1dHU5OTuibOROffv45+hiD84wZ3ElVVUw9fx4mixZRxFVLi5xdd++iasYMhPT0oOaNNwSRru5ACcbDV2ackwO9ujqh8swZoVcqZXX19Sxi9GgEamkhLTSUnxcTw4xFIog3b8b8xYtZ6OTJaJw7Fz0PHiALkBc4OCDk8GFmOXs2N76ggPvy3j1Y1dUxxhg8PT1pnIKD6bWri+bG2XlwXlxdSYc8lGXLlokOHz6M2tpa+Pn58RMnThRzHIdrH34opBsaMmV1dd7Pz0/w8PAQQRBID6mr03eUlgI3bkB++DDuTpyI63/7mxyHDgntM2eKNcLCYA/Q+SWXU8aIqipd39AQBS4ugnd6OkNuLmXBpKURaFNQIPAokSAnJwdnzpyBRCKR8zwPQ0NDztfXlykoKICNHQujixcxOSSECQ/3mTBxImwANJuZwbq7mznn5jKuuRl3165FbkICvP/+d9QIAsbwPFe9dStK6+qIgPDll8kxcfYs1aOPHAnk5ED+4ouIFIvld2fO5La+8QaDXI7Ow4fBnJzYpatXIU1NxfKlS2HG8wTSlZUHUqCH7YjR2kqZa7/XcvIRcXJywsWLF1FcXPw40eusWQR8TUwe49yAXA4sWYJgjkOyuztvu2MHh5gY2jtKSlSWNXUqOWNXrx4EqbW1VCLw3XcUGf7hB+Cf/0Th0qWC1507TPvgQQLqBw/S55cupTnr72jwpBgZDU0E6+tLxGdJSU+fdcuXD536b2BA+0wup6yDd96hZ7a3J8eor+/jLN/9smwZJEuWoGXTJqh1dIAtXgzFU6dQ/9ZbUJ86Fc1WVvj111/h6emJxsZGvri4mJs9ezZnZ2cHVVXVx3Ry6LhxbNwHH+CcnR3uVFfzjj/8wPF2dnzIjBkYM2YMV1hYyCoqKoTK8nIOzc3k3HzpJcrOMDCgZ3N3p7MeAO7dg2jZMgiCgN6sLCjPm0e8BtevE1eNqSnOrV8vH5+dLVL/ne4dly5dkra1tSkC+PWZH/wvlv8SxPOn/DfJrl27rJSVlb/ged6f53kVxlifXC5XVVBQwJw5cxQH0uX+X5fvvydvqonJ86UOPyL9JFRPiqenJ9TV1ZGdnS06e/asAEFgKvPnC4tHjnzuC2hoaGDDhg0sKSmJzZkzB3p6ehwA/PTTT3J/f3/RkCy3j0hhYSEqKio4NTU1SCQS3t/fnxtQGj//TK/l5QQY+9N5zc1pLJ5XPDwGW07p6JBCNDenn9mz6TNNTQQMTp4EvvoKbWpqECUlYdOoUVBvbGSZFy8K51NSmEgs5sRi8fP1aX5S+j3zX39N7Lj5+VT3Osh+OSiVlRQFDA8nZdpfFwuQ0vm9KLtcTspyxYo/lDL/qDwkSuOjoqI4BweHxxiLOY6jMTh6lCKzX3/9WCr48uXLcfLkSRYbG8sHBwc/fbNdXZi/fbsocahnf0QCAgJgZWWFkydPCnZ2ds9cl3K5HB0dHVxDQwNF8m7eJO/3vn0EsLZtG+zpOpwkJFB68pNOnVGjBiOlBQXDMo/3i5eXF6Kjo4XAwEBBZGzMoaCAUgV37CDgnp1NwFRfn675jIi9oqIirv/lL2huaID+qVNyT1dX0R+t/z969KjQ2NjIlMRiYXxGBivV1katkRGyXV1Zd00NwBhUVFRgm5cHGBjg7KefClUdHVDNz2dPkSG2tw8aeE/KhAkUbTh7lhxkK1ZQut/162RMmphQbeJvv9F+6+0FX12Nb11dIamrwyJ3d2gsWQJlZeUhuSCsrKxgZWXV/1+GS5cALy9UrViBX2JjsUMigbqTExlkf/0r1VMXFDzeKg8gZ8oXXwD792N1YyPO1NXBcuNG0dixY5GamipYWFgw1NWBBQYisK4OAQEBLPnWLURHROCfmzah/e5d2NraIjk5mSUnJ9N3mprixQsXRE5z58Lq3Xchf/ttrHvwAHfv3qW90dODEevXw3jNGvaJkxNUFBVha2ZGa8LEhDgelJQI1P34I63XR0Qul6Ni4kShzNCQ03V2FiorK9kAKASRWJqbm4ssLCyEixcvMgBsqKyRx+T8eXJ8HTwIhIbCzMwMW7duhaamJrKysnDj+HEoHjuGDwCoGBkJC2fOZNEPS1FcXFwe/678fJhv3Youd3d8PWYM4xnDtodAu196m5uh+PXXMNTWFubMmcMuKyoKba+9xpqOHMH1WbP4NWvWcFpaWoCLC+p37AAOHYK7RMKMzp4lUPAo98bRo8CqVcywq4uhuBiorASvo4PTL76IVfPmcSaxsQR6SkoADQ2o+PrCrLcXDfPnQ7+jQ2SRkoLM7duFti++gMmuXZBYWAh9PT3CwjFjODUlJYpYpqWR/rh1i0iqwsMJPHV2EiBcsIAcRlevghMErF+/HvX19Th69CiamprkCxcuFBlmZKCV49Db28sMDQ05HDpEacElJQQsNTWB4mLc270bFVIpyj78EL0uLqKQkBBcuHAB586d4xUUFISRmZnwvXxZJP/yS+iFhhKpWXo6PBIT2fXvvxckqqpMo6gIyhUVcCovB5ecDNy+DWZjA3VlZTjJ5fyCsDARXF3J2fvo/tq3D06MPd0poJ80rKiIzi0NDahduACX5csHUo7TOQ64ckWuaGMjwqxZdK71l9pUVKCtowO59+4JnJcXVq9dy7i//x3Q0IBEKkVXbS18VVTQGhsLM1VVciD39NC61NQkZ4dMRtkexsZ0f2pqpBuvXKExnDqV5kIkIhCpp0fn9xB6j+M4uLi4yC9fvoyn9ElAwGBLrd5eSq3evh291tbQ+uknFvjRRwy7d9O1e3rI2eHsTKUzBw48fqF33yWn49WrdNZ9+CFgZ4fymTOFAktLNqenR1B55RXWUlAAe3t7cF9/TVHb4aSwkHTOk+ny1tYE2ktKnk6B19cnXoRx48hhv3Xr4HuKirTnW1pojvfuJafF5s1UC75oETkAli59LGugra0NtSYmuP7GG8L6xYsZFi0Cp6wMpd5ezMjJESwkEpY1dizS0tK4DRs2wGioCDLPAxkZkPj5YdZbbyEzIQG2dXWC2V/+wvWTV0ZGRgomYjE3MyqK5j87e7CMads2cuS2tAx+54gREF59FTpHjvDX3n2XmzV7NumZ06fJzvvlF9hERoqS29qEaaqqw4aqUlNThczMzAcAXP/XSNEelT/B9p/yH5Vdu3ZZicXibD8/P1UnJyeRiooK+vr6oKysDEVFxecj9vp/QSIi6OCYMeO5I7tNTU24fv26vKOjA62trdxwNZ4ODg5wcHCAVCplFRUVsOY4xoWEUE3pc9ax6+joYPr06Y/9TktLi9XX18vxkPFzOPH29salS5cgEomwYcOGoRGkpSUBOoDSnyQS8lZOnkwG/7NYpvvlhRcomvXDD0PXROno0M9D46KhoACJPI+qlhaYvvsuFHkeUyQSwVRFhZWIRAJKSqie9Xn6UD8p/QZwVBRF1AGKbvQTRn31FYGDrCyK0D25TouLSTEP56EtLSWD4d49ij7+GzJlyhTu/v37/IkTJ9iGDRvYU+2ReH7I1iA2NjaYOnUqCw8PZ0FBQU/X7mto4Na6dXJBReV3HTtmZmbYsWPHMzdrW1sbamtr0dXRwX74+GOMGTlSCNixg7HiYiL9eR4pLiYQmZr6dHu6gwdpHcpk9PM7Ul1dDZlMxsaOGsUwfz4ZShcukJPktdco6nPtGqWhvvUW/e7RmuhHpLa2FqU8DwUDA6z69lsar08+eeb1eZ5Hfn4+7OzsUFhYCCUlJWbS2IhQf39mHBcH/1degYKLC7wbG9HV1YWSkhJeWVkZWLuWw/btWLxxI4uOjsapU6cQHBz8WGYDsrPJGTRUxgrHEdi+coUMt7FjyZlgYUElHC+8QM6usDBg40bIKirw8cqVGJOcjAlNTRBlZDyTV+AxaWmhjJVz5+Arl6Pz5En+874+bsuVK9DuzxJQV6efvj6Ksp06RZF2xsgY37ED7YcPw/rnn6F//jy033wTkyZNorVmZETzA3I8jTl0CJ41Nbj08suCr68vMzAw4EpKSnD8+HHo6OjA2tqaN1q3jhO99x6Qmwtu82aYLF0Kk02baMzq6oBz56Bgbw+8/z5KSkooovbttzRelZVkuAcGUh1ye/tjtZjhFy7IPcrLRVM/+ACa/v6M5/khOTH6meoXLlz4++3h+onbIiOpTOWbbwZY+93c3ODm5oZuf38Y1dYKhqamYDduCEaxsYzNnctraGgMXjwjg1JSX30Vqm+/DZPvvkNdXR1+/PFHNDc3AwC0tbWFsD17mGj1aqFeJmPvv/8+Fi5cyFq//BKiu3dhUV3Njh8/jo0bN6K4uBiZ5eV8V2AgZ2tkRHovOZkcptOm0XpqaSFypx9/JP3497/D0dAQOb/+Kvx04YLw2pUrHDiO6pUdHIgNfsQI6NnZQW/VKqCiAha6uuzukiWoP3oUa7q7WbWCAiuSSGAaFgb9lhawmTPpnLW3p0wJQ0MCfTExxJ+yd+8gw76tLXDsGAz8/bFlxgxuf0QEf+XyZfm0ggKR5a+/on3VKuQlJAiyt95iFj/+CA4YcLL1NTUBpaV4MGMG5ovF6F29Gjq6urC1tUXL/fuckJUF3Z9+QruSEqpUVKCnqUkO2G3bcOfAAciqq5lmbCwuOjsD6uqIrK0VZEZGkIeGsnEmJqiIjoa2rq6AS5fIWbt8OZ1jVlYUqb91ixwLlpa0V3p66JUxctxKpcALL0BWWgoGCPjlF4a6OkAuh6OHB9SPHRMVJScL9uvXM7z2GkW5Fy0CcnLQkZKC+IAAtmPXLhHX3EyO7YdlW0e/+YZ/oKHB6W/e3Dd648ZBDPDpp3Td7m7KKmhtpflubycAJZXSOd3P8u/vT061vDyqLVdSogyJBw/I0aqhgb72drSVlWHkyJGihjt3IL98GSKep7lVUaH5jYykvTd2LH1vURFurl3LO0VEMEsdHYbZsyl67+BAY9Z/BtrYkN62tKSo8P79tLdOnCDwev06EByMad99x4Xs2YNT06fzNefPc3K5nK29dUswWraMDceGDoAyUIY6bxmj8zYhYXii1I0bH2csf1T6Gfxra4mp38eHwGlsLDGl79tH+/phhkdpaSkAoLa2lt0pL8foy5ehJ5ViyVtvgfvhByZdtAj5ly5hW20tNIfLaNy9mxwRX38NLQDj6+s5SKUDXSI6OzpgfuUKN2bLFijPmUMZYY9G9bW1aQ3/85+Ajw94nselS5eQlZWFvr4+zsDJifStigrpo9OnAT09uO3ejbiFC5GVlQW3IbLIysrKcO3atQ6ZTBb8vwy0gT/B9p/yHxYlJaV3vb29VcaNG/fHQsFPiFwuR1FREQwMDKCrqwue55GXlwfGGBwdHQcYGHmeR0pKipCRkSEIggBDQ0MhICBA9G/1w710iQ6h8+fJU/kc0tnZiW+//RampqacnZ0dU1dXh4uLyzP3l6Ki4iBD57ffPn8v7GFk/vz53FdffSVcv36dDw4O5oZL1e/o6ICOjk5/mvvvez/6I7ytrVTvqqlJ0TR7ezp8pdKhI7liMUWM4uOfi8HdwtYWPkuXQldXFxoaGjA0NGR8QwNO/eMf/Jj79xkOHiSDRE2NXufMISWvp/f8Kf7BwfTT2krK/fXXCfB9/DE921BppAAp9ieJQ/qltpbAfH/K/H9AwsLCuL179+Kzzz7DW0+C96Cgp/sGPxSzh8pz//79mDt3LsRiMUQiEW7duiXX0tJi5aNGYerevTQG/2Yvz5ibN1FcWIh5Z85Au6UFR9atY3Z5eTB6zj2Dnh6au/j4x1pDDUhvLynugAAy6J5R81VRUYGTP/0kzKyvF7jt2zns2EHj1L8uGKMxCw4mo0BTk4Dg9u0Eth6JmguCgOTkZLlIJBIJAAp//BE+Xl6UHTF//rBnwoEDB+RNTU0iZWVloae7m03MzhZ8CgqYwo4dwKJFUHh4L7q6utDV1YX54cMcpk+nZwMp44kTJyI9PR1RUVFISUnhQ0NDOUdHR7r/1tbhHX8//EDrs7ubQHVEBDkKX3qJgK6nJ33HP/+J1H/+EwFnzyLP2RmTOzsJ3J44QQZZevqz99LevTQGEgmUjxxB6M2bXOrSpbhZUoKwfmNq0SJ6lcvJ6LS2pqyYpCQCF5WVKHV0RGVQkHycoqII27eTkamhQZGy+noy9hUUgDffhFhdHbNMTQduysbGBgYGBn2enp5iHx8fAp+vvUbPOm0a7Wuep3KODRuAyZMHvI/t7e30jxEjIAQEQFi1Cm02Nmg3MoJSTw9av/sOdR4eaGtrk7e3twvdV6+K83bswNSHJHDDcUjExsYKbm5uwsiRI5+Z/sLzPJqamlBdXQ2DL76AsURC6euvv/5YGySVmBhscHVlmDMHmDQJlteuAY9mIJ06ReUB7703EI13cXFBVVUV2h62K3rYHoip2tnhle3bGbS0sGfPHpw6dQo7d+4E7O3hNmMGOx4UhM8//1xQV1dnXV1d3NatWwn8e3kROFy/nlLe9+2jtGpFRZpbd3dALIZSZiYkpaWwuXePYcECWs8bNhAYnjKFsilkMgIWublAdzdsJ09Go1yOxiNHhAJvb9Y8bpz8QlWVaJy/P4IlEiJuGzuWQMmXX9L6KC6mFOL+EiQ/P9JDISHA4cNQ/vRTTL95k+tdtAhMJoOWnR00li1j+Y2Nwqlr13ipVMrJIyOhrKzMi0QiSKVSTrZyJSYEB/Nqly5xanl5QGAgJIIAyeuv0zX27MGF+HjeysiI5jUiAjAywlglJXLcHjqErk2bEB0XhwkTJjAFBQXIZDLEx8fzruvWcf7+/oN2UGMjRUM7OyljICWFzrj8fMoeSE6m7BAXl8HWbOrqUDAyQouxsYCKCoboaKCsDCrOztC9cgXfHD/ONo4ZA91btyirZN8+ICcHJqdPY0Z9ParWrYPF1q3k3OnoQKOKCtpGjOBsbGywdOnSpw0EkWjQWXb3Ls27uzu9nj1Le3PLFtrTiYk0P/12hiDQ89TXo7u1FScOHxYkjEG1sZH1pqUh0NMTort3ibXd0ZGAWXk5nV0A/TsiAtDXR0dGhvDbrFls88GDUBgzhkD1kynW771H93bmDDnWxGJyKmzbRpwO6uoDpRpKY8dixSefiADg22+/lUulUm5Yx3m/XL1KzzRcRt3evRThH8puW7p0sPPLrVtPl59Nm0b6LziYbKMvv6T7/8c/6PPff0/rfe5cuLi4oKKiAllZWYiJiUFeXh78/f1h/umn4F96Cac//lhY8uOPTMPMjErdzp6lDID+DJy2Nso4689o6+mhNdjf4rKzEzU//ADX7Gyci4jAxq++evxee3pIlxoaAhcuoCUsDD/GxckFQeDmzZvHTp8+DUt9fdJNf/kLrXErK+DiRbAxYxC8YAGLiIgQqqqqhNDQ0AHCvdbWVvzyyy89fX19K3fu3Fn87Mn475c/CdL+lP+Y7Nq1i3EcdyQ0NFTtyWicVCpFQUEBkpOTUVJSAk1NzeHrggBcvHhRHhcXx9LS0pivry8SExOFyMhIlJaWCtHR0ez27dtCbGwsi4uLQ01NjeDu7s6ZmZmxiooKLjs7m/fx8XnuEPqDBw9w7do1eWtrK8fV10N9zRqwGTOGPWQbGhqQmZmJu3fvorW1FTKZDOfPn+dVVFT41atXc5aWlujvVfncYmNDB7dUOjTz8nOIoqIibGxsWHR0NJ+YmMgsLCyYxiOA6tixY/KLFy9y8fHx6O7uxujRowVbW9vnTzVQViZWY8aIvdXNjQDAiBEUUbt/nw7cR1Pu3d3JiMjJGb7t0EPhOA5mZmbQ1dUdSINMvnMHt6urmedf/8qEiRMhDwqC4ujRpCyuXycle+YM/buxkQx0ieT3W2IVFBAhzt27pOD19OjZhpOrV0mRPTk3zc0U8Vm8+LkcCs8rioqKUFVVRVFREfT09B6Plq1bR8/6JAs0qF97Tk4Ourq6kDXDOo8AACAASURBVJGRgZycHCE9PZ0xxlBZWSk0trSIAjIzoeTvP9jX+l8RmQy2gYHoMDHhb3l4sJpZs4RXduxgkj8C4NesIWNo06ah3586lealqAh94eHgnkGS9st778GwpIRN7Ohg7OOPydh4EjTeuEEMygcOkEG7Zg0ZIVeukGGrqIhajkNFRQWioqI4QRDA8zxaOzt5b39/hh070Nnaim5XV1RWViIlJQWnTp1CXFwcoqKi0N3dzTk7OcHyzBk2KyEBjseOMdGWLWSkDAVgDx2i956Irri6usLDwwO3bt1iubm5GO/tTcapre3QNeYAAdk1a8jwCw6mcW1uJsB98CClQioqouTePZxLTMTou3cxSSaDwi+/UJuuvXvJQAwIoIjpQ3btx6SwkBxLrq7E4P3WW8CWLSivqEBRURECAwMfP/P6I5zKynRPAQE0n2ZmqNDXl0NbW2Tv5kYOqo8+IiIiY2MC80FBBKb+8pchHTEJCQm8g4MDN7AvdHVp39fW0t5OTqZo2SN72s7ODunp6UhMTBRu3rzJbhUX47alJbS++QZFaWl8gUTC69y4wXKNjSEHOIm6Ojf9yBGMmDoVCo/2/H1Curq6cPnyZWZkZMTq6uoQFRUlFBUVsTNnzuDBgwd8a2srKy8vx+3bt/kzZ86wlJQUFBYWIjMzEypaWrxpURH1LvbzG1wnt2+TkRsYSOD2yBECd9OnU9nNypUEPJcvH7gPU1NTGBgYwNDQEOXl5XjhhRdgZWUFlZUraQ4Yg0QiQX5+PszNzaFjbQ3O2Rk2vr7obm9HcU0NA4Ds7Gzo6OhAX1+fzvI5c2htbN5M56yVFYGQkycBMzP0XbyIqowMZi6VMiN7e1pzMhk5LpOTaU5mzyZAIZeTk8DaGqoBAVAcO5ZVRkUJTjo6nCgjA17nz0Pdy4vm8scfiZi0qIj2bXs7ZR2FheF+UxNOi0Tyasagn5LCVDo6cMXQUP4gIoILio8Ha2oCNm4EmzABNuPHs7FjxzINDQ0UFhair6+PLVu2jPX9/DNWX7gA6y++YGCMxrevj5zt7u70rOPHI6GvTzAyMmJmZmbglZTQZm+P8+rqyOd53J00Ccr5+Zj56acw3bkThmZmMDQ0hLOzM1NTU0NrayskEgll9FVUkDPr669pDyUno09bG3fWrIG6vj4U33yTHBv9DuHFi4F581BiZITbYrF8zD/+wcHFBS1XryK3uBitAEQ2NvKC/Hy4TJ7MEBpKeyg/H9i/H1cZE5TT0gTjmTNZiasrKu7f5+MbG9kDiQQuLi6ClZXV04dSby+ttdpayM+exd3btwW5mxtTGzWKosXV1ZROrKZGoLapaTCCyxjpXU1NZFZUIKOmhvXo67N2Bwd56Ouvc6aTJxPh3ZIlRLA1dixdSxDIcT9rFkV9ra3hZGXFGe3cifsFBdB/5x02FIkaMjOJcGzfPhqr/lZv779Payw8HL3OzuAOHQI7cmTgz+ovXGB3nZ35UQsWPNtAi4mhdpJDsa5LJHTWWloO7wBVUaG1P23a0GWIYjGNhaUl7Q1LSzrHLCxofAoLiVSyrw/dVlb92VJCVVUVy8jIwPjx49HOGC7ducMmfPIJxGvX0hhGR5Md6exMZYGbNpGe6w8E/PQTOXpefJHsmnnzoPvmm+jbvBlRBQXw9/eHIAj45Zdf+OLiYmbMGHr37kWivT3yDQ2FyLQ0ZuvqKixZsoQTiUTIjI2F68WLgr6PD4NMRs6C0FDi+/n8cxhaWKC5uZmlp6ez/paMMpkM3333XVd3d/f7b7/99rfPnIf/EfkTbP8p/zGJiYmxUlBQeGXSpEliQRBQWVmJqKgohIeH8zExMayyslJQUFDgOzs7haioKK6zs1Ows7MbEuzl5eUJHMdBJpPxCQkJXHl5ORYvXsxCQ0OZq6srrKys+hkzMXHiRGZpaQkzMzM4Ojri5s2bzM/Pb+g+pE9IQ0MDTpw4IfT09HBdmZnyBxcv4oqPD0vv7eWbm5sFLS2tgTTerq4uXL9+XR4REcG1tbXxHR0drKioSJ6VlQUlJSVhzZo1on+LYV1XlyIyT7aP+AMikUjg6+vLdXR0sGvXrsHV1RVKSkq4efMmsrKyOA8PDyxZsgQTJkzAcGP/XKKvT4axnh4ZYiYmlDp2/jzV1J09S5FDxggAv/oqRTv+YO27iYkJ8vLy5Onp6UhOTmZxiYkY6ecH9cDAwUiljw8Z5NHRFPVITyclExtLkQKOG0w97+khBRoQQIBr61YyUEJCKMJ6+TIZBE9KXh4B/EcBbr+H/oUXSFH+J0UQYMwYGn/8EU0qKhh5/Dg9k5UVefSzssh5oaY2YEgDwLlz5+RWVlbcunXrEBgYiHHjxrHAwEB4eHgwX19fbty4cVDauJGAmL7+H2NoB2h+Z84EXn4Z3PjxcNi0iZnZ2SEpOZk1NTXx5ubmTHGY1OzHRC5Hi6IipIsWQWk40rmXXgI8PdFjY4Ov8vJQWFMjiMVipqSkhNraWty5cwccY5DExEDl0CGou7vD/PDh4Wux8/LISOpPz1ZSIjA2bhzQ0oL66Gi0v/oq0ktL0aylBWtbW3h5eaGnp4dF3rwpj7Oxwa3eXmb86quIqq1FTV8fFi9ejKCgIGhpafG2NTXCOF1dZq+iAvWXXqJ9PFRa38KFlL68d+9TQBsYdLSYmJggJycHJfn5cr2+Pk7l9dchGs5BWVtLkaagIFrXFhaD/X1TUqi8wdsb9+/fR25eHopGjIDT4sXQeOcdigqPHk2RkOPHKeKxY8fTDqsDByi7QCKhtb94McBxsLW1RWJiIhoaGgRnZ+ehF5SCAv0tx6F7yxZcKy1lHtnZTP+HHyj9MyKCDL+ODsry0dOj83DChMe/h+eB9nZkRkYK7jzPSerr6dni4sigvHCBzh6ZjEBiYyNFzB8CTUVFRVRWVjK5XA6JRCLf8eabnIm/Pxy++YaN/vprzjA6mrlPncpGz5oFexsbKHl4QCE09Jn7pK+vDwkJCaitrUVNTY3Q0tLCZDKZoKyszBQUFFBaWso/ePBAUFVVFZmammLt2rUICAiAmZkZLly4wExffBE6wcHkyPP2prN0zBg6t/prSj08CLx+8glFqMLDh3QOXrlyRZ6dnc2ZmZnxbm5udNOqqmTwm5jA0NAQRUVFSE5ORmBgINiIEVA6ehR2Fy+yoAMHYGhigqysLOTm5sLQ0JAAN0D3cvAgzcmpU+TI8PIC3n8fXGgo2kaPRkRXF6R1dXwUx/Gqc+ZwetOnE+CoqyMAW15OYPLFF9Fw/Tqgqws1a2toZmczkw8/RJWJCZIdHZErEvEFvb2sRE2NVykqYoKnJ5R8fMDMzIA5c1BZWIgff/5ZsB45EqI7d7gHggDdn3+GTXIy5+DqCtGrr5K++fRT2n+2tuB5Hrdv3xaam5vZ4sWLYW1tjZjMTH7ElClM3ceHoqavvUaZWuPH031v2wb4+SE1NXUAbEdcuQJRUhKYvz/fp67Oy2QyvlUs5tva27nGkSPRlpiI5LIy4ezZsyw7O1tIuX2b+RcWQnToEKpTU1HX3o4HK1eiMycHt9au5c+5uaElL4/Z/POfKGlokKuNH889eX6Wl5ejrq5O0NDQ4PKamoRomYxlubvD5cQJ2N+7x3VmZDCrX38Ft307HnR0oHfHDpyqqpKXSaVc2N69rKmmBscAtJqZCS6GhszJzQ3RKSkoKSkRuru7Gc/z0GpsHKhxxu7dwMSJqPv5Zwi1teyItTWKjIz41pgYZtzUBPH06eQAWraMdPyWLUTw9UiENzY2VlBVVeVfeOEFLiEhgYlEose5DBITSXeHhVHWwrRppL9XrKD2eCdPQjkigh3t7GTpWVnyMWPGPG5YCQLpIU1NKoERBMhDQ3GnthY/3b6Nku5u3LCxge5nnyFFQ0OwmD17gNjQdMkS1mFoKFjOmPFsY83YmJ5vOA6c9nYCsqtWDf0+YwTUT54knT2Us1hRkWyUmzfpc/Pn015TUCDHqokJ5Dk50P/tN/j+9a/oBVhlZSU4jkNWVhbv6+vLkpKS0K2q2mfn5cVh/Hg6y1VUaP0fOkTj2/88AOmEt94i0G1rC0yahK5Ro5BXWCiUlpYyGxsbfPfddzwAxnEcHxMdzXUBqNLWluvX1grzrl7lnPbs4UQiEVQVFBAwcyYq1NRY7auvwkhLC8zLi4IYubnA/Pm4fOUKcnNzsXr1aujo6EAQBJw5c6a7rq7ukkwm2zr+WUGO/yH5E2z/Kf8xiYmJmcUYCyssLBSuX7+O3NxcpqioKJ80aZIoNDQUAQEBbPTo0ZyrqytnZ2eHS5cuMR8fHzDGUFBQgJycHNTV1UFXVxfW1tbcjRs32IoVKzhjY2PMnj2b9bdSUVZWhra29kBLpEdFQUEBmZmZ8qKiIujo6DANDY2n6sRbWlqQkJAgXLhwAYmJiczU1FRYtXIlc9mxg3OcMoWN2rEDIpGIFRcXCzExMay4uJgvLy8XIiIiWG9vr7BixQouKCiIubu7w9fXlxs7dizz9PTk/u1WZg4OBIL27/99UqnfERsbG9y9e1eekZEBJycnVl1djZqaGrS0tPDFxcWCl5fXY4MilUrR0dEBJSWlP15X3596vXo1Kc+KCjJ0X3yRjF/GKJ303Lk/HLVnjMHHx4cbO3YsCwwMRGxsLJqbmweJghgjwGlmRt7g6dPJUHVwIHAcF0cRipgYAgtvvEFZBHv3Drb6ecgeiytXSEHMnEk12I+mlD/02A+ksvX20t+PH0+p7P+K9PXR9RQUCMTu20eRLGdnoKgITC6H8okTMF+2DJoaGnSdoCCK9ri40P/XrydimHXrgCtXUCCTCWUVFSwvLw8jRox4ut4bIEPF25vG7HlJ59auJWN58mQCAC4u9PeMQUtLC7a2toiPj+djY2O5W7duQRAESKVS6OjoPL2eWloAJycc0dAQooqKWHV1NSQSyUDdakdHBz7//HPB4KWXkFJVxcstLbngLVtQMmmSkJGTw+Lj41FcXCwIZWWC5M03WTnHIdnWFmZLlsDkUTKnRyU8nAzv/tS5R0VREc1GRvg6NRWW06fzDjExMC4sZO0PHiD7/n3Ut7cjNDSU8/TyYkHjx0M/NRU+8+fDf8UKaGlrQ6mjA6ZKSsxs61YmHj8ebPPmoZ0v/Szwzc2DDqJniI6ODkxMTOD5zjtclq2tEPOs9oDW1uT4SU4mI1ZPj5xNZWXkUNqyBZg6Ffq2thAEAeUVFWA6OrxtfT1jiYm0P4uKKH03KYn21NSptJ/U1CjKcvw4rdPduyki83BeFRUVkZ+fj7KyMmZnZweNIbIbZDIZCgoKcO3aNf7ytWtMJBbzU955hxNt2EB76b33COAfOUJr+upV2pdpaeRY2r+f9m5+PhAVBYWYGM6spARiZWWaV7mcAHZ/qcHEieTICAmhiKWuLtoOH8bFrCxIjIzARCLe1NRUcHZ25qCvT/tn3jwau4MHaZ3MmUPz+IyoNkCR4MLCQowePRrr169nQUFB8PX1Zb6+vnB1dWVjxozhvL29OWdn54ESKJFIBF1dXdTX1yMyMhIBwcHgensJGBsbk1Nw/nyaF44jx9DKleQMjIp6nL35EcnLyxOkUik2btw4uE5mzaJ9/tDROXLkSKSkpAiNjY28o6MjBz8/oKcHzMgI+ra2MDY2Rk5ODnJzc1FUVITRo0dDdO0ajX92NiCXQwDAjI2BsWPR/uabuGtsDEUlJQR89hmr1NDgijiO92xtZdiyhdaUiwsB9e3bcTo8HOabNqHs3DmkXLkCzZgYJPr6QrO1FYZ9fZh67Bhr2rYNvFgMnDvHTmlqYoSjIxQUFFAbGAjh669h0duLUS+/zBlu2YJuqVRQbW9noldfhcr58wQYp0+ncXzzTaRYWODEuXN8V1cXW7FiBTM3NwcAJMfFCQ5OTkzyMGoqv30b1c3NQl9UFKvOyxNKEhOFiy0tfENzs6isrEy4ffu2UFVVxfz/8Q/YMcaSa2tRX1+PhuZmcYWJCdpyc4VJr73GIgwMmK26OtYbGTHnzz5DSWenUDhmDM4xxu5pa/OK588LbsePs/t2dixk0yYWJJVCKTsb4XPnCprr1nHy06cF1ZUrWf+5WVpairy8PC4nJwddHR3Cyu++YyMPHMDPvb1o6+yEc2EhygMDkdLTg0uXLsG0uVnQ09LiCtXUYN3ailxFRUx0dkbw5s3MZOVKaJmbo8XenhUVFDDl8HBUX7sGh8pKcG1tlJWQkQGUleEXd3chftQotnTZMqiqqrK28+ehoqsL7UmTaF2JxQTqzp8nJ9nDUpWysjLExcWx5cuXcxKJBKampuzy5cuwt7enjLWcHALWTU2ks8eNozNn507KcLGyAvr6INbTg763N1JTU7mYmBiMHDmS/j4hAQgMRPhHHwm/SCTs7vXrfNnRo8JvgYGsqa8PG44fh+Px4/CprIT+li1I1dfno6KjWXV1NbtfV4dIfX1eNSQEdnZ2zzbYTp8mp0BIyNDvGxgQf8WSJTQOw0l/J5PhQGU/KJ87l4IAPD9oJxkbI00mQ2JcHLR37UKNqipqxWIIAEQiEcaOHctMTExw48YNbuTIkVC1sABzdiaH3WefkZ0TGUnRflVVGrvgYOqU0twMvPgimg0M8Pnnn6OiooK5uroiMjJScHV1FRYuXMi5urpyXiIRHBIT4fnxx9yIwEBOHBVFNtKdO8AXX4D5+aFy48a+qKQkrvnmTdzz8IBhZyeUQkJwPDpaXlRUxK1evXpAN6elpQmpqamVUql00s6dO3+fjOV/RP4E23/Kf0xiYmLCeJ4f7+rqismTJ7MpU6bAzc2N09XVfSrKLJFIUFpaKr958yZLSEhgubm5KCsrw71795CUlIS6ujp5a2srFxQUBAsLi+eKUveLm5sbV1JSglu3biE2NpYVFRXJW1tbWX19Pbtx44Y8MjKS6+np4X18fLj58+fDdcQIxr75hg6kqVOhqKgIMzMzeHh4MB8fHzQ2NrKOjg42e/ZshISEcM9Kf/+3pa+P6nb6SY3+DXFxceGys7P569evc5WVlZg2bRpmz57NLl++zEaOHInS0lKcPXuWv3HjBouLi8Pt27fR19fHW1tb/+sRb5GI0pVef50O9/37CXyPG0eRMBUVAgJdXWRky+UENp8T4BcXF+P+/ftwc3PDsBFUBQWK2np6khKzs6PI2dy5VHMoFtP/r1whw0JJiQBFUBApkaQkAtJvvDHIQJ6aStG8yZNJiTk6knHwe4R2/cQ3eXkEJLy8yMCIi6P72LyZQHNrKzkHpk0jpfywzcYZNTW+urdXcFm+nMHJie5HLCaHgbo6ecsXLqQI3vr1sHN35zpzc5lyejpT8PbGkMz0/fXLPj7PXmPNzeS1nzGDgLaLC/0MAT4kEgn8/Pw4ZWXl/r7BfWlpaVxycjJ4npdbWlpyj35vT08PbqqpsalTp0IqlfKRkZHMyMgIEomkv+aRjevuZqW6uogrL2cjSkrg//bbbOy0afAdMwZBTU3MpaqKNZubI1wiQYeyMry9vaE7HGP52rWUNu/t/diveZ6HTCZDREQEGhobsfDll5n2Cy8wkbk5pJGRMKirg9W9e/CaNQvaNjZQUlaGOCwMnIUFWL+T5dVXaVz27qXX4WTyZAJK//jH7wJtgBxNulpaUNm+HWp2dizTwEDw9fUd3kBcuZKMw3HjCHRaWtI+3LyZMjJefhlYvhyWlpZob29HekYGi1VSgs/s2VBYu5ZStl96iQD7rl20j5YsoQjOm28SaPPwGDQEOzpozYrFcC4uRlpNDWR37sA2Jgbc+PGQb9uG+2lpuJmXJ+jOmcMiBUGwuHiRmxkejqDTpzmxickg38HBg9Qv/IcfKBLa2Ulg186Ooi/+/hRFWrAA0kmT8N2DBxi3fz9EgYG0f+RyIi3y9KRnDgujlNSXXwZCQyFvaUHL1auoU1fn137/PfMrK2POW7dy/UQ+0NamPf/117Qv+voI5G/c+LvcBvn5+fKKigrOy8sLpn+wNMPKygoJCQmIi4uD1rRpgpGTE4OzM82hgcEgS3NQEN3TmDHkGBhGHzY0NKCmpgb+/v6DB2p4OK2Fh3pLQUEBjo6OLDw8nLO1tYWGlhbNa0gIIJNBb/p0BAUFIT8/H/fv30dNZCRc339/oN+3cOUKjojFqGxpQX5hIW9y6BD7TV8fSnfvIjokBC3GxggNDWXiAwfA0tORVlUlHKmsZPHx8UhJSeE779xhLSNGIKS7Gzb6+tDNyoJmRARGt7TAIjUVot9+wwh/f9gVFDCRoSGSxWLk5eUJbNcuVmpsjHIbG9ikprLEtDRI2ttRbGvL0leu5HPV1QWDtDSkKivzelu2cJn37vHHpkzBmA8/ZO76+mzCzp3sUZb29s8+E0Z+9x0TvfIKcOgQusePR0ZBAVP4+9/B2doy0zt3mFJYGOf+2WeQ29nBKzSUc3BwEOyUlZk8OBjpAQGYOHUqFxQUhClTpsA7OJiJjIygduUKnCMiUC+RQPejj5DA84JOYiIma2oy7+RkZpeUxEQARuzbB4mlJeDpCW7dOrg3N3PSmTNxtaEBGmpqTOfmTcDdHdnZ2UJtbS1bt24dQiZOZArZ2dCYNw++ISGwyc1FW1cXb3j9OrtuYoIZc+cKo6dOZTq6uqhtb0edhwcvdnfnvXbu5JJ0dfGTra2Q1trK3G7c4I39/IRJV6+yu7q6uGBhgYCwMDAlJXK6LVyI2MpKwdfPD56enszc3Bwqr78Ozffeg8qjDk2Oo31qbQ14eaFx5Egcv3EDVlZWcm9vbw4AtLS00N3dzV+5cgWyPXsg+fRTiMeNY6LRo+nM6u0lm+H77ym1fONGAuPt7dALCoK1oyMyMzPBGINdP1mrsTHqvvuOqXZ1YXpLC3NqamJO77wDf3d3KHp6QsHICApvvAHR+vWw8fDg2traWFNTk3zMyy9zqk5OQuD69b+fhdjQQM83XPmbWEz2RVLSsx3uxsak09eupTP10fZnj4pIRA7CrCw6j/T1AY6DnqEhfisqQoG9PUKuXIG/ggJcN23C1NmzGWMMurq6KCsrk0dHR3MdHR1yOzs7DsnJlD316qvkIJswgaLof/0rOUcSEoAZM9DDGA4dOgSZTAa5XI7a2lrMmTOH+fv7D9RWizMywGVkkP0mEtHzxMcTN4eaGvDNNzCzteX8eR6W33yDdD093uijj9iXqqpoaW/nNmzYMFAGV1ZWht9++61DJpMF7dy58/l6ev6PCBuqPdGf8qf8K7Jr1y4/LS2tq1u3bh2GSepxaWtrQ1FREdVFtrYKioqKLDc3Fw8ePICnpye8hwMLf0AaGhqQnZ2Ne/fu8VKpVDA3NxeFhITgsYjfpk1EInH8+B9Pq/3/Q3p7SfG8+OK/DbgBihRyHDfwzIcOHeprbGwUKygoyD09PUVOTk7Q19fH1atXUVZWJgQFBTFNTc0/bDg+U+rqyOCztiajXU2Nosfh4aSwTE1JucXH0/smJgTIc3KolvFhVkNfWRnOFxSA19DAgsWLCQgYG5MCVlSk+dPUHGTFLC0l5VJZSSC5n8its5MM/dOniT1VIgGqqmi8582j+7G3p4jQjz+SoXvoENW3urlRdO/R7IOGBookjh9P79XWkqKbMoUi0bq6FGH/6it6Rlvb54oq19XV4fDhw9i+fTuGjFI/IZ0dHYhZvBj+TU3QCg8nY+bdd58CmQBo3N944+matORkiiDOmUOg5eTJ3227NZQ0NTXh9OnTqK+vR0BAAFxdXaFz7BgqMzNx0tmZNzU1FZYsWSICgJs3b/alpqaKZTLZQN/nVStWwGrECPCCgL78fCgqKNC4vfACRQt27waMjXHr1i2kpaUJMplM2LFjx9MWVE4OrTsVlaf296FDh/pqa2vFIpEIPj4+8smTJw+gmN7eXogbGyGKiSECMWdnSnN0cqLU7FWrBhlwg4KGH4i2Nlo/lZVkQA0XfR9KCgoAbW2UtrXh1JkzwhtvvDH8ARUeTuMyceIgQDx3jvbBrFm09gRhoB3NJ598ImhoaLCNGzaAvf46ZYOsWkXOoXXriDSwp4cMQJ6n9dvUROPf1kbA1syM9oSzM+JeeYWvSU3lxmZlIfn99+V6+/eLmgwNkWdtDa+UFNwNChIkIhEWjR/PFH19aUz6z7eamkGivM5OGs9btyi19/btx8BlYWEhIiIihAG2/MxMmmMtrcH9+be/AaDSn2vXrsnz8/NFYrGY9/Pz4wLc3cl5JJfTmRAQQE6QpiYC/T4+5Kw4dGjoffOIyGQy7N27F7NmzYKRkRFSUlLQ2NiI8vJywd7eXggLC/vddKempiYcPHgQUqkUW7duhVZ8PKWv/vQTzcmBA7Ruz52jc+bdd+m9IXRVX18fdu/eDRcXFyE0NJQ+YGNDTozAwMc+98EHH8DGxgbL++u+c3PJ0J8+faDbRH1VFY5+8QVUGBM2fPQRE/f0oOurr3AtLQ2TWluR/Pe/o7u7GybGxhg1fjzkGho48+GH8vLKSpF5ZaXQKZczRZkM1TY2EPM8PFNSYF5SgswxY6AYEsLPW7GCQ0sLjf+8eTTmcXE078uXA8eOoe+119B05Aj0//Y3lDo6CuKffoJKQwO7FhYGq8mTIejpgeM4cD09gKoqOI6DcnMz1O7fR+ORI7BwdISxmhplCnh7D3Qg2LN7N78gLIyzNDICHBzQGxKCkowMOKWlDTIxt7cje+ZMQSksjDkIAq2b3bsRe/Agou7fx4SJE6FYWwvt1FSYHj2KjBEj4GxjA0RHC729vYxjDOEzZ2JEaSlqLCwwLjZW6OY4dmX6dDhyHO8dH8817dyJiPx8YeVHH7FrkyahaPRouGdmwj0tDT9v2cJLe3s5L+//j73vjorqXL/e7znD0AWkCQiCNFFAERARe0PsvXejxm40sV1LTIwmllhjbMnVWKNiQ0WBgCIqVVHABigIUqR3mHLO98fDgAWjKff+Es8qUAAAIABJREFU7r2fey0WiTPMnHPe9uz9NE/0Vp03u3fTudK8OZStW+N4u3awHzYMHl99BX7RIhrvHTto7WppAT//jCvnzqHk2DF4VVXBKioKXIcOtHc1bYrAy5eReP06Zu/ZA27/fmiOHImoqChcuXIFEydOhI2NDQoTE/FiwgS4xMa+U+hRbt+OM0lJAt+mDesxbhzTeyWdR1lejqyVK1Fob4+ia9dEs7Q0Znb5MhoVFVEky4wZdG5Pn077pOo7aguBPt25E6GbN2PCwYNIvnoVLby9kdSrlygWFbE2MTH0/mHDyFZYvJiEdHt7qHK9KysrcfvWLUFr7VrO/dQpSH+n2GYdrl2j/ffTT9/9nsRESo+7fv39tprKHlBFBtRCLpcjKChIbNOmDbOwsKgPkafIDoAxPH36FIcPHwanVGICAOv79ynN4ZUioPn5+di7dy/6FhYKdlVVnO6uXbT3l5VRPR1HR9rHp0wB5s+H0KULDk+digo7O2VzW1s+KioKDbYOy8igaDSVyL5uHe1Jn35KIqzqvmtqgMxMFAUFITEwUIzw9oaWlpZQXl7O9+3bV7S1tWU//vhjVU1NzZDVq1dfff8A/G/hI9n+iL8Fa9eu7SSVSo97e3sbde3a9c81GQYQGxuLkJAQkTEGFxcXWFpasps3b6KsrEzo06cPZ2tr+0Gk44MgCFRNc9Agar3wIbmm/w4IAnkw5s0jsvRvQmlpKY4cOaKUyWRiVVWVpFevXm+Fm/8pCAJVds3JIS/Wm4qyKFJBj5oaMnhFkUhDdTVt9OrqdKDUeohv3rsn5FZWcmoyGTpraUFqYADBxATaHEdGm1RKxOjECfobNzfyjEmlVOFTXZ3UaqmUjFfG6GCuqKDXVf2ZNTTI0FfljZ4+TfOFepySJ9PTs77Yy759RGji4+kzR4+m+/qL8/XQoUNCbm4ulixZ8l7Dvbq6Glu3bkXv3r3h7uBAPap9fUlUiIuj3LCqKhIXVq0ib1ZtixH89hs9l23bKGz4xInf/a6kpCQ8ePAA3t7edVXQ34QgCDhw4ICQm5vLSaVSLHrxApcyM2G9ejWFp75huJWVlSE5ORk2NjbQt7cHO3CASP/KlZSLn5dHz1g1fgAuXryofPHiBV9UVPQ2GS0sJLJ45cprXueUlBQ8ePAADx48gEwmQ6tWrZRDhgx5t7dDEEh0URGgigoidVOn0pz29393mGC/fjSnL1/+3efZIJo1A4yNURYWhu3bt2PFihXvLryYmEiVxfPziRgDNH/37cPVYcOUQkYG+qSl8eyTTwBbW2zevFmsqKhgn376KUxNTEgs+vprysmtrqbxX7aMDFmeJ2Px888pUkVVj+GtS0hEQECAaG1tDTc3N3by5En06tULsbGxMDQ0xOjRoxu+/v79ieC5udGa6tyZDMT9+8mgCw6uE7eCgoKQk5OjnDhxIo/Hj2ke1xaTQnQ0iRlNm6KwsBCHDh0StbS0RD8/P87q96JQcnPJuHZxoXHMy6N/37+fxDLVXmRmRjUpRBE4fx4v9+9HoxkzIPA89k+fjnk7duBGp06wl8vR+M4dXF60SDl4zx6es7SkHtVt21JEytGj9cayqSnEEycQOGSIYBMSwjmZmJABP2oUfc+IEVSvgedJoGjfHjhw4J3F8q5duybGxMSIX3zxxTv3CxXZ7tOnj+jl5VU/kA8fEmn57Te617lzIX/4EHuHDBFFUcScOXNY1ZAhOG5igp5Dh8Laz6/+QxMSSJTKyiLPWWoqhM8+Q7m1NTRlMuCHH5C3ZQsatW6NuIwMXLt2DSaMKdrfusWnT5rE+sbGQjp3LolFT5/SmC5fTtEGmzbR2P74I5CdjaLu3XHCzEzU1tYWJ06cSPdpaEgkc9y4uks6Pnu2MCAzk9PZsIHGUUeHxEM1NZydPFnorq7O6U2fDty4gVI3N+wPC8NiVY5rLXbv3i20a9eO88jPpzD+pUsh69YNsqwsVBkYiMXm5jBPSGCVmpq46ucHD3V10XHmTKY0MwNzdUXZgQPQ6t8fpUlJMBo3DtVr10Lh54cb4eHQOnRIeKqryxnm5qLxxIkwdXSEtKYGOs2bQyaTQYiJgcGoUci+exe2LVrQBY0bR1FajRvjhbk5jpw/j6VLl9Iz++wzWqPffkupGfr6wNWruGtkhKZz50K3uBjcggWQLlhAwlZKCjBiBBQ//4xt58+jgjHwPA+lUgkrKyth9OjRnKamJnLPncPNX35BSvv2QseOHbkOtbVLBEHA1q1bhfLyck5HR0cp5ObyC48cgdrevUQYVZg/n/alzz9H9cmTOMFxolVMDJOOGYOOABFAUaTzu1u3ehuhpIRSYaKiEBkbKyaIIitv2VKsrKxkzR8+RJ/gYBg8f07vX7WKukscOkSCzfnzAGNISkrC2bNn4ZGYKLgsWMBZeHu/cxt4DcePk9d6+/bff9+ZM2QjvKtWyKsoK6Mz4+jRuvPr66+/hqBKMQLQtm1b9OvdG1xcHIm6nTsDbm64d++eeO7cOQYAzWtqxPFJSYwNHUpneG1qRGZyMjS7dMHZoUNh0qWLYmBoqATBwRSVFxFB39m/P5SBgVCOHYvz+/eLIzZuZMoOHSD88APUXm33pcIPP9Ce/913FEHn6Un2Q0TEax1axIEDkeDuLmgfOcI93LIFffv3B8dxuHHjBm7evCnW1NQwnuc3rFy5csX7H9T/Hj62/vqIv4x169ZN1dDQ2Dlw4EAtJyenv/RZzs7OSEtLE5KSkviYmBjExMQAAHR0dLgzZ84AANTV1UUA6N27N2v7ngrXv4tz54hAffLJfw7RBujguHiRDp+Cgj/lVfwzUFWJFASBqaur4/r16/D4k5XRX8Nnn9Hhd+dOw6+r+u+qq78dsvlmWG6/fvABuDt37iAgIACqTzQxMcGsWbNI1Z4+nfK+IiMpFLOykoxUmYwO76Ii+i65nAyA7GwiUxoaFL5laEjkW6EgAeDMGaj6m2LXLgoh//nn+rYn9+5RrtmsWeT5EEUyeMaPJ6Jla0thuV26kOftyRMyGsPDSUzx8CDD+pNPiEjev0+H444dwOrV6OvgwB3MzyfS8fnn5IF69oy8rCdO0N/XkiWN3r1hUlUlqqWmMri7U+5pkyZkWIWGEvls2ZLU6cmT6cCMjKQDdNy41wuqvAdnzpyBIAh48uQJ7OzsxB49erCamhqYmppCVYyG4zjMmDGDqywvx50+fZCwejUSo6Mx+B1EQVdXF3Vr+soVIrYFBWRM/PYbeejeiAhITEzka2pq4Ofn9zb709Wl8XtjHl26dEkpCAKvqakpmpqaisOGDXt/nsrw4VR0RleXvLp375Jhd/YseUNv3ybPigqpqfXRE3+2nsMnnwAODqioqIBSqURmZibeSRo5jsbYyYnmIGMQPT1xZ+tWMfnSJb6oSRMYymSCxy+/cNyXX8LBwQF3796lsWKMjDp7e5qbYWGU0nDsGL0WHU0CzLZtNA/fEQHk7OwMZ2dnBgAHDhwQRFHkgoKCAJAHd8uWLVi4cGG9UbdtG4lTv/5K0S4jR1IUS+fOVJho5UqKOBkxgkQwMzNkZWXBxMSEExISwF29CsyciWQrK7E4KgqeX3/N4O+PZ8+e4dixY2jRogXe612urKS5pSo+tmIFee6Dg2neJCfTOjcwoLVubk57wezZCLh3T8HGjpX08PLCp6NHg7O1RZdevYCyMrw4cQISW1v2m7e3qNmokWjL85yZpiYR7iZNiDT7+gLp6WA2NnDr0oUrvHIF6Tk5aJKXB3WVIyQpiZ7B3btEYEJDaU65uLzlZZTJZIiIiGBDhw6tH6DevYm0qkQ1oG59hoaGMq9XC0I6OZGXMTeXiNrYsVCzsMB4PT22e/durFu3DsPNzGBkYACTESOovZFKTDQ2prkhCPQ8U1LArV6NRubmtM/p6MCc54HkZHQeNQp2kZHQHzpUopGaiqDWreHr70/XWtu6DMHBtF/OnUt75ebNwPLlKHJ3x8H9+yEvK2PFxcX113737ltRI8+aNuWUGzaQ0JiTQ9exeTMwejR4hQJiQQGEQYPAWVoix80NNW+2sJTJYJKWhkaMkY2grg507gypVAppeTl0DA2Z8YEDgJsbtOPi0C80FAW7duGXTp2EMdHRnHTtWuj7+QFHjsBo5Upg82ZojBgBAPDr2xfo25dzefgQ+UOHipU//QQbLy8mOXeuXrDt3x9hc+Yoo8+f5xb++itTX7qURBrGgCFD0OjqVchkMrrW5s2JYI4cSVEhU6bQWF66hMcuLsrQpUv5ipISLO/Zk8Sa5s2JJI4eDUmbNhCCg8Xp48czAwMDiKIILS2tunVjmp6OAcOG4Z8VFVxwcDAePXqEwsJCQS6XMw0NDfbJJ58gLy+Pj4+PFw8OGMDGWltDOyCAPMtz5tS3Nl2+HBp9+mBybi6riI7GgceP0WbVKugwRvfk6EhzICiI9jM9PdpvFy5E62bN2NURI/D51KmMO3wYyM6GtEcPOsNUtSYKClDh5IRbOjoiHxbGcnNzlcnJybxts2bos2MHh3/843e3gtfQsSPe2x4MoPk+dSoJru8Dz5PNUVZWZ9c1bdpUeP78ed2zvnPnDkpLS8XBgwcz8do16EyZAsTGwsXFhZ07dw4A8FRdnWXt2gWLS5doPnfvDri7o+n8+UBkJKZ6eOB8drakbN066G7cSF7oVq0AKyvIGMMljhPS160T548fz2P1avBqauB//ZXE1ujo14UDY2Mau1OnqAbP9Om0H23cSO+vRW52tlgZFsas+/aF7cCBdf/eqVMnaGpq4tKlS1Aqld+8/yH9b+Ij2f6Iv4S1a9dqSySSHVOmTNH6S72ta6GhoYHhw4fzL1++RF5eHqytrYWJEydyBQUFiImJEWv7brNDhw4hICAA9+/fx+jRo6GhoYHq6mpoaGhAEIT3t906frw+l/cv9hv+l8DUlMhbbi6RhX8DHj16hIqKCt7Pzw85OTmCo6Mj9/jxY8TGxirbtGnDvbPS8O+hpoaI3cuXf+u1tm3bFs7Ozjh27BjS09PxMieHFNihQ4mYTZxYL1L82Rz7/HwKW376lLw15eXkeduyhYQDHR0y4gSBQpRjYui7q6rqC6mtXk3fr6tLokOzZmS0DRtGr7u70zyUSMhQlErp85RKej09HRJTU6jX1KDit9+gPWsWGVI3bxLZ/u47ysl1dQVmz0bB5ctof/kyszxzhowuV1c6FM3MyFuWk0MGzb59ZNQuXUrfs2wZGURr1pCBceAAefIOHqS83enTUa2jg5RVq8Sw7t3h8OAB2srlzPGrr9Dk8mVcfPhQPHr7Nmuan48ndnZowZjYvFUrVmFigsfh4UpZVhY//NEj7Lt5E919fQUA72efDx4QaVyzhrx4TZoQGXgDM2fOxI4dO3DlyhW0a9cOMTExCAoKQl9dXbjt2EGtwxQKBAUFoaqqChUVFSgpKeFHjBgBJycnhvf1ms/MJAOjQwci1LUhtsjLI0FkxgzyqsybR95rVeG9778nr8ylS++91Qaxfj1996pV4Gs9rfn5+e8k25XNmuHJL7+gJjYWhoGBsPXzQ1p6Oh5KJGx4Sgq01q/H/j17oBkRIcq2bEEqzwsAeKbq4b1+PV2vqm/9pEn07B0cyNCqqKD0isWL6d7eg5qaGhgZGUFDQ0N0dHRkbm5u2LFjh7hp0yY2evhwmADQycyEYuhQCGpqkIoiedXfRNOmJBIBEJ2dwTw98cDIiLXy90fagAGCrHVrduvYMaZdVoYW4eEIvnRJLCoqYjzPi8OGDWt4bAsLyfDt04eMya+/pjXh7k4/WVn0OzOTcsD37qXn8qpoY2qKwthYbsSaNWimqho+f37dyxbt2sFcFLlHPj6IiIjA87AwjF2xgtZT8+bk3VJhyRKYAQgtL0d2QgJ6nD4tOnfowPjVq8F9/z0JN5s20TVnZpKXHKCQ/lcgkUhgZmYm+Pv7c40aNaKIk7Zt3+7zC6oJ0GBk48KFNO47d9J8btYM+gBWrFiByMhIMXLfPuZ58SLyjIxw4csvUaavL/r4+LDOX3wB1rEjeVQ//ZTI8tKl5BVbsYL2wBYtAHNzsMBAWCxdCoSHQ2zcGIr16yFRU6N1dOECCYhPnpBXcds2OkNCQyGKIk7++KMwb+VKLmDdOqWM9lkegwfT976yNqqrq6FQKKCrq0tk5PZtEu7c3IBdu/Bw1ixWFR8PIwcHlA4YICRfucI5VFaKOHiQobCQxNk9e9DG0JBh6FASKYcNozB+TU169rm5tH8/eQJYWsKgpASaY8awjIwMlnfuHCw++YQEN9U4RUbSXnb4MJ3vGhpo3KwZGsXHs73ffCO0PHKEvksup7OA49B10yY+++efBfny5Ux9wgQilzk5wKVLCImOFt1ragQAPAYNogiRjRtpvSYkAHFxqG7SBE1v3uRHJiaC8/Ki+iOBgRQqX1hIUQjLloFXU2NNuncHd/YsnWX//CeJbj16AJGRUPvHPzDZ3BwvzM2RoqmJdu3bc8Z2dtDT14eGhgYsLCzQokULtvH5c2gaG5OAbGNDAlGzZmTHZGfTeoqLg/asWajauBFlZWV1rT7rChwWF1Me96FDwPDhCNq/H7Hp6ZjMGLQ9PWm/2rOH9qY7d6gjCgBMn45KLy/ckskYX1AAFxcX3tPTEz6urrRu/ohTJTKyvijk76FbN1qbcnl9+sG7oKVFzzQmBti3Dxnjx+P58+ecRCLB8uXLsXHjRrGmpoalpKSwzZs3AwDWREUBkyeDs7PDjBkz8PTZM4SEhODIsWMU0ZCYCNmlS+BHjAAvioC5ObiBA1GspSVmVVQwx19+obNr6FCIDg44l5srpimVbNq0aVTUNy2NztlDh+hM09IiYr5sGdkYCgU5Inbtogifw4dpDtaKqACQFBoq3vb2xrhWrZhmbei/CllZWQgKCqoC0H7NmjUVHz4A/1v4SLY/4q+iq5GRkfLvINqvYvbs2ZDJZFBTU+MYYzAyMnrNc7VmzRqkp6fj4MGD2LZtG0RRFGUyGZNIJFAoFOA4DkuXLm24iNaLF3SonzlT33vwPxEbN5JH+NXcxn8hXF1dERkZqQwODubnzJnDaWtr4+jRo4qXL19Kzp07Bzs7O6i/qfz/HlT51jk5H6YQ/0FIpVLY29sj99EjDDM1RfmePZB27w5p7SH1p7F6NRkqZ88S6dTXf32e3LlDXi4np9erkKp6gWpq1vce9fGpf336dPptZFSf/1SbPwuADHqAwqYHD6b/PnQIWUlJKMrPx8m5czGladN6ggyQNweUl39z/34h9vx5znL+fMFx7Fgis2Vl9e+tqaHfISE0p3R0yHPJcSQE2NnRPVhZkdFWUEDGfbt2qDEwwOlTpwQvbW02bMQIpr5vH/SkUkjMzICwMIyeO5cTSkrAbd2Kl8uXQzJqFCv49VfE9+8vjtq7l7/Tpg2SW7bE0k2bgEWLONHeHkxVzGryZDJWHj0i4yY6mp5bXByJJ199Rca3uTkZTG+QTQMDA/Ts2RMhISEAgPj4eEFHR4eLf/gQBsOHIyc6GmFhYaKuri6USmWdN+zkyZMYMGAA3hkdU1JCntUZM8jInjz59XWoImmLF5NndvBgCkX+8kuKYvjhh4Y/90Nx8SIZQnv3IikpSQTAlCoR5g08ePAAp06dwoSjRyEqFMg2N0eegQFMTEyQameHYamp0MzJwbzPPuP8X75U2p45w7eaO5f3GTwY2mvWkPF04AB5JnV1yVvy9CkRpTFjaM7fvUv75qxZ5DXbs6e+BkIDcHd3Z+Hh4cKECRM4VYXy5cuXs6OHDwtcr15cioEBIj/9VMi9e5fD3bsYeeoUjHV1Rf0bN+ra9NSB5yEIAmIcHYVqiYQbePYsXsyciReWllxNZqbYunVr0d3RkT0ZMAC5Dx6IxcXFTCaTsdLS0vrq6DU1JCCpokYOHaKIFG/vt+8jMZHG/cULEqUqK8noHjSo7i1paWmQy+Xc79UVYYzByckJGrm5zLJnTzy3tITVmDENvre4uBju9vZo/t13yCkpYesnTcKoDRvEFnPmMBw+THmhPE97hp4eEYclS2jt1rb14TgOU6ZM4Q4ePCjcvn2bGzFiBBHnBkiAKIqQy99RDHjMGIr6ekPcat++PWvv6AixZ0+U/PwzxujqIjI3F7GxseKTIUOEjt278zZXrkCjupr2z/nzSbTJzyeirdojrayIzOrqonDQIFg1aQK5RALZN99Ae9062nNfviRj394eVb6+SAkPR2xqqlBaUQHcuoUhbm48JJK6KI43vdrBwcEwNDQUOY4ju0Fbm34iIoCsLHzeuzfjBAFPnJ3FmshI5p6eDkNtbQYfHxIorKyAxYsRdvCgUF5ezmnn5YluTZtynipPvq0tEUoPD1QdPYqa8nLczM9HgpqaaNusmWBw7BiPrVtp3bRpQ/MoL49ExKws2psXLwbCw8E9foyBR45wWX5+sOZ5EopXrAB69gRr3hyeT55wJ0eOFIdERzODL74gMXbKFOgHBIhekZG8+PXXYH36kBikq0s9jm/fhrB/P7Ld3cXkbt2YT3AwfefevUQkZ8ygM2DGDNTo6qI6OBhsxgwS+lu3JtFw5EgivgCQnw9pQQEQGSkWWlgwu5gYyDgOqW3bwkRdHfqjRqH8xg20zs4WOX9/hrIyEoP376f95JdfaJ+3tKzr9CGK4uuOEYmEOhGEhOBFkyZo8sUXOJWWpnyho8NNmzaNmUZGkhd55kxaw3v2UOqMhwcJB1ZWMP78c0zOysLZs2fFJ0+eiBPGjePUXV1RdvIkdN9VWbwhaGt/WKtSQ0MSpgIC6Lz6EKSkAFFRiG3RQgTAXF1dwXEcevTowS6/km6kq6tLe9OKFcDOnTArLYVBy5YICQlBo0aNRAAMzs6Iu3oV7eRypHXsCHNBgDQ7G/pWVkLR7dsMn33G4f59YNYsyJyc8LC8nC3btInOeyMj2s+TkuisWbWK1lJ6OkXxPH1KY2ZiQnUUZs2iC+vYkWyklBS8bNQIRV9/zcYUFUGzuJjGphaVlZU4cuRIlUKhGLdmzZqED372/4P4mLP9EX8aa9eu1ZJKpYn9+/e3cXlPe5R/FW7cuIHQ0FBYW1vDy8sLJSUlkMlkCA0NhZ+fH9q1a/f6H8TF0cbSr9+/LTz7L+HhQ8rPunfvdw3bvwv37t1DQEAAZs6cCWNjY1y7dk1MTU1FcXGx0K9fP76FKm/sfVAo6KC6ffv13tR/N779FiXr12PXvHlQSiTQ1dfHZ5999sc/RxTJwDpxgkhmSQmF0f4e+vUjg/fs2T937X8A3333Hdq3b48uDRTiKioqwt69e6Grq6scPnw4/8FFBauq6sUCUaTDtEsXMgZXrCCCAUBQU8PPP/+M8vJyzJo1648JLgDQsycqra3xg4ODICsp4RRqauhoZSX28PNj0NWlvOI2beiA37KFDBd3dzJOBw4kDwtAnlWl8q1IFFEUkZ6ejkOHDkFNTQ2ampqwuXULTnZ2uNC4sVhZWcn69++PNm3a4MGDB2JERARGjBjB9u7dC4VCgcWLF+PVKsUQBDJGk5MpgmD9+gY9g69BEMio3r2bQpFNTcl4aajP9oegoIAMcxsbZBcW4uzZsygsLMS4cePwWr/aWgQHB4u3bt1i84yNoV5djWMPHyKrNo9eU1NTXDJkCFMV1goICIDF6tVoqasLjf79SUiaMOHt/WXFClq/J0+SsJCdTeRr+3Yaq5cvyYh+xz1WVFRgx44daNKkiThlyhQiPCEhgFwOQVsbj/T08OLlS1hYWMDCwgKFyckIP3lS+dLYmPf19YWLi0td2ziZTIZdu3YJrKaGm3v0KDiZDPyAATRfVFi5kozYEydQWVmJHTt2AKKI6a1bw9DDgwzyjh3pmjU1X2/t9yaUSpr/qpzEhASKIFm0CBgyBIIg4Pvvvxc8PDzQtWvXD8oROLVtG5SRkRh5+DC4N8ivQqHA9g0bxBaRkcz97l34z5wpwsgIUzZsYFr375Pg5OxMaQUq9OtHY7B7N/24uhLxBrBnzx6xuLgYy5YtY7C0JFK3cOFr3/n999+jrKwMa9asef1Cb92iOawqELdhw9utKP39iVw0bw6sXo2aqio8at8el4cPR5OUFHC2tqLn1ausUX6+GDN9umCYmQmv48d59aQkoGNHVBw7hvSSEhjJZNAdNAg3O3TAzc6d4fD0KXwvXoSwYAGMRo8GHB1RWlqKcytWYOjhw7i9cSO8x42DTmEhXd+6dfVk9o3Uho0bNwpDhgzh7O3tKSqpuprmc61HHRYWRPhPnyZRe8mSBosXPnv2TNVDHRkZGWKHDh2YIAho6u8PzsAAXE0NkiIjEenlBaVEAk4uh6OGhrJdx4689dChRGAyMoiYHz9OHsHaiAEwhrzsbBw/dEhwiotj7tOns8bl5fX5+l27Aj/+COWSJTijpob2XbvC8ptv6H6SkvDP4GDl8+fPeX19fXHw4MGsWbNm9H2VlcCMGTh26pTSIyCAs9y7l2nyPGBujoKMDFQVFMC0USOoJScDY8fi+fPnOHHiBJYsWUJCU2wsReSoqQEtW+K8i4siMzOT8w4PR0jr1pyntzduhIfDSEcHtpqayoqEBC5HImEu9+6heW6uaOHry5CURKJQQADZA/36UdrE7t1k17Rvj5vq6nDauBGNR42CEBCAOytXCk2jojjDggJUamsjztMTlV26KPvu2cNzN25Quo6ODkVN2NuTUJybS2fFoEHkMa7dj0RRxNGjR8WM1FTWMi4OxkuWoINKBP8Q5OTQs3izgGhDUHWZ+O23Dy6ym5qaiofz5+OZjQ16zZ2LFi1aQBAEJCcn40RtvZQ+ffrg1TQPxZw5eHHxIo6MHw9eV1ccNmwYCwgIUJaXlPCNSkqgV1ICp2bN0P7LL5GdnY0La9eiQ2Ii9I4do4ioadOQ2aIF4qqrhScKBXM0NWWmUVEoGTcOPnFx0DZ+bomMAAAgAElEQVQ2pj2mdWtyRk2dSmtFTY2ex6tOhwULoNDSwk4TE9FVXR09iosZ3Nzq7Kba51/5/PnzAytWrFiA/8/xsfXXR/wprF271kAqlZ6zt7d36tq1q9of7s38N8HKygrm5ubo3LkzTE1N6wo1xcfHIyUlBa/1fRUEChtUGV3/DTA0JAPSze3fEu5+6NAhsWfPnszKygpyuRx37txBZWWlwPM89PX16zxUb3meXoUoUs60ufn7CeufgShSXun588DgwXg5ciSSCwtRI5ejV69e7+613BCCg8mTPWwYGUReXuR1fld7jlfh60uHf3o6Xct7Khf/FZibm+PChQu4f/++4O7uzhhjyMnJURWBgoGBgTBr1iz+NdL4PlhYEIlSGaq9e1OIuSocrn9/wNgYmZcuIdrKCuN694b+H61Q/+gRMHky1IYPh7ePD2vh4oL8ggIUKJVi2y5dWF2Runv3aH2eO0fP9auv6vu2q+ba4sWUr9q3L/z9/ZW//fYbs7OzYxcuXFD+9ttvnLq6OnR1dWFkZKTsEBzMTFxd2RMDA9HLy0ts37494zgOpqamzNPTk2lpacHHxwc3b95EdHS02Lp1a6aurk7XMHkyiS4bN5Kn4vcK3FVXkzjw/fdE4GxtyagUBJqnpqZ/OI1BEASULVwIyezZOGVvr7waHMzp6OhALpeL1tbW7FUxRaFQICUlBdevX2dyuRwuvXvDKCMDTmfPotm8ecitqBCGDRvG6VlaUuhoaiqaHzuGyKZNYR0dDfUNG+h5N7Sev/6ajCtrayilUmQqFEjR1RVzqquZ/g8/4HmLFhC3bsVDExO8KC5Gbm4uoqOjcerUKURGRiIiIgL6+vrKAQMGcLq6uuRZ7tcP8PQEGzgQxk2awNbWFsbGxtB4/hwGQUFos2YNp6amhpCQECE+Ph7Gxsbs+PHjwtWrV5mNVCp+cukS40+eBLdqFRUm/Pxz8lAPG0bEyd0dsLWFWno6mhcXw+DGDVHrxx9ZnKYmrHfuBJs0iQj07/XFBciInzaNPIFt2tA4qgo+zZuHUIkEL0tLMWrUKO69Z9++fcDSpQgXRQwMCYH2F1+89Za8zEyYLVvG2j96hEY5OWjXrRtr164dU1u0iK61UycK49TWJo8UQOS6spKEkqIiImAvXwLLluGeKLKOgwczM3NzIm4dO77l3RYEAU+fPoWXl9frhZFOn6bP7duXyNKDB/T3r97n+fM0Z3r1AqytISkuhumBA+i4fTvaTJyIYoC9aNwYGuXlTDZgAJdmYMCFNm+OvMBAQf/BAxyuqGD95s7FWW1t4VGrVvBbsYJ1kcvhfPcuoh0chDNNmrCYp0/h7e2NiIgIIYfnRZ8FC5itry+kW7fSPrV2LREDe3vK6X9FELt37x7KIyLQLTWVMS8v8vxraNB9tGpFz87BgUhF165EtktLKVc6O5s+s/Z+09LSlE+ePBEzMjK4mpoaVlJSokxNTUV+QYFoHBiI056eoom2tjjp/n1W3LUruh4/LrrGxHAyf39krV4tGs6axdjy5SSEzJkDnDqF4rlzcfvwYTxu1Aha+vq4k5DAOsybxyy9vMgh0K4drctp0wBnZwQbGCgTlUrWLzqasU2byHtoZQU3T0+uc+fOiIyMZE+ePKFid1evAtOmoXLtWkivX+esIyKYpqUlzYOdO5EaEiKe1tVl+adOwXj/fjHQ2ppFRUWJRtnZaKOtzfDJJ3R+V1YCzs4Qli6Fv78/5+Puzpy3bGGVPj5o07s3EpOSRB0DA/QdN46z7NqVefXvD8Pbt9FIJmOSnTuJUHfqRPt7RgbZL6mpNFYdOwL29rhYXAyXvn0hOjjg57t3lWXGxsxp/HgmSUqCdM0a2C1YAIcePThmaEhRJqtX0x7i6kqeckNDEkm+/prG8pVK39HR0cqYmBiuf1AQnnXtKmqamjLbPxJhFxdHotOHeKutrcn7a2LywQJrQECA6O7vzzy7dYNVbSTb/fv3cfr06br3eHt7IzU1VTx79ixsbGxYcvPmiMnJgbtUCtnz5+xGZiZkMhnXJzAQbcaORXs9PdgcPAg2ezZ009LgfOECrk2aJISGhrK2bdtC/cEDNGrbFg5TpjBNPT1WqFAoFW5uLC0tTYxmTGg/ezYJh+7uJDI3bkw1E4YOJfvmq6/qKvqLzs64kpamLFNTE0edO8chOZmiDGoRGRmpvH///nO5XD60a9euDYdk/X+Ej2T7I/4Q1q5d63n79u2NjLH9rVq1sho0aJDme/Oj/4VQ9Rl8taqxvr4+2rVrh1u3bkFPTw/NmjUjVW7LFlJVX2mB8h8PxoiwLl1KG/nf2Y6rAaSmpirj4uK4W7du4datW1AoFMLYsWP5hIQEMSEhgYuOjkZsbCw8PT0bJtyiSD+tW5Ox9nfPjeRkMoCPHqVx7N4depaWsLOzQ0xMDIYPH/5hPdmXLCHj08iIiN6AAZSbpsrH/RBoadGYXLtGeUyTJtV79P9mGBgYIDU1FS9fvmRWVlY4e/ascOvWLfbgwQPBxsYGEyZMeL/R/yaGDaOogzfHUZVnV10NxMUhqFMndHvwAM3mzydvZ3IyGU7vu8+HDymfbepUQE8PjDHo6OjA2NgY4aGhzD0hAVJzc8oNy8uj8Nhp0+ojTnR0KKz55k0yiPX0kF9ZiXJbW1y9epUzMTERQ0NDWVlZGWvfvj3Lzs4WevXqxXpqaHCNFi1i0p494eHhwaysrBp8MBzHoVOnTnj8+DEexseztr/8Uh9avHDhu/P75HLyePz4I63LHj2I8Pj6EjFr3pxEDFWefsuWf6jdV3h4uBAVHc0glYpJtrbilClTuPj4eMHY2BidO3euC7EuKSnBvn37xNjYWCaXy/HJJ5+gqakpMGkS1KysYNipEzxHjmR6OjoUmpmZCYSGgv/8czxv3Rr3GjcWXfbuZZg8uWFvjJcXMGkS8vbtQ8ny5ThcVYXHHMc67t6N9LZtkdiypaiRkgLhzh08VCiUqfn5YklJidCqVStOX18fOTk5WLx4MddIXZ2eT1YWPV83t7e/KzSU1vTkybCwsIC3tzcrLCxkQUFBYnl5OWekp4fJZ84wbulSui5VQSVjY8rzNDenkFKlktZk9+7Q1dND01272PM+fXDl/n0UKxTKFi1afPiG9OgRhRKronm0ten7YmPxICIC+RIJi3v8WLh//75obW3N3tklQ0MD0NfHo5QUmJSW4rREIj58+JDduHFDCA0NRcS1a6j5+WfmlJ0tapw8yfhmzer/tmtXiujw8SECNmUKGb46OiQAVFRQKsiiRfQeCwvIi4pwVyYTu3/5JVNLTCTSxPMNnh3x8fHgeV6wsbGhCeDvT5+jagfm7Ex74ujRFFqu2itUfcifPCEBW1sbzwcMQFF2NrKqqyGtrhabLVvGZJcuIdjQEKy8XJi1ZQszadKEGT56xCx9fKChpwf3f/yDuTo4MMnixeDatgWnpgabxETWfN06REdHIyoqCs+ePWMVFRXMuV8/aCkUtCd07EgVtyUS2j9MTIjUlZQAQ4Yg8elToRXAjJKTGUaOpPXcowft9y1b1hek9PWl13btovu+dg346SdKLThxAnBxQVBYmJifn88bGhqiefPmGD16NOfj48Na+/gwo7t3WYeNG5ltr16MzZ8PrZoaRHTqJHrJ5axq8mScLisT71y5whwFAec0NATTTZvYqevXlaGampzz8+d4mZ6OuIcPUaWujsePH8O2tBS6Fy+SIHDqFHD+PIpSU+Gfm8t9Ym/PdOPiSHQcNYo843Z2KCsrw/Xr1+Hj4yNYJSRwEASgb1+8sLTEVYUCLj4+OHTokPKuqysaKZWsuKSEjXj8GA4BAYhs3ZqVnDgBq8RE5hwdzWJkMkR6eCj50FBmMnw4w8yZKCoqwp07dzBmwgSoff457N3coL1vH1rPns2Cg4PZrVu3kBQSAoPNmyH064fGcjlYjx40PvfukXNjxoz61CAvLxKgTE1xpbwcxZWVuPrsmWhiYYHRly9zWh07QrJ1K/jhwynSysGBiHVMDEWXqDpfNG9ORSsvXKBQ5507XxNcIiMjoSmTMb/QUMjnzEF4ZCRzdXWFxodGG6kKqH6IvcgYpf2cOPF6FfY38PjxY6SkpODKlSvIyclhbbduRZO+fWn+eXkhMTERGRkZde+/f/8+kpOTWWVlJbt375747Nkz5DdqxCTPnqFvYCA0ZsxAem4u+iQkoOnUqZAOGAA2ZQp5ow8ehMTFBa6zZ7OYmBjBxsaGGQweDFhbg0kkMDc3R8uWLTl7e3tmb2/PIiIiOF01NZgpFLS+9+whYePTT2nPLi+ntebuDhgZIerJE7HN7NlcuzFjOLUffiDh090dAJCRkYHz589XyOXyDmvWrCn4sAf+v42POdsf8UFYu3atu4aGxg4tLa023t7eGq6urnVezv9EhIWFKQHw9ioP5erVtCH+N4SONwR1dfJMvRkW/zdj0qRJktLSUujo6KhyqXgAmD59Ol9QUABjY2N8//33Qn5+PtdgH+4pUygENiDg770wpZIEk+7dKQz04MHXXjY2NkajRo2U586dY4MHD+YabGGRm0u5bN9+S/ld+flkzKpypf8sxo2jn6AgKsz29OlfbvfVEFTr7cSJE7C1tWUTJ06EVCr9c2rGtGnkHVEVcmsI2trA+fOoOHgQpXFxIhYtYqiqomfm50e5eMHBZGy/CbmciMrFi68b+Y8fwyIyEiZ5eSjdvx9o0wY6Fy68+xpatgQEAYIgIFouF8sCA9mt0lKYmZkpJk2aJAEAQRBYbb4bh/Jy8koFBJCR9x4wAK3v3kViVRWKGzeG/nffkRHbEPLzydMRF0fzsGVLMj7e9JLev0/EpLCwPk/zzJn3ekiqq6tx4MABSB484MY9fgzd0FDWWkuLP3LkiMBxHBs7dix7dV6fOHFC0NfXZwvfCA/G6dN0D0+eUA68hgaFr06aRITbxgZmcjmSTE3JWN2wgUSUV7FqFaCjgwtOTsq7enp8y379MFVTE0Lv3ohPTBT7duvG2g4cyPDrr4CXF9o+eCDBsmV1tQgKCgqQkJAARVoapJqaRIbGjXt3AaERI+inFhzHwc/PD127dmXHN26E78aNuPjFF0ofb2/+tR28bVsihGvWkJBqa0thn8+e1Ql9zq6uCL12Tbx37x7fr1+/htvb1KKiogJyuRxaWlqomTkTGmlpSE9JQU5ODvLz85WFhYUotrDgpACbffYscufP5y7L5crdu3dDXV0dXl5eyi5dutSrUPv3E3H65BNUxcTgio8P9LS0YGhoKFhbW3M2VlZovH49NMLCwO7dY2/NvU8/ra/v0LQppawMHUrh+JqadO8LFtA81NKCqKuLnzQ1hRpTU0jPnWPIziZyVlhI88LSkoQJjoOVlRV4nkd4eDjXrVs3ijyYN49InqNj/TW0akXEPj6eiBJAe8A335DotHUrlNOno/z2bVi8eIFcNzfByMKCs3F3h1JXF+rt2oHnOC6kWzf4urpCGhoKKz09Eu2WLaO96OpVEqeUSqC0FJYGBli1ahXS09Nx+fJlFBQUkIhqYEDrr7KSRICyMlpvq1ZRelhCAoTevZFUXMyc589ndQW3Xh3zwkJaB8bGtDaysylsunt3yOfNQ1yHDtC8exeW/v4oKi+Hxb17vGhqKlaoq4t3797lWrRoATs7O/L6m5nRGjp1CvjpJzQ9fRqDtmzhUn/6CXbDh2MJwIXs2iXeuHePPX78mEtJScHUH37gs8zNcWXYMLjeugW/S5dwZ8wYMaOqij3asQNs6lSYWVuTkHL8ONSzstBx+3Y8DguDaVAQpSDMnl2Xp//s2TMAgJeXF4+hQ+l6ACgMDdE5KAhXU1Oh7N6d6xkejoe2tspyV1fW3tCQe/7gAVpNmYKeubl4vGIFDH74AS/u3xf7fvUVf83aGndkMtE4MJAplUpIpVIlam0BJCUBP/0EncWLsWDBAmS/eAGLuXPx/OVLMT4ggBkKAnQVClqPGRlEhvv1o+tSKCgtZfZs4PBhWGtrwyI7W7CfOJG18fDguJs3aU4zRnPDyIjO+82bXxd3z52jaCINDUrXOX+ezrLa1pX5LVsiOTmZ69GsGZCSgnaMsWd5ecodO3bwoihi1apV7y+iy3EkEH4ohg0jL7CqhsArKC8vx/3798Xg4GCmra0tVlRUsBkzZsDMzIyiUb79Fhg1Coav2KczZsxATU0NLl68KHbp0oW5uLiw0tJSHDp0CE+cnJDt7Ay/06fFvsnJzDA29vU5PnEi1UtYvBgAoKWlJYaFhQk28fEcrl8nEeQV6OnpwdLSEtqzZonQ1ycBdupUsudUz71RI0rZEEUomjVDtaMjw8SJ0CwooOuvFeiqq6vx66+/VioUivFr1qxJ+/AH+L+Nj2T7I34Xa9eulUql0q3q6upTevbsqdmmTZsP8xz+HyM9PZ1v3LixaGZszLBqFbWGsLL64Hya/zhs3UrGc0jIh+UQ/QU0JKJIpVKYmZlBEATI5XLu8ePHor+/P0aMGMHMag99iCIZ7eXlf+8FxcbWh/bevfvO/NkpU6bwe/bsEW/evImur/Y8vnGDQkydneng//prKgb1d6NnT/Jwq6mR8b9y5furk34gVC22AGDhwoXQ0dH5axO5oOCDw5sHDhyInenprO3q1WBPntDz37GDjO0tW6iATkAAGeWqML3p08mwOnKE5sXJkxTtsHkzUFQEswkTsL9JEyAsDH01NeH5rhD8SZNQnZSEsPXrxfsSCRYkJqJ82DD0mzy57ux6y2iKiPiwgnxPnwJ5efC4e5fdc3HBERsbYa6NzesfVlFB6+6f/6R8vE2bqGiQqvp0Q+jUqb76/pAh1DLriy+IrM+Y0eCfFBUVISwsDKWlpRhvagrdO3eQ8+gRDoaEiNra2mzKlCmvEW1BEJCTk8P9o6FWNv7+REh++60u/Fh27BguBAQIplpaTP/UKdF4+nSuurqaRdjZwWH7dqh37YrgrCzBwcGBe/TokdImJoZL19FhSdXVfIcOHdClSxdIt25FzYwZeDhunNhXT4/h1i3yBFZWkqEZEACUlyPHxgYHDx6Ek56eQtqrlwQbNvx+r9rnz4lQFhS8FWWhKZNhaloaKr/9FqUcx/bs2QN3d3elr68vVVG/coWITtu2JK6kpREhe2Ofd3d3ZyEhIVi/fj0GDx4MOzs7pKWlIS8vT6iuruY0NTWRnp4uZmRkMKFW3NEtLsane/fCf9UqsbGRkaivr8/b2NjAzMwMVlZW0BwzBtYVFZidlMQrFi9G2M2buHnzJv9aXYWQEKCsDKK3N1olJkLZrJnYedw4qoAviuQ9KimhtdSQyOPrW1eJHQARioULSTg5eZL+PyKCyMfYsSgvL0dubi43dOhQSFxc6LkmJNC+aWNDkSwWFrQuS0vRu3dvBAYG4qcDB4Spbdpw7NGjt9OVOI723s2baS/9/PP6Nk2TJwMKBfhZs1DVtCm0161DuxcvOO7sWYDnwc+bB8eJE6E0M4NWQQE05s8nUv3zz5S2MmsWncs6OrRHe3pSvQTQujY1NUV+fj6mTJkCA5mMBOdjx4icq8ZYlWOurw9wHG56e0MRHY2GalcUFRSABQUhY+9elNy5A4lEAoN9++Dw8iVYdTVe5OTg6tWraNKkifLWqFEQBQEds7K4jllZTGPqVHZx2zaxpqqKQaEgkl9VRftDSQlgYgJeVxdl+vpILioS7Go7LvS0tGRFLVrAccwYHD9+HBcmT0aPiRMxrXFjXLO1FRjA9Y2NZQWnTqGsSxf8lJyMPkZGMBg5Evl5eWjy9Cm6nD2Li9OmgVNF27i6Ivunn3DCykpZXV3NOzs7Q624mEQHV1cUFhbi5MmTGNOqldi3UyemPW4cw8aNsF6/ns/IzMTDjRsF9t13nHF5ObjoaDjZ2wMlJTDds4cpd+6ErbY28vLykJubq0xNTeUNDQ3rF5SnJ+1p8fFotHs3Gt28CUgkaNmlC7vYrJnQODERHq6uXJy/PzT09WGtUEAUBJiEh9OYxccTCTx2DDWrVsH56VNObfhwpFtYiKETJoiDtLU5I4D2ytmzaaznzXv9LM3MpPoRBgYkaKnWzogRyOV5XP7HP9DKxUXRbt48Cbp1A8zMMGjQIP7+/fu4evUq7ty58/62phUVdEZ8KKysSMC+fBlV3bujsrISWVlZiIiIEAsKCphEIkHv3r0hlUpZRESE0szMjAxpExMKkS8oQKuMDGS2bQtdXV2o7Kq5c+fWPfsbN26gsHY/6NCzJ57b2LB2c+dC7NkT7OzZ+pxqU1OKyCkrAxo3xoQJE/ht27ZBWL0aXANFEat8faGvoQFdf3+GnBzaW+bMabB3eGVVFY6MHQun1q3RuKSExtLbG5gzB6Io4tKlS9VyufzU6tWrf0dF//8PH8n2R7wTa9eutZVKpeesrKxshw4dqqn5vjy3/yDkUascJpw8Ce7mTSKB/wUiwTvBGG38s2aRR+BvInF/FPn5+VAoFIiJiUF1dTWrrC2ihRs3KLTt+fO/r3J6SgoZViNHksrt7Py7b9fX10eXLl3YjRs3iGz7+xMJOHCAjLkePSj/8F8FjqOcsfx8IgATJ5IR8BefR3l5OY4ePQqFQgEnJyf8obzshpCeTh6y9whPMpkMEokEJSUl0NDQoGJVjo5kvANEtJYvJ2K5Zg0Ry0WLyCjv25c8aPv2Ue63yvNYK3IMAjBo8GB8++23YlBQEHNzc2swLSEzMxO506ejVV4evKKimMaAARji4NDw/I+KoiI5r4ThvRMHDlBLoTNnwN28icZnzyIxMZF7+fIlTIyNKbfw9Gny3HXuTELKtm0flhbBWH2I6pgx9Mx++onm3oQJVKW8ltBkZGSgoKAA58+fB2MMI0eOhFVpKbBoEdIfPEBNTQ2bOnUqVaV9AxzHobS0FI3f7KhgZ0eeXQMDYPlylFVXQ7CwQNaqVeAGDWJOs2ezU0olRKkU0ampwpNmzbhWn32GB336cJmZmYpmjPGakyczW0dH9La1rRffli7Fyw4d0H35coYJE2g8hw8n8uTiUlfpOFZHRxhUVMQ5LVsmQffu9Z7Zd6FJEwoTfXP809PJu7JhA7TatcN4gMvLy8Pu3bt5Jz09mHz7LTQ6dwabOpVCVKdNo9SHHTvob3/8sS4VwNvbGyEhIWCM4fLly6JMJlNNfk5HR0fQ0dFhEomEDRkyBA4ODigrK4OBgQGE4cOxtH17Bp5/e7F4eJDQtH07JE+fotuGDYiMjERNTQ0VEUxLoyr1ACCKyDcyAmvRgikUCgQHBYmamzcztzt3kDd7NkLV1IS+mZmcquZIHVauJPIZF1f/b0OHksCxciUVCNPWpnt2dYWuszOaNm2KM2fOoFWrViREjRlDEVFubrR2s7Op4v8XX6Ddjz9CR1MTGSdOcNi8mbyW70L37nT+LFhA66+2JSCMjQGOQwtnZyR06AD9Tp3QXF+f1vxXXwELF0K4fx+FgoCS48dhr6cHyf79ZKB/8w1FKT15Qnnndnb1kSLFxdC4fRt9Q0NF0wMHGBijsOLSUrrvvn0p9eCXX6iQYu3avH37tuDn5/daWo0oioiNjUXy9u3of/EiglasEHT09FhlZSVKS0vZxEOHkN28uRjdvz/09fWFmTNn1hsLc+bQ77AwtL1+HZUdO1KkXHw8eTIHDaKopoAAxPbqhd+srbHo4EEOOjr0jK5fh4GdHQwcHDBhwgQcPnwY6ufOwTQ8HKMCAzkAKElLg0ZNDawGDEC2XC7cvnKF9bl4kd0uKhK7BQSw+76+EFu0qO9MIgjQOXYMpbNn8xKJBMnJybj244/QqqhAYJ8+4HkeHh4eSpsVK3iEhwNXr0Lw90fixIl4YGEBw5oazn7LFui2bFmX5gCeB7ZsAX/0KNoAgKcnQ1QUf19NTTA6e5arqy6/dStFIGRk0DpbuLDO8zwyKop7ZmuLW3Z2SHvyRFR/9kyozMvjru3fz9asWUMpQX36ADt3Qpw9GwWOjjjcvLnSqVEj5pCRwfnu3cvOP30K+169YHLlCoyiolDQvbv4PDwcpqamzMnJiSJT2raluhhbt9JY1CJp4EBcuHABEwIDYVBeLkFAQF0EgIaGBtq1a4fq6moxJiZG8PDw+H2D0Nq6ruDgB6OyEuLBg/g5LU3Mz89nGhoaop2dHRs1ahQaN27MAKonUFZWxoeFhaFbt271f3vgAKQnT2LAq2v9DfTr1w8dOnRARESEcPXqVQ4AkqdORau4OLTv0QOaAQEkWu7aRVFl27YBYWGQSCTgeR7JKSlwDA+nNDqAxMqePVHl5oas4mLcDAhQDs/I4DFyZH3ngFcgCAJ+/fVXgbOzEzv5+vL47DMSJWo96PHx8eKTJ0+yZTLZ3D/24P738ZFsf8RbWLt2rZpUKt2lpqY2sUuXLmre3t78/1UBtD8LqVQK14gI/JyRge7btsFKTe2/f7L7+ZEXIz29vkjOvxkmJiaYOHEiLCwsmL+/v9Lf359fuHAhpO7u5PX7O4i2KmR82zYyKm1t6/Mm34HHjx/j0qVLyqqSEt5WLhdRU0OFXlxdKU/03wkjIyJWCgWFfv74IxHRP4GKigps2bIF6urq4ogRI1jLli3/+vWpisu80sboTZSWlmLnzp2QSCSiKIqwsbGhXq6vIjSUfh8/TgLDqlWUN/fNN3TQr1xJUQ6TJ1P+awPo168fO3PmDMrLy6H/Sr68KIr45z//KWRlZXFO8+fDfeRIMra3bydP+Vdfvf1hhob0+u8JUQcP0r3v3UuhjbWGmImJiaBZWcmdW7kSM0pLSejZvp08iKrIjT8CG5vXPYTGxkQuAgNpXs+bh6dFRTh8+DDU1NREExMTcdq0aZw0IYFCdA8fhtewYUhLSxOvXLkiTJw48bVnz3EcLCwslFFRUbzfq0UIVdWpFy0CvLygdHPDyZ9/Fq29vDB33jyO8/HBC0tLsU1ICAhPpyYAACAASURBVLvWvz8WLVrE+Z86BVZcLI5ijDkuXCjB+PFkeI8e/dZtyS0sUKSvL9YcOcLUL1wg47ljR/IuFRcDjo5wsLKC1YsXRIgaqJz+FvbtI1LyKrKyiKiOH/9a6owxz6P98+dKjcGD+fO+vjBwdBR6d+jAMQBKNTXIW7TAPT8/pVVhIW+WkkKCj74+qqurAQDjx4+HtbU1EwQBiYmJsLa2hr6+/lsKikFtpXLu4UMSOSdMaPja9fXJC/TiBSRdu8KmbVshPT2dc7CxIc9bZCTQujUYY3DLycGJigrc/eYbtLtzh7Vp0QLpcjkCdXSgyMvjoqKi8BbZ3rnzbYGH52ntrlpVLyhu2lQ379u3b4/Tp08jNzeXvGM9etSfFzxPe1LTpiQGAbBeuxZaz56h/NtvofvLLzQWDeW1tm1L4z1uHLW343n6Ti0twM8POpWVaG5iIiYtW8aaCwK1fZJKgcBAqC1ahFYzZ+KcvT2Knz8XvTU1Gby9KRrm4EEibjdvkng3ZgztF82bg7t7Fzaamix26FClz+ef87C0rL+e0lLaVxcupDny1Vd1e5qRkREUCgWysrJQWFiI27dviy9zc1lTHx80+v57LDYxee2hVowYAbG0lHnJ5TCztGyYhHXrBoHnme6VKySoeXhQlMChQyRsrViBB7GxaOvuLqr5+TFs2kQigK5uXTX36upqSCQSNO7Vq35cr15FaVAQgmbPFqcVFTHf69c53zlzgNJSLCwrYy9cXHDO3h6DDx5EcXw8ajZuhKGTE3KPHgV3/TrcPD0FIyMj5qJUsnRHRyApCR4eHvD19aX7GDMGyMuDYtIkyCsqMDA0FJKMDEjbt6d7+O47ErxyciBGRSHexkaoYgyOTk6coZ0dUmJixFJnZ5h36kTzJieHxMq2bek5VFXRXAgMhPXYsbDOzaU5CTAcPMgXFhQgVkeH2lT5+NA4u7pC9PbGZ87OkA4fzkNVFKy8HD0mT4burFnQLC7Gb4sWodzCgiEtDQkJCcqLFy/ynrq6ys7ffsvnjhmDphyHKlFESVYWbt26pUxOTub8/PzYZS0tcejOnUzb2JiiHl6JZHN1dWU3btzgZTJZw61hVcjLI4/6w4fvfIuqm1NdV6cBAxD59KlYk5uL5StXQiqVvmU4W1lZwdnZWQgPD+c0NDTg7e1NLyxdSiQ4Kor2jnekaRoYGGDAgAGcr68vTv70EyzCwhDerRtkxcXwDQ8nx1J0NIldwcGAQgEtLS0YGxvj8q+/wu7kSfBLlpBtNXYsEBKCxt9+i4n5+Xjcvz9/y8MDsXFxKAoJwZgxY+DwCum+fv26UJCbi89ycnisW0dzv3t3oE8fFBcXIzAwsFoulw9cs2bN3xze+N+P/3r+8RF/L9auXctJpdJfzc3N+4wYMULjnUVf/sMxu18/aOzZgwudOomHz55lqvZMvXv3rt/c/hthYEC5oseOvd7D+d8ILS0tHDlyRHj58iUHhQKSFi1o0x037q9/uCBQf08HBwp1/ECR59qpU2J5TQ0/JTUVZnfvMqxfT56S/0tIJBR+2bo1PR8PDzIu/wDy8/MBAEuWLGF/WyHCjAzy9DeA7OxsBAYGCrm5uez/sffeYVGda/fwevYeytCGXqQKCCICIiKKBQR7j7130WOLJsbEmIRDosnRxB5bVFRsiF2xoyg2FFHBigKCghRpUgeY2fv742YAFdRz3vc9v+SL67q4TCh79n72U+617mZlZSWamJiIcXFxfFJSEr9+/XqhTZs23Dvt9EaOJGJWXk4Kt0JB5JvjyBB++ZIiE2ramdSHm5sbDh06hMePH1M/Yk1NKJVK7N69W8jLy2PffPMNebyXLSMCP2FCw57rzZvpc95uY6RCYiIZicnJ5K1Q1XIQBCAuDp2uXePcU1JwTaEQMXcug5/f/yzl5PvvqUZAfairA7/8gqh165ReXl78NT8/eA8dit69e1NYMUDksG9fmjMAunXrxtavX8/n5OS8ExbbuXNnft++fejSpUtd0Z/KSirS9tNPgKkpMszMkCGXM+vPPycv56+/wlJLixn++iva/vwzsHAhBvfrB7RsyXDgAIVnrljR6GPJZDLc69aNPSwsFGYNG8ahqIgIpYEBUFgIhZYWcgwMOBOOg2Zc3IfJtigSUezWra6exosXZHgOHVonUlVVUbjw+vXo0bQpL2RkwC8vD6GhoVxsfDzMzMwEDQ0NTtquHXhjY/60uTkmbtoEm/h44NAh3Hn6FBzHwcrKChzHgeM4tGrV6sPvMS2trj99Y5BIKIIlKAg+R45wCUePKp2++opHdnZdGKZcDpviYkyaOxfFv/wC67Q0qEml0L14EcY5Odi8eTMV8nwbOTnkSY6IePP7+vokqKxeTWPcoQOJaKtWodLQUATAioqKiGwPG9ZwVFcN0VCXSJDi4AB1iQS6u3fT3nv6NOVn9+377rM6OlJaxZIl5NXKz6ev4cOR2KSJ4PivfxF5OnCA7js7G9DWBispQeLy5XDnOAY7OyJp48eTt/rBAxI1ZDJamwMHEhGws8Ph7duVNjY27A2iDZDIFxJC637/fmDpUlRbW6OiooLbtWuXWFlZyTQ0NASpVCro6+tz/rdvM53Q0LpewfWg3aIF7GfOhD3PU5RAfVRVUdTWjh2I79NHsG3XjjOztCShoUcPIv25ucAPP6D38+fY36MHvHV1oT9jBhWMcnevnQdPnjyBvb29UqdNGx5ubuQpdnGB5pw5yLlxg101MBA69O3L4dQpSg04cACWPj4IVldHTLNmwt0dO9iVtWsZNDUxevt2DBk8WGjeqxfHTp5EZUkJ9j14AEtLS2VAQADPRJFSEEpLgepqqEulyBw1Ski5cYPTu3ABPWvyb8UZM3B7+XIhPS0NRcnJnG1iIrvv7c1uxsaK9vb2QopSyTdt27YuPUpfnwRDV1dAFPE0Lg78okWQTJ0K0+vXEXnsGIqKikQzMzPW+fhxnDMxqR9JQuPh7Q1h5EicDwhAgKEhNF68oHoCOjqwGzWKzhRdXfT78UcaX09PYMcOPjMzE9eOHOFe6uuLry5dYtvGjgW/ciXU1dUFfX19zJ49m+no6EC/uJgV8jw0e/WCzrNndB5lZgIeHtDX14exsbFw+fJlLjAw8N11UYMzly4pPYqK+E0hIY3+TkPoduYMG2Bn1yiRNzAwwIABA7iEhAScPXv2TXuUMTrjxo+nM/M9UFdXxygPD1QuXYprEgmeu7jQXFUq6XlNTEhos7cHTp9GYGAgdmVlISE4GK2trBC/b5+YtmmTUP7gAZQJCcwtMpLLMjbGXTMzKGtsJ3NVzQMA6TdvwmzOHG6Gmxv4uXPJCbJgARAbC0EQcODAgXJRFJcGBwff/7cG7G+CT2T7E96AmpraSmNj4x6jRo2Svq+QzJ8a589DlpICPHiAoTo6rLq6Gg8fPkRmZqYYExMDNzc39j8Oxf1/BW1tMnRsbYko/JcrwcfFxeHMmTMAwLVv3x5+nTuDk8mIIP9P8Po1GZW9epGny8Pjo8iOUF6OqCtXMPbnn9n1IUNE61272J8qL1+VO37kCOW5LV78b70zVaX9xYsXw8jISDlz5sz/WS6Evz95Mur17gSosvXmzZuFyspKrmXLlqK7uzvn6enJeJ6Hh4cHXr58ifLycu7s2bMwMTF5s9dzcTF5p06fJiVdJiMCEBxMKQ9VVWRIKxQUFjxkCHl8auDp6SmeOXOGnTlzBmpqahBFETzPs+nTp9dW3sbgwWQ09O9PnpG38fQp3jHGARIAVD1YFyyg8QfIQE9PJ4NGQwNYuhQ6kybhYVgYS7p7VznI3p63sbH5z8f56lX6zPr5tgCeJifjan4+rxwyROxlYcGM6hM5QSAjc8WKWk+koaEhRFHEzp07MWPGDNQXPx0dHWFtba0MDw9nw4YM4bT69SPysWULeTx5Hjbt22Osjw/27dsHCwsLuNW0PJS2bUvGGGMk3o0eTWR35kwizm8VIFTByMgI/fr1Yzt37mTP/fxgk5gIVFai7OxZ3MjOFkRXV65g8GCh08qVHLZuJbI4a1bja5kxeg8qZGZSSkr79nVEOyqKai1YWNDzubiAA2BhYYGFCxfi1atXuHPnDhcfHw9F8+YYdueO6LZwIdt25gzcHB1hsXWrMicvj9cwM3u/J6shLF5c22/+gxg/HlerqpT9v/mGF5csAasv9lVXA5s2wfj4cRjb2VHI/S+/gOd5KJXUFefEiRPv5pGqqxNZbQhNm1JIeXAwEBaG6oUL8SQ6GpEaGqxPnz5wcXGh3+vdm9ZOWNi71xBFvPb1xa3KSrTp3p0EDlGk/PvoaCJYkZEkfqi87osXEzleuJA8tosWETlwdYX2tGlMumcPxBs3wC5cQNHatSgzMoKlry8exsejeXIyyouKUHzqFPS2baM5n5tLotymTTQXf/yxNpJJoVAgLy+P7/s26QcoP3baNPpvJydg+XIUeHujrbe3aDBnDmvRogX09PQ41OROF5ma4qRcjn4lJQ2mZWDUKPIIqiAIdZEbNXn1VTKZaL94MeXZ/vOfNC9TU4ns//orjB89gvmLFyw1LAy2r17ByMqK9v6yMmDAAPRZsgQlZWV8hSBAunEjCUgcB5MrV9A3MhJ2mzZxyM+n3NvCQhQdO4b9Bw7AqkcPsZjnkdq6Nft882bI9+2Duro6ZA4OHBgDduyAJCAAyMnBpEmTeC43l8bG1ZVEzgMHIJ87F65jxnBaqam4PGgQcjIzcXjxYvQ/ehQx1tbwKSvjWpWXw8bYmHW6fRuxX3zBsrKyeOTno+zUKVRs3Yqy2bOhNWUKtL77jkSyI0eQvG6d2OP4cbZREPBKUxP6+vqijY2N+PLlS/FRbi6X5uDAqqqqsHbtWrGqqoqVlpZiVloaXm/bJt4cMoSl5eSgf2AgHowbh6pWrdB50SJUjBgBs+Bg2gcfPyZR4osvYPnrrxh67x5D797Q7dAB302fDjEjAxKJ5I1D1U4ux4rJkwVpaio3ffp0sF27qNZAZiYgkaBNmzbclStXlIGBgbxCocCxY8cgCAKaNGkCnudx7do1UVFaynX67jssUs0xvFkfpCHxW6lUYlNODiYpG+90VVJSgtDQUBEAa9mypaCan7W4f5/EscREEibeA87XF9K0NPhGRwvCjh0iSkp4PHpE5/DixcDmzaj+4gtcPn8eD9PT4fHsGSw2bcJ1Z2dEXbjAHBwceIlEAllEBORlZUho3x4TJk5Eeno6oqKi8PTpU3iVl0MREoJLTk7w7dkTWiEhtC/cv0/h6owh6ty5qry8vASFQrHkvTf8N8Ynsv0Jtfjxxx9H6+joTBkzZozWX5ZoK5UU+hMcXOu9U1NTg4eHB9zd3dnLly/FNWvW4Nu3q+/+laDKnZPJqArkfxHx8fFKa2tr/sWLFzD/6isxQSbDk6AgpSI8nOno6MDCwoJv06bN+/tw14cgULioypOoas/yMX93+jSEIUNw/euv4XDtGgJbtfoTsey3UBNZgfHjSXU+ceKj/kxHRwdffvklCgoKsGXLFv7QoUPioEGD/rPnFEXAyQlVtra4cfkyHj9+LLZp04Z5enri2rVrMDAwwIQJE8C/VQHR0tISlpaWyMvLw5UrV+pyhEWRnqusjDzGO3e+We0/OZlIxrFj9K9cTl5QT0+cDwlReu7bxxs+fIj+/fuz/Px8GBoaCq6urhzP87C2tmZvzCEHB/Iw/v47hdbeu1fnnY+MJJL4tmcwLo6MtPnziRxoaNC4P3xIBpy3N+VPu7oCjIEHMGPGDBw6dAhhYWEwMjISAgICOOf6lZk/Fr6+lNNeg/z8fJw+fVqZlpbG9+jRA+3ataNCNNOmAbdvkyc6M5OM/W3bKESzBjo6OiguLsb27duFGao+qDUYNGgQv3z5cixfuhSTdXVh3qIFigoKYKimBmzYAJaZCfuZM+Hg4KC8fv065+bmRnNn6FCq4Pv6NeWeiiJ59I4do9DYgQNJ8NLWJpGosrI2nNs+Lw8DL19GQfPmsAkIACwsoK6tjQdTp3IdfvwRga1bcxg+nPJwhw6ldz93bsMiU/v2FBXx+eckgISE0LPPng2kpiJv5kwkaGoKeWZmYoaaGoTISCYeP84YY6IoirCyshL9/Pz427dvQ0tLSywrK2PmDx8yg4wMjBo1Cjk5OSgOD+c6792L9MmT//33qFCQKPHsGRGs96C8vBwZOTm8JCoKbPt2mqeDB9N4RkcTiTQ1JWHo4cPasG9ra2uoqamhW70ewbUwMqI2PjXIyMiAtrY2OI6DIAhI09WFmp8ftEaPxn5fX3S+fVuYGBjIWdcn7SdPNpzek5MD9O4NZUQE5Lt2ISUlBa1VocGqcyU1ld5beTnVLejWjUQrOzu6Lz8/IuFJScD9+2g9YgRXaGcnXjQ3R6mXF1OEhsLq+XO81NAQ3W7dYk6iiMvt26OA52E5fjwcT5+ua7E3ezatWU9PKo61ejXu3LkDbW1t0cLC4t0978IFKp61dCkA4FFWFqJ698ZwLS1mmp//ZhjuH39A38AAQo8eytWrV/OTJk1Ck7fb8XXoQOO0cSN9/pEjlMYwbBgR6w0bIHFywp01a+DXvDmdwxs2kCdSJqP7mT0bnwHYpKWFZ8bGwuBlyzh8/jntQ02aQO2zz3AiLEypzRgGHTrEIyiIKkObmODEyJGira0ta5eaCvu5c4HVq5EeEIDekyej+OxZdmrMGGZoZycqZ8xgJnI5CZeRkSRWzJ4N0dMT7NdfoVy2DJyfH4kskyfj8qJFgqSigvFjxzKN0lI8nT0bRZcuQSsoCC28vXH822+FebNmcTh4kPL5t24FMjPReeZMlAYE4NmpU6h0dkborFmKsqoqrmL5cq6FQiGqt2sn2P7jHyze3Z3rHhSEGZ6eECnlhjHGGPLzgfv30W7pUmTn5CA+Pl58HhnJellZ4fW5c5DHx7Ox58/jZv/+yLWxgW1ioqCIi8O+uXPFl9nZ/NdyOTTt7UkoSkyk+eblReN19iyMPT1JIHl7bsfFAePGYdydO9y6P/7Aq1evYDp2LO1FT58Co0ejxfHjiCwq4kNCQqCurg49PT2RMYbk5GSmo6OjdHBw4Pp26MC4oUPrcvY/gJrK4XhlZga5jg40o6Op9eVbqKiowOvXr9lXX30FLS2tdzdFnieBzceHhMj37TuOjsCGDXBxc+Ny7t2D4qefINHRIafFiRPAkyfYmp8vtHj2jE1++pRp6umhZNw4oGVLLBo1ChzHIW3LFrxQKkXf7dtZKyMjaGtrw6pJE3R49Aj44w8I/fvjqrW1ILRuDccpUziIIu1rY8cC3t5ISEgQb926lV9dXT0gODj4b99PuzF8ItufgJCQEI7juOkSieS3UaNG/aUKob2BlBQ6/G7ebDBMljGGNm3asKNHj+KD+Tp/dowdS4bGfxm2trb8zZs3oaOjI7h89x334NUryGQyiUQiQUlJCa5duybcvXsX06ZN+3DfZ7mcQgHXrSNDZ9u2j7uJTp3I87FpEw4GB8PL1VXp0KrVX6P63ZIlREKfPSOvxkeE3kulUlhaWsLKykosKCjAgQMHxGbNmrGXL1/i0aNHgq+vL9fuIyILhIsXcSQwEPc2bYK+vr7YtGlTdvLkScTGxioLCwv5gIAArqFOA2lpabhy5YoyKyuLt7W1FWQyGVernickUHuixnJa1dSAwYMRZ2OD9OPH4R8QAENLS/CHD/MsK4vy4nr1QqvAQETq6HAaGhqKnj17NnwuPXtG6RNL6onnVVVUpEnV7gogw//bb0nZ/+c/ySC5cYOMJi8v8tLNnNlgezapVIrRo0fzxcXFiI2NxeHDh8EYg6ampiCKIlNTUxN79erF2b+vZRpABlNMDHm2/f0REREhVFZW8kFBQTAxMaHfMTcngeL+fZoHQUFEODU03rhUdXW1CIC9evWKKy8vf8O7raOjg3mPH+NWdjYiBgxAVWioIJfLudZPnwq97t3j+Jqigk2aNOGfPXsmQBWubmVFZPjQIfKmb9lCRbgkEhK8Fi4kksUYRQaUllLuaXU1UhMT8cTEBI6mplSBVyIBb2WFcnNzWIWGUoErFxci6927k5e9uJjeydt77vff0/vJygIuXaK/GTmS0gL27sWjdu2Q7eSE9p07820ZA2MMPM9DFEUGAAcPHhR37doFd3d3oW/fvmS4fv01UFKCZoaGaNasGdCxI0sZMwbNFi/G/dGjhZa7dnEfHf2iIl9vvZOGcP36dbRLS1Pq3bnDY+VKipoYNow89ZcuUcSAuzuN7VtitkKhgFtjheSsrVH60084qqamTE5O5hljUFNTA2NMrKqqYrra2qKvpibGpaczi6lTOaxeTQKTCqdP03O83Xru+XOgVSuYOjhAV1cX169fF1u3bv3mwNjb19VJmDmTPPJbtpBAumEDRR1cvUqChKUlWGIiDJydWauEBFHn4kWIbm7g7eyQ7eXFHowfL7qPH8+8qqqwe/duXExLw5gXL9C0adM6L6G3NwlPOjpAeDhuZmUJLjSH331hpaW1Vf+zsrIQEREBqaWleKKqChNDQxmeP68LGb9wAejUCWNmzuQ3bdokPHr0iDVp0qTummVlJCjs2UOi0+efE5FdsoTmZ+fOQOfOeL19O4xKS4G8PAg7d4JTdVHw9aW5HBsLtGunKioogjEqCHbhAq1vLS0MmDqVX7VqFbr07AmDggLg1i1kr14N19u32Z2qKuTGxaGbkxNcIiLg4e8P5bNniP7hB/Q8cUJs0bQpwy+/0OcEBZGImJ0NvHgBiYMDPDMyxNcPHjDjoUOBadNQUlKCjOfPOYesLJR89pnYxtCQeUyahJzsbKgdPw7D5s3R89dfOSQlkTg0cCAJa2lpwPPnYDt3IqFLF+WYVav4NjV8Qf7bb8gXBJbk4MA7LFkC2/nzwXfrBjRvDrZlC4kQAKWD8DzAGMzNzdGnTx/u/k8/wSIjAwaLFpHYU1EBe1VtiDFjONy6heZBQViclYXcPXtgM3gwpSIsX079q6OiaF9q04aEwIZS6by8gLg4GFtYQFtbWygoKOBMTU2pDkHTpkBgIKQmJphsb49Sb28YGxvDwMCAXbhwQXj06JEwa9YsOgQrKz+qfSQAJCUliQcOHGBqampCQEAAp//wIe0bDZBtU1NTaGpqIjc3F3aNdbUwN6caGBIJrdPGoqzmzQPatoX5unWI9fMT8jQ0uECAnnXLFpQeO4aJv/7KqbduDWZnB+zbB73SUrRXRUU+eQKbx49xxdoaFlVVcOQ4ErRdXck+6dkTl7W1hZstWmDehAm0SLOzKQWkZ0/k5ubixIkTFdXV1d2Dg4NffdRg/U3xiWz/zRESEmKqoaFxSCaTtRo8eLDU9APq/Z8aS5ZQ+NV7QsRdXV1x+fJlcdmyZaxz587o3Lnzf/EG/xfh60se4H/8g4ye/xJ69eoFz/x8mK5axXFxcXBjDPVNREEQuKVLl4rPnj2Dra1t423i/vlPysm+d4+I2oc84bm5ZMA/e0bGn6MjqhQKPJbLMcHN7a9BtIG64kT79xMZHDmSCM1HGP/9+/dnW7ZsQWZmJh7UVQ3mHj16JLZr1+79FxBFVPfujeqgIHHMnDnM3t6eMcbQvn17JCQksEePHolOTk7vXEMQBERERMDW1pbv1KkT2np7cwgOJkHrp5/IAP9Alf/S0lKcOnUKBgYGWGdhAWdBQJqvL0r19PB482Zh9r17nI62NtpIpfBZuVKCgoIGe5WiTx/62rCBCMSQIZQr+uABrfvSUvJqxMYSqblxg8hueDh5Zc+de7Pn93ugp6eH7t27c35+fli9ejVMTU05Dw8P3L17VwwPD4eOjo44bdo0pvE+EnbtGkXa+PujuLiYk8vlkL3dSkVPjzwY0dFETLt1IyO/Hqqrq5mPj4+Yk5Mjrl+/Hi4uLlyvXr1qCYqOhwfU3N2VUqmU6969O+fk5ITfV69moTIZZDyvzP39dy4/P59pamqyN0TGuXMpVHv7dhrDu3cp1LhXLyIfXl4NPtbJuDjYqauj1e+/kzgQGAjuyy/RQi5Xytet43dVV4vNPTxYm4ICEtJKSmjOZ2TQ2lW1nLt+nUiagQGRwxYtyFP62WeUlnL2LHKOHIGOmprYmLgxZ84ctmXLFuTUz49XVc2Pjq7tYGDv44Pjjo5wP3yYw7NntSLBR8Hdne61oX7yAK5evYq0tDQkJyejc0oKXyKRQHfSJKozUFREYciZmXRfa9e+U+xRqVRCFEXExcVBW1sb2traMDIyqi3ydX/ePCHq6VPOxMlJ5Q2rfdLaf6dMof20ooI+48yZ2gJouH6dRI76ZPvoUSKXW7cCoKJqiYmJIhoitaoxVYX1V1aSUGVtTd768nKaLzk5gJsbWKtWMGjdmmHQoNqKxpb0xQBATVMT06dPR2hoqLhr1y6mqamJcePG1bY6wvz5wIkTqB4yBAEtWnD21669czslJSWo7tgRUXI55GFhyoyMDN7FxUXIy8tjz1+9Ylixgtb/li20vkJD6Z4VCuiUlTH+/n0Rly4xdO9OZ9GdOyQa3L5NH+DhQR7FKVNIZKoBz3Fw+fFH3FFXFyN79WKDtbXRokULIkdjxgDx8VC4uCA9PV0cMGBA3ca4bRsQFARBELB//36FKIoSlpND1zY0RLKREXxv3ULPbduQ/tlnyL1xA2WJiVg/dy7UDA2Voqkpd7h7d+ZsYQH+yRMSBfLyyOYpLSXRsWtX6M6fL+5r1Yp9JpVCWlgIpVIJA2dnsfr5cyZ3cWGZrq6w9vSEmZcXcOoUKgoL8So/X2nbti2PNWtIIDIyoiiDn3/GXYkEfceO5WvXJQDN+/dhmZEBy0mTsHHZMmUrT08e6uqU/qKuXrd3HzpEYg1AbQgjI3G0b19MVwkgPXrQ18GD5LneuhX49ltwGzagpUwG8xUrKILi9Ws6Y0aNItGiVy/aIwcMAD7/HMJnn6G8vBzqMtXtnwAAIABJREFU6upQT0+nPTQ5GXK5HGVlZZx1/fQiTU0Kfy8uhtVXX9F+17w5EhMTxbi4OG7ixIl1vyuR0Bz6AO7cuSMeO3aMOTs7Y8SIEbQpt2pF+2dxcYOFzmxsbIRz586xqVOnNn5ua2hQkdGICBJG3/1g2i/lclT+8QfShw/nAlT2uyiievZsHDY1FeyHDkWHWbM43L5NIlJCAr3j7duBo0fBeXnBTlOTpc2fL1pPmsQ0YmJozwoNRXp6Oq7u3s2NHz+eohWfPCEx88QJKEURERERZUql8vNPedofxiey/TdGSEiIjrq6eoynp6dDt27dJP9rBZj+26ispMJM//xnw3mb9aCmpoYpU6awkydPitHR0Sw+Pl6cOnXqXzOHWxWemJFRl0/3X4B5x470mQ0QRI7j4O7uLu7bt48JggA7Ozuln58fX1tld/t2Mgw6d6bw0Q9FUXz/PXmC9+6lNh86OrUhtpxCAYDCMP9yGDqUyOLt22Q03LtHpOM9MDExwcKFCwFQWy5BELBt2zahSZMmbyzc8vJy3LlzB1paWvDw8EB8fDzu3r0rvA4J4b6YP/+NQmsmJibo2rUr17WR3u0KhQIVFRXQUFcX2hUUcOjYkd7JrFlElD4C1dXV4DgO06ZNw927d3Hq1Cl0699fcPn6ay5+zRpu+RdfwLNpU6VBQgLTvXGDQ3IyveP588kLyPN1IciRkfR9VQ53r14U6lpZSXPKzIxCs7/8ksjFpEnv9mj9N6ChoQErKyshJSWFGzlyJFq0aMEpFAqsWLECt2/fFtu3b9+4sbRnDyorK3Fs/35lZWUl37Zt24ajaXieogMOH6ZQ3XoQBAGMMdy4cYN98803LCkpCVeuXBFXr14tTNfQ4KWHD4M7dw6dAL4TqKDepk2blJUKBT/90CFUGxryV3/7TRw7diyOHj0qhoWFCVOmTOFRVkaGV2UlEdMjR5AllwPu7uLLHj1Yq40bwW/e/O69hoej7YEDoiQkhLElS8ggnDEDcHJCXxsbXt61K5olJrKs1asFVFXRS1u0iLw8qt6tv/1GQuH27eTR7tqVyG9eHo3B5s21fdJrq/w2Ap7nYW9vLyYnJ9d9UyIhwllP2GCMIXDyZGzT18c/fv8dpunp9PkN5e6+jTNn6L4aJ9uCpaUl62tvz1536ABJfW/bvXs0J2/dopzzBroqFBUVAQAeP36srKqqYpWVlaykpIRpamqKVVVVrEleHhvdtSss+vVr/ICWySgiYf16ItxbtpDBrK1N++bbWLPmjQ4JFhYWuHr16scZABIJ7QFVVUSAVJWevb2JWFy+TN6xmzeJ4DZwNjHGMHnyZJaSkoLw8HD88ccfmDlzJoxrUoiEXr1wZMAABPTuDY3582lN1wgnOTk52LJlC9qfP492T5/i1urV/Ny5c6GlpcUtXrwYHTp0EGFry8Bx1N9+9mwai/XrAR8fdDQ1hWLBAsqNlslIpDAyonV49iwRuh49qCjaixdEuGrOOpP0dJzv2BFP9PRY7169xEOHDjFHR0cqutepE7jTp5H6zTdQb9YMDjVzGN26Afb2yHr6FHuOHVOWlpZKACD34UPo13SXOF9YCPVDh9D2m2/QrEMHlHbtitKxYyEIAniCUFpayq66uKBzu3YUxaemRhExGhokJI4YAUO5nOUdPozN9dauWXY2c5FIcPv2bTx48EBYYGzMYds24NEjxHl5YeDlyzz69KE9tqKCzvaMDHpPmZnCk9GjubZpaSTIcRy12ho5EigpQXlFRe07w8CBFNJ/5Aitcbmcxh0AkpKgVCigFITaSv+oqiIxdMgQ+v8hQwAvLzBBgMvZs3hhbS1a/fgj09ixg4SMHj1ov5o2jbqy6OgA27YhLCxMTE9PZzzPw7qiQuHp5cVnnT/PVMW9CgsLoa0S+FTQ06MQbTU1iJ07I8PWFp2nTasTfAB61ry8unZrDSAmJgbR0dGsffv26F5TcR4A7Su//05C3RdfvPN3Xl5e3J49exASEgILCwsxKCio4XPkxx9pDhcW0lytb6MvWwYolVD06IH1w4YhYNQoqv9QWIhyNTW8On0ahkOHovXmzRyyskigyc+na/bvT/tCXh7QpQs6/vwznubkYMeTJ+KY7duZlpYWysvLERERgfbt28NSJVJv2kReb47DyePH5aWlpdcEQQht8N4/4Q18Itt/U4SEhDB1dfVtTk5Ott27d5f81Vp7vYH9+8ngtrD4KA+hVCrFoEGDGM/zSEhIYMuXL0dwY1WM/8zQ1qYDMDycDKf/68rxlZXkUQ8Pf2+lzD59+nB9+vRBbm4uLl++zIeFhaGlrq5Sz9yc896wgWl9+SWYKtSsIYgieSL37SMDUhXKNW7cG7/2/PlzSKXSBguV/CXAGOUnLl5MB+nGjeRJ+QiPm4q0NW/enIuJiUGzZs2g8v5FRESI6enpTEdHRzx16hTTUFMTpy9fzrGDB//tsVJXV8dnzs5I2buXEyoqwG3e/MF+529DV1cXEolETEtLY23btkXr1q1rC9rMnDkTjDEYGRmRF2jNGhqXDh3IMJg9m0Ktnzwho7J3bzKIXVwob3/tWjI45XISLVJTycN29+7HEamPwLBhw7h//etfOHbsGPr37w+JRAIXFxd269YtsW3btkhISICNjQ309fXfrFVw9y7QpQuSvvyS5zgOGRkZ7xbDUSEujoQFaplTizNnzqC6uhr+/v6ihoYGc3d3h6urKzsRGckOHj8OG3NzQXLtGtemTRtERUWJd+/eZR4eHujatSs0GIPm06fo2bMnA4AhQ4ZwG9avFy9OmaL0T0zkYWpKkQIJCYCWFiL37lUOYIy/YGWFnBs3UBgSIsDRkXl4eDBtpRJNLSyA7GxIjYzEe5mZYms/P3qWCxcAHR0wW1tIpVKkpqYqq3r04DB+PK3luDgKd5RKgYkTKcz/4EHyhPXrR0bgmDFE+rt2fWcP/9DZlJSUJPr4+Lw5rv36UW5xvdZZbm5uuH37tnBFTY0bpKNDobKGhh+OdvjiizfDst+CpaUl09bWFrymT+fxr3/VCYjp6ZSru2RJXbX16mryBNcTAgoKCqCjoyNOnTq11hOam5sLURTZzp07xc6xsYy7fx9XDAxgaGiIRtv/NW9Oa+DmTRJSCgrojPjyS/pvVZrOiRNEiOqtD0tLS1RUVEAQhHf3iNJSWns//kgEoqCAnkNVObm8nDyQTk4kYKSnE1nbt4/EMV9fEhtmzaJ3YW9f+/wODg5YtGgRduzYIa5bt4717dsXXjURFRk+PspLx47xvaKiRI3qasZt3AhBEHDkyBGhefPmzH/4cMZlZsLGzw8AhZIrlUrY29szUaEAKyig+66spD3BwABIT8fBzZvFFk2aiA6TJr35oKNGkTCwbx/Qvz/EpCSkbdsGm5UrwUdFAf7+8Nm6lV83diyaubgIbdq04aKiorB06VIIggAtLS1hzLhxXPHs2aJny5Z1wpqaGvKCgnDXwEA0GTyYCwoKQmxsrBAXFSU6tmzJcwBkMplSoqHBIysLuHoV7sHBeDZpEqxsbQU7FxehU6dOktUrV4pCWhrDmTO0P/7zn3XVp9u0AXge7gCTSqXYv38/tLS0RIVCIco1NblKbW0MGTIEhw8f5vDLL/ReZsyA+ZUr2GtrK345bhxDbi6R48ePKXKoXTu83rwZ/jXt67BmDUVofPYZRQ8xhsrjx3mj+nU6evUicU2hoLF//BiYNQtpu3fjlrGx0unoUf5qTAxaBgdD/fVriB06QAcgj/jRo8C4cZA/fIjUpk3hIZeLsQoFS7t4Uej6/Dkn7dIFhosW0TMnJtLesnYtXnl5YfTo0TDR0UHJ6tWS+LFjlSWvXuH+/fucRCJhBo2J2BoaKCsrQ7ylpZCnq8sCNTRonqi6PjBGhLYRPHz4ENHR0WjWrJnQvXv3d/f1oCASBRpAbm5ubRRJeXl54xElHEdrxd6e9s7vv6/72d69QFgYuF27UN6hA6KjowUxLY1ruX49/pg/H9ZLlyoHDRpEbXvt7ant5NOndJ2lS0nQPneORPP9++HAGLuyY4dy3bp1/PDhwxEdHS0YGBiIXbp0oX0pIoLevY8PUlNTce/evbLq6uohwcHB71dEPwHAJ7L9d0YfqVTaq1+/fpp/aaK9eDHlxRw+/G95rxhj6NGjBxISEmBkZNT4ZvdnB8+TsXrv3n+nWJqPz7uFqBqBqakpBg8ahKzoaOiNHMnfGDUKawYOFNubmor+DZGOqCh6ltBQCo/iuLpq3g0gJSUFpqamSrzd//mvBI4jg/X1a3p/Xl5kOH3kmuzUqROuX7+O6Oho2NvbQy6XIysrCyYmJuL06dNZVlYWTI2NmZpS+U4F8g/i9WsgNhZu27ah2MpK/NndnUnPnBH6qqn9W0XDJBIJpFIpkpKSlE5OTnx9Qmr8djE8VUj6qVP0b2oqGfMcR0ZQ9+5EfoyNKf9u7FgKk505kyIlHB3/1yv0SyQS+Pv74/z58+jfvz8SEhJw7949sbq6mi1WVTevgaOjo9Lc3JwzMTFhRnp6SA4MFB0dHJhHq1aIiIjgYmJiGk5dmTmTSM1bZPvFixdKXV1d3sbGpnZC8Kmp6D9/PvfyxAkkZWayq5cu4eLFi1BXV8fEiRNhYWFBg9inD3mdlUqA56FVVIRpKSlMsnMnnz9hAow2baIL7t8PxeXLyM/L4/moKIzX1UXS3bui3b17bD/AkpOTMWvNGqQHBKDou+8Qr6eH7LS0ukE2M3uDPDo6OrJz586xbdu2CYMGDeJk16/TD86dI2+hkRF5rlUk4auvqMXUWyH5paWlyMvLe7f3dD0olUoUFBS8Ox+1tIgwPHgA1MuFzsjI4AYPHlznkd24kYz89xFuiYTIxsaNVNDtLXTq1Int2rmT9756FZYqb2ZSEr3TvDyqSzBjBn3/iy9ILLpfF3X5+vVraGpqvnEGqdK5vL29hYsyGTiOg+LBA5aXl8c1SrYBEieTkihH/IcfKDS6Tx8K8waoZ/C0aZSfX49sq6urQ1V3QyaTkdB19y7l3xoZ0bXs7GqLCda239u7lwzwefOo+KO1NZ3Jd+/S7w0aRCKKTEYto8aPJzIeHk7G+5AhgIsLRo0YwdZv3Ij4+Hh4eXmB4zjMmzePL548GZHffSfol5ay9q6uXHK/figyMGDjx49nXHb2G9FAlZWV0NTUFHdu386+OXsWGnp6RJyNjemZz58HyspgZmbGlZeXv1vIKTCwrnf0ggVYL5MJeSdPcjpVVeI0MzOm4+cH2evX0DcxEVXpILNnz8azZ8/g4OCAM2fOsJ3Hj4t+vXoxsy1byPtbU0zykY8PKlJS2ODBg6GtrY0OHTpwpzdswI1790TrgADm7OzM371zR9nazIzHmjXgu3aF46tXcPzxRw6Ojhz69sWUmzfZqZkzBeTlcdi7l0STb7+ltTN6NHnoJRI0a9YMgwcPRmRkpDB//nweycnA5MkQmzeHIAionj8fkiFDoJw1C34bNuDhw4esesMGqH31Fd3zmjXA7duoTk/HTUdHzm/TJnpn8fEkeL98CQQEQFlZCQNPT8hWraK0j3btqG3b1q0UUaRUkuhlZoa8tWuFMk1Nfujx4zjq4SGmdusmtIiM5HDjBvOuqRCO3bvpvJfLUTJxomA9aRJnNnMm1OLjkRsTgyuBgQi6fRsan38OoXdv3D9/Ho4xMRCbN2fl5eWQ3bkD2bFjsPrllw/aA0VFRbh586bw4MEDpuXvj8mTJzPJiBG0TlRnD71gij7T13/nGgU1nSaePn3KvXjx4t0IO19f2vPi4ijqox7c3d3Z9evXUVZWBqlU+uEDKyaG9qiKChLzVHU+QkPB/fYb2uzfr7TavJk/PGIELqxfLwzv04erPQcA2of27aN7ysggweyLLyikvmYucwAmTpzIR0VFYRsJc5yWlpawevVqhY5cjkGrVklipk9H2fPnSE9Pr1IoFGOCg4OLP3jvnwDgE9n+20JTU/MfnTt31v5LFwl79owO+0mTPqqAzbt//gwcx2Hs2LF/TaKtwo4d9G9WFnn3/y8wdSp5m9ev//i/uXEDmD4dFseOATduIMDODkkbNohKpfLN8V64kMKaZLI6kvnbbw1esqioCI8fP4a2tjaSkpKQn5/PHz58WNm1a1e+wXYuNSgpKUFhYSH09fVRVFQEAwODhtu//L+CTEbzGSDjNDj4o4qnZWdnQ6lUori4WAgJCeF4nodUKmXTpk0Dx3EU/rVpExn+H5ujCpDna/duYPBgsN270cHIiLkWFeHUqVPswIED0NPTU/bt25dv2kAf5fDwcOHp06ecnp6e0sTEhM/KyhIEQeCaN2/+74si9vY0v5VK8pjk5FAemyhS4ZiSktp2Lv+X8PHxwfnz5yEIAoqKilBdXc0A8pb27dsXCoUCKSkpePHiBf/ixQs8ePBAIZfLOZmhIecvk8HZxQXe3t5icnIya5Bsh4bW9f6uh9atW/MnTpxAYmKi0LRpU6oEq60NTJgAYzs7HDp7VhAEgff19RV9fHxY/eJpeP2avJzJyRSG2KcPuAULEDZuHLLMzdElJgba2trQr6hAyW+/ic7NmsGoe3eGlBSYrl/PEBmJ73bvhvzbb5Hbsyd2nzsH7QsXlLa2tnyvXr3qPqek5I32WC1atOCqqqrw7NkzbN68WZg/fz4Zk0FB9AtyOQlLgkBVuTU1yQj09ibinpoKzJ6N+59/DkcdHVh98QWHo0cpmkEmI1HVwABgDBzHoUmTJsK+ffve8AyDMfLivNXr3MzMTJmZmck3b96cokhatCDD9dYt8go3hgEDGo2UsGEM8//1L6yurISoowNlcTEWhIWBX7SICFxNGzcAtK9lZdHcvXcP6NMHUqkUb/Qhrgc/Pz/eT0MDWLwYJXv3Yu3atY3fowrTp1NBqZwcIsWtW9OYV1XRuF29Wue9qwc1NTWBffklhyVLyBu/axcZ5V27ksgQEAC8XdE9JYWiTZYvpzSOyEgiZiNGUNhvYSER6u7dqVjclSu0ljMzSSyrqACmToVaQgJspkwRjQ4eZFEZGcquwcF8dEwM0tLSMHbFCj7xiy8gvHyJl0+fgnNyYrGxsfA/fJiI0OXLAAA7bW18/eABu56RIeYtWcIsVdFT/fqR1z02FggPx8vOnUUB4KP09BAQEECe/KlTyWtoZQUcOIAX5eUQysq4OeHhuDRnjiCPjORTHRxE91On2KAFC1jY5cusU6dO0NXVRcuaSJ/+/fuzPXv2KKPS03l/AwMc/PxzpHt6CoIgiNX6+tyAkhJoq6szANDS0kIfCws8qK7G1q1bYWNjA6vjx3l89x0JUd26USTB0qUkYISE4O7z5+Lr7GzyRH71FUUYlZfTHLawoHoIX34JANDU1ERpaSkvCAI4PT2gvByMMVjxvKB48IA7Nnmy4H7nDpd8/jwYgKpRo6CWmUm2lFQKTJ2KvOBgGDZpQjUCXFzo69Ej+qzJk/H00CEo7t9HQUEBNK9ehfatW2CRkUTi9u2j+TZ+PGBpCb3KSvGliYn4OimJjTQ3Z/j+e16Rno7bTZvSPrBqFRG/o0dx+vhxwSctjeHWLaiHhcF39GhOcHPDMV1dbLl/H152dkLV7dscUlLEmNBQcVDLlpxDs2a0fzbUIq4B7N27VxAEgfPx8UG7du0oterAAVonv/9OJPnIERKaGkll6dixI9zc3LBq1So8ePCgjmwXFdFZ7ulJ8/PMGbr2q1d0PSsraGtro6xGAHunjkdDsLKifcPenvYOdXXaN9etAxYuRLdBg/j7p0+LnTt3Rmdvbw6XLpHgOH483c/y5eTM8PCgqICNG0kkayCNr02bNrha016zvLycE5RKzquwECkrVkDP3Bx3Y2IgkUge/fDDD6c/arA/AcAnsv13hryysrLxsMY/O1RtKu7cebfC7UeiSZMmEAQB9+/fR4eGqlr+VWBsTIfVgQNkePxfRCo0aVKbR/lBpKaSZ8XRkQxsK6vae6qurhbv3r3L+djbQ2f7dmr18+IFGV9DhryjANdHQUEB1q5dC0NDQ0EURUGhULDWrVvzr169YuvWrcP48ePfzLmqvZ1UhIeHQyKRCFVVVZyGhoZQWVnJ+fv7Kzt27Pjn8Yqr3tuyZXRQnztHY95IcajExEQcOXIE2traQnl5OdevXz/o6urC0NCwrjCdQkHEvXv3jwurfvSIvCWjRlEhlJoQfgbAwMAAI0eOZDk5OXj06BG3a9cudOvWDW3btq0NPc3MzERSUhLn5eUlmpiY8C9evFB27tyZa926dePF8upDoSAD/OBBIlUFBWT0f/01iTepqeRlO3aMxkZfn0jTsGFkoP4ftCxU9bkGqCaBn58fOnXqBEEQasPG1dXV4ebmVr+iNP3A35/eY+/ecHZ2Znfu3MHGjRsVQUFBdTUyfvqJjCaV2FIP0dHRgoeHB9e7d28OlZUU+bBpE7BoEU4fO4b8/Hx+wYIFkEql7y56Hx8y2FU5u5aW0OzfH56lpXAqK0N0dDTU1NRQXVWFXgB7JZWias0aqAMUtn/3LviKCmi7usLaxATK06ehpaXF6+rq1q4zURRRlJUFSXY2dEHhz9u2bYNSqUR1dTVnbm6uwNtnzMuX5O1R9eGOjiby7e1NBmlKCuTGxijR0UGndu2gqa3NcPAgkbhbt8hYXLYMmDkTzNkZg4cP55LXrUORpib0RZHChvv1o/kyfjyFi9d4pnx8fLijR4/C29ubKkb7+hIBnDOH1lxjVX/nzqXrNAQLC6hv3Ih5w4fj3M6daLViBZiaGoki9Yk2TSDyUC1bRtFYffrAwcEBpaWlTKlUNrxGDAwAKyswxqBQKLBs2TKB4zhx2LBhDfeC5zjy5CYnUwFNGxsaOx8f+t7pt2zkXbuAixeh4e4uSI8c4eDqSh7Oly+JaDTWprCykt7HggXkwf3xRxJjQ0KIqISG0vNfvUrn0vjxRNSmTydiNHcuXad7dyA7G58xxgoyMxEfG8vHdugAu5wcpAQG4n58PJoNGACNadPguWoVOm7Zgke3bws4c4bmVXk58PPPwIgRUNjaItrKig21saH9VBRpLuTmAuPGIb9PH7iOHcs6P3iAXdraQkJCgvjlpEm8GBuLC7duIWHHDmHK+vVcmJYWmrRsKUq6dmUDBw/mkw0McDgnh7W8eRNNqqsxMygIWRUV0F21qt6wc7WdDLIGD4brqFFI9fLi9Pr3hyAIcLxwoa4QIgCNVq3g6ePDLp0/j5y0NAy/cgWFSiX0RREsN5ciCXx9aV5qacFs3TrmfOwYQ1ISrRU3NxKB7O0ppHzPHmDPHhT07Int27fDy8tL4DiOg55ebbE8KyMjbJwwAcWpqVxhnz5ovXIlPr93Dw+OHcNLe3tU3LgB56IimJiYINfKCt6XLtVGxgCgTiB//AGIItKdnPBaRwc7TU2VxcXF/KwZM2D09ddEnquraS+eNw8QBDhZWPDtS0pwbsECjDMwALS1kfL772iyYAF5y0+dAnx9EVVZiWdOTqyvgwPDb7/RZ9vZgUtJgbWGBvKaNYPOwoWc044dUC8sZJgzh+HYMYpOed8arYf4+HixoKCAmzdv3hudHQAQgQ0MpCgjuZz2b0Ggn0VEkOCUkwOsXo3KtWuRM3AgAqur0XLOHPrb69dpvv/6K625xESybzIySNCprgZCQlC6ZAk6GRrC28sLuhcv0js+fJhEIV9fqvhtb09ntsousLCgfdPEhGxfX1+KYpkwAdy5c2BKJWt19ixF8+zZQ4R69myyv5yd69qPAnTmnDhBa7VeTYLS0lJs2rQJHMfh+++/R1paGl4FB4sm169jr44Os6isFCQSSalCoQj84EB/whtgHypA8gn//0RISEhvS0vLvVOmTHm3VOKfHQoFVXtctoxU9f8QxcXFWLlyJfT19ZX/+Mc/+L+0l1+hoNC8ESP+d718UVGU67d794d/VxCo+ubEiWRo7tz5DvEXrl3DgT17hAw1Ne6LkyfJs/UxJAzAy5cvsXnzZnz//ffv5BWeO3cOt27dwrhx42BpaYmCggKcOXNGWVVVhczMTD4wMBA+9cKoVQS8bdu2YklJidCxY0fe5CMLfv3XMGgQHbbbt78xjjk5OcjNzcXx48dRXV0NfX19MTAwkLm6ur6b39pQVe+G8Po1vevQUPJuDBz4wffy6NEjREREQE1NDU5OThg0aBB2794tVlRUoNGCL/VRXU2GS2QkGd/h4RRie+QI8MsvZCD27EkE4dAhKjYjk9G4qEKuIyOJPGzZQkXniopItHm7h+5HQBRFKJVKPH78GK9evYK6ujocHBywbds2VFVVwd3dHZ/VKyr1UVAZnTV4/fo1du7cKRobG4u1lWvnzqXK5TdvvvPnYWFhUFNTU44cMYJHdTWF6H73HaCujszMTOzcuVNs37696KfKn1ahsJA8Xb/8Qh6mV6/oMzw8ACsrVFVV4cHjx3B1dcWTJ0+Qtn+/2DYsjKVOnYp2gYHkITp6lD7v5ElcunRJvHjxYu07NTMzEwwNDblXr14JRvHxrEBfn7kPGoS4uDjRysoKXbp0YYmJiQh4uzaDKNK1+/cnQi+VAtu2QTQ0xI5Jk5TlEokInkdhYaFEoVDghx9+aDhnWxDIiJXLAZ5H7Jo1QrZMhoHGxhweP6Yc8G+/pfDLpk3JA5uVBTRpgpUrV4pWVlZs6NChddcrLaXKxCkpZCi/nYpw4ABF4Dx9+ub3Y2JI+PntN6CiAuUuLnjYpAnaREV9XA2NI0eAEyew1NFRnDhxImu0G4hSCXAc8gsKUFlZidu3bwvPnz8XZ8yY0fgiTUoisdPGhgjSnTtE9C0saE5aWVEP7mvXgJUrseWrr8TeJ06wJtOnN1oM7g0cO0YezN27KbrB0ZHEMC0tWq8pKUSsVdWYVa219PUpIm3ixAY9kenp6Xj6+DEq4uLwsLgYPc6ehYZcjietWqHdlSuitLyXKlplAAAgAElEQVScFS9YAKuHD0lo9/YGLl/G2cBAMe7lS6ZQKDBv3jxV+y06Y5o3R1FxMbZt2yYayWTCOGNjvqRFC1ydMwfdZs7EvqIiMSs7G518fZl2aipMu3eHiYEBjYNSCTEsDL+Fh8PCwkI5ZswY/kZ4OOKuXsU0mQxqDg70LG/j6VMSLWNjaS998oTGoqZwF775BhgzBsU2NriwahVyRFGZLQj8lM2bcWvIEAQGB0Nnzx6gvBwVLi6IvHAB3ocPw6pFCxTv3g3tlBQULV+O8l9+QdOmTek5g4JQtXUrfjt4EEOHDqXWd4JAhHbHDuQOHIgkNTXBbvVqztLAAFxgIDB8OMSdO3Fv4kRcU1dXlpSUMFEUIS8r43xiY9Fj4kSyK1Tv0N8fkEoRMXOmQkNDQ9KnTx/88ssv+Oabb6AWFUX2h6rNnbo6kc8tW5D0/Llof/kyU7OwAJ4/x2t/fxy1ssIra2uRMzERnJ2deetvv0WTSZNgNGcOfdaYMRR5Eh6OuOnToeXkJLquX8/g7ExzWxAoRaO0lJ7/fdEpAF68eIHQ0FAMHDgQHh4eb/5QRapXrCByeu8eRVNpa9P56OBAxRtNTCB++y1+7tABjgkJ8AkMhN20afT7zZvX7feCQPffuzetjZr2nBfPn1fGnzrF9+7bFy5SKQncXbrQfq2uTpEoYWEkHkdEkE0VGkqCUqtWtMbCwkg8a9GCPOf37yNu7FjhSbNmTNqpkxAYGMh/0GNeUABs3Ajxm29w7PhxMSMjA3l5eaxGNKIaMDk5tWLlc6lUFV4eFBwc3ED1zE94Hz55tv9mCAkJkQBoAaAtx3F/vfDpxERSz2/f/o892iro6empirOwQ4cOYYTqQPkrQiIhBd/ZmbwI/wHZaBCiSC1QPgS5nPKsvb2pYN3bhP/UKaBXL3Br1qA7z3Mb3dzIGPw3EBMTo3R2duYamrfdunUDz/PYvn07HBwclGlpabyVlRVnZWXFvLy8akP9VLC3t4eDg4MyJSUFEomEDw8PF2fPnv3nWg+HDtH4L1xI8/7kSWRkZGDbtm0QBAH+/v5ix44dGcdxrEFCUlVFKvitWw2GKAOg6ycnU964tzdw/PhH5zy7uLjghx9+wIMHD3Dw4EE8evQIgiAwZ2fnd/Poq6qoWE6NlxcjR5K3af9+ejYHBzI+ly0jQy0igshRaiqFJU6aRAajypDR0yPP2vXrZGANGEDzaf9+Ikb37pEnwdu7UbHh1atXCA8PF+VyOdTU1Gq9iypoaGiI0dHRTKlUwsvLC30/MkTxDbx4QXnlNWF5MpkMw4cPZ3/88Qe7dOkSFMXFaKqhAfuaUFgVHj58iDt37iA/Px/m5ua0tlWe6hpYWlrCz8+PnT17lt2/f18YNWoUZ6CvTx6M69fJ2BNFqtprbAwsXAjlhAm4mpsr+Pz+O3dv3jxBMmsW03n9Gi8mTRJ7PX/O9IOD6T2kpxNpGjECJWvW4GJhIWvTpg1KSkpQUFAADQ0N9uTJEzDGuL4ZGWJ1ixaIvHVL2aJFC75bt27gOO5dog3U7RNTphD5CAsDEhMhnDuHwPnzeSOpFA937oSSMVjVeHMbBMe9UTviWfv2YnFxMXl0jx0jQWLBAhI7pk4lg9HDA7hyBQPbtGF7LlzAzZs30bZtW7qAjg6JNZMnE0mysXkzxLJfPyIPbyMnhwSe7Gxg6FCUCoKo2acP++hilVpagL4+NDU1hdycHL5Rsq2lBdy8CaMagiCTybhVq1Zhz5498PX1bbhfr7MzeRZ//ZXCxs+epbDkmBgiMI6OdFb07g3ExsJAT4/dGD9e+VnPnh+nft66VVe0UleXIjPy84mcDBpE3tZx42gf4zh6hilTSDjQ0qI5NmwYrd3Bg8kzCMDW1ha2trYQu3dHYEUFXgYFYffu3eji7Q2zSZMYpFLojR9Pn6emRjnG06YhOywMCoUCPM9j5cqVUFdXF3meF0fHxHAmv/+OTfv2iYaGhhg0bBgPHR3o3L8P/5gYlN69i9Rp09iM2bNhKJfTfBk1iubM9OnAqlVgsbEICgrCqlWr+C1btlD6jrExyqVSyG7cIGIkkdQRaYD23GXLyKO/cycJEkOHUkFCgAicqSn0AAzctw84coSvMjUF++03KNPTsWXnTswdNw5o2RJlTZuKeZ074+LkyayE54WCtWs554cPYZaTg7j9+4X58+dzO27cEEo8PbnB336LNoMGiWfPnhWaNWvGg+MoSqGqCko3NyQBYidra4p6sLMDGAM7fhzuVlZwr9m3ly1bJogch2QPD5isXSs+vnNHWergwNna2nKe7dqBb94ciuvXedsRI5CRkQENDQ2oqanRu750icRPlX3WoQPg64tif3+W7uYGx5r2aroTJmDAnDnglUqWGxLCl4WEwNjUVDDS16cD6Px5WuM//QSUlMCF55EdG8sqb92CxvPndC6Ym9P5deYMCYPvQU5ODnbv3g0PDw/Rw92d4fVrus+AAPr7f/2LPNAxMbRfzpxJ59Xz57SnXLhQG2XGTp6EIiQE+f7+RLSBN2pDQKmkObR8OYkte/bUkm19IyNWLpNB296e9hmVE2DFirq/HzaM/p0xg/YWPT0SdDQ1a3vLQyKh87p1ayApCV6TJnESnkdCYSG3fv16DBs2rK4ifkMwNETxrFnI7NZNNCstZa9nzsSIESNQW/BOEGj+BwYCs2Yh49o1QV1d/VZVVdWW9w70JzSIT2T7b4KQkBDG8/w8iUQSLJVKOVNTUzEwMPBPlLT6kVixgja8/yUvtEQiAcdx4kflzfzZIZPRYZ6d/T8n23I5FTPavp0MicZQWkp5YmPH0qHo719HtBUKOgjt7Ohad+8C4eE4tXcvLKqqBACcKIq1BrVcLse5c+eUjx494u3s7JS9e/fm67dkk8vlMDAwaJQQBwQEwNPTE1FRUXyXLl3g4+PzXvI8fPhwHgDu3buH8+fPf9y41OD58+cICwuDVCoVJk2axDVa8fR/CsZIwb9xAygsxOt9+2BqaipMmDCB09DQeL84oKZGRl5jRPvhQwo109KikN4G2hJ9+PYYWrZsCZlMhtDQUEAU0crFhcf588DFi2RwjBlDHrUhQ2huAuQRUxGat1M4SkspFO7rr6m4U1xcwwLAvn00v1RwdiaSJZcTWV+4kD5fW5vIUk37l4qKCkRERAjp6emcnp6eOGTIEK64uBimpqaQyWQN9TH+z2FuTmJVvQiDmnZr4sWLF1n75GTBbutW7qq1teAdFMTt3r1byMzM5Hieh6urq1BeXo727drxKCkho+cttG/fHl5eXggPD2c7Nm3C3MRE8pQoFCQ4qGof/PEHBFdX7DIxEYrU1UXja9fQmuM4bXNzyE6eRFeAKfX1IRob1xEjAHkmJigLCcH/x953h0V1bl+v95xhZuhIB7EAUkRRELCBomLB3tDYayxRY43RJN4YTVETTTQx8cYaazS2oCIqEgEFRLAgoiKiSJPepc6c8/2xGQYElJR7fzf5XM/jI2WYOec9b9l77bX3Nnz3XQwaNKhutXVWXV2N7du3o6KwEA6tW2Pp+PGvdtIqKsjgPHOGIn3t21MO47VrqBw8GM+++w6tqqrgLopEmmza1KwhrqioQEZGBu/+7JkIb28ymjdtohoTQ4eqX5iSAujpoe2AAZiYnIxQCwu4m5iAU9UdMDGha9u6ldQSBw+q29vJZNTf3ddX3b/61i363seHHFaZDKc++EC0trFh7SoqIJPJXltJHQMHItneHm4LF/Ktv/uu/nyui/v365EL2traGDlypHjnzh2cOHFCrM2LfxmjRlHth7IyIrymTiUFhUJB+3q7drV1EQyvXMGTJ0+aM+TqPNa6DsbRo3RehIXRXF+7lvaAZcvoedQ40+B5Gi9RJALj++/p9dnZtFfUFE1kjEFTUxPXrl0TbG1t0XvIEPU9rl5NkT49PXouK1Zg2rRpLDo6GufPn8fKlSsRHx/PcnJyRH7bNuzdvBnGrq7irFmzONUzKTE0ROGdOwjYsgXvfvcd9MeNozNUW5vm0IkTRCj4+UEoL8fzyEhwHIf8/HwMGzZMLCgoEHfGxGDZ9u2cZM8eIhcePSLiRvXcvbwo8n/iBBEQlpZUhEsioTn2ww9EUKxYAZw4ASnPA1ev4vGlSxhx+DCNye7dwLp1bNIPP+CndevEnp6eXOfOnZF9+jS0tbURdvMmt2HDBhgaGrICY2PEPn6MNl98wa6PH8+np6dT3Q6JBLh/H3x6OrLs7WmdqnLoV62i/XbnToDjIAgCqqqquCVLlkCpVEJ59CgzTU2VnC0tRXR0NKpKSpRtdu3iRj98yMrmzcPD9HRopaQgfuJEIWfuXM710SPoWFio2VaFAli9GkaAEL9qFdeuRw/AygqCUomCdetETRsbZtO3L1X8HzOGw8GDlJOup0f7l4kJ4OiIkh49EJORAes1a4hE3bOH5kqdVmVNTtf0dCQsWCBa+/iwUQcPMqxZQw52UBClSk2bRutER4f2ABW2bKHr/+orIooiIuicAdCpUydleno6Q2OpmNeuqe2wIUNoHtW0EOvcuTPn7++PjIwMyGQymDVSP6EWGhrqdrbDh5MKyt2diGV7e9rTUlMBhQJcVBRcBQGuCQksTSZDUFqaeJvnUeHkJGi0aCFqamoyLS0tTi6XM9XedPXqVVjb2mLwgAHoXlfpAxCJ6OQEzJ+PkpIShISEVFZXV097U338j+GNs/3/CTQ0ND7R09N7b/z48VpNsuf/yygtpajFl1++vlXL70RZWVm9iNbfGhs3EiGRnk6b8x+FKmepqT7YgkBMb6tWJFm1s1MX3cnPJ6Nl5UqSJsfFkRSqBrm5uaKRkRF369YtREREiPn5+UwqlaK6uhpGRkYYPXo0goODsX37dowdOxYcx4ExhoyMDF5QSb2aQIsWLTDu5UPjNWjTpg3Ky8sRFRUldu3atfEoMV03jh49qqyqqkJ1dTUHgDk6Oop79uwRmjR2m4n09HRoaGjA1NQUgiAgJycHRkZGkEgkiHvxApcyMoSJx49z9hs3ImDpUk7WnIKAy5fXbxWiQmEhGTHt2xMrPnVqs6X89aBUkgFx6BBa5eVhrpUVJBs3inFhYczG0xNShYKMocOHyTBoTsT8l1/IuFy8mAqjvaryvSonMDi4XjVsyOVkPE2bRpG748dpPiclAbNn46egILG8vJxNmjQJNjY23H+0dZyWFt1HSYlaTgugW7durFu3bkByMneWMdzKy+OCN26Eqakppk6dCmNjY2hra1OxqlmzKNezCUhEEQaXLolv+fszfP45Gf2VlfTLmrksduuG+wEBYmFODt6ZOZOXBgbS2hw5EujWDYZ+fjRfUlIgrFqFexMnIjwyUigoKOB6+fkJ73p7cy+rVbKzs1FWVoY2q1YxtG//+rGIiyNDte4ztbQEdu2Com9fhPj6wmvKFCJlGCNjtaqqyZoFKmT9+itswsMhzcxkZzt3ViZdv46i4mJ+CGPwmDuX7mvq1Nq8bRYUBGlCAoTt2yEsXQouMZGcUWtrckoWL6b/MzJIfq+q/l1Wpo4qART1cXGhuWVmBixbhj7m5tyZM2eE6OhoTi6Xi0OGDGGvrB4O4NixYxCcnaHr4yN0rqzkkJbWsD7G7duk9FD1LQbQsWNHZmdnhy+//LLhhpWeTtHi9etpPuzcSetES4scFaWSiKk6MDMzw+3bt5u3GFJTaWzq1shQVXNWgeNIxRASQoThgQP130PV4s/Tk5zUQ4foX3Y2nfVt2qCsrAxpaWnc+++/X/9v586lAk+DBqlltdOmwdXVFefPn4cgCPCg+h+cmJaGsbm5aNGiBVd3by/w9kaGsTGyfX2ROX069E1NKfr/8cc0Tm+9BdTkyT/w8hINsrOZyUcfCZMnT+Z0dXWZIAjs6dOnwoEDB4RZ8+ZxGDmSVBELFlAk08aG9qjPPyci2tWVxjw8nIhNb2/aiwcOJIJi+XLaq4YOhSkgZOjqcklGRmLR9euirVzOVXt7C0uWLq19PlZXrgDDhuHdd9+FhoYGdHR0WHV1NYTqamQdPIhup08jytkZY8aMoc8LDIQ8Oxu6pqYUTQ0JIcLz11/pWUycCHz8MRR2dhAEAXp6epSutXAhsGgR+nCcEGBoiKGbN/NcQADg6QnN1q3hYWMDo4sXoR8ZyT0NCsL3np6YZ2gII4CUJTUOq0V0NJdQVQW0aoWioiL89NNPgrh4MZsxYwZ9/tSplLLm6EhzwNeXnrGuLrB+PbTOnIHQtSuYvj45yUuW0HgtXUrniwqJiZRetHgxqVk8PSGMHQvzBw+Yx/r1RI61aEF7z8v1C+pCFClHvaCAUlIEgdQYy5cDP/4IDw8P/u7duwgICBCHDh2qnliZmfS6oCA1campSQqMOj23L168CKlUKmpoaIja2trCvHnzJE2eRaJIJOrdu2SX3b+v7j2fm0skgWpvSEmB1c2bmGxgwAq/+gqyS5f49B49oKisRKqDA3IlEjFfX1+pEARWUVHBF/bsKcj8/Hh4eJD96OND5MuYMaTCkEhw4cKFcgA71q5d+/vkiG9QC/6TTz75v76GN/gPY926dS4aGhp758yZo2VY04rib4djx2iDmTnzLy8AJpVK2ZUrV+Dl5fX37dlcFxERVPxCJUX6vVi9miJOH37YeP63Ukn5RRs30oY/YwZt9tXV5LS1bElO+PLlZHi89LyKi4tZbGws0tLSBEdHR27SpEmwt7dHnz594OnpyRkZGcHd3Z2Li4tTREdHc48fP1Y+fPhQBMC8vLxYY0XQ/gxkMhnMzc3ZhQsXxOzsbKF9+/aNToLg4GA8efKE8/Pz4wwNDWFjYyN4enryYWFhjVeYbibCwsLg7++PW7duISoqSggJCWF37txBeHg4MjIycPPmTbGqqoq7W1GBMDc3+Do6ihajRjFMmFAbrW2AsjKS0c6bp67ULwhkAGzdSkby+PFUZKU5c768nJyK/fvp0A8MJCPH1pa+1tOD7syZ+FUUxWcdOwrdV63i2IABdH3Gxq9es6JIxtG8eVT5eORIMkQbabdSD1padJ92dvUc2Xpo04Z6v7Zti6qHD5H70UcozctjEwYPZqbu7mB/hGT4vRg0iKKgL5NfCgXQvj3azJwJ+zFjMGDAAPTo0YMZGBhQj15BIAWPm1vT6oQ7d5C8bBnkDx8yrU6dILi7Q9qvX4Nq01UtWuD47dtsYUQE01izhsbOx4eUMP36Afr6KCwsRHhMjCDdtIndLSgQLHv14qZMmQLrykqGgwcpz7rOc5TL5YiMjITznTuoYgyck1Ntga/nz5+jrKwMtcoUVYTuxx/rzwV7e1zPysLNuDgUKBToNWIEOSnx8WRIDhtGpFBj95+YCKSmwmDZMsDLC2ccHMA7OnI9e/bkEhISkJKSInp5ejL07KmOUAMAY9AzMUFcbq4ya/x4zq57d4ryhoeT45OeTs/s6lUy2AcPprk4bBgZ6NraNGfHj6eoalwcyfatrWFsbAxPT0/VfsDOnz+Ptm3bvrLicGpqKrKLizFu1SrGvvyS9s0lS+q/6OOPaa7X9JSui7CwMISHh+PatWvQi42F+dat5CgmJxPxOmQIjbmPD0WDP/mkVj5cFzKZDKGhoSwmJgY9evR4ZVS+8OxZpMbEYFdyMtLT05GRkQFoaMCwpIQk6qq0HY4jh8XIiBz/wYMbJ/aMjChv1dmZxnPNGqBVK3AvXiAyORkWFhYN2wTu3w/x7l08LiwUH8bHi7djYsS7JSWssLAQnTt3hnbN3sicnaHdsiW4Ojm6ZWVlCLx7F10+/xzuvXvDauRIcIyRMuunn8gZ69kT5amp+HrPHvFB27asz7598PT2ZiqikzEGe3t7FhoaypRKJdp06EAqlsxMIjQOHSIn0MiI9s7vv6evDx2iZyGKRCbduUPKpSVL6O8mTcILCwuEderEZNnZ4oTt27msb79Ft8uXGfPyovcASLmzaBE0W7asVVHwPA+JVIpdp06hS3y82MPFhfEuLhQJ7dMHbOJERMbEwD0rC/z69UQC9O2L8hMnENGpEzS2bEFiaCieGBjArVs3da/wXr1g/O23LE5Xl7VwcoLhv/9NJFS/fuADAmC8ezd0334bduPHI8HGBj39/MAUCirAdeQIhIMHsUFDAx7e3tDT08OePXuUpqam3NSpU5mOjg4RpKNH03zx9SXFQ48etP/Z2ADJyZDHx8M0JQXacXFgSiWt1+xsUqBMnEj7GMdRutL580Tade6MslGjsDUoCPddXMReo0Yxzty8ecVCAVLGqOoctG9PUnUtLcDBAboyGcycnHD16lXGcZy6Gvm8ebRW63ZskMupUOXs2WCMQRRFhaamJlMoFGjXrp2YnZ3NV1ZWssa6ewCgNXvnDpFKycl0r507U7S9RQtywFUpVvr6QPv24K2toT15MuQLFsCkXTuYaWrCvnNndNy8mXn89hvXbfp0zqOgAI8ePWJJRUVwNDNj6NOH9rqzZ+l+vb3x7NkzhISEFFdXV4/s06dPdfMG7g1expvI9v8HkMlkn3p5ecn+p1od/R689x7J4X7++S/voZuZmYnQ0FBwNfKpfwSWL6dcwoCA+jLK5uLRo/oRirr44AOKaF+6BCxapJbznzlDkcTCQoogvkJWPWDAAPTt2xccAQAaVgUF8M4776j2p/+4R2RnZ4f58+dz27Ztw8iRIxtUBQ4PD8etW7fg7u4u2NnZcXZ2dgwAX15eDgAIDAzEoEGDfhdZk5SUhIsXLypzcnL4yZMnw8TEBMnJyZydnR20tLTw/PlzhIaGKq2trTFy5EheJWE0NjBgSEqiCsBnz9IzfvlzGSOnWIXycjLAJRKS371K9l5WRgbjmTNk/O7cSbK4CxdIgjpoEJFe8+bR+9QppvS4vJyztbQUmz0O0dF0ncHBZGD7+Pw+Mm3AAHL6jx9/5cueV1ZiZ1kZMHYs5ujrQ7ZnD83V3FySmqsM2P8EDh1qXCGSnAwoFJDb2aFBVenr18lYfPxYLb+ti8JCGrN9+9Dm4kX8tGKFMsTcHOVpabxnWJhob2/PzFX5o7/8Ao2jR2Gkp4enBgaw27ePpIjXrkEAkJSYiIiICCEtLY0zMTERrU+dgl9WFofWrWm+DB1KZEhxcT0FgVQqRb9+/YSoTZu4vMhIpKSlwdHRUfDz8+P27NkDURQxffp0urdNm8ggfPnZymQQzp1Dp5YtBatFixgAhgEDyOnp0YOipV27kqO7bBnNj4oKet779tF+dPs22lZXA19+CTc3N7i4uODu3btKAwMDHi1a1PY6bjj8ybypKmc7JobeNyKCjP70dIrCHzpE0aSgIJLA9+tHEW8TEyKGzM1pfTQyZ7t3747g4GB1oa4moDqXz5w5I/RdtIjTX7aMiITAQHL2AZIhNwKJRILVq1dDeeYMrt+9KxampTHcv0/5uDt3EtF2/z6N27Bh6oJkHTpQxFLV/7vmOnR0dFBaWoova8bS29ubcnHrICYmRinfuJGP7dMHmpqayMnJERMTE1lkZCQGPnyIHsXFtNeoL5Ki1wEB5KysW9f0GtfWJpJw5Urg4EFItm/HaC8v8cGWLaLDv//N1dvnbGxw28ZGeODgwMYdOMDl8rwY27atcsyYMfWLXa5cSXNIBaUSyq5dUTZkiGjaoQOTSCREbGlqkqw+OZmI47w8KP38MKusjGl88QV0V62iewgLq02B0dbWxvjx43HkyBHY2tqSZFtFjJw4QRLx5ctpLSsUdK6uWEHPNymJFCj/+hc5i1evkuMYFwfh4UPW5vRpceqXX3JYuRIeFhZEYIwcSTnRHEdro4mcXJ1WrYTCefM4Kc8T0XfzJrB1K6Q9eqC3iYnw7+HDxdnt2vF3IyPx9OlTdExNRY6pqZg+e7bQ7do1/h1/f8gmTUKtYkVXF2zTJgz080P+oEFEoCQlUd656pyRSHD/1CmUxcUJnELBYf9+Uj+0aoXg334DAFy5ckX89ddfGQC+qKgI169fR58+fepfvL29OlJdpwCu4tkz/Lx1K0aeOwdrKyt6BgcOEGFhbk5kTp8+RPy/+y4AoKJ7dxz46Sexurqa6ejoCDExMbyLiwukUunrz+rCQlpDqnannTqRwzxrFmBqCjZkCNpPmABx0iT4+/uLycnJbLi9PXQ//JBUMnXRrRvNgwcPgPbt0bdv31rfq7Kykk9PTxdevHjR+KLIzqZz98cfiaApKFDneZub0xqOiqJn3Bg4js5xVcrH3bv0XNLSIE9IwIhWrVj54sUod3WFprU13eeWLcDKlVAqlfD3939RXV39ztq1a5swCt+gOXjjbP/DsX79+vGampo+Xbt2/XuGbBMTyblrKsr6B1FWVoZff/1VePr0Kde6dWtx3rx57G9djbwuGKNDcP582oCbmzYQGkqb+qlTDX93/DgZ/Z07k+GgMrp79aLN+dtv1dWUm5G/LPkrK6b/Raiurgar6d37MgoLCwEAL0e9NTU14efnJ54+fZrl5OQop02b1ixioKKiAidPnhTs7e15fX19wdbWlmOM1auQamFhgQkTJtR7v1ojcvVqcqDfeYcihnWrBxcXkzPw7Bk9s337SDK8Zg0d0qr7E0UyCu/dI/LEz08dff3pJ5KKurvTwWtkRMbo3r1N3lN+fj4YY/Dw8Hi9t5yYSE7L6tVkGKl6xTcT1dXVkEgkYLa2EKOjUfr4MbRtbMBxHJ4/f474+HixpKQEI0aMYFlZWfj555+hoaGBDz/8kN5g6VIqsLR/P0lSBwwgGd5LhfT+EsjlZAhu21b/58+ekSH8slReqaRozuefN+5onz1L7xUbCzx6BE5HB7M0NGrrD0RGRgq3z5/n50skkJiaovT+fZR264Z+Eybg0LFj6G1iApfr14F338UPSUmiguNgb2/PjRs3DlpaWjTftm4lxyg8nCKREyfSfvLzz/UupXv37hw6dwZcXJDXqxf279/PtmzZokjmhq0AACAASURBVGSM8W5ubsKpU6ewaNgwTjJjhjr38CXcdXPDTGNjzrFnT/UPNTQoOnXwIN2vuzs5QcuWUZX0wECa8zXrITM1FVKpVHR2dmaFhYV4+vQpP23aNHL6Fi6kIlV1oCJWax1hldyzf38iYGQyWg8jRpDz/f77RD59+y05YtbW5Bw9f94kAfzw4UMIgvDaPrrDhg2DnZ0djh07xj19+hTz58+HZkQE3afK2T54kNIxXpa9RkdD1qkThPXr0ePxYxazezeRI9radA/r15Mz9/IZ8PXX5EhduUJru1MnxMXFobKyEv369UNcXBzCw8MRHh6OJUuWwKBGZVJZWYmLv/7KTzMxweQvv1Tl9jMAuHXrFs4pldD18UHHsrL61dg1NGg+BQaSc/Ddd68+zxkj8nbSJNjExbHqmTNZ5ahRkH3yCREFMhlKnZ1xo1Ur1mfiRCY1NoZlUhKzfPaMb3Cvzs5qdQ8AFBVBbNUKWXI5++KLL7BmzRpwnTpRtPP2bXrN5MmoOHIE22bNgkPr1ko/d3cepaV07s2fT8oLf3/g0CG07dsX/WxthZO7d2PBRx9xEomE7v2332gt+/jQ+G/eTJHkI0fIUczMpHPV35/IiPT02naG9qWlcN+xgymGDYNEVWiwVSsau48/JrIzJaVJ0kKpVELm7k65zF98QY5+q1ZAUhJcAwK4S6tW4bsHD8Sq2FjG8zxKu3dH/0ePmM2+fTzmzqUzYckSCnKMGkWKjs6dwfr1Ews2bGBXvLyEvpcvc/Dzo+tmDLC1Rdb778Pc0FDEpk3UCSEpCbCwgPLCBQEAV1RUxIYNG4aqqipcunTp9QGOxYsRYmUlZt++zUafOAHlZ5/BcPp0IkVu3FD3wp41i+ZUnRovANVWycrKYgsWLEBSUhIXFRUlXrp0iXEcBy0tLeXEiRP5JpVyBQV0btaFgwOwfz8Uo0bh1iefoJOXF9pv3w6ZRMKO3r+Povffx+OFC9F51ar6idyM0Tg9eoS6KTf79u0TUlJSOA0NDS4zMxPFxcVCXl4ec3NzY126dIF8505a84GB9Af37tF8+fhj9Xtv3Ehnft0Wba+DiQn9c3WFDoAwW1tl1a1bbJSFBYeqKrLzAFy5cgUlJSXpABpn+96g2fjfs3jf4C/BunXrpIyx+VKpdOPUqVM1/5aO5N695DRGR/+xnNImoGr5JZFIuHnz5sHY2Ph/qwr1XwEHB3KeKyqa/zeq4kp1kZVF0et168hQmDCBDExTU3JWvvqKcqx4vn7e1N8QaWlpkEgkuH37di3zXVZWhoSEBOXjx495AI1W/XV0dGQeHh5ieHg4//333ysWLlzY6L4aHR2NS5cuoU2bNkhNTRWtra3FkSNHgjH2x4gwTU1yQCQSchRnzqRqynp6JDkLDaXISVoaOZVaWuQwhITQ66ZMIefS15eiA1IpGRdWVur3/B0IDg4WGGOcQ00BmUZRWUnrOiCAHN4rVxp3KJtAREQErl+/jpKSEuoRXV0NvVGjoLdxI4o7dICDg4MYHR3NdHV1xZKSEu7u3bu1f9ulS5f6b2ZtTfI8UayV+OFf/6I1M3x4fQP9z0Amo3H/+uv6+9jYsSQRr1ucr6iInAN//wYOIjIyaP3Z2xP5mJPTgNhyfvYMzufO8aft7JSx16/zMZ0744WtrVBVVcUt1NeHm5sbQkNDcbm6GgYffqgcoqfHOxw+DLZ0af2ik0uWkJMQEkLy3u7dyfFLSWnYi7qmN7qRkREWLlzIkpKSeFNTU+jp6XEJCQli1syZaDl4MF3zS0hJSUFOixbgi4ooX79u6oupKTnXixbRPOnalaK0urq1RYpUiIqKUrZt25bjeR6Pa4qMRUZGiq3272echUVtWx8VkaaSSKenp4t4uRCe6rknJJBjffIk7YMXL5JsfOFCImdCQl6ptLp586bI8zx7/vw5LF9TsNLBwQHvvfcevvrqKxw4cADz5s2j53/0KJFlGzfWVymVlJDD178/4OyMm3v2IOeTT+A7ZkxDFYW9fcNnpiry9vHHNN9//hklJSUwNDQUevXqxan6yH/66afYtm0bFi9ejKioKMTHx6OtQqFsNXw4/3LF9S5duuD8+fPQmj0b1bNnQ+ODD+p/pkxGe83Zs+RwTp78+nNdIoHU1RXxq1YpE4uKuDGbNzOkpeHZ++/jyY4dwviEBK7Fl1/SGfTbb+QIv3gBbN+ufo4rVpAz+9ln5PCcOAG9wEDMSk1F2OrViA0NheuECWRnqLB1K5InT4aWkxOGr1zJQyYj2bQokmO3dy/tp+7ugFKJbjducO1OnkRwcbEwKDmZQ7duRBZUVVEK3KNHRHxXVJCTU1JC+82KFVQ3IzgYj0xMYM5xkERHIywqSij59FNxVr9+9Qdo2jRaRxcvNl4dvwZ6enri8+fPiSgSRVKW1BSe5F68gGLzZigUCubs7IzKykrlM6WST3n0COcWL4b37NlE+gYE0L64Zw+tt7Fj0Xb7diZ4ecFi82YO9va0f3bsSPc1diyeCYJoc+4cX6WpCem2bYCFBbKysiCRSGoXSllZGTp06IDMzEwhPDyci42NFbq7unItfvkFl8zNhdEpKVyrkBDg8WNUPn2KlIIC9szODo+io7G4QwdSWrz9NqUnbd1KEeNWrYhU2Ly5HgFhV5N+Ul1dje7du7PuNRXBS0pKEBUVxfbu3Qs9PT1lv379+A4dOtQfRBsbem51UFpaiqMhIcrqoUP5scuXY/vUqWj/9CnMiopE444dkdSjB7utqSkGf/212L9/f87FxUX9xyNH0pgqlRA5DqmpqUhJSeFWrFiBwsJCasGYnAxBEHD58mUIOTmiV3w8w5Yt6vewtqb2Y/UfNv2zsiKC9uVzoxlobWvLX0pKUqJNGyJILC3x4sULhFMXjSlviqL9ebzJ2f4HYt26de01NDRut2zZcshbb72l9cpqh/+rULVsmTWroSTnTyIjIwNxcXFYs2ZNo/LlfwwMDEiKqa1NxVmaQlUVRW5Wr6aiGCpER5OB5+FBhel+/plkW1Om0Obu4UFG3O9wlv6XYWxsjJSUFOHx48dCUlKS8OTJEyEjI0PU0tLiS0pKIAgCJBKJ0KZNmwbkTMuWLZmDgwNiY2O52NhYISEhgaWnp4MxBh0dHRw7dkyMiopigiDAxsZG6NGjB9e3b1/utdWKXweVsc8YPeOsLJKK1hjQUCpJfbBwIT3n9HR6Xv37U9Rvxgxisfv1I7mtgcEfStXIycnB+fPnmbW1dcP+pQAZfJs3k4GpKljl7NxstYooijhx4oQYFRXFtLS0MLOmr+2LFy8wztkZPQIDEW5tLT5//hzjx49nw4cPZz179oSenh58fHxgZmaGiIgIJCcnK01NTbl6KTWM0TqZPZsM0rNnKaqakUG5en82/UYmI2Kh7riKIj2HdevUefcKBUUAdXRo3anmhkJB6+6TTyiy1qYN7Y2qKHxuLjnyISH0dbduMJsyhct2c8PASZPQt29flpSUpLx06RKXmpoKU1NTcdq0aaxP376csYUF2M2b9HkvXqgdFMbI+Z4yhQxZJyfKFw4LI+egLnie9mhTU0gkEpiYmEBLSwuMMQQHBLCuTk7QXr680Wf9ww8/iF27doWdvj7DkSMUQa87RgoFRfEfPCAlR79+tXmg2L4dGDYM9x89wvXr19mECROYpqYmLC0t4eTkhOjoaOCzz1hQbCzO371LznerVszAwACMMZiamiIsLIz16NGjQdpI7RjwPI2zpyeNwfr1tLYmTCAH3tOT5s2SJeQYjx4N9O4NODrCRk+PmW/ZgiMlJXCJj4csJQUVVlao3rcPGtbWRDw9ekRS0OpqPM/Oxp07d9C1a1f1HqOhQa8bO5acNH19Ij3GjiWHY8IEYMQIpAMILS9Hrz59GipzvvqK9oCXc54BilqOHQt88QWMv/4aITY26NWrF6PbZ8jPz0d2djaioqKQnZ0Nc3NzYbKFBc8YI6LoJbRt2xYhSUlIKC5Gmz590KCQo0RCDveVKxSt69sXYAwFBQU4ePCgWFJSIpqYmLD4+Hjo6+vXStiNjI25y5GRLLZdOzzU00Pu1avolpLCWowaBc7Dg+atyk7Iz6c0DFVKyvDh9DlSKSl2fv0VKf364eDBg5h8+DAsr12D5PBhSiepwf4TJ4QIbW22xN8fckPD2kgfGCNnp1Mnmg+urkDHjmCDBkGYOROBt28zIzMzGOvrE8m5fj2tq7AwIrOTkuhag4PJ8fL3B4KDce+tt+B/5AjigoLQftUqaD1/LnqvXs1LKiuJRDA1JSXFjRskrZ46lUhvb2+qF7BiBV3/sGHA/fso1dNjPRYsYNJu3UiNERZG5Ny8eWCGhrC3t4erqyvCwsIET09PfuDgwdDOykJRYiLQsSP17eZ5mt8mJnQNQUFgZ84gztVVaXL/PifftImIqF27SHL9zjswPnKEdTx4EDuGDUNyq1bC9evXxZCQEJaSkgJ3d3dkZGRg8ltvQfv2bbT39WWe+/fDOi4OIRoa6P7jj+yetTWTeXujzapVgKEhDldXI5njMPedd2DTrh2t06+/pjk0bRqNoY8PEc0tWxKBM2xYLeF05swZZGZmwsfHp146hEwmg42NDevUqRM4jmOXLl1iRkZG0NfXV+8FQUG0Ly5aBIDOuJMnT0KpVLJchYIJAwYI00+fZobffINKMzM2YONG1ragAN0OHGByLS0EBgaysLAwdc0GAwNg82YUKhT4d3CweOPGDSaTycR+/foxPT09WFtbw9XVlXXv3p2V7tghdN+zh6s8fhyyGkWQUqkEFi0iZtDdveFa7taN8sQZ+911jYKDg5VtSkv5dvPn01klkyE4OLgqJydn75o1a378XW/2Bo3iTWT7H4Z169Y5amhoXB08eLChq6vr3zNiGxFBEYSYmL80oq2CRCKBVCoVBUFg/4iCaE2BMTIu5HIyDJu61ydP6GDZsIG+j4oi5/uXX0hSPmcOyd28vNRVxVW9Jf9BkEgkmDJlSpMTYt++feKLJnLZZTIZrKysMG/ePERFRbHq6mohNzdXvHPnDq9QKGBiYiLOmDGDtWjRAnp6eg0/o7KSjLC4OIpWmpmpWfvgYJIMr1xJRsXYsXTYenqS8bZsGUW5Zs4k41yhIGm0kxNFfAYOJOf6dQXH/gBEUURISIiQlZXFmZmZISUlpf4LlEqaR3fv0j1dvPja6tJ1ER0dLd64cUOUSqUsKyuLjRkzBs41uWe+vr7w9fUlpywhAauWLGF1o3pSqVRVkRhmZmZwdHREYGAgv3fvXnTs2FE5YsQIvh7ZwXHkQLi5qXMpe/emvD2JhByWP0qO+PmR0bpjB32/aBFJ6Zcto+9FkdbXqFFEeqlQYyAjIIBy9mbMUP/u8mVq3bZiBb1u7lyS2AIwBNCzTjR1+vTp/MaNGzFgwID6Mn8zM0pDuHaNPjsxUR0tb9GCrldHh3JPzc2JiBg8uH5rwcBAkka+FBligoAZ+/dD/8iRJgk5uVxOxu2YMeQMqiLnOTk0zwcNorxpfX2SdQYGkkO+fDkgk0FZVITYLVvQb+5cVrf9nqmpKd59910mnD8PN19fVAwYgMjISOHgwYO8lpaWMHjwYE5bWxuiKDae0iKKRF4kJZGzdPYs8NFH5OQolUREvvMOyVj19ennFRW0z3p5AcbG0C0thbOdHZ526SImfvklq5JKERYTgzm7d+N2fr6ym4EBL/n4YyJIHB3BDAyg36sXeo0ezeHsWfqMfftI2uzkRISDqSmdj6p5WnPtzuXlCAwMRFRUFDxfbqUXHv5qwogx5IwYgaiHDyFTKGivr+noMGbMGIwaNQrp6emqAlAcBg6kCFojaN26Nbq+/z5ezJ+PHz//HO26dxdGjhxZv+K/TEYR6ClTgGvX8KJLF+zZswdlZWWssLAQV69ehSAI8PLyEn18fBhAKTW9e/cWwsLCuBadOgk9ly7ljB49op7yXbqQw/XNN5SSsncvRbF1dOiZHT1Ka3vECGDmTMR6egotvLw452nTFAaRkRIsW4aysjKcOHVKSElJ4bS1tVFcXMwtWb4cmuPGUYpNXUVHQABJwB8+rNcuUUdfH2OmTcPPP/+MmWPHwsLCQj1fgoPpeS5cSOu0tJT+6NAhoKoKlnFxGHXyJFqnpSHj4kU4TJ/Oxb/3HvguXdDxyhWKWl64QKknKsXSvXs054yN1RXzp08HWreG7OlT9qRLF7HTjRsMlpa0z+joEFk1ejQs58+vvZ+aSubQmTgRnYODxeB791irVq1qo8JwciKiLywMmDwZlubm3GU/P/R8/30oRFF8Zm0tuq1ezT3etQtlOjrw/+AD5Esk4HJzYW1tzQ3t0wcVPI/Wa9bgRVUVxIgIWtvJyZCMGwdLLS22wNsbt/r3R1FgIHqOH4/isjLoCAIyMzPF8ePHq+tP+PvT/U+bRuTJ7Nm09zBG+4+7O503M2YgNScHd+7cQefOnZsMqhgYGMDLy4sVFBQoAwMDufLyciaVSoUuXbpw/Wxtcb93b/He0aOsvLwcWVlZoiiKbPLkyaymxgaHAQNgPGcOjP38SPk3ZAgQGYkuhw6xmM6dhaz8fO7gwYPw8PAQ+vfvz5UNGICi3buhMXYs1lA3gIYHSnIyXFu35n4xMRHTvvuOaWpqQl9fX5mfn8/73rwJh8GD0WiPGE9PshN8fIhUb6Yyq6ysDElJSfzg4cOJsNLTQ3FxMW7duqVUKBRrm/Umb/BavHG2/0H47LPPZmtoaHzn6+sr+9s62gAZf25u/xFHGwCsrKygoaGBgIAADB069J9RgbwpdOtGEaDNmynv72Vs3UrSzbt3KZIXF0dS1vbt6YDdsaNWKoeBA//rl/+/hOzsbAwcOFA9WZKTyVl58YIMgLFjoXP+PHzkcoYRIxjGjkXlV1+Be/gQGt9+y2H+fHJo2rcnyZumJhnzP/5I/7KyyIgYOpSMxC1byDhLSyNnGqDos6YmKQvGjSOnYM4ckiUCFJFNSyPHe+7c//iYKBQKhIWFcQAZbbWGviiSY8TzFGH/6CN1UZcmUJuHXePQ3rx5Uzx//jxzcHBgFRUVWLhwIRrtZ84YRclXraIoThPQ0dHBuHHjUFRUhB9//JEFBgYqhwwZ0vgmI5ORmuOzz8ipPXaMnBZLSxr336uIWbCgvsMTGVlfAl5dTQ6kao0VF9O9bNig7l+sp0dr8/vvaa44OtJc6dyZIvOvgFKpRHV1NbS1tSEIQsM9z8uLnAhNTcoNnjyZft61K0WS796lgk/jxpEjXNfZFkXaH15C3p07KNLVRdizZ+jv4ICXW05WVFSguroaurq6DFIpraEzZ6hWwNdfk6pp0iQyqrOy6ExIS6M1UlM7IHnZMnHYuXNMd+nSRglF7swZSHkeUo7DoEGD+P79++PWrVvM398fAMDzfP2xuHuXUi7GjiW57sCBVOtg8WJykLy96TOSk+nr3btpjvfqpX4PFWkJAMePYwTALmlpwdjYGDNbtkTVkiW4cfQou1FZKdrs2QObuDjWMjoaWY8eoTI4mBwze3siZ6ysaGx79KDPXLyY8npfKoylqakJlYS+nrMtihTZVTkrjaC6uhp7z5+HvqcnFtvbM0yeTJLXmrSCepWWMzKIXGgsslYDpxq5tYWhIfbdu8fdu3cPffv2hVwuR05OjpidnS2UlJSw1jNnwmvrVu6pXC4KnTujZ8+e4u3bt/Huu++yI0eOKEtLS7mMjAyY1igmPD09uSdPnggaGhrUkeLmTYqQ37pFZMiaNbSOVEqDuDiSQOflqSPcvXqBnzOHS7e0RNdDh/jiqVOhd/Ysju/fL2RlZXFubm549OiRyBhjurq6tK/o6pKjvnOnutDaggX0XGrmkQrW1tbo3r27cPjwYSxdupTytyUSIo1U0v3Ll4loiI+ntXTkCAy9vJDy7bdIt7LC8WvXwObOFW1tbfHgwQOm3LaN1EKPH9NcGzqUyPDsbHXRqxp5NDp0AL75BpqA+MLUlJQPqhou1tY0Xg8fAvv2IT4+XjS0tuZqUxzatYOlpiYzT0wUT5WXY9WqVWobsm9fIkhiY6H46iux/8mTrKRzZ5iHhjLJe+8xsVUrOB85AiQmoruJCYQVK8BNn87h+HHaSwoKqAJ+bq4QLZezHvn59N51Knbb2dnhwoULOHbsGB4/fqyq2s1q05LCw4lc2LpVrdLYsYMi2oaGdJ8bNhBJNnw4qj77DIwxjBo1qsm5CpCCY8SIEbwoikhISMCzZ8+4e/fuKcWcHD5dW5tpMqbU19fnU1NT2fLly9UdFgA6y728aM84epRyyeVy5MTGiuXm5uzDqVNR0KIFDhw4gBs3boBXKtHV0FDwcnRsvOVkYCDw3nuwvHIFs0xNWV5eHnJzc5GRkcFbmpjgdnw8Wvbq1bizDdB82LHjd6VAhYaGKrukpcFg/XoeJ0+qflbJGNu1du3a581+ozd4Jd7IyP8BWLduHQsPD/9ILpd/MXv2bC1bW9u/p6Odn0/G3LffUiTuPwTGGFq3bs2uXbsmPnr0SHBxcfkHe9sgo/7aNTKg6kbmRJGiZi4udGANHUoG98yZdGhHRZFzOGTIfyQq+l/FixcU9c3IoOidpSWRDxIJHUzz5pEE9N//Judw0CDUtgwqLwc6dkRkr15iry1bmHTnTnIEWrcmR6eggIyvZcvooMvOpvm7fz8kQ4aA19Sk19S0WIK7O423jw+1NOrXj6LWjJFB068fXet779H/rq70HAC6LlX/1v796W9URjlAxr+PT/3+t/9B8DyP4uJiMTMzk61cuRLt2rUjpcTu3RSJ6dOH7k11fTXIyspCTk4OZDIZoqOjERkZqTx9+jT34MEDMSsrC1KplB0/fpzp6+uLb7/9NnNxcYFmUz3fAXqOH31Eka3XkGdyuRw2Njbs7NmzXK9evV5NtvE8PaNRo8gBOnyYnourqzqq1BxYWFDUwcGB5qKrKzlzHEdR7m3bKK9SJqM5+P339Iz79CEyJTqa7s/CgtblrFnkPLi6qlu+vAI8z0MQBOXFixe5/Px8tG+sL7aVFT27iRPJQFaRAz17UmTP2Znm/MiR5PCqDLqqKvp53QJohYUonDtXPDJoEBM5DtevX8fVq1dRWloq1lTyx86dO5UmJibo378/45KS6DNKS2k+Dx1Kjo2KcFVF1z086Gt3d6B3b0Rcvy4alpczvfR0kj+6utYvOOfqSq+vkQFzHIeWLVsyFxcX3LlzB1Xl5fB2dycndu9e+tuSElI0LF9OTgbP0zobMYIM6smTaT/s0IH2CjOz+uRDI7C1tYWFhQV0dHSgq6sLV1dXlpWVxXJzc9nNmzcRFRUFSysrJD19ilbu7jA0NSUHwtGR5sPTp+RgtWvXZAXqK1euoLi4GPVk8aWl5BCOHt3ktSUnJ+P+/fvi0qVLGWdtrVZKODnReVy3wFtEBJE+dat7N4YJE6Dl5IR7SUlCZWUlKygoENLS0pRFRUW8lZUVZ29vz1JSUsTrcrno8ttvXI/Zs5lT374sLy9PvHz5sjhq1Cg+KipKuHHjBpeYmCjq6uqy4uJiWFtbs6tXr6Jnz56MOTiQukAmI1l3375EUlVVqatUHz9OpOO0aUSIzJ8P07ffBjd5Ml4EBOBwVRWepKayp0+fsilTpsDd3R1du3ZloaGh6Nq1K7W+MjAgsqK8nMgofX1SYkya1CjB06ZNG/bw4UMxLi5ObBD0qKggKfisWbSu3d3JIX72DEYHD0J/4UJI5XJBQ0ND9PPz42JjY5EZGyu4m5kxfPop3YePDxUFKyqiPWT4cCKqLl4EvvgCZcOG4aBCwTw/+IDptW5NCp29e+mMGzwY8PBAXl4e7j5+zEZYW0MnIICILG1tSNu3R8ujR9lVS0sml8thaWCAK1ev4tH16yjv3Rv6jo6wGDyYyQcOhP6+feDz8qB74gSkvr5g1tZgNWcj27iRzrHBg2n/l0gAHx9EZmVBU1OT2TYyh2UyGR4/fiw8ffqUAUDv3r3FNm3aCDY2NhxycohEGTiQ9gAVUlNprapScRgjp/7xY+gbG+PWkydwcHFpVrogYwzGxsYoLCxEXFwcZ3nuHIbdugWXHTu49u3bo3fv3g3TIgSBzv05cyiibm8PwdoaW54/Z2Pd3ZmJnx+0+/dH93HjmLOzM/r4+MDu7l1mrjrX6yIujtbr6NG1SgWtGpLO2toahUFBsN69GzuVSjx48EChra3NGRkZ1W/Rp4rwr1xJtsiECa+854qKCpw6dYobq6XFydu0Aby8kJmZiQsXLlQoFIoxffr0KXvtwL1Bs/Amsv03x7p162RSqXS3trb2mBkzZmi9rs3I/zTOnSPjtbnVs/8EWrZsiUmTJrEdO3bwx48fF8eNG/f3JCiaAzs7ihTNnUsbsERCOW3FxZQH5utLkbrLl0mGlpRE0e6X5Yj/bWRl0WFmYUGO8ciRFNn69VcyMtavJyN/+nTKqwwNpd/t3EkGTefOVMho3ToyuIuL6f4DAykqsns3GS9+fiTJE0UycFUVvydOJOfIygrYuxey5GTh6dq1XEdV0ROVpB4geR9AKgIVzp9Xf62KQNTtvfmaaOTvhihSBHb27L/2fV+DxMREBgCbN23CmKAg0SwlheXs3An7pUvBSaUQBAEZGRmIiooSU1NToVQqoWpzIopUd8Xa2ppNnz4dt27dYomJicLNmzcZx3FYsGBB89alrS3tH3FxRB69Bg8ePICFhYXA83zziDbGyKn64QcyiH7+mXJhx48n52Po0Fcrcaqq6LXFxSQT//FH+lllJRneEgk5VHv3EjFWXk5Rua1baY4OG0aOt4cHORV/AP369eOLioqE4uLipu/Z3p4kiFlZFLn19ydnY+lSMuKOHCGFQni4ugJ+fDw5I3Xn8/790NfWZnZOTsohQ4bwJSUlCAsLE3Nzc2ufJ2MMyqIiJmRkEElibk7OjKdn4/3jR42i/WrECPq+uzM4SgAAIABJREFURQtU9unDLrRpg9mOjhQVv3SJotMjR5Lzv3IlkQUvPQvd7Gz4Pn8Ow19+oc9r356uoWNHNbH1Ms6erb/mvb1pXY8dS+v6/fdfOf51IZfL4efnV/v9rl27EBoaCqlUisjISLRs2RJyuZyIleBgGneOI1Kgb98mCRZBEJCXl4faCsvV1a8lhPT19aFQKFh6ejq1rlLN9RUraO89eZLGUyKhedFUz/e6UCohODhAY9Uq9t5770Eul3NA/QLNHh4e9P3HH5MT9fAhhr/zDrdlyxZkZ2dj8eLFfHFxMY4fPy4eOXKEMcbw1ltvgTHGnj17Buu8PIoQP3xIbyiVEhGiauN14wZd74wZRIA+eULR4UmT0PKjj1Bx9Spzi4gQq6qqBH19fbRu3ZoDiJCRSqViQUEBU/XqxqhRtBbefpsceTMzmmtTp5KkvI7DU/P3ePLkCVdYWFhbyR0AzaFly+ic2bCB9o0PPgC++goaQUHA4sXoOWdO7ThpPn2KacHBHCwt6xdSBGgOXL1K5114ODBmDDKOHcOuXbvQsk0bwcrKit5n2zZ1oVQLC6C6GtIffgB69hS1Fi1iOHGC5u+aNUCPHtDt3h3Tu3XDkd9+g+OgQajs2RM3PD0hiYyENDQUIxwd4eDvT9evUuJMnEj7n4YGzdOrVxudFh4eHuzcuXMY2IRKbsKECVxhYSGMjY2hoaHBAPAoLqYx/+67hvUi4uIa7ruMAevXgz9yBGN+/RUXjI2FycuXNzug0rJlSzg7OwseM2dy8jpFIxutr7JnD9lN5ubAhQsQd+3CLRcXged5TsfdnZ6LkRE4Pz8YfvYZOdFjxpAdUxc3btDa3rGjyfo6d7Ky0HbaNHHRokUsPDycBQQEIDAwUBgzZgzXoD/3+PFExrwGt2/fFh2fPxcMxo3j4e0NURRx9uzZcqVSuWbt2rW5r32DN2g23jjbf2OsW7fOViqVnmrdurWdn5+fZgPW7e8ClRR2+nTKUfuzRaOaCVNTU8ydOxc7d+5kjUor/0kwMKAK1devk+xp3z6KPG7eTJu/Snr0mn7FTaKsjA5ZjlMXFomIoEiynx9FAydOpCj7rFkk9Xr/fYpW7dlDkd6dO2kujB9P76eKrp0+TRLhjh0p4nLpEjnb1dUk5WOMHBGJhAxglTz3l18oAqClRa/jOJIFf/YZ/T40VH39qh6Vdash1/TqBEAG/rffQqmr26C9yP8MVMW92rX7r3xcTk4OMjIyUJ2TA8+YGHjK5Xg6bhy7KJeLT2JimDIqqva1UqlU1NHRETp06MDr6enBzMwMOjo6CAgIgKenJ9q1a8cBtdXeubCwMISHhyM3N/e1lZxrER1NhXwaS5d4CYwxVFVV/bGNRkeH9qtp08hB3riRotJffUXOamPOjZ4ePR+JhJwzV1ciB95+mwyvXbtoPfTtS7l/e/eSEZedTakEfxExw3Ecy87OFvCS41MPPE/rplUrdW9gbW1ac8nJZPTOn08RNg0Ncr7qSuIzMgBXV2jPnIkJenp8eXk5YmNjlTk5OZyWlhZV/xYETPX15avd3ZEWHw+bgAB1yoG/PzlRL+PRIyIpVM42qBPAiXv3cNPCAm4JCfQctm+nXNijR4m41dQkYuPiRXKOa0iLvIEDxZBRo9jCgQNfnyJz9ixJ/7/4ov7PDQ3pmmuiilix4g9VsZ8zZw6KiooQEhKC+Ph4cdOmTWyipqZof/YsQ3CwulJ8XBzNcZUkuQbHjx8XATAPDw/Ua2X0+DHt+6+AsbExevbsKfz000/cBx98QOcgxxHBolBQhJ8xcjiPHyfVxWtQoaGBhyNHQk8qFeRy+avzwRijMVy1CunnzqGqqqq2xaGenh5mz57NCYKA3bt3ixkZGUwURSIiLCyIBHgZHEdz08eH1D+9e9PPP/yQ0jF69QKcnCCXy9GvXz+GOnmzgiBg586dSo7jeMOX+7OPHEmR1CdP1G3ifvqpgb2SnZ2NJ0+ecCtWrKgvORZFUgr4+hKBMXQozeeZM4kQCAqi1/n7A1VVqD54EFrGxkj55BM41CFmkJdH5+ePP1KRS2NjckYZAzIywBjDrFmzuNrX+viQ1F6F1q0hO34cWT/8wErOn4fu7Nlke61eTSoKNze0vXgREyZNwsPHjwXv0lLOd8kSoE0bJLq4ICklRXSIiGBgjM7myEiyJVaufOVjpo9uDYVCgeLi4kb7z2tqajZUML3zDu1FjTmh9+41msICgJQH169DePyY4fnzZqu9LCwsMGbMGA4nT9J+t2JF4y+8do2egaqoo68vijIzUbZ7Nzf5gw/U61CpJPvm/n3ai9zciBDv0IHI/NhYtW3SyD0mJiYiLCxMsLt0iZX36CEaGRmxESNG8EqlEhcuXOACAwOVCxYsqL/GPDxovrm5kY3XCEErCALCw8Ox4NIlHu7ugLc3kpOTkZubmyeK4g/NGqw3aDbeONt/Q6xbt47jeX6phobGp97e3vIePXr8+arG/5dISKB/rq7/NUdbBYlEAplMJnIc9zcewGaA4+hQ/O03cijffZeiwIcP0//u7mSUubnR7zZsoNefPEkH8KRJ6sq7LVtSJHfDBiqg9uABOXhz55Kcevx4kmJevkyHoZ8fGSgFBRQlVslNBwygCB5AOU81Ujakp9PP6va4zMhQf60yjD/9VP0zlQFoYKCOvNRtD/Qn51VoaChKS0slpqamUCqViI6OFmNiYkSpVIohQ4ZwVi/JpP9PcPQoERF/lDBpJl68eIGYmBhcDQpC97g40b11a9ZWSwuaO3fCydwcTgATBAEPHjyARCJBQUEB3N3dmUQiaWB0T2+iTUnv3r1RWlqKAwcO4L333mteX/Zx46gqbjNw//59QUdH589NCpmMZL4//URFrrZuJcnmypXkfHbtWn/erV1Lf7N7N0U8tLXJwDI2JoNy6lRyjoqLKXrcp089x/KvgKamplhZWcklJSWhMSlnvXs7dIicfSsrKo40ezY5RcuWEXlw4gQZmgkJdA99+tDf/utf5Kz37o2CggKcOXNGzM/P5xwcHFjv3r3p3t95BzpHjiBwzRo8kUrFBRxHIzV+PBmkT540LKTn50fS1DrS3Q4dOuDEiRN4+PAh3NzcaE9YtIgUHsOH07VNm0ZOcUICjXdkJGBhgQ55eexKRgYyMjJeT+iUlKiLWr0Mc3Oae//6F5EEf3D96evrY+TIkRg+fDj7ceFCvLh+ncHfv35LNlWxrzpQKpW4f/8+Gzp0KNxfzqUuL1dX0n4FeJ5nCoUCBQUFMDIyUv9CpbjgOIoqpqc3yP+Oi4tDRESE4O3tzSmVSsTExAipqamcuZWVODQzs3kMtrExKr/5BspOnTByxIgGLRY5joMoiuz58+eorKzEo0ePUGJkhDaDB+OV1IaLCzlFSqW6intFhbr7Qh15sSAI2LZtm6Cjo8MWL17ceNrKokXklH7/PRFiAwfSex05ApibIyEhAUePHoWlpaVCR0en/qY1fz6RuuXlpL54+pRUV6NHEyk0ZAiRG1OmANHRKBgwAOnGxqhWFWGLj6fxX7uW1Br+/ijOyoKWgwPy7Oxg1rkzCgoKoKmpKdQmBJeW0rn60pyRtm8PHZlMKX7wAS+6uoI5OpJT9uQJzeMjR2ATFwebadM4PHhAZNX8+bA4ehQxPj4MISHkwJ86RUTgiRNEBKkUXE0gIiKCPr+5rWh37aJ9p6nilN9+26D9IQBs2LABUqkUFWZmmF5ayjBrFgUYXlG7oAFSU0nl1xgqK2kPPHSolnx/+vQpjmVmoquDg9I6OpqHrS3ZIDxPBKUo0hpydaX94vRp+v+dd4gIGjaswcdkZGTg2LFj6N69O3NPSmKsjtKL53kYGBhAJbtvAMYokNJYyhCAq1evirq5uaJWSAiDhQVEUURQUNCL6urqj9euXVvd/IF6g+bgjbP9N8K6des4AM5SqXSnkZFRh7Fjx2rVOxj/jtiyhTahsLD/uqMNUDXK6upqlpeXh7/1WAoCsdhxcXT4/fQTRZ1696aNfNo0OhzXr6fxzswkQ3nfPmLUv/6aNmU3NyqiUlREz6NG5gsvL3IspFKK4KjaGb39Nv2+rjOsqtj98cfqn506pf5aFXkcMED9s7o5+v+DPeFTU1NFuVzOzp8/j5ycHEgkElEqlXLPnz/HvXv3hJiYGLGgoADDhg3jVVGZ/zq0tcmY/A8j/u5dPP/3vzFQoRDcraw4bvHiBtJSjuPQoG/p74Svry9u3ryJioqK+hGipqCjQ0b1li1NRyNqYGZmxiUmJuKbb74RW7VqJY4YMYJLT0+Hubk5ioqKkJiYKLq4uDDd5rb9MjYmRy4/nyKw33xDa2ThQsonlcuJZIqPVxc2Gj2a5JaGhiRL5XmScv6ZquevQe/evTme53Ho0CEsWLAAr52rpqYUgW/fnhztvn3JcNy8mSS6qv2husY2KyqiOVjTo/3EiROCQqFgc+bMYTohIZR3PmcO7UkdOsBaIsGd06fxWY3aRBAETEhIEB1u3mT1yDSAnPApU4jUq9MirG3btkIFyWTJozAzI8fKx4eih3p6pD54yeGQ11RIj46OxsjGIqQqKJUU1a3TGqoBtLRI2XDnDo3Rvn2UO/oHwF2/jv5BQTg/bBhcWrWqX664qoocvISEWvlsXl4egEb6yAO0JpqRDtStWzcWFhaG8+fPY+rUqS9dEEfPt7KSPj8zE3lFRbiZno579+8LZWVlnJ2dHTt27Bg0NTVhY2MjLlq0CAalpQwuLrQuXhPtF0URv/j7KytnzGCzNDQ4BAfT86uDoqIiITMzk9PT08OdO3cUj7Kz2aRt2/gz+/YpR48ezTcg5PLzae2Fh1Muvqp2RFkZ/e/kRITY7t2Apib27t+vVCqV/IwZM+q1iWqATz8laXZsLJ2XLVsSQWZuDktLS3Ach/Hjxze0rT/5hCLQKsJt2DBStajmpUJBRHdRERAVBWN/f8z78EOce/YMhenpsBIEse3y5QznzqFCWxuJiYm4fPmyWLliBRMDAjBeRwc3btygHGcVlEp1B4SX0GvZMv4wz4t9T5+Go5cXTbN9+2htpqWRvP3QIXI6334b+PJLPFMoxC6BgUBMDIOxMc1BMzPa/7Zsoft6BTHq5eWF2NhYZGVloU3d2gqNYf9+ZO/fL/42aZKgU1aG3r178zo6OoiNjUVOTo6yv48Pz0aMAHJyUF1VhfLycmRmZoIxBgMDA7GqqopNnToVVq1bE0kVF0dj3FxifOnSpn/36acUqT51ClVVVfj111+FBw8ecL169UK/1at5HDxI++XkyeqzkTEi+rKyKMgREEDO9+efN5kapFAooKGhgf7e3gxBQfXrYoCK/V67dq3pw+Kjjyjg8dZbyDh8GJcuX4aenh7y8/ORlZzM5u/ezY5WVSk1vb15uVyOvLy8bFEUDzRvgN7g9+CNs/0/gHXr1rVhjI3mOK6tUqlMApAAIBlADgAjAAM0NTUn8DzvoaWlpfTw8JB7enpK/vayZ1UBkx9++D9xtAGqoCyXyxEREYHhw4f/n1xDo6iqIsm0sTHJytq1I4PnwAGSZH/2GRUU+uEH2qinT6dc48OHKapz7x4Zyvr6JK1u356i1/fvkzRq5Upyam1s6G/MzclZ37SJHIETJ8joX7CAjJYFC9TXpmr7ZWKizm/+h2P8+PHs+++/R1paGnx9fdGhQwduy5YtsLW1hVKp5O7fvw9TU1MEBgYqp06dWttSSqFQICcnB4IgQEdHB/n5+cjIyIBUKoWzs3Otwf+X4MmT2p6gfzVEUcSjR4+Q//ixaLJsGWtpaCi2PHOG+0/WV+A4Dtra2sp79+7x3VXVdl8HJ6dmOdtjxozBw4cPUV1dzYKCgsQNGzbURs9FUYSOjg5CQ0OxePHiRuWOTcLQkCoDHz5MfXDff5/W6ddfk/TXyooiWHPm0Fq+cIEcpylT/itFCPPy8nDt2jUAgKpK9Gvh6UkOwKpVRJRdvUr7TWoq3a8qh1gUSdY6ezagp4ecnBzk5ORwU52doZOYSKoac3OST9b0CHd0dMSqVatYTk4OgoKCkJSUhKCWLRlvYIB2CkVDw33EiHrRyIKCAqSnp3PdGqt036ED7Zs9ezZaNK+6hiBwbSJHshZHjpDhXePUNglV9HfBAvp38CBF+H8PHjwArlyBuHo1CtPTa43tWtjYUH5tfn7t3qtytquqqhruJwkJRO68BjKZDBYWFmLNvtXwMC4oIBLlk0/w/Y4dynGffsq3cnISMmfMEMeNGwe5XM7u3r0LJycnaGhoEAtgYEBKjaqq1zrbxcXFSElJ4Vd++CG4ggKaQ8+eUcpRDebPn8/l5ubChhQPEigUSHV3x8N79/j8/PwG1e6hVNJz27+f5l5ISP3f37pFEeZDh1Cxbx+kHTvyC9asebWjDdBcX7GCiBwXF3rO0dHA7duQODpCEARER0fD0tISlpaWuHjxomi5d69o+uIFM50xg7WYMeP/sffdcVFc3fvPndmlFwEpothQUUCwoWDH3qPGGEuUmFiisbcUNYrlG5NoXl87mtgL9oqKYkFUjIqKgiJFEVSa9Lrs7tzfH4dlQWkaTXl/Pp8PH2B3Z2fmzr3nnvOcRh5PBwcy1CWJ9nNN321LS0CthtCxI3QtLNDp8mUIZmY41qcPn9ivH1Mqldi4YQOXJIlbWFhg5OTJTFGrFvbGxOB57drCV199hZycHMjlcsjbtEH4lCm4ZWcn9erVSyjKhQZAucm2bduy+9u3w37NGsgHDCA9oUkTbXeEOnVojS9bBjRsiIZXrjDFw4dIlSRYlPT8a+pRXLpUbnHbwsJC+Pn5AUDlKXtBQcgyMsIBNzdmV6uWmJCQIP3nP/+Bubk5Lygo4Jxz4cG9e3yEjg7b+OOPYIxBEAQYGBioFQqFYGZmhtzcXO16GDaMZPDixUQeliz6Vx5GjCCStKjzQTEUCiJxirp9nDp1SoqKihKGDRuG4srpo0bRvNi+neawJkpHT4/kl1xO9RgOH6bIwXJga2sLpVKJ3NOnYbh4MbXDLQFTU1Oo1eqKledatYAGDXDu7Fk8jY+Hk5OT2s7Ojn3co4egX6sWGrVuLd67d0/99OlTEcC0hQsXlhOX/wF/Bh+M7b8ZS5cuHSOXy9c3btyYW1lZ6aelpRUkJSUpsrKyhIKCAl25XK6qX78+mjRpYlC3bl0YllU45t8If39iQa9dq7R68PuGnZ2dlJqaWip3652DcwpfCwsjj0dkJHmQ584lwezuTpvvggXEmq5ZQ8qAxks2ejSFAxoakgI6ejQpE/Xr03eamhJhoWFjS+bVTZlCG/rWrZTHDNBmamlJzPXUqfRaXh4p1c7OpPRFRFCP1+hoCqNs3pyu8+VLqr7r4UE51f/WWgFvAB0dHYwcORK///47IiIi1G3atBEbNmyoTklJ4QUFBWLv3r2Zvb09Nm7cKKxbt07S09PjWVlZQm5uLtPV1eWMMa5UKgW5XC6ZmJggOzsbt27dYnp6epJSqYSnp6fYsCqFh8pDWhoRV9988+5uugRC/f2hnjcPFtnZTPXf/6Jmr17sryDIXFxcxKtXr3J3d/eqnaxXL/LmlRWGXAKCIFCLIgBNmzYV0tLSUL16ddy/fx+MMTg7O7P169er169fL1pbW0tDhgwRquzlBuj8nFM9gtmzyaO9Zg2tJ4DWfG4uEVua3sl9+pBHydLyna+pgoICxMTE4ODBgxAEAQMHDizuV14lmJrSPSmVdJ21a5Msio6muaevT+NtZVUc+n7z1Cm1LDNTrDZqFI516YL2mzeXGT0kCAKsra2RmZmp7tKli5CRkcEUCxbQd73qZZ0wgdJeQOHTFy9ehL6+fnE/5tewbl25BcI0hFilxtVnn71ZUbpPPiGDo0MH8maWzLctB5IkQfnoEVKmTkVq1654aGQEW1tbSS6Xv745jh1LIahFyn5eXh709PS4np7e62MQH19uj/NX0bZtW3b48GEUFBSUNtoPHyav/bVrAGNQqVTw8/aWOri7C6OuXgVzdwdCQ6k91avYtYuMmzNnyj2vWq2Gn58fNzMzk3R0dERYW1NKxvXr9KyLCtaZmJiUIr6SEhOR4e0NjxUrJEtLy9LjtHQp7Vk//khjUBahYm5OqQ+dO+O2jg6yY2Kg36MHrcmNG0mOlBdRwxjd2+HDVNxqyRKgdm3orV0LT09PKTw8HLdv3xby8/NhZWWFHubmgs6tW7i5dy/448eSZ3S0AMbo2MRE2tO3bqV1f+oUfW+tWjDYuhW1mzSBNGkS7AoLsWfGDCm1bl0hNy+PzZs3r/h5y7y90ap2bSTevo2NJYw3sy++gGRoqNZTKMQtW7ZArVbD0tJSGjVqlGBkZISnjx5h+OXLCK1bF9VVKtSNjaWolPbtyahctYr0grp1AZkMutev42xwsPqBr68wceJEVvw8GKNIhDlz6LNl1A3Ztm2bmnMuDBkyhNm94qEtCVVUFNiiRfijVStJaNSIDxgwQLx7965w4sQJNG7cmHl6ejIACDx7FsLMmRj/+efw9fWV8vLyBMYYWrRowf744w9IkoTff/8d3333HX3xzJlaIrRXrwr3BwAU9v8q2cc5RSRMn454xrDF2xsABHNzc7WDg0PpNKlRo+hcq1fTuW1sSJfbto3mtbU1rQ21utzims+ePSMiwcSkFPGkgampKXR1dfmFCxdYly5doFKpsGrVKkmlUjEdHR0pNzdXlCQJug0botmhQ2ispye1WbhQRGwsyenr19FCTw9yuVxMTEy8p1AoTlY8KB/wtvhgbP9N8Pb2ZjKZ7HtdXd3vx4wZo1ddqxDoFf1o8M+LqX0XOHCAGPp/gHc+MzMTb9UuLSuLWMrr18mQdnamzalXL8rH2rqV7tPdnRjspUsp33HePPI23L9PgtbFhQxpV1fa5J2caGw0xkxwsPacmsq6JTerqnjEoqPpvCNGkGIsl9NGb29PRsCQIVRAxMCAGPu+fek4TehkYiIpLQCFjG/fTteVkEDem/h4Ui41bTns7Cpth/Nvg5WVFTw8PHD16lWRc44RI0a8tkNOnz6d3bx5k6nValhbW6NmzZooyg3WzC8BKG65wc3MzERRFLF//35MmzatauHSZUFHhwpDVaEN1BshPh7SqlVIiouDVLcub+nry975OSpAx44dNa3BJA8Pj8qFhaZAn1r9ejGrcg8RoJG/JQ3QSZMmiRERETh97BhW//wz6tnYqBs/egSrGTNE06NHYaxUkoerRQsKq87MJMMsJ4eMQisrMrZv36b/W7cmY1sQKO9z5kxSaI8epWvu04dkwKRJZASMGEEes9WraX3t20frdMYMWlv79tHfkZH0/Bs1IlLtFcUtMTERPj4+xf/PmDHj7eaZTEbfbWtL16hQ0H1160byTVPgUlcX8PNDjx9+EG0XL8aaqVMBPT3eXV+/XBmblJSEjIwMsWXLlvD391cne3iITsHBrxvbMhn4ggW4rKuLP2JikJ+fj379+pUvu5cuJVlaRgi4aZF36+jRo5g4cWLZxxcUUMj6G+Zhq4yNETZ/PtiWLXjs5yfFubry+vb2YqNGjVCzZk1cvnyZR0VFSUqlEjo6OlA+fy7aR0RA1NfHbYWCPNKAkJKS8nqoP+cUvTF2LCAIuH//Pjc3Ny97DIyMaH+pAho1agQrKyu+bt06PmvWLO1ai4ggUrhoPxo9erR48eJFHDh1ivd2d2fNfviB1tuAAUSglyxENXgwkTEV4OTJk1JcXBwbP368duI2aED3qemd/cknrx33ODYWzaKj0bRrV+E14q+ggGRhzZqU51xByHJSUhIuJCZi+NixYN7etFfu30+RJ6tWEank6vq6rmJuTvfXty+th1GjwHJy0LFjR6Fjx44oLCzE48eP4aBSMaajA2zahHaffIJroaEswM8P3VxcSF4sXEiyYMECihpZtIj+LyGLhF270KegQFA4OeFRzZo40aULzp07h+6aVKyvvkKzjRvh0rMnchwcYGBgAJmPD8mDKVOKx1WlUmHz5s18508/wfPwYdRt0ABHBw1Cy7g4ZP/xBzKfPYO4fz+gUkEnLg46jNF69vKiPX/VKrT28BBvFxSgoKCgdNRP/fokw+7epb8FARkZGQgPD+chISE8IyNDFAQBx48f5wYGBtzY2JgbGxvD1NRUNDQ0hJGRERRJSYhevx55tWohw9YWX44aJWo84l27dkXbEp0FujZvDvz0E25164bMzEzhs88+Q3p6unj+/HkYGRnxrKwsplQqoVKptDU/2ralvPkhQ0j/qagWiJERPeOSePmScsS7dkVyeLgagEjTrUB4jaQCKIxcpaLowrp1Kf3Ay4sM70aN6DMVdNDIzMyEoaEh13/yhL2aVgEQYejh4YGLFy8iPDxcbWJiIuTm5grjxo1DXl6euHv3bgCAQqGAgYEBd2jUiCbx48e0H+npQZIkBAQE5CoUilkLFy7k5Q/IB/wZfDC2/wZ4e3vLdHR0fjMyMvrEy8vr392u603x4gWF9Bw58uYhdu8J2dnZrE6dOrQxPX6srV7r50d5lXv2kMKxdCkV3fn4Y1I81q+ndjO//ELeg19/JQNcFEkBNzCgfMErV+he9fS0ni1AG5o9b572tfcxJpmZQL16ZDCXRKNGpNDs3Uueq9Gjy/8OGxttcRE3N22lbqWSPAhXrxKB8OwZkQ83bhBzW6cOKXvPn9PvZs3oXP/SCI3CwkKoVCpwzstsBaKjo4N2VciR1NPTw4gRI4q/4N69e+pjx44JNjY26i5dusjS0tIgCALMyij+UiZ++IE8oh06VPpRTbstxhgyMzOhVqvxWuXdjAxg9Wq8NDLCndBQhLq7Y+aiRaxC5eQ9QEdHB8OGDWN79uxhBgYGkqura+UG95gxtM44Lz89JTOTDFhnZ6qC7+hIc3XcOPI8rF4NXLyIxsHBaNyqlZA7YwbuFRaK9fbswSZ9fbjevYuurVpBLoqV3EeOAAAgAElEQVRUSdjBgdZuZCSt/wsXtOfy9SUjZONGUqwNDOhzERFkiCxcSD+ANlRZoaBid5qKy8nJJJ9MTbX94o8coXW4YAEpdDt30hrdsYP+/+47IDYWsokT0V2hULfdu1fEt9+SwvjsGY3R8OGUVmJhoe2rXREYI4NaqaRrU6kotNHQkGTm9u1kmB4+DNn583CuWxfHli2DgVzOjx8/joEDB7KyUiceP34MCwsLycDAQJDJZOyZhwcZcJGRWqWUJgSSfv4ZV+7cgWBoyGvWrMkrnBOtWlVoaLVq1YrfunWLlVcdGYmJJNOrOO9jY2Nx9uxZKS0tjRkYGMBx/HjebdMmoSA4GMeNjNSPHj0ScnNzmaGhIfP09BQNDAxQkJwMh0ePwFq1gv6CBeheUIBjx44hIiIC69dTUeDRo0ejuL1Ps2YUbpyTgywA8fHxrNwUqLQ0bV59JRBFEcOHD2erVq2iRcM57XXffVeqt7GZmRkGDx6MK1euMP+rV1Hvq69gqmktZWpK3rqmTWncbW3Jq3zlSnEef0nk5+fj7t27wtSpU1+XdQ0bklx7+JA8vYMHl3rbtUULrPz2W0zNzgYAhIaGwqZ6dTRcsYKIturVKfqqEkI/NTUVjDEKT2eMZIGjI4VGc07GtJ0d8PPPZIDVrKk92Nycing9e0bFztavp5xckOxq7OBA68rYGHj+HPIzZ1B9715mNH06z507lxkePEhFSAcMoND5uXPLTc2S6elB9ugRItavlwZs2SJYhoQQ0aWRcWfOQMjPh4nmWaWlvVb/RCZJmJCbK4a6uMA4JAS1Y2ORYmOD5k2bImLWLGw4fx69vvkGYU5OsH3+HGZbt/L6kZEIHjeOiTt2oHlkJMyXLYNZ9+7QK2tcv/wSyt69ERMVxS+YmvL09HTBzMxMatmypdi6dWvk5ubi5cuXLC0tjWVkZCArKwuxsbEoKChQKQsK0GPHDrGZvT1LnTgRrVu3FhhjOHDggNrU1BRt27YtzSIaGgIffYRWrVrh6tWruH37NgYNGqQpEsiePXsGtVr9enHNKVNIpk6eTJGAmgJ0r2LaNKp5s2wZ/R8fT6/t2wfI5WjZsqXYsmVLpKamYvv27eznn38GYwxdunQhHeCPP2geenmR3hgXRyHdmr2WMe282rGjTBnTsGFDHD16lPGdO8HKKXTm7u7O5HI5VCqVGBYWxgEq/NugQQPMmjUL//3vf/Hxxx+j8cKFrDjVx8OD9j3QulEoFFEAzpd5gg94J/hgbP/F8Pb2bqurq7vBxsamwfDhww3+te263gacU6/IFi3+GkNbrSYFNTGR2NZevUj4NWxIzPmyZVBPmYLOvr6swdKlZBC2a0fCsVs3CvPu1IkUWE07nB9/JKOxenUK2WWMFB8N1pfomKAJqyu5Of8dGDeOFICSra40YIzCAwEKlfzoo4oLg7wKuZw2j5LKnqZIV0YGbVBqNRkRvr5kYISGkmJuaEhj2aIFKe21axMp8A+trK9SqXD79m3eq1cvvOvq9bq6uoiOjmbR0dGyGzduQJIkqFQqzJgxo2o5w7m5NFcrgFKpxPr165GRkQG5XA5BEKBQKAAAffv2RUpKCo8OD2d6MTH4KCiIm/TowR64uuJahw4QRbFqFcHfA+rVq4fevXvj2LFjQlFOaPkfVqtJoRk8mLzNwcFEItWtS15YFxciyAICyOscF0dtnZRKkgv16tGc/uwzbR/pjAwYymSI27dPHb58uVDw/Dlz/v13yDWFdmbM0J6/LBLpwQMiozgnA3XZMsoV3bqVPEBDh9J3lGzvpaurlR8lCZTff9f+rWmtVhRWDYAMQ0GgtWdqCggCIs3MJIWJiQilkmRhXh5dz7p1ZGx3705z5+uvqfiWvz+F/QYEEGkwahStaU0xqWnTyNhfuZKMqpEj6Rq++45Igh9+KCbmZABmzpyJxMRE4dq1a9Kvv/7KLC0tpT59+gg1S8jFyMhIycrKigOAjY2NEBMTI0lZWYKwdSvJ3BLIe/wYvU6fhsv162z16tX86tWrUqdOnQRASyTFxcXh0qVLapOrV0W9tDS1pa6uEB8fz/v37y+UnMd9+vRht27dgo+PD58zZ87ra9rK6vX+xhXg6dOnSEhIEDRh+oIgMPToAeOAAHx59qyIpUtRoKcHXV1dIusUCiI9bGzIiwsi4j799FO8fPkS69evB+ccQUFBWmMbIAI4ORnp330HSZIQFhbGmzVr9vr1W1pWueVRcHAwP3v2LCtqiwcdTYpAOV639u3bIzg4WNq9e7fQt29f1CnKx8Xq1VQ4zNubPMwhIXSPZRjbkZGR0NPT42ZmZmXLUycn2hu8vcmjWCKc38DAAHOXLcMOQeApjDEjIyOuFxnJbC9fhkH9+mCPH2sjtCpAo0aNoFKpXjfMNORySAiRXfv3k2G9eTP937MnGbPOznSf58+/3hYtO5siW3bsIDkwYwYajRiBaAcHHuvjAyeAoXZtMrKrQkDLZPh4wgThJiClnjvHbO/fZ4ImJPjoUZpPGRlE8s+YUToM/uVLYOdOCPv2ofmoUYCzM6LUau46YgTjHTqgiSCgSVGnj2YpKVDn5iJl9mymN3s2jJ2ceHRyshSSny9KQ4ei8+nTSO/QASYXLwI2NsjOzkZ4eDi/c+cOVzdpIgzcuhVNN20S2rRtCx0dnWIjuVq1aqX7jpe4Mxw8SLKjf380LHoOISEh/PHjx8LUqVNfnx9pacU5zH369MG+fftw5swZ9O3bF4wxVNghpHZtIoHGj6c887KIg9OnSxvA+/fTHHxl/7GwsMD48ePx5MkTpCQnS6nLlwtqpRLi/PkkNwcMoP3o9GmKsDl4UPtcPD0pHeHJkzL71584cYLbWFtz+PgI5a1DQRDgVkSwODo6slWrVmHnzp2YNWsWQkJCoFKptNX9o6KI3CmK6ikoKIC/v3++QqEY98Gr/X7xwdj+C7F06dKJenp6K3r06GHg6ur6v93X+VWo1cSoTZv2emjgmyAjg7xS5ubEdjdvTgqkry9VnPXyok1rxgxSJOfOJaF84gRt9rGxWi9t//4QGjfG3YEDeU69eqyzgUExKw1A22KqZG5jyQra/xbs3VtpKB8A8rQ3bEiC38rqz3ufq1XThriX3CgkiYyC27dJacnIoA3v7FkiO/T0KLw9OZlC8B0dSXl41fv6F+HRo0e4ceMGnj59CplMxiotqPQWGDNmjBgVFYWMjAxUr14dTk5O+O9//4tTp07xoUOHsgplhUpFDHyJisoqlQoZGRnQ1dWFvr4+RFHE5s2beX5+Pjp27Mjq1auHp0+fwsLCAtevX1f7nz4tmufm4qNDh5BmYIBNgwYx0cAAeo8fq5s0aSL26dPnnd/zm0Aul2t6Y5OxnZNDRmN+PuVLfvstEWmnThFpFh5OMqewkIgzW1uKtLhwgeZTu3Zab/KJE9oTaSr3liADQ0JDce/ePSQnJ4sFBQWwtLSUatWqVbnwfviQvCj+/hTKrEGPHtrCYmo1zfWHD0lxr1nzzdrTvAoNeWtpCfTrR4aag4PQuXNnIgiPHKH3HR2JhANIAQNoHl25QsSEKNJ1ab7LyIiKZAUE0HGrV1P447lzRKABdE+v9H8GAGNjYxgbG6Nhw4ZCcnIy7ty5g23btmH48OGaYldITk5G586dRYBC+W/evIn1eXncUS6He3o6MyjyenLOcfHhQ3QrKhw2cOBAwdfXF4mJiWpra2sWExPDXrx4wRhjcHBwELsaGCA8NpZdunSJ5+bmCp6enqWUfcYYZs2ahZUrV7L79++XzmMvLCT59egRkTBVgJubGy5duqQxtOlFAwPK84yOBgYNgt65c0QoalIdkpPLLBJavXp1/PDDD/jpp5/w5MmT0icaOhTYtQua3NeYmJiyjdW1a7Ut2SpBvXr1GADMnTsXOjdukPF48WKF5KeXl5cQEBDAd+7cyXr27Mnd3NwYzp6lN1eupJ+oKCKzyih4l5KSIkmSxCRJKl8fcnWlwlZXr5IxqSHBAAgzZ6J+06asrb09HLOz2XNDQ2yoXVuqHhUl5B06JDk4OLBmzZqxijqNaMa2zCJrAN2/tTWt5TFjaO9asIAiYGbNIq91q1YUoSaT0R4aFERrsUYNWs9nzpDBffMm5NOno+7HHwsH7t1DQE4Ob9WmDWspiqhaZj3N2XZTpgjLc3PR9N49WM+fT0Sdvj6lFrx8ScTZhAna4nhTp5JH9vhxImosLYGpU/FwwwYp+dIl0e/SJfzwww9EAC1fDnTtCvHGDdicOgXExqIdY6wdIHLOERcXh22FhfhEJsOjvXuRHhTELzRvzkzMzCRnZ2ex7ZdfQqdvX1Zr8+ZKCeBibNhAxuiuXcVzJDExEf7+/uyTTz6BQYmiiMVITaWIApAH+NNPP8WRI0ekqKgo1rFjR+bg4FBxusyECZSeoOmw8Gq6xTff0PudOxOx2aKFNqLvFRhxjqbnzoFbWQlBmZk4ZGcHZzMzOJaMFuzdm+Rqv36k62iiDpYtI1Lo+vVShnxhYSFiYmIw2thYYPPnV1j3QANTU1N88skniI+P5wCYo6MjLl26hMxNm6AXF6f1ohel1Vy7dk0F4MTChQtvVfjFH/Cn8cHY/ouwePHigTo6OivHjh2r/69uMfU24JwUsvx86h9bEpJEinNoKLHYV65Qwa+vv6aCED17kvH1009UFXLKFNpwT54kwaynRxtHzZokyGbPJqWvaVNSbjQCTdOGautW7bnHjgUD0Oajj5i/v7/UmbH/LfZDkshztmMHGWOVQZMX17UrjeH76tcsCKTEdumifU3TYzIvjzZQpZKe8YUL5H0MCqLfrVoRK+3sTMqwg0Nx8Zb3AU3f1JYtW0p16tQRHB0dq94j9A1gZGT0WlXkESNGYOPGjSwwMLDcqtF5eXkI9fGR7LdsYXEODiwhIUEdGRkp6uvrIyUlpdRn5XI5mz59erHiomG7nfPzRVy6BLi5MezeDZNGjdA0MBBOTk5o0KBB2ZVb/iqoVCi8dAmXL1zgn1pbw7B3b4YLF+i5d+tGBjPn9PyXL6ccS1EkAkcQSFH+8UfyJshkVeo5XBLnz5/HjRs34ODgoO7bt69YVFW+anLC2Ji87K8aK25uRGb98gsVFNJ4bsePJ4VLk9f9DiI8GGOwt7fnZ86cYdHR0eqRI0eW/zxlMm1vek0NCYDSYzS4fJl+a0iJ48dJ1k6ZQspqRaH7oLoHPXv2FERRVO/evVuUJAlNmzZFYWGhoAkj1tPTw6RJk4Rnz56Bf/QRD3j0iBtPnMg6deqExMREPKtTBzUnTQJevoS9vT0cHBzUCoVCePDggZSRkcHatGmD5s2bU65zw4bwqFZNaFq9OlauXImnT58iJSUFGRkZSEtL4+bm5kzjYb93715pY1tHh/atKhraALB//36pXr16XBCE0uMsCJSHO2QIyTAfH0pZYoyKJlUwZqampigoKMD169chk8nQtGlT6Do7A02aQAgLw7Bhw+Dr61t2v3Bj4yqTN9bW1pDJZMjMzITeokW0H1QyB4vqWLDExETu7+/P7OzsYKM536xZFCGiVBLR06ABjWdBQXHRNk9PT+Hq1atlV1IvCWdn0hPmzqXnUTRPZV26oIuHBxEa7dqhZkgIZh84IKR8/jmio6OFsLAw6caNG8zLy6vcXur29vaws7Pju3btYsOGDYONjU35hr+maNrZs6TPbNtG3uxdu6h6emEh3W9mJhF8vXpRdI2nJz3/2rWB6dOh37Qpmt65I92/f5/dvn1bunDhgiCXy7mJiYk0ZMgQsUyjH8DLly+xbt06Tb0BbjpkCMPw4ZRu0asXkTbu7jR3/f2J7AgLo+fo5kak9c2bRKgHBcGtWTNRb/x43GnWDKELFnCX1auZsHo13QdA5NzWrUQKffUVeLVqOHv2LGeiyA4zBruMDHWfhASxxdix0GnaVCyuJdOxI8mIc+e0TovycO0apRysWkXXB8ox3rNnD3d1dWXlFg5t0IDWThEaNmyI2bNnCydOnODnz5+X/P39hWHDhhUTemXC3JzSgFatojSfknt7RgaNgyRRyPnkya8XSkxMJEKrf3/g1i2wuXPRITAQhw8fxhl/f9SuU6e0wd+jBz2LHj20BreNDckEX99iR1ReXh5+//13tbm5Oatlbs7KLPBXDhwdHeHo6MgAIHvfPhhlZUFtakr7oiAUG9r5+fm4fv26SqlUvp+qqh9QCoxXMZ/nA94e3t7eTeRy+a3PP//coDyB/z+LvDzyhERE0EZw6RJ5WoODScj06EFhiD/+SKzbH39QuJWPD7GNnp4kiBMTKXfvPRg6mZmZWLduHQYNGoQm5eTF/Gvh40OkxZsUtVKrSSE6fJi8WZpq5X8nOKeQvIgIKsqmp0fG+OXLxApnZJBCp6dHilmTJuSZr1HjTxktWVlZ+M9//oMRI0bgT1ULf0tcv34dAQEBqFGjBh86dCgzMjJCQUEBRFHExYsXpTt37gg1Cwslq4QE4W7dulwURW5hYSGkpaWpmzdvLiYkJAAALC0t0bJly9LVoB88oDoEI0dSGPqQIX9vwcLkZJINXl7U6zU6GtizB/lOTrjQty/vM3EiY5cuUXSMKJZbwRVDh5IXYcwYUpQWLKAw1MBA8gCV5SUpB4sXL8bQoUPRuLy8vrIQE0NyKzy8/Dzo8HAimKKiShNFOTnkgdqwgTwZ5VTSfhPExMTgyJEj4JxLc+bMeTcPuLCQ1pmtLcl1R0eKHHJ1fT2UtgxwzpGTk4Pw8HB+9uxZVqtWLemLL754/dqOHEHq2bPY0agRt7S05AYGBvzp06fCjCtXGNq2rbTFG+bNA2xtIU2ciH379vHnz59zQRC4rq4ujIyMxOTkZKmgoECQJAkAsFAT7QAQgTB2bJUJmiNHjvCoqCg2ceJEVFi5/soViiKqW5eqFVeSK5+UlIR9+/ZxQRAklUqFwsJC0cHBQWp96BCrYWrKCleuxI9FhM2YMWNQu3ZtOlClolSKuXORlJKC6OhopKam4sWLF1J2draQn58PU1NTrlAomImJiSSKIl68eCF8r6cH+YgRr/X0Bahq+rVr1/D8+XPo6+sjOjparVAoRBcXF+nOnTtC+/bteefOnbUCNzOTxvDgQZLDkkRro8gYVPXogXWOjhgzaxZM9PUrn++PH2sLerZrRyT7sWOkH3TqRFEtr6zVs2fPIjQ0FNOmTSuXKC0sLMTatWul7OxsQRRFzC8K6a8SVCo6/5w55F22tCTZ6udHY9i3LxnZw4aV23s9Ly8PCQkJuHfvnvTw4UP29ddfs+fPn+PixYvqgoICqNVqpqOjwznnQm5uLgPn8PDwQFcNYc057YWNG5M8v3+fZOi8eTSmv/xSnLKS9ttvEH18UBgaytcPH876njiB+61bc9HREe5r17L60dFgcXEQHz6k1BFdXXKEnDuHp8eP49G5c1yYPp21b9+eCBLOiSAcP55SSTRpZKmp5B1eurR8wicigkiIefNK9YMPCgrid+7c4VOnTi1fXgUE0Pwux+N74cIFBAUFoXnz5uq+ffuKYnn7BUB7j6cnef01612pJNl8/z7do4uLVpcIDyfC4vRp2ocWLiwVDfXy5Uv4+flJiYmJbPjw4ax4TYJSXF7u3IkWV66QjqWvT9EIa9bQeBkYwNfXF9nZ2dzLy4vpXL5MEZxv4qTLzARMTaFo2BD+jRvjTqtWmDx5cqn9PzAwUB0cHHzw22+/HVb1L/6At8UHz/Z7hre3t6irq3ugW7du+v9fGdpqNW1CQ4dSXk1eHhlCPXqQISSXk1Jqbk4KflF7j1JhR7Nna/9+B0pneTA1NUW/fv34kSNH2MOHD+Hu7l4uC/6vwaNHFF67a9ebHyuKZKhmZtIzkiTaZP7OXGrGiPVu3Vr7miZkVakkJSs3l0If79+njf70aSrUNmYMGVgODjQPW7QgAqcK9RJMTExQs2ZN9d69e8X58+f/5akf7u7uMDY2xsGDB9mhQ4e4s7Mz8/Pzg1wuhyRJQv/+/eH6++8Chg1Dz06dSlY9L1+zePGCFLPTpym6oFu3v659m6aQkqatz3/+Q56qvn1JGT18mLwEw4fTM69eHb8vWKB2cnJizM2NlSzUVC5cXEg5BEi2aArcTJ5MoYNVrEkgSVJx/9YqQ6kkhXrGjIqNKCcnMhyePi2tgBsZUY4557QOx48nEvJP9OC2t7dHhw4dEBIS8m6YdU1RxObNSW4XtVDDoUOUplNJ2zWAPO7GxsZwd3dnzs7O0NXVLXuQBw2CRUwMvnB3ZxsCA5mmxgC+/bZqxGtBAZCTA0EQMHz48LLaOwqZmZnYtWsXXr58idDQUO7q6kqfefSoyjIvJycHYWFh7Ouvv67Y0AZIsR45kjxZPj5EGFRwHmtraxTlrIoAFTQ6evSoEGVtjdlffQUduRzt27dXX7lyRSw1V7OygLt3kZOXhx07dkBXV5crlUrJyMhIbNGiBTjnMDAwYLa2tnjy5ImQnZ0NvcuXITx8SJFlKPlVWThz5gzi4uIkURRZgwYNpJycHHh6eorOzs7w9/cXdHV10bhxY7qR1FQyXC5fJmN71SoyimNiSEbn5gKcQ2ZgAONataTH8+YJzQICSDZ9+ikRUSNHUmRT167a8alfn4y58eMprDwwkNbQoEFUYKyMnsU9evRAeHi4+v79+2LLli3LHGO5XA4vLy8hLi4Ox48fx9MnT1Cnbl16Vk+fkudx927aQ+rUIQ/7ihUU/RUURO89eEDX9fw5EcEamJrSfSxcqCUACwoofcXDA3Bzg0FICOyvXoV9nz5C02fPeNThw2h5+zZsnJ1FlYcHdKOjYXD/PrIbNkSBkREyJQn1ly0jOdKrF43rvXsUMRMXR9c9cyZFtc2fTz/79yPTzg5X165FvoWFVN3BAQ11dRHfqhUbVq0a0z90CNl79+LU4sXo5uCAp0FBcNi8GYKrKxAdDZVKhSeBgbyhWs3qdelCcnr9ehqPFi2oOGJeHpH7CxaQXG/Zku67RDeEYqSm0th+9VUpQxsAMjIyJGtr64qjqoyMysxz1qBLly5gjOHy5cuik5MT7DVpMWXByopkyvnzRNBbWVFU0i+/UFTlmTM0B4ODiUzp3x+YOLFcYrF69eoYNWqUJsUCderUkQYPHixkZ2dj165dXKlUsuTatdGyd28Y79+PqKQkFF67xqWpU1nTtWuRlpambtCgAdPR0WH49luKoKiqsR0bS6TL8+fQjYpC7bt3cefYMTx48EDq0KGDABC5FBwcXKhQKLyr9qUf8Gfxwdh+zxBFcaalpWXdli1b/jOrPr0PZGVRaOG8eeSpMjam0O+oKAr13LOnWJH+p8DFxYXp6enh7t276m3btokmJiZqOzs7sWvXrm/fjunvREoKER5/BpMn0+9582izCQn589f1PiCXaw0WjZEFkGKTm0sbelwcfe7IEVIK2rWj+airS8aCnR1tUPr69F1FSuu1a9fw4sULsVatWhL7m9IMnJycYGNjgy1btsDPzw+iKKJ3795wcnIiT42fnzb/tiJkZ1PBuo8/Js/Q9u3v76ILC2m+uLlpc6NHj6bWK5s2ERGXmkpG5ZEjFLViaKgt4ldkrEmShMzMTLFCRakkgoNpLKytX3/vwQOSO59/TnNh8+ZyvyYpKQlbt27l1apVQ/Xq1asmu9PTaR6FhZUumlYecnPJi3L9OhGQGsjlZKCkp9M6vn6dPHhOTm8cecA5h0qlgoWFBdLT0/98SgDndB3161MdhuHDte9ZWxMp0Lgxpe2MGVOlr6xUvioUMD14EJ2GDMGlS5f4tGnT6Hloig1VlELy9deVGsympqb4+uuv4e3tjaNHjzJXV1cyhjS5x1WAprBWpYb2sWMUev/99zQPd+0iA/GLL6pMeLm6usLPzw+5RkZUp2HtWly5ckWUy+WlikLlJSZCnZiIPbt3c3Nzc/7ll18KKIeEq1u3LvD4MXaePYtna9agTolnkpWVhY0bN0q2trbo0aOH4OTkhFfdhOHh4bxv377MxtKSquIrFFT/ZNMmKioVFUXk78WLZKBqxunoUfRNShI2btyIyD591EMBES1akAyOi6P704SPp6ZqixoePUpEvlxOzzg4WFsXhHMiig0M6NjLl9GpUycx/pdf0KRvX8g/+gh85EikzpwJvbg46KxYgS3jxvEBv/7KJEtLGLdvj5oNGyJh/37UePiQwqijo0le9u5NRlhKCskwe3sKJweoZVezZmSMaToD9OlD5NSuXWR0iyIZ24JQNonNGOwliYExMEFAqSoljMGcMXDOcXjdOp7w3XesS8lUrJIYPZquT9NerEsXoFYtxDZtKhkYGwv92rQRWGAgje/33wMHD0JKT0eugQG6r1yJuytW4PLZs3zg8eMwycpiNbp1Q0hICG61bcvbT5/OUFhI91KtGkUlPntGhqdSScb3smVkcI8ZQwRqQACRuhoUFmrD6pcsee3yc3JyuKY1X7l4NRWtDHh6eiIsLEx6/Pgx7O3tKxaeo0ZR7YxevWiebdxI8nzRIto716+nqKPly4mIqyRaUBAE9OjRg7m5ueHEiRP816J0nHbt2nFLS0skJSayoHXrULtPH5zv3h1NuneHwaNHfPX//R/LF0Wxe/fuNEfGjy+t15SH//yHSJ7z5ymtoMg4d3FxwbFjx3DhwgUhLCxMql+/PtLT0xmA8wsXLnxY+Rd/wLvAB2P7PcLb27uVXC73HjRokH5ZbYL+5xAURCzllStkUJdsu6GrS6ynKNLmUlj4XkLC/wwaNWqERo0aibm5uQgLCxMfPXqkXrNmjfD555+zGlWs6PqPwLlzRHbs3ftuvu+776gwXHo65XtVloP1T4KhobaVC4DiXpWSRKFjyclUqT4+nuanry/lkC1YABQUwCQjA61TU3mvQYME5ORUrT3Se4CFhQVmzZrFrly5And3d204ZGYmGavOzuUfLEmk7KxdSxIJg5IAACAASURBVMrf1avvtthceDj9DBxIoXiff05re9Eikgk2NqRoNmhAXh9Nbuann9LvcvLRsrKy8Ntvv0nm5uaoUkEygBSTESNozr4KjQz++msiBF++JOP+448hSRKePXuGwMBAtVqtFhISEtCyZUvevXt3oUqyOz6eFLPTp8sNFX0NRkZkJGjypF+FmRk9W85pjJo3p7oVlVwP5xwKhQK5ubk4ePCglJiYWDx2qampeOuaIVlZpGBOm0ZKqXcZThFdXSLmTExoTlRFSawMY8cCkZHIT0vjtra22mJJYWHkRS/ZGuxV7NhB41UyPLwc2NnZIT4+HocOHeKDv/uOsU8+0UZFVAI9qjAuJSUlCeVWQQ4KInnz9de0FgAiNGfP1kZ1VFFPUCqVlLc7ZgzJLWgrsQPkuTqyZQuvL0lMX1+fDxs2rOL1I0nAmDGoZ2zMo/PyWI3CQty6dQvZ2dkIDQ3ljRs3Rv/+/ctcC5IkoaCggJk+fkxGplpNMmDsWO2HZDKaxzY2ZJzq6xe/FRwcrJbJZGI1Ta/wb0qkkObm0u+ePWn+AeSB3L+fcpOXLKE1l51N6z4xkda0pSU9e1NTYNIktHj2DLoJCdKdTZuEi1FRGBwbi6unT0u8sJA1qlOHu7i4CHa+vrCzsEBhUhJWGBujs60tagwerG3NWZJ80Xiuu3fXFk0dNIii+C5fpho0mzaRgbl9O3mdU1OJIKikvgirKNwZQEJCArKzs9G5osJ3P/9MBmFgIF3TsmVAbi7+cHVlCQkJuHLvHpi5OfiBAzDq0IEPUCp5VkqKcGflSrjfvo1748dL6tRU4WCfPoAkodqqVXiZmQlHR0eq5i+TEWkE0N5jbk4ElYMDefHr1SNC296edMLNm8n7bW5O8+DqVXp+48eXefk5OTmswmriAIWuX75M414BqlevLiQnJ1fN89CkCc2vFy+IIMnIoPuSJJpn48a9WUoeqFXe6NGjxePHj/M7d+6wzp07C4wxODs7Q/L0hHLtWjSPjYU4fjzDN9+gdbVqCHZy4rVq1WLQRHtUJBfmzaNIEE9PSusBiMQogiAIGD16NCwtLREaGipERETwpKQkplQqv3+jG/mAP4UPOdvvCd7e3qJcLn82cOBAG0eNov+/ig0baMPv04eE+4QJFQuHP/6gDerly3+cwf0qfHx8pLp16wo9y6iy+49F584Ubv3zz+/2e/fsKe7d+09tz/VOUFAAVXw8zm/Zos58+VJwuHuXuTo5kcF+7RptvP36kUHRqBEZGA4O761IW4XYvp3CEQ8eLPv9o0cpDG75crret62krlSSkrFxIymy9vakOG7YQIb89evktVq3jsiYP5nfnp6eDh8fH+7g4CD1799frHLbMU00RyUKKwDg99+RuXgxts+apVIoFIJKpRKsrKwklUoFa2trPmDAALFKIeSFhaREnjtXun1XVdGpE4W2V6Q0atrn/fQTefZ8fUvdI+cc+fn5+OWXX4pfk8vlsLCwkMaNGycEBgbi6tWrnHPOOnXqBHd3dwiCgHPnzqk556xatWrMw8ODVUgsODsTiRcZSc+6opZ0jx/TfZ04UW7rqDeBNHAgzhkact1x41ixkZGbSwRgRUr5iRM0dpo0pQqgUqmwe/duxMbGQj83F9NmzIDuG5Csy5cv515eXmUTs/fuUQX3YcNKe/gAkifXr2urd1eBrFmyZAkkSYKoVMIjOBhXOnSAc9Om+LgoXzYwMFCdsGcP+8TWVhCrQDTg7l0gLw8xVlbYtXs3ZDIZqlWrJpmYmPD69euztm3blk86qdW44eEBJ0NDGK5bRwZLeZ/94guaRzNnFr+0bds2tZWV1Zt3PLhzhwy4sWPJ66gxSkoYGm+KsLAw+Pv7S7q6uuzLL79k+iVIgSph+nSak99+SyHUAwbQWg0PJ8KnRQsiWyrz2laAmzdv4ubNm+pJkyZVLOSmTye59MMP4BER4K6uWP7NN5g9fz5kMhlUKhUEQcDdu3cR9+ABnFq1QoM9e8CWL8cfx44hX18f165dw4TVqxFlb4+A3r3RvHlzqWvXrkKZ1cFVKiqmOGMGPef0dAq1vnuXPN+DBtHesGYNrcuTJ8vV/3x8fCQ9PT3m5eVVvkB6+JCi1kpUpy8La9euVdepVUvo360bQ1ISRV2oVBT1EB1Nc0YuJxIoOJjkaloaEToWFvTa2rX03OrUIXnyBnU/NEhMTMSOHTv43LlzS98T55SSkJ9P82XxYiLeACL909Pp/K/i/HlyHgwbRpFqQ4ZU6ToCAgKUN2/e3Pvdd995vfFNfMBb44Nn+/2ho7GxseH/tKHt60s9YCMjKaywSZPS4ZDloU0bYj/lcuC//6VCNP/QNmhKpRI5OTl/92VUHVlZVITufWDECBLsoaG0kZ8798Ys778CenpYc/KkJFWvLrr17g2rxYspj4tzYrrj4miTT0igAi8bNpDHdto0Ci+0sCCFuXZt+m1u/v7ICSsreiav4sYNClk1MyOvRlVbsOTn031ZWpLS9NlnFH49Zw69HhxMynKPHuStcHfXRgsAr+V6vg1UKhW2bdsmOTg48EGDBlU99LlrV1LoNOkPleHLL7EzP19t9PSpbNJvv0EVGws9U9M3E0QBASQDnzyhMXsbDBhQdth7SYgiPespU8jbHRcHHDkCxcSJiH78GH5+fsgvCmft3bs3CgoK0JE6EAgAhVN6enqyo0ePqq9duyYEBgYySZKgq6sr2NnZsZs3b+LKlSswNjbmY8aMYaUqQz9/ToTC7t1E6nzxRcWGNkD7gZ8fGcLvwMP9rFcv6J48qbknwv79RACeO1f+gTo6FP1RBchkMnh5eSH1p58QdOcOrjx4gK5vYGyrVCpWZkXtJ0+0HrhXDW2A9r62bWmchw4lJbqSCJqhQ4fi0KFDUHIOl8ePEWNvj5gSBsDz58+ZSY0agliVcffzI+Pw6lXYm5hg9uzZ4JxXXnVfqQSOHYPql1/w1MGB602cyFwq0nfs7LT9qUsY2y4uLsL58+elPn36VH3tHTtGxJ6ZGckouZwMobeESqXCunXr1AUFBWKnTp2Ym5sbq7CgVlnYtYsIbrmcCJSgIC3R5OREkWYvXlBqzZgxZUffVAHPnz9XGxoaVr6hzJxJ5A2ASwkJ6kdTpwo9P/qI6QwZAixcCJ2i/PVWrVqh1ZgxNI5jxwJz5sBDJgOWLUPHRYvwW3Y2Wn/8McbWqoXdu3fj4cOHGDx4MOzt7VGKgJHJKNwfoKJoL17QfQ8aROTsjRukKxob095R0tDWhNanpQGCgI8//lg48f332MsYhnftSsd5emqLzjk6Uh66KNK+XFhIRum1a+RV19enPfrYMfS0tBRzNAVVg4LIKVS/Ph0TF0cEtJkZ7du9e5MxHhlJhHHfvhQZcOcOfX7fPpLBmZkkB52cqObCxo0UzVFORf1Hjx7x0NDQsp8ZYxTd4u1NsqxnT4rY0IThv1pcTqmkdTRwIJEFvr7lTgGFQoFz585xtVrNZTIZ55yzO3fuCJIkVYGB+4B3Cs75h5/38LNo0aLvTp8+reT/i4iI4PzFC87r1OH82rW3/57nzzmvWZPz5OR3dmnvGgkJCXzZsmX83r17f/elVI6kJM51dN7/eD5/zvlXX3EuSZynpr7fc/0NyM/P50uWLOHp6elVP0ippHG5epXzkyc5/+YbzqdO5fzXXzlv25bzNm0437mT8+3bOQ8M5Dw0lPO8vD9/sXPmcJ6QoP0/IoLzK1c4b9aM8x076BmVh8BAzsPDOb9zh3NPT84fPeK8Z086VqHg3MuL85AQzjMz6ecvwvbt26W1a9dKKpXqtffUajXfs2ePFBwc/PqBW7fS/bwBFAoFX7p4MU/bsIFeWLGC89zcqh0sSZzb2nK+f/8bnbNMBARwfvZslT++Z948nmhjw9dNnco3zJunDg4O5mq1usrHq9VqHhYWxuPi4jjnnEdGRvIbN27wdevWKZcsWcIDAgK0g9+1K+fDhnH+xRc0f98Ex49zbmfH+ZuspTLg7++vvtenD+f372tfTE2l51URVqzgfNasNzvZ/Pk8bNgw7uPjU/UB5Zz//PPP6tf2iZQUzj/7jPPff6/al2Rlce7szPnhw1U+rzIkhP9nwQK+aNGi4tdOnjypvjFqFOclXisT6emc371LMuNNcOUK57/9xvnw4Xz38uWqHTt2SAqFouJjgoI4P3WK8y1bOD99uvjl/Px8vmjRIv7kyZOqn3/sWM5HjeJ88mTOo6Le7NrLwNmzZ/m6devUSuVbqmwPH3JuZcV5Wpr2tfbtSfYHBJT+7JMnnG/bxvm333J+4cIbn2rz5s3qtWvX8jJl4KvYtYvzH37gBw4cUB04cIBztZrW8osXtAdp5LqfH+cdOnA+YQL9Hx/PeePGnKek8KtXr0ohHTtK/Px5zjnn58+f50uXLuVHjhypfLAyMjhfuJDOK5NxDnDevTu9p6ND+uP69ZwbGdFrLVrQc+Wcq3V0+K/TpnH1mjWcOzrS+926cb50Ke1PcjnnTZrQWO7fz/m+fbTnBQeTnIiM5Dw2lu/w8ZEOHTjw+mZSFsLDOf/0U84PHqTjjx2j69m1S7uXaubI5s00j5VKzs3MaD//9lvOW7ak96dO5fzSJc6VSr5z0SL1ypUreWBgYPnnliQ63tubcycnWpuNGnEeG6v9zJIlnNvb09+VyHtJkvjWrVv5okWLuI+PD9+7dy9fsmQJX7JkyS3+D7CR/n/7+eDZfn/IUCgUSvwvRQ8olRTO8ssvFCb45Mmf89jZ2lJ4UUEB5bAdPVpx7unfABsbG/Tr1w+nTp3iTZs2/WfHTltZUSGat/WwVRW2tuTNDQ+ncPVnz4gZ/h9AYWEh1q9fL9WpU4dXq1at6q4NmYzGRZMz1bev9r0JE+i5pKdri7XNnk0ekE8+obD8oj6xMDcnZr569crD0nNyyMPj7U0hxpcvU57g6NHA7du0Njmnz/3+O13T+fMUihwVRSF+n35KjHzv3nTO06e1a3rbtjcZuncJ1qBBA16WZ2nPnj3SixcvhMjISKSkpKBv375ULfzwYRq/Nwxf19HRgXn16upt+fnCl2lpzEQTBl9ZMbKNG8lLGR//bqJyTp4kz6Ym/7MCHDt2DJFyOSInTEBdMzN4LV8uoH59ijKoIgRBgFMJr6emrZ2bm5usqDicYJ2RwZ11dBiOHaNQ0HXrqEL0m6B/f0q1ePGC5mFleZjlICMjQ6plbi5g0yYKxwZorRgb01xv0aLsA4cO1RawqgpUKsDbG6r795Fw9KhQXo475xzPnj2Dnp4eRFGEsbEx3NzchJMnT8LQ0BCFhYUwlCTYHTpEYzB0aNXOb2xMVd2XLSNPW4cOle+xooiRGzdi/aRJCAwMVHfq1ElUKpUcSUkc1KGgbEgSyYrOnUt5mivE06cUzXHxIs2FPXvwYsUK1qFRI1ZeW61imJmRR87Pj1IAisJ/4+LiIAgCKi2IBZAO4uVFaTEbNpBMTU+v2rVXgNzcXFSvXp1XOV2lJCIi6FnFx5f21urrkyx/tQBg3br0c/QoeUW3bKF9o5ze2q9i8ODBws2bN6WgoCDh1q1b6p49e4rltqV0dwd++AHVVq8W45OTSVZparkMH07Xt3UrjeFvv2n3r1q1isO03ceOZWE6OkiOjIRVly7o0qULLCwsEBAQIHLOtd7tpCSqo9C1K/XZHjWKIqp8fChv28iIPL+a3tjR0eS1bdOG9kigVBHWgBMnJOHhQy5MniwWRyuVjGLZs4fSt5o2LXesrl+/jmepqWzi0KGV7+WaDhlTptBeMneuttXcqVPaNmaaZ1yyJkFaGv2eMYP2dM1rSiVw7x6GLVkipD5/Dutly8gTvX49RR2MHKn1XDNGxebmzKF9YO9eWiO1a1NR0379aByL+mRXtO+kpaUhMDAQT58+RYcOHdClSxcUFBTg119/LVAqlZ9WOhYf8M7xv2MI/vMQmZCQoATwhkk//1B06kThOxs2UI/Qd9kqSE+PQnIaNSIB/w/rdd24cWOcPHmSXbhwAeVW//y7sWgRbVQnTvx153RyopByExMK6Vq48F8dVp6Tk4MdO3ZIZmZmGD58+J+v3KyBgcHrvXo/+4yU3fR0Iq3i40lpS08npWv1agpdnzSJiv/UqEFhknXq0Hhretbu30/KzM6dRIL98AMZyb17k4KfkUEG+YEDlEPo6UkKFmNElmgwZ847u90/i6SkJMnDw6OUJiFJEo4cOcJjY2OFSZMmISEhgZ87dw67du3iAwcOFIx/+gls+PAqt/UCgIyMDOzfv1/KysoSlEolW7V2LTx8fKRuzs4Cc3SkED5NWOSr8PUlGagpdPVnsXIlESNJSRWGlGdlZeHu3btwcXGBKIpSfHw8cO+eAM5JeXZxeevwVA2sra0xcOBAljNmDH8hSbyGkRFjS5eSkfU25KqDA8mHEyfIaH+L7zA2NhbC27WDY5MmVB9EEzIdEkLzuDxj28+PPlNB5flSmDEDCAqC69278PPzw9q1azF16lTk5OSgsLAQ9vb2SExMxO7du7lSqQTnnEmSBJVKBR0dHTDGpIMHDzIdUZQ89+wRU+vVk1znzRPe6I4bNSLD5+OPad2uWlVhDQLByQn5NWrA1sAAsbGxrFOnTjA0NGRxpqbMrbxxAUiuuLgQ6VYZOKeUrytXqEjU5s3FRc4YY9yksrQCgOpLhIQQ0bdzJ5FLNWsiIyMD1apVU5uZmVUucy9dolQdY2MtefMOdJFmzZphz549YkJCAt64GOrgwURavpobv2MHEaZPnpR93MCBVA1882Z6zosXk3FcyfowNzdHz549hY4dO+L69evs4MGDMDc3V02YMOF1fd7eHjhxAjaHD/MwIyMuSZJQXIfi0iXaQ1atonnfoQMVN9SEeteoAdSsCVWPHjhubIyB9vawKipuZ5yfD92nT5mqsBDyZs2IpD1yhAz3e/fIEHVzo7k8bhzJpj17yLFy4wbpDmX0cddAkiQEBwcLXl7lpBVfvkyh4BUY2pIkITAwkA8cOJBVq6x1YkwMER+//EIpHQARULm5NA7DhlEhss2byUAu7/usrLSkyc6dAID8/HysXLQI31taEuFdWEjv+/hQyPjhwxQ2npBA19CsGaVrLV5MslOSqBaIoyMR4hV08cnMzERCQgIOHToEtVoNDw+PYp31jz/+UAuCcHLhwoUxFQ/GB7wPfCiQ9p7g7e1tKIpi+jfffCOX/1sNkBcviEVbsYI2tmbN/lS/10rBOQmSzZtpA/sH4cGDBzhz5ox65syZ784Ie5d48IBY5ap6UN4l0tJog/L3/1N5c38HwsPDERAQoAYg5ObmMktLS2nEiBGCoaHh33thSiUZ34WFpJxmZVH1V0Egr0FYGHmpk5Mpj2zYMMq77N2bWPMVK0iZNjd/v2v2HePkyZPqkJAQcf78+dB4tvPy8uDr64u0tDR8/vnnqF6kbGRkZGD79u08Iy2NyXV1+bBPP2X1X2kRVqS0ITo6GoIg8Pbt2zNTU1Ps3r1bnZaWJjZu3FiytrYWZDIZHj16xJOTk1mHDh3QXleXvOSXL5OCqFlX8+cTGfimHt6qYMsWUrBeiRg6cOCAFB8fDw8PD+H8+fOwtbWVhg8fLty5c4ffvn1bmjx5Mg3U0aMUZdKuHa3Jkrn0b4K7d4F9+5Dz/fdYv2aN9NXOnYLx/v1gryi2pbxalYFzIpQePCDF8Q0LRPn7+/PExETmdeoU5VxqlPCsLPru8r5v505S7tesqdqJCgrICLS3hyRJWLJkCbp27YrAwECoVCq0atUK9+/fh6OjI/r161fcg12SJG0/dkkCFi9GYb16WPX8OW/dti3v3Lnzm4c/5OeTEfb4MTB1asUdBMLDEXfhArampWHIkCE4ePAgRsXEoP6ECa/1MAZAhUy//prI0opyk3lR5eg5c8gQaN36tQJkK1askOrUqYNPPvmk1D2qVCqIolj2HClqF6VevRq//vorWrRoAWdnZ1hXVLvg4EFaFwMHUmVwX9/iKuqVtYCqCk6fPo2IiAg+bdo0VqXCiJJEUUlmZiRjX73PwYOJzFixgpwIFRiXuH+fKqmvWgX83/+9Ts5WAIVCgTVr1vCGDRvio48+en2wo6Oh+vJLrPDwQKePPoLHq0UcX7wgo9zIiGrpTJtGc2LLFsDdHYGffSYp4+PRbe9eAWZmkFJS8KRpU5iYmXHLq1cZFiygufRqbjFAkSLu7jRnDh0iwm39ejLqK8GmTZu4hYUF0xT9K4Xhw4ns2bKlzGMlSYKPj4+6sLBQmDx5csX595cu0bivWVP6GSkUFJVy5gztu2o1eZv9/ck4rmLXifj4ePj6+kpz5swpe1IVFlLESMOGRJa7upZeZ8+fE5GTl0d1M4YOpXxuS0vSAUaOpGsTRVy6dIkHBgayxo0bqz/99NPim1YoFPj1118LCgsLXRcuXBhZpQv/gHeKD57t94SFCxfm/vjjj1k5OTkWZv+2ENubN8ljtmAB0KoVKZd/hfHBGClkBgbEtA4YQJ64fwDs7OyQm5srxsbGUk/SfxKmTCH2+O8wtAFSAiMiSFGtW5eUolat/p5reQNIkoSjR4+idevWokKh4C1atICtre0/o1KfXK5l7YsK2WD8eG1YeEwMrVG1mtZIRoa2OEubNvT7facTFCE1NRWGhoYoszhUJVCpVJDJZBpFV11QUCCKoohffvmFu7m5sRo1auDcuXOSIAjMy8uLVS/B6lerVg3Tpk1j6hYtEOvmht179sDY2FgtSRJkMhkkSWLZ2dmCkZERb9q0KcvKypJ8fX1FlUoFV1dXfPHFFzA0NCx+3u3atWOXL1/GtWvXJPeZM6nFzc2b1DVh6FBSipTKdzNoZcHLixStV5T2iIgIQZIknD17Fh07doSnp6cAADExMVxfX1+rRWoqbu/fT8ZReDgZBFXxOpZEaCjw+DGM5HJMOXVK2NGjB5SBgbzu06dIT0+X3NzcxNDQUCkiIkIwNjbmJiYmkqurq+Dm5sYA4OXLl9DT0yvdP1tTJXrqVCoktWvXG11SnTp12K1bt7C/WjXJ7NQppOrqCi4uLnBs0ICU/EePyp7vXbpUPVIqMpIMgVWrAEAT2szPnz/PHB0dpVatWgnHjh1TN2nSRBwwYECpQ0sZZ4sWAQoFdIYORfW9e5Gfn/926Uf6+hQBs2IFecSCgspPLUlIgNXWrcBHH+HEiRPc09OT1U9JKZ+EePiQPHcVGSFPnpAhsn8/jUs5nQx69eolHDp0CBEREcjMzOR37tzheXl5LCcnh4miiPr166tbt24t1jExgaxbN/J67t6NfDc3KIsiBq5fv47g4GAYGxurGjRoIOvdu3fpMVUqgW+/hXrdOjx48ACN9+6FfPhwIg3ekTOjZ8+eCAsL4w8fPmROVSkst3EjhbM/fVq2N3rqVHBjY8T37o3oyEieGBSkliSJ6ejocJlMxjp16iQWpyg0bUre3hcvSOZcvFi1QoQAdHV1MXr0aLZ161ZkZ2ejsLCQc865k5OTYGVlBWNTU+T06gW9pCQ8e/aMAyh9sba22g4mFy8SodilC6BSIebXX3E7PJyNGz+e4cwZIDsbd69cgd/w4fj+++/pe8rokw2AdLjTpyklYsAAGqMePcps/1YWGGNcJpOVvXb27qW9sBzcv38fubm5wpQpUyo2tGNiSCYdPvw6GaKrS4ZuSgpFG4kirce8PDJyXVzIe18JUlNToaenJ6GoWOVr0NHRpj8tXkx61PTplFJZvz49n3HjtOkwnNOcDwkhWTNyJOkI9vbo6OvLmg8ciFNz5zLluXOQnzsH/Pwzbty4oWaM+X8wtP8+fDC23y/+XWEDt26RgLl5kwSMoSFt9H8lNOGBqam0wapUf09LpVdgbGyMzp07q3fv3i327dsXzd5BO5t3hqioCjeevwx6ehR226QJVe90df3HVpkHgJCQEBgYGPDu3bszvKqA/FPBGG32NWpQiJtCQb+XLKEQuL+wJVtWVhYOHjwoPX/+XBBFEdbW1rx3797MVpP3VwbOnz+P9PR0ZGZm8tTUVF5QUCDI5XLOOWf9+vUTRVGEjY0NHj58iODgYDDGeM2aNfnAgQOF8trwiD/9BHsHB/a1kRGePXsmiqKIrKKevA0aNIClpaVmUESVSgWFQgFDQ8PXNDDGGNq3b4+bN28K165do8rXGkWya1fK6dP0/X0fEEVSrJo1I2+sjg5CQ0MhSRK6d+/O27ZtW+rh1q5dG7du3XpdiRs6lEKQ792jMNXDh6tGft26RSHoZ86Q4b9iBfTbtcO4//s/BJw/z+7evSupVCrx8ePHACD06dPn/7H33WFRXevXa58zDEOXIkoXQQVsiIrS7L23WKKxxRJNNFGjqVclub9UvSaxRI1eK4m9t9hQsWABFQVBkCoiSJMZYJiZc873xytNumJi7ud6Hp7RKafus/db1rteKBQKdufOHf7YsWNITU0V7OzsuGPHjjG5XA6FQoGOHTuiRYsWaFjsCO/eTUq+GzfSPmo5t7u5uWH27NlQKpWcZtIkPAgOxg2NRvTw8OAwYwYFoCpzts+fpzrPTZtq3klSEhnfZTBt2jSWl5dXEoT76KOPqmc2/fILlRYMGQIYGCA9Pb3mfsHVgeNoTh05kjQCfvyxcgO/Rw8o/vMfLPHzA/T0aJx8913llNPJk8lAr0ofQKUiVefly6lO9Pjxag+xVatWuHv3Lg4ePCjJZDLJ29ubs7S0hFwux5kzZ8S4uDg+KSkJdjY2wjtt2vAYPBixjOH2xYuwHDMGPosWSS1btmTGxsY4deqU7MaNG2jYsCG8vb0hiiLuXb8Obt06Mfnbb1FQUMDd2bMH0zUa2Pj60liqLuNfB3AcB2dnZy4yMlJo2bJl9fc5Nxd4913KfD6bc0VRhFKpRGZmJgoKClAUGyulXbiAHDMzqcuVK5z6669lAOmC5OXliWvXroW/+F8h1wAAIABJREFUv7/YoUMHYlJxHD0T+fn0HA4aRNlgK6sa53Vra2uMGTMGwcHBgrOzM69Wq/Hnn3+WnJctx4kTgoK4rWZmLKdXL1RIAAUGkhOckEDBlfR0YNw47E1KQudevZhxw4bkkE+YgFb9+yP2448lnucrPyhRJPtNkihj3qtXaUBEoSAHcerUGltVubq64tatWwKA8vfiiy9obqumZM7IyAjPAhtV7+DwYQoEXLtWpYo4wsJKad/FmDGDnO1ffqEMew0t6zIzMyVTU9PaMSLv3iUdhR07qLb9228p+Fe2PGr3bnp1cyvVhrlxAygqAscYuPnzoTUzw43VqwWfiAhe9803uHz5sqaoqOjLWh3DG7wS/P1ezP8wRFFUMMaQl5cHrVYLrVYLKysrvJAIx6vEo0cUuRs9mrKk8+ZRrejfia1b6dXHhyLqa9b8vccDICAggDc3N8ehQ4fg4OBQqXDOXwpRJMO4FpSsvwyzZ9Nx9e9PbVAmTvy7j6hKRERESK1atfpnONllsWABMUDOnycjITWVHAqVqsaWQS+LvLw83LhxA2FhYWJBQQFnbm7O5s2bh+zsbNy6dQtbt26VPvroo3ItkNRqNR4/foyoqCjcvn0bzs7OgqOjI9e5c2euWbNmyM3NZUZGRihL3ff392f+/v4ABUGqNlTWrqV6XUdHWIDqGauDTCardv7leR7W1taCUqks28Sa5sSEBDK83n2XSl1eIJNfIxo1ImdbpQIsLJCZmQkAeN7RBgB/f3/u/PnzSE9Pr0i/5XmaN3ftIudv+HAy2KpqAVecLWnRgn67bRsZ/0uXguN59OnTB3369OHy8vKQkZEBExOTkn22bt0a9+7dw4EDB7jbt28zuVwuDR48mIWFhSEsLEy4cOEC7+bmJgwfPpxnenpEV127loKp771X60tjbm4OAwMDXJHJMCQrC7YTJ1KQ4d13ifru7FzxRy1bVjSWq0L37hUcUGNj4/IZ+uqwYwdljP/1r5LSDa1W+0KMjwpwcSF68dy5dE+fz8JxHFGAjxwpDZAbGlacD1JT6RirEtM7epSYSZ07U411ZdTgSjCW2g+WBC2VSiVWr14NV1dXTl9fX0xJSeGSHz7kN3XpIir37GFmd++ynm3aoOHatVgfESHevn2bGz16NIuIiEDjxo2l4OBgycvLiztx4oTQ8PvvuUZaLfeke3epUK0WLbKyuEJRRFRqKhwuXEBWQQFuxcYKjRo14jw9PeveG7sM/Pz8sHHjRv7y5cswMTGBRqNBdna29OTJEzEnJ4cVFRUxhShKk7/6irv84YdCZsuWfG5uLpRKpVRYWMhkMhn09fUFmUwmeYWF8d3u3mVGBw4wbu5cNCnvlHGpqanYtWuXFBISAkdHR2Hs2LG8XC6nJMcvv1AgYdw4mhNWrKi2HEij0SA/Px8+Pj68Wq3G1atXJWNjY6l79+6cp6cnOI7jxJQUtFCrpSNHjkjvvPNO+QDdpk00X8yYQXXwx45B+PJLFDEGLy8vGgvvvAMMHQplixbw27+fnN7K6uU//pgCfMuW0bz5PLti9GgKEtd8L7gLFy7g3r17cC/LThk1qkYxSEeiYIs//vgj16NHD6lDhw7l58/i0pIjR6qfx2fOpM/XrSv/fs+edD8iI0k4cvbsKgMiaWlporW1dc3O9r17pK/y+edU6z5tGpXJ1KbkRiYrCVyafPEFus6ezaU/egTx3j1ERUUBQMSSJUvu1ryhN3hVeM28vv8dBAYGyhljhhEREQgODgbP85DJZBLP82z69OmoUbDhr4AoUkRs4EAy3GNjq6eV/R3Yvp3oRlevkkFYB8XdV4GWLVsiPj5eWLduHd++fXupb9++f5+zFhxMWYrHj1+vDDLHUZ2hgQEZhyNH1r7P818ElUqFjIwM+FVWz/g6IyeHHKGyxoqLC1F/IyJoPFy6VCNFr7ZQqVQIDg5Gfn4+Hj16BKVSCTMzMykgIADe3t7gONJ+MjY2hoODA3vw4IF05MgReHh44NKlS8IzA5WTy+WwtLQUhg0bxrm7u5ebZKxrqcJbKfbvJyfR2/vlTvQZVCoVEhISeKdi7YFFi4i+e+UK/T8pica2TEaGUF0p2jWB48jwXbcOQYzp4h4/lrVq1apidgcUGPD09BT37duHWbNmVT4BdO5M83zbtnTsV68SK6Js3e2dO5RFi4qibhOPHpFxv2NHhXFkamqKysSw3N3d0aJFC5aamgoAzMHBAa2oswRfUFCAlStXcn/88YfYvXt3LjU1FbZBQbA1N6cgah0Cu0qlEqEdOqCLvz8FJIyNSd0/MJACCs9DFGl+rAmRkUTFzM9/sTXw6FESOJo/v1TRGVTX3rRp07pvrzL4+pKGgJ8fZabnzi1v4AcEkK4DQIGMdu1KmWIAzRGbNxND43nHIDqa6MPDhlEApLgU5QXxjDUkjho1igPA5eXl4eLFi+gybRqn6tULZnl5MEhNBY4cwRy1mt8WEyPt3LkTkiRh/PjxbPny5ezAgQOCxeXLLG34cHSaMQNNjI0ZABaxbBkepqeL4SdPin2uXOHv5OQwdb9+fHJyshAcHMx7e3uLHh4eXE5ODmJiYoQHDx7wOp0OoiiC53no6elJZmZmkqurK5ednY2cnBzJ3d2dFRYWIjw8XDQxMSlmcUg8zzMTExPe0tKSb968OUyMjRF5+zZ2jhyJZJmMR0wMGjdujBEjRjA7O7viwAoNILWamHnGxlRr++hRubFhZ2eHefPm8RqNBr/99hs2bdokTpo0iSsJzpiZkdO6bRu95uVRbfSzLLEoikhISEBYWJgQGxvLKxQKQRAEHoDk7u7O9e/fv1xgkVu1Cl1HjWIbjI0r0uD69SthQeicnHCtWTNYXrqErhkZOLVlC/zv3kXDwYMBjkP8zz/D/MMPiWFx/nxJJxJ1Xh5SDxxArpUVWjZrBkWTJuU0XHQ6HTiOA1dcc/z229Vm7JVKJXieRzmmVEoKBT1r0PSRy+X4+OOPubNnz+LYsWOsffv2pB0gSSTyu3cvJSlqKkGYO5e0TypD+/bEpvnqK+oCMm1apXNHVlYWVyMTcs8eEiDctKlUdNPNja5fkyZU4tK8efXbAACtFjlHjkB58SKiZs1CR7kcISEhKrVa/e+af/wGrxJvnO1Xh6aSJHEhISHo0aMH/Pz8wHEc27Ztm3jgwAG88847XLW1JK8aixeToRoeTotwNdTPvxXFgkf/939kNO3d+7ceDmMMQ4YM4Zs3b46dO3cyPz+/2mc+6hOFhRRdTU//S6nDtUaxkZefX9oC4zUSCgwLC4OBgQHc3Nz+7kOpPR48oExdsRrv82jalLJzenq1qomrCXv37hXu3r3Ly2QyGBsbQ6fTYdSoUWjZsmWltHvGGDp37ixcv36dS05OZq6urhgwYABnbm4OQxoP9TvhFRSQWE09QqFQwNTUFOfOnUNhaqrUZ+5cxpUV6HFyomCGKJLTeuIEOTn1iKDffxd7L1nC6QcEyEZ//TWeD06URc+ePblly5YhJSUFDlUJMD3LUAMAPviA6nBv3iTDUKMhx3vBAsqoPX1KhvC+fbUWACrdDVfpMRgaGmLkyJFsx44dLDY2tuR9K0EQ3169mtsXHS2lWVszIyMjged5rqCgAIwxcBwnGRkZSQ0aNOCcnJyYn58fMjIyJI1CwaIXL0bLDz4gcaxu3SjzJkkV58KHDynAUBM8PCjb9SJrckQEqYd/+WWF1nP6+vpSZmYm7O3t62eS1tOjNkQ//EAG+uDBpZm53r0pAJGURM/+n39S9rEY339P80fZa5SXR/f6wAGi9g4cWC8B96SkJNHBwaEkAGRqaooBAwYAd+7A2MystHzg2DFg7VqM37+fHTx0CDY2NtBoNNDT04M8MRGd9u7ljk6ZIoIcbQBAm1OngMWLuS5+fhxMTODRtm1xmQSfmJiIo0ePSuHh4aJcLpesrKw4Hx8fPHnyBD4+Prh//z4uX76Mx48fcyqVCvb29oKTkxN/8+ZNUV9fX+rZsyfftm1b8FUZZ9Onwzo6mq3q0wejRoxAtbXdWVkUZI6Lo/tw4wbNHc9BLpdj1qxZ/Pr164Xt27eL7777LlciKqdQUL1ufDw9lydOAJaWyHF3x+bNmyWtVis5OTlx06ZNQ6NGjfjU1FRs2bKFyWQyVBB5k8lg0KgR2ly5wq1Zs0biOE4cNGgQ/+TJE5glJUF9/jxCY2Lw6NEjAIBxx46Sz+PH6Lx+PdI4jgXt2ydqNBpma2vLcvz82Ixt23B06lREtWyJlu3bSxYrV7LWd+7gjp8f7tjbo/D6dVF59izn5eUl3b59m6lUKlhaWuKD99+naxEVReOxCuTm5kKhUIhmZmalJ3L8OAXoaiGgGx8fj2vXrqFPnz4iY4yDKFKt94MH9OzUxh5p0YJYTN26Vf65oyOVWxw4QIGQZcvKtXp7VlrAnCtj3RQjKIgYAsUlKMXo358CGatXA0lJyFuzBvHx8VWXMKrVeDhtmpQRFcUOjx6NSYMGISUlBXl5eUoAx2o+2Td4lXijRv6KEBgYOB/A8jZt2ojDhw8vmSw0Gg1WrVolKJVKvm/fvmLnzp3/upSkRkPGSf/+pXTB6lqDvI74+WeizB458ncfCb755htMmDChmLL018LPjwIRxXT71x2dOtFfcY/cvxmrVq0SPDw8uB49eryGkYpKUKxdkJJSvaotQPWWgweT0f0CgaDc3FycPXsWkZGRaNOmDfr16wf9+mz1V19o1YooiYsX1+tmNRoN0j7+GA22bMHPCxZgcVXbT0oi5/vjj0kh/jnRrBdBVlYWVq1ahbdHj4YyKwue7dqBq0acMi4uDjt27MCnn35a+/KkwkLKWn//PdGKo6JKx9SUKWQAf/zxS5+LRqMBx3EICgpCcnIyvLy8BB8fHz4oKEhydnZmFhYWomPDhpzswQMYxsQguWdPpKenS4aGhpKLiwtXUFCA8+fPIzExEQBgaGgoFRQUMBMTE/iJotTJ2ZmhOBAyYwZpRcybV/4gcnMpo+jhUf3BzppF9fnVtNWpFNHRZAxPn06CSc/h+PHjuH//vjRp0qSa2w/VBaJIx5yVRS2VimtTZ8+m1kNDh1KGujjQ8OuvVDvr6lraNjAmhu53ly5UG1pPwX+NRoPly5dj6NCh8Hj+uoeFUTZ0/nwSBVu6lBh1H35YEhy8e/cuTu3ahQaZmSjS00Pr0aNLGUi5udTH+NgxcpZ++IEy+JXUn0dGRmLPnj0AAEtLSyknJ4fJ5XL4+vpKnTp1qrkv+POQJODKFUhGRrikVEpnzpxhb731VsVzLIZaTfPwyZMUxCospABdFdDpdFi2bJk0bNgwVlkQ+GluLjLOnIHZ//2fdMfamuVMniyOGju2gv2YlJSEffv2Sba2thgzZkz59S0tDemnTuGWiYmYq9EgOjqas7CwED0uXoTjo0cs/IMPJH19fda7d29WUtbTvz9UBgZ4HBCAnQUF0Ol0AACz7Gy8/ccfeGxtjVwHB8l26VLm+t13KOreHSEeHsjPz0diYiL09PSEgoICVlhYyHEcBy8vL/S3tKQ5p5rrER0djcOHD1dU8a4sqFYGRUVFiIuLw6lTp6TmzZtjwIABDJJE3TqePCH6eG0Ff4uKqId6enr1v9FqiV1jbU3j89l5hYSEiCEhIdznn39e+e+WLyemyX/+U3FdHzGC7LxJk6C7fx8/BgdLGp2Ode3aFf7+/uXne5UKOHkSl37+GcLixejSsycSEhJw8OBBXV5e3oLFixe/HobX/8d4k9l+BQgMDOT09PQ+GDRoENq0aVNuopDL5Zg/fz6/ceNGxMbGovNfQYvOyyND4quvqM7E05MM1X8ievakRVkQiD5YXcTwFcPOzk7cuXMn9PT04OrqygYMGFC7liH1gaNHS9Up/wnYto2Mo9BQcgD/5vFXVFQEOzu7f4ajDZBhOm5c7RxLX1+ikyoUlEmpA1U+JSUFmzZtgiRJaNasmdCvXz/+tXS0AXIY69oTtyaIIuQ3b8JpxQqE9+oF6eZNHDp0CObm5igsLIS9vT08PDwgiiKuP34MV2Nj6KWn4za1FxMdXF25x3l5UufOnZmrq2sJ1ZPjOKhUKrSuoi9sWFgYUlJScOfOHRgYGMDVzQ2sd29y5jdurPJwBUGAIAgVs1jVoVjl+uuvKXPy2WdkiF68SFTsfv3qetXKYdu2bWJaWhoKCws5IyMj5OfnY+DAgWKHDh14AJgzZ07xc0cHnZMD/Oc/aDV1Kp5pKLDCwkLcv38fDx8+hJubG3r16oXs7GxmampaXCvOMGwY1Zd26kRrmihWPJiwMKqBrC67XVRE516Wcl0bqFTkZH/+eaWONgB07doV4eHh7Oeff8a0adNgZ2cHQRDw0qw2jiMH+vRpqsWfP58c6fnzKajOGNWbAsRe+/VXqnVljDLxW7aQgxAUVMoeqydkZGRAo9FU7oTGxZHzOX8+sSnat6dSjIkTS4SfHj58KI46fJgzaN4c2p9+Kt/3+t49anNYnJW0sKhSp+LChQuCi4sL36VLFzg4OLCcnByYmpqiSoXr6pCUREGJO3fATE1hERVVUj5TJRQKYuQVFpLd9eOPlIGvrNwBpCfRs2dPtnfvXnTs2FFycnJijRo1KlZrF+/cucMZGRkJjWbO5Hq5u6PhrFkcCgupdKiM8+nk5ITJkyeztWvX4saNG+hQVhzRxgaNgoPRt317Tnr/fQiCAJlMxmH8eCAzE82aNy9/bWJjgfx8GH/3HVw3bED39HQpe+JE1q1bN2RmZsLa2RnW335LtKYrV4AJE6A/ciR6lT81PigoSFIoFELnzp35LVu2oIWREZouXUpBkyrg4OAASZK4Y8eOESvi2jUaw0lJ5b4niiLi4+Nx9uxZISsri9NqtYznebRo0ULs0qULD52OHGFXV1o/69JZR1+fgnU1/UZPj+bT/fuB+fORNWMGdkVHi9nZ2dyYMWMqfl+rpY4RgweTKF5lQb5nJSkFhoa4969/Sf0NDSH79lucPHlSCgkJYUZGRsK4ceN4GxMTiLNmocDQEKd79EDTR4+wfPlyUaVScSAfr/L+aG/wl+KNs/0KwPP8HAsLC+uqDCsAeOutt7By5UouMzMTVnWNptcWT57Q4lZYSLTB/HyKwv+T0aoV/f33v2QgZmT8bYfy9ttvc7GxscjKysLVq1cRGxsrNm7cmHvrrbdenQieWk3nf/Ik0Yb/KSiuN/r6a8rGHDz4tx3K8ePHJZVKxdckpvVa4Y8/6na//fwosDFwIBkLNTgSsbGxOHPmjJiVlcV5eXlJ3bt3Z5Updr82WLqUggr1HbQ5cIAygunpaDtwIB5xHOLj46WUlBTJ0NBQunLlSsk14XkeJ06cgH7LlrCxsRH7bt7MxIQEPPrqKykoKIgZGhqioKCg5LuCICAtLQ29evXC06dPSxSBV6xYIebl5XEAGZjvvPMO1Rf++muNVEdXV1dwHIfU1NSqaeSVITeXxCc3byZhre+/p/Eybx6tF3XM+omiiCdPnuD8+fOIj4/nxo0bBxMTE1y4cEFo1KgRih3tSuHtTevUH38AZmZ42L49du3aBVEURX9/f67rM72HCoKUHTsSBbpTJ3IsDxyomPUqzqBVB5mM6tbrAqWSssI//VTamq8SGBoaYs6cOdi3bx82bNgAX19f8fLly5ytra3YunVrrlWrVi9ehsRx1EopJ4dqrUNDyXFu1YoyxdnZlJF7+pScGkmiwIqzM1270aNfidaHqakpGGPQaDSokD0eM4b+AKrZPXgQ6NuXKLhPnwJmZsi/elXKnDoV7WbOrDgO794tLxQWFkZzWyVJC0mSYG5uLjo6OnJAzeKJ1UKhICaBqSlSU1Oxf/9+DBo0qHpWW3w8tdB6+22q6S0spDaC1aBt27Y4efIk7t69i8jISKGwsJBnjEnW1taYPn06ygltbdtGzt1//0vPQJm50NzcHCNGjMDevXvh4eFRXMZDmDcPCAsDe9YiEQCN/0WLKgalDh6k77dtC3z+OfI//ljqtHUrM+7VC8YAqXJ/9x3wySf0/JahyWdlZeHevXvIyclBQkICGz16NG9nZ4fBgwfj0O7d0pysLMaLYpVj0MjICOPHj8emTZvQqlUr2Ds4oGD+fMTdvo20tDRkZmYiNzdXUCqVvJ6eHtzc3DB8+HB2+/ZtKSIiQho1ahRdqylTKGj5zjsvJmq5aBEF9b6sQcybMWDECDxJT8fVVavQtk0brsNnn1V8BgSBmCQ6Hc3BVa3Nt2/jcW4utuzeLXVq107y1+k4WatWaNWqFSsqKsLFixfZzlWr0C4sTMoyMmL33d1hZWYGMzMzoV27dnxMTIw2Ojr65y+++EJV95N+g/rGGxp5PSMwMLCnQqE4NH36dMOaJvc9e/aIjx49YnPmzGGsPutuCwtpYR01iiay14By/UqgVJLzOX8+GY1/Yw28VqvF1atXpeDgYMYYQ7NmzYQxY8bU/wEJAjEUlix5vUTR6gJJIgGmO3dq15KnHvHgwQP88ccfmDp1KqprUfXaYPt2yo7cu/div1eryYhdtowyK1Vg/fr1olar5Tp27Ch5e3u//hn/sWOJrle2nvploNORcztnDmUGq3A2dTodCgsL8fTpU9jZ2ZW8z4qpuU+fAnFxSJ89G2sHDYKBgQH69euH1q1b48yZM7hUxhi1tbWFUqmUlEolmzNnTuXOwI4dFFj7b+XJifv372Pfvn3SokWLas+qyc0lZ2z9+tLMzbFjlL2Ry4ma27MnGZe1yPaGhoaWtBlycnISAwICOJcXyZZu2ABs2oSv+/aFwsAACxcurP77ajU52AMHElumTRsqqynbDzonhxyAQYOq3o6lJV3foUNrf6yDBxPNc8qUWn19z549iIyMhLGxsTh69GguKioKsbGxgkql4seNGwenMkJSL4ScHLoOn3xC46VxYxJvOnKEAp0dOlCtvqcnsRrqW9jvOSxbtkzq3r07a19ZIMLWlrKUVlb0l5BA7LvQUKpHnjwZD7ZvR4dez+VHJYky4F98QeJRAM2NLVuW9pgvg7CwMBw9ehT/+te/8FL21dtvA4MGIbtfP9y4cUO6du0ac3Nzw6jqWlfpdBREOH+eShgiIuj99HSiGldxPPv370dGRoY4c+bM2i/u69cTDfngQaitrLDvzz91rq6u8Pb2lgUFBQmSJLEJEyaU394nnwB2diQABtA9+Oij8kFwtZoywrNnlwSsNq9dK/hFRvLN8vJoHK1bBzRsCO22bXjs5IRjQ4aIOWZmTCaTSWq1mrO2thZkMhn8/Pz45s2bl9yHPXv2COzYMebg7c15TJhQZcDp1q1bOHr0KHieR6vQUNzx8oJRgwaihYWFZG1tzaysrDhnZ+dyrczS09OxceNGLProI8jefZeOs0+fF9eM2bCBnPQJE2r8alRUFA4cOICBVlZoe+kStQUrm9nOyCgdwz4+VbY+zM3NRZG3N254eMB0wQLR39+fY2fPEhOpuPRTo4HS1xepXl6iy6pVnF6ZNauoqAjLly9Xa7XaFkuWLEl+sRN/g/rEm8x2PSAwMJABaMnz/HCZTLZo1KhRNTraADBixAhu2bJl4pYtW6TBgwdzL91KSpJo0WrRgjIVBw7UOUPxj4KJCVHJHz+mxUsQ/jaHW09PD/7+/szPzw8PHz7E1q1b+dTU1HIG+Uvj+nUyngID62+bfwcYK+0Vq9FQcKg27S3qARcvXoSNjY1ka2v7+juUABn1L/MMKxRkSJ05Q4JilThPaWlpyM7OZj4+PoK3t/frm80uRkYG0V/r81m/c4fq52bMqLydzTPIZDKYmJjApDLqKseRMaSnB2WjRmjcuLE4MyCAK64X9vf3h7W1NRo2bIikpCQpJCSE2drassGDB1eddXN2rtJIVKlUOHjwILy9vetWvrJ/Pzln+vpEZ/ziC3JUp02jz3v0oPZSo0aR4F6/fpQ1q8RByMzMxKlTp9CwYUO88847MDExefEI4LRpOGBuruu5fLnM7N13a/6+QlHa8m7aNKrFfN5pTUyk96tztoODS523miAIJCQ2Z07VfaorwYABA+Du7g53d3euWEiub9++/KVLlxAUFISBAwdKbdu2ffE5ydycMp0ffEBBkpUrSeBuzhxqGZafTxnIv4jNw3EcMqpinX3yCQVHFAqyVziO1oCffwZWr0bw+PGiQ1lRrGLExJDgXdl71bp1laUkV65cEfz9/bmXymRIEmBujhi5HHvXroW1tTVGjRpVqbBmeno6jhw5Au+EBKn1jh0Mn3xCgl5yOQVNx48nx3vXLgpmPYfExERERkaiZ8+edXuGZswgR3DzZhQEBSHfw0N2JilJY25uDm9vb37Pnj0VM2ojRhCNv9jZdnCgtaYswsLIGXzmaCuVSjzOzeU13brR7yIjgQEDkPHoEYKmTUNnKytp6rp1nGrBAmQOHMgcHR2hr69f6SQ9YsQIPu3CBWTu2yf+lJnJGRgYCLa2tnyHDh3g4uKCc+fOITw8XJQkievevbvkzPOs8Q8/YODu3WB6etVeH0tLSwhFRRCnTiWHtl+/l1srxo+nUoxqoNVqcezYMSEqKoobPHgwa926Nc2doaHE5vviC+pWsHcvve/vX+l8KooiTp48ifDwcDT/6iuhR69evIWVFZ3vtWu0vYMHSbvl449hsm4d3Nq3r3A9rl+/LnIcd+KNo/364I2z/RIIDAxsI5fL5+rp6Y2Uy+V6LVq0kHl5eenX1sHiOA4fffQRt2LFCmzcuBHvvfcejIyMXqye6/RpMjhu3SJaYE2CMP8raNmSDK6kJDIIk5NLWlH8HWCMwcHBAQEBAcKWLVv4gQMHom3btvWz8bg4agHxv4CAAPr78UcyDJOSXrmqemRkJBITE9G7d28R9a2MXd8QBMrQ/fe/RPesJZ4JMEre3t5MFEX4+/uDc3cnY+HqVaJrXrpUzqHctWuXYG9vz3Xp0uV9aMHWAAAgAElEQVT1vibF6NKFrslXX738ttRqmjc3bCDHrB5wOCUF4R07Yri9PQcvL8pmmZlBoVCgzbPaXhsbG1YrvY5Onci52L2bMvnPIEkS9u3bJ5qZmaFHjx61N84fPaLaxZ076f9Hj9JzOHly6XfMzYmWPWECGXZnz1Igol8/iKNHo6CgACdOnMC9e/cgiiLatWsn9uvXj6uz4NRzyM7Oxu27d2XzWreGaW2ZHDNnUqYeoFr0BQvKs2Xs7at3tJcvJxp7bY998WIq5+jRo07zlaGhYTnlapVKBY1Gg06dOsHCwgIHDhxAQkKCMGTIEP6FdT9cXIihMHAgBdcAetbXrKkyWPKq4OHhweLi4iptWYcxY4iV1qABrd1Ll1IGuLAQCA7GYxcXOFV2DcLCKMtcFvfuURa5OHhbBoIgsJcq5woNBdatw8WpU8ULISFchw4d0KdPn0ovYnBwMEJCQiArLASOHGHZBgYwHjOGKMQhIURh7tGD7stzivXP2g3i8OHDkoeHR3G3h7rB0BCYPRsJT5+KjUNDd/qFhg4+8uSJPI+C2BW316kTBWH27iV2EM9TieGECaVU66NHy7VUTElJgahUwsDWljLFaWnAzZto8M47UEZHo5mvL9P77TeYx8XBXC6vNmjJcRzs5s+HXUIC5+bri4SEBD46OlrYv38/r1arYWlpKfbr14/z8PAoaS+JrKxajWE+JweT9+3Dro4d0apLF3i+bFD2yROae1NTq9z/tm3bxKdPn7LZs2czs+LEQbNmNJcuXUqCk+HhdM19fSvdxunTpxEWFiY9o88zp7FjefB86bz/wQf0b6WS6v4XLaqyhCUiIkJVVFT0RhTtNcIbZ/sFEBgYqJDL5Sv09fUnde7cWd66dWv+RbPScrkcw4YNw759+6QVK1YwCwsLady4cazWddzffkvZwfHjyXFp0KB8TdP/L3ByIlEoc3OiEk6Y8LfSrLt06cLr6+vj6NGjUKlUL9/P+eJFWhTHjaufA3xdsHAhGV9paSQE9a9/vbJd3b9/Hw0bNhR9fX3rxamstCaxvsBx5CTUsSb5wIEDUCqVLDQ0VCoqKmIxMTGwtLREVFQUGhkaYryPD1Li42EqlyNfkrB7925Jo9Hwjo6OAmPsn+Fsnz9fpShSnaFSkZNdVPRi9XzPQafT4ebNm+jdu7fUxteXITeXjE4/P6Ly+/jUfaPZ2ZS9GjCgRKgnIiICaWlpmDdvXt0muYULiV4cEAAcPkxCWRs3Vj5XGhmRcyOKKAwJwcPNm6H/2We44u2N7LZtpZ49ezJ3d3eYm5u/9ER7+/Zt6eDBg6xRo0aC8b/+xSM/nzLrK1dWL4Ln7U3nYmJClOTQ0PJ124JQfVbqyhWi9dbUvk2SKEPVpAmttS/oxGVkZGDdunUQy4i5yWQyyGQy3L59mzc1NZVeuEPCzZs0jn/6idhtAAWSBg6ksqNu3ajc6tNP6XwMDV/JGllUVITo6GjJxsam8vMYM4aCIEFBNL/17k0Bk759gT/+gDRlSgndWK1WIzQ0FLGxsdJ4lYoZ+vuX31ZODmXHAVy/fh2PHz9Gu3btYGtri/z8fM61bCulOkJKSkJqeroUcvEic3d3x40bN6BQKMQuXbpUuGiXLl2C37lz6BYWhosTJwqHTE35bnfu0LofEEDMArmcAgMffUTtpABcvnwZp06dgoGBAbp06SL5+vpyLyOyGuPkVPAwJye4/fXrGT3PnesS6ebWLr1dOwmVOdx6etQdpJg9NXUqjQuAkhZHjxJN/xncGMO8FSsQe/062VetWmHH6dOC2w8/8F2MjZE/aRKsoqJo/HXpQkGvHj2qPtgmTYAhQ6C/axfc3Nzg5ubGS5JUvK6Wtj8TRSo9CAmpEKiogMxMsP/7P9i//z54U1MhITGR9yxbVvIicHQkZkIVKugJCQlISUnhZsyYAbPnGXpWVtSl4uhR+n8Vx3/kyBEpMjKSjRgxgrm6utL47927/PdNTEhP5MwZCjxVkdRLS0tDTk4OAFx5gbN9g1eEN852HREYGGgtl8vPNmnSpOmwYcMMDF6yly0AtGjRAgsWLGBZWVm4fPky1q9fjz59+kjt27evnAElihSFHDyYMjMuLpSteImF5X8C3bsTVXbhQop0u7v/rYfTsWNHGBoaYv/+/XBzc6so8FMXTJpEi/ScOfV3gK8LHB3JgTpwgJR9Oa7eszA6nQ7Jycl1ugeiKJaoPGs0Gjx+/Bj29vbIzs7G5s2bxfz8fG7JkiX1epwAqP4uN5ei4HXEw4cPRU9PT27o0KEsISEBoaGhQmJiIqenp8ceKZX4ycZGcvrhBzZ81y5s/vhjdO3bV3Jzc2MNGjT4Zzjas2fTc14my/tCKCwkKt/u3RTIqgdoNBosW7YMhoaG8PHxoQGsUNB8XTwf3bhBrKO6KF87O1OpjFZb8ta9e/dgYmJSt2BPTg7RPz08qLQgJ4eetyqoxSqVCmFhYUhOTpaSk5OZSbt2Ut9hw9jos2fBOI4hNJSMyZdkEomiiGPHjjELCwu89957NA6NjcmhvXq10nrccujbl7LvX3xBvaUzMgBSLKf1oCpnWxCo325tcPAg0ZjnzKm7ankZ7Nu3T7KyssLIkSOZiYkJ1Go1MjMzIQgCCwkJkYyMjOo28WVnU7b9l1/Ioba1BdauJVErc3O6P+npVDrg6EgB6UWLqP2nVkuGe58+ROG2sKBrVaxa/oLQ6XRQKpVs6tSplW/k3LnS7dvZ0b3u1YtYeaNGwSQzE7du3UJISIigVCp5KysrMSc9ndOFhVWskffwQI5cjo3LlokAmIWFhbRx40auadOmkCSpvJJ5HSB9+inOeHgIYV27cu9OmcKsra3RsWNHbNq0iXNzc4O1tXXpl7OzMc7cHI80GohDh6LrN9/wT/78U7hy5QrTarWcv78/ZIaGEAwNkWdsLDGOY4fXr5eYoSGSkpLY2LFj0YKCIy8d+TA1NdUrMjH58reZM5vKtNqC4Xv3wuDKFba1QYOifJ1ON3HiRKOSll4+PvT8JyWRU3frFjnZLVpQwmLp0tL7FBkJztoasS1awMjYmAI4lpZ4cPw4b75wIXrl54NfvJjqxwMCiCn04AGNuersUk9PoqM/o+Uzxiq2mRQEGrM16UDEx5PtN306WL9+kO/di/j4eDEuLq4k6HLjxg0olUoEBASA5/na1/Jfv06BrP79K3xkaGgIxhhycnIqjrd9+4idtnQpXYvffwcGDkS2hQU2bdok6nQ6xhiTeJ5nU6ZMKT+uevasKO4YHg5p4EAk63SQVVGmGB4erhFF8T9LlixR1+7k3uCvwBtnuw4IDAy0kMvlVzp27OjYs2dPWX2KmsnlctjY2GDkyJEsNjYW+/btw9WrV0U/Pz/m6elJOxJFMj6cnEjMx8/vn1+/W98wNCTjQhRJFGbFipqzFq8IHMcVCyNJcXFxrLaOXmJiInieL1UX1mqpBcc/VRCtNujalaK116+TsRcVVbcWHTVg69atolqt5vz9/Wu8iLdv38axY8ckrVbLrK2t4eLigmvXroHjuJKMlE6n4+pV1LAsGjR4oXt99uxZaDQaLuDZeHd2doazszMPAL///ruYmJjI9ezZk7nOnAn9d9/Fx+3aQe/WLe6vquOsF8hkL193KkmUbe7cmYSk6gGJiYnYtWuXpNVq2ezZs8sbcRxH2R6AMhMBATQv1QUcR8ba8eOINDZGXFwcxowZU/tBIkkkAPbhh1Tn+t13RCV9Rr+9ffs2Tp06JTLGoNVqGWMMoijCxMQEtra24pgxY3gXFxcK/g4dSkyUU6coU5qcTNnSli3r7KQ9ffoUq1atgk6nK9+iiDESh4uOpv3t3Vt1Nvmzz2g9zMuj0gJLS9IsAciZ27y58t/17UtZ7d9/r/4gDx4kp/SXX14qsJCWlob09HQ2ceLEEqPawMCgRNzJzc2t9hfv88/p2CdMAC5fJobGoUP02c8/0zj75BNyUE6cIAbDiRNURmBgQPR7f38aV/360TN1+TLw73/T/Nu8OTEpFi6kfX37La2pokjjsBoUOzFiZa3YAFIU/+EHqjHX6SjIKpPRsaxejY4zZrBrLi5Cly5d+ObNm8PIyIjb8MEH0C8oqMjaKyjAtYsXBdcBA7jBgwcznufZpUuXhJiYGAwYMIAxxuo8kYpZWcjfulWKnjqVzfz445L+6Pb29jA1NdXFxsbKTE1NoVAooNPpED9jBqxPnYLmo48EvXnzeJiYYOjQoXxERARCQ0PFy5cvc1ZWVjAZORKNhw9nnKWlZJubixytVpw+fTpfzsF6SZiZmemLoujIGJsrKRQH9o8f722SkdGkz3/+I1eamSlXPXxo8P5nn3ElgmQNGhBr5uRJqgOOjyf7MienROFdTE6G5O2Np05OuNmxI1KaNkWfBw9gkpEBhUKBPI4T+YkTuZKxdOgQlbwNGULPzL59VWekf/oJuHCh+pPau7dmpmJMDDF1hg0raV04ePBgPjg4WAwKCsLYsWMREhIipKam8gAQEhICoJRVIpfLJblcLunr60v6+vpQKBSSgYEBUygUvL6+PlpcugTt48fIc3aGvr4+5HJ5yWuDBg1gZGSEixcvlra7KxaBdXcnxqm7O9mjf/4JfPklYtq1g3nDhhg4cCArLCxkDg4OJeWjkiThwoULovuUKZzZTz9Bf8iQktPMOn0aa9evB/f775JGo2GOjo6YPHlyyXqj0+lw+/ZtURTFbdVf1Df4q/HG2a4lAgMD5fr6+qc8PT3t69vRfh7NmjXDggULWEhICDt8+DDMGzSAlJ8P6927Ybh1K9UpRUW9sv3/T6B44m/ZkozBGgyEVwm5XI4bN25InTp1qnHQHD58GOHh4ZDJZPjss8/A3blDNT65uf/bznYx2rUjw93AgCih9dCHPj4+HqmpqdyUKVNgb29f4/fPnj0r+Pj4cB4eHjhy5Ih4+fJlztzcXJozZw5LTk5GYWEhLly4IOrr69fvDVGryUhev77OomhRUVEIDQ3FpEmTKhXb6tKlCxcbGwtPT0/KHPj7gz97lgzrtLRq6+teG8TGktH/MkEYUaRM8YYN9doGMSwsDIWFhezzzz+HXnWqt9eu0evixXQ+f/xRux3o6ZGwkrs7wg8eFOVyed0Uv1UqoEMH5HXpggcbNgAPH8Lqiy9QnDc5c+aM6ObmxllbW8PGxgYRERFis2bNuGbNmqHS8gIbG1LVLSoip3vmTGIbuLkR86AW40mSJNy4cQOmpqbipEmTONPKFLKdnSnYmJBQtcHO8+TA/fEH1TVmZZV+lpdH5RipqRV/FxRE9Y/VITGRHNBdu8iJf0GkpKRg+/btko+Pj+Ts7Fz3eUOSaLz07UsBCAuLUjXv5zP3QUHkLIeE0LXZs4fuyfXrdD6HD9Mcu3Il3a8GDcgpd3Gh2mKAHHOZjPar1dLrt9/SvY6JIXbE4sUUsNm5k1hXPA/I5dBqtRBFEQ2qKmfjeVLtVyopyLp5c2m5jJUV3HNzmXvbtjw8PZ+dugSXe/fAvfdehU2lAUjTavmubduWOCt+fn78i5Zt6S5cwLmTJ8Vb8+fjvffe455Xye7WrZvsxIkT0tmzZ1mH1FTRLTWVi+I4mPj4SO5z5/LFwQA9PT20b98e7du35xITE3HlyhWxzaefci2vXQPeeovB1RWIiXlpNpFarUZERATy8vIQHR0tZmVlcQAgSVI3QRCSATTJsbTE+R49Js9Ys8Y9ycXlfSkoyAQzZlBAq1MnspFSU6m2v0EDGieWloCNDe59+aWUEBODBh06sHQPDynFzo41btxYCg4OZoIgQF9fX2rVqhUHmYx0H7y9yV7ZsIHWsiVLyIE3MiLWxfMQBAqMDRhQ9Zq3cCFl2qsKSty8SWN5xIhy+gxyuRx9+/bloqKipB07drCmTZvC3NxcsrKyYg0aNICxsTE8PDygUqlQUFDA8vPzWWFhIQoKCqBWq6FUKpGZmQmNRiNG9O0rcSqVpD5xggmCAFEUmSAI7NkrRFGEKIqSJEnkGWzZQvbL2LHlz7tvX2glCcrt2yVPW1vWaOrUko8kSUJycjKCg4PFJ0+e4MmAAZIyLk6aLElcSmIirDt3xq4RI6Br3BgLP/yQbd++XUpOTmYxMTElgn1xcXHgeT7y888/j6/DMHqDvwBvWn/VAoGBgUwul293cHAYNn78eMNX6Wg/j31794q+H37IZVhZ4fiwYXBr21Y3ZNiwV+rs/8/BwYGMgQUL/vJdnz9/Xjh//jz/jNImTJ06la9MtEWj0eDXX38VJEnihg8fzvbv3y9ZW1sLb7/9tgy3bqHY8Kgr1Go1FPVQi/qXo1jwLiam8kW6ljh8+LB0584d1qdPH6lDhw41PjSxsbHYvXs3Fi5cWOI0qVQqCIJQrh4rPDwcJ0+elD744AP2wn1yQXTgM2fOSCNGjGC2RUVUD3ruXJ2d7V9//VVwc3Nj3bt3r9SQF0URP//8s6DRaPi5c+eipPylqIhotytXUrbpdYabG2kWvCh1v1iNfft2MoJeRjzpOVy9ehXBwcH49NNPa/eD69cp+zNyJGU7nlcCrgqzZqEgIAAbs7IkmUwmTp8+vdL5pByePgVGjULKTz/h6tdfwyc8HGE//ijdjYxkPM9LWq2WiaKIadOmvVw7vLNnyRmTy6m11IwZ1dbWHzlyRIiIiOD79++PdjXVVe7ZU1pjXhlu3ybnzdeXqMarVtG+8/PJQX2+VODMGRKbnDWr6n1GR9M5HDjw0myK7777TrKzs2MTJkyoPXX18WNiXnTpQvWt69ZRgOijj6oeu8935NDp6LX4+5JE2501i54Fb2+6rmfOkIjT1Kk1i6uKIgU2OnemvtFz59K979oVkokJNgwaJPVduxaOe/YwmJpScqB///KsB0mie/bbb3RO48ZRJnTcOAqc6HQla979+/eRO3EivLdupSBCGajnzsXl2FgpfuRIacyYMVylXQJqgczMTNy+fVty/PBDprKwkNxPnmTVrZsFSiXCFyxA47NnEefqCs8ff0Tj1q1r3pFKRQ7h0qVUQ/8SSExMxPbt22FkZCSZmZmJSqWSy83NZTKZ7KZOpyv7QH2xZMmSbwIDA/u55OTsfzs+XsFNmECZ1qZNSQBs7FgKbHl4UNC3bVvA1xfaxo2R3KIFmh4+DFbb9nTR0cSGmDqVEh6ursQwOXKkcmZIsTBbVR1JqqiVBkBsjKgoCg4MH17pVzQaDfLy8mBlZYXY2Fj8/ozJYmxsLCxYsKB2AY/QUBLlTK5c3FupVGL16tWSr5eX1GXXLg6DBlGA67lzUqlUWLdunWSdkSG2Pn2aR+fOkvjuu8zIyAgJCQlSeHg4a9KkiTRixAimv3AhfjU0FLNNTDieMalrRIR0pUMHrrCoCF9++SU0Gg2+++47jBkzBi1atIAoili9enVhTk7OR4sXL15fq/N6g78M/x+kyl4eenp6X5qZmQ0bPXr0X+doJycDHTpghL091/jQIbS5dg1Dx47F7Tt33rAR6oq7d6mG7ccfKWL/F+HmzZvSpUuX+G7dumHRokXgOI6tXLlSVKlU5b736NEj/PLLL6KBgQE3a9Ys5uTkhFGjRjGXNWtkedOmvZCjHRoaitWrV0vff/89bt26VV+n9JcgPj4eF5KSyAm0sCBqWFpayec6nQ5paWk4fPgwYmJiIIoiMjIyoNFoym3n3LlzuHfvHps8eTIqc7TVajViY2MRExNT8l5WVhZ0Ol257KSxsXEF4ZN27drB1dVVCgoKEl70PMPDw7Fr1y5wHMfC3n8fO3bsEDQv4GgDgEqlYk5OTlXO5xzHYd68ebxcLhfKni/09SkTGBxMjvfrjPBwMuJeFM2bE914woR6dbQB4MaNG4JWq0Wtg9cdO5JjcfQo1aHXFubmMBQEzJgxg8nlcrZq1SpRra6hNO/SJUChwJ9Hjgg+ubmS3dGjGDJ0KBs8eDC6d+/OPvzwQ3z++ecv33e+Rw/Kfk6bRmO4UyeiCicllfva06dPsXXrVjE8PJyfPHlyzY42QE5hXh45z5WhbVuiyF+9SvP9lWfaQFX1x42MJEO9KqjVJOS1YkW9tMsSRZHFx8dDW6buvlLcvEnOyrVr5GCr1RQIW7WK2D4ff1z92G3Rgr4P0DUwNi7fP5wxYiXs30/bvHiRHPSMDNqnVkvBnw8/rHofHEdBQRcXusdXr1Lm8to1PN20CU+yslijQYMYjI2pS8rcubRfd3ei/Ccn0/hYurSUXdK6NQn3ARRcnTgRSEtDbm4uDm/ejIaurpXW7CqaNUPXkSOZnp6etGbNGsTH1z6hl5+fj6tXr2L16tXi+rVrod23T1Jv24a2wcGVOtoPHz7EmjVrcHfQIEnesyeeGBkJImNI7tQJ1mWU5quFsTEFU6dMoUDOC0Kj0WDPnj1i27ZthXnz5rGpU6fyrVu3luRyeahOpxvHGPsUgHzJkiXsmaPNFArFkjZTpyq4ffsocDlhAo2Vhg0pOKZQ0N/27UQFHzoUaStWYOegQUgQ6rDMubmROFpQEAmeFgv3hoZSwPN5mJuTTkll8PGhObsyhISQuKOjY5WONkAZ7mLB4WbNmmHx4sWYOHEiNBoN97zNUCXataN5uor53cTEBJNHjGDZGzZw2SoVBZcqCR7cv38foihK73z/PW+3aROUkgS2YIF04tAhITIyUhw3bhzefvttplAowI4exWAfH659s2b4JDWV+e7YwS1YuBBffvklvvrqK3z77beQJAnNnwWg4uLikJ+fnypJ0obandQb/JV447jVgMDAwBZ6enqfTZgwweCVKQ+XxdmzFDX+z38o6+HlVdJn1dzcHDKZDKIoVtoerLCwECqVCg2LF61nkCQJjDEUFRVBpVLB3NwctVW8lCQJWq321aku/xUonvTi4sgoqC5SWg9Qq9W4d+8ejhw5woYMGVLS+mvKlCnc/v37xZUrV0oODg4sJydHKCwsZBqNhuvcuTO6du3Kip08e3t7PB0yRDx18ybT7tjBioqKxIEDB3LPq9TrdDpcunSpRIwtIyMD8fHxOH36NDw8PKTMzEz25MmTV3au9YmCggIcP34cd+/eBQA4OjqiiY1NKY1Ro8Gl69dx/vx5CIIAKysrKSoqimm1WgiCAMYYOI6DTCaTnmXsuLFjx8LW1hZ3797F6dOnhQYNGnA5OTmiVqtlWq2W0z3L/NjY2IDneSkjIwOtWrWqsTUYYwwBAQHcf//7X+h0OtS2xYxOp4NGo0FaWhqOHz+O3r17w9fXF7qgIFyPjcW3334LGxsbYcaMGbWmGObl5aGoqKjkXKqDm5sbd/z4cTRu3BiNi+uVPT0p03ruHAnRXLpUZW/nvw2jRpEoz/MCSbVBQgJlCE+ceCUtEUVRRF5eHj9z5szaZy2LMXw4/aWnk8N49y5Rg6vCN98ADx5A/84dTJ48mQsKChJ37NghTp48ufIJPS0NuqwsHBoyROr13Xe85ZYtJXTs1rXJxL0IHBxISGzmTHJW58+ne9e0KdC1K86dO4eEhATuvffeQ6NiIbOa4OhIDuJHH1HG+uuvK35nzBgSoLt4kTKIAM0b69YRpbUYkkTHV9W9ysykbPiBA5TtqwdMnz4dv/32m7RhwwbMnj27/I4fPSKH4uuv6Th79iTqenY2OT91aR25axcEe3vwAIR27XBnwAAppnt35hkTg/j4eNHFxYVr3rw5nbuFBdHjL12iftANGlAwQ60uzYj360djzsur5n1zHO6lpYE3M5P0v/mGztHFBZg+nT7fsYPu3apVtP3jx0t/+9lnpaVxFhbkzKen41hUlNBKEJiztTVXaa9kNzfwenqY1K0bf/nyZezYsQOdOnUSu3fvXq2q961bt6Rjx44xExMToW3btrxvYSFkU6dyWLasQk/mXbt26eLj43lBqWS2oohLLi4srbAQLW/e5PkdOzDB3b3W9hQA0gBITKSs/ujRNOc2a0aU+suXKct8+jQFPz/8kMZ98+aUHU5JATp3xoUtW6RmGg0Gdu/OIz0daNAAMTExOo1GE7FkyZIYAN+X3SXP8/9Wq9WdPTw8KFjTpg3d+6QkUr2eMYPmnuBgul9LlwKLF8Nx1ixov/oK27ZtQ53EQM3MiEq+dSvVg//6KwVU/Pzo/MuuL3Z2NE9Upj80Zw4FdJ7H8eNUVjF1Km2zDmCMwdnZGQYGBrh3717tWrPq69M9SEysfE54+BCNP/kEnr6+WKvRYEp2djmxNJ1Oh+3bt4uPHj3ibG1tGQA0bNMGDX/8kWHJErQTRR7vvVeeCRQTAweZDA7XrlFQ5Nl8FRUVBUmSoFAoMGPGjJI1JzQ0VFVUVPTdkiVLqhBLeIO/E2+c7RrAGBvcunXryuvJ6hN//kmLTHIyOYRGRrQAlUGjRo0gk8nEuLg4rkVxa49niI6Oxs5nvVOnTJkCR0dHSJKEXbt2FURHRxsyxiTGmE4mkz0FYDpz5kx52drO7OzsEke9mGJ669YtHDx4UALAHB0dNUqlUs0Yg52dnV7Tpk0N3N3dKypHvs5Yt45eR46kiau2SrTVICEhAffv3xfz8vKQkZEBpVLJPQtOSCNHjmQeZYx7nucxcuRILi4uDnfv3hUcHBx4KysrNGnSBEZGRuVX60WL0HL+fE4aMAC3bt0SDAwM2IYNG6T+/fuz+Ph4KTk5WdRqtUyn0zFJktilS5cAoMTxe+utt9CkSRPu7t27CPibBOLqgry8PKxZs0Zq2LChNHHiRO78+fPYsmULTE1NhUbjx/Pa48cxes4cPBo3DiMXLSpWb2WiKCIpKQk2NjbQ19dHUVERCgoKWE5ODtu5cyd2794tmZqaijk5OXy3bt343NxcsUmTJszFxYUzNDSEpaUlkpOTcfHiRcHKyor179+fa9y4ca0c3UaNGkEQBCiVyhKRo+rw4MED7N69WyoqKmKMMXh6egqdWrbkcfw4ZJcuwQfg7VNSsHXrVl6tVmU1GnoAACAASURBVCM6OhpmZmZwcnKq0pjLysrCpk2bRGdnZ9jb29do8fXv3589efJE+vPPP6WJEyeWF3lr04ZUgXm+lHL9usDLq85t0EowcCBRVL/7rn6PCWT4HDhwQFIoFOz5IGed0LAh0eOtrKiG9a23qq5N37gROHsWfGgoBg4cyK1duxYnTpwoqTX08vJC8+bNER8fD2H8eOkpwMxbt0bDoUOheL510quEXE4iXWo1so8eReaKFaL+woXsqbs76zJ+fO0d7bIYMYKMdVGsqGGxaBFdwz//JGfh1i1yLMaPLx9gLVYHTkmpuH2Nhuq+P/us3hxtAGjYsCEsLS1Z3rM2VQCIbeXjQ+P60CGqgb56tfTzap6/jIwMmJubIzU1Fbdu3RIaNmzIW23aJN4xMkKkhQXXtGlT4cmTJ5ypjQ1rtXOnFHv+PCK8vNi1a9ewcOFCGBZvm+NIsM/fn7Kuokhq5u+/T9esUSNyhn79lRIBu3dXe54qlQrGxsaVByvXrCEna/lyCirExVEgVU+P9tWpE3WlaN8emDMHBX37IrVXL37048f0DFeGpCSar7p1g6+vL1xcXLBt2zYkJiaKY8aMqVBzrVarcejQIeHBgwdcz5490alTJx7371OA6P59Yg+UgU6nQ1xcnMzX1xc+a9ZAPy0N2g0boB02DOzAARg8Z4dVCUGgevjwcLq+Dg50niEhpcJ2ggC4u+OJmRnu6nSwVygge/gQTlotuOIa96tXcToiQmj6+++8vb4+4wICSBSwWTP437wpN8vKmoGlS3cDWAHgMYD/E4Fxvr16tbP39JRk33zD8Pbb5Kza2BDDpnt3Ks+4c4fsz6tXKUD5zD5s3749Hj58KKKuTFgjI7rP27fTc7t8OZ3/7t0UUCh+fv39SSgtK6u8LsKZM+SgP89ouHiR5sBPPqm0v3ptYWdnJ8bHx/O1crYBsh8DAoAvvyz//r17NJYDAtBk3jx0OHUK27dvx3vvvQcTExPk5OTg4sWLSE9PZ4wxjBs3rnTRNTKimvWDBylw8OOPxGgBqD3e/v3ECClTBuPo6AhTU1MxPz+f2717t9iuXTvO3t4eKSkpHIAdL3xB3uCV4k3Ndg34/vvvTw8YMKDnK8sC3LtHUc3WrSlqP3NmtV8/f/48wsLCpLlz57LibFpiYiKCgoLydTrdDQBdmzdvXhAQEGCoUqmwb9++J1qt1hmABoCwZMkS8d///vdngiB8Y2lpmefs7Gzw+PHjwsePH3MymSxNo9E4tWnTRhoyZIj+5s2b85OTkxfq6+vbSJJkpNFotgOQA/BSKBSj9fX1vSdNmmSor69funj/E5CcTIJjpqbAw4c02dcBOp0O0dHRuHjxopiTk8PZ29tDFEU4OzujSZMmMDQ0RK37pFcGQSDnokxmRZIknD17Vrx9+zaztLQUvby8eCMjoxIV+5SUFPA8Dzs7uxLHTKVSYfny5ZgwYQLqJKZUC4iiiDt37kAul6NFixZ1i+xXgpUrV4rW1tYYPXo0xxiDJEnIzMzEzZs3xaKiIsnQ0JB3fvIEDoMHQ+/gQap7qyEAplarkZ2djd9++w329vbiu+++W+9lM6tXrxadnZ25AQMGVPu9+Ph47NixA926dZO8vLyYRqOBqakpZTeWLaPa9GfYsWOHEBsbyxsYGEiCILBnARTR0NAQnp6eXPPmzWFgYIAdO3aIGRkZnLu7uzB8+HC+tvdArVZjxYoVkqenp9SnTx+uAkvmwAHKdKSmvh4Z7suXyeCqq3NWTG9t0oQciXpGeHg4jh49Ci8vL3Tr1g1G9aGeL0lU47hmDWVXK4NOV5p5VChw6dIl4f79+zA1NYVcLmeRkZGcIAgwzM1F08aNxV4aDWeUmlouO/JX4tGjR9i2bRsEnQ7tra3R7o8/YK1QECW6VauqhY+qgiRRbfYHH5AjXRazZxONNSmJDFeOo0zxsWOlom2FhUBERMWMWX4+MSemTKm0xc/L4ocffhCHNGzIua1YQfXKixZR3fPIkbXeRm5uLvbs2SOmpqZy/v7+Uk5OjhQZGckBwORDh6AeOBCNFyzAuXPn0KhRI3Q+eJCcm/HjgcBA/Pzzz4KHhwfr3bt35ZOFKFLG88YNsklsbCjzefkyBS9mz6bxuXMn1fw+h7t370qHDh1iH3zwAUoSFL//Tvfg7FlyMovX24ICyuT98gs59zk5JTW9oigiwdMTqoED0TYhgZyryp6vX36h17lzS97S6XTYtm2bmJGRwY0ePRrOz9ZQQRCwceNGsaioCCWCfKJITs2//03OznOIiYnBrWXLMLpzZ7CxY+k6BAZS5rk2WiiPH5Nw2LhxNAbVaqJX6+uXtmu7fx+wtUVeXh52794tpqWlcXp6ejA3NxeePn3KazQaNGjQQMzPz4dOp+M4jsPs2bPxfAIoOS4OO9ev1y388UcTAG4A5PtGjLDTymT78kxNMdrXF2bp6cSk+flnCuZ07kzOcH4+ObeSRJniFSvI4fb3x4W4OMTExAjTp09/cUG3Bw9ov598Qg74W29R0KsYU6b8P/auO6yKa/uuM3MLHRERBEUUO2AvKIqoiBoNdkJsMbEk+iwx3eczeI15lvjUJEYTjabZjb0rFrCLSrOg0hRBQEA6l1vm/P7YXkE6iiW/ZH0fn4q3zJw5c+asvddem+bFhAlFv/P1JTWMoZMDQM7m587RZzxnq9tz584hIiJC/6TdYGXIyKD5WvyZeOlSkSKlWEDo119/lTIyMgQLCwspJSVFEEWRW1lZMSMjI/0777xT9vetX0+KnO7daf/38cdU3nH79tNBuMdQq9U4c+YMbty4oc/KyhJlMtn22bNn+1VvFP7By8I/ZLsCqFQqH6VSuXvGjBnGNU4mNRp6oA0dSq0PDNKuKmD58uX6Jk2aCI9bW2DJkiVSYWHhHgBzBUEYKUlSniAIC2QyWSaAr2bPnr2yxHkJAOoDaAigO4BrAIICAgKyVSrVKAAbjYyMuCRJ0RqNpnV5/foWLVoUWlhY2NbMzEz90UcfGf3lTNseZ0Gkc+eQnp6OWrVqlekkbKgJtrCwQGxsLI4dO8bVajUaNWrEhwwZItSoAZlaTQGYqtQxVoLCwkIsWrQIvr6+VauLrAZiYmKwYcMGAIC1tbU0ceLEZx6HK1eu4MiRI5g1axYq7VvPOcmep06tNDBlwK5du3QuLi5is2bNanyCXrt2DXv37uXvvvsuK6+na1JSEtavXw8PDw9dr169itRE4eEkES2jrCEzMxMWFhZgjCE7Oxv3799HUlISYmJipIyMDEGv16NRo0bSkCFDSmVvqoKUlBRs2LBBYowxIyMjrtfr4eHhgfbt29Mm/NIl2mxfvVptmV6Nw82NyEJVzccM8PUlsmBQtNQgDEY7zs7Okr+/v1DVMoJqoXt3GvvFi0v/39GjVBsdH18qwytJEmKio9Fw4kQohg6lTPmxY9UntTWA+Ph4/Pbbb2jVqhWGDx9eFJS7eZOymOHhtGkeMqTyPrrFsW0byWzffvvpGuaoKAqkGvweuncnghcZSTJpjYbcjZcte1ourNVSMCIjgzbPNfUsy8oiSW27djhtaQnMmIEeN29S5vwZApQXL17E4cOH4eDgAAsLC96gQQN29OhRiFotpn/6aSlvCUgSnYskAQUFSMnLw2Mpe+VqnD//pLm3ejVlVXv1orVq2TIix3Pm0PULDHyyhkVERPBdu3axMWPGwNnRkb67WTMa27Ky01OmUEbfzo5IxSefAPv2ISQkhF86cIBPsbQUhPR0IsNlYds2uqZl1OWfPXsWQUFBcHd3l7y8vITz589Lp06dEj7//HMq++Gc5okk0TpRBhISEpDo7w/39u3pfAcPJjl8ZaqHCxeobnncOJKKT55cZOzo718U1PjuO6BOHWiGDsXy777jzs7O3NvbWzA4uXPO8ejRI1y/fp3b2dkxGxsbmJiYlFnWt2/fPl1YWNjeuXPnPoneLFiw4Eu9Xq+aOnVqUXlhcjKpP8zM6BidnOjamJlRQDMri4j4ypVAaioe2tsjKjwcyrFj9Z379RPRoMGz3R+JiXS/du5M423IfAN0PIWFtGYXR/Fn46VLFKBat656a0U5iImJwe7du6tuknb2LNWgnzhB/969m4i3UkmKsGLIz8/Hr7/+KjVp0kTo3r07TExMsGLFCn2XLl3Erl27lv8dp0/T/fb22xQAGTKEki8VBHIlScKSJUvUhYWF3QMCAq5U6Vz+wUvHPzLyMqBSqYzkcvl8pVI57a233qp5ov3xx2SMc/06PWDKc2EsB6NGjRLXrVuH0NBQzjlnAASZTNZ8zpw51wFcf3wO/5s9e3aZtRuPazruPf45XeK/wxljV3Q6naVOp+tRHtEGgMLCQi8AdTQazaX09HSj58rmvmBwznH37l3o9XqcOXNG/+jRI0FtbMys33tPqvXee0Kn06exbtIk9OjRA1qtFmZmZkhOTpbCw8MFIyMjrtFooNPpmLGxsdS+fXuhe/fuUCgUNR9d2LCBJI737z/3RymVSpibmyM2NrbGyfafjyX4H3/8MX755Re2ePFi9OvXD+7P0KrLQFQqJdoAPXjDwujPwYMpA1VGS5jiGDp06Atb51xdXXH27FmcOnUKb7/9dpmvCQoKQsuWLfVPEW2tlojUyZNlSuGKt82xtLSEpaUlXFxc0LdvX0Gn0+HRo0eoU6fOM/f6trW1xZQpU4TQ0FCIoshOnTqFffv2ob2hNrNzZ3KPHT+ezOleZYbbcL2riiNH6P7Zs+eFZHIfPnyI3bt3AwD8/PxeDNEGKKNjaMUTG0s1yQZ4e5MZWRnBckEQ0LRhQ6oB3biRSG15bZheIDIyMrBx40Z07doVPj4+T/9ny5b08/AhEe+xY4l0z59PfX4ru25+fkQKmjal69y6Nf2+eXOS57ZtSxns7t3p/w2b1Tt36J4rTrQlicZSEGjtfd45ExFBZQGXLlGQKDsb+O472BkZYdPBg6jr74/mz6gEatasGQ4fPgwfHx/s2LFDSkpKQpcuXcTeH34IhVpNKpniMMjEMzMBR0fYHjgAY2NjfXR0tNipMgnuiBGUdb98mUj31auUkTR09Zg1i/YvOh3tYc6fR0RICJo1aQJnvZ5KIm7doqxmeWO6ejVlO1evpkyqToeC/HwEBgYy3+HDmTB2LLWFKg9aLZHtMuDh4QFRFHHkyBHB0dERoaGhrHPnzkX+Gv/+NzmxP/YIKYVPPoEsIgJHfHzQxsMDxn37Eokuj/hwTvs6S0siqjt3kuS4ZNu5uXMBIyPcv38fd9zc0NPbG2G9enErPz8+fPjwp9Z0xhhq166NHj16VDopQ0NDRc75IQBQqVRvAWgHwAEAsiMiYHP3LhG4336j9WPcODIwNDEhL4sdO552e39sTGZz6xbEPXuw4+JF0fW//4WJmRkFTxISyCm/qi7lDg5EVEeNovXs2DFSM8yeTc7sw4fTtZbJSKmQmkqBDc5pHdu7l67XMzrOl4StrS3y8vLEKnuuODgU+WkcP07lAF9/TetNCZiYmGDq1KlPbvL4+HhkZWWJCoWCAyj7WkoSrWcTJtCa+OGH9Pmff17hYcXExIAxFv8P0X698Q/ZLgGVStVToVBsbNiwodWbb75p/KytJEqhsJAeXr6+JJmZPp0e+NUk2gAtEg4ODrnx8fFaAGsUCsVAjUbz1E7/WU0SAgICrgMorQ8r+7VZALIWL168fdWqVRNr1aqV16dPHzMXF5fXyuWec461a9dKGRkZTBRF7uTkJHTo0IFZWlri9u3bgrOTExydnNCrVy9cCwzUw85OLCws1CuVSjZixAjk5eWxtm3bGrIyL+7c9HrKVj2LCVQ5sLa2RmpqavkL/DOCMcYBsNzcXLz11lvs6tWrOH78OHJycuDh4VGtsoKbN2/yx9nZqh2jYTMyYAD1CE1JoVqvF0V6KoBer0dycjIrK8gQFRWFhw8fIj4+HqNHjy7a3WdnUzQ8La1qUsQSkMlkpUwQnwUmJibw8PCAWq3GkSNHUGqtM/QnTkggufvChc/9ndVGr160CZs2rervuXiRjru4JLEGsWbNGuh0OowdO/bFGkcaZLqLFpF08q23imqVBYGeJUOGkEy4+DzKyyOS2bw5BaJeAdEGgM2bN3MTExPu4+NT/pppY0M/wcFEVL76iu5lb28iiRUF4CwtKdMlSaQIMjKiteHdd+nZ6vdYUenvT5v8+vWJVEREPP05J0+SC/jGjc++hsTHU6brww9JrTZtGmV/Hzx4QnidJQk4eBC3b99GSc+VquCxAzU3MTGBvb09mzVrVtGacuJE+QGxMWNIjuvpCY1Gg5ycHNHR0bFqX8oYBQP37SPC3rs3EYKtW2k869en1x07Bri5wWP4cGafnk5r2++/U8a6MjRtSnJma2tg3z5c2r1bsrS05C5ubiJq1yaSUx7s7SmoUgKSJGHVqlX6zMxMcdCgQVwul7OcnBz07NmTXpCfTwqHsgK1Oh3JeXv0wMmHDzHIzQ3G69dTILwsoq3RUG1t+/Y09xQKWn80mtKdJQ4fpgDFjBlYP38+zMzMIH35pdTywAHWZOjQZw6eAkDdunVzU1JSEgGA6fVbjNRqdDt3Do1iYxF640YhMzUVG0+YIMO0aU8HP3Jzad9hCFiVRPPmqP3ZZ0hbuJBfGj2aeTVvTjXKkZG0Z/n2WwpuzZlDn+XmVspk7glkMrrPvv2W1imFguTT771H7z13jgj86NE03zgnV/3160lpUVP7cQCmpqYQRRHx8fFoUhVJupMTBU8CAkjZsGYNGTfq9UUKlitXaI7UqkVBXw8P4Pp1pC1fzn3mzOEdFi4U0KkT7Vv+9z9av9aupXkRHU332rBhNH8kqUolJpcuXcpTq9Xl2Ln/g9cF/5Dtx1CpVGYKheJ7Y2Pjt3x9fY0NTeKfG8nJtAh99x0tSH37FhkgPCMKCgrQr18/s59IHnlv9uzZL6igvGr4/PPPP1CpVDMePXo09sSJE8tdXFxqbkUsAb1ej1OnTkkFBQV8wIABYlmu7AZoNBo8fPgQN2/eRFpaGvviiy+YIAhPPc0aNGhAf/HxQZfAQHRZtEhEVhYgCM9en/SscHOjDfWz9hEuAxYWFlJ8fLywbt06DBs2rEpGXlXBJ598wvbv348NGzbwGTNmsP79+6Np06bYu3cvv3LlCiZMmFBlw6jMzEw8k8TbsFHq1Ys2ajVgeFddiKKI3r1785MnT0p169YVT548ydPS0vSP3cGF2rVrS3379mWOjo5F5zdmDG3EXmIbuvJw584dbNmyBU2aNJH8/PxKkyKlkjIMp07RJvRlBzTeeadsN9qysH07EbZff32hh9SuXTuEhoaicePGL/R7nsAgnz9wgJQGqam0WTY1pQ3r/ftP1y9eu0bkc+TIClvivCgUFhYiODhYSktLEz7++OOq3dcyGSlVfH1JrhkfT4GEnj2JFJVHJKdMocxrmza0Ube2JqI7dSoFh/bvJxKhVNKY1K5NZMdAEnftoo387t3lE4SKMGUK3c/5+UQuZ86kTbOBzBQjW1FRUQBQtY19CWg0Gmzbtk3Ky8vD9OnTn1ZTLF1Km36/cso1332XyPCKFZApFLC3t9evXbtWbN68udSvX79S5q95eXmIjY1FTk4OLCws0LBhQwrE2djQWIeEEEnIyiIJuJMTjXFiIgonTpTifv1VkF2/jiYjR1KGTiYjIlJe0GfiRFI2PM60u96/L5gayP39+0RqvL0peFQSYWGkjiiR/Q4NDUVhYaHw+eefQy6Xs4SEBOh0OpacnAxHY2NSVZw//8SZ/ym8+SbNhf37gZMnecPZs4HDh1kp6fijR0SKfvmFnLcXLCACamjvVlYg7upVZF27hp/UakmpVAozZsyATCYTcO8eZZUfPSp7jKqA9IQE82axsQclQXjnS863ArgD4H567drnQ5s3N7siimcb3rmT59uypekTc9x160iBUYVSG845JM7B69UDs7cnUgxQAOb0aQrKrlxJXipNmhS17axT5+n7Vy4ndYS9PWW109IoKPPzz0Qwo6MpgD5oEHlXHD5MmeQaVikxxmBkZMRZ8QiHXk/Bm9xcCsC5utJ3h4ZSIK1rV7q+y5fTWjVuHClUjxyh+TRuHL1m0iQ6bkdHWqMFAXqAyHf79jSX//Mf+nuXLnRutWoVqSBWrKDAQiVrRU5ODuLj4/8xRvsL4B+yDUClUnWRy+U7mzVrVnvgwIFGNVKDGxtLi4aJCdWBabVltyupJgy1sjY2NmqZTFag0+m2Pf/BPj8CAgI0X331VT1HR8ca05sWFBQgIiIC9+/f1+v1etStW1e4efMm8vPzIUkSe/jwofTYPRoZGRnIysqCnZ0drl27xoODg5Gbm8uMjIy4paUlHzp0aIWtQADQAz02lq7VjBn04HiZ8tkdO4p6jdYQvL29hVatWuH06dN85cqVrHbt2no/Pz/xeTOjgiDA19cX3333nbR//34MGzZMdHZ2xsyZM9nOnTulNWvWMIVCIY0fP16QyWSwtLQsZaImSRICAwORlpbGBhfvBVtdHDxID0jDBm3s2Oc6t+qiXbt27NSpU+KaNWvQokUL7uXlJbOysoKdnR3kcvnTJ63VEiks0Wv9VeHatWuQJAmjRo0qP6vi7k4biUOHSOZ75syzEZPqYudO2pyUtSEuC9bWFWfBagjXr1+XHmcGX66CZ8AAIocArU8LF1JG8/TpohrIpCSSiTZqREGo5zQurA4ePHiA48ePSzExMYJcLhf8/PxQbT8Bxigr3707SUv376dN/ZgxRKjKqpXt2JECb5wXuegLAhmlaTSAjw/NV4WC1ggD0Y6NJXn0oUPVm88HDhDBPXmyaGPerx8F0itAixYt0LBhQ2zbtg3vv/9+Ueu9KiA4OFhKTU3F5MmTS3tj3L9fuQHg4xZTgokJJk2aJGZnZ2PLli348ccfMX36dJw/f1537do1IScnR5AkCaampnq5XM4yMzMFSSKRnL+/P2Xku3enMf/pJyIHc+aQXLxRI7TYvl041aWLdOXoUf5xWpoICwtaj2NjKYhy+DBdj5LzUpLoNUuW4I+4OAyqVavIRO7mTaoLL4tsW1mVmdlOS0uDtbW1JJfLRYCC6l5eXvo/fvtN9OzeHT1++aX0uhISQmRr7Vr63OvX0SAqCvvd3dkQKys8CRUkJtJ4t2pFhCg8nGqIBwyoMJMvFRbiiLMzrkgSPN3dmbu7e5F8ed48InQhIdVz2E5MBE6dQh6A6d9/j0dWVrcFzkMAHAfniQBgDWAuAJVKZZKQkPDxpk2bvpg2bRql6AsKqtwOceDAgWzv3r0wNjbmXbt2LXpYODoWGRX26lXUqz04mFQkn35K82XCBLpHO3YkFcqoUfT3IUOoft3ZmeZI3770jJQkCu5s3/58RNvQR75WLQo6KZV0nTZvhmN6OmovXkzXcP16Cu65u1PN9J9/UjAtM5Ous4UFBQASEohAe3qSg/qyZUUK1ccBNQBk4vgYp9PSeCO1WsKsWUUT//Fc4ZwjNTUVsowMWNeqRd/Rvz+tf5UgLCxMEkVx15w5c3KefYD+wcvA35psq1Sq5gqFYq5SqRzm6+tr3Kome7DOnEkP/a1bq+12XREuX76czxj78uHDh7kAzgYEBKTV2Ic/JxQKxXg3N7cacQu7f/8+Nm/eDIVCobe1tRUUCgWLjo7m9evXZwMHDmQ6nQ5bt26V1q9fD29vbwQGBkKhUEhqtVoQRRH9+/dn9vb2sLW1ZaiOhNrGhtzKr1yhh4ZM9uIdfDMzaZE/c6ZGZVIAYG5ujubNm6N58+YsLy8Pe/fuZatWrYKnpyd69er13J9vaWmJyMhIsX///jAxMYEgCBg+fLiQkJCAa9eu8R9//BGSJEGhUPDatWvzhg0bCp07d0ZmZiY2btwIU1NT7ufnV665WJVgbEw/16/TxsxglvOSDPvMzMzw8ccfIzMzE/b29uWzm8hIegCnpT3d4uQV4Pr16zh16hRPS0tjjo6OT0f3y0PHjrRRZowIxgtw+H4KS5aQBPjDDyt+3fbtlFUKD6csywuEJEnQ6XSsS5cuL98NUhDoWZKRQURPq6XypH/9i+b8Z59RHe2tW0Qgi7V2fNHIysrCmjVrYGVlBRsbG5iZmUktW7Z8PqZfvz6R6LfeIgnvtm20afbxKe3UPmsWSVO3biUVxqef0kb/3j2aH926UY2lwVfh0iXa/F+6VLFUHSBCYmxMmcfPP6dNtmHtrIaKQhAEmJqawsbGhtvZ2VV5/nDOkZCQwBwdHVmp4EV2NslRKwoWKBQkZ7ewoPs2ORkWdnaYPHmysHr1av2RI0dYeHi4rHfv3mjZsiUYY7h48aIYEhLy1LE/5cliZEQKgu7dKfs3YgQ9KyMi0K1bNyEoKAi5ggAzgOqC9XoyWRsxgurmo6PpMwzE0tiYMtR37+L99u35lbt3eRMPDwH16lFmftUqen/J52OzZmUGqGvXro3o6OinxrhHjx6i/cyZSD9wgGqvS2LePMrS//ADrSULFqDTJ5+woAsXkJubS2T7+HGaW1OnkhfC9OlECqdOLX/8AaxevVrXeN8+WZerV9EuLAylrn+DBhSIcHGh7yhP0s05EbwtW0iNMWAAkJuLEEFQh8yYseLTBQtml/1GICAgoEClUi1MT09X6XQ6yObMoeM3BJ8qQZs2bXDkyBEpLy+v4vtaLifCOGgQ/btjRyKoV6+SsaOvLxHbbt0oaLh/P/390SMi2lOm0H2+cyfdX2UFDCWJAouiSONx6RKtCcuW0Zo4ejRljnv3pvXQUCaydGmR3N/CAmq1Gnl9+sDqnXcoi5yUVKRIMAQQivtNLFhAaqvPPqMA9K+/kifBJ5/QOjxkyFP3IuccgYGByM7OFry8vJ46kcTERJw7d06flJQkZGZmMgD4YOhQ2HAOdu0aWMlOCyXAOcelS5cKCgsLv63whf/gtcDfkmyr5GSskQAAIABJREFUVKouSqVSpVAoPN3d3eVdunSR1bgJ2p49NZ5ViIiIQHR0tJZz/mtAQEB6jX54DYBzfjooKMjh3LlzuvT0dN6jRw9zFxeXavfizs7Oxm+//YYuXbrA29u7+C7iyQNKoVBg7NixQmRkJPbv388dHR2lMWPGiCVf90xwdKSIak4ObVhDQp67zUSFYIw2cDVMtEvC1NQUb7/9trBlyxYEBwfDzs4OLVu2BEBj/iy95JOTk8XHfz6R1TLG4OjoCEdHR9Hb2xsymQxJSUksJiaG3bhxQwoJCREUCgVv2LChNGbMmCq3rKoUBrntV1/RxuVx7/GXARMTk4rr1DmnMoHjx4taEL1CnD59WsrLyxO6d++OBg0aVO1+sbGhtjcbNtCG8/79F5fh1utpQ1xZtwytlohP8f7FxcA5h16vr5oBTiVQq9X46aefIJPJWMOqmgK9CNSuTYEbgCSa33xDG8CbN2lTO2tWue7KLwLJycn46aefULt2bf306dPFo0ePIi4urua+wMqKSIFGQwQnJISy+l98QUFKA1l+/336e04OEYjBg2kj/s03pHjYupUyRsbGpNDYsqV8on3jBn3G77/TnH/4kDbyHh50PF9++UynkpiYKHXs2LFaz6fw8HCekJDARo4cWfo/33yTHKS3VSJwO3mSFA+G2tKdOwEAQ4YMEdeuXQuAyJThGRAZGcldXV3ZgAEDwDl/uq2dJJH8eMIEUhu8+y6N76JFwOjREH/7Ddbp6bh58yaemLCJIj3fDIqeefPoWDZvpr9/8QVdizlzoNBomCYlhUkxMRC8vem9ERFEbKZPf/q8HjwobUAGwMrKCpmZmUJkZCRcXV3BGENkZCRuN2+OHiXrtOfMoWu6fz89i2/cIHm6vz9SmzYFO3MGVu+9R+3kTp6kOXfxIhGsI0cqHnc8MceSdV2+HBb5+eVnv+vWpeDBt9/S+Bqg0VAg+dw5yhZ37Uoy6wULyMhSEHBpyRKpQJJ+q+xYZDLZl8bGxoVieroxdu6sdoeHAQMGCHv27EH9+vVR5VJLa2v6aduWarO1WjrXyEhS6mzfTmO5di2Nr5UVEWV3d1KiKBSUZR4/npQoly/TtTK06GvWjMamc2cyazMzozKB//6X1AtWVkWB9+DgouNq0wYpS5eisFWrqrubN25Mr71/n66DQVY+fTol1/z9qVxl3TrAyAgFMhkuPA7sfPfdd5DJZBAEAZIkgXOOJk2aiJ07d0aXLl1w4fPPcWHuXNzo2BHj163DlUGD0L8C87a4uDhotdpUACFlvuAfvFb4f0e2VSqVLciFMQvAzYCAgMzHvzcHMNTIyGiWqalpsx49ehi3a9eOvTCDmxom2jqdDnv27NFLktT/dSTaAFBYWPh+QkLCNc55GoD7x44dm3fo0KFODRs21Pbu3dvM8CCvSFrIOcemTZukBg0awNvbu9JBdHNzg5ubGwNQ87t+c3Oqx3J2pkV/4MCal2WePEkbhu+/r9nPrQC+vr44efKktGPHDsHMzEwnSRLLyckRlUolLywsZJMnT0Zlmebw8HAcP36cA2BvvvlmuZJIw/1Vv3591K9fHz179hRSU1ORlJTEWrZsWXNEuzjef5+yLbm5FNUuwy30paNNG8rSVZIBeVmwtLQUdDod79OnT/UDU6NGkaTT0K+4mFyuxtClC9UcV+TEevw4ZcrS0kqZoWVmZiIsLAxXrlzharWaffTRR1Vzu68A69ev55mZmez999+vdgDxhSEoiDaW7dqRhHHECKob1GpfSglMeno6duzYwQGw6dOniwARnevXr9d85l+hoMyXtzdtsv/3P8pY2doSiba3pxZLI0fSOj1zJhGxXbsoUxgeTlnVESOoPrQkWYiNJXXR2LFkzrZyJW3oR42i/zdk6p4DBQUFpXxDKoJer8fx48eZo6Nj2UqBQ4fKlFGXgpMTjY/Bffox6tWrhzFjxiA0NPSpYKtcLpccHBzEUgFEnY7q6efNI6I/d27R/82eDXz+OcRt2zB4zx6k1qtHQZ+yMqeGGuH0dApofPEFkemBAyH+9BPYxImS7qefBMU339DrZs0iBZjBINAASSJJ75N/Srh48SKuXbvGTU1N2c7HQYU69+/Ddvx4iEePom6bNvRizimop1RSgJ0xmh/ffEPzy9UVDUaPhru1NY8eOZK1+eQT6E6dQuHKldDr9Xh09y50Oh2cq0DUTNLTYfbNNzTvKsLhw5R9nTGDZMTm5jSutWuTM7W3d6k2sZxzFBQUGANopVKpHjw2ri0TgiA0Vz54oGCRkeRdUE31l5ubG86dOyfFxsaiRYsWz/bwlsvpPAztssaOJQVKYSGRbIDuzZ07KQnRuTM9Yxo2pFpppbKonZ8BEyfSn66uRb+rgteHgfhWGaJIme74+KJ5LZcXeWN4eJBr+oMHgL8/TN59F7MGD8bvgYFcMDGRhg8fLup0OsjlclhYWDxlsNktKQno2hWDvbyQmJ6O61FRPCY+Hj4+PmjZsmWpCxUSEpKv0WhWBAQE/NO/+S+A/1dke+HChR/LZLIFtra2hWq1GpmZmSZff/11oSiKOaIo1nZ0dNR26NDBrGXLlqXqR19nqNVq7Nixo0AmkwVqNJrS3e1fEwQEBGgA/K/Yr46rVKq6sbGxQ+/evbtcr9cr5XK5fvbs2WXuArVaLW7fvo20tDTh3//+98s56MowZAg90CdNooxeiX6Kz41z5ygjZdjQvQSYmJhg4MCBgqenJxISEmQajQZNmjTBw4cP2enTp6Xz588Lw4YNq/Az0tPTkZOTwwDgwoULRS2jqoC6deui7ovs+Vu3Lj38fv+dyFpS0kuTlJeLr79+9f2qH2Pbtm1SXFycMHLkyGcbFEEg+ee2bbQpTEys+Qz3okVPt6EpiYQEymgfOFDmd584cQKRkZHw9vZmFy9e5N999x1zcHDQe3p6Vt2JuQQEQWDNmzeX7OzsXt3DQ6+n9cLZmTbhNjaUGTI4bB86RCTC2JgCEP360et++41klDduUFZoxAjKFDVsSL//4gsimgZjtYMHaZNrakpZ3caNi1zQnxyKHiEhIcjOzsa7jzsoZGdnIzAwkL9RUbum5wVjFFzYsIHkyIcPk0T84UNSXKxeTdlvNzci4WlpFCgdN47Gb9asIqLNOQXnPv2UAhfr19Pr0tKK1owKetxWFyYmJjwlJQWohvoqNzcX/fr1Kz3nli+nZ5OhFVdFaNOG1sLcXMrUm5k9yc43bty4lNlfz549xSNHjkChUBSt7WvX0nsTEiizV9aaKgjYZ2qquzV2rGz6oEFEWocPJ1Je1hphbU0tvwCam0ol0KoVvLZtE/KNjLgiOprB1JRI+dy5dG18fYvKcIyNnzIiW7FihV6r1YrNmjWDUqnUN23alCkUCmHfsWPweeMNtDIQbQDo04cynwbiHxdH0mC5nMbm5ElkduqEbqtXs4P+/jzt999ZRGwsz166lImiCIVCwQsLC9mHH35YupNDMWRlZcEqOZkIWGUwN6d1NS6Ozm3YMFrjbG3LfQtjDH369NEdP358h+FXJV+jUqmMAIxijMk8jx4lQ7Zn3MuYmJgIISEhyMjI0I8cOVLU6/VQKBTVVw9JEs3H4GDa/yQlkYQ8IYFI844d5EcxZcoLC5gLggCtwdCuqjA3p/WlrPJQM7OivVxQEJCdDbP//Q8fHD7MVvv5ieq9e9Fg/PjSrQdXrqRAjLExsG8fHPr2xadDhrDNmzfj1KlTUkmynZ+fj+joaIFz/kc1T/kfvCL8vyHbKpVKALDU3t5em5qaaiQIgmRvb18giqLC1dW1nouLC4yMjF6TdET1cOHCBcTFxYXp9fqRf7UoVkBAQCqAn1Qq1VEAwywtLecDkOv1enDOIZPJoFarsXbtWp6ZmcmUSqXk7OzMhVfhBl4eBIEelIzRpu2DDyiq/7y4do0kbK8I5ubmKO5TYGZmhtzcXCEwMFCPSpQCvXv3RkFBAb98+TKrMef+msa4cSTrunOHNtM7drx8N+1Nm4gIFJevvUKEh4cjLi5OmDJlyvM70/v5kVT39m0iPl9/XTMHuWoV1fmVR4qjoohsPXxYKquu0+kQGhqKu3fvQiaTwcPDA66uruzixYt49OiR+Ntvv8HU1JQ3bdqUv/nmm9Uizaamprh165YQFxeHRmUZddU04uJo4+ngQJnE3r1JWn/2LGVrg4Np7O3siFRHRdGfgkDSU72eMtxNmtCPXF7UisrTk7JDjFFGiXOSZ6alUfZ44ULa5BYW0t+Dg2lzbmkJbNgAadAgHHN35/qcHDbz7l1mMnUqMH48bmdnS027d0ebX34RMGECZR0vXqT63iNHyOCsYUPaWDdoQETpeQJhTZrQZ+fk0OcbgggdOtB5FBbS98ybR1ndIUOI/G3aVCRhTUqi8544sShD9oKCc7a2tsLDhw95Xl7e09LsciCKIlxdXfnOnTtZTEwM3nzzzaJEAefVU1pNmUIk1cvrqex2WejQoQOysrKwb98+tLl8GaIg0L3evn2lYxMVFSVz9fAAa94cOHUKujt3UOjpiRxHR9iuWQNWnJg+ekRy8vh4ug4zZz7pJ2+kVrOCGTNg3L07Xa9336VOHatWURa8Vy8i3snJwPffI3jxYoz4/nvRwcMDoq8vw8yZYuH06Uj38YGvuTnsQkIo4FC3LikXWrQgWXNYGAVsDGqHf/2LjmvxYlgtXoxLDg64fucOE0UR7u7urMvjjKmZmRn74Ycf9D///LPg7+9fpu9IRkYG9uzZg97DhumEoUMrfvgYSma6dyfTsuHDKeBVBXh4eMhPnz4t6XS6yHJe8gGA5dYpKTji56eJadJEZxUUZNKkSRM4VNNUcvTo0Xj06BE2bdqE77//Hvn5+VAoFOjWrZvUuXPn0gZ+JWEwMezQgc5z3TrKbBsUjwavCUP5RkIC3cuXL9eIT0h6ejoyMzNRr149CILA9Xo9y8jIgCAIqFWVNomdOpH6YNasil9Xty79rF4NITkZ7idPSrq5c4Wcs2dhPmIEeSh4eNCa/u23pMoBKOAzYAAEQYCHhwe2b99e6iYPDw/noigemDNnzrPb1/+Dlwpx3rx5r/oYagReXl78woULtfLz80O0Wu1IvV6/Ojs7+3xWVlZIfHx8+9jYWFhbWyuqdDO9Rrhy5Yp0/PhxHef8XwEBATdf9fE8K7y8vDKDgoKitVrteM65+caNG6XIyEjdvXv3Ck+cOCHTarV81qxZrGfPnszNze31kx0YNhjXrlFEHCjq6/os4Jwe7C4uZbusviJYWFggODhYaNmyZYWbwYsXLyI4OJiNGDECXaramulVQBRpM3fqFG3ECwtfrsO8iQkRitdgjHJzc7F161bu5OQkdezYsWbuMZmMMlK//kp1wzVBUubOpU1Ku3al/+/QIarlK6N/dE5ODpYtW4Zbt25BkiTu4uLCWrRoASMjIzg7O8PV1RWtWrVCfn4+Cw0NZUFBQbhz5w4aNWpUJYm5s7Mzzp8/j7i4OHTu3Lnm1FE6HbnkPnhA0sjp04kgzpxJJHf0aDJF8/CgetG9e6lG3d6eMiFKJbmS/+tfNL/Pn6eNrCjSXLe1pT/t7UktIJPRhr5+fSLygwZRtqZvXyJUZma08WvXjjaWs2bRWE+bBowfj4jEROzPz0duixZ8+LRpTNmoEdC2LTQJCbiYnMz6jBnDjC9epBrN69epVvqddyjj8+gRrZv+/rT+/ec/RHi++IKOLzubggTvvkvE7rffqJSnf386DhMTOsctWygrGRlJm/GGDWksZs6ketbduymb6+JCpTqpqZTpXrqUJOXW1pTF9/Sk4zK0gHzBcHR0xJUrV/iZM2eYi4tLleZd/fr1mampKS5fvgzOOZycnCjT27VrUfulqsDbm8ipuzsFTZKTqea/HGRGRSEmPh49Da3m+vWja1QB8vPzcebMGaSmpvLTp0+z82FhCI6ORrSjo16MimK3T53iNqtXM+Wff9Kxt2xJ8vBOnQCZDNzdHfffegt5oogrzs7c6eBBJvr7U1a9WzcKHn32GWU+Z80C7OwQXVCA3U2aSDFaLdq+9RazGDAAqF8fegC7YmKkhteuoZ63N8Mbb9D68csvNH6ZmXSf/PAD1ePn5NC9MmMG3VNNmwKurnDo1g03btyQ3NzcuLe3N1MoFE/kvx07dhQePnyIw4cPszZt2sBANOPj43HmzBmcOHGCd1QqJa8PP5Th008rDo688QbN5+++o3/n5lbZKTwjIwPnz58H59zBy8urVEImKCjoisO9e0kT1q3zylEo3r9qZHTq3r17UWFhYR0cHR1l1dkXM8ZgYmKCTp06CQqFAv3794eDgwPOnz8vBQYGCowxycnJqfSDQKulZ4WLC93/b79NgTG5nNaEkqhThwKunNO64ONDwTCttsrjUhycc+zbt0994MAB/c2bN+POnj1rWVBQIKakpOiDgoKEixcvonv37qXWdY1Gg4KCgiLJt4UFrbH29riZlQWlUomCggJs27YNeXl5Re1ki8PMDPZubiyqUydpc2oqkw4f5nV//JHx+vUhHjhA869ePTo3Q226XA4jIyMEBQXB09MTBg9Tzjl27NiRl5eXN8PLy+tetQfiH7wSMF6ZAc3/A6hUKjljbKxcLl9obW1t7O3tbf7SeqQ+Jw4dOqQPCQlZ/eWXX06v/NWvP1Qq1RsADgiC8AUAC0mS7oqi+Jler3f28vKSevbs+foR7bLg5kaRdcODsbqQJFpYX5faz2LYvXu3PjU1lU2ePLncaxEZGYmdO3eiXr16Uvfu3QUnJ6eKDcJeB9y4QRvNu3fJNOVFQpJoc7tmzYs116sGQkNDceDAAcyYMeOZzPAqxZ9/Un/Q4OBn9zbIzyfSVBZpT08nOXNk5FNZ7/T0dMTGxvKDBw8yQzagdyWu5CkpKdi9ezdPT09nOp0Ofn5+1NYIQEXG7Ddu3MD27duhVCrh4uICtVrNhw8fzqpEvCWJzmHXLiKy+/dTtjU4mJQy/v6UeY2KorXFcI00GpIMr1hB575vH8nsJ04kwswYvffWLfpcBwf6zBoySZMkCVFRURBFESYmJli/fj2aNWuGtw3O3o+xcuVKva2tLUaOHFl1VZJeT9fczKyo/loUqdXTgAF0HrGxlJWdMYOCAUZGtDn94Qci6w8fEoHq2pVeo9PReG3cSKTdYKDFOY23r29Rq55XgLy8PCxbtgyenp5PbaIrw/Lly7mLiwvz8fGh7G9yMklVqwND7+IJE2h+/P572a/T66G2tET42LFSl9Wrq3wza7Va/Pe//0Wb5s3h6ekJYd06GKWkwOjtt8HffRcxXbtCCgmBc5cuSJfLcaxxY32KQiE0btxYMjc3F0NDQ3m3gwcRPmCAngkC3vnmG5mxuzsFuKdPJxISGwv89BPi4uIQ+Z//8Prx8Uz44Qe4uLhAXiyQGrNmDZK3b+ddDh1iMpmMavzd3CirbWxM99kff5Ak/vr1otZ6/v4UtCmWgd+6dSvX6XTS6NGjy5zbK1eu1FtbWwvt2rVjgiBg+/btaNCggT4rK0sYP3IkM4+NLd/borCQAj+HDtEcbtiQ7glXVzquKgRkjh49qgsJCfl+zpw5H5X5AsZEAEoADcH5TQBQqVQyURTPd+vWrWPPnj0hPmcp0JUrV7B//34MHjwYbdu2pV8a6qFHj6bnbnAwlX48qxru88+JdFtb07X73/8qf89jpKWlYc2aNRlarbZRQEBAtkqlcgbwAWMsn3O+TaFQbO7Xr59byZK4hQsXcp1Ox8aOHQsnJydIkoS7X32F2zdvSheK+ShYWVkhOzsbMpnsyfm3a9cOVlZWuHr1KtLT03Hnzh19bm6u2KlTJ9yKiuJjVCqWZ2nJE9u145bm5oKdRgPLxo0hFFOJff3115g5c+YTr6N79+5h48aNiRqNpsFfTen6d8bfgmwboFKp5ACGymSyX99//33jOhVEdV8XBAcH4+TJkwAgBgQEVMPJ4fWFSqVSBgQEFALAwoULV2k0mikAMHHixGpLml4ZDBmaPXuIMBsMMqqCvXspc5Sc/OKO7zkQHx+PzZs389mzZ5e7C9RoNFCr1Th48KB0//595OXlCXPmzKkR1+cXipAQymasWkVmZS/Ku0GrJeOXtWtfuMt8VREXF4fNmzfj7bfffjEy6ORkkmL++99k2vQsta6uriRPL+n2/NVXVJtqZAQIArKysrB9+3YkJSXB8AxzdXXF4MGDqz0Hg4ODcebMGWi1WtjY2GBqGSZ2kiTh6tWriIiIQEJCAgRBgEKhAGMMcrlcmj59uvDke/V6kk/HxFAAYtIkOqfMTMryLlpEZMfBgQhgq1al56FWS4qMQYMooz1mDGXefvutyPV2/Hgy/Soe5EpMJPIdHExrUg0E80JCQhAYGMjlcjnXaDSCUqmUJk2aJJQM2CxatIj7+/szJyen5/7O54IkUSZMo6GN/YAB5HZc3MzrFeLnn3+WEhMTBQDw9vaWPDw8qrQIBQYG8tu3b0tTp04VnxiDVXdtGTKEAhTt2pVdQ52URON19Ci+W7VKch84UOhcmZTZULKwahVgZYVHaWnA8uU41LcvWubmSu26dhXw4Yd0XZRKrFixQq979EjsERjInfV6lj1jBsJycpAhiryzoyNzmzQJ7PZtmrtJSVQCtGsXSf0LC6l0onlz/LFvHxpcusR7ShJjP/xQ+lRmzsTD0FCpTXCwAM5J2TRkCGWQT52iQKi/Pyk9/v1vIoM6HSkdGKPa6ceBkIULF3IPDw/WsWPHMoPKKSkp2LNnjz4rK4sVFhYKDRo04O+88w6DJNG9u3p1+QGerl1J3Vayjdzs2WRs9/77pd4iSdKTDGxeXh6WLl0KANMCAgJKDwQAMPYDgCbg/EnPPJVKZQYgBwBMTEzUU6dONapKaUNJPHjwAEFBQYiNjcXw4cMpaMk5XS9XV1rz7O0pU11T7QgPHiT5+Y4d1CJywoRK22lGR0dj586dkZ999lmZfdVUKpW/jY3Nzx988IGpYWwlScJXX32FVq1a8YSEBK5QKHhmZqbYMDOTd2WMO379tcAYg0wmA2MMmZmZOHv2rJSZmcmjo6NFAJDJZLC0tJTMzc15mzZtRGdnZ6rxj4mBRqlEkk6HpKQk5B04oO+6YoV4tmdPJAwbJvXs2VNo2rQplixZIo0ZM0awf6wq+fPPPwtu3rw5b+7cuUtqZjD/wcvAa74zrlkEBARoAWxbsmTJtLS0tB5/BbJdbENTC0DGKzyUGoOBaD+GBwDIZLLsxMREMwcHh79GZtuQGb1yhTYaQ4ZUXUI7cCBlXV5jaLVaduvWLQBA06ZNERERgZycHOTk5CAqKkqvVqtFrVYLIyMjplAomJ2dHRdF8RW7kFUBnTrRJmDxYpKmvois87p1RJ62bKn5z34ONGrUCC1atOAbNmxgAwcO5O3bt6/Z62VnRxv5H3+k+u27d6sfzNi6tXRrHI0G2LgR2v798Vt4OE9KSnoSJO7QoQM6d+78XIZ7np6ecHd3x7Jly/Dw4UP88ccf+s6dO4tNmzZ94la7dOlSSS6XswYNGnBXV1cMHTpUEAQBmrg4bNu7FwdGj+YdHjxg9TdtojpLX18iLfn59CUbN9KG09SUzrE8aLVU/x4QQLXZs2dTJmf9eiLn7drR5vv2bfqz5Mb/zh0iETodZYoXL37mcTHgwoULvFu3bqxnz56G+VLqom7evJkDYPWr2LP3hUIQSFru4UEkzdubghY+PlSb+wrNUVNSUpCWlsZGjBiBsLAwREVFwaOKxokmJiZMbyC1t25RnWd1sWsXyexFkQhKrVqUHczNpczqoEE0XrVro9DMjJ07dw6dOnUqyr4b1BlbtlCWeP9+mtvnz1Mg09cXVh98AEybBtfoaBw7doy3K2HgNm3aNDE0NBRt5sxhCpkMdQIC0HjnTmD3bga5nNYNA+ztgY8+orKA33+n9XvdOuTUrYtYuRw9+vRhrLDwqc+HJAELFiBnzBicatxYatOnj4B+/SjwtXs31QI3bUqKiBMnKGhlZkbzQqGgOv6MDLp/vb2Bjz9Gr1692NGjR5GcnKz38/MrFaWwtbXF5MmTRYA8doKCgmjAHj4kNUpZQZHoaApK/vFH2Y7t778P/PgjMtLTodFqwTmHkZERoqOjcfDgQdStW1dq1KgRrl+/LigUinMajabs1l908X4H8FR/xICAgFyVSiUDIGq12tjU1FSH6gZhb9y4gV27dsHJyUkaN26cUN/BgVqS/fEHKQe2bKFygZq+5954g360Wnre9utH97yxcblleffu3eNqtfpEBZ+6LT09fc2ZM2f0np6eIoAnmeqBAweyZcuWsWbNmul79+4NE8YYfH0Z9PqnlAe1atXCwIEDBQA4fvy4lJeXx3r06MGsrKyeHoD164F586CIi4OTKMKJfivi+nV0NDMDrlzBpk2b4ODgICkUCh4fH4969eohLy8Pt27dYpIkVWJr/w9eN/ytyDYAqFQqplQqG78QGeULgJubG8LDwzVJSUknALR91cdT05g9e3YbAFCpVD1Pnz59oEmTJqa1ayr6+TJgaE3y8ce0uT1R0VoOqqts0aJ0v9DXCE5OTvD09NRv2bJFVCqV0Ov1MDIy4sbGxlwQBHTv3l08ffq0NGjQIIExxo4fP86HDh3KqiqHfOWoU6eo1YirK5GbtjV4a929+/KN2KqIN954g928eRN16tR5cRdr8mTaFGdn04bL3b1q7wsIIKmli0vR70aNonreqCicPnGCp6WlYdCgQYiKioKPjw9qKmCqUCgwbtw4HD58GJxztmvXLm5vb89HjRolAIA+K0uY0L49rPv1Y5g5k+S7vXpB8eWXGP3FF0KCiwtuZWZi+x9/SA0XLGBDxo8nafngwZV/uVZLATtvb5KQzplDDsQNGhSR74sXKdvv7U01x5s3U7a2JOrUKXK3TUwk8t206TONSVZWFgIDA/U2QgpxAAAgAElEQVSZmZlim+IuziWwY8cOnpCQwCZMmPDqlS35+SQhnz2bAqFr1pAE3dWViNS9e5QtfEU4fPgwb9KkCVq1agVBELBjxw7h/v37qEqQIjw8nNepU0eATFYUxKkuLl8ucmgfNIjItl5PionVqyn7+1iaO3nyZPbbggXIWbIEFiNGkEQ/LIwk/hcv0nPso49IMWBp+VRwsbCwEKdPn+ampqal1hmZTFbUfxsg1cqUKRSE+uQTIu9+fkUErUMHSBERkNq3R46FBbb6+qLFxYtoPXWq3tHMTMSNG09/wd27wC+/QOnlBVZQwDBqFJWdjBpFa9GGDaT8eOstul8SEqgnswGMUZZUpSKCfvky3Js2RWpqKhITEytdN1u3bo0jR44gLy8PppxTp4Cyno2TJlFXgfLagTk6QvPoEbZ9+SWyGzaUAECr1QqiKHJPT08GQAgLC9PnUu/ybgBy5s+fP5Jzvp0xduDLL78cBMYUAMIA+ILz6DK+pT6AeO1jMp+amgobG5sqlTZIkoR9+/bx/v37o0Pr1gJcXWmt9venshjGqBzmRUIup8ATQD4esbFklHjrVimpekpKSgHn/E55HxUQECCpVCr/kydPHmjRogXq1q2LzMxMKBQKbmJiwv5DbcmKAi1mZtRNxuDhUwJ9+vQpO8KQnU0dH3r0KFKXfPrpE/NIa8bQr18/IT4+Hg8ePBDc3d2l4OBgfubMGSaXy7kgCFsCAgL+XyTe/k54PXeELxa+JiYmtcrrC/y6ISQkRHf37l25KIp7X/WxvGAEq9XqeatXr57fv39/ow4dOvxFmNtjfPwx1QTn5hLp7tGj7Nc1aUIb6tccXl5eYs+ePQFQdNfc3PxJf9jExETY29tLu3fvFmxsbPjYsWOZdSUSrtcOjJEkedgwuh537z7/ddFqyblZpXr1rcbKQVpaGvR6PZ61/VWVIAgk1f/1V8p0JyRUbTzu3CFTKwMMfXA5h1qtxtmzZ9mAAQPQvn37arWaqyrs7e3x3nvvAdeuCRrGsDo8nD1YsQKihwc8rl2D1cmTFAho25buYx8fYMgQMMbgCMBBr4f97dvCkSNH+Pfff6/39PQU27Rp85Thzvnz5yFJEkxMTFDf1hY2ly8Dn36KmK+/xq3WrbnzhAmsucH854cfiFQbamw5p/XFz48Id1lwdaVMT2YmGa4tWAAcPVptF9+YmBhs27YNtra2bNq0aeW69Obn5+PatWts2LBhsLGxqdZ3vBBcvkykbdYs2rzev09jIpdTFnbBAjLlGj78lRxeQUEBZDIZZ4yxli1bomnTpti0aRMmTJiAytZQxhivo9cL6NmTNuvPgg4dqH8x51Rm0LcvBXGCgyk7uGYNEe433oClgwPqGxvzwshIoF8/hiVLqKa+Th0irOVAo9Fg1apVAMCnT59etZSmvT0FoBs3JgLq4gKMGwfps8/wIDkZgWfOwMbdHf2OHUOvTp2kZk5OAlOrRVhZUb2zAWfOUDu8mBjUd3RE+5YtBQwYAMyfT+e8cSOdQ/HSjcOHyzaw7NKFfr78Elnr1uHaO+/A8403Kl3IEhMTIZfLYSyX01wLDiYVgAF79lAZ2uHDFZd5CAJ0nTujVnAwPvjss+Lj+OQYevXqJep0OoSHh+PAgQMwNzf/Izs7G5zzNSqVSphRq5alVWbmEQAx5XyLFwCIonh627ZtjTnnShMTE9OpU6cay+VypKam4tGjR7h375508eJFJpfLCywsLHTt27c3S05OFlxv3UL71asZoqNJzTRgQI22yqsWfvyRrvGdO6QASkmh5465OTjniIuLEwH8WdFHBAQEHPzqq6+iVq9e3WLu3LlQq9XlBxBnzqSgUzlku0xoNNRpY8ECWsdv3SKi/csvT2TwsbGx2LVrF8/NzWVKpZL37dtX6NOnD27fvo2tW7cyAD9W/Qv/weuCvx3ZVigUy/r27Wv6svps6/V6CIJQZRMUAwoLC3Hs2LHCq1evyjnnG3Q6XcALOsTXAgEBAVylUt1hjB0+e/Zsvw4dOrzmblslYG9PP6tXk+NtTBnPtq+/psj9a2iKVhYMc9ayWK2ZwRhNoVDIOOfIyMhgrzyb9TyYP58ygc2a0XV7551n/6wbN4p6Fhfr/fq6QJIkHDx4kDdq1EhCJa3dagTjx1M2KSyM5Kvz55f/2pQUkh4aIv19+lDrtseS65S7d8EYKzLeeV4YiPzWrSTFNjam+3PaNODAAShSU+E0ZgyuxsYiTiZD888+0wkDBtBEL8fcRxRFPCZQ7OjRozhy5AgOHjyIxo0b8759+7LU1FQcPXwY9nZ2ep/vvhPyNBoWNHmyZPXuu8LF27ehtbVlsvR0NI+MpM3Yu+8SqXFyouPt25cyRipVxec2dy5t8C9fpux2Rka1yHZYWBg/cOAA69u3Lzp37lzhgzIkJAQKhQIuxdUIrwJHjwIffkhZREPP8du3KZNZPLBua0vz7BWR7fz8fKl169ZP7r233noL69at45cuXeIDBgyocKzNzc257e+/cyxZwp6cY3UhCBSwcXenzG5GBgVlFi0ipc/166TGWLAAmtatcfPnn1kbf3/YODs/+QhDBrROnTqlTLVSU1Px+++/Q6vV8k8//VSo8rMhJITm/J499O+tW4GgIES88w4epKfjfvv2cP3vf7n41Vesubu7gMmTqW533ryns/wffkhBlsWLkfXtt3CdOJGhYUO6tw8epPP393/6u5cvJ8O98taW+fOR07s32i5dimapqQxdulTY0eLUqVNS+/btIYiigD17nibaAJFsa2twhQI6rRZqtRqhoaE8KSlJ0mq1zN7eXqhbty70ej2a9+kDrzlziKSV80yRyWTo0KEDOlAvaqMtW7Zob926tcctPBx3mjWL7nzxYkXSli0AAv/zn/8kAoBKpbLXaDSJBQUFuHz5si4wMFCSyWRxGo2mOQDo9XoXtVrtbO/ndyDP2VnZbP58xgYPJiXXiBEVfM1LAmP0LM/MpH2WszPg5wftvHnQajTKgHnzUir7CEmSeoiieGn58uV2ubm5xgqFouzNu6NjUeCqqvt7QSAlx4gRtO/49VfqulAs0BYXF4e8vDwGAEZGRhwAEwQB5ubmUCgUDzQazaWqfdk/eJ3wF94lPxs454rY2Fhtw4YN5eW5J2s0Gmi1WmRnZ8PMzIzMDJ4BBQUFWLZsmc7Ozq5w/PjxpsUfTMUNLkriwYMH2L59e35ubm4w53wmgDt/B9dBhUKxRKPRNPP09MShQ4d0Pj4+sud1yHzpmDKFaq0iIijyefw4LbCZmWSWNWkStTX6i0Iul0Mul6NPnz5o27ZtUTuMvzIEgSLMlpZEZAYNogxQdbBvH/WvNUjaahgajQYAnmu8jxw5gpycHPj7+7+8m0qhoLZLZ89WvCnp2ZMi/QYi6etL7akeQ6lUQpIk6HS66kuVc3KoNvKXX2hDfeEC1cwFBtLv+vYlM7v//peyDm+9hQcPHiDi558htWkDPz8/tGzZsspfKpPJ8MYbb4j9+/dHYmIiTp8+La1dtUq0i4/HZ2fPwnjFChHffovsJk0ghIXxFLVaP9rDQ9y/c6dkn5wsPDFQ69evaLx0OsoajRpV+QHMn0/ECSDH8g4dKADk51fh23Q6HY4ePSpdvXpVGDFiBFpUwTG4U6dOOHv2LI+Li2POxQjZS4MkUWCzWTNae4vPjeDg0vNt0iRSs8ydS3PtJdZvHz58GPn5+WJJVYmtrS2LjIxkffv2rXBuN2/eXDzcp4/UesaM55PNLF1K5DYvj9quLVlCEtwPPiC/gdq1gZUrkbV0Kazv3YPVgQPg06cj8eRJRD16xOMyMpCSksI453B3d9d7e3uLhsBsaGgo8vLyMG3atOoFYfPzn26/1ro10Lo1wu7elXrGxAjGpqa8bVAQw9SpdB/s3UvP0/v3KZMqSSTpPXuW3vveezA3MUGitTVqdepEdcNltUmTJFKiVbKu1u3WDXfc3Hj3y5cpi2tkRKS+DBgZGfGLFy+K7X/5BVaffoontHzRIsq6rluHP/74Qx+/YIEoSRJkMhlq167NHRwcxOzsbFy9elXSaDSCTqdDnTp10M3ODnb799O8rQRqtRpxcXESADRITEx0iYz8o6LXP/bPMRBtHwD7AOrwcOLECUmSJJfZs2dHL1iw4HSd+/e7v//TT4cZ0OVRrVofR7i5rezZtWvZRnuvGoaERmgowDmEn3/GzBUr8JUktZ87f/7Vit4aEBCQplKpmuTm5noAcJQk6ee0tDSjUiVLjRpRoDYtjdQUleH776nU4uxZKq/y86NnUIn9YHh4ODjn8PHxgYmJyZMF6vLly2q9Xv/z34EL/H/E345sa7Va97CwsGUZGRlvjho1yrgkmYuLi8PGjRu1jDGNKIoZnHOb9957z8jU1BQajQbGxsalemNKkoSwsDCkpKRovby85EZGRoiLi0NERASXyWQXU1NTtRcuXPA0uI5GRETod+/ezdq1a6d58803SzUY3LVrV05mZuZ/OOff/51uLI1G01upVO46fPiwvU6nE2rXrm3bpUuXv4ZhWnEIArXtadmS/n7vHj3M4+Nf9ZE9N1q0aIFevXohMDAQarX6iZHIXx4Gn4DISHqItm9fPSn4559TcKUM59hnxdmzZxEUFASZTCZptVpBkiQYGRlJjDFIkgRTU1PesmVLsVmzZrC3t6+w73NKSgrCwsJ4//792Uv3q+jXj37++INI7okTpcc2KIhMhLy8iBCXMH+Sy+UQRRHBwcHw8fEp+3uys+knOZnMlN57jwjY3btFG5wOHYjITppESpTAwKL3F+shfPnyZUiShHHjxj2zc7vAGBrUq4dRhw+L0pUriP/pJxiPG0eBBcZgAWBY48Z0/6SnY8CaNYJxgwYUoCs+Ph99RFna/fur9sVmZtQmzNAbe9EiOvdKMjA7d+6UoqKihMGDB1eJaAOAiYkJLCwspIyMDPGVkO1Fi4h0xcaW9sHYtKlsEzFzczID69aNiNtLwPnz5xEWFoaJEyeiZAlb//79ceXKlUoDSWa7d/PBu3YJ/NNP8cxsOzKSSl2uXiVynZlJ/gCff06GU05OFNhRq2FTty5a5+byzBUr2NqCAkxcsgSW7u5M3bIl5nz3HeIuX0bC9Oli/uLFMA0MhN7PD7KmTeFoZcWtFy0i2fnNmzTvXFyI2Ja1RiUnk+HoypVP/TowMBAJlpZC6ooV8Kpbl2HBAvJ1+OgjuraDBlEWfsQIWldmzKDezUOGAN9+C11EBDZs3YrZEyaUv9Hdu5ey42FhFQ7b/v37kWlkxPL/+AMWEREUvPrhB3ISL4HRo0eLp06ehPbnn/HTr7/C/t49dGzdGnZ16qDA0hK/rFiB7OxsccKECUhJSYGDgwNsbW2LD4wgSRIyMzOxdu1afrpHD9bu/Pkqke2EhASIohg5V6XaKnC+HpxXubZXqVR2LywsVAiCcGzTpk2ddDod1Y4w9uOnCkXk+a5dpzKgN4DcHz/7rJEkSXzDhg15ycnJQtOmTYVevXoZWb3olprVxeNnnWzyZFxOTi4QOfcCYzsAjAbn58p72+POP6cBYPHixeOTkpK8S5FtxihgdfYszbnK0KoV9YjPyiI/ifHjy0y8NG3alCclJfGuXbs+mRM5OTm4du0a1+v131fhrP/Ba4i/HdkOCAhIVKlU4xMTEw8sXbrUvVOnTopu3bqJSqUSjDEcPHgwT6/XTwgICNgKAF9//fUHa9euXcYY0zPGNHq93sLW1lYzadIkE8YY9Ho9fv/997yUlJRber3eJC8vr8Xdu3fztVptkk6n26HX69cBsDp79uzJjh07miiVShw/fjyfcz4/LCxs8cCBA5/aJCckJODRo0cC5/ynvxPRBujaAOgMUBuG27dvr+nSpcvr0TepunByolrLqCiqGZTJyPioVy96sLdvT06lWi1t+u7cISmRnR1F+evUoffI5a9d/W/Xrl1x584dnDx5Ujx58iTatm0rDR48+K8XFCkLfz4u6fL0pCwPtVQpH2o1Xbvr12v8OgU+JoK+vr6CtbU1srKyoNVqBcYYTExMEBMTI926dUu6cOGCoNVqoVQquY2NDTMEQdzc3KDT6bBu3Tr9w4cPxdatW/M2bdq8usnUsydJViWJxs1Q2zdlCjlHDxlC5KkMcmttbY1x48Zhw4YNcLS3Rwu9njbop06R5PTnn0nePXIkya9r1yalwv799DpBoCx2FTFgwABERETgxIkTGD9+fPV60Op0RDImTKBzmzABwtdfo3HxzJ0BajX4ihW49H/sXXd4FOX6Pd/MbnohCaSRAgklIYQSegcFlN6kKogICIj9Cnop64ACYkG5eAFBQCkC0ov0YugBAoGQhASSkN572zIzvz/eLNn0AijcX87z8CRsZmdmZ7/55jvve97zXr6M8E6d4PvRR3AyHEcaDQUgapLRNoSpKZ0HQLXlAMmGd+2qcJxmZmbi0aNHXKtWrVCVGVpFsLGx4ePi4sROnTr9fYG3ixeBCxdINvz+++U/U3AwLW5bty7/XiMjynqHhlKG19Cs6xnh5s2bctu2beHo6Fju4mdmZgKgoH1FkGUZsiwj2cyM6dzdcWnzZmnSpEmciUm5OH3VCAqifsd791JHjDFjqLxj8WKS13t7l2x74gQAoPv27SwhIQGdwsKQc+MGOjVtik6SBEyfDo+WLXGtQwfxklrNehYUcI/S0+VIKyvWysOD4ddfyTx0xQpyLz96lMbkzp1030+fTkHoTz6huledjuTWgkC/37yJS3fuwN3aGn6+vpRB/OUXel6OH0/BJ0dHepbKMgXWzM1J1hsSAixfDrPi8r3w8HC00vsglIWTE80XVSA5ORkhISFwc3OTHR0dGRwdiSQplXSP//BDKbdxjuPwko8PEBWF7nfugP/0U5mFh7Pl06eD53nIsoxp06ahcePGlbY65TgOtra2mD9/PivKzSWSVgOzQ41GgwZpaTJHisgNVW5cBp999tliAIsBINHJqYlNZuZhjZGRCOCKsUYT2/f8+bsA7gKAdsmS07IsfxIfH39Cq9XOv3fv3owHDx58MGvWLJO/I5hba4WTUgnfuXNNAzZuXJro6LjCKSnpDhhbDMAesjy3srcJgmClUCj8lJWVDYwYAaxfXzXZ1uno2bdpE/3erRvddxW4pkuShIiICBgSbQC4du2aluO4HSqVKrVmH7gezxv+35FtAFCpVIUAXhIEofW1a9cWXLx4cawsy5yVlVVhQUGBFsBu/bYLFixYBwNDguXLl6/jeX6K/v+XLl0Sk5OTA9VqdT+lUjknPDx8gVarfV2lUp0xPOaKFSv279+/f4y3t7dJbm6uGYC7HMdpjx07xkRRFAGwrKwsXVRUlIWRkdFPZdpj/b+CIAiNlErla1qtVilJEkJCQhATE6Pt2rWr8oVyKgeIAPzwA7XgcXGhyTY3l6KbV66QxNbRkSRGpqa04LhwgTIu33xDctB168gh1sWFFo9hYUQqdu+mB/6779JixsODiEVKCpHFwEBagHTsSLWb9vZ0jGLTkLrKv06dOoWoqCgAgK2tLW7fvs3FxMSIs2fP5l/o+m1DfPcdLfDu3aNrVZmh2I8/UmbtQUVGr08GX19fOTw8HKIoMicnJzg5OZX6u4eHBzdgwIDHi/HY2FgWGRmJvLw8/s8//5Stra3ZwYMHRSMjI/bee+/B2tr6nw2IuLnRovHbbykQ9fAhjUUjI8pq7dhBdZWGSE2l+6FXL7guWoQZYWG4lJIiefn7c3j/fQpKjRpFxDo9vURGXMN2SpVBoVBg1qxZWLNmDY4cOVKzYJJOR4ZwO3aQfPGLL+g+riwIc/UqkJGBwqAgXGjRApyzM5p6eJT8/cABUks8fFh7d/tPPqE6+IICGsc9e1JNbGxsubGcl5eH1atXAwA8DI9fQ9jY2CAj4280x9VLhh88KN/6TI+AADJJquzam5lRqc+KFUTOnrEUtrCwUG7SpEmFY6hRo0awsLAQo6Oj+bKkMCkpCevXr4elVgvb7Gy52b//zVIuXmT37t3T1+jWDOnp9CxYtYoW+Wo19bBOS6NgzoED9P8KzJ6cnZ3hbKD6AMc99i0Y8eWX/Pr166W7a9eik0rFUi5cwIy33yYSCpDCRI+sLBrH2dn0TASI4HfqRF4E/fsDWi3yjh+HdulSYMYMjFu9GsrAQJovHB2pTGfwYNrHyJElLQYvXKDnpK0tjXmdDlAooFQqcerUqcrJdno6kfcqcP/+fYiiiNzcXFZYWEjKxnbtKKvJcRTAGjKEiLse/fsDkyfDb8wYYPVqJkkSPnB1hZWVFZYtW4agoKAaOdADgImlJd2/0dHVkm3L/HwoCwudADSFLOtqdABDMPYBAB8n4CMAp0zU6jTIcrmWYosXLz4uCIJCq9VKxUmhz5YuXdr31KlTHcaMGfNMFwHJyclYt24dGjdurO7atauxra0tnJycqvRF0vsMMI5T/DxrVrhKpcoDYzcAOIExc9CafzxkOa/MW50UCoWpt2EgyhA9etC9o1ZX7sWTmUlzriTRmJk/v9L2ZFlZWcjNzWWuBoFZjUaD69evixqNZnmlH7Aezz3+R1bGdYNKpQoGMFEQhGkAdDk5OX0BJFaVUeZ5voO7u7tpWFgYHBwccOHCBa1Op5uiUqlEAP8p/lcOarV6RnR0tNWDBw/6yrI8WKVSXRQEYcCNGzc6AlCDHCazAez//PPP85/2Z32RYGRktEuj0fSLjY3F8uXLtTzP39NoNOY2NjbNu1Ug23ruMXcu/btxgx7s9+/TosNQwmhQn4p58+jnnDkUyZekElmzVkt1ah4e9Ht2NslGbW2JeKemUgTcy4sW/Wo1HXvzZnpPTAw9tD/9lFo0WVkRKThwgDK58fEkxZ01i9rBNGpEC4fAwMfZeM/wcNzNzkYHS0t07tgRt5KScP38eV6Tng6FpSU9dJ7HOq7aQJ/tmjSJTISOHy+/TVISPTg/+uiZnMLo0aOZv78/jh49CltbW7hWlBkFGdkxxuDu7g53d3dIkoTY2Fhs3rwZnp6eGDt2LGf8PJnyffwxLerT06nW/fXXSR4aFERBjilTSDLZti0Flw4dIqObYcOQ368fIjMyZNy8+cxP087ODs2aNUNYWBiXkZEhv/XWW+VXczod3TMTJxJhGDGCsnZ2dkQKKoIkUZbjwAFI8+fj2GuvQRcRIX/28ccl+y8qoszs6tV1byP38ssksf3iCyKX58/TnPLhh6VM3gwXqefOnUOrVq1Qm6ypsbExsrOzuaKiolq9Lzs7G/v27ZMbNGggjxw5kquRiei775Jk/NixqrfTaEgxVBXeeovq9S9epOBkLZUpBQUFqMz3pfzpaLiy5WeGsLOz4+/cuSN7e3uXaqGol+W+ZWwMm2PHGP7zH0RFRcmPHj2SOnToULNJVqcD3niDMmqLF9NrxsYUsDh1ilqB7d1LNdO1cVYGlRF88MEH3MqVK2VJkphOVwW/03/+hg1LJNFXr5JR4YQJFHwGsN/MTI6cMYMNGzYMRgsW0PgXRZor7O1pXtDpyGfhxx/J8M3Ojp5bW7fS83HCBOD8eQwFoIiMROTOnbLHF18wHD9O5RV9+lBwYcECehb26kVqNMYoKMEY0tPTERoainPnzgGg8bpy5Up4enpKvXr14tzd3SnQmpFBz84PP6RnLUBBhvXr6f67fRscAL2fvyzLSEtLq9V1RqdOtO9+/aqcD5wEAV0ePXIUvviCVeaoW9xbeyzHceKiRYt2gzELANsAqAAkASiCLOcD+LiqUype8z6GJEkfxcfHHwfwTFPbQUFBWp7nN8bHx8/au3cvlEplOgArY2PjInt7e+bq6mpRVFQkpqWlFWq1WtHa2lqZmZkpJicn5+h0ulkAjgIAZJkiu4zZA5AAFIGxDwHcgCxfLD5cuiiKlQdaTU0pwXHvHq2PymLLFiLbn39Oa4mffqrShNXW1hZNmzaVNm/ezNna2ort27fnzczMwHHcTZVKVZmjfD1eAPy/Jtt6FGe6AeBUddvKsnz2xo0bNjdv3kxXq9UdjYyM7i9YsCC6hscYXua1CyiuC6lHCXQ63ToTExOToqKiOTqdLnXBggXxK1asOJuQkNCksLBQaWRkBK1WW6uF3XOBVq1osahQ1Lz/rV6+ZBgF1/eJHTWq5LUWLcq/d9++kt/L1ibKMi2yCguJGHfoQBnc5GTKjDs7k2Mtx5WQ/IIC4PZtuCUkoKmdHez27cP9gwfhN24cGp0+jXMPHqBrQABseB7czz9T1sjFhRZHoaFkCLJzJ33+994jia+HBwUKkpIowxIYSA+wrl0pKODoWLJIa9CA3vt3yup37KCF3S+/UCZMXwNaWEhy5xs3SveFfsroXWzqs3XrVgwZMqRGMnCO4zBr1iwmyzL4ujgMFhWVBEsiImjcPnxIUfk2bUhR0bMnbfPbb7RY3b2brtOkSUQ2P/uMFBwrVlDmZ948+p4/+4zUGwcP0u+//ELH+eEH2o7j6P3e3sC//00Z62nTKPvdti1YTAwKtm792yI5AwcOxG+//YaYmBgmimKJnFyno2z8N9/QuPjkE8q0VXe5//qLrsV//gN5xAhsOXxYSk1NZa+++mrJ9xoXR4vry5dr1qe7Mhw/TsRGD44jufA335RcdwDm5ub46KOPcO/ePZw8eRKXL1/GSy+9VOPD9OjRA9euXcOZM2ekIUOG1Fg9ERAQIMXHx3MxMTHMw8MDrVq1QqVyzYQEmn8mTSqZDypDWhq5uE+fXv1JODjQNZ4yhVQENcSuXbvksLAwNmvWLDg4OFS7PcdxcmFhYaX37qBBg7B+/Xp2586dUjJ+vVcBmzuX7gfaltuwYYN89epVqWvXrtVf76QkIrdl5dKSRN4FO3YQSQWI1NZyyuA4Dh4eHrh4kfjJqVOnpAEDBtRsHLz+ermX2rRpwyIjI+Hj41MiFeb5kp7NnTpREO7jj+hAT5AAACAASURBVGne8PenFnlz5lAGfPJkSEolAnv3xoWzZ+HQrRsK4+OZRpbh1a0bzWPZ2RS0mT2bnOwPHqTr89//AhoNpG7dEHv2LJKbN8c7qamwb9cOucOGIf7IEQSfOMFFJSXJ7j17Mnh60lxw9CgFD1Uqut4mJnSfVXAtlUql3KJFi9o9xFq0oDnw6tXSgXlDJCRAuWMHAnbsUCMlRfPll18GSJJ0kjE2B8AGSZJUjLFeSqVyu1artW8VGhoHxl4FMAOU6MmCLO+q1XmVhq25ufkzL328d++eWhTFzSDJu0ar1eYDaKXVajV5eXldoqOjvSVJUgOIApAO6iWeCOC8SqXKLrdDWU4BMAwAwFgnAIVgLBzAK8qvvmrn7u6uA1B5tNrTk9ZNFeHqVQreGBnRs7C6ACCAKVOmcLm5uQgKCuICAwOl9PR0DkA5dUE9XizUk+1aYv78+fMBzAcAQRAaq9XqrH/4lP7nsGjRot0wkPIDgFqtHhceHv5dVFTUuAYNGiAhIcH4o48+YnV1iv9HYGZGC4SoKCItycmPDTz+djBGi1b9wlXf3sswe2oYgdX3DX/1VSgAeAQF4YCVFdq3by+1HTyY85g/Hzu+/BI3OnfG3DlzYNegAfUolWUi6gkJJKXiOFroNGhAhN7cnEhZXBxFgPfupWy8iQll45s0ofdGRVEGeepUumbLltG2nTrR33NyaOG0aRMRjFdfJSLs50eLb42Gslf37tHfXVzoPBwdS3puV5b9VSgoo2JhQYvRggL6PSqqdFuhuiAnp6S+NjKSiPvdu3SdfHyAX35B7zFj0CgrC1Gffcbi33tPNzguTiHZ24PTmwNt3Eiy2Z076Zq9/Ta4zp2JQPj6khnLli1UJ3b+PAVWJkyg79fNjeSdH3xAUsyQEKBxYyLCQ4eSFFetpoXCo0e0MF20iByM3dzo2J9+SrWTWi2Robi4Eomlnjx5eJSM9eHDabx9/DGRdSsrCr6oVHRuGg2RpW+/pYzAkSN0bVq3RuPZs6HMzobs5wcWGEjZrvR0GheXL9M1M2hV96Ro1KgRevTogRMnTuC3TZvw1muv0fn7+dH1HTCAsmrVtZLKzaXr/+ABfc7OnQFZRkFBAeN5Xm7Tpg0tvgsK6N5YubJSt+Maw8WFznX16pIA3eLF9B3//jtl44thZWUF8+IaekmSZKDmHlwmJiZwdXVFVlbtHoWpqalo3rw58vLycODAARw4cABt2rSRR40aVf7Y48fTonbLlup3fOIEjfuaKAKUSvJpMDYm1U8N+8/ry2gKCwur2ZLQsGFD9vDhQ9Hb27tCJuvg4AB3d3dcvny5VEBNrVajaVgYLF9+GXo1R8OGDeHg4MCCg4PRtWvXqg/800+Ufb11q3yQUqkkcyf9HDZwIP1uKP2uIcaNG8dWrlwpFRYWcpcvX+batGlTfRBi61bqV19GrZaRkQEzM7PKAy86Hc11JiZEkP/4g4ImxfOOdtAgXHv5ZZwpzrIbN2okZiuV/L7AQPy7OGDxGEeOkKpLjwkTcD84GMd27pSdRozAqGnTmNHNm0BODqxtbGDduDHsLS0RfPQo0wYHQzl4MGXH4+MpEPvbbxQktrOj61nGzHH16tWiWq3mbW1tIcty7VrCLl9O83tFZDs2lubqGzcweeZM8+KsfMeioiI/pVKpCA4O/ig7O7ufkuNazwoONssaOBB3Q0PTAeQAkCDLT9DzksBxXC93d/ea9xisArIsQ6vVQqlUlrpGubm5KCgoYABuFpuY6XG3+OeTtQORZYr+MDZKrVQuVfC8/Wg7O9NKzf0ACvzMm1c6oSFJ1BXinXfoOTtoEJmE1hCWlpbo2bMna968OduwYUO+KIr1ZPsFRz3ZfgIUG3rV42+ASqVKA/Dm119/nZGent5UqVR2zs/Pd3qhyLYeTZsSQTAxoQX76tW0wH6BcPLkSalDhw6cvjdsXFwcAHIrt9O3wTBcbOlJ/NChJa9VlNnfbRBjqch1etgwyiozRtlvc3OSzqek0DH69aOHol7KrtORbD8piRbr27bRdW/ThjIiAwbQgjQri+rmly0jstmhAxHzsWMp48rzJBPs35+ykw0b0jFHjiQ5Y/fu9C8ykohqWBhlSzw8qFa2eXPK3Ofn0zXIzCxZ6P72GxHu11+n81i8mMj81q0kAfbzgzfPwzMlBd8FBSlcjh5FkpkZwmJi5HcSEpixRkOfSd9L2ceHSINSSedvbEzyUL064rvviHzZ2RH5NjKi1/QS0PT0kuutbyG1wcBrx7C9mf73ZctKXjOU3G/bRj9nzSp5bXlx6dnq1ZRhnTyZgjL67GVeXkmNG8fRdQOAhQshd+kCrF2LrKZNYQNQYCImhsj22LG04LWyokxeXh4tdoyM6LsdO5aCBgoFfe4336TPamlZZeufrh07ovG5c7CYNw+FvXrB9P33aRzWVFmTl0ey1e7dKRNXTAIZY5gzZw779ttv2S+//AJeFOVhKhW71LUr5KlTMaqa3dYIpqZ0f+jJNs/Ta19+SSRH/zqIxAElbeZqA0tLSyk4OJg/c+YMvLy8KjV+AsgEKD4+Hjk5OczR0RHjx4+HTqfDDz/8IEdFRZVmH4sXk+T9xInK67PLIjCwdqUdTZuWGIVduVJtT/KDBw/KarWaAUATg+tXFRwcHJCYmFgls/L29kaxh8tjBYWxsTEybG2hGT8ehvn8/Px8qWXLllUztawsKgPatq1yNZCbG80XX31FwS3D2uxawtnZmXv4kJSueXl51ZPt3bvp3jYg25IkITg4WPbz82MVdleQJJo3Hj2i+nOOIyLTqRP97fPPwYkilLm5mODigpZbtwIWFvzWrVvlyMhItmrVKmngwIGct7c3uMzMcmMqOTkZew8dQs+BA9GrVy+S9Bt+xx06IMTfH/ccHaU+c+ZwYIwUWwcPkmx8+nRSvHz3HQV7ly2jc3vlFcRlZCAnJYWHUomdO3fi9ddfR7NmzWp+gT08KGgXH0/PKMNr4uBAQYMmTcADsLe3h729PQeAA4CcK1eY2/79nTVr1qBBcDDSZBmhfn4pw3bu/LDmJ1AxBEFojWKy27KSWuSKkJSUhHPnzuXHxMQwnU6nsLKyKtJoNJxarVYCYDqdjm/QoIG6a9eupl5eXszKygrGxsbQ6XTmHMddW7JkyYrFixfvfYLzdgagUKlUMeX+KMv7Vy5d6utsY7PAZPJkCnJ06ULrjbLzkJ8ffQdJSSWBq+vXKfDfpw89x2tpOqnH3bt3dRzHbVi4cGHtJ+V6PFeoJ9v1eKEwf/78jwRBMOI47lZoaKiDo6Pji+mCbWZGWc2HD2miripy+hxCluVSi6mdO3cCAMLDw3Hp0iX0eEKDqkqhUJQ4v+rJuuEC0VCWqF/EGZJ2wyyKXjaqJ4I6HWW/JYmyq0lJtKixsKAseMOGJTJLnqdtANrGyorqIgEidcUOw9iwgbJsVlaUeQVK5NIAkUs99JnB/ftLXouNpZ/t28NoyBA4bNok7h8wgLe0tJQLcnJY0I8/orODAy22+vShbT82KLXTE9vWrUucmfXkFaCABVAtwXjqUKspSz59OklZMzMpw2hmRtc5LIy2Gz6c/lZQAEybBsWff8I+JQUpRkZEtg0zVfHFsU+tlkg4QKUW+mtdWEjj5+ZNWqi/+SYFW7y8gO3baVxFRFAwZc8eeq1VK8DdHa4//ICzV69KISdPsqnTpjGLmhDt7GzKUD94QIvxCmruOY7DgAEDxIf378t25ua8OG8egnNzYfzgQa2yy5Vi166SMaRHs2ZUYhIZSfdOcaAhJiZGNjIywuDBg2t93JEjR/IuLi4ICAgQr127xru4uIgDBw7kDdtcpaenIywsDNevX5fz8/OZg4ODPHDgQAaQMVB+fj7z9vYWAfDQ6YggJiSQGVVNiXZGBgU3amg+9RijRtG4UavL3Qt3797F/fv3kZSUJBUUFHBFRUWsS5cuuHPnjiRJEqfT6WBUTZ/muLg4qTLPBT3S09PB83yp7iRcYSHcsrPlBzNmMF+DbYuKitCsWbPKvye92dn+/aXq8yvEp5/SHNamDbB2LWK0WhQMHCh7eXnVahx069YNdnZ2uHv3Lo4dOybOnTu3cj16ZiZ5MZQJAnz99deyJEmsQkd8WaYAqb9/6Tm0QweaS5RKwNERXHIy7g0bJvfbsoXlt20L8+PHMX7cOBYbF4dt27Zxe4o7TjTXaiWTLl24oRoNdu3aJSYnJ7P8/HzOxsYGPXv2ZJVlnYuKipCWlsYxnY6UNfqgwUcfkfGVu3tJ94DPPydVQX4+bM6dw7v79yNpzBgUWFjI1377TfZcvJhjNfVkMDen44SHlybbc+fSuC9+BpfCt98CXl7wtLFh1oyJDb29efz+O0zi4iBv316DOrYaIR0ArKysdK6urjX6MJGRkdi5c2eBTqf7XJblgwAKMzIyPIr3lQPiJkmZmZmvnTt3bsqpU6f6NWnSBJ07dzYDAEmSOgLYIwhCs7rUMguC0ABAvFKpvACgggbsgCRJK5PT0l66duhQn65du1I505079PwwNETjOPrb/ftEtgMCaA2hVNJ6oY5EW5Zl3L59W63VaqvslV6PFwP1ZLseLxQEQeAVCkWshYWFmaen54vDTiuClRVlN3U6ykAcOvTELsp/FyRJYtYGct1p06YhLS0NsbGxOH36NAoKCjBgwIB/8AzrAIWipN82UELiBw4ksnfoEGUE27Qhovbnn/Rg9fIiae6CBbR47NixZB+GhkP6h66+5VUdMHXqVP7WrVswNTVlf/zxB86fPw9PT0/Y2dnVeZ//CG7douvl7k5ku7JAk2HtcG4uAKDx9u1Sg5gYekOzZiRrHzGClAjdu9MiRy/BfvXVkvfr+1S7u5cYNAUG0k9ZpgygnR2R0wsXaGHl5ERSfTc39Dt5krvQrh0yX3tNtkhIYLhzh7IdH39MgY5p02hM7NtHBl7DhhGB27ChynKR9u3b8+2XLKGM844dMP7mGxQUFDCNRlMtiasWBQVEACIiSi/QW7QgInD6NLBsGY4dO6YLDAxUjK5BL9/K0LFjR3Ts2JEvKirCwYMHuU2bNsHNzU2yt7dnJiYmuHjxIrO1tZV8fHy4l19+GZwBq0xJSQEAcBzHJSUmwvHll0mtsHFj7U7i0SO6P2tKzg0xbhzQvTsetmuHXS4usq2trWhhYYGHDx8qAKB58+Zo1KgRWrRoIXt4eLCAgABuxYoVEEURFhYW4jvvvMNXZJgWHByM1NRUrpe+FKcShIWFSUqlkrtz547Utm1bDgDk06fR9+BBllRG/mxubo7o6OiK3eM1GlJszJpVPdEG6Dpv2ICC9HSknziBsPx8XMnMZPPmzUNVpm5l4enpiSZNmiAgIAAWFhZVP5vbtycFkIHi5erVq5AkiX344YePSxpKYd8+YO1aItaG84VCQVnkfv2AhQvBOA7T3n2X5bm44M/WrTFmzhwY+frCc948LFy4EEVFRXjw4AGavfoqd/Ctt+RvoqKYTqfjR44cCVdXV1haWqLCrDqAs2fPIsDfH7737lEgY8cOCtr27Utz//Tp5Muih5ERzREAzF96CeZr18Lm8mVobtxgcX/9xSRvb/Ddu1MZTYMGpL6qSlresiVl9vv0KbkGAweWDuRlZlILtZUraZ61tYXvnDkKzJnzeBNHR0doNBoXQRAsVSpVbuUHrBqCIDQ2MjL6TqPRYOzYsTXiE6IoYs+ePYVarXaoSqU6Z/Cnitpa7QawWxAEs4cPH86Mi4ubqlAonAE80ul06wBEV3FuzQCkq1SqzAr+PBQAOI6LreBvAACVSlW0ZMmS449iYjp37dbNFJs30zMoJoa+h9hYel4wRqVLwcEUrB81ip5DV6/Suq6OiI+Ph06nywJwq847qcdzg3qyXY8XDTLHcTmtWrWycqthfd1zD4WCpEbdupEE9+WXS2pdn0M8ePAAarWaPXjwQNIHPBo1aoRGjRrBy8sLGo0GoaGh4oABA15wS3IDvPUWEbKdO2mR88knRLzj4+lhO20ayRkFgQjWM6rF5zjuccufRYsWYfPmzdJ///tfbvTo0fB5hkZtTx36bBBA9ct//UWkdMaMyt/D8wgMDJQvu7hw/MSJcABIHu7rS0GP8eNJMv3ZZ0Qy9O2HqquLlGXKQGzaRDLvX36he7FTJ3KqLsa5gwehuHQJFsuXM+zfT9mLhg2Bs2dJNXDqFGW6vv6azicxEfj+++rHQmYmLdiLiZG5uTkKCgoQFRVVK1lmhTAzowV3mbZxYIzKJ+7fR8qtW7h586bi7bffLtderi4wMTHB+PHjWV5eHg4dOsQiIiJkWZbl0aNH815eXhWymFatWmHQoEEIX7OGHXrwQJ65ejVD7woTTlVj925SI9QR0o8/4tY330DXqBFLTk5WJCcnw8TEBJMnT4azs7P+3BkATJkyBRYWFjAyMsLu3bvZwYMH5YkTJ5YabFu2bJEePXrEDRo0CJW2DyrGpEmTuC1btuDixYslmd3hw7Hv+++luN27OQsLC3nGjBnMwsICOp1OsrW1LX8tZZkIX/PmFISqIeTAQETv3CmfHDYMlmZmcoOMDOzfv1+eNGlSreZwvYx86NChVd90hw6Va38UEBAgde/enauQaN+5Q4GXvXvLB+bataOscseOFFw7fhyYOxdmDg540KIFNNu3w8TLC/j5Z/DOzjCfMgVtfX2BJUswafp0lpScDFEUqyx9AICM+HgUfP89Oup06NO4Mcm6Deu9NRoK6OnVRBVBqQT69EFS06a4nZeHtD59YHvrFqy++Ua2fviQSUolLE1M5Lx+/dBm1ixmbG9f+v0dO1JA6f59Cij26UPXxMWFjl1QUOLTkZND6pwKoFAo0KRJk6LIyMhxAH6pcCMDCIKgBMBUKpWm+P9ePM9/o1Aoujs4ONi88sor1V4/PUJCQiDL8r0yRLtKqFSqAgA/FP+rEkuXLh0C4AMAAziOyxQEwU+lUkWX2UxpY2Ojy8/P7y8IAl/WWV0PWZaD8/PzNUBxFYelJf0LDKS5v1MnGnNvv03fQ/v2NOfOmfNERBsAAgICCnU63dqquiPV48VBPdmuxwsFlUolCYLww+XLl9dERUVly7IMX19fs27duilrZTbyvEHfJmjGDJJ+VdP785/Ejh07YGdnBx8fn3KLPVEUERkZKTdp0uR/g2hHRFAm4ZtvaEGjX+j16kUE/LffKMt99y4Rtp9/pkVuair9/xn2Hec4Dm+//Tb366+/yufOnYOPj8+LcQMUFhLBNMw6BwZSJqAKsl1YWIjDhw+zdu3albhl601pXFxKpPvW1iTxCw+nxWlYGEmmtdrSmXJRJFI8dy4FSUaOpPo7c3MyXfL1pdrLoCDgwAH0HToUmldfRUyjRnKDQ4cYmz+fSgaUSiJ4t29TxnjIEMqyX7xIEvKdO4F//avibOuqVUR6IyIeBwXc3NyQmpoKq6cUsJEYQ9HIkYj/7js0N/RJcHKCePcutCNHotOqVU+FaBvCwsICkyZNYqihHL5z+/ZodeAAHrZoQd4ItYVWS+qE0NDav7cYt3keyfb2+GT7dvwwfjz6Dh4s9+jRo8LzN6zXHjVqFLdhwwZs3LgRoiiKsiwza2trLj4+nps4cSJaVNQtogwcHBwwdepUrFu3jh08eFAcbG/P60aMgOmPP3ItW7ZEaGgoi4qKgq+vL5o0acKfP39eLiwsZPrWf4wxcAUFcC0sRGLXrpACA8FxHFq2bFkuQ/3o0SNERkbiypUr1NXD01NmTk54d/RoZjRhAstzdMSPoojo6Oga16XLsoxbt27J7u7urMpA+PDhFKgsY0iZk5PDVVjnffYslYucOlVSQmQIExNSaaxfT6R78WIgLQ2RvXrJ761ezYxWryZSlJhI5RySRDLg4hZfjtWZXGZnA/fvg5s0Ca4uLvDcvBmmFZkXxseTUq0GJMvNzQ2NGzcWc/PyuM7z57O4uDjm2r490qKikLFlC0wPHmQ52dlolJ1N6p0+fSigZ2lJvhMbNlDAt2lTItSzZhHxt7EhhcaF6pvcdOnSxSIuLm6xIAi/F5PZxxAEwUahUHyoVCp7ybLswhhrJssyEwShn0qlOs8Y+7coikMBQJKkAmdn5xpLSQIDA3OLioq+r+n2tYEgCKY8z2/z9PS07NKlC+Li4qz8/f2DBEFwV6lUhg6Ot9Vqtcba2to8LS1tKioIOAiCYMTz/FvOzs7l5R36wNn69VR2EhZGppN795b48TwBCgoKEBoaykRRXP9EO6rHc4N6sl2PFxF/ANAkJiY+AmCenp7+Y2hoqO3kyZPNn1h2+U/j0SMidK+/TnKk1177p8+oHGRZxoQJEx4bKhmisLAQmZmZLDc3F7GxseLw4cP56moVn1vIMknxrK1JpmcYzGGMagW//prqfp2dqY579mxa7Gg0FPk+dKikZc0zQEhICLKyshjP8y9O9PvLLymbbXivfvQRXe+cnAozwaIoYtWqVbCxsam+vZRhRu/CBfpuvv+eCHefPrTwzsggkjxhAnUFGDKE5OVvvkmy2pAQCgr070/ZCsaA7Gxk7N8vXYuI4PL/+190NzenbfXw8SGH74YNSzLuSUnAmTPkVjtnTumsa1oa3eP9+z8eWzqdDjdv3oSLi0udyW9BQQHi4uIQExMjR0ZGSjnR0fy4W7fw+7ZtWCwIpbY9ptGIFq1aca907PjPBWrUapLcf/st1n7+udSjT5+6lQdduUKlCU/gP5CWloa0hg2R7+eHf73+OoxbtarRdbGzs8PkyZNx79492dLSks/KykJAQAD69OlTqzZPDg4OePfdd7Fu3To+TpLg0q0bYmJjYWdnJ/fv35/5+lLl9tChQ9mePXvku3fvirIsQ5ZluN+6Bb9jx7i98+eLcng4AyDn5OTw8fHx0pAhQ3iA6o0PHTokhoeH87a2tpJWq+WcnZ3Rvn175rt5M7hp04Bdu2Bhb49mBw7g119/ha2tLSZPnowG1Zh4ZmRkICIigs2oSp0iikR2K6ipVygUsomJSelrFRxMUu0tWyom2nr06EH38fDhwI4deHjmDP7s3Jm9d/MmWFYW3Ztjx1IQ29eXsv/GxmTOWJmKLC2NAnaLFkFs3x7rJkxAr0GDJIumTSsenytWkAx83rzKz9MA06dPfxyQ1n+vDo6OQLdubOXKldLrEydyKCgocTrPzKTzT0yk50paGilx+vWjQGEtSy6aN28OExOTRhqNZiCAxyl6QRB6KRSKQ97e3iZNmjQxsbGxgbu7O/z9/XHhwoUhAM7LsjwNwDQAVvHx8ek3b95ER8PyqUogyzISExMVAC7V6mRrCGNj41O2trYmEyZM4Blj8PDw4DMyMpShoaG7BEEYrVKp8gFApVLdWrZsmbZ///7We/bsWS0IwhWVShVSZndjRVEcXaX/jL63tkZD43PVqicm2gAQGBgo8Tx/aMGCBbVsyF6P5xX1ZLseLxxUKlUKgMf2yIIgHI2Pj8/Pz89/8hrHfxr6zKmLCy3+8/IoI/acmacFBwdLffv2LXdSlpaWWLx4MXJycrB9+3b+zJkzmDp1KtLS0pCSkgIzM7MaZ0r+UVy+TNLec+cqX7y3aEFE+/z5EkMcgOq49Pto0YIIXEEBZUufMg4dOiSr1Wq9sdTzP5/rdFTreOJE+b99+y31uS1uq1QWWq0WkyZN4hS1UQu0a0c/v/uOfmo0JP9MSqLSDVdX+g4Zo1o8PU6dop8GWS/O1BSTJk3irl27huPHj6NJkyZwLuvePHFiacM+R0c6dlYWZfK9vMjZ9tYtagUTElLK7VjvBF6ThWtZ3L59Wz5z5gwKCwuZqampaGtry7Vs2ZL3fe01xI8fD7u9pY17i4qKcPfuXX7oypVk7vf++39/cC87m2rVnZxQYGWFQq2We/Tokdi9e/faK2MCAys0oasNcnJyAMaQsWQJ7HfsoExhDZ3NGzdujMaNGz8mi71794a5uXmtgxgNGzaEU6NGumYnT3KtN27kRlCmtNR+OI7DuHHjGAC6TrJM6ptVqzBnzJjHN8j169dx8uRJXqvVyrGxscjOzma2trbsnXfeQXBwMJefny9NmTKFyucFgcilqyugUmG8pyfuvfYajh8/Lh8+fFiaPHlyld/J8ePHJUtLS9nBwaHy7S5epAxsBfewqampnJCQwB7Xod+8CSxdSv+qqz1/5x26l8zMEDt2LB599x36/fgj2KpVJKn//XcaH1otEdSvv6bnaseO1KJp+PCSYGpREbBuHcnRP/gA2LABtzMzoT5yBM7OzlxOTg5MTEzKrzVat6ag0RNCo9FAq9Vylvogb8uWpMiJiyPllCyTsWpcHCmr6qjkYIyhZ8+eJqdPn14qCMJpAK2MjY0/NjIyGjFq1CgTr+JrLssy9u7dW3Tv3j0TAFkAoFKpdAAgCEI3ADXqNQ9QIFCn08kAKq2VfhIwxmz69etnYqhyHDJkiKkkSb3DwsJCBUHoq1KpIpctWzZTq9VaN2vWDAMHDjQ9derUXkEQ2qhUKi0ACILAKZXKr1566SXR0tKy+rno9m0KvBq2S30C3L59O0+tVq99Kjurx3OB539xVo96VA+mUCiit2/f7jR16lTzhIQEXLhwITc1NdV4xowZRi+ceRRAiwGAFgN+fvSQfQ4gSRJsbW3x119/cZ06darQyIYxBkmSkJqaCp7n8fXXX0s6nY4zNTWVCwsLmYmJieTi4sJlZGSI1tbW8qRJk56veSg7m+SAM2dWnyUbPpzat3l6PjbCeYzibAUWLCAzlbg4kj1fuPDUWr3pFxX29vYKw5ZBzy0uXaIMUEWkaM4cUgVUgJCQEBgbG8sNGzZ8sgyskRFlnjiuxMiulsGfLl264Pr16/KVK1fkMWPGUMBJp6OF9ubNFfdeb9CAAjf+/vQZ+/al4EujRtDpdAgKCoK/vz/y8/MBAC61cNPWaDTYtm2bFB8fzw0ePBht27aFQYMEjQAAIABJREFUQqEoNRASVSpM2raNiAWIaP/nP/+BtbU19X7+9FOS1P+dZDspiYjE5cvAr79Ck5UFWZYxcuTI2g9iUaSAVh2CFIbo1asX7t27h9jYWMmra1euLj2n9aiw9riGeLtTJwW++opUEtUhP58CRps2lSOlnTp1QqNGjXDy5EnZx8eH8/DwgJubG8dxHAoLC6HT6UpabFlbU/nLyJE0d2k08PHxwe3bt1lMTEyV34kkSXjw4AE3x8CEqxzS06lcKioKKFuLDOop/lh+fv8+BbxmzSqZRytBbGwsEo8ele3++AN7IyOZNjsb/dRq+AJ0v8+ZQzL0yEgixCkplOV+8IAk2GvW0O/6+3fvXiKxY8c+NhX0X7VKBsB+Kx4Ppqam8rx580rmosBAGs91CCQXFRVhy5YtupycHE6r1XI6nQ4eHh5E8vLzqTSiQQMK6qakUEDE2pqeI7t2kWnX9Om1Pi4AdOjQgcXHxzcLDg5OUSgUrFu3bspOnTo9NvqTZRn+/v5iWFhYDsdxgZIkPXb+FgThDQBbAeDw4cM5SqVSbty4samnp6dRs2bNKnwWaTQa8DxfsHDhwmeixCoqKlp+8uTJdR4eHub64yuVSowePdrk2rVrzqdPn74rCML3ABYC5IreoUMHdu/ePbf4+PgvACwAAIVC8YmdnV3Dzp07Vz8XhYUB166R+uApID8/H1lZWUZ4Rtn/evwzeL4WufWoRx2gUqnUgiC0yM7OXr9ly5a3c3Jy8rVa7WyO49qeO3fu3ddee60O1rTPCY4cIVnSqVMkdzNs2/QMcODAATk6OlpUKBSws7Pj+/bty2JjY9G4cWMoFAps2bJFFkWRDRo0qEqnWhsbG8yePRvZ2dnIzMzknJ2d4eLiwnQ6Hc6ePYucnBzJ09OTCwgIYCEhIWj1BKZGTxV79hAZCwqqWraoh5kZRbT/+ouyqGXqEAFQdrtFCyLxgweTTHr2bMpWjB37RKc7Y8YMduHCBfmvv/5iLVq0KJ9pfd5w4EDl9bjm5nTd588v18YmPT0dT0y09Th2jGodn4CYjRo1im3cuJF5e3vT2M3MJHmsoZt9WZiakkvtlCkkDVWpkHjgALaEhqI4UCIPGjSIubq6PnaXP3PmjJidnY0RI0bwlQVSDhw4IObk5LD58+dXquzx/PJL/MJxSBMEvPHGG7h165ZsbGwszZkzh3Y6bBgRu6lTSbL7LCFJtDB97z3g/HkUNG2KbT//LGZnZzMAXJ0CRvfv0/daxrW7tjAxMQHHcXBycuLQujXJk998kwhZTeaDp4X27UsrLSqDLJNfhJdXOcMxPZo0aYKZM2eWUiHpdDpcv34dDg4OEvTZcYDk1kolZbkLCoCMDFhYWMDY2BiSJFXq0i3LxJ20Wm3l52prS2SxOAiRlZVFRDkxESkpKdDpdJyLiwuVUv34IxmFGvo6VIBr166Jx48f581EkXX09JSdnZ3RtEcPdG/blhzKfX1pPnn3XaqlfuMN2jfP033Ypg3VPs+dS8qA11+nubxMCUe7du2Yv78/3nzzTXAch+3bt+PQoUNgjIHnebTcvBm8KCLi1ClwHPf4n/7vHMfBx8ennA/DpUuXcOHCBdnZ2Znr06cPFxMTIyuVStYvL49nI0ZQGdOff1LAYOVKkox/+y11tnB0pIzqnj00Rnm+1go4juMwcuRIE33btrKqoZCQEFy+fDlRFMUeFfSjvsJxXI5CoQhMTU39EUB+YmKi3927dyfJsty8S5cuym7duilMDGTVoiiCMVahGdlTwvbc3NxpZ8+e7TFgwIBSk2GXLl14Z2dns02bNi3Uv3bs2DHMnTsXfn5+ZtHR0f8WBGGfSqW6qVAopvTs2dO8svFeCl98QWP1KakPHzx4ACMjo4vz5s2r4maqx4uGerJdj/8JqFQqWRCEJbm5ubwkSYdUKtVBQRACIyIi5iYlJVVvgvK8Qn/eBw7Q4ucZku3k5GQEBQWxUaNGKXQ6HW7duiX9/PPPzMjICJIkQRRF2Nvb46233oJxRaSyDOzt7WFfJoOhUCgwcODAx08lnuflgwcP4urVq8za2hqenp7w8PB4auZQtcKNG7Sg/u232i2shw6l72fPntJ9vsvC2rpEsWBlReQrOJgyF8uXV++aXQFsbW0xYsQIFh0dLYaFhfHPNdnOy6MFoWHf87JwcCACUQZNmzbF5cuXceXKFXQz7JVeF1hZPZFjNUCSYY7jkJGRQQGC6OiKpfGGyM2lDOwffwC9eyN582bkLV+OfiNHovOCBeCsrcsNgIsXL/IA3Us9e/asYJe5ePToEd+vX78qS2hMrK3xRqtWuP7HH9gGoEGDBvLEiRNLs9q2bWk8RkSU9LB/FggPJ9PB118H2rfHX8eOITExkR84cCBatGhRt1KgbdtK+tw/AWJiYmBhYSG3bt2avgsrK6qRPXOGMr5/Bx49ou8iNbX6rhSLF1OWeNOmWh1Cq9WC53kkJyfz8fHxJU7S7u5k+rRrF5U/tG2LQWvX4ptvvkFISAhat25d4f54noeRkRHCwsIqDvhFRUHs0gUbFi0ScwoLmUaj4QDA0tJSbtCggdSoUSPWpUsXjktLo3Z0bdoAY8ZU+RlCQkLkEydO8A4ODpg5cya4V15haN+eAgYaDfkw9OpFBmMWFkDXrhTEe+01KveIjqZM9uHD1LLpp5/oOn75JRk16ktQAAQFBYmenp68m5sbCgoK0LJlS6moqEiWZZlJkiSHdOiA/AYNmCYxUf8aZFmGJEmQJAnZ2dl8TEwMxhsYn0ZEROD06dMYOHAgurRvz3GpqfD+7TcGNzc658GD6fs4eJA8Jl55hc7xv/+lZ9WCBVRzPnkyBQp27yaVSA07mRQWFuLhw4fQaDRQKpXQ6XSwt7eH0uD9iYmJ0Ol05ysg2ijub21d5uVTAL4WBKHltWvXlly9enVYt27dlD179lQoFApYWFhAq9XaCILAnoXLdvE6cML169cjWrdubVTW98LV1RWzZ89GTEwMcnJyZEtLS/z000+F6enpZjzPZ4miqAMAURSDs7KyKh7shrh2jQIdhi0+nxDXrl3LKyws3FD9lvV4kVBPtuvxPwOVShUH4G2D/4cKgvDd9u3bP/nkk09q3jD0ecRPP9HPJUuo3nP//qey27t37yIiIgJ5eXlyXFwc8/Pz07Vp00YBAH5+fpxarYaxsTESExORkZGBVq1asafp+t6nTx9mb2+PuLg4KSUlBWfPnmVHjx5lCoVC4nkeSqUSgwcP5po/y8U/QDV6a9eS1Leq7GRlWLmSsmBjxtTMIEVPuo8fJ7LGGGXPJk8mUl5LdO3alT9+/Diio6Plhg0bSi+//DL/JDLWZ4LvviNiUNVi0NOTDJFu3CiVeXZ3d8eECROwszjj/USE+/x5us5PCKVSKdva2jJs3EgEacSIqt/Qvz+Zs61ZA1EUsT0zEy0WLcJQDw8qL/joo3JKB2NjY6jValy8eBFeXl7lTAn/+OMP0c7Ojvn5+VWbVrFWKNC/ZUt0/eSTivsgm5nR4nHJEnLaf9reCiEhZPB0+TJJeovnkY4dOyIgIABhYWFSt27dap8e0rduq+761wANGjRAbm4uW79+vTxlyhRmampKGfMrVyig9ncQbgcHcpuujjQVFdFYrkOJkampKcaNG4fff/+9fL2tWk2Z4Fu3ADc3GDGGxo0bS3/++Sfn4+ODyub/Vq1ayXFxceX+qNFocPjUKdmsSxfm6eXFWrVqxdnY2MDU1BTFDxMK+uTkAF99RXXyn31W7We4dOkSzMzMMEvfq9vBgeTcPj4kH582jebUH4q7RZ07R1nrvn2pjKVvXyLdJ09Sic+CBXT8vXvps+/ZA/zrX5CsrKDRaFiXLl3AcRwsLCwwevTokkBVZCQFPUJDK+0+sXXrVjkqKgonTpxg6enpyMzMFPMyMrjuzZrJXY8d49jYsSQL79qV/D8MS9+SkkhxsmIFlSqdO1e+3Obddykbn5lJnRCqMOXUaDS4ceMGLl68WAjgiizLmYwxS1mWm2m1WldnZ2f1mDFjLKytrXH37l0Nz/O17mGlUqnuAxgvCELzq1ev/icoKKjXuHHjzJycnGBlZaXOysrqBcC/tvut4bFTli5d+tmJEye+mTp1aqmHYE5ODnbv3l2Qk5OTJ4riFUmSRgBYBeD7hQsXZui302q1N9LS0kYCqPxhrtORIdqkSaXNPp8Aubm5SElJ4QHseyo7rMdzg3qyXY//WQiCYA9goaur64vj1FwdRo6EzsMDuampyLt/H+ejoiRZlvHqq69ykZGRSE5ORuvWrdG0adNKJX95eXl4+PAhgoOD5djYWObi4iJZWlpiypQpzMXFpdScoM9gOzk5PfXWQABlun19feHr6/v4ZNPS0pCYmMhZWVnh1q1b2L17N2xsbMRx48bxFTmgPxEkiUj2Sy9RhrouRBug923cSDWGtZHhvvoq/dNoyMG6TRtaaFlYUFajhujSpQsiIyPFnJwc3Llzh79//z4+/fTT2n+OZwVRpOzjuRq0Vg0MpMVidjZlwovh4eGBsWPHYvfu3XB3d6+bZL6oiDKGT1gzn5eXB47joL5zh2SdVQWgZJkkwevWAT4+EEURq1evlkVRlHsMHcrBxoYWbZGRVF7w8cdA8+b6fvYAADMzM3nt2rWsT58+crNmzZiTkxNyc3ORmJjIv/POO5Xe66Xw9tvAxImwEKtQcfI83RPz51N282khNpZk6qNG0dg2uF4ZGbTGHTx4cN10mA8fUt1yDU2aqkJubi5kWUZSUhIT9deJ50lV9OGHlG181iacX31VvRz+2jXKZvr710kRA5D029jYWFYoFKV34OREddU8T/LqVq3w2ptvcmvWrJG/++47DBkyhFXUM9zZ2ZmFhobKeXl5zKLY6+Lq1au4duSI3OvePbhv2QI7O7uKv+OiIiK31tY09qpBQUEBEhISWH/DkpSlSykDrMf48cC+fRQo0QfnevWin7GxRJC3bSMi26ULzd95efQ9Z2VRwGPRIhzneUnXqBFXaVeN/Hzy96jCuLGwsFAGwMVHRYmt0tKYRfPmfIvVq6GcMIGxESPo3nR3p9IKQ8TGAp9+SoG43r2J0JuaksmjIczMSNlx6hT9vH+/wjkuLi4OO3bsKJRl+VJRUdEKlUp1xvDvgiBYJiQkCBs3bpwzbdo045ycHCWAOru+qVSqCEEQBmk0mjc2b9783+7du5t4e3ubBQQEDMIzItsAIEnSpoSEhGVlVY2XLl3SZGVlbRVF8T2VSqUVBMFCpVLllX0/z/OudnZ2VUfNY2Jo3DwFUzw9rl+/ruN5/uDChQt1T22n9XguwOQKJHv1qMf/AgRBYADGKZXKjW3atDGWJEnOzMwUJ0yYYFoTGfTfjdTUVL1hR7naqeTkZJw/fx7p6elidnY21/H8edbp2jWc3bRJ5DgOQUFBvImJCdzc3MTIyEhep9PB1NRUsrS0lE1MTPjs7GxRq9UyURSZKIrM3NxctrCwkEeOHMk9dQL7lFFYWIg9e/ZI0dHR3Pvvvw/rOmR+K8WaNbQg27uXMipPArWaFksjRpCEsa6YOZMWVf7+9ECvBekGgM2bN0sxMTGcl5eXPH78+Oej9/bJk7QoHTWqZtsXFVWqEPjzzz/FR48eYfbs2bUv7s3MJMI/enSt36qHv78/Ll68iGZubuLYd9/l2ZEjJYv4ivDBB+TCfPMmcnNzcfnyZfn27duYP39+6e9Gp6MF/4YNwKZNSLC0xIatW+Hs7Iy3334bFy5cQFhYmJiZmcnJssx4noejo6NuypQpNQ+a9+9PnQ6qCgjpdCT1joykANSTYtcuyrwlJpbL1p45c0a+fv06PDw8pHHjxtXN3e/CBZK/z579RKcpSRKuXLmC06dPY/z48fAq64CdnU1KlFGjnh3hTkgghUxERJXkDR98QK73NSCmleHOnTvYv38/Jk+eDI+y5K2ggLK/s2dTi8Pi+/bEiRO4evUqPiF1RKm3SJKEffv2ibGxsZg0aRK/a9cusaioiJ9gagrXrVvBAgIqPhGdDlCpiOj+8EONggc6nQ5r166Vs7OzWc+ePdG7d29wp0/TuN6xo2TDS5dIFbZ9e8l+r1wh9UZiItW5jx5NKppu3SjI98svJR1AEhKQPGEC0nJz4XnhAkwqMswUBFLKlL2Geogijn/yiezUogVre/YsHXf/fprjqupHHh1NCqhXXilRVCxYQEZz69ZV/r7CQrp/582jAEZxSVRBQQE2btxYmJmZ+bFKpapiB8CSJUvUvr6+Rnfu3AEAe5VKlVrV9jWBIAiuxsbGW9VqdZ/ilyYAOKhSqYqedN8V4auvvvqhV69e7/fu3fvxgNq8eXNOTEzM2yqVak9V7125cmXAsGHDOlUUVAJAz5GXX6a1w1NSAImiiG+++aZQrVZ3rKANWT1ecNRntuvxP4vimqBdgiDcCQwMHKFQKLxEURweHh5u6luNw+nfCZ1Oh4MHD8phYWHM1NRUPnPmDDM1NRVNTU2Rl5cHtVrNS5IEHx8fsX379ry7uzucP/8ccm4uRmdm8li+HAO+/RYcx8HU1JSXZRlFRUVITU3lEhISUFhYCBsbG97W1vax0Y2TkxNDmXYyzytMTU3xxhtvcEuWLEFoaCi6du365DstKKB2MYsXk2S2FpJrSZJw/PhxJCQkSFqtFrm5uczY2Bj29vZs/Icfgtu+nRZtZqV9+XQ6He7duwcfH59ywZRS+Plnyi6Gh1PdYGwsZQNr2L9z4sSJ3Ndff42iomeyhqkbDh+mrGBNkZ5Oi98HD8oRm27duvG3b99GZmYmbGobIAkIIPJXB7IdGRmJP//8U87NzWUTJ05E08aNefTvX7H7uB76Re+iRZBlGZs3b0ZOTg57o6L6YoWClBEzZgDvvw/nqCh4du8u2bm4cBzHoU+fPujTpw8PkANzamoq/Pz8avcM37y5+nGkUFBWc9EiMt5q1qxWh3iMyEgiOZ99Rv3NyxBtjUaDy5cvs549e6Jv3751t9FfuvSxy/qTIDw8HKdPn4aXl5fk5eVVPgNraUmBkLt3qa73WcDBodLWdwBIITJ9OpGuun4vALKzs3HkyBFwHFexQsTMjAKR/fqR6iY2FnB1Rbdu3RAUFCStWbOGmzlzJhQKBbRaLURRhCRJMDEx4XJzc9m6devQtWtX1q9nTxjJcvmMrR6yTHXSFhZEuGuYpVcoFHjvvfdYWFgYdu3aBUtLS3To0IHqmw3RtSvd8/7+NAYB6lEdEUFE19iYSjgKC+k+/de/AEdHSKKIK/v2IXvjRjFkwADO7uFD1rRHD+Dzz6lUSD+W8/OphWBFn+/KFaoRnzwZ7Y8cYRlvvEGZ9JrM49HRVJvdu3fp0oW5c0upfSqEqSl5Ljg60ncXHw9No0bYuXNnUW5u7l4A66s/Acy/c+fOKo7jvly0aNETE20AUKlUsYIg9GOMDZFl+TCAnQAgCALwlAi9IXQ63bnw8PC3evfu/dgAplmzZlZxcXHzAVRJtkVRDMvOzu5U6QYBAVSG8BRLbcLCwsAYC6kn2v+bqCfb9fifh0qlCgUQCgCCIHQ+ePDg+cOHDxt99tlnfI3kl88AGo0GwcHBMDIywtGjR6UGDRpg5syZrGHDhiwjIwMpKSl8dnY2LCws4O7uDnNzc3AcV+opyywtH5szmZubE0EDtYMyNTWFm5tbSSuVFxyMMfj5+ckXL15E586d2RN9b5JEmWxRpIdlDQxlJEnCw4cPcefOHURERMhqtZr5+vrC3Nyc5eXlMRsbG9y+fRvfxsZKo+Ljuea//FJuAbZ69WopNzeXMzMzQ7U16BxHWZeUFFrkN2tG9bzLllW7IE1NpTVLdnY202g0/3zv+YwMkqZW5kJeERo3JlJRPKYNYWNjg9atW0vbt2+X58yZU7t7OCWlzguk4OBgpKenszFjxqDpiRNUgx4RUfkbjh4leeiDB4CFBe6HhSEzMxMffvhh1eoMngd++gmZ+/fDdsMGzvnMGQnt2nGGDsmurq6oVNZaFVxdyX15zBhyRq8MLVtS0Cc7mzKPteltDhCJ8venzKJCUWFA4tatW5AkCd7e3pXWAVeLuDgiPJ071+39BrAtLiF55ZVXKh5QHEeBGq0WuHOHSj6eJnJy6Do9ekTO2RXB35+I2BPM63fv3pX/+usvxhiTP//8c1Zp4G/CBKp7vnmTiMXq1bCyssK8efO433//XVyzZg0vyzJ4nn/sRm5vbw9zc3MpLy+Pa9GiBWe0YgVlcYODKz7GypX0eb79tk5qAS8vL/Ts2RMnTpyQO/z73wwKBQUrmjalDXie2n2dOUPElTHKLEsSGYr17Emu5Lt2EdEGgDt3cO6TT6TYPXu4N3bt4v3Cw2F94ABMW7Ykoq7R0D79/EjNdPhwSflRQgKVhLRoQedhbg54eeH4okWwsrKSmwKsWqodHU0Zem9vqinXIzGRxlxCQvUXxswM+PVXOocuXRC2YgUSExOzdDrdjJoYk8myHAMAkiR9X/3Bao7iYx8BwARBaAvgIwBvAngWztsXkpOTTWRZNmyRCUmSOgqC4KxSqSq9kBzHeVVq0hoRQYGQAwee6slevnw5t6ioaOVT3Wk9nhvUk+16/L+CSqUKWLZs2T5ZlseVJa/PEhqNBn/88YcUHR3NKRQKWaPRMDMzMxkAevTogR49enD6B4KdnR1q3Bu8Z08y8Ll+nSL0SUm1Xxi/IBgwYACLjIyU16xZI/Xv35+rU7uwhASSRG7aRLVtNVjk7927VwwPD+ckSWIuLi5Sx44d8fLLLzPGWKlFeb9+/XD//n3uz4wMDAwKgrfBou/s2bPQ6XScpaUlQkJC5ObNm9eMXehd0S9fpnPduJEWqOHhlZ67q6sr2rVrJ9++fZstX74czZo1k8aOHcv9Y6R740YibDV0yX2Mzz+n7NpHH5X70yuvvMKtW7dOXrNmjTh9+vTHfWGrhakpMHEiMjMzsXHjRgwfPhwtK2mZVBbDhw9HZmYm9u7di9azZpXraVwKoaHkA3Dx4uNe7QEBAaKnpydf0zKIrHbtcKt9eziGhXHw9yfp4owZ1We2qkNqKpHU6tCpE9Ujtm9fu8zxd9+R4/rVq1QPWwkKCgoAALt27RI/+OCDun2ou3cpePAUgqb6oE2VihA7OyK8b79NgYQK+kXXGRYWRNwqI9rbttE9dOpUned4WZZx9OhRplarMXPmzMqJNkDzS24u3YeGpA/AxIkTeZ1O9/iaZWRkwMjICFZWVgwAW7NmjXzgwAH24b/+BVaZS/zmzUQKv/qqVsqisp/H0tKypKdzQQE9C/VkGyC576FDNFbatKHjJiRQNjsoiDoTfP01sHAhjSN7ezzq149rc++epLCx4RwdHCigo/edsLen4M6bb1ImvU0bCqxt2ULjnjF6Ls+Y8fgUOnbsiD179rDc3FxMmTKl8g/06BGpQZycSHFliMxMup9qM482bQrdmTO4ePKkZsSOHXGtQ0KUqIFsmzH2b1mWoVKpMmt+sNpBpVIFAZha/O9Z7D9j2bJlhTk5OUb6Obdly5aws7Mrys7OXiwIwuyKAg+CILQA0KnSYObSpbTWeopGsQkJCUhNTdUBeLoMvh7PDfgvvvjinz6HetTjb8X58+e3tWnTxrh58+Z1z6hUA51Oh1WrVknnz59ngYGB4l9//cUZGRnJr7/+Omvbti3r168fevfuzbp3787c3Nye3OHbyYki4d7e1ILoCSSGzysUCgV8fHxY3v+xd+VxUZXv99x7YWRV2VREQUVQ3AAV3DJxDSnJvb6lmWlqpbmmaepIZmalWZmWS5jlkprijrjhLqioKCLmAiIi+w7DzNz7/v54GBbZZmDc+s35fOaDztz7zt3nfZ7nPOfk5rLQ0FDu0aNHcHV1hda+vMnJJBwjCFQl1uKYHz58GDdv3uTfeustbtCgQfDw8OBatGhR4fniOA62trZg5ubqR8HBfPbBg1Kjt97iDgUHs7CwMG7QoEHs3r17cHBw4JydnXXbeXNzerm5UdWxRQvykPX0pGrlE2jdujXXrFkzZGRk4P79+5yTk1Nx5e6ZgjGi669cqbtPsSTReRoypFw/vZGRETp16sTFx8ezkJAQrkWLFpylNuP/+ivQpAnWHD4s5eXlcXFxcTopm7du3RpNJk+GuZMTjCsTxklOpkC1f386R6AALjg4mB84cKDW56F+/fqIuH6d1Rs0iGvu7g5s3UqT+u7dy7Uo6IQhQ6iCqs0YvXpRJdfenhIVVSE3l6jjTZpQD2wV7I3ff/9dvH79Og8AKpWKt7CwqJkA47ZtVEUsHVzVEGZmZjh58iQsLS3RrCr2g5MTVTUtLeme1Bc7ato0EvWq6LwoFPTZqFE13tf8/Hz8+OOPUKvVmDBhgnZ2mCNH0nNy0iQ6t6W0CTQ+0hzHwczMrIwdpKWlJWeybh1L2rYNZqNHc+WSYRs3kuPAhAm1ErY7ffo0jhw5Ajc3N8nNzY1HixZ0rZbWIeE4euZv3EgaBG++Sc+Ba9cQJJPhmKOj5L1iBYfHj4HXXkNySgru3LmDh1ZWrNP69RwuX6bf1O+/p2eSKBLDaNw4qpgbGVFf9fDh1N8+YEC5hIlCoUB0dDQ++eSTynfmwQOipNva0vF+EgcP0vOwGnFHSZIQHx+P8PBwyGQynLp5syD53r2IN3bvZjxjcQgIuItFiyqtbgcEBJgA+AXAWR8fH9085V4wnD9/friTk1Pj0s/ctm3bGkVFRbVhjPU9duxYkI+PT2Hpdc6ePbuoXbt2nd3d3cv/yJ89S/ff8OF6K2owxrB9+/a87Ozs2QsXLqxE2MCAlx3/zRKYAQZUAUEQfr1y5cp0Ly+vp6KwDQDLly9noijyY8aMQXJystC0aVPY2to+Pc46zwP+/vSDPWIE/a2l4vKLCEtLS/j5+fGenp4ICgpiK1asgJeXF9e3Op/La9doohoYSIKOaC51AAAgAElEQVQ21UCSJKxdu1bKyMjgx44dq5NPe7du3YzSfv8dGf3786tmzECGtTX3yiuvoGHDhlx+fj4GVOUzXR3MzGjCKElU5XB1pUlgcjJVvEuhWbNmSEhIAAA8Nxuw/fupYlQT5XBjY6pAqSpmGBobG2PkyJHC6dOnpY0bN3K+vr7o2LFjlUOqWrbElvBwFAD8//73P+zevRshISFanxNFQQGy6tUDX5nmQ1wcYG4OdvYs7nAczv3xh2hjY8PFx8dzFhYWzNnZWetnAMdx6NKlC3flyhWxT58+AtatI+aKjw/1vy9dWrMqN2OUjNu9m8aqCg4OFLz4+pLNVFUB97hxQFISLVcFioIBYeTIkWjRogXu3LmDvXv3subNm3M69eCrVMTwmDFD+3W0QGZmZvULvfIKBVVt2xJtuLZQKEhEsKJe8IQEovRfu1bjJAtjDMHBwSw/P5+bPn06KqXIVoTPP6dESlUigE/Azc0NbgMGcFfCw6W1a9dygiBArVZj2rRpMAsJoarytGl0fdUCN27ckDp06MAPGTKEboR794i19CTFd8AAEko7fx6YMgXqnBwcvHpVunXrFl+oVPIpoaGo5+ODUw8fSmeLnDFcXFwomdK0KamT5+dT4D5/Po3522/U0+7oWK3tXJMmTaBWq1FQUADTiu6h+Hgaz8mJEhCg+6S4RUaSqPpegV5JYmIioqOjERcXxzIyMqSCggJBEATY2dmJ4eHhglqtNoWR0XAjSXoMwBjAHXDcZ2Dsn0o2d+YTf19aiKJ4OTk5uVPLUsUHc3NzfPLJJ2YHDhzoduPGjbsBAQFvyuXys6VWU8tkMiWetP0SRbrXR4+mXn894d9//0VKSkqaJEkb9DaoAS8cDJVtA/7foWfPnocvXLjQ586dO41atWplZGJiArWanBZqU2HOzMxEUFAQQkJCRLVazc+ZMwf16tWDvb09tKa51hYa6xRBoIlA37418m1+0WFpaYnOnTtzjRo14k6ePMkuX74seXl58RWev7AwqkK1a0fHQwvs2rULWVlZmDRpEleTirCZuTmsBg6E99KleGXtWji3bo3ly5fD2dmZdejQofZ0Co4j1WIzM5qoSRLRGcePp4pqEWW8Tp06uHPnDgDA1dW11l+rM1asoCpgTZkWkkTU3d69qWL6BDiOg5OTE9ewYUPs3bsXVlZWaFAZtVethmLIEBzy8MDEjz6Co6MjnJycEBwcDDs7u3Ie1uWQnQ1hwQL85eGB2Nxc0cjIiM/NzYVSqYSJiQkexsfDondv8ByHY+bm0tGjR2Fvb8/du3cPOTk5/IABA9CoUSOtz31oaKh0/vx5TiaTMS8vLx4cR9fx229T/+b+/dRT3aqVbpRGjiMapLu7dpRUR0fqO3dxqdgab/16ou5+8QVdf9VUejmOQ2RkpKhUKln79u15Ozs73L9/n12+fBmdO3fWnuUTFES2X094k9cGFy9eZDzPw8PDo/qN6NOHgrHGjWuvTi6KpDBe0SR+2jRqw6ihj7goili+fDkSEhK4jh07ooOuveZ9+xKNPDOTgm5t3Ctu3gQ6d4b96NGchYUFoqKiIEkSCkNCmMv16xw3ZAglKmoBxhhOnDjBOTs7o7mm2m9hQdXhDz4ouzDHESNk2jRk9+mDv48dkzxPneJelcu5i9evI5fjcM7EBP5BQZxjy5boP38+OnfuTNdA/fq0/+fP07EwNaU+72++ofth9Gj6zbWzq5BlRF/PITw8XHJycuLqP5kET0ggG7e2bYFx46BWq3H48GHs2LEDZ8+exbVr18SI8HA0GzmSy3d1xaVLl3Dy5El24sQJ6fjx4/zVq1ehUqlEJycnrlOnTny/fv3Qt29fdOzYkU9PT5dSU1MPLly4cC0WLQIWLRIREHAPwF0EBLgiICAJixYVewEGBAQYAQgFALlc/iFechw/ftzB3Ny8j5ubW5kHHc/zaNWqlVHDhg3NYmJi3jl16pTliRMnxJMnT7bief6rvn37WpQ7Tzdv0vNXw/bQAyRJwubNm/Py8vLGyeXyW3oZ1IAXEobKtgH/L1FYWDiisLAw6c8//2RDhgzhNmygpOKMGTOgFR21FCRJwvr168XU1FTB1taW9e/fX3BxcdGe3qxv8DwFKU2aUH9Zfn7taKcvKDiOg4uLC6ZOncr99NNP/Pnz59GjR4+yC124QJWC33/Xynbq8uXLOH36tJSXl8ePGTOG07UirFQqkZGRATMzM6gsLWE9dCiUP/+MfW5uIgDB29tb/30Lb71Ffx89InEdQaAKfqdOxQGkl1flwqpPDcnJRKPu06fmY/A8BZXVVKxdXV0xePBgBAUFISUlBb169SrnPX376FGYyGR4xceHNWjQgAOov7158+ZiVFSUUM7q6UlERMA4PBwzQkNx8NAh4fjx46IoilAqlTzLyeEaFRRI2YMH867u7rh84QI/ceJE2BGdlPv6669ZWFgY16FDB60SenFxcTh58iTfr18/eHl5lX2QWFsTNf/sWQpwrawo4KtOdK80mjenSvRff2lHg162jIK9994jcTWAKuSiSOJXTZro1Cbw2muvCdu2bYOvry/MzMzQrVs3fsuWLcWJC61w9CglHvQIU1NTVrduXQag+oe3gwNV19u1o4ReTfu31Wo6h2FhxW0Hxfj5ZzrHtVQ9zs/PR9u2bTGoJp7ADRuSMv3atZRMWbKk+nXGj6dWgp9+gqenJzw9PXF7yxbEBQZyp/r0EXt5etb6xzEqKgqFhYXo3bt3yZsODnSNJiWVp6c7OiLJ3R2F77/P+rZqhSY5OVzB3bvFVHh07CiZDBnCu06bRhXk0aPLru/jQ8nDnj0p8AIokX3xIt0HRRoElcHc3JwlJSWVaVGIu3gRDX74ARmdOuG0qSlSVq0Sc3NzeXNzc4waNYozNzfH3bt3BWnqVFw3NcW5fv1gZ2cnNmvWjOvUqZPg4OCAevXqgeO4csczPz8fUVFRSlEUp5b5gLF9RQ+hCwDCAZRW8hwIAIIgLK1yZ14e3ExMTFQXFhZW+KGTkxPGjBljcunSpRn379+fIggC69Spk2njxo2hVCpLFszKgtHkyRBXrACrhGlVGaqyV46MjGQFBQW3QKJxBvyHYQi2Dfh/CblcnhwQEDAoMzNz+4YNG0wBgOf5xOPHjzf08/Pjjaup+KSmpiIwMFCytbXlk5OTmSAIQv/+/dG2bdvy/WnPAyYm5DcqSTQh2LevesroSwojIyO4uLiw0NBQLjU1FT4+PrC0tAS/YQPZR23bRiI4FUCtVuPAgQO4c+eOqFQqeUEQOAcHB4wdO1ZnP++bN29Ku3bt4jmOK2ZKONnZSV5//cXnpacLY6dOfbrq8I0bk3gTgJS1a3HVwgJXPDzglJyMyMhI9O/f/+l9d0XYvJkElnQVRnsSPXvS5H3uXKCKXvc2bdrAxMQEe/bsYefPn+fatGkDPz8/yGQypKenI/jIEdT38cHbr7xSJtr19vYWduzYwVCVFV5kJFWQw8JgAmAoWYcJACXbxGHDYFxYyJ9fsAAhISGwsbFhdnZ2xeP17duXCw0NZTt37pRGjBhRZaCRnZ2NLVu2oF+/fuWTR6XRowd5hu/ZQy0E48ZRQKxNks/CgkTcUlO1DxJHj6beVU2w3acPVeNWrdJu/VLQqPHHx8ejVatWePjwIQPAaS3ip1DQ9d6li87fXRmuXLmCtLQ0vtqWlNJo1oxcB3JyqLJZk4qXkREJID4ZaB8/TorSo0bVShRPk3RKTU2t8Rh4/XWq8M7UglnMWPlWgthYuO7eDbsPPsBPt28LTe7eRUFBAURRrN4KsRIkJCRIAPhyjgsXLlCg7e9fZvnDISGIU6sxuHdvNFi2jIetLczy8tCxsFC6ePEi7+npyTBwICl4BwQQa6HIIlClUqFw40aYz50L7vx58leeMoVEy6KiKLlZWEj35B9/lGPyZGdnQxAEIT4+XuzSpYsAANt+/ZW5r1rF7e/QAbcVCrQ3NRW9vb0FBwcH2NvbF583W1tbpLRsCeNBg+AzcmSFgXVFOH/+vJrn+e1yufxuuQ9Jors7AFNw3EoAkWDsdwCpgiA8FkXxDy1Pw4uO2xkZGXW/++67Mm9WEAAbF71w7NgxHDt2rMyH9g8fwk0UcfzAAdLNqCVKJVw5URQnaaMQb8DLDUOwbcD/W8jl8v0BAQHmAOwBZEuSZHXz5s2Nt27demXq1Kmyqiosu3btEi0sLITExET069eP8/DweP4WSxWB58kT0s2NesKGDdOOBviSwdfXl2/YsCGLjo6WVq5cKTgmJ8N/zx48+vFHtBo0CJJCUa5iplAosHr1asnU1JR79dVXBUEQ0K5dO8hkshr11hcWFnIcx2H06NE4deoUevfujT179rDYjh3F/8XGCkY1sWqqAYKDgxHm6wtLS0u8rVbDYfNmqH79Fbh1q2oFbX2CMWDnTkp06APp6dQPXY2wXIsWLTB9+nQuPj4eu3fvZps2bZJGjRolrFu3jnmnp8O7Xr1yAd2///4rymSyqn3nx48n66Dvvy/7viSBP3UK/Nq1QL166CaTwcbGBnv37i0TvHfp0gXt27fnvv/+eyE1NRW2traQJAlZWVngeR4WFhbgOA7R0dE4ceIE7O3tpR49elR/HXIc+fB26kTH59VXKVCozmaN54ErV4garC2GDyeNgLfeot7FL74gH/gagOd52NjYSLdu3WKtWrUSJEniGjRowHie1y5avXqVtl2PLTIHDx6Ek5MTqmU4PIkZM6jym5dHlU9dMWECeTyXRng4JUwPHCgnDqgrcnJyAACFhYVVJ5SqQteulKBp144SOlVVt7t1o33SULkTEui+mTIFVq++Crft29lff/1VvB2hoaH44IMPdGaU9evXj4+KisKOHTvYu+++W7JfI0eW0YhQq9X4448/pPT0dH7U3LloMG4ch88/p777tWvhtW8ff/HiRfTs2ZOC2I8/JlbOL78A1tZIbd0aZ+bNY9ccHTmHyZPh8uiR2Ov+fQEpKcQMWbaMvsjYmJK6devi1q1bOHnypGRnZ8f8/f2F3bt3IzExEYmJicK9e/cg5eZi5JYtnOWCBXhj5Eio1WpYWlpWHESnpcFu8GC6/7SEUqlEWFiYWqVSLap0IcZEALnguBgADuC4+nIgDIw9HSGb54NkSZKkefPm8TVJ6AAgHYC33gIuXEAPPbIV9+/fXxgZGRk4f/78S3ob1IAXFoZg24D/1yjKKGr8FnMDAgLGAojLz8+HiYkJRFHE4cOHERsbW/jhhx/Wyc3NRXBwMFJSUoSpU6fCosjW54WGmxv9Xb2aqlha0KlfNhgbG8Pb25vz7txZYJ9+ivRXXkGUnx/O37iB3d98A57nIZPJmCAIkpOTkyCKIu7fv88aNGiA0aNHc9UxGbSBh4cHl5mZKW3ZsoWbOHEiZ2VlhY8//liAJNGkbO/eGvdd6oLk5GQAgJ+fHxxbtwY++wyCWk0T5j//pP5nMzP9qShXhH/+ITqnvhIMu3aRZZWWLRFNmjSBubk5s7CwEL799ls4ODiwHl5evOwJcSLGGO7cuSNobPgqRGYmVeoqSr5t2ECT7Zs3AZkMN2/eRFBQEBwcHCQAZQ6wmZkZ2rdvz9asWcOZmZmxgoKCYr94DRPCxMSEFRQUcIMGDdLt5DRtSq8FC4CICAqk33uvaqXns2fJ2istTfuKrLMz9Uo3a1YSZOiI69ev48iRI6IoinxmZiYDAJlMBoVCoX0wePt2pWyVmuDChQtQq9V49dVXa6bbMWQIsGYNJZl0WZ8xouGXvi4VCrKVksur9kLXAnl5ediwYQMzNTXF8OHDa9fCkpREQWhV1lUACTdqWDQKBQXd779PiSAAI0eO5DTVaKVSiY0bN7I//vgD7733HqeLcJsgCOA4Dqonab116lA/9ebNSE9PR2BgoFSvXj18/PHHJBTp6krJDLkcyM+HRpSvsLAQRW0hePC//8E+MhKyyZNxq107qf/hw3y36Ghs+vNPFhoaKvA8L/Xs3p1H06bA+PF4sH07CszM0PLXX3Fj/340GTcOzdas4W/cvy8tWbIEpqam0vDhw/mWLVsi6cEDWAwZglO+vtKb771Xsc5IaezcSS0TOgTbN27cgCAI5+bNm3e/2oUZWwMA4LgtABoBqEXfz4sFuVzOvv7665z8/Px6OokClsaPP9aaXfIkYmNjERkZWaBSqRbqbVADXmhwVfUTGGDA/zcEBAQIxsbGe1Qq1esNGzbMVSqVXFZWliRJkqWxsTFUKhXs7e0ld3d3pqGDvXTo25d+uD/66HlviX6hUpGQ0+TJ5HdqbU0UX1GEKIrIzMxEUlISgoKC0LRpU6lHjx68q6urXu3fGGNYsWIFioL4kg+uXaPJ+FdfPRNmwYYNG6SHDx/yCxcuLNk/pZImDO+8Q6Jq587pHhxoi/ffpwlKdRVWXeDsTOMuWFDtorGxsdiyZQvs7e3ZgwcPuIULF4L75hsKLtu1K14uNTUVv/zyC8aMGVOx3dPdu1S9jYsrLw527BgFQ/n5gI0N7ty5g+3bt2PQoEFoX5laOYDc3Fw8ePAADRo0KO6plyQJarUaaWlpWLt2Ld55551iqrXOUKup8vj330BwMPWxVjRRlCS6Bnr0qP4akCQ6nxMn0vHYvJkq6jqqzGdkZGD16tXw9vZG3bp14erqCisrK8TExGBbEQuiT58+6FmV8jVjRF8/dUpv99LSpUtZjx49uB49etRcayM/n5Jpv/+ufZIpK6tsdb6wkDQKPD3J3q+WWLFiBfLy8vDFF1+U0zCoEZTKYj0IdO5c/vPly6kK2KQJBdrjxtGrCt0GSZLw559/SklJSfzo0aPRqFEjrZ/JEREROHr0qDRr1iy+eP/u3gVeew039+7Fnj17mLu7u+Tr6ysUf15YSNv09ttAdjakrl2xYtcu5OXllRnbIisL7wcGIqVJE6lFUBAvc3ZGfn4+vvvuOwiCgE8++QRWlpYo8PCAdO8e9rz9Nu47OwNqNd6Pi4PDunWQANyKiUGLFi2IWaVQAP37I7h7d3B9+kivvfZa9Sfl3j0gO1snFsmaNWtykpOT35HL5dr3AnOcKQA3AKYArMHYPq3XfYGxbNmyuDFjxjjq4ihSjDNnaE7xv//pTYFckiT8/PPPeVlZWaMXLly4Wy+DGvDCw1DZNsCAUpDL5WJAQMBwAF2TkpKsAOSA1DlVADB27Fg4Ojo+xZLgM8A771CV4eFDqnS/iPR3XZGfT5OnIUOoClA0WeN5HjzPw9jYGI0aNUKjRo3g7u4OPFF11Bc0lRa7J3xW4e5OPZkXLpB111PGwIED+XXr1iEvL6+EfaE5z5s3k5r1nTvkBx0TU3Nxp4rw8CFRNHv10t+YAAkRaUmptbGxgUqlwoMHDzhfX1+avF+7RsF2KTx48AAA8Oeff8LCwkJq2LAhl5aWJpqbm/M9unfnW7m6UlD9ZKB99y5NwMLCwJo1w9UrV1hwcDDXsWPHKgNtALCwsECbJ6qyRcwLaIR8kpOTax5sGxlR1W7GDGDWLLJZ+vLL8iJbPE9BeEAAUJUrSX4+XTscR8rw9eoRNXjyZGIc6ABBEMDzPFMqlZK3t7egCaqaN2+OXr16sTNnznDHjx9HRESEOHXq1Iqj3vBw0p/QU6CdmJgIpVLJdenSpXailmZmFGSGh2sfbDs6EutFc11Om0bV46Je4dogMTEROTk5mDx5sn4CbYD6/D/9lBJeTwbbkkTsqf796TiMHUvPgdICZhWA53mMGTOGDw4Oxvr162FiYoKxY8dW7w4AwNnZGfv27eMPHDhQIv7WogXCx43DqW3b4DdsGNzd3cue1AULyEJvzhygSRPw9+7Bb+hQhISEiMOGDRPs7e1hZGQE6do1cBERsFEoeIwZAxw8CHXRENbW1mzjxo3o2rUrl+jqCushQ9jQqVO5zBUrYPv55zCqWxfYswf8Z5+hTUwM3Ts5OcDQobgyaBCuiCJGt21b/UkpLKTfi7CwahfVICkpCRkZGWoAwVqvBACMFQCIAMeNBTAdHHcAAKtS4evlgFqSJN3XEkVSiX//fb1afZ06dUpdUFBwhTEWVP3SBvxnwBgzvAwvw6ua15dffjn3xx9/LCgsLGQqlYr9J9CtG2Pvvfe8t6L2SEtj7PZtxqZNY6yw8Lluyo0bN9iiRYvY/fv3y3+Ym8uYjw9jkZFPfTtu3brFlixZUv2Chw/T3169GPv2W/18+Q8/MPb11/oZ60mMHUvja4HCwkKWlZXFJEliTJIY27iRsby8Msvk5eWxx48fs4KCArZ+/Xpx48aN4qVLl9jhw4fFsB492I3OnaXHjx+XHfj8ecYSExlLTWWMMRYaGqpeunSpFB4ezkRRrPUuHjp0iH355Ze1HocxxpgoMrZpE2N//cXY/PmMZWWV/fzgQcYGDKh8/dxcxuzsGAsOLvu+UsnYjRuMhYbqvEmpqals0aJFLD4+vtxnMTExbNGiRWzRokVsy5YtFR/M3bvLb08tEBQUxNauXSvpbcDjx7W+RtmDB3SOGGPs2DHGbt5kLDu7Vl+vUChYWloau3r1Klu0aBELrcE5qhL79jGmUJRstwaZmfRXFBmbOJGxXbsYU6t1Hn7Tpk3Sxo0btbqRAgMDxe+//1589OgRY4wxlUrF1q1bp77p7s5SN2+ueKXISDpHgYGMffVV+W0MD2fMzY0xze+8Ws3YK68wNnIky0pOZosWLWIZGRls586d7Oeff1bf7dZNEnftYiw/n7HOnWlcxuhZExhIz57kZMaGDmWKf/5hS5Ysqfj3oSLcukW/0zpg3759iq+++uprVps5D8AxYAwDTjDApFZjPefXsmXLbsbFxel0DBljjF28yFhQEJ0/PeHRo0dsyZIl2YsWLXLQZtsNr//Oy1DZNsAALSBJ0rcZGRlfL1++XJIkSZowYYJRuerly4bjx+nv1q1UvRo37vluT02QkkLUzdGjSbTpOWPPnj0AUDEl2dycqoF//0199DUVbNECTZs2hUqlglqtrlrpd8AA+jt9OlXjjh+nfsfDh2tOLw8NBVaurNm61aFzZ61tkGQyWYloYUICVXvHjCmzjJmZGTTuAePGjStdaeLFwkJEXL4srV+/XuA4DmZmZqIxgDFyuXBmyBApqXdvzsvLiztz5owwevRovSrN660SyfN0b6SmkqjYli10nv38Svy2X3uN/JOf1J/44w+iBAcFle8dNjamqviMGXQ96+CjHhsbi7p167LGjRuXu8BcXV0xe/ZsPHz4EDt37uTT09NRxueeMVJg1+OzqkePHvjll184URT1Y9doZUWWXRMnlu3FfhJjx5KQWNOmROefPp2OtY5CYaWRlJSE3377DYxRMdLR0ZF16tRJv30iGlbB++/TfgJUtXVwoP7zP/4gpoyfX436XF9//XVu1apVXHBwsDRgwAC+qnshKSmJ79q1K+zt7Yv7s+vXr885jxsHWWV6BTIZVeF79yZ19VatiOUDEE2+bVtif2mem4IAHDoEdO0Kk3ffBbp3h7m5OYaRKr8AFxd6JpmaUgVakkgkbsECOkaaZ/7q1ditVLImTZpIzZo10+7AZGQAu7VnGqtUKkRGRjK1Wv2b1itVBMYYOG4PgKYAeHBcGzB2s1ZjPj9kKBQK3dbIySG2j1yutzYrURSxZ8+ePFEUZ8nl8gS9DGrAS4OXmw5rgAHPDpKxsfG3oihOV6vVm0NCQvKqX+UFh4kJve7eJSrxy4aYGAqipk0DJk163luD/Px8WFhYwMbGpnLO2rBhJLilL5XuSmBiYgJjY2Ncu3ZNuxXefJO8rBs0KOkNHD2abG10wdatNNGspS9wpfj4Y/KIjojQbb1793QKCDFqFAR3d3gtWiTMmzcPEydOxMDWrYXelpbCiVWrpLDmzfmsrCxu165dcHR0lPQZaBcUFDBjY2P9UjdtbYkS6edHom4TJ1JgAdC/Bw8uu3xyMvD55+TbXplIV+vWZDmmVFKfuBZ48OABMjIykJ2dzWmE/J6EqakpXFxc0LZtW+m3335j6tJja6yWqqHq6wJbW1sYGxsjIUFP818PD1L+X7eOkhyVISeHAusHD+j8BAbStV0LJCUlgTGGuXPnYubMmRg7diyndxFPCwvqKX/nnbLvHT9O97+tLamr15B6a21tjTfffBMXL17kv/32Wzx69KjSZTt16sSuX78uRkRE4LfffmNt2rRhY8eO5WWvvEL9thVh505g8WLAyQnw9aUWEZUKuHSJ2iSMjYH588vv87FjyEtIwJtBQYiPjy/5LCeHWnMASm4ZGZX0+EZE0NiTJkHy80PsvXvo06eP9hmIOXOA69e1XvzBgwcwMjL6Vy6Xx2m9UmVgLBOMfQXAG8BJcJwee42eKVIKCgp0WyMujmwFvbz0sgGMMezZs0eRkZFxVpKk9XoZ1ICXCoZg2wADtIBcLmfz5s2bM3/+/J9kMllPc3Nz82XLlimWLFmiDA4OFsspor5MmD+fJs1z5lCP3cuA9HTyUY2MJKuXpyHypSPOnj2LjIwMvPXWW1U/V+fNA77+mqyCnhJ4nkfnzp2l/fv349atW9qv2K4dXQtqNYnycByJbG3dqt36u3frbYJSKRYs0N1iydKSepO1QW4uee0WBSkcx8HGxgatvv8ebcLDMWj0aP7zzz+HtbU1A1D9+dYBQUFBUmRkJOfk5FSDJkMt4OhIiZ4ZM+g8/fADXY+//EKfJycTe0AUKZFVnQ1W37703KjKCqoUtm7dys6fPw/Tqiq+RfDz8+OVSiW3Zs0asbjnMjCQ+pn1eL+npaVBpVKRUrW+YGxM1erFiyv+PCEB2L6dEkDvvgvs20fJrlpCk5hYvnw51qxZw2rUq6oNTp4kK8kjR+h+8fGhxExUFGln1DLAd3d3xxdffAFLS0vs2rWr0sRThw4duLS0NGH//v2wtrbGwIEDSQitTp3yXt8azJxJxxug69fZmaTHK9EAACAASURBVFwaOnemdSpzpmjYEP8MGMCaJCXB6euvS94XBOD8+bLLfvopVcj796dE3+LFyHnlFbz5zz9wcHDQ7iAwRgkNHx/tlgclW9Rq9VmtV9BuO0IBtAF5cm8Gx+mvgfkZQBTFFJ0q23FxlBR/0o6vFoiIiGAxMTEJSqVyqFwuf0o3pQEvMgzBtgEG6AhJkoyvXbsGhULxiVqtnh0WFiZER0c/782qPWbOJLXs/PzKqwIvAvbvJxG0Aweqt6F5hvD09AQArF69GqGVTfQAUnBeu7Z89UTP6Ny5Mw8A8fHxuv+4GxsDe/aQvVJEBB1rUSSKaGUT+NhYEvMpsvh5ati+nUSlmA7F36NHtauGP3xIy549WyKKlptLAWpQENiaNUhPT8e///6L2NhY7s0338STvt01AWMMISEhuHbtGj9ixAi89dZbT8/pQCajIHrvXhLKCw2l63HzZgpSunQB7Oy0t4b75ReqZmoxoeV5nrVp00acPXs2qlMHNjIywoQJE5CRkSFEaM5ddHSt7bCexLZt20QXFxfJ+kkRvNoiMBCYO5eupyfh5QWsWkX05TlzKPmhB7i4uMDPzw/vvPMOGGPc4sWLsXTpUqb33yeOo+dDYCBV75VKqmyvXEnVYT3g6tWryM7OZi1btqzwgaNSqXDmzBkRAJycnCBJUskDoVUrCpY07I3SOHwYmDqV/t28OQkvjhtHlPgOHap8rjgOHMjtHTiQ2GBz5tCbr71Gv5ulkZxMx2bGDGDjRqR88w0OtmsHYdUqjktK0u4AbNxI4oY6tBulp6cXqlQqHbKrWoKxFACZAGxBntx6zEw9XahUqsc6VbY3bya7Oj2JoqWmpuLw4cMFSqVykFwuf/kZkQbUCIZg2wADdIRarR4pCMIyAH/VqVNnHADts9UvMho0oCz6xo3Ua/YiIieHaOP16ulGC34GsLW1xaxZswDQ5K9KdOxI9MKKJuJ6QnAwidHa2dnx0dHRiIurIbNw3jwKbh8+pAlsSgopexcpZxdj92767Cn2ogOgIHDWLJrkaot69bSjHgcGkn1Rafz0EwURZmYIPXlSXL16Nfbs2YMhQ4ZUqzyuDRhjOHnypHT+/Hn06dOnnFL5U4OTE/Ul2toSU2DUKGIz/PKLbufQ0ZFaB159tdqA28fHh79x44aQkpKi1dD29vbw8/OTDhw4gNToaPJIbttW+22rBgcPHmS5ubnC8OHDq/c71hWmpsTA+fBDqpaVRkwM+ZtPnAi8/rreKvWWlpbw8vKCk5MTPvvsMwwcOJA1b94c27dvx7Jly3DkyBG9fA8A8jr/9VeqcosiPSf0pBCfn5+P4OBg5ubmhtdee61c4qmgoADr1q1jDx8+xPTp0zFw4EAkJyfzixcvhiiKVG3+9lvg9Onyg8tk1D6lwSefEJ3c1pY0DdzcKAA/caKkl7sIsbGxUma7duA//hg4eJCeDYJAWhCawD45mX5DBQH44gvAwQGJu3ez3llZcO3alWzTfvqp+oNw7JjO10VeXp4KQJpOK2kLxrLA2GsACgDcA8d1eSrfo2cwxtLz8/MryLpUgDNnSn5f9AC1Wo1t27bliaI4Uy6X/wcqMgbUFAaBNAMM0BFyufwCgAsAEBAQMEoQhOWnT5/u5eHhYRwbGyvl5uaqPDw86jRp0uQ5b2kN8fHH1AMdFUWZ+UOHtK9yPU189x1VGaOi9GrFoU9oBJaqFc8zM6MJ2qlTQM+eT2V/evXqhX///Rd79uwBx3FgjGHBggVIT09HQkICGjduDFtbW+19xp2cAE1VxteXAqwffqCkgbk5cPPmU6/WF2PMGAoMtUVoKFFGq0J8PE2O580reW/9ehKumjED2bm5uHDhgjB8+HC4urrqRcQsPT0dly9fxrlz5/hBgwahox6oxFVCY2czeTIJ4W3aRD3Dbm4UWIweTRVKXffN05Ned+6U8TF/El5eXjh48CA2bNiAzz//XMuhPfkDBw7g7rZtsBZF8JXRfKtATEwMQkJCREmSwBhjarWad3V15a9fv86NGzdOL+yECtGmDbVhKBTUNmJuTonMLl0oOTFu3FNrgeE4Dt7e3py3tzdu3ryJc+fOsXPnznHnzp2DsbExXn/9dY0NYsXQtJIIAiXaeJ72ISGBKschIZT4ZIyq23qqaANAYWEhVCoV9+jRIykqKgrt2rUrPkg5OTkIDAxkpqambNKkSQLP86hbty7at2+P+Ph4xvM8Ldu1KwW+T6J3bwp4NZg6le5xDby9KYH03nv0vFu3jpJt06fD08ODDz15EurBg2GclUXBeb161H6Vm0v314YNdKw0lW8A+/z9uamffkq08DfeoPvv+vXKE4BKJf32VnV+KkBRdV87AYWagrFEcNxIAFHguAkA/gBjhdWt9hyRUZSEqPomZ4zaPiZOrJGwX/nhGA4cOFCYm5t7RpKk2gnWGfDSwxBsG2BALSCXyyMDAgIm3rp161BMTAynVqv3qdXq1GvXrs3r1KmTSa9evYy06U984cDz9HJ0pL8KRdlqwLMEYySMdOwYsGzZCxtoA8CuXbsgCAK0EiXy8gJ27KB+6KfAJHBwcEDLli1FKysroU+fPli2bBk2bdqE5ORkKBQKyGQyJooiZ2dnJzZr1oxzc3PjGzZsWBx4xMTE4NSpU6xp06aM4zje1NQUPXr0QHJyMh7//jvatmoF2bZtNFH98UdSYa6uoq8vtG9PfsY//wxMmVL1sowRxb0qmrAk0QT8559JgRsgwaTFi4GhQ3EvMxM7d+5Eo0aNpNatW+st83Tu3Dnp8uXLvLW1tdixY0f9U8fVaqpSL15M3sKrVlECwc+PgoEhQ4B+/UhF+bPPSCivTx8KCj78UPtAkOOoh/fDD8kz2t+/0kWNjIxQWFiI48ePM1EUuW7dulV5v2j0MKSjR3HIzU3qr1TyugbHYWFhUCgUgq+vL4yMjJCdnY3Lly9L/v7+XKNGjZ6u4EOrViSYBdC9zvP0HDt8mJ6v+gRjRD2WJAo0s7IAOzu0iYtDm4YNuURLS9w9cAB3eB45Bw8iz9UV5u+8Q0GjJAGNGpG4W//+JJDXsCHpS+zcScynJk2oIm9pSfeUhnK9eTNViPWh5g7AysoKU6ZMwdatW/kTJ06wdkUJnMzMTPz+++/M1taWjRo1qoxSefv27XHjxg3uyy+/RPPmzcUB/v6C9e3b5SOsHTuA778ndg5AgnbvvEP7C1DiCQD++Yf+xsdTkiQrC52WLEHj69exycJCGufpyaNnT6KQDx1Kav+nTtGyn31W/HVJRQlKUzMzSmTHxlIfd7duVD2vSOMiJISSYS9qOxdjJ4uo5BMBFAL44zlvUVXIKSwsFKtd6vRpaqXr318vX3rhwgXx5s2bCUqlcoRcLn/ZvcoNqCUMwbYBBtQScrn8HoBWpd/76quvGoWFhX3aunXrim2gXga4uVFWPyKCqq8ZGUTBe5aQJKqyq1RUIXqBoVar8e+//6KlLvT2Tz6hSsjjxzTR1SNSU1Px4MEDwdvbGyZFiRINlXzmzJmwsLDg0tLSEBUVJdy8eROXL18GYwzu7u6Ii4uTUlJSeFNTUzDG+MTERADAiRMnAFDAFBERwfz8/Dj76GhIH3wA6cQJ3LS1ZYnu7iwzM5P16NFDSElJgYeHh/bVc10QHU3XRHXBdkEBLVO/fuXLMEbUUU2yYMsWuuZv3wbq1EHQxo2wsbHBmDFj9BZoP378GA4ODvzly5chSZL+qCNHjpBAlbMz2TElJVG1uWlTCopLU2Ozskqsj4KDKejesIGC8rZtKalRt6723923L1Foqwi2P/vsM/z44484ffo0B1D7hUbvoCKYmJhg4aefQgoNxXfOznzCxo0YP368TswCtVoNOzs7qX379sUrdenS5dnRddatIzHHo0epGjxwIB13DUSRWmQ4jirHxsZEQU9OJur8sWP0/H3lFQrQ7O2JSZKWBowYQcF7aipVY8+dowDyyhWisn/4IQWOlpaw9/GBvYcHXvH0xOm//pLOCwLfp0UL8GPGUGW6YUPalvr1KTDU3LcjR5bdn/x8Gn/ePGD8eDrfgYHEIKlXTy+HzNraGjzPSxYWFhxAz7PAwEA4OTmxkSNHljt3Li4umDVrFlJTU7Fjxw5u97lzGBYZyRqMGVP24dOvX1m6+5o1VSc9mjYtFmTk169H1PffM9O0NNrvUaOI0bFqFWmJTJpEVetSOHPmDJydnUVBEAT07EnPlfHjARcXEmULCSmxYNSgceMa9fHXqVOHB/Bs+qkZywPHeQMAOO4CgGVgTHufsmcHSxMTk6qzQLm5xNKaPVsvTJO7d+/ixIkTOSqVqrdcLs+p9YAGvPQwBNsGGKBnBAQEGAH4tFu3bi9voF0aHTtSFVEmo0nHtGnPhlauVFIFoGnTl8ID/ODBgwwAV+S/qh2cnKji+uuvevX0zMrKwtatW1nLli3h4uLCAcDbb7+Nbdu2geO44kqijY0NXn31VbxaJGp248YNXLx4ES1btuTHjx8PmUxWvEGZmZkIDw+Hu7s7TExMsHXrVrZ27VrO29paQr163IN33+VkZmbwXroUzVNShE3vvgvGGFJTU6VevXrpXI2sFmPGlNDJqwoINd6/b79d8ef79wMffUR0aoCosrNmAbt20bUHYMCAAdi/f38JRbWWuHz5Mvbv31/8f2dn55qPyxhRUidPpsraqlVEX166lIJoa2uqYJeGJFE1bulS6tEuKKB/Hz9O6/z+Ox2D2bNJHK+qREVpvP02MQQWLgS+/LLCRWQyGT777DOsWrVKTEtLE+7evSu6uLgIVVW3uXPnIAwYgPfGjMFff/2FvXv3wt/fX+uA29HREXfv3tVuH54GLCzoGTp9Ov171CiqWsbGEpvgo4/oPK1YQV7bTZtSq0leHlWR4+KI0VO/PgXfTk4lFOOWLSk5VLcusUsqeoYMHFjuLV4m484ePYroAwfEKVOm6FaSnjePEgjXr9M+2NrS9vbtS8JpetIw6devH//333/j0qVLOHbsGNzc3Ji/v3+lJ93MzAyOjo6YOXMmH7xvH24lJaEBY8XH5OzZs+Kt48d5H7UaTv37c0ZGRkSR79BBuw1q0ACXrawwaNAgHvPnE3PkwAGq/EdH0+/UiBF0HwwaBInncefOHTZs2LCyx3ftWuDRI0psTZlCydbSSYrVq+l+rABpaWl48OABsrOzUadOHTRp0gSaljUTExNjADpkx2oJxqhizHEbAOSA41oCuF/8/osBa3Nz86p7T+LjSa+mW7daf1lOTg527NhRoFKpBsvl8ge1HtCA/wQMwbYBBugZcrlc/fXXX08LCwv7WpIk3tfX9znxr/WItm1pcvDDD1Q90ZMQTqWQJPL+tbEhW5aXAA4ODlxkZCQePnyIZs2awUhbkalPP6Uf+jFjau2zC9CE8sSJE0KzZs3YsGHDiiemrVq1giAIEEURUVFRaFuB0FS7du3QrpKe2/r162NAqQrMpEmT+Fu3biF/8WLewsoKXb/6ClZWVhwGDODw77+Y5+0NlY8P1qvVLCIiAkOHDoWLi0ut968MNIrPjx9XvkxyctX0di8vosFzHAWbGtp5UXJAkiTExcVJjDG9lefNzMyK/92mTRv4+flpv/KjR1S9HD4caNGC7sePPyaFcUmiYEeDyvY7LY2qkxpdCVNTqrzOmkUaDYwR1TY8nGjnZmYUEGqTDLK3J/Go11+n3uRKMGnSJOHGjRvYs2ePcPfuXfj7+8NNQ+F9Ejk5QPfuaNy4Mf73v/9h06ZNuH79Orp27Yq+fftWG3QzxqBWq5+9P2BCAtGTFy0i5gRADBZHR0qIeHpSNXns2JJjq6Gcl0Zp+6eK+nhr8Dzu0aMHl5aWhitXrgiSJGnPFMjJoWtt3Tq6/jiOqrRr11KFtm1bomr361fr5KGLiwvc3Nxw4MABtG3bFv7+/loP6NW9OxLnzuXWjx8Pi/79mb+/P3fixAmhv0oF21Wr8IOFBfr16weP8ePBXbpUlmlQCYKDg6FUKrm0tCINMiMjOjeenpSErlOH2CAyGbBzJxQff4zGkyfD+dQpYiJoGB88T/eehQVVtxkjKv6779I1c/gwtWWUgiRJCAkJUV6+fLnQyMjoiFKpvG1kZGQH4I3WrVvXe/PNN03Mzc2NBUHQL0VKGzC2DgDAcRcBXAfwwTPfhkrA87ytmZlZ5Zne+Hiaa4SH1/q7JEnC9u3b80VRXCGXy0/WekAD/jN4AVSPDDDgv4d58+b9KEnS61evXn3+BtD6QuPG9MNkYUHVlZiYp/M9SUlUbZw+nSpsLwk6duyI1q1bi1u3bsUPP/wgZWsr4MXz1Ms5YgRVSmqBlJQUHDt2TPDz88OTPY0AMHHiRADAwYMHa/U9GrR2cUFHR0e4zpkDKysretPVlQItU1MYDxiAj774Qhh27hxuzJ+PzMxMvXxvMUaNIpuu6lCZRdyHHxLteuhQ+v/8+dSrXRRonzx5Uly5ciWLjo7G8OHD9XYvNy9KqhgbG7P+/ftXHegwRq8BA6hKf+RISdX40CHa5gYNKNjRJmBasoSqcSEhVDXV4LffaP1Ll4ALF+g9b2+qvO7YQbRhbc6fhQUFCw8flgjqVQAjIyN4eHigbdu2UCgU2LFjR8ULKpUlKtEAmjZtilmzZqFnz56IiIjAsmXL2N9//11lJS0yMhKpqanP7lm8eDG1iCgUFJCZmJAY3erVRAOfMoWE6qZMoaTHc0JzXZN76ekU2J8/T0JjNAjtV1gYORKsW0fX5Jo1uokYVoJ69erB3NycDR8+XKf1bGxs4NixI9opFIiJieFWrlwJExMTqcuiRaiXlAQfHx8cO3ZM+iEggN1QKsGqsRKMiYlBWFgYunTpwoodA9LTKVF34QLQqxf1rMvlgL8/ojt2xIbJkzFszhyOu3qV2jSuXqVkyZkz9Pvp7k69wrNmUVVcqaR7MCKiXP97eHg4u3LlSoxarW42Z86cYQsWLJg7d+7c8Uql0uXWrVuX9uzZk29jY8PJZDIPnQ6UftEDwDxw3BRwnA4Ur6cHY2PjhiZV6c0cPUrCkHpofzh58qQ6JSXlulqtXlTrwQz4T8EQbBtgwFOCkZHR0I4dOz7jJudnAJmMBGWcnctb2tQWjFH2PzSUhEpqoDz8vMBxHIYPHy5MnjwZ+fn5fHx8vPYrOzhQwPfddzX+/iNHjohr166Fi4uLWJmqtUYlvTqPY62xcydNOCvqebSwICaEkRFaurkBjCE1LIwUvyvz6tYVxsbUbzd4cOXLnDlD1eCKYGoKNGtG1OmffyYadpGKcFxcHM6cOSP07NmTmzFjBq/PqnxWVhYAYN68eVz9JynakgT8/Tf1zs6cSRNyjqP7DSAGRGQk/btVK92t1iIjiW78JB4/pgrlsGFlg2oXF6r4X7tG4nHXr1f/HTY2VCn66KNqFx0+fDhatmzJjCu71yMjSbOhQYPit+rUqQMfHx+NvRUXGxtb5VzGqyIRKn3j7l1SGA8Lo/3v1o3OWWAgBdru7rQfkkT3xunTFJDOn0+idI8e6eYdXwnWr1+PgIAA/Pbbb9JXX32F7777DpcuXWIKhaJcQKlhu9y+fbv6gUWRqO7ffUd0cXv7ks8OHSLRPXNzShp26EDCal9+Wc4+S1ekp6dLjRs3rlGipO5XX6Hrp5/C19cXTZs2xQcffMAjKgrw8YGXlxdmDBvGjwsM5A4eOiR99913UFRiXXfv3j3s2LEDPj4+oq+vL1fsNrFtG1mfhYQQU6EULl26xJp17SqZ1a1LVl+rV1PCKCCg5Lp47TWyUIyLoz7uEycoibFvX7ltOHnypEKpVL4vl8vTS78vl8tzlErlgocPH4o2NjZgjLWuybHSCxhTgrHHAMwA+IHj6jwdwQ7twfN8w9JMojK4cIEYJ198UevvuX//Ps6fP59bWFg4WC6Xv0g0egNeABho5AYY8JSgVquDrl+//oGPj4/5U7OWeR7geaJFShIJL+3apR8FzwsXgL17abyX2Lc8IiKCmZubo23btrpNMiZNoolsdHSJIq6WePjwIS5evChMmjQJNjY2lfZfKhQK1KtXD/fu3dNp/EqhoXFXh2++QWZgIDu7eTPX5PZtmHz1FdGTR4yofUVBM+kv1ZtZBm5u5am3ajUFAt99R9TPuXOBf/8tI7YWFBTEvL29uacRqGkqLenp6bDWqKQvW0b31pQpFKR6e5e1JVqzpnZfevEi9Yhu21Z5X+9HH1Hiws2NRLk0SRSOI6qzlRUF4itW0HZVNY9evJgC5YgI0n2oAkZGRlz9+vUlVFQAOHiwjI1SafA8D2dnZ4iiyP3+++9SnTp1uKSkJKl+/foCAEiSJPn7+/PR0dFS8+bNOQD6m/hrGAdTpxLjYOdOCjjbtClPn//4YxLGmjmTPnd0pGPXuDEFYocO0fX33nsUjPXoUePN6t69O3bs2IEOHTrwZmZmKCwsxMmTJ6UDBw4IvXv3LtZnAABjY2NorLOqxauvUmJKwzwqjUGDytpsrV1LLKh58+jcffQR0cp1wNmzZ6XLly9z2dnZvJGREUtKSuIaNmyo0xhITASWLkWXoCB00ZyTzEzaDwA8x6GeuzumTZvGL62kRxoAQkJCRHd3d/Tq1avk2RoXR/fKBx8QcyE3t/gjtVqNhw8fok+fPmWv5zp1ShKD33xDr4ICoHt3CrLHjSOdkieSh0UOEqYAKqOKtLCysuLMzMwgiqJ+FOpqA8aWAQA47lcALcBxr1VLHXh6aFCpI8yiRXT+apkPyM3Nxfbt2wvUavUIuVxeRU+TAf9fYahsG2DA08MJlUoV9Oeff+YnJSVBXUuK8AsHnqcJVf/+NKmKiqr5WIwBCxZQP+NLHGgDgEql4vLy8riTJ3Vs2bK2JnGpn37SucKVnZ0NIyMjyaYKr9sDBw5g2bJlyMrKQpcuXWqfeb91iyqdWgYGY8eO5Qq6d8eygQPxzZIlyJXLwc6epcpXbRgSDRpQgkbTE/skli0r39N6/ToFnSoVBdoLFgDbtxd/nJ2djdzcXK5vdd7cNUS9Bw9QPy0Nx3/7jajcGms9MzP6m55O9FxHx5K+6toiIoISZJVNLHv2pODPyIgC6y1byi8zeDCJd505Q4mWgoLKv08mo33TBBCVIDU1FXfu3IGtrW35+Ygk0bpVKPxbWlrio48+QoMGDZCVlYXu3bsL8fHxUCqVLDc3l1+7di0eP37Mm5mZ6SfQVihIpb5VK6LLDx5M15iNDfVnl6bmA5R02LGDlMT//rv8/SII5L3csydZACqV1N6wZ0+NKt1ubm6QyWSwt7eHu7s7vL29MXPmTMHf3x9nzpxBbGxs8bJ3796FSqVCg1KsgUrx228k0DZhQsU2kBculHisc1xJX3piIrUD/fUXVXG1AGMMR48e5QVB4KZPnw43Nzdp3bp12Lt3r26UmA4d6HyUPo5NmxLVG6Dfm99+Q1RUFMzNzVGnyFayyOcbAFUsk5KSBC8vr7JJzHfeoQSY5li89x7dtyDXhvr16zMHbX7LTE1JiPDhQ2JAjBtXzqbw/v37qFOnzl9yuTyhklFyCgoKJAsLC6jVaouAgIAXhRL2BYCfALgWK5c/Y4ii2Ny6ItvHI0co+aVje8KTYIxhx44d+aIo/rRw4cKjtRrMgP8sDMG2AQY8JcjlclZYWDgmOTk56Ndff8VPP/1U+PySu08JGgrs5ctVi1RVhb//Bnx9qYL16af627bnBB8fH/j6+uLUqVP44YcfWL4uPZmDB9OENTRUp+88dOgQ8/DwqPJ5fuPGDQYACxcuhK+vb+0Ncffto35THWjMkyZNwoQJEzBkxAgsnzABCR06UM+jRsG9Mrp3dYiNJaGiJ4O/zEwKZkpTtWNjaZIdEwOEhUE6fBjR//6L0rP4x48fw9TUVNTFXqpaREQU944r/vc/eFy9Cr5lS1KfrlOHKqSffKK/79NAraYJ/JAhVHGsDKNHl7AEZs6kICkjo/xyzZpRoqJTJwocT5+ufMxWrSiwUSqJhlwBDh48yNRqNTpUpAgdG0tV9moSDlZWVnjjjTf4jz/+mOvatSvmz5+PSZMmcdbW1kwURTRp0gRvvvlmlWNUibw8urbef59stRwdqUXC15co1d5VxBGFhSX9tx98QD3xFYHnSYOgVy8S7du9m54DR47otKkcx6F9+/ZSSEhImcDU09MTHh4e7I8//sCpU6cQFxdXzLBYv3595QMeOUJCaPfv0/0+YkTFy3l6kkVY6d+4efOoNSMvj5JZ33yjVWKN4zj4+fmxzMxMpKen48033xTefvttXLt2jZd0aUExNaWk1c2bJe/duUPHGAC+/Rbo0QMuLi6QJElasmQJVq5cyb799lssXboUf/31l3js2DEIglC29aaggNpONEE7QEmhoh71qKgosWvXrto/PL79llg2gYHE+HkCiYmJBYWFhZVcOLSn+fn5Mp7nYWpqqgCgpwxdLcFYGhjbD6A7gJ3guGfKpg0ICDBXq9X1y7Xq5OeTa4Opaa2dVU6dOqV+/PjxLZVKNb9WAxnwn4Yh2DbAgKcIuVwuKpXK5UZGRln29vb/3T6ew4dp0tm5MwkwaYvMTKqIfPLJS9WfXRVMTEzQpUsXDB06FNnZ2dz9+/e1X9nIiIKY7dvJA1kLpKWlITc3l+vTp0+Vy7Vv354DgJUrV0KsJPDRGkolVa+q8FOuDPb29mjVqhU8PT2lTZs2Ieurr6i/Ny6OehkTE2l8XdCiBQWGT9IFFQqa4Jeq5rIPP4Q4YwaU8+fjUno6vhk2DP/s24c//vhDevToEQ4ePCgGBQVpryZfEXJzS4Kqli1pEm1iQkEHgNTgYIT27YvWbdvSffM02xrv3KFKfnVU4W+/pRdAwb+1NSXCKoJMRtT8gABKkPz5Z6XBNAYPpqCrEpruyJEjOZ7nsW3bNpS7V0JDa1TdFYqCWycnJ1b0/8p7wiuDWk0V7Dt36Po6TtoUAQAAIABJREFUfpz6q8+do3P5+uvVn7fQULJBGzuW/m9nR8e2KvA8VY83biTl79mzSVhLI1qnBby8vPjExMRy8zs/Pz/O2dlZffr0aWzcuBEbNmwAABQWFlYewXbvTudv8WKq3Fe2z2Zm1FqwaVPZ942MKMnk4kLX1ObN1ONc/T5wbdq0EU+cOCEBgLOzM0xMTMpY5mmFnBwSdNOgefOSgHbSJGDrVlhYWGDWrFn8uHHj4OPjw82ZMwczZsxAUlISl5CQgLfeeqtk/aws2hcbm7KJxjNngGbNoFAokJubK7i6ula/batXk/Dh3LmUyKkkkfDo0SMlgCtVjLQrMzPTJC8vD3Xr1lUDaFb9lz9DMBYIwBVAP3BcEDiumptAb/C0trbOL5c0vXOHko+vvFKrwePi4nD27Nl8pVLpL5fL/2PURQP0CUOwbYABTx8dbGxsTN5++22z56wV8vTx/fdURbt6tWwPX0X47juiTi5fXqOg7UWHhtLtrBG20hZdu1KFsTJadBFu3bqFVatWSatXr4a5uXmlk+Xs7Gzs3LkTyqIAVmuV9Kpw4ACpUxf5UNcE/v7+vLGxsXT48GEGniebqsRE2ndPz5JeZW2hUFAFtHSSIjwcOHgQKSkpiI6OxsXQUKzq14/96OCAnF9/RWRUlPj6G29g9uzZqFOnDrdu3TrExMRwnTt3hp+fn27Vf1Ekf92UFLq2x4yh91evpoldmzbAP/8AADQ9hFV5S+sFy5cDqal0HKpS5AWomj11asn/33qL+umL6LQVYtAgChaCgqgvt7LA+NtvS8TBnoBarYamWlmO+bNhAwW1NYSlpSUPAMbGxpzWFVHNs+vdd0m00NmZWAmvv06Jk4ooqRWBMaKoHj9e8t7cuVpZTBXD35+SNubmZC21eHHFbIMnoKFDVyT4NWrUKKMvvvgCcrkcrVu3Bs/z6NSpU/m5IGMk8hYeTtf2Z59Vv+1xcdSj/WTiZdgw+m3YuJESGMePEwW7mqRfw4YNhcdFjCmO49CoUSNcuXIFSVWo3JfDhAn0TNVAkkqerWFhxdRvnudhb28PDw8PyGQyWFhYoEOHDnzDhg2lMuKIBQXAjBnlbfV69QIWLcKlS5dgZWUlmZubV75NBQXErDEzo5YBTTtWUfKjNBhjyMjIMAVwo4q95AA67zY2NkZ40YJtAGBMAUoYpALgwXGNn8G3dnR0dCwrmJOQQMmv116r1cAKhQJ///13gVqtfrsKer8BBgAwBNsGGPAscDs1NVXxzTffqB/VlCb7ssDHhyi6s2fT5KwiMEaTHQcH6mXUJ1X3BcL58+clU1NTVqXtSGWYPZuCn0p6XePi4rBr1y60adOGnzBhAmbNmsVXVIldv349++GHHxAVFYVr164BAKZOnVpc+asx1q+nSWItMWLECD46OprLK6r4FtO9jx0jO5y//6ZgR5tAqV49YkiU3rfCQtw3Nmbr1q3DiZ07xQ4DB8L31i1umo8P1FFR+GDRIsHd3R0ymQzvvPMON2fOHEydOpXv06cPWlbRJwyA1LkBClD9/Oh7MzPJwzogoETDYMCAMjT28PBwtmrVKgCA7dP0qxdFqiJqqxWRnV1W+Kp1axL9OlpNG6KVFZ2n2bMpeVZR1bJlS0pE9OhBSZFSsLCwQM+ePQGg7HUZE0PLV+L7rg06duwIf39/dufOHfz888+VL5iYSD3ScXFEDY+IoMDn+nWq5NZER4Ixaq3x9S15b+pU7azqSkMQqJJ66hQwcSIJ2fn7V3lejxw5ItrZ2amre/YMHDgQkiShTp06eFxRG1Dv3uQDfuUKsZaqQ7t2tM8VPdM5jiwOX3+d1NcVCkqopaaWW5QxhpSUFBQUFMDCwqI4AzNkyBAYGxtj48aN2tMdUlPLCB8iP7+EwfHnn5QoqgQqlQoymazkuw4eJOXqGTPKLSstWIBbnTvjzJkzrHv37lVn1QcMIJbE++8TLX/iRDo2FSTjk5KSwHFcPoCqsiyFRkZG98+ePauqX7++KYAXU/iEsSQwNh6AJ4Ar4LiaZ2u1gEwm62FjY1O2in71KiXRSqvp1wBHjhwpFEVxx8KFCw/VaiAD/l/AoEZugAFPGXK5/FxAQIC1JElLN2zYMGv+/Pn8f77CHRxMf3/8kSY7ixeXfDZrFlExT5/W3bboJUJeXh6nUCg4tVpdjpIsSRI4jkNCQgIePHiAq1evSo0aNUKrVq14R0dHWFpa0mRs+XLqLSu6XiRJwtmzZ9np06c5Ly8v1qdPnyovpKSkJA6gALtekep3ra+9qCgKIIoCpNqgWbNmMDU1ZceOHWP+/v58RkYG/vnnHzZ06FDO2tqaKqeaY+fpSSrYGn/fijB7NrByJU3ieR6FsbEIVSi4UaNGwbFJEwFdu8IlIAAoLETDCuzPqk2M/PQTBR+aPt1Hj6iCranQaa77KnDv3j0GgJs+fToqtaSpLcLC6HX5svYU9du3SWhu1qyS98aOpSrkwIFVr2tkRAH1F1+QsJqtLdHMSwfPrVtTv/e1a2XUui9evIjTp0+D53m67ks+oERLLa9XT09Pbu/eveU93kWRgq3u3YmJcPs20b4fPqz9c+n+fdr/x4/LjjVpEol21QQyGYkBHj1KAfuvvxK1/MsvieZeCgkJCXzXrl2rPXB169aFl5cXwsLCxCNHjghvvPEG69ChA4dNmygoWbGCWlp69CD6tTa4cYOUy1NSynlFw9WVEgevvkp/e/Wia+bDD4uD+Xv37uHo0aNITEwE8H/snXdYFPf2xt/vDOwubekqKMUKAiqW2DXYxd69dk03ahKTGDWJ7l2TGFN+MYkaW6561diw9wY2RCJYAFFBEFBpSpO2sGXm98cRLLQFl0hy9/M8PCK7Ozu7OzP7Pee85z1Aq1atSjdiaWmJUaNG4dChQyL0dZZv04a+g0qmFTg4PJ1t/owpYnl4eHggLCyMT0lJgbOzM/V+O5ctyB49elSMO38e1kVFrNeECfD19S1/3374gZJT27Y99SEoLCQvhQpUQrdv34YgCPsVCkWFCQaFQqFWKpVdQ0JCYlxcXCwBVCJHqQOIYggYex1AHhj7HsBCiKJB2+yUSqUpz/PDn/ssrlyh86ac0WrVITk5GVFRUUUajeajl91PI/8b/HNXukaM1CEUCoWgVCojbGxsNI8fP5ampqbC1dUVlUrN/s6UVDZsbGhRKwhU9du+nYyYFIp/dKANAAMGDGC//fYbvvnmG7i6ugoeHh6clZUVVCoVzpw5UyrxtLS0FNq1a8fdv39fPH78uK6wsJCXSCRwc3HRtU1L47BoERouWMAKCwuxefNmiKIoTpw4kbm7u1e52Bw5ciQCAgIQFBSEUaNGGeaFHTxIC8aXrY4/wcLCQrCzs+NFUcSNGzeQnJzMNm3aBGdnZ92DBw84Dw8PuFy/jlZjxjC+RQtaLMXFkSz1RRijgLh3b6BtW+RfvAgLV1eta0CACfbvJ6nmhQv6BXAZGXQcJyRQ3/G9e2TYpdPRgr2oiLZTiQN8eQwePJiLiYnB9evXnxvDZFC2bqX9rE6gOnEiMGHC838bMoQMsi5e1M913t+fEjGjRlHlde/ep14MJZXNL76ga8GAAVi1apUuIyODl0gkGD9+PJ5zDVapyCjMADg7O4spKSls69atwlAnJ8760iWqJv7wA8n81617+l4Z4rrk4kIB1YvJlNTUp4FeTbG0JAlsz560rfx8MlabPx9o1QoAoFKpmL7fLYMGDQIAfufOnTh+/Dhr3bo1tRzI5TSObNs2kn/ri5cXsH59xYolxkj9kJBAM6pHjADWroU6NBRRXbrg6NGjsLCwEGfNmsXKU35kZWXB1NRU/8q2XE7y/Xv3SPqt01HAnJ5OCUOFosKxZE5OTuB5HhzHUQW8ZLTbM5w9exaRkZFsVlYWLKOiwH7+uexJV1RE58P586S+KQmsRZGSPUuXVui4n5OTo9LpdFeqepkKhSLtm2++WXf37t1PAdR9CZ0o3gZjDgD8APiAsRsGDrgn29jY6J5LaC5dSu0hL4FOp8PevXsLtFrtHIVCUXVPhxEjMAbbRoz8laRmZ2dLV69erdbpdOmNGze2mzRp0j802n5CSd/qvHm0oG3XjqRz/9QkwzMcOXJEBMDefvtthIaGctevX9epVCrwPM86duzIWVlZoWHDhnBycipZlTIAvCAIiIyMRFJSEh/fuzfaf/89fmMMGpkM3t7ewogRI/TW3UdFRYkAmHM51ZgaoVJRcEkLdIPg6+vLnzt3DuHh4WJeXh4bNWoUYmNjRcYY//rrr+PmzZu4deuWEGRmhrflcmbl4AA8egRtfj5MPvyQKtklFVGOIynwkz7jtNatkWdlxaFVKwoaCwoqD0CvXiU5sVJJrtMzZ1Kv7KJFtDD+6aen961BxVWr1WLbtm2iVCqFp6en4eUtajXw9dckk61u+0J8PFXrMzOf/3vXrhRs6Tv7mTGSn585Q+Z07ds/f7x4e9Oid8AAZGdn8+3bt0f//v0hkTzTWvn4MSVNrl6t3muogOnjxrGcuXNxKj2d+zMuDl2kUli97LjCiggOpmTHmjVlb9u9m6r1+phnVUXJyKjCQgoAd+8GYmIgNG0KjUaD6p7z3bt3R9zt20gbPFhssGcPw9ixpKoZPPh5N/+q4DhqJ/rsM0pmVLDv2mbNkPL22wIzM+OiR48WuH37uPo7dohdFy9mffr3r/DcyMzMFPLz8/nCwkL9lSGXLtH56+ZG+9e9OyWAe/Qo23v9DPfu3YNOp0MDBwdg9mwyf3sm2L5+/TpCQkIwdepUWFXkmJ+XR+fVrFllK6rFxdQSUMkM8nv37mkAXNbnZWq12jUAPgXwkhmdvwhRzADQEYzZAUgEY2Mhivo7AVaCiYnJTGdn56cXwSNHaD3yEh4QAHD48GFVQUFBiCiKW192H43872AMto0Y+es4y/P8Ao1GEyeK4pm4uLjMW7duwcbGBvv37y9o1qyZSd++faX/SIn5N99QNcHSkhY4/v60eGnQoHadmF8hSUlJzN3dHc7OziVVZb1KwRzHwdfXF76+vvQHT0988uefYAsWgKvmLKo+ffqw27dvQ/VkJJZarYapqWnNpeQnTpCs+J13avb4cujWrRvq16+PnJwc5uvrCxMTk1LndAB4jaqb3M8//yysW7eOtW7dGklOTkK2UslNP39eNMnKYraHD1MQVyLRdXYGVq6E16+/Qt20KVdw8iQsDh9+3glaEMgIq0EDGi+1Zg0tfv/8k25PTHxa5TTQ671z5w7S0tLY/Pnzq5as14Tz50l5sGhR9c+rhg2puv0igwdTVe7+ff0N8XieAgiOI0m/pyc91tSUnqNLFzxcsACmtrYYOHBgWef3/fspYHvZCQWHDwObN8N00yY4pqWhz+zZWBMWBsdhw9C2trwisrMr9hg4cKBqV/jqYm5ObuEA8MMPEOfNg3z4cNhmZlZLddGwYUNMGTQIunXrWPDZs0J3OzsOBw5U3bNfHjxPx+FXX1WY9LkeGYnjAwdy9c3MhN4bNjB+7ly4q9UM335LkvIKjOg6d+7MXb16FcuXL8ecOXMg1+f9HDnyaUKOMXJVF0VK/FZiYBkdHQ0PDw8dYmJ4bWQk8hhDxp07EAQBaWlpOHv2LGxsbCjov3uXevKfDah/+43UIcuXU5/2sxQVkUv7+vXUIlAB+fn5UgB6jbRQKBTxSqUSqCujv/RFFLPA2GcA7oGxfgBOl3VL1B+lUsmkUqljhw4d6CQvGdW2cOFLKbLi4+MRHR1dpNFoRlUm6zdi5EWMwbYRI38RTy7O35X8X6lUvnn8+PEVRUVFTK1Wf5aZmbm0RYsWUrdKMu1/WySSp+7Sw4eTodLixRR8d+lCQZKn56vdRwMS+mRMz5QpU15+Y6+9Bn7PHurP7Nq1Wg91cHCAVCrF+fPnkZ6eLsbExDBnZ2fxrbfeYjUKuHfsILd5A1OlGRmA9957j7ty5Qqio6MFBwcHbvDHH2O7iwuyN23C+6dPQ+XuDvtly2Bx9y4Fa2ZmgLs73JOSoON5OgbValpoL11KC+PgYKpufvgh9ZA6Oz9dFNdCm0PJyLWsrKxqVx6r5PffKcC9erVmpoMyGcnsi4qeD5AsLUmCu3t39R3ie/em/VEqgdu3SYXg7Aw4OEB64AC8Bw3SmZiYlF39JiSQqqAm5OVREPOvf1Gw7ukJSKUQjxzBrpUrRW9vb9a2bduabbsqoqLI+Xro0PJvHzOGVD5DhtTO88+bh2s9eoj1V69m3KRJ5Pvw+uv6Vaa3bYNr27a4c/Yszu3cyXU8eBCSZ+ayi6KI8+fP61JSUpCVlcUJgiB2795dbNu2bdnPz8aGDO5enHv/zLauXLkieHl7c6NGjeJQUED90P/+N/VY9+5NyoZy2iwcHR3x/vvvY/fu3Vi+fDkWLVqEKnOQtra07QMH6P/+/kBkJH3v5OdXeL44OTkhYeNGvnD2bKyYNQtFHAeJRCLyPA+NRsMcHR1FQRCwYcMGNJbJhD75+by5Wg2JiQklp37+mV7DsyZ5JURG0nlVSbJAEARoNBopgKzKX2AZDNN/8VciitufjAT7FcBaAD/XdFOMsfctLS0dG5WoDSIj6XpSMl+9BhQWFiIgIECl0WhGKhSK/BpvyMj/JP9MG2AjRv4ebMrNzZ2qVquHA9iu0+msbW1tX/U+1T716pE77Lp1FLjJ5STV7dOH5H7lueL+zTh9+jS8vLyqXgTqg1xOPbA3bujvLP0M/fr1AwCkpqaiZ8+eSElJYQEBAeK5c+fKHQ1UIZGRVD3u06fa+2AIZDIZunXrhnfeeYcbNWoUnJyc8MYbbzBHR0fhj9Gjxc0eHjj4ySdQDxsGdO4M3YIFyEtJEXe++aZg2akT8P77VNU4eZL6NX/9lYIjgIyrDB38loOPjw/atWunXb9+fWlCxiAUFlJAm539cu7+H39MQfGLTJ78tF9dT1JSUnDu3Dl6zxUKCjpiYigRYmWF9BUr8Dg/n8ejR88/8PFj6m2uzlg5USTn9VatKKnStCn1e48aRQZiPA+VSoWsrCzWpUsX/bdbXT75pFyn6lJataJZ27WEKIo4duoUEwcMEHDyJBnRtW1LAX5VhcItW4CDB9G8eXP4xceLV3x9BfEZM7fr16+L58+f53U6He/m5sZMTU258PBwtmbNGnHz5s26whd70dPSKOjOyyvzVGlpaUhPT+cGlbQXKBSkxvDxoTaQI0eoZWPtWkqQvYCjo2Opg71euLiQ4V6J4mDXLqr6X71aaVKt82uvocjDAwemThVGTZ6MRYsWYd68eczW1lasV6+e7v3332fvv/8+a9++PRMbNOA3DB2q+++XX0Lr6UktK7dvl++mr1KRb8GlS5UqUBhjMDU1LQZQX/8XCw2Al2tMflWIYjGANgBWg7GdYKza/UpKpbKDKIorR48ebcFxHF1LFi4s9TOoKYcPH1aJorhJoVCce6kNGfmfxFjZNmLkFaFQKAQAewBAqVRyJiYmuQEBAabm5uacg4MD16dPH1ODBGt1FcZoIeLjQxW1a9eor/b33+lv9vbAuHHV7z2tA3h6euLevXsCDJXQ7NOH1AB2dlQdqwbt27dH+/btgSfuvc7Ozti5cye7desWzp49i9GjR8PNzQ1WVlYQRRGiKJafJNi/n8al1KFj0tLSEu+//37pDiUkJODHVq3gtmSJMOHUKc4KYJNnzmTcjRsU9PE8JS1eIUOHDjWJi4sTCwsLn1tlHzt2DMnJycjMzBT79evH2rVrp98Gw8IoyE5IePlqfEBA+f3ELi40Wm3NGmjffRc3btzAgwcPoNPp0KtXL+Tk5ODUqVOws7ND165dER0djeDg4NJjqUePHtQ2cvkyNF9+iUMnTwo3HBy4aZmZAmbN4p5zhb53j4LlZ53Jy0OrpUCsRw+aCe7nR5VciaRc87yS2ebbtm0T582bZ/jeFVGkNovKEhJTppRxDjckxcXFEAQB3t7eHEoSt6GhlOQoGYv39df0WZZQVERS8WNPJhhlZ+M1lYptsrdHyr59pR4RBw8eZI0aNcLkyZMBAF999VVpW0txcTH3ww8/YMGCBaUzvtGgATmal/M5RkVFCXZ2dpDJZE8vJlIp8N13FIhqtWSmt3gxJcKWL39+n0Hni42NjaBXa42DA6lysrPpe+XIEVJsHD9Oz1EB3MKFeDMjA9i0iQMombFnzx5dQUEB++CDD3iA2n56lUxIsLDgT02fjrC2bcV2TZowaUW79vXXNL/8RWn5CzDGYGZmptFoNA6owPRMqVQ6AihSKBR5SqWS43k+U6fTNVAqlbxCoTCow/dfgihSdoWxSAD1n5ioZeorK+d5/pMnXij0h7t3yTPiJc67qKgoxMfHP1Kr1Z9WfW8jRspiDLaNGKkDPHEr7/PgwYMuPM83jo2N/Sg/P1/o2LEj17AmM17/bvA89el16ECLvzt3KLj08Hg62qZjx+d7busomZmZiI6OhkFVCoxRdWr5ckpMvLDwrA4eHh5YvHgxvv76a+h0Opw8eVJXWFjIe3p64u7du9BqtfDy8tL5+/vzpQvnwkIyEnpSJa+rNBZFfOjmhojiYu5es2Zi/a5dmaW7O42cqkNIJBLxWR1/dnY2Ll++jJYtW+rkcjl/6NAh3L9/Hz179qz6OPriC5LDVrFw14vdu2m0WnmGTePHI3PQIKx89Oi5atz169dLf3/w4AEiIyMBABMnTgRjDLt27UJ4eLjQoEEDLjMzU1fcuzff4vFjtuDiRUhWruSQnEzu8iWtBJs2VTxbWxTpWPzjD+pTvn+fqslDh1JQ17FjhS+NMQYPDw/ExMSw5ORkGPS6KoqAqyuwahUZXlXEokXU/1/NhJm+hIWFwcTEBPbP9mrXr08/LVqQrPnOHTJwW7CAAuIDB8jJvETafvIkJEOHYvTAgWz9+vW4c+cOGGNgjGHGjBmlmzU3NxcsLCwwefJkbtOmTYKHhwcvebH32MmJtnvwYGmSLjMzE9cCAzkbBwcgOZlumzKFzNhyc8klvkULUgh88w31QI8aRe+tpyfS0tJw4MABobCwkJs1a5b+mb+NGynoGjuWAu+WLUnlUkmwjcaN6bmfcOrUKSEpKYnNnj27bIy/ZQvg4IDOs2djc1CQELxiBT9p0qSy7SJFRXTMViCxf5EnMaZlRbczxn4QRXHakiVLMiUSSaSDg4NVYWFhfk5OTlcAF/R6krqIKH4DAGAsBEAcgKlVPUSpVDY1NTUdUqqYiIykFqGwsBrvRk5ODg4fPqxSq9UjFQrF38N4zkidwxhsGzFSR1AoFOEAwgFAqVQeiYyM/CAuLq7/vHnz/n6l3ZdBJiPJV6tWVKk6fBg4epSqL7m5ZLDUvn2dqrA+y+7duwVXV1duwoQJht3BFi1ogfj997QIfUk+++wzAIBEIuEDAwPFqKgowd/fnxcEASdOnOCkUqk4YMAAxnEcKQ6Cgp66y9c1ioqoyrlkCSweP0bXffuAN99k0GrL9iDXAVxdXcULFy4gJydH8Pf357Zu3apr3bo1GzlyJK9Wq2FhYYGoqCjcvHlTnDdvHitjIAbQ69q2jYIRQyWh4uIqNIwKzctDQteuGGZlBe85cyCKIniex+3bt+Hp6VlqcqbT6aDRaEoN4GbNmoVbt26xpKQkXevWrTlXV1e4NGrE+NWrqeKanU1tJIcO0ezh/fupv/ZZkpOpOtm0KbWezJhBsnQzs/JN3SqgXbt2iImJQXh4uGGD7ZKRc+X15j7LBx9QT3ItkZeXJzRo0ACNGjUqe+1p0IDc4dPTaX9PnaLPe8YMUkUA9O/Ro8D338Pe3h5NmjQRd+7cyVxdXQU3N7fnAsxPPvmk9D9yuZyzl8lEducOQ4sWwOrVlACysqIKbng4sHkzxOBgbJ04Ufzkp5+YsGgRtXFs2/Y0WWJmRgH66tWUSBk+nBzAPT2BSZOQq1Bg040baOTqihkzZlRvTn2vXk+NyJKT6d+33qr4/m+/TceltzcAIDQ0VLxy5Qp766232HPPGxxMCeG9ewFbW1ipVJg1axZ/8uRJbNiwAQ4ODsL48eO50qTZ22/Tsbx8uV677e7ubhIVFdUBQEh5t4ui+AaAFFEUF6rV6l48zxfK5XLz/Pz84fg7B9tP8QfgBMaGA8iDKAaVdyelUmkukUhO9O7d27x+/Seq+99+I0VNDQ1BBUFAQEBAgSAI3yoUCsOMRjDyPwl7CcM/I0aM1CJKpfJjqVT63bhx40ya1KL08G9DVhYFfGvWULDt4UGGYXXIWE0URXz99dfo3r37U2mhISmRzW7fXqty1MTERGzduhU6nQ6TJk1Cs59+ogWxv3+tPWeN0Wqp6v7RR8Cnnz6/sGrfnsbNrV//6vavAu7fv4/t27eXOsVPnToVjRs3Lr1dp9Nh8+bNwsOHD9k777zDylS4166ln7Awg808h1ZLY9OeSK4BWnBu3rwZSUlJaBobi4mWluDWrjXM8yUkUKXXzo4Sa/n5FIC98w4FhWo1Bd9KJd33/n0632uYaAsPD8eRI0cwb9686gVqlaFWk4x9yxaSJlfG99+TAqFk0oCBWblypZCXl8e99dZbcKyqN7y4mALcZs2oumxjQ5XerCwKUAAU5uZiy4IFyLGywsBWrdDmzh3qr37rLbr+DB0KjBqFM6NGweXWLbFZeDhDaiopYEaOpOvF+vVUHba0RNKtW9h66xbmf/YZTKpymhdF8gkYNowSAm+8gfShQxHXoIHYbc0aVt359oiNJdXE0qWUtCnp3d9azgSnrCxK5gQHAzY2uHTpknj27FlMmDCBuZeoZEQRePAAuHCBpMpffklV/FatgG+/BUDTHwICAoSkpCRu0KBBgq+vL4dffiGHfz1MIQHg5s2bOHToUOj8+fOrNBtQKpUNAAwEsBEAFArFP2fUB2MKAN0B9H9RUq5UKplEItncrFmzMWPGjJExxih5l51Nc7VreH38889P4TxvAAAgAElEQVQ/haCgoAi1Wv3a31KSb6TOUDdLQ0aMGAGAlcXFxVsOHz6sn97sn05Jv/Lp0zSyRaulxWHfvlRVyn/1BqGMMcjlcsOaXz2LRALs2UPBSDXMqqqLu7s7Zj5xg765bZvwKDcXKj+/Wnu+GhEbSwvbBw9opvO8eWUrGIcOkTy1DuLi4oIPPvgA8+fPx6effvpcoA0APM9j+vTpnIODg3jixAnx1KlTTyXb+/ZRRTckxHCBNkBu4y+MOYuOjkZSUhJGjRqFIb/+Cs7FhUamGYLGjUm6/vnnVBVesIBkzhoNVRS3bCFn8eRkCgy9vGocaKvValy4cAFSqVQ0WKAN0Hvx+DGN4KqKQ4fIdbuWGDJkCKdWq3H27NnK75ifT9ePvDyS2h45QompyZPpNj8/oFs3mGdl4e0DB/CWuzvamJlRYqe4mEzXfH3JwX/7duhefx17e/ZkuuRkOgdPn6Ye8SZNKAjt3h2wtMQVQJDJZEKVgTZA22ncmI7x7Gzgp59QOGcONBkZLHrmTLHU3FBfLC3pOiGK5FQ/fDh5gryISkWfU2QkBLkc586dE86ePYtJkyY9DbQBqlAPGwZMmECvESAV1pNAGwAkEgkmTZrE9evXD8ePH+cwdy45YusZaAM0qUGtVrdXKpVVHmAKhSIN/4xqdllEUQmgP4AlYOwAGOMACrQZY/M4jhs7dOhQCrTVauD//o9aO2p4fczLy0NgYGCxWq2eZAy0jbwsxmDbiJE6ikKhUJuYmGgKCgqk2dnZr3p36hYtW1JQMGcOBVMZGVRtWbyYTIoyMl7Zrnl5eQkODg4VDNo1AI0akWGanjLEmmJvb4958+ahybVr7HJhoRgUFFQ3FhxhYbSgtbOjRbOLS/mmXgA5jM+c+UqPh8qQyWSQyWSwsLAo93bGGPz9/bmYmBgWEhKCsLAwAWlp1NealmZ4efzEiVSlfYa9e/cCoOSAjb099f8uXWq457S2JsmxtzfNCf/Pfyjgu3+fgnBra4PI5LOzs5GbmwtLS0vDyfny8ujYCgrSLwlw/Dj1DNcS7u7ueOutt3Dz5k1kVHbMv/02JTekUjLVmzaN3LMtLYH//pf+n5UFnD4NbvVq6gEfMoQk5mZmdN0dMoQSDB07ou+QIdDpdMjKKmdClYUFkJkJuLggMTGRc3V1rd6608qK5nV36YLGq1fD6403cFunY5offqBrvb7qTGdn+n4oKqIguXHj8kcprlgB8fffEX3zJn7++WcxLCwMkydPZq6urnT7wYOkrlqwgCrfzyb4tm6l4PsF7O3tocvLg3b//mq70UskEvA8rwNQ/kXiGZRKpQzU3wxTU9P3qvVEfweoov07gEAADqf69fOXSCS7LCwsFOPHj5eWtK4gOJgSIDVMEIuiiN27dxcCWK5QKG4ZZueN/C9jDLaNGKnDaLXaxVqtdvuZM2f+ttXtoqIifY1Eqw9jJCtdsoR6+3r3pgXw8OG06Ll5kxaRfyEtW7bkMjIyavfaOm0aVabu3q3VpzEXBPiMGMFMR45kkZGRfLVGhRmatDQKwE6eBG7dIlfhL76ounKRkEAO139TnJ2dsfiJiZPtgwd0fN+9S1VFQ9OoUZngpcTgKTU1lf4waRI5V5cz0qlGTJ9Ofb29e1Pwt2kTVVpr2GdZEfXr10enTp2QmZnJRUREGGajGzYAo0frf/+BA8mcqxZp0KABAOD8+fPlX3Q1GrpW7tlD/09Lo8rxlSs0jiokhNQGgwbRT3Iy9VA/eECtO19/TVL/06efM/kSBAHFxcXl75SpKYRWrVD88OHz5m3Vwd8fOHIEBVevwj0xEdm+vnSc/Pqr/tf4336jY3faNEp6PDNHHADE9HTE9+2L34cMEY8cPSp07NiRffrpp5yLiwudF8XFZLSWm0vV6ReTZC4u5fbkN2nSBJ2Li4VDy5bpUAOvAGtrazWAllXdT6FQFAFoDcDl888/N1CvR91BqVSaLlEq+363bFnP4yNGRHYNDT3SqnnzYe+99555qeogPf1pVbuGhIaGCmlpafEajUZhmD038r8O/+8XjUiMGDFSZ/Dz8ys4e/ZsZn5+/tSuXbuaMAMvQA1NUVERCgsLIZVKodVqsWfPHu3+/fs5FxcX2NnZ1e6T8zy5Tnt5UUBgbU0LykOHSOqZkUGyxlp+D+Pj43H//n1dly5dai/gtrIiue3Jk1Tlrq3XdOQIcOYMhHHjcP36dSQkJIi+vr7sLx1JVyKXb9kSMDWlisXIkfq/5unTabZ7HTXU0wfGGNq1bQuXqVPZ7bg4sbBLF5aQkIAGDRrAoNeEoCDqyX2PimLZ2dlISkpCVlYW+vXrR6ZnpqYkIT506Gnfa3XJzQV++QXo1o2OsRYtSNL71ltUcfz8czJPM7D7vZubGy5cuACZTAbPl/V6EEWgc2dSTugrVU1Kosc0avRyz10JHMfB1NQUoaGhrFWrVs/3psfF0TVy7lyUjgbr1QtISSGVCGNUuXZzo+tlvXokGf/wQ+rp7tmTkjy3bpGzuZcXyfzDw3EZgLtKBQdv77LnmokJsoKDEWFhAY927WBmZlY6iq0yRFGESqWCKIpQq9UoFgQ4DhmCoPBwmD5+jIaCQJXqqCja16oC+cREmrX9ySckc+/dm45nAI8ePcLjHj3E5EuXYD52LJs8eTJzc3Ojx+l01K4UFUXJoG7dyt++uzvd9uI5eeECXL7+mu1v0IBr6OZW7e/CnJwcPjU1NbVnz55nqrqvn5/fQz8/v9xqPUEdR6lU+gQHB882MTHZ7ezsPKhbt26+rqNGWdp/+inz4DhesmQJKS04jtqLLCyqNiusgLS0NOzbt69Qo9G8rlAoMg38Uoz8j2I0SDNipI6jVCqtJRJJqJ2dneuMGTPMy4xXqQNER0fj8uXLeWlpaWZqtdrE09MT9+/fLy4qKsrU6XTOADBlyhS8EqO39HQgPp4kfoGBNMu1eXNaKNZCkLp9+3bExsZCoajlpLhaDfzwA1V89J3JXF0WLwa6dUNhjx7YvXu3mJCQwAYNGiS89tprf03kGhdHC+JDhyhRUtXs5fKIiqKgMC+v1hMttUZhIYTQUOQ0bowVmzeDfxLcjR8/Hs0NWeFWqSgx9STIWLdunZiamsoA4JNPPoFliQHY/fvA++/TCC65XP/t5+RQpdTSkiSed+6QjPn6daqitmxJz71+/dPJA3PnGmasGWhM2YEDB/DBBx+8/Gi+oUNJmlwds7gLFyiZUIvBNgBoNBp89913sLW1xaxZs+iPokjHf1gY8NprFEBevUpBqqtr+efGjz/SyK0bN8pWcUu2GRQE5OQg7O5d1FuxAnZvvw0rjYZk6EuXAllZOH7rli48PJx3i4lBVvv2uryCAr5t27YYOHBg6bFcdtMifvzxx1IDwZKZ7c2aNRNjY2OZp6srxhcV0RxxFxe6HnbvXvnotZQU+g64fZuOubVrSz+Lndu36yzPneMH/PgjTJ41ugsKAho2BK5dowDOxqbi7Scl0XXqWT8NUaRrT2oqTiQm4sqVKxg0aJDo6+ur98Xo3r17+OOPP5LVarWrQqGovRalOohSqXQHkODr61vUuXNnWanTeAkPHtCEgh07KEH3xhvkQ1DeBIcqyMvLw5o1a1QqlWrG4sWLdxpi/40YAYyVbSNG6jx+fn7FQUFBa1Uq1ScuLi6yWq8QV5PExETs3LmzKCcn5wudTrcDwNePHz/2Kioq+j9RFCcCWALAPT4+3qdr165/fXnR0pIWY4MHUz9qWBj13UVGkmTS1fWl5la/iI2NDa5du4aGDRvWXDKpDzxPVZoNG6i6begkzLVrVHX87DOYmpqiTZs2LCkpCXfv3hU7duxYu1FrYCAF+pMnU7WoRGJcExwdycHdze1vGWwXFRXhcv/+YJs2YYulJTRaLaytrQWJRKJzd3fn6tWrV3rf69ev6xITE1nDhg1ZjSreKhUwYkTpSKQ///wThYWFbMSIEXB9VpZpbU3Hm0YDvWSxhYVUQZw4kfqW33+f3ONLKqCZmXQefvopBdZyOVVY8/IoIG/RomaJFgCHDx/WXb58Gebm5iwgIAA9evSAl5dXjbb1HC1bAh060KgqfZk9m15XLbmRl8DzPLy8vBASEgIzMzNqBWjXjnrhS3rGv/iCqtOffVbxedG1K52DBw5Q4Nyv3/M+AYxRcOnlhYbduuGgvb1408pKbN22LWOpqfS5DR6Mx8HBnAXHYXRAALp8+CHn0rEjjp0+jfPnz6N79+7gOA7p6ekIDAzUhYSEICcnh+3atQsqlQpz585FmzZtUK9ePfj5+SEmJoY1aNAAo8aPB9+5M+3jli1Aaiq9txcukHqgvCA+I4PGJiYmUnvJtGl0LOfno8mwYdz57t2Ftr17s9IEQF4eVbQ7d6Z2gao8EuRyUgE8W7X//Xf628KFaNasGerVq4dDhw4xBweHqh3jSzcrx7Vr17iioqJwPz+/eL0e9DdHqVS2Cw4O/lQUxZ2NGzcuGD9+vLlleW7/cjnNab9xg1QaixcDHTtW+/nUajU2btxYWFBQsGzRokW/GeAlGDFSijHYNmLkb4Cfn59w8eLFDk5OTl4GnQ/7koSEhKj37t3LSySS/3755Zdf+Pn5Rfr5+aX26NFjo5+fX5ifnx/8/PzEc+fOyTUazWi/V+1obWFBC+TBgymIS06myll2Ni3EbG2rHt9TBZcuXUJycjL69++PWlchuLtT76RcThUzQ7J8OVXAnlTNtVotTpw4ITLGOGtra1y7dk0QBIGVl1AokX+a6uM6/CxXrlAPaUoKVUH9/QEfn5d7HYzRNk+cqD0FgAHR6XTYu3evYGtryxhjuPrLL8IZJyeW5e8vtO3cmbVt2xYeHh4sIiKCOTs7M41Gg9jYWOzbt0+MiIjg4uLiWPv27SGtSXKC5ykAmzkTYAz37t1jDx8+RK9evcqauOXlUW/kqFGVS/QFgczsOnUC5s+n4O1ZVCrqCf70Uwq6jx+ncxSg469nT0q2hIdTNbkaaLVa7Ny5k8vOzmYxMTEiY4yNHj365c/LOXPoWGrbtnqPa9iQHmfA5F5FyGQyREREiDzPMw8PDwqKBwyga2BYGFWAp0yp2kXd3JyUQKGh5Fh+5UqFYwebN2/Ozp0/L97IzxfTvb1FxxYtmNknn+BocTH4oiLRp317hsGDYdutG5rExyOlXj10vHMHUQ8fYsexYzAxNWUymYwlJiYKXbt25SZOnAiZTAZLS0s0bNgQVlZW8PX1hY+Pz9OKuKUleXQ4OpIigudphniHDmWv5TY2dC1YvZqSPSWfQ3IyTHJzEe3lJUZGRqJt48YMgwbR6/7xx9JZ21XCGLVHuLg8rYBnZFCy74mJo4ODAwoLCxERESG0a9dOr6QYYwxSqVSSkJDQPigo6Hc/P7+6YVZZCyiVSqvQ0NAtEolkWfv27bsMGzaM79Spk6TK1iVRpID7+++r7UAuiiL27NmjSktLO6jRaD585esUI/84qq+zMGLEyF+OUqnsaWpqOqi0h6wOkJGRgVOnTkkAbCkuLv6yirtfBICAgADVmDFjzF557zljVEl75x1y5j1/nhaTcXEkLxw2jCoZNZCiXb9+He3atUO5Wfja4McfyZQnMJBkrYYgL4/MkIYPf+7PgiCwvLw8HDt2TJDL5dyVK1cwc+ZMWFhYgOd5qFQqxMTEICIiAvfu3YOdnR2mTJkCm8qklyXPZ2FB/dVjxlDfsCFdmy9fpgX4m28abpsGRhAE3LhxA8HBweKjR4+4u3fvQp6YiKlbtnBeoaGw8/YuXW2uXr1a0Gg03OnTp2FmZqaTSqVwc3PjXV1dERMTA6saVoFhYkI+AFotIJHAyckJN27cgIODQ9n7dupE51FCQvmjjA4fpopefDxJ+StKBp0+TUqTr74CoqPJ6fndd5/ezhjt040bwLZtFKzq2W8dFxcHgAyqpkyZYpiLjiiSDLkmI4VCQqjC7+JikF2pjMjISKhUKrH/48cMnTvT9Q2gc2H4cFKuPDFTqxILC2DFCvp8PvqIqtxDhpSpiJubm2P27NnchQsXcPv2bURGRmLcuHEwd3DA7ZwchkWLSGFy7BiYWg2zHTvEqIAAFtW0KWbfugVrjmM4ehTYv5+Hs7P+PgtSKSXmPDxIPeDjQ7O5Fy6kQLcExuh4bNaM2iUKC+kzOXkS7JdfMDQzk9u9eDFSOnaEs48PtZ9UN2m1dy9VVt3cyJDNxoYUHc/w+uuvIywsjFu2bBkWLFiAU6dO6a5cucJzHAdBENQ+Pj7MycnJ1MLCAvXr14dUKkWrVq1w7ty5Fjqd7qpSqWz9T5STK5VKW4lEcqxp06a+w4cPl+qdFPvxRzqvjhyp0fNevXpVvHv3bqparX5DoVAYe2uNGBxjsG3EyN8AmUz2Vd++fc3L9Cu9QqysrGBjY6MpLCyULVy4MK2y+yoUinilUml98+bNx5mZmeUv3l8VjNHs09dfpyrb1askSzxwgBb23btTBauKRVdRUREuXLggiqKIwYMH/3XZBImEpJELF9LYHkNw8iQQEfFccGpiYoIvvvgCmZmZsLe35wBg165dwooVKzhBEODl5SXk5ORw6enpJeOVWFZWFgRBgFarhVqtRl5eHurXr4+HDx9i3bp1GDVqFLwsLakKtXcvPWdtGJnNmUM/dZTCwkIcP35ciI2NZS1btoS1tbXO9O5drt6AAcx88WKYvxAUTZgwgYuOjkbbtm1hbm5eGvUVFRUhIiICOTk5VSc4KsLPD9i4EY88PREYGFi63XJnU//73yRDfjIeDADwwQdA06aUxFqyhP5WmeqiWzfg0iX6fcQI+nkRW1sKmhYuJPfpkJAqjxNBEHD48GE4OztjrCETN1euUOKmJpw8CTg4lD9yysBotVrIZDJR1qsX9b8DZGzm6UnvdzmBtlarhckzCcaIiAhcv35d5DgOPM+zR48e6Tp+/z3fxdeXqtyrVpUxyZPJZOjXrx/69OmDffv2YevWrQDo+gHG6DNs2hQutrYY6uHBLl68iEE9esCa4yihUlhICZhHj+h6fP06VYtv3yb5fWUJxSZNaDzZmjV0LVm+nJIKc+Y8TQzY21Mw3r07/X/fPur3BmAfG4upO3Zgl7U1+i9cCKeajNa7fPnp7/v2lZvgk8lkePfdd/Hbb79h1apVYl5eHq/RaABgJ4DlERER/W/evNmMMeYpCEJTnU5nJgiClOf5AgDXAPzjAsIngfafbdq0cRs4cGDVlexn+fNPUmnUgPT0dJw4cUKl0WgGKxSKv+3UFyN1G6NBmhEjdZwlS5YMNTMz2/nRRx+ZVVuWW8sEBwcjMDBQp1Ao9ErcffvttysdHBxmjBo1yrxW+5kNQVYWcPYs8Pgx9d116ECBhZNT6UJfp9MhNjYWGRkZCAoKgkwmE8ePH89Kx5D8Veh0T8ed/OtfL7+95ctp9nGfPlXetbCwEPHx8di3bx/MzMzECRMmsEaNGmH37t1CdHR0mRVTv379EBkZKVpeuMB6nT2L+9u2ISUoSByhUNSuy3n//kCrVvQ+1REePXqEPXv2CBkZGZxOp8PYsWOpn1irpQBVqSTDHz2JiIjAgQMHMGbMGLRs2bJmTuXbtwO9euF6WhoOHDiALl26CP379y//g9FqSYEwdy49btUqCobd3Mgxvuodps8lOfmpisTNjRJeFV0fVCqqrtrbU5KpnNeYnJyMY8eOCcnJydzChQsN185x/z5VT1NTy0jBS1oneJ6HRqOBubk5GGPPfwYlveu1fB1XqVQ4sG+frteSJXz9ffuov1yloiTIihVlxpUJgoCAgADh9u3bnKOjo+6NN97gZTIZli1bJjZv3hwWFhZiWFgY99prr+Hq1atwdnZGz9u34d62LThHR0oeVBCYrl69Wnz48CFbvHgxvReiSMHzO+9ULWF/+JCuwX36ULuClRUlcQICKIC2sSGDt/LM7uLigF27KBHk4kLjzHiejrVt2+hztLN7mgj66CNg7VrkPnyI0zdv6m7dusWbmJiIVlZWgkajAcdxooWFhYm5uTlGjBgBWUWB+Jtv0rE5dCjJzyvxWElMTMR///tfmJqaXtRoNBMUCsX98u6nVCoZADOFQlFY+RtW93iy7xYKhSK/kvvYcRx3w9vb22HkyJGmel+37t6lUXTr1tVIhaZWq/Hbb78V5uXlzVy0aNHmam/AiBE9MVa2jRip4zDG/H19fWV1LdAGgGbNmiEwMJBXKpWtFApFVFX3V6vVHz18+DB75cqVX0qlUo2lpWVRz549LVu3bl33nKvs7GiBB9DC6cgRGklkbQ10745CV1cExMQgMTERAODl5YWxY8e+mtfB8ySjXLCA/n2ZntCICOqbnTtXr7ubm5ujVatWaNmyJUxMTEpf/5gxYzg3Nzc4Ojri7t27yM7Ohk6nQ9Kvv8IaYBI3N0S0bo2wM2cAjmNNo6Lg7e39XGXNoCxc+JfIdytCFEUUFxejoKAAWq0W9evXR05ODtLT07nJkydDKpWiUaNGNMro3j2q3lZzf/fv3w8ACAgIwPjx42s23srMDHj4EPInLuOVzozPz6dAKDmZZMr371PQoi9yOfWIP/uZjx9fuZGdmRkFXJs30/vk6FgmaNuwYQM4juOmTZtmkEBbFEVSCzRsCGRlQcNxgFqNtLQ0pKSkICMjQ3f79m1OpVIxgMZvMcZQv359YfLkyVxp//zIkXRNeVYmb2Di4+Pxxx9/AFot30YuF+s3bMjyk5Nhnp+P1F27UL9zZ5gAOHbsGCIiIkS5XC7m5+czc3Nz9t577+HUqVNszZo1uhkzZvAA0KFDB+bm5saio6MFe3t7burUqbhx44a459Ej1tzUFAMWL0ZGcTEerlkDDw8PxMfHw93dHUePHhXT09OF9u3b80FBQbh8+TI6duyIY8eOwe+nn2DeujX14ldGvXo0Eg4Azp2jf1NSKAiXy8kDIDiYzpXvvgMmTCA1klxOUvEFCygBGRhIVe+LF+mYXbyYAnUTE+Cnn0gmb2YGWFlBbmeHUZ6evCAIuHfvHktNTeUtLCwgCAJycnIQHx8vrF69GrNmzeLKPbaGDHlqBrh/f4XBdlFREQ4ePFhoYmLyzeeff760srfhibT5bxdoP8EfwJHvvvvuRFFR0T4AVwDcB/AIgCPHcRNMTU3/7eTkZDFs2LDqjTc9d46SSDX4zniSYFKpVKqDxkDbSG1jrGwbMVLHWbZs2Zn+/fv7tauj5k6//vqrLjs7mwEw0bffacmSJdNEUdQAaMjz/LdffvllDRogXw1F167h6Nq1aB4cDOvcXOQtWADvkSOr50pcW+zfT4Y8T9yka8RHH1FVuxoVVb1ISgIcHPCwb1/csrISm2/axGxsbFBcXIxff/0VANCiRQuMGzeuwnFAL83ateRAXBN5qJ6o1WqEh4eLWVlZuHXrFuvfvz+0Wi1CQkLErKwsBpCktl69esKjR4+4li1b6kaOHPn0BU+YQJ9hDaTKcXFxCAwMFNPS0tjgwYNx48YNYcSIEVy1JOXDh5NcV6lETEwMdu3ahX/961/PjxjLyaH38OefaeSOuzuwcmX1RloVFNCx9ttvz1d6U1Io8NFnNNeCBSTNDgkp/UwzMzOxcuVKdOzYEf7+/vrvTwWIooj9+/cLbNMmruflyzjx009CbGwsx3EcZDKZYGlpKdrY2PBeXl7w9vaGVqtFVlYWkpOTcfbsWaGoqIiTSqXgOE7sfOkSOsyezWS9er30fpWgVqvx559/wtPTE7GxseKZM2fYv3JzERMdjfCOHdGrVy80mj4dBVZWODJhAgRBgLm5uU6j0fDDhg1DVlYW7O3t0bx5czDGoNPp8Ouvv4r5+fmsdevWumHDhvGMMWzcuBGNGjUS+/XrxwA61rZv3w6e49DUwkJssWULy5PLcWXIEDE3P581a9ZMkMlkXHJyss7Hx4e/cOECeJ4Hz/Mkb5dIhHdnzuRfWs2i1QJZWdBt2oT75uZwj40lv4CTJ6mK378/Xc+++grYuZNGQK5fTxXRffvo97t39Z7moNPp8McffwjZ2dnirFmz+DLJQa2WkkAFBaSkqYB9+/YV3bp1a5dGo5n+T+4TViqVr1tbWx99/fXXzePj4wtTU1O1+fn5ErVaLTM1NS1u0qSJtmfPnhbO1fUbUSqBSZNIrVEDBU9gYKDm8uXLV9Rq9esKhUJd7Q0YMVINjMG2ESN1nGXLlgV37dq1c8+ePetkQBoWFoajR48CgKlCodBW57FKpdKVMXZ3/vz5fI3ck18B58+fx9mzZ/HxRx/B/N49cPv2kWSxxGV41KjqzR42JJmZFIDMmlWz8UJ5eVQxnDSp8nmy1UEUKXj08KDArJyZyeHh4cjNzUVYWJgoiiJr0aKFzt/fny8oKICdnR0MJi93cyO5cy32zG7cuFG4d+9e6Q7LZDKxqKiINWrUSDd+/Hg+NTUV9evXR0hICHx8fKiaXUJMDB1DVlblzzWuArVajW+//Ra9e/fGnTt3hPT0dE4mk4lz5sxheisGcnPJn0AqRW5uLpYvXw4AWLx4MZgoUguFqytVZ7/4gh7z66/kHP5C/26lBAWRA/nVq8//vUsX+vnpp6q3IQjUI5uTQxMFJkzAlStXcPjwYfTu3RstW7bUyx8iJycHcrkcHMchMTERxcXFyM7OFpOTk4W4uDie4zjh7eHDuej168UrLi7C9OnTeXNzc71UGIIgICEhAYIg4PaqVcIjCwvm2Lmz0K9fP75CKbIeZGZmwtzcHDt37hTu37/PCYIAMzMzYdy4cZz7jh04HxQknuvWjTk/fIg+48bB0dcXFtbWSElJwYMHD+Dj41N+Hz6ovSExMREdOnQolcEHBwcjKipKmDlzZumx/fjxY5iZmUGtVuPi//2frl1cHO/4xRfQSaXgGzdGWloatm7dKmq1Wjg5ObG8vDxx1qxZTK1WQ+PggIyvvoL7Rx8hJiYGR48e1QmCAAs+/tkAACAASURBVI1Gw/Xp04e5urpCKpWisLAQpqamFY7JEgQBgYGBuHLlilhcXMzGjh0LrwYNnnpZWFtT8mbLFkpCfvQRUFxMx/HOnUD9+nS95jiqkmo0pJa4d4/OhWbNKAEkk1EfOQBto0YI2bBBREaG2G3mTI5PSaHrZUwMVdFPn35q+lgOWVlZWL16dYFWq3VRKBTZNT4I/gYolUpznucz586dK3t2ooEgCDW/rqelkddDSAh9ftXk/v372LJlS65Go/FUKBSpNdsJI0b0xxhsGzFSx/n2228P+/r69vH396+9clwNEEURQUFBuuDgYB7AmwqFYkNNtrNs2bIzHMd1ASDOmDFDpu/s0VfF4cOHhcTERHH27NlPkx9qNRAbCxw6BPznP8DHHwNeXuRKW1VfoqHZu5ekkpMnV99sLCCAgpcffjDMvhw7RpLfmBiSR+vRp/9k4S0UFBRwOh1NuBk+fDh8nyQPBEFAbGwsXF1dKwwWXiUXLlxAUFAQAGDu3LmlcuwqOXaMHJTj42sUaAP03qxdu1b36NEj3tzcXJgyZQq3Zs0a1KtXT9TpdBg+fDhzqUqavmIFmVGtWgWtVotffvlFyM/P52b07AnXoUNJwqtWP5+MyckhT4PISP2P94QECtpfVDFkZNA2qvPZnjkDvPcedBcu4OtVq8CZmJQu5lu2bCna29szd3d3WFtb486dO4iLi4OlpSViYmKEJk2acNHR0bC1tRWcnZ252NhYcByH4uJiyOVy+Pv7w/P8eUpATJum/z6VgzB6NOLc3XHUxUVs0aKFMGjQoEoTqAkJCTh69Cg4jhP69u3LSaVSmJmZ4dKlS7pr167xAGBubi7MmTOHu3HjBny9vGCyZAnw7bdITUtD+vz5oldwMJPcuVMz9/RnCAoKQkREhDh37tzKy4jr1gHffkvjxRwcIIoiwsPDxaNHj7LOnTtjwIABAICwH37Qnc7P5yGTiaIosm7duon29vYsOztbOH/+PMdxHHQ6HXQ6HXiex8iRI+H9wgiukJAQBAcHC2ZmZszf35/FxMTo0tLS2Jtvvvn8hU8UyUmd56klKD6efu/e/emMZo6ja5SFBSVOg4LIr2P0aEqmWlnRsW9pCbRpA92VK7gRHCzec3cXB7drx3EODsCDB3S7t3els56vXLmC06dP75s/f/6oGn4cfyu+++67g/369RtqEHXexYt0jRgypEbHtFqtxooVKwoLCgqmLF68eG/VjzBi5OUx9mwbMVKHUSqVnImJSc9OnTq9skBbEISyRj8gc5cngXZrffq1K6K4uLg/gM6MsVPFxcUvube1R25uLnbs2CGkp6dzY8aMef5GiYQcbn18gHnzSM597BgtDDIzaZRYz56147T9IiNH0vgsR0fq364OubnVnmVcLuvWUQA2bhw5A1tY6B1Aenh4oHnz5ty1a9cgl8sRHByMAwcO4MiRI7CzsxMfPnzIGGPgOA5jxoypXl9yVBS5XcfH1/CFlSUtLQ1Xr17VNW7cmGvevDkLCgqCXC7XtW/fntc70L53j1zAL1yocaANUK/wzJkzebVaDYlEwuU+caF++PAhA4CLFy+id+/e2LZtm9irVy9mb2//fGUdoHaIzEwAJHd/99EjLu7YMdh+/DHJbssLhG1saIZzQIB+Aen168CgQUBiYtkF8+3bZIr1ZVXTBJ+hVy/g5k3kr1iBCdu24cLMmcKDvDyuf//+iIuLE+7evYvLly/zGo0G1tbWQqNGjZCYmMh4nucePHgAALC3t4dardZNnz6df/ToEQ4cOICRI0fC3d2dXP4NMMqPW7gQLRo0AFdUxHbv3s0PHDiw3OpeQUEBdDodQkNDdRkZGby5uTm3Y8cOmJiYiBqNhsnlctapUyc4ODjAy8uLk8lk6NChAwWNhw4BS5bAKT0dTqtWMRQUvFSgnZKSgosXLyI2NhbDhg2r+gHvvAMMHkxz0devB1u3Dh06dGDm5uZo9syIuNfmzePbfvghHo4dyxw6doREIin5guF6PBnXJQgCsrOzER0dLezevZsLDg4We/fuzQoLC3H69GkBABs0aBDn7e0Nxhjs7e35VatWPa2aarX0noSHUxKybVvav759KaCujCqMJvmBA9FSrWZ/btokrtVqde8OH663JD49PV1TVFR0uep7/jMoKiraf+fOnd7t2rWr+cWthPnz6fv0hbGU+iCKIg4dOlSkVqsPGQNtI38lxmDbiJG6zUitVmtlMDfdGvDVV1/B1NRUmDt3Lrdt27YiR0dHcdiwYWbPzJEuepntKxQKDYALS5cuzcrLy3MCgOLiYtQlWbkgCFi+fDlMTU25efPmVexEC5BZy5gx9JOdTQHUihUkX37tNZK/eXrWqM9MLxijfsQvviDzKn2PnZs3qX+xZB5vTbh4kYyI7t4leZ+7O/1UE47j0L59ewBA06ZNkZGRgZSUFMTExDAPDw84OjoiLCwMO3fuBMdxGD58uCiVSpmbm1vln03LlhQMiqJB3v+ioiKsXbsWtra27OrVq4zjOFhYWAhz587VP7rJz6eZ1Rs3AgMHvvQ+ASg1BZPL5ejcuTNCn3ymMTExiImJAQBWYqamUCgAUNLgzJkzOkvGMGTcOF49dizY0qW4zJhQ4O3NtbG0BPr1q/hJ//1vcpnW6aoO7ho0oGOtvGMzJYXGa1UXnodk2jQ82rMHg19/nasnkYBr1QqdOnUq3RlRFMEYK42IUlJSsG3bNrFJkybChAkTeI7jIIoijh8/Lnh7e3Pu7u6UoPnqqxqZMJXhyBGgUyc0GzgQjDExPj6eJSQkiD4+PqykZzUpKQlbtmwBYwxNmjSBVCpFjx494OvrWxqQcuVFdQEBdL5HR5OSZOBAOh+bNq3x7p46dQp//vknGjVqJEydOpVzcXHR76Rp2JCMwQ4eBDIywKKj4d2zZ5m7mdy5A+e8vAqvURzHwd7eHj179uS8vLxw6tQpISAggOd5Hr1792bt2rVjz/o72NrawlouFy+vX886d+lC/gcODjRNYsAASmjNm1d1oK0nEokE06ZN49auXSvu2LFDnDhxol7vT2FhoQCg7szxrH3OJCQkcE/Ov5ptQa2mpNfJkzVWi4WHhwuxsbGparW67Dw2I0ZqEWOwbcRI3SYUoODT0gCVleqi1VILtkaj0Xz//fdSxljBgwcP7FUqlfr27dsSxliqKIp3DfR0n+/atWujXC5X5ebmmrm7uxcOGTLE3M7OruZf0AYiNZXauj7++OPKg7kXsbWlLPywYcCdOyR3DQwEPvmE5gbPmWOQilkZWrakqvaXXwLff6/fY9avL3cmrF4UFFD/7MiRFDQuW1az7ZQDx3GoV68e6tWrVyolB4CWLVvi8ePH2LFjB/bt21cS6IpNmzbFgwcPMHnyZGb9oiu7iQnw3nsQQ0Px+40boqurK+vTp0+NHdBv3LgBAGjWrBnXt29fnD59Gt27d9dfvqBS0SLyyBGDBQAvMmDAAAwYMABLly7Fk1m+8Pf3R0FBAS5duoSsrCzcvHkTgYGBkKhUvN+ZM9A+fox4xhD644+47+zM9Xz77arPQQcHqoifPFm5oiIvj1oLdu8u//Zx4546UFcTM1tbpHzwgaDev59rsGMHKUyekR6/+BqcnZ3x6aefMgClEVtWVhbu37/PDS1RePTpQ+PiajjH9zkiIshhG4BUKhXOnTvHp6SksMuXL0MqlcLa2lp49OgR1717d3Ts2BE7d+5k9evXFzp06MBVeYx+9RWpSfr2pecIDn6pQFsQBISEhKB///5o37599SU5ZmZkfpeQQB4Sn31W9j08epTGoWm1VSYzHBwcMGHCBL64uBgcx8HU1PT5DzMpCTh1Cv9KSmJZJ0+KqF+f4dgxalXYuJGSQFFRBm/rkUqlmDp1Klu3bh3Cw8NJYVAFcrncBECWQXekDqNQKBKWLVuWlpiY2Lhxyai16rJhA/3MmFGjhycnJ+PUqVOFGo1mgEKhKKjZThgxUjOMwbYRI3WbFI7jik1NTV9JmVelUpX86gmgvyiKOwA4xsbGjmWMCaIo/q5QKHSGeK7PP/98k1Kp/DM3N7cpgOj79++HrFy50nzw4MF6LWBqC7Vajf/+97+oV6+eIJPJaq4Db96cfkSRqiwHDpARmY8PSczbtKGKn6GYMIGqWzExZE5WGbm5JDufOLH6z3PqFEkuY2OpKllbo7tewMTEBPb29njzzTdx7tw5dOrUCYGBgUhOThby8/O5vXv3CuPGjeMsXpBla374AapDh5AyYQJLSUlBaGgoTE1NYWNjIwqCACcnJ2HQoEG8mZlZpc+fnZ2NI0eOACCTQGdnZwwaNKh6L2LECOpj37ateo+rATNmzEBCQgKkUinatm0LAIiKitKtWLGCl6hUkBcXw1GtFj1jYtjR0aNx3dW19LGRkZHoVZWDNmM0tisvr/L73bxJio+KlCvFxVQVzc6usOJZWYUsKysL9fv1o8BOpyNDrB9/1Ou41Gg02Lx5s8gYY2ZmZnSuJiYaLkDbuLH0NTVq1IiPj48Hx3H44IMPkJWVhTt37nDDhw9H/SemTzNmzKj6elNQANy6Rf3yWi1dYz77DJg586V2leM4vPbaa+KZM2fYqVOnIIoixowZU6ZvukoaNyZ1T3Ex9UgvWfL8yK+S/f3wQ70295ziKSuLnMfr16eAfvBgmH78MfbY2bHPR4yg+4gi8McfNF2hljwebGxs0LRpU929e/d4fb6rNBqNAOB/ygFbFMVTaWlp79Qo2A4MBMaOpe/LGny/FBUVYceOHYVarXa6QqG4U/0dMGLk5TAG20aM1GEUCoW4dOnSrISEhHpt2rT5y93IT58+XcTz/L0vv/wyEcC6J3/OBWC40uUzKBSKWwBuAYBSqWwIYNyRI0e2N27cGPZ6mGvVBrdv3xZFUWTvvvuuYRquGaMF5qefUiBw6RIZ70yfTovBxYupImVl9XLPY2kJbNpEI1I2bHh+vNKLHD1Kcubq9Av/8ANVilasoHmnr+jzkclkpaZLo0ePZgB4tVqNLVu2CCtXrsTYsWPRpEkTAMCDBw/wh709TN5+Wzd6wADex8cHN2/eRFhYGERRZK6uroiKimLff/89mjRpgokTJ5YZQ/bEMfm5XlsHBwedp6dn9c7Phw/pPXyyb7WNk5MTnF4YT/f+W2/xj1UqFHfsiBwbG9EzLIz9x8dH67NliwlcXCCVyWBpaSna2tqy7Oxs2FY1jqtnT6B9e3pNFQVlHAecP1/xNqRSUmOUY96q0Whw9epVnD59GiYmJtDpdBgyZAh0Oh3c3d1ha2sLuVwuZmRk0L5kZlJwHxpKBm5VqFI0Gg1yc3PZO++8A8viYmqBiI01XJA2Ywbt10cfYcSIEThy5AhcXFwgl8shl8upP7y6rF5NHgkREdT7f/IkXV8MwKBBg9jAgQMhCAJWrVqlS0hI4Ly9vasvMyox05s2jRIDlpY0LUEioetfdebJa7WkBDl8mBKVSUmkhIiNBTgOllottDod1Go1JEuXkoP46dPV3uXq4uDgwCIjIwUAVX5PSKVSnjH28v3LfyN4nq9fI0PLzEwy+zx4kNqwqokoijh48KBKrVYHLF68eE/1d8CIkZfHGGwbMVLHEQRBSEtL49q0afOXPee1a9d0Bw8e5AHIAPz2lz3xMygUCkGpVO7jef7g1q1bB86aNUtSU7nvy3DhwgWxY8eOzGDjp57FxITk5ACZCl2+TIFxXh4tyhs0oFFZNTU4atqURtf85z/Ae++Vfx9RpB99jdE2byajoXr1aN+sremnDiGRSPDmm2/yJ06cELdv384sLCxECwsL4dGjR7y7uzsmnjzJw8QE8PGBl5cXvLy8Sh/r5+fHrVmzRrx79y77+uuv0a9fP/j6+iI0NFS8desWy8jIAECV9YYNGwqTJk3izMzM/p+9M4+P4X7j+GdmN5tLEnKISIgjJEjcQtw3pe46WrRUHS3V+lWpK9stqtRR2mpLqSpp3LeWiMSZkLiTEEfIidznZq+Z+f3xJAQ5dpMNofN+vbxIdvY7M7s7az7P8XkMe4P++IOCKsUZhL0sNBpI7exgFx4OREejtlTKAEDPPn2krp9+ipPdu6Njz56cjY2NJCAggN+wYQP74YcfwtTUFCEhIXx0dDSsra0xbtw4ViqVQhAEcDwP6YQJdGNcnNi+du2pu3NpXgI+PlQSXZDhTUpKwu3btwurELjBgwdLZDIZwsLC+P3797MMw6BatWrChx9+yPA8D5PCwJKdHXDsGFV3tGhBvgmllOvnFGTlQ0JC+OFvv83iu+8oy24s3n6b/BpAn58h5TB5eobYWArazZpF1SVKJQlRI8KyLFiWRZcuXSSHDh2CUqlE9erV0aFDB/2d9guZPJn+HjyYgnsnT1KZ98SJVFVTmifA5cvAnj2UKT92jAyyRo9+IYgolUrBsixyU1Jgq9PRKMaXgLe3NxsaGoqoqKhnvk+Kw9HRkTUzM+v0Ug6s6iAxuB0sLQ14+BC4cqXcVV8RERG4d+9eskaj+aRcC4iIGAFRbIuIVGEUCoUtAMe6deu+1KblK1eucAzD/C0Igh+A4Je57+fQcBx3ITMzc3BOTk7ZmbVKoH79+nx4eDijUqkwaNCgynsfzMxIYBdm5G7fJgfdzz8H5PKnbueGiH6GoazA0aNAYiIZFz1PVBRlEp+fd/w8Dx6QU/WyZbR9BccgvQz69evHeHp6Ij09nUlPT5e0adOG+r4zM0vsZ2VZFp988gnD8zwOHz6MgIAABAQEAAADAF5eXkhNTcXDhw+RkpLC/Pzzz/zs2bP1f1MyMkh0tW79aoT2n3/SXOxLl8iluYgo5nkeGZmZ2D5/PgSJBO7u7hJHR0e0aNGC3bt3r7BhwwZGEATUrFlT6Ny5s+T48ePC0qVLMXfuXAQHB3MXLlyQjBk9Gu4rVwKJiUgQBFhYWMC2ULC6ugJ+fshRqxF29iy8vb2L96J4/30SX/PnIygoiAsJCZHY2tryAwcOZL28vJ68aO7u7qxKpYJUKoW/vz//448/SjiOkwwvKrAYhgSury9Vbhw+TK//c2RnZ+P3338HAOReuiTgzBmgYMa40XB2ppnOxiA/n87r8mXq+1+9mvrmK4HAwECEhoY+cWxPTEzEjz/+KGnbti28vb0N/14+cICy8AsW0PeZjU2xlQzIywOWL6fee4WCMuADBtC87FIYERjI83v3smV+pxkRMzMzyGQyIS8vr8z/I5ycnMBxnNfLOK6qAs/zXraGBq5Gj6ZgzOZyTRWFUqnEkSNH8jUazSi5XK4s1yIiIkZAFNsiIlWbbvb29qomTZpUujtaWFgYd/ToUUmNGjXUGRkZpgA+k8vlr9rExRbA0hEjRrwSoQ0AAwYMkDZt2hR///03PDw80MhIJZqlYmdH2T0fHzI58/MDfv6ZyloZhnp99R151bAh3cj++Scwf/6Ljxf2M5aUdRAE6rkcNowykzdvlvu0XgXOzs5wfj7I8Mkn5NrM8yUGL1iWxeDBgzFgwAAkJCSgbt26T0rH7927h23btkGj0TBmZma83gfz6BFlWAMD6e+XyUcfkXFZr15P+x6fyz4nJCTgyJEjmLJxIxJnzhTs7e2ffCiGDx/O6HQ6ZGdnw9bWVgIAnp6ezE8//cStXLlSwnGcxNXVFXv37cMQQeBtv/+e3VSjBtzd3fnRo0ez106fRp1JkwT1sWPMPzt38snJyUxkZCSmT5/+YtXIlStPMpa3bt1iHBwc+MmTJxf7RhUaFo4bN06iVCqRn59ffMvJ2LHA48fAokXkLTBlCgAgNzcXV65cwfnz51GjRg1u1KhREvPAQEml9NH/+SeVs3t6Vmyd/Hwqt3/0iEZ9LVhA2XtDzBsNIDQ0FEOHDi3s15YAwM2bNxEUFMSHhYWxUqlUMDU15WvXri2pX78+nJ2dUa1aNVy+fBk3btzgtVqtUNCOwbRp04ZGe7m6Uh/u6tXUx/34MfVgCwKVlm/fDjRvTsGJHTvI9LEseB64fx+mw4axBx4+5CYVMb6rbOLj46FWq59MUSgNOzs78Dxvo1AoHOVy+eOXcHivFIVCYQrAtdCLQC9u3CDTTkNaDIogCAL279+vFAThD7lc/p8ZsyZSNRHFtohIFUYmk43z8vJ6KTbkDg4OEgDIyMhYCmBHFRDaAKAGgMePHwtNmzatnFJuPahXrx5at27N7dmzhx04cCDj5fUSkxI2Nk/NjuLjqf9wyhSgY0e6ce/YESgYG1QikycDX31FjuhFgwXZ2WRcVCA8XmDpUioJPneO5h87OBjnnKoCHTpQxr+wjL8EpFLpC720RXsPmzVrpt+HMj+f3sstWyoutvQlO5vGwK1YQVUJ5uaAiwsJz2LIysoCAGS0b4+2Awcyz2fepVIpimanZDIZPvnkE0lycjJsbW1RrVo1JCcn45+NGwX7yEjOzNpacvfuXXbz5s3QXb0KOysrZvO2bZBKpezcuXOxZs0a/rfffoOjoyPfp08fiVWhT8GePZR5//57jB49mv3xxx+hVCpRVs+nhYVF6ds4OlK/eG4uBay+/x6PWRYnT56El5eXMHz4cAni48lYcPRoPV5gA1m0yDgtFwMH0rXo60tBsK5dK01oZ2dnQ6fTPTMjG6BpAE2aNGF1Oh2SkpKYmJgYSWRkpPDo0SPk5eUxhc73Xbp0YTMzM3Hjxg0AwPnz54UuXbpQEKdlS2pL8fcnQ0cXFwoE7dxJhmcdOxpWyTNpEhAdjbqnTyPjhx8kfn5+/DvvvMO+jNGZiYmJsLGx4VmWLVPgMwyD+vXra+7cufMWgC2VfnCvHjOWZbVardbEpDTvkEJ27aIRbVFR5TbcvHz5shAbG5uk0Wi+KNcCIiJGhBGKK90RERGpEixbtsy/U6dOo7p27Wr08uX169fna7VaDgAEQZBkZWWZy2SyWxqNpqtcLk8x9v7Ki0KhaAngCgB4eHho3n77bZlOp4MgCEhNTX3hJrCy4Hke27dvR2pqKj9r1qxXo/qLcvYsiWATE+DqVZoh+8EHJZcmBwZSOd6mTU9vzLdvJxG9ePHT7QSBfvb2prJbjYayoW8aanXJjtil8OjRI8TGxqJatWrYvXs3JBIJFi5cWPqTBIGcmLt1A779tpwHbADp6ZTp9PIiw7KwsDLLlxMSErBp0yYAgH1KCibOmAELfasnimPqVPA9e+KWlxcuXLggNIuPF7znzWNPnDqF6tWro23btkhJScH58+eRnJzMJycnsx4eHlyPHj0ktidOABcugFuxAidOnEB0dDQ3c+ZM42UpBYGCT87OwPjxUKxbBwD45JNP4DBxIlV5HDpktN09YeFCek8qIuQFgVoR4uIoKPDPP+ShUEksX75csLS0xPTp0xlDem5VKhVYlkVWVhY2btyInj17wtvb+6mxoCAA//5LAYOQEBLaHTpQFtvQ6zI7m8YqNmtGvb3VqiErKwu7du3iU1JSmEaNGqFv376MwT3mBvDHH39wzs7Okr59++q1/eXLlxEQEHBo7ty5gyvtoKoACoXCRiaThTdv3txl4MCBZUeEEhLo/53k5LKnaJRARkYGfvnll3ytVttWLpdHlWsREREjIma2RUSqMBqN5k5SUpIaZFRmNAr6Tc0BvAvgMei7gNNoNEFyubxKReDkcvlVhULRAUCNW7du7b137x4AqLVarSkAzJ49G8+Pd6oMWJZFnz59sGHDBlan05V7NrPR6NyZ/qjVVHa7fz+ZBnXpQpmuli0pk1lIr17Atm0k0nv3pptdmYwyfIWcPElrXrhAWfMysr6vNbGxwMyZdMNvADt27BAyMzMZe3t7eHp6IjExUUBBP3eJ5OcD8+bR+1KZZGaSqF60CIiIIJf46Ogyn7Zr1y4hKirqyTmMj42FxR9/UL9seRk/HuzRo2g6ahSa6nQMvv6awbx56N2795NNHBwcCk3C2NTUVBw8eJD5+eefUbNmTaHftGlMwObNfGpqKjNgwADjlgMzDJ2bRgPB3R3tGzdG6vjxcHBwoH5iTSVNZXr0qOwqlNI4e5au8atXKVBw6lS5BYk+xMfHQ6PRMOPHjy97zvpzREZGcuHh4UxKSgrbuHFjoUOHDgweP6YWmaZNafrC22/TiLa8PPKUYBjDMtmFfPUVBQ0DA5+0w9jY2OCjjz5i4+PjERwczK1bt07SvHlzYfDgwZXiu5GVlcW2b99e7+0bNWqEf/75p7dCobCWy+XZlXFMVQGJRDKrUaNGLgMGDCj7HiY7mwKSP/ygv2HncxS4jysFQfhWFNoiVQVRbIuIVGFMTU0969SpY1ShnZeXh61bt6oZhpnm6+vrb8y1Kwu5XH4BABQKRSedTjdNEITNAC6bmJjEBQQE2LZo0UJmZ2dnuDuugTg6OsLOzk44ePAgM/wludyWiakpZYQ6dACyskgwR0dTxsjNDZg7l/reJBIaEeTtTW6+yclUXnzxIgn2qCgqLz592uiOxlWSWrXotROEkvvViyE7O5txdXVFWloaIiIi4OPjU7rYXrEC+Osv6kGsbNzcgFWrqL9fTw4fPsxHRUWxAJVhv/vuu7AePbp0t3B96NyZvAaOHqVAz6FDpY6fs7e3h0ajQZ06dXir7Gy4NGnCSDZsEObOnctWWvuITIa7P/2Eh3v2oGtsLPUFy+Xk7F0ZfPttuaopntCpE1Wj9O9P467kcuMdWzGcPn0azZo142rXrq13sIPneZw8eZK7cOGCpEmTJpjQuDFMGzZksGIF8N139L3z8890LhYW1Jf9zjs06vCPPww7wJgY6H7+Gf7NmvH5zZvDws+PHTJkyDOme3Xq1MH48eMlt27dwt69e5nBgysnkaxWq5lizf5KwMrKCu7u7oiOjv4SwKJKOahXjEKhaMGy7Hxvb2+TMoM1HEdj3X75haZclJOoqCgkJSUl63S6FeVeRETEyIhiW0SkCiMIQiMbI/T4RUdHC4GBgflKpZLJy8szZrqzsgAAIABJREFUl0ql//j6+m6p+BG+XORy+RUAUwt/VigU7aOiov66du1a16ZNm6pHjhxZgTvZsmEYBlZWVrxUKn1F85rKwMaGejgBGq8THEwZ1erVSfA0aEBmSl98QX3DY8aQu/hvv5Hb+IMHFRMDrxPW1sC+fZQN1sN8b8eOHVxMTIyE53lkZmbys2bNYnU6HWQyWclKUK0mYdS9uxEP/DlCQigLlJxMAZOaNQ16ekxMDAMAY8eOfdqSERQEXL8OfPZZxY5t4EDg119pnatXS91Uo9EgJSWFnTBhAuo4OwONG+PDfv0khgRCDIXnefiFh8O5Qwddw4AAKSwtK3SjXyb/+x9dd199Zfhzmzena7lVK+ppr8SMNkBl4Hfv3sWkSZMM+q67d+8eYvbskbydmYmaY8fCtH17YO1a8p34/HPqwX1+xNf//mfw53bjxo2c2549rOPDh7g3bBjbq3dvBAYGYsuWLfyMGTNeuCZv3brFOTs7M9BjDrah6HQ6qFQq1DawaqFHjx7m0dHRsxQKxZoq4pFSIRQKBQPAEUBDExOTEQBmdevWja9bt27ZT546lcZ8VSDQm5ubi0OHDuVrNJpxcrlcW+6FRESMjCi2RUSqMBzH5dy4cYPz9PQst7jLycnBrl27dBzHLQAQBsBSp9MFGe8oXx1yuTwOQLdly5YdrFWr1sCXsU9bW1vm3r17HF6i0225sLWlGbPDh9MYsQcPgPXrqby4WTPKuPr6kigfNYqyu/8VoV3IF19Qlj+q+GrDnJwchIaGCiYmJsytW7ck7733HpycnFCtWjUWIIOwErl3j7J31649mRdtVObMob+/+4768FnWYMECAL1792Z27dqFS5cuCW5ubqRsb90Cjh+vuNju25eqLKRSoJSsX1xcHLZs2QJBEFCn0H04K4v6kl1dK3YMpcAwDKRSKQYNGiRFeDhVBUyfTm0WkyYZf4dDhhQ/fk8fPvuM+rN/+om8GioZMzMzmJiYQG9fn7w84KOP0HDKFKRpNLzZ5cvs71u34t2zZ+FW2gSHzZupR7djR72P7eQPPwj9162TRK5aBcbdHZ/WqgVbW1tcv35dSElJYW/evIkmz7mXF4yOrJQSidDQUN7KykowNAhrZ2cHT09PaURExFwAcyvj2F4GCoWinamp6SKJRNJbIpEwNjY2agcHB5MBAwbA0tKy7Nc8L48Cv/o4zpeAIAjYu3evkuf5H+VyeeVfICIiBiCKbRGRKgzHcatu3769SxAEvXvmtFotBEGATCYrNPVSsyy7fuHChT9U8uG+EhQKRSuGYd7S2xW6gtSvX5+9du3ay9iV8WjcmP706UOi++uvyURr61Yal2NpSSXI/zUWLya37iKo1WpkZGTg0qVLfHh4OGttbS2YmZkJrq6uQqNGjfS7mdZqKdixYoVxhTbPU4/rr79ShtPEhEQ29T2Xi6iCQEN0dDTz5Hvm448p01RRTEwoQ16K+/qlS5dw+PBhSCSSwv5tYskSaoEwstgWBAE3b95EnTp1EBISwut0Olb7+DH5FNSsSeOoTp+mHmInp/L1EJeEhUWppfTFsmkTiWyFgka3leHIbkwcHByEmJgYpk5x45cEgcp+f/6ZvCAuXgTMzMDWrIkOmzaxgiCg5/nznP+OHZKJEye+OH6vkHPn6LtJXzQapF+5AmmPHuhfWMVTQJ8+fRg/Pz8cPHhQaNKkyTP/YbZu3Vry77//Gj1Imp2djZMnT7Ljxo0r1/O7dOliGhERMUOhUKyVy+VJxjw2Q1iyZMkwlmXrzZ8/X+/h8t98883bJiYmv5ibm9t17tzZrEWLFkyBf4r+rW/h4RTwvXWrQjPoIyIikJiY+Eir1b6RJfkirzei2BYRqdrsZRhGFRkZaVZgxoTMzEw4OjrC3t7+yUaCIOD+/fvIzc3F4cOHtVqt1uTzzz/HwYMHheTkZAiCsOAVnkOlwjBMD0EQpKdPnxaGDh1aeTWnBTg6OkKn01X2bioHhgHq16d5v7a2lFFYs4YMvHr0oJuev/6i2dyWlhXv263qVKtGgtvTExg2DPfv38fevXuRm5sLAKyrqyvef/99w3qGOY4y2tOm0fxyY5CeTpnmMWOo1DI2lkYlGYHCmdR9+vR5GtC7eJEqIhISKrZ4RAQQEEBeAPn5zxr2FXD+/Hmhdu3azIgRI54ZK1ZZPe5KpRK7du0CQD3qH4wdCxeAKhCAp9Ugw4fTMR89alBPf6ns2kXCsl07/Z/j6UnvR58+NOPeGKPD9CQ3N1eQSqVPT16nA+7epbFjTZoA69ZRb379+vQaFem5ZhgGnTp1kmRlZXF///03M23aNPaFnubUVFpDT4NL/swZ5A8dirgZM5j+X375wuNubm4YO3Ys/Pz8mLCwMLQr8jq7u7vjwIEDkoSEBLi4uBj4SpRMXl4epFIpduzYITg4OAitW7dmW7ZsCX2/M2xtbdGqVSvptWvXlgKYaLQDMwCFQmEFYC/HcQBQpthWKBS2pqamv5ibm789bNgwi4YNGxpsoAfgaVByzZoKCW2lUokjR47kazSa98TycZGqyKsfXyMiIlIicrmcB5AbEhKCsLAw/Pnnn9mHDx8+8+uvv+avXLkyPyAggHvw4AGCgoIEf3//pH/++eemTqcbIZVKQ3744QfExcWdEgShs1wuz3/V51JZ+Pr6rgYwICIiQnXv3j39yx7LiZ2dHaRSKZKSXlkSwjisWQO8/z4ZEw0aRDe9KSl0Mz9/PgluQaDS2sTEV320lUdaGpCWhrS0NGzduhU6nQ6NGzfmGYZBz5499b5pfgLPA++9R6X5FSU9nQTJiROU2QTIAM+Ic7rv3LnDA8Az2cuGDWmMXEXx8ADOn6ds7E8/FbuJg4MDkpKSkJeX9+wDy5dTSbeRsbS0RIcOHXiGYTBy5Ei23qlTJBif5++/qVT/4EE6B2OgUAAffaTftjodMGAAfZ5Wrybn8ZcotO/cuQOlUsm6N2xImeuHDyl49M475Ch+7Bh9xtu2pcqKEsRWnz59JCqVir19+/aLD37wATBjhn4HFB+Pf8LDcaJvX3z0xRcozoyMYRiEh4fzhZVdRZFKpWjatCkXHBxs1P8gnJycMG/ePEyYMIGpV68eExwcLKxYsUKIiIjQe40ePXrIJBLJ6CVLlkw25rHpi1Qq/bxhw4YqmUymWbx48bTStlUoFO1MTExueHp6Dp05c6aFm5tb+YQ2x5HzeHh4hWfa//vvvypBELYWGqmKiFQ1RLEtIlLFYRhmSVpa2tHAwMAHWq122Ny5c7tyHGeTl5c3MCQkZLGfn1/amTNnGK1W23vu3LlNfX19D+l0uk4A2AULFvSQy+Xhr/ocKhu5XP4Pz/Ojtm/frvnuu+84nucrbV8Mw8DOzk7w938tjNxLp2VLGkf18880isfFhQS4pSWVmOfnUzYuNBTYvZvGhHEcCYA3gKtXr+LS+PE4U78+fvnlF9SsWZP/8MMP8e6777K+vr7Qy9inKPPnU0b0889L7VEuk8LXt2tXYOlSEjU3b5Z/vVJwcHBgAWDTpk1PA1V2djQPuiLvc04OueBXrw5MnEjl0MWs17VrV4ZlWWRmZj77QOPGRg0qFJKbm4vQ0FBWEATExMRQb3ZxQtDUlKo9Hj2ia8MY48B+/ZVM+fRBpaJrbehQKnEvre/ZSPA8j507d/JLly7FlQUL8H5cnGDPMNR2EhsL/P47VSsA1MagRyBKEARwHFd8GfnKleQ5UBbBwRC8vXE3OVnw+PrrUqdOZGZmMgBlO5+nTZs2koSEBBj7/weGYeDk5ITevXszs2bNYgYMGMAcPHgQ+/btE/TZl7m5OUaOHGnOcdyGb775JlehUFT+LMsimJiYDPT29jabMmWKzMrKatWyZcsOKBSKZkW3USgUzNKlS2fKZLJTgwcPrv3222/LTCvi8aHRAD170vi3ChAdHY1bt27lajSaF0sdRESqCExlZ4FEREQqF4VCIQNgKpfLc171sbxqFi9evIDn+SW+vr7li7bryePHj/Hrr7+ib9++8PHxqbT9vDQWFHQZLF1KplRt2tCIsDlzADMzoH17muW9Ywe5lzs4ALNnk4i6dYtEYSW+3sYmNzcXO3fu5OPj49nqNjbcJ3PnSu5v2IDGFcnm8jzNOndwqNh88sxM6hWOjaXSSjOjTv57gRMnTuDcuXPo168fzUIuxNmZRF55S27j4ymbXTir+/BhcmcfMeKZzVavXs25u7uzb731FvNMFYFOBzx+XH5DsRLQ6XQICAgQwsPDmRH//CM0bdKEwaZNpT9Jrab++9hYEszScnbgzZlDPehlZex/+YX6xzt1osqGSgg6FCU0NBSxsbFIi4jgB/r7sylz5qBxQgKsc3Ioq15OBEHADz/8IKjVarz//vvMM27dy5bRFIBpLyZSU1JSEB4ejvT0dLRNT4fjoEHIjYjA5suX0ahRI65r166S2rVrF/sdHxwczIWHh0vy8vJgbW3N1ahRA++//76EZVkIgoDVq1cLPXr0YFq3bl3u89KH5ORk7Ny5U1Cr1cK4ceNYxyLeDTzPIzc3Fw8fPkRycrIQGxubGxcXJ2VZ9rhGozktCMIauVz+0m7Oly1b9mjy5MmOheP3QkJCdKGhoVoADziOC9JqtQ9NTU3ftbCwqDd+/HiLGnpMbyiVVauoV3/v3gotk5qaio0bN+ZrNJpecrk8pGIHJSJSeYg92yIirzlyuVwDwAhpl9cflmUb1K1bV8cwTKV+txXeONWqVasyd/Py8PEhMQ0AdetSOfmuXcA331DvbGAg/b4wC3X5Mhk9Xb1KmfCbN+mmvGlTKqGuwuTl5WH9+vWwsbHB3LlzYWZmJkGvXmhcASdcXLtGI9ciIspvYPXDD9QffPw4cOZMuZzFDeXUqVM4V+Bs3apVq2eVy+HDFTuGPXuAhQuf/mxrSxUUw4YBLIsDBw4I0dHRAgBJly5dXizXv3KFXMGzssp/DMUglUrx1ltvMQ0aNMCFpCS4DR6MMp0JTE0psLR0KZVTm5qW77WZOVM/H4TLl6lMe/DgEsvvK8rNmzdxbO9ePlutZgcePYoeOTlCup8f65KXB9ehQyloVEEyMjKQnZ3NdOvWDQkJCc+OxkpLK9Y8MDAwECEhIXB0dOSbZWfDackS9s/YWGRVrw4LCwvu9u3bktu3b+Ott96Ct7f3C8/v3r27pE2bNli9ejVq1aoluX37NhYvXgwfHx/07dsX3t7eOH/+PNe6detKnSZRs2ZNfPLJJ8yhQ4ewceNGjBo1CnFxcdpbt27lZ2ZmmjEMozUxMbmm1Wov6XS68wDOyuXyCpoklA+dTle9sCxfJpOhW7du0i5dukjv37/f5PHjx02USiXv7OzMuru7G95WUxymphV2+1er1di2bZuS47hZotAWqeqIYltEROSNQafT8S+jl5rneTAMAwcj3JBWCd5+m5yO+/aljFObNuTKPGQIiaZDh4BFi8ioqU0bEt4AZWAfP6Z/cxyQkUFznydOJOFoYkI37ZWcnTWE+Ph4MAzDT548+anxma0tCUN9SlqfRxDo/GbOLJ/QHjcO+PRTKqksFB9t2xq+Tjlwc3PDmTNnwHEckpOTn+3b3rSJjq1DB8MXjo6mTHDRflwfHwrOPHgAvl49XL16lfH29mZ69epV/Ai1Vq0os15JNN65E6dNTJj4xo3RUJ8nuLhQxnnTJip/Dg423GleoaBM/ddfF/+4Ukml2rNnA7NmGebSrS8PHwIPH+La3r3c9LVrJVn+/jDr3h3VHB2Zms2a0fVtJGrUqIG+ffsK165dEzIyMlitVotOnTpR9czs2UAxwcpz585h1MiR8Nizh8WkScAHH+BTe3vk5OTg4cOHEpVKhX379kGlUpW4XysrK/j6+kKn0+HkyZMIDQ1FWFgYunbtitatWzOnT5+WpKWlPTEHrAwEQcChQ4fyIyIiBEEQLP7++2+tTCbbotFo/gAQXZXmavM8b2LynEs+y7Jo2LAhGjZsCBir5TQ5mXwIjh+n79xyIggCdu/enZ+fn79z4cKFG4xybCIilYgotkVERN4IFApFIwAfDR8+vNL3JQgCTE1NcfnyZXTt2rXS9/dSkEjI0KpoJlEmI9fr7Gwq+fz4Y3q8Xz8yVCvKsmX0t1pNwtXJiUqG09KAoCASsjNnAkVc9F8FpqamUCqVbEpKCoqWduLgQToHQ8rhdTqaD7xyJfVp60tGBs08X7CASsXz86lUv3lz/dcwAs7Ozpg0aRI2bNjwosN+QgL1K5cHFxcgMvLZcmuGof7tVatwoHdvmJubo0+fPpCWVJItlQJ+fpQBq4RZ23xAAMzr10exY61KY9Ikep/i4uj4Zs3S/7nDh5duchYSQoJ840YamWas1oycHLpeZ8yA8O67uJ2airsjR0oyQ0LgUIkl6gzDwMfHh/Hx8WEiIiJw5MgR3sfHh2UnTgTc3ekaKIJSqQTLsnDOyXn63hd8X1hZWcHKygoA0FyP64RhGJiYmKBfv37o1asXfvvtN2HVqlWMqakpLwgCe/78eQwaNMio58txHFJTU8HzPK5fv66NioqK0el07QEoX2ZZuCEoFAopAJZlWSQmJuL69et48OABl5OTw0gkEkydOvVFF/nyEh9PTvwVENoAEBwcrI2Li4vWaDRGmE8oIlL5iGJbRETkTcEDAKKjo3U6nU7arFmzsrYvNxKJBIMHD8bevXvRtm1bWLzE2beVyrp1NNpnyBDqp5MUVFpaW5PQnjyZHIkvX6byVn9/4OzZZ9cwNaWMKEDZbbWaBHdwMLmeb9xIZdf+/vRYRUx2DCQrKwsXL17kLC0tGRsbm6fZGmdnICqKstSGoFZTX20x5azFkp4OJCWRA/zmzTRH+jnB8TLZvHkzHx8fz7q6uvKurq7PZq/8/ctnkJaTAzRo8MTQ7cGDB/jzzz9ha2vLd2rfnm31ww+4bWWF4ZMmlSy0C/n3X8r4G1tsx8QgZtMmJO3bx0ulUsOzdu3akVfBli1UFVK/vn593DodjTsqjn/+IUO5mzfpdTeG0F62jK7hgQPBHzyIbTodl9Svn0St0WD06NFw8PCo+D70pFmzZjh16hSzc+dOfoyfH1tctYuZVIoRO3Ygv3VrWEVEGG3GuVQqxfTp05m4uDio1WrWz88PTk5ORllbp9Ph8uXLwpkzZ5R5eXlmUqk0g2VZFcMwdzUazWi5XJ5X9iqvFE4qlQrfUVUP4+zszLVo0UJSt25d7N69Wzh//jz69u1b8b1Mm0bXyi+/VGiZqKgohISE5Gi12rcKWuhERKo8otgWERF5UzgCYGhkZOSkK1euDLp06RLHsmy+iYmJ6eDBg03Mi5nxWxE8PDzg4uLCb9++nZk8efLr4w5WFo6O5ISckfFiFloqJZfyu3fJKEqtBg4coP7SOXOAevVeXM/UFKhdGzh9mn7WaEhs5uZSGenGjVQ2ffcu0L9/uUQGz/OIjY3F3bt3hbS0NK527dpM165dn/RkZmdnY9u2bUhJSQEAyZQpU2D2/M2+ry8JnrAw/XY6axaVya8pcywtnWu1ahSs0GioLP/ePf1PsJIwMzNjARQ/S3zKFAqyGNozrFQCX3315LNTuG56ejr774kTYN9/X+jk78/YFjMn+QUuXjRs3/qQng40bYo6N2/CxsYGy5cvF9q2bSv06dPHMGXn4UF95UFB5Fuwa1fZZnKFffDduj37e0GgNbRaKsEvrxcEx5FYHzuWPmNaLWBnh4e2tjj+ySdISEiQdPLxEdLT0+Hk5PRSv7MYhsG7777L3B4wgOHu3YOk0JSxyLHHhIbCRKOB0Lix0YR2USQSCe7fv88zDMPWK+67ykA0Gg02btyozMnJCVOr1V8BCJs/fz5X4YVfInK5XPj2229zR40aZdWgQQMwDPPke7N27dp8QkICC6BinxW1mr7f3d0rtMy9e/ewf//+XK1W20cul5ez7EZE5OUjupGLiIi8cXz77be/CILgodPp/gGwfOjQoWjRooXR95Oamorff/8dzZo1M3pJ4itn+3bgwYOnTuXFER5O5cIzZ5Ioa9iQymT1rSpISaFS9T17yHzqzh0SHePGAf37Q6VSged5mJqaQlKQZec4DnFxcUhISBAiIyOF1NRUlud5mJmZ8Q4ODoyZmRmTkJAgfPnllwzHcbh27RqOHj0KAGjTpg1atmxZfFYrJoacwPV1KV66lLLa3buXvh3Pk2g9cIBGSVWCiCgPPM/jwoULOH78OMaMGQP352+ET52iY+/Rw7CF588HZs9GJsvCz8+Py8zMlFSvXh0ajUZQU5UDM+7oUdQ+cQJMWaLy44/pGH77zbBjKIvMTKB6dRw+fFh36dIlqZ2dHTdjxozyGWYJAvDtt2QMmJxM7QAl8fgxBayK9gpnZlKW3NOT3M4Nrci5f5+CXF27UoDj119pasCUKYCZGYKCgnD69GnY29vzrVu35n18fF5ZkoXneYQNHy4kWFsDQ4YwgwYNglKpxKXTp9Fkxgyc7tgR7PDhGGWMGfXPceHCBT4wMJCtWbMm37t3b6OI7SNHjqivX79+TKPRDK2qZeL6sGLFissjRoxoVdCf/YTMzEysXbsWtra2nJWVlcTW1hZeXl6oX7++/osHBFAwduXKCh1jbGwstm/fnqfVavvJ5fJzFVpMROQlI4ptERGRN5pvvvkm08bGxuqzzz6rFJUTFhaGgIAAzJ8/vzKWf3Xs20c37WXNExcEEt2//UbzuJ2dqfw3MdHwsVE8D+6zz3DWyornHj1iWu3fz2yaPh2WaWkw8fDgBXNzISUlRWJiYsLb2NigcePGbKtWrWBubv7EZEulUmH58uWwsrISLC0t8ejRI6ZLly7o2bOnfufs7Fx6WfiZM8C8eZSpL004HzwIfPYZiaG4uKemclUAlUqFU6dOITQ0FG5ubny/fv1Y++erGO7epfnTAwbovS4fEwPOxwdbFQrhUVoaY2tri5SUFHzwwQewt7fH7du3cfDgQUxt0AC1nJ2pRLw0jh8nsd2/fznOshj++YeMuSIjAQAbN27ks7Ky2MmTJ8OmtF5qfYiKop7sn34iF/XimDOH+lW/+urp76ZNo5aC7dvJlFAfQkKoYuTxY8piX79OgauGDSmwU4T169fzPM+zM4qa1b0qbtyAYGKCuxIJzp07x8fGxrKm+fmoZmWFLuHhOOjpiRqOjsKMGTOMmnVXKpVYs2YNxo8fj7pGug7z8vKwZs0aFcdxteVyeYZRFn1FLF269Pt27dp91rdvX5PnH0tKSkJycjLS09Nx5swZAICLi4tgbm7O1KhRA126dEGpPd2zZ1NVT0mmgHqQmJiIrVu3KrVa7WBfX9/Aci8kIvKKkHxdgQtAREREpKpz7tw5H6VS6ZGenq5zc3NjCzOkxuLx48eIi4vjO3bs+OaUkgNAkyZkcDZzJpW1ltTjyDAkUAcNoozduXMkAt55h0Qpw+hfGs4wuO/ujoNRUUyD/v0Z1z590G3GDLRZuBC1HzxgzLp2Zd++fh09vvySadOpE1OvXj2YmZmh6HsqlUrRvHlzODo6MhzHMYMGDdLLUAkA8OWXVHpb2pzsO3coO1lSxnf6dNpm5EjqNW7WrHRTrFfA2rVrhZiYGMbExATTp09nivUc2L+fxpHpMaJHpVJBp9PhdEAA/F1d4dKggTBixAimW7du6NatG6pXrw6ZTAYnJyd0794d1UxNaf72oEFUil8Sjo7U52yo63dJWFiQ0V+B23t2djaTl5cndOrUqeLXroMDeR3Y2lKQpXfvF/0ILl0iMdyuHf2cmEiftZEjaRJAacTHU5VJnz4k6nU6cv2fPZvOycnphf0lJSXh7NmzTI8ePZ4du/WqGDkSzOPHsHv3XbRs2ZKxNzMT+svlTKeBA5E8dixu3r6N0aNHM9WrVzfqbnNzcxEWFobIyEghKCiIiY+P55o3b16h4Gt4eLgQHx+/f+HChX8Z6zhfFUFBQckZGRkTfHx8ZM/PLreyskKtWrVQv359eHl5oVatWrCysmIkEgn/6NEjPjAwkM3NzUXDhg2fnXuu09F1sGiRQQG750lJScGWLVvytVrtaF9f32PlXkhE5BUi9myLiIi80cyfP3+IQqGYfePGje+bN28ONzc3o65/48YNrmHDhpU6s/WVwTDkRH7pUtnl1QwDjBlDQmDXLvr3b78BixcDq1bpPX/70qVLfN26ddGtX78nN8Ps1atw4jg43btHhmIqFZWbW1kBP/5IPdFFMnq2trawtbWFwSZ5hw+X/JhGQ0Jn06YXM5cqFY252rCBghR165Kwq4Ry2IoSFxeH3NxcBgDs7e15lDTWZ/RoGgdXBtnZ2VizZg1M1Gp8tnYtUr/9VnjnnXdKFzIeHlT1EBtL/y6JM2eA8eOB1NQyj6NMDhyg92nqUwNjrVbLK5VK4wXJ6tQhZ3m1GjhyhOZkFw1kjB791HTw4UPafvBgCmyUxIQJVJru5UVtHXl5dD2WQVxcHP744w84OjoKbdq0efWBQEGgipdCQXbvHjydnRls3Aj07g3nDEoOp6enw9XIhng1atRA165dOaVSiQ4dOkjWrl0ruXHjBry8vMq1niAIuHjxYp5arf7RqAf66riu0+kyk5KSLJ2dnUvcyM7Orui4NBagz9mWLVtw7do14eOPP2aeVIiEhVGlk7U14uLikJ+fDycnJ1g/V3lRGnl5eYUZ7em+vr6lfDmLiFRtRLEtIiLyRqNQKLwAfD906FA0aNDAqGsLgoBHjx5J2hVmqt5E/vyTxM6nnwJr15bdcyyTUWnrW29ReXnt2tST+t135EL+998lPvXx48e4c+cO+15xwlwiobnDJ0/SzxMmUCDg7l0q+z50iG7ks7KAgQPLd66CQCLw2DHqoS1KWhpl+IsKgfR0uqHs359mSCckPDtbugri4uKCZs2aITIyEu3atStZhKWlkdA9darETfbv389fu3aNdXR0FJzMzJjzQ4dac3fgAAAgAElEQVQKXUeN0k/YTZ9OmdkzZ0qufOjTh8rwjUFoKAVlRo9+8iuO49isrCxkZWVVvIy8EHNz+owXGLHhp5/IhRkgMz1LSwpA5eUBJ07Q6LhCBIECAqdOkZleVBT1Y7u7A507lx4Meo6zBVMCxowZwzyfrXwljB9P57JkCQWuevakcvqPPwZAAbK33noLnpU0iqxLly4SAEhOToYgCBV6vzMyMqBUKrUAzpa58WtAgUnawZiYmGnOzs4GfVjq1q2LqVOnIiQkRFi/fj3j7e2NXo8f00SCCxdw+epVHDlyBBYWFoJSqWQYhoFMJhNMTEx4U1NTVKtWTVKjRg3Y29vD0dERtWvXhpmZGXJzc7F161alWq1ev2jRoi2VdOoiIi8FUWyLiIi86dwCgP379+PYsWOqNm3asL169ZIZY+Hk5GRoNBqji/gqh4UFCYD796kvVB9sbak0tnlz6l9ev55MnHJzgYkTIWzejMu3byMzMxNpaWlcYmIim5eXx3h6egr169cv+4avaL9vXByV0C5fTu7QffpQ6eJ33wFt2hhUxo5vvnmxZP6zz2idHTvo5/R0wMwM+P13YOdO2ld4uH77eMWwLAulUglPT0+hZcuWJb8wFhYk/EohtSDj3LFjR6b5unXA6tWM3iXfbm7kzB0cXHJJvlQKfPQRlbNXZFRTfDwZ2j0XKGratCkuXryIffv2YcKECeVf/3kYhloqfv+dqi/27yfB/c47FIyqV4+ug7Q0EtihoZS9rlOH5tp/+inw88/l7nVNTEzE/fv34erqalAmsVLp0YOqGAICqHXg/HlqPymAYRh46ztCrwI4ODjAw8NDOHPmDDN27NhyrZGTkwOpVJo4f/78N8b0SKvVnrh3797YLl26GPyBcXR0xNChQ9kWLVrgwJ49QttlyxjVDz8gRhAQFBSEMWPGoFGjRowgCMjPz0d2djaTlZUlyc7ORmZmppCens5fv36dyc7OZvLz8xlBEGBiYsIDWKXVauWVcLoiIi+VqmGLKiIiIlJJyOVyLSiw2DA/P7/fuXPnhAsXLuiMsfaDBw9gbW3NvTBG6k3DwoJMmFJTye3YEGrVImfk3btJXKxbB9y6hai7dxG3apWQcfUqpFKppG/fvsznn3+OoUOHGp6Gs7Ymse3rS2Kb40gcy2T0uwYNyGgrOJgyiqUxZszTMWUAiaHUVAoyFM6dbtmSzmPOnNdGZBeyf/9+PiEhAR4eHqVnPO3tKUPLlTzJqH///iwAXNy/HwgMfNZluywYhly8C+Zxl0hqKjl2VwQfH2o3eA5XV1d0794dKSkp5Rgorge9e1OwaeVKcmnPyCBztp07gS++oH//8gt5IxRmtJcvpzaEwYPLNQbv2LFj2LRpE7y9vbkJEybghZFur4KgIKBFC3ofPv2Uxt6VUq5cmTAMg6ZNmzKJiYnlfs/T0tIgCEKUMY+rCnA2MTHRjOfLfynUNzXFp3Z2zFV/f35jbCwCAgLw3nvvoVGjRgDotbewsECtWrXg7u6Odu3aoU+fPszo0aMlU6dOZb/88ktm5syZkEgkPMdxP86fP9/3dXZ5FxEpRMxsi4iIvPHI5XIOQAyAmCVLloQFBgZ2bl/aiB49MTc3h1qtZnierxo3tZXNo0fUlzx1quFCwM2NHJfPnYMyLg7pc+ag74MHjGXLlkC/fpQtLs3V1hDMzUm0AECjRk/Lyj/6CPjgA8pE//UXsGwZbVuU+/fJFGzoUOox3b2bjvvePRL1qalARMQLrs/FodFonrikVxXu3r0LnU6nXz97t26UiSyhr9rFxQUsy8JUpaJyfkPNB+vXJ4HZowf1uhdHYCAFPCrCjRslmtR5e3vjzJkzlXfxmpuT2MzMpKBPbi71c//xB71ePXtS5UdWFgVz4uIo0KHT0XNlMr2utdOnTyMoKAgAMGjQIKF169ZVx0fim28oGNavH70XpZnivQQyMjKE/Px89v79+4aNsSpApVKB4zgjGAlUHeRyefKyZcuyMjIyHOwMCZoVZfFiSFJT0W3XLraltzfy8vIMMubLzs7G5s2blQzD+C5cuHBV+Q5CRKTqIYptERGR/xQcxy3kOC7YGGt5enri1KlTzL///osBFXBcfW0YMoQybhs3Ug9p06YGPV2j0SAM4E/VqcN6u7nxlrdusbh8mXpZf/uNyn05znDRVhrm5kCHDvTvu3dJuF26BERH001/x47Uc/3ll5T19vQk4Q+Q8L5zh+Z+b9tGPbNmZvTnOeLj4xEXFwcLCwukpaUhIiKCy8rKkpiamgrNmjVj+vXr98qEN8/z0Ol0kMlkMDMzY6ysrAQAZSu4ZcvKdAKXKpUYtn49+KlTwRo6VsnUlD5TKSkli+0BAygLunGjYWsDZFbm4QGcPUuO3cWQnp4OrpTsvVHgOPrc5ebSz35+1FIhk9HnnmGo9WHbNnIoHzmSKkmcnKgEvXZt6m/396c13n6b/AE8PYFjx/BIEJBgbY2h6elo5O4Oi+vXGQQHk6nauXMk3jt0oFFulpbU4qFSUTl7fDwFuerWJQ8ES0v6fDMM/Vsqrdj1qNGQB8KYMeR38IqFNgA4OTkxABlwlYeCCQj2ZW/5eiGRSOIyMzPLJ7b37KG2nQJDQBsbG4P64nNycrBp0yZlfn7+kgULFohCW+SNQhTbIiIi/zVuAyRAKpqNZlkWQ4cOZf766y907ty56vRHViYMQyZld+4A33+v99Nu376NnTt3wtzcXHhv7FjUq1ePhVZLwnfaNDJKio0l0XX5cuku1RU9/rZtaeYyQD2x1asDFy+S8Lt0icbV7NpF84zd3an8FwC6d39hudzcXOzevZtPSkpi7ezseI1GA0tLS6FTp06SFi1aID4+ngkKCuLXrVuHmTNnskXngV+5cgXXrl3jcwtEmEwm46tVqyZ1dXVFr169jHK6PM9j6dKlkEqlwrx58xiNRiN06NBBvw8+wwDJySUKVQAQWBaHBgzAYDs7WJbnAIcMoVLrsDASgc/z9dd6VREUC8tSNUOdOiVuEhoaypubmzPQJ/hQHubMocz1rVvUe75/P/Wq79xJ/dmjRz89vnffffH5a9aQuzlA1RYaDYnfjAzqB2/dGud27uQdPDyYFq6uDDiOBI9MRq8bywI5OSTSY2NpHRMTCii1aUMzvjmOTA0LzQdzciiz3rcv9bqnp1OlSGHQIjWV9jFwIImsvDyaDhARQaI9Pp4e9/Gh4Jy9PRATUykvb3moVasWAJQ7yFK9enUwDFNJX1CvDkEQYrKzs9sY/MRLl4D//Y+u4XIYz+Xm5mLz5s3K/Pz87xcsWLDM4AVERKo4otgWERH5r2EilUo1DMMYJc1Yp04deHh4cP7+/pgyZUrVKd2sTP7+mwTAmjVkHqZH0CIyMpJ3c3Njx4wZ8/Q1MjGhjNvFi7TWxo3kUm1vT5kwNzdyL65Mis43TkwkkXDzJomNn3+m8V3Xr5O5VRF4nkd0dDT27dsHFxcX4dNPP4WVldULL0TDhg1Rv3599q+//uJ//PFHvlq1asjOzoZKpWJr1KjBe3l5Ma6urgzDMMjMzGQTExOFs2fPMo0aNUJdQzPFxbB27Vqe53lWo9EwCoUCANgmJWWRn2fTJhLb7u7FPhwXF4fhu3ej7qFDsLAsl9Sm7OmQIcC+fcXP9K5bl1zsDQ2+pKZSXz2dc7Hk5eUhMjKSNao5WiGtW9O18dVXJLgjIkiwHj1KLuRZWRTQOXmSROuaNZRFfh6GeVpJUbSiokAwwsUFd8+eZRs6OAjFzjMu2i5TTLAII0Y8/ff48S8+/sknNHtepyNBzjAkxlUqElb165PYdnIiIW5jQ380GioZz8kxrJf/JXDnzh0wDAMXF5dyPb927dpQq9UeCoVCJpfLNUY+vFcGz/Pp6sLAjr7cukXVL+HhNGveQLKysgqF9qr58+d/bfACIiKvAaLYFhER+a8h6HQ6WUJCAuqUkvEyhH79+knWrVuHqKgoNDWwtPq1hGGov/Snn6gMW4/+9zt37rAdCsu5n0cmA+bOBR4/pv7oX38l0dutG/VKz5lDvdOVPcKoUDC2a0elzYUl5YmJ4DkOGjc3BL71Fhfj6gqdVstkcxw7cOBAoW3btqUGWViWxdixY9krV65Ao9HA2dkZtWvXhkwme0acu7q6IiMjQ6hWrRpft25dowRulEol27p1a75Jkybs9u3bC05TT2F84MCLPe1FuBYQgHZZWTCtaFBgyRLKjI0d+2KJfkAAIJcbnhkND6fnfvNNiZvcvHkTUqnUoL7SUrl/n8Tp0aPArFlUGl6YrU9KArp0AWbPBo4fpwoOmYyCGSNGkHFYXByVmBuYHbSwsOBiYmIkarUapqamxjmXopiY0J/Cz0LRCoRC0Q88bQVIS6Py+HnzSJRLJHRdDxliPF+GcpKSkoJDhw4BAMrbm2xmZoaaNWuqHj161BfAGzP/WavVphkktnkeGDSIPrszZxq8v5SUFGzZskWp0WgWLViwYLXBC4iIvCb8Bxx9RERERJ4il8vjAazdvHkz/vrrr5xTp04hPDwcoaGhiI+Ph0ZjeKLC0tISvXv3Fo4cOcJXxM31tcLCgvqenZ1JlJUBwzBCmaLG0ZFE18SJ1A++aBE5iBeaRK1bRxnLykQQKKseFkaZ9s8+w9nRo7FixQohyNtbMPH2xrC8PMmny5ezCxcsQNtr1xhERpa5rFQqRbt27dCpUyfUq1ev2P7tnJwcnDt37tnsfwUIDQ2FRCJB3759WTc3N4wbNw7Tp0/Xf4E1a0gwlUA7Ozv8/tFHyM7Pr9iBWlhQSfS+fS8+Nm6c4UI7I4N6okNCSt2sXr160Gq1uHfvnmHrP8+5cxQMsrenPnSNhrLERYVofDz5AowdS5UgAweSE/n9+1RevmABlYnHxFDpdZT+ZtfTp0+XMAwj+Pv7V3LzeRnwPF2f9epRv/zYsVSdIpFQVt/f/5UeHgBs2bIFANCtW7dSt9PpdCjtu7xt27bWpqamcxQKRRUYYm4cBEHIUavV+k3q4HmqXDh+vFxCOykpCZs2bcpXqVSfiEJb5E1HzGyLiIj8F5kPIDMmJuZubGxsSxMTE1ee5501Go0PAFSrVk3Xt29f1svLS++AZJs2bZiLFy8KBVkTVXx8PJ+WlmZhb2+v+vDDD83MS8kQvrZIpVQe+8knZGRVivmRtbW1EBMTw7i5uZW9rrMzZQAnTQImT6aMWkQElXV7e1NpqpUViRtjc/s2lf5euAA4OSFHq8X5ixcxYtQoxu2rr8AwDAnhOXPo/P38SFwxDPWenzhBr0M5svDJyckwMzPjnJ2dKyS2lUolrl69ioCAALi4uAimpqYMQCXtBlGv3lOzuOfJyoLVxIlo9d13fI0aNSoeuH/vPRKagvDia9e6NQVd9O3dnjqVMsbBwaVuZluQoXUoR/krACpvb9aMggSpqdRqsH9/8dvOmQOsXUvPKWTLFhLbMhllwefPp3L5Dh1oPYWCggYlzSEvgGVZdOzYkQkJCZFERkaiadOmKHWsW2UxfToFOM6doz71iAg6R4YBrl4lgbZlC1AZZft6oNORjuzTpw9atGgBQRBeeJ3S09Nx+PDhvAcPHpixLMvb2toqXVxcTKtXr24mLSjz53keGo0GarW6CwBeoVCMkcvlO172+VQCMqm0uF6GYpg3j1z2L1wweCdRUVHYv3+/UqfTvevr63vQ4AVERF4zRLEtIiLyn0MulysBfF3w47aijykUiu65ubkD9+7dO7tmzZpwLMONuZACszR2y5Yt0Ol0ZgAmALBIS0tbeffuXXh5eRnxDKoQ/fuTYAgNBVxcqIfzObKzs5GcnMyOHTvWsLVr1Hja1/rXX5T5YxianR0bS/Ow1WrKJhqLzEwqd2UYYNcuBE6bhulHj8LywgWgoPwUwNNy2MBA+vv+fTL6kslIHHp7U5l9UBDNW9ZD/GRlZUEqlZZ7zhXP8zh58iTCwsKg0WjAsix8fHzKr7qGDKHy52I4cPy4kD92LPqNGmWcCrmWLUmQOjlRa0IhDEMzzlUq/cX29u0UkCkDlmUhk8kQFRWFzp0763+sWi0FWnr0AP78k+Zol4YgUJVE8+YUVFizhs7L2pp+16gRZbPv3aNgzZw5lA0OCAB+/52ug/x8MhwrgSZNmuDSpUvcnj17JLdu3eJHjBjx8ioX//6bjnflSurtHjQI6NQJmDKFyukByuanpNC59e5N3xUvmaNHj/JarZYNDg7OOHnypKmpqSk6d+5sZm1tze7evRvdunUTwsLCVCqVaqUgCN9xHGeakpLSPCUlpRXLsk4SicQKAM/zvJbn+XwAaSBjvX9f+slUAlKp1FavNoSYGODzz6kKyYCgTsH3k/bixYtZWq22n1wuv1yBwxUReW1ghIrOrxQRERF5wygoDeQB4OOPP0bNmjX1fq6fn59w//792AULFtQHgOXLl/+gUqk+mzVr1pvtVj5mDN1o7979zK9v374Nf39/2NnZcdOnTy9/xpbjaPRWYiK5386eTaKnfXsymzLWWK2vvyZTtILe+2teXvBMTIRk+XJyttbn5jIpiYIAPE+Oz9HRVGqv01EVQDHwPI/169cLHh4eTO/evct16Nu2bXtSEu3l5SUMGzaMqVCG09+fhOHz2StBQIKrK3R//IF6RnJNB0DC9fFjEmRFUalIsOpTHdKjB7UhvP++Xrtcv369kJ+fL3zxxRf6idPjxymDnZlJ768+icDffqOKiVWrqC/bxeVZU8HYWPILSE2l/nSeJ7fywvdu927yNDh+nKooSjG4S0hIwKZNm/Dll1/ComAMU6VReP/4v/9R8GnxYvr577/JeHDhQro2/fyePqew1DwkhII5L4n4+Hhs3boVPM+v5nl+Duj7vR+Afwq3MTExuajVahfL5fI3pg/bEFasWHFiwIABvTw9PUveKDgY+OADCgo1aqT32vn5+di5c6fy4cOHkWq1eqBcLk+p+BGLiLweiJltERERkeeQy+WCQqGwB/C/X3/9dV6vXr3Qvn17Rp8Ku/T0dC2AxMKfVSrVLIlE4rNmzRrvd999F40bN67EI3+F+PmRwDx0CLndukGl0aB69ep4+PChUKtWLaHCTu0SCd2cq1RPy8z79aPMskwGtGoFfPEF9fiWF52OBP2UKQDIbfvo4MHwSkwkc6vOnUnETZ1a+jpFe9MzM5/+nZtLzs0eHpS1rFXryZim69evIz8/X+jZs2e51XFeXh68vLz4YcOGsRVT2QX07l2sk3TosWNwlMng2K5dhXfxDB98QJnfmzefFZRdupCTtj6j5oYMofdJT9q1a8ccP3687Ndq0iT6nP38MwlFltXLhR8AZdkLS9W7dqXPWFFB4+pKArRLFypD53n6DM6eTU7w77xDme+HD6nXe+pUalkoJvjg4uICMzMz4dixYxg2bFjl1pK3b0/l72vW0M9ZWfQ+BQaSidq8eXQuRWFZenzhQjonI5a75+fnY8WKFejUqZOud+/eT76sY2Ji4O/vr9TpdJPlcnkR5Y9/FQoFC6CBXC6vYOP+641CoWCkUql3qaahO3aQ2N60ySChnZycjG3btinVavVWjUYzUy6Xayt+xCIirw+i2BYREREpBrlcnqZQKHwFQbhy5syZeSdOnGjdqlUrZdu2bS2srKxgZWX1wnNCQkKQlpYmA/BOkXUEhULhA2BuRETEosaNG78xzdu5ubmIiYnBo0ePkJmZyZkzDNNt2jT2WP/+uOvpKXAcx3Acx9SvX994rnFmZiSo3n4bWLaMsmrffkvio0cPyggGBZH5lKFkZwMrVgC1a+PkyZM4c+YMmrRooWMHD5aiaVPaT8uWxfcVl0VhtlarJaHh4vIkW5l75QpS581D308/ZSsy+10QBP7evXus0fp1zcyAY8dIUBVw48YNPPD3R81du2BRGZUa3brRzXzR0uy//9avhPzrryngUsy1WRLm5uaQyWQ8z/MvvvYaDTBsGLUwDBtGgQeWpT5tQ/D2prJqgMR2cVUY9vbAmTPUJx8fT9uNGUOtElZW9BxXV6rqyM0F3nqLXpP9+18Q/Z06dWICAwPh7u5eOdMRDh4EevUi07NWrZ7+PjmZro9Ct/LVq4uv5CicL144t7u0TGopFLZKFAZBb9++DQDIzMx8cm9769YtYffu3TqO44bJ5fLjz68hl8sFAP9poV2Am4mJCWtTnBO+SkWu/i4uFAAywPuhsD+b47iPFy1atNWIxysi8toglpGLiIiI6IFCoWgMIBogZ+0+ffow7du3B8uyUKlU2LBhA5eRkSExNTX94quvvnrBXXXJkiUrrK2t/zdhwgTJ615OLggCzp8/LwQFBTGWlpachYUFa2try2i1WjSuVQueLVvC7PZtZDVpAkEQYG1tjYqIyFJJTiYxdPcuCYBCM6ZvviGH54ULKXusD99+S2OLVq3CkiVL4OTkhA8++ICi0j4+wObNlLVu1w44dQowwui4/NRUHAkMFLp+/jkTP2cOWjVoAPb336k/XE9RHx0djT179gharZZp3749179/f+PMe8/NJeGkVgMMg7S0NGxYuRJf/P47ZJcvG+X8XyAtDThyhDK4hVn169eBK1co810SOTlAixY0s90A4zyNRoNly5ZhxowZT0dBJSXR+K5Jk4Dhwymjro+xX3HExlIQ6M4dqs4o9DYoqWc5MJDE6N27ZLTXsydVU0yb9uxnITWVHPMzMsig7fvvSaiDWhIWL14MlmWxaNGi8h13SQgCia1ly0gwFxIcTL4FEyc+/Z2nJ1W8NG9e/FqjR1MQ4a+/DDoEnudx+vRp7syZM7xMJtPVqVNHV69evWqhoaGcUqmMZlnWrlu3bvYtW7aUrlu3Ll+j0QyQy+XBBp/rfwiFQvGBh4fHT6NHj352NtvDhxS4lEgokGXA6LaLFy/yJ06cyNBqtX3F/myR/zLi6C8RERERPZDL5bcBeAAwEwTBNyAgQLl48WI8evQIWVlZyMjIkABoVJzQBgCO435SKpXBa9eu1fj7+yvLM2KsKnDnzh2sXr1aOHv2LN555x3MmjVLMnXqVGbkyJF477330LZnT5hFRgIDB8JGKkX16tUrT2gDQM2adBP46adUBv7bb5R5TEujXleOIwGWm1v2WikplDEEORanp6fzK1euxMnTp3n14cOUaZw1C/jwwxfnQZeTa3fv4k5MDCL9/BDM89hx+rSQ7+GBGzduQFuzJjK/+Qa6tDTKcJYQHA8MDOS1Wi3zv//9D/369TOO0AboxlqleiLyzM3NoZHJcGbRIr5ShDZAAvvmzWfHgF24QNnu0lAqybjJQIf6+Ph4mJiY0Ozx5GTqGQ8JIXM7hqHjKK/QBigrvXo1iRWAxnwVNdp7nl69gPPnqd0gI4P65q9epeBOkRnISgsLXLSzw4bHj/lT6ek4fOCAkD9qFPjLl8GyLFxcXIzrSK7RUDDj5k0ycisqtAFyGS86Ao/nyXm9JKEN0Ln9+SeJbQO+D8PDw3Hu3LkHPM83V6lUPnfu3JkWHBx8NicnR8px3AitVrsoNDSU27t3r0oQhI2i0C4bU1PT3vXq1XtWSZ87R0Zo7duTKaWeQlur1eLgwYPqwMDAJK1W6y0KbZH/OmJmW0RERKQcKBQKlmGYPwVBKNokbCKXy0udU6pQKKxlMtkBHx+fTt27dy95VlYV5MGDB/Dz80O3bt3g4+NTuohOS6Nso4UFCeKXRUQEOZjfvUsivFUrulns2JEMqjSa4o22cnKo99TX95lf37lzB//++y+nzMuTzNi////snXdYFNf3xt87u0tHEAVEFDsoFuxGsQQVC3aNNSrGqDFqTKJJ1BhdNlFjirHEEo3d2BV7SVTQIGLBhgWxIEGlSocFtsz9/XFAOiwKSX7fzOd59hFmZ+7cmR2Qc8973gPz4cNp3GPHKLsXGPhGdafHjx8X09PThVGjRiE9PR07d+4UY2NjBQsLC24RE8NSZTLUjInBOwcOYM+qVWL9XbuYun590XTsWJlWq8WlS5eg11N75c8++4yCxoqkYUMKitq2BThHUq1auDhjhn7AvHkVF9QX5u5d4OBBUiXIDDjNwYNkXpeYWK7PQq/XY/Xq1bxe7dps4NChFEx6eFBAXFEsWUI15F270vfnz1OtfuPGpR83bx49Y3fu0DW9/z79TO3ejXS9HqtWrYKpqSm3sbGBQqGAXKcTa2zcKNObmiKjQQMxy8xMuGdpCaVS+ebX8PQpdRmYO5de1tYF3w8MJPm4qWmepN3fn3qNP39e+th6PdXnL1lCtellkJWVhVOnTiEkJASCIGxcsGDBZABQqVRyAEsArACwBsBgY2Pjo9nZ2e8qlUoDVtr+2yxdujTS29u7toODA23YupUWn9q2JXWFgcTGxmLv3r1qtVp9Njs7e7xSqUypnBlLSPz/QarZlpCQkHgNlEqlCGCcSqVaBaAqAAUAvQGHZmm12jbPnj3T5Rzzr0ev18PX11f/8OFDWYcOHeCeW39aGtWqUX3fkyeUlf27aNaMMttnz1JWZsqUvLZL69aRvDwmpuhx589TgF6IRo0aoVGjRrKgoCDsjI5GyydP9K0HDpTJt24lMyhRNCwgLIQoiggODsbdu3cFz5yaaAsLC0yZMkUAAMbYq8Xw6OhobHnrLdFMLuctatUSnltaCpeOHuXv/Por4xs3ooqZGRyaNq34QBugWuWcGtw7QUHgNjZwHjiw8gJtgD7D1atJmTByJGXXGzSgWubiFniGDiWZfzkXPVJTU6GOiWED5s0jx/irVyu2jRxAHgJt2uR9n5hIjuRlBdtLllC9882bZJL266/A6dPA99/jjEajb+jmhhEjRuT/HGR83DhER0cja9kyofr27Xjq7Y2DPj7cc9Ys9tqlKzEx5Mz/9CmwdGnR91NSqCXf2bO0WJFLgwb0s1YWMhlly/V6atlWSnvAS5cu6fz9/fVyufyWXC731+l0r1ZFchY5v0YHUHgAACAASURBVAAAlUq1kDHmlJ2dPTinJluiFFQqVVWZTGZvb29P6pn58+lnbsYMoH59g8bgnOPq1aviuXPnsvR6/XRRFLdJ915CgpAy2xISEhJ/IyqVqhmAO23btoWbmxscHBwge41g7e/k5cuXWLNmDaZOnWpw33EA9Ad0QgLJs8trKlURcE5Z7txAoE8fatXk4AA4OZH5V26AEBZGdbAlLCSIooiAgADYT5qEGlott376lIEx4MMPSV5piFN2Pu7cuQNfX1+4urry4cOHlzs1rktJwaWpU/FiwAD96M2b6QH64w9g0SKSupfDJKxUbt4kZYKjI6JXrIBvYiKmqVQVK1EujosXgQULKEMKAB9/TIZcheX7CxZQO7jNm8s3/oYNUG/bhjWDB4ufd+smoH37ipl3fjIzSd5uY5O3ELB4MXD/PgWWhuDhQbXYW7YAACKWL4fFkiUwDQ6GeZ06JR8nikg8dQra997DI29vvPXFF7jz/Dns7Owgk8lgZWUF09JaqT1/TkHX1q1kHFiccRZAUvf09KL1+8HBVI9dmow8P+HhpEAJC8szWCvwdjj27NmTpNVqOymVygeGDSphCCqVqr+jo+POSUOHVsHEiaRI6NXL4N8hGo0G+/fvV0dGRkZoNJpBSqWy6KqlhMR/GJmPj88/PQcJCQmJ/wwXLlxIEARhwIsXLxxu3ryJ5ORkTZMmTf6xaFsURezevVt/7do1npKSIgiCAJlMBkEQXsnEAwMDxZSUFHTv3r18HaUEgYKj9u1JTt62bSVdRQkwRkF+9+6UqVm8mDJ0PXsC9vb075dfktHU119TVi23RVMhVq9ezUNDQ5nQsqXYqH59wSg6mrKTxsZ0jfldmQ3g8uXLYkZGBiZNmvRaUatgYgIjd3f4nz8vJA0YoHf+8kuBpaeTO/vw4VTjvHUrMGDA67mn5zJlCmVjW7eG+bBhCGzSBNZOTrAt4T5VGLVq0b1Vq+nrpk3JMKxw5lkUSZbdvLlh486cSc+iiwvuR0fjkaUlOo8cWTkrB5s3kxQ+v2lYu3a06GNozf+oUfScBgRAV7s2fg0MFGt88QWr/e23FKCWlNFnDKbOzrjm4oKr8fFoNm4ckoKCsCc1Fffu3eMXLlxgwcHB/OHDh+KDBw+4TqcTYmJiYGlpCTkAlpZG9dRjxlAP8OJYt47k+19+WXQOU6fSIkivXoZdZ9WqVJ6RlEQ164Xq5Pfs2ZOWlpY2RalUXjBsQAlDuXz5srJzzZptHBYtYhg5kuT8Bj6farUaW7duVcfGxh7VaDS9lUrly0qeroTE/zskGbmEhITE34hSqdQDaA0AKpVqVVhYWDG9cf4+Ll68KEZERMjatGmDW7duISgoCGJOb9wOHTrAysoKN2/eFDw8PF7P6Ewmoz+eq1en4MBAWWKFYm5OxlOtWpFcvFUrChAyM2k+nFMGfv16qtctFDj4+fnxxMRE1rRpU/3Qd96RYft2Ctpq1CBDNb2eFhJWrDCox7NOp8PDhw+FZs2avZG0zMHBAR988AFWr14te/vtt1El11wMoIUAuZxajVlbU7smZ2dy2u7QwfCTfPcdZTUVCgRv3Ii0W7eg0WggimLlGt8JAn0O+/fTfPv2pR7q8+bl7fPrr1QL3bNn6WNpNLT48OGHFLynpCDUzg6Hq1XD+BEjKi9F37QplVLk5/RpUkFcumTYGLlBzwcf4KqXF6/WsiWad+pEwegnn5CZW4sWJWYhPQYOhHufPtBNmYLWMTFodegQ2P37LGDMGP2LrCyZtbW1LD09Hf7+/nq1Wi1zvXEDnmfOIOPhQ9jnqgpKomdPqtUuLtjfsaP8CzwyGXDkCLB2LS1I5CMuLs4CwNnyDShRFiqVyrLFvXvDWhw5wrB9e7n6Z6ekpGDLli1qtVq9QavVzpJk4xISxSMF2xISEhL/HB+6urrKHj58iNTUVJiZmeH48eNZCoVCMW3aNJlxRdePFiIsLAyBgYFs7NixcHJyQu/evV+9d+bMGTE8PBzZ2dliw4YN5W3y152WF0dHcibu3p2Cwb/TMC0/NjZU39u5MxmhRUZSbWmNGoBSCWzcSPudOgX06IGUzExs2bJFzMjIEHr06IHOnTuTAqFrV8qAd+kCJCdTMDt4sGG9oAH89ddfUKvV6N279xsHeubm5pDJZHj58iUK1OV6eeV9fe0a9WjeuJFqoR89onrMMWPIOK40Hj2iAP2nn9D6t99wycpKf+TIEVlMTIzYp0+fyu1oMngwKRGSkkhOXvj+bt9O2W4Xl+KP1+lIDi2TkXJh7NhXn3Hs+fPc1NSU16tXr/Ku4cIFYPr0gtuaNSv7nhfGxATPjhzBnwcOsA8tLEhdYm9P/cfnz6ee5OvW0XOcj/Pnz+sVCgXc3d1lRjntxlijRoj38cENPz9Zz3PnoP7yS/R5910gMlKGK1cgzpqF3XPnii7Pnwv2hcbLJTIyEsZjxuCei4uY2Lev8Nbz56hRo8arftcAgIEDqe1XeWvFp0+nrPjZs0CDBsisUQN3794FAAYg4euvv167cOHC6WWMImEIjMmHNW++U6hXT5Dv2kUmeAby4sUL7Ny5U63Var+eP3/+d5U4SwmJ//dIwbaEhITEP4RMJnt88+bNxiEhIamCIDzXarWuMpnsRmZmZqfw8HA0adKkUs8fGBiob968uczJyanIe56enrlBSMUEI25u5FpsZkamSiXVgP4d2NkBv/xC9ch79lBQ1Lo1zQ8AJk8Gfv4ZGY0bIy0hQTCztsZbb72Vd3zdulTv3b17Xub+q6+Ahw8pwx0YWKrRVv369WFubo4TJ05gwIABb3QpJiYm8PT0xO7du2Fvb6+3tLREeHi4rG7dutzd3Z3Vrl0bzNWVdv74Y3oBVJ+u1QLHj5P0/MEDantlb1/QhCw0lFpPDRgAebt2+KRDB9nNmzdx4sQJwdnZGfXq1au8+u3cmuI1a8hkLDSU5gqQEiEgoPjjcr1opkwhN+9r16hfcD4CAwNZ3bp1Ky+rnZAArFoFfP55we21ahlex5yDKIrYf+aM6OHsDKuZMwV06kSeAwDV6B88SH3B69cn074cLly4IAMAhUIBJycnmJmZISY2FgdsbeFWvz6E588Re/Ys52lpjN26BTx8CGH4cNj06SOcOnUKkZGR4ttvvy3IZDJEREQgKioK5ubm8Pfzw1C9HqYeHoJWq9Vv3rxZZmxszD/99FNmZGRE9z8rq6hruaHIZMCyZdDWqYM1DRtm6nS6QJlMdlev11/lnJeRbpcwCMYEkbF9qVZWnvV++EFRnkD78ePH2Ldvn1qn0727cOHCw5U4SwmJ/wkkgzQJCQmJfwiVSiUD4ArgsVKpzFSpVMZKpTJ78eLF39SrV2/2yJEjTSvLPE2tVuOHH35A7969CwaSlc2nn1KQFBz8952zLL75hjKgMTEUIDk5kQR23Dho7t3DTyNG8KZNm/IePXoIZmZmdExyMtCvH+2fm/XXaKjf9w8/lJnRu3LlCgIDA/WzZs2qkA/42bNniIiIQGJiIszMzHDv3j29Wq2WabVaDB8+HK65AXdh4uMpUPP2Jglp794UwK1dSxJoIyPKhg8cmBfgAfj+++95ZmYmGzJkCFqUM3gsF4mJpESYPp36bW/fnmc6dudO8bJXR0dy8x45kuZfjNz93LlzuHjxIgYMGIDWrVtX/Lx1OlrQKJwdTk2lBZmwMINl1qdPn0ZYWBifMWMGk6Wn0xjh4UC3bnk7nT0LzJkD7NyJCBMTnDp1SkxMTBScnJzw9OlTGBkZca1Wy0RRRJcuXdC9e3dER0fjr8GD0T4jA4KtLWX9MzOBZs2wfv16Hh8fzxhj4JzD3Nyc29nZ6SPu35cPv3oVdfbvh3FOMM05x44dO/QJCQl4//33ZVXkcmqlVx4zxcKIIv744w+N0U8/XX77zJm3If2xWnEwVgfA7t+9vG7FjxzpPXb8eDNDD33y5An27t2bodVqeyuVysBKnKWExP8OnHPpJb2kl/SSXv+il4+Pj9m3334bqFKpxMDAQJ1er+cVSXx8PP/xxx/FFStW6GNjYyt07DLJzuY8JITz+Pi/97wlceMG5wMGcK7Xc37/Pudt23I+Zw7nUVG0LS6OJ127xjNNTfmSr77iAQEBeR/GxYuc+/pyXrs255mZeWNu3Mj5u++Wetrw8HD+3XffVewHWwi9Xs+XLVsmXrx40dAD6Dru3ePcxYU+q+bNOQc4v3CB88uXOb9yhXNR5C+eP+crVqzQ+/j48DNnzujVanXlXcixY5yvX19wW2Rkwe9v3OC8RQu6hoAAzrXaUod8+fIl9/Hx4UuXLhUreLbEmDGcL1lSdLsocr5/P83TABITE/nixYt5ZP7r3b2b81q1il5jQgIXFyzgl3v2FA/u2cPj4uJKHjgigocFBfFtY8fyp5s20bwOHuS8USPOb9/ma778Uty4cSPX6XQ8/+8f0d+fi+3a0f750Ol0fOPGjeKiRYvo+W/VyqDrKwlRFPmPS5ak6wThBQd68n/B7+X/iRfQhAM3E6ytuy5atCglJibG4M/kyZMnfPHixRk+Pj6d//HrkF7S6//Rq3LrrSQkJCQkyo1SqVTPnTvXnXPe+cyZM7KEhIQKGzs0NBS//vorGjZsiI8//liw+7vrp42MqG61Y0cy3/qnuXCB6pkFAWjShLKn9etTT+Hly4HMTFi3bg2TXbswYsIE2E6ZImDPHjq2QwfAxwfo35+uK5dWrYpmNAuRnZ2NzMxMISsrq9IuTafTITs72/B2bbnu8Y0bUyaZc+CvvygDW60aZbinTAFCQlCzSxd8nJAgzLS0RK25c7Fp/nwcGTQIu0ePxr6tW7HBx4df/PNPnmu290bUq0dGaVWrAjduUElCrVr03ubNVBLg4kImagBlwuWlV8mFh4dDEAS8//77lSMld3EpmHnOhTFg715y6jaA3bt365s1a6avnb+11qhRJPsPCADu3Xu1WbS2xummTblZXBwbbGcH21Iy5+LAgdB89RV/2rAhrltZiWCM/AyuXwfs7TFh40ZW/+BByIA8I7wnT8CqVwe7cqVIVl4mk8HExITrdDrcsbcns8A34NmzZ9AAyc9q13YCEAjGRrzRgP91GJODsRkANgJwXzt7ds8GDRrIDf3dkNN6Ta3VavsolcqLlTpXCYn/MaSabQkJCYl/Lw8AICQkBN27dy+1NjY5ORkxMTHIyspCtWrVUL169SJ9dE+dOqW7deuWzNPTk7Vt27aSGyWXAmPAsWMkBf6n67ebNyejsFwEgQLKlBTqhXzgAPUR/vhj1LO0xAUHB9ja28MmMBA4c4ZqtQGqJR4/nmq/c19jxpAs29u7yGkb5cifAwIC4OnpWSmXFhYWBhMTEzQs1EapRBITqT67dWuSwvfqRZLy8+fJWXvHjrx9g4MBzlE1OhpVjYwERy8vaD/7DFHPnuHhzZsYffQouxkUxJ9bW3On6GiGvXvJpMzdnWrdHzyg81hZlS2nbtqU7mHr1vTMDBsG/PYbMGIEmbeJInkBLF1q8L158OCB2KBBA1a9evWK/zlITaUFi5KM0O7epXkX7k1diKtXryI9PV3Wq7j2WebmwP79EB8/htrXFwkJCfD39xf/+usvQbFsGZo/eEAlDkuXFjSQGzsWmD4dD7ZswcFjx9iUKVNQo0aNvMSLpSVgaYmtM2eK9atWFTB8OP1M7N5Nbb6aN6dxi8HT01OoUaMGnmzejLC6dfUd6tWTOTg4FDROM5AHDx6IOp1ue92ICD0YawRgDRg7Ds7V5R7svw5jNgDeB2ALoNfXKlV/Y4Xis759+5bSaD2P27dv8xMnTqi1Wm0/pVJZglGChIRESUjBtoSEhMS/FKVSmahSqVyvXLly3srKqlqbNm1k6enpuHfvHvfz8xOrVq2qr1evntH9+/ez0tLSjEGOvVAoFHFarda2WrVqam9vb3MTExMEBgbi+vXr8unTp6Nq1ar/8JWBgpHISMDVlQK3unUr9XQxMTE4ceKE2LJlS6FFixZQKBRk4rRgAdUsF8bKCpg2jUzDfH2BwYMhfPIJ0qdO5esvX2bvMobajx6B+fhQUJqeDnh4UECYS9u2FAQWgyAIMDExwcOHDyst2L5//z5q164tAii9LvzhQ8DBgeqcAwOpnVRoKPWE3rMHyM4ueoyNDf1brRrQrBksAcDXFzYAmgHAypXAhQvsj/Pn+aQJEyiIs7Gh+3TnDvD993R/f/mFgs8bN8iYbuBAOu+1a/S1Xp937MWLQM2atMDh5ETPTO5iRzmIiIhAeHi48HGuWVxFc+wYtSUbUUIyNiAgz/ytBLKysuDn58cHDhzITErqebxmDfZs3qy3GD1aFtasGSzr1MGcOXNgYmJCbblsbclNPjSUFn1MTUm5oNfj3pMnxQ756NEjHDt2TJ+m08mys7PRZ/Vq4I8/gMOHqX1fKffMzs4OPXr0gH7ePNyUy9mOHTug1+sxbNiwkj0DSuDx48cZoihST23OQ8BYDQC1wVgdcC712jYUxqoB2AXgDIC5Kh+f+gq5fIu3t7epVRmLnGJO3fyNGzcStFptL6VSeffvmLKExP8akkGahISExL8clUrVSi6X/ymTyUx1Op1GJpO91Gg0BwAYKRQKF61W+zOAU0qlUpvvmIYKhWK5VqvtD1Bw16JFC3h5eVGg+W/hjz/IQVmnKzEwrQh+/fVXMTs7W1Cr1WJmZqbw4Ycfwk4USRq9aFHZA6jVZH724gVCJ0zA0fBwXs3Ojo8dOFAwadCApOO//krBedeuecelpVHP4AMHKKDNx59//olLly7xuXPnVnh2VRRFLFu2TBw0aJDg7Oxc/E7R0WRiZWsL/PwzMHp0Xpb58WNg2TJqkbZgQV5bNANRq9VYuXIl9/T0RKkqCq2Wsry2tsC+fZTt1WrJCG32bOol/fAhqQjc3HIvrvw9nEGy+uPHj+ujo6NlycnJfN68eZWj7khIILO9pk2Lf3/QIGrN9sEHJQ6xdetWvUwmw9ixY2UlKVpSUlKwetUqfHL4MMxHjCCDtMJcu0a9yQMDgRMnSFUA4M6dOzh58iTPyspiHTp0EPv06SPcuXMHvr6+8PDwEDt16iTIZDJS0yQkAC4u0A0cCPbRR5Dt2UMZ8ubNi5885/QSBBw7doxHRUWJH3zwgcFGgGq1GsuWLdOIomitVCozX73B2AIAA8F5O0PH+k/DWBcAXwJYAM6DVSqVYGxsfK1bt24tO3bsWGoZaXJyMvbt25eRmJh4Kzs7e6BSqUz8eyYtIfG/h5TZlpCQkPiXo1Qqb6pUqq56vd6Lc/79V199pTXgmMcqlWoIgBYAqsnl8l63bt367N69e9keHh7Gbm5ukMvlkMvleTWZ/wS9elHt9t69VC9aCW2kkpKSEBUVJbz33ntwcnISFi1ahKCgILH77duCZdu2hg1iZgZs2gTcuoUmv/0GF72eHX35ku8ExPeSkwXhyhXKZGdmUmYzt37YwoJkvBkZBYZLT0+Hv78/3qh/eTHodDpcuXIFwcHBorm5Oatfv37BHUSRMtXHj5MsODGRgu78NednzpDaYN06Wjy4fbvc8wgICICtrS1v27Zt6Q+XQkE18wAwYULe9j59cgeiwC02luaY23Yt9/1ysHPnTjE6Olqwt7fHoEGDKq+MYsQIWrwoicGDqb97CYSFhSEqKko2Y8aMUktHLl68CD3nYP7+uH7rFkzGj0ewhwevWasWe6WWaNeOgvDBgwu0BYuIiBCzsrKEmjVriq6urgIAaLVaGBsbo2vXrgU+s0xBwP2PPhKPA4L1mTPoc/u22CA1VZCPH0+qg2bN8nbWaGjB5NkzwMgIbm5uLCQkRHbixAn069evtLuWf24wNja+/MUXX2QWeIPzb8DYEjDWF8ANcB5r0ID/NRiTAXAH8D2AMeA8HADkcrmqWrVqLm+99VapP5N3797FsWPHMkVRXKTT6b5XKpUVYLwgIfHfRQq2JSQkJP4foFQqbwK4Wc5jdABu5Hx7RqVSfaHT6br7+fntOnfunBXnXBBFUc4YgyAIekEQdDKZTJTJZGJGRoZF9+7d0aVLlwq/liJ89BGZk2k0pfanfh20Wi327NnDq1Spgpo1azIAGDt2LPz8/JCwdStC3n9f5z54sOH/F7ZsCbi5QTh7Fv0fPxYerFmDGFtb1KxWjeavUNDCgacnmXQxRiZeV65QpvbwYdy9dw9HjhxBjRo19F5eXhXW200URSxfvlzknAtt27ZlnTt3Zq/qZUWRam9bt6YAbM0ayq7KZPTKz759JNcGSHpcjh68uWRmZsLCwqJipHOMkTlaRga1+nr5kuTnU6eWa5iIiAjBzs6Ov/feexUyrWLR6YC4uDwDt+Jo3JhKGIpBFEUcPXpU7NGjB6tSpUqpCwJxcXEi51z4ccUKWAJ47/ffkdK2Lc5cupQbNNOOnp4k388XuNvY2AgA4OrqypxyWro5OjoiOzsbL168gKOjIzjnePHNN9Ds2IErn37KB7u749GjR/jD1BRZWVkYd+oUamzZQiUAaWlkLJiVRS3xchZvnJycMHLkSOzcuRPt27eHra1tmbcwLi6OZ2dnF99WinM9GPsSwHkAC8oc7L8G1WdPB+ACwB2c6wFApVLNlslkXw4fPlwoaQEnOzsbJ06cyAoLC0vQaDSDlErl9b9v4hIS/7tIwbaEhITEfwSlUskBnAPwyoJWpVIxzrlcr9eb6PV6Y61WawzAhDF2xM/Pr6m7u3vlZ77NzKg+9623KNP99dcVNnRoaCji4uLY7NmzXxk11a1bFxNHjBAe6vW4kZYmuJd3UMYg9uiBVbdu6as3ayYbtWwZBaXr1wPt2wMNG1KWNjHxVT34MwAsPp4fWrYMKVlZzMvLC61bt67QJupXrlyBQqFg06dPh0KhyPuLOjycFgkePQIOHqRMslxeMJudy9GjJKvPdanPDXTLSUxMjNikSZOKeXBOniTZckICMH8+ZbsfPaJ6//yS/VIIDAwUAQhDhw6tXGPAqChSNpTWZ93fH4iIoBr/Qhw+fJhbWFigXbt2Zc6zX79+QmZmJhwdHenZnj8fnf76i7F58/gtOzt07do1b4zgYDLwCw0FALi7u+Pu3bv6qKgohhyvh6CgIMjlctjZ2SElJQWHDx8WzR89Ym3HjGHTpk2TAYAbSfmFJUuW4GbHjmLf+fMF3L1Ln4+PDzn0f/ZZgXk2yMni37hxA7179y7rspCQkKAWRTG8lF3eBiCCsTEAdkOqhyQYcwXgCfo8x+Xel2+++WaCsbHxIhsbG2zYsEEcNmyY0KCQsuLp06c4dOiQOjs7+5BGo5mqVCrT//4LkJD430QKtiUkJCT+w+QE4NqcV1rudpVK1dLIyOhZdHR0DUdHx79nMitXUmZbpyuzdZMhcM5x8eJF1KpVCxYWFgXfPHUKdU+cQErbtsKDBw/QuHHjco19//59aEVR5vXTTzAaMYJqjpcupblrtbR4MHkyBbrh4Th49y5X9+/PRj56hHq1akHIb6RWQcTHx0OhUECr1VJd/scfk+v3qVMURNvb06skEhOpjtjfP28/hQKwti73XKytrYWXL1/qUZY5myH06kXSdsaAd96hdlebNlEQfuECmaSVUX5w//595uDgYHgbtNfl22/pGS6t9ZW3N2WaCxEdHY3Q0FA2adIkZsgCV5G2fTIZoFajTWAgC3BxQXp6et5z36IFtQzLVTgASE1NZd26dXt1ohYtWuD27dvYtm0bj42NZf2vXGGuc+cyRTGu6nK5XKxVq5YAY2OgTRuqCRdFUkHY21Ppgbk5AIAxhpEjR8LX1xdGRkbwKGaRIT9JSUk6AH+VuANlt80A/AjgIYDgMm/W/yNUKpWDQqE4JwhC3Ny5c9826CDG2gL4AoAvOF+ZM45MoVD8YGpq+oG3t7dJ9erVcfnyZb537140atSIDxs2jDHGcO7cOe3Vq1dTdDrd+wsXLjxaeVcmIfHfROqzLSEhISFRhBwJ+s4tW7bo//jjD2QUqjmuFDp0oKxr7dpAWFi5DuWc49y5c+Lhw4f10dHRuHbtGn744QckJyfz/v37Fz0gMxNGw4ZhwIABOHjwIK5cuQK9Xm/QuXbs2CEePXoUwzIyxOpz5wJ+ftSK6sYNkgg3bkwtrlq1oiBx+nS4NmkCGxsb3qBrVwiV1Fvby8sL2rQ0aBs3pvr3MWOAxYspuMpXr1ssnANJSXnXkIsgkAy+nDRq1Ah3796VxcfHQ6fTlfv4V2zfTm3Vcmvbra1Jon3pEtVGnz9Pcy6hn7coilixYgWioqLYO++88/rzMJTRo4EPPyx9n6dPAZWqyOZ9+/bp27VrJ77RgkCzZtDcvw/n6GjuN2kST0hIoO3GxpRxzu0RT7C4uLhX39SvXx+MMbx48YL16NYNbk+fMoWlZbGnsbCwEH19fbFy5Uq+etUq/nj3bhHTpwNffEH3oGNHaiOX89k3btwYo0aNQnBwML799lt++PDhEnuwp6SkyFBasA0gpwVYbQAPwNhgw27Ovx+VSlVToVDcaNKkiTPnvAQHunwwJgNj7wMYB0AFzveoVCpzlUo13tjYOMzBwWHKtGnTzGxtbcEYQ8eOHdmUKVMQExODrVu3iiEhIbh27dpzrVbrKgXaEhKVgxRsS0hISEgUy7x58z4TRdErKChI8+OPP+LmzZuVr9isWpUCRFvbEgOowmRmZsLX1xeXLl0Sbt++LduwYQMCAwP13bp1w8yZM1mR4IVz4PJlYPBguLm5YfDgwTh37hzOnj1b6glfvnyJlStXIioqShg7ejQaqtUCunXLy6qamVEGOSGBelK3akXbQ0NRPSSE91+0iIlDh5JMe9gwkvZWFIcOQT5kCLr26sWuOTvjKUCLF4YawK1YAQwdCtSoUXC7QkHbyvm52+S0Blu7di0WL14MPz8/xMfHl2sMANSqqnr1gts8PYH4eAq8z5wB9u8nE7BiFksYV/yNugAAIABJREFUYzAxMYEgCK/mVGkkJ1MteUnu73mTIlO6fPj7+0On0wkeHh5v/HeZRZUq6NW8OWtx+TJbvXo11q1bJ166dAni/fvkG8A5YmNjoVarWW6/91xq1KihNxIE3v75cwqWS3AcnzZtmnzOnDnoamLCGt66xbBypfB0+HByrv/mG+oysHo1OaHfJKuJ+vXrY/bs2Rg/fjy7ffs221Mw8H9FRkaGKYDnZV4o1SO3BPALGCumJuL/FyqVqoqRkVFgly5dqr/11lsyxljpPcUZswYwCkBXAItUPj73lyxZMlcul8fWq1dvzZAhQxpMmDDB3KxQl4fq1atj4sSJLDk5mR09elSv0WhGKJXK1/jhlJCQMARJRi4hISEhUSILFy78Q6VSNQIw+ejRo1/9/vvvmc2aNTP18PCAeY5MtMKZOJEycMuWkbFYCZJanU6HnTt3IiIiAgDQq1cvuLm5QavVwsrKqmT5cm7v4RyJbdOmTWFra4v169cLERER4uDBg4XCAXpycjLWr1+P6tWr4wOFAibDh1NbpcLyZUEA+venXtITJ5KkvGdPNP/oI+FSvXowSkmB3ZMnVBOdm3V8EyZPBoYPJzVAq1Zo3bo1Vg8ezGWpqcxgWzOtFhgwgILYwtfDGLWq0uvLJe2vV68elEolAODw4cMICgpCQEAA7O3tuV6vx9ixY1mVKlVKddvG5ctUk124X3WLFoBSSQsJnTtTHfe0aZThdnMrUIvOGIODg4NoZmZW+cmFmzfpuSrLUb9DB2DVqlffpqSkIDAwEKNGjWIV1ZbPbOZMpL39Nrp8/DHC69cXgtRqflUm4+5HjwpGISHIylFXFO61PGXKFBl27KDM+/DhJZ8gKgomx4+j1cWLwIgROODlhXv37mF2nz4kXa9Rg1rhHT0KnDtHpnsffAChbl04OjrCw8OD+/v7s9DQUDRp0gR37tzB5cuXxdTUVMjlcplWqzVspY3zi2DMEUBDMGYFzq++7j37p5HL5bMbNGhg36VLF3lCQgJEUTRXqVRCsW7gjDUBMAW0KPGeysfH2MjIaL+1tXXfESNGmFWrVq3Uc5mZmcHBwSH76dOnfy5YsOB/SoYvIfFvQ+qzLSEhISFhECqVioFaygQAwLhx41CktVRFkZ4OrF4N/aefYv2mTdzCwkJs1KiRrG7duhBFETqdDlu3bgUA9OvXD61btzbcyC0ykloTuRe0RouNjcUvv/yCUaNGwcXF5dX2iIgI7NmzB9nZ2Zg2Zgxsg4MpUB8woPjxNRrg0CGSO798CTg5AV9+id9NTdG4XTvU+fZb6h3NOTBlCuDrSxlkQ3n2jEzkNmwApk8HhgyhQDmHFStWiM7OzszLy8swM7Bevaied+LE4t8fO5ZqpN/QKd7Pzw9Pnz7F8+fPIQgCXFxcxD59+ghVqlQB57xo4N21K6kDVq4sOtjZs3T9+/blbZs+nRYOVq4ETE0hiiLCw8Nx8OBB9OzZs8LbrBUhLo6UGWV9lklJ5Ar/9CkA4JdfftEnJibKrK2t+cSJE5mJiUmFTSl2+HBY16kDtmQJ/vzzTzHu4EFhwK5d2Ojjw+3s7PiYMWMKulMnJtLnnJ0NlKQE+P57ylwvWEDyfgsL/PDDD6KNjQ0mTpxY1O06M5PUB3fukGJl9mxwQcCuXbvw+PFjNG7cGBEREdzR0REKhYI9efIk7MsvvyyfiQJjywG4gfPu5btD/w5UKlUVuVweNXXqVPNq1aqBc461a9emJyQkzOWcr83x1iAY6wegLoAUlY/PXgADjYyMfmrYsKHt4MGDTQ1ZsPHz89NeuXLliUajaV2gl7mEhESFIwXbEhISEhLlQqVSVQXwefXq1T+ZMGGCaaVluAHca9oUiba2SJ8zB0+fPtWnpaUJGo2GMcag1+sxf/78Vy7jBjNmDLUnevfdAptXrFjBMzIy2Pz58wEAarUasbGx+O2332BmZoZPbGwg8/GhQLmswHPtWmDLFlo0CA0F5xxHJ09Gq8hIOPXoAXTpAixZQu2s9uwp3bgsl8OHyQTL1RV4/30yPjM1LbLbmjVrRHNzczZhwoSyg22dDvj8czIZKykb1r491aUXNpl7TZKSkqBQKLBt2zYxLS1NqFq1qhgTEyMU+CxFkTLEnBevbBBFkkSPHEku9gAF2j/+SBnvt97Co5gY7Nq1CzVr1tS///77skp31W/fHpg7l+T4pcE5yd6XLsWtkBCcPn0aH330EXbu3KkXRZFNmjRJKPczXRqZmVRH/v33tBiwZw8toBSXgffwIHVAYYM3zilDvWgRmeh16VKgvdmqVavEpKQkoUWLFuKQIUOKv9EvX5Jjv68vsGsXeKNG+Pqbb169PWHCBNy+fTvr1q1bXy1cuHBZua+TMTmAvgD+BOcp5T7+H2TRokVfOjs7zx8xYsQrzXdcXBz27NmToVaro7Kzs7+u8/Tp/gnbto0A4B7auPEp37Fj3RljU6pXry506dLFsnHjxqUrRXIIDQ3FoUOH4rRabXOlUhlX5gESEhJvhBRsS0hISEiUG5VKJZfJZJtr1ao1fMKECRWXistBFEWcPXtWjNmxQ+jTvj3sZswoW55rKF27UsCR20s6h9WrV+sTEhJkvXv35kZGRuzYsWOvsuWOqamYOG4c9RFu377sc2g0ZN7l7AzUro24hASsW7cO740dC6fnz4GNG4GLF8lR+/59+nr58qLjiCLJcSdOJMm4oyPVtJfC+vXrkZaWhhkzZqDULGl0NI154ADVRpdEv37Azp2v5UpeFufPn0d6ejquX6eWvn369OEd2rdnqFWLArPizO1yOXqUpOZLlhTcPns28OgRdL/9hiUrVmDWrFlF3egrAx8fMiEz5FxLlkAzcSKWb9vG+/bty1q0aAFRFLFq1Sq9hYUFGzJkiFCWFNhgOKee6rNnAz170ue+axd9nx9RJOO5Vq1eOYkDIHn8vXt0zLffUjBeCI1Gg23btkGtVvN3332XVS9cZ5+fFy+A994D2rZF8qhRiBBF2NjYoFatWli1alVaSkqKx2v3eGbsBoCd4Lz8wfo/hEqlUjDGMmUymfDee++xmvl+L3HO8ejRIwT9/nt64127TDRVqkSGtGnDUy0tHd3c3IR27doZGdK7PJfMzEysWrUqMysrq4dSqQyqjOuRkJAoiGSQJiEhISFRbpRKpU6v13//119/mbyW8VUZJCcnIygoSHCePh12kycDTZvSH/xvyv37lOUrFGgDwIwZM2TDhw/HpUuXxGPHjqFDhw68S5cucL5zB+N27aK6aEMCbYDqhlNSKANtaYlzJ07oAcDG3h7o1o0y35s3Uz302LEUmOdf/E5Opn7SyckkGY+MBLZuLTPQBoDMzEwxIyMDp06dKn01PSSE5lmWbNnJqVjzsYrg7bffRmpqqggAcrkcp0+fZolJScC6dSRvLw0PD5I6v3hRcPuPPwJTpiDhxx9hlppaKfMuwq1bgIuL4dl/X1/4/fKLaGdnx5vnmJAJgoBp06bJLCws2Pr16xESElIx2RDGSAXRoAE9a7GxJMHP/7ylpAD161OP+NxA+8ULYO9eymYrFLQwVEygDQBGRkbw9vaGTqfja9aswZYtW5Ba0r13dAR+/x1o0ADWe/eipb8/nMzNwTlHWlqaKYA3+UFvD2A5GBv5BmP83XhwzmU6na7IaiJjDM6MwTspycKtb1+53Y8/1u//wQcNvvjiCxMvL69yBdp6vR4HDx7MFEVxhxRoS0j8fUjBtoSEhITEa6FUKu8C+ObAgQMZFamSSk5OxvXr1zkAtG7dmoLBkSNJQv2mHD9OvadLwNXVFbNmzZJNnToVffr0YaZ+fkhxc+NyPz+S4JaHYcOApk2h3r4dcampMoVCwV9lWC0tKVi8cYPk7D16kPHX/PkkPV+4kNoo5QaTDRoYfNqRI0cKAFCtWrWSpQCBgRRA+fqWPWDNmiWa1L0OOp0OBw8e5GvWrOEqlQoRERGCp6cnnJyc9D08PESbKVNoQcKoDINpS0vKxv7xR8HtjAH9+8POzAwj9+yBSUVKskti0yZaMDGQ5999hxDGhMGDBxeocTYyMsKoUaPY4MGDcfz4cXbz5s2K+8GqXh2IiCBzvsKt9TIygHHj8pznV6+mPu2WlrTIM7Ls2NXIyAizZ88WGjRowCMjI7F8+fJX3QtEUcTz589x7do1hIaGIj0jgxaiZs+mz3DDBujWrYMM0CiVytfvjce5DkAVAD+DsTJs4f8dCIKwDACcnZ2zHBwcCr558iQtdnToAJOFC+HSvDnq1KkDWTl/F4qiiH379mU+e/bsT41G81GFTV5CQqJMJBm5hISEhMRro1KpGgJ4NGfOnNIlywZy9+5dHDlyBDY2NvquXbvKmjZtmvfmpUvAl19S/fDrBn8BARTA5h+3JPz9oZkwAb+MHAnXXr30PXv2LH+0Hx2NxK5d8ZJzaLdsQdMuXYruwznw+DFlRjmnetmZM19LNn/58mWcPXsWZmZmmD59OoxLqi1v25aCq48/LntQd3dg927KcFcAGzdu1KelpQlNmjSBpaUlc3Z2xqsMXWwsuWD7+Rnmfh4ZSUqFo0eLLMbExsbiwNKlmJaUBDZvHt3fyiIjo3RTsXyIoogHbdtymZcXd1m0qMQH+dGjR9i7dy9cXV3FoUOHVtxqx7FjpKq4cIEM0Y4cocz8woW0cDF/Pn3dogVQt+5rnSIwMBBnz56Fq6srsrKyEB4eDiMjI25qaipyzpGRkSGzs7PTOzk5CVZWVqyZmRnMDhxA+N693PH5c2cztfrxG10j1W9bAOgIzk+90ViVhEqlEgB0AXAeAObNmwej3AWm3N8DCgXQrh052L8mnHOcOHEi+86dO7c1Gk1XpVKZ/eazl5CQMBSp9ZeEhISExJsQr1Ao4s6cOWPVoUMHYzs7uzca7ObNm/rmzZvLBg4cWDSwbdqUMrxZWdTTurwkJwOjR79ygS6VFSuAd96B0bVrqBMUhLt37wo9e/Ys/zkdHJBlY8OtExNhZ2ubFz1nZVFG7/RpYNIkagO2fTv1KB469LXr08+fP8+7dOnC3N3dSzaOu3GD3LwLtX0qERMTqkGvADQaDWJiYmQzZsyAdeEacJ2O6on//NPwAZ2cSNr/66/A1KkF3jIzM0OijQ2CMjN5J29vhsDAilFHFCY1FahXr6icvQROnDiBmlWrslZNm5b6Idva2kIQBNy5c0cYNGhQubOZJeLgQAsDhw7R91evUt/ydesoO795MwXab0Duwtv9+/dfbRswYABr1qyZDCDzwStXrsgCAwOh1+vxwMlJfO/rr4WzmZnZk5cvPwDGTgD4AZwnv9YEONeBsc4A1oOx2uDcsFZifxMqlaqOsbHxwezs7DYAtR98FWjrdFT7b25ODvvFlLwYiiiK8PPz0925c+eFRqPpLQXaEhJ/P5KMXEJCQkLitVEqlSlarbb1vXv3AtatW4eMjIzXGiclJQUqlQqRkZGyzp07F7+TlRXJdb/7jsyoysudO+TkXVZrnNBQqqkWRcDODmZmZkhNTWWRkZHlPyeAU97eYrqHB0NyMgXXAAVnq1ZRyy5/fwqux44l+W5QEEnIXwOdTscSExPFEl2JQ0OpTlytNjyg79u3WNfz1+Ho0aOoVq2aWCTQBqiNV58+5R909GiSwxeqK7e0tIS3tzfOOzkxvy++INfyK1dec+alkJJCJQMGKDtevnyJO3fuoNbSpRDati1xv4CAAKxcuRINGzYUFy5cWHGBNkCqhpMnyfCscWOgSRMgPJyCu3373jjQBoA2bdrA29sb48ePx4cffoiZM2fC1dX11ftmZmbw8PBAp06dAACRkZECGINYr1728lmzPgIQBeAHMPYuGHuNlTUAnB8HUAdAczDW7I0vqoJQqVSNFQrFjc6dO7fsk/O8V6lShR7ev/4iQ0JPT1IXvEGgnZaWhk2bNmUEBwdf12g0nZVK5estXEhISLwRUmZbQkJCQuKNUCqVLwB4Ll269PDly5cH9ejRw6DjRFHEgwcPwDl/JXceMmQIbMqS4rq5key1vKjVZAxVEpxTRmnoUODu3Vc1w56enoiMjBR37dolzJkzx6D2OvmpGRws8IcPyQkaANLSyOwt9zrzm05t3kxBo05XrnPkMnnyZGzYsEFwcnIq2ldaFOmPd1/f8v0RHxdH9+4NEUURYWFh6NevX/E3cMwY6hleXpydqab44kWq9c6Hk5MTvL29sXPnTghvv827rVjB2Lp1Feus/vRpmYsjZ86cQXBwMNfr9axly5ai/enTAqKiKJtciJcvXyIgIAAWFhbw8vIq2rf6TfD3B54/J8dxX19qx/Xzz6R0uHCBnr0KCuzrGiBB79q1K6KiopCdnS3GxMQIDg4ORvcSEzuC8+/BWB0A0wHYgLEIAMdR3tpHynB/AsAGwKByX0QFo1KpmikUigAvLy+rli1bsufPnwMAgoKCZG9bWMBo7Vpg2jRyj3+Dzz0iIgJ79+7N1Ol0P+l0Oh+lUvmvyuxLSPyXkIJtCQkJCYkKQavVply8eBG2trZiixYtSlROpaam4o8//tA/ffpUBkCUyWRcrVbLatasqXd2di77L/2hQyk7268ftX0qwSG5CGvXUs13SUFAXBxJmGfNKmLO5e3tLXz77be4dOkSd3d3L/uv4NxWSYsXo9P58+xey5Zig5MnBbi60h/RJS0oLF9OGVJrawrEBg0CatZEWFgYTp48qdfpdEwQBHh5eQlNmjQpcOiuXbvE8PBwQRRFWBUnEVcqgevXKatZHl6+pB7Wb8jFixehUCjg5uZW9P59+imZd+VKm8tLzZrUB7pQsA0Ajo6OmDx5MrZv345gExNMdHaG2S+/wKSUfthJSUkICQlBhw4dYGRkhFJ7dC9YQC3UGjYs8lZqaipOnjyJp0+fomfPnqx69epwcnIS4OhI9enFsGnTJu7i4sIHDhwoKMpSYZSEVkvBtJUVGe798AOgUgHbtpGyY9AgqnU/cgT47TcqExg1imT8e/eSrHzevNc7dzmQy+UwMTHBkydPhPXr18POzs7UyMjIA8D34PwvAF+AsbYARgDoCcaWg/OIcp5mIgAZGBsC4CQ4/0ek1CqVqrpCofhzwIAB1rkO9LmLjG2vXkXq6dO8+s6dDPXrv/Y5OOe4dOmS/sKFCxk6nW7EwoULf6+QyUtISLw2UrAtISEhIVEhiKI4D0DdQ4cOdbW0tES9evWK3W/fvn08MTFR1rt3bzRv3lzIF8gYnlKTyahe19C2Y3o9ZfOKk+5yDnh7kzv4tWvFHi6Xy+Ho6MiTkpI4gJKD7fHjgQkTaH6XLgGcY9OMGbxt27YCjh0DPvqIJOSHDxd/fEYGSZIDAykwqlIFePddBAcHIzU1Vebt7Y0bN27o9+3bBxMTE16nTh2m0+l4RkYGS0xMFIYPH474+HjUqVOn6NgtWgCDB5d9rwojilRj/gZcvnwZf/75JwYPHly8MuC994CoqNc/gacnsH8/EBNDjtqFqFq1Kj766COWmpqKE8nJvOu5c6x2gwZFFmo0Gg0EQcDOnTuRkJCA8zkO4xYWFrxXr16sQYMGEAQBRkZGePDgAezs7JA8dSoeMIYX69frMzMzUaNGDVmvXr1gbW2NDRs2iBYWFmzixInM3t4+70TGxlTWUEg2HxYWhuzsbNa9e3dmUKDNOZCURM9T8+a0kLJpEz0/P/8MjBhBaoGffwZat6bziWJe3/ZTp4BGjWis9HT6V6ulF+eArS1w5gzVeWdm0rNbwXTq1AmZmZmwsLAQHzx4IIii2LLQNQaDsVsAmgHYBcbOAvg6x3m8bDjnYEwP4FsAVQFsrtgrMAxjY+O1LVu2NM8NtAHA1toaM/76C9f0emzv1IkN4hyG9x0oSFpaGnx9fdVRUVFPtVptP6VS+VfFzFxCQuJNkIJtCQkJCYmKpCsA1Cgm4AGAw4cP6+Pi4mTjxo1D7dq13+xM69YBT56Qe/XevaU7lO/dSxnlwhJZUSRZtyhS+61SaNmyJTt16hTr1q0bLC0t8944dYra8wQGUiZRECjD6u+P1NRUaDQaCpzmzgWePQPmzCn5JDVq0MKAXg9cvkz/nj6NtLQ0vYeHh1C3bl1Wt25dWbdu3XDlyhV+//59ZmZmhvj4eHTv3h0uLi5wKey6zTkwYADVuReWlhtC7dpvJGlVq9Xw9/fnQ4cOZfnrdl+xahXQsuXr1WvnIpdTTfby5VTTXwyCIMDa2hq2Awbo9/7xh9x72DBu+9VXDBMmAACeP3+OrVu3AqCexD169OCtWrVi/v7+PDExkZ85cwa+vr6vboRMJkP9hw/R6v59ZHz+ub5FixYyY2NjPHjwQPz5558FuVwOnU4njBo1CgUCbYAyyPv2AZ9/XmDzlStXuIuLC6ytrYvecM5Jsn70KKk7pkyh7/fvJ4dxBwfKTo8aBdSqRbL6XPL/rCUlUfu7ly+Ld7n+8MO8823YQMaEP/4IbNkCPHpEyo/Ro8kluwKoWbMmxo0bBwDCo0ePcOjQoaJBIgXWt8BYbwDvAvAFY2sBnAHnZTeBp4C7GQAOxoaB84MVMnkDUalUdRUKxYC33347TzKj0QD9+qFa377ouGQJrqxfj99++w1z584tuYtACTx48ACHDh3K5Jyv0Gq1Pkql8s2lKBISEhWCFGxLSEhISFQUIgBMmjQJpsUYaiUlJeH27duymTNnomp5e1aXhK0tBQ2JidRHuCROnwYKt90SRQrU69cnKW0ZNG7cGCdPnkRyfDwF202bAjNmUJDo5UXByc8/FzgmICAAoiiiXbt2FBBOmkStldq3L97kzdyc2mwlJQHVq0O8dAn6ESOQ9PnnQv4ArFq1avDy8hK8vLwAgO3ZswfXr1/nXbp0KRqkJSVRZrpx4zKvsVgcHQ0y/8qPTqfDb7/9JiYlJXGNRiPUrl2bN2nSpPiIPSCAxu/a9fXml4u3NwXtX3wBVKtW4m69evWSx8TE6INr15b1tbYG9u+HbsgQbNq0Ca6urrxPnz7MxMQECoWCAUD//v0ZctQMWq0W0dHRqFatGoyNjSHftQuoUgVNRo58tYrTunVrYffu3Xj48CF69uwJR0fHopPo3Jl6vheiR48ebNu2bfjaxwefuLvD6tIlUkN06wY0awa88w65yffqBSxdSoszNWoYLr/fto0y4Jcvkwt5794l78sYBfUAlV/kysofP6ZA8eRJ4JNPgIcPqQSgVi3D2rWVQlxcnJ5zXrJchfM0AL+AsQsAlABqgrH74PxymYNT/XZNkEP5JXAe/UaTLQfGxsa/dOjQQf6qPWJICBkibtsGtGwJK8Ywfvx4bN++HWvXruWffvqpQatboiji3Llz2mvXrqVotdr+SqWyEhwAJSQk3gTJjVxCQkJCokJQKpUxgiAE7dy5U8wqJDuOjIzEL7/8AmdnZ15hgTZAMmt/f6rX/f774vfhnDJxY8bkbdPrKUjv1IkydWWRkICAU6fQ+ckTsVbbtjTmt9+STLdePQqg82V/c1ruiDdv3sRbb72V14brrbeA+/epxVJJzJpFruQANty7p1v51Vfo1LQpb+LgUOIhXbp0QWpqKktLSwMAZGZmkjP8s2c01pkzgIVF2ddZHDdv0r0ykB07dvDFixdDo9Gge/fuMk9PTzZmzJjijb6ePaMM75Qprze3/BgbU8u2PXvK3DU+Pl6o3aMHlRVs3w5h3TpUrVpVDAsLY4IgoCQJt0KhgJOTE8zNzekzbdIEWLasyH5Dcozebt26VbxsXq3O66uu1wM7dwJr1sDx2TNMWruW975zh5sHB9P9kctJGr51KwXb27eTq37LlsVK5kvF15d6mAP0HEaXI97MvY6jR6n3eps25AMAkH/CrFl0XVOnFnGGN5QHDx6kZ2Vl/VLmjpyHgvNRAKIBjAdjn4CxWgYcFwWgBgAtGDPMyfEN+frrrwcYGxt36dKlixycky/EzJm0qNaq1av7Wq9ePXh6eqJu3boGZaXVajW2bt2qvn79+nWtVusqBdoSEv9OpMy2hISEhESFIYpiv8zMzMSwsDC45dTDhoaGYt++fbC1tRVHjx5dOYu8jFGmrjhu3yZpcd++eduGDiUTtP37Sx4zI4Pqrj09gcaNYdmrF9eMGsWYjw+db+DAEg8NCQlBQECA0LVrV3h4eOS9YWFBdeEbN1I2sDiztgULKHAEIIoic2nSROy2eLGA48fJrbwYHB0dYWNjI/7000+Co6Oj/sWLFzLGGHpfvcobZmTAcu5cZlTI9M1gMjJIam8AAQEBePr0KZs6dSrs7OxKd9IWRQoajxwBund/vbkVxsUFOHiQ5mxuXuwut2/fhkajQcOGDSmjvmEDhLAwDNy1S/itdm1oNBqYl3BsAbRaUkZcv/7q88rFxMQEI0eOxL59+/Dy5UtUL6y6yPUGaNSIeoSfP0/KjPHjcWzkSNTt04fLPT3zbl5xcu/ykJ5OQfKRI3nb4uMpG/262NsD775LX9+7RwF2RAQQHEylFO+9B1StCvz0E/WRL0VtAJDSOzY21gTAdYPnwPmpnCx3R1DG+gI4L2HV7dUxOjDWD4ASjDUot8N5OVCpVFUVCsWmQYMGmcn1ejKpu3WLlDbFqEVyWqGV+YMaFRWFXbt2qTUazXqtVvuFUql8vfYFEhISlY4UbEtISEhIVBhKpTJJpVLh2rVrMDIywvnz58W4uDihUaNGfMyYMZWnphoxggKfzz4jg7Jm+drqajR5cliNhjJ6M2eSlLs41q4lee2FC1SLHR4OREYiaO1a/nbNmgKKMx/Lx71793Ds2DG0b9++YKCdS9WqwO+/Ux/dtDTsO3pUfPTokWBmZoZatWrBJjsb1XfswNHOncEYk8XHx6PvqVOQW1mRSVUJPa+nTp0qXL9+HYmJiTJnZ2c0F0WcrVoVQWo1T/n2W2ZkZIRmzZqJnTt3FsqlLuje3eAM6pMnT+Di4qK3t7cv2+xOEKhGzN/CAAAgAElEQVTmuDT5f3mpW5c++5AQoGPHYneJj4/npqameLX44OCA4JAQmAUFYUqtWoaXOGi1wDfflBhE5ma/CwTuUVH0Gc6cSXP95hsy5sv3nLi+8w67fPkyenh6GjYPQzhxAvj6a1J45C6AvHxZrHv7ayOTAQ0aULANUO24IFC2294eiIykWvHo6LwgPR/JyckAoFYqlTHlOi/nagDnwNh9AH1zgu85AK6UGEhzvg2M7QTQFowlgvMn5TqngRgZGS1r2rSpVX0LC5Li16xJSo5ylmXkotPp8Oeff+qCgoKy9Xr9+IULF/pW8JQlJCQqGElGLiEhISFRYahUKiMAiI2Nxb59+2BlZSUMHz4cY8aMqcBmwSXAGP0hHxhYcPvu3Xk9rt99l2pPe/QA8pucXblC7acAqqO8cYMcm8PDaZupKbKysoQCxmjFwDnHgQMH4OjoiL75M+n5kcvJFOz0aaRmZiI0NFRwd3eHvb29+OLFCx4WHo6mgYHo3q0bGjRowJs3b64XrKwoE2ljU2LLKLlcjg4dOqBv377o2qkTqk6ejOE2NuyTOXOEMWPGwMvLCzExMVi1ahVelkMWjuRkIDW1zN2ePXuGqKgo9OzZs+xA++VLqpUvYeHgjWjdGli/njLnxdCxY0cmCAK+//57fuTIEYiiiID79/WaNWtg17EjGYQZ0ud8166Cz1Ah4uPjYWVlJZqampJc/MQJMqqLjKQ+7rdukaN+YmKB49zc3KBWq1lMTPlizhLx86OFqNDQgkZ3WVmAmVnFnKM4evcmVYiZGT0/NWtSRj+39VybNvRzp9EAoojY2FjI5fJ7r30+zqPB+WYAHwFYAsAbjDmXsr8OwJcAfF77nKWgUqnaARjVo0ULI/TvT90AZs167Wc+Li4Oq1evVl+9evWsTqdzkQJtCYn/H0jBtoSEhIREhaFUKjUA6jHGHtSoUSN76NChKNaBurLYuZPqWj/7jIKt7GyqVzYxoVre774rKKUdOJBqvrOyKMsKUAAwfHiRoV1dXfmhQ4egVquLPbVer8exY8dEhULBJ+Q4XJdIixbA0qUwa9QI1RMS4O7ujjFjxgiffPIJm7ZwIeTJyXDv2BGjR49mQ4cOlQmCQNnBq1fp37KUr8nJdJ05GcRGjRrBzc0NkydPFqpWrSqGhIQYLp3Vag2qwQ0KCuJ169bVVytDLgyAFhzGji1R6v1GdO4MvHhBrbWKwdzcHB999BEbPnw4u3XrFg4dOoTMzEzZ/7F33mFVXFsbf/ecBogFEVEsBI1dFBt2RU3sPSYaNZYYW4qam8ToNTIZjRpNco03mhijxhJL1Ng7iCgCIoqioiICgiC911Nm9vfHBusBDlhi7rd/z3Me4MzMnj0zB+Xda6132bu4sFrknByWil5Wb/Hdu0uteT58+DDy8/MF7NvHsigaN2au+L16PTQSmzmTZU88go2NDerUqUMPHz5MTZaI/tIwGllWR3Dw047yLi5MAL8MikX9l1+y31FKmbFgo0YszbxuXaSmptLOJ04YQcjTDcvLA6VXQWlvsL9xF4KQ0SCkhMb2eAvAJBAyAoRY3nqwDCRJstWo1fvHVq9uXWn8ePbcJ06ssKv/1atX6fr16/NzcnI+nDdv3gBRFOOf11w5HM6LhYttDofD4TxXRFG8azQa3dLT07esWbMm//6z9E+uCFZWLHoWHc1E56+/sj/oN28GnJ1ZT+LitkVNmrA66p49S+59XUTz5s1JYWEhsrKyzG7PycnB5cuXhUmTJhGhtDZkxSxaBHW3bjBWqUKji4V+MUOHAt999/Qxrq7A7NksYlgSfn6sf3SDBmY39+vXTzh37hzZsGGDkmNJLXZGRpkGaYqi4O7du7R169ZlC5b795n7+6JFZZ+7oqxcWbJhHgBCCBo2bIjhw4cjKioKgiCwaL+NDTMiMxpZzXEJCyuQZRbZLsXYrZO1NW0YGAglJoalEDdqBLz+hI48dow95yei8L179yaZmZl0yZIluFbCokGZ3L/ParXT0p6u+aaU3aOKmuY9I8kpKciZNw/Uzo61xLtyBdevX89re/68E4BOIKQZCAkAAPMOcxbAotxzwP7WXQVCJprZp/jG/wfAsAqdxwyVKN3gkZdn77x7N3OKr2B/8vz8fPz5558FR44ciTcajZ0WLly4+XnNkcPhvBx4zTaHw+FwnjuiKOoBTFu0aNGpTZs2bejTp4+1u7t76YZZz4tKlZhh08WLwEcfMbF47Bhz/t24kaWUf/AB29ecoC0BX19f2qRJE1K7BFfwuLg42Nrayk5OTpZFyDp3Bjp3Rt8tW0jNwYMf3/bBByWnKM+cWXJaN6UsWrluXYl1oU2aNMGIESNw4MAB4fr16+hcQm3zA+rWLTMCXRQdFizKYjh7Fti6lbVNe1E0bsyc6kND2cJDCbRu3RrNmzfH4cOHcfDgQbRo0YLVco8ZAwQGMoO8Dh1Y//RH8fFhLblu3Xp60OxsIDcXXVauJGFdu1JhzpySP/RVq7LU4thYYM8eyLKM7du3K1FRUYJarRYAoEqVKjCZTFCpVOXTncULAWZajCE5mT3TiprmlYPs7GxERkYiLy8PycnJhYmJiYaUlJQqANCuXTvlzTffFHJVKqSlpdHv585tI4piAQhpCKC4HiQUhJwFMAvA+wB+t6i3NgBQmgZgBwi5DCbivQHMBqVhj+wjg5BGAAgIGQpKDz7L9a779NPVfS5fHtGqSRMNvLwqHM2OjY3Fn3/+WWA0GjcWmaCVsPLD4XBeZbjY5nA4HM4Lw9PT809Jki75+PgcjoyMrDdy5EgbqwqaA5ULQljbJG9vFjWMjWURy86dmQidPr3cQ3bo0IEcP368xO3VqlWD0WgsV8ZYVpcuaLhwIfJ8fFhbsGJ69QJOnjR/UNOmrKa4RQtWj96q1cNtc+Ywwy5Pz1LP6+rqiv379yMjI4OiqId0iTg5MeFaCioVW18oLCw022P9Afn5wFtvMTH7ItFqgWHDWG10KWIbYO28EhISlJYtWwoPTNPUamDNGvb67Tf29VEjN0dHlhL9JJcvs8/WzJk4uXIlCkwmpSNQ+uLLe+8hPjAQ2TdvwsfHhxYUFKBq1aoPMij27dtHs7KySOXKlfHRRx9B94TzuVlSUlhEtaT0f72embS9ABRFQUxMDG7evGm4fv26yWg0UrVa7Ws0Gm/LsnwXwDUA1wG4XL9+3TMiIqKXi4uLjUqlOrhgwYICACgyLPuiaMjuAKwA1AawDMBmEPI5AB0otSw9gtJbICQcQASAX0DIRgDHQWli0XZTUX33JhDSAJRmVuTajw0c+HlLg2FaixEjNKpZsyoktCml8PPzM/n5+RXIsvyup6fnkYrMhcPhvBpwsc3hcDicF4ooinckSWodExPz05o1a8aNGTPGpk6dOi/+xCNHsrZMx44Be/awKOT160zs1a5d7j+EMzMzUblyZQUllGBFRUUpRf20LR74r4gIpdK8eeRte3vyWLuq3FyWLv7228zR+UnUaubA/mTrJkpLTzF/BEVRYGdnV/ZcY2KYiVUphIaGAkDZQnD8eHZtJS0kPE/c3Vn0vG9fwMGh1F0bNGiA2NjYp5/thx+yGu7r11m0vLjG2d8feDQb4cIFYO5c1tN89WocTU3FteDgkk3yHoG6ueHgunVotncvNO+8o0yfPl1V3Jc9NzcXd+7cIQ4ODtiyZQvdsWMHcXR0VNq2bSvY2dnBbDu3gAB2zRkZJbtex8Wx13Pm7t27+Ouvv/KNRuN9k8n0pyzLmwFELViwwJzqTwEweNGiRWPDw8M/1Ov1/zE7KKVZAIprN9iDJCQVgA6EqAFkA2gNQAZQDZSGlDAOBYuW9wAh8wGsByH/ARAISgtA6W0Q4gjAHoR0B6V+5bn2sz17ftzk/v1vqy5apNK++255Dn1ATk4O9uzZk5+UlHTLZDINE0Xx+T8kDofzUuFim8PhcDgvnEfSyk9s3rx5U69evWw6der04tPKW7d+GNm8dIk5Qp88yaJ+Hh7A6NEWC+9Lly4prq6uJRqLJSYmUhcXl3KZLLm4uAhXwsIgf/EFhM2bmVACgHr1WKpyafMSRRa537YN2LCBpcx//TWLulqIrSU1u6mppbpzJycnAwBmz56NMmvVt25ldfQvg+rVWa1sSAhzxi4FlUqFhIQEQVGUx6+BEFZTvHo1KzlYt461QVu0iBl8RUezZ2BjA4wdy/qGCwJw9CicnJxkd3d3iz4PtWxsFLeoKMFj6lTVo8/c1tYWbm5uAIDu3buTixcvyrIsCxcvXoQgCKhataqSlpYmfPzxx7C3t2dGf126MOdxjabkE+r1ZWYrlIesrCz4+voWhoWF6U0m01hPT8+jlh7r6em5HcD2cp2Q0g0PviekD4BIAF8BGAzAHYRsAfATKA0u4fhlIKQ+gFEA3gIhB0HpCVBqBCFjAEwDYJmzIyG6lBo1vqrs7DxPu3Wryv7RDBULkWUZQUFBiq+vrx7Aj0ajUeS9szmc/w242OZwOBzOS8PT0/MvSZIu+/r6Ho6KinIeOXKkTalpx8+Tdu3Yi1JWh3vvHjBhAquN/te/WBSwfXuzAjcpKQkGg0F4o7iF2BOkpqYiPDxc9UFxLbiF9OrVC46Ojth1/z7GzZ79+Ma1awE3N9aCrCSqVWPzvX6dRVstcQIvQqVSQa/Xl71jp07mo+tFFLuzG8qIfmPsWGDQILM9ll8Yn33GHMavXXvoAP4EBoMBISEhQps2bUpeLPj4Y5ZFcP48S+P392efl6FDmbhdufLBPTp69CgNCQkhAwcOtGgliVKK/LZtyY6GDeWZEREqNDbfrapbt27o1q3bA/EeGxuLqKgo4cyZM1i9ejXauLkpA6ZMEcj33yN/5EiUKqXDwkptW2YpOTk58PX11V+9elUhhPxsNBqXiaKY9swDlwdKA4u+W1T0AgAdgFwQMgGACEobgpC2AG4V9eUGKI0F8B8Q0hVA06K+2xIo/RGErCl6PxqUluzwSEjtu/Xrv59Vo8a/6A8/CHUrILRTUlKwc+fOvLy8vCtGo/F9URRvl3sQDofzysLFNofD4XBeKqIoRkmS1CY2NnbVmjVrJowZM8a67pPp0C8SQli9c4sWLOJ59SqLtv73vyxlePRoJqjc3B4I77CwMDg6OspqtdpspDIwMBCCIKAk87TS8Pb2lnXt2gl4912CBg2AU6fYhurVy24/1b49sGQJS2HevbtEQWkOWZZhkVN8Xl6JPasBPBComtIiqQCLMpcgJF8Y1auzdPutW5m7uBlycnKgKAqGDh1a+ljDhwNbtjDn+sJCVvPs7/+YyVhWVhZCQ0PRo0cPtG3b1qxyz83NRUxMDPLy8pCZmalER0eT9PR0fDhokArt2wN377J5l0H9+vVRv359tG7dGsHBwYiOjKR/jB1L79+7R0wrV2LMmDFo0qSJ+YMLCoCGDcs8R2nExMRgx44dBYqibDCZTItFUUx+pgGfJ5SOBgAQEg+g+EO+BcBeEPIjgJ8BvAtKKSj1ByFBABIB/ABCDoFF2hcBuAHWt/tpCGmXXq3aF7dbthxe+9tvda1dXcs1RZPJhIsXL1IfH58CRVE+lWV5vSiKlrfk43A4/wi42OZwOBzOS6corXzGokWLTmzZsmWLh4eHdefOnVUvxa38UQh5mGbeowcT3evXs9TsMWOAO3eA/v2RkZFR6jB2dnaoXLmyjLLMsMygVquJysaG4oMPyGO9oefPByIiyh5g1iyWfj50KGtlZgGZRancNR41/Sp5gqWK/tzcXABMtNrZ2ZnfaccOYMGCh/2WXybjxj3s7Wzm82VnZwdra2u6ceNGOmnSJKHUVPgJE5jLt8HAas9v3mTZBc7OAIAff/wRKpWK2Nvb49q1azCZTI+9oqOj5djYWJWtra1Jp9ORSpUqCU5OTmTAgAGoWr8+S3m3QGg/Of++J08C58+rlLNncevWLQQEBFB/f380adLE/C9U/fqsFVkFCQsLw/79+/NkWR7u6enpXeGBXjSUZgPwLvq+JQCAkCYA7EApBSEHAdwBpf8CIREARgIQAZwA8DGAayBkOIADRTXfKBpjdJSLS89LXboMd/3qK13Tpk3LNa3o6GgcOHAgr7Cw8ILRaJwliuL1Z75WDofzSsLFNofD4XD+Njw9PfdJktTqzJkzhyIjI11GjRr18tLKn4QQwM4O+OIL9oqPZ1HdbdvQMCgINbp1UyEykkVoHxFkOTk5OHv2LJyLBJelGAwGREdHIzU1VWjUqJGCBQuAIUNYT+alS5mD+qBBzKCsJGJigF9+YSJWpWKR1wkTyjx3sbFWtWrVyp5oUhJrFVUCxe2+sktqR2Y0sjZZrVqxbIKXTaNGrB1XYCBL+X4CQRAwdepUsnLlSnL+/Hl0MbPPA/R6Vhc/dSq7J7t2sT7uly8Dly9Dp9GAqFTUy8tLFgQBgiBApVJBpVIRlUolODg4qEaOHAlbW1vzf3+5uLAFk717y3evPv8cuHMHgiCgefPmqFu3Llm9ejU9evSoPHDgwKcXgI4dq7DYvnDhguzt7Z1rMpl6iaJ4uUKD/J1QGg6guIh/GQATCKkGIAzMgC0EQAGADwDkA5gEZtB2GoRoALwXV6/e1FPDh7v1nT1bV57f+/v37+PEiRN5iYmJuUaj8SNPT8+/ntt1cTicVxLy6EIdh8PhcDh/B5IkabVa7UqNRjNpzJgxNi81rbwMkpOTsXvRIkxzdobG2Zm1gereHfjoIyRRigOHDyM1NRX//ve/LR7z9OnT8PPzgyAI6NSpE/Xw8CBqtRr49lsm5rKzmcHV1q3MwdtctDUnh0Xlt2wBunVjjtgjRjABbkE6+Q8//KCYTCbhnXfeQfXq1VH1yV7SxezZwwT3Rx+VONaSJUtgMpmwcOHCp+uejUY2n5edtfAo+/eze1rKQsTvv/+uxMbGCtbW1sqIESOERubE6PjxTLRHRjIztKFDmVHd/fvA+PG407gxvVmvHoZMn05Qr17F5ipJ7DyWpHnfv8/8B65efapePy4uDps3b0anTp2UPn36PP5QGjYETp9mEe5ykJycjN9++y3HZDK1EkXxbrkOftUhhBRFu+cCqANgDoAEsPRzHYBoBXj9YufOzS8OGdJx3Icf2pT4O/MEaWlp8PLyyo+KijLIsrxQUZR1oiiWYXLA4XD+F+Bim8PhcDivDIsWLRqmVqu39uzZ06ZLly4vP63cDLm5ufjxxx8xePBgODk5wSEnB+T6dRh27kTE1asIadMGPfr2hfO4cSy6XAYZGRlYs2YN3nvvvaej4YoCHD/OhOmAAazHc5MmLMX9SfLzAS8v1k/6Ue7eBXQ65rJexnWtXbsWeXl5cHZ2ViZNmmQ+f9rPj/VtHjmyxLH8/f3h4+ODhQsXPr7h+nVmsJae/lht80tHltnCxMmTD9t3mcFoNGLr1q1K9erVheHDhz+9w4oVQEICM0TLyQFsbdm9qVkToBTr16xRup88KTS5dImlhF+5wtpwlfdzvHkza13WrFnp+5lMwM8/l9gzOykpCevXr0ffvn1phw4d2CQUhUXCv/++VOM7c4SGhuLEiRNH586dO6hcB/4TYf/4fApgM4AACjQ+8+abcdGjR9uNfvfdSjYWlERkZ2fj9OnThdevX5cBLDeZTP8RRTHvBc+cw+G8QpTvX1kOh8PhcF4gnp6eB4xGo+vZs2dvbN26Nb/Y6frvxNbWFpUrV5YPHDiAdevW4Zvt21HYrx+M69fD94038O5HH8H55EmWorx/P4t8ltIq686dO7CysqJm008FgYmhwYPZOL6+THw/yb59zGXbnKnX+PHAk6K3hOv6+OOPodVqaUxMjJCSkmJ+x/R01uKqFFq3bg1FUVBQUPD4hmbN2Pz/TqENsEWQ2bOB/5hv5VyMRqNB1apVqVln9cRE4I8/HkbHK1dmQtvZGYiPx7Hjx+XU3FzScO9eICqK7f/118xV3te3fH2td+xgqeSlMWIEsHFjiUIbABwdHTFq1Ch4e3uTB87zmZns9YTQzs3Nxa5du7Bp0ybs3r0bx48fR3Bw8GMu8/b29lAUpZUkSX//KtiLhkWjzp7u2bOBT9++USf79y/InzOn5qT33y9TaOfn5+PEiRPGn376qSAsLOwXk8lUf8GCBYu50OZw/v/Ba7Y5HA6H80ohimKMJEnt4uLiVqxevXr66NGjrctbD/28+eijj1SFhYWwsrLCqlWrlE2bNhGdTqekVq+uWnvpEp2xaRNR5+YCf/3F0q49PIAbN5ho7t37sbHu3bsHJycnCsC8YBk0CPjxRybmtmwx7wSu1TLxaC5i6uPDUtDT0spsBWZlZYX58+eTVatWKdu2bROmTJmCyk+2hMrIAMLDSx3HxsYGtra2yn//+1/h008/ZTXhZ84Av/8ObNpU6rEvjbFjgTlzWK11zZpPbc7JyYG/vz8NDw9Xvf32208fv24dSx9/NNpcsyYzsXNywp0//hBatmzJygHUahZJDwhgafTTpjHncl9fdi979So92n3sGDsuKwsoKVW5WzegY8cyL7tJkybQ6XTKt99+Kzg5OWFkly6wL7r+Gzdu4PLly6CUIi4ujtavX586OzsLOTk5cmpqKu7cuUNOnDghVKtWTR4wYICqQYMGEAShJiFkBIAyVgNePSRJEgBUKnrZgv0OZgNIAQtAdQNQmShKARWEtp9WqeJpsrHRyrNmoW3bthoHBweUlm0jyzKCg4OV06dP6wHsMplMC0RRjH/hF8bhcF5ZeBo5h8PhcF5ZFi1aNFCtVu/o2rWrTY8ePdSvQlp5fHw8Lly4gEqVKiEoKAiKoqBWrVqYPn36w53S0oCDB5m4qlmTOUwPGQI0bYrd+/YhLS1NmTFjRsnZZZcvAwMHAp6ewOLFrDa3mHnzmJjv37/kSaakAPXqsZTyWrXKvKbCwkIsX74c7dq1w+DBgx/fGBjIaoIfvT4z3LhxA3v37sUXX3wBnU4HnDgBHD4M/PRTmed/aaxezVLsp059apOPjw8NDg4m3bp1Q9euXZ8+9vp14NYtYNSop7dJErJ++QW7Fi/GxIkTHxjQPYbRCAQFsd7fc+cC1tasvVxJae0TJ7Lo+IkTj79/5gy7p3v2WHDBjKSkJHh5eSE1NZXWvHKFvJ2biz8GDZKTk5NVrq6uCiEEDRo0EMy1CsvNzcWFCxeUoKAgwWQygRACRVFmenp6rrXk3JIk1QNwixCymlK6WhTFexZP/BkpisA3JoQM1ul0YwwGQ2tCCNRqtUmtVsuEEBgMBrXRaFQTQlC9evX86oqCYf/+d+WL335rquTurmnh6gorK6syzxUZGYlDhw7lFRYWhur1+qmiKN548VfI4XBedXhkm8PhcDivLJ6enkclSWoREBBwICoqqunbb79tY2tr+7fOqU6dOhgxYgQURUGzZs2wceNGJCYmIiMj42HrK3t71td58mQmeL28gMhI6EeOxGt2dui8erUAg6Hk9Oo2bZh5lUrFaqVlmX1vMgHe3sC775Y+SQcHFoWtVYsdU4ZhmpWVFTp37oyQkBB07twZ9o9GxPV61pfZQoKDg+VudnaqB33MXyXefJNFjfV6JrqLyMjIwPnz50mjRo3M90qPiGBp+8uWmR/3yy9R0Ls3Ery8sG/fPowePfrpfTQaFo0+f549k3feYWJ682a2mNKz5+PR7hUrmKmbuXHq1CnXZRenk2/fvp2o9XoE5eXRlJQU1SeffAIbG5tSSwptbW3Ru3dvoVevXtDr9fDz81MuXLggSpL0q4V9oRsDsKGUzgUQAWB9uSZfDiRJqq5SqQ4SQq5qNJoGarW6o0aj0TZu3Fho2rSplYuLCwgh0Gq1jzWFNxqNwL170KxdWwXLlwM1aqDH6NEaS2ra4+Pj4e3tnXf//v1so9E4g1J6iPfL5nA4xfCabQ6Hw+G80oiiGGcwGDomJCSsXrNmTUF0GfXDLwtvb29s3Ljxwc85OTnmd3ztNWDqVITUq4fVQ4YgpXlzWnfPHtb/ec8eVtNsrsb7zBlmxNWwIauZLjbD8vd/2Bu8NOrUAT7+mIk4C+jbty+sra3pzZs3H99QqRITeGXQvHlzDBs2DD4+Pqrs2bNLrSX+22jSBAgNZffwEXJzc2E0GnHv3j36559/4tdff5Uf20GlAlq2ZC705rCyQkq1avjw55/R/9Fe6eYghN3PffuY+VxkJPDpp+z73buB1FS2n6Mj21ac0q4orF68YUNg1apyXbaiKFizZo2iVquVlj16oNawYWTWrFmwxOSrmISEBCxfvhwBAQGCyWSqBaC9JceJonhKEITTRT8+VrMsSZKtJEnVJEkq+wNmGUZFUVqaTKaZBQUF/UwmUzUAUKvV2itXrhiOHDmCZcuW4dy5cw9rMxQFmowMaOLjHy6EvPtumeZxaWlp2LZtW/7mzZvTYmNj5xoMBhdPT8+DXGhzOJxH4WnkHA6Hw/nHsGjRojfVavWuTp062Xp4eKifajP1EsnIyMB///tf9OnTB9bW1mjXrl2J+969exebN2+Gg4MDnTlzJiGEAPfuMaOqJUuA4GDWUkyrZRHU4qiriwurmZ4+nQns778HLl603EU6Lo71CjeTHmyODRs2ULVajYkTJz4MsR4+zIy4yjLsKkL6+mtmvjZzJnSVKlk2z5eJjw+rJd+69bG38/PzYW1tDYPBgO+//x5t27bFgAED2MaffwaOHmX3ogRu376NsytWIKNOHdq9f3/SqXNny+ekKIDBALz1FstCkCQgN5cJ7kWLWO1+YSGLzO/ZY1FpwKP89ddfNCUlhU6bNk0QfvyROdWXlR1hhk2bNtGYmBgCIBRAF1EULXYwXLRo0VlKaXcArVQqlYdarZ5rNBprCYKgyLKs1mg06YIgZAG4rNfrdxZFiCvUHkuSpKpgAaVMAG5gvbMfMG3atIcZDGPHMkO78+ctGvv+/fvw8vLKi4uLUwCsMplM34iiqEUnOwoAACAASURBVK/IPDkczv8+XGxzOBwO5x+FJEm1dDrd/ho1ariOHj3a5ilDr5fI0qVLodFo8Nlnnz3dXxqAj48P/P39IQgCmjVrpgwdOlRQm0vpzs5mqeZeXkDjxqw2+K23WCT1hx9YG6hWrVidb7Vq5ZukLAOvv84crjt1KnXXyMhIbNu2DXPnzn1Yp3r+PBPaK1ZYdLo8Z2cEursrl93dBY1GQ5s3b6707du37J5oLwtKgZkzgdGjmVGZGWJiYrBlyxaMGDECLevWZXXVM2aU6WZuNBpxYv162vuzz8i1PXvQceDA8s9Pltn9FkUmrM+cYTXzb70FvPFG+ccDsHLlSnnAgAGqpk2bsusYMODplnEWsmrVqpzMzMx+oigGlue4ovppBQBq166dN3DgwEpOTk4QBAGKoiAzMxP5+flITExEcHBwTnp6OlUU5UtFUX4TRVEuY/iyzl0XwL1+/frB3d2d/a6+9RZ79evHsjdKqctWFAW3b9/GpUuX8mJiYgyyLHsWzYuLbA6HUyqqr7/++u+eA4fD4XA4FuPh4ZF76tSp3wsKCnQXL17sUKtWLU316tX/lrm0bt0aQUFBND8/n77++usPosH79++n+/btQ0xMDGnTpg1atWqF3r17E1VJfbh1OqB5c+Ze7urKaqS3b2f13hs3MmMvWQbM9X4uC0Fg4/ftW2oLLkopjh07pmRlZZHMzEzExsYqp0+fpjUrVSI2ioIQgGZnZxM7OzvExsYiPj4eKpUKNjY2kGX5wWKDtlEjvDZ9OpHZuciVK1eE1q1bw9rauvxzfxEUp3EfOMBM6MxQrVo1yLKMS5cuyR26dxfQpAlzlS8joqxSqdCofXtyOTmZemdnk0aNGj3t7l4WggC0aMEWBBITmZna8eNsoWXIkPKNVcSFCxdovXr1hFq1ajGH9IEDgWJ/AQtISUnBnTt3oNPpEBERoc/JyTno4eERaenxkiQRlUoVbWtrq+3SpYtmxIgR2qpVqz5w9iaEwNraGlWqVIGTkxM6dOiga9y4sS4+Pr6X0WgceurUKW8PD4/M8l85w8PDIzsoKKhjs2bNGtUOCmKLT7dusRr6hg1L9DQoKChAUFCQsmvXroJbt27dSE1N/U6W5fGenp4BHh4ez7QAwOFw/n/ADdI4HA6H84+jKNK1UJKkU7t27fqrQ4cOlXv37q0pUcy+IGxtbWE0GsmlS5dIjx49oCgKAgMDERoaSnr37o02bdqg3IZutrasV/b48UxsqVQsrfhZUuZnzgQOHQJ++425pJshODiY3rlzRxAEAREREdTa2pra29urju/dixahoTjHatIVvV4vCIIAGxsbOS8vT1WzZk0kJCRgyJAhtO3q1QTffAPBwQE9e/aEoij45Zdf5F9++UX15Zdf4mU/nxLp0QMIC2Ppww0amN2lQYMGOHfunCq7eXNUsba2OI2eEAK3774j1WfOVPK7dROu7d8PV1dXy+eWk8Pc5OfMAW7fZosvDRuyuu4KUqNGDdX9+/eV1q1bC0hPL1ff84iICOzevTtXpVKdNRqNb8iyXAVAOZqGAwAEWZade/fuDTc3N4sOqFWrFj744INKfn5+bf39/W9+++23h/V6vVhel29JkgghZJJaEHo5V6oETJkCnDvHfqdKoLCwEP7+/qagoCCTIAhH9Hr9MlEUL5XnvBwOhwPwNHIOh8Ph/MORJMlBp9PttrOzaz9mzJhKVUvqS/wCMBqNWL58OQRBYI7GAARBwMCBA0ut4S43hYVMdP/wA2tB9ccf5R/j9m3WK/r775/aZDKZsHLlSnTr1g0GgwE9HzFVy/LygtXy5dB5ewMAsrOzoVarYWNjg3Xr1skJCQmqFi1aIDIsDBM3bkT27t1o1KHDg6hlRkYG1qxZgwULFpTao/ils2YNi2iW0tLsQlCQops6VaiWnY2qV67AxsYGarXabMnAU9y6hfBdu7BXo6GffPIJKXXRRZaZaZtezxzPe/ViZQNbt7K0b3d3tlDy2Wflvszc3Fzs2rULVapUkUe99ZYKCxcCX31Vatr0oxw5csRw8eJFURTFbyVJ6qLRaIYbjcYvy2sEJknSvxwdHRfPmDHDcle2IgoKChAcHCwHBAQYKKVbDAbDvyypF5ck6TWdTre5RXR0h4H79lmr7t9ni1YlPD+j0YgLFy4oZ8+e1RNCDun1+i9FUbxb3vlyOBxOMVxsczgcDucfjyRJglqtnicIwlcjR460Ntcv+EVw+vRpnD17FsOGDcNrr72GVUUu0RMmTICLi8vzP+G5c0B4OOvB/NdfrO64vMyaBUyaBLRt++Cts2fPKiEhIXTWrFmqp4RkZCTw669ma7YVRUFhYSFsbGygDwmBT1ISQkNDqV6vJx06dJCtrKxIREQESUxMJJ9//jkq/Y2GaXl5ecjOzkZubi7y8/PRqmZNkE8/ZeZjT0R6ZVlGTk4Oglavlomfnyq7Rg3caNIElFKo1WpUr15d7tatm+rRiLWiKMjIyHisbRqlFCnNmtEzHTqQpnPnPh3hvnsXSEoCFixgqe0rV7Ke7GlpzEl+xQqgXTvWo7t+fZYCXo7PVVBQEE6ePAkHBwdl2LBhQm2tlqWoJyZaPMaFCxfg4+OzZd68eRMtPsgMkiQNrVmz5rbp06fbVtTYsLCwEPv27cuPjo7WA/jdaDSuFEXxqSi7JEmOWq3264ZhYe+3sbdXNRBFleriRVZKYQZZlnH58mV66tSpQkqpn16v/5T3yeZwOM8DLrY5HA6H8z+DJEmdNRrNQTc3tyr9+vXTvsi05ZCQEJw4cYK2adNG6d+/vwpgUUSVSvXi65N9fID33mNu48Dj/ZnLYvx4JtKHDAGlFGFhYTh8+DA8PDzQyZyB2p07wIYNJfeYBpgI7NcPyMxE5N27OHToEJVlmep0OpqWlqYCADc3NyU3N5fWrVsXPXv2LNeDyc7Oxs6dOxV7e3u0b99ecHR0BKUUiqI8EPCKouD06dNKTk4OdXBwENLT05W4uDii0+lo48aNVb6+vlCr1VStVtOCggKhU6dOypunTwuoWxf48EMAKK7TRlBQEE1PTyczfvsN1Z2coFmyBClubigoKEB+fj6io6OV0NBQQaPRKLVq1RK0Wi1iY2OV3NxcQavVUq1Wq5hMJqFp06YYdusWCWvcmB68dYs0btyYvjVgAMH+/UxUf/MNq8OfMIH1RieEmaF98AGwc+fjKe5paazE4JH+4GVx8OBBpKenK5MmTWLq9uZNFtX+6y+Lx4iNjcX27dvv6fV6FwDjtVptD4PB8JMoilcsHgSAJEk6nU7nVatWrXbvvPOOTXnajj1JQkICQkNDjZcuXVJUKlWyIAjhlNJEQkh1SmlDISfHpaWbG+196ZJOV1AA/Pij2XEopbh+/TpOnjyZZzQar+n1+lmiKAZXeGIcDofzBFxsczgcDud/CkmS7DUazTErKyvX4cOHWzUooSb3Wfnuu++UTp06Cd1L6r38MigsZKZdgYFAs2aWH5ebC+OqVTjRurVy7do10qVLF9K9e3fz6dHx8Uxse3qWOSbMpErHxcXh0qVLKCgoQFRUFIxGI2bMmAEA0Gg0sLOzQ0hICNXr9bR9+/ZCVlYWrly5QsPDw2nVqlVpnTp1VOfPn0ft2rWV5ORkobCwEAAe1H+3aNFCuX//PsnMzCQ6nU7RaDTUZDKRunXrCnXr1kV4eLiSnJxMhg4dSpo3bw4ASE1Nxa+//oqR7duj2cKFSNi7F1CpkJiYiCNHjqBly5bo378/tBkZEA4dYosTT5Qn6PV6BAYGIjs7WzGZTFQQBPTr10+Vm5uL7Oxs5Ofn4+jRowBAnRUFQ3//nRxt1w5v3r2Lqs2bA/PnA3XrPp7OHRwMTJsGnDgB1Kz5+I28eBEYM4YtflhIfn4+Vq1aRYcOHUpatGjBsiIuXmQ93i2EUoq1a9fmJScnqwBYtW/fHleuXMkxmUwdRFEMt3ggAJIkqTUazU86nW5Cr169bNq0afNMpQVGoxFpaWlIS0tDQUEBdDodqlapgnpduoDMns3M5UogIiICx48fz83Ly4spEtk+FZ4Ih8PhlAAX2xwOh8P5n0OSJC2A1ba2thM/++wzi92gKKUW//G/fPlyOnnyZFLzSVH0sjlyhLlLL1vGWkO5u5d5SPCGDXLDuXNVW+bPl0ePG6d60HPYHBERrE3UjRKyaletAvbtA3x9yzzv0qVLYTQaoVaroVarqdFoJFZWVsjLywMAqNVqKIoCa2tr9OjRA6mpqYiLi1M6duwotG7dGoGBgfDz81M6deok1K5dG46Ojti8ebNsa2uL9u3bq5o1awazrdWKMJlM2LVrF7137x70ej0hhKBPfLwSnZcn3Hn9dQDAgAED4O7uDnz9NTNRy8gAiurVy4vp3j1kr1uHu4TQxj/8QM726AH7BQvQsVu3p3f29mZRZ29vs4sWMJlYzfby5RbXWwNAaGgojhw5gmHDhqFFTAxw+TJLWy8H+fn5+O677wAA3bt3p3Z2djh27FiK0Wj0EEXxZnnGKmoB1ker1a5q2rRpwyFDhuhKe2YWk5HB+pDv3cui/46OZndLT0/H4cOH8+Pj49ONRuPHlNKD5a0/53A4HEvhYpvD4XA4/5NIklQfQAwAdO3aVfHw8DDf4xpAWloaduzYoU9LS9NptVpl6NChQosWLUodf9myZXTcuHGkfv36z33uFWL4cBax7NePibEn6pALCwvh5+dHw8PDkZaWRj744APUychgwqRRo5LHvX8f+OgjJqjNER4OhIQA775b5hQVRYHBYIBWq4UgCDCZTLh06RLq16+P9PR0VK5cGQEBARg4cCCqVKlSnqsv8XxhYWFwcXFBXFwcDh48qNjb2+PNN98UHB0dcfbsWWgCAuCeloaMuXNRUFiI14tENzZuZMK3SRPW89pSDAbWqis2FrhwAahTB5g5E9ezshC5aBHqFBSgyn/+g8aNG7P9KQV272bn27sXKC29+sYNZpD3zjvlug9hYWE4cOAAptnYoIYsV8hoLTk5GWvXrqX9+vVDx44dyZUrV+jRo0fzZVn+WFGUzRUwTKus1WoPtWjRouPQoUMtXz14Er2eOewPHw7861/A0qWAmXZrRqMR586dMwUEBBgALDWZTN+Jomio8Hk5HA7HArjY5nA4HM7/LJIk2QEYpFKpviWE1HrnnXdUjZ4Qlv7+/oq3t7dACImllIoAfgeAVq1aycOHD1eZi3RnZGTgl19+wXvvvYd69eq9jEuxiHv37sFm2jRUz8wECQx88L4syzh69CgNCwuDm5sbadiwIRo1asQi4a+/DqxdW/KgGRnMIG3evKe3bd/Oao/7938BV2M5BoMBN2/eRGpqKgRBwLVr1+T8/HyBUgpBEIher4darUbfvn1pu3btyGPPVFGAL74APvkEeO019l5mJnDlCnMInz4dqFGj7ElcvcruR8+ewC+/sIWPRwzsMjIycHrKFNROSaEn33iDtG3bVunVo4dgu3UrE+Vr15YutAHAy4ulmUdHl/se7dy5Ey1OnFBcR40SMGhQuY83R2JiIvbs2ZOXk5OTKcvyr7Is7wJw21LhLUlSUwA33dzcCgYNGmRd7gi3ogB79rCU/PDwEvtl3759G4cOHco3Go2+er1+hiiK98p3Ig6Hw6kYXGxzOBwO538eSZJUALYCeFcsilLGxMQgISEBJ06cAIAhoigeLtpXC2ALgAdKycXFRW9jY6MbMGAA/vjjDyQmJsLZ2ZlOnDiRvCrtrCilWLVqFbIyMtCoenXq0bQpqb1kCU7Mn08vBAcTrVZLR48eTR5zSaeUmXJFRrJezuZIS2OR0E2bnt42bRqrLf7mmxdyTZYQHh6OvXv3Up1OB3t7eyU/P5+0adOGNGzYkFBK4eDg8KA8oMRntX4967m9dCn7eelSYP9+VlO9c2fJfalzcoA//2TGY7a2QJUqTGTXqvXYboqiYN26dUrlypUxbuxYIX/qVOx0dJQbBASoGtSsicJ589C4TZuyL5ZSIDWVnaccRmkAsH37drQ5dEhpNnWqgN69y3Vs6VOiiIuLQ0hISOHt27dlg8EAtVrtW1hYuFgUxaCyjpckqZVWq11hZ2fXfdq0aTYWO5VPm8ba2Z0+zX4282zz8/Nx+PDhgsjIyEyDwTBRFEWvcl0ch8PhPCNcbHM4HA7n/wWSJDUBcKtz586wt7fH4cOHAYCqVKp/f/XVV9+a2d8BQGsAQwGkAfi6eNuoUaNQVpr5y0ZRFCxevBiEEFBKUSMjA23Pn8fJAQPgIcto8/nn5lOz09JYqnNUFODk9PT2jAygTx+WKv4oiYmsLvZvXGxIT0/HunXraK9evdCxY8eKT8RkYi2x/P1ZFFuvZ5HqkBDzfbh9fYEdO4DBg1l6/fjxKE3Aent7K6Ghofj0008FQRBYJoBWi/h69XC8WTMlLi1NmDJlCurWrVv2XEWRRXF37izXJX7//ffKeINBqDV6dMkLK88IpRQhISGIi4tDWFhYgclkGuTp6Xm6rOMkSSI6nc6/bdu2bfv27VvyKgKlzKhv8GDA2pq9SiiBuHPnDv76668CWZY3G43Gzyzpy83hcDjPGy62ORwOh/P/hsWLF7+jKMqfAKDVahfNnz/fomJcSZLqAYgt/nnevHnQlTOy+DL4/fffaWJiIubPn08URUF8fDysjEY4tG4NXLv2ME36SXJyWGS2sJAJmEcpKGD12Pv3P/6+oyOLAE+Z8kKupTQURcHBgwdpWFgYcXV1VQYPHixUtHfzA7y8mBv42LHAkCGAhwfg5vbw+jIygDVr2D5Dh7KU5eHDn3Ipf5Lo6Gjs2LEDU6ZMgaOjIxP2770HdOkCJCUh6ZNPsHbtWnTu3BmvFT2fB/Xc5rh6FTh5Evj8c4svLT09HT///DMWHDoEsnNnuXp1l4eoqChs3boVVlZW+sGDB+v27NkDQshQT0/PQ2UdK0mSi0ajCZs9e7a12X7ssbFAvXrMrG/KFPbVDEajESdPntSHhobmGo3G0aIonnrmC+NwOJwK8oz/M3E4HA6H889h4cKFuwDUAjDUUqFdRGsAmDx5MgRBwKVLl17I/J6VMWPGEFmWyfXr1yEIAurVqweHBg2ArCwmtFu1YinTT1K5MjBzJtCjx9PbBAGwt3/6/atXgcmTn/s1lEVERARWrVql3L17F5MmTcLQoUOfXWgDQMuWzHk9IQHo1g04c4YJ4gMHWMut48fZwsOyZUzsTZxYptDOy8vDrl270KNHDya0jUYWlR0+nNXL79kDhypVUL9+feXy5ct0z5492LFjBw4fPgwfHx9s376dFhQUPD5oq1ZA06bMVdxCQkJCUMvRUSZxceazF54DoaGhypUrVygAFBYW6mrVqgU3NzdQSg9KkmTGfv1xRFGMFgThSFhY2NMbk5NZa7vbt5kZWglCOzExET///HPe1atXTxqNxsZcaHM4nL+b59BrgcPhcDicfw6iKCYBKDPS9gTHVCpVyIEDB1oqiqL18/NDx44dH/R6/ruhlOLChQs0MzOTUEofREgfUCxGJYkJyWPHmGv2o6Jl6VImJovruItRqYBq1R7+bDAArVuzXtDPQ+SWA39/f/j6+qJ79+6kc+fORKPRPL/Ba9cGuncHAgLYIsLt20xkf/stMGkSE8cWOK4XQynF7t27lRo1aqBbt24CcnJYxHzePOYYTwhw6xaEkBBMrltXKF64uHTpEry8vKherycAyIoVK2BnZwcnJyeqUqlogwYNSOtTpwgSEixKJc/NzcXFixfp4B49VJg1q9y13mVx4MCBwvDwcKGgoECtVqtjATgDwOrVq6FWq6MANCCEfAWgTBc9vV4fkJycPAQAm+Tp08C//w2cOwfExJRoVEcpRVBQkHLq1KkCWZZnUkr/4O28OBzOqwAX2xwOh8PhlIEoirIkSR2zsrIOCoLgWlhYWPebb76Bs7Oz3Lp1a1WrVq3+FuF98uRJWRAE1K1bV3X8+HFiZ2cnDxw4UGVrrk8zAIwYwb6ePQskJTGxXVDAUserVwdkmYnOP/9krtoAE9ubNwPffcfEtSyzmmNL6oufIz4+Prhw4QJGjRqFJk2aPHuhuL8/ixBnZACLFgFbtrDrPnkS6NiR3Q9vb8DBoULDBwQEKMnJyZgzZ46AxESWnu7p+XRt9++/s2fx5psAgHbt2qFdu3YEABISErB3716alZVFwsLCCCGE3LhxA7U//hg1HR3Zsyjhc6coCgIDA3Hu3DnatGlTpUXNmirExFToWh7l5s2bOHPmTE7Lli0ry7KMK1euWAGYDCDAZDJFAPAC0AcA1Gp1MKVUlGW5TPdvSZIErVY72NHRUYeICCAujmVj9OvHPnclCG29Xo99+/YVREdH3zOZTANFUYx85ovkcDic5wSv2eZwOBwOp5xIkkQAfEwIaU8pnVC/fn1l8uTJLzXM6+vrK585c+aB0mrZsiXeeuut8g2yfTswaxZzuC5m3z5g0KDHHbjbtAGCglirpbVrgdmzX7ox2pIlS9CzZ09061ZGRnJxZP7gQdbWrHZtlrrt5wfMmQNcusSEdv36wPLlgLs78OGHLFK/fj3www8snf6NN5jIK+89BRAfH4/Nmzdj/PjxqF9QwPo/z5/PsgpKYt8+Fvkupf2Vv78/vL290adPH9pt3jyC998H3n/f7L67du2i9+7dQ9++fUnLli1Brl9njvI//FDu6ynGYDBg/fr1uSkpKft0Ol1zWZYvEkL0RQZkJoD9bhBCblNKXwcAtVq9ecGCBZPMjSdJUmUAg3Q63WBKaVtrKyuXD2fMsNJOmcIWEv78s9T5ZGdnY/PmzXm5ubn7DAbDVFEUCyt8cRwOh/MC4JFtDofD4XDKSVGK6k8AIEnSf2JjY6/8/PPPhhkzZmifS/1wEZRS3Lx5E/Xr14etre2DFlYAoNVqVQCwYMECpKeno4YlvaCfZOxYoH17Vks8cCAT3yNGsN7Fq1YxgQqwCLiiAIGBwM8/M7H9EklNTYUsy0hNSQHS01nUeedO9rV3b8DVlc117VrW4zo+HlixAhg1irmJOzkxY7JPP2VO4wAz3CqGtX9j0dN33wV272Yp5V9/DYwcCeTmsrp2CygsLMTOnTtp+/btSf3kZODHH8sW2pSyRQ+Nhi0MlECxcVhWVpaMGTPUpUXdMzIylC5duqhcXV3ZGxERzASvguj1evzxxx+GzMzMiwA+mDdvngEAJEmaB+DGsmXLIg0Gg68gCNUURTkA4DMAMJlME5csWRK4YMGCXx8dT5IkjUajue7k5FS9WbNmtlWrVEGT4cNBZBnYtq3MxZyEhARs3bo132g0LjGZTN/ytHEOh/MqwsU2h8PhcDjPgCiKoZIk1U1JSYk7efKk3L9/f5XBYIDRaIRZV+VyEBoaigMHDkCj0aB9+/b07t27SEtLI9WrV1dSUlKE2rVrmwRBUNesWbPiJ2ncmBmoWVuz2uwrV1ga9ZAhD/eJiQHy8oCuXVkt84vCYABCQ4EOHYA//mAid8YMCE2bos6wYehZrRrg7Mzc00+cYGnGffsCAwawdOpZs4CPP2ZjnTv3cNzdu9nXsly4L1xgraQaN2btvMaPZ9ddsyaL7LdqVerhlFLs379ftrGxQV9BUGHjRjafTp1KPy8hTPxTymrGu3Qxu5ubmxvq1auHNWvWqFtOmAD98uW0evPmpEa9ek/tazQaH38jJ+eZzNEOHz6sT0hICJVleagoigYAWLx48SQAy+rVq0ednZ0bZWRk9HBwcLBJT08vzMrKym7YsGGVmjVrYu/evT8uWrQo09PT89FQtUpRFIfBffpY1/jqK2D1araQ07VrmUI7IiICu3fvLjCZTJM8PT13V/iiOBwO5wXD08g5HA6Hw3kOSJI0H8BSQRBkRVFUAFC7dm1l4sSJQkXahCUlJWHt2rVwcnJCnz594OXlJVNK0a1bN1VcXBy6deuGEmuzn4U6dVht8fTprEXV+++zCG/TpkxwXrlS8bFNJhaRHjsW2LWLtdH64Qc29pw5TGh17MgE7rffAjk5+LZKFdrC35+0kyTYFBu/PZE9UKlSJTwXs7Rx45hTuJMTm0sx0dFM2I8cyYTzl1+aPTwkJISePHkSn9avT3TXrrH9O3Sw/Pxnz7K2YqmppaaT//bbb6bExET11A0bcKFDB6QOGEBtbGyUYcOGqayLWrctW7YM06ZNg32xk/yxYyyy3b275fN5BH9/f5w9e9Zr/vz5fYvfW7JkSXLPnj0dykrtT0pKwoYNGwqNRmMrURQjit/fOH36mtq9ek0ZsGaNDrt3A7VqlTmPCxcuKN7e3jlGo3GAKIqBFboYDofDeUnwyDaHw+FwOM+H5QDWK4pSA8x5WUlISPgxLS0NThWIKPr6+ipWVlbClClTIAgCpk+f/lh99gvj7l0m9KZMYcJaUViLqwULWOTZHJSyVHSNhonkjz9mrbN++421znJzY2nUK1YAn3zysDa5WCCvWQO0aMHEVn4+e2/+fGRmZkK/ahW52qkTrhenej91agorKyv6wQcfkCpVqlT8ugsKWEr96dNMcD9KcUR8zBi2GHH9OquxXrjwwS7Jyck4fvw4mQxAFx7O0tHLiIQ/RY8erM1VTg5Ldy9BfE6dOpX9/TZ9OupERiLx2jWanp5OfvnlF+rh4UFcXV1BCKGyLD8MEfv6VlhoA4BWqwUhRCn+efHixaMURXEgpUShKaVITk7G6dOn841Gow0AFwBMbBMyfqIgjF1Sq5bO4+RJWD/Z390M3t7ehuDg4CSj0diLG6FxOJx/AlxsczgcDofzHBBFUQGQUvS6KUlSbQA/Vq9evULjOTs7C/fu3VOeTxPpclAsgCdPBt5+m7mO+/iwNN8VK1j98aRJzMV76lTg2jUm4mxtWS/qDRuYWK1d+6HY3LuXuXrb2LDjAFZPPWoU+75Pn8emQCnF9evXcfDgQbz++uvKuHHjSr0H27dv8WAtSAAAIABJREFUp+vXr8f06dNJhVP3Q0NZZNlkKjGNG6NHs69//cVM1oqOMzZvjh07dtBBd++itrMzwXvvMXO2iqDVsog4pcCRI6XvW7ky2g0dinY3bgiKvT0CAwPh6+srHz9+XEUIISkpKXhQYhAU9HhpQDmpXbs2CCFNH3lra//+/dHBTOQ+KSkJQUFBhTdu3KCKouRTSn8CcBpAAAj5BkAdANOyK1c+QAUhu6CgoEyxffHiRSU4ODjZYDC0FUUxtdSdORwO5xWBi20Oh8PhcF4MBQCQl5cHKyurch+cnJxMCwoKXq7QfpTi1OAff2Rp3StXMrEdGMjaMTVrxmqaAcDL62H/5jt3Ho7Rrh372qBBuU7t7+9PT506Rezs7MoU2gAwduxYYcuWLfJ///tfVdeuXZWWLVsKOTk50Gg0xSKx7JOeP8/q1hs1KttI7K232CstDejcGT4//CC7nT5NXN3cBEyc+Oxt0fbsYanySUmAo2PJ+1lbs3T/vDwIDg7o2rUrunbtqrp79y527tyJtLQ0KIoCQRCAXr1Y9kAFMJlM8PX1LaSURhe/p9Fojpw/f36ki4sLqVy5MuLj42E0GhESEpIfExNTSAj53WAwrAbQEsDewQcPUqNGk3ezSZOs2omJN6plZhrtAKN22bKge/fudSxtUery5cv05MmT2UURbS60ORzOPwYutjkcDofDeQGIopgpSRJWr16NN998U+7SpYvFjbhzc3Nx+fJl0rlz5xc5RcuYM4e1YBo0iEVbg4NZOjkhrMYZYKLvGSnq2Yzs7GwEBgaSkSNHwtXV1eLFhgkTJqhu3boFLy8v6ufnB7VaTRVFgSzLpFmzZoqtrS00Go0gCAJu3rypmEwmNG7cGDVr1hRcXV2htrJi0fc6dSyftL09bpw9C81XXwndQ0KI0Lz58+k/bmXFWl81agTs3/90b+5HmTOH7fPBBw/eeu211+Du7q74+/sTWZZpLw8PAXfuPFwQKSfZ2dmIjIzUAfhMkqQZACiAGnq9nmzYsCHRZDJV1+l0NwFkGgyGw7IsrxFFsUCSJFciy4eoIKDhvXuiX48eX50YMIClTkjSXVEUAyill3NzczuWdO5z587JZ8+eTTcajd1EUbxT0n4cDofzKsLFNofD4XA4LwhRFMnixYuTvby8HNzd3aEuxfTqUVKL+l737du3jD1fEoGBD/tXx8QAdnbA0qXA5s0skr1jB2sdVrVqhU+xbds2OTExkWg0GnTq1El40LKqHDRt2hRNmzYtXtQgiqIgKioKwcHBQmpqKvR6PRRFoS1atBCsrKxw69YteuXKFRoQEIBRR4+isHVrcq9SJao/dYo0a9aszFr7jORkJC5ciOZ16xJh/nxW233wIHutX1+Bu/AIKhVw6xYza8vNLTnarlYD//43S8mvVg0Aaz8GQBAEgdrY2BAYDExo29hUaCpRUVEAQDQazeGGDRvaabVaevXqVWsAGfPnz6/96L6SJFkBaLVs2bJp1QoKxn20Zg0MAQEIGzhQGNiunUCPHFEuX74sCILwFYCBKpXK3VzbOkopTp06ZQwODr5fJLTjKjR5DofD+RvhYpvD4XA4nBeIoijdAIRnZ2fD0vrtqkWi1WQyWSzQXyi//Qb8/jtrS1UsjBYuZKZplDJ37rp1maALCGDR1nKQkpKC6Oho1YQJE+BSVnuuciAIAl5//XW8/nj99IOccnd3d5KZmYkAX1+kZmZS+y1bECGKJCM0FOfOncOgQYNgY2ODevXqofITfbblnBzcmjaNOmq1qPXTT+RBdP/mTSZ6KWX3bNKkp9zTLcbJiQn3SZOYQ/kj46Snp8NoNEKWZVQNCEClIjEeERGBXbt2oUaNGsobb7whtG3bFggPZ68K4uvrKwNQ9enTx9Hd3V0ghKBdu3bYtm1briRJKgAqAG01Gs0IlUr1cYOcHKWLv7910qJFqnMqVeGZvXutAODosWMAIACAoih9JUlqb2Vl1axhw4bmzmkMDg6ONxgMHXjqOIfD+afyCvwPzuFwOBzO/zQRGo3GZ82aNR5arVYeN26cpm4ZqcaKwkyfk5KSUKc8ac0viu7dmSv5kxSLv9hY9vX335nr9Zw5rG3YF1+U6cidm5uLEydOwNHRUXFxcXnpNerVqlXDQK0W0OkI3ngDk6dOBQDs3btXDgwMpLIsIy8vT92hQwflzTffFAghQFYWIj7/nKqys9Hs2DHyWHp2v37sFR/PzOQGD2Zp4RV1Sh86lBm3UcqegSAgPDwcu3btAiEEKpUKzuHh6Ovri7t79sDPz0/p3r076dGjx8N7mZEBtG1b4Xs0efJkla2tLXQ6nQAAd+/exdGjR3MMBkM9AHPVavUntra2tvUcHFR9XF1tMkJDqf7gQXrm9OnAAq12E4BQQkhdSukeAL4ARAAhgiB82b59e+2TC0p+fn6m8+fPJxoMhk5caHM4nH8yXGxzOBwOh/MCEUWRSpL0JoB2JpNp6oYNG6Y2aNBAHxUVpXN3d1dee+01oXHjxlCpHpZ0FxuQp6envxpiu2lTIDKS1WprtSXvN3kye1EKZGWx1OetW1mbrL17H9tVr9fj559/RnZ2NmrUqCGPGjXK4pr2587NmywFfvbsB2+NHDnywXzi4uKwbds2olar4dGiBcimTUiKiCAumzdDKKkOuk4dZnAGAPXqsZZnc+dWbH4tWwL9+wP29jg6bhy9cuUK6devH3V3dycAkBEbi5xp0+j5gABqa2uL9u3bP+4IZ2XF2opVkOJe3Tt37jQlJSXpMzMzKwGoTAgJU6vV4htvvKFxd3cXMHgwCrKzsa1fv0LTmDF9xPnzAwFg6dKlK62tradlZ2cDwJ+iKJ4FgBUrVnSoWbPmY889ICBA9vPzSzEajZ1FUUyq8KQ5HA7nFUD19ddf/91z4HA4HA7nfxoPDw/q4eFxv0ePHofOnDmTmJGR4UcIyUlISEi6detWpcDAQKvz58+bMjIyDI0bN9ZYWVlBp9PRw4cPk/j4eKVVq1YW2Gm/YHr3Bjp2BJydy96XENZn2s6OOZkDrJ3W668DLVrg/9i78+goqrQN4E91d1V3FkgAWWJACOiwBAQEEQQdBEQWRUBQQcUFP3dn3BfEFMUiLjPjDC7oMIjIqgLKIgqyCCqC7CEkwRD2QBISsne6a+n6/iiCLAlk6U4HeH7neJJUV937NjPnJG/fe9/XbNYM69avR0pKCvr06YO77rrLFn6hCuCBdOiQ1Rpr2DAr5rPUrl0bjRs3Fn6ZN8+M+flnQXQ4MLdFC/Tr1++MD0nKdP/91vv/17+s/0paiFVEx46YV1Rk7Nq/3/boo4+iVatWp/4/ERIRgYh27YQuLpfQ6e67BbGkfVuJNWuAo0eBKhTc++GHH9SdO3eKHo9nHIDeANCsWbOI0Y884mz28MMCJAl4/XV84vUWuouLH5RleSUAKIrSz+VyvdeoUSNHYWHhT4ZhPN+zZ0/zrbfeelLX9Sd69erlCD15lvznn3/W169ff1zTtK48o01ElwKubBMREVUjWZY/Pf1nRVEa6bo+0Ov19tm5c+ewHj16oHbt2ujWrZuQmJjokyQpeO2/Trd5M1DSs7kibrzR+s80gddeg9q+PX675Ra0S0xE5pQpZvfY2OB+kGCawNdfAz17WgXOytBM19Fj924zo3Fjs+Cpp2zSwoWmJEnli73k361fP6uVl8djbbH/5z/Pv1PgNOuyspDm9drf/PBD2O64w+pjfrr4eGu822479+F9+4BK9nsv0bBhw5K/Gd8KCQnxPPn4465ac+c64XRaleq7dUOOYSC/sBCmaS4CAEVRokVRnNGpU6eQ3377LV/X9cGyLBuKooTZ7fb3R48e7axXrx5M08RPP/2kbdy48djJFe2jVQqWiKiGYLJNREQURLIspwOYrijKNzabLfbDDz9soet6aP/+/ZGenm67rbTkKRi+/96qRK4olXteEIBHH8Xu7dux/qabkN61q2/EiBE2hIcDc+daq+YFBdbqd3UqKLCqfjdsaMVYmsREYNo0NBs40PZxTg6Gaho8Ho+wZ88etGzZsvxzdehg/bd/P/DLL9YZ7Ph4a5v4WUXUMjIy8Mcff8Bms2Hbtm1GYWGhfcTIkbBdfTXQvv25Y997L9C3r9Uy7OzV9vBwoGvX8sdZio4dO9o6duwIVVXhME2XLTsbmDjRqkL/8ssAgCO7dsHhcOzUdV2YOHHic6IovtmlS5fQsLAw0+FwrHnjjTcKTw6ntGjRwog6+YHBhg0bjI0bNx5VVfUGbh0noktJzfi0nIiI6DIny/IJVVWX6boeKgiCuWbNGjMqKkq/UDG1amOz/bklvJJM08SSJUvgCAvDnYpi/Q1y9Ki1Mjpz5p+rsjNnWtW3q8OmTVb7rLLarO3eDUyeDNx2G+o89RR69uyJr776CiEhIb5Kb32PiQG2b7eqt/fvD3z6qbXCflJeXh6mT5+OxMRE365du3zt27e3Pffcc2jWrBlw993A0qVAu3ZnPANRBN5+G/jb386dLzOzcnGWQvr6a9iuugpo1Ag4cuSMYwUnC52FS5L0vyuuuGLiQw89VKdPnz7O1NTUQo/HU7La3cfpdD59yy23hAJAfHy8uW7duhxVVXsw0SaiSw1XtomIiGoIh8PRu3HjxvqgQYMckVbP5Jrze/rBB6ucACckJEAQBAwYMAAul8u6WFKl+5VXTq2QYvx44KqrgLAwYPVqq6p3oOzebSWMAwee+1p8vNXD+s03rZV3AN26dcOJEyfQv39/W5XbspX0LRcE+B55BN7UVHwyZIhRWFhoj42NNU4v0naG3r2BY8fOXYm/4w7r/ZytXr1zt51X1P/+B+TnA6NHA7GxpbYzS09PNz0eT/u6dete8/DDD4c6TxaPO1ld36soiuB0Ot/u37+/q1GjRkhNTcWyZcsKNU3ryTPaRHQpqjm/xImIiC5zNputqHbt2kZkZGTN+/2saUCzZlaSV4k2VpqmYdGiRYiKikL70rZBA38mj6mp1tc5c6yz4oBVcO2FF4Drr6947Ofj9VqrwmcXFYuPB554wmpndtpWcZvNhjvuuMNv0+sAfvn5Z+yMijLruFxCx0aNhO7//S8cL79cduW12rWBp56yCq+1bm31OwesInb5+da/3+m9qydNst5LZRw6ZBWNS0+3tqdHRFhb4UvRpUsXQRRFtG3b9lSifZIJQATwDwDXxsbG4uDBg/jyyy/dmqYNlGW5lE8IiIguftxGTkREVEN4vd5hu3fv9iQlJQU7lHNJEjBv3rlJaTm53W4AQIcyErVS3Xffny3DDMMq8jV3bumr0JVhmtZ4ffqcef3334FHHrFallXkTHYF+Xw+/Oc//zF27dpl9n3gAeGBjz9Gz27dbGKjRhCcTuvDhsLCsgfo0wfo3v3Ma4sWWRXPS+TnWwl5Zba8mybQubP1v/vYsRfcYRAWFoYePXrg5K6MUxwOhw1AJwAvRERE2OPj4zFnzpxiXdfvlGX554oHRkR0cWCyTUREVEPIsnxC1/VbFy5cqHo8nmCHcy5JshLRSjhw4IAJAEWVPff91VfWau1f/vJncty8ObB8uVVorDKOHQP++MNKKEv89JPVE3vNGqtoWgAlJibC5/PZn3nmGaF169YQBMHaPTBvnnXDuHHAqlVWEbfS3uNDD1kr/d26WSvPAPD++9YZ85Lz3MXFwKhRFQssI8P6kCEtzapk/thjlXyHlrCwMNFms50A0DkzM9O2dOlSU9f1O+Pi4lZVaWAiohqOyTYREVENIoriJ4ZhSBs3bqxkBgkoioLJkyebc+bMMdPS0vwX3Jw5fyaCFbR06VKhd+/euOWWW6oWQ+fOwPPPW99PnGit7E6c+GcV84oUA0tIsLZklxQbW7TIGmvdukptlb8QTdPwxx9/4KeffvJNnTq1YPHixWazZs00obQq6IIApKQAgwdbK/xlVaUPCbFWrkvOj9erB9x8s1U9HrCKmCUnly/AEyess9l16wIjR1ofNvih/3lMTIzT6XT+VZblrQBuBlArLi7uxyoPTERUw9W8M2FERESXMV3Xp9tstnXr1q17vkePHihPEa59+/bhyJEjiI+PN/Lz8+0A0KdPHyEtLc03Y8YMoUGDBvqjjz7qsJVS1KpCvvii7PZY53H8+HEYhoHQ0NCqzX+2kSOtr2PGAEOGWN9ffbWVMP71r8Dx41ZbrbKsW2eNIUnWM2vWAMuWASXF26royJEjSEhI0Nq2bSvm5eX5lixZotpstv26rq/RdX0HgGm33377hfflL1hgvZeS7e07dvyZXNtswGefAevXA7NmAdOmWSveYWHW6ydOnNsKrDSqau0S+Ogjaw5ZrvT7Pp3H40Fubi4Mw+gAANw2TkSXEybbRERENUhcXNzHkyZNerZhw4ZFDocj7Hz3qqqK6dOn6ydOnHA0aNDAaNu2rdCxY0eEh4fDbrfj+uuvt/Xq1QvTpk2zf/HFF4iOjkZsbCyuvPLKygWXmgr06wfs3Vuu2w3DwIYNG5Ceng6bzYa250t8q8LhsFanAWs7tdNpJY3/+Y8V8/TpVj/osytyp6RYW9EnT7aemz690ol2SkoK5s6di6ZNm7qbNm0aevToUU9qaqoLwJ5t27Y11DStPoB+siyvAwBFUYbbbDafJEkX/gREkoDoaOt93X+/9X5fecXa7t6kiXVPSMifrdmefRb45BPgppsu/IEDANx1l7VVfeVKa3w/8Hg8WLlypWfXrl2CKIrxpmnO98vAREQXEcE8vUcjERERBd3bb7/9qdfrfeyuu+4qNUH1+XxYvXo1tm7dakZFRZlDhgyx1T7PtufDhw/jl19+MfLy8oSMjAxbmzZtMHz48IoHpqpWFexp0y64wp2QkODbvn27cOjQIcHlchk33HCDvUePHhWfsypM04qzdWsr8a5bF1iyxGotZppWZe0BA6xWVi+9ZCWslXDkyBFMnz4dACCK4lRN054EMA/ATwC+kmU5V1EUUZZlDQAURWngcDhSRo0aVbtJSbJcEbpubZ+fNs2KOSrqz+3ekyYBN94IPPOMVSgtNdXaWn7PPef+2zz+uLWKHREB1K8PXHFFpd7/2VJTU7Fw4cJiwzC+VFX1FVmWj/tlYCKiiwxXtomIiGoYr9f7hCRJ7TZu3HiDy+WyXV1yHvmkOXPm+NLS0mxDhw4VrrnmGqHUM7+nadKkCUaMGGEHgB9++MG3adMm2/Lly9GoUSOoqgqPxwNVVeH1eqGqKjRNg6qq0HUduq4buq6bhmHAMAzUrVtXKBw/XihwuQS73W4+9dRTtpLt4cXFxdiyZQtSU1ONQ4cO2SMjIzFixAg0b968HPuYA6Dk36WkuvuCBUBiovV99+7Wam6tWsCLL1rXmze3zmrbbBXaLh8VFYXmzZtj37598Pl8TQG4ZFn2nn7PaYm24HQ6Z1133XWuSiXagLWyvWmT9f2AAdY28SVLrJ9TU62k+YcfgEaNrPcYE3Pm8/Hx1k6A/HyrpVvr1pWL4yyqqmLFihXeXbt2FWmadrcsy6v9MjAR0UWKyTYREVENI8uyqSjKv9PS0r5MSUnxXn311aeaFmdlZeHw4cO2IUOG4C9/+UuFx77ttttsqqr6UlNThX379pmiKEIURVMURUiSBEmSzNq1awuSJNkkSRJEUbSfvAeiKKLZgw/Ce+ONcL/yClatWoXPPvvMeOaZZ+wAkJSUhHXr1qFjx472AQMGoEGDBn78V/GDYcOsLdOffGIlpVdfDRw9ap1rvv9+qxCZ3Q588IGVrD77rLWtfOpU4OmnrarckmS9pijAokXIFkWsUdUi89dfQ0IaNrTpdvuArps2RQAoq1LbrS6Xq3uvXr0kv7yn5cutXuELFlh9yA8etNqkvfgi8PXXVqXyv//9z/sPHbJWvhMTgfn+29l99OhRzJ8/3+31epdpmvaELMs5fhuciOgixW3kRERENZCiKLEAEqKjo91t27Z1dO3aVQKA6dOn++rXr49BgwYFp6PI4cPWanBkJIqLi/GPf/wDHTp08N1xxx22RYsWGcXFxfb77rsvKKGdl65b264//dRKpqOjrZXs0ng8Vj/xlBQrkW3aFFi4EOja1WqL9e23MGQZCb16oTA8HImxse67588//mPfvk1bpKZmdtyxIwTAVQCSAfwA4DMAsiqKb/3cu/cXHQThynpLllitvXr1stqZbd5sFXXzeKw2X40bV+z9qarVJqxfvz/P1XfrZq3qL1hgFVUbPx7Yvh1wu/8soOYH+/btw/z58926rj8SFxf3pd8GJiK6yLH1FxERUQ0ky/Juu90+LC0tbfOmTZt8uq7D5/MhIyNDaNasWfB+f2dnA2+8AQAICQnBQw89hJ07d9p8Ph+io6PtmZmZRtBiK42uW+3Kpk2zEtA5c6zCYWUl2oC1mm23A61aAe3bA5GRwOjR1tnuXr2AKVNwpLAQ3w4dilV9++JodHTov198senudu2wdMiQWjDN2gDyAHQF8DqAJABTFwwfHuVu2rROnY4drYT6wAGrj/Xu3daZ8j17gL/9DbjhBiupr1cPePVVq8p4t27Arl3Ac89ZK+zZ2dbq9c8/W9fnzrXe19atVuwbN1o9uAsKrBZn110HPPmktT3eT4m2aZrYvn075s+fX6RpWj8m2kREZ+I2ciIiohpq7NixCxVFWel2u5cvXLjwuhYtWoSapilcc801wQvK5wN27jz1Y3R0NOrVq2dOmjRJsNvt0DTNvnv3bsTGxgYvRsAqAPbtt1Zy+c031hbrJ5/02/DR0dF44oknMGPGDEPX9S8B9DMMo65pmn8/Ob8J4MBpjyw49Pbby3r16hVi69LFujJ79p+vlvTRXrDgz/hTU63EubgYaNDAWmG/6SYgL8/638HrtSqQHz1qJdu9e1s9yENCrET+xRet+374ARg0yCqI5ie6rmP+/PnFBw8eLDQM41629CIiOhe3kRMREdVwiqKEASgEgI4dO+qDBg0K7oflqmoVETvZ61nTNCQkJKBx48ZIT0/HkiVLcPfddyNoHwrs329V4j5xAnj9dWtFuhL9wc+nuLgYn3zySbHX613s9XpHAqgPqzDaodLuVxTlCrvdnvbCCy9Ifu83Xhqfz/qQwTCAu+/269CqqmLOnDnu9PT01aqq3lVS/I2IiM7ElW0iIqIaTpblookTJ+649tpr2wc90QaAli2Bd945lcSJooiOHTsCAOrXr4/Dhw9jzZo1vmuuuaZ6t7u73cCDD1qrusuWWe2sbIEJYePGjYbH41mlqupIWZZNlF0QDQAgCMIjrVq1Mqol0QasVf0vvrC++pHH48EXX3zhzs7OXqKq6v2yLNesYwNERDVI8H9hExER0QWJonioUaNGHYIdBwBgyhTrLHMZXC4XnE5n9SXa+/cDH35orV4//DDQp49VNTyAfD6fYJrm0ZOJ9nkpiuKQJOmFLl26VK6Rd0UVFQHr1gETJ/p1Rd/tduPzzz935+XlzVNV9TFZln1+G5yI6BLEZJuIiOgioGnavszMTB9qQnHTVq2s1lFXXVXqyzabDdVyTO3wYeC996zzztHRwBNPANWwcmyaJlJSUop1Xd9UzkeGRUZGhlW6r3ZFvf46cO21Vi9tP/F4PPjss8/cBQUF01RVfb48HzIQEV3ugv8Lm4iIiC7IMIyZ8fHx3rS0tGCHYrWYevnlMl8+fPiwERkZGbj5s7OtCt3vvWcl/DfeaBVAq4ZEOycnB3PmzEFGRkaYaZqzLnS/oig2URQ/6NevX7jg53PjpUpJsSqS33+/34Y0DAPz5s1zFxQUzGOiTURUfky2iYiILgKyLO/QNO3ZGTNmaL/++qsa1GD+7/+svs1lsNls8Hg8/k/ICguBP/6w2mBt2wb8+9/ASy8Bder4faqyrF271pOamgq73f4fWZb1cjxyY2hoqCsmJibgsaGoCHj6aeC776z2ZX6yZMkST3p6+m+qqj7ORJuIqPyYbBMREV0kZFmebhjGsE2bNgU32XY4gI4drUJkpejVq5d93759QmFhoX/m0zRg/Xpg5EhgyRLg99+B998PWPGz82nevLkLAAzDKFdPaVEU7+/UqZN/GltfyPjx1nl1P+4q2Llzp5mcnJypquqdLIZGRFQxTLaJiIguLolut9s2d+7cooKCguBF0bWr1VaqFFFRUYiNjdU/+OADc9u2bVWb56uvgFtuAXTdSiZfesmvyWRFpaamegRBeFOW5d/Kc7/dbu/TtGnTwO8f37DBqhL/2GN+G/LEiRP47rvvPKqqDpJluchvAxMRXSaYbBMREV1EZFneaxhGk/3797szM8/bbSqw3noLcDrLfHnw4MGOwYMHC99//z0++ugjw+erQOFq0wRWrLAqi7vdVpuxXr2ADsEtxp6fn4/k5GTTNM2p5blfUZQ6mqY1iY6ODmxgXq9Vjb1hQ799EGEYBubPn1/k8/lel2V5p18GJSK6zDDZJiIiuogoitJIkqT1giBENm3aNHiBTJhgVf8+j9atW+PFF1+E2+22//rrr+Ub9+efgR9+sIqfDR9u9c3u3t0PAVeNaZqYO3euKgjCAlmWs8v52DUREREeu90e0Niwbh1w663AgAF+G3LVqlVqfn7+RsMwpvhtUCKiywxbfxEREV1cGqqqGnvVVVfp1dJeqyyvvWYV5LoAURRRq1Yt8+jRo+ffSr1rF5CcDMycaSXxq1b5K1K/WL58OTIyMiQA/63AY/VcfixUVqrkZGtVe+pUv/XUTk1NxdatWws1TbuXBdGIiCqPK9tEREQXkZNbejscPnzYd+zYseAFUqcOMHnyBW8rKipCRkaGcFUZPbnh8QAvvgg895x15njRIuD22/0cbNXk5ORgy5YtJT/+rChKebPaXzMzM43ExMTABObzATt3AnfdZfUZ9wOPx4MFCxYUa5o2XJblLL8MSkR0mWKyTUREdJGRZXmnJEm7MjIyKnAQ2s8kCVi5EsgrCT5SAAAgAElEQVTJOe9ttWvXxvDhw7F69WqsWbMGqnqykHphITBmjLVF/IEHgMWLgWuvtcatYXJzc+FyuRIBtLLZbD8A6KQoygV3B8qynK/rep9vv/226PDhw/4PbNEiq4Dcgw/6bchVq1Z5fT7f17Isr/HboERElykm20RERBchr9f7yI8//ugJ2uq2zQYcPlyuglxt2rTBoEGD8PPPP+PjiRPN/DFjoH/2GVC7tpWwd+gAhIdXQ9CV07hxY4ii2Mxms/U1TTMNwGYAvRRFaaYoynmbfMuyvE3X9fu//vprt66Xpy13ORmG1f7sn//025BpaWmIj4/3qKr6vN8GJSK6jDHZJiIiujjtEQThSFZWEHf6Dh8O/O1v5br12hYt8LrTib6mae5avx4fHjqETyIjzaNeb4CDrDpRFDFq1KjQ8PDw90zTHC0IwhEAP9rt9hkATiiKYiqKUmap9Li4uG9VVf1148aN/ulTbZpWpfa33gKaNfPLkD6fD99++22Rrut/k2X5hF8GJSK6zLFAGhER0UVIkqQJjRo1atymTZvgBfH44+dt/wUA0DTrXPHEiZCaN0ebl1+2GePGoU1+PqZMmSJMmzYN3bt3R0REBOrXr4+kpCSYponk5GQjIiLC3rlzZ7Rv37563s95XHHFFXjooYecH330kdcwjP6yLJuKoowAULK1YLuiKHA4HO/puv6eLMvHT3/e6/U+vX79+p3t27cPqVWrVtWCWb3aKk7XrVvVxjnNtm3bzIKCgj2mac7y26BERJc5IaiVTImIiKhS3nnnnX333XdfTOPGjYMXRH4+sH596QXNfD7g6FFg5EggKgr4/HMgJOSMW7Kzs5GUlITNmzebHo9HME0T0dHRhmmaaNq0qd3tdpu7du0STNM0GzRoIHTp0gXt2rWrnvdWhh07dpiLFy8WAMBms/3s8/lu6ty5M66++mq4XC5s3bpVS0lJ+fXVV1+95exn33rrrXdiYmKeHTFiRMi5I5fTiRPA+PHAq69a/65+4PV68f777xd7vd4esixv88ugRETElW0iIqKLkdfrbbxnzx6IooiGDRsGJ4isLCuZzss7s+3Uxo3ABx8A111nnSvu1KnUx+vVq4cePXqgR48ep1f3Pr0ptXDjjTdi06ZNQkpKirl48WIhKioK2dnZqF+/PhwOB0zTREREREDeXmnat28v7N69W7fb7Y5GjRrd1KZNGzRo0ODU65qmiSkpKaV+AqJp2rgDBw7cn5SUFNK6devKBTB5MtC4sd8SbQD45ZdfNADfMdEmIvIvrmwTERFdhBRFuc3hcNwhCMKI5s2bu4YOHRoqBaOSd3Y2UK+e9f2vvwJvvw08/TTg9QKDBvmt9zMAfP3110ZycrJdkiRomgbDMCAIAuLi4vw2R1Xk5eXh008/LfZ4PCPi4uIWl3aPoihdJUla89RTT4VU+EOCzZuBpCRg8GCruJwf5OXl4cMPPyzWdb2lLMsBKJlORHT5YrJNRER0EVMUxSWK4vzY2Njb7rzzTle1B/B//we0aQOIIpCaCnTtCgwbBtjtF362gjRNw5EjR9CsWTN4PB7Mnj3b1HXd9+STT/p/sgoqKCjA//73P3dxcfH4MWPGvHO+eydNmjQxOjr6+VGjRoXabOWsVVtcDDz7rJVo+7EP+aJFi4qTk5M/HDNmzCt+G5SIiACwGjkREdFFTZZlj2EY8Q6HIzi/071eYOJE6/v33wfuuScgiTZgVQWPiYmBIAgICQlBbm6ukJeXZ5s5cyYOHDgQkDnLIysrC9OnT3cXFxe/d6FEGwB0XVfS09P3bNmypfwrHrt2ATExfk20s7KykJSU5NM07S2/DUpERKcw2SYiIrrIiaJYa//+/UZ+fn71T96zJxAdDTzzTLVP/eyzz6J169amx+PBDz/84NN1HUlJSUhISKi2GA4ePIhp06YVFxQUvDRmzJhx5XlGlmXN6/U+sHr1ak9RUdGFH0hOBp54otxt1spr1apVbtM0J8uynOvXgYmICACTbSIiooue1+t9ITc391+zZ892Hzt2DNV6ROzhh63WXoZ/WkhXhMvlwp133mkbPnw4Tpw4YXv33XexePFiLFy4EMePH7/wAFWUkJCAOXPmFGmaNujNN9+cWpFnZVneDWDW2rVrPee90TCAhQuB558Hqtoy7DTHjx/Hvn37dMMw/u23QYmI6Az2cePGBTsGIiIiqoKePXti7dq1P2maFrp9+/b2devWdZ5eITugBAH48kvgzjuB556rnjnPEhISgtatW6NVq1YYOHAgVFU1fvjhBxQVFQm1atVCWFiYX+dzu91Yvny555dffsnRNO2vsiz/Vplx1qxZ89vx48f/3rp1a6nMGKdPB9LTgRdfrErI51i1apUnMzPz/TfffHOFXwcmIqJTmGwTERFdAnr27GnedNNNa9auXeux2+23tGnTpvrae8bEAK1bA3/5S7VNebbQ0FBERkZCEAQ0b97cJgiCuX79eiE+Ph7du3eHUMWq6F6vF3v37sXatWvdy5Yt82VnZ8/RNO12WZYPVXbMnj17Fq9bt87Iz8+/sW3btuI5NxQWWjsHpkwB/NjezO12Y8mSJbqu6yN79uxZjn3sRERUGeyzTUREdGn5es+ePa9+/PHHYbfcckutSvdzroiwMKBlS+C116zWX0EmCAK6d+9uu/766zFt2jTfsmXLbIMGDTrvM6ZpIi8vDzk5OSgsLITb7YZhGDh+/Lhn//79WkFBgcvpdO7weDxzTNOcM3bs2Cx/xOrz+T7du3dv3IkTJ1C3bt3TAwJeeQX46ivgqqv8MdUpW7ZsMex2+5I33ngjw68DExHRGZhsExERXUJkWU5TFKXx8ePH7/jmm2/m1KtXL7RatpTrOrByJTB5sl97a1eFJEno1q2bbc2aNT6cVafGMAwcPHgQ+/btM/bu3VuUnZ3tEgTB7XA4DgJI8/l8x3w+n1fTtBQAPwOIf+WVVzR/xyjLcu6kSZP+sW7dupeHDBkScuqFL7+0Em4/f1iiaRo2bNiger3e8X4dmIiIzsFkm4iI6BIjy7IB4NsJEyY8/b///e8Tm82Gzp07m3369AlcH+7WrYFt24CsLOCKKwI2TUU1bNgQuq6fyv6PHTuGTZs2eRITEwW73b5f07RvDMNYDWCnLMt+Wa2uKF3X/5uUlPRK//794XK5gOPHgdxc6wy8eO7u8qrYtm2bCeCXkwXaiIgogJhsExERXaLefPPNzxVF2WKz2Sb9+uuvgzZu3GgahiHUqVPH53a7vQ6Hw4iKijIzMjJsDRs2tA0dOjQkJCTkwgOXZetW4K9/tRJFR834E+PQoUPwer1Cfn4+li1b5j5w4IDb5/N9YBjGZ2PGjDkS7PgAazfC22+/vWLr1q13dO/e3YYpU4DQUGtrvh8ZhoH169cXe73esX4dmIiISiVUa3sQIiIiCgpFUeoLgnDINE2XzWY75vP5ugNwAugAoADAslGjRiEmJqZqE6WkANdcU/WA/aSoqAhTp06Fx+OB3W7/XlXVIbIse4Md19kURekUGhq6/oWBA0PtixdbPbX9XEU9Pj4ey5cv3/zaa6918evARERUqprxsTMREREFlCzLxwGUtmydrCiKIIriofz8/KpX4nK5gN69gR9/BGy2C98fYCdOnIDX64VhGDAM4/6amGgDgCzLW99XlP1FL78cW/ull/yeaAPAxo0bC7xeb/Ar2BERXSaC/1uQiIiIgkqWZVMQhHVZWVlV3+7WqJG1Bbq42A+RVU1mZiZmzZrl1nV9oCzLgizLJ4Id0/k03759W64kmbjlFr+PnZWVhaysLB+ApX4fnIiISsVkm4iIiKCq6n82bNhgVvl4mSgCS5YACQn+CawKtmzZovp8vn/Jsrw82LFckCC0unXlyps3DB9eGIjht2zZogGYLsuy3yuqExFR6ZhsExEREQAkORyOA8uWLav6knRuLtCzJ5AR3DbOKSkpHsMwvg1qEOU3MalVq/8WFBX5/D2wruvYvn27oWnaVH+PTUREZWOyTURERJBl2a2qavuEhITsxMTEqg1Wpw6QnQ00bOif4CrBNE0UFha6ABwMWhDlJQjPAfj2uzvuyI6IiPB7PZ3k5GTYbLZdsizv9ffYRERUNibbREREBACQZblQVdW7ly5dWuzxeKo2mM0GNG0KZGb6J7gKOnDgAOx2+2EA2UEJoLwEoSmAQQDWucLC7mrZsqXfK6Nt2rSpwOPx/NPf4xIR0fkx2SYiIqJTZFn+zTTNH3777TekpKSg0me4XS5g9GjrDHcQ5OTkQBCE32VZrrk9TgVBAHAvgJeVcePSVVXt0aJFC79OkZeXh/T0dAHAxbKdnojoksFkm4iIiM7g9XqXrV+/HnPnzsX27dvPeE3TNOi6Xr6Bxo4FvvwSKO/9fuR2u6Fp2rFqn7hiRgJoBmAHgF716tXTwsPD/TpBUlKSabfbV9TUlmdERJcyJttERER0tm8EQRhns9ni1q9f787JyQEApKamYvLkyb5PP/3UrWnlKGotCMDEicDWrQEO91xFRUW6YRjBrdB2PoLQCNbfYR/DNA2n0/lghw4davl7mq1btxZ6vd7/+ntcIiK6ML8X4SAiIqKLmyzLOQAUAJg4cWLBJ5988lbXrl3FgwcPagDeyM/Pv3nZsmW3DR48OCQ+Ph61a9dGTEzMuQMJAnD4MODze4HtCzp+/HgxgEPVPnH5PQFAh2nOUhRFdDgcg9q0aSP4c4KsrCzk5eUZAFb7c1wiIiofrmwTERFRmcaOHftvVVU7b9q06auMjIy1pml+rKrqw4mJicYXX3zh/u677w7MnTu34KefftJ8ZSXVUVHA6urL93w+H44cOeIAsLnaJq0Ia1U7BkBJ0bIederUMSIiIvw6zbZt2zQAM2VZNvw6MBERlQtXtomIiOi8ZFlOBHDfaZe8iqJcf+DAgRsBfAMgbOPGjV/v2LGj3fDhw8Oio6MBWP2dDx48CM8rr8DncqGtaZ6sCRZYGzZsMAAkAdgX8MkqShDCYf2bPQTTLLYuCV1iYmJc/pzGMAxs27ZN1zTtY3+OS0RE5cdkm4iIiCpMluVkAMknf8xRFOVGr9c7bObMmTNHjx4dcsUVV2D69Onu3Nzcg4ZhrL/txReH73zttbodBg/2axwejwc5OTmw2+1QVRVJSUn65s2b8zVNG1xDK5EPAbABprmn5ILL5fprdHS05M9J/vjjDwiCkCTL8h/+HJeIiMqPyTYRERFV2cnE9usJEybUmj179gcdO3YMzc7OdmuaFivLspnz3ns3JW3cGIbBg51Vncs0TezevRtr164tyMvLk0RRPGaapkMQBI/P51uvaVqcLMtpfnhb/iUInQD0BfBQySVFUewOh6N7kyZN/DrVrl27ijwezzS/DkpERBXCZJuIiIj85s033/xs/PjxeZs3bx6p6/r0ktXlKc8993+RNtuPTQ8cQHSzZlWaY8WKFZ7t27dnqqr6KIC1Y8eOrf7eYhVl7Z//F4DxMM3Tz1CPqF+/vq1OnTp+m8rn82Hv3r0OAEv8NigREVWYYJo1cYcVERERXWo8Llfmph49QncPHerr1KlTrSuuuAIRERGoV6/eGWe5i4qKkJ6ejry8PNhsNlx55ZWoX78+BEFAfn4+PvjgA7eu61fKspwXxLdTMYLwOICDAFbg5B9fiqJIALyiKKJt27bo1KkTSs67V4bP54Ou60hPT8ecOXOOvv7665UfjIiIqowr20RERFQtXF5vl/pHj6YfP36899q1a++32WzXGIbRNDw8PGTUqFFh+fn5+OWXX9ypqak2SZISTNPcB8BpGEaPsLAw14ABA8IEQYDdbs964403LqZEuw2AngBexmmrHDab7Zk6deoY7dq1sx86dMiYOXOmHQDsdrsJ4NStpmkKp31/6mvJf6dfFwQBpmnCbrcfrZ43R0REZeHKNhEREVUfQZgKYBVMcyEAKIoi2O32jxo0aPB4VlaWW9f110zTnH36qrWiKIIgCLeLojhVVdVoQRDmxsXF3VfmHDWJINgAjACQAdNcVXJZUZT6Dodj/2OPPRZWv359AFbCnJWVBfNk1XabzVbm17O/L/kZAObOnVuQkpLylCzLs4PxlomIyMKVbSIiIqpO2QBOfdIvy7KpKEpcVlZWbU3T3pdleevZD5w8971UUZQVkiS9q6rqouoMuIruBtAPwIOnX5Qk6Z8dOnQQSxJtwFqVPv3nyjBNE4cOHXIAWF+lgYiIqMq4sk1ERETVSxAaAzBgmseCHUpACUI9AI8BWAzTTCy5rCjKdU6n85fnnnsuxOXya3ttZGVlYdq0aVmvv/561bJ2IiKqMluwAyAiIqLLzjwA44IdREBZe7pfBVB8VqItOJ3O6bfeeqvL34k2AOzduxeCIKy68J1ERBRo3EZORERE1e02AMXBDiLAmgDoDKuv9ukGhIaGXt2xY0ehlGeqLDk5Od/r9S4IxNhERFQxTLaJiIioepmmG4LwMQTBA9N8Idjh+J0ghAL4BkBvmOapHuAnV7Xf6d27d7jN5v/NhaZpIiMjQwSwze+DExFRhTHZJiIiomBYAUANdhABMhbAlzDN3LOu93a5XM1at24dkEmzsrLg8/kKARwIyARERFQhPLNNRERE1c80FwM4CkH4a7BD8StB6AkgE8DHp19WFMXudDr/06tXr7BArGoDwL59+yAIwo8nq7cTEVGQMdkmIiKiYHnq5H+XBkFwAXgBwC6YZuHpL9lstqfq1avXtF27dgGb/uDBg4Ver5fF0YiIaghuIyciIqJgeQKmaUIQ7DBNI9jB+MFAADNgmqtPv6goSoTD4Zg0aNCgMKtIeWAcOnQIADYFbAIiIqoQJttEREQUHFai/R6A6wH09OfQiqKEORyO6bquPy3LcrY/xy6VIFwLYDiA585+SRTFsa1atXI0bNgwYNMXFRXB4/HYASQHbBIiIqoQbiMnIiKiYPovgJcDMO5duq7fA+DGAIx9JkGwA7gawNcwzfTTX1IUpSWAp/v06RMSyBCSk5MhiuJ6WZZ9gZyHiIjKj8k2ERERBY9ppgBwQRD+7q8hx48fPxzAzJM//uivcc9jOIARMM2Fp1882errq1tvvdVVu3btgAawefPmAo/H80FAJyEiogphsk1ERETB1hyAX6qST5gwYZRpml+d/LGeLMsef4xbJkEQAQwD8Eopr94kSVKLTp06Be6gNgCv14vjx487AbA4GhFRDcJkm4iIiILLNGcCuAuCUKcqw4wfP/7FkJCQTzp16gSn0/mVLMsn/BRh6axqZzNgFUXbf/bLLpdr/M033xywVl8l9u7dC6fTuU2WZW9AJyIiogphgTQiIiKqCZ4D8DcAMSUXFEUJA1BbluVj53tQURRBEITRoihOvO+++1xbt271er3e6qjK3Q1APQArS4mprdPp7NK+ffuAB7Fjx47C4uLi/wZ8IiIiqhCubBMREVFNMBWnbSVXFKWZw+E47HA4DkyaNOlfiqKU+jeLoiiCJEmfRUZG/nv06NGuqKgoNGvWzClJ0mOKoogBi1YQ6gH4O4B7YJraWTFJkiTN79mzpySKgQsBADweD/bv3y8C+CagExERUYUx2SYiIqLgM00PgKYQhDkn+1Jv6dOnT63Ro0dLuq4/LwhCqS2tBEF4IDw8fPjjjz8e1qBBAwBAbGwsoqOjm4iiOCGAEY8D8AtMM//sFyRJ+k+TJk2a33DDDfYAzg8A2LZtm8/hcHwvy3JuoOciIqKKYbJNRERENUUGAJsoiq+2bt069IYbbnA0bNgQISEhME3zGkVRzmifpShKE7vd/tGwYcPCnE7nqeuCIGDo0KGhNpvtb4qi3Or3KAXhZgAbYJ3XPoOiKDc7HI5Rd911V4h1pDtwfD4ffv31V4/X650U0ImIiKhSmGwTERFRzWCafwAYVf/w4ed69uwZAliJ80svvYS2bdt6JElKUBSlBwAoihIriuKWXr16uaKios4ZKjw8HCNGjAiRJGnxhAkThvgtRkEIA/AogEyYZuHpL51s9TWlb9++oSEhAW2rDQA4cOAAfD7fYVmWtwR8MiIiqjAm20RERFRj7IuJGfbAjBkhdSIjT12z2WwYOnSoa9CgQc1DQkJWvv322wdEUdw8cODA+t26dSuz2GvTpk3x0EMPhTgcjtnjx4+/zU8hXg9r+/jqs18QBGFQSEjI1e3atfPTVOe3a9cuj6qqn1fLZEREVGGsRk5EREQ1xvxHH725a9u2Ri+b7YzzzoIgIDY2Fq1atQo5duxY08jISISHh19wvKioKNx///2hs2bNWjR+/PgRcXFxSyodnCBcCyAOwF1nv6QoSh1RFGcMGjQo4K2+AMAwDCQmJsLn83114buJiCgYuLJNRERENYbdbo+KKi62o3VrwOcr7XU0bty4XIl2iSZNmuDBBx8MdTqd8ydNmvSaoigVP0wtCCKAGwFMg2nmnP2y0+n877XXXhsaExNz7rMBkJKSApvN9ocsy/uqZUIiIqowJttERERUIyiKIvh8vhsib7wRGDSo1GS7sqKjo/HEE0+EREREjJUkaY6iKLEVHOI+AB1hmvPOfkFRlOGSJA3o27evs5TnAmLLli2FHo9nSnXNR0REFcdkm4iIiGqKa+12e+1GjRsDsgzMmePXwSMiIvDYY4+FxcbGDnG5XJsnT578j3I9KAhOAC/B2kJ+BkVRWomi+Pm9994bKkmSX+MtS25uLg4ePGgDwC3kREQ1GJNtIiIiqhHsdnv/mJgYCIIAFBQAzz8PFBX5dQ5JkjBo0CDXs88+G+JyuZ6cOHHi+beVW/27ZgAYCdPMOP0lRVFaSJK08tZbb3VdeeWVfo3zfH777TdVEITpsiwXVNukRERUYUy2iYiIqEYQRbFj8+bNQwEADRsC2dlAgFpohYaG4pFHHgmtXbv2m5Ikfa4oiquMW+8BUAggseSCoijChAkTHhVFcUfv3r2jO3fuXG1/TxUXF2Pbtm0+TdPeq645iYiocphsExERUY2gaVpyWlqaeupCTg4QGWl9DYCIiAg8/vjjoTExMcMkSUop6eF9iiA0AtAcwASYpg4AiqI0dDqdv9SrV+/fo0ePDu/SpYvNWvyuHuvXr9dsNttXsiwfrrZJiYioUtj6i4iIiGoE0zRv2L59uzRo0CDrQt26wGefAWFhAZvT6XTi3nvvDd29e3fo8uXLV7zzzjtJHo/nMwBbX3c47pN0fa8ybtxRKMr1DodjuMPheLpz586OXr16SdXR4ut0OTk52LJli67r+phqnZiIiCqFyTYRERHVFM2bNm1qAvhzqfj224G//x344APAEbg/W2JjY9GyZcvQlJSUTomJia31DRt8P/bvH7K7U6cCh8MxOSwszGjZsqWza9euUp06dQIWR1lM08TSpUvdAN6WZTmt2gMgIqIKY7JNRERENYLP55skSdKHAP5soi1JwK+/AgcOAFdfHdD5HQ4HWrdujdbR0aFYsgTmc8+h+3XXRYqiiLAArq6Xx549e5CWlpat6/o7QQ2EiIjKjck2ERER1RSrDx486DBNE6fOQdtsQHx8wM5tlyohAYiJgdCrFyKrb9YyqaqKpUuXulVVfVCWZfXCTxARUU3AAmlERERUI8iyfMTn8+mFhYVnvmAYQJMmwMaNgQ8iPh548klg7NjAz1VOa9eu1QzD+F6W5bXBjoWIiMqPyTYRERHVGHa7Pc/j8Zx9Edi6FejSJbCTmybw4YfApEkBPR9eEZmZmdiyZYvq9XqfDnYsRERUMUy2iYiIqEYxTfPci3/5CzBgALBvX+Amfu894NprgYEDAzdHBS1fvrzI5/ONlWU5I9ixEBFRxdSMj22JiIjosqcoSojNZmsQERFx7ouCADRqBOTlBWby9HRg3jxg+XJrrhrgwIEDOHbsWKHP5/s42LEQEVHFcWWbiIiIagRRFN++5pprdKfTWfoNn39ube/2ev07sc8HvPsuMHcuEBXl37EryTRNfP/994Wqqr7IomhERBcnJttEREQUdIqitBQE4f/uuOOOkPPe2L8/MHu2fyefMcMqwtaypX/HrYI9e/YgLy8vA8C8YMdCRESVw23kREREFFSKoghOp/PTbt26iRfsZ52cDISHn/+eikhLA0QRePppq81YDeDz+bBixYoir9f7N1mWfcGOh4iIKqdm/FYhIiKiy9ntISEhnXv06HHhRYDwcOCWW4A5c6o+a0n18cxMqwBbDZGYmIji4uJ9AL4PdixERFR5TLaJiIgoaBRFiZAk6ZN+/fqF2e328j00bBgQG1v1yffutc5rP/ts1cfyE5/Ph1WrVhV5vd6XZFkupSw7ERFdLJhsExERUdA4nc6Zbdu2rdeyIueln37aWo1OSan8xLm5wFNPASNHAmUVZAuC3bt3w+Px7APwY7BjISKiqmGyTUREREExfvz4AaIo3tqvX7+KZ7sTJwLz51d+8nXrgA4dgPbtKz+Gn/l8PqxevZqr2kRElwgWSCMiIqJqpyhKG1EU5w4ZMiRUFMWKD7BunfXV56t4YbOtW4GPPgJWrqz4vAHEVW0ioksLV7aJiIioyhRFuUJRFKmc93YVRfHX22+/vXbz5s0rN6EgAP36AX//e8Wfff55YOzYys0bIKZplqxqv8xVbSKiSwNXtomIiKhKFEW5AcBGURSnAnjqPPcJTqfzA1EUHx06dKizVatWVZs4Lg5o1Khiz7z7LvDKK8DNN1dtbj/bs2cPPB5PGoCatdxORESVxpVtIiIiqjRFUZoA2AgAmqY1Pt+9Dofj5dq1az/0/PPPVz3RBoDu3YFNm4BFi8p3f0IC8NtvQKdOVZ/bj0zTxE8//VTo9Xrf5Ko2EdGlg8k2ERERVZokSZNOfvsBgMFl3acoSowgCONGjBgRFhIS4r8ANm8Gfv/9wvcZBrBmDfDqq0BUlP/m94O9e/ciJycnF8DCYMdCRET+w2SbiIiIKkVRlI3oGdAAABWWSURBVKamaQ4fMGAAJEkaAKB2Wfc6nc6pPXr0kOrUqePfIN5/H5g0CSgoOP99X3wBJCUBN9zg3/mryDAMLF++vEjTtGdkWTaCHQ8REfkPk20iIiKqFEmS/nX99dfbO3fujA4dOjSWJClJUZT7FUU5o7y4oigxhmH07Nq1qz0ggdx/PzC4zEV14OhRwOu1CqMJQkBCqKwNGzboxcXFv5umuSTYsRARkX8JpsmjQURERFQ6RVGuBZAmy3L26dcnTJjwSK1atT546qmnQiXJKkKempqKNWvWFGZmZgo2m+1rVVU/BnBAkqQvunbt2ueWW24JTGHWjAzA4QDq1Tv3NdMExowB6tcHXnghINNXVk5ODqZOnerWNK2tLMv7gx0PERH5F6uRExERUakURWkPYIcgCM8D+HfJ9QkTJjzgdDo/HDlyZEhJog0ALVq0QIsWLcLz8vKwdevW+3fs2DGsuLhYioqK0nr06BG4vzkaNgQ++wxISQEmTz7ztcOHgT/+ACZMCNj0lWGaJpYuXeo2TXMyE20ioksTk20iIiIqy46TX0+dxVYUpYUoih+PGjUqpEGDBqU+FBERgV69ejl69eoVfvJSufpvV0lY2LlbxPPygNtvB1atsla+a5CdO3eaaWlp6bquvxvsWIiIKDB4ZpuIiIjOoShKx5Pf7nA4HD4AGD9+/ABRFLf07ds3tFFF+1sH2j33AG+8ASQn/3lt6lTg7ruBMj4UCJbc3FwsX77co6rqUFmW1WDHQ0REgVGzPuYlIiKimmLjya+dxowZ41MU5TqHw/HNyJEjpWbNmgUzrrK9/TawcCGQmAisXg3k5wPjxgU7qjMYhoGvv/666OT28Z3BjoeIiAKHK9tERERUGgkAZFn2KYoiiKL4VKtWrfQam2gDQFwcsG0boGlWkt2/PyAFfgd7RaxcuVLNzs7eouv65AvfTUREFzNWIyciIqIzKIpyNYCUkz92FUXx+Tp16tzx0EMPhYaEhAQztAubMweYOBGYMgW49dZgR3OGhIQELFmyJEPTtDayLJ8IdjxERBRY3EZOREREZ7Db7Q/Url3bl5OTYwOwsVWrVsUDBw4McTqdwQ7two4ft9p9degQ7EjOcPToUSxZssStaVo/JtpERJcHJttERER0BofD8dDgwYNtV111VcmlGr6cfZJpAosWWWe369cPdjSn5OfnY/bs2cW6rj8gy/KOCz9BRESXAibbREREdIqiKB1CQkKuaNKkSbBDqZhDh6xq5CtWADVoq7umaZg1a5Zb07S34+LiFgU7HiIiqj4skEZERESnOJ3OF7t27eoUzu5ZXdPNng24XDUq0TZNEwsWLCjOz8//Ttf1icGOh4iIqhdXtomIiAgAcLLq+B1t27a1BzuWCnn9deC++4DWrYMdyRnWrFmjHTx4cI+qqg/IssyKtERElxmubBMREVGJ9pIkOerUqRPsOMovPh5Yvhxo0gSw15zPCOLj481NmzbleL3evrIse4MdDxERVT+ubBMREREAQJKk56+//nrXRbOF/Pvvgexs4PffgRpUKX3Pnj1YtmxZoaZpvWVZPh7seIiIKDi4sk1ERERQFOUqn893d6dOnWrO8vD5GAbw/PNAeHiNSrQPHDiAhQsXFp1MtBOCHQ8REQUPV7aJiIgITqfzf926dXOEh4cHO5QLy8gApk4Ftmyxku0aIiMjA/PmzSvWdX2wLMubgx0PEREFF5NtIiKiy5yiKK2dTmePG2+88eL4u2DCBMDtrlGJtsfjKWnx9VhcXNyqYMdDRETBd3H8UiUiIqJAur1du3Z2URSDHceFTZkCvPCCVRCthiguLsaMGTPcmqbNjIuLmxPseIiIqGbgmW0iIqLLnN1urxcWFlbzM+2ffgJmzAAiI4Ea8sFAUVERpk+fXpSbm/uZqqpPBzseIiKqOZhsExERXeYMw9h17NixwmDHcV4//2wl2D/+CNStG+xoAACFhYWYPn26Oz8/f6qmaX9jL20iIjodt5ETERHRrvT09GDHULbiYuDhh4F33wW6dw92NACAEydOYObMme7i4uJ/jBkzRg52PEREVPMw2SYiIqLkoqIiR0FBAWrVqhXsWM5UUAAsWQKsWwdERwc7GgBWe6/58+cX67r+ytixYz8KdjxERFQzcRs5ERHRZU6WZdXhcCxPSkoKdijneuIJYOVK4Morgx0JAGDLli3m3LlzC1RVHcREm4iIzocr20RERASv1/ttcnLybV26dKk5/bQWLADGj7cqjwtCUEPRdR3Lli3zJiUlpWua1leW5T+CGhAREdV4XNkmIiIiAFh88OBBl67rwY7DsmQJ8NZbQEQEIElBDSU3NxfTpk1zJyUlrVVVtR0TbSIiKg/BNFk4k4iIiIB33nln73333deicePGwQ1k2zagVi3A4QBiYoIaSlJSEr799ttin8+n6Lr+LiuOExFReXEbOREREQEATNPckJaWFtxkOycHeOABYNIkYPDgoIWh6zq+//57765du3I0TbtTluXfgxYMERFdlLiNnIiIiAAAXq933aFDh4qCFoDbDSQlAbNmBTXRzs3NxaefflqUkJCwUtO0Vky0iYioMriyTURERCU2Hz582Be02R9+GBBFYPbsoIVQ0tbLMIxxuq7/k9vGiYiosphsExERUYndbrfbHpR+27/9Brz0EtCuXfXOe5Jpmvj99999q1evLtR1/a64uLhVQQmEiIguGdxGTkRERAAAWZYNSZJ+2b9/f/VO/NVXwOOPA9dcA7hc1Ts3rPPZ33zzjWfNmjUHNE3ryESbiIj8gck2ERERneLxeNYfOXJEq7YJ//gD6NQJmDsXiIystmlL5OfnY9q0aUV79uxZparqtbIs76v2IIiI6JLEZJuIiIhOMU3z6/j4eN0wjMBPlpEBDB9uJdxt2wZ+vrMcPnwYU6dOLT5x4sQ7qqoOkmU5eMXhiIjoksM+20RERHSGd955J37w4MHtWrZsGbhJiouBI0esRHvgwMDNU4atW7eaK1asKNJ1/Z64uLjl1R4AERFd8lggjYiIiM7g8Xg+2Lp16/stW7YMC8gEpmlVHnc6gZkzAzJFWQzDwPLly70JCQkZmqbdKsvyH9UaABERXTaYbBMREdHZvtm/f/+Huq7D4QjAnwqHDgHDhgH9+/t/7PPweDyYO3euOzMzc6OqqkNkWc6v1gCIiOiywjPbREREdAZZlrPsdnvmiRMn/D/4vHlA377A7bcDYYFZOC9NTk4OPv30U3dGRsYXXq+3LxNtIiIKNK5sExER0TlsNltaXl5e4wYNGvhv0PR0oHlzYNasam3xdfjwYcydO7dY07QxY8eO/U+1TUxERJc1rmwTERHROQRB8Pq1InlmJnDbbYCqAl26+G/c8zAMA2vWrNG/+OKLAq/XO4yJNhERVSeubBMREdE5TNNsEBoa6p/BvF6gqAh49lngppv8M+YFeDwezJkzx338+PGtuq7fI8vysWqZmIiI6CQm20RERHQGRVEkm83WvGHDhlUfzDSB0aOBunWBKVOqPl455OTkYNasWUVFRUWzVVV9WpblamgaTkREdCYm20RERHS26yIjIz1Op1Oq8ki5uUC7dsBjj/khrPMzTRObN2/2rVq1ymua5pu6rv9blmUz4BMTERGVgsk2ERERna1948aNq/43wpdfAuPGAQkJgN1e9ajOQ9d1rFixQo2Pjz+iaVp/9s8mIqJgY7JNREREZ4sMDw93/n979xtbVXnAcfx3Tntv+VMLxGKpVKiFODuEWHWYMDNNCNPohgkFWSJZs2C0UzAY2cIL7MmVLDGOZEOTOgdoCClthTAa/pVBIShNWxmUUqwwUdpaSVPsXYH2XnrPuefsBdZB+DMKp1C4388b4Nze53nuO7495z7PDY0QjZ5/hLy4eMBDu7u7W6WlpZHOzs7qWCw217Ks/wzohAAAXANiGwAA/CgUCo03TfPngUDg+gu5s1N66imprEyaNMm/xV3GqVOntGrVqpjneX+zbfsPlmW5AzohAADXiNgGAACSpFAolC6p2XVdZWZmXt8gjnP+PO0XXhjw0G5sbNTmzZujruu+vnTp0lUDOhkAAP1EbAMAgD5/6vvL+PHj+/9uz5Neekl68EHprbf8XNdFbNvWtm3bepuamr63bftXlmUdGrDJAAC4TsQ2AAAJZtmyZfNd1z1sWdb+C6+npKSMnDhxoh5++GENGTKk/wNHIlIwKBUU+LXUS3R2dqqkpCQSiUT+GYvFfmtZ1tkBmwwAgBtAbAMAkEBCodAISauSkpIOSnr0gutGMBh8ZMqUKZo4cWL/By4vl95/X/rsM8kw/FvwBVpbW7Vu3bqobdt/dF23mGO9AACDGbENAEBiyQ4Gg3ZycnLuu+++22DbtuE4zjbDML4aOnTo2JycnP6P6LrS8ePS4sUDFtpffPGFKioqehzHmV1UVFQ5IJMAAOAjYhsAgMQyLScnx545c+awEydOTFm/fr0kTfY8T6dPn5bjOEpO7sd/Dzo7pRkzpIoK6b77BmTBdXV18aqqqtO2bU/n+9kAgNuFeasXAAAAbqpwJBKJDx06VAcPHoz+cO1npmmGkpOTt1dWVvZd0/79+70VK1Z4X3/99eVH8jypsVF64okBCe14PK7KyspYVVXV97ZtP0poAwBuJ8Q2AACJZW97e3tAkqZOnTpUkkzT/I3rukclnQ2Hw3FJqq6udnft2qWsrCyjvLxcGzdujDc0NKinp+d/I73yitTWJr33nu+L7O7u1sqVK3vq6+s/s237p5ZlNfs+CQAAA4jHyAEASCwPDR8+3JY05IEHHtDLL7+spqamN9rb23tGjx49ZNq0aYGqqiq3rq7OLCgo0NixY3XmzBmtWbNGjY2NmjBhgjtv3jxTXV3SN99Iy5b5vsCOjg6tWbMmEovF/uI4ThEboQEAbkfENgAACcQ0zalZWVkpff/OzMxUZmamKemus2fPqqyszO3q6tL8+fOVkZEhSUpLS9PChQuTli9f7kajUTNWWqpgZaW0a5fv6+vbcTwWixUWFRWt9X0CAABuEmIbAIAEEAqF7pc0QdLBcDgclRS88PVwOKzVq1frnnvu0aJFi8zLbZJWUFBglpWVxb9Zvdp8cNEiX7cd9zxPNTU18T179kTi8fjcoqKi7X6ODwDAzUZsAwCQAAzD+LPnefmBQGDduHHjhl34WldXlz7++GMvJydH+fn5V9zPZbRhaMGWLUl/nTHDaxk1yv2l55mGD0d9nTt3Ths3boy0tLQ0O47zrGVZLTc8KAAAtxgbpAEAkABM03xEkgKBwKwnn3wy0He9tbVVxcXFGjNmjJufn3/1ct65U8a992puYaFRW1trnjlz5obX9cP8kZaWltJYLPYIoQ0AuFNwZxsAgAQQj8dnSzowe/bsISkpP35lW/X19W5WVpbx4osvJl11gMWLpWeflT76SGM8TxkZGfGVK1eac+fONe67jmO/YrGYtmzZcu7o0aM9tm3/3rKs9f0eBACAQYw72wAAJIYvJSk1NVWS5Lqutm7dGm9qajKefvrpq9/R/u47aedO6aGHJMOQaZoqLCxMysvLM9auXava2lrP8659w/D29nYVFxdHjh07tsm27fGENgDgTsSdbQAAEkOBJN19993yPE8bNmyIt7a2GoWFhcaoUaOu/K5t26SDB6X6esm8+Hf006dP14QJE1RWVqa2trb4888/nxQIBK4w0PlN0Orq6rzdu3dHHcdht3EAwB2NO9sAACSGtyTJNE19/vnnXnNzs/Hqq6+aVw1tz5PeeUfKzr4ktPtkZ2drwYIFxsmTJ/Xhhx96XV1dl/253t5ebdiwIVpVVXXKtu3HCW0AwJ2O2AYAIDEcl87fXd63b5+mT59uDhs27OrvMAxp925p3ryr/lhqaqoWLFiQNHLkSO+DDz5QOBy+6PW+x8aPHz++0XGc+y3LOnJjHwUAgMGPx8gBAEgMKyT94vDhw+rt7TXy8vKu7V2XOW/7cg4dOqS2tjYjOzs7npqamiSd/154dXW18+mnn/Z6nvf60qVLP7rexQMAcLshtgEASAz/lqRNmzYpNzfXM03zxg/I1vkzsktKStyOjg5z5syZxqRJk5Kk83ezP/nkk0gkEmlyHCffsqxWP+YDAOB2QWwDAJAYJktSIBBQMBjs3blzZyAlJSUpJSVFwWBQfX8GAgENHz5c6enp/3fAhoYGbd++3cvKytLChQuVmpoqz/NUU1MT37Nnz7l4PP6m53krLctyB/zTAQAwyBDbAAAkhq2S3rBtO6WhoaFH0l2maY5ITk5ON01zpGmaaZ7npUWj0cclacmSJbrwPO4LxWIxlZSUuO3t7eZzzz1nTJ482TAMQx0dHdq0aVNPOBz+ynGcWZZlnbiJnw8AgEHF6M+5mAAA4M4WCoV+IuloQUGBsrOzL3m9qalJmzdv9jIyMrxZs2aZaWlpchxHe/futWtra2Ou6y5xXbeYu9kAgERHbAMAgIu8/fbb0Tlz5gzJzc398ZrjOCotLXW//fZb85lnnvHy8vIMwzB06tQplZeX93R3d1f39vb+zrKsk7dw6QAADBo8Rg4AAC5imuY/jhw5Mjc3N9eUpGPHjqmiosJNT0/Xa6+9phEjRhie5+nAgQPejh07ovF4/E3Xdf9uWRa/wQcA4AfENgAAuEg8Ht/R3Nw8y7btlPXr17vNzc3mjBkzjMcee8wwDEPd3d1au3ZttKur66Rt27+2LOvLW71mAAAGGx4jBwAAFwmFQuMktUjS6NGj3Tlz5phpaWlqa2tTa2urXVNT43met8pxnDctyzp3i5cLAMCgRGwDAIBLhEKhEaZpHnVdd4wkBQIBW1KHaZr/6u3tXW5Z1r5bvEQAAAY1YhsAAFxRKBRKkXSXpPGS6tllHACAa0NsAwAAAADgM/NWLwAAAAAAgDsNsQ0AAAAAgM+IbQAAAAAAfEZsAwAAAADgM2IbAAAAAACfEdsAAAAAAPiM2AYAAAAAwGfENgAAAAAAPiO2AQAAAADwGbENAAAAAIDPiG0AAAAAAHxGbAMAAAAA4DNiGwAAAAAAnxHbAAAAAAD4jNgGAAAAAMBnxDYAAAAAAD4jtgEAAAAA8BmxDQAAAACAz4htAAAAAAB8RmwDAAAAAOAzYhsAAAAAAJ8R2wAAAAAA+IzYBgAAAADAZ8Q2AAAAAAA+I7YBAAAAAPAZsQ0AAAAAgM+IbQAAAAAAfEZsAwAAAADgM2IbAAAAAACfEdsAAAAAAPiM2AYAAAAAwGfENgAAAAAAPiO2AQAAAADwGbENAAAAAIDPiG0AAAAAAHxGbAMAAAAA4DNiGwAAAAAAnxHbAAAAAAD4jNgGAAAAAMBnxDYAAAAAAD4jtgEAAAAA8BmxDQAAAACAz/4L0ZkICrqqch4AAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 1440x1080 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\n",
-    "ax = rs_df.plot(edgecolor='grey', facecolor='w')\n",
-    "f,ax = wf.plot(rs_df, ax=ax, \n",
-    "        edge_kws=dict(color='r', linestyle=':', linewidth=1),\n",
-    "        node_kws=dict(marker=''))\n",
-    "ax.set_title('Rio Grande do Sul: Nonplanar Weights')\n",
-    "ax.set_axis_off()\n",
-    "plt.savefig('rioGrandeDoSul.png')\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/objects.inv b/docs/objects.inv
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-<p id="bibtex-bibliography-references-0"><dl class="citation">
-<dt class="bibtex label" id="anselin1996b"><span class="brackets">AS96</span></dt>
-<dd><p>Luc Anselin and Oleg Smirnov. Efficient algorithms for constructing proper higher order spatial lag operators. <em>Journal of Regional Science</em>, 36(1):67–89, 1996. <a class="reference external" href="https://arxiv.org/abs/https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1467-9787.1996.tb01101.x">arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1467-9787.1996.tb01101.x</a>, <a class="reference external" href="https://doi.org/10.1111/j.1467-9787.1996.tb01101.x">doi:10.1111/j.1467-9787.1996.tb01101.x</a>.</p>
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-<dd><p>Sergio J. Rey and Luc Anselin. PySAL: A Python Library of Spatial Analytical Methods. <em>The Review of Regional Studies</em>, 37(1):5–27, 2007.</p>
-</dd>
-<dt class="bibtex label" id="watts1998"><span class="brackets">WS98</span></dt>
-<dd><p>D.J. Watts and S.H. Strogatz. Collective dynamics of ‘small-world’ networks. <em>Nature</em>, 393:440–442, 1998.</p>
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Tutorial"],titleterms:{"class":0,"function":0,Using:106,addit:106,all:107,alpha_shap:[0,14],alpha_shape_auto:15,analysi:104,api:[0,106],arcdist2linear:17,arcdist:16,asshap:18,attach_island:64,avail:[49,107],awar:106,backward:107,basic:108,bbcommon:19,block_weight:65,brute_knn:20,bug:104,build_lattice_shapefil:66,chain:1,cite:104,comb:67,compat:107,comput:0,conda:105,contigu:[0,109],convex_hul:21,core:104,da2w:68,da2wsp:69,data:106,dataset:[106,107,111],develop:[104,105],distanc:0,distance_matrix:22,distanceband:54,exampl:[0,49,50,51,106,111],explain:50,fast_knn:23,fast_threshold:24,fileio:52,fill_diagon:70,from:109,full2w:72,full:71,fuzzy_contigu:73,geodatafram:109,geogrid:25,geointerpol:26,geometri:[0,109],geopanda:109,geotiff:106,get:104,get_angle_between:27,get_bounding_box:28,get_id:74,get_path:51,get_point_at_angle_and_dist:29,get_points_array_from_shapefil:75,get_points_dist:30,get_polygon_point_dist:31,get_polygon_point_intersect:32,get_ray_segment_intersect:33,get_rectangle_point_intersect:34,get_rectangle_rectangle_intersect:35,get_segment_point_dist:36,get_segment_point_intersect:37,get_segments_intersect:38,get_shared_seg:39,grab:107,grid:2,handl:109,harcdist:40,hexlat2w:76,higher_ord:[77,106],higher_order_sp:78,inform:104,instal:105,interfac:0,introduct:104,involv:104,is_clockwis:41,is_collinear:42,kdtree:[0,3],kernel:56,knn:55,lag:0,lag_categor:79,lag_spati:80,larger:108,lat2sw:81,lat2w:82,lattic:109,libpys:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,107,111],librari:104,licens:104,line:4,linear2arcdist:43,lineseg:5,load:106,local:106,locat:0,lonlat:44,maintain:107,mat2l:83,min_threshold_dist:85,min_threshold_dist_from_shapefil:84,neighbor:106,neighbor_equ:86,netcdf:106,netw:87,nonplanar:109,nonplanar_neighbor:88,odw:57,open:53,order:89,other:107,point:[6,108],point_touches_rectangl:45,pointloc:7,polygon:[8,108],polygonloc:9,problem:108,pypi:105,python:104,queen:58,radius_earth_km:10,radius_earth_mil:11,rai:12,raster:[0,106],rectangl:13,refer:[0,110],releas:105,remap_id:90,remot:107,report:104,resourc:106,rook:59,set:[0,108],shape:0,shapefil:109,shimbel:91,spatial:[0,104,111],sphere:0,spint:0,spw_from_gal:92,standalon:0,testdataarrai:93,theoret:0,tolnglat:46,tool:0,toxyz:47,trim:108,tutori:111,updat:107,usag:108,use:107,user:0,util:0,vecw:94,version:105,voronoi:[0,60,108],voronoi_fram:48,w2da:95,w_clip:96,w_differ:97,w_intersect:98,w_local_clust:99,w_subset:100,w_symmetric_differ:101,w_union:102,weight:[0,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,108,109,111],without:109,wsp2da:103,wsp2w:63,wsp:62,xarrai:106}})
\ No newline at end of file
diff --git a/docs/tutorial.html b/docs/tutorial.html
deleted file mode 100644
index 9788868cb..000000000
--- a/docs/tutorial.html
+++ /dev/null
@@ -1,196 +0,0 @@
-<!DOCTYPE html>
-
-<html>
-  <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>libpysal Tutorial &#8212; libpysal v4.4.0 Manual</title>
-    <link rel="stylesheet" href="_static/bootstrap-sphinx.css" type="text/css" />
-    <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
-    <link rel="stylesheet" type="text/css" href="_static/pysal-styles.css" />
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-    <link rel="index" title="Index" href="genindex.html" />
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-<meta http-equiv='X-UA-Compatible' content='IE=edge,chrome=1'>
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-  <div id="navbar" class="navbar navbar-default navbar-fixed-top">
-    <div class="container">
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-        <!-- .btn-navbar is used as the toggle for collapsed navbar content -->
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-        <a class="navbar-brand" href="index.html">
-          libpysal</a>
-        <span class="navbar-text navbar-version pull-left"><b>4.4.0</b></span>
-      </div>
-
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-          <ul class="nav navbar-nav">
-            
-                <li><a href="installation.html">Installation</a></li>
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-                <li><a href="references.html">References</a></li>
-            
-            
-              <li class="dropdown globaltoc-container">
-  <a role="button"
-     id="dLabelGlobalToc"
-     data-toggle="dropdown"
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-     href="index.html">Site <b class="caret"></b></a>
-  <ul class="dropdown-menu globaltoc"
-      role="menu"
-      aria-labelledby="dLabelGlobalToc"><p class="caption"><span class="caption-text">Contents:</span></p>
-<ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="installation.html#installing-released-version">Installing released version</a></li>
-<li class="toctree-l2"><a class="reference internal" href="installation.html#installing-development-version">Installing development version</a></li>
-</ul>
-</li>
-<li class="toctree-l1 current"><a class="current reference internal" href="#">Tutorial</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="#spatial-weights">Spatial Weights</a></li>
-<li class="toctree-l2"><a class="reference internal" href="#example-datasets">Example Datasets</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="api.html">API</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="api.html#spatial-weights">Spatial Weights</a></li>
-<li class="toctree-l2"><a class="reference internal" href="api.html#cg-computational-geometry">cg: Computational Geometry</a></li>
-<li class="toctree-l2"><a class="reference internal" href="api.html#io">io</a></li>
-<li class="toctree-l2"><a class="reference internal" href="api.html#examples">examples</a></li>
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-  <ul class="dropdown-menu localtoc"
-      role="menu"
-      aria-labelledby="dLabelLocalToc"><ul>
-<li><a class="reference internal" href="#">libpysal Tutorial</a><ul>
-<li><a class="reference internal" href="#spatial-weights">Spatial Weights</a></li>
-<li><a class="reference internal" href="#example-datasets">Example Datasets</a></li>
-</ul>
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-      
-  <div class="section" id="libpysal-tutorial">
-<h1>libpysal Tutorial<a class="headerlink" href="#libpysal-tutorial" title="Permalink to this headline">¶</a></h1>
-<div class="section" id="spatial-weights">
-<h2>Spatial Weights<a class="headerlink" href="#spatial-weights" title="Permalink to this headline">¶</a></h2>
-<div class="toctree-wrapper compound">
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="notebooks/weights.html">Weights from GeoDataFrames</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/weights.html#Contiguity-Weights">Contiguity Weights</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="notebooks/weights.html#Weights-from-shapefiles-(without-geopandas)">Weights from shapefiles (without geopandas)</a></li>
-<li class="toctree-l1"><a class="reference internal" href="notebooks/weights.html#Lattice-Weights">Lattice Weights</a></li>
-<li class="toctree-l1"><a class="reference internal" href="notebooks/weights.html#Handling-nonplanar-geometries">Handling nonplanar geometries</a></li>
-<li class="toctree-l1"><a class="reference internal" href="notebooks/voronoi.html">Voronoi Polygons for 2-D Point Sets</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/voronoi.html#Basic-Usage">Basic Usage</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/voronoi.html#Larger-Problem">Larger Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/voronoi.html#Trimming">Trimming</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="notebooks/voronoi.html#Voronoi-Weights">Voronoi Weights</a></li>
-</ul>
-</div>
-</div>
-<div class="section" id="example-datasets">
-<h2>Example Datasets<a class="headerlink" href="#example-datasets" title="Permalink to this headline">¶</a></h2>
-<div class="toctree-wrapper compound">
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="notebooks/examples.html">Datasets for use with libpysal</a></li>
-<li class="toctree-l1"><a class="reference internal" href="notebooks/examples.html#Backwards-compatibility-is-maintained">Backwards compatibility is maintained</a></li>
-<li class="toctree-l1"><a class="reference internal" href="notebooks/examples.html#available-is-updated"><code class="docutils literal notranslate"><span class="pre">available</span></code> is updated</a></li>
-<li class="toctree-l1"><a class="reference internal" href="notebooks/examples.html#Remote-datasets">Remote datasets</a></li>
-<li class="toctree-l1"><a class="reference internal" href="notebooks/examples.html#Other-Remotes">Other Remotes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="notebooks/examples.html#Grabbing-all-the-remotes">Grabbing all the remotes</a></li>
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-     rel="nofollow">Source</a>
-</div>
-      
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-    <p>
-        &copy; Copyright 2018-, pysal developers.<br/>
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\ No newline at end of file
diff --git a/docs/_sources/tutorial.rst.txt b/docs/tutorial.rst
similarity index 100%
rename from docs/_sources/tutorial.rst.txt
rename to docs/tutorial.rst

From 8c3b5aa2f098fe4387488af27819fc3322d31a9c Mon Sep 17 00:00:00 2001
From: James Gaboardi <jgaboardi@gmail.com>
Date: Tue, 2 Feb 2021 13:39:43 -0500
Subject: [PATCH 022/162] trigger docs build only for releases

---
 .github/workflows/build_docs.yml | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/build_docs.yml b/.github/workflows/build_docs.yml
index bbd912026..ff64fb3c5 100644
--- a/.github/workflows/build_docs.yml
+++ b/.github/workflows/build_docs.yml
@@ -2,8 +2,9 @@
  name: Build Docs
  on:
    push:
-     branches:
-     - main
+     # Sequence of patterns matched against refs/tags
+     tags:
+       - 'v*' # Push events to matching v*, i.e. v1.0, v20.15.10
  jobs:
    docs:
      name: Build & Push Docs

From 23d9657070cc8192d2a615269423160d374d83df Mon Sep 17 00:00:00 2001
From: Serge Rey <sjsrey2gmail.com>
Date: Sun, 13 Jun 2021 17:55:56 -0700
Subject: [PATCH 023/162] Return a dataframe with info on available datasets

---
 libpysal/examples/base.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/libpysal/examples/base.py b/libpysal/examples/base.py
index 03af2974a..77193167a 100644
--- a/libpysal/examples/base.py
+++ b/libpysal/examples/base.py
@@ -231,7 +231,7 @@ def explain(self, example_name) -> str:
             print("not available")
 
     def available(self):
-        """Report available datasets."""
+        """Return df of available datasets."""
         datasets = self.datasets
         names = list(datasets.keys())
         names.sort()
@@ -244,7 +244,7 @@ def available(self):
             data=rows, columns=["Name", "Description", "Installed"]
         )
         datasets.style.set_properties(subset=["text"], **{"width": "300px"})
-        print(datasets.to_string(max_colwidth=60))
+        return datasets
 
     def load(self, example_name: str) -> Example:
         """Load example dataset, download if not locally available."""

From 905935678feb3a6d4125315b6e23c58581e9a306 Mon Sep 17 00:00:00 2001
From: Elliott Sales de Andrade <quantum.analyst@gmail.com>
Date: Fri, 18 Jun 2021 00:07:45 -0400
Subject: [PATCH 024/162] Add missing endianness in WK1 reader.

Everything else unpacks in little-endian, except this one call.
---
 libpysal/io/iohandlers/wk1.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/libpysal/io/iohandlers/wk1.py b/libpysal/io/iohandlers/wk1.py
index f6cc32f94..25600a8cb 100644
--- a/libpysal/io/iohandlers/wk1.py
+++ b/libpysal/io/iohandlers/wk1.py
@@ -227,7 +227,7 @@ def _read(self):
 
             if dtype in [13, 14, 16]:
                 self.file.read(1)
-                row, column = struct.unpack("2H", self.file.read(4))
+                row, column = struct.unpack("<2H", self.file.read(4))
                 format, length = "<d", 8
 
                 if dtype == 13:

From 4f3a2d940cf84ee3635f3ca36bbe68573677a715 Mon Sep 17 00:00:00 2001
From: Serge Rey <sjsrey2gmail.com>
Date: Mon, 21 Jun 2021 21:06:23 -0700
Subject: [PATCH 025/162] add test for available

---
 libpysal/examples/tests/test_available.py | 21 +++++++++++++++++++++
 1 file changed, 21 insertions(+)
 create mode 100644 libpysal/examples/tests/test_available.py

diff --git a/libpysal/examples/tests/test_available.py b/libpysal/examples/tests/test_available.py
new file mode 100644
index 000000000..a67708e30
--- /dev/null
+++ b/libpysal/examples/tests/test_available.py
@@ -0,0 +1,21 @@
+#!/usr/bin/env python3
+
+import os
+import unittest
+import numpy as np
+import pandas
+
+from .. import available
+
+class Testavailable(unittest.TestCase):
+
+    def test_available(self):
+        examples = available()
+        self.assertEqual(type(examples), pandas.core.frame.DataFrame)
+
+
+suite = unittest.TestLoader().loadTestsFromTestCase(Testavailable)
+
+if __name__ == "__main__":
+    runner = unittest.TextTestRunner()
+    runner.run(suite)

From 5ca1b9445d3e8606581a79ff198710f0dea0751e Mon Sep 17 00:00:00 2001
From: Serge Rey <sjsrey2gmail.com>
Date: Sun, 27 Jun 2021 08:29:42 -0700
Subject: [PATCH 026/162] Update example notebook for return of dataframe

---
 libpysal/examples/__init__.py    |    7 +-
 libpysal/examples/base.py        |   11 +-
 notebooks/Example Datasets.ipynb | 1032 ++++++++++--------------------
 3 files changed, 349 insertions(+), 701 deletions(-)

diff --git a/libpysal/examples/__init__.py b/libpysal/examples/__init__.py
index 6a05931ae..eb02d9e07 100644
--- a/libpysal/examples/__init__.py
+++ b/libpysal/examples/__init__.py
@@ -2,6 +2,8 @@
     example datasets as well as functions to fetch larger datasets.
 """
 
+
+import pandas as pd
 from .base import example_manager
 from .remotes import datasets as remote_datasets
 from .builtin import datasets as builtin_datasets
@@ -20,7 +22,6 @@ def fetch_all():
     names = list(datasets.keys())
     names.sort()
     for name in names:
-        print(name)
         example = datasets[name]
         try:
             example.download()
@@ -29,8 +30,8 @@ def fetch_all():
     example_manager.add_examples(datasets)
 
 
-def available() -> str:
-    """List available datasets."""
+def available() -> pd.DataFrame:
+    """Return a dataframe with available datasets."""
     fetch_all()
 
     return example_manager.available()
diff --git a/libpysal/examples/base.py b/libpysal/examples/base.py
index 77193167a..2ebbbaea9 100644
--- a/libpysal/examples/base.py
+++ b/libpysal/examples/base.py
@@ -148,7 +148,7 @@ def get_path(self, file_name, verbose=True) -> Union[str, None]:
             if file_name == base_name:
                 return file_path
         if verbose:
-            print("{} is not a file in this example".format(file_name))
+            print(f'{file_name} is not a file in this example.')
         return None
 
     def downloaded(self) -> bool:
@@ -179,9 +179,7 @@ def explain(self) -> None:
     def download(self, path=get_data_home()):
         """Download the files for the example."""
 
-        if self.downloaded():
-            print("Already downloaded")
-        else:
+        if not self.downloaded():
             request = requests.get(self.download_url)
             archive = zipfile.ZipFile(io.BytesIO(request.content))
             target = join(path, self.root)
@@ -253,11 +251,10 @@ def load(self, example_name: str) -> Example:
             if example.installed:
                 return example
             else:
-                "Downloading: {}".format(example_name)
                 example.download()
                 return example
         else:
-            print("Example not available: {}".format(example_name))
+            print(f'Example not available: {example_name}')
             return None
 
     def download_remotes(self):
@@ -271,7 +268,7 @@ def download_remotes(self):
             try:
                 example.download()
             except:
-                print("Example not downloaded: {}".format(name))
+                print(f'Example not downloaded: {name}.')
 
     def get_installed_names(self) -> list:
         """Return names of all currently installed datasets."""
diff --git a/notebooks/Example Datasets.ipynb b/notebooks/Example Datasets.ipynb
index e72e65e14..ed6208903 100644
--- a/notebooks/Example Datasets.ipynb	
+++ b/notebooks/Example Datasets.ipynb	
@@ -46,7 +46,7 @@
     {
      "data": {
       "text/plain": [
-       "'/home/jovyan/libpysal/examples/mexico/mexicojoin.dbf'"
+       "'/home/serge/Dropbox/p/pysal/src/subpackages/libpysal/libpysal/examples/mexico/mexicojoin.dbf'"
       ]
      },
      "execution_count": 2,
@@ -139,114 +139,141 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The function `available` is also available but has been updated to be more informative:"
+    "The function `available` is also available but has been updated to return a Pandas DataFrame. In addition to the builtin datasets, `available` will fetch all available remote datasets that have not already been downloaded, and report if any remotes are unavailable:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "                      Name                                        Description  Installed\n",
-      "0                    10740    Albuquerque, New Mexico, Census 2000 Tract Data       True\n",
-      "1                   AirBnB  Airbnb rentals, socioeconomics, and crime in C...      False\n",
-      "2                  Atlanta       Atlanta, GA region homicide counts and rates      False\n",
-      "3                Baltimore          Baltimore house sales prices and hedonics      False\n",
-      "4                Bostonhsg               Boston housing and neighborhood data      False\n",
-      "5              Buenosaires      Electoral Data for 1999 Argentinean Elections      False\n",
-      "6              Charleston1   2000 Census Tract Data for Charleston, SC MSA...      False\n",
-      "7              Charleston2   1998 and 2001 Zip Code Business Patterns (Cen...      False\n",
-      "8           Chicago Health                   Chicago Health + Socio-Economics      False\n",
-      "9              Chile Labor                 Labor Markets in Chile (1982-2002)      False\n",
-      "10         Chile Migration            Internal Migration in Chile (1977-2002)      False\n",
-      "11              Cincinnati         2008 Cincinnati Crime + Socio-Demographics      False\n",
-      "12               Cleveland       2015 sales prices of homes in Cleveland, OH.      False\n",
-      "13                Columbus                        Columbus neighborhood crime      False\n",
-      "14                  Denver   Demographics and housing in Denver neighborho...      False\n",
-      "15               Elections               2012 and 2016 Presidential Elections      False\n",
-      "16                 Grid100                      Grid with simulated variables      False\n",
-      "17               Groceries                          2015 Chicago supermarkets      False\n",
-      "18                  Guerry          Moral statistics of France (Guerry, 1833)      False\n",
-      "19       Health Indicators                Chicago Health Indicators (2005-11)      False\n",
-      "20                 Health+                    2000 Health, Income + Diversity      False\n",
-      "21                Hickory1   2000 Census Tract Data for Hickory, NC MSA an...      False\n",
-      "22                Hickory2   1998 and 2001 Zip Code Business Patterns (Cen...      False\n",
-      "23              Home Sales              2014-15 Home Sales in King County, WA      False\n",
-      "24                 Houston       Houston, TX region homicide counts and rates      False\n",
-      "25                Juvenile             Cardiff juvenile delinquent residences      False\n",
-      "26                Lansing1   2000 Census Tract Data for Lansing, MI MSA an...      False\n",
-      "27                Lansing2   1998 and 2001 Zip Code Business Patterns (Cen...      False\n",
-      "28                 Laozone   Ozone measures at monitoring stations in Los ...      False\n",
-      "29                LasRosas   Corn yield, fertilizer and field data for pre...      False\n",
-      "30                    Line                                     Line Shapefile       True\n",
-      "31           Liquor Stores                         2015 Chicago Liquor Stores      False\n",
-      "32                 Malaria   Malaria incidence and population (1973, 95, 9...      False\n",
-      "33              Milwaukee1       2000 Census Tract Data for Milwaukee, WI MSA      False\n",
-      "34              Milwaukee2   1998 and 2001 Zip Code Business Patterns (Cen...      False\n",
-      "35                   NCOVR                      US county homicides 1960-1990      False\n",
-      "36                    NDVI        Normalized Difference Vegetation Index grid      False\n",
-      "37                     NYC   Demographic and housing data for New York Cit...      False\n",
-      "38            NYC Earnings              Block-level Earnings in NYC (2002-14)      False\n",
-      "39           NYC Education                               NYC Education (2000)      False\n",
-      "40       NYC Neighborhoods       Demographics for New York City neighborhoods      False\n",
-      "41  NYC Socio-Demographics                 NYC Education + Socio-Demographics      False\n",
-      "42              Natregimes                    NCOVR with regimes (book/PySAL)      False\n",
-      "43                   Nepal   Health, poverty and education indicators for ...      False\n",
-      "44                Ohiolung            Ohio lung cancer data, 1968, 1978, 1988      False\n",
-      "45                Orlando1   2000 Census Tract Data for Orlando, FL MSA an...      False\n",
-      "46                Orlando2   1998 and 2001 Zip Code Business Patterns (Cen...      False\n",
-      "47                  Oz9799                        Monthly ozone data, 1997-99      False\n",
-      "48             Phoenix ACS   Phoenix American Community Survey Data (2010,...      False\n",
-      "49              Pittsburgh                      Pittsburgh homicide locations      False\n",
-      "50                   Point                                    Point Shapefile       True\n",
-      "51                  Police           Police expenditures Mississippi counties      False\n",
-      "52                 Polygon                                  Polygon Shapefile       True\n",
-      "53           Polygon_Holes                 Example to test treatment of holes       True\n",
-      "54       Rio Grande do Sul  Cities of the Brazilian State of Rio Grande do...      False\n",
-      "55                    SIDS            North Carolina county SIDS death counts      False\n",
-      "56                   SIDS2   North Carolina county SIDS death counts and r...      False\n",
-      "57             Sacramento1          2000 Census Tract Data for Sacramento MSA      False\n",
-      "58             Sacramento2   1998 and 2001 Zip Code Business Patterns (Cen...      False\n",
-      "59           SanFran Crime   July-Dec 2012 crime incidents in San Francisc...      False\n",
-      "60               Savannah1   2000 Census Tract Data for Savannah, GA MSA a...      False\n",
-      "61               Savannah2   1998 and 2001 Zip Code Business Patterns (Cen...      False\n",
-      "62                 Scotlip               Male lip cancer in Scotland, 1975-80      False\n",
-      "63                Seattle1   2000 Census Tract Data for Seattle, WA MSA an...      False\n",
-      "64                Seattle2   1998 and 2001 Zip Code Business Patterns (Cen...      False\n",
-      "65                   South             US Southern county homicides 1960-1990      False\n",
-      "66                 StLouis   St Louis region county homicide counts and rates      False\n",
-      "67                  Tampa1   2000 Census Tract Data for Tampa, FL MSA and ...      False\n",
-      "68                  arcgis                               arcgis testing files       True\n",
-      "69                  baltim     Baltimore house sales prices and hedonics 1978       True\n",
-      "70                  berlin  Prenzlauer Berg neighborhood AirBnB data from ...       True\n",
-      "71                    book       Synthetic data to illustrate spatial weights       True\n",
-      "72                 burkitt  Burkitt's lymphoma in the Western Nile distric...       True\n",
-      "73                  calemp         Employment density for California counties       True\n",
-      "74                 chicago                              Chicago neighborhoods       True\n",
-      "75              clearwater                               mgwr testing dataset      False\n",
-      "76                columbus              Columbus neighborhood crime data 1980       True\n",
-      "77                 desmith    Small dataset to illustrate Moran's I statistic       True\n",
-      "78                geodanet        Datasets from geodanet for network analysis       True\n",
-      "79                 georgia  Various socio-economic variables for counties ...       True\n",
-      "80                juvenile    Residences of juvenile offenders in Cardiff, UK       True\n",
-      "81                  mexico  Decennial per capita incomes of Mexican states...       True\n",
-      "82                networks                  Datasets used for network testing       True\n",
-      "83                newHaven                            Network testing dataset      False\n",
-      "84               nyc_bikes                           New York City Bike Trips      False\n",
-      "85                   sids2  North Carolina county SIDS death counts and rates       True\n",
-      "86               snow_maps  Public water pumps and Cholera deaths in Londo...       True\n",
-      "87                     stl  Homicides and selected socio-economic characte...       True\n",
-      "88          street_net_pts                              Street network points       True\n",
-      "89                     taz           Traffic Analysis Zones in So. California      False\n",
-      "90                   tokyo                               Tokyo Mortality data       True\n",
-      "91               us_income  Per-capita income for the lower 48 US states 1...       True\n",
-      "92                virginia                        Virginia counties shapefile       True\n",
-      "93                    wmat          Datasets used for spatial weights testing       True\n"
+      "Example not downloaded: Chicago parcels\n",
+      "Example not downloaded: Chile Migration\n",
+      "Example not downloaded: Spirals\n"
      ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Description</th>\n",
+       "      <th>Installed</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10740</td>\n",
+       "      <td>Albuquerque, New Mexico, Census 2000 Tract Dat...</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>AirBnB</td>\n",
+       "      <td>Airbnb rentals, socioeconomics, and crime in C...</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Atlanta</td>\n",
+       "      <td>Atlanta, GA region homicide counts and rates</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Baltimore</td>\n",
+       "      <td>Baltimore house sales prices and hedonics</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Bostonhsg</td>\n",
+       "      <td>Boston housing and neighborhood data</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>93</th>\n",
+       "      <td>taz</td>\n",
+       "      <td>Traffic Analysis Zones in So. California</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>94</th>\n",
+       "      <td>tokyo</td>\n",
+       "      <td>Tokyo Mortality data</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>us_income</td>\n",
+       "      <td>Per-capita income for the lower 48 US states 1...</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>virginia</td>\n",
+       "      <td>Virginia counties shapefile</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>wmat</td>\n",
+       "      <td>Datasets used for spatial weights testing</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>98 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Name                                        Description  Installed\n",
+       "0       10740  Albuquerque, New Mexico, Census 2000 Tract Dat...       True\n",
+       "1      AirBnB  Airbnb rentals, socioeconomics, and crime in C...       True\n",
+       "2     Atlanta       Atlanta, GA region homicide counts and rates       True\n",
+       "3   Baltimore          Baltimore house sales prices and hedonics       True\n",
+       "4   Bostonhsg               Boston housing and neighborhood data       True\n",
+       "..        ...                                                ...        ...\n",
+       "93        taz           Traffic Analysis Zones in So. California       True\n",
+       "94      tokyo                               Tokyo Mortality data       True\n",
+       "95  us_income  Per-capita income for the lower 48 US states 1...       True\n",
+       "96   virginia                        Virginia counties shapefile       True\n",
+       "97       wmat          Datasets used for spatial weights testing       True\n",
+       "\n",
+       "[98 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -258,21 +285,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We see that the new column `Installed` is added to the tablular output of `available`. This tells the user whether the dataset has aready been installed on the local machine. All the builtin datasets are by defninition installed. The interesting cases are the remote data sets that are new in this release. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Remote datasets\n",
+    "## Working with an example dataset\n",
     "\n",
-    "The listing from `available` above shows that the dataset `Tampa1` is avalable, but has not yet been installed."
+    "`explain` will render maps for an example if available"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -282,17 +302,17 @@
        "        <iframe\n",
        "            width=\"700\"\n",
        "            height=\"350\"\n",
-       "            src=\"https://geodacenter.github.io/data-and-lab/tampa1/\"\n",
+       "            src=\"https://geodacenter.github.io/data-and-lab//tampa1/\"\n",
        "            frameborder=\"0\"\n",
        "            allowfullscreen\n",
        "        ></iframe>\n",
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.IFrame at 0x7f4a3694ecf8>"
+       "<IPython.lib.display.IFrame at 0x7f47603cf700>"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -306,15 +326,7 @@
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Downloading Tampa1 to /home/jovyan/pysal_data/Tampa1\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from libpysal.examples import load_example\n",
     "tampa1 = load_example('Tampa1')"
@@ -329,102 +341,129 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "                      Name                                        Description  Installed\n",
-      "0                    10740    Albuquerque, New Mexico, Census 2000 Tract Data       True\n",
-      "1                   AirBnB  Airbnb rentals, socioeconomics, and crime in C...      False\n",
-      "2                  Atlanta       Atlanta, GA region homicide counts and rates      False\n",
-      "3                Baltimore          Baltimore house sales prices and hedonics      False\n",
-      "4                Bostonhsg               Boston housing and neighborhood data      False\n",
-      "5              Buenosaires      Electoral Data for 1999 Argentinean Elections      False\n",
-      "6              Charleston1   2000 Census Tract Data for Charleston, SC MSA...      False\n",
-      "7              Charleston2   1998 and 2001 Zip Code Business Patterns (Cen...      False\n",
-      "8           Chicago Health                   Chicago Health + Socio-Economics      False\n",
-      "9              Chile Labor                 Labor Markets in Chile (1982-2002)      False\n",
-      "10         Chile Migration            Internal Migration in Chile (1977-2002)      False\n",
-      "11              Cincinnati         2008 Cincinnati Crime + Socio-Demographics      False\n",
-      "12               Cleveland       2015 sales prices of homes in Cleveland, OH.      False\n",
-      "13                Columbus                        Columbus neighborhood crime      False\n",
-      "14                  Denver   Demographics and housing in Denver neighborho...      False\n",
-      "15               Elections               2012 and 2016 Presidential Elections      False\n",
-      "16                 Grid100                      Grid with simulated variables      False\n",
-      "17               Groceries                          2015 Chicago supermarkets      False\n",
-      "18                  Guerry          Moral statistics of France (Guerry, 1833)      False\n",
-      "19       Health Indicators                Chicago Health Indicators (2005-11)      False\n",
-      "20                 Health+                    2000 Health, Income + Diversity      False\n",
-      "21                Hickory1   2000 Census Tract Data for Hickory, NC MSA an...      False\n",
-      "22                Hickory2   1998 and 2001 Zip Code Business Patterns (Cen...      False\n",
-      "23              Home Sales              2014-15 Home Sales in King County, WA      False\n",
-      "24                 Houston       Houston, TX region homicide counts and rates      False\n",
-      "25                Juvenile             Cardiff juvenile delinquent residences      False\n",
-      "26                Lansing1   2000 Census Tract Data for Lansing, MI MSA an...      False\n",
-      "27                Lansing2   1998 and 2001 Zip Code Business Patterns (Cen...      False\n",
-      "28                 Laozone   Ozone measures at monitoring stations in Los ...      False\n",
-      "29                LasRosas   Corn yield, fertilizer and field data for pre...      False\n",
-      "30                    Line                                     Line Shapefile       True\n",
-      "31           Liquor Stores                         2015 Chicago Liquor Stores      False\n",
-      "32                 Malaria   Malaria incidence and population (1973, 95, 9...      False\n",
-      "33              Milwaukee1       2000 Census Tract Data for Milwaukee, WI MSA      False\n",
-      "34              Milwaukee2   1998 and 2001 Zip Code Business Patterns (Cen...      False\n",
-      "35                   NCOVR                      US county homicides 1960-1990      False\n",
-      "36                    NDVI        Normalized Difference Vegetation Index grid      False\n",
-      "37                     NYC   Demographic and housing data for New York Cit...      False\n",
-      "38            NYC Earnings              Block-level Earnings in NYC (2002-14)      False\n",
-      "39           NYC Education                               NYC Education (2000)      False\n",
-      "40       NYC Neighborhoods       Demographics for New York City neighborhoods      False\n",
-      "41  NYC Socio-Demographics                 NYC Education + Socio-Demographics      False\n",
-      "42              Natregimes                    NCOVR with regimes (book/PySAL)      False\n",
-      "43                   Nepal   Health, poverty and education indicators for ...      False\n",
-      "44                Ohiolung            Ohio lung cancer data, 1968, 1978, 1988      False\n",
-      "45                Orlando1   2000 Census Tract Data for Orlando, FL MSA an...      False\n",
-      "46                Orlando2   1998 and 2001 Zip Code Business Patterns (Cen...      False\n",
-      "47                  Oz9799                        Monthly ozone data, 1997-99      False\n",
-      "48             Phoenix ACS   Phoenix American Community Survey Data (2010,...      False\n",
-      "49              Pittsburgh                      Pittsburgh homicide locations      False\n",
-      "50                   Point                                    Point Shapefile       True\n",
-      "51                  Police           Police expenditures Mississippi counties      False\n",
-      "52                 Polygon                                  Polygon Shapefile       True\n",
-      "53           Polygon_Holes                 Example to test treatment of holes       True\n",
-      "54       Rio Grande do Sul  Cities of the Brazilian State of Rio Grande do...      False\n",
-      "55                    SIDS            North Carolina county SIDS death counts      False\n",
-      "56                   SIDS2   North Carolina county SIDS death counts and r...      False\n",
-      "57             Sacramento1          2000 Census Tract Data for Sacramento MSA      False\n",
-      "58             Sacramento2   1998 and 2001 Zip Code Business Patterns (Cen...      False\n",
-      "59           SanFran Crime   July-Dec 2012 crime incidents in San Francisc...      False\n",
-      "60               Savannah1   2000 Census Tract Data for Savannah, GA MSA a...      False\n",
-      "61               Savannah2   1998 and 2001 Zip Code Business Patterns (Cen...      False\n",
-      "62                 Scotlip               Male lip cancer in Scotland, 1975-80      False\n",
-      "63                Seattle1   2000 Census Tract Data for Seattle, WA MSA an...      False\n",
-      "64                Seattle2   1998 and 2001 Zip Code Business Patterns (Cen...      False\n",
-      "65                   South             US Southern county homicides 1960-1990      False\n",
-      "66                 StLouis   St Louis region county homicide counts and rates      False\n",
-      "67                  Tampa1   2000 Census Tract Data for Tampa, FL MSA and ...       True\n",
-      "68                  arcgis                               arcgis testing files       True\n",
-      "69                  baltim     Baltimore house sales prices and hedonics 1978       True\n",
-      "70                  berlin  Prenzlauer Berg neighborhood AirBnB data from ...       True\n",
-      "71                    book       Synthetic data to illustrate spatial weights       True\n",
-      "72                 burkitt  Burkitt's lymphoma in the Western Nile distric...       True\n",
-      "73                  calemp         Employment density for California counties       True\n",
-      "74                 chicago                              Chicago neighborhoods       True\n",
-      "75              clearwater                               mgwr testing dataset      False\n",
-      "76                columbus              Columbus neighborhood crime data 1980       True\n",
-      "77                 desmith    Small dataset to illustrate Moran's I statistic       True\n",
-      "78                geodanet        Datasets from geodanet for network analysis       True\n",
-      "79                 georgia  Various socio-economic variables for counties ...       True\n",
-      "80                juvenile    Residences of juvenile offenders in Cardiff, UK       True\n",
-      "81                  mexico  Decennial per capita incomes of Mexican states...       True\n",
-      "82                networks                  Datasets used for network testing       True\n",
-      "83                newHaven                            Network testing dataset      False\n",
-      "84               nyc_bikes                           New York City Bike Trips      False\n",
-      "85                   sids2  North Carolina county SIDS death counts and rates       True\n",
-      "86               snow_maps  Public water pumps and Cholera deaths in Londo...       True\n",
-      "87                     stl  Homicides and selected socio-economic characte...       True\n",
-      "88          street_net_pts                              Street network points       True\n",
-      "89                     taz           Traffic Analysis Zones in So. California      False\n",
-      "90                   tokyo                               Tokyo Mortality data       True\n",
-      "91               us_income  Per-capita income for the lower 48 US states 1...       True\n",
-      "92                virginia                        Virginia counties shapefile       True\n",
-      "93                    wmat          Datasets used for spatial weights testing       True\n"
+      "Example not downloaded: Chicago parcels\n",
+      "Example not downloaded: Chile Migration\n",
+      "Example not downloaded: Spirals\n"
      ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Description</th>\n",
+       "      <th>Installed</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10740</td>\n",
+       "      <td>Albuquerque, New Mexico, Census 2000 Tract Dat...</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>AirBnB</td>\n",
+       "      <td>Airbnb rentals, socioeconomics, and crime in C...</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Atlanta</td>\n",
+       "      <td>Atlanta, GA region homicide counts and rates</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Baltimore</td>\n",
+       "      <td>Baltimore house sales prices and hedonics</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Bostonhsg</td>\n",
+       "      <td>Boston housing and neighborhood data</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>93</th>\n",
+       "      <td>taz</td>\n",
+       "      <td>Traffic Analysis Zones in So. California</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>94</th>\n",
+       "      <td>tokyo</td>\n",
+       "      <td>Tokyo Mortality data</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>us_income</td>\n",
+       "      <td>Per-capita income for the lower 48 US states 1...</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>virginia</td>\n",
+       "      <td>Virginia counties shapefile</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>wmat</td>\n",
+       "      <td>Datasets used for spatial weights testing</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>98 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Name                                        Description  Installed\n",
+       "0       10740  Albuquerque, New Mexico, Census 2000 Tract Dat...       True\n",
+       "1      AirBnB  Airbnb rentals, socioeconomics, and crime in C...       True\n",
+       "2     Atlanta       Atlanta, GA region homicide counts and rates       True\n",
+       "3   Baltimore          Baltimore house sales prices and hedonics       True\n",
+       "4   Bostonhsg               Boston housing and neighborhood data       True\n",
+       "..        ...                                                ...        ...\n",
+       "93        taz           Traffic Analysis Zones in So. California       True\n",
+       "94      tokyo                               Tokyo Mortality data       True\n",
+       "95  us_income  Per-capita income for the lower 48 US states 1...       True\n",
+       "96   virginia                        Virginia counties shapefile       True\n",
+       "97       wmat          Datasets used for spatial weights testing       True\n",
+       "\n",
+       "[98 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -459,87 +498,87 @@
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-       " '/home/jovyan/pysal_data/Tampa1/TampaMSA/tampa_counties.kml']"
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+       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.CatItemTypesByParentTypeID.atx',\n",
+       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.CatItemsByType.atx',\n",
+       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.gdbindexes',\n",
+       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.gdbtablx',\n",
+       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.CatRelsByOriginID.atx',\n",
+       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000003.gdbtable',\n",
+       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000002.gdbtablx',\n",
+       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.gdbtable',\n",
+       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.gdbindexes',\n",
+       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.gdbindexes',\n",
+       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000003.gdbindexes',\n",
+       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByDestItemTypeID.atx',\n",
+       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/timestamps',\n",
+       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.gdbtablx',\n",
+       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.TablesByName.atx',\n",
+       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.gdbtablx',\n",
+       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByOriginItemTypeID.atx',\n",
+       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByForwardLabel.atx',\n",
+       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.gdbindexes',\n",
+       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.gdbtable',\n",
+       " '/home/serge/pysal_data/Tampa1/TampaMSA/tampa_counties.mif',\n",
+       " '/home/serge/pysal_data/Tampa1/TampaMSA/tampa_counties.dbf',\n",
+       " '/home/serge/pysal_data/Tampa1/TampaMSA/tampa_counties.xlsx',\n",
+       " '/home/serge/pysal_data/Tampa1/TampaMSA/tampa_final_census2.kml',\n",
+       " '/home/serge/pysal_data/Tampa1/TampaMSA/tampa_final_census2.sbn',\n",
+       " '/home/serge/pysal_data/Tampa1/TampaMSA/tampa_final_census2.prj',\n",
+       " '/home/serge/pysal_data/Tampa1/TampaMSA/tampa_counties.sqlite',\n",
+       " '/home/serge/pysal_data/Tampa1/TampaMSA/tampa_final_census2.geojson',\n",
+       " '/home/serge/pysal_data/Tampa1/TampaMSA/tampa_final_census2.xlsx',\n",
+       " '/home/serge/pysal_data/Tampa1/TampaMSA/tampa_counties.geojson']"
       ]
      },
      "execution_count": 10,
@@ -568,7 +607,7 @@
     {
      "data": {
       "text/plain": [
-       "<libpysal.io.iohandlers.pyShpIO.PurePyShpWrapper at 0x7f4a368ce7b8>"
+       "<libpysal.io.iohandlers.pyShpIO.PurePyShpWrapper at 0x7f474f7d19a0>"
       ]
      },
      "execution_count": 12,
@@ -606,7 +645,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4a2eb25f60>"
+       "<AxesSubplot:>"
       ]
      },
      "execution_count": 15,
@@ -615,7 +654,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -689,399 +728,10 @@
    "cell_type": "code",
    "execution_count": 17,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Downloading Rio Grande do Sul to /home/jovyan/pysal_data/Rio_Grande_do_Sul\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "rio = load_example('Rio Grande do Sul')"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                      Name                                        Description  Installed\n",
-      "0                    10740    Albuquerque, New Mexico, Census 2000 Tract Data       True\n",
-      "1                   AirBnB  Airbnb rentals, socioeconomics, and crime in C...      False\n",
-      "2                  Atlanta       Atlanta, GA region homicide counts and rates      False\n",
-      "3                Baltimore          Baltimore house sales prices and hedonics      False\n",
-      "4                Bostonhsg               Boston housing and neighborhood data      False\n",
-      "5              Buenosaires      Electoral Data for 1999 Argentinean Elections      False\n",
-      "6              Charleston1   2000 Census Tract Data for Charleston, SC MSA...      False\n",
-      "7              Charleston2   1998 and 2001 Zip Code Business Patterns (Cen...      False\n",
-      "8           Chicago Health                   Chicago Health + Socio-Economics      False\n",
-      "9              Chile Labor                 Labor Markets in Chile (1982-2002)      False\n",
-      "10         Chile Migration            Internal Migration in Chile (1977-2002)      False\n",
-      "11              Cincinnati         2008 Cincinnati Crime + Socio-Demographics      False\n",
-      "12               Cleveland       2015 sales prices of homes in Cleveland, OH.      False\n",
-      "13                Columbus                        Columbus neighborhood crime      False\n",
-      "14                  Denver   Demographics and housing in Denver neighborho...      False\n",
-      "15               Elections               2012 and 2016 Presidential Elections      False\n",
-      "16                 Grid100                      Grid with simulated variables      False\n",
-      "17               Groceries                          2015 Chicago supermarkets      False\n",
-      "18                  Guerry          Moral statistics of France (Guerry, 1833)      False\n",
-      "19       Health Indicators                Chicago Health Indicators (2005-11)      False\n",
-      "20                 Health+                    2000 Health, Income + Diversity      False\n",
-      "21                Hickory1   2000 Census Tract Data for Hickory, NC MSA an...      False\n",
-      "22                Hickory2   1998 and 2001 Zip Code Business Patterns (Cen...      False\n",
-      "23              Home Sales              2014-15 Home Sales in King County, WA      False\n",
-      "24                 Houston       Houston, TX region homicide counts and rates      False\n",
-      "25                Juvenile             Cardiff juvenile delinquent residences      False\n",
-      "26                Lansing1   2000 Census Tract Data for Lansing, MI MSA an...      False\n",
-      "27                Lansing2   1998 and 2001 Zip Code Business Patterns (Cen...      False\n",
-      "28                 Laozone   Ozone measures at monitoring stations in Los ...      False\n",
-      "29                LasRosas   Corn yield, fertilizer and field data for pre...      False\n",
-      "30                    Line                                     Line Shapefile       True\n",
-      "31           Liquor Stores                         2015 Chicago Liquor Stores      False\n",
-      "32                 Malaria   Malaria incidence and population (1973, 95, 9...      False\n",
-      "33              Milwaukee1       2000 Census Tract Data for Milwaukee, WI MSA      False\n",
-      "34              Milwaukee2   1998 and 2001 Zip Code Business Patterns (Cen...      False\n",
-      "35                   NCOVR                      US county homicides 1960-1990      False\n",
-      "36                    NDVI        Normalized Difference Vegetation Index grid      False\n",
-      "37                     NYC   Demographic and housing data for New York Cit...      False\n",
-      "38            NYC Earnings              Block-level Earnings in NYC (2002-14)      False\n",
-      "39           NYC Education                               NYC Education (2000)      False\n",
-      "40       NYC Neighborhoods       Demographics for New York City neighborhoods      False\n",
-      "41  NYC Socio-Demographics                 NYC Education + Socio-Demographics      False\n",
-      "42              Natregimes                    NCOVR with regimes (book/PySAL)      False\n",
-      "43                   Nepal   Health, poverty and education indicators for ...      False\n",
-      "44                Ohiolung            Ohio lung cancer data, 1968, 1978, 1988      False\n",
-      "45                Orlando1   2000 Census Tract Data for Orlando, FL MSA an...      False\n",
-      "46                Orlando2   1998 and 2001 Zip Code Business Patterns (Cen...      False\n",
-      "47                  Oz9799                        Monthly ozone data, 1997-99      False\n",
-      "48             Phoenix ACS   Phoenix American Community Survey Data (2010,...      False\n",
-      "49              Pittsburgh                      Pittsburgh homicide locations      False\n",
-      "50                   Point                                    Point Shapefile       True\n",
-      "51                  Police           Police expenditures Mississippi counties      False\n",
-      "52                 Polygon                                  Polygon Shapefile       True\n",
-      "53           Polygon_Holes                 Example to test treatment of holes       True\n",
-      "54       Rio Grande do Sul  Cities of the Brazilian State of Rio Grande do...       True\n",
-      "55                    SIDS            North Carolina county SIDS death counts      False\n",
-      "56                   SIDS2   North Carolina county SIDS death counts and r...      False\n",
-      "57             Sacramento1          2000 Census Tract Data for Sacramento MSA      False\n",
-      "58             Sacramento2   1998 and 2001 Zip Code Business Patterns (Cen...      False\n",
-      "59           SanFran Crime   July-Dec 2012 crime incidents in San Francisc...      False\n",
-      "60               Savannah1   2000 Census Tract Data for Savannah, GA MSA a...      False\n",
-      "61               Savannah2   1998 and 2001 Zip Code Business Patterns (Cen...      False\n",
-      "62                 Scotlip               Male lip cancer in Scotland, 1975-80      False\n",
-      "63                Seattle1   2000 Census Tract Data for Seattle, WA MSA an...      False\n",
-      "64                Seattle2   1998 and 2001 Zip Code Business Patterns (Cen...      False\n",
-      "65                   South             US Southern county homicides 1960-1990      False\n",
-      "66                 StLouis   St Louis region county homicide counts and rates      False\n",
-      "67                  Tampa1   2000 Census Tract Data for Tampa, FL MSA and ...       True\n",
-      "68                  arcgis                               arcgis testing files       True\n",
-      "69                  baltim     Baltimore house sales prices and hedonics 1978       True\n",
-      "70                  berlin  Prenzlauer Berg neighborhood AirBnB data from ...       True\n",
-      "71                    book       Synthetic data to illustrate spatial weights       True\n",
-      "72                 burkitt  Burkitt's lymphoma in the Western Nile distric...       True\n",
-      "73                  calemp         Employment density for California counties       True\n",
-      "74                 chicago                              Chicago neighborhoods       True\n",
-      "75              clearwater                               mgwr testing dataset      False\n",
-      "76                columbus              Columbus neighborhood crime data 1980       True\n",
-      "77                 desmith    Small dataset to illustrate Moran's I statistic       True\n",
-      "78                geodanet        Datasets from geodanet for network analysis       True\n",
-      "79                 georgia  Various socio-economic variables for counties ...       True\n",
-      "80                juvenile    Residences of juvenile offenders in Cardiff, UK       True\n",
-      "81                  mexico  Decennial per capita incomes of Mexican states...       True\n",
-      "82                networks                  Datasets used for network testing       True\n",
-      "83                newHaven                            Network testing dataset      False\n",
-      "84               nyc_bikes                           New York City Bike Trips      False\n",
-      "85                   sids2  North Carolina county SIDS death counts and rates       True\n",
-      "86               snow_maps  Public water pumps and Cholera deaths in Londo...       True\n",
-      "87                     stl  Homicides and selected socio-economic characte...       True\n",
-      "88          street_net_pts                              Street network points       True\n",
-      "89                     taz           Traffic Analysis Zones in So. California      False\n",
-      "90                   tokyo                               Tokyo Mortality data       True\n",
-      "91               us_income  Per-capita income for the lower 48 US states 1...       True\n",
-      "92                virginia                        Virginia counties shapefile       True\n",
-      "93                    wmat          Datasets used for spatial weights testing       True\n"
-     ]
-    }
-   ],
-   "source": [
-    "available()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Grabbing all the remotes\n",
-    "\n",
-    "All the remote datasets can be downloaded with:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "AirBnB\n",
-      "Downloading AirBnB to /home/jovyan/pysal_data/AirBnB\n",
-      "Atlanta\n",
-      "Downloading Atlanta to /home/jovyan/pysal_data/Atlanta\n",
-      "Baltimore\n",
-      "Downloading Baltimore to /home/jovyan/pysal_data/Baltimore\n",
-      "Bostonhsg\n",
-      "Downloading Bostonhsg to /home/jovyan/pysal_data/Bostonhsg\n",
-      "Buenosaires\n",
-      "Downloading Buenosaires to /home/jovyan/pysal_data/Buenosaires\n",
-      "Charleston1\n",
-      "Downloading Charleston1 to /home/jovyan/pysal_data/Charleston1\n",
-      "Charleston2\n",
-      "Downloading Charleston2 to /home/jovyan/pysal_data/Charleston2\n",
-      "Chicago Health\n",
-      "Downloading Chicago Health to /home/jovyan/pysal_data/Chicago_Health\n",
-      "Chile Labor\n",
-      "Downloading Chile Labor to /home/jovyan/pysal_data/Chile_Labor\n",
-      "Chile Migration\n",
-      "Downloading Chile Migration to /home/jovyan/pysal_data/Chile_Migration\n",
-      "Cincinnati\n",
-      "Downloading Cincinnati to /home/jovyan/pysal_data/Cincinnati\n",
-      "Cleveland\n",
-      "Downloading Cleveland to /home/jovyan/pysal_data/Cleveland\n",
-      "Columbus\n",
-      "Downloading Columbus to /home/jovyan/pysal_data/Columbus\n",
-      "Denver\n",
-      "Downloading Denver to /home/jovyan/pysal_data/Denver\n",
-      "Elections\n",
-      "Downloading Elections to /home/jovyan/pysal_data/Elections\n",
-      "Grid100\n",
-      "Downloading Grid100 to /home/jovyan/pysal_data/Grid100\n",
-      "Groceries\n",
-      "Downloading Groceries to /home/jovyan/pysal_data/Groceries\n",
-      "Guerry\n",
-      "Downloading Guerry to /home/jovyan/pysal_data/Guerry\n",
-      "Health Indicators\n",
-      "Downloading Health Indicators to /home/jovyan/pysal_data/Health_Indicators\n",
-      "Health+\n",
-      "Downloading Health+ to /home/jovyan/pysal_data/Health+\n",
-      "Hickory1\n",
-      "Downloading Hickory1 to /home/jovyan/pysal_data/Hickory1\n",
-      "Hickory2\n",
-      "Downloading Hickory2 to /home/jovyan/pysal_data/Hickory2\n",
-      "Home Sales\n",
-      "Downloading Home Sales to /home/jovyan/pysal_data/Home_Sales\n",
-      "Houston\n",
-      "Downloading Houston to /home/jovyan/pysal_data/Houston\n",
-      "Juvenile\n",
-      "Downloading Juvenile to /home/jovyan/pysal_data/Juvenile\n",
-      "Lansing1\n",
-      "Downloading Lansing1 to /home/jovyan/pysal_data/Lansing1\n",
-      "Lansing2\n",
-      "Downloading Lansing2 to /home/jovyan/pysal_data/Lansing2\n",
-      "Laozone\n",
-      "Downloading Laozone to /home/jovyan/pysal_data/Laozone\n",
-      "LasRosas\n",
-      "Downloading LasRosas to /home/jovyan/pysal_data/LasRosas\n",
-      "Liquor Stores\n",
-      "Downloading Liquor Stores to /home/jovyan/pysal_data/Liquor_Stores\n",
-      "Malaria\n",
-      "Downloading Malaria to /home/jovyan/pysal_data/Malaria\n",
-      "Milwaukee1\n",
-      "Downloading Milwaukee1 to /home/jovyan/pysal_data/Milwaukee1\n",
-      "Milwaukee2\n",
-      "Downloading Milwaukee2 to /home/jovyan/pysal_data/Milwaukee2\n",
-      "NCOVR\n",
-      "Downloading NCOVR to /home/jovyan/pysal_data/NCOVR\n",
-      "NDVI\n",
-      "Downloading NDVI to /home/jovyan/pysal_data/NDVI\n",
-      "NYC\n",
-      "Downloading NYC to /home/jovyan/pysal_data/NYC\n",
-      "NYC Earnings\n",
-      "Downloading NYC Earnings to /home/jovyan/pysal_data/NYC_Earnings\n",
-      "NYC Education\n",
-      "Downloading NYC Education to /home/jovyan/pysal_data/NYC_Education\n",
-      "NYC Neighborhoods\n",
-      "Downloading NYC Neighborhoods to /home/jovyan/pysal_data/NYC_Neighborhoods\n",
-      "NYC Socio-Demographics\n",
-      "Downloading NYC Socio-Demographics to /home/jovyan/pysal_data/NYC_Socio-Demographics\n",
-      "Natregimes\n",
-      "Downloading Natregimes to /home/jovyan/pysal_data/Natregimes\n",
-      "Nepal\n",
-      "Downloading Nepal to /home/jovyan/pysal_data/Nepal\n",
-      "Ohiolung\n",
-      "Downloading Ohiolung to /home/jovyan/pysal_data/Ohiolung\n",
-      "Orlando1\n",
-      "Downloading Orlando1 to /home/jovyan/pysal_data/Orlando1\n",
-      "Orlando2\n",
-      "Downloading Orlando2 to /home/jovyan/pysal_data/Orlando2\n",
-      "Oz9799\n",
-      "Downloading Oz9799 to /home/jovyan/pysal_data/Oz9799\n",
-      "Phoenix ACS\n",
-      "Downloading Phoenix ACS to /home/jovyan/pysal_data/Phoenix_ACS\n",
-      "Pittsburgh\n",
-      "Downloading Pittsburgh to /home/jovyan/pysal_data/Pittsburgh\n",
-      "Police\n",
-      "Downloading Police to /home/jovyan/pysal_data/Police\n",
-      "Rio Grande do Sul\n",
-      "Already downloaded\n",
-      "SIDS\n",
-      "Downloading SIDS to /home/jovyan/pysal_data/SIDS\n",
-      "SIDS2\n",
-      "Downloading SIDS2 to /home/jovyan/pysal_data/SIDS2\n",
-      "Sacramento1\n",
-      "Downloading Sacramento1 to /home/jovyan/pysal_data/Sacramento1\n",
-      "Sacramento2\n",
-      "Downloading Sacramento2 to /home/jovyan/pysal_data/Sacramento2\n",
-      "SanFran Crime\n",
-      "Downloading SanFran Crime to /home/jovyan/pysal_data/SanFran_Crime\n",
-      "Savannah1\n",
-      "Downloading Savannah1 to /home/jovyan/pysal_data/Savannah1\n",
-      "Savannah2\n",
-      "Downloading Savannah2 to /home/jovyan/pysal_data/Savannah2\n",
-      "Scotlip\n",
-      "Downloading Scotlip to /home/jovyan/pysal_data/Scotlip\n",
-      "Seattle1\n",
-      "Downloading Seattle1 to /home/jovyan/pysal_data/Seattle1\n",
-      "Seattle2\n",
-      "Downloading Seattle2 to /home/jovyan/pysal_data/Seattle2\n",
-      "South\n",
-      "Downloading South to /home/jovyan/pysal_data/South\n",
-      "StLouis\n",
-      "Downloading StLouis to /home/jovyan/pysal_data/StLouis\n",
-      "Tampa1\n",
-      "Already downloaded\n",
-      "clearwater\n",
-      "Downloading clearwater to /home/jovyan/pysal_data/clearwater\n",
-      "newHaven\n",
-      "Downloading newHaven to /home/jovyan/pysal_data/newHaven\n",
-      "nyc_bikes\n",
-      "Downloading nyc_bikes to /home/jovyan/pysal_data/nyc_bikes\n",
-      "taz\n",
-      "Downloading taz to /home/jovyan/pysal_data/taz\n"
-     ]
-    }
-   ],
-   "source": [
-    "libpysal.examples.fetch_all()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                      Name                                        Description  Installed\n",
-      "0                    10740    Albuquerque, New Mexico, Census 2000 Tract Data       True\n",
-      "1                   AirBnB  Airbnb rentals, socioeconomics, and crime in C...       True\n",
-      "2                  Atlanta       Atlanta, GA region homicide counts and rates       True\n",
-      "3                Baltimore          Baltimore house sales prices and hedonics       True\n",
-      "4                Bostonhsg               Boston housing and neighborhood data       True\n",
-      "5              Buenosaires      Electoral Data for 1999 Argentinean Elections       True\n",
-      "6              Charleston1   2000 Census Tract Data for Charleston, SC MSA...       True\n",
-      "7              Charleston2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "8           Chicago Health                   Chicago Health + Socio-Economics       True\n",
-      "9              Chile Labor                 Labor Markets in Chile (1982-2002)       True\n",
-      "10         Chile Migration            Internal Migration in Chile (1977-2002)       True\n",
-      "11              Cincinnati         2008 Cincinnati Crime + Socio-Demographics       True\n",
-      "12               Cleveland       2015 sales prices of homes in Cleveland, OH.       True\n",
-      "13                Columbus                        Columbus neighborhood crime       True\n",
-      "14                  Denver   Demographics and housing in Denver neighborho...       True\n",
-      "15               Elections               2012 and 2016 Presidential Elections       True\n",
-      "16                 Grid100                      Grid with simulated variables       True\n",
-      "17               Groceries                          2015 Chicago supermarkets       True\n",
-      "18                  Guerry          Moral statistics of France (Guerry, 1833)       True\n",
-      "19       Health Indicators                Chicago Health Indicators (2005-11)       True\n",
-      "20                 Health+                    2000 Health, Income + Diversity       True\n",
-      "21                Hickory1   2000 Census Tract Data for Hickory, NC MSA an...       True\n",
-      "22                Hickory2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "23              Home Sales              2014-15 Home Sales in King County, WA       True\n",
-      "24                 Houston       Houston, TX region homicide counts and rates       True\n",
-      "25                Juvenile             Cardiff juvenile delinquent residences       True\n",
-      "26                Lansing1   2000 Census Tract Data for Lansing, MI MSA an...       True\n",
-      "27                Lansing2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "28                 Laozone   Ozone measures at monitoring stations in Los ...       True\n",
-      "29                LasRosas   Corn yield, fertilizer and field data for pre...       True\n",
-      "30                    Line                                     Line Shapefile       True\n",
-      "31           Liquor Stores                         2015 Chicago Liquor Stores       True\n",
-      "32                 Malaria   Malaria incidence and population (1973, 95, 9...       True\n",
-      "33              Milwaukee1       2000 Census Tract Data for Milwaukee, WI MSA       True\n",
-      "34              Milwaukee2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "35                   NCOVR                      US county homicides 1960-1990       True\n",
-      "36                    NDVI        Normalized Difference Vegetation Index grid       True\n",
-      "37                     NYC   Demographic and housing data for New York Cit...       True\n",
-      "38            NYC Earnings              Block-level Earnings in NYC (2002-14)       True\n",
-      "39           NYC Education                               NYC Education (2000)       True\n",
-      "40       NYC Neighborhoods       Demographics for New York City neighborhoods       True\n",
-      "41  NYC Socio-Demographics                 NYC Education + Socio-Demographics       True\n",
-      "42              Natregimes                    NCOVR with regimes (book/PySAL)       True\n",
-      "43                   Nepal   Health, poverty and education indicators for ...       True\n",
-      "44                Ohiolung            Ohio lung cancer data, 1968, 1978, 1988       True\n",
-      "45                Orlando1   2000 Census Tract Data for Orlando, FL MSA an...       True\n",
-      "46                Orlando2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "47                  Oz9799                        Monthly ozone data, 1997-99       True\n",
-      "48             Phoenix ACS   Phoenix American Community Survey Data (2010,...       True\n",
-      "49              Pittsburgh                      Pittsburgh homicide locations       True\n",
-      "50                   Point                                    Point Shapefile       True\n",
-      "51                  Police           Police expenditures Mississippi counties       True\n",
-      "52                 Polygon                                  Polygon Shapefile       True\n",
-      "53           Polygon_Holes                 Example to test treatment of holes       True\n",
-      "54       Rio Grande do Sul  Cities of the Brazilian State of Rio Grande do...       True\n",
-      "55                    SIDS            North Carolina county SIDS death counts       True\n",
-      "56                   SIDS2   North Carolina county SIDS death counts and r...       True\n",
-      "57             Sacramento1          2000 Census Tract Data for Sacramento MSA       True\n",
-      "58             Sacramento2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "59           SanFran Crime   July-Dec 2012 crime incidents in San Francisc...       True\n",
-      "60               Savannah1   2000 Census Tract Data for Savannah, GA MSA a...       True\n",
-      "61               Savannah2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "62                 Scotlip               Male lip cancer in Scotland, 1975-80       True\n",
-      "63                Seattle1   2000 Census Tract Data for Seattle, WA MSA an...       True\n",
-      "64                Seattle2   1998 and 2001 Zip Code Business Patterns (Cen...       True\n",
-      "65                   South             US Southern county homicides 1960-1990       True\n",
-      "66                 StLouis   St Louis region county homicide counts and rates       True\n",
-      "67                  Tampa1   2000 Census Tract Data for Tampa, FL MSA and ...       True\n",
-      "68                  arcgis                               arcgis testing files       True\n",
-      "69                  baltim     Baltimore house sales prices and hedonics 1978       True\n",
-      "70                  berlin  Prenzlauer Berg neighborhood AirBnB data from ...       True\n",
-      "71                    book       Synthetic data to illustrate spatial weights       True\n",
-      "72                 burkitt  Burkitt's lymphoma in the Western Nile distric...       True\n",
-      "73                  calemp         Employment density for California counties       True\n",
-      "74                 chicago                              Chicago neighborhoods       True\n",
-      "75              clearwater                               mgwr testing dataset       True\n",
-      "76                columbus              Columbus neighborhood crime data 1980       True\n",
-      "77                 desmith    Small dataset to illustrate Moran's I statistic       True\n",
-      "78                geodanet        Datasets from geodanet for network analysis       True\n",
-      "79                 georgia  Various socio-economic variables for counties ...       True\n",
-      "80                juvenile    Residences of juvenile offenders in Cardiff, UK       True\n",
-      "81                  mexico  Decennial per capita incomes of Mexican states...       True\n",
-      "82                networks                  Datasets used for network testing       True\n",
-      "83                newHaven                            Network testing dataset       True\n",
-      "84               nyc_bikes                           New York City Bike Trips       True\n",
-      "85                   sids2  North Carolina county SIDS death counts and rates       True\n",
-      "86               snow_maps  Public water pumps and Cholera deaths in Londo...       True\n",
-      "87                     stl  Homicides and selected socio-economic characte...       True\n",
-      "88          street_net_pts                              Street network points       True\n",
-      "89                     taz           Traffic Analysis Zones in So. California       True\n",
-      "90                   tokyo                               Tokyo Mortality data       True\n",
-      "91               us_income  Per-capita income for the lower 48 US states 1...       True\n",
-      "92                virginia                        Virginia counties shapefile       True\n",
-      "93                    wmat          Datasets used for spatial weights testing       True\n"
-     ]
-    }
-   ],
-   "source": [
-    "available()"
-   ]
   }
  ],
  "metadata": {
@@ -1100,9 +750,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.8.10"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

From 133de8ad7b51ab1860eaad44a6c633d6c796c276 Mon Sep 17 00:00:00 2001
From: Serge Rey <sjsrey2gmail.com>
Date: Sun, 27 Jun 2021 08:42:41 -0700
Subject: [PATCH 027/162] Update testing dependicies

---
 ci/36.yaml | 1 +
 ci/37.yaml | 1 +
 ci/38.yaml | 1 +
 3 files changed, 3 insertions(+)

diff --git a/ci/36.yaml b/ci/36.yaml
index 7af1f515c..811c08a0e 100644
--- a/ci/36.yaml
+++ b/ci/36.yaml
@@ -4,6 +4,7 @@ channels:
 dependencies:
   - python=3.6
   - beautifulsoup4
+  - jinja2
   - pandas>=1.0
   - scipy>=1.0
   - xarray
diff --git a/ci/37.yaml b/ci/37.yaml
index 2d3f3023c..dbcc6c5ad 100644
--- a/ci/37.yaml
+++ b/ci/37.yaml
@@ -4,6 +4,7 @@ channels:
 dependencies:
   - python=3.7
   - beautifulsoup4
+  - jinja2
   - pandas>=1.0
   - scipy>=1.0
   - xarray
diff --git a/ci/38.yaml b/ci/38.yaml
index 3fcbc99d3..362863812 100644
--- a/ci/38.yaml
+++ b/ci/38.yaml
@@ -4,6 +4,7 @@ channels:
 dependencies:
   - python=3.8
   - beautifulsoup4
+  - jinja2
   - pandas>=1.0
   - scipy>=1.0
   - xarray

From d47143d2e25df7b86abb861d29a4daab0d739904 Mon Sep 17 00:00:00 2001
From: James Gaboardi <jgaboardi@gmail.com>
Date: Sun, 27 Jun 2021 12:50:51 -0400
Subject: [PATCH 028/162] add manual trigger to doc build action

---
 .github/workflows/build_docs.yml | 42 +++++++++++++++-----------------
 1 file changed, 20 insertions(+), 22 deletions(-)

diff --git a/.github/workflows/build_docs.yml b/.github/workflows/build_docs.yml
index ff64fb3c5..acabbc6ca 100644
--- a/.github/workflows/build_docs.yml
+++ b/.github/workflows/build_docs.yml
@@ -5,6 +5,13 @@
      # Sequence of patterns matched against refs/tags
      tags:
        - 'v*' # Push events to matching v*, i.e. v1.0, v20.15.10
+   workflow_dispatch:
+     inputs:
+       version:
+         description: Manual Doc Build Reason
+         default: test
+         required: false
+
  jobs:
    docs:
      name: Build & Push Docs
@@ -18,31 +25,20 @@
      defaults:
        run:
          shell: bash -l {0}
+     
      steps:
-       - uses: actions/checkout@v2
-       - uses: actions/cache@v2
-         env:
-           CACHE_NUMBER: 0
-         with:
-           path: ~/conda_pkgs_dir
-           key: ${{ matrix.os }}-conda-${{ env.CACHE_NUMBER }}-${{ hashFiles(matrix.environment-file) }}
-       - uses: conda-incubator/setup-miniconda@v2
+       - name: Checkout repo
+         uses: actions/checkout@v2
+       
+       - name: Setup micromamba
+         uses: mamba-org/provision-with-micromamba@main
          with:
-            miniconda-version: 'latest'
-            mamba-version: '*'
-            channels: conda-forge
-            channel-priority: true
-            auto-update-conda: false
-            auto-activate-base: false
-            environment-file: ${{ matrix.environment-file }}
-            activate-environment: test
-            use-only-tar-bz2: true
-       - run: mamba info --all
-       - run: mamba list
-       - run: conda config --show-sources
-       - run: conda config --show
+           environment-file: ${{ matrix.environment-file }}
+           micromamba-version: 'latest'
+       
        - name: Make Docs
          run: cd docs; make html
+       
        - name: Commit Docs
          run: |
            git clone https://github.com/ammaraskar/sphinx-action-test.git --branch gh-pages --single-branch gh-pages
@@ -54,7 +50,9 @@
            git commit -m "Update documentation" -a || true
            # The above command will fail if no changes were present,
            # so we ignore the return code.
-       - uses: ad-m/github-push-action@master
+       
+       - name: push to gh-pages
+         uses: ad-m/github-push-action@master
          with:
             branch: gh-pages
             directory: gh-pages

From b01cb03d169b102d610cf0fce14cb9aef5b5d92c Mon Sep 17 00:00:00 2001
From: Serge Rey <sjsrey2gmail.com>
Date: Sun, 27 Jun 2021 10:22:28 -0700
Subject: [PATCH 029/162] bump version

---
 libpysal/__init__.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/libpysal/__init__.py b/libpysal/__init__.py
index 3b84c152c..4cd055b25 100644
--- a/libpysal/__init__.py
+++ b/libpysal/__init__.py
@@ -1,4 +1,4 @@
-__version__ = "4.4.0"
+__version__ = "4.5.0"
 
 # __version__ has to be define in the first line
 

From 03d99bbaabffa50063c7ad467def35172affd638 Mon Sep 17 00:00:00 2001
From: James Gaboardi <jgaboardi@gmail.com>
Date: Sun, 27 Jun 2021 13:27:03 -0400
Subject: [PATCH 030/162] add 39 env

---
 ci/39.yaml | 28 ++++++++++++++++++++++++++++
 1 file changed, 28 insertions(+)
 create mode 100644 ci/39.yaml

diff --git a/ci/39.yaml b/ci/39.yaml
new file mode 100644
index 000000000..24801573d
--- /dev/null
+++ b/ci/39.yaml
@@ -0,0 +1,28 @@
+name: test
+channels:
+  - conda-forge
+dependencies:
+  - python=3.9
+  - beautifulsoup4
+  - jinja2
+  - pandas>=1.0
+  - scipy>=1.0
+  - xarray
+  # testing
+  - codecov
+  - matplotlib
+  - pytest
+  - pytest-cov
+  # optional
+  - geopandas>=0.7.0
+  - numba
+  - xarray
+  - joblib
+  - zstd
+  # for docs build action (this env only)
+  - nbsphinx
+  - numpydoc
+  - sphinx>=1.4.3
+  - sphinxcontrib-bibtex<2.0.0
+  - sphinx_bootstrap_theme
+  

From 338027d8ce6ef5060401ce75e0943b14b8cd3924 Mon Sep 17 00:00:00 2001
From: James Gaboardi <jgaboardi@gmail.com>
Date: Sun, 27 Jun 2021 13:27:28 -0400
Subject: [PATCH 031/162] update and modernize unittests

---
 .github/workflows/unittests.yml | 55 +++++++++++++++------------------
 1 file changed, 25 insertions(+), 30 deletions(-)

diff --git a/.github/workflows/unittests.yml b/.github/workflows/unittests.yml
index 2b7a61743..aa5b74238 100644
--- a/.github/workflows/unittests.yml
+++ b/.github/workflows/unittests.yml
@@ -10,45 +10,40 @@
 
  jobs:
    unittests:
-     name: conda (${{ matrix.os }}, ${{ matrix.environment-file }})
+     name: ${{ matrix.os }}, ${{ matrix.environment-file }}
      runs-on: ${{ matrix.os }}
-    #  timeout-minutes: 25
+     #timeout-minutes: 25
      strategy:
        matrix:
          os: ['macos-latest', 'ubuntu-latest', 'windows-latest']
-         environment-file: [ci/36.yaml, ci/37.yaml, ci/38.yaml]
+         environment-file: [ci/36.yaml, ci/37.yaml, ci/38.yaml, ci/39.yaml]
          exclude:
            - environment-file: ci/36.yaml
              os: windows-latest
-     defaults:
-       run:
-         shell: bash -l {0}
+     
      steps:
-       - uses: actions/checkout@v2
-       - uses: actions/cache@v2
-         env:
-           CACHE_NUMBER: 0
-         with:
-           path: ~/conda_pkgs_dir
-           key: ${{ matrix.os }}-conda-${{ env.CACHE_NUMBER }}-${{ hashFiles(matrix.environment-file) }}
-       - uses: conda-incubator/setup-miniconda@v2
+       - name: checkout repo
+         uses: actions/checkout@v2
+       
+       - name: setup micromamba
+         uses: mamba-org/provision-with-micromamba@main
          with:
-            miniconda-version: 'latest'
-            mamba-version: '*'
-            channels: conda-forge
-            channel-priority: true
-            auto-update-conda: false
-            auto-activate-base: false
-            environment-file: ${{ matrix.environment-file }}
-            activate-environment: test
-            use-only-tar-bz2: true
-       - run: mamba info --all
-       - run: mamba list
-       - run: conda config --show-sources
-       - run: conda config --show
-       - run: python -c 'import libpysal; libpysal.examples.fetch_all()'
-       - run: py.test -v libpysal --cov=libpysal --cov-report=xml
-       - name: codecov (${{ matrix.os }}, ${{ matrix.environment-file }})
+           environment-file: ${{ matrix.environment-file }}
+           micromamba-version: 'latest'
+      
+      - name: run tests - bash
+         shell: bash -l {0}
+         run: python -c 'import libpysal; libpysal.examples.fetch_all()'
+         run: pytest -v libpysal --cov=libpysal --cov-report=xml
+         if: matrix.os != 'windows-latest'
+       
+       - name: run tests - powershell
+         shell: powershell
+         run: python -c 'import libpysal; libpysal.examples.fetch_all()'
+         run: pytest -v libpysal --cov=libpysal --cov-report=xml
+         if: matrix.os == 'windows-latest'
+      
+       - name: ${{ matrix.os }}, ${{ matrix.environment-file }}
          uses: codecov/codecov-action@v1
          with:
            token: ${{ secrets.CODECOV_TOKEN }}

From 7eebc6e76e5dec735466e34b047c72694e3257dc Mon Sep 17 00:00:00 2001
From: James Gaboardi <jgaboardi@gmail.com>
Date: Sun, 27 Jun 2021 13:27:46 -0400
Subject: [PATCH 032/162] update and modernize release and publish

---
 .github/workflows/release_and_publish.yml | 115 ++++++++++------------
 1 file changed, 54 insertions(+), 61 deletions(-)

diff --git a/.github/workflows/release_and_publish.yml b/.github/workflows/release_and_publish.yml
index 27253b3ae..45cb95d38 100644
--- a/.github/workflows/release_and_publish.yml
+++ b/.github/workflows/release_and_publish.yml
@@ -7,66 +7,59 @@
  # under the user's name, not the organzation.
  
  #--------------------------------------------------
- name: release_and_publish
+ name: Release & Publish
 
- on:
-   push:
-     # Sequence of patterns matched against refs/tags
-     tags:
-       - 'v*' # Push events to matching v*, i.e. v1.0, v20.15.10
+on:
+  push:
+    # Sequence of patterns matched against refs/tags
+    tags:
+      - "v*" # Push events to matching v*, i.e. v1.0, v20.15.10
 
- jobs:
-   build:
-     runs-on: ubuntu-latest
-     steps:
-     - uses: actions/checkout@v2
-     - name: Set up Python
-       uses: actions/setup-python@v2
-       with:
-         python-version: '3.x'
-     - name: Install dependencies
-       run: |
-         python -m pip install --upgrade pip
-         pip install setuptools wheel twine jupyter urllib3 pandas pyyaml
-         python setup.py sdist bdist_wheel
-     - name: Run Changelog
-       run: |
-         jupyter nbconvert --to notebook --execute --inplace --ExecutePreprocessor.timeout=-1 --ExecutePreprocessor.kernel_name=python3 tools/gitcount.ipynb
-     - name: Cat Changelog
-       uses: pCYSl5EDgo/cat@master
-       id: changetxt
-       with:
-         path: ./tools/changelog.md
-       env:
-         TEXT: ${{ steps.changetxt.outputs.text }}
-     - name: Create Release
-       id: create_release
-       uses: actions/create-release@v1
-       env:
-         GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} # token is provided by GHA, DO NOT create
-       with:
-         tag_name: ${{ github.ref }}
-         release_name: Release ${{ github.ref }}
-         body: ${{ steps.changetxt.outputs.text }}
-         draft: false
-         prerelease: false
-     - name: Get Asset name
-       run: |
-         export PKG=$(ls dist/)
-         set -- $PKG
-         echo "name=$1" >> $GITHUB_ENV
-     - name: Upload Release Asset
-       id: upload-release-asset
-       uses: actions/upload-release-asset@v1
-       env:
-         GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-       with:
-         upload_url: ${{ steps.create_release.outputs.upload_url }} # This pulls from the CREATE RELEASE step above, referencing it's ID to get its outputs object, which include a `upload_url`. See this blog post for more info: https://jasonet.co/posts/new-features-of-github-actions/#passing-data-to-future-steps
-         asset_path: dist/${{ env.name }}
-         asset_name: ${{ env.name }}
-         asset_content_type: application/zip
-     - name: Publish distribution 📦 to PyPI
-       uses: pypa/gh-action-pypi-publish@master
-       with:
-         user: __token__
-         password: ${{ secrets.PYPI_PASSWORD }}
+jobs:
+  build:
+    name: Create release & publish to PyPI
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout repo
+        uses: actions/checkout@v2
+      
+      - name: Set up python
+        uses: actions/setup-python@v2
+        with:
+          python-version: "3.x"
+      
+      - name: Install Dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install setuptools wheel twine jupyter urllib3 pandas pyyaml
+          python setup.py sdist bdist_wheel
+      
+      - name: run Changelog
+        run: |
+          jupyter nbconvert --to notebook --execute --inplace --ExecutePreprocessor.timeout=-1 --ExecutePreprocessor.kernel_name=python3 tools/gitcount.ipynb
+      
+      - name: cat Changelog
+        uses: pCYSl5EDgo/cat@master
+        id: changetxt
+        with:
+          path: ./tools/changelog.md
+        env:
+          TEXT: ${{ steps.changetxt.outputs.text }}
+      
+      - name: Get the tag name
+        run: echo "TAG=${GITHUB_REF/refs\/tags\//}" >> $GITHUB_ENV
+      
+      - name: Release
+        uses: softprops/action-gh-release@v1
+        with:
+          body: ${{ steps.changetxt.outputs.text }}
+          body_path: ${{ steps.changetxt.outputs.path }}
+          name: Release ${{ env.TAG }}
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+      
+      - name: Publish distribution 📦 to PyPI
+        uses: pypa/gh-action-pypi-publish@master
+        with:
+          user: __token__
+          password: ${{ secrets.PYPI_PASSWORD }}

From d32abf204a6d2ba49951c058a081371cc537fd6a Mon Sep 17 00:00:00 2001
From: James Gaboardi <jgaboardi@gmail.com>
Date: Sun, 27 Jun 2021 13:40:46 -0400
Subject: [PATCH 033/162] corretn syntax in unittests

---
 .github/workflows/unittests.yml | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/unittests.yml b/.github/workflows/unittests.yml
index aa5b74238..79e14123e 100644
--- a/.github/workflows/unittests.yml
+++ b/.github/workflows/unittests.yml
@@ -31,11 +31,11 @@
            environment-file: ${{ matrix.environment-file }}
            micromamba-version: 'latest'
       
-      - name: run tests - bash
-         shell: bash -l {0}
-         run: python -c 'import libpysal; libpysal.examples.fetch_all()'
-         run: pytest -v libpysal --cov=libpysal --cov-report=xml
-         if: matrix.os != 'windows-latest'
+       - name: run tests - bash
+          shell: bash -l {0}
+          run: python -c 'import libpysal; libpysal.examples.fetch_all()'
+          run: pytest -v libpysal --cov=libpysal --cov-report=xml
+          if: matrix.os != 'windows-latest'
        
        - name: run tests - powershell
          shell: powershell

From 77a389f2a59ff74d2ba1eb8fa94a597aa2473dd8 Mon Sep 17 00:00:00 2001
From: James Gaboardi <jgaboardi@gmail.com>
Date: Sun, 27 Jun 2021 13:41:38 -0400
Subject: [PATCH 034/162] adjsut setup(app) in conf.py

---
 docs/conf.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/conf.py b/docs/conf.py
index 1d91a393b..9aac84899 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -273,7 +273,7 @@
 
 
 def setup(app):
-    app.add_stylesheet("pysal-styles.css")
+    app.add_css_file("pysal-styles.css")
 
 
 # Example configuration for intersphinx: refer to the Python standard library.

From 32596e3ebcd885996b4560282117fcb6dc9bc18c Mon Sep 17 00:00:00 2001
From: James Gaboardi <jgaboardi@gmail.com>
Date: Sun, 27 Jun 2021 13:41:56 -0400
Subject: [PATCH 035/162] add Python 3.9 to setup

---
 setup.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/setup.py b/setup.py
index ad36e7daf..e4b946410 100644
--- a/setup.py
+++ b/setup.py
@@ -90,6 +90,7 @@ def setup_package():
             "Programming Language :: Python :: 3.6",
             "Programming Language :: Python :: 3.7",
             "Programming Language :: Python :: 3.8",
+            "Programming Language :: Python :: 3.9",
         ],
         package_data={"libpysal": list(example_data_files)},
         install_requires=install_reqs,

From 78ed137d5c07cfc9b00fbf28d7fd97b49f27b22e Mon Sep 17 00:00:00 2001
From: James Gaboardi <jgaboardi@gmail.com>
Date: Sun, 27 Jun 2021 13:44:44 -0400
Subject: [PATCH 036/162] correct syntax for GHA [2]

---
 .github/workflows/release_and_publish.yml | 110 +++++++++++-----------
 .github/workflows/unittests.yml           |   8 +-
 2 files changed, 59 insertions(+), 59 deletions(-)

diff --git a/.github/workflows/release_and_publish.yml b/.github/workflows/release_and_publish.yml
index 45cb95d38..da5ea4667 100644
--- a/.github/workflows/release_and_publish.yml
+++ b/.github/workflows/release_and_publish.yml
@@ -8,58 +8,58 @@
  
  #--------------------------------------------------
  name: Release & Publish
-
-on:
-  push:
-    # Sequence of patterns matched against refs/tags
-    tags:
-      - "v*" # Push events to matching v*, i.e. v1.0, v20.15.10
-
-jobs:
-  build:
-    name: Create release & publish to PyPI
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout repo
-        uses: actions/checkout@v2
-      
-      - name: Set up python
-        uses: actions/setup-python@v2
-        with:
-          python-version: "3.x"
-      
-      - name: Install Dependencies
-        run: |
-          python -m pip install --upgrade pip
-          pip install setuptools wheel twine jupyter urllib3 pandas pyyaml
-          python setup.py sdist bdist_wheel
-      
-      - name: run Changelog
-        run: |
-          jupyter nbconvert --to notebook --execute --inplace --ExecutePreprocessor.timeout=-1 --ExecutePreprocessor.kernel_name=python3 tools/gitcount.ipynb
-      
-      - name: cat Changelog
-        uses: pCYSl5EDgo/cat@master
-        id: changetxt
-        with:
-          path: ./tools/changelog.md
-        env:
-          TEXT: ${{ steps.changetxt.outputs.text }}
-      
-      - name: Get the tag name
-        run: echo "TAG=${GITHUB_REF/refs\/tags\//}" >> $GITHUB_ENV
-      
-      - name: Release
-        uses: softprops/action-gh-release@v1
-        with:
-          body: ${{ steps.changetxt.outputs.text }}
-          body_path: ${{ steps.changetxt.outputs.path }}
-          name: Release ${{ env.TAG }}
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-      
-      - name: Publish distribution 📦 to PyPI
-        uses: pypa/gh-action-pypi-publish@master
-        with:
-          user: __token__
-          password: ${{ secrets.PYPI_PASSWORD }}
+ 
+ on:
+   push:
+     # Sequence of patterns matched against refs/tags
+     tags:
+       - "v*" # Push events to matching v*, i.e. v1.0, v20.15.10
+ 
+ jobs:
+   build:
+     name: Create release & publish to PyPI
+     runs-on: ubuntu-latest
+     steps:
+       - name: Checkout repo
+         uses: actions/checkout@v2
+ 
+       - name: Set up python
+         uses: actions/setup-python@v2
+         with:
+           python-version: "3.x"
+ 
+       - name: Install Dependencies
+         run: |
+           python -m pip install --upgrade pip
+           pip install setuptools wheel twine jupyter urllib3 pandas pyyaml
+           python setup.py sdist bdist_wheel
+ 
+       - name: run Changelog
+         run: |
+           jupyter nbconvert --to notebook --execute --inplace --ExecutePreprocessor.timeout=-1  --ExecutePreprocessor.kernel_name=python3 tools/gitcount.ipynb
+ 
+       - name: cat Changelog
+         uses: pCYSl5EDgo/cat@master
+         id: changetxt
+         with:
+           path: ./tools/changelog.md
+         env:
+           TEXT: ${{ steps.changetxt.outputs.text }}
+ 
+       - name: Get the tag name
+         run: echo "TAG=${GITHUB_REF/refs\/tags\//}" >> $GITHUB_ENV
+ 
+       - name: Release
+         uses: softprops/action-gh-release@v1
+         with:
+           body: ${{ steps.changetxt.outputs.text }}
+           body_path: ${{ steps.changetxt.outputs.path }}
+           name: Release ${{ env.TAG }}
+         env:
+           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+ 
+       - name: Publish distribution 📦 to PyPI
+         uses: pypa/gh-action-pypi-publish@master
+         with:
+           user: __token__
+           password: ${{ secrets.PYPI_PASSWORD }}
diff --git a/.github/workflows/unittests.yml b/.github/workflows/unittests.yml
index 79e14123e..bc302e15c 100644
--- a/.github/workflows/unittests.yml
+++ b/.github/workflows/unittests.yml
@@ -32,10 +32,10 @@
            micromamba-version: 'latest'
       
        - name: run tests - bash
-          shell: bash -l {0}
-          run: python -c 'import libpysal; libpysal.examples.fetch_all()'
-          run: pytest -v libpysal --cov=libpysal --cov-report=xml
-          if: matrix.os != 'windows-latest'
+         shell: bash -l {0}
+         run: python -c 'import libpysal; libpysal.examples.fetch_all()'
+         run: pytest -v libpysal --cov=libpysal --cov-report=xml
+         if: matrix.os != 'windows-latest'
        
        - name: run tests - powershell
          shell: powershell

From 187e66f02e579db1e71d5de43c95dcc8989f2f64 Mon Sep 17 00:00:00 2001
From: James Gaboardi <jgaboardi@gmail.com>
Date: Sun, 27 Jun 2021 13:46:30 -0400
Subject: [PATCH 037/162] correct syntax for GHA [3]

---
 .github/workflows/unittests.yml | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/unittests.yml b/.github/workflows/unittests.yml
index bc302e15c..bd1ab9444 100644
--- a/.github/workflows/unittests.yml
+++ b/.github/workflows/unittests.yml
@@ -33,14 +33,16 @@
       
        - name: run tests - bash
          shell: bash -l {0}
-         run: python -c 'import libpysal; libpysal.examples.fetch_all()'
-         run: pytest -v libpysal --cov=libpysal --cov-report=xml
+         run: |
+           python -c 'import libpysal; libpysal.examples.fetch_all()'
+           pytest -v libpysal --cov=libpysal --cov-report=xml
          if: matrix.os != 'windows-latest'
        
        - name: run tests - powershell
          shell: powershell
-         run: python -c 'import libpysal; libpysal.examples.fetch_all()'
-         run: pytest -v libpysal --cov=libpysal --cov-report=xml
+         run:  |
+           python -c 'import libpysal; libpysal.examples.fetch_all()'
+           pytest -v libpysal --cov=libpysal --cov-report=xml
          if: matrix.os == 'windows-latest'
       
        - name: ${{ matrix.os }}, ${{ matrix.environment-file }}

From 2f861b231cc4b5bcb78c57c7bbd13b1f6a359393 Mon Sep 17 00:00:00 2001
From: James Gaboardi <jgaboardi@gmail.com>
Date: Sun, 27 Jun 2021 13:57:52 -0400
Subject: [PATCH 038/162] remove Python 3.6 from actions

---
 .github/workflows/unittests.yml | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/.github/workflows/unittests.yml b/.github/workflows/unittests.yml
index bd1ab9444..43ed84842 100644
--- a/.github/workflows/unittests.yml
+++ b/.github/workflows/unittests.yml
@@ -16,10 +16,7 @@
      strategy:
        matrix:
          os: ['macos-latest', 'ubuntu-latest', 'windows-latest']
-         environment-file: [ci/36.yaml, ci/37.yaml, ci/38.yaml, ci/39.yaml]
-         exclude:
-           - environment-file: ci/36.yaml
-             os: windows-latest
+         environment-file: [ci/37.yaml, ci/38.yaml, ci/39.yaml]
      
      steps:
        - name: checkout repo

From 947125da718507ef880d8acc49927ceae0446110 Mon Sep 17 00:00:00 2001
From: James Gaboardi <jgaboardi@gmail.com>
Date: Sun, 27 Jun 2021 13:59:06 -0400
Subject: [PATCH 039/162] remove Python 3.6 setup & envs

---
 .github/workflows/build_docs.yml |  2 +-
 ci/36.yaml                       | 19 -------------------
 setup.py                         |  3 +--
 3 files changed, 2 insertions(+), 22 deletions(-)
 delete mode 100644 ci/36.yaml

diff --git a/.github/workflows/build_docs.yml b/.github/workflows/build_docs.yml
index acabbc6ca..09b7ef2ee 100644
--- a/.github/workflows/build_docs.yml
+++ b/.github/workflows/build_docs.yml
@@ -20,7 +20,7 @@
      strategy:
        matrix:
          os: ['ubuntu-latest']
-         environment-file: [ci/38.yaml]
+         environment-file: [ci/39.yaml]
          experimental: [false]
      defaults:
        run:
diff --git a/ci/36.yaml b/ci/36.yaml
deleted file mode 100644
index 811c08a0e..000000000
--- a/ci/36.yaml
+++ /dev/null
@@ -1,19 +0,0 @@
-name: test
-channels:
-  - conda-forge
-dependencies:
-  - python=3.6
-  - beautifulsoup4
-  - jinja2
-  - pandas>=1.0
-  - scipy>=1.0
-  - xarray
-  # testing
-  - codecov
-  - matplotlib
-  - pytest
-  - pytest-cov
-  # optional
-  - geopandas>=0.7.0
-  - numba
-  - zstd
diff --git a/setup.py b/setup.py
index e4b946410..778594709 100644
--- a/setup.py
+++ b/setup.py
@@ -87,7 +87,6 @@ def setup_package():
             "Topic :: Scientific/Engineering :: GIS",
             "License :: OSI Approved :: BSD License",
             "Programming Language :: Python",
-            "Programming Language :: Python :: 3.6",
             "Programming Language :: Python :: 3.7",
             "Programming Language :: Python :: 3.8",
             "Programming Language :: Python :: 3.9",
@@ -96,7 +95,7 @@ def setup_package():
         install_requires=install_reqs,
         extras_require=extras_reqs,
         cmdclass={"build_py": build_py},
-        python_requires=">3.5",
+        python_requires=">=3.7",
     )
 
 

From a5f51f973d32ebdebeb6343508fc6ca740d7a986 Mon Sep 17 00:00:00 2001
From: James Gaboardi <jgaboardi@gmail.com>
Date: Sun, 27 Jun 2021 14:11:07 -0400
Subject: [PATCH 040/162] classify repo as Python

---
 .gitattributes | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 .gitattributes

diff --git a/.gitattributes b/.gitattributes
new file mode 100644
index 000000000..f4c7e5f01
--- /dev/null
+++ b/.gitattributes
@@ -0,0 +1 @@
+*.ipynb linguist-language=Python

From 5474b2662ef58b091efcaaf7b02713f73e4fcd05 Mon Sep 17 00:00:00 2001
From: Serge Rey <sjsrey2gmail.com>
Date: Sun, 27 Jun 2021 11:27:45 -0700
Subject: [PATCH 041/162] bump

---
 libpysal/__init__.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/libpysal/__init__.py b/libpysal/__init__.py
index 4cd055b25..e83518298 100644
--- a/libpysal/__init__.py
+++ b/libpysal/__init__.py
@@ -1,4 +1,4 @@
-__version__ = "4.5.0"
+__version__ = "4.5.1"
 
 # __version__ has to be define in the first line
 

From b9b61a3302dd5b05739b599b89002146cc31ccae Mon Sep 17 00:00:00 2001
From: Serge Rey <sjsrey2gmail.com>
Date: Sun, 27 Jun 2021 11:40:46 -0700
Subject: [PATCH 042/162] rename examples

---
 notebooks/{Example Datasets.ipynb => examples.ipynb} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename notebooks/{Example Datasets.ipynb => examples.ipynb} (100%)

diff --git a/notebooks/Example Datasets.ipynb b/notebooks/examples.ipynb
similarity index 100%
rename from notebooks/Example Datasets.ipynb
rename to notebooks/examples.ipynb

From 540bf092d00ce28a433a0874d83d2b81fd1a7ea9 Mon Sep 17 00:00:00 2001
From: James Gaboardi <jgaboardi@gmail.com>
Date: Sun, 27 Jun 2021 18:09:30 -0400
Subject: [PATCH 043/162] update conf.py

---
 docs/conf.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/docs/conf.py b/docs/conf.py
index 9aac84899..0dd80f9d3 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -291,8 +291,7 @@ def setup(app):
 
 # This is processed by Jinja2 and inserted before each notebook
 nbsphinx_prolog = r"""
-{% set docname = env.doc2path(env.docname, base='') %}
-{% set fullpath = env.doc2path(env.docname, base='tree/master/') %}
+{% set docname = env.doc2path(env.docname, base=None) %}
 
 .. only:: html
 
@@ -305,7 +304,7 @@ def setup(app):
         Interactive online version:
         :raw-html:`<a href="https://mybinder.org/v2/gh/pysal/libpysal/master?filepath={{ docname }}"><img alt="Binder badge" src="https://mybinder.org/badge_logo.svg" style="vertical-align:text-bottom"></a>`
 
-    __ https://github.com/pysal/libpysal/{{ fullpath }}
+    __ https://github.com/pysal/libpysal/blob/master/{{ docname }}
 
 .. raw:: latex
 

From bd1e0f8d3f89ae696e2a7331b9d89de638edeff8 Mon Sep 17 00:00:00 2001
From: Serge Rey <sjsrey2gmail.com>
Date: Sun, 27 Jun 2021 15:32:27 -0700
Subject: [PATCH 044/162] debug failing ci for lat2SW

---
 libpysal/weights/tests/test_util.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/libpysal/weights/tests/test_util.py b/libpysal/weights/tests/test_util.py
index 33534c28f..afee1e549 100644
--- a/libpysal/weights/tests/test_util.py
+++ b/libpysal/weights/tests/test_util.py
@@ -34,6 +34,7 @@ def test_lat2SW(self):
         w9 = util.lat2SW(3, 3)
         rows, cols = w9.shape
         n = rows * cols
+        self.assertEqual(w9.nnz, 24)
         pct_nonzero = w9.nnz / float(n)
         self.assertEqual(pct_nonzero, 0.29629629629629628)
         data = w9.todense().tolist()

From 1c5da0246342c4a00576808650e120bb22d1b38b Mon Sep 17 00:00:00 2001
From: Serge Rey <sjsrey2gmail.com>
Date: Sun, 27 Jun 2021 16:28:18 -0700
Subject: [PATCH 045/162] skipping lat2SW for 1.7.0 scipy

---
 libpysal/weights/tests/test_util.py | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/libpysal/weights/tests/test_util.py b/libpysal/weights/tests/test_util.py
index afee1e549..10fa3d850 100644
--- a/libpysal/weights/tests/test_util.py
+++ b/libpysal/weights/tests/test_util.py
@@ -7,6 +7,7 @@
 from ...io.fileio import FileIO as psopen
 from ... import examples
 import numpy as np
+import scipy
 import unittest
 
 
@@ -17,6 +18,9 @@
 except:
     HAS_GEOPANDAS = False
 
+SCIPY1_7 = False
+if scipy.version.version == '1.7.0':
+    SCIPY1_7 = True
 
 class Testutil(unittest.TestCase):
     def setUp(self):
@@ -29,7 +33,9 @@ def test_lat2W(self):
         self.assertEqual(w9.pct_nonzero, 29.62962962962963)
         self.assertEqual(w9[0], {1: 1.0, 3: 1.0})
         self.assertEqual(w9[3], {0: 1.0, 4: 1.0, 6: 1.0})
-
+    @unittest.skipIf(
+        SCIPY1_7, "Known failure due to dia change (TOFIX)"
+    )
     def test_lat2SW(self):
         w9 = util.lat2SW(3, 3)
         rows, cols = w9.shape

From cf7e4235357d8740066762577a192ff6bf4b9909 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Mon, 19 Jul 2021 19:01:18 +0000
Subject: [PATCH 046/162] Bump codecov/codecov-action from 1 to 2.0.1

Bumps [codecov/codecov-action](https://github.com/codecov/codecov-action) from 1 to 2.0.1.
- [Release notes](https://github.com/codecov/codecov-action/releases)
- [Changelog](https://github.com/codecov/codecov-action/blob/master/CHANGELOG.md)
- [Commits](https://github.com/codecov/codecov-action/compare/v1...v2.0.1)

---
updated-dependencies:
- dependency-name: codecov/codecov-action
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>
---
 .github/workflows/unittests.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/unittests.yml b/.github/workflows/unittests.yml
index 43ed84842..debaa2959 100644
--- a/.github/workflows/unittests.yml
+++ b/.github/workflows/unittests.yml
@@ -43,7 +43,7 @@
          if: matrix.os == 'windows-latest'
       
        - name: ${{ matrix.os }}, ${{ matrix.environment-file }}
-         uses: codecov/codecov-action@v1
+         uses: codecov/codecov-action@v2.0.1
          with:
            token: ${{ secrets.CODECOV_TOKEN }}
            file: ./coverage.xml

From df6b57262624d26bc05b9d0cf7e33d13fc7efcf9 Mon Sep 17 00:00:00 2001
From: James Gaboardi <jgaboardi@gmail.com>
Date: Mon, 19 Jul 2021 18:12:06 -0400
Subject: [PATCH 047/162] major version only

---
 .github/workflows/unittests.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/unittests.yml b/.github/workflows/unittests.yml
index debaa2959..897197d5d 100644
--- a/.github/workflows/unittests.yml
+++ b/.github/workflows/unittests.yml
@@ -43,7 +43,7 @@
          if: matrix.os == 'windows-latest'
       
        - name: ${{ matrix.os }}, ${{ matrix.environment-file }}
-         uses: codecov/codecov-action@v2.0.1
+         uses: codecov/codecov-action@v2
          with:
            token: ${{ secrets.CODECOV_TOKEN }}
            file: ./coverage.xml

From a4ac23b63721ff80ae3c8fd14441c4e8819e419d Mon Sep 17 00:00:00 2001
From: James Gaboardi <jgaboardi@gmail.com>
Date: Mon, 19 Jul 2021 18:13:05 -0400
Subject: [PATCH 048/162] Update unittests.yml

---
 .github/workflows/unittests.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/unittests.yml b/.github/workflows/unittests.yml
index 897197d5d..2cf2c5436 100644
--- a/.github/workflows/unittests.yml
+++ b/.github/workflows/unittests.yml
@@ -43,7 +43,7 @@
          if: matrix.os == 'windows-latest'
       
        - name: ${{ matrix.os }}, ${{ matrix.environment-file }}
-         uses: codecov/codecov-action@v2
+         uses: codecov/codecov-action@v2 
          with:
            token: ${{ secrets.CODECOV_TOKEN }}
            file: ./coverage.xml

From f026d79b3dbc8d1510fb71931499e1f6a7574adc Mon Sep 17 00:00:00 2001
From: eli knaap <ek@knaaptime.com>
Date: Tue, 17 Aug 2021 10:34:11 -0700
Subject: [PATCH 049/162] use toarray

---
 libpysal/weights/weights.py | 13 +++----------
 1 file changed, 3 insertions(+), 10 deletions(-)

diff --git a/libpysal/weights/weights.py b/libpysal/weights/weights.py
index 72aadaafe..b2e11c4e7 100644
--- a/libpysal/weights/weights.py
+++ b/libpysal/weights/weights.py
@@ -1142,16 +1142,9 @@ def full(self):
         >>> ids
         ['first', 'second', 'third']
         """
-        wfull = np.zeros([self.n, self.n], dtype=float)
-        keys = list(self.neighbors.keys())
-        if self.id_order:
-            keys = self.id_order
-        for i, key in enumerate(keys):
-            n_i = self.neighbors[key]
-            w_i = self.weights[key]
-            for j, wij in zip(n_i, w_i):
-                c = keys.index(j)
-                wfull[i, c] = wij
+        wfull = self.sparse.toarray()
+        keys = sorted(list(self.neighbors.keys()))
+
         return (wfull, keys)
 
     def to_WSP(self):

From 8153a8a285274a7aaea9e85a583ad53bfabf9b12 Mon Sep 17 00:00:00 2001
From: eli knaap <ek@knaaptime.com>
Date: Tue, 17 Aug 2021 10:37:39 -0700
Subject: [PATCH 050/162] idorder

---
 libpysal/weights/weights.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/libpysal/weights/weights.py b/libpysal/weights/weights.py
index b2e11c4e7..f0f42e4ee 100644
--- a/libpysal/weights/weights.py
+++ b/libpysal/weights/weights.py
@@ -1143,7 +1143,9 @@ def full(self):
         ['first', 'second', 'third']
         """
         wfull = self.sparse.toarray()
-        keys = sorted(list(self.neighbors.keys()))
+        keys = list(self.neighbors.keys())
+        if self.id_order:
+            keys = self.id_order
 
         return (wfull, keys)
 

From 614408e26e339a4f51c8e2b627567489c1b3b7c5 Mon Sep 17 00:00:00 2001
From: James Gaboardi <jgaboardi@gmail.com>
Date: Fri, 27 Aug 2021 20:28:15 -0400
Subject: [PATCH 051/162] skip scipy 1.7.1

---
 libpysal/weights/tests/test_util.py | 22 +++++++++++++---------
 1 file changed, 13 insertions(+), 9 deletions(-)

diff --git a/libpysal/weights/tests/test_util.py b/libpysal/weights/tests/test_util.py
index 10fa3d850..85f00e69c 100644
--- a/libpysal/weights/tests/test_util.py
+++ b/libpysal/weights/tests/test_util.py
@@ -19,9 +19,10 @@
     HAS_GEOPANDAS = False
 
 SCIPY1_7 = False
-if scipy.version.version == '1.7.0':
+if scipy.version.version.startswith("1.7"):
     SCIPY1_7 = True
 
+
 class Testutil(unittest.TestCase):
     def setUp(self):
         self.w = Rook.from_shapefile(examples.get_path("10740.shp"))
@@ -33,9 +34,8 @@ def test_lat2W(self):
         self.assertEqual(w9.pct_nonzero, 29.62962962962963)
         self.assertEqual(w9[0], {1: 1.0, 3: 1.0})
         self.assertEqual(w9[3], {0: 1.0, 4: 1.0, 6: 1.0})
-    @unittest.skipIf(
-        SCIPY1_7, "Known failure due to dia change (TOFIX)"
-    )
+
+    @unittest.skipIf(SCIPY1_7, "Known failure due to dia change (TOFIX)")
     def test_lat2SW(self):
         w9 = util.lat2SW(3, 3)
         rows, cols = w9.shape
@@ -352,12 +352,16 @@ def test_fuzzy_contiguity(self):
         wf = fuzzy_contiguity(rs_df)
         self.assertEqual(wf.islands, [])
         self.assertEqual(set(wf.neighbors[0]), set([239, 59, 152, 23]))
-        buff = fuzzy_contiguity(rs_df, buffering=True, buffer=.2)
-        self.assertEqual(set(buff.neighbors[0]), set([175, 119, 239, 59, 152, 246, 23, 107]))
-        rs_index = rs_df.set_index('NM_MUNICIP')
+        buff = fuzzy_contiguity(rs_df, buffering=True, buffer=0.2)
+        self.assertEqual(
+            set(buff.neighbors[0]), set([175, 119, 239, 59, 152, 246, 23, 107])
+        )
+        rs_index = rs_df.set_index("NM_MUNICIP")
         index_w = fuzzy_contiguity(rs_index)
-        self.assertEqual(set(index_w.neighbors['TAVARES']), set(['SÃO JOSÉ DO NORTE', 'MOSTARDAS']))
-        wf_pred = fuzzy_contiguity(rs_df, predicate='touches')
+        self.assertEqual(
+            set(index_w.neighbors["TAVARES"]), set(["SÃO JOSÉ DO NORTE", "MOSTARDAS"])
+        )
+        wf_pred = fuzzy_contiguity(rs_df, predicate="touches")
         self.assertEqual(set(wf_pred.neighbors[0]), set([]))
         self.assertEqual(set(wf_pred.neighbors[1]), set([142, 82, 197, 285, 386, 350]))
 

From ee8a3b76c55b1e10ddac0063396c3afc7c1a3531 Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Mon, 30 Aug 2021 17:01:54 +0100
Subject: [PATCH 052/162] cast sw to csc and eliminate zeros

---
 libpysal/weights/util.py | 21 ++++++++++++++++-----
 1 file changed, 16 insertions(+), 5 deletions(-)

diff --git a/libpysal/weights/util.py b/libpysal/weights/util.py
index f6bdc9eca..b3f2991cb 100644
--- a/libpysal/weights/util.py
+++ b/libpysal/weights/util.py
@@ -1241,6 +1241,8 @@ def lat2SW(nrows=3, ncols=5, criterion="rook", row_st=False):
     m = m + m.T
     if row_st:
         m = sparse.spdiags(1.0 / m.sum(1).T, 0, *m.shape) * m
+    m = m.tocsc()
+    m.eliminate_zeros()
     return m
 
 
@@ -1510,8 +1512,17 @@ def nonplanar_neighbors(w, geodataframe, tolerance=0.001, **kwargs):
     w.non_planar_joins = fixes
     return w
 
-@requires('geopandas')
-def fuzzy_contiguity(gdf, tolerance=0.005, buffering=False, drop=True, buffer=None, predicate='intersects', **kwargs):
+
+@requires("geopandas")
+def fuzzy_contiguity(
+    gdf,
+    tolerance=0.005,
+    buffering=False,
+    drop=True,
+    buffer=None,
+    predicate="intersects",
+    **kwargs,
+):
     """
     Fuzzy contiguity spatial weights
 
@@ -1625,7 +1636,7 @@ def fuzzy_contiguity(gdf, tolerance=0.005, buffering=False, drop=True, buffer=No
         new_geometry = gdf.geometry.buffer(buffer)
         gdf["_buffer"] = new_geometry
         old_geometry_name = gdf.geometry.name
-        gdf.set_geometry('_buffer', inplace=True)
+        gdf.set_geometry("_buffer", inplace=True)
 
     neighbors = {}
     if GPD_08:
@@ -1641,8 +1652,8 @@ def fuzzy_contiguity(gdf, tolerance=0.005, buffering=False, drop=True, buffer=No
             ids = gdf.index[res[inp == i]].tolist()
             neighbors[ix] = ids
     else:
-        if predicate != 'intersects':
-            raise ValueError(f'Predicate `{predicate}` requires geopandas >= 0.8.0.')
+        if predicate != "intersects":
+            raise ValueError(f"Predicate `{predicate}` requires geopandas >= 0.8.0.")
         tree = gdf.sindex
         for i, (ix, geom) in enumerate(gdf.geometry.iteritems()):
             hits = list(tree.intersection(geom.bounds))

From 0f858fba03e001e4b5517ba1580f8e6f34a7fa89 Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Mon, 30 Aug 2021 17:07:12 +0100
Subject: [PATCH 053/162] remove unnecessary skip

---
 libpysal/weights/tests/test_util.py | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/libpysal/weights/tests/test_util.py b/libpysal/weights/tests/test_util.py
index 85f00e69c..95ac0fad3 100644
--- a/libpysal/weights/tests/test_util.py
+++ b/libpysal/weights/tests/test_util.py
@@ -18,10 +18,6 @@
 except:
     HAS_GEOPANDAS = False
 
-SCIPY1_7 = False
-if scipy.version.version.startswith("1.7"):
-    SCIPY1_7 = True
-
 
 class Testutil(unittest.TestCase):
     def setUp(self):

From 238e017350374a36f71075a2145403e1dde44b00 Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Mon, 30 Aug 2021 17:12:35 +0100
Subject: [PATCH 054/162] remove skip

---
 libpysal/weights/tests/test_util.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/libpysal/weights/tests/test_util.py b/libpysal/weights/tests/test_util.py
index 95ac0fad3..1ecb92787 100644
--- a/libpysal/weights/tests/test_util.py
+++ b/libpysal/weights/tests/test_util.py
@@ -31,7 +31,6 @@ def test_lat2W(self):
         self.assertEqual(w9[0], {1: 1.0, 3: 1.0})
         self.assertEqual(w9[3], {0: 1.0, 4: 1.0, 6: 1.0})
 
-    @unittest.skipIf(SCIPY1_7, "Known failure due to dia change (TOFIX)")
     def test_lat2SW(self):
         w9 = util.lat2SW(3, 3)
         rows, cols = w9.shape

From f565254618b741bc13744f8bd4b0ee81793195d3 Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Fri, 3 Sep 2021 11:34:16 +0100
Subject: [PATCH 055/162] update adjlist function with island handling options

---
 libpysal/weights/weights.py | 36 ++++++++++++++++++++++++------------
 1 file changed, 24 insertions(+), 12 deletions(-)

diff --git a/libpysal/weights/weights.py b/libpysal/weights/weights.py
index 43f638109..50e45d319 100644
--- a/libpysal/weights/weights.py
+++ b/libpysal/weights/weights.py
@@ -157,8 +157,7 @@ def __init__(
         if (not self.silence_warnings) and (self.n_components > 1):
             message = (
                 "The weights matrix is not fully connected: "
-                "\n There are %d disconnected components."
-                % self.n_components
+                "\n There are %d disconnected components." % self.n_components
             )
             ni = len(self.islands)
             if ni == 1:
@@ -276,6 +275,7 @@ def from_adjlist(
     def to_adjlist(
         self,
         remove_symmetric=False,
+        drop_islands=True,
         focal_col="focal",
         neighbor_col="neighbor",
         weight_col="weight",
@@ -293,6 +293,11 @@ def to_adjlist(
             a ``W`` created from this adjacency list **MAY NOT BE THE SAME**
             as the original ``W``. If you would like to consider (1,2) and
             (2,1) as distinct links, leave this as ``False``.
+        drop_islands : bool
+            Whether or not to preserve islands as entries in the adjacency
+            list. By default, observations with no neighbors do not appear
+            in the adjacency list. If islands are kept, they are coded as
+            self-neighbors with zero weight.
         focal_col : str
             Name of the column in which to store "source" node ids.
         neighbor_col : str
@@ -301,21 +306,28 @@ def to_adjlist(
             Name of the column in which to store weight information.
         """
         try:
-            import pandas as pd
+            import pandas
         except (ImportError, ModuleNotFoundError):
             raise ImportError(
                 "pandas must be installed & importable to use this method"
             )
         links = []
-        for idx, neighb in self:
-            if len(neighb) == 0:
-                links.append((idx, idx, 0))
-                continue
-            for n, w in neighb.items():
-                links.append((idx, n, w))
-        adjlist = pd.DataFrame(links, columns=[focal_col, neighbor_col, weight_col])
-
-        return adjtools.filter_adjlist(adjlist) if remove_symmetric else adjlist
+        focal_ix, neighbor_ix = self.sparse.nonzero()
+        names = np.asarray(self.id_order)
+        focal = names[focal_ix]
+        neighbor = names[neighbor_ix]
+        weights = self.sparse.data
+        adjlist = pandas.DataFrame(
+            {focal_col: focal, neighbor_col: neighbor, weight_col: weights}
+        )
+        if remove_symmetric:
+            adjlist = adjtools.filter_adjlist(adjlist)
+        if not drop_islands:
+            island_adjlist = pandas.DataFrame(
+                {focal_col: self.islands, neighbor_col: self.islands, weight_col: 0}
+            )
+            adjlist = pandas.concat((adjlist, island_adjlist)).reset_index(drop=True)
+        return adjlist.sort_values([focal_col, neighbor_col])
 
     def to_networkx(self):
         """Convert a weights object to a ``networkx`` graph.

From b400dc0e1c21e9313137e89324d73ec32b3a9833 Mon Sep 17 00:00:00 2001
From: Martin Fleischmann <martin@martinfleischmann.net>
Date: Fri, 3 Sep 2021 20:08:14 +0100
Subject: [PATCH 056/162] add versioneer

---
 .gitattributes       |    1 +
 MANIFEST.in          |    2 +
 libpysal/__init__.py |    8 +-
 libpysal/_version.py |  623 +++++++++++++
 setup.cfg            |    7 +
 setup.py             |   10 +-
 versioneer.py        | 2064 ++++++++++++++++++++++++++++++++++++++++++
 7 files changed, 2704 insertions(+), 11 deletions(-)
 create mode 100644 libpysal/_version.py
 create mode 100644 setup.cfg
 create mode 100644 versioneer.py

diff --git a/.gitattributes b/.gitattributes
index f4c7e5f01..f847a0dac 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -1 +1,2 @@
 *.ipynb linguist-language=Python
+libpysal/_version.py export-subst
diff --git a/MANIFEST.in b/MANIFEST.in
index 9b9838046..8cf15845b 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -1,2 +1,4 @@
 include authors.txt LICENSE.txt THANKS.txt CHANGELOG.md MANIFEST.in pysal/COPYING requirements_dev.txt requirements_plus.txt requirements.txt requirements_plus_conda.txt requirements_plus_pip.txt requirements_docs.txt
 
+include versioneer.py
+include libpysal/_version.py
diff --git a/libpysal/__init__.py b/libpysal/__init__.py
index e83518298..1bd171e0c 100644
--- a/libpysal/__init__.py
+++ b/libpysal/__init__.py
@@ -1,7 +1,3 @@
-__version__ = "4.5.1"
-
-# __version__ has to be define in the first line
-
 """
 libpysal: Python Spatial Analysis Library (core)
 ================================================
@@ -28,3 +24,7 @@
 from . import io
 from . import weights
 from . import examples
+
+from . import _version
+
+__version__ = _version.get_versions()["version"]
diff --git a/libpysal/_version.py b/libpysal/_version.py
new file mode 100644
index 000000000..295654257
--- /dev/null
+++ b/libpysal/_version.py
@@ -0,0 +1,623 @@
+
+# This file helps to compute a version number in source trees obtained from
+# git-archive tarball (such as those provided by githubs download-from-tag
+# feature). Distribution tarballs (built by setup.py sdist) and build
+# directories (produced by setup.py build) will contain a much shorter file
+# that just contains the computed version number.
+
+# This file is released into the public domain. Generated by
+# versioneer-0.20 (https://github.com/python-versioneer/python-versioneer)
+
+"""Git implementation of _version.py."""
+
+import errno
+import os
+import re
+import subprocess
+import sys
+
+
+def get_keywords():
+    """Get the keywords needed to look up the version information."""
+    # these strings will be replaced by git during git-archive.
+    # setup.py/versioneer.py will grep for the variable names, so they must
+    # each be defined on a line of their own. _version.py will just call
+    # get_keywords().
+    git_refnames = "$Format:%d$"
+    git_full = "$Format:%H$"
+    git_date = "$Format:%ci$"
+    keywords = {"refnames": git_refnames, "full": git_full, "date": git_date}
+    return keywords
+
+
+class VersioneerConfig:  # pylint: disable=too-few-public-methods
+    """Container for Versioneer configuration parameters."""
+
+
+def get_config():
+    """Create, populate and return the VersioneerConfig() object."""
+    # these strings are filled in when 'setup.py versioneer' creates
+    # _version.py
+    cfg = VersioneerConfig()
+    cfg.VCS = "git"
+    cfg.style = "pep440"
+    cfg.tag_prefix = "v"
+    cfg.parentdir_prefix = "libpysal-"
+    cfg.versionfile_source = "libpysal/_version.py"
+    cfg.verbose = False
+    return cfg
+
+
+class NotThisMethod(Exception):
+    """Exception raised if a method is not valid for the current scenario."""
+
+
+LONG_VERSION_PY = {}
+HANDLERS = {}
+
+
+def register_vcs_handler(vcs, method):  # decorator
+    """Create decorator to mark a method as the handler of a VCS."""
+    def decorate(f):
+        """Store f in HANDLERS[vcs][method]."""
+        if vcs not in HANDLERS:
+            HANDLERS[vcs] = {}
+        HANDLERS[vcs][method] = f
+        return f
+    return decorate
+
+
+# pylint:disable=too-many-arguments,consider-using-with # noqa
+def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False,
+                env=None):
+    """Call the given command(s)."""
+    assert isinstance(commands, list)
+    process = None
+    for command in commands:
+        try:
+            dispcmd = str([command] + args)
+            # remember shell=False, so use git.cmd on windows, not just git
+            process = subprocess.Popen([command] + args, cwd=cwd, env=env,
+                                       stdout=subprocess.PIPE,
+                                       stderr=(subprocess.PIPE if hide_stderr
+                                               else None))
+            break
+        except EnvironmentError:
+            e = sys.exc_info()[1]
+            if e.errno == errno.ENOENT:
+                continue
+            if verbose:
+                print("unable to run %s" % dispcmd)
+                print(e)
+            return None, None
+    else:
+        if verbose:
+            print("unable to find command, tried %s" % (commands,))
+        return None, None
+    stdout = process.communicate()[0].strip().decode()
+    if process.returncode != 0:
+        if verbose:
+            print("unable to run %s (error)" % dispcmd)
+            print("stdout was %s" % stdout)
+        return None, process.returncode
+    return stdout, process.returncode
+
+
+def versions_from_parentdir(parentdir_prefix, root, verbose):
+    """Try to determine the version from the parent directory name.
+
+    Source tarballs conventionally unpack into a directory that includes both
+    the project name and a version string. We will also support searching up
+    two directory levels for an appropriately named parent directory
+    """
+    rootdirs = []
+
+    for _ in range(3):
+        dirname = os.path.basename(root)
+        if dirname.startswith(parentdir_prefix):
+            return {"version": dirname[len(parentdir_prefix):],
+                    "full-revisionid": None,
+                    "dirty": False, "error": None, "date": None}
+        rootdirs.append(root)
+        root = os.path.dirname(root)  # up a level
+
+    if verbose:
+        print("Tried directories %s but none started with prefix %s" %
+              (str(rootdirs), parentdir_prefix))
+    raise NotThisMethod("rootdir doesn't start with parentdir_prefix")
+
+
+@register_vcs_handler("git", "get_keywords")
+def git_get_keywords(versionfile_abs):
+    """Extract version information from the given file."""
+    # the code embedded in _version.py can just fetch the value of these
+    # keywords. When used from setup.py, we don't want to import _version.py,
+    # so we do it with a regexp instead. This function is not used from
+    # _version.py.
+    keywords = {}
+    try:
+        with open(versionfile_abs, "r") as fobj:
+            for line in fobj:
+                if line.strip().startswith("git_refnames ="):
+                    mo = re.search(r'=\s*"(.*)"', line)
+                    if mo:
+                        keywords["refnames"] = mo.group(1)
+                if line.strip().startswith("git_full ="):
+                    mo = re.search(r'=\s*"(.*)"', line)
+                    if mo:
+                        keywords["full"] = mo.group(1)
+                if line.strip().startswith("git_date ="):
+                    mo = re.search(r'=\s*"(.*)"', line)
+                    if mo:
+                        keywords["date"] = mo.group(1)
+    except EnvironmentError:
+        pass
+    return keywords
+
+
+@register_vcs_handler("git", "keywords")
+def git_versions_from_keywords(keywords, tag_prefix, verbose):
+    """Get version information from git keywords."""
+    if "refnames" not in keywords:
+        raise NotThisMethod("Short version file found")
+    date = keywords.get("date")
+    if date is not None:
+        # Use only the last line.  Previous lines may contain GPG signature
+        # information.
+        date = date.splitlines()[-1]
+
+        # git-2.2.0 added "%cI", which expands to an ISO-8601 -compliant
+        # datestamp. However we prefer "%ci" (which expands to an "ISO-8601
+        # -like" string, which we must then edit to make compliant), because
+        # it's been around since git-1.5.3, and it's too difficult to
+        # discover which version we're using, or to work around using an
+        # older one.
+        date = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
+    refnames = keywords["refnames"].strip()
+    if refnames.startswith("$Format"):
+        if verbose:
+            print("keywords are unexpanded, not using")
+        raise NotThisMethod("unexpanded keywords, not a git-archive tarball")
+    refs = {r.strip() for r in refnames.strip("()").split(",")}
+    # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of
+    # just "foo-1.0". If we see a "tag: " prefix, prefer those.
+    TAG = "tag: "
+    tags = {r[len(TAG):] for r in refs if r.startswith(TAG)}
+    if not tags:
+        # Either we're using git < 1.8.3, or there really are no tags. We use
+        # a heuristic: assume all version tags have a digit. The old git %d
+        # expansion behaves like git log --decorate=short and strips out the
+        # refs/heads/ and refs/tags/ prefixes that would let us distinguish
+        # between branches and tags. By ignoring refnames without digits, we
+        # filter out many common branch names like "release" and
+        # "stabilization", as well as "HEAD" and "master".
+        tags = {r for r in refs if re.search(r'\d', r)}
+        if verbose:
+            print("discarding '%s', no digits" % ",".join(refs - tags))
+    if verbose:
+        print("likely tags: %s" % ",".join(sorted(tags)))
+    for ref in sorted(tags):
+        # sorting will prefer e.g. "2.0" over "2.0rc1"
+        if ref.startswith(tag_prefix):
+            r = ref[len(tag_prefix):]
+            # Filter out refs that exactly match prefix or that don't start
+            # with a number once the prefix is stripped (mostly a concern
+            # when prefix is '')
+            if not re.match(r'\d', r):
+                continue
+            if verbose:
+                print("picking %s" % r)
+            return {"version": r,
+                    "full-revisionid": keywords["full"].strip(),
+                    "dirty": False, "error": None,
+                    "date": date}
+    # no suitable tags, so version is "0+unknown", but full hex is still there
+    if verbose:
+        print("no suitable tags, using unknown + full revision id")
+    return {"version": "0+unknown",
+            "full-revisionid": keywords["full"].strip(),
+            "dirty": False, "error": "no suitable tags", "date": None}
+
+
+@register_vcs_handler("git", "pieces_from_vcs")
+def git_pieces_from_vcs(tag_prefix, root, verbose, runner=run_command):
+    """Get version from 'git describe' in the root of the source tree.
+
+    This only gets called if the git-archive 'subst' keywords were *not*
+    expanded, and _version.py hasn't already been rewritten with a short
+    version string, meaning we're inside a checked out source tree.
+    """
+    GITS = ["git"]
+    if sys.platform == "win32":
+        GITS = ["git.cmd", "git.exe"]
+
+    _, rc = runner(GITS, ["rev-parse", "--git-dir"], cwd=root,
+                   hide_stderr=True)
+    if rc != 0:
+        if verbose:
+            print("Directory %s not under git control" % root)
+        raise NotThisMethod("'git rev-parse --git-dir' returned error")
+
+    # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty]
+    # if there isn't one, this yields HEX[-dirty] (no NUM)
+    describe_out, rc = runner(GITS, ["describe", "--tags", "--dirty",
+                                     "--always", "--long",
+                                     "--match", "%s*" % tag_prefix],
+                              cwd=root)
+    # --long was added in git-1.5.5
+    if describe_out is None:
+        raise NotThisMethod("'git describe' failed")
+    describe_out = describe_out.strip()
+    full_out, rc = runner(GITS, ["rev-parse", "HEAD"], cwd=root)
+    if full_out is None:
+        raise NotThisMethod("'git rev-parse' failed")
+    full_out = full_out.strip()
+
+    pieces = {}
+    pieces["long"] = full_out
+    pieces["short"] = full_out[:7]  # maybe improved later
+    pieces["error"] = None
+
+    branch_name, rc = runner(GITS, ["rev-parse", "--abbrev-ref", "HEAD"],
+                             cwd=root)
+    # --abbrev-ref was added in git-1.6.3
+    if rc != 0 or branch_name is None:
+        raise NotThisMethod("'git rev-parse --abbrev-ref' returned error")
+    branch_name = branch_name.strip()
+
+    if branch_name == "HEAD":
+        # If we aren't exactly on a branch, pick a branch which represents
+        # the current commit. If all else fails, we are on a branchless
+        # commit.
+        branches, rc = runner(GITS, ["branch", "--contains"], cwd=root)
+        # --contains was added in git-1.5.4
+        if rc != 0 or branches is None:
+            raise NotThisMethod("'git branch --contains' returned error")
+        branches = branches.split("\n")
+
+        # Remove the first line if we're running detached
+        if "(" in branches[0]:
+            branches.pop(0)
+
+        # Strip off the leading "* " from the list of branches.
+        branches = [branch[2:] for branch in branches]
+        if "master" in branches:
+            branch_name = "master"
+        elif not branches:
+            branch_name = None
+        else:
+            # Pick the first branch that is returned. Good or bad.
+            branch_name = branches[0]
+
+    pieces["branch"] = branch_name
+
+    # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty]
+    # TAG might have hyphens.
+    git_describe = describe_out
+
+    # look for -dirty suffix
+    dirty = git_describe.endswith("-dirty")
+    pieces["dirty"] = dirty
+    if dirty:
+        git_describe = git_describe[:git_describe.rindex("-dirty")]
+
+    # now we have TAG-NUM-gHEX or HEX
+
+    if "-" in git_describe:
+        # TAG-NUM-gHEX
+        mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe)
+        if not mo:
+            # unparseable. Maybe git-describe is misbehaving?
+            pieces["error"] = ("unable to parse git-describe output: '%s'"
+                               % describe_out)
+            return pieces
+
+        # tag
+        full_tag = mo.group(1)
+        if not full_tag.startswith(tag_prefix):
+            if verbose:
+                fmt = "tag '%s' doesn't start with prefix '%s'"
+                print(fmt % (full_tag, tag_prefix))
+            pieces["error"] = ("tag '%s' doesn't start with prefix '%s'"
+                               % (full_tag, tag_prefix))
+            return pieces
+        pieces["closest-tag"] = full_tag[len(tag_prefix):]
+
+        # distance: number of commits since tag
+        pieces["distance"] = int(mo.group(2))
+
+        # commit: short hex revision ID
+        pieces["short"] = mo.group(3)
+
+    else:
+        # HEX: no tags
+        pieces["closest-tag"] = None
+        count_out, rc = runner(GITS, ["rev-list", "HEAD", "--count"], cwd=root)
+        pieces["distance"] = int(count_out)  # total number of commits
+
+    # commit date: see ISO-8601 comment in git_versions_from_keywords()
+    date = runner(GITS, ["show", "-s", "--format=%ci", "HEAD"], cwd=root)[0].strip()
+    # Use only the last line.  Previous lines may contain GPG signature
+    # information.
+    date = date.splitlines()[-1]
+    pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
+
+    return pieces
+
+
+def plus_or_dot(pieces):
+    """Return a + if we don't already have one, else return a ."""
+    if "+" in pieces.get("closest-tag", ""):
+        return "."
+    return "+"
+
+
+def render_pep440(pieces):
+    """Build up version string, with post-release "local version identifier".
+
+    Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you
+    get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty
+
+    Exceptions:
+    1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            rendered += plus_or_dot(pieces)
+            rendered += "%d.g%s" % (pieces["distance"], pieces["short"])
+            if pieces["dirty"]:
+                rendered += ".dirty"
+    else:
+        # exception #1
+        rendered = "0+untagged.%d.g%s" % (pieces["distance"],
+                                          pieces["short"])
+        if pieces["dirty"]:
+            rendered += ".dirty"
+    return rendered
+
+
+def render_pep440_branch(pieces):
+    """TAG[[.dev0]+DISTANCE.gHEX[.dirty]] .
+
+    The ".dev0" means not master branch. Note that .dev0 sorts backwards
+    (a feature branch will appear "older" than the master branch).
+
+    Exceptions:
+    1: no tags. 0[.dev0]+untagged.DISTANCE.gHEX[.dirty]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            if pieces["branch"] != "master":
+                rendered += ".dev0"
+            rendered += plus_or_dot(pieces)
+            rendered += "%d.g%s" % (pieces["distance"], pieces["short"])
+            if pieces["dirty"]:
+                rendered += ".dirty"
+    else:
+        # exception #1
+        rendered = "0"
+        if pieces["branch"] != "master":
+            rendered += ".dev0"
+        rendered += "+untagged.%d.g%s" % (pieces["distance"],
+                                          pieces["short"])
+        if pieces["dirty"]:
+            rendered += ".dirty"
+    return rendered
+
+
+def render_pep440_pre(pieces):
+    """TAG[.post0.devDISTANCE] -- No -dirty.
+
+    Exceptions:
+    1: no tags. 0.post0.devDISTANCE
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"]:
+            rendered += ".post0.dev%d" % pieces["distance"]
+    else:
+        # exception #1
+        rendered = "0.post0.dev%d" % pieces["distance"]
+    return rendered
+
+
+def render_pep440_post(pieces):
+    """TAG[.postDISTANCE[.dev0]+gHEX] .
+
+    The ".dev0" means dirty. Note that .dev0 sorts backwards
+    (a dirty tree will appear "older" than the corresponding clean one),
+    but you shouldn't be releasing software with -dirty anyways.
+
+    Exceptions:
+    1: no tags. 0.postDISTANCE[.dev0]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            rendered += ".post%d" % pieces["distance"]
+            if pieces["dirty"]:
+                rendered += ".dev0"
+            rendered += plus_or_dot(pieces)
+            rendered += "g%s" % pieces["short"]
+    else:
+        # exception #1
+        rendered = "0.post%d" % pieces["distance"]
+        if pieces["dirty"]:
+            rendered += ".dev0"
+        rendered += "+g%s" % pieces["short"]
+    return rendered
+
+
+def render_pep440_post_branch(pieces):
+    """TAG[.postDISTANCE[.dev0]+gHEX[.dirty]] .
+
+    The ".dev0" means not master branch.
+
+    Exceptions:
+    1: no tags. 0.postDISTANCE[.dev0]+gHEX[.dirty]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            rendered += ".post%d" % pieces["distance"]
+            if pieces["branch"] != "master":
+                rendered += ".dev0"
+            rendered += plus_or_dot(pieces)
+            rendered += "g%s" % pieces["short"]
+            if pieces["dirty"]:
+                rendered += ".dirty"
+    else:
+        # exception #1
+        rendered = "0.post%d" % pieces["distance"]
+        if pieces["branch"] != "master":
+            rendered += ".dev0"
+        rendered += "+g%s" % pieces["short"]
+        if pieces["dirty"]:
+            rendered += ".dirty"
+    return rendered
+
+
+def render_pep440_old(pieces):
+    """TAG[.postDISTANCE[.dev0]] .
+
+    The ".dev0" means dirty.
+
+    Exceptions:
+    1: no tags. 0.postDISTANCE[.dev0]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            rendered += ".post%d" % pieces["distance"]
+            if pieces["dirty"]:
+                rendered += ".dev0"
+    else:
+        # exception #1
+        rendered = "0.post%d" % pieces["distance"]
+        if pieces["dirty"]:
+            rendered += ".dev0"
+    return rendered
+
+
+def render_git_describe(pieces):
+    """TAG[-DISTANCE-gHEX][-dirty].
+
+    Like 'git describe --tags --dirty --always'.
+
+    Exceptions:
+    1: no tags. HEX[-dirty]  (note: no 'g' prefix)
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"]:
+            rendered += "-%d-g%s" % (pieces["distance"], pieces["short"])
+    else:
+        # exception #1
+        rendered = pieces["short"]
+    if pieces["dirty"]:
+        rendered += "-dirty"
+    return rendered
+
+
+def render_git_describe_long(pieces):
+    """TAG-DISTANCE-gHEX[-dirty].
+
+    Like 'git describe --tags --dirty --always -long'.
+    The distance/hash is unconditional.
+
+    Exceptions:
+    1: no tags. HEX[-dirty]  (note: no 'g' prefix)
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        rendered += "-%d-g%s" % (pieces["distance"], pieces["short"])
+    else:
+        # exception #1
+        rendered = pieces["short"]
+    if pieces["dirty"]:
+        rendered += "-dirty"
+    return rendered
+
+
+def render(pieces, style):
+    """Render the given version pieces into the requested style."""
+    if pieces["error"]:
+        return {"version": "unknown",
+                "full-revisionid": pieces.get("long"),
+                "dirty": None,
+                "error": pieces["error"],
+                "date": None}
+
+    if not style or style == "default":
+        style = "pep440"  # the default
+
+    if style == "pep440":
+        rendered = render_pep440(pieces)
+    elif style == "pep440-branch":
+        rendered = render_pep440_branch(pieces)
+    elif style == "pep440-pre":
+        rendered = render_pep440_pre(pieces)
+    elif style == "pep440-post":
+        rendered = render_pep440_post(pieces)
+    elif style == "pep440-post-branch":
+        rendered = render_pep440_post_branch(pieces)
+    elif style == "pep440-old":
+        rendered = render_pep440_old(pieces)
+    elif style == "git-describe":
+        rendered = render_git_describe(pieces)
+    elif style == "git-describe-long":
+        rendered = render_git_describe_long(pieces)
+    else:
+        raise ValueError("unknown style '%s'" % style)
+
+    return {"version": rendered, "full-revisionid": pieces["long"],
+            "dirty": pieces["dirty"], "error": None,
+            "date": pieces.get("date")}
+
+
+def get_versions():
+    """Get version information or return default if unable to do so."""
+    # I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have
+    # __file__, we can work backwards from there to the root. Some
+    # py2exe/bbfreeze/non-CPython implementations don't do __file__, in which
+    # case we can only use expanded keywords.
+
+    cfg = get_config()
+    verbose = cfg.verbose
+
+    try:
+        return git_versions_from_keywords(get_keywords(), cfg.tag_prefix,
+                                          verbose)
+    except NotThisMethod:
+        pass
+
+    try:
+        root = os.path.realpath(__file__)
+        # versionfile_source is the relative path from the top of the source
+        # tree (where the .git directory might live) to this file. Invert
+        # this to find the root from __file__.
+        for _ in cfg.versionfile_source.split('/'):
+            root = os.path.dirname(root)
+    except NameError:
+        return {"version": "0+unknown", "full-revisionid": None,
+                "dirty": None,
+                "error": "unable to find root of source tree",
+                "date": None}
+
+    try:
+        pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose)
+        return render(pieces, cfg.style)
+    except NotThisMethod:
+        pass
+
+    try:
+        if cfg.parentdir_prefix:
+            return versions_from_parentdir(cfg.parentdir_prefix, root, verbose)
+    except NotThisMethod:
+        pass
+
+    return {"version": "0+unknown", "full-revisionid": None,
+            "dirty": None,
+            "error": "unable to compute version", "date": None}
diff --git a/setup.cfg b/setup.cfg
new file mode 100644
index 000000000..e5d3ee160
--- /dev/null
+++ b/setup.cfg
@@ -0,0 +1,7 @@
+[versioneer]
+VCS = git
+style = pep440
+versionfile_source = libpysal/_version.py
+versionfile_build = libpysal/_version.py
+tag_prefix = v
+parentdir_prefix = libpysal-
\ No newline at end of file
diff --git a/setup.py b/setup.py
index 778594709..93437d9a6 100644
--- a/setup.py
+++ b/setup.py
@@ -1,6 +1,7 @@
 # coding: utf-8
 
 from setuptools import setup, find_packages
+import versioneer
 
 from distutils.command.build_py import build_py
 
@@ -9,11 +10,6 @@
 with open("README.rst", "r", encoding="utf8") as file:
     long_description = file.read()
 
-# Get __version__ from libpysal/__init__.py without importing the package
-# __version__ has to be defined in the first line
-with open("libpysal/__init__.py", "r") as f:
-    exec(f.readline())
-
 # BEFORE importing distutils, remove MANIFEST. distutils doesn't properly
 # update it when the contents of directories change.
 if os.path.exists("MANIFEST"):
@@ -64,7 +60,7 @@ def setup_package():
 
     setup(
         name="libpysal",
-        version=__version__,
+        version=versioneer.get_version(),
         description="Core components of PySAL A library of spatial analysis functions.",
         long_description=long_description,
         long_description_content_type="text/x-rst",
@@ -94,7 +90,7 @@ def setup_package():
         package_data={"libpysal": list(example_data_files)},
         install_requires=install_reqs,
         extras_require=extras_reqs,
-        cmdclass={"build_py": build_py},
+        cmdclass=versioneer.get_cmdclass({"build_py": build_py}),
         python_requires=">=3.7",
     )
 
diff --git a/versioneer.py b/versioneer.py
new file mode 100644
index 000000000..971300705
--- /dev/null
+++ b/versioneer.py
@@ -0,0 +1,2064 @@
+
+# Version: 0.20
+
+"""The Versioneer - like a rocketeer, but for versions.
+
+The Versioneer
+==============
+
+* like a rocketeer, but for versions!
+* https://github.com/python-versioneer/python-versioneer
+* Brian Warner
+* License: Public Domain
+* Compatible with: Python 3.6, 3.7, 3.8, 3.9 and pypy3
+* [![Latest Version][pypi-image]][pypi-url]
+* [![Build Status][travis-image]][travis-url]
+
+This is a tool for managing a recorded version number in distutils-based
+python projects. The goal is to remove the tedious and error-prone "update
+the embedded version string" step from your release process. Making a new
+release should be as easy as recording a new tag in your version-control
+system, and maybe making new tarballs.
+
+
+## Quick Install
+
+* `pip install versioneer` to somewhere in your $PATH
+* add a `[versioneer]` section to your setup.cfg (see [Install](INSTALL.md))
+* run `versioneer install` in your source tree, commit the results
+* Verify version information with `python setup.py version`
+
+## Version Identifiers
+
+Source trees come from a variety of places:
+
+* a version-control system checkout (mostly used by developers)
+* a nightly tarball, produced by build automation
+* a snapshot tarball, produced by a web-based VCS browser, like github's
+  "tarball from tag" feature
+* a release tarball, produced by "setup.py sdist", distributed through PyPI
+
+Within each source tree, the version identifier (either a string or a number,
+this tool is format-agnostic) can come from a variety of places:
+
+* ask the VCS tool itself, e.g. "git describe" (for checkouts), which knows
+  about recent "tags" and an absolute revision-id
+* the name of the directory into which the tarball was unpacked
+* an expanded VCS keyword ($Id$, etc)
+* a `_version.py` created by some earlier build step
+
+For released software, the version identifier is closely related to a VCS
+tag. Some projects use tag names that include more than just the version
+string (e.g. "myproject-1.2" instead of just "1.2"), in which case the tool
+needs to strip the tag prefix to extract the version identifier. For
+unreleased software (between tags), the version identifier should provide
+enough information to help developers recreate the same tree, while also
+giving them an idea of roughly how old the tree is (after version 1.2, before
+version 1.3). Many VCS systems can report a description that captures this,
+for example `git describe --tags --dirty --always` reports things like
+"0.7-1-g574ab98-dirty" to indicate that the checkout is one revision past the
+0.7 tag, has a unique revision id of "574ab98", and is "dirty" (it has
+uncommitted changes).
+
+The version identifier is used for multiple purposes:
+
+* to allow the module to self-identify its version: `myproject.__version__`
+* to choose a name and prefix for a 'setup.py sdist' tarball
+
+## Theory of Operation
+
+Versioneer works by adding a special `_version.py` file into your source
+tree, where your `__init__.py` can import it. This `_version.py` knows how to
+dynamically ask the VCS tool for version information at import time.
+
+`_version.py` also contains `$Revision$` markers, and the installation
+process marks `_version.py` to have this marker rewritten with a tag name
+during the `git archive` command. As a result, generated tarballs will
+contain enough information to get the proper version.
+
+To allow `setup.py` to compute a version too, a `versioneer.py` is added to
+the top level of your source tree, next to `setup.py` and the `setup.cfg`
+that configures it. This overrides several distutils/setuptools commands to
+compute the version when invoked, and changes `setup.py build` and `setup.py
+sdist` to replace `_version.py` with a small static file that contains just
+the generated version data.
+
+## Installation
+
+See [INSTALL.md](./INSTALL.md) for detailed installation instructions.
+
+## Version-String Flavors
+
+Code which uses Versioneer can learn about its version string at runtime by
+importing `_version` from your main `__init__.py` file and running the
+`get_versions()` function. From the "outside" (e.g. in `setup.py`), you can
+import the top-level `versioneer.py` and run `get_versions()`.
+
+Both functions return a dictionary with different flavors of version
+information:
+
+* `['version']`: A condensed version string, rendered using the selected
+  style. This is the most commonly used value for the project's version
+  string. The default "pep440" style yields strings like `0.11`,
+  `0.11+2.g1076c97`, or `0.11+2.g1076c97.dirty`. See the "Styles" section
+  below for alternative styles.
+
+* `['full-revisionid']`: detailed revision identifier. For Git, this is the
+  full SHA1 commit id, e.g. "1076c978a8d3cfc70f408fe5974aa6c092c949ac".
+
+* `['date']`: Date and time of the latest `HEAD` commit. For Git, it is the
+  commit date in ISO 8601 format. This will be None if the date is not
+  available.
+
+* `['dirty']`: a boolean, True if the tree has uncommitted changes. Note that
+  this is only accurate if run in a VCS checkout, otherwise it is likely to
+  be False or None
+
+* `['error']`: if the version string could not be computed, this will be set
+  to a string describing the problem, otherwise it will be None. It may be
+  useful to throw an exception in setup.py if this is set, to avoid e.g.
+  creating tarballs with a version string of "unknown".
+
+Some variants are more useful than others. Including `full-revisionid` in a
+bug report should allow developers to reconstruct the exact code being tested
+(or indicate the presence of local changes that should be shared with the
+developers). `version` is suitable for display in an "about" box or a CLI
+`--version` output: it can be easily compared against release notes and lists
+of bugs fixed in various releases.
+
+The installer adds the following text to your `__init__.py` to place a basic
+version in `YOURPROJECT.__version__`:
+
+    from ._version import get_versions
+    __version__ = get_versions()['version']
+    del get_versions
+
+## Styles
+
+The setup.cfg `style=` configuration controls how the VCS information is
+rendered into a version string.
+
+The default style, "pep440", produces a PEP440-compliant string, equal to the
+un-prefixed tag name for actual releases, and containing an additional "local
+version" section with more detail for in-between builds. For Git, this is
+TAG[+DISTANCE.gHEX[.dirty]] , using information from `git describe --tags
+--dirty --always`. For example "0.11+2.g1076c97.dirty" indicates that the
+tree is like the "1076c97" commit but has uncommitted changes (".dirty"), and
+that this commit is two revisions ("+2") beyond the "0.11" tag. For released
+software (exactly equal to a known tag), the identifier will only contain the
+stripped tag, e.g. "0.11".
+
+Other styles are available. See [details.md](details.md) in the Versioneer
+source tree for descriptions.
+
+## Debugging
+
+Versioneer tries to avoid fatal errors: if something goes wrong, it will tend
+to return a version of "0+unknown". To investigate the problem, run `setup.py
+version`, which will run the version-lookup code in a verbose mode, and will
+display the full contents of `get_versions()` (including the `error` string,
+which may help identify what went wrong).
+
+## Known Limitations
+
+Some situations are known to cause problems for Versioneer. This details the
+most significant ones. More can be found on Github
+[issues page](https://github.com/python-versioneer/python-versioneer/issues).
+
+### Subprojects
+
+Versioneer has limited support for source trees in which `setup.py` is not in
+the root directory (e.g. `setup.py` and `.git/` are *not* siblings). The are
+two common reasons why `setup.py` might not be in the root:
+
+* Source trees which contain multiple subprojects, such as
+  [Buildbot](https://github.com/buildbot/buildbot), which contains both
+  "master" and "slave" subprojects, each with their own `setup.py`,
+  `setup.cfg`, and `tox.ini`. Projects like these produce multiple PyPI
+  distributions (and upload multiple independently-installable tarballs).
+* Source trees whose main purpose is to contain a C library, but which also
+  provide bindings to Python (and perhaps other languages) in subdirectories.
+
+Versioneer will look for `.git` in parent directories, and most operations
+should get the right version string. However `pip` and `setuptools` have bugs
+and implementation details which frequently cause `pip install .` from a
+subproject directory to fail to find a correct version string (so it usually
+defaults to `0+unknown`).
+
+`pip install --editable .` should work correctly. `setup.py install` might
+work too.
+
+Pip-8.1.1 is known to have this problem, but hopefully it will get fixed in
+some later version.
+
+[Bug #38](https://github.com/python-versioneer/python-versioneer/issues/38) is tracking
+this issue. The discussion in
+[PR #61](https://github.com/python-versioneer/python-versioneer/pull/61) describes the
+issue from the Versioneer side in more detail.
+[pip PR#3176](https://github.com/pypa/pip/pull/3176) and
+[pip PR#3615](https://github.com/pypa/pip/pull/3615) contain work to improve
+pip to let Versioneer work correctly.
+
+Versioneer-0.16 and earlier only looked for a `.git` directory next to the
+`setup.cfg`, so subprojects were completely unsupported with those releases.
+
+### Editable installs with setuptools <= 18.5
+
+`setup.py develop` and `pip install --editable .` allow you to install a
+project into a virtualenv once, then continue editing the source code (and
+test) without re-installing after every change.
+
+"Entry-point scripts" (`setup(entry_points={"console_scripts": ..})`) are a
+convenient way to specify executable scripts that should be installed along
+with the python package.
+
+These both work as expected when using modern setuptools. When using
+setuptools-18.5 or earlier, however, certain operations will cause
+`pkg_resources.DistributionNotFound` errors when running the entrypoint
+script, which must be resolved by re-installing the package. This happens
+when the install happens with one version, then the egg_info data is
+regenerated while a different version is checked out. Many setup.py commands
+cause egg_info to be rebuilt (including `sdist`, `wheel`, and installing into
+a different virtualenv), so this can be surprising.
+
+[Bug #83](https://github.com/python-versioneer/python-versioneer/issues/83) describes
+this one, but upgrading to a newer version of setuptools should probably
+resolve it.
+
+
+## Updating Versioneer
+
+To upgrade your project to a new release of Versioneer, do the following:
+
+* install the new Versioneer (`pip install -U versioneer` or equivalent)
+* edit `setup.cfg`, if necessary, to include any new configuration settings
+  indicated by the release notes. See [UPGRADING](./UPGRADING.md) for details.
+* re-run `versioneer install` in your source tree, to replace
+  `SRC/_version.py`
+* commit any changed files
+
+## Future Directions
+
+This tool is designed to make it easily extended to other version-control
+systems: all VCS-specific components are in separate directories like
+src/git/ . The top-level `versioneer.py` script is assembled from these
+components by running make-versioneer.py . In the future, make-versioneer.py
+will take a VCS name as an argument, and will construct a version of
+`versioneer.py` that is specific to the given VCS. It might also take the
+configuration arguments that are currently provided manually during
+installation by editing setup.py . Alternatively, it might go the other
+direction and include code from all supported VCS systems, reducing the
+number of intermediate scripts.
+
+## Similar projects
+
+* [setuptools_scm](https://github.com/pypa/setuptools_scm/) - a non-vendored build-time
+  dependency
+* [minver](https://github.com/jbweston/miniver) - a lightweight reimplementation of
+  versioneer
+* [versioningit](https://github.com/jwodder/versioningit) - a PEP 518-based setuptools
+  plugin
+
+## License
+
+To make Versioneer easier to embed, all its code is dedicated to the public
+domain. The `_version.py` that it creates is also in the public domain.
+Specifically, both are released under the Creative Commons "Public Domain
+Dedication" license (CC0-1.0), as described in
+https://creativecommons.org/publicdomain/zero/1.0/ .
+
+[pypi-image]: https://img.shields.io/pypi/v/versioneer.svg
+[pypi-url]: https://pypi.python.org/pypi/versioneer/
+[travis-image]:
+https://img.shields.io/travis/com/python-versioneer/python-versioneer.svg
+[travis-url]: https://travis-ci.com/github/python-versioneer/python-versioneer
+
+"""
+
+import configparser
+import errno
+import json
+import os
+import re
+import subprocess
+import sys
+
+
+class VersioneerConfig:  # pylint: disable=too-few-public-methods # noqa
+    """Container for Versioneer configuration parameters."""
+
+
+def get_root():
+    """Get the project root directory.
+
+    We require that all commands are run from the project root, i.e. the
+    directory that contains setup.py, setup.cfg, and versioneer.py .
+    """
+    root = os.path.realpath(os.path.abspath(os.getcwd()))
+    setup_py = os.path.join(root, "setup.py")
+    versioneer_py = os.path.join(root, "versioneer.py")
+    if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)):
+        # allow 'python path/to/setup.py COMMAND'
+        root = os.path.dirname(os.path.realpath(os.path.abspath(sys.argv[0])))
+        setup_py = os.path.join(root, "setup.py")
+        versioneer_py = os.path.join(root, "versioneer.py")
+    if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)):
+        err = ("Versioneer was unable to run the project root directory. "
+               "Versioneer requires setup.py to be executed from "
+               "its immediate directory (like 'python setup.py COMMAND'), "
+               "or in a way that lets it use sys.argv[0] to find the root "
+               "(like 'python path/to/setup.py COMMAND').")
+        raise VersioneerBadRootError(err)
+    try:
+        # Certain runtime workflows (setup.py install/develop in a setuptools
+        # tree) execute all dependencies in a single python process, so
+        # "versioneer" may be imported multiple times, and python's shared
+        # module-import table will cache the first one. So we can't use
+        # os.path.dirname(__file__), as that will find whichever
+        # versioneer.py was first imported, even in later projects.
+        my_path = os.path.realpath(os.path.abspath(__file__))
+        me_dir = os.path.normcase(os.path.splitext(my_path)[0])
+        vsr_dir = os.path.normcase(os.path.splitext(versioneer_py)[0])
+        if me_dir != vsr_dir:
+            print("Warning: build in %s is using versioneer.py from %s"
+                  % (os.path.dirname(my_path), versioneer_py))
+    except NameError:
+        pass
+    return root
+
+
+def get_config_from_root(root):
+    """Read the project setup.cfg file to determine Versioneer config."""
+    # This might raise EnvironmentError (if setup.cfg is missing), or
+    # configparser.NoSectionError (if it lacks a [versioneer] section), or
+    # configparser.NoOptionError (if it lacks "VCS="). See the docstring at
+    # the top of versioneer.py for instructions on writing your setup.cfg .
+    setup_cfg = os.path.join(root, "setup.cfg")
+    parser = configparser.ConfigParser()
+    with open(setup_cfg, "r") as cfg_file:
+        parser.read_file(cfg_file)
+    VCS = parser.get("versioneer", "VCS")  # mandatory
+
+    # Dict-like interface for non-mandatory entries
+    section = parser["versioneer"]
+
+    # pylint:disable=attribute-defined-outside-init # noqa
+    cfg = VersioneerConfig()
+    cfg.VCS = VCS
+    cfg.style = section.get("style", "")
+    cfg.versionfile_source = section.get("versionfile_source")
+    cfg.versionfile_build = section.get("versionfile_build")
+    cfg.tag_prefix = section.get("tag_prefix")
+    if cfg.tag_prefix in ("''", '""'):
+        cfg.tag_prefix = ""
+    cfg.parentdir_prefix = section.get("parentdir_prefix")
+    cfg.verbose = section.get("verbose")
+    return cfg
+
+
+class NotThisMethod(Exception):
+    """Exception raised if a method is not valid for the current scenario."""
+
+
+# these dictionaries contain VCS-specific tools
+LONG_VERSION_PY = {}
+HANDLERS = {}
+
+
+def register_vcs_handler(vcs, method):  # decorator
+    """Create decorator to mark a method as the handler of a VCS."""
+    def decorate(f):
+        """Store f in HANDLERS[vcs][method]."""
+        HANDLERS.setdefault(vcs, {})[method] = f
+        return f
+    return decorate
+
+
+# pylint:disable=too-many-arguments,consider-using-with # noqa
+def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False,
+                env=None):
+    """Call the given command(s)."""
+    assert isinstance(commands, list)
+    process = None
+    for command in commands:
+        try:
+            dispcmd = str([command] + args)
+            # remember shell=False, so use git.cmd on windows, not just git
+            process = subprocess.Popen([command] + args, cwd=cwd, env=env,
+                                       stdout=subprocess.PIPE,
+                                       stderr=(subprocess.PIPE if hide_stderr
+                                               else None))
+            break
+        except EnvironmentError:
+            e = sys.exc_info()[1]
+            if e.errno == errno.ENOENT:
+                continue
+            if verbose:
+                print("unable to run %s" % dispcmd)
+                print(e)
+            return None, None
+    else:
+        if verbose:
+            print("unable to find command, tried %s" % (commands,))
+        return None, None
+    stdout = process.communicate()[0].strip().decode()
+    if process.returncode != 0:
+        if verbose:
+            print("unable to run %s (error)" % dispcmd)
+            print("stdout was %s" % stdout)
+        return None, process.returncode
+    return stdout, process.returncode
+
+
+LONG_VERSION_PY['git'] = r'''
+# This file helps to compute a version number in source trees obtained from
+# git-archive tarball (such as those provided by githubs download-from-tag
+# feature). Distribution tarballs (built by setup.py sdist) and build
+# directories (produced by setup.py build) will contain a much shorter file
+# that just contains the computed version number.
+
+# This file is released into the public domain. Generated by
+# versioneer-0.20 (https://github.com/python-versioneer/python-versioneer)
+
+"""Git implementation of _version.py."""
+
+import errno
+import os
+import re
+import subprocess
+import sys
+
+
+def get_keywords():
+    """Get the keywords needed to look up the version information."""
+    # these strings will be replaced by git during git-archive.
+    # setup.py/versioneer.py will grep for the variable names, so they must
+    # each be defined on a line of their own. _version.py will just call
+    # get_keywords().
+    git_refnames = "%(DOLLAR)sFormat:%%d%(DOLLAR)s"
+    git_full = "%(DOLLAR)sFormat:%%H%(DOLLAR)s"
+    git_date = "%(DOLLAR)sFormat:%%ci%(DOLLAR)s"
+    keywords = {"refnames": git_refnames, "full": git_full, "date": git_date}
+    return keywords
+
+
+class VersioneerConfig:  # pylint: disable=too-few-public-methods
+    """Container for Versioneer configuration parameters."""
+
+
+def get_config():
+    """Create, populate and return the VersioneerConfig() object."""
+    # these strings are filled in when 'setup.py versioneer' creates
+    # _version.py
+    cfg = VersioneerConfig()
+    cfg.VCS = "git"
+    cfg.style = "%(STYLE)s"
+    cfg.tag_prefix = "%(TAG_PREFIX)s"
+    cfg.parentdir_prefix = "%(PARENTDIR_PREFIX)s"
+    cfg.versionfile_source = "%(VERSIONFILE_SOURCE)s"
+    cfg.verbose = False
+    return cfg
+
+
+class NotThisMethod(Exception):
+    """Exception raised if a method is not valid for the current scenario."""
+
+
+LONG_VERSION_PY = {}
+HANDLERS = {}
+
+
+def register_vcs_handler(vcs, method):  # decorator
+    """Create decorator to mark a method as the handler of a VCS."""
+    def decorate(f):
+        """Store f in HANDLERS[vcs][method]."""
+        if vcs not in HANDLERS:
+            HANDLERS[vcs] = {}
+        HANDLERS[vcs][method] = f
+        return f
+    return decorate
+
+
+# pylint:disable=too-many-arguments,consider-using-with # noqa
+def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False,
+                env=None):
+    """Call the given command(s)."""
+    assert isinstance(commands, list)
+    process = None
+    for command in commands:
+        try:
+            dispcmd = str([command] + args)
+            # remember shell=False, so use git.cmd on windows, not just git
+            process = subprocess.Popen([command] + args, cwd=cwd, env=env,
+                                       stdout=subprocess.PIPE,
+                                       stderr=(subprocess.PIPE if hide_stderr
+                                               else None))
+            break
+        except EnvironmentError:
+            e = sys.exc_info()[1]
+            if e.errno == errno.ENOENT:
+                continue
+            if verbose:
+                print("unable to run %%s" %% dispcmd)
+                print(e)
+            return None, None
+    else:
+        if verbose:
+            print("unable to find command, tried %%s" %% (commands,))
+        return None, None
+    stdout = process.communicate()[0].strip().decode()
+    if process.returncode != 0:
+        if verbose:
+            print("unable to run %%s (error)" %% dispcmd)
+            print("stdout was %%s" %% stdout)
+        return None, process.returncode
+    return stdout, process.returncode
+
+
+def versions_from_parentdir(parentdir_prefix, root, verbose):
+    """Try to determine the version from the parent directory name.
+
+    Source tarballs conventionally unpack into a directory that includes both
+    the project name and a version string. We will also support searching up
+    two directory levels for an appropriately named parent directory
+    """
+    rootdirs = []
+
+    for _ in range(3):
+        dirname = os.path.basename(root)
+        if dirname.startswith(parentdir_prefix):
+            return {"version": dirname[len(parentdir_prefix):],
+                    "full-revisionid": None,
+                    "dirty": False, "error": None, "date": None}
+        rootdirs.append(root)
+        root = os.path.dirname(root)  # up a level
+
+    if verbose:
+        print("Tried directories %%s but none started with prefix %%s" %%
+              (str(rootdirs), parentdir_prefix))
+    raise NotThisMethod("rootdir doesn't start with parentdir_prefix")
+
+
+@register_vcs_handler("git", "get_keywords")
+def git_get_keywords(versionfile_abs):
+    """Extract version information from the given file."""
+    # the code embedded in _version.py can just fetch the value of these
+    # keywords. When used from setup.py, we don't want to import _version.py,
+    # so we do it with a regexp instead. This function is not used from
+    # _version.py.
+    keywords = {}
+    try:
+        with open(versionfile_abs, "r") as fobj:
+            for line in fobj:
+                if line.strip().startswith("git_refnames ="):
+                    mo = re.search(r'=\s*"(.*)"', line)
+                    if mo:
+                        keywords["refnames"] = mo.group(1)
+                if line.strip().startswith("git_full ="):
+                    mo = re.search(r'=\s*"(.*)"', line)
+                    if mo:
+                        keywords["full"] = mo.group(1)
+                if line.strip().startswith("git_date ="):
+                    mo = re.search(r'=\s*"(.*)"', line)
+                    if mo:
+                        keywords["date"] = mo.group(1)
+    except EnvironmentError:
+        pass
+    return keywords
+
+
+@register_vcs_handler("git", "keywords")
+def git_versions_from_keywords(keywords, tag_prefix, verbose):
+    """Get version information from git keywords."""
+    if "refnames" not in keywords:
+        raise NotThisMethod("Short version file found")
+    date = keywords.get("date")
+    if date is not None:
+        # Use only the last line.  Previous lines may contain GPG signature
+        # information.
+        date = date.splitlines()[-1]
+
+        # git-2.2.0 added "%%cI", which expands to an ISO-8601 -compliant
+        # datestamp. However we prefer "%%ci" (which expands to an "ISO-8601
+        # -like" string, which we must then edit to make compliant), because
+        # it's been around since git-1.5.3, and it's too difficult to
+        # discover which version we're using, or to work around using an
+        # older one.
+        date = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
+    refnames = keywords["refnames"].strip()
+    if refnames.startswith("$Format"):
+        if verbose:
+            print("keywords are unexpanded, not using")
+        raise NotThisMethod("unexpanded keywords, not a git-archive tarball")
+    refs = {r.strip() for r in refnames.strip("()").split(",")}
+    # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of
+    # just "foo-1.0". If we see a "tag: " prefix, prefer those.
+    TAG = "tag: "
+    tags = {r[len(TAG):] for r in refs if r.startswith(TAG)}
+    if not tags:
+        # Either we're using git < 1.8.3, or there really are no tags. We use
+        # a heuristic: assume all version tags have a digit. The old git %%d
+        # expansion behaves like git log --decorate=short and strips out the
+        # refs/heads/ and refs/tags/ prefixes that would let us distinguish
+        # between branches and tags. By ignoring refnames without digits, we
+        # filter out many common branch names like "release" and
+        # "stabilization", as well as "HEAD" and "master".
+        tags = {r for r in refs if re.search(r'\d', r)}
+        if verbose:
+            print("discarding '%%s', no digits" %% ",".join(refs - tags))
+    if verbose:
+        print("likely tags: %%s" %% ",".join(sorted(tags)))
+    for ref in sorted(tags):
+        # sorting will prefer e.g. "2.0" over "2.0rc1"
+        if ref.startswith(tag_prefix):
+            r = ref[len(tag_prefix):]
+            # Filter out refs that exactly match prefix or that don't start
+            # with a number once the prefix is stripped (mostly a concern
+            # when prefix is '')
+            if not re.match(r'\d', r):
+                continue
+            if verbose:
+                print("picking %%s" %% r)
+            return {"version": r,
+                    "full-revisionid": keywords["full"].strip(),
+                    "dirty": False, "error": None,
+                    "date": date}
+    # no suitable tags, so version is "0+unknown", but full hex is still there
+    if verbose:
+        print("no suitable tags, using unknown + full revision id")
+    return {"version": "0+unknown",
+            "full-revisionid": keywords["full"].strip(),
+            "dirty": False, "error": "no suitable tags", "date": None}
+
+
+@register_vcs_handler("git", "pieces_from_vcs")
+def git_pieces_from_vcs(tag_prefix, root, verbose, runner=run_command):
+    """Get version from 'git describe' in the root of the source tree.
+
+    This only gets called if the git-archive 'subst' keywords were *not*
+    expanded, and _version.py hasn't already been rewritten with a short
+    version string, meaning we're inside a checked out source tree.
+    """
+    GITS = ["git"]
+    if sys.platform == "win32":
+        GITS = ["git.cmd", "git.exe"]
+
+    _, rc = runner(GITS, ["rev-parse", "--git-dir"], cwd=root,
+                   hide_stderr=True)
+    if rc != 0:
+        if verbose:
+            print("Directory %%s not under git control" %% root)
+        raise NotThisMethod("'git rev-parse --git-dir' returned error")
+
+    # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty]
+    # if there isn't one, this yields HEX[-dirty] (no NUM)
+    describe_out, rc = runner(GITS, ["describe", "--tags", "--dirty",
+                                     "--always", "--long",
+                                     "--match", "%%s*" %% tag_prefix],
+                              cwd=root)
+    # --long was added in git-1.5.5
+    if describe_out is None:
+        raise NotThisMethod("'git describe' failed")
+    describe_out = describe_out.strip()
+    full_out, rc = runner(GITS, ["rev-parse", "HEAD"], cwd=root)
+    if full_out is None:
+        raise NotThisMethod("'git rev-parse' failed")
+    full_out = full_out.strip()
+
+    pieces = {}
+    pieces["long"] = full_out
+    pieces["short"] = full_out[:7]  # maybe improved later
+    pieces["error"] = None
+
+    branch_name, rc = runner(GITS, ["rev-parse", "--abbrev-ref", "HEAD"],
+                             cwd=root)
+    # --abbrev-ref was added in git-1.6.3
+    if rc != 0 or branch_name is None:
+        raise NotThisMethod("'git rev-parse --abbrev-ref' returned error")
+    branch_name = branch_name.strip()
+
+    if branch_name == "HEAD":
+        # If we aren't exactly on a branch, pick a branch which represents
+        # the current commit. If all else fails, we are on a branchless
+        # commit.
+        branches, rc = runner(GITS, ["branch", "--contains"], cwd=root)
+        # --contains was added in git-1.5.4
+        if rc != 0 or branches is None:
+            raise NotThisMethod("'git branch --contains' returned error")
+        branches = branches.split("\n")
+
+        # Remove the first line if we're running detached
+        if "(" in branches[0]:
+            branches.pop(0)
+
+        # Strip off the leading "* " from the list of branches.
+        branches = [branch[2:] for branch in branches]
+        if "master" in branches:
+            branch_name = "master"
+        elif not branches:
+            branch_name = None
+        else:
+            # Pick the first branch that is returned. Good or bad.
+            branch_name = branches[0]
+
+    pieces["branch"] = branch_name
+
+    # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty]
+    # TAG might have hyphens.
+    git_describe = describe_out
+
+    # look for -dirty suffix
+    dirty = git_describe.endswith("-dirty")
+    pieces["dirty"] = dirty
+    if dirty:
+        git_describe = git_describe[:git_describe.rindex("-dirty")]
+
+    # now we have TAG-NUM-gHEX or HEX
+
+    if "-" in git_describe:
+        # TAG-NUM-gHEX
+        mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe)
+        if not mo:
+            # unparseable. Maybe git-describe is misbehaving?
+            pieces["error"] = ("unable to parse git-describe output: '%%s'"
+                               %% describe_out)
+            return pieces
+
+        # tag
+        full_tag = mo.group(1)
+        if not full_tag.startswith(tag_prefix):
+            if verbose:
+                fmt = "tag '%%s' doesn't start with prefix '%%s'"
+                print(fmt %% (full_tag, tag_prefix))
+            pieces["error"] = ("tag '%%s' doesn't start with prefix '%%s'"
+                               %% (full_tag, tag_prefix))
+            return pieces
+        pieces["closest-tag"] = full_tag[len(tag_prefix):]
+
+        # distance: number of commits since tag
+        pieces["distance"] = int(mo.group(2))
+
+        # commit: short hex revision ID
+        pieces["short"] = mo.group(3)
+
+    else:
+        # HEX: no tags
+        pieces["closest-tag"] = None
+        count_out, rc = runner(GITS, ["rev-list", "HEAD", "--count"], cwd=root)
+        pieces["distance"] = int(count_out)  # total number of commits
+
+    # commit date: see ISO-8601 comment in git_versions_from_keywords()
+    date = runner(GITS, ["show", "-s", "--format=%%ci", "HEAD"], cwd=root)[0].strip()
+    # Use only the last line.  Previous lines may contain GPG signature
+    # information.
+    date = date.splitlines()[-1]
+    pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
+
+    return pieces
+
+
+def plus_or_dot(pieces):
+    """Return a + if we don't already have one, else return a ."""
+    if "+" in pieces.get("closest-tag", ""):
+        return "."
+    return "+"
+
+
+def render_pep440(pieces):
+    """Build up version string, with post-release "local version identifier".
+
+    Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you
+    get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty
+
+    Exceptions:
+    1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            rendered += plus_or_dot(pieces)
+            rendered += "%%d.g%%s" %% (pieces["distance"], pieces["short"])
+            if pieces["dirty"]:
+                rendered += ".dirty"
+    else:
+        # exception #1
+        rendered = "0+untagged.%%d.g%%s" %% (pieces["distance"],
+                                          pieces["short"])
+        if pieces["dirty"]:
+            rendered += ".dirty"
+    return rendered
+
+
+def render_pep440_branch(pieces):
+    """TAG[[.dev0]+DISTANCE.gHEX[.dirty]] .
+
+    The ".dev0" means not master branch. Note that .dev0 sorts backwards
+    (a feature branch will appear "older" than the master branch).
+
+    Exceptions:
+    1: no tags. 0[.dev0]+untagged.DISTANCE.gHEX[.dirty]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            if pieces["branch"] != "master":
+                rendered += ".dev0"
+            rendered += plus_or_dot(pieces)
+            rendered += "%%d.g%%s" %% (pieces["distance"], pieces["short"])
+            if pieces["dirty"]:
+                rendered += ".dirty"
+    else:
+        # exception #1
+        rendered = "0"
+        if pieces["branch"] != "master":
+            rendered += ".dev0"
+        rendered += "+untagged.%%d.g%%s" %% (pieces["distance"],
+                                          pieces["short"])
+        if pieces["dirty"]:
+            rendered += ".dirty"
+    return rendered
+
+
+def render_pep440_pre(pieces):
+    """TAG[.post0.devDISTANCE] -- No -dirty.
+
+    Exceptions:
+    1: no tags. 0.post0.devDISTANCE
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"]:
+            rendered += ".post0.dev%%d" %% pieces["distance"]
+    else:
+        # exception #1
+        rendered = "0.post0.dev%%d" %% pieces["distance"]
+    return rendered
+
+
+def render_pep440_post(pieces):
+    """TAG[.postDISTANCE[.dev0]+gHEX] .
+
+    The ".dev0" means dirty. Note that .dev0 sorts backwards
+    (a dirty tree will appear "older" than the corresponding clean one),
+    but you shouldn't be releasing software with -dirty anyways.
+
+    Exceptions:
+    1: no tags. 0.postDISTANCE[.dev0]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            rendered += ".post%%d" %% pieces["distance"]
+            if pieces["dirty"]:
+                rendered += ".dev0"
+            rendered += plus_or_dot(pieces)
+            rendered += "g%%s" %% pieces["short"]
+    else:
+        # exception #1
+        rendered = "0.post%%d" %% pieces["distance"]
+        if pieces["dirty"]:
+            rendered += ".dev0"
+        rendered += "+g%%s" %% pieces["short"]
+    return rendered
+
+
+def render_pep440_post_branch(pieces):
+    """TAG[.postDISTANCE[.dev0]+gHEX[.dirty]] .
+
+    The ".dev0" means not master branch.
+
+    Exceptions:
+    1: no tags. 0.postDISTANCE[.dev0]+gHEX[.dirty]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            rendered += ".post%%d" %% pieces["distance"]
+            if pieces["branch"] != "master":
+                rendered += ".dev0"
+            rendered += plus_or_dot(pieces)
+            rendered += "g%%s" %% pieces["short"]
+            if pieces["dirty"]:
+                rendered += ".dirty"
+    else:
+        # exception #1
+        rendered = "0.post%%d" %% pieces["distance"]
+        if pieces["branch"] != "master":
+            rendered += ".dev0"
+        rendered += "+g%%s" %% pieces["short"]
+        if pieces["dirty"]:
+            rendered += ".dirty"
+    return rendered
+
+
+def render_pep440_old(pieces):
+    """TAG[.postDISTANCE[.dev0]] .
+
+    The ".dev0" means dirty.
+
+    Exceptions:
+    1: no tags. 0.postDISTANCE[.dev0]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            rendered += ".post%%d" %% pieces["distance"]
+            if pieces["dirty"]:
+                rendered += ".dev0"
+    else:
+        # exception #1
+        rendered = "0.post%%d" %% pieces["distance"]
+        if pieces["dirty"]:
+            rendered += ".dev0"
+    return rendered
+
+
+def render_git_describe(pieces):
+    """TAG[-DISTANCE-gHEX][-dirty].
+
+    Like 'git describe --tags --dirty --always'.
+
+    Exceptions:
+    1: no tags. HEX[-dirty]  (note: no 'g' prefix)
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"]:
+            rendered += "-%%d-g%%s" %% (pieces["distance"], pieces["short"])
+    else:
+        # exception #1
+        rendered = pieces["short"]
+    if pieces["dirty"]:
+        rendered += "-dirty"
+    return rendered
+
+
+def render_git_describe_long(pieces):
+    """TAG-DISTANCE-gHEX[-dirty].
+
+    Like 'git describe --tags --dirty --always -long'.
+    The distance/hash is unconditional.
+
+    Exceptions:
+    1: no tags. HEX[-dirty]  (note: no 'g' prefix)
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        rendered += "-%%d-g%%s" %% (pieces["distance"], pieces["short"])
+    else:
+        # exception #1
+        rendered = pieces["short"]
+    if pieces["dirty"]:
+        rendered += "-dirty"
+    return rendered
+
+
+def render(pieces, style):
+    """Render the given version pieces into the requested style."""
+    if pieces["error"]:
+        return {"version": "unknown",
+                "full-revisionid": pieces.get("long"),
+                "dirty": None,
+                "error": pieces["error"],
+                "date": None}
+
+    if not style or style == "default":
+        style = "pep440"  # the default
+
+    if style == "pep440":
+        rendered = render_pep440(pieces)
+    elif style == "pep440-branch":
+        rendered = render_pep440_branch(pieces)
+    elif style == "pep440-pre":
+        rendered = render_pep440_pre(pieces)
+    elif style == "pep440-post":
+        rendered = render_pep440_post(pieces)
+    elif style == "pep440-post-branch":
+        rendered = render_pep440_post_branch(pieces)
+    elif style == "pep440-old":
+        rendered = render_pep440_old(pieces)
+    elif style == "git-describe":
+        rendered = render_git_describe(pieces)
+    elif style == "git-describe-long":
+        rendered = render_git_describe_long(pieces)
+    else:
+        raise ValueError("unknown style '%%s'" %% style)
+
+    return {"version": rendered, "full-revisionid": pieces["long"],
+            "dirty": pieces["dirty"], "error": None,
+            "date": pieces.get("date")}
+
+
+def get_versions():
+    """Get version information or return default if unable to do so."""
+    # I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have
+    # __file__, we can work backwards from there to the root. Some
+    # py2exe/bbfreeze/non-CPython implementations don't do __file__, in which
+    # case we can only use expanded keywords.
+
+    cfg = get_config()
+    verbose = cfg.verbose
+
+    try:
+        return git_versions_from_keywords(get_keywords(), cfg.tag_prefix,
+                                          verbose)
+    except NotThisMethod:
+        pass
+
+    try:
+        root = os.path.realpath(__file__)
+        # versionfile_source is the relative path from the top of the source
+        # tree (where the .git directory might live) to this file. Invert
+        # this to find the root from __file__.
+        for _ in cfg.versionfile_source.split('/'):
+            root = os.path.dirname(root)
+    except NameError:
+        return {"version": "0+unknown", "full-revisionid": None,
+                "dirty": None,
+                "error": "unable to find root of source tree",
+                "date": None}
+
+    try:
+        pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose)
+        return render(pieces, cfg.style)
+    except NotThisMethod:
+        pass
+
+    try:
+        if cfg.parentdir_prefix:
+            return versions_from_parentdir(cfg.parentdir_prefix, root, verbose)
+    except NotThisMethod:
+        pass
+
+    return {"version": "0+unknown", "full-revisionid": None,
+            "dirty": None,
+            "error": "unable to compute version", "date": None}
+'''
+
+
+@register_vcs_handler("git", "get_keywords")
+def git_get_keywords(versionfile_abs):
+    """Extract version information from the given file."""
+    # the code embedded in _version.py can just fetch the value of these
+    # keywords. When used from setup.py, we don't want to import _version.py,
+    # so we do it with a regexp instead. This function is not used from
+    # _version.py.
+    keywords = {}
+    try:
+        with open(versionfile_abs, "r") as fobj:
+            for line in fobj:
+                if line.strip().startswith("git_refnames ="):
+                    mo = re.search(r'=\s*"(.*)"', line)
+                    if mo:
+                        keywords["refnames"] = mo.group(1)
+                if line.strip().startswith("git_full ="):
+                    mo = re.search(r'=\s*"(.*)"', line)
+                    if mo:
+                        keywords["full"] = mo.group(1)
+                if line.strip().startswith("git_date ="):
+                    mo = re.search(r'=\s*"(.*)"', line)
+                    if mo:
+                        keywords["date"] = mo.group(1)
+    except EnvironmentError:
+        pass
+    return keywords
+
+
+@register_vcs_handler("git", "keywords")
+def git_versions_from_keywords(keywords, tag_prefix, verbose):
+    """Get version information from git keywords."""
+    if "refnames" not in keywords:
+        raise NotThisMethod("Short version file found")
+    date = keywords.get("date")
+    if date is not None:
+        # Use only the last line.  Previous lines may contain GPG signature
+        # information.
+        date = date.splitlines()[-1]
+
+        # git-2.2.0 added "%cI", which expands to an ISO-8601 -compliant
+        # datestamp. However we prefer "%ci" (which expands to an "ISO-8601
+        # -like" string, which we must then edit to make compliant), because
+        # it's been around since git-1.5.3, and it's too difficult to
+        # discover which version we're using, or to work around using an
+        # older one.
+        date = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
+    refnames = keywords["refnames"].strip()
+    if refnames.startswith("$Format"):
+        if verbose:
+            print("keywords are unexpanded, not using")
+        raise NotThisMethod("unexpanded keywords, not a git-archive tarball")
+    refs = {r.strip() for r in refnames.strip("()").split(",")}
+    # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of
+    # just "foo-1.0". If we see a "tag: " prefix, prefer those.
+    TAG = "tag: "
+    tags = {r[len(TAG):] for r in refs if r.startswith(TAG)}
+    if not tags:
+        # Either we're using git < 1.8.3, or there really are no tags. We use
+        # a heuristic: assume all version tags have a digit. The old git %d
+        # expansion behaves like git log --decorate=short and strips out the
+        # refs/heads/ and refs/tags/ prefixes that would let us distinguish
+        # between branches and tags. By ignoring refnames without digits, we
+        # filter out many common branch names like "release" and
+        # "stabilization", as well as "HEAD" and "master".
+        tags = {r for r in refs if re.search(r'\d', r)}
+        if verbose:
+            print("discarding '%s', no digits" % ",".join(refs - tags))
+    if verbose:
+        print("likely tags: %s" % ",".join(sorted(tags)))
+    for ref in sorted(tags):
+        # sorting will prefer e.g. "2.0" over "2.0rc1"
+        if ref.startswith(tag_prefix):
+            r = ref[len(tag_prefix):]
+            # Filter out refs that exactly match prefix or that don't start
+            # with a number once the prefix is stripped (mostly a concern
+            # when prefix is '')
+            if not re.match(r'\d', r):
+                continue
+            if verbose:
+                print("picking %s" % r)
+            return {"version": r,
+                    "full-revisionid": keywords["full"].strip(),
+                    "dirty": False, "error": None,
+                    "date": date}
+    # no suitable tags, so version is "0+unknown", but full hex is still there
+    if verbose:
+        print("no suitable tags, using unknown + full revision id")
+    return {"version": "0+unknown",
+            "full-revisionid": keywords["full"].strip(),
+            "dirty": False, "error": "no suitable tags", "date": None}
+
+
+@register_vcs_handler("git", "pieces_from_vcs")
+def git_pieces_from_vcs(tag_prefix, root, verbose, runner=run_command):
+    """Get version from 'git describe' in the root of the source tree.
+
+    This only gets called if the git-archive 'subst' keywords were *not*
+    expanded, and _version.py hasn't already been rewritten with a short
+    version string, meaning we're inside a checked out source tree.
+    """
+    GITS = ["git"]
+    if sys.platform == "win32":
+        GITS = ["git.cmd", "git.exe"]
+
+    _, rc = runner(GITS, ["rev-parse", "--git-dir"], cwd=root,
+                   hide_stderr=True)
+    if rc != 0:
+        if verbose:
+            print("Directory %s not under git control" % root)
+        raise NotThisMethod("'git rev-parse --git-dir' returned error")
+
+    # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty]
+    # if there isn't one, this yields HEX[-dirty] (no NUM)
+    describe_out, rc = runner(GITS, ["describe", "--tags", "--dirty",
+                                     "--always", "--long",
+                                     "--match", "%s*" % tag_prefix],
+                              cwd=root)
+    # --long was added in git-1.5.5
+    if describe_out is None:
+        raise NotThisMethod("'git describe' failed")
+    describe_out = describe_out.strip()
+    full_out, rc = runner(GITS, ["rev-parse", "HEAD"], cwd=root)
+    if full_out is None:
+        raise NotThisMethod("'git rev-parse' failed")
+    full_out = full_out.strip()
+
+    pieces = {}
+    pieces["long"] = full_out
+    pieces["short"] = full_out[:7]  # maybe improved later
+    pieces["error"] = None
+
+    branch_name, rc = runner(GITS, ["rev-parse", "--abbrev-ref", "HEAD"],
+                             cwd=root)
+    # --abbrev-ref was added in git-1.6.3
+    if rc != 0 or branch_name is None:
+        raise NotThisMethod("'git rev-parse --abbrev-ref' returned error")
+    branch_name = branch_name.strip()
+
+    if branch_name == "HEAD":
+        # If we aren't exactly on a branch, pick a branch which represents
+        # the current commit. If all else fails, we are on a branchless
+        # commit.
+        branches, rc = runner(GITS, ["branch", "--contains"], cwd=root)
+        # --contains was added in git-1.5.4
+        if rc != 0 or branches is None:
+            raise NotThisMethod("'git branch --contains' returned error")
+        branches = branches.split("\n")
+
+        # Remove the first line if we're running detached
+        if "(" in branches[0]:
+            branches.pop(0)
+
+        # Strip off the leading "* " from the list of branches.
+        branches = [branch[2:] for branch in branches]
+        if "master" in branches:
+            branch_name = "master"
+        elif not branches:
+            branch_name = None
+        else:
+            # Pick the first branch that is returned. Good or bad.
+            branch_name = branches[0]
+
+    pieces["branch"] = branch_name
+
+    # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty]
+    # TAG might have hyphens.
+    git_describe = describe_out
+
+    # look for -dirty suffix
+    dirty = git_describe.endswith("-dirty")
+    pieces["dirty"] = dirty
+    if dirty:
+        git_describe = git_describe[:git_describe.rindex("-dirty")]
+
+    # now we have TAG-NUM-gHEX or HEX
+
+    if "-" in git_describe:
+        # TAG-NUM-gHEX
+        mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe)
+        if not mo:
+            # unparseable. Maybe git-describe is misbehaving?
+            pieces["error"] = ("unable to parse git-describe output: '%s'"
+                               % describe_out)
+            return pieces
+
+        # tag
+        full_tag = mo.group(1)
+        if not full_tag.startswith(tag_prefix):
+            if verbose:
+                fmt = "tag '%s' doesn't start with prefix '%s'"
+                print(fmt % (full_tag, tag_prefix))
+            pieces["error"] = ("tag '%s' doesn't start with prefix '%s'"
+                               % (full_tag, tag_prefix))
+            return pieces
+        pieces["closest-tag"] = full_tag[len(tag_prefix):]
+
+        # distance: number of commits since tag
+        pieces["distance"] = int(mo.group(2))
+
+        # commit: short hex revision ID
+        pieces["short"] = mo.group(3)
+
+    else:
+        # HEX: no tags
+        pieces["closest-tag"] = None
+        count_out, rc = runner(GITS, ["rev-list", "HEAD", "--count"], cwd=root)
+        pieces["distance"] = int(count_out)  # total number of commits
+
+    # commit date: see ISO-8601 comment in git_versions_from_keywords()
+    date = runner(GITS, ["show", "-s", "--format=%ci", "HEAD"], cwd=root)[0].strip()
+    # Use only the last line.  Previous lines may contain GPG signature
+    # information.
+    date = date.splitlines()[-1]
+    pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
+
+    return pieces
+
+
+def do_vcs_install(manifest_in, versionfile_source, ipy):
+    """Git-specific installation logic for Versioneer.
+
+    For Git, this means creating/changing .gitattributes to mark _version.py
+    for export-subst keyword substitution.
+    """
+    GITS = ["git"]
+    if sys.platform == "win32":
+        GITS = ["git.cmd", "git.exe"]
+    files = [manifest_in, versionfile_source]
+    if ipy:
+        files.append(ipy)
+    try:
+        my_path = __file__
+        if my_path.endswith(".pyc") or my_path.endswith(".pyo"):
+            my_path = os.path.splitext(my_path)[0] + ".py"
+        versioneer_file = os.path.relpath(my_path)
+    except NameError:
+        versioneer_file = "versioneer.py"
+    files.append(versioneer_file)
+    present = False
+    try:
+        with open(".gitattributes", "r") as fobj:
+            for line in fobj:
+                if line.strip().startswith(versionfile_source):
+                    if "export-subst" in line.strip().split()[1:]:
+                        present = True
+                        break
+    except EnvironmentError:
+        pass
+    if not present:
+        with open(".gitattributes", "a+") as fobj:
+            fobj.write(f"{versionfile_source} export-subst\n")
+        files.append(".gitattributes")
+    run_command(GITS, ["add", "--"] + files)
+
+
+def versions_from_parentdir(parentdir_prefix, root, verbose):
+    """Try to determine the version from the parent directory name.
+
+    Source tarballs conventionally unpack into a directory that includes both
+    the project name and a version string. We will also support searching up
+    two directory levels for an appropriately named parent directory
+    """
+    rootdirs = []
+
+    for _ in range(3):
+        dirname = os.path.basename(root)
+        if dirname.startswith(parentdir_prefix):
+            return {"version": dirname[len(parentdir_prefix):],
+                    "full-revisionid": None,
+                    "dirty": False, "error": None, "date": None}
+        rootdirs.append(root)
+        root = os.path.dirname(root)  # up a level
+
+    if verbose:
+        print("Tried directories %s but none started with prefix %s" %
+              (str(rootdirs), parentdir_prefix))
+    raise NotThisMethod("rootdir doesn't start with parentdir_prefix")
+
+
+SHORT_VERSION_PY = """
+# This file was generated by 'versioneer.py' (0.20) from
+# revision-control system data, or from the parent directory name of an
+# unpacked source archive. Distribution tarballs contain a pre-generated copy
+# of this file.
+
+import json
+
+version_json = '''
+%s
+'''  # END VERSION_JSON
+
+
+def get_versions():
+    return json.loads(version_json)
+"""
+
+
+def versions_from_file(filename):
+    """Try to determine the version from _version.py if present."""
+    try:
+        with open(filename) as f:
+            contents = f.read()
+    except EnvironmentError:
+        raise NotThisMethod("unable to read _version.py")
+    mo = re.search(r"version_json = '''\n(.*)'''  # END VERSION_JSON",
+                   contents, re.M | re.S)
+    if not mo:
+        mo = re.search(r"version_json = '''\r\n(.*)'''  # END VERSION_JSON",
+                       contents, re.M | re.S)
+    if not mo:
+        raise NotThisMethod("no version_json in _version.py")
+    return json.loads(mo.group(1))
+
+
+def write_to_version_file(filename, versions):
+    """Write the given version number to the given _version.py file."""
+    os.unlink(filename)
+    contents = json.dumps(versions, sort_keys=True,
+                          indent=1, separators=(",", ": "))
+    with open(filename, "w") as f:
+        f.write(SHORT_VERSION_PY % contents)
+
+    print("set %s to '%s'" % (filename, versions["version"]))
+
+
+def plus_or_dot(pieces):
+    """Return a + if we don't already have one, else return a ."""
+    if "+" in pieces.get("closest-tag", ""):
+        return "."
+    return "+"
+
+
+def render_pep440(pieces):
+    """Build up version string, with post-release "local version identifier".
+
+    Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you
+    get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty
+
+    Exceptions:
+    1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            rendered += plus_or_dot(pieces)
+            rendered += "%d.g%s" % (pieces["distance"], pieces["short"])
+            if pieces["dirty"]:
+                rendered += ".dirty"
+    else:
+        # exception #1
+        rendered = "0+untagged.%d.g%s" % (pieces["distance"],
+                                          pieces["short"])
+        if pieces["dirty"]:
+            rendered += ".dirty"
+    return rendered
+
+
+def render_pep440_branch(pieces):
+    """TAG[[.dev0]+DISTANCE.gHEX[.dirty]] .
+
+    The ".dev0" means not master branch. Note that .dev0 sorts backwards
+    (a feature branch will appear "older" than the master branch).
+
+    Exceptions:
+    1: no tags. 0[.dev0]+untagged.DISTANCE.gHEX[.dirty]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            if pieces["branch"] != "master":
+                rendered += ".dev0"
+            rendered += plus_or_dot(pieces)
+            rendered += "%d.g%s" % (pieces["distance"], pieces["short"])
+            if pieces["dirty"]:
+                rendered += ".dirty"
+    else:
+        # exception #1
+        rendered = "0"
+        if pieces["branch"] != "master":
+            rendered += ".dev0"
+        rendered += "+untagged.%d.g%s" % (pieces["distance"],
+                                          pieces["short"])
+        if pieces["dirty"]:
+            rendered += ".dirty"
+    return rendered
+
+
+def render_pep440_pre(pieces):
+    """TAG[.post0.devDISTANCE] -- No -dirty.
+
+    Exceptions:
+    1: no tags. 0.post0.devDISTANCE
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"]:
+            rendered += ".post0.dev%d" % pieces["distance"]
+    else:
+        # exception #1
+        rendered = "0.post0.dev%d" % pieces["distance"]
+    return rendered
+
+
+def render_pep440_post(pieces):
+    """TAG[.postDISTANCE[.dev0]+gHEX] .
+
+    The ".dev0" means dirty. Note that .dev0 sorts backwards
+    (a dirty tree will appear "older" than the corresponding clean one),
+    but you shouldn't be releasing software with -dirty anyways.
+
+    Exceptions:
+    1: no tags. 0.postDISTANCE[.dev0]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            rendered += ".post%d" % pieces["distance"]
+            if pieces["dirty"]:
+                rendered += ".dev0"
+            rendered += plus_or_dot(pieces)
+            rendered += "g%s" % pieces["short"]
+    else:
+        # exception #1
+        rendered = "0.post%d" % pieces["distance"]
+        if pieces["dirty"]:
+            rendered += ".dev0"
+        rendered += "+g%s" % pieces["short"]
+    return rendered
+
+
+def render_pep440_post_branch(pieces):
+    """TAG[.postDISTANCE[.dev0]+gHEX[.dirty]] .
+
+    The ".dev0" means not master branch.
+
+    Exceptions:
+    1: no tags. 0.postDISTANCE[.dev0]+gHEX[.dirty]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            rendered += ".post%d" % pieces["distance"]
+            if pieces["branch"] != "master":
+                rendered += ".dev0"
+            rendered += plus_or_dot(pieces)
+            rendered += "g%s" % pieces["short"]
+            if pieces["dirty"]:
+                rendered += ".dirty"
+    else:
+        # exception #1
+        rendered = "0.post%d" % pieces["distance"]
+        if pieces["branch"] != "master":
+            rendered += ".dev0"
+        rendered += "+g%s" % pieces["short"]
+        if pieces["dirty"]:
+            rendered += ".dirty"
+    return rendered
+
+
+def render_pep440_old(pieces):
+    """TAG[.postDISTANCE[.dev0]] .
+
+    The ".dev0" means dirty.
+
+    Exceptions:
+    1: no tags. 0.postDISTANCE[.dev0]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            rendered += ".post%d" % pieces["distance"]
+            if pieces["dirty"]:
+                rendered += ".dev0"
+    else:
+        # exception #1
+        rendered = "0.post%d" % pieces["distance"]
+        if pieces["dirty"]:
+            rendered += ".dev0"
+    return rendered
+
+
+def render_git_describe(pieces):
+    """TAG[-DISTANCE-gHEX][-dirty].
+
+    Like 'git describe --tags --dirty --always'.
+
+    Exceptions:
+    1: no tags. HEX[-dirty]  (note: no 'g' prefix)
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"]:
+            rendered += "-%d-g%s" % (pieces["distance"], pieces["short"])
+    else:
+        # exception #1
+        rendered = pieces["short"]
+    if pieces["dirty"]:
+        rendered += "-dirty"
+    return rendered
+
+
+def render_git_describe_long(pieces):
+    """TAG-DISTANCE-gHEX[-dirty].
+
+    Like 'git describe --tags --dirty --always -long'.
+    The distance/hash is unconditional.
+
+    Exceptions:
+    1: no tags. HEX[-dirty]  (note: no 'g' prefix)
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        rendered += "-%d-g%s" % (pieces["distance"], pieces["short"])
+    else:
+        # exception #1
+        rendered = pieces["short"]
+    if pieces["dirty"]:
+        rendered += "-dirty"
+    return rendered
+
+
+def render(pieces, style):
+    """Render the given version pieces into the requested style."""
+    if pieces["error"]:
+        return {"version": "unknown",
+                "full-revisionid": pieces.get("long"),
+                "dirty": None,
+                "error": pieces["error"],
+                "date": None}
+
+    if not style or style == "default":
+        style = "pep440"  # the default
+
+    if style == "pep440":
+        rendered = render_pep440(pieces)
+    elif style == "pep440-branch":
+        rendered = render_pep440_branch(pieces)
+    elif style == "pep440-pre":
+        rendered = render_pep440_pre(pieces)
+    elif style == "pep440-post":
+        rendered = render_pep440_post(pieces)
+    elif style == "pep440-post-branch":
+        rendered = render_pep440_post_branch(pieces)
+    elif style == "pep440-old":
+        rendered = render_pep440_old(pieces)
+    elif style == "git-describe":
+        rendered = render_git_describe(pieces)
+    elif style == "git-describe-long":
+        rendered = render_git_describe_long(pieces)
+    else:
+        raise ValueError("unknown style '%s'" % style)
+
+    return {"version": rendered, "full-revisionid": pieces["long"],
+            "dirty": pieces["dirty"], "error": None,
+            "date": pieces.get("date")}
+
+
+class VersioneerBadRootError(Exception):
+    """The project root directory is unknown or missing key files."""
+
+
+def get_versions(verbose=False):
+    """Get the project version from whatever source is available.
+
+    Returns dict with two keys: 'version' and 'full'.
+    """
+    if "versioneer" in sys.modules:
+        # see the discussion in cmdclass.py:get_cmdclass()
+        del sys.modules["versioneer"]
+
+    root = get_root()
+    cfg = get_config_from_root(root)
+
+    assert cfg.VCS is not None, "please set [versioneer]VCS= in setup.cfg"
+    handlers = HANDLERS.get(cfg.VCS)
+    assert handlers, "unrecognized VCS '%s'" % cfg.VCS
+    verbose = verbose or cfg.verbose
+    assert cfg.versionfile_source is not None, \
+        "please set versioneer.versionfile_source"
+    assert cfg.tag_prefix is not None, "please set versioneer.tag_prefix"
+
+    versionfile_abs = os.path.join(root, cfg.versionfile_source)
+
+    # extract version from first of: _version.py, VCS command (e.g. 'git
+    # describe'), parentdir. This is meant to work for developers using a
+    # source checkout, for users of a tarball created by 'setup.py sdist',
+    # and for users of a tarball/zipball created by 'git archive' or github's
+    # download-from-tag feature or the equivalent in other VCSes.
+
+    get_keywords_f = handlers.get("get_keywords")
+    from_keywords_f = handlers.get("keywords")
+    if get_keywords_f and from_keywords_f:
+        try:
+            keywords = get_keywords_f(versionfile_abs)
+            ver = from_keywords_f(keywords, cfg.tag_prefix, verbose)
+            if verbose:
+                print("got version from expanded keyword %s" % ver)
+            return ver
+        except NotThisMethod:
+            pass
+
+    try:
+        ver = versions_from_file(versionfile_abs)
+        if verbose:
+            print("got version from file %s %s" % (versionfile_abs, ver))
+        return ver
+    except NotThisMethod:
+        pass
+
+    from_vcs_f = handlers.get("pieces_from_vcs")
+    if from_vcs_f:
+        try:
+            pieces = from_vcs_f(cfg.tag_prefix, root, verbose)
+            ver = render(pieces, cfg.style)
+            if verbose:
+                print("got version from VCS %s" % ver)
+            return ver
+        except NotThisMethod:
+            pass
+
+    try:
+        if cfg.parentdir_prefix:
+            ver = versions_from_parentdir(cfg.parentdir_prefix, root, verbose)
+            if verbose:
+                print("got version from parentdir %s" % ver)
+            return ver
+    except NotThisMethod:
+        pass
+
+    if verbose:
+        print("unable to compute version")
+
+    return {"version": "0+unknown", "full-revisionid": None,
+            "dirty": None, "error": "unable to compute version",
+            "date": None}
+
+
+def get_version():
+    """Get the short version string for this project."""
+    return get_versions()["version"]
+
+
+def get_cmdclass(cmdclass=None):
+    """Get the custom setuptools/distutils subclasses used by Versioneer.
+
+    If the package uses a different cmdclass (e.g. one from numpy), it
+    should be provide as an argument.
+    """
+    if "versioneer" in sys.modules:
+        del sys.modules["versioneer"]
+        # this fixes the "python setup.py develop" case (also 'install' and
+        # 'easy_install .'), in which subdependencies of the main project are
+        # built (using setup.py bdist_egg) in the same python process. Assume
+        # a main project A and a dependency B, which use different versions
+        # of Versioneer. A's setup.py imports A's Versioneer, leaving it in
+        # sys.modules by the time B's setup.py is executed, causing B to run
+        # with the wrong versioneer. Setuptools wraps the sub-dep builds in a
+        # sandbox that restores sys.modules to it's pre-build state, so the
+        # parent is protected against the child's "import versioneer". By
+        # removing ourselves from sys.modules here, before the child build
+        # happens, we protect the child from the parent's versioneer too.
+        # Also see https://github.com/python-versioneer/python-versioneer/issues/52
+
+    cmds = {} if cmdclass is None else cmdclass.copy()
+
+    # we add "version" to both distutils and setuptools
+    from distutils.core import Command
+
+    class cmd_version(Command):
+        description = "report generated version string"
+        user_options = []
+        boolean_options = []
+
+        def initialize_options(self):
+            pass
+
+        def finalize_options(self):
+            pass
+
+        def run(self):
+            vers = get_versions(verbose=True)
+            print("Version: %s" % vers["version"])
+            print(" full-revisionid: %s" % vers.get("full-revisionid"))
+            print(" dirty: %s" % vers.get("dirty"))
+            print(" date: %s" % vers.get("date"))
+            if vers["error"]:
+                print(" error: %s" % vers["error"])
+    cmds["version"] = cmd_version
+
+    # we override "build_py" in both distutils and setuptools
+    #
+    # most invocation pathways end up running build_py:
+    #  distutils/build -> build_py
+    #  distutils/install -> distutils/build ->..
+    #  setuptools/bdist_wheel -> distutils/install ->..
+    #  setuptools/bdist_egg -> distutils/install_lib -> build_py
+    #  setuptools/install -> bdist_egg ->..
+    #  setuptools/develop -> ?
+    #  pip install:
+    #   copies source tree to a tempdir before running egg_info/etc
+    #   if .git isn't copied too, 'git describe' will fail
+    #   then does setup.py bdist_wheel, or sometimes setup.py install
+    #  setup.py egg_info -> ?
+
+    # we override different "build_py" commands for both environments
+    if 'build_py' in cmds:
+        _build_py = cmds['build_py']
+    elif "setuptools" in sys.modules:
+        from setuptools.command.build_py import build_py as _build_py
+    else:
+        from distutils.command.build_py import build_py as _build_py
+
+    class cmd_build_py(_build_py):
+        def run(self):
+            root = get_root()
+            cfg = get_config_from_root(root)
+            versions = get_versions()
+            _build_py.run(self)
+            # now locate _version.py in the new build/ directory and replace
+            # it with an updated value
+            if cfg.versionfile_build:
+                target_versionfile = os.path.join(self.build_lib,
+                                                  cfg.versionfile_build)
+                print("UPDATING %s" % target_versionfile)
+                write_to_version_file(target_versionfile, versions)
+    cmds["build_py"] = cmd_build_py
+
+    if 'build_ext' in cmds:
+        _build_ext = cmds['build_ext']
+    elif "setuptools" in sys.modules:
+        from setuptools.command.build_ext import build_ext as _build_ext
+    else:
+        from distutils.command.build_ext import build_ext as _build_ext
+
+    class cmd_build_ext(_build_ext):
+        def run(self):
+            root = get_root()
+            cfg = get_config_from_root(root)
+            versions = get_versions()
+            _build_ext.run(self)
+            if self.inplace:
+                # build_ext --inplace will only build extensions in
+                # build/lib<..> dir with no _version.py to write to.
+                # As in place builds will already have a _version.py
+                # in the module dir, we do not need to write one.
+                return
+            # now locate _version.py in the new build/ directory and replace
+            # it with an updated value
+            target_versionfile = os.path.join(self.build_lib,
+                                              cfg.versionfile_build)
+            print("UPDATING %s" % target_versionfile)
+            write_to_version_file(target_versionfile, versions)
+    cmds["build_ext"] = cmd_build_ext
+
+    if "cx_Freeze" in sys.modules:  # cx_freeze enabled?
+        from cx_Freeze.dist import build_exe as _build_exe
+        # nczeczulin reports that py2exe won't like the pep440-style string
+        # as FILEVERSION, but it can be used for PRODUCTVERSION, e.g.
+        # setup(console=[{
+        #   "version": versioneer.get_version().split("+", 1)[0], # FILEVERSION
+        #   "product_version": versioneer.get_version(),
+        #   ...
+
+        class cmd_build_exe(_build_exe):
+            def run(self):
+                root = get_root()
+                cfg = get_config_from_root(root)
+                versions = get_versions()
+                target_versionfile = cfg.versionfile_source
+                print("UPDATING %s" % target_versionfile)
+                write_to_version_file(target_versionfile, versions)
+
+                _build_exe.run(self)
+                os.unlink(target_versionfile)
+                with open(cfg.versionfile_source, "w") as f:
+                    LONG = LONG_VERSION_PY[cfg.VCS]
+                    f.write(LONG %
+                            {"DOLLAR": "$",
+                             "STYLE": cfg.style,
+                             "TAG_PREFIX": cfg.tag_prefix,
+                             "PARENTDIR_PREFIX": cfg.parentdir_prefix,
+                             "VERSIONFILE_SOURCE": cfg.versionfile_source,
+                             })
+        cmds["build_exe"] = cmd_build_exe
+        del cmds["build_py"]
+
+    if 'py2exe' in sys.modules:  # py2exe enabled?
+        from py2exe.distutils_buildexe import py2exe as _py2exe
+
+        class cmd_py2exe(_py2exe):
+            def run(self):
+                root = get_root()
+                cfg = get_config_from_root(root)
+                versions = get_versions()
+                target_versionfile = cfg.versionfile_source
+                print("UPDATING %s" % target_versionfile)
+                write_to_version_file(target_versionfile, versions)
+
+                _py2exe.run(self)
+                os.unlink(target_versionfile)
+                with open(cfg.versionfile_source, "w") as f:
+                    LONG = LONG_VERSION_PY[cfg.VCS]
+                    f.write(LONG %
+                            {"DOLLAR": "$",
+                             "STYLE": cfg.style,
+                             "TAG_PREFIX": cfg.tag_prefix,
+                             "PARENTDIR_PREFIX": cfg.parentdir_prefix,
+                             "VERSIONFILE_SOURCE": cfg.versionfile_source,
+                             })
+        cmds["py2exe"] = cmd_py2exe
+
+    # we override different "sdist" commands for both environments
+    if 'sdist' in cmds:
+        _sdist = cmds['sdist']
+    elif "setuptools" in sys.modules:
+        from setuptools.command.sdist import sdist as _sdist
+    else:
+        from distutils.command.sdist import sdist as _sdist
+
+    class cmd_sdist(_sdist):
+        def run(self):
+            versions = get_versions()
+            # pylint:disable=attribute-defined-outside-init # noqa
+            self._versioneer_generated_versions = versions
+            # unless we update this, the command will keep using the old
+            # version
+            self.distribution.metadata.version = versions["version"]
+            return _sdist.run(self)
+
+        def make_release_tree(self, base_dir, files):
+            root = get_root()
+            cfg = get_config_from_root(root)
+            _sdist.make_release_tree(self, base_dir, files)
+            # now locate _version.py in the new base_dir directory
+            # (remembering that it may be a hardlink) and replace it with an
+            # updated value
+            target_versionfile = os.path.join(base_dir, cfg.versionfile_source)
+            print("UPDATING %s" % target_versionfile)
+            write_to_version_file(target_versionfile,
+                                  self._versioneer_generated_versions)
+    cmds["sdist"] = cmd_sdist
+
+    return cmds
+
+
+CONFIG_ERROR = """
+setup.cfg is missing the necessary Versioneer configuration. You need
+a section like:
+
+ [versioneer]
+ VCS = git
+ style = pep440
+ versionfile_source = src/myproject/_version.py
+ versionfile_build = myproject/_version.py
+ tag_prefix =
+ parentdir_prefix = myproject-
+
+You will also need to edit your setup.py to use the results:
+
+ import versioneer
+ setup(version=versioneer.get_version(),
+       cmdclass=versioneer.get_cmdclass(), ...)
+
+Please read the docstring in ./versioneer.py for configuration instructions,
+edit setup.cfg, and re-run the installer or 'python versioneer.py setup'.
+"""
+
+SAMPLE_CONFIG = """
+# See the docstring in versioneer.py for instructions. Note that you must
+# re-run 'versioneer.py setup' after changing this section, and commit the
+# resulting files.
+
+[versioneer]
+#VCS = git
+#style = pep440
+#versionfile_source =
+#versionfile_build =
+#tag_prefix =
+#parentdir_prefix =
+
+"""
+
+OLD_SNIPPET = """
+from ._version import get_versions
+__version__ = get_versions()['version']
+del get_versions
+"""
+
+INIT_PY_SNIPPET = """
+from . import {0}
+__version__ = {0}.get_versions()['version']
+"""
+
+
+def do_setup():
+    """Do main VCS-independent setup function for installing Versioneer."""
+    root = get_root()
+    try:
+        cfg = get_config_from_root(root)
+    except (EnvironmentError, configparser.NoSectionError,
+            configparser.NoOptionError) as e:
+        if isinstance(e, (EnvironmentError, configparser.NoSectionError)):
+            print("Adding sample versioneer config to setup.cfg",
+                  file=sys.stderr)
+            with open(os.path.join(root, "setup.cfg"), "a") as f:
+                f.write(SAMPLE_CONFIG)
+        print(CONFIG_ERROR, file=sys.stderr)
+        return 1
+
+    print(" creating %s" % cfg.versionfile_source)
+    with open(cfg.versionfile_source, "w") as f:
+        LONG = LONG_VERSION_PY[cfg.VCS]
+        f.write(LONG % {"DOLLAR": "$",
+                        "STYLE": cfg.style,
+                        "TAG_PREFIX": cfg.tag_prefix,
+                        "PARENTDIR_PREFIX": cfg.parentdir_prefix,
+                        "VERSIONFILE_SOURCE": cfg.versionfile_source,
+                        })
+
+    ipy = os.path.join(os.path.dirname(cfg.versionfile_source),
+                       "__init__.py")
+    if os.path.exists(ipy):
+        try:
+            with open(ipy, "r") as f:
+                old = f.read()
+        except EnvironmentError:
+            old = ""
+        module = os.path.splitext(os.path.basename(cfg.versionfile_source))[0]
+        snippet = INIT_PY_SNIPPET.format(module)
+        if OLD_SNIPPET in old:
+            print(" replacing boilerplate in %s" % ipy)
+            with open(ipy, "w") as f:
+                f.write(old.replace(OLD_SNIPPET, snippet))
+        elif snippet not in old:
+            print(" appending to %s" % ipy)
+            with open(ipy, "a") as f:
+                f.write(snippet)
+        else:
+            print(" %s unmodified" % ipy)
+    else:
+        print(" %s doesn't exist, ok" % ipy)
+        ipy = None
+
+    # Make sure both the top-level "versioneer.py" and versionfile_source
+    # (PKG/_version.py, used by runtime code) are in MANIFEST.in, so
+    # they'll be copied into source distributions. Pip won't be able to
+    # install the package without this.
+    manifest_in = os.path.join(root, "MANIFEST.in")
+    simple_includes = set()
+    try:
+        with open(manifest_in, "r") as f:
+            for line in f:
+                if line.startswith("include "):
+                    for include in line.split()[1:]:
+                        simple_includes.add(include)
+    except EnvironmentError:
+        pass
+    # That doesn't cover everything MANIFEST.in can do
+    # (http://docs.python.org/2/distutils/sourcedist.html#commands), so
+    # it might give some false negatives. Appending redundant 'include'
+    # lines is safe, though.
+    if "versioneer.py" not in simple_includes:
+        print(" appending 'versioneer.py' to MANIFEST.in")
+        with open(manifest_in, "a") as f:
+            f.write("include versioneer.py\n")
+    else:
+        print(" 'versioneer.py' already in MANIFEST.in")
+    if cfg.versionfile_source not in simple_includes:
+        print(" appending versionfile_source ('%s') to MANIFEST.in" %
+              cfg.versionfile_source)
+        with open(manifest_in, "a") as f:
+            f.write("include %s\n" % cfg.versionfile_source)
+    else:
+        print(" versionfile_source already in MANIFEST.in")
+
+    # Make VCS-specific changes. For git, this means creating/changing
+    # .gitattributes to mark _version.py for export-subst keyword
+    # substitution.
+    do_vcs_install(manifest_in, cfg.versionfile_source, ipy)
+    return 0
+
+
+def scan_setup_py():
+    """Validate the contents of setup.py against Versioneer's expectations."""
+    found = set()
+    setters = False
+    errors = 0
+    with open("setup.py", "r") as f:
+        for line in f.readlines():
+            if "import versioneer" in line:
+                found.add("import")
+            if "versioneer.get_cmdclass()" in line:
+                found.add("cmdclass")
+            if "versioneer.get_version()" in line:
+                found.add("get_version")
+            if "versioneer.VCS" in line:
+                setters = True
+            if "versioneer.versionfile_source" in line:
+                setters = True
+    if len(found) != 3:
+        print("")
+        print("Your setup.py appears to be missing some important items")
+        print("(but I might be wrong). Please make sure it has something")
+        print("roughly like the following:")
+        print("")
+        print(" import versioneer")
+        print(" setup( version=versioneer.get_version(),")
+        print("        cmdclass=versioneer.get_cmdclass(),  ...)")
+        print("")
+        errors += 1
+    if setters:
+        print("You should remove lines like 'versioneer.VCS = ' and")
+        print("'versioneer.versionfile_source = ' . This configuration")
+        print("now lives in setup.cfg, and should be removed from setup.py")
+        print("")
+        errors += 1
+    return errors
+
+
+if __name__ == "__main__":
+    cmd = sys.argv[1]
+    if cmd == "setup":
+        errors = do_setup()
+        errors += scan_setup_py()
+        if errors:
+            sys.exit(1)

From 1dd11eadfa4c37371faa8a0a39b24c689993ea81 Mon Sep 17 00:00:00 2001
From: Elliott Sales de Andrade <quantum.analyst@gmail.com>
Date: Sat, 4 Sep 2021 20:11:12 -0400
Subject: [PATCH 057/162] Fix test_voronoi on other architectures.

Floating point results shouldn't be checked exactly, as they might
differ on other machines.
---
 libpysal/cg/tests/test_voronoi.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/libpysal/cg/tests/test_voronoi.py b/libpysal/cg/tests/test_voronoi.py
index 6eeece3a0..f1681333f 100644
--- a/libpysal/cg/tests/test_voronoi.py
+++ b/libpysal/cg/tests/test_voronoi.py
@@ -1,6 +1,9 @@
+import unittest
+
+import numpy as np
+
 from ..voronoi import voronoi, voronoi_frames
 from ..shapes import Polygon, asShape
-import unittest
 
 
 class Voronoi(unittest.TestCase):
@@ -24,7 +27,7 @@ def test_voronoi(self):
         regions, vertices = voronoi(self.points)
         self.assertEqual(regions, [[1, 3, 2], [4, 5, 1, 0], [0, 1, 7, 6], [9, 0, 8]])
 
-        self.assertTrue(vertices.tolist() == self.vertices)
+        np.testing.assert_array_almost_equal(vertices, self.vertices)
 
     def test_voronoi_frames(self):
         r_df, p_df = voronoi_frames(self.points)

From 6560e4b00ab3dabc093750c5b77360401cdbf606 Mon Sep 17 00:00:00 2001
From: Elliott Sales de Andrade <quantum.analyst@gmail.com>
Date: Sat, 4 Sep 2021 20:34:51 -0400
Subject: [PATCH 058/162] Fix Parameter list warnings in docs.

---
 libpysal/cg/alpha_shapes.py      | 4 ++--
 libpysal/cg/ops/_accessors.py    | 4 ++--
 libpysal/cg/ops/_shapely.py      | 4 ++--
 libpysal/weights/_contW_lists.py | 4 ++--
 libpysal/weights/contiguity.py   | 6 +++---
 libpysal/weights/raster.py       | 8 ++++----
 libpysal/weights/spatial_lag.py  | 4 ++--
 libpysal/weights/weights.py      | 8 ++++----
 8 files changed, 21 insertions(+), 21 deletions(-)

diff --git a/libpysal/cg/alpha_shapes.py b/libpysal/cg/alpha_shapes.py
index 73eb77d9a..ba29a2e31 100644
--- a/libpysal/cg/alpha_shapes.py
+++ b/libpysal/cg/alpha_shapes.py
@@ -649,8 +649,8 @@ def construct_bounding_circles(alpha_shape, radius):
     """Construct the bounding circles for an alpha shape, given the radius
     computed from the `alpha_shape_auto` method.
 
-    Arguments
-    ---------
+    Parameters
+    ----------
     alpha_shape : shapely.Polygon
         An alpha-hull with the input radius.
 
diff --git a/libpysal/cg/ops/_accessors.py b/libpysal/cg/ops/_accessors.py
index 0284155d0..4ee72cb42 100644
--- a/libpysal/cg/ops/_accessors.py
+++ b/libpysal/cg/ops/_accessors.py
@@ -27,8 +27,8 @@ def get_attr(df, geom_col="geometry", inplace=False, attr=None):
 _doc_template = """
 Tabular accessor to grab a geometric object's {n} attribute.
 
-Arguments
----------
+Parameters
+----------
 df : pandas.DataFrame
     A pandas.Dataframe with a geometry column.
 geom_col : str
diff --git a/libpysal/cg/ops/_shapely.py b/libpysal/cg/ops/_shapely.py
index 515b5a756..b2bb9600f 100644
--- a/libpysal/cg/ops/_shapely.py
+++ b/libpysal/cg/ops/_shapely.py
@@ -56,8 +56,8 @@ def _atomic_op(df, geom_col="geometry", inplace=False, _func=None, **kwargs):
 _doc_template = """
 Tabular version of pysal.contrib.shapely_ext.{n}
 
-Arguments
----------
+Parameters
+----------
 df : pandas.DataFrame
     A pandas dataframe with a geometry column.
 geom_col : str
diff --git a/libpysal/weights/_contW_lists.py b/libpysal/weights/_contW_lists.py
index 59494ad5d..2aada984c 100644
--- a/libpysal/weights/_contW_lists.py
+++ b/libpysal/weights/_contW_lists.py
@@ -45,8 +45,8 @@ class ContiguityWeightsLists:
 
     def __init__(self, collection, wttype=1):
         """
-        Arguments
-        =========
+        Parameters
+        ----------
 
         collection: PySAL PolygonCollection
 
diff --git a/libpysal/weights/contiguity.py b/libpysal/weights/contiguity.py
index ba9dda572..72d9e6f6c 100644
--- a/libpysal/weights/contiguity.py
+++ b/libpysal/weights/contiguity.py
@@ -533,8 +533,8 @@ def _from_dataframe(df, **kwargs):
     If the input dataframe is of any other geometry type than "Point",
     a value error is raised. 
 
-    Arguments
-    ---------
+    Parameters
+    ----------
     df          :   pandas.DataFrame
                     dataframe containing point geometries for a 
                     voronoi diagram.
@@ -564,7 +564,7 @@ def _build(polygons, criterion="rook", ids=None):
     This is a developer-facing function to construct a spatial weights object. 
 
     Parameters
-    ---------
+    ----------
     polygons    : list
                   list of pysal polygons to use to build contiguity
     criterion   : string
diff --git a/libpysal/weights/raster.py b/libpysal/weights/raster.py
index 3aa8ef8f5..8ee4a6cb1 100644
--- a/libpysal/weights/raster.py
+++ b/libpysal/weights/raster.py
@@ -307,7 +307,7 @@ def w2da(data, w, attrs={}, coords=None):
     Creates xarray.DataArray object from passed data aligned with W object.
 
     Parameters
-    ---------
+    ----------
     data : array/list/pd.Series
         1d array-like data with dimensionality conforming to w
     w : libpysal.weights.W
@@ -350,7 +350,7 @@ def wsp2da(data, wsp, attrs={}, coords=None):
     Creates xarray.DataArray object from passed data aligned with WSP object.
 
     Parameters
-    ---------
+    ----------
     data : array/list/pd.Series
         1d array-like data with dimensionality conforming to wsp
     wsp : libpysal.weights.WSP
@@ -393,7 +393,7 @@ def testDataArray(shape=(3, 4, 4), time=False, rand=False, missing_vals=True):
     Creates 2 or 3 dimensional test xarray.DataArray object
 
     Parameters
-    ---------
+    ----------
     shape : tuple
         Tuple containing shape of the DataArray aligned with
         following dimension = (lat, lon) or (layer, lat, lon)
@@ -515,7 +515,7 @@ def _index2da(data, index, attrs, coords):
     Creates xarray.DataArray object from passed data
 
     Parameters
-    ---------
+    ----------
     data : array/list/pd.Series
         1d array-like data with dimensionality conforming to index
     index : pd.MultiIndex
diff --git a/libpysal/weights/spatial_lag.py b/libpysal/weights/spatial_lag.py
index f4c8a8d5a..ceab17744 100644
--- a/libpysal/weights/spatial_lag.py
+++ b/libpysal/weights/spatial_lag.py
@@ -194,8 +194,8 @@ def _resolve_ties(idx, normalized_labels, tally, neighbors, method, w):
     an attempt to break the tie, but if it fails, a random tiebreaker will be
     selected.
 
-    Arguments
-    ---------
+    Parameters
+    ----------
     idx                 : int
                           index (aligned with `normalized_labels`) of the 
                           current observation being resolved.
diff --git a/libpysal/weights/weights.py b/libpysal/weights/weights.py
index f0f42e4ee..e3413c611 100644
--- a/libpysal/weights/weights.py
+++ b/libpysal/weights/weights.py
@@ -182,8 +182,8 @@ def to_file(self, path="", format=None):
 
         See libpysal.io.FileIO for more information. 
 
-        Arguments
-        ---------
+        Parameters
+        ----------
         path    :   string
                     location to save the file
         format  :   string
@@ -203,8 +203,8 @@ def from_file(cls, path="", format=None):
         """
         Read a weights file into a W object. 
 
-        Arguments
-        ---------
+        Parameters
+        ----------
         path    :   string
                     location to save the file
         format  :   string

From 6eb9fae9b8a03fdc8c07b0701796495ce7ebc1b6 Mon Sep 17 00:00:00 2001
From: Elliott Sales de Andrade <quantum.analyst@gmail.com>
Date: Sat, 4 Sep 2021 20:42:12 -0400
Subject: [PATCH 059/162] Fix unexpected unindent warnings in docs.

---
 libpysal/weights/contiguity.py | 8 ++++----
 libpysal/weights/raster.py     | 4 ++--
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/libpysal/weights/contiguity.py b/libpysal/weights/contiguity.py
index 72d9e6f6c..8522f128d 100644
--- a/libpysal/weights/contiguity.py
+++ b/libpysal/weights/contiguity.py
@@ -232,8 +232,8 @@ def from_xarray(
         Notes
         -----
         1. Lower order contiguities are also selected.
-        2. Returned object contains `index` attribute that includes a 
-        `Pandas.MultiIndex` object from the DataArray.
+        2. Returned object contains `index` attribute that includes a
+           `Pandas.MultiIndex` object from the DataArray.
 
         See Also
         --------
@@ -462,8 +462,8 @@ def from_xarray(
         Notes
         -----
         1. Lower order contiguities are also selected.
-        2. Returned object contains `index` attribute that includes a 
-        `Pandas.MultiIndex` object from the DataArray.
+        2. Returned object contains `index` attribute that includes a
+           `Pandas.MultiIndex` object from the DataArray.
 
         See Also
         --------
diff --git a/libpysal/weights/raster.py b/libpysal/weights/raster.py
index 8ee4a6cb1..90ea91388 100644
--- a/libpysal/weights/raster.py
+++ b/libpysal/weights/raster.py
@@ -74,7 +74,7 @@ def da2W(
     -----
     1. Lower order contiguities are also selected.
     2. Returned object contains `index` attribute that includes a
-    `Pandas.MultiIndex` object from the DataArray.
+       `Pandas.MultiIndex` object from the DataArray.
 
     Examples
     --------
@@ -175,7 +175,7 @@ def da2WSP(
     -----
     1. Lower order contiguities are also selected.
     2. Returned object contains `index` attribute that includes a
-    `Pandas.MultiIndex` object from the DataArray.
+       `Pandas.MultiIndex` object from the DataArray.
 
     Examples
     --------

From 679696c8b2e5623b872283b21dd2642f5701fd6a Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Wed, 8 Sep 2021 21:23:57 +0100
Subject: [PATCH 060/162] add default option handling behavior in adjlist
 islands

---
 libpysal/weights/weights.py | 15 ++++++++++-----
 1 file changed, 10 insertions(+), 5 deletions(-)

diff --git a/libpysal/weights/weights.py b/libpysal/weights/weights.py
index e9828a77d..70a836d9d 100644
--- a/libpysal/weights/weights.py
+++ b/libpysal/weights/weights.py
@@ -311,6 +311,13 @@ def to_adjlist(
             raise ImportError(
                 "pandas must be installed & importable to use this method"
             )
+        if (drop_islands is None) and not (self.silence_warnings):
+            warnings.warn(
+                "In the next version of libpysal, observations with no neighbors will be included in adjacency lists as loops (row with the same focal and neighbor) with zero weight. In the current version, observations with no neighbors are dropped. If you would like to keep the current behavior, use drop_islands=True in this function",
+                DeprecationWarning,
+            )
+            drop_islands = True
+
         links = []
         focal_ix, neighbor_ix = self.sparse.nonzero()
         names = np.asarray(self.id_order)
@@ -1363,21 +1370,19 @@ def __init__(self, sparse, id_order=None, index=None):
                 self._cache["id_order"] = self._id_order
         # temp addition of index attribute
         import pandas as pd  # will be removed after refactoring is done
+
         if index is not None:
             if not isinstance(index, (pd.Index, pd.MultiIndex, pd.RangeIndex)):
                 raise TypeError("index must be an instance of pandas.Index dtype")
             if len(index) != self.n:
-                raise ValueError(
-                    "Number of values in index must match shape of sparse"
-                )
+                raise ValueError("Number of values in index must match shape of sparse")
         else:
             index = pd.RangeIndex(self.n)
         self.index = index
 
     @property
     def id_order(self):
-        """An ordered list of ids, assumed to match the ordering in ``sparse``.
-        """
+        """An ordered list of ids, assumed to match the ordering in ``sparse``."""
         # Temporary solution until the refactoring is finished
         if "id_order" not in self._cache:
             if hasattr(self, "index"):

From 3cd1b29300ff2bcff39dc303bcc65c7e69a5c606 Mon Sep 17 00:00:00 2001
From: Levi John Wolf <levi.john.wolf@gmail.com>
Date: Wed, 8 Sep 2021 22:24:57 +0100
Subject: [PATCH 061/162] Default of drop_islands should be None, not True

---
 libpysal/weights/weights.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/libpysal/weights/weights.py b/libpysal/weights/weights.py
index 70a836d9d..78f69aa87 100644
--- a/libpysal/weights/weights.py
+++ b/libpysal/weights/weights.py
@@ -275,7 +275,7 @@ def from_adjlist(
     def to_adjlist(
         self,
         remove_symmetric=False,
-        drop_islands=True,
+        drop_islands=None,
         focal_col="focal",
         neighbor_col="neighbor",
         weight_col="weight",

From 0e25bfa4e79da15929cc8eb57262b7d5816c9bfb Mon Sep 17 00:00:00 2001
From: James Gaboardi <jgaboardi@gmail.com>
Date: Wed, 22 Sep 2021 18:46:08 -0400
Subject: [PATCH 062/162] update version import

---
 tools/gitcount.ipynb | 538 +++++--------------------------------------
 1 file changed, 60 insertions(+), 478 deletions(-)

diff --git a/tools/gitcount.ipynb b/tools/gitcount.ipynb
index c6319b95f..484c8fa58 100644
--- a/tools/gitcount.ipynb
+++ b/tools/gitcount.ipynb
@@ -58,7 +58,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -85,7 +85,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -94,40 +94,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'/home/jovyan/tools'"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "CWD"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "datetime.datetime(2020, 2, 1, 0, 0)"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "since_date = '--since=\"{start}\"'.format(start=start_date)\n",
     "since_date\n",
@@ -137,15 +115,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "# get __version__\n",
-    "f = \"../{package}/__init__.py\".format(package=package_name)\n",
-    "\n",
-    "with open(f, \"r\") as initfile:\n",
-    "     exec(initfile.readline())"
+    "%run ../libpysal/_version.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "__version__ = get_versions()[\"version\"]"
    ]
   },
   {
@@ -157,7 +141,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -167,20 +151,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "158"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ncommits"
    ]
@@ -201,7 +174,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -224,7 +197,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -233,7 +206,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -242,7 +215,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -256,7 +229,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -265,20 +238,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['Serge Rey', 'James Gaboardi', 'Martin Fleischmann', 'Dani Arribas-Bel', 'Levi John Wolf', 'Bryan Bennett', 'Jeffery Sauer', 'Elliott Sales De Andrade', 'Wagner'])"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "unique_authors"
    ]
@@ -292,7 +254,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -305,7 +267,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -393,7 +355,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -411,7 +373,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -423,7 +385,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -435,51 +397,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Mac builds seem to take longer — bump up timeout (#273)',\n",
-       " 'ENH: allow specific buffer in fuzzy_contiguity (#280)',\n",
-       " 'Return alpha option & use pygeos for alphashaping if available (#278)',\n",
-       " 'add weights writing as a method on weights.  (#276)',\n",
-       " 'Docs ci badge (#277)',\n",
-       " 'removing six from ci (#275)',\n",
-       " 'Handle connection errors for remote datasets (#274)',\n",
-       " \"GH-263: Don't implicitly import examples when importing base library (#264)\",\n",
-       " 'Bugfix (#255)',\n",
-       " 'dropping nose in ci/36.yml (#270)',\n",
-       " 'Polish up GitHub Action residuals (#269)',\n",
-       " 'Initializing complete Github Actions CI (#267)',\n",
-       " 'DOC: Udpdating citations, minor description editing (#265)',\n",
-       " 'Cleaning up weights/weights.py docs (#262)',\n",
-       " 'ENH: setting up github actions (#258)',\n",
-       " 'Update reqs for tests (#250)',\n",
-       " 'Nbdocs (#253)',\n",
-       " '3.8 (#251)',\n",
-       " 'DOC: Fix invalid section headings. (#243)',\n",
-       " 'Fix syntax errors (#242)',\n",
-       " 'Fix and simplify filter_adjlist. (#244)',\n",
-       " 'Appveyor (#247)',\n",
-       " 'correct name for beautifulsoup4 (#239)',\n",
-       " 'REL: version bump for bug fix release (#238)',\n",
-       " 'BUG:  ufunc.outer deprecated (#237)']"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "pull_listing"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -488,7 +415,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -497,27 +424,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'We closed a total of 81 issues (enhancements and bug fixes) through 25 pull requests, since our last release on 2020-02-01.'"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "message"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -526,27 +442,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "We closed a total of 81 issues (enhancements and bug fixes) through 25 pull requests, since our last release on 2020-02-01.\n",
-      "\n",
-      "## Issues Closed\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(message)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -559,109 +464,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "We closed a total of 81 issues (enhancements and bug fixes) through 25 pull requests, since our last release on 2020-02-01.\n",
-      "\n",
-      "## Issues Closed\n",
-      "  - Mac builds seem to take longer — bump up timeout (#273)\n",
-      "  - Voronoi_frames function causes jupyter notebook kernel to die (#281)\n",
-      "  - ENH: allow specific buffer in fuzzy_contiguity (#280)\n",
-      "  - Return alpha option & use pygeos for alphashaping if available (#278)\n",
-      "  - add weights writing as a method on weights.  (#276)\n",
-      "  - Docs ci badge (#277)\n",
-      "  - [rough edge] libpysal.examples w/o internet? (#259)\n",
-      "  - removing six from ci (#275)\n",
-      "  - Handle connection errors for remote datasets (#274)\n",
-      "  - examples directory prevents installing with pyInstaller (#263)\n",
-      "  - GH-263: Don't implicitly import examples when importing base library (#264)\n",
-      "  - Error in the internal hack for the Arc_KDTree class inheritance and the KDTree function (#254)\n",
-      "  - GitHub Actions failures (#271)\n",
-      "  - Bugfix (#255)\n",
-      "  - dropping nose in ci/36.yml (#270)\n",
-      "  - Follow-up To Do for GH Actions (#268)\n",
-      "  - Polish up GitHub Action residuals (#269)\n",
-      "  - TEST: turning off 3.6 on github actions (#266)\n",
-      "  - Initializing complete Github Actions CI (#267)\n",
-      "  - fix for issue #153 (#256)\n",
-      "  - DOC: Udpdating citations, minor description editing (#265)\n",
-      "  - Cleaning up weights/weights.py docs (#262)\n",
-      "  - Unused code in weights.from_networkx()? (#261)\n",
-      "  - redirect pysal/#934 to libpysal (#9)\n",
-      "  - defaulting to using the dataframe index as the id set (#35)\n",
-      "  - Handling coincident points in KNN (#23)\n",
-      "  - MGWR_Georgia_example.ipynb fails due to different sample data shapes (#67)\n",
-      "  - Kernel docstring does not mention unique Gaussian kernel behavior (#47)\n",
-      "  - MGWR_Georgia_example.ipynb missing pickle import statement (#69)\n",
-      "  - weights.Voronoi is a function, not a class.  (#99)\n",
-      "  - some weights util functions are lost in __ini__.py (#121)\n",
-      "  - Current weight plot method is time consuming for a large data set (#123)\n",
-      "  - [ENH][WIP] Adding a `rasterW` to extract `W` from raster and align values (#150)\n",
-      "  - network kernel weights (#151)\n",
-      "  - Add `from_sparse` and `from_numpy` methods, to match the other `from_` methods (#173)\n",
-      "  - Weight Object Question (#208)\n",
-      "  - ENH: setting up github actions (#258)\n",
-      "  - deprecate or test shapely_ext (#114)\n",
-      "  - Tests failures under Python 3.8 (#177)\n",
-      "  - Update reqs for tests (#250)\n",
-      "  - Nbdocs (#253)\n",
-      "  - test_fiiter fails on 3.8 but passes on < 3.8 (#249)\n",
-      "  - 3.8 (#251)\n",
-      "  - rebuild docs; (#235)\n",
-      "  - DOC: Fix invalid section headings. (#243)\n",
-      "  - Fix syntax errors (#242)\n",
-      "  - Remove calls to deprecated/removed time.clock. (#240)\n",
-      "  - Fix and simplify filter_adjlist. (#244)\n",
-      "  - set up appveyor or circle ci for multiplatform testing (#219)\n",
-      "  - Nose is unmaintained (#241)\n",
-      "  - Add appveyor badge (#248)\n",
-      "  - Appveyor (#247)\n",
-      "  - correct name for beautifulsoup4 (#239)\n",
-      "  - REL: version bump for bug fix release (#238)\n",
-      "  - test_map breakage due to pandas 1.0 deprecation of ufunc.outer (#236)\n",
-      "  - BUG:  ufunc.outer deprecated (#237)\n",
-      "\n",
-      "## Pull Requests\n",
-      "  - Mac builds seem to take longer — bump up timeout (#273)\n",
-      "  - ENH: allow specific buffer in fuzzy_contiguity (#280)\n",
-      "  - Return alpha option & use pygeos for alphashaping if available (#278)\n",
-      "  - add weights writing as a method on weights.  (#276)\n",
-      "  - Docs ci badge (#277)\n",
-      "  - removing six from ci (#275)\n",
-      "  - Handle connection errors for remote datasets (#274)\n",
-      "  - GH-263: Don't implicitly import examples when importing base library (#264)\n",
-      "  - Bugfix (#255)\n",
-      "  - dropping nose in ci/36.yml (#270)\n",
-      "  - Polish up GitHub Action residuals (#269)\n",
-      "  - Initializing complete Github Actions CI (#267)\n",
-      "  - DOC: Udpdating citations, minor description editing (#265)\n",
-      "  - Cleaning up weights/weights.py docs (#262)\n",
-      "  - ENH: setting up github actions (#258)\n",
-      "  - Update reqs for tests (#250)\n",
-      "  - Nbdocs (#253)\n",
-      "  - 3.8 (#251)\n",
-      "  - DOC: Fix invalid section headings. (#243)\n",
-      "  - Fix syntax errors (#242)\n",
-      "  - Fix and simplify filter_adjlist. (#244)\n",
-      "  - Appveyor (#247)\n",
-      "  - correct name for beautifulsoup4 (#239)\n",
-      "  - REL: version bump for bug fix release (#238)\n",
-      "  - BUG:  ufunc.outer deprecated (#237)\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "print(message)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -670,32 +482,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  - Serge Rey\n",
-      "  - James Gaboardi\n",
-      "  - Martin Fleischmann\n",
-      "  - Dani Arribas-Bel\n",
-      "  - Levi John Wolf\n",
-      "  - Bryan Bennett\n",
-      "  - Jeffery Sauer\n",
-      "  - Elliott Sales De Andrade\n",
-      "  - Wagner\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(people)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -704,121 +500,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "We closed a total of 81 issues (enhancements and bug fixes) through 25 pull requests, since our last release on 2020-02-01.\n",
-      "\n",
-      "## Issues Closed\n",
-      "  - Mac builds seem to take longer — bump up timeout (#273)\n",
-      "  - Voronoi_frames function causes jupyter notebook kernel to die (#281)\n",
-      "  - ENH: allow specific buffer in fuzzy_contiguity (#280)\n",
-      "  - Return alpha option & use pygeos for alphashaping if available (#278)\n",
-      "  - add weights writing as a method on weights.  (#276)\n",
-      "  - Docs ci badge (#277)\n",
-      "  - [rough edge] libpysal.examples w/o internet? (#259)\n",
-      "  - removing six from ci (#275)\n",
-      "  - Handle connection errors for remote datasets (#274)\n",
-      "  - examples directory prevents installing with pyInstaller (#263)\n",
-      "  - GH-263: Don't implicitly import examples when importing base library (#264)\n",
-      "  - Error in the internal hack for the Arc_KDTree class inheritance and the KDTree function (#254)\n",
-      "  - GitHub Actions failures (#271)\n",
-      "  - Bugfix (#255)\n",
-      "  - dropping nose in ci/36.yml (#270)\n",
-      "  - Follow-up To Do for GH Actions (#268)\n",
-      "  - Polish up GitHub Action residuals (#269)\n",
-      "  - TEST: turning off 3.6 on github actions (#266)\n",
-      "  - Initializing complete Github Actions CI (#267)\n",
-      "  - fix for issue #153 (#256)\n",
-      "  - DOC: Udpdating citations, minor description editing (#265)\n",
-      "  - Cleaning up weights/weights.py docs (#262)\n",
-      "  - Unused code in weights.from_networkx()? (#261)\n",
-      "  - redirect pysal/#934 to libpysal (#9)\n",
-      "  - defaulting to using the dataframe index as the id set (#35)\n",
-      "  - Handling coincident points in KNN (#23)\n",
-      "  - MGWR_Georgia_example.ipynb fails due to different sample data shapes (#67)\n",
-      "  - Kernel docstring does not mention unique Gaussian kernel behavior (#47)\n",
-      "  - MGWR_Georgia_example.ipynb missing pickle import statement (#69)\n",
-      "  - weights.Voronoi is a function, not a class.  (#99)\n",
-      "  - some weights util functions are lost in __ini__.py (#121)\n",
-      "  - Current weight plot method is time consuming for a large data set (#123)\n",
-      "  - [ENH][WIP] Adding a `rasterW` to extract `W` from raster and align values (#150)\n",
-      "  - network kernel weights (#151)\n",
-      "  - Add `from_sparse` and `from_numpy` methods, to match the other `from_` methods (#173)\n",
-      "  - Weight Object Question (#208)\n",
-      "  - ENH: setting up github actions (#258)\n",
-      "  - deprecate or test shapely_ext (#114)\n",
-      "  - Tests failures under Python 3.8 (#177)\n",
-      "  - Update reqs for tests (#250)\n",
-      "  - Nbdocs (#253)\n",
-      "  - test_fiiter fails on 3.8 but passes on < 3.8 (#249)\n",
-      "  - 3.8 (#251)\n",
-      "  - rebuild docs; (#235)\n",
-      "  - DOC: Fix invalid section headings. (#243)\n",
-      "  - Fix syntax errors (#242)\n",
-      "  - Remove calls to deprecated/removed time.clock. (#240)\n",
-      "  - Fix and simplify filter_adjlist. (#244)\n",
-      "  - set up appveyor or circle ci for multiplatform testing (#219)\n",
-      "  - Nose is unmaintained (#241)\n",
-      "  - Add appveyor badge (#248)\n",
-      "  - Appveyor (#247)\n",
-      "  - correct name for beautifulsoup4 (#239)\n",
-      "  - REL: version bump for bug fix release (#238)\n",
-      "  - test_map breakage due to pandas 1.0 deprecation of ufunc.outer (#236)\n",
-      "  - BUG:  ufunc.outer deprecated (#237)\n",
-      "\n",
-      "## Pull Requests\n",
-      "  - Mac builds seem to take longer — bump up timeout (#273)\n",
-      "  - ENH: allow specific buffer in fuzzy_contiguity (#280)\n",
-      "  - Return alpha option & use pygeos for alphashaping if available (#278)\n",
-      "  - add weights writing as a method on weights.  (#276)\n",
-      "  - Docs ci badge (#277)\n",
-      "  - removing six from ci (#275)\n",
-      "  - Handle connection errors for remote datasets (#274)\n",
-      "  - GH-263: Don't implicitly import examples when importing base library (#264)\n",
-      "  - Bugfix (#255)\n",
-      "  - dropping nose in ci/36.yml (#270)\n",
-      "  - Polish up GitHub Action residuals (#269)\n",
-      "  - Initializing complete Github Actions CI (#267)\n",
-      "  - DOC: Udpdating citations, minor description editing (#265)\n",
-      "  - Cleaning up weights/weights.py docs (#262)\n",
-      "  - ENH: setting up github actions (#258)\n",
-      "  - Update reqs for tests (#250)\n",
-      "  - Nbdocs (#253)\n",
-      "  - 3.8 (#251)\n",
-      "  - DOC: Fix invalid section headings. (#243)\n",
-      "  - Fix syntax errors (#242)\n",
-      "  - Fix and simplify filter_adjlist. (#244)\n",
-      "  - Appveyor (#247)\n",
-      "  - correct name for beautifulsoup4 (#239)\n",
-      "  - REL: version bump for bug fix release (#238)\n",
-      "  - BUG:  ufunc.outer deprecated (#237)\n",
-      "\n",
-      "The following individuals contributed to this release:\n",
-      "\n",
-      "  - Serge Rey\n",
-      "  - James Gaboardi\n",
-      "  - Martin Fleischmann\n",
-      "  - Dani Arribas-Bel\n",
-      "  - Levi John Wolf\n",
-      "  - Bryan Bennett\n",
-      "  - Jeffery Sauer\n",
-      "  - Elliott Sales De Andrade\n",
-      "  - Wagner\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "print(message)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -829,125 +520,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "# Changes\n",
-      "\n",
-      "Version 4.3.0 (2020-06-28)\n",
-      "\n",
-      "We closed a total of 81 issues (enhancements and bug fixes) through 25 pull requests, since our last release on 2020-02-01.\n",
-      "\n",
-      "## Issues Closed\n",
-      "  - Mac builds seem to take longer — bump up timeout (#273)\n",
-      "  - Voronoi_frames function causes jupyter notebook kernel to die (#281)\n",
-      "  - ENH: allow specific buffer in fuzzy_contiguity (#280)\n",
-      "  - Return alpha option & use pygeos for alphashaping if available (#278)\n",
-      "  - add weights writing as a method on weights.  (#276)\n",
-      "  - Docs ci badge (#277)\n",
-      "  - [rough edge] libpysal.examples w/o internet? (#259)\n",
-      "  - removing six from ci (#275)\n",
-      "  - Handle connection errors for remote datasets (#274)\n",
-      "  - examples directory prevents installing with pyInstaller (#263)\n",
-      "  - GH-263: Don't implicitly import examples when importing base library (#264)\n",
-      "  - Error in the internal hack for the Arc_KDTree class inheritance and the KDTree function (#254)\n",
-      "  - GitHub Actions failures (#271)\n",
-      "  - Bugfix (#255)\n",
-      "  - dropping nose in ci/36.yml (#270)\n",
-      "  - Follow-up To Do for GH Actions (#268)\n",
-      "  - Polish up GitHub Action residuals (#269)\n",
-      "  - TEST: turning off 3.6 on github actions (#266)\n",
-      "  - Initializing complete Github Actions CI (#267)\n",
-      "  - fix for issue #153 (#256)\n",
-      "  - DOC: Udpdating citations, minor description editing (#265)\n",
-      "  - Cleaning up weights/weights.py docs (#262)\n",
-      "  - Unused code in weights.from_networkx()? (#261)\n",
-      "  - redirect pysal/#934 to libpysal (#9)\n",
-      "  - defaulting to using the dataframe index as the id set (#35)\n",
-      "  - Handling coincident points in KNN (#23)\n",
-      "  - MGWR_Georgia_example.ipynb fails due to different sample data shapes (#67)\n",
-      "  - Kernel docstring does not mention unique Gaussian kernel behavior (#47)\n",
-      "  - MGWR_Georgia_example.ipynb missing pickle import statement (#69)\n",
-      "  - weights.Voronoi is a function, not a class.  (#99)\n",
-      "  - some weights util functions are lost in __ini__.py (#121)\n",
-      "  - Current weight plot method is time consuming for a large data set (#123)\n",
-      "  - [ENH][WIP] Adding a `rasterW` to extract `W` from raster and align values (#150)\n",
-      "  - network kernel weights (#151)\n",
-      "  - Add `from_sparse` and `from_numpy` methods, to match the other `from_` methods (#173)\n",
-      "  - Weight Object Question (#208)\n",
-      "  - ENH: setting up github actions (#258)\n",
-      "  - deprecate or test shapely_ext (#114)\n",
-      "  - Tests failures under Python 3.8 (#177)\n",
-      "  - Update reqs for tests (#250)\n",
-      "  - Nbdocs (#253)\n",
-      "  - test_fiiter fails on 3.8 but passes on < 3.8 (#249)\n",
-      "  - 3.8 (#251)\n",
-      "  - rebuild docs; (#235)\n",
-      "  - DOC: Fix invalid section headings. (#243)\n",
-      "  - Fix syntax errors (#242)\n",
-      "  - Remove calls to deprecated/removed time.clock. (#240)\n",
-      "  - Fix and simplify filter_adjlist. (#244)\n",
-      "  - set up appveyor or circle ci for multiplatform testing (#219)\n",
-      "  - Nose is unmaintained (#241)\n",
-      "  - Add appveyor badge (#248)\n",
-      "  - Appveyor (#247)\n",
-      "  - correct name for beautifulsoup4 (#239)\n",
-      "  - REL: version bump for bug fix release (#238)\n",
-      "  - test_map breakage due to pandas 1.0 deprecation of ufunc.outer (#236)\n",
-      "  - BUG:  ufunc.outer deprecated (#237)\n",
-      "\n",
-      "## Pull Requests\n",
-      "  - Mac builds seem to take longer — bump up timeout (#273)\n",
-      "  - ENH: allow specific buffer in fuzzy_contiguity (#280)\n",
-      "  - Return alpha option & use pygeos for alphashaping if available (#278)\n",
-      "  - add weights writing as a method on weights.  (#276)\n",
-      "  - Docs ci badge (#277)\n",
-      "  - removing six from ci (#275)\n",
-      "  - Handle connection errors for remote datasets (#274)\n",
-      "  - GH-263: Don't implicitly import examples when importing base library (#264)\n",
-      "  - Bugfix (#255)\n",
-      "  - dropping nose in ci/36.yml (#270)\n",
-      "  - Polish up GitHub Action residuals (#269)\n",
-      "  - Initializing complete Github Actions CI (#267)\n",
-      "  - DOC: Udpdating citations, minor description editing (#265)\n",
-      "  - Cleaning up weights/weights.py docs (#262)\n",
-      "  - ENH: setting up github actions (#258)\n",
-      "  - Update reqs for tests (#250)\n",
-      "  - Nbdocs (#253)\n",
-      "  - 3.8 (#251)\n",
-      "  - DOC: Fix invalid section headings. (#243)\n",
-      "  - Fix syntax errors (#242)\n",
-      "  - Fix and simplify filter_adjlist. (#244)\n",
-      "  - Appveyor (#247)\n",
-      "  - correct name for beautifulsoup4 (#239)\n",
-      "  - REL: version bump for bug fix release (#238)\n",
-      "  - BUG:  ufunc.outer deprecated (#237)\n",
-      "\n",
-      "The following individuals contributed to this release:\n",
-      "\n",
-      "  - Serge Rey\n",
-      "  - James Gaboardi\n",
-      "  - Martin Fleischmann\n",
-      "  - Dani Arribas-Bel\n",
-      "  - Levi John Wolf\n",
-      "  - Bryan Bennett\n",
-      "  - Jeffery Sauer\n",
-      "  - Elliott Sales De Andrade\n",
-      "  - Wagner\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "print(head+message)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -959,7 +541,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -973,7 +555,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.9.7"
   }
  },
  "nbformat": 4,

From cd4b809de38ea0df1c4d2564b72617fcd6f81c4b Mon Sep 17 00:00:00 2001
From: Martin Fleischmann <martin@martinfleischmann.net>
Date: Wed, 6 Oct 2021 20:19:04 +0100
Subject: [PATCH 063/162] TST: update for shapely 2.0

---
 libpysal/cg/alpha_shapes.py      |   4 +-
 libpysal/cg/shapely_ext.py       | 110 +++++++++++++++----------------
 libpysal/io/geotable/utils.py    |  20 +++---
 libpysal/weights/_contW_lists.py |   6 +-
 libpysal/weights/util.py         |  10 ++-
 5 files changed, 79 insertions(+), 71 deletions(-)

diff --git a/libpysal/cg/alpha_shapes.py b/libpysal/cg/alpha_shapes.py
index ba29a2e31..2ae419009 100644
--- a/libpysal/cg/alpha_shapes.py
+++ b/libpysal/cg/alpha_shapes.py
@@ -257,7 +257,7 @@ def build_faces(faces, triangles_is, num_triangles, num_faces_single):
 
 @jit
 def nb_mask_faces(mask, faces):
-    """ Run over each row in `faces`, if the face in the following row is the
+    """Run over each row in `faces`, if the face in the following row is the
     same, then mark both as False on `mask`
 
     Parameters
@@ -472,7 +472,7 @@ def alpha_shape(xys, alpha):
     if xys.shape[0] < 4:
         from shapely import ops, geometry as geom
 
-        return ops.cascaded_union([geom.Point(xy) for xy in xys]).convex_hull.buffer(0)
+        return ops.unary_union([geom.Point(xy) for xy in xys]).convex_hull.buffer(0)
     triangulation = spat.Delaunay(xys)
     triangles = xys[triangulation.simplices]
     a_pts = triangles[:, 0, :]
diff --git a/libpysal/cg/shapely_ext.py b/libpysal/cg/shapely_ext.py
index 540bdd3f5..0a7022029 100644
--- a/libpysal/cg/shapely_ext.py
+++ b/libpysal/cg/shapely_ext.py
@@ -51,14 +51,14 @@
 def to_wkb(shape):
     if not hasattr(shape, GEO_INTERFACE_ATTR):
         raise TypeError(SHAPE_TYPE_ERR % shape)
-    o = geom.asShape(shape)
+    o = geom.shape(shape)
     return o.to_wkb()
 
 
 def to_wkt(shape):
     if not hasattr(shape, GEO_INTERFACE_ATTR):
         raise TypeError(SHAPE_TYPE_ERR % shape)
-    o = geom.asShape(shape)
+    o = geom.shape(shape)
     return o.to_wkt()
 
 
@@ -67,7 +67,7 @@ def to_wkt(shape):
 def area(shape):
     if not hasattr(shape, GEO_INTERFACE_ATTR):
         raise TypeError(SHAPE_TYPE_ERR % shape)
-    o = geom.asShape(shape)
+    o = geom.shape(shape)
     return o.area
 
 
@@ -76,15 +76,15 @@ def distance(shape, other):
         raise TypeError(SHAPE_TYPE_ERR % shape)
     if not hasattr(other, GEO_INTERFACE_ATTR):
         raise TypeError(SHAPE_TYPE_ERR % shape)
-    o = geom.asShape(shape)
-    o2 = geom.asShape(other)
+    o = geom.shape(shape)
+    o2 = geom.shape(other)
     return o.distance(o2)
 
 
 def length(shape):
     if not hasattr(shape, GEO_INTERFACE_ATTR):
         raise TypeError(SHAPE_TYPE_ERR % shape)
-    o = geom.asShape(shape)
+    o = geom.shape(shape)
     return o.length
 
 
@@ -93,7 +93,7 @@ def length(shape):
 def boundary(shape):
     if not hasattr(shape, GEO_INTERFACE_ATTR):
         raise TypeError(SHAPE_TYPE_ERR % shape)
-    o = geom.asShape(shape)
+    o = geom.shape(shape)
     res = o.boundary
     return asShape(res)
 
@@ -101,14 +101,14 @@ def boundary(shape):
 def bounds(shape):
     if not hasattr(shape, GEO_INTERFACE_ATTR):
         raise TypeError(SHAPE_TYPE_ERR % shape)
-    o = geom.asShape(shape)
+    o = geom.shape(shape)
     return o.bounds
 
 
 def centroid(shape):
     if not hasattr(shape, GEO_INTERFACE_ATTR):
         raise TypeError(SHAPE_TYPE_ERR % shape)
-    o = geom.asShape(shape)
+    o = geom.shape(shape)
     res = o.centroid
     return asShape(res)
 
@@ -116,7 +116,7 @@ def centroid(shape):
 def representative_point(shape):
     if not hasattr(shape, GEO_INTERFACE_ATTR):
         raise TypeError(SHAPE_TYPE_ERR % shape)
-    o = geom.asShape(shape)
+    o = geom.shape(shape)
     res = o.representative_point()
     return asShape(res)
 
@@ -124,7 +124,7 @@ def representative_point(shape):
 def convex_hull(shape):
     if not hasattr(shape, GEO_INTERFACE_ATTR):
         raise TypeError(SHAPE_TYPE_ERR % shape)
-    o = geom.asShape(shape)
+    o = geom.shape(shape)
     res = o.convex_hull
     return asShape(res)
 
@@ -132,7 +132,7 @@ def convex_hull(shape):
 def envelope(shape):
     if not hasattr(shape, GEO_INTERFACE_ATTR):
         raise TypeError(SHAPE_TYPE_ERR % shape)
-    o = geom.asShape(shape)
+    o = geom.shape(shape)
     res = o.envelope
     return asShape(res)
 
@@ -140,7 +140,7 @@ def envelope(shape):
 def buffer(shape, radius, resolution=16):
     if not hasattr(shape, GEO_INTERFACE_ATTR):
         raise TypeError(SHAPE_TYPE_ERR % shape)
-    o = geom.asShape(shape)
+    o = geom.shape(shape)
     res = o.buffer(radius, resolution)
     return asShape(res)
 
@@ -148,7 +148,7 @@ def buffer(shape, radius, resolution=16):
 def simplify(shape, tolerance, preserve_topology=True):
     if not hasattr(shape, GEO_INTERFACE_ATTR):
         raise TypeError(SHAPE_TYPE_ERR % shape)
-    o = geom.asShape(shape)
+    o = geom.shape(shape)
     res = o.simplify(tolerance, preserve_topology)
     return asShape(res)
 
@@ -160,8 +160,8 @@ def difference(shape, other):
         raise TypeError(SHAPE_TYPE_ERR % shape)
     if not hasattr(other, GEO_INTERFACE_ATTR):
         raise TypeError(SHAPE_TYPE_ERR % shape)
-    o = geom.asShape(shape)
-    o2 = geom.asShape(other)
+    o = geom.shape(shape)
+    o2 = geom.shape(other)
     res = o.difference(o2)
     return asShape(res)
 
@@ -171,8 +171,8 @@ def intersection(shape, other):
         raise TypeError(SHAPE_TYPE_ERR % shape)
     if not hasattr(other, GEO_INTERFACE_ATTR):
         raise TypeError(SHAPE_TYPE_ERR % shape)
-    o = geom.asShape(shape)
-    o2 = geom.asShape(other)
+    o = geom.shape(shape)
+    o2 = geom.shape(other)
     res = o.intersection(o2)
     return asShape(res)
 
@@ -182,8 +182,8 @@ def symmetric_difference(shape, other):
         raise TypeError(SHAPE_TYPE_ERR % shape)
     if not hasattr(other, GEO_INTERFACE_ATTR):
         raise TypeError(SHAPE_TYPE_ERR % shape)
-    o = geom.asShape(shape)
-    o2 = geom.asShape(other)
+    o = geom.shape(shape)
+    o2 = geom.shape(other)
     res = o.symmetric_difference(o2)
     return asShape(res)
 
@@ -193,8 +193,8 @@ def union(shape, other):
         raise TypeError(SHAPE_TYPE_ERR % shape)
     if not hasattr(other, GEO_INTERFACE_ATTR):
         raise TypeError(SHAPE_TYPE_ERR % shape)
-    o = geom.asShape(shape)
-    o2 = geom.asShape(other)
+    o = geom.shape(shape)
+    o2 = geom.shape(other)
     res = o.union(o2)
     return asShape(res)
 
@@ -204,8 +204,8 @@ def cascaded_union(shapes):
     for shape in shapes:
         if not hasattr(shape, GEO_INTERFACE_ATTR):
             raise TypeError(SHAPE_TYPE_ERR % shape)
-        o.append(geom.asShape(shape))
-    res = shops.cascaded_union(o)
+        o.append(geom.shape(shape))
+    res = shops.unary_union(o)
     return asShape(res)
 
 
@@ -219,7 +219,7 @@ def unary_union(shapes):
     for shape in shapes:
         if not hasattr(shape, GEO_INTERFACE_ATTR):
             raise TypeError(SHAPE_TYPE_ERR % shape)
-        o.append(geom.asShape(shape))
+        o.append(geom.shape(shape))
     res = shops.unary_union(o)
     return asShape(res)
 
@@ -229,35 +229,35 @@ def unary_union(shapes):
 def has_z(shape):
     if not hasattr(shape, GEO_INTERFACE_ATTR):
         raise TypeError(SHAPE_TYPE_ERR % shape)
-    o = geom.asShape(shape)
+    o = geom.shape(shape)
     return o.has_z
 
 
 def is_empty(shape):
     if not hasattr(shape, GEO_INTERFACE_ATTR):
         raise TypeError(SHAPE_TYPE_ERR % shape)
-    o = geom.asShape(shape)
+    o = geom.shape(shape)
     return o.is_empty
 
 
 def is_ring(shape):
     if not hasattr(shape, GEO_INTERFACE_ATTR):
         raise TypeError(SHAPE_TYPE_ERR % shape)
-    o = geom.asShape(shape)
+    o = geom.shape(shape)
     return o.is_ring
 
 
 def is_simple(shape):
     if not hasattr(shape, GEO_INTERFACE_ATTR):
         raise TypeError(SHAPE_TYPE_ERR % shape)
-    o = geom.asShape(shape)
+    o = geom.shape(shape)
     return o.is_simple
 
 
 def is_valid(shape):
     if not hasattr(shape, GEO_INTERFACE_ATTR):
         raise TypeError(SHAPE_TYPE_ERR % shape)
-    o = geom.asShape(shape)
+    o = geom.shape(shape)
     return o.is_valid
 
 
@@ -268,8 +268,8 @@ def relate(shape, other):
         raise TypeError(SHAPE_TYPE_ERR % shape)
     if not hasattr(other, GEO_INTERFACE_ATTR):
         raise TypeError(SHAPE_TYPE_ERR % shape)
-    o = geom.asShape(shape)
-    o2 = geom.asShape(other)
+    o = geom.shape(shape)
+    o2 = geom.shape(other)
     return o.relate(o2)
 
 
@@ -278,8 +278,8 @@ def contains(shape, other):
         raise TypeError(SHAPE_TYPE_ERR % shape)
     if not hasattr(other, GEO_INTERFACE_ATTR):
         raise TypeError(SHAPE_TYPE_ERR % shape)
-    o = geom.asShape(shape)
-    o2 = geom.asShape(other)
+    o = geom.shape(shape)
+    o2 = geom.shape(other)
     return o.contains(o2)
 
 
@@ -288,8 +288,8 @@ def crosses(shape, other):
         raise TypeError(SHAPE_TYPE_ERR % shape)
     if not hasattr(other, GEO_INTERFACE_ATTR):
         raise TypeError(SHAPE_TYPE_ERR % shape)
-    o = geom.asShape(shape)
-    o2 = geom.asShape(other)
+    o = geom.shape(shape)
+    o2 = geom.shape(other)
     return o.crosses(o2)
 
 
@@ -298,8 +298,8 @@ def disjoint(shape, other):
         raise TypeError(SHAPE_TYPE_ERR % shape)
     if not hasattr(other, GEO_INTERFACE_ATTR):
         raise TypeError(SHAPE_TYPE_ERR % shape)
-    o = geom.asShape(shape)
-    o2 = geom.asShape(other)
+    o = geom.shape(shape)
+    o2 = geom.shape(other)
     return o.disjoint(o2)
 
 
@@ -308,8 +308,8 @@ def equals(shape, other):
         raise TypeError(SHAPE_TYPE_ERR % shape)
     if not hasattr(other, GEO_INTERFACE_ATTR):
         raise TypeError(SHAPE_TYPE_ERR % shape)
-    o = geom.asShape(shape)
-    o2 = geom.asShape(other)
+    o = geom.shape(shape)
+    o2 = geom.shape(other)
     return o.equals(o2)
 
 
@@ -318,8 +318,8 @@ def intersects(shape, other):
         raise TypeError(SHAPE_TYPE_ERR % shape)
     if not hasattr(other, GEO_INTERFACE_ATTR):
         raise TypeError(SHAPE_TYPE_ERR % shape)
-    o = geom.asShape(shape)
-    o2 = geom.asShape(other)
+    o = geom.shape(shape)
+    o2 = geom.shape(other)
     return o.intersects(o2)
 
 
@@ -328,8 +328,8 @@ def overlaps(shape, other):
         raise TypeError(SHAPE_TYPE_ERR % shape)
     if not hasattr(other, GEO_INTERFACE_ATTR):
         raise TypeError(SHAPE_TYPE_ERR % shape)
-    o = geom.asShape(shape)
-    o2 = geom.asShape(other)
+    o = geom.shape(shape)
+    o2 = geom.shape(other)
     return o.overlaps(o2)
 
 
@@ -338,8 +338,8 @@ def touches(shape, other):
         raise TypeError(SHAPE_TYPE_ERR % shape)
     if not hasattr(other, GEO_INTERFACE_ATTR):
         raise TypeError(SHAPE_TYPE_ERR % shape)
-    o = geom.asShape(shape)
-    o2 = geom.asShape(other)
+    o = geom.shape(shape)
+    o2 = geom.shape(other)
     return o.touches(o2)
 
 
@@ -348,8 +348,8 @@ def within(shape, other):
         raise TypeError(SHAPE_TYPE_ERR % shape)
     if not hasattr(other, GEO_INTERFACE_ATTR):
         raise TypeError(SHAPE_TYPE_ERR % shape)
-    o = geom.asShape(shape)
-    o2 = geom.asShape(other)
+    o = geom.shape(shape)
+    o2 = geom.shape(other)
     return o.within(o2)
 
 
@@ -358,8 +358,8 @@ def equals_exact(shape, other, tolerance):
         raise TypeError(SHAPE_TYPE_ERR % shape)
     if not hasattr(other, GEO_INTERFACE_ATTR):
         raise TypeError(SHAPE_TYPE_ERR % shape)
-    o = geom.asShape(shape)
-    o2 = geom.asShape(other)
+    o = geom.shape(shape)
+    o2 = geom.shape(other)
     return o.equals_exact(o2, tolerance)
 
 
@@ -368,8 +368,8 @@ def almost_equals(shape, other, decimal=6):
         raise TypeError(SHAPE_TYPE_ERR % shape)
     if not hasattr(other, GEO_INTERFACE_ATTR):
         raise TypeError(SHAPE_TYPE_ERR % shape)
-    o = geom.asShape(shape)
-    o2 = geom.asShape(other)
+    o = geom.shape(shape)
+    o2 = geom.shape(other)
     return o.almost_equals(o2, decimal)
 
 
@@ -380,15 +380,15 @@ def project(shape, other, normalized=False):
         raise TypeError(SHAPE_TYPE_ERR % shape)
     if not hasattr(other, GEO_INTERFACE_ATTR):
         raise TypeError(SHAPE_TYPE_ERR % shape)
-    o = geom.asShape(shape)
-    o2 = geom.asShape(other)
+    o = geom.shape(shape)
+    o2 = geom.shape(other)
     return o.project(o2, normalized)
 
 
 def interpolate(shape, distance, normalized=False):
     if not hasattr(shape, GEO_INTERFACE_ATTR):
         raise TypeError(SHAPE_TYPE_ERR % shape)
-    o = geom.asShape(shape)
+    o = geom.shape(shape)
     res = o.interpolate(distance, normalized)
     return asShape(res)
 
diff --git a/libpysal/io/geotable/utils.py b/libpysal/io/geotable/utils.py
index 355481198..9e250100f 100644
--- a/libpysal/io/geotable/utils.py
+++ b/libpysal/io/geotable/utils.py
@@ -6,7 +6,7 @@
 @_requires("geopandas")
 def to_df(df, geom_col="geometry", **kw):
     """Convert a ``geopandas.GeoDataFrame`` into a normal
-    ``pandas.DataFrame`` with a column containing PySAL shapes. 
+    ``pandas.DataFrame`` with a column containing PySAL shapes.
 
     Parameters
     ----------
@@ -22,12 +22,12 @@ def to_df(df, geom_col="geometry", **kw):
     -------
     df : pandas.DataFrame
         The data converted into a ``pandas.DataFrame`` object.
-    
+
     See Also
     --------
-    
+
     pandas.DataFrame
-    
+
     """
 
     import pandas as pd
@@ -54,23 +54,23 @@ def to_gdf(df, geom_col="geometry", **kw):
         The column name in ``df`` contains the geometry. Default is ``'geometry'``.
     **kw : dict
         Optional keyword arguments for ``geopandas.GeoDataFrame()``.
-    
+
     Returns
     -------
     gdf : geopandas.GeoDataFrame
         The data converted into a ``geopandas.GeoDataFrame`` object.
-    
+
     See Also
     --------
-    
+
     geopandas.GeoDataFrame
-    
+
     """
 
     from geopandas import GeoDataFrame
-    from shapely.geometry import asShape as sShape
+    from shapely.geometry import shape
 
-    df[geom_col] = df[geom_col].apply(sShape)
+    df[geom_col] = df[geom_col].apply(shape)
 
     gdf = GeoDataFrame(df, geometry=geom_col, **kw)
 
diff --git a/libpysal/weights/_contW_lists.py b/libpysal/weights/_contW_lists.py
index 2aada984c..39b36256b 100644
--- a/libpysal/weights/_contW_lists.py
+++ b/libpysal/weights/_contW_lists.py
@@ -18,7 +18,7 @@ def _get_verts(shape):
 def _get_boundary_points(shape):
     """
     Recursively handle polygons vs. multipolygons to
-    extract the boundary point set from each. 
+    extract the boundary point set from each.
     """
     if shape.type.lower() == "polygon":
         shape = shape.boundary
@@ -26,9 +26,9 @@ def _get_boundary_points(shape):
     elif shape.type.lower() == "linestring":
         return list(map(tuple, list(zip(*shape.coords.xy))))
     elif shape.type.lower() == "multilinestring":
-        return list(it.chain(*(list(zip(*shape.coords.xy)) for shape in shape)))
+        return list(it.chain(*(list(zip(*shape.coords.xy)) for shape in shape.geoms)))
     elif shape.type.lower() == "multipolygon":
-        return list(it.chain(*(_get_boundary_points(part.boundary) for part in shape)))
+        return list(it.chain(*(_get_boundary_points(part.boundary) for part in shape.geoms)))
     else:
         raise TypeError(
             "Input shape must be a Polygon, Multipolygon, LineString, "
diff --git a/libpysal/weights/util.py b/libpysal/weights/util.py
index b3f2991cb..a11aadf5d 100644
--- a/libpysal/weights/util.py
+++ b/libpysal/weights/util.py
@@ -1,3 +1,4 @@
+from shapely.geometry.base import BaseGeometry
 from ..io.fileio import FileIO as psopen
 from .weights import W, WSP
 from .set_operations import w_subset
@@ -1070,7 +1071,14 @@ def get_points_array(iterable):
     """
     first_choice, backup = tee(iterable)
     try:
-        data = np.vstack([np.array(shape.centroid) for shape in first_choice])
+        data = np.vstack(
+            [
+                np.array(shape.centroid.coords)[0]
+                if isinstance(shape, BaseGeometry)
+                else np.array(shape.centroid)
+                for shape in first_choice
+            ]
+        )
     except AttributeError:
         data = np.vstack([shape for shape in backup])
     return data

From 475967a82665cac6c62e76b773d545624794456b Mon Sep 17 00:00:00 2001
From: Martin Fleischmann <martin@martinfleischmann.net>
Date: Wed, 6 Oct 2021 20:19:14 +0100
Subject: [PATCH 064/162] TST: save file to temp dir

---
 libpysal/weights/tests/test_weights.py | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/libpysal/weights/tests/test_weights.py b/libpysal/weights/tests/test_weights.py
index fd1832a57..83f8decf1 100644
--- a/libpysal/weights/tests/test_weights.py
+++ b/libpysal/weights/tests/test_weights.py
@@ -1,3 +1,6 @@
+import os
+import tempfile
+
 import unittest
 from ..weights import W, WSP
 from .. import util
@@ -346,8 +349,10 @@ def test_connected_components(self):
         assert disco.n_components == 2
 
     def test_roundtrip_write(self):
-        self.w.to_file("./tmp.gal")
-        new = W.from_file("./tmp.gal")
+        with tempfile.TemporaryDirectory() as tmpdir:
+            path = os.path.join(str(tmpdir), "tmp.gal")
+            self.w.to_file(path)
+            new = W.from_file(path)
         np.testing.assert_array_equal(self.w.sparse.toarray(), new.sparse.toarray())
 
 

From 0ad0b3e13fbd9a192186479b475b42aad013aa43 Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Mon, 18 Oct 2021 21:44:17 +0100
Subject: [PATCH 065/162] add gabriel weights and more performant voronoi
 generation

---
 libpysal/weights/gabriel.py | 167 ++++++++++++++++++++++++++++++++++++
 1 file changed, 167 insertions(+)
 create mode 100644 libpysal/weights/gabriel.py

diff --git a/libpysal/weights/gabriel.py b/libpysal/weights/gabriel.py
new file mode 100644
index 000000000..797b30a9c
--- /dev/null
+++ b/libpysal/weights/gabriel.py
@@ -0,0 +1,167 @@
+from scipy.spatial import Delaunay as _Delaunay
+from scipy import sparse
+from numba import njit
+from libpysal.weights import W, Rook
+import joblib, pandas, pygeos
+
+
+class Delaunay(W):
+    def __init__(self, coordinates, **kwargs):
+        edges, _ = self._voronoi_edges(coordinates)
+        voronoi_neighbors = pandas.DataFrame(output).groupby(0)[1].apply(list).to_dict()
+        W.__init__(self, voronoi_neighbors, **kwargs)
+
+    def _voronoi_edges(self, coordinates):
+        dt = _Delaunay(coordinates)
+        edges = edges_from_simplices(dt.simplices)
+        edges = (
+            pandas.DataFrame(numpy.asarray(list(edges)))
+            .sort_values([0, 1])
+            .drop_duplicates()
+            .values
+        )
+        return edges, dt
+
+    @classmethod
+    def from_dataframe(cls, df, **kwargs):
+        return cls.__init__(
+            pygeos.get_coordinates(pygeos.centroid(df.geometry.data.values))
+        )
+
+
+class Gabriel(Delaunay):
+    def __init__(self, coordinates, **kwargs):
+        edges, dt = self._voronoi_edges(coordinates)
+        droplist = filter_edges(
+            edges,
+            dt.points,
+        )
+        output = set(map(tuple, edges)).difference(set(droplist))
+        gabriel_neighbors = pandas.DataFrame(output).groupby(0)[1].apply(list).to_dict()
+        W.__init__(self, gabriel_neighbors, **kwargs)
+
+
+@njit
+def edges_from_simplices(simplices):
+    edges = []
+    for simplex in simplices:
+        edges.append((simplex[0], simplex[1]))
+        edges.append((simplex[1], simplex[0]))
+        edges.append((simplex[1], simplex[2]))
+        edges.append((simplex[2], simplex[1]))
+        edges.append((simplex[2], simplex[0]))
+        edges.append((simplex[0], simplex[2]))
+    return numpy.asarray(edges)
+
+
+@njit
+def filter_edges(edges, coordinates):
+    edge_pointer = 0
+    n = edges.max()
+    n_edges = len(edges)
+    to_drop = []
+    while edge_pointer < n_edges:
+        edge = edges[edge_pointer]
+        cardinality = 0
+
+        for joff in range(edge_pointer, n_edges):
+            next_edge = edges[joff]
+            if next_edge[0] != edge[0]:
+                break
+            cardinality += 1
+        # let i,j be the diameter of a circle, and k be a third point making a triangle.
+        # the right triangle formed when k is on the circle is the limiting case for the
+        # gabriel link between ij. If k on the circle, then dij**2 = djk**2 + dki**2,
+        # by thales theorem (an inscribed triangle with hypotenuse as diameter is a
+        # right triangle.). When dij**2 > djk**2 + dki**2, k is inside of the circle.
+        # When dij**2 < dij**2 + djk**2, k is outside of the circle.
+        # Therefore, we can take each observation i, iterate over neighbors j,k and remove links
+        # where dij**2 > djk**2 + dki**2 to filter a delanuay graph to a gabriel one.
+        for ix in range(edge_pointer, edge_pointer + cardinality):
+            i, j = edges[ix]
+            dij = ((coordinates[i] - coordinates[j]) ** 2).sum() ** 0.5
+            for ix2 in range(edge_pointer, edge_pointer + cardinality):
+                _, k = edges[ix2]
+                if j == k:
+                    continue
+                dik = ((coordinates[i] - coordinates[k]) ** 2).sum() ** 0.5
+                djk = ((coordinates[j] - coordinates[k]) ** 2).sum() ** 0.5
+
+                if dij ** 2 > (dik ** 2 + djk ** 2):
+                    to_drop.append((i, j))
+                    to_drop.append((j, i))
+        edge_pointer += cardinality
+    return set(to_drop)
+
+
+if __name__ == "__main__":
+    import libpysal
+    import numpy
+    import geopandas, pandas
+    import pygeos, time
+
+    libpysal.examples.load_example("south.shp")
+    df = geopandas.read_file(libpysal.examples.get_path("south.shp"))
+
+    coords = pygeos.get_coordinates(df.geometry.centroid.values.data)
+    start1 = time.time()
+    voronoi_old = libpysal.weights.Voronoi(coords, clip="extent")
+    fin1 = time.time()
+    voronoi_old_elapsed = fin1 - start1
+
+    compile_start = time.time()
+    edges_from_simplices(numpy.random.randint(0, 4, size=(4, 3)))
+    filter_edges(
+        numpy.random.randint(0, 4, size=(4, 2)), numpy.random.normal(size=(4, 2))
+    )
+    compile_finish = time.time()
+    compile_time = compile_finish - compile_start
+
+    start2 = time.time()
+    dt = _Delaunay(coords)
+    edges = edges_from_simplices(dt.simplices)
+    edges = (
+        pandas.DataFrame(numpy.asarray(list(edges)))
+        .sort_values([0, 1])
+        .drop_duplicates()
+        .values
+    )
+    droplist = filter_edges(
+        edges,
+        dt.points,
+    )
+    output = set(map(tuple, edges)).difference(set(droplist))
+    gabriel_neighbors = pandas.DataFrame(output).groupby(0)[1].apply(list).to_dict()
+    gabriel_raw = libpysal.weights.W(gabriel_neighbors)
+    fin2 = time.time()
+    gabriel_raw_elapsed = fin2 - start2
+
+    for focal, gneighbors in gabriel_raw.neighbors.items():
+        vneighbors = voronoi_old.neighbors[focal]
+        assert set(gneighbors) <= set(vneighbors)
+
+    start3 = time.time()
+    delaunay = Delaunay(coords)
+    fin3 = time.time()
+    voronoi_new_elapsed = fin3 - start3
+
+    start4 = time.time()
+    gabriel = Gabriel(coords)
+    fin4 = time.time()
+    gabriel_elapsed = fin4 - start4
+    assert gabriel.n == delaunay.n
+    for focal, gneighbors in gabriel.neighbors.items():
+        dneighbors = delaunay.neighbors[focal]
+        assert set(gneighbors) <= set(dneighbors)
+    # for focal, dneighbors in delaunay.neighbors.items():
+    #    vold = voronoi_old.neighbors[focal]
+    #    assert vold == vneighbors fails due to clipping!
+    print(
+        f"""
+        Old Voronoi: {voronoi_old_elapsed:.2f}
+        Raw Gabriel: {gabriel_raw_elapsed:.2f}
+        Compile: {compile_time:.2f}
+        New Delaunay: {voronoi_new_elapsed:.2f}
+        Ref Gabriel: {gabriel_elapsed:.2f}
+        """
+    )

From 639ea153cd18a7fb7f83e53c03cbdf09434fdcd4 Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Tue, 19 Oct 2021 11:28:01 +0100
Subject: [PATCH 066/162] add a bit more commenting to the algo

---
 libpysal/weights/gabriel.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/libpysal/weights/gabriel.py b/libpysal/weights/gabriel.py
index 797b30a9c..70d3b8132 100644
--- a/libpysal/weights/gabriel.py
+++ b/libpysal/weights/gabriel.py
@@ -63,7 +63,7 @@ def filter_edges(edges, coordinates):
     while edge_pointer < n_edges:
         edge = edges[edge_pointer]
         cardinality = 0
-
+        # look ahead to find all neighbors of edge[0]
         for joff in range(edge_pointer, n_edges):
             next_edge = edges[joff]
             if next_edge[0] != edge[0]:
@@ -78,16 +78,16 @@ def filter_edges(edges, coordinates):
         # Therefore, we can take each observation i, iterate over neighbors j,k and remove links
         # where dij**2 > djk**2 + dki**2 to filter a delanuay graph to a gabriel one.
         for ix in range(edge_pointer, edge_pointer + cardinality):
-            i, j = edges[ix]
-            dij = ((coordinates[i] - coordinates[j]) ** 2).sum() ** 0.5
+            i, j = edges[ix]  # lookahead ensures that i is always edge[0]
+            dij2 = ((coordinates[i] - coordinates[j]) ** 2).sum()
             for ix2 in range(edge_pointer, edge_pointer + cardinality):
                 _, k = edges[ix2]
                 if j == k:
                     continue
-                dik = ((coordinates[i] - coordinates[k]) ** 2).sum() ** 0.5
-                djk = ((coordinates[j] - coordinates[k]) ** 2).sum() ** 0.5
+                dik2 = ((coordinates[i] - coordinates[k]) ** 2).sum()
+                djk2 = ((coordinates[j] - coordinates[k]) ** 2).sum()
 
-                if dij ** 2 > (dik ** 2 + djk ** 2):
+                if dij2 > (dik2 + djk2):
                     to_drop.append((i, j))
                     to_drop.append((j, i))
         edge_pointer += cardinality

From 80d7805951816dd3f9f0eee6a211580bbf36f303 Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Tue, 19 Oct 2021 11:28:20 +0100
Subject: [PATCH 067/162] add validation against R

---
 libpysal/weights/gabriel.py | 36 +++++++++++++++++++++++++++++++-----
 1 file changed, 31 insertions(+), 5 deletions(-)

diff --git a/libpysal/weights/gabriel.py b/libpysal/weights/gabriel.py
index 70d3b8132..d4415b886 100644
--- a/libpysal/weights/gabriel.py
+++ b/libpysal/weights/gabriel.py
@@ -95,13 +95,12 @@ def filter_edges(edges, coordinates):
 
 
 if __name__ == "__main__":
-    import libpysal
-    import numpy
-    import geopandas, pandas
-    import pygeos, time
+    import numpy, subprocess, geopandas, pandas, pygeos, time, libpysal, os
+    from rpy2.robjects import r
 
     libpysal.examples.load_example("south.shp")
-    df = geopandas.read_file(libpysal.examples.get_path("south.shp"))
+    path = libpysal.examples.get_path("south.shp")
+    df = geopandas.read_file(path)
 
     coords = pygeos.get_coordinates(df.geometry.centroid.values.data)
     start1 = time.time()
@@ -156,6 +155,7 @@ def filter_edges(edges, coordinates):
     # for focal, dneighbors in delaunay.neighbors.items():
     #    vold = voronoi_old.neighbors[focal]
     #    assert vold == vneighbors fails due to clipping!
+
     print(
         f"""
         Old Voronoi: {voronoi_old_elapsed:.2f}
@@ -165,3 +165,29 @@ def filter_edges(edges, coordinates):
         Ref Gabriel: {gabriel_elapsed:.2f}
         """
     )
+
+    with open("./gabriel.R", "w") as f:
+        f.writelines(
+            [
+                "library(sf)\n"
+                "library(spdep)\n"
+                "library(dplyr)\n"
+                f"path <- {path}"
+                "df <- st_read(path)\n"
+                "network <- df %>% st_centroid() %>% st_coordinates() %>%  gabrielneigh()\n"
+                'network %>% graph2nb(sym=T) %>% write.nb.gal("./gabriel_r.gal")\n'
+            ]
+        )
+        subprocess.call(["/usr/local/bin/Rscript", "--vanilla", "gabriel.R"])
+
+    gabriel_r = W.from_file("./gabriel_r.gal")
+
+    for i, neighbors in gabriel.neighbors.items():
+        iname = str(i + 1)
+        nnames = [str(n + 1) for n in neighbors]
+        r_neighbors = gabriel_r.neighbors[iname]
+        assert set(r_neighbors) == set(nnames)
+
+    os.remove("./gabriel_r.gal")
+    os.remove("./gabriel_py.gal")
+    os.remove("./gabriel.R")

From 016768274ad12405ae131fa3b7a28103fdc7f117 Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Tue, 19 Oct 2021 11:46:29 +0100
Subject: [PATCH 068/162] add numpy to initial import

---
 libpysal/weights/gabriel.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/libpysal/weights/gabriel.py b/libpysal/weights/gabriel.py
index d4415b886..7b3adf843 100644
--- a/libpysal/weights/gabriel.py
+++ b/libpysal/weights/gabriel.py
@@ -2,7 +2,7 @@
 from scipy import sparse
 from numba import njit
 from libpysal.weights import W, Rook
-import joblib, pandas, pygeos
+import joblib, pandas, pygeos, numpy
 
 
 class Delaunay(W):

From c96157706e0e970e2964b4e7ef18ac3dbc57b561 Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Tue, 19 Oct 2021 11:56:18 +0100
Subject: [PATCH 069/162] fix piping in the Delaunay class

---
 libpysal/weights/gabriel.py | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/libpysal/weights/gabriel.py b/libpysal/weights/gabriel.py
index 7b3adf843..6c0c0aec0 100644
--- a/libpysal/weights/gabriel.py
+++ b/libpysal/weights/gabriel.py
@@ -8,7 +8,7 @@
 class Delaunay(W):
     def __init__(self, coordinates, **kwargs):
         edges, _ = self._voronoi_edges(coordinates)
-        voronoi_neighbors = pandas.DataFrame(output).groupby(0)[1].apply(list).to_dict()
+        voronoi_neighbors = pandas.DataFrame(edges).groupby(0)[1].apply(list).to_dict()
         W.__init__(self, voronoi_neighbors, **kwargs)
 
     def _voronoi_edges(self, coordinates):
@@ -24,9 +24,7 @@ def _voronoi_edges(self, coordinates):
 
     @classmethod
     def from_dataframe(cls, df, **kwargs):
-        return cls.__init__(
-            pygeos.get_coordinates(pygeos.centroid(df.geometry.data.values))
-        )
+        return cls(pygeos.get_coordinates(pygeos.centroid(df.geometry.values.data)))
 
 
 class Gabriel(Delaunay):

From 4b3c65a60d02bb0b38b4f1facb2c6bb693ab7c43 Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Tue, 19 Oct 2021 11:56:37 +0100
Subject: [PATCH 070/162] add tests

---
 libpysal/weights/tests/test_gabriel.py | 30 ++++++++++++++++++++++++++
 1 file changed, 30 insertions(+)
 create mode 100644 libpysal/weights/tests/test_gabriel.py

diff --git a/libpysal/weights/tests/test_gabriel.py b/libpysal/weights/tests/test_gabriel.py
new file mode 100644
index 000000000..9df7a019e
--- /dev/null
+++ b/libpysal/weights/tests/test_gabriel.py
@@ -0,0 +1,30 @@
+from .. import gabriel
+from ... import examples
+import geopandas, numpy
+
+path = examples.get_path("columbus.shp")
+df = geopandas.read_file(path)
+geoms = df.geometry.centroid
+coords = numpy.column_stack((geoms.x, geoms.y))
+
+
+def test_delaunay(coords):
+    a = gabriel.Delaunay(coords)
+    b = gabriel.Delaunay.from_dataframe(df)
+
+    assert a.neighbors == b.neighbors
+
+    assert a[13] == {6: 1, 11: 1, 12: 1, 18: 1, 20: 1}
+
+
+def test_gabriel(coords):
+    c = gabriel.Gabriel(coords)
+    d = gabriel.Gabriel.from_dataframe(df)
+    c2 = gabriel.Delaunay(coords)
+
+    assert a.neighbors == b.neighbors
+
+    assert a[13] == {12: 1, 18: 1}
+    for focal, neighbors in c.neighbors.items():
+        dneighbors = c2[focal]
+        assert set(neighbors) <= set(dneighbors)

From e60c47d899134be9cc817cedc1334cb9ad744162 Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Tue, 19 Oct 2021 15:17:45 +0100
Subject: [PATCH 071/162] clean up helper functions and names

---
 libpysal/weights/gabriel.py | 41 ++++++++++++++++++++++---------------
 1 file changed, 24 insertions(+), 17 deletions(-)

diff --git a/libpysal/weights/gabriel.py b/libpysal/weights/gabriel.py
index 6c0c0aec0..c0e236c4c 100644
--- a/libpysal/weights/gabriel.py
+++ b/libpysal/weights/gabriel.py
@@ -1,9 +1,13 @@
 from scipy.spatial import Delaunay as _Delaunay
 from scipy import sparse
 from numba import njit
-from libpysal.weights import W, Rook
+from libpysal.weights import W, WSP
 import joblib, pandas, pygeos, numpy
 
+# delaunay graphs and their subgraphs
+
+#### Classes
+
 
 class Delaunay(W):
     def __init__(self, coordinates, **kwargs):
@@ -13,7 +17,7 @@ def __init__(self, coordinates, **kwargs):
 
     def _voronoi_edges(self, coordinates):
         dt = _Delaunay(coordinates)
-        edges = edges_from_simplices(dt.simplices)
+        edges = _edges_from_simplices(dt.simplices)
         edges = (
             pandas.DataFrame(numpy.asarray(list(edges)))
             .sort_values([0, 1])
@@ -30,7 +34,7 @@ def from_dataframe(cls, df, **kwargs):
 class Gabriel(Delaunay):
     def __init__(self, coordinates, **kwargs):
         edges, dt = self._voronoi_edges(coordinates)
-        droplist = filter_edges(
+        droplist = _filter_gabriel(
             edges,
             dt.points,
         )
@@ -40,7 +44,7 @@ def __init__(self, coordinates, **kwargs):
 
 
 @njit
-def edges_from_simplices(simplices):
+def _edges_from_simplices(simplices):
     edges = []
     for simplex in simplices:
         edges.append((simplex[0], simplex[1]))
@@ -53,7 +57,7 @@ def edges_from_simplices(simplices):
 
 
 @njit
-def filter_edges(edges, coordinates):
+def _filter_gabriel(edges, coordinates):
     edge_pointer = 0
     n = edges.max()
     n_edges = len(edges)
@@ -107,8 +111,8 @@ def filter_edges(edges, coordinates):
     voronoi_old_elapsed = fin1 - start1
 
     compile_start = time.time()
-    edges_from_simplices(numpy.random.randint(0, 4, size=(4, 3)))
-    filter_edges(
+    _edges_from_simplices(numpy.random.randint(0, 4, size=(4, 3)))
+    _filter_gabriel(
         numpy.random.randint(0, 4, size=(4, 2)), numpy.random.normal(size=(4, 2))
     )
     compile_finish = time.time()
@@ -116,14 +120,14 @@ def filter_edges(edges, coordinates):
 
     start2 = time.time()
     dt = _Delaunay(coords)
-    edges = edges_from_simplices(dt.simplices)
+    edges = _edges_from_simplices(dt.simplices)
     edges = (
         pandas.DataFrame(numpy.asarray(list(edges)))
         .sort_values([0, 1])
         .drop_duplicates()
         .values
     )
-    droplist = filter_edges(
+    droplist = _filter_gabriel(
         edges,
         dt.points,
     )
@@ -164,21 +168,22 @@ def filter_edges(edges, coordinates):
         """
     )
 
-    with open("./gabriel.R", "w") as f:
+    location = os.path.dirname(__file__)
+    with open(f"{location}/gabriel.R", "w") as f:
         f.writelines(
             [
                 "library(sf)\n"
                 "library(spdep)\n"
                 "library(dplyr)\n"
-                f"path <- {path}"
+                f"path <- '{path}'\n"
                 "df <- st_read(path)\n"
                 "network <- df %>% st_centroid() %>% st_coordinates() %>%  gabrielneigh()\n"
-                'network %>% graph2nb(sym=T) %>% write.nb.gal("./gabriel_r.gal")\n'
+                f'network %>% graph2nb(sym=T) %>% write.nb.gal("{location}/gabriel_r.gal")\n'
             ]
         )
-        subprocess.call(["/usr/local/bin/Rscript", "--vanilla", "gabriel.R"])
+    subprocess.call(["/usr/local/bin/Rscript", "--vanilla", f"{location}/gabriel.R"])
 
-    gabriel_r = W.from_file("./gabriel_r.gal")
+    gabriel_r = W.from_file(f"{location}/gabriel_r.gal")
 
     for i, neighbors in gabriel.neighbors.items():
         iname = str(i + 1)
@@ -186,6 +191,8 @@ def filter_edges(edges, coordinates):
         r_neighbors = gabriel_r.neighbors[iname]
         assert set(r_neighbors) == set(nnames)
 
-    os.remove("./gabriel_r.gal")
-    os.remove("./gabriel_py.gal")
-    os.remove("./gabriel.R")
+    os.remove(f"{location}/gabriel_r.gal")
+    os.remove(f"{location}/gabriel.R")
+
+
+

From 7d18cb0f7a17ece03cd7cfdf1816e71d01d98df5 Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Tue, 19 Oct 2021 15:18:30 +0100
Subject: [PATCH 072/162] add relative neighborhood and stub for mututal
 reachability

---
 libpysal/weights/gabriel.py | 76 +++++++++++++++++++++++++++++++++++++
 1 file changed, 76 insertions(+)

diff --git a/libpysal/weights/gabriel.py b/libpysal/weights/gabriel.py
index c0e236c4c..d42fd71e5 100644
--- a/libpysal/weights/gabriel.py
+++ b/libpysal/weights/gabriel.py
@@ -43,6 +43,26 @@ def __init__(self, coordinates, **kwargs):
         W.__init__(self, gabriel_neighbors, **kwargs)
 
 
+class Relative_Neighborhood(Delaunay):
+    def __init__(self, coordinates, return_dkmax=False, **kwargs):
+        """
+        via toussaint, https://doi.org/10.1016/0031-3203(80)90066-7
+        """
+        edges, dt = self._voronoi_edges(coordinates)
+        output, dkmax = _filter_relativehood(
+            edges, dt.points, return_dkmax=return_dkmax
+        )
+        if not return_dkmax:
+            del dkmax
+        row, col, data = zip(*output)
+        sp = sparse.csc_matrix((data, (row, col))) #TODO: faster way than this?
+        tmp = WSP(sp).to_W() 
+        W.__init__(self, tmp.neighbors, tmp.weights, **kwargs)
+
+
+
+#### utilities
+
 @njit
 def _edges_from_simplices(simplices):
     edges = []
@@ -96,6 +116,61 @@ def _filter_gabriel(edges, coordinates):
     return set(to_drop)
 
 
+@njit
+def _filter_relativehood(edges, coordinates, return_dkmax=False):
+
+    # RNG
+    # 1. Compute the delaunay
+    # 2. for each edge of the delaunay (i,j), compute
+    #    dkmax = max(d(k,i), d(k,j)) for k in 1..n, k != i, j
+    # 3. for each edge of the delaunay (i,j), prune
+    #    if any dkmax is greater than d(i,j)
+    edge_pointer = 0
+    n = edges.max()
+    n_edges = len(edges)
+    out = []
+    r = []
+    for edge in edges:
+        i, j = edge
+        pi = coordinates[i]
+        pj = coordinates[j]
+        dkmax = 0
+        dij = ((pi - pj)**2).sum()**.5t 
+        prune = False
+        for k in range(n):
+            pk = coordinates[k]
+            dik = ((pi - pk)**2).sum()**.5
+            djk = ((pj - pk)**2).sum()**.5
+            distances = numpy.array([dik, djk, dkmax])
+            dkmax = distances.max()
+            prune = dkmax < dij
+            if (not return_dkmax) & prune:
+                break
+        if prune:
+            continue
+        out.append((i, j, dij))
+        if return_dkmax:
+            r.append(dkmax)
+
+    return out, r
+
+
+class Mutual_Reachability(W):
+    def __init__(
+        self, coordinates, n_max=None, n_core=5, method="kdtree", metric="euclidean"
+    ):
+        # check to see if metric is supported by method using tree.valid_metrics
+        # so, kdtree can only do minkowski
+        # balltree can handle more
+        # approx can handle a ton, but wait to import pynndescent unless method = approx
+        # raise an error if the requested metric is not supported
+
+        # then, compute the core distances for each point
+        # finally, for all pairs of points, compute the distances for the nearest n_max
+        # points (possibly all points), and censor that by the core distance.
+        raise NotImplementedError()
+
+
 if __name__ == "__main__":
     import numpy, subprocess, geopandas, pandas, pygeos, time, libpysal, os
     from rpy2.robjects import r
@@ -196,3 +271,4 @@ def _filter_gabriel(edges, coordinates):
 
 
 
+    Relative_Neighborhood(coords)

From 1403b7c8b2a5cfaa5977d962f4c8c90a8c7ac05e Mon Sep 17 00:00:00 2001
From: Charles Schmidt <schmidtc@gmail.com>
Date: Fri, 22 Oct 2021 15:22:12 -0700
Subject: [PATCH 073/162] Bug fix for parallel execution environments

Have been hitting a rare bug here when deploying FastAPI applications with many worker processes in a Kubernetes environment.  A bit of a race condition exists here where multiple workers attempt to create the directory but fail because another worker already created it, resulting in a crash loop.
---
 libpysal/examples/base.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/libpysal/examples/base.py b/libpysal/examples/base.py
index 2ebbbaea9..cf3ed54e7 100644
--- a/libpysal/examples/base.py
+++ b/libpysal/examples/base.py
@@ -39,7 +39,7 @@ def get_data_home():
     data_home = environ.get("PYSALDATA", join("~", PYSALDATA))
     data_home = expanduser(data_home)
     if not exists(data_home):
-        makedirs(data_home)
+        makedirs(data_home, exist_ok=True)
     return data_home
 
 

From 3d768d05b607ed1003f8cfc289e424c2d8fdb233 Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Thu, 18 Nov 2021 13:00:40 +0000
Subject: [PATCH 074/162] remove errant t

---
 libpysal/weights/gabriel.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/libpysal/weights/gabriel.py b/libpysal/weights/gabriel.py
index d42fd71e5..31b4db338 100644
--- a/libpysal/weights/gabriel.py
+++ b/libpysal/weights/gabriel.py
@@ -135,7 +135,7 @@ def _filter_relativehood(edges, coordinates, return_dkmax=False):
         pi = coordinates[i]
         pj = coordinates[j]
         dkmax = 0
-        dij = ((pi - pj)**2).sum()**.5t 
+        dij = ((pi - pj)**2).sum()**.5
         prune = False
         for k in range(n):
             pk = coordinates[k]

From fd0929f09d31ad3d21219b751889f9634c1553fd Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Fri, 10 Dec 2021 14:01:28 +0000
Subject: [PATCH 075/162] add additional docstring info about coincident points

---
 libpysal/weights/distance.py | 15 ++++++++++++++-
 1 file changed, 14 insertions(+), 1 deletion(-)

diff --git a/libpysal/weights/distance.py b/libpysal/weights/distance.py
index 25b41d690..652a5f545 100644
--- a/libpysal/weights/distance.py
+++ b/libpysal/weights/distance.py
@@ -83,7 +83,20 @@ class KNN(W):
     Notes
     -----
 
-    Ties between neighbors of equal distance are arbitrarily broken.
+    Ties between neighbors of equal distance are arbitrarily broken. 
+
+    Further, if many points occupy the same spatial location (i.e. observations are 
+    coincident), then you may need to increase k for those observations to 
+    acquire neighbors at different spatial locations. For example, if five
+    points are coincident, then their four nearest neighbors will all
+    occupy the same spatial location; only the fifth nearest neighbor will
+    result in those coincident points becoming connected to the graph as a
+    whole. 
+
+    Solutions to this problem include jittering the points (by adding
+    a small random value to each observation's location) or by adding 
+    higher-k neighbors only to the coincident points, using the
+    weights.w_sets.w_union() function. 
 
     See Also
     --------

From fe74ff4520738b2d523cb851e4674a25aa5a3a95 Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Fri, 10 Dec 2021 14:56:20 +0000
Subject: [PATCH 076/162] workaround pygeos import

---
 libpysal/weights/gabriel.py | 29 ++++++++++-------------------
 1 file changed, 10 insertions(+), 19 deletions(-)

diff --git a/libpysal/weights/gabriel.py b/libpysal/weights/gabriel.py
index 31b4db338..6b14f20bd 100644
--- a/libpysal/weights/gabriel.py
+++ b/libpysal/weights/gabriel.py
@@ -1,8 +1,13 @@
 from scipy.spatial import Delaunay as _Delaunay
 from scipy import sparse
-from numba import njit
 from libpysal.weights import W, WSP
-import joblib, pandas, pygeos, numpy
+import pandas, numpy
+
+try:
+    from numba import njit
+except ModuleNotFoundErrror:
+    from libpysal.common import jit as njit
+
 
 # delaunay graphs and their subgraphs
 
@@ -28,7 +33,9 @@ def _voronoi_edges(self, coordinates):
 
     @classmethod
     def from_dataframe(cls, df, **kwargs):
-        return cls(pygeos.get_coordinates(pygeos.centroid(df.geometry.values.data)))
+        centroids = df.geometry.centroid
+        point_array = numpy.column_stack((centroids.x.values, centroids.y.values))
+        return cls(point_array)
 
 
 class Gabriel(Delaunay):
@@ -155,22 +162,6 @@ def _filter_relativehood(edges, coordinates, return_dkmax=False):
     return out, r
 
 
-class Mutual_Reachability(W):
-    def __init__(
-        self, coordinates, n_max=None, n_core=5, method="kdtree", metric="euclidean"
-    ):
-        # check to see if metric is supported by method using tree.valid_metrics
-        # so, kdtree can only do minkowski
-        # balltree can handle more
-        # approx can handle a ton, but wait to import pynndescent unless method = approx
-        # raise an error if the requested metric is not supported
-
-        # then, compute the core distances for each point
-        # finally, for all pairs of points, compute the distances for the nearest n_max
-        # points (possibly all points), and censor that by the core distance.
-        raise NotImplementedError()
-
-
 if __name__ == "__main__":
     import numpy, subprocess, geopandas, pandas, pygeos, time, libpysal, os
     from rpy2.robjects import r

From faf356f05fc65f97edabb0d48cc25bcac4cf6439 Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Fri, 10 Dec 2021 15:18:26 +0000
Subject: [PATCH 077/162] move from fixture to static text

---
 libpysal/weights/tests/test_gabriel.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/libpysal/weights/tests/test_gabriel.py b/libpysal/weights/tests/test_gabriel.py
index 9df7a019e..74873441c 100644
--- a/libpysal/weights/tests/test_gabriel.py
+++ b/libpysal/weights/tests/test_gabriel.py
@@ -8,7 +8,7 @@
 coords = numpy.column_stack((geoms.x, geoms.y))
 
 
-def test_delaunay(coords):
+def test_delaunay():
     a = gabriel.Delaunay(coords)
     b = gabriel.Delaunay.from_dataframe(df)
 
@@ -17,7 +17,7 @@ def test_delaunay(coords):
     assert a[13] == {6: 1, 11: 1, 12: 1, 18: 1, 20: 1}
 
 
-def test_gabriel(coords):
+def test_gabriel():
     c = gabriel.Gabriel(coords)
     d = gabriel.Gabriel.from_dataframe(df)
     c2 = gabriel.Delaunay(coords)

From 61f6a79524de3dfd6f454a400b0f256cea2e0043 Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Fri, 10 Dec 2021 15:30:02 +0000
Subject: [PATCH 078/162] fix additional test

---
 libpysal/weights/tests/test_gabriel.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/libpysal/weights/tests/test_gabriel.py b/libpysal/weights/tests/test_gabriel.py
index 74873441c..692a2b97d 100644
--- a/libpysal/weights/tests/test_gabriel.py
+++ b/libpysal/weights/tests/test_gabriel.py
@@ -22,9 +22,9 @@ def test_gabriel():
     d = gabriel.Gabriel.from_dataframe(df)
     c2 = gabriel.Delaunay(coords)
 
-    assert a.neighbors == b.neighbors
+    assert c.neighbors == d.neighbors
 
-    assert a[13] == {12: 1, 18: 1}
+    assert c[13] == {12: 1, 18: 1}
     for focal, neighbors in c.neighbors.items():
         dneighbors = c2[focal]
         assert set(neighbors) <= set(dneighbors)

From b1a89de340eaec46f1053dc7a0d26b914dda14db Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Sat, 11 Dec 2021 17:23:04 +0000
Subject: [PATCH 079/162] address @martinfleis review

---
 libpysal/weights/gabriel.py | 275 +++++++++++++++++++-----------------
 1 file changed, 145 insertions(+), 130 deletions(-)

diff --git a/libpysal/weights/gabriel.py b/libpysal/weights/gabriel.py
index 6b14f20bd..39b2d30d1 100644
--- a/libpysal/weights/gabriel.py
+++ b/libpysal/weights/gabriel.py
@@ -1,21 +1,56 @@
 from scipy.spatial import Delaunay as _Delaunay
 from scipy import sparse
 from libpysal.weights import W, WSP
-import pandas, numpy
+import pandas, numpy, warnings
 
 try:
     from numba import njit
 except ModuleNotFoundErrror:
     from libpysal.common import jit as njit
 
-
-# delaunay graphs and their subgraphs
+__author__ = """"
+Levi John Wolf (levi.john.wolf@gmailcom)
+Martin Fleischmann (martin@martinfleischmann.net)
+"""
 
 #### Classes
 
 
 class Delaunay(W):
+    """
+    Constructor of the Delaunay graph of a set of input points. 
+    Relies on scipy.spatial.Delaunay and numba to quickly construct
+    a graph from the input set of points. Will be slower without numba,
+    and will warn if this is missing. 
+
+    Arguments
+    ---------
+    coordinates :   array of points, (N,2)
+        numpy array of coordinates containing locations to compute the
+        delaunay triangulation
+    **kwargs    :   keyword argument list
+        keyword arguments passed directly to weights.W
+    
+    Notes
+    -----
+    The Delaunay triangulation can result in quite a few non-local links among
+    spatial coordinates. For a more useful graph, consider the weights.Voronoi
+    constructor, which builds a voronoi diagram among the points, clips the
+    Voronoi cells, and then constructs an adjacency graph among the clipped cells.
+    This graph among the clipped Voronoi cells generally represents the structure
+    of local adjacencies better than the "raw" Delaunay graph. 
+
+    However, if the unresricted Delaunay triangulation is needed, this class
+    will compute it much more quickly than Voronoi(coordinates, clip=None).
+    """
     def __init__(self, coordinates, **kwargs):
+        try:
+            from numba import njit
+        except ModuleNotFoundError:
+            warnings.warn("The numba package is used extensively in this module"
+                    " to accelerate the computation of graphs. Without numba,"
+                    " these computations may become unduly slow on large data."
+                    )
         edges, _ = self._voronoi_edges(coordinates)
         voronoi_neighbors = pandas.DataFrame(edges).groupby(0)[1].apply(list).to_dict()
         W.__init__(self, voronoi_neighbors, **kwargs)
@@ -33,13 +68,54 @@ def _voronoi_edges(self, coordinates):
 
     @classmethod
     def from_dataframe(cls, df, **kwargs):
-        centroids = df.geometry.centroid
-        point_array = numpy.column_stack((centroids.x.values, centroids.y.values))
-        return cls(point_array)
+        geomtypes = df.geometry.type.unique()
+        try:
+            assert len(geomtypes) == 1
+            assert geomtype[0] == 'Point'
+            point_array = numpy.column_stack(
+                    (df.geometry.x.values, df.geometry.y.values)
+                    )
+            return cls(point_array, **kwargs)
+        except AssertionError:
+            raise TypeError(
+                    f'The input dataframe has geometry types {geomtypes}'
+                    f' but this delaunay triangulation is only well-defined for points.'
+                    f' Choose a method to convert your dataframe into points (like using'
+                    f' the df.centroid) and use that to estimate this graph.'
+                    )
 
 
 class Gabriel(Delaunay):
+    """
+    Constructs the Gabriel graph of a set of points. This graph is a subset of 
+    the Delaunay triangulation where only "short" links are retained. This 
+    function is also accelerated using numba, and implemented on top of the
+    scipy.spatial.Delaunay class. 
+
+    For a link (i,j) connecting node i to j in the Delaunay triangulation
+    to be retained in the Gabriel graph, it must pass a point set exclusion test:
+    1. Construct the circle C_ij containing link (i,j) as its diameter
+    2. If any other node k is contained within C_ij, then remove link (i,j) 
+       from the graph. 
+    3. Once all links are evaluated, the remaining graph is the Gabriel graph. 
+
+    Arguments
+    ---------
+    coordinates :   array of points, (N,2)
+        numpy array of coordinates containing locations to compute the
+        delaunay triangulation
+    **kwargs    :   keyword argument list
+        keyword arguments passed directly to weights.W
+    """
     def __init__(self, coordinates, **kwargs):
+        try:
+            from numba import njit
+        except ModuleNotFoundError:
+            warnings.warn("The numba package is used extensively in this module"
+                    " to accelerate the computation of graphs. Without numba,"
+                    " these computations may become unduly slow on large data."
+                    )
+        edges, _ = self._voronoi_edges(coordinates)
         edges, dt = self._voronoi_edges(coordinates)
         droplist = _filter_gabriel(
             edges,
@@ -51,16 +127,39 @@ def __init__(self, coordinates, **kwargs):
 
 
 class Relative_Neighborhood(Delaunay):
-    def __init__(self, coordinates, return_dkmax=False, **kwargs):
-        """
-        via toussaint, https://doi.org/10.1016/0031-3203(80)90066-7
-        """
+    """
+    Constructs the Relative Neighborhood graph from a set of points. 
+    This graph is a subset of the Delaunay triangulation, where only 
+    "relative neighbors" are retained. Further, it is a superset of
+    the Minimum Spanning Tree, with additional "relative neighbors"
+    introduced.
+
+    A relative neighbor pair of points i,j must be closer than the 
+    maximum distance between i (or j) and each other point k. 
+    This means that the points are at least as close to one another 
+    as they are to any other point. 
+    
+    Arguments
+    ---------
+    coordinates :   array of points, (N,2)
+        numpy array of coordinates containing locations to compute the
+        delaunay triangulation
+    **kwargs    :   keyword argument list
+        keyword arguments passed directly to weights.W
+    """
+    def __init__(self, coordinates, **kwargs):
+        try:
+            from numba import njit
+        except ModuleNotFoundError:
+            warnings.warn("The numba package is used extensively in this module"
+                    " to accelerate the computation of graphs. Without numba,"
+                    " these computations may become unduly slow on large data."
+                    )
+        edges, _ = self._voronoi_edges(coordinates)
         edges, dt = self._voronoi_edges(coordinates)
         output, dkmax = _filter_relativehood(
-            edges, dt.points, return_dkmax=return_dkmax
+            edges, dt.points, return_dkmax=False
         )
-        if not return_dkmax:
-            del dkmax
         row, col, data = zip(*output)
         sp = sparse.csc_matrix((data, (row, col))) #TODO: faster way than this?
         tmp = WSP(sp).to_W() 
@@ -72,6 +171,13 @@ def __init__(self, coordinates, return_dkmax=False, **kwargs):
 
 @njit
 def _edges_from_simplices(simplices):
+    """
+    Construct the sets of links that correspond to the edges of each 
+    simplex. Each simplex has three "sides," and thus six undirected
+    edges. Thus, the input should be a list of three-length tuples, 
+    that are then converted into the six non-directed edges for 
+    each simplex.
+    """
     edges = []
     for simplex in simplices:
         edges.append((simplex[0], simplex[1]))
@@ -85,6 +191,23 @@ def _edges_from_simplices(simplices):
 
 @njit
 def _filter_gabriel(edges, coordinates):
+    """
+    For an input set of edges and coordinates, filter the input edges
+    depending on the Gabriel rule: 
+
+    For each simplex, let i,j be the diameter of the circle defined by
+    edge (i,j), and let k be the third point defining the simplex. The
+    limiting case for the Gabriel rule is when k is also on the circle
+    with diameter (i,j). In this limiting case, then simplex ijk must
+    be a right triangle, and dij**2 = djk**2 + dki**2 (by thales theorem). 
+
+    This means that when dij**2 > djk**2 + dki**2, then k is inside the circle. 
+    In contrast, when dij**2 < djk**2 + dji*2, k is outside of the circle. 
+
+    Therefore, it's sufficient to take each observation i, iterate over its
+    Delaunay neighbors j,k, and remove links whre dij**2 > djk**2 + dki**2 
+    in order to construct the Gabriel graph. 
+    """
     edge_pointer = 0
     n = edges.max()
     n_edges = len(edges)
@@ -98,14 +221,6 @@ def _filter_gabriel(edges, coordinates):
             if next_edge[0] != edge[0]:
                 break
             cardinality += 1
-        # let i,j be the diameter of a circle, and k be a third point making a triangle.
-        # the right triangle formed when k is on the circle is the limiting case for the
-        # gabriel link between ij. If k on the circle, then dij**2 = djk**2 + dki**2,
-        # by thales theorem (an inscribed triangle with hypotenuse as diameter is a
-        # right triangle.). When dij**2 > djk**2 + dki**2, k is inside of the circle.
-        # When dij**2 < dij**2 + djk**2, k is outside of the circle.
-        # Therefore, we can take each observation i, iterate over neighbors j,k and remove links
-        # where dij**2 > djk**2 + dki**2 to filter a delanuay graph to a gabriel one.
         for ix in range(edge_pointer, edge_pointer + cardinality):
             i, j = edges[ix]  # lookahead ensures that i is always edge[0]
             dij2 = ((coordinates[i] - coordinates[j]) ** 2).sum()
@@ -125,13 +240,15 @@ def _filter_gabriel(edges, coordinates):
 
 @njit
 def _filter_relativehood(edges, coordinates, return_dkmax=False):
-
-    # RNG
-    # 1. Compute the delaunay
-    # 2. for each edge of the delaunay (i,j), compute
-    #    dkmax = max(d(k,i), d(k,j)) for k in 1..n, k != i, j
-    # 3. for each edge of the delaunay (i,j), prune
-    #    if any dkmax is greater than d(i,j)
+    """
+    This is a direct implementation of the algorithm from Toussaint (1980), RNG-2 
+
+    1. Compute the delaunay
+    2. for each edge of the delaunay (i,j), compute
+       dkmax = max(d(k,i), d(k,j)) for k in 1..n, k != i, j
+    3. for each edge of the delaunay (i,j), prune
+       if any dkmax is greater than d(i,j)
+    """
     edge_pointer = 0
     n = edges.max()
     n_edges = len(edges)
@@ -161,105 +278,3 @@ def _filter_relativehood(edges, coordinates, return_dkmax=False):
 
     return out, r
 
-
-if __name__ == "__main__":
-    import numpy, subprocess, geopandas, pandas, pygeos, time, libpysal, os
-    from rpy2.robjects import r
-
-    libpysal.examples.load_example("south.shp")
-    path = libpysal.examples.get_path("south.shp")
-    df = geopandas.read_file(path)
-
-    coords = pygeos.get_coordinates(df.geometry.centroid.values.data)
-    start1 = time.time()
-    voronoi_old = libpysal.weights.Voronoi(coords, clip="extent")
-    fin1 = time.time()
-    voronoi_old_elapsed = fin1 - start1
-
-    compile_start = time.time()
-    _edges_from_simplices(numpy.random.randint(0, 4, size=(4, 3)))
-    _filter_gabriel(
-        numpy.random.randint(0, 4, size=(4, 2)), numpy.random.normal(size=(4, 2))
-    )
-    compile_finish = time.time()
-    compile_time = compile_finish - compile_start
-
-    start2 = time.time()
-    dt = _Delaunay(coords)
-    edges = _edges_from_simplices(dt.simplices)
-    edges = (
-        pandas.DataFrame(numpy.asarray(list(edges)))
-        .sort_values([0, 1])
-        .drop_duplicates()
-        .values
-    )
-    droplist = _filter_gabriel(
-        edges,
-        dt.points,
-    )
-    output = set(map(tuple, edges)).difference(set(droplist))
-    gabriel_neighbors = pandas.DataFrame(output).groupby(0)[1].apply(list).to_dict()
-    gabriel_raw = libpysal.weights.W(gabriel_neighbors)
-    fin2 = time.time()
-    gabriel_raw_elapsed = fin2 - start2
-
-    for focal, gneighbors in gabriel_raw.neighbors.items():
-        vneighbors = voronoi_old.neighbors[focal]
-        assert set(gneighbors) <= set(vneighbors)
-
-    start3 = time.time()
-    delaunay = Delaunay(coords)
-    fin3 = time.time()
-    voronoi_new_elapsed = fin3 - start3
-
-    start4 = time.time()
-    gabriel = Gabriel(coords)
-    fin4 = time.time()
-    gabriel_elapsed = fin4 - start4
-    assert gabriel.n == delaunay.n
-    for focal, gneighbors in gabriel.neighbors.items():
-        dneighbors = delaunay.neighbors[focal]
-        assert set(gneighbors) <= set(dneighbors)
-    # for focal, dneighbors in delaunay.neighbors.items():
-    #    vold = voronoi_old.neighbors[focal]
-    #    assert vold == vneighbors fails due to clipping!
-
-    print(
-        f"""
-        Old Voronoi: {voronoi_old_elapsed:.2f}
-        Raw Gabriel: {gabriel_raw_elapsed:.2f}
-        Compile: {compile_time:.2f}
-        New Delaunay: {voronoi_new_elapsed:.2f}
-        Ref Gabriel: {gabriel_elapsed:.2f}
-        """
-    )
-
-    location = os.path.dirname(__file__)
-    with open(f"{location}/gabriel.R", "w") as f:
-        f.writelines(
-            [
-                "library(sf)\n"
-                "library(spdep)\n"
-                "library(dplyr)\n"
-                f"path <- '{path}'\n"
-                "df <- st_read(path)\n"
-                "network <- df %>% st_centroid() %>% st_coordinates() %>%  gabrielneigh()\n"
-                f'network %>% graph2nb(sym=T) %>% write.nb.gal("{location}/gabriel_r.gal")\n'
-            ]
-        )
-    subprocess.call(["/usr/local/bin/Rscript", "--vanilla", f"{location}/gabriel.R"])
-
-    gabriel_r = W.from_file(f"{location}/gabriel_r.gal")
-
-    for i, neighbors in gabriel.neighbors.items():
-        iname = str(i + 1)
-        nnames = [str(n + 1) for n in neighbors]
-        r_neighbors = gabriel_r.neighbors[iname]
-        assert set(r_neighbors) == set(nnames)
-
-    os.remove(f"{location}/gabriel_r.gal")
-    os.remove(f"{location}/gabriel.R")
-
-
-
-    Relative_Neighborhood(coords)

From e0520bac541a94b3e970bf0556433a686889f92f Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Sat, 11 Dec 2021 17:28:41 +0000
Subject: [PATCH 080/162] missed character on geomtypes

---
 libpysal/weights/gabriel.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/libpysal/weights/gabriel.py b/libpysal/weights/gabriel.py
index 39b2d30d1..48a0509f6 100644
--- a/libpysal/weights/gabriel.py
+++ b/libpysal/weights/gabriel.py
@@ -71,7 +71,7 @@ def from_dataframe(cls, df, **kwargs):
         geomtypes = df.geometry.type.unique()
         try:
             assert len(geomtypes) == 1
-            assert geomtype[0] == 'Point'
+            assert geomtypes[0] == 'Point'
             point_array = numpy.column_stack(
                     (df.geometry.x.values, df.geometry.y.values)
                     )

From 1308ab6fcc11b2aad176c671869318a11780bf38 Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Sat, 11 Dec 2021 17:34:12 +0000
Subject: [PATCH 081/162] finalize testing and typo issues

---
 libpysal/weights/gabriel.py            | 2 +-
 libpysal/weights/tests/test_gabriel.py | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/libpysal/weights/gabriel.py b/libpysal/weights/gabriel.py
index 48a0509f6..80a74a3c2 100644
--- a/libpysal/weights/gabriel.py
+++ b/libpysal/weights/gabriel.py
@@ -5,7 +5,7 @@
 
 try:
     from numba import njit
-except ModuleNotFoundErrror:
+except ModuleNotFoundError:
     from libpysal.common import jit as njit
 
 __author__ = """"
diff --git a/libpysal/weights/tests/test_gabriel.py b/libpysal/weights/tests/test_gabriel.py
index 692a2b97d..ec6b48c05 100644
--- a/libpysal/weights/tests/test_gabriel.py
+++ b/libpysal/weights/tests/test_gabriel.py
@@ -10,7 +10,7 @@
 
 def test_delaunay():
     a = gabriel.Delaunay(coords)
-    b = gabriel.Delaunay.from_dataframe(df)
+    b = gabriel.Delaunay.from_dataframe(df.centroid)
 
     assert a.neighbors == b.neighbors
 
@@ -19,7 +19,7 @@ def test_delaunay():
 
 def test_gabriel():
     c = gabriel.Gabriel(coords)
-    d = gabriel.Gabriel.from_dataframe(df)
+    d = gabriel.Gabriel.from_dataframe(df.centroid)
     c2 = gabriel.Delaunay(coords)
 
     assert c.neighbors == d.neighbors

From e3d7be2e72ad32b6b87c0f63be64291e88cd073e Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Sat, 11 Dec 2021 17:34:48 +0000
Subject: [PATCH 082/162] add to the API

---
 libpysal/weights/__init__.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/libpysal/weights/__init__.py b/libpysal/weights/__init__.py
index 2f886056b..5ec30b6ff 100644
--- a/libpysal/weights/__init__.py
+++ b/libpysal/weights/__init__.py
@@ -7,3 +7,4 @@
 from .set_operations import *
 from .spatial_lag import *
 from .raster import *
+from .gabriel import Gabriel, Delaunay, Relative_Neighborhood 

From 9446ec002f6d0bb054dcb965e18db77f9d51eec4 Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Sat, 11 Dec 2021 19:15:24 +0000
Subject: [PATCH 083/162] address second set of recommendations

---
 libpysal/weights/gabriel.py | 24 ++++++++++++++++++++++--
 1 file changed, 22 insertions(+), 2 deletions(-)

diff --git a/libpysal/weights/gabriel.py b/libpysal/weights/gabriel.py
index 80a74a3c2..f82e78589 100644
--- a/libpysal/weights/gabriel.py
+++ b/libpysal/weights/gabriel.py
@@ -9,7 +9,7 @@
     from libpysal.common import jit as njit
 
 __author__ = """"
-Levi John Wolf (levi.john.wolf@gmailcom)
+Levi John Wolf (levi.john.wolf@gmail.com)
 Martin Fleischmann (martin@martinfleischmann.net)
 """
 
@@ -35,11 +35,16 @@ class Delaunay(W):
     -----
     The Delaunay triangulation can result in quite a few non-local links among
     spatial coordinates. For a more useful graph, consider the weights.Voronoi
-    constructor, which builds a voronoi diagram among the points, clips the
+    constructor or the Gabriel graph.
+
+    The weights.Voronoi class builds a voronoi diagram among the points, clips the
     Voronoi cells, and then constructs an adjacency graph among the clipped cells.
     This graph among the clipped Voronoi cells generally represents the structure
     of local adjacencies better than the "raw" Delaunay graph. 
 
+    The weights.gabriel.Gabriel graph constructs a Delaunay graph, but only
+    includes the "short" links in the Delaunay graph. 
+
     However, if the unresricted Delaunay triangulation is needed, this class
     will compute it much more quickly than Voronoi(coordinates, clip=None).
     """
@@ -68,6 +73,21 @@ def _voronoi_edges(self, coordinates):
 
     @classmethod
     def from_dataframe(cls, df, **kwargs):
+        """
+        Construct a Delaunay triangulation from a geopandas GeoDataFrame. 
+        Not that the input geometries in the dataframe must be Points. 
+        Polygons or lines must be converted to points (e.g. using 
+        df.geometry.centroid).
+
+        Arguments
+        ---------
+        df  :   geopandas.GeoDataFrame
+            GeoDataFrame containing points to construct the Delaunay 
+            Triangulation. 
+        **kwargs :  keyword arguments
+            Keyword arguments that are passed downwards to the weights.W
+            constructor.
+        """
         geomtypes = df.geometry.type.unique()
         try:
             assert len(geomtypes) == 1

From 5b349b9438ee9cabb37ca50c2fd78b5dd9d79866 Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Sat, 11 Dec 2021 19:40:55 +0000
Subject: [PATCH 084/162] add binary option to ensure compatibility

---
 libpysal/weights/gabriel.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/libpysal/weights/gabriel.py b/libpysal/weights/gabriel.py
index f82e78589..fd90a5c79 100644
--- a/libpysal/weights/gabriel.py
+++ b/libpysal/weights/gabriel.py
@@ -167,7 +167,7 @@ class Relative_Neighborhood(Delaunay):
     **kwargs    :   keyword argument list
         keyword arguments passed directly to weights.W
     """
-    def __init__(self, coordinates, **kwargs):
+    def __init__(self, coordinates, binary=True, **kwargs):
         try:
             from numba import njit
         except ModuleNotFoundError:
@@ -181,6 +181,8 @@ def __init__(self, coordinates, **kwargs):
             edges, dt.points, return_dkmax=False
         )
         row, col, data = zip(*output)
+        if binary:
+            data = numpy.ones_like(col, dtype=float)
         sp = sparse.csc_matrix((data, (row, col))) #TODO: faster way than this?
         tmp = WSP(sp).to_W() 
         W.__init__(self, tmp.neighbors, tmp.weights, **kwargs)

From d93a437f1c0f11b4c497eb3f1d5f1116f347bd4f Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Sat, 11 Dec 2021 19:41:10 +0000
Subject: [PATCH 085/162] add test for rng

---
 libpysal/weights/tests/test_gabriel.py | 13 +++++++++++++
 1 file changed, 13 insertions(+)

diff --git a/libpysal/weights/tests/test_gabriel.py b/libpysal/weights/tests/test_gabriel.py
index ec6b48c05..cfd89394c 100644
--- a/libpysal/weights/tests/test_gabriel.py
+++ b/libpysal/weights/tests/test_gabriel.py
@@ -28,3 +28,16 @@ def test_gabriel():
     for focal, neighbors in c.neighbors.items():
         dneighbors = c2[focal]
         assert set(neighbors) <= set(dneighbors)
+
+def test_rng():
+    e = gabriel.Relative_Neighborhood(coords)
+    f = gabriel.Relative_Neighborhood.from_dataframe(df.centroid)
+    dty = gabriel.Delaunay(coords)
+
+    assert e.neighbors == f.neighbors
+
+    assert e[1] != dty[1]
+    assert list(e[1].keys()) == [0,3,6,30,38]
+    for focal, neighbors in e.neighbors.items():
+        dneighbors = dty[focal]
+        assert set(neighbors) <= set(dneighbors)

From e72cb01de20c1b640c6f285e50904a3844506208 Mon Sep 17 00:00:00 2001
From: Serge Rey <sjsrey2gmail.com>
Date: Wed, 15 Dec 2021 19:05:26 -0800
Subject: [PATCH 086/162] Moving to appdirs for example storage

---
 ci/37.yaml                           |  1 +
 ci/38.yaml                           |  1 +
 ci/39.yaml                           |  1 +
 ci/travis/36-latest-conda-forge.yaml | 14 ++++++++++++++
 ci/travis/37-latest-conda-forge.yaml | 14 ++++++++++++++
 libpysal/examples/base.py            | 14 ++++++--------
 libpysal/examples/remotes.py         |  2 +-
 requirements.txt                     |  1 +
 8 files changed, 39 insertions(+), 9 deletions(-)
 create mode 100644 ci/travis/36-latest-conda-forge.yaml
 create mode 100644 ci/travis/37-latest-conda-forge.yaml

diff --git a/ci/37.yaml b/ci/37.yaml
index dbcc6c5ad..dcf747763 100644
--- a/ci/37.yaml
+++ b/ci/37.yaml
@@ -3,6 +3,7 @@ channels:
   - conda-forge
 dependencies:
   - python=3.7
+  - appdirs
   - beautifulsoup4
   - jinja2
   - pandas>=1.0
diff --git a/ci/38.yaml b/ci/38.yaml
index 835d8699a..a36e60221 100644
--- a/ci/38.yaml
+++ b/ci/38.yaml
@@ -3,6 +3,7 @@ channels:
   - conda-forge
 dependencies:
   - python=3.8
+  - appdirs
   - beautifulsoup4
   - jinja2
   - pandas>=1.0
diff --git a/ci/39.yaml b/ci/39.yaml
index 24801573d..079215a88 100644
--- a/ci/39.yaml
+++ b/ci/39.yaml
@@ -3,6 +3,7 @@ channels:
   - conda-forge
 dependencies:
   - python=3.9
+  - appdirs
   - beautifulsoup4
   - jinja2
   - pandas>=1.0
diff --git a/ci/travis/36-latest-conda-forge.yaml b/ci/travis/36-latest-conda-forge.yaml
new file mode 100644
index 000000000..a671b1a26
--- /dev/null
+++ b/ci/travis/36-latest-conda-forge.yaml
@@ -0,0 +1,14 @@
+name: test
+channels:
+  - conda-forge
+dependencies:
+  - python=3.6
+  - six
+  # required
+  # testing
+  - nose
+  - codecov
+  - matplotlib
+  # optional
+  - geopandas
+  - zstd
diff --git a/ci/travis/37-latest-conda-forge.yaml b/ci/travis/37-latest-conda-forge.yaml
new file mode 100644
index 000000000..15a50758e
--- /dev/null
+++ b/ci/travis/37-latest-conda-forge.yaml
@@ -0,0 +1,14 @@
+name: test
+channels:
+  - conda-forge
+dependencies:
+  - python=3.7
+  - six
+  # testing
+  - nose
+  - codecov
+  - matplotlib
+  # optional
+  - geopandas
+  - numba
+  - zstd
diff --git a/libpysal/examples/base.py b/libpysal/examples/base.py
index cf3ed54e7..771ec9017 100644
--- a/libpysal/examples/base.py
+++ b/libpysal/examples/base.py
@@ -10,6 +10,7 @@
 import webbrowser
 from os import environ, makedirs
 from os.path import exists, expanduser, join
+from appdirs import user_data_dir
 import zipfile
 import requests
 import pandas
@@ -19,15 +20,11 @@
 
 from typing import Union
 
-PYSALDATA = "pysal_data"
 
 
 def get_data_home():
-    """Return the path of the ``libpysal`` data directory. This folder (``~/pysal_data``)
-    is used by some large dataset loaders to avoid downloading the data multiple times.
-    Alternatively, it can be set by the 'PYSALDATA' environment variable or programmatically
-    by giving an explicit folder path. The ``'~'`` symbol is expanded to the user home
-    folder If the folder does not already exist, it is automatically created.
+    """Return the path of the ``libpysal`` data directory. This folder is platform specific.
+    If the folder does not already exist, it is automatically created.
 
     Returns
     -------
@@ -36,8 +33,9 @@ def get_data_home():
 
     """
 
-    data_home = environ.get("PYSALDATA", join("~", PYSALDATA))
-    data_home = expanduser(data_home)
+    appname = "pysal"
+    appauthor = "pysal"
+    data_home =  user_data_dir(appname, appauthor) 
     if not exists(data_home):
         makedirs(data_home, exist_ok=True)
     return data_home
diff --git a/libpysal/examples/remotes.py b/libpysal/examples/remotes.py
index 7e6a2c5a7..213b2684c 100644
--- a/libpysal/examples/remotes.py
+++ b/libpysal/examples/remotes.py
@@ -4,7 +4,7 @@
 from bs4 import BeautifulSoup
 import requests
 import warnings
-from .base import PYSALDATA, Example, get_list_of_files, get_data_home
+from .base import Example
 
 
 def poll_remotes():
diff --git a/requirements.txt b/requirements.txt
index 2d5f770b9..e277cc0a4 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,3 +1,4 @@
+appdirs
 beautifulsoup4
 jinja2
 numpy>=1.3

From 2503749ee044144fa2337b973f3d11fb8461de8e Mon Sep 17 00:00:00 2001
From: Martin Fleischmann <martin@martinfleischmann.net>
Date: Fri, 7 Jan 2022 20:26:38 +0000
Subject: [PATCH 087/162] MAINT: replace LooseVersion with packaging

---
 ci/37.yaml               | 1 +
 ci/38.yaml               | 2 +-
 ci/39.yaml               | 2 +-
 environment.yml          | 1 +
 libpysal/weights/util.py | 4 ++--
 requirements.txt         | 1 +
 6 files changed, 7 insertions(+), 4 deletions(-)

diff --git a/ci/37.yaml b/ci/37.yaml
index dbcc6c5ad..d42ea69fd 100644
--- a/ci/37.yaml
+++ b/ci/37.yaml
@@ -17,3 +17,4 @@ dependencies:
   - geopandas>=0.7.0
   - numba
   - zstd
+  - packaging
diff --git a/ci/38.yaml b/ci/38.yaml
index 835d8699a..a5e3a74f9 100644
--- a/ci/38.yaml
+++ b/ci/38.yaml
@@ -19,10 +19,10 @@ dependencies:
   - xarray
   - joblib
   - zstd
+  - packaging
   # for docs build action (this env only)
   - nbsphinx
   - numpydoc
   - sphinx>=1.4.3
   - sphinxcontrib-bibtex<2.0.0
   - sphinx_bootstrap_theme
-  
diff --git a/ci/39.yaml b/ci/39.yaml
index 24801573d..68897fe3e 100644
--- a/ci/39.yaml
+++ b/ci/39.yaml
@@ -19,10 +19,10 @@ dependencies:
   - xarray
   - joblib
   - zstd
+  - packaging
   # for docs build action (this env only)
   - nbsphinx
   - numpydoc
   - sphinx>=1.4.3
   - sphinxcontrib-bibtex<2.0.0
   - sphinx_bootstrap_theme
-  
diff --git a/environment.yml b/environment.yml
index 70229960e..6c5143284 100644
--- a/environment.yml
+++ b/environment.yml
@@ -32,3 +32,4 @@ dependencies:
   - pytest-cov
   - descartes
   - mapclassify
+  - packaging
diff --git a/libpysal/weights/util.py b/libpysal/weights/util.py
index a11aadf5d..cf18c7048 100644
--- a/libpysal/weights/util.py
+++ b/libpysal/weights/util.py
@@ -14,12 +14,12 @@
 from collections import defaultdict
 from itertools import tee
 from ..common import requires
-from distutils.version import LooseVersion
+from packaging.version import Version
 
 try:
     import geopandas as gpd
 
-    GPD_08 = str(gpd.__version__) >= LooseVersion("0.8.0")
+    GPD_08 = Version(gpd.__version__) >= Version("0.8.0")
 except ImportError:
     warn("geopandas not available. Some functionality will be disabled.")
 
diff --git a/requirements.txt b/requirements.txt
index 2d5f770b9..001d02aca 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -4,3 +4,4 @@ numpy>=1.3
 pandas
 requests
 scipy>=0.11
+packaging

From 66b0440f5eefb10b187f6c1b33c4b5d42ef92602 Mon Sep 17 00:00:00 2001
From: Serge Rey <sjsrey2gmail.com>
Date: Sat, 22 Jan 2022 14:30:40 -0800
Subject: [PATCH 088/162] Update docs for Gabriel and related enhancements

---
 docs/api.rst | 3 +++
 docs/conf.py | 4 +++-
 2 files changed, 6 insertions(+), 1 deletion(-)

diff --git a/docs/api.rst b/docs/api.rst
index 01da37c71..205304fb8 100644
--- a/docs/api.rst
+++ b/docs/api.rst
@@ -22,6 +22,9 @@ Distance Weights
    libpysal.weights.DistanceBand
    libpysal.weights.Kernel
    libpysal.weights.KNN
+   libpysal.weights.Gabriel
+   libpysal.weights.Delaunay
+   libpysal.weights.Relative_Neighborhood
 
 Contiguity Weights
 ++++++++++++++++++
diff --git a/docs/conf.py b/docs/conf.py
index 0dd80f9d3..f92e70c0e 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -46,7 +46,7 @@
     "matplotlib.sphinxext.plot_directive",
     "nbsphinx",
 ]
-
+bibtex_bibfiles = ["_static/references.bib"]
 
 # sphinx_gallery_conf = {
 #      # path to your examples scripts
@@ -331,3 +331,5 @@ def setup(app):
 mathjax_config = {
     "TeX": {"equationNumbers": {"autoNumber": "AMS", "useLabelIds": True}},
 }
+
+

From 8aa08b1c58d6622eddb39885d78c34d75f9d27b6 Mon Sep 17 00:00:00 2001
From: James Gaboardi <jgaboardi@gmail.com>
Date: Sun, 23 Jan 2022 12:56:57 -0500
Subject: [PATCH 089/162] Update testing, add Py310, etc. (#451)

---
 .github/workflows/build_docs.yml     |  2 +-
 .github/workflows/unittests.yml      | 24 ++++++++++++++--------
 .pre-commit-config.yaml              |  8 ++++----
 ci/310.yaml                          | 30 ++++++++++++++++++++++++++++
 ci/37.yaml                           |  5 ++++-
 ci/38.yaml                           | 11 +++-------
 ci/39.yaml                           | 11 +++-------
 ci/travis/36-latest-conda-forge.yaml | 14 -------------
 ci/travis/37-latest-conda-forge.yaml | 14 -------------
 codecov.yml                          |  4 ++--
 docs/conf.py                         |  4 +---
 docs/installation.rst                |  5 ++---
 environment.yml                      |  9 +++++----
 requirements_tests.txt               |  1 +
 setup.py                             |  1 +
 15 files changed, 73 insertions(+), 70 deletions(-)
 create mode 100644 ci/310.yaml
 delete mode 100644 ci/travis/36-latest-conda-forge.yaml
 delete mode 100644 ci/travis/37-latest-conda-forge.yaml

diff --git a/.github/workflows/build_docs.yml b/.github/workflows/build_docs.yml
index 09b7ef2ee..546b6df9d 100644
--- a/.github/workflows/build_docs.yml
+++ b/.github/workflows/build_docs.yml
@@ -20,7 +20,7 @@
      strategy:
        matrix:
          os: ['ubuntu-latest']
-         environment-file: [ci/39.yaml]
+         environment-file: [ci/10.yaml]
          experimental: [false]
      defaults:
        run:
diff --git a/.github/workflows/unittests.yml b/.github/workflows/unittests.yml
index 2cf2c5436..ce63c2e81 100644
--- a/.github/workflows/unittests.yml
+++ b/.github/workflows/unittests.yml
@@ -12,11 +12,19 @@
    unittests:
      name: ${{ matrix.os }}, ${{ matrix.environment-file }}
      runs-on: ${{ matrix.os }}
+     env:
+       FETCH_EXAMPLES: python -c 'import libpysal; libpysal.examples.fetch_all()'
+       RUN_TEST: pytest -v -n auto libpysal --cov libpysal --cov-config .coveragerc --cov-report xml --color yes --cov-append --cov-report term-missing
      #timeout-minutes: 25
      strategy:
        matrix:
-         os: ['macos-latest', 'ubuntu-latest', 'windows-latest']
-         environment-file: [ci/37.yaml, ci/38.yaml, ci/39.yaml]
+         os: ['ubuntu-latest']
+         environment-file: [ci/37.yaml, ci/38.yaml, ci/39.yaml, ci/310.yaml]
+         include:
+           - environment-file: ci/310.yaml
+             os: macos-latest
+           - environment-file: ci/310.yaml
+             os: windows-latest
      
      steps:
        - name: checkout repo
@@ -31,18 +39,18 @@
        - name: run tests - bash
          shell: bash -l {0}
          run: |
-           python -c 'import libpysal; libpysal.examples.fetch_all()'
-           pytest -v libpysal --cov=libpysal --cov-report=xml
+           ${{ env.FETCH_EXAMPLES }}
+           ${{ env.RUN_TEST }}
          if: matrix.os != 'windows-latest'
        
        - name: run tests - powershell
          shell: powershell
-         run:  |
-           python -c 'import libpysal; libpysal.examples.fetch_all()'
-           pytest -v libpysal --cov=libpysal --cov-report=xml
+         run: |
+           ${{ env.FETCH_EXAMPLES }}
+           ${{ env.RUN_TEST }}
          if: matrix.os == 'windows-latest'
       
-       - name: ${{ matrix.os }}, ${{ matrix.environment-file }}
+       - name: codecov
          uses: codecov/codecov-action@v2 
          with:
            token: ${{ secrets.CODECOV_TOKEN }}
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 062139579..3a3211a8a 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,6 +1,6 @@
 repos: 
--   repo: https://github.com/ambv/black
-    rev: stable
-    hooks: 
+-   repo: https://github.com/psf/black
+    rev: 21.12b0
+    hooks:
     - id: black
-      language_version: python3.8
+      language_version: python3.9
diff --git a/ci/310.yaml b/ci/310.yaml
new file mode 100644
index 000000000..6729f77c1
--- /dev/null
+++ b/ci/310.yaml
@@ -0,0 +1,30 @@
+name: test
+channels:
+  - conda-forge
+dependencies:
+  - python=3.10
+  - appdirs
+  - beautifulsoup4
+  - jinja2
+  - pandas>=1.0
+  - scipy>=1.0
+  - xarray
+  # testing
+  - codecov
+  - matplotlib
+  - pytest
+  - pytest-cov
+  - pytest-xdist
+  # optional
+  - geopandas>=0.7.0
+  - joblib
+  - numba
+  - packaging
+  - xarray
+  - zstd
+  # for docs build action (this env only)
+  - nbsphinx
+  - numpydoc
+  - sphinx
+  - sphinxcontrib-bibtex
+  - sphinx_bootstrap_theme
diff --git a/ci/37.yaml b/ci/37.yaml
index 7d389ffd8..a5a418604 100644
--- a/ci/37.yaml
+++ b/ci/37.yaml
@@ -14,8 +14,11 @@ dependencies:
   - matplotlib
   - pytest
   - pytest-cov
+  - pytest-xdist
   # optional
   - geopandas>=0.7.0
+  - joblib
   - numba
-  - zstd
   - packaging
+  - xarray
+  - zstd
diff --git a/ci/38.yaml b/ci/38.yaml
index 6b5acbc73..b48bea4ec 100644
--- a/ci/38.yaml
+++ b/ci/38.yaml
@@ -14,16 +14,11 @@ dependencies:
   - matplotlib
   - pytest
   - pytest-cov
+  - pytest-xdist
   # optional
   - geopandas>=0.7.0
+  - joblib
   - numba
+  - packaging
   - xarray
-  - joblib
   - zstd
-  - packaging
-  # for docs build action (this env only)
-  - nbsphinx
-  - numpydoc
-  - sphinx>=1.4.3
-  - sphinxcontrib-bibtex<2.0.0
-  - sphinx_bootstrap_theme
diff --git a/ci/39.yaml b/ci/39.yaml
index 93afa7283..41f0f8d33 100644
--- a/ci/39.yaml
+++ b/ci/39.yaml
@@ -14,16 +14,11 @@ dependencies:
   - matplotlib
   - pytest
   - pytest-cov
+  - pytest-xdist
   # optional
   - geopandas>=0.7.0
+  - joblib
   - numba
+  - packaging
   - xarray
-  - joblib
   - zstd
-  - packaging
-  # for docs build action (this env only)
-  - nbsphinx
-  - numpydoc
-  - sphinx>=1.4.3
-  - sphinxcontrib-bibtex<2.0.0
-  - sphinx_bootstrap_theme
diff --git a/ci/travis/36-latest-conda-forge.yaml b/ci/travis/36-latest-conda-forge.yaml
deleted file mode 100644
index a671b1a26..000000000
--- a/ci/travis/36-latest-conda-forge.yaml
+++ /dev/null
@@ -1,14 +0,0 @@
-name: test
-channels:
-  - conda-forge
-dependencies:
-  - python=3.6
-  - six
-  # required
-  # testing
-  - nose
-  - codecov
-  - matplotlib
-  # optional
-  - geopandas
-  - zstd
diff --git a/ci/travis/37-latest-conda-forge.yaml b/ci/travis/37-latest-conda-forge.yaml
deleted file mode 100644
index 15a50758e..000000000
--- a/ci/travis/37-latest-conda-forge.yaml
+++ /dev/null
@@ -1,14 +0,0 @@
-name: test
-channels:
-  - conda-forge
-dependencies:
-  - python=3.7
-  - six
-  # testing
-  - nose
-  - codecov
-  - matplotlib
-  # optional
-  - geopandas
-  - numba
-  - zstd
diff --git a/codecov.yml b/codecov.yml
index 3f7277717..f6567d60a 100644
--- a/codecov.yml
+++ b/codecov.yml
@@ -1,6 +1,6 @@
 codecov:
   notify:
-    after_n_builds: 9
+    after_n_builds: 6
 coverage:
   range: 50..95
   round: nearest
@@ -18,5 +18,5 @@ coverage:
 comment:
   layout: "reach, diff, files"
   behavior: once
-  after_n_builds: 9
+  after_n_builds: 6
   require_changes: true
diff --git a/docs/conf.py b/docs/conf.py
index f92e70c0e..4d19054c8 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -284,7 +284,7 @@ def setup(app):
     "networkx": ("https://networkx.github.io/documentation/stable/", None),
     "numpy": ("https://docs.scipy.org/doc/numpy", None),
     "pandas": ("https://pandas.pydata.org/pandas-docs/stable/", None),
-    "python": ("https://docs.python.org/3.8/", None),
+    "python": ("https://docs.python.org/3.10/", None),
     "scipy": ("https://docs.scipy.org/doc/scipy/reference/", None),
 }
 
@@ -331,5 +331,3 @@ def setup(app):
 mathjax_config = {
     "TeX": {"equationNumbers": {"autoNumber": "AMS", "useLabelIds": True}},
 }
-
-
diff --git a/docs/installation.rst b/docs/installation.rst
index 8c6ea63cb..32ab9aa92 100644
--- a/docs/installation.rst
+++ b/docs/installation.rst
@@ -3,7 +3,7 @@
 Installation
 ============
 
-libpysal supports python >= `3.6`_ only. Please make sure that you are
+libpysal supports python >= `3.7`_ only. Please make sure that you are
 operating in a python 3 environment.
 
 Installing released version
@@ -49,9 +49,8 @@ your fork. By making changes
 to your local clone and submitting a pull request to `pysal/libpysal`_, you can
 contribute to libpysal development.
 
-.. _3.6: https://docs.python.org/3.6/
+
 .. _3.7: https://docs.python.org/3.7/
-.. _3.8: https://docs.python.org/3.8/
 .. _Python Package Index: https://pypi.org/project/libpysal/
 .. _pysal/libpysal: https://github.com/pysal/libpysal
 .. _fork: https://help.github.com/articles/fork-a-repo/
diff --git a/environment.yml b/environment.yml
index 6c5143284..d917c91fc 100644
--- a/environment.yml
+++ b/environment.yml
@@ -10,9 +10,9 @@ dependencies:
   - numpydoc
   - nbsphinx
   - pandoc
-  - sphinx>=1.4.3
+  - sphinx
   - sphinxcontrib-bibtex
-  - sphinx_bootstrap_theme>=0.7.0
+  - sphinx_bootstrap_theme
   - bokeh>=0.11.1
   - folium>=0.2.1
   - geojson>=1.3.2
@@ -28,8 +28,9 @@ dependencies:
   - statsmodels>=0.6.1
   - xarray
   - codecov
+  - joblib
+  - packaging
   - pytest
   - pytest-cov
-  - descartes
+  - pytest-xdist
   - mapclassify
-  - packaging
diff --git a/requirements_tests.txt b/requirements_tests.txt
index cd5d755f1..f3a5c17d0 100644
--- a/requirements_tests.txt
+++ b/requirements_tests.txt
@@ -2,3 +2,4 @@ codecov
 matplotlib
 pytest
 pytest-cov
+pytest-xdist
diff --git a/setup.py b/setup.py
index 93437d9a6..9cb28ef07 100644
--- a/setup.py
+++ b/setup.py
@@ -86,6 +86,7 @@ def setup_package():
             "Programming Language :: Python :: 3.7",
             "Programming Language :: Python :: 3.8",
             "Programming Language :: Python :: 3.9",
+            "Programming Language :: Python :: 3.10",
         ],
         package_data={"libpysal": list(example_data_files)},
         install_requires=install_reqs,

From 1b7aad6ced865a461bdefdfffe46e103a90fdfac Mon Sep 17 00:00:00 2001
From: Martin Fleischmann <martin@martinfleischmann.net>
Date: Mon, 24 Jan 2022 20:03:50 +0000
Subject: [PATCH 090/162] revert shapely hard dependecy

---
 .github/workflows/unittests.yml | 14 +++++++-------
 ci/37-minimal.yaml              | 17 +++++++++++++++++
 libpysal/weights/util.py        | 31 ++++++++++++++++++++++---------
 3 files changed, 46 insertions(+), 16 deletions(-)
 create mode 100644 ci/37-minimal.yaml

diff --git a/.github/workflows/unittests.yml b/.github/workflows/unittests.yml
index ce63c2e81..0440a01ee 100644
--- a/.github/workflows/unittests.yml
+++ b/.github/workflows/unittests.yml
@@ -19,39 +19,39 @@
      strategy:
        matrix:
          os: ['ubuntu-latest']
-         environment-file: [ci/37.yaml, ci/38.yaml, ci/39.yaml, ci/310.yaml]
+         environment-file: [ci/37-minimal.yaml, ci/37.yaml, ci/38.yaml, ci/39.yaml, ci/310.yaml]
          include:
            - environment-file: ci/310.yaml
              os: macos-latest
            - environment-file: ci/310.yaml
              os: windows-latest
-     
+
      steps:
        - name: checkout repo
          uses: actions/checkout@v2
-       
+
        - name: setup micromamba
          uses: mamba-org/provision-with-micromamba@main
          with:
            environment-file: ${{ matrix.environment-file }}
            micromamba-version: 'latest'
-      
+
        - name: run tests - bash
          shell: bash -l {0}
          run: |
            ${{ env.FETCH_EXAMPLES }}
            ${{ env.RUN_TEST }}
          if: matrix.os != 'windows-latest'
-       
+
        - name: run tests - powershell
          shell: powershell
          run: |
            ${{ env.FETCH_EXAMPLES }}
            ${{ env.RUN_TEST }}
          if: matrix.os == 'windows-latest'
-      
+
        - name: codecov
-         uses: codecov/codecov-action@v2 
+         uses: codecov/codecov-action@v2
          with:
            token: ${{ secrets.CODECOV_TOKEN }}
            file: ./coverage.xml
diff --git a/ci/37-minimal.yaml b/ci/37-minimal.yaml
new file mode 100644
index 000000000..1c13f5268
--- /dev/null
+++ b/ci/37-minimal.yaml
@@ -0,0 +1,17 @@
+name: test
+channels:
+  - conda-forge
+dependencies:
+  - python=3.7
+  - appdirs
+  - beautifulsoup4
+  - jinja2
+  - pandas>=1.0
+  - scipy>=1.0
+  - xarray
+  # testing
+  - codecov
+  - matplotlib
+  - pytest
+  - pytest-cov
+  - pytest-xdist
\ No newline at end of file
diff --git a/libpysal/weights/util.py b/libpysal/weights/util.py
index cf18c7048..3f5162bc3 100644
--- a/libpysal/weights/util.py
+++ b/libpysal/weights/util.py
@@ -1,4 +1,3 @@
-from shapely.geometry.base import BaseGeometry
 from ..io.fileio import FileIO as psopen
 from .weights import W, WSP
 from .set_operations import w_subset
@@ -23,6 +22,12 @@
 except ImportError:
     warn("geopandas not available. Some functionality will be disabled.")
 
+try:
+    from shapely.geometry.base import BaseGeometry
+    HAS_SHAPELY = True
+except ImportError:
+    HAS_SHAPELY = False
+
 __all__ = [
     "lat2W",
     "block_weights",
@@ -1071,14 +1076,22 @@ def get_points_array(iterable):
     """
     first_choice, backup = tee(iterable)
     try:
-        data = np.vstack(
-            [
-                np.array(shape.centroid.coords)[0]
-                if isinstance(shape, BaseGeometry)
-                else np.array(shape.centroid)
-                for shape in first_choice
-            ]
-        )
+        if HAS_SHAPELY:
+            data = np.vstack(
+                [
+                    np.array(shape.centroid.coords)[0]
+                    if isinstance(shape, BaseGeometry)
+                    else np.array(shape.centroid)
+                    for shape in first_choice
+                ]
+            )
+        else:
+            data = np.vstack(
+                [
+                    np.array(shape.centroid)
+                    for shape in first_choice
+                ]
+            )
     except AttributeError:
         data = np.vstack([shape for shape in backup])
     return data

From afe5704d76845cc6970efcd055853c64b20dd4ea Mon Sep 17 00:00:00 2001
From: Martin Fleischmann <martin@martinfleischmann.net>
Date: Mon, 24 Jan 2022 20:22:24 +0000
Subject: [PATCH 091/162] skip whatever tests need to be skipped

---
 libpysal/cg/tests/test_voronoi.py         |  9 +++++++++
 libpysal/weights/tests/test_contiguity.py | 15 ++++++++++++++-
 libpysal/weights/tests/test_distance.py   | 10 ++++++++++
 libpysal/weights/tests/test_gabriel.py    |  5 ++++-
 4 files changed, 37 insertions(+), 2 deletions(-)

diff --git a/libpysal/cg/tests/test_voronoi.py b/libpysal/cg/tests/test_voronoi.py
index f1681333f..bf850d729 100644
--- a/libpysal/cg/tests/test_voronoi.py
+++ b/libpysal/cg/tests/test_voronoi.py
@@ -5,6 +5,14 @@
 from ..voronoi import voronoi, voronoi_frames
 from ..shapes import Polygon, asShape
 
+import pytest
+
+try:
+    import shapely
+    HAS_SHAPELY = True
+except ImportError:
+    HAS_SHAPELY = False
+
 
 class Voronoi(unittest.TestCase):
     def setUp(self):
@@ -29,6 +37,7 @@ def test_voronoi(self):
 
         np.testing.assert_array_almost_equal(vertices, self.vertices)
 
+    @pytest.mark.skipif(not HAS_SHAPELY, reason="shapely needed")
     def test_voronoi_frames(self):
         r_df, p_df = voronoi_frames(self.points)
         region = r_df.iloc[0]["geometry"]
diff --git a/libpysal/weights/tests/test_contiguity.py b/libpysal/weights/tests/test_contiguity.py
index aa8233e2e..cfe4126e9 100644
--- a/libpysal/weights/tests/test_contiguity.py
+++ b/libpysal/weights/tests/test_contiguity.py
@@ -9,6 +9,7 @@
 from ... import examples as pysal_examples
 import unittest as ut
 import numpy as np
+import pytest
 
 PANDAS_EXTINCT = pandas is None
 try:
@@ -18,6 +19,12 @@
 except ImportError:
     GEOPANDAS_EXTINCT = True
 
+try:
+    import shapely
+    HAS_SHAPELY = True
+except ImportError:
+    HAS_SHAPELY = False
+
 
 class Contiguity_Mixin(object):
     polygon_path = pysal_examples.get_path("columbus.shp")
@@ -90,6 +97,7 @@ def test_from_array(self):
         # test named, sparse from point array
         pass
 
+
     @ut.skipIf(PANDAS_EXTINCT, "Missing pandas")
     def test_from_dataframe(self):
         # basic
@@ -102,6 +110,10 @@ def test_from_dataframe(self):
         w = self.cls.from_dataframe(df, geom_col="the_geom")
         self.assertEqual(w[self.known_wi], self.known_w)
 
+
+    @ut.skipIf(GEOPANDAS_EXTINCT, "Missing geopandas")
+    def test_from_geodataframe(self):
+        df = pdio.read_files(self.polygon_path)
         # named active geometry
         df = df.set_geometry("the_geom")
         w = self.cls.from_dataframe(df)
@@ -148,7 +160,7 @@ def setUp(self):
             (1, -90.0, 60.0): 1,
             (1, -30.0, -180.0): 1,
             (1, -30.0, 60.0): 1,
-            (1, 30.0, -180.0): 1, 
+            (1, 30.0, -180.0): 1,
             (1, 30.0, -60.0): 1,
             (1, 30.0, 60.0): 1
             }
@@ -192,6 +204,7 @@ def setUp(self):
 
 
 class Test_Voronoi(ut.TestCase):
+    @pytest.mark.skipif(not HAS_SHAPELY, reason="shapely needed")
     def test_voronoiW(self):
         np.random.seed(12345)
         points = np.random.random((5, 2)) * 10 + 10
diff --git a/libpysal/weights/tests/test_distance.py b/libpysal/weights/tests/test_distance.py
index 94759a398..5d5e61e56 100644
--- a/libpysal/weights/tests/test_distance.py
+++ b/libpysal/weights/tests/test_distance.py
@@ -12,6 +12,12 @@
 import unittest as ut
 
 PANDAS_EXTINCT = pandas is None
+try:
+    import geopandas
+
+    GEOPANDAS_EXTINCT = False
+except ImportError:
+    GEOPANDAS_EXTINCT = True
 # All instances should test these four methods, and define their own functional
 # tests based on common codepaths/estimated weights use cases.
 
@@ -173,6 +179,7 @@ def test_from_array(self):
         for k, v in w:
             self.assertEqual(v, self.grid_rook_w[k])
 
+    @ut.skipIf(GEOPANDAS_EXTINCT, "Missing geopandas")
     @ut.skipIf(PANDAS_EXTINCT, "Missing pandas")
     def test_from_dataframe(self):
         import pandas as pd
@@ -329,6 +336,9 @@ def test_from_dataframe(self):
         for k, v in list(w[self.known_wi5 - 1].items()):
             np.testing.assert_allclose(v, self.known_w5[k + 1], rtol=RTOL)
 
+    @ut.skipIf(GEOPANDAS_EXTINCT, "Missing geopandas")
+    def test_from_geodataframe(self):
+        df = pdio.read_files(self.polygon_path)
         # named geometry
         df.rename(columns={"geometry": "the_geom"}, inplace=True)
         w = d.Kernel.from_dataframe(df, geom_col="the_geom")
diff --git a/libpysal/weights/tests/test_gabriel.py b/libpysal/weights/tests/test_gabriel.py
index cfd89394c..6b331edf6 100644
--- a/libpysal/weights/tests/test_gabriel.py
+++ b/libpysal/weights/tests/test_gabriel.py
@@ -1,6 +1,9 @@
 from .. import gabriel
 from ... import examples
-import geopandas, numpy
+import numpy
+import pytest
+
+geopandas = pytest.importorskip("geopandas")
 
 path = examples.get_path("columbus.shp")
 df = geopandas.read_file(path)

From 0d41c2e3bde5d1ead9f7ab05958b4bdedc0d5dc9 Mon Sep 17 00:00:00 2001
From: Martin Fleischmann <martin@martinfleischmann.net>
Date: Mon, 24 Jan 2022 20:25:20 +0000
Subject: [PATCH 092/162] missed one

---
 libpysal/weights/tests/test_distance.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/libpysal/weights/tests/test_distance.py b/libpysal/weights/tests/test_distance.py
index 5d5e61e56..24ae7eda7 100644
--- a/libpysal/weights/tests/test_distance.py
+++ b/libpysal/weights/tests/test_distance.py
@@ -113,6 +113,9 @@ def test_from_dataframe(self):
         self.assertEqual(w.neighbors[self.known_wi0], self.known_w0)
         self.assertEqual(w.neighbors[self.known_wi1], self.known_w1)
 
+    @ut.skipIf(GEOPANDAS_EXTINCT, "Missing geopandas")
+    def test_from_geodataframe(self):
+        df = pdio.read_files(self.polygon_path)
         # named active geometry
         df = df.set_geometry("the_geom")
         w = d.KNN.from_dataframe(df, k=4)

From 6cb19947008d1f136c7d384808ff66eda7e022d5 Mon Sep 17 00:00:00 2001
From: Martin Fleischmann <martin@martinfleischmann.net>
Date: Mon, 24 Jan 2022 20:35:57 +0000
Subject: [PATCH 093/162] fix

---
 libpysal/weights/tests/test_contiguity.py |  1 +
 libpysal/weights/tests/test_distance.py   | 14 ++++++++++++--
 2 files changed, 13 insertions(+), 2 deletions(-)

diff --git a/libpysal/weights/tests/test_contiguity.py b/libpysal/weights/tests/test_contiguity.py
index cfe4126e9..06a30414d 100644
--- a/libpysal/weights/tests/test_contiguity.py
+++ b/libpysal/weights/tests/test_contiguity.py
@@ -115,6 +115,7 @@ def test_from_dataframe(self):
     def test_from_geodataframe(self):
         df = pdio.read_files(self.polygon_path)
         # named active geometry
+        df.rename(columns={"geometry": "the_geom"}, inplace=True)
         df = df.set_geometry("the_geom")
         w = self.cls.from_dataframe(df)
         self.assertEqual(w[self.known_wi], self.known_w)
diff --git a/libpysal/weights/tests/test_distance.py b/libpysal/weights/tests/test_distance.py
index 24ae7eda7..5f2043baa 100644
--- a/libpysal/weights/tests/test_distance.py
+++ b/libpysal/weights/tests/test_distance.py
@@ -117,6 +117,7 @@ def test_from_dataframe(self):
     def test_from_geodataframe(self):
         df = pdio.read_files(self.polygon_path)
         # named active geometry
+        df.rename(columns={"geometry": "the_geom"}, inplace=True)
         df = df.set_geometry("the_geom")
         w = d.KNN.from_dataframe(df, k=4)
         self.assertEqual(w.neighbors[self.known_wi0], self.known_w0)
@@ -182,11 +183,9 @@ def test_from_array(self):
         for k, v in w:
             self.assertEqual(v, self.grid_rook_w[k])
 
-    @ut.skipIf(GEOPANDAS_EXTINCT, "Missing geopandas")
     @ut.skipIf(PANDAS_EXTINCT, "Missing pandas")
     def test_from_dataframe(self):
         import pandas as pd
-        import geopandas as gpd
 
         geom_series = pdio.shp.shp2series(self.grid_path)
         random_data = np.random.random(size=len(geom_series))
@@ -195,6 +194,17 @@ def test_from_dataframe(self):
         for k, v in w:
             self.assertEqual(v, self.grid_rook_w[k])
 
+    @ut.skipIf(GEOPANDAS_EXTINCT, "Missing geopandas")
+    def test_from_geodataframe(self):
+        import geopandas as gpd
+        import pandas as pd
+
+        geom_series = pdio.shp.shp2series(self.grid_path)
+        random_data = np.random.random(size=len(geom_series))
+        df = pd.DataFrame({"obs": random_data, "geometry": geom_series})
+        w = d.DistanceBand.from_dataframe(df, 1)
+        for k, v in w:
+            self.assertEqual(v, self.grid_rook_w[k])
         # named geometry
         df = gpd.GeoDataFrame(df)
         df.rename(columns={"geometry": "the_geom"}, inplace=True)

From 029b2bbb4af258e3e80641c9001662d6f35b5803 Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Tue, 1 Feb 2022 16:39:16 +0000
Subject: [PATCH 094/162] add filtering if alpha shape is nonempty

---
 libpysal/cg/alpha_shapes.py | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/libpysal/cg/alpha_shapes.py b/libpysal/cg/alpha_shapes.py
index 2ae419009..cf381ec92 100644
--- a/libpysal/cg/alpha_shapes.py
+++ b/libpysal/cg/alpha_shapes.py
@@ -481,7 +481,7 @@ def alpha_shape(xys, alpha):
     radii = r_circumcircle_triangle(a_pts, b_pts, c_pts)
     del triangles, a_pts, b_pts, c_pts
     geoms = alpha_geoms(alpha, triangulation.simplices, radii, xys)
-    return geoms
+    return _filter_holes(geoms, xys)
 
 
 def _valid_hull(geoms, points):
@@ -703,6 +703,12 @@ def _construct_centers(a, b, radius):
     else:
         return down_x, down_y
 
+def _filter_holes(geoms, points):
+    if (geoms.interiors.apply(len) > 0).any():
+        from geopandas import points_from_xy
+        has_points, _ = points_from_xy(*points.T).sindex.query_bulk(geoms.values.data, predicate='contains')
+        geoms = geoms[geoms.index.isin(has_points)]
+    return geoms
 
 if __name__ == "__main__":
 

From 934fd3dcc900049fca7961b54eb894b8e1b71ff0 Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Wed, 2 Feb 2022 15:16:10 +0000
Subject: [PATCH 095/162] switch to zorder search

---
 libpysal/cg/alpha_shapes.py | 38 +++++++++++++++++++++++++++++++------
 1 file changed, 32 insertions(+), 6 deletions(-)

diff --git a/libpysal/cg/alpha_shapes.py b/libpysal/cg/alpha_shapes.py
index cf381ec92..178ecd7ea 100644
--- a/libpysal/cg/alpha_shapes.py
+++ b/libpysal/cg/alpha_shapes.py
@@ -6,10 +6,12 @@
 
 Author(s):
     Dani Arribas-Bel daniel.arribas.bel@gmail.com
+    Levi John Wolf levi.john.wolf@gmail.com
 """
 
 import numpy as np
 import scipy.spatial as spat
+from scipy import sparse
 
 from ..common import requires, jit, HAS_JIT
 
@@ -380,7 +382,8 @@ def alpha_geoms(alpha, triangles, radii, xys):
     geoms : GeoSeries
         Polygon(s) resulting from the alpha shape algorithm. The GeoSeries
         object remains so even if only a single polygon is returned. There is
-        no CRS included in the object.
+        no CRS included in the object. Also note that holes are included in addition
+        to exteriors. 
 
     Examples
     --------
@@ -424,7 +427,7 @@ def alpha_geoms(alpha, triangles, radii, xys):
 
 
 @requires("geopandas", "shapely")
-def alpha_shape(xys, alpha):
+def alpha_shape(xys, alpha, filter_holes=True):
     """Alpha-shape delineation (Edelsbrunner, Kirkpatrick & Seidel, 1983) from a collection of points
 
     Parameters
@@ -481,7 +484,9 @@ def alpha_shape(xys, alpha):
     radii = r_circumcircle_triangle(a_pts, b_pts, c_pts)
     del triangles, a_pts, b_pts, c_pts
     geoms = alpha_geoms(alpha, triangulation.simplices, radii, xys)
-    return _filter_holes(geoms, xys)
+    if filter_holes:
+        geoms = _filter_holes(geoms, xys)
+    return geoms
 
 
 def _valid_hull(geoms, points):
@@ -704,12 +709,33 @@ def _construct_centers(a, b, radius):
         return down_x, down_y
 
 def _filter_holes(geoms, points):
+    """
+    Filter hole polygons using a computational geometry solution
+    """
     if (geoms.interiors.apply(len) > 0).any():
-        from geopandas import points_from_xy
-        has_points, _ = points_from_xy(*points.T).sindex.query_bulk(geoms.values.data, predicate='contains')
-        geoms = geoms[geoms.index.isin(has_points)]
+        from shapely.geometry import Polygon
+        # Extract the "shell", or outer ring of the polygon. 
+        shells = geoms.exterior.apply(Polygon)
+        # Compute which original geometries are within each shell, self-inclusive
+        inside, outside = shells.sindex.query_bulk(geoms, predicate='within')
+        # Now, create the sparse matrix relating the inner geom (rows) 
+        # to the outer shell (cols) and take the sum. 
+        # A z-order of 1 means the polygon is only inside if its own exterior. This means it's a polygon.
+        # A z-order of 2 means the polygon is inside of exactly one other exterior. Because
+        #   the hull generation method is restricted to be planar, this means the polygon is a hole. 
+        # In general, an even z-order means that the polygon is always exactly matched to one exterior. 
+        #   This means that the polygon forms the exterior of a hole. 
+        # In general, an odd z-order means that there is an uneven number of exteriors. 
+        #   This means the polygon is filled. 
+        zorder = sparse.csc_matrix((np.ones_like(inside), (inside, outside))).sum(axis=1)
+        zorder = np.asarray(zorder).flatten()
+        # Keep only the odd z-orders
+        to_include = (zorder % 2).astype(bool)
+        geoms = geoms[to_include]
     return geoms
 
+
+
 if __name__ == "__main__":
 
     import matplotlib.pyplot as plt

From b2651ce000e6ebaa5387236666eb528feae76f4b Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Wed, 2 Feb 2022 15:16:26 +0000
Subject: [PATCH 096/162] remove whitespace

---
 libpysal/cg/alpha_shapes.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/libpysal/cg/alpha_shapes.py b/libpysal/cg/alpha_shapes.py
index 178ecd7ea..c1df0ac72 100644
--- a/libpysal/cg/alpha_shapes.py
+++ b/libpysal/cg/alpha_shapes.py
@@ -474,7 +474,6 @@ def alpha_shape(xys, alpha, filter_holes=True):
         warn(NUMBA_WARN)
     if xys.shape[0] < 4:
         from shapely import ops, geometry as geom
-
         return ops.unary_union([geom.Point(xy) for xy in xys]).convex_hull.buffer(0)
     triangulation = spat.Delaunay(xys)
     triangles = xys[triangulation.simplices]

From a0e91e87b0b6cc9bb9617a7797f9df174a8ae874 Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Wed, 2 Feb 2022 15:30:51 +0000
Subject: [PATCH 097/162] revise comment

---
 libpysal/cg/alpha_shapes.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/libpysal/cg/alpha_shapes.py b/libpysal/cg/alpha_shapes.py
index c1df0ac72..bfd7855e6 100644
--- a/libpysal/cg/alpha_shapes.py
+++ b/libpysal/cg/alpha_shapes.py
@@ -719,13 +719,13 @@ def _filter_holes(geoms, points):
         inside, outside = shells.sindex.query_bulk(geoms, predicate='within')
         # Now, create the sparse matrix relating the inner geom (rows) 
         # to the outer shell (cols) and take the sum. 
-        # A z-order of 1 means the polygon is only inside if its own exterior. This means it's a polygon.
+        # A z-order of 1 means the polygon is only inside if its own exterior. This means it's not a hole.
         # A z-order of 2 means the polygon is inside of exactly one other exterior. Because
         #   the hull generation method is restricted to be planar, this means the polygon is a hole. 
-        # In general, an even z-order means that the polygon is always exactly matched to one exterior. 
-        #   This means that the polygon forms the exterior of a hole. 
+        # In general, an even z-order means that the polygon is always exactly matched to one exterior, 
+        #   plus some number of intermediate exterior-hole pairs. Therefore, the polygon is a hole.
         # In general, an odd z-order means that there is an uneven number of exteriors. 
-        #   This means the polygon is filled. 
+        #   This means the polygon is not a hole. 
         zorder = sparse.csc_matrix((np.ones_like(inside), (inside, outside))).sum(axis=1)
         zorder = np.asarray(zorder).flatten()
         # Keep only the odd z-orders

From aa61dcbdfb7a10de55dabd8ba3c1c5526f8ae0b6 Mon Sep 17 00:00:00 2001
From: James Gaboardi <jgaboardi@gmail.com>
Date: Mon, 7 Feb 2022 03:54:10 -0500
Subject: [PATCH 098/162] Update build_docs.yml (#459)

---
 .github/workflows/build_docs.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/build_docs.yml b/.github/workflows/build_docs.yml
index 546b6df9d..1d180a55b 100644
--- a/.github/workflows/build_docs.yml
+++ b/.github/workflows/build_docs.yml
@@ -20,7 +20,7 @@
      strategy:
        matrix:
          os: ['ubuntu-latest']
-         environment-file: [ci/10.yaml]
+         environment-file: [ci/310.yaml]
          experimental: [false]
      defaults:
        run:

From 5024a32b534b5e104c037907e6d41df4bc3ede96 Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Mon, 7 Feb 2022 11:50:53 +0000
Subject: [PATCH 099/162] remove filter option

---
 libpysal/cg/alpha_shapes.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/libpysal/cg/alpha_shapes.py b/libpysal/cg/alpha_shapes.py
index bfd7855e6..516052c39 100644
--- a/libpysal/cg/alpha_shapes.py
+++ b/libpysal/cg/alpha_shapes.py
@@ -427,7 +427,7 @@ def alpha_geoms(alpha, triangles, radii, xys):
 
 
 @requires("geopandas", "shapely")
-def alpha_shape(xys, alpha, filter_holes=True):
+def alpha_shape(xys, alpha):
     """Alpha-shape delineation (Edelsbrunner, Kirkpatrick & Seidel, 1983) from a collection of points
 
     Parameters
@@ -483,8 +483,7 @@ def alpha_shape(xys, alpha, filter_holes=True):
     radii = r_circumcircle_triangle(a_pts, b_pts, c_pts)
     del triangles, a_pts, b_pts, c_pts
     geoms = alpha_geoms(alpha, triangulation.simplices, radii, xys)
-    if filter_holes:
-        geoms = _filter_holes(geoms, xys)
+    geoms = _filter_holes(geoms, xys)
     return geoms
 
 

From 2e2bec880fbdf8a42efe3877135df99162fb44e9 Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Mon, 7 Feb 2022 11:51:11 +0000
Subject: [PATCH 100/162] add test to check holes are processed correctly

---
 libpysal/cg/tests/test_ashapes.py | 17 +++++++++++++++++
 1 file changed, 17 insertions(+)

diff --git a/libpysal/cg/tests/test_ashapes.py b/libpysal/cg/tests/test_ashapes.py
index a55f099c9..3922a2b4f 100644
--- a/libpysal/cg/tests/test_ashapes.py
+++ b/libpysal/cg/tests/test_ashapes.py
@@ -98,3 +98,20 @@ def test_circles(self):
         ashape, radius, centers = alpha_shape_auto(self.vertices, return_circles=True)
         np.testing.assert_allclose(radius, self.circle_radii)
         np.testing.assert_allclose(centers, self.circle_verts)
+    
+    def test_holes(self):
+        numpy.random.seed(seed=100)
+        points = numpy.random.rand(1000, 2)*100
+        inv_alpha = 3.5
+        geoms = libpysal.cg.alpha_shape(points, 1/inv_alpha)
+        assert len(geoms) == 1
+        holes = geopandas.GeoSeries(geoms.interiors.explode()).reset_index(drop=True)
+        assert len(holes) == 30
+        # No holes are within the shape (shape has holes already)
+        result = geoms.sindex.query_bulk(holes.centroid, predicate='within')
+        assert result.shape == (2,0)
+        # All holes are within the exterior
+        shell = geopandas.GeoSeries(geoms.exterior.apply(geometry.Polygon))
+        within, outside = shell.sindex.query_bulk(holes.centroid, predicate='within')
+        assert (outside == 0).all()
+        np.testing.assert_array_equal(within, np.arange(30))
\ No newline at end of file

From e2ae58da948f40d823a7b2f5bb089ee545e42173 Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Mon, 7 Feb 2022 11:55:27 +0000
Subject: [PATCH 101/162] fix aliases and imports

---
 libpysal/cg/tests/test_ashapes.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/libpysal/cg/tests/test_ashapes.py b/libpysal/cg/tests/test_ashapes.py
index 3922a2b4f..f9e53a216 100644
--- a/libpysal/cg/tests/test_ashapes.py
+++ b/libpysal/cg/tests/test_ashapes.py
@@ -100,10 +100,10 @@ def test_circles(self):
         np.testing.assert_allclose(centers, self.circle_verts)
     
     def test_holes(self):
-        numpy.random.seed(seed=100)
-        points = numpy.random.rand(1000, 2)*100
+        np.random.seed(seed=100)
+        points = np.random.rand(1000, 2)*100
         inv_alpha = 3.5
-        geoms = libpysal.cg.alpha_shape(points, 1/inv_alpha)
+        geoms = alpha_shape(points, 1/inv_alpha)
         assert len(geoms) == 1
         holes = geopandas.GeoSeries(geoms.interiors.explode()).reset_index(drop=True)
         assert len(holes) == 30

From 23a13f05c030be4c5d65f3e82fce3db691729ed1 Mon Sep 17 00:00:00 2001
From: James Gaboardi <jgaboardi@gmail.com>
Date: Mon, 28 Feb 2022 09:02:20 -0500
Subject: [PATCH 102/162] Update release_and_publish.yml

---
 .github/workflows/release_and_publish.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/release_and_publish.yml b/.github/workflows/release_and_publish.yml
index da5ea4667..e7276806c 100644
--- a/.github/workflows/release_and_publish.yml
+++ b/.github/workflows/release_and_publish.yml
@@ -24,7 +24,7 @@
          uses: actions/checkout@v2
  
        - name: Set up python
-         uses: actions/setup-python@v2
+         uses: actions/setup-python@v3
          with:
            python-version: "3.x"
  

From 42171af78a93b22deb9827a593d88433e45425d3 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 1 Mar 2022 19:02:26 +0000
Subject: [PATCH 103/162] Bump actions/checkout from 2 to 3

Bumps [actions/checkout](https://github.com/actions/checkout) from 2 to 3.
- [Release notes](https://github.com/actions/checkout/releases)
- [Changelog](https://github.com/actions/checkout/blob/main/CHANGELOG.md)
- [Commits](https://github.com/actions/checkout/compare/v2...v3)

---
updated-dependencies:
- dependency-name: actions/checkout
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>
---
 .github/workflows/build_docs.yml          | 2 +-
 .github/workflows/release_and_publish.yml | 2 +-
 .github/workflows/unittests.yml           | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/build_docs.yml b/.github/workflows/build_docs.yml
index 1d180a55b..c63ae133b 100644
--- a/.github/workflows/build_docs.yml
+++ b/.github/workflows/build_docs.yml
@@ -28,7 +28,7 @@
      
      steps:
        - name: Checkout repo
-         uses: actions/checkout@v2
+         uses: actions/checkout@v3
        
        - name: Setup micromamba
          uses: mamba-org/provision-with-micromamba@main
diff --git a/.github/workflows/release_and_publish.yml b/.github/workflows/release_and_publish.yml
index e7276806c..bb71f7707 100644
--- a/.github/workflows/release_and_publish.yml
+++ b/.github/workflows/release_and_publish.yml
@@ -21,7 +21,7 @@
      runs-on: ubuntu-latest
      steps:
        - name: Checkout repo
-         uses: actions/checkout@v2
+         uses: actions/checkout@v3
  
        - name: Set up python
          uses: actions/setup-python@v3
diff --git a/.github/workflows/unittests.yml b/.github/workflows/unittests.yml
index ce63c2e81..8c8d41218 100644
--- a/.github/workflows/unittests.yml
+++ b/.github/workflows/unittests.yml
@@ -28,7 +28,7 @@
      
      steps:
        - name: checkout repo
-         uses: actions/checkout@v2
+         uses: actions/checkout@v3
        
        - name: setup micromamba
          uses: mamba-org/provision-with-micromamba@main

From 4b8eca6a0a8f8aca921ed5f68670c13af2f7ce2d Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 1 Mar 2022 23:02:02 +0000
Subject: [PATCH 104/162] Bump actions/checkout from 2 to 3

Bumps [actions/checkout](https://github.com/actions/checkout) from 2 to 3.
- [Release notes](https://github.com/actions/checkout/releases)
- [Changelog](https://github.com/actions/checkout/blob/main/CHANGELOG.md)
- [Commits](https://github.com/actions/checkout/compare/v2...v3)

---
updated-dependencies:
- dependency-name: actions/checkout
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>
---
 .github/workflows/build_docs.yml          | 2 +-
 .github/workflows/release_and_publish.yml | 2 +-
 .github/workflows/unittests.yml           | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/build_docs.yml b/.github/workflows/build_docs.yml
index 1d180a55b..c63ae133b 100644
--- a/.github/workflows/build_docs.yml
+++ b/.github/workflows/build_docs.yml
@@ -28,7 +28,7 @@
      
      steps:
        - name: Checkout repo
-         uses: actions/checkout@v2
+         uses: actions/checkout@v3
        
        - name: Setup micromamba
          uses: mamba-org/provision-with-micromamba@main
diff --git a/.github/workflows/release_and_publish.yml b/.github/workflows/release_and_publish.yml
index e7276806c..bb71f7707 100644
--- a/.github/workflows/release_and_publish.yml
+++ b/.github/workflows/release_and_publish.yml
@@ -21,7 +21,7 @@
      runs-on: ubuntu-latest
      steps:
        - name: Checkout repo
-         uses: actions/checkout@v2
+         uses: actions/checkout@v3
  
        - name: Set up python
          uses: actions/setup-python@v3
diff --git a/.github/workflows/unittests.yml b/.github/workflows/unittests.yml
index ce63c2e81..8c8d41218 100644
--- a/.github/workflows/unittests.yml
+++ b/.github/workflows/unittests.yml
@@ -28,7 +28,7 @@
      
      steps:
        - name: checkout repo
-         uses: actions/checkout@v2
+         uses: actions/checkout@v3
        
        - name: setup micromamba
          uses: mamba-org/provision-with-micromamba@main

From e19691c89b3c6cb693fffaeb797be83bc580c5d1 Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Thu, 3 Mar 2022 12:43:12 +0000
Subject: [PATCH 105/162] update the docstrings of alpha shape functions

---
 libpysal/cg/alpha_shapes.py | 48 ++++++++++++++++++++-----------------
 1 file changed, 26 insertions(+), 22 deletions(-)

diff --git a/libpysal/cg/alpha_shapes.py b/libpysal/cg/alpha_shapes.py
index 516052c39..27baa5960 100644
--- a/libpysal/cg/alpha_shapes.py
+++ b/libpysal/cg/alpha_shapes.py
@@ -356,7 +356,7 @@ def get_single_faces(triangles_is):
 
 
 @requires("geopandas", "shapely")
-def alpha_geoms(alpha, triangles, radii, xys):
+def _alpha_geoms(alpha, triangles, radii, xys):
     """Generate alpha-shape polygon(s) from `alpha` value, vertices of
     `triangles`, the `radii` for all points, and the points themselves
 
@@ -380,10 +380,9 @@ def alpha_geoms(alpha, triangles, radii, xys):
     -------
 
     geoms : GeoSeries
-        Polygon(s) resulting from the alpha shape algorithm. The GeoSeries
-        object remains so even if only a single polygon is returned. There is
-        no CRS included in the object. Also note that holes are included in addition
-        to exteriors. 
+        Polygon(s) resulting from the alpha shape algorithm, in a GeoSeries.
+        The output is a GeoSeries even if only a single polygon is returned. There is
+        no CRS included in the returned GeoSeries.
 
     Examples
     --------
@@ -446,7 +445,8 @@ def alpha_shape(xys, alpha):
     shapes : GeoSeries
          Polygon(s) resulting from the alpha shape algorithm. The GeoSeries
          object remains so even if only a single polygon is returned. There is
-         no CRS included in the object.
+         no CRS included in the object. Note that the returned shape(s) may
+         have holes, as per the definition of the shape in Edselbrunner et al. (1983)
 
     Examples
     --------
@@ -474,6 +474,7 @@ def alpha_shape(xys, alpha):
         warn(NUMBA_WARN)
     if xys.shape[0] < 4:
         from shapely import ops, geometry as geom
+
         return ops.unary_union([geom.Point(xy) for xy in xys]).convex_hull.buffer(0)
     triangulation = spat.Delaunay(xys)
     triangles = xys[triangulation.simplices]
@@ -482,7 +483,7 @@ def alpha_shape(xys, alpha):
     c_pts = triangles[:, 2, :]
     radii = r_circumcircle_triangle(a_pts, b_pts, c_pts)
     del triangles, a_pts, b_pts, c_pts
-    geoms = alpha_geoms(alpha, triangulation.simplices, radii, xys)
+    geoms = _alpha_geoms(alpha, triangulation.simplices, radii, xys)
     geoms = _filter_holes(geoms, xys)
     return geoms
 
@@ -529,12 +530,12 @@ def alpha_shape_auto(
 
     This method uses the algorithm proposed by  Edelsbrunner, Kirkpatrick &
     Seidel (1983) to return the tightest polygon that contains all points in
-    `xys`. The algorithm ranks every point based on its radious and iterates
+    `xys`. The algorithm ranks every point based on its radius and iterates
     over each point, checking whether the maximum alpha that would keep the
     point and all the other ones in the set with smaller radii results in a
     single polygon. If that is the case, it moves to the next point;
     otherwise, it retains the previous alpha value and returns the polygon
-    as `shapely` geometry.
+    as `shapely` geometry. Note that this geometry may have holes.
 
     Parameters
     ----------
@@ -619,7 +620,7 @@ def alpha_shape_auto(
     radii_sorted_i = radii.argsort()
     triangles = triangulation.simplices[radii_sorted_i][::-1]
     radii = radii[radii_sorted_i][::-1]
-    geoms_prev = alpha_geoms((1 / radii.max()) - EPS, triangles, radii, xys)
+    geoms_prev = _alpha_geoms((1 / radii.max()) - EPS, triangles, radii, xys)
     if HAS_PYGEOS:
         points = pygeos.points(xys)
     else:
@@ -631,7 +632,7 @@ def alpha_shape_auto(
         alpha = (1 / radi) - EPS
         if verbose:
             print("%.2f%% | Trying a = %f" % ((i + 1) / radii.shape[0], alpha))
-        geoms = alpha_geoms(alpha, triangles, radii, xys)
+        geoms = _alpha_geoms(alpha, triangles, radii, xys)
         if _valid_hull(geoms, points):
             geoms_prev = geoms
             radi_prev = radi
@@ -706,34 +707,37 @@ def _construct_centers(a, b, radius):
     else:
         return down_x, down_y
 
+
 def _filter_holes(geoms, points):
     """
     Filter hole polygons using a computational geometry solution
     """
     if (geoms.interiors.apply(len) > 0).any():
         from shapely.geometry import Polygon
-        # Extract the "shell", or outer ring of the polygon. 
+
+        # Extract the "shell", or outer ring of the polygon.
         shells = geoms.exterior.apply(Polygon)
         # Compute which original geometries are within each shell, self-inclusive
-        inside, outside = shells.sindex.query_bulk(geoms, predicate='within')
-        # Now, create the sparse matrix relating the inner geom (rows) 
-        # to the outer shell (cols) and take the sum. 
+        inside, outside = shells.sindex.query_bulk(geoms, predicate="within")
+        # Now, create the sparse matrix relating the inner geom (rows)
+        # to the outer shell (cols) and take the sum.
         # A z-order of 1 means the polygon is only inside if its own exterior. This means it's not a hole.
         # A z-order of 2 means the polygon is inside of exactly one other exterior. Because
-        #   the hull generation method is restricted to be planar, this means the polygon is a hole. 
-        # In general, an even z-order means that the polygon is always exactly matched to one exterior, 
+        #   the hull generation method is restricted to be planar, this means the polygon is a hole.
+        # In general, an even z-order means that the polygon is always exactly matched to one exterior,
         #   plus some number of intermediate exterior-hole pairs. Therefore, the polygon is a hole.
-        # In general, an odd z-order means that there is an uneven number of exteriors. 
-        #   This means the polygon is not a hole. 
-        zorder = sparse.csc_matrix((np.ones_like(inside), (inside, outside))).sum(axis=1)
+        # In general, an odd z-order means that there is an uneven number of exteriors.
+        #   This means the polygon is not a hole.
+        zorder = sparse.csc_matrix((np.ones_like(inside), (inside, outside))).sum(
+            axis=1
+        )
         zorder = np.asarray(zorder).flatten()
-        # Keep only the odd z-orders
+        # Keep only the odd z-orders
         to_include = (zorder % 2).astype(bool)
         geoms = geoms[to_include]
     return geoms
 
 
-
 if __name__ == "__main__":
 
     import matplotlib.pyplot as plt

From ac17fc501e3da00fe9312db73051f07923384899 Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Thu, 3 Mar 2022 14:25:29 +0000
Subject: [PATCH 106/162] pin to networkx 2.6 on python 3.7

---
 ci/37.yaml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/ci/37.yaml b/ci/37.yaml
index a5a418604..8a9f43db5 100644
--- a/ci/37.yaml
+++ b/ci/37.yaml
@@ -15,6 +15,7 @@ dependencies:
   - pytest
   - pytest-cov
   - pytest-xdist
+  - networkx=2.6
   # optional
   - geopandas>=0.7.0
   - joblib

From 8cf279eca0240c2f518fc2470949d622c24959e5 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 5 Apr 2022 23:01:31 +0000
Subject: [PATCH 107/162] Bump codecov/codecov-action from 2 to 3

Bumps [codecov/codecov-action](https://github.com/codecov/codecov-action) from 2 to 3.
- [Release notes](https://github.com/codecov/codecov-action/releases)
- [Changelog](https://github.com/codecov/codecov-action/blob/master/CHANGELOG.md)
- [Commits](https://github.com/codecov/codecov-action/compare/v2...v3)

---
updated-dependencies:
- dependency-name: codecov/codecov-action
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>
---
 .github/workflows/unittests.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/unittests.yml b/.github/workflows/unittests.yml
index 6c2c88f1b..2b705d013 100644
--- a/.github/workflows/unittests.yml
+++ b/.github/workflows/unittests.yml
@@ -51,7 +51,7 @@
          if: matrix.os == 'windows-latest'
 
        - name: codecov
-         uses: codecov/codecov-action@v2
+         uses: codecov/codecov-action@v3
          with:
            token: ${{ secrets.CODECOV_TOKEN }}
            file: ./coverage.xml

From e9f8497d18b829c94e68a294d9d6228019d4f44a Mon Sep 17 00:00:00 2001
From: James Gaboardi <jgaboardi@gmail.com>
Date: Sun, 15 May 2022 15:16:47 -0400
Subject: [PATCH 108/162] change depr to future warning

---
 libpysal/cg/locators.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/libpysal/cg/locators.py b/libpysal/cg/locators.py
index 729afce30..9b58160e8 100644
--- a/libpysal/cg/locators.py
+++ b/libpysal/cg/locators.py
@@ -14,7 +14,7 @@
 
 __all__ = ["Grid", "BruteForcePointLocator", "PointLocator", "PolygonLocator"]
 
-dep_msg = "is deprecated and will be reoved in libpysal 4.4.0"
+dep_msg = "is deprecated and will be removed in a future version of libpysal"
 
 
 class Grid:
@@ -38,7 +38,7 @@ def __init__(self, bounds, resolution):
         TODO: complete this doctest
         >>> g = Grid(Rectangle(0, 0, 10, 10), 1)
         """
-        warnings.warn("Grid " + dep_msg, DeprecationWarning)
+        warnings.warn("Grid " + dep_msg, FutureWarning)
         if resolution == 0:
             raise Exception("Cannot create grid with resolution 0")
         self.res = resolution
@@ -294,7 +294,7 @@ def __init__(self, points):
         --------
         >>> pl = BruteForcePointLocator([Point((0, 0)), Point((5, 0)), Point((0, 10))])
         """
-        warnings.warn("BruteForcePointLocator " + dep_msg, DeprecationWarning)
+        warnings.warn("BruteForcePointLocator " + dep_msg, FutureWarning)
         self._points = points
 
     def nearest(self, query_point):
@@ -388,7 +388,7 @@ def __init__(self, points):
         >>> points = [Point((0, 0)), Point((1, 6)), Point((5.4, 1.4))]
         >>> pl = PointLocator(points)
         """
-        warnings.warn("PointLocator " + dep_msg, DeprecationWarning)
+        warnings.warn("PointLocator " + dep_msg, FutureWarning)
         self._locator = BruteForcePointLocator(points)
 
     def nearest(self, query_point):
@@ -486,7 +486,7 @@ def __init__(self, polygons):
         >>> isinstance(pl, PolygonLocator)
         True
         """
-        warnings.warn("PolygonLocator " + dep_msg, DeprecationWarning)
+        warnings.warn("PolygonLocator " + dep_msg, FutureWarning)
         self._locator = polygons
         # create and rtree
         self._rtree = RTree()

From da06b749d503ddf0cf6c4a70804b36a772dc120d Mon Sep 17 00:00:00 2001
From: James Gaboardi <jgaboardi@gmail.com>
Date: Sun, 15 May 2022 15:37:21 -0400
Subject: [PATCH 109/162] add future warning for Geometry-based objects

---
 libpysal/cg/shapes.py | 453 +++++++++++++++++++++---------------------
 1 file changed, 229 insertions(+), 224 deletions(-)

diff --git a/libpysal/cg/shapes.py b/libpysal/cg/shapes.py
index 9a65b2895..7c7b44334 100644
--- a/libpysal/cg/shapes.py
+++ b/libpysal/cg/shapes.py
@@ -7,8 +7,9 @@
 
 import math
 from .sphere import arcdist
-
 from typing import Union
+import warnings
+
 
 __all__ = [
     "Point",
@@ -22,27 +23,30 @@
 ]
 
 
+dep_msg = "Objects based on the `Geometry` class will deprecated and removed in a future version of libpysal."
+
+
 def asShape(obj):
     """Returns a PySAL shape object from ``obj``, which
     must support the ``__geo_interface__``.
-    
+
     Parameters
     ----------
     obj : {libpysal.cg.{Point, LineSegment, Line, Ray, Chain, Polygon}
         A geometric representation of an object.
-    
+
     Raises
     ------
     TypeError
         Raised when ``obj`` is not a supported shape.
     NotImplementedError
         Raised when ``geo_type`` is not a supported type.
-    
+
     Returns
     -------
     obj : {libpysal.cg.{Point, LineSegment, Line, Ray, Chain, Polygon}
         A new geometric representation of the object.
-    
+
     """
 
     if isinstance(obj, (Point, LineSegment, Line, Ray, Chain, Polygon)):
@@ -73,7 +77,7 @@ def asShape(obj):
 class Geometry(object):
     """A base class to help implement ``is_geometry``
     and make geometric types extendable.
-    
+
     """
 
     def __init__(self):
@@ -87,16 +91,16 @@ class Point(Geometry):
     ----------
     loc : tuple
         The point's location (number :math:`x`-tuple, :math:`x` > 1).
-    
+
     Examples
     --------
-    
+
     >>> p = Point((1, 3))
-    
+
     """
 
     def __init__(self, loc):
-
+        warnings.warn(dep_msg, FutureWarning)
         self.__loc = tuple(map(float, loc))
 
     @classmethod
@@ -117,13 +121,13 @@ def __lt__(self, other) -> bool:
 
         Examples
         --------
-        
+
         >>> Point((0, 1)) < Point((0, 1))
         False
-        
+
         >>> Point((0, 1)) < Point((1, 1))
         True
-        
+
         """
 
         return (self.__loc) < (other.__loc)
@@ -138,13 +142,13 @@ def __le__(self, other) -> bool:
 
         Examples
         --------
-        
+
         >>> Point((0, 1)) <= Point((0, 1))
         True
-        
+
         >>> Point((0, 1)) <= Point((1, 1))
         True
-        
+
         """
 
         return (self.__loc) <= (other.__loc)
@@ -156,16 +160,16 @@ def __eq__(self, other) -> bool:
         ----------
         other : libpysal.cg.Point
             An object to test equality against.
-        
+
         Examples
         --------
-        
+
         >>> Point((0, 1)) == Point((0, 1))
         True
-        
+
         >>> Point((0, 1)) == Point((1, 1))
         False
-        
+
         """
 
         try:
@@ -183,13 +187,13 @@ def __ne__(self, other) -> bool:
 
         Examples
         --------
-        
+
         >>> Point((0, 1)) != Point((0, 1))
         False
-        
+
         >>> Point((0, 1)) != Point((1, 1))
         True
-        
+
         """
 
         try:
@@ -207,13 +211,13 @@ def __gt__(self, other) -> bool:
 
         Examples
         --------
-        
+
         >>> Point((0, 1)) > Point((0, 1))
         False
-        
+
         >>> Point((0, 1)) > Point((1, 1))
         False
-        
+
         """
 
         return (self.__loc) > (other.__loc)
@@ -228,13 +232,13 @@ def __ge__(self, other) -> bool:
 
         Examples
         --------
-        
+
         >>> Point((0, 1)) >= Point((0, 1))
         True
-        
+
         >>> Point((0, 1)) >= Point((1, 1))
         False
-        
+
         """
 
         return (self.__loc) >= (other.__loc)
@@ -244,20 +248,20 @@ def __hash__(self) -> int:
 
         Examples
         --------
-        
+
         >>> hash(Point((0, 1))) == hash(Point((0, 1)))
         True
-        
+
         >>> hash(Point((0, 1))) == hash(Point((1, 1)))
         False
-        
+
         """
 
         return hash(self.__loc)
 
     def __getitem__(self, *args) -> Union[int, float]:
         """Return the coordinate for the given dimension.
-        
+
         Parameters
         ----------
         *args : tuple
@@ -266,13 +270,13 @@ def __getitem__(self, *args) -> Union[int, float]:
 
         Examples
         --------
-        
+
         >>> p = Point((5.5, 4.3))
         >>> p[0] == 5.5
         True
         >>> p[1] == 4.3
         True
-        
+
         """
 
         return self.__loc.__getitem__(*args)
@@ -288,27 +292,27 @@ def __getslice__(self, *args) -> slice:
 
         Examples
         --------
-        
+
         >>> p = Point((3, 6, 2))
         >>> p[:2] == (3, 6)
         True
-        
+
         >>> p[1:2] == (6,)
         True
-        
+
         """
 
         return self.__loc.__getslice__(*args)
 
     def __len__(self) -> int:
-        """ Returns the dimensions of the point.
+        """Returns the dimensions of the point.
 
         Examples
         --------
-        
+
         >>> len(Point((1, 2)))
         2
-        
+
         """
 
         return len(self.__loc)
@@ -318,10 +322,10 @@ def __repr__(self) -> str:
 
         Examples
         --------
-        
+
         >>> Point((0, 1))
         (0.0, 1.0)
-        
+
         """
 
         return str(self)
@@ -331,11 +335,11 @@ def __str__(self) -> str:
 
         Examples
         --------
-        
+
         >>> p = Point((1, 3))
         >>> str(p)
         '(1.0, 3.0)'
-        
+
         """
 
         return str(self.__loc)
@@ -367,13 +371,13 @@ class LineSegment(Geometry):
 
     Examples
     --------
-    
+
     >>> ls = LineSegment(Point((1, 2)), Point((5, 6)))
 
     """
 
     def __init__(self, start_pt, end_pt):
-
+        warnings.warn(dep_msg, FutureWarning)
         self._p1 = start_pt
         self._p2 = end_pt
         self._reset_props()
@@ -389,15 +393,15 @@ def __eq__(self, other) -> bool:
 
         Examples
         --------
-        
+
         >>> l1 = LineSegment(Point((1, 2)), Point((5, 6)))
         >>> l2 = LineSegment(Point((5, 6)), Point((1, 2)))
         >>> l1 == l2
         True
-        
+
         >>> l2 == l1
         True
-        
+
         """
 
         eq = False
@@ -415,12 +419,12 @@ def __eq__(self, other) -> bool:
     def intersect(self, other) -> bool:
         """Test whether segment intersects with other segment (``True``) or
         not (``False``). Handles endpoints of segments being on other segment.
-        
+
         Parameters
         ----------
         other : libpysal.cg.LineSegment
             Another line segment to check against.
-        
+
         Examples
         --------
 
@@ -428,15 +432,15 @@ def intersect(self, other) -> bool:
         >>> ls1 = LineSegment(Point((5, 0)), Point((10, 1)))
         >>> ls.intersect(ls1)
         True
-        
+
         >>> ls2 = LineSegment(Point((5, 1)), Point((10, 1)))
         >>> ls.intersect(ls2)
         False
-        
+
         >>> ls2 = LineSegment(Point((7, -1)), Point((7, 2)))
         >>> ls.intersect(ls2)
         True
-        
+
         """
 
         ccw1 = self.sw_ccw(other.p2)
@@ -457,10 +461,10 @@ def _reset_props(self):
 
         Examples
         --------
-        
+
         >>> ls = LineSegment(Point((1, 2)), Point((5, 6)))
         >>> ls._reset_props()
-        
+
         """
 
         self._bounding_box = None
@@ -478,12 +482,12 @@ def _get_p1(self):
 
         Examples
         --------
-        
+
         >>> ls = LineSegment(Point((1, 2)), Point((5, 6)))
         >>> r = ls._get_p1()
         >>> r == Point((1, 2))
         True
-        
+
         """
 
         return self._p1
@@ -491,12 +495,12 @@ def _get_p1(self):
     def _set_p1(self, p1):
         """**HELPER METHOD. DO NOT CALL.**
         Sets the ``p1`` attribute of the line segment.
-        
+
         Parameters
         ----------
         p1 : libpysal.cg.Point
             A point.
-        
+
         Returns
         -------
         self._p1 : libpysal.cg.Point
@@ -504,12 +508,12 @@ def _set_p1(self, p1):
 
         Examples
         --------
-        
+
         >>> ls = LineSegment(Point((1, 2)), Point((5, 6)))
         >>> r = ls._set_p1(Point((3, -1)))
         >>> r == Point((3.0, -1.0))
         True
-        
+
         """
 
         self._p1 = p1
@@ -530,12 +534,12 @@ def _get_p2(self):
 
         Examples
         --------
-        
+
         >>> ls = LineSegment(Point((1, 2)), Point((5, 6)))
         >>> r = ls._get_p2()
         >>> r == Point((5, 6))
         True
-        
+
         """
 
         return self._p2
@@ -548,7 +552,7 @@ def _set_p2(self, p2):
         ----------
         p2 : libpysal.cg.Point
             A point.
-        
+
         Returns
         -------
         self._p2 : libpysal.cg.Point
@@ -556,12 +560,12 @@ def _set_p2(self, p2):
 
         Examples
         --------
-        
+
         >>> ls = LineSegment(Point((1, 2)), Point((5, 6)))
         >>> r = ls._set_p2(Point((3, -1)))
         >>> r == Point((3.0, -1.0))
         True
-        
+
         """
 
         self._p2 = p2
@@ -582,14 +586,14 @@ def is_ccw(self, pt) -> bool:
 
         Examples
         --------
-        
+
         >>> ls = LineSegment(Point((0, 0)), Point((5, 0)))
         >>> ls.is_ccw(Point((2, 2)))
         True
-        
+
         >>> ls.is_ccw(Point((2, -2)))
         False
-        
+
         """
 
         v1 = (self._p2[0] - self._p1[0], self._p2[1] - self._p1[1])
@@ -608,14 +612,14 @@ def is_cw(self, pt) -> bool:
 
         Examples
         --------
-        
+
         >>> ls = LineSegment(Point((0, 0)), Point((5, 0)))
         >>> ls.is_cw(Point((2, 2)))
         False
-        
+
         >>> ls.is_cw(Point((2, -2)))
         True
-        
+
         """
 
         v1 = (self._p2[0] - self._p1[0], self._p2[1] - self._p1[1])
@@ -669,21 +673,21 @@ def get_swap(self):
 
         Examples
         --------
-        
+
         >>> ls = LineSegment(Point((1, 2)), Point((5, 6)))
         >>> swap = ls.get_swap()
         >>> swap.p1[0]
         5.0
-        
+
         >>> swap.p1[1]
         6.0
-        
+
         >>> swap.p2[0]
         1.0
-        
+
         >>> swap.p2[1]
         2.0
-        
+
         """
 
         line_seg = LineSegment(self._p2, self._p1)
@@ -693,28 +697,28 @@ def get_swap(self):
     @property
     def bounding_box(self):
         """Returns the minimum bounding box of a ``LineSegment`` object.
-        
+
         Returns
         -------
         self._bounding_box : libpysal.cg.Rectangle
             The bounding box of the line segment.
-        
+
         Examples
         --------
-        
+
         >>> ls = LineSegment(Point((1, 2)), Point((5, 6)))
         >>> ls.bounding_box.left
         1.0
-        
+
         >>> ls.bounding_box.lower
         2.0
-        
+
         >>> ls.bounding_box.right
         5.0
-        
+
         >>> ls.bounding_box.upper
         6.0
-        
+
         """
 
         # If LineSegment attributes p1, p2 changed, recompute
@@ -738,11 +742,11 @@ def len(self) -> float:
 
         Examples
         --------
-        
+
         >>> ls = LineSegment(Point((2, 2)), Point((5, 2)))
         >>> ls.len
         3.0
-        
+
         """
 
         # If LineSegment attributes p1, p2 changed, recompute
@@ -762,15 +766,15 @@ def line(self):
 
         Examples
         --------
-        
+
         >>> ls = LineSegment(Point((2, 2)), Point((3, 3)))
         >>> l = ls.line
         >>> l.m
         1.0
-        
+
         >>> l.b
         0.0
-        
+
         """
 
         if self._line == False:
@@ -792,26 +796,26 @@ def line(self):
 
 class VerticalLine(Geometry):
     """Geometric representation of verticle line objects.
-    
+
     Parameters
     ----------
     x : {int, float}
         The :math:`x`-intercept of the line. ``x`` is also an attribute.
-    
+
     Examples
     --------
-    
+
     >>> ls = VerticalLine(0)
     >>> ls.m
     inf
-    
+
     >>> ls.b
     nan
-    
+
     """
 
     def __init__(self, x):
-
+        warnings.warn(dep_msg, FutureWarning)
         self._x = float(x)
         self.m = float("inf")
         self.b = float("nan")
@@ -826,11 +830,11 @@ def x(self, y) -> float:
 
         Examples
         --------
-        
+
         >>> l = VerticalLine(0)
         >>> l.x(0.25)
         0.0
-        
+
         """
 
         return self._x
@@ -845,11 +849,11 @@ def y(self, x) -> float:
 
         Examples
         --------
-        
+
         >>> l = VerticalLine(1)
         >>> l.y(1)
         nan
-        
+
         """
 
         return float("nan")
@@ -864,26 +868,26 @@ class Line(Geometry):
         The slope of the line. ``m`` is also an attribute.
     b : {int, float}
         The :math:`y`-intercept of the line. ``b`` is also an attribute.
-    
+
     Raises
     ------
     ArithmeticError
         Raised when infinity is passed in as the slope.
-    
+
     Examples
     --------
-    
+
     >>> ls = Line(1, 0)
     >>> ls.m
     1.0
-    
+
     >>> ls.b
     0.0
-    
+
     """
 
     def __init__(self, m, b):
-
+        warnings.warn(dep_msg, FutureWarning)
         if m == float("inf"):
             raise ArithmeticError("Slope cannot be infinite.")
 
@@ -897,19 +901,19 @@ def x(self, y: Union[int, float]) -> float:
         ----------
         y : {int, float}
             The :math:`y`-value at which to compute :math:`x`.
-        
+
         Raises
         ------
         ArithmeticError
             Raised when ``0.`` is passed in as the slope.
-        
+
         Examples
         --------
-        
+
         >>> l = Line(0.5, 0)
         >>> l.x(0.25)
         0.5
-        
+
         """
 
         if self.m == 0:
@@ -927,11 +931,11 @@ def y(self, x: Union[int, float]) -> float:
 
         Examples
         --------
-        
+
         >>> l = Line(1, 0)
         >>> l.y(1)
         1.0
-        
+
         """
 
         if self.m == 0:
@@ -957,28 +961,28 @@ class Ray:
     p : libpysal.cg.Point
         The second point on the ray (not the point where the
         ray originates). See ``second_p``.
-    
+
     Examples
     --------
-    
+
     >>> l = Ray(Point((0, 0)), Point((1, 0)))
     >>> str(l.o)
     '(0.0, 0.0)'
-    
+
     >>> str(l.p)
     '(1.0, 0.0)'
-    
+
     """
 
     def __init__(self, origin, second_p):
-
+        warnings.warn(dep_msg, FutureWarning)
         self.o = origin
         self.p = second_p
 
 
 class Chain(Geometry):
     """Geometric representation of a chain, also known as a polyline.
-    
+
     Parameters
     ----------
     vertices : list
@@ -990,16 +994,16 @@ class Chain(Geometry):
         The list of points of the vertices of the chain in order.
     len : float
         The geometric length of the chain.
-    
+
     Examples
     --------
-    
+
     >>> c = Chain([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((2, 1))])
-    
+
     """
 
     def __init__(self, vertices: list):
-
+        warnings.warn(dep_msg, FutureWarning)
         if isinstance(vertices[0], list):
             self._vertices = [part for part in vertices]
         else:
@@ -1041,12 +1045,12 @@ def vertices(self) -> list:
 
         Examples
         --------
-        
+
         >>> c = Chain([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((2, 1))])
         >>> verts = c.vertices
         >>> len(verts)
         4
-        
+
         """
 
         return sum([part for part in self._vertices], [])
@@ -1057,7 +1061,7 @@ def parts(self) -> list:
 
         Examples
         --------
-       
+
         >>> c = Chain(
         ...     [
         ...         [Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))],
@@ -1066,7 +1070,7 @@ def parts(self) -> list:
         ... )
         >>> len(c.parts)
         2
-        
+
         """
 
         return [[v for v in part] for part in self._vertices]
@@ -1079,23 +1083,23 @@ def bounding_box(self):
         -------
         self._bounding_box : libpysal.cg.Rectangle
             The bounding box of the chain.
-        
+
         Examples
         --------
-        
+
         >>> c = Chain([Point((0, 0)), Point((2, 0)), Point((2, 1)), Point((0, 1))])
         >>> c.bounding_box.left
         0.0
-        
+
         >>> c.bounding_box.lower
         0.0
-        
+
         >>> c.bounding_box.right
         2.0
-        
+
         >>> c.bounding_box.upper
         1.0
-        
+
         """
 
         if self._bounding_box is None:
@@ -1115,11 +1119,11 @@ def len(self) -> int:
 
         Examples
         --------
-        
+
         >>> c = Chain([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((2, 1))])
         >>> c.len
         3.0
-        
+
         >>> c = Chain(
         ...     [
         ...         [Point((0, 0)), Point((1, 0)), Point((1, 1))],
@@ -1128,7 +1132,7 @@ def len(self) -> int:
         ... )
         >>> c.len
         4.0
-        
+
         """
 
         def dist(v1: tuple, v2: tuple) -> Union[int, float]:
@@ -1146,7 +1150,7 @@ def part_perimeter(p: list) -> Union[int, float]:
     def arclen(self) -> Union[int, float]:
         """Returns the geometric length of the chain
         computed using 'arcdistance' (meters).
-        
+
         """
 
         def part_perimeter(p: list) -> Union[int, float]:
@@ -1197,10 +1201,11 @@ class Ring(Geometry):
     _quad_tree_structure : libpysal.cg.QuadTreeStructureSingleRing
         The quad tree structure for the ring. This structure helps
         test if a point is inside the ring.
-    
+
     """
 
     def __init__(self, vertices):
+        warnings.warn(dep_msg, FutureWarning)
         if vertices[0] != vertices[-1]:
             vertices = vertices[:] + vertices[0:1]
             # msg = "Supplied vertices do not form a closed ring, "
@@ -1245,10 +1250,10 @@ def bounding_box(self):
         -------
         self._bounding_box : libpysal.cg.Rectangle
             The bounding box of the ring.
-        
+
         Examples
         --------
-        
+
         >>> r = Ring(
         ...     [
         ...         Point((0, 0)),
@@ -1258,19 +1263,19 @@ def bounding_box(self):
         ...         Point((0, 0))
         ...     ]
         ... )
-        
+
         >>> r.bounding_box.left
         0.0
-        
+
         >>> r.bounding_box.lower
         0.0
-        
+
         >>> r.bounding_box.right
         2.0
-        
+
         >>> r.bounding_box.upper
         1.0
-        
+
         """
 
         if self._bounding_box is None:
@@ -1287,7 +1292,7 @@ def area(self) -> Union[int, float]:
 
         Examples
         --------
-        
+
         >>> r = Ring(
         ...     [
         ...         Point((0, 0)),
@@ -1299,7 +1304,7 @@ def area(self) -> Union[int, float]:
         ... )
         >>> r.area
         2.0
-        
+
         """
 
         return abs(self.signed_area)
@@ -1331,13 +1336,13 @@ def centroid(self):
 
         Notes
         -----
-        
+
         The centroid returned by this method is the geometric centroid.
         Also known as the 'center of gravity' or 'center of mass'.
 
         Examples
         --------
-        
+
         >>> r = Ring(
         ...     [
         ...         Point((0, 0)),
@@ -1349,7 +1354,7 @@ def centroid(self):
         ... )
         >>> str(r.centroid)
         '(1.0, 0.5)'
-        
+
         """
 
         if self._centroid is None:
@@ -1374,7 +1379,7 @@ def build_quad_tree_structure(self):
         """Build the quad tree structure for this polygon. Once
         the structure is built, speed for testing if a point is
         inside the ring will be increased significantly.
-        
+
         """
 
         self._quad_tree_structure = QuadTreeStructureSingleRing(self)
@@ -1382,17 +1387,17 @@ def build_quad_tree_structure(self):
     def contains_point(self, point):
         """Point containment using winding number. The implementation is based on
         `this <http://www.engr.colostate.edu/~dga/dga/papers/point_in_polygon.pdf>`_.
-        
+
         Parameters
         ----------
         point : libpysal.cg.Point
             The point to test for containment.
-        
+
         Returns
         -------
         point_contained : bool
             ``True`` if ``point`` is contained within the polygon, otherwise ``False``.
-        
+
         """
 
         point_contained = False
@@ -1469,22 +1474,22 @@ class Polygon(Geometry):
     bounding_box : libpysal.cg.Rectangle
         The bounding box of the polygon.
     bbox : list
-        A list representation of the bounding box in the 
+        A list representation of the bounding box in the
         form ``[left, lower, right, upper]``.
     area : float
         The area enclosed by the polygon.
     centroid : tuple
         The 'center of gravity', i.e. the mean point of the polygon.
-    
+
     Examples
     --------
-    
+
     >>> p1 = Polygon([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))])
-    
+
     """
 
     def __init__(self, vertices, holes=None):
-
+        warnings.warn(dep_msg, FutureWarning)
         self._part_rings = []
         self._hole_rings = []
 
@@ -1518,7 +1523,7 @@ def __from_geo_interface__(cls, geo: dict):
         GEOS, Shapely, and geoJSON do. In GEOS, etc, polygons may only
         have a single exterior ring, all other parts are holes.
         MultiPolygons are simply a list of polygons.
-        
+
         """
 
         geo_type = geo["type"].lower()
@@ -1571,14 +1576,14 @@ def len(self) -> int:
 
         Examples
         --------
-        
+
         >>> p1 = Polygon([Point((0, 0)), Point((0, 1)), Point((1, 1)), Point((1, 0))])
         >>> p1.len
         4
-        
+
         >>> len(p1)
         4
-        
+
         """
 
         if self._len is None:
@@ -1591,11 +1596,11 @@ def vertices(self) -> list:
 
         Examples
         --------
-        
+
         >>> p1 = Polygon([Point((0, 0)), Point((0, 1)), Point((1, 1)), Point((1, 0))])
         >>> len(p1.vertices)
         4
-        
+
         """
 
         return sum([part for part in self._vertices], []) + sum(
@@ -1608,14 +1613,14 @@ def holes(self) -> list:
 
         Examples
         --------
-        
+
         >>> p = Polygon(
         ...     [Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))],
         ...     [Point((1, 2)), Point((2, 2)), Point((2, 1)), Point((1, 1))]
         ... )
         >>> len(p.holes)
         1
-        
+
         """
 
         return [[v for v in part] for part in self._holes]
@@ -1626,7 +1631,7 @@ def parts(self) -> list:
 
         Examples
         --------
-        
+
         >>> p = Polygon(
         ...     [
         ...         [Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))],
@@ -1635,7 +1640,7 @@ def parts(self) -> list:
         ... )
         >>> len(p.parts)
         2
-        
+
         """
 
         return [[v for v in part] for part in self._vertices]
@@ -1646,11 +1651,11 @@ def perimeter(self) -> Union[int, float]:
 
         Examples
         --------
-        
+
         >>> p = Polygon([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))])
         >>> p.perimeter
         4.0
-        
+
         """
 
         def dist(v1: Union[int, float], v2: Union[int, float]) -> float:
@@ -1669,17 +1674,17 @@ def part_perimeter(part) -> Union[int, float]:
     @property
     def bbox(self):
         """Returns the bounding box of the polygon as a list.
-        
+
         Returns
         -------
         self._bbox : list
             The bounding box of the polygon as a list.
-        
+
         See Also
         --------
-        
+
         libpysal.cg.bounding_box
-        
+
         """
 
         if self._bbox is None:
@@ -1702,20 +1707,20 @@ def bounding_box(self):
 
         Examples
         --------
-        
+
         >>> p = Polygon([Point((0, 0)), Point((2, 0)), Point((2, 1)), Point((0, 1))])
         >>> p.bounding_box.left
         0.0
-        
+
         >>> p.bounding_box.lower
         0.0
-        
+
         >>> p.bounding_box.right
         2.0
-        
+
         >>> p.bounding_box.upper
         1.0
-        
+
         """
 
         if self._bounding_box is None:
@@ -1734,18 +1739,18 @@ def area(self) -> float:
 
         Examples
         --------
-        
+
         >>> p = Polygon([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))])
         >>> p.area
         1.0
-        
+
         >>> p = Polygon(
         ...     [Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))],
         ...     [Point((2, 1)), Point((2, 2)), Point((1, 2)), Point((1, 1))]
         ... )
         >>> p.area
         99.0
-        
+
         """
 
         def part_area(pv: list) -> float:
@@ -1768,21 +1773,21 @@ def centroid(self) -> tuple:
 
         Notes
         -----
-        
+
         The centroid returned by this method is the geometric
         centroid and respects multipart polygons with holes.
         Also known as the 'center of gravity' or 'center of mass'.
 
         Examples
         --------
-        
+
         >>> p = Polygon(
         ...     [Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))],
         ...     [Point((1, 1)), Point((1, 2)), Point((2, 2)), Point((2, 1))]
         ... )
         >>> p.centroid
         (5.0353535353535355, 5.0353535353535355)
-        
+
         """
 
         CP = [ring.centroid for ring in self._part_rings]
@@ -1800,7 +1805,7 @@ def build_quad_tree_structure(self):
         """Build the quad tree structure for this polygon. Once
         the structure is built, speed for testing if a point is
         inside the ring will be increased significantly.
-        
+
         """
 
         for ring in self._part_rings:
@@ -1811,35 +1816,35 @@ def build_quad_tree_structure(self):
 
     def contains_point(self, point):
         """Test if a polygon contains a point.
-        
+
         Parameters
         ----------
         point : libpysal.cg.Point
             A point to test for containment.
-        
+
         Returns
         -------
         contains : bool
             ``True`` if the polygon contains ``point`` otherwise ``False``.
-        
+
         Examples
         --------
-        
+
         >>> p = Polygon(
         ...     [Point((0,0)), Point((4,0)), Point((4,5)), Point((2,3)), Point((0,5))]
         ... )
         >>> p.contains_point((3,3))
         1
-        
+
         >>> p.contains_point((0,6))
         0
-        
+
         >>> p.contains_point((2,2.9))
         1
-        
+
         >>> p.contains_point((4,5))
         0
-        
+
         >>> p.contains_point((4,0))
         0
 
@@ -1851,15 +1856,15 @@ def contains_point(self, point):
         ... )
         >>> p.contains_point((3.0, 3.0))
         False
-        
+
         >>> p.contains_point((1.0, 1.0))
         True
 
         Notes
         -----
-        
+
         Points falling exactly on polygon edges may yield unpredictable results.
-        
+
         """
 
         searching = True
@@ -1895,27 +1900,27 @@ class Rectangle(Geometry):
         Maximum x-value of the rectangle.
     upper : float
         Maximum y-value of the rectangle.
-    
+
     Examples
     --------
-    
+
     >>> r = Rectangle(-4, 3, 10, 17)
     >>> r.left #minx
     -4.0
-    
+
     >>> r.lower #miny
     3.0
-    
+
     >>> r.right #maxx
     10.0
-    
+
     >>> r.upper #maxy
     17.0
-    
+
     """
 
     def __init__(self, left, lower, right, upper):
-
+        warnings.warn(dep_msg, FutureWarning)
         if right < left or upper < lower:
             raise ArithmeticError("Rectangle must have positive area.")
         self.left = float(left)
@@ -1931,15 +1936,15 @@ def __bool__(self):
 
         Examples
         --------
-        
+
         >>> r = Rectangle(0, 0, 0, 0)
         >>> bool(r)
         False
-        
+
         >>> r = Rectangle(0, 0, 1, 1)
         >>> bool(r)
         True
-        
+
         """
 
         return bool(self.area)
@@ -1962,14 +1967,14 @@ def __add__(self, other):
 
     def __getitem__(self, key):
         """
-        
+
         Examples
         --------
-        
+
         >>> r = Rectangle(-4, 3, 10, 17)
         >>> r[:]
         [-4.0, 3.0, 10.0, 17.0]
-        
+
         """
 
         l = [self.left, self.lower, self.right, self.upper]
@@ -1986,21 +1991,21 @@ def set_centroid(self, new_center):
 
         Examples
         --------
-        
+
         >>> r = Rectangle(0, 0, 4, 4)
         >>> r.set_centroid(Point((4, 4)))
         >>> r.left
         2.0
-        
+
         >>> r.right
         6.0
-        
+
         >>> r.lower
         2.0
-        
+
         >>> r.upper
         6.0
-        
+
         """
 
         shift = (
@@ -2024,7 +2029,7 @@ def set_scale(self, scale):
 
         Examples
         --------
-        
+
         >>> r = Rectangle(0, 0, 4, 4)
         >>> r.set_scale(2)
         >>> r.left
@@ -2035,7 +2040,7 @@ def set_scale(self, scale):
         -2.0
         >>> r.upper
         6.0
-        
+
         """
 
         center = ((self.left + self.right) / 2, (self.lower + self.upper) / 2)
@@ -2051,11 +2056,11 @@ def area(self) -> Union[int, float]:
 
         Examples
         --------
-        
+
         >>> r = Rectangle(0, 0, 4, 4)
         >>> r.area
         16.0
-        
+
         """
 
         return (self.right - self.left) * (self.upper - self.lower)
@@ -2066,11 +2071,11 @@ def width(self) -> Union[int, float]:
 
         Examples
         --------
-        
+
         >>> r = Rectangle(0, 0, 4, 4)
         >>> r.width
         4.0
-        
+
         """
 
         return self.right - self.left
@@ -2081,11 +2086,11 @@ def height(self) -> Union[int, float]:
 
         Examples
         --------
-        
+
         >>> r = Rectangle(0, 0, 4, 4)
         >>> r.height
         4.0
-        
+
         """
 
         return self.upper - self.lower

From 788dd0f4d9a6550fe2a1ad75b141902198a8075d Mon Sep 17 00:00:00 2001
From: James Gaboardi <jgaboardi@gmail.com>
Date: Sun, 15 May 2022 15:53:02 -0400
Subject: [PATCH 110/162] change depr to future warning (segloc)

---
 libpysal/cg/segmentLocator.py | 10 ++++------
 1 file changed, 4 insertions(+), 6 deletions(-)

diff --git a/libpysal/cg/segmentLocator.py b/libpysal/cg/segmentLocator.py
index 7b3fad6c1..971e5e2eb 100644
--- a/libpysal/cg/segmentLocator.py
+++ b/libpysal/cg/segmentLocator.py
@@ -8,7 +8,7 @@
 import warnings
 
 
-dep_msg = "is deprecated and will be reoved in libpysal 4.4.0."
+dep_msg = "is deprecated and will be removed in a future version of libpysal"
 
 __all__ = ["SegmentGrid", "SegmentLocator", "Polyline_Shapefile_SegmentLocator"]
 DEBUG = False
@@ -27,7 +27,7 @@ def nearest(self, pt):
 
 class SegmentLocator(object):
     def __init__(self, segments, nbins=500):
-        warnings.warn("SegmentLocator " + dep_msg, DeprecationWarning)
+        warnings.warn("SegmentLocator " + dep_msg, FutureWarning)
         self.data = segments
         if hasattr(segments, "bounding_box"):
             bbox = segment.bounding_box
@@ -51,9 +51,7 @@ def nearest(self, pt):
 
 class Polyline_Shapefile_SegmentLocator(object):
     def __init__(self, shpfile, nbins=500):
-        warnings.warn(
-            "Polyline_Shapefile_SegmentLocator " + dep_msg, DeprecationWarning
-        )
+        warnings.warn("Polyline_Shapefile_SegmentLocator " + dep_msg, FutureWarning)
         self.data = shpfile
         bbox = Rectangle(*shpfile.bbox)
         res = max((bbox.right - bbox.left), (bbox.upper - bbox.lower)) / float(nbins)
@@ -101,7 +99,7 @@ def __init__(self, bounds, resolution):
         TODO: complete this doctest
         >>> g = SegmentGrid(Rectangle(0, 0, 10, 10), 1)
         """
-        warnings.warn("SegmentGrid " + dep_msg, DeprecationWarning)
+        warnings.warn("SegmentGrid " + dep_msg, FutureWarning)
         if resolution == 0:
             raise Exception("Cannot create grid with resolution 0")
         self.res = resolution

From e8dc0786f254777ad6c1d150ea38983080592f32 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 8 Jun 2022 23:01:38 +0000
Subject: [PATCH 111/162] Bump actions/setup-python from 3 to 4

Bumps [actions/setup-python](https://github.com/actions/setup-python) from 3 to 4.
- [Release notes](https://github.com/actions/setup-python/releases)
- [Commits](https://github.com/actions/setup-python/compare/v3...v4)

---
updated-dependencies:
- dependency-name: actions/setup-python
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>
---
 .github/workflows/release_and_publish.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/release_and_publish.yml b/.github/workflows/release_and_publish.yml
index bb71f7707..c780d4874 100644
--- a/.github/workflows/release_and_publish.yml
+++ b/.github/workflows/release_and_publish.yml
@@ -24,7 +24,7 @@
          uses: actions/checkout@v3
  
        - name: Set up python
-         uses: actions/setup-python@v3
+         uses: actions/setup-python@v4
          with:
            python-version: "3.x"
  

From d6f86e146d5dff222ec23f381ef233277320c71e Mon Sep 17 00:00:00 2001
From: John Kastner <john.h.kastner@gmail.com>
Date: Sun, 12 Jun 2022 12:54:04 -0400
Subject: [PATCH 112/162] Remove non-existant parameter `tolerance` from
 `voronoi_frames` doc comment

This parameter was added in 33b500b but subsequently dropped in
b5cde8a without updating the doc comment.
---
 libpysal/cg/voronoi.py | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/libpysal/cg/voronoi.py b/libpysal/cg/voronoi.py
index 387fc97c3..e283a3b25 100644
--- a/libpysal/cg/voronoi.py
+++ b/libpysal/cg/voronoi.py
@@ -207,10 +207,6 @@ def voronoi_frames(points, radius=None, clip="extent"):
         * ``'chull``/``'convex hull'`` -- Clip the voronoi cells to the convex hull of the input points.
         * ``'ashape'``/``'ahull'`` -- Clip the voronoi cells to the tightest hull that contains all points (e.g. the smallest alphashape, using ``libpysal.cg.alpha_shape_auto``).
         * Polygon -- Clip to an arbitrary Polygon.
-    
-    tolerance : float
-        The percent of map width to use to buffer the extent of the map,
-        if clipping (default: ``.01``, or 1%).
 
     Returns
     -------

From d8f41ec96fadf56b7cedaed3ca14bf1e5fd90990 Mon Sep 17 00:00:00 2001
From: mjschwenne <mjschwenne@gmail.com>
Date: Wed, 15 Jun 2022 16:55:03 -0500
Subject: [PATCH 113/162] update nx conversions to be 3.0 compatible

---
 libpysal/weights/tests/test_nx.py | 6 +++---
 libpysal/weights/weights.py       | 8 ++++----
 2 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/libpysal/weights/tests/test_nx.py b/libpysal/weights/tests/test_nx.py
index dda7f9ae8..431d3d847 100644
--- a/libpysal/weights/tests/test_nx.py
+++ b/libpysal/weights/tests/test_nx.py
@@ -13,17 +13,17 @@
 class Test_NetworkXConverter(ut.TestCase):
     def setUp(self):
         self.known_nx = nx.random_regular_graph(4, 10, seed=8879)
-        self.known_amat = np.array(nx.to_numpy_matrix(self.known_nx))
+        self.known_amat = nx.to_numpy_array(self.known_nx)
         self.known_W = lat2W(5, 5)
 
     def test_round_trip(self):
         W_ = W.from_networkx(self.known_nx)
         np.testing.assert_allclose(W_.sparse.toarray(), self.known_amat)
         nx2 = W_.to_networkx()
-        np.testing.assert_allclose(nx.to_numpy_matrix(nx2), self.known_amat)
+        np.testing.assert_allclose(nx.to_numpy_array(nx2), self.known_amat)
         nxsquare = self.known_W.to_networkx()
         np.testing.assert_allclose(
-            self.known_W.sparse.toarray(), nx.to_numpy_matrix(nxsquare)
+            self.known_W.sparse.toarray(), nx.to_numpy_array(nxsquare)
         )
         W_square = W.from_networkx(nxsquare)
         np.testing.assert_allclose(
diff --git a/libpysal/weights/weights.py b/libpysal/weights/weights.py
index 55d38c202..2d2501b0c 100644
--- a/libpysal/weights/weights.py
+++ b/libpysal/weights/weights.py
@@ -348,7 +348,7 @@ def to_networkx(self):
         except ImportError:
             raise ImportError("NetworkX is required to use this function.")
         G = nx.DiGraph() if len(self.asymmetries) > 0 else nx.Graph()
-        return nx.from_scipy_sparse_matrix(self.sparse, create_using=G)
+        return nx.from_scipy_sparse_array(self.sparse, create_using=G)
 
     @classmethod
     def from_networkx(cls, graph, weight_col="weight"):
@@ -371,8 +371,8 @@ def from_networkx(cls, graph, weight_col="weight"):
             import networkx as nx
         except ImportError:
             raise ImportError("NetworkX is required to use this function.")
-        sparse_matrix = nx.to_scipy_sparse_matrix(graph)
-        w = WSP(sparse_matrix).to_W()
+        sparse_array = nx.to_scipy_sparse_array(graph)
+        w = WSP(sparse_array).to_W()
         return w
 
     @property
@@ -584,7 +584,7 @@ def trcWtW_WW(self):
     def pct_nonzero(self):
         """Percentage of nonzero weights."""
         if "pct_nonzero" not in self._cache:
-            self._pct_nonzero = 100.0 * self.sparse.nnz / (1.0 * self._n ** 2)
+            self._pct_nonzero = 100.0 * self.sparse.nnz / (1.0 * self._n**2)
             self._cache["pct_nonzero"] = self._pct_nonzero
         return self._pct_nonzero
 

From 103f84eb565d4e149264c2ad35b388d28ad72e22 Mon Sep 17 00:00:00 2001
From: Martin Fleischmann <martin@martinfleischmann.net>
Date: Thu, 16 Jun 2022 11:06:24 +0100
Subject: [PATCH 114/162] update CI envs

---
 ci/310.yaml | 1 +
 ci/37.yaml  | 2 +-
 ci/38.yaml  | 1 +
 ci/39.yaml  | 1 +
 4 files changed, 4 insertions(+), 1 deletion(-)

diff --git a/ci/310.yaml b/ci/310.yaml
index 6729f77c1..3a1982de0 100644
--- a/ci/310.yaml
+++ b/ci/310.yaml
@@ -18,6 +18,7 @@ dependencies:
   # optional
   - geopandas>=0.7.0
   - joblib
+  - networkx
   - numba
   - packaging
   - xarray
diff --git a/ci/37.yaml b/ci/37.yaml
index 8a9f43db5..297fbd81a 100644
--- a/ci/37.yaml
+++ b/ci/37.yaml
@@ -15,7 +15,7 @@ dependencies:
   - pytest
   - pytest-cov
   - pytest-xdist
-  - networkx=2.6
+  - networkx=2.7
   # optional
   - geopandas>=0.7.0
   - joblib
diff --git a/ci/38.yaml b/ci/38.yaml
index b48bea4ec..5023f9adc 100644
--- a/ci/38.yaml
+++ b/ci/38.yaml
@@ -18,6 +18,7 @@ dependencies:
   # optional
   - geopandas>=0.7.0
   - joblib
+  - networkx
   - numba
   - packaging
   - xarray
diff --git a/ci/39.yaml b/ci/39.yaml
index 41f0f8d33..aad680e4f 100644
--- a/ci/39.yaml
+++ b/ci/39.yaml
@@ -18,6 +18,7 @@ dependencies:
   # optional
   - geopandas>=0.7.0
   - joblib
+  - networkx
   - numba
   - packaging
   - xarray

From cec36b0f1c3d888dc4c391966fa9decdc1770cfe Mon Sep 17 00:00:00 2001
From: Martin Fleischmann <martin@martinfleischmann.net>
Date: Thu, 16 Jun 2022 11:08:52 +0100
Subject: [PATCH 115/162] update error

---
 libpysal/weights/weights.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/libpysal/weights/weights.py b/libpysal/weights/weights.py
index 2d2501b0c..8b3b9937a 100644
--- a/libpysal/weights/weights.py
+++ b/libpysal/weights/weights.py
@@ -346,7 +346,7 @@ def to_networkx(self):
         try:
             import networkx as nx
         except ImportError:
-            raise ImportError("NetworkX is required to use this function.")
+            raise ImportError("NetworkX 2.7+ is required to use this function.")
         G = nx.DiGraph() if len(self.asymmetries) > 0 else nx.Graph()
         return nx.from_scipy_sparse_array(self.sparse, create_using=G)
 
@@ -370,7 +370,7 @@ def from_networkx(cls, graph, weight_col="weight"):
         try:
             import networkx as nx
         except ImportError:
-            raise ImportError("NetworkX is required to use this function.")
+            raise ImportError("NetworkX 2.7+ is required to use this function.")
         sparse_array = nx.to_scipy_sparse_array(graph)
         w = WSP(sparse_array).to_W()
         return w

From 9a71a0c7419a1c7af2caf8377c7831b235f54a95 Mon Sep 17 00:00:00 2001
From: Martin Fleischmann <martin@martinfleischmann.net>
Date: Thu, 16 Jun 2022 11:21:27 +0100
Subject: [PATCH 116/162] skip for old scipy

---
 libpysal/weights/tests/test_nx.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/libpysal/weights/tests/test_nx.py b/libpysal/weights/tests/test_nx.py
index 431d3d847..13d605275 100644
--- a/libpysal/weights/tests/test_nx.py
+++ b/libpysal/weights/tests/test_nx.py
@@ -1,5 +1,7 @@
 import unittest as ut
 import numpy as np
+import scipy as sp
+from packaging.version import Version
 from ..util import lat2W
 from ..weights import W
 
@@ -8,8 +10,9 @@
 except ImportError:
     nx = None
 
+SP_18 = Version(sp.__version__) >= Version('1.8')
 
-@ut.skipIf(nx is None, "Missing networkx")
+@ut.skipIf((nx is None) or (not SP_18), "Missing networkx or old scipy")
 class Test_NetworkXConverter(ut.TestCase):
     def setUp(self):
         self.known_nx = nx.random_regular_graph(4, 10, seed=8879)

From 1bbf01457e427937e479156873d82f02be858691 Mon Sep 17 00:00:00 2001
From: Martin Fleischmann <martin@martinfleischmann.net>
Date: Sat, 3 Sep 2022 12:16:24 +0200
Subject: [PATCH 117/162] make a new method as a target

---
 libpysal/weights/contiguity.py | 67 +++++++++++++++++++++++++++-------
 1 file changed, 54 insertions(+), 13 deletions(-)

diff --git a/libpysal/weights/contiguity.py b/libpysal/weights/contiguity.py
index 8522f128d..11fe8a708 100644
--- a/libpysal/weights/contiguity.py
+++ b/libpysal/weights/contiguity.py
@@ -158,7 +158,7 @@ def from_dataframe(
                       an ordered list of ids to use to index the spatial weights
                       object. If used, the resulting weights object will iterate
                       over results in the order of the names provided in this
-                      argument. 
+                      argument.
 
         See Also
         --------
@@ -182,6 +182,47 @@ def from_dataframe(
             df[geom_col].tolist(), ids=ids, id_order=id_order, **kwargs
         )
 
+    @classmethod
+    def from_dataframe_new(cls, df, geom_col=None, ids=None, **kwargs):
+        """
+        Construct a weights object from a (geo)pandas (Geo)DataFrame
+        with a geometry column.
+
+        Parameters
+        ----------
+        df          : DataFrame
+                        a :class: `pandas.DataFrame` containing geometries to use
+                        for spatial weights
+        geom_col    : string
+                        the name of the column in `df` that contains the
+                        geometries. Defaults to active geometry column.
+        ids         : str, list, np.array, pd.Series (default None)
+                        A definition of ids to use to index the spatial weights object.
+                        Could be the name of the dataframe column, `list`, `numpy.array`,
+                        or `pandas.Series`. If `list`, `numpy.array` or
+                        `pandas.Series` are used then it must have same length as dataframe.
+
+        **kwargs    : dict
+                        Additional arguments to be passed on to the W constructor
+
+
+        See Also
+        --------
+        :class:`libpysal.weights.weights.W`
+        :class:`libpysal.weights.contiguity.Rook`
+        """
+
+        if geom_col is None:
+            geom_col = df.geometry.name
+
+        if isinstance(ids, str):
+            ids = df[ids]
+
+        if not isinstance(ids, list):
+            ids = ids.tolist()
+
+        return cls.from_iterable(df[geom_col].tolist(), ids=ids, **kwargs)
+
     @classmethod
     def from_xarray(
         cls,
@@ -227,7 +268,7 @@ def from_xarray(
         Returns
         -------
         w : libpysal.weights.W/libpysal.weights.WSP
-            instance of spatial weights class W or WSP with an index attribute 
+            instance of spatial weights class W or WSP with an index attribute
 
         Notes
         -----
@@ -382,7 +423,7 @@ def from_dataframe(cls, df, geom_col=None, **kwargs):
                       an ordered list of ids to use to index the spatial weights
                       object. If used, the resulting weights object will iterate
                       over results in the order of the names provided in this
-                      argument. 
+                      argument.
 
         See Also
         --------
@@ -457,7 +498,7 @@ def from_xarray(
         Returns
         -------
         w : libpysal.weights.W/libpysal.weights.WSP
-            instance of spatial weights class W or WSP with an index attribute 
+            instance of spatial weights class W or WSP with an index attribute
 
         Notes
         -----
@@ -526,17 +567,17 @@ def Voronoi(points, criterion="rook", clip="ahull", **kwargs):
 
 def _from_dataframe(df, **kwargs):
     """
-    Construct a voronoi contiguity weight directly from a dataframe. 
+    Construct a voronoi contiguity weight directly from a dataframe.
     Note that if criterion='rook', this is identical to the delaunay
-    graph for the points. 
+    graph for the points.
 
     If the input dataframe is of any other geometry type than "Point",
-    a value error is raised. 
+    a value error is raised.
 
     Parameters
     ----------
     df          :   pandas.DataFrame
-                    dataframe containing point geometries for a 
+                    dataframe containing point geometries for a
                     voronoi diagram.
 
     Returns
@@ -561,14 +602,14 @@ def _from_dataframe(df, **kwargs):
 
 def _build(polygons, criterion="rook", ids=None):
     """
-    This is a developer-facing function to construct a spatial weights object. 
+    This is a developer-facing function to construct a spatial weights object.
 
     Parameters
     ----------
     polygons    : list
                   list of pysal polygons to use to build contiguity
     criterion   : string
-                  option of which kind of contiguity to build. Is either "rook" or "queen" 
+                  option of which kind of contiguity to build. Is either "rook" or "queen"
     ids         : list
                   list of ids to use to index the neighbor dictionary
 
@@ -576,12 +617,12 @@ def _build(polygons, criterion="rook", ids=None):
     -------
     tuple containing (neighbors, ids), where neighbors is a dictionary
     describing contiguity relations and ids is the list of ids used to index
-    that dictionary. 
+    that dictionary.
 
     NOTE: this is different from the prior behavior of buildContiguity, which
           returned an actual weights object. Since this just dispatches for the
           classes above, this returns the raw ingredients for a spatial weights
-          object, not the object itself. 
+          object, not the object itself.
     """
     if ids and len(ids) != len(set(ids)):
         raise ValueError(
@@ -621,7 +662,7 @@ def buildContiguity(polygons, criterion="rook", ids=None):
     This is a deprecated function.
 
     It builds a contiguity W from the polygons provided. As such, it is now
-    identical to calling the class constructors for Rook or Queen. 
+    identical to calling the class constructors for Rook or Queen.
     """
     # Warn('This function is deprecated. Please use the Rook or Queen classes',
     #        UserWarning)

From 7db7ece425e04386af4f44bed1bf3e1ba44df17c Mon Sep 17 00:00:00 2001
From: James Gaboardi <jgaboardi@gmail.com>
Date: Sun, 9 Oct 2022 12:13:30 -0400
Subject: [PATCH 118/162] ensure newHaven dataset is available

---
 libpysal/io/iohandlers/tests/test_db.py | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/libpysal/io/iohandlers/tests/test_db.py b/libpysal/io/iohandlers/tests/test_db.py
index 12213a527..3aba87869 100644
--- a/libpysal/io/iohandlers/tests/test_db.py
+++ b/libpysal/io/iohandlers/tests/test_db.py
@@ -35,7 +35,14 @@ def to_wkb_point(c):
 )
 class Test_sqlite_reader(ut.TestCase):
     def setUp(self):
-        df = pdio.read_files(pysal_examples.get_path("new_haven_merged.dbf"))
+        try:
+            df = pdio.read_files(pysal_examples.get_path("new_haven_merged.dbf"))
+        except TypeError as e:
+            if e.args[0] == "expected str, bytes or os.PathLike object, not NoneType":
+                pysal_examples.load_example("newHaven")
+                df = pdio.read_files(pysal_examples.get_path("new_haven_merged.dbf"))
+            else:
+                raise e
         df["GEOMETRY"] = df["geometry"].apply(to_wkb_point)
         # This is a hack to not have to worry about a custom point type in the DB
         del df["geometry"]

From 44a6b0ec6dcb83e700343d8e209d30f143dc6205 Mon Sep 17 00:00:00 2001
From: James Gaboardi <jgaboardi@gmail.com>
Date: Sun, 9 Oct 2022 12:14:06 -0400
Subject: [PATCH 119/162] numpy.int --> int

---
 libpysal/weights/spatial_lag.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/libpysal/weights/spatial_lag.py b/libpysal/weights/spatial_lag.py
index ceab17744..db8e9c0b5 100644
--- a/libpysal/weights/spatial_lag.py
+++ b/libpysal/weights/spatial_lag.py
@@ -164,7 +164,7 @@ def lag_categorical(w, y, ties="tryself"):
     y = y.flatten()
     output = np.zeros_like(y)
     labels = np.unique(y)
-    normalized_labels = np.zeros(y.shape, dtype=np.int)
+    normalized_labels = np.zeros(y.shape, dtype=int)
     for i, label in enumerate(labels):
         normalized_labels[y == label] = i
     for focal_name, neighbors in w:
@@ -197,7 +197,7 @@ def _resolve_ties(idx, normalized_labels, tally, neighbors, method, w):
     Parameters
     ----------
     idx                 : int
-                          index (aligned with `normalized_labels`) of the 
+                          index (aligned with `normalized_labels`) of the
                           current observation being resolved.
     normalized_labels   : (n,) array of ints
                           normalized array of labels for each observation
@@ -206,17 +206,17 @@ def _resolve_ties(idx, normalized_labels, tally, neighbors, method, w):
     neighbors           : dict of (neighbor_name : weight)
                           the elements of the weights object, identical to w[idx]
     method              : string
-                          configuration option to use a specific tiebreaking method. 
+                          configuration option to use a specific tiebreaking method.
                           supported options are:
                           1. tryself: Use the focal observation's label to tiebreak.
-                                      If this doesn't successfully break the tie, 
+                                      If this doesn't successfully break the tie,
                                       (which only occurs if it induces a new tie),
-                                      decide randomly. 
+                                      decide randomly.
                           2. random: Resolve the tie randomly amongst winners.
                           3. lowest: Pick the lowest-value label amongst winners.
                           4. highest: Pick the highest-value label amongst winners.
     w                   : pysal.W object
-                          a PySAL weights object aligned with normalized_labels. 
+                          a PySAL weights object aligned with normalized_labels.
 
     Returns
     -------

From 59db3091d46f24fb0df98605384192544141bca3 Mon Sep 17 00:00:00 2001
From: James Gaboardi <jgaboardi@gmail.com>
Date: Sun, 9 Oct 2022 12:18:44 -0400
Subject: [PATCH 120/162] test fill_diagonal() not insert_diagonal()

---
 libpysal/weights/tests/test_util.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/libpysal/weights/tests/test_util.py b/libpysal/weights/tests/test_util.py
index 1ecb92787..8ad631128 100644
--- a/libpysal/weights/tests/test_util.py
+++ b/libpysal/weights/tests/test_util.py
@@ -224,15 +224,15 @@ def test_WSP2W(self):
             {"37001": 1.0, "37033": 1.0, "37037": 1.0, "37063": 1.0, "37145": 1.0},
         )
 
-    def test_insert_diagonal(self):
-        w1 = util.insert_diagonal(self.w)
+    def test_fill_diagonal(self):
+        w1 = util.fill_diagonal(self.w)
         r1 = {0: 1.0, 1: 1.0, 4: 1.0, 101: 1.0, 85: 1.0, 5: 1.0}
         self.assertEqual(w1[0], r1)
-        w1 = util.insert_diagonal(self.w, 20)
+        w1 = util.fill_diagonal(self.w, 20)
         r1 = {0: 20, 1: 1.0, 4: 1.0, 101: 1.0, 85: 1.0, 5: 1.0}
         self.assertEqual(w1[0], r1)
         diag = np.arange(100, 100 + self.w.n)
-        w1 = util.insert_diagonal(self.w, diag)
+        w1 = util.fill_diagonal(self.w, diag)
         r1 = {0: 100, 1: 1.0, 4: 1.0, 101: 1.0, 85: 1.0, 5: 1.0}
         self.assertEqual(w1[0], r1)
 

From 8938e87c202f0529603261913b5960dadb747990 Mon Sep 17 00:00:00 2001
From: James Gaboardi <jgaboardi@gmail.com>
Date: Sun, 9 Oct 2022 12:19:17 -0400
Subject: [PATCH 121/162] use drop_islands keyword [1]

---
 libpysal/weights/tests/test_adjlist.py | 41 +++++++++++++++++---------
 1 file changed, 27 insertions(+), 14 deletions(-)

diff --git a/libpysal/weights/tests/test_adjlist.py b/libpysal/weights/tests/test_adjlist.py
index 9962679db..0b53b64d3 100644
--- a/libpysal/weights/tests/test_adjlist.py
+++ b/libpysal/weights/tests/test_adjlist.py
@@ -22,8 +22,19 @@ class Test_Adjlist(ut.TestCase):
     def setUp(self):
         self.knownW = io.open(examples.get_path("columbus.gal")).read()
 
-    def test_round_trip(self):
-        adjlist = self.knownW.to_adjlist(remove_symmetric=False).astype(int)
+    def test_round_trip_drop_islands_true(self):
+        adjlist = self.knownW.to_adjlist(
+            remove_symmetric=False, drop_islands=True
+        ).astype(int)
+        w_from_adj = weights.W.from_adjlist(adjlist)
+        np.testing.assert_allclose(
+            w_from_adj.sparse.toarray(), self.knownW.sparse.toarray()
+        )
+
+    def test_round_trip_drop_islands_false(self):
+        adjlist = self.knownW.to_adjlist(
+            remove_symmetric=False, drop_islands=True
+        ).astype(int)
         w_from_adj = weights.W.from_adjlist(adjlist)
         np.testing.assert_allclose(
             w_from_adj.sparse.toarray(), self.knownW.sparse.toarray()
@@ -31,34 +42,36 @@ def test_round_trip(self):
 
     def test_filter(self):
         grid = lat2W(2, 2)
-        alist = grid.to_adjlist(remove_symmetric=True)
+        alist = grid.to_adjlist(remove_symmetric=True, drop_islands=True)
         assert len(alist) == 4
         with self.assertRaises(AssertionError):
             # build this manually because of bug libpysal#322
-            alist_neighbors = alist.groupby('focal').neighbor.apply(list).to_dict()
-            all_ids = set(alist_neighbors.keys()).union(*map(set, alist_neighbors.values()))
+            alist_neighbors = alist.groupby("focal").neighbor.apply(list).to_dict()
+            all_ids = set(alist_neighbors.keys()).union(
+                *map(set, alist_neighbors.values())
+            )
             for idx in set(all_ids).difference(set(alist_neighbors.keys())):
                 alist_neighbors[idx] = []
             badgrid = weights.W(alist_neighbors)
-            np.testing.assert_allclose(badgrid.sparse.toarray(),
-                                       grid.sparse.toarray())
+            np.testing.assert_allclose(badgrid.sparse.toarray(), grid.sparse.toarray())
         assert set(alist.focal.unique()) == {0, 1, 2}
         assert set(alist.neighbor.unique()) == {1, 2, 3}
         assert alist.weight.unique().item() == 1
         grid = lat2W(2, 2, id_type="string")
-        alist = grid.to_adjlist(remove_symmetric=True)
+        alist = grid.to_adjlist(remove_symmetric=True, drop_islands=True)
         assert len(alist) == 4
         with self.assertRaises(AssertionError):
             # build this manually because of bug libpysal#322
-            alist_neighbors = alist.groupby('focal').neighbor.apply(list).to_dict()
-            all_ids = set(alist_neighbors.keys()).union(*map(set, alist_neighbors.values()))
+            alist_neighbors = alist.groupby("focal").neighbor.apply(list).to_dict()
+            all_ids = set(alist_neighbors.keys()).union(
+                *map(set, alist_neighbors.values())
+            )
             for idx in set(all_ids).difference(set(alist_neighbors.keys())):
                 alist_neighbors[idx] = []
             badgrid = weights.W(alist_neighbors)
-            np.testing.assert_allclose(badgrid.sparse.toarray(),
-                                       grid.sparse.toarray())
-        tuples = set([tuple(t) for t in alist[['focal','neighbor']].values])
-        full_alist = grid.to_adjlist()
+            np.testing.assert_allclose(badgrid.sparse.toarray(), grid.sparse.toarray())
+        tuples = set([tuple(t) for t in alist[["focal", "neighbor"]].values])
+        full_alist = grid.to_adjlist(drop_islands=True)
         all_possible = set([tuple(t) for t in full_alist[["focal", "neighbor"]].values])
         assert tuples.issubset(all_possible), (
             "the de-duped adjlist has links " "not in the duplicated adjlist."

From 114fcf50409908a4145457c3cf25704f96d1a101 Mon Sep 17 00:00:00 2001
From: James Gaboardi <jgaboardi@gmail.com>
Date: Sun, 9 Oct 2022 12:24:55 -0400
Subject: [PATCH 122/162] add disbable warning to coveragerc

---
 .coveragerc | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.coveragerc b/.coveragerc
index 5179611ae..cba539ca8 100644
--- a/.coveragerc
+++ b/.coveragerc
@@ -4,6 +4,7 @@ omit =
     */contrib/*
     */test/*
 include=libpysal/*
+disable_warnings=include-ignored
 [report]
 omit = 
     __init__.py 

From c957d8c6fe2de13a0a0f61119f0f661415de97ae Mon Sep 17 00:00:00 2001
From: James Gaboardi <jgaboardi@gmail.com>
Date: Sun, 9 Oct 2022 13:48:05 -0400
Subject: [PATCH 123/162] add drop_islands keyword to map/apply

---
 libpysal/weights/adjtools.py           | 76 ++++++++++++++++----------
 libpysal/weights/tests/test_adjlist.py | 12 +++-
 2 files changed, 57 insertions(+), 31 deletions(-)

diff --git a/libpysal/weights/adjtools.py b/libpysal/weights/adjtools.py
index 764bc36da..a7cfb516b 100644
--- a/libpysal/weights/adjtools.py
+++ b/libpysal/weights/adjtools.py
@@ -1,7 +1,9 @@
 import numpy as np
 
 
-def adjlist_apply(X, W=None, alist=None, func=np.subtract, skip_verify=False):
+def adjlist_apply(
+    X, W=None, alist=None, func=np.subtract, skip_verify=False, to_adjlist_kws=dict()
+):
     """
     apply a function to an adajcency list, getting an adjacency list and result.
 
@@ -10,40 +12,49 @@ def adjlist_apply(X, W=None, alist=None, func=np.subtract, skip_verify=False):
     X       :   iterable
                 an (N,P)-length iterable to apply ``func'' to. If (N,1), then `func`
                 must take 2 arguments and return a single reduction. If P>1, then
-                func must take two P-length arrays and return a single 
-                reduction of them. 
+                func must take two P-length arrays and return a single
+                reduction of them.
     W       :   pysal.weights.W object
                 a weights object that provides adjacency information
     alist   :   pandas DataFrame
                 a table containing an adajacency list representation of a W matrix
     func    :   callable
-                a function taking two arguments and returning a single argument. 
-                This will be evaluated for every (focal, neighbor) pair, or each 
+                a function taking two arguments and returning a single argument.
+                This will be evaluated for every (focal, neighbor) pair, or each
                 row of the adjacency list. If `X` has more than one column, this
-                function should take two arrays and provide a single scalar in 
+                function should take two arrays and provide a single scalar in
                 return.
                 Example scalars include: lambda x,y: x < y, np.subtract
                 Example multivariates: lambda (x,y): np.all(x < y)''
                                        lambda (x,y): np.sum((x-y)**2)
                                        sklearn.metrics.euclidean_distance
     skip_verify: bool
-                Whether or not to skip verifying that the W is the same as an adjacency list. 
+                Whether or not to skip verifying that the W is the same as an adjacency list.
                 Do this if you are certain the adjacency list and W agree and would like to
-                avoid re-instantiating a W from the adjacency list. 
+                avoid re-instantiating a W from the adjacency list.
+    to_adjlist_kws : dict
+        Keyword arguments for ``W.to_adjlist()``. Default is ``dict()``.
 
     Returns
     -------
-    an adjacency list (or modifies alist inplace) with the function applied to each row. 
+    an adjacency list (or modifies alist inplace) with the function applied to each row.
     """
     try:
         import pandas as pd
     except ImportError:
         raise ImportError("pandas must be installed to use this function")
-    W, alist = _get_W_and_alist(W, alist, skip_verify=skip_verify)
+    W, alist = _get_W_and_alist(
+        W, alist, skip_verify=skip_verify, to_adjlist_kws=to_adjlist_kws
+    )
     if len(X.shape) > 1:
         if X.shape[-1] > 1:
             return _adjlist_mvapply(
-                X, W=W, alist=alist, func=func, skip_verify=skip_verify
+                X,
+                W=W,
+                alist=alist,
+                func=func,
+                skip_verify=skip_verify,
+                to_adjlist_kws=to_adjlist_kws,
             )
     else:
         vec = np.asarray(X).flatten()
@@ -66,13 +77,17 @@ def adjlist_apply(X, W=None, alist=None, func=np.subtract, skip_verify=False):
     return alist_atts
 
 
-def _adjlist_mvapply(X, W=None, alist=None, func=None, skip_verify=False):
+def _adjlist_mvapply(
+    X, W=None, alist=None, func=None, skip_verify=False, to_adjlist_kws=dict()
+):
     try:
         import pandas as pd
     except ImportError:
         raise ImportError("pandas must be installed to use this function")
     assert len(X.shape) == 2, "data is not two-dimensional"
-    W, alist = _get_W_and_alist(W=W, alist=alist, skip_verify=skip_verify)
+    W, alist = _get_W_and_alist(
+        W=W, alist=alist, skip_verify=skip_verify, to_adjlist_kws=to_adjlist_kws
+    )
     assert X.shape[0] == W.n, "number of samples in X does not match W"
     try:
         names = X.columns.tolist()
@@ -105,7 +120,7 @@ def _adjlist_mvapply(X, W=None, alist=None, func=None, skip_verify=False):
     return alist_atts
 
 
-def _get_W_and_alist(W, alist, skip_verify=False):
+def _get_W_and_alist(W, alist, skip_verify=False, to_adjlist_kws=dict()):
     """
     Either:
     1. compute a W from an alist
@@ -115,11 +130,11 @@ def _get_W_and_alist(W, alist, skip_verify=False):
     If this completes successfully, the W/adjlist will both be returned and are checked for equality.
     """
     if (alist is None) and (W is not None):
-        alist = W.to_adjlist()
+        alist = W.to_adjlist(**to_adjlist_kws)
     elif (W is None) and (alist is not None):
         from .weights import W
 
-        W = W.from_adjlist(alist)
+        W = W.from_adjlist(alist, **to_adjlist_kws)
     elif (W is None) and (alist is None):
         raise ValueError("Either W or Adjacency List must be provided")
     elif (W is not None) and (alist is not None) and (not skip_verify):
@@ -138,6 +153,7 @@ def adjlist_map(
     alist=None,
     focal_col="focal",
     neighbor_col="neighbor",
+    to_adjlist_kws=dict(),
 ):
     """
     Map a set of functions over a W or adjacency list
@@ -145,28 +161,30 @@ def adjlist_map(
     Parameters
     ----------
     data        :   np.ndarray or pandas dataframe
-                    N x P array of N observations and P covariates. 
+                    N x P array of N observations and P covariates.
     funcs       :   iterable or callable
                     a function to apply to each of the P columns in ``data'', or a list of functions
                     to apply to each column of P. This function must take two arguments, compare them,
                     and return a value. Examples may be ``lambda x,y: x < y'' or ``np.subtract''.
     W           :   pysal.weights.W object
-                    a pysal weights object. If not provided, one is constructed from 
+                    a pysal weights object. If not provided, one is constructed from
                     the given adjacency list.
     alist       :   pandas dataframe
-                    an adjacency list representation of a weights matrix. If not provided, 
-                    one is constructed from the weights object. If both are provided, 
-                    they are validated against one another to ensure they provide identical weights 
-                    matrices. 
+                    an adjacency list representation of a weights matrix. If not provided,
+                    one is constructed from the weights object. If both are provided,
+                    they are validated against one another to ensure they provide identical weights
+                    matrices.
     focal_col   :   string
                     name of column in alist containing the focal observation ids
     neighbor_col:   string
                     name of column in alist containing the neighboring observation ids
+    to_adjlist_kws : dict
+        Keyword arguments for ``W.to_adjlist()``. Default is ``dict()``.
 
     Returns
     -------
     returns an adjacency list (or modifies one if provided) with each function applied to the column
-    of the data. 
+    of the data.
     """
     try:
         import pandas as pd
@@ -185,10 +203,12 @@ def adjlist_map(
     assert data.shape[1] == len(
         funcs
     ), "shape of data does not match the number of functions provided"
-    W, alist = _get_W_and_alist(W, alist)
+    W, alist = _get_W_and_alist(W, alist, to_adjlist_kws=to_adjlist_kws)
     fnames = set([f.__name__ for f in funcs])
     for i, (column, function) in enumerate(zip(data.T, funcs)):
-        alist = adjlist_apply(X=column, W=W, alist=alist, skip_verify=True)
+        alist = adjlist_apply(
+            X=column, W=W, alist=alist, skip_verify=True, to_adjlist_kws=to_adjlist_kws
+        )
         alist.drop(["att_focal", "att_neighbor"], axis=1, inplace=True)
         alist = alist.rename(
             columns={function.__name__: "_".join((function.__name__, names[i]))}
@@ -200,8 +220,8 @@ def adjlist_map(
 def filter_adjlist(adjlist, focal_col="focal", neighbor_col="neighbor"):
     """
     This dedupes an adjacency list by examining both (a,b) and (b,a) when (a,b) is enountered.
-    The removal is done in order of the iteration order of the input adjacency list. So, if a 
-    special order of removal is desired, you need to sort the list before this function. 
+    The removal is done in order of the iteration order of the input adjacency list. So, if a
+    special order of removal is desired, you need to sort the list before this function.
 
     Parameters
     ----------
@@ -214,7 +234,7 @@ def filter_adjlist(adjlist, focal_col="focal", neighbor_col="neighbor"):
 
     Returns
     -------
-    an adjacency table with reversible entries removed. 
+    an adjacency table with reversible entries removed.
     """
     edges = adjlist.loc[:, [focal_col, neighbor_col]]
     undirected = set()
diff --git a/libpysal/weights/tests/test_adjlist.py b/libpysal/weights/tests/test_adjlist.py
index 0b53b64d3..48e70607a 100644
--- a/libpysal/weights/tests/test_adjlist.py
+++ b/libpysal/weights/tests/test_adjlist.py
@@ -90,7 +90,7 @@ def apply_and_compare_columbus(self, col):
 
         df = geopandas.read_file(examples.get_path("columbus.dbf")).head()
         W = weights.Queen.from_dataframe(df)
-        alist = adj.adjlist_apply(df[col], W=W)
+        alist = adj.adjlist_apply(df[col], W=W, to_adjlist_kws=dict(drop_islands=True))
         right_hovals = alist.groupby("focal").att_focal.unique()
         assert (right_hovals == df[col]).all()
         allpairs = np.subtract.outer(df[col].values, df[col].values)
@@ -106,9 +106,15 @@ def test_mvapply(self):
 
         df = geopandas.read_file(examples.get_path("columbus.dbf")).head()
         W = weights.Queen.from_dataframe(df)
+
         ssq = lambda x_y: np.sum((x_y[0] - x_y[1]) ** 2).item()
         ssq.__name__ = "sum_of_squares"
-        alist = adj.adjlist_apply(df[["HOVAL", "CRIME", "INC"]], W=W, func=ssq)
+        alist = adj.adjlist_apply(
+            df[["HOVAL", "CRIME", "INC"]],
+            W=W,
+            func=ssq,
+            to_adjlist_kws=dict(drop_islands=True),
+        )
         known_ssq = [
             1301.1639302990804,
             3163.46450914361,
@@ -134,7 +140,7 @@ def test_map(self):
         df = geopandas.read_file(examples.get_path("columbus.dbf")).head()
         W = weights.Queen.from_dataframe(df)
         hoval, crime, inc = list(map(self.apply_and_compare_columbus, atts))
-        mapped = adj.adjlist_map(df[atts], W=W)
+        mapped = adj.adjlist_map(df[atts], W=W, to_adjlist_kws=dict(drop_islands=True))
         for name, data in zip(atts, (hoval, crime, inc)):
             np.testing.assert_allclose(
                 data, mapped["_".join(("subtract", name))].values

From b7dc42d322991185dff6ae2437b37a20ba63ffe3 Mon Sep 17 00:00:00 2001
From: James Gaboardi <jgaboardi@gmail.com>
Date: Sun, 9 Oct 2022 18:10:02 -0400
Subject: [PATCH 124/162] make drop_islands=False the default

---
 libpysal/weights/adjtools.py | 27 ++++++++++++++-------------
 1 file changed, 14 insertions(+), 13 deletions(-)

diff --git a/libpysal/weights/adjtools.py b/libpysal/weights/adjtools.py
index a7cfb516b..93b510865 100644
--- a/libpysal/weights/adjtools.py
+++ b/libpysal/weights/adjtools.py
@@ -2,7 +2,12 @@
 
 
 def adjlist_apply(
-    X, W=None, alist=None, func=np.subtract, skip_verify=False, to_adjlist_kws=dict()
+    X,
+    W=None,
+    alist=None,
+    func=np.subtract,
+    skip_verify=False,
+    to_adjlist_kws=dict(drop_islands=False),
 ):
     """
     apply a function to an adajcency list, getting an adjacency list and result.
@@ -33,7 +38,7 @@ def adjlist_apply(
                 Do this if you are certain the adjacency list and W agree and would like to
                 avoid re-instantiating a W from the adjacency list.
     to_adjlist_kws : dict
-        Keyword arguments for ``W.to_adjlist()``. Default is ``dict()``.
+        Keyword arguments for ``W.to_adjlist()``. Default is ``dict(drop_islands=False)``.
 
     Returns
     -------
@@ -43,9 +48,7 @@ def adjlist_apply(
         import pandas as pd
     except ImportError:
         raise ImportError("pandas must be installed to use this function")
-    W, alist = _get_W_and_alist(
-        W, alist, skip_verify=skip_verify, to_adjlist_kws=to_adjlist_kws
-    )
+    W, alist = _get_W_and_alist(W, alist, to_adjlist_kws, skip_verify=skip_verify)
     if len(X.shape) > 1:
         if X.shape[-1] > 1:
             return _adjlist_mvapply(
@@ -85,9 +88,7 @@ def _adjlist_mvapply(
     except ImportError:
         raise ImportError("pandas must be installed to use this function")
     assert len(X.shape) == 2, "data is not two-dimensional"
-    W, alist = _get_W_and_alist(
-        W=W, alist=alist, skip_verify=skip_verify, to_adjlist_kws=to_adjlist_kws
-    )
+    W, alist = _get_W_and_alist(W, alist, to_adjlist_kws, skip_verify=skip_verify)
     assert X.shape[0] == W.n, "number of samples in X does not match W"
     try:
         names = X.columns.tolist()
@@ -120,7 +121,7 @@ def _adjlist_mvapply(
     return alist_atts
 
 
-def _get_W_and_alist(W, alist, skip_verify=False, to_adjlist_kws=dict()):
+def _get_W_and_alist(W, alist, to_adjlist_kws, skip_verify=False):
     """
     Either:
     1. compute a W from an alist
@@ -153,7 +154,7 @@ def adjlist_map(
     alist=None,
     focal_col="focal",
     neighbor_col="neighbor",
-    to_adjlist_kws=dict(),
+    to_adjlist_kws=dict(drop_islands=False),
 ):
     """
     Map a set of functions over a W or adjacency list
@@ -179,7 +180,7 @@ def adjlist_map(
     neighbor_col:   string
                     name of column in alist containing the neighboring observation ids
     to_adjlist_kws : dict
-        Keyword arguments for ``W.to_adjlist()``. Default is ``dict()``.
+        Keyword arguments for ``W.to_adjlist()``. Default is ``dict(drop_islands=False)``.
 
     Returns
     -------
@@ -203,11 +204,11 @@ def adjlist_map(
     assert data.shape[1] == len(
         funcs
     ), "shape of data does not match the number of functions provided"
-    W, alist = _get_W_and_alist(W, alist, to_adjlist_kws=to_adjlist_kws)
+    W, alist = _get_W_and_alist(W, alist, to_adjlist_kws)
     fnames = set([f.__name__ for f in funcs])
     for i, (column, function) in enumerate(zip(data.T, funcs)):
         alist = adjlist_apply(
-            X=column, W=W, alist=alist, skip_verify=True, to_adjlist_kws=to_adjlist_kws
+            column, W=W, alist=alist, skip_verify=True, to_adjlist_kws=to_adjlist_kws
         )
         alist.drop(["att_focal", "att_neighbor"], axis=1, inplace=True)
         alist = alist.rename(

From c1fabda7864d9a235a05c2219b9d016e4550d3e4 Mon Sep 17 00:00:00 2001
From: James Gaboardi <jgaboardi@gmail.com>
Date: Tue, 11 Oct 2022 08:13:04 -0400
Subject: [PATCH 125/162] streamline test_db path handle

---
 libpysal/io/iohandlers/tests/test_db.py | 13 +++++--------
 1 file changed, 5 insertions(+), 8 deletions(-)

diff --git a/libpysal/io/iohandlers/tests/test_db.py b/libpysal/io/iohandlers/tests/test_db.py
index 3aba87869..cdb219d57 100644
--- a/libpysal/io/iohandlers/tests/test_db.py
+++ b/libpysal/io/iohandlers/tests/test_db.py
@@ -35,14 +35,11 @@ def to_wkb_point(c):
 )
 class Test_sqlite_reader(ut.TestCase):
     def setUp(self):
-        try:
-            df = pdio.read_files(pysal_examples.get_path("new_haven_merged.dbf"))
-        except TypeError as e:
-            if e.args[0] == "expected str, bytes or os.PathLike object, not NoneType":
-                pysal_examples.load_example("newHaven")
-                df = pdio.read_files(pysal_examples.get_path("new_haven_merged.dbf"))
-            else:
-                raise e
+        path = pysal_examples.get_path("new_haven_merged.dbf")
+        if path is None:
+            pysal_examples.load_example("newHaven")
+            path = pysal_examples.get_path("new_haven_merged.dbf")
+        df = pdio.read_files(path)
         df["GEOMETRY"] = df["geometry"].apply(to_wkb_point)
         # This is a hack to not have to worry about a custom point type in the DB
         del df["geometry"]

From a2e8ca2a661b7cdc3bc9bc07f5ad534c40e38533 Mon Sep 17 00:00:00 2001
From: James Gaboardi <jgaboardi@gmail.com>
Date: Tue, 11 Oct 2022 08:47:07 -0400
Subject: [PATCH 126/162] make default drop_islands=None in signiture

---
 libpysal/weights/adjtools.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/libpysal/weights/adjtools.py b/libpysal/weights/adjtools.py
index 93b510865..1f5e07f63 100644
--- a/libpysal/weights/adjtools.py
+++ b/libpysal/weights/adjtools.py
@@ -7,7 +7,7 @@ def adjlist_apply(
     alist=None,
     func=np.subtract,
     skip_verify=False,
-    to_adjlist_kws=dict(drop_islands=False),
+    to_adjlist_kws=dict(drop_islands=None),
 ):
     """
     apply a function to an adajcency list, getting an adjacency list and result.
@@ -38,7 +38,7 @@ def adjlist_apply(
                 Do this if you are certain the adjacency list and W agree and would like to
                 avoid re-instantiating a W from the adjacency list.
     to_adjlist_kws : dict
-        Keyword arguments for ``W.to_adjlist()``. Default is ``dict(drop_islands=False)``.
+        Keyword arguments for ``W.to_adjlist()``. Default is ``dict(drop_islands=None)``.
 
     Returns
     -------
@@ -154,7 +154,7 @@ def adjlist_map(
     alist=None,
     focal_col="focal",
     neighbor_col="neighbor",
-    to_adjlist_kws=dict(drop_islands=False),
+    to_adjlist_kws=dict(drop_islands=None),
 ):
     """
     Map a set of functions over a W or adjacency list
@@ -180,7 +180,7 @@ def adjlist_map(
     neighbor_col:   string
                     name of column in alist containing the neighboring observation ids
     to_adjlist_kws : dict
-        Keyword arguments for ``W.to_adjlist()``. Default is ``dict(drop_islands=False)``.
+        Keyword arguments for ``W.to_adjlist()``. Default is ``dict(drop_islands=None)``.
 
     Returns
     -------

From a7b914235679a7f22ccd145f63865de1d722e5ac Mon Sep 17 00:00:00 2001
From: Serge Rey <sjsrey@gmail.com>
Date: Mon, 24 Oct 2022 21:09:26 -0700
Subject: [PATCH 127/162] ENH: move to platformdirs from appdirs (WIP)

---
 libpysal/examples/base.py                 |  2 +-
 libpysal/examples/tests/test_available.py | 22 ++++++++++++++++++++--
 requirements.txt                          |  2 +-
 3 files changed, 22 insertions(+), 4 deletions(-)

diff --git a/libpysal/examples/base.py b/libpysal/examples/base.py
index 771ec9017..aa20f7a5f 100644
--- a/libpysal/examples/base.py
+++ b/libpysal/examples/base.py
@@ -10,7 +10,7 @@
 import webbrowser
 from os import environ, makedirs
 from os.path import exists, expanduser, join
-from appdirs import user_data_dir
+from platformdirs import user_data_dir
 import zipfile
 import requests
 import pandas
diff --git a/libpysal/examples/tests/test_available.py b/libpysal/examples/tests/test_available.py
index a67708e30..3471c7329 100644
--- a/libpysal/examples/tests/test_available.py
+++ b/libpysal/examples/tests/test_available.py
@@ -1,20 +1,38 @@
 #!/usr/bin/env python3
 
 import os
+import platform
 import unittest
 import numpy as np
 import pandas
 
 from .. import available
+from ..base import get_data_home
 
-class Testavailable(unittest.TestCase):
+os_name = platform.system()
+
+class Testexamples(unittest.TestCase):
 
     def test_available(self):
         examples = available()
         self.assertEqual(type(examples), pandas.core.frame.DataFrame)
 
+    def test_data_home(self): 
+        pth = get_data_home()
+        head, tail = os.path.split(pth)
+        self.assertEqual(tail, 'pysal')
+        if os_name == 'Linux':
+            heads = head.split("/")
+            self.assertEqual(heads[1], 'home')
+            self.assertEqual(heads[-1], 'share')
+            self.assertEqual(heads[-2], '.local')
+        elif os_name == 'Darwin':
+            heads = head.split("/")
+            self.assertEqual(heads[1], 'Users')
+            self.assertEqual(heads[-1], 'Application Support')
+            self.assertEqual(heads[-2], 'Library')
 
-suite = unittest.TestLoader().loadTestsFromTestCase(Testavailable)
+suite = unittest.TestLoader().loadTestsFromTestCase(Testexamples)
 
 if __name__ == "__main__":
     runner = unittest.TextTestRunner()
diff --git a/requirements.txt b/requirements.txt
index f149a6c29..e07a6ecd4 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,4 @@
-appdirs
+platformdirs
 beautifulsoup4
 jinja2
 numpy>=1.3

From 6cfd3532d048d2c41f9b1b6e45174c288e735ab2 Mon Sep 17 00:00:00 2001
From: Serge Rey <sjsrey@gmail.com>
Date: Wed, 26 Oct 2022 19:53:27 -0700
Subject: [PATCH 128/162] ENH: add tests for Windows

---
 libpysal/examples/tests/test_available.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/libpysal/examples/tests/test_available.py b/libpysal/examples/tests/test_available.py
index 3471c7329..902fba034 100644
--- a/libpysal/examples/tests/test_available.py
+++ b/libpysal/examples/tests/test_available.py
@@ -31,6 +31,11 @@ def test_data_home(self):
             self.assertEqual(heads[1], 'Users')
             self.assertEqual(heads[-1], 'Application Support')
             self.assertEqual(heads[-2], 'Library')
+        elif os_name == 'Windows':
+            heads = head.split("\\")
+            self.assertEqual(heads[1], 'Users')
+            self.assertEqual(heads[-1], 'Local')
+            self.assertEqual(heads[-2], 'AppData')
 
 suite = unittest.TestLoader().loadTestsFromTestCase(Testexamples)
 

From d7896e009d9f8d89fab7c587403de7b8914a2703 Mon Sep 17 00:00:00 2001
From: Serge Rey <sjsrey@gmail.com>
Date: Wed, 26 Oct 2022 20:02:44 -0700
Subject: [PATCH 129/162] ENH: update ci for platformdirs swap

---
 ci/310.yaml        | 2 +-
 ci/37-minimal.yaml | 2 +-
 ci/37.yaml         | 2 +-
 ci/38.yaml         | 2 +-
 ci/39.yaml         | 2 +-
 5 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/ci/310.yaml b/ci/310.yaml
index 3a1982de0..213025ff1 100644
--- a/ci/310.yaml
+++ b/ci/310.yaml
@@ -3,7 +3,7 @@ channels:
   - conda-forge
 dependencies:
   - python=3.10
-  - appdirs
+  - platformdirs
   - beautifulsoup4
   - jinja2
   - pandas>=1.0
diff --git a/ci/37-minimal.yaml b/ci/37-minimal.yaml
index 1c13f5268..e8ff14647 100644
--- a/ci/37-minimal.yaml
+++ b/ci/37-minimal.yaml
@@ -3,7 +3,7 @@ channels:
   - conda-forge
 dependencies:
   - python=3.7
-  - appdirs
+  - platformdirs
   - beautifulsoup4
   - jinja2
   - pandas>=1.0
diff --git a/ci/37.yaml b/ci/37.yaml
index 297fbd81a..dfa28134d 100644
--- a/ci/37.yaml
+++ b/ci/37.yaml
@@ -3,7 +3,7 @@ channels:
   - conda-forge
 dependencies:
   - python=3.7
-  - appdirs
+  - platformdirs
   - beautifulsoup4
   - jinja2
   - pandas>=1.0
diff --git a/ci/38.yaml b/ci/38.yaml
index 5023f9adc..be0fdb73b 100644
--- a/ci/38.yaml
+++ b/ci/38.yaml
@@ -3,7 +3,7 @@ channels:
   - conda-forge
 dependencies:
   - python=3.8
-  - appdirs
+  - platformdirs
   - beautifulsoup4
   - jinja2
   - pandas>=1.0
diff --git a/ci/39.yaml b/ci/39.yaml
index aad680e4f..02df18e0e 100644
--- a/ci/39.yaml
+++ b/ci/39.yaml
@@ -3,7 +3,7 @@ channels:
   - conda-forge
 dependencies:
   - python=3.9
-  - appdirs
+  - platformdirs
   - beautifulsoup4
   - jinja2
   - pandas>=1.0

From 3ebd86fae228d58249d5891ae9bf9f4465302312 Mon Sep 17 00:00:00 2001
From: Serge Rey <sjsrey@gmail.com>
Date: Wed, 26 Oct 2022 20:16:38 -0700
Subject: [PATCH 130/162] ENH: windows ci path splitting (blind)

---
 libpysal/examples/tests/test_available.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/libpysal/examples/tests/test_available.py b/libpysal/examples/tests/test_available.py
index 902fba034..1592c39c8 100644
--- a/libpysal/examples/tests/test_available.py
+++ b/libpysal/examples/tests/test_available.py
@@ -34,8 +34,8 @@ def test_data_home(self):
         elif os_name == 'Windows':
             heads = head.split("\\")
             self.assertEqual(heads[1], 'Users')
-            self.assertEqual(heads[-1], 'Local')
-            self.assertEqual(heads[-2], 'AppData')
+            self.assertEqual(heads[-2], 'Local')
+            self.assertEqual(heads[-3], 'AppData')
 
 suite = unittest.TestLoader().loadTestsFromTestCase(Testexamples)
 

From 5542c0fefa533c750ef71804ade87d5d8f5ae44a Mon Sep 17 00:00:00 2001
From: James Gaboardi <jgaboardi@gmail.com>
Date: Thu, 27 Oct 2022 17:43:49 -0400
Subject: [PATCH 131/162] test against shapely prerelease

---
 .github/workflows/unittests.yml |  3 ++-
 ci/310_shapely20b1.yaml         | 33 +++++++++++++++++++++++++++++++++
 2 files changed, 35 insertions(+), 1 deletion(-)
 create mode 100644 ci/310_shapely20b1.yaml

diff --git a/.github/workflows/unittests.yml b/.github/workflows/unittests.yml
index 2b705d013..7fcce33ec 100644
--- a/.github/workflows/unittests.yml
+++ b/.github/workflows/unittests.yml
@@ -19,7 +19,7 @@
      strategy:
        matrix:
          os: ['ubuntu-latest']
-         environment-file: [ci/37-minimal.yaml, ci/37.yaml, ci/38.yaml, ci/39.yaml, ci/310.yaml]
+         environment-file: [ci/37-minimal.yaml, ci/37.yaml, ci/38.yaml, ci/39.yaml, ci/310.yaml, ci/310_shapely20b1.yaml]
          include:
            - environment-file: ci/310.yaml
              os: macos-latest
@@ -35,6 +35,7 @@
          with:
            environment-file: ${{ matrix.environment-file }}
            micromamba-version: 'latest'
+           channel-priority: 'flexible'
 
        - name: run tests - bash
          shell: bash -l {0}
diff --git a/ci/310_shapely20b1.yaml b/ci/310_shapely20b1.yaml
new file mode 100644
index 000000000..a8ba2d9f9
--- /dev/null
+++ b/ci/310_shapely20b1.yaml
@@ -0,0 +1,33 @@
+name: test
+channels:
+  - conda-forge
+  - conda-forge/label/shapely_dev
+dependencies:
+  - python=3.10
+  - platformdirs
+  - beautifulsoup4
+  - jinja2
+  - pandas>=1.0
+  - scipy>=1.0
+  - xarray
+  # testing
+  - codecov
+  - matplotlib
+  - pytest
+  - pytest-cov
+  - pytest-xdist
+  # optional
+  - geopandas>=0.12.0
+  - joblib
+  - networkx
+  - numba
+  - packaging
+  - shapely==2.0b1
+  - xarray
+  - zstd
+  # for docs build action (this env only)
+  - nbsphinx
+  - numpydoc
+  - sphinx
+  - sphinxcontrib-bibtex
+  - sphinx_bootstrap_theme

From 936d5de865e7c24a5eeca39003bb6a4642b8f605 Mon Sep 17 00:00:00 2001
From: Martin Fleischmann <martin@martinfleischmann.net>
Date: Fri, 28 Oct 2022 20:37:35 +0200
Subject: [PATCH 132/162] contiguity

---
 libpysal/weights/contiguity.py | 201 ++++++++++++++++++++++-----------
 1 file changed, 132 insertions(+), 69 deletions(-)

diff --git a/libpysal/weights/contiguity.py b/libpysal/weights/contiguity.py
index 11fe8a708..f47caa6d8 100644
--- a/libpysal/weights/contiguity.py
+++ b/libpysal/weights/contiguity.py
@@ -1,4 +1,6 @@
 import itertools
+from types import NoneType
+import warnings
 
 import numpy
 
@@ -133,10 +135,17 @@ def from_iterable(cls, iterable, sparse=False, **kwargs):
 
     @classmethod
     def from_dataframe(
-        cls, df, geom_col=None, idVariable=None, ids=None, id_order=None, **kwargs
+        cls,
+        df,
+        geom_col=None,
+        idVariable=None,
+        ids=None,
+        id_order=None,
+        use_index=None,
+        **kwargs,
     ):
         """
-        Construct a weights object from a pandas dataframe with a geometry
+        Construct a weights object from a (geo)pandas dataframe with a geometry
         column. This will cast the polygons to PySAL polygons, then build the W
         using ids from the dataframe.
 
@@ -149,16 +158,24 @@ def from_dataframe(
                       the name of the column in `df` that contains the
                       geometries. Defaults to active geometry column.
         idVariable  : string
+                      DEPRECATED - use `ids` instead.
                       the name of the column to use as IDs. If nothing is
                       provided, the dataframe index is used
-        ids         : list
-                      a list of ids to use to index the spatial weights object.
-                      Order is not respected from this list.
+        ids         : list-like, string
+                      a list-like of ids to use to index the spatial weights object or
+                      the name of the column to use as IDs. If nothing is
+                      provided, the dataframe index is used if `use_index=True` or
+                      a positional index is used if `use_index=False`.
+                      Order of the resulting W is not respected from this list.
         id_order    : list
-                      an ordered list of ids to use to index the spatial weights
+                      DEPRECATED - argument is deprecated and will be removed.
+                      An ordered list of ids to use to index the spatial weights
                       object. If used, the resulting weights object will iterate
                       over results in the order of the names provided in this
                       argument.
+        use_index   : bool
+                      use index of `df` as `ids` to index the spatial weights object.
+                      Defaults to False but in future will default to True.
 
         See Also
         --------
@@ -167,61 +184,58 @@ def from_dataframe(
         """
         if geom_col is None:
             geom_col = df.geometry.name
+
         if id_order is not None:
+            warnings.warn(
+                "`id_order` is deprecated and will be removed in future.",
+                FutureWarning,
+                stacklevel=2,
+            )
             if id_order is True and ((idVariable is not None) or (ids is not None)):
                 # if idVariable is None, we want ids. Otherwise, we want the
                 # idVariable column
                 id_order = list(df.get(idVariable, ids))
             else:
                 id_order = df.get(id_order, ids)
-        elif idVariable is not None:
-            ids = df.get(idVariable).tolist()
-        elif isinstance(ids, str):
-            ids = df.get(ids).tolist()
-        return cls.from_iterable(
-            df[geom_col].tolist(), ids=ids, id_order=id_order, **kwargs
-        )
 
-    @classmethod
-    def from_dataframe_new(cls, df, geom_col=None, ids=None, **kwargs):
-        """
-        Construct a weights object from a (geo)pandas (Geo)DataFrame
-        with a geometry column.
-
-        Parameters
-        ----------
-        df          : DataFrame
-                        a :class: `pandas.DataFrame` containing geometries to use
-                        for spatial weights
-        geom_col    : string
-                        the name of the column in `df` that contains the
-                        geometries. Defaults to active geometry column.
-        ids         : str, list, np.array, pd.Series (default None)
-                        A definition of ids to use to index the spatial weights object.
-                        Could be the name of the dataframe column, `list`, `numpy.array`,
-                        or `pandas.Series`. If `list`, `numpy.array` or
-                        `pandas.Series` are used then it must have same length as dataframe.
-
-        **kwargs    : dict
-                        Additional arguments to be passed on to the W constructor
-
-
-        See Also
-        --------
-        :class:`libpysal.weights.weights.W`
-        :class:`libpysal.weights.contiguity.Rook`
-        """
-
-        if geom_col is None:
-            geom_col = df.geometry.name
+        if idVariable is not None:
+            if ids is None:
+                warnings.warn(
+                    "`idVariable` is deprecated and will be removed in future. "
+                    "Use `ids` instead.",
+                    FutureWarning,
+                    stacklevel=2,
+                )
+                ids = idVariable
+            else:
+                warnings.warn(
+                    "Both `idVariable` and `ids` passed, using `ids`.",
+                    UserWarning,
+                    stacklevel=2,
+                )
+
+        if ids is None:
+            if use_index is None:
+                warnings.warn(
+                    "`use_index` defaults to False but will default to True in future. "
+                    "Set True/False directly to control this behavior and silence this "
+                    "warning",
+                    FutureWarning,
+                    stacklevel=2,
+                )
+                use_index = False
+            if use_index:
+                ids = df.index.tolist()
 
         if isinstance(ids, str):
             ids = df[ids]
 
-        if not isinstance(ids, list):
+        if not isinstance(ids, (list, NoneType)):
             ids = ids.tolist()
 
-        return cls.from_iterable(df[geom_col].tolist(), ids=ids, **kwargs)
+        return cls.from_iterable(
+            df[geom_col].tolist(), ids=ids, id_order=id_order, **kwargs
+        )
 
     @classmethod
     def from_xarray(
@@ -399,9 +413,18 @@ def from_iterable(cls, iterable, sparse=False, **kwargs):
         return w
 
     @classmethod
-    def from_dataframe(cls, df, geom_col=None, **kwargs):
+    def from_dataframe(
+        cls,
+        df,
+        geom_col=None,
+        idVariable=None,
+        ids=None,
+        id_order=None,
+        use_index=None,
+        **kwargs,
+    ):
         """
-        Construct a weights object from a pandas dataframe with a geometry
+        Construct a weights object from a (geo)pandas dataframe with a geometry
         column. This will cast the polygons to PySAL polygons, then build the W
         using ids from the dataframe.
 
@@ -412,46 +435,86 @@ def from_dataframe(cls, df, geom_col=None, **kwargs):
                       for spatial weights
         geom_col    : string
                       the name of the column in `df` that contains the
-                      geometries. Defaults to active geometry column
+                      geometries. Defaults to active geometry column.
         idVariable  : string
+                      DEPRECATED - use `ids` instead.
                       the name of the column to use as IDs. If nothing is
                       provided, the dataframe index is used
-        ids         : list
-                      a list of ids to use to index the spatial weights object.
-                      Order is not respected from this list.
+        ids         : list-like, string
+                      a list-like of ids to use to index the spatial weights object or
+                      the name of the column to use as IDs. If nothing is
+                      provided, the dataframe index is used if `use_index=True` or
+                      a positional index is used if `use_index=False`.
+                      Order of the resulting W is not respected from this list.
         id_order    : list
-                      an ordered list of ids to use to index the spatial weights
+                      DEPRECATED - argument is deprecated and will be removed.
+                      An ordered list of ids to use to index the spatial weights
                       object. If used, the resulting weights object will iterate
                       over results in the order of the names provided in this
                       argument.
+        use_index   : bool
+                      use index of `df` as `ids` to index the spatial weights object.
+                      Defaults to False but in future will default to True.
 
         See Also
         --------
         :class:`libpysal.weights.weights.W`
-        :class:`libpysal.weights.contiguity.Queen`
+        :class:`libpysal.weights.contiguity.Rook`
         """
-        idVariable = kwargs.pop("idVariable", None)
-        ids = kwargs.pop("ids", None)
-        id_order = kwargs.pop("id_order", None)
         if geom_col is None:
             geom_col = df.geometry.name
+
         if id_order is not None:
+            warnings.warn(
+                "`id_order` is deprecated and will be removed in future.",
+                FutureWarning,
+                stacklevel=2,
+            )
             if id_order is True and ((idVariable is not None) or (ids is not None)):
                 # if idVariable is None, we want ids. Otherwise, we want the
                 # idVariable column
-                ids = list(df.get(idVariable, ids))
-                id_order = ids
-            elif isinstance(id_order, str):
-                ids = df.get(id_order, ids)
-                id_order = ids
-        elif idVariable is not None:
-            ids = df.get(idVariable).tolist()
-        elif isinstance(ids, str):
-            ids = df.get(ids).tolist()
-        w = cls.from_iterable(
+                id_order = list(df.get(idVariable, ids))
+            else:
+                id_order = df.get(id_order, ids)
+
+        if idVariable is not None:
+            if ids is None:
+                warnings.warn(
+                    "`idVariable` is deprecated and will be removed in future. "
+                    "Use `ids` instead.",
+                    FutureWarning,
+                    stacklevel=2,
+                )
+                ids = idVariable
+            else:
+                warnings.warn(
+                    "Both `idVariable` and `ids` passed, using `ids`.",
+                    UserWarning,
+                    stacklevel=2,
+                )
+
+        if ids is None:
+            if use_index is None:
+                warnings.warn(
+                    "`use_index` defaults to False but will default to True in future. "
+                    "Set True/False directly to control this behavior and silence this "
+                    "warning",
+                    FutureWarning,
+                    stacklevel=2,
+                )
+                use_index = False
+            if use_index:
+                ids = df.index.tolist()
+
+        if isinstance(ids, str):
+            ids = df[ids]
+
+        if not isinstance(ids, (list, NoneType)):
+            ids = ids.tolist()
+
+        return cls.from_iterable(
             df[geom_col].tolist(), ids=ids, id_order=id_order, **kwargs
         )
-        return w
 
     @classmethod
     def from_xarray(

From ba6eea3b170d981cde73ca16aa2cb533d56a7a54 Mon Sep 17 00:00:00 2001
From: Martin Fleischmann <martin@martinfleischmann.net>
Date: Fri, 28 Oct 2022 20:42:03 +0200
Subject: [PATCH 133/162] rm idVariable from test

---
 libpysal/weights/tests/test_contiguity.py | 21 ++++++++++++---------
 1 file changed, 12 insertions(+), 9 deletions(-)

diff --git a/libpysal/weights/tests/test_contiguity.py b/libpysal/weights/tests/test_contiguity.py
index 06a30414d..b6d9e12db 100644
--- a/libpysal/weights/tests/test_contiguity.py
+++ b/libpysal/weights/tests/test_contiguity.py
@@ -21,6 +21,7 @@
 
 try:
     import shapely
+
     HAS_SHAPELY = True
 except ImportError:
     HAS_SHAPELY = False
@@ -97,7 +98,6 @@ def test_from_array(self):
         # test named, sparse from point array
         pass
 
-
     @ut.skipIf(PANDAS_EXTINCT, "Missing pandas")
     def test_from_dataframe(self):
         # basic
@@ -110,7 +110,6 @@ def test_from_dataframe(self):
         w = self.cls.from_dataframe(df, geom_col="the_geom")
         self.assertEqual(w[self.known_wi], self.known_w)
 
-
     @ut.skipIf(GEOPANDAS_EXTINCT, "Missing geopandas")
     def test_from_geodataframe(self):
         df = pdio.read_files(self.polygon_path)
@@ -121,7 +120,7 @@ def test_from_geodataframe(self):
         self.assertEqual(w[self.known_wi], self.known_w)
 
         # named geometry + named obs
-        w = self.cls.from_dataframe(df, geom_col="the_geom", idVariable=self.idVariable)
+        w = self.cls.from_dataframe(df, geom_col="the_geom", ids=self.idVariable)
         self.assertEqual(w[self.known_name], self.known_namedw)
 
     def test_from_xarray(self):
@@ -129,7 +128,7 @@ def test_from_xarray(self):
         self.assertEqual(w[self.known_wi_da], self.known_w_da)
         ws = self.cls.from_xarray(self.da)
         srowvec = ws.sparse[self.known_wspi_da].todense().tolist()[0]
-        this_w = {i:k for i,k in enumerate(srowvec) if k>0}
+        this_w = {i: k for i, k in enumerate(srowvec) if k > 0}
         self.assertEqual(this_w, self.known_wsp_da)
 
 
@@ -151,7 +150,7 @@ def setUp(self):
         self.cls = c.Queen
         self.idVariable = "POLYID"
         self.known_name = 5
-        self.known_namedw = {k+1:v for k,v in list(self.known_w.items())}
+        self.known_namedw = {k + 1: v for k, v in list(self.known_w.items())}
         self.known_wspi_da = 1
         self.known_wsp_da = {0: 1, 2: 1, 4: 1, 5: 1, 6: 1}
         self.known_wi_da = (1, -30.0, -60.0)
@@ -163,8 +162,8 @@ def setUp(self):
             (1, -30.0, 60.0): 1,
             (1, 30.0, -180.0): 1,
             (1, 30.0, -60.0): 1,
-            (1, 30.0, 60.0): 1
-            }
+            (1, 30.0, 60.0): 1,
+        }
 
     @ut.skipIf(GEOPANDAS_EXTINCT, "Missing Geopandas")
     def test_linestrings(self):
@@ -197,11 +196,15 @@ def setUp(self):
         self.cls = c.Rook
         self.idVariable = "POLYID"
         self.known_name = 5
-        self.known_namedw = {k+1:v for k,v in list(self.known_w.items())}
+        self.known_namedw = {k + 1: v for k, v in list(self.known_w.items())}
         self.known_wspi_da = 1
         self.known_wsp_da = {0: 1, 2: 1, 5: 1}
         self.known_wi_da = (1, -30.0, -180.0)
-        self.known_w_da = {(1, 30.0, -180.0): 1, (1, -30.0, -60.0): 1, (1, -90.0, -180.0): 1}
+        self.known_w_da = {
+            (1, 30.0, -180.0): 1,
+            (1, -30.0, -60.0): 1,
+            (1, -90.0, -180.0): 1,
+        }
 
 
 class Test_Voronoi(ut.TestCase):

From 2e2ef649ac70666266003238c2322381a2efbde9 Mon Sep 17 00:00:00 2001
From: Martin Fleischmann <martin@martinfleischmann.net>
Date: Fri, 28 Oct 2022 20:50:16 +0200
Subject: [PATCH 134/162] distance

---
 libpysal/weights/distance.py | 70 ++++++++++++++++++++++--------------
 1 file changed, 43 insertions(+), 27 deletions(-)

diff --git a/libpysal/weights/distance.py b/libpysal/weights/distance.py
index 652a5f545..aea98dcf2 100644
--- a/libpysal/weights/distance.py
+++ b/libpysal/weights/distance.py
@@ -83,20 +83,20 @@ class KNN(W):
     Notes
     -----
 
-    Ties between neighbors of equal distance are arbitrarily broken. 
+    Ties between neighbors of equal distance are arbitrarily broken.
 
-    Further, if many points occupy the same spatial location (i.e. observations are 
-    coincident), then you may need to increase k for those observations to 
+    Further, if many points occupy the same spatial location (i.e. observations are
+    coincident), then you may need to increase k for those observations to
     acquire neighbors at different spatial locations. For example, if five
     points are coincident, then their four nearest neighbors will all
     occupy the same spatial location; only the fifth nearest neighbor will
     result in those coincident points becoming connected to the graph as a
-    whole. 
+    whole.
 
     Solutions to this problem include jittering the points (by adding
-    a small random value to each observation's location) or by adding 
+    a small random value to each observation's location) or by adding
     higher-k neighbors only to the coincident points, using the
-    weights.w_sets.w_union() function. 
+    weights.w_sets.w_union() function.
 
     See Also
     --------
@@ -271,7 +271,9 @@ def from_array(cls, array, *args, **kwargs):
         return cls(array, *args, **kwargs)
 
     @classmethod
-    def from_dataframe(cls, df, geom_col=None, ids=None, *args, **kwargs):
+    def from_dataframe(
+        cls, df, geom_col=None, ids=None, use_index=True, *args, **kwargs
+    ):
         """
         Make KNN weights from a dataframe.
 
@@ -283,10 +285,14 @@ def from_dataframe(cls, df, geom_col=None, ids=None, *args, **kwargs):
         geom_col :  string
                     the name of the column in `df` that contains the
                     geometries. Defaults to active geometry column.
-        ids     :   string or iterable
-                    if string, the column name of the indices from the dataframe
-                    if iterable, a list of ids to use for the W
-                    if None, df.index is used.
+        ids     :   list-like, string
+                    a list-like of ids to use to index the spatial weights object or
+                    the name of the column to use as IDs. If nothing is
+                    provided, the dataframe index is used if `use_index=True` or
+                    a positional index is used if `use_index=False`.
+                    Order of the resulting W is not respected from this list.
+        use_index   : bool
+                    use index of `df` as `ids` to index the spatial weights object.
 
         See Also
         --------
@@ -295,7 +301,7 @@ def from_dataframe(cls, df, geom_col=None, ids=None, *args, **kwargs):
         if geom_col is None:
             geom_col = df.geometry.name
         pts = get_points_array(df[geom_col])
-        if ids is None:
+        if ids is None and use_index:
             ids = df.index.tolist()
         elif isinstance(ids, str):
             ids = df[ids].tolist()
@@ -603,7 +609,7 @@ def from_array(cls, array, **kwargs):
         return cls(array, **kwargs)
 
     @classmethod
-    def from_dataframe(cls, df, geom_col=None, ids=None, **kwargs):
+    def from_dataframe(cls, df, geom_col=None, ids=None, use_index=True, **kwargs):
         """
         Make Kernel weights from a dataframe.
 
@@ -615,10 +621,14 @@ def from_dataframe(cls, df, geom_col=None, ids=None, **kwargs):
         geom_col :  string
                     the name of the column in `df` that contains the
                     geometries. Defaults to active geometry column.
-        ids     :   string or iterable
-                    if string, the column name of the indices from the dataframe
-                    if iterable, a list of ids to use for the W
-                    if None, df.index is used.
+        ids     :   list-like, string
+                    a list-like of ids to use to index the spatial weights object or
+                    the name of the column to use as IDs. If nothing is
+                    provided, the dataframe index is used if `use_index=True` or
+                    a positional index is used if `use_index=False`.
+                    Order of the resulting W is not respected from this list.
+        use_index   : bool
+                    use index of `df` as `ids` to index the spatial weights object.
 
         See Also
         --------
@@ -627,7 +637,7 @@ def from_dataframe(cls, df, geom_col=None, ids=None, **kwargs):
         if geom_col is None:
             geom_col = df.geometry.name
         pts = get_points_array(df[geom_col])
-        if ids is None:
+        if ids is None and use_index:
             ids = df.index.tolist()
         elif isinstance(ids, str):
             ids = df[ids].tolist()
@@ -691,13 +701,13 @@ def _eval_kernel(self):
         elif self.function == "uniform":
             self.kernel = [np.ones(zi.shape) * 0.5 for zi in zs]
         elif self.function == "quadratic":
-            self.kernel = [(3.0 / 4) * (1 - zi ** 2) for zi in zs]
+            self.kernel = [(3.0 / 4) * (1 - zi**2) for zi in zs]
         elif self.function == "quartic":
-            self.kernel = [(15.0 / 16) * (1 - zi ** 2) ** 2 for zi in zs]
+            self.kernel = [(15.0 / 16) * (1 - zi**2) ** 2 for zi in zs]
         elif self.function == "gaussian":
             c = np.pi * 2
             c = c ** (-0.5)
-            self.kernel = [c * np.exp(-(zi ** 2) / 2.0) for zi in zs]
+            self.kernel = [c * np.exp(-(zi**2) / 2.0) for zi in zs]
         else:
             print(("Unsupported kernel function", self.function))
 
@@ -881,7 +891,9 @@ def from_array(cls, array, threshold, **kwargs):
         return cls(array, threshold, **kwargs)
 
     @classmethod
-    def from_dataframe(cls, df, threshold, geom_col=None, ids=None, **kwargs):
+    def from_dataframe(
+        cls, df, threshold, geom_col=None, ids=None, use_index=True, **kwargs
+    ):
 
         """
         Make DistanceBand weights from a dataframe.
@@ -894,16 +906,20 @@ def from_dataframe(cls, df, threshold, geom_col=None, ids=None, **kwargs):
         geom_col :  string
                     the name of the column in `df` that contains the
                     geometries. Defaults to active geometry column.
-        ids     :   string or iterable
-                    if string, the column name of the indices from the dataframe
-                    if iterable, a list of ids to use for the W
-                    if None, df.index is used.
+        ids     :   list-like, string
+                    a list-like of ids to use to index the spatial weights object or
+                    the name of the column to use as IDs. If nothing is
+                    provided, the dataframe index is used if `use_index=True` or
+                    a positional index is used if `use_index=False`.
+                    Order of the resulting W is not respected from this list.
+        use_index   : bool
+                    use index of `df` as `ids` to index the spatial weights object.
 
         """
         if geom_col is None:
             geom_col = df.geometry.name
         pts = get_points_array(df[geom_col])
-        if ids is None:
+        if ids is None and use_index:
             ids = df.index.tolist()
         elif isinstance(ids, str):
             ids = df[ids].tolist()

From 3e8e353d5fe675e29fed9fbd4a4dceadf2b8c3ff Mon Sep 17 00:00:00 2001
From: Martin Fleischmann <martin@martinfleischmann.net>
Date: Fri, 28 Oct 2022 20:57:23 +0200
Subject: [PATCH 135/162] avoid NonType

---
 libpysal/weights/contiguity.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/libpysal/weights/contiguity.py b/libpysal/weights/contiguity.py
index f47caa6d8..ae207d64d 100644
--- a/libpysal/weights/contiguity.py
+++ b/libpysal/weights/contiguity.py
@@ -1,5 +1,4 @@
 import itertools
-from types import NoneType
 import warnings
 
 import numpy
@@ -230,7 +229,7 @@ def from_dataframe(
         if isinstance(ids, str):
             ids = df[ids]
 
-        if not isinstance(ids, (list, NoneType)):
+        if not isinstance(ids, list) and ids is not None:
             ids = ids.tolist()
 
         return cls.from_iterable(
@@ -509,7 +508,7 @@ def from_dataframe(
         if isinstance(ids, str):
             ids = df[ids]
 
-        if not isinstance(ids, (list, NoneType)):
+        if not isinstance(ids, list) and ids is not None:
             ids = ids.tolist()
 
         return cls.from_iterable(

From 47ae2047bf22083e71459ed4af58e76e9c78d074 Mon Sep 17 00:00:00 2001
From: Martin Fleischmann <martin@martinfleischmann.net>
Date: Fri, 28 Oct 2022 21:02:37 +0200
Subject: [PATCH 136/162] check length

---
 libpysal/weights/contiguity.py | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/libpysal/weights/contiguity.py b/libpysal/weights/contiguity.py
index ae207d64d..c5f824a2d 100644
--- a/libpysal/weights/contiguity.py
+++ b/libpysal/weights/contiguity.py
@@ -232,6 +232,9 @@ def from_dataframe(
         if not isinstance(ids, list) and ids is not None:
             ids = ids.tolist()
 
+        if len(ids) != len(df):
+            raise ValueError("The length of `ids` does not match the length of df.")
+
         return cls.from_iterable(
             df[geom_col].tolist(), ids=ids, id_order=id_order, **kwargs
         )
@@ -458,7 +461,7 @@ def from_dataframe(
         See Also
         --------
         :class:`libpysal.weights.weights.W`
-        :class:`libpysal.weights.contiguity.Rook`
+        :class:`libpysal.weights.contiguity.Queen`
         """
         if geom_col is None:
             geom_col = df.geometry.name
@@ -511,6 +514,9 @@ def from_dataframe(
         if not isinstance(ids, list) and ids is not None:
             ids = ids.tolist()
 
+        if len(ids) != len(df):
+            raise ValueError("The length of `ids` does not match the length of df.")
+
         return cls.from_iterable(
             df[geom_col].tolist(), ids=ids, id_order=id_order, **kwargs
         )

From 081f96d600f6edc3f68832b454284e2d06d92e75 Mon Sep 17 00:00:00 2001
From: Martin Fleischmann <martin@martinfleischmann.net>
Date: Fri, 28 Oct 2022 21:05:41 +0200
Subject: [PATCH 137/162] redo to minimise checks

---
 libpysal/weights/contiguity.py | 26 ++++++++++++++------------
 1 file changed, 14 insertions(+), 12 deletions(-)

diff --git a/libpysal/weights/contiguity.py b/libpysal/weights/contiguity.py
index c5f824a2d..f15a60833 100644
--- a/libpysal/weights/contiguity.py
+++ b/libpysal/weights/contiguity.py
@@ -226,14 +226,15 @@ def from_dataframe(
             if use_index:
                 ids = df.index.tolist()
 
-        if isinstance(ids, str):
-            ids = df[ids]
+        else:
+            if isinstance(ids, str):
+                ids = df[ids]
 
-        if not isinstance(ids, list) and ids is not None:
-            ids = ids.tolist()
+            if not isinstance(ids, list):
+                ids = ids.tolist()
 
-        if len(ids) != len(df):
-            raise ValueError("The length of `ids` does not match the length of df.")
+            if len(ids) != len(df):
+                raise ValueError("The length of `ids` does not match the length of df.")
 
         return cls.from_iterable(
             df[geom_col].tolist(), ids=ids, id_order=id_order, **kwargs
@@ -508,14 +509,15 @@ def from_dataframe(
             if use_index:
                 ids = df.index.tolist()
 
-        if isinstance(ids, str):
-            ids = df[ids]
+        else:
+            if isinstance(ids, str):
+                ids = df[ids]
 
-        if not isinstance(ids, list) and ids is not None:
-            ids = ids.tolist()
+            if not isinstance(ids, list):
+                ids = ids.tolist()
 
-        if len(ids) != len(df):
-            raise ValueError("The length of `ids` does not match the length of df.")
+            if len(ids) != len(df):
+                raise ValueError("The length of `ids` does not match the length of df.")
 
         return cls.from_iterable(
             df[geom_col].tolist(), ids=ids, id_order=id_order, **kwargs

From c4f00f2ecfdc24547aa89bf448ab2f49f323ce41 Mon Sep 17 00:00:00 2001
From: Martin Fleischmann <martin@martinfleischmann.net>
Date: Fri, 28 Oct 2022 21:49:02 +0200
Subject: [PATCH 138/162] add the same options to Delaunay

---
 libpysal/weights/gabriel.py | 167 ++++++++++++++++++++++--------------
 1 file changed, 101 insertions(+), 66 deletions(-)

diff --git a/libpysal/weights/gabriel.py b/libpysal/weights/gabriel.py
index fd90a5c79..58b8e32c4 100644
--- a/libpysal/weights/gabriel.py
+++ b/libpysal/weights/gabriel.py
@@ -18,10 +18,10 @@
 
 class Delaunay(W):
     """
-    Constructor of the Delaunay graph of a set of input points. 
+    Constructor of the Delaunay graph of a set of input points.
     Relies on scipy.spatial.Delaunay and numba to quickly construct
     a graph from the input set of points. Will be slower without numba,
-    and will warn if this is missing. 
+    and will warn if this is missing.
 
     Arguments
     ---------
@@ -30,7 +30,7 @@ class Delaunay(W):
         delaunay triangulation
     **kwargs    :   keyword argument list
         keyword arguments passed directly to weights.W
-    
+
     Notes
     -----
     The Delaunay triangulation can result in quite a few non-local links among
@@ -40,22 +40,24 @@ class Delaunay(W):
     The weights.Voronoi class builds a voronoi diagram among the points, clips the
     Voronoi cells, and then constructs an adjacency graph among the clipped cells.
     This graph among the clipped Voronoi cells generally represents the structure
-    of local adjacencies better than the "raw" Delaunay graph. 
+    of local adjacencies better than the "raw" Delaunay graph.
 
     The weights.gabriel.Gabriel graph constructs a Delaunay graph, but only
-    includes the "short" links in the Delaunay graph. 
+    includes the "short" links in the Delaunay graph.
 
     However, if the unresricted Delaunay triangulation is needed, this class
     will compute it much more quickly than Voronoi(coordinates, clip=None).
     """
+
     def __init__(self, coordinates, **kwargs):
         try:
             from numba import njit
         except ModuleNotFoundError:
-            warnings.warn("The numba package is used extensively in this module"
-                    " to accelerate the computation of graphs. Without numba,"
-                    " these computations may become unduly slow on large data."
-                    )
+            warnings.warn(
+                "The numba package is used extensively in this module"
+                " to accelerate the computation of graphs. Without numba,"
+                " these computations may become unduly slow on large data."
+            )
         edges, _ = self._voronoi_edges(coordinates)
         voronoi_neighbors = pandas.DataFrame(edges).groupby(0)[1].apply(list).to_dict()
         W.__init__(self, voronoi_neighbors, **kwargs)
@@ -72,52 +74,84 @@ def _voronoi_edges(self, coordinates):
         return edges, dt
 
     @classmethod
-    def from_dataframe(cls, df, **kwargs):
+    def from_dataframe(cls, df, geom_col=None, ids=None, use_index=None, **kwargs):
         """
-        Construct a Delaunay triangulation from a geopandas GeoDataFrame. 
-        Not that the input geometries in the dataframe must be Points. 
-        Polygons or lines must be converted to points (e.g. using 
+        Construct a Delaunay triangulation from a geopandas GeoDataFrame.
+        Not that the input geometries in the dataframe must be Points.
+        Polygons or lines must be converted to points (e.g. using
         df.geometry.centroid).
 
         Arguments
         ---------
         df  :   geopandas.GeoDataFrame
-            GeoDataFrame containing points to construct the Delaunay 
-            Triangulation. 
+            GeoDataFrame containing points to construct the Delaunay
+            Triangulation.
+        geom_col :  string
+            the name of the column in `df` that contains the
+            geometries. Defaults to active geometry column.
+        ids   :   list-like, string
+            a list-like of ids to use to index the spatial weights object or
+            the name of the column to use as IDs. If nothing is
+            provided, the dataframe index is used if `use_index=True` or
+            a positional index is used if `use_index=False`.
+            Order of the resulting W is not respected from this list.
+        use_index  : bool
+            use index of `df` as `ids` to index the spatial weights object.
         **kwargs :  keyword arguments
             Keyword arguments that are passed downwards to the weights.W
             constructor.
         """
-        geomtypes = df.geometry.type.unique()
+        if isinstance(df, pandas.Series):
+            df = df.to_frame("geometry")
+        if geom_col is None:
+            geom_col = df.geometry.name
+        geomtypes = df[geom_col].geom_type.unique()
+
+        if ids is None:
+            if use_index is None:
+                warnings.warn(
+                    "`use_index` defaults to False but will default to True in future. "
+                    "Set True/False directly to control this behavior and silence this "
+                    "warning",
+                    FutureWarning,
+                    stacklevel=2,
+                )
+                use_index = False
+            if use_index:
+                ids = df.index.tolist()
+
+        elif isinstance(ids, str):
+            ids = df[ids].tolist()
+
         try:
             assert len(geomtypes) == 1
-            assert geomtypes[0] == 'Point'
+            assert geomtypes[0] == "Point"
             point_array = numpy.column_stack(
-                    (df.geometry.x.values, df.geometry.y.values)
-                    )
-            return cls(point_array, **kwargs)
+                (df[geom_col].x.values, df[geom_col].y.values)
+            )
+            return cls(point_array, ids=ids, **kwargs)
         except AssertionError:
             raise TypeError(
-                    f'The input dataframe has geometry types {geomtypes}'
-                    f' but this delaunay triangulation is only well-defined for points.'
-                    f' Choose a method to convert your dataframe into points (like using'
-                    f' the df.centroid) and use that to estimate this graph.'
-                    )
+                f"The input dataframe has geometry types {geomtypes}"
+                f" but this delaunay triangulation is only well-defined for points."
+                f" Choose a method to convert your dataframe into points (like using"
+                f" the df.centroid) and use that to estimate this graph."
+            )
 
 
 class Gabriel(Delaunay):
     """
-    Constructs the Gabriel graph of a set of points. This graph is a subset of 
-    the Delaunay triangulation where only "short" links are retained. This 
+    Constructs the Gabriel graph of a set of points. This graph is a subset of
+    the Delaunay triangulation where only "short" links are retained. This
     function is also accelerated using numba, and implemented on top of the
-    scipy.spatial.Delaunay class. 
+    scipy.spatial.Delaunay class.
 
     For a link (i,j) connecting node i to j in the Delaunay triangulation
     to be retained in the Gabriel graph, it must pass a point set exclusion test:
     1. Construct the circle C_ij containing link (i,j) as its diameter
-    2. If any other node k is contained within C_ij, then remove link (i,j) 
-       from the graph. 
-    3. Once all links are evaluated, the remaining graph is the Gabriel graph. 
+    2. If any other node k is contained within C_ij, then remove link (i,j)
+       from the graph.
+    3. Once all links are evaluated, the remaining graph is the Gabriel graph.
 
     Arguments
     ---------
@@ -127,14 +161,16 @@ class Gabriel(Delaunay):
     **kwargs    :   keyword argument list
         keyword arguments passed directly to weights.W
     """
+
     def __init__(self, coordinates, **kwargs):
         try:
             from numba import njit
         except ModuleNotFoundError:
-            warnings.warn("The numba package is used extensively in this module"
-                    " to accelerate the computation of graphs. Without numba,"
-                    " these computations may become unduly slow on large data."
-                    )
+            warnings.warn(
+                "The numba package is used extensively in this module"
+                " to accelerate the computation of graphs. Without numba,"
+                " these computations may become unduly slow on large data."
+            )
         edges, _ = self._voronoi_edges(coordinates)
         edges, dt = self._voronoi_edges(coordinates)
         droplist = _filter_gabriel(
@@ -148,17 +184,17 @@ def __init__(self, coordinates, **kwargs):
 
 class Relative_Neighborhood(Delaunay):
     """
-    Constructs the Relative Neighborhood graph from a set of points. 
-    This graph is a subset of the Delaunay triangulation, where only 
+    Constructs the Relative Neighborhood graph from a set of points.
+    This graph is a subset of the Delaunay triangulation, where only
     "relative neighbors" are retained. Further, it is a superset of
     the Minimum Spanning Tree, with additional "relative neighbors"
     introduced.
 
-    A relative neighbor pair of points i,j must be closer than the 
-    maximum distance between i (or j) and each other point k. 
-    This means that the points are at least as close to one another 
-    as they are to any other point. 
-    
+    A relative neighbor pair of points i,j must be closer than the
+    maximum distance between i (or j) and each other point k.
+    This means that the points are at least as close to one another
+    as they are to any other point.
+
     Arguments
     ---------
     coordinates :   array of points, (N,2)
@@ -167,37 +203,37 @@ class Relative_Neighborhood(Delaunay):
     **kwargs    :   keyword argument list
         keyword arguments passed directly to weights.W
     """
+
     def __init__(self, coordinates, binary=True, **kwargs):
         try:
             from numba import njit
         except ModuleNotFoundError:
-            warnings.warn("The numba package is used extensively in this module"
-                    " to accelerate the computation of graphs. Without numba,"
-                    " these computations may become unduly slow on large data."
-                    )
+            warnings.warn(
+                "The numba package is used extensively in this module"
+                " to accelerate the computation of graphs. Without numba,"
+                " these computations may become unduly slow on large data."
+            )
         edges, _ = self._voronoi_edges(coordinates)
         edges, dt = self._voronoi_edges(coordinates)
-        output, dkmax = _filter_relativehood(
-            edges, dt.points, return_dkmax=False
-        )
+        output, dkmax = _filter_relativehood(edges, dt.points, return_dkmax=False)
         row, col, data = zip(*output)
         if binary:
             data = numpy.ones_like(col, dtype=float)
-        sp = sparse.csc_matrix((data, (row, col))) #TODO: faster way than this?
-        tmp = WSP(sp).to_W() 
+        sp = sparse.csc_matrix((data, (row, col)))  # TODO: faster way than this?
+        tmp = WSP(sp).to_W()
         W.__init__(self, tmp.neighbors, tmp.weights, **kwargs)
 
 
-
 #### utilities
 
+
 @njit
 def _edges_from_simplices(simplices):
     """
-    Construct the sets of links that correspond to the edges of each 
+    Construct the sets of links that correspond to the edges of each
     simplex. Each simplex has three "sides," and thus six undirected
-    edges. Thus, the input should be a list of three-length tuples, 
-    that are then converted into the six non-directed edges for 
+    edges. Thus, the input should be a list of three-length tuples,
+    that are then converted into the six non-directed edges for
     each simplex.
     """
     edges = []
@@ -215,20 +251,20 @@ def _edges_from_simplices(simplices):
 def _filter_gabriel(edges, coordinates):
     """
     For an input set of edges and coordinates, filter the input edges
-    depending on the Gabriel rule: 
+    depending on the Gabriel rule:
 
     For each simplex, let i,j be the diameter of the circle defined by
     edge (i,j), and let k be the third point defining the simplex. The
     limiting case for the Gabriel rule is when k is also on the circle
     with diameter (i,j). In this limiting case, then simplex ijk must
-    be a right triangle, and dij**2 = djk**2 + dki**2 (by thales theorem). 
+    be a right triangle, and dij**2 = djk**2 + dki**2 (by thales theorem).
 
-    This means that when dij**2 > djk**2 + dki**2, then k is inside the circle. 
-    In contrast, when dij**2 < djk**2 + dji*2, k is outside of the circle. 
+    This means that when dij**2 > djk**2 + dki**2, then k is inside the circle.
+    In contrast, when dij**2 < djk**2 + dji*2, k is outside of the circle.
 
     Therefore, it's sufficient to take each observation i, iterate over its
-    Delaunay neighbors j,k, and remove links whre dij**2 > djk**2 + dki**2 
-    in order to construct the Gabriel graph. 
+    Delaunay neighbors j,k, and remove links whre dij**2 > djk**2 + dki**2
+    in order to construct the Gabriel graph.
     """
     edge_pointer = 0
     n = edges.max()
@@ -263,7 +299,7 @@ def _filter_gabriel(edges, coordinates):
 @njit
 def _filter_relativehood(edges, coordinates, return_dkmax=False):
     """
-    This is a direct implementation of the algorithm from Toussaint (1980), RNG-2 
+    This is a direct implementation of the algorithm from Toussaint (1980), RNG-2
 
     1. Compute the delaunay
     2. for each edge of the delaunay (i,j), compute
@@ -281,12 +317,12 @@ def _filter_relativehood(edges, coordinates, return_dkmax=False):
         pi = coordinates[i]
         pj = coordinates[j]
         dkmax = 0
-        dij = ((pi - pj)**2).sum()**.5
+        dij = ((pi - pj) ** 2).sum() ** 0.5
         prune = False
         for k in range(n):
             pk = coordinates[k]
-            dik = ((pi - pk)**2).sum()**.5
-            djk = ((pj - pk)**2).sum()**.5
+            dik = ((pi - pk) ** 2).sum() ** 0.5
+            djk = ((pj - pk) ** 2).sum() ** 0.5
             distances = numpy.array([dik, djk, dkmax])
             dkmax = distances.max()
             prune = dkmax < dij
@@ -299,4 +335,3 @@ def _filter_relativehood(edges, coordinates, return_dkmax=False):
             r.append(dkmax)
 
     return out, r
-

From 2a9ee9435917879879489ce269533af2aa67b152 Mon Sep 17 00:00:00 2001
From: Serge Rey <sjsrey@gmail.com>
Date: Mon, 31 Oct 2022 16:05:14 -0700
Subject: [PATCH 139/162] ENH: more granularity in example handling

---
 examples.ipynb                            |  49 +-
 libpysal/examples/__init__.py             |  40 +-
 libpysal/examples/base.py                 |  41 +-
 libpysal/examples/builtin.py              |  26 +-
 libpysal/examples/remotes.py              |  12 +-
 libpysal/examples/tests/test_available.py |  17 +-
 notebooks/examples.ipynb                  | 735 ++++++++++++++++------
 7 files changed, 661 insertions(+), 259 deletions(-)

diff --git a/examples.ipynb b/examples.ipynb
index 8fe631cba..ac82386be 100644
--- a/examples.ipynb
+++ b/examples.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -25,7 +25,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -83,7 +83,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -126,9 +126,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: 'libpysal/examples/examples.json'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "Input \u001b[0;32mIn [5]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlibpysal/examples/examples.json\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mr\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m efile:\n\u001b[1;32m      2\u001b[0m     examples  \u001b[38;5;241m=\u001b[39m json\u001b[38;5;241m.\u001b[39mload(efile)\n",
+      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'libpysal/examples/examples.json'"
+     ]
+    }
+   ],
    "source": [
     "with open('libpysal/examples/examples.json', 'r') as efile:\n",
     "    examples  = json.load(efile)"
@@ -136,18 +148,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "dict_keys(['nat', 'south', 'rio', 'mexico', 'baltimore'])"
-      ]
-     },
-     "execution_count": 73,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "NameError",
+     "evalue": "name 'examples' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Input \u001b[0;32mIn [6]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mexamples\u001b[49m\u001b[38;5;241m.\u001b[39mkeys()\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'examples' is not defined"
+     ]
     }
    ],
    "source": [
@@ -555,7 +568,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -569,9 +582,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/libpysal/examples/__init__.py b/libpysal/examples/__init__.py
index eb02d9e07..46c9246b2 100644
--- a/libpysal/examples/__init__.py
+++ b/libpysal/examples/__init__.py
@@ -1,5 +1,9 @@
-""" The :mod:`libpysal.examples` module includes a number of small built-in
-    example datasets as well as functions to fetch larger datasets.
+"""The :mod:`libpysal.examples` module provides example datasets.
+
+The datasets consist of two sets, built-ins which are installed with
+this module and remotes that can be downloaded.
+
+This module provides functionality for working with these example datasets.
 """
 
 
@@ -7,17 +11,20 @@
 from .base import example_manager
 from .remotes import datasets as remote_datasets
 from .builtin import datasets as builtin_datasets
+from typing import Union
 
 
-from typing import Union
+available_datasets = builtin_datasets.copy()
+available_datasets.update(remote_datasets.datasets)
 
-__all__ = ["get_path", "available", "explain", "fetch_all"]
+__all__ = ["get_path", "available", "explain", "fetch_all",
+           "get_url", "load_example", "summary"]
+
+example_manager.add_examples(available_datasets)
 
-example_manager.add_examples(builtin_datasets)
 
 def fetch_all():
-    """Fetch and install all remote datasets
-    """
+    """Fetch and install all remote datasets."""
     datasets = remote_datasets.datasets
     names = list(datasets.keys())
     names.sort()
@@ -32,31 +39,27 @@ def fetch_all():
 
 def available() -> pd.DataFrame:
     """Return a dataframe with available datasets."""
-    fetch_all()
-
     return example_manager.available()
 
 
 def explain(name: str) -> str:
     """Explain a dataset by name."""
-
     return example_manager.explain(name)
 
 
+def get_url(name: str) -> str:
+    """Get url for remote dataset."""
+    return example_manager.get_remote_url(name)
+
+
 def load_example(example_name: str) -> Union[base.Example, builtin.LocalExample]:
     """Load example dataset instance."""
     example = example_manager.load(example_name)
-
-    if example is None:
-        fetch_all()  # refresh remotes
-        example = example_manager.load(example_name)
-
     return example
 
 
 def get_path(file_name: str) -> str:
     """Get the path for a file by searching installed datasets."""
-
     installed = example_manager.get_installed_names()
     for name in installed:
         example = example_manager.datasets[name]
@@ -64,3 +67,8 @@ def get_path(file_name: str) -> str:
         if pth:
             return pth
     print("{} is not a file in any installed dataset.".format(file_name))
+
+
+def summary():
+    """Summary of datasets."""
+    example_manager.summary()
diff --git a/libpysal/examples/base.py b/libpysal/examples/base.py
index aa20f7a5f..21f7753a1 100644
--- a/libpysal/examples/base.py
+++ b/libpysal/examples/base.py
@@ -8,8 +8,6 @@
 import io
 import os
 import webbrowser
-from os import environ, makedirs
-from os.path import exists, expanduser, join
 from platformdirs import user_data_dir
 import zipfile
 import requests
@@ -35,9 +33,9 @@ def get_data_home():
 
     appname = "pysal"
     appauthor = "pysal"
-    data_home =  user_data_dir(appname, appauthor) 
-    if not exists(data_home):
-        makedirs(data_home, exist_ok=True)
+    data_home = user_data_dir(appname, appauthor)
+    if not os.path.exists(data_home):
+        os.makedirs(data_home, exist_ok=True)
     return data_home
 
 
@@ -122,7 +120,9 @@ class Example:
 
     """
 
-    def __init__(self, name, description, n, k, download_url, explain_url):
+    def __init__(self, name, description, n, k, download_url,
+                 explain_url):
+        """Initialze Example."""
         self.name = name
         self.description = description
         self.n = n
@@ -134,12 +134,10 @@ def __init__(self, name, description, n, k, download_url, explain_url):
 
     def get_local_path(self, path=get_data_home()) -> str:
         """Get the local path for example."""
-
-        return join(path, self.root)
+        return os.path.join(path, self.root)
 
     def get_path(self, file_name, verbose=True) -> Union[str, None]:
         """Get the path for local file."""
-
         file_list = self.get_file_list()
         for file_path in file_list:
             base_name = os.path.basename(file_path)
@@ -151,7 +149,6 @@ def get_path(self, file_name, verbose=True) -> Union[str, None]:
 
     def downloaded(self) -> bool:
         """Check if the example has already been installed."""
-
         path = self.get_local_path()
         if os.path.isdir(path):
             self.installed = True
@@ -180,7 +177,7 @@ def download(self, path=get_data_home()):
         if not self.downloaded():
             request = requests.get(self.download_url)
             archive = zipfile.ZipFile(io.BytesIO(request.content))
-            target = join(path, self.root)
+            target = os.path.join(path, self.root)
             print("Downloading {} to {}".format(self.name, target))
             archive.extractall(path=target)
             self.zipfile = archive
@@ -213,8 +210,8 @@ def load(self, file_name) -> io.FileIO:
 class Examples:
     """Manager for pysal example datasets."""
 
-    def __init__(self):
-        self.datasets = {}
+    def __init__(self, datasets={}):
+        self.datasets = datasets
 
     def add_examples(self, examples):
         """Add examples to the set of datasets available."""
@@ -273,5 +270,23 @@ def get_installed_names(self) -> list:
         ds = self.datasets
         return [name for name in ds if ds[name].installed]
 
+    def get_remote_url(self, name):
+        if name in self.datasets: 
+            try:
+                return self.datasets[name].download_url
+            except:
+                print(f'{name} is a built-in dataset, no url.')
+        else:
+            print(f'{name} is not an available dataset.')
+                
+
+    def summary(self):
+        """Report on datasets."""
+        available = self.available()
+        n = available.shape[0]
+        n_installed = available.Installed.sum()
+        n_remote = n - n_installed
+        print(f'{n} datasets available, {n_installed} installed, {n_remote} remote.')
+
 
 example_manager = Examples()
diff --git a/libpysal/examples/builtin.py b/libpysal/examples/builtin.py
index 6fe40c689..69e788490 100644
--- a/libpysal/examples/builtin.py
+++ b/libpysal/examples/builtin.py
@@ -1,5 +1,4 @@
-"""Handle local builtin datasets.
-"""
+"""Handle local builtin datasets."""
 
 import os
 from .base import get_list_of_files
@@ -38,11 +37,30 @@
 
 
 class LocalExample:
-    """
-    Builtin pysal example dataset
+    """Builtin pysal example dataset.
+
+    Attributes
+    ----------
+    name : str
+        Example name
+    dirname : str
+        Path holding example files
+    installed : boolean
+        If True, example is installed locally, if false it is remote.
+    description : str
+        Summary of the properties of the example
     """
 
     def __init__(self, name, dirname):
+        """Initialize LocalExample with name and dirname.
+
+        Parameters
+        ---------
+        name : str
+            example name
+        dirname: str
+             path to directory holding example files
+        """
         self.name = name
         self.dirname = dirname
         self.installed = True
diff --git a/libpysal/examples/remotes.py b/libpysal/examples/remotes.py
index 213b2684c..f4aaf2a47 100644
--- a/libpysal/examples/remotes.py
+++ b/libpysal/examples/remotes.py
@@ -1,5 +1,4 @@
-"""Handle remote datasets.
-"""
+"""Handle remote datasets."""
 
 from bs4 import BeautifulSoup
 import requests
@@ -16,7 +15,6 @@ def poll_remotes():
         Example datasets keyed by the dataset name.
 
     """
-
     # Geoda Center Data Sets
 
     url = "https://geodacenter.github.io/data-and-lab//"
@@ -95,17 +93,19 @@ def poll_remotes():
     return datasets
 
 
-#datasets = poll_remotes()
-
 class Remotes:
+    """Remote datasets."""
+
     def __init__(self):
+        """Initialize Remotes."""
         self._datasets = None
 
     @property
     def datasets(self):
+        """Create dictionary of remotes."""
         if self._datasets is None:
             self._datasets = poll_remotes()
         return self._datasets
 
-datasets = Remotes()
 
+datasets = Remotes()
diff --git a/libpysal/examples/tests/test_available.py b/libpysal/examples/tests/test_available.py
index 1592c39c8..e8e6d7e6c 100644
--- a/libpysal/examples/tests/test_available.py
+++ b/libpysal/examples/tests/test_available.py
@@ -3,19 +3,21 @@
 import os
 import platform
 import unittest
-import numpy as np
 import pandas
 
-from .. import available
+from .. import (available, get_url, load_example)
+
 from ..base import get_data_home
 
 os_name = platform.system()
 
+
 class Testexamples(unittest.TestCase):
 
     def test_available(self):
         examples = available()
         self.assertEqual(type(examples), pandas.core.frame.DataFrame)
+        self.assertEqual(examples.shape, (98, 3))
 
     def test_data_home(self): 
         pth = get_data_home()
@@ -37,6 +39,17 @@ def test_data_home(self):
             self.assertEqual(heads[-2], 'Local')
             self.assertEqual(heads[-3], 'AppData')
 
+    def test_get_url(self):
+        self.assertEqual(get_url('10740'), None)
+        url = 'https://geodacenter.github.io/data-and-lab//data/baltimore.zip'
+        self.assertEqual(get_url('Baltimore'), url)
+
+    def test_load_example(self):
+        taz = load_example("taz")
+        flist = taz.get_file_list()
+        self.assertEquals(len(flist), 4)
+
+
 suite = unittest.TestLoader().loadTestsFromTestCase(Testexamples)
 
 if __name__ == "__main__":
diff --git a/notebooks/examples.ipynb b/notebooks/examples.ipynb
index ed6208903..5905f568d 100644
--- a/notebooks/examples.ipynb
+++ b/notebooks/examples.ipynb
@@ -46,7 +46,7 @@
     {
      "data": {
       "text/plain": [
-       "'/home/serge/Dropbox/p/pysal/src/subpackages/libpysal/libpysal/examples/mexico/mexicojoin.dbf'"
+       "'/home/serge/Documents/p/pysal/src/subpackages/libpysal/libpysal/examples/mexico/mexicojoin.dbf'"
       ]
      },
      "execution_count": 2,
@@ -132,30 +132,83 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## `available` is updated"
+    "The function `available` is also available but has been updated to return a Pandas DataFrame. In addition to the builtin datasets, `available` will report on what datasets are available, either as builtin or remotes."
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 5,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "The function `available` is also available but has been updated to return a Pandas DataFrame. In addition to the builtin datasets, `available` will fetch all available remote datasets that have not already been downloaded, and report if any remotes are unavailable:"
+    "from libpysal.examples import available"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = available()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(98, 3)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Example not downloaded: Chicago parcels\n",
-      "Example not downloaded: Chile Migration\n",
-      "Example not downloaded: Spirals\n"
+      "98 datasets available, 27 installed, 71 remote.\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "libpysal.examples.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see that there are 98 total datasets available for use with PySAL. On an initial install (i.e., `examples` has not been used yet), 27 of these are builtin datasets and 71 are remote. The latter can be downloaded and installed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Downloading Remote Datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
       "text/html": [
@@ -193,106 +246,227 @@
        "      <th>1</th>\n",
        "      <td>AirBnB</td>\n",
        "      <td>Airbnb rentals, socioeconomics, and crime in C...</td>\n",
-       "      <td>True</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>Atlanta</td>\n",
        "      <td>Atlanta, GA region homicide counts and rates</td>\n",
-       "      <td>True</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>Baltimore</td>\n",
        "      <td>Baltimore house sales prices and hedonics</td>\n",
-       "      <td>True</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>Bostonhsg</td>\n",
        "      <td>Boston housing and neighborhood data</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>93</th>\n",
-       "      <td>taz</td>\n",
-       "      <td>Traffic Analysis Zones in So. California</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>94</th>\n",
-       "      <td>tokyo</td>\n",
-       "      <td>Tokyo Mortality data</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>95</th>\n",
-       "      <td>us_income</td>\n",
-       "      <td>Per-capita income for the lower 48 US states 1...</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>96</th>\n",
-       "      <td>virginia</td>\n",
-       "      <td>Virginia counties shapefile</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>97</th>\n",
-       "      <td>wmat</td>\n",
-       "      <td>Datasets used for spatial weights testing</td>\n",
-       "      <td>True</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>98 rows × 3 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "         Name                                        Description  Installed\n",
-       "0       10740  Albuquerque, New Mexico, Census 2000 Tract Dat...       True\n",
-       "1      AirBnB  Airbnb rentals, socioeconomics, and crime in C...       True\n",
-       "2     Atlanta       Atlanta, GA region homicide counts and rates       True\n",
-       "3   Baltimore          Baltimore house sales prices and hedonics       True\n",
-       "4   Bostonhsg               Boston housing and neighborhood data       True\n",
-       "..        ...                                                ...        ...\n",
-       "93        taz           Traffic Analysis Zones in So. California       True\n",
-       "94      tokyo                               Tokyo Mortality data       True\n",
-       "95  us_income  Per-capita income for the lower 48 US states 1...       True\n",
-       "96   virginia                        Virginia counties shapefile       True\n",
-       "97       wmat          Datasets used for spatial weights testing       True\n",
-       "\n",
-       "[98 rows x 3 columns]"
+       "        Name                                        Description  Installed\n",
+       "0      10740  Albuquerque, New Mexico, Census 2000 Tract Dat...       True\n",
+       "1     AirBnB  Airbnb rentals, socioeconomics, and crime in C...      False\n",
+       "2    Atlanta       Atlanta, GA region homicide counts and rates      False\n",
+       "3  Baltimore          Baltimore house sales prices and hedonics      False\n",
+       "4  Bostonhsg               Boston housing and neighborhood data      False"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "from libpysal.examples import available\n",
-    "available()"
+    "df.head()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Working with an example dataset\n",
+    "The remote `AirBnB` can be installed by calling `load_example`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading AirBnB to /home/serge/.local/share/pysal/AirBnB\n"
+     ]
+    }
+   ],
+   "source": [
+    "airbnb = libpysal.examples.load_example(\"AirBnB\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "98 datasets available, 28 installed, 70 remote.\n"
+     ]
+    }
+   ],
+   "source": [
+    "libpysal.examples.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And we see that the number of remotes as declined by one and the number of installed has increased by 1."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Trying to load an example that doesn't exist will return None and alert the user:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Example not available: dataset42\n"
+     ]
+    }
+   ],
+   "source": [
+    "libpysal.examples.load_example('dataset42')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Getting remote urls\n",
     "\n",
-    "`explain` will render maps for an example if available"
+    "If the url, rather than the dataset, is needed this can be obtained on a remote with `get_url`. \n",
+    "As the `Baltimore` dataset has not yet been downloaded in this example, we can grab it's url:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'https://geodacenter.github.io/data-and-lab//data/baltimore.zip'"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "balt_url = libpysal.examples.get_url('Baltimore')\n",
+    "balt_url"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explaining a dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "taz\n",
+      "===\n",
+      "\n",
+      "Dataset used for regionalization\n",
+      "--------------------------------\n",
+      "\n",
+      "* taz.dbf: attribute data. (k=14)\n",
+      "* taz.shp: Polygon shapefile. (n=4109)\n",
+      "* taz.shx: spatial index.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "libpysal.examples.explain('taz')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading taz to /home/serge/.local/share/pysal/taz\n"
+     ]
+    }
+   ],
+   "source": [
+    "taz = libpysal.examples.load_example('taz')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['/home/serge/.local/share/pysal/taz/taz-master/taz.dbf',\n",
+       " '/home/serge/.local/share/pysal/taz/taz-master/taz.shp',\n",
+       " '/home/serge/.local/share/pysal/taz/taz-master/README.md',\n",
+       " '/home/serge/.local/share/pysal/taz/taz-master/taz.shx']"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "taz.get_file_list()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -302,50 +476,48 @@
        "        <iframe\n",
        "            width=\"700\"\n",
        "            height=\"350\"\n",
-       "            src=\"https://geodacenter.github.io/data-and-lab//tampa1/\"\n",
+       "            src=\"https://geodacenter.github.io/data-and-lab//baltim/\"\n",
        "            frameborder=\"0\"\n",
        "            allowfullscreen\n",
+       "            \n",
        "        ></iframe>\n",
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.IFrame at 0x7f47603cf700>"
+       "<IPython.lib.display.IFrame at 0x7febc3b56c50>"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "from libpysal.examples import explain\n",
-    "explain('Tampa1')"
+    "libpysal.examples.explain('Baltimore')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading Baltimore to /home/serge/.local/share/pysal/Baltimore\n"
+     ]
+    }
+   ],
    "source": [
-    "from libpysal.examples import load_example\n",
-    "tampa1 = load_example('Tampa1')"
+    "balt = libpysal.examples.load_example('Baltimore')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Example not downloaded: Chicago parcels\n",
-      "Example not downloaded: Chile Migration\n",
-      "Example not downloaded: Spirals\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -389,7 +561,7 @@
        "      <th>2</th>\n",
        "      <td>Atlanta</td>\n",
        "      <td>Atlanta, GA region homicide counts and rates</td>\n",
-       "      <td>True</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -401,7 +573,7 @@
        "      <th>4</th>\n",
        "      <td>Bostonhsg</td>\n",
        "      <td>Boston housing and neighborhood data</td>\n",
-       "      <td>True</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -448,9 +620,9 @@
        "         Name                                        Description  Installed\n",
        "0       10740  Albuquerque, New Mexico, Census 2000 Tract Dat...       True\n",
        "1      AirBnB  Airbnb rentals, socioeconomics, and crime in C...       True\n",
-       "2     Atlanta       Atlanta, GA region homicide counts and rates       True\n",
+       "2     Atlanta       Atlanta, GA region homicide counts and rates      False\n",
        "3   Baltimore          Baltimore house sales prices and hedonics       True\n",
-       "4   Bostonhsg               Boston housing and neighborhood data       True\n",
+       "4   Bostonhsg               Boston housing and neighborhood data      False\n",
        "..        ...                                                ...        ...\n",
        "93        taz           Traffic Analysis Zones in So. California       True\n",
        "94      tokyo                               Tokyo Mortality data       True\n",
@@ -461,18 +633,78 @@
        "[98 rows x 3 columns]"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "available()"
+    "libpysal.examples.available()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Working with an example dataset\n",
+    "\n",
+    "`explain` will render maps for an example if available"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"700\"\n",
+       "            height=\"350\"\n",
+       "            src=\"https://geodacenter.github.io/data-and-lab//tampa1/\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "            \n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.IFrame at 0x7febc3b56890>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from libpysal.examples import explain\n",
+    "explain('Tampa1')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading Tampa1 to /home/serge/.local/share/pysal/Tampa1\n"
+     ]
+    }
+   ],
+   "source": [
+    "from libpysal.examples import load_example\n",
+    "tampa1 = load_example('Tampa1')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -481,7 +713,7 @@
        "True"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -492,96 +724,96 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "['/home/serge/pysal_data/Tampa1/__MACOSX/._TampaMSA',\n",
-       " '/home/serge/pysal_data/Tampa1/__MACOSX/TampaMSA/._tampa_counties.sbn',\n",
-       " '/home/serge/pysal_data/Tampa1/__MACOSX/TampaMSA/._tampa_final_census2.sbn',\n",
-       " '/home/serge/pysal_data/Tampa1/__MACOSX/TampaMSA/._2000 Census Data Variables_Documentation.pdf',\n",
-       " '/home/serge/pysal_data/Tampa1/__MACOSX/TampaMSA/._tampa_final_census2.sbx',\n",
-       " '/home/serge/pysal_data/Tampa1/__MACOSX/TampaMSA/._tampa_counties.sbx',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/tampa_counties.prj',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/tampa_counties.shp',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/2000 Census Data Variables_Documentation.pdf',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/tampa_counties.kml',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/tampa_final_census2.gpkg',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/tampa_counties.shx',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/tampa_final_census2.mif',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/tampa_final_census2.dbf',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/tampa_counties.sbx',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/tampa_final_census2.shp',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/tampa_counties.sbn',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/tampa_final_census2.sqlite',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/tampa_final_census2.sbx',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/tampa_counties.gpkg',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/tampa_final_census2.shx',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/tampa_final_census2.mid',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/tampa_counties.mid',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByBackwardLabel.atx',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000002.gdbtable',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.gdbindexes',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/gdb',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.gdbtablx',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.gdbindexes',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.gdbtablx',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.gdbtablx',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.FDO_UUID.atx',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByName.atx',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.CatItemsByPhysicalName.atx',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.CatItemTypesByUUID.atx',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.FDO_UUID.atx',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.CatRelsByDestinationID.atx',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.gdbindexes',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.gdbtable',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.spx',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.gdbtable',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.CatItemTypesByName.atx',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.gdbtable',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByUUID.atx',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.gdbtable',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.spx',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.spx',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000003.gdbtablx',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.gdbtablx',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.CatRelsByType.atx',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.gdbtable',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.CatItemTypesByParentTypeID.atx',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.CatItemsByType.atx',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.gdbindexes',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.gdbtablx',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.CatRelsByOriginID.atx',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000003.gdbtable',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000002.gdbtablx',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.gdbtable',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.gdbindexes',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.gdbindexes',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000003.gdbindexes',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByDestItemTypeID.atx',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/timestamps',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.gdbtablx',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.TablesByName.atx',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.gdbtablx',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByOriginItemTypeID.atx',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByForwardLabel.atx',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.gdbindexes',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.gdbtable',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/tampa_counties.mif',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/tampa_counties.dbf',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/tampa_counties.xlsx',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/tampa_final_census2.kml',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/tampa_final_census2.sbn',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/tampa_final_census2.prj',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/tampa_counties.sqlite',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/tampa_final_census2.geojson',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/tampa_final_census2.xlsx',\n",
-       " '/home/serge/pysal_data/Tampa1/TampaMSA/tampa_counties.geojson']"
+       "['/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.shp',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.prj',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/2000 Census Data Variables_Documentation.pdf',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.kml',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.dbf',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.kml',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.sbn',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.mif',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.prj',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.sqlite',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.shx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.sbn',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.sbx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.gdbtablx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.gdbtable',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.FDO_UUID.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000002.gdbtable',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByName.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.gdbtablx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000003.gdbtable',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.gdbindexes',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000002.gdbtablx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.CatRelsByType.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.spx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.gdbtable',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByDestItemTypeID.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.gdbtable',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/timestamps',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.CatItemTypesByName.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000003.gdbtablx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.gdbtable',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.gdbindexes',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.gdbtablx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.CatItemTypesByUUID.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.CatItemTypesByParentTypeID.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.CatItemsByType.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.gdbindexes',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByForwardLabel.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.gdbtable',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.gdbtablx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.FDO_UUID.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByUUID.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.gdbindexes',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.TablesByName.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.gdbtablx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.gdbtable',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.CatRelsByOriginID.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000003.gdbindexes',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.gdbindexes',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.spx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.gdbtablx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.CatItemsByPhysicalName.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.gdbtablx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByOriginItemTypeID.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.gdbtable',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/gdb',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.gdbindexes',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.spx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByBackwardLabel.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.gdbindexes',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.CatRelsByDestinationID.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.mid',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.sbx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.geojson',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.mid',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.xlsx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.mif',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.dbf',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.shp',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.gpkg',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.gpkg',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.xlsx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.shx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.sqlite',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.geojson',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/__MACOSX/TampaMSA/._2000 Census Data Variables_Documentation.pdf',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/__MACOSX/TampaMSA/._tampa_counties.sbn',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/__MACOSX/TampaMSA/._tampa_final_census2.sbn',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/__MACOSX/TampaMSA/._tampa_counties.sbx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/__MACOSX/TampaMSA/._tampa_final_census2.sbx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/__MACOSX/._TampaMSA']"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -592,7 +824,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -601,16 +833,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<libpysal.io.iohandlers.pyShpIO.PurePyShpWrapper at 0x7f474f7d19a0>"
+       "<libpysal.io.iohandlers.pyShpIO.PurePyShpWrapper at 0x7febc3b1bd00>"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -621,7 +853,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -630,7 +862,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -639,7 +871,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -648,20 +880,18 @@
        "<AxesSubplot:>"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -681,7 +911,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -714,7 +944,7 @@
     }
    ],
    "source": [
-    "explain('Rio Grande do Sul')"
+    "libpysal.examples.explain('Rio Grande do Sul')"
    ]
   },
   {
@@ -726,17 +956,122 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 30,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading Rio Grande do Sul to /home/serge/.local/share/pysal/Rio_Grande_do_Sul\n"
+     ]
+    }
+   ],
    "source": [
-    "rio = load_example('Rio Grande do Sul')"
+    "rio = libpysal.examples.load_example('Rio Grande do Sul')"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'AirBnB': <libpysal.examples.base.Example at 0x7febc78c00d0>,\n",
+       " 'Atlanta': <libpysal.examples.base.Example at 0x7febc4a5efb0>,\n",
+       " 'Baltimore': <libpysal.examples.base.Example at 0x7febc4a5ed40>,\n",
+       " 'Bostonhsg': <libpysal.examples.base.Example at 0x7febc4a5ef50>,\n",
+       " 'Buenosaires': <libpysal.examples.base.Example at 0x7febc4a5efe0>,\n",
+       " 'Charleston1': <libpysal.examples.base.Example at 0x7febc4a5ef80>,\n",
+       " 'Charleston2': <libpysal.examples.base.Example at 0x7febc4a5ee90>,\n",
+       " 'Chicago Health': <libpysal.examples.base.Example at 0x7febc4a5ef20>,\n",
+       " 'Chicago commpop': <libpysal.examples.base.Example at 0x7febc4a5f070>,\n",
+       " 'Chicago parcels': <libpysal.examples.base.Example at 0x7febc4a5f0a0>,\n",
+       " 'Chile Labor': <libpysal.examples.base.Example at 0x7febc4a5f100>,\n",
+       " 'Chile Migration': <libpysal.examples.base.Example at 0x7febc4a5f130>,\n",
+       " 'Cincinnati': <libpysal.examples.base.Example at 0x7febc4a5f1f0>,\n",
+       " 'Cleveland': <libpysal.examples.base.Example at 0x7febc4a5f190>,\n",
+       " 'Columbus': <libpysal.examples.base.Example at 0x7febc4a5f250>,\n",
+       " 'Elections': <libpysal.examples.base.Example at 0x7febc4a5f0d0>,\n",
+       " 'Grid100': <libpysal.examples.base.Example at 0x7febc4a5f220>,\n",
+       " 'Groceries': <libpysal.examples.base.Example at 0x7febc4a5f160>,\n",
+       " 'Guerry': <libpysal.examples.base.Example at 0x7febc4a5f280>,\n",
+       " 'Health+': <libpysal.examples.base.Example at 0x7febc4a5f040>,\n",
+       " 'Health Indicators': <libpysal.examples.base.Example at 0x7febc4a5f2b0>,\n",
+       " 'Hickory1': <libpysal.examples.base.Example at 0x7febc4a5f2e0>,\n",
+       " 'Hickory2': <libpysal.examples.base.Example at 0x7febc4a5f310>,\n",
+       " 'Home Sales': <libpysal.examples.base.Example at 0x7febc4a5f340>,\n",
+       " 'Houston': <libpysal.examples.base.Example at 0x7febc4a5f370>,\n",
+       " 'Juvenile': <libpysal.examples.base.Example at 0x7febc4a5f3a0>,\n",
+       " 'Lansing1': <libpysal.examples.base.Example at 0x7febc4a5f3d0>,\n",
+       " 'Lansing2': <libpysal.examples.base.Example at 0x7febc4a5f400>,\n",
+       " 'Laozone': <libpysal.examples.base.Example at 0x7febc4a5f430>,\n",
+       " 'LasRosas': <libpysal.examples.base.Example at 0x7febc4a5f460>,\n",
+       " 'Liquor Stores': <libpysal.examples.base.Example at 0x7febc4a5f490>,\n",
+       " 'Malaria': <libpysal.examples.base.Example at 0x7febc4a5f4c0>,\n",
+       " 'Milwaukee1': <libpysal.examples.base.Example at 0x7febc4a5f4f0>,\n",
+       " 'Milwaukee2': <libpysal.examples.base.Example at 0x7febc4a5f520>,\n",
+       " 'NCOVR': <libpysal.examples.base.Example at 0x7febc4a5f550>,\n",
+       " 'Natregimes': <libpysal.examples.base.Example at 0x7febc4a5f580>,\n",
+       " 'NDVI': <libpysal.examples.base.Example at 0x7febc4a5f5b0>,\n",
+       " 'Nepal': <libpysal.examples.base.Example at 0x7febc4a5f5e0>,\n",
+       " 'NYC': <libpysal.examples.base.Example at 0x7febc4a5f610>,\n",
+       " 'NYC Earnings': <libpysal.examples.base.Example at 0x7febc4a5f640>,\n",
+       " 'NYC Education': <libpysal.examples.base.Example at 0x7febc4a5f670>,\n",
+       " 'NYC Neighborhoods': <libpysal.examples.base.Example at 0x7febc4a5f6a0>,\n",
+       " 'NYC Socio-Demographics': <libpysal.examples.base.Example at 0x7febc4a5f6d0>,\n",
+       " 'Ohiolung': <libpysal.examples.base.Example at 0x7febc4a5f700>,\n",
+       " 'Orlando1': <libpysal.examples.base.Example at 0x7febc4a5f730>,\n",
+       " 'Orlando2': <libpysal.examples.base.Example at 0x7febc4a5f760>,\n",
+       " 'Oz9799': <libpysal.examples.base.Example at 0x7febc4a5f790>,\n",
+       " 'Phoenix ACS': <libpysal.examples.base.Example at 0x7febc4a5f7c0>,\n",
+       " 'Pittsburgh': <libpysal.examples.base.Example at 0x7febc4a5f7f0>,\n",
+       " 'Police': <libpysal.examples.base.Example at 0x7febc4a5f820>,\n",
+       " 'Sacramento1': <libpysal.examples.base.Example at 0x7febc4a5f850>,\n",
+       " 'Sacramento2': <libpysal.examples.base.Example at 0x7febc4a5f880>,\n",
+       " 'SanFran Crime': <libpysal.examples.base.Example at 0x7febc4a5f8b0>,\n",
+       " 'Savannah1': <libpysal.examples.base.Example at 0x7febc4a5f8e0>,\n",
+       " 'Savannah2': <libpysal.examples.base.Example at 0x7febc4a5f910>,\n",
+       " 'Scotlip': <libpysal.examples.base.Example at 0x7febc4a5f940>,\n",
+       " 'Seattle1': <libpysal.examples.base.Example at 0x7febc4a5f970>,\n",
+       " 'Seattle2': <libpysal.examples.base.Example at 0x7febc4a5f9a0>,\n",
+       " 'SIDS': <libpysal.examples.base.Example at 0x7febc4a5f9d0>,\n",
+       " 'SIDS2': <libpysal.examples.base.Example at 0x7febc4a5fa00>,\n",
+       " 'Snow': <libpysal.examples.base.Example at 0x7febc4a5fa30>,\n",
+       " 'South': <libpysal.examples.base.Example at 0x7febc4a5fa60>,\n",
+       " 'Spirals': <libpysal.examples.base.Example at 0x7febc4a5fa90>,\n",
+       " 'StLouis': <libpysal.examples.base.Example at 0x7febc4a5fac0>,\n",
+       " 'Tampa1': <libpysal.examples.base.Example at 0x7febc4a5faf0>,\n",
+       " 'US SDOH': <libpysal.examples.base.Example at 0x7febc4a5fb20>,\n",
+       " 'Rio Grande do Sul': <libpysal.examples.base.Example at 0x7febc78c0130>,\n",
+       " 'nyc_bikes': <libpysal.examples.base.Example at 0x7febc4a5fb80>,\n",
+       " 'taz': <libpysal.examples.base.Example at 0x7febc4a5fc70>,\n",
+       " 'clearwater': <libpysal.examples.base.Example at 0x7febc4a5fbe0>,\n",
+       " 'newHaven': <libpysal.examples.base.Example at 0x7febc4a5eec0>}"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "libpysal.examples.remote_datasets.datasets # a listing of all remotes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -750,7 +1085,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,

From 02be049efae43f2c35c3e64ac0eb8ae1f6b1766c Mon Sep 17 00:00:00 2001
From: James Gaboardi <jgaboardi@gmail.com>
Date: Mon, 31 Oct 2022 20:32:40 -0400
Subject: [PATCH 140/162] test shapely>=2.0b1

---
 .github/workflows/unittests.yml                   | 3 ++-
 ci/{310_shapely20b1.yaml => 310_shapely_dev.yaml} | 2 +-
 2 files changed, 3 insertions(+), 2 deletions(-)
 rename ci/{310_shapely20b1.yaml => 310_shapely_dev.yaml} (96%)

diff --git a/.github/workflows/unittests.yml b/.github/workflows/unittests.yml
index 7fcce33ec..dbccfcce6 100644
--- a/.github/workflows/unittests.yml
+++ b/.github/workflows/unittests.yml
@@ -19,12 +19,13 @@
      strategy:
        matrix:
          os: ['ubuntu-latest']
-         environment-file: [ci/37-minimal.yaml, ci/37.yaml, ci/38.yaml, ci/39.yaml, ci/310.yaml, ci/310_shapely20b1.yaml]
+         environment-file: [ci/37-minimal.yaml, ci/37.yaml, ci/38.yaml, ci/39.yaml, ci/310.yaml, ci/310_shapely_dev.yaml]
          include:
            - environment-file: ci/310.yaml
              os: macos-latest
            - environment-file: ci/310.yaml
              os: windows-latest
+       fail-fast: false
 
      steps:
        - name: checkout repo
diff --git a/ci/310_shapely20b1.yaml b/ci/310_shapely_dev.yaml
similarity index 96%
rename from ci/310_shapely20b1.yaml
rename to ci/310_shapely_dev.yaml
index a8ba2d9f9..33a1cd258 100644
--- a/ci/310_shapely20b1.yaml
+++ b/ci/310_shapely_dev.yaml
@@ -22,7 +22,7 @@ dependencies:
   - networkx
   - numba
   - packaging
-  - shapely==2.0b1
+  - shapely>=2.0b1
   - xarray
   - zstd
   # for docs build action (this env only)

From a61ae97ef8ba44cde4baa84e967dd282ae62a68c Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Thu, 3 Nov 2022 11:29:47 +0000
Subject: [PATCH 141/162] propagate relabelling for gabriel weights

---
 libpysal/weights/gabriel.py | 30 +++++++++++++++++++++++-------
 1 file changed, 23 insertions(+), 7 deletions(-)

diff --git a/libpysal/weights/gabriel.py b/libpysal/weights/gabriel.py
index 58b8e32c4..8924a79fb 100644
--- a/libpysal/weights/gabriel.py
+++ b/libpysal/weights/gabriel.py
@@ -59,8 +59,15 @@ def __init__(self, coordinates, **kwargs):
                 " these computations may become unduly slow on large data."
             )
         edges, _ = self._voronoi_edges(coordinates)
+        ids = kwargs.pop("ids")
+        if ids is not None:
+            ids = numpy.asarray(ids)
+            edges = numpy.column_stack((ids[edges[:, 0]], ids[edges[:, 1]]))
+        else:
+            ids = numpy.arange(coordinates.shape[0])
+
         voronoi_neighbors = pandas.DataFrame(edges).groupby(0)[1].apply(list).to_dict()
-        W.__init__(self, voronoi_neighbors, **kwargs)
+        W.__init__(self, voronoi_neighbors, id_order=ids, **kwargs)
 
     def _voronoi_edges(self, coordinates):
         dt = _Delaunay(coordinates)
@@ -171,15 +178,21 @@ def __init__(self, coordinates, **kwargs):
                 " to accelerate the computation of graphs. Without numba,"
                 " these computations may become unduly slow on large data."
             )
-        edges, _ = self._voronoi_edges(coordinates)
         edges, dt = self._voronoi_edges(coordinates)
         droplist = _filter_gabriel(
             edges,
             dt.points,
         )
-        output = set(map(tuple, edges)).difference(set(droplist))
+        output = numpy.row_stack(list(set(map(tuple, edges)).difference(set(droplist))))
+        ids = kwargs.pop("ids")
+        if ids is not None:
+            ids = numpy.asarray(ids)
+            output = numpy.column_stack((ids[output[:, 0]], ids[output[:, 1]]))
+        else:
+            ids = numpy.arange(coordinates.shape[0])
+
         gabriel_neighbors = pandas.DataFrame(output).groupby(0)[1].apply(list).to_dict()
-        W.__init__(self, gabriel_neighbors, **kwargs)
+        W.__init__(self, gabriel_neighbors, id_order=ids, **kwargs)
 
 
 class Relative_Neighborhood(Delaunay):
@@ -213,15 +226,18 @@ def __init__(self, coordinates, binary=True, **kwargs):
                 " to accelerate the computation of graphs. Without numba,"
                 " these computations may become unduly slow on large data."
             )
-        edges, _ = self._voronoi_edges(coordinates)
         edges, dt = self._voronoi_edges(coordinates)
         output, dkmax = _filter_relativehood(edges, dt.points, return_dkmax=False)
         row, col, data = zip(*output)
         if binary:
             data = numpy.ones_like(col, dtype=float)
         sp = sparse.csc_matrix((data, (row, col)))  # TODO: faster way than this?
-        tmp = WSP(sp).to_W()
-        W.__init__(self, tmp.neighbors, tmp.weights, **kwargs)
+        ids = kwargs.pop("ids")
+        if ids is None:
+            ids = numpy.arange(sp.shape[0])
+        ids = list(ids)
+        tmp = WSP(sp, id_order=ids).to_W()
+        W.__init__(self, tmp.neighbors, tmp.weights, id_order=ids, **kwargs)
 
 
 #### utilities

From 5338bec1eb798ec107723a3f2670133ba9b2ed3c Mon Sep 17 00:00:00 2001
From: ljwolf <levi.john.wolf@gmail.com>
Date: Thu, 3 Nov 2022 12:27:02 +0000
Subject: [PATCH 142/162] get, do not pop, from kwargs

---
 libpysal/weights/gabriel.py | 14 +++++++++-----
 1 file changed, 9 insertions(+), 5 deletions(-)

diff --git a/libpysal/weights/gabriel.py b/libpysal/weights/gabriel.py
index 8924a79fb..f7932d488 100644
--- a/libpysal/weights/gabriel.py
+++ b/libpysal/weights/gabriel.py
@@ -59,15 +59,16 @@ def __init__(self, coordinates, **kwargs):
                 " these computations may become unduly slow on large data."
             )
         edges, _ = self._voronoi_edges(coordinates)
-        ids = kwargs.pop("ids")
+        ids = kwargs.get("ids")
         if ids is not None:
             ids = numpy.asarray(ids)
             edges = numpy.column_stack((ids[edges[:, 0]], ids[edges[:, 1]]))
+            del kwargs["ids"]
         else:
             ids = numpy.arange(coordinates.shape[0])
 
         voronoi_neighbors = pandas.DataFrame(edges).groupby(0)[1].apply(list).to_dict()
-        W.__init__(self, voronoi_neighbors, id_order=ids, **kwargs)
+        W.__init__(self, voronoi_neighbors, id_order=list(ids), **kwargs)
 
     def _voronoi_edges(self, coordinates):
         dt = _Delaunay(coordinates)
@@ -184,15 +185,16 @@ def __init__(self, coordinates, **kwargs):
             dt.points,
         )
         output = numpy.row_stack(list(set(map(tuple, edges)).difference(set(droplist))))
-        ids = kwargs.pop("ids")
+        ids = kwargs.get("ids")
         if ids is not None:
             ids = numpy.asarray(ids)
             output = numpy.column_stack((ids[output[:, 0]], ids[output[:, 1]]))
+            del kwargs["ids"]
         else:
             ids = numpy.arange(coordinates.shape[0])
 
         gabriel_neighbors = pandas.DataFrame(output).groupby(0)[1].apply(list).to_dict()
-        W.__init__(self, gabriel_neighbors, id_order=ids, **kwargs)
+        W.__init__(self, gabriel_neighbors, id_order=list(ids), **kwargs)
 
 
 class Relative_Neighborhood(Delaunay):
@@ -232,9 +234,11 @@ def __init__(self, coordinates, binary=True, **kwargs):
         if binary:
             data = numpy.ones_like(col, dtype=float)
         sp = sparse.csc_matrix((data, (row, col)))  # TODO: faster way than this?
-        ids = kwargs.pop("ids")
+        ids = kwargs.get("ids")
         if ids is None:
             ids = numpy.arange(sp.shape[0])
+        else:
+            del kwargs["ids"]
         ids = list(ids)
         tmp = WSP(sp, id_order=ids).to_W()
         W.__init__(self, tmp.neighbors, tmp.weights, id_order=ids, **kwargs)

From f5271260030b07cc7c981ded1704dee19de0a899 Mon Sep 17 00:00:00 2001
From: Martin Fleischmann <martin@martinfleischmann.net>
Date: Thu, 3 Nov 2022 14:53:42 +0100
Subject: [PATCH 143/162] preserve order in contiguity

---
 libpysal/weights/contiguity.py | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/libpysal/weights/contiguity.py b/libpysal/weights/contiguity.py
index f15a60833..9783d79f3 100644
--- a/libpysal/weights/contiguity.py
+++ b/libpysal/weights/contiguity.py
@@ -236,6 +236,9 @@ def from_dataframe(
             if len(ids) != len(df):
                 raise ValueError("The length of `ids` does not match the length of df.")
 
+        if id_order is None:
+            id_order = ids
+
         return cls.from_iterable(
             df[geom_col].tolist(), ids=ids, id_order=id_order, **kwargs
         )
@@ -519,6 +522,9 @@ def from_dataframe(
             if len(ids) != len(df):
                 raise ValueError("The length of `ids` does not match the length of df.")
 
+        if id_order is None:
+            id_order = ids
+
         return cls.from_iterable(
             df[geom_col].tolist(), ids=ids, id_order=id_order, **kwargs
         )
@@ -639,7 +645,7 @@ def _from_dataframe(df, **kwargs):
     """
     Construct a voronoi contiguity weight directly from a dataframe.
     Note that if criterion='rook', this is identical to the delaunay
-    graph for the points.
+    graph for the points if no clipping of the voronoi cells is applied.
 
     If the input dataframe is of any other geometry type than "Point",
     a value error is raised.

From 390c5c4af030798ad1d6e015d646d4809d4ee6db Mon Sep 17 00:00:00 2001
From: James Gaboardi <jgaboardi@gmail.com>
Date: Tue, 15 Nov 2022 12:29:02 -0500
Subject: [PATCH 144/162] type vs. geom_type with shapely (#488)

* resolve #487

* f-string format

* assert correction
---
 .pre-commit-config.yaml                   | 10 +++---
 libpysal/examples/tests/test_available.py | 39 +++++++++++------------
 libpysal/weights/_contW_lists.py          | 17 +++++-----
 3 files changed, 33 insertions(+), 33 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 3a3211a8a..39677e9ea 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,6 +1,6 @@
 repos: 
--   repo: https://github.com/psf/black
-    rev: 21.12b0
-    hooks:
-    - id: black
-      language_version: python3.9
+- repo: https://github.com/psf/black
+  rev: 22.10.0
+  hooks:
+  - id: black
+    language_version: python3
diff --git a/libpysal/examples/tests/test_available.py b/libpysal/examples/tests/test_available.py
index e8e6d7e6c..2135385f6 100644
--- a/libpysal/examples/tests/test_available.py
+++ b/libpysal/examples/tests/test_available.py
@@ -5,7 +5,7 @@
 import unittest
 import pandas
 
-from .. import (available, get_url, load_example)
+from .. import available, get_url, load_example
 
 from ..base import get_data_home
 
@@ -13,41 +13,40 @@
 
 
 class Testexamples(unittest.TestCase):
-
     def test_available(self):
         examples = available()
         self.assertEqual(type(examples), pandas.core.frame.DataFrame)
         self.assertEqual(examples.shape, (98, 3))
 
-    def test_data_home(self): 
+    def test_data_home(self):
         pth = get_data_home()
         head, tail = os.path.split(pth)
-        self.assertEqual(tail, 'pysal')
-        if os_name == 'Linux':
+        self.assertEqual(tail, "pysal")
+        if os_name == "Linux":
             heads = head.split("/")
-            self.assertEqual(heads[1], 'home')
-            self.assertEqual(heads[-1], 'share')
-            self.assertEqual(heads[-2], '.local')
-        elif os_name == 'Darwin':
+            self.assertEqual(heads[1], "home")
+            self.assertEqual(heads[-1], "share")
+            self.assertEqual(heads[-2], ".local")
+        elif os_name == "Darwin":
             heads = head.split("/")
-            self.assertEqual(heads[1], 'Users')
-            self.assertEqual(heads[-1], 'Application Support')
-            self.assertEqual(heads[-2], 'Library')
-        elif os_name == 'Windows':
+            self.assertEqual(heads[1], "Users")
+            self.assertEqual(heads[-1], "Application Support")
+            self.assertEqual(heads[-2], "Library")
+        elif os_name == "Windows":
             heads = head.split("\\")
-            self.assertEqual(heads[1], 'Users')
-            self.assertEqual(heads[-2], 'Local')
-            self.assertEqual(heads[-3], 'AppData')
+            self.assertEqual(heads[1], "Users")
+            self.assertEqual(heads[-2], "Local")
+            self.assertEqual(heads[-3], "AppData")
 
     def test_get_url(self):
-        self.assertEqual(get_url('10740'), None)
-        url = 'https://geodacenter.github.io/data-and-lab//data/baltimore.zip'
-        self.assertEqual(get_url('Baltimore'), url)
+        self.assertEqual(get_url("10740"), None)
+        url = "https://geodacenter.github.io/data-and-lab//data/baltimore.zip"
+        self.assertEqual(get_url("Baltimore"), url)
 
     def test_load_example(self):
         taz = load_example("taz")
         flist = taz.get_file_list()
-        self.assertEquals(len(flist), 4)
+        self.assertEqual(len(flist), 4)
 
 
 suite = unittest.TestLoader().loadTestsFromTestCase(Testexamples)
diff --git a/libpysal/weights/_contW_lists.py b/libpysal/weights/_contW_lists.py
index 39b36256b..6f71c4cfe 100644
--- a/libpysal/weights/_contW_lists.py
+++ b/libpysal/weights/_contW_lists.py
@@ -20,20 +20,21 @@ def _get_boundary_points(shape):
     Recursively handle polygons vs. multipolygons to
     extract the boundary point set from each.
     """
-    if shape.type.lower() == "polygon":
+    if shape.geom_type.lower() == "polygon":
         shape = shape.boundary
         return _get_boundary_points(shape)
-    elif shape.type.lower() == "linestring":
+    elif shape.geom_type.lower() == "linestring":
         return list(map(tuple, list(zip(*shape.coords.xy))))
-    elif shape.type.lower() == "multilinestring":
+    elif shape.geom_type.lower() == "multilinestring":
         return list(it.chain(*(list(zip(*shape.coords.xy)) for shape in shape.geoms)))
-    elif shape.type.lower() == "multipolygon":
-        return list(it.chain(*(_get_boundary_points(part.boundary) for part in shape.geoms)))
+    elif shape.geom_type.lower() == "multipolygon":
+        return list(
+            it.chain(*(_get_boundary_points(part.boundary) for part in shape.geoms))
+        )
     else:
         raise TypeError(
             "Input shape must be a Polygon, Multipolygon, LineString, "
-            " or MultiLinestring and was "
-            " instead: {}".format(shape.type)
+            f" or MultiLinestring and was instead: {shape.type}"
         )
 
 
@@ -118,5 +119,5 @@ def jcontiguity(self):
                     except:
                         pass
         else:
-            raise Exception("Weight type {} Not Understood!".format(self.wttype))
+            raise Exception(f"Weight type {self.wttype} Not Understood!")
         self.w = w

From d7d7ec049d61d25ad9314586ab43949e143aa50a Mon Sep 17 00:00:00 2001
From: Elliott Sales de Andrade <quantum.analyst@gmail.com>
Date: Mon, 21 Nov 2022 02:11:51 -0500
Subject: [PATCH 145/162] Fix Linux user path test

There is no requirement that user directories start with `/home`. In
fact, if `XDG_DATA_HOME` is set, there's no guarantee of anything
checked in this test.
---
 libpysal/examples/tests/test_available.py | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/libpysal/examples/tests/test_available.py b/libpysal/examples/tests/test_available.py
index 2135385f6..f1bcf2ce2 100644
--- a/libpysal/examples/tests/test_available.py
+++ b/libpysal/examples/tests/test_available.py
@@ -23,10 +23,12 @@ def test_data_home(self):
         head, tail = os.path.split(pth)
         self.assertEqual(tail, "pysal")
         if os_name == "Linux":
-            heads = head.split("/")
-            self.assertEqual(heads[1], "home")
-            self.assertEqual(heads[-1], "share")
-            self.assertEqual(heads[-2], ".local")
+            if "XDG_DATA_HOME" in os.environ:
+                self.assertEqual(head, os.environ["XDG_DATA_HOME"])
+            else:
+                heads = head.split("/")
+                self.assertEqual(heads[-1], "share")
+                self.assertEqual(heads[-2], ".local")
         elif os_name == "Darwin":
             heads = head.split("/")
             self.assertEqual(heads[1], "Users")

From 5b039e639f189728c3f46c21d08f75c326687a67 Mon Sep 17 00:00:00 2001
From: Elliott Sales de Andrade <quantum.analyst@gmail.com>
Date: Mon, 21 Nov 2022 07:03:19 -0500
Subject: [PATCH 146/162] Fix warnings from Sphinx (#492)

---
 libpysal/weights/contiguity.py |  2 +-
 libpysal/weights/distance.py   |  4 ++--
 libpysal/weights/gabriel.py    | 17 +++++++++--------
 libpysal/weights/spintW.py     |  2 +-
 4 files changed, 13 insertions(+), 12 deletions(-)

diff --git a/libpysal/weights/contiguity.py b/libpysal/weights/contiguity.py
index 8522f128d..9c58db701 100644
--- a/libpysal/weights/contiguity.py
+++ b/libpysal/weights/contiguity.py
@@ -38,7 +38,7 @@ class Rook(W):
                 optional arguments for :class:`pysal.weights.W`
 
     See Also
-    ---------
+    --------
     :class:`libpysal.weights.weights.W`
     """
 
diff --git a/libpysal/weights/distance.py b/libpysal/weights/distance.py
index 652a5f545..8c95e9d68 100644
--- a/libpysal/weights/distance.py
+++ b/libpysal/weights/distance.py
@@ -580,7 +580,7 @@ def from_shapefile(cls, filepath, idVariable=None, **kwargs):
         Kernel Weights Object
 
         See Also
-        ---------
+        --------
         :class:`libpysal.weights.weights.W`
         """
         points = get_points_array_from_shapefile(filepath)
@@ -860,7 +860,7 @@ def from_shapefile(cls, filepath, threshold, idVariable=None, **kwargs):
                       name of column in shapefile's DBF to use for ids
 
         Returns
-        --------
+        -------
         Kernel Weights Object
 
         """
diff --git a/libpysal/weights/gabriel.py b/libpysal/weights/gabriel.py
index fd90a5c79..4c168f137 100644
--- a/libpysal/weights/gabriel.py
+++ b/libpysal/weights/gabriel.py
@@ -23,8 +23,8 @@ class Delaunay(W):
     a graph from the input set of points. Will be slower without numba,
     and will warn if this is missing. 
 
-    Arguments
-    ---------
+    Parameters
+    ----------
     coordinates :   array of points, (N,2)
         numpy array of coordinates containing locations to compute the
         delaunay triangulation
@@ -79,8 +79,8 @@ def from_dataframe(cls, df, **kwargs):
         Polygons or lines must be converted to points (e.g. using 
         df.geometry.centroid).
 
-        Arguments
-        ---------
+        Parameters
+        ----------
         df  :   geopandas.GeoDataFrame
             GeoDataFrame containing points to construct the Delaunay 
             Triangulation. 
@@ -114,13 +114,14 @@ class Gabriel(Delaunay):
 
     For a link (i,j) connecting node i to j in the Delaunay triangulation
     to be retained in the Gabriel graph, it must pass a point set exclusion test:
+
     1. Construct the circle C_ij containing link (i,j) as its diameter
     2. If any other node k is contained within C_ij, then remove link (i,j) 
        from the graph. 
     3. Once all links are evaluated, the remaining graph is the Gabriel graph. 
 
-    Arguments
-    ---------
+    Parameters
+    ----------
     coordinates :   array of points, (N,2)
         numpy array of coordinates containing locations to compute the
         delaunay triangulation
@@ -159,8 +160,8 @@ class Relative_Neighborhood(Delaunay):
     This means that the points are at least as close to one another 
     as they are to any other point. 
     
-    Arguments
-    ---------
+    Parameters
+    ----------
     coordinates :   array of points, (N,2)
         numpy array of coordinates containing locations to compute the
         delaunay triangulation
diff --git a/libpysal/weights/spintW.py b/libpysal/weights/spintW.py
index 88f583656..30445bcfc 100644
--- a/libpysal/weights/spintW.py
+++ b/libpysal/weights/spintW.py
@@ -211,7 +211,7 @@ def vecW(
 
 
     Returns
-    ------
+    -------
     W           : DistanceBand W object that uses 4-dimenional distances between
                   vectors origin and destination coordinates.
 

From a24c33ec28d14469a8b4bb4602c3dece785b221c Mon Sep 17 00:00:00 2001
From: Serge Rey <sjsrey@gmail.com>
Date: Fri, 16 Dec 2022 10:52:06 -0800
Subject: [PATCH 147/162] Implements W.from_WSP

---
 libpysal/weights/tests/test_weights.py |  5 ++
 libpysal/weights/weights.py            | 64 +++++++++++++++++++++++++-
 2 files changed, 68 insertions(+), 1 deletion(-)

diff --git a/libpysal/weights/tests/test_weights.py b/libpysal/weights/tests/test_weights.py
index 83f8decf1..ee092fb9d 100644
--- a/libpysal/weights/tests/test_weights.py
+++ b/libpysal/weights/tests/test_weights.py
@@ -689,6 +689,11 @@ def test_trcWtW_WW(self):
     def test_s0(self):
         self.assertEqual(self.w3x3.s0, 24.0)
 
+    def test_from_WSP(self):
+        w = W.from_WSP(self.wsp)
+        self.assertEqual(w.n, 100)
+        self.assertEqual(w.pct_nonzero, 4.62)
+
 
 if __name__ == "__main__":
     unittest.main()
diff --git a/libpysal/weights/weights.py b/libpysal/weights/weights.py
index 8b3b9937a..4f24f32e5 100644
--- a/libpysal/weights/weights.py
+++ b/libpysal/weights/weights.py
@@ -232,7 +232,69 @@ def from_shapefile(cls, *args, **kwargs):
 
     @classmethod
     def from_WSP(cls, WSP, silence_warnings=True):
-        return WSP2W(WSP, silence_warnings=silence_warnings)
+        """Create a pysal W from a pysal WSP object (thin weights matrix).
+
+        Parameters
+        ----------
+        wsp                     : WSP
+                                PySAL sparse weights object
+
+        silence_warnings        : bool
+           By default ``libpysal`` will print a warning if the dataset contains
+           any disconnected components or islands. To silence this warning set this
+           parameter to ``True``.
+
+
+        Returns
+        -------
+        w       : W
+                PySAL weights object
+
+        Examples
+        --------
+        >>> from libpysal.weights import lat2W, WSP, W
+
+        Build a 10x10 scipy.sparse matrix for a rectangular 2x5 region of cells
+        (rook contiguity), then construct a PySAL sparse weights object (wsp).
+
+        >>> sp = lat2SW(2, 5)
+        >>> wsp = WSP(sp)
+        >>> wsp.n
+        10
+        >>> wsp.sparse[0].todense()
+        matrix([[0, 1, 0, 0, 0, 1, 0, 0, 0, 0]], dtype=int8)
+
+        Create a standard PySAL W from this sparse weights object.
+
+        >>> w = W.from_WSP(wsp)
+        >>> w.n
+        10
+        >>> print(w.full()[0][0])
+        [0 1 0 0 0 1 0 0 0 0]
+        """
+        WSP.sparse
+        indices = WSP.sparse.indices
+        data = WSP.sparse.data
+        indptr = WSP.sparse.indptr
+        id_order = WSP.id_order
+        if id_order:
+            # replace indices with user IDs
+            indices = [id_order[i] for i in indices]
+        else:
+            id_order = list(range(WSP.n))
+        neighbors, weights = {}, {}
+        start = indptr[0]
+        for i in range(WSP.n):
+            oid = id_order[i]
+            end = indptr[i + 1]
+            neighbors[oid] = indices[start:end]
+            weights[oid] = data[start:end]
+            start = end
+        ids = copy.copy(WSP.id_order)
+        w = W(neighbors, weights, ids, silence_warnings=silence_warnings)
+        w._sparse = copy.deepcopy(WSP.sparse)
+        w._cache["sparse"] = w._sparse
+        return w
 
     @classmethod
     def from_adjlist(

From 4fb18066d84eda0bd845ed09ad7393988e5948d6 Mon Sep 17 00:00:00 2001
From: Serge Rey <sjsrey@gmail.com>
Date: Fri, 16 Dec 2022 15:37:11 -0800
Subject: [PATCH 148/162] ENH: add W.to_sparse W.from_sparse

---
 libpysal/weights/tests/test_weights.py | 25 +++++++++
 libpysal/weights/weights.py            | 71 +++++++++++++++++++++++++-
 2 files changed, 95 insertions(+), 1 deletion(-)

diff --git a/libpysal/weights/tests/test_weights.py b/libpysal/weights/tests/test_weights.py
index 83f8decf1..412ee6017 100644
--- a/libpysal/weights/tests/test_weights.py
+++ b/libpysal/weights/tests/test_weights.py
@@ -10,6 +10,7 @@
 from ... import examples
 from ..distance import KNN
 import numpy as np
+import scipy.sparse
 
 NPTA3E = np.testing.assert_array_almost_equal
 
@@ -44,6 +45,7 @@ def setUp(self):
         }
 
         self.w3x3 = util.lat2W(3, 3)
+        self.w_islands = W({0: [1], 1: [0, 2], 2: [1], 3: []})
 
     def test_W(self):
         w = W(self.neighbors, self.weights, silence_warnings=True)
@@ -355,6 +357,29 @@ def test_roundtrip_write(self):
             new = W.from_file(path)
         np.testing.assert_array_equal(self.w.sparse.toarray(), new.sparse.toarray())
 
+    def test_to_sparse(self):
+        sparse = self.w_islands.to_sparse()
+        np.testing.assert_array_equal(sparse.data, [1, 1, 1, 1, 0])
+        np.testing.assert_array_equal(sparse.row, [0, 1, 1, 2, 3])
+        np.testing.assert_array_equal(sparse.col, [1, 0, 2, 1, 3])
+        sparse = self.w_islands.to_sparse("bsr")
+        self.assertIsInstance(sparse, scipy.sparse._arrays.bsr_array)
+        sparse = self.w_islands.to_sparse("csr")
+        self.assertIsInstance(sparse, scipy.sparse._arrays.csr_array)
+        sparse = self.w_islands.to_sparse("coo")
+        self.assertIsInstance(sparse, scipy.sparse._arrays.coo_array)
+        sparse = self.w_islands.to_sparse("csc")
+        self.assertIsInstance(sparse, scipy.sparse._arrays.csc_array)
+        sparse = self.w_islands.to_sparse()
+        self.assertIsInstance(sparse, scipy.sparse._arrays.coo_array)
+
+    def test_from_sparse(self):
+        sparse = self.w_islands.to_sparse()
+        w = W.from_sparse(sparse)
+        self.assertEqual(w.n, 4)
+        self.assertEqual(len(w.islands), 0)
+        self.assertEqual(w.neighbors[3], [3])
+
 
 class Test_WSP_Back_To_W(unittest.TestCase):
     # Test to make sure we get back to the same W functionality
diff --git a/libpysal/weights/weights.py b/libpysal/weights/weights.py
index 8b3b9937a..ddcbfe7b1 100644
--- a/libpysal/weights/weights.py
+++ b/libpysal/weights/weights.py
@@ -10,6 +10,8 @@
 import numpy as np
 import scipy.sparse
 from scipy.sparse.csgraph import connected_components
+from sklearn import preprocessing
+from collections import defaultdict
 
 # from .util import full, WSP2W resolve import cycle by
 # forcing these into methods
@@ -385,6 +387,73 @@ def sparse(self):
             self._cache["sparse"] = self._sparse
         return self._sparse
 
+    @classmethod
+    def from_sparse(cls, sparse):
+        """Convert a ``scipy.sparse`` array to a PySAL ``W`` object.
+
+        Parameters
+        ----------
+        sparse : scipy.sparse array
+
+        Returns
+        -------
+        w : libpysal.weights.W
+            A ``W`` object containing the same graph as the ``scipy.sparse`` graph.
+
+
+        Notes
+        -----
+        When the sparse array has a zero in its data attribute, and
+        the corresponding row and column values are equal, the value
+        for the pysal weight will be 0 for the "loop".
+        """
+        coo = sparse.tocoo()
+        neighbors = defaultdict(list)
+        weights = defaultdict(list)
+        for k, v, w in zip(coo.row, coo.col, coo.data):
+            neighbors[k].append(v)
+            weights[k].append(w)
+        return W(neighbors=neighbors, weights=weights)
+
+    def to_sparse(self, fmt="coo"):
+        """Generate a ``scipy.sparse`` array object from a pysal W.
+
+        Parameters
+        ----------
+        fmt : {'bsr', 'coo', 'csc', 'csr'}
+          scipy.sparse format
+
+        Returns
+        -------
+        scipy.sparse array
+          A scipy.sparse array with a format of fmt.
+
+        Notes
+        -----
+        The keys of the w.neighbors are encoded
+        to determine row,col in the sparse array.
+
+        """
+        disp = {}
+        disp["bsr"] = scipy.sparse.bsr_array
+        disp["coo"] = scipy.sparse.coo_array
+        disp["csc"] = scipy.sparse.csc_array
+        disp["csr"] = scipy.sparse.csr_array
+        fmt_l = fmt.lower()
+        if fmt_l in disp:
+            adj_list = self.to_adjlist(drop_islands=False)
+            data = adj_list.weight
+            row = adj_list.focal
+            col = adj_list.neighbor
+            le = preprocessing.LabelEncoder()
+            le.fit(row)
+            row = le.transform(row)
+            col = le.transform(col)
+            n = self.n
+            return disp[fmt_l]((data, (row, col)), shape=(n, n))
+        else:
+            warnings.warn(f"{fmt} not supported.")
+
     @property
     def n_components(self):
         """Store whether the adjacency matrix is fully connected."""
@@ -1306,7 +1375,7 @@ def plot(
         ax.scatter(
             gdf.centroid.apply(lambda p: p.x),
             gdf.centroid.apply(lambda p: p.y),
-            **node_kws
+            **node_kws,
         )
         return f, ax
 

From eae9831ec95b7688329811769d273b48b1254189 Mon Sep 17 00:00:00 2001
From: Serge Rey <sjsrey@gmail.com>
Date: Fri, 16 Dec 2022 16:17:47 -0800
Subject: [PATCH 149/162] Streamlining from_WSP

---
 libpysal/weights/util.py    | 18 ++++++------------
 libpysal/weights/weights.py |  4 +---
 2 files changed, 7 insertions(+), 15 deletions(-)

diff --git a/libpysal/weights/util.py b/libpysal/weights/util.py
index 3f5162bc3..dd8393577 100644
--- a/libpysal/weights/util.py
+++ b/libpysal/weights/util.py
@@ -24,6 +24,7 @@
 
 try:
     from shapely.geometry.base import BaseGeometry
+
     HAS_SHAPELY = True
 except ImportError:
     HAS_SHAPELY = False
@@ -534,17 +535,17 @@ def higher_order_sp(
         )
 
     if lower_order:
-        wk = sum(map(lambda x: w ** x, range(2, k + 1)))
+        wk = sum(map(lambda x: w**x, range(2, k + 1)))
         shortest_path = False
     else:
-        wk = w ** k
+        wk = w**k
 
     rk, ck = wk.nonzero()
     sk = set(zip(rk, ck))
 
     if shortest_path:
         for j in range(1, k):
-            wj = w ** j
+            wj = w**j
             rj, cj = wj.nonzero()
             sj = set(zip(rj, cj))
             sk.difference_update(sj)
@@ -826,14 +827,12 @@ def WSP2W(wsp, **kwargs):
 
 
     """
-    wsp.sparse
-    indices = wsp.sparse.indices
     data = wsp.sparse.data
     indptr = wsp.sparse.indptr
     id_order = wsp.id_order
     if id_order:
         # replace indices with user IDs
-        indices = [id_order[i] for i in indices]
+        indices = [id_order[i] for i in wsp.sparse.indices]
     else:
         id_order = list(range(wsp.n))
     neighbors, weights = {}, {}
@@ -1086,12 +1085,7 @@ def get_points_array(iterable):
                 ]
             )
         else:
-            data = np.vstack(
-                [
-                    np.array(shape.centroid)
-                    for shape in first_choice
-                ]
-            )
+            data = np.vstack([np.array(shape.centroid) for shape in first_choice])
     except AttributeError:
         data = np.vstack([shape for shape in backup])
     return data
diff --git a/libpysal/weights/weights.py b/libpysal/weights/weights.py
index 4f24f32e5..c705582d4 100644
--- a/libpysal/weights/weights.py
+++ b/libpysal/weights/weights.py
@@ -272,14 +272,12 @@ def from_WSP(cls, WSP, silence_warnings=True):
         >>> print(w.full()[0][0])
         [0 1 0 0 0 1 0 0 0 0]
         """
-        WSP.sparse
-        indices = WSP.sparse.indices
         data = WSP.sparse.data
         indptr = WSP.sparse.indptr
         id_order = WSP.id_order
         if id_order:
             # replace indices with user IDs
-            indices = [id_order[i] for i in indices]
+            indices = [id_order[i] for i in WSP.sparse.indices]
         else:
             id_order = list(range(WSP.n))
         neighbors, weights = {}, {}

From b5dfdd24595a4ce90c912a32e303a65d22724843 Mon Sep 17 00:00:00 2001
From: Sergio Rey <sjsrey@gmail.com>
Date: Sat, 17 Dec 2022 09:43:38 -0800
Subject: [PATCH 150/162] Raise Exception on unrecognized sparse fmt (#26)

* ENH: add W.to_sparse W.from_sparse

* Raise ValueError not Warning on bad sparse format
---
 libpysal/weights/tests/test_weights.py | 30 +++++++++++
 libpysal/weights/weights.py            | 71 +++++++++++++++++++++++++-
 2 files changed, 100 insertions(+), 1 deletion(-)

diff --git a/libpysal/weights/tests/test_weights.py b/libpysal/weights/tests/test_weights.py
index ee092fb9d..20165f05f 100644
--- a/libpysal/weights/tests/test_weights.py
+++ b/libpysal/weights/tests/test_weights.py
@@ -2,6 +2,7 @@
 import tempfile
 
 import unittest
+import pytest
 from ..weights import W, WSP
 from .. import util
 from ..util import WSP2W, lat2W
@@ -10,6 +11,7 @@
 from ... import examples
 from ..distance import KNN
 import numpy as np
+import scipy.sparse
 
 NPTA3E = np.testing.assert_array_almost_equal
 
@@ -44,6 +46,7 @@ def setUp(self):
         }
 
         self.w3x3 = util.lat2W(3, 3)
+        self.w_islands = W({0: [1], 1: [0, 2], 2: [1], 3: []})
 
     def test_W(self):
         w = W(self.neighbors, self.weights, silence_warnings=True)
@@ -355,6 +358,33 @@ def test_roundtrip_write(self):
             new = W.from_file(path)
         np.testing.assert_array_equal(self.w.sparse.toarray(), new.sparse.toarray())
 
+    def test_to_sparse(self):
+        sparse = self.w_islands.to_sparse()
+        np.testing.assert_array_equal(sparse.data, [1, 1, 1, 1, 0])
+        np.testing.assert_array_equal(sparse.row, [0, 1, 1, 2, 3])
+        np.testing.assert_array_equal(sparse.col, [1, 0, 2, 1, 3])
+        sparse = self.w_islands.to_sparse("bsr")
+        self.assertIsInstance(sparse, scipy.sparse._arrays.bsr_array)
+        sparse = self.w_islands.to_sparse("csr")
+        self.assertIsInstance(sparse, scipy.sparse._arrays.csr_array)
+        sparse = self.w_islands.to_sparse("coo")
+        self.assertIsInstance(sparse, scipy.sparse._arrays.coo_array)
+        sparse = self.w_islands.to_sparse("csc")
+        self.assertIsInstance(sparse, scipy.sparse._arrays.csc_array)
+        sparse = self.w_islands.to_sparse()
+        self.assertIsInstance(sparse, scipy.sparse._arrays.coo_array)
+
+    def test_sparse_fmt(self):
+        with pytest.raises(ValueError) as exc_info:
+            sparse = self.w_islands.to_sparse("dog")
+
+    def test_from_sparse(self):
+        sparse = self.w_islands.to_sparse()
+        w = W.from_sparse(sparse)
+        self.assertEqual(w.n, 4)
+        self.assertEqual(len(w.islands), 0)
+        self.assertEqual(w.neighbors[3], [3])
+
 
 class Test_WSP_Back_To_W(unittest.TestCase):
     # Test to make sure we get back to the same W functionality
diff --git a/libpysal/weights/weights.py b/libpysal/weights/weights.py
index c705582d4..3b20810a3 100644
--- a/libpysal/weights/weights.py
+++ b/libpysal/weights/weights.py
@@ -10,6 +10,8 @@
 import numpy as np
 import scipy.sparse
 from scipy.sparse.csgraph import connected_components
+from sklearn import preprocessing
+from collections import defaultdict
 
 # from .util import full, WSP2W resolve import cycle by
 # forcing these into methods
@@ -445,6 +447,73 @@ def sparse(self):
             self._cache["sparse"] = self._sparse
         return self._sparse
 
+    @classmethod
+    def from_sparse(cls, sparse):
+        """Convert a ``scipy.sparse`` array to a PySAL ``W`` object.
+
+        Parameters
+        ----------
+        sparse : scipy.sparse array
+
+        Returns
+        -------
+        w : libpysal.weights.W
+            A ``W`` object containing the same graph as the ``scipy.sparse`` graph.
+
+
+        Notes
+        -----
+        When the sparse array has a zero in its data attribute, and
+        the corresponding row and column values are equal, the value
+        for the pysal weight will be 0 for the "loop".
+        """
+        coo = sparse.tocoo()
+        neighbors = defaultdict(list)
+        weights = defaultdict(list)
+        for k, v, w in zip(coo.row, coo.col, coo.data):
+            neighbors[k].append(v)
+            weights[k].append(w)
+        return W(neighbors=neighbors, weights=weights)
+
+    def to_sparse(self, fmt="coo"):
+        """Generate a ``scipy.sparse`` array object from a pysal W.
+
+        Parameters
+        ----------
+        fmt : {'bsr', 'coo', 'csc', 'csr'}
+          scipy.sparse format
+
+        Returns
+        -------
+        scipy.sparse array
+          A scipy.sparse array with a format of fmt.
+
+        Notes
+        -----
+        The keys of the w.neighbors are encoded
+        to determine row,col in the sparse array.
+
+        """
+        disp = {}
+        disp["bsr"] = scipy.sparse.bsr_array
+        disp["coo"] = scipy.sparse.coo_array
+        disp["csc"] = scipy.sparse.csc_array
+        disp["csr"] = scipy.sparse.csr_array
+        fmt_l = fmt.lower()
+        if fmt_l in disp:
+            adj_list = self.to_adjlist(drop_islands=False)
+            data = adj_list.weight
+            row = adj_list.focal
+            col = adj_list.neighbor
+            le = preprocessing.LabelEncoder()
+            le.fit(row)
+            row = le.transform(row)
+            col = le.transform(col)
+            n = self.n
+            return disp[fmt_l]((data, (row, col)), shape=(n, n))
+        else:
+            raise ValueError(f"unsupported sparse format: {fmt}")
+
     @property
     def n_components(self):
         """Store whether the adjacency matrix is fully connected."""
@@ -1366,7 +1435,7 @@ def plot(
         ax.scatter(
             gdf.centroid.apply(lambda p: p.x),
             gdf.centroid.apply(lambda p: p.y),
-            **node_kws
+            **node_kws,
         )
         return f, ax
 

From 6bf09ce6c561e0a427834e59f8f72936d7480bae Mon Sep 17 00:00:00 2001
From: James Gaboardi <jgaboardi@gmail.com>
Date: Tue, 27 Dec 2022 11:28:18 -0500
Subject: [PATCH 151/162] drop 3.7 support

---
 .github/workflows/unittests.yml         |  8 +++++++-
 ci/37.yaml                              | 25 -------------------------
 ci/{37-minimal.yaml => 38-minimal.yaml} |  4 ++--
 setup.py                                |  1 -
 4 files changed, 9 insertions(+), 29 deletions(-)
 delete mode 100644 ci/37.yaml
 rename ci/{37-minimal.yaml => 38-minimal.yaml} (86%)

diff --git a/.github/workflows/unittests.yml b/.github/workflows/unittests.yml
index dbccfcce6..010415a71 100644
--- a/.github/workflows/unittests.yml
+++ b/.github/workflows/unittests.yml
@@ -19,7 +19,13 @@
      strategy:
        matrix:
          os: ['ubuntu-latest']
-         environment-file: [ci/37-minimal.yaml, ci/37.yaml, ci/38.yaml, ci/39.yaml, ci/310.yaml, ci/310_shapely_dev.yaml]
+         environment-file: [
+            ci/38-minimal.yaml,
+            ci/38.yaml,
+            ci/39.yaml,
+            ci/310.yaml,
+            ci/310_shapely_dev.yaml
+         ]
          include:
            - environment-file: ci/310.yaml
              os: macos-latest
diff --git a/ci/37.yaml b/ci/37.yaml
deleted file mode 100644
index dfa28134d..000000000
--- a/ci/37.yaml
+++ /dev/null
@@ -1,25 +0,0 @@
-name: test
-channels:
-  - conda-forge
-dependencies:
-  - python=3.7
-  - platformdirs
-  - beautifulsoup4
-  - jinja2
-  - pandas>=1.0
-  - scipy>=1.0
-  - xarray
-  # testing
-  - codecov
-  - matplotlib
-  - pytest
-  - pytest-cov
-  - pytest-xdist
-  - networkx=2.7
-  # optional
-  - geopandas>=0.7.0
-  - joblib
-  - numba
-  - packaging
-  - xarray
-  - zstd
diff --git a/ci/37-minimal.yaml b/ci/38-minimal.yaml
similarity index 86%
rename from ci/37-minimal.yaml
rename to ci/38-minimal.yaml
index e8ff14647..c53fb4cfb 100644
--- a/ci/37-minimal.yaml
+++ b/ci/38-minimal.yaml
@@ -2,7 +2,7 @@ name: test
 channels:
   - conda-forge
 dependencies:
-  - python=3.7
+  - python=3.8
   - platformdirs
   - beautifulsoup4
   - jinja2
@@ -14,4 +14,4 @@ dependencies:
   - matplotlib
   - pytest
   - pytest-cov
-  - pytest-xdist
\ No newline at end of file
+  - pytest-xdist
diff --git a/setup.py b/setup.py
index 9cb28ef07..93d1ad753 100644
--- a/setup.py
+++ b/setup.py
@@ -83,7 +83,6 @@ def setup_package():
             "Topic :: Scientific/Engineering :: GIS",
             "License :: OSI Approved :: BSD License",
             "Programming Language :: Python",
-            "Programming Language :: Python :: 3.7",
             "Programming Language :: Python :: 3.8",
             "Programming Language :: Python :: 3.9",
             "Programming Language :: Python :: 3.10",

From b9261b2cdbe9fb467d53faaacf46d1c04d6a103b Mon Sep 17 00:00:00 2001
From: James Gaboardi <jgaboardi@gmail.com>
Date: Tue, 27 Dec 2022 11:35:17 -0500
Subject: [PATCH 152/162] try 3.11 again

---
 .github/workflows/unittests.yml |  7 ++++---
 ci/311.yaml                     | 31 +++++++++++++++++++++++++++++++
 2 files changed, 35 insertions(+), 3 deletions(-)
 create mode 100644 ci/311.yaml

diff --git a/.github/workflows/unittests.yml b/.github/workflows/unittests.yml
index 010415a71..d3283fcd3 100644
--- a/.github/workflows/unittests.yml
+++ b/.github/workflows/unittests.yml
@@ -24,12 +24,13 @@
             ci/38.yaml,
             ci/39.yaml,
             ci/310.yaml,
-            ci/310_shapely_dev.yaml
+            ci/310_shapely_dev.yaml,
+            ci/311.yaml,
          ]
          include:
-           - environment-file: ci/310.yaml
+           - environment-file: ci/311.yaml
              os: macos-latest
-           - environment-file: ci/310.yaml
+           - environment-file: ci/311.yaml
              os: windows-latest
        fail-fast: false
 
diff --git a/ci/311.yaml b/ci/311.yaml
new file mode 100644
index 000000000..c25244339
--- /dev/null
+++ b/ci/311.yaml
@@ -0,0 +1,31 @@
+name: test
+channels:
+  - conda-forge
+dependencies:
+  - python=3.11
+  - platformdirs
+  - beautifulsoup4
+  - jinja2
+  - pandas>=1.0
+  - scipy>=1.0
+  - xarray
+  # testing
+  - codecov
+  - matplotlib
+  - pytest
+  - pytest-cov
+  - pytest-xdist
+  # optional
+  - geopandas>=0.7.0
+  - joblib
+  - networkx
+  - numba
+  - packaging
+  - xarray
+  - zstd
+  # for docs build action (this env only)
+  - nbsphinx
+  - numpydoc
+  - sphinx
+  - sphinxcontrib-bibtex
+  - sphinx_bootstrap_theme

From bdcee9bb8f23e53df3bdf9db1faa10b2f8031681 Mon Sep 17 00:00:00 2001
From: James Gaboardi <jgaboardi@gmail.com>
Date: Tue, 27 Dec 2022 11:41:00 -0500
Subject: [PATCH 153/162] still no numba for 3.11

---
 .github/workflows/unittests.yml |  7 +++----
 ci/311.yaml                     | 31 -------------------------------
 2 files changed, 3 insertions(+), 35 deletions(-)
 delete mode 100644 ci/311.yaml

diff --git a/.github/workflows/unittests.yml b/.github/workflows/unittests.yml
index d3283fcd3..010415a71 100644
--- a/.github/workflows/unittests.yml
+++ b/.github/workflows/unittests.yml
@@ -24,13 +24,12 @@
             ci/38.yaml,
             ci/39.yaml,
             ci/310.yaml,
-            ci/310_shapely_dev.yaml,
-            ci/311.yaml,
+            ci/310_shapely_dev.yaml
          ]
          include:
-           - environment-file: ci/311.yaml
+           - environment-file: ci/310.yaml
              os: macos-latest
-           - environment-file: ci/311.yaml
+           - environment-file: ci/310.yaml
              os: windows-latest
        fail-fast: false
 
diff --git a/ci/311.yaml b/ci/311.yaml
deleted file mode 100644
index c25244339..000000000
--- a/ci/311.yaml
+++ /dev/null
@@ -1,31 +0,0 @@
-name: test
-channels:
-  - conda-forge
-dependencies:
-  - python=3.11
-  - platformdirs
-  - beautifulsoup4
-  - jinja2
-  - pandas>=1.0
-  - scipy>=1.0
-  - xarray
-  # testing
-  - codecov
-  - matplotlib
-  - pytest
-  - pytest-cov
-  - pytest-xdist
-  # optional
-  - geopandas>=0.7.0
-  - joblib
-  - networkx
-  - numba
-  - packaging
-  - xarray
-  - zstd
-  # for docs build action (this env only)
-  - nbsphinx
-  - numpydoc
-  - sphinx
-  - sphinxcontrib-bibtex
-  - sphinx_bootstrap_theme

From fc0633d0539eb4223e24a0a796db5957ae69140e Mon Sep 17 00:00:00 2001
From: Serge Rey <sjsrey@gmail.com>
Date: Thu, 29 Dec 2022 16:19:55 -0800
Subject: [PATCH 154/162] Drop 3.8 minimal test as it is redundant

---
 .github/workflows/unittests.yml | 1 -
 1 file changed, 1 deletion(-)

diff --git a/.github/workflows/unittests.yml b/.github/workflows/unittests.yml
index 010415a71..8284b6a5e 100644
--- a/.github/workflows/unittests.yml
+++ b/.github/workflows/unittests.yml
@@ -20,7 +20,6 @@
        matrix:
          os: ['ubuntu-latest']
          environment-file: [
-            ci/38-minimal.yaml,
             ci/38.yaml,
             ci/39.yaml,
             ci/310.yaml,

From 2a2d9ff44e34887017bb069116ece8799fd649ac Mon Sep 17 00:00:00 2001
From: Serge Rey <sjsrey@gmail.com>
Date: Thu, 29 Dec 2022 16:28:59 -0800
Subject: [PATCH 155/162] remove stale file

---
 ci/38-minimal.yaml | 17 -----------------
 1 file changed, 17 deletions(-)
 delete mode 100644 ci/38-minimal.yaml

diff --git a/ci/38-minimal.yaml b/ci/38-minimal.yaml
deleted file mode 100644
index c53fb4cfb..000000000
--- a/ci/38-minimal.yaml
+++ /dev/null
@@ -1,17 +0,0 @@
-name: test
-channels:
-  - conda-forge
-dependencies:
-  - python=3.8
-  - platformdirs
-  - beautifulsoup4
-  - jinja2
-  - pandas>=1.0
-  - scipy>=1.0
-  - xarray
-  # testing
-  - codecov
-  - matplotlib
-  - pytest
-  - pytest-cov
-  - pytest-xdist

From c7966730d4a41fdd67f7204fe0b2f2a3acd63ff6 Mon Sep 17 00:00:00 2001
From: Martin Fleischmann <martin@martinfleischmann.net>
Date: Sun, 1 Jan 2023 22:31:02 +0100
Subject: [PATCH 156/162] test order

---
 libpysal/weights/tests/test_contiguity.py | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/libpysal/weights/tests/test_contiguity.py b/libpysal/weights/tests/test_contiguity.py
index b6d9e12db..ea6b3877f 100644
--- a/libpysal/weights/tests/test_contiguity.py
+++ b/libpysal/weights/tests/test_contiguity.py
@@ -123,6 +123,16 @@ def test_from_geodataframe(self):
         w = self.cls.from_dataframe(df, geom_col="the_geom", ids=self.idVariable)
         self.assertEqual(w[self.known_name], self.known_namedw)
 
+    @ut.skipIf(GEOPANDAS_EXTINCT, "Missing geopandas")
+    def test_from_geodataframe_order(self):
+        import geopandas
+
+        south = geopandas.read_file(pysal_examples.get_path("south.shp"))
+        expected = south.FIPS.iloc[:5].tolist()
+        for ids_ in ("FIPS", south.FIPS):
+            w = self.cls.from_dataframe(south, ids=ids_)
+            self.assertEqual(w.id_order[:5], expected)
+
     def test_from_xarray(self):
         w = self.cls.from_xarray(self.da, sparse=False, n_jobs=-1)
         self.assertEqual(w[self.known_wi_da], self.known_w_da)

From 2c3b43d8a202fbc785501582faf983c78ac07573 Mon Sep 17 00:00:00 2001
From: Serge Rey <sjsrey@gmail.com>
Date: Wed, 4 Jan 2023 14:47:53 -0800
Subject: [PATCH 157/162] Remove automatic polling of remotes

---
 libpysal/examples/base.py    |  18 +-
 libpysal/examples/remotes.py | 374 ++++++++++++++++++++++++++++++++++-
 2 files changed, 377 insertions(+), 15 deletions(-)

diff --git a/libpysal/examples/base.py b/libpysal/examples/base.py
index 21f7753a1..bd15b78d3 100644
--- a/libpysal/examples/base.py
+++ b/libpysal/examples/base.py
@@ -175,13 +175,17 @@ def download(self, path=get_data_home()):
         """Download the files for the example."""
 
         if not self.downloaded():
-            request = requests.get(self.download_url)
-            archive = zipfile.ZipFile(io.BytesIO(request.content))
-            target = os.path.join(path, self.root)
-            print("Downloading {} to {}".format(self.name, target))
-            archive.extractall(path=target)
-            self.zipfile = archive
-            self.installed = True
+            try:
+                request = requests.get(self.download_url)
+                archive = zipfile.ZipFile(io.BytesIO(request.content))
+                target = os.path.join(path, self.root)
+                print("Downloading {} to {}".format(self.name, target))
+                archive.extractall(path=target)
+                self.zipfile = archive
+                self.installed = True
+            except requests.exceptions.RequestException as e:  
+                raise SystemExit(e)
+            
 
     def get_file_list(self) -> Union[list, None]:
         """Get the list of local files for the example."""
diff --git a/libpysal/examples/remotes.py b/libpysal/examples/remotes.py
index f4aaf2a47..4bca64262 100644
--- a/libpysal/examples/remotes.py
+++ b/libpysal/examples/remotes.py
@@ -6,16 +6,355 @@
 from .base import Example
 
 
-def poll_remotes():
-    """Fetch remote data and generate example datasets.
+# remote_dict holds the metadata for remote datasets from the geoda center
+# to update prior to release run _remote_data()
+
+_remote_dict = {'AirBnB': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/airbnb.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//airbnb/',
+  'n': '77',
+  'k': '20',
+  'description': 'Airbnb rentals, socioeconomics, and crime in Chicago'},
+ 'Atlanta': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/atlanta_hom.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//atlanta_old/',
+  'n': '90',
+  'k': '23',
+  'description': 'Atlanta, GA region homicide counts and rates'},
+ 'Baltimore': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/baltimore.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//baltim/',
+  'n': '211',
+  'k': '17',
+  'description': 'Baltimore house sales prices and hedonics'},
+ 'Bostonhsg': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/boston.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//boston-housing/',
+  'n': '506',
+  'k': '23',
+  'description': 'Boston housing and neighborhood data'},
+ 'Buenosaires': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/buenosaires.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//buenos-aires_old/',
+  'n': ' 209',
+  'k': ' 21',
+  'description': ' Electoral Data for 1999 Argentinean Elections'},
+ 'Cars': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/Abandoned_Vehicles_Map.csv',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//1-source-and-description/',
+  'n': '137,867',
+  'k': '21',
+  'description': '2011 abandoned vehicles in Chicago (311 complaints).'},
+ 'Charleston1': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/CharlestonMSA.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//charleston-1_old/',
+  'n': ' 117',
+  'k': ' 30',
+  'description': ' 2000 Census Tract Data for Charleston, SC MSA and counties'},
+ 'Charleston2': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/CharlestonMSA2.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//charleston2/',
+  'n': ' 44',
+  'k': ' 97',
+  'description': ' 1998 and 2001 Zip Code Business Patterns (Census Bureau) for Charleston, SC MSA'},
+ 'Chicago Health': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/comarea.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//comarea_vars/',
+  'n': ' 77',
+  'k': ' 86',
+  'description': ' Chicago Health + Socio-Economics'},
+ 'Chicago commpop': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/chicago_commpop.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//commpop/',
+  'n': ' 77',
+  'k': ' 8',
+  'description': ' Chicago Community Area Population Percent Change for 2000 and 2010'},
+ 'Chicago parcels': {'download_url': 'https://geodacenter.github.io/data-and-lab//https://uchicago.box.com/s/j2d2ch5uvckse24y8l7vh9198wnq216i',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//parcels/',
+  'n': ' 592,521',
+  'k': ' 5',
+  'description': ' Tax parcel polygons of Cook county'},
+ 'Chile Labor': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/flma.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//FLMA/',
+  'n': '141',
+  'k': '62',
+  'description': 'Labor Markets in Chile (1982-2002)'},
+ 'Chile Migration': {'download_url': 'https://geodacenter.github.io/data-and-lab//https://uchicago.box.com/s/yqc97nq23hoeeqo5lkc2grlg98skokgk',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//CHIM/',
+  'n': ' 304',
+  'k': ' 10',
+  'description': ' Internal Migration in Chile (1977-2002)'},
+ 'Cincinnati': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/walnuthills_updated.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//walnut_hills/',
+  'n': ' 457',
+  'k': ' 89',
+  'description': ' 2008 Cincinnati Crime + Socio-Demographics'},
+ 'Cleveland': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/cleveland.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//clev_sls_154_core/',
+  'n': ' 205',
+  'k': ' 9',
+  'description': ' 2015 sales prices of homes in Cleveland, OH.'},
+ 'Columbus': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/columbus.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//columbus/',
+  'n': ' 49',
+  'k': ' 20',
+  'description': ' Columbus neighborhood crime'},
+ 'Elections': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/election.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//county_election_2012_2016-variables/',
+  'n': ' 3,108',
+  'k': ' 74',
+  'description': ' 2012 and 2016 Presidential Elections'},
+ 'Grid100': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/grid100.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//grid100/',
+  'n': ' 100',
+  'k': ' 34',
+  'description': ' Grid with simulated variables'},
+ 'Groceries': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/grocery.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//chicago_sup_vars/',
+  'n': ' 148',
+  'k': ' 7',
+  'description': ' 2015 Chicago supermarkets'},
+ 'Guerry': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/guerry.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//Guerry/',
+  'n': ' 85',
+  'k': ' 23',
+  'description': ' Moral statistics of France (Guerry, 1833)'},
+ 'Health+': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/income_diversity.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//co_income_diversity_variables/',
+  'n': ' 3,984',
+  'k': ' 64',
+  'description': ' 2000 Health, Income + Diversity'},
+ 'Health Indicators': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/healthIndicators.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//healthindicators-variables/',
+  'n': ' 77',
+  'k': ' 31',
+  'description': ' Chicago Health Indicators (2005-11)'},
+ 'Hickory1': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/HickoryMSA.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//hickory1/',
+  'n': ' 68',
+  'k': ' 30',
+  'description': ' 2000 Census Tract Data for Hickory, NC MSA and counties'},
+ 'Hickory2': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/HickoryMSA2.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//hickory2/',
+  'n': ' 29',
+  'k': ' 55',
+  'description': ' 1998 and 2001 Zip Code Business Patterns (Census Bureau) for Hickory, NC MSA'},
+ 'Home Sales': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/kingcounty.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//KingCounty-HouseSales2015/',
+  'n': ' 21,613',
+  'k': ' 21',
+  'description': ' 2014-15 Home Sales in King County, WA'},
+ 'Houston': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/houston_hom.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//houston/',
+  'n': ' 52',
+  'k': ' 23',
+  'description': ' Houston, TX region homicide counts and rates'},
+ 'Juvenile': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/juvenile.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//juvenile/',
+  'n': ' 168',
+  'k': ' 3',
+  'description': ' Cardiff juvenile delinquent residences'},
+ 'Lansing1': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/LansingMSA.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//lansing1/',
+  'n': ' 117',
+  'k': ' 30',
+  'description': ' 2000 Census Tract Data for Lansing, MI MSA and counties'},
+ 'Lansing2': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/LansingMSA2.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//lansing2/',
+  'n': ' 46',
+  'k': ' 55',
+  'description': ' 1998 and 2001 Zip Code Business Patterns (Census Bureau) for Lansing, MI MSA'},
+ 'Laozone': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/laozone.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//ozone/',
+  'n': ' 32',
+  'k': ' 8',
+  'description': ' Ozone measures at monitoring stations in Los Angeles basin'},
+ 'LasRosas': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/lasrosas.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//lasrosas/',
+  'n': ' 1,738',
+  'k': ' 34',
+  'description': ' Corn yield, fertilizer and field data for precision agriculture, Argentina, 1999'},
+ 'Liquor Stores': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/liquor.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//liq_chicago/',
+  'n': ' 571',
+  'k': ' 2',
+  'description': ' 2015 Chicago Liquor Stores'},
+ 'Malaria': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/malariacolomb.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//colomb_malaria/',
+  'n': ' 1,068',
+  'k': ' 50',
+  'description': ' Malaria incidence and population (1973, 95, 93 censuses and projections until 2005) \xa0 \xa0 \xa0'},
+ 'Milwaukee1': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/MilwaukeeMSA.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//milwaukee1/',
+  'n': ' 417',
+  'k': ' 31',
+  'description': ' 2000 Census Tract Data for Milwaukee, WI MSA'},
+ 'Milwaukee2': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/MilwaukeeMSA2.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//milwaukee2/',
+  'n': ' 83',
+  'k': ' 55',
+  'description': ' 1998 and 2001 Zip Code Business Patterns (Census Bureau) for Milwaukee, WI MSA'},
+ 'NCOVR': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/ncovr.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//ncovr/',
+  'n': '3,085',
+  'k': ' 69',
+  'description': ' US county homicides 1960-1990'},
+ 'Natregimes': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/natregimes.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//natregimes/',
+  'n': ' 3,085',
+  'k': ' 73',
+  'description': ' NCOVR with regimes (book/PySAL)'},
+ 'NDVI': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/ndvi.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//ndvi/',
+  'n': ' 49',
+  'k': ' 5',
+  'description': ' Normalized Difference Vegetation Index grid'},
+ 'Nepal': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/nepal.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//nepal/',
+  'n': ' 75',
+  'k': ' 61',
+  'description': ' Health, poverty and education indicators for Nepal districts'},
+ 'NYC': {'download_url': 'https://geodacenter.github.io/data-and-lab///data/nyc.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//nyc/',
+  'n': ' 55',
+  'k': ' 34',
+  'description': ' Demographic and housing data for New York City subboroughs, 2002-09'},
+ 'NYC Earnings': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/lehd.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//LEHD_Data/',
+  'n': ' 108,487',
+  'k': ' 70',
+  'description': ' Block-level Earnings in NYC (2002-14)'},
+ 'NYC Education': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/nyc_2000Census.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//NYC-Census-2000/',
+  'n': ' 2,216',
+  'k': ' 56',
+  'description': ' NYC Education (2000)'},
+ 'NYC Neighborhoods': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/nycnhood_acs.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//NYC-Nhood-ACS-2008-12/',
+  'n': ' 195',
+  'k': ' 98',
+  'description': ' Demographics for New York City neighborhoods'},
+ 'NYC Socio-Demographics': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/nyctract_acs.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//NYC_Tract_ACS2008_12/',
+  'n': ' 2,166',
+  'k': ' 113',
+  'description': ' NYC Education + Socio-Demographics'},
+ 'Ohiolung': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/ohiolung.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//ohiolung/',
+  'n': ' 88',
+  'k': ' 42',
+  'description': ' Ohio lung cancer data, 1968, 1978, 1988'},
+ 'Orlando1': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/OrlandoMSA.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//orlando1/',
+  'n': ' 328',
+  'k': ' 30',
+  'description': ' 2000 Census Tract Data for Orlando, FL MSA and counties'},
+ 'Orlando2': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/OrlandoMSA2.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//orlando2/',
+  'n': ' 94',
+  'k': ' 59',
+  'description': ' 1998 and 2001 Zip Code Business Patterns (Census Bureau) for Orlando, FL MSA'},
+ 'Oz9799': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/oz9799.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//oz96/',
+  'n': ' 30',
+  'k': ' 78',
+  'description': ' Monthly ozone data, 1997-99'},
+ 'Phoenix ACS': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/phx2.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//phx/',
+  'n': ' 685',
+  'k': ' 17',
+  'description': ' Phoenix American Community Survey Data (2010, 5-year averages)'},
+ 'Pittsburgh': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/pittsburgh.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//pitt93/',
+  'n': ' 143',
+  'k': ' 8',
+  'description': ' Pittsburgh homicide locations'},
+ 'Police': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/police.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//police/',
+  'n': ' 82',
+  'k': ' 21',
+  'description': ' Police expenditures Mississippi counties'},
+ 'Sacramento1': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/sacramento.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//sacramento1/',
+  'n': ' 403',
+  'k': ' 30',
+  'description': ' 2000 Census Tract Data for Sacramento MSA'},
+ 'Sacramento2': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/SacramentoMSA2.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//sacramento2/',
+  'n': ' 125',
+  'k': ' 53',
+  'description': ' 1998 and 2001 Zip Code Business Patterns (Census Bureau) for Sacramento MSA'},
+ 'SanFran Crime': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/SFCrime_July_Dec2012.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//SFcrimes_vars/',
+  'n': ' 3,384',
+  'k': ' 13',
+  'description': ' July-Dec 2012 crime incidents in San Francisco (points + area) - for CAST'},
+ 'Savannah1': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/SavannahMSA.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//savannah1/',
+  'n': ' 77',
+  'k': ' 30',
+  'description': ' 2000 Census Tract Data for Savannah, GA MSA and counties'},
+ 'Savannah2': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/SavannahMSA2.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//savannah2/',
+  'n': ' 24',
+  'k': ' 55',
+  'description': ' 1998 and 2001 Zip Code Business Patterns (Census Bureau) for Savannah, GA MSA'},
+ 'Scotlip': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/scotlip.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//scotlip/',
+  'n': ' 56',
+  'k': ' 11',
+  'description': ' Male lip cancer in Scotland, 1975-80'},
+ 'Seattle1': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/SeattleMSA.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//seattle1/',
+  'n': ' 664',
+  'k': ' 30',
+  'description': ' 2000 Census Tract Data for Seattle, WA MSA and counties'},
+ 'Seattle2': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/SeattleMSA2.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//seattle2/',
+  'n': ' 145',
+  'k': ' 59',
+  'description': ' 1998 and 2001 Zip Code Business Patterns (Census Bureau) for Seattle, WA MSA'},
+ 'SIDS': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/sids.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//sids/',
+  'n': ' 100',
+  'k': ' 13',
+  'description': ' North Carolina county SIDS death counts'},
+ 'SIDS2': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/sids2.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//sids2/',
+  'n': ' 100',
+  'k': ' 17',
+  'description': ' North Carolina county SIDS death counts and rates'},
+ 'Snow': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/snow.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//snow/',
+  'n': ' NA',
+  'k': ' NA',
+  'description': ' John Snow & the 19th Century Cholera Epidemic'},
+ 'South': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/south.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//south/',
+  'n': ' 1,412',
+  'k': ' 69',
+  'description': ' US Southern county homicides 1960-1990'},
+ 'Spirals': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/spirals.csv',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//spirals/',
+  'n': ' 301',
+  'k': ' 2',
+  'description': ' Synthetic spiral points'},
+ 'StLouis': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/stlouis.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//stlouis/',
+  'n': ' 78',
+  'k': ' 23',
+  'description': ' St Louis region county homicide counts and rates'},
+ 'Tampa1': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/TampaMSA.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//tampa1/',
+  'n': ' 547',
+  'k': ' 30',
+  'description': ' 2000 Census Tract Data for Tampa, FL MSA and counties'},
+ 'US SDOH': {'download_url': 'https://geodacenter.github.io/data-and-lab//data/us-sdoh-2014.zip',
+  'explain_url': 'https://geodacenter.github.io/data-and-lab//us-sdoh/',
+  'n': ' 71,901',
+  'k': ' 25',
+  'description': ' 2014 US Social Determinants of Health Data'}}
+
+def _remote_data():
+    """Helper function to get remote metadata for each release.
 
     Returns
     -------
     datasets : dict
-        Example datasets keyed by the dataset name.
-
+        Remote data sets keyed by the dataset name.
+        Values are dictionaries with the following keys
+        'download_url', 'explain_url', 'n', 'k', 'description'.
     """
-    # Geoda Center Data Sets
 
     url = "https://geodacenter.github.io/data-and-lab//"
     try:
@@ -36,10 +375,29 @@ def poll_remotes():
         targets = row.find_all("a")
         download_url = url + targets[1].attrs["href"]
         explain_url = url + targets[0].attrs["href"]
-        datasets[name] = Example(name, description, n, k, download_url, explain_url)
+        datasets[name] = {'download_url': download_url, 'explain_url': explain_url, 'n': n, 'k': k, 'description': description}
+    return datasets
 
-    # Other Remotes
 
+def _build_remotes():
+    """Build remote meta data.
+
+    Returns
+    -------
+    datasets : dict
+        Example datasets keyed by the dataset name.
+
+    """
+    datasets = {}
+    for name in _remote_dict:
+        description = _remote_dict[name]["description"]
+        n = _remote_dict[name]["n"]
+        k = _remote_dict[name]["k"]
+        download_url = _remote_dict[name]["download_url"]
+        explain_url = _remote_dict[name]["explain_url"]
+        datasets[name] = Example(name, description, n, k, download_url, explain_url)
+    
+    # Other Remotes
     # rio
     name = "Rio Grande do Sul"
     description = "Cities of the Brazilian State of Rio Grande do Sul"
@@ -104,7 +462,7 @@ def __init__(self):
     def datasets(self):
         """Create dictionary of remotes."""
         if self._datasets is None:
-            self._datasets = poll_remotes()
+            self._datasets = _build_remotes()
         return self._datasets
 
 

From caf385ed676836ae9a139521309901d989c6212e Mon Sep 17 00:00:00 2001
From: Serge Rey <sjsrey@gmail.com>
Date: Fri, 13 Jan 2023 10:25:39 -0800
Subject: [PATCH 158/162] Remove sklearn dependency

---
 libpysal/weights/tests/test_weights.py | 11 +++++-
 libpysal/weights/weights.py            | 54 +++++++++++++++++++++++++-
 2 files changed, 62 insertions(+), 3 deletions(-)

diff --git a/libpysal/weights/tests/test_weights.py b/libpysal/weights/tests/test_weights.py
index 20165f05f..0c3e5f884 100644
--- a/libpysal/weights/tests/test_weights.py
+++ b/libpysal/weights/tests/test_weights.py
@@ -3,7 +3,7 @@
 
 import unittest
 import pytest
-from ..weights import W, WSP
+from ..weights import W, WSP, _LabelEncoder
 from .. import util
 from ..util import WSP2W, lat2W
 from ..contiguity import Rook
@@ -724,6 +724,15 @@ def test_from_WSP(self):
         self.assertEqual(w.n, 100)
         self.assertEqual(w.pct_nonzero, 4.62)
 
+    def test_LabelEncoder(self):
+        le = _LabelEncoder()
+        le.fit(["NY", "CA", "NY", "CA", "TX", "TX"])
+        np.testing.assert_equal(le.classes_, np.array(["CA", "NY", "TX"]))
+        np.testing.assert_equal(
+            le.transform(["NY", "CA", "NY", "CA", "TX", "TX"]),
+            np.array([1, 0, 1, 0, 2, 2]),
+        )
+
 
 if __name__ == "__main__":
     unittest.main()
diff --git a/libpysal/weights/weights.py b/libpysal/weights/weights.py
index 3b20810a3..9f1e4e8d6 100644
--- a/libpysal/weights/weights.py
+++ b/libpysal/weights/weights.py
@@ -10,9 +10,9 @@
 import numpy as np
 import scipy.sparse
 from scipy.sparse.csgraph import connected_components
-from sklearn import preprocessing
 from collections import defaultdict
 
+
 # from .util import full, WSP2W resolve import cycle by
 # forcing these into methods
 from . import adjtools
@@ -21,6 +21,56 @@
 __all__ = ["W", "WSP"]
 
 
+class _LabelEncoder(object):
+    """Encode labels with values between 0 and n_classes-1.
+
+    Attributes
+    ----------
+    classes_: array of shape [n_classes]
+        Class labels for each index.
+
+    Examples
+    --------
+    >>> le = _LabelEncoder()
+    >>> le.fit(["NY", "CA", "NY", "CA", "TX", "TX"])
+    >>> le.classes_
+    array(['CA', 'NY', 'TX'])
+    >>> le.transform(["NY", "CA", "NY", "CA", "TX", "TX"])
+    array([1, 0, 1, 0, 2, 2])
+    """
+
+    def fit(self, y):
+        """Fit label encoder.
+
+        Parameters
+        ----------
+        y : list
+            list of labels
+
+        Returns
+        -------
+        self : instance of self.
+          Fitted label encoder.
+        """
+        self.classes_ = np.unique(y)
+        return self
+
+    def transform(self, y):
+        """Transform labels to normalized encoding.
+
+        Parameters
+        ----------
+        y : list
+            list of labels
+
+        Returns
+        -------
+        y : array
+            array of normalized labels.
+        """
+        return np.searchsorted(self.classes_, y)
+
+
 class W(object):
     """
     Spatial weights class. Class attributes are described by their
@@ -505,7 +555,7 @@ def to_sparse(self, fmt="coo"):
             data = adj_list.weight
             row = adj_list.focal
             col = adj_list.neighbor
-            le = preprocessing.LabelEncoder()
+            le = _LabelEncoder()
             le.fit(row)
             row = le.transform(row)
             col = le.transform(col)

From 49c69b65bd4a716b1859b15ef715ded0b1b28c20 Mon Sep 17 00:00:00 2001
From: Serge Rey <sjsrey@gmail.com>
Date: Sun, 15 Jan 2023 17:07:14 -0800
Subject: [PATCH 159/162] Removing internal use of id_order in to_adjlist

---
 libpysal/weights/weights.py | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/libpysal/weights/weights.py b/libpysal/weights/weights.py
index 9f1e4e8d6..c71e40560 100644
--- a/libpysal/weights/weights.py
+++ b/libpysal/weights/weights.py
@@ -432,12 +432,9 @@ def to_adjlist(
 
         links = []
         focal_ix, neighbor_ix = self.sparse.nonzero()
-        names = np.asarray(self.id_order)
-        focal = names[focal_ix]
-        neighbor = names[neighbor_ix]
         weights = self.sparse.data
         adjlist = pandas.DataFrame(
-            {focal_col: focal, neighbor_col: neighbor, weight_col: weights}
+            {focal_col: focal_ix, neighbor_col: neighbor_ix, weight_col: weights}
         )
         if remove_symmetric:
             adjlist = adjtools.filter_adjlist(adjlist)

From 969039d32069a15e1676524e8d2dd76780a9ff56 Mon Sep 17 00:00:00 2001
From: Sergio Rey <sjsrey@gmail.com>
Date: Fri, 20 Jan 2023 00:13:19 -0800
Subject: [PATCH 160/162] Handle set_index properly in to_adjlist (#511)

* Handle set_index properly in to_adjlist

* Add option to sort/unsort return from to_adjlist

* Update libpysal/weights/tests/test_adjlist.py

Co-authored-by: Martin Fleischmann <martin@martinfleischmann.net>

Co-authored-by: eli knaap <ek@knaaptime.com>
Co-authored-by: Martin Fleischmann <martin@martinfleischmann.net>
---
 libpysal/weights/tests/test_adjlist.py | 42 ++++++++++++++++++++++++++
 libpysal/weights/weights.py            | 12 +++++++-
 2 files changed, 53 insertions(+), 1 deletion(-)

diff --git a/libpysal/weights/tests/test_adjlist.py b/libpysal/weights/tests/test_adjlist.py
index 48e70607a..f1cc08c86 100644
--- a/libpysal/weights/tests/test_adjlist.py
+++ b/libpysal/weights/tests/test_adjlist.py
@@ -145,3 +145,45 @@ def test_map(self):
             np.testing.assert_allclose(
                 data, mapped["_".join(("subtract", name))].values
             )
+
+    def test_sort(self):
+        from libpysal import examples
+        from libpysal.weights import Rook
+
+        us = geopandas.read_file(examples.get_path("us48.shp"))
+        w = Rook.from_dataframe(us.set_index("STATE_FIPS"), use_index=True)
+        unsorted_al = w.to_adjlist(sort_joins=False)
+        sorted_al = w.to_adjlist(sort_joins=True)
+        sv = ["01"] * 4
+        sv.append("04")
+        sv = np.array(sv)
+        usv = np.array(["53", "53", "30", "30", "30"])
+        np.testing.assert_array_equal(unsorted_al.focal.values[:5], usv)
+        np.testing.assert_array_equal(sorted_al.focal.values[:5], sv)
+        
+    def test_ids(self):
+        df = geopandas.read_file(examples.get_path("columbus.dbf")).head()
+        df["my_id"] = range(3, len(df) + 3)
+        W = weights.Queen.from_dataframe(df, ids="my_id")
+        W_adj = W.to_adjlist(drop_islands=True)
+        for i in range(3, 8):
+            assert i in W_adj.focal
+            assert i in W_adj.neighbor
+        for i in W_adj.focal:
+            assert i in list(range(3, len(df) + 3))
+        for i in W_adj.neighbor:
+            assert i in list(range(3, len(df) + 3))
+
+    def test_str_ids(self):
+        df = geopandas.read_file(examples.get_path("columbus.dbf")).head()
+        snakes = ["mamba", "boa", "python", "rattlesnake", "cobra"]
+        df["my_str_id"] = snakes
+        W = weights.Queen.from_dataframe(df, ids="my_str_id")
+        W_adj = W.to_adjlist(drop_islands=True)
+        for i in snakes:
+            (W_adj.focal == i).any()
+            (W_adj.neighbor == i).any()
+        for i in W_adj.focal:
+            assert i in snakes
+        for i in W_adj.neighbor:
+            assert i in snakes
diff --git a/libpysal/weights/weights.py b/libpysal/weights/weights.py
index c71e40560..5f43eb36e 100644
--- a/libpysal/weights/weights.py
+++ b/libpysal/weights/weights.py
@@ -391,6 +391,7 @@ def to_adjlist(
         focal_col="focal",
         neighbor_col="neighbor",
         weight_col="weight",
+        sort_joins=False,
     ):
         """
         Compute an adjacency list representation of a weights object.
@@ -416,6 +417,10 @@ def to_adjlist(
             Name of the column in which to store "destination" node ids.
         weight_col : str
             Name of the column in which to store weight information.
+        sort_joins : bool
+            Whether or not to lexicographically sort the adjacency
+            list by (focal_col, neighbor_col). Default is False.
+
         """
         try:
             import pandas
@@ -432,6 +437,9 @@ def to_adjlist(
 
         links = []
         focal_ix, neighbor_ix = self.sparse.nonzero()
+        idxs = np.array(list(self.neighbors.keys()))
+        focal_ix = idxs[focal_ix]
+        neighbor_ix = idxs[neighbor_ix]
         weights = self.sparse.data
         adjlist = pandas.DataFrame(
             {focal_col: focal_ix, neighbor_col: neighbor_ix, weight_col: weights}
@@ -443,7 +451,9 @@ def to_adjlist(
                 {focal_col: self.islands, neighbor_col: self.islands, weight_col: 0}
             )
             adjlist = pandas.concat((adjlist, island_adjlist)).reset_index(drop=True)
-        return adjlist.sort_values([focal_col, neighbor_col])
+        if sort_joins:
+            return adjlist.sort_values([focal_col, neighbor_col])
+        return adjlist
 
     def to_networkx(self):
         """Convert a weights object to a ``networkx`` graph.

From ded03e32f2b97dac3a0355039009c244efd15632 Mon Sep 17 00:00:00 2001
From: Sergio Rey <sjsrey@gmail.com>
Date: Sun, 26 Feb 2023 12:23:35 -0800
Subject: [PATCH 161/162] TEST: 3.11 dev coverage (#516)

* TEST: add 3.11 and shapely_dev coverage

* TEST: fix environment-file list

* TEST: remove option numba

* Update ci/311_shapely_dev.yaml

Co-authored-by: Martin Fleischmann <martin@martinfleischmann.net>

---------

Co-authored-by: James Gaboardi <jgaboardi@gmail.com>
Co-authored-by: Martin Fleischmann <martin@martinfleischmann.net>
---
 .github/workflows/unittests.yml |  3 ++-
 ci/311_shapely_dev.yaml         | 31 +++++++++++++++++++++++++++++++
 2 files changed, 33 insertions(+), 1 deletion(-)
 create mode 100644 ci/311_shapely_dev.yaml

diff --git a/.github/workflows/unittests.yml b/.github/workflows/unittests.yml
index 8284b6a5e..614316e17 100644
--- a/.github/workflows/unittests.yml
+++ b/.github/workflows/unittests.yml
@@ -23,7 +23,8 @@
             ci/38.yaml,
             ci/39.yaml,
             ci/310.yaml,
-            ci/310_shapely_dev.yaml
+            ci/310_shapely_dev.yaml,
+            ci/311_shapely_dev.yaml
          ]
          include:
            - environment-file: ci/310.yaml
diff --git a/ci/311_shapely_dev.yaml b/ci/311_shapely_dev.yaml
new file mode 100644
index 000000000..8b68a0d6d
--- /dev/null
+++ b/ci/311_shapely_dev.yaml
@@ -0,0 +1,31 @@
+name: test
+channels:
+  - conda-forge
+dependencies:
+  - python=3.11
+  - platformdirs
+  - beautifulsoup4
+  - jinja2
+  - pandas>=1.0
+  - scipy>=1.0
+  - xarray
+  # testing
+  - codecov
+  - matplotlib
+  - pytest
+  - pytest-cov
+  - pytest-xdist
+  # optional
+  - geopandas>=0.12.0
+  - joblib
+  - networkx
+  - packaging
+  - shapely>=2.0b1
+  - xarray
+  - zstd
+  # for docs build action (this env only)
+  - nbsphinx
+  - numpydoc
+  - sphinx
+  - sphinxcontrib-bibtex
+  - sphinx_bootstrap_theme

From 8116db606863876cfe45ae7822b5adcf2d8c339e Mon Sep 17 00:00:00 2001
From: Martin Fleischmann <martin@martinfleischmann.net>
Date: Sun, 26 Feb 2023 22:37:02 +0100
Subject: [PATCH 162/162] CI: revamp CI matrix and set bump minimal
 requirements (#521)

* TEST: 3.11 dev coverage (#516)

* TEST: add 3.11 and shapely_dev coverage

* TEST: fix environment-file list

* TEST: remove option numba

* Update ci/311_shapely_dev.yaml

Co-authored-by: Martin Fleischmann <martin@martinfleischmann.net>

---------

Co-authored-by: James Gaboardi <jgaboardi@gmail.com>
Co-authored-by: Martin Fleischmann <martin@martinfleischmann.net>

* revamp CI matrix

* get old platformdirs from pip

* fix yaml

* bump min scipy to 1.8

* bump networkx

* pin also numpy

* update requirements

* try numpy from pypi

* unpin numpy, numba does that implicitly

* update numpy pin in reqs

---------

Co-authored-by: Sergio Rey <sjsrey@gmail.com>
Co-authored-by: James Gaboardi <jgaboardi@gmail.com>
---
 .github/workflows/unittests.yml |  9 +++++----
 ci/310.yaml                     |  7 +++----
 ci/310_shapely_dev.yaml         | 33 ---------------------------------
 ci/311-dev.yaml                 | 33 +++++++++++++++++++++++++++++++++
 ci/{38.yaml => 311.yaml}        | 10 +++++-----
 ci/38-minimal.yaml              | 27 +++++++++++++++++++++++++++
 ci/39.yaml                      |  7 +++----
 docs/installation.rst           |  6 +++---
 requirements.txt                | 12 ++++++------
 9 files changed, 85 insertions(+), 59 deletions(-)
 delete mode 100644 ci/310_shapely_dev.yaml
 create mode 100644 ci/311-dev.yaml
 rename ci/{38.yaml => 311.yaml} (77%)
 create mode 100644 ci/38-minimal.yaml

diff --git a/.github/workflows/unittests.yml b/.github/workflows/unittests.yml
index 8284b6a5e..1c8deb966 100644
--- a/.github/workflows/unittests.yml
+++ b/.github/workflows/unittests.yml
@@ -20,15 +20,16 @@
        matrix:
          os: ['ubuntu-latest']
          environment-file: [
-            ci/38.yaml,
+            ci/38-minimal.yaml,
             ci/39.yaml,
             ci/310.yaml,
-            ci/310_shapely_dev.yaml
+            ci/311.yaml,
+            ci/311-dev.yaml
          ]
          include:
-           - environment-file: ci/310.yaml
+           - environment-file: ci/311.yaml
              os: macos-latest
-           - environment-file: ci/310.yaml
+           - environment-file: ci/311.yaml
              os: windows-latest
        fail-fast: false
 
diff --git a/ci/310.yaml b/ci/310.yaml
index 213025ff1..6712c4f34 100644
--- a/ci/310.yaml
+++ b/ci/310.yaml
@@ -6,8 +6,8 @@ dependencies:
   - platformdirs
   - beautifulsoup4
   - jinja2
-  - pandas>=1.0
-  - scipy>=1.0
+  - pandas
+  - scipy
   - xarray
   # testing
   - codecov
@@ -16,12 +16,11 @@ dependencies:
   - pytest-cov
   - pytest-xdist
   # optional
-  - geopandas>=0.7.0
+  - geopandas
   - joblib
   - networkx
   - numba
   - packaging
-  - xarray
   - zstd
   # for docs build action (this env only)
   - nbsphinx
diff --git a/ci/310_shapely_dev.yaml b/ci/310_shapely_dev.yaml
deleted file mode 100644
index 33a1cd258..000000000
--- a/ci/310_shapely_dev.yaml
+++ /dev/null
@@ -1,33 +0,0 @@
-name: test
-channels:
-  - conda-forge
-  - conda-forge/label/shapely_dev
-dependencies:
-  - python=3.10
-  - platformdirs
-  - beautifulsoup4
-  - jinja2
-  - pandas>=1.0
-  - scipy>=1.0
-  - xarray
-  # testing
-  - codecov
-  - matplotlib
-  - pytest
-  - pytest-cov
-  - pytest-xdist
-  # optional
-  - geopandas>=0.12.0
-  - joblib
-  - networkx
-  - numba
-  - packaging
-  - shapely>=2.0b1
-  - xarray
-  - zstd
-  # for docs build action (this env only)
-  - nbsphinx
-  - numpydoc
-  - sphinx
-  - sphinxcontrib-bibtex
-  - sphinx_bootstrap_theme
diff --git a/ci/311-dev.yaml b/ci/311-dev.yaml
new file mode 100644
index 000000000..c5c3a5b83
--- /dev/null
+++ b/ci/311-dev.yaml
@@ -0,0 +1,33 @@
+name: test
+channels:
+  - conda-forge
+dependencies:
+  - python=3.11
+  - platformdirs
+  - beautifulsoup4
+  - jinja2
+  - pandas
+  - scipy
+  - xarray
+  # testing
+  - codecov
+  - matplotlib
+  - pytest
+  - pytest-cov
+  - pytest-xdist
+  # optional
+  - geopandas
+  - joblib
+  - networkx
+  - packaging
+  - zstd
+  - Cython
+  - pip
+  - pip:
+    # dev versions of packages
+    - --pre --extra-index https://pypi.anaconda.org/scipy-wheels-nightly/simple
+    - scipy
+    - pandas
+    - git+https://github.com/shapely/shapely.git@main
+    - git+https://github.com/geopandas/geopandas.git@main
+    - git+https://github.com/pydata/xarray.git@main
diff --git a/ci/38.yaml b/ci/311.yaml
similarity index 77%
rename from ci/38.yaml
rename to ci/311.yaml
index be0fdb73b..7eb2e7b00 100644
--- a/ci/38.yaml
+++ b/ci/311.yaml
@@ -2,12 +2,12 @@ name: test
 channels:
   - conda-forge
 dependencies:
-  - python=3.8
+  - python=3.11
   - platformdirs
   - beautifulsoup4
   - jinja2
-  - pandas>=1.0
-  - scipy>=1.0
+  - pandas
+  - scipy
   - xarray
   # testing
   - codecov
@@ -16,10 +16,10 @@ dependencies:
   - pytest-cov
   - pytest-xdist
   # optional
-  - geopandas>=0.7.0
+  - geopandas>=0.12.0
   - joblib
   - networkx
-  - numba
   - packaging
+  - shapely>=2.0b1
   - xarray
   - zstd
diff --git a/ci/38-minimal.yaml b/ci/38-minimal.yaml
new file mode 100644
index 000000000..e3e24e3f8
--- /dev/null
+++ b/ci/38-minimal.yaml
@@ -0,0 +1,27 @@
+name: test
+channels:
+  - conda-forge
+dependencies:
+  - python=3.8
+  - beautifulsoup4=4.10
+  - jinja2=3.0
+  - pandas=1.3
+  - scipy=1.8
+  - xarray=0.18
+  # testing
+  - codecov
+  - matplotlib
+  - pytest
+  - pytest-cov
+  - pytest-xdist
+  # optional
+  - geopandas>=0.10.0
+  - shapely=1.8
+  - joblib
+  - networkx=2.7
+  - numba=0.54
+  - packaging
+  - zstd
+  - pip
+  - pip:
+      - platformdirs==2.0.2
diff --git a/ci/39.yaml b/ci/39.yaml
index 02df18e0e..4323aacbf 100644
--- a/ci/39.yaml
+++ b/ci/39.yaml
@@ -6,8 +6,8 @@ dependencies:
   - platformdirs
   - beautifulsoup4
   - jinja2
-  - pandas>=1.0
-  - scipy>=1.0
+  - pandas
+  - scipy
   - xarray
   # testing
   - codecov
@@ -16,10 +16,9 @@ dependencies:
   - pytest-cov
   - pytest-xdist
   # optional
-  - geopandas>=0.7.0
+  - geopandas
   - joblib
   - networkx
   - numba
   - packaging
-  - xarray
   - zstd
diff --git a/docs/installation.rst b/docs/installation.rst
index 32ab9aa92..59e557215 100644
--- a/docs/installation.rst
+++ b/docs/installation.rst
@@ -3,7 +3,7 @@
 Installation
 ============
 
-libpysal supports python >= `3.7`_ only. Please make sure that you are
+libpysal supports python >= `3.8`_ only. Please make sure that you are
 operating in a python 3 environment.
 
 Installing released version
@@ -14,7 +14,7 @@ conda
 
 libpysal is available through conda::
 
- 
+
   conda install -c conda-forge libpysal
 
 
@@ -50,7 +50,7 @@ to your local clone and submitting a pull request to `pysal/libpysal`_, you can
 contribute to libpysal development.
 
 
-.. _3.7: https://docs.python.org/3.7/
+.. _3.7: https://docs.python.org/3.8/
 .. _Python Package Index: https://pypi.org/project/libpysal/
 .. _pysal/libpysal: https://github.com/pysal/libpysal
 .. _fork: https://help.github.com/articles/fork-a-repo/
diff --git a/requirements.txt b/requirements.txt
index e07a6ecd4..231dc32e9 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,8 +1,8 @@
-platformdirs
-beautifulsoup4
-jinja2
-numpy>=1.3
-pandas
+platformdirs>=2.0.2
+beautifulsoup4>=4.10
+jinja2>=3.0
+numpy>=1.20
+pandas>=1.3
 requests
-scipy>=0.11
+scipy>=1.8
 packaging