Functional

esda/functions.py

@@ -0,0 +1,66 @@
+from .moran import (
+    Moran,
+    Moran_Local,
+    Moran_BV,
+    Moran_Local_BV,
+    Moran_Rate,
+    Moran_Local_Rate,
+)
+from .geary import Geary
+from .gamma import Gamma
+from .geary_local import Geary_Local
+from .geary_local_mv import Geary_Local_MV
+from .getisord import G, G_Local
+from .join_counts import Join_Counts
+from .join_counts_local import Join_Counts_Local
+from .join_counts_local_bv import Join_Counts_Local_BV
+from .join_counts_local_mv import Join_Counts_Local_MV
+
+# from .lee import Spatial_Pearson # no solution yet for sklearn style classes
+# from .losh import LOSH
+import inspect
+
+for klass in (
+    Moran,
+    Moran_Local,
+    Moran_BV,
+    Moran_Local_BV,
+    Moran_Rate,
+    Moran_Local_Rate,
+    Geary,
+    Gamma,
+    Geary_Local,
+    Geary_Local_MV,
+    G,
+    G_Local,
+    Join_Counts,
+    Join_Counts_Local,
+    Join_Counts_Local_BV,
+    Join_Counts_Local_MV,
+):
+    assert hasattr(klass, "_statistic"), f"{klass} has no _statistic"
+    assert not callable(klass._statistic), f"{klass}._statistic is callable"
+    klassname = klass.__name__
+    name = klass.__name__.lower()
+    if klassname == "LOSH":
+        defn = f"def {name}(*args, **kwargs):\n\tobj = {klassname}(*args, **kwargs)\n\treturn obj._statistic, obj.pval"
+    elif klassname == "Spatial_Pearson":
+        defn = f"def {name}(*args, **kwargs):\n\tobj = {klassname}(*args, **kwargs)\n\treturn obj._statistic, obj.significance_"
+    else:
+        defn = f"def {name}(*args, **kwargs):\n\tobj = {klassname}(*args, **kwargs)\n\treturn obj._statistic, obj.p_sim"
+    exec(defn)
+    exec(f"{name}.__doc__ = {klassname}.__doc__")
+    init_sig = inspect.signature(klass)
+    globals()[name].__signature__ = init_sig
+    del globals()[klassname]
+
+for klass in (LOSH, Spatial_Pearson):
+    # sklearn style...
+    pass
+
+del klassname
+del klass
+del name
+del init_sig
+del defn
+del inspect

esda/geary_local.py

@@ -121,7 +121,7 @@
         n_jobs = self.n_jobs
         seed = self.seed
 
-        self.localG = self._statistic(x, w)
+        self.localG = self._stat_func(x, w)
 
         if permutations:
             self.p_sim, self.rlocalG = _crand_plus(
@@ -150,8 +150,12 @@
 
         return self
 
+    @property
+    def _statistic(self):
+        return self.localG
+
     @staticmethod
-    def _statistic(x, w):
+    def _stat_func(x, w):
         # Caclulate z-scores for x
         zscore_x = (x - np.mean(x)) / np.std(x)
         # Create focal (xi) and neighbor (zi) values

esda/geary_local_mv.py

@@ -68,21 +68,21 @@
         >>> lG_mv.localG[0:5]
         >>> lG_mv.p_sim[0:5]
         """
-        self.variables = np.array(variables, dtype='float')
+        self.variables = np.array(variables, dtype="float")
 
         w = self.connectivity
-        w.transform = 'r'
+        w.transform = "r"
 
         self.n = len(variables[0])
         self.w = w
-        
+
         permutations = self.permutations
 
         # Caclulate z-scores for input variables
         # to be used in _statistic and _crand
         zvariables = stats.zscore(variables, axis=1)
 
-        self.localG = self._statistic(variables, zvariables, w)
+        self.localG = self._stat_func(variables, zvariables, w)
 
         if permutations:
             self._crand(zvariables)
@@ -95,27 +95,30 @@
 
         return self
 
+    @property
+    def _statistic(self):
+        return self.localG
+
     @staticmethod
-    def _statistic(variables, zvariables, w):
+    def _stat_func(variables, zvariables, w):
         # Define denominator adjustment
         k = len(variables)
         # Create focal and neighbor values
         adj_list = w.to_adjlist(remove_symmetric=False)
         zseries = [pd.Series(i, index=w.id_order) for i in zvariables]
-        focal = [zseries[i].loc[adj_list.focal].values
-                 for i in range(len(variables))]
-        neighbor = [zseries[i].loc[adj_list.neighbor].values
-                    for i in range(len(variables))]
+        focal = [zseries[i].loc[adj_list.focal].values for i in range(len(variables))]
+        neighbor = [
+            zseries[i].loc[adj_list.neighbor].values for i in range(len(variables))
+        ]
         # Carry out local Geary calculation
-        gs = adj_list.weight.values * \
-            (np.array(focal) - np.array(neighbor))**2
+        gs = adj_list.weight.values * (np.array(focal) - np.array(neighbor)) ** 2
         # Reorganize data
         temp = pd.DataFrame(gs).T
-        temp['ID'] = adj_list.focal.values
-        adj_list_gs = temp.groupby(by='ID').sum()
+        temp["ID"] = adj_list.focal.values
+        adj_list_gs = temp.groupby(by="ID").sum()
         localG = np.array(adj_list_gs.sum(axis=1) / k)
 
-        return (localG)
+        return localG
 
     def _crand(self, zvariables):
         """
@@ -146,13 +149,15 @@
             np.random.shuffle(idsi)
             vars_rand = []
             for j in range(nvars):
-                vars_rand.append(zvariables[j][idsi[rids[:, 0:wc[i]]]])
+                vars_rand.append(zvariables[j][idsi[rids[:, 0 : wc[i]]]])
             # vars rand as tmp
             # Calculate diff
             diff = []
             for z in range(nvars):
-                diff.append((np.array((zvariables[z][i] - vars_rand[z])**2
-                                      * w[i])).sum(1) / nvars)
+                diff.append(
+                    (np.array((zvariables[z][i] - vars_rand[z]) ** 2 * w[i])).sum(1)
+                    / nvars
+                )
             # add up differences
             temp = np.array([sum(x) for x in zip(*diff)])
             # Assign to object to be returned

esda/join_counts_local.py

@@ -115,7 +115,7 @@
         self.n = len(y)
         self.w = w
 
-        self.LJC = self._statistic(y, w)
+        self.LJC = self._stat_func(y, w)
 
         if permutations:
             self.p_sim, self.rjoins = _crand_plus(
@@ -132,8 +132,12 @@
 
         return self
 
+    @property
+    def _statistic(self):
+        return self.LJC
+
     @staticmethod
-    def _statistic(y, w):
+    def _stat_func(y, w):
         # Create adjacency list. Note that remove_symmetric=False - this is
         # different from the esda.Join_Counts() function.
         adj_list = w.to_adjlist(remove_symmetric=False)

esda/join_counts_local_bv.py

@@ -133,7 +133,7 @@
         n_jobs = self.n_jobs
         seed = self.seed
 
-        self.LJC = self._statistic(x, z, w, case=case)
+        self.LJC = self._stat_func(x, z, w, case=case)
 
         if permutations:
             if case == "BJC":
@@ -168,8 +168,12 @@
 
         return self
 
+    @property
+    def _statistic(self):
+        return self.LJC
+
     @staticmethod
-    def _statistic(x, z, w, case):
+    def _stat_func(x, z, w, case):
         # Create adjacency list. Note that remove_symmetric=False - this is
         # different from the esda.Join_Counts() function.
         adj_list = w.to_adjlist(remove_symmetric=False)

esda/join_counts_local_mv.py

@@ -115,7 +115,7 @@
         # np.array() of dtype='float' for numba
         self.ext = np.array(np.prod(np.vstack(variables), axis=0), dtype="float")
 
-        self.LJC = self._statistic(variables, w)
+        self.LJC = self._stat_func(variables, w)
 
         if permutations:
             self.p_sim, self.rjoins = _crand_plus(
@@ -132,8 +132,12 @@
 
         return self
 
+    @property
+    def _statistic(self):
+        return self.LJC
+
     @staticmethod
-    def _statistic(variables, w):
+    def _stat_func(variables, w):
         # Create adjacency list. Note that remove_symmetric=False -
         # different from the esda.Join_Counts() function.
         adj_list = w.to_adjlist(remove_symmetric=False)

esda/lee.py

@@ -22,10 +22,10 @@
         ---------
         connectivity:   scipy.sparse matrix object
                         the connectivity structure describing the relationships
-                        between observed units. Will be row-standardized. 
+                        between observed units. Will be row-standardized.
         permutations:   int
                         the number of permutations to conduct for inference.
-                        if < 1, no permutational inference will be conducted. 
+                        if < 1, no permutational inference will be conducted.
 
         Attributes
         ----------
@@ -36,10 +36,10 @@
                       smoothing factor"
         reference_distribution_: numpy.ndarray (n_permutations, 2,2)
                       distribution of correlation matrices for randomly-shuffled
-                      maps. 
+                      maps.
         significance_: numpy.ndarray (2,2)
                        permutation-based p-values for the fraction of times the
-                       observed correlation was more extreme than the simulated 
+                       observed correlation was more extreme than the simulated
                        correlations.
         """
         self.connectivity = connectivity
@@ -77,7 +77,7 @@
         )
         if self.connectivity is None:
             self.connectivity = sparse.eye(Z.shape[0])
-        self.association_ = self._statistic(Z, self.connectivity)
+        self.association_ = self._stat_func(Z, self.connectivity)
 
         standard_connectivity = sparse.csc_matrix(
             self.connectivity / self.connectivity.sum(axis=1)
@@ -100,8 +100,12 @@
             self.significance_ = (extreme + 1.0) / (self.permutations + 1.0)
         return self
 
+    @property
+    def _statistic(self):
+        return self.association_
+
     @staticmethod
-    def _statistic(Z, W):
+    def _stat_func(Z, W):
         ctc = W.T @ W
         ones = numpy.ones(ctc.shape[0])
         return (Z.T @ ctc @ Z) / (ones.T @ ctc @ ones)
@@ -118,13 +122,13 @@
         ---------
         connectivity:   scipy.sparse matrix object
                         the connectivity structure describing the relationships
-                        between observed units. Will be row-standardized. 
+                        between observed units. Will be row-standardized.
         permutations:   int
                         the number of permutations to conduct for inference.
-                        if < 1, no permutational inference will be conducted. 
+                        if < 1, no permutational inference will be conducted.
         significance_: numpy.ndarray (2,2)
                        permutation-based p-values for the fraction of times the
-                       observed correlation was more extreme than the simulated 
+                       observed correlation was more extreme than the simulated
                        correlations.
         Attributes
         ----------
@@ -135,10 +139,10 @@
                       smoothing factor"
         reference_distribution_: numpy.ndarray (n_permutations, n_samples)
                       distribution of correlation matrices for randomly-shuffled
-                      maps. 
+                      maps.
         significance_: numpy.ndarray (n_samples,)
                        permutation-based p-values for the fraction of times the
-                       observed correlation was more extreme than the simulated 
+                       observed correlation was more extreme than the simulated
                        correlations.
 
 
@@ -151,28 +155,28 @@
 
     def fit(self, x, y):
         """
-        bivariate local pearson's R based on Eq. 22 in Lee (2001), using 
+        bivariate local pearson's R based on Eq. 22 in Lee (2001), using
         site-wise conditional randomization from Moran_Local_BV.
-        
+
         L_i = \dfrac{
                      n \cdot
                        \Big[\big(\sum_i w_{ij}(x_j - \bar{x})\big)
                             \big(\sum_i w_{ij}(y_j - \bar{y})\big) \Big]
-                     } 
+                     }
                     {
                      \sqrt{\sum_i (x_i - \bar{x})^2}
                      \sqrt{\sum_i (y_i - \bar{y})^2}}
             = \dfrac{
                      n \cdot
                        (\tilde{x}_j - \bar{x})
                        (\tilde{y}_j - \bar{y})
-                     } 
+                     }
                     {
                      \sqrt{\sum_i (x_i - \bar{x})^2}
                      \sqrt{\sum_i (y_i - \bar{y})^2}}
 
-        Lee, Sang Il. (2001), "Developing a bivariate spatial 
-        association measure: An integration of Pearson's r and 
+        Lee, Sang Il. (2001), "Developing a bivariate spatial
+        association measure: An integration of Pearson's r and
         Moran's I." Journal of Geographical Systems, 3(4):369-385.
 
         Arguments