Use (longitude, latitude) coordinate order for WKT

datasets/leona-selected-grid-mv-lines.csv

@@ -1,4 +1,4 @@
 WKT
-"LINESTRING (15.7230180309079 -16.5240428342476, 15.7204712822643 -16.45979485215119)"
-"LINESTRING (15.7204712822643 -16.45979485215119, 15.6990162260261 -16.39475446682649)"
-"LINESTRING (15.6990162260261 -16.39475446682649, 15.67694993024816 -16.36668175282426)"
+"LINESTRING (-16.5240428342476 15.7230180309079, -16.45979485215119 15.7204712822643)"
+"LINESTRING (-16.45979485215119 15.7204712822643, -16.39475446682649 15.6990162260261)"
+"LINESTRING (-16.39475446682649 15.6990162260261, -16.36668175282426 15.67694993024816)"

infrastructure_planning/electricity/report.py

@@ -392,9 +392,9 @@ def save_total_lines(
 
         node1_d, node2_d = order_nodes(node1_d, node2_d, edge_order)
         wkt = LineString([(
-            node1_d['latitude'], node1_d['longitude'],
+            node1_d['longitude'], node1_d['latitude'],
         ), (
-            node2_d['latitude'], node2_d['longitude'],
+            node2_d['longitude'], node2_d['latitude'],
         )]).wkt
         rows.append([line_length, discounted_cost, edge_order, wkt])
     properties_folder = make_folder(join(target_folder, 'properties'))
@@ -535,7 +535,7 @@ def save_total_map(
             'Proposed MV Line Length (m)': node_d.get(
                 'grid_mv_line_adjusted_length_in_meters'),
             'Levelized Cost Per kWh Consumed': levelized_cost,
-            'WKT': Point(latitude, longitude).wkt,
+            'WKT': Point(longitude, latitude).wkt,
             'FillColor': color_by_technology[technology],
             'RadiusInPixelsRange5-10': node_d['peak_demand_in_kw'],
         })
@@ -552,8 +552,8 @@ def save_total_map(
             node2_d['peak_demand_in_kw'])
         line_length = edge_d['grid_mv_line_adjusted_length_in_meters']
         geometry_wkt = LineString([
-            (node1_d['latitude'], node1_d['longitude']),
-            (node2_d['latitude'], node2_d['longitude'])]).wkt
+            (node1_d['longitude'], node1_d['latitude']),
+            (node2_d['longitude'], node2_d['latitude'])]).wkt
         rows.append({
             'Name': name,
             'Peak Demand (kW)': peak_demand,

infrastructure_planning/macros.py

@@ -10,7 +10,6 @@
 from invisibleroads_macros.disk import (
     make_enumerated_folder_for, make_folder, remove_safely,
     replace_file_extension)
-from invisibleroads_macros.geometry import flip_xy, transform_geometries
 from invisibleroads_macros.iterable import merge_dictionaries, sort_dictionary
 from invisibleroads_macros.table import normalize_key
 from networkx import Graph, write_gpickle
@@ -133,18 +132,17 @@ def run(main_functions, g):
             g.update(compute(f, g))
             continue
         for node_id, node_d in g['infrastructure_graph'].cycle_nodes():
-            l = merge_dictionaries(node_d, {
+            v = merge_dictionaries(node_d, {
                 'node_id': node_id,
                 'local_overrides': dict(g['demand_point_table'].ix[node_id])})
-            node_d.update(compute(f, l, g))
+            node_d.update(compute(f, v, g))
     return g
 
 
 def save_shapefile(target_path, geotable):
     if 'wkt' in geotable:
-        geometries = [wkt.loads(x) for x in geotable['wkt']]
         # Shapefiles expect (x, y) or (longitude, latitude) coordinate order
-        geometries = transform_geometries(geometries, flip_xy)
+        geometries = [wkt.loads(x) for x in geotable['wkt']]
     else:
         xys = geotable[['longitude', 'latitude']].values
         geometries = [Point(xy) for xy in xys]

infrastructure_planning/parsers.py

@@ -1,6 +1,5 @@
 import geometryIO
 import re
-from invisibleroads_macros.geometry import flip_xy, transform_geometries
 from invisibleroads_macros.iterable import merge_dictionaries
 from pandas import DataFrame, read_csv
 
@@ -47,10 +46,7 @@ def load_geotable(path):
     elif path.endswith('.zip'):
         proj4, geometries, fields, definitions = geometryIO.load(path)
         normalize_geometry = geometryIO.get_transformGeometry(proj4)
-        # Convert to (longitude, latitude)
         geometries = [normalize_geometry(x) for x in geometries]
-        # Convert to (latitude, longitude)
-        geometries = transform_geometries(geometries, flip_xy)
         table = DataFrame(fields, columns=[x[0] for x in definitions])
         table['WKT'] = [x.wkt for x in geometries]
     else:

infrastructure_planning/preprocessors.py

@@ -74,7 +74,7 @@ def normalize_solar_mini_grid_panel_table(solar_mini_grid_panel_table):
 
 
 def normalize_grid_mv_line_geotable(grid_mv_line_geotable, demand_point_table):
-    'Make sure that grid mv lines use (latitude, longitude) coordinate order'
+    'Make sure that grid mv lines use (longitude, latitude) coordinate order'
     raw_geometries = [wkt.loads(x) for x in grid_mv_line_geotable['wkt']]
     # Remove incompatible geometries
     geometries, xs = [], []
@@ -93,10 +93,10 @@ def normalize_grid_mv_line_geotable(grid_mv_line_geotable, demand_point_table):
     if geometries:
         regular = tuple(GeometryCollection(geometries).centroid.coords[0])[:2]
         flipped = regular[1], regular[0]
-        reference = tuple(demand_point_table[['latitude', 'longitude']].mean())
+        reference = tuple(demand_point_table[['longitude', 'latitude']].mean())
         # If the flipped coordinates are closer,
         if get_distance(reference, flipped) < get_distance(reference, regular):
-            # Flip coordinates to get (latitude, longitude) coordinate order
+            # Flip coordinates to get (longitude, latitude) coordinate order
             geometries = transform_geometries(geometries, flip_xy)
     grid_mv_line_geotable['wkt'] = [x.wkt for x in geometries]
     return {'grid_mv_line_geotable': grid_mv_line_geotable}