adapted remaining examples

WZBSocialScienceCenter · Jan 28, 2021 · 56c5379 · 56c5379
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diff --git a/examples/duplicate_points.py b/examples/duplicate_points.py
@@ -4,7 +4,7 @@
 Duplicate points, i.e. points with exactly the same coordinates will belong to the same Voronoi region.
 
 Author: Markus Konrad <markus.konrad@wzb.eu>
-January 2020
+January 2021
 """
 
 

diff --git a/examples/random_points_across_italy.py b/examples/random_points_across_italy.py
@@ -3,7 +3,7 @@
 and their points will be plotted using the `plotting` sub-module of `geovoronoi`.
 
 Author: Markus Konrad <markus.konrad@wzb.eu>
-March 2018
+January 2021
 """
 
 

diff --git a/examples/random_points_and_area.png b/examples/random_points_and_area.png
diff --git a/examples/random_points_and_area.py b/examples/random_points_and_area.py
@@ -6,10 +6,11 @@
 Note that it is important to use an *equal area* projection before calculating the areas of the Voronoi regions!
 
 Author: Markus Konrad <markus.konrad@wzb.eu>
-March 2018
+January 2021
 """
 
 import logging
+from pprint import pprint
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -24,6 +25,8 @@
 geovoronoi_log.setLevel(logging.INFO)
 geovoronoi_log.propagate = True
 
+#%%
+
 N_POINTS = 20
 COUNTRY = 'Spain'
 
@@ -48,39 +51,44 @@
 
 # use only the points inside the geographic area
 pts = [p for p in coords_to_points(coords) if p.within(area_shape)]  # converts to shapely Point
+n_pts = len(pts)
 
-print('will use %d of %d randomly generated points that are inside geographic area' % (len(pts), N_POINTS))
+print('will use %d of %d randomly generated points that are inside geographic area' % (n_pts, N_POINTS))
 coords = points_to_coords(pts)   # convert back to simple NumPy coordinate array
 
 del pts
 
+#%%
+
 #
 # calculate the Voronoi regions, cut them with the geographic area shape and assign the points to them
 #
 
-poly_shapes, pts, poly_to_pt_assignments = voronoi_regions_from_coords(coords, area_shape)
+poly_shapes, poly_to_pt_assignments = voronoi_regions_from_coords(coords, area_shape)
 
 # calculate area in km², too
 poly_areas = calculate_polygon_areas(poly_shapes, m2_to_km2=True)   # converts m² to km²
 
 print('areas in km²:')
-print(poly_areas)
+pprint(poly_areas)
 
 print('sum:')
-print(sum(poly_areas))
+print(sum(poly_areas.values()))
+
+#%%
 
 #
 # plotting
 #
 
 fig, ax = subplot_for_map(show_x_axis=True, show_y_axis=True)
 
-voronoi_labels = ['%d km²' % round(a) for a in poly_areas]
+voronoi_labels = {poly_i: '%d km²' % round(a) for poly_i, a in poly_areas.items()}
 plot_voronoi_polys_with_points_in_area(ax, area_shape, poly_shapes, coords, poly_to_pt_assignments,
                                        voronoi_labels=voronoi_labels, voronoi_label_fontsize=7,
                                        voronoi_label_color='gray')
 
-ax.set_title('%d random points and their Voronoi regions in %s' % (len(pts), COUNTRY))
+ax.set_title('%d random points and their Voronoi regions in %s' % (n_pts, COUNTRY))
 
 plt.tight_layout()
 plt.savefig('random_points_and_area.png')

diff --git a/examples/toyexample.py b/examples/toyexample.py
@@ -1,3 +1,11 @@
+"""
+Artificial mini-example useful for debugging.
+
+Author: Markus Konrad <markus.konrad@wzb.eu>
+January 2021
+"""
+
+
 import logging
 
 import matplotlib.pyplot as plt
@@ -21,16 +29,17 @@
 
 points = np.array([[1, 1], [1.1, 0.9], [1.15, 0.8], [2.5, 0]])
 
+# surrounding shape
 shape = Polygon([[-1, -1], [3, -1], [3, 3], [-1, 3]])
 
-#%%
+#%% Voronoi region generation
 
 region_polys, region_pts = voronoi_regions_from_coords(points, shape)
 
 print('Voronoi region to point assignments:')
 print(region_pts)
 
-#%%
+#%% plotting
 
 fig, ax = subplot_for_map(show_x_axis=True, show_y_axis=True)
 

diff --git a/examples/using_geopandas.png b/examples/using_geopandas.png
diff --git a/examples/using_geopandas.py b/examples/using_geopandas.py
@@ -2,7 +2,7 @@
 Example script to show how to incorporate GeoPandas with geovoronoi.
 
 Author: Markus Konrad <markus.konrad@wzb.eu>
-March 2018
+January 2021
 """
 
 import logging
@@ -20,6 +20,8 @@
 geovoronoi_log.setLevel(logging.INFO)
 geovoronoi_log.propagate = True
 
+#%%
+
 #
 # load geo data
 #
@@ -35,6 +37,7 @@
 south_am_shape = cascaded_union(south_am.geometry)
 south_am_cities = cities[cities.geometry.within(south_am_shape)]   # reduce to cities in South America
 
+#%%
 
 #
 # calculate the Voronoi regions, cut them with the geographic area shape and assign the points to them
@@ -44,16 +47,19 @@
 coords = points_to_coords(south_am_cities.geometry)
 
 # calculate the regions
-poly_shapes, pts, poly_to_pt_assignments = voronoi_regions_from_coords(coords, south_am_shape)
+poly_shapes, poly_to_pt_assignments = voronoi_regions_from_coords(coords, south_am_shape)
+
 
+#%%
 
 #
 # Plotting
 #
 
 fig, ax = subplot_for_map()
 
-plot_voronoi_polys_with_points_in_area(ax, south_am_shape, poly_shapes, pts, poly_to_pt_assignments)
+plot_voronoi_polys_with_points_in_area(ax, south_am_shape, poly_shapes, coords, poly_to_pt_assignments,
+                                       point_labels=south_am_cities.name.tolist())
 
 ax.set_title('Cities data for South America from GeoPandas\nand Voronoi regions around them')
 

diff --git a/examples/vregion_assignment.py b/examples/vregion_assignment.py
diff --git a/geovoronoi/_geom.py b/geovoronoi/_geom.py
@@ -76,8 +76,6 @@ def calculate_polygon_areas(poly_shapes, m2_to_km2=False):
     Return the area of the respective polygons in `poly_shapes`. Returns a NumPy array of areas in m² (if `m2_to_km2` is
     False) or km² (otherwise).
     """
-    areas = np.array([p.area for p in poly_shapes])
-    if m2_to_km2:
-        return areas / 1000000    # = 1000²
-    else:
-        return areas
+
+    unit_convert = 1000000 if m2_to_km2 else 1
+    return {i_poly: p.area / unit_convert for i_poly, p in poly_shapes.items()}