Commit @4d6d1c216d2dbc675961cf3892501224de871922 - yjyoon/Choropleth_Chart

윤영준 2024-04-29

# 보로노이 계산 병렬화

@4d6d1c216d2dbc675961cf3892501224de871922

fe5703b

4d6d1c2

algorithms/voronoi.py

--- algorithms/voronoi.py

+++ algorithms/voronoi.py


     coords = list(geom.exterior.coords)
     return (coords)
 
-# def vor_ver(furthest_pnt, i):
-#     return furthest_pnt[i].vertices
+def find_lec_for_polygon(points, polygon):
+    voronoi = Voronoi(points, furthest_site=False)
+    furthest_pnt = voronoi.vertices
+
+    voronoi_nodes = points_from_xy(extract(furthest_pnt, 0), extract(furthest_pnt, 1))
+    voronoi_nodes_if_inside = polygon.contains(voronoi_nodes)
+    voronoi_nodes = list(compress(voronoi_nodes, voronoi_nodes_if_inside))
+    leccc = [item for sublist in extract_coord(voronoi_nodes) for item in sublist]
+
+    dist = cdist(np.array([list(item) for item in points]), np.array([list(item) for item in leccc]))
+    voronoi_nodes_dist = [min(dist[:, j]) for j in range(dist.shape[1])]
+    lecc_ind = np.argmax(voronoi_nodes_dist)
+    lecc = leccc[lecc_ind]
+    lecc_dist = np.max(voronoi_nodes_dist)
+
+    return lecc, lecc_dist
 
 def get_lecc(shp):
-    """
-    This functions finds largest enclosed circle center from shape file.
-    It is used for optimizing text placement and size using Voronoi diagram in this project
-    Arguments:
-    shp -- shape object
+    polygons_coord = [coord_lister(geom) for geom in shp.geometry]
 
-    returns -
-    lecc : which is largest enclosed circle
-    lecc_dist : which is radius of largest enclosed circle
-    """
-    num_area = len(shp)
-    polygons_coord = shp.geometry.apply(coord_lister)
-    furthest_pnt = [None] * num_area
-    voronoi_ori = [None] * num_area
-    for i, points in enumerate(polygons_coord):
-        voronoi_ori[i] = Voronoi(points, furthest_site=False)
-        furthest_pnt[i] = voronoi_ori[i].vertices
+    with ProcessPoolExecutor() as executor:
+        results = list(executor.map(find_lec_for_polygon, polygons_coord, shp.geometry))
 
-    voronoi_nodes = [None] * num_area
-    leccc = [None] * num_area
-    # get voronoi nodes that is inside the polygon.
-    for i in range(num_area):
-        voronoi_nodes[i] = points_from_xy(extract(furthest_pnt[i], 0), extract(furthest_pnt[i], 1))
-        voronoi_nodes_if_inside = shp.geometry[i].contains(voronoi_nodes[i])
-        voronoi_nodes[i] = list(compress(voronoi_nodes[i], voronoi_nodes_if_inside))
-        leccc[i] = [item for sublist in extract_coord(voronoi_nodes[i]) for item in sublist]
-
-    dist = [None] * num_area
-    for i, points in enumerate(polygons_coord):
-        # explaining this mess : I need to get the LEC from candidate I got before, but the thing is,
-        # cdist from scipy only accepts exact dimension with array like object,
-        # which means I need to explicitly set the shape of ndarray, convert list of points into ndarray
-        dist[i] = cdist(np.array([list(item) for item in points]), np.array([list(item) for item in leccc[i]]))
-
-    lecc = [None] * num_area
-    lecc_dist = [None] * num_area
-    for i in range(num_area):
-        voronoi_nodes_dist = [None] * (dist[i].shape[1])
-        for j in range(dist[i].shape[1]):
-            voronoi_nodes_dist[j] = min(dist[i][:, j])
-        lecc_ind = np.argmax(voronoi_nodes_dist)
-        lecc[i] = leccc[i][lecc_ind]
-        lecc_dist[i] = np.max(voronoi_nodes_dist)
-
+    lecc, lecc_dist = zip(*results)
     return lecc, lecc_dist

Add a comment

Open 0
Closed 0

List

...	...	@@ -15,52 +15,28 @@
15	15	coords = list(geom.exterior.coords)
16	16	return (coords)
17	17
18		-# def vor_ver(furthest_pnt, i):
19		-# return furthest_pnt[i].vertices
	18	+def find_lec_for_polygon(points, polygon):
	19	+ voronoi = Voronoi(points, furthest_site=False)
	20	+ furthest_pnt = voronoi.vertices
	21	+
	22	+ voronoi_nodes = points_from_xy(extract(furthest_pnt, 0), extract(furthest_pnt, 1))
	23	+ voronoi_nodes_if_inside = polygon.contains(voronoi_nodes)
	24	+ voronoi_nodes = list(compress(voronoi_nodes, voronoi_nodes_if_inside))
	25	+ leccc = [item for sublist in extract_coord(voronoi_nodes) for item in sublist]
	26	+
	27	+ dist = cdist(np.array([list(item) for item in points]), np.array([list(item) for item in leccc]))
	28	+ voronoi_nodes_dist = [min(dist[:, j]) for j in range(dist.shape[1])]
	29	+ lecc_ind = np.argmax(voronoi_nodes_dist)
	30	+ lecc = leccc[lecc_ind]
	31	+ lecc_dist = np.max(voronoi_nodes_dist)
	32	+
	33	+ return lecc, lecc_dist
20	34
21	35	def get_lecc(shp):
22		- """
23		- This functions finds largest enclosed circle center from shape file.
24		- It is used for optimizing text placement and size using Voronoi diagram in this project
25		- Arguments:
26		- shp -- shape object
	36	+ polygons_coord = [coord_lister(geom) for geom in shp.geometry]
27	37
28		- returns -
29		- lecc : which is largest enclosed circle
30		- lecc_dist : which is radius of largest enclosed circle
31		- """
32		- num_area = len(shp)
33		- polygons_coord = shp.geometry.apply(coord_lister)
34		- furthest_pnt = [None] * num_area
35		- voronoi_ori = [None] * num_area
36		- for i, points in enumerate(polygons_coord):
37		- voronoi_ori[i] = Voronoi(points, furthest_site=False)
38		- furthest_pnt[i] = voronoi_ori[i].vertices
	38	+ with ProcessPoolExecutor() as executor:
	39	+ results = list(executor.map(find_lec_for_polygon, polygons_coord, shp.geometry))
39	40
40		- voronoi_nodes = [None] * num_area
41		- leccc = [None] * num_area
42		- # get voronoi nodes that is inside the polygon.
43		- for i in range(num_area):
44		- voronoi_nodes[i] = points_from_xy(extract(furthest_pnt[i], 0), extract(furthest_pnt[i], 1))
45		- voronoi_nodes_if_inside = shp.geometry[i].contains(voronoi_nodes[i])
46		- voronoi_nodes[i] = list(compress(voronoi_nodes[i], voronoi_nodes_if_inside))
47		- leccc[i] = [item for sublist in extract_coord(voronoi_nodes[i]) for item in sublist]
48		-
49		- dist = [None] * num_area
50		- for i, points in enumerate(polygons_coord):
51		- # explaining this mess : I need to get the LEC from candidate I got before, but the thing is,
52		- # cdist from scipy only accepts exact dimension with array like object,
53		- # which means I need to explicitly set the shape of ndarray, convert list of points into ndarray
54		- dist[i] = cdist(np.array([list(item) for item in points]), np.array([list(item) for item in leccc[i]]))
55		-
56		- lecc = [None] * num_area
57		- lecc_dist = [None] * num_area
58		- for i in range(num_area):
59		- voronoi_nodes_dist = [None] * (dist[i].shape[1])
60		- for j in range(dist[i].shape[1]):
61		- voronoi_nodes_dist[j] = min(dist[i][:, j])
62		- lecc_ind = np.argmax(voronoi_nodes_dist)
63		- lecc[i] = leccc[i][lecc_ind]
64		- lecc_dist[i] = np.max(voronoi_nodes_dist)
65		-
	41	+ lecc, lecc_dist = zip(*results)
66	42	return lecc, lecc_dist (No newline at end of file)

Delete comment