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Colorized Voronoi diagram with Scipy, in 2D, including infinite regions
import numpy as np
import matplotlib.pyplot as plt
from scipy.spatial import Voronoi
def voronoi_finite_polygons_2d(vor, radius=None):
"""
Reconstruct infinite voronoi regions in a 2D diagram to finite
regions.
Parameters
----------
vor : Voronoi
Input diagram
radius : float, optional
Distance to 'points at infinity'.
Returns
-------
regions : list of tuples
Indices of vertices in each revised Voronoi regions.
vertices : list of tuples
Coordinates for revised Voronoi vertices. Same as coordinates
of input vertices, with 'points at infinity' appended to the
end.
"""
if vor.points.shape[1] != 2:
raise ValueError("Requires 2D input")
new_regions = []
new_vertices = vor.vertices.tolist()
center = vor.points.mean(axis=0)
if radius is None:
radius = vor.points.ptp().max()*2
# Construct a map containing all ridges for a given point
all_ridges = {}
for (p1, p2), (v1, v2) in zip(vor.ridge_points, vor.ridge_vertices):
all_ridges.setdefault(p1, []).append((p2, v1, v2))
all_ridges.setdefault(p2, []).append((p1, v1, v2))
# Reconstruct infinite regions
for p1, region in enumerate(vor.point_region):
vertices = vor.regions[region]
if all(v >= 0 for v in vertices):
# finite region
new_regions.append(vertices)
continue
# reconstruct a non-finite region
ridges = all_ridges[p1]
new_region = [v for v in vertices if v >= 0]
for p2, v1, v2 in ridges:
if v2 < 0:
v1, v2 = v2, v1
if v1 >= 0:
# finite ridge: already in the region
continue
# Compute the missing endpoint of an infinite ridge
t = vor.points[p2] - vor.points[p1] # tangent
t /= np.linalg.norm(t)
n = np.array([-t[1], t[0]]) # normal
midpoint = vor.points[[p1, p2]].mean(axis=0)
direction = np.sign(np.dot(midpoint - center, n)) * n
far_point = vor.vertices[v2] + direction * radius
new_region.append(len(new_vertices))
new_vertices.append(far_point.tolist())
# sort region counterclockwise
vs = np.asarray([new_vertices[v] for v in new_region])
c = vs.mean(axis=0)
angles = np.arctan2(vs[:,1] - c[1], vs[:,0] - c[0])
new_region = np.array(new_region)[np.argsort(angles)]
# finish
new_regions.append(new_region.tolist())
return new_regions, np.asarray(new_vertices)
# make up data points
np.random.seed(1234)
points = np.random.rand(15, 2)
# compute Voronoi tesselation
vor = Voronoi(points)
# plot
regions, vertices = voronoi_finite_polygons_2d(vor)
print "--"
print regions
print "--"
print vertices
# colorize
for region in regions:
polygon = vertices[region]
plt.fill(*zip(*polygon), alpha=0.4)
plt.plot(points[:,0], points[:,1], 'ko')
plt.axis('equal')
plt.xlim(vor.min_bound[0] - 0.1, vor.max_bound[0] + 0.1)
plt.ylim(vor.min_bound[1] - 0.1, vor.max_bound[1] + 0.1)
plt.savefig('voro.png')
plt.show()
@liubenyuan

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@liubenyuan liubenyuan commented Sep 19, 2015

and edge_project is a function projecting points back on the boundary of shape using numerical gradient

def edge_project(pts, fd, h0=1.0):
    """
    project points back on the boundary (where fd=0) using numerical gradient

    note : you should specify h0 according to your actual mesh size
    """
    deps = sqrt(np.finfo(float).eps)*h0
    d = fd(pts)
    dgradx = (fd(pts + [deps, 0]) - d) / deps
    dgrady = (fd(pts + [0, deps]) - d) / deps
    dgrad2 = dgradx**2 + dgrady**2
    dgrad2[dgrad2 == 0] = 1.
    # calculate gradient vector (minus)
    pgrad = np.vstack([d*dgradx/dgrad2, d*dgrady/dgrad2]).T
    return pgrad
@geertn444

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@geertn444 geertn444 commented Dec 17, 2019

The procedure voronoi_finite_polygons_2d creates remote points, but it creates duplicate remote points.
For example:

35 [-0.60794158 -0.24345993]
36 [-3.41872139 -0.21721891]
37 [-3.41872139 -0.21721891]
38 [-0.99550093 0.83040914]

p36 and p37 are the same.

p37 belongs to polygon 8:
8 [37, 11, 1, 5, 7, 38]

p36 belongs to polygon 6:
6 [11, 36, 35, 9, 10]

Because they also share p11, in reality polygon 6 & 8 are adjacent. However, this point is missed and this influences the coloring !
Would it be possible to run the procedure with a parameter to "merge" common points together ?

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