Voronoi Diagrams

scikit-geometry comes with methods to efficiently compute the voronoi diagram of points.

import numpy as np
from skgeom import *
from matplotlib import pyplot as plt

npoints = np.random.rand(100, 2) * 20 - 10
points = []
for r in npoints:
    points.append(Point2(*r))

vdiag = voronoi.VoronoiDiagram()

for p in points:
    vdiag.insert(p)

for he in vdiag.edges:
    source, target = he.source(), he.target()
    if source and target:
        plt.plot([source.point().x(), target.point().x()], [source.point().y(), target.point().y()])

plt.scatter(npoints[:, 0], npoints[:, 1])

plt.axis('equal')
plt.gca().set_adjustable("box")
plt.gca().set_xlim([-10, 10])
plt.gca().set_ylim([-10, 10])
plt.show()