I have spent a great deal of time now trying to obtain edges from the scipy.spatial.Voronoi diagram to no avail. Here is the main documentation: http://docs.scipy.org/doc/scipy-dev/reference/generated/scipy.spatial.Voronoi.html
If you create a Voronoi Diagram like so:
points = np.array([[0, 0], [0, 1], [0, 2], [1, 0], [1, 1], [1, 2],
[2, 0], [2, 1], [2, 2]]) //Or feel free to use any set of points
then you have access to the following object properties:
vor.regions
vor.max_bound
vor.ndim
vor.ridge_dict
vor.ridge_points
vor.ridge_vertices
vor.npoints
vor.point_region
vor.points
But is unclear how to combine these to get edges in the form (point1, point2) for 2d voronoi diagrams? I know edges exist because you can plot the voronoi diagram and its edgres and vertices because you can do the following:
voronoi_plot_2d(vor)
plt.show()
which clearly depicts voronoi edgres - how to get a list of them and their starting and endpoints? Its okay if I only get the solid edges (not the dotted ones which go unbounded off the plot)
We know that the intersection of any number of half-planes forms a convex region bounded by a set of connected line segments. These line segments form the boundaries of Voronoi regions and are called Voronoi edges. The endpoints of these edges are called Voronoi vertices.
A voronoi diagram has the following properties: Voronoi Diagram is a planar graph where every vertex is of degree 3. For 'n' sites, there are 'n' faces at most 2n-5 vertices, and at most 3n-6 edges. Sites in the unbounded voronoi cells correspond to the vertices on the convex hull.
scipy. spatial can compute triangulations, Voronoi diagrams, and convex hulls of a set of points, by leveraging the Qhull library. Moreover, it contains KDTree implementations for nearest-neighbor point queries, and utilities for distance computations in various metrics.
Take a look at the ridge_vertices
attribute:
ridge_vertices (list of list of ints, shape (nridges, *))
Indices of the Voronoi vertices forming each Voronoi ridge.
Each element in that list is a pair of integers. Each integer is an index
into the vertices
list. So each element defines a line to be draw in the
Voronoi diagram. An index of -1 means a point that is "at infinity".
Here's script that draws the lines of the Voronoi diagram:
import numpy as np
import matplotlib.pyplot as plt
from scipy.spatial import Voronoi
points = np.array([[0, 0], [0, 1], [0, 2],
[1, 0], [1, 1], [1, 2],
[2, 0], [2, 1], [2, 2]])
vor = Voronoi(points)
fig = plt.figure()
# Mark the Voronoi vertices.
plt.plot(vor.vertices[:,0], vor.vertices[:, 1], 'ko', ms=8)
for vpair in vor.ridge_vertices:
if vpair[0] >= 0 and vpair[1] >= 0:
v0 = vor.vertices[vpair[0]]
v1 = vor.vertices[vpair[1]]
# Draw a line from v0 to v1.
plt.plot([v0[0], v1[0]], [v0[1], v1[1]], 'k', linewidth=2)
plt.show()
It creates:
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