from a set of points I built the Voronoi tessellation using scipy:
from scipy.spatial import Voronoi
vor = Voronoi(points)
Now I would like to build a Polygon in Shapely from the regions the Voronoi algorithm created. The problem is that the Polygon class requires a list of counter-clockwise vertices. Although I know how to order these vertices, I can't solve the problem because often this is my result:
(overlapping polygon). This is the code (ONE RANDOM EXAMPLE):
def order_vertices(l):
mlat = sum(x[0] for x in l) / len(l)
mlng = sum(x[1] for x in l) / len(l)
# https://stackoverflow.com/questions/1709283/how-can-i-sort-a-coordinate-list-for-a-rectangle-counterclockwise
def algo(x):
return (math.atan2(x[0] - mlat, x[1] - mlng) + 2 * math.pi) % 2*math.pi
l.sort(key=algo)
return l
a = np.asarray(order_vertices([(9.258054711746084, 45.486245994138976),
(9.239284166975443, 45.46805963143515),
(9.271640747003861, 45.48987234571072),
(9.25828782103321, 45.44377372506324),
(9.253993275176263, 45.44484395950612),
(9.250114174032936, 45.48417979682819)]))
plt.plot(a[:,0], a[:,1])
How can I solve this problem?
A Voronoi diagram is sometimes also known as a Dirichlet tessellation. The cells are called Dirichlet regions, Thiessen polytopes, or Voronoi polygons.
In hydrology, Voronoi diagrams are used to calculate the rainfall of an area, based on a series of point measurements. In this usage, they are generally referred to as Thiessen polygons.
Voronoi diagrams have applications in almost all areas of science and engineering. Biological structures can be described using them. In aviation, they are used to identify the nearest airport in case of diversions. In mining, they can aid estimation of overall mineral resources based on exploratory drill holes.
If you're just after a collection of polygons you don't need to pre-order the point to build them.
The scipy.spatial.Voronoi
object has a ridge_vertices
attribute containing indices of vertices forming the lines of the Voronoi ridge. If the index is -1
then the ridge goes to infinity.
First start with some random points to build the Voronoi object.
import numpy as np
from scipy.spatial import Voronoi, voronoi_plot_2d
import shapely.geometry
import shapely.ops
points = np.random.random((10, 2))
vor = Voronoi(points)
voronoi_plot_2d(vor)
You can use this to build a collection of Shapely LineString objects.
lines = [
shapely.geometry.LineString(vor.vertices[line])
for line in vor.ridge_vertices
if -1 not in line
]
The shapely.ops
module has a polygonize
that returns a generator for Shapely Polygon objects.
for poly in shapely.ops.polygonize(lines):
#do something with each polygon
Or if you wanted a single polygon formed from the region enclosed by the Voronoi tesselation you can use the Shapely unary_union
method:
shapely.ops.unary_union(list(shapely.ops.polygonize(lines)))
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