spatial

Nearest-neighbor Queries

KDTree
cKDTree
distance
Rectangle

Delaunay Triangulation, Convex Hulls and Voronoi Diagrams

Delaunay
ConvexHull
Voronoi
SphericalVoronoi
HalfspaceIntersection

Plotting Helpers

delaunay_plot_2d
convex_hull_plot_2d
voronoi_plot_2d

See also

Tutorial

Simplex representation

The simplices (triangles, tetrahedra, …) appearing in the Delaunay tesselation (N-dim simplices), convex hull facets, and Voronoi ridges (N-1 dim simplices) are represented in the following scheme:

tess = Delaunay(points)
hull = ConvexHull(points)
voro = Voronoi(points)

# coordinates of the j-th vertex of the i-th simplex
tess.points[tess.simplices[i, j], :]        # tesselation element
hull.points[hull.simplices[i, j], :]        # convex hull facet
voro.vertices[voro.ridge_vertices[i, j], :] # ridge between Voronoi cells

For Delaunay triangulations and convex hulls, the neighborhood structure of the simplices satisfies the condition:

tess.neighbors[i,j] is the neighboring simplex of the i-th simplex, opposite to the j-vertex. It is -1 in case of no neighbor.

Convex hull facets also define a hyperplane equation:

(hull.equations[i,:-1] * coord).sum() + hull.equations[i,-1] == 0

Similar hyperplane equations for the Delaunay triangulation correspond to the convex hull facets on the corresponding N+1 dimensional paraboloid.

The Delaunay triangulation objects offer a method for locating the simplex containing a given point, and barycentric coordinate computations.

Functions

tsearch
distance_matrix
minkowski_distance
minkowski_distance_p
procrustes

Package Contents