Computational Geometry in Python

Abstraction: Python scipy/sympy toolkit for computational geometry algorithms

Key points:

• Divides into Combinatorial (points, segments, polygons) and Numerical (curves, surfaces) computational geometry
• scipy.spatial wraps Qhull libraries for convex hulls, Voronoi diagrams, and Delaunay triangulations
• KDTree / cKDTree in scipy.spatial enables fast nearest-neighbor and range searching; cKDTree is C-backed and significantly faster
• Constrained conforming Delaunay triangulations (with angle/area constraints) require the triangle library by Richard Shewchuck, wrapping a CMU mesh generator
• Bezier curves implemented via matplotlib.path using de Casteljau algorithm; higher-degree curves use piecewise Bezier splines
• Shortest paths on triangulations use scipy.sparse weighted adjacency matrices and scipy.sparse.csgraph

Connections: Scipy · Sympy · Computational Geometry · Delaunay Triangulation · Algorithms

Source: http://blancosilva.github.io/post/2014/10/28/Computational-Geometry-in-Python.html?