scipy.spatial – Spatial data structures and algorithms
In this article, we are going to see spatial data structure and algorithms, it is used to represent data in a geometric space.
What is spatial data structure?
The spatial package computes the 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.
Example 1: Delaunay Triangulations
In mathematics and computational geometry, a Delaunay triangulation for a given set p of discrete points, in a plane is a triangulation DT(p) such that no point p is inside the circumcircle of any triangle in DT(p).
Example 2: Coplanar points
Coplanar points are three or more points that lie in the same plane. Recall that, a plane is a flat surface, which extends without end in all directions.
[[3 1 0] [2 3 0]] [[4 0 3]]
Example 3: Convex Hulls
The convex hull or convex envelope of a set of points X in the euclidean space(or, more generally in affine space over the reals) is the smallest convex set that contains X.
Example 4: KPTrees
kd-tree is a quick nearest-neighbor lookup. And Kdtree() methods return the kd-tree object
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