Spatial queries in Python

Distances, nearest neighbours and hulls over point sets.

import numpy as np
from scipy import spatial

points = np.array([[0.0, 0.0], [1.0, 0.5], [3.0, 3.0], [4.0, 0.5]])
query = np.array([[1.2, 0.6]])

print(spatial.distance.cdist(query, points).round(3))
print(round(spatial.distance.euclidean(points[0], points[2]), 4))

tree = spatial.KDTree(points)
distance, index = tree.query(query, k=2)
print(distance.round(3), index)

hull = spatial.ConvexHull(points)
print(sorted(hull.vertices.tolist()), round(hull.volume, 2))

How it works

cdist gives every pairwise distance between two sets.
KDTree answers nearest-neighbour queries in log time.
ConvexHull returns the vertices of the enclosing polygon.

Keywords and builtins used here

as
print
round
sorted

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Step 3 of 4 in Sparse & spatial, step 21 of 23 in Scientific computing with SciPy.

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