SciPy

scipy.spatial.KDTree.query_pairs

KDTree.query_pairs(self, r, p=2.0, eps=0)[source]

Find all pairs of points in self whose distance is at most r.

Parameters
rpositive float

The maximum distance.

pfloat, optional

Which Minkowski norm to use. p has to meet the condition 1 <= p <= infinity.

epsfloat, optional

Approximate search. Branches of the tree are not explored if their nearest points are further than r/(1+eps), and branches are added in bulk if their furthest points are nearer than r * (1+eps). eps has to be non-negative.

Returns
resultsset

Set of pairs (i,j), with i < j, for which the corresponding positions are close.

Examples

You can search all pairs of points in a kd-tree within a distance:

>>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> from scipy.spatial import KDTree
>>> np.random.seed(21701)
>>> points = np.random.random((20, 2))
>>> plt.figure(figsize=(6, 6))
>>> plt.plot(points[:, 0], points[:, 1], "xk", markersize=14)
>>> kd_tree = KDTree(points)
>>> pairs = kd_tree.query_pairs(r=0.2)
>>> for (i, j) in pairs:
...     plt.plot([points[i, 0], points[j, 0]],
...             [points[i, 1], points[j, 1]], "-r")
>>> plt.show()
../_images/scipy-spatial-KDTree-query_pairs-1.png

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