scipy.spatial.cKDTree.query_ball_tree#

cKDTree.query_ball_tree(self, other, r, p=2., eps=0)#

Find all pairs of points between self and other whose distance is at most r

Parameters:

othercKDTree instance: The tree containing points to search against.
rfloat: The maximum distance, has to be positive.
pfloat, optional: Which Minkowski norm to use. p has to meet the condition 1 <= p <= infinity. A finite large p may cause a ValueError if overflow can occur.
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:

resultslist of lists: For each element self.data[i] of this tree, results[i] is a list of the indices of its neighbors in other.data.

Examples

You can search all pairs of points between two kd-trees within a distance:

>>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> from scipy.spatial import cKDTree
>>> rng = np.random.default_rng()
>>> points1 = rng.random((15, 2))
>>> points2 = rng.random((15, 2))
>>> plt.figure(figsize=(6, 6))
>>> plt.plot(points1[:, 0], points1[:, 1], "xk", markersize=14)
>>> plt.plot(points2[:, 0], points2[:, 1], "og", markersize=14)
>>> kd_tree1 = cKDTree(points1)
>>> kd_tree2 = cKDTree(points2)
>>> indexes = kd_tree1.query_ball_tree(kd_tree2, r=0.2)
>>> for i in range(len(indexes)):
...     for j in indexes[i]:
...         plt.plot([points1[i, 0], points2[j, 0]],
...             [points1[i, 1], points2[j, 1]], "-r")
>>> plt.show()

../../_images/scipy-spatial-cKDTree-query_ball_tree-1.png