`scipy.spatial._ckdtree:cKDTree.query_ball_tree`

Signature

def query_ball_tree ( self , other , r , p = 2.0 , eps = 0.0 )

Summary

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

Parameters

other : cKDTree instance: The tree containing points to search against.
r : float: The maximum distance, has to be positive.
p : float, 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.
eps : float, 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

results : list 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()

✓

Aliases

scipy.spatial.cKDTree.query_ball_tree