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bundles / scipy 1.18.0 / scipy / spatial / _kdtree / KDTree / query_ball_tree

function

scipy.spatial._kdtree:KDTree.query_ball_tree

source: /scipy/spatial/_kdtree.py:639

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 : KDTree 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.

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 KDTree
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)
✗
[<matplotlib.lines.Line2D object at 0x7fa8f6853230>]

kd_tree1 = KDTree(points1)
kd_tree2 = KDTree(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")
✗
[<matplotlib.lines.Line2D object at 0x7fa8f6851160>]
[<matplotlib.lines.Line2D object at 0x7fa8f6852120>]
[<matplotlib.lines.Line2D object at 0x7fa8f6850c20>]
[<matplotlib.lines.Line2D object at 0x7fa8f6852ba0>]
[<matplotlib.lines.Line2D object at 0x7fa8f6850980>]
[<matplotlib.lines.Line2D object at 0x7fa8f6852510>]
[<matplotlib.lines.Line2D object at 0x7fa8f68534d0>]
[<matplotlib.lines.Line2D object at 0x7fa8f6850d70>]
[<matplotlib.lines.Line2D object at 0x7fa8f6853620>]
[<matplotlib.lines.Line2D object at 0x7fa8f68538c0>]
[<matplotlib.lines.Line2D object at 0x7fa8f68527b0>]
[<matplotlib.lines.Line2D object at 0x7fa8f6851550>]
[<matplotlib.lines.Line2D object at 0x7fa8e4729d30>]
[<matplotlib.lines.Line2D object at 0x7fa8e472bcb0>]
[<matplotlib.lines.Line2D object at 0x7fa8e47281a0>]
[<matplotlib.lines.Line2D object at 0x7fa8e4729160>]
[<matplotlib.lines.Line2D object at 0x7fa8e47296a0>]
[<matplotlib.lines.Line2D object at 0x7fa8e4728830>]
[<matplotlib.lines.Line2D object at 0x7fa8e47297f0>]
[<matplotlib.lines.Line2D object at 0x7fa8e472aa50>]
[<matplotlib.lines.Line2D object at 0x7fa8e47292b0>]

plt.show()
✓
fig-a1a35713cbe3c614.png

Aliases

  • scipy.spatial.KDTree.query_ball_tree