`scipy.spatial._kdtree:KDTree.query_pairs`

Signature

   def     query_pairs (    self    ,    r    ,    p    =  2.0   ,    eps    =  0.0   ,    output_type    =  set     )

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

r : positive float: The maximum distance.
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.
output_type : string, optional: Choose the output container, 'set' or 'ndarray'. Default: 'set'
versionadded 1.6.0

results : set or ndarray: Set of pairs (i,j), with i < j, for which the corresponding positions are close. If output_type is 'ndarray', an ndarray is returned instead of a set.

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
rng = np.random.default_rng()
points = rng.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()

✓