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scipy.spatial.KDTree.

# query_ball_tree

KDTree.query_ball_tree(other, r, p=2.0, eps=0)

  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)
      >>> 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")
      >>> plt.show()