# scipy.spatial.cKDTree.query_pairs

cKDTree.query_pairs(self, r, p=2., eps=0)

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

  Parameters
    r positive float

      The maximum distance.

    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.

    output_type string, optional

      Choose the output container, ‘set’ or ‘ndarray’. Default: ‘set’

  Returns
    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 ndarry is returned instead of a set.

  Examples

  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 cKDTree
      >>> 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 = cKDTree(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()

scipy.spatial.cKDTree.query_ball_tree scipy.spatial.cKDTree.sparse_distance_matrix