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scipy.spatial.KDTree

# scipy.spatial.KDTree.count_neighbors

`KDTree.``count_neighbors`(other, r, p=2.0)

  Count how many nearby pairs can be formed.

  Count the number of pairs (x1,x2) can be formed, with x1 drawn from self and x2 drawn from other, and where `distance(x1, x2, p) <= r`. This is the “two-point correlation” described in Gray and Moore 2000, “N-body problems in statistical learning”, and the code here is based on their algorithm.

  Parameters: | other : KDTree instance The other tree to draw points from. r : float or one-dimensional array of floats The radius to produce a count for. Multiple radii are searched with a single tree traversal. p : float, 1<=p<=infinity, optional Which Minkowski p-norm to use
  Returns: | result : int or 1-D array of ints The number of pairs. Note that this is internally stored in a numpy int, and so may overflow if very large (2e9).