KDTree.count_neighbors(other, r, p=2.0)[source]

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

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).

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