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

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 : integer or one-dimensional array of integers

The number of pairs. Note that this is internally stored in a numpy int, and so may overflow if very large (two billion).

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