This is documentation for an old release of SciPy (version 0.10.0). Read this page Search for this page in the documentation of the latest stable release (version 1.15.1).

scipy.spatial.cKDTree.query

cKDTree.query(self, x, k=1, eps=0, p=2, distance_upper_bound=np.inf)

Query the kd-tree for nearest neighbors.

Parameters :

x : array_like, last dimension self.m

An array of points to query.

k : int

The number of nearest neighbors to return.

eps : non-negative float

Return approximate nearest neighbors; the k-th returned value is guaranteed to be no further than (1 + eps) times the distance to the real k-th nearest neighbor.

p : float, 1 <= p <= infinity

Which Minkowski p-norm to use. 1 is the sum-of-absolute-values “Manhattan” distance. 2 is the usual Euclidean distance. infinity is the maximum-coordinate-difference distance.

distance_upper_bound : non-negative float

Return only neighbors within this distance. This is used to prune tree searches, so if you are doing a series of nearest-neighbor queries, it may help to supply the distance to the nearest neighbor of the most recent point.

Returns :

d : ndarray of floats

The distances to the nearest neighbors. If x has shape tuple+(self.m,), then d has shape tuple+(k,). Missing neighbors are indicated with infinite distances.

i : ndarray of ints

The locations of the neighbors in self.data. If x has shape tuple+(self.m,), then i has shape tuple+(k,). Missing neighbors are indicated with self.n.