scipy.spatial.KDTree.query

KDTree.query(x, k=1, eps=0, p=2, distance_upper_bound=inf)

Query the kd-tree for nearest neighbors

Parameters

xarray_like, last dimension self.m

An array of points to query.

kint, optional

The number of nearest neighbors to return.

epsnonnegative float, optional

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

pfloat, 1<=p<=infinity, optional

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_boundnonnegative float, optional

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

dfloat or array of floats

The distances to the nearest neighbors. If x has shape tuple+(self.m,), then d has shape tuple if k is one, or tuple+(k,) if k is larger than one. Missing neighbors (e.g. when k > n or distance_upper_bound is given) are indicated with infinite distances. If k is None, then d is an object array of shape tuple, containing lists of distances. In either case the hits are sorted by distance (nearest first).

iinteger or array of integers

The locations of the neighbors in self.data. i is the same shape as d.

Examples

>>> from scipy import spatial
>>> x, y = np.mgrid[0:5, 2:8]
>>> tree = spatial.KDTree(list(zip(x.ravel(), y.ravel())))
>>> tree.data
array([[0, 2],
       [0, 3],
       [0, 4],
       [0, 5],
       [0, 6],
       [0, 7],
       [1, 2],
       [1, 3],
       [1, 4],
       [1, 5],
       [1, 6],
       [1, 7],
       [2, 2],
       [2, 3],
       [2, 4],
       [2, 5],
       [2, 6],
       [2, 7],
       [3, 2],
       [3, 3],
       [3, 4],
       [3, 5],
       [3, 6],
       [3, 7],
       [4, 2],
       [4, 3],
       [4, 4],
       [4, 5],
       [4, 6],
       [4, 7]])
>>> pts = np.array([[0, 0], [2.1, 2.9]])
>>> tree.query(pts)
(array([ 2.        ,  0.14142136]), array([ 0, 13]))
>>> tree.query(pts[0])
(2.0, 0)