scipy.spatial.KDTree.query¶

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

Query the kd-tree for nearest neighbors

Parameters :

Parameters :	x : array_like, last dimension self.m An array of points to query. k : integer The number of nearest neighbors to return. eps : nonnegative float 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. 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 : nonnegative 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 : 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 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). i : array of integers The locations of the neighbors in self.data. i is the same shape as d.

x : array_like, last dimension self.m

An array of points to query.

k : integer

The number of nearest neighbors to return.

eps : nonnegative float

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.

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 : nonnegative 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 : 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 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).

i : 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(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]))

Next topic

scipy.spatial.KDTree.query_ball_point