scipy.spatial.KDTree.query#

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

Query the kd-tree for nearest neighbors.

Parameters:

xarray_like, last dimension self.m: An array of points to query.
kint or Sequence[int], optional: Either the number of nearest neighbors to return, or a list of the k-th nearest neighbors to return, starting from 1.
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 distance (“Manhattan” distance). 2 is the usual Euclidean distance. infinity is the maximum-coordinate-difference distance. A large, finite p may cause a ValueError if overflow can occur.
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.
workersint, optional: Number of workers to use for parallel processing. If -1 is given all CPU threads are used. Default: 1.

New in version 1.6.0.

Returns:

dfloat or array of floats: The distances to the nearest neighbors. If x has shape tuple+(self.m,), then d has shape tuple+(k,). When k == 1, the last dimension of the output is squeezed. Missing neighbors are indicated with infinite distances. Hits are sorted by distance (nearest first).

Changed in version 1.9.0: Previously if k=None, then d was an object array of shape tuple, containing lists of distances. This behavior has been removed, use query_ball_point instead.
iinteger or array of integers: The index of each neighbor in self.data. i is the same shape as d. Missing neighbors are indicated with self.n.

Examples

>>> import numpy as np
>>> from scipy.spatial import KDTree
>>> x, y = np.mgrid[0:5, 2:8]
>>> tree = KDTree(np.c_[x.ravel(), y.ravel()])

To query the nearest neighbours and return squeezed result, use

>>> dd, ii = tree.query([[0, 0], [2.2, 2.9]], k=1)
>>> print(dd, ii, sep='\n')
[2.         0.2236068]
[ 0 13]

To query the nearest neighbours and return unsqueezed result, use

>>> dd, ii = tree.query([[0, 0], [2.2, 2.9]], k=[1])
>>> print(dd, ii, sep='\n')
[[2.        ]
 [0.2236068]]
[[ 0]
 [13]]

To query the second nearest neighbours and return unsqueezed result, use

>>> dd, ii = tree.query([[0, 0], [2.2, 2.9]], k=[2])
>>> print(dd, ii, sep='\n')
[[2.23606798]
 [0.80622577]]
[[ 6]
 [19]]

To query the first and second nearest neighbours, use

>>> dd, ii = tree.query([[0, 0], [2.2, 2.9]], k=2)
>>> print(dd, ii, sep='\n')
[[2.         2.23606798]
 [0.2236068  0.80622577]]
[[ 0  6]
 [13 19]]

or, be more specific

>>> dd, ii = tree.query([[0, 0], [2.2, 2.9]], k=[1, 2])
>>> print(dd, ii, sep='\n')
[[2.         2.23606798]
 [0.2236068  0.80622577]]
[[ 0  6]
 [13 19]]