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bundles / scipy 1.17.1 / scipy / spatial / _kdtree / KDTree / query

function

scipy.spatial._kdtree:KDTree.query

source: /scipy/spatial/_kdtree.py :369

Signature

def   query ( self ,  x ,  k = 1 ,  eps = 0.0 ,  p = 2.0 ,  distance_upper_bound = inf ,  workers = 1 )

Summary

Query the kd-tree for nearest neighbors.

Parameters

x : array_like, last dimension self.m

An array of points to query.

k : int 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.

eps : nonnegative 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.

p : float, 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_bound : nonnegative 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.

workers : int, optional

Number of workers to use for parallel processing. If -1 is given all CPU threads are used. Default: 1.

Returns

d : float 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).

i : integer 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')
✗
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')
✗
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')
✗
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')
✗
or, be more specific 
dd, ii = tree.query([[0, 0], [2.2, 2.9]], k=[1, 2])
✓
print(dd, ii, sep='\n')
✗

Aliases

  • scipy.spatial.KDTree.query