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bundles / scipy 1.17.1 / scipy / spatial / _ckdtree / cKDTree / query

cython_function_or_method

scipy.spatial._ckdtree:cKDTree.query

Summary

Query the kd-tree for nearest neighbors

Parameters

x : array_like, last dimension self.m

An array of points to query.

k : list of integer or integer

The list of k-th nearest neighbors to return. If k is an integer it is treated as a list of [1, ... k] (range(1, k+1)). Note that the counting starts from 1.

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 A finite large p may cause a ValueError if overflow can occur.

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.

workers : int, optional

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

Returns

d : 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.

i : ndarray of ints

The index of each neighbor in self.data. If x has shape tuple+(self.m,), then i has shape tuple+(k,). When k == 1, the last dimension of the output is squeezed. Missing neighbors are indicated with self.n.

Notes

If the KD-Tree is periodic, the position x is wrapped into the box.

When the input k is a list, a query for arange(max(k)) is performed, but only columns that store the requested values of k are preserved. This is implemented in a manner that reduces memory usage.

Examples

import numpy as np
from scipy.spatial import cKDTree
x, y = np.mgrid[0:5, 2:8]
tree = cKDTree(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.cKDTree.query

Referenced by

This package