scipy.spatial.cKDTree.query¶

cKDTree.query(self, x, k=1, eps=0, p=2, distance_upper_bound=np.inf, n_jobs=1)¶

Query the kd-tree for nearest neighbors

Parameters:

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 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. n_jobs : int, optional Number of jobs to schedule for parallel processing. If -1 is given all processors 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+(len(k),). When k == 1, the last dimension of the output is squeezed. Missing neighbors are indicated with infinite distances. i : ndarray of ints The locations of the neighbors in self.data. If x has shape tuple+(self.m,), then i has shape tuple+(len(k),). When k == 1, the last dimension of the output is squeezed. Missing neighbors are indicated with self.n.

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

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.

n_jobs : int, optional

Number of jobs to schedule for parallel processing. If -1 is given all processors 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+(len(k),). When k == 1, the last dimension of the output is squeezed. Missing neighbors are indicated with infinite distances.

i : ndarray of ints

The locations of the neighbors in self.data. If x has shape tuple+(self.m,), then i has shape tuple+(len(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 :py:code:`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

>>> tree = cKDTree(data)

To query the nearest neighbours and return squeezed result, use

>>> dd, ii = tree.query(x, k=1)

To query the nearest neighbours and return unsqueezed result, use

>>> dd, ii = tree.query(x, k=[1])

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

>>> dd, ii = tree.query(x, k=[2])

To query the first and second nearest neighbours, use

>>> dd, ii = tree.query(x, k=2)

or, be more specific

>>> dd, ii = tree.query(x, k=[1, 2])