scipy.spatial.cKDTree.query¶

cKDTree.
query
(self, x, k=1, eps=0, p=2, distance_upper_bound=np.inf, n_jobs=1)¶ Query the kdtree for nearest neighbors
 Parameters
 xarray_like, last dimension self.m
An array of points to query.
 klist of integer or integer
The list of kth 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.
 epsnonnegative 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.
 pfloat, 1<=p<=infinity
Which Minkowski pnorm to use. 1 is the sumofabsolutevalues “Manhattan” distance 2 is the usual Euclidean distance infinity is the maximumcoordinatedifference distance A finite large p may cause a ValueError if overflow can occur.
 distance_upper_boundnonnegative float
Return only neighbors within this distance. This is used to prune tree searches, so if you are doing a series of nearestneighbor queries, it may help to supply the distance to the nearest neighbor of the most recent point.
 n_jobsint, optional
Number of jobs to schedule for parallel processing. If 1 is given all processors are used. Default: 1.
 Returns
 darray of floats
The distances to the nearest neighbors. If
x
has shapetuple+(self.m,)
, thend
has shapetuple+(k,)
. When k == 1, the last dimension of the output is squeezed. Missing neighbors are indicated with infinite distances. indarray of ints
The locations of the neighbors in
self.data
. Ifx
has shapetuple+(self.m,)
, theni
has shapetuple+(k,)
. When k == 1, the last dimension of the output is squeezed. Missing neighbors are indicated withself.n
.
Notes
If the KDTree 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], [2.1, 2.9]], k=1) >>> print(dd, ii) [2. 0.14142136] [ 0 13]
To query the nearest neighbours and return unsqueezed result, use
>>> dd, ii = tree.query([[0, 0], [2.1, 2.9]], k=[1]) >>> print(dd, ii) [[2. ] [0.14142136]] [[ 0] [13]]
To query the second nearest neighbours and return unsqueezed result, use
>>> dd, ii = tree.query([[0, 0], [2.1, 2.9]], k=[2]) >>> print(dd, ii) [[2.23606798] [0.90553851]] [[ 6] [12]]
To query the first and second nearest neighbours, use
>>> dd, ii = tree.query([[0, 0], [2.1, 2.9]], k=2) >>> print(dd, ii) [[2. 2.23606798] [0.14142136 0.90553851]] [[ 0 6] [13 12]]
or, be more specific
>>> dd, ii = tree.query([[0, 0], [2.1, 2.9]], k=[1, 2]) >>> print(dd, ii) [[2. 2.23606798] [0.14142136 0.90553851]] [[ 0 6] [13 12]]