query#

KDTree.query(x,k=1,eps=0.0,p=2.0,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 thek-th nearest neighbors to return, starting from 1.

epsnonnegative float, optional

Return approximate nearest neighbors; the kth returned valueis guaranteed to be no further than (1+eps) times thedistance 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 prunetree searches, so if you are doing a series of nearest-neighborqueries, it may help to supply the distance to the nearest neighborof the most recent point.

workersint, optional

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

Added in version 1.6.0.

Returns:

dfloat or array of floats

The distances to the nearest neighbors.Ifx 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.Hits are sorted by distance (nearest first).

Changed in version 1.9.0:Previously ifk=None, thend was an object array ofshapetuple, containing lists of distances. This behaviorhas been removed, usequery_ball_point instead.

iinteger or array of integers

The index of each neighbor inself.data.i is the same shape as d.Missing neighbors are indicated withself.n.

Examples

Try it in your browser!

>>>importnumpyasnp>>>fromscipy.spatialimportKDTree>>>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]]

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