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scipy.spatial.KDTree.

# query

KDTree.query(x, k=1, eps=0, p=2, distance_upper_bound=inf, workers=1)

  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 the k-th nearest neighbors to return, starting from 1.

    epsnonnegative 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.

    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 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.

    workersint, optional

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

      Added in version 1.6.0.

  Returns:
    dfloat 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).

      Changed in version 1.9.0: Previously if `k=None`, then d was an object array of shape `tuple`, containing lists of distances. This behavior has been removed, use `query_ball_point` instead.

    iinteger 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')
      [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]]