# scipy.spatial.cKDTree.query

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

  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.

      Changed in version 1.6.0: The “n_jobs” argument was renamed “workers”. The old name “n_jobs” is deprecated and will stop working in SciPy 1.8.0.

  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')
      [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]]

scipy.spatial.cKDTree.count_neighbors scipy.spatial.cKDTree.query_ball_point