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scipy.spatial.cKDTree.query_ball_point

# 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: | x : array_like, last dimension self.m An array of points to query. k : integer The number of nearest neighbors to return. 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+(k,). 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+(k,). Missing neighbors are indicated with self.n.