scipy.spatial.KDTree.query¶
- KDTree.query(x, k=1, eps=0, p=2, distance_upper_bound=inf)[source]¶
Query the kd-tree for nearest neighbors
Parameters: x : array_like, last dimension self.m
An array of points to query.
k : int, optional
The number of nearest neighbors to return.
eps : nonnegative 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.
p : float, 1<=p<=infinity, optional
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, 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.
Returns: d : float or array of floats
The distances to the nearest neighbors. If x has shape tuple+(self.m,), then d has shape tuple if k is one, or tuple+(k,) if k is larger than one. Missing neighbors (e.g. when k > n or distance_upper_bound is given) are indicated with infinite distances. If k is None, then d is an object array of shape tuple, containing lists of distances. In either case the hits are sorted by distance (nearest first).
i : integer or array of integers
The locations of the neighbors in self.data. i is the same shape as d.
Examples
>>> from scipy import spatial >>> x, y = np.mgrid[0:5, 2:8] >>> tree = spatial.KDTree(list(zip(x.ravel(), y.ravel()))) >>> tree.data array([[0, 2], [0, 3], [0, 4], [0, 5], [0, 6], [0, 7], [1, 2], [1, 3], [1, 4], [1, 5], [1, 6], [1, 7], [2, 2], [2, 3], [2, 4], [2, 5], [2, 6], [2, 7], [3, 2], [3, 3], [3, 4], [3, 5], [3, 6], [3, 7], [4, 2], [4, 3], [4, 4], [4, 5], [4, 6], [4, 7]]) >>> pts = np.array([[0, 0], [2.1, 2.9]]) >>> tree.query(pts) (array([ 2. , 0.14142136]), array([ 0, 13])) >>> tree.query(pts[0]) (2.0, 0)