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SciPy Reference Guide, Release 0.8.dev query(x, k=1, eps=0, p=2, distance_upper_bound=inf ) 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 : nonnegative float 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> from <strong>scipy</strong>.spatial import KDTree >>> x, y = np.mgrid[0:5, 2:8] >>> tree = KDTree(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], 418 Chapter 3. Reference
[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])) query_ball_point(x, r, p=2.0, eps=0) Find all points within r of x Parameters x : array_like, shape tuple + (self.m,) The point or points to search for neighbors of r : positive float The radius of points to return p : float 1
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