query_ball_point#
- KDTree.query_ball_point(x, r, p=2.0, eps=0, workers=1, return_sorted=None, return_length=False)[source]#
- Find all points within distance r of point(s) x. - Parameters:
- xarray_like, shape tuple + (self.m,)
- The point or points to search for neighbors of. 
- rarray_like, float
- The radius of points to return, must broadcast to the length of x. 
- pfloat, optional
- Which Minkowski p-norm to use. Should be in the range [1, inf]. A finite large p may cause a ValueError if overflow can occur. 
- epsnonnegative float, optional
- Approximate search. Branches of the tree are not explored if their nearest points are further than - r / (1 + eps), and branches are added in bulk if their furthest points are nearer than- r * (1 + eps).
- workersint, optional
- Number of jobs to schedule for parallel processing. If -1 is given all processors are used. Default: 1. - Added in version 1.6.0. 
- return_sortedbool, optional
- Sorts returned indices if True and does not sort them if False. If None, does not sort single point queries, but does sort multi-point queries which was the behavior before this option was added. - Added in version 1.6.0. 
- return_lengthbool, optional
- Return the number of points inside the radius instead of a list of the indices. - Added in version 1.6.0. 
 
- Returns:
- resultslist or array of lists
- If x is a single point, returns a list of the indices of the neighbors of x. If x is an array of points, returns an object array of shape tuple containing lists of neighbors. 
 
 - Notes - If you have many points whose neighbors you want to find, you may save substantial amounts of time by putting them in a KDTree and using query_ball_tree. - Examples - >>> import numpy as np >>> from scipy import spatial >>> x, y = np.mgrid[0:5, 0:5] >>> points = np.c_[x.ravel(), y.ravel()] >>> tree = spatial.KDTree(points) >>> sorted(tree.query_ball_point([2, 0], 1)) [5, 10, 11, 15] - Query multiple points and plot the results: - >>> import matplotlib.pyplot as plt >>> points = np.asarray(points) >>> plt.plot(points[:,0], points[:,1], '.') >>> for results in tree.query_ball_point(([2, 0], [3, 3]), 1): ... nearby_points = points[results] ... plt.plot(nearby_points[:,0], nearby_points[:,1], 'o') >>> plt.margins(0.1, 0.1) >>> plt.show() 