scipy.spatial.cKDTree.
query_ball_tree#
- cKDTree.query_ball_tree(self, other, r, p=2., eps=0)#
- Find all pairs of points between self and other whose distance is at most r - Parameters:
- othercKDTree instance
- The tree containing points to search against. 
- rfloat
- The maximum distance, has to be positive. 
- pfloat, optional
- Which Minkowski norm to use. p has to meet the condition - 1 <= p <= infinity. A finite large p may cause a ValueError if overflow can occur.
- epsfloat, 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). eps has to be non-negative.
 
- Returns:
- resultslist of lists
- For each element - self.data[i]of this tree,- results[i]is a list of the indices of its neighbors in- other.data.
 
 - Examples - You can search all pairs of points between two kd-trees within a distance: - >>> import matplotlib.pyplot as plt >>> import numpy as np >>> from scipy.spatial import cKDTree >>> rng = np.random.default_rng() >>> points1 = rng.random((15, 2)) >>> points2 = rng.random((15, 2)) >>> plt.figure(figsize=(6, 6)) >>> plt.plot(points1[:, 0], points1[:, 1], "xk", markersize=14) >>> plt.plot(points2[:, 0], points2[:, 1], "og", markersize=14) >>> kd_tree1 = cKDTree(points1) >>> kd_tree2 = cKDTree(points2) >>> indexes = kd_tree1.query_ball_tree(kd_tree2, r=0.2) >>> for i in range(len(indexes)): ... for j in indexes[i]: ... plt.plot([points1[i, 0], points2[j, 0]], ... [points1[i, 1], points2[j, 1]], "-r") >>> plt.show() 