scipy.spatial.KDTree.
query_pairs#
- KDTree.query_pairs(r, p=2.0, eps=0, output_type='set')[source]#
- Find all pairs of points in self whose distance is at most r. - Parameters:
- rpositive float
- The maximum distance. 
- pfloat, optional
- Which Minkowski norm to use. p has to meet the condition - 1 <= p <= infinity.
- 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.
- output_typestring, optional
- Choose the output container, ‘set’ or ‘ndarray’. Default: ‘set’ - Added in version 1.6.0. 
 
- Returns:
- resultsset or ndarray
- Set of pairs - (i,j), with- i < j, for which the corresponding positions are close. If output_type is ‘ndarray’, an ndarry is returned instead of a set.
 
 - Examples - You can search all pairs of points in a kd-tree within a distance: - >>> import matplotlib.pyplot as plt >>> import numpy as np >>> from scipy.spatial import KDTree >>> rng = np.random.default_rng() >>> points = rng.random((20, 2)) >>> plt.figure(figsize=(6, 6)) >>> plt.plot(points[:, 0], points[:, 1], "xk", markersize=14) >>> kd_tree = KDTree(points) >>> pairs = kd_tree.query_pairs(r=0.2) >>> for (i, j) in pairs: ... plt.plot([points[i, 0], points[j, 0]], ... [points[i, 1], points[j, 1]], "-r") >>> plt.show() 