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Classe « KDTree »

Méthode scipy.spatial.KDTree.query_pairs

Signature de la méthode query_pairs

def query_pairs(self, r, p=2.0, eps=0, output_type='set') 

Description

help(KDTree.query_pairs)

Find all pairs of points in `self` whose distance is at most r.

Parameters
----------
r : positive float
    The maximum distance.
p : float, optional
    Which Minkowski norm to use.  `p` has to meet the condition
    ``1 <= p <= infinity``.
eps : 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)``.  `eps` has to be non-negative.
output_type : string, optional
    Choose the output container, 'set' or 'ndarray'. Default: 'set'

    .. versionadded:: 1.6.0

Returns
-------
results : set 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()

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