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

query_pairs.__doc__

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()