Classe « KDTree »

Méthode scipy.spatial.KDTree.query_ball_point

Signature de la méthode query_ball_point

def query_ball_point(self, x, r, p=2.0, eps=0, workers=1, return_sorted=None, return_length=False) 

Description

query_ball_point.__doc__

Find all points within distance r of point(s) x.

        Parameters
        ----------
        x : array_like, shape tuple + (self.m,)
            The point or points to search for neighbors of.
        r : array_like, float
            The radius of points to return, must broadcast to the length of x.
        p : float, 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.
        eps : nonnegative 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)``.
        workers : int, optional
            Number of jobs to schedule for parallel processing. If -1 is given
            all processors are used. Default: 1.

            .. versionadded:: 1.6.0
        return_sorted : bool, optional
            Sorts returned indicies 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.

            .. versionadded:: 1.6.0
        return_length: bool, optional
            Return the number of points inside the radius instead of a list
            of the indices.

            .. versionadded:: 1.6.0

        Returns
        -------
        results : list 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
        --------
        >>> 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()