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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

help(KDTree.query_ball_point)

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 indices 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
--------
>>> import numpy as np
>>> 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()

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