parameters: (self, x, r, p=2., eps=0)
docstring: 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 : positive float
              The radius of points to return.
          p : float, optional
              Which Minkowski p-norm to use.  Should be in the range [1, inf].
          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)``.
  
          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 = zip(x.ravel(), y.ravel())
          >>> tree = spatial.KDTree(points)
          >>> 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()
docstring_summary: Find all points within distance r of point(s) x.
docstring_tokens: Find all points within distance r of point ( s ) x .
function: def query_ball_point(self, x, r, p=2., eps=0):
          """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 : positive float
              The radius of points to return.
          p : float, optional
              Which Minkowski p-norm to use.  Should be in the range [1, inf].
          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)``.
  
          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 = zip(x.ravel(), y.ravel())
          >>> tree = spatial.KDTree(points)
          >>> 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()
  
          """
          x = np.asarray(x)
          if x.shape[-1] != self.m:
              raise ValueError("Searching for a %d-dimensional point in a "
                               "%d-dimensional KDTree" % (x.shape[-1], self.m))
          if len(x.shape) == 1:
              return self.__query_ball_point(x, r, p, eps)
          else:
              retshape = x.shape[:-1]
              result = np.empty(retshape, dtype=object)
              for c in np.ndindex(retshape):
                  result[c] = self.__query_ball_point(x[c], r, p=p, eps=eps)
              return result
function_tokens: def query_ball_point ( self , x , r , p = 2. , eps = 0 ) : x = np . asarray ( x ) if x . shape [ - 1 ] != self . m : raise ValueError ( "Searching for a %d-dimensional point in a " "%d-dimensional KDTree" % ( x . shape [ - 1 ] , self . m ) ) if len ( x . shape ) == 1 : return self . __query_ball_point ( x , r , p , eps ) else : retshape = x . shape [ : - 1 ] result = np . empty ( retshape , dtype = object ) for c in np . ndindex ( retshape ) : result [ c ] = self . __query_ball_point ( x [ c ] , r , p = p , eps = eps ) return result