8aa27bbac3e52d7f214ba22028afcbd4cf13b97d,scipy/spatial/kdtree.py,KDTree,query_ball_point,#KDTree#Any#Any#Any#Any#,575

Before Change

        x = np.asarray(x)
        if x.dtype.kind == "c":
            raise TypeError("KDTree does not work with complex data")
        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

    def query_ball_tree(self, other, r, p=2., eps=0):
        
        Find all pairs of points between `self` and `other` whose distance is at most r

After Change

        x = np.asarray(x)
        if x.dtype.kind == "c":
            raise TypeError("KDTree does not work with complex data")
        return super().query_ball_point(x, r, p, eps)

    def query_ball_tree(self, other, r, p=2., eps=0):
        

Instances