dgrad.py 文件源码

def __init__(self, n, axis, order=2, mode='valid'):
        """
        Construct gradient operator for signal of dimension *n* for
        dimension *axis*. Use a filter kernel of length *order* (must
        be even). Use convolution type *mode*.
        """
        self.n = n
        self.ndim = len(self.n)
        self.axis = axis
        if axis < 0 or axis >= self.ndim:
            raise ValueError('0 <= axis (= {0}) < ndim = {1}'.format(axis, self.ndim))

        self.d = differentiator(order)

        h_list = []
        for i in reversed(range(self.ndim)):
            if i == axis:
                h_list.append(self.d)
            else:
                h_list.append(NP.array([1]))

        super(GradientFilter, self).__init__(n, h_list, mode=mode)