dgrad.py 文件源码

def __init__(self, n, axis, order=3, mode='valid'):
        """
        Construct a second-order gradient operator for signal of dimension
        *n* for dimension *axis*. Use a filter kernel of length
        *order* (must be odd). Use convolution type *mode*.
        """
        # assert that the filter length is odd
        assert(order % 2 == 1)
        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(int(order/2) + 1)
        self.d2 = NP.convolve(self.d, self.d)

        self.mode = mode

        h_list = []
        m = []
        for i in reversed(range(self.ndim)):
            if i == axis:
                h_list.append(self.d2)
            else:
                h_list.append(NP.array([1]))
            m.append(len(h_list[-1]))
        self.m = m

        if mode == 'circ':
            n_prime = array(n) - m + 1
            super(Gradient2Filter, self).__init__(n_prime, h_list, mode=mode)
        else:
            super(Gradient2Filter, self).__init__(n, h_list, mode=mode)