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# scipy.ndimage.filters.generic_gradient_magnitude

`scipy.ndimage.filters.``generic_gradient_magnitude`(input, derivative, output=None, mode='reflect', cval=0.0, extra_arguments=(), extra_keywords=None)

  Calculate a gradient magnitude using the provided function for the gradient.

  Parameters: | input : array-like input array to filter derivative : callable Callable with the following signature:: derivative(input, axis, output, mode, cval, *extra_arguments, **extra_keywords) See `extra_arguments`, `extra_keywords` below `derivative` can assume that `input` and `output` are ndarrays. Note that the output from `derivative` is modified inplace; be careful to copy important inputs before returning them. output : array, optional The `output` parameter passes an array in which to store the filter output. mode : {‘reflect’,’constant’,’nearest’,’mirror’, ‘wrap’}, optional The `mode` parameter determines how the array borders are handled, where `cval` is the value when mode is equal to ‘constant’. Default is ‘reflect’ cval : scalar, optional Value to fill past edges of input if `mode` is ‘constant’. Default is 0.0 extra_keywords : dict, optional dict of extra keyword arguments to pass to passed function extra_arguments : sequence, optional Sequence of extra positional arguments to pass to passed function

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