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# scipy.ndimage.generic_gradient_magnitude

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

  Gradient magnitude using a provided gradient function.

  > 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. Output array should have different name as compared to input array to avoid aliasing errors. mode : str or sequence, optional The mode parameter determines how the array borders are handled. Valid modes are {‘reflect’, ‘constant’, ‘nearest’, ‘mirror’, ‘wrap’}. cval is the value used when mode is equal to ‘constant’. A list of modes with length equal to the number of axes can be provided to specify different modes for different axes. 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