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# scipy.ndimage.filters.generic_filter

`scipy.ndimage.filters.``generic_filter`(input, function, size=None, footprint=None, output=None, mode='reflect', cval=0.0, origin=0, extra_arguments=(), extra_keywords=None)

  Calculates a multi-dimensional filter using the given function.

  At each element the provided function is called. The input values within the filter footprint at that element are passed to the function as a 1D array of double values.

  Parameters : | input : array_like Input array to filter. function : callable Function to apply at each element. size : scalar or tuple, optional See footprint, below footprint : array, optional Either size or footprint must be defined. size gives the shape that is taken from the input array, at every element position, to define the input to the filter function. footprint is a boolean array that specifies (implicitly) a shape, but also which of the elements within this shape will get passed to the filter function. Thus `size=(n,m)` is equivalent to `footprint=np.ones((n,m))`. We adjust size to the number of dimensions of the input array, so that, if the input array is shape (10,10,10), and size is 2, then the actual size used is (2,2,2). 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 origin : scalar, optional The origin parameter controls the placement of the filter. Default 0.0. extra_arguments : sequence, optional Sequence of extra positional arguments to pass to passed function extra_keywords : dict, optional dict of extra keyword arguments to pass to passed function

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