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# scipy.ndimage.filters.maximum_filter

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

  Calculates a multi-dimensional maximum filter.

  Parameters : | input : array-like input array to filter 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 :

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