scipy.ndimage.filters.minimum_filter

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

Calculates a multi-dimensional minimum 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 0.0.