# scipy.ndimage.minimum_filter1d¶

scipy.ndimage.minimum_filter1d(input, size, axis=-1, output=None, mode='reflect', cval=0.0, origin=0)[source]

Calculate a one-dimensional minimum filter along the given axis.

The lines of the array along the given axis are filtered with a minimum filter of given size.

Parameters: input : array_like The input array. size : int length along which to calculate 1D minimum axis : int, optional The axis of input along which to calculate. Default is -1. output : array or dtype, optional The array in which to place the output, or the dtype of the returned array. By default an array of the same dtype as input will be created. mode : {‘reflect’, ‘constant’, ‘nearest’, ‘mirror’, ‘wrap’}, optional The mode parameter determines how the input array is extended when the filter overlaps a border. Default is ‘reflect’. Behavior for each valid value is as follows: ‘reflect’ (d c b a | a b c d | d c b a) The input is extended by reflecting about the edge of the last pixel. ‘constant’ (k k k k | a b c d | k k k k) The input is extended by filling all values beyond the edge with the same constant value, defined by the cval parameter. ‘nearest’ (a a a a | a b c d | d d d d) The input is extended by replicating the last pixel. ‘mirror’ (d c b | a b c d | c b a) The input is extended by reflecting about the center of the last pixel. ‘wrap’ (a b c d | a b c d | a b c d) The input is extended by wrapping around to the opposite edge. cval : scalar, optional Value to fill past edges of input if mode is ‘constant’. Default is 0.0. origin : int, optional Controls the placement of the filter on the input array’s pixels. A value of 0 (the default) centers the filter over the pixel, with positive values shifting the filter to the left, and negative ones to the right.

Notes

This function implements the MINLIST algorithm [1], as described by Richard Harter [2], and has a guaranteed O(n) performance, n being the input length, regardless of filter size.

References

Examples

>>> from scipy.ndimage import minimum_filter1d
>>> minimum_filter1d([2, 8, 0, 4, 1, 9, 9, 0], size=3)
array([2, 0, 0, 0, 1, 1, 0, 0])


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