# scipy.ndimage.minimum_filter1d#

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

Calculate a 1-D 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:
inputarray_like

The input array.

sizeint

length along which to calculate 1D minimum

axisint, optional

The axis of input along which to calculate. Default is -1.

outputarray 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 beyond its boundaries. 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. This mode is also sometimes referred to as half-sample symmetric.

‘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. This mode is also sometimes referred to as whole-sample symmetric.

‘wrap’ (a b c d | a b c d | a b c d)

The input is extended by wrapping around to the opposite edge.

For consistency with the interpolation functions, the following mode names can also be used:

‘grid-mirror’

This is a synonym for ‘reflect’.

‘grid-constant’

This is a synonym for ‘constant’.

‘grid-wrap’

This is a synonym for ‘wrap’.

cvalscalar, optional

Value to fill past edges of input if mode is ‘constant’. Default is 0.0.

originint, 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.

Returns:
resultndarray.

Filtered image. Has the same shape as input.

Notes

This function implements the MINLIST algorithm , as described by Richard Harter , 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])
```