scipy.ndimage.maximum_filter1d¶

scipy.ndimage.
maximum_filter1d
(input, size, axis=1, output=None, mode='reflect', cval=0.0, origin=0)[source]¶ Calculate a onedimensional maximum filter along the given axis.
The lines of the array along the given axis are filtered with a maximum filter of given size.
Parameters:  input : array_like
The input array.
 size : int
Length along which to calculate the 1D maximum.
 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.
Returns:  maximum1d : ndarray, None
Maximumfiltered array with same shape as input. None if output is not None
Notes
This function implements the MAXLIST 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
[1] (1, 2) http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.42.2777 [2] (1, 2) http://www.richardhartersworld.com/cri/2001/slidingmin.html Examples
>>> from scipy.ndimage import maximum_filter1d >>> maximum_filter1d([2, 8, 0, 4, 1, 9, 9, 0], size=3) array([8, 8, 8, 4, 9, 9, 9, 9])