skdh.utility.math.moving_median

skdh.utility.math.moving_median(a, w_len, skip=1, trim=True, axis=-1)

Compute the moving mean.

Parameters:

aarray-like

Signal to compute moving mean for.

w_lenint

Window length in number of samples.

skipint

Window start location skip in number of samples. Default is 1.

trimbool, optional

Trim the ends of the result, where a value cannot be calculated. If False, these values will be set to NaN. Default is True.

axisint, optional

Axis to compute the moving mean along. Default is -1.

Returns:

mmednumpy.ndarray

Moving median. Note that if the moving axis is not the last axis, then the result will not be c-contiguous.

Notes

On the moving axis, the output length can be computed as follows:

\[\frac{n - w_{len}}{skip} + 1\]

where n is the length of the moving axis. For cases where skip != 1 and trim=False, the length of the return on the moving axis can be calculated as:

\[\frac{n}{skip}\]

Examples

Compute the with non-overlapping windows:

>>> import numpy as np
>>> x = np.arange(10)
>>> moving_median(x, 3, 3)
array([1., 4., 7.])

Compute with overlapping windows:

>>> moving_median(x, 3, 1)
array([1., 2., 3., 4., 5., 6., 7., 8.])

Compute without trimming:

>>> moving_median(x, 3, 1)
array([1., 2., 3., 4., 5., 6., 7., 8., nan, nan])

Compute on a nd-array to see output shape. On the moving axis, the output should be equal to \((n - w_{len}) / skip + 1\).

>>> n = 500
>>> window_length = 100
>>> window_skip = 50
>>> shape = (3, n, 5, 10)
>>> y = np.random.random(shape)
>>> res = moving_median(y, window_length, window_skip, axis=1)
>>> print(res.shape)
(3, 9, 5, 10)

Check flags for different axis output

>>> z = np.random.random((10, 10, 10))
>>> moving_median(z, 3, 3, axis=0).flags['C_CONTIGUOUS']
False

>>> moving_median(z, 3, 3, axis=1).flags['C_CONTIGUOUS']
False

>>> moving_median(z, 3, 3, axis=2).flags['C_CONTIGUOUS']
True