median_absolute_deviation¶

astropy.stats.
median_absolute_deviation
(data, axis=None, func=None, ignore_nan=False)[source]¶ Calculate the median absolute deviation (MAD).
The MAD is defined as
median(abs(a  median(a)))
.Parameters:  data : arraylike
Input array or object that can be converted to an array.
 axis : {int, sequence of int, None}, optional
Axis along which the MADs are computed. The default (
None
) is to compute the MAD of the flattened array. func : callable, optional
The function used to compute the median. Defaults to
numpy.ma.median
for masked arrays, otherwise tonumpy.median
. ignore_nan : bool
Ignore NaN values (treat them as if they are not in the array) when computing the median. This will use
numpy.ma.median
ifaxis
is specified, ornumpy.nanmedian
ifaxis==None
and numpy’s version is >1.10 because nanmedian is slightly faster in this case.
Returns: See also
Examples
Generate random variates from a Gaussian distribution and return the median absolute deviation for that distribution:
>>> import numpy as np >>> from astropy.stats import median_absolute_deviation >>> rand = np.random.RandomState(12345) >>> from numpy.random import randn >>> mad = median_absolute_deviation(rand.randn(1000)) >>> print(mad) 0.65244241428454486