mad_std¶
-
astropy.stats.
mad_std
(data, axis=None, func=None, ignore_nan=False)[source] [edit on github]¶ Calculate a robust standard deviation using the median absolute deviation (MAD).
The standard deviation estimator is given by:
\[\sigma \approx \frac{\textrm{MAD}}{\Phi^{-1}(3/4)} \approx 1.4826 \ \textrm{MAD}\]where \(\Phi^{-1}(P)\) is the normal inverse cumulative distribution function evaluated at probability \(P = 3/4\).
Parameters: - data : array-like
Data array or object that can be converted to an array.
- axis : {int, sequence of int, None}, optional
Axis along which the robust standard deviations are computed. The default (
None
) is to compute the robust standard deviation 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: Examples
>>> import numpy as np >>> from astropy.stats import mad_std >>> rand = np.random.RandomState(12345) >>> madstd = mad_std(rand.normal(5, 2, (100, 100))) >>> print(madstd) # doctest: +FLOAT_CMP 2.0232764659422626