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Module « scipy.stats »

Fonction sigmaclip - module scipy.stats

Signature de la fonction sigmaclip

def sigmaclip(a, low=4.0, high=4.0) 

Description

help(scipy.stats.sigmaclip)

Perform iterative sigma-clipping of array elements.

Starting from the full sample, all elements outside the critical range are
removed, i.e. all elements of the input array `c` that satisfy either of
the following conditions::

    c < mean(c) - std(c)*low
    c > mean(c) + std(c)*high

The iteration continues with the updated sample until no
elements are outside the (updated) range.

Parameters
----------
a : array_like
    Data array, will be raveled if not 1-D.
low : float, optional
    Lower bound factor of sigma clipping. Default is 4.
high : float, optional
    Upper bound factor of sigma clipping. Default is 4.

Returns
-------
clipped : ndarray
    Input array with clipped elements removed.
lower : float
    Lower threshold value use for clipping.
upper : float
    Upper threshold value use for clipping.

Examples
--------
>>> import numpy as np
>>> from scipy.stats import sigmaclip
>>> a = np.concatenate((np.linspace(9.5, 10.5, 31),
...                     np.linspace(0, 20, 5)))
>>> fact = 1.5
>>> c, low, upp = sigmaclip(a, fact, fact)
>>> c
array([  9.96666667,  10.        ,  10.03333333,  10.        ])
>>> c.var(), c.std()
(0.00055555555555555165, 0.023570226039551501)
>>> low, c.mean() - fact*c.std(), c.min()
(9.9646446609406727, 9.9646446609406727, 9.9666666666666668)
>>> upp, c.mean() + fact*c.std(), c.max()
(10.035355339059327, 10.035355339059327, 10.033333333333333)

>>> a = np.concatenate((np.linspace(9.5, 10.5, 11),
...                     np.linspace(-100, -50, 3)))
>>> c, low, upp = sigmaclip(a, 1.8, 1.8)
>>> (c == np.linspace(9.5, 10.5, 11)).all()
True

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