Module « scipy.stats »

Fonction sigmaclip - module scipy.stats

Signature de la fonction sigmaclip

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

Description

sigmaclip.__doc__

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
    --------
    >>> 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