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scipy.stats.

# sigmaclip

scipy.stats.sigmaclip(a, low=4.0, high=4.0)

  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