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- sigma_clip

# sigma_clip

`astropy.stats.``sigma_clip`(data, sigma=3, sigma_lower=None, sigma_upper=None, iters=5, cenfunc=np.ma.median, stdfunc=np.std, axis=None, copy=True)

  Perform sigma-clipping on the provided data.

  The data will be iterated over, each time rejecting points that are discrepant by more than a specified number of standard deviations from a center value. If the data contains invalid values (NaNs or infs), they are automatically masked before performing the sigma clipping.

  Note

  scipy.stats.sigmaclip provides a subset of the functionality in this function.

  Parameters:

  data : array-like

  > The data to be sigma clipped.

  sigma : float, optional

  > The number of standard deviations to use for both the lower and upper clipping limit. These limits are overridden by `sigma_lower` and `sigma_upper`, if input. Defaults to 3.

  sigma_lower : float or `None`, optional

  > The number of standard deviations to use as the lower bound for the clipping limit. If `None` then the value of `sigma` is used. Defaults to `None`.

  sigma_upper : float or `None`, optional

  > The number of standard deviations to use as the upper bound for the clipping limit. If `None` then the value of `sigma` is used. Defaults to `None`.

  iters : int or `None`, optional

  > The number of iterations to perform sigma clipping, or `None` to clip until convergence is achieved (i.e., continue until the last iteration clips nothing). Defaults to 5.

  cenfunc : callable, optional

  > The function used to compute the center for the clipping. Must be a callable that takes in a masked array and outputs the central value. Defaults to the median (`numpy.ma.median`).

  stdfunc : callable, optional

  > The function used to compute the standard deviation about the center. Must be a callable that takes in a masked array and outputs a width estimator. Masked (rejected) pixels are those where:

  >     deviation < (-sigma_lower * stdfunc(deviation))
  >     deviation > (sigma_upper * stdfunc(deviation))

  > where:

  >     deviation = data - cenfunc(data [,axis=int])

  > Defaults to the standard deviation (`numpy.std`).

  axis : int or `None`, optional

  > If not `None`, clip along the given axis. For this case, `axis` will be passed on to `cenfunc` and `stdfunc`, which are expected to return an array with the axis dimension removed (like the numpy functions). If `None`, clip over all axes. Defaults to `None`.

  copy : bool, optional

  > If `True`, the `data` array will be copied. If `False`, the returned masked array data will contain the same array as `data`. Defaults to `True`.

  Returns:

  filtered_data : `numpy.ma.MaskedArray`

  > A masked array with the same shape as `data` input, where the points rejected by the algorithm have been masked.

  Notes

  1. The routine works by calculating:

         deviation = data - cenfunc(data [,axis=int])

     and then setting a mask for points outside the range:

         deviation < (-sigma_lower * stdfunc(deviation))
         deviation > (sigma_upper * stdfunc(deviation))

     It will iterate a given number of times, or until no further data are rejected.

  2. Most numpy functions deal well with masked arrays, but if one would like to have an array with just the good (or bad) values, one can use:

         good_only = filtered_data.data[~filtered_data.mask]
         bad_only = filtered_data.data[filtered_data.mask]

     However, for multidimensional data, this flattens the array, which may not be what one wants (especially if filtering was done along an axis).

  Examples

  This example generates random variates from a Gaussian distribution and returns a masked array in which all points that are more than 2 sample standard deviations from the median are masked:

      >>> from astropy.stats import sigma_clip
      >>> from numpy.random import randn
      >>> randvar = randn(10000)
      >>> filtered_data = sigma_clip(randvar, sigma=2, iters=5)

  This example sigma clips on a similar distribution, but uses 3 sigma relative to the sample mean, clips until convergence, and does not copy the data:

      >>> from astropy.stats import sigma_clip
      >>> from numpy.random import randn
      >>> from numpy import mean
      >>> randvar = randn(10000)
      >>> filtered_data = sigma_clip(randvar, sigma=3, iters=None,
      ...                            cenfunc=mean, copy=False)

  This example sigma clips along one axis on a similar distribution (with bad points inserted):

      >>> from astropy.stats import sigma_clip
      >>> from numpy.random import normal
      >>> from numpy import arange, diag, ones
      >>> data = arange(5) + normal(0., 0.05, (5, 5)) + diag(ones(5))
      >>> filtered_data = sigma_clip(data, sigma=2.3, axis=0)

  Note that along the other axis, no points would be masked, as the variance is higher.