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- sigma_clipped_stats

# sigma_clipped_stats

`astropy.stats.``sigma_clipped_stats`(data, mask=None, mask_value=None, sigma=3.0, sigma_lower=None, sigma_upper=None, iters=5, cenfunc=<function median>, stdfunc=<function std>, axis=None)

  Calculate sigma-clipped statistics from data.

  For example, sigma-clipped statistics can be used to estimate the background and background noise in an image.

  Parameters:

  data : array-like

  > Data array or object that can be converted to an array.

  mask : `numpy.ndarray` (bool), optional

  > A boolean mask with the same shape as `data`, where a `True` value indicates the corresponding element of `data` is masked. Masked pixels are excluded when computing the image statistics.

  mask_value : float, optional

  > An image data value (e.g., `0.0`) that is ignored when computing the image statistics. `mask_value` will be masked in addition to any input `mask`.

  sigma : float, optional

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

  sigma_lower : float, 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, 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, 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) when calculating the image statistics. 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`.

  Returns:

  mean, median, stddev : float

  > The mean, median, and standard deviation of the sigma-clipped image.