sigma_clipped_stats

astropy.stats.sigma_clipped_stats(data, mask=None, mask_value=None, sigma=3.0, sigma_lower=None, sigma_upper=None, maxiters=5, cenfunc='median', stdfunc='std', std_ddof=0, axis=None, grow=False)

Calculate sigma-clipped statistics on the provided data.

Parameters:

dataarray_like or MaskedArray

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

masknumpy.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 statistics.

mask_valuefloat, optional

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

sigmafloat, 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. The default is 3.

sigma_lowerfloat 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. The default is None.

sigma_upperfloat 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. The default is None.

maxitersint or None, optional

The maximum number of sigma-clipping iterations to perform or None to clip until convergence is achieved (i.e., iterate until the last iteration clips nothing). If convergence is achieved prior to maxiters iterations, the clipping iterations will stop. The default is 5.

cenfunc{‘median’, ‘mean’} or callable(), optional

The statistic or callable function/object used to compute the center value for the clipping. If using a callable function/object and the axis keyword is used, then it must be able to ignore NaNs (e.g., numpy.nanmean) and it must have an axis keyword to return an array with axis dimension(s) removed. The default is 'median'.

stdfunc{‘std’, ‘mad_std’} or callable(), optional

The statistic or callable function/object used to compute the standard deviation about the center value. If using a callable function/object and the axis keyword is used, then it must be able to ignore NaNs (e.g., numpy.nanstd) and it must have an axis keyword to return an array with axis dimension(s) removed. The default is 'std'.

std_ddofint, optional

The delta degrees of freedom for the standard deviation calculation. The divisor used in the calculation is N - std_ddof, where N represents the number of elements. The default is 0.

axisNone or int or tuple of int, optional

The axis or axes along which to sigma clip the data. If None, then the flattened data will be used. axis is passed to the cenfunc and stdfunc. The default is None.

growfloat or False, optional

Radius within which to mask the neighbouring pixels of those that fall outwith the clipping limits (only applied along axis, if specified). As an example, for a 2D image a value of 1 will mask the nearest pixels in a cross pattern around each deviant pixel, while 1.5 will also reject the nearest diagonal neighbours and so on.

Returns:

mean, median, stddevfloat

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

See also

SigmaClip, sigma_clip

Notes

The best performance will typically be obtained by setting cenfunc and stdfunc to one of the built-in functions specified as as string. If one of the options is set to a string while the other has a custom callable, you may in some cases see better performance if you have the bottleneck package installed.