SigmaClippedStats

class astropy.stats.sigma_clipping.SigmaClippedStats(data: ArrayLike, *, mask: NDArray | None = None, mask_value: float | None = None, sigma: float = 3.0, sigma_lower: float | None = None, sigma_upper: float | None = None, maxiters: int = 5, cenfunc: Literal['median', 'mean'] | Callable = 'median', stdfunc: Literal['std', 'mad_std'] | Callable = 'std', axis: int | tuple[int, ...] | None = None, grow: float | Literal[False] | None = False)

Bases: object

Class to calculate sigma-clipped statistics on the provided data.

Parameters:

datanumpy:array_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_valuepython:float, 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.

sigmapython: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. The default is 3.

sigma_lowerpython:float or python: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_upperpython:float or python: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.

maxiterspython:int or python: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 python: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 python: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'.

axispython:None or python:int or python:tuple of python: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.

growpython:float 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.

See also

sigma_clipped_stats, 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 a 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. To preserve accuracy, bottleneck is only used for float64 computations.

Methods Summary

biweight_location([c, M])	Calculate the biweight location of the data.
biweight_scale([c, M])	Calculate the biweight scale of the data.
mad_std()	Calculate the median absolute deviation (MAD) based standard deviation of the data.
max()	Calculate the maximum of the data.
mean()	Calculate the mean of the data.
median()	Calculate the median of the data.
min()	Calculate the minimum of the data.
mode([median_factor, mean_factor])	Calculate the mode of the data using a estimator of the form (median_factor * median) - (mean_factor * mean).
std([ddof])	Calculate the standard deviation of the data.
sum()	Calculate the sum of the data.
var([ddof])	Calculate the variance of the data.

Methods Documentation

biweight_location(c: float = 6.0, M: float | None = None) → float | NDArray

Calculate the biweight location of the data.

NaN values are ignored.

Parameters:

cpython:float, optional

Tuning constant for the biweight estimator. Default value is 6.0.

Mpython:float or python:None, optional

Initial guess for the biweight location. Default value is None.

Returns:

biweight_locationpython:float or ndarray

The biweight location of the data.

biweight_scale(c: float = 6.0, M: float | None = None) → float | NDArray

Calculate the biweight scale of the data.

NaN values are ignored.

Parameters:

cpython:float, optional

Tuning constant for the biweight estimator. Default value is 6.0.

Mpython:float or python:None, optional

Initial guess for the biweight location. Default value is None.

Returns:

biweight_scalepython:float or ndarray

The biweight scale of the data.

mad_std() → float | NDArray

Calculate the median absolute deviation (MAD) based standard deviation of the data.

NaN values are ignored.

Returns:

mad_stdpython:float or ndarray

The MAD-based standard deviation of the data.

max() → float | NDArray

Calculate the maximum of the data.

NaN values are ignored.

Returns:

maxpython:float or ndarray

The maximum of the data.

mean() → float | NDArray

Calculate the mean of the data.

NaN values are ignored.

Returns:

meanpython:float or ndarray

The mean of the data.

median() → float | NDArray

Calculate the median of the data.

NaN values are ignored.

Returns:

medianpython:float or ndarray

The median of the data.

min() → float | NDArray

Calculate the minimum of the data.

NaN values are ignored.

Returns:

minpython:float or ndarray

The minimum of the data.

mode(median_factor: float = 3.0, mean_factor: float = 2.0) → float | NDArray

Calculate the mode of the data using a estimator of the form (median_factor * median) - (mean_factor * mean).

NaN values are ignored.

Parameters:

median_factorpython:float, optional

The multiplicative factor for the data median. Defaults to 3.

mean_factorpython:float, optional

The multiplicative factor for the data mean. Defaults to 2.

Returns:

modepython:float or ndarray

The estimated mode of the data.

std(ddof: int = 0) → float | NDArray

Calculate the standard deviation of the data.

NaN values are ignored.

Parameters:

ddofpython:int, optional

The delta degrees of freedom for the standard deviation calculation. The divisor used in the calculation is N - ddof, where N represents the number of elements. For a population standard deviation where you have data for the entire population, use ddof=0. For a sample standard deviation where you have a sample of the population, use ddof=1. The default is 0.

Returns:

stdpython:float or ndarray

The standard deviation of the data.

sum() → float | NDArray

Calculate the sum of the data.

NaN values are ignored.

Returns:

sumpython:float or ndarray

The sum of the data.

var(ddof: int = 0) → float | NDArray

Calculate the variance of the data.

NaN values are ignored.

Parameters:

ddofpython:int, optional

The delta degrees of freedom. The divisor used in the calculation is N - ddof, where N represents the number of elements. For a population variance where you have data for the entire population, use ddof=0. For a sample variance where you have a sample of the population, use ddof=1. The default is 0.

Returns:

varpython:float or ndarray

The variance of the data.