Class to perform sigma clipping.

The data will be iterated over, each time rejecting values that are less or more than a specified number of standard deviations from a center value.

Clipped (rejected) pixels are those where:

data < center - (sigma_lower * std)
data > center + (sigma_upper * std)

where:

center = cenfunc(data [, axis=])
std = stdfunc(data [, axis=])

Invalid data values (i.e., NaN or inf) are automatically clipped.

For a functional interface to sigma clipping, see sigma_clip().

sigclip = SigmaClip(sigma=4., cenfunc='mean', maxiters=None)
sigclip(data, axis=None, masked=False, return_bounds=True)

Parameters¶

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'.

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.

See Also¶

sigma_clip, sigma_clipped_stats

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.

Examples¶

This example uses a data array of random variates from a Gaussian distribution. We clip all points that are more than 2 sample standard deviations from the median. The result is a masked array, where the mask is True for clipped data:

>>> from astropy.stats import SigmaClip
>>> from numpy.random import randn
>>> randvar = randn(10000)
>>> sigclip = SigmaClip(sigma=2, maxiters=5)
>>> filtered_data = sigclip(randvar)

This example clips all points that are more than 3 sigma relative to the sample mean, clips until convergence, returns an unmasked ~numpy.ndarray, and modifies the data in-place:

>>> from astropy.stats import SigmaClip
>>> from numpy.random import randn
>>> from numpy import mean
>>> randvar = randn(10000)
>>> sigclip = SigmaClip(sigma=3, maxiters=None, cenfunc='mean')
>>> filtered_data = sigclip(randvar, masked=False, copy=False)

This example sigma clips along one axis:

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

Note that along the other axis, no points would be clipped, as the standard deviation is higher.

Public members¶

SigmaClip(sigma=3.0, sigma_lower=None, sigma_upper=None, ...)

Initialize self. See help(type(self)) for accurate signature.

__repr__()

Return repr(self).

__str__()

Return str(self).

__call__(data, axis=None, masked=True, return_bounds=False, ...)

Perform sigma clipping on the provided data.