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)[source] [edit on github]¶

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

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.
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.

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sigma_clip¶