scipy.stats.

sigmaclip#

scipy.stats.sigmaclip(a, low=4.0, high=4.0)[source]#

Perform iterative sigma-clipping of array elements.

Starting from the full sample, all elements outside the critical range are removed, i.e. all elements of the input array c that satisfy either of the following conditions:

c < mean(c) - std(c)*low
c > mean(c) + std(c)*high

The iteration continues with the updated sample until no elements are outside the (updated) range.

Parameters:

aarray_like: Data array, will be raveled if not 1-D.
lowfloat, optional: Lower bound factor of sigma clipping. Default is 4.
highfloat, optional: Upper bound factor of sigma clipping. Default is 4.

Returns:

clippedndarray: Input array with clipped elements removed.
lowerfloat: Lower threshold value use for clipping.
upperfloat: Upper threshold value use for clipping.

Notes

Array API Standard Support

sigmaclip has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable SCIPY_ARRAY_API=1 and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.

Library	CPU	GPU
NumPy	✅	n/a
CuPy	n/a	⛔
PyTorch	⛔	⛔
JAX	⛔	⛔
Dask	⛔	n/a

See Support for the array API standard for more information.

Examples

>>> import numpy as np
>>> from scipy.stats import sigmaclip
>>> a = np.concatenate((np.linspace(9.5, 10.5, 31),
...                     np.linspace(0, 20, 5)))
>>> fact = 1.5
>>> c, low, upp = sigmaclip(a, fact, fact)
>>> c
array([  9.96666667,  10.        ,  10.03333333,  10.        ])
>>> c.var(), c.std()
(0.00055555555555555165, 0.023570226039551501)
>>> low, c.mean() - fact*c.std(), c.min()
(9.9646446609406727, 9.9646446609406727, 9.9666666666666668)
>>> upp, c.mean() + fact*c.std(), c.max()
(10.035355339059327, 10.035355339059327, 10.033333333333333)

>>> a = np.concatenate((np.linspace(9.5, 10.5, 11),
...                     np.linspace(-100, -50, 3)))
>>> c, low, upp = sigmaclip(a, 1.8, 1.8)
>>> (c == np.linspace(9.5, 10.5, 11)).all()
True