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bundles / scipy 1.17.1 / scipy / stats / _stats_py / sigmaclip

function

scipy.stats._stats_py:sigmaclip

source: /scipy/stats/_stats_py.py :3382

Signature

def   sigmaclip ( a ,  low = 4.0 ,  high = 4.0 )

Summary

Perform iterative sigma-clipping of array elements.

Extended Summary

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

a : array_like

Data array, will be raveled if not 1-D.

low : float, optional

Lower bound factor of sigma clipping. Default is 4.

high : float, optional

Upper bound factor of sigma clipping. Default is 4.

Returns

clipped : ndarray

Input array with clipped elements removed.

lower : float

Lower threshold value use for clipping.

upper : float

Upper threshold value use for clipping.

Notes

This function iteratively removes observations. Once observations are removed, they are not re-added in subsequent iterations. Consequently, although it is often the case that clipped is identical to a[(a >= lower) & (a <= upper)], this property is not guaranteed to be satisfied; clipped may have fewer elements.

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                   ⚠️ no JIT             ⚠️ no JIT           
Dask                  ⛔                     n/a                 
====================  ====================  ====================

See dev-arrayapi 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
c.var(), c.std()
low, c.mean() - fact*c.std(), c.min()
upp, c.mean() + fact*c.std(), c.max()
✗
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()
✗

Aliases

  • scipy.stats.sigmaclip

Referenced by

This package