`scipy.stats._stats_py:sigmaclip`

source: build-install/usr/lib/python3.14/site-packages/scipy/stats/_stats_py.py:3389

Signature

def sigmaclip(a, low = 4.0, high = 4.0, *, nan_policy = propagate)

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.

nan_policy : {'propagate', 'omit', 'raise'}

Defines how to handle input NaNs.

propagate: if a NaN is present in the input, the clipped array will be empty, and the upper and lower thresholds will be NaN.
omit: NaNs will be omitted when performing the calculation.
raise: if a NaN is present, a ValueError will be raised.

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.

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()

✗

(np.float64(10.035355339059327), np.float64(10.035355339059327), np.float64(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()

✗

np.True_

Aliases

scipy.stats.sigmaclip