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.
Examples
>>> 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