scipy.special.smirnovi#

scipy.special.smirnovi(n, p, out=None) = <ufunc 'smirnovi'>#

Inverse to smirnov

Returns d such that smirnov(n, d) == p, the critical value corresponding to p.

Parameters:

nint: Number of samples
pfloat array_like: Probability
outndarray, optional: Optional output array for the function results

Returns:

scalar or ndarray: The value(s) of smirnovi(n, p), the critical values.

See also

smirnov: The Survival Function (SF) for the distribution
scipy.stats.ksone: Provides the functionality as a continuous distribution
kolmogorov, kolmogi: Functions for the two-sided distribution
scipy.stats.kstwobign: Two-sided Kolmogorov-Smirnov distribution, large n

Notes

smirnov is used by stats.kstest in the application of the Kolmogorov-Smirnov Goodness of Fit test. For historical reasons this function is exposed in scpy.special, but the recommended way to achieve the most accurate CDF/SF/PDF/PPF/ISF computations is to use the stats.ksone distribution.

Examples

>>> from scipy.special import smirnovi, smirnov

>>> n = 24
>>> deviations = [0.1, 0.2, 0.3]

Use smirnov to compute the complementary CDF of the Smirnov distribution for the given number of samples and deviations.

>>> p = smirnov(n, deviations)
>>> p
array([0.58105083, 0.12826832, 0.01032231])

The inverse function smirnovi(n, p) returns deviations.

>>> smirnovi(n, p)
array([0.1, 0.2, 0.3])