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 - smirnovis 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 - smirnovto 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])