# 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:
    n int

      Number of samples

    p float array_like

      Probability

    out ndarray, 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 historial 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])