# scipy.special.kolmogorov

scipy.special.kolmogorov(y, out=None) = <ufunc 'kolmogorov'>

  Complementary cumulative distribution (Survival Function) function of Kolmogorov distribution.

  Returns the complementary cumulative distribution function of Kolmogorov’s limiting distribution (`D_n*\sqrt(n)` as n goes to infinity) of a two-sided test for equality between an empirical and a theoretical distribution. It is equal to the (limit as n->infinity of the) probability that `sqrt(n) * max absolute deviation > y`.

  Parameters:
    y float array_like

      Absolute deviation between the Empirical CDF (ECDF) and the target CDF, multiplied by sqrt(n).

    out ndarray, optional

      Optional output array for the function results

  Returns:
    scalar or ndarray

      The value(s) of kolmogorov(y)

  See also

  `kolmogi`

    The Inverse Survival Function for the distribution

  `scipy.stats.kstwobign`

    Provides the functionality as a continuous distribution

  `smirnov`, `smirnovi`

    Functions for the one-sided distribution

  Notes

  `kolmogorov` 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.kstwobign distribution.

  Examples

  Show the probability of a gap at least as big as 0, 0.5 and 1.0.

      >>> import numpy as np
      >>> from scipy.special import kolmogorov
      >>> from scipy.stats import kstwobign
      >>> kolmogorov([0, 0.5, 1.0])
      array([ 1.        ,  0.96394524,  0.26999967])

  Compare a sample of size 1000 drawn from a Laplace(0, 1) distribution against the target distribution, a Normal(0, 1) distribution.

      >>> from scipy.stats import norm, laplace
      >>> rng = np.random.default_rng()
      >>> n = 1000
      >>> lap01 = laplace(0, 1)
      >>> x = np.sort(lap01.rvs(n, random_state=rng))
      >>> np.mean(x), np.std(x)
      (-0.05841730131499543, 1.3968109101997568)

  Construct the Empirical CDF and the K-S statistic Dn.

      >>> target = norm(0,1)  # Normal mean 0, stddev 1
      >>> cdfs = target.cdf(x)
      >>> ecdfs = np.arange(n+1, dtype=float)/n
      >>> gaps = np.column_stack([cdfs - ecdfs[:n], ecdfs[1:] - cdfs])
      >>> Dn = np.max(gaps)
      >>> Kn = np.sqrt(n) * Dn
      >>> print('Dn=%f, sqrt(n)*Dn=%f' % (Dn, Kn))
      Dn=0.043363, sqrt(n)*Dn=1.371265
      >>> print(chr(10).join(['For a sample of size n drawn from a N(0, 1) distribution:',
      ...   ' the approximate Kolmogorov probability that sqrt(n)*Dn>=%f is %f' %  (Kn, kolmogorov(Kn)),
      ...   ' the approximate Kolmogorov probability that sqrt(n)*Dn<=%f is %f' %  (Kn, kstwobign.cdf(Kn))]))
      For a sample of size n drawn from a N(0, 1) distribution:
       the approximate Kolmogorov probability that sqrt(n)*Dn>=1.371265 is 0.046533
       the approximate Kolmogorov probability that sqrt(n)*Dn<=1.371265 is 0.953467

  Plot the Empirical CDF against the target N(0, 1) CDF.

      >>> import matplotlib.pyplot as plt
      >>> plt.step(np.concatenate([[-3], x]), ecdfs, where='post', label='Empirical CDF')
      >>> x3 = np.linspace(-3, 3, 100)
      >>> plt.plot(x3, target.cdf(x3), label='CDF for N(0, 1)')
      >>> plt.ylim([0, 1]); plt.grid(True); plt.legend();
      >>> # Add vertical lines marking Dn+ and Dn-
      >>> iminus, iplus = np.argmax(gaps, axis=0)
      >>> plt.vlines([x[iminus]], ecdfs[iminus], cdfs[iminus], color='r', linestyle='dashed', lw=4)
      >>> plt.vlines([x[iplus]], cdfs[iplus], ecdfs[iplus+1], color='r', linestyle='dashed', lw=4)
      >>> plt.show()