code: def k_s(X):
      
      xbar, sigma = pmag.gausspars(X)
      d, f = 0, 0.
      for i in range(1, len(X) + 1):
          b = old_div(float(i), float(len(X)))
          a = gaussfunc(X[i - 1], xbar, sigma)
          if abs(f - a) > abs(b - a):
              delta = abs(f - a)
          else:
              delta = abs(b - a)
          if delta > d:
              d = delta
          f = b
      return d, xbar, sigma
docs: Kolmorgorov-Smirnov statistic. Finds the
      probability that the data are distributed
      as func - used method of Numerical Recipes (Press et al., 1986)