old_contents: from scipy.stats import chisquare as _chisquare, kstest as _kstest
  from numpy import inf, zeros_like, histogram, log2, ceil
  
  def kolsmi(dist, fit_result, data):
      """
      Perform a Kolmogorow–Smirnow-Test for goodness of fit.
  
      This tests the H0 hypothesis, if data is a sample of dist
  
      Args:
          dist:         A mle.Distribution instance
          fit_result:   The solution dict, returned by the Distribution.fit method
          data:         The data used in Distribution.fit
      Returns:
          teststat:     the test statistic, e.g. the max distance between the
                        cumulated distributions
          p-value:      the p-value, probability that dist describes the data
      """
      teststat, pvalue = _kstest(data, lambda x: dist.cdf(x, **fit_result["x"]))
      return teststat, pvalue
  
  def chisquare(dist, fit_result, data, bins=None, range=None):
      """
      Perform a Chi^2 test for goodness of fit.
  
      Tests the H0 hypothesis if the distances between fit result and
      data are compatible  with random fluctuations.
  
      Args:
          dist:         A mle.Distribution instance
          fit_result:   The solution dict, returned by the Distribution.fit method
          data:         The data used in Distribution.fit
          bins:         Number of bins for the histogram (default: 1+log2(N))
          range:        Range for the histogram (default: min(data), max(data))
      Returns:
          chisquare:    the test statistic, chi^2/ndf
          p-value:      the p-value, probability that differences between dist
                        and data are compatible with random fluctuation
      """
  
      if bins is None:
          bins = ceil(1 + log2(len(data)))
          print(bins)
  
      entries, edges = histogram(data, bins=bins, range=range, density=True)
      center = 0.5 * (edges[:-1] + edges[1:])
  
      chisq, pvalue = _chisquare(entries,
                                 dist.pdf(center, **fit_result["x"]),
                                 ddof=len(fit_result["x"]))
  
      return chisq, pvalue
  
new_contents: from scipy.stats import chisquare as _chisquare, kstest as _kstest
  from numpy import inf, zeros_like, histogram, log2, ceil
  
  def kolsmi(dist, fit_result, data):
      """
      Perform a Kolmogorow–Smirnow-Test for goodness of fit.
  
      This tests the H0 hypothesis, if data is a sample of dist
  
      Args:
          dist:         A mle.Distribution instance
          fit_result:   The solution dict, returned by the Distribution.fit method
          data:         The data used in Distribution.fit
      Returns:
          teststat:     the test statistic, e.g. the max distance between the
                        cumulated distributions
          p-value:      the p-value, probability that dist describes the data
      """
      teststat, pvalue = _kstest(data, lambda x: dist.cdf(x, **fit_result["x"]))
      return teststat, pvalue
  
  def chisquare(dist, fit_result, data, bins=None, range=None):
      """
      Perform a Chi^2 test for goodness of fit.
  
      Tests the H0 hypothesis if the distances between fit result and
      data are compatible  with random fluctuations.
  
      Args:
          dist:         A mle.Distribution instance
          fit_result:   The solution dict, returned by the Distribution.fit method
          data:         The data used in Distribution.fit
          bins:         Number of bins for the histogram (default: 1+log2(N))
          range:        Range for the histogram (default: min(data), max(data))
      Returns:
          chisquare:    the test statistic, chi^2/ndf
          p-value:      the p-value, probability that differences between dist
                        and data are compatible with random fluctuation
      """
  
      if bins is None:
          bins = ceil(2*len(data)**(1.0/3.0))
  
      entries, edges = histogram(data, bins=bins, range=range)
      cdf = dist.cdf(edges, **fit_result["x"])
      exp_entries = len(data) * (cdf[1:] - cdf[:-1])
  
      chisq, pvalue = _chisquare(entries,
                                 exp_entries,
                                 ddof=len(fit_result["x"])
                                 )
      chisq = chisq/(bins - len(fit_result["x"]) - 1)
      return chisq, pvalue
  
subject: fix chi square test implementation