code: def gof_mc(randfn, distr, nobs=100):
      # print '\nIs it correctly sized?'
      from collections import defaultdict
  
      results = defaultdict(list)
      for i in range(1000):
          rvs = randfn(nobs)
          goft = GOF(rvs, distr)
          for ti in all_gofs:
              results[ti].append(
                  goft.get_test(ti, "stephens70upp")[0][1]
              )
  
      resarr = np.array([results[ti] for ti in all_gofs])
      print("         ", "      ".join(all_gofs))
      print("at 0.01:", (resarr < 0.01).mean(1))
      print("at 0.05:", (resarr < 0.05).mean(1))
      print("at 0.10:", (resarr < 0.1).mean(1))
  
imports: from statsmodels.compat.python import lmap
  import numpy
  from scipy.stats import distributions
  from statsmodels.tools.decorators import cache_readonly
  from scipy.special import kolmogorov
label_desc: Use image node_id 10 for calling a global function with example usage: gof_mc(randfn, distr, nobs) without return types