code: def compare_ordered(vals, alpha):
      """simple ordered sequential comparison of means
  
      vals : array_like
          means or rankmeans for independent groups
  
      incomplete, no return, not used yet
      """
      vals = np.asarray(vals)
      alphaf = alpha  # Notation ?
      sortind = np.argsort(vals)
      pvals = vals[sortind]
      sortrevind = sortind.argsort()
      ntests = len(vals)
      # alphacSidak = 1 - np.power((1. - alphaf), 1./ntests)
      # alphacBonf = alphaf / float(ntests)
      v1, v2 = np.triu_indices(ntests, 1)
      # v1,v2 have wrong sequence
      for i in range(4):
          for j in range(4, i, -1):
              print(i, j)
  
imports: from collections import namedtuple
  from statsmodels.compat.python import lzip, lrange
  import copy
  import math
  import numpy
  from numpy.testing import assert_almost_equal, assert_equal
  from scipy import stats, interpolate
  from statsmodels.iolib.table import SimpleTable
  from statsmodels.stats.multitest import multipletests, _ecdf, fdrcorrection, fdrcorrection_twostage
  from statsmodels.graphics import utils
  from statsmodels.tools.sm_exceptions import ValueWarning
label_desc: Use image node_id 15 for calling a global function with example usage: compare_ordered(vals, alpha) without return types