code: def handle_hotmaps_results(permutation_result):
      
      if len(permutation_result[0])  == 6:
          mycols = ['gene', 'window length', 'codon position', 'mutation count',
                    'windowed sum', 'p-value']
      else:
          mycols = ['gene', 'window length', 'codon position', 'index', 'mutation count',
                    'windowed sum', 'p-value']
  
      permutation_df = pd.DataFrame(permutation_result, columns=mycols)
  
      # get benjamani hochberg adjusted p-values
      permutation_df['q-value'] = 1
      for w in permutation_df['window length'].unique():
          is_window = permutation_df['window length'] == w
          permutation_df.loc[is_window, 'q-value'] = mypval.bh_fdr(permutation_df.loc[is_window, 'p-value'])
      #permutation_df['q-value'] = mypval.bh_fdr(permutation_df['p-value'])
  
      # order output
      #permutation_df = permutation_df.set_index('gene', drop=False)  # make sure genes are indices
      col_order = mycols + ['q-value']
      permutation_df = permutation_df.sort_values(by=['window length', 'p-value'])
      return permutation_df[col_order]
docs: Takes in output from multiprocess_permutation function and converts to
      a better formatted dataframe.
  
      Parameters
      ----------
      permutation_result : list
          output from multiprocess_permutation
  
      Returns
      -------
      permutation_df : pd.DataFrame
          formatted output suitable to save