text: # -*- coding: utf-8 -*-
  
  import os
  import pandas as pd
  import numpy as np
  
  model_list = os.scandir()
  model_list = sorted(f.name for f in model_list if f.is_dir())
  #model_list = [model_list[0]]
  
  for benchmark_model in model_list:
  
      print(benchmark_model)
  
      parameter_file_name = (benchmark_model +
                             '/parameters_' +
                             benchmark_model + '.tsv')
  
      if os.path.isfile(parameter_file_name):
          # create a copy of the original parameter file
          os.system("cp {} {}".format(
              parameter_file_name,
              benchmark_model + '/parametersOriginal_' +
              benchmark_model + '.tsv')
          )
          # read the parameter file
          parameter_df = pd.read_csv(parameter_file_name, sep='\t')
  
          # loop over rows in parameter data-frame
          for row in range(parameter_df.shape[0]):
              for column_name in ['upperBound', 'lowerBound', 'nominalValue']:
                  # set column type to float64
                  parameter_df = parameter_df.astype({column_name: 'float64'})
                  if parameter_df['parameterScale'][row] in 'log':
                      # exponential of log parameter
                      parameter_df.at[row, column_name] = \
                          np.exp(float(parameter_df[column_name][row]))
                  elif parameter_df['parameterScale'][row] in 'log10':
                      # exponential of log10 parameter
                      parameter_df.at[row, column_name] = \
                          np.power(10, float(parameter_df[column_name][row]))
  
          # write results in parameter file
          parameter_df.to_csv(r'./'+benchmark_model +
                              '/parameters_'+benchmark_model+'.tsv',
                              index=None,
                              header=True,
                              sep='\t')
      else:
          # in case parameter file not found
          print('Parameter file not found in {}'.format(
              benchmark_model)
          )
  