docs: def generate_timing_stats(file_list, var_list):
      """
      Parse all of the timing files, and generate some statistics
      about the run.
  
      Args:
          file_list: A list of timing files to parse
          var_list: A list of variables to look for in the timing file
  
      Returns:
          A dict containing values that have the form:
              [mean, min, max, mean, standard deviation]
      """
      timing_result = dict()
      timing_summary = dict()
      for file in file_list:
          timing_result[file] = functions.parse_gptl(file, var_list)
      for var in var_list:
          var_time = []
          for f, data in timing_result.items():
              try:
                  var_time.append(data[var])
              except:
                  continue
          if len(var_time):
              timing_summary[var] = {'mean': np.mean(var_time),
                                     'max': np.max(var_time),
                                     'min': np.min(var_time),
                                     'std': np.std(var_time)}
      return timing_summary