code: def log_results(self, output_path=None, run_id=None):
          
          best_ind = self.halloffame[0]
          model_params = self.parse_individual(
              best_ind)  # need to change name of 'params'
          if output_path is None:
              output_path = os.getcwd()
          if run_id is None:
              run_id = '{:%Y%m%d-%H%M%S}'.format(
                  datetime.datetime.now())
          with open('{0}/{1}_opt_log.txt'.format(
                  output_path, run_id), 'w') as log_file:
              log_file.write('\nEvaluated {0} models in total\n'.format(
                  self._model_count))
              log_file.write('Run ID is {0}\n'.format(run_id))
              log_file.write('Best fitness is {0}\n'.format(
                  self.halloffame[0].fitness))
              log_file.write(
                  'Parameters of best model are {0}\n'.format(model_params))
              log_file.write(
                  'Best individual is {0}\n'.format(self.halloffame[0]))
              for i, entry in enumerate(self.halloffame[0]):
                  if entry > 0.95:
                      log_file.write(
                          "Warning! Parameter {0} is at or near maximum allowed "
                          "value\n".format(i + 1))
                  elif entry < -0.95:
                      log_file.write(
                          "Warning! Parameter {0} is at or near minimum allowed "
                          "value\n".format(i + 1))
              log_file.write('Minimization history: \n{0}'.format(self.logbook))
          with open('{0}/{1}_opt_best_model.pdb'.format(
                  output_path, run_id), 'w') as output_file:
              output_file.write(self.best_model.pdb)
          return
docs: Saves files for the minimization.
  
          Notes
          -----
          Currently saves a logfile with best individual and a pdb of
          the best model.