Saves files for the minimization.

        Notes
        -----
        Currently saves a logfile with best individual and a pdb of
        the best model.

def log_results(self, output_path=None, run_id=None):
        """Saves files for the minimization.

        Notes
        -----
        Currently saves a logfile with best individual and a pdb of
        the best model.
        """
        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