def log_final_log(self, prev_result, prev_score):
    logging.info('Final best fits:')
    [logging.info(c.get_pars()) for c in prev_result['comps']]
    logging.info('Final age med and span:')
    if (self.fit_pars['optimisation_method'] == 'emcee'):
        [logging.info(row[(- 1)]) for row in prev_result['med_and_spans']]
    logging.info('Membership distribution: {}'.format(prev_result['memb_probs'].sum(axis=0)))
    logging.info('Final membership:')
    logging.info('\n{}'.format(np.round((prev_result['memb_probs'] * 100))))
    logging.info('Final lnlikelihood: {}'.format(prev_score['lnlike']))
    logging.info('Final lnposterior:  {}'.format(prev_score['lnpost']))
    logging.info('Final BIC: {}'.format(prev_score['bic']))
    logging.info('#########################')
    logging.info('### END #################')
    logging.info('#########################')
def log_final_log(self, prev_result, prev_score):
    logging.info('Final best fits:')
    [logging.info(c.get_pars()) for c in prev_result['comps']]
    logging.info('Final age med and span:')
    if (self.fit_pars['optimisation_method'] == 'emcee'):
        [logging.info(row[(- 1)]) for row in prev_result['med_and_spans']]
    logging.info('Membership distribution: {}'.format(prev_result['memb_probs'].sum(axis=0)))
    logging.info('Final membership:')
    logging.info('\n{}'.format(np.round((prev_result['memb_probs'] * 100))))
    logging.info('Final lnlikelihood: {}'.format(prev_score['lnlike']))
    logging.info('Final lnposterior:  {}'.format(prev_score['lnpost']))
    logging.info('Final BIC: {}'.format(prev_score['bic']))
    logging.info('#########################')
    logging.info('### END #################')
    logging.info('#########################')