code: def mark_fit_good(self, fit, spec=None):
          
          if spec == None:
              for spec, fits in list(self.pmag_results_data['specimens'].items()):
                  if fit in fits:
                      break
          samp = self.Data_hierarchy['sample_of_specimen'][spec]
          if 'sample_orientation_flag' not in self.Data_info['er_samples'][samp]:
              self.Data_info['er_samples'][samp]['sample_orientation_flag'] = 'g'
          samp_flag = self.Data_info['er_samples'][samp]['sample_orientation_flag']
          if samp_flag == 'g':
              self.bad_fits.remove(fit)
              return True
          else:
              self.user_warning(
                  "Cannot mark this interpretation good its sample orientation has been marked bad")
              return False
docs: Marks fit good so it is used in high level means
  
          Parameters
          ----------
          fit : fit to mark good
          spec : specimen of fit to mark good (optional though runtime will
              increase if not provided)