docs: def compute_stability_classification(self, predicted_data, record, dataframe_record):
          '''Calculate the stability classification for the analysis cases. Must be called after get_experimental_ddg_values.'''
  
          new_idxs = []
          stability_classication_x_cutoff, stability_classication_y_cutoff = self.stability_classication_x_cutoff, self.stability_classication_y_cutoff
          for analysis_set in self.get_analysis_sets(record):
              ddg_details = record['DDG'][analysis_set]
              exp_ddg_fieldname = BenchmarkRun.get_analysis_set_fieldname('Experimental', analysis_set)
              stability_classification_fieldname = BenchmarkRun.get_analysis_set_fieldname('StabilityClassification', analysis_set)
              new_idxs.append(stability_classification_fieldname)
              dataframe_record[stability_classification_fieldname] = None
  
              if ddg_details:
                  stability_classification = None
                  if dataframe_record[exp_ddg_fieldname] != None:
                      stability_classification = fraction_correct([dataframe_record[exp_ddg_fieldname]], [predicted_data[self.ddg_analysis_type]], x_cutoff = stability_classication_x_cutoff, y_cutoff = stability_classication_y_cutoff)
                      stability_classification = int(stability_classification)
                      assert(stability_classification == 0 or stability_classification == 1)
                  dataframe_record[stability_classification_fieldname] = stability_classification
  
          # Update the CSV headers
          try:
              idx = self.csv_headers.index('StabilityClassification')
              self.csv_headers = self.csv_headers[:idx] + new_idxs + self.csv_headers[idx + 1:]
          except ValueError, e: pass