code: def roc_curve(self):
          
          pod = self.contingency_tables["TP"].astype(float) / (self.contingency_tables["TP"] +
                                                               self.contingency_tables["FN"])
          pofd = self.contingency_tables["FP"].astype(float) / (self.contingency_tables["FP"] +
                                                                self.contingency_tables["TN"])
          return pd.DataFrame({"POD": pod, "POFD": pofd, "Thresholds": self.thresholds},
                              columns=["POD", "POFD", "Thresholds"])
docs: Generate a ROC curve from the contingency table by calculating the probability of detection (TP/(TP+FN)) and the
          probability of false detection (FP/(FP+TN)).
  
          Returns:
              A pandas.DataFrame containing the POD, POFD, and the corresponding probability thresholds.