also store competitive skill scores

mlair/run_modules/post_processing.py

@@ -121,6 +121,7 @@ class PostProcessing(RunEnvironment):
             self.skill_scores = (skill_score_competitive, skill_score_climatological)
         self.report_error_metrics(errors)
         self.report_error_metrics({self.forecast_indicator: skill_score_climatological})
+        self.report_error_metrics({"skill_score": skill_score_competitive})
 
         # plotting
         self.plot()
@@ -842,6 +843,12 @@ class PostProcessing(RunEnvironment):
                     dim = station_errors.dims[0]
                     sel_index = [sel for sel in station_errors.coords[dim] if "CASE" in str(sel)]
                     station_errors = {str(i.values): station_errors.sel(**{dim: i}) for i in sel_index}
+                elif isinstance(station_errors, pd.DataFrame):
+                    sel_index = station_errors.index.tolist()
+                    ahead = station_errors.columns.values
+                    station_errors = {k: xr.DataArray(station_errors[station_errors.index == k].values.flatten(),
+                                                      dims=["ahead"], coords={"ahead": ahead}).astype(float)
+                                      for k in sel_index}
                 for metric, vals in station_errors.items():
                     if metric == "n":
                         metric = "count"