For debugging on HPC

mlair/helpers/statistics.py

@@ -355,10 +355,7 @@ class SkillScores:
 
         :return: all CASES as well as all terms
         """
-        if self.external_data is None:
-            ahead_names = []
-        else:
-            ahead_names = list(self.external_data[self.ahead_dim].data)
+        ahead_names = list(internal_data[self.ahead_dim].data)
 
         all_terms = ['AI', 'AII', 'AIII', 'AIV', 'BI', 'BII', 'BIV', 'CI', 'CIV', 'CASE I', 'CASE II', 'CASE III',
                      'CASE IV']

mlair/plotting/postprocessing_plotting.py

@@ -121,7 +121,9 @@ class PlotOversamplingContingency(AbstractPlotClass):
     def _min_max_threshold(self):
         min_threshold = 0
         max_threshold = 0
+        logging.info("min_max thresholds")
         for station in self._stations:
+            logging.info(f"{station}")
             file = os.path.join(self._file_path, self._file_name % station)
             forecast_file = xr.open_dataarray(file)
             obs = forecast_file.sel(type=self._obs_name)
@@ -139,17 +141,23 @@ class PlotOversamplingContingency(AbstractPlotClass):
         for station in self._stations:
             file = os.path.join(self._file_path, self._file_name % station)
             forecast_file = xr.open_dataarray(file)
+            logging.info(f"{station}: load obs")
             obs = forecast_file.sel(type=self._obs_name)
+            logging.info(f"{station}: load pred")
             predictions = [forecast_file.sel(type=self._model_name)]
+            logging.info(f"{station}: load comp")
             competitors = [self._load_competitors(station, [comp]).sel(type=comp) for comp in self._comp_names]
             predictions.extend(competitors)
+            logging.info(f"itearate over thresholds")
             for threshold in range(self._min_threshold, self._max_threshold):
-                for pred in predictions:
+                for i, pred in enumerate(predictions):
+                    logging.info(i)
                     ta, fa, fb, tb = self._single_contingency(obs, pred, threshold)
                     contingency_array.loc[dict(thresholds=threshold, contingency_cell="ta", type=pred.type.values)] = ta
                     contingency_array.loc[dict(thresholds=threshold, contingency_cell="fa", type=pred.type.values)] = fa
                     contingency_array.loc[dict(thresholds=threshold, contingency_cell="fb", type=pred.type.values)] = fb
                     contingency_array.loc[dict(thresholds=threshold, contingency_cell="tb", type=pred.type.values)] = tb
+            logging.info(f"{station}: finished")
         return contingency_array
 
     def _single_contingency(self, obs, pred, threshold):