remove logging from postprocessing_plotting.py, minor_tail_loss=MSE

mlair/plotting/postprocessing_plotting.py

@@ -139,9 +139,7 @@ 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)
@@ -159,17 +157,12 @@ 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, comp_list:{self._comp_names}")
             for comp in self._comp_names:
                 c = self._load_competitors(station, [comp])
                 if c is not None:
-                    logging.info(f"{station}: {comp} is not None")
                     predictions.append(c.sel(type=comp))
-            logging.info(f"itearate over thresholds")
             for threshold in range(self._min_threshold, self._max_threshold):
                 for pred in predictions:
                     ta, fa, fb, tb = self._single_contingency(obs, pred, threshold)
@@ -177,7 +170,6 @@ class PlotOversamplingContingency(AbstractPlotClass):
                     contingency_array.loc[dict(thresholds=threshold, contingency_cell="fa", type=pred.type.values)] = fa + 1
                     contingency_array.loc[dict(thresholds=threshold, contingency_cell="fb", type=pred.type.values)] = fb + 1
                     contingency_array.loc[dict(thresholds=threshold, contingency_cell="tb", type=pred.type.values)] = tb + 1
-            logging.info(f"{station}: finished")
         return contingency_array
 
     def _single_contingency(self, obs, pred, threshold):