skill score statistics require now more explicitly named indicators for...

skill score statistics require now more explicitly named indicators for forecast, observation and reference to prevent error arising from auto-filling

mlair/helpers/statistics.py

@@ -209,7 +209,7 @@ class SkillScores:
 
     def skill_scores(self, window_lead_time: int) -> pd.DataFrame:
         """
-        Calculate skill scores for all combinations of CNN, persistence and OLS.
+        Calculate skill scores for all combinations of model names.
 
         :param window_lead_time: length of forecast steps
 
@@ -228,7 +228,7 @@ class SkillScores:
         return skill_score
 
     def climatological_skill_scores(self, external_data: Data, window_lead_time: int,
-                                    forecast_name: str = "cnn") -> xr.DataArray:
+                                    forecast_name: str) -> xr.DataArray:
         """
         Calculate climatological skill scores according to Murphy (1988).
 
@@ -273,8 +273,8 @@ class SkillScores:
         kwargs = {"external_data": external_data} if external_data is not None else {}
         return self.__getattribute__(f"skill_score_mu_case_{mu_type}")(data, observation_name, forecast_name, **kwargs)
 
-    @staticmethod
-    def general_skill_score(data: Data, observation_name: str, forecast_name: str, reference_name: str) -> np.ndarray:
+    def general_skill_score(self, data: Data, forecast_name: str, reference_name: str,
+                            observation_name: str = None) -> np.ndarray:
         r"""
         Calculate general skill score based on mean squared error.
 
@@ -285,6 +285,8 @@ class SkillScores:
 
         :return: skill score of forecast
         """
+        if observation_name is None:
+            observation_name = self.observation_name
         data = data.dropna("index")
         observation = data.sel(type=observation_name)
         forecast = data.sel(type=forecast_name)