introduced new class method to set data handler fir pos

mlair/data_handler/data_handler_mixed_sampling.py

@@ -316,10 +316,17 @@ class DataHandlerMixedSamplingWithClimateAndFirFilter(DataHandlerMixedSamplingWi
 
     @classmethod
     def _split_chem_and_meteo_variables(cls, **kwargs):
+        """
+        Select all used variables and split them into categories chem and other.
+
+        Chemical variables are indicated by `cls.data_handler_climate_fir.chem_vars`. To indicate used variables, this
+        method uses 1) parameter `variables`, 2) keys from `statistics_per_var`, 3) keys from
+        `cls.data_handler_climate_fir.DEFAULT_VAR_ALL_DICT`. Option 3) is also applied if 1) or 2) are given but None.
+        """
         if "variables" in kwargs:
             variables = kwargs.get("variables")
         elif "statistics_per_var" in kwargs:
-            variables = kwargs.get("statistics_per_var")
+            variables = kwargs.get("statistics_per_var").keys()
         else:
             variables = None
         if variables is None:
@@ -348,14 +355,7 @@ class DataHandlerMixedSamplingWithClimateAndFirFilter(DataHandlerMixedSamplingWi
                 cls.prepare_build(sp_keys, chem_vars, cls.chem_indicator)
                 sp_chem_unfiltered = cls.data_handler_unfiltered(station, **sp_keys)
         if len(meteo_vars) > 0:
-            if cls.data_handler_fir_pos is None:
-                if "extend_length_opts" in kwargs:
-                    if isinstance(kwargs["extend_length_opts"], dict) and cls.meteo_indicator not in kwargs["extend_length_opts"].keys():
-                        cls.data_handler_fir_pos = 0  # use faster fir version without climate estimate
-                    else:
-                        cls.data_handler_fir_pos = 1  # use slower fir version with climate estimate
-                else:
-                    cls.data_handler_fir_pos = 0  # use faster fir version without climate estimate
+            cls.set_data_handler_fir_pos(**kwargs)
             sp_keys = {k: copy.deepcopy(kwargs[k]) for k in cls.data_handler_fir[cls.data_handler_fir_pos].requirements() if k in kwargs}
             sp_keys = cls.build_update_transformation(sp_keys, dh_type="filtered_meteo")
             cls.prepare_build(sp_keys, meteo_vars, cls.meteo_indicator)
@@ -369,8 +369,36 @@ class DataHandlerMixedSamplingWithClimateAndFirFilter(DataHandlerMixedSamplingWi
         dp_args = {k: copy.deepcopy(kwargs[k]) for k in cls.own_args("id_class") if k in kwargs}
         return cls(sp_chem, sp_meteo, sp_chem_unfiltered, sp_meteo_unfiltered, chem_vars, meteo_vars, **dp_args)
 
+    @classmethod
+    def set_data_handler_fir_pos(cls, **kwargs):
+        """
+        Set position of fir data handler to use either faster FIR version or slower climate FIR.
+
+        This method will set data handler indicator to 0 if either no parameter "extend_length_opts" is given or the
+        parameter is of type dict but has no entry for the meteo_indicator. In all other cases, indicator is set to 1.
+        """
+        p_name = "extend_length_opts"
+        if cls.data_handler_fir_pos is None:
+            if p_name in kwargs:
+                if isinstance(kwargs[p_name], dict) and cls.meteo_indicator not in kwargs[p_name].keys():
+                    cls.data_handler_fir_pos = 0  # use faster fir version without climate estimate
+                else:
+                    cls.data_handler_fir_pos = 1  # use slower fir version with climate estimate
+            else:
+                cls.data_handler_fir_pos = 0  # use faster fir version without climate estimate
+
     @classmethod
     def prepare_build(cls, kwargs, var_list, var_type):
+        """
+        Prepares for build of class.
+
+        `variables` parameter is updated by `var_list`, which should only include variables of a specific type (e.g.
+        only chemical variables) indicated by `var_type`. Furthermore, this method cleans the `kwargs` dictionary as
+        follows: For all parameters provided as dict to separate between chem and meteo options (dict must have keys
+        from `cls.chem_indicator` and/or `cls.meteo_indicator`), this parameter is removed from kwargs and its value
+        related to `var_type` added again. In case there is no value for given `var_type`, the parameter is not added
+        at all (as this parameter is assumed to affect only other types of variables).
+        """
         kwargs.update({"variables": var_list})
         for k in list(kwargs.keys()):
             v = kwargs[k]
@@ -382,17 +410,6 @@ class DataHandlerMixedSamplingWithClimateAndFirFilter(DataHandlerMixedSamplingWi
                     except KeyError:
                         pass
 
-    @staticmethod
-    def adjust_window_opts(key: str, parameter_name: str, kwargs: dict):
-        try:
-            if parameter_name in kwargs:
-                window_opt = kwargs.pop(parameter_name)
-                if isinstance(window_opt, dict):
-                    window_opt = window_opt[key]
-                kwargs[parameter_name] = window_opt
-        except KeyError:
-            pass
-
     def _create_collection(self):
         collection = super()._create_collection()
         if self.id_class_other is not None:
@@ -419,9 +436,10 @@ class DataHandlerMixedSamplingWithClimateAndFirFilter(DataHandlerMixedSamplingWi
 
         # meteo transformation
         if len(meteo_vars) > 0:
+            cls.set_data_handler_fir_pos(**kwargs)
             kwargs_meteo = copy.deepcopy(kwargs)
             cls.prepare_build(kwargs_meteo, meteo_vars, cls.meteo_indicator)
-            dh_transformation = (cls.data_handler_fir[cls.data_handler_fir_pos or 0], cls.data_handler_unfiltered)
+            dh_transformation = (cls.data_handler_fir[cls.data_handler_fir_pos], cls.data_handler_unfiltered)
             transformation_meteo = super().transformation(set_stations, tmp_path=tmp_path,
                                                           dh_transformation=dh_transformation, **kwargs_meteo)