applied corrections to load data correctly

mlair/data_handler/data_handler_mixed_sampling.py

@@ -60,7 +60,7 @@ class DataHandlerMixedSamplingSingleStation(DataHandlerSingleStation):
         self.set_inputs_and_targets()
 
     def load_and_interpolate(self, ind) -> [xr.DataArray, pd.DataFrame]:
-        vars = [self.variables, self.target_var][ind]
+        vars = [self.variables, self.target_var]
         stats_per_var = helpers.select_from_dict(self.statistics_per_var, vars[ind])
         data, self.meta = self.load_data(self.path[ind], self.station, stats_per_var, self.sampling[ind],
                                          self.station_type, self.network, self.store_data_locally, self.data_origin,
@@ -115,7 +115,7 @@ class DataHandlerMixedSamplingWithFilterSingleStation(DataHandlerMixedSamplingSi
 
     def make_input_target(self):
         """
-        A FIR filter is applied on the input data that has hourly resolution. Lables Y are provided as aggregated values
+        A FIR filter is applied on the input data that has hourly resolution. Labels Y are provided as aggregated values
         with daily resolution.
         """
         self._data = tuple(map(self.load_and_interpolate, [0, 1]))  # load input (0) and target (1) data
@@ -143,7 +143,7 @@ class DataHandlerMixedSamplingWithFilterSingleStation(DataHandlerMixedSamplingSi
     def load_and_interpolate(self, ind) -> [xr.DataArray, pd.DataFrame]:
 
         start, end = self.update_start_end(ind)
-        vars = [self.variables, self.target_var][ind]
+        vars = [self.variables, self.target_var]
         stats_per_var = helpers.select_from_dict(self.statistics_per_var, vars[ind])
 
         data, self.meta = self.load_data(self.path[ind], self.station, stats_per_var, self.sampling[ind],
@@ -353,6 +353,7 @@ class DataHandlerMixedSamplingWithClimateAndFirFilter(DataHandlerMixedSamplingWi
                 sp_keys = {k: copy.deepcopy(kwargs[k]) for k in cls.data_handler_unfiltered.requirements() if k in kwargs}
                 sp_keys = cls.build_update_transformation(sp_keys, dh_type="unfiltered_chem")
                 cls.prepare_build(sp_keys, chem_vars, cls.chem_indicator)
+                cls.correct_overwrite_option(sp_keys)
                 sp_chem_unfiltered = cls.data_handler_unfiltered(station, **sp_keys)
         if len(meteo_vars) > 0:
             cls.set_data_handler_fir_pos(**kwargs)
@@ -364,11 +365,18 @@ class DataHandlerMixedSamplingWithClimateAndFirFilter(DataHandlerMixedSamplingWi
                 sp_keys = {k: copy.deepcopy(kwargs[k]) for k in cls.data_handler_unfiltered.requirements() if k in kwargs}
                 sp_keys = cls.build_update_transformation(sp_keys, dh_type="unfiltered_meteo")
                 cls.prepare_build(sp_keys, meteo_vars, cls.meteo_indicator)
+                cls.correct_overwrite_option(sp_keys)
                 sp_meteo_unfiltered = cls.data_handler_unfiltered(station, **sp_keys)
 
         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 correct_overwrite_option(cls, kwargs):
+        """Set `overwrite_local_data=False`."""
+        if "overwrite_local_data" in kwargs:
+            kwargs["overwrite_local_data"] = False
+
     @classmethod
     def set_data_handler_fir_pos(cls, **kwargs):
         """

mlair/data_handler/data_handler_single_station.py

@@ -395,11 +395,11 @@ class DataHandlerSingleStation(AbstractDataHandler):
             era5_stats, join_stats = statistics_per_var, statistics_per_var
 
         # load data
-        if era5_origin is not None and len(era5_origin) > 0:
+        if era5_origin is not None and len(era5_stats) > 0:
             # load era5 data
             df_era5, meta_era5 = era5.load_era5(station_name=station, stat_var=era5_stats, sampling=sampling,
                                       data_origin=era5_origin)
-        if join_origin is None or len(join_stats.keys()) > 0:
+        if join_origin is None or len(join_stats) > 0:
             # load join data
             df_join, meta_join = join.download_join(station_name=station, stat_var=join_stats, station_type=station_type,
                                                     network_name=network, sampling=sampling, data_origin=join_origin)