data origin era5 loads now data from TOARDB, era5_local uses former data loader from local path

mlair/data_handler/data_handler_single_station.py

@@ -375,37 +375,37 @@ class DataHandlerSingleStation(AbstractDataHandler):
         :return: downloaded data and its meta data
         """
         df_all = {}
-        df_era5, df_toar = None, None
-        meta_era5, meta_toar = None, None
+        df_era5_local, df_toar = None, None
+        meta_era5_local, meta_toar = None, None
         if data_origin is not None:
-            era5_origin = filter_dict_by_value(data_origin, "era5", True)
-            era5_stats = select_from_dict(statistics_per_var, era5_origin.keys())
-            toar_origin = filter_dict_by_value(data_origin, "era5", False)
-            toar_stats = select_from_dict(statistics_per_var, era5_origin.keys(), filter_cond=False)
-            assert len(era5_origin) + len(toar_origin) == len(data_origin)
-            assert len(era5_stats) + len(toar_stats) == len(statistics_per_var)
+            era5_local_origin = filter_dict_by_value(data_origin, "era5_local", True)
+            era5_local_stats = select_from_dict(statistics_per_var, era5_local_origin.keys())
+            toar_origin = filter_dict_by_value(data_origin, "era5_local", False)
+            toar_stats = select_from_dict(statistics_per_var, era5_local_origin.keys(), filter_cond=False)
+            assert len(era5_local_origin) + len(toar_origin) == len(data_origin)
+            assert len(era5_local_stats) + len(toar_stats) == len(statistics_per_var)
         else:
-            era5_origin, toar_origin = None, None
-            era5_stats, toar_stats = statistics_per_var, statistics_per_var
+            era5_local_origin, toar_origin = None, None
+            era5_local_stats, toar_stats = statistics_per_var, statistics_per_var
 
         # load data
-        if era5_origin is not None and len(era5_stats) > 0:
+        if era5_local_origin is not None and len(era5_local_stats) > 0:
             # load era5 data
-            df_era5, meta_era5 = data_sources.era5.load_era5(station_name=station, stat_var=era5_stats,
-                                                             sampling=sampling, data_origin=era5_origin)
+            df_era5_local, meta_era5_local = data_sources.era5.load_era5(
+                station_name=station, stat_var=era5_local_stats, sampling=sampling, data_origin=era5_local_origin)
         if toar_origin is None or len(toar_stats) > 0:
             # load combined data from toar-data (v2 & v1)
             df_toar, meta_toar = data_sources.toar_data.download_toar(station=station, toar_stats=toar_stats,
                                                                       sampling=sampling, data_origin=toar_origin)
 
-        if df_era5 is None and df_toar is None:
-            raise data_sources.toar_data.EmptyQueryResult(f"No data available for era5 and toar-data")
+        if df_era5_local is None and df_toar is None:
+            raise data_sources.toar_data.EmptyQueryResult(f"No data available for era5_local and toar-data")
 
-        df = pd.concat([df_era5, df_toar], axis=1, sort=True)
-        if meta_era5 is not None and meta_toar is not None:
-            meta = meta_era5.combine_first(meta_toar)
+        df = pd.concat([df_era5_local, df_toar], axis=1, sort=True)
+        if meta_era5_local is not None and meta_toar is not None:
+            meta = meta_era5_local.combine_first(meta_toar)
         else:
-            meta = meta_era5 if meta_era5 is not None else meta_toar
+            meta = meta_era5_local if meta_era5_local is not None else meta_toar
         meta.loc["data_origin"] = str(data_origin)
         meta.loc["statistics_per_var"] = str(statistics_per_var)