update toar service url

mlair/configuration/toar_data_v2_settings.py

@@ -10,7 +10,7 @@ def toar_data_v2_settings(sampling="daily") -> Tuple[str, Dict]:
     :return: Service url and optional headers
     """
     if sampling == "daily":  # pragma: no branch
-        TOAR_SERVICE_URL = "https://toar-data.fz-juelich.de/statistics/api/v1/"
+        TOAR_SERVICE_URL = "https://toar-data.fz-juelich.de/api/v2/analysis/statistics/"
         headers = {}
     elif sampling == "hourly" or sampling == "meta":
         TOAR_SERVICE_URL = "https://toar-data.fz-juelich.de/api/v2/"

mlair/data_handler/data_handler_single_station.py

@@ -331,7 +331,7 @@ class DataHandlerSingleStation(AbstractDataHandler):
         file_name = self._set_file_name(path, station, statistics_per_var)
         meta_file = self._set_meta_file_name(path, station, statistics_per_var)
         if self.overwrite_local_data is True:
-            logging.debug(f"overwrite_local_data is true, therefore reload {file_name}")
+            logging.debug(f"{self.station[0]}: overwrite_local_data is true, therefore reload {file_name}")
             if os.path.exists(file_name):
                 os.remove(file_name)
             if os.path.exists(meta_file):
@@ -339,22 +339,22 @@ class DataHandlerSingleStation(AbstractDataHandler):
             data, meta = data_sources.download_data(file_name, meta_file, station, statistics_per_var, sampling,
                                             store_data_locally=store_data_locally, data_origin=data_origin,
                                             time_dim=self.time_dim, target_dim=self.target_dim, iter_dim=self.iter_dim)
-            logging.debug(f"loaded new data")
+            logging.debug(f"{self.station[0]}: loaded new data")
         else:
             try:
-                logging.debug(f"try to load local data from: {file_name}")
+                logging.debug(f"{self.station[0]}: try to load local data from: {file_name}")
                 data = xr.open_dataarray(file_name)
                 meta = pd.read_csv(meta_file, index_col=0)
                 self.check_station_meta(meta, station, data_origin, statistics_per_var)
-                logging.debug("loading finished")
+                logging.debug(f"{self.station[0]}: loading finished")
             except FileNotFoundError as e:
-                logging.debug(e)
-                logging.debug(f"load new data")
+                logging.debug(f"{self.station[0]}: {e}")
+                logging.debug(f"{self.station[0]}: load new data")
                 data, meta = data_sources.download_data(file_name, meta_file, station, statistics_per_var, sampling,
                                                         store_data_locally=store_data_locally, data_origin=data_origin,
                                                         time_dim=self.time_dim, target_dim=self.target_dim,
                                                         iter_dim=self.iter_dim)
-                logging.debug("loading finished")
+                logging.debug(f"{self.station[0]}: loading finished")
         # create slices and check for negative concentration.
         data = self._slice_prep(data, start=start, end=end)
         data = self.check_for_negative_concentrations(data)
@@ -372,7 +372,7 @@ class DataHandlerSingleStation(AbstractDataHandler):
             if v is None or k not in meta.index:
                 continue
             if meta.at[k, station[0]] != v:
-                logging.debug(f"meta data does not agree with given request for {k}: {v} (requested) != "
+                logging.debug(f"{station[0]}: meta data does not agree with given request for {k}: {v} (requested) != "
                               f"{meta.at[k, station[0]]} (local). Raise FileNotFoundError to trigger new "
                               f"grapping from web.")
                 raise FileNotFoundError

mlair/data_handler/data_handler_with_filter.py

@@ -374,7 +374,7 @@ class DataHandlerClimateFirFilterSingleStation(DataHandlerFirFilterSingleStation
     def apply_filter(self):
         """Apply FIR filter only on inputs."""
         self.apriori = self.apriori.get(str(self)) if isinstance(self.apriori, dict) else self.apriori
-        logging.info(f"{self.station}: call ClimateFIRFilter")
+        logging.info(f"{self.station[0]}: call ClimateFIRFilter")
         climate_filter = ClimateFIRFilter(self.input_data.astype("float32"), self.fs, self.filter_order,
                                           self.filter_cutoff_freq,
                                           self.filter_window_type, time_dim=self.time_dim, var_dim=self.target_dim,