cleanup, remove outdated validate_station_old method

mlair/run_modules/pre_processing.py

@@ -285,38 +285,6 @@ class PreProcessing(RunEnvironment):
             for k, v in attrs.items():
                 self.data_store.set(k, v)
 
-    def validate_station_old(self, data_handler: AbstractDataHandler, set_stations, set_name=None,
-                             store_processed_data=True):
-        """
-        Check if all given stations in `all_stations` are valid.
-
-        Valid means, that there is data available for the given time range (is included in `kwargs`). The shape and the
-        loading time are logged in debug mode.
-
-        :return: Corrected list containing only valid station IDs.
-        """
-        t_outer = TimeTracking()
-        logging.info(f"check valid stations started{' (%s)' % (set_name if set_name is not None else 'all')}")
-        # calculate transformation using train data
-        if set_name == "train":
-            logging.info("setup transformation using train data exclusively")
-            self.transformation(data_handler, set_stations)
-        # start station check
-        collection = DataCollection()
-        valid_stations = []
-        kwargs = self.data_store.create_args_dict(data_handler.requirements(), scope=set_name)
-        for station in set_stations:
-            try:
-                dp = data_handler.build(station, name_affix=set_name, store_processed_data=store_processed_data,
-                                        **kwargs)
-                collection.add(dp)
-                valid_stations.append(station)
-            except (AttributeError, EmptyQueryResult):
-                continue
-        logging.info(f"run for {t_outer} to check {len(set_stations)} station(s). Found {len(collection)}/"
-                     f"{len(set_stations)} valid stations.")
-        return collection, valid_stations
-
     def transformation(self, data_handler: AbstractDataHandler, stations):
         if hasattr(data_handler, "transformation"):
             kwargs = self.data_store.create_args_dict(data_handler.requirements(), scope="train")