diff --git a/mlair/data_handler/abstract_data_handler.py b/mlair/data_handler/abstract_data_handler.py index 53d4597060f9452e4a59cfa1001da9aaa86056b8..419db059a58beeb4ed7e3e198e41b565f8dc7d25 100644 --- a/mlair/data_handler/abstract_data_handler.py +++ b/mlair/data_handler/abstract_data_handler.py @@ -6,7 +6,6 @@ import inspect from typing import Union, Dict from mlair.helpers import remove_items -import logging class AbstractDataHandler: @@ -14,7 +13,6 @@ class AbstractDataHandler: _requirements = [] def __init__(self, *args, **kwargs): - logging.info("Initialise AbstractDataHandler") pass @classmethod diff --git a/mlair/data_handler/data_handler_single_station.py b/mlair/data_handler/data_handler_single_station.py index 163a0a7754fc34e61383e99c7bc608d13b9f03f6..9fdba43cc2b6b3f9ed7a6d97dbd43ec86f03a1f7 100644 --- a/mlair/data_handler/data_handler_single_station.py +++ b/mlair/data_handler/data_handler_single_station.py @@ -62,7 +62,6 @@ class DataHandlerSingleStation(AbstractDataHandler): overwrite_local_data: bool = False, transformation=None, store_data_locally: bool = True, min_length: int = 0, start=None, end=None, variables=None, data_origin: Dict = None, lazy_preprocessing: bool = False, aggregation_dim=None, **kwargs): - logging.info(f"Initialise DataHandlerSingleStation") super().__init__() self.station = helpers.to_list(station) self.path = self.setup_data_path(data_path, sampling) diff --git a/mlair/data_handler/data_handler_wrf_chem.py b/mlair/data_handler/data_handler_wrf_chem.py index 69d2cb7fece19d88e0c41af188d0705c237e1a25..d1f9508d97037dcaab5faac956d66e916268623c 100644 --- a/mlair/data_handler/data_handler_wrf_chem.py +++ b/mlair/data_handler/data_handler_wrf_chem.py @@ -167,6 +167,7 @@ class BaseWrfChemDataLoader: else: return dist.argmin(dim) + @TimeTrackingWrapper def _set_dims_as_coords(self): if self._data is None: raise IOError(f'{self.__class__.__name__} can not set dims as coords. Use must use `open_data()` before.') @@ -174,7 +175,7 @@ class BaseWrfChemDataLoader: for k, _ in data.dims.items(): data = data.assign_coords({k: data[k]}) self._data = data - logging.info('set dimensions as coordinates') + # logging.info('set dimensions as coordinates') def apply_staged_transormation(self, mapping_of_stag2unstag=None): if mapping_of_stag2unstag is None: @@ -396,7 +397,6 @@ class DataHandlerSingleGridColumn(DataHandlerSingleStation): def __init__(self, *args, external_coords_file=None, var_logical_z_coord_selector=None, targetvar_logical_z_coord_selector=None, **kwargs): - logging.info("Initialise DataHandlerSingleGridColumn") self.external_coords_file = external_coords_file self.var_logical_z_coord_selector = self._ret_z_coord_select_if_valid(var_logical_z_coord_selector, as_input=True) @@ -608,7 +608,6 @@ class DataHandlerSectorGrid(DataHandlerSingleGridColumn): _requirements = DataHandlerWRF.requirements() def __init__(self, *args, radius=None, sectors=None, wind_sector_edge_dim_name=None, **kwargs): - logging.info("Initialise DataHandlerSectorGrid") if radius is None: radius = 100 # km self.radius = radius @@ -726,6 +725,7 @@ class DataHandlerSectorGrid(DataHandlerSingleGridColumn): self.windsector.is_in_sector(sect, loader.geo_infos.bearing.drop('points').squeeze())) return sec_data + @TimeTrackingWrapper def preselect_and_transform_neighbouring_data_based_on_radius(self, loader): """ Select neighbouring grid boxes which have a maximal distance of pre-selected radius from full model field diff --git a/mlair/data_handler/default_data_handler.py b/mlair/data_handler/default_data_handler.py index 02b15993a1a3db66c2b6e7dc564361e959ab9fa1..66f6b5b0b64c1126237880c2a722487254a71d21 100644 --- a/mlair/data_handler/default_data_handler.py +++ b/mlair/data_handler/default_data_handler.py @@ -60,14 +60,14 @@ class DefaultDataHandler(AbstractDataHandler): @classmethod def build(cls, station: str, **kwargs): - logging.info(f"build dh: DefaultDataHandler") sp_keys = {k: copy.deepcopy(kwargs[k]) for k in cls._requirements if k in kwargs} sp = cls.data_handler(station, **sp_keys) dp_args = {k: copy.deepcopy(kwargs[k]) for k in cls.own_args("id_class") if k in kwargs} - try: - return dask.compute(cls(sp, **dp_args))[0] - except Exception: - return cls(sp, **dp_args) + return cls(sp, **dp_args) + # try: + # return dask.compute(cls(sp, **dp_args))[0] + # except Exception: + # return cls(sp, **dp_args) def _create_collection(self): return [self.id_class]