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..a1b7f96966cb15960096941b408fbd886df45749 100644
--- a/mlair/data_handler/data_handler_wrf_chem.py
+++ b/mlair/data_handler/data_handler_wrf_chem.py
@@ -148,10 +148,12 @@ class BaseWrfChemDataLoader:
status = 'FAIL'
print(f'{i}: {filenamestr} {status}')
+ @TimeTrackingWrapper
def get_distances(self, lat, lon):
dist = haversine_dist(lat1=self._data.XLAT, lon1=self._data.XLONG, lat2=lat, lon2=lon)
return dist
+ @TimeTrackingWrapper
def get_bearing(self, lat, lon, points_last=True):
bearing = bearing_angle(lat1=lat, lon1=lon, lat2=self._data.XLAT, lon2=self._data.XLONG)
if points_last:
@@ -167,6 +169,7 @@ 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.')
@@ -174,8 +177,9 @@ 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')
+ @TimeTrackingWrapper
def apply_staged_transormation(self, mapping_of_stag2unstag=None):
if mapping_of_stag2unstag is None:
mapping_of_stag2unstag = {'U': 'U10', 'V': 'V10', 'U10': 'U10', 'V10': 'V10'}
@@ -216,6 +220,7 @@ class BaseWrfChemDataLoader:
self._data[u_ll_name] = ull
self._data[v_ll_name] = vll
+ @TimeTrackingWrapper
def set_interpolated_field(self, staged_field: xr.DataArray, target_field: xr.DataArray,
dropped_staged_attrs: List[str] =None, **kwargs):
stagger_attr_name = kwargs.pop('stagger_attr_name', 'stagger')
@@ -276,6 +281,7 @@ class SingleGridColumnWrfChemDataLoader(BaseWrfChemDataLoader):
# self._set_geoinfos()
logging.debug("SingleGridColumnWrfChemDataLoader Initialised")
+ @TimeTrackingWrapper
def __enter__(self):
self.open_data()
@@ -295,6 +301,7 @@ class SingleGridColumnWrfChemDataLoader(BaseWrfChemDataLoader):
self.data.close()
gc.collect()
+ @TimeTrackingWrapper
def _set_geoinfos(self):
# identify nearest coords
self._set_nearest_icoords(dim=[self.logical_x_coord_name, self.logical_y_coord_name])
@@ -329,6 +336,7 @@ class SingleGridColumnWrfChemDataLoader(BaseWrfChemDataLoader):
def geo_infos(self):
return self._geo_infos
+ @TimeTrackingWrapper
def _apply_external_coordinates(self):
ds_coords = xr.open_dataset(self.external_coords_file, chunks={'south_north': 36, 'west_east': 40})
data = self._data
@@ -336,7 +344,7 @@ class SingleGridColumnWrfChemDataLoader(BaseWrfChemDataLoader):
data = data.assign_coords({k: (remove_items(list(v.dims), 'Time'), v.values.squeeze())})
self._data = data
ds_coords.close()
- logging.debug('setup external coords')
+ # logging.debug('setup external coords')
def _set_coords(self, coords):
__set_coords = dict(lat=None, lon=None)
@@ -359,6 +367,7 @@ class SingleGridColumnWrfChemDataLoader(BaseWrfChemDataLoader):
else:
return self.__coords
+ @TimeTrackingWrapper
def _set_nearest_icoords(self, dim=None):
lat, lon = self.get_coordinates(as_arrays=True)
self._nearest_icoords = dask.compute(self.compute_nearest_icoordinates(lat, lon, dim))[0]
@@ -396,7 +405,6 @@ 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)
@@ -436,6 +444,7 @@ class DataHandlerSingleGridColumn(DataHandlerSingleStation):
def setup_data_path(self, data_path: str, sampling: str):
return data_path
+ @TimeTrackingWrapper
def extract_data_from_loader(self, loader):
data = loader.data.isel(loader.get_nearest_icoords()).squeeze()[self.variables]
return data
@@ -608,7 +617,6 @@ 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
@@ -624,6 +632,7 @@ class DataHandlerSectorGrid(DataHandlerSingleGridColumn):
self.wind_dir_name = None
super().__init__(*args, **kwargs)
+ @TimeTrackingWrapper
def extract_data_from_loader(self, loader):
wind_dir_name = self._get_wind_dir_var_name(loader)
full_data = loader.data.isel(loader.get_nearest_icoords()).squeeze()
@@ -673,6 +682,7 @@ class DataHandlerSectorGrid(DataHandlerSingleGridColumn):
opts=trafo_opts, transformation_dim=self.target_dim)
return trafo
+ @TimeTrackingWrapper
def modify_history(self):
if self.transformation_is_applied:
ws_edges = self.get_applied_transdormation_on_wind_sector_edges()
@@ -721,11 +731,13 @@ class DataHandlerSectorGrid(DataHandlerSingleGridColumn):
else:
return self.history
+ @TimeTrackingWrapper
def get_section_data_from_circle(self, grid_data, loader, sect):
sec_data = grid_data.where(
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
@@ -750,14 +762,17 @@ class DataHandlerSectorGrid(DataHandlerSingleGridColumn):
return grid_data
+ @TimeTrackingWrapper
def apply_aggregation_method_on_sector_data(self, data, loader):
data = data.mean(dim=(loader.logical_x_coord_name, loader.logical_y_coord_name))
return data
+ @TimeTrackingWrapper
def compute_wind_dir_of_interest(self):
wind_dir_of_intrest = self.history.sel({self.target_dim: self.wind_dir_name, self.window_dim: 0})
return wind_dir_of_intrest
+ @TimeTrackingWrapper
def get_applied_transdormation_on_wind_sector_edges(self):
ws_edges = self._get_left_and_right_wind_sector_edges(return_as='xr.da', dim=self.wind_sector_edge_dim_name)
ws_edges = self.apply_transformation_on_data(ws_edges)
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]