diff --git a/.gitmodules b/.gitmodules
new file mode 100644
index 0000000000000000000000000000000000000000..45f0660c7938d17d16827418ea9bb17aac9d098c
--- /dev/null
+++ b/.gitmodules
@@ -0,0 +1,3 @@
+[submodule "mlair/external/toarstats"]
+ path = mlair/external/toarstats
+ url = git@gitlab.jsc.fz-juelich.de:esde/toar-public/toarstats.git
diff --git a/HPC_setup/requirements_HDFML_additionals.txt b/HPC_setup/requirements_HDFML_additionals.txt
index fd22a309913efa6478a4a00f94bac70433e21774..a0bbb3b21daaa5181d5180b22d69c40652e387d7 100644
--- a/HPC_setup/requirements_HDFML_additionals.txt
+++ b/HPC_setup/requirements_HDFML_additionals.txt
@@ -59,6 +59,7 @@ termcolor==1.1.0
toml==0.10.2
toolz==0.11.1
typing-extensions==3.7.4.3
+tzwhere==3.0.3
urllib3==1.26.3
Werkzeug==1.0.1
wget==3.2
diff --git a/HPC_setup/requirements_JUWELS_additionals.txt b/HPC_setup/requirements_JUWELS_additionals.txt
index fd22a309913efa6478a4a00f94bac70433e21774..a0bbb3b21daaa5181d5180b22d69c40652e387d7 100644
--- a/HPC_setup/requirements_JUWELS_additionals.txt
+++ b/HPC_setup/requirements_JUWELS_additionals.txt
@@ -59,6 +59,7 @@ termcolor==1.1.0
toml==0.10.2
toolz==0.11.1
typing-extensions==3.7.4.3
+tzwhere==3.0.3
urllib3==1.26.3
Werkzeug==1.0.1
wget==3.2
diff --git a/mlair/data_handler/abstract_data_handler.py b/mlair/data_handler/abstract_data_handler.py
index 36d6e9ae5394705af4b9fbcfd1d8ff77572642b5..a6f49d2d756200a8a4e5f15c13ecf385ef08dabc 100644
--- a/mlair/data_handler/abstract_data_handler.py
+++ b/mlair/data_handler/abstract_data_handler.py
@@ -3,8 +3,12 @@ __author__ = 'Lukas Leufen'
__date__ = '2020-09-21'
import inspect
+import logging
from typing import Union, Dict
+import dask
+from dask.diagnostics import ProgressBar
+
from mlair.helpers import remove_items
@@ -84,3 +88,13 @@ class AbstractDataHandler:
def _hash_list(self):
return []
+
+ @staticmethod
+ def _force_dask_computation(data):
+ try:
+ with ProgressBar():
+ logging.info(f"DefaultDH: _force_dask_computation")
+ data = dask.compute(data)[0]
+ except:
+ logging.info("can't execute dask.compute")
+ return data
diff --git a/mlair/data_handler/data_handler_wrf_chem.py b/mlair/data_handler/data_handler_wrf_chem.py
index 8188ddf39e96b769e9d47ada881bfb10ceaff438..f191d6d0d40eea7cd7165f969474da17f910688e 100644
--- a/mlair/data_handler/data_handler_wrf_chem.py
+++ b/mlair/data_handler/data_handler_wrf_chem.py
@@ -8,6 +8,14 @@ import itertools
import glob
import matplotlib.pyplot as plt
from dask.diagnostics import ProgressBar
+from tzwhere import tzwhere
+import datetime as dt
+from toarstats import toarstats
+
+import hashlib
+from functools import reduce, partial
+import dill
+
import dask
import inspect
@@ -16,6 +24,7 @@ import gc
from mlair.helpers.geofunctions import haversine_dist, bearing_angle, WindSector, VectorRotateLambertConformal2latlon
from mlair.helpers.helpers import convert2xrda, remove_items, to_list
from mlair.helpers import TimeTrackingWrapper, TimeTracking
+from mlair.configuration import check_path_and_create
from mlair.data_handler.data_handler_single_station import DataHandlerSingleStation
@@ -69,7 +78,6 @@ class BaseWrfChemDataLoader:
vars_to_rotate: Tuple[Tuple[Tuple[str, str], Tuple[str, str]]] = DEFAULT_VARS_TO_ROTATE,
staged_dimension_mapping=None, stag_ending='_stag',
date_format_of_nc_file=None,
-
):
"""
Initialisze data loader
@@ -166,7 +174,7 @@ class BaseWrfChemDataLoader:
"""
if (self.start_time is None) and (self.end_time is None):
path_list = os.path.join(self.data_path, self.common_file_starter + '*')
- logging.info(f"Reading file(s): {path_list}")
+ #logging.info(f"Reading file(s): {path_list}")
return path_list
elif (self.start_time is not None) and (self.end_time is not None):
path_list = set()
@@ -176,7 +184,7 @@ class BaseWrfChemDataLoader:
self.date_format_of_nc_file) + '*'
))[0])
path_list = sorted(list(path_list))
- logging.info(f"Reading file(s): {path_list}")
+ #logging.info(f"Reading file(s): {path_list}")
return path_list
else:
raise ValueError(f"`start_time' and `end_time' must both be given or None.")
@@ -192,10 +200,16 @@ class BaseWrfChemDataLoader:
# see also https://github.com/pydata/xarray/issues/1385#issuecomment-438870575
data = xr.open_mfdataset(paths=self.dataset_search_str, combine='nested', concat_dim=self.time_dim_name,
parallel=True, decode_cf=False)
+ data = xr.decode_cf(data)
+ logging.info(f"Reading file(s): {self.dataset_search_str}")
else:
data = xr.open_mfdataset(paths=self.dataset_search_str, combine='nested', concat_dim=self.time_dim_name,
- parallel=True, decode_cf=False, preprocess=self.preprocess_fkt_for_loader, )
- data = xr.decode_cf(data)
+
+ parallel=True, preprocess=self.preprocess_fkt_for_loader,
+ #decode_cf=False,
+ )
+ logging.info(f"Reading file(s): {self.dataset_search_str}")
+ #data = xr.decode_cf(data)
self._data = data
def preprocess_fkt_for_loader(self, ds):
@@ -394,12 +408,20 @@ class SingleGridColumnWrfChemDataLoader(BaseWrfChemDataLoader):
DEFAULT_ITER_DIM = "points"
DEFAULT_WINDOW_DIM = "window"
+ _hash = ["data_path", "external_coords_file", "time_dim_name",
+ "rechunk_values", "variables", "z_coord_selector", "date_format_of_nc_file",
+ "wind_sectors", "wind_sector_edge_dim_name", "statistics_per_var",
+ "aggregation_sampling", "time_zone",
+ ]
+
def __init__(self, coords: Tuple[float_np_xr, float_np_xr],
target_dim=DEFAULT_TARGET_DIM, target_var=DEFAULT_TARGET_VAR,
window_history_size=DEFAULT_WINDOW_HISTORY_SIZE,
window_lead_time=DEFAULT_WINDOW_LEAD_TIME,
external_coords_file: str = None,
- wind_sectors=None, wind_sector_edge_dim_name=None, **kwargs):
+ wind_sectors=None, wind_sector_edge_dim_name=None, statistics_per_var: Dict = None,
+ aggregation_sampling: str = None, time_zone: str = None, target_time_type: str = None,
+ station: str = None, lazy_preprocessing: bool = False, **kwargs):
"""
@@ -417,6 +439,10 @@ class SingleGridColumnWrfChemDataLoader(BaseWrfChemDataLoader):
:type external_coords_file:
:param wind_sectors:
:type wind_sectors:
+ :param statistics_per_var: Dict containing the (TOAR-) statistics as value for each variable (key)
+ :param aggregation_sampling: sampling period for statistics (e.g. "daily", "seasonal" etc. See torstats for more details)
+ :param time_zone:
+ :param: station
:param kwargs:
:type kwargs:
"""
@@ -431,6 +457,18 @@ class SingleGridColumnWrfChemDataLoader(BaseWrfChemDataLoader):
self.external_coords_file = external_coords_file
self.wind_sectors = wind_sectors
self.wind_sector_edge_dim_name = wind_sector_edge_dim_name
+ self.statistics_per_var = statistics_per_var
+ self.aggregation_sampling = aggregation_sampling
+ self.time_zone = time_zone
+ self.target_time_type = target_time_type
+ self.station = station
+
+ self.lazy = lazy_preprocessing
+ self.lazy_path = None
+ if self.lazy is True:
+ self.lazy_path = os.path.join(self.data_path, "lazy_data", self.__class__.__name__)
+ check_path_and_create(self.lazy_path)
+
logging.debug("SingleGridColumnWrfChemDataLoader Initialised")
@@ -450,11 +488,62 @@ class SingleGridColumnWrfChemDataLoader(BaseWrfChemDataLoader):
self._set_dims_as_coords()
if self.external_coords_file is not None:
self._apply_external_coordinates()
+
self.apply_staged_transormation()
- # self.rechunk_data(self.rechunk_values)
self._set_geoinfos()
+
+ if self.lazy is False:
+ self.reset_data_by_other(self.apply_toarstats())
+ else:
+ self.load_lazy()
+ self.store_lazy()
+
+ # self.rechunk_data(self.rechunk_values)
return self
+ def reset_data_by_other(self, other: xr.Dataset):
+ attrs = self._data.attrs
+ self._data = other
+ # for var in other:
+ # self._data[var] = other[var]
+
+ def store_lazy(self):
+ hash = self._get_hash()
+ filename = os.path.join(self.lazy_path, hash + ".nc")
+ if not os.path.exists(filename):
+ self._data.to_netcdf(filename, format="NETCDF4", engine="netcdf4")
+ logging.info(f"{self.station[0]}: store lazy data: {filename}")
+ # dill.dump(self._create_lazy_data(), file=open(filename, "wb"))
+
+ # def _create_lazy_data(self):
+ # return [self._data ]#, self.meta, self.input_data, self.target_data]
+
+ def load_lazy(self):
+ hash = self._get_hash()
+ filename = os.path.join(self.lazy_path, hash + ".nc")
+ try:
+ self._data = xr.open_mfdataset(filename)
+ logging.info(f"{self.station[0]}: used lazy data: {filename}")
+ except FileNotFoundError:
+ logging.debug(f"{self.station[0]}: could not use lazy data")
+ self.reset_data_by_other(self.apply_toarstats())
+ except OSError:
+ logging.debug(f"{self.station[0]}: could not use lazy data")
+ self.reset_data_by_other(self.apply_toarstats())
+
+ def _extract_lazy(self, lazy_data):
+ # _data, self.meta, _input_data, _target_data = lazy_data
+ # f_prep = partial(self._slice_prep, start=self.start, end=self.end)
+ self._data = lazy_data[0]
+ # self._data, self.input_data, self.target_data = list(map(f_prep, [_data, _input_data, _target_data]))
+
+ def _hash_list(self):
+ return sorted(list(set(self._hash)))
+
+ def _get_hash(self):
+ hash = "".join([str(self.__getattribute__(e)) for e in self._hash_list()]).encode()
+ return hashlib.md5(hash).hexdigest()
+
def __exit__(self, exc_type, exc_val, exc_tb):
self.data.close()
gc.collect()
@@ -599,6 +688,121 @@ class SingleGridColumnWrfChemDataLoader(BaseWrfChemDataLoader):
else:
return {k: list(v.values) for k, v in self._nearest_coords.items()}
+ def apply_toarstats(self):
+ dh = self.prepare_and_apply_toarstats(self.data, target_sampling=self.aggregation_sampling)
+ return dh
+
+ @staticmethod
+ def _toarstats_wrapper(data, sampling, statistics, metadata, seasons=None, crops=None,
+ data_capture=None):
+
+ return toarstats(sampling, statistics, data, metadata, seasons, crops, data_capture)
+
+
+ @TimeTrackingWrapper
+ def prepare_and_apply_toarstats(self, data, target_sampling="daily"):
+ meta = pd.DataFrame({self.physical_x_coord_name: self.get_coordinates()['lon'].tolist(),
+ self.physical_y_coord_name: self.get_coordinates()['lat'].tolist()},
+ index=self.station)
+ if self.target_time_type is None:
+ target_time_type_or_zone = self.get_local_time_zone_from_lat_lon(meta=meta)
+ else:
+ target_time_type_or_zone = self.target_time_type
+ collector = []
+ for var in self.statistics_per_var.keys():
+ collector.append(self.__toarstats_aggregation(data, target_time_type_or_zone, meta, target_sampling, var))
+ sampling_data = xr.merge(collector).dropna(self.physical_t_coord_name)
+ # sampling_data.attrs = data.attrs
+ missing_squeezed_coords = data.coords._names - sampling_data.coords._names
+ for coord in missing_squeezed_coords:
+ sampling_data.coords[coord] = data.coords[coord]
+
+ return sampling_data
+
+ def __toarstats_aggregation(self, data, target_time_type_or_zone, meta, target_sampling, var):
+ with TimeTracking(name=f"{self.station}: apply toarstats `{self.statistics_per_var[var]}({var})`"):
+ spatial_dims = list(remove_items(data[var].dims, self.physical_t_coord_name))
+ try:
+ df = data[var].to_dataframe()[[var]].reset_index(level=spatial_dims)
+ except Exception as e:
+ logging.info(f"can't reset_index in __toarstats_aggregation: {e}")
+ df = data[var].to_dataframe()[[var]].reset_index(level=spatial_dims)
+
+ df = df[[var] + spatial_dims]
+ df = self.set_external_time_zone_and_convert_to_target_time(df, target_time_type_or_zone)
+ df = df.groupby(spatial_dims)
+ df = df.apply(self._toarstats_wrapper, sampling=target_sampling, statistics=self.statistics_per_var[var],
+ metadata=(meta[self.physical_y_coord_name], meta[self.physical_x_coord_name]))
+ df.columns = [var]
+ df.index.set_names(df.index.names[:len(spatial_dims)] + [self.physical_t_coord_name], inplace=True)
+ # df = df.to_xarray().to_array(self.target_dim)
+ df = df.to_xarray()
+ df = df.chunk({self.logical_x_coord_name: -1})
+ # collector.append(df)
+ return df
+
+ def set_external_time_zone_and_convert_to_target_time(self, data, target_time_type_or_zone):
+ """
+
+ :param data:
+ :type data:
+ :param target_time_type_or_zone:
+ :type target_time_type_or_zone:
+ :return:
+ :rtype:
+ """
+ hdata = data
+ # hdata = data.squeeze().to_pandas()
+ if target_time_type_or_zone == "solar_time":
+ lon = self.get_coordinates()['lon']
+ toffset = dt.timedelta(hours=np.floor(lon/15.))
+ hdata.index = hdata.index + toffset
+ else:
+ hdata.index = self.set_time_zone(hdata.index)
+ hdata.index = hdata.index.tz_convert(target_time_type_or_zone)
+ logging.debug(f"Set local time zone to: {target_time_type_or_zone}")
+ return hdata
+
+ def get_local_time_zone_from_lat_lon(self, lat=None, lon=None, meta=None):
+ """
+ Retuns name of time zone for given lat lon coordinates.
+
+ Method also accepts a meta data pd.DataFrame where lat and lon are extracted with the loader's physical x and y
+ coord names
+
+ :param lat:
+ :type lat:
+ :param lon:
+ :type lon:
+ :param meta:
+ :type meta: pd.DataFrame
+ :return:
+ :rtype: str
+ """
+ if (lat is None and lon is None) and (meta is not None):
+ lat = meta[self.physical_y_coord_name]
+ lon = meta[self.physical_x_coord_name]
+
+ tz_where = tzwhere.tzwhere()
+ local_time_zone = tz_where.tzNameAt(latitude=lat, longitude=lon)
+ logging.debug(f"Detect local time zone '{local_time_zone}' based on lat={lat}, lon={lon}")
+ return local_time_zone
+
+ def set_time_zone(self, time_index):
+ """
+ Sets time zone information on a given index
+
+ :param time_index:
+ :type time_index:
+ :return:
+ :rtype:
+ """
+
+ dti = pd.to_datetime(time_index)
+ dti = dti.tz_localize(self.time_zone)
+ logging.debug(f"Set external time zone for {self.station} to: {self.time_zone}")
+ return dti
+
class DataHandlerSingleGridColumn(DataHandlerSingleStation):
_requirements = remove_items(inspect.getfullargspec(DataHandlerSingleStation).args, ["self", "station"])
@@ -609,6 +813,9 @@ class DataHandlerSingleGridColumn(DataHandlerSingleStation):
rechunk_values=None,
date_format_of_nc_file=None,
as_image_like_data_format=True,
+ time_zone=None,
+ target_time_type=None,
+ input_output_sampling4toarstats : tuple = None,
**kwargs):
self.external_coords_file = external_coords_file
self.var_logical_z_coord_selector = self._return_z_coord_select_if_valid(var_logical_z_coord_selector,
@@ -622,6 +829,10 @@ class DataHandlerSingleGridColumn(DataHandlerSingleStation):
self.rechunk_values = rechunk_values
self.date_format_of_nc_file = date_format_of_nc_file
self.as_image_like_data_format = as_image_like_data_format
+
+ self.time_zone = time_zone
+ self.target_time_type = target_time_type
+ self.input_output_sampling4toarstats = input_output_sampling4toarstats
super().__init__(*args, **kwargs)
@staticmethod
@@ -702,7 +913,13 @@ class DataHandlerSingleGridColumn(DataHandlerSingleStation):
# end_time=self.end,
date_format_of_nc_file=self.date_format_of_nc_file,
wind_sectors=helper_wind_sectors,
- wind_sector_edge_dim_name=helper_wind_sector_edge_dim_name ,
+ wind_sector_edge_dim_name=helper_wind_sector_edge_dim_name,
+ statistics_per_var=self.statistics_per_var,
+ aggregation_sampling=self.input_output_sampling4toarstats[1],
+ time_zone=self.time_zone,
+ target_time_type=self.target_time_type,
+ station=self.station,
+ lazy_preprocessing=True,
)
self.__loader = loader
@@ -722,18 +939,107 @@ class DataHandlerSingleGridColumn(DataHandlerSingleStation):
logging.info(f"start compute data for {self.station} in load_data")
data = dask.compute(data)[0]
+ # set time zone to attrs
+ data.attrs["time_zone"] = self.time_zone
+
# expand dimesion for iterdim
data = data.expand_dims({self.iter_dim: station}).to_array(self.target_dim)
# transpose dataarray: set first three fixed and keep remaining as is
data = data.transpose(self.iter_dim, self.time_dim, self.target_dim, ...)
- data = self._slice_prep(data, start=start, end=end)
+
+
# ToDo add metadata
- _meta = {'station_lon': self.loader.get_coordinates()['lon'].tolist(),
- 'station_lat': self.loader.get_coordinates()['lat'].tolist()}
- meta = pd.DataFrame(_meta, index=station).T
+ _meta = {self.loader.physical_x_coord_name: self.loader.get_coordinates()['lon'].tolist(),
+ self.loader.physical_y_coord_name: self.loader.get_coordinates()['lat'].tolist()}
+ meta = pd.DataFrame(_meta, index=station)
+
+ # if isinstance(self.input_output_sampling4toarstats, tuple) and len(self.input_output_sampling4toarstats) == 2:
+ # if self.var_logical_z_coord_selector != 0:
+ # raise NotImplementedError(
+ # f"Method `apply_toarstats` is not implemented for var_logical_z_coord_selector != 0: "
+ # f"Is {self.var_logical_z_coord_selector}")
+ # data = self.prepare_and_apply_toarstats(data, meta, self.input_output_sampling4toarstats[1])
+ data = self._slice_prep(data, start=start, end=end)
return data, meta
+ # @TimeTrackingWrapper
+ # def prepare_and_apply_toarstats(self, data, meta, target_sampling="daily"):
+ # local_time_zone = self.get_local_time_zone_from_lat_lon(meta=meta)
+ # hdata = self.set_external_time_zone_and_convert_to_target_time(data, local_time_zone)
+ # hsampling_data = []
+ # for i, var in enumerate(hdata.columns):
+ # hdf = toarstats(target_sampling, self.statistics_per_var[var], hdata[var],
+ # (meta[self.loader.physical_y_coord_name], meta[self.loader.physical_x_coord_name]))
+ # hsampling_data.append(xr.DataArray(hdf, coords=[hdf.index, [var]],
+ # dims=[self.loader.physical_t_coord_name, self.target_dim]))
+ # sampling_data = xr.concat(hsampling_data, dim=self.target_dim)
+ # sampling_data = sampling_data.broadcast_like(data, exclude=self.loader.physical_t_coord_name).dropna(
+ # self.loader.physical_t_coord_name)
+ # sampling_data.attrs = data.attrs
+ # missing_squeezed_coords = data.coords._names - sampling_data.coords._names
+ # for coord in missing_squeezed_coords:
+ # sampling_data.coords[coord] = data.coords[coord]
+ #
+ # return self._force_dask_computation(sampling_data)
+ #
+ # def set_external_time_zone_and_convert_to_target_time(self, data, local_time_zone):
+ # """
+ #
+ # :param data:
+ # :type data:
+ # :param local_time_zone:
+ # :type local_time_zone:
+ # :return:
+ # :rtype:
+ # """
+ # hdata = data.squeeze().to_pandas()
+ # hdata.index = self.set_time_zone(hdata.index)
+ # hdata.index = hdata.index.tz_convert(local_time_zone)
+ # logging.debug(f"Set local time zone for {self.station} to: {local_time_zone}")
+ # return hdata
+ #
+ # def get_local_time_zone_from_lat_lon(self, lat=None, lon=None, meta=None):
+ # """
+ # Retuns name of time zone for given lat lon coordinates.
+ #
+ # Method also accepts a meta data pd.DataFrame where lat and lon are extracted with the loader's physical x and y
+ # coord names
+ #
+ # :param lat:
+ # :type lat:
+ # :param lon:
+ # :type lon:
+ # :param meta:
+ # :type meta: pd.DataFrame
+ # :return:
+ # :rtype: str
+ # """
+ # if (lat is None and lon is None) and (meta is not None):
+ # lat = meta[self.loader.physical_y_coord_name]
+ # lon = meta[self.loader.physical_x_coord_name]
+ #
+ #
+ # tz_where = tzwhere.tzwhere()
+ # local_time_zone = tz_where.tzNameAt(latitude=lat, longitude=lon)
+ # logging.debug(f"Detect local time zone '{local_time_zone}' based on lat={lat}, lon={lon}")
+ # return local_time_zone
+ #
+ # def set_time_zone(self, time_index):
+ # """
+ # Sets time zone information on a given index
+ #
+ # :param time_index:
+ # :type time_index:
+ # :return:
+ # :rtype:
+ # """
+ #
+ # dti = pd.to_datetime(time_index)
+ # dti = dti.tz_localize(self.time_zone)
+ # logging.debug(f"Set external time zone for {self.station} to: {self.time_zone}")
+ # return dti
+
@staticmethod
def _extract_largest_coord_extractor(var_extarctor, target_extractor) -> Union[List, None]:
if var_extarctor is not None and target_extractor is not None:
@@ -827,9 +1133,8 @@ class DataHandlerSingleGridColumn(DataHandlerSingleStation):
"""
return all(self._transformation)
- @staticmethod
- def interpolate(data, dim: str, method: str = 'linear', limit: int = None, use_coordinate: Union[bool, str] = True,
- **kwargs):
+ def interpolate(self, data, dim: str, method: str = 'linear', limit: int = None,
+ use_coordinate: Union[bool, str] = True, sampling="daily", **kwargs):
"""
Interpolate values according to different methods.
@@ -866,6 +1171,7 @@ class DataHandlerSingleGridColumn(DataHandlerSingleStation):
:return: xarray.DataArray
"""
+ # data = self.create_full_time_dim(data, dim, sampling)
return data.interpolate_na(dim=dim, method=method, limit=limit, use_coordinate=use_coordinate, **kwargs)
diff --git a/mlair/data_handler/default_data_handler.py b/mlair/data_handler/default_data_handler.py
index 188283703cb55481c6e21524077663f8ae2a9f17..1b4715a05d7e072add90137b89f7944299f449f5 100644
--- a/mlair/data_handler/default_data_handler.py
+++ b/mlair/data_handler/default_data_handler.py
@@ -7,15 +7,12 @@ import inspect
import gc
import logging
import os
-import pickle
import dill
import shutil
from functools import reduce
from typing import Tuple, Union, List
import multiprocessing
import psutil
-import dask
-from dask.diagnostics import ProgressBar
import numpy as np
import xarray as xr
@@ -111,16 +108,6 @@ class DefaultDataHandler(AbstractDataHandler):
attr_dict[attr] = val
return attr_dict
- @staticmethod
- def _force_dask_computation(data):
- try:
- with ProgressBar():
- logging.info(f"DefaultDH: _force_dask_computation")
- data = dask.compute(data)[0]
- except:
- logging.info("can't execute dask.compute")
- return data
-
def _load(self):
try:
with open(self._save_file, "rb") as f:
diff --git a/mlair/external/toarstats b/mlair/external/toarstats
new file mode 160000
index 0000000000000000000000000000000000000000..a3c2020686d300db6ca5a1d0b81128f4740e2b16
--- /dev/null
+++ b/mlair/external/toarstats
@@ -0,0 +1 @@
+Subproject commit a3c2020686d300db6ca5a1d0b81128f4740e2b16
diff --git a/mlair/model_modules/model_class.py b/mlair/model_modules/model_class.py
index 8f464b16cbc74a241f70ba1c5bb9f8fe611dba95..fd43674e009631f4fd7f1c5648f500a0eaff2ec1 100644
--- a/mlair/model_modules/model_class.py
+++ b/mlair/model_modules/model_class.py
@@ -518,7 +518,7 @@ class MyLuongAttentionLSTMModel(AbstractModelClass):
self.initial_lr = 0.01
self.clipnorm = 1
- self.n_hidden = 100
+ self.n_hidden = 32
# apply to model
self.set_model()
diff --git a/mlair/run_modules/pre_processing.py b/mlair/run_modules/pre_processing.py
index e817d2a16664b751396303f3c95313a85841625c..db9e9e940f7675e618e48f41f0cffb4e4d9ac644 100644
--- a/mlair/run_modules/pre_processing.py
+++ b/mlair/run_modules/pre_processing.py
@@ -327,8 +327,8 @@ def f_proc(data_handler, station, name_affix, store, **kwargs):
except (AttributeError, EmptyQueryResult, KeyError, requests.ConnectionError, ValueError, IndexError) as e:
formatted_lines = traceback.format_exc().splitlines()
logging.info(
- f"remove station {station} because it raised an error: {e} -> {' | '.join(f_inspect_error(formatted_lines))}")
- logging.debug(f"detailed information for removal of station {station}: {traceback.format_exc()}")
+ #f"remove station {station} because it raised an error: {e} -> {' | '.join(f_inspect_error(formatted_lines))}")
+ f"remove station {station} because it raised an error: {e} -> {' | '.join(formatted_lines)}")
res = None
return res, station
diff --git a/requirements.txt b/requirements.txt
index dba565fbb535db7d7782baec8690971d4393b3e0..a2ccc2b04bcde470228bc8684f80f33f6a1945e8 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -64,6 +64,7 @@ termcolor==1.1.0
toml==0.10.2
toolz==0.11.1
typing-extensions==3.7.4.3
+tzwhere==3.0.3
urllib3==1.26.3
Werkzeug==1.0.1
wget==3.2
diff --git a/requirements_gpu.txt b/requirements_gpu.txt
index f170e1b7b67df7e17a3258ca849b252acaf3e650..be648ff8f7809ba9e80a0adfc356e7091e2415f7 100644
--- a/requirements_gpu.txt
+++ b/requirements_gpu.txt
@@ -64,6 +64,7 @@ termcolor==1.1.0
toml==0.10.2
toolz==0.11.1
typing-extensions==3.7.4.3
+tzwhere==3.0.3
urllib3==1.26.3
Werkzeug==1.0.1
wget==3.2
diff --git a/run_wrf_dh.py b/run_wrf_dh.py
index b9d4cd02a9e103201d98ff629fa5c2de12410e9d..e769338b254a04de8cad50d29e70f2e9d22953a5 100644
--- a/run_wrf_dh.py
+++ b/run_wrf_dh.py
@@ -8,7 +8,7 @@ from mlair.workflows import DefaultWorkflow
from mlair.helpers import remove_items
from mlair.configuration.defaults import DEFAULT_PLOT_LIST
-from mlair.model_modules.model_class import MyPaperModel, MyLSTMModel, MyCNNModel
+from mlair.model_modules.model_class import IntelliO3TsArchitecture, MyLSTMModel, MyCNNModel, MyLuongAttentionLSTMModel
import os
@@ -33,6 +33,7 @@ def main(parser_args):
evaluate_bootstraps=True, number_of_bootstraps=30, create_new_bootstraps=True, #
plot_list=plots,
model_name_for_plots='SecModel',
+ time_zone="UTC",
# competitors=["test_model", "test_model2"],
# competitor_path=os.path.join(os.getcwd(), "data", "comp_test"),
competitors=["baseline", "sector_baseline"],
@@ -40,10 +41,11 @@ def main(parser_args):
train_min_length=1, val_min_length=1, test_min_length=1,
# data_handler=DataHandlerSingleStation,
# data_handler=DataHandlerSingleGridColumn,
- upsampeling=True,
+ upsampeling=False,
epochs=20,
- window_lead_time=2,
+ window_lead_time=4,
window_history_size=6,
+
# ('Germany', (5.98865807458, 47.3024876979, 15.0169958839, 54.983104153))
stations=["coords__48_8479__10_0963", "coords__51_8376__14_1417",
# "coords__50_7536__7_0827", "coords__51_4070__6_9656",
@@ -65,9 +67,10 @@ def main(parser_args):
# data_path='/home/felix/Data/WRF-Chem/upload_aura_2021-02-24/2009/',
# data_path='/home/felix/Data/WRF-Chem/test_cut_nc/',
# data_path='/home/felix/Data/WRF-Chem/test_cut_nc_joint',
- data_path="/home/felix/Data/WRF-Chem/test_cut_nc_joint/short_test",
+ # data_path="/home/felix/Data/WRF-Chem/test_cut_nc_joint/short_test",
+ # data_path="/media/felix/INTENSO/WRF_CHEM/hourly/cdo_output_test/jan_test",
# data_path = "/p/scratch/deepacf/kleinert1/IASS_proc_monthyl",
- # data_path="/media/felix/INTENSO/WRF_CHEM/JFM_2009",
+ data_path="/media/felix/INTENSO/WRF_CHEM/JFM_2009",
# external data coords
external_coords_file='/home/felix/Data/WRF-Chem/test_cut_nc/coords.nc',
@@ -89,12 +92,18 @@ def main(parser_args):
'no2': {"method": "standardise"},
'co': {"method": "standardise"},
'PSFC': {"method": "standardise"},
- 'CLDFRA': {"method": "min_max"},
+ # 'CLDFRA': {"method": "min_max", "min": 0., "max": 1.},
},
- variables=['T2', 'o3', 'wdir10ll', 'wspd10ll', 'no', 'no2', 'co', 'PSFC', 'PBLH', 'CLDFRA'],
+ # variables=['T2', 'o3', 'wdir10ll', 'wspd10ll', 'no', 'no2', 'co', 'PSFC', 'PBLH', 'CLDFRA'],
+ variables=['T2', 'o3', 'wdir10ll', 'wspd10ll', 'no', 'no2', 'co', 'PSFC', 'PBLH'],
target_var='o3',
- statistics_per_var={'T2': None, 'o3': None, 'wdir10ll': None, 'wspd10ll': None,
- 'no': None, 'no2': None, 'co': None, 'PSFC': None, 'PBLH': None, 'CLDFRA': None, },
+ # statistics_per_var={'T2': None, 'o3': None, 'wdir10ll': None, 'wspd10ll': None,
+ # 'no': None, 'no2': None, 'co': None, 'PSFC': None, 'PBLH': None, 'CLDFRA': None, },
+ statistics_per_var={'T2': "average_values", 'o3': "dma8eu", 'wdir10ll': "average_values",
+ 'wspd10ll': "average_values", 'no': "dma8eu", 'no2': "dma8eu", 'co': "dma8eu",
+ 'PSFC': "average_values", 'PBLH': "average_values",
+ # 'CLDFRA': "average_values",
+ },
# separate_vars=["o3", "o3Sect", "o3SectLeft", "o3SectRight"],
separate_vars=["o3", "o3Sect"],
# variables=['T2', 'Q2', 'PBLH', 'U10ll', 'V10ll', 'wdir10ll', 'wspd10ll'],
@@ -105,46 +114,62 @@ def main(parser_args):
var_logical_z_coord_selector=0,
targetvar_logical_z_coord_selector=0,
aggregation_dim='bottom_top',
- radius=100, # km
+ radius=200, # km
start='2009-01-01',
- end='2009-01-04',
+ # end='2009-01-04',
+
#end='2009-01-31',
- # end='2009-03-31',
+ end='2009-03-31',
train_start='2009-01-01',
- train_end='2009-01-02',
+ # train_end='2009-01-02',
# train_start='2009-01-01',
#train_end='2009-01-15',
- # train_end='2009-02-28',
+ # train_end='2009-02-15',
+ # train_end="2009-01-31",
+ train_end='2009-02-28',
- val_start='2009-01-02',
- val_end='2009-01-03',
+ # val_start='2009-01-02',
+ # val_end='2009-01-03',
###################################
#val_start='2009-01-15',
#val_end='2009-01-22',
###################################
- # val_start='2009-03-01',
- # val_end='2009-03-14',
+ val_start='2009-03-01',
+ val_end='2009-03-14',
+ ###################################
+ # val_start='2009-02-15',
+ # val_end='2009-03-02',
+ ###################################
+ # val_start='2009-02-01',
+ # val_end='2009-02-28',
+
+ # test_start='2009-01-03',
+ # test_end='2009-01-04',
+ ###################################
+ # test_start='2009-01-22',
+ # test_end='2009-01-31',
- test_start='2009-01-03',
- test_end='2009-01-04',
###################################
- #test_start='2009-01-22',
- #test_end='2009-01-31',
+ test_start='2009-03-15',
+ test_end='2009-03-31',
###################################
- # test_start='2009-03-15',
+ # test_start='2009-03-02',
# test_end='2009-03-31',
- sampling='hourly',
+ # sampling='hourly',
+ sampling="daily",
+ input_output_sampling4toarstats=("hourly", "daily"),
interpolation_limit=0,
- # as_image_like_data_format=False,
+ as_image_like_data_format=False,
# model=MyLSTMModel,
+ model=MyLuongAttentionLSTMModel,
use_multiprocessing=True,
# as_image_like_data_format=True,
# model=MyLSTMModel,
- model=MyCNNModel,
+ # model=MyCNNModel,
**parser_args.__dict__)
workflow.run()
diff --git a/run_wrf_dh_sector.py b/run_wrf_dh_sector.py
index d77bfd74653b0183402dc3d9180badf6548129d7..7c7fd3c8b06f9568b17124252879ee39b80feaeb 100644
--- a/run_wrf_dh_sector.py
+++ b/run_wrf_dh_sector.py
@@ -7,7 +7,8 @@ from mlair.data_handler.data_handler_wrf_chem import DataHandlerWRF, DataHandler
from mlair.workflows import DefaultWorkflow
from mlair.helpers import remove_items
from mlair.configuration.defaults import DEFAULT_PLOT_LIST
-from mlair.model_modules.model_class import MyPaperModel, MyLSTMModel, MyCNNModel, MyCNNModelSect
+
+from mlair.model_modules.model_class import IntelliO3TsArchitecture, MyLSTMModel, MyCNNModel, MyCNNModelSect
import os
@@ -37,13 +38,13 @@ def main(parser_args):
train_min_length=1, val_min_length=1, test_min_length=1,
# data_handler=DataHandlerSingleStation,
# data_handler=DataHandlerSingleGridColumn,
- epochs=1000,
- window_lead_time=2,
+ epochs=10,
+ window_lead_time=4,
window_history_size=6,
stations=["coords__48_8479__10_0963", "coords__51_8376__14_1417",
- "coords__50_7536__7_0827", "coords__51_4070__6_9656",
- "coords__49_8421__7_8662", "coords__49_7410__7_1935",
- "coords__51_1566__11_8182", "coords__51_4065__6_9660",
+ # "coords__50_7536__7_0827", "coords__51_4070__6_9656",
+ # "coords__49_8421__7_8662", "coords__49_7410__7_1935",
+ # "coords__51_1566__11_8182", "coords__51_4065__6_9660",
# "coords__50_7333__7_1000", "coords__50_0000__8_0000",
# "coords__48_7444__7_6000", "coords__51_0000__11_0000",
# "coords__52_7555__8_1000", "coords__50_0000__2_0000",
@@ -52,14 +53,16 @@ def main(parser_args):
],
# data_handler=DataHandlerWRF,
data_handler=DataHandlerMainSectWRF, #,
- data_path="/p/scratch/deepacf/kleinert1/IASS_proc_monthyl",
+ # data_path="/p/scratch/deepacf/kleinert1/IASS_proc_monthyl",
#data_path="/p/scratch/deepacf/kleinert1/IASS_proc",
#data_path="/p/project/deepacf/intelliaq/kleinert1/DATA/WRF_CHEM_soft_ln_small_test",
+ data_path="/media/felix/INTENSO/WRF_CHEM/hourly/cdo_output_test/jan_test",
common_file_starter="wrfout_d01",
date_format_of_nc_file="%Y-%m",
time_dim='XTIME',
#external_coords_file='/p/project/deepacf/inbound_data/IASS_upload/coords.nc',
- external_coords_file="/p/scratch/deepacf/kleinert1/IASS_proc/coords.nc",
+ # external_coords_file="/p/scratch/deepacf/kleinert1/IASS_proc/coords.nc",
+ external_coords_file="/media/felix/INTENSO/WRF_CHEM/monthly/coords.nc",
transformation={
"T2": {"method": "standardise"},
"Q2": {"method": "standardise"},
@@ -72,12 +75,18 @@ def main(parser_args):
'no2': {"method": "standardise"},
'co': {"method": "standardise"},
'PSFC': {"method": "standardise"},
- 'CLDFRA': {"method": "min_max"},
+ # 'CLDFRA': {"method": "min_max", "min": 0., "max": 1.},
},
- variables=['T2', 'o3', 'wdir10ll', 'wspd10ll', 'no', 'no2', 'co', 'PSFC', 'PBLH', 'CLDFRA'],
+ # variables=['T2', 'o3', 'wdir10ll', 'wspd10ll', 'no', 'no2', 'co', 'PSFC', 'PBLH', 'CLDFRA'],
+ variables=['T2', 'o3', 'wdir10ll', 'wspd10ll', 'no', 'no2', 'co', 'PSFC', 'PBLH'],
target_var='o3',
- statistics_per_var={'T2': None, 'o3': None, 'wdir10ll': None, 'wspd10ll': None,
- 'no': None, 'no2': None, 'co': None, 'PSFC': None, 'PBLH': None, 'CLDFRA': None, },
+ # statistics_per_var={'T2': None, 'o3': None, 'wdir10ll': None, 'wspd10ll': None,
+ # 'no': None, 'no2': None, 'co': None, 'PSFC': None, 'PBLH': None, 'CLDFRA': None, },
+ statistics_per_var={'T2': "average_values", 'o3': "dma8eu", 'wdir10ll': "average_values",
+ 'wspd10ll': "average_values", 'no': "dma8eu", 'no2': "dma8eu", 'co': "dma8eu",
+ 'PSFC': "average_values", 'PBLH': "average_values",
+ # 'CLDFRA': "average_values",
+ },
# variables=['T2', 'Q2', 'PBLH', 'U10ll', 'V10ll', 'wdir10ll', 'wspd10ll'],
# target_var=["T2"],
# statistics_per_var={'T2': None, 'Q2': None, 'PBLH': None,
@@ -87,44 +96,47 @@ def main(parser_args):
targetvar_logical_z_coord_selector=0,
aggregation_dim='bottom_top',
- radius=100, # km
+ radius=200, # km
start='2009-01-01',
- #end='2009-01-04',
- #end='2009-01-31',
- end='2009-03-31',
+ # end='2009-01-04',
+ end='2009-01-31',
+ # end='2009-03-31',
#train_start='2009-01-01',
#train_end='2009-01-02',
train_start='2009-01-01',
- #train_end='2009-01-15',
- train_end='2009-02-28',
+ train_end='2009-01-15',
+ # train_end='2009-02-28',
#val_start='2009-01-02',
#val_end='2009-01-03',
###################################
- #val_start='2009-01-15',
- #val_end='2009-01-22',
+ val_start='2009-01-15',
+ val_end='2009-01-22',
###################################
- val_start='2009-03-01',
- val_end='2009-03-14',
+ # val_start='2009-03-01',
+ # val_end='2009-03-14',
#test_start='2009-01-03',
#test_end='2009-01-04',
###################################
- #test_start='2009-01-22',
- #test_end='2009-01-31',
+ test_start='2009-01-22',
+ test_end='2009-01-31',
###################################
- test_start='2009-03-15',
- test_end='2009-03-31',
+ # test_start='2009-03-15',
+ # test_end='2009-03-31',
- sampling='hourly',
+ # sampling='hourly',
+ sampling="daily",
+ input_output_sampling4toarstats=("hourly", "daily"),
use_multiprocessing=True,
interpolation_limit=0,
# as_image_like_data_format=False,
# model=MyLSTMModel,
- model=MyCNNModelSect,
+# model=MyCNNModelSect,
+ model=MyCNNModel,
**parser_args.__dict__)
workflow.run()
diff --git a/supplement/WRF_coord_list_from_IntelliO3.json b/supplement/WRF_coord_list_from_IntelliO3.json
new file mode 100644
index 0000000000000000000000000000000000000000..00f83a5a56cf468f82fb21d85d37b33290691e9c
--- /dev/null
+++ b/supplement/WRF_coord_list_from_IntelliO3.json
@@ -0,0 +1 @@
+["coords__52_598476__11_864362", "coords__51_33308__10_867189", "coords__49_515053__8_402374", "coords__53_24696__10_4565", "coords__49_240677__9_446661", "coords__52_489451__13_430844", "coords__49_741035__7_193486", "coords__54_527298__9_549379", "coords__53_715302__7_21398", "coords__48_472086__8_412808", "coords__47_508499__11_142889", "coords__47_84687__11_16085", "coords__54_309673__10_116564", "coords__53_562454__8_569406", "coords__50_791618__11_661233", "coords__49_872299__8_664619", "coords__47_915565__9_754411", "coords__48_972401__13_128934", "coords__52_08918__13_171803", "coords__47_693474__10_034748", "coords__52_362919__9_70612", "coords__51_536911__7_457464", "coords__48_173195__11_648036", "coords__51_83728__11_891525", "coords__53_315823__13_870311", "coords__53_077148__8_815781", "coords__48_001511__7_832122", "coords__51_462734__13_526797", "coords__51_897598__14_057064", "coords__50_497711__9_935862", "coords__50_32061__11_897491", "coords__48_169689__8_619859", "coords__50_10215__8_761242", "coords__52_82943__9_622951", "coords__48_708653__9_414453", "coords__47_777027__9_608422", "coords__51_483192__11_978358", "coords__48_773072__8_220214", "coords__47_518013__10_404233", "coords__51_592201__7_569839", "coords__48_524399__9_058547", "coords__49_632389__7_002141", "coords__54_07312__9_791584", "coords__52_541363__14_057456", "coords__48_663841__10_156297", "coords__52_684196__13_583772", "coords__49_107878__9_733231", "coords__51_182625__6_693875", "coords__48_899233__9_172792", "coords__48_72739__9_334039", "coords__51_103531__6_804103", "coords__51_551067__9_949761", "coords__50_561752__10_3753", "coords__52_348282__13_048529", "coords__48_154533__11_554669", "coords__53_835491__10_695936", "coords__50_587589__8_670028", "coords__51_57066__8_148061", "coords__48_02166__12_538176", "coords__51_855583__13_706503", "coords__54_186714__7_876328", "coords__48_476569__7_937806", "coords__47_898846__11_1112", "coords__51_798626__10_618275", "coords__49_842133__7_866208", "coords__48_710049__8_741153", "coords__51_524265__12_925879", "coords__53_868069__10_901677", "coords__47_58815__7_633272", "coords__51_485287__12_004464", "coords__48_890575__8_705113", "coords__53_630737__10_110595", "coords__51_897018__11_057462", "coords__51_403008__7_208572", "coords__47_985657__8_807748", "coords__52_484165__13_123697", "coords__48_645608__8_904442", "coords__51_453461__6_865108", "coords__53_1413__13_03166", "coords__51_740765__14_322827", "coords__51_406967__6_965641", "coords__51_175671__14_443772", "coords__51_054798__13_728719", "coords__54_43667__12_72528", "coords__53_362347__7_207261", "coords__51_862__6_874553", "coords__51_051247__13_729886", "coords__48_507703__9_051178", "coords__52_339951__14_531917", "coords__50_499954__11_134591", "coords__51_50758__10_238539", "coords__51_183773__7_052633", "coords__48_81707__13_2181", "coords__50_010323__8_451528", "coords__50_754704__6_093923", "coords__52_407719__12_157347", "coords__49_605911__10_963528", "coords__49_4645__8_754019", "coords__50_826__12_542056", "coords__51_156605__11_818216", "coords__50_828465__13_586547", "coords__50_619179__10_787137", "coords__50_017078__8_216497", "coords__52_653269__13_296081", "coords__52_609566__12_876509", "coords__48_562061__7_827066", "coords__51_168083__11_120728", "coords__49_237225__7_036322", "coords__51_657169__11_147792", "coords__53_030094__13_990561", "coords__52_59317__11_17235", "coords__48_345778__9_207639", "coords__53_060043__8_916967", "coords__53_568123__9_786234", "coords__51_448761__7_582294", "coords__52_420444__12_552303", "coords__48_853733__8_225808", "coords__51_547493__7_693836", "coords__50_532963__8_684398", "coords__52_268368__11_864795", "coords__51_93655__7_61158", "coords__51_096863__8_772858", "coords__50_655861__12_2052", "coords__50_933514__11_597061", "coords__51_406544__6_965991", "coords__52_194225__12_561389", "coords__47_723186__12_858856", "coords__51_249214__6_732383", 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