diff --git a/mlair/data_handler/data_handler_mixed_sampling.py b/mlair/data_handler/data_handler_mixed_sampling.py
index 8633c647a33ea3c310d318c2629178fbbe99f7f0..908ae4095e694a1ff128b51a5c58a3d49567a7bb 100644
--- a/mlair/data_handler/data_handler_mixed_sampling.py
+++ b/mlair/data_handler/data_handler_mixed_sampling.py
@@ -244,11 +244,11 @@ class DataHandlerMixedSamplingWithClimateFirFilter(DataHandlerClimateFirFilter):
def build(cls, station: str, **kwargs):
sp_keys = {k: copy.deepcopy(kwargs[k]) for k in cls.data_handler.requirements() if k in kwargs}
filter_add_unfiltered = kwargs.get("filter_add_unfiltered", False)
- sp_keys = cls.build_update_kwargs(sp_keys, dh_type="filtered")
+ sp_keys = cls.build_update_transformation(sp_keys, dh_type="filtered")
sp = cls.data_handler(station, **sp_keys)
if filter_add_unfiltered is True:
sp_keys = {k: copy.deepcopy(kwargs[k]) for k in cls.data_handler_unfiltered.requirements() if k in kwargs}
- sp_keys = cls.build_update_kwargs(sp_keys, dh_type="unfiltered")
+ sp_keys = cls.build_update_transformation(sp_keys, dh_type="unfiltered")
sp_unfiltered = cls.data_handler_unfiltered(station, **sp_keys)
else:
sp_unfiltered = None
@@ -256,7 +256,7 @@ class DataHandlerMixedSamplingWithClimateFirFilter(DataHandlerClimateFirFilter):
return cls(sp, data_handler_class_unfiltered=sp_unfiltered, **dp_args)
@classmethod
- def build_update_kwargs(cls, kwargs_dict, dh_type="filtered"):
+ def build_update_transformation(cls, kwargs_dict, dh_type="filtered"):
if "transformation" in kwargs_dict:
trafo_opts = kwargs_dict.get("transformation")
if isinstance(trafo_opts, dict):
@@ -313,6 +313,8 @@ class DataHandlerMixedSamplingWithClimateAndFirFilter(DataHandlerMixedSamplingWi
data_handler_unfiltered = DataHandlerMixedSamplingSingleStation
_requirements = list(set(data_handler_climate_fir.requirements() + data_handler_fir[0].requirements() +
data_handler_fir[1].requirements() + data_handler_unfiltered.requirements()))
+ chem_indicator = "chem"
+ meteo_indicator = "meteo"
def __init__(self, data_handler_class_chem, data_handler_class_meteo, data_handler_class_chem_unfiltered,
data_handler_class_meteo_unfiltered, chem_vars, meteo_vars, *args, **kwargs):
@@ -351,32 +353,32 @@ class DataHandlerMixedSamplingWithClimateAndFirFilter(DataHandlerMixedSamplingWi
if len(chem_vars) > 0:
sp_keys = {k: copy.deepcopy(kwargs[k]) for k in cls.data_handler_climate_fir.requirements() if k in kwargs}
- sp_keys = cls.build_update_kwargs(sp_keys, dh_type="filtered_chem")
+ sp_keys = cls.build_update_transformation(sp_keys, dh_type="filtered_chem")
- cls.prepare_build(sp_keys, chem_vars, "chem")
+ cls.prepare_build(sp_keys, chem_vars, cls.chem_indicator)
sp_chem = cls.data_handler_climate_fir(station, **sp_keys)
if filter_add_unfiltered is True:
sp_keys = {k: copy.deepcopy(kwargs[k]) for k in cls.data_handler_unfiltered.requirements() if k in kwargs}
- sp_keys = cls.build_update_kwargs(sp_keys, dh_type="unfiltered_chem")
- cls.prepare_build(sp_keys, chem_vars, "chem")
+ sp_keys = cls.build_update_transformation(sp_keys, dh_type="unfiltered_chem")
+ cls.prepare_build(sp_keys, chem_vars, cls.chem_indicator)
sp_chem_unfiltered = cls.data_handler_unfiltered(station, **sp_keys)
if len(meteo_vars) > 0:
if cls.data_handler_fir_pos is None:
if "extend_length_opts" in kwargs:
- if isinstance(kwargs["extend_length_opts"], dict) and "meteo" not in kwargs["extend_length_opts"].keys():
+ if isinstance(kwargs["extend_length_opts"], dict) and cls.meteo_indicator not in kwargs["extend_length_opts"].keys():
cls.data_handler_fir_pos = 0 # use faster fir version without climate estimate
else:
cls.data_handler_fir_pos = 1 # use slower fir version with climate estimate
else:
cls.data_handler_fir_pos = 0 # use faster fir version without climate estimate
sp_keys = {k: copy.deepcopy(kwargs[k]) for k in cls.data_handler_fir[cls.data_handler_fir_pos].requirements() if k in kwargs}
- sp_keys = cls.build_update_kwargs(sp_keys, dh_type="filtered_meteo")
- cls.prepare_build(sp_keys, meteo_vars, "meteo")
+ sp_keys = cls.build_update_transformation(sp_keys, dh_type="filtered_meteo")
+ cls.prepare_build(sp_keys, meteo_vars, cls.meteo_indicator)
sp_meteo = cls.data_handler_fir[cls.data_handler_fir_pos](station, **sp_keys)
if filter_add_unfiltered is True:
sp_keys = {k: copy.deepcopy(kwargs[k]) for k in cls.data_handler_unfiltered.requirements() if k in kwargs}
- sp_keys = cls.build_update_kwargs(sp_keys, dh_type="unfiltered_meteo")
- cls.prepare_build(sp_keys, meteo_vars, "meteo")
+ sp_keys = cls.build_update_transformation(sp_keys, dh_type="unfiltered_meteo")
+ cls.prepare_build(sp_keys, meteo_vars, cls.meteo_indicator)
sp_meteo_unfiltered = cls.data_handler_unfiltered(station, **sp_keys)
dp_args = {k: copy.deepcopy(kwargs[k]) for k in cls.own_args("id_class") if k in kwargs}
@@ -385,10 +387,20 @@ class DataHandlerMixedSamplingWithClimateAndFirFilter(DataHandlerMixedSamplingWi
@classmethod
def prepare_build(cls, kwargs, var_list, var_type):
kwargs.update({"variables": var_list})
- cls.adjust_window_opts(var_type, "window_history_size", kwargs)
- cls.adjust_window_opts(var_type, "window_history_offset", kwargs)
- cls.adjust_window_opts(var_type, "window_history_end", kwargs)
- cls.adjust_window_opts(var_type, "extend_length_opts", kwargs)
+ for k in list(kwargs.keys()):
+ v = kwargs[k]
+ if isinstance(v, dict):
+ if len(set(v.keys()).intersection({cls.chem_indicator, cls.meteo_indicator})) > 0:
+ try:
+ new_v = kwargs.pop(k)
+ kwargs[k] = new_v[var_type]
+ except KeyError:
+ pass
+ #
+ # cls.adjust_window_opts(var_type, "window_history_size", kwargs)
+ # cls.adjust_window_opts(var_type, "window_history_offset", kwargs)
+ # cls.adjust_window_opts(var_type, "window_history_end", kwargs)
+ # cls.adjust_window_opts(var_type, "extend_length_opts", kwargs)
@staticmethod
def adjust_window_opts(key: str, parameter_name: str, kwargs: dict):
@@ -420,7 +432,7 @@ class DataHandlerMixedSamplingWithClimateAndFirFilter(DataHandlerMixedSamplingWi
# chem transformation
if len(chem_vars) > 0:
kwargs_chem = copy.deepcopy(kwargs)
- cls.prepare_build(kwargs_chem, chem_vars, "chem")
+ cls.prepare_build(kwargs_chem, chem_vars, cls.chem_indicator)
dh_transformation = (cls.data_handler_climate_fir, cls.data_handler_unfiltered)
transformation_chem = super().transformation(set_stations, tmp_path=tmp_path,
dh_transformation=dh_transformation, **kwargs_chem)
@@ -428,7 +440,7 @@ class DataHandlerMixedSamplingWithClimateAndFirFilter(DataHandlerMixedSamplingWi
# meteo transformation
if len(meteo_vars) > 0:
kwargs_meteo = copy.deepcopy(kwargs)
- cls.prepare_build(kwargs_meteo, meteo_vars, "meteo")
+ cls.prepare_build(kwargs_meteo, meteo_vars, cls.meteo_indicator)
dh_transformation = (cls.data_handler_fir[cls.data_handler_fir_pos or 0], cls.data_handler_unfiltered)
transformation_meteo = super().transformation(set_stations, tmp_path=tmp_path,
dh_transformation=dh_transformation, **kwargs_meteo)
diff --git a/mlair/run_modules/post_processing.py b/mlair/run_modules/post_processing.py
index dfcc9edb15b33d8020c094ca81af5332e01782bc..5f2873a56e4dcfd1d5621f0aad79fb70c8ea263d 100644
--- a/mlair/run_modules/post_processing.py
+++ b/mlair/run_modules/post_processing.py
@@ -184,7 +184,7 @@ class PostProcessing(RunEnvironment):
against the number of observations and diversity ot stations.
"""
path = self.data_store.get("forecast_path")
- all_stations = self.data_store.get("stations")
+ all_stations = self.data_store.get("stations", "test")
start = self.data_store.get("start", "test")
end = self.data_store.get("end", "test")
index_dim = self.index_dim