diff --git a/mlair/data_handler/data_handler_mixed_sampling.py b/mlair/data_handler/data_handler_mixed_sampling.py
index 0bdd9b216073bd6d045233afb3fd945718117a98..84596ad081b922a92a91b3df0513a4e730b8eb53 100644
--- a/mlair/data_handler/data_handler_mixed_sampling.py
+++ b/mlair/data_handler/data_handler_mixed_sampling.py
@@ -316,10 +316,17 @@ class DataHandlerMixedSamplingWithClimateAndFirFilter(DataHandlerMixedSamplingWi
@classmethod
def _split_chem_and_meteo_variables(cls, **kwargs):
+ """
+ Select all used variables and split them into categories chem and other.
+
+ Chemical variables are indicated by `cls.data_handler_climate_fir.chem_vars`. To indicate used variables, this
+ method uses 1) parameter `variables`, 2) keys from `statistics_per_var`, 3) keys from
+ `cls.data_handler_climate_fir.DEFAULT_VAR_ALL_DICT`. Option 3) is also applied if 1) or 2) are given but None.
+ """
if "variables" in kwargs:
variables = kwargs.get("variables")
elif "statistics_per_var" in kwargs:
- variables = kwargs.get("statistics_per_var")
+ variables = kwargs.get("statistics_per_var").keys()
else:
variables = None
if variables is None:
@@ -348,14 +355,7 @@ class DataHandlerMixedSamplingWithClimateAndFirFilter(DataHandlerMixedSamplingWi
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 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
+ cls.set_data_handler_fir_pos(**kwargs)
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_transformation(sp_keys, dh_type="filtered_meteo")
cls.prepare_build(sp_keys, meteo_vars, cls.meteo_indicator)
@@ -369,8 +369,36 @@ class DataHandlerMixedSamplingWithClimateAndFirFilter(DataHandlerMixedSamplingWi
dp_args = {k: copy.deepcopy(kwargs[k]) for k in cls.own_args("id_class") if k in kwargs}
return cls(sp_chem, sp_meteo, sp_chem_unfiltered, sp_meteo_unfiltered, chem_vars, meteo_vars, **dp_args)
+ @classmethod
+ def set_data_handler_fir_pos(cls, **kwargs):
+ """
+ Set position of fir data handler to use either faster FIR version or slower climate FIR.
+
+ This method will set data handler indicator to 0 if either no parameter "extend_length_opts" is given or the
+ parameter is of type dict but has no entry for the meteo_indicator. In all other cases, indicator is set to 1.
+ """
+ p_name = "extend_length_opts"
+ if cls.data_handler_fir_pos is None:
+ if p_name in kwargs:
+ if isinstance(kwargs[p_name], dict) and cls.meteo_indicator not in kwargs[p_name].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
+
@classmethod
def prepare_build(cls, kwargs, var_list, var_type):
+ """
+ Prepares for build of class.
+
+ `variables` parameter is updated by `var_list`, which should only include variables of a specific type (e.g.
+ only chemical variables) indicated by `var_type`. Furthermore, this method cleans the `kwargs` dictionary as
+ follows: For all parameters provided as dict to separate between chem and meteo options (dict must have keys
+ from `cls.chem_indicator` and/or `cls.meteo_indicator`), this parameter is removed from kwargs and its value
+ related to `var_type` added again. In case there is no value for given `var_type`, the parameter is not added
+ at all (as this parameter is assumed to affect only other types of variables).
+ """
kwargs.update({"variables": var_list})
for k in list(kwargs.keys()):
v = kwargs[k]
@@ -382,17 +410,6 @@ class DataHandlerMixedSamplingWithClimateAndFirFilter(DataHandlerMixedSamplingWi
except KeyError:
pass
- @staticmethod
- def adjust_window_opts(key: str, parameter_name: str, kwargs: dict):
- try:
- if parameter_name in kwargs:
- window_opt = kwargs.pop(parameter_name)
- if isinstance(window_opt, dict):
- window_opt = window_opt[key]
- kwargs[parameter_name] = window_opt
- except KeyError:
- pass
-
def _create_collection(self):
collection = super()._create_collection()
if self.id_class_other is not None:
@@ -419,9 +436,10 @@ class DataHandlerMixedSamplingWithClimateAndFirFilter(DataHandlerMixedSamplingWi
# meteo transformation
if len(meteo_vars) > 0:
+ cls.set_data_handler_fir_pos(**kwargs)
kwargs_meteo = copy.deepcopy(kwargs)
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)
+ dh_transformation = (cls.data_handler_fir[cls.data_handler_fir_pos], cls.data_handler_unfiltered)
transformation_meteo = super().transformation(set_stations, tmp_path=tmp_path,
dh_transformation=dh_transformation, **kwargs_meteo)