def build_model_data(model_run, debug=False):
"""
Take a Calliope model_run and convert it into an xarray Dataset, ready for
constraint generation. Timeseries data is also extracted from file at this
point, and the time dimension added to the data
Parameters
----------
model_run : AttrDict
preprocessed model_run dictionary, as produced by
Calliope.core.preprocess_model
debug : bool, default = False
Used to debug steps within build_model_data, particularly before/after
time dimension addition. If True, more information is returned
Returns
-------
data : xarray Dataset
Dataset with optimisation parameters as variables, optimisation sets as
coordinates, and other information in attributes.
data_dict : dict, only returned if debug = True
dictionary of parameters, prior to time dimension addition. Used here to
populate the Dataset (using `from_dict()`)
data_pre_time : xarray Dataset, only returned if debug = True
Dataset, prior to time dimension addition, with optimisation parameters
as variables, optimisation sets as coordinates, and other information
in attributes.
"""
# We build up a dictionary of the data, then convert it to an xarray Dataset
# before applying time dimensions
data = xr.Dataset(
coords=add_sets(model_run),
attrs=add_attributes(model_run)
)
data_dict = dict()
data_dict.update(constraints_to_dataset(model_run))
data_dict.update(costs_to_dataset(model_run))
data_dict.update(location_specific_to_dataset(model_run))
data_dict.update(tech_specific_to_dataset(model_run))
data_dict.update(carrier_specific_to_dataset(model_run))
data.merge(xr.Dataset.from_dict(data_dict), inplace=True)
add_lookup_arrays(data, model_run)
if debug:
data_pre_time = data.copy(deep=True)
add_time_dimension(data, model_run)
# Carrier information uses DataArray indexing in the function, so we merge
# these directly into the main xarray Dataset
if debug:
return data, data_dict, data_pre_time
else:
return data
def build_model_data(model_run, debug=False):
"""
Take a Calliope model_run and convert it into an xarray Dataset, ready for
constraint generation. Timeseries data is also extracted from file at this
point, and the time dimension added to the data
Parameters
----------
model_run : AttrDict
preprocessed model_run dictionary, as produced by
Calliope.core.preprocess_model
debug : bool, default = False
Used to debug steps within build_model_data, particularly before/after
time dimension addition. If True, more information is returned
Returns
-------
data : xarray Dataset
Dataset with optimisation parameters as variables, optimisation sets as
coordinates, and other information in attributes.
data_dict : dict, only returned if debug = True
dictionary of parameters, prior to time dimension addition. Used here to
populate the Dataset (using `from_dict()`)
data_pre_time : xarray Dataset, only returned if debug = True
Dataset, prior to time dimension addition, with optimisation parameters
as variables, optimisation sets as coordinates, and other information
in attributes.
"""
# We build up a dictionary of the data, then convert it to an xarray Dataset
# before applying time dimensions
data = xr.Dataset(
coords=add_sets(model_run),
attrs=add_attributes(model_run)
)
data_dict = dict()
data_dict.update(constraints_to_dataset(model_run))
data_dict.update(costs_to_dataset(model_run))
data_dict.update(location_specific_to_dataset(model_run))
data_dict.update(tech_specific_to_dataset(model_run))
data_dict.update(carrier_specific_to_dataset(model_run))
data.merge(xr.Dataset.from_dict(data_dict), inplace=True)
add_lookup_arrays(data, model_run)
if debug:
data_pre_time = data.copy(deep=True)
add_time_dimension(data, model_run)
# Carrier information uses DataArray indexing in the function, so we merge
# these directly into the main xarray Dataset
if debug:
return data, data_dict, data_pre_time
else:
return data