def process_spec_techs(raw_data: RawData, model_data: Data,
year_vector: List[int], first_model_year: int,
par_list: List[str],
duration_period_sum: pd.DataFrame) -> dict:
df = raw_data['base_input']['spec_techs'].copy()
df = df.set_index('technology', drop=True)
data = df_to_nested_dict(df)
# load base techs default settings
_base_tech = model_data['technology']
default = raw_data['base_tech']['default']
# load units
units = model_data['units']
technology = {}
for k, v in data.items():
if 'base_techs' in v.keys():
tech = _base_tech[v['base_techs']]
else:
tech = get_base_techs(default, v['commodity_out1'], year_vector,
first_model_year, duration_period_sum)
tech_dict = copy.deepcopy(dict(tech))
tech_dict = _parse_spec_techs(tech_dict, v, units, year_vector,
first_model_year, par_list,
duration_period_sum)
technology[k] = tech_dict
logger.debug('Created helper model_data structure: \'spec techs\'')
return technology
def process_units(_data: RawData) -> dict:
df = _data['base_input']['unit'].copy()
df = df.set_index(['parameter'], drop=True)
data = df_to_nested_dict(df)
data = {'units': data}
logger.debug('Created helper data structure: \'unit_techs\'')
return data['units']
def process_spatial_locations(_data: RawData) -> dict:
df = _data['base_input']['locations'].copy()
df['tech'] = 'technology'
df['override'] = 'override'
df = df.set_index(['location', 'tech', 'technology', 'override'],
drop=True)
data = df_to_nested_dict(df)
data = {'locations': data}
logger.debug('Created helper data structure: \'locations\'')
return data['locations']
def process_demand(raw_data: RawData) -> dict:
df = raw_data['base_input']['demand'].copy()
df = df.assign(y='year')
df = df.assign(c=df.commodity)
df = df.assign(d='demand')
df = df.assign(n=df.node)
df = df.set_index(['d', 'n', 'y', 'year', 'c'])
# create nested dict from DataFrame
data = df_to_nested_dict(df)
logger.debug('Created helper data structure: \'demand\'')
return data['demand']