class PathwaysModel(object):
"""
Highest level classification of the definition of an energy system.
Includes the primary geography of the energy system (i.e. country name) as well as the author.
"""
def __init__(self, db_path, cfgfile_path, custom_pint_definitions_path=None, name=None, author=None):
self.model_config(db_path, cfgfile_path, custom_pint_definitions_path)
self.name = cfg.cfgfile.get("case", "scenario") if name is None else name
self.author = cfg.cfgfile.get("case", "author") if author is None else author
self.demand = Demand()
self.supply = Supply()
def model_config(self, db_path, cfgfile_path, custom_pint_definitions_path):
cfg.init_cfgfile(cfgfile_path)
cfg.init_db(db_path)
cfg.init_pint(custom_pint_definitions_path)
cfg.init_geo()
cfg.init_shapes()
cfg.init_outputs_id_map()
def configure_energy_system(self):
print "configuring energy system"
self.configure_demand()
self.configure_supply()
cfg.init_outputs_id_map()
def populate_energy_system(self):
self.populate_demand_system()
self.populate_supply_system()
def populate_measures(self):
self.populate_demand_measures()
self.populate_supply_measures()
def calculate(self):
self.calculate_demand_only()
self.pass_results_to_supply()
self.calculate_supply()
def configure_demand(self):
"""Read in and initialize data"""
# Drivers must come first
self.demand.add_drivers()
# Sectors requires drivers be read in
self.demand.add_sectors()
for sector in self.demand.sectors.values():
# print 'configuring the %s sector' %sector.name
sector.add_subsectors()
def configure_supply(self):
self.supply.add_nodes()
def populate_demand_system(self):
print "remapping drivers"
self.demand.remap_drivers()
print "populating energy system data"
for sector in self.demand.sectors.values():
print " " + sector.name + " sector"
for subsector in sector.subsectors.values():
print " " + subsector.name
subsector.add_energy_system_data()
self.demand.precursor_dict()
def populate_supply_system(self):
self.supply.add_energy_system_data()
def populate_demand_measures(self):
for sector in self.demand.sectors.values():
for subsector in sector.subsectors.values():
subsector.add_measures()
def populate_supply_measures(self):
self.supply.add_measures()
def calculate_demand_only(self):
self.demand.manage_calculations()
def calculate_supply(self):
self.supply.calculate()
def pass_results_to_supply(self):
for sector in self.demand.sectors.values():
sector.aggregate_subsector_energy_for_supply_side()
self.demand.aggregate_sector_energy_for_supply_side()
self.supply.demand_df = self.demand.energy_demand
def pass_results_to_demand(self):
self.demand.aggregate_results()
self.demand.link_to_supply(
self.supply.emissions_demand_link, self.supply.energy_demand_link, self.supply.cost_demand_link
)
def export_results(self, specified_directory=None):
if specified_directory is None:
specified_directory = os.path.join(os.getcwd())
else:
specified_directory = os.path.join(specified_directory)
attributes = dir(self.demand.outputs)
for att in attributes:
if isinstance(getattr(self.demand.outputs, att), pd.core.frame.DataFrame):
output = self.demand.outputs.return_cleaned_output(att)
ExportMethods.writedataframe(att, output, specified_directory)
class PathwaysModel(object):
"""
Highest level classification of the definition of an energy system.
Includes the primary geography of the energy system (i.e. country name) as well as the author.
"""
def __init__(self,
db_path,
cfgfile_path,
custom_pint_definitions_path=None,
name=None,
author=None):
self.model_config(db_path, cfgfile_path, custom_pint_definitions_path)
self.name = cfg.cfgfile.get('case',
'scenario') if name is None else name
self.author = cfg.cfgfile.get('case',
'author') if author is None else author
self.demand = Demand()
self.supply = Supply()
def model_config(self, db_path, cfgfile_path,
custom_pint_definitions_path):
cfg.init_cfgfile(cfgfile_path)
cfg.init_db(db_path)
cfg.init_pint(custom_pint_definitions_path)
cfg.init_geo()
cfg.init_shapes()
cfg.init_outputs_id_map()
def configure_energy_system(self):
print 'configuring energy system'
self.configure_demand()
self.configure_supply()
cfg.init_outputs_id_map()
def populate_energy_system(self):
self.populate_demand_system()
self.populate_supply_system()
def populate_measures(self):
self.populate_demand_measures()
self.populate_supply_measures()
def calculate(self):
self.calculate_demand_only()
self.pass_results_to_supply()
self.calculate_supply()
def configure_demand(self):
"""Read in and initialize data"""
# Drivers must come first
self.demand.add_drivers()
# Sectors requires drivers be read in
self.demand.add_sectors()
for sector in self.demand.sectors.values():
# print 'configuring the %s sector' %sector.name
sector.add_subsectors()
def configure_supply(self):
self.supply.add_nodes()
def populate_demand_system(self):
print 'remapping drivers'
self.demand.remap_drivers()
print 'populating energy system data'
for sector in self.demand.sectors.values():
print ' ' + sector.name + ' sector'
for subsector in sector.subsectors.values():
print ' ' + subsector.name
subsector.add_energy_system_data()
self.demand.precursor_dict()
def populate_supply_system(self):
self.supply.add_energy_system_data()
def populate_demand_measures(self):
for sector in self.demand.sectors.values():
for subsector in sector.subsectors.values():
subsector.add_measures()
def populate_supply_measures(self):
self.supply.add_measures()
def calculate_demand_only(self):
self.demand.manage_calculations()
def calculate_supply(self):
self.supply.calculate()
def pass_results_to_supply(self):
for sector in self.demand.sectors.values():
sector.aggregate_subsector_energy_for_supply_side()
self.demand.aggregate_sector_energy_for_supply_side()
self.supply.demand_df = self.demand.energy_demand
def pass_results_to_demand(self):
self.demand.aggregate_results()
self.demand.link_to_supply(self.supply.emissions_demand_link,
self.supply.energy_demand_link,
self.supply.cost_demand_link)
def export_results(self, specified_directory=None):
if specified_directory is None:
specified_directory = os.path.join(os.getcwd())
else:
specified_directory = os.path.join(specified_directory)
attributes = dir(self.demand.outputs)
for att in attributes:
if isinstance(getattr(self.demand.outputs, att),
pd.core.frame.DataFrame):
output = self.demand.outputs.return_cleaned_output(att)
ExportMethods.writedataframe(att, output, specified_directory)
class PathwaysModel(object):
"""
Highest level classification of the definition of an energy system.
Includes the primary geography of the energy system (i.e. country name) as well as the author.
"""
def __init__(self, cfgfile_path, custom_pint_definitions_path=None, name=None, author=None):
self.cfgfile_path = cfgfile_path
self.custom_pint_definitions_path = custom_pint_definitions_path
self.model_config(cfgfile_path, custom_pint_definitions_path)
self.name = cfg.cfgfile.get('case', 'scenario') if name is None else name
self.author = cfg.cfgfile.get('case', 'author') if author is None else author
self.scenario_dict = dict(zip(util.sql_read_table('Scenarios','id', return_iterable=True, is_active=True),
util.sql_read_table('Scenarios','name', return_iterable=True, is_active=True)))
self.outputs = Output()
self.geography = cfg.cfgfile.get('case', 'primary_geography')
def model_config(self, cfgfile_path, custom_pint_definitions_path):
cfg.init_cfgfile(cfgfile_path)
cfg.init_db()
cfg.path = custom_pint_definitions_path
cfg.init_pint(custom_pint_definitions_path)
cfg.init_geo()
cfg.init_date_lookup()
if shape.shapes.rerun:
shape.shapes.create_empty_shapes()
# shape.shapes.activate_shape(cfg.electricity_energy_type_shape_id)
cfg.init_outputs_id_map()
def configure_energy_system(self):
print 'configuring energy system'
self.demand = Demand(self.cfgfile_path, self.custom_pint_definitions_path)
self.supply = Supply(os.path.join(os.getcwd(),'outputs'),self.cfgfile_path, self.custom_pint_definitions_path)
self.configure_demand()
self.configure_supply()
def populate_energy_system(self):
self.populate_demand_system()
self.populate_supply_system()
def populate_shapes(self):
print 'processing shapes'
if shape.shapes.rerun:
shape.shapes.initiate_active_shapes()
shape.shapes.process_active_shapes()
def populate_measures(self, scenario_id):
self.scenario_id = scenario_id
self.scenario = self.scenario_dict[self.scenario_id]
self.demand_case_id = util.sql_read_table('Scenarios','demand_case',id=self.scenario_id)
self.populate_demand_measures()
self.supply_case_id = util.sql_read_table('Scenarios','supply_case',id=self.scenario_id)
self.populate_supply_measures()
def calculate(self):
self.calculate_demand_only()
self.pass_results_to_supply()
self.calculate_supply()
def configure_demand(self):
"""Read in and initialize data"""
# Drivers must come first
self.demand.add_drivers()
# Sectors requires drivers be read in
self.demand.add_sectors()
for sector in self.demand.sectors.values():
# print 'configuring the %s sector' %sector.name
sector.add_subsectors()
def configure_supply(self):
self.supply.add_node_list()
def populate_demand_system(self):
print 'remapping drivers'
self.demand.remap_drivers()
print 'populating energy system data'
for sector in self.demand.sectors.values():
print ' '+sector.name+' sector'
# print 'reading energy system data for the %s sector' %sector.name
for subsector in sector.subsectors.values():
print ' '+subsector.name
subsector.add_energy_system_data()
sector.precursor_dict()
def populate_supply_system(self):
self.supply.add_nodes()
def populate_demand_measures(self):
for sector in self.demand.sectors.values():
# print 'reading %s measures' %sector.name
for subsector in sector.subsectors.values():
subsector.add_measures(self.demand_case_id)
def populate_supply_measures(self):
self.supply.add_measures(self.supply_case_id)
def calculate_demand_only(self):
self.demand.calculate_demand()
print "aggregating demand results"
self.demand.aggregate_results()
def calculate_supply(self):
self.supply.initial_calculate()
self.supply.calculate_loop()
self.supply.final_calculate()
def pass_results_to_supply(self):
for sector in self.demand.sectors.values():
sector.aggregate_subsector_energy_for_supply_side()
self.demand.aggregate_sector_energy_for_supply_side()
self.supply.demand_object = self.demand
def pass_results_to_demand(self):
print "calculating link to supply"
self.demand.link_to_supply(self.supply.emissions_demand_link, self.supply.demand_emissions_rates, self.supply.energy_demand_link, self.supply.cost_demand_link)
def calculate_combined_results(self):
print "calculating combined emissions results"
self.calculate_combined_emissions_results()
print "calculating combined cost results"
self.calculate_combined_cost_results()
print "calculating combined energy results"
self.calculate_combined_energy_results()
def export_results(self):
for attribute in dir(self.outputs):
if isinstance(getattr(self.outputs,attribute), pd.DataFrame):
result_df = getattr(self.outputs, attribute)
keys = [self.scenario.upper(),str(datetime.now().replace(second=0,microsecond=0))]
names = ['SCENARIO','TIMESTAMP']
for key, name in zip(keys,names):
result_df = pd.concat([result_df],keys=[key],names=[name])
ExportMethods.writeobj(attribute,result_df, os.path.join(os.getcwd(),'combined_outputs'), append_results=True)
for attribute in dir(self.demand.outputs):
if isinstance(getattr(self.demand.outputs,attribute), pd.DataFrame):
result_df = self.demand.outputs.return_cleaned_output(attribute)
keys = [self.scenario.upper(),str(datetime.now().replace(second=0,microsecond=0))]
names = ['SCENARIO','TIMESTAMP']
for key, name in zip(keys,names):
result_df = pd.concat([result_df],keys=[key],names=[name])
ExportMethods.writeobj(attribute,result_df, os.path.join(os.getcwd(),'demand_outputs'), append_results=True)
for attribute in dir(self.supply.outputs):
if isinstance(getattr(self.supply.outputs,attribute), pd.DataFrame):
result_df = self.supply.outputs.return_cleaned_output(attribute)
keys = [self.scenario.upper(),str(datetime.now().replace(second=0,microsecond=0))]
names = ['SCENARIO','TIMESTAMP']
for key, name in zip(keys,names):
result_df = pd.concat([result_df],keys=[key],names=[name])
ExportMethods.writeobj(attribute,result_df, os.path.join(os.getcwd(),'supply_outputs'), append_results=True)
def calculate_combined_cost_results(self):
#calculate and format export costs
cost_unit = cfg.cfgfile.get('case','currency_year_id') + " " + cfg.cfgfile.get('case','currency_name')
if self.supply.export_costs is not None:
setattr(self.outputs,'export_costs',self.supply.export_costs)
self.export_costs_df = self.outputs.return_cleaned_output('export_costs')
del self.outputs.export_costs
util.replace_index_name(self.export_costs_df, 'FINAL_ENERGY','SUPPLY_NODE_EXPORT')
keys = ["EXPORT","SUPPLY"]
names = ['EXPORT/DOMESTIC', "SUPPLY/DEMAND"]
for key,name in zip(keys,names):
self.export_costs_df = pd.concat([self.export_costs_df],keys=[key],names=[name])
self.export_costs_df.columns = [cost_unit.upper()]
else:
self.export_costs_df = None
#calculate and format emobodied supply costs
self.embodied_energy_costs_df = self.demand.outputs.return_cleaned_output('demand_embodied_energy_costs')
self.embodied_energy_costs_df.columns = [cost_unit.upper()]
# del self.demand.outputs.demand_embodied_energy_costs
keys = ["DOMESTIC","SUPPLY"]
names = ['EXPORT/DOMESTIC', "SUPPLY/DEMAND"]
for key,name in zip(keys,names):
self.embodied_energy_costs_df = pd.concat([self.embodied_energy_costs_df],keys=[key],names=[name])
#calculte and format direct demand emissions
self.demand_costs_df= self.demand.outputs.return_cleaned_output('levelized_costs')
# del self.demand.outputs.levelized_costs
keys = ["DOMESTIC","DEMAND"]
names = ['EXPORT/DOMESTIC', "SUPPLY/DEMAND"]
for key,name in zip(keys,names):
self.demand_costs_df = pd.concat([self.demand_costs_df],keys=[key],names=[name])
# levels_to_keep = cfg.output_levels
# levels_to_keep = [x.upper() for x in levels_to_keep]
# levels_to_keep += names + [self.geography.upper() +'_SUPPLY', 'SUPPLY_NODE']
keys = ['EXPORTED', 'SUPPLY-SIDE', 'DEMAND-SIDE']
names = ['COST TYPE']
self.outputs.costs = util.df_list_concatenate([self.export_costs_df, self.embodied_energy_costs_df, self.demand_costs_df],keys=keys,new_names=names)
# util.replace_index_name(self.outputs.costs, self.geography.upper() +'_EARNED', self.geography.upper() +'_SUPPLY')
# util.replace_index_name(self.outputs.costs, self.geography.upper() +'_CONSUMED', self.geography.upper())
self.outputs.costs[self.outputs.costs<0]=0
self.outputs.costs= self.outputs.costs[self.outputs.costs[cost_unit.upper()]!=0]
# self.outputs.costs.sort(inplace=True)
def calculate_combined_emissions_results(self):
#calculate and format export emissions
if self.supply.export_emissions is not None:
setattr(self.outputs,'export_emissions',self.supply.export_emissions)
if 'supply_geography' not in cfg.output_combined_levels:
util.remove_df_levels(self.outputs.export_emissions,self.geography +'_supply')
self.export_emissions_df = self.outputs.return_cleaned_output('export_emissions')
del self.outputs.export_emissions
util.replace_index_name(self.export_emissions_df, 'FINAL_ENERGY','SUPPLY_NODE_EXPORT')
keys = ["EXPORT","SUPPLY"]
names = ['EXPORT/DOMESTIC', "SUPPLY/DEMAND"]
for key,name in zip(keys,names):
self.export_emissions_df = pd.concat([self.export_emissions_df],keys=[key],names=[name])
else:
self.export_emissions_df = None
#calculate and format emobodied supply emissions
self.embodied_emissions_df = self.demand.outputs.return_cleaned_output('demand_embodied_emissions')
# del self.demand.outputs.demand_embodied_emissions
keys = ["DOMESTIC","SUPPLY"]
names = ['EXPORT/DOMESTIC', "SUPPLY/DEMAND"]
for key,name in zip(keys,names):
self.embodied_emissions_df = pd.concat([self.embodied_emissions_df],keys=[key],names=[name])
#calculte and format direct demand emissions
self.direct_emissions_df= self.demand.outputs.return_cleaned_output('demand_direct_emissions')
# del self.demand.outputs.demand_direct_emissions
keys = ["DOMESTIC","DEMAND"]
names = ['EXPORT/DOMESTIC', "SUPPLY/DEMAND"]
for key,name in zip(keys,names):
self.direct_emissions_df = pd.concat([self.direct_emissions_df],keys=[key],names=[name])
if 'supply_geography' in cfg.output_combined_levels:
keys = self.direct_emissions_df.index.get_level_values(self.geography.upper()).values
names = self.geography.upper() +'_SUPPLY'
self.direct_emissions_df[names] = keys
self.direct_emissions_df.set_index(names,append=True,inplace=True)
# levels_to_keep = cfg.output_levels
# levels_to_keep = [x.upper() for x in levels_to_keep]
# levels_to_keep += names + [self.geography.upper() +'_SUPPLY', 'SUPPLY_NODE']
keys = ['EXPORTED', 'SUPPLY-SIDE', 'DEMAND-SIDE']
names = ['EMISSIONS TYPE']
self.outputs.emissions = util.df_list_concatenate([self.export_emissions_df, self.embodied_emissions_df, self.direct_emissions_df],keys=keys,new_names = names)
# util.replace_index_name(self.outputs.emissions, "ENERGY","FINAL_ENERGY")
util.replace_index_name(self.outputs.emissions, self.geography.upper() +'_EMITTED', self.geography.upper() +'_SUPPLY')
util.replace_index_name(self.outputs.emissions, self.geography.upper() +'_CONSUMED', self.geography.upper())
self.outputs.emissions= self.outputs.emissions[self.outputs.emissions['VALUE']!=0]
emissions_unit = cfg.cfgfile.get('case','mass_unit')
self.outputs.emissions.columns = [emissions_unit.upper()]
# self.outputs.emissions.sort(inplace=True)
def calculate_combined_energy_results(self):
self.embodied_energy = self.demand.outputs.return_cleaned_output('demand_embodied_energy')
self.embodied_energy = self.embodied_energy[self.embodied_energy ['VALUE']!=0]
self.final_energy = self.demand.outputs.return_cleaned_output('energy')
self.final_energy = self.final_energy[self.final_energy.index.get_level_values('YEAR')>=int(cfg.cfgfile.get('case','current_year'))]
self.embodied_energy['ENERGY ACCOUNTING'] = 'EMBODIED'
self.final_energy['ENERGY ACCOUNTING'] = 'FINAL'
self.embodied_energy.set_index('ENERGY ACCOUNTING',append=True,inplace=True)
self.final_energy.set_index('ENERGY ACCOUNTING',append=True,inplace=True)
# self.outputs.energy = pd.concat([self.embodied_energy, self.final_energy],keys=['DROP'],names=['DROP'])
for name in [x for x in self.embodied_energy.index.names if x not in self.final_energy.index.names]:
self.final_energy[name] = "N/A"
self.final_energy.set_index(name,append=True,inplace=True)
self.final_energy = self.final_energy.groupby(level=self.embodied_energy.index.names).sum()
self.final_energy = self.final_energy.reorder_levels(self.embodied_energy.index.names)
self.outputs.energy = pd.concat([self.embodied_energy,self.final_energy])
self.outputs.energy= self.outputs.energy[self.outputs.energy['VALUE']!=0]
energy_unit = cfg.cfgfile.get('case','energy_unit')
self.outputs.energy.columns = [energy_unit.upper()]
class PathwaysModel(object):
"""
Highest level classification of the definition of an energy system.
Includes the primary geography of the energy system (i.e. country name) as well as the author.
"""
def __init__(self,
cfgfile_path,
custom_pint_definitions_path=None,
name=None,
author=None):
self.cfgfile_path = cfgfile_path
self.custom_pint_definitions_path = custom_pint_definitions_path
self.model_config(cfgfile_path, custom_pint_definitions_path)
self.name = cfg.cfgfile.get('case',
'scenario') if name is None else name
self.author = cfg.cfgfile.get('case',
'author') if author is None else author
self.scenario_dict = dict(
zip(
util.sql_read_table('Scenarios',
'id',
return_iterable=True,
is_active=True),
util.sql_read_table('Scenarios',
'name',
return_iterable=True,
is_active=True)))
self.outputs = Output()
self.geography = cfg.cfgfile.get('case', 'primary_geography')
def model_config(self, cfgfile_path, custom_pint_definitions_path):
cfg.init_cfgfile(cfgfile_path)
cfg.init_db()
cfg.path = custom_pint_definitions_path
cfg.init_pint(custom_pint_definitions_path)
cfg.init_geo()
cfg.init_date_lookup()
if shape.shapes.rerun:
shape.shapes.create_empty_shapes()
# shape.shapes.activate_shape(cfg.electricity_energy_type_shape_id)
cfg.init_outputs_id_map()
def configure_energy_system(self):
print 'configuring energy system'
self.demand = Demand(self.cfgfile_path,
self.custom_pint_definitions_path)
self.supply = Supply(os.path.join(os.getcwd(),
'outputs'), self.cfgfile_path,
self.custom_pint_definitions_path)
self.configure_demand()
self.configure_supply()
def populate_energy_system(self):
self.populate_demand_system()
self.populate_supply_system()
def populate_shapes(self):
print 'processing shapes'
if shape.shapes.rerun:
shape.shapes.initiate_active_shapes()
shape.shapes.process_active_shapes()
def populate_measures(self, scenario_id):
self.scenario_id = scenario_id
self.scenario = self.scenario_dict[self.scenario_id]
self.demand_case_id = util.sql_read_table('Scenarios',
'demand_case',
id=self.scenario_id)
self.populate_demand_measures()
self.supply_case_id = util.sql_read_table('Scenarios',
'supply_case',
id=self.scenario_id)
self.populate_supply_measures()
def calculate(self):
self.calculate_demand_only()
self.pass_results_to_supply()
self.calculate_supply()
def configure_demand(self):
"""Read in and initialize data"""
# Drivers must come first
self.demand.add_drivers()
# Sectors requires drivers be read in
self.demand.add_sectors()
for sector in self.demand.sectors.values():
# print 'configuring the %s sector' %sector.name
sector.add_subsectors()
def configure_supply(self):
self.supply.add_node_list()
def populate_demand_system(self):
print 'remapping drivers'
self.demand.remap_drivers()
print 'populating energy system data'
for sector in self.demand.sectors.values():
print ' ' + sector.name + ' sector'
# print 'reading energy system data for the %s sector' %sector.name
for subsector in sector.subsectors.values():
print ' ' + subsector.name
subsector.add_energy_system_data()
sector.precursor_dict()
def populate_supply_system(self):
self.supply.add_nodes()
def populate_demand_measures(self):
for sector in self.demand.sectors.values():
# print 'reading %s measures' %sector.name
for subsector in sector.subsectors.values():
subsector.add_measures(self.demand_case_id)
def populate_supply_measures(self):
self.supply.add_measures(self.supply_case_id)
def calculate_demand_only(self):
self.demand.calculate_demand()
print "aggregating demand results"
self.demand.aggregate_results()
def calculate_supply(self):
self.supply.initial_calculate()
self.supply.calculate_loop()
self.supply.final_calculate()
def pass_results_to_supply(self):
for sector in self.demand.sectors.values():
sector.aggregate_subsector_energy_for_supply_side()
self.demand.aggregate_sector_energy_for_supply_side()
self.supply.demand_object = self.demand
def pass_results_to_demand(self):
print "calculating link to supply"
self.demand.link_to_supply(self.supply.emissions_demand_link,
self.supply.demand_emissions_rates,
self.supply.energy_demand_link,
self.supply.cost_demand_link)
def calculate_combined_results(self):
print "calculating combined emissions results"
self.calculate_combined_emissions_results()
print "calculating combined cost results"
self.calculate_combined_cost_results()
print "calculating combined energy results"
self.calculate_combined_energy_results()
def export_results(self):
for attribute in dir(self.outputs):
if isinstance(getattr(self.outputs, attribute), pd.DataFrame):
result_df = getattr(self.outputs, attribute)
keys = [
self.scenario.upper(),
str(datetime.now().replace(second=0, microsecond=0))
]
names = ['SCENARIO', 'TIMESTAMP']
for key, name in zip(keys, names):
result_df = pd.concat([result_df],
keys=[key],
names=[name])
ExportMethods.writeobj(attribute,
result_df,
os.path.join(os.getcwd(),
'combined_outputs'),
append_results=True)
for attribute in dir(self.demand.outputs):
if isinstance(getattr(self.demand.outputs, attribute),
pd.DataFrame):
result_df = self.demand.outputs.return_cleaned_output(
attribute)
keys = [
self.scenario.upper(),
str(datetime.now().replace(second=0, microsecond=0))
]
names = ['SCENARIO', 'TIMESTAMP']
for key, name in zip(keys, names):
result_df = pd.concat([result_df],
keys=[key],
names=[name])
ExportMethods.writeobj(attribute,
result_df,
os.path.join(os.getcwd(),
'demand_outputs'),
append_results=True)
for attribute in dir(self.supply.outputs):
if isinstance(getattr(self.supply.outputs, attribute),
pd.DataFrame):
result_df = self.supply.outputs.return_cleaned_output(
attribute)
keys = [
self.scenario.upper(),
str(datetime.now().replace(second=0, microsecond=0))
]
names = ['SCENARIO', 'TIMESTAMP']
for key, name in zip(keys, names):
result_df = pd.concat([result_df],
keys=[key],
names=[name])
ExportMethods.writeobj(attribute,
result_df,
os.path.join(os.getcwd(),
'supply_outputs'),
append_results=True)
def calculate_combined_cost_results(self):
#calculate and format export costs
cost_unit = cfg.cfgfile.get(
'case', 'currency_year_id') + " " + cfg.cfgfile.get(
'case', 'currency_name')
if self.supply.export_costs is not None:
setattr(self.outputs, 'export_costs', self.supply.export_costs)
self.export_costs_df = self.outputs.return_cleaned_output(
'export_costs')
del self.outputs.export_costs
util.replace_index_name(self.export_costs_df, 'FINAL_ENERGY',
'SUPPLY_NODE_EXPORT')
keys = ["EXPORT", "SUPPLY"]
names = ['EXPORT/DOMESTIC', "SUPPLY/DEMAND"]
for key, name in zip(keys, names):
self.export_costs_df = pd.concat([self.export_costs_df],
keys=[key],
names=[name])
self.export_costs_df.columns = [cost_unit.upper()]
else:
self.export_costs_df = None
#calculate and format emobodied supply costs
self.embodied_energy_costs_df = self.demand.outputs.return_cleaned_output(
'demand_embodied_energy_costs')
self.embodied_energy_costs_df.columns = [cost_unit.upper()]
# del self.demand.outputs.demand_embodied_energy_costs
keys = ["DOMESTIC", "SUPPLY"]
names = ['EXPORT/DOMESTIC', "SUPPLY/DEMAND"]
for key, name in zip(keys, names):
self.embodied_energy_costs_df = pd.concat(
[self.embodied_energy_costs_df], keys=[key], names=[name])
#calculte and format direct demand emissions
self.demand_costs_df = self.demand.outputs.return_cleaned_output(
'levelized_costs')
# del self.demand.outputs.levelized_costs
keys = ["DOMESTIC", "DEMAND"]
names = ['EXPORT/DOMESTIC', "SUPPLY/DEMAND"]
for key, name in zip(keys, names):
self.demand_costs_df = pd.concat([self.demand_costs_df],
keys=[key],
names=[name])
# levels_to_keep = cfg.output_levels
# levels_to_keep = [x.upper() for x in levels_to_keep]
# levels_to_keep += names + [self.geography.upper() +'_SUPPLY', 'SUPPLY_NODE']
keys = ['EXPORTED', 'SUPPLY-SIDE', 'DEMAND-SIDE']
names = ['COST TYPE']
self.outputs.costs = util.df_list_concatenate([
self.export_costs_df, self.embodied_energy_costs_df,
self.demand_costs_df
],
keys=keys,
new_names=names)
# util.replace_index_name(self.outputs.costs, self.geography.upper() +'_EARNED', self.geography.upper() +'_SUPPLY')
# util.replace_index_name(self.outputs.costs, self.geography.upper() +'_CONSUMED', self.geography.upper())
self.outputs.costs[self.outputs.costs < 0] = 0
self.outputs.costs = self.outputs.costs[
self.outputs.costs[cost_unit.upper()] != 0]
# self.outputs.costs.sort(inplace=True)
def calculate_combined_emissions_results(self):
#calculate and format export emissions
if self.supply.export_emissions is not None:
setattr(self.outputs, 'export_emissions',
self.supply.export_emissions)
if 'supply_geography' not in cfg.output_combined_levels:
util.remove_df_levels(self.outputs.export_emissions,
self.geography + '_supply')
self.export_emissions_df = self.outputs.return_cleaned_output(
'export_emissions')
del self.outputs.export_emissions
util.replace_index_name(self.export_emissions_df, 'FINAL_ENERGY',
'SUPPLY_NODE_EXPORT')
keys = ["EXPORT", "SUPPLY"]
names = ['EXPORT/DOMESTIC', "SUPPLY/DEMAND"]
for key, name in zip(keys, names):
self.export_emissions_df = pd.concat(
[self.export_emissions_df], keys=[key], names=[name])
else:
self.export_emissions_df = None
#calculate and format emobodied supply emissions
self.embodied_emissions_df = self.demand.outputs.return_cleaned_output(
'demand_embodied_emissions')
# del self.demand.outputs.demand_embodied_emissions
keys = ["DOMESTIC", "SUPPLY"]
names = ['EXPORT/DOMESTIC', "SUPPLY/DEMAND"]
for key, name in zip(keys, names):
self.embodied_emissions_df = pd.concat(
[self.embodied_emissions_df], keys=[key], names=[name])
#calculte and format direct demand emissions
self.direct_emissions_df = self.demand.outputs.return_cleaned_output(
'demand_direct_emissions')
# del self.demand.outputs.demand_direct_emissions
keys = ["DOMESTIC", "DEMAND"]
names = ['EXPORT/DOMESTIC', "SUPPLY/DEMAND"]
for key, name in zip(keys, names):
self.direct_emissions_df = pd.concat([self.direct_emissions_df],
keys=[key],
names=[name])
if 'supply_geography' in cfg.output_combined_levels:
keys = self.direct_emissions_df.index.get_level_values(
self.geography.upper()).values
names = self.geography.upper() + '_SUPPLY'
self.direct_emissions_df[names] = keys
self.direct_emissions_df.set_index(names,
append=True,
inplace=True)
# levels_to_keep = cfg.output_levels
# levels_to_keep = [x.upper() for x in levels_to_keep]
# levels_to_keep += names + [self.geography.upper() +'_SUPPLY', 'SUPPLY_NODE']
keys = ['EXPORTED', 'SUPPLY-SIDE', 'DEMAND-SIDE']
names = ['EMISSIONS TYPE']
self.outputs.emissions = util.df_list_concatenate([
self.export_emissions_df, self.embodied_emissions_df,
self.direct_emissions_df
],
keys=keys,
new_names=names)
# util.replace_index_name(self.outputs.emissions, "ENERGY","FINAL_ENERGY")
util.replace_index_name(self.outputs.emissions,
self.geography.upper() + '_EMITTED',
self.geography.upper() + '_SUPPLY')
util.replace_index_name(self.outputs.emissions,
self.geography.upper() + '_CONSUMED',
self.geography.upper())
self.outputs.emissions = self.outputs.emissions[
self.outputs.emissions['VALUE'] != 0]
emissions_unit = cfg.cfgfile.get('case', 'mass_unit')
self.outputs.emissions.columns = [emissions_unit.upper()]
# self.outputs.emissions.sort(inplace=True)
def calculate_combined_energy_results(self):
self.embodied_energy = self.demand.outputs.return_cleaned_output(
'demand_embodied_energy')
self.embodied_energy = self.embodied_energy[
self.embodied_energy['VALUE'] != 0]
self.final_energy = self.demand.outputs.return_cleaned_output('energy')
self.final_energy = self.final_energy[
self.final_energy.index.get_level_values('YEAR') >= int(
cfg.cfgfile.get('case', 'current_year'))]
self.embodied_energy['ENERGY ACCOUNTING'] = 'EMBODIED'
self.final_energy['ENERGY ACCOUNTING'] = 'FINAL'
self.embodied_energy.set_index('ENERGY ACCOUNTING',
append=True,
inplace=True)
self.final_energy.set_index('ENERGY ACCOUNTING',
append=True,
inplace=True)
# self.outputs.energy = pd.concat([self.embodied_energy, self.final_energy],keys=['DROP'],names=['DROP'])
for name in [
x for x in self.embodied_energy.index.names
if x not in self.final_energy.index.names
]:
self.final_energy[name] = "N/A"
self.final_energy.set_index(name, append=True, inplace=True)
self.final_energy = self.final_energy.groupby(
level=self.embodied_energy.index.names).sum()
self.final_energy = self.final_energy.reorder_levels(
self.embodied_energy.index.names)
self.outputs.energy = pd.concat(
[self.embodied_energy, self.final_energy])
self.outputs.energy = self.outputs.energy[
self.outputs.energy['VALUE'] != 0]
energy_unit = cfg.cfgfile.get('case', 'energy_unit')
self.outputs.energy.columns = [energy_unit.upper()]
