def calculate_d_payback_energy(self):
initial_vintage = min(cfg.supply_years)
demand_side_df = self.demand.d_all_energy_demand_payback
demand_side_df.columns = ['value']
demand_side_df = demand_side_df[demand_side_df.index.get_level_values('vintage')>=initial_vintage]
demand_side_df = demand_side_df[demand_side_df.index.get_level_values('year')>=initial_vintage]
sales_df = copy.deepcopy(self.demand.outputs.d_sales)
util.replace_index_name(sales_df,'vintage','year')
sales_df = sales_df[sales_df.index.get_level_values('vintage')>=initial_vintage]
sales_df = util.add_and_set_index(sales_df,'year',cfg.supply_years)
# sales_df.index = sales_df.index.reorder_levels(demand_side_df.index.names)
# sales_df = sales_df.reindex(demand_side_df.index).sort_index()
self.demand.outputs.d_payback_energy = util.DfOper.divi([demand_side_df, sales_df])
self.demand.outputs.d_payback_energy = self.demand.outputs.d_payback_energy[np.isfinite(self.demand.outputs.d_payback_energy.values)]
self.demand.outputs.d_payback_energy = self.demand.outputs.d_payback_energy.replace([np.inf,np.nan],0)
for sector in self.demand.sectors.values():
for subsector in sector.subsectors.values():
if hasattr(subsector,'stock') and subsector.sub_type!='link':
indexer = util.level_specific_indexer(self.demand.outputs.d_payback_energy,'subsector',subsector.id)
self.demand.outputs.d_payback_energy.loc[indexer,'unit'] = subsector.stock.unit.upper()
self.demand.outputs.d_payback_energy = self.demand.outputs.d_payback_energy.set_index('unit', append=True)
self.demand.outputs.d_payback_energy.columns = [cfg.calculation_energy_unit.upper()]
self.demand.outputs.d_payback_energy['lifetime_year'] = self.demand.outputs.d_payback_energy.index.get_level_values('year')-self.demand.outputs.d_payback_energy.index.get_level_values('vintage')+1
self.demand.outputs.d_payback_energy = self.demand.outputs.d_payback_energy.set_index('lifetime_year',append=True)
self.demand.outputs.d_payback_energy = util.remove_df_levels(self.demand.outputs.d_payback_energy,'year')
self.demand.outputs.d_payback_energy = self.demand.outputs.d_payback_energy.groupby(level = [x for x in self.demand.outputs.d_payback_energy.index.names if x !='lifetime_year']).transform(lambda x: x.cumsum())
self.demand.outputs.d_payback_energy = self.demand.outputs.d_payback_energy[self.demand.outputs.d_payback_energy[cfg.calculation_energy_unit.upper()]!=0]
self.demand.outputs.d_payback_energy = self.demand.outputs.return_cleaned_output('d_payback_energy')
def calculate_combined_emissions_results(self):
export_emissions = self.calc_and_format_export_emissions()
embodied_emissions_list = self.calc_and_format_embodied_supply_emissions()
direct_emissions_list = self.calc_and_format_direct_demand_emissions()
export_emissions = util.add_and_set_index(export_emissions,['EMISSIONS_TYPE'],['EXPORTED'])
embodied_emissions_list = [util.add_and_set_index(x, ['EMISSIONS_TYPE'], ['SUPPLY_SIDE']) for x in embodied_emissions_list]
direct_emissions_list = [util.add_and_set_index(x,['EMISSIONS_TYPE'],['DEMAND_SIDE']) for x in direct_emissions_list]
if export_emissions is not None:
for name in [x for x in embodied_emissions_list[0].index.names if x not in export_emissions.index.names]:
export_emissions[name] = "N/A"
export_emissions.set_index(name,append=True,inplace=True)
export_emissions = export_emissions.groupby(level=embodied_emissions_list[0].index.names).sum()
if direct_emissions_list is not None:
for df in direct_emissions_list:
for name in [x for x in embodied_emissions_list[0].index.names if x not in df.index.names]:
df[name] = "N/A"
df.set_index(name,append=True,inplace=True)
self.outputs.c_emissions = [export_emissions] + embodied_emissions_list + direct_emissions_list
self.outputs.c_emissions = [util.replace_index_name(x, GeoMapper.combined_outputs_geography.upper() +'-EMITTED', GeoMapper.combined_outputs_geography.upper() +'_SUPPLY',inplace=True) for x in self.outputs.c_emissions]
self.outputs.c_emissions = [util.replace_index_name(x, GeoMapper.combined_outputs_geography.upper() +'-CONSUMED', GeoMapper.combined_outputs_geography.upper(),inplace=True) for x in self.outputs.c_emissions]
self.outputs.c_emissions = [x[x['VALUE']!=0] for x in self.outputs.c_emissions]
emissions_unit = cfg.getParam('mass_unit')
for x in self.outputs.c_emissions:
x.columns = [emissions_unit.upper()]
for x in self.outputs.c_emissions: x.index = x.index.reorder_levels([l for l in embodied_emissions_list[0].index.names if l in x.index.names])
def clean_df(df, stack_years=False):
if type(df) is not pd.core.frame.DataFrame:
raise ValueError('output_type must be a pandas dataframe')
if stack_years and 'year' not in df.index.names:
df = df.stack()
util.replace_index_name(df, 'year')
df.columns = ['value']
if 'year' in df.index.names:
df = df[df.index.get_level_values('year') >= cfg.getParamAsInt(
'current_year')]
dct = cfg.outputs_id_map
index = df.index
index.set_levels([[dct[name].get(item, item) for item in level]
for name, level in zip(index.names, index.levels)],
inplace=True)
index.names = [
x.upper() if isinstance(x, basestring) else x for x in index.names
]
if isinstance(df.columns, pd.MultiIndex):
columns = df.columns
columns.set_levels(
[[dct[name].get(item, item) for item in level]
for name, level in zip(columns.names, columns.levels)],
inplace=True)
columns.names = [
x.upper() if isinstance(x, basestring) else x
for x in columns.names
]
else:
df.columns = [
x.upper() if isinstance(x, basestring) else x
for x in df.columns
]
return df
def clean_rio_df(df,
stack_years=False,
add_geography=True,
replace_gau=True):
if type(df) is not pd.core.frame.DataFrame:
raise ValueError('output_type must be a pandas dataframe')
if stack_years and 'year' not in df.index.names:
df = df.stack()
util.replace_index_name(df, 'year')
df.columns = ['value']
dct = cfg.outputs_id_map
df = df.reset_index()
for col_name in df.columns.get_level_values(0):
# if col_name in cfg.outputs_id_map.keys():
# try:
# df[col_name] = [dct[col_name][x] if x in dct[col_name].keys() else x for x in df[col_name].values]
# except:
# pdb.set_trace()
if col_name in GeoMapper.geography_to_gau.keys():
if replace_gau:
util.replace_column_name(df, 'gau', col_name)
if add_geography:
df['geography'] = col_name
df.columns = [x.lower() for x in df.columns]
return df
def calculate_d_payback_energy(self):
initial_vintage = min(cfg.supply_years)
demand_side_df = self.demand.d_all_energy_demand_payback
demand_side_df.columns = ['value']
demand_side_df = demand_side_df[demand_side_df.index.get_level_values('vintage')>=initial_vintage]
demand_side_df = demand_side_df[demand_side_df.index.get_level_values('year')>=initial_vintage]
sales_df = copy.deepcopy(self.demand.outputs.d_sales)
util.replace_index_name(sales_df,'vintage','year')
sales_df = sales_df[sales_df.index.get_level_values('vintage')>=initial_vintage]
sales_df = util.add_and_set_index(sales_df,'year',cfg.supply_years)
# sales_df.index = sales_df.index.reorder_levels(demand_side_df.index.names)
# sales_df = sales_df.reindex(demand_side_df.index).sort_index()
self.demand.outputs.d_payback_energy = util.DfOper.divi([demand_side_df, sales_df])
self.demand.outputs.d_payback_energy = self.demand.outputs.d_payback_energy[np.isfinite(self.demand.outputs.d_payback_energy.values)]
self.demand.outputs.d_payback_energy = self.demand.outputs.d_payback_energy.replace([np.inf,np.nan],0)
for sector in self.demand.sectors.values():
for subsector in sector.subsectors.values():
if hasattr(subsector,'stock') and subsector.sub_type!='link':
indexer = util.level_specific_indexer(self.demand.outputs.d_payback_energy,'subsector',subsector.id)
self.demand.outputs.d_payback_energy.loc[indexer,'unit'] = subsector.stock.unit.upper()
self.demand.outputs.d_payback_energy = self.demand.outputs.d_payback_energy.set_index('unit', append=True)
self.demand.outputs.d_payback_energy.columns = [cfg.calculation_energy_unit.upper()]
self.demand.outputs.d_payback_energy['lifetime_year'] = self.demand.outputs.d_payback_energy.index.get_level_values('year')-self.demand.outputs.d_payback_energy.index.get_level_values('vintage')+1
self.demand.outputs.d_payback_energy = self.demand.outputs.d_payback_energy.set_index('lifetime_year',append=True)
self.demand.outputs.d_payback_energy = util.remove_df_levels(self.demand.outputs.d_payback_energy,'year')
self.demand.outputs.d_payback_energy = self.demand.outputs.d_payback_energy.groupby(level = [x for x in self.demand.outputs.d_payback_energy.index.names if x !='lifetime_year']).transform(lambda x: x.cumsum())
self.demand.outputs.d_payback_energy = self.demand.outputs.d_payback_energy[self.demand.outputs.d_payback_energy[cfg.calculation_energy_unit.upper()]!=0]
self.demand.outputs.d_payback_energy = self.demand.outputs.return_cleaned_output('d_payback_energy')
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]
def calc_and_format_export_costs(self):
#calculate and format export costs
if self.supply.export_costs is None:
return None
export_costs = GeoMapper.geo_map(self.supply.export_costs.copy(), GeoMapper.supply_primary_geography, GeoMapper.combined_outputs_geography, 'total')
export_costs = Output.clean_df(export_costs)
util.replace_index_name(export_costs, 'FINAL_ENERGY', 'SUPPLY_NODE_EXPORT')
export_costs = util.add_to_df_index(export_costs, names=['EXPORT/DOMESTIC', "SUPPLY/DEMAND"], keys=["EXPORT", "SUPPLY"])
cost_unit = cfg.getParam('currency_year') + " " + cfg.getParam('currency_name')
export_costs.columns = [cost_unit.upper()]
return export_costs
def calc_and_format_export_energy(self):
if self.supply.export_energy is None:
return None
export_energy = GeoMapper.geo_map(self.supply.export_energy.copy(), GeoMapper.supply_primary_geography, GeoMapper.combined_outputs_geography, 'total')
export_energy = Output.clean_df(export_energy)
util.replace_index_name(export_energy, 'FINAL_ENERGY','SUPPLY_NODE_EXPORT')
export_energy = util.add_to_df_index(export_energy, names=['EXPORT/DOMESTIC', "ENERGY ACCOUNTING"], keys=["EXPORT", "EMBODIED"])
for x in cfg.output_combined_levels:
if x not in export_energy.index.names:
export_energy = util.add_and_set_index(export_energy,[x],["N/A"])
return export_energy
def calc_and_format_export_emissions(self):
#calculate and format export emissions
if self.supply.export_emissions is None:
return None
export_emissions = GeoMapper.geo_map(self.supply.export_emissions.copy(), GeoMapper.supply_primary_geography, GeoMapper.combined_outputs_geography, 'total')
if 'supply_geography' not in cfg.output_combined_levels:
util.remove_df_levels(export_emissions, GeoMapper.supply_primary_geography +'_supply')
export_emissions = Output.clean_df(export_emissions)
util.replace_index_name(export_emissions, 'FINAL_ENERGY','SUPPLY_NODE_EXPORT')
index_names = export_emissions.index.names
export_emissions = export_emissions.reset_index()
export_emissions['FINAL_ENERGY'] = 'export ' + export_emissions['FINAL_ENERGY']
export_emissions = export_emissions.set_index(index_names).sort_index()
export_emissions = util.add_to_df_index(export_emissions, names=['EXPORT/DOMESTIC', "SUPPLY/DEMAND"], keys=["EXPORT", "SUPPLY"])
return export_emissions
def calculate_payback(self):
# self.embodied_emissions_df = self.demand.outputs.return_cleaned_output('demand_embodied_emissions_tco')
# del self.demand.outputs.demand_embodied_emissions
#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
# emissions = util.DfOper.add([self.embodied_emissions_df,self.direct_emissions_df])
# #calculate and format export costs
cost_unit = cfg.cfgfile.get('case','currency_year_id') + " " + cfg.cfgfile.get('case','currency_name')
initial_vintage = min(cfg.supply_years)
supply_side_df = self.demand.outputs.demand_embodied_energy_costs_payback
supply_side_df = supply_side_df[supply_side_df.index.get_level_values('vintage')>=initial_vintage]
supply_side_df = supply_side_df[supply_side_df.index.get_level_values('year')>=initial_vintage]
supply_side_df = supply_side_df.sort_index()
demand_side_df = self.demand.d_annual_costs_payback
demand_side_df.columns = ['value']
demand_side_df = demand_side_df[demand_side_df.index.get_level_values('vintage')>=initial_vintage]
demand_side_df = demand_side_df[demand_side_df.index.get_level_values('year')>=initial_vintage]
demand_side_df = demand_side_df.reindex(supply_side_df.index).sort_index()
sales_df = copy.deepcopy(self.demand.outputs.d_sales)
util.replace_index_name(sales_df,'vintage','year')
sales_df = sales_df[sales_df.index.get_level_values('vintage')>=initial_vintage]
sales_df = util.add_and_set_index(sales_df,'year',cfg.supply_years)
sales_df.index = sales_df.index.reorder_levels(supply_side_df.index.names)
sales_df = sales_df.reindex(supply_side_df.index).sort_index()
keys = ['SUPPLY-SIDE', 'DEMAND-SIDE']
names = ['COST TYPE']
self.outputs.c_payback = pd.concat([util.DfOper.divi([supply_side_df, sales_df]), util.DfOper.divi([demand_side_df, sales_df])],keys=keys,names=names)
self.outputs.c_payback = self.outputs.c_payback[np.isfinite(self.outputs.c_payback.values)]
self.outputs.c_payback = self.outputs.c_payback.replace([np.inf,np.nan],0)
for sector in self.demand.sectors.values():
for subsector in sector.subsectors.values():
if hasattr(subsector,'stock') and subsector.sub_type!='link':
indexer = util.level_specific_indexer(self.outputs.c_payback,'subsector',subsector.id)
self.outputs.c_payback.loc[indexer,'unit'] = subsector.stock.unit.upper()
self.outputs.c_payback = self.outputs.c_payback.set_index('unit', append=True)
self.outputs.c_payback.columns = [cost_unit.upper()]
self.outputs.c_payback['lifetime_year'] = self.outputs.c_payback.index.get_level_values('year')-self.outputs.c_payback.index.get_level_values('vintage')+1
self.outputs.c_payback = self.outputs.c_payback.set_index('lifetime_year',append=True)
self.outputs.c_payback = util.remove_df_levels(self.outputs.c_payback,'year')
self.outputs.c_payback = self.outputs.c_payback.groupby(level = [x for x in self.outputs.c_payback.index.names if x !='lifetime_year']).transform(lambda x: x.cumsum())
self.outputs.c_payback = self.outputs.c_payback[self.outputs.c_payback[cost_unit.upper()]!=0]
self.outputs.c_payback = self.outputs.return_cleaned_output('c_payback')
def calculate_payback(self):
# self.embodied_emissions_df = self.demand.outputs.return_cleaned_output('demand_embodied_emissions_tco')
# del self.demand.outputs.demand_embodied_emissions
#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
# emissions = util.DfOper.add([self.embodied_emissions_df,self.direct_emissions_df])
# #calculate and format export costs
cost_unit = cfg.cfgfile.get('case','currency_year_id') + " " + cfg.cfgfile.get('case','currency_name')
initial_vintage = min(cfg.supply_years)
supply_side_df = self.demand.outputs.demand_embodied_energy_costs_payback
supply_side_df = supply_side_df[supply_side_df.index.get_level_values('vintage')>=initial_vintage]
supply_side_df = supply_side_df[supply_side_df.index.get_level_values('year')>=initial_vintage]
supply_side_df = supply_side_df.sort_index()
demand_side_df = self.demand.d_annual_costs_payback
demand_side_df.columns = ['value']
demand_side_df = demand_side_df[demand_side_df.index.get_level_values('vintage')>=initial_vintage]
demand_side_df = demand_side_df[demand_side_df.index.get_level_values('year')>=initial_vintage]
demand_side_df = demand_side_df.reindex(supply_side_df.index).sort_index()
sales_df = copy.deepcopy(self.demand.outputs.d_sales)
util.replace_index_name(sales_df,'vintage','year')
sales_df = sales_df[sales_df.index.get_level_values('vintage')>=initial_vintage]
sales_df = util.add_and_set_index(sales_df,'year',cfg.supply_years)
sales_df.index = sales_df.index.reorder_levels(supply_side_df.index.names)
sales_df = sales_df.reindex(supply_side_df.index).sort_index()
keys = ['SUPPLY-SIDE', 'DEMAND-SIDE']
names = ['COST TYPE']
self.outputs.c_payback = pd.concat([util.DfOper.divi([supply_side_df, sales_df]), util.DfOper.divi([demand_side_df, sales_df])],keys=keys,names=names)
self.outputs.c_payback = self.outputs.c_payback[np.isfinite(self.outputs.c_payback.values)]
self.outputs.c_payback = self.outputs.c_payback.replace([np.inf,np.nan],0)
for sector in self.demand.sectors.values():
for subsector in sector.subsectors.values():
if hasattr(subsector,'stock') and subsector.sub_type!='link':
indexer = util.level_specific_indexer(self.outputs.c_payback,'subsector',subsector.id)
self.outputs.c_payback.loc[indexer,'unit'] = subsector.stock.unit.upper()
self.outputs.c_payback = self.outputs.c_payback.set_index('unit', append=True)
self.outputs.c_payback.columns = [cost_unit.upper()]
self.outputs.c_payback['lifetime_year'] = self.outputs.c_payback.index.get_level_values('year')-self.outputs.c_payback.index.get_level_values('vintage')+1
self.outputs.c_payback = self.outputs.c_payback.set_index('lifetime_year',append=True)
self.outputs.c_payback = util.remove_df_levels(self.outputs.c_payback,'year')
self.outputs.c_payback = self.outputs.c_payback.groupby(level = [x for x in self.outputs.c_payback.index.names if x !='lifetime_year']).transform(lambda x: x.cumsum())
self.outputs.c_payback = self.outputs.c_payback[self.outputs.c_payback[cost_unit.upper()]!=0]
self.outputs.c_payback = self.outputs.return_cleaned_output('c_payback')
def calculate_combined_energy_results(self):
energy_unit = cfg.calculation_energy_unit
if self.supply.export_costs is not None:
setattr(self.outputs,'export_energy',self.supply.export_energy)
self.export_energy = self.outputs.return_cleaned_output('export_energy')
del self.outputs.export_energy
util.replace_index_name(self.export_energy, 'FINAL_ENERGY','SUPPLY_NODE_EXPORT')
keys = ["EXPORT","EMBODIED"]
names = ['EXPORT/DOMESTIC', 'ENERGY ACCOUNTING']
for key,name in zip(keys,names):
self.export_energy = pd.concat([self.export_energy],keys=[key],names=[name])
else:
self.export_energy = None
self.embodied_energy = self.demand.outputs.return_cleaned_output('demand_embodied_energy')
self.embodied_energy = self.embodied_energy[self.embodied_energy ['VALUE']!=0]
keys = ['DOMESTIC','EMBODIED']
names = ['EXPORT/DOMESTIC', 'ENERGY ACCOUNTING']
for key,name in zip(keys,names):
self.embodied_energy = pd.concat([self.embodied_energy],keys=[key],names=[name])
self.final_energy = self.demand.outputs.return_cleaned_output('d_energy')
self.final_energy = self.final_energy[self.final_energy.index.get_level_values('YEAR')>=int(cfg.cfgfile.get('case','current_year'))]
keys = ['DOMESTIC','FINAL']
names = ['EXPORT/DOMESTIC', 'ENERGY ACCOUNTING']
for key,name in zip(keys,names):
self.final_energy = pd.concat([self.final_energy],keys=[key],names=[name])
# self.outputs.c_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)
if self.export_energy is not None:
for name in [x for x in self.embodied_energy.index.names if x not in self.export_energy.index.names]:
self.export_energy[name] = "N/A"
self.export_energy.set_index(name,append=True,inplace=True)
self.export_energy = self.export_energy.groupby(level=self.embodied_energy.index.names).sum()
self.export_energy = self.export_energy.reorder_levels(self.embodied_energy.index.names)
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.c_energy = pd.concat([self.embodied_energy,self.final_energy,self.export_energy])
self.outputs.c_energy= self.outputs.c_energy[self.outputs.c_energy['VALUE']!=0]
self.outputs.c_energy.columns = [energy_unit.upper()]
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]
def calculate_combined_energy_results(self):
energy_unit = cfg.calculation_energy_unit
if self.supply.export_costs is not None:
setattr(self.outputs,'export_energy',self.supply.export_energy)
self.export_energy = self.outputs.return_cleaned_output('export_energy')
del self.outputs.export_energy
util.replace_index_name(self.export_energy, 'FINAL_ENERGY','SUPPLY_NODE_EXPORT')
keys = ["EXPORT","EMBODIED"]
names = ['EXPORT/DOMESTIC', 'ENERGY ACCOUNTING']
for key,name in zip(keys,names):
self.export_energy = pd.concat([self.export_energy],keys=[key],names=[name])
else:
self.export_energy = None
self.embodied_energy = self.demand.outputs.return_cleaned_output('demand_embodied_energy')
self.embodied_energy = self.embodied_energy[self.embodied_energy ['VALUE']!=0]
keys = ['DOMESTIC','EMBODIED']
names = ['EXPORT/DOMESTIC', 'ENERGY ACCOUNTING']
for key,name in zip(keys,names):
self.embodied_energy = pd.concat([self.embodied_energy],keys=[key],names=[name])
self.final_energy = self.demand.outputs.return_cleaned_output('d_energy')
self.final_energy = self.final_energy[self.final_energy.index.get_level_values('YEAR')>=int(cfg.cfgfile.get('case','current_year'))]
keys = ['DOMESTIC','FINAL']
names = ['EXPORT/DOMESTIC', 'ENERGY ACCOUNTING']
for key,name in zip(keys,names):
self.final_energy = pd.concat([self.final_energy],keys=[key],names=[name])
# self.outputs.c_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)
if self.export_energy is not None:
for name in [x for x in self.embodied_energy.index.names if x not in self.export_energy.index.names]:
self.export_energy[name] = "N/A"
self.export_energy.set_index(name,append=True,inplace=True)
self.export_energy = self.export_energy.groupby(level=self.embodied_energy.index.names).sum()
self.export_energy = self.export_energy.reorder_levels(self.embodied_energy.index.names)
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.c_energy = pd.concat([self.embodied_energy,self.final_energy,self.export_energy])
self.outputs.c_energy= self.outputs.c_energy[self.outputs.c_energy['VALUE']!=0]
self.outputs.c_energy.columns = [energy_unit.upper()]
def clean_df(df, stack_years=False):
if type(df) is not pd.core.frame.DataFrame:
raise ValueError('output_type must be a pandas dataframe')
if stack_years and 'year' not in df.index.names:
df = df.stack()
util.replace_index_name(df,'year')
df.columns = ['value']
if 'year' in df.index.names:
df = df[df.index.get_level_values('year')>=int(cfg.cfgfile.get('case','current_year'))]
dct = cfg.outputs_id_map
index = df.index
try:
index.set_levels([[dct[name].get(item, item) for item in level] for name, level in zip(index.names, index.levels)], inplace=True)
index.names = [x.upper() if isinstance(x, basestring) else x for x in index.names]
except:
pass
if isinstance(df.columns,pd.MultiIndex):
columns = df.columns
columns.set_levels([[dct[name].get(item, item) for item in level] for name, level in zip(columns.names, columns.levels)], inplace=True)
columns.names = [x.upper() if isinstance(x, basestring) else x for x in columns.names]
else:
df.columns = [x.upper() if isinstance(x, basestring) else x for x in df.columns]
return df
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()]
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,cfg.primary_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",cfg.primary_geography.upper() +'_EMITTED']
for key,name in zip(keys,names):
self.direct_emissions_df = pd.concat([self.direct_emissions_df],keys=[key],names=[name])
if cfg.primary_geography+'_supply' in cfg.output_combined_levels:
keys = self.direct_emissions_df.index.get_level_values(cfg.primary_geography.upper()).values
names = cfg.primary_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 + [cfg.primary_geography.upper() +'_SUPPLY', 'SUPPLY_NODE']
keys = ['EXPORTED', 'SUPPLY-SIDE', 'DEMAND-SIDE']
names = ['EMISSIONS TYPE']
self.outputs.c_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.c_emissions, "ENERGY","FINAL_ENERGY")
util.replace_index_name(self.outputs.c_emissions, cfg.primary_geography.upper() +'-EMITTED', cfg.primary_geography.upper() +'_SUPPLY')
util.replace_index_name(self.outputs.c_emissions, cfg.primary_geography.upper() +'-CONSUMED', cfg.primary_geography.upper())
self.outputs.c_emissions= self.outputs.c_emissions[self.outputs.c_emissions['VALUE']!=0]
emissions_unit = cfg.cfgfile.get('case','mass_unit')
self.outputs.c_emissions.columns = [emissions_unit.upper()]