def export_io(self):
io_table_write_step = int(cfg.cfgfile.get('output_detail','io_table_write_step'))
io_table_years = sorted([min(cfg.supply_years)] + range(max(cfg.supply_years), min(cfg.supply_years), -io_table_write_step))
df_list = []
for year in io_table_years:
sector_df_list = []
keys = self.supply.demand_sectors
name = ['sector']
for sector in self.supply.demand_sectors:
sector_df_list.append(self.supply.io_dict[year][sector])
year_df = pd.concat(sector_df_list, keys=keys,names=name)
year_df = pd.concat([year_df]*len(keys),keys=keys,names=name,axis=1)
df_list.append(year_df)
keys = io_table_years
name = ['year']
df = pd.concat(df_list,keys=keys,names=name)
for row_sector in self.supply.demand_sectors:
for col_sector in self.supply.demand_sectors:
if row_sector != col_sector:
df.loc[util.level_specific_indexer(df,'sector',row_sector),util.level_specific_indexer(df,'sector',col_sector,axis=1)] = 0
self.supply.outputs.io = df
result_df = self.supply.outputs.return_cleaned_output('io')
keys = [self.scenario.name.upper(), cfg.timestamp]
names = ['SCENARIO','TIMESTAMP']
for key, name in zip(keys,names):
result_df = pd.concat([result_df], keys=[key],names=[name])
Output.write(result_df, 's_io.csv', os.path.join(cfg.workingdir, 'supply_outputs'))
def export_result_to_csv(self, result_name):
if result_name=='combined_outputs':
res_obj = self.outputs
elif result_name=='demand_outputs':
res_obj = self.demand.outputs
elif result_name=='supply_outputs':
res_obj = self.supply.outputs
else:
raise ValueError('result_name not recognized')
for attribute in dir(res_obj):
if not isinstance(getattr(res_obj, attribute), pd.DataFrame):
continue
result_df = getattr(res_obj, 'return_cleaned_output')(attribute)
keys = [self.scenario.name.upper(), cfg.timestamp]
names = ['SCENARIO','TIMESTAMP']
for key, name in zip(keys, names):
result_df = pd.concat([result_df], keys=[key], names=[name])
if attribute in ('hourly_dispatch_results', 'electricity_reconciliation', 'hourly_marginal_cost', 'hourly_production_cost'):
# Special case for hourly dispatch results where we want to write them outside of supply_outputs
Output.write(result_df, attribute + '.csv', os.path.join(cfg.workingdir, 'dispatch_outputs'))
else:
Output.write(result_df, attribute+'.csv', os.path.join(cfg.workingdir, result_name))
def export_io(self):
io_table_write_step = int(cfg.cfgfile.get('output_detail','io_table_write_step'))
io_table_years = sorted([min(cfg.supply_years)] + range(max(cfg.supply_years), min(cfg.supply_years), -io_table_write_step))
df_list = []
for year in io_table_years:
sector_df_list = []
keys = self.supply.demand_sectors
name = ['sector']
for sector in self.supply.demand_sectors:
sector_df_list.append(self.supply.io_dict[year][sector])
year_df = pd.concat(sector_df_list, keys=keys,names=name)
year_df = pd.concat([year_df]*len(keys),keys=keys,names=name,axis=1)
df_list.append(year_df)
keys = io_table_years
name = ['year']
df = pd.concat(df_list,keys=keys,names=name)
for row_sector in self.supply.demand_sectors:
for col_sector in self.supply.demand_sectors:
if row_sector != col_sector:
df.loc[util.level_specific_indexer(df,'sector',row_sector),util.level_specific_indexer(df,'sector',col_sector,axis=1)] = 0
self.supply.outputs.io = df
result_df = self.supply.outputs.return_cleaned_output('io')
keys = [self.scenario.name.upper(), cfg.timestamp]
names = ['SCENARIO','TIMESTAMP']
for key, name in zip(keys,names):
result_df = pd.concat([result_df], keys=[key],names=[name])
Output.write(result_df, 's_io.csv', os.path.join(cfg.workingdir, 'supply_outputs'))
def export_result_to_csv(self, result_name):
if result_name=='combined_outputs':
res_obj = self.outputs
elif result_name=='demand_outputs':
res_obj = self.demand.outputs
elif result_name=='supply_outputs':
res_obj = self.supply.outputs
else:
raise ValueError('result_name not recognized')
for attribute in dir(res_obj):
if not isinstance(getattr(res_obj, attribute), pd.DataFrame):
continue
result_df = getattr(res_obj, 'return_cleaned_output')(attribute)
keys = [self.scenario.name.upper(), cfg.timestamp]
names = ['SCENARIO','TIMESTAMP']
for key, name in zip(keys, names):
result_df = pd.concat([result_df], keys=[key], names=[name])
if attribute in ('hourly_dispatch_results', 'electricity_reconciliation', 'hourly_marginal_cost', 'hourly_production_cost'):
# Special case for hourly dispatch results where we want to write them outside of supply_outputs
Output.write(result_df, attribute + '.csv', os.path.join(cfg.workingdir, 'dispatch_outputs'))
else:
Output.write(result_df, attribute+'.csv', os.path.join(cfg.workingdir, result_name))
def export_stacked_io(self):
df = copy.deepcopy(self.supply.outputs.io)
df.index.names = [x + '_input'if x!= 'year' else x for x in df.index.names ]
df = df.stack(level=df.columns.names).to_frame()
df.columns = ['value']
self.supply.outputs.stacked_io = df
result_df = self.supply.outputs.return_cleaned_output('stacked_io')
keys = [self.scenario.name.upper(), cfg.timestamp]
names = ['SCENARIO','TIMESTAMP']
for key, name in zip(keys,names):
result_df = pd.concat([result_df], keys=[key],names=[name])
Output.write(result_df, 's_stacked_io.csv', os.path.join(cfg.workingdir, 'supply_outputs'))
def export_stacked_io(self):
df = copy.deepcopy(self.supply.outputs.io)
df.index.names = [x + '_input'if x!= 'year' else x for x in df.index.names ]
df = df.stack(level=df.columns.names).to_frame()
df.columns = ['value']
self.supply.outputs.stacked_io = df
result_df = self.supply.outputs.return_cleaned_output('stacked_io')
keys = [self.scenario.name.upper(), cfg.timestamp]
names = ['SCENARIO','TIMESTAMP']
for key, name in zip(keys,names):
result_df = pd.concat([result_df], keys=[key],names=[name])
Output.write(result_df, 's_stacked_io.csv', os.path.join(cfg.workingdir, 'supply_outputs'))
def clean_and_write(result_df, attribute):
"""
:param result_df: pandas dataframe
:param attribute: string
"""
if cfg.rio_supply_run and self.supply is not None:
keys = [self.supply.rio_scenario.upper(), cfg.timestamp]
else:
keys = [self.scenario.name.upper(), cfg.timestamp]
names = ['SCENARIO', 'TIMESTAMP']
for key, name in zip(keys, names):
result_df = pd.concat([result_df], keys=[key], names=[name])
result_df = result_df.fillna(0)
if attribute in (
'hourly_dispatch_results', 'electricity_reconciliation', 'hourly_marginal_cost', 'hourly_production_cost'):
# Special case for hourly dispatch results where we want to write them outside of supply_outputs
Output.write(result_df, attribute + '.csv', os.path.join(cfg.workingdir, 'dispatch_outputs'))
else:
Output.write(result_df, attribute + '.csv', os.path.join(cfg.workingdir, result_name))
def export_result_to_csv(self, result_name):
if result_name=='combined_outputs':
res_obj = self.outputs
elif result_name=='demand_outputs':
res_obj = self.demand.outputs
elif result_name=='supply_outputs':
res_obj = self.supply.outputs
else:
raise ValueError('result_name not recognized')
for attribute in dir(res_obj):
if not isinstance(getattr(res_obj, attribute), pd.DataFrame):
continue
result_df = getattr(res_obj, '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])
Output.write(result_df, attribute+'.csv', os.path.join(cfg.workingdir, result_name))
