def calculate_EEIO_model(model, year, location, demandtype, perspective,
include_data_quality):
"""
Calculates an EEIO model result
:param model: A useeopy Model object
:param year: int, year of demand
:param location: string, location acronym of demand
:param demandtype: string, either 'production' or 'consumption'
:param perspective: string, either 'DIRECT' or 'INDIRECT'
:return: A useeiopy Result object
"""
result = {}
demandfile = model.path + model.name + "_FinalDemand.csv"
demandcolumnname = str(year) + '_' + location + "_" + demandtype
demand = dd.demandtodict(demandcolumnname, demandfile)
if (perspective == 'FINAL'):
p = calc.FINAL_PERSPECTIVE
r = calc.calculate(model.model, demand, p)
result['LCI_f'] = r.lci_contributions.transpose()
result['LCIA_f'] = r.lcia_contributions.transpose()
elif (perspective == 'DIRECT'):
p = calc.DIRECT_PERSPECTIVE
r = calc.calculate(model.model, demand, p)
result['LCI_d'] = r.lci_contributions.transpose()
result['LCIA_d'] = r.lcia_contributions.transpose()
else:
log.error('Perspective must be DIRECT or FINAL.')
new_result = Result(result, model, location, demandtype, perspective, year,
include_data_quality)
return new_result
def calculate(model, year, location, demandtype, perspective, modelname,
modelpath):
demandfile = modelpath + modelname + "_FinalDemand.csv"
demandcolumnname = str(year) + '_' + location + "_" + demandtype
demand = dd.demandtodict(demandcolumnname, demandfile)
if (perspective == 'FINAL'):
p = calc.FINAL_PERSPECTIVE
else:
p = calc.DIRECT_PERSPECTIVE
result = calc.calculate(model, demand, p)
lciacontributions = result.lcia_contributions.transpose()
lciaflowcontributions = result.lcia_flow_contributions.transpose()
resultsfolder = modelpath + "results/"
if os.path.exists(resultsfolder) is False:
os.mkdir(resultsfolder)
lciacontributions.to_csv(resultsfolder + modelname + "_" + str(year) +
"_" + location + "_" + demandtype + "_" +
perspective + "_" + "lciacontributions.csv")
lciaflowcontributions.to_csv(resultsfolder + modelname + "_" + str(year) +
"_" + location + "_" + demandtype + "_" +
perspective + "_" +
"lciaflowcontributions.csv")
eeio0 = useeiopy.assemble("USEEIO2012_scen0") # Baseline
eeio1 = useeiopy.assemble("USEEIO2012_scen1")
eeio2 = useeiopy.assemble("USEEIO2012_scen2")
eeio3 = useeiopy.assemble("USEEIO2012_scen3")
drc = eeio0.drc_matrix.copy()
demand2012_file = 'C:/Users/qread/Dropbox/projects/foodwaste/Code/USEEIO-master/useeiopy/Model Builds/USEEIO2012/USEEIO2012_FinalDemand.csv'
# Results across all sectors of the economy.
demand_dict2012 = dd.demandtodict('2012_US_Consumption', demand2012_file)
demand_dict2012_corrected = correct_demand_names(demand_dict2012, drc)
result0 = iomb.calculate(eeio0, demand_dict2012_corrected)
result1 = iomb.calculate(eeio1, demand_dict2012_corrected)
result2 = iomb.calculate(eeio2, demand_dict2012_corrected)
result3 = iomb.calculate(eeio3, demand_dict2012_corrected)
result0.lcia_total
result1.lcia_total
result2.lcia_total
result3.lcia_total
all_results = pandas.concat([result0.lcia_total, result1.lcia_total, result2.lcia_total, result3.lcia_total], axis = 1)
all_results.columns = ['result0','result1','result2','result3']
def get_impacts(scenario_name, scenario_csv):
demand_dict = dd.demandtodict(scenario_name, scenario_csv)
demand_dict_corrected = correct_demand_names(demand_dict, drc)
result = iomb.calculate(useeio1pt1, demand_dict_corrected)
return (result.lcia_total)