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']
# Write the output.
all_results.to_csv('Q:/IO_output/structural_scenarios_2012.csv') # Remote version
all_results.to_csv('C:/Users/qread/Dropbox/projects/foodwaste/Data/structural_scenarios_2012.csv') # Local version
def eeio_lcia_contributions(model_path, demand_values, demand_codes):
eeio = useeiopy.assemble(model_path)
demand_dict = dict(zip(demand_codes, demand_values))
result = iomb.calculate(eeio, demand_dict)
lcia_contr = result.lcia_contributions
return lcia_contr
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)
# Create dictionary to map the old and new name
key_mapping = dict(zip(key_demand, drc_index_sorted))
# Correct names in original dictionary
dict_corrected = dict(
(key_mapping[key], value) for (key, value) in demand_dict.items())
return (dict_corrected)
# Get names of the sectors
sector_names = drc.index.values.tolist()
# Get dairy code
dairy_code = [x for x in sector_names if re.search('dairies', x)]
dairy_result = iomb.calculate(useeio1pt1, {dairy_code[0]: 1})
# Get inputs and outputs of dairy sector
dairy_inputs = drc.loc[:, dairy_code[0]]
dairy_outputs = drc.loc[dairy_code[0], :]
# Milk products that use output from dairies
dairyproducts_code = [x for x in sector_names if re.search('^3115', x)]
cheese_result = iomb.calculate(useeio1pt1, {dairyproducts_code[0]: 1})
# Inputs and outputs of cheese sector
cheese_inputs = drc.loc[:, dairyproducts_code[
0]] # 33 cents of dairy output is required to produce 1 dollar of cheese.
cheese_outputs = drc.loc[dairyproducts_code[
0], :] # 1/2 cent of cheese output is required to produce 1 dollar of restaurant value