outputs_path = output_directory
varname = 'Total_Emissions_by_A'
scaling = 1
ylabel = 'MMT CO$_2$e'
index_name = 'ARB_Sectors'
year = 2050
title = 'Economywide GHG Emissions in ' + str(year)
#base_case = 'Current Policy Reference'
invar = pd.read_csv(os.path.join(input_directory, varname + '.csv'),
na_values='NAN')
plot_util.stacked_bar(invar,
year,
varname,
output_directory,
index_name,
fmt=fmt,
xkeys=cases,
ykeys=keys,
labels_dict=labels_dict,
scaling=scaling,
ylabel=ylabel,
title=title,
xlabels=xlabels,
other_key=other_key,
fontsize=fontsize,
filename=filename)
'''
cases = ['FONG Medium Building Electrification', 'FONG Medium Buildings Branching High',
'FONG Medium Buildings Branching Low']
xlabels = ['Delayed\nElectrification', 'Slower\nElectrification', 'Mixed with\nGas HPs']
'''
ykeys = ['Biomethane', 'Renewable Diesel', 'Renewable Gasoline and Ethanol', 'Renewable Jet Fuel']
filename = 'Biofuel_Production_Bookends' # 'Biofuel_Production_Multiprong'
labels_dict = {
'Jet Fuel (Kerosene)': 'Renewable Jet Fuel', 'Gasoline': 'Renewable Gasoline and Ethanol',
'Diesel': 'Renewable Diesel', 'Pipeline Gas': 'Biomethane'
}
outputs_path = output_directory
varnames = ['Annual_Biofuel_Produ']
scaling = [1000 / 1.055] # EJ to TBTU
ylabel = ['TBTU']
index_name = 'Final_Biofuels'
year = 2050
title = 'Biofuel Production in ' + str(year)
for i in range(len(varnames)):
varname = varnames[i]
invar = pd.read_csv(os.path.join(input_directory, varname + '.csv'), na_values='NAN')
scaling_in = scaling[i]
ylabel_in = ylabel[i]
plot_util.stacked_bar(invar, year, varname, output_directory, index_name, fmt=fmt, xkeys=cases, ykeys=ykeys,
scaling=scaling_in, ylabel=ylabel_in, title=title, xlabels=xlabels, labels_dict=labels_dict, filename=filename)