metric_dict = {}
all_dict = {}
front_commods = ['sourceout', 'enrichmentout', 'frmixerout', 'moxmixerout']
add = '-buffer0'
# add = sys.argv[1]
name = 'eg01-eg29-flatpower-d3ploy' + add
for calc_method in calc_methods:
output_file = name + '-' + calc_method + '.sqlite'
all_dict['power'] = tester.supply_demand_dict_driving(
output_file, demand_eq, 'power')
metric_dict = tester.metrics(all_dict['power'], metric_dict, calc_method,
'power', True)
for commod in front_commods:
all_dict[commod] = tester.supply_demand_dict_nondriving(
output_file, commod, True)
metric_dict = tester.metrics(all_dict[commod], metric_dict,
calc_method, commod, True)
commod = 'lwrpu'
all_dict[commod] = tester.supply_demand_dict_nond3ploy(output_file, commod)
metric_dict = tester.metrics(all_dict[commod], metric_dict, calc_method,
commod, True)
commod = 'frpu'
all_dict[commod] = tester.supply_demand_dict_nond3ploy(output_file, commod)
metric_dict = tester.metrics(all_dict[commod], metric_dict, calc_method,
True, True, False, 1)
front_commods = ['sourceout', 'enrichmentout']
back_commods = [
'lwrstorageout', 'frstorageout', 'lwrout', 'frout', 'lwrreprocessingwaste',
'frreprocessingwaste', 'frtru', 'lwrtru'
]
for commod in front_commods:
all_dict[commod] = tester.supply_demand_dict_nondriving(
output_file, commod, True)
name = 'B2000-' + commod
plotter.plot_demand_supply_agent(all_dict[commod],
agent_entry_dict[commod], commod, name,
True, True, False, 1)
metric_dict = tester.metrics(all_dict[commod], metric_dict, calc_method,
commod, True)
for commod in back_commods:
all_dict[commod] = tester.supply_demand_dict_nondriving(
output_file, commod, False)
name = 'B2000-' + commod
plotter.plot_demand_supply_agent(all_dict[commod],
agent_entry_dict[commod], commod, name,
False, True, False, 1)
metric_dict = tester.metrics(all_dict[commod], metric_dict, calc_method,
commod, False)
df = pd.DataFrame(metric_dict)
df.to_csv('B2000-' + calc_method + '.csv')
name = 'scenario_1_input_' + calc_method
input_file = name + '.json'
output_file = name + '.sqlite'
with open(input_file, 'w') as f:
json.dump(scenario_1_input[calc_method], f)
s = subprocess.check_output(['cyclus', '-o', output_file, input_file],
universal_newlines=True, env=ENV)
# Initialize dicts
all_dict_fuel = {}
all_dict_fuel = tester.supply_demand_dict_driving(output_file, demand_eq, 'fuel')
plotter.plot_demand_supply(
all_dict_fuel, 'fuel', name,True)
metric_dict = tester.metrics(all_dict_fuel,metric_dict,calc_method,'fuel',True)
df = pd.DataFrame(metric_dict)
df.to_csv('scenario_1_output.csv')
##########################################################################################
######################################SCENARIO 2##########################################
# scenario 2, source -> reactor (cycle time = 1, refuel time = 0) -> sink
scenario_2_input = {}
demand_eq = "1000*t"
for calc_method in calc_methods:
scenario_2_input[calc_method] = copy.deepcopy(scenario_template)
scenario_2_input[calc_method]["simulation"].update({"facility": [{
"config": {"Source": {"outcommod": "fuel",
True,
False,
False)
plotter.plot_demand_supply_agent(
all_dict_spent_fuel,
agent_entry_dict["spent_fuel"],
"spent_fuel",
name + " Spent Fuel",
False,
False,
False,
)
metric_dict = tester.metrics(
all_dict_power,
metric_dict,
calc_method,
"power",
True)
metric_dict = tester.metrics(
all_dict_fuel,
metric_dict,
calc_method,
"fuel",
True)
metric_dict = tester.metrics(
all_dict_spent_fuel, metric_dict, calc_method, "spent_fuel", False
)
df = pd.DataFrame(metric_dict)
df.to_csv("scenario.csv")
all_dict_power = tester.supply_demand_dict_driving(
output_file, demand_eq, 'power')
all_dict_fuel = tester.supply_demand_dict_nondriving(
output_file, 'fuel',True)
all_dict_spentfuel = tester.supply_demand_dict_nondriving(
output_file, 'spentfuel',False)
# plots demand, supply, calculated demand, calculated supply for the scenario for each calc method
plotter.plot_demand_supply(all_dict_power, 'power', name, True)
name2 = "scenario_5_input_"+ calc_method +"_fuel"
plotter.plot_demand_supply(all_dict_fuel, 'fuel', name2, True)
name3 = "scenario_5_input_"+ calc_method +"_spentfuel"
plotter.plot_demand_supply(all_dict_spentfuel, 'spentfuel', name3, False)
metric_dict = tester.metrics(all_dict_power,metric_dict,calc_method,'power',True)
metric_dict = tester.metrics(all_dict_fuel,metric_dict,calc_method,'fuel',True)
metric_dict = tester.metrics(all_dict_spentfuel,metric_dict,calc_method,'spentfuel',False)
df = pd.DataFrame(metric_dict)
df.to_csv('scenario_5_output.csv')
######################################SCENARIO 6##########################################
# scenario 6, source -> reactor (cycle time = 1, refuel time = 0) -> storage -> sink
scenario_6_input = {}
demand_eq = "1000*t"
for calc_method in calc_methods:
scenario_6_input[calc_method] = copy.deepcopy(scenario_template)
scenario_6_input[calc_method]["simulation"].update({"facility": [
False,
False,
False)
name5 = "scenario_7_input_" + calc_method + "_reactor2output"
plotter.plot_demand_supply_agent(
all_dict['reactor2output'],
agent_entry_dict['reactor1output'],
'reactor2output',
name5,
False,
False,
False)
metric_dict = tester.metrics(
all_dict['power'],
metric_dict,
calc_method,
'power',
True)
metric_dict = tester.metrics(
all_dict['sourceoutput'],
metric_dict,
calc_method,
'sourceoutput',
True)
metric_dict = tester.metrics(
all_dict['reactor1output'],
metric_dict,
calc_method,
'reactor1output',
False)
metric_dict = tester.metrics(
env=ENV)
# Initialize dicts
all_dict = {}
agent_entry_dict = {}
all_dict['power'] = tester.supply_demand_dict_driving(
output_file, demand_eq, 'power')
all_dict['sourceoutput'] = tester.supply_demand_dict_nondriving(
output_file, 'sourceoutput', True)
agent_entry_dict['power'] = tester.get_agent_dict(output_file,
['reactor1', 'reactor2'])
agent_entry_dict['sourceoutput'] = tester.get_agent_dict(
output_file, ['source'])
# plots demand, supply, calculated demand, calculated supply for the scenario for each calc method
plotter.plot_demand_supply_agent(all_dict['power'],
agent_entry_dict['power'], 'power', name,
True)
name2 = "scenario_7_input_" + calc_method + "_sourceoutput"
plotter.plot_demand_supply_agent(all_dict['sourceoutput'],
agent_entry_dict['sourceoutput'],
'sourceoutput', name2, True)
metric_dict = tester.metrics(all_dict['power'], metric_dict, calc_method,
'power', True)
metric_dict = tester.metrics(all_dict['sourceoutput'], metric_dict,
calc_method, 'sourceoutput', True)
df = pd.DataFrame(metric_dict)
df.to_csv('scenario_7_output.csv')
