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setup parser and subparsers #6

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setup parser and subparsers #6

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1f97d2f
setup parser and subparsers
gonuke Sep 3, 2020
5e17b61
replace all input() commands
gonuke Sep 3, 2020
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132 changes: 99 additions & 33 deletions economic_analysis/economic_analysis.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,6 @@
import numpy as np
import yaml
import argparse
import pandas as pd
import datetime
import statistics
Expand Down Expand Up @@ -70,19 +71,19 @@ def load_inputs():

return inputs

def initialize_real_fuel_costs(my_data):
fuel_type = input("Enter: f for fuel in $/Kwh or Alt for $/mmBTU",)
if fuel_type == 'f':
def initialize_real_fuel_costs(my_data, fuel_type):
if fuel_type == 'std':
for generation_type, generation_type_data in my_data['generation_data'].items():
for generation_variation in generation_type_data:
my_data['generation_data'][generation_type][generation_variation]['RealFuelCost'] = my_data['generation_data'][generation_type][generation_variation]['FuelCost']
elif fuel_type == 'Alt':
elif fuel_type == 'alt':
for generation_type, generation_type_data in my_data['generation_data'].items():
for generation_variation in generation_type_data:
my_data['generation_data'][generation_type][generation_variation]['RealFuelCost'] = my_data['generation_data'][generation_type][generation_variation]['AltFuelCost']* my_data['generation_data'][generation_type][generation_variation]['HeatRate']
else:
print("error enter proper fuel cost")
return my_data

def Mean(x):
return statistics.mean(x)
def StandardDeviation(x):
Expand All @@ -96,7 +97,7 @@ def UpperCILimit(x, iterations):
def LowerCILimit(x, iterations):
return Mean(x) - NinetyFivePercentCIHalfWidth(x, iterations)

def monte_carlo_analysis(my_data):
def monte_carlo_analysis(my_data, case_list, iterations):
OneNuclear = []
OneDiesel = []
OneNaturalGas = []
Expand All @@ -110,10 +111,9 @@ def monte_carlo_analysis(my_data):
FourDiesel = []
FourNaturalGas = []
ScenarioList = [OneNuclear, OneDiesel, OneNaturalGas , TwoNuclear, TwoDiesel , TwoNaturalGas, ThreeNuclear ,ThreeDiesel ,ThreeNaturalGas,FourNuclear ,FourDiesel ,FourNaturalGas]
iterations = int(input("Enter Number of Iterations for Monte Carlo Analysis:", ))
for j in range (1, iterations+1):
Monte_Carlo_List = []
Monte_Carlo_List = monte_carlo_distribution(my_data)
Monte_Carlo_List = monte_carlo_distribution(my_data, case_list)
OneNuclear.append(Monte_Carlo_List[0])
OneDiesel.append(Monte_Carlo_List[1])
OneNaturalGas.append(Monte_Carlo_List[2])
Expand Down Expand Up @@ -148,7 +148,7 @@ def monte_carlo_analysis(my_data):
dfResults['Lower CI Limit'] = dfResults.apply(lambda x: "{0:,.2f}".format(x['Lower CI Limit']), axis=1)
print(dfResults)

def monte_carlo_distribution(my_data):
def monte_carlo_distribution(my_data, case_list):
Results = []
for Scenario, cases in case_list.items():
Scenario == 'Scenario1'
Expand Down Expand Up @@ -179,7 +179,7 @@ def monte_carlo_distribution(my_data):
generation_type_two_data, interest))
return Results

def sensitivity_analysis(my_data):
def sensitivity_analysis(my_data, case_list):
interest_rates = list(my_data['model_assumptions']['SensitivityInterestRates'])
for Scenario, cases in case_list.items():
for generation_type_one, generation_type_two in cases:
Expand Down Expand Up @@ -208,10 +208,10 @@ def sensitivity_analysis(my_data):
dfResults.to_excel(writer_orig, index=False, sheet_name='Sensitivity Inputs')
writer_orig.save()

def main_code(my_data):
generation_variation_nuclear = input("Enter Nuclear: Best, Median, Worst",)
generation_variation_diesel = input("Enter Diesel: Best, Median, Worst",)
generation_variation_naturalgas = input("Enter Natural Gas: Best, Median, Worst",)
def main_code(my_data, case_list,
generation_variation_nuclear,
generation_variation_naturalgas,
generation_variation_diesel):
generation_variation_none = 'Median'
generation_variation = { 'Nuclear': generation_variation_nuclear,
'Diesel': generation_variation_diesel,
Expand All @@ -228,25 +228,91 @@ def main_code(my_data):
dfResults = pd.DataFrame(Results, columns = ['Scenario', 'Power Type','Backup', 'interest','Annual Payment'])
dfResults['Annual Payment'] = dfResults.apply(lambda x: "{0:,.2f}".format(x['Annual Payment']), axis=1)
print(dfResults)


def read_args():

default_interest_rate = 0.02

parser = argparse.ArgumentParser()

subparsers = parser.add_subparsers()

parser_mc = subparsers.add_parser('monte_carlo')
parser_sens = subparsers.add_parser('sensitivity')
parser_single = subparsers.add_parser('single')

for subparser in [parser_mc, parser_single]:
subparser.add_argument('-i', '--interest_rate', type=float,
help="interest rate to use for this analysis (default: {:.2f})".format(default_interest_rate),
default=default_interest_rate)
default_samples = 20
parser_mc.add_argument('-N', '--num_samples', type=int,
help="number of Monte Carlo samples (default: {:d})".format(default_samples),
default=default_samples)
parser_mc.set_defaults(func=run_mc)

if __name__ == "__main__":
Results = []
my_data = load_inputs()
my_data = initialize_real_fuel_costs(my_data)
case_list = {
'Scenario1': list(zip(['None','None','None'], ['Nuclear','Diesel','NaturalGas'])),
'Scenario2': list(zip(['Nuclear','Diesel','NaturalGas'],['None','None','None'])),
'Scenario3': list(zip(['Nuclear','Diesel','NaturalGas'], ['Diesel','NaturalGas','Diesel'])),
'Scenario4': list(zip(['Nuclear','Diesel','NaturalGas'], ['Diesel','NaturalGas','Diesel']))

parser_sens.set_defaults(func=run_sensitivity)

case_choices = ['Best', 'Median', 'Worst']
parser_single.add_argument('--nuclear-case',choices=case_choices,
help="Which case to use for nuclear costs",
default='Median')
parser_single.add_argument('--gas-case',choices=case_choices,
help="Which case to use for natural gas costs",
default='Median')
parser_single.add_argument('--diesel-case',choices=case_choices,
help="Which case to use for diesel costs",
default='Median')
parser_single.set_defaults(func=run_single)

for subparser in [parser_mc, parser_sens, parser_single]:
subparser.add_argument('-f', '--fuelcost_type', choices=['std', 'alt'],
default='std')
subparser.add_argument('filename', help="YAML file with economic data")

return parser.parse_args()

def init(fuelcost_type):

my_data = load_inputs()
my_data = initialize_real_fuel_costs(my_data, fuelcost_type)

case_list = {
'Scenario1': list(zip(['None','None','None'], ['Nuclear','Diesel','NaturalGas'])),
'Scenario2': list(zip(['Nuclear','Diesel','NaturalGas'],['None','None','None'])),
'Scenario3': list(zip(['Nuclear','Diesel','NaturalGas'], ['Diesel','NaturalGas','Diesel'])),
'Scenario4': list(zip(['Nuclear','Diesel','NaturalGas'], ['Diesel','NaturalGas','Diesel']))
}
model_type = input("s / m / mc-- sensitivity or main or monte carlo",)
if model_type == "m":
interest = float(input("Enter interest rate in decimal: ex. .02 for 2%",))
main_code(my_data)
elif model_type == "s":
sensitivity_analysis(my_data)
elif model_type == 'mc':
interest = float(input("Enter interest rate in decimal: ex. .02 for 2%",))
monte_carlo_analysis(my_data)
else:
print("error: s or m ONLY")

return my_data, case_list

def run_mc(args):

my_data, case_list = init(args.fuelcost_type)

interest = args.interest_rate
monte_carlo_analysis(my_data, case_list, args.num_samples)

def run_sensitivity(args):

my_data, case_list = init(args.fuelcost_type)

sensitivity_analysis(my_data, case_list)

def run_single(args):

my_data, case_list = init(args.fuelcost_type)

interest = args.interest_rate
main_code(my_data, case_list, args.nuclear_case, args.gas_case, args.diesel_case)


if __name__ == "__main__":
args = read_args()

Results = []

args.func(args)