'''
    DS2500
    Spring 2025

    Sample code from class -- monte carlo simulation
    to determine whether we should open a bar on Huntington Ave
    (but NOT on campus property)

    We sell:
    * Laney's LI Iced Tea
    * Soda
    * Wings

    Prob distribution Iced tea: 100 @ 40%,200 @ 20%, 500 @ 2%
    Profit for wings: $3.50

    EV comes from weighted average: .4 * 100 + .2 * 200 + .02 * 500
    Simulation comes from choosing based on prob distribution:
        - sim for a day of sales: 100 OR 200 OR 500
        - with a 40% chance of 100, 20% chance of 200, 2% chance of 500
'''

import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt

SIMS = 10000
DAYS = 365
START = 750000

NUMS = [100, 200, 500]
LI = [.6, .38, .02]
SODA = [.7, .1, .2]
WINGS = [.35, .15, .5]
LI_PROF = 8
SODA_PROF = 2
WING_PROF = 3.5
DAILY_OP = 500

def main():
    # start off with expected value (always the same, based as a weighted average)
    ev_li = np.average(NUMS, weights = LI)
    ev_soda = np.average(NUMS, weights = SODA)
    ev_wings = np.average(NUMS, weights = WINGS)
    print(f"Expected values: {ev_li, ev_soda, ev_wings}")

    # how much profit do we expect to make in a year
    ev_profit = (ev_li * LI_PROF + ev_soda * SODA_PROF + ev_wings * WING_PROF - DAILY_OP) * \
                DAYS - START
    print(f"Expected profit in a year: ${ev_profit}")

    # now, on to simulations
    # run this simulation a bunch of times
    outcomes = []
    for _ in range(SIMS):
        # simulate the one day 365 times
        yearly_profit = 0
        for _ in range(DAYS):
            # start with one day
            daily_li = np.random.choice(NUMS, p = LI) * LI_PROF
            daily_soda = np.random.choice(NUMS, p = SODA) * SODA_PROF
            daily_wings = np.random.choice(NUMS, p = WINGS) * WING_PROF
            daily_profit = daily_li + daily_soda + daily_wings - DAILY_OP
            yearly_profit += daily_profit
        yearly_profit -= START
        outcomes.append(yearly_profit)
        # print(f"After a year of simulated business, we made ${yearly_profit}")

    # how did we do?
    sns.histplot(outcomes, color = "magenta")
    plt.show()




if __name__ == "__main__":
    main()