import numpy as np

def main():
    part_a()
    #part_b()
    part_c()

def part_a():
    samples = np.array([0])
    samples = np.delete(samples,0)
    for i in range(1000):
        seats_sold,profit,time_elapsed = run_simulation(100)
        samples = np.append(samples,time_elapsed)
    avg_min = np.average(samples)
    avg_days = avg_min/1440.0
    print('Average number of days to sell 150 tickets at $100 each is: ' + str(avg_days) + '!')

def part_b():

    #first round
    #cur_cost = 450
    #dist = 5

    #second round
    #cur_cost = 410
    #dist = 1
    max_list = [0] * 32
    max_list = np.array(max_list)
    for k in range(32):
        print('iteration ' + str(k))
        cur = 400
        dist = 5
        results = [0] * 13
        results = np.array(results)
        for i in range(13):
            print('(' + str(i) + ')')
            samples = np.array([0])
            samples = np.delete(samples,0)
            for j in range(500):
                seats_sold,profit,time_elapsed = run_simulation(cur)
                samples = np.append(samples,profit)
            avg_prof = np.average(samples)
            results[i] = avg_prof
            cur = cur + dist
        max_val = dist*(np.argmax(results)) + 400
        max_list[k] = max_val
    print(max_list)

    '''
    while(True):
        to_test = np.array([cur_cost - dist, cur_cost, cur_cost + dist])
        results = np.array([0.0,0.0,0.0])
        for i in range(3):
            samples = np.array([0])
            samples = np.delete(samples,0)
            for j in range(500):
                seats_sold,profit,time_elapsed = run_simulation(to_test[i])
                samples = np.append(samples,profit)
            avg_prof = np.average(samples)
            results[i] = avg_prof
        if(results[0] < results[1] and results[1] > results[2]):
            print('maximum of $' + str(results[1]) + ' identified at X = $' + str(to_test[1]))
            break
        if(results[0] > results[1] and results[1] < results[2]):
            print('minimum of $' + str(results[1]) + ' identified at X = $' + str(to_test[1]))
            if(results[0] > results[2]):
                cur_cost = to_test[0]
            else:
                cur_cost = to_test[2]
        if(results[0] < results[1] and results[1] < results[2]):
            print('moving up to X = $' + str(to_test[2]))
            cur_cost = to_test[2]
        if(results[0] > results[1] and results[1] > results[2]):
            print('moving down to X = $' + str(to_test[0]))
            cur_cost = to_test[1]
    '''




def part_c():
    pass

def run_simulation(init_cost):
    total_time = 60 * 24.0 * 30
    mean_time = 120.0
    seats = 150
    sold = 0
    cur_cost = init_cost
    profit = 0.0
    time_elapsed = 0.0

    calls = get_calls(mean_time, total_time)

    for i in range(len(calls)):
        time_elapsed = time_elapsed + calls[i]
        probability = get_probability(cur_cost,sold)
        drawn_sample = np.random.uniform()

        if(drawn_sample <= probability):
            profit = profit + cur_cost
            sold = sold + 1
            if(sold == seats):
                break

    return sold,profit,time_elapsed

def get_calls(mean, time):
    calls = np.array([0])
    calls = np.delete(calls,0)
    cur_sum = 0.0
    while(True):
        val = (np.random.exponential(mean,1))[0]
        if(cur_sum + val > time):
            break
        calls = np.append(calls,val)
        cur_sum = cur_sum + val
    return calls

def get_probability(X,N):
    f = (300 - np.e**(X/100.0))/600
    g = N/350.0
    p = np.min([1, np.max([f,g])])
    return p

if __name__ == '__main__':
    main()