Archero simulation of chests

archero_chest_simulation.py

import random
import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt

number_of_different_items = 16
price_of_golden_chest = 60
price_of_obsidian_chest = 300
golden_probabilities = {0.8: 1, 0.2:3}
obsidian_probabilities = {0.5:3, 0.44:9, 0.06:27}
number_of_runs = 10000

#Simulating golden
expended_list = []
for i in range(number_of_runs):
    items = dict.fromkeys(range(number_of_different_items),0)
    expended = 0
    while(all([x < 27 for x in items.values()])):
        expended += price_of_golden_chest
        item = random.randrange(0,number_of_different_items)
        quality = random.random()
        if(quality > 0.8):
            items[item] += 3
        else:
            items[item] += 1

    expended_list.append(expended)

print(f"Median of expended to get an item: {np.percentile(expended_list, 50)}")
print(f"90% of the time you will get it for less than: {np.percentile(expended_list, 90)}")
print(f"10% of the time you will get it for less than: {np.percentile(expended_list, 10)}")

h = expended_list
h.sort()
hmean = np.mean(h)
hstd = np.std(h)
pdf = stats.norm.pdf(h, hmean, hstd)
plt.plot(h, pdf) 

#Simulating obsidian
expended_list = []
for i in range(number_of_runs):
    items = dict.fromkeys(range(number_of_different_items),0)
    expended = 0
    while(all([x < 27 for x in items.values()])):
        expended += price_of_obsidian_chest
        item = random.randrange(0,number_of_different_items)
        quality = random.random()
        if(quality <= 0.5):
            items[item] += 3
        elif(quality <= 0.94):
            items[item] += 9
        else:
            items[item] += 27
    expended_list.append(expended)
    
print(f"Median of expended to get an item: {np.percentile(expended_list, 50)}")
print(f"90% of the time you will get it for less than: {np.percentile(expended_list, 90)}")
print(f"10% of the time you will get it for less than: {np.percentile(expended_list, 10)}")

h = expended_list
h.sort()
hmean = np.mean(h)
hstd = np.std(h)
pdf = stats.norm.pdf(h, hmean, hstd)
plt.plot(h, pdf)