In [12]:

    

import numpy as np
import tqdm
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()


    

In [13]:

    

# flags
people_num = 500
init_coin = 50
iterations = 10000
plot_every = 5000
give_out_coin_each_iteration = 1
get_coin_each_iteration = 1


    

In [14]:

    

class Person():
    def __init__(self, initmoney : int):
        self.coins = init_coin
    
    def give_out_coin(self, coin_num : int, allow_negative=False) -> int:
        if allow_negative == False:
            if self.coins - coin_num >= 0:
                self.coins -= coin_num
                return coin_num
            else:
                return 0
        else:
            self.coins -= coin_num
            return coin_num
    
    def get_coin(self, coin_got : int) -> bool:
        self.coins += coin_got
        return True


    

In [15]:

    

people = [Person(init_coin) for _ in range(people_num)]
coin_distribution_1 = []

# go for test with 1000 iteration
for i in range(iterations):
    coin_pool = 0 # init coin_pool
    
    # for every iteration everybody loses 1 coin, unless he doesn't have one
    for person in people:
        # optional: 20% tax for person's current coin
#         coin_pool += person.give_out_coin(person.coins // 5)
        
        coin_pool += person.give_out_coin(give_out_coin_each_iteration)
    
    # now distribute coin from coin_pool to each person
    coin_goes = np.random.choice(people_num, coin_pool)
    for go in coin_goes:
        people[go].get_coin(get_coin_each_iteration)
    
    if i % plot_every == 0:
        coin_distribution = [person.coins for person in people]
        sns.distplot(coin_distribution, bins=20)
        plt.xlabel('coins')
        plt.ylabel('distribution')
        plt.title('% of people with certain coin number distplot')
        plt.show()

people.sort(key=lambda x:x.coins)


    

In [16]:

    

coin_dist = [person.coins for person in people]
plt.plot(coin_dist)


    

    
Out[16]:





[<matplotlib.lines.Line2D at 0x114ed8208>]

In [17]:

    

plt.xlabel('person index')
plt.ylabel('his coins')
plt.show()


    

In [18]:

    

people_2 = [Person(init_coin) for _ in range(people_num)]
coin_distribution_2 = []

# go for test with 1000 iteration
for i in range(iterations):
    coin_pool = 0 # init coin_pool
    
    # for every iteration everybody loses 1 coin, unless he doesn't have one
    for person in people_2:
        # optional: 20% tax for person's current coin
#         coin_pool += person.give_out_coin(person.coins // 5)
        
        coin_pool += person.give_out_coin(give_out_coin_each_iteration, allow_negative=True)
    
    # now distribute coin from coin_pool to each person
    coin_goes = np.random.choice(people_num, coin_pool)
    for go in coin_goes:
        people_2[go].get_coin(get_coin_each_iteration)
    
    if i % plot_every == 0:
        coin_distribution_2 = [person.coins for person in people_2]
        sns.distplot(coin_distribution_2, bins=20)
        plt.xlabel('coins')
        plt.ylabel('distribution')
        plt.title('% of people with certain coin number distplot')
        plt.show()


    

In [19]:

    

sns.distplot(coin_distribution)
sns.distplot(coin_distribution_2)
plt.show()


    

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