Setup

import math
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.ticker import StrMethodFormatter as SMF
import numpy as np
import pandas as pd
pd.set_option('display.precision', 2)
from random import shuffle as RS
import seaborn as sns
import time

Constants and Helpers

def getDeck():
    deck = []
    for m in range(13):
        deck.append(str(m))
    deck = deck * 4
    return(deck)

def collCardG(deck) :
    
    p = len(deck)/2
    
    while len(deck) > 0 :
        if deck.pop() == deck.pop() :
            deal = p - len(deck)/2
            return("Loss", deal)
    
    return("Win", p+0.1)

def monteCarlo(n, t) :
    record = []
    for i in range(n):
        deck = getDeck()
        if t == 'ec':
            deck *= 2
        RS(deck)
        record.append(collCardG(deck)[1])
        
    freqDist = {}
    for r in record :
        if r in freqDist.keys():
            freqDist[r] += 1
        else :
            freqDist[r] = 1
                
    freqDist = dict(sorted(freqDist.items()))
    df = pd.DataFrame(data = {"Round": freqDist.keys(),
                              "Frequency": freqDist.values(),
                              "Outcome": ["Loss"]*(len(freqDist)-1)+["Win"]})
    
    df.loc[len(df)-1, 'Round'] -= 0.1
    return(df)

def barHelper(df, n, t) :
    
    df['Percent'] = round(100.00*df['Frequency']/df['Frequency'].sum(), 1).astype(str) + '%'
    
    pad = df.loc[len(df)-2, 'Frequency']
    
    if t == 'fiddler' :
        d = 1
        xmax = 26
        ymax = 0.25 * n
        title = ' (1 Deck)'
        x1 = 0.5
        y1 = n/1000
        x2 = 0.88*xmax
        y2 = n/8 
    else :
        d = 2
        xmax = 52
        ymax = 0.075 * n
        title = ' (2 Decks)'
        x1 = 0.9
        y1 = n/2000
        x2 = 0.91*xmax
        y2 = n/80
    sns.set()
    fig = plt.figure(figsize = (1 
    
    fig.savefig("2024.04.20" + t + ".png", bbox_inches = 'tight')

start_time = time.time()
df = monteCarlo(1000000000, 'fiddler')
print(df)
print(time.time() - start_time)
barHelper(df, 1000000000, 'fiddler')

    Round  Frequency Outcome
0     1.0   58826949    Loss
1     2.0   55327340    Loss
2     3.0   52028581    Loss
3     4.0   48930957    Loss
4     5.0   46023654    Loss
5     6.0   43295451    Loss
6     7.0   40713412    Loss
7     8.0   38301018    Loss
8     9.0   36027836    Loss
9    10.0   33889430    Loss
10   11.0   31887613    Loss
11   12.0   30012269    Loss
12   13.0   28231474    Loss
13   14.0   26560500    Loss
14   15.0   25007064    Loss
15   16.0   23522135    Loss
16   17.0   22137058    Loss
17   18.0   20836691    Loss
18   19.0   19615296    Loss
19   20.0   18465453    Loss
20   21.0   17377988    Loss
21   22.0   16367205    Loss
22   23.0   15402573    Loss
23   24.0   14499283    Loss
24   25.0   13652776    Loss
25   26.0   12856677    Loss
26   26.0  210203317     Win
19522.50635576248

start_time = time.time()
df2 = monteCarlo(1000000000, 'ec')
print(df2)
print(time.time() - start_time)
barHelper(df2, 1000000000, 'ec')

    Round  Frequency Outcome
0     1.0   67965157    Loss
1     2.0   63343445    Loss
2     3.0   59007147    Loss
3     4.0   54981412    Loss
4     5.0   51261835    Loss
5     6.0   47759221    Loss
6     7.0   44521266    Loss
7     8.0   41468445    Loss
8     9.0   38654442    Loss
9    10.0   36029327    Loss
10   11.0   33576942    Loss
11   12.0   31290397    Loss
12   13.0   29167309    Loss
13   14.0   27172757    Loss
14   15.0   25333365    Loss
15   16.0   23613007    Loss
16   17.0   22013277    Loss
17   18.0   20514757    Loss
18   19.0   19125547    Loss
19   20.0   17818223    Loss
20   21.0   16612738    Loss
21   22.0   15481175    Loss
22   23.0   14427718    Loss
23   24.0   13451323    Loss
24   25.0   12537282    Loss
25   26.0   11685440    Loss
26   27.0   10899545    Loss
27   28.0   10152410    Loss
28   29.0    9463953    Loss
29   30.0    8824934    Loss
30   31.0    8233891    Loss
31   32.0    7673357    Loss
32   33.0    7155056    Loss
33   34.0    6670221    Loss
34   35.0    6215454    Loss
35   36.0    5794870    Loss
36   37.0    5405290    Loss
37   38.0    5038693    Loss
38   39.0    4698832    Loss
39   40.0    4382015    Loss
40   41.0    4083250    Loss
41   42.0    3808199    Loss
42   43.0    3551525    Loss
43   44.0    3310298    Loss
44   45.0    3087566    Loss
45   46.0    2880665    Loss
46   47.0    2687162    Loss
47   48.0    2504066    Loss
48   49.0    2330862    Loss
49   50.0    2173025    Loss
50   51.0    2026110    Loss
51   52.0    1892437    Loss
52   52.0   26243360     Win
35273.89794301987

Rohan Lewis

2024.04.22