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# -*- coding: utf-8 -*-
"""
@author: Colton Smith
"""
#http://pydealer.readthedocs.io/en/latest/index.html
import pydealer
import numpy as np
import pandas as pd
import seaborn as sb
import matplotlib.pyplot as plt
deck = pydealer.Deck()
pen = [0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95]
multi = [1,2,3,4,5,6,7,8]
sims = 10000
def new_shoe(m_s):
shoe = pydealer.Stack()
for i in range(0,m_s):
shoe.add(deck)
shoe.shuffle()
return shoe
counts = {
"Ace": -1,
"King": -1,
"Queen": -1,
"Jack": -1,
"10": -1,
"9": 0,
"8": 0,
"7": 0,
"6": 1,
"5": 1,
"4": 1,
"3": 1,
"2": 1
}
### Save Parameter Combination Results ###
s_pen = []
s_multi = []
s_points = []
s_time = []
s_var = []
for m in range(0,len(multi)):
for p in range(0,len(pen)):
m_s = multi[m]
p_s = pen[p]
points = []
time = []
var = []
for i in range(0,sims):
rcount_hist = []
tcount_hist = []
rcount = 0
tcount = 0
shoe = new_shoe(m_s)
cut = int(round(shoe.size*p_s,0))
for j in range(0,cut):
hand = shoe.deal(1)
card = hand[0].value
c_count = counts.get(card)
rcount += c_count
if (shoe.size == 0):
tcount = 0
else:
tcount = rcount / (shoe.size/deck.size)
rcount_hist.append(rcount)
tcount_hist.append(tcount)
favor = len([k for k in tcount_hist if k > 2])
points.append(favor)
time.append((favor/len(tcount_hist))*100)
var.append(np.std(tcount_hist))
# plt.plot(tcount_hist)
# plt.title(str(m_s) + ' Decks with ' + str(p_s) + ' Penetration')
# plt.xlabel('Cards Dealt')
# plt.ylabel('True Count')
s_pen.append(p_s)
s_multi.append(m_s)
s_points.append(np.mean(points))
s_time.append(np.mean(time))
s_var.append(np.mean(var))
### Plot Heatmaps of Parameters ###
results = pd.DataFrame(
{'Number of Decks': s_multi,
'Penetration': s_pen,
'Advantage_Points': s_points,
'Advantage_Time': s_time,
'Variance': s_var
})
fig, axes = plt.subplots(nrows=1, ncols=3, sharey=False, squeeze=True)
adv_h = results[['Number of Decks','Penetration','Advantage_Points']]
adv_h = adv_h.pivot(index='Number of Decks', columns='Penetration', values='Advantage_Points')
fig1 = sb.heatmap(adv_h, annot = True, cbar = False, cmap = 'RdYlGn', ax = axes[0])
axes[0].set_title('Number of Points with TC > 2 during ' + str(sims) + ' Simulations')
adv_p = results[['Number of Decks','Penetration','Advantage_Time']]
adv_p = adv_p.pivot(index='Number of Decks', columns='Penetration', values='Advantage_Time')
fig2 = sb.heatmap(adv_p, annot = True, cbar = False, cmap = 'RdYlGn', ax = axes[1], yticklabels = False)
axes[1].set_title('Percent of Shoe with TC > 2 during ' + str(sims) + ' Simulations')
axes[1].set_ylabel('')
axes[1].set_xlabel('')
flip = results[['Number of Decks','Penetration','Variance']]
flip = flip.pivot(index='Number of Decks', columns='Penetration', values='Variance')
fig3 = sb.heatmap(flip, annot = True, cbar = False, cmap = 'RdYlGn', ax = axes[2], yticklabels = False)
axes[2].set_title('Standard Deviation of the True Count during ' + str(sims) + ' Simulations')
axes[2].set_ylabel('')
axes[2].set_xlabel('')