Switch Table Probabilities

switch.py

#!/usr/bin/python3

# Calculating and plotting "Switch Tables" (see Marcia's blog)
# Ian McDougall, Feb 2023

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt

sns.set_theme()

def cat_desc_single(a, b):	# greater first
	return int(str(a)+str(b)) if a>b else int(str(b)+str(a))
cat_desc = np.vectorize(cat_desc_single)

def cat_asc_single(a, b):	# lesser first
	return int(str(a)+str(b)) if a<b else int(str(b)+str(a))
cat_asc = np.vectorize(cat_asc_single)

def cat_arb_single(a, b):	# first first
	return int(str(a)+str(b))
cat_arb = np.vectorize(cat_arb_single)

d4 = np.arange(1, 5, dtype=int)
d6 = np.arange(1, 7, dtype=int)
d8 = np.arange(1, 9, dtype=int)
d10 = np.arange(10, dtype=int)	# the only die with zero
d12 = np.arange(1, 13, dtype=int)

#d6x = d6[:, np.newaxis]
#d6y = d6[np.newaxis, :]

#d66 = cat_arb(d6x, d6y)
#d66d = cat_desc(d6x, d6y)
#d66a = cat_asc(d6x, d6y)

d10x = d10[:, np.newaxis]
d10y = d10[np.newaxis, :]

d100 = np.ravel(cat_arb(d10x, d10y))
d100d = np.ravel(cat_desc(d10x, d10y))
d100a = np.ravel(cat_asc(d10x, d10y))

d100_frame = {
	"Descending": d100d,
	"Arbitrary": d100,
	"Ascending": d100a
}

fig1 = plt.figure()
ax1 = sns.ecdfplot(data=d100_frame, complementary=True)
ax1.set_xlim(0,100)
plt.xlabel('Result (At Least)')
plt.ylabel('Odds')
fig1.savefig('figure1.png', bbox_inches='tight')