modeldice.py

#!/usr/bin/python3
import sys
import re
from discreteprobabilitydistribution import Distribution
from random import randint as randint
from dice import interpretinstructions, dropdice
import matplotlib.pyplot as plt

# Iterate every potential rolled set, drop dice, and populate a distribution.
def comprehensiveIterateModel(N,d,drop,dropN):
	if isinstance(N, int):
		new = Distribution()
		new.populateIndividualChoice(N)
		N = new
	N.inormalize()
	if isinstance(d, int):
		new = Distribution()
		new.populateIndividualChoice(d)
		d = new
	d.inormalize()
	# print(N.distribution)
	distribution = Distribution()
	for x in N.distribution:
		for y in d.distribution:
			set_distribution = Distribution()
			rolled = [1 for x in range(x)]
			numberofchoices=y**x
			for n in range(numberofchoices):
				set_distribution.populateIndividualChoice(sum(dropdice(rolled, drop, dropN)))
				# print(rolled) #DEBUG
				rolled[0] += 1
				for index in range(x):
					if rolled[index] > y:
						rolled[index] = 1
						if index + 1 < x:
							rolled[index+1] +=1
			# set_distribution.inormalize(scale = N.distribution[x] * d.distribution[y])
			for y in set_distribution.distribution:
				distribution.populateIndividualChoice(y, count=set_distribution.distribution[y])
	print(distribution.distribution)
	return distribution					

# Random number generated roll.  
def roll(N,d,drop,Ndrop):
	output = comprehensiveIterateModel(N,d,drop,Ndrop)
	return output

# Parse instructions for rolling random numbers and displaying them in the output
def parseinstructions(strargs):
	instructions=[x.split(' ',1) for x in re.split(', *',strargs)]
	print(instructions)
	for item in instructions:
		interpreted = interpretinstructions(item[0])
		# print(interpreted)
		rolled = eval(interpreted)
		rolled.inormalize()
		formatted = rolled.plotformat()
		plt.plot(formatted[0], formatted[1])
		if len(item) > 1:
			plt.title(item[1:])
		plt.show()

parseinstructions(' '.join(sys.argv[1:]))