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simulate_dice.py
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simulate_dice.py
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import itertools
from collections import Counter
import pandas as pd
import numpy as np
from multiprocessing import Pool, Process, Queue
from gen_function import roll_dice
NTHREADS=6
def pad_cut_probs(probs, length):
probs = probs[:length]
probs = np.pad(probs, (0, length - len(probs)), mode='constant', constant_values=(0,0)).astype(float)
return probs
# A flexible function to return a function that will effeciently generate random numbers
def roller(faces=6):
SIZE = 100000
rolls = np.random.randint(1, faces + 1, size=SIZE)
i = -1
def get_a_roll():
nonlocal i, rolls
if i >= SIZE - 1:
rolls = np.random.randint(1, faces + 1, size=SIZE)
i = -1
i += 1
return rolls[i]
return get_a_roll
# To test our math, let's compare against a simulation
# Function returns number of successes from a single die roll
# reroll_fail is the number of failures to reroll
def roll_die(roll_func, rolls, success=4, explode=6, reroll_fail=0):
roll = roll_func()
if roll < success:
# Roll failed
if reroll_fail == 0:
rolls.append(roll)
return 0
else:
return roll_die(roll_func, rolls, success=success, explode=explode, reroll_fail=reroll_fail - 1)
else:
# Success
if roll >= explode:
# Explode the die
rolls.append(roll)
return 1 + roll_die(roll_func, rolls, success=success, explode=explode, reroll_fail=reroll_fail)
else:
rolls.append(roll)
return 1
# https://stackoverflow.com/questions/5228158/cartesian-product-of-a-dictionary-of-lists
def product_dict(**kwargs):
keys = kwargs.keys()
vals = kwargs.values()
for instance in itertools.product(*vals):
yield dict(zip(keys, instance))
def sim_roll_dice(num_dice=1, shade='black', open_ended=False, luck=False, boon=0, divine_inspiration=False, saving_grace=False, high_ob=10, trials=100000, log=False):
# What counts as a success
shade_to_faces = {
'black': 3,
'grey': 4,
'white': 5,
}
success_count = shade_to_faces[shade]
# Check the open-ended/luck behaviour
explode_count = 0
reroll_one = False
if open_ended:
explode_count = 1
if luck:
reroll_one = True
elif luck:
explode_count = 1
# Boon and divine_inspiration impact number of dice rolled
if divine_inspiration:
num_dice = num_dice * 2
num_dice += boon
reroll_fail = 0
if saving_grace:
reroll_fail = 1
# Do the simulation
roll_func = roller()
totals = []
pre_results = []
included_failure = []
for i in range(trials):
rolls = []
total = 0
for i in range(num_dice):
total += roll_die(roll_func, rolls, success=(6+1 - success_count), explode=(6+1-explode_count), reroll_fail=reroll_fail)
# Reroll a die for luck
pre_results.append(total)
if reroll_one:
# Check if any fail, then reroll one die for that one.
if min(rolls) < 6+1-success_count:
included_failure.append(total)
total += roll_die(roll_func, rolls, success=(6+1 - success_count), explode=(6+1-explode_count), reroll_fail=0)
totals.append(total)
results = np.array(totals)
odds = np.bincount(results)/trials
#if reroll_one:
# pre_results_r = pad_cut_probs(np.bincount(np.array(pre_results)), 11)
# failure_r = pad_cut_probs(np.bincount(np.array(included_failure)), 11)
# post_results_r = pad_cut_probs(np.bincount(np.array(totals)), 11)
# odds_r = pad_cut_probs(odds, 11)
# fl = lambda x: list(map(float,x))
# with np.errstate(divide='ignore', invalid='ignore'):
# print("pre_results_r: ", fl(pre_results_r))
# print("failure_r: ", fl(failure_r))
# print("post_results_r: ", fl(post_results_r))
# print("odds_r: ", fl(odds_r))
# print("Reroll odds: ", fl(post_results_r/pre_results_r))
# print("Reroll to non-reroll diff: ", fl((post_results_r - pre_results_r)/pre_results_r))
# print("Odds of n successes including a failure: ",fl(failure_r/pre_results_r))
return odds
params = {
'num_dice': list(range(1,4)),
'shade': ['black', 'grey', 'white'],
'open_ended': [True, False],
'luck': [True, False],
'divine_inspiration': [True, False],
'saving_grace': [True, False],
}
#params = {
# 'num_dice': [1],
# 'shade': ['black'], #, 'grey', 'white'],
# 'open_ended': [True],
# 'luck': [True],
# 'divine_inspiration': [False],
# 'saving_grace': [True],
# }
procs = []
p_params = product_dict(**params)
def sim_roll_dice_d(kwargs):
sim = sim_roll_dice(**kwargs)
sim = pad_cut_probs(sim, 11)
exact = roll_dice(**kwargs)
exact = pad_cut_probs(exact, 11)
#print(float(sum(exact)))
diff = sim - exact
if max(diff) > 1e-02:
print(kwargs)
print("Simulated: ", list(map(float,sim)))
print("Exact: ", list(map(float,exact)))
print("Diff: ", list(map(float,sim-exact)))
with Pool(processes=NTHREADS) as pool:
pool.map(sim_roll_dice_d, p_params)
for p in procs:
p.join()
import sys
sys.exit(1)