#!/usr/bin/python

from random import randint

def array_sum(array):
	sum=0
	for sample in array:
		sum=sum+sample
	return sum

def pink_noise(number_of_dice,size_of_die):
	array=[]
	dice=[]
	for x in range(number_of_dice):
		dice.append(randint(1,size_of_die))
	array.append(array_sum(dice))
	for sample in range(1,2**number_of_dice):
		for x in range(number_of_dice):
			if sample%(2**x) == 0:
				dice[x]=randint(1,size_of_die)
		array.append(array_sum(dice))
	##### scale between 0 and array_max:
	array_min=min(array)
	for item in range(len(array)):
		array[item]=array[item]-array_min
	return array
		
def selfsimilar_pink_noise(depth,phrase_length):
	array=[]
	phrase=[]
	pointers=[]
	for x in range(phrase_length):
		phrase.append(randint(1,6))
	for x in range(depth):
		pointers.append(0)
	for sample in range(2**depth):
		for x in range(depth):
			if sample%(2**x) == 0:
				pointers[x]=(pointers[x]+1) % phrase_length
		sum=0
		for point in pointers:
			sum=sum+phrase[point]
		array.append(sum)
	#### scaled between 0 and array_max:
	array_min=min(array)
	for item in range(len(array)):
		array[item]=array[item]-array_min
	return array

def sspn_linear(total_length, phrase_length, size_of_die):
	array=[]
	phrase=[]
	pointers=[]
	for x in range(phrase_length):
		phrase.append(randint(1,size_of_die))
		pointers.append(0)
	for sample in range(total_length):
		for x in range(1,phrase_length+1):
			if sample % x == 0:
				pointers[x-1]=(pointers[x-1]+1) % phrase_length
		sum=0
		for point in pointers:
			sum=sum+phrase[point]
		array.append(sum)
	#### scaled between 0 and array_max:
	array_min=min(array)
	for item in range(len(array)):
		array[item]=array[item]-array_min
	return array