Derrick Schultz (he/him) dvschultz

Please consider using http://lygia.xyz instead of copy/pasting this functions. It expand suport for voronoi, voronoise, fbm, noise, worley, noise, derivatives and much more, through simple file dependencies. Take a look to https://github.com/patriciogonzalezvivo/lygia/tree/main/generative

Generic 1,2,3 Noise

float rand(float n){return fract(sin(n) * 43758.5453123);}

float noise(float p){
	float fl = floor(p);
  float fc = fract(p);

I'm using Ubuntu 16.04 with a GTX 1060

	/* Following canvas-based Perlin generation code originates from
	* iron_wallaby's code at: http://www.ozoneasylum.com/30982
	*/
	function randomNoise(canvas, x, y, width, height, alpha) {
	x = x \|\| 0;
	y = y \|\| 0;
	width = width \|\| canvas.width;
	height = height \|\| canvas.height;
	alpha = alpha \|\| 255;
	var g = canvas.getContext("2d"),

	import re
	import json
	import numpy as np
	from BeautifulSoup import BeautifulStoneSoup as Stone

	f = open('new-basemap-borders-01.svg')
	svg = f.read()
	f.close()

	soup = Stone(svg)

	The only requirements for popup footnotes in iBooks are:
	* Ebook has to be an EPUB3
	* epub:type "noteref" and "footnote"

	So you can link to a totally separate document, as you normally would for endnotes,
	but include the attributes so the <a> link behaves differently in iBooks, instead triggering the popup.

	Original reference link would look something like this (in a file called ch001.html):

	<a epub:type="noteref" href="footnote.html#note1">1</a></div>


	# ported from http://www.gizma.com/easing/
	# by http://th0ma5w.github.io
	#
	# untested :P


	import math

	linearTween = lambda t, b, c, d : c*t/d + b

	var c = function(n) {
	var string = '';
	n = Math.floor(n * Math.pow(256, 3));

	for (var i = 5; i >= 0; i--) {
	string += (Math.floor(n / Math.pow(16, i)) % 16).toString(16);
	}

	return '#' + string;
	}

	from PIL import Image
	import numpy as np
	import pyprind
	import random
	import os
	import pygame
	from collections import defaultdict, Counter


	class MarkovChain(object):

	from bs4 import BeautifulSoup
	import requests
	import re
	import urllib2
	import os
	import argparse
	import sys
	import json

	# adapted from http://stackoverflow.com/questions/20716842/python-download-images-from-google-image-search