Athan kgryte

The range sliders at the top change the values for the force-directed algorithm and the buttons load new graphs and apply various techniques. This will hopefully serve as a tool for teaching network analysis and visualization principles during my Gephi courses and general Networks in the Humanities presentations.

Notice this includes a pretty straightforward way to load CSV node and edge lists as exported from Gephi.

It also includes a pathfinding algorithm built for the standard data structure of force-directed networks in D3. This requires the addition of .id attributes for the nodes, however.

Now with Clustering Coefficients!

Also, it loads images for nodes but the images are not in the gist. The code also refers to different network types but the data files on Gist only refer to the transportation network.

	Latency Comparison Numbers (~2012)
	----------------------------------
	L1 cache reference 0.5 ns
	Branch mispredict 5 ns
	L2 cache reference 7 ns 14x L1 cache
	Mutex lock/unlock 25 ns
	Main memory reference 100 ns 20x L2 cache, 200x L1 cache
	Compress 1K bytes with Zippy 3,000 ns 3 us
	Send 1K bytes over 1 Gbps network 10,000 ns 10 us
	Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD

	#!/usr/bin/ruby
	# Convert a Markdown README to HTML with Github Flavored Markdown
	# Github and Pygments styles are included in the output
	#
	# Requirements: json gem (`gem install json`)
	#
	# Input: STDIN or filename
	# Output: STDOUT
	# Arguments: "-c" to copy to clipboard (or "\| pbcopy"), or "> filename.html" to output to a file
	# cat README.md \| flavor > README.html

	#!/bin/bash

	# Downloads and install a .dmg from a URL
	#
	# Usage
	# $ dmginstall [url]
	#
	# For example, for installing alfred.app
	# $ dmginstall http://cachefly.alfredapp.com/alfred_1.3.1_261.dmg
	#

	// Language is OpenGL ES, but gist.github.com doesn't know that language
	//
	// Approximation is based on multiply/accumulate, because thats the only
	// thing Videocore can do quickly!

	precision highp float;

	float sinf(float x)
	{
	x*=0.159155;

	var svg = document.getElementById('graph'),
	xml = new XMLSerializer().serializeToString(svg),
	data = "data:image/svg+xml;base64," + btoa(xml),
	img = new Image()

	img.setAttribute('src', data)
	document.body.appendChild(img)

	#include <stdio.h>
	#include "cblas.h"
	#include "lapacke.h"

	int main(int argc, char* argv[])
	{
	int* ipiv;
	int info;
	int i, j;
	int n;

	// Linear Congruential Generator
	// Variant of a Lehman Generator
	var lcg = (function() {
	// Set to values from http://en.wikipedia.org/wiki/Numerical_Recipes
	// m is basically chosen to be large (as it is the max period)
	// and for its relationships to a and c
	var m = 4294967296,
	// a - 1 should be divisible by m's prime factors
	a = 1664525,
	// c and m should be co-prime

	import numpy as np
	import scipy.linalg.blas

	cdef extern from "f2pyptr.h":
	void *f2py_pointer(object) except NULL

	ctypedef int dgemm_t(
	char transa, char transb,
	int m, int n, int *k,
	double *alpha,

	BIN = ./node_modules/.bin
	SRC = $(wildcard src/*.coffee)
	LIB = $(SRC:src/%.coffee=lib/%.js)

	build: $(LIB)

	lib/%.js: src/%.coffee
	@mkdir -p $(@D)
	@$(BIN)/coffee -bcp $< > $@