{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#

     Graph plots in Lightning" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##
Setup" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import os\n", "from lightning import Lightning\n", "\n", "from numpy import random, asarray, argmin\n", "from colorsys import hsv_to_rgb\n", "import networkx as nx" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Connect to server" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
Lightning initialized
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Connected to server at http://public.lightning-viz.org\n" ] }, { "data": { "application/javascript": [ "(function e(t,n,r){function s(o,u){if(!n[o]){if(!t[o]){var a=typeof require==\"function\"&&require;if(!u&&a)return a(o,!0);if(i)return i(o,!0);throw new Error(\"Cannot find module '\"+o+\"'\")}var f=n[o]={exports:{}};t[o][0].call(f.exports,function(e){var n=t[o][1][e];return s(n?n:e)},f,f.exports,e,t,n,r)}return n[o].exports}var i=typeof require==\"function\"&&require;for(var o=0;o" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "lgn = Lightning(ipython=True, host='http://public.lightning-viz.org')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##
Random spatial graphs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Spatial graphs have nodes with fixed spatial positions, and links between them.\n", "
\n", "They are useful for spatial network data, where position has meaning (unlike force-directed graphs, where position is used dynamically during rendering).\n", "
\n", "First we'll generate a random geometric graph using `networkx` and plot with basic styling.\n", "
\n", "The random geometric graph places links between nodes with probability that depends on their spatial distance." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "G = nx.random_geometric_graph(100, 0.2)\n", "pos = asarray(nx.get_node_attributes(G, 'pos').values())\n", "mat = nx.adjacency_matrix(G).todense()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lgn.graph(pos[:,0], pos[:,1], mat)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can add a color to each node. Here we color the same graph based on distance from the origin." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dists = [(x - 0.5)**2 + (y - 0.5)**2 for x, y in pos]\n", "lgn.graph(pos[:,0], pos[:,1], mat, values=dists, colormap='Greens')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As with other plots, we can also color using group labels.\n", "
\n", "Here we assign a label to each point based on the shortest path from it to the center." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "center = argmin(dists)\n", "p = nx.single_source_shortest_path_length(G, center)\n", "xy = asarray([pos[i,:] for i in p.keys()])\n", "g = p.values()\n", "lgn.graph(xy[:,0], xy[:,1], mat, group=g)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##
Edge bundling" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Graphs with many edges can become hard to visualize (due to hairballs).\n", "
\n", "Lightning helps with this by letting you click on points and see only the links to that node. Try it!" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "G = nx.random_geometric_graph(50, 0.5)\n", "pos = asarray(nx.get_node_attributes(G, 'pos').values())\n", "dists = [(x - 0.5)**2 + (y - 0.5)**2 for x, y in pos]\n", "mat = nx.adjacency_matrix(G).todense()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lgn.graph(pos[:,0], pos[:,1], mat)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another option is to bundle edges together using an algorithm by Holton and Van Wijk, emphasizing large tracts.\n", "See this [link](https://github.com/upphiminn/d3.ForceBundle) for the implementaiton.\n", "
\n", "As with the unbundled version, you can click on points to highlight links." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lgn.graphbundled(pos[:,0], pos[:,1], mat)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }