1 Read raw data and create Graph.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fri Sep 30 2016 \n",
      "\n",
      "CPython 2.7.12\n",
      "IPython 4.2.0\n",
      "\n",
      "networkx 1.11\n",
      "matplotlib 1.5.1\n",
      "urllib2 2.7\n"
     ]
    }
   ],
   "source": [
    "%reload_ext watermark\n",
    "%watermark -p networkx,matplotlib,urllib2 -v -n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Creating Graphs from Stanford GraphBase\n",
    "\n",
    "- http://ftp.cs.stanford.edu/pub/sgb/homer.dat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "import networkx as nx \n",
    "import matplotlib.pyplot as plt \n",
    "import urllib2\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "homer = urllib2.urlopen('http://ftp.cs.stanford.edu/pub/sgb/homer.dat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def read_nodes(homer):\n",
    "    \"\"\"Reads in the nodes of the graph from the input file.\n",
    "    \n",
    "    Args:\n",
    "        homer: A handle for the file containing the graph data, starting at the top.\n",
    "        \n",
    "    Returns:\n",
    "        A generator of the nodes in the graph, yielding a list of the form:\n",
    "            ['CH', 'AG, 'ME', ...]\n",
    "    \"\"\"\n",
    "    line = homer.readline()\n",
    "    while line != '\\n':\n",
    "        line = line.replace('\\n', '')\n",
    "        if len(line) != 0:\n",
    "            if line[0] != '*':\n",
    "                if line[:2] != '1:':\n",
    "                    yield line[:2]\n",
    "                else: break\n",
    "        line = homer.readline()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def read_edges(homer):\n",
    "    \"\"\"Reads in the edges of the graph from the input file.\n",
    "    \n",
    "    Args:\n",
    "        homer: A handle for the file containing the graph data, starting at the top \n",
    "            of the edges section.\n",
    "            \n",
    "    Returns:\n",
    "        A generator of the edges in the graph, yielding a list of pairs of the form:\n",
    "            [('CH', 'AG'), ('AG', 'ME'), ...]\n",
    "    \"\"\"\n",
    "\n",
    "    for line in homer.readlines():\n",
    "        line = line.replace('\\n', '')\n",
    "        if len(line) > 1:\n",
    "            if ':' in line:\n",
    "                idx = line.index(':')\n",
    "                line = line[(idx+1):]\n",
    "                all_related_nodes = line.split(';')\n",
    "                for related_nodes in all_related_nodes:\n",
    "                    related_nodes = related_nodes.split(',')\n",
    "                    for idx_node1 in range(len(related_nodes)-1):\n",
    "                        for idx_node2 in range(idx_node1+1, len(related_nodes)):\n",
    "                            pair = (related_nodes[idx_node1], related_nodes[idx_node2])\n",
    "                            yield pair"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "G = nx.Graph()\n",
    "G.add_nodes_from(read_nodes(homer))\n",
    "G.add_edges_from(read_edges(homer))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['G2', 'G1', 'G0', 'GS', 'GR', 'GL', '3Z', '3Y', '3X', '3S']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G.nodes()[:10]\n",
    "#list(read_nodes(homer))[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('G2', 'AC'),\n",
       " ('G2', 'F2'),\n",
       " ('G1', 'PA'),\n",
       " ('G0', 'TM'),\n",
       " ('GS', 'DI'),\n",
       " ('GS', 'EO'),\n",
       " ('GS', 'HT'),\n",
       " ('GS', 'TS'),\n",
       " ('GS', 'HE'),\n",
       " ('GS', 'RU')]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G.edges()[:10]\n",
    "#list(read_edges(homer))[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: \n",
      "Type: Graph\n",
      "Number of nodes: 561\n",
      "Number of edges: 1629\n",
      "Average degree:   5.8075\n"
     ]
    }
   ],
   "source": [
    "# get graph info\n",
    "print(nx.info(G))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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HztiVwNOcWWErU6uwz+ka7O1YK0m+QtCL3SB2P6cXKXH/nMvwhUR6scVJ7g8EaAmHWbV6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\nEQ8piEVERDykIBYREfGQglhERMRD/x+rTTXNPYZvFAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8f8cc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nx.draw(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tGqyEfBMWpNOxcegO3vH7eD9P8b7ehgXa92P+zTZ8/Q+tOa98m2vMeTv22q3A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\nKRCLiIj4SIFYRETERwrEIiIiPvp/VJJfJdr5VYEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x94a8668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nx.draw(G, with_labels=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "nx.write_gexf(G, 'data/homer.gexf')"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python [python2]",
   "language": "python",
   "name": "Python [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": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}