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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Interactive data visualization in the notebook"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In this part, we will see several options to create interactive plots in the notebook."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Making matplotlib figures interactive in the notebook"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The [**mpld3**](http://mpld3.github.io) library allows you to make your matplotlib figures interactive in the notebook. It leverages the JavaScript visualization library [**d3.js**](http://d3js.org/) by converting a matplotlib figure into an abstract representation that is understood by d3.js."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We import NumPy and matplotlib as usual. We also need to activate matplotlib's inline backend in the notebook."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now, we import mpld3. If this fails, type in a terminal: `pip install mpld3`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import mpld3\n",
      "mpld3.enable_notebook()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "All we have to do now is create a normal matplotlib plot. It will show up directly in the notebook as an interactive figure."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "N = 100\n",
      "scatter = plt.scatter(np.random.normal(size=N),\n",
      "                      np.random.normal(size=N),\n",
      "                      c=np.random.random(size=N),\n",
      "                      s=1000 * np.random.random(size=N),\n",
      "                      alpha=0.3,\n",
      "                      cmap=plt.cm.jet)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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        "}\n",
        "\n",
        "if(typeof(mpld3) !== \"undefined\"){\n",
        "   // already loaded: just create the figure\n",
        "   create_fig59481671897527989593649();\n",
        "}else if(typeof define === \"function\" && define.amd){\n",
        "   // require.js is available: use it to load d3/mpld3\n",
        "   require.config({paths: {d3: \"http://d3js.org/d3.v3.min\"}});\n",
        "   require([\"d3\"], function(d3){\n",
        "      window.d3 = d3;\n",
        "      mpld3_load_lib(\"http://mpld3.github.io/js/mpld3.v0.1.js\", create_fig59481671897527989593649);\n",
        "    });\n",
        "}else{\n",
        "    // require.js not available: dynamically load d3 & mpld3\n",
        "    mpld3_load_lib(\"http://d3js.org/d3.v3.min.js\", function(){\n",
        "        mpld3_load_lib(\"http://mpld3.github.io/js/mpld3.v0.1.js\", create_fig59481671897527989593649);})\n",
        "}\n",
        "</script>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "png": 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ZuVyO//gfv8TrPYvF8uqdbLLZBAbDNH/7t29/Te79QhdwnR0pl8ssLi0yuzKB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aFDfN3zoEpl3H4nlkdypLrexCSJiOLWKLCu1smm0zQrFSJWG1OzOWauJ/jg3R4kSUSt1ajMzlJo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\nAArMTdzl8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IMe1pdSBEMdzC1lCbR30HA4+PKrK5xtAYfTzonzx1mK+vnlf/xvtDY1rHbb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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x9f71cf8>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Click on the small arrow on the bottom left to pan and zoom in the figure."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## A crash course on d3.js"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "d3.js is a popular visualization library in JavaScript. In principle, it has nothing to do with Python. Yet, we will see here how to mix Python and d3.js in the IPython notebook."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "First, let's show three discs with [**SVG**](http://en.wikipedia.org/wiki/Scalable_Vector_Graphics). To show SVG figures in the IPython notebook, we can use the `%%SVG` cell magic."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "> SVG is a XML-based **vector image format** understood by all modern browsers."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%SVG\n",
      "<svg id=\"svg\" height=\"60\">\n",
      "  <circle cx=\"40\" cy=\"30\" r=\"10\" />\n",
      "  <circle cx=\"80\" cy=\"30\" r=\"10\" />\n",
      "  <circle cx=\"120\" cy=\"30\" r=\"10\" />\n",
      "</svg>"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "svg": [
        "<svg height=\"60\" id=\"svg\">\n",
        "  <circle cx=\"40\" cy=\"30\" r=\"10\"/>\n",
        "  <circle cx=\"80\" cy=\"30\" r=\"10\"/>\n",
        "  <circle cx=\"120\" cy=\"30\" r=\"10\"/>\n",
        "</svg>"
       ],
       "text": [
        "<IPython.core.display.SVG at 0x9fc0278>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now, let's show how to manipulate dynamically these objects with d3.js. This all happens client-side, in the browser. At this point, Python knows nothing about what we're doing here."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%javascript\n",
      "require.config({paths: {d3: \"http://d3js.org/d3.v3.min\"}});\n",
      "require([\"d3\"], function(d3) {\n",
      "\n",
      "    // We select the #svg element.\n",
      "    var svg = d3.select(\"#svg\");\n",
      "\n",
      "    // We select all circles...\n",
      "    var circle = svg.selectAll(\"circle\")\n",
      "                    // and we assign them some data.\n",
      "                    .data([\"red\", \"green\", \"blue\"]);\n",
      "\n",
      "    // Now, we assign the \"fill\" attribute of each circle to \n",
      "    // the value associated to it, and the \"r\" attribute\n",
      "    // to some value depending on the data.\n",
      "    circle.transition().duration(1000).\n",
      "        attr(\"fill\", function(d) { return d; }).\n",
      "        attr(\"r\", function(d) { return 4*d.length; });\n",
      "    \n",
      "});"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "javascript": [
        "require.config({paths: {d3: \"http://d3js.org/d3.v3.min\"}});\n",
        "require([\"d3\"], function(d3) {\n",
        "\n",
        "    // We select the #svg element.\n",
        "    var svg = d3.select(\"#svg\");\n",
        "\n",
        "    // We select all circles...\n",
        "    var circle = svg.selectAll(\"circle\")\n",
        "                    // and we assign them some data.\n",
        "                    .data([\"red\", \"green\", \"blue\"]);\n",
        "\n",
        "    // Now, we assign the \"fill\" attribute of each circle to \n",
        "    // the value associated to it, and the \"r\" attribute\n",
        "    // to some value depending on the data.\n",
        "    circle.transition().duration(1000).\n",
        "        attr(\"fill\", function(d) { return d; }).\n",
        "        attr(\"r\", function(d) { return 4*d.length; });\n",
        "    \n",
        "});"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0xa64a048>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In d3.js, the central idea is to bind data values to visual objects. Here, we assign a string to each circle. Then, we can assign graphical attributes of the objects as a function of the data items associated to them."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Animations can be easily added. Here, we just need to add:\n",
      "\n",
      "`transition().duration(1000).`\n",
      "\n",
      "after `circle.`."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Showing a networkX graph with d3.js"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now, we will load a graph with the [**networkX**](http://networkx.github.io) package, display it with matplotlib first, and then d3.js."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import json\n",
      "import networkx as nx"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Couldn't import dot_parser, loading of dot files will not be possible.\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We will load an example graph, representing relations between members of a Karate club. Involved in a dispute, these members are split into two groups."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "g = nx.karate_club_graph()\n",
      "nx.draw(g)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
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        "}\n",
        "\n",
        "if(typeof(mpld3) !== \"undefined\"){\n",
        "   // already loaded: just create the figure\n",
        "   create_fig5948215190944540370072();\n",
        "}else if(typeof define === \"function\" && define.amd){\n",
        "   // require.js is available: use it to load d3/mpld3\n",
        "   require.config({paths: {d3: \"http://d3js.org/d3.v3.min\"}});\n",
        "   require([\"d3\"], function(d3){\n",
        "      window.d3 = d3;\n",
        "      mpld3_load_lib(\"http://mpld3.github.io/js/mpld3.v0.1.js\", create_fig5948215190944540370072);\n",
        "    });\n",
        "}else{\n",
        "    // require.js not available: dynamically load d3 & mpld3\n",
        "    mpld3_load_lib(\"http://d3js.org/d3.v3.min.js\", function(){\n",
        "        mpld3_load_lib(\"http://mpld3.github.io/js/mpld3.v0.1.js\", create_fig5948215190944540370072);})\n",
        "}\n",
        "</script>"
       ],
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24wY2R0bCNJ39VQD6kPg+NBRtmzTBgwcPAAB6enro0qULrl27hl69eqF79+5o0aIFLl68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\n",
       "text": [
        "<matplotlib.figure.Figure at 0xcd38da0>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Displaying this graph as a dynamic object in d3.js is not straightforward. We first need to export it in a language-independent representation (JSON). Then, we will load this JSON document and render the graph with d3.js."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's export the graph in an adequate format. NetworkX integrates a convenient function for this."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from networkx.readwrite import json_graph"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "json_graph.node_link_data(g)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "{'directed': False,\n",
        " 'graph': [('name', \"Zachary's Karate Club\")],\n",
        " 'links': [{'source': 0, 'target': 1},\n",
        "  {'source': 0, 'target': 2},\n",
        "  {'source': 0, 'target': 3},\n",
        "  {'source': 0, 'target': 4},\n",
        "  {'source': 0, 'target': 5},\n",
        "  {'source': 0, 'target': 6},\n",
        "  {'source': 0, 'target': 7},\n",
        "  {'source': 0, 'target': 8},\n",
        "  {'source': 0, 'target': 10},\n",
        "  {'source': 0, 'target': 11},\n",
        "  {'source': 0, 'target': 12},\n",
        "  {'source': 0, 'target': 13},\n",
        "  {'source': 0, 'target': 17},\n",
        "  {'source': 0, 'target': 19},\n",
        "  {'source': 0, 'target': 21},\n",
        "  {'source': 0, 'target': 31},\n",
        "  {'source': 1, 'target': 2},\n",
        "  {'source': 1, 'target': 3},\n",
        "  {'source': 1, 'target': 7},\n",
        "  {'source': 1, 'target': 13},\n",
        "  {'source': 1, 'target': 17},\n",
        "  {'source': 1, 'target': 19},\n",
        "  {'source': 1, 'target': 21},\n",
        "  {'source': 1, 'target': 30},\n",
        "  {'source': 2, 'target': 3},\n",
        "  {'source': 2, 'target': 32},\n",
        "  {'source': 2, 'target': 7},\n",
        "  {'source': 2, 'target': 8},\n",
        "  {'source': 2, 'target': 9},\n",
        "  {'source': 2, 'target': 13},\n",
        "  {'source': 2, 'target': 27},\n",
        "  {'source': 2, 'target': 28},\n",
        "  {'source': 3, 'target': 7},\n",
        "  {'source': 3, 'target': 12},\n",
        "  {'source': 3, 'target': 13},\n",
        "  {'source': 4, 'target': 10},\n",
        "  {'source': 4, 'target': 6},\n",
        "  {'source': 5, 'target': 16},\n",
        "  {'source': 5, 'target': 10},\n",
        "  {'source': 5, 'target': 6},\n",
        "  {'source': 6, 'target': 16},\n",
        "  {'source': 8, 'target': 32},\n",
        "  {'source': 8, 'target': 30},\n",
        "  {'source': 8, 'target': 33},\n",
        "  {'source': 9, 'target': 33},\n",
        "  {'source': 13, 'target': 33},\n",
        "  {'source': 14, 'target': 32},\n",
        "  {'source': 14, 'target': 33},\n",
        "  {'source': 15, 'target': 32},\n",
        "  {'source': 15, 'target': 33},\n",
        "  {'source': 18, 'target': 32},\n",
        "  {'source': 18, 'target': 33},\n",
        "  {'source': 19, 'target': 33},\n",
        "  {'source': 20, 'target': 32},\n",
        "  {'source': 20, 'target': 33},\n",
        "  {'source': 22, 'target': 32},\n",
        "  {'source': 22, 'target': 33},\n",
        "  {'source': 23, 'target': 32},\n",
        "  {'source': 23, 'target': 25},\n",
        "  {'source': 23, 'target': 27},\n",
        "  {'source': 23, 'target': 29},\n",
        "  {'source': 23, 'target': 33},\n",
        "  {'source': 24, 'target': 25},\n",
        "  {'source': 24, 'target': 27},\n",
        "  {'source': 24, 'target': 31},\n",
        "  {'source': 25, 'target': 31},\n",
        "  {'source': 26, 'target': 33},\n",
        "  {'source': 26, 'target': 29},\n",
        "  {'source': 27, 'target': 33},\n",
        "  {'source': 28, 'target': 33},\n",
        "  {'source': 28, 'target': 31},\n",
        "  {'source': 29, 'target': 32},\n",
        "  {'source': 29, 'target': 33},\n",
        "  {'source': 30, 'target': 33},\n",
        "  {'source': 30, 'target': 32},\n",
        "  {'source': 31, 'target': 33},\n",
        "  {'source': 31, 'target': 32},\n",
        "  {'source': 32, 'target': 33}],\n",
        " 'multigraph': False,\n",
        " 'nodes': [{'club': 'Mr. Hi', 'id': 0},\n",
        "  {'club': 'Mr. Hi', 'id': 1},\n",
        "  {'club': 'Mr. Hi', 'id': 2},\n",
        "  {'club': 'Mr. Hi', 'id': 3},\n",
        "  {'club': 'Mr. Hi', 'id': 4},\n",
        "  {'club': 'Mr. Hi', 'id': 5},\n",
        "  {'club': 'Mr. Hi', 'id': 6},\n",
        "  {'club': 'Mr. Hi', 'id': 7},\n",
        "  {'club': 'Mr. Hi', 'id': 8},\n",
        "  {'club': 'Officer', 'id': 9},\n",
        "  {'club': 'Mr. Hi', 'id': 10},\n",
        "  {'club': 'Mr. Hi', 'id': 11},\n",
        "  {'club': 'Mr. Hi', 'id': 12},\n",
        "  {'club': 'Mr. Hi', 'id': 13},\n",
        "  {'club': 'Officer', 'id': 14},\n",
        "  {'club': 'Officer', 'id': 15},\n",
        "  {'club': 'Mr. Hi', 'id': 16},\n",
        "  {'club': 'Mr. Hi', 'id': 17},\n",
        "  {'club': 'Officer', 'id': 18},\n",
        "  {'club': 'Mr. Hi', 'id': 19},\n",
        "  {'club': 'Officer', 'id': 20},\n",
        "  {'club': 'Mr. Hi', 'id': 21},\n",
        "  {'club': 'Officer', 'id': 22},\n",
        "  {'club': 'Officer', 'id': 23},\n",
        "  {'club': 'Officer', 'id': 24},\n",
        "  {'club': 'Officer', 'id': 25},\n",
        "  {'club': 'Officer', 'id': 26},\n",
        "  {'club': 'Officer', 'id': 27},\n",
        "  {'club': 'Officer', 'id': 28},\n",
        "  {'club': 'Officer', 'id': 29},\n",
        "  {'club': 'Officer', 'id': 30},\n",
        "  {'club': 'Officer', 'id': 31},\n",
        "  {'club': 'Officer', 'id': 32},\n",
        "  {'club': 'Officer', 'id': 33}]}"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We export this dictionary to a JSON file, understandable by d3.js."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "with open('graph.json', 'w') as f:\n",
      "    json.dump(_, f, indent=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now, let's create the SVG element containing the graph, along with a couple of CSS styles for the nodes and edges. It is empty for now."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%html\n",
      "<svg id=\"graph\" width=\"600\" height=\"300\"></svg>\n",
      "<style>\n",
      ".node {stroke: #fff; stroke-width: 1.5px;}\n",
      ".link {stroke: #999; stroke-opacity: .6;}\n",
      "</style>"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<svg id=\"graph\" width=\"600\" height=\"300\"></svg>\n",
        "<style>\n",
        ".node {stroke: #fff; stroke-width: 1.5px;}\n",
        ".link {stroke: #999; stroke-opacity: .6;}\n",
        "</style>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0xd031278>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The JavaScript code to load and display the graph is below."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%javascript\n",
      "require([\"d3\"], function(d3) {\n",
      "\n",
      "    // We select the SVG element.\n",
      "    var svg = d3.select(\"#graph\");\n",
      "\n",
      "    // We create a color scale.\n",
      "    var color = d3.scale.category10();\n",
      "\n",
      "    // We create a force-directed dynamic graph layout.\n",
      "    var force = d3.layout.force()\n",
      "        .charge(-120)\n",
      "        .linkDistance(30)\n",
      "        .size([600, 300]);\n",
      "\n",
      "    // We load the JSON file.\n",
      "    d3.json(\"graph.json\", function(error, graph) {\n",
      "\n",
      "        // We initialize the force field.\n",
      "        force.nodes(graph.nodes)\n",
      "             .links(graph.links)\n",
      "             .start();\n",
      "\n",
      "        // We create the links.\n",
      "        var link = svg.selectAll(\".link\")\n",
      "            .data(graph.links)\n",
      "            .enter().append(\"line\")  // adding a <line/> element for every graph link\n",
      "            .attr(\"class\", \"link\");\n",
      "\n",
      "        // We create the nodes.\n",
      "        var node = svg.selectAll(\".node\")\n",
      "            .data(graph.nodes)\n",
      "            .enter().append(\"circle\")  // adding a <circle/> element for every graph node\n",
      "            .attr(\"class\", \"node\")\n",
      "            .attr(\"r\", 5)  // radius\n",
      "            .style(\"fill\", function(d) {  // color depending on the person's group\n",
      "                return color(d.club); \n",
      "            })\n",
      "            .call(force.drag);\n",
      "\n",
      "        // Binding the force field to the graph.\n",
      "        force.on(\"tick\", function() {\n",
      "            link.attr(\"x1\", function(d) { return d.source.x; })\n",
      "                .attr(\"y1\", function(d) { return d.source.y; })\n",
      "                .attr(\"x2\", function(d) { return d.target.x; })\n",
      "                .attr(\"y2\", function(d) { return d.target.y; });\n",
      "\n",
      "            node.attr(\"cx\", function(d) { return d.x; })\n",
      "                .attr(\"cy\", function(d) { return d.y; });\n",
      "        });\n",
      "    });\n",
      "\n",
      "});"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "javascript": [
        "require([\"d3\"], function(d3) {\n",
        "\n",
        "    // We select the SVG element.\n",
        "    var svg = d3.select(\"#graph\");\n",
        "\n",
        "    // We create a color scale.\n",
        "    var color = d3.scale.category10();\n",
        "\n",
        "    // We create a force-directed dynamic graph layout.\n",
        "    var force = d3.layout.force()\n",
        "        .charge(-120)\n",
        "        .linkDistance(30)\n",
        "        .size([600, 300]);\n",
        "\n",
        "    // We load the JSON file.\n",
        "    d3.json(\"graph.json\", function(error, graph) {\n",
        "\n",
        "        // We initialize the force field.\n",
        "        force.nodes(graph.nodes)\n",
        "             .links(graph.links)\n",
        "             .start();\n",
        "\n",
        "        // We create the links.\n",
        "        var link = svg.selectAll(\".link\")\n",
        "            .data(graph.links)\n",
        "            .enter().append(\"line\")  // adding a <line/> element for every graph link\n",
        "            .attr(\"class\", \"link\");\n",
        "\n",
        "        // We create the nodes.\n",
        "        var node = svg.selectAll(\".node\")\n",
        "            .data(graph.nodes)\n",
        "            .enter().append(\"circle\")  // adding a <circle/> element for every graph node\n",
        "            .attr(\"class\", \"node\")\n",
        "            .attr(\"r\", 5)  // radius\n",
        "            .style(\"fill\", function(d) {  // color depending on the person's group\n",
        "                return color(d.club); \n",
        "            })\n",
        "            .call(force.drag);\n",
        "\n",
        "        // Binding the force field to the graph.\n",
        "        force.on(\"tick\", function() {\n",
        "            link.attr(\"x1\", function(d) { return d.source.x; })\n",
        "                .attr(\"y1\", function(d) { return d.source.y; })\n",
        "                .attr(\"x2\", function(d) { return d.target.x; })\n",
        "                .attr(\"y2\", function(d) { return d.target.y; });\n",
        "\n",
        "            node.attr(\"cx\", function(d) { return d.x; })\n",
        "                .attr(\"cy\", function(d) { return d.y; });\n",
        "        });\n",
        "    });\n",
        "\n",
        "});"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0xd0310b8>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## References\n",
      "\n",
      "* [Visualizing a NetworkX graph in the IPython notebook with d3.js](http://nbviewer.ipython.org/github/ipython-books/cookbook-code/blob/master/notebooks/chapter06_viz/04_d3.ipynb), recipe from the [IPython Cookbook](http://ipython-books.github.io).\n",
      "* [Converting matplotlib figures to d3.js visualizations with mpld3](http://nbviewer.ipython.org/github/ipython-books/cookbook-code/blob/master/notebooks/chapter06_viz/05_mpld3.ipynb), recipe from the [IPython Cookbook](http://ipython-books.github.io)."
     ]
    }
   ],
   "metadata": {}
  }
 ]
}