signed-laplacian-eigen-value.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import networkx as nx\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "from numpy.linalg import eigh\n",
    "from scipy import sparse as sp\n",
    "from scipy.sparse import issparse\n",
    "from matplotlib import pyplot as plt\n",
    "from collections import defaultdict\n",
    "from functools import reduce\n",
    "from scipy.sparse.linalg import eigs\n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "from scipy.sparse.linalg import eigs\n",
    "\n",
    "from helpers import (\n",
    "    signed_layout, draw_nodes, draw_edges, flatten, \n",
    "    conductance_by_sweeping,\n",
    "    get_borderless_fig, \n",
    "    labels2groups,\n",
    "    num_ccs, cc_sizes, \n",
    "    signed_normalized_laplacian,\n",
    "    signed_laplacian\n",
    ") \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from graph_generator.community_graph import make"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "g, groundtruth = make(4, 2, 1, 0, 0.3, 1)\n",
    "g.add_edge(8, 0, sign=1)\n",
    "g.add_edge(9, 0, sign=-1)\n",
    "g.add_edge(10, 4, sign=-1)\n",
    "g.add_edge(11, 5, sign=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
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       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
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       "\n",
       "        var msg = JSON.parse(evt.data);\n",
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       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
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       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
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       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
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       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/matplotlib/cbook/__init__.py:424: MatplotlibDeprecationWarning: \n",
      "Passing one of 'on', 'true', 'off', 'false' as a boolean is deprecated; use an actual boolean (True/False) instead.\n",
      "  warn_deprecated(\"2.2\", \"Passing one of 'on', 'true', 'off', 'false' as a \"\n",
      "/usr/local/lib/python3.5/dist-packages/networkx/drawing/nx_pylab.py:611: MatplotlibDeprecationWarning: isinstance(..., numbers.Number)\n",
      "  if cb.is_numlike(alpha):\n"
     ]
    }
   ],
   "source": [
    "fig, ax = get_borderless_fig()\n",
    "pos = {\n",
    "    0: [0, 0], 1: [0, 1], 2: [1, 0], 3: [1, 1],\n",
    "    4: [2, 0], 5: [2, 1], 6: [3, 0], 7: [3, 1],\n",
    "    8: [0, 2], 9: [0.5, -2], 10: [2, -1], 11: [2, 2]\n",
    "}\n",
    "draw_nodes(g, pos=pos, ax=ax)\n",
    "# nx.draw_networkx_labels(g, pos, ax=ax)\n",
    "draw_edges(g, pos=pos, ax=ax)\n",
    "\n",
    "fig.savefig('figs/eigenvector-illustration-graph.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = nx.adjacency_matrix(g, weight='sign')\n",
    "# L = signed_normalized_laplacian(A)\n",
    "L = signed_laplacian(g)\n",
    "eig_val, eig_vec = eigs(L, k=1, which='SM')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eigen value [-9.2381486e-16+0.j]\n"
     ]
    }
   ],
   "source": [
    "print('eigen value', eig_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/matplotlib/cbook/__init__.py:424: MatplotlibDeprecationWarning: \n",
      "Passing one of 'on', 'true', 'off', 'false' as a boolean is deprecated; use an actual boolean (True/False) instead.\n",
      "  warn_deprecated(\"2.2\", \"Passing one of 'on', 'true', 'off', 'false' as a \"\n",
      "/usr/local/lib/python3.5/dist-packages/networkx/drawing/nx_pylab.py:611: MatplotlibDeprecationWarning: isinstance(..., numbers.Number)\n",
      "  if cb.is_numlike(alpha):\n"
     ]
    }
   ],
   "source": [
    "fig, ax = get_borderless_fig()\n",
    "\n",
    "values = eig_vec.real.flatten()\n",
    "labels = {i: '{:.2f}'.format(v) for i, v in enumerate(values)}\n",
    "\n",
    "nx.draw_networkx_nodes(g, pos, node_color=values, node_size=300, cmap=plt.cm.coolwarm, ax=ax)\n",
    "offset_x, offset_y = 0.2, 0.1\n",
    "label_pos = {n: [x+offset_x, y+offset_y] for n, (x, y) in pos.items()}\n",
    "for n in [6, 7]:\n",
    "    x, y = label_pos[n]\n",
    "    label_pos[n] = [x-offset_x, y+offset_y]\n",
    "nx.draw_networkx_labels(g, label_pos, labels=labels, ax=ax, )\n",
    "draw_edges(g, pos=pos, ax=ax)\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig('figs/eigenvector-illustration.pdf')"
   ]
  }
 ],
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