PCA to the max.ipynb

{
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   "metadata": {
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Couldn't import dot_parser, loading of dot files will not be possible.\n"
     ]
    }
   ],
   "source": [
    "%matplotlib notebook  \n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cross_decomposition import CCA\n",
    "\n",
    "import networkx as nx\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "G = nx.read_gexf(\"datasets/influences.gexf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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   "source": [
    "A = nx.to_numpy_matrix(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pca = PCA()\n",
    "Z = pca.fit(A).transform(A)\n",
    "Z = zip(*Z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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   "outputs": [
    {
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     "output_type": "stream",
     "text": [
      "3358\n"
     ]
    },
    {
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       "            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 overriden (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",
       "    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 + '\">');\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 dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\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>"
      ]
     },
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     "output_type": "display_data"
    },
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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print len(pca.explained_variance_ratio_)\n",
    "\n",
    "# i = 0\n",
    "# s = 0\n",
    "# while s < .99:\n",
    "#   i = i + 1\n",
    "#   s = np.sum(pca.explained_variance_ratio_[0:i])\n",
    "  \n",
    "#   print (i, s)\n",
    "\n",
    "def plot_explained_vr(pca):\n",
    "  (x, y) = zip(*list(enumerate(pca.explained_variance_ratio_)))\n",
    "  \n",
    "  y = [ np.sum(y[0:i]) for i, dummy in enumerate(y) ]\n",
    "  \n",
    "  fig = plt.figure()\n",
    "  plt.plot(x, y)\n",
    "  \n",
    "  plt.show()\n",
    "\n",
    "plot_explained_vr(pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\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",
       "            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",
       "        this.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 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);\n",
       "        canvas.attr('height', height);\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'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\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;\n",
       "    var y = canvas_pos.y;\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 overriden (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",
       "    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 + '\">');\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 dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\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"
    },
    {
     "data": {
      "text/html": [
       "<img 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TQlqvQiIgAiIgAgUmIAEoASgqclJAJoS1HoREAEREAERKDABCUAJQFOTkwA0Jaj1IiACIiACIlBgAhKAEoCmJicBaEpQ60VABERABESgwAQkACUATU1OAtCUoNaLgAiIgAiIQIEJSABKAJqanASgKUGtFwEREAEREIECE5AAlAA0NTkJQFOCWi8CIiACIiACBSYgASgBaGpyEoCmBLVeBERABERABApMQAJQAtDU5CQATQlqvQiIgAiIgAgUmIAEoASgqclJAJoS1HoREAEREAERKDABCUAJQFOTKxkBGIlEMH/+fNTV1WHo0KE4/fTTEQ6HTfev9SIgAiIgAiJQcgQkACUATY22JAQgxd8xx9RixYp1iESOQzj8Empq+mHx4oUSgaYWoPUiIAIiIAIlR0ACUALQ1GhLQgA+8sgjmDjxWtTXvwOgK4CdqK4+HA88MAMTJkwwZaD1IiACIiACIlBSBCQAJQBNDbYkBOD06dMxa9ZmxGJ3N+83FJqMadP2xowZM0wZaL0IiIAIiIAIlBQBCUAJQFODLQkBKA+g6WvWehEQAREQgXIiIAEoAWhqzyUhABM5gGsRiRyPcHgRamr6KwfQ9O1rvQiIgAiIQEkSkACUADQ13JIQgNykqoBNX7XWi4AIiIAIlAsBCUAJQFNbLhkBaLpRrRcBERABERCBciEgASgBaGrLEoCmBLVeBERABERABApMQAKwfATgLwFMAkBBtgzAFADvtWJPNwI4CcChAN4EcLTPeWcA4Hl9AawF8HMAj/ucJwFY4A+tbicCIiACIiACpgQkAMtDAE4DcBmAEwB8BOB6AD8EcCCAXT5Gcj6ATQDGAhjiIwBHA3gVwNkAFgAYD+APAL4DYHnK9SQATT+FWi8CIiACIiACBSYgAVgeAnA1gNkA7nDsJwTgEwBTAcxrw6YoFI/zEYC/B9ANwA88a+cD2AzgIgnAAn9KdTsREAEREAERyDMBCcDSF4D0wG0FcKQTznVNZCGAdwFclYMApJfvEQC3eNZOdwThCAnAPH8KdTkREAEREAERKDABCcDSF4D7A1gP4BAA//DYzx8BbAcwOQcBuArALAB3edZe4ngUGVb2HgoBF/hDq9uJgAiIgAiIgCkBCcDSF4BF4QGcMmUKKisrLXusra21/ugQAREQAREQAREoHgILFy4E//BobGzEnDlz+FemfNFh1OmOQBns2C8H8F8AfmqQA0hhyUpg9yjpHEA1gS4DK9cWREAEREAE8kZAHsDS9wDSGJjnxypgtnahGLwOwLkADmqlCrgCAP+wtcsxAI53LKrB+V9WAb/iVAE/41QBPwTgqFKsAk6MgVuHSOQ4hMMvoaamn8bA5e3XiC4kAiIgAiJQagQkAMtDANLufgHgYgB7AFjq6QPYB8D7TsuXvzgGej8AtoKJO/+fXlD+ndXD7sEK4JsA9HP6AF4L4AkfAy/6HMBHHnkEEydei/r6dwB0BbAT1dWH44EHZmDChAml9pnV84qACIiACIiAMQEJwPIRgMbGkOMFil4ATp8+HbNmbUYsdnfzFkOhyZg2bW/MmDEj7bbpQXz00Ufx2GOPWeeeeeaZOOussxAOh9Ou1QkiIAIiIAIiUIwEJAAlAE3tsugFoIkHkOLv6KPH4M03P0Q8zsKWFxEIbMeoUUPx+usvSgSaWo/Wi4AIiIAIdAgBCUAJQFPDK3oBmMgBXItI5HiEw4tQU9M/oxxAisfzzrsGkQin6tnhY+BwhMM7MHfu7Qohm1qP1ouACIiACHQIAQlACUBTwyt6AcgNZlMF7D33gw8+wFNP7YN4/F4Pp8kIBJbgmmtOyCiEbApY60VABERABEQg3wQkACUATW2qJARgpptMrRgOBp9ENBpGPM4e2/IAZspR54mACIiACBQ3AQlACUBTCy0rAdgyX3ArAoH+lviLx8cCeAGBwJfKATS1Gq0XAREQARHoUAISgBKApgZYVgLQr2I4GJyEk076DMFg0GKlKmBTk9F6ERABERCBjiYgASgBaGqDZSUATSqGTUEWYn02uZCFeB7dQwREQAREoGMISABKAJpaXlkJQJOKYVOQ7b1eE1Ham7CuLwIiIAKlQ0ACUALQ1FpLQgBm4/nK5lxTeIVcX+7ezUKy1L1EQAREoNQJSABKAJracNELQHm+7FdsOhHF1FC0XgREQAREoHgISABKAJpaY9ELQHm+7FcsDqamrvUiIAIiUD4EJAAlAE2tuegFoDxf9isu5/xGUyPWehEQARHobAQkACUATW2+6AWgPF+JV1yu+Y2mRqz1IiACItDZCEgASgCa2nzRC8B0ni+JIlMT0HoREAEREIFSIyABKAFoarNFLwC5wV27dllFEEuWLMGoUaOsGb5dunTxhEXXIRI5DuHwS6ip6YfFixciHA6bstF6ERABERABEShKAhKAEoCmhln0ArCtKuD58+dj4sRrUV//TvOs3+rqw/HAAzMwYcIEUzZaLwIiIAIiIAJFSUACUALQ1DCLXgC2lQNYV1eHWbM2Ixa7u5lDKDQZ06btbXkJdYiACIiACIhAORKQAJQANLXroheAbVUBDx06VB5AUwvQehEQAREQgZIjIAEoAWhqtEUvANvyAJ5++uk45pharFixFpHI8QiHF6Gmpr9yAE2tQutFQAREQASKmoAEoASgqYEWvQBUFbDpK9Z6ERABERCBciMgASgBaGrTRS8AuUG1ejF9zVovAiIgAiJQTgQkACUATe25JASg6Sa1XgREQAREQATKiYAEoASgqT2XjQCUl9DUFLReBERABESgVAhIAEoAmtpqWQjAtnoFqiG0qYlovQiIgAiIQLERkACUADS1yZIUgKnevmg0ikmTrlNDaFNr0HoREAEREIGSICABKAFoaqglJwD9vH09ekTx6adjEIvd08xDDaFNTUPrRUAEREAEipWABKAEoKltlpwA9OsLGA4PAMDZwO+V9Eg45TGamrPWi4AIiEDnICABKAFoauklJwD9JoMEg5Pwta+9gi++iJdsQ2jlMZqastaLgAiIQOchIAEoAWhq7SUjAF3v2MMPP4xnn/0bIpF/ANgLwE5UVx+Oe++9ERUVFeB8YI6I45SQUioAaWviyYQJE0zfs9aLgAiIgAiUEQEJQAlAU3MuCQGY7B37HpqangOwA4HAGaisfLnV8W8mIVWTtbm8lLZmHs+YMSOXS2qNCIiACIhAmRKQAJQANDXtkhCA/nl/38CJJ9bg7LPP9vX2mYRUTdbm+kLkAcyVnNaJgAiIQOcjIAEoAWhq9SUhAHPxjpkIKpO1ub6QdDOPc72u1omACIiACJQfAQlACUBTqy4JAZiLIMtFNLowTdZm8kJaCy8XOuycybPqHBEQAREQgeIjIAEoAWhqlSUhAHPxjuUiGtMVmjzwwAyYFmR0RHjZ1EhKYb3Ecym8JT2jCIhAvghIAEoAmtpSSQhAbjLbL/hsRWOuhSbZvoBchGm29+hs50tUd7Y3rv2KgAhIAEoAmn4KSkYA5rLRbERjLoUmuTxTe4eXc3mmUl8jUV3qb1DPLwIikC0BCUAJwGxtJvX8shaA2cAplDCTWMnmrWR2bqHeXWZPo7NEQAREoP0JSABKAJpamQSgQ7BQwizb0LTpC+4M67N9d9l4hjsDP+1RBESg9AhIAJaPAPwlgEkAKMiWAZgCgINt/Q6Ov5gD4CQATQCeAXAZgG3OyccAeMXqlGwfAQBbAPT1uVjJC8Bsv8y95x922GEWkpUrV4J/v+OOe1FXt67dx8ll+8yl96upsE+cjahWvmBh343uJgIi0D4EJADLQwBOcwTcCQA+AnA9gB8COBDALh/ToeALA+B8MIq7R6x5aMCpHgH4MoAKAPE0plcSArCttinHHFOLFSso2o5DOPwSamr6YfHihb5j4Nou9HgFQ4b0wU9+cpElCEtxnFz7/JopjatmKqqz9RaWxu71lCIgAp2NgARgeQjA1QBmA7jDMeAQgE8ATAUwL8Wo6cVbC+CbdFw5P+Pf6xwP38cA6AGkAKwEECt1AdiWx2b+/PmYOPFa1Ne/A6Br81zg1tq1+H35A4cD4Ki1cdZM4Xy0eulsv4hKab/KFyylt6VnFQERaI2ABGDpC0B64LYCOBLAm54XvRDAuwCuSnn5pwD4I4AuKf+9HsAZAJ72CECKwSrnOjcCeM3HkIreA9hade79999seepmzdqMWOzu5q2FQpMxbdre8Juf6/flD0wGsLclAtta21l/DWXqWSsVPvIAlsqb0nOKgAi0RUACsPQF4P4A1gM4BMA/PC+bIm87bHXiPc4FMAtA75T/vtHxGP4vgF4A9nVyCHcDcAmAmwCMBkBXmfcoegFoi7ZNiMXuBDDfcXa+gd69P8KMGTfioouuRiRyHoCRAGpRXT0M9957IyoqKlBXV5cUypUHMLtfqOWYL5dNvmB2tHS2CIiACBSOgARg6QvA9vAA+lkgQ8J/BfBzPwE4ZcoUVFYyYgzU1tZaf4rloGg799yrEY32B0Cn5vcAPGvVvAwa1B+rV29FPP59AAusupdBgwagR4+eePfd/2uRF8g92TmDa62fNTU9Z60JBM5AZeXLqKnp32r+YDHwKLQ3rly9ZeT46KOP4rHHHrNe65lnnomzzjrLN2+0GN67nkEEREAESGDhwoXWHx6NjY2YM4f1oOjmOIw6HSQWQZT64ZcD+C8AP20lB3ANgCGeHED+fTmAfo5C8uPxEoA3APzMTwBu27YNe+5JLVp8B7+se/XaH1u2sKbln825fkAfBINd0dTESPnpANYB+C5CoRfR1LQd8ThTJVkwvTMpt6+1KuBiL/roCG9cuebLdQTL4vtk6YlEQARKmYA8gKXvAaT9Mc+PbVzY1oVi8DoADPUe1EoVMF1drAI+x6kCfthp+XKaY8xjHKVERVQN4GKnyuHbjlD02nzRh4D5Zb3vvvth69aTAfze8+ysfRlhhX2Ba53otl0IAhwM4FanUBrtlttXSI9cR3jjOuKehfiFXK77KgQ73UMERKA4CEgAlocApDX9whFqewBY6ukD2AfA+wDGAviLY3Z0a7FieJzT5oWCkAKSOYM8GOa9CEAPAF85RSA3AFjsY7ZFLwD5ZX3eeZcjEtk9ReTR4ckal7OdrScKQYALAXR3RGCyBzDTj246cVdoL1JHeOPKNV+uI1hmanc6TwREQAQyISABWD4CMJP33R7nFL0A5Jf1zJmfo6mJzlGGdY8HwNw9OkGjCAR2IB6nOPygOTwcCByEiopGNDWdinB4Uda5fZmIu0J7kQp9P9fY0gnh9jDK9r5mR7Fs733p+iIgAp2HgASgBKCptRe9AEx8WXNACpNf3wLwEABGvFn0HEU4zAKWvdDUNMZqBj10aD9cdtmkVhs6pxM1mQiEQnuRytUbZ2rAuawXy1yoaY0IiEAxEZAAlAA0tceiF4D8sj766DF44w0WgAx2IuScgMfCpxMBvArgc/z4x+eiW7duaSd4ZOLdy0TcZSISU19OOuGZ7mWark93/c70c7HsTG9bexWB8iMgASgBaGrVRS8AucH77rsPkyZd4Yg+Fn08D2CgM/K40Sr66N79K3z66b/StvLw8yiGww/h/vtvwznnsK4GyETcZetFykR4mr5MrRcBERABEegcBCQAJQBNLb0kBOCQIUPwzjubUtrA0Bv4KwAXgEUfweDD+N//vR8TJnBEcutHck4hC6WPswTlfvuFsXbt3y0Bmam4y8aLlImoNH2Zpuuz2Y/pvbReBERABEQgdwISgBKAuVuPvbLoBSBFSVVVNeLx81PawPzICf+yD+BBCAQOxDXXjPYdAeeFlFxVnMgrDIUewIMP/rbZC5hvMZRJWNn0ZWa63m9v/G9Dh47E6tWfoKlpGCor12DYsOJujJ3pfnWeCIiACJQbAQlACUBTmy56AThv3jycey7bIrIjTqLS1+71x37ZX7d6YFdVbcCDD/46rQeQQqd//wPwySecKEIPoL8X0BRs6vpi8QD6haKHDu2LTZs2YdWqL5y8StL7GUcAACAASURBVA6O2R9VVeszYppvVrqeCIiACIhA2wQkACUATT8jRS8Ax40bh2eeYdsXNnnex2kDswgAQ8JfcrgbgsFVGDVqMF577YW0OYAERlF5/vlXIhbj9jke2W4gHQ5/A3PnzkwrInOBnmlYOdtrZ+up9BOi3HcsthNNTRTDbjPtwxEIDMA114xK61XN9pn9zs92H/m4p64hAiIgAqVKQAJQAtDUdoteAA4aNAirV++y2r0AMccTuB5AvTMIhSPiohg9ehRef/3FjARgwgvInoL3NTMMhS7CtGn7tJvgybfIyaWwxC8UHQhMQjzO9jpve+zpIoRCj2HevLvaRRB7DTeXfZgavtaLgAiIQCkTkACUADS136IXgIMHD8aqVUcDuB3AdABLAGxzCkK+BeAja+pHVdXOrMKV9AJecMHPEIm81+z1qq4+HA88MKPdBU+6l5apUMwlrNyaB5CCOhJhs216ALdaVdbduwdx++2/xVlnnZWRsE63r9Z+nss+cr2X1mVOIFM7zPyKOlMERCBfBCQAJQBNbanoBeBFF12Ee+99BsCHnvDkN5w2MEc4k++yD1e2V0jW9IVk4w3LpbDEb99snB2Px/H22+tRX/9dAH9GILAHgsETEA6/jJqafli8eGHGIjBb4ZDLPkw5a33bBLKxQ7EUAREoPAEJQAlAU6sregE4ZswYvPjiXz2Nn1/yhIF/DYBtX3ILV2YrVExhZ7I+G29YNuemhlznz5+Purq65sbZ/Dn/28MPP4xnn63L2TOai3DIdR+Z8NQ5uRHQO8mNm1aJQKEISABKAJraWtELwK9//etOxe4rTuHHIACbrdYvAItB2Ah6MAYP7o733qvL2EuVCi4TMZjJOaYvJBtvWD68mKl7WrZsGWbP3opY7O7mrYRCkzFt2t4Z5UbmIhzysQ9T7lqfTCAbOxQ7ERCBwhOQAJQANLW6oheAtbW1eOGFvzkVwMc684DZroSPfhKA5xAK7cDGjavx0ksvJXm12NQ5k6M1r9WiRU9jwYIF1jUPO+ww3HHHvairW49I5Dhr5nC2odFMniVbAWUiSv323adPV6xfvwMNDeyvaFcEZ5MbmatwMNlHJlxzOacYnymXfeSyJls7zOUeWiMCIpA7AQlACcDcrcdeWfQC8J577sHkydcDeN8Rf6xWZeXudgBDAPwYodANGDiwOzZs2JmTOPP7squqOgx9++7RfM1g8ElEo2HE4//ISRhl+qK83rDGxu8hFHoKPXvuhpkzb857MUZrX/J9+uyGDRu+QiRyPMLhRaipybwhdLkIh1xC2Zm+41I4T17ZUnhLesbOTEACUALQ1P6LXgCecsopWLCgBwC2fmGfOnoBOQuYXkD36Gd5AWOxvzeLMwq4iy8+BV26dLHy3E4++eRmbx7//+mnn94cLvZvjXIcQqEPEY26zaevArAlpW1M5qHRbF4Uv3wfffRRXH31dfj88xiamr6fUzFGunsmxuJ9H0AdgKEIBl/Ef/xHDwwfPtzQm7rWV0B2tFct0/uXi5BNZwNt/TxTVib30FoREIHcCEgASgDmZjmJVUUvAEeOHImlS+1WL96mzcz7swXZSACrADQA+B2As5zdHYCKijji8bGoqFiEcHgnotHdfT2Efl/2odDX0dR0BuLxe53rPQJgWtI0ktTQaD6/MAshQNgK57zzpiAe39uZifwSAoHNmDt3TvNIvFwMrDUOHe1Vy+b+uYayc+GlNSIgAiKQLQEJQAnAbG0m9fyiF4B2FfBfnGrf33ue/0IA/+tUBP8MwLUAIgAOA8DRcf/p9ApkHtuDzs//mRS+vffeG1FRUQEWPjzxxDNYv34XolF62xbBDoPWo77enRSyFYFAf1RUdENTU22L0Ghr+XSnnjrW8qZ5PY6ZvLR0AiQfYtMWgNckhbUDgYMwd+4tRgKwtf0VQtS2xZb3P//86WhoYEoBvcUHo6rqF779Izv6WTOxEZ0jAiLQeQlIAEoAmlp/0QvAfv36Yf16NiYOAfiR4/HjHF96AHcHUAvgVWdSiHseJ4f8OwBXMLKB9Gctpn706vUCvviiotkryAIIV7AxZHz88eOwYkUilDlkSF/85CcXYeXKlc3tU9xCEz/BAByIQIAiY23WBSNtCRCKyWOOqcWKFetyynl0jebqq6/Grbd+4fFyAu05DSWdqE015nyIXO81ud9Zs+jJ5fSY4wCwpVAU06ZNwMyZM5Nurxw4018tHb8+3/bT8TvSE4hAgoAEoASg6eeh6AVg165dsWsXv7DpyRvrtH5h6JePnvDoUWzZE0KqnRnBXwfgVrK29ABy/m3y9IuW1a7ZfIH4iRv2J7TnF/88q0pavtTWGjZfdtkkPPbYY3j22b8hEmFByl5ZV+q61z/00KH48EOK5mTPaHtNQ8nGq5ZNuDbTD8EVV1yB3/3uTy3s5vLLz8Bvf/vbFpfJ5v1n+gw6rzAE2sN+CvPkuosIZEZAAlACMDNLaf2soheAtgeQc4C9Yo/ePwo+ev7cgyFhVgjTs/MoAOa17QDAeb/PIhzegVCoR3NhQo8eAWzceCyamtwcP3q/ci/q8PcAHg5ghhO+vtDqpZfqaWrrBXoFCNvQ3H77PXj77Q1oaDgW8fgLAA5wKqPDWT97IhzaB8AGh9uzGDx4b7z33rKc+ymm24/tufQvEPGuzUYsZvohsD2A7CGZmP8MZP9eMr2fzus4Au1hPx23G91ZBFoSkACUADT9XBS9AJwyZQruvPMrTziXW2Yo+DEAGz3j4SgKGfp1Q8MUX9+2RGE4/BDuuWcWqqurmytbo9EoJk26zpPjl12/u1TwXo+dLdCeA8Cm1ZcAWGE9/yWXTMB3v/vdnKpr/QUmvZg3Azg1aw9jwmM5hzNArCrgQGAJfvrToRg2bJjlZWxqakLfvn2tSupc8hj9jDNTr1q24eJMPggSBZlQKo9z2sN+yoOMdlEuBCQAJQBNbbnoBeCxxx6LV1+l9y/VA8gwcG/Hw8eJIJucqSDM/VuIQIB9As9ERcXTvn300uV47dq1C/wSWbJkCUaNGmVNwaAQ8h6pYsZtNXPbbbfhzTc5uzjmhKrZZuU5hMO7EArtnVPenv2Ftgmx2D2eR6AQ/jOqqnpg2LABWc3rbb334e5YtWoL4nHmVrLdzpcAfoDq6teyzmM0Mc72EGttvXM+a+p4vEwbiZvsU2vbh0B72E/7PKmuKgK5EZAAlADMzXISq4peAFZWViIS2c0J6TKcS7FHz9+ZAE5s7l9HgWUn9a+2hCBz/HbfvR47duzWah+91rxRFH+9eg3Ajh3MPWTe4fOorv4KGzeuQbdu3Sx6fjlGQ4b0sYpE6D1bsOBlNDWxf+F7jpfSvxI503w7fqGxYjcSca+3E8DhCAa34bLLzsWtt96aVdjWTwyx8nntWjbTTr6HHcYel7WX0cQDmE6g52r4fu+c18pHUU2uz6R1+SfQXvaT/yfVFUUgNwISgBKAuVlOCQnAYDCIePx8R4gtcwo7mPtH7ZooggAYDmWo+DZnd2wL8wmAuwCcZxVKhMMH4sQTv4Wzzz67zcbQ/sUCg9G161e46647rIkc9BZNnHitT5uYvSzBGYs9DGAEgCOtBssAnz05/4wVt+PGfY5DDjmkRVVx6ovlF1r//gfjk084+/gERwj3RzA4EFdf3TOjOb1+13S9XswxpMhcsKBnSo7cZEd8z8g6z9DvftkIrUzDxaYfAnmLTAkW5/pC2U9x7l5PVe4EJAAlAE1tvOg9gIFAAMB+jiftdKfpM0OqC50ijzOcv1fSj+Pks8UB0FPnnsdiCR6rEQiMsRpDs4l0PF6FpqZTUFn5SlJ488gjj8Qbb1BQeosFWNH7R4TDXTBixKH4zndGYPbsrYjF7nau7W0UzWepcRpV00v5MgD+N4alk0PZFRVRxOOnZjRbmH37LrhgKiIRClo2wK5FdfUwuP0MObM4dcpJWwbifkHafRCfx9q1nyISqUpqdk0vY748gLkIrdbC7NnutS0Oyhcz/TWi9SIgAoUmIAEoAWhqcyUiANnahcKEff8Ss3htrx8FyhsAbnGaPzNnbd+UqSGsGKbnbK2naORg578daolGCik3HHvppZfiv//7CQDM42P7GTvcCvSzCk3C4TWYPPks3Hffcx4PoHdUHMUgPZBuE2mupwhlm5pejjB18xbpsWQFc9tFKBRCifFwUTQ1jUEw+AJ69qzAbrtVYt26zYjF9kNFxb8wbNgheP31F5NCwn5Cyu5zuA4NDf0Rj7MxMkO/FNnk9F0nB5CV1D+wRtHxXjNn3pDzTOJshZZfmL2iYgcika6IRjmn+KW85CXmIkxNP3haLwIiIAImBCQAJQBN7IdrS0QAMgeQx7+lVANTOFFsMdeOU0B43h4ARgNwPXNcx2KJpY4gc5FNcnIJV1riMRR6yWrTwmIPWwA+5HgR6cFjbiHbpdB7d4jlhdxvvzD69OmDt99eb7WWCQafQDRa6UzVuMkpSvEWbPBZ2Z6mL4CTAAx3BNbXHA9bBIHAWIwevQtXXnll0uQQrxBqbDwWodACy/sZj1MI8flYxctXydDwyy3GuflPKan2TDrxPi858no3Y9CgneAklieffAaff16PWOzkFt5SPwNsLfSWrdBqrbm2XfnMtACzym332VvLF1u06OlW50ebfvCKab1CpcX0NvQsIpAZAQlACcDMLKX1s0pEAFJ0rXcEWWo18L+c3R3r9Nu7EgBDwBR9tzp/b8sDSM/iQIRCdfjxj8/DEUccAfaL++QTFpxQNP6fI9boeWTTZOb1PYZQ6BArn3DAgAHWOLmnnnoOH330CeJxeinp7aP30PusHFFHgcbxdRQvvwRA7yPFDCuXeb9Vlgisqno1ybPVthCidzTV23gwxo8fjieeoBcTVm5fcr7iTiRmHf83AHovKXh/4zxLY3PBB9enrq2qOgwXX3yKVRWdGnJuqwEvfzZ06BFYs2YLYrFaVFW9jJqa/q1WL/s3177ACa3TJoYiGHwx5xxI78eiLQ9pJHJc3ryNph/YfK9Xw+R8E9X1RKAwBCQAJQBNLa1EBCDDsP2dliQcCedWA7NBNAUg8wQphCj8uCW7ctduBE2vIPPvGH51p4mwiITXo6eLYWSOiTsfgYCdVxiPs2iDAo4hUf43NphmFS/Do/Q48h5xBAIUMa+AI+TWrfsSjY3MVaRHkc/BFjD0TI5xcgDZo5DikKFVtqn5El26VCEa7WqFYG2x6D+r+De/+Q3efPPQpJFtdtiYza7Jg8UlXo/nhRg/fnOzAPQTUoHAcQgG/4lYbCCAjz0h3wpUVQWb28pcd911VvPkRK4jPYQHoKIijnh8bAth1JqXj3mKc+bch+XL16KxcQCCweUYOHA/1NW9ldRexyvEWI19991PJxXa2B5UcqUNvIBAYAvuv/8OnH8+RXX+jmy9lfm7c2Gv1Fn2WViqupsItD8BCUAJQFMrKxEBSJFDgcfHZUsS5qsxh+//OSFZeufYF5Bj0bx5e2wKTYHHFjHMw6PQYmsXjv06C0DYyb971hGSbq7fjY6g4rXssKrdZNoVcBSX9PIxj68RodDBiMUYembVMUUZvZEUjhSCLPw41/n/iZzAYPAg3HPPDVbLmOeffxHA0c1TPfhSA4FJ6N37RWtWcSJHL9X7yUKXnwO4LinfMBA4CHPn3oJzzjmnVQ8gvXjdu8ewcSOFauK63MuUKac3t5VpKRDabmfTWp7fiSduxPPPv5PUYqa6+vDmvEs+aKo3isU64fBOSyS7YfZIhO8smcMBB3TH++/XZdUGJ90HJ9t8xba8iZzdXKw9BU32mY6hfi4CItB+BCQAJQBNratEBCDFHw8Wd1AMfs8Rdd2dXoAMpVIMUhS+4mFCLxm9eD91+gWyWITePObf2eLNnhwyxLkel7ptT25w2rcw9MyqWwo6b1FH8pg320NID+AHnsIRhn15sEk1Bae3qvhH2G+/xfjsswiiUVYrJ4tKCsRQKIJIhH0N3ariL5z8QTcnkQL1IAQCnDTS1fLIBYMLMWhQd9TVvdnsWXOF1fLla9DYOBDB4DIMHPh1jBtXi9/8ZnuSZzF1HJ43P66+niL1D87zsOL6j1ZY3rvGr1K5qqoGXbrsxJYtJycxYBucadP2aW5h01pzajfc/MEHH+Cpp/ZJ8YRehFDoMcybdxcmTJhg+nloXp+rZ6zUQqq57jNvoHUhERCBnAhIAEoA5mQ4nkUlIgAZxqWnit41egCZt7bK8QgyFEhBxHAwQ6GfpoyHc8PDFI309FE08jyKRTevj/mCR1ltVYBhzW1PKisPRY8eTdi4kY2nKQJT28Ls43jgKCLp6aPI+70HL69Lwcn7sL9ewnMVDg8A0CWl6TKvc5B1fii0HbEY8w3pHaTX62rnWkc4wvR0BAI/xujR7+GSSy7BTTfNxJo1n6CpaRgqK9dg6NC+VlPqlStXWnl6Y8eOxciRR2HNGrauGeOErr2FIHa1c6pXzvXMPfTQQ7jooisQj9Pr6IbYued/oLr6O5Ynj56uo48eY01BsSeJvGhNZBk0aD+rSjkSoQc1IaLZrHvu3JnNwq0tb9QNN9yAq666Crff/men0CZRnR0IDMA119jTWvJ15FoY0lGCKtdCDjVMzpfF6DoiUFgCEoASgKYWVyICkB5ACjeGXdc5oVgWZKSGROlpY5sV5t0x7EuBwtAsxaK3nQvzyBY7worCjIKGQovXZPh1BAKBZQgGI4jFwojHKT4pfFLvx+ISeggpKCn+KDAT5wQCB2LcuOH4wQ9+gJtumoWPPvoX4vH9EQh8jG7dgvjyy1N9Rrs97YhZiiWKWu77Gsfb9hoA9iOkCEz0AOS4ujlzHkIs5hZx7EQg0A+BwJ6WELPzFLtgzZrtiEbfb2bBMHDfvntgwwa2VmFblUWtFmWccMIJVgg3lQFz8I444kiwYpYC7s47H0c0mvCCUuSNHTsEzzyzL5qa6M1kixmK9uew336VWLv2783h0UzyBxsa+C6Yh8jQPL29fVBVtb7VohT3A5KLQMqlMKQjQqqmXsdc2Jj+4tF6ERABMwISgBKAZhZUMm1gKIYosD5yQqzMrRvn422jQPk2gCCABxzRdlwLz10g8AjYX7qpieIvOS9v333D+PzzKGIxXoMCjF45ijr+7wYAvN4zjrjkc1BM0hP4rhNSpmDjuUtxwAF98fbbSy2B8+1vH4e33uLz0zP2glOUQv3tFZWJpst230Hm97GqmfunF5DPw/WLLM/awIH7g4USGzc2Oh43FrewVyFD39NTwtGudzERImcIdurU7hg+fDjaaqxMgdCrVy9s2XJaCssL0b374/j444/BnoJLlqxALMZCmYSnlOHhceM+w8KF76K+ntNQ7KKacPgh3H//bc15iq6n0Z4UsjZJkE6ZciEmTbrOKQZhOJyV1JdZe62q4oSXXW32BjQVSO6HLBPvXrpz2kNspbun6S+JfK1Pt/d0P8/Xc+g6IlAOBCQAJQBN7bhEPIAMzzLPrckpsGAxB71tFF2uZ48Ch61B6GGiCOIf5qvRI+htHk0v4mYEAqxi/Y4jxiiu6Pn7HkKhVYhGWQTCHECKQ7dH3p1O1XAdgCUAvgngSaeXIEUfhRkFGL2IFGz0qL2MYcP6Y/jwQ3HnnRSkFKf0SjLMzBYtnyEQ6IZ4/BQnJ5F7oEDi8zAXkdXJywF4n8e7Xxa+sCI2VUSy8IX7Ts45BB53Kn7ta6SGYP2MyRVPb7zxBuJxFtkkF+EMGdIL06ZNcwQai1HY3ub65kKdqqpf4L77brIqgFOF3eLFC1sUR/iJgJaVyHaRDMPfo0aNwl13LUBDQ8IWUsPY+RJImXj32gqpkm82o/Ay/XBn8lyZXqu9zksnwtP9vL2eS9cVgVIlIAEoAWhqu0UtAPmlUFlJjw/DuPzjhmopfJjbx8dnuJcFFBRPbmEHvWc850ynbYvb/oWeN1bzUsjY7ViSq3m/jqamMxCP0zPotlbxm+rheuoYNmZz59sdwcNWM94JIlutUCwbNic8eN4WNfQefoFAYKs1lo6j6uxnc6uRXSHH4peWrV5sgctKX2/eIb1/9JAxX5LC1+sxYxib4pJFJy1DsH7GlBBPrITm8/D53D2QcQW6d6/Etm3j0dREDsxtZKW1fc7uu0fx6adrLKHnzh3OZlwdn6ktAUfPZXKbGrSYWZwvgZSpkMxXI+xMP9yZPlem12uP89I9Y7qft8cz6ZoiUMoEJAAlAE3tt6gF4IMPPoiJEycC6J3ieWJOHL1u9PYxH+xIp1WL7cmzm0DPBcApGxQkrzuj3NiehTl23mreA8G2KRUVH2H33euxY8duiEToxXI9Wawi5uQNevY4dYMhVLeHIIXgV55QM4UohSlzDvks7nxgevCSPWN21TL7Df7JEa/MX+TrYCUxQ80UvLw2heGvPB5Jby4jRRa9kKm5ia6Xkdeh19QNHdNDyfV9EAy+iksvvRCzZ89us0UJxdMtt3yOeJyiMbXhNHMg7aKVQID9EznWjs+a4OtXVJKt0bblVaOoPP/86WhoSPY6Pvjgr5uLS/IlLkwLJvIlRFP5mT5Xtu8jl/PT7T3dz3O5p9aIQDkTkACUADS176IWgMxNW76cIVeKIrZsoRBk42dXdFEAMRRMwUOx43rP6A2k6GF7GE7zYK4eBRW9ZTw30TSZocSuXR9DQ0M1olGOaJuPeJxFJ2wi7a145T27ON658U7lMduy8By39yBFIgUnQ72sGmbbGd6Pz0WhR88YcwhZtUzhytxBhnjptbvf2R/3xAIWXotePApDrqcnz1v8QBFKMcuwN/87xSnXbUOvXj2waROriLlnFk2k9kasB3Ca1cS5Z89Qm/N9KZ7OPXcaolHmYCazswtSvJXQ9Ahyqkki9Ey+11zT07hClyKHs5DZN5HHmWeeac0ktvMTB2DHjpZeR04q4ZEPgeR69ZYtW4aGhgZUVVVZuZPZ9PjLlxD1+9AXe/5cur2n+7npLzqtF4FyIyABWD4CkO4hDqelIGOm/BSngZ2fzVINzHEawtG9w4oEZsQz/ugeZziJY4wx0k3GagImgKUeRS0Ae/fujY0bmQdHrxXDsq54+hwAvU0UTgyXUvDZoiZR/UvvGsWi2/6FoV93Uoi3V99ghEIs+ljj3IfXZv4cvYde4cS2MbwXhQavRZH4pScESxHGPEB6/9xJJRREREwhSk9iqqeOApItZhhapbhKbWFDDxuvtw277RbHV19RBPIZ2IOP3kfmIdIrR28oxV4lwuE6hEL7oKGB3s7nEY+zb6LrkeTrJzOezzF5vNbBCIcbMWLEob4j2SgsDj10OD78kNNCmNvoZZfaC/EpZ/JK4pyKioPxhz/catyjr7UcseQCkUQrm8mTT04aVced+4WgMxFOmeSnZXed5CIXv1xI94OayXVzObfQX4bpRHi6nxf6eXU/ETAhkM3nNtf7SACWhwCc5gg49rVgmShjWT8EwG9/Ko3Ug4KP8UV2vWW8kXFGfpOf6pw42qlGONuJXdJdxWQxVjzQ3eQ9iloADhkyBO+8w3BinxThQTHGZs72rFs75DvPaQ1CEcjGz9waRZcbMuUa6mV61ui1Yu4gPXH0sBEnhRhF3EjnXgw9e3PrqM+ZW3ePEwamIOU1KcAo7PgsP0u5J4UdvVAUYHyl3qIMCjEWhvA56d2kZmczafegd41CjeLxCsfryYIPikA3pMscRnomeY1fIhT6DwSDu/v0FmThBkel2YLPFn9u02QWm+yJ6ur5SVM5vEaybds27LtvHzQ20pvIZ2qZd8mCknj8K0Sj9Ei61dLPYvDgvfHee8usMLPJL8XWPES1td/E00/v64yq48eF1c8PW6I5EDgHVVWLk+Yqe/eVibDj+em8U5leh9fKhoHfdTl28NRTx7bwPmbzDLl+4ZiuS7f3dD83vb/Wi0AhCBTqsygBWB4CkJn/swHc4RgnXTacKTbVUTVem3U9enT9cOgsD/6dcVL+jG4aqhbGJX/gWciht6wioKooGQF4yCGH4O9/59g3ihfqY26Tnj2GOincGAJ1RQ2FED1+FHOc7EEhd69nrxRwDzkeO4ZZ3WuxMIQa2n0FxESPIr1/rseOopEzc9kaZqbjUJ3ltKZhVTGFEb2BRJ5akMEiET4jw9epHkBej8KTFca3pOQmcooIvXpcy4IV5he6YtGbw8hnbURV1R7Ye+8mfPrpGJ/egnyGCVafv2h0G+JxOoW9xSYzEAq9hGnT9vYN1VIA2Xl2FLjMOWTvQ/YTpJC25wEPHz4Q8XgcdXXrkqaNuLN+TX8ptpYjlmgxw3A7mSVCwbZQXonq6tG+4jYTYUevoT2LuQvicb4DO8/UO/3E7zoUxPfff3NSm5tsv3z8rst/FwYCB6Oqam2SsE23l2zvrfNFQARyI1Coz6IEYOkLQH6jU12wiuFNj7nRtcO+FlelmCD7hXD+lp3clDgY/6QLiYqIXj4qGioK96BbhOqEoyVKQgCyv13XrvQ20SvH0GNqCJg6mVtmDiCj3xRw9GZRMNFjyOkhqYKLDlOuc0OURE9HK8+ldubroGeOoU0KPYZw6e2ifnarkCkCeD9G4Xl/CkBek94nPq9XnLFAgtdiHiKfi6KLQtbO1bND0hcAoJhk/iFFp9vEmuLQ+6xc/3Xnnl5h+yMMGvQafvWrXyEajeKCC36W4gHk/SmWIxgz5mhs3Vrf3I7FW9BSXT2sVQ9gQnxxzwxzsxk3x8KxOCaCk08+Bn/+858t7xbPZWNqtmfh9I7nn3/e6jHI93n33U87vfxanzrS2q/cdE2ilyxZ6uQ8et853+0ZCAZ34uSTN4H/oPBWIKebPOK2bGE4PR7nO0vMf/YWt9jX2ZQivC/Efvstwtq1q7KeA+x6wmzheWiL0Xdu3mXLZ9jseEJtiqlj/XL7OtMqERCBbAgUqqBJArD0BeD+jjuFcTP27HAPijwmhVHReI9zY8Ca8QAAIABJREFUHbVAd5L3oBuJHkPGKBlvpKK4y3PCJc7P+Y1YEgLwiiuuwO9+R48dPWvMyUsVc/T2sZL2bwD+P0cgshCCAo2ikYKRAs4tFKEIoqeOIpCeK86l/bMjLim6qLl5L/boozeJopCeRF6b+YDeXoIMowYRDgP9+++JHj264s03Ge6lZy0x6cIO1/JaLAzh9ZjzR9FH/U6xyWfjRBI6bxkGpjBlwQudwhR/qfODmc/I15ucw9i165e49NIfgyHzq6++Dp98wmtRaLItDotF6L18C6HQeowYMQSXXvojTJ/+n/j8868Qi52CysqXW50AQjHCEWz2pBFmFZCvt//igbj88jNw6623tuhxV1HBCSNdEY0yJ/Ix3ybRrXkdU3/h8jmOOur7WL78fUQivREKrcG+++6FWbNm4LTTTrOE3Ycfsl9kapj9HQQCHyIY3BNNTWNQUbEIw4cPwGuvvWDlBE6ceK2vKOX9U3+W8L6tSeJFcXreede0EN7h8A7MnXt7VvmPXk9pQ0N/xOP0gPs1C5+Q1guZjwrsbL74dK4IiED6lJF8MZIALH0BmE8PID18zA/M2gM4ZcoUp98eUFtba/3p6OPII4/EG2/Q68VQI0PAqV/sDLWyUpchUHry3Pw0TqJgmJIiiOuZ+vgXn/51DU5Y1hV2vAbFEl8JW564UzUo3ChAk2f87rbbH3H++eeDz0kh8eyzdYhEGFa2J13Yz0udTs8in4NFI/xDAcrWLt4vdXoK6UGk8GMKKOt1/Jo/0wPFPEY3zMnwNYVtdwQCJ1oj3/bfvwtWr/4XmpqYw8gwuDvfmKxmobr6l81ze9MVRRx22GG4/fZ78PbbG1Bf74ahee/kohKKOFbE+gkm4Gbn/TG/0c2XzN4DSGHEOcPLlq1BJMJiIArRClRVhZqbbd9xBz21yf9Q4Ki6eDy5WTbb/syde4tVRew3eYRFGW01n77yyiuTqn/5bP37H9xCeAeDA3H11dlVQCd7OvkPCIrnVQgExjohaH8vpIooOvo3lu4vAjaB9vwsLly4EPzDo7GxEXPmMCpjpXzRYdTpDn6blvrhlwNI9xYrGVjZ4D3oKmK5Kl1Tbg4g/07Rx9EXbg4gVQzjk+5RcjmACQ8gQ6V++XNERM8aBRBFEUOmbPxM4Uaxw+phhkwZlqXnzc+DyOIRV1i6PftSq1zt1jD+c4cphuwpIIEAz9kdweAJVk5cLLYZkQifi1F3d4oJvZIMJbOAJXVKB8PEbluYGke8Uc9TGH4PweBCNDVRpPKZafbuuDu+Yr72hKjaf//d8NFHzPVzx87xdwNZ2B7SUaO6YurUqS1amCR7n45FILAQ8TibVNNE+exuxXWiqMT1MiUaMvMXEllQDNP7SRHKcDq9sgegoqIJ8TgZtT532O8D7Z8Px/D29Zao/Z//uR6XXXa1px3Mc9aIuMMOG4gVK8gz+R8Q48dvxhNPPNFqUUa2eTzz5s3DBRdMRSTC9j+28G4rrN7aL62W4aOIJf5GjdqJL774EuvX70I0+v0W/Pju/NrksPhGhwiIQGEJFKKgSR7A0vcA0iqZ58c2LlQa/KblPC2Gel23UKrlMvGKv9XZ1I5KgCWPVDwsf+XBKmB2K2a8jgqCVcCMpTI+VjJVwIkcQHrymNNHMeUVNBQUrMhlmJPeJfbRcytd3bm3FH2MjtMb5udB9FYX8zUwZOyex+szREvRxTCcOwGD//qiKOPPvbN8tyIcPhBjxx6Bt96qw8aNLEDgK+UEDZ5PscjrME0ztaEyn5cilR5C3sft68e19ECuRW3td7Bly1dYtmw1YjF6KOn9IxOGepNn706dupcVDr7tttuwbBk9qK5QZiibFbq7o6qqwvKceVuQ+IssPhu9q8yxZEj6EoRCDJ2flSRCEg2ZyZSC1G2/Q4FMAcxClcNw8cWnJLVnyVSg+OXV2BkSeyMY/NzK8Rs0aBDef/99bNmyBaNHj7YKWv793/8dTz5Js/cK+4MxfvxwSwC2dmT7r/hsz2/tvi3fgW1XJ574Lav3IY+VK1cm5TKaFtgU9qtRdxMBEcgHAQnA8hCAtIVfALjYcQ/RFeT2AeS3Kb/BqUQYx+RBhcCK4XFOQhkFIQWk1wXMcDCH2NIryKQ0Kg6/b7uibgMTCFDfUusyZ45h04R3xS6u4BZZ30LxxPw919vhbVDMMWpEluoBZN0MRRoLTIiXYVfeh+clQm+26GS4kUKU5/N/eVDQsZUKRah9sOnxKadswjPPLEc06hUc1PIUUtTlbr9Ab8EHn4X7Y6iSgjP1WRsxbdqPrEIPenmuvvpaK38vEmGLGIawE/N5OXvXnYJBYfCNbwzBqlUUZN4iGlY634Jw+D8tYXH22Wdb3kCGPe2pH94iE7Kkd5RClNWn631FHAX7oEEHYuNGemxpnmwITVaDEQgcgqqq5Ly5bH8Btu4B/DkCgamoqNjLyvGj97Wmpl+zsKVn7rzzpjhhYLs/YyDwBebOnZO2Qjfbf8Vne74fA6+QbGz8HuJxNhB3PcvM1UzszV2frbcyW/Y6XwREoPgISACWjwDsKOsqAQFIgUaBRPGX2mKFwoReMAo/ChVvzhtz6CjSKBLZXjG1ApfeQoZiKSBHOUXYuxAMdkdTE8UaRVhq8j3z83g+vYqs2+FzJap+2fT4pJOG46mn9kkRUWxBw2flOoZq3bYy9I4xVM09sj6H12IOY7K3kh63efPuai4mcIUGq23vvPN+1NdTnCXP3nWnYJxyyilYsIDh2GQPmC3OqhAIjLFyByks2FR54sTpKeKVQpu5kSyO+V8ccEB3vP9+XVJlqyta3nprlRWeTORPLkQg8GOMHv0eUvPmMjF4b1izqakJf//7GmzYsBP19XaTa77fiopdiMWqEI8ninS8xQ/e4pFodD9UVHyCYcO+gddffzHr6txMnjkf57jv9+GHH3ZySxP9LP0KOwpVdZiPvekaIiAC+SEgASgBaGpJJSAAuUV6xpg7523sTJHGPDjmAnIbbiokPWzsFchwKvvVcWoGW3jwXDpUKWTo7XOrdunVoyePouwNjB8/EK+99hq2bGFE3SvE7HCjPa2Dw1o4McT1ILIowW56fP3112DSpOuSKktt7x+9lNwHhRrDyPz/bu4iuwCxtQqj/zektJIZ7Cu6SCWd54dC4lvf+haWLqXYoxOY4pJimXmEDOOy3WQid5B5dBdf/FNrLJ4dvmajbDJj0Qfz+F6yQq1PPcWJH4mmxn5CxfbQ2vl5DzwwI6tKWPfarPpdsqQO8bhbmPM8DjhgL4wff4KVu1dRUWG9q2XLIojHKczt/aW2P6F30tuehqFhVyBn+wHKh5cv03tmKuzS2UGm99N5NoFCvmMxF4FcCUgASgDmajvuuqIWgBdffDHuvptze5nDR4HkbbHCkC89X/TssaKX4WI374y5fBRrzLlzJ4F4x5alzrBlA+ElCARimDv3f6zGv0uXUhx5PYA8h+FgplMy9MrOOmyMzPusR7duu2HixH9HTU0NfvWrW/HRR1uskCTwolMowvM5kY+eRword6QbxRSf/9+chtC8B/dJL5fdL3DkyOF49dXn8fjjjyfNwn377bcxe/ZW395v7MHHCtelS1c7VbNuVTM9kfQ2UlCzW1BCNLGp8tNP/wWxGJ+NYXB6SJmWSp7/ZWUquLlzFFVDhx6BNWu2IhqlJ5PFLi09l6NGDfMdMZfOcO3WKpcjEqF3NOFldRsscz1b3nz6acTJiXT3Nz+p+MJbPcx2NN42MH7TSU4++WQsWLDA6l3o7RnoCgO3NyArkVPDzen2lO3PMxV2+co/zPb5yvF85VOW41stzz1JAEoAmlp2UQtAekB+/ev/cXLz6P3ztlhh+JUihl641LFvbJXBvDuKAvfwij6KQYaIeVzq9Nw7BYHAIowePRjnnXcmpkxhUYhb+EEhxkIOihEKNBaeMOWS96c4ooeNHkbmG9Kzx6p83p/pnPSgsS0M78dwL0WhtwiEnkf2/qOIcvMP2c6R498oxHjdJgwYMBhr1253KntfRCCwHYMG7YePP27Ioo8dPZHM0XPHubEHIdNEbdHEsWpPPbUW8TjFHEO57rQUhlsfB1MymTvH9imHHsq+e26+YsvClmDwIFx66WmYPXt2TqFWvvtbbnkO8TjD8/xHgH0wz7J37xfx+ef1iESYN5kc2g6HGzBixGEpOYDXJIWIW7aBWWeJZAo6b+/CVIGXqSAz/VC667MRdvJa5Yd6od9xfp5aV+mMBCQAJQBN7b6oBSB/Gf/bv/3EEUIUVe4UDQoPCiuKGQow5gcmT8dg4+FEzp3bvoSijLligxzRR+8WCwNYbctK2cctITRsWG/89a+sXKX44p8jHKHh9SgyhMz703vH0C6FodtMmjmHvCaFHz2GtzmF2v/hiFU2kua5btEKvYKs0aG3kiFiVvhyX+6eGbJmWJaFIwmPZkXFlxgwoDc2bGDD5eOTqnL9+tjZXsXU3MaD4Yom5gDarUySvW4MYYdCOzBixDArd44Vv+eccwliMXYaYhEMw+4UtmTLd/QCAoEvMWrU0Jxz7VrzAIZCByMYbHTarVCEJ4tDFuE89thjzaLz1FNPxZNP0kvbsg0Mi1/a7l24Ex09bcMVdsuWLUNDQwOqqqpazAE2/SWg9QkCmYbdxUwEOpqABKAEoKkNFrUA5JdfZSW9bPxD0UdvH70+nKbhNkSm+EidFEKBxRw9N+eOHqwYgsF6hEIxRCIMb6aOiqM4vAWh0GJnpBe9cuykQ5FBAcHreUUmR8bRU0dvmleY8d7ESg+l27CY4Wp6CV2PIp8n0dCXoqZ//yps2vQFtm1jRx/uib0P3evSG0nPorcIZjICgSW46qoxliBIDVn6V836j5IbPvwd/O1vnKgCa9qGnXdH5mMQCDyP3r2rMXPmDZbnj2FT2zu3xJkp7IZnKbCmOfmFuffB83q/Wj7LQvTuXYWNG49FUxM9lMntdPwKJGwB6N8GhqPhZs1KHp9mjwKkJ5eV1eln/hZi2obCkqa/5jJfLw9g5qx0ZscSkACUADS1wKIWgHbIz20FQ28Z+9FR+LmhTDcPkB4+d7Qaw7X8//RiUUjx7xRwFI+HoaLi7+jTpwJr1tAbltob8FNUV7+P+nr22mbeIa9JDx6FILvtpOYUMk+Q7U6Sp4TYRSaJ5sytzyY+GIEAPXI7EI8zF49eQXon2fHH27vQv0l1W6PGEpW5HyIapXeOLWgoepMrl9naxdsTL5OGwvySPP/86WhooBeWfQUpdDm9MHl0XWoxRrZhSr9noU3YRTYsxCEzO1+SjaVHjBjUIt+wrTYwLCLJxgOYTUjW9IPpXS9Rkk+abV+ro95x4XaoO5ULAQlACUBTWy4xAUgPGqdZMLzKMcoUamyHyPBpohee3VaRf1hVS88dvXR2mxSGbQcO7IXVqykOU1ujbLIaCC9Z8hricQpI9s5zR6Ax3Ove352xS0HKCtnUvn30Jro9/zgVg2Fszh5OFpxVVfPQ2BhCPP47AD90vIQsNqGw9Y6Lo/eTQ2D4TJmHWPlllpjj+xtnUgrX07Noc8y0J57X0NwvyeXL16CxcSCCwWXo2XN3bN1a5ZuPOGHCBM94pES+nV9Pu3QGndonLxR6Cj177oaZM29u9lCmPqs7Qzi1DQzPSx0FV1GxE5FIF99pGzw/FxHrN3Iv3T69P1dYMhta5udm+47N76griED2BCQAJQCzt5rkFSUiAL/tiL3UvnzsT/eV09LFr2ULiy4ebZELSA/gxx+zoIIhYrs5sD1SLoauXbti3Ljv4ZFHXk8RdgzZUpixmtftN/hNJ1ePQtG9DgUnw6fs300PFauXKd5Sc+/oxaQ3ju1m3ApWFlOwvyC9nPR80mtJwfYsgM2orf0eNm/ejF69eqF///5WKxOGf9nEubWJGskejeMQi/FazC8cjHD4X+jXrxdOO+2EtNdJNbTUL0lWzx5//DisWLG2RT4iny2fXqxsv6DbOt9vH61VAWf7YctX6Daf7LLdg84XAREoTgISgBKAppZZIgKQOXucfOcNtbKql4UeDME+2UrT5v9nTa+wvXHucSEqKuaif/8DsWrV/znijL0EeR7DrpejW7cKbNtGYcbxcol1du88txKY16RIO9rxLroVsxRYfC568CgE6WV0J4vQ8+YWsjDXj30K6VVkmJqVyfRSunvhGuYYXorKygoEg3s7wqrtSlU/g/CKnMMOo4cRYAuZJ554BuvXfwW2R8lHS5O2hFZn9GLlS7iVc1gyWzFv+gtP60WgXAhIAEoAmtpyiQhAiiR66xL94Ow8QHrbGIal2PKGeZkvyKILhk5TizTspsw1Nd/AihUcicafs3iBBR4UZG7Il94+r0Bz8w7pzWP1MCt02Q8w1bPH6mC2VqEgZJWsK1rdSlm2s+Ezs5qX1cJuJfCFCATmIR6n0GXrG4aOeY0FCIW2IxbzhqvZx+/m5tnHuRQitCZO7r33RqvBsl8fPBNjy5cYMnmGQq/Np+gtR6GULw9pod+r7icCxUBAAlAC0NQOS0QA+jWCZsiW4s31qLFKliFTCjBWiDI8y+kQDLO6o9LsxsrAqQgE/uRUulLI+U/gsMPLDOMyd5DX5pSOyx2PHq/JSl2KQXr63NxEFkasRyi0GU1Ne3r6z1GMUrh9ywn70jv562YRx7553//+YXj11X86xRUsImFjaxZXnOkzBs+/UjVdqNYNF/uJk2BwEr72tVfwxRfMdctvo+PWvFiLFj3dauNlU+Pu6PXFJnqLTUQWG5+OthfdXwSyISABKAGYjb34nVv0AjAYDCIep9DyNoKmV439937sycej8KNHkA2bXa8aR54xZMtQ7RvONdhPj944jl+jOKSXjTl3FFmJPEI2HN5336fx6adsOcMpHSwy6eKMnOM4OYZruZYhVfai4/V5TfbC24WRI4eAz85CiUiEQo4ePQpFClYWsfAZ6IFk5SyngezEwIEDrKrnjz6iSHXzHR902p2kFprwedg/MLlXnfcl+3lYhg7ti8sum2T1ynv22b8hEuHz2mHocJi9ELsgEml79myuRtd63qBZYUiuz9Pe6zo6dJsa+r/jjntRV7c+a3HfXsIxnx7S9n6Xur4IFBsBCUAJQFObLHoByJy1995jqHQfRzxxtBqLO9jahSKMY9UYcuUfiiWGbt1myazGpcgb7+QBsmiEQoxi8QcAZjtFIpy64a26ZU7egfja1wItKlv9RJKdv8cqXwrP+/DjH5+N3/72t9a7efTRRzF58iXYtYuoU0UcBSB781GA0mv5LLp3j2PLFu7LGzpmAQrPoXhkwcpmhEK215HtT2pq+vuOW/PzsHAKRkVFBE1Np7QoCNlnn2p89lmt0wfRNq3UVi5+BperQOgMHqBc2Zh+sFPFfzD4JKLRcNJElExSB9ozTFuo999R78D0HWq9CLRFQAJQAtD0E1L0ArBfv35Yv57hUxZFnOv04mMuYGpFMCtb6emjN4s9/thTj3l7FErM5WM42J57C9AzyNAvJ3TsRFXVYejeHdi4kXmDDOVSVPZBZeU69Ou3Z9KkjSFD+lpeurq6dWho+B7icfc+9mSPUOgiTJu2D2bMsBsJ80vunHMuRixGwemtVOYzUMzxeRK5jQwFh0IRRCIMLbtClvs41Xlm9g6cjm9+c19UV1dbbWt4L1YEpx5+Hha70TFD53w+Vi6zjc1Yqwikf/89sH79TjQ0vNt873QiwUQgyANk+vFtfX1LccVm4vyMJGwwE3HfniKtEB5SE/tsv7ejK4uAOQEJQAlAUysqegG41157Yds2ih82/PUP19qC7g8A2JiZ4u1mBAKrEI+zypfCzJ0Y4VbWXoZQKIB4/CxUVr5sedCOPLIGt922AvE4kfA4E8HgK7jyym6IRqNYsmSJ1Spl1KhRWLlypTWWa82aNXj++TeTwqipgsl/agY9jPROMo+RY+w4Ts0+3Fm3X3wRam6n4vamo+AEvI2ux6K6+lW01k/PfxoIq51vde7WcpJGnz67YcOGr3xbufgZW6YCwc8Lw/54qY2Y0wlOU4PvLOtbiuuWzcQzYd3eIr29vXOZ2mdnsQvts3wISABKAJpac9ELwG7dumH7dnq3WHHLSlhWv7IYI7UimGLmsuacuL32imLjRrePHtu30JtW4RRz2B6vnj1DzSPOGKo977wpiMcZWqbQYqh5E3r12gfbtoXQ2Hgs4vE/WTmFweAJCAZfxD77BNHU1IDPP+ckjxGorFyNYcMGJIVj/admPGOFqisrA2hspIcy4c3kl3JqJS577PGLeM6chxCLcSoJRW1i/619ke/atQtDhx6BNWu2IBarRUXFi4hGtzkj3G5xBGhCfNIjNHXqXr6j5VoztEwEQmteGBaAtNU70NS4O/P6lsJnKwKB/qio6Iampto2Uwe83EpdQGVin53ZTrT30iUgASgBaGq9RS8A2fB43Tp6yhjeZcEEW6TUNo8AA15wKnuDVl5fKPQCRo48AGvXrsLGjcydY9NmVvP+1WkmzdCqXfTgFU72yLBrknKkbC8di0o47oxNmpM9ZnbvQHrz9kAo1ISBA7ujru6NpHCsK37sqRks8liGbt2qcdttM8E5tSNHHtUs0KqqbG/k4sULWzR2vvrqq3HrrUsRj7M6+Ki0obzEfdda9w0Gl2PAgP3QvfveePfdj9HQMADxOEfbUXzantFQ6ApMmXI+br311lYbS6caXCYCoa1zWJVsOinD9EOQr/Xt7c3K5jn9wqtMX/jJTy6yPNhDhw5ts4G4e69ChGmz2Ve252Zin9leU+eLQDEQkACUADS1w6IXgH379sWGDeyb5/X8uaFcFm+wEna7EwIOoW/fXrj55ptxwQVTEYnQ68e2LBRNzAukWKQodPP1JmPatL2tHDrbU7ApqQACmARgqRN6Zg4hizZY8esek515wSwsuR7V1b/EAw/MAEefeY+2hEEmooHnHHroMHz4IVvdsJVMcu9BPw9guj5/y5YtwxNPPI/1679EQwMLTOgtbTuk7GdsmQiEzuCFKcZcs0xsK5NfIPm6Tib3yvc5mdhnvu/Z1vVKmWUhOele6QlIAEoApreSts8oegHYvXt3bN3K4g0/zx+LI9yqXlb/DkDv3ptw7rnnWmIOeNNpBp1aMMICiHFJHsCEYFrmCMS3ADwAgJ5Fzhz28wCy+pfXYt4hQ8efWYJy5syZpu8laT2fjd5Juz2LO1WExRsM5b2EESMGtfAaZiK6+GXEWcF33vk4otFEo+nWQsqtfXml+1LrKC9MuufK50vqqD3mcw/leq1C2kE68WfPni7Ptkflaj/Fui8JQAlAU9ssegHIubeffcbcvUSo0s71o9eKnjbm6tEDyDzB7Rgz5jtYs+ZjfPghK4c5iYMj17zVt5y4sQxVVduTwq38kjjqqO9jyRIWgrBCmG1Z6ClkZTDvPwb2KDjm7LFNC+f3um1l2BfweitEfPnlZzS3gDF9Oe56u5Dkc8TjnFbCgz0E+QwrMX78kVZPv9RZwJkKkkyEonXHSAS5fnl1hBfG5HlzeW+Zcszl2lpTHgQy/UyWx261i/YmIAEoAWhqY0UvAMeMGYMXX/yLMwrO7YPHnn9s0MzcPvYI/C8Ai60w8H33/Q8uvfQmNDSwvx69eWx5kvAAhsPfwIkn1uDss89ukQPFPMALLvhZUiNku5UMDzaQZk9B/n82fabgZH9BhpXZyoWewiZMnXoG/uu/+Dz5O5I9gG5rmMMRDu/A3Lm3twg5Jwu2tW1W9Pp9KbEtzsUXn2LlMrq5YqYVu4X2whT6y7bQ98ufdelKhSKgfyQUinTnuI8EoASgqaUXvQAcO3YsFi78myOyOBKORR0sAuEEDubEcXrFqwgGa6zq2X/+85+YNWszYrFjnKINeulYxHGc1Wh58OC98d57y5I8Zq44+c1vfoM33+yCeJyj37wzeh9GPM5ZxJyaQQFG7yLvSwE6CADbzXBayVZcfvlZefcA8vmOPnoM3nzzQ8Tj3PsLCAS+xKhRQ/H66y+2WrCRiehK9c6xUjgc3oWGht0QjR6PiopFGD58AI46aiRmz96KWCyRA9laH7lM7mtquOnWF/rLtiO8nOkY6OfFRUD/SCiu91HqTyMBKAFoasNFLwBHjhyJpUvf50wKpxEyQ7MUaAztMjeQ+Xdb0Lt3D8yaZTdfnjTpOtTXn+aEhuc4eYJ1CASW4Kqrhifl6HlDhQ0NbPXCPnssFGGT5kYrT/DQQ3tg2bIhLULJtneRPfXsySEcTzdt2o/S5gDmIpC4hq1qGO7lceaZZ+Kss87KuFq3LUPxPs+OHTswZ87jSdXQnB4yZcppuPfeZ1Ff33b7mUKHXlvbV0d82ebyXrP5ALf39bN5Fp2bPQH9IyF7ZlrROgEJQAlA089H0QvAE044Ac8//zaAm5yZuZzqwaIM/rHbudhh2YNRXb0OQ4b0webNn2H16rVoamKYlsUSLdu+uOD8myUfiEDgYFRVrbHyBKdMudAnNOw2VHYrfichFPoT5s27yzck694vXwKpvcQAW9M8+SQLWpLzJk8++XNs2rQDK1ZkH1LOpOGwqSGnri+3L9t82U2+Oet62RFor89tdk+hs8uBgASgBKCpHRe9ALziiivwu9897uTYcXQZCzOYb0dh54ZpL3JmBf8c7qzbaPRky5sXCOxAIHAGKitfaS76IDS399wHH3yAp5/umdT+hdM4Ro9+D1deeaWVJ8gjEYJlMYjrgaQYdUXogRg8eC+8915dm165fHim2lMM2AJwudN02803PBjjxw+3vI/pevYVOvSaqWcz0753ph+o9lqfD7tpr2fTdUVABApPQAJQAtDU6opeANbW1uKFF1amzP6lx4/hXvYApAfQbcdCb5w769YOzaYWfRCYt5o1GHwS0Wg4KeRJj9XkyScnFUFQdA0ZMhKrV3+MpqYaAMsRCBDfWIRCCzFgQHe89dYjsPztAAAgAElEQVTrePrpp9sM0+ZDILWnGLAbYnMiCnMeKbgXIRD4AnPnzsE555yT1t7a89nS3ryMT/C3m4swbtznOOSQQzJu7FzGiLQ1EehUBCQAJQBNDb7oBeDgwYOxahUnX/zes1fO/mVLljOd2biJnD17OgfFnx2aTS1UyGRElj17t6tVBME+e5y1yzCwnVvo5sBtRTh8IMaOPQIDBgxARUUFFix4AatWbXEKNV5EILC9RaFGpgKprVBRPkRka4bjtsNZvvx9RKP7oaLiEwwb9o02i0281yq30KvpByxf6/3sxvV2NzWNb7ZTvyky+XoGXUcERKB4CEgASgCaWmPRC0C7COTTFiFJoKfjoVrgVOWe5PQE3OaMiUvk/Xm9eZyAkVrNGgxOwsknb7I8KZyfe9ddC9DQ8K5T8WuPjKut/SaefnrfpCpYrvva117BF18A9fV9ATBXkeLUrVQe1qJVSyYCKV2IN1MRmatxmOYpma7P9bnLeV2q3QSDCz1znVvPcS1nJtqbCHRmAhKAEoCm9l/0AvDBBx/ExIk/cXL87JAkwCkfHF3GUDBbvHBOMEO/rNS9x+kNOMYaeF9VVZ/kzevTpxobNtS3Ws3amndt3LjPsHDhu0nrwmG2gumCSKQOwHBPg2ZWJrNX4UAEAktxzTUnWOPm3COdQEon8DIRkaaGofXFR8BrN8xdXbBgHzQ1uc3BW3q7i28HeiIREIF8EZAAlAA0taWiF4Dbtm3DXnv1crxxnOv7CYBdTksYTsNgQQbHwLkFGZ87EzriCASAeJzekQ+bvXlscty37x7YsGGHb4PktmbozplzX1IVbI8eMXz66RjEYhSm1wDgqLZEo2a2qgmH0Wqz5tZeXiYh3nQi0tQwOuP6UmKa7h8JnfH9ac8i0JkISABKAJrae9ELQLsKmHOAOYKNjaApspiH553vS08gR7adCuBPjgjj1BBO6aB3MFExzJzAqVP3wvDhw1FXV9cieb4t7xphe6tgo9Gop+cgQ8/0ProHPZLzMHr0ERnnz7krM/lyLyWxYmqkhVifLuxeiGfI5h7yAmdDS+eKQPkRkACUADS16qIXgEceeSTeeKPSya9jZSqrfsf5NGXmKDhOB2E41uuJS64YzqQnXabiyv0SXrp0JSIRjor7oNkDGAwehEsvPQ2zZ8/Oullzui/3UhMrpkZaiPWZiO58PUem9pXufvm6Trr76OciIALFR0ACUALQ1CqLXgDaHkBOv+DINY5iawJQ1cIDGAzuQFMTc/KYi5dcMRwM/hmBwAQrJ5CNnfNRKel++bKo5KuvvsKf//wMNm2KoamJuYesHDa7T1tf7qZiRcKh5ccmk7C76YeN6yXe80FR1xABEZAAlAA0/RQUvQBkVW7Pnv2s6lygwhkBRwHI3D43zLsNBxzQFwcd1B/PPlvnO8Zs9913z1uvNL8v8aFD++KyyyZh5cqVebtPay/XRKxIgPhTNRXVmX4QC3WfTJ9H54mACJQmAQlACUBTyy0JAdir1wDs2EHx5xZ9fOFMA2HI9wgAP0d19WjcddcvMGXKNOzYwbnBFIfPYffdWaixxmrqnOuR6jFL5P61PRe3tfuZeuBMRITJ2lz5lcK6dGH3fO3BRLzn6xl0HREQgdInIAEoAWhqxUUvAO0QMAs73KKPrQDYc4+PfiIAt+VKf4wf/wWef/4dNDRcD+Dv1nzgqqpf4MEHf93mfN62IPp5zHr0iDrVv4mij9SG022JP3cSSWPjsQiFFqBnz2rMnHkzzjrrrIzyBU3EigRI62/bVJhn8mGUAM+Eks4RARFIR0ACUAIwnY2k+3nRC0DOcH377WGevL5HAExLaQx9OAKBbTjllKN8mzW7TZ5zmQfr94Wd6P+XKDbJpLiELyNxvWUAOGd4HYDvWnmDI0YMzDg/MVexIgGS7iPRvj83Ee/t+2S6ugiIQCkRkACUADS116IXgOFwGNHovh4P4FUAGAJOLvTo2nU+7rprTotxbYFAf1RU7OUpzuiXscgiXD+PWWICSNy3l2BbLyVxveMAXOu0tLF7B2YqIk1eugSICb38rM1VvOfn7rqKCIhAORCQACx9AXgGgBudmOZaK5kNeDyNcf4SwCQnBko30hSn74m7jGWy9c6oDDbBiwM4MuUc99yiF4ABdnO2Qr7M62PD5ScAhFOqgA/CSScNxeOPPw47vLrWEmbB4BOIRiuTikKyFVltNYbm/F+/XoJtvb/E9U4DsB3A3c2nZxpGNv3lJQFiSlDrRUAERKBjCUgAlrYAHA3gVQBnA+BA2/EA/gDgOwCWt2JajH1e5lQ4fASAyW4/BHCgMx6DyygA6V56JQPzLHoBuMcee2DHDjaB5ig15vUdAOCnAHZ3cgBfRiDwBebOnYNzzvn/27sXGLmq+47jP7wee8UzgqZtUsAWAge7EFhCQ1EAi+cmheLWUCGSKI4UHi6bVy1syS4l4REWsEMakSW8oa4pmDaGFhBsy8s0RXLCKwWqQiGF0AJtZIWAqWyv167+6zN4PMzO3jv/O2fumf1eyRLYc+7c8zlnZn977jnnfmFsm43qZs32uKz77/+oRkfzz9Wr2hU9YtZs78C84TRD+/ISBBBAAIEuFCAAph0A7R7mXpLOqOmbayStl3TuOP3155KukfSD8O82LGbPRls09tiJ7YcFQBsqezRDny99AHz11Vd14IGH1mz78qAkWwhi1fyEKpW3dMQRcxo+baOo+W5Fj5jZ+e6++24tWXKxfvnLLYXtHZihvXkJAggggEAXCBAA0w6ANspnKxququmLS0MgPLJB/7SwZsnHbueuq/n3YUnPS7LJcdUA+Ha4T2orDOw5ajueGL/ziUsfAO1yLQTaAo4NGzaMXf2cOXM0d+5c2d5+9ki3+fPnN1w9W/ToXdHfGUUHy6KvL8v5uqEOWerJaxBAAIEyCRAAyxkAb5O0IMy9swls9Yfd9j0hPKB2uaQbal6wMIzm2S3d+mNfSb+QNDs8EqP673eFyWTnhb84XtKTkkYlnRxGBi1Y1r5PtWwSAdBCxnHHnaJ16/5D27ZZleyO+QYdeOAB+tnPnmq6x18nA0on3zvGFxWbSsdQ5j0QQACBDwsQAMsZAG3HYXsw7HjHiKT3wjy/dowA1r/vxZJOCXML6/9tLAAODAxo2jR73q7U398/9qdMxx133KEFC5ZqdNQ2Xt6xdYr0kA46aB+9+OLTmfbPi1mnyRCOirrFHrNdeC8EEEAgVYHh4WHZHzs2b96soaEh+0+bSmYrCifd0WiELRUEmwNoAcxWAlePVuYAvhVWRVTnADYKgJboPtMApvQjgBakZs48UG++aQOb1s9XSvpLSZ+3j4CmTj1Yq1ataHmj53Z1lskQjthUul29h/MigAACzQUYASznCGDWfmurgG2lrq0CfiCsArZ0c2yTVcA2z89WAZ8qyRaE2OjeF8dWQ0j2sNw+bTexOYHV1cB3hteN/apQd5Q6AFr4u/DCC3Xttau0bduWEADtFrDdRZ8hyX4TOn/sCSCzZ89u+zN4szasva7M4chza7q2rD2f+cYb79fGja09Ei+PJ69FAAEEENghQABMOwBaS9oK4MtDmrF9AG1nYNvornq8ELaGubLm7749lnok2xvlqbp9AE+TdLUkmy9oickWgVwnacc+KDt/gkobAKu3UH/yk2c1OnpYmDL5kqTtmyZLtjL4Iu2yyyLXRs/t+kIp6wig59Z0fdmpUx9WpfK+tmzZLfeG2O1y57wIIIDAZBAgAKYfADvdT0sbAHcEKBvk/Ga4U16bY7+iKVPu1LZte7s2em5XA7SyAtkzMpe1Hp5g2qjs9OmH6PzzTx9biNPKo/ayXjevQwABBBBgBLC2D6Q8B7AMfbm0AXDHLVSb7/exsPHzyx+MAPb0HKy+vt/Ws88e7trouZ2NkCfQeUbm8tTBc2vaUzbPNfJaBBBAAIHmAowAMgLo/YyUNgDuPAJoo4C2P7Ydvzf2RJDe3k26/vrvaeHCS7tiDppnZC5PJ/C8j6dsnmvktQgggAACBMCJ+gAjgBMJNf/30gbA6ojYunVPa+tW21VnangC3j+OLZ6eMuVXWrnyag0N3fLBs38rlYfV1zdTa9cOl25bmImaKdboWiu3pqvX7ik7Uf35dwQQQACB7AKMADICmL23NH5laQOgXa4FjtNPP10PPWSrTHfc/t2+AGSm5s3bU7NmzdKmTZs0ffr0pk8F8UK1u3zM0bU8t6br6+0p225Dzo8AAghMFgECIAHQ29dLHQCtckuWLNHy5Xb795aautqjku9SpbKrtm6dp0rlEfX1zUhy5I/RNW8XpjwCCCAw+QQIgARAb68vfQBsNDImHRQei/ympI+MbQvT23uobr99sO0bQrdzBKyd527WUTr1vt7OS3kEEEBgsgoQAAmA3r5f+gBYO+9s06YT1NMzrD32GNU773xW27bZw1S2Hz0952nx4n00ODjoNRm3fKyVum2rQIMTd2OdYvrxXggggEAnBAiABEBvvyt9ALQK1o9QbdmyReecc3HU1b/Vp5IMDa3U6OiOR9HFGnn0NvR45WPOPWxXHTgvAgggMNkECIAEQG+fTyIA1lcy9mrU6vv99KevaMuWnR9F19Mz0PaRR28jNysfa/VxO+vAuRFAAIHJJkAAJAB6+3ySAbDRqOD8+fPbtvVL43mIthL5W+rtvSTK3ENvQzMC2C5BzosAAgjEFyAAEgC9vS7ZAOiteJ7yjUbJpK+op+dH+vSnj2D1cR5MXosAAggg4BYgABIAvZ2IAJhBsNEI4NSpB+uCC+ZrxYoVbRt5zHBphbwktVXAqV1vIY3ESRBAAIEaAQIgAdD7gSAAZhCMPecwwyVN2pewannSNj0VRwABAuBOfYBHwfk+EgTAjH6MOmWEavPLWLXcZmBOjwACSQgwAsgIoLejEgC9gpSPKsCq5ajcvBkCCJRUgABIAPR2TQKgV5DyUQUYAYzKzZshgEBJBQiABEBv1yQAegUpH1WA+ZhRuXkzBBAoqQABkADo7ZoEQK8g5aMLMB8zOjlviAACJRMgABIAvV2SAOgVpDwCCCCAAAKRBQiABEBvl0siADLi421myiOAAAIIdJMAAZAA6O3PpQ+A7PvmbWLKI4AAAgh0mwABkADo7dOlD4Cs+vQ2MeURQAABBLpNgABIAPT26dIHQPZ98zYx5RFAAAEEuk2AAEgA9Pbp0gdARgC9TUx5BBBAAIFuEyAAEgC9fbr0AZB937xNTHkEEEAAgW4TIAASAL19uvQB0CrIKmBvM1MeAQQQQKCbBAiABEBvf04iAHorSXkEEEAAAQS6SYAASAD09mcCoFeQ8ggggAACCEQWIAASAL1djgDoFaQ8AggggAACkQUIgARAb5cjAHoFKY8AAggggEBkAQIgAdDb5QiAXkHKI4AAAgggEFmAAEgA9HY5AqBXkPIIIIAAAghEFiAAEgC9XY4A6BWkPAIIIIAAApEFCIAEQG+XIwB6BR3l2d/QgUdRBBBAYBILEAAJgN7uTwD0CrZYfscTTl7XyMiJqlQeUV/fDK1dO6xKpdLiWSmGAAIIIDAZBAiABEBvPycAegVbLM8zjluEoxgCCCCAgAiABEDvx4AA6BVssfzSpUu1fPl6jY7e+MEZenrO0+LF+2hwcLDFs1IMAQQQQGAyCBAACYDefk4A9Aq2WJ4RwBbhKIYAAgggwAig0g+AZ0q6TNL+kl6TdJGke5r07bMkDUg6TNLukmyy2Na6139S0rWSPiXpHUk3SbpknHMSADv0RbJjDuBrGhk5SZXKw+rrm8kcwA61B2+LAAIIpCTACGDaAfAoSY9LOlvSfZLmSVol6RhJz4zTEU+WtLekXSXd3CAAWih8WdKtki6VNEvSg5JWSPp+g3MSADv4iWcVcAfxeWsEEEAgYQECYNoB0ELaXpLOqOmDayStl3TuBP1yrqRHGwTABZKukvTxmpHBr0v6mqSDCIAJf9q5dAQQQAABBIIAATDtAGijfKtDYKt26qUhEB7ZYgC8RtJsSZ+rKX+0pB+HsLmh7ryMAPJ1ggACCCCAQGICBMByBsDbJNlI3LZx5ijabd8TJL0iabmkG2r63UJJi8Kt22bdcbwRQLstvFu4rVwtf7CkFyXtJ+lNAmBin3IuFwEEEEAAgToBAmA5A6DNz+tt0ltHJL0X5vmVYgRwYGBA06ZNG7vk/v7+sT8cCCCAAAIIIFAegeHhYdkfOzZv3qyhoSH7T5tK9m55rjLelewS760KfyebA2i3YG0lcPXwzgH8kqSr6+YAfkPSV5kDWHj7cUIEEEAAAQQ6IsAIYDlHALN2BlsF/Fi4XftAWAW8UtKxTVYBTwkLP+wWsK3u3UPSqP0yEG452yrgl8Iq4O+E0Gfn/i6rgLM2C69DAAEEEECg3AIEwLQDoPUuWwF8uaQZYR/AZZLurel2L4StYa4Mf2dzC22Ooc0vtMNGQO2/j5f0RPi7QyRdF/YB/LWkH4a9Bhv1ZhaBlPszztUhgAACCCDwIQECYPoBsNPdmgDY6Rbg/RFAAAEEEMgpQAAkAObsMh96OQHQK0h5BBBAAAEEIgsQAAmA3i5HAPQKUh4BBBBAAIHIAgRAAqC3yxEAvYKURwABBBBAILIAAZAA6O1yBECvIOURQAABBBCILEAAJAB6uxwB0CtIeQQQQAABBCILEAAJgN4uRwD0ClIeAQQQQACByAIEQAKgt8sRAL2ClEcAAQQQQCCyAAGQAOjtcgRAryDlEUAAAQQQiCxAACQAerscAdArSHkEEEAAAQQiCxAACYDeLkcA9ApSHgEEEEAAgcgCBEACoLfLEQC9gpRHAAEEEEAgsgABkADo7XIEQK8g5RFAAAEEEIgsQAAkAHq7HAHQK0h5BBBAAAEEIgsQAAmA3i5HAPQKUh4BBBBAAIHIAgRAAqC3yxEAvYKURwABBBBAILIAAZAA6O1yBECvIOURQAABBBCILEAAJAB6uxwB0CtIeQQQQAABBCILEAAJgN4uRwD0ClIeAQQQQACByAIEQAKgt8sRAL2ClEcAAQQQQCCyAAGQAOjtcgRAryDlEUAAAQQQiCxAACQAerscAdArSHkEEEAAAQQiCxAACYDeLkcA9ApSHgEEEEAAgcgCBEACoLfLEQC9gpRHAAEEEEAgsgABkADo7XIEQK8g5RFAAAEEEIgsQAAkAHq7HAHQK0h5BBBAAAEEIgsQAAmA3i5HAPQKUh4BBBBAAIHIAgRAAqC3yxEAvYKURwABBBBAILIAAZAA6O1yBECvIOURQAABBBCILEAAJAB6uxwB0CtIeQQQQAABBCILEAAJgN4uRwD0ClIeAQQQQACByAIEQAKgt8sRAL2ClEcAAQQQQCCyAAGQAOjtcgRAryDlEUAAAQQQiCxAACQAerscAdArSHkEEEAAAQQiCxAACYDeLkcA9ApSHgEEEEAAgcgCBEACoLfLEQC9gpRHAAEEEEAgsgABkADo7XIEQK8g5RFAAAEEEIgsQAAkAHq7HAHQK0h5BBBAAAEEIgsQAAmA3i6XRAAcGRnRmjVr9Nxzz+nwww/X/PnzValUvHWnPAIIIIAAAkkKEADTD4BnSrpM0v6SXpN0kaR7mvTGsyQNSDpM0u6SLAVtrXu9/f9GSVu03WebpKMlvdjgvKUPgBb+5s7t17PPvq6RkRNVqTyivr4ZWrt2mBCY5NcWF40AAggg4BUgAKYdAI+S9LiksyXdJ2mepFWSjpH0zDid42RJe0vaVdLNTQLgiZIey9DBSh8AV69erS9/eZk2bvxXSbtJel+9vYfq9tsHddZZloc5EEAAAQQQmFwCBMC0A+CtkvaSdEZNt10jab2kcyfoynMlPdokAJ4U/n2iT0TpA+DSpUu1fPl6jY7e+EFdenrO0+LF+2hwcHCi+vHvCCCAAAIIdJ0AATDtAGijfKslXVXTM5eGQHikMwC+HcLh65KuD6OFjU5Z+gDICGDXfW9RIQQQQAABpwABsJwB8DZJC8LcO5uDV3/Ybd8TJL0iabmkG2pesFDSIkmzHAHweElPShqVZLeM75BkwbL2faqnL30A3DEH8DWNjJykSuVh9fXNZA6g88uD4ggggAAC6QoQAMsZAG1+Xm+TbjUi6b0wz68dI4D1b32xpFPC3ML6fxsLgAMDA5o2bdrYv/X394/9KdPBKuAytQbXggACCCDQCYHh4WHZHzs2b96soaEh+0+bSvZuJ66n0+/ZaISt09eU9f1tDqAFMFsJXD2KmAPYKABaovtMgwsr/QhgVkxehwACCCCAwGQRYASwnCOAWfufrQK2lbq2CviBsAp4paRjm6wCnhLm9tkikAcl7RFu9W4Ot5z7wtYvz4ftYWw18J2SbBRw7FeFuoMAmLW1eB0CCCCAAAIlESAAph0ArRvZCuDLJc0I+wAuk3RvTf96IWwNc2X4O5tbaHMMbW8/O6r7/Nm8vycknSbpakn7hn0AbRHIdZJuGqfPEgBL8mHmMhBAAAEEEMgqQABMPwBmbet2vY4A2C5ZzosAAggggECbBAiABEBv1yIAegUpjwACCCCAQGQBAiAB0NvlCIBeQcojgAACCCAQWYAASAD0djkCoFeQ8ggggAACCEQWIAASAL1djgDoFaQ8AggggAACkQUIgARAb5cjAHoFKY8AAggggEBkAQIgAdDb5QiAXkHKI4AAAgggEFmAAEgA9HY5AqBXkPIIIIAAAghEFiAAEgC9XY4A6BWkPAIIIIAAApEFCIAEQG+XIwB6BSmPAAIIIIBAZAECIAHQ2+UIgF5ByiOAAAIIIBBZgABIAPR2OQKgV5DyCCCAAAIIRBYgABIAvV2OAOgVpDwCCCCAAAKRBQiABEBvlyMAegUpjwACCCCAQGQBAiAB0NvlCIBeQcojgAACCCAQWYAASAD0djkCoFeQ8ggggAACCEQWIAASAL1djgDoFaQ8AggggAACkQUIgARAb5cjAHoFKY8AAggggEBkAQIgAdDb5QiAXkHKI4AAAgggEFmAAEgA9HY5AqBXkPIIIIAAAghEFiAAEgC9XY4A6BWkPAIIIIAAApEFCIAEQG+XIwB6BSmPAAIIIIBAZAECIAHQ2+UIgF5ByiOAAAIIIBBZgABIAPR2OQKgV5DyCCCAAAIIRBYgABIAvV2OAOgVpDwCCCCAAAKRBQiABEBvlyMAegUpjwACCCCAQGQBAiAB0NvlCIBeQcojgAACCCAQWYAASAD0djkCoFeQ8ggggAACCEQWIAASAL1djgDoFaQ8AggggAACkQUIgARAb5cjAHoFKY8AAggggEBkAQIgAdDb5QiAXkHKI4AAAgggEFmAAEgA9HY5AqBXkPIIIIAAAghEFiAAEgC9XY4A6BWkPAIIIIAAApEFCIAEQG+XIwB6BSmPAAIIIIBAZAECIAHQ2+UIgF5ByiOAAAIIIBBZgABIAPR2OQKgV5DyCCCAAAIIRBYgABIAvV2OAOgVpDwCCCCAAAKRBQiABEBvlyMAegUpjwACCCCAQGQBAiAB0NvlCIBeQcojgAACCCAQWYAAmH4APFPSZZL2l/SapIsk3dOkHw1KOlXSDEkbJK2VtETSf9WU+aSkayV9StI7km6SdMk45yQARv7Q8nYIIIAAAgh4BQiAaQfAoyQ9LulsSfdJmidplaRjJD0zTuf4jqS/k/S8pF0l/VDSHEl94fW7S3pZ0q2SLpU0S9KDklZI+n6Dc3Z1ABweHlZ/f7/3c1bK8tStlM0y4UV1c7tZ5bu5ftRtwu5dyhd0a7sRANMOgBbS9pJ0Rs2nZo2k9ZLOzfhJOiyExb0l/VrSAklXSfq4pK3hHF+X9DVJB022ALho0SJdc801GSnTehl1S6u9qlfbze1mdezm+lE3PnNlEiAAph0AbZRvdQhs1X61NATCIzN2NLv9u1DSAeH1lnZmS/pcTfmjJf04hE27bVx7dPUIIF/YGXtRyV5Gu5WsQXJcDm2XA6tEL6XdStQYGS+FAFjOAHhbGInbpsbXZ7d9T5D0iqTlkm6oaW8Lc4vCrduJusFJYb7gfEn/FF58s6Tdwm3lavmDJb0oaT9JbzYKgG+88Yb23NOyYHcdy5Yt0xVXXNFdlQq1oW5pNms3t5u1SDfXj7rxmSuTgAXA/fazH+tjdxLfLdO1xbqWXWK9UY73sbl5vU1ePyLpvXDrttURwNMk/XUImv9Q8155RwB/p24BSY5q8lIEEEAAAQQQ6LDAvpL+u8PX0JG3L2MAzAphcwBt2M1WAlePLHMAvyDpB5L+RNLDdW/2JUlX180B/Iakr44zB9D8bL6gBVIOBBBAAAEEEEhHYI9wZ8/uOE66I+UAaKuAHwu3ax8Iq4BXSjq2ySpgC3K2uvcPJf1Lg9a2VcAvhVXAtmLYFn7Yub87zirgSddhqDACCCCAAAIIpC+QcgA0fVsBfHnY18/2AVwm6d6aZnkhbA1zZfg7W9lrt5A3hf+3+lvyt0Uf1UB4iKTrwj6AtjLYtoqxvQY5EEAAAQQQQACBrhBIPQB2RSNQCQQQQAABBBBAIKYAATC/dt6nj3xL0l9I+r+wqtlGHG3japuL2OnDnnByTphL+bSkgbDiudF1fUTSUHiSio2k2q1xu6Vuo6RlPPLUzVaW23Y/NjJcHRW2LYKuL2HFzgrtZHtY2pSFSs2elam3W966pdRuWZ5CVN9+qXzmWqlbKm13sSSbG/4bkkbD96NNI6qfP17bdqm0Wyt1S6Xd6j9L9oQwe1iE7f7x6Djf66m0W2E/lgiA+ShbefqIBcATJR2X763a/urFIcDZ7e9XJdl12hedPf3Ewmr9YYHPwob9kLZ+Yyuw35f0R22/0vxvkLduNpf0iWCQ/93iljhZkm1cbqvlbduiiQJgSu2Wt24ptdtETyFq1ItSabtW6pZK29k88P8Nv+hOlWQPBrC9sfYJ338pt1srdUul3WrbxX6ufV6Sfb/Yn/ECYCqft5fzT50AAAZ9SURBVMJ+4hAA81G28vSRsgbAn0uybW9sRbQdPWE1lO2jeEcdS/VZy/acZJtXaYf993PhOcy1z1LOJ9qeV+epm12Bfan9syT7jTiVY274ImsWAFNrt6p9lrql2m7VOtY/hai+36XadlaPieqWattNl/Snkr4ZFgjafPJuabcsdUux3WyLF3uQgz0i9hdNRgBT/ry1/DOLAJiPrpWnj1gAvDCMqtnI2pOS/lySLVrp1GHb57wTbnuuq7mI4fCcZLve2uN0SXeFUafav98YtuG5v1MVafC+eetW/VKzxT9TJP2PpL8Pi4tshLOsR5aQlFK71TpnqVuq7VatZ/1TiOr7WaptZ/WYqG6ptd0fhF+KbcPg18M0mH8b54shtXbLU7fU2s2u136m3S3pljBVZrxbwKm1WyE/lwiA2xnb+fSROWGfwDckfSw8veT3wwhao1uthTTsBCex34rstyF77J1te1M9LOTZjujn1ZX/Yrhuu/7a4+3w5JW/iXHRGd8jb93stNYe/x5C8aGS/iq4nJ3xPTvxsiwhKaV2ayUApthuVs9GTyGq70Optl2WuqX6mbM5YrYjhG0j9rvj3AJOtd2y1C21drsgzPvrDx8um7s+XgBMtd1cP3sIgNv5Yjx9pNpQ08J8EvsSaTaR2NWwExTOO0qW0m9HeevWiMrma1rb2Cah1S2D2tkerZw7SwBMqd1aCYD1bim023hPIaqvS4ptl7VuqX7m7LrtZ6YtfFsQHiXaDe1WrcNEdUup3Q4It35t3r4NvtjRLACm+Hlr5efGTmUIgPkIW336SO27VAOgdbjqM4jzXUUxr240T+4tSX82zhzA/wxze6pzAKvzfGaU8HF4eerW7EvNwqTd5i7jkSUA2ryWlNqt6pylbim2W7OnENXXJ7W2y1O3FNuues22EMQCoC1+a/T9nVq71bbFRHVLqd0soN8Q7mhVc44t3LG2swWMC+sqk3K7tfzziQCYj66Vp4/YI+ds1dF6Sb8VbqXahFS71djJOWY2z8+2cTlVkgUmWwBhw+CfGGcVsG1dYwsO7Ive+s2dkjZI+uN8hFFenaduvympLywCsVvydmvn9jBH09qubIfNU7R2sJD0YBiltO0pNodNzeuvN6V2y1O31NptoqcQNepnqbRd3rql1Ha26temxthK4I9KshXPtquDfX+PN4UnlXbLW7eU2q037JZQ+7myxYq2i4UFd5sDn/J3ZSE/lwiA+RnzPn3EFhTYXKXdJP0qbDdi+wJa6Or08W1J54cQ8VTNPoD7SbJJzp+teUKKzRGxFcN2m6e6l6F98ducwTIeWetmv/n9bdj+xlZC27zGH5V4EYj9ZmtzVqvPrqzuW3h8GO1Lud3y1C21dpvoKUQpf+by1i2ltrMwd2TYc9O+v9eGX5ZtZN2OlNstb91SardGP5PsF+XqNjApt1thP28JgIVRciIEEEAAAQQQQCANAQJgGu3EVSKAAAIIIIAAAoUJEAALo+RECCCAAAIIIIBAGgIEwDTaiatEAAEEEEAAAQQKEyAAFkbJiRBAAAEEEEAAgTQECIBptBNXiQACCCCAAAIIFCZAACyMkhMhgAACCCCAAAJpCBAA02gnrhIBBBBAAAEEEChMgABYGCUnQgABBBBAAAEE0hAgAKbRTlwlAggggAACCCBQmAABsDBKToQAAggggAACCKQhQABMo524SgQQQAABBBBAoDABAmBhlJwIAQQQQAABBBBIQ4AAmEY7cZUIIIAAAggggEBhAgTAwig5EQIIIIAAAgggkIYAATCNduIqEUAAAQQQQACBwgQIgIVRciIEEEAAAQQQQCANAQJgGu3EVSKAAAIIIIAAAoUJEAALo+RECCCAAAIIIIBAGgIEwDTaiatEAAEEEEAAAQQKEyAAFkbJiRBAAAEEEEAAgTQECIBptBNXiQACCCCAAAIIFCZAACyMkhMhgAACCCCAAAJpCBAA02gnrhIBBBBAAAEEEChMgABYGCUnQgABBBBAAAEE0hAgAKbRTlwlAggggAACCCBQmAABsDBKToQAAggggAACCKQhQABMo524SgQQQAABBBBAoDABAmBhlJwIAQQQQAABBBBIQ4AAmEY7cZUIIIAAAggggEBhAgTAwig5EQIIIIAAAgggkIYAATCNduIqEUAAAQQQQACBwgQIgIVRciIEEEAAAQQQQCANAQJgGu3EVSKAAAIIIIAAAoUJ/D9jopAZiWFEMgAAAABJRU5ErkJggg==\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "plt.scatter(Z[0], Z[1027])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\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",
       "            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",
       "        this.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 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);\n",
       "        canvas.attr('height', height);\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'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\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;\n",
       "    var y = canvas_pos.y;\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 overriden (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",
       "    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 + '\">');\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 dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\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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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "ax.scatter(Z[0], Z[1], Z[2])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>z1</th>\n",
       "      <th>z2</th>\n",
       "      <th>z3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Israr_Ahmed</th>\n",
       "      <td>-0.126169</td>\n",
       "      <td>0.008341</td>\n",
       "      <td>0.000280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Muhammad_Tahir</th>\n",
       "      <td>-0.125927</td>\n",
       "      <td>0.008490</td>\n",
       "      <td>0.000328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Javed_Ahmad_Ghamidi</th>\n",
       "      <td>-0.123644</td>\n",
       "      <td>0.007843</td>\n",
       "      <td>-0.000096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ghulam_Ali_Dihlawi</th>\n",
       "      <td>-0.123364</td>\n",
       "      <td>0.008188</td>\n",
       "      <td>0.000312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sadat_Husaini</th>\n",
       "      <td>-0.123238</td>\n",
       "      <td>0.008005</td>\n",
       "      <td>0.000264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Haji_Dost_Muhammad_Qandhari</th>\n",
       "      <td>-0.122584</td>\n",
       "      <td>0.008101</td>\n",
       "      <td>0.000308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sayyad_Laal_Shah_Hamdani</th>\n",
       "      <td>-0.121684</td>\n",
       "      <td>0.007994</td>\n",
       "      <td>0.000302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anthony_Giddens,_Baron_Giddens</th>\n",
       "      <td>-0.119880</td>\n",
       "      <td>0.024274</td>\n",
       "      <td>-0.033115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Muhammad_Usman_Damani</th>\n",
       "      <td>-0.119564</td>\n",
       "      <td>0.007745</td>\n",
       "      <td>0.000290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>François_Roustang</th>\n",
       "      <td>-0.118420</td>\n",
       "      <td>0.023820</td>\n",
       "      <td>-0.032556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jean-Claude_Michéa</th>\n",
       "      <td>-0.118420</td>\n",
       "      <td>0.023820</td>\n",
       "      <td>-0.032556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daniel_Cohn-Bendit</th>\n",
       "      <td>-0.118420</td>\n",
       "      <td>0.023820</td>\n",
       "      <td>-0.032556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Francisco_Varela</th>\n",
       "      <td>-0.118420</td>\n",
       "      <td>0.023820</td>\n",
       "      <td>-0.032556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vincent_Descombes</th>\n",
       "      <td>-0.118420</td>\n",
       "      <td>0.023820</td>\n",
       "      <td>-0.032556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hans_G._Furth</th>\n",
       "      <td>-0.118420</td>\n",
       "      <td>0.023820</td>\n",
       "      <td>-0.032556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Raphaël_Gély</th>\n",
       "      <td>-0.118420</td>\n",
       "      <td>0.023820</td>\n",
       "      <td>-0.032556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>David_Ames_Curtis</th>\n",
       "      <td>-0.118420</td>\n",
       "      <td>0.023820</td>\n",
       "      <td>-0.032556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Danilo_Martuccelli</th>\n",
       "      <td>-0.118420</td>\n",
       "      <td>0.023820</td>\n",
       "      <td>-0.032556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chris_Pallis</th>\n",
       "      <td>-0.118420</td>\n",
       "      <td>0.023820</td>\n",
       "      <td>-0.032556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Myriam_Revault_d'Allonnes</th>\n",
       "      <td>-0.118420</td>\n",
       "      <td>0.023820</td>\n",
       "      <td>-0.032556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nicole_Loraux</th>\n",
       "      <td>-0.118420</td>\n",
       "      <td>0.023820</td>\n",
       "      <td>-0.032556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pierre_Vidal-Naquet</th>\n",
       "      <td>-0.118420</td>\n",
       "      <td>0.023820</td>\n",
       "      <td>-0.032556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Theofanis_Tasis</th>\n",
       "      <td>-0.118420</td>\n",
       "      <td>0.023820</td>\n",
       "      <td>-0.032556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Claude_Lefort</th>\n",
       "      <td>-0.118420</td>\n",
       "      <td>0.023820</td>\n",
       "      <td>-0.032556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jacques_Le_Goff</th>\n",
       "      <td>-0.118420</td>\n",
       "      <td>0.023820</td>\n",
       "      <td>-0.032556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andreas_Kalyvas</th>\n",
       "      <td>-0.118420</td>\n",
       "      <td>0.023820</td>\n",
       "      <td>-0.032556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Georges_Lapassade</th>\n",
       "      <td>-0.118420</td>\n",
       "      <td>0.023820</td>\n",
       "      <td>-0.032556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sophie_Klimis</th>\n",
       "      <td>-0.118420</td>\n",
       "      <td>0.023820</td>\n",
       "      <td>-0.032556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Laurent_Van_Eynde</th>\n",
       "      <td>-0.118420</td>\n",
       "      <td>0.023820</td>\n",
       "      <td>-0.032556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yorgos_Oikonomou</th>\n",
       "      <td>-0.118420</td>\n",
       "      <td>0.023820</td>\n",
       "      <td>-0.032556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Frieder_Otto_Wolf</th>\n",
       "      <td>-0.118420</td>\n",
       "      <td>0.023820</td>\n",
       "      <td>-0.032556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>René_Lourau</th>\n",
       "      <td>-0.118420</td>\n",
       "      <td>0.023820</td>\n",
       "      <td>-0.032556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E._P._Thompson</th>\n",
       "      <td>-0.118420</td>\n",
       "      <td>0.023820</td>\n",
       "      <td>-0.032556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Elias_Petropoulos</th>\n",
       "      <td>-0.118420</td>\n",
       "      <td>0.023820</td>\n",
       "      <td>-0.032556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shimshon_Bichler</th>\n",
       "      <td>-0.118420</td>\n",
       "      <td>0.023820</td>\n",
       "      <td>-0.032556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mathieu_Potte-Bonneville</th>\n",
       "      <td>-0.118420</td>\n",
       "      <td>0.023820</td>\n",
       "      <td>-0.032556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lorraine_Code</th>\n",
       "      <td>-0.118420</td>\n",
       "      <td>0.023820</td>\n",
       "      <td>-0.032556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jonathan_Nitzan</th>\n",
       "      <td>-0.118420</td>\n",
       "      <td>0.023820</td>\n",
       "      <td>-0.032556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Johann_Pál_Arnason</th>\n",
       "      <td>-0.118420</td>\n",
       "      <td>0.023820</td>\n",
       "      <td>-0.032556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>John_Thompson_(sociologist)</th>\n",
       "      <td>-0.118420</td>\n",
       "      <td>0.023820</td>\n",
       "      <td>-0.032556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vincent_de_Gaulejac</th>\n",
       "      <td>-0.118420</td>\n",
       "      <td>0.023820</td>\n",
       "      <td>-0.032556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Edgar_Morin</th>\n",
       "      <td>-0.118420</td>\n",
       "      <td>0.023820</td>\n",
       "      <td>-0.032556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sudipta_Kaviraj</th>\n",
       "      <td>-0.118420</td>\n",
       "      <td>0.023820</td>\n",
       "      <td>-0.032556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Graham_Ward_(theologian)</th>\n",
       "      <td>-0.118420</td>\n",
       "      <td>0.023820</td>\n",
       "      <td>-0.032556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marcel_Gauchet</th>\n",
       "      <td>-0.118420</td>\n",
       "      <td>0.023820</td>\n",
       "      <td>-0.032556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Harald_Wolf</th>\n",
       "      <td>-0.118420</td>\n",
       "      <td>0.023820</td>\n",
       "      <td>-0.032556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Robin_Hahnel</th>\n",
       "      <td>-0.118420</td>\n",
       "      <td>0.023820</td>\n",
       "      <td>-0.032556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mirza_Mazhar_Jan-e-Janaan</th>\n",
       "      <td>-0.118330</td>\n",
       "      <td>0.007605</td>\n",
       "      <td>0.000282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sayyid_Ghulam_Hussain_Shah_Bukhari</th>\n",
       "      <td>-0.118104</td>\n",
       "      <td>0.007793</td>\n",
       "      <td>0.000323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abdul_Quader_Molla</th>\n",
       "      <td>-0.118061</td>\n",
       "      <td>0.007569</td>\n",
       "      <td>0.000189</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          z1        z2        z3\n",
       "Israr_Ahmed                        -0.126169  0.008341  0.000280\n",
       "Muhammad_Tahir                     -0.125927  0.008490  0.000328\n",
       "Javed_Ahmad_Ghamidi                -0.123644  0.007843 -0.000096\n",
       "Ghulam_Ali_Dihlawi                 -0.123364  0.008188  0.000312\n",
       "Sadat_Husaini                      -0.123238  0.008005  0.000264\n",
       "Haji_Dost_Muhammad_Qandhari        -0.122584  0.008101  0.000308\n",
       "Sayyad_Laal_Shah_Hamdani           -0.121684  0.007994  0.000302\n",
       "Anthony_Giddens,_Baron_Giddens     -0.119880  0.024274 -0.033115\n",
       "Muhammad_Usman_Damani              -0.119564  0.007745  0.000290\n",
       "François_Roustang                  -0.118420  0.023820 -0.032556\n",
       "Jean-Claude_Michéa                 -0.118420  0.023820 -0.032556\n",
       "Daniel_Cohn-Bendit                 -0.118420  0.023820 -0.032556\n",
       "Francisco_Varela                   -0.118420  0.023820 -0.032556\n",
       "Vincent_Descombes                  -0.118420  0.023820 -0.032556\n",
       "Hans_G._Furth                      -0.118420  0.023820 -0.032556\n",
       "Raphaël_Gély                       -0.118420  0.023820 -0.032556\n",
       "David_Ames_Curtis                  -0.118420  0.023820 -0.032556\n",
       "Danilo_Martuccelli                 -0.118420  0.023820 -0.032556\n",
       "Chris_Pallis                       -0.118420  0.023820 -0.032556\n",
       "Myriam_Revault_d'Allonnes          -0.118420  0.023820 -0.032556\n",
       "Nicole_Loraux                      -0.118420  0.023820 -0.032556\n",
       "Pierre_Vidal-Naquet                -0.118420  0.023820 -0.032556\n",
       "Theofanis_Tasis                    -0.118420  0.023820 -0.032556\n",
       "Claude_Lefort                      -0.118420  0.023820 -0.032556\n",
       "Jacques_Le_Goff                    -0.118420  0.023820 -0.032556\n",
       "Andreas_Kalyvas                    -0.118420  0.023820 -0.032556\n",
       "Georges_Lapassade                  -0.118420  0.023820 -0.032556\n",
       "Sophie_Klimis                      -0.118420  0.023820 -0.032556\n",
       "Laurent_Van_Eynde                  -0.118420  0.023820 -0.032556\n",
       "Yorgos_Oikonomou                   -0.118420  0.023820 -0.032556\n",
       "Frieder_Otto_Wolf                  -0.118420  0.023820 -0.032556\n",
       "René_Lourau                        -0.118420  0.023820 -0.032556\n",
       "E._P._Thompson                     -0.118420  0.023820 -0.032556\n",
       "Elias_Petropoulos                  -0.118420  0.023820 -0.032556\n",
       "Shimshon_Bichler                   -0.118420  0.023820 -0.032556\n",
       "Mathieu_Potte-Bonneville           -0.118420  0.023820 -0.032556\n",
       "Lorraine_Code                      -0.118420  0.023820 -0.032556\n",
       "Jonathan_Nitzan                    -0.118420  0.023820 -0.032556\n",
       "Johann_Pál_Arnason                 -0.118420  0.023820 -0.032556\n",
       "John_Thompson_(sociologist)        -0.118420  0.023820 -0.032556\n",
       "Vincent_de_Gaulejac                -0.118420  0.023820 -0.032556\n",
       "Edgar_Morin                        -0.118420  0.023820 -0.032556\n",
       "Sudipta_Kaviraj                    -0.118420  0.023820 -0.032556\n",
       "Graham_Ward_(theologian)           -0.118420  0.023820 -0.032556\n",
       "Marcel_Gauchet                     -0.118420  0.023820 -0.032556\n",
       "Harald_Wolf                        -0.118420  0.023820 -0.032556\n",
       "Robin_Hahnel                       -0.118420  0.023820 -0.032556\n",
       "Mirza_Mazhar_Jan-e-Janaan          -0.118330  0.007605  0.000282\n",
       "Sayyid_Ghulam_Hussain_Shah_Bukhari -0.118104  0.007793  0.000323\n",
       "Abdul_Quader_Molla                 -0.118061  0.007569  0.000189"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(index=G.nodes())\n",
    "\n",
    "df[\"z1\"] = Z[0]\n",
    "df[\"z2\"] = Z[1]\n",
    "df[\"z3\"] = Z[2]\n",
    "\n",
    "df.sort_values(\"z1\", inplace=True)\n",
    "\n",
    "df.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>z1</th>\n",
       "      <th>z2</th>\n",
       "      <th>z3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ion_Dragoumis</th>\n",
       "      <td>1.192918</td>\n",
       "      <td>-0.559894</td>\n",
       "      <td>-0.911085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Giovanni_Gentile</th>\n",
       "      <td>1.198342</td>\n",
       "      <td>0.395272</td>\n",
       "      <td>-0.598512</td>\n",
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       "    <tr>\n",
       "      <th>Damaris_Cudworth_Masham</th>\n",
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       "      <td>-2.396113</td>\n",
       "      <td>-0.563355</td>\n",
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       "    <tr>\n",
       "      <th>Cornelius_Castoriadis</th>\n",
       "      <td>1.199587</td>\n",
       "      <td>0.296502</td>\n",
       "      <td>-1.416812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Catherine_Malabou</th>\n",
       "      <td>1.205827</td>\n",
       "      <td>0.685684</td>\n",
       "      <td>0.194916</td>\n",
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       "    <tr>\n",
       "      <th>Gottfried_Wilhelm_Leibniz</th>\n",
       "      <td>1.222833</td>\n",
       "      <td>-2.452834</td>\n",
       "      <td>-0.590228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leszek_Kołakowski</th>\n",
       "      <td>1.224622</td>\n",
       "      <td>0.763705</td>\n",
       "      <td>0.316435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Juan_José_Sebreli</th>\n",
       "      <td>1.233607</td>\n",
       "      <td>0.856086</td>\n",
       "      <td>0.310605</td>\n",
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       "    <tr>\n",
       "      <th>John_McDowell</th>\n",
       "      <td>1.243974</td>\n",
       "      <td>-0.108425</td>\n",
       "      <td>0.743246</td>\n",
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       "    <tr>\n",
       "      <th>Alfred_North_Whitehead</th>\n",
       "      <td>1.261306</td>\n",
       "      <td>-1.265261</td>\n",
       "      <td>0.743177</td>\n",
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       "    <tr>\n",
       "      <th>Kostas_Axelos</th>\n",
       "      <td>1.261412</td>\n",
       "      <td>0.984766</td>\n",
       "      <td>-0.783851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Immanuel_Kant</th>\n",
       "      <td>1.275194</td>\n",
       "      <td>-2.091894</td>\n",
       "      <td>0.375737</td>\n",
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       "    <tr>\n",
       "      <th>Karl_Marx</th>\n",
       "      <td>1.289819</td>\n",
       "      <td>-0.659949</td>\n",
       "      <td>-0.039330</td>\n",
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       "    <tr>\n",
       "      <th>Xavier_Zubiri</th>\n",
       "      <td>1.291926</td>\n",
       "      <td>-0.521271</td>\n",
       "      <td>-0.592131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>William_Desmond_(philosopher)</th>\n",
       "      <td>1.325088</td>\n",
       "      <td>-0.544502</td>\n",
       "      <td>0.366285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Joseph_Dietzgen</th>\n",
       "      <td>1.325381</td>\n",
       "      <td>0.469264</td>\n",
       "      <td>0.667744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Søren_Kierkegaard</th>\n",
       "      <td>1.325998</td>\n",
       "      <td>-0.406932</td>\n",
       "      <td>0.606610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Benedetto_Croce</th>\n",
       "      <td>1.347021</td>\n",
       "      <td>0.655599</td>\n",
       "      <td>0.500021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>György_Lukács</th>\n",
       "      <td>1.368202</td>\n",
       "      <td>0.963876</td>\n",
       "      <td>0.322257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slavoj_Žižek</th>\n",
       "      <td>1.371311</td>\n",
       "      <td>0.852189</td>\n",
       "      <td>-0.791035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Friedrich_Nietzsche</th>\n",
       "      <td>1.421026</td>\n",
       "      <td>-0.215107</td>\n",
       "      <td>1.311245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Costanzo_Preve</th>\n",
       "      <td>1.433872</td>\n",
       "      <td>-0.392594</td>\n",
       "      <td>-0.518546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Judith_Butler</th>\n",
       "      <td>1.434596</td>\n",
       "      <td>1.342448</td>\n",
       "      <td>-1.025513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gillian_Rose</th>\n",
       "      <td>1.452782</td>\n",
       "      <td>0.395396</td>\n",
       "      <td>0.276963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marcel_Paquet</th>\n",
       "      <td>1.481340</td>\n",
       "      <td>0.644556</td>\n",
       "      <td>0.231138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alasdair_MacIntyre</th>\n",
       "      <td>1.515037</td>\n",
       "      <td>-0.053218</td>\n",
       "      <td>-0.877911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T._K._Seung</th>\n",
       "      <td>1.519529</td>\n",
       "      <td>0.033941</td>\n",
       "      <td>0.574338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Karl_Popper</th>\n",
       "      <td>1.597377</td>\n",
       "      <td>-0.389197</td>\n",
       "      <td>0.927601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Michael_Oakeshott</th>\n",
       "      <td>1.620609</td>\n",
       "      <td>-0.805236</td>\n",
       "      <td>0.512777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Herbert_Marcuse</th>\n",
       "      <td>1.626267</td>\n",
       "      <td>1.078498</td>\n",
       "      <td>-0.132770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peter_Rollins</th>\n",
       "      <td>1.645246</td>\n",
       "      <td>1.346098</td>\n",
       "      <td>-1.294755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Allan_Bloom</th>\n",
       "      <td>1.648364</td>\n",
       "      <td>-0.485074</td>\n",
       "      <td>-0.879820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Michel_Aflaq__4</th>\n",
       "      <td>1.649057</td>\n",
       "      <td>0.705379</td>\n",
       "      <td>0.352544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leonardo_Polo</th>\n",
       "      <td>1.684991</td>\n",
       "      <td>-0.378898</td>\n",
       "      <td>0.018251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gilles_Deleuze</th>\n",
       "      <td>1.710664</td>\n",
       "      <td>0.443516</td>\n",
       "      <td>0.411562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Charles_Taylor_(philosopher)</th>\n",
       "      <td>1.716631</td>\n",
       "      <td>-0.092317</td>\n",
       "      <td>-0.883582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Karl_Jaspers</th>\n",
       "      <td>1.724040</td>\n",
       "      <td>0.642434</td>\n",
       "      <td>0.260587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jürgen_Habermas</th>\n",
       "      <td>1.759664</td>\n",
       "      <td>1.168210</td>\n",
       "      <td>0.335592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jean-Paul_Sartre</th>\n",
       "      <td>1.796306</td>\n",
       "      <td>1.291671</td>\n",
       "      <td>-1.342204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Georg_Wilhelm_Friedrich_Hegel</th>\n",
       "      <td>1.848736</td>\n",
       "      <td>-1.685908</td>\n",
       "      <td>1.024502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hans-Georg_Gadamer</th>\n",
       "      <td>1.904082</td>\n",
       "      <td>-0.495156</td>\n",
       "      <td>-0.062817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Richard_J._Bernstein</th>\n",
       "      <td>1.920509</td>\n",
       "      <td>0.687492</td>\n",
       "      <td>0.289457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Simone_de_Beauvoir</th>\n",
       "      <td>2.081901</td>\n",
       "      <td>1.054889</td>\n",
       "      <td>-0.204537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Michael_Marder</th>\n",
       "      <td>2.130968</td>\n",
       "      <td>-0.600794</td>\n",
       "      <td>-0.226694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Theodor_W._Adorno</th>\n",
       "      <td>2.155209</td>\n",
       "      <td>1.114976</td>\n",
       "      <td>0.168955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Boris_Groys</th>\n",
       "      <td>2.337228</td>\n",
       "      <td>0.032551</td>\n",
       "      <td>-0.531624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leo_Strauss</th>\n",
       "      <td>2.356254</td>\n",
       "      <td>-0.685890</td>\n",
       "      <td>-0.177409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Simon_Critchley</th>\n",
       "      <td>2.464945</td>\n",
       "      <td>0.045068</td>\n",
       "      <td>-0.670834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Martin_Heidegger</th>\n",
       "      <td>2.671536</td>\n",
       "      <td>-0.920451</td>\n",
       "      <td>0.419891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mário_Ferreira_dos_Santos</th>\n",
       "      <td>3.778563</td>\n",
       "      <td>-1.342523</td>\n",
       "      <td>-0.443463</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     z1        z2        z3\n",
       "Ion_Dragoumis                  1.192918 -0.559894 -0.911085\n",
       "Giovanni_Gentile               1.198342  0.395272 -0.598512\n",
       "Damaris_Cudworth_Masham        1.199565 -2.396113 -0.563355\n",
       "Cornelius_Castoriadis          1.199587  0.296502 -1.416812\n",
       "Catherine_Malabou              1.205827  0.685684  0.194916\n",
       "Gottfried_Wilhelm_Leibniz      1.222833 -2.452834 -0.590228\n",
       "Leszek_Kołakowski              1.224622  0.763705  0.316435\n",
       "Juan_José_Sebreli              1.233607  0.856086  0.310605\n",
       "John_McDowell                  1.243974 -0.108425  0.743246\n",
       "Alfred_North_Whitehead         1.261306 -1.265261  0.743177\n",
       "Kostas_Axelos                  1.261412  0.984766 -0.783851\n",
       "Immanuel_Kant                  1.275194 -2.091894  0.375737\n",
       "Karl_Marx                      1.289819 -0.659949 -0.039330\n",
       "Xavier_Zubiri                  1.291926 -0.521271 -0.592131\n",
       "William_Desmond_(philosopher)  1.325088 -0.544502  0.366285\n",
       "Joseph_Dietzgen                1.325381  0.469264  0.667744\n",
       "Søren_Kierkegaard              1.325998 -0.406932  0.606610\n",
       "Benedetto_Croce                1.347021  0.655599  0.500021\n",
       "György_Lukács                  1.368202  0.963876  0.322257\n",
       "Slavoj_Žižek                   1.371311  0.852189 -0.791035\n",
       "Friedrich_Nietzsche            1.421026 -0.215107  1.311245\n",
       "Costanzo_Preve                 1.433872 -0.392594 -0.518546\n",
       "Judith_Butler                  1.434596  1.342448 -1.025513\n",
       "Gillian_Rose                   1.452782  0.395396  0.276963\n",
       "Marcel_Paquet                  1.481340  0.644556  0.231138\n",
       "Alasdair_MacIntyre             1.515037 -0.053218 -0.877911\n",
       "T._K._Seung                    1.519529  0.033941  0.574338\n",
       "Karl_Popper                    1.597377 -0.389197  0.927601\n",
       "Michael_Oakeshott              1.620609 -0.805236  0.512777\n",
       "Herbert_Marcuse                1.626267  1.078498 -0.132770\n",
       "Peter_Rollins                  1.645246  1.346098 -1.294755\n",
       "Allan_Bloom                    1.648364 -0.485074 -0.879820\n",
       "Michel_Aflaq__4                1.649057  0.705379  0.352544\n",
       "Leonardo_Polo                  1.684991 -0.378898  0.018251\n",
       "Gilles_Deleuze                 1.710664  0.443516  0.411562\n",
       "Charles_Taylor_(philosopher)   1.716631 -0.092317 -0.883582\n",
       "Karl_Jaspers                   1.724040  0.642434  0.260587\n",
       "Jürgen_Habermas                1.759664  1.168210  0.335592\n",
       "Jean-Paul_Sartre               1.796306  1.291671 -1.342204\n",
       "Georg_Wilhelm_Friedrich_Hegel  1.848736 -1.685908  1.024502\n",
       "Hans-Georg_Gadamer             1.904082 -0.495156 -0.062817\n",
       "Richard_J._Bernstein           1.920509  0.687492  0.289457\n",
       "Simone_de_Beauvoir             2.081901  1.054889 -0.204537\n",
       "Michael_Marder                 2.130968 -0.600794 -0.226694\n",
       "Theodor_W._Adorno              2.155209  1.114976  0.168955\n",
       "Boris_Groys                    2.337228  0.032551 -0.531624\n",
       "Leo_Strauss                    2.356254 -0.685890 -0.177409\n",
       "Simon_Critchley                2.464945  0.045068 -0.670834\n",
       "Martin_Heidegger               2.671536 -0.920451  0.419891\n",
       "Mário_Ferreira_dos_Santos      3.778563 -1.342523 -0.443463"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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