quora/quora_1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "import pandas as pd\n",
    "#import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.feature_extraction.text import TfidfTransformer, TfidfVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('../input/train.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check train data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>qid1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>qid2</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>question1</th>\n",
       "      <td>What is the step by step guide to invest in sh...</td>\n",
       "      <td>What is the story of Kohinoor (Koh-i-Noor) Dia...</td>\n",
       "      <td>How can I increase the speed of my internet co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>question2</th>\n",
       "      <td>What is the step by step guide to invest in sh...</td>\n",
       "      <td>What would happen if the Indian government sto...</td>\n",
       "      <td>How can Internet speed be increased by hacking...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_duplicate</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                              0  \\\n",
       "id                                                            0   \n",
       "qid1                                                          1   \n",
       "qid2                                                          2   \n",
       "question1     What is the step by step guide to invest in sh...   \n",
       "question2     What is the step by step guide to invest in sh...   \n",
       "is_duplicate                                                  0   \n",
       "\n",
       "                                                              1  \\\n",
       "id                                                            1   \n",
       "qid1                                                          3   \n",
       "qid2                                                          4   \n",
       "question1     What is the story of Kohinoor (Koh-i-Noor) Dia...   \n",
       "question2     What would happen if the Indian government sto...   \n",
       "is_duplicate                                                  0   \n",
       "\n",
       "                                                              2  \n",
       "id                                                            2  \n",
       "qid1                                                          5  \n",
       "qid2                                                          6  \n",
       "question1     How can I increase the speed of my internet co...  \n",
       "question2     How can Internet speed be increased by hacking...  \n",
       "is_duplicate                                                  0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:3].T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('How can I increase the speed of my internet connection while using a VPN?',\n",
       " 'How can Internet speed be increased by hacking through DNS?')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[2,'question1'],df.loc[2,'question2'],"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 404290 entries, 0 to 404289\n",
      "Data columns (total 6 columns):\n",
      "id              404290 non-null int64\n",
      "qid1            404290 non-null int64\n",
      "qid2            404290 non-null int64\n",
      "question1       404290 non-null object\n",
      "question2       404288 non-null object\n",
      "is_duplicate    404290 non-null int64\n",
      "dtypes: int64(4), object(2)\n",
      "memory usage: 18.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "there are 2 null object in q2. fill that"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['question2'] = df['question2'].fillna('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 404290 entries, 0 to 404289\n",
      "Data columns (total 6 columns):\n",
      "id              404290 non-null int64\n",
      "qid1            404290 non-null int64\n",
      "qid2            404290 non-null int64\n",
      "question1       404290 non-null object\n",
      "question2       404290 non-null object\n",
      "is_duplicate    404290 non-null int64\n",
      "dtypes: int64(4), object(2)\n",
      "memory usage: 18.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## transform\n",
    "use tfidf vectorizer (without using tf-idf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "vect = TfidfVectorizer(use_idf=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 18.7 s, sys: 667 ms, total: 19.4 s\n",
      "Wall time: 40.4 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "TfidfVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
       "        dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',\n",
       "        lowercase=True, max_df=1.0, max_features=None, min_df=1,\n",
       "        ngram_range=(1, 1), norm='l2', preprocessor=None, smooth_idf=True,\n",
       "        stop_words=None, strip_accents=None, sublinear_tf=False,\n",
       "        token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b', tokenizer=None, use_idf=False,\n",
       "        vocabulary=None)"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time vect.fit(df.question1.append(df.question2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 16s, sys: 827 ms, total: 1min 17s\n",
      "Wall time: 1min 23s\n"
     ]
    }
   ],
   "source": [
    "%time df['q1_vect'] = list(vect.transform(df.question1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 18s, sys: 775 ms, total: 1min 19s\n",
      "Wall time: 1min 29s\n"
     ]
    }
   ],
   "source": [
    "%time df['q2_vect'] = list(vect.transform(df['question2']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def question_cosine_distance(df):\n",
    "    return cosine_distances(df.q1_vect, df.q2_vect)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 12min 28s, sys: 0 ns, total: 12min 28s\n",
      "Wall time: 12min 32s\n"
     ]
    }
   ],
   "source": [
    "%time df['tf_nonstop_dist'] = df.apply(question_cosine_distance, axis=1)['id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics.pairwise import cosine_distances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "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 src=\"data:image/png;base64,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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/anaconda3/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5dc6016d30>"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "plt.subplot(1,2,1)\n",
    "sns.violinplot(x = 'is_duplicate', y = 'tf_nonstop_dist', data = df[0:50000])\n",
    "plt.subplot(1,2,2)\n",
    "sns.distplot(df[df['is_duplicate'] == 1.0]['tf_nonstop_dist'][0:10000], color = 'green')\n",
    "sns.distplot(df[df['is_duplicate'] == 0.0]['tf_nonstop_dist'][0:10000], color = 'red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## transform (tf-idf, stopwords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 22.4 s, sys: 133 ms, total: 22.6 s\n",
      "Wall time: 24.3 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "TfidfVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
       "        dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',\n",
       "        lowercase=True, max_df=1.0, max_features=None, min_df=1,\n",
       "        ngram_range=(1, 1), norm='l2', preprocessor=None, smooth_idf=True,\n",
       "        stop_words=None, strip_accents=None, sublinear_tf=False,\n",
       "        token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b', tokenizer=None, use_idf=True,\n",
       "        vocabulary=None)"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vect = TfidfVectorizer(use_idf=True)\n",
    "%time vect.fit(df.question1.append(df.question2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 17s, sys: 585 ms, total: 1min 18s\n",
      "Wall time: 1min 21s\n",
      "CPU times: user 1min 15s, sys: 661 ms, total: 1min 16s\n",
      "Wall time: 1min 35s\n",
      "CPU times: user 12min 13s, sys: 0 ns, total: 12min 13s\n",
      "Wall time: 12min 32s\n"
     ]
    }
   ],
   "source": [
    "%time df['q1_vect'] = list(vect.transform(df.question1))\n",
    "%time df['q2_vect'] = list(vect.transform(df['question2']))\n",
    "%time df['tfidf_nonstop_dist'] = df.apply(question_cosine_distance, axis=1)['id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "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 src=\"data:image/png;base64,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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/anaconda3/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5dc648fcc0>"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "plt.subplot(1,2,1)\n",
    "sns.violinplot(x = 'is_duplicate', y = 'tfidf_nonstop_dist', data = df)\n",
    "plt.subplot(1,2,2)\n",
    "sns.distplot(df[df['is_duplicate'] == 1.0]['tfidf_nonstop_dist'], color = 'green')\n",
    "sns.distplot(df[df['is_duplicate'] == 0.0]['tfidf_nonstop_dist'], color = 'red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vect = TfidfVectorizer(use_idf=True, stop_words='english')\n",
    "%time vect.fit(df.question1.append(df.question2))\n",
    "%time df['q1_vect'] = list(vect.transform(df.question1))\n",
    "%time df['q2_vect'] = list(vect.transform(df['question2']))\n",
    "%time df['tfidf_stop_dist'] = df.apply(question_cosine_distance, axis=1)['id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "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 src=\"data:image/png;base64,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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/anaconda3/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5dc6249518>"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#plot\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.subplot(1,2,1)\n",
    "sns.violinplot(x = 'is_duplicate', y = 'tfidf_stop_dist', data = df)\n",
    "plt.subplot(1,2,2)\n",
    "sns.distplot(df[df['is_duplicate'] == 1.0]['tfidf_stop_dist'], color = 'green')\n",
    "sns.distplot(df[df['is_duplicate'] == 0.0]['tfidf_stop_dist'], color = 'red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vect = TfidfVectorizer(use_idf=False, stop_words='english')\n",
    "%time vect.fit(df.question1.append(df.question2))\n",
    "%time df['q1_vect'] = list(vect.transform(df.question1))\n",
    "%time df['q2_vect'] = list(vect.transform(df['question2']))\n",
    "%time df['idf_stop_dist'] = df.apply(question_cosine_distance, axis=1)['id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "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 src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA8AAAAKACAYAAABT+RFDAAAgAElEQVR4nOy9e3BV157fedLdk0y6ezKppN1OfH3HIO210t1J37xmqqYmPTWZdCY1k/zOW9I5kpCEEALxkAAh8caYNzbmaWPewoBssI3txubiJ8Y2NhhjYwM2mIcNGAwScHXBqVSq+vre3/yxJdlsXtpw9v7ttfT9VH3KhtKRfmuds5Z+X/YrFgMAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAIDNKKWWaa1PK6V+N3DgwF/c7GuKi4v/hVLqPaXUUa31Ya31Osdx/l7YtQIAALCL4uLi/1drfUAp9blSau+tfg8BAAAAABSEoqKiv/r5z3/+gNb6m9sEYKe4uPhfdP/x72ittzqO83CIZQIAALCMAQMG/EOt9ZUBAwb8WSzm/j5SSh2RrgsAAAAA/QCt9em+/su71nq8Uqot6JoAAADYS3Fx8b/VWn/1079TSl1TSv0rqZoAAAAA0E/oawC+//77/0hrfcxxnEQYdQEAALATx3H+gdb6suM4/3v3nxNKqd8WFxenpGsDAAAAgOX0MQD/D0qpV5VSS0IpCgAAgNU4jvN/KaXe7b4OeLlS6ohSiqTrAgAAAIDl9CEA/4FS6iWl1Co/3/eHH37LAAAA7p573N5N4u9qrbu01kW3+6Lf/e530m8JAAAYTVibOgCR5g4B+PeVUi9qrdf6/b6XLn3Ply9DCCG8W+9xe480AwYM+Cc9/6+UmqOUeuFOr2FmvnJF/n25G69c+Z5RP8aA+s2t34YxXLli9+8VAO6IUmqVUuqc1vpvtdYXlVInYrFYTGs9U2s9rPtrKpRSv1VKfdbtQcdxnujL97906XuGEEJ49wb5O0Aax3HWaK2PKaVOOI6z0XGcf3Cn1zAzX74s/77cjZcvu40z6scYUL98Lf11DJcv2/17BQBxpBc5hBCarvQ+HjVMbzxRP8aA+s2t34YxIAADEDDSixxCCE1Xeh+PGqY3nqgfY0D95tZvwxgQgAEIGOlFDiGEpiu9j0cN0xtP1I8xoH5z67dhDAjAAASM9CKHEELTld7Ho4bpjSfqxxhQv7n12zAGBGAAAkZ6kUMIoelK7+NRw/TGE/VjDKjf3PptGAMCMAABI73IIYTQdKX38ahheuOJ+jEG1G9u/TaMAQEYgICRXuQQQmi60vt41DC98UT9GAPqN7d+G8aAAAxAwEgvcgghNF3pfTxqmN54on6MAfWbW78NY0AABiBgpBc5hBCarvQ+HjVMbzxRP8aA+s2t34YxIAADEDDSixxCCE1Xeh+PGqY3nqgfY0D95tZvwxgQgAEIGOlFDiGEpiu9j0cN0xtP1I8xoH5z67dhDAjAAASM9CKHEELTld7Ho4bpjSfqxxhQv7n12zAGBGAAAkZ6kUMIoelK7+NRw/TGE/VjDKjf3PptGAMCMAABI73IIYTQdKX38ahheuOJ+jEG1G9u/TaMAQEYgICRXuQQQmi60vt41DC98UT9GAPqN7d+G8aAAAxAwEgvcgghNF3pfTxqmN54on6MAfWbW78NY0AABiBgpBc5hBCarvQ+HjVMbzxRP8aA+s2t34YxIAADEDDSixxCCE1Xeh+PGqY3nqgfY0D95tZvwxgQgAEIGOlFDiGEpiu9j0cN0xtP1I8xoH5z67dhDAjAAASM9CKHEELTld7Ho4bpjSfqxxhQvzn1d3Rc5RMnzl7nyZNn+be//a0xY7jZeyC9jwNgNdKLHEIITVd6H48aJjXPXk1r/m2r34YxoP5wPXHiLB+Z8jCfnTW/1yNTH+auri5jxnCz90B6HwfAaqQXOYQQmq70Ph41TGqevZrW/NtWvw1jQP3heuLEWT47az5///iyXs/Ono8ADAC4NdKLHEIITVd6H48aJjXPXk1r/m2r34YxoP5wRQAGAPhGepFDCKHpSu/jUcOk5tmrac2/bfXbMAbUH64IwAAA30gvcgghNF3pfTxqmNQ8ezWt+betfhvGgPrDFQEYAOAb6UUOIYSmK72PRw2TmmevpjX/ttVvwxhQf7giAAMAfCO9yCGE0HSl9/GoYVLz7NW05t+2+m0YA+oPVwRgAIBvpBc5hBCarvQ+HjVMap69mtb821a/DWNA/eGKAAwA8I30IocQQtOV3sejhknNs1fTmn/b6rdhDKg/XBGAAQC+kV7kEEJoutL7eNQwqXn2alrzb1v9NowB9YcrAjAAwDfSixxCCE1Xeh+PGiY1z15Na/5tq9+GMaD+cEUABgD4RnqRQwih6Urv41HDpObZq2nNv2312zAG1B+uCMAAAN9IL3IIITRd6X08apjUPHs1rfm3rX4bxoD6wxUBGADgG+lFDiGEpiu9j0cNk5pnr6Y1/7bVb8MYUH+4IgADAHwjvcghhNB0pffxqGFS8+zVtObftvptGAPqD1cEYACAb6QXOYQQmq70Ph41TGqevZrW/NtWvw1jQP3higAMAPCN9CKHEELTld7Ho4ZJzbNX05p/2+q3YQyoP1wRgAEAvpFe5BBCaLrS+3jUMKl59mpa829b/TaMAfWHKwIwAMA30oscQghNV3ofjxomNc9eTWv+bavfhjGg/nBFAAYA+EZ6kUMIoelK7+NRw6Tm2atpzb9t9dswBtQfrgjAAADfSC9yCCE0Xel9PGqY1Dx7Na35t61+G8aA+sMVARgA4BvpRQ4hhKYrvY9HDZOaZ6+mNf+21W/DGFB/uCIAAwB8I73IIYTQdKX38ahhUvPs1bTm37b6bRgD6g9XBGAAgG+kFzmEEJqu9D4eNUxqnr2a1vzbVr8NY0D94YoADADwjfQihxBC05Xex6OGSc2zV9Oaf9vqt2EMqD9cEYABAL6RXuQQQmi60vt41DCpefZqWvNvW/02jAH1hysCMADAN9KLHEIITVd6H48aJjXPXk1r/m2r34YxoP5wRQAGAPhGepFDCKHpSu/jUcOk5tmrac2/bfXbMAbUH64IwAAA30gvcgghNF3pfTxqmNQ8ezWt+betfhvGgPrDFQEYAOAb6UUOIYSmK72PB4nW+j8rpT5VSn2mtT6sta6+02tMap69mtb821a/DWNA/eGKAAwA8I30IocQQtOV3seDRGv9K8dx/nksFosNHDjwIaXUf7///vv/6HavMal59mpa829b/TaMAfWHKwIwAMA30ovcJE+dOsdHjpwoiCdOnBUfD4SwMErv40Gitb5cVFT0V7FYLDZw4MBfKKXOxWKxP7jda0xqnr2a1vzbVr8NY0D94YoADADwjfQij7qnTp3nrVtf5HHjxjMRFdRRoxp58+YtfPz4GfFxQgjvXul9PEiKior+Wmt9WWt9Ril1tbi4+D/c6TUmNc9eTWv+bavfhjGg/nBFAAYA+EZ6kUfRs2c7efv2nTx58lROJBI/htZEhhPZqoIYT2Z7v288HueWlgm8bdt2Pn36gvj4IYT+lN7HA+T3lVK7i4uL/10sFosVFRX9r1rrCw8++OA/ut2LmJmvXHGbONO8csVt/lE/xoD65WvpiydPnuWzs+fz94uW9fptdwA2ZQw3ew/C2eIB6KdIN45R8cKFLn7jjd08a9YcTqczP4beeJJTFSO5tOFRzrds5PLW9oKYb9nEpSMf51RlI1Mi3fvzkskkT58+g3fseJPPnbssPi8QwjsrvY8HRXFx8b/VWn/107/TWn9cVFT017d7HQMAQEh0dXVx1+LFzKtX99q1eDF3dXVJl3ZPBLu7A9DPkW4cJe3ouMrvv/8RP/bYIi4ry/3k9OQ4J/PDuGTYHM6P31Cw0HvLMDx+I5cMn8+p8gam+I9HnLPZEp47dwG//fb7fOFCl/h8QQhvrvQ+HhTFxcV/qpS6NmDAgD/r/rOjtb7iOM6Dt3sdszlHj2525AX1Ywyo35z6cQQYAOAb6cYxTL/77grv2bOf29o28aRJU7m0tPS6a3KTpbWcHfoI58atDzz03jIMN7dxSf0sTubqmCj+kzCc5dbWibx27QZ+9929fO7cJfH5hObZ0XGVW1sn8uDBtfzFFyfF67FF6X08SIqLi3Na68Pdj0E6VFxcnLvTa5jNuX7Q6+XLZl3/aFv9NowB9YcrrgEGAPhGepEH6ZkzF/mtt97jFStW89ixzZxKpW64EVWipIqzQ6ZybuxqsdB7K3Pj1nK27mFOlNZcF4aJiBOJJDc2juHly5/i11/fxadOnRefbxh9jx79uvcztG3bdvF6bFF6H48aJjXPXk1r/m2r34YxoP5wRQAGAPhGepEX0q++Os2vvvo6L1q0jBsaRnA8fn1opHiCk6WDOVPTyqUjHuN8c5t4yO37keENXDricc4MnsjJsiFM8eQNYb6+fhg/9tgifvnlHfzll6e4s/Oa+HsCo+W+fZ/2fl7WrXtavB5blN7Ho4ZJzbNX05p/2+q3YQyoP1wRgAEAvpFe5HdrZ+c1/vzzY/z88y/z3LkLePDg2hsfNZRIczJXz5naKVw6agnnxxfuJlbS5ls2ctnoZZwdMo1T+eEcT2ZuGH9VVRXPmjWHt2zZxp9++gV3dFwVf9+grK+9tqv387Fo0VLxemxReh+PGiY1z15Na/5tq9+GMaD+cEUABgD4RnqR99WzZzv53Xf38vr1G3nKlGmcy+VvCHzxVCmnKkZytu5hLmt8gvMtm8SDaniBeDOXNa3g7NBHOFUxiuPpshvmp7S0lCdOnMxr1rTxrl178MilfujWrS/2fh6mT58hXo8tSu/jUcOk5tmrac2/bfXbMAbUH64IwAAA30gv8pvZ2XmNDx36il966VV+/PEl3NAw8sbTmYk4kSnn9KAmLqmfzbkxqzjfslk8iEbJ3NjVXDJsLqerxnIiW3njEXIirq8fzgsWPM7btm3ngwe/xFFiy33yyZW97/2IEaPE67FF6X08apjUPHs1rfm3rX4bxoD6wxUBGADgG+lFfulS347uUiLFybI6ztRMcK/fFbxTs6nmm9u6ryOexMlc/XXPH8ZR4v7h9Okzet/rTCaL68QLpPQ+HjVMap69mtb821a/DWNA/eGKAAwA8E3Yi9rP0d1UZSNnhz7CZY1P9qvTmUMLxN2nTZfUz+L0oKY7HCVeyC+88Dc4Smy49fXDr3tvjx8/I16TDUrv41HDpObZq2nNv2312zAG1B+uCMAAAN+EsZA7O6/xRx8d5KVLn+TKykE4uhth+3KUOJfL88KFi3nPnv0IwwbZ0XHVfRRYnDg9xH0G9vvvfyRelw1K7+NRw6Tm2atpzb9t9dswBtQfrgjAAFiIUmqZ1vq0Uup3AwcO/MVtvo601se01seVUtvuu+++P+7L9w9yAR8+fJzXrGnjurqh19+sKlPOqcrROLprgHc6SlxdXcMrVqzmTz/9QvwXBry9x4+fcc+uyKU421jBRMQvvfSqeF02WLgd3w5Map69mtb821a/DWNA/eGKAAyAhRQVFf3Vz3/+8we01t/cKgDff//9f6SU6igqKlKxWCzmOM4TSqnH+vL9g9iI2tuf49Gjm64Pvek8ZwZP5FzTU+KhDt6buTGrOFM75YYwPHz4CN6wYTMfPfq1+C8PeKP79h1kIuJUbQmXTaxiIuL16zeK12WDhdzzbcCk5tmrac2/bfXbMAbUH64IwABYjNb69K0CsOM4JVrrnT1/Vkr9uVLqXF++byEW6tmzHfzSS6/yhAmTrrueN57McrqqmctGL8Mdmi21rPFJztS0cjx1/WOXxo0bz8899xJ/88134r9IoOvOnW+5N78amef8I+5zsxcuXCxelw0Wap+3BZOaZ6+mNf+21W/DGFB/uCIAA2AxdwjAzUqplT1/fvDBB/++1vo3sVjs9+70fe91ke7atYfT6fR11/OmKkdz6YiFOLW5H5lv2cyloxZzetAYpkTmx5uZJZK8fftO8V8m8Htub3/OvdP3+EFc8Wg9ExFPnjxVvC4bLOBWbwUmNc9eTWv+bavfhjGg/nBFAAbAYoIMwJcv373PPPNc9ynOZVwyfB7nxz8tHsYkTZU3cKq8QbwO2TC8kUsbHuVExr3O9Kmn1tzTZwwWxg0bNjERcdnEaq5c5N4NuqVlgnhdNljArd4KTGqeb9Z4on6MAfWbUz8CMAAW04dToF/7yZ//Qin1bV++7w8//Jbvhc7Ozu4jvxnOt2wUD1/SUjzBFE+I1yFtvmUzx9M5JiI+efLkPX3GQGF4/vnn3SPAE6q44vFhTEQ8ZcoU6bKsoFD7vC0wm9M836zxRP0YA+o3p34EYAAs5nYB+L777vvj7ptg6VjM/02w7vXoR3Nzi9tYNzwqHrykRQB2LRu9lImIhw6t50uXrokfoYPf86ZNz7jrtPXHADxx4mTxumywkHu9DZjUPN+s8UT9GAPqN6d+BGAALEQptUopdU5r/bda64tKqROxWCymtZ6ptR72k68jrfUxpdQJpdRLf/Inf/I/9eX7F2KhPvfcS+71npkKztbN4NzY1eIBDAFYxtzYtZwdOpMTJdVMRLxu3dPiv0ig66ZNW9wA3DKIKxa6AXjSpCniddlgUPu/qZjUPHs1rfm3rX4bxoD6wxUBGADgm0Is1G++ucA1NbXX3QU4kR3EmZoJ/e4O0P0tAOdbNnNZ4xOcGTypN/T2WF5ewceOfSP+iwS6vvjiK0xEnG2q4PzsOiYinj17nnhdNii9j0cNk5pnr6Y1/7bVb8MYUH+4IgADAHxTqMX67beX+LXXdvGCBQu5vKLi+mcAJ0s4XdnEJQ0LrL9JVn8IwPnxG7l0xEJODxp7w+OPyspyPGfOPN6x4w0+c6ZD/JcI/NH33tvHRMTpoWVcNqWGiYhXrlwjXpcNSu/jUcOk5tmrac2/bfXbMAbUH64IwAAA3wSxcDs6rvK+fZ/yypVrefjwEdcFJIonOJmr5+zQRzg3do14mEMA7pu5ceu4pH4Wp8obmBKp697Turp6fuKJlbxnz36+ePHX4r844M394ouTTEScrEhzyRj3H6mef/5l8bpsUHofjxomNc9eTWv+bavfhjGg/nBFAAYA+CaMhXz06Nf87LMv8MSJkzmZTN5wqnS6ahyXDJvDuTErjT9d2oYAnG/ZzLmxq7lk+DxOVzffcGpzPB7n5uYW3rTpWT506CvxXxSwb1640MXxeJwpGed0vXvkfteuPeJ12aD0Ph41TGqevZrW/NtWvw1jQP3higAMAPBN2Iv67NkO/uUv3+S5cxdwPp+//ugwEceTWU7mh3GmdjKXjlzE+eYN4oHQ9gCcH/80l45azNkhUzlV3sDxVMkN70tpaSnPmjWHt2/fyd98c0H8lwO8O2tqBrvrLOv+Q9Thw8fFa7JB6X08apjUPHs1rfm3rX4bxoD6wxUBGADgG8kF3tFxlT/++HPesmUbz527gIcMqbsheBHF3aPEg8ZwSf0sLmt6KtJHiaMegPMtmzk3ZiWXDJvD6apxnCipYqL4DfNeUzOYZ82aw+3tz/G+fQf5woUu8V8I8N5tbZ3449kXiQTe1wIpvY9HDZOaZ6+mNf+21W/DGFB/uCIAAwB8I73IvZ46dZ7ffPNdXrVqHbe2TuRs9sajkZTIcDJXz5nBk7h0xELON7eJB8uoBuD8+A1cOnIRZ2onczI/jOPJ7A3zmclkuLm5hVesWM2vvbaLT5w4K/45gMH4+ONLet/32toh4vXYovQ+HjVMap69mtb821a/DWNA/eGKAAwA8I30Ir+THR1X+eDBL/j551/mBQsWcn39sJscJXafQZweNJZLhs/j3Lh1/TYA55vbuKRhAaermjmRHXTTo7tDhtTx3LkLeMuWbXzgwCEcBexH9jwLmIh44sTJ4vXYovQ+HjVMap69mtb821a/DWNA/eGKAAwA8I30Ir8bT5++wO+8s4fXrGnjSZOmcFlZ2Y2BOFvJ6apmLm14NNQjxGEH4Pz4De4jiapbuk9nvn4estkSbm2dyCtXruU333yXT506L/7+QTl37Hiz97Px2GOLxOuxRel9PGqY1Dx7Na35t61+G8aA+sMVARgA4BvpRV4IOzqu8mefHeUtW7bxjBmzOJfz3lwrzomSas7UtLinTAf4LOKgA3B+/Eb3lOaaCZwoHXzDEd7S0lKeNu1hbm/fyp98cpg7Oq6Kvz8wOu7Zs7/3s7Jq1TrxemxReh+PGiY1z15Na/5tq9+GMaD+cEUABgD4RnqRB2FHx1X+5JPDvHnzFp4yZfqN1xHHE5wsHcyZwRPdO02P3xjZAJxv2cRlo5a61/CWDXG//0/Gks5keOLEybxhQzt/9NFBPIcX3tZDh471fnaeffYF8XpsUXofjxomNc9eTWv+bavfhjGg/nBFAAYA+EZ6kYfhhQtdvG/fp9zWtoknTJjE6XTaE4iTnMzVccmwOfcchgsRgPMtm7hk+HxO5ocxxVPX1ZpMJrm5uYXXrt3Ae/bs5++++5X4/EJzPH36Qu9naceON8XrsUXpfTxqmNQ8ezWt+betfhvGgPrDFQEYAOAb6UUu4Xff/Yr37NnPa9a08bhx4zmRSF73HOJMzQTOjV0degDOjVvHmdrJHE+V/lhPPM6NjWP4qafW8O7dH/K5c5fF5w+aa0fH1d7P1jvv7BGvxxal9/GoYVLz7NW05t+2+m0YA+oPVwRgAIBvpBd5FDx37hLv2PEmNze3XHfdcKq8wT1F2sdzh/0G4HzLZi4btZRTFaOuO725sbGJX355B5850yE+P9Auez5jH3zwsXgttii9j0cNk5pnr6Y1/7bVb8MYUH+4IgADAHwjvcij5sGDX/Djjy/hTCZz3SOWsnUzON+8oWABOD/+aS6pn8WJ7I93bk6lUjxv3qP80UcHxecB2mvP523fPnzOCqX0Ph41TGqevZrW/NtWvw1jQP3higAMAPCN9CKPqmfOXORnnnmO6+qG/nhUOJHidHUL51s23XUAzrdsdk9zTv4YsKura3jDhnY8ogiG4o8B+FPxWmxReh+PGiY1z15Na/5tq9+GMaD+cEUABgD4RnqRR92Ojqv8zjsf8LRpD3M87j5yKFXecMsQfLsAnG/ZzOlBY3oDyIQJk/j113fhzs0wVHs+fzjToHBK7+NRw6Tm2atpzb9t9dswBtQfrgjAAADfSC9yk/z00y+4oqLSDcH54ZxvufGO0bcKwPmWzZyubGIi4pKSUlx/CcVEAC680vt41DCpefZqWvNvW/02jAH1hysCMADAN9KL3DQPHTrGlZWD3EcS5Yfd8NikmwXgfMsmTlWOZiLisrIy3r//M/FxwP4rAnDhld7Ho4ZJzbNX05p/2+q3YQyoP1wRgAEAvpFe5CZ65MhxHlTl3rwqmau/7nRobwDOt2z+SfjN8YEDh8Trh/1b3ASr8Erv41HDpObZq2nNv2312zAG1B+uCMAAAN9IL3JT/eKLk1xVVc1ExKUjF90yAJc1regNv598cli8bghxBLjwSu/jUcOk5tmrac2/bfXbMAbUH64IwAAA30gvcpNdsWI1ExFnh0y7ZQAuGTabiYgXLFgoXi+Ely7hCHAQSu/jUcOk5tmrac2/bfXbMAbUH64IwAAA30gvcpPdseNN94ZYFSNvGYDT1c1MRPzssy+I1wvhpUsIwEEovY9HDZOaZ6+mNf+21W/DGFB/uCIAAwB8I73ITfbIkeNMRJzIVNwyACfLapmI+MMPD4jXC+GlS3gOcBBK7+NRw6Tm2atpzb9t9dswBtQfrgjAAADfSC9yk71wocsNE/HkLQNwPF3GRMTHj58VrxfCS5cQgINQeh+PGiY1z15Na/5tq9+GMaD+cEUABgD4RnqRm+zBg1+6R4Czg259BDhXx0TE7723T7xeCC9dQgAOQul9PGqY1Dx7Na35t61+G8aA+sMVARgA4BvpRW6yf/M3v3SvAa5svGUAztS0MhHxpk1bxOuF8NIlBOAglN7Ho4ZJzbNX05p/2+q3YQyoP1wRgAEAvpFe5Ca7fLn7iKNs3cO3vgv08HlMRDxnznzxeiG8dAkBOAil9/GoYVLz7NW05t+2+m0YA+oPVwRgAIBvpBe5yY4e3eQ+B3jUklsG4NyYlUxEXF1dw52d18RrhhB3gS680vt41DCpefZqWvNvW/02jAH1hysCMADAN9KL3FSPHDnBRMTxZJbzLZtuGYDzLZs5kalgIuK9ez8RrxtCBODCK72PRw2TmmevpjX/ttVvwxhQf7giAAMAfCO9yE113bqnmYg4XTWuN+zeLACXt7ZzZvAkJiJesmS5eN0QIgAXXul9PGqY1Dx7Na35t61+G8aA+sMVARgA4BvpRW6inZ3XuL5++A2nP98qAPecBl1eUcEXL/5avH7Yv8U1wDnaj2EAACAASURBVIVXeh+PGiY1z15Na/5tq9+GMaD+cEUABgD4RnqRm+j+/Z+7pz+nyjjfsvmOAbi8tZ0TJdVMRPz22++J1w/7tzgCXHil9/GoYVLz7NW05t+2+m0YA+oPVwRgAIBvpBe5iS5Y8DgTEWcGT7oh6N4qAGeHPsJExFOnThevH/ZvEYALr/Q+HhQPPvjgP1JKfaaUOqiUOqi1Pq61/tsBAwb8w9u9zqTm2atpzb9t9dswBtQfrgjAAADfSC9y0zx9+gKn0xkminNu7No+B+D8+A1MiRTH43E+evRr8XHA/isCcOGV3sfDQms9Xim1/U5fZ1Lz7NW05t+2+m0YA+oPVwRgAIBvpBe5aba3P8dExKnyETeE3NsF4PLWdk5XjWUi4pUr14iPA/ZfcQ1w4ZXex8NCKXXUcZz4nb7OpObZq2nNv2312zAG1B+uCMAAAN9IL3KT7Oi4ykOG1Lk3vxq5yHcALmt8komI8/lyPn/+ivh4YP8UR4ALr/Q+HgbFxcX/h9b6QiwW+707fa1JzbNX05p/2+q3YQyoP1wRgAEAvpFe5Cb5xhu7mYg4ka284eZXfQnA5a3tnCwbwkTE27ZtFx8P7J8iABde6X08DLTW67TWC/rytczMV664TZxpXrniNv+oH2NA/fK19MWTJ8/y2dnz+ftFy3r9tjsAmzKGm70HQe/pAPRrpBtHkxw/vpWJiEvqZ90y4N4pAJc2PMZExPX1w7mz85r4mGD/EwG48Erv40Fz//33/5HW+vuioiLdl69nAAAIia6uLu5avJh59epeuxYv5q6uLunS7omg93UA+jXSjaMpHjhwyH30UTLL+fEb7zoA51s2cyJTzkTEu3btER8X7H8iABde6X08aBzHqVNKvd/Xr2c25+jRzY68oH6MAfWbUz+OAAMAfCPdOJrinDnzbvnoIz8B+KePRJowYZL4uGD/sycAf/QRAnChlN7Hg0Zr/YHWurqvX89szvWDXi9fNuv6R9vqt2EMqD9ccQ0wAMA30ovcBI8e/ZoTiQRTPMm5cevuOQDnxz/N8WSWiYgPHDgsPj7Yv8QR4MIrvY9HDZOaZ6+mNf+21W/DGFB/uCIAAwB8I73ITXDpUvfuzemqcbcNtn0NwOWt7ZwZPImJiGfPnic+Pti/RAAuvNL7eNQwqXn2alrzb1v9NowB9YcrAjAAwDfSizzqfvPNd5zJZJgozrkxqwoWgPPj1jPFk5xIJPjLL0+JjxP2H/Ec4MIrvY9HDZOaZ6+mNf+21W/DGFB/uCIAAwB8I73Io+66dU8zEXGqYuQdQ62fAFze2s7pqnFMRLxkyXLxccL+I44AF17pfTxqmNQ8ezWt+betfhvGgPrDFQEYAOAb6UUeZc+du8z5vHvH5rLRywsegHNjVjFRnNPpDJ86dV58vLB/iABceKX38ahhUvPs1bTm37b6bRgD6g9XBGAAgG+kF3mU3bJlGxMRJ3N1fQq0fgNweWs7pypGMRHxmjVt4uOF/UME4MIrvY9HDZOaZ6+mNf+21W/DGFB/uCIAAwB8I73Io+rFi7/mwYNrmYi4dOTjgQXgskb3BltlZTn+9ttL4uOG9osAXHil9/GoYVLz7NW05t+2+m0YA+oPVwRgAIBvpBd5VH3lldeZiDhRUsX5ls2BBeDy1nZO5uqZiLi9/TnxcUP7xXOAC6/0Ph41TGqevZrW/NtWvw1jQP3higAMAPCN9CKPop2d13jkSPfU5JLh83yF2bsJwKUjFzMRcXV1DV+40CU+fmi3CMCFV3ofjxomNc9eTWv+bavfhjGg/nBFAAYA+EZ6kUfR3bs/ZCLieDrH+ZZNgQfgfMtmTpRUMxHx9u07xccP7RanQBde6X08apjUPHs1rfm3rX4bxoD6wxUBGADgG+lFHkUnTJjERMTZoTN8Bdm7DcDlre1cMnw+ExE3NIzkzs5r4nMA7bUnAH/44QHxWmxReh+PGiY1z15Na/5tq9+GMaD+cEUABgD4RnqRR80DBw67R3+TWc6Pfzq0AJxv2cTxjPvIpXfe2SM+D9BeewLwnj37xWuxRel9PGqY1Dx7Na35t61+G8aA+sMVARgA4BvpRR4158xxj8RmBk/yHWLvJQCXt7ZzdugjTEQ8ceJk8XmAdtrZea03AO/e/aF4PbYovY9HDZOaZ6+mNf+21W/DGFB/uCIAAwB8I73Io+RXX53mRCLJFE9wbty60ANwfvzTHE9mmIj44MEvxOcD2ueFC129Afjtt98Tr8cWpffxqGFS8+zVtObftvptGAPqD1cEYACAb6QXeZRcsWI1ExGnBzXdVYC91wBc3trOmZoJTES8YMFC8fmA9nnu3OXeAPz667vE67FF6X08apjUPHs1rfm3rX4bxoD6wxUBGADgG+lFHhXPnbvEZWVlTERc1rRCLADnxq1liic4lUrxyZPfis8LtMtvvrnQG4B37HhDvB5blN7Ho4ZJzbNX05p/2+q3YQyoP1wRgAEAvpFe5FHxuedeYiLiZK7+rsNrIQJweWs7pypHMxHxunVPi88LtMvjx8/0BuCXXnpVvB5blN7Ho4ZJzbNX05p/2+q3YQyoP1wRgAEAvpFe5FGws/MaDx/ewETEpSMeEw/AZaOXMxFxZeUgvnChS3x+oD1+8cXJ3gD83HMviddji9L7eNQwqXn2alrzb1v9NowB9YcrAjAAwDfSizwKvvfePvfRR5lyzrdsFg/A5a3tnCitYSLiV199XXx+oD0ePPhlbwDevHmLeD22KL2PRw2TmmevpjX/ttVvwxhQf7giAAMAfCO9yKPgww/PZCLibN30ew6uhQrAJcPmMhHx2LHN4vMD7XHv3k96AzBOsS+c0vt41DCpefZqWvNvW/02jAH1hysCMADAN9KLXNoTJ85yIpFgiic539wWmQCcb9nI8WRJ9yORvhSfJ2iH77zzQW8AfvLJVeL12KL0Ph41TGqevZrW/NtWvw1jQP3higAMAPCN9CKXtq1tExMRpyob7zm0FjIAl7e2c6amlYmIly59QnyeoB3u3Pl2bwB+/PEl4vXYovQ+HjVMap69mtb821a/DWNA/eGKAAwA8I30Ipe0o+MqDx5c6z76aNTSyAXg3JiVbm1lOT5//or4fEHzffHFV3oD8Jw588TrsUXpfTxqmNQ8ezWt+betfhvGgPrDFQEYAOAb6UUu6e7dHzIRcSJbec83vwoiAJe3tnOyrI6JiLdv3yk+X9B8N23a0huAJ02aKl6PLUrv41HDpObZq2nNv2312zAG1B+uCMAAWEhxcbGjtf5Qa31cKbVfKfXnN/s6x3EmKqW+VEp9ppTaW1RU9L/15ftLL3JJ58171L351ZB7v/lVUAG452ZYCCuwEK5cubY3ADc2NonXY4uF3fXNx6Tm2atpzb9t9dswBtQfrgjAAFiI1nqX4zhVsVgsppTKaq0/vsnX/Eut9ZkHH3zw78disZjjOJVKqf19+f7Si1zKc+cuczZbwkRxzo1bG9kAnB//NFMixYlEgk+dOic+b9BsH398SW8AHjKkTrweWyzsrm8+JjXPXk1r/m2r34YxoP5wRQAGwDIcx7lPKXU1Fov9Xs/faa0vaq2Lfvp1AwcO/IVS6nxxcfGfdr9ulFJqW19+hvQil3LHjjeYiDiZqytYWA0iAJe3tnOqYhQTET/77Avi8wbN9pFHZvUG4NLSUvF6bLGgG78FmNQ83+g1PnryKO/Z/xG//5Hrno/288WLv45AbXfWtPBi4xhQf7giAANgGY7j/But9bGf/p1Sar/jOP/e+7VKqRal1H9TSn2rlDr50EMP/dO+/AzpRS7ltGkzmIi4pH5W5ANw6YjHmIh4zJix4vMGzXbcuPG9AZiIcHO1AlmgLd8aTGqevV682MVLdy7lhbuW8GNvu856aR5/88134rX1RdPCi41jQP3higAMgGX0NQAPGDBggFJq78CBA+/vft0orfWevvyMnoXWnzx//hKnUmmmeKIgz/7tMTt0Rm+wyA6dUbjToFs2cTyZYSLiEyfOis8fNNchQ+quC8DHjn0tXpMNFmzTtwSTmmevFy928fI3lvOid5fx47td52xfgACMMaD+iIoADIBl9PUUaK31eKXUqp4/P/DAA3+olPpdLBb7gzv9jB9++C33N/bu3dt9+vPQQMJvECG45zTo1157TXr6gMGUlpZ6AvAx6ZKsoJD7vg0wm9M8e0UAltf0MaD+cEUABsBCtNbvaK1rYrFYzHGcklvcBCutlDpy//33/1EsFosVFxfnvEeOb0XPQutPPvro425ArStcQE2UVN8QgBMl1QX7/iXD5zER8dSp08XnD5rpt9923vAZfe21t8XrssHC7vrmY1Lz7BUBWF7Tx4D6wxUBGAALKSoq0kqpvVrr41rrjx3H+YtYLBbTWs/UWg/r+TrHceZqrY8ppT7TWn/gOM6/7sv3l17kYdvRcZXLKyqYiDg3dnXBAmo8mb0hXMST2cKdBt28gSme4HQ6zefOXRKfR2ien39+7MfPZ9z97zPPPC9elw0Gtf+biknNs1cEYHlNHwPqD1cEYACAb6QXedh+8skR9+hstrJg4TSMAFze2s7JsiFMRLx794fi8wjNc/fuD28IwE8+uUq8LhuU3sejhknNs1cEYHlNHwPqD1cEYACAb6QXedg+88xzTEScrhpnXADO1E5mIuKVK9eKzyM0zxdffOWGADxr1lzxumxQeh+PGiY1z14RgOU1fQyoP1wRgAEAvpFe5GE7ffoj7uOPhs83LgCXjlrCRMRjxzaLzyM0z3Xrnv4x/HYHYHyWCqP0Ph41TGqevSIAy2v6GFB/uCIAAwB8I73Iw7Sj4yrncnn3+t9x640LwPnxG5niSU4kkrgOGPq25+ZvFCemhPsZrakZLF6XDUrv41HDpObZKwKwvKaPAfWHKwIwAMA30os8TA8d+sq9/jdTUdBgGlYALm/98TrgPXv2i88nNMuJEydfH4CTcU4kEnzx4q/FazNd6X08apjUPHtFAJbX9DGg/nBFAAYA+EZ6kYfp9u07mYg4VTna2ACcrm5hIuL29q3i8wnNcujQ+usCcCKfZiLir746LV6b6Urv41HDpObZKwKwvKaPAfWHKwIwAMA30os8TJcvX9H9/N+HjQ3APc8DnjNnnvh8QnPs7LzGmUzmx6O/CeL0kFImIv7gg4/F6zNd6X08apjUPHtFAJbX9DGg/nBFAAYA+EZ6kYdpc7N79LR01BJjA3BuzEomIh46tF58PqE5fv31eff0/9JUbwDOjipnIuJXXnldvD7Tld7Ho4ZJzbNXBGB5TR8D6g9XBGAAgG+kF3lYdnZe49LSMiYizo/fYGwAzrdsZkqkOR6P8/nzV8TnFZrhgQOH3NP/a0p6A3BpaxUTET/9dLt4faYrvY9HDZOaZ68IwPKaPgbUH64IwAAA30gv8rA8fvysG0rTuYKH0jADcHlrOydKapiI+JNPDovPKzTD11/fxUTEmRH53gCcm1HLRMSLFi0Tr890pffxqGFS8+wVAVhe08eA+sMVARgA4BvpRR6W7767l4mIk7l64wNwqrIRp65CXz7zzPPu86+bK3sDcMUC96ZYU6ZMF6/PdKX38ahhUvPsFQFYXtPHgPrDFQEYAOAb6UUellu2vMBExOnq8cYH4MyQqUxEvHr1evF5hWb4xBPuteO5qTW9AXjQ8hFMRNzQMFK8PtOV3sejhknNs1cEYHlNHwPqD1cEYACAb6QXeVguWfKEewfooTOND8ClDY8yEfHMmXPE5xWa4cyZc5iIuHxuXW8Arl47momIy8py4vWZrvQ+HjVMap69IgDLa/oYUH+4IgADAHwjvcjDcsqU6e4doEcuMj4AlzU+yUTEjY1N4vMKzXDMmLFMRFy5eHhvAK5pa+J4NsFExN9+e0m8RpOV3sejhknNs1cEYHlNHwPqD1cEYACAb6QXeVjW1w93TwEds8r4AJwf/7R7N+t8XnxeoRlWVg5iIuKqVaOuC8DJQe7n9rPPjorXaLLS+3jUMKl59ooALK/pY0D94YoADADwjfQiD8OOjqucTqeZKM75lk3GB2D355UwEfGZMx3i8wuj7fnzV7rvgJ7gmram6wJwZniOiYjffPNd8TpNVnofjxomNc9eEYDlNX0MqD9cEYABAL6RXuRhePLkt4E+AkkiACdKex6FdER8fmG0/fzzY+4d0AdlbgjAJc2VTET8zDPPi9dpstL7eNQwqXn2igAsr+ljQP3higAMAPCN9CIPw48+OugGgLJaawJwqnwEjtzBPvnWW++5zwAenrshAOemD2Yi4qVLnxSv02Sl9/GA+buO4zyhlDqhtT6ktd50pxeY1Dx7RQCW1/QxoP5wRQAGAPhGepGH4c6dbzERcapytDUBOF09nomIt259UXx+YbTdtGlL9zOAB90QgHueBTxp0hTxOk1Weh8PEqXUEqXUsp4/FxcX/+mdXmNS8+wVAVhe08eA+sMVARgA4BvpRR6G7e1b3SNgNROsCcDZOveu1k89tUZ8fmG0nTt3gXvTtJlDbgjAVStHuXeHrhwkXqfJSu/jQfHAAw/8oVLq2n333ffHfl5nUvPsFQFYXtPHgPrDFQEYAOAb6UUehsuXr+h+BvAj1gTgkuHzmYh4zpz54vMLo21Dg3u6/KBlDTcE4Jq2Jk6UJpmI+OTJb8VrNVXpfTwoioqK/lJrfVprPV9rfUAp9V5xcfF/uNPrTGqevSIAy2v6GFB/uCIAAwB8I73Iw3DmzDnuM4AbHrUmAJeNXsZExC0tE8TnF0bX7777FSeSSaZknKvXN940AKeHljER8a5de8TrNVXpfTwoHMf510qp3zmOUxmLxWJKqX+ltb7sOM59t3sdM/OVK24TZ5odHT8G4B7nbF/Ap09/J15bX7xyxQ0vps6/DWNA/eF68uRZPjt7Pn+/aFmv33YHYFPGcLP3IJxdHoB+inTjGIZjxzYzEXFZ4xPWBODc2DVMRDxkSJ34/MLounfvJ+7179XZ3sDrDcClLe4zgteu3SBer6lK7+NB8bOf/ewfa61/E4vF/k7P32mtP77TUWA2mN/85je8/I3lvPqT1b0u3bWUv//+e+nSAAA3oauri7sWL2ZevbrXrsWLuaurS7q0eyLwDR6A/ox04xiGgwfXMhFxbtw6awJwvmUTE8U5k8lwZ+c18TmG0bStbVP3DbAqbxmAe26EhbMJ7l7pfTxIlFKvFxcX/3+xWCz20EMPDVRKXXrooYf+6e1ew2zO0SOvOAIsr+ljQP3hiiPAAADfSDeOQdvZeY1TqRQTJTjfstmaAFze2s7xVCkTEZ8+fUF8nmE0nThxMhMRl88bessAXL1mNFOcOJ1O83ff/Uq8ZhOV3seD5KGHHhqotX5Ha31YKfVZcXFx6k6vYTbn+kGvuAZYXtPHgPrDFdcAAwB8I73Ig/b06QtuGE2VBhtGBQJwoqSKiYgPHTomPs8wel640MXZbJYpTly9evQtA3BNWxOnqt3P7wcffCxet4lK7+NRw6Tm2SsCsLymjwH1hysCMADAN9KLPGgPHTrGRMSJkirrAnAy5566umfPfvF5htFz375PmYg4WZW9LuzeLACXjnevA16/fqN43SYqvY9HDZOaZ68IwPKaPgbUH64IwAAA30gv8qB9//2P3BCQq7cuAKcqG5mIeMeON8XnGUbPDRvab7j+91YBuHz+UCYibm2dKF63iUrv41HDpObZKwKwvKaPAfWHKwIwAMA30os8aHfseNO9C25lo3UBOFPTykTEzz77gvg8w+g5adKUG67/vVUAxnXA96b0Ph41TGqevSIAy2v6GFB/uCIAAwB8I73Ig3br1heZiDhd3WJdAM4OmcpExGvWtInPM4yWP73+t+on1//eKgD/9DpgnFLvX+l9PGqY1Dx7RQCW1/QxoP5wRQAGAPhGepEH7fr1G5mIOFM7xboAXFI/i4mIlyx5QnyeYbTcs2e/e+aD5/rf2wXgnucBr1q1Trx+05Tex6OGSc2zVwRgeU0fA+oPVwRgAIBvpBd50C5btoKJiLNDZ9oXgBsWMBHxnDnzxOcZRssVK1YzEXFpa1WfA3DFwmFMRDxq1Gjx+k1Teh+PGiY1z14RgOU1fQyoP1wRgAEAvpFe5EE7d64bEkuGz7cuAJeOWsJExBMnThafZxgtGxpGMBFx5aLhfQ7A1esbOZ5JMBHxiRNnxcdgktL7eNQwqXn2igAsr+ljQP3higAMAPCN9CIP2okTJ7tHwkYtsS4A58asZCLiESNGic8zjI5Hj37tfv6ySa5e39jnAFzT1sSZhhwTEW/btl18HCYpvY9HDZOaZ68IwPKaPgbUH64IwAAA30gv8qAdMWIUExHnxqy0LgDnmze4R/kqB4nPM4yOCxcudk/7byy/aci9XQDOz65jIuK6unq+cKFLfCymKL2PRw2TmmevCMDymj4G1B+uCMAAAN9IL/Kgrax0b+yTb95gXwBu2cwUT3IikeCOjqvicw3lPXToGCcSCaZEnAc9OdJ3AK5e38jJQRkmIn7++ZfFx2OK0vt41DCpefaKACyv6WNA/eGKAAwA8I30Ig/Sixd/7YaBeJLzLZutC8Dlre0cT7unrJ48eU58vqG8M2bMdK95b668acC9UwCuaWvi8gVDu88sqORz5y6Jj8kEpffxqGFS8+wVAVhe08eA+sMVARgA4BvpRR6kX355iomIE5mK4IOoUABOlg1hIuJ9+w6KzzeU9d1397qfu3SCq1aNuusAXNPWxOkhpUxEvHLlGvFxmaD0Ph41TGqevSIAy2v6GFB/uCIAAwB8I73Ig7QnECTzw6wNwOnKRiYifuWV18XnG8p56NBXXFbmng1QNqXmtuG2LwG4cvFwpkS8+7P1mvj4oq70Ph41TGqevSIAy2v6GFB/uCIAAwB8I73Ig/SFF/6GiYjTVc3WBuBMrXuX6/XrN4nPN5Tx1KnzXFvrngmQHZm/6Z2f/QbgmrYmzj9Sy0TEqVSKP/jgY/FxRlnpfTxqmNQ8e0UAltf0MaD+cEUABgD4RnqRB+mKFavdUFA33doAXDJsLhMRL1iwUHy+YfieP3+Fx4wZ5wbVwSVcve724ddPAK5pa+LS1ir3Luq5HB8+fFx8vFFVeh+PGiY1z14RgOU1fQyoP1wRgAEAvpFe5EE6Y8Ys9xnADY9ZG4DLRi9jIuLm5hbx+Ybh2tFxlWfNmuOe5l+evu11v3cbgKvXN3JmZJ6JiGuHDDEmBISt9D4eNUxqnr0iAMtr+hhQf7giAAMAfCO9yIO09xnATU9ZG4DzzW14FnA/dc0a972PZ5M86IkRfQq0fgNwTVsTV69t5NTgkt5/aMHzgW9Ueh+PGiY1z14RgOU1fQyoP1wRgAEAvpFe5EHZ2XmNs9ksE8U537LR2gDs/mz3ua3ffotH1vQXX3nlNfdzlohzxcJhfQ6zdxOAa9qauGrlKE7kUr2n23d2XhOfgygpvY9HDZOaZ68IwPKaPgbUH64IwAAA30gv8qA8depc9yNhciGFULkAnCipZiLiTz/9QnzeYfDu3fsJp1JuGM0/UusryN5tAK5pa+JByxo4nk4wEfHGjc+Iz0OUlN7Ho4ZJzbNXBGB5TR8D6g9XBGAAgG+kF3lQfv75MfcZwCXV1gfgZH4YExG///5H4vMOg/Xs2Q6uqKh0r20fP8h3iL2XAFzT1sTlC4YyxYnj8Tjv2bNffD6iovQ+HjVMap69IgDLa/oYUH+4IgADAHwjvciDct++T92bA+XqrA/AqQr3Wuc33nhHfN5hsG7btt19tFdt6R0fdxREAK5pa+Kyie4ZBzNnzhGfj6govY9HDZOaZ68IwPKaPgbUH64IwAAA30gv8qB855097qNhyhusD8DpQWOZiPjll3eIzzsM1rFjm5mIuHxO3V0H2HsNwFWrRjEl4pxKpfj06QvicxIFpffxqGFS8+wVAdi/HR1X+cSJszfY0XHVmDGY/h705/oRgAEAvpFe5EG5c+dbbgCubLQ+AGdqWpmIeMuWF8TnHQbnkSPHu+/6nOjT836DCsA1bU2cGZ5jIuLnnntRfF6ioPQ+HjVMap69IgD798SJszxly8M865X5vU7Z8jCfOHHWmDGY/h705/oRgAEAvpFe5EH51lvvdR8BHmF9AE5XjWMi4hdffEV83mFwrl27gYmIS8ZW3FN4LUQALp87lImIx45tFp+XKCi9j0cNk5pnrwjA/j1x4izPemV+73w9vnsZz3plPgIw6g9FBGAAgG+kF3lQ9s9rgHeLzzsMzuXLn3Kfaz21RjwAD1o+gomIGxpGiM9LFJTex6OGSc2zVwRg/yIAo35JEYABAL6RXuRBefiwe7poIltlfQBO5tyjcR988LH4vMPg3Lr1RfcI8LhK8QBcPs/9zE2f/oj4vERB6X08apjUPHtFAPYvAjDqlxQBGADgG+lFHpTffPOdG0JTZdYH4J7nAH/22VHxeYfBuWuXe2O3dH2ZeADOTa1hIuLly58Sn5coKL2PRw2TmmevCMD+RQBG/ZIiAAMAfCO9yIOys/Map9NpJopzvmWT1QG452efPdshPu8wOHtugpWsSIsH4JKx7rOIt27FTbAuXUKj4sWk5tkrArB/EYBRv6QIwAAA30gv8iCtrx/mXjM5drW1ATg//ml3jLm8+HzDYL148ddcWlrKRMRVq0aJBuBUlft537v3E/F5iYLS+3jUMKl5vnGdIQD7FQEY9UuKAAwA8I30Ig/SSZOmMhFx6chF1gbgsqYVTEQ8atRo8fmGwTtt2sNMRJyfLfwc4DhxNlvCFy50ic9JFJTex6OGSc2zVwRg/yIAo35JEYABAL6RXuRBumjRUvemQfWzrA3ApSMeYyLiGTNmis83DN5nnnmOiYizTXf/KKR7DcDlc+qYiHjKlOni8xEVpffxqGFS8+wVAdi/CMCoX1IEYACAb6QXeZCuXLnWDQt1D1sbgEuGiF7pYAAAIABJREFUz2Mi4sceWyQ+3zB4Dx780r0OuPzurwO+1wBcMqaCiYjb27eKz0dUlN7Ho4ZJzbNXBGD/IgCjfkkRgAEAvpFe5EG6bp17fWx2yFR7A3D9bCYiXrx4mfh8w+A9cuSEfAAe6wbg557DDbB6lN7Ho4ZJzbNXBGD/IgCjfkkRgAEAvpFe5EG6YUM7ExFnaidbG4CzQ2d2P45mhfh8w+B988133c/0sJxYAM5NG8xExEuXPiE+H1FReh+PGiY1z14RgP2LAIz6JUUABgD4RnqRB+nmzVvcsFAzwd4AXDeDiYhXrFgtPt8weDdtcj/TpeMHiQXgikfrmYh40qQp4vMRFaX38ahhUvPsFQHYvwjAqF9SBGAAgG+kF3mQLl/+VL+5Bnju3AXi8w2DtaPjKk+YMMm9C/QjtWIBuOqpkW4N+TyfPHlOfF6ioPQ+HjVMap69IgD7FwEY9UuKAAwA8I30Ig/SfvEYpMYn8RikfmBn5zVeuHCx+7nKJrnqKbnnAFevb+T0EPd5xCNGjOIzZzrE50da6X08apjUPHtFAPYvAjDqlxQBGADgG+lFHqTV1TVMRJwbt87aAJxv2chEcc5kMtzRcVV8zmEw9tzRPJ5KcOXi4XcdXgsRgGvamrhq9WhOVmaYiLi5uYXPn78iPkeSSu/jUcOk5tkrArB/EYBRv6QIwAAA30gv8qA8c6ajO4RmON+y2doAXN7azomMe1feI0eOi887LLw917JTMs4Vj9XfU3AtVAB2T4UexYl8momIp017mC9c6BKfKyml9/GoYVLz7BUB2L8IwKhfUgRgACykuLjY0Vp/qLU+rpTar5T681t83c+VUq9orb9SSn3hOM6ovnx/6UUelG+8sdt9XEyuPpQQKhmAU5WjmYh427bt4vMOC+fFi7/mp55a436e4sTlc+vuObQWMgDXtDXxoCdHcKIkyUTEEydO5lOn+uc1wYXd9c3HpOb5xnWHAOxXBGDULykCMAAWorXe5ThOVSwWiymlslrrj2/xdZ8opTI9f3Yc576+fH/pRR6UixYt7b4B1nTrA3DJsLlMRDxjxizxeYeF8auvTvPYsc3uZykR5/zMIQUJrIUOwDVtTVy5tIETpSkmIq6sHMR79uwXn7+wLcxubw8mNc9eEYD9iwCM+iVFAAbAMhzHuU8pdTUWi/1ez99prS9qrYt++nVFRUV/rbX+4G5+hvQiD8LOzms8eHCte/1v01PWB+D8uPXuzb5KS/v1aai2+Pbb73Eun2ci4kQ+fc/X/AYdgGvamrhq5ShODy1zP/fxOK9b93S/uib9Hrd66zCpefaKAOxfBGDULykCMACW4TjOv9FaH/vp3yml9juO8+89f9eotf4bpdQWpdRBpdSLDz300MC+/AzpRR6Ehw8fdxvxdC6U63+lA3B5azsnStwbfn3wwcfi8w/vzgsXuvjJJ1f1fn4yDTmuXj26oEE1qABc0+beHbpscjVT3K1/woRJ/eYxSQXc9q3ApObZKwKwfxGAUb+kCMAAWEZfA7DjOOOUUtcGDBjwZ91fM1xrfaAvP6Nnodnk9u07mYg4VTk6tAAqHYAzNa1MRNzevkV8/qF/L126xjNnzu495Tk3fTBXr28seEgNMgD3WPH4sN5Tomtrh/Dp09+Jz2/QFmzTtwSTmmevCMD+RQBG/ZIiAANgGX09BVoplVVKvdvz5wceeOAPlVI/xGKx37/Tz/jhh9+ybaxbty7U63+jEIB7rgNetGiR9PSDu2Dr1q29z/gt9CnPYQfgmrYmrlo1ilODS5iIeMqUKfzDDz9IT3GgFHDbtwJmc5pnrwjA/kUARv2SIgADECJKqdk3+3ut9cxC/hyt9Tta65pYLBZzHKfkZjfB6g68p37+858/0P11pUqpI335/j0LzSYnTJjkXhM7clG/CcBlTSuYiHjEiJHi8w/9+dZbuzkejzPFqSCPOYpCAK5pc68LTpS5R4KXL18hPs9BWsg93wZMap69IgD7FwEY9UuKAAxAiGitv7/F33cV8ucUFRVppdRerfVxrfXHjuP8RffPmam1HvaTn/sflVKfdfuu4zj/vC/fX3qRB2F5uftc3HxzW78JwPmWTUzxJCcSSf7uu1+Jvwewb37++TEuKXGPlOamDQ48lIYZgGvamrhyyXCOJ+NMRPzSS6+Kz3dQFnLPtwGTmmevCMD+RQBG/ZIiAAMQAo7j/CfHcf6TUuq/KaX+n54/dztCa31GukY/SC/yIEylUkyUCC18RiEAl7e2czxVykTEp09fEH8PYN+cNGmKe7p+Y3kogTTsAFzT1sT52UOYiDidzvD581fE5zwIpffxqGFS8+wVAdi/CMCoX1IEYABCQGt9Wmt9Win1257/7/YbpdQ+x3ES0jX6QXqRB2G++xEy+fEb+1UApkSaiYgvXvy1+HsA++a4cePd5+cuCva6X8kAXNPWxPF0gokIAdhAtNZntNbHus8uOug4TumdXmNS8+wVAdi/CMCoX1IEYABCRCn1rHQNhUB6kQdh7zOAx63vNwE437LZfXROJis+/7Dvzpkz3/3Hmtl11gbgqtWjmYi4oqJCfL6DUnofDxKt9TdFRUV/6ec1JjXPXhGA/YsAjPolRQAGQJDi4uL/W2v9f0rX4RfpRR6EDQ0j3QA8ZlX/CcDjN3SHjErx+Yd9d9Uq947lZZOrrQ3AlUsamIi4sXGM+HwHpfQ+HiRa69MDBw78hZ/XmNQ8e0UA9i8CMOqXFAEYgBBRSr1bXFz872KxWMxxnIla6ytKqQ7HcSZK1+YH6UUehFOmTHfvAt3waOgBuLa2lqdNm8a1tbXh3gV69DImIh49ukl8/mHf3bZtu3sN8CiLrwGe5V4D/Mgjs8XnOyil9/Eg6b7E51C3ax944IE/udNrTGqevSIA+xcBGPVLigAMQIhora/Eup+zq5Q6NXDgwF8MGDBgAG6CJe/Gjc+4N92pHh9qAK6treUzZ84wM/OZM2d4SF19aD8/M2QqExE/+eRK8fmHfffo0a85Ho9zPBXnqtWjrQzA6SHuzdm2bdsuPt9BKb2PB4njOA92/+/va60XKKV+eafXMDNfuSL/eKq7saPjxwDc45ztC/j06e/Ea+uLV6644SXM+T958izPfnX+dXM2+9X5fPLkWWPGYPp70J/rP3nyLJ+dPZ+/X7Ss12+7A7ApY7jZexDw1g7A3aGUuhqLxX7PcZxipdS3PX+vtf6vgmX5RrpxDML9+z9jIuJESVWoAXjatGn8U6ZPnxHaz0/m6pmI+K233hOff+jPadNmuKdBT62xLgBXLnVPf87l8tbeAOvSpf7TqAwYMOCfKKWu3enr2GB+85vf8PI3lvPqT1b3unTXUv7++++lS4ssXV1dvPidxdfN2eJ3FnNXV5d0aaAf0NXVxV2LFzOvXt1r12LzP39h7OkA+EZrvUsp9ZTW+mWl1FOxmPsv5Uqpc9K1+UG6cQzCixd/3fts1bCeBXyzI8C1IR0BzrdsYkqkOB6P85kzHeLzD/25e/eH7j/Y5NNcvb7RqgCcbaroPjNhlfg8B6n0Ph4UDzzwwB8WFRX9zz1/dhynWSn17p1ex2zO0SOvOALsXxwBRv2S4ggwACHy0EMPDVRKPauUerrnmijHcUq11guka/ODdOMYlD1H1UqGzQ0tAEtdA1w6chETETc1jRWfd+jfzs5rXF8/zL2J2cJh1gTg6vWNHE/FOR6P89GjX4vPc5BK7+NB0f177qBS6vPua4BfLioq+l/u9Dpmc64f9IprgP2La4BRv6S4BhgA4BvpRR6UL774ChMRp8pHhBqAJe4Cna4ax0TEGza0i887vDuXLHnCPVV4+mBrAvCgJ0YwEXF9/TDx+Q1a6X08apjUPHtFAPYvAjDqlxQBGICAUUrV9vy/1nrYrZSs0S/SizwoT506z/F4nCmR4vz4jdYG4HzLZo6nypiI+NChY+LzDu/OZ5553j1jobnSmgBcPn8oExFPnTpdfH6DVnofjxomNc9eEYD9iwCM+iVFAAYgYLTWO3v+Xym1+2Zqrd+RrNEv0os8SJubW9zHIY14zNoAXDZ6eb85ymazb7/9PhMRZ4aVWROAc9MGMxHx8uUrxOc3aKX38ahhUvPsFQHYvwjAqF9SBGAAgG+kF3mQbt68xX0cUtU4awNwZvAkJiJesWK1+HzDu/fw4eNMRJyszFgTgEvGVTIR8ZYt28TnN2il9/GoYVLz7BUB2L8IwKhfUgRgAIBvpBd5kH766Rfu3XUzFdYG4GRZLRMR7979ofh8w7v31Klz7mc1l7ImAGcby5mIePv2neLzG7TS+3jUMKl59ooA7F8EYNQvKQIwAAGjlPqdUuq3d1K6Tj9IL/Ig7ei4yhUV7mNYcmPXWheA8+OfZoonOJVKW/2M1f7g2bMd3c+uTtoTgEfmmYh45863xec3aKX38ahhUvPsFQHYvwjAqF9SBGAAAmbgwIH/rEfHcUYppfY6jpPQWv/L7v9+4DjOSOk6/SC9yIN21qy53Y9Dmm1dAC4dsZCJiFtaJojPM7w3L1zocj8zmYQ1ATjTkGMi4rfeek98foNWeh+PGiY1z14RgP2LAIz6JUUABiBEtNbHe57/24PjOPdprY9L1XQ3SC/yoN269cXu64CbrQvAmdopTES8Zk2b+DzDe7Oz85p71/Jk3JoAnK4v6zen50vv41HDpObZKwKwfxGAUb+kCMAAhIjW+lc/+9nP/vFP/+6BBx74E611l1RNd4P0Ig/aXbv2dD8PuMG6AJweNJaJiF9+eYf4PMN7N5VKMSUsCsBD3QC8Z89+8bkNWul9PGqY1Dx7RQD2LwIw6pcUARiAENFar1NK7dNa/+eioqK/VEr9F631h0qp9dK1+UF6kQftZ58d7b62stq6AJzMD2Mi4vfe2yc+z/DezWTcz5A1AXhIKRMR7937ifjcBq30Ph41TGqevSIA+xcBGPVLigAMQIg4jvP3HMeZq5T6Win135VSXzuOM2/AgAH/o3RtfpBe5EHbc3OheLLEugCcyA5iIuIjR46LzzO8d0tL3cBYvb7RjgBc647no48Ois9t0Erv41HDpObZKwKwfxGAUb+kCMAARAzHcSZJ13AnpBd50HZ0XHWvrYwnrQvA8bR7iumpU+fE5xnemydPnnNPgbYpAHcfAf7lL98Un9+gld7Ho4ZJzbNXBGD/IgCjfkkRgAGIGFrr76VruBPSizxojx8/03133XLrAnCydDATER84cEh8nuG9OXfuAiYizozIBx5MwwrAuRnuM6oHD67lc+cui89xkErv41HDpObZKwKwfxGAUb+kCMAARAyt9X+VruFOSC/yoP3wwwNMRJzM1VkXgFMVo5iI+LXX7H/Oqs3u2bPf/bykEly1YqQ1Abh6fSOnakqYiHjVqnXi8xyk0vt41DCpefaKAOxfBGDULykCMAARA0eA5X3lldfcxyBVNlkXgDM1E5iIuL19q/g8w7vz/PkrPGzYcCYiLptaE3goDTMA17Q1ceWSBqY4cTKZ5M8+Oyo+30EpvY9HDZOaZ68IwP5FAEb9kiIAAxAxEIDlXb58hXtqae1U6wJwSf0sJiKePXue+DxDf3Z2XuOdO9/mwYPd04STlRmuXhfstb8SAbimrYlLxlUyEXE2m+X16zfx+fNXxOe/0Erv41HDpObZKwKwfxGAUb+kCMAARAwEYFk7Oq5yVVWVe3StaYV1ATg3bh0TxTmbLbH+GkubPHDgMI8f39r7OUlVZ3nQ8hGhBdKwA3D12tGcHVXeO96amsG8Y8eb3Nl5Tfy9KJTS+3jUMKl59ooA7F8EYNQvKQIwABFDKfWFdA13QnqRB+kHH3zsPgM4WxloEJUKwOWt7Zwsq2Mi4p073xKfb3h7T548x489tsi9KzkRJ0qTnJ85JPC7PksH4B4rFw3nVM2P66S5ucWaG7hJ7+NRw6Tm2SsCsH8RgFG/pAjAAISM4zjFWuspWusVWuspRUVF/z97bx4f1XXe/x87TpykSftr7NSN4yyGe2+bNGma5dslTdK0Tdw0PrNKs2gbCQkEWgEhzA7DKsyO2EHIBoPBKwYTbDCGYGNjLGzMYowQBgQIJBnLoDSrY39+f1xpkAYBuhrNPPeMns/r9X4VqyPpmXPmnJy37r3n6NQ1WQ31II8n8+YtbLv9eUzSCnDKwMmQUiIcnkLe3kzXnDvXjIcfXofU1NS2z4UDvpFZCK0sTriEUgpwdrW5MVZwSi6cPvPIJ4fDgZkzZ+P48dPk/RQL1PO43aLS4jkaFmDrsABz/ZSwAHM4CYymaT5d13+n6/rTuq7P13X9KV3Xf6tpmp+6NiuhHuTxorHxMtLTzWcPA0OXJa0AB8tWQ0oHPB4Pzp1rJm935iqnTl1AVdXDCAaDkc+EtyCIzMXx3+nZrgIcEeFVxfA9kAWHq+1quNOFiopZOHToHfJ+6wnU87jdotLiORoWYOuwAHP9lLAAczgJjK7rdbqu/7zj1wzD+Jmu6yepaupJqAd5vLh6+3NW3CWUUoDTRq6DKzCQb4O2EbW19Vi0aBlSUlIjnwVPrg/pc/JJpdNOAtxO5pJCpJSkQTodkSvCkyZNweuvv0Xej1agnsftFpUWz9GwAFuHBZjrp4QFmMNJYHRd/0AI8YmoL9/W9nVlQj3I48WCBYsSdvsztQC33wY9Zcp08nbvyxw9WofZs+fB7XZfveI7OICMuYPJRdOuAtxO1pJCpA7PgMPtiLTdqFFjsGfPPiU2y6Kex+0WlRbP0bAAW4cFmOunhAWYw0lgDMOYq2naqI5f03X9AU3T5lDV1JNQD/J4YO7+HGrb/Xlp0gtwx92gGxqS74gZu3Ps2LuYMmV6ZHMr6ZRIKUpD5sIh5GKpigBHRHh5kXlrtNcZGUelpcOwZ88+8n6+EdTzuN2i0uI5GhZg67AAc/2UsABzOAmMruv7DMP4UNf1Rl3X39B1vdEwjD/puv5qR6jrvFmoB3k8OHq0zhRQTzAhAkotwGkj18Hpy4GUEq++eoC8/fsSp05dwIDcXLPPXQ6kDksnf8ZXZQFuJ7SyGP6x2XD6XOY5yS6XrT/b1PO43aLS4jkaFmDrsABz/ZSwAHM4CYxhGNndgbrOm4V6kMeDdgF2pmT2GQFufw7YzpKQbFy8+AEeeGC0eZZvTiqylhWRi2OyCHBEhKtKkFpmbmaXnp5h292iqedxu0WlxfO145oF2CoswFw/JSzAHA7HcqgHeTx4551TpgB70/uOAPvNq5D79x8kb/++wsKFS8zPmd+tjPyqJsDZ1ebRSd58P6SUKCoqwfnz9rvNn3oet1tUWjxHwwJsHRZgrp8SFmAOJ8Hp37//f2matsYwjJ2apq3p16/ff1PXZDXUgzxek6GUEg5vWt8R4LZboA8cOELe/n2BTZu2Rs70zZhnr02ukk2As6vNW6Jd6V5IKTFtWgV5/0dDPY/bLSotnqNhAbYOCzDXTwkLMIeTwGialmsYxnuGYVTouj647f82a5qWR12blVAP8nhw5kxj22ZEHgRHPJT0AhwsXwuH27xCduRILXn79wWKi0vN443yfAhVlZALYrILcHZ1KfyjQ5GxderUBfLPQEeo53G7RaXFczQswNZhAeb6KWEB5nASGMMwjvfv3//7Hb+madr3DMOopaqpJ6Ee5PFi9OhxkFIiJXd80gtwav60yO2hKhwZkwzs3v1K5JxfT54PoRXF5ILYHQLjcyKfz8D4HPJ6ukNodQl8I7MidVdVPUze/9FQz+N2i0qL52hYgK3DAsz1U8ICzOEkMIZhtAghbov68if5HGB78MorNW0SmorgiDVJK8DB8kfg9KZDSonnnttJ3u59iQMHjkSO23JleG2/+3NH+VVFgkOrSuAtDEZ2gn7mmV+R93tXUM/jdotKi+doWICtwwLM9VPCAszhJDC6ru/QNG2SEOKWti/domnaRMMwdlLWZTXUgzyejBgx0rwKPHBS0gpw6uAKSCmRnz8YjY2Xydu8r1FbW4+iomJzM6xUF9IfHEQujdfDnXXtZ9SdlUJe1/XIXFQAd455lT0QCODll/eT9/f1oJ7H7RaVFs/RsABbhwWY66eEBZjDSWD69etnGIZRq+t6U9s5wE2GYdT269fPoK7NSqgHeTzZvfuVyFXgwPCqpBPg4IiH4PCYV8c2b95G3t59lXPnmjF+/KSrUpmTaksRdnid135GvU7yuroS35SiNEiHWWNubh4OH7b3s+3U87jdotLiORoWYOuwAHP9lLAAcziJz239+/f/d03T/P379/93ce0t0bYP9SCPJ01NVyJi4g4ORrD8kaQSYE+muRHTsGFlfPWXmMbGy1i//nFkZGR0FuGZgxBabY9NsuwuwJmVBUgpDEbE1+12Y/78RUqIB/U8breotHiOhgXYOizAXD8lLMAcTgKjadqarr6u6/pDia4lllAP8nhz8uR5pKdntN0KPTlpBNg35EFIKZGamoq33z5J3s6Myfnzl9pEOPOqCGfbQ4TtKsDR4uvxeLBgwWLU1vZs8UwB9Txut6i0eI6GBdg6LMBcPyUswBxOAmMYRut1vv5+omuJJdSDPBG88MKetmOR3AgMXaa8AAeGr4r8vqeffpa8fZlraWi4hEcffQKZmVkdRDgFGXPpzgy2mwBnLS26RnwXLlzS40UzJdTzuN2i0uI5GhZg67AAc/2UsABzOAmIYRj5hmHk67r+u/Z/d/jag4ZhHKeu0UqoB3mimDt3gbmTbGCg8gLszjA3XZo0aQofe2RzGhouYcOGJyO7RUunA8FJA/q8AGfMGwynz90mvl5UVi5FXd1Z8v7qKdTzuN2i0uI5GhZg67AAc/2UsABzOAmIruu72/hzh3/vNgxjl67rGzRN+1fqGq2EepAnirNnm5GWZh4X5C9aoKwAB0qXQkoHPB4v6urOkbcr0z0aGt7HsmWrIp+N1OEZCb8l2i4CHJySC4fLASklRo0akxSfY+p53G5RafEcDQuwdViAuX5KWIA5nATGMIyZ1DX0RqgHeSJZs2Z93K4CJ0qA3elFkFJi8eJl5O3JWGfr1h3weLzmlc88H7KWF/UZAQ6tLoGv/Oqz0ZWVS3Hx4gfkfdIbUM/jdotKi+doWICtwwLM9VPCAszhJDD33HPPF+6+++7Ptv3nJ3RdH6BpWpa4ei6wEqEe5Ink3Ln4XQVOhAC3X/31er04eVL9q2Z9lZqawwiFss2zg4MeZC4uTHoBDlWVwJvvj+zuvGnTVvJ+6E2o53G7RaXFczQswNZhAeb6KWEB5nASGF3X92ma9t22fz/YdibwUU3T5lDXZiXUgzzRrF27wVyEpxcqJ8CerLK2q7/LyduRiY26unMYNGiw+ceY0aGkF+CMeeZ7dblc2LfvDfL2722o53G7RaXFczQswNZhAeb6KWEB5nASGMMwWkTb1V5d189/7Wtfu1fTtC8ahtFAXJqlUA/yRLN//1vmQtyfq5wAu9MKIKXEzp17yNuRiY2LFz+IHJWUMX9I0gtwaHUJnKkuSCnxxhtHydu/t6Gex+0WlRbP145NFmCrsABz/ZSwAHM4CYxhGC2apt3er1+/b+u6fqLty7cYhvEb0sIshnqQJ5pDh94xbz1NDSknwK7AIEgp8fLL+8nbkYmNnTvNo7lcWSkJEVBqAc6uLoWvzBT+hQuXkLd/b0M9j9stKi2eo2EBtg4LMNdPCQswh5PAGIbxjK7rmw3D2Nu+IVa/fv10Xdffpa7NSqgHeaI5fvy0KcDedPUE2D8AUkrU1Bwib0em51y40IIxY8ZBSonA+Jw+I8CZleYdDMFgMOmeYaeex+0WlRbP0bAAW4cFmOunhAWYw0lg7rnnni9omjbDMIxw+2ZYuq7fr+v6UOrarIR6kCeaurpz5sLf7VdOgJ2p5lmyb76ZfLeQ9hV27nwJAwcOavsMOvvULtDZ1aVwD0iFlBI+nx/r1m3EhQst5H3SG1DP43aLSovnaFiArcMCzPVTwgLM4dgsuq7/irqGm4V6kCeabdteaDsKaZByAuzOKIGUEhs3PkXejow1jhypxdixEyKfDVemFxlz8hMqn3YQ4KylhfAWBCO/f9CgfOza9TJ5/8QK9Txut6i0eI6GBdg6LMBcPyUswByOzWIYRit1DTcL9SBPNNOnz4SUEikDw8oJcOoQs/ZRo8aQtyPTPerrG7F48XK4XOYGUI4UFwITcxBaXZJQ8bSLALeTPjsfrkxvpI7x4yfiyJET5P3VU6jncbtFpcVzNCzA1mEB5vopYQHmcGwWFmB7ceFCC/z+gPns5bBVyglwcMTDkA4XXC4XzpxpJG9P5vo0Nl7GU09tQXp6hvl5cEikDs9A1opiEuG0mwBnV5s7Qwcm5MCR4oockbRkyQqcOXORvP+sQj2P2y0qLZ6jYQG2Dgsw108JCzCHY7OwANuLl156LS47QCdKgNNGroMrmA8pJbZu3U7ensy1NDVdwQsv7MHgwQWRz4Enz4fMhYk56kglAW4na3kRUoelQzrMmgKBANat24iGhvfJ+7O7UM/jdotKi+doWICtwwLM9VPCAszh2CwswPaisnIppJTwDhijrACnDJwMKSWmT59J3p5MZ/bvP4jy8geuPueb7kHajIHkgml3AW4nc+EQeAb5I7Xl5AzAli3Po7HxMnnf3gzqeTze0XV9gK7rH2ua5uzO61VaPEfDAmwdFmCunxIWYA7HZlHhTGDqQZ5I8vLM3Xf9JYuUFeDAsJVtV8mCSohBX+Do0TpMmTIt0u/OVBcCkwaQPOersgC3kz4rH+7Q1fFUVFSCPXv2kffzjaCex+OZe++992uGYbxiGMYrLMD09d0MFmB6uP7EwgLM4cQ5uq4/3/5vTdOGdeP1y+JbUeyhHuSJ4vDh2rajZ3wIlj+irACnjVwHZ2oWpJR45ZUa8nbt66xYsfrqBlduJ/yjQgitonvONxkEOLvafD44ODUPzqAnUuvYsRM+IAsFAAAgAElEQVRQX2/PZ9+p5/E45hZd11/QNO27uq7vZgGmr+9msADTw/UnFhZgDifO0XX9AyHEJ4RQ4/bm7oR6kCeKDRueMJ/HzBwaFylNpAB7c0ZBSonly6vI27Uvs2vXXrOvnRKpw9KRtSxxZ/omuwBHRLiqBIFx2ZGNshYvXkbe711BPY/HK4ZhjNA0baIQQrAAswBfDxZgrp8SFmAOJ84xDOMZXdff0HX9UcMw/qTr+qNdQV2nlVAP8kTx8MPr4vb8b6IFuP054MrKJeTt2ldpbLyMIUMKzdvRx+eQi2KyCnA7mQuHQDok3G43jh17l7z/o6Gex+MRTdP+Qdf1V0XbH32tCvClS+YiTjUaG68KcDvTNs/E6dMN5LV1h0uXTHlJZPvX1dVj6rMVndps6rMVqKurV+Y9qN4Hfbn+urp61E+tQOvchRHOtgmwKu+hqz6I6wTP4ViJpmm367qeruv6OMMw/qhp2qSuoK7TSqgXjonisceeMq8Ah8rVF+Dc8ZBSYuXKavJ27as8/fSz5kZXaR6Equz1rG8yCnB2dSlSStMhpcS0aTPI+z8a6nk8HtF1fYhhGA2GYZwyDOO0ruu/13W9Udf1wTf7XiicDz/8EJXbK7HiwIoIC15cgNbWVurSbJuWlhbM2zWvU5vN2zUPLS0t1KVx+kBaWlrQMm8esGJFhJZ56n/+EjHPcziWo+v6NOoaeiPUC8dEsXnztqS7BXrduo3k7doXaWi4hKws8znstOl55GLYVwQ4a1kRHG7zPdTUHCL/HHSEeh5PRPgKMF8Bvh58BZjrp4SvAHM4HMuhXjgmih07fg0pJdxpQ5QXYE/mMEgp8eSTm8nbtS9SW3sGDocDUkoEJ+eSi2FfEeDMhUMi72HTpq3kn4OOUM/jiYhhGLv4GWD6+m7Ge+/xM8DUcP2JhZ8B5nDiHF3XP9Z1/aObQV2nlVAP8kRRW3vG3K3X4URg2CplBThY9hCk09wZ98033yZv177KY489bfazQyI4VY2rwCoLcGZlAZyp5kZYkydPw8WLH5B/BjpCPY/bLSotnqNhAbYOCzDXTwkLMIcT59x7771/146maUW6rr+qaZrTMIzvtP3fvZqmFfbm7+zfv7/WdgZjra7r+3Vd/8aNXq/r+sO6rn+sadpfdufnUw/yRDJr1ty254BHKCvA3gFjIaXE+PGTyNuzr7Nu3ca2XaAdSJsxkFwSk1WAMxcXwulzRz73Fy60kPd9NL0z2ydPVFo8R8MCbB0WYK6fEhZgDieBMQyj9u67776z49c0TfuiYRi1vfx7XtQ0LUsIIXRdTzEM4/UbvNajadpKXdc/YgG+lqNH6+B0OiEdLgSGVyknwMERD0d+z/79B8nbk2lFdfXaiAQHJ+citNq+G2KpKMAZ8wbDGTDld9SoMWhoeJ+8z7uit+b7ZIlKi+doWICtwwLM9VPCAszhJDCGYbz/5S9/+Y6OX7v77rvvNAyjpbd+h6ZpX9R1/bIQ4tYOv/eiYRj9ol9777333mUYxut33XXXX/AV4OszffpM8ypwVplyAuwdMAZSSowePZa8HZmrLF9eFelzz0A/MhcXkouj6gIcWlmM1LIMSIdZ54gRI3Hu3HvkfX09emvOT5aotHiOhgXYOizAXD8lLMAcTgJjGEaVruv7dF2/v1+/ft/Wdf1+wzBe0XV9dW/9Dk3TvmcYxjsdv6br+n5N034a/Vpd1zdrmvYfbf9mAb4Ohw/Xwu02ryj5CucqI8D+kkWQDiccDgf27XuTvB2ZqzQ1XcHWrTuQnp7R1u8O+Mdm2+5qsCoCnFYxMHLV1+VyYeXKajQ0XCLv5xvRKxN+EkWlxXM0LMDWYQHm+ilhAeZwEhhN027XNG26ruvvtp2PeFLTtBmapt3ei7+jWwKsaVqeruuVHV5jSYCpt3tPNI8++ri5+Hf7ECxbbXsBDo54GE6veQbq8uVV5O3HdM2ZMxcwe/bcSP+7MlOQMW8wuVCqIsBZy4rgLQxGahs+vAxvvXWMvF+7Q69M+EkUlRbP0bAAW4cFmOunhAWYw4lz+vfv/1/t/9Y07b4O/E/H/+6t39fdW6B1XV+n63q9YRinDMM4rev6x4ZhnDEM4zs3+x1//vNH1xy+nez5+OOPMXHixLZjkQoQLH/E1gLsyRreJgTD8eGHH1I3H+cmOXLkCAYPHhzZJTq1LBOhVfRXg+0swMEpuZH6/H4/tm3bho8+Umdu6q05P1kCqLN4joYF2DoswFw/JSzAHE6co+v60fZ/G4Zx+jqc6s3faRjGLsMwsoUQQtO01BttgtWhzo/vvPPOz3fn57cPtL5GXV09gsE0SCmRmj/NtgLsK5gNKSW8Xi+OHKklbzeme1y48D5Wrao2j96SEq4MLzLmD2EBjr7qu7wI3iGBSD3h8BScOFFP3n9WiXWeT7aotHiOhgXYOizAXD8lLMAcThKmX79+hq7rrxqGUWsYxuuapn1TCCEMw5hsGEZ+V9/Du0B3j+ee29kmqqkIllXbToCDI9bA4TUl/bHHniZvL8Y6b755FEOGFEZ2ivaPCZE9G2w3AU6rGBg529fvD2Dr1h3k/dVTenPOT4aotHiOhgXYOizAXD8lLMAcDsdyqAc5JU1NVzB27PheORs4HgLcfuZvSUkpGhsvk7cX0zMaGt7HkiUr4HA4zNvuc1KRubigzwpwaGUxUoemR2oYPXosamt7tlC2C9TzuN2i0uI5GhZg67AAc/2UsABzOBzLoR7k1Bw50r4rtAP+0iW2EeDAsJWQDhccDgef+Zsk7N37OnJyBrRtwOZE2oyBfU6AMxcXwpXmMf/o5PFi48an0NR0hbxvYoV6HrdbVFo8R8MCbB0WYK6fEhZgDodjOdSD3A4sW7bKfE4zMNA2AuzOKIaUErNmzSVvH6b3qK9vwsyZcyLHJaXPye8zApy1vCgivyUlpTh8uJa8P3oL6nncblFp8RwNC7B1WIC5fkpYgDkcjuVQD3I7cO5cM7xeL6R0IDhiDbkAB8sfgcPlhZQSdXVnyduH6X0qK5ean5EUFzIrE3M7NKUAh1YVw52TCiklSkuH4dy598j7oDehnsftFpUWz9GwAFuHBZjrp4QFmMPhWA71ILcLRUUl5mY8JYvJBTgwvApSSmRlhcjbhYkPjY2XMXnyNEgp4Qy4kbWsKGkFOLS6BN7B5k7Publ5OHnyPHn79zbU87jdotLiORoWYOuwAHP9lLAAczgcy6Ee5HZhxowH245Emk4uwL7COW2bA40jbxcmfjQ0XEJZWbm5MVYoJe67Q1MJsK88E1JKpKWl4ejROvJ2jwfU87jdotLiORrVBLix8TJOnKiPUFdXj48++ogFmBCuP7GwAHM4HMuhHuR2Yc2a9eZ5u9kPkAtwSt5ESCmxaNEy8nZh4ssrr9SYnxWPM2kF2Jvvh5QSK1dWk7d3vKCex+0WlRbP0agmwCdO1GPshomYsqUCU7ZUYNyGiQlf+LMAc/2UsABzOBzLoR7kdmHBgkWmAOeMphfggWFIKTFlyjTydmHiSzg8FVJK+Moz4y6iVAKcPmsQpJTIyMjA+fOXyNs8HlDP43aLSovnaFQU4I7yOfXZxC/8WYC5fkpYgDkcjuVQD3I7cP78Jfj95lWqwNBl5AIcHL4a0uGEy+VKyuclGZODB4/B4XDA4XYia3nyPgOcXV0K9wBzA6z16x8nb/d4QD2P2y0qLZ6jYQGOvQYWYK4/kbAAczgcy6Ee5HZg06atbccg5fVIWHtbgNNGroM7vQhSSqxZs568fZje5cKFFjz33IuRjdd8I+J/9ZdagNMfNK8CB4Np2LDhSZw6dYG8H3oT6nncblFp8RwNC3DsNbAAc/2JhAWYw+FYDvUgp6ax8TJKSoaaG2ANrrCNAPuK5kV2zb1woYW8nZjYOXDgMBYsWIxgMC3yGXH6ErMDNLUAZ1eXwjPIH/m9brcb4fBU7Ny5BxcvfkDeN7FCPY/bLSotnqNhAY69BhZgrj+RsABzOBzLoR7k1CxbttIUAbcfwfKenQEcDwEOlj8CZ4q5e+7MmXPQ1HSFvK0Y65w8eR7r1j2GgoKiTp8Nd3YqAhNzkLWiOGECSi3AoaoSpM0YaB6J5HREasjIyMDixctx8OAx8v7qKdTzuN2i0uI5Ghbg2GtgAeb6EwkLMIfDsRzqQU7J5s3bzEW4wwV/8cIey2o8BDht5DoESpfC4fRASol16x4jby+me1y40ILt23dhwoQwnE5Xh6u9LvhGZCKzsiBh0mknAe5I1vIiBMbnwJ3VedyUlAzFY489hdOn1bpFmnoet1tUWjxHwwIcew0swFx/ImEB5nA4lkM9yKl47bU34Xa7287+nRaTqMZLgNNGroOvYDakdMDhcGDXrpfJ243pmmPH3sWTT27G1KkzOt3iLF0OeAuCSJ85KO7HHKkkwB3JWDAEqcMz4Ei5+scCt9uN0aPHYs2a9aipOYTGxsvkfXwjqOdxu0WlxXM0LMCx18ACzPUnEhZgDodjOdSDnILa2jPIyMiM6dzfRAlw2sh1SMmbYG6W5PPj0KHj5O3HtOLkyXN49tnnMWvWXOTkDLim793ZKQhMSOwtzqoKcDvXu0VaSgm/P4BJkyZj48ancOjQcds9EkA9j9stKi2eo2EBjr0GFmCuP5GwAHM4HMuhHuSJpqHhEkpKSk1JCQ5GsPwR2wtwsPwReDLNmvPyBuHMmYvk7djXqK9vxI4du7FgwWIMGVJwTV87fS6kFAYRDA9A5pJCcplUUYA7yfCKYqRNH4jUYRlwZXivqTsrKwvTp8/Epk1bcfz4afLPB/U8breotHiOhgU49hpYgLn+RMICzOFwLId6kCeSpqYrmD59piksKRkIlj3UK4IabwE2JXgNXL4cSCkxZsw4298SqjoNDZewZ88+LFu2CkOHDoPT6bxGHL2DAwiMz0FmZQH57c3JJsDRZC0rQnBKLlKK0+AMuK95H3l5gzBnznxs2/YCiahQz+N2i0qL52hYgGOvgQWY608kLMAcDsdyqAd5InnkkQ1tYupFYOiyXpPTRAhw2sh1CAyvgsPtg5QSS5asIG/PZOTgwWOYMeNBeDyezv3pcsCT54N/dAgZ8wYrIbzJJMDRZC4uQGBiDrwFwU7PDksp4XA4MGLESOzcuSdht0pTz+N2i0qL52hYgGOvIR4C3Nh4GSdO1HfCrn8IVk0gVa+fBZjD4VgO9SBPFHv27IPD4YCUDvgK5/SqmCZKgNNGroO/uBLSYS74t27dQd6uycK+fW9gwoRJV/vPYR5V5BuZhfRZ+QhVqSe8ySzAHQmtLkHGgiHwj8uGZ5AfDs/V91lQUIRnn30+7mcNU8/jdotKi+doWIBjryEeAnziRD3GbpiIKVsqMGVLBcZumNjjnx9vVBNI1etnAeZwOJZDPcgTRXn5A5BSIiV3fK9LaSIFOG3kOqTmT4WUEvn5g8nbVWWamq5g1669kc+GlBIOtxO+skxkLbXnc7wswN0Q4lXFCEzI6XSrdG5uHh5/fBMaGi7F5bNEPY/bLSotnqNhAY69hngJcMffEcvPjzeqCaTq9bMAczgcy6Ee5ImaHB0OB6TTjeCINcoLcLD8ETjcfkgp8eabb5O3r2o0Nl7Gtm0voKio5GpfpbjgHxWy1a7N8RLgAQMGYPz48RgwYEBSCnBEhFeXIDglt9MmWhkZmVizZj3q65t69TNFPY/bLSotnqNhAY69BhZgtQRS9fpZgDkcjuVQD/JEsH794+auzxnFcRHStJHr4EwNXbszcGoobr/PEyqHlBLLl1eRt69KNDVdQWnpsKt95HcjMD4HoVXJK74dBXjAgAE4c+YMAODMmTPIHZRHXlciRDhtxkC4c1Ij/R4IBvH22yd77XNFPY/bLSotnqNhAY69BhZgtQRS9fpZgDkcjuVQD/JEMHTocPMc3YJZcRPSlIGTrhHglIGT4vb7/MWVkVs77XYmqp1pbLyM/PwhkY2t0ufkkwtaIgV4/Pjx6JgJkyaQ15UospYVwZVubm7m8Xhw5Ehtr32uqOdxu0WlxXM0LMCx18ACrJZAql4/CzCHw7Ec6kGeCFJTzSs/wbLquAlptATHU37TRpq3QbdvhnXu3HvkbawSp041YNSoMREJDoYHkMtZogQ4+grwgEG55HUlgox5g+H0m88EZ2fnoKbmcK9+pqjncbtFpcVzNCzAsdfAAqyWQKpePwswh8OxHOpBngjaNzmKx+7P0UiHE9LhjPvv8ZcshpQSgwcPIW9fFbl48QMsXrw88geL1GEZSbHT880EuC89A9xOcEouHC4HpJQoL38A7757vtc/T9TzuN2i0uL52rmBBTjWGliA1RJI1etnAeZwOJZDPcgTQVXVw5BSwpv9QNIIcEreBEgpMX9+JXn7qsyzzz4Pj8fcJMmV5oHvgSxkVhaQS1s8BTjZd4HOri5F1vIiBCbkwJPri7zXhQuXxO04JOp53G5RafEcDQtw7DWwAKslkKrXzwLM4XAsh3qQJ4K9e183Nzzy5SSNALuDgyGlxLZtO8nbV3UOHDiMvLxBncTQlemFf3QImYuSR4aTXYCzVhQjOGkAPHk+SMfV95jq8+GZZ34V188Q9Txut6i0eI6GBTj2GliA1RJI1etnAeZwOJZDPcgTwYULLZHngP3FlcoLcGDockiHE06nMy63c/ZFGhsvY+/e1zF/fiXS0zM6SaI7lAL/2GxkLlH7bOBkFODQymIEJ+fCM8gP6XRE3pfH60U4PBXbtu3E+fPxOfu3I9TzuN2i0uI5Ghbg2GtgAVZLIFWvnwWYw+FYDvUgTxTV1WvbrgJnI1j+iNIC3H71d8GCReTtmow0Nl7Gnj37MHv2PASDwc4ynJOKwLhsZC0tIpe/virAoVXFCE7NhXdwANJ1VXrdbjcmTJiELVuex9mzzQn9zFDP43aLSovnaFiAY6+BBVgtgVS9fhZgDodjOdSDPFE0NLyP3Nw885bIQVOVFWBfwSxIKZGWlo4zZy6St2uyc+FCC3btehkVFbPg8/mvyqNDwjskgIx5g8mFsK8IcNbSQqSWZcLhufo+nE4XxowZh02btuLMmUayzwn1PG63qLR4joYFOPYaWIDVEkjV62cB5nA4lkM9yBPJjh2/Nhf9rtS4HYkUTwEOjlgDhzcNUko8+eRm8vbsazQ0vI8dO3Zj2rQKeL3eq7fb5vqQ/uAghFbbexdpVQU4s7IAKSVpnW5xHjlyFJ544hnbSAn1PG63qLR4joYFOPYaWIDVEkjV62cB5nA4lkM9yBPN2LHm7smeULlyApySOw5SSpSUlKKx8TJ5W/ZlTp1qQFXVw51ukXZlpSA4Nc+2IqyaAGfMHWze5uy4erW3omIW3nrrHfL+j4Z6HrdbVFo8R8MCHHsNLMBqCaTq9bMAczgcy6Ee5Inm8OFauFwuSIcTgdKlyghwYPgqSKcbUkrs2/cGeTsyJufONePRR59AKJR99bbcoAeBCTkIrbKXCKsgwKHVJUifOajT8UUpKSmorFyC48dPk/f39aCex+0WlRbP0bAAx14DC7BaAql6/SzAHA7HcqgHOQWLFi0zr9gF85URYE9GCaSUmD59Jnn7Mddy4UILNm/ehsGDC65eEU7zIGuZfTbLsrsAh1aXwFtw9Yp6MJiG1avX4NSpC+T9ezOo53G7RaXFczQswLHXwAKslkCqXj8LMIfDsRzqQU7BmTONSEtLh5QSvoLZthdgf3ElpJTwer2orT1D3n7M9WlquoIXXtiDwsKiyBFKoVXF5HKpggCnDjePnwoEg9iw4UmcO/ceeX92F+p53G5RafEcDQtw7DWwAKslkKrXzwLM4XAsh3qQU/H445vMq3S+HNsLsDttCKSUWL16DXm7Md3j9OkLGDQo3/zDRb7fFs8F21mAA+PMW8g9Hi9ee+1N8v6zCvU8breotHiOhgU49hpYgNUSSNXrZwHmcDiWQz3IqbhwoSWygVFg6DLbCnBw+GpIhxNut4ePPVKMY8feRXq6eVUzdVg6uWTaVYDTpuVBOiQcDge2b99F3m89gXoet1tUWjxHwwIcew0swGoJpOr1swBzOBzLoR7klMyf33Zrcc4o2wpwSt4kSCkxefI08vZirLN//1uRI5OCU/NYgKPIWloIh8s83ujRR58g76+eQj2P2y0qLZ6jYQGOvQYWYLUEUvX6WYA5HI7lUA9ySvbvP2hKgCeAYPkjthRgZ6p5a+gLL+whby+mZ6xf/7i5m3FJGgtwFMGpeZBSYuzYCeT9FAvU87jdotLiORoW4NhrYAFWSyBVr58FmMPhWA71IKekqekKBg4cBCkl/CWLbCfAgWGrIKVEWlo6LlxoIW8vpme0/6HFHUphAY7CV54JKSWqq9eS91MsUM/jdotKi+doWIBjr4EFWC2BVL1+FmAOh2M51IOcmoqKWeYzmvnTbSfAvsI5bVfHxpO3E9Nzzp+/BIfDAelykG6GZUcB9gzyQ0qJnTvVvsOBeh6PZ3Rd367r+lu6rh/UdX2Pruv/dLPvUWnxHA0LcOw1sACrJZCq188CzOFwLId6kFPz0EPrevU54N4U4Pbnfysrl5C3ExMbeXnmnQaZiwpYgDvgDLghpcQ775wi76NYoJ7H4xlN0/6y/d/9+/d367r+1s2+R6XFczQswLHXwAKslkCqXj8LMIfDsRzqQU7N88+/aN6eml5oOwH2ZA2HlBKPP76JvJ2YnlNf3wi32w3pkMhaQXcmsB0F2J2dmhTPuFPP44mKrus5uq6/ebPXqbR4joYFOPYaWIDVEkjV62cB5nA4lkM9yKk5ePAYpJRwpmbZToBdAfOq4UsvvUbeTkzPeeaZX5ln3A70k8qmHQXY33b+7/TpM8n7KRao5/F4R9O0Nbqun9V1vV7TtH+42etVWjxHwwIcew0swGoJpOr1swBzOBzLoR7k1NTVnW3bCTpoOwF2+nIgpcQbbxwlbyem54wdO948BmlyLgtwFFlLCyEdEqmpqWhouETeVz2Feh5PVDRNy9J1/Vc3ex0AXLpkLuJUo7HxqgC3M23zTJw+3UBeW1fU1dVj6rMVkVrbBTiR7R9dQ3sddXX1Pfp5ly6ZAtbxPXT1Pnv68+NNV/WrhGr119XVo35qBVrnLoxwdmrix0Fv90Ei5nQOp8+GeuFIzdmzzaYIuFLtJ8Ap5g65R4/WkbcT0zNqa8/A6XRBOh0IEd7+bFcBzq4uhXuAeRv05s3byPurp1DP44mMruu/++pXv/rXN3oNFM6HH36Iyu2VWHFgRYQFLy5Aa2srdWldpqWlBfN2zYvUOm/XPLS0tJDWEI867PA+OfZMS0sLWubNA1asiNAyT/3PR6LmdA6nT4Z64UhNY+NlUwacbtsJsMMThJQSdXXnyNuJ6Rntu4ynFNOeAWxnAQ5OyYWUEjk5A5S9Ckw9j8cr/fr1+6uvfe1rX2r/77ZNsM7e7PsAda4eRcNXgGOvwU5XgJuaLqOurv4ampoux609VLuCqnr9fAWYw+FYDvXC0Q54vSnmLarlj9hLgF3mlbGzZ5vJ24ixzuuvv9V2d4EDWUsKyUXTrgIcWl0Cd8gcg2vWrCfvt55APY/HK/369fuqruv7DcM41HYU0o577733H2/2fYA6zw9Gw88Ax16DnZ4BPnGiHmM3TMSULRURxm6YGNfnh7uqXyVUq5+fAeZwOJZDPcjtQEaGeatxsOwhWwmwdDjhdDrR1HSFvI0YazQ1XUFZWTmklPA9kEUumXYW4OzqUqTPyTfP4/b5cPKkenc8UM/jdotKi+doWIBjr8FuAtybtfW0fpVQrX4WYA6HYznUg9wO5OcPhpQSgWErbCPAwRFrTCkPppG3D2Odl1/eb+4u7nMhtIr22V8VBDi7uhTeAvOW/2XLVpH3n1Wo53G7RaXFczQswLHXwAKslkCqXj8LMIfDsRzqQW4Hhg0rg5QS/pJFthHgwLBVkFIiL28gefsw1tmy5TnbPPurigAHp5rPAldUzCLvP6tQz+N2i0qL52hYgGOvgQVYLYFUvX4WYA6HYznUg9wOjBs3wbxVtXCubQTYX7oEUkqUlAwlbx/GOps3bzMFuIQFuPsCnMcCnCRRafEcDQtw7DWwAKslkKrXzwLM4XAsh3qQ24E5c+abzx8OmmobAfYVmLsHT5o0mbx9GOs888yv2gQ4nVwsVRPgGTMeJO8/q1DP43aLSovnaFiAY6+BBVgtgVS9fhZgDodjOdSD3A6sWbMeUkp4c0bZRoBT8iZBSolFi5aRtw9jnfYrwJ48H0KrS8jlUgUB9o8JsQAnSVRaPEfDAhx7DSzAagmk6vWzAHM4HMuhHuR2YNu2FyClhDu9yDYC7AmNgJQSGzc+Rd4+jHVqa+vh9wfMZ8vHZpPLpd0FOGPuYEinA1JK7Nixm7z/rEI9j9stKi2eo2EBjr0GFmC1BFL1+lmAORyO5VAPcjtQU3MIUkq4fDm2EWB32hBIKbFz50vk7cP0jN27X4HD4YB0SKTPHEQumXYV4KylhXD6XG07QK8k77eeQD2P2y0qLZ6jYQGOvQYWYLUEUvX6WYA5HI7lUA9yO1BbW98mA2m2EWCXLwdSStTUHCZvH6bnrF27ISKamYsKWICjCFWVwJ2dCiklxowZh8bGy+R91hOo53G7RaXFczQswLHXwAKslkCqXj8LMIfDsRzqQW4HGhreN4XA5bWNADu96ZBS4vjx0+Ttw/ScpqYrmDatwrzDIN2DrBV0ZwLbTYBDq0uQUpwWOe7rzJmL5P3VU6jncbtFpcVzNCzAsdfAAqyWQKpePwswh8OxHOpBbhdSUsyrUMHytbYQYIcrBVJKnDv3HnnbMLFx/vwlFBeXkm+KZTcBbt/0KtXnw8GDx8j7KRao53G7RaXFczQswLHXwAKslkCqXj8LMIfDsRzqQW4XsrPNW44Dw1eTC3Cw/BFI6YDb7SFvF6Z3qK2tR1ZWlil8w2iORrKTAKdNN9JUs0kAACAASURBVI88cjqd2LXrZfL+iRXqedxuUWnxHA0LcOw1sACrJZCq188CzOFwLId6kNuBxsbL8Hq9kNKBYPkacgHueAX4zJlG8vZheoeamsNtnzOJwPicPivAGfMGw+FyJNUu59TzuN2i0uI5Ghbg2GtgAVZLIFWvnwWYw+FYDvUgtwNvv33SvBrlTbfNM8Au/wBIKbFv3xvk7cP0Htu37zJ3hnY6EFqZ2OeB7SLA7hzzcYP58xeR90dvQT2P2y0qLZ6jYQGOvQYWYLUEUvX6WYA5HI7lUA9yO/DCC3vMc4DThthGgD2ZwyClxFNPbSFvH6Z3ycjIhJQy4Rti2UWAXVnm3Q1vvvk2eV/0FtTzuN2i0uI5Ghbg2GtgAVZLIFWvnwWYw+FYDvUgtwPV1WshpYQ3+wHbCHBK3kRIKbFgQfJcJWPM2+0dDgeky5HwzbDsIsCeQX5IKbF79yvk/dFbUM/jdotKi+doWIBjr4EFWC2BVL1+FmAOh2M51IOcmsbGy8jLGwgpJXyFc20jwP6Sxeau1ME0NDRcIm8npneoqztr3m4fcCdcPO0iwCkl5tFHzzzzK/L+6C2o53G7RaXFczQswNY5fvw0xjw6EVO2VEQY8+jEHh/jxwJMi2r1swBzOEmY/v37a4ZhvGIYRq2u6/t1Xf9GF6/5lq7re3RdP2YYxmHDMKo0Tbu9Oz+fepBTs3OnefuzMyUDwfJHbCPAaSPXweXP5dugk4impitYtmylebt9TmqfFWDfA+Zu2OXlD6C+Pjk2eev9mV/tqLR4joYF2Do1NYdRlXkfniz0RKjKvA81NYd79PNYgGlRrX4WYA4nCWMYxouapmUJIYSu6ymGYbwe/Zr+/ftr/fv3/1bbf95iGMZGTdMmdufnUw9yakaNGgMpJVIGTu4Vae1NAU4dMhNSShQUFKGp6Qp5WzE958KFFsyY8aApnk4H0mYM7LMCnLm4EE6/G1JKDBlS2OOrRHaiF6f8pIhKi+doWICtU1NzGBty78f2skCEDbn3swArOgZUq58FmMNJsmia9kVd1y8LIW5t/5phGBcNw+h3o+8zDGOEruvV3fkd1IOckoMHj5kS4PIiOOJh2wlwsHwtHJ4ApJR46aXXyNuL6RlnzlzEyJGjzM+ax4n0WYMSLp12EuDs6lJkLS2EK9M8EiorKwtvvHGUvJ9iIcapPumi0uI5GhZg69TUsADfrH6VUK3+TgL84Dz8LiOE9/xpuLJ9O9678D55fT3tA+p5nMMhi6Zp3zMM452OX9N1fb+maT+93vfcddddf2EYxjuapjm78zuoBzkl06fPbNv8amSvCGtvC3DayHVIyZsAKSVGjRpD3l6Mdd555xTy84eYt9n73chcOIREOO0mwNnVpQitLIZnoLkhVmpqKnbt2kveXz0lxqk+6aLS4jmaY6ffhTHfQNrGTBbgblJTwwJ8s/pVQrX6Owrw77JyACEi/MHtJa+vp31APY9zOGTpgQB/Utf1Z3Vdn9/d39E+0Poab79dB6fTCelwITC8yrYCHBzxMBwu89iY/fvfJG83pvscOvQOsrLM513dWSnIWlpIJpt2FODs6lKEqkoim2I5nU5s3bqdvN96QizzfDJGpcVzNNNemAkRFug3vz8LcDepqWEBvln9KqFa/R0F+A8//x9ACPzxb78ECIE/f+Wr5PX1tA+o53EOhywWb4G+Tdf1p3VdX27ld/z5zx+hL2bRokWQUsITKus1WY2HAKeNXAfvgLGQUmLq1KnUzcbpZurq6pCWZoqdZ6AfoZWJPfNXFQHOri5FaHUJ/KPMPxQ4HA4899xz1N1nObHO9ckWQJ3FczQ/WvkTiLDAHTPvYAHuJjU1LMA3q18lVKu/owD/6Xs/AIRAcyAdH3/2s/j41lvR3KDebdAswJw+H8MwdhmGkS2EEJqmpXa1CZYQ4hO6rj9lGMYqqz+/faD1JU6ePAuXywXpcCIwbKXtBThYVg3p9EBKibfeOkbefsyN2bt3P1JTU83b6wuCCFUl9rxf1QS4ncD4nEhda9asJ+9HK8Q80SdZVFo8d+T42dP4xORPQIQFbpt8G2bvWsAC3A1qaliAb1a/SqhWf0cB/vPX7wWEwIWCYvz57/8eEAKX9r9FXmNP+oB6HudwSNOvXz9D1/VXDcOoNQzjdU3TvimEEIZhTDYMI18IIXRdT9d1/SNd1w+28aamaYu68/OpBzkFmzZtNW9LTS/sVVGNlwCnjVwHT1YZpJRYu/ZR8vZjrs/OnXvg8Zh/rEgpSUdotT3kVwUBzq4uRXByLqTDrG3p0pXK7H4ez/8NUDEqLZ47UvnSMoiwiDB5x/SECHBj42WcOFF/DY2Nl2/6vXYS4PADP8KdEz+DhSN/xgKs6BhQsf6OAvzR5/8SEAJnx07EH3/xC0AIfPDEZvIae9IH1PM4h5PUoR7kFMycObtXjz5KhAD7hphH6IwdO4G8/ZjrEwgEzU2dyjJtJb+qCHB2dSnSpg+EdDogpcTrr6vxl3vqedxuUWnx3JH/XSMhwgK3hG+BCAsMf3ZkQgT4xIl6jN0wEVO2VEQYu2FijwSPUoB/PO4eiLDAP4/7EguwomNAxfojAjxjNiAEPv7MZ1E/tQK/z88HhEDr3EryGnvSB9TzOIeT1KEe5BQMGJALKSUCQ5cpI8DBsmpIKeHz+bp1VYChoaioBFJKpM/OJxdJVQU4VFUCh9sJh8Nh29tOo6Gex+0WlRbP7ZxpaMRnpn0Gt025DX83/+8gwgK5Tw5KmAD3VNJ6KsCxXHWOpqbGFOC/nfgX5h8QJgksGPSfLMCKjQFV628X4P8rH21ufHXPV1A/tQK/nT4dEAK/HTqCvMae9AH1PM7hJHWoB3miOX78tLnwd/sQLH9EGQFOG7kOzpRMSClRU3OIvB2Zrlm+vMr8Q0V5JrlIqirA6bPyIaVESclQ8v7sLtTzuN2i0uK5nXWvPwYRFvhJ1U/xnyv+EyIskLLen7QCHMtV52hqag5j1cCfd7p9XJb1YwFWbAyoWn+7AP92wCBACPzpH/8J9VMr8Jt16wAh8HtPCnmNPekD6nmcw0nqUA/yRHPgwBHzyBVfdlwkNZ4C7A4OhpQSu3e/Qt6OTNfs3ft65NgjapGMxp2Vco0A27FO3wjzDz0rV1aT92d3oZ7H7RaVFs/tVLw4ByIsUPzMULgfckOEBX5WfV9SC3BviWFNzWGMLfgXiLDAlyd8DiIs8LkJn8TLr73eo/ZgAaZFtfrbBfj3Lo959u9//Qz1Uytw5eWXTSH+/g/Ia+xJH1DP4xxOUod6kCea9ivATm+6cgLs8g9Q6rnIvsjFix/A7/ebz5iXpiO0iv74o3Y67rLcTmB8Dnld7YRWlyAwMQcOl/n876uvHiDvz+5CPY/bLSotntsZtW0cRFhgwnNh5KzPgQgL/GDZP7MAd4OamsNIG/r3EGGBwJhv4J/G3wURFpi0aWqP2oMFmBbV6m8X4D/+6CfmFd/UAOqnVqClvh4QAh/d+UXyGnvSB9TzOIeT1KEe5Inm3Llm89ZPV6pyAuxMyYCUEkeP1pG3I3N9tm3biZQU82qrK92DjAVDyOWyKwm2k/xmLS+Cd3AgUltl5VJldoBubuaFSnRUWjy3k/90AURYYO7uhRj25DCIsICx8O9YgLtBTc1h/OvIL0GEBcY/8EMMG/UD8xbyan+P2oMFmBbV6m8X4A+/8U3zmd/BhaYAt7Tgoy98wTwX+NQF8jqt9gH1PM7hJHWoB3miaWq6ArfbDSmdyj0D7HD7IKXEmTMXyduRuTGHD9eiqKjYFDqXA4HxObbZFVo6JaRTktfRTvrsfDj9blPKg0Hs2LGbvP+sQj2P2y0qLZ7b8T0ahAgLrH51LcJbwhBhgb+ZdRcLcDeoqTmMu8Z9FiIssGbE/agY+R/m89TLftqj9mABpkW1+tsF+M9/cxcgBH4zZmJEgP/0T98FhMD7e14jr9NqH1DP4xxOUod6kFOQk2PeShwYtkIZAQ6OeBhSOpCSksK7QCtCQ8P7WLRoWeSqpjffj6zlReTCaRcBDq0uge+BrMi5vyNHjkJtbfwWpfGEeh63W1RaPLfz84f+ByIs8NQbz2Lec/NwS/gW3D71dhbgbrDr1b3mc7+TPoXny/xYVf6/EGGBb8z/Zo/agwWYFtXqP3GiHvXh6fj4tk/i41tvReuD8yIC/Aen+Vzw5UceI6/Tah9Qz+McTlKHepBTMGnSFPOs1iEzlRFgf9ECSCkxfLh62/n3dV588WWkpaWbm05lp5I/F2wXAW7f7MrpdOKhh9Yp/Ycd6nncblFp8dzOD5b9M0RYYNfRvajcXonPz/hLiLDAtBceZAG+CUufWwURFvju+LuwvSyATWVeiLDAFyru6FF7sADTolr9J07U43z5GPN53zvuQOuchREB/m3JcPOq8PQHyeu02gfU8ziHk9ShHuQUVFevNa/I5YxSRoBT8iZBSokFCxaRtx9jnbq6s8jLG2R+7oYESG+HtoMAByaYzyK73W68/PJ+8v6JFep53G5RafHcTv8FGkRY4ODJt1G5vRL3zPkKRFhg5NYxLMA3oXTjcIiwgH/M32N7WQDbywL4zMTbcEv4Fpy/eMnyz2MBpkW1+k+cqEdj3mBACHxo/F0nAf7NnAWR54Kp67TaB9TzOIeT1KEe5BS8+OLL5gZFwXxlBNidUQIpJTZt2krefkzPOHq0DmlpaebdB2V05wRTC3BaxUBIh4TD4cC2bS+Q90tvQD2P2y0qLZ7buWPmHRBhgVPnGlC5vRL/sOjbEGGB/KcKWIBvwv+s/CVEWGDcAz+MCPCXx5nHIb3x7lHLP08lAW5svIwTJ+o7UVdXj48++ki5MXCj9rczJ07U45LXBwiBP/7rDzsJ8OXHN5lHI/3ifvI6rfYB9TzO4SR1qAc5Be1HITm8acoIsMuXw0cgJQH79r0Bj8djPoM+gWYXZkoBzpg/GA63eczR2rUbyPujt6Cex+0WlRbPzc2taGy6jFsn34rPTPsMLl5sQeX2Svz7yh+bx/psSGcBvgnfXvAdiLDA0vL7IgL8rVF3QoQFth6y/kculQT4xIl6HBk7EfVTKiIcGTexW31gV1QU4Ms//W9TdO93dhLg919707wy/M1vkddptQ+o53EOJ6lDPcgpuHChBQ6HA9LpVkaAHZ4gpJRxvW2LSQxPP/2s+QeYFFefE2Bvofk5njZthlLHHN0M6nncblFp8dzc3IqT585BhAW+NPvuiAD/co2ECAv84uFfsgDfhL+Z+TcQYYEnytwRAf5J+T0QYYGqV9dY/nmqCXD9lAq0zlkYoV2+VBoDN2t/O3PiRD1+8/3/BwiB36Vnde6Dhkv4+JZb8NHnPk9ep9U+oJ7HOZykDvUgp6J9U6LgiIeVEGDpcMPhcCi9URBjsmzZKkgpkVIY7HMCHAybO7CXlJSyACdxVFo8Nze3oubkYXPX4sp/iAhw2sZMiLDAv674IQvwDWi4+D5uDd+K2yd+As+X+SMC7B5uPlM95YUZlttDJQE+fvwMjo+ZiMYpFRFqx07EpUuXlBoDN2t/O3PiRD1+234GcH7BNX+E+OiOO8yzgOubyGu10gfU8ziHk9ShHuRUDBlSYN6GOnSZ7QU4OOIhSCmRnp5B3m5MbNTXN8Hv90NKiYwFQ/qcAIeqSiJn/u7Zs4+8P3oL6nncblFp8dzc3IodR38NERb495U/jghwwabiNin+JgvwDXjj3aMQYYEvj/tcRH63lwWQW/otiLBAwaYSy+2hkgDX1BzGI1n34dlCT4RHsu7DqVOnlBoDN2t/O3PiRD1+/9WvA0Lg/4aPvEaAP9QNQAhceusd8lqt9AH1PM7hJHWoBzkVZWXlkFLCX1xpewEODDOvGA4cOIi83ZjYWLt2A6SU8Az0kwgotQBnV5fCPy4bUkqMHj2WvD96C+p53G5RafHc3NyKxw5sgggL3L/WGRHg0dvGQYQF7p7zZVsL8PHjpzHm0YmYsqUCU7ZUYOyG7l197C0x3PLW8xBhgX8c9cVOAlxe9AOIsIB7nddye6gmwBty7+/03jfm3c8CnEBOnKjHn+78onnc0fjJ1wjwn/7l3wAh8P6Le8lrtdIH1PM4h5PUoR7kVAwbVmYKcMli+wvwcFOA8/IGkrcb03MOH65Fqs9nXs2fnd9nBTi0shgOrxNSSmzevI28X3oD6nncblFp8dzc3IoVr1RDhAUyH8uOCPDUF2ZAhAX+v4q/trUA19QcRlXmfXiy0IMnCz1Y3c2rj70lhkv3mmcA/1f5VzpJ4IzBP4IIC/zLin+z3B4swLSoKMB//uxnASHQWjH7GgH+w/9KQAh88MRm8lqt9AH1PM7hJHWoBzkVpaXDTAEuXaKAAK+GlBI5OQPI243pGfX1TRg0KN989rc4jUw+7SDA2dWlCE7JNa+EezyoqTlM3j+xQj2P2y0qLZ6bm1sxc9dciLBA8eZhEQGetWseRFjg01M/Y3sB7ihg3ZWv3hLDcc9NgggL+IYZnSRw+cCfQYQFvjrv65bbgwWYFuUE+J1T+PiWW/Dxpz7V5UZkv8vMBoTAlRXV5LVa6QPqeZzDSepQD3IqiotLzWeAS5faXoCDZdWQUiI7O4e83RjrNDVdwaRJkyGlhDsrBaFVJX1egLOrS5E6PCPyuX733fPk/RQL1PO43aLS4rm5uRWj2m53Hv/85IgAz/31Qtw+9XbcEr4FU56ZwQJ8HbKfyDOf9S35TicJXJ/7S9w2+TZ8asqnLG94xwJMi2oCfPK1g4AQ+Oivv9ClAP+2tCxydZi6Vit9QD2PczhJHepBToVaV4CrWIAV5qGH1kWOPcpcUkgunnYR4FBVCTwDzFvCR44cpfQO59TzuN2i0uK5ubkVg58uhAgLzPv1ok4C/Fcz/soU46fDLMDX4WfV95ltVPAvnSRwQ+79+NKDd0OEBY7Vn7L0M1mAaVFNgE9v2wkIgT/f85UuBfg34enmBlnlo8lrtdIH1PM4h5PUoR7kVIwZMw5SSviK5tlegP2lSyGlRFFRMXm7MdbJzMwyn/t9cBC5dNpJgLOrS5G1rAiOFBeklDh48Bh5X/UU6nncblFp8dzc3Ar/hjSIsED1vnWdBPiuWX8LERYY8cQoFuDr8I3Kb0KEBRbk//QaAf7Owu9ChAVefPtlSz+zKwGL3uxrzKMTcfz46bi2bXeIbn8W4MRzdv0TgBD48O++0aUAX6lcZp4RnKvORqIswBxOnEM9yKmYPn0mpJRIHTzT9gLsK5oPKSVGjRpD3m6MdYqKis1jj+bTHHtkZwHOri6NCHA8BSPeUM/jdotKi+fm5lbc99AvIMICmw7+qpMAf33eveazwRuH9lkBbmy8jBMn6q+h/Y6Nv2y7Sr429xfXCPDPVvwPRFhg3euPWWqPrgSspqbzZl9Vmfd1a/8AFmDrqCbADYtXAELgT9/7QZcCfHn944AQ+L3bS16rlT6gnsc5nKQO9SCnYsGCxeaGRAMn21+AhzwIKSWmTp1B3m6MdWbOnA0pJYLhAeSyaTcBzlpaCCklsrJC5P0UC9TzuN2i0uK5ubkVP1j2zxBhgd3HXu0kwH/fdnUzb31+nxXgEyfqMXbD1Suv7UctnThRj5PnzkGEBe6ouPMaCdyQez/S14QgwgKzds231B7XE+COv2ND7v0swHFCNQFunFIBCIE//uSnXQpwy3Mvmv//H/+UvFYrfUA9j3M4SR3qQU5F5DzWrOG2F2BvzmhIKbF48XLydmOss379Y+bdBsMyyIXTbgKcNmMgpJQYO3YCeT/FAvU8breotHhubm6FtkCHCAscOnW8kwB/d+n3zOOR1ob6tABf73V73nkNIizwDwu+1aUAD3usHCIsUPbsSEvtwQJMi2oC/N7wkYAQ+MP/3t+lAF/a/5Z5i/Q/fJu8Vit9QD2PczhJHepBTsWRIyfMjYlcKQiWr7WtAAfLH4HTmw4pJV599QB5uzHW2bfvDUgpIZ0OW1wFtosAZ8wfDKfPDSklqqoeJu+nWKCex+MVTdNuNwxjk2EYx3VdP6jr+nZN0/rf7PtUWjw3N7fizgfvhAgL1F9o6iTA/7by3yHCAikP+VmAu3jdozVPQIQF/nv5z7sU4BmbZ0OEBYIbMyy1BwswLaoJcEtOnnmLc2qgSwF+7+Q5c5OsL91NXquVPkjEHM/h9NlQD3JK2o9C8hXOsa0A+0sWR3aAtnqUBGMfVqwwz3KWUsI3IhOh1XRHIdlBgNOm58HhckSeba+vbyTvo1ignsfjFU3Tbu/fv/8vOvx3ka7ru2/2fSotnpuaruATkz+BT0/9NJqbWzsJ8H+tNs+ylVVOFuAuXjdr13yIsED6mlCXArxy+0OmIFf/3FJ7sADTopoAX3F6zE2usnO7FODmpiv4+Lbb8PHtt6NZkXUUCzCHE+dQD3JKIrdBZ5TYVoC92SMhpcSSJSvI24uJjS1bnoPbbV7x9Ob7EVpZ3OcEOLS6BP7RocgfA+bPr8TFix+Q902sUM/jiUr//v2/bxjGqZu9TqXF87vnz0OEBf529pfQ3NxZgH+5xmEK3IqfswB38bqhW0ZAhAWGPz6ySwF+YvdmiLDAd5Z811J7sADTopoA/9+P/wMQAr8tLO1agJtb8ee/uQsQAs2nLpDX290+SMSczuH02VAPckqOHz8Np9MU1kDpUtsJcGDYKjicHkgpceDAEfL2YmJn3743kJ6eASklXBleZC4q6DMCHFpZjJSiIKSUcDqd2LjxKfL+6C2o5/FExTCMtYZhzLvZ61RaPNecPAwRFvhG5TfR3NxZgL3rfRBhgR8t/QkLcBevS1nvhwgLPLhlXpcC/OKreyHCAl+ec8916+hql+m6unp89NFHLMBEqCbAv//WP5rn/I4cc10B/vAb3wSEwCVF1lIswBxOnEM9yKmprDTP2HX5ByBY/oitBNidVgApJaZNqyBvJ6b3eOedUygoMHc/drgdCIzPSegt0RQCnD47H86AefXb7/dj9+5XyPuhN6GexxMRwzDGGobxyte//vVP3+y1AHDpkrmIszs7394DERb44cof4b33WtHYeFWAMx/LhggL/GDRP+P06Ya41VBXV4+pz1Zg7q8XRpj6bAXq6upv+r0HDhzGxrz7sWNEADtGXJWvm7V/d3/njV73ryt+aB6BtHNDpxra66ipOYTbptyGT035FJqbr1y3jnEbJmLqsxURxm2YiJaWlk7voav3eeDA4bi2bXeIrstKH9iVS5dMAVal/j/dcw8gBH4zeTpa5y5E69yFONsmwO3v4Y///mNACHzwwq/J6+1uHyRiXudw+myoF47UnD3bjOzsHHOX3kFTbCPAviGzIKVEIBDEyZPnyNuJ6f3P3ezZ8yK3AntyfchcXJh0AhxaVYzU4RmR9zlsWBmOHDlB3v69DfU8Hu/oul5uGMbrd9555+e783oolB0nd0CEBTwbPQCADz/8EJXbK7HiwAqUbCuBCAv80+J/Qmtra9xqaGlpwbxd87DiwIoI83bNQ0tLy02/99SpU3i60IU9YzOwZ2wGni504dSpU732O2/0uq/M+wpEWKDmWE2nGjrWcffcuyHCApd/fzmmOuL9Pnua6Lqs1MbpnXz8F3+Bj2+5BVi2DFixAlixAi3zovo4NRUQAnjuObpCLSbe8zqH06dDvXC0Azt37mnbEdqLwPAqcgEOjngIDrcfUko89dQW8vZh4seuXS8jKyur7WqwE4FJA+J+NThRApw+Jx+uNPMWfrfbjbVrH0Vj42XyNo8H1PN4PKNpWplhGAf69ev3V939HkCdq0crXzU3asp4LIT33ut8Bbh48zCIsIA2V+crwFGvO3r8BG4J34LPTf88amoOdXkV9MCBw/j24u9AhAVeq3vTUh18BZgOpa4An20yd3j+7GcjV3+7ugL8u7adoluXrqSvuZt9EM95ncPp86FeONqFyZOntd0KnRfTsUixCnCw/BG4083bY8vLH+Cdn/sAZ85cREXFrKtXgwf6kbU0fleD4y3AoaoS+EZkQjrM91NSUoqDB4+Rt3M8oZ7H4xXDML6s6/rHuq7X6br+ZttRSPtu9n2AOs8Pztw1FyIsUPTMUDQ3d34GuHzrGIiwwD2zvsLPAEe9bvuB3eYZwIu+3eVzsO3P6P7n6v+GCAs8e2hHt+toF+CO7yH6d3T3GeDjx09jzKMTMWVLRYQxj07E8eOne6Xvunrv/Axw4rj01juAEPjTnV+MPP/b1TPA/1dmnhX8m6lqPFL23nvJ+78rHI4tQj3I7cKpUw2RW6G92SPJBDgldwKklAgG0/DOO6fI24VJHNu3745skOVIcSGtYqByApy5qADuUIr5xySXC6tXr02KXZ5vBvU8breosnhubm7F6G3jIcIC458Po7m5swBPeH4yRFjgzoovsgBHva7q12sgwgL/u0beUIB9jwYhwgIPvba+23X0pgDX1BxGVeZ9eLLQE6Eq875ufW936Oq9swAnjpYXXzbPAP7q128owL+ZNtPcKGt4OXnN3e0D6nmcw0nqUA9yO1FTcyhyTE3q4IqEC7CvcB6kdMDhcODXv36VvD2YxHPqVAMmTJgUuRqcOjwDoarevSU6XgIcnJwLh9sJKSUGDhzUawtMFaCex+0WVRbPzc2tKHpmKERYYNau+Whu7izA03fOgggLfG7a51iAo143aetUiLDAkE3FNxTggk0lndq3O3X0tgBfr7be6Luufj4LcOL44PFnzCOQvvHNGwrwlaWrzLOCQ7nkNXe3D6jncQ4nqUM9yO3G008/a8qH0w1/D45G6qkAB4athMNlXjlbs6brv5QzfYOmpivYuPGpyB9jXJkpyKzsveOSeluAOx5vJKXEgw/OwblzzeTtmEio53G7RZXFc3NzK0KP50KEBZbtrUJzc2cBnr1rAW4N34rbJt/GAhz1urzH8iHCAhUvzrmhZE7YPgUiLPDAr8Z2uw4WHXn5+AAAIABJREFUYFpUEuAry1ebtzZ////dUIA/2Pg0IAT+IF3kNXe3D6jncQ4nqUM9yO3InDnzzbNKvekIlj0UdwEOjlgDZ2o2pJSYNGkKP/fLoLm5FW++eRT5+YMjxyUFJ+faToAz5g+BM2hudOXz+bF1a9fP+SU71PO43aLK4rm5uRWedSkQYYF1rz+G5ubOAjxn90J8dtpfQIQF3qmL3yMpKgrwfat/AREW2FDz5A0ls/KlZRBhgdwnB3W7jo4CfLrhItI3ZiHwcDqGFX0Pz5R5WYDjjEoC/JvpDwJC4PKPf3pDAW55YQ8gBP74wx+R19zdPqCexzmcpA71ILcjFy60YOjQYebutWlDLJ0P3BMB9mQOhZQSgwblo76+ifz9M/bh3Ln3In+QkVLCNyIz5l2ie0uA06bnweF2QEqJoUOH49ixd8nbiwrqedxuUWXx3Nzcip9V3wcRFtj81nNobr5WgO+YeQdEWOD1tw/FrQYVBfgbC78JERZ4tfaNG0rmhponIcICzkc83a6jowDP2b0QIiwifH/83/ZYgJ8r8yNn9LdRXvQDFuAboJIA/1/ZA4AQaPmfX95QgC+9cRQQAh/+/TfIa+5uH1DP4xxOUod6kNuV2tozkQ2JvAPGxk2AUwZNNp/1TE3FoUPHyd83Y082b94WuSXam+9HaGUxmQCHVpfAPzoUkfL58ytx4UILeRtRQj2P2y2qLJ6bm1vxLyv+DSIs8OKxvWhuvlaAvzznHoiwwM63XopbDaoJ8OTNM/C56Z+HCAucvdB8QwF+4egeiLDAD1d2feXtZgL8X9U/gwgLuFZ78MmJt+ITk27B5rKUHgnwlAd+DBEW+OzE2/DSvtd6pe+6eu8swImj/XijS17fDQW4+fRFQAh89MW/Ia+5u31APY9zOEkd6kFuZ/bufR1Op8u88lYwu9cF2F+80Hy9lHj++RfJ3y9jb/bteyPyRxlXhheZi3t2VFIsAhxaVRJ53tfpdOKxx54ibxc7QD2P2y2qLJ6bm1vxzUXfMq/w1plXeKMFuP8CHSIssOn1X8WtBtUEePTT5s7ZX5p9d5c1dBTgg+8egwgL6AuMbtfRLsDvnj+HT075JD499dN4ad9r+NZo82r89JE/6ZEAf3/830auJI95ckKv9F1X750FOHH8weEGhEBTZvaNBbjpCj7+9Kfx8Sc+gWYFHjNjAeZw4hzqQW53Hn30CfMZTFcKAsOrek2AgyMegsMTgJQSy5dXkb9PRg3eeecUCgoKI0clZcwbnDABzlpRDHd2KqSU8Pv92L37FfL2sAvU87jdosriubm5FV+d+zXzGd9681zYaAH+1uJ/hAgLrNm7IW41qCbAgzcUQoQF/nXFD7usoaMAn73QjP+fvfOOi+J4//gDdo1JTCyJphohanr/JlHzS9FEXXqvKthAQURRQYWlCBZU7AU0sWvsvWssieWsYAFUEAvCoSiXWGLh8/tj4eS4vUI5d4/M5/X6/OHt3s4zuzfjvpmZZ4gnNEloYnQcpQA8+88UEE/45bduUChS4RbyLognuIa3rTAAJw/tCuIJllEWIJ7wzsQ21ZJvQ6zuDICfnf/9tiNAhBt9A/UDsFKFxy1bAUQouHBF8riNeQZS9+NMTDVaUjdyuTs/vwhRUdFGrwc2FoBL1/2Ghg5FXt4dyevJbD6+ckWJUaOErZJsHO3gldTf5ADsO3cg7HsIWcr9/Xuz6frlLHU/LjeZy8uzUqlCk4QmIJ5w9UYBlEptAP581pcgnjB97xyTxWBuAOy6wAPEE9yWeYrGUH6N7vPxL4B4wvUbt4yKoxSAbRbZg3jC1P2zoFCkIrr/NyCe8O7olyoMwDYRbUA8wTn8XbQY2RDEE9ad3FLlZydWdwbAz86P2r0HEOFaSJhBAH74/ocAEW4dPiF53MY8A6n7cSamGi2pG7k5+OLFa/Dw8BTW6vaNrTIAuwSMB8dxcHJywpkzFySvH7P5OS/vDmJj44WpyM528J5m/DZJFQVg3+QgOPi5qPf3vXjxquT1l5ul7sflJnN5ec7PL0LtmNqoF1tP/Vl5AO6Y/J2w3c/2CSaLw9wA+Ke5P2tsbVQ+hvIA3HryOyCekJadaVQcsRsTcF15HQ3jGqFWdC2kX8mGQpGKRX5dUT+qNiyjLDDPv4vRAJzi30X9vYVDOPgMagfiCdzCqm+JI1Z3BsDPzo9fbQkQ4Up4pEEA/rfj/wkJs7bKf8kZA2AmJhNL6kZuLt6+fU/J/sAOcAuZU2kAdg+dBxt7YRrp77+vlbxezObr3NxCjBw5WoBgN3v4zDBuTXBFANg3JQiOfYWp+j169ER6erbk9Zajpe7H5SZzeXnOyc0H8YSm45qqPysPwJ1/Fbb7idhYPWtGxWxuAPzZtC9APGHGgbmiMZQH4C9n/w/EE/ac0142oQuAlx1fBuIJHZI7aZTxeck63rBA4zI5KxSpCB3wGYgnfDnyVWwPdUOKfxfUjhb+8JGbV7UEfmJ1ZwD87FzcoAGKLS2Rw48xCMD3bR2ELZOWrZI8bmOegdT9OBNTjZbUjdycnJAwvmQqdEClAdjBJxQcxyE8fCTb75e5yr527SaGDh0mJMbydIDPHMPZoSsCwE4DPcBxHDw9vZCWliF5feVqqftxuclcXp7PXL4I4glvTXpb/Vl5ALZd5ADiCQNXDzJZHOYGwK0nCiO6G0/vEI2hPAB3W2AD4gkrjmn/0VcXAIdsDAHxhFHbojXK6D3iIxBP6Bb6ttEA7DRYSGTWY8QH6tisJlqDeMKfGceq9OzE6s4A+Bk5J1/Y2qjJS8iJSTAIwPd8/QAiFM1Mlj52I56B1P04E1ONltSN3JyclXUdzs4lSYCCplcYgN1C5oKzsYWtrR3Onr0oeX2Ya4ZzcvIRFBQsTKsP9qw2APaI81cnvDpx4ozk9ZSzpe7H5SazeHlWqnA48wSIJ3ww/SP1Z+UB2HWZsN61x3I/k8VhbgDcJL6JxpTm8jGUB2Df3/00RowNXT92YwK+mydMPV99YqNGGdOGdhb+aBHxvNEA/PmwFiCeED2sgzq2n+d2A/GEeYcWVunZidWdAfCz8c3T6QAR/n27tVEAfHfQEIAIqvjxksduzDOQuh9nYqrRkrqRm5tnz07ROwqsD4AdfAaD4ziMHz9R8now1yyfPXsRDg6OwmjthL5VBmCfOQNh6yxsAbZ+fdUTxdR0S92Py03m8PKsVKpE96gtD8A9Vgrw5rDQyWRxmBMAx+5MAPGE58c8r57FVD6G8gAcummYAKA74o2MIx4vJrwI4gkXrl7RKGNLqAvqRdWCZZQF/jxsePRWoUhF85KkV4uGcOrYApcFg3jC0E0jqvTsxOrOAPjZ+NYfhwAi3Pv4E6MA+O+oOIAI/4SFSx67Mc9A6n6cialGS+pGbm7OysqFs7OLzlFgXQDsFjIHnI0t7OzscO7cJcnrwVzzvGDBEvVUaN+UoCoBsPMgIenb8OHhbKq+EZa6H5ebzOHlWalUYc3JTSCe8PNvXdWflQfgfmsGgHjCTyldTBaHOQFw39UBwtrc2Z10xlAegBN2J4J4QtD6wUbFMXhVGIgnvD25tWgZ1qOFEeiluw2v5dz7118gnvBcVB1sC3VVxzZx09RqSYQlVncGwM/Gt9dtEYD2u++NAmBV0gyACHf79Jc8dmOegdT9OBNTjZbUjdwcPWfOPHAcBwevYKMB2LFHGDiOQ2LiZMnjZ66Zzs0tRP/+wh7BriN8Kw3AXhP7Cb9vBweWpdxIS92Py03m8PKsVKqw4IiQaMl5qZv6s/IAHLJhqJBAadb/TBaHOQHwL78JU4cHrArWGUN5AE75awGIJ3gs9zYqjtJtlhyWOImW0TniLRBPiF2XYPD+pGwXyv5wVDON2Nbt2wLiCW2SrKr07MTqzgD42fjO/MXCml47R6MA+M5vSwEi3Hd2kyzmijwDqftxJqYaLakbuTn6zJkLwh6sDm5GA7Ctsy84jsNff1Ut4QYzsz4fPHhUPQpcWQB2DvECx3GYO3e+5PUxF0vdj8tN5vDyrFSqMP3AHBBP6LWqt/qz8gA8YstoEE9oN6W9yeKoKgDP6PMj+o74GOtDnUwOwO2nvQ/iCXP2zteIQR8ArzspwOZP87VH0cXi6DCrU8mU6TGiZfQpSYTVc5G/wfszbGUEiCfYh1tpxHbk6Ck0jGsEy2hLXMlVVvrZidWdAfCzsWrSNGFbox5+RgHw7fVbASI8+Ml0szmq8xlI3Y8zMdVoSd3IzdH5+UXw9e0BjuPgNmi2QQB2D/0VHGcDJycn5OZWbcsFZmZ9vnHjNuztHcDZcDqnQRsC4NI9f/fvPyx5fczFUvfjcpM5vDwrlU+n5gavDy3ThjQBOHpHPIgnvJb4usniqAoAHz16Gu+OEKYE20S0MSkAT9iThOfGPAfiCYdTT6jPUSj0A/CfGceEUdgZHxsVR2mW6XWnNouWMSZMAOSOM78zeH8c5juDeMLg4V9oxfbpzM90bs9krMXqzgD42fjv0TEAEW4GhxoFwLf2HQaI8PCzLySP3ZhnIHU/zsRUoyV1IzdXx8ePA8dxcO4TYxCAXfoL2yeNGBEhedzMNd+BgQPAcRy8kvpXCoBtSpJfXbx4TfK6mIul7sflJnN4eVYqVRi5NQrEE0ZujVJ/Vh6Ax+2eJCR9in/BZHFUBYDj1o0F8QTiCRZRhJiAb0wGwBFbI0E8oUl8E43zFAr9AJx1PVdrv2VdcUzYk4T6sfVBPCHr+jXRMpYMEbZVajmulcH7837SByCeMG1oZ63YPJf7gHjCzIOV3xZHrO4MgJ+N7w4MAYiQHxljFADfTM0Qtk16p43ksRvzDKTux5mYarSkbuTm6tWrNwjrJL2DDQKwYw9hn9b586u23QIzszGOi4sHx3Fwj/GrMAD7zBbg2cPDU/J6mJOl7sflJnN4eVYqVQheHwriCQm7E9WflQfgxL1TUDemLiyjLZGXf8ckcVQWgLOv30Dzsc1BPOGzUa+AeMLrEY2RcTHDJADsvaKHsG3UlA8rBMBKpUqd1TknN19vHCO2jCpZm9tGow5ly9gW6opGkbVLIDlXZx3z8u+gfmx9WEQRNoQ6acUWszNeawZARS1WdwbAz8b3vHsARMidNNUoAC7dN/jJyy9LHrsxz0DqfpyJqUZL6kZurl63bnMJAIcYBuCew8FxHBYuXCp53Mw120eOnFJnKfcY07vCAOw7ZyA4WxtwHIfly1dLXh9zsdT9uNxkDi/PSqUKPVf6g3jC9ANz1J+JAXDjMc+DeMKla6aZFVFZAB66aQSIJ7w34mVsDnVB69ECZEZviTYagCO2RsFtmSfid00wCMCdkv8PxBN+SeleYQB+b5owEns484TeunuVQLbzMmedALw91E097Xtb2h6ddSzd57nVyOdEY/v9+DoQT+jy6y+VfnZidWcA/Gz8oLstQIRr8xYaB8BKFYobNECxpSWUeab5Y1Z1PgOp+3EmphotqRu5uXrx4uXgOA6OPYcbBGAn/0hwHIcZM+ZIHjdzzfWJE2fg5uYGjuPgFOQB33mVWwPsHuMHzoYDx3FYs2aj5PUyB0vdj8tN5vDyrFSq4LTEFcQTFhxZpv5MDIBLR1lPXjpnkjgqC8DvJLUB8YS4/t9ie6gb+GEdQDzh2znfGgXAI9fyeHnsy8K05oSX4LHQRy8AvzXpbRBP6L20f4UB+OffuoJ4wqoTG/TW/buUH4Q67Y7TC8A/DXkDxBOS9s3QWcd5hxaCeMLXYS1FY0vLzgTxhDcmvlnpZydWdwbAz8b/ftMBIELO6o1GA/DjV1sCRCi4eFXy+A09A6n7cSamGi2pG7m5etasZAE0/EcbBGDnvmPAcRzGj58oedzMNdOpqRnw9BSyNzsGuuuEX2P3AXaL6iVkOrexwaZNOySvn9wtdT8uN5nDy7NS+RTK1pzcpP5MDIBfnyDA1h/nD5kkjsoA8IlLZ4V1tQnNsNSvG7aHumF9qBNqR1mibkxdZOuZGlxa5sdTPwXxhHqx9dTriBO2ThCNLX7XBNSOqQPLaEuMXhdTYQD2W9UHxBOm7Jupt+7vTBagflPaJr0A3Cv4PRBP6Ld2gM46hm4U9hN2C3lXNLb8/CI0SRBGkrOuXa/UsxOru6kBOC/vDjIzc7ScV02jmuYCwI/avQcQIWvXAaMBuPQ7N8v8NuVoBsBMTCaW1I3cXB0XlyAkweobZzgJVmAiOI7D8OHhksfNXLOcmpqBBQuWwNvbR4Dfvm46sz9XBIB7zA+G20gh07mtrS2mTp2BgwePVtsLVk2z1P243GQOL89KpQrfzBVGTHee2af+TAyArSZag3jChtPbTRJHZQA4ad8MEE/olsxpANgHI5qCeMLioyv0lpm0U/h+/dgGiNgapR4NfyH+BZy9fEkrNsclLiCeYD2lrVZsCoVhAB61LRrEE8I2h+us+/g9k4U1u7wFcvJy9ALw6ID/gXjCdynf66xj6R84hgV+oTO2L2cL19metrdSz06s7lUBYDG4Ld/vZmbmIC0iEjkxCWqnRUQalTTNGJsLAJeO5l48lmo0AJeOGhfu3CdJzBV5BlL340xMNVpSN3JzdHZ2LhwdHcFxNnALmWvcNki29rCxscG5c5ckj5/ZfJ2fX4QjR05h1qxk9O3bDxzHqe3Q29Ug/FYEgHvMD4brCF+NMjw8PTF+/ETs2rUP16/fkvx+yMVS9+Nykzm8PCuVKnww/SOtdaliAPxB0kdGQWVlXRkAdlwibO/Dr43TADCfQe1K9jbWvUduXv4dtJrwGogneK3ooS6zNGOyy1J3jdj49WPw8lgBrPusDqgUAM/+c15Jeb466z5880gQT2g2trkWvJQvY07vziCe8MqEV3XW8/WJwsj9jD4/6oytNLFX2XXgFbFY3asCwOXhVgxsMzNztIEvxris4cbYXAC4uH59FNeqhcyMy0YD8INuNgARbv++TvL4DT0DqftxJqYaLakbuTl60aJl4DgO9h4BGqCrC4A9whbDwSeErQNmrpRzcwuxb98hTJ48FT4+mkBq62wHp2BPeIztrXfac2UBuMf8YHhN7g+XMB/YeTlqlO3k5Izo6Dhs3LgNOTl5kt8nKS11Py43mcPLs1KpUq9pPXP5ovqzGzcKkbg+EQnbJiB+q+DPpnxe5e1y9LmiAJyXfwdNxwlAuvnATg0Am9jvO/W61vz8ItHvrz25GcQTmo9tgQl7ktRlhq0Ox3NjGoN4wjLFKnVsbgu8hG2HJrTChD1JlQLgjad3iI7Ylq176bZEH0392CAAL/PrjpcShPXL53Oytep44eoVYUR7zIvqKeJisfE7xoB4wqANQyr17MTqXlUALgtzYmDLAFilkdFZ9H7oAOB7Xr4AEYrm/ip9HQw8A6n7cSamGi2pG7m5OS/vDnr18gPHcXAJnGQ0ALsGzwDHcXBzc8e1azclrwezvH31agG2bduN+PhxcHV10wBPOw8HuIR6wyuxr9HQWxUALmvvaQFwjegB+57O6kRZHMfBzs4OI0aMxO+/r8WFC1ckv3/P2lL343KT7F+eS1wKkWW35rl6VYmpve2wZICD2p1DWmttl1SdLguBUdtj8V3K9whcHqwTaPae+wvEE96d0lYbDP274bVEYXT3r4zjot8vBc0uyV21oDt6swCEDeMaYdPpnUg7n4FW44XreSz3FoXz8jGIAfDJS+dKtjey0ln3TilCluluKZxRAPztjI4gnrD8mHbG+vWntoJ4wufTvtQb21LFSqHMBTaVenZidWcAbHrfPJ2u3tO3IgB8d8AggAiqcZMkr4OhZyB1P87EVKMldSM3N2/evEMYeXPygvvQRUYDsEfYYti5+oPjOCxdulLyejDLxzdu3MbJk+ewfv0WTJ8+G8OHh8PBUXO01d7HCa7DfeE9pX+lwLW6ALisfWYNgFtkTzj0dlVvnVSaOCsoKBiJiZOxYsUaHDp0HFevKiW/z6a01P243CT3l+dS14uth9oxtTVGSq9eVWJBgBN2DHkKNK6D24J4QsTWSJPEkZ6ejfClkQhfE4kW44T9fJ+PewHH086Inh+9I75kOnJ/LQBb7t8dXkuFEdvYnQla3718PQ/PjWkMy2hLDF0VrgXAGRmX0W9NIIgnNI5/Hi+WJIl6IeFFjNs9qdIAnJtXCMtoSzSIa6Bxv8sCcOvJ7whZppf1MwqA+yzpD+IJw7eM1Kpn/K7xIJ7gucBHb2yKi6kgnmCVZF2pZydWdwbApvetvX8BRHj4+ZcVAuC/R/EAEf4JHy15HQw9A6n7cSamGi2pG7k5OScnHz4+PiXJr8aIQq4+AHYJnCiMHLu4/idHyZhVuHw5DwcPHsXSpSsxdmwiBgwIgr29gwbschwHzoaDg58L3Eb1hPeMwCrDqikAuKx95w6Ee6wfHAPcYeNgq1UfGxsb+Pv3QXR0HObPX4hdu/YhI+Oyzima5map+3G5Se4vz0qlCldvFJRs/9NE83MRAO456H0QTxiwbpBJYlEoUjHD50e0jXgJxBMsooSMzG6/eYqe/8P8n0A8YdHRFaIAnLI/BcQT/jfnG63vlq7F/XZ2R53TrvPzi9RrY4knvJXYGsHrQ3VOzy4fgxgAK5UqtCoZmS47ZbkUgMfvmYx6sfVgwVtg1DreKACevGk6iCf8/FtXrXp6rfAF8YTINbF6Y8vLv4P6sfVRO6Y2rt+oeF4DsbozADa9b6/dDBDhQeefKwTAqsQpABHu9h8oeR0MPQOp+3EmphotqRu5OXnKFGEas52bv+joryEA9ghbDHvPAeA4DnFx8ZLXh9l0zs8vwrlzl7Bt224kJ/+K0aN59OzZSxt0S2DXzssRToHucI3oAc9xfeAzZ2C1AqqpAVgDhlOC4DWxH9wie8J5kCfsezjDxt5GtO7u7h4ICxuOadNmYf36LThx4gxycwslf34VtdT9uNwk95dnpVKF8znZonvAigFwYNAnIJ7g83svk8SiUKTCY5Awyvza6MaYHPYj6kRawoK3wObUnRrnZl7JQZ2YOqgXWw9Z166LAnD6hXT19j7HLqZpfL8Unidsn6x33fGNvNtYeGQ5th/ba3B9cvkYdAFwacbl3WcPPK1PCQAP2xwB4gktxr+C2I3a8FK+jGV+3bHt4F5hLfP4Flp/TPt4hvDMFu9aYTC296YJyb/+zDhWqWfHAPjZ+878xQAR7ru4VwiA78xbKHzP3UvyOhh6BlL340xMNVpSN3JzsUKRChsbG3A2tnAbNEsn4BoCYLfBKeBshRG/vXv/lLxezFX39es3oVCcxurVG5CUNA2hoUPh4uIqCnw2jraw7+UM5xAvuEf1gtfkfkZlbjYnABaF4nlB8J4WAPdYf7gM9YZDH1fYutqL3iN7e3sEBg7E2LETsGTJChw4cASXL9+Q/Dnrs9T9uNwk95dnpVKFoxdOg3hC+2nva3wuBsBDB34B4gl2ixxNEotCkYr2I4TR3zFhnbA91E0NxB/P+EQD7ib9MQ3EE7iFdurvlgfgrKws9FzprzVt+8+MY7DgLfDcmMY4de68UYm3jEnQVT4GXQBcmrl6wZFlWtcvXZf82awvjAZghSJVPap8Kuu8+twbebfVo7p/Hj5mMDaHxU4gnvDr4SUa8RqzJZFY3RkAm96qiVOFkdx+gRUC4NurNwojx790k7wOhp6B1P04E1ONltSN3Bycn1+EkJBQYZ/VniN0wq0xAOwRthhOvXlwHIfevfuwfVXNzLm5hVAoTuP339di7NhE9O8fAFtb7Sm/HMfB1t0Bjv3c4DLMBx5j/OE9PbBSSatqAgDrss/sAfAc1weuET3gNMBdyDRtKz5a3KNHT0RFxWDBgiXYv/8wcnLyJf89lFrqflxukvvLs1Kpwu5zB0WnCYsBcGTgNyCe8P28H00Sy/5Dh1ErygJ1oiyxIdQJ20PdsMivK1qOawniCSuPr1ef2yG5E4gnzD+0GEqlbgDelLqjZN/ed9UA7bHcWz2V29jM09UJwAPXh4B4Qvyu8VrX75gsZK+2X+xUIQDuvtAWxBN+O7xUfe6fGcdAPKHd1PZGxVY6+lx+jXdmZg4ilkUiZkMCYjYkIGKZ9pZEYtdnAGx6/z0qWljLO3xkhQD41u6Dwtrhr76WvA6GnoHU/TgTU42W1I3cHLxv3yFh9M7eFe5DFlQZgN2HLoKtkzc4jsOmTdslrx+zuPPzi5CWloH167cgKWkaBg0aDAcHkfW6djaw93GC00APuI3qCc8JfeE713RTmGsSAIvZNyUIXpP7w53vBefBXnDwc4GNo/i64r59+yEhYTxWrFiNI0dOSTZ9Wup+XG6S+8uzUvk0S3DnX3/W+FwMgMf2/65kdPJzk8SStHmGMNo7qoUGpIWtFMCsY/J3UCpVSM3OgAVvgcbxz+NKrpBYTqEQB2ClsghvTHxTPeX45KVzqB1TG/Vi6yEtO1MSAE7YnQjiCYHrgrWu/1ri6yCeELw+tEIAPGobD+IJIRuGqs+d++evIJ7gtMRVNLalfl2xe/dB9ahu0k7h/jsvddNbd7H7I3Z9BsCm992BIUI254TECgHwzRNnhezR77aVvA6GnoHU/TgTk6R655132lhbW/9pbW2dYWVldcTKyqqd2HlWVlactbX1+ZLzVjVr1uw5Y64vdSM3Bw8fHi7se+ofqRdsjQVgj7DFcO4XD47jEBAwoMYkAjJ3Z2bmYPv2vZg9OwXDh4drbT+kXq/r7QSnYE+4RfWCV1J/WYzq1iQAFoXieUHwnh4I91h/OId6w76XM2zstaHY3t4ewcEhmDx5Gtat24zTp9OfySyL6u31zV9yf3lWKlVYfHQFiCc4LnHW+FwMgKf0+7FKmYIN2XOBMP3Xf8SHGoB34PBRNB3XDMQTtqXtQcxOIfuz+/Kn6xcVCnEALihQIXRjGIgndEjuhK4LOBBP6LHSX93fPWsAXlRyz+0XO2pcP3xNJCx4C9SPrY9xeyZVCIBhJ8trAAAgAElEQVRXndigtb+w36o+QgKs7bGisS307oKA6cHqkd0BKwaBeML7Uz/UiLc0O3fpeeFLI5GerrnnsNj1GQCb3ur9fGfPqxAAK7NyASI8bt5C8joYegZS9+NMTJLK2tp6d5s2bXyIiKysrJysra2Plj+nRYsWjaysrPJat25tRUTUpk2baVZWVuONub7UjVzuPnLklDDaZOcE9yG/VRsAuw9dCBsHd3Ach1279klez/+ac3LysG/fIfz222JERUXDx8dX9zTmQHe4juwBz8S+8E2W78huTQZgnVA8pT/co3rBKdgTdj5OotOnXVxcMWzYCMyaNRfbtu1GRkb1vCSWdfX2+uYvub88K5UqzDooZEr2/d1P43MxAE7p84s62ZIpYnlnYhsQT5gxtIsW4I3cGqXOVv3cmMYgnrDi2Fr1dxUK3QB8OPMEasfUVmdztoy2xNELp6FUasOdLsCrTgDec+5PYW/eWV9qXN99obd6PXbi3ikVAuALV6+AeMKLCS8iP78IV3KVeDHhRRBPOHLhlE4ADpkXpq5Pwu5EWPAWaBTXSOMP0gpFKlK8u2BVoANWBTogxbuLVp3Eri8XABZbwyy2jrm8zQGAH3SzAYhwe8XaigFwfhGK69RBcd26UMp48IEBMNN/Wm3atGlmZWV1h4gsSz+ztra+YW1t3brcec7W1tZbSv9tZWXVzsrK6qoxZUjdyOXuuLgEYe1vr3CDUFsRABbWAkeD4ziEhQ2XvJ7/FV+9qkRISKiQ0Kz8lFpnOzj0cYXrcB94ju0Dn9kDJIc8BsAVhOLkIHgl9oXbyB5wCnSHnYfIlPWS9cRHj56qtt9VtXX6NURyf3lWKlUYv2ey6NZGYgC8qHd31IquhQZxDao9jtTsDGHf39F1sTXUVQvwLl69ipYTWqkh9p2kNsjNezrVX6HQDcBKpZDsK2F3IuwWOWLMrnEa3ysLd7oArzoBOPNKDognNB3XTA2amZk5+GrG1yVJxhwqDMBKpUq9f/DWtN3qbZ46JHfSGVt5AE7cOwUvJQhJyE5eOmdUmfrqLhcAzszMQVpEJHJiEtROi9Bex1ze5gDA/37TASBC4Y4/KgbAShUeN28BEEGZLd/kigyAmf7TatOmzafW1tbny35mZWV1pE2bNv9X7rxQKyurWaX/fu211xpYW1s/ojLgrEtSN3K5uzT5lWvQtGoHYLeQZHAcBz8/f8nr+V/xhg3bBAiyt4FDLxe4hHrDI86/2vfalbNrMgCL2WfOQHiO7QPX4b5w7OsKG2c7cByHsWMnVNvvqrr6/Joiub88K5UqjNoWDeIJI7aM0vhcDIAX9+bwQrwwqnjtxs1qjWPa/tkgnvB1WEudAHntxk2cvXwJqdkZWuUrFPoBWJfLf08ntFYjACuVKrRJshLWJZ87qL5+s7HNQTxhyMbhlQLgEVtGqUeQv5nbAcQTZh1M0RmbGAC3m/yeVoZqfWXqq7ucALgyU6XNAYAftXsPIMLNo6crDMCP3m0rfPfkuWcet7FmAMz0n9azAuCCAmZdHjQopASAp1c/AA9OAcdx6NXLT/J6/lccHR0LjuPgFtVLcjBjACyNvacHluxB7I68vNvV8ruqrj6/pkjuL89KpQohG4YK2w6VGRVVKnUDcKvxwnY753KyqjWO0u1/+gZ9YBRAlrdCYV4AHLguGMQThm8ZCaVShYOnj4J4QqMxz2H8nsmVAuBrN26i/bT31aPkLyS8iJzcfJ2xiQFwl+SuWgm69JWpr+4MgE3vx6+8ChCh4OLVCgPww6++Bohwa498t6Jk/68w/adVwSnQW8v8u72VldUVY8p4/PgJmHQrNLRkBDh4hlFQa+/RH/Ye/Y06133wvJIRYD+pq/mf0IMHD+Ds7AzOhoPPrJo5vdkYO/Z1g2NfN8njkNJ2Pk7gOA6nT5+ult9WtXX6NUSAvF+elUoV/Ff3BfGEqftnaXyuC4DbTm4P4gmHM09Uaxyl4Da+XyeTAbDYWtDduw9imV+3Zw7A605uAfGET2Z+CqVShYk7poB4wkczPlFfv6IArFSqsPvsAfV6Z//VffXGJgbAvZf211qfbKjM0nPWePyEC1+/jysftMa1997GyS/aIvvUKQbApnJ+EYrr1UNxrVpQlvy2KwLAD37pJqwfXrNJ+rroeQZS9+NMTJLK2tp6j7W1dQ8iNehqJcFq1qzZcyVJsKxLzqtQEiypR+Xk7IEDgys0AlwRl44A+/r2kLye/wXv2rWvZDsrG7iE+cAjoTd85ph/Uitm4+ybEgTPxL5wjegBO09hbfCUKdOr5bdVvb2++UvWL88ldl3mobGfbql1AfAX078E8YQdZ/6othhycvNRK7oW6sfWxxIjYFTMCoVhAC6/n23MhgQEJAVhoW8Xg2UakyyrfAz64s/NK1QnqUq7fAGOC12ELYiWuFUJgJVKFWJ3Ctsplf0jhVhsYgAcuS4WdWPqoU5MHZ2jx1plXruJq4FBeFinNkCk4ceNG+OfYeFQXqvYlHkGwIZdkHUdIMKTZs1111MPAN939wKIcGfeIsnrou8ZSN2PMzFJqtatW1tbWVn9ZW1tnWFtbX20TZs27YmIrK2to62trfuWnle6DZKVlVWmlZXVmqZNmzY25vpSN3K5e+zYCeA4Dg6+Q6odgB39RoLjOEREjJK8nv8FHzp0HPb29loJkew8HeA0wANukT3NclsjZnH7zAiEe6wfnAd7wd5XPEP0ggVLquW3Zar+31wl55fnUv/yWzcQT1h9YqPG57oA+Ic5P4F4wqoTG6othi2pu0A84cMpHxsNkOWtUBgHwOVHcUPmhhkFwAqF4WRZ5WMwFL/jEpeSLahcYBltCQveAhFbI6sMwMbcn+2DXbHW9lss7NMdyycHaYxqfzbjCxBP2HBqm8EyCy5cwb8dOqmBN7ftGzjd9X843e1rXH2zhfrzhx99gptHjE+4xwDYsG8ePS3s5duuve566gHgu/0HCnsIT0iSvC76noHU/TgTU42W1I1c7k5Ly4StrS04Gzu4hSRXG/y6h/4KGztHcByHQ4eqd0ods25fvnwDe/YcwNy58zF8eDhcXFy1s0E72MLBz4WNEpuRy47uOvZ3g62LyB867OwQHByCKVNmYNOmHVrbvVTFUvfjcpOcX55L/b853wjJmM4e0PhcFwDbzncA8YR5h6pv1ChhdyKIJ7j95ilrADYUmzHnlPWcP+er1+sST/glpbtGbKYC4H3+HO6+8JzGSG36dx9j5upYxGxIQJ/fA0A8YdQ2Xm+ZN0+dx6N27QEi3G/1GrbZdtBaA3xt0yY8attOGKl8rjGKxk5EZnq23i2JCjIu4/L6rbgWOhyqhAkMgHW4cOtugAj/duiku556APjvUTxAhH/CR0teF33PQOp+nImpRkvqRm4OHjs2URgF9hlcfaO/vcJLRn/l2wH/F5yXdwcnT57DqlXrMW5cIvr27Se6bY6dpwOcBrJRYrlYY3S3h/jorre3D6KiYrBw4TL89dcxXL9evdl7y1rqflxukvPLc6nbTRWy/ioupmp8rguAvRb5gnhC0r4ZVe5zSuHHqWT677ClI41ajytmhcL8APjC1Svq9bpuiz0RvT7e5AC8M8gJRc1eFEb+nmuAY1+1w73nGwJEuPtCI8ydNBCz9whbKHX59RedZaau3YzHr78hjO5+8ilObt+rOwlWTh7u+fqpYftuq9dQ4OyGq8NH4kpEFDL79Ede7Fg8sLHH41dbaoB5sYUFHr3dGre62eDSYc0/klcFgLP5MVAoUvWDuMwB+M7iFcIfH+wcdd8PPQCsmjRNeB59AySviy4zAGZiMrGkbuTm4HPnLsHOzg6cjS3cgmdWfe1vSDJsbIU1iArFacnrx6xpY0eJ7Xs4wynIE24je8BzfM3dN1hK+yYPhNfEfnCP6gXnwV5w8HeBbck2Rs9qdNcYS92Py01yfnkudatEIatz5hVNaNAFwP2WDwDxhOgd8VUqt+x63BbjXgHxBNeJnkbBqJgVCvMDYKVShcjtsei50h9n0y9qxVbtANyrG3LfFaC1qNmLWOL+I0LmhWHm6lhkdvhAgKmG9XB20QIQT2iS0AR5+Xe0ytzs0An/Nm0mjD5+9z2U2TegUKTCe1A7fDOyFdzD2yF2WEetZ3B75Xr8+8abWuuEy/tJs+a49/EneNisOYrr1n36eb16uOfdA7f2HVY/z8oCcGp4JI4OHqZ3b2C5A7AqaQZAhHv+fXXfDz0AfGfBMuGZO7pIXhddZgDMxGRiSd3IzcVTp84Ax3GwdfSEe+ivlZ/6PHQBbF16gOM4xMZW7UWK+dnY2FFijuNg42wHBz8XOId4wS2yF7wS+8J3LptCbRB0U4LgldQf7jF+cBnqDce+rrB1d9B5n5/l6K4xlrofl5vk/PJc6kZjnoMFb4G8/Dsan+sC4LDV4cJetZuGV6ncUhhN2DUBlrwl6sTUQfCcIf85ANYXW3UD8F/ffSxAa/262OfPaSbB2j0Zp7sL2+I8adgQgX4tQTzhQPpRjTJP2nyrTnb14JfuUF4tgFKpwvBVIzWmcxNPiAz8n9YzuJCagZt2jnj44cd40qgRnjR+Hg9atkKRvROKZibjZlqm+n7kxCRANXYi7vr1xT8ffITi2k+TbP3b6Xtcn5mMnMjYSgNwenik3u/KHYD/HhUtTGEeFqGznvoAuHDTTuFe/t8PktdFlxkAMzGZWFI3cnPx9eu3EBQ0CBzHwd4jAO5DF1UKgB18hH2F/f37ICcnT/J6MVfOV64ocfjwCaxatR5TpszAsGEj4OXlrRPYbN3s4dDHFS5DvOEe4ydMo075702j9p0XBO/pAfAY4w/X4b5wDHCHnZejsDexyH1zcnLGoEEhGD9+IpYs+R379h3CxYtXJX/+5S11Py43yfnlWakUMhETT3g+/gWtY7oAOHrDGBBP6LO6f5XKLgW+oPWDQTzhzUlvGQ2jYlYoGADr87FDx/F3Y2Gq80nuG/Es0Lsn4wj3tRoyp31BGDHXF4qjp7HFviOufPiO+pjSwRnK67egVKqw5OjvsOQtYRFF6DviY3SOeAvEE74e9qroM6jMqG1OTAIuHTiKfwYPxZOXXno6Kly/Ph5+/iXu+fSCKnbsfwqA7wYECVPZx03SfW/1APCtQ8eFKewffCR5XXSZATATk4kldSM3J6enZ8PDw1N4MfcbWWH4de4To36pP3nynOT1Ya5+X7p0Dfv3H8ayZSuRmDgZISGhcHFxEQdjG2FtsWN/N7gM84FHnD+8pwXUmPXFPjMHwHNsH7hG9IDTQA/Y+zrBxl57rS7HcbC3t0dAwADExSXg118XY+fOfTh37hLy84skf6bGWOp+XG6S88uzUqnC+ZxsEE94feIbWsd0AfCkrdOENavLPKtUdinwOSx2BvGEDsmdGACbEICzw0cLW960eAnbB7uKA3BJFugbYyficYP6ash81KDB022NatXC4Y4fqsvckroLDeMagniC76D22B7qhpWh9qgTZYlaURY4cvZItQGw+pzLeVBNnIp7n3+JYguLp+uFLS1x/623oRwWjlv7j0BZ0m/WVAC+7+pRso3RQt33Vg8AF2TmCM+0ZSvJ66LLDICZmEwsqRu5uXn//sNCVmjOBi6BE42GX9egaeBsbMFxHLZs2SV5PZifnfPzi5Ceno1du/ZjwYIlGDNmLAIDB8LeXnyKr429DRz8XeA6wleYQm0GQOw7LwjeUwPgNronHAPcYSOyTpfjONjY2MDfvzeioqIxd+58bN68A6dOnceNG7clf05VsdT9uNwk55dnpVKFwxdOgnjC+9M/1DqmC4Dn7vpNyFi8oHuVyi4Fvi9mf1UC1F4MgE0FwDn5+LdZc4AIxxy/U39XFwBnZubg1l/Hcfibt3HpxZLEVQ3r49IX7bDfr7u6zD/OH1LvZey5wFcjrp9KRoHD1odVPwCXOefa0HDcd3TBo/bvo7jO0/XCIMLj117H3T79kbN2E3Ki42scAD/4sTNAhNvrtui+t3oAWJlfhOJatVBcv776jwVyMwNgJiYTS+pGbo5etGhZCag4w21winFbHjl6gOM4zJgxR/L4meXhvLw7SE3NwNatu5Cc/Ct4PgZ9+vSFjY1NOSC2hUNvV7iG+8JrUj/ZALH39AC4RfWCU6A7bF20gdfHxxcjRozE9OmzsHbtJigUp3HtmrRrdU1lqftxuUnOL89KpQo7zvwB4gnfzu2odUwXAC/bt0rndyriUuB7dUJL9ZriGgHAvbphV5CTepRVDgD8d3Q8QIT8V56O/m4PdcNvXp3Rf2oQYjYkqB2+NFKdPC/t8gU0jGuIRhGEgIEfYGOoM7aHuiG5d2cELB2IJglNQDzBY7k3jh49rRHX5LAfQTzhtcTXkK+8o1HP6gRgjWslJCLfpxcKfXvh8Vtva8Dwvy1ewX0Xd6jiJ9QYAH748ScAEW4dOKr73uoDYKUKT0r+MKLMypW8PmJmAMzEZGJJ3cjN0fn5RRg1KlLIPuvWx+B6YHuvgeA4DiEhocjNLZQ8fmZ5+/LlPOzY8QemTp2JwMCB2kDsYAuHPq5wjegBr8nPbksm7xmBcOd7wWmAB2xdtffZ7dmzF8aOnYB16zY/8yzMUlvqftxUsrKymmJtbZ1tZWVV/Pbbb39o7Pfk/PKsVKrw+/F1IJ7QbYGN1jFdALzprx06R40r4szMHIxeF6NOgDVuzySzB+Dj+w8j/5Wn61P/fvl5bHT+P0kBuCDrOp68/DJAhJ3dvtaIf5bL95jm2BGrAh3UTvHuonG9YZsj1EmtGkbVQePIuhqJrpyWuCI3r1Arrm2hrngz4nkQT1h5Yp1GPU0GwOXOu/XnMfwTOgwPW732dJp0w4Z48Es3nAkdrgXA5bdGunAhB0+ePJFtGy7dhqrgXJbu+2EAgEv3cb55LE3y+oj+fhkAMzGZVlI3cnN1VtZ1+Pj4lKwHHqVn3W8sOI6Dq6srzp27JHnczObnrKxcbNu2G0lJ09C/f4D2tGJHWzj2c4PbyB7Vukexz8xAuEf7wWmgh2hGZh8fX4wZMxarV2/4z/+2pe7HTaXWrVt3eP3111taW1tn1SQATvlrgXoEr/wxXQD8h+IQiCe8MemtKpWdmZmDPssC1AmwKgKjYlYoJAbgvDso/L8fhKm3tWvhce1aApw0fxGKwycNxp+eno3wpZEao7ERyyJx8+bNKgHwPyNGCetEv+mgFf8sl+8xz+17vXXPy7+DMevH470RL6uht0Fkbfw052fsOPOH3vvjEfIuiCcMXD9I4xkYA8Dp6ZeRHh6JvJgE5MUkID08Eunpl7V+Q0bBdHo28r164JH1u0/XNdevj/wfO0M1dqLOrZHSRkbqhUepXdygAYotLaEsWTpTGQD+99uOABEKt++VvD5iZgDMxGRiSd3IzdkHDhwpGZ2zgWvQNO39fgfNBmcrjJRt3crW/TJXjy9evIYtW3Zi4sQp6NNH95ZM1W1PTy/ExIzB77+vRVpahtkkqHoWlrofN7Wsra2zaxIAT/xjKogn9Fs7QOuYLgA+fioNxBNeTHixSmVnZuaAm2ernk5t7gB8d9AQYU/WBvXwRx8b7Axywj8vPgcQ4eqAQUbFn+LdRWM0dp5PF806ZN9AQXdb3GtQDwVvvoLMbz/AynIjtmVdkHEZTxo/DxDh7KLlRgHwkl7dsHv3QfUoaGZmDnbvPoilfl2xOtQBG0KdjL4/sQHfgHjCJzM/1XgGxkCrQpGKRT5dsDHQARsDHbDIR7uelRlN/idkKB6+977GnsN3+waKTos2BI+SOvuGEH/Tpvrvh4E63Ld1EP5AsnSl9HUS+w0zAGZiMq2kbuTm7tmzU4Sp0O59RaY+B4HjOIwfP1HyOJlrri9cuIKNG7dhwoRJ8PfvDWdnl2qxl5c3eD4Gy5evxqlT5xnw6rHU/bipVdMAePT2GBBPGL5lpNYxXQCcmpqBBnENYBltWaW2kJmZg0+nfa7OKG3OAFy4dbcAI/XqYbvNt+rjh91/xBMLwpPatXFr3+EKxV++DjcVqXjU7j2Nda0gwu0mjXF6/VbRa97tP1DYr5ezE72+GAAne3XBgYBg9ShoTkwCdgUEY6F3lwrfn8X+XdEorhEsoy1x6do19TMwFoDLXk8MzBWKVFwul9zK2KnSF3164d4rLZ+uEf76W5wZMsJsAPjmsTRhJPvdtnrraagO93r1BohQNG225HUSMwNgJiYTS+pGbu7OycmHm5u7MM154FTN0V/OBg4ODlr/KTEzM9csS92Pm1qVAeCbN4WXODl60IYhIJ4Qv3u81rFr154CcKkX9+aQlpaBFuNfAfGE7OvXK132hQs5aDFOuM7QTcMx8Y8pGJwchkW+XTTKXO7fHceOpRq83rFjqVju313je1lZWRr3/8KFHMRuTMDEP6aobWyZ5a+vPk9xGg+//lYY6R0YonXOuQ9aA0S47+pRofg16pBzA49fex0gwj9t22GLYyccd+qEOyXrjR82aYLb67dqXK9ozjxhzWudOig8eFT0+rNdBQAu+1mKdxecHhwG1cQpah8dHIZFPl0qdX++m/sdiCcsVaxUP4Oc2ASN6+fEJuDChRy910vx7oIDgcHIiU1Q+2BoGDIiIg1eS6zM1IhIZISPxn0nFxTXFbJHP3jpJWT36a8+50oJPMqxDd/esVd49t900FtPQ3W4O3S48LuKHiN5ncR882bN/3+FiUlSSf3iWBM8f/5CrVFgB+9gcByHpKRpksfHzMxsWkvdj5talQFgOStgk7AGd+GphVrH7t+/jwUBTtgX4aX274H2uH79Ot6b8Z4AwLezK132deV1dQKsmUdnYs6xORi5ZCSW9+6qUeaaQDtkZWUZvF5WVhbWBNrp/V5hYSEm7ZmEOcfmqG1smeWvX3rejeRkYRTx9deRfe6c1jmbe/6M4tq1gfr1gcJCo+PXiIPnhTI6dNAo40CYG662bqneA/fekCG4c/o0/l6wQA11+O03nddP8foJi3x+0vhsoV9XXBg1CpgzR+20kSOx3K9rpe7PsA3DhO2QdoSpn0HhpEka1y+cNAmF5e5N+euJxZUVF4ercXEGryVWpsZ34+KAd94RRvHr1gX69dN5Ldlo82bh+To7qz8y9t5qaNo04TojRjyDoCsnU/bpTEz/eUn94lgTnJOTB1dXV3AcB1snb9g6+YDjbGBvb4+MjMuSx8fMzGxaS92Pm1o1bQTYYYkTiCcsUazQOqZvBLhDcicQT9iWtrvSZa88tE6dAKuio7FilmIEeEWvrrj3tjDCq5o2S+coaGHJfq1/x401Ov7S7+b8+SeKGzZEsYUFCvf+qXXeIu/OWG3fQZ10q6zvho3Qe32xEeBkr59wODAI+bEJah8ICMIC78qNAK89uhbEEz6e+YnOUUpjR4DLj0ynRkRWbQS47HfHTUTBZ1+o7919B2dZjwCrps0S1pz38tdbT0N1KJr7q1BfL1/J6yRmNgLMxGRiSf3iWFO8YMESraRBU6fOlDwuZmZm01vqftxUsrKymm1lZXXV2tr6obW19Q0rK6tMY74HyHsN8A/zfwLxhA2nt2sd07cG2GGxAM6Ljq6odNlRm2I1EmBVdg1wwaVruJOyAJdiE7DZoRO2h7g8szXAR779QFiH2f59KG/c1rlOOH36XOE863eh1LFuWuy7y/27Q+UgJCm6591D9LyF3l0QMi8MC+cMxbX338bNN1sg64PWKBgyXKMsseuLrQGe5fI95jh2VCef2hjogKn2HTTOM/b+LPfvjsyLmWg05jlYRlvi4tWrlV4DnOzVBadCwjS+Z8xevqXPvXyZYt9NDY/Eja4cii0shPXVP3ZG4a1bsmzDf0fGClOXh47QW09Da4Bvr9ogTP/+pZvkdRJt3wU19/8VJiZZSOpGXlOcn1+E8+ezkJaWibS0TJw7d4klDWJm/o9Y6n5cbpI7AH82S0hCte+8doImfQDcd40wdXrSH5Vf2uK80FUjAVaFADi/CLdXrseDrhyK69XTGPW817gh/vLuYnIA3hXkhHsNhLJvL18jeo76WkdO4VHJSPHtDdtE74fYd7c4dhKm5TZ6DgVpF0TPKwXgsnWK2WAYKPUBsKHPKgLAWVlZ+HF+Z2GmwdHfRSGt/P67ZTNPP2sATg+PxD2fXiiuJYyo342MlGUbvjtgkDDzICFRbz0NAfCtvX8Ja4m/+EryOomZATATk4kldSNnZmZmNndL3Y/LTXIH4DZJViCecDorXeuYPgAeuTUKxBPCt4yudNltk9qDeMKQjcONg9ErShRu2YV/wsI1siEX162LB11+QUF3GxS90EgYaa1bG3t/+arSALzUr6voVkDL/Lqpz8n8RthKp+jL/6lHWhUK3ZmiS0fs7ju5it4Pse9mWb0mQFjIUJ3nmQMAR5ZkGw9cF6wTRsvuvyuWefpZArAqcQru+vdTQ7Bq0lTJ22p533f3En5/yb/prachAL6ZmiG0mdbvSF4nMTMAZmIysaRu5MzMzMzmbqn7cblJ7gDcbFzzkmzON7SO6QPgpH0zQDyh9+p+lSo3+/oNWEZbom5MXYzbM0kURneEuOCYYydktWmFBy1bqaellvrR262hSpiAgotXoVQKALa8ZzfkfNQGIMJjSwvkLltWKQBe6N0FAdODEbMhQe2ApCD1eXv72+FRndoAEc4sfjoNXKHQDcAF57JQXLcuiuvVQ0F6ttY9Kf/dP/ra4rGlBYrr1sXNMxd0nmcOALz9zB5hHfCMT4yG0SMhYZICsCpxCgpc3FFsYYFiCwvcWbBM8vZa1g86/yzMKFizSW89DW7ldO2mMMvghart620qMwBmYjKxpG7kzMzMzOZuqftxuUnOAJyfX4S6MXVRJ6aO6DIVfQC8+OgKEE+wX+xYqbLXndwC4glvJbYWhdH9ft3xT5PGGsD75LnGePjZ57g7aAhur9sC5Y3bGtdUKEoAbLArLn7VXoDkV0WC2mYAACAASURBVF7BzYynsFkRAC4PlerzBrvihrWwJVH2Oy01QFAdg47p1PcdnIRkWNHxWvek/HezvmgrjD66umr8hsqfZw4AfCO/UL0O+PiZNLMB4JzYBNwdN049Dd3QXs7P0g8//Qwg0oipUgCsVOHJ8y8ARFBevyV5vcqbATATk4kldSNnZmZmNndL3Y/LTXIG4JzcfBBPaDquqehxfQC8PW2vOoFVZcoetS0axBM6zPpOCzI3cv/Dg4b1Bfh7+QUc/6o9Tm/crjN5VKkVijIANtgVua81E5L7dO2u/m51APCZzp8DRPi3QT2sdf+xQgB8e62wdc2jd9po1afsd3cNdMTDenWEvYV37DB7AC4oUKnXAc/ZO9+sALiwsBD3evUWZhW8+Zbo6L0UfvzGWwARCs5c1FtPYwD48VtvC9dKu2DyuCtqBsBMTCaW1I2cmZmZ2dwtdT8uN8kZgNOyM0E8ofVk8bV/+gD4xKWzIJ5gPeXdSpXddQEH4gkeC701wC02rjf+LZlanNfmNewMdjaYBbrUCoUmgK31/AmPmjYVQHrsRCiVVQfg9bbfqLcbOm7fUSu28jFowWJ+ER61sdKaulr+uxkdPgSIcO315lrrmMuXYS4APLpkHbDf8j5mB8AFubfw77cdhT98fP8jlHl3JG+/Txo9J4za5hbqracxAPywZPsnOY1wl5oBMBOTiSV1I2dmZmY2d0vdj8tNcgbgA+lHQTzh05mfiR7XB8BXcpUgntAkoUmFy83PL0Lz8S1APCFsdfhTcNuThHPvCSNR19u+qd7OqLIAvNy/O3IXLBASZdWrh1t/HKoSAE8b4o779YVR2cufWovGVj4GsXP+jokXkmHZOojGvzvQQT36u9322xoDwKWzBt6f+oH5AXCBCgXns/G4ZSthCvuoaGnbb06+MC37pZcM1tMYAH7wc1fRP8rIwQyAmZhMLKkbOTMzM7O5W+p+XG6SMwBvOr0TxBP+b94Posf1AbBSqcLz8S+AeML1GxVbN1g6etxqwmsaMLou2k/IeNygLnYPcNALW2JWKLQBOCsrC3eDQoRpx++2xYXT6RUG4DkrePzl8zMeWwpJuG5Yv44dwc6VBuCCjMsobihkqy7cvlfru6Xrl/PatBLdyql8GeYCwLl5hXhuTGNYRlsiNVo/eMoRgJVKFQo3bEdxrVoorlULhZt2StZ2b544K/ymrawN1tMYAL7n4a2VUVouZgDMxGRiSd3ImZmZmc3dUvfjcpOcAXipYiWIJ9gtchQ9bgiAW09+B8QTUrMzKlRu8l+/gXhC1/mcGkanbBkPVbMXASLs7/iBXoDUZYVCHIALrt/Ew08+FaZC/9wVY1bH6gXgvf3tsK/jh9jT5XNce/9tdfbpJxaE459aYftgV52xlY9BV/x/j4wS9l79/EuNLZTWePyER3Vqo5gIB31/qVEArFSq8NP8LiCeMD/a1ywBuOyze/z6Gyi4dE2Stlu4ZZcwHftbzTX4lQXguwNDNJYKyMkMgJmYTCypGzkzMzOzuVvqflxukjMAzzyYDOIJvr/7iR43BMBfzfkaxBN2nz1QoXL7rx0o7CG8YbQagPf35gAiZLR9Awt9OlcvABeocPPIKTwpWQ+c9UFrTN04VgOA1zh0QOY376OoBMLL+n6jBjjFfYOJET669yjWEYPO+K8o1UmMimYmQ6lU4fj+w7jx6svCFPB2b4oCpFgZUgDwkl7dRPdJXurXVS8AR26PFX5z/P/MFoCVeXfwb4dOABHu+fSUpO0WJf8m/DbdPA3W0xgALt2j+p+wcMn7pfJmAMzEZGJJ3ciZmZmZzd1S9+Nyk5wBOGF3IognBK0fLHrcEABzC+1APGH5sdUVKveL2V+BeMKKP9cgZkMCJu2cBFVTYRuWSSO8DEKmLisUugFYqVTh5uGT+Pf1N4TM0I3q49JX7XG28+fIb95EA3gf1quLy280xxr377F06iBM3p6oc6p0pQFYqcKdX5cII8uNn8fd/gOhKhmlvvtCI/zRx0bWAJzs1QUHAoKRE5Og9q6AYA1gFYv/8IWTsOAt8BxfD1cnjJU9AGdHj0FWVhYuXMjRgH3l0dPqJFS3l6955m33b36MAKyhYQbraQwAF02dJQB9T3/J+6XyZgDMxGRiSd3ImZmZmc3dUvfjcpOcAXjEllEgnjBqm3hCH0MA3GtVbxBPmLp/ltFlXr1RgHqx9VAnpg5On01H+NJIrBniBhDhSts30H9yEBb4mAaAlUoVLv11DJc+aqOe1lzqe/XrIuejNjji9gO2h7jo3we4mgBYmV+E++5eGnEUPf8UfuUOwOVhtDyw6or/xxRhGnR8vL3sATg1IhJnR41CTuxT0E+LiERmZg5UiVOEqdCvtkTBhSvPtO3e7dNfmLI8carBehoDwLdXbRD+MPRzV8n7pfJmAMzEZGJJ3ciZmZmZzd1S9+Nyk5wBOGBtEIgnTNibJHrcEACHbQ4H8YTI7bFGl7n+1FYQT/hqztdQKFKR4tUZt0qmHR/q8gUm2nfQgq/qBODSLNDTNiRgzZg+2DTSB3FxvbWmXZsagPPy7qhHE7O37kahf1/k/d8PWOv+g0GALF+GuQHwkoO/g3jC69FNUDhhkgZ4nh8fjZHx3TBozA/YNThIFgB8NS4Oqoki188vwr/f/yg6FdnUftCVEx19riwA3/rruDD74YOPJO+XypsBMBOTiSV1I2dmZmY2d0vdj8tNcgZgrxW+IJ4w989fRY8bAuDxeyaDeELA2iCjyxy2OQLEE0I3hkGhSMXO7l8LI7CNG2JHiIsofJkCgCu7D3B1AXBmZg4ilkUiZkOC2gFJQVrXr4kAnJFxGR/yrUA8YWaCB1SJU7A1PBhdR7eHJW8B4gnEE5pFPoehfT+VLwArVbh56jyePC9M37+zaMUza7sPP/xY2Lf3oMJgPY0BYGX2DWE6ftOmkvdL5c0AmInJxJK6kTMzMzObu6Xux+UmOQNw94W2wlrcY2tFjxsC4F8PLwHxBOelbkaX+e3cjiCesOrEBigUqbjcuqWQ/KrjRzrhq6YCsDFx1EQAzszMwdRoNzXofhjTCvWiaoN4QiO+LnrEfY0fYtuCeIJFFGFS2I+yBWCl8un62UfNmuHi0dMaa4Xz8u6YpO2WJnRTZuUarKdRAKxU4cmLwkwM5RWl5H1TWTMAZmIysaRu5MzMzMzmbqn7cblJzgDcIbkTiCdsS9sjetwQAJfuI/xdyvdGlXclV6le/3v5eh5O7DmIR7Us8cTSEnsC7BkA/4cAODt6DKLiObwS/TyIJ1hGWcBz9Be4PCEeqsQpKJqQhJ4j/gfiCR+Pai4rAM7mx0ChSH0KuhmXcbskK/Tfn32htVa42ttuTr4wWtukidaxqgDwo3bvAUS4eeSU5H1TWTMAZmIysaRu5MzMzMzmbqn7cblJzgD87hRhlO34pTOixw0B8JELp0A84b1pHxhV3rqTW9Trf5VKFbLDRwNEyGvzml74YgCsH4B/8+qM/lODNKZThy+NRHp6tt77IyUAl0Ja4YRJ2Dx2IDZEDNCC0X0hIXhhVF0QT0gM+142AJwaHomjg4dpZMA+2rsfntSpI2TxDgzWGUd1+NYhYb3uo/e0211VAPjBj52FdcXrtkjeN5U1A2AmJhNL6kbOzMzMbO6Wuh+Xm+QMwC8kvAjiCVdvFIgeNwTAWdeug3hC8/EtjCqvNGlW6EZh65a/P/gQIMIJuw4MgKsAwLNcvsc0x45YFeigdop3F60yxWKTGoD1weiRkDB4DnhXmCY9qrmsAFjse/k/dhGyQjdvAdXYiSYD4Nsr1+vM2FwVAL7n00vIRD5jruR9U1kzAGZiMrGkbuTMzMzM5m6p+3G5Sa4AnJObD+IJL419Sec5hgA4P78IDeIaoFZ0LeTlG17rWHb9760/jwnZc+vXxY5BLgyAqwjAxtwzsdikAOD09MtID49EXkyC2kdCwnB+xGit66X4/IiXIuuDeMIw/y9kDcDpw0fhcavXBDj9pbvJAFiVNENIHOfXR+tYVQD4n+EjhWnco3jJ+6eyZgDMxGRiSd3ImZmZmc3dUvfjcpNcAfjohdMgntBuanud5xgCYKVShTcmvgniCedysvSWV7r+t25MXVy+noe7QYMBIpx//22D8MUAuGYBsEKRikU+XbAx0EHt2a7f67ye/4gPQTzh28Gt5A3A4ZH4J2Qoii0sUFy7Nq4PGmISAP4nLFwA1dExWseqAsBFU2bqBGspzQCYicnEkrqRMzMzM5u7pe7H5Sa5AvDG0zsMJrAyBoA/m/U5iCfsO39Yb3lLFSsFiJnbEcobt/H4lVcBImyx78gA+D8IwOXjmOv1Ew4HBGmMCu/vH4QF3l2wZIgNLKIIDUbXxuGQEFkDsCpxCv4tSYh1r3UbZGZcrva2e8/TR5iqPGe+1rGqALB6avUv3STvn8qaATATk4kldSNnZmZmNndL3Y/LTXIF4HmHFoJ4gtsyT53nGAPA9osdQTxh/qHFesvz/d0PxBNidsbj9vLVABH+adfeKCBjAFzzAXiWy/eY49hRY1R4qn0HdZkfj2oB4gnjw2xlD8CquHF48oKwN/CNxKRqb7v/dvoeIELhxh1ax6oCwKXLEh5++LHk/VNZMwBmYjKxpG7kzMzMzOZuqftxuUmuADxm1zgQTwheH6rzHGMAeNQ2XiOxlZjz8u/glQmvgnjCkQuncN/eESDC5bAIo4BsqV9X7N59UGN/VbE9VhUKBsD67tnu3QexzK+bbAFYX5lDhn8J4gn/N7KNQYjV2qYoM0fYczo6/tkAcOIU3OvhJ2RqfrkpCqp5GvSjd9oI2xWdPKd1rCoAXJB1XdheqWkzyfsnjbgYADMxmVZSN3JmZmZmc7fU/bjcJFcADl4fCuIJ8bvG6zzHGABefmw1iCd0+fUXndfZnrYXxBPaTLbCRUUqntSthyd16mLfms1GAdlC7y4ImB6ssc1PxDLtPVYVCgbA+u5ZQFKQ1vXNBYDXhDqiTqQlakdZInvCGIMwWn6booODwwxCa3UCsGpCEu6+206YCt3Dv/rabn4RiuvXR3GtWlDmFmodrwoAK5UqPHleGLlWXhXPDC+FGQAzMZlYUjdyZmZmZnO31P243CRXAHZd5gHiCfMOLdR5jjEAfObyRRBPeHVCS53XCd0YBuIJ30z9Fpv72wFEOPvN+0YDmRiMxmzQnuaqUDAArmj8lQVgfWt2TQHA20Pd8MWQV0A8YVKCS8VgtAqfVRqAE6fgWugwPGnQAMUWFijcsqta2m3B+Wxhq6XXXhc9XlUAftSuvTC6fPS05H2Uus4MgJmYTCupGzkzMzOzuVvqflxukisAf5fyPYgnbDq9U+c5xgCwUqlC8/Et9GaCbj/tfRBP6LMsALlt3wSIsDq+r9FAxgBYfgBsaM2uKQB4YN9PQDzhq5i3zQKAc2ISUFCSsflR+/dFR2wr6sJd+4V1ul99LXq8qgD87w8/AUS4vX6r5H1UqRkAMzGZWFI3cmZmZmZzt9T9uNwkVwBuO7UdiCcoLqbqPOfa+Sxss+uEY87/pxeAf5j/E4gn/H58ndY1Srdbajq2KZLi+wBEUDVpjNi1ceg/OQgLfJ4tAKenZyN8aaTG1GCxOBgAGwZgY75XnQA8y+snvBjZAMQTTo8fbRYAnHn2Ih61FaZC/x0dX+V2e+fXJcL+2Y7OoserCsD3vHsIGaZnJkveR5WaATATk4kldSNnZmZmNndL3Y/LTXIF4CYJTUA84UquUvt4biHu2zmi2MICIAKIcPF/72H7YFdRAB60YQiIJ0Ruj9W6Vukx51/dcM7qNYAI5z57F6sCHTBRZMTQ1ACsUKQixbsLVgU6qC0WBwNg+QFwslcXuIULo8Cj4ruZBwBn5qBw4w6ACMUNG4kmrqqIS/cA/mf4SNHjVQXgf4ZFCLA+KlryPqrUDICZmEwsqRs5MzMzs7lb6n5cbpIjAF/JVYJ4wosJL4oe/yd8tJANtmFD3GjZDE9qWQJEuPRVeyz2764FwMl//QbiCY5LNEelrt4oQNNxTUE8YeWmxXhYuxaKLSywrzdXIYj6zasz+k8N0hi1DV8aifT0bI3yFArjANgY+GIALE8AXjjEC8QTrKKbo2hCkqwBuGw26jsu7gJYdv5ZK3t5Rfygyy8AEe4sWyV6vKoArEqaISTu8u8reT9VagbATEwmltSNnJmZmdncLXU/LjfJEYCPXUwD8YS2U9tpHSvctR/FtWujuFYt5G/YigUBTjju1AmPa9UCiPBXp4+0APhQ5nEQT7Ce8q7G5zMPJoN4wtdzv0V2CVTnt25ZKfia5thRY9R2rldn0W1+lvp1ZQCs448Gxk45lzMAnwwZirejhT+q/DFuiKwBuGw26qsjRuFxw0YAEa7Pnle5tptfhMfNWwBEKDh7SfScqgLw7RVrASI8+KW75P1UqRkAMzGZWFI3cmZmZmZzt9T9uNwkRwDedHoniCd0Svle89i1m3hk/a4wxXLIcI0kWCdsvxXWHtavizMHFRrfy8u/g4ZxjWAZbYnL1/PUn38+S9i7NfnPX3HXSrju/7d352FSVOf+wM8siCDgAsPIzDBL13nfqCQY4zWYCAIKowJJFAUSAfdfFBBkVYZlaBj2wbB5BRQQxAA/IyjKjTGKRlkUUIhgZJV9bfZxhQvz3j+6uumZ6aVm6O6qhu/ned7nmW6qqk+3nlP97ao69cW9zS44fPnC0PJuvcrc6uaDbr3K3IbnQsKX1QDp1AAc7EcDq6ecOzkA/7v3AHlu1F2i3EoeH3mbfDlwqPy794Ays1Gv7j1ANg0c6ogAXObewJ06eyewysgUz86Dle63R7/c7J0BOjMr5DIXGoCPffqFd9Ku6yr+OGZXIQADxJjdnRyFQqESvewex53GiQF41qfzRLmVdFzwpzLPn5o63fsF/Rc3imf/sQqzQHtcGd6jT10fqbDNW6Y3EeVW8s6X/xSP53zIrj8+XTyLlnhDda0a8l7vDlELwJHuQ3uh4ctKgKxqALY6GdeFBOBYPmdnAN44vlBS3MlyubuavNunu8zq2LLMbNTTO7as8P+GEwJwSfEk+TE3T0Qp+f7p3pXutydfXeg9OtvmdyGXudAA7Dl4wnuf4dRUx9wLGAEYIMbs7uQoFAqV6GX3OO40TgzAoz8YL8qtpOeSPuefP3xKzvy8sff6wlf+Kh5PxdsgffJoGzmbnCylqalybPmaMtvsuaSP/zToj75eJenjvfdsHfIPt5xu1tx79LfJDVELZFbuQxvN8BXquaoG4LVrrU3GhQBcMQCXTJgsfxx5iyi3kocGNinzo0eoH0ccEYAnTJZ9PZ6Rc6mpUpqaKruWvOs/fd/KdcHf9X3W+0NSwdCQy1xwAPaUyJlf3iSilBz7cKXtY5XHgwAMEHN2d3IUCoVK9LJ7HHcaJwZg38zMoz4Y53/uxJv/4z29MjtXPAdPiMcT/D7AX91oiCglp29vKZ7Dp/zr79x/UG6e5g0lycOTRbmV3D2njRx9y7vd03XryesP3R3VQBbpPrROD8BW2oEAHDwArx1fIEnuJKk57DKZ9lDkswOcEoA3FBTK/ia/8faJBhmye9hI2TiosMKM5sHqpztbe+/Ru3BxyGWiEYB/+FMX762QXphh+1jl8SAAA8Sc3Z0chUKhEr3sHsedxokBuNOCB0W5lcxcNdf/3E93t/XOUls0xv9csAC88OG75Uz9+mWOFPtq856dQpNYlFvJz19oLDv2H5DTzVqIKCV7+gyIavC08hwCsP0BeP5jbWT9+vWybVvoycqqEoBLJkyW3xc19s4+3tNIqAC8ZcAgOZt+rfd05nva+W+XFLbfHj4l5+qleS9B2LQz5HLRCMDfFo3xnqbd4xnbxyqPBwEYIObs7uQoFAqV6GX3OO40TgzALWbdUeZ63aOr/y2lSUlyrlZtOfLNPv9ywQLwa0+0kz1jis2jxTni2X24zLY37twqhe8VyVe7tvuPKp9Lqy9fLF99yQbg+Y/dE3TG6gWPtbGw/XuCBsjAdZ0cgGd2yZcv+veX3UWhJyuragD+ZFx/UW4l1QqTZVr/uxImAG8uKJTvevWV0qQkKU1Jkf1P944YgI+u/9rb5xpmh10uGgH4xKJ3vEeoW95p+1jl8SAAA8Sc3Z0chUKhEr3sHsedxokB+PopN4hyK1mz7UvxeErk+249vUd8/tytzHKhAvCGLzfLmVua+GeLDvYah7fskjOZWd5bHw1xWw58F2MAfrVLvnR7oVeZCa+6Tepp6Z6887rmS79Z/aTondDrOj0AbxsypEyALD9ZWVUDcMmEydJ1oHem8eyhdWRJ3/sTJgCXTJgsp1vc4Q2a1zaQbRu3hO2zJ1/5q/eIcbs/hF0uGgH4yNc7vGE7/VrbxyqPBwEYIObs7uQoFAqV6GX3OO40TgvABw4dlxoja0jqiFTZe/CIePYekXPXXOOd9Gbl52WWDRmAN2zx3i84JUVKk5LkxCuvlTm6uXXLLjnW+m4RpWRHY0NGvDXKcuC7WAOwlXsKhwrAgxcOluf/FXrdSzkAr+zdV1wD64hyK2k9KFf+0bdjwgTgkrHPy9mMTBGl5Pgjj4ftt98/2d17icLgYWGXi0YA9nhK/KdohzvdOl6FAAwQY3Z3chQKhUr0snscdxqnBeCPN30myq2k0dRfiMdTIqemz/Iehfpt0wrLhgvAHk+JlIyf6D3F+fLL5cWix2XE22Nk1BsjZPkD3ut+v6tdQ6a/PrxSgQ8BGAG4MgF4de8BUvxIU6k5rJoot5K2gwyZ0bl1YgTgCZPlu/4Fcq5aNe819X99PWifPbJ9r5yrXUdKk5Iq/EhVvqIVgH1Hp08sXmr7mIUADBBjdndyFAqFSvSyexx3GqcF4CmfTBPlVvLgwq7i8ZTI6d829c74On1WhWUjBWCPp0S+795LRCkpTUqS/TfkyvHMet5QnJQkc5/tVOnAhwCMAFzZAPxql3yZNOBOfwhu1idTPu/dLyECcMmEyXLsd/d6+0yt2nLso1UV+uG3Q4b7J8yK1L+jFYB9/frbkWNtH7MQgAFizO5OjkKhUIledo/jTuO0APzEoidFuZWMWTZBjq383PvFu25d8ew9UmFZKwHYc+ikHH/kcfnxistFlBJRSg5clyNTR/0/Kfj/hQjACMBxCcDv9e0kkwe08ofg5oMNOVg8PiEC8O7ho+WEeeuhsw0y5Oi/N53vX3s8crZ+uvc06b9/ELF/RysAn3phhohS8sODXW0fsxCAAWLM7k6OQqFQiV52j+NO47QA/Ovpt4pyK/n7hg/k+yee9E5+1b1X0GUtBWCP90v3yMUjZeHEp+WNcU/JhGUTZeBrQ6X3rAH+iZuemthT5nZFAEYAjl0Afq9vJ3mxf75cNaS6KLeSm0Y0lM3jhzs+AO90j5K1n66T78zTjs80yJDjr70unj0e+W5AQchLFIJVtALwsQ9Xetvyy5tsH7MQgAFizO5OjkKhUIledo/jTuOkAHzw0AmpObKmpAxPkb1fbZLSmjWlNCVFjq7dEHT5ygTgEW+PqRDwpv+xpbzR/T55o/t98vy9TeMe0hCAL64A/FLnVvJZt55yaMQYf33yVE+ZW25b4x6+XWhoPVFuJfWH15b3xvVydADeUFAoa/o8K3sGu+UHQ/vPpCitWdP/94mFiyz18WgFYM/eI1JarZqUVq8unh0HbB23EIABYszuLycoFAqV6GX3OO40TgrAyzevEeVWcv2URvLds4NElJIf2z8QcvkLDcB2h7SLIQDP7dJa+s7sW+Y2SOWPpl8qAXhah5Yyo30zeaf7ff6aEuSHlZc758vK3s/I74oai3IrSXUnS7fBzWVt775lwvPq3gNk08ChjgjA/ueKJ8nRP7SXs+akV6ebNZdTL8+x3MejFoA9JfLTna298wPMmG3ruIUADBBjdn85QaFQqEQvu8dxp3FSAP7v5S+Jcit5aG4nOVe3rve6wmXLQy5vNQBv3rxTCuYXlrnPrRNC2oLH7pH169fLtm3nb9EU7fsRz3/sHlm2bEXY7c/p3FqemtIz7OcTavvTO7aUGZ2ay6Ie94U8mn4pBWArr+k7Vfpk8UQZOrqtpLiTvfcKfq6WjH3mfICe3rFlxNmi4x6AJ0yW3SPGyLYNW+TIll2V7uObN++SzQWFZYL+lkGFcvTo0UqPQ6emTPNOvtXmd7aOWwjAADFm95cTFAqFSvSyexx3GicF4CcXdxflVvLe03/wXlfYvGXY5a0G4LVrN8jMLvn+gOaUkDava770m9WvzNHTaN+P+NUu+dLthV4Rtz+1fbOwn0+4ADyva6sy/w2qGgwvtQDse72Px/UXwzwlWrmVNBmcIZMHtLJ0v2C7AvDWrbur1MfXrt0g87rmlzlKPq9rfoUzIazUka27/adBH9mx37ZxCwEYIMbs/nKCQqFQiV52j+NO46QAfOuM38rVzyr56ZqrvNcVvvF22OUrE4AjnVZsVwCOdP1sNAJw4OnN0d5+ogfglzu3kg39+snhovDX7cYyAJdMmCzLe/eRB3v8TOoUXuYPwq7nrpRRve+WfcNHhjwtOhEDcPm+GOxSAKv1U+u7Qt4mLV6FAAwQY3Z/OUGhUKhEL7vHcadxSgDetf+QXDGqlsy+yTupzk93txHP4VNh10EARgC+0Nec3rGlzO7UQpb2CH/dbqwDsO+o8+K+98mjA38hdQtr+INw/aE15ZH+jeS1Hm0qnBZ9qQfgU1OnW74HcawKARggxuz+goJCoVCJXnaP407jlAA84v3RcsdD3vB7rlbtsvcaDVEIwAjA0QjAkdofzwDsW2Zp3wfk0Seul5znavmDcLVhyfLrftfKrH5/lFPFk2wLwDvdo2Tt2g3+68p9dejQyYh9NlhfvJAAfGTbnvOnQf/nG1vGLgRguJQlaa2nEtF2Itqqte4RakEims3MW4hoPTMvd7lc/2X1Rez+goJCeE3ekQAAEaNJREFUoVCJXtEZ8p3JMAzNzCvNfcxqIro+0jpOCMC7DxyWW56rK7vreANwydjnLa2X6AHYygzKF/qa5Se4ivb2EYBjE4B9687s1EKeH3CH3DEoR6oNS/aH4Z8NT5dxo9vLikED4h6AfbdF2j1ijL82Diq0dFTY1xff7Nteip5tJh0LrpMW/bPkidefkDHLiuXLHZsrPX782P4B75wBd7QSj4UQHu1CAIZLFjM/RETvK6VUdnb21cy8K9QXDyJqp5RKNv9uy8w7rb6O3V8cUSgUKtErKoO+QzHzMq11V6WUIqL7mXlNpHWcEICnzeor+2qrSn+JDRaAX32sraxYsabMkSkrMyvbEdKszKAcjdcMnOAq2ttHAI5tAA5c9/V+98oDPUiyhlwZcFQ4RVq5fybTx3aWPcVj4haAyy9j9bTov320RFr3y5Hqw1L87yGwktxJ0uzl5jJ39QI5dNjaOHBk00452yBDRCn5trAo7uMXAjBcsohoqda6Y8DjcUQ0ItJ6mZmZdZn5tDIDcSR2f3FEoVCoRK8LGOodTWudRkQnVcD+hJkPMrMr3Hp2BuAjX++Qkr4D5ESNJBGl5GDzJuLZ47G8frAAPLtLvvSa1rvMLX2szKxsVwCOVviKdkhDAI7da77UuZV81q1nmVsBVWbirWntm8rIZ26T2wZmSGrh+aPC1dwp0qroeukzpJUsGPSo7C8e54gAfODQcVn4+WJpNTvf39bkYUly85Br5bGBjaXX0zdJ0btF8qeFXaT26Dr+ZfQkkqL3x8jmPTsjjgUn3v6HlCYnS2lqqhz7bF18xzEEYLhUMfMGImrie6y17kZEcyKtR0RFzPym1dex+4sjCoVCJXpVbZR3Pq31r5h5U+BzRLRaa90i3HrxDsAnFi+Vsw0ypPTyy0WU8tf7t2WKZ/+xSm0rWACe2bm19CwXgK2c9osAjAAcz892RvtmZW4FVNWJt6Z0bSFjn2sjbdyN5HJ3aoUjqg2GXym3F5F0GHqzPDOkhfxlTAeZM+YReWdsD1kw6AlZMuhJ+WL8YPlq/DDZNr5IVhQMkPUFBXK4uFg8xRPkSPHz8nnBINlQMFiOFf9Fjhf/RdYVDJH/FAyRk8UT/dcib3OPkGWrVshH61fK0jX/lHnLF0rh0hHScf6f5Jqx1/jbU2tkbWnX1yWv9mvnfw+B1wDvOeCRKZ9Mk+umXO9f57IRl8ltLzWT/ksHyrQVM+WNdW/LnM/my6gPxsnSL9/3jwffjhwrZ+uny7FVX8R1n4IADBctIlpFRJ7AYuYjROTRWmdVJQBrrbsw8yatdZrVdvg6GgqFQqGqVlXfEzjbhQTgo0fj9/mfmjtfSqtXl9KUFDl35VXyY4dO4ln2sRw6fKLS29q3zyMvdG0jCx9v66/RbX8jY9v8WmZ2yffX8PxbZFK735RZbnzbW8s8V/5xPJ4b3/ZWeaF907i/ZrS3H+k92PXZRqv9dn62Vl5zcrsmMq9Lvrzc9Q7p8+dfyu1PNZBGBely1bAaQU8ztqNumNhIBvytQBYuXiQzOrUs855mdGopO3bsKDMOeTynZNG6t+W+v94vl424LOR2jUna9n3K0aMX734FIKzKngJtGEYnZt7CzJnxaSEAAFzMqnoKNAAAAEClMfPD5iRYyVlZWdcw8y6tdaNgy2qtOxLRVsMwGsa5mQAAcBFj5g+Z+WGllNJaP2BlEiwAAACAqkg2b4P0DRFtY+anff9gGMbNRLTU95iZzxDRbiJaR0TriWhddnb21fY0GwAALhYul4uJaJV5htGaUD/EAgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAxJZhGJqZVzLzFiJaTUTX290mAB8imszMO4moNC8vr7Hd7YGLRpJ5x4LtRLRVa90j1IJENNscH9cz83KXy/Vf8WxoIKvjNRG1Y+ZN5nJvpKWl1Yp3W4Ox0n7DMH5ORB8T0dfMvIGZZ2qtq9vR3mAqu88kojlEVKq1rhOvNoZjtf2GYTQkoreZeTMRfRWuj8ST1fZrrZ8jov+YdxlZ5XK5bol3W4Oxuk9zah+20n6n92EAAMXMy7TWXZVSiojux/0ywUlcLlfThg0bZjDzDgRgiBZmfsi8Z73Kzs6+mpl3hQuTSqlk8++2zLwzjk0tw8p4nZ6efgURHXK5XKSUUmbQHx/vtgZjpf2GYWjDMH5uPkxi5oVa68K4NjSMyuwzmfk+rfVLRHTOKQHYavuZ+XMiau97rLVOi1cbw7HSfma+kZl3ZWVl1VBKKa11ZyJaHe+2BmNln+bkPmyl/U7vwwBwidNapxHRSWV+uVNKKWY+yMwuG5sFUAEz70QAhmghoqVa644Bj8cR0YhI62VmZtZl5tMqYMyMF6vjtdb6AWb+u+8xEV1PRHvj2NSgqrq/YeZ+RDQ75g20oDLvIS8vL52Z15hhxhFHgK223+Vy3cnMK+LewAistj8vL68xEe0zDKO+uV4PInojzs0NK9w+zal9OFBl9slO6sMAAEpr/Stm3hT4HBGt1lq3sKlJAEEhAEM0MfMGImrie6y17kZEcyKtR0RFzPxmLNsWitXxWmvdl4im+R5nZWXVYOb/VTaE9kBV2d+kp6dfwcybtNa/j3kDLajMeyCiJVrr5ubfTgnAltpPRD2Z+S0iWkBE64hoUU5OTl5cGxtEJT///kT0PRHtIaJtOTk5DeLWUAsiBGBH9uFAVvfJTuvDAAAIwJAwEIChMohoFRF5AouZjxCRR2udVZUArLXuYn6Rs+VU0EswAFcjoneIaGLsW2dNJf4bPE5EUwKWSagArLXuQ0SncnNzrzOXeZKZ18axqUFZbX9ubm4uEa3Ky8tLN9frwczL49jUiC6RAOy4PgwAgFOgIWEgAEM0VfYUaMMwOjHzFmbOjE8LK6rkKdDvBjy+gYj2xLGpQVVyf5NKRIuJaHr8WhiZ1fdARK8R0W5m3uGbMIiZdzHzjXFvdIBKtP9+IvqX73FGRkZNIjqrlEqJW2ODsNp+85Rb//87ZvtLlVKp8WtteBZOgXZcHw5kYZ/syD4MAKCUUoqZP2Tmh5XyD7qYBAscBwEYoomZHzYnwUrOysq6hpl3aa0bBVtWa92RiLYahtEwzs2swMp4nZaWVsucQIfN5RwzgY7F/U0KES1i5pfj3DxLqrLPJKLSevXq1Y596yKz0n4zMG5v2LBhhrlcByLaGO+2BmOl/cx8HxFtTE9Pv0Ip/w9Ym8ovZ6dw+zQn92GfCPtkR/dhAADlcrmYiFaZRzfWhPoSCGAHIppORHuZ+QwzHySirXa3CS4KyeaXym+IaBszP+37B8Mwbiaipb7HzHyGiHab10KuJ6J12dnZV9vR6CDj9Q1mG4cz8599y5F5CxUi2kpEi50Svqy0n4geJKJz5me9nojWaa2n2tvy86z+NwhEDpoF2mr7mblVwH+Dfznlu4HV9mutR5l9YD0zr9Ba32Rfq88LtU9LlD5spf1O78MAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACgFBF9ZRjGHdHYFjOPIaLZ0dgWEb2itR6tlFIul6spEW2LxnYBACA2Qu1P8vLyfkZEpb7HWuvqzPwWEZ0gon/Et5UXpvx7Yea/E9GjdrYJAAAAbBKrABwtsdgmAACEZ4bGc77HRPQgEX2hlEqp6jaJ6CNm/nNUGlgJ5d+LVcy8U2udH4s2AQAAgE0QgAEAoLwgAXgwEc2/kG0iAAMAAECV+HbKhmHcTESfEdEpIjrMzPMirZuXl9eYmdeY67xDRNN9AVhr3ZyZDwYuT0SfMvND5us+bD5+3jwNbpvWukPAsv6wWn5bOTk5DYhoAREdYuZjRLQ4YL35zHzA3ObHWutG5ja6MfMZIvqJmUuI6F9KKVWvXr3aZrv3MvMBrfVUpdRlF/KZAgBcinz7E/MU55nMfIyZt2ite/hCIxGNZObTzHyGmUu01r1DbS8rK+saZn6LmY8z83Ei+tR8biwRnSWiH8xtzFVKKZfLxUT0T3P5zVrrxwLaFnafE0q492K+H38Qz8nJyWPmZUR0kpmP+vYzRDSfiM4R0ffm/qeoyh8yAAAAXBhm3qG1zieiVcxcYD59mdb6NxFWTTXXfU4plUJErYno+3IB+EDgCuUDMDP/b7n1f9BaG+ay5QOwb1vJzPy51npqenr6FUqpFGZuFvB+Hjafr0ZExUS0MeD1KxwBJqJFRDQrPT39Cq11HWZ+F19OAAAqLyAAjyKiTzMzM+tmZGTUI6JVgaFRaz3MyhFgrfVoIlqita6ulErSWt+UkZFRU6mgR4BTzYA6TCmVahjGzcx8xHdNcqR9Tpg2hH0vge0wg+6LSqlkpVSKYRi3BX42RNTayucIAAAAMeT7wkJEHxHRdGbOtLheMyI6FPgcES2uTAAOtj4zDzL/DhqAtda3MvNRpVRqpDa6XK4riag0Ozv76vLbNLeVxsxnzMDsW6cpEX1j5TMAAIDzAvYn3xBRW9/zWuvfVyUAM7ObmVe4XK5flP+38gHYMIzbiOiwUiopYP0xvqPDkfY5oRDRN8zcJtR7KReA5zDzm8FCNU6BBgAAcAjfTtk8dWseER0moo2RZrU0DKOTOYmJHxFNrGQArrA+M/+3+XeoANyBiNaFaFayeWrcdiI6aZ7mds7lclH5bSqllMvluoWIzvlOrzNPsTtJRKcsfHQAABDA3J/cRUQ/BoZWZr6xKgE4IyOjpjmmbyWifUQ0TpkTZ5UPwFrrjkH2KU8y8wdmG8Luc0KJ9F4C22EeIX6RiPYQ0XbzaHPgZ4MADAAAYLdgO2UzcJ5mZleY9YIdAV7kC8DmNcUny79WhCPAiyIdASaiJuYR4Aqzh2qtuzDz5ry8vBzzcR0iKnW5XGyuOzswAOfl5aUT0U/BtgUAAJUTcAR4ezSOAAcyf6Td4vtxlpk/jMIRYP8+J5RIR7ODnIqtlPLPkXHUMIyW5uvvQAAGAABwgIBrtrpqrdOUUoqIfklEP/qCZAip5jVNz5p/twq8BrhevXq1mflbwzDuVUqlaK17mJOeBF4DfKb8+oZhaLMNIa8BJqIviGhyWlpaLeW91vd2c7luzPyl1rpORkZGTa31VPMIMJuvOYaZFwa+CTO0v5ibm3uVUkoZhtFQa31XdD5dAIBLR8D+ZDQzr8zIyKhnXmqyoioBmIjammfwJGmt04joK611V/PfFphHhH181wAXKqWqaa1/RUQel8t1p9m2oPsc3xlCoUR6L4EBWGvdQWudpZRSubm5ueSdqLGZudynWutuVj9LAAAAiBHfr9LM/Kq5sy4xf2V/JNK65i/ca81ZLd8homkUcBskrXVnItrHzEeYeTh5J9oKPAK8Sms9wTxVebvWuqNv3TABWDVs2DCDiP5GRB5zps2/KaVUenr6FeydMfRb84tYl8AAbBiGJqJ17J3Nc5lS/lmgJzPzLvP056+Y+ekofbwAAJcM3/5Ea12diGaZl5Zs0Vp3r2IAfsa8BvdbZt6vtZ6gzCO85nwQm9l7J4BXlPLOAs3MHxDRCfN1Hw9oW9h9Tii5ubmXh3svgUeizdO195rt3RV4CrTW+vfMvMvcznCLHykAAABcLHxfRuxuBwAAXPywzwEAAABb4csIAADEC/Y5AAAAEJHL5WpqnspVElDfMnOJ7zqnqsKXEQAAIKJpQfYzJUT0P9F8nXD7HPPSl2D7uYHRbAMAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAEA8/R8FHiL1dbUACAAAAABJRU5ErkJggg==\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/anaconda3/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5dc5fc6828>"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#plot\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.subplot(1,2,1)\n",
    "sns.violinplot(x = 'is_duplicate', y = 'idf_stop_dist', data = df)\n",
    "plt.subplot(1,2,2)\n",
    "sns.distplot(df[df['is_duplicate'] == 1.0]['idf_stop_dist'], color = 'green')\n",
    "sns.distplot(df[df['is_duplicate'] == 0.0]['idf_stop_dist'], color = 'red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import log_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_back = df.copy(deep=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#del(df['cos_dist'])\n",
    "del(df['q1_vect'])\n",
    "del(df['q2_vect'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>qid1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>qid2</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>question1</th>\n",
       "      <td>What is the step by step guide to invest in sh...</td>\n",
       "      <td>What is the story of Kohinoor (Koh-i-Noor) Dia...</td>\n",
       "      <td>How can I increase the speed of my internet co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>question2</th>\n",
       "      <td>What is the step by step guide to invest in sh...</td>\n",
       "      <td>What would happen if the Indian government sto...</td>\n",
       "      <td>How can Internet speed be increased by hacking...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_duplicate</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tf_nonstop_dist</th>\n",
       "      <td>0.0550888</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.634852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tfidf_nonstop_dist</th>\n",
       "      <td>0.0214848</td>\n",
       "      <td>0.250245</td>\n",
       "      <td>0.745323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tfidf_stop_dist</th>\n",
       "      <td>0.0182158</td>\n",
       "      <td>0.20806</td>\n",
       "      <td>0.723911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>idf_stop_dist</th>\n",
       "      <td>0.057191</td>\n",
       "      <td>0.367544</td>\n",
       "      <td>0.634852</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                    0  \\\n",
       "id                                                                  0   \n",
       "qid1                                                                1   \n",
       "qid2                                                                2   \n",
       "question1           What is the step by step guide to invest in sh...   \n",
       "question2           What is the step by step guide to invest in sh...   \n",
       "is_duplicate                                                        0   \n",
       "tf_nonstop_dist                                             0.0550888   \n",
       "tfidf_nonstop_dist                                          0.0214848   \n",
       "tfidf_stop_dist                                             0.0182158   \n",
       "idf_stop_dist                                                0.057191   \n",
       "\n",
       "                                                                    1  \\\n",
       "id                                                                  1   \n",
       "qid1                                                                3   \n",
       "qid2                                                                4   \n",
       "question1           What is the story of Kohinoor (Koh-i-Noor) Dia...   \n",
       "question2           What would happen if the Indian government sto...   \n",
       "is_duplicate                                                        0   \n",
       "tf_nonstop_dist                                              0.416667   \n",
       "tfidf_nonstop_dist                                           0.250245   \n",
       "tfidf_stop_dist                                               0.20806   \n",
       "idf_stop_dist                                                0.367544   \n",
       "\n",
       "                                                                    2  \n",
       "id                                                                  2  \n",
       "qid1                                                                5  \n",
       "qid2                                                                6  \n",
       "question1           How can I increase the speed of my internet co...  \n",
       "question2           How can Internet speed be increased by hacking...  \n",
       "is_duplicate                                                        0  \n",
       "tf_nonstop_dist                                              0.634852  \n",
       "tfidf_nonstop_dist                                           0.745323  \n",
       "tfidf_stop_dist                                              0.723911  \n",
       "idf_stop_dist                                                0.634852  "
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:3].T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "training_fields = ['tf_nonstop_dist','tfidf_nonstop_dist','tfidf_stop_dist','idf_stop_dist']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## learn 4 features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "clf = SGDClassifier(loss='log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "        df[training_fields], df['is_duplicate'], test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDClassifier(alpha=0.0001, average=False, class_weight=None, epsilon=0.1,\n",
       "       eta0=0.0, fit_intercept=True, l1_ratio=0.15,\n",
       "       learning_rate='optimal', loss='log', n_iter=5, n_jobs=1,\n",
       "       penalty='l2', power_t=0.5, random_state=None, shuffle=True,\n",
       "       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_test['predict_proba'] = list(clf.predict_proba(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_test['predict_proba'] = X_test['predict_proba'].apply(lambda x:x[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.56809686443975316"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_loss(y_test, X_test['predict_proba'])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}